diff --git a/.circleci/config.yml b/.circleci/config.yml index d99c485af94..6c7bbddb9f1 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -112,6 +112,24 @@ jobs: python -m mypy . cd .. no_output_timeout: 10m + + semgrep: + docker: + - image: cimg/python:3.12 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + steps: + - checkout + - setup_google_dns + - run: + name: Install Semgrep + command: pip install semgrep + - run: + name: Run Semgrep (custom rules only) + command: semgrep scan --config .semgrep/rules . --error + local_testing_part1: docker: - image: cimg/python:3.12 @@ -1255,7 +1273,15 @@ jobs: ls # Add --timeout to kill hanging tests after 120s (2 min) # Add --durations=20 to show 20 slowest tests for debugging - python -m pytest -vv tests/llm_translation --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread + # Subdirectories with dedicated jobs (maintain this list as new jobs are added) + IGNORE_DIRS=( + "tests/llm_translation/realtime" + ) + IGNORE_ARGS="" + for dir in "${IGNORE_DIRS[@]}"; do + IGNORE_ARGS="$IGNORE_ARGS --ignore=$dir" + done + python -m pytest -vv tests/llm_translation $IGNORE_ARGS --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread no_output_timeout: 120m - run: name: Rename the coverage files @@ -1271,6 +1297,54 @@ jobs: paths: - llm_translation_coverage.xml - llm_translation_coverage + realtime_translation_testing: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest==7.3.1" + pip install "pytest-retry==1.6.3" + pip install "pytest-cov==5.0.0" + pip install "pytest-asyncio==0.21.1" + pip install "respx==0.22.0" + pip install "pytest-xdist==3.6.1" + pip install "pytest-timeout==2.2.0" + pip install "websockets" + # Run pytest and generate JUnit XML report + - run: + name: Run realtime tests + command: | + pwd + ls + # Add --timeout to kill hanging tests after 120s (2 min) + # Add --durations=20 to show 20 slowest tests for debugging + python -m pytest -vv tests/llm_translation/realtime --cov=litellm --cov-report=xml -v --junitxml=test-results/junit.xml --durations=20 -n 4 --timeout=120 --timeout_method=thread + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml realtime_translation_coverage.xml + mv .coverage realtime_translation_coverage + + # Store test results + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - realtime_translation_coverage.xml + - realtime_translation_coverage mcp_testing: docker: - image: cimg/python:3.11 @@ -1316,6 +1390,51 @@ jobs: paths: - mcp_coverage.xml - mcp_coverage + agent_testing: + docker: + - image: cimg/python:3.11 + auth: + username: ${DOCKERHUB_USERNAME} + password: ${DOCKERHUB_PASSWORD} + working_directory: ~/project + + steps: + - checkout + - setup_google_dns + - run: + name: Install Dependencies + command: | + python -m pip install --upgrade pip + python -m pip install -r requirements.txt + pip install "pytest==7.3.1" + pip install "pytest-retry==1.6.3" + pip install "pytest-cov==5.0.0" + pip install "pytest-asyncio==0.21.1" + pip install "respx==0.22.0" + pip install "pydantic==2.11.0" + pip install "a2a-sdk" + # Run pytest and generate JUnit XML report + - run: + name: Run tests + command: | + pwd + ls + python -m pytest -vv tests/agent_tests --ignore=tests/agent_tests/local_only_agent_tests --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit.xml --durations=5 + no_output_timeout: 120m + - run: + name: Rename the coverage files + command: | + mv coverage.xml agent_coverage.xml + mv .coverage agent_coverage + + # Store test results + - store_test_results: + path: test-results + - persist_to_workspace: + root: . + paths: + - agent_coverage.xml + - agent_coverage guardrails_testing: docker: - image: cimg/python:3.11 @@ -2176,6 +2295,7 @@ jobs: - run: python ./tests/code_coverage_tests/router_code_coverage.py - run: python ./tests/code_coverage_tests/test_chat_completion_imports.py - run: python ./tests/code_coverage_tests/info_log_check.py + - run: python ./tests/code_coverage_tests/check_guardrail_apply_decorator.py - run: python ./tests/code_coverage_tests/test_ban_set_verbose.py - run: python ./tests/code_coverage_tests/code_qa_check_tests.py - run: python ./tests/code_coverage_tests/check_get_model_cost_key_performance.py @@ -3532,7 +3652,7 @@ jobs: python -m venv venv . venv/bin/activate pip install coverage - coverage combine llm_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage + coverage combine llm_translation_coverage realtime_translation_coverage llm_responses_api_coverage ocr_coverage search_coverage mcp_coverage logging_coverage audio_coverage litellm_router_coverage litellm_router_unit_coverage local_testing_part1_coverage local_testing_part2_coverage litellm_assistants_api_coverage auth_ui_unit_tests_coverage langfuse_coverage caching_coverage litellm_proxy_unit_tests_part1_coverage litellm_proxy_unit_tests_part2_coverage image_gen_coverage pass_through_unit_tests_coverage batches_coverage litellm_security_tests_coverage guardrails_coverage litellm_mapped_tests_coverage coverage xml - codecov/upload: file: ./coverage.xml @@ -3700,7 +3820,6 @@ jobs: - run: name: Get new version command: | - cd litellm-proxy-extras NEW_VERSION=$(python -c "import toml; print(toml.load('pyproject.toml')['tool']['poetry']['version'])") echo "export NEW_VERSION=$NEW_VERSION" >> $BASH_ENV @@ -3725,7 +3844,6 @@ jobs: - run: name: Publish to PyPI command: | - cd litellm-proxy-extras echo -e "[pypi]\nusername = $PYPI_PUBLISH_USERNAME\npassword = $PYPI_PUBLISH_PASSWORD" > ~/.pypirc python -m pip install --upgrade pip build twine setuptools wheel rm -rf build dist @@ -3832,6 +3950,9 @@ jobs: image: ubuntu-2204:2023.10.1 resource_class: xlarge working_directory: ~/project + parameters: + browser: + type: string steps: - checkout - setup_google_dns @@ -3861,7 +3982,7 @@ jobs: echo "Expires at: $EXPIRES_AT" neon branches create \ --project-id $NEON_PROJECT_ID \ - --name preview/commit-${CIRCLE_SHA1:0:7} \ + --name preview/commit-${CIRCLE_SHA1:0:7}-<< parameters.browser >> \ --expires-at $EXPIRES_AT \ --parent br-fancy-paper-ad1olsb3 \ --api-key $NEON_API_KEY || true @@ -3871,7 +3992,7 @@ jobs: E2E_UI_TEST_DATABASE_URL=$(neon connection-string \ --project-id $NEON_PROJECT_ID \ --api-key $NEON_API_KEY \ - --branch preview/commit-${CIRCLE_SHA1:0:7} \ + --branch preview/commit-${CIRCLE_SHA1:0:7}-<< parameters.browser >> \ --database-name yuneng-trial-db \ --role neondb_owner) echo $E2E_UI_TEST_DATABASE_URL @@ -3883,7 +4004,7 @@ jobs: -e UI_USERNAME="admin" \ -e UI_PASSWORD="gm" \ -e LITELLM_LICENSE=$LITELLM_LICENSE \ - --name litellm-docker-database \ + --name litellm-docker-database-<< parameters.browser >> \ -v $(pwd)/litellm/proxy/example_config_yaml/simple_config.yaml:/app/config.yaml \ litellm-docker-database:ci \ --config /app/config.yaml \ @@ -3899,7 +4020,7 @@ jobs: sudo rm dockerize-linux-amd64-v0.6.1.tar.gz - run: name: Start outputting logs - command: docker logs -f litellm-docker-database + command: docker logs -f litellm-docker-database-<< parameters.browser >> background: true - run: name: Wait for app to be ready @@ -3908,6 +4029,7 @@ jobs: name: Run Playwright Tests command: | npx playwright test \ + --project << parameters.browser >> \ --config ui/litellm-dashboard/e2e_tests/playwright.config.ts \ --reporter=html \ --output=test-results @@ -4014,6 +4136,12 @@ workflows: only: - main - /litellm_.*/ + - semgrep: + filters: + branches: + only: + - main + - /litellm_.*/ - local_testing_part1: filters: branches: @@ -4113,6 +4241,20 @@ workflows: - main - /litellm_.*/ - e2e_ui_testing: + name: e2e_ui_testing_chromium + browser: chromium + context: e2e_ui_tests + requires: + - ui_build + - build_docker_database_image + filters: + branches: + only: + - main + - /litellm_.*/ + - e2e_ui_testing: + name: e2e_ui_testing_firefox + browser: firefox context: e2e_ui_tests requires: - ui_build @@ -4196,12 +4338,24 @@ workflows: only: - main - /litellm_.*/ + - realtime_translation_testing: + filters: + branches: + only: + - main + - /litellm_.*/ - mcp_testing: filters: branches: only: - main - /litellm_.*/ + - agent_testing: + filters: + branches: + only: + - main + - /litellm_.*/ - guardrails_testing: filters: branches: @@ -4307,7 +4461,9 @@ workflows: - upload-coverage: requires: - llm_translation_testing + - realtime_translation_testing - mcp_testing + - agent_testing - google_generate_content_endpoint_testing - guardrails_testing - llm_responses_api_testing @@ -4378,13 +4534,16 @@ workflows: - publish_to_pypi: requires: - mypy_linting + - semgrep - local_testing_part1 - local_testing_part2 - build_and_test - e2e_openai_endpoints - test_bad_database_url - llm_translation_testing + - realtime_translation_testing - mcp_testing + - agent_testing - google_generate_content_endpoint_testing - llm_responses_api_testing - ocr_testing @@ -4408,7 +4567,8 @@ workflows: - litellm_assistants_api_testing - auth_ui_unit_tests - db_migration_disable_update_check - - e2e_ui_testing + - e2e_ui_testing_chromium + - e2e_ui_testing_firefox - litellm_proxy_unit_testing_key_generation - litellm_proxy_unit_testing_part1 - litellm_proxy_unit_testing_part2 diff --git a/.dockerignore b/.dockerignore index 76e31546c2f..a487d2a859a 100644 --- a/.dockerignore +++ b/.dockerignore @@ -48,7 +48,7 @@ dist/ build/ *.egg-info/ .DS_Store -node_modules/ +**/node_modules *.log .env .env.local diff --git a/.github/actions/helm-oci-chart-releaser/action.yml b/.github/actions/helm-oci-chart-releaser/action.yml index 059277ed882..1823e262832 100644 --- a/.github/actions/helm-oci-chart-releaser/action.yml +++ b/.github/actions/helm-oci-chart-releaser/action.yml @@ -40,38 +40,33 @@ outputs: runs: using: composite steps: + - name: Helm | Setup + uses: azure/setup-helm@v4 + with: + version: v3.20.0 + - name: Helm | Login shell: bash run: echo ${{ inputs.registry_password }} | helm registry login -u ${{ inputs.registry_username }} --password-stdin ${{ inputs.registry }} - env: - HELM_EXPERIMENTAL_OCI: '1' - + - name: Helm | Dependency if: inputs.update_dependencies == 'true' shell: bash run: helm dependency update ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Package shell: bash run: helm package ${{ inputs.path == null && format('{0}/{1}', 'charts', inputs.name) || inputs.path }} --version ${{ inputs.tag }} --app-version ${{ inputs.app_version }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Push shell: bash run: helm push ${{ inputs.name }}-${{ inputs.tag }}.tgz oci://${{ inputs.registry }}/${{ inputs.repository }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Logout shell: bash run: helm registry logout ${{ inputs.registry }} - env: - HELM_EXPERIMENTAL_OCI: '1' - name: Helm | Output id: output shell: bash - run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT \ No newline at end of file + run: echo "image=${{ inputs.registry }}/${{ inputs.repository }}/${{ inputs.name }}:${{ inputs.tag }}" >> $GITHUB_OUTPUT diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index b91b16c955c..f13039f4516 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -9,6 +9,7 @@ - [ ] I have Added testing in the [`tests/litellm/`](https://github.com/BerriAI/litellm/tree/main/tests/litellm) directory, **Adding at least 1 test is a hard requirement** - [see details](https://docs.litellm.ai/docs/extras/contributing_code) - [ ] My PR passes all unit tests on [`make test-unit`](https://docs.litellm.ai/docs/extras/contributing_code) - [ ] My PR's scope is as isolated as possible, it only solves 1 specific problem +- [ ] I have requested a Greptile review by commenting `@greptileai` and received a **Confidence Score of at least 4/5** before requesting a maintainer review ## CI (LiteLLM team) diff --git a/.github/workflows/test-litellm-matrix.yml b/.github/workflows/test-litellm-matrix.yml new file mode 100644 index 00000000000..b442c7dd5f5 --- /dev/null +++ b/.github/workflows/test-litellm-matrix.yml @@ -0,0 +1,109 @@ +name: LiteLLM Unit Tests (Matrix) + +on: + pull_request: + branches: [main] + +# Cancel in-progress runs for the same PR +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + test: + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + fail-fast: false + matrix: + test-group: + # tests/test_litellm split by subdirectory (~560 files total) + - name: "llms" + path: "tests/test_litellm/llms" + workers: 4 + # tests/test_litellm/proxy split by subdirectory (~180 files total) + - name: "proxy-guardrails" + path: "tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers" + workers: 4 + - name: "proxy-core" + path: "tests/test_litellm/proxy/auth tests/test_litellm/proxy/client tests/test_litellm/proxy/db tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine" + workers: 4 + - name: "proxy-misc" + path: "tests/test_litellm/proxy/_experimental tests/test_litellm/proxy/agent_endpoints tests/test_litellm/proxy/anthropic_endpoints tests/test_litellm/proxy/common_utils tests/test_litellm/proxy/discovery_endpoints tests/test_litellm/proxy/experimental tests/test_litellm/proxy/google_endpoints tests/test_litellm/proxy/health_endpoints tests/test_litellm/proxy/image_endpoints tests/test_litellm/proxy/middleware tests/test_litellm/proxy/openai_files_endpoint tests/test_litellm/proxy/pass_through_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/response_api_endpoints tests/test_litellm/proxy/spend_tracking tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/vector_store_endpoints tests/test_litellm/proxy/test_*.py" + workers: 4 + - name: "integrations" + path: "tests/test_litellm/integrations" + workers: 4 + - name: "core-utils" + path: "tests/test_litellm/litellm_core_utils" + workers: 2 + - name: "other" + path: "tests/test_litellm/caching tests/test_litellm/responses tests/test_litellm/secret_managers tests/test_litellm/vector_stores tests/test_litellm/a2a_protocol tests/test_litellm/anthropic_interface tests/test_litellm/completion_extras tests/test_litellm/containers tests/test_litellm/enterprise tests/test_litellm/experimental_mcp_client tests/test_litellm/google_genai tests/test_litellm/images tests/test_litellm/interactions tests/test_litellm/passthrough tests/test_litellm/router_strategy tests/test_litellm/router_utils tests/test_litellm/types" + workers: 4 + - name: "root" + path: "tests/test_litellm/test_*.py" + workers: 4 + # tests/proxy_unit_tests split alphabetically (~48 files total) + - name: "proxy-unit-a" + path: "tests/proxy_unit_tests/test_[a-o]*.py" + workers: 2 + - name: "proxy-unit-b" + path: "tests/proxy_unit_tests/test_[p-z]*.py" + workers: 2 + + name: test (${{ matrix.test-group.name }}) + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" + + - name: Install Poetry + uses: snok/install-poetry@v1 + + - name: Cache Poetry dependencies + uses: actions/cache@v4 + with: + path: | + ~/.cache/pypoetry + ~/.cache/pip + .venv + key: ${{ runner.os }}-poetry-${{ hashFiles('poetry.lock') }} + restore-keys: | + ${{ runner.os }}-poetry- + + - name: Install dependencies + run: | + poetry config virtualenvs.in-project true + poetry install --with dev,proxy-dev --extras "proxy semantic-router" + poetry run pip install pytest-retry==1.6.3 pytest-xdist google-genai==1.22.0 \ + google-cloud-aiplatform>=1.38 fastapi-offline==1.7.3 python-multipart==0.0.22 openapi-core + + - name: Setup litellm-enterprise + run: | + cd enterprise && poetry run pip install -e . && cd .. + + - name: Run tests - ${{ matrix.test-group.name }} + run: | + poetry run pytest ${{ matrix.test-group.path }} \ + --tb=short -vv \ + --maxfail=10 \ + -n ${{ matrix.test-group.workers }} \ + --durations=20 + + # Aggregate job to require all matrix jobs pass + test-complete: + needs: test + runs-on: ubuntu-latest + if: always() + steps: + - name: Check test results + run: | + if [ "${{ needs.test.result }}" != "success" ]; then + echo "Some test groups failed" + exit 1 + fi + echo "All test groups passed!" diff --git a/.github/workflows/test-litellm.yml b/.github/workflows/test-litellm.yml index d9cf2e74a11..dc9b48c28f6 100644 --- a/.github/workflows/test-litellm.yml +++ b/.github/workflows/test-litellm.yml @@ -1,8 +1,12 @@ name: LiteLLM Mock Tests (folder - tests/test_litellm) +# DEPRECATED: This workflow is replaced by test-litellm-matrix.yml which runs +# the same tests in parallel across 10 jobs for faster CI times. +# Kept for manual debugging only. on: - pull_request: - branches: [ main ] + workflow_dispatch: # Manual trigger only + # pull_request: + # branches: [ main ] jobs: test: diff --git a/.semgrep/rules/README.md b/.semgrep/rules/README.md new file mode 100644 index 00000000000..0dbb77cdd48 --- /dev/null +++ b/.semgrep/rules/README.md @@ -0,0 +1,22 @@ +# Custom Semgrep rules for LiteLLM + +Add custom rule YAML files here. Semgrep loads all `.yml`/`.yaml` files under this directory. + +**Run only custom rules (CI / fail on findings):** + +```bash +semgrep scan --config .semgrep/rules . --error +``` + +**Run with registry + custom rules:** + +```bash +semgrep scan --config auto --config .semgrep/rules . +``` + +**Layout:** + +- `python/` – Python-specific rules (security, patterns) +- Add more subdirs as needed (e.g. `generic/` for language-agnostic rules) + +See [Semgrep rule syntax](https://semgrep.dev/docs/writing-rules/rule-syntax/). diff --git a/.semgrep/rules/python/reliability/unbounded-memory.yml b/.semgrep/rules/python/reliability/unbounded-memory.yml new file mode 100644 index 00000000000..f13c38471fb --- /dev/null +++ b/.semgrep/rules/python/reliability/unbounded-memory.yml @@ -0,0 +1,17 @@ +# Unbounded memory growth – data structures without a clear max limit +# Can lead to OOM under load. + +rules: + - id: unbounded-asyncio-queue + message: asyncio.Queue() with no maxsize can grow unbounded. Use asyncio.Queue(maxsize=N) for integrations (e.g. log queues). + severity: ERROR + languages: [python] + pattern-either: + - pattern: asyncio.Queue() + - pattern: asyncio.Queue(maxsize=0) + metadata: + category: reliability + cwe: "CWE-400: Uncontrolled Resource Consumption" + tags: [python, reliability] + confidence: HIGH + source: https://docs.python.org/3/library/asyncio-queue.html diff --git a/.semgrep/rules/python/unbounded-memory.yml b/.semgrep/rules/python/unbounded-memory.yml new file mode 100644 index 00000000000..811ef689344 --- /dev/null +++ b/.semgrep/rules/python/unbounded-memory.yml @@ -0,0 +1,14 @@ +# Unbounded memory growth – data structures without a clear max limit +# Can lead to OOM under load. + +rules: + - id: unbounded-asyncio-queue + message: asyncio.Queue() with no maxsize can grow unbounded. Use asyncio.Queue(maxsize=N) for integrations (e.g. log queues). + severity: ERROR + languages: [python] + pattern-either: + - pattern: asyncio.Queue() + - pattern: asyncio.Queue(maxsize=0) + metadata: + category: correctness + cwe: "CWE-400: Uncontrolled Resource Consumption" \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index 23a0e97eaee..3cb67908076 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -90,6 +90,7 @@ LiteLLM is a unified interface for 100+ LLM providers with two main components: - Pydantic v2 for data validation - Async/await patterns throughout - Type hints required for all public APIs +- **Avoid imports within methods** — place all imports at the top of the file (module-level). Inline imports inside functions/methods make dependencies harder to trace and hurt readability. The only exception is avoiding circular imports where absolutely necessary. ### Testing Strategy - Unit tests in `tests/test_litellm/` diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a418c8c57af..77bc15ff50b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -7,11 +7,20 @@ Thank you for your interest in contributing to LiteLLM! We welcome contributions Here are the core requirements for any PR submitted to LiteLLM: - [ ] **Sign the Contributor License Agreement (CLA)** - [see details](#contributor-license-agreement-cla) +- [ ] **Keep scope isolated** - Your changes should address 1 specific problem at a time + +#### Proxy (Backend) PRs + - [ ] **Add testing** - Adding at least 1 test is a hard requirement - [see details](#adding-testing) - [ ] **Ensure your PR passes all checks**: - [ ] [Unit Tests](#running-unit-tests) - `make test-unit` - [ ] [Linting / Formatting](#running-linting-and-formatting-checks) - `make lint` -- [ ] **Keep scope isolated** - Your changes should address 1 specific problem at a time + +#### UI PRs + +- [ ] **Ensure the UI builds successfully** - `npm run build` +- [ ] **Ensure all UI unit tests pass** - `npm run test` +- [ ] **Add tests for new components or logic** - If you are adding a new component or new logic, add corresponding tests ## **Contributor License Agreement (CLA)** @@ -245,6 +254,43 @@ docker run \ --config /app/config.yaml --detailed_debug ``` +## UI Development + +### 1. Setup Your Local UI Development Environment + +```bash +# Clone the repo (if you haven't already) +git clone https://github.com/YOUR_USERNAME/litellm.git +cd litellm + +# Navigate to the UI dashboard directory +cd ui/litellm-dashboard + +# Install dependencies +npm install + +# Start the development server +npm run dev +``` + +### 2. Adding UI Tests + +If you are adding a **new component** or **new logic**, you must add corresponding tests. + +### 3. Running UI Unit Tests + +```bash +npm run test +``` + +### 4. Building the UI + +Ensure the UI builds successfully before submitting your PR: + +```bash +npm run build +``` + ## Submitting Your PR 1. **Push your branch**: `git push origin your-feature-branch` diff --git a/Dockerfile b/Dockerfile index 4bfda939110..5e93a0c627e 100644 --- a/Dockerfile +++ b/Dockerfile @@ -3,6 +3,7 @@ ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base # Runtime image ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base + # Builder stage FROM $LITELLM_BUILD_IMAGE AS builder @@ -48,7 +49,22 @@ USER root # Install runtime dependencies (libsndfile needed for audio processing on ARM64) RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \ - npm install -g npm@latest tar@latest + npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \ + # SECURITY FIX: npm bundles tar, glob, and brace-expansion at multiple nested + # levels inside its dependency tree. `npm install -g ` only creates a + # SEPARATE global package, it does NOT replace npm's internal copies. + # We must find and replace EVERY copy inside npm's directory. + GLOBAL="$(npm root -g)" && \ + find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done && \ + npm cache clean --force WORKDIR /app # Copy the current directory contents into the container at /app @@ -62,10 +78,28 @@ COPY --from=builder /wheels/ /wheels/ # Install the built wheel using pip; again using a wildcard if it's the only file RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels +# Replace the nodejs-wheel-binaries bundled node with the system node (fixes CVE-2025-55130) +RUN NODEJS_WHEEL_NODE=$(find /usr/lib -path "*/nodejs_wheel/bin/node" 2>/dev/null) && \ + if [ -n "$NODEJS_WHEEL_NODE" ]; then cp /usr/bin/node "$NODEJS_WHEEL_NODE"; fi + # Remove test files and keys from dependencies RUN find /usr/lib -type f -path "*/tornado/test/*" -delete && \ find /usr/lib -type d -path "*/tornado/test" -delete +# SECURITY FIX: nodejs-wheel-binaries (pip package used by Prisma) bundles a complete +# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/. +# Patch every copy of tar, glob, and brace-expansion inside that tree. +RUN GLOBAL="$(npm root -g)" && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done + # Install semantic_router and aurelio-sdk using script # Convert Windows line endings to Unix and make executable RUN sed -i 's/\r$//' docker/install_auto_router.sh && chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh diff --git a/Makefile b/Makefile index 0da83c363cd..74031f418d6 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,12 @@ # LiteLLM Makefile # Simple Makefile for running tests and basic development tasks -.PHONY: help test test-unit test-integration test-unit-helm lint format install-dev install-proxy-dev install-test-deps install-helm-unittest check-circular-imports check-import-safety +.PHONY: help test test-unit test-unit-llms test-unit-proxy-guardrails test-unit-proxy-core test-unit-proxy-misc \ + test-unit-integrations test-unit-core-utils test-unit-other test-unit-root \ + test-proxy-unit-a test-proxy-unit-b test-integration test-unit-helm \ + info lint lint-dev format \ + install-dev install-proxy-dev install-test-deps \ + install-helm-unittest check-circular-imports check-import-safety # Default target help: @@ -22,9 +27,26 @@ help: @echo " make check-import-safety - Check import safety" @echo " make test - Run all tests" @echo " make test-unit - Run unit tests (tests/test_litellm)" + @echo " make test-unit-llms - Run LLM provider tests (~225 files)" + @echo " make test-unit-proxy-guardrails - Run proxy guardrails+mgmt tests (~51 files)" + @echo " make test-unit-proxy-core - Run proxy auth+client+db+hooks tests (~52 files)" + @echo " make test-unit-proxy-misc - Run proxy misc tests (~77 files)" + @echo " make test-unit-integrations - Run integration tests (~60 files)" + @echo " make test-unit-core-utils - Run core utils tests (~32 files)" + @echo " make test-unit-other - Run other tests (caching, responses, etc., ~69 files)" + @echo " make test-unit-root - Run root-level tests (~34 files)" + @echo " make test-proxy-unit-a - Run proxy_unit_tests (a-o, ~20 files)" + @echo " make test-proxy-unit-b - Run proxy_unit_tests (p-z, ~28 files)" @echo " make test-integration - Run integration tests" @echo " make test-unit-helm - Run helm unit tests" +# Keep PIP simple for edge cases: +PIP := $(shell command -v pip > /dev/null 2>&1 && echo "pip" || echo "python3 -m pip") + +# Show info +info: + @echo "PIP: $(PIP)" + # Installation targets install-dev: poetry install --with dev @@ -34,19 +56,19 @@ install-proxy-dev: # CI-compatible installations (matches GitHub workflows exactly) install-dev-ci: - pip install openai==2.8.0 + $(PIP) install openai==2.8.0 poetry install --with dev - pip install openai==2.8.0 + $(PIP) install openai==2.8.0 install-proxy-dev-ci: poetry install --with dev,proxy-dev --extras proxy - pip install openai==2.8.0 + $(PIP) install openai==2.8.0 install-test-deps: install-proxy-dev - poetry run pip install "pytest-retry==1.6.3" - poetry run pip install pytest-xdist - poetry run pip install openapi-core - cd enterprise && poetry run pip install -e . && cd .. + poetry run $(PIP) install "pytest-retry==1.6.3" + poetry run $(PIP) install pytest-xdist + poetry run $(PIP) install openapi-core + cd enterprise && poetry run $(PIP) install -e . && cd .. install-helm-unittest: helm plugin install https://github.com/helm-unittest/helm-unittest --version v0.4.4 || echo "ignore error if plugin exists" @@ -62,8 +84,40 @@ format-check: install-dev lint-ruff: install-dev cd litellm && poetry run ruff check . && cd .. +# faster linter for developing ... +# inspiration from: +# https://github.com/astral-sh/ruff/discussions/10977 +# https://github.com/astral-sh/ruff/discussions/4049 +lint-format-changed: install-dev + @git diff origin/main --unified=0 --no-color -- '*.py' | \ + perl -ne '\ + if (/^diff --git a\/(.*) b\//) { $$file = $$1; } \ + if (/^@@ .* \+(\d+)(?:,(\d+))? @@/) { \ + $$start = $$1; $$count = $$2 || 1; $$end = $$start + $$count - 1; \ + print "$$file:$$start:1-$$end:999\n"; \ + }' | \ + while read range; do \ + file="$${range%%:*}"; \ + lines="$${range#*:}"; \ + echo "Formatting $$file (lines $$lines)"; \ + poetry run ruff format --range "$$lines" "$$file"; \ + done + +lint-ruff-dev: install-dev + @tmpfile=$$(mktemp /tmp/ruff-dev.XXXXXX) && \ + cd litellm && \ + (poetry run ruff check . --output-format=pylint || true) > "$$tmpfile" && \ + poetry run diff-quality --violations=pylint "$$tmpfile" --compare-branch=origin/main && \ + cd .. ; \ + rm -f "$$tmpfile" + +lint-ruff-FULL-dev: install-dev + @files=$$(git diff --name-only origin/main -- '*.py'); \ + if [ -n "$$files" ]; then echo "$$files" | xargs poetry run ruff check; \ + else echo "No changed .py files to check."; fi + lint-mypy: install-dev - poetry run pip install types-requests types-setuptools types-redis types-PyYAML + poetry run $(PIP) install types-requests types-setuptools types-redis types-PyYAML cd litellm && poetry run mypy . --ignore-missing-imports && cd .. lint-black: format-check @@ -72,11 +126,14 @@ check-circular-imports: install-dev cd litellm && poetry run python ../tests/documentation_tests/test_circular_imports.py && cd .. check-import-safety: install-dev - poetry run python -c "from litellm import *" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) + @poetry run python -c "from litellm import *; print('[from litellm import *] OK! no issues!');" || (echo '🚨 import failed, this means you introduced unprotected imports! 🚨'; exit 1) # Combined linting (matches test-linting.yml workflow) lint: format-check lint-ruff lint-mypy check-circular-imports check-import-safety +# Faster linting for local development (only checks changed code) +lint-dev: lint-format-changed lint-mypy check-circular-imports check-import-safety + # Testing targets test: poetry run pytest tests/ @@ -84,6 +141,38 @@ test: test-unit: install-test-deps poetry run pytest tests/test_litellm -x -vv -n 4 +# Matrix test targets (matching CI workflow groups) +test-unit-llms: install-test-deps + poetry run pytest tests/test_litellm/llms --tb=short -vv -n 4 --durations=20 + +test-unit-proxy-guardrails: install-test-deps + poetry run pytest tests/test_litellm/proxy/guardrails tests/test_litellm/proxy/management_endpoints tests/test_litellm/proxy/management_helpers --tb=short -vv -n 4 --durations=20 + +test-unit-proxy-core: install-test-deps + poetry run pytest tests/test_litellm/proxy/auth tests/test_litellm/proxy/client tests/test_litellm/proxy/db tests/test_litellm/proxy/hooks tests/test_litellm/proxy/policy_engine --tb=short -vv -n 4 --durations=20 + +test-unit-proxy-misc: install-test-deps + poetry run pytest tests/test_litellm/proxy/_experimental tests/test_litellm/proxy/agent_endpoints tests/test_litellm/proxy/anthropic_endpoints tests/test_litellm/proxy/common_utils tests/test_litellm/proxy/discovery_endpoints tests/test_litellm/proxy/experimental tests/test_litellm/proxy/google_endpoints tests/test_litellm/proxy/health_endpoints tests/test_litellm/proxy/image_endpoints tests/test_litellm/proxy/middleware tests/test_litellm/proxy/openai_files_endpoint tests/test_litellm/proxy/pass_through_endpoints tests/test_litellm/proxy/prompts tests/test_litellm/proxy/public_endpoints tests/test_litellm/proxy/response_api_endpoints tests/test_litellm/proxy/spend_tracking tests/test_litellm/proxy/ui_crud_endpoints tests/test_litellm/proxy/vector_store_endpoints tests/test_litellm/proxy/test_*.py --tb=short -vv -n 4 --durations=20 + +test-unit-integrations: install-test-deps + poetry run pytest tests/test_litellm/integrations --tb=short -vv -n 4 --durations=20 + +test-unit-core-utils: install-test-deps + poetry run pytest tests/test_litellm/litellm_core_utils --tb=short -vv -n 2 --durations=20 + +test-unit-other: install-test-deps + poetry run pytest tests/test_litellm/caching tests/test_litellm/responses tests/test_litellm/secret_managers tests/test_litellm/vector_stores tests/test_litellm/a2a_protocol tests/test_litellm/anthropic_interface tests/test_litellm/completion_extras tests/test_litellm/containers tests/test_litellm/enterprise tests/test_litellm/experimental_mcp_client tests/test_litellm/google_genai tests/test_litellm/images tests/test_litellm/interactions tests/test_litellm/passthrough tests/test_litellm/router_strategy tests/test_litellm/router_utils tests/test_litellm/types --tb=short -vv -n 4 --durations=20 + +test-unit-root: install-test-deps + poetry run pytest tests/test_litellm/test_*.py --tb=short -vv -n 4 --durations=20 + +# Proxy unit tests (tests/proxy_unit_tests split alphabetically) +test-proxy-unit-a: install-test-deps + poetry run pytest tests/proxy_unit_tests/test_[a-o]*.py --tb=short -vv -n 2 --durations=20 + +test-proxy-unit-b: install-test-deps + poetry run pytest tests/proxy_unit_tests/test_[p-z]*.py --tb=short -vv -n 2 --durations=20 + test-integration: poetry run pytest tests/ -k "not test_litellm" diff --git a/README.md b/README.md index 77adddf8978..7790c67afd5 100644 --- a/README.md +++ b/README.md @@ -309,7 +309,7 @@ Support for more providers. Missing a provider or LLM Platform, raise a [feature | [Deepgram (`deepgram`)](https://docs.litellm.ai/docs/providers/deepgram) | ✅ | ✅ | ✅ | | | ✅ | | | | | | [DeepInfra (`deepinfra`)](https://docs.litellm.ai/docs/providers/deepinfra) | ✅ | ✅ | ✅ | | | | | | | | | [Deepseek (`deepseek`)](https://docs.litellm.ai/docs/providers/deepseek) | ✅ | ✅ | ✅ | | | | | | | | -| [ElevenLabs (`elevenlabs`)](https://docs.litellm.ai/docs/providers/elevenlabs) | ✅ | ✅ | ✅ | | | | ✅ | | | | +| [ElevenLabs (`elevenlabs`)](https://docs.litellm.ai/docs/providers/elevenlabs) | ✅ | ✅ | ✅ | | | ✅ | ✅ | | | | | [Empower (`empower`)](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ | | | | | | | | | [Fal AI (`fal_ai`)](https://docs.litellm.ai/docs/providers/fal_ai) | ✅ | ✅ | ✅ | | ✅ | | | | | | | [Featherless AI (`featherless_ai`)](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ | | | | | | | | diff --git a/ci_cd/.grype.yaml b/ci_cd/.grype.yaml index 642e2dd9d03..b9bc9db58f5 100644 --- a/ci_cd/.grype.yaml +++ b/ci_cd/.grype.yaml @@ -1,3 +1,36 @@ ignore: - vulnerability: CVE-2026-22184 reason: no fixed zlib package is available yet in the Wolfi repositories, so this is ignored temporarily until an upstream release exists + # Wolfi base image: Python 3.13 and Node from apk have no fixed builds in Wolfi yet / not applicable + - vulnerability: CVE-2025-55130 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: CVE-2025-59465 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: CVE-2025-55131 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: CVE-2025-59466 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: CVE-2026-21637 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: CVE-2025-55132 + reason: Node in Wolfi apk; only used for Admin UI build/prisma + - vulnerability: GHSA-hx9q-6w63-j58v + reason: orjson dumps recursion; allowlisted + - vulnerability: GHSA-73rr-hh4g-fpgx + reason: diff npm transitive dep; override in package.json, allowlisted + - vulnerability: CVE-2026-0865 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2025-15282 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2026-0672 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2025-15366 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2025-15367 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2025-11468 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2025-12781 + reason: Python 3.13 in Wolfi base; no fixed apk build yet + - vulnerability: CVE-2026-1299 + reason: Python 3.13 in Wolfi base; no fixed apk build yet diff --git a/ci_cd/security_scans.sh b/ci_cd/security_scans.sh index 3a212a56f64..2db72ae5c69 100755 --- a/ci_cd/security_scans.sh +++ b/ci_cd/security_scans.sh @@ -140,12 +140,14 @@ run_grype_scans() { "GHSA-34x7-hfp2-rc4v" # node-tar hardlink path traversal - not applicable, tar CLI not exposed in application code "GHSA-r6q2-hw4h-h46w" # node-tar not used by application runtime, Linux-only container, not affect by macOS APFS-specific exploit "GHSA-8rrh-rw8j-w5fx" # wheel is from chainguard and will be handled by then TODO: Remove this after Chainguard updates the wheel - "CVE-2025-59465" # We do not use Node in application runtime, only used for building Admin UI - "CVE-2025-55131" # We do not use Node in application runtime, only used for building Admin UI - "CVE-2025-59466" # We do not use Node in application runtime, only used for building Admin UI - "CVE-2025-55130" # We do not use Node in application runtime, only used for building Admin UI - "CVE-2025-59467" # We do not use Node in application runtime, only used for building Admin UI - "CVE-2026-21637" # We do not use Node in application runtime, only used for building Admin UI + "CVE-2025-59465" # Node only used for Admin UI build/prisma + "CVE-2025-55131" # Node only used for Admin UI build/prisma + "CVE-2025-59466" # Node only used for Admin UI build/prisma + "CVE-2025-55130" # Node only used for Admin UI build/prisma + "CVE-2025-59467" # Node only used for Admin UI build/prisma + "CVE-2026-21637" # Node only used for Admin UI build/prisma + "CVE-2025-55132" # Node only used for Admin UI build/prisma + "GHSA-hx9q-6w63-j58v" # orjson dumps recursion; allowlisted "CVE-2025-15281" # No fix available yet "CVE-2026-0865" # No fix available yet "CVE-2025-15282" # No fix available yet @@ -154,6 +156,8 @@ run_grype_scans() { "CVE-2025-15367" # No fix available yet "CVE-2025-12781" # No fix available yet "CVE-2025-11468" # No fix available yet + "CVE-2026-1299" # Python 3.13 email module header injection - not applicable, LiteLLM doesn't use BytesGenerator for email serialization + "CVE-2026-0775" # npm cli incorrect permission assignment - no fix available yet, npm is only used at build/prisma-generate time ) # Build JSON array of allowlisted CVE IDs for jq diff --git a/cookbook/livekit_agent_sdk/README.md b/cookbook/livekit_agent_sdk/README.md new file mode 100644 index 00000000000..1c3f0bf9564 --- /dev/null +++ b/cookbook/livekit_agent_sdk/README.md @@ -0,0 +1,114 @@ +# LiveKit Voice Agent with LiteLLM Gateway + +Simple example showing how to use LiveKit's xAI realtime plugin with LiteLLM as a proxy. This lets you switch between xAI, OpenAI, and Azure realtime APIs without changing your code. + +## Quick Start + +### 1. Install dependencies + +```bash +pip install livekit-agents[xai] websockets +``` + +### 2. Start LiteLLM proxy + +```bash +# With xAI +export XAI_API_KEY="your-xai-key" +litellm --config config.yaml --port 4000 +``` + +### 3. Run the voice agent + +```bash +python main.py +``` + +Type your message and get a voice response from Grok! + +## Configuration + +Set these environment variables if needed: + +```bash +export LITELLM_PROXY_URL="http://localhost:4000" +export LITELLM_API_KEY="sk-1234" +export LITELLM_MODEL="grok-voice-agent" +``` + +Or use the defaults - connects to `http://localhost:4000` by default. + +## Example Config File + +Create a `config.yaml` with your realtime models: + +```yaml +model_list: + - model_name: grok-voice-agent + litellm_params: + model: xai/grok-2-vision-1212 + api_key: os.environ/XAI_API_KEY + model_info: + mode: realtime + + - model_name: openai-voice-agent + litellm_params: + model: gpt-4o-realtime-preview + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: realtime + +general_settings: + master_key: sk-1234 +``` + +Then start: `litellm --config config.yaml --port 4000` + +## How It Works + +LiveKit's xAI plugin connects through LiteLLM proxy by setting `base_url`: + +```python +from livekit.plugins import xai + +model = xai.realtime.RealtimeModel( + voice="ara", + api_key="sk-1234", # LiteLLM proxy key + base_url="http://localhost:4000", # Point to LiteLLM +) +``` + +## Switching Providers + +Just change the model in your config - no code changes needed: + +**xAI Grok:** +```yaml +model: xai/grok-2-vision-1212 +``` + +**OpenAI:** +```yaml +model: gpt-4o-realtime-preview +``` + +**Azure OpenAI:** +```yaml +model: azure/gpt-4o-realtime-preview +api_base: https://your-endpoint.openai.azure.com/ +``` + +## Why Use LiteLLM? + +- ✅ **Switch providers** without changing agent code +- ✅ **Cost tracking** across all voice sessions +- ✅ **Rate limiting** and budgets +- ✅ **Load balancing** across multiple API keys +- ✅ **Fallbacks** to backup models + +## Learn More + +- [LiveKit xAI Realtime Tutorial](/docs/tutorials/livekit_xai_realtime) +- [xAI Realtime Docs](/docs/providers/xai_realtime) +- [LiveKit Agents Documentation](https://docs.livekit.io/agents/) +- [LiteLLM Realtime API](/docs/realtime) diff --git a/cookbook/livekit_agent_sdk/config.example.yaml b/cookbook/livekit_agent_sdk/config.example.yaml new file mode 100644 index 00000000000..1361f36af34 --- /dev/null +++ b/cookbook/livekit_agent_sdk/config.example.yaml @@ -0,0 +1,21 @@ +model_list: + - model_name: grok-voice-agent + litellm_params: + model: xai/grok-2-vision-1212 + api_key: os.environ/XAI_API_KEY + model_info: + mode: realtime + + - model_name: openai-voice-agent + litellm_params: + model: gpt-4o-realtime-preview + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: realtime + +litellm_settings: + drop_params: True + telemetry: False + +general_settings: + master_key: sk-1234 # Change this to a secure key diff --git a/cookbook/livekit_agent_sdk/main.py b/cookbook/livekit_agent_sdk/main.py new file mode 100644 index 00000000000..0e2d7ebdfaf --- /dev/null +++ b/cookbook/livekit_agent_sdk/main.py @@ -0,0 +1,112 @@ +""" +Simple xAI Voice Agent using LiveKit SDK with LiteLLM Gateway + +This example shows how to use LiveKit's xAI realtime plugin through LiteLLM proxy. +LiteLLM acts as a unified interface, allowing you to switch between xAI, OpenAI, +and Azure realtime APIs without changing your agent code. +""" +import asyncio +import json +import os +import websockets + +# Configuration +PROXY_URL = os.getenv("LITELLM_PROXY_URL", "http://localhost:4000") +API_KEY = os.getenv("LITELLM_API_KEY", "sk-1234") +MODEL = os.getenv("LITELLM_MODEL", "grok-voice-agent") + + +async def run_voice_agent(): + """ + Simple voice agent that: + 1. Connects to xAI realtime API through LiteLLM proxy + 2. Sends a user message + 3. Streams back the response + """ + + url = f"ws://{PROXY_URL.replace('http://', '').replace('https://', '')}/v1/realtime?model={MODEL}" + headers = {"Authorization": f"Bearer {API_KEY}"} + + print(f"🎙️ Connecting to voice agent...") + print(f" Model: {MODEL}") + print(f" Proxy: {PROXY_URL}") + print() + + async with websockets.connect(url, additional_headers=headers) as ws: + # Receive initial connection event + initial = json.loads(await ws.recv()) + print(f"✅ Connected! Event: {initial['type']}\n") + + # Get user input + user_message = input("💬 Your message: ").strip() + if not user_message: + user_message = "Tell me a fun fact about AI!" + + print(f"\n🤖 Sending to {MODEL}...\n") + + # Send user message + await ws.send(json.dumps({ + "type": "conversation.item.create", + "item": { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": user_message}] + } + })) + + # Request response + await ws.send(json.dumps({ + "type": "response.create", + "response": {"modalities": ["text", "audio"]} + })) + + # Stream response + print("🎤 Response: ", end='', flush=True) + transcript = [] + + try: + while True: + msg = await asyncio.wait_for(ws.recv(), timeout=15.0) + event = json.loads(msg) + + # Capture transcript deltas + if event['type'] == 'response.output_audio_transcript.delta': + delta = event.get('delta', '') + if delta: + print(delta, end='', flush=True) + transcript.append(delta) + + # Done when response completes + elif event['type'] == 'response.done': + break + + except asyncio.TimeoutError: + pass + + print("\n") + + if transcript: + print(f"✅ Complete response: {''.join(transcript)}") + + await ws.close() + + +def main(): + """Run the voice agent""" + print("=" * 70) + print("LiveKit xAI Voice Agent via LiteLLM Proxy") + print("=" * 70) + print() + + try: + asyncio.run(run_voice_agent()) + except KeyboardInterrupt: + print("\n\n👋 Goodbye!") + except Exception as e: + print(f"\n❌ Error: {e}") + print("\nMake sure LiteLLM proxy is running:") + print(f" litellm --config config.yaml --port 4000") + + +if __name__ == "__main__": + main() diff --git a/cookbook/livekit_agent_sdk/requirements.txt b/cookbook/livekit_agent_sdk/requirements.txt new file mode 100644 index 00000000000..9e3542fac27 --- /dev/null +++ b/cookbook/livekit_agent_sdk/requirements.txt @@ -0,0 +1,2 @@ +livekit-agents[xai]>=1.3.12 +websockets>=15.0.1 diff --git a/cookbook/nova_sonic_realtime.py b/cookbook/nova_sonic_realtime.py index 0ea0badfb01..c7a73c1d00f 100644 --- a/cookbook/nova_sonic_realtime.py +++ b/cookbook/nova_sonic_realtime.py @@ -16,10 +16,14 @@ Usage: import asyncio import base64 import json +import os import pyaudio import websockets from typing import Optional +# Bounded queue size for audio chunks (configurable via env to avoid unbounded memory) +AUDIO_QUEUE_MAXSIZE = int(os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 10_000)) + # Audio configuration (matching Nova Sonic requirements) INPUT_SAMPLE_RATE = 16000 # Nova Sonic expects 16kHz input OUTPUT_SAMPLE_RATE = 24000 # Nova Sonic outputs 24kHz @@ -40,7 +44,7 @@ class RealtimeClient: self.api_key = api_key self.ws: Optional[websockets.WebSocketClientProtocol] = None self.is_active = False - self.audio_queue = asyncio.Queue() + self.audio_queue = asyncio.Queue(maxsize=AUDIO_QUEUE_MAXSIZE) self.pyaudio = pyaudio.PyAudio() self.input_stream = None self.output_stream = None diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index 8a08f0b4e29..0f6db331e50 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -26,6 +26,10 @@ version: 1.1.0 # It is recommended to use it with quotes. appVersion: v1.80.12 +annotations: + org.opencontainers.image.source: "https://github.com/BerriAI/litellm" + org.opencontainers.image.url: "https://docs.litellm.ai/" + dependencies: - name: "postgresql" version: ">=13.3.0" diff --git a/docker/Dockerfile.custom_ui b/docker/Dockerfile.custom_ui index 57926bcd170..177d7b7b12a 100644 --- a/docker/Dockerfile.custom_ui +++ b/docker/Dockerfile.custom_ui @@ -6,7 +6,18 @@ WORKDIR /app # Install Node.js and npm (adjust version as needed) RUN apt-get update && apt-get install -y nodejs npm && \ - npm install -g npm@latest tar@latest + npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \ + GLOBAL="$(npm root -g)" && \ + find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done && \ + npm cache clean --force # Copy the UI source into the container COPY ./ui/litellm-dashboard /app/ui/litellm-dashboard diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index 24bf706434d..a6fcd98ab6d 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -50,7 +50,18 @@ USER root # Install runtime dependencies RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip libsndfile && \ - npm install -g npm@latest tar@latest + npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 && \ + GLOBAL="$(npm root -g)" && \ + find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done && \ + npm cache clean --force WORKDIR /app # Copy the current directory contents into the container at /app @@ -64,6 +75,20 @@ COPY --from=builder /wheels/ /wheels/ # Install the built wheel using pip; again using a wildcard if it's the only file RUN pip install *.whl /wheels/* --no-index --find-links=/wheels/ && rm -f *.whl && rm -rf /wheels +# SECURITY FIX: nodejs-wheel-binaries (pip package used by Prisma) bundles a complete +# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/. +# Patch every copy of tar, glob, and brace-expansion inside that tree. +RUN GLOBAL="$(npm root -g)" && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done + # Install semantic_router and aurelio-sdk using script # Convert Windows line endings to Unix and make executable RUN sed -i 's/\r$//' docker/install_auto_router.sh && chmod +x docker/install_auto_router.sh && ./docker/install_auto_router.sh diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev index ae557d4647f..bc1d22d5e05 100644 --- a/docker/Dockerfile.dev +++ b/docker/Dockerfile.dev @@ -62,7 +62,18 @@ RUN apt-get update && apt-get install -y --no-install-recommends \ nodejs \ npm \ && rm -rf /var/lib/apt/lists/* \ - && npm install -g npm@latest tar@latest + && npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 \ + && GLOBAL="$(npm root -g)" \ + && find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done \ + && find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done \ + && find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done \ + && npm cache clean --force WORKDIR /app @@ -80,6 +91,20 @@ RUN pip install --no-cache-dir *.whl /wheels/* --no-index --find-links=/wheels/ rm -f *.whl && \ rm -rf /wheels +# SECURITY FIX: nodejs-wheel-binaries (pip package used by Prisma) bundles a complete +# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/. +# Patch every copy of tar, glob, and brace-expansion inside that tree. +RUN GLOBAL="$(npm root -g)" && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done + # Generate prisma client and set permissions # Convert Windows line endings to Unix for entrypoint scripts RUN prisma generate && \ diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 9ff27e07494..004377e19b3 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -47,7 +47,6 @@ RUN mkdir -p /var/lib/litellm/ui && \ if [ -f "/app/enterprise/enterprise_ui/enterprise_colors.json" ]; then \ cp /app/enterprise/enterprise_ui/enterprise_colors.json ./ui_colors.json; \ fi && \ - rm -f package-lock.json && \ npm install --legacy-peer-deps && \ npm run build && \ cp -r /app/ui/litellm-dashboard/out/* /var/lib/litellm/ui/ && \ @@ -60,7 +59,8 @@ RUN mkdir -p /var/lib/litellm/ui && \ mkdir -p "$folder_name" && \ mv "$html_file" "$folder_name/index.html"; \ fi; \ - done ) && \ + done && \ + touch .litellm_ui_ready ) && \ cd /app/ui/litellm-dashboard && rm -rf ./out # Build litellm wheel and place it in wheels dir (replace any PyPI wheels) @@ -105,7 +105,18 @@ RUN for i in 1 2 3; do \ && for i in 1 2 3; do \ apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor && break || sleep 5; \ done \ - && npm install -g npm@latest tar@latest + && npm install -g npm@latest tar@7.5.7 glob@11.1.0 @isaacs/brace-expansion@5.0.1 \ + && GLOBAL="$(npm root -g)" \ + && find "$GLOBAL/npm" -type d -name "tar" -path "*/node_modules/tar" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done \ + && find "$GLOBAL/npm" -type d -name "glob" -path "*/node_modules/glob" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done \ + && find "$GLOBAL/npm" -type d -name "brace-expansion" -path "*/node_modules/@isaacs/brace-expansion" | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done \ + && npm cache clean --force # Copy artifacts from builder COPY --from=builder /app/requirements.txt /app/requirements.txt @@ -147,6 +158,20 @@ RUN pip install --no-index --find-links=/wheels/ -r requirements.txt && \ fi; \ fi +# SECURITY FIX: nodejs-wheel-binaries (pip package used by Prisma) bundles a complete +# npm with old vulnerable deps at /usr/lib/python3.*/site-packages/nodejs_wheel/. +# Patch every copy of tar, glob, and brace-expansion inside that tree. +RUN GLOBAL="$(npm root -g)" && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/tar" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/tar" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/glob" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/glob" "$d"; \ + done && \ + find /usr/lib -path "*/nodejs_wheel/*/node_modules/@isaacs/brace-expansion" -type d | while read d; do \ + rm -rf "$d" && cp -rL "$GLOBAL/@isaacs/brace-expansion" "$d"; \ + done + # Permissions, cleanup, and Prisma prep # Convert Windows line endings to Unix for entrypoint scripts RUN sed -i 's/\r$//' docker/entrypoint.sh && \ diff --git a/docker/README.md b/docker/README.md index 6d81276bb4b..7027a30fdd7 100644 --- a/docker/README.md +++ b/docker/README.md @@ -70,9 +70,12 @@ docker compose -f docker-compose.yml -f docker-compose.hardened.yml up -d This setup: - Builds from `docker/Dockerfile.non_root` with Prisma engines and Node toolchain baked into the image. -- Runs the proxy as a non-root user with a read-only rootfs and only two writable tmpfs mounts: +- Runs the proxy as a non-root user with a read-only rootfs and only writable tmpfs mounts: - `/app/cache` (Prisma/NPM cache; backing `PRISMA_BINARY_CACHE_DIR`, `NPM_CONFIG_CACHE`, `XDG_CACHE_HOME`) - `/app/migrations` (Prisma migration workspace; backing `LITELLM_MIGRATION_DIR`) +- Pre-builds and serves the admin UI from read-only paths: + - `/var/lib/litellm/ui` (pre-restructured Next.js UI with `.litellm_ui_ready` marker) + - `/var/lib/litellm/assets` (UI logos and assets) - Routes all outbound traffic through a local Squid proxy that denies egress, so Prisma migrations must use the cached CLI and engines. You should also verify offline Prisma behaviour with: diff --git a/docs/my-website/blog/claude_opus_4_6/index.md b/docs/my-website/blog/claude_opus_4_6/index.md new file mode 100644 index 00000000000..82320472e13 --- /dev/null +++ b/docs/my-website/blog/claude_opus_4_6/index.md @@ -0,0 +1,730 @@ +--- +slug: claude_opus_4_6 +title: "Day 0 Support: Claude Opus 4.6" +date: 2026-02-05T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg +description: "Day 0 support for Claude Opus 4.6 on LiteLLM AI Gateway - use across Anthropic, Azure, Vertex AI, and Bedrock." +tags: [anthropic, claude, opus 4.6] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +LiteLLM now supports Claude Opus 4.6 on Day 0. Use it across Anthropic, Azure, Vertex AI, and Bedrock through the LiteLLM AI Gateway. + +## Docker Image + +```bash +docker pull ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 +``` + +## Usage - Anthropic + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-opus-4-6 + litellm_params: + model: anthropic/claude-opus-4-6 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +## Usage - Azure + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-opus-4-6 + litellm_params: + model: azure_ai/claude-opus-4-6 + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE # https://.services.ai.azure.com +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e AZURE_AI_API_KEY=$AZURE_AI_API_KEY \ + -e AZURE_AI_API_BASE=$AZURE_AI_API_BASE \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +## Usage - Vertex AI + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-opus-4-6 + litellm_params: + model: vertex_ai/claude-opus-4-6 + vertex_project: os.environ/VERTEX_PROJECT + vertex_location: us-east5 +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e VERTEX_PROJECT=$VERTEX_PROJECT \ + -e GOOGLE_APPLICATION_CREDENTIALS=/app/credentials.json \ + -v $(pwd)/config.yaml:/app/config.yaml \ + -v $(pwd)/credentials.json:/app/credentials.json \ + ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +## Usage - Bedrock + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-opus-4-6 + litellm_params: + model: bedrock/anthropic.claude-opus-4-6-v1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: us-east-1 +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \ + -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:litellm_stable_release_branch-v1.80.0-stable.opus-4-6 \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +## Advanced Features + +### Compaction + + + + +Litellm supports enabling compaction for the new claude-opus-4-6. + +**Enabling Compaction** + +To enable compaction, add the `context_management` parameter with the `compact_20260112` edit type: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "What is the weather in San Francisco?" + } + ], + "context_management": { + "edits": [ + { + "type": "compact_20260112" + } + ] + }, + "max_tokens": 100 +}' +``` +All the parameters supported for context_management by anthropic are supported and can be directly added. Litellm automatically adds the `compact-2026-01-12` beta header in the request. + + + + +Enable compaction to reduce context size while preserving key information. LiteLLM automatically adds the `compact-2026-01-12` beta header when compaction is enabled. + +:::info +**Provider Support:** Compaction is supported on Anthropic, Azure AI, and Vertex AI. It is **not supported** on Bedrock (Invoke or Converse APIs). +::: + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 4096, + "messages": [ + { + "role": "user", + "content": "Hi" + } + ], + "context_management": { + "edits": [ + { + "type": "compact_20260112" + } + ] + } +}' +``` + + + + + +**Response with Compaction Block** + +The response will include the compaction summary in `provider_specific_fields.compaction_blocks`: + +```json +{ + "id": "chatcmpl-a6c105a3-4b25-419e-9551-c800633b6cb2", + "created": 1770357619, + "model": "claude-opus-4-6", + "object": "chat.completion", + "choices": [ + { + "finish_reason": "length", + "index": 0, + "message": { + "content": "I don't have access to real-time data, so I can't provide the current weather in San Francisco. To get up-to-date weather information, I'd recommend checking:\n\n- **Weather websites** like weather.com, accuweather.com, or wunderground.com\n- **Search engines** – just Google \"San Francisco weather\"\n- **Weather apps** on your phone (e.g., Apple Weather, Google Weather)\n- **National", + "role": "assistant", + "provider_specific_fields": { + "compaction_blocks": [ + { + "type": "compaction", + "content": "Summary of the conversation: The user requested help building a web scraper..." + } + ] + } + } + } + ], + "usage": { + "completion_tokens": 100, + "prompt_tokens": 86, + "total_tokens": 186 + } +} +``` + +**Using Compaction Blocks in Follow-up Requests** + +To continue the conversation with compaction, include the compaction block in the assistant message's `provider_specific_fields`: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "How can I build a web scraper?" + }, + { + "role": "assistant", + "content": [ + { + "type": "text", + "text": "Certainly! To build a basic web scraper, you'll typically use a programming language like Python along with libraries such as `requests` (for fetching web pages) and `BeautifulSoup` (for parsing HTML). Here's a basic example:\n\n```python\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https://example.com'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, 'html.parser')\n\n# Extract and print all text\ntext = soup.get_text()\nprint(text)\n```\n\nLet me know what you're interested in scraping or if you need help with a specific website!" + } + ], + "provider_specific_fields": { + "compaction_blocks": [ + { + "type": "compaction", + "content": "Summary of the conversation: The user asked how to build a web scraper, and the assistant gave an overview using Python with requests and BeautifulSoup." + } + ] + } + }, + { + "role": "user", + "content": "How do I use it to scrape product prices?" + } + ], + "context_management": { + "edits": [ + { + "type": "compact_20260112" + } + ] + }, + "max_tokens": 100 +}' +``` + +**Streaming Support** + +Compaction blocks are also supported in streaming mode. You'll receive: +- `compaction_start` event when a compaction block begins +- `compaction_delta` events with the compaction content +- The accumulated `compaction_blocks` in `provider_specific_fields` + +### Adaptive Thinking + +:::note +When using `reasoning_effort` with Claude Opus 4.6, all values (`low`, `medium`, `high`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets with `type: "enabled"`, pass the native `thinking` parameter directly (see "Native thinking param" tab below). +::: + + + + +LiteLLM supports adaptive thinking through the `reasoning_effort` parameter: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "Solve this complex problem: What is the optimal strategy for..." + } + ], + "reasoning_effort": "high" +}' +``` + + + + +Use the `thinking` parameter with `type: "adaptive"` to enable adaptive thinking mode: + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 16000, + "thinking": { + "type": "adaptive" + }, + "messages": [ + { + "role": "user", + "content": "Explain why the sum of two even numbers is always even." + } + ] +}' +``` + + + + +Use the `thinking` parameter directly for adaptive thinking via the SDK: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "Solve this complex problem: What is the optimal strategy for..."}], + thinking={"type": "adaptive"}, +) +``` + + + + +### Effort Levels + + + + +Four effort levels available: `low`, `medium`, `high` (default), and `max`. Pass directly via the `output_config` parameter: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "Explain quantum computing" + } + ], + "output_config": { + "effort": "medium" + } +}' +``` + +You can use reasoning effort plus output_config to have more control on the model. + + + + +Four effort levels available: `low`, `medium`, `high` (default), and `max`. Pass directly via the `output_config` parameter: + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 4096, + "messages": [ + { + "role": "user", + "content": "Explain quantum computing" + } + ], + "output_config": { + "effort": "medium" + } +}' +``` + + + + +### 1M Token Context (Beta) + +Opus 4.6 supports 1M token context. Premium pricing applies for prompts exceeding 200k tokens ($10/$37.50 per million input/output tokens). LiteLLM supports cost calculations for 1M token contexts. + + + + +To use the 1M token context window, you need to forward the `anthropic-beta` header from your client to the LLM provider. + +**Step 1: Enable header forwarding in your config** + +```yaml +general_settings: + forward_client_headers_to_llm_api: true +``` + +**Step 2: Send requests with the beta header** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--header 'anthropic-beta: context-1m-2025-08-07' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "Analyze this large document..." + } + ] +}' +``` + + + + +To use the 1M token context window, you need to forward the `anthropic-beta` header from your client to the LLM provider. + +**Step 1: Enable header forwarding in your config** + +```yaml +general_settings: + forward_client_headers_to_llm_api: true +``` + +**Step 2: Send requests with the beta header** + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'anthropic-beta: context-1m-2025-08-07' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 16000, + "messages": [ + { + "role": "user", + "content": "Analyze this large document..." + } + ] +}' +``` + +:::tip +You can combine multiple beta headers by separating them with commas: +```bash +--header 'anthropic-beta: context-1m-2025-08-07,compact-2026-01-12' +``` +::: + + + + +### US-Only Inference + +Available at 1.1× token pricing. LiteLLM automatically tracks costs for US-only inference. + + + + +Use the `inference_geo` parameter to specify US-only inference: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "What is the capital of France?" + } + ], + "inference_geo": "us" +}' +``` + +LiteLLM will automatically apply the 1.1× pricing multiplier for US-only inference in cost tracking. + + + + +Use the `inference_geo` parameter to specify US-only inference: + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 4096, + "messages": [ + { + "role": "user", + "content": "What is the capital of France?" + } + ], + "inference_geo": "us" +}' +``` + +LiteLLM will automatically apply the 1.1× pricing multiplier for US-only inference in cost tracking. + + + + +### Fast Mode + +:::info +Fast mode is **only supported on the Anthropic provider** (`anthropic/claude-opus-4-6`). It is not available on Azure AI, Vertex AI, or Bedrock. +::: + +**Pricing:** +- Standard: $5 input / $25 output per MTok +- Fast: $30 input / $150 output per MTok (6× premium) + + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "claude-opus-4-6", + "messages": [ + { + "role": "user", + "content": "Refactor this module..." + } + ], + "max_tokens": 4096, + "speed": "fast" +}' +``` + +**Using OpenAI SDK:** + +```python +import openai + +client = openai.OpenAI( + api_key="your-litellm-key", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="claude-opus-4-6", + messages=[{"role": "user", "content": "Refactor this module..."}], + max_tokens=4096, + extra_body={"speed": "fast"} +) +``` + +**Using LiteLLM SDK:** + +```python +from litellm import completion + +response = completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "Refactor this module..."}], + max_tokens=4096, + speed="fast" +) +``` + +LiteLLM automatically tracks the higher costs for fast mode in usage and cost calculations. + + + + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'x-api-key: sk-12345' \ +--header 'content-type: application/json' \ +--data '{ + "model": "claude-opus-4-6", + "max_tokens": 4096, + "speed": "fast", + "messages": [ + { + "role": "user", + "content": "Refactor this module..." + } + ] +}' +``` + +LiteLLM automatically: +- Adds the `fast-mode-2026-02-01` beta header +- Tracks the 6× premium pricing in cost calculations + + + diff --git a/docs/my-website/blog/fastapi_middleware_performance/index.mdx b/docs/my-website/blog/fastapi_middleware_performance/index.mdx new file mode 100644 index 00000000000..b0c5ba13634 --- /dev/null +++ b/docs/my-website/blog/fastapi_middleware_performance/index.mdx @@ -0,0 +1,220 @@ +--- +slug: fastapi-middleware-performance +title: "Your Middleware Could Be a Bottleneck" +date: 2026-02-07T10:00:00 +authors: + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg + - name: Ryan Crabbe + title: "Performance Engineer, LiteLLM" + url: https://www.linkedin.com/in/ryan-crabbe-0b9687214 + image_url: https://media.licdn.com/dms/image/v2/D5603AQHt1t9Z4BJ6Gw/profile-displayphoto-shrink_400_400/profile-displayphoto-shrink_400_400/0/1724453682340?e=1772064000&v=beta&t=VXdmr13rsNB05wyA2F1TENOB5UuDHUZ0FCHTolNyR5M +description: "How we improved LiteLLM proxy latency and throughput by replacing a single middleware base class" +tags: [performance, fastapi, middleware] +hide_table_of_contents: false +--- + +import { BaseHTTPMiddlewareAnimation, PureASGIAnimation, BenchmarkVisualization } from '@site/src/components/MiddlewareDiagrams'; + +> How we improved LiteLLM proxy latency and throughput by replacing a single, simple middleware base class + +--- + +## Our Setup + +The LiteLLM proxy server has two middleware layers. The first is Starlette's `CORSMiddleware` (re-exported by FastAPI), which is a pure ASGI middleware. Then we have a simple BaseHTTPMiddleware called PrometheusAuthMiddleware. + +The job of `PrometheusAuthMiddleware` is to authenticate requests to the `/metrics` endpoint. It's not on by default, you enable it with a flag in your proxy config: + +
+Proxy config flag + +```yaml +litellm_settings: + require_auth_for_metrics_endpoint: true +``` + +
+ +The middleware checks two things: is the request hitting `/metrics`, and is auth even enabled? If both checks fail, which they do for the vast majority of requests, it just passes the request through unchanged. + +
+PrometheusAuthMiddleware source + +```python +class PrometheusAuthMiddleware(BaseHTTPMiddleware): + async def dispatch(self, request: Request, call_next): + if self._is_prometheus_metrics_endpoint(request): + if self._should_run_auth_on_metrics_endpoint() is True: + try: + await user_api_key_auth(request=request, api_key=...) + except Exception as e: + return JSONResponse(status_code=401, content=...) + response = await call_next(request) + return response + + @staticmethod + def _is_prometheus_metrics_endpoint(request: Request): + if "/metrics" in request.url.path: + return True + return False +``` + +
+ +Looks harmless. Subclass `BaseHTTPMiddleware`, implement `dispatch()`, done. This is what you will see in Starlette's documentation[1](#footnote-1). + +{/* truncate */} + +--- + +## What BaseHTTPMiddleware Actually Does + +When you write a `dispatch()` method, you'd expect the request to flow straight through your function and out the other side. What actually happens is much more involved. + +On every request, even a pure passthrough (meaning nothing happens), `BaseHTTPMiddleware` creates **7 intermediate objects and tasks**: + + + +It wraps the request in a new object to track body state, creates a synchronization event, allocates an in-memory channel to pass messages between your middleware and the inner app, sets up a task group to manage the lifecycle, and then runs your actual route handler in a *separate background task* when you call `call_next()`. The response body then flows back through that in-memory channel, gets re-wrapped in a streaming response object, and finally reaches the caller. That's a lot. + +For a middleware that for us, does nothing on 99.9% of requests, paying this cost doesn't make sense. + +Compare that to a pure ASGI middleware, which we can have just check the request path and continue along. + + + +Our middleware is doing something really simple. For the vast majority of requests it doesn't need to do anything at all but just let the request pass through. It doesn't need task groups, memory streams, or cancel scopes. It needs a function call. + +--- + +## Comparing Both + +We replaced the `BaseHTTPMiddleware` subclass with a pure ASGI middleware. To benchmark the difference, we used Apache Bench[2](#footnote-2) to compare both configurations of LiteLLM's middleware stack: the old setup (1 pure ASGI + 1 `BaseHTTPMiddleware`) against the new setup (2 pure ASGI). + +A minimal FastAPI app serves `GET /health` → `PlainTextResponse("ok")`. The endpoint does zero work to isolate the middleware overhead: any difference between configs is purely the cost of the middleware plumbing itself. Both middlewares are just calling the next layer. Same work, different base class. + +Apache Bench (`ab`) fires requests at the server with 1,000 concurrent connections and a single uvicorn worker. One worker means one event loop, so the benchmark directly measures how each middleware design handles concurrent load on a single thread. + + + +
+Try it yourself + +Save the script below as `benchmark_middleware.py`, then run: + +```bash +# Terminal 1 — start the "before" server (1 ASGI + 1 BaseHTTPMiddleware) +python benchmark_middleware.py --middleware mixed + +# Terminal 2 — benchmark it +ab -n 50000 -c 1000 http://localhost:8000/health + +# Stop the server, then start the "after" server (2x pure ASGI) +python benchmark_middleware.py --middleware asgi + +# Terminal 2 — benchmark again +ab -n 50000 -c 1000 http://localhost:8000/health +``` + +```python +import argparse +import uvicorn +from fastapi import FastAPI +from fastapi.responses import PlainTextResponse +from starlette.middleware.base import BaseHTTPMiddleware +from starlette.requests import Request +from starlette.types import ASGIApp, Receive, Scope, Send + + +class NoOpBaseHTTPMiddleware(BaseHTTPMiddleware): + async def dispatch(self, request: Request, call_next): + return await call_next(request) + + +class NoOpPureASGIMiddleware: + def __init__(self, app: ASGIApp) -> None: + self.app = app + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + await self.app(scope, receive, send) + + +def create_app(middleware_type: str | None = None, layers: int = 2) -> FastAPI: + app = FastAPI() + + @app.get("/health") + async def health(): + return PlainTextResponse("ok") + + if middleware_type == "mixed": + app.add_middleware(NoOpBaseHTTPMiddleware) + app.add_middleware(NoOpPureASGIMiddleware) + elif middleware_type == "asgi": + for _ in range(layers): + app.add_middleware(NoOpPureASGIMiddleware) + + return app + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--middleware", choices=["asgi", "mixed"], default=None) + parser.add_argument("--layers", type=int, default=2) + parser.add_argument("--port", type=int, default=8000) + args = parser.parse_args() + + app = create_app(middleware_type=args.middleware, layers=args.layers) + uvicorn.run(app, host="0.0.0.0", port=args.port, workers=1, log_level="warning") +``` + +
+ +--- + +## Our Change + +Here's what we replaced it with: + +```python +class PrometheusAuthMiddleware: + def __init__(self, app: ASGIApp) -> None: + self.app = app + + async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None: + if scope["type"] != "http" or "/metrics" not in scope.get("path", ""): + await self.app(scope, receive, send) + return + + if litellm.require_auth_for_metrics_endpoint is True: + request = Request(scope, receive) + api_key = request.headers.get("Authorization") or "" + try: + await user_api_key_auth(request=request, api_key=api_key) + except Exception as e: + # send 401 directly via ASGI protocol + ... + return + + await self.app(scope, receive, send) +``` + +For the 99.9% of requests that aren't hitting `/metrics`, the middleware is now one dict lookup, one string check, and one function call. No objects allocated, no tasks spawned. + +It's important to evaluate if the tools you're using are the right fit for the job as your software grows and handles more responsiblity. We're now putting in a static analysis check to prevent this from happening again with any newly introduced middlewares. If we find the use case is necessary then that's okay and we'll reevalute but for everything LiteLLM needs to do at the moment it's not. + +This middleware change was one part of a broader optimization effort on the LiteLLM proxy. Across all optimizations combined, we've measured about a **30% reduction in proxy overhead** over the past two weeks. + +--- + + +1 [Starlette Middleware — BaseHTTPMiddleware](https://starlette.dev/middleware/#basehttpmiddleware) + + +2 [Apache HTTP server benchmarking tool (`ab`)](https://httpd.apache.org/docs/2.4/programs/ab.html) diff --git a/docs/my-website/blog/litellm_observatory/index.md b/docs/my-website/blog/litellm_observatory/index.md new file mode 100644 index 00000000000..4554f77fb85 --- /dev/null +++ b/docs/my-website/blog/litellm_observatory/index.md @@ -0,0 +1,136 @@ +--- +slug: litellm-observatory +title: "Improve release stability with 24 hour load tests" +date: 2026-02-06T10:00:00 +authors: + - name: Alexsander Hamir + title: "Performance Engineer, LiteLLM" + url: https://www.linkedin.com/in/alexsander-baptista/ + image_url: https://github.com/AlexsanderHamir.png + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +description: "How we built a long-running, release-validation system to catch regressions before they reach users." +tags: [testing, observability, reliability, releases] +hide_table_of_contents: false +--- + +![LiteLLM Observatory](https://raw.githubusercontent.com/AlexsanderHamir/assets/main/Screenshot%202026-01-31%20175355.png) + +# Improve release stability with 24 hour load tests + +As LiteLLM adoption has grown, so have expectations around reliability, performance, and operational safety. Meeting those expectations requires more than correctness-focused tests, it requires validating how the system behaves over time, under real-world conditions. + +This post introduces **LiteLLM Observatory**, a long-running release-validation system we built to catch regressions before they reach users. + +--- + +## Why We Built the Observatory + +LiteLLM operates at the intersection of external providers, long-lived network connections, and high-throughput workloads. While our unit and integration tests do an excellent job validating correctness, they are not designed to surface issues that only appear after extended operation. + +A subtle lifecycle edge case discovered in v1.81.3 reinforced the need for stronger release validation in this area. + +--- + +## A Real-World Lifecycle Edge Case + +In v1.81.3, we shipped a fix for an HTTP client memory leak. The change passed unit and integration tests and behaved correctly in short-lived runs. + +The issue that surfaced was not caused by a single incorrect line of logic, but by how multiple components interacted over time: + +- A cached `httpx` client was configured with a 1-hour TTL +- When the cache expired, the underlying HTTP connection was closed as expected +- A higher-level client continued to hold a reference to that connection +- Subsequent requests failed with: + +``` +Cannot send a request, as the client has been closed +``` + +**Before (with bug):** + +| Provider | Requests | Success | Failures | Fail % | +|----------|----------|---------|----------|--------| +| OpenAI | 720,000 | 432,000 | 288,000 | 40% | +| Azure | 692,000 | 415,200 | 276,800 | 40% | + +**After (fixed):** + +| Provider | Requests | Success | Failures | Fail % | +|----------|------------|-----------|----------|---------| +| OpenAI | 1,200,000 | 1,199,988 | 12 | 0.001% | +| Azure | 1,150,000 | 1,149,982 | 18 | 0.002% | + +Our focus moving forward is on being the first to detect issues, even when they aren’t covered by unit tests. LiteLLM Observatory is designed to surface latency regressions, OOMs, and failure modes that only appear under real traffic patterns in **our own production deployments** during release validation. + + +--- + +### How the Observatory Works + +[LiteLLM Observatory](https://github.com/BerriAI/litellm-observatory) is a testing service that runs long-running tests against our LiteLLM deployments. We trigger tests by sending API requests, and results are automatically sent to Slack when tests complete. + +#### How Tests Run + +1. **Start a Test**: We send a request to the Observatory API with: + - Which LiteLLM deployment to test (URL and API key) + - Which test to run (e.g., `TestOAIAzureRelease`) + - Test settings (which models to test, how long to run, failure thresholds) + +2. **Smart Queueing**: + - The system checks whether we are attempting to run the exact same test more than once + - If a duplicate test is already running or queued, we receive an error to avoid wasting resources + - Otherwise, the test is added to a queue and runs when capacity is available (up to 5 tests can run concurrently by default) + +3. **Instant Response**: The API responds immediately—we do not wait for the test to finish. Tests may run for hours, but the request itself completes in milliseconds. + +4. **Background Execution**: + - The test runs in the background, issuing requests against our LiteLLM deployment + - It tracks request success and failure rates over time + - When the test completes, results are automatically posted to our Slack channel + +#### Example: The OpenAI / Azure Reliability Test + +The `TestOAIAzureRelease` test is designed to catch a class of bugs that only surface after sustained runtime: + +- **Duration**: Runs continuously for 3 hours +- **Behavior**: Cycles through specified models (such as `gpt-4` and `gpt-3.5-turbo`), issuing requests continuously +- **Why 3 Hours**: This helps catch issues where HTTP clients degrade or fail after extended use (for example, a bug observed in LiteLLM v1.81.3) +- **Pass / Fail Criteria**: The test passes if fewer than 1% of requests fail. If the failure rate exceeds 1%, the test fails and we are notified in Slack +- **Key Detail**: The same HTTP client is reused for the entire run, allowing us to detect lifecycle-related bugs that only appear under prolonged reuse + +#### When We Use It + +- **Before Deployments**: Run tests before promoting a new LiteLLM version to production +- **Routine Validation**: Schedule regular runs (daily or weekly) to catch regressions early +- **Issue Investigation**: Run tests on demand when we suspect a deployment issue +- **Long-Running Failure Detection**: Identify bugs that only appear under sustained load, beyond what short smoke tests can reveal + + +### Complementing Unit Tests + +Unit tests remain a foundational part of our development process. They are fast and precise, but they don’t cover: + +- Real provider behavior +- Long-lived network interactions +- Resource lifecycle edge cases +- Time-dependent regressions + +LiteLLM Observatory complements unit tests by validating the system as it actually runs in production-like environments. + +--- + +### Looking Ahead + +Reliability is an ongoing investment. + +LiteLLM Observatory is one of several systems we’re building to continuously raise the bar on release quality and operational safety. As LiteLLM evolves, so will our validation tooling, informed by real-world usage and lessons learned. + +We’ll continue to share those improvements openly as we go. + diff --git a/docs/my-website/blog/minimax_m2_5/index.md b/docs/my-website/blog/minimax_m2_5/index.md new file mode 100644 index 00000000000..50084fcc1e5 --- /dev/null +++ b/docs/my-website/blog/minimax_m2_5/index.md @@ -0,0 +1,394 @@ +--- +slug: minimax_m2_5 +title: "Day 0 Support: MiniMax-M2.5" +date: 2026-02-12T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://pbs.twimg.com/profile_images/2001352686994907136/ONgNuSk5_400x400.jpg + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +description: "Day 0 support for MiniMax-M2.5 on LiteLLM" +tags: [minimax, M2.5, llm] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +LiteLLM now supports MiniMax-M2.5 on Day 0. Use it across OpenAI-compatible and Anthropic-compatible APIs through the LiteLLM AI Gateway. + +## Supported Models + +LiteLLM supports the following MiniMax models: + +| Model | Description | Input Cost | Output Cost | Context Window | +|-------|-------------|------------|-------------|----------------| +| **MiniMax-M2.5** | Advanced reasoning, Agentic capabilities | $0.3/M tokens | $1.2/M tokens | 1M tokens | +| **MiniMax-M2.5-lightning** | Faster and More Agile (~100 tps) | $0.3/M tokens | $2.4/M tokens | 1M tokens | + +## Features Supported + +- **Prompt Caching**: Reduce costs with cached prompts ($0.03/M tokens for cache read, $0.375/M tokens for cache write) +- **Function Calling**: Built-in tool calling support +- **Reasoning**: Advanced reasoning capabilities with thinking support +- **System Messages**: Full system message support +- **Cost Tracking**: Automatic cost calculation for all requests + +## Docker Image + +```bash +docker pull litellm/litellm:v1.81.3-stable +``` + +## Usage - OpenAI Compatible API (/v1/chat/completions) + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: minimax-m2-5 + litellm_params: + model: minimax/MiniMax-M2.5 + api_key: os.environ/MINIMAX_API_KEY + api_base: https://api.minimax.io/v1 +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e MINIMAX_API_KEY=$MINIMAX_API_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.3-stable \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "minimax-m2-5", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +### With Reasoning Split + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "minimax-m2-5", + "messages": [ + { + "role": "user", + "content": "Solve: 2+2=?" + } + ], + "extra_body": { + "reasoning_split": true + } +}' +``` + +## Usage - Anthropic Compatible API (/v1/messages) + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: minimax-m2-5 + litellm_params: + model: minimax/MiniMax-M2.5 + api_key: os.environ/MINIMAX_API_KEY + api_base: https://api.minimax.io/anthropic/v1/messages +``` + +**2. Start the proxy** + +```bash +docker run -d \ + -p 4000:4000 \ + -e MINIMAX_API_KEY=$MINIMAX_API_KEY \ + -v $(pwd)/config.yaml:/app/config.yaml \ + ghcr.io/berriai/litellm:v1.81.3-stable \ + --config /app/config.yaml +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "minimax-m2-5", + "max_tokens": 1000, + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] +}' +``` + + + + +### With Thinking + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data '{ + "model": "minimax-m2-5", + "max_tokens": 1000, + "thinking": { + "type": "enabled", + "budget_tokens": 1000 + }, + "messages": [ + { + "role": "user", + "content": "Solve: 2+2=?" + } + ] +}' +``` + +## Usage - LiteLLM SDK + +### OpenAI-compatible API + +```python +import litellm + +response = litellm.completion( + model="minimax/MiniMax-M2.5", + messages=[ + {"role": "user", "content": "Hello, how are you?"} + ], + api_key="your-minimax-api-key", + api_base="https://api.minimax.io/v1" +) + +print(response.choices[0].message.content) +``` + +### Anthropic-compatible API + +```python +import litellm + +response = litellm.anthropic.messages.acreate( + model="minimax/MiniMax-M2.5", + messages=[{"role": "user", "content": "Hello, how are you?"}], + api_key="your-minimax-api-key", + api_base="https://api.minimax.io/anthropic/v1/messages", + max_tokens=1000 +) + +print(response.choices[0].message.content) +``` + +### With Thinking + +```python +response = litellm.anthropic.messages.acreate( + model="minimax/MiniMax-M2.5", + messages=[{"role": "user", "content": "Solve: 2+2=?"}], + thinking={"type": "enabled", "budget_tokens": 1000}, + api_key="your-minimax-api-key" +) + +# Access thinking content +for block in response.choices[0].message.content: + if hasattr(block, 'type') and block.type == 'thinking': + print(f"Thinking: {block.thinking}") +``` + +### With Reasoning Split (OpenAI API) + +```python +response = litellm.completion( + model="minimax/MiniMax-M2.5", + messages=[ + {"role": "user", "content": "Solve: 2+2=?"} + ], + extra_body={"reasoning_split": True}, + api_key="your-minimax-api-key", + api_base="https://api.minimax.io/v1" +) + +# Access thinking and response +if hasattr(response.choices[0].message, 'reasoning_details'): + print(f"Thinking: {response.choices[0].message.reasoning_details}") +print(f"Response: {response.choices[0].message.content}") +``` + +## Cost Tracking + +LiteLLM automatically tracks costs for MiniMax-M2.5 requests. The pricing is: + +- **Input**: $0.3 per 1M tokens +- **Output**: $1.2 per 1M tokens +- **Cache Read**: $0.03 per 1M tokens +- **Cache Write**: $0.375 per 1M tokens + +### Accessing Cost Information + +```python +response = litellm.completion( + model="minimax/MiniMax-M2.5", + messages=[{"role": "user", "content": "Hello!"}], + api_key="your-minimax-api-key" +) + +# Access cost information +print(f"Cost: ${response._hidden_params.get('response_cost', 0)}") +``` + +## Streaming Support + +### OpenAI API + +```python +response = litellm.completion( + model="minimax/MiniMax-M2.5", + messages=[{"role": "user", "content": "Tell me a story"}], + stream=True, + api_key="your-minimax-api-key", + api_base="https://api.minimax.io/v1" +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="") +``` + +### Streaming with Reasoning Split + +```python +stream = litellm.completion( + model="minimax/MiniMax-M2.5", + messages=[ + {"role": "user", "content": "Tell me a story"}, + ], + extra_body={"reasoning_split": True}, + stream=True, + api_key="your-minimax-api-key", + api_base="https://api.minimax.io/v1" +) + +reasoning_buffer = "" +text_buffer = "" + +for chunk in stream: + if hasattr(chunk.choices[0].delta, "reasoning_details") and chunk.choices[0].delta.reasoning_details: + for detail in chunk.choices[0].delta.reasoning_details: + if "text" in detail: + reasoning_text = detail["text"] + new_reasoning = reasoning_text[len(reasoning_buffer):] + if new_reasoning: + print(new_reasoning, end="", flush=True) + reasoning_buffer = reasoning_text + + if chunk.choices[0].delta.content: + content_text = chunk.choices[0].delta.content + new_text = content_text[len(text_buffer):] if text_buffer else content_text + if new_text: + print(new_text, end="", flush=True) + text_buffer = content_text +``` + +## Using with Native SDKs + +### Anthropic SDK via LiteLLM Proxy + +```python +import os +os.environ["ANTHROPIC_BASE_URL"] = "http://localhost:4000" +os.environ["ANTHROPIC_API_KEY"] = "sk-1234" # Your LiteLLM proxy key + +import anthropic + +client = anthropic.Anthropic() + +message = client.messages.create( + model="minimax-m2-5", + max_tokens=1000, + system="You are a helpful assistant.", + messages=[ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Hi, how are you?" + } + ] + } + ] +) + +for block in message.content: + if block.type == "thinking": + print(f"Thinking:\n{block.thinking}\n") + elif block.type == "text": + print(f"Text:\n{block.text}\n") +``` + +### OpenAI SDK via LiteLLM Proxy + +```python +import os +os.environ["OPENAI_BASE_URL"] = "http://localhost:4000" +os.environ["OPENAI_API_KEY"] = "sk-1234" # Your LiteLLM proxy key + +from openai import OpenAI + +client = OpenAI() + +response = client.chat.completions.create( + model="minimax-m2-5", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hi, how are you?"}, + ], + extra_body={"reasoning_split": True}, +) + +# Access thinking and response +if hasattr(response.choices[0].message, 'reasoning_details'): + print(f"Thinking:\n{response.choices[0].message.reasoning_details[0]['text']}\n") +print(f"Text:\n{response.choices[0].message.content}\n") +``` diff --git a/docs/my-website/blog/model_cost_map_incident/index.md b/docs/my-website/blog/model_cost_map_incident/index.md new file mode 100644 index 00000000000..b9ff20e4128 --- /dev/null +++ b/docs/my-website/blog/model_cost_map_incident/index.md @@ -0,0 +1,95 @@ +--- +slug: model-cost-map-incident +title: "Incident Report: Invalid model cost map on main" +date: 2026-02-10T10:00:00 +authors: + - name: Ishaan Jaffer + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/ishaanjaffer/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +tags: [incident-report, stability] +hide_table_of_contents: false +--- + +**Date:** January 27, 2026 +**Duration:** ~20 minutes +**Severity:** Low +**Status:** Resolved + +## Summary + +A malformed JSON entry in `model_prices_and_context_window.json` was merged to `main` ([`562f0a0`](https://github.com/BerriAI/litellm/commit/562f0a028251750e3d75386bee0e630d9796d0df)). This caused LiteLLM to silently fall back to a stale local copy of the model cost map. Users on older package versions lost cost tracking for newer models only (e.g. `azure/gpt-5.2`). No LLM calls were blocked. + +- **LLM calls and proxy routing:** No impact. +- **Cost tracking:** Impacted for newer models not present in the local backup. Older models were unaffected. The incident lasted ~20 minutes until the commit was reverted. + +{/* truncate */} + +--- + +## Background + +The model cost map is not in the request path. It is used after the LLM response comes back, inside a try/catch, to calculate spend. A missing entry never blocks a call. + +```mermaid +flowchart TD + A["1. litellm.completion() receives request + litellm/main.py"] --> B["2. Route to provider + litellm/litellm_core_utils/get_llm_provider_logic.py"] + B --> C["3. LLM returns response + litellm/main.py"] + C --> D["4. Post-call: look up model in cost map + litellm/cost_calculator.py"] + D -->|"found"| E["5a. Attach cost to response"] + D -->|"not found (try/catch)"| F["5b. Log warning, set cost=0"] + E --> G["6. Return response to caller"] + F --> G + + style D fill:#fff3cd,stroke:#ffc107 + style F fill:#fff3cd,stroke:#ffc107 + style E fill:#d4edda,stroke:#28a745 + style G fill:#d4edda,stroke:#28a745 +``` + +Both paths return a response to the caller. When the cost map lookup fails, the only difference is `cost=0` on that request. + +--- + +## Root cause + +LiteLLM fetches the model cost map from GitHub `main` at import time. If the fetch fails, it falls back to a local backup bundled with the package. Before this incident, the fallback was completely silent -- no warning was logged. + +A contributor PR introduced an extra `{` bracket, producing invalid JSON. The remote fetch failed with `JSONDecodeError`, triggering the silent fallback. Users on older package versions had backup files missing newer models. + +**Timeline:** + +1. Malformed JSON merged to `main` +2. LiteLLM installations fall back to local backup on next import +3. Users report `"This model isn't mapped yet"` for newer models +4. Bad commit identified and reverted (~20 minutes) + +--- + +## Remediation + +| # | Action | Status | Code | +|---|---|---|---| +| 1 | CI validation on `model_prices_and_context_window.json` | ✅ Done | [`test-model-map.yaml`](https://github.com/BerriAI/litellm/blob/main/.github/workflows/test-model-map.yaml) | +| 2 | Warning log on fallback to local backup | ✅ Done | [`get_model_cost_map.py#L57-L68`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm_core_utils/get_model_cost_map.py#L57-L68) | +| 3 | `GetModelCostMap` class with integrity validation helpers | ✅ Done | [`get_model_cost_map.py#L24-L149`](https://github.com/BerriAI/litellm/blob/main/litellm/litellm_core_utils/get_model_cost_map.py#L24-L149) | +| 4 | Resilience test suite (bad hosted map, fallback, completion) | ✅ Done | [`test_model_cost_map_resilience.py#L150-L291`](https://github.com/BerriAI/litellm/blob/main/tests/llm_translation/test_model_cost_map_resilience.py#L150-L291) | +| 5 | Test that backup model cost map always exists and contains common models | ✅ Done | [`test_model_cost_map_resilience.py#L213-L228`](https://github.com/BerriAI/litellm/blob/main/tests/llm_translation/test_model_cost_map_resilience.py#L213-L228) | + +Enterprises that require zero external dependencies at import time can set `LITELLM_LOCAL_MODEL_COST_MAP=True` to skip the GitHub fetch entirely. + +--- + +## Other dependencies on external resources + +| Dependency | Impact if unavailable | Fallback | +|---|---|---| +| Model cost map (GitHub) | Cost tracking for newer models | Local backup (now with warning) | +| JWT public keys (IDP/SSO) | Auth fails | None | +| OIDC UserInfo (IDP/SSO) | Auth fails | None | +| HuggingFace model API | HF provider calls fail | None | +| Ollama tags (localhost) | Ollama model list stale | Static list | diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md index a7e8b52d99a..b1166a7809c 100644 --- a/docs/my-website/docs/a2a.md +++ b/docs/my-website/docs/a2a.md @@ -68,116 +68,9 @@ Follow [this guide, to add your pydantic ai agent to LiteLLM Agent Gateway](./pr ## Invoking your Agents -Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM. - -This example shows how to: -1. **List available agents** - Query `/v1/agents` to see which agents your key can access -2. **Select an agent** - Pick an agent from the list -3. **Invoke via A2A** - Use the A2A protocol to send messages to the agent - -```python showLineNumbers title="invoke_a2a_agent.py" -from uuid import uuid4 -import httpx -import asyncio -from a2a.client import A2ACardResolver, A2AClient -from a2a.types import MessageSendParams, SendMessageRequest - -# === CONFIGURE THESE === -LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL -LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key -# ======================= - -async def main(): - headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} - - async with httpx.AsyncClient(headers=headers) as client: - # Step 1: List available agents - response = await client.get(f"{LITELLM_BASE_URL}/v1/agents") - agents = response.json() - - print("Available agents:") - for agent in agents: - print(f" - {agent['agent_name']} (ID: {agent['agent_id']})") - - if not agents: - print("No agents available for this key") - return - - # Step 2: Select an agent and invoke it - selected_agent = agents[0] - agent_id = selected_agent["agent_id"] - agent_name = selected_agent["agent_name"] - print(f"\nInvoking: {agent_name}") - - # Step 3: Use A2A protocol to invoke the agent - base_url = f"{LITELLM_BASE_URL}/a2a/{agent_id}" - resolver = A2ACardResolver(httpx_client=client, base_url=base_url) - agent_card = await resolver.get_agent_card() - a2a_client = A2AClient(httpx_client=client, agent_card=agent_card) - - request = SendMessageRequest( - id=str(uuid4()), - params=MessageSendParams( - message={ - "role": "user", - "parts": [{"kind": "text", "text": "Hello, what can you do?"}], - "messageId": uuid4().hex, - } - ), - ) - response = await a2a_client.send_message(request) - print(f"Response: {response.model_dump(mode='json', exclude_none=True, indent=4)}") - -if __name__ == "__main__": - asyncio.run(main()) -``` - -### Streaming Responses - -For streaming responses, use `send_message_streaming`: - -```python showLineNumbers title="invoke_a2a_agent_streaming.py" -from uuid import uuid4 -import httpx -import asyncio -from a2a.client import A2ACardResolver, A2AClient -from a2a.types import MessageSendParams, SendStreamingMessageRequest - -# === CONFIGURE THESE === -LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL -LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key -LITELLM_AGENT_NAME = "ij-local" # Agent name registered in LiteLLM -# ======================= - -async def main(): - base_url = f"{LITELLM_BASE_URL}/a2a/{LITELLM_AGENT_NAME}" - headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} - - async with httpx.AsyncClient(headers=headers) as httpx_client: - # Resolve agent card and create client - resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url) - agent_card = await resolver.get_agent_card() - client = A2AClient(httpx_client=httpx_client, agent_card=agent_card) - - # Send a streaming message - request = SendStreamingMessageRequest( - id=str(uuid4()), - params=MessageSendParams( - message={ - "role": "user", - "parts": [{"kind": "text", "text": "Hello, what can you do?"}], - "messageId": uuid4().hex, - } - ), - ) - - # Stream the response - async for chunk in client.send_message_streaming(request): - print(chunk.model_dump(mode="json", exclude_none=True)) - -if __name__ == "__main__": - asyncio.run(main()) -``` +See the [Invoking A2A Agents](./a2a_invoking_agents) guide to learn how to call your agents using: +- **A2A SDK** - Native A2A protocol with full support for tasks and artifacts +- **OpenAI SDK** - Familiar `/chat/completions` interface with `a2a/` model prefix ## Tracking Agent Logs diff --git a/docs/my-website/docs/a2a_invoking_agents.md b/docs/my-website/docs/a2a_invoking_agents.md new file mode 100644 index 00000000000..3bb248e4561 --- /dev/null +++ b/docs/my-website/docs/a2a_invoking_agents.md @@ -0,0 +1,280 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Invoking A2A Agents + +Learn how to invoke A2A agents through LiteLLM using different methods. + +:::tip Deploy Your Own A2A Agent + +Want to test with your own agent? Deploy this template A2A agent powered by Google Gemini: + +[**shin-bot-litellm/a2a-gemini-agent**](https://github.com/shin-bot-litellm/a2a-gemini-agent) - Simple deployable A2A agent with streaming support + +::: + +## A2A SDK + +Use the [A2A Python SDK](https://pypi.org/project/a2a-sdk) to invoke agents through LiteLLM using the A2A protocol. + +### Non-Streaming + +This example shows how to: +1. **List available agents** - Query `/v1/agents` to see which agents your key can access +2. **Select an agent** - Pick an agent from the list +3. **Invoke via A2A** - Use the A2A protocol to send messages to the agent + +```python showLineNumbers title="invoke_a2a_agent.py" +from uuid import uuid4 +import httpx +import asyncio +from a2a.client import A2ACardResolver, A2AClient +from a2a.types import MessageSendParams, SendMessageRequest + +# === CONFIGURE THESE === +LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL +LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key +# ======================= + +async def main(): + headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} + + async with httpx.AsyncClient(headers=headers) as client: + # Step 1: List available agents + response = await client.get(f"{LITELLM_BASE_URL}/v1/agents") + agents = response.json() + + print("Available agents:") + for agent in agents: + print(f" - {agent['agent_name']} (ID: {agent['agent_id']})") + + if not agents: + print("No agents available for this key") + return + + # Step 2: Select an agent and invoke it + selected_agent = agents[0] + agent_id = selected_agent["agent_id"] + agent_name = selected_agent["agent_name"] + print(f"\nInvoking: {agent_name}") + + # Step 3: Use A2A protocol to invoke the agent + base_url = f"{LITELLM_BASE_URL}/a2a/{agent_id}" + resolver = A2ACardResolver(httpx_client=client, base_url=base_url) + agent_card = await resolver.get_agent_card() + a2a_client = A2AClient(httpx_client=client, agent_card=agent_card) + + request = SendMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello, what can you do?"}], + "messageId": uuid4().hex, + } + ), + ) + response = await a2a_client.send_message(request) + print(f"Response: {response.model_dump(mode='json', exclude_none=True, indent=4)}") + +if __name__ == "__main__": + asyncio.run(main()) +``` + +### Streaming + +For streaming responses, use `send_message_streaming`: + +```python showLineNumbers title="invoke_a2a_agent_streaming.py" +from uuid import uuid4 +import httpx +import asyncio +from a2a.client import A2ACardResolver, A2AClient +from a2a.types import MessageSendParams, SendStreamingMessageRequest + +# === CONFIGURE THESE === +LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL +LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key +LITELLM_AGENT_NAME = "ij-local" # Agent name registered in LiteLLM +# ======================= + +async def main(): + base_url = f"{LITELLM_BASE_URL}/a2a/{LITELLM_AGENT_NAME}" + headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} + + async with httpx.AsyncClient(headers=headers) as httpx_client: + # Resolve agent card and create client + resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url) + agent_card = await resolver.get_agent_card() + client = A2AClient(httpx_client=httpx_client, agent_card=agent_card) + + # Send a streaming message + request = SendStreamingMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Tell me a long story"}], + "messageId": uuid4().hex, + } + ), + ) + + # Stream the response + async for chunk in client.send_message_streaming(request): + print(chunk.model_dump(mode="json", exclude_none=True)) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## /chat/completions API (OpenAI SDK) + +You can also invoke A2A agents using the familiar OpenAI SDK by using the `a2a/` model prefix. + +### Non-Streaming + + + + +```python showLineNumbers title="openai_non_streaming.py" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # Your LiteLLM Virtual Key + base_url="http://localhost:4000" # Your LiteLLM proxy URL +) + +response = client.chat.completions.create( + model="a2a/my-agent", # Use a2a/ prefix with your agent name + messages=[ + {"role": "user", "content": "Hello, what can you do?"} + ] +) + +print(response.choices[0].message.content) +``` + + + + +```typescript showLineNumbers title="openai_non_streaming.ts" +import OpenAI from 'openai'; + +const client = new OpenAI({ + apiKey: 'sk-1234', // Your LiteLLM Virtual Key + baseURL: 'http://localhost:4000' // Your LiteLLM proxy URL +}); + +const response = await client.chat.completions.create({ + model: 'a2a/my-agent', // Use a2a/ prefix with your agent name + messages: [ + { role: 'user', content: 'Hello, what can you do?' } + ] +}); + +console.log(response.choices[0].message.content); +``` + + + + +```bash showLineNumbers title="curl_non_streaming.sh" +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "a2a/my-agent", + "messages": [ + {"role": "user", "content": "Hello, what can you do?"} + ] + }' +``` + + + + +### Streaming + + + + +```python showLineNumbers title="openai_streaming.py" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # Your LiteLLM Virtual Key + base_url="http://localhost:4000" # Your LiteLLM proxy URL +) + +stream = client.chat.completions.create( + model="a2a/my-agent", # Use a2a/ prefix with your agent name + messages=[ + {"role": "user", "content": "Tell me a long story"} + ], + stream=True +) + +for chunk in stream: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) +``` + + + + +```typescript showLineNumbers title="openai_streaming.ts" +import OpenAI from 'openai'; + +const client = new OpenAI({ + apiKey: 'sk-1234', // Your LiteLLM Virtual Key + baseURL: 'http://localhost:4000' // Your LiteLLM proxy URL +}); + +const stream = await client.chat.completions.create({ + model: 'a2a/my-agent', // Use a2a/ prefix with your agent name + messages: [ + { role: 'user', content: 'Tell me a long story' } + ], + stream: true +}); + +for await (const chunk of stream) { + const content = chunk.choices[0]?.delta?.content; + if (content) { + process.stdout.write(content); + } +} +``` + + + + +```bash showLineNumbers title="curl_streaming.sh" +curl -X POST http://localhost:4000/v1/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "a2a/my-agent", + "messages": [ + {"role": "user", "content": "Tell me a long story"} + ], + "stream": true + }' +``` + + + + +## Key Differences + +| Method | Use Case | Advantages | +|--------|----------|------------| +| **A2A SDK** | Native A2A protocol integration | • Full A2A protocol support
• Access to task states and artifacts
• Context management | +| **OpenAI SDK** | Familiar OpenAI-style interface | • Drop-in replacement for OpenAI calls
• Easier migration from LLM to agent workflows
• Works with existing OpenAI tooling | + +:::tip Model Prefix + +When using the OpenAI SDK, always prefix your agent name with `a2a/` (e.g., `a2a/my-agent`) to route requests to the A2A agent instead of an LLM provider. + +::: diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md index 482dedaa8a9..0931c349e48 100644 --- a/docs/my-website/docs/adding_provider/generic_guardrail_api.md +++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md @@ -93,6 +93,12 @@ Implement `POST /beta/litellm_basic_guardrail_api` "user_api_key_end_user_id": "end user id associated with the litellm virtual key used", "user_api_key_org_id": "org id associated with the litellm virtual key used" }, + "request_headers": { // optional: inbound request headers (allowlist). Allowed headers show their value; all others show "[present]" to indicate the header existed. + "User-Agent": "OpenAI/Python 2.17.0", + "Content-Type": "application/json", + "X-Request-Id": "[present]" + }, + "litellm_version": "1.x.y", // optional: LiteLLM library version running this proxy "input_type": "request", // "request" or "response" "litellm_call_id": "unique_call_id", // the call id of the individual LLM call "litellm_trace_id": "trace_id", // the trace id of the LLM call - useful if there are multiple LLM calls for the same conversation diff --git a/docs/my-website/docs/adding_provider/simple_guardrail_tutorial.md b/docs/my-website/docs/adding_provider/simple_guardrail_tutorial.md index 9c654cd1560..884a7397bde 100644 --- a/docs/my-website/docs/adding_provider/simple_guardrail_tutorial.md +++ b/docs/my-website/docs/adding_provider/simple_guardrail_tutorial.md @@ -101,12 +101,11 @@ model_list: - model_name: gpt-4 litellm_params: model: gpt-4 - api_key: os.environ/OPENAI_API_KEY + api_key: os.environ/OPENAI_API_KEY -litellm_settings: - guardrails: +guardrails: - guardrail_name: my_guardrail - litellm_params: + litellm_params: guardrail: my_guardrail mode: during_call api_key: os.environ/MY_GUARDRAIL_API_KEY diff --git a/docs/my-website/docs/benchmarks.md b/docs/my-website/docs/benchmarks.md index a1489081b4c..1f818cef498 100644 --- a/docs/my-website/docs/benchmarks.md +++ b/docs/my-website/docs/benchmarks.md @@ -5,6 +5,13 @@ import Image from '@theme/IdealImage'; Benchmarks for LiteLLM Gateway (Proxy Server) tested against a fake OpenAI endpoint. +## Setting Up a Fake OpenAI Endpoint + +For load testing and benchmarking, you can use a fake OpenAI proxy server. LiteLLM provides: + +1. **Hosted endpoint**: Use our free hosted fake endpoint at `https://exampleopenaiendpoint-production.up.railway.app/` +2. **Self-hosted**: Set up your own fake OpenAI proxy server using [github.com/BerriAI/example_openai_endpoint](https://github.com/BerriAI/example_openai_endpoint) + Use this config for testing: ```yaml @@ -12,7 +19,7 @@ model_list: - model_name: "fake-openai-endpoint" litellm_params: model: openai/any - api_base: https://your-fake-openai-endpoint.com/chat/completions + api_base: https://exampleopenaiendpoint-production.up.railway.app/ # or your self-hosted endpoint api_key: "test" ``` diff --git a/docs/my-website/docs/completion/web_search.md b/docs/my-website/docs/completion/web_search.md index db50c7b5bc5..1f5ba2dee4e 100644 --- a/docs/my-website/docs/completion/web_search.md +++ b/docs/my-website/docs/completion/web_search.md @@ -18,16 +18,46 @@ Each provider uses their own search backend: | Provider | Search Engine | Notes | |----------|---------------|-------| -| **OpenAI** (`gpt-4o-search-preview`) | OpenAI's internal search | Real-time web data | +| **OpenAI** (`gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview`) | OpenAI's internal search | Real-time web data | | **xAI** (`grok-3`) | xAI's search + X/Twitter | Real-time social media data | | **Google AI/Vertex** (`gemini-2.0-flash`) | **Google Search** | Uses actual Google search results | | **Anthropic** (`claude-3-5-sonnet`) | Anthropic's web search | Real-time web data | | **Perplexity** | Perplexity's search engine | AI-powered search and reasoning | +:::warning Important: Only Search Models Support `web_search_options` +For OpenAI, only dedicated search models support the `web_search_options` parameter: +- `gpt-4o-search-preview` +- `gpt-4o-mini-search-preview` +- `gpt-5-search-api` + +**Regular models like `gpt-5`, `gpt-4.1`, `gpt-4o` do not support `web_search_options`** +::: + +:::tip The `web_search_options` parameter is optional +Search models (like `gpt-4o-search-preview`) **automatically search the web** even without the `web_search_options` parameter. + +Use `web_search_options` when you need to: +- Adjust `search_context_size` (`"low"`, `"medium"`, `"high"`) +- Specify `user_location` for localized results +::: + :::info **Anthropic Web Search Models**: Claude models that support web search: `claude-3-5-sonnet-latest`, `claude-3-5-sonnet-20241022`, `claude-3-5-haiku-latest`, `claude-3-5-haiku-20241022`, `claude-3-7-sonnet-20250219` ::: +## OpenAI Web Search: Two Approaches + +OpenAI offers two distinct ways to use web search depending on the endpoint and model: + +| Approach | Endpoint | Models | How to enable | +|----------|----------|--------|---------------| +| **Search Models** | `/chat/completions` | `gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview` | Pass `web_search_options` parameter | +| **Web Search Tool** | `/responses` | `gpt-5`, `gpt-4.1`, `gpt-4o`, and other regular models | Pass `web_search_preview` tool | + +:::tip Search models search automatically +Search models like `gpt-5-search-api` **automatically search the web** even without the `web_search_options` parameter. Use `web_search_options` to set `search_context_size` (`"low"`, `"medium"`, `"high"`) or specify `user_location` for localized results. +::: + ## `/chat/completions` (litellm.completion) ### Quick Start @@ -39,7 +69,7 @@ Each provider uses their own search backend: from litellm import completion response = completion( - model="openai/gpt-4o-search-preview", + model="openai/gpt-5-search-api", messages=[ { "role": "user", @@ -59,31 +89,36 @@ response = completion( ```yaml model_list: - # OpenAI + # OpenAI search models + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + - model_name: gpt-4o-search-preview litellm_params: model: openai/gpt-4o-search-preview api_key: os.environ/OPENAI_API_KEY - + # xAI - model_name: grok-3 litellm_params: model: xai/grok-3 api_key: os.environ/XAI_API_KEY - + # Anthropic - model_name: claude-3-5-sonnet-latest litellm_params: model: anthropic/claude-3-5-sonnet-latest api_key: os.environ/ANTHROPIC_API_KEY - + # VertexAI - model_name: gemini-2-flash litellm_params: model: gemini-2.0-flash vertex_project: your-project-id vertex_location: us-central1 - + # Google AI Studio - model_name: gemini-2-flash-studio litellm_params: @@ -91,13 +126,13 @@ model_list: api_key: os.environ/GOOGLE_API_KEY ``` -2. Start the proxy +2. Start the proxy ```bash litellm --config /path/to/config.yaml ``` -3. Test it! +3. Test it! ```python showLineNumbers from openai import OpenAI @@ -109,13 +144,18 @@ client = OpenAI( ) response = client.chat.completions.create( - model="grok-3", # or any other web search enabled model + model="gpt-5-search-api", # or any other web search enabled model messages=[ { "role": "user", "content": "What was a positive news story from today?" } - ] + ], + extra_body={ + "web_search_options": { + "search_context_size": "medium" + } + } ) ``` @@ -132,7 +172,7 @@ from litellm import completion # Customize search context size response = completion( - model="openai/gpt-4o-search-preview", + model="openai/gpt-5-search-api", messages=[ { "role": "user", @@ -240,6 +280,12 @@ response = client.chat.completions.create( ## `/responses` (litellm.responses) +Use the `web_search_preview` tool with models like `gpt-5`, `gpt-4.1`, `gpt-4o`, etc. + +:::info +Search-dedicated models like `gpt-5-search-api` and `gpt-4o-search-preview` do **not** support the `/responses` endpoint. Use them with `/chat/completions` + `web_search_options` instead (see above). +::: + ### Quick Start @@ -249,18 +295,14 @@ response = client.chat.completions.create( from litellm import responses response = responses( - model="openai/gpt-4o", - input=[ - { - "role": "user", - "content": "What was a positive news story from today?" - } - ], + model="openai/gpt-5", + input="What is the capital of France?", tools=[{ "type": "web_search_preview" # enables web search with default medium context size }] ) ``` + @@ -268,19 +310,24 @@ response = responses( ```yaml model_list: - - model_name: gpt-4o + - model_name: gpt-5 litellm_params: - model: openai/gpt-4o + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY + + - model_name: gpt-4.1 + litellm_params: + model: openai/gpt-4.1 api_key: os.environ/OPENAI_API_KEY ``` -2. Start the proxy +2. Start the proxy ```bash litellm --config /path/to/config.yaml ``` -3. Test it! +3. Test it! ```python showLineNumbers from openai import OpenAI @@ -292,11 +339,11 @@ client = OpenAI( ) response = client.responses.create( - model="gpt-4o", + model="gpt-5", tools=[{ "type": "web_search_preview" }], - input="What was a positive news story from today?", + input="What is the capital of France?", ) print(response.output_text) @@ -314,13 +361,8 @@ from litellm import responses # Customize search context size response = responses( - model="openai/gpt-4o", - input=[ - { - "role": "user", - "content": "What was a positive news story from today?" - } - ], + model="openai/gpt-5", + input="What is the capital of France?", tools=[{ "type": "web_search_preview", "search_context_size": "low" # Options: "low", "medium" (default), "high" @@ -341,12 +383,12 @@ client = OpenAI( # Customize search context size response = client.responses.create( - model="gpt-4o", + model="gpt-5", tools=[{ "type": "web_search_preview", "search_context_size": "low" # Options: "low", "medium" (default), "high" }], - input="What was a positive news story from today?", + input="What is the capital of France?", ) print(response.output_text) @@ -400,14 +442,14 @@ model_list: web_search_options: search_context_size: "high" # Options: "low", "medium", "high" - # Different context size for different models - - model_name: gpt-4o-search-preview + # OpenAI search model with custom context size + - model_name: gpt-5-search-api litellm_params: - model: openai/gpt-4o-search-preview + model: openai/gpt-5-search-api api_key: os.environ/OPENAI_API_KEY web_search_options: search_context_size: "low" - + # Gemini with medium context (default) - model_name: gemini-2-flash litellm_params: @@ -432,6 +474,7 @@ Use `litellm.supports_web_search(model="model_name")` -> returns `True` if model ```python showLineNumbers # Check OpenAI models +assert litellm.supports_web_search(model="openai/gpt-5-search-api") == True assert litellm.supports_web_search(model="openai/gpt-4o-search-preview") == True # Check xAI models @@ -455,13 +498,20 @@ assert litellm.supports_web_search(model="gemini/gemini-2.0-flash") == True ```yaml model_list: # OpenAI + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + model_info: + supports_web_search: True + - model_name: gpt-4o-search-preview litellm_params: model: openai/gpt-4o-search-preview api_key: os.environ/OPENAI_API_KEY model_info: supports_web_search: True - + # xAI - model_name: grok-3 litellm_params: @@ -516,6 +566,12 @@ Expected Response ```json showLineNumbers { "data": [ + { + "model_group": "gpt-5-search-api", + "providers": ["openai"], + "max_tokens": 128000, + "supports_web_search": true + }, { "model_group": "gpt-4o-search-preview", "providers": ["openai"], diff --git a/docs/my-website/docs/extras/contributing_code.md b/docs/my-website/docs/extras/contributing_code.md index 930a47eec7e..673a83aca05 100644 --- a/docs/my-website/docs/extras/contributing_code.md +++ b/docs/my-website/docs/extras/contributing_code.md @@ -1,27 +1,36 @@ # Contributing Code -## **Checklist before submitting a PR** +## Checklist before submitting a PR -Here are the core requirements for any PR submitted to LiteLLM +Here are the core requirements for any PR submitted to LiteLLM: -- [ ] Sign the Contributor License Agreement (CLA) - [see details](#contributor-license-agreement-cla) -- [ ] Add testing, **Adding at least 1 test is a hard requirement** - [see details](#2-adding-testing-to-your-pr) -- [ ] Ensure your PR passes the following tests: - - [ ] [Unit Tests](#3-running-unit-tests) - - [ ] [Formatting / Linting Tests](#35-running-linting-tests) -- [ ] Keep scope as isolated as possible. As a general rule, your changes should address 1 specific problem at a time +- [ ] Sign the [Contributor License Agreement (CLA)](#contributor-license-agreement-cla) +- [ ] Keep scope as isolated as possible — your changes should address **one specific problem** at a time -## **Contributor License Agreement (CLA)** +### Proxy (Backend) PRs + +- [ ] Add testing — **at least 1 test is a hard requirement** ([details](#2-adding-tests)) +- [ ] Ensure your PR passes: + - [ ] [Unit Tests](#3-running-unit-tests) — `make test-unit` + - [ ] [Formatting / Linting Tests](#4-running-linting-tests) — `make lint` + +### UI PRs + +- [ ] Ensure the UI builds successfully — `npm run build` +- [ ] Ensure all UI unit tests pass — `npm run test` +- [ ] If you are adding a **new component** or **new logic**, add corresponding tests + +## Contributor License Agreement (CLA) Before contributing code to LiteLLM, you must sign our [Contributor License Agreement (CLA)](https://cla-assistant.io/BerriAI/litellm). This is a legal requirement for all contributions to be merged into the main repository. The CLA helps protect both you and the project by clearly defining the terms under which your contributions are made. -**Important:** We strongly recommend reviewing and signing the CLA before starting work on your contribution to avoid any delays in the PR process. You can find the CLA [here](https://cla-assistant.io/BerriAI/litellm) and sign it through our CLA management system when you submit your first PR. +**Important:** We strongly recommend signing the CLA **before** starting work on your contribution to avoid delays in the review process. You can find and sign the CLA [here](https://cla-assistant.io/BerriAI/litellm). -## Quick start +--- -## 1. Setup your local dev environment +## Proxy (Backend) -Here's how to modify the repo locally: +### 1. Setting up your local dev environment Step 1: Clone the repo @@ -29,56 +38,53 @@ Step 1: Clone the repo git clone https://github.com/BerriAI/litellm.git ``` -Step 2: Install dev dependencies: +Step 2: Install dev dependencies ```shell poetry install --with dev --extras proxy ``` -That's it, your local dev environment is ready! +### 2. Adding tests -## 2. Adding Testing to your PR +- Add your tests to the [`tests/test_litellm/` directory](https://github.com/BerriAI/litellm/tree/main/tests/litellm). +- This directory mirrors the `litellm/` directory 1:1 and should **only** contain mocked tests. +- **Do not** add real LLM API calls to this directory. -- Add your test to the [`tests/test_litellm/` directory](https://github.com/BerriAI/litellm/tree/main/tests/litellm) +#### File naming convention for `tests/test_litellm/` -- This directory 1:1 maps the the `litellm/` directory, and can only contain mocked tests. -- Do not add real llm api calls to this directory. +The test directory follows the same structure as `litellm/`: -### 2.1 File Naming Convention for `tests/test_litellm/` - -The `tests/test_litellm/` directory follows the same directory structure as `litellm/`. - -- `litellm/proxy/test_caching_routes.py` maps to `litellm/proxy/caching_routes.py` - `test_{filename}.py` maps to `litellm/{filename}.py` +- `litellm/proxy/test_caching_routes.py` maps to `litellm/proxy/caching_routes.py` -## 3. Running Unit Tests +### 3. Running unit tests -run the following command on the root of the litellm directory +Run the following command from the root of the `litellm` directory: ```shell make test-unit ``` -## 3.5 Running Linting Tests +### 4. Running linting tests -run the following command on the root of the litellm directory +Run the following command from the root of the `litellm` directory: ```shell make lint ``` -LiteLLM uses mypy for linting. On ci/cd we also run `black` for formatting. +LiteLLM uses `mypy` for type checking. CI/CD also runs `black` for formatting. -## 4. Submit a PR with your changes! +### 5. Submit a PR -- push your fork to your GitHub repo -- submit a PR from there +- Push your changes to your fork on GitHub +- Open a Pull Request from your fork -## Advanced +--- -### Building LiteLLM Docker Image +## UI -Some people might want to build the LiteLLM docker image themselves. Follow these instructions if you want to build / run the LiteLLM Docker Image yourself. +### 1. Setting up your local dev environment Step 1: Clone the repo @@ -86,17 +92,72 @@ Step 1: Clone the repo git clone https://github.com/BerriAI/litellm.git ``` -Step 2: Build the Docker Image +Step 2: Navigate to the UI dashboard directory -Build using Dockerfile.non_root +```shell +cd ui/litellm-dashboard +``` + +Step 3: Install dependencies + +```shell +npm install +``` + +Step 4: Start the development server + +```shell +npm run dev +``` + +### 2. Adding tests + +If you are adding a **new component** or **new logic**, you must add corresponding tests. + +### 3. Running UI unit tests + +```shell +npm run test +``` + +### 4. Building the UI + +Ensure the UI builds successfully before submitting your PR: + +```shell +npm run build +``` + +### 5. Submit a PR + +- Push your changes to your fork on GitHub +- Open a Pull Request from your fork + +--- + +## Advanced + +### Building the LiteLLM Docker Image + +Follow these instructions if you want to build and run the LiteLLM Docker image yourself. + +Step 1: Clone the repo + +```shell +git clone https://github.com/BerriAI/litellm.git +``` + +Step 2: Build the Docker image + +Build using `Dockerfile.non_root`: ```shell docker build -f docker/Dockerfile.non_root -t litellm_test_image . ``` -Step 3: Run the Docker Image +Step 3: Run the Docker image -Make sure config.yaml is present in the root directory. This is your litellm proxy config file. +Make sure `config.yaml` is present in the root directory. This is your LiteLLM proxy config file. ```shell docker run \ @@ -107,18 +168,19 @@ docker run \ litellm_test_image \ --config /app/config.yaml --detailed_debug ``` -### Running LiteLLM Proxy Locally -1. cd into the `proxy/` directory +### Running the LiteLLM Proxy Locally -``` +1. Navigate to the `proxy/` directory: + +```shell cd litellm/litellm/proxy ``` -2. Run the proxy +2. Run the proxy: ```shell python3 proxy_cli.py --config /path/to/config.yaml # RUNNING on http://0.0.0.0:4000 -``` \ No newline at end of file +``` diff --git a/docs/my-website/docs/integrations/websearch_interception.md b/docs/my-website/docs/integrations/websearch_interception.md new file mode 100644 index 00000000000..0c5d8927013 --- /dev/null +++ b/docs/my-website/docs/integrations/websearch_interception.md @@ -0,0 +1,411 @@ +# Web Search Integration + +Enable transparent server-side web search execution for any LLM provider. LiteLLM automatically intercepts web search tool calls and executes them using your configured search provider (Perplexity, Tavily, etc.). + +## Quick Start + +### 1. Configure Web Search Interception + +Add to your `config.yaml`: + +```yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + callbacks: + - websearch_interception: + enabled_providers: + - openai + - minimax + - anthropic + search_tool_name: perplexity-search # Optional + +search_tools: + - search_tool_name: perplexity-search + litellm_params: + search_provider: perplexity + api_key: os.environ/PERPLEXITY_API_KEY +``` + +### 2. Use with Any Provider + +```python +import litellm + +response = await litellm.acompletion( + model="gpt-4o", + messages=[ + {"role": "user", "content": "What's the weather in San Francisco today?"} + ], + tools=[ + { + "type": "function", + "function": { + "name": "litellm_web_search", + "description": "Search the web for information", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + } + } + ] +) + +# Response includes search results automatically! +print(response.choices[0].message.content) +``` + +## How It Works + +When a model makes a web search tool call, LiteLLM: + +1. **Detects** the `litellm_web_search` tool call in the response +2. **Executes** the search using your configured search provider +3. **Makes a follow-up request** with the search results +4. **Returns** the final answer to the user + +```mermaid +sequenceDiagram + participant User + participant LiteLLM + participant LLM as LLM Provider + participant Search as Search Provider + + User->>LiteLLM: Request with web_search tool + LiteLLM->>LLM: Forward request + LLM-->>LiteLLM: Response with tool_call + Note over LiteLLM: Detect web search
tool call + LiteLLM->>Search: Execute search + Search-->>LiteLLM: Search results + LiteLLM->>LLM: Follow-up with results + LLM-->>LiteLLM: Final answer + LiteLLM-->>User: Final answer with search results +``` + +**Result**: One API call from user → Complete answer with search results + +## Supported Providers + +Web search integration works with **all providers** that use: +- ✅ **Base HTTP Handler** (`BaseLLMHTTPHandler`) +- ✅ **OpenAI Completion Handler** (`OpenAIChatCompletion`) + +### Providers Using Base HTTP Handler + +| Provider | Status | Notes | +|----------|--------|-------| +| **OpenAI** | ✅ Supported | GPT-4, GPT-3.5, etc. | +| **Anthropic** | ✅ Supported | Claude models via HTTP handler | +| **MiniMax** | ✅ Supported | All MiniMax models | +| **Mistral** | ✅ Supported | Mistral AI models | +| **Cohere** | ✅ Supported | Command models | +| **Fireworks AI** | ✅ Supported | All Fireworks models | +| **Together AI** | ✅ Supported | All Together AI models | +| **Groq** | ✅ Supported | All Groq models | +| **Perplexity** | ✅ Supported | Perplexity models | +| **DeepSeek** | ✅ Supported | DeepSeek models | +| **xAI** | ✅ Supported | Grok models | +| **Hugging Face** | ✅ Supported | Inference API models | +| **OCI** | ✅ Supported | Oracle Cloud models | +| **Vertex AI** | ✅ Supported | Google Vertex AI models | +| **Bedrock** | ✅ Supported | AWS Bedrock models (converse_like route) | +| **Azure OpenAI** | ✅ Supported | Azure-hosted OpenAI models | +| **Sagemaker** | ✅ Supported | AWS Sagemaker models | +| **Databricks** | ✅ Supported | Databricks models | +| **DataRobot** | ✅ Supported | DataRobot models | +| **Hosted VLLM** | ✅ Supported | Self-hosted VLLM | +| **Heroku** | ✅ Supported | Heroku-hosted models | +| **RAGFlow** | ✅ Supported | RAGFlow models | +| **Compactif** | ✅ Supported | Compactif models | +| **Cometapi** | ✅ Supported | Comet API models | +| **A2A** | ✅ Supported | Agent-to-Agent models | +| **Bytez** | ✅ Supported | Bytez models | + +### Providers Using OpenAI Handler + +| Provider | Status | Notes | +|----------|--------|-------| +| **OpenAI** | ✅ Supported | Native OpenAI API | +| **Azure OpenAI** | ✅ Supported | Azure-hosted OpenAI | +| **OpenAI-Compatible** | ✅ Supported | Any OpenAI-compatible API | + +## Configuration + +### WebSearch Interception Parameters + +| Parameter | Type | Required | Description | Example | +|-----------|------|----------|-------------|---------| +| `enabled_providers` | List[String] | Yes | List of providers to enable web search for | `[openai, minimax, anthropic]` | +| `search_tool_name` | String | No | Specific search tool from `search_tools` config. If not set, uses first available. | `perplexity-search` | + +### Provider Values + +Use these values in `enabled_providers`: + +| Provider | Value | Provider | Value | +|----------|-------|----------|-------| +| OpenAI | `openai` | Anthropic | `anthropic` | +| MiniMax | `minimax` | Mistral | `mistral` | +| Cohere | `cohere` | Fireworks AI | `fireworks_ai` | +| Together AI | `together_ai` | Groq | `groq` | +| Perplexity | `perplexity` | DeepSeek | `deepseek` | +| xAI | `xai` | Hugging Face | `huggingface` | +| OCI | `oci` | Vertex AI | `vertex_ai` | +| Bedrock | `bedrock` | Azure | `azure` | +| Sagemaker | `sagemaker_chat` | Databricks | `databricks` | +| DataRobot | `datarobot` | VLLM | `hosted_vllm` | +| Heroku | `heroku` | RAGFlow | `ragflow` | +| Compactif | `compactif` | Cometapi | `cometapi` | +| A2A | `a2a` | Bytez | `bytez` | + +## Search Providers + +Configure which search provider to use. LiteLLM supports multiple search providers: + +| Provider | `search_provider` Value | Environment Variable | +|----------|------------------------|----------------------| +| **Perplexity AI** | `perplexity` | `PERPLEXITYAI_API_KEY` | +| **Tavily** | `tavily` | `TAVILY_API_KEY` | +| **Exa AI** | `exa_ai` | `EXA_API_KEY` | +| **Parallel AI** | `parallel_ai` | `PARALLEL_AI_API_KEY` | +| **Google PSE** | `google_pse` | `GOOGLE_PSE_API_KEY`, `GOOGLE_PSE_ENGINE_ID` | +| **DataForSEO** | `dataforseo` | `DATAFORSEO_LOGIN`, `DATAFORSEO_PASSWORD` | +| **Firecrawl** | `firecrawl` | `FIRECRAWL_API_KEY` | +| **SearXNG** | `searxng` | `SEARXNG_API_BASE` (required) | +| **Linkup** | `linkup` | `LINKUP_API_KEY` | + +See [Search Providers Documentation](../search/index.md) for detailed setup instructions. + +## Complete Configuration Example + +```yaml +model_list: + # OpenAI + - model_name: gpt-4o + litellm_params: + model: openai/gpt-4o + api_key: os.environ/OPENAI_API_KEY + + # MiniMax + - model_name: minimax + litellm_params: + model: minimax/MiniMax-M2.1 + api_key: os.environ/MINIMAX_API_KEY + + # Anthropic + - model_name: claude + litellm_params: + model: anthropic/claude-sonnet-4-5 + api_key: os.environ/ANTHROPIC_API_KEY + + # Azure OpenAI + - model_name: azure-gpt4 + litellm_params: + model: azure/gpt-4 + api_base: https://my-azure.openai.azure.com + api_key: os.environ/AZURE_API_KEY + +litellm_settings: + callbacks: + - websearch_interception: + enabled_providers: + - openai + - minimax + - anthropic + - azure + search_tool_name: perplexity-search + +search_tools: + - search_tool_name: perplexity-search + litellm_params: + search_provider: perplexity + api_key: os.environ/PERPLEXITY_API_KEY + + - search_tool_name: tavily-search + litellm_params: + search_provider: tavily + api_key: os.environ/TAVILY_API_KEY +``` + +## Usage Examples + +### Python SDK + +```python +import litellm + +# Configure callbacks +litellm.callbacks = ["websearch_interception"] + +# Make completion with web search tool +response = await litellm.acompletion( + model="gpt-4o", + messages=[ + {"role": "user", "content": "What are the latest AI news?"} + ], + tools=[ + { + "type": "function", + "function": { + "name": "litellm_web_search", + "description": "Search the web for current information", + "parameters": { + "type": "object", + "properties": { + "query": { + "type": "string", + "description": "Search query" + } + }, + "required": ["query"] + } + } + } + ] +) + +print(response.choices[0].message.content) +``` + +### Proxy Server + +```bash +# Start proxy with config +litellm --config config.yaml + +# Make request +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gpt-4o", + "messages": [ + {"role": "user", "content": "What is the weather in San Francisco?"} + ], + "tools": [ + { + "type": "function", + "function": { + "name": "litellm_web_search", + "description": "Search the web", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"} + }, + "required": ["query"] + } + } + } + ] + }' +``` + +## How Search Tool Selection Works + +1. **If `search_tool_name` is specified** → Uses that specific search tool +2. **If `search_tool_name` is not specified** → Uses first search tool in `search_tools` list + +```yaml +search_tools: + - search_tool_name: perplexity-search # ← This will be used if no search_tool_name specified + litellm_params: + search_provider: perplexity + api_key: os.environ/PERPLEXITY_API_KEY + + - search_tool_name: tavily-search + litellm_params: + search_provider: tavily + api_key: os.environ/TAVILY_API_KEY +``` + +## Troubleshooting + +### Web Search Not Working + +1. **Check provider is enabled**: + ```yaml + enabled_providers: + - openai # Make sure your provider is in this list + ``` + +2. **Verify search tool is configured**: + ```yaml + search_tools: + - search_tool_name: perplexity-search + litellm_params: + search_provider: perplexity + api_key: os.environ/PERPLEXITY_API_KEY + ``` + +3. **Check API keys are set**: + ```bash + export PERPLEXITY_API_KEY=your-key + ``` + +4. **Enable debug logging**: + ```python + litellm.set_verbose = True + ``` + +### Common Issues + +**Issue**: Model returns tool_calls instead of final answer +- **Cause**: Provider not in `enabled_providers` list +- **Solution**: Add provider to `enabled_providers` + +**Issue**: "No search tool configured" error +- **Cause**: No search tools in `search_tools` config +- **Solution**: Add at least one search tool configuration + +**Issue**: "Invalid function arguments json string" error (MiniMax) +- **Cause**: Fixed in latest version - arguments weren't properly JSON serialized +- **Solution**: Update to latest LiteLLM version + +## Related Documentation + +- [Search Providers](../search/index.md) - Detailed search provider setup +- [Claude Code WebSearch](../tutorials/claude_code_websearch.md) - Using with Claude Code +- [Tool Calling](../completion/function_call.md) - General tool calling documentation +- [Callbacks](./custom_callback.md) - Custom callback documentation + +## Technical Details + +### Architecture + +Web search integration is implemented as a custom callback (`WebSearchInterceptionLogger`) that: + +1. **Pre-request Hook**: Converts native web search tools to LiteLLM standard format +2. **Post-response Hook**: Detects web search tool calls in responses +3. **Agentic Loop**: Executes searches and makes follow-up requests automatically + +### Supported APIs + +- ✅ **Chat Completions API** (OpenAI format) +- ✅ **Anthropic Messages API** (Anthropic format) +- ✅ **Streaming** (automatically converted) +- ✅ **Non-streaming** + +### Response Format Detection + +The handler automatically detects response format: +- **OpenAI format**: `tool_calls` in assistant message +- **Anthropic format**: `tool_use` blocks in content + +### Performance + +- **Latency**: Adds one additional LLM call (follow-up request with search results) +- **Caching**: Search results can be cached (depends on search provider) +- **Parallel Searches**: Multiple search queries executed in parallel + +## Contributing + +Found a bug or want to add support for a new provider? See our [Contributing Guide](https://github.com/BerriAI/litellm/blob/main/CONTRIBUTING.md). diff --git a/docs/my-website/docs/load_test.md b/docs/my-website/docs/load_test.md index 4641a70366c..071b097904b 100644 --- a/docs/my-website/docs/load_test.md +++ b/docs/my-website/docs/load_test.md @@ -4,8 +4,9 @@ import Image from '@theme/IdealImage'; ## Locust Load Test LiteLLM Proxy -1. Add `fake-openai-endpoint` to your proxy config.yaml and start your litellm proxy -litellm provides a free hosted `fake-openai-endpoint` you can load test against +1. Add `fake-openai-endpoint` to your proxy config.yaml and start your litellm proxy. + +LiteLLM provides a free hosted `fake-openai-endpoint` you can load test against. You can also self-host your own fake OpenAI proxy server using [github.com/BerriAI/example_openai_endpoint](https://github.com/BerriAI/example_openai_endpoint). ```yaml model_list: diff --git a/docs/my-website/docs/load_test_advanced.md b/docs/my-website/docs/load_test_advanced.md index 3171bc33594..d35b5f74784 100644 --- a/docs/my-website/docs/load_test_advanced.md +++ b/docs/my-website/docs/load_test_advanced.md @@ -29,12 +29,16 @@ Tutorial on how to get to 1K+ RPS with LiteLLM Proxy on locust **Note:** we're currently migrating to aiohttp which has 10x higher throughput. We recommend using the `openai/` provider for load testing. +:::tip Setting Up a Fake OpenAI Endpoint +You can use our hosted fake endpoint or self-host your own using [github.com/BerriAI/example_openai_endpoint](https://github.com/BerriAI/example_openai_endpoint). +::: + ```yaml model_list: - model_name: "fake-openai-endpoint" litellm_params: model: openai/any - api_base: https://your-fake-openai-endpoint.com/chat/completions + api_base: https://exampleopenaiendpoint-production.up.railway.app/ # or your self-hosted endpoint api_key: "test" ``` diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index d63b55ee29e..84d10c25931 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -506,7 +506,14 @@ Your OpenAPI specification should follow standard OpenAPI/Swagger conventions: - **Operation IDs**: Each operation should have a unique `operationId` (this becomes the tool name) - **Parameters**: Request parameters should be properly documented with types and descriptions -## MCP Oauth +## MCP OAuth + +LiteLLM supports OAuth 2.0 for MCP servers -- both interactive (PKCE) flows for user-facing clients and machine-to-machine (M2M) `client_credentials` for backend services. + +See the **[MCP OAuth guide](./mcp_oauth.md)** for setup instructions, sequence diagrams, and a test server. + +
+Detailed OAuth reference (click to expand) LiteLLM v 1.77.6 added support for OAuth 2.0 Client Credentials for MCP servers. @@ -588,6 +595,8 @@ sequenceDiagram See the official [MCP Authorization Flow](https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization#authorization-flow-steps) for additional reference. +
+ ## Forwarding Custom Headers to MCP Servers @@ -1486,7 +1495,7 @@ async with stdio_client(server_params) as (read, write): **Q: How do I use OAuth2 client_credentials (machine-to-machine) with MCP servers behind LiteLLM?** -At the moment LiteLLM only forwards whatever `Authorization` header/value you configure for the MCP server; it does not issue OAuth2 tokens by itself. If your MCP requires the Client Credentials grant, obtain the access token directly from the authorization server and set that bearer token as the MCP server’s Authorization header value. LiteLLM does not yet fetch or refresh those machine-to-machine tokens on your behalf, but we plan to add first-class client_credentials support in a future release so the proxy can manage those tokens automatically. +LiteLLM supports automatic token management for the `client_credentials` grant. Configure `client_id`, `client_secret`, and `token_url` on your MCP server and LiteLLM will fetch, cache, and refresh tokens automatically. See the [MCP OAuth M2M guide](./mcp_oauth.md#machine-to-machine-m2m-auth) for setup instructions. **Q: When I fetch an OAuth token from the LiteLLM UI, where is it stored?** diff --git a/docs/my-website/docs/mcp_oauth.md b/docs/my-website/docs/mcp_oauth.md new file mode 100644 index 00000000000..9cd7b1e77be --- /dev/null +++ b/docs/my-website/docs/mcp_oauth.md @@ -0,0 +1,244 @@ +# MCP OAuth + +LiteLLM supports two OAuth 2.0 flows for MCP servers: + +| Flow | Use Case | How It Works | +|------|----------|--------------| +| **Interactive (PKCE)** | User-facing apps (Claude Code, Cursor) | Browser-based consent, per-user tokens | +| **Machine-to-Machine (M2M)** | Backend services, CI/CD, automated agents | `client_credentials` grant, proxy-managed tokens | + +## Interactive OAuth (PKCE) + +For user-facing MCP clients (Claude Code, Cursor), LiteLLM supports the full OAuth 2.0 authorization code flow with PKCE. + +### Setup + +```yaml title="config.yaml" showLineNumbers +mcp_servers: + github_mcp: + url: "https://api.githubcopilot.com/mcp" + auth_type: oauth2 + client_id: os.environ/GITHUB_OAUTH_CLIENT_ID + client_secret: os.environ/GITHUB_OAUTH_CLIENT_SECRET +``` + +[**See Claude Code Tutorial**](./tutorials/claude_responses_api#connecting-mcp-servers) + +### How It Works + +```mermaid +sequenceDiagram + participant Browser as User-Agent (Browser) + participant Client as Client + participant LiteLLM as LiteLLM Proxy + participant MCP as MCP Server (Resource Server) + participant Auth as Authorization Server + + Note over Client,LiteLLM: Step 1 – Resource discovery + Client->>LiteLLM: GET /.well-known/oauth-protected-resource/{mcp_server_name}/mcp + LiteLLM->>Client: Return resource metadata + + Note over Client,LiteLLM: Step 2 – Authorization server discovery + Client->>LiteLLM: GET /.well-known/oauth-authorization-server/{mcp_server_name} + LiteLLM->>Client: Return authorization server metadata + + Note over Client,Auth: Step 3 – Dynamic client registration + Client->>LiteLLM: POST /{mcp_server_name}/register + LiteLLM->>Auth: Forward registration request + Auth->>LiteLLM: Issue client credentials + LiteLLM->>Client: Return client credentials + + Note over Client,Browser: Step 4 – User authorization (PKCE) + Client->>Browser: Open authorization URL + code_challenge + resource + Browser->>Auth: Authorization request + Note over Auth: User authorizes + Auth->>Browser: Redirect with authorization code + Browser->>LiteLLM: Callback to LiteLLM with code + LiteLLM->>Browser: Redirect back with authorization code + Browser->>Client: Callback with authorization code + + Note over Client,Auth: Step 5 – Token exchange + Client->>LiteLLM: Token request + code_verifier + resource + LiteLLM->>Auth: Forward token request + Auth->>LiteLLM: Access (and refresh) token + LiteLLM->>Client: Return tokens + + Note over Client,MCP: Step 6 – Authenticated MCP call + Client->>LiteLLM: MCP request with access token + LiteLLM API key + LiteLLM->>MCP: MCP request with Bearer token + MCP-->>LiteLLM: MCP response + LiteLLM-->>Client: Return MCP response +``` + +**Participants** + +- **Client** -- The MCP-capable AI agent (e.g., Claude Code, Cursor, or another IDE/agent) that initiates OAuth discovery, authorization, and tool invocations on behalf of the user. +- **LiteLLM Proxy** -- Mediates all OAuth discovery, registration, token exchange, and MCP traffic while protecting stored credentials. +- **Authorization Server** -- Issues OAuth 2.0 tokens via dynamic client registration, PKCE authorization, and token endpoints. +- **MCP Server (Resource Server)** -- The protected MCP endpoint that receives LiteLLM's authenticated JSON-RPC requests. +- **User-Agent (Browser)** -- Temporarily involved so the end user can grant consent during the authorization step. + +**Flow Steps** + +1. **Resource Discovery**: The client fetches MCP resource metadata from LiteLLM's `.well-known/oauth-protected-resource` endpoint to understand scopes and capabilities. +2. **Authorization Server Discovery**: The client retrieves the OAuth server metadata (token endpoint, authorization endpoint, supported PKCE methods) through LiteLLM's `.well-known/oauth-authorization-server` endpoint. +3. **Dynamic Client Registration**: The client registers through LiteLLM, which forwards the request to the authorization server (RFC 7591). If the provider doesn't support dynamic registration, you can pre-store `client_id`/`client_secret` in LiteLLM (e.g., GitHub MCP) and the flow proceeds the same way. +4. **User Authorization**: The client launches a browser session (with code challenge and resource hints). The user approves access, the authorization server sends the code through LiteLLM back to the client. +5. **Token Exchange**: The client calls LiteLLM with the authorization code, code verifier, and resource. LiteLLM exchanges them with the authorization server and returns the issued access/refresh tokens. +6. **MCP Invocation**: With a valid token, the client sends the MCP JSON-RPC request (plus LiteLLM API key) to LiteLLM, which forwards it to the MCP server and relays the tool response. + +See the official [MCP Authorization Flow](https://modelcontextprotocol.io/specification/2025-06-18/basic/authorization#authorization-flow-steps) for additional reference. + +## Machine-to-Machine (M2M) Auth + +LiteLLM automatically fetches, caches, and refreshes OAuth2 tokens using the `client_credentials` grant. No manual token management required. + +### Setup + +You can configure M2M OAuth via the LiteLLM UI or `config.yaml`. + +### UI Setup + +Navigate to the **MCP Servers** page and click **+ Add New MCP Server**. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/d1f1e89c-a789-4975-8846-b15d9821984a/ascreenshot_630800e00a2e4b598baabfc25efbabd3_text_export.jpeg) + +Enter a name for your server and select **HTTP** as the transport type. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/2008c9d6-6093-4121-beab-1e52c71376aa/ascreenshot_516ffd6c7b524465a253a56048c3d228_text_export.jpeg) + +Paste the MCP server URL. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/b0ee8b7d-6de8-492b-8962-287987feec29/ascreenshot_b3efca82078a4c6bb1453c58161909f9_text_export.jpeg) + +Under **Authentication**, select **OAuth**. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/e1597814-ff8e-40b9-9d7b-864dcdbe0910/ascreenshot_2097612712264d8f9e553f7ca9175fb0_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/f6ea5694-f28a-4bc3-9c9a-bb79f199bd65/ascreenshot_9be839f55b1b4f96bfe24030ba2c7f8d_text_export.jpeg) + +Choose **Machine-to-Machine (M2M)** as the OAuth flow type. This is for server-to-server authentication using the `client_credentials` grant — no browser interaction required. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/9853310c-1d86-4628-bad1-7a391eca0e4d/ascreenshot_f302a286fa264fdd8d56db53b8f9395c_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/df64dc65-ef86-475d-adaf-12e227d5e873/ascreenshot_9e2f41d43a76435f918a00b52ffcc639_text_export.jpeg) + +Fill in the **Client ID** and **Client Secret** provided by your OAuth provider. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/0de5a7bd-9898-4fc7-8843-b23dd5aac47f/ascreenshot_b9087aaa81a14b5b9c199929efc4a563_text_export.jpeg) + +Enter the **Token URL** — this is the endpoint LiteLLM will call to fetch access tokens using `client_credentials`. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/0aea70f1-558c-4dca-91bc-1175fe1ddc89/ascreenshot_b3fcf8a1287e4e2d9a3d67c4a29f7bff_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/e842ef09-1fd7-47a6-909b-252d389f0abc/ascreenshot_2a87dad3624847e7ac370591d1d1aedd_text_export.jpeg) + +Scroll down and review the server URL and all fields, then click **Create MCP Server**. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/0857712b-4b53-40f8-8c1f-a4c72edaa644/ascreenshot_47be3fcd5de64ed391f70c1fb74a8bfc_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/9d961765-955f-4905-a3dc-1a446aa3b2cc/ascreenshot_43fd39d014224564bc6b35aced1fb6d3_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/3825d5fa-8fd1-4e71-b090-77ff0259c3f6/ascreenshot_2509a7ebd9bf421eb0e82f2553566745_text_export.jpeg) + +Once created, open the server and navigate to the **MCP Tools** tab to verify that LiteLLM can connect and list available tools. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/8107e27b-5072-4675-8fd6-89b47692b1bd/ascreenshot_f774bc76138f430d808fb4482ebfcdca_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/ce94bb7b-c81b-4396-9939-178efb2cdfce/ascreenshot_28b838ab6ae34c76858454555c4c1d79_text_export.jpeg) + +Select a tool (e.g. **echo**) to test it. Fill in the required parameters and click **Call Tool**. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/c459c1d3-ec29-4211-9c28-37fbe7783bbc/ascreenshot_e9b138b3c2cc4440bb1a6f42ac7ae861_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/5438ac60-e0ac-4a79-bf6f-5594f160d3b5/ascreenshot_9133a17d26204c46bce497e74685c483_text_export.jpeg) + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/a8f6821b-3982-4b4d-9b25-70c8aff5ac31/ascreenshot_28d474d0e62545a482cff6128527883a_text_export.jpeg) + +LiteLLM automatically fetches an OAuth token behind the scenes and calls the tool. The result confirms the M2M OAuth flow is working end-to-end. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-10/c3924549-a949-48d1-ac67-ab4c30475859/ascreenshot_8f6eca9d717f45478d50a881bd244bb3_text_export.jpeg) + +### Config.yaml Setup + +```yaml title="config.yaml" showLineNumbers +mcp_servers: + my_mcp_server: + url: "https://my-mcp-server.com/mcp" + auth_type: oauth2 + client_id: os.environ/MCP_CLIENT_ID + client_secret: os.environ/MCP_CLIENT_SECRET + token_url: "https://auth.example.com/oauth/token" + scopes: ["mcp:read", "mcp:write"] # optional +``` + +### How It Works + +1. On first MCP request, LiteLLM POSTs to `token_url` with `grant_type=client_credentials` +2. The access token is cached in-memory with TTL = `expires_in - 60s` +3. Subsequent requests reuse the cached token +4. When the token expires, LiteLLM fetches a new one automatically + +```mermaid +sequenceDiagram + participant Client as Client + participant LiteLLM as LiteLLM Proxy + participant Auth as Authorization Server + participant MCP as MCP Server + + Client->>LiteLLM: MCP request + LiteLLM API key + LiteLLM->>Auth: POST /oauth/token (client_credentials) + Auth->>LiteLLM: access_token (expires_in: 3600) + LiteLLM->>MCP: MCP request + Bearer token + MCP-->>LiteLLM: MCP response + LiteLLM-->>Client: MCP response + + Note over LiteLLM: Token cached for subsequent requests + Client->>LiteLLM: Next MCP request + LiteLLM->>MCP: MCP request + cached Bearer token + MCP-->>LiteLLM: MCP response + LiteLLM-->>Client: MCP response +``` + +### Test with Mock Server + +Use [BerriAI/mock-oauth2-mcp-server](https://github.com/BerriAI/mock-oauth2-mcp-server) to test locally: + +```bash title="Terminal 1 - Start mock server" showLineNumbers +pip install fastapi uvicorn +python mock_oauth2_mcp_server.py # starts on :8765 +``` + +```yaml title="config.yaml" showLineNumbers +mcp_servers: + test_oauth2: + url: "http://localhost:8765/mcp" + auth_type: oauth2 + client_id: "test-client" + client_secret: "test-secret" + token_url: "http://localhost:8765/oauth/token" +``` + +```bash title="Terminal 2 - Start proxy and test" showLineNumbers +litellm --config config.yaml --port 4000 + +# List tools +curl http://localhost:4000/mcp-rest/tools/list \ + -H "Authorization: Bearer sk-1234" + +# Call a tool +curl http://localhost:4000/mcp-rest/tools/call \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{"name": "echo", "arguments": {"message": "hello"}}' +``` + +### Config Reference + +| Field | Required | Description | +|-------|----------|-------------| +| `auth_type` | Yes | Must be `oauth2` | +| `client_id` | Yes | OAuth2 client ID. Supports `os.environ/VAR_NAME` | +| `client_secret` | Yes | OAuth2 client secret. Supports `os.environ/VAR_NAME` | +| `token_url` | Yes | Token endpoint URL | +| `scopes` | No | List of scopes to request | diff --git a/docs/my-website/docs/mcp_public_internet.md b/docs/my-website/docs/mcp_public_internet.md new file mode 100644 index 00000000000..69dd7464657 --- /dev/null +++ b/docs/my-website/docs/mcp_public_internet.md @@ -0,0 +1,251 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Exposing MCPs on the Public Internet + +Control which MCP servers are visible to external callers (e.g., ChatGPT, Claude Desktop) vs. internal-only callers. This is useful when you want a subset of your MCP servers available publicly while keeping sensitive servers restricted to your private network. + +## Overview + +| Property | Details | +|-------|-------| +| Description | IP-based access control for MCP servers — external callers only see servers marked as public | +| Setting | `available_on_public_internet` on each MCP server | +| Network Config | `mcp_internal_ip_ranges` in `general_settings` | +| Supported Clients | ChatGPT, Claude Desktop, Cursor, OpenAI API, or any MCP client | + +## How It Works + +When a request arrives at LiteLLM's MCP endpoints, LiteLLM checks the caller's IP address to determine whether they are an **internal** or **external** caller: + +1. **Extract the client IP** from the incoming request (supports `X-Forwarded-For` when configured behind a reverse proxy). +2. **Classify the IP** as internal or external by checking it against the configured private IP ranges (defaults to RFC 1918: `10.0.0.0/8`, `172.16.0.0/12`, `192.168.0.0/16`, `127.0.0.0/8`). +3. **Filter the server list**: + - **Internal callers** see all MCP servers (public and private). + - **External callers** only see servers with `available_on_public_internet: true`. + +This filtering is applied at every MCP access point: the MCP registry, tool listing, tool calling, dynamic server routes, and OAuth discovery endpoints. + +```mermaid +flowchart TD + A[Incoming MCP Request] --> B[Extract Client IP Address] + B --> C{Is IP in private ranges?} + C -->|Yes - Internal caller| D[Return ALL MCP servers] + C -->|No - External caller| E[Return ONLY servers with
available_on_public_internet = true] +``` + +## Walkthrough + +This walkthrough covers two flows: +1. **Adding a public MCP server** (DeepWiki) and connecting to it from ChatGPT +2. **Making an existing server private** (Exa) and verifying ChatGPT no longer sees it + +### Flow 1: Add a Public MCP Server (DeepWiki) + +DeepWiki is a free MCP server — a good candidate to expose publicly so AI gateway users can access it from ChatGPT. + +#### Step 1: Create the MCP Server + +Navigate to the MCP Servers page and click **"+ Add New MCP Server"**. + +![Click Add New MCP Server](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/28cc27c2-d980-4255-b552-ebf542ef95be/ascreenshot_30a7e3c043834f1c87b69e6ffc5bba4f_text_export.jpeg) + +The create dialog opens. Enter **"DeepWiki"** as the server name. + +![Enter server name](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/8c733c38-310a-40ef-8a5b-7af91cc7f74f/ascreenshot_16df83fed5bd4683a22a042e07063cec_text_export.jpeg) + +For the transport type dropdown, select **HTTP** since DeepWiki uses the Streamable HTTP transport. + +![Select transport type](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/e473f603-d692-40c7-a218-866c2e1cb554/ascreenshot_e93997971f2f44beac6152786889addf_text_export.jpeg) + +Now scroll down to the MCP Server URL field. + +![Configure server](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/b08d3c1f-9279-45b6-8efb-f73008901da6/ascreenshot_ce0de66f230a41b0a454e76653429021_text_export.jpeg) + +Enter the DeepWiki MCP URL: `https://mcp.deepwiki.com/mcp`. + +![Enter MCP server URL](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/e59f8285-cfde-4c57-aa79-24244acc9160/ascreenshot_8d575c66dc614a4183212ba282d22b41_text_export.jpeg) + +With the name, transport, and URL filled in, the basic server configuration is complete. + +![Server URL configured](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/0f1af7ed-760d-4445-bdec-3da706d4eef4/ascreenshot_d7d6db69bc254ded871d14a71188a212_text_export.jpeg) + +#### Step 2: Enable "Available on Public Internet" + +Before creating, scroll down and expand the **Permission Management / Access Control** section. This is where you control who can see this server. + +![Expand Permission Management](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/cc10dea2-6028-4a27-a33b-1b1b7212efb5/ascreenshot_0fdd152b862a4bf39973bc805ce64c57_text_export.jpeg) + +Toggle **"Available on Public Internet"** on. This is the key setting — it tells LiteLLM that external callers (like ChatGPT connecting from the public internet) should be able to discover and use this server. + +![Toggle Available on Public Internet](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/39c14543-c5ae-4189-8f85-9efc87135820/ascreenshot_9991f54910c24e21bba5c05ea4fa8e28_text_export.jpeg) + +With the toggle enabled, click **"Create"** to save the server. + +![Click Create](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/843be209-aade-44f4-98da-e55d1644854c/ascreenshot_8cfc90345a5f4d069b397e80d0a6e449_text_export.jpeg) + +#### Step 3: Connect from ChatGPT + +Now let's verify it works. Open ChatGPT and look for the MCP server icon to add a new connection. The endpoint to use is `/mcp`. + +![ChatGPT add MCP server](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/58b5f674-edf4-4156-a5fa-5fdc8ed5d7b9/ascreenshot_36735f7c37394e919793968794614126_text_export.jpeg) + +In the dropdown, select **"Add an MCP server"** to configure a new connection. + +![ChatGPT MCP server option](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/f89da8af-bc61-44a7-a765-f52733f4970d/ascreenshot_6410a917b782437eb558de3bfcd35ffd_text_export.jpeg) + +ChatGPT asks for a server label. Give it a recognizable name like "LiteLLM". + +![Enter server label](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/88505afe-07c1-4674-a89c-8035a5d05eb6/ascreenshot_143aefc38ddd4d3f9f5823ca2cc09bc2_text_export.jpeg) + +Next, enter the Server URL. This should be your LiteLLM proxy's MCP endpoint — `/mcp`. + +![Enter LiteLLM MCP URL](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/9048be4a-7e40-43e7-9789-059fed2741a6/ascreenshot_e81232c17fd148f48f0ae552e9dc2a10_text_export.jpeg) + +Paste your LiteLLM URL and confirm it looks correct. + +![URL pasted](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/7707e796-e146-47c8-bce0-58e6f4076272/ascreenshot_0710dc58b8ed4d6887856b1388d59329_text_export.jpeg) + +ChatGPT also needs authentication. Enter your LiteLLM API key in the authentication field so it can connect to the proxy. + +![Enter API key](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/f6cfcb81-021d-4a41-94d7-d4eaf449d025/ascreenshot_d635865abfb64732a7278922f08dbcaa_text_export.jpeg) + +Click **"Connect"** to establish the connection. + +![Click Connect](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/1146b326-6f0c-4050-9729-af5c88e1bc81/ascreenshot_e19fb857e5394b9a9bf77b075b4fb620_text_export.jpeg) + +ChatGPT connects and shows the available tools. Since both DeepWiki and Exa are currently marked as public, ChatGPT can see tools from both servers. + +![ChatGPT shows available MCP tools](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/43ac56b7-9933-4762-903a-370fc52c79b5/ascreenshot_39073d6dc3bc4bb6a79d93365a26a4f8_text_export.jpeg) + +--- + +### Flow 2: Make an Existing Server Private (Exa) + +Now let's do the reverse — take an existing MCP server (Exa) that's currently public and restrict it to internal access only. After this change, ChatGPT should no longer see Exa's tools. + +#### Step 1: Edit the Server + +Go to the MCP Servers table and click on the Exa server to open its detail view. + +![Exa server overview](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/65844f13-b1ec-4092-b3fd-b1cae3c0c833/ascreenshot_cc8ea435c5e14761a1394ca80fe817c0_text_export.jpeg) + +Switch to the **"Settings"** tab to access the edit form. + +![Click Settings](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/d5b65271-561e-4d2a-b832-96d32611f6e4/ascreenshot_a200942b17264c1eb7a3ffdb2c2141f5_text_export.jpeg) + +The edit form loads with Exa's current configuration. + +![Edit server](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/119184f6-f3cd-45b7-9cfa-0ea08de27020/ascreenshot_c39a793da03a4f0fb84b5ee829af9034_text_export.jpeg) + +#### Step 2: Toggle Off "Available on Public Internet" + +Scroll down and expand the **Permission Management / Access Control** section to find the public internet toggle. + +![Expand permissions](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/bf7114cc-8741-4fa0-a39a-fe625482e88a/ascreenshot_8a987649c03e46558a2ec9a6f2f539a4_text_export.jpeg) + +Toggle **"Available on Public Internet"** off. This will hide Exa from any caller outside your private network. + +![Toggle off public internet](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/f36af5ad-028f-4bb1-aed1-43e38ff9b733/ascreenshot_9128364a049f489bb8483e18e5c88015_text_export.jpeg) + +Click **"Save Changes"** to apply. The change takes effect immediately — no proxy restart needed. + +![Save changes](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/126a71b3-02e1-4d61-a208-942b92e9ef25/ascreenshot_f349ef69e08044dd8e4903f4286b7b97_text_export.jpeg) + +#### Step 3: Verify in ChatGPT + +Go back to ChatGPT to confirm Exa is no longer visible. You'll need to reconnect for ChatGPT to re-fetch the tool list. + +![ChatGPT verify](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/15518882-8b19-44d3-9bba-245aeb62b4b1/ascreenshot_f98f59c51e6543e1be4f3960ba375fc9_text_export.jpeg) + +Open the MCP server settings and select to add or reconnect a server. + +![Reconnect to server](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/784d3174-77c0-42e6-a059-4c906db8f72a/ascreenshot_d77db951b83e4b15a00373222712f6b5_text_export.jpeg) + +Enter the same LiteLLM MCP URL as before. + +![Reconnect URL](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/17ef5fb0-b240-4556-8d20-753d359b7fcf/ascreenshot_583466ce9e8f40d1ba0af8b1e7d04413_text_export.jpeg) + +Set the server label. + +![Reconnect name](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/d7907637-c957-4a3c-ab4f-1600ca9a70a0/ascreenshot_e429eea43f3f4b3ca4d3ac5a77fbde2d_text_export.jpeg) + +Enter your API key for authentication. + +![Reconnect key](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/9cfff77a-37aa-4ca6-8032-0b46c50f37e3/ascreenshot_250664183399496b8f5c9f86f576fc0b_text_export.jpeg) + +Click **"Connect"** to re-establish the connection. + +![Click Connect](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/686f6307-b4ae-448b-ac6c-2c9d7b4f6b57/ascreenshot_3f499d0812af42ab89fed103cc21c249_text_export.jpeg) + +This time, only DeepWiki's tools appear — Exa is gone. LiteLLM detected that ChatGPT is calling from a public IP and filtered out Exa since it's no longer marked as public. Internal users on your private network would still see both servers. + +![Only DeepWiki tools visible](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/667d79b6-75f9-4799-9315-0c176e7a5e34/ascreenshot_efa43050ac0b4445a09e542fa8f270ff_text_export.jpeg) + +## Configuration Reference + +### Per-Server Setting + + + + +Toggle **"Available on Public Internet"** in the Permission Management section when creating or editing an MCP server. + + + + +```yaml title="config.yaml" showLineNumbers +mcp_servers: + deepwiki: + url: https://mcp.deepwiki.com/mcp + available_on_public_internet: true # visible to external callers + + exa: + url: https://exa.ai/mcp + auth_type: api_key + auth_value: os.environ/EXA_API_KEY + available_on_public_internet: false # internal only (default) +``` + + + + +```bash title="Create a public MCP server" showLineNumbers +curl -X POST /v1/mcp/server \ + -H "Authorization: Bearer sk-..." \ + -H "Content-Type: application/json" \ + -d '{ + "server_name": "DeepWiki", + "url": "https://mcp.deepwiki.com/mcp", + "transport": "http", + "available_on_public_internet": true + }' +``` + +```bash title="Update an existing server" showLineNumbers +curl -X PUT /v1/mcp/server \ + -H "Authorization: Bearer sk-..." \ + -H "Content-Type: application/json" \ + -d '{ + "server_id": "", + "available_on_public_internet": false + }' +``` + + + + +### Custom Private IP Ranges + +By default, LiteLLM treats RFC 1918 private ranges as internal. You can customize this in the **Network Settings** tab under MCP Servers, or via config: + +```yaml title="config.yaml" showLineNumbers +general_settings: + mcp_internal_ip_ranges: + - "10.0.0.0/8" + - "172.16.0.0/12" + - "192.168.0.0/16" + - "100.64.0.0/10" # Add your VPN/Tailscale range +``` + +When empty, the standard private ranges are used (`10.0.0.0/8`, `172.16.0.0/12`, `192.168.0.0/16`, `127.0.0.0/8`). diff --git a/docs/my-website/docs/observability/langfuse_integration.md b/docs/my-website/docs/observability/langfuse_integration.md index a81336c5bc6..d3c5a44d481 100644 --- a/docs/my-website/docs/observability/langfuse_integration.md +++ b/docs/my-website/docs/observability/langfuse_integration.md @@ -215,6 +215,66 @@ The following parameters can be updated on a continuation of a trace by passing Any other key value pairs passed into the metadata not listed in the above spec for a `litellm` completion will be added as a metadata key value pair for the generation. +#### Multiple Langfuse Projects (Per-Request Credentials) + +You can send traces to different Langfuse projects per request by passing credentials directly to `completion()` or `acompletion()`. This works alongside (or instead of) the global env vars and is useful when different teams or business processes use different Langfuse projects. + +Pass **`langfuse_public_key`**, **`langfuse_secret_key`** (or **`langfuse_secret`**), and optionally **`langfuse_host`** as keyword arguments: + +```python +import litellm +from litellm import completion + +# Optional: set a default via env for requests that don't pass credentials +# os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-default..." +# os.environ["LANGFUSE_SECRET_KEY"] = "sk-default..." + +litellm.success_callback = ["langfuse"] +litellm.failure_callback = ["langfuse"] + +# Request 1 → Langfuse Project A +response_a = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello from team A"}], + langfuse_public_key="pk-lf-project-a...", + langfuse_secret_key="sk-lf-project-a...", + langfuse_host="https://us.cloud.langfuse.com", # optional +) + +# Request 2 → Langfuse Project B (different project) +response_b = completion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello from team B"}], + langfuse_public_key="pk-lf-project-b...", + langfuse_secret_key="sk-lf-project-b...", + langfuse_host="https://eu.cloud.langfuse.com", # optional, can differ per project +) +``` + +Async usage with per-request credentials: + +```python +import litellm +from litellm import acompletion + +litellm.success_callback = ["langfuse"] +litellm.failure_callback = ["langfuse"] + +response = await acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hi"}], + langfuse_public_key="pk-lf-...", + langfuse_secret_key="sk-lf-...", + langfuse_host="https://us.cloud.langfuse.com", # optional +) +``` + +- **`langfuse_public_key`** – Langfuse project public key (required for per-request override). +- **`langfuse_secret_key`** or **`langfuse_secret`** – Langfuse secret key (either name is accepted). +- **`langfuse_host`** – Langfuse host URL (e.g. `https://us.cloud.langfuse.com`); optional, defaults to env or Langfuse cloud. + +When these are passed, that request uses this project (and host) for the Langfuse callback; when omitted, the callback uses the global Langfuse client (from env vars if set). LiteLLM caches a Langfuse client per credential set to avoid creating a new client on every request. + #### Disable Logging - Specific Calls To disable logging for specific calls use the `no-log` flag. diff --git a/docs/my-website/docs/pass_through/bedrock.md b/docs/my-website/docs/pass_through/bedrock.md index b8d20d77da0..65c5d8caadc 100644 --- a/docs/my-website/docs/pass_through/bedrock.md +++ b/docs/my-website/docs/pass_through/bedrock.md @@ -556,3 +556,147 @@ for event in response.get("completion"): print(completion) ``` + +## Using LangChain AWS SDK with LiteLLM + +You can use the [LangChain AWS SDK](https://python.langchain.com/docs/integrations/chat/bedrock/) with LiteLLM Proxy to get cost tracking, load balancing, and other LiteLLM features. + +### Quick Start + +**1. Install LangChain AWS**: + +```bash showLineNumbers +pip install langchain-aws +``` + +**2. Setup LiteLLM Proxy**: + +Create a `config.yaml`: + +```yaml showLineNumbers +model_list: + - model_name: claude-sonnet + litellm_params: + model: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0 + aws_region_name: us-east-1 + custom_llm_provider: bedrock +``` + +Start the proxy: + +```bash showLineNumbers +export AWS_ACCESS_KEY_ID="your-access-key" +export AWS_SECRET_ACCESS_KEY="your-secret-key" + +litellm --config config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +**3. Use LangChain with LiteLLM**: + +```python showLineNumbers +from langchain_aws import ChatBedrockConverse +from langchain_core.messages import HumanMessage + +# Your LiteLLM API key +API_KEY = "Bearer sk-1234" + +# Initialize ChatBedrockConverse pointing to LiteLLM proxy +llm = ChatBedrockConverse( + model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0", + endpoint_url="http://localhost:4000/bedrock", + region_name="us-east-1", + aws_access_key_id=API_KEY, + aws_secret_access_key="bedrock" # Any non-empty value works +) + +# Invoke the model +messages = [HumanMessage(content="Hello, how are you?")] +response = llm.invoke(messages) + +print(response.content) +``` + +### Advanced Example: PDF Document Processing with Citations + +LangChain AWS SDK supports Bedrock's document processing features. Here's how to use it with LiteLLM: + +```python showLineNumbers +import os +import json +from langchain_aws import ChatBedrockConverse +from langchain_core.messages import HumanMessage + +# Your LiteLLM API key +API_KEY = "Bearer sk-1234" + +def get_llm() -> ChatBedrockConverse: + """Initialize LLM pointing to LiteLLM proxy""" + llm = ChatBedrockConverse( + model_id="us.anthropic.claude-3-7-sonnet-20250219-v1:0", + base_model_id="anthropic.claude-3-7-sonnet-20250219-v1:0", + endpoint_url="http://localhost:4000/bedrock", + region_name="us-east-1", + aws_access_key_id=API_KEY, + aws_secret_access_key="bedrock" + ) + return llm + +if __name__ == "__main__": + # Initialize the LLM + llm = get_llm() + + # Read PDF file as bytes (Converse API requires raw bytes) + with open("your-document.pdf", "rb") as file: + file_bytes = file.read() + + # Prepare messages with document attachment + messages = [ + HumanMessage(content=[ + {"text": "What is the policy number in this document?"}, + { + "document": { + "format": "pdf", + "name": "PolicyDocument", + "source": {"bytes": file_bytes}, + "citations": {"enabled": True} + } + } + ]) + ] + + # Invoke the LLM + response = llm.invoke(messages) + + # Print response with citations + print(json.dumps(response.content, indent=4)) +``` + +### Supported LangChain Features + +All LangChain AWS features work with LiteLLM: + +| Feature | Supported | Notes | +|---------|-----------|-------| +| Text Generation | ✅ | Full support | +| Streaming | ✅ | Use `stream()` method | +| Document Processing | ✅ | PDF, images, etc. | +| Citations | ✅ | Enable in document config | +| Tool Use | ✅ | Function calling support | +| Multi-modal | ✅ | Text + images + documents | + +### Troubleshooting + +**Issue**: `UnknownOperationException` error + +**Solution**: Make sure you're using the correct endpoint URL format: +- ✅ Correct: `http://localhost:4000/bedrock` +- ❌ Wrong: `http://localhost:4000/bedrock/v2` + +**Issue**: Authentication errors + +**Solution**: Ensure your API key is in the correct format: +```python +aws_access_key_id="Bearer sk-1234" # Include "Bearer " prefix +``` diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index 446d663c5ac..de5a4dc610c 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -1473,6 +1473,20 @@ LiteLLM translates OpenAI's `reasoning_effort` to Anthropic's `thinking` paramet | "medium" | "budget_tokens": 2048 | | "high" | "budget_tokens": 4096 | +:::note +For Claude Opus 4.6, all `reasoning_effort` values (`low`, `medium`, `high`) are mapped to `thinking: {type: "adaptive"}`. To use explicit thinking budgets, pass the native `thinking` parameter directly: + +```python +from litellm import completion + +resp = completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the capital of France?"}], + thinking={"type": "enabled", "budget_tokens": 1024}, +) +``` +::: + @@ -1614,8 +1628,65 @@ curl http://0.0.0.0:4000/v1/chat/completions \ +#### Adaptive Thinking (Claude Opus 4.6) + + +```python +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the optimal strategy for solving this problem?"}], + thinking={"type": "adaptive"}, +) +``` + + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "anthropic/claude-opus-4-6", + "messages": [{"role": "user", "content": "What is the optimal strategy for solving this problem?"}], + "thinking": {"type": "adaptive"} + }' +``` + + + + +#### Enabled Thinking with Budget + + + + +```python +response = litellm.completion( + model="anthropic/claude-opus-4-6", + messages=[{"role": "user", "content": "What is the capital of France?"}], + thinking={"type": "enabled", "budget_tokens": 5000}, +) +``` + + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_KEY" \ + -d '{ + "model": "anthropic/claude-opus-4-6", + "messages": [{"role": "user", "content": "What is the capital of France?"}], + "thinking": {"type": "enabled", "budget_tokens": 5000} + }' +``` + + + ## **Passing Extra Headers to Anthropic API** diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 487212ad655..e546ed97656 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -9,7 +9,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor | Description | Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs). | | Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/qwen2/`](./bedrock_imported.md#qwen2-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc), [`bedrock/moonshot`](./bedrock_imported.md#moonshot-kimi-k2-thinking) | | Provider Doc | [Amazon Bedrock ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) | -| Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations` | +| Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations`, `/v1/realtime`| | Rerank Endpoint | `/rerank` | | Pass-through Endpoint | [Supported](../pass_through/bedrock.md) | diff --git a/docs/my-website/docs/tutorials/bedrock_realtime_with_audio.md b/docs/my-website/docs/providers/bedrock_realtime_with_audio.md similarity index 98% rename from docs/my-website/docs/tutorials/bedrock_realtime_with_audio.md rename to docs/my-website/docs/providers/bedrock_realtime_with_audio.md index 07e29af5320..a2d9813ffd9 100644 --- a/docs/my-website/docs/tutorials/bedrock_realtime_with_audio.md +++ b/docs/my-website/docs/providers/bedrock_realtime_with_audio.md @@ -1,8 +1,4 @@ -# Call Bedrock Nova Sonic Realtime API with Audio Input/Output - -:::info -Requires LiteLLM Proxy v1.70.1+ -::: +# Bedrock Realtime API ## Overview diff --git a/docs/my-website/docs/providers/dashscope.md b/docs/my-website/docs/providers/dashscope.md index 565776d6c4c..3df0fbab1ba 100644 --- a/docs/my-website/docs/providers/dashscope.md +++ b/docs/my-website/docs/providers/dashscope.md @@ -1,7 +1,7 @@ -# Dashscope (Qwen API) +# Dashscope API (Qwen models) https://dashscope.console.aliyun.com/ -**We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests** +**We support ALL Qwen models (from Alibaba Cloud), just set `dashscope/` as a prefix when sending completion requests** ## API Key ```python @@ -9,6 +9,26 @@ https://dashscope.console.aliyun.com/ os.environ['DASHSCOPE_API_KEY'] ``` +## API Base +You can optionally specify the API base URL depending on your region: + +| Region | API Base | +|--------|----------| +| **International** | `https://dashscope-intl.aliyuncs.com/compatible-mode/v1` | +| **China/Beijing** | `https://dashscope.aliyuncs.com/compatible-mode/v1` | + +```python +# Set via environment variable +os.environ['DASHSCOPE_API_BASE'] = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1" + +# Or pass directly in the completion call +response = completion( + model="dashscope/qwen-turbo", + messages=[{"role": "user", "content": "hello"}], + api_base="https://dashscope-intl.aliyuncs.com/compatible-mode/v1" +) +``` + ## Sample Usage ```python from litellm import completion @@ -43,9 +63,7 @@ for chunk in response: ``` -## Supported Models - ALL Qwen Models Supported! -We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests - +## All supported Models [DashScope Model List](https://help.aliyun.com/zh/model-studio/compatibility-of-openai-with-dashscope?spm=a2c4g.11186623.help-menu-2400256.d_2_8_0.1efd516e2tTXBn&scm=20140722.H_2833609._.OR_help-T_cn~zh-V_1#7f9c78ae99pwz) diff --git a/docs/my-website/docs/providers/elevenlabs.md b/docs/my-website/docs/providers/elevenlabs.md index 5cf62f51203..b4ed3d3346b 100644 --- a/docs/my-website/docs/providers/elevenlabs.md +++ b/docs/my-website/docs/providers/elevenlabs.md @@ -243,6 +243,13 @@ ElevenLabs provides high-quality text-to-speech capabilities through their TTS A | Supported Operations | `/audio/speech` | | Link to Provider Doc | [ElevenLabs TTS API ↗](https://elevenlabs.io/docs/api-reference/text-to-speech) | +### Supported Models + +| Model | Route | Description | +|-------|-------|-------------| +| Eleven v3 | `elevenlabs/eleven_v3` | Most expressive model. 70+ languages, audio tags support for sound effects and pauses. | +| Eleven Multilingual v2 | `elevenlabs/eleven_multilingual_v2` | Default TTS model. 29 languages, stable and production-ready. | + ### Quick Start #### LiteLLM Python SDK @@ -265,6 +272,26 @@ with open("test_output.mp3", "wb") as f: f.write(audio.read()) ``` +#### Using Eleven v3 with Audio Tags + +Eleven v3 supports [audio tags](https://elevenlabs.io/docs/overview/capabilities/text-to-speech#audio-tags) for adding sound effects and pauses directly in the text: + +```python showLineNumbers title="Eleven v3 with audio tags" +import litellm +import os + +os.environ["ELEVENLABS_API_KEY"] = "your-elevenlabs-api-key" + +audio = litellm.speech( + model="elevenlabs/eleven_v3", + input='Welcome back. applause Today we have a special guest. Let me introduce them.', + voice="alloy", +) + +with open("eleven_v3_output.mp3", "wb") as f: + f.write(audio.read()) +``` + #### Advanced Usage: Overriding Parameters and ElevenLabs-Specific Features ```python showLineNumbers title="Advanced TTS with custom parameters" diff --git a/docs/my-website/docs/providers/github_copilot.md b/docs/my-website/docs/providers/github_copilot.md index 306c9f949ec..e9fd3444f5f 100644 --- a/docs/my-website/docs/providers/github_copilot.md +++ b/docs/my-website/docs/providers/github_copilot.md @@ -35,11 +35,10 @@ from litellm import completion response = completion( model="github_copilot/gpt-4", - messages=[{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}], - extra_headers={ - "editor-version": "vscode/1.85.1", - "Copilot-Integration-Id": "vscode-chat" - } + messages=[ + {"role": "system", "content": "You are a helpful coding assistant"}, + {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"} + ] ) print(response) ``` @@ -50,11 +49,7 @@ from litellm import completion stream = completion( model="github_copilot/gpt-4", messages=[{"role": "user", "content": "Explain async/await in Python"}], - stream=True, - extra_headers={ - "editor-version": "vscode/1.85.1", - "Copilot-Integration-Id": "vscode-chat" - } + stream=True ) for chunk in stream: @@ -134,11 +129,7 @@ client = OpenAI( # Non-streaming response response = client.chat.completions.create( model="github_copilot/gpt-4", - messages=[{"role": "user", "content": "How do I optimize this SQL query?"}], - extra_headers={ - "editor-version": "vscode/1.85.1", - "Copilot-Integration-Id": "vscode-chat" - } + messages=[{"role": "user", "content": "How do I optimize this SQL query?"}] ) print(response.choices[0].message.content) @@ -156,11 +147,7 @@ response = litellm.completion( model="litellm_proxy/github_copilot/gpt-4", messages=[{"role": "user", "content": "Review this code for bugs"}], api_base="http://localhost:4000", - api_key="your-proxy-api-key", - extra_headers={ - "editor-version": "vscode/1.85.1", - "Copilot-Integration-Id": "vscode-chat" - } + api_key="your-proxy-api-key" ) print(response.choices[0].message.content) @@ -174,8 +161,6 @@ print(response.choices[0].message.content) curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer your-proxy-api-key" \ - -H "editor-version: vscode/1.85.1" \ - -H "Copilot-Integration-Id: vscode-chat" \ -d '{ "model": "github_copilot/gpt-4", "messages": [{"role": "user", "content": "Explain this error message"}] @@ -211,9 +196,11 @@ export GITHUB_COPILOT_API_KEY_FILE="api-key.json" ### Headers -GitHub Copilot supports various editor-specific headers: +LiteLLM automatically injects the required GitHub Copilot headers (simulating VSCode). You don't need to specify them manually. -```python showLineNumbers title="Common Headers" +If you want to override the defaults (e.g., to simulate a different editor), you can use `extra_headers`: + +```python showLineNumbers title="Custom Headers (Optional)" extra_headers = { "editor-version": "vscode/1.85.1", # Editor version "editor-plugin-version": "copilot/1.155.0", # Plugin version diff --git a/docs/my-website/docs/providers/litellm_proxy.md b/docs/my-website/docs/providers/litellm_proxy.md index bfefc8a787c..918ac6755a5 100644 --- a/docs/my-website/docs/providers/litellm_proxy.md +++ b/docs/my-website/docs/providers/litellm_proxy.md @@ -227,6 +227,28 @@ response = litellm.completion( ) ``` +## OAuth2/JWT Authentication + +If your LiteLLM Proxy requires OAuth2/JWT authentication (e.g., Azure AD, Keycloak, Okta), the SDK can automatically obtain and refresh tokens for you. + +```python +import litellm +from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=AzureADCredential(), + scope="api://my-litellm-proxy/.default" +) +litellm.api_base = "https://my-proxy.example.com" + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +[Learn more about SDK Proxy Authentication (OAuth2/JWT Auto-Refresh) →](../proxy_auth) + ## Sending `tags` to LiteLLM Proxy Tags allow you to categorize and track your API requests for monitoring, debugging, and analytics purposes. You can send tags as a list of strings to the LiteLLM Proxy using the `extra_body` parameter. diff --git a/docs/my-website/docs/providers/openai.md b/docs/my-website/docs/providers/openai.md index 80645a51ac5..23940e1c54e 100644 --- a/docs/my-website/docs/providers/openai.md +++ b/docs/my-website/docs/providers/openai.md @@ -230,7 +230,70 @@ os.environ["OPENAI_BASE_URL"] = "https://your_host/v1" # OPTIONAL These also support the `OPENAI_BASE_URL` environment variable, which can be used to specify a custom API endpoint. -## OpenAI Vision Models +### OpenAI Web Search Models + +OpenAI has two ways to use web search, depending on the endpoint: + +| Approach | Endpoint | Models | How to enable | +|----------|----------|--------|---------------| +| **Search Models** | `/chat/completions` | `gpt-5-search-api`, `gpt-4o-search-preview`, `gpt-4o-mini-search-preview` | Pass `web_search_options` parameter | +| **Web Search Tool** | `/responses` | `gpt-5`, `gpt-4.1`, `gpt-4o`, and other regular models | Pass `web_search_preview` tool | + + + + +```python showLineNumbers +from litellm import completion + +response = completion( + model="openai/gpt-5-search-api", + messages=[{"role": "user", "content": "What is the capital of France?"}], + web_search_options={ + "search_context_size": "medium" # Options: "low", "medium", "high" + } +) +``` + + + + +```python showLineNumbers +from litellm import responses + +response = responses( + model="openai/gpt-5", + input="What is the capital of France?", + tools=[{ + "type": "web_search_preview", + "search_context_size": "low" + }] +) +``` + + + + +```yaml +model_list: + # Search model for /chat/completions + - model_name: gpt-5-search-api + litellm_params: + model: openai/gpt-5-search-api + api_key: os.environ/OPENAI_API_KEY + + # Regular model for /responses with web_search_preview tool + - model_name: gpt-5 + litellm_params: + model: openai/gpt-5 + api_key: os.environ/OPENAI_API_KEY +``` + + + + +For full details, see the [Web Search guide](../completion/web_search.md). + +## OpenAI Vision Models | Model Name | Function Call | |-----------------------|-----------------------------------------------------------------| | gpt-4o | `response = completion(model="gpt-4o", messages=messages)` | diff --git a/docs/my-website/docs/providers/openai/responses_api.md b/docs/my-website/docs/providers/openai/responses_api.md index 75eab1afac5..7799c93ccf2 100644 --- a/docs/my-website/docs/providers/openai/responses_api.md +++ b/docs/my-website/docs/providers/openai/responses_api.md @@ -37,6 +37,24 @@ for event in response: print(event) ``` +#### Web Search +```python showLineNumbers title="OpenAI Responses with Web Search" +import litellm + +response = litellm.responses( + model="openai/gpt-5", + input="What is the capital of France?", + tools=[{ + "type": "web_search_preview", + "search_context_size": "medium" # Options: "low", "medium", "high" + }] +) + +print(response) +``` + +For full details, see the [Web Search guide](../../completion/web_search.md). + #### Image Generation with Streaming ```python showLineNumbers title="OpenAI Streaming Image Generation" import litellm diff --git a/docs/my-website/docs/providers/perplexity.md b/docs/my-website/docs/providers/perplexity.md index 2fcb49c60fa..68adf9939c6 100644 --- a/docs/my-website/docs/providers/perplexity.md +++ b/docs/my-website/docs/providers/perplexity.md @@ -120,6 +120,293 @@ All models listed here https://docs.perplexity.ai/docs/model-cards are supported +## Agentic Research API (Responses API) + +Requires v1.72.6+ + + +### Using Presets + +Presets provide optimized defaults for specific use cases. Start with a preset for quick setup: + + + + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +# Using the pro-search preset +response = responses( + model="perplexity/preset/pro-search", + input="What are the latest developments in AI?", + custom_llm_provider="perplexity", +) + +print(response.output) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: perplexity-pro-search + litellm_params: + model: perplexity/preset/pro-search + api_key: os.environ/PERPLEXITY_API_KEY +``` + +2. Start proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl http://0.0.0.0:4000/v1/responses \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer anything" \ + -d '{ + "model": "perplexity-pro-search", + "input": "What are the latest developments in AI?" + }' +``` + + + + +### Using Third-Party Models + +Access models from OpenAI, Anthropic, Google, xAI, and other providers through Perplexity's unified API: + + + + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/openai/gpt-4o", + input="Explain quantum computing in simple terms", + custom_llm_provider="perplexity", + max_output_tokens=500, +) + +print(response.output) +``` + + + + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/anthropic/claude-3-5-sonnet-20241022", + input="Write a short story about a robot learning to paint", + custom_llm_provider="perplexity", + max_output_tokens=500, +) + +print(response.output) +``` + + + + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/google/gemini-2.0-flash-exp", + input="Explain the concept of neural networks", + custom_llm_provider="perplexity", + max_output_tokens=500, +) + +print(response.output) +``` + + + + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/xai/grok-2-1212", + input="What makes a good AI assistant?", + custom_llm_provider="perplexity", + max_output_tokens=500, +) + +print(response.output) +``` + + + + +### Web Search Tool + +Enable web search capabilities to access real-time information: + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/openai/gpt-4o", + input="What's the weather in San Francisco today?", + custom_llm_provider="perplexity", + tools=[{"type": "web_search"}], + instructions="You have access to a web_search tool. Use it for questions about current events.", +) + +print(response.output) +``` + + +### Reasoning Effort (Responses API) + +Control the reasoning effort level for reasoning-capable models: + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/openai/gpt-5.2", + input="Solve this complex problem step by step", + custom_llm_provider="perplexity", + reasoning={"effort": "high"}, # Options: low, medium, high + max_output_tokens=1000, +) + +print(response.output) +``` + +### Multi-Turn Conversations + +Use message arrays for multi-turn conversations with context: + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/anthropic/claude-3-5-sonnet-20241022", + input=[ + {"type": "message", "role": "system", "content": "You are a helpful assistant."}, + {"type": "message", "role": "user", "content": "What are the latest AI developments?"}, + ], + custom_llm_provider="perplexity", + instructions="Provide detailed, well-researched answers.", + max_output_tokens=800, +) + +print(response.output) +``` + +### Streaming Responses + +Stream responses for real-time output: + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +response = responses( + model="perplexity/openai/gpt-4o", + input="Tell me a story about space exploration", + custom_llm_provider="perplexity", + stream=True, + max_output_tokens=500, +) + +for chunk in response: + if hasattr(chunk, 'type'): + if chunk.type == "response.output_text.delta": + print(chunk.delta, end="", flush=True) +``` + +### Supported Third-Party Models + +| Provider | Model Name | Function Call | +|----------|------------|---------------| +| OpenAI | gpt-4o | `responses(model="perplexity/openai/gpt-4o", ...)` | +| OpenAI | gpt-4o-mini | `responses(model="perplexity/openai/gpt-4o-mini", ...)` | +| OpenAI | gpt-5.2 | `responses(model="perplexity/openai/gpt-5.2", ...)` | +| Anthropic | claude-3-5-sonnet-20241022 | `responses(model="perplexity/anthropic/claude-3-5-sonnet-20241022", ...)` | +| Anthropic | claude-3-5-haiku-20241022 | `responses(model="perplexity/anthropic/claude-3-5-haiku-20241022", ...)` | +| Google | gemini-2.0-flash-exp | `responses(model="perplexity/google/gemini-2.0-flash-exp", ...)` | +| Google | gemini-2.0-flash-thinking-exp | `responses(model="perplexity/google/gemini-2.0-flash-thinking-exp", ...)` | +| xAI | grok-2-1212 | `responses(model="perplexity/xai/grok-2-1212", ...)` | +| xAI | grok-2-vision-1212 | `responses(model="perplexity/xai/grok-2-vision-1212", ...)` | + +### Available Presets + +| Preset Name | Function Call | +|----------------|--------------------------------------------------------| +| fast-search | `responses(model="perplexity/preset/fast-search", ...)`| +| pro-search | `responses(model="perplexity/preset/pro-search", ...)` | +| deep-research | `responses(model="perplexity/preset/deep-research", ...)`| + +### Complete Example + +```python +from litellm import responses +import os + +os.environ['PERPLEXITY_API_KEY'] = "" + +# Comprehensive example with multiple features +response = responses( + model="perplexity/openai/gpt-4o", + input="Research the latest developments in quantum computing and provide sources", + custom_llm_provider="perplexity", + tools=[ + {"type": "web_search"}, + {"type": "fetch_url"} + ], + instructions="Use web_search to find relevant information and fetch_url to retrieve detailed content from sources. Provide citations for all claims.", + max_output_tokens=1000, + temperature=0.7, +) + +print(f"Response ID: {response.id}") +print(f"Model: {response.model}") +print(f"Status: {response.status}") +print(f"Output: {response.output}") +print(f"Usage: {response.usage}") +``` + :::info For more information about passing provider-specific parameters, [go here](../completion/provider_specific_params.md) diff --git a/docs/my-website/docs/providers/xai_realtime.md b/docs/my-website/docs/providers/xai_realtime.md new file mode 100644 index 00000000000..b36908c4686 --- /dev/null +++ b/docs/my-website/docs/providers/xai_realtime.md @@ -0,0 +1,308 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# xAI Voice Agent (Realtime API) + +xAI's Grok Voice Agent provides real-time voice conversation capabilities through WebSocket connections, enabling natural bidirectional audio interactions. + +| Feature | Description | Comments | +| --- | --- | --- | +| LiteLLM AI Gateway | ✅ | | +| LiteLLM Python SDK | ✅ | Full support via `litellm.realtime()` | + +## Quick Start + +### Supported Model + +| Model | Context | Features | +|-------|---------|----------| +| `xai/grok-4-1-fast-non-reasoning` | 2M tokens | Voice conversation, Function calling, Vision, Audio, Web search, Caching | + +**Note:** xAI Realtime API uses the non-reasoning variant for optimal real-time performance. + +## Python SDK Usage + +### Basic Realtime Connection + +```python +import asyncio +from litellm import realtime + +async def test_xai_realtime(): + """ + Test xAI Grok Voice Agent via LiteLLM SDK + """ + # Initialize realtime connection + ws = await realtime( + model="xai/grok-4-1-fast-non-reasoning", + api_key="your-xai-api-key", # or set XAI_API_KEY env var + ) + + # Connection established, xAI sends "conversation.created" event + print("Connected to xAI Grok Voice Agent") + + # Send a message + await ws.send_text(json.dumps({ + "type": "conversation.item.create", + "item": { + "type": "message", + "role": "user", + "content": [{ + "type": "input_text", + "text": "Hello! How are you?" + }] + } + })) + + # Request a response + await ws.send_text(json.dumps({ + "type": "response.create" + })) + + # Listen for responses + async for message in ws: + data = json.loads(message) + print(f"Received: {data['type']}") + + if data['type'] == 'response.done': + break + + await ws.close() + +# Run the async function +asyncio.run(test_xai_realtime()) +``` + +### With Audio Input/Output + +```python +import asyncio +import json +from litellm import realtime + +async def xai_voice_conversation(): + """ + Voice conversation with xAI Grok Voice Agent + """ + ws = await realtime( + model="xai/grok-4-1-fast-non-reasoning", + api_key="your-xai-api-key", + ) + + # Send audio data (base64 encoded PCM16 24kHz) + await ws.send_text(json.dumps({ + "type": "conversation.item.create", + "item": { + "type": "message", + "role": "user", + "content": [{ + "type": "input_audio", + "audio": "base64_encoded_audio_data_here" + }] + } + })) + + # Request response with audio + await ws.send_text(json.dumps({ + "type": "response.create", + "response": { + "modalities": ["text", "audio"], + "instructions": "Please respond in a friendly tone." + } + })) + + # Process streaming audio response + async for message in ws: + data = json.loads(message) + + if data['type'] == 'response.audio.delta': + # Handle audio chunks + audio_chunk = data['delta'] + # Process audio_chunk (play it, save it, etc.) + + elif data['type'] == 'response.done': + break + + await ws.close() + +asyncio.run(xai_voice_conversation()) +``` + +## LiteLLM Proxy (AI Gateway) Usage + +Load balance across multiple xAI deployments or combine with other providers. + +### 1. Add Model to Config + +```yaml +model_list: + - model_name: grok-voice-agent + litellm_params: + model: xai/grok-4-1-fast-non-reasoning + api_key: os.environ/XAI_API_KEY + model_info: + mode: realtime + + # Optional: Add fallback to OpenAI + - model_name: grok-voice-agent + litellm_params: + model: openai/gpt-4o-realtime-preview-2024-10-01 + api_key: os.environ/OPENAI_API_KEY + model_info: + mode: realtime +``` + +### 2. Start Proxy + +```bash +litellm --config /path/to/config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + +### 3. Test Connection + +#### Python Client + +```python +import asyncio +import websockets +import json + +async def test_proxy(): + url = "ws://0.0.0.0:4000/v1/realtime?model=grok-voice-agent" + + async with websockets.connect( + url, + extra_headers={ + "Authorization": "Bearer sk-1234", # Your LiteLLM proxy key + "OpenAI-Beta": "realtime=v1" + } + ) as ws: + # Wait for conversation.created event from xAI + message = await ws.recv() + print(f"Connected: {message}") + + # Send a message + await ws.send(json.dumps({ + "type": "conversation.item.create", + "item": { + "type": "message", + "role": "user", + "content": [{ + "type": "input_text", + "text": "Hello from LiteLLM proxy!" + }] + } + })) + + # Request response + await ws.send(json.dumps({ + "type": "response.create" + })) + + # Listen for response + async for message in ws: + data = json.loads(message) + print(f"Event: {data['type']}") + + if data['type'] == 'response.done': + break + +asyncio.run(test_proxy()) +``` + +#### Node.js Client + +```javascript +// test.js - Run with: node test.js +const WebSocket = require("ws"); + +const url = "ws://0.0.0.0:4000/v1/realtime?model=grok-voice-agent"; + +const ws = new WebSocket(url, { + headers: { + "Authorization": "Bearer sk-1234", + "OpenAI-Beta": "realtime=v1", + }, +}); + +ws.on("open", function open() { + console.log("Connected to xAI via LiteLLM proxy"); + + // Send a message + ws.send(JSON.stringify({ + type: "conversation.item.create", + item: { + type: "message", + role: "user", + content: [{ + type: "input_text", + text: "What's the weather like?" + }] + } + })); + + // Request response + ws.send(JSON.stringify({ + type: "response.create", + response: { + modalities: ["text"], + instructions: "Please assist the user." + } + })); +}); + +ws.on("message", function incoming(message) { + const data = JSON.parse(message.toString()); + console.log(`Event: ${data.type}`); + + if (data.type === 'response.done') { + ws.close(); + } +}); + +ws.on("error", function handleError(error) { + console.error("Error: ", error); +}); +``` + +## Key Differences from OpenAI + +xAI's Grok Voice Agent has some differences from OpenAI's Realtime API: + +| Feature | xAI | OpenAI | LiteLLM Handling | +|---------|-----|--------|------------------| +| Initial Event | `conversation.created` | `session.created` | ⚠️ Passed through as-is | +| WebSocket URL | `wss://api.x.ai/v1/realtime` | `wss://api.openai.com/v1/realtime` | ✅ Auto-configured | +| Model | `grok-4-1-fast-non-reasoning` | `gpt-4o-realtime-preview` | ✅ Via model prefix | +| Audio Format | PCM16 24kHz mono | PCM16 24kHz mono | ✅ Compatible | +| Context Window | 2M tokens | 128K tokens | N/A | + +**What LiteLLM Handles:** +- ✅ Automatic URL routing to correct provider +- ✅ Authentication headers (no `OpenAI-Beta` header for xAI) +- ✅ WebSocket connection management +- ✅ All other event types are compatible + +**What You Need to Handle:** +- ⚠️ Initial event type difference (`conversation.created` vs `session.created`) + +**Tip:** Make your client compatible with both event types: +```python +# Handle both providers +if event['type'] in ['session.created', 'conversation.created']: + print("Connection established") +``` + +## Related Documentation + +- [xAI Chat/Text Models](/docs/providers/xai) +- [LiteLLM Realtime API Overview](/docs/realtime) +- [xAI Official Documentation](https://docs.x.ai/docs) + +## Support + +For issues or questions: +- [LiteLLM GitHub Issues](https://github.com/BerriAI/litellm/issues) +- [xAI Documentation](https://docs.x.ai/docs) diff --git a/docs/my-website/docs/proxy/admin_ui_sso.md b/docs/my-website/docs/proxy/admin_ui_sso.md index 7b299429db7..f88d3480446 100644 --- a/docs/my-website/docs/proxy/admin_ui_sso.md +++ b/docs/my-website/docs/proxy/admin_ui_sso.md @@ -23,26 +23,75 @@ From v1.76.0, SSO is now Free for up to 5 users. -1. Add Okta credentials to your .env +#### Step 1: Create an OIDC Application in Okta + +In your Okta Admin Console, create a new **OIDC Web Application**. See [Okta's guide on creating OIDC app integrations](https://help.okta.com/en-us/content/topics/apps/apps_app_integration_wizard_oidc.htm) for detailed instructions. + +When configuring the application: +- **Sign-in redirect URI**: `https:///sso/callback` +- **Sign-out redirect URI** (optional): `https://` + + + +After creating the app, copy your **Client ID** and **Client Secret** from the application's General tab: + + + +#### Step 2: Assign Users to the Application + +Ensure users are assigned to the app in the **Assignments** tab. If Federation Broker Mode is enabled, you may need to disable it to assign users manually. + +#### Step 3: Configure Authorization Server Access Policy + +:::warning Important +This step is required. Without an Access Policy for your app, users will get a `no_matching_policy` error when attempting to log in. +::: + +1. Go to **Security** → **API** + + + +2. Select the **default** authorization server (or your custom one) + + + +3. Click on **Access Policies** tab, create a new policy assigned to your LiteLLM app +4. Add a rule that allows the **Authorization Code** grant type + + + +See [Okta's Access Policy documentation](https://help.okta.com/en-us/content/topics/security/api-access-management/access-policies.htm) for more details. + +#### Step 4: Configure LiteLLM Environment Variables ```bash -GENERIC_CLIENT_ID = "" -GENERIC_CLIENT_SECRET = "" -GENERIC_AUTHORIZATION_ENDPOINT = "/authorize" # https://dev-2kqkcd6lx6kdkuzt.us.auth0.com/authorize -GENERIC_TOKEN_ENDPOINT = "/token" # https://dev-2kqkcd6lx6kdkuzt.us.auth0.com/oauth/token -GENERIC_USERINFO_ENDPOINT = "/userinfo" # https://dev-2kqkcd6lx6kdkuzt.us.auth0.com/userinfo -GENERIC_CLIENT_STATE = "random-string" # [OPTIONAL] REQUIRED BY OKTA, if not set random state value is generated -GENERIC_SSO_HEADERS = "Content-Type=application/json, X-Custom-Header=custom-value" # [OPTIONAL] Comma-separated list of additional headers to add to the request - e.g. Content-Type=application/json, etc. +GENERIC_CLIENT_ID="" +GENERIC_CLIENT_SECRET="" +GENERIC_AUTHORIZATION_ENDPOINT="https:///oauth2/default/v1/authorize" +GENERIC_TOKEN_ENDPOINT="https:///oauth2/default/v1/token" +GENERIC_USERINFO_ENDPOINT="https:///oauth2/default/v1/userinfo" +GENERIC_CLIENT_STATE="random-string" +PROXY_BASE_URL="https://" ``` -You can get your domain specific auth/token/userinfo endpoints at `/.well-known/openid-configuration` +:::tip +You can find all OAuth endpoints at `https:///.well-known/openid-configuration` +::: -2. Add proxy url as callback_url on Okta +#### Step 5: Test the SSO Flow -On Okta, add the 'callback_url' as `/sso/callback` +1. Start your LiteLLM proxy +2. Navigate to `https:///ui` +3. Click the SSO login button +4. Authenticate with Okta and verify you're redirected back to LiteLLM +#### Troubleshooting - +| Error | Cause | Solution | +|-------|-------|----------| +| `redirect_uri` error | Redirect URI not configured | Add `/sso/callback` to Sign-in redirect URIs in Okta | +| `access_denied` | User not assigned to app | Assign the user in the Assignments tab | +| `no_matching_policy` | Missing Access Policy | Create an Access Policy in the Authorization Server (see Step 3) | @@ -174,6 +223,7 @@ GENERIC_USER_FIRST_NAME_ATTRIBUTE = "first_name" GENERIC_USER_LAST_NAME_ATTRIBUTE = "last_name" GENERIC_USER_ROLE_ATTRIBUTE = "given_role" GENERIC_USER_PROVIDER_ATTRIBUTE = "provider" +GENERIC_USER_EXTRA_ATTRIBUTES = "department,employee_id,manager" # comma-separated list of additional fields to extract from SSO response GENERIC_CLIENT_STATE = "some-state" # if the provider needs a state parameter GENERIC_INCLUDE_CLIENT_ID = "false" # some providers enforce that the client_id is not in the body GENERIC_SCOPE = "openid profile email" # default scope openid is sometimes not enough to retrieve basic user info like first_name and last_name located in profile scope @@ -190,6 +240,40 @@ Use `GENERIC_USER_ROLE_ATTRIBUTE` to specify which attribute in the SSO token co Nested attribute paths are supported (e.g., `claims.role` or `attributes.litellm_role`). +**Capturing Additional SSO Fields** + +Use `GENERIC_USER_EXTRA_ATTRIBUTES` to extract additional fields from the SSO provider response beyond the standard user attributes (id, email, name, etc.). This is useful when you need to access custom organization-specific data (e.g., department, employee ID, groups) in your [custom SSO handler](./custom_sso.md). + +```shell +# Comma-separated list of field names to extract +GENERIC_USER_EXTRA_ATTRIBUTES="department,employee_id,manager,groups" +``` + +**Accessing Extra Fields in Custom SSO Handler:** + +```python +from litellm.proxy.management_endpoints.types import CustomOpenID + +async def custom_sso_handler(userIDPInfo: CustomOpenID): + # Access the extra fields + extra_fields = getattr(userIDPInfo, 'extra_fields', None) or {} + + user_department = extra_fields.get("department") + employee_id = extra_fields.get("employee_id") + user_groups = extra_fields.get("groups", []) + + # Use these fields for custom logic (e.g., team assignment, access control) + # ... +``` + +**Nested Field Paths:** + +Dot notation is supported for nested fields: + +```shell +GENERIC_USER_EXTRA_ATTRIBUTES="org_info.department,org_info.cost_center,metadata.employee_type" +``` + - Set Redirect URI, if your provider requires it - Set a redirect url = `/sso/callback` ```shell diff --git a/docs/my-website/docs/proxy/cli.md b/docs/my-website/docs/proxy/cli.md index 9244f75b756..d3624000a32 100644 --- a/docs/my-website/docs/proxy/cli.md +++ b/docs/my-website/docs/proxy/cli.md @@ -1,7 +1,10 @@ # CLI Arguments -Cli arguments, --host, --port, --num_workers -## --host +This page documents all command-line interface (CLI) arguments available for the LiteLLM proxy server. + +## Server Configuration + +### --host - **Default:** `'0.0.0.0'` - The host for the server to listen on. - **Usage:** @@ -14,7 +17,7 @@ Cli arguments, --host, --port, --num_workers litellm ``` -## --port +### --port - **Default:** `4000` - The port to bind the server to. - **Usage:** @@ -27,9 +30,9 @@ Cli arguments, --host, --port, --num_workers litellm ``` -## --num_workers - - **Default:** `1` - - The number of uvicorn workers to spin up. +### --num_workers + - **Default:** Number of logical CPUs in the system, or `4` if that cannot be determined + - The number of uvicorn / gunicorn workers to spin up. - **Usage:** ```shell litellm --num_workers 4 @@ -40,55 +43,273 @@ Cli arguments, --host, --port, --num_workers litellm ``` -## --api_base +### --config + - **Short form:** `-c` - **Default:** `None` - - The API base for the model litellm should call. + - Path to the proxy configuration file (e.g., config.yaml). + - **Usage:** + ```shell + litellm --config path/to/config.yaml + ``` + +### --log_config + - **Default:** `None` + - **Type:** `str` + - Path to the logging configuration file for uvicorn. + - **Usage:** + ```shell + litellm --log_config path/to/log_config.conf + ``` + +### --keepalive_timeout + - **Default:** `None` + - **Type:** `int` + - Set the uvicorn keepalive timeout in seconds (uvicorn timeout_keep_alive parameter). + - **Usage:** + ```shell + litellm --keepalive_timeout 30 + ``` + - **Usage - set Environment Variable:** `KEEPALIVE_TIMEOUT` + ```shell + export KEEPALIVE_TIMEOUT=30 + litellm + ``` + +### --max_requests_before_restart + - **Default:** `None` + - **Type:** `int` + - Restart worker after this many requests. This is useful for mitigating memory growth over time. + - For uvicorn: maps to `limit_max_requests` + - For gunicorn: maps to `max_requests` + - **Usage:** + ```shell + litellm --max_requests_before_restart 10000 + ``` + - **Usage - set Environment Variable:** `MAX_REQUESTS_BEFORE_RESTART` + ```shell + export MAX_REQUESTS_BEFORE_RESTART=10000 + litellm + ``` + +## Server Backend Options + +### --run_gunicorn + - **Default:** `False` + - **Type:** `bool` (Flag) + - Starts proxy via gunicorn instead of uvicorn. Better for managing multiple workers in production. + - **Usage:** + ```shell + litellm --run_gunicorn + ``` + +### --run_hypercorn + - **Default:** `False` + - **Type:** `bool` (Flag) + - Starts proxy via hypercorn instead of uvicorn. Supports HTTP/2. + - **Usage:** + ```shell + litellm --run_hypercorn + ``` + +### --skip_server_startup + - **Default:** `False` + - **Type:** `bool` (Flag) + - Skip starting the server after setup (useful for database migrations only). + - **Usage:** + ```shell + litellm --skip_server_startup + ``` + +## SSL/TLS Configuration + +### --ssl_keyfile_path + - **Default:** `None` + - **Type:** `str` + - Path to the SSL keyfile. Use this when you want to provide SSL certificate when starting proxy. + - **Usage:** + ```shell + litellm --ssl_keyfile_path /path/to/key.pem --ssl_certfile_path /path/to/cert.pem + ``` + - **Usage - set Environment Variable:** `SSL_KEYFILE_PATH` + ```shell + export SSL_KEYFILE_PATH=/path/to/key.pem + litellm + ``` + +### --ssl_certfile_path + - **Default:** `None` + - **Type:** `str` + - Path to the SSL certfile. Use this when you want to provide SSL certificate when starting proxy. + - **Usage:** + ```shell + litellm --ssl_certfile_path /path/to/cert.pem --ssl_keyfile_path /path/to/key.pem + ``` + - **Usage - set Environment Variable:** `SSL_CERTFILE_PATH` + ```shell + export SSL_CERTFILE_PATH=/path/to/cert.pem + litellm + ``` + +### --ciphers + - **Default:** `None` + - **Type:** `str` + - Ciphers to use for the SSL setup. Only used with `--run_hypercorn`. + - **Usage:** + ```shell + litellm --run_hypercorn --ssl_keyfile_path /path/to/key.pem --ssl_certfile_path /path/to/cert.pem --ciphers "ECDHE+AESGCM" + ``` + +## Model Configuration + +### --model or -m + - **Default:** `None` + - The model name to pass to LiteLLM. + - **Usage:** + ```shell + litellm --model gpt-3.5-turbo + ``` + +### --alias + - **Default:** `None` + - An alias for the model, for user-friendly reference. Use this to give a litellm model name (e.g., "huggingface/codellama/CodeLlama-7b-Instruct-hf") a more user-friendly name ("codellama"). + - **Usage:** + ```shell + litellm --alias my-gpt-model + ``` + +### --api_base + - **Default:** `None` + - The API base for the model LiteLLM should call. - **Usage:** ```shell litellm --model huggingface/tinyllama --api_base https://k58ory32yinf1ly0.us-east-1.aws.endpoints.huggingface.cloud ``` -## --api_version - - **Default:** `None` +### --api_version + - **Default:** `2024-07-01-preview` - For Azure services, specify the API version. - **Usage:** ```shell litellm --model azure/gpt-deployment --api_version 2023-08-01 --api_base https://" ``` -## --model or -m +### --headers - **Default:** `None` - - The model name to pass to Litellm. + - Headers for the API call (as JSON string). - **Usage:** ```shell - litellm --model gpt-3.5-turbo + litellm --model my-model --headers '{"Authorization": "Bearer token"}' ``` -## --test - - **Type:** `bool` (Flag) - - Proxy chat completions URL to make a test request. - - **Usage:** - ```shell - litellm --test - ``` - -## --health - - **Type:** `bool` (Flag) - - Runs a health check on all models in config.yaml - - **Usage:** - ```shell - litellm --health - ``` - -## --alias +### --add_key - **Default:** `None` - - An alias for the model, for user-friendly reference. + - Add a key to the model configuration. - **Usage:** ```shell - litellm --alias my-gpt-model + litellm --add_key my-api-key ``` -## --debug +### --save + - **Type:** `bool` (Flag) + - Save the model-specific config. + - **Usage:** + ```shell + litellm --model gpt-3.5-turbo --save + ``` + +## Model Parameters + +### --temperature + - **Default:** `None` + - **Type:** `float` + - Set the temperature for the model. + - **Usage:** + ```shell + litellm --temperature 0.7 + ``` + +### --max_tokens + - **Default:** `None` + - **Type:** `int` + - Set the maximum number of tokens for the model output. + - **Usage:** + ```shell + litellm --max_tokens 50 + ``` + +### --request_timeout + - **Default:** `None` + - **Type:** `int` + - Set the timeout in seconds for completion calls. + - **Usage:** + ```shell + litellm --request_timeout 300 + ``` + +### --max_budget + - **Default:** `None` + - **Type:** `float` + - Set max budget for API calls. Works for hosted models like OpenAI, TogetherAI, Anthropic, etc. + - **Usage:** + ```shell + litellm --max_budget 100.0 + ``` + +### --drop_params + - **Type:** `bool` (Flag) + - Drop any unmapped params. + - **Usage:** + ```shell + litellm --drop_params + ``` + +### --add_function_to_prompt + - **Type:** `bool` (Flag) + - If a function passed but unsupported, pass it as a part of the prompt. + - **Usage:** + ```shell + litellm --add_function_to_prompt + ``` + +## Database Configuration + +### --iam_token_db_auth + - **Default:** `False` + - **Type:** `bool` (Flag) + - Connects to an RDS database using IAM token authentication instead of a password. This is useful for AWS RDS instances that are configured to use IAM database authentication. + - When enabled, LiteLLM will generate an IAM authentication token to connect to the database. + - **Required Environment Variables:** + - `DATABASE_HOST` - The RDS database host + - `DATABASE_PORT` - The database port + - `DATABASE_USER` - The database user + - `DATABASE_NAME` - The database name + - `DATABASE_SCHEMA` (optional) - The database schema + - **Usage:** + ```shell + litellm --iam_token_db_auth + ``` + - **Usage - set Environment Variable:** `IAM_TOKEN_DB_AUTH` + ```shell + export IAM_TOKEN_DB_AUTH=True + export DATABASE_HOST=mydb.us-east-1.rds.amazonaws.com + export DATABASE_PORT=5432 + export DATABASE_USER=mydbuser + export DATABASE_NAME=mydb + litellm + ``` + +### --use_prisma_db_push + - **Default:** `False` + - **Type:** `bool` (Flag) + - Use `prisma db push` instead of `prisma migrate` for database schema updates. This is useful when you want to quickly sync your database schema without creating migration files. + - **Usage:** + ```shell + litellm --use_prisma_db_push + ``` + +## Debugging + +### --debug - **Default:** `False` - **Type:** `bool` (Flag) - Enable debugging mode for the input. @@ -102,10 +323,10 @@ Cli arguments, --host, --port, --num_workers litellm ``` -## --detailed_debug +### --detailed_debug - **Default:** `False` - **Type:** `bool` (Flag) - - Enable debugging mode for the input. + - Enable detailed debugging mode to view verbose debug logs. - **Usage:** ```shell litellm --detailed_debug @@ -116,80 +337,76 @@ Cli arguments, --host, --port, --num_workers litellm ``` -#### --temperature - - **Default:** `None` - - **Type:** `float` - - Set the temperature for the model. - - **Usage:** - ```shell - litellm --temperature 0.7 - ``` - -## --max_tokens - - **Default:** `None` - - **Type:** `int` - - Set the maximum number of tokens for the model output. - - **Usage:** - ```shell - litellm --max_tokens 50 - ``` - -## --request_timeout - - **Default:** `6000` - - **Type:** `int` - - Set the timeout in seconds for completion calls. - - **Usage:** - ```shell - litellm --request_timeout 300 - ``` - -## --drop_params +### --local + - **Default:** `False` - **Type:** `bool` (Flag) - - Drop any unmapped params. + - For local debugging purposes. - **Usage:** ```shell - litellm --drop_params + litellm --local ``` -## --add_function_to_prompt +## Testing & Health Checks + +### --test - **Type:** `bool` (Flag) - - If a function passed but unsupported, pass it as a part of the prompt. + - Proxy chat completions URL to make a test request to. - **Usage:** ```shell - litellm --add_function_to_prompt + litellm --test ``` -## --config - - Configure Litellm by providing a configuration file path. +### --test_async + - **Default:** `False` + - **Type:** `bool` (Flag) + - Calls async endpoints `/queue/requests` and `/queue/response`. - **Usage:** ```shell - litellm --config path/to/config.yaml + litellm --test_async ``` -## --telemetry +### --num_requests + - **Default:** `10` + - **Type:** `int` + - Number of requests to hit async endpoint with (used with `--test_async`). + - **Usage:** + ```shell + litellm --test_async --num_requests 100 + ``` + +### --health + - **Type:** `bool` (Flag) + - Runs a health check on all models in config.yaml. + - **Usage:** + ```shell + litellm --health + ``` + +## Other Options + +### --version + - **Short form:** `-v` + - **Type:** `bool` (Flag) + - Print LiteLLM version and exit. + - **Usage:** + ```shell + litellm --version + ``` + +### --telemetry - **Default:** `True` - **Type:** `bool` - - Help track usage of this feature. + - Help track usage of this feature. Turn off for privacy. - **Usage:** ```shell litellm --telemetry False ``` - -## --log_config - - **Default:** `None` - - **Type:** `str` - - Specify a log configuration file for uvicorn. - - **Usage:** - ```shell - litellm --log_config path/to/log_config.conf - ``` - -## --skip_server_startup +### --use_queue - **Default:** `False` - **Type:** `bool` (Flag) - - Skip starting the server after setup (useful for DB migrations only). + - To use celery workers for async endpoints. - **Usage:** ```shell - litellm --skip_server_startup - ``` \ No newline at end of file + litellm --use_queue + ``` diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 1a1bf975d2d..5499e541335 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -94,7 +94,7 @@ litellm_settings: # /chat/completions, /completions, /embeddings, /audio/transcriptions mode: default_off # if default_off, you need to opt in to caching on a per call basis ttl: 600 # ttl for caching - disable_copilot_system_to_assistant: False # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior. + disable_copilot_system_to_assistant: False # DEPRECATED - GitHub Copilot API supports system prompts. callback_settings: otel: @@ -197,7 +197,7 @@ router_settings: | disable_add_transform_inline_image_block | boolean | For Fireworks AI models - if true, turns off the auto-add of `#transform=inline` to the url of the image_url, if the model is not a vision model. | | disable_hf_tokenizer_download | boolean | If true, it defaults to using the openai tokenizer for all models (including huggingface models). | | enable_json_schema_validation | boolean | If true, enables json schema validation for all requests. | -| disable_copilot_system_to_assistant | boolean | If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior. Useful for tools (like Claude Code) that send system messages, which Copilot does not support. | +| disable_copilot_system_to_assistant | boolean | **DEPRECATED** - GitHub Copilot API supports system prompts. | ### general_settings - Reference @@ -395,7 +395,7 @@ router_settings: | ATHINA_API_KEY | API key for Athina service | ATHINA_BASE_URL | Base URL for Athina service (defaults to `https://log.athina.ai`) | AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key) -| AUTO_REDIRECT_UI_LOGIN_TO_SSO | Flag to enable automatic redirect of UI login page to SSO when SSO is configured. Default is **true** +| AUTO_REDIRECT_UI_LOGIN_TO_SSO | Flag to enable automatic redirect of UI login page to SSO when SSO is configured. Default is **false** | AUDIO_SPEECH_CHUNK_SIZE | Chunk size for audio speech processing. Default is 1024 | ANTHROPIC_API_KEY | API key for Anthropic service | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com @@ -545,6 +545,13 @@ router_settings: | DEFAULT_MAX_TOKENS | Default maximum tokens for LLM calls. Default is 4096 | DEFAULT_MAX_TOKENS_FOR_TRITON | Default maximum tokens for Triton models. Default is 2000 | DEFAULT_MAX_REDIS_BATCH_CACHE_SIZE | Default maximum size for redis batch cache. Default is 1000 +| DEFAULT_MCP_SEMANTIC_FILTER_EMBEDDING_MODEL | Default embedding model for MCP semantic tool filtering. Default is "text-embedding-3-small" +| DEFAULT_MCP_SEMANTIC_FILTER_SIMILARITY_THRESHOLD | Default similarity threshold for MCP semantic tool filtering. Default is 0.3 +| DEFAULT_MCP_SEMANTIC_FILTER_TOP_K | Default number of top results to return for MCP semantic tool filtering. Default is 10 +| MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL | Default TTL in seconds for MCP OAuth2 token cache. Default is 3600 +| MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE | Maximum number of entries in MCP OAuth2 token cache. Default is 200 +| MCP_OAUTH2_TOKEN_CACHE_MIN_TTL | Minimum TTL in seconds for MCP OAuth2 token cache. Default is 10 +| MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS | Seconds to subtract from token expiry when computing cache TTL. Default is 60 | DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT | Default token count for mock response completions. Default is 20 | DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT | Default token count for mock response prompts. Default is 10 | DEFAULT_MODEL_CREATED_AT_TIME | Default creation timestamp for models. Default is 1677610602 @@ -637,6 +644,7 @@ router_settings: | GENERIC_TOKEN_ENDPOINT | Token endpoint for generic OAuth providers | GENERIC_USER_DISPLAY_NAME_ATTRIBUTE | Attribute for user's display name in generic auth | GENERIC_USER_EMAIL_ATTRIBUTE | Attribute for user's email in generic auth +| GENERIC_USER_EXTRA_ATTRIBUTES | Comma-separated list of additional fields to extract from generic SSO provider response (e.g., "department,employee_id,groups"). Accessible via `CustomOpenID.extra_fields` in custom SSO handlers. Supports dot notation for nested fields | GENERIC_USER_FIRST_NAME_ATTRIBUTE | Attribute for user's first name in generic auth | GENERIC_USER_ID_ATTRIBUTE | Attribute for user ID in generic auth | GENERIC_USER_LAST_NAME_ATTRIBUTE | Attribute for user's last name in generic auth @@ -738,6 +746,7 @@ router_settings: | LITERAL_API_URL | API URL for Literal service | LITERAL_BATCH_SIZE | Batch size for Literal operations | LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX | Disable automatic URL suffix appending for Anthropic API base URLs. When set to `true`, prevents LiteLLM from automatically adding `/v1/messages` or `/v1/complete` to custom Anthropic API endpoints +| LITELLM_ASSETS_PATH | Path to directory for UI assets and logos. Used when running with read-only filesystem (e.g., Kubernetes). Default is `/var/lib/litellm/assets` in Docker. | LITELLM_CLI_JWT_EXPIRATION_HOURS | Expiration time in hours for CLI-generated JWT tokens. Default is 24 hours | LITELLM_DD_AGENT_HOST | Hostname or IP of DataDog agent for LiteLLM-specific logging. When set, logs are sent to agent instead of direct API | LITELLM_DD_AGENT_PORT | Port of DataDog agent for LiteLLM-specific log intake. Default is 10518 @@ -752,6 +761,7 @@ router_settings: | LITELLM_MIGRATION_DIR | Custom migrations directory for prisma migrations, used for baselining db in read-only file systems. | LITELLM_HOSTED_UI | URL of the hosted UI for LiteLLM | LITELLM_UI_API_DOC_BASE_URL | Optional override for the API Reference base URL (used in sample code/docs) when the admin UI runs on a different host than the proxy. Defaults to `PROXY_BASE_URL` when unset. +| LITELLM_UI_PATH | Path to directory for Admin UI files. Used when running with read-only filesystem (e.g., Kubernetes). Default is `/var/lib/litellm/ui` in Docker. | LITELM_ENVIRONMENT | Environment of LiteLLM Instance, used by logging services. Currently only used by DeepEval. | LITELLM_KEY_ROTATION_ENABLED | Enable auto-key rotation for LiteLLM (boolean). Default is false. | LITELLM_KEY_ROTATION_CHECK_INTERVAL_SECONDS | Interval in seconds for how often to run job that auto-rotates keys. Default is 86400 (24 hours). @@ -777,6 +787,7 @@ router_settings: | LITELLM_USER_AGENT | Custom user agent string for LiteLLM API requests. Used for partner telemetry attribution | LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD | If true, prints the standard logging payload to the console - useful for debugging | LITELM_ENVIRONMENT | Environment for LiteLLM Instance. This is currently only logged to DeepEval to determine the environment for DeepEval integration. +| LITELLM_ASYNCIO_QUEUE_MAXSIZE | Maximum size for asyncio queues (e.g. log queues, spend update queues, and cookbook examples such as realtime audio in `nova_sonic_realtime.py`). Bounds in-memory growth to prevent OOM. Default is 1000. | LOGFIRE_TOKEN | Token for Logfire logging service | LOGFIRE_BASE_URL | Base URL for Logfire logging service (useful for self hosted deployments) | LOGGING_WORKER_CONCURRENCY | Maximum number of concurrent coroutine slots for the logging worker on the asyncio event loop. Default is 100. Setting too high will flood the event loop with logging tasks which will lower the overall latency of the requests. @@ -803,6 +814,8 @@ router_settings: | MAXIMUM_TRACEBACK_LINES_TO_LOG | Maximum number of lines to log in traceback in LiteLLM Logs UI. Default is 100 | MAX_RETRY_DELAY | Maximum delay in seconds for retrying requests. Default is 8.0 | MAX_LANGFUSE_INITIALIZED_CLIENTS | Maximum number of Langfuse clients to initialize on proxy. Default is 50. This is set since langfuse initializes 1 thread everytime a client is initialized. We've had an incident in the past where we reached 100% cpu utilization because Langfuse was initialized several times. +| MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH | Maximum header length for MCP semantic filter tools. Default is 150 +| MAX_POLICY_ESTIMATE_IMPACT_ROWS | Maximum number of rows returned when estimating the impact of a policy. Default is 1000 | MIN_NON_ZERO_TEMPERATURE | Minimum non-zero temperature value. Default is 0.0001 | MINIMUM_PROMPT_CACHE_TOKEN_COUNT | Minimum token count for caching a prompt. Default is 1024 | MISTRAL_API_BASE | Base URL for Mistral API. Default is https://api.mistral.ai @@ -819,6 +832,8 @@ router_settings: | MICROSOFT_USER_ID_ATTRIBUTE | Field name for user ID in Microsoft SSO response. Default is `id` | MICROSOFT_USER_LAST_NAME_ATTRIBUTE | Field name for user last name in Microsoft SSO response. Default is `surname` | MICROSOFT_USERINFO_ENDPOINT | Custom userinfo endpoint URL for Microsoft SSO (overrides default Microsoft Graph userinfo endpoint) +| MODEL_COST_MAP_MAX_SHRINK_RATIO | Maximum allowed shrinkage ratio when validating a fetched model cost map against the local backup. Rejects the fetched map if it is smaller than this fraction of the backup. Default is 0.5 +| MODEL_COST_MAP_MIN_MODEL_COUNT | Minimum number of models a fetched cost map must contain to be considered valid. Default is 50 | NO_DOCS | Flag to disable Swagger UI documentation | NO_REDOC | Flag to disable Redoc documentation | NO_PROXY | List of addresses to bypass proxy diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index a5674bf2bc5..56a8b9566db 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -469,6 +469,7 @@ credential_list: api_version: "2023-05-15" credential_info: description: "Production credentials for EU region" + custom_llm_provider: "azure" ``` #### Key Parameters diff --git a/docs/my-website/docs/proxy/custom_sso.md b/docs/my-website/docs/proxy/custom_sso.md index bbd7f41bee1..8b7adeb0c5a 100644 --- a/docs/my-website/docs/proxy/custom_sso.md +++ b/docs/my-website/docs/proxy/custom_sso.md @@ -142,6 +142,18 @@ async def custom_sso_handler(userIDPInfo: OpenID) -> SSOUserDefinedValues: f"No ID found for user. userIDPInfo.id is None {userIDPInfo}" ) + ################################################# + # Access extra fields from SSO provider (requires GENERIC_USER_EXTRA_ATTRIBUTES env var) + # Example: Set GENERIC_USER_EXTRA_ATTRIBUTES="department,employee_id,groups" + extra_fields = getattr(userIDPInfo, 'extra_fields', None) or {} + user_department = extra_fields.get("department") + employee_id = extra_fields.get("employee_id") + user_groups = extra_fields.get("groups", []) + + print(f"User department: {user_department}") # noqa + print(f"Employee ID: {employee_id}") # noqa + print(f"User groups: {user_groups}") # noqa + ################################################# ################################################# # Run your custom code / logic here diff --git a/docs/my-website/docs/proxy/forward_client_headers.md b/docs/my-website/docs/proxy/forward_client_headers.md index 5477ffe87aa..2155a7517be 100644 --- a/docs/my-website/docs/proxy/forward_client_headers.md +++ b/docs/my-website/docs/proxy/forward_client_headers.md @@ -6,6 +6,52 @@ Control which model groups can forward client headers to the underlying LLM prov By default, LiteLLM does not forward client headers to LLM provider APIs for security reasons. However, you can selectively enable header forwarding for specific model groups using the `forward_client_headers_to_llm_api` setting. +## How it Works + +LiteLLM does **not** forward all client headers to the LLM provider. Instead, it uses an **allowlist** approach — only headers matching specific rules are forwarded. This ensures sensitive headers (like your LiteLLM API key) are never accidentally sent to upstream providers. + +```mermaid +sequenceDiagram + participant Client as Client (SDK / curl) + participant Proxy as LiteLLM Proxy + participant Filter as Header Filter (Allowlist) + participant LLM as LLM Provider (OpenAI, Anthropic, etc.) + + Client->>Proxy: Request with all headers
(Authorization, x-trace-id,
x-custom-header, anthropic-beta, etc.) + + Proxy->>Filter: Check forward_client_headers_to_llm_api
setting for this model group + + Note over Filter: Allowlist rules:
1. Headers starting with "x-" ✅
2. "anthropic-beta" ✅
3. "x-stainless-*" ❌ (blocked)
4. All other headers ❌ (blocked) + + Filter-->>Proxy: Return only allowed headers + + Proxy->>LLM: Request with filtered headers
(x-trace-id, x-custom-header,
anthropic-beta) + + LLM-->>Proxy: Response + Proxy-->>Client: Response +``` + +### Header Allowlist Rules + +The following rules determine which headers are forwarded (see [`_get_forwardable_headers`](https://github.com/litellm/litellm/blob/main/litellm/proxy/litellm_pre_call_utils.py) in `litellm/proxy/litellm_pre_call_utils.py`): + +| Rule | Example | Forwarded? | +|---|---|---| +| Headers starting with `x-` | `x-trace-id`, `x-custom-header`, `x-request-source` | ✅ Yes | +| `anthropic-beta` header | `anthropic-beta: prompt-caching-2024-07-31` | ✅ Yes | +| Headers starting with `x-stainless-*` | `x-stainless-lang`, `x-stainless-arch` | ❌ No (causes OpenAI SDK issues) | +| Standard HTTP headers | `Authorization`, `Content-Type`, `Host` | ❌ No | +| Other provider headers | `Accept`, `User-Agent` | ❌ No | + +### Additional Header Mechanisms + +| Mechanism | Description | Reference | +|---|---|---| +| **`x-pass-` prefix** | Headers prefixed with `x-pass-` are always forwarded with the prefix stripped, regardless of settings. E.g., `x-pass-anthropic-beta: value` → `anthropic-beta: value`. Works for all pass-through endpoints. | [Source code](https://github.com/litellm/litellm/blob/main/litellm/passthrough/utils.py) | +| **`openai-organization`** | Forwarded only when `forward_openai_org_id: true` is set in `general_settings`. | [Forward OpenAI Org ID](#enable-globally) | +| **User information headers** | When `add_user_information_to_llm_headers: true`, LiteLLM adds `x-litellm-user-id`, `x-litellm-org-id`, etc. | [User Information Headers](#user-information-headers-optional) | +| **Vertex AI pass-through** | Uses a separate, stricter allowlist: only `anthropic-beta` and `content-type`. | [Source code](https://github.com/litellm/litellm/blob/main/litellm/constants.py) | + ## Configuration ## Enable Globally diff --git a/docs/my-website/docs/proxy/guardrails/custom_code_guardrail.md b/docs/my-website/docs/proxy/guardrails/custom_code_guardrail.md new file mode 100644 index 00000000000..8cbc247ae5e --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/custom_code_guardrail.md @@ -0,0 +1,332 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Custom Code Guardrail + +Write custom guardrail logic using Python-like code that runs in a sandboxed environment. + +## Quick Start + +### 1. Define the guardrail in config + +```yaml +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + +guardrails: + - guardrail_name: block-ssn + litellm_params: + guardrail: custom_code + mode: pre_call + custom_code: | + def apply_guardrail(inputs, request_data, input_type): + for text in inputs["texts"]: + if regex_match(text, r"\d{3}-\d{2}-\d{4}"): + return block("SSN detected") + return allow() +``` + +### 2. Start proxy + +```bash +litellm --config config.yaml +``` + +### 3. Test + +```bash +curl -X POST http://localhost:4000/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "My SSN is 123-45-6789"}], + "guardrails": ["block-ssn"] + }' +``` + +## Configuration + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `guardrail` | string | ✅ | Must be `custom_code` | +| `mode` | string | ✅ | When to run: `pre_call`, `post_call`, `during_call` | +| `custom_code` | string | ✅ | Python-like code with `apply_guardrail` function | +| `default_on` | bool | ❌ | Run on all requests (default: `false`) | + +## Writing Custom Code + +### Function Signature + +Your code must define an `apply_guardrail` function. It can be either sync or async: + +```python +# Sync version +def apply_guardrail(inputs, request_data, input_type): + # inputs: see table below + # request_data: {"model": "...", "user_id": "...", "team_id": "...", "metadata": {...}} + # input_type: "request" or "response" + + return allow() # or block() or modify() + +# Async version (recommended when using HTTP primitives) +async def apply_guardrail(inputs, request_data, input_type): + response = await http_post("https://api.example.com/check", body={"text": inputs["texts"][0]}) + if response["success"] and response["body"].get("flagged"): + return block("Content flagged") + return allow() +``` + +### `inputs` Parameter + +| Field | Type | Description | +|-------|------|-------------| +| `texts` | `List[str]` | Extracted text from the request/response | +| `images` | `List[str]` | Extracted images (for image guardrails) | +| `tools` | `List[dict]` | Tools sent to the LLM | +| `tool_calls` | `List[dict]` | Tool calls returned from the LLM | +| `structured_messages` | `List[dict]` | Full messages with role info (system/user/assistant) | +| `model` | `str` | The model being used | + +### `request_data` Parameter + +| Field | Type | Description | +|-------|------|-------------| +| `model` | `str` | Model name | +| `user_id` | `str` | User ID from API key | +| `team_id` | `str` | Team ID from API key | +| `end_user_id` | `str` | End user ID | +| `metadata` | `dict` | Request metadata | + +### Return Values + +| Function | Description | +|----------|-------------| +| `allow()` | Let request/response through | +| `block(reason)` | Reject with message | +| `modify(texts=[], images=[], tool_calls=[])` | Transform content | + +## Built-in Primitives + +### Regex + +| Function | Description | +|----------|-------------| +| `regex_match(text, pattern)` | Returns `True` if pattern found | +| `regex_replace(text, pattern, replacement)` | Replace all matches | +| `regex_find_all(text, pattern)` | Return list of matches | + +### JSON + +| Function | Description | +|----------|-------------| +| `json_parse(text)` | Parse JSON string, returns `None` on error | +| `json_stringify(obj)` | Convert to JSON string | +| `json_schema_valid(obj, schema)` | Validate against JSON schema | + +### URL + +| Function | Description | +|----------|-------------| +| `extract_urls(text)` | Extract all URLs from text | +| `is_valid_url(url)` | Check if URL is valid | +| `all_urls_valid(text)` | Check all URLs in text are valid | + +### Code Detection + +| Function | Description | +|----------|-------------| +| `detect_code(text)` | Returns `True` if code detected | +| `detect_code_languages(text)` | Returns list of detected languages | +| `contains_code_language(text, ["sql", "python"])` | Check for specific languages | + +### Text Utilities + +| Function | Description | +|----------|-------------| +| `contains(text, substring)` | Check if substring exists | +| `contains_any(text, [substr1, substr2])` | Check if any substring exists | +| `word_count(text)` | Count words | +| `char_count(text)` | Count characters | +| `lower(text)` / `upper(text)` / `trim(text)` | String transforms | + +### HTTP Requests (Async) + +Make async HTTP requests to external APIs for additional validation or content moderation. + +| Function | Description | +|----------|-------------| +| `await http_request(url, method, headers, body, timeout)` | General async HTTP request | +| `await http_get(url, headers, timeout)` | Async GET request | +| `await http_post(url, body, headers, timeout)` | Async POST request | + +**Response format:** +```python +{ + "status_code": 200, # HTTP status code + "body": {...}, # Response body (parsed JSON or string) + "headers": {...}, # Response headers + "success": True, # True if status code is 2xx + "error": None # Error message if request failed +} +``` + +**Note:** When using HTTP primitives, define your function as `async def apply_guardrail(...)` for non-blocking execution. + +## Examples + +### Block PII (SSN) + +```python +def apply_guardrail(inputs, request_data, input_type): + for text in inputs["texts"]: + if regex_match(text, r"\d{3}-\d{2}-\d{4}"): + return block("SSN detected") + return allow() +``` + +### Redact Email Addresses + +```python +def apply_guardrail(inputs, request_data, input_type): + pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}" + modified = [] + for text in inputs["texts"]: + modified.append(regex_replace(text, pattern, "[EMAIL REDACTED]")) + return modify(texts=modified) +``` + +### Block SQL Injection + +```python +def apply_guardrail(inputs, request_data, input_type): + if input_type != "request": + return allow() + for text in inputs["texts"]: + if contains_code_language(text, ["sql"]): + return block("SQL code not allowed") + return allow() +``` + +### Validate JSON Response + +```python +def apply_guardrail(inputs, request_data, input_type): + if input_type != "response": + return allow() + + schema = { + "type": "object", + "required": ["name", "value"] + } + + for text in inputs["texts"]: + obj = json_parse(text) + if obj is None: + return block("Invalid JSON response") + if not json_schema_valid(obj, schema): + return block("Response missing required fields") + return allow() +``` + +### Check URLs in Response + +```python +def apply_guardrail(inputs, request_data, input_type): + if input_type != "response": + return allow() + for text in inputs["texts"]: + if not all_urls_valid(text): + return block("Response contains invalid URLs") + return allow() +``` + +### Call External Moderation API (Async) + +```python +async def apply_guardrail(inputs, request_data, input_type): + # Call an external moderation API + for text in inputs["texts"]: + response = await http_post( + "https://api.example.com/moderate", + body={"text": text, "user_id": request_data["user_id"]}, + headers={"Authorization": "Bearer YOUR_API_KEY"}, + timeout=10 + ) + + if not response["success"]: + # API call failed - decide whether to allow or block + return allow() + + if response["body"].get("flagged"): + return block(response["body"].get("reason", "Content flagged")) + + return allow() +``` + +### Combine Multiple Checks + +```python +def apply_guardrail(inputs, request_data, input_type): + modified = [] + + for text in inputs["texts"]: + # Redact SSN + text = regex_replace(text, r"\d{3}-\d{2}-\d{4}", "[SSN]") + # Redact credit cards + text = regex_replace(text, r"\d{16}", "[CARD]") + modified.append(text) + + # Block SQL in requests + if input_type == "request": + for text in inputs["texts"]: + if contains_code_language(text, ["sql"]): + return block("SQL injection blocked") + + return modify(texts=modified) +``` + +## Sandbox Restrictions + +Custom code runs in a restricted environment: + +- ❌ No `import` statements +- ❌ No file I/O +- ❌ No `exec()` or `eval()` +- ✅ HTTP requests via built-in `http_request`, `http_get`, `http_post` primitives +- ✅ Only LiteLLM-provided primitives available + +## Per-Request Usage + +Enable guardrail per request: + +```bash +curl -X POST http://localhost:4000/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "guardrails": ["block-ssn"] + }' +``` + +## Default On + +Run guardrail on all requests: + +```yaml +litellm_settings: + guardrails: + - guardrail_name: block-ssn + litellm_params: + guardrail: custom_code + mode: pre_call + default_on: true + custom_code: | + def apply_guardrail(inputs, request_data, input_type): + ... +``` diff --git a/docs/my-website/docs/proxy/guardrails/grayswan.md b/docs/my-website/docs/proxy/guardrails/grayswan.md index d6efaf15504..6c0ccbc293d 100644 --- a/docs/my-website/docs/proxy/guardrails/grayswan.md +++ b/docs/my-website/docs/proxy/guardrails/grayswan.md @@ -13,20 +13,26 @@ Cygnal returns a `violation` score between `0` and `1` (higher means more likely ### 1. Obtain Credentials -1. Create a Gray Swan account and generate a Cygnal API key. +1. Log in to our Gray Swan platform and generate a Cygnal API key. + + For existing customers, you should already have access to our [platform](https://platform.grayswan.ai). + + For new users, please register at this [page](https://hubs.ly/Q03-sX1J0) and we are more than happy to give you an onboarding! + + 2. Configure environment variables for the LiteLLM proxy host: -```bash -export GRAYSWAN_API_KEY="your-grayswan-key" -export GRAYSWAN_API_BASE="https://api.grayswan.ai" -``` + ```bash + export GRAYSWAN_API_KEY="your-grayswan-key" + export GRAYSWAN_API_BASE="https://api.grayswan.ai" + ``` ### 2. Configure `config.yaml` -Add a guardrail entry that references the Gray Swan integration. Below is a balanced example that monitors both input and output but only blocks once the violation score reaches the configured threshold. +Add a guardrail entry that references the Gray Swan integration. Below is our recommmended settings. ```yaml -model_list: +model_list: # this part is a standard litellm configuration for reference - model_name: openai/gpt-4.1-mini litellm_params: model: openai/gpt-4.1-mini @@ -40,13 +46,14 @@ guardrails: api_key: os.environ/GRAYSWAN_API_KEY api_base: os.environ/GRAYSWAN_API_BASE # optional optional_params: - on_flagged_action: monitor # or "block" + on_flagged_action: passthrough # or "block" or "monitor" violation_threshold: 0.5 # score >= threshold is flagged reasoning_mode: hybrid # off | hybrid | thinking - categories: - safety: "Detect jailbreaks and policy violations" - policy_id: "your-cygnal-policy-id" + policy_id: "your-cygnal-policy-id" # Optional: Your Cygnal policy ID. Defaults to a content safety policy if empty. + streaming_end_of_stream_only: true # For streaming API, only send the assembled message to Cygnal (post_call only). Defaults to false. default_on: true + guardrail_timeout: 30 # Defaults to 30 seconds. Change accordingly. + fail_open: true # Defaults to true; set to false to propagate guardrail errors. general_settings: master_key: "your-litellm-master-key" @@ -65,13 +72,13 @@ litellm --config config.yaml --port 4000 ## Choosing Guardrail Modes -Gray Swan can run during `pre_call`, `during_call`, and `post_call` stages. Combine modes based on your latency and coverage requirements. +Gray Swan can run during `pre_call`, `during_call`, and `post_call` stages. Combine modes based on your latency and coverage requirements. | Mode | When it Runs | Protects | Typical Use Case | |--------------|-------------------|-----------------------|------------------| | `pre_call` | Before LLM call | User input only | Block prompt injection before it reaches the model | | `during_call`| Parallel to call | User input only | Low-latency monitoring without blocking | -| `post_call` | After response | Full conversation | Scan output for policy violations, leaked secrets, or IPI | +| `post_call` | After response | Model Outputs | Scan output for policy violations, leaked secrets, or IPI | When using `during_call` with `on_flagged_action: block` or `on_flagged_action: passthrough`: @@ -81,87 +88,110 @@ When using `during_call` with `on_flagged_action: block` or `on_flagged_action: - The guardrail exception prevents the response from reaching the user, but **does not cancel the running LLM task** - This means you pay full LLM costs while returning an error/passthrough message to the user -**Recommendation:** For cost-sensitive applications, use `pre_call` and `post_call` instead of `during_call` for blocking or passthrough modes. Reserve `during_call` for `monitor` mode where you want low-latency logging without impacting the user experience. +**Recommendation:** Use `pre_call` and `post_call` instead of `during_call` for `passthrough` (or `block`) `on_flagged_action` (see our recommended configuration above). Reserve `during_call` for `monitor` mode ONLY when you want low-latency logging without impacting the user experience. - - +--- -```yaml -guardrails: - - guardrail_name: "cygnal-monitor-only" - litellm_params: - guardrail: grayswan - mode: "during_call" - api_key: os.environ/GRAYSWAN_API_KEY - optional_params: - on_flagged_action: monitor - violation_threshold: 0.6 - default_on: true +## Work with Claude Code + +Follow the official litellm [guide](https://docs.litellm.ai/docs/tutorials/claude_responses_api) on setting up Claude Code with litellm, with the guardrail part mentioned above added to your litellm configuration. Cygnal natively supports coding agent policies defense. Define your own policy or use the provided coding policies on the platform. The example config we show above is also the recommended setup for Claude Code (with the `policy_id` replaced with an appropriate one). + +--- + +## Per-request overrides via `extra_body` + +You can override parts of the Gray Swan guardrail configuration on a per-request basis by passing `litellm_metadata.guardrails[*].grayswan.extra_body`. + +`extra_body` is merged into the Cygnal request body and takes precedence over specific fields from `config.yaml`, which are `policy_id`, `violation_threshold`, and `reasoning_mode`. + +If you include a `metadata` field inside `extra_body`, it is forwarded to the Cygnal API as-is under the request body's `metadata` field. + +Example: + +```bash +curl -X POST "http://0.0.0.0:4000/v1/messages?beta=true" \ + -H "Authorization: Bearer token" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "openrouter/anthropic/claude-sonnet-4.5", + "messages": [{"role": "user", "content": "hello"}], + "litellm_metadata": { + "guardrails": [ + { + "cygnal-monitor": { + "extra_body": { + "policy_id": "specific policy id you want to use", + "metadata": { + "user": "health-check" + } + } + } + } + ] + } + }' ``` -Best for visibility without blocking. Alerts are logged via LiteLLM’s standard logging callbacks. +OpenAI client: - - +```python +from openai import OpenAI -```yaml -guardrails: - - guardrail_name: "cygnal-block-input" - litellm_params: - guardrail: grayswan - mode: "pre_call" - api_key: os.environ/GRAYSWAN_API_KEY - optional_params: - on_flagged_action: block - violation_threshold: 0.4 - categories: - pii: "Detect sensitive data" - default_on: true +client = OpenAI(api_key="anything", base_url="http://0.0.0.0:4000") + +resp = client.responses.create( + model="openrouter/anthropic/claude-sonnet-4.5", + input="hello", + extra_body={ + "litellm_metadata": { + "guardrails": [ + { + "cygnal-monitor": { + "extra_body": { + "policy_id": "69038214e5cdb6befc5e991e", + "metadata": {"trace_id": "trace-123"}, + } + } + } + ] + } + }, +) ``` -Stops malicious or sensitive prompts before any tokens are generated. +Anthropic client: - - +```python +from anthropic import Anthropic -```yaml -guardrails: - - guardrail_name: "cygnal-full-coverage" - litellm_params: - guardrail: grayswan - mode: [pre_call, post_call] - api_key: os.environ/GRAYSWAN_API_KEY - optional_params: - on_flagged_action: block - violation_threshold: 0.5 - reasoning_mode: thinking - policy_id: "policy-id-from-grayswan" - default_on: true +client = Anthropic(api_key="anything", base_url="http://0.0.0.0:4000") + +resp = client.messages.create( + model="openrouter/anthropic/claude-sonnet-4.5", + max_tokens=256, + messages=[{"role": "user", "content": "hello"}], + extra_body={ + "litellm_metadata": { + "guardrails": [ + { + "cygnal-monitor": { + "extra_body": { + "policy_id": "69038214e5cdb6befc5e991e", + "metadata": {"trace_id": "trace-123"}, + } + } + } + ] + } + }, +) ``` -Provides the strongest enforcement by inspecting both prompts and responses. +Notes: - - - -```yaml -guardrails: - - guardrail_name: "cygnal-passthrough" - litellm_params: - guardrail: grayswan - mode: [pre_call, post_call] - api_key: os.environ/GRAYSWAN_API_KEY - optional_params: - on_flagged_action: passthrough - violation_threshold: 0.5 - default_on: true -``` - -Allows requests to proceed without raising a 400 error when content is flagged. Instead of blocking, the model response content is replaced with a detailed violation message including violation score, violated rules, and detection flags (mutation, IPI). **Supported Response Formats:** OpenAI chat/text completions, Anthropic Messages API. Other response types (embeddings, images, etc.) will log a warning and return unchanged. - - - +- The guardrail name (for example, `cygnal-monitor`) must match the `guardrail_name` in `config.yaml`. +- Per-request guardrail overrides may require a premium license, depending on your proxy settings. --- @@ -170,9 +200,14 @@ Allows requests to proceed without raising a 400 error when content is flagged. | Parameter | Type | Description | |---------------------------------------|-----------------|-------------| | `api_key` | string | Gray Swan Cygnal API key. Reads from `GRAYSWAN_API_KEY` if omitted. | +| `api_base` | string | Override for the Gray Swan API base URL. Defaults to `https://api.grayswan.ai` or `GRAYSWAN_API_BASE`. | | `mode` | string or list | Guardrail stages (`pre_call`, `during_call`, `post_call`). | | `optional_params.on_flagged_action` | string | `monitor` (log only), `block` (raise `HTTPException`), or `passthrough` (replace response content with violation message, no 400 error). | -| `.optional_params.violation_threshold`| number (0-1) | Scores at or above this value are considered violations. | +| `optional_params.violation_threshold` | number (0-1) | Scores at or above this value are considered violations. | | `optional_params.reasoning_mode` | string | `off`, `hybrid`, or `thinking`. Enables Cygnal's reasoning capabilities. | | `optional_params.categories` | object | Map of custom category names to descriptions. | | `optional_params.policy_id` | string | Gray Swan policy identifier. | +| `guardrail_timeout` | number | Timeout in seconds for the Cygnal request. Defaults to 30. | +| `fail_open` | boolean | If true, errors contacting Cygnal are logged and the request proceeds; if false, errors propagate. Defaults to treu. | +| `streaming_end_of_stream_only` | boolean | For streaming `post_call`, only send the final assembled response to Cygnal. Defaults to false. | +| `default_on` | boolean | Run the guardrail on every request by default. | diff --git a/docs/my-website/docs/proxy/guardrails/guardrail_policies.md b/docs/my-website/docs/proxy/guardrails/guardrail_policies.md index 56be11c85a7..e2cb839203e 100644 --- a/docs/my-website/docs/proxy/guardrails/guardrail_policies.md +++ b/docs/my-website/docs/proxy/guardrails/guardrail_policies.md @@ -1,3 +1,7 @@ +import Image from '@theme/IdealImage'; +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + # [Beta] Guardrail Policies Use policies to group guardrails and control which ones run for specific teams, keys, or models. @@ -10,6 +14,9 @@ Use policies to group guardrails and control which ones run for specific teams, ## Quick Start + + + ```yaml showLineNumbers title="config.yaml" model_list: - model_name: gpt-4 @@ -43,6 +50,26 @@ policy_attachments: scope: "*" # apply to all requests ``` + + + +**Step 1: Create a Policy** + +Go to **Policies** tab and click **+ Create New Policy**. Fill in the policy name, description, and select guardrails to add. + +![Enter policy name](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/4ba62cc8-d2c4-4af1-a526-686295466928/ascreenshot_401eab3e2081466e8f4d4ffa3bf7bff4_text_export.jpeg) + +![Add a description for the policy](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/51685e47-1d94-4d9c-acb0-3c88dce9f938/ascreenshot_a5cd40066ff34afbb1e4089a3c93d889_text_export.jpeg) + +![Select a parent policy to inherit from](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/1d96c3d3-187a-4f7c-97d2-6ac1f093d51e/ascreenshot_8a3af3b2210547dca3d4709df920d005_text_export.jpeg) + +![Select guardrails to add to the policy](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/23781274-e600-4d5f-a8a6-4a2a977a166c/ascreenshot_a2a45d2c5d064c77ab7cb47b569ad9e9_text_export.jpeg) + +![Click Create Policy to save](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/1d1ae8a8-daa5-451b-9fa2-c5b607ff6220/ascreenshot_218c2dd259714be4aa3c4e1894c96878_text_export.jpeg) + + + + Response headers show what ran: ``` @@ -58,6 +85,9 @@ x-litellm-applied-guardrails: pii_masking,prompt_injection You have a global baseline, but want to add extra guardrails for a specific team. + + + ```yaml showLineNumbers title="config.yaml" policies: global-baseline: @@ -81,6 +111,30 @@ policy_attachments: - finance # team alias from /team/new ``` + + + +**Option 1: Create a team-scoped attachment** + +Go to **Policies** > **Attachments** tab and click **+ Create New Attachment**. Select the policy and the teams to scope it to. + +![Select teams for the attachment](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/50e58f54-3bc3-477e-a106-e58cb65fde7e/ascreenshot_85d2e3d9d8d24842baced92fea170427_text_export.jpeg) + +![Select the teams to attach the policy to](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/f24066bb-0a73-49fb-87b6-c65ad3ca5b2f/ascreenshot_242476fbdac447309f65de78b0ed9fdd_text_export.jpeg) + +**Option 2: Attach from team settings** + +Go to **Teams** > click on a team > **Settings** tab > under **Policies**, select the policies to attach. + +![Open team settings and click Edit Settings](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/c31c3735-4f9d-4c6a-896b-186e97296940/ascreenshot_4749bb24ce5942cca462acc958fd3822_text_export.jpeg) + +![Select policies to attach to this team](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/da8d5d7a-d975-4bfe-acd2-f41dcea29520/ascreenshot_835a33b6cec545cbb2987f017fbaff90_text_export.jpeg) + + + + + + Now the `finance` team gets `pii_masking` + `strict_compliance_check` + `audit_logger`, while everyone else just gets `pii_masking`. ## Remove guardrails for a specific team @@ -201,6 +255,60 @@ policy_attachments: - "test-*" # key alias pattern ``` +**Tag-based** (matches keys/teams by metadata tags, wildcards supported): + +```yaml showLineNumbers title="config.yaml" +policy_attachments: + - policy: hipaa-compliance + tags: + - "healthcare" + - "health-*" # wildcard - matches health-team, health-dev, etc. +``` + +Tags are read from key and team `metadata.tags`. For example, a key created with `metadata: {"tags": ["healthcare"]}` would match the attachment above. + +## Test Policy Matching + +Debug which policies and guardrails apply for a given context. Use this to verify your policy configuration before deploying. + + + + +Go to **Policies** > **Test** tab. Enter a team alias, key alias, model, or tags and click **Test** to see which policies match and what guardrails would be applied. + + + + + + +```bash +curl -X POST "http://localhost:4000/policies/resolve" \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{ + "tags": ["healthcare"], + "model": "gpt-4" + }' +``` + +Response: + +```json +{ + "effective_guardrails": ["pii_masking"], + "matched_policies": [ + { + "policy_name": "hipaa-compliance", + "matched_via": "tag:healthcare", + "guardrails_added": ["pii_masking"] + } + ] +} +``` + + + + ## Config Reference ### `policies` @@ -233,14 +341,18 @@ policy_attachments: scope: ... teams: [...] keys: [...] + models: [...] + tags: [...] ``` | Field | Type | Description | |-------|------|-------------| | `policy` | `string` | **Required.** Name of the policy to attach. | | `scope` | `string` | Use `"*"` to apply globally. | -| `teams` | `list[string]` | Team aliases (from `/team/new`). | +| `teams` | `list[string]` | Team aliases (from `/team/new`). Supports `*` wildcard. | | `keys` | `list[string]` | Key aliases (from `/key/generate`). Supports `*` wildcard. | +| `models` | `list[string]` | Model names. Supports `*` wildcard. | +| `tags` | `list[string]` | Tag patterns (from key/team `metadata.tags`). Supports `*` wildcard. | ### Response Headers @@ -248,6 +360,7 @@ policy_attachments: |--------|-------------| | `x-litellm-applied-policies` | Policies that matched this request | | `x-litellm-applied-guardrails` | Guardrails that actually ran | +| `x-litellm-policy-sources` | Why each policy matched (e.g., `hipaa=tag:healthcare; baseline=scope:*`) | ## How it works diff --git a/docs/my-website/docs/proxy/guardrails/policy_tags.md b/docs/my-website/docs/proxy/guardrails/policy_tags.md new file mode 100644 index 00000000000..11840116c31 --- /dev/null +++ b/docs/my-website/docs/proxy/guardrails/policy_tags.md @@ -0,0 +1,139 @@ +# Tag-Based Policy Attachments + +Apply guardrail policies automatically to any key or team that has a specific tag. Instead of attaching policies one-by-one, tag your keys and let the policy engine handle the rest. + +**Example:** Your security team requires all healthcare-related keys to run PII masking and PHI detection. Tag those keys with `health`, create a single tag-based attachment, and every matching key gets the guardrails automatically. + +## 1. Create a Policy with Guardrails + +Navigate to **Policies** in the left sidebar. You'll see a list of existing policies along with their guardrails. + +![Policies list page showing existing policies and the + Add New Policy button](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/d7aa1e1f-011e-40bf-a356-6dfe9d5d54f1/ascreenshot_8db95c231a7f4a79a36c2a98ba127542_text_export.jpeg) + +Click **+ Add New Policy**. In the modal, enter a name for your policy (e.g., `high-risk-policy2`). You can also type to search existing policy names if you want to reference them. + +![Create New Policy modal — enter the policy name and optional description](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/18f1ff69-9b83-4a98-9aad-9892a104d3ff/ascreenshot_1c6b85231cad4ec695750b53bbbda52c_text_export.jpeg) + +Scroll down to **Guardrails to Add**. Click the dropdown to see all available guardrails configured on your proxy — select the ones this policy should enforce. + +![Guardrails to Add dropdown showing available guardrails like OAI-moderation, phi-pre-guard, pii-pre-guard](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/55cedad7-9939-44a1-8644-a184cde82ab7/ascreenshot_eab4e55b82b8411893eccb6234d60b82_text_export.jpeg) + +After selecting your guardrails, they appear as chips in the input field. The **Resolved Guardrails** section below shows the final set that will be applied (including any inherited from a parent policy). + +![Selected guardrails shown as chips: testing-pl, phi-pre-guard, pii-pre-guard. Resolved Guardrails preview below.](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/c06d5b08-1c85-4715-b827-3e6864880428/ascreenshot_7a082e55f3ad425f9009346c68afae23_text_export.jpeg) + +Click **Create Policy** to save. + +![Click Create Policy to save the new policy](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/7e6eae64-4bba-4d72-b226-d1308ac576a8/ascreenshot_22d0ed686c594221bbbd2f40df214d75_text_export.jpeg) + +## 2. Add a Tag Attachment for the Policy + +After creating the policy, switch to the **Attachments** tab. This is where you define *where* the policy applies. + +![Switch to the Attachments tab — shows the attachment table and scope documentation](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/871ae6d9-16d1-44e2-baf2-7bb8a9e72087/ascreenshot_76e124619d70462ea0e2fbb46ded1ac9_text_export.jpeg) + +Click **+ Add New Attachment**. The Attachments page explains the available scopes: Global, Teams, Keys, Models, and **Tags**. + +![Attachments page showing scope types including Tags — click + Add New Attachment](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/d45ab8bc-fc1e-425b-8a3f-44d18df810ec/ascreenshot_425824030f3144b7ab3c0ac570349b00_text_export.jpeg) + +In the **Create Policy Attachment** modal, first select the policy you just created from the dropdown. + +![Select the policy to attach from the dropdown (e.g., high-risk-policy2)](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/e0dcac40-e39c-4a6a-9d9c-4bbb9ec0ee91/ascreenshot_445b19894e0b466196a13e20c8e67f2d_text_export.jpeg) + +Choose **Specific (teams, keys, models, or tags)** as the scope type. This expands the form to show fields for Teams, Keys, Models, and Tags. + +![Select "Specific" scope type to reveal the Tags field](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/f685e02a-e22e-4c6c-9742-d5268746214b/ascreenshot_14d63d9d06dd4fc7854cfeb5e8d9ef85_text_export.jpeg) + +Scroll down to the **Tags** field and type the tag to match — here we enter `health`. You can enter any string, or use a wildcard pattern like `health-*` to match all tags starting with `health-` (e.g., `health-team`, `health-dev`). + +![Tags field with "health" entered. Supports wildcards like prod-* matching prod-us, prod-eu.](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/14581df7-732c-4ea5-b36d-58270b00e92c/ascreenshot_e734c81418f046549b61a84b9d352a29_text_export.jpeg) + +## 3. Check the Impact of the Attachment + +Before creating the attachment, click **Estimate Impact** to preview how many keys and teams would be affected. This is your blast-radius check — make sure the scope is what you expect before applying. + +![Click Estimate Impact — the tag "health" is entered and ready to preview](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/6ccb81d7-3d11-48b0-b634-fc4d738aa530/ascreenshot_2eb89e6ff13a4b12b61004660a36c30c_text_export.jpeg) + +The **Impact Preview** appears inline, showing exactly how many keys and teams would be affected. In this example: "This attachment would affect **1 key** and **0 teams**", with the key alias `hi` listed. + +![Impact Preview showing "This attachment would affect 1 key and 0 teams." Keys: hi](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/8834d85a-2c15-48dd-8d6b-810cf11ee5c4/ascreenshot_d814b42ca9f34c23b0c2269bfa3e64fb_text_export.jpeg) + +Once you're satisfied with the impact, click **Create Attachment** to save. + +![Click Create Attachment to finalize](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/4a8918f2-eedb-4f49-a53b-4e46d0387d2a/ascreenshot_b08d490d836d4f46b4e5cbb14f61377a_text_export.jpeg) + +The attachment now appears in the table with the policy name `high-risk-policy2` and tag `health` visible. + +![Attachments table showing the new attachment with policy high-risk-policy2 and tag "health"](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/45867887-0aec-44a4-963b-b6cc6c302e3e/ascreenshot_981caeff98574ec89a8a53cd295e5043_text_export.jpeg) + +## 4. Create a Key with the Tag + +Navigate to **Virtual Keys** in the left sidebar. Click **+ Create New Key**. + +![Virtual Keys page showing existing keys — click + Create New Key](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/4c1f9448-e590-4546-9357-6f68aa395b27/ascreenshot_4a7bc5be9e4347f3a9fe46f78d938d7c_text_export.jpeg) + +Enter a key name and select a model. Then expand **Optional Settings** and scroll down to the **Tags** field. + +![Create New Key modal — enter the key name](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/f84f7a2b-8057-4926-9f80-d68e437c77cf/ascreenshot_a277c8611b6e41059663b0759cd85cab_text_export.jpeg) + +In the **Tags** field, type `health` and press Enter. This is the tag the policy engine will match against. + +![Tags field in key creation — type "health" to add the tag](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/3ad3bf10-76d2-4f15-9a66-ed6c99bb25c4/ascreenshot_8a8773fb65fc49329cb1716da92b2723_text_export.jpeg) + +The tag `health` now appears as a chip in the Tags field. Confirm your settings look correct. + +![Tags field showing "health" selected with a checkmark](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/de3e58a9-6013-4d0c-882e-5517ea286684/ascreenshot_c7eef1736fce4aa894ac3b118b3800a2_text_export.jpeg) + +Click **Create Key** at the bottom of the form. + +![Click Create Key to generate the new virtual key with the health tag](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/51d419ea-ee80-4e24-8e93-b99a844881bc/ascreenshot_097d4564289943a88e30b5d2e3eab262_text_export.jpeg) + +A dialog appears with your new virtual key. Click **Copy Virtual Key** — you'll need this to test in the next step. + +![Save your Key dialog — click Copy Virtual Key to copy it to clipboard](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/e87a0cc1-4d12-4066-bfa2-973159808fd1/ascreenshot_7b616a7291d0497a9c61bdcdb59394d7_text_export.jpeg) + +## 5. Test the Key and Validate the Policy is Applied + +Navigate to **Playground** in the left sidebar to test the key interactively. + +![Navigate to Playground from the sidebar](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/e6f8a3ee-e9e8-4107-93d1-bfca734c5ce9/ascreenshot_539bde38abe646e49148a912fff2d257_text_export.jpeg) + +Under **Virtual Key Source**, select "Virtual Key" and paste the key you just copied into the input field. + +![Paste the virtual key into the Playground configuration](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/a6612c4a-d499-4e54-8019-f54fde674ad9/ascreenshot_e85ebb9051554594bab0da57823fafad_text_export.jpeg) + +Select a model from the **Select Model** dropdown. + +![Select a model (e.g., bedrock-claude-opus-4.5) from the dropdown](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/325e330f-3eff-4c5e-b177-21916138a2f5/ascreenshot_693478f89c034e949e08f3ed0dd05120_text_export.jpeg) + +Type a message and press Enter. If a guardrail blocks the request, you'll see it in the response. In this example, the `testing-pl` guardrail detected an email pattern and returned a 403 error — confirming the policy is working. + +![Guardrail in action — the request was blocked with "Content blocked: email pattern detected"](https://colony-recorder.s3.amazonaws.com/files/2026-02-11/2cf16809-d2e5-4eae-a7dd-6a16dfcca7ce/ascreenshot_727d7d4ed20b4a52b2b41e39fd36eccb_text_export.jpeg) + +**Using curl:** + +You can also verify via the command line. The response headers confirm which policies and guardrails were applied: + +```bash +curl -v http://localhost:4000/chat/completions \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "say hi"}] + }' +``` + +Check the response headers: + +``` +x-litellm-applied-policies: high-risk-policy2 +x-litellm-applied-guardrails: pii-pre-guard,phi-pre-guard,testing-pl +x-litellm-policy-sources: high-risk-policy2=tag:health +``` + +| Header | What it tells you | +|--------|-------------------| +| `x-litellm-applied-policies` | Which policies matched this request | +| `x-litellm-applied-guardrails` | Which guardrails actually ran | +| `x-litellm-policy-sources` | **Why** each policy matched — `tag:health` confirms it was the tag | diff --git a/docs/my-website/docs/proxy/guardrails/zscaler_ai_guard.md b/docs/my-website/docs/proxy/guardrails/zscaler_ai_guard.md index 94f31c3bfdf..2e626004238 100644 --- a/docs/my-website/docs/proxy/guardrails/zscaler_ai_guard.md +++ b/docs/my-website/docs/proxy/guardrails/zscaler_ai_guard.md @@ -100,7 +100,7 @@ In cases where encounter other errors when apply Zscaler AI Guard, return exampl } } ``` -## 6. Sending User Information to Zscaler AI Guard for Analysis (Optional) +## 6. Sending User Information to Zscaler AI Guard (Optional) If you need to send end-user information to Zscaler AI Guard for analysis, you can set the configuration in the environment variables to True and include the relevant information in custom_headers on Zscaler AI Guard. - To send user_api_key_alias: @@ -133,4 +133,30 @@ curl -i http://localhost:8165/v1/chat/completions \ "zguard_policy_id": } }' +``` + +## 8. Set Custom Zscaler AI Guard Policy on Litellm Team OR Key Metadata (Optional) +In addition to setting `zguard_policy_id` in a request or the configuration file, you can also set it in the metadata for LiteLLM Team or Key. The `zguard_policy_id` is determined using the following order of precedence: request, Key, Team, config file. This logic is illustrated below: +``` +user_api_key_metadata = metadata.get("user_api_key_metadata", {}) or {} +team_metadata = metadata.get("team_metadata", {}) or {} +policy_id = ( + metadata.get("zguard_policy_id") + if "zguard_policy_id" in metadata + else ( + user_api_key_metadata.get("zguard_policy_id") + if "zguard_policy_id" in user_api_key_metadata + else ( + team_metadata.get("zguard_policy_id") + if "zguard_policy_id" in team_metadata + else self.policy_id + ) + ) + ) +``` +You can leverage this feature to apply multiple policies configured on the Zscaler AI Guard (ZGuard) to traffic from different applications. (Note: It is recommended to map policies using either Team or Key metadata, but not a mix of both.) + +Example set in Team/Key Metadata, you can set From UI: +``` +{"zguard_policy_id": 100} ``` \ No newline at end of file diff --git a/docs/my-website/docs/proxy/prod.md b/docs/my-website/docs/proxy/prod.md index a42d91a7d5f..994788a3ad9 100644 --- a/docs/my-website/docs/proxy/prod.md +++ b/docs/my-website/docs/proxy/prod.md @@ -250,11 +250,133 @@ The migrate deploy command: ### Read-only File System -If you see a `Permission denied` error, it means the LiteLLM pod is running with a read-only file system. +Running LiteLLM with `readOnlyRootFilesystem: true` is a Kubernetes security best practice that prevents container processes from writing to the root filesystem. LiteLLM fully supports this configuration. -To fix this, just set `LITELLM_MIGRATION_DIR="/path/to/writeable/directory"` in your environment. +#### Quick Fix for Permission Errors -LiteLLM will use this directory to write migration files. +If you see a `Permission denied` error, it means the LiteLLM pod is running with a read-only file system. LiteLLM needs writable directories for: +- **Database migrations**: Set `LITELLM_MIGRATION_DIR="/path/to/writable/directory"` +- **Admin UI**: Set `LITELLM_UI_PATH="/path/to/writable/directory"` +- **UI assets/logos**: Set `LITELLM_ASSETS_PATH="/path/to/writable/directory"` + +#### Complete Read-Only Filesystem Setup (Kubernetes) + +For production deployments with enhanced security, use this configuration: + +**Option 1: Using EmptyDir Volumes with InitContainer (Recommended)** + +This approach copies the pre-built UI from the Docker image to writable emptyDir volumes at pod startup. + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: litellm-proxy +spec: + template: + spec: + initContainers: + - name: setup-ui + image: ghcr.io/berriai/litellm:main-stable + command: + - sh + - -c + - | + cp -r /var/lib/litellm/ui/* /app/var/litellm/ui/ && \ + cp -r /var/lib/litellm/assets/* /app/var/litellm/assets/ + volumeMounts: + - name: ui-volume + mountPath: /app/var/litellm/ui + - name: assets-volume + mountPath: /app/var/litellm/assets + + containers: + - name: litellm + image: ghcr.io/berriai/litellm:main-stable + env: + - name: LITELLM_NON_ROOT + value: "true" + - name: LITELLM_UI_PATH + value: "/app/var/litellm/ui" + - name: LITELLM_ASSETS_PATH + value: "/app/var/litellm/assets" + - name: LITELLM_MIGRATION_DIR + value: "/app/migrations" + - name: PRISMA_BINARY_CACHE_DIR + value: "/app/cache/prisma-python/binaries" + - name: XDG_CACHE_HOME + value: "/app/cache" + securityContext: + readOnlyRootFilesystem: true + runAsNonRoot: true + runAsUser: 101 + capabilities: + drop: + - ALL + volumeMounts: + - name: config + mountPath: /app/config.yaml + subPath: config.yaml + readOnly: true + - name: ui-volume + mountPath: /app/var/litellm/ui + - name: assets-volume + mountPath: /app/var/litellm/assets + - name: cache + mountPath: /app/cache + - name: migrations + mountPath: /app/migrations + + volumes: + - name: config + configMap: + name: litellm-config + - name: ui-volume + emptyDir: + sizeLimit: 100Mi + - name: assets-volume + emptyDir: + sizeLimit: 10Mi + - name: cache + emptyDir: + sizeLimit: 500Mi + - name: migrations + emptyDir: + sizeLimit: 64Mi +``` + +**Option 2: Without UI (API-only deployment)** + +If you don't need the admin UI, you can run with minimal configuration: + +```yaml +env: + - name: LITELLM_NON_ROOT + value: "true" + - name: LITELLM_MIGRATION_DIR + value: "/app/migrations" +securityContext: + readOnlyRootFilesystem: true +``` + +The proxy will log a warning about the UI but API endpoints will work normally. + +#### Environment Variables for Read-Only Filesystems + +| Variable | Purpose | Default | +|----------|---------|---------| +| `LITELLM_UI_PATH` | Admin UI directory | `/var/lib/litellm/ui` (Docker) | +| `LITELLM_ASSETS_PATH` | UI assets/logos | `/var/lib/litellm/assets` (Docker) | +| `LITELLM_MIGRATION_DIR` | Database migrations | Package directory | +| `PRISMA_BINARY_CACHE_DIR` | Prisma binary cache | System default | +| `XDG_CACHE_HOME` | General cache directory | System default | + +#### Important Notes + +1. **Migrations**: Always set `LITELLM_MIGRATION_DIR` to a writable emptyDir path +2. **Prisma Cache**: Set `PRISMA_BINARY_CACHE_DIR` and `XDG_CACHE_HOME` to writable paths +3. **Server Root Path**: If using a custom `server_root_path`, you must pre-process UI files in your Dockerfile as the proxy cannot modify files at runtime with read-only filesystem +4. **Automatic Detection**: The UI is automatically detected as pre-restructured if it contains a `.litellm_ui_ready` marker file (created by the official Docker images) ## 10. Use a Separate Health Check App :::info diff --git a/docs/my-website/docs/proxy/ui_logs.md b/docs/my-website/docs/proxy/ui_logs.md index b6d3d2ae7ca..8cfe818ebfd 100644 --- a/docs/my-website/docs/proxy/ui_logs.md +++ b/docs/my-website/docs/proxy/ui_logs.md @@ -37,6 +37,40 @@ general_settings: +## Tracing Tools + +View which tools were provided and called in your completion requests. + + + +**Example:** Make a completion request with tools: + +```bash +curl -X POST 'http://localhost:4000/chat/completions' \ + -H 'Authorization: Bearer sk-1234' \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "gpt-4", + "messages": [{"role": "user", "content": "What is the weather?"}], + "tools": [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + } + } + } + } + ] + }' +``` + +Check the Logs page to see all tools provided and which ones were called. ## Stop storing Error Logs in DB diff --git a/docs/my-website/docs/proxy/ui_team_soft_budget_alerts.md b/docs/my-website/docs/proxy/ui_team_soft_budget_alerts.md new file mode 100644 index 00000000000..17c42e57c9a --- /dev/null +++ b/docs/my-website/docs/proxy/ui_team_soft_budget_alerts.md @@ -0,0 +1,130 @@ +import Image from '@theme/IdealImage'; + +# Team Soft Budget Alerts + +Set a soft budget on a team and get email alerts when spending crosses the threshold — without blocking any requests. + +## Overview + +A **soft budget** is a spending threshold that triggers email notifications when exceeded, but **does not block requests**. This is different from a hard budget (`max_budget`), which rejects requests once the limit is reached. + + + +Team soft budget alerts let you: + +- **Get notified early** — receive email alerts when a team's spend crosses the soft budget threshold +- **Keep requests flowing** — unlike hard budgets, soft budgets never block API calls +- **Target specific recipients** — send alerts to specific email addresses (e.g. team leads, finance), not just the team members +- **Work without global alerting** — team soft budget alerts are sent via email independently of Slack or other global alerting configuration + +:::warning Email integration required +Team soft budget alerts are sent via email. You must have an active email integration (SendGrid, Resend, or SMTP) configured on your proxy for alerts to be delivered. See [Email Notifications](./email.md) for setup instructions. +::: + +:::info Automatically active +Team soft budget alerts are **automatically active** once you configure a soft budget and at least one alerting email on a team. No additional proxy configuration or restart is needed — alerts are checked on every request. +::: + +## How It Works + +On every API request made with a key belonging to a team, the proxy checks: + +1. Does the team have a `soft_budget` set? +2. Is the team's current `spend` >= the `soft_budget`? +3. Are there any emails configured in `soft_budget_alerting_emails`? + +If all three conditions are met, an email alert is sent to the configured recipients. Alerts are **deduplicated** so the same alert is only sent once within a 24-hour window. + +## How to Set Up Team Soft Budget Alerts + +### 1. Navigate to the Admin UI + +Go to the Admin UI (e.g. `http://localhost:4000/ui` or your `PROXY_BASE_URL/ui`). + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/f06d75ad-25ef-4ee8-90c3-9604f8e46a1c/ascreenshot_1a6defaed1494d6da0001459511ecfd5_text_export.jpeg) + +### 2. Go to Teams + +Click **Teams** in the sidebar. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/f06d75ad-25ef-4ee8-90c3-9604f8e46a1c/ascreenshot_2d258fa280f6463b966bf7a05bb102d5_text_export.jpeg) + +### 3. Select a team + +Click on the team you want to configure soft budget alerts for. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/490f09fb-6bf5-45a8-a384-676889f34c88/ascreenshot_15cceb22abe64df0bf7d7c742ecb5b2f_text_export.jpeg) + +### 4. Open team Settings + +Click the **Settings** tab to view the team's configuration. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/28dd1bc5-7d07-462f-b277-33f885bdc07e/ascreenshot_12f2b762b5d24686801d93ad5b067e06_text_export.jpeg) + +### 5. Edit Settings + +Click **Edit Settings** to modify the team's budget configuration. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/30a483ea-7e01-4fdc-ac5f-a5572388d138/ascreenshot_0915eadd9e754a798489853b82de3cb5_text_export.jpeg) + +### 6. Set the Soft Budget + +Click the **Soft Budget (USD)** field and enter your desired threshold. For example, enter `0.01` for testing or a higher value like `500` for production. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/8b306d80-4943-4ad0-a51a-94b5ebdd6680/ascreenshot_5bb6e65c6428473fac2607f6a7f4b98a_text_export.jpeg) + +### 7. Add alerting emails + +Click the **Soft Budget Alerting Emails** field and enter one or more comma-separated email addresses that should receive the alert. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/a97c6efa-cc93-45d7-979e-d2a533f423b9/ascreenshot_2d8223ce8e934aa1bfadfb2f78aee5fc_text_export.jpeg) + +### 8. Save Changes + +Click **Save Changes**. The soft budget alert is now active — no proxy restart required. + +![](https://colony-recorder.s3.amazonaws.com/files/2026-02-07/865ba6f1-3fc6-4c19-8e08-433561d6c3f7/ascreenshot_b2f0503ada3a479a83dc8b7d01c1f8da_text_export.jpeg) + +### 9. Verify: email alert received + +Once the team's spend crosses the soft budget, an email alert is sent to the configured recipients. Below is an example of the alert email: + + + +## Settings Reference + +| Setting | Description | +| ------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | +| **Soft Budget (USD)** | The spending threshold that triggers an email alert. Requests are **not** blocked when this limit is exceeded. | +| **Soft Budget Alerting Emails** | Comma-separated email addresses that receive the alert when the soft budget is crossed. At least one email is required for alerts to be sent. | + +:::tip Soft Budget vs. Max Budget + +- **Soft Budget**: Advisory threshold — sends email alerts but does **not** block requests. +- **Max Budget**: Hard limit — blocks requests once the budget is exceeded. + +You can set both on the same team to get early warnings (soft) and a hard stop (max). +::: + +## API Configuration + +You can also configure team soft budgets via the API when creating or updating a team: + +```bash +curl -X POST 'http://localhost:4000/team/update' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "team_id": "your-team-id", + "soft_budget": 500.00, + "metadata": { + "soft_budget_alerting_emails": ["lead@example.com", "finance@example.com"] + } + }' +``` + +## Related Documentation + +- [Email Notifications](./email.md) – Configure email integrations (Resend, SMTP) for LiteLLM Proxy +- [Alerting](./alerting.md) – Set up Slack and other alerting channels +- [Cost Tracking](./cost_tracking.md) – Track and manage spend across teams, keys, and users diff --git a/docs/my-website/docs/proxy_auth.md b/docs/my-website/docs/proxy_auth.md new file mode 100644 index 00000000000..91084b34a37 --- /dev/null +++ b/docs/my-website/docs/proxy_auth.md @@ -0,0 +1,333 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# SDK Proxy Authentication (OAuth2/JWT Auto-Refresh) + +Automatically obtain and refresh OAuth2/JWT tokens when using the LiteLLM Python SDK with a LiteLLM Proxy that requires JWT authentication. + +## Overview + +When your LiteLLM Proxy is protected by an OAuth2/OIDC provider (Azure AD, Keycloak, Okta, Auth0, etc.), your SDK clients need valid JWT tokens for every request. Instead of manually managing token lifecycle, `litellm.proxy_auth` handles this automatically: + +- Obtains tokens from your identity provider +- Caches tokens to avoid unnecessary requests +- Refreshes tokens before they expire (60-second buffer) +- Injects `Authorization: Bearer ` headers into every request + +## Quick Start + +### Azure AD + + + + +Uses the [DefaultAzureCredential](https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential) chain (environment variables, managed identity, Azure CLI, etc.): + +```python +import litellm +from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler + +# One-time setup +litellm.proxy_auth = ProxyAuthHandler( + credential=AzureADCredential(), # uses DefaultAzureCredential + scope="api://my-litellm-proxy/.default" +) +litellm.api_base = "https://my-proxy.example.com" + +# All requests now include Authorization headers automatically +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + + + + +Use a specific Azure AD app registration: + +```python +import litellm +from azure.identity import ClientSecretCredential +from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler + +azure_cred = ClientSecretCredential( + tenant_id="your-tenant-id", + client_id="your-client-id", + client_secret="your-client-secret" +) + +litellm.proxy_auth = ProxyAuthHandler( + credential=AzureADCredential(credential=azure_cred), + scope="api://my-litellm-proxy/.default" +) +litellm.api_base = "https://my-proxy.example.com" + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + + + + +**Required package:** `pip install azure-identity` + +### Generic OAuth2 (Okta, Auth0, Keycloak, etc.) + +Works with any OAuth2 provider that supports the `client_credentials` grant type: + +```python +import litellm +from litellm.proxy_auth import GenericOAuth2Credential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=GenericOAuth2Credential( + client_id="your-client-id", + client_secret="your-client-secret", + token_url="https://your-idp.example.com/oauth2/token" + ), + scope="litellm_proxy_api" +) +litellm.api_base = "https://my-proxy.example.com" + +response = litellm.completion( + model="gpt-4", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +### Custom Credential Provider + +Implement the `TokenCredential` protocol to use any authentication mechanism: + +```python +import time +import litellm +from litellm.proxy_auth import AccessToken, ProxyAuthHandler + +class MyCustomCredential: + """Any class with a get_token(scope) -> AccessToken method works.""" + + def get_token(self, scope: str) -> AccessToken: + # Your custom logic to obtain a token + token = my_auth_system.get_jwt(scope=scope) + return AccessToken( + token=token, + expires_on=int(time.time()) + 3600 + ) + +litellm.proxy_auth = ProxyAuthHandler( + credential=MyCustomCredential(), + scope="my-scope" +) +``` + +## Supported Endpoints + +Auth headers are automatically injected for: + +| Endpoint | Function | +|----------|----------| +| Chat Completions | `litellm.completion()` / `litellm.acompletion()` | +| Embeddings | `litellm.embedding()` / `litellm.aembedding()` | + +## How It Works + +``` +┌──────────┐ ┌──────────────────┐ ┌──────────────┐ ┌──────────────┐ +│ Your │ │ ProxyAuthHandler │ │ Identity │ │ LiteLLM │ +│ Code │────▶│ (token cache) │────▶│ Provider │ │ Proxy │ +│ │ │ │◀────│ (Azure AD, │ │ │ +│ │ │ │ │ Okta, etc) │ │ │ +│ │ └────────┬─────────┘ └──────────────┘ │ │ +│ │ │ Authorization: Bearer │ │ +│ │──────────────┼───────────────────────────────────▶│ │ +│ │◀─────────────┼────────────────────────────────────│ │ +└──────────┘ │ └──────────────┘ +``` + +1. You set `litellm.proxy_auth` once at startup +2. On each SDK call (`completion()`, `embedding()`), the handler checks its cached token +3. If the token is missing or expires within 60 seconds, it requests a new one from your identity provider +4. The `Authorization: Bearer ` header is injected into the request +5. If token retrieval fails, a warning is logged and the request proceeds without auth headers + +## API Reference + +### ProxyAuthHandler + +The main handler that manages the token lifecycle. + +```python +from litellm.proxy_auth import ProxyAuthHandler + +handler = ProxyAuthHandler( + credential=, # required - credential provider + scope="" # required - OAuth2 scope to request +) +``` + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `credential` | `TokenCredential` | Yes | A credential provider (AzureADCredential, GenericOAuth2Credential, or custom) | +| `scope` | `str` | Yes | The OAuth2 scope to request tokens for | + +**Methods:** + +| Method | Returns | Description | +|--------|---------|-------------| +| `get_token()` | `AccessToken` | Get a valid token, refreshing if needed | +| `get_auth_headers()` | `dict` | Get `{"Authorization": "Bearer "}` headers | + +### AzureADCredential + +Wraps any `azure-identity` credential with lazy initialization. + +```python +from litellm.proxy_auth import AzureADCredential + +# Uses DefaultAzureCredential (recommended) +cred = AzureADCredential() + +# Or wrap a specific azure-identity credential +from azure.identity import ManagedIdentityCredential +cred = AzureADCredential(credential=ManagedIdentityCredential()) +``` + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `credential` | Azure `TokenCredential` | No | An azure-identity credential. If `None`, uses `DefaultAzureCredential` | + +### GenericOAuth2Credential + +Standard OAuth2 client credentials flow for any provider. + +```python +from litellm.proxy_auth import GenericOAuth2Credential + +cred = GenericOAuth2Credential( + client_id="your-client-id", + client_secret="your-client-secret", + token_url="https://your-idp.com/oauth2/token" +) +``` + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `client_id` | `str` | Yes | OAuth2 client ID | +| `client_secret` | `str` | Yes | OAuth2 client secret | +| `token_url` | `str` | Yes | Token endpoint URL | + +### AccessToken + +Dataclass representing an OAuth2 access token. + +```python +from litellm.proxy_auth import AccessToken + +token = AccessToken( + token="eyJhbG...", # JWT string + expires_on=1234567890 # Unix timestamp +) +``` + +### TokenCredential Protocol + +Any class implementing this protocol can be used as a credential provider: + +```python +from litellm.proxy_auth import AccessToken + +class MyCredential: + def get_token(self, scope: str) -> AccessToken: + ... +``` + +## Provider-Specific Examples + +### Keycloak + +```python +from litellm.proxy_auth import GenericOAuth2Credential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=GenericOAuth2Credential( + client_id="litellm-client", + client_secret="your-keycloak-client-secret", + token_url="https://keycloak.example.com/realms/your-realm/protocol/openid-connect/token" + ), + scope="openid" +) +``` + +### Okta + +```python +from litellm.proxy_auth import GenericOAuth2Credential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=GenericOAuth2Credential( + client_id="your-okta-client-id", + client_secret="your-okta-client-secret", + token_url="https://your-org.okta.com/oauth2/default/v1/token" + ), + scope="litellm_api" +) +``` + +### Auth0 + +```python +from litellm.proxy_auth import GenericOAuth2Credential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=GenericOAuth2Credential( + client_id="your-auth0-client-id", + client_secret="your-auth0-client-secret", + token_url="https://your-tenant.auth0.com/oauth/token" + ), + scope="https://my-proxy.example.com/api" +) +``` + +### Azure AD with Managed Identity + +```python +from azure.identity import ManagedIdentityCredential +from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler + +litellm.proxy_auth = ProxyAuthHandler( + credential=AzureADCredential( + credential=ManagedIdentityCredential() + ), + scope="api://my-litellm-proxy/.default" +) +``` + +## Combining with `use_litellm_proxy` + +You can use `proxy_auth` together with [`use_litellm_proxy`](./providers/litellm_proxy#send-all-sdk-requests-to-litellm-proxy) to route all SDK requests through an authenticated proxy: + +```python +import os +import litellm +from litellm.proxy_auth import AzureADCredential, ProxyAuthHandler + +# Route all requests through the proxy +os.environ["LITELLM_PROXY_API_BASE"] = "https://my-proxy.example.com" +litellm.use_litellm_proxy = True + +# Authenticate with OAuth2/JWT +litellm.proxy_auth = ProxyAuthHandler( + credential=AzureADCredential(), + scope="api://my-litellm-proxy/.default" +) + +# This request goes through the proxy with automatic JWT auth +response = litellm.completion( + model="vertex_ai/gemini-2.0-flash-001", + messages=[{"role": "user", "content": "Hello!"}] +) +``` diff --git a/docs/my-website/docs/realtime.md b/docs/my-website/docs/realtime.md index f4627c78da3..b191c82c670 100644 --- a/docs/my-website/docs/realtime.md +++ b/docs/my-website/docs/realtime.md @@ -3,11 +3,12 @@ import TabItem from '@theme/TabItem'; # /realtime -Use this to loadbalance across Azure + OpenAI. +Use this to loadbalance across Azure + OpenAI + xAI and more. Supported Providers: - OpenAI - Azure +- xAI ([see full docs](/docs/providers/xai_realtime)) - Google AI Studio (Gemini) - Vertex AI - Bedrock @@ -46,6 +47,21 @@ model_list: api_key: os.environ/OPENAI_API_KEY ``` + + + +```yaml +model_list: + - model_name: grok-voice-agent + litellm_params: + model: xai/grok-4-1-fast-non-reasoning + api_key: os.environ/XAI_API_KEY + model_info: + mode: realtime +``` + +**[See full xAI Realtime documentation →](/docs/providers/xai_realtime)** + diff --git a/docs/my-website/docs/response_api.md b/docs/my-website/docs/response_api.md index 140dfd4faf8..dd2b77712c4 100644 --- a/docs/my-website/docs/response_api.md +++ b/docs/my-website/docs/response_api.md @@ -1023,6 +1023,134 @@ curl http://localhost:4000/v1/responses \ +## Server-side compaction + +For long-running conversations, you can enable **server-side compaction** so that when the rendered context size crosses a threshold, the server automatically runs compaction in-stream and emits a compaction item—no separate `POST /v1/responses/compact` call is required. + +Supported on the OpenAI Responses API when using the `openai` or `azure` provider. Pass `context_management` with a compaction entry and `compact_threshold` (token count; minimum 1000). When the context crosses the threshold, the server compacts in-stream and continues. Chain turns with `previous_response_id` or by appending output items to your next input array. See [OpenAI Compaction guide](https://developers.openai.com/api/docs/guides/compaction) for details. + +For explicit control over when compaction runs, use the standalone compact endpoint (`POST /v1/responses/compact`) instead. + +### Python SDK + +```python showLineNumbers title="Server-side compaction with LiteLLM Python SDK" +import litellm + +# Non-streaming: enable compaction when context exceeds 200k tokens +response = litellm.responses( + model="openai/gpt-4o", + input="Your conversation input...", + context_management=[{"type": "compaction", "compact_threshold": 200000}], + max_output_tokens=1024, +) +print(response) + +# Streaming: same context_management, compaction runs in-stream if threshold is crossed +stream = litellm.responses( + model="openai/gpt-4o", + input="Your conversation input...", + context_management=[{"type": "compaction", "compact_threshold": 200000}], + stream=True, +) +for event in stream: + print(event) +``` + +### LiteLLM Proxy (AI Gateway) + +Use the OpenAI SDK with your proxy as `base_url`, or call the proxy with curl. The proxy forwards `context_management` to the provider. + +**OpenAI Python SDK (proxy as base_url):** + +```python showLineNumbers title="Server-side compaction via LiteLLM Proxy" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", # LiteLLM Proxy (AI Gateway) + api_key="your-proxy-api-key", +) + +response = client.responses.create( + model="openai/gpt-4o", + input="Your conversation input...", + context_management=[{"type": "compaction", "compact_threshold": 200000}], + max_output_tokens=1024, +) +print(response) +``` + +**curl (proxy):** + +```bash title="Server-side compaction via curl to LiteLLM Proxy" +curl -X POST "http://localhost:4000/v1/responses" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "openai/gpt-4o", + "input": "Your conversation input...", + "context_management": [{"type": "compaction", "compact_threshold": 200000}], + "max_output_tokens": 1024 + }' +``` + +## Shell tool + +The **Shell tool** lets the model run commands in a hosted container or local runtime (OpenAI Responses API). You pass `tools=[{"type": "shell", "environment": {...}}]`; the `environment` object configures the runtime (e.g. `type: "container_auto"` for auto-provisioned containers). See [OpenAI Shell tool guide](https://developers.openai.com/api/docs/guides/tools-shell) for full options. + +Supported when using the `openai` or `azure` provider with a model that supports the Shell tool. + +### Python SDK + +```python showLineNumbers title="Shell tool with LiteLLM Python SDK" +import litellm + +response = litellm.responses( + model="openai/gpt-5.2", + input="List files in /mnt/data and run python --version.", + tools=[{"type": "shell", "environment": {"type": "container_auto"}}], + tool_choice="auto", + max_output_tokens=1024, +) +``` + +### LiteLLM Proxy (AI Gateway) + +Use the OpenAI SDK with your proxy as `base_url`, or call the proxy with curl. The proxy forwards `tools` (including `type: "shell"`) to the provider. + +**OpenAI Python SDK (proxy as base_url):** + +```python showLineNumbers title="Shell tool via LiteLLM Proxy" +from openai import OpenAI + +client = OpenAI( + base_url="http://localhost:4000", + api_key="your-proxy-api-key", +) + +response = client.responses.create( + model="openai/gpt-5.2", + input="List files in /mnt/data.", + tools=[{"type": "shell", "environment": {"type": "container_auto"}}], + tool_choice="auto", + max_output_tokens=1024, +) +``` + +**curl:** + +```bash title="Shell tool via curl to LiteLLM Proxy" +curl -X POST "http://localhost:4000/v1/responses" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "openai/gpt-5.2", + "input": "List files in /mnt/data.", + "tools": [{"type": "shell", "environment": {"type": "container_auto"}}], + "tool_choice": "auto", + "max_output_tokens": 1024 + }' +``` + ## Session Management LiteLLM Proxy supports session management for all supported models. This allows you to store and fetch conversation history (state) in LiteLLM Proxy. diff --git a/docs/my-website/docs/troubleshoot.md b/docs/my-website/docs/troubleshoot.md index f9ed47972eb..179f1c7897c 100644 --- a/docs/my-website/docs/troubleshoot.md +++ b/docs/my-website/docs/troubleshoot.md @@ -45,6 +45,58 @@ Full error logs, stack traces, and any images from service metrics (CPU, memory, --- +## UI Issues + +If you're experiencing issues with the LiteLLM Admin UI, please include the following information in addition to the general details above. + +### 1. Steps to Reproduce + +A clear, step-by-step description of how to trigger the issue (e.g., "Navigate to Settings → Team, click 'Create Team', fill in fields, click submit → error appears"). + +### 2. LiteLLM Version + +The current version of LiteLLM you're running. Check via `litellm --version` or the UI's settings page. + +### 3. Architecture & Deployment Setup + +Distributed environments are a known source of UI issues. Please describe: + +- **Number of LiteLLM instances/replicas** and how they are deployed (e.g., Kubernetes, Docker Compose, ECS) +- **Load balancer** type and configuration (e.g., ALB, Nginx, Cloudflare Tunnel) — include whether sticky sessions are enabled +- **How the UI is accessed** — directly via LiteLLM, through a reverse proxy, or behind an ingress controller +- **Any CDN or caching layers** between the user and the LiteLLM server + +### 4. Network Tab Requests + +Open your browser's Developer Tools (F12 → Network tab), reproduce the issue, and share: + +- The **failing request(s)** — URL, method, status code, and response body +- **Screenshots or HAR export** of the relevant network activity +- Any **CORS or mixed-content errors** shown in the Console tab + +### 5. Environment Variables + +Non-sensitive environment variables related to the UI and proxy setup, such as: + +- `LITELLM_MASTER_KEY` +- `PROXY_BASE_URL` / `LITELLM_PROXY_BASE_URL` +- `UI_BASE_PATH` +- Any SSO-related variables (e.g., `GOOGLE_CLIENT_ID`, `MICROSOFT_TENANT`) + +Do **not** include passwords, secrets, or API keys. + +### 6. Browser & Access Details + +- **Browser** and version (e.g., Chrome 120, Firefox 121) +- **Access URL** used to reach the UI (redact sensitive parts) +- Whether the issue occurs for **all users or specific roles** (Admin, Internal User, etc.) + +### 7. Screenshots or Screen Recordings + +A screenshot or short screen recording of the issue is extremely helpful. Include any visible error messages, toasts, or unexpected behavior. + +--- + ## Support Channels [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) diff --git a/docs/my-website/docs/troubleshoot/max_callbacks.md b/docs/my-website/docs/troubleshoot/max_callbacks.md new file mode 100644 index 00000000000..4b0f3e24b73 --- /dev/null +++ b/docs/my-website/docs/troubleshoot/max_callbacks.md @@ -0,0 +1,68 @@ +# MAX_CALLBACKS Limit + +## Error Message + +``` +Cannot add callback - would exceed MAX_CALLBACKS limit of 30. Current callbacks: 30 +``` + +## What This Means + +LiteLLM limits the number of callbacks that can be registered to prevent performance degradation. Each callback runs on every LLM request, so having too many callbacks can cause exponential CPU usage and slow down your proxy. + +The default limit is **30 callbacks**. + +## When You Might Hit This Limit + +- **Large enterprise deployments** with many teams, each having their own guardrails +- **Multiple logging integrations** combined with custom callbacks +- **Per-team callback configurations** that add up across your organization + +## How to Override + +Set the `LITELLM_MAX_CALLBACKS` environment variable to increase the limit: + +```bash +# Docker +docker run -e LITELLM_MAX_CALLBACKS=100 ... + +# Docker Compose +environment: + - LITELLM_MAX_CALLBACKS=100 + +# Kubernetes +env: + - name: LITELLM_MAX_CALLBACKS + value: "100" + +# Direct +export LITELLM_MAX_CALLBACKS=100 +litellm --config config.yaml +``` + +## Recommendations + +1. **Start conservative** - Only increase as much as you need. If you have 60 teams with guardrails, try `LITELLM_MAX_CALLBACKS=75` to leave headroom. + +2. **Monitor performance** - More callbacks means more processing per request. Watch your CPU usage and response latency after increasing the limit. + +3. **Consolidate where possible** - If multiple teams use identical guardrails, consider using shared callback configurations rather than per-team duplicates. + +## Example: Large Enterprise Setup + +For an organization with 60+ teams, each with a guardrail callback: + +```yaml +# config.yaml +litellm_settings: + callbacks: ["prometheus", "langfuse"] # 2 global callbacks + +# Each team adds 1 guardrail callback = 60+ callbacks +# Total: 62+ callbacks needed +``` + +Set the environment variable: + +```bash +export LITELLM_MAX_CALLBACKS=100 +``` diff --git a/docs/my-website/docs/tutorials/claude_code_beta_headers.md b/docs/my-website/docs/tutorials/claude_code_beta_headers.md new file mode 100644 index 00000000000..4cc6f7ff92b --- /dev/null +++ b/docs/my-website/docs/tutorials/claude_code_beta_headers.md @@ -0,0 +1,230 @@ +import Image from '@theme/IdealImage'; + +# Claude Code - Managing Anthropic Beta Headers + +When using Claude Code with LiteLLM and non-Anthropic providers (Bedrock, Azure AI, Vertex AI), you need to ensure that only supported beta headers are sent to each provider. This guide explains how to add support for new beta headers or fix invalid beta header errors. + +## What Are Beta Headers? + +Anthropic uses beta headers to enable experimental features in Claude. When you use Claude Code, it may send beta headers like: + +``` +anthropic-beta: prompt-caching-scope-2026-01-05,advanced-tool-use-2025-11-20 +``` + +However, not all providers support all Anthropic beta features. LiteLLM uses `anthropic_beta_headers_config.json` to manage which beta headers are supported by each provider. + +## Common Error Message + +```bash +Error: The model returned the following errors: invalid beta flag +``` + +## How LiteLLM Handles Beta Headers + +LiteLLM uses a strict validation approach with a configuration file: + +``` +litellm/litellm/anthropic_beta_headers_config.json +``` + +This JSON file contains a **mapping** of beta headers for each provider: +- **Keys**: Input beta header names (from Anthropic) +- **Values**: Provider-specific header names (or `null` if unsupported) +- **Validation**: Only headers present in the mapping with non-null values are forwarded + +This enforces stricter validation than just filtering unsupported headers - headers must be explicitly defined to be allowed. + +## Adding Support for a New Beta Header + +When Anthropic releases a new beta feature, you need to add it to the configuration file for each provider. + +### Step 1: Locate the Config File + +Find the file in your LiteLLM installation: + +```bash +# If installed via pip +cd $(python -c "import litellm; import os; print(os.path.dirname(litellm.__file__))") + +# The config file is at: +# litellm/anthropic_beta_headers_config.json +``` + +### Step 2: Add the New Beta Header + +Open `anthropic_beta_headers_config.json` and add the new header to each provider's mapping: + +```json title="anthropic_beta_headers_config.json" +{ + "description": "Mapping of Anthropic beta headers for each provider. Keys are input header names, values are provider-specific header names (or null if unsupported). Only headers present in mapping keys with non-null values can be forwarded.", + "anthropic": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "new-feature-2026-03-01": "new-feature-2026-03-01", + ... + }, + "azure_ai": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "new-feature-2026-03-01": "new-feature-2026-03-01", + ... + }, + "bedrock_converse": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "new-feature-2026-03-01": null, + ... + }, + "bedrock": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "new-feature-2026-03-01": null, + ... + }, + "vertex_ai": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "new-feature-2026-03-01": null, + ... + } +} +``` + +**Key Points:** +- **Supported headers**: Set the value to the provider-specific header name (often the same as the key) +- **Unsupported headers**: Set the value to `null` +- **Header transformations**: Some providers use different header names (e.g., Bedrock maps `advanced-tool-use-2025-11-20` to `tool-search-tool-2025-10-19`) +- **Alphabetical order**: Keep headers sorted alphabetically for maintainability + +### Step 3: Restart Your Application + +After updating the config file, restart your LiteLLM proxy or application: + +```bash +# If using LiteLLM proxy +litellm --config config.yaml + +# If using Python SDK +# Just restart your Python application +``` + +The updated configuration will be loaded automatically. + +## Fixing Invalid Beta Header Errors + +If you encounter an "invalid beta flag" error, it means a beta header is being sent that the provider doesn't support. + +### Step 1: Identify the Problematic Header + +Check your logs to see which header is causing the issue: + +```bash +Error: The model returned the following errors: invalid beta flag: new-feature-2026-03-01 +``` + +### Step 2: Update the Config + +Set the header value to `null` for that provider: + +```json title="anthropic_beta_headers_config.json" +{ + "bedrock_converse": { + "new-feature-2026-03-01": null + } +} +``` + +### Step 3: Restart and Test + +Restart your application and verify the header is now filtered out. + +## Contributing a Fix to LiteLLM + +Help the community by contributing your fix! + +### What to Include in Your PR + +1. **Update the config file**: Add the new beta header to `litellm/anthropic_beta_headers_config.json` +2. **Test your changes**: Verify the header is correctly filtered/mapped for each provider +3. **Documentation**: Include provider documentation links showing which headers are supported + +### Example PR Description + +```markdown +## Add support for new-feature-2026-03-01 beta header + +### Changes +- Added `new-feature-2026-03-01` to anthropic_beta_headers_config.json +- Set to `null` for bedrock_converse (unsupported) +- Set to header name for anthropic, azure_ai (supported) + +### Testing +Tested with: +- ✅ Anthropic: Header passed through correctly +- ✅ Azure AI: Header passed through correctly +- ✅ Bedrock Converse: Header filtered out (returns error without fix) + +### References +- Anthropic docs: [link] +- AWS Bedrock docs: [link] +``` + + +## How Beta Header Filtering Works + +When you make a request through LiteLLM: + +```mermaid +sequenceDiagram + participant CC as Claude Code + participant LP as LiteLLM + participant Config as Beta Headers Config + participant Provider as Provider (Bedrock/Azure/etc) + + CC->>LP: Request with beta headers + Note over CC,LP: anthropic-beta: header1,header2,header3 + + LP->>Config: Load header mapping for provider + Config-->>LP: Returns mapping (header→value or null) + + Note over LP: Validate & Transform:
1. Check if header exists in mapping
2. Filter out null values
3. Map to provider-specific names + + LP->>Provider: Request with filtered & mapped headers + Note over LP,Provider: anthropic-beta: mapped-header2
(header1, header3 filtered out) + + Provider-->>LP: Success response + LP-->>CC: Response +``` + +### Filtering Rules + +1. **Header must exist in mapping**: Unknown headers are filtered out +2. **Header must have non-null value**: Headers with `null` values are filtered out +3. **Header transformation**: Headers are mapped to provider-specific names (e.g., `advanced-tool-use-2025-11-20` → `tool-search-tool-2025-10-19` for Bedrock) + +### Example + +Request with headers: +``` +anthropic-beta: advanced-tool-use-2025-11-20,computer-use-2025-01-24,unknown-header +``` + +For Bedrock Converse: +- ✅ `computer-use-2025-01-24` → `computer-use-2025-01-24` (supported, passed through) +- ❌ `advanced-tool-use-2025-11-20` → filtered out (null value in config) +- ❌ `unknown-header` → filtered out (not in config) + +Result sent to Bedrock: +``` +anthropic-beta: computer-use-2025-01-24 +``` + +## Provider-Specific Notes + +### Bedrock +- Beta headers appear in both HTTP headers AND request body (`additionalModelRequestFields.anthropic_beta`) +- Some headers are transformed (e.g., `advanced-tool-use` → `tool-search-tool`) + +### Azure AI +- Uses same header names as Anthropic +- Some features not yet supported (check config for null values) + +### Vertex AI +- Some headers are transformed to match Vertex AI's implementation +- Limited beta feature support compared to Anthropic \ No newline at end of file diff --git a/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md b/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md new file mode 100644 index 00000000000..bbb29489856 --- /dev/null +++ b/docs/my-website/docs/tutorials/claude_code_prompt_cache_routing.md @@ -0,0 +1,43 @@ +# Claude Code - Prompt Cache Routing + +Claude's [Prompt Caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) feature helps to optimize API usage through attempting to cache prompts and re-use cached prompts during subsequent API calls. This feature is used by Claude Code. + +When LiteLLM [load balancing](../proxy/load_balancing.md) is enabled, to ensure this prompt caching feature still works with Claude Code, LiteLLM needs to be configured to use the `PromptCachingDeploymentCheck` pre-call check. This pre-call check will ensure that API calls that used prompt caching are remembered and that subsequent API calls that try to use that prompt caching are routed to the same model deployment where a cache write occurred. + +## Set Up + +1. Configure the router so that it uses the `PromptCachingDeploymentCheck` (via setting the `optional_pre_call_checks` property), and configure the models so that they can access multiple deployments of Claude; below, we show an example for multiple AWS accounts (referred to as `account-1` and `account-2`, using the `aws_profile_name` property): +```yaml +router_settings: + optional_pre_call_checks: ["prompt_caching"] + +model_list: +- litellm_params: + model: us.anthropic.claude-sonnet-4-5-20250929-v1:0 + aws_profile_name: account-1 + aws_region_name: us-west-2 + model_info: + litellm_provider: bedrock + model_name: us.anthropic.claude-sonnet-4-5-20250929-v1:0 +- litellm_params: + model: us.anthropic.claude-sonnet-4-5-20250929-v1:0 + aws_profile_name: account-2 + aws_region_name: us-west-2 + model_info: + litellm_provider: bedrock + model_name: us.anthropic.claude-sonnet-4-5-20250929-v1:0 +``` +2. Utilize Claude Code: + 1. Launch Claude Code, which will do a warm-up API call that tries to cache its warm-up prompt and its system prompt. + 2. Wait a few seconds, then quit Claude Code and re-open it. + 3. You'll notice that the warm-up API call successfully gets a cache hit (if using Claude Code in an IDE like VS Code, ensure that you don't do anything between step 2.1 and 2.2 here, otherwise there may not be a cache hit): + 1. Go to the [LiteLLM Request Logs page](../proxy/ui_logs.md) in the Admin UI + 2. Click on the individual requests to see (a) the cache creation and cache read tokens; and (b) the Model ID. In particular, the API call from step 2.1 should show a cache write, and the API call from step 2.2 should show a cache read; in addition, the Model ID should be equal (meaning the API call is getting forwarded to the same AWS account). + +## Related + +- [Claude Code - Quickstart](./claude_responses_api.md) +- [Claude Code - Customer Tracking](./claude_code_customer_tracking.md) +- [Claude Code - Plugin Marketplace](./claude_code_plugin_marketplace.md) +- [Claude Code - WebSearch](./claude_code_websearch.md) +- [Proxy - Load Balancing](../proxy/load_balancing.md) diff --git a/docs/my-website/docs/tutorials/copilotkit_sdk.md b/docs/my-website/docs/tutorials/copilotkit_sdk.md new file mode 100644 index 00000000000..fc4db8bfe3e --- /dev/null +++ b/docs/my-website/docs/tutorials/copilotkit_sdk.md @@ -0,0 +1,99 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# CopilotKit SDK with LiteLLM + +Use CopilotKit SDK with any LLM provider through LiteLLM Proxy. + +> **Note:** CopilotKit SDK integration with LiteLLM Proxy works with LiteLLM v1.81.7-nightly or higher. + + +## Quick Start + +### 1. Add Model to Config + +```yaml title="config.yaml" +model_list: + - model_name: claude-sonnet-4-5 + litellm_params: + model: "anthropic/claude-sonnet-4-5-20250514-v1:0" + api_key: "os.environ/ANTHROPIC_API_KEY" +``` + +### 2. Start LiteLLM Proxy + +```bash +litellm --config config.yaml +``` + +### 3. Use CopilotKit SDK + +```typescript +import OpenAI from "openai"; +import { + CopilotRuntime, + OpenAIAdapter, + copilotRuntimeNextJSAppRouterEndpoint, +} from "@copilotkit/runtime"; +import { NextRequest } from "next/server"; + +const model = "claude-sonnet-4-5"; + +const openai = new OpenAI({ + apiKey: process.env.OPENAI_API_KEY || "sk-12345", + baseURL: process.env.OPENAI_BASE_URL || "http://localhost:4000/v1", +}); + +const serviceAdapter = new OpenAIAdapter({ openai, model }); +const runtime = new CopilotRuntime(); + +export const POST = async (req: NextRequest) => { + const { handleRequest } = copilotRuntimeNextJSAppRouterEndpoint({ + runtime, + serviceAdapter, + endpoint: "/api/copilotkit", + }); + return handleRequest(req); +}; +``` + +### 4. Test + +```bash +curl -X POST http://localhost:3000/api/copilotkit \ + -H "Content-Type: application/json" \ + -d '{ + "method": "agent/run", + "params": { + "agentId": "default" + }, + "runId": "your_run_id", + "threadId": "your_thread_id", + "runId": ""your_run_id"", + "tools": [], + "context": [], + "forwardedProps": {}, + "state": {}, + "messages": [ + { + "id": "166e573e-f7c6-4c0f-8685-04dbefec18be", + "content": "Hi", + "role": "user" + } + ] + } +}' +``` + +## Environment Variables + +| Variable | Value | Description | +|----------|-------|-------------| +| `OPENAI_API_KEY` | `sk-12345` | Your LiteLLM API key | +| `OPENAI_BASE_URL` | `http://localhost:4000/v1` | LiteLLM proxy URL | + + +## Related Resources + +- [CopilotKit Documentation](https://docs.copilotkit.ai) +- [LiteLLM Proxy Quick Start](../proxy/quick_start) diff --git a/docs/my-website/docs/tutorials/livekit_xai_realtime.md b/docs/my-website/docs/tutorials/livekit_xai_realtime.md new file mode 100644 index 00000000000..1d70186382f --- /dev/null +++ b/docs/my-website/docs/tutorials/livekit_xai_realtime.md @@ -0,0 +1,190 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# LiveKit xAI Realtime Voice Agent + +Use LiveKit's xAI Grok Voice Agent plugin with LiteLLM Proxy to build low-latency voice AI agents. + +The LiveKit Agents framework provides tools for building real-time voice and video AI applications. By routing through LiteLLM Proxy, you get unified access to multiple realtime voice providers, cost tracking, rate limiting, and more. + +## Quick Start + +### 1. Install Dependencies + +```bash +pip install livekit-agents[xai] +``` + +### 2. Start LiteLLM Proxy + +Create a config file with your xAI realtime model: + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: grok-voice-agent + litellm_params: + model: xai/grok-2-vision-1212 + api_key: os.environ/XAI_API_KEY + model_info: + mode: realtime + +litellm_settings: + drop_params: True + +general_settings: + master_key: sk-1234 # Change this to a secure key +``` + +Start the proxy: + +```bash +litellm --config config.yaml --port 4000 +``` + +### 3. Configure LiveKit xAI Plugin + +Point LiveKit's xAI plugin to your LiteLLM proxy: + +```python +from livekit.plugins import xai + +# Configure xAI to use LiteLLM proxy +model = xai.realtime.RealtimeModel( + voice="ara", # Voice option + api_key="sk-1234", # Your LiteLLM proxy master key + base_url="http://localhost:4000", # LiteLLM proxy URL +) +``` + +## Complete Example + +Here's a complete working example: + + + + +```python +#!/usr/bin/env python3 +""" +Simple xAI realtime voice agent through LiteLLM proxy. +""" +import asyncio +import json +import websockets + +PROXY_URL = "ws://localhost:4000/v1/realtime" +API_KEY = "sk-1234" +MODEL = "grok-voice-agent" + +async def run_voice_agent(): + """Connect to xAI realtime API through LiteLLM proxy""" + url = f"{PROXY_URL}?model={MODEL}" + headers = {"Authorization": f"Bearer {API_KEY}"} + + async with websockets.connect(url, extra_headers=headers) as ws: + # Wait for initial connection event + initial = json.loads(await ws.recv()) + print(f"✅ Connected: {initial['type']}") + + # Send user message + await ws.send(json.dumps({ + "type": "conversation.item.create", + "item": { + "type": "message", + "role": "user", + "content": [{ + "type": "input_text", + "text": "Hello! Tell me a joke." + }] + } + })) + + # Request response + await ws.send(json.dumps({ + "type": "response.create", + "response": {"modalities": ["text", "audio"]} + })) + + # Collect response + transcript = [] + async for message in ws: + event = json.loads(message) + + # Capture text response + if event['type'] == 'response.output_audio_transcript.delta': + transcript.append(event['delta']) + print(event['delta'], end='', flush=True) + + # Done when response completes + elif event['type'] == 'response.done': + break + + print(f"\n\n✅ Full response: {''.join(transcript)}") + +if __name__ == "__main__": + asyncio.run(run_voice_agent()) +``` + + + + + +```python +from livekit.agents import Agent, AgentSession, WorkerOptions, cli +from livekit.plugins import xai + +class VoiceAgent(Agent): + def __init__(self): + super().__init__( + instructions="You are a helpful voice assistant.", + llm=xai.realtime.RealtimeModel( + voice="ara", + api_key="sk-1234", + base_url="http://localhost:4000", + ), + ) + +if __name__ == "__main__": + cli.run_app( + WorkerOptions( + agent_factory=VoiceAgent, + ) + ) +``` + + + + +## Running the Example + +1. **Start LiteLLM Proxy** (if not already running): + ```bash + litellm --config config.yaml --port 4000 + ``` + +2. **Run the example**: + ```bash + python your_script.py + ``` + +## Expected Output + +``` +✅ Connected: conversation.created +Hello! Here's a joke for you: Why don't scientists trust atoms? +Because they make up everything! + +✅ Full response: Hello! Here's a joke for you: Why don't scientists trust atoms? Because they make up everything! +``` + + +## Complete Working Example + +**[LiveKit Agent SDK Cookbook](https://github.com/BerriAI/litellm/tree/main/cookbook/livekit_agent_sdk)** + + +## Learn More + +- [xAI Realtime API](/docs/providers/xai_realtime) +- [LiveKit xAI Plugin](https://docs.livekit.io/agents/models/realtime/plugins/xai/) +- [LiteLLM Realtime API](/docs/realtime) diff --git a/docs/my-website/img/okta_access_policies.png b/docs/my-website/img/okta_access_policies.png new file mode 100644 index 00000000000..e09adc2ce7f Binary files /dev/null and b/docs/my-website/img/okta_access_policies.png differ diff --git a/docs/my-website/img/okta_authorization_server.png b/docs/my-website/img/okta_authorization_server.png new file mode 100644 index 00000000000..bddb3e07a4a Binary files /dev/null and b/docs/my-website/img/okta_authorization_server.png differ diff --git a/docs/my-website/img/okta_client_credentials.png b/docs/my-website/img/okta_client_credentials.png new file mode 100644 index 00000000000..a00a9f4657e Binary files /dev/null and b/docs/my-website/img/okta_client_credentials.png differ diff --git a/docs/my-website/img/okta_redirect_uri.png b/docs/my-website/img/okta_redirect_uri.png new file mode 100644 index 00000000000..a1e58560c72 Binary files /dev/null and b/docs/my-website/img/okta_redirect_uri.png differ diff --git a/docs/my-website/img/okta_security_api.png b/docs/my-website/img/okta_security_api.png new file mode 100644 index 00000000000..7f9e218074c Binary files /dev/null and b/docs/my-website/img/okta_security_api.png differ diff --git a/docs/my-website/img/policy_team_attach.png b/docs/my-website/img/policy_team_attach.png new file mode 100644 index 00000000000..4e337931ed8 Binary files /dev/null and b/docs/my-website/img/policy_team_attach.png differ diff --git a/docs/my-website/img/policy_test_matching.png b/docs/my-website/img/policy_test_matching.png new file mode 100644 index 00000000000..5d024ae78b4 Binary files /dev/null and b/docs/my-website/img/policy_test_matching.png differ diff --git a/docs/my-website/img/release_notes/mcp_internet.png b/docs/my-website/img/release_notes/mcp_internet.png new file mode 100644 index 00000000000..d24d2a20870 Binary files /dev/null and b/docs/my-website/img/release_notes/mcp_internet.png differ diff --git a/docs/my-website/img/ui_team_soft_budget_alerts.png b/docs/my-website/img/ui_team_soft_budget_alerts.png new file mode 100644 index 00000000000..9627b5f1daa Binary files /dev/null and b/docs/my-website/img/ui_team_soft_budget_alerts.png differ diff --git a/docs/my-website/img/ui_team_soft_budget_email_example.png b/docs/my-website/img/ui_team_soft_budget_email_example.png new file mode 100644 index 00000000000..0cd83487112 Binary files /dev/null and b/docs/my-website/img/ui_team_soft_budget_email_example.png differ diff --git a/docs/my-website/img/ui_tools.png b/docs/my-website/img/ui_tools.png new file mode 100644 index 00000000000..6f4d0f87410 Binary files /dev/null and b/docs/my-website/img/ui_tools.png differ diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 4c3db680565..4af7a168f83 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -61,6 +61,8 @@ "mermaid": ">=11.10.0", "gray-matter": "4.0.3", "glob": ">=11.1.0", + "tar": ">=7.5.7", + "@isaacs/brace-expansion": ">=5.0.1", "node-forge": ">=1.3.2", "mdast-util-to-hast": ">=13.2.1", "lodash-es": ">=4.17.23" diff --git a/docs/my-website/release_notes/v1.81.3-stable/index.md b/docs/my-website/release_notes/v1.81.3-stable/index.md index 22b6f43deef..c4b9013590c 100644 --- a/docs/my-website/release_notes/v1.81.3-stable/index.md +++ b/docs/my-website/release_notes/v1.81.3-stable/index.md @@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -docker.litellm.ai/berriai/litellm:v1.81.3.rc.2 +docker.litellm.ai/berriai/litellm:v1.81.3-stable ``` diff --git a/docs/my-website/release_notes/v1.81.6.md b/docs/my-website/release_notes/v1.81.6.md index ef19276f2cf..d349afa65f9 100644 --- a/docs/my-website/release_notes/v1.81.6.md +++ b/docs/my-website/release_notes/v1.81.6.md @@ -1,5 +1,5 @@ --- -title: "v1.81.6 - Logs v2 with Tool Call Tracing" +title: "[Preview] v1.81.6 - Logs v2 with Tool Call Tracing" slug: "v1-81-6" date: 2026-01-31T00:00:00 authors: diff --git a/docs/my-website/release_notes/v1.81.9.md b/docs/my-website/release_notes/v1.81.9.md new file mode 100644 index 00000000000..c34d3056cae --- /dev/null +++ b/docs/my-website/release_notes/v1.81.9.md @@ -0,0 +1,372 @@ +--- +title: "[Preview] v1.81.9 - Control which MCP Servers are exposed on the Internet" +slug: "v1-81-9" +date: 2026-02-07T00:00:00 +authors: + - name: Krrish Dholakia + title: CEO, LiteLLM + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: CTO, LiteLLM + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +hide_table_of_contents: false +--- + +## Deploy this version + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + + + + +``` showLineNumbers title="docker run litellm" +docker run \ +-e STORE_MODEL_IN_DB=True \ +-p 4000:4000 \ +ghcr.io/berriai/litellm:main-v1.81.9.rc.1 +``` + + + + +``` showLineNumbers title="pip install litellm" +pip install litellm==1.81.9 +``` + + + + +## Key Highlights + +- **Claude Opus 4.6** - [Full support across Anthropic, AWS Bedrock, Azure AI, and Vertex AI with adaptive thinking and 1M context window](../../blog/claude_opus_4_6) +- **A2A Agent Gateway** - [Call A2A (Agent-to-Agent) registered agents through the standard `/chat/completions` API](../../docs/a2a_invoking_agents) +- **Expose MCP servers on the public internet** - [Launch MCP servers with public/private visibility and IP-based access control for internet-facing deployments](../../docs/mcp_public_internet) +- **UI Team Soft Budget Alerts** - [Set soft budgets on teams and receive email alerts when spending crosses the threshold — without blocking requests](../../docs/proxy/ui_team_soft_budget_alerts) +- **Performance Optimizations** - Multiple performance improvements including ~40% Prometheus CPU reduction, LRU caching, and optimized logging paths +- **LiteLLM Observatory** - [Automated 24-hour load tests](../../blog/litellm-observatory) +- **30% Faster Request Processing for Callback-Heavy Deployments** - [Performance improvement for callback heavy deployments][PR #20354](https://github.com/BerriAI/litellm/pull/20354) + +--- + +## 30% Faster Request Processing for Callback-Heavy Deployments + + If you use logging callbacks like Langfuse, Datadog, or Prometheus, every request was paying an unnecessary cost: three loops that re-sorted your callbacks on every single request, even though the callback list hadn't changed. The more callbacks you had configured, the more time was wasted. We moved this work to happen once at startup instead of on every request. For deployments with the default callback set, this is a ~30% speedup in request setup. For deployments with many callbacks configured, the improvement is even larger. + +--- + +## LiteLLM Observatory + +LiteLLM Observatory is a long-running release-validation system we built to catch regressions before they reach users. The system is built to be extensible—you can add new tests, configure models and failure thresholds, and queue runs against any deployment. Our goal is to achieve 100% coverage of LiteLLM functionality through these tests. We run 24-hour load tests against our production deployments before all releases, surfacing issues like resource lifecycle bugs, OOMs, and CPU regressions that only appear under sustained load. + +--- + +## MCP Servers on the Public Internet + +This release makes it safe to expose MCP servers on the public internet by adding public/private visibility and IP-based access control. You can now run internet-facing MCP services while restricting access to trusted networks and keeping internal tools private. + +[Get started](../../docs/mcp_public_internet) + + + +## UI Team Soft Budget Alerts + +Set a soft budget on any team to receive email alerts when spending crosses the threshold — without blocking any requests. Configure the threshold and alerting emails directly from the Admin UI, with no proxy restart needed. + +[Get started](../../docs/proxy/ui_team_soft_budget_alerts) + + + +Let's dive in. + +--- + +## New Models / Updated Models + +#### New Model Support (13 new models) + +| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | +| -------- | ----- | -------------- | ------------------- | -------------------- | +| Anthropic | `claude-opus-4-6` | 1M | $5.00 | $25.00 | +| AWS Bedrock | `anthropic.claude-opus-4-6-v1` | 1M | $5.00 | $25.00 | +| Azure AI | `azure_ai/claude-opus-4-6` | 200K | $5.00 | $25.00 | +| Vertex AI | `vertex_ai/claude-opus-4-6` | 1M | $5.00 | $25.00 | +| Google Gemini | `gemini/deep-research-pro-preview-12-2025` | 65K | $2.00 | $12.00 | +| Vertex AI | `vertex_ai/deep-research-pro-preview-12-2025` | 65K | $2.00 | $12.00 | +| Moonshot | `moonshot/kimi-k2.5` | 262K | $0.60 | $3.00 | +| OpenRouter | `openrouter/qwen/qwen3-235b-a22b-2507` | 262K | $0.07 | $0.10 | +| OpenRouter | `openrouter/qwen/qwen3-235b-a22b-thinking-2507` | 262K | $0.11 | $0.60 | +| Together AI | `together_ai/zai-org/GLM-4.7` | 200K | $0.45 | $2.00 | +| Together AI | `together_ai/moonshotai/Kimi-K2.5` | 256K | $0.50 | $2.80 | +| ElevenLabs | `elevenlabs/eleven_v3` | - | $0.18/1K chars | - | +| ElevenLabs | `elevenlabs/eleven_multilingual_v2` | - | $0.18/1K chars | - | + +#### Features + +- **[Anthropic](../../docs/providers/anthropic)** + - Full Claude Opus 4.6 support with adaptive thinking across all regions (us, eu, apac, au) - [PR #20506](https://github.com/BerriAI/litellm/pull/20506), [PR #20508](https://github.com/BerriAI/litellm/pull/20508), [PR #20514](https://github.com/BerriAI/litellm/pull/20514), [PR #20551](https://github.com/BerriAI/litellm/pull/20551) + - Map reasoning content to anthropic thinking block (streaming + non-streaming) - [PR #20254](https://github.com/BerriAI/litellm/pull/20254) + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Add 1hr tiered caching costs for long-context models - [PR #20214](https://github.com/BerriAI/litellm/pull/20214) + - Support TTL (1h) field in prompt caching for Bedrock Claude 4.5 models - [PR #20338](https://github.com/BerriAI/litellm/pull/20338) + - Add Nova Sonic speech-to-speech model support - [PR #20244](https://github.com/BerriAI/litellm/pull/20244) + - Fix empty assistant message for Converse API - [PR #20390](https://github.com/BerriAI/litellm/pull/20390) + - Fix content blocked handling - [PR #20606](https://github.com/BerriAI/litellm/pull/20606) + +- **[Google Gemini / Vertex AI](../../docs/providers/gemini)** + - Add Gemini Deep Research model support - [PR #20406](https://github.com/BerriAI/litellm/pull/20406) + - Fix Vertex AI Gemini streaming content_filter handling - [PR #20105](https://github.com/BerriAI/litellm/pull/20105) + - Allow using OpenAI-style tools for `web_search` with Vertex AI/Gemini models - [PR #20280](https://github.com/BerriAI/litellm/pull/20280) + - Fix `supports_native_streaming` for Gemini and Vertex AI models - [PR #20408](https://github.com/BerriAI/litellm/pull/20408) + - Add mapping for responses tools in file IDs - [PR #20402](https://github.com/BerriAI/litellm/pull/20402) + +- **[Cohere](../../docs/providers/cohere)** + - Support `dimensions` param for Cohere embed v4 - [PR #20235](https://github.com/BerriAI/litellm/pull/20235) + +- **[Cerebras](../../docs/providers/cerebras)** + - Add reasoning param support for GPT OSS Cerebras - [PR #20258](https://github.com/BerriAI/litellm/pull/20258) + +- **[Moonshot](../../docs/providers/moonshot)** + - Add Kimi K2.5 model entries - [PR #20273](https://github.com/BerriAI/litellm/pull/20273) + +- **[OpenRouter](../../docs/providers/openrouter)** + - Add Qwen3-235B models - [PR #20455](https://github.com/BerriAI/litellm/pull/20455) + +- **[Together AI](../../docs/providers/togetherai)** + - Add GLM-4.7 and Kimi-K2.5 models - [PR #20319](https://github.com/BerriAI/litellm/pull/20319) + +- **[ElevenLabs](../../docs/providers/elevenlabs)** + - Add `eleven_v3` and `eleven_multilingual_v2` TTS models - [PR #20522](https://github.com/BerriAI/litellm/pull/20522) + +- **[Vercel AI Gateway](../../docs/providers/vercel_ai_gateway)** + - Add missing capability flags to models - [PR #20276](https://github.com/BerriAI/litellm/pull/20276) + +- **[GitHub Copilot](../../docs/providers/github_copilot)** + - Fix system prompts being dropped and auto-add required Copilot headers - [PR #20113](https://github.com/BerriAI/litellm/pull/20113) + +- **[GigaChat](../../docs/providers/gigachat)** + - Fix incorrect merging of consecutive user messages for GigaChat provider - [PR #20341](https://github.com/BerriAI/litellm/pull/20341) + +- **[xAI](../../docs/providers/xai_realtime)** + - Add xAI `/realtime` API support - works with LiveKit SDK - [PR #20381](https://github.com/BerriAI/litellm/pull/20381) + +- **[OpenAI](../../docs/providers/openai)** + - Add `gpt-5-search-api` model and docs clarifications - [PR #20512](https://github.com/BerriAI/litellm/pull/20512) + +### Bug Fixes + +- **[Anthropic](../../docs/providers/anthropic)** + - Fix extra inputs not permitted error for `provider_specific_fields` - [PR #20334](https://github.com/BerriAI/litellm/pull/20334) + +- **[AWS Bedrock](../../docs/providers/bedrock)** + - Fix: Managed Batches inconsistent state management for list and cancel batches - [PR #20331](https://github.com/BerriAI/litellm/pull/20331) + +- **[OpenAI Embeddings](../../docs/providers/openai)** + - Fix `open_ai_embedding_models` to have `custom_llm_provider` None - [PR #20253](https://github.com/BerriAI/litellm/pull/20253) + +--- + +## LLM API Endpoints + +#### Features + +- **[Messages API](../../docs/providers/anthropic)** + - Filter unsupported Claude Code beta headers for non-Anthropic providers - [PR #20578](https://github.com/BerriAI/litellm/pull/20578) + - Fix inconsistent response format in `anthropic.messages.acreate()` when using non-Anthropic providers - [PR #20442](https://github.com/BerriAI/litellm/pull/20442) + - Fix 404 on `/api/event_logging/batch` endpoint that caused Claude Code "route not found" errors - [PR #20504](https://github.com/BerriAI/litellm/pull/20504) + +- **[A2A Agent Gateway](../../docs/a2a)** + - Allow calling A2A agents through LiteLLM `/chat/completions` API - [PR #20358](https://github.com/BerriAI/litellm/pull/20358) + - Use A2A registered agents with `/chat/completions` - [PR #20362](https://github.com/BerriAI/litellm/pull/20362) + - Fix A2A agents deployed with localhost/internal URLs in their agent cards - [PR #20604](https://github.com/BerriAI/litellm/pull/20604) + +- **[Files API](../../docs/providers/gemini)** + - Add support for delete and GET via file_id for Gemini - [PR #20329](https://github.com/BerriAI/litellm/pull/20329) + +- **General** + - Add User-Agent customization support - [PR #19881](https://github.com/BerriAI/litellm/pull/19881) + - Fix search tools not found when using per-request routers - [PR #19818](https://github.com/BerriAI/litellm/pull/19818) + - Forward extra headers in chat - [PR #20386](https://github.com/BerriAI/litellm/pull/20386) + +--- + +## Management Endpoints / UI + +#### Features + +- **SSO Configuration** + - SSO Config Team Mappings - [PR #20111](https://github.com/BerriAI/litellm/pull/20111) + - UI - SSO: Add Team Mappings - [PR #20299](https://github.com/BerriAI/litellm/pull/20299) + - Extract user roles from JWT access token for Keycloak compatibility - [PR #20591](https://github.com/BerriAI/litellm/pull/20591) + +- **Auth / SDK** + - Add `proxy_auth` for auto OAuth2/JWT token management in SDK - [PR #20238](https://github.com/BerriAI/litellm/pull/20238) + +- **Virtual Keys** + - Key `reset_spend` endpoint - [PR #20305](https://github.com/BerriAI/litellm/pull/20305) + - UI - Keys: Allowed Routes to Key Info and Edit Pages - [PR #20369](https://github.com/BerriAI/litellm/pull/20369) + - Add Key info endpoint object permission data - [PR #20407](https://github.com/BerriAI/litellm/pull/20407) + - Keys and Teams Router Setting + Allow Override of Router Settings - [PR #20205](https://github.com/BerriAI/litellm/pull/20205) + +- **Teams & Budgets** + - Add `soft_budget` to Team Table + Create/Update Endpoints - [PR #20530](https://github.com/BerriAI/litellm/pull/20530) + - Team Soft Budget Email Alerts - [PR #20553](https://github.com/BerriAI/litellm/pull/20553) + - UI - Team Settings: Soft Budget + Alerting Emails - [PR #20634](https://github.com/BerriAI/litellm/pull/20634) + - UI - User Budget Page: Unlimited Budget Checkbox - [PR #20380](https://github.com/BerriAI/litellm/pull/20380) + - `/user/update` allow for `max_budget` resets - [PR #20375](https://github.com/BerriAI/litellm/pull/20375) + +- **UI Improvements** + - Default Team Settings: Migrate to use Reusable Model Select - [PR #20310](https://github.com/BerriAI/litellm/pull/20310) + - Navbar: Option to Hide Community Engagement Buttons - [PR #20308](https://github.com/BerriAI/litellm/pull/20308) + - Show team alias on Models health page - [PR #20359](https://github.com/BerriAI/litellm/pull/20359) + - Admin Settings: Add option for Authentication for public AI Hub - [PR #20444](https://github.com/BerriAI/litellm/pull/20444) + - Adjust daily spend date filtering for user timezone - [PR #20472](https://github.com/BerriAI/litellm/pull/20472) + +- **SCIM** + - Add base `/scim/v2` endpoint for SCIM resource discovery - [PR #20301](https://github.com/BerriAI/litellm/pull/20301) + +- **Proxy CLI** + - CLI arguments for RDS IAM auth - [PR #20437](https://github.com/BerriAI/litellm/pull/20437) + +#### Bugs + +- Fix: Remove unnecessary key blocking on UI login that prevented access - [PR #20210](https://github.com/BerriAI/litellm/pull/20210) +- UI - Team Settings: Disable Global Guardrail Persistence - [PR #20307](https://github.com/BerriAI/litellm/pull/20307) +- UI - Model Info Page: Fix Input and Output Labels - [PR #20462](https://github.com/BerriAI/litellm/pull/20462) +- UI - Model Page: Column Resizing on Smaller Screens - [PR #20599](https://github.com/BerriAI/litellm/pull/20599) +- Fix `/key/list` `user_id` Empty String Edge Case - [PR #20623](https://github.com/BerriAI/litellm/pull/20623) +- Add array type checks for model, agent, and MCP hub data to prevent UI crashes - [PR #20469](https://github.com/BerriAI/litellm/pull/20469) +- Fix unique constraint on daily tables + logging when updates fail - [PR #20394](https://github.com/BerriAI/litellm/pull/20394) + +--- + +## Logging / Guardrail / Prompt Management Integrations + +#### Bug Fixes (3 fixes) + +- **[Langfuse](../../docs/proxy/logging#langfuse)** + - Fix Langfuse OTEL trace export failing when spans contain null attributes - [PR #20382](https://github.com/BerriAI/litellm/pull/20382) + +- **[Prometheus](../../docs/proxy/logging#prometheus)** + - Fix incorrect failure metrics labels causing miscounted error rates - [PR #20152](https://github.com/BerriAI/litellm/pull/20152) + +- **[Slack Alerts](../../docs/proxy/alerting)** + - Fix Slack alert delivery failing for certain budget threshold configurations - [PR #20257](https://github.com/BerriAI/litellm/pull/20257) + +#### Guardrails (7 updates) + +- **Custom Code Guardrails** + - Add HTTP support to custom code guardrails + Unified guardrails for MCP + Agent guardrail support - [PR #20619](https://github.com/BerriAI/litellm/pull/20619) + - Custom Code Guardrails UI Playground - [PR #20377](https://github.com/BerriAI/litellm/pull/20377) + +- **Team-Based Guardrails** + - Implement team-based isolation guardrails management - [PR #20318](https://github.com/BerriAI/litellm/pull/20318) + +- **[OpenAI Moderations](../../docs/apply_guardrail)** + - Ensure OpenAI Moderations Guard works with OpenAI Embeddings - [PR #20523](https://github.com/BerriAI/litellm/pull/20523) + +- **[GraySwan / Cygnal](../../docs/apply_guardrail)** + - Fix fail-open for GraySwan and pass metadata to Cygnal API endpoint - [PR #19837](https://github.com/BerriAI/litellm/pull/19837) + +- **General** + - Check for `model_response_choices` before guardrail input - [PR #19784](https://github.com/BerriAI/litellm/pull/19784) + - Preserve streaming content on guardrail-sampled chunks - [PR #20027](https://github.com/BerriAI/litellm/pull/20027) + +--- + +## Spend Tracking, Budgets and Rate Limiting + +- **Support 0 cost models** - Allow zero-cost model entries for internal/free-tier models - [PR #20249](https://github.com/BerriAI/litellm/pull/20249) + +--- + +## MCP Gateway (9 updates) + +- **MCP Semantic Filtering** - Filter MCP tools using semantic similarity to reduce tool sprawl for LLM calls - [PR #20296](https://github.com/BerriAI/litellm/pull/20296), [PR #20316](https://github.com/BerriAI/litellm/pull/20316) +- **UI - MCP Semantic Filtering** - Add support for MCP Semantic Filtering configuration on UI - [PR #20454](https://github.com/BerriAI/litellm/pull/20454) +- **MCP IP-Based Access Control** - Set MCP servers as private/public available on internet with IP-based restrictions - [PR #20607](https://github.com/BerriAI/litellm/pull/20607), [PR #20620](https://github.com/BerriAI/litellm/pull/20620) +- **Fix MCP "Session not found" error** on VSCode reconnect - [PR #20298](https://github.com/BerriAI/litellm/pull/20298) +- **Fix OAuth2 'Capabilities: none' bug** for upstream MCP servers - [PR #20602](https://github.com/BerriAI/litellm/pull/20602) +- **Include Config Defined Search Tools** in `/search_tools/list` - [PR #20371](https://github.com/BerriAI/litellm/pull/20371) +- **UI - Search Tools**: Show Config Defined Search Tools - [PR #20436](https://github.com/BerriAI/litellm/pull/20436) +- **Ensure MCP permissions are enforced** when using JWT Auth - [PR #20383](https://github.com/BerriAI/litellm/pull/20383) +- **Fix `gcs_bucket_name` not being passed** correctly for MCP server storage configuration - [PR #20491](https://github.com/BerriAI/litellm/pull/20491) + +--- + +## Performance / Loadbalancing / Reliability improvements (14 improvements) + +- **Prometheus ~40% CPU reduction** - Parallelize budget metrics, fix caching bug, reduce CPU usage - [PR #20544](https://github.com/BerriAI/litellm/pull/20544) +- **Prevent closed client errors** by reverting httpx client caching - [PR #20025](https://github.com/BerriAI/litellm/pull/20025) +- **Avoid unnecessary Router creation** when no models or search tools are configured - [PR #20661](https://github.com/BerriAI/litellm/pull/20661) +- **Optimize `wrapper_async`** with `CallTypes` caching and reduced lookups - [PR #20204](https://github.com/BerriAI/litellm/pull/20204) +- **Cache `_get_relevant_args_to_use_for_logging()`** at module level - [PR #20077](https://github.com/BerriAI/litellm/pull/20077) +- **LRU cache for `normalize_request_route`** - [PR #19812](https://github.com/BerriAI/litellm/pull/19812) +- **Optimize `get_standard_logging_metadata`** with set intersection - [PR #19685](https://github.com/BerriAI/litellm/pull/19685) +- **Early-exit guards in `completion_cost`** for unused features - [PR #20020](https://github.com/BerriAI/litellm/pull/20020) +- **Optimize `get_litellm_params`** with sparse kwargs extraction - [PR #19884](https://github.com/BerriAI/litellm/pull/19884) +- **Guard debug log f-strings** and remove redundant dict copies - [PR #19961](https://github.com/BerriAI/litellm/pull/19961) +- **Replace enum construction with frozenset lookup** - [PR #20302](https://github.com/BerriAI/litellm/pull/20302) +- **Guard debug f-string in `update_environment_variables`** - [PR #20360](https://github.com/BerriAI/litellm/pull/20360) +- **Warn when budget lookup fails** to surface silent caching misses - [PR #20545](https://github.com/BerriAI/litellm/pull/20545) +- **Add INFO-level session reuse logging** per request for better observability - [PR #20597](https://github.com/BerriAI/litellm/pull/20597) + +--- + +## Database Changes + +### Schema Updates + +| Table | Change Type | Description | PR | Migration | +| ----- | ----------- | ----------- | -- | --------- | +| `LiteLLM_TeamTable` | New Column | Added `allow_team_guardrail_config` boolean field for team-based guardrail isolation | [PR #20318](https://github.com/BerriAI/litellm/pull/20318) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205091235_allow_team_guardrail_config/migration.sql) | +| `LiteLLM_DeletedTeamTable` | New Column | Added `allow_team_guardrail_config` boolean field | [PR #20318](https://github.com/BerriAI/litellm/pull/20318) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205091235_allow_team_guardrail_config/migration.sql) | +| `LiteLLM_TeamTable` | New Column | Added `soft_budget` (double precision) for soft budget alerting | [PR #20530](https://github.com/BerriAI/litellm/pull/20530) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205144610_add_soft_budget_to_team_table/migration.sql) | +| `LiteLLM_DeletedTeamTable` | New Column | Added `soft_budget` (double precision) | [PR #20653](https://github.com/BerriAI/litellm/pull/20653) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207110613_add_soft_budget_to_deleted_teams_table/migration.sql) | +| `LiteLLM_MCPServerTable` | New Column | Added `available_on_public_internet` boolean for MCP IP-based access control | [PR #20607](https://github.com/BerriAI/litellm/pull/20607) | [Migration](https://github.com/BerriAI/litellm/blob/main/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207093506_add_available_on_public_internet_to_mcp_servers/migration.sql) | + +--- + +## Documentation Updates (14 updates) + +- Add FAQ for setting up and verifying LITELLM_LICENSE - [PR #20284](https://github.com/BerriAI/litellm/pull/20284) +- Model request tags documentation - [PR #20290](https://github.com/BerriAI/litellm/pull/20290) +- Add Prisma migration troubleshooting guide - [PR #20300](https://github.com/BerriAI/litellm/pull/20300) +- MCP Semantic Filtering documentation - [PR #20316](https://github.com/BerriAI/litellm/pull/20316) +- Add CopilotKit SDK doc as supported agents SDK - [PR #20396](https://github.com/BerriAI/litellm/pull/20396) +- Add documentation for Nova Sonic - [PR #20320](https://github.com/BerriAI/litellm/pull/20320) +- Update Vertex AI Text to Speech doc to show use of audio - [PR #20255](https://github.com/BerriAI/litellm/pull/20255) +- Improve Okta SSO setup guide with step-by-step instructions - [PR #20353](https://github.com/BerriAI/litellm/pull/20353) +- Langfuse doc update - [PR #20443](https://github.com/BerriAI/litellm/pull/20443) +- Expose MCPs on public internet documentation - [PR #20626](https://github.com/BerriAI/litellm/pull/20626) +- Add blog post: Achieving Sub-Millisecond Proxy Overhead - [PR #20309](https://github.com/BerriAI/litellm/pull/20309) +- Add blog post about litellm-observatory - [PR #20622](https://github.com/BerriAI/litellm/pull/20622) +- Update Opus 4.6 blog with adaptive thinking - [PR #20637](https://github.com/BerriAI/litellm/pull/20637) +- `gpt-5-search-api` docs clarifications - [PR #20512](https://github.com/BerriAI/litellm/pull/20512) + +--- + +## New Contributors +* @Quentin-M made their first contribution in [PR #19818](https://github.com/BerriAI/litellm/pull/19818) +* @amirzaushnizer made their first contribution in [PR #20235](https://github.com/BerriAI/litellm/pull/20235) +* @cscguochang made their first contribution in [PR #20214](https://github.com/BerriAI/litellm/pull/20214) +* @krauckbot made their first contribution in [PR #20273](https://github.com/BerriAI/litellm/pull/20273) +* @agrattan0820 made their first contribution in [PR #19784](https://github.com/BerriAI/litellm/pull/19784) +* @nina-hu made their first contribution in [PR #20472](https://github.com/BerriAI/litellm/pull/20472) +* @swayambhu94 made their first contribution in [PR #20469](https://github.com/BerriAI/litellm/pull/20469) +* @ssadedin made their first contribution in [PR #20566](https://github.com/BerriAI/litellm/pull/20566) + +--- + +## Full Changelog +[v1.81.6-nightly...v1.81.9](https://github.com/BerriAI/litellm/compare/v1.81.6-nightly...v1.81.9) diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 49265ddf63f..579b5699101 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -42,48 +42,62 @@ const sidebars = { label: "Guardrails", items: [ "proxy/guardrails/quick_start", - "proxy/guardrails/guardrail_policies", "proxy/guardrails/guardrail_load_balancing", + "proxy/guardrails/test_playground", + "proxy/guardrails/litellm_content_filter", { type: "category", - "label": "Contributing to Guardrails", + label: "Providers", + items: [ + ...[ + "proxy/guardrails/qualifire", + "proxy/guardrails/aim_security", + "proxy/guardrails/onyx_security", + "proxy/guardrails/aporia_api", + "proxy/guardrails/azure_content_guardrail", + "proxy/guardrails/bedrock", + "proxy/guardrails/enkryptai", + "proxy/guardrails/ibm_guardrails", + "proxy/guardrails/grayswan", + "proxy/guardrails/hiddenlayer", + "proxy/guardrails/lasso_security", + "proxy/guardrails/guardrails_ai", + "proxy/guardrails/lakera_ai", + "proxy/guardrails/model_armor", + "proxy/guardrails/noma_security", + "proxy/guardrails/dynamoai", + "proxy/guardrails/openai_moderation", + "proxy/guardrails/pangea", + "proxy/guardrails/pillar_security", + "proxy/guardrails/pii_masking_v2", + "proxy/guardrails/panw_prisma_airs", + "proxy/guardrails/secret_detection", + "proxy/guardrails/custom_guardrail", + "proxy/guardrails/custom_code_guardrail", + "proxy/guardrails/prompt_injection", + "proxy/guardrails/tool_permission", + "proxy/guardrails/zscaler_ai_guard", + "proxy/guardrails/javelin" + ].sort(), + ], + }, + { + type: "category", + label: "Contributing to Guardrails", items: [ "adding_provider/generic_guardrail_api", "adding_provider/simple_guardrail_tutorial", "adding_provider/adding_guardrail_support", ] }, - "proxy/guardrails/test_playground", - "proxy/guardrails/litellm_content_filter", - ...[ - "proxy/guardrails/qualifire", - "proxy/guardrails/aim_security", - "proxy/guardrails/onyx_security", - "proxy/guardrails/aporia_api", - "proxy/guardrails/azure_content_guardrail", - "proxy/guardrails/bedrock", - "proxy/guardrails/enkryptai", - "proxy/guardrails/ibm_guardrails", - "proxy/guardrails/grayswan", - "proxy/guardrails/hiddenlayer", - "proxy/guardrails/lasso_security", - "proxy/guardrails/guardrails_ai", - "proxy/guardrails/lakera_ai", - "proxy/guardrails/model_armor", - "proxy/guardrails/noma_security", - "proxy/guardrails/dynamoai", - "proxy/guardrails/openai_moderation", - "proxy/guardrails/pangea", - "proxy/guardrails/pillar_security", - "proxy/guardrails/pii_masking_v2", - "proxy/guardrails/panw_prisma_airs", - "proxy/guardrails/secret_detection", - "proxy/guardrails/custom_guardrail", - "proxy/guardrails/prompt_injection", - "proxy/guardrails/tool_permission", - "proxy/guardrails/zscaler_ai_guard", - "proxy/guardrails/javelin" - ].sort(), + ], + }, + { + type: "category", + label: "Policies", + items: [ + "proxy/guardrails/guardrail_policies", + "proxy/guardrails/policy_tags", ], }, { @@ -95,6 +109,11 @@ const sidebars = { "proxy/prometheus" ] }, + { + type: "doc", + id: "integrations/websearch_interception", + label: "Web Search Integration" + }, { type: "category", label: "[Beta] Prompt Management", @@ -124,10 +143,12 @@ const sidebars = { "tutorials/claude_responses_api", "tutorials/claude_code_max_subscription", "tutorials/claude_code_customer_tracking", + "tutorials/claude_code_prompt_cache_routing", "tutorials/claude_code_websearch", "tutorials/claude_mcp", "tutorials/claude_non_anthropic_models", "tutorials/claude_code_plugin_marketplace", + "tutorials/claude_code_beta_headers", ] }, "tutorials/opencode_integration", @@ -150,7 +171,9 @@ const sidebars = { }, items: [ "tutorials/claude_agent_sdk", + "tutorials/copilotkit_sdk", "tutorials/google_adk", + "tutorials/livekit_xai_realtime", ] }, @@ -219,6 +242,7 @@ const sidebars = { label: "Configuration", items: [ "set_keys", + "proxy_auth", "caching/all_caches", ], }, @@ -283,40 +307,52 @@ const sidebars = { label: "Admin UI", items: [ "proxy/ui", - "proxy/admin_ui_sso", - "proxy/custom_root_ui", - "proxy/custom_sso", - "proxy/ai_hub", - "proxy/model_compare_ui", - "proxy/ui_credentials", - "tutorials/scim_litellm", { type: "category", - label: "UI User/Team Management", + label: "Setup & SSO", items: [ - "proxy/access_control", - "proxy/public_teams", + "proxy/admin_ui_sso", + "proxy/custom_sso", + "proxy/custom_root_ui", + "tutorials/scim_litellm", + ] + }, + { + type: "category", + label: "Models", + items: [ + "proxy/ui_credentials", + "proxy/ai_hub", + "proxy/model_compare_ui", + ] + }, + { + type: "category", + label: "Teams & Organizations", + items: [ + "proxy/access_control", "proxy/self_serve", + "proxy/public_teams", "proxy/ui/bulk_edit_users", "proxy/ui/page_visibility", ] }, { type: "category", - label: "UI Usage Tracking", + label: "Observability: Usage", items: [ "proxy/customer_usage", - "proxy/endpoint_activity" + "proxy/endpoint_activity", ] }, { type: "category", - label: "UI Logs", + label: "Logs", items: [ "proxy/ui_logs", "proxy/ui_spend_log_settings", "proxy/ui_logs_sessions", - "proxy/deleted_keys_teams" + "proxy/deleted_keys_teams", ] } ], @@ -364,6 +400,7 @@ const sidebars = { items: [ "proxy/users", "proxy/team_budgets", + "proxy/ui_team_soft_budget_alerts", "proxy/tag_budgets", "proxy/customers", "proxy/dynamic_rate_limit", @@ -372,6 +409,16 @@ const sidebars = { ], }, "proxy/caching", + { + type: "link", + label: "Guardrails", + href: "https://docs.litellm.ai/docs/proxy/guardrails/quick_start", + }, + { + type: "link", + label: "Policies", + href: "https://docs.litellm.ai/docs/proxy/guardrails/guardrail_policies", + }, { type: "category", label: "Create Custom Plugins", @@ -469,6 +516,7 @@ const sidebars = { label: "/a2a - A2A Agent Gateway", items: [ "a2a", + "a2a_invoking_agents", "a2a_cost_tracking", "a2a_agent_permissions" ], @@ -538,6 +586,8 @@ const sidebars = { items: [ "mcp", "mcp_usage", + "mcp_oauth", + "mcp_public_internet", "mcp_semantic_filter", "mcp_control", "mcp_cost", @@ -717,6 +767,7 @@ const sidebars = { "providers/bedrock_agents", "providers/bedrock_writer", "providers/bedrock_batches", + "providers/bedrock_realtime_with_audio", "providers/aws_polly", "providers/bedrock_vector_store", ] @@ -849,7 +900,14 @@ const sidebars = { "providers/watsonx/audio_transcription", ] }, - "providers/xai", + { + type: "category", + label: "xAI", + items: [ + "providers/xai", + "providers/xai_realtime", + ] + }, "providers/xiaomi_mimo", "providers/xinference", "providers/zai", @@ -1047,6 +1105,18 @@ const sidebars = { "troubleshoot/cpu_issues", "troubleshoot/memory_issues", "troubleshoot/spend_queue_warnings", + "troubleshoot/max_callbacks", + ], + }, + { + type: "category", + label: "Blog", + items: [ + { + type: "link", + label: "Incident: Broken Model Cost Map", + href: "/blog/model-cost-map-incident", + }, ], }, ], diff --git a/docs/my-website/src/components/MiddlewareDiagrams/BaseHTTPMiddlewareAnimation.tsx b/docs/my-website/src/components/MiddlewareDiagrams/BaseHTTPMiddlewareAnimation.tsx new file mode 100644 index 00000000000..0821cf353c6 --- /dev/null +++ b/docs/my-website/src/components/MiddlewareDiagrams/BaseHTTPMiddlewareAnimation.tsx @@ -0,0 +1,133 @@ +import React, { useState, useEffect, useCallback, useRef } from 'react'; +import styles from './styles.module.css'; + +interface Stage { + label: string; + subtitle: string; + code: string; +} + +const STAGES: Stage[] = [ + { + label: 'Request Wrapping', + subtitle: '_CachedRequest', + code: 'request = _CachedRequest(scope, receive)', + }, + { + label: 'Sync Event', + subtitle: 'anyio.Event()', + code: 'response_sent = anyio.Event()', + }, + { + label: 'Memory Stream', + subtitle: 'create_memory_object_stream()', + code: 'send_stream, recv_stream = anyio.create_memory_object_stream()', + }, + { + label: 'Task Group', + subtitle: 'create_task_group()', + code: 'async with anyio.create_task_group() as task_group:', + }, + { + label: 'Background Task', + subtitle: 'task_group.start_soon(coro)', + code: 'task_group.start_soon(coro) # app runs in separate task', + }, + { + label: 'Nested Task Group', + subtitle: 'receive_or_disconnect()', + code: 'async with anyio.create_task_group() as task_group: ...', + }, + { + label: 'Response Wrapping', + subtitle: '_StreamingResponse', + code: 'response = _StreamingResponse(status_code=..., content=body_stream())', + }, +]; + +const INTERVAL_MS = 1200; +const PAUSE_MS = 600; + +export default function BaseHTTPMiddlewareAnimation() { + const [activeStage, setActiveStage] = useState(0); + const [paused, setPaused] = useState(false); + const [expandedStage, setExpandedStage] = useState(null); + const timerRef = useRef | null>(null); + + const clearTimer = useCallback(() => { + if (timerRef.current !== null) { + clearTimeout(timerRef.current); + timerRef.current = null; + } + }, []); + + useEffect(() => { + if (paused) return; + + const advance = () => { + setActiveStage((prev) => { + const next = (prev + 1) % STAGES.length; + // If wrapping around, add extra pause + if (next === 0) { + timerRef.current = setTimeout(() => { + timerRef.current = setTimeout(advance, INTERVAL_MS); + }, PAUSE_MS); + return next; + } + timerRef.current = setTimeout(advance, INTERVAL_MS); + return next; + }); + }; + + timerRef.current = setTimeout(advance, INTERVAL_MS); + return clearTimer; + }, [paused, clearTimer]); + + const handleStageClick = (index: number) => { + clearTimer(); + setPaused(true); + setActiveStage(index); + + if (expandedStage === index) { + // Close panel and resume + setExpandedStage(null); + setPaused(false); + } else { + setExpandedStage(index); + } + }; + + return ( +
+
7 steps per request
+
+ {STAGES.map((stage, i) => ( +
+
handleStageClick(i)} + role="button" + tabIndex={0} + onKeyDown={(e) => { + if (e.key === 'Enter' || e.key === ' ') handleStageClick(i); + }} + > +
{i + 1}
+
{stage.label}
+
{stage.subtitle}
+
+
+ ))} +
+
+ {expandedStage !== null && ( + 
+            {STAGES[expandedStage].code}
+
+ )} +
+
+ ); +} diff --git a/docs/my-website/src/components/MiddlewareDiagrams/BenchmarkVisualization.tsx b/docs/my-website/src/components/MiddlewareDiagrams/BenchmarkVisualization.tsx new file mode 100644 index 00000000000..b2b34d9d044 --- /dev/null +++ b/docs/my-website/src/components/MiddlewareDiagrams/BenchmarkVisualization.tsx @@ -0,0 +1,337 @@ +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import styles from './styles.module.css'; + +/* ── Constants ── */ +const TOTAL_REQUESTS = 50_000; +const DURATION_AFTER_MS = 8_000; // "After" column finishes in 8s +const DURATION_BEFORE_MS = 13_920; // 74% slower → 8000 * 1.74 +const TICK_MS = 50; +const RESET_PAUSE_MS = 2_000; +const MAX_DOTS = 14; + +const BEFORE_RPS = 3_785; +const AFTER_RPS = 6_577; +const BEFORE_P50 = 21; +const AFTER_P50 = 13; + +const BEFORE_LAYERS = [ + { label: 'ab client', warning: false }, + { label: 'uvicorn \u00B7 1 worker', warning: false }, + { label: 'ASGI Middleware', warning: false }, + { label: 'BaseHTTPMiddleware', warning: true }, + { label: 'GET /health \u2192 "ok"', warning: false }, +]; + +const AFTER_LAYERS = [ + { label: 'ab client', warning: false }, + { label: 'uvicorn \u00B7 1 worker', warning: false }, + { label: 'ASGI Middleware', warning: false }, + { label: 'ASGI Middleware', warning: false }, + { label: 'GET /health \u2192 "ok"', warning: false }, +]; + +const BENCHMARK_RUNS = [ + { config: 'Before (1 ASGI + 1 BaseHTTP)', run: 1, rps: 3596, p50: 21 }, + { config: 'Before (1 ASGI + 1 BaseHTTP)', run: 2, rps: 3599, p50: 21 }, + { config: 'Before (1 ASGI + 1 BaseHTTP)', run: 3, rps: 4161, p50: 21 }, + { config: 'After (2x Pure ASGI)', run: 1, rps: 6504, p50: 13 }, + { config: 'After (2x Pure ASGI)', run: 2, rps: 6631, p50: 13 }, + { config: 'After (2x Pure ASGI)', run: 3, rps: 6595, p50: 13 }, +]; + +/* ── Dot type ── */ +interface Dot { + id: number; + progress: number; // 0..1 (top to bottom) +} + +/* ── Component ── */ +export default function BenchmarkVisualization() { + const [elapsed, setElapsed] = useState(0); + const [running, setRunning] = useState(false); + const [afterDone, setAfterDone] = useState(false); + const [beforeDone, setBeforeDone] = useState(false); + const [tableOpen, setTableOpen] = useState(false); + const [beforeDots, setBeforeDots] = useState([]); + const [afterDots, setAfterDots] = useState([]); + const dotIdRef = useRef(0); + const observerRef = useRef(null); + const wrapperRef = useRef(null); + const timerRef = useRef | null>(null); + const hasStartedRef = useRef(false); + + const beforeProgress = Math.min(elapsed / DURATION_BEFORE_MS, 1); + const afterProgress = Math.min(elapsed / DURATION_AFTER_MS, 1); + const beforeCompleted = Math.round(beforeProgress * TOTAL_REQUESTS); + const afterCompleted = Math.round(afterProgress * TOTAL_REQUESTS); + const beforeCurrentRPS = running && !beforeDone + ? Math.round(BEFORE_RPS * (0.9 + Math.random() * 0.2)) + : beforeDone ? 0 : 0; + const afterCurrentRPS = running && !afterDone + ? Math.round(AFTER_RPS * (0.9 + Math.random() * 0.2)) + : afterDone ? 0 : 0; + + const reset = useCallback(() => { + setElapsed(0); + setAfterDone(false); + setBeforeDone(false); + setBeforeDots([]); + setAfterDots([]); + dotIdRef.current = 0; + }, []); + + // Start/restart loop + const startSimulation = useCallback(() => { + reset(); + setRunning(true); + }, [reset]); + + // IntersectionObserver to auto-start on scroll + useEffect(() => { + observerRef.current = new IntersectionObserver( + ([entry]) => { + if (entry.isIntersecting && !hasStartedRef.current) { + hasStartedRef.current = true; + startSimulation(); + } + }, + { threshold: 0.3 } + ); + + if (wrapperRef.current) { + observerRef.current.observe(wrapperRef.current); + } + + return () => { + observerRef.current?.disconnect(); + }; + }, [startSimulation]); + + // Main tick + useEffect(() => { + if (!running) return; + + timerRef.current = setInterval(() => { + setElapsed((prev) => { + const next = prev + TICK_MS; + + if (next >= DURATION_AFTER_MS) setAfterDone(true); + if (next >= DURATION_BEFORE_MS) setBeforeDone(true); + + // Both done → schedule reset + if (next >= DURATION_BEFORE_MS) { + setTimeout(() => { + startSimulation(); + }, RESET_PAUSE_MS); + setRunning(false); + return next; + } + return next; + }); + }, TICK_MS); + + return () => { + if (timerRef.current) clearInterval(timerRef.current); + }; + }, [running, startSimulation]); + + // Dot animation + useEffect(() => { + if (!running) return; + + const dotInterval = setInterval(() => { + const spawnBefore = !beforeDone && Math.random() < 0.4; + const spawnAfter = !afterDone && Math.random() < 0.65; + + if (spawnBefore) { + setBeforeDots((prev) => { + const dots = [...prev, { id: dotIdRef.current++, progress: 0 }]; + return dots.slice(-MAX_DOTS); + }); + } + if (spawnAfter) { + setAfterDots((prev) => { + const dots = [...prev, { id: dotIdRef.current++, progress: 0 }]; + return dots.slice(-MAX_DOTS); + }); + } + + // Advance existing dots + setBeforeDots((prev) => + prev + .map((d) => ({ ...d, progress: d.progress + 0.08 })) + .filter((d) => d.progress <= 1) + ); + setAfterDots((prev) => + prev + .map((d) => ({ ...d, progress: d.progress + 0.14 })) + .filter((d) => d.progress <= 1) + ); + }, 100); + + return () => clearInterval(dotInterval); + }, [running, beforeDone, afterDone]); + + const renderFlowStack = ( + layers: { label: string; warning: boolean }[], + dots: Dot[], + isBefore: boolean + ) => ( +
+
+ {dots.map((dot) => ( +
0.85 ? (1 - dot.progress) * 6 : 0.8, + }} + /> + ))} +
+ {layers.map((layer, i) => ( + + {i > 0 &&
↓
} +
+ {layer.label} + {layer.warning && ← overhead} +
+ + ))} +
+ ); + + const formatNum = (n: number) => n.toLocaleString(); + + return ( +
+
+ 50,000 requests · 1,000 concurrent · 1 worker +
+ +
+ {/* Before column */} +
+
+ Before (1 ASGI + 1 BaseHTTP) + {beforeDone && ( + done + )} +
+ {renderFlowStack(BEFORE_LAYERS, beforeDots, true)} +
+
+
{formatNum(beforeCurrentRPS)}
+
RPS
+
+
+
{formatNum(beforeCompleted)}
+
Completed
+
+
+
{BEFORE_P50}ms
+
P50
+
+
+
+
+
+
+ + {/* After column */} +
+
+ After (2x Pure ASGI) + {afterDone && ( + done + )} +
+ {renderFlowStack(AFTER_LAYERS, afterDots, false)} +
+
+
{formatNum(afterCurrentRPS)}
+
RPS
+
+
+
{formatNum(afterCompleted)}
+
Completed
+
+
+
{AFTER_P50}ms
+
P50
+
+
+
+
+
+
+
+ + {/* Summary stats */} +
+
+
+74%
+
Throughput (RPS)
+
+
+
-38%
+
Median Latency (P50)
+
+
+ + {/* Collapsible per-run data */} +
+ +
+ + + + + + + + + + + {BENCHMARK_RUNS.map((row, i) => ( + + + + + + + ))} + +
ConfigRunRPSP50 (ms)
{row.config}{row.run}{formatNum(row.rps)}{row.p50}
+
+
+ +
+ ); +} diff --git a/docs/my-website/src/components/MiddlewareDiagrams/PureASGIAnimation.tsx b/docs/my-website/src/components/MiddlewareDiagrams/PureASGIAnimation.tsx new file mode 100644 index 00000000000..c936519a651 --- /dev/null +++ b/docs/my-website/src/components/MiddlewareDiagrams/PureASGIAnimation.tsx @@ -0,0 +1,67 @@ +import React, { useState, useEffect, useRef, useCallback } from 'react'; +import styles from './styles.module.css'; + +interface Stage { + label: string; + subtitle: string; +} + +const STAGES: Stage[] = [ + { label: 'Scope Check', subtitle: 'scope["type"] != "http"' }, + { label: 'Direct Call', subtitle: 'await self.app(scope, receive, send)' }, +]; + +const INTERVAL_MS = 1200; +const PAUSE_MS = 600; + +export default function PureASGIAnimation() { + const [activeStage, setActiveStage] = useState(0); + const timerRef = useRef | null>(null); + + const clearTimer = useCallback(() => { + if (timerRef.current !== null) { + clearTimeout(timerRef.current); + timerRef.current = null; + } + }, []); + + useEffect(() => { + const advance = () => { + setActiveStage((prev) => { + const next = (prev + 1) % STAGES.length; + if (next === 0) { + timerRef.current = setTimeout(() => { + timerRef.current = setTimeout(advance, INTERVAL_MS); + }, PAUSE_MS); + return next; + } + timerRef.current = setTimeout(advance, INTERVAL_MS); + return next; + }); + }; + + timerRef.current = setTimeout(advance, INTERVAL_MS); + return clearTimer; + }, [clearTimer]); + + return ( +
+
2 steps per request
+
+ {STAGES.map((stage, i) => ( +
+
+
{i + 1}
+
{stage.label}
+
{stage.subtitle}
+
+
+ ))} +
+
+ ); +} diff --git a/docs/my-website/src/components/MiddlewareDiagrams/index.tsx b/docs/my-website/src/components/MiddlewareDiagrams/index.tsx new file mode 100644 index 00000000000..ad20d62adfd --- /dev/null +++ b/docs/my-website/src/components/MiddlewareDiagrams/index.tsx @@ -0,0 +1,3 @@ +export { default as BaseHTTPMiddlewareAnimation } from './BaseHTTPMiddlewareAnimation'; +export { default as PureASGIAnimation } from './PureASGIAnimation'; +export { default as BenchmarkVisualization } from './BenchmarkVisualization'; diff --git a/docs/my-website/src/components/MiddlewareDiagrams/styles.module.css b/docs/my-website/src/components/MiddlewareDiagrams/styles.module.css new file mode 100644 index 00000000000..a9b9249f97a --- /dev/null +++ b/docs/my-website/src/components/MiddlewareDiagrams/styles.module.css @@ -0,0 +1,494 @@ +/* ── Shared custom properties ── */ +:root { + --mw-stage-bg: #f8f9fa; + --mw-stage-border: #dee2e6; + --mw-stage-active-bg: #e8f4fd; + --mw-stage-active-border: #3b82f6; + --mw-stage-green-active-bg: #ecfdf5; + --mw-stage-green-active-border: #10b981; + --mw-dot-color: #3b82f6; + --mw-warning-accent: #ef4444; + --mw-success-accent: #10b981; + --mw-text-primary: #1a1a2e; + --mw-text-secondary: #6b7280; + --mw-code-bg: #f1f5f9; + --mw-panel-bg: #ffffff; + --mw-panel-border: #e5e7eb; + --mw-bar-bg: #e5e7eb; + --mw-arrow-color: #9ca3af; + --mw-column-bg: #fafafa; + --mw-column-border: #e5e7eb; + --mw-layer-bg: #f3f4f6; + --mw-layer-border: #d1d5db; + --mw-layer-warning-bg: #fef2f2; + --mw-layer-warning-border: #fca5a5; + --mw-progress-bg: #e5e7eb; +} + +[data-theme='dark'] { + --mw-stage-bg: #1e1e2e; + --mw-stage-border: #374151; + --mw-stage-active-bg: #1e3a5f; + --mw-stage-active-border: #60a5fa; + --mw-stage-green-active-bg: #064e3b; + --mw-stage-green-active-border: #34d399; + --mw-dot-color: #60a5fa; + --mw-warning-accent: #f87171; + --mw-success-accent: #34d399; + --mw-text-primary: #e5e7eb; + --mw-text-secondary: #9ca3af; + --mw-code-bg: #1e293b; + --mw-panel-bg: #111827; + --mw-panel-border: #374151; + --mw-bar-bg: #374151; + --mw-arrow-color: #6b7280; + --mw-column-bg: #111827; + --mw-column-border: #374151; + --mw-layer-bg: #1f2937; + --mw-layer-border: #4b5563; + --mw-layer-warning-bg: #451a1a; + --mw-layer-warning-border: #b91c1c; + --mw-progress-bg: #374151; +} + +/* ── Pipeline (shared between BaseHTTP and PureASGI) ── */ +.pipelineWrapper { + margin: 1.5rem 0; +} + +.pipelineLabel { + text-align: center; + font-size: 0.85rem; + font-weight: 600; + color: var(--mw-text-secondary); + margin-bottom: 0.75rem; + text-transform: uppercase; + letter-spacing: 0.05em; +} + +.pipeline { + display: flex; + flex-wrap: wrap; + justify-content: center; + align-items: stretch; + gap: 0.75rem; + padding: 0.5rem 0; +} + +.pipelineTwoCol { + max-width: 480px; + margin: 0 auto; +} + +.stageWrapper { + display: flex; + align-items: center; + width: 160px; + flex-shrink: 0; +} + +.pipelineTwoCol .stageWrapper { + width: 200px; +} + +.arrow { + display: none; +} + +.stage { + flex: 1; + padding: 0.85rem 0.75rem; + min-height: 100px; + display: flex; + flex-direction: column; + justify-content: center; + background: var(--mw-stage-bg); + border: 2px solid var(--mw-stage-border); + border-radius: 8px; + text-align: center; + cursor: pointer; + transition: background 0.4s ease, border-color 0.4s ease, box-shadow 0.4s ease; + user-select: none; +} + +.stage:hover { + border-color: var(--mw-stage-active-border); +} + +.stageActive { + background: var(--mw-stage-active-bg); + border-color: var(--mw-stage-active-border); + box-shadow: 0 0 0 3px rgba(59, 130, 246, 0.15); +} + +.stageActiveGreen { + background: var(--mw-stage-green-active-bg); + border-color: var(--mw-stage-green-active-border); + box-shadow: 0 0 0 3px rgba(16, 185, 129, 0.15); +} + +.stageNoClick { + cursor: default; +} + +.stageNumber { + font-size: 0.7rem; + font-weight: 700; + color: var(--mw-text-secondary); + margin-bottom: 0.3rem; +} + +.stageLabel { + font-size: 0.85rem; + font-weight: 600; + color: var(--mw-text-primary); + margin-bottom: 0.25rem; + line-height: 1.3; +} + +.stageSubtitle { + font-size: 0.72rem; + color: var(--mw-text-secondary); + font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace; + word-break: break-word; + line-height: 1.3; +} + +/* ── Code panel (accordion) ── */ +.codePanel { + max-height: 0; + overflow: hidden; + transition: max-height 0.35s ease, padding 0.35s ease; + background: var(--mw-code-bg); + border-radius: 0 0 8px 8px; + margin-top: 0.5rem; +} + +.codePanelOpen { + max-height: 120px; + padding: 0.75rem 1rem; +} + +.codePanelCode { + font-family: 'SFMono-Regular', Consolas, 'Liberation Mono', Menlo, monospace; + font-size: 0.8rem; + color: var(--mw-text-primary); + white-space: pre; + margin: 0; + line-height: 1.5; +} + +/* ── Benchmark Visualization ── */ +.benchmarkWrapper { + margin: 1.5rem 0; +} + +.benchmarkConfig { + text-align: center; + font-size: 0.85rem; + color: var(--mw-text-secondary); + margin-bottom: 1rem; + font-weight: 500; +} + +.benchmarkColumns { + display: flex; + gap: 1.5rem; +} + +.benchmarkColumn { + flex: 1; + background: var(--mw-column-bg); + border: 1px solid var(--mw-column-border); + border-radius: 12px; + padding: 1.25rem; + position: relative; + overflow: hidden; +} + +.columnTitle { + font-size: 0.9rem; + font-weight: 700; + color: var(--mw-text-primary); + text-align: center; + margin-bottom: 1rem; +} + +.columnTitleBefore { + color: var(--mw-warning-accent); +} + +.columnTitleAfter { + color: var(--mw-success-accent); +} + +/* ── Request flow stack ── */ +.flowStack { + display: flex; + flex-direction: column; + align-items: center; + gap: 0; + position: relative; + min-height: 280px; +} + +.flowLayer { + width: 100%; + max-width: 260px; + padding: 0.6rem 0.75rem; + background: var(--mw-layer-bg); + border: 1px solid var(--mw-layer-border); + border-radius: 6px; + text-align: center; + font-size: 0.78rem; + font-weight: 500; + color: var(--mw-text-primary); + position: relative; + z-index: 1; +} + +.flowLayerWarning { + background: var(--mw-layer-warning-bg); + border-color: var(--mw-layer-warning-border); + font-weight: 700; +} + +.flowArrow { + display: flex; + justify-content: center; + color: var(--mw-arrow-color); + font-size: 0.9rem; + padding: 0.15rem 0; + position: relative; + z-index: 0; + min-height: 20px; +} + +.overheadTag { + font-size: 0.65rem; + color: var(--mw-warning-accent); + margin-left: 0.4rem; +} + +/* ── Dots layer (canvas for flowing dots) ── */ +.dotsCanvas { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + pointer-events: none; + z-index: 2; +} + +.dot { + position: absolute; + width: 6px; + height: 6px; + border-radius: 50%; + background: var(--mw-dot-color); + opacity: 0.8; +} + +.dotSlow { + background: var(--mw-warning-accent); +} + +.dotFast { + background: var(--mw-success-accent); +} + +/* ── Stats & progress ── */ +.statsRow { + display: flex; + justify-content: space-around; + margin-top: 1rem; + padding-top: 0.75rem; + border-top: 1px solid var(--mw-panel-border); +} + +.stat { + text-align: center; +} + +.statValue { + font-size: 1.1rem; + font-weight: 700; + color: var(--mw-text-primary); + font-variant-numeric: tabular-nums; +} + +.statLabel { + font-size: 0.7rem; + color: var(--mw-text-secondary); + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.progressBar { + width: 100%; + height: 6px; + background: var(--mw-progress-bg); + border-radius: 3px; + margin-top: 0.75rem; + overflow: hidden; +} + +.progressFill { + height: 100%; + border-radius: 3px; + transition: width 0.1s linear; +} + +.progressFillBefore { + background: var(--mw-warning-accent); +} + +.progressFillAfter { + background: var(--mw-success-accent); +} + +/* ── Summary stats below simulation ── */ +.summaryStats { + display: flex; + justify-content: center; + gap: 2rem; + margin-top: 1.5rem; + flex-wrap: wrap; +} + +.summaryItem { + text-align: center; + padding: 0.75rem 1.25rem; + background: var(--mw-stage-bg); + border-radius: 8px; + border: 1px solid var(--mw-panel-border); +} + +.summaryValue { + font-size: 1.5rem; + font-weight: 800; + color: var(--mw-success-accent); +} + +.summaryLabel { + font-size: 0.8rem; + color: var(--mw-text-secondary); + margin-top: 0.2rem; +} + +/* ── Collapsible table ── */ +.collapsible { + margin-top: 1.5rem; +} + +.collapsibleToggle { + background: none; + border: 1px solid var(--mw-panel-border); + border-radius: 6px; + padding: 0.5rem 1rem; + cursor: pointer; + font-size: 0.85rem; + color: var(--mw-text-primary); + width: 100%; + text-align: left; + display: flex; + align-items: center; + gap: 0.5rem; + transition: background 0.2s; +} + +.collapsibleToggle:hover { + background: var(--mw-stage-bg); +} + +.collapsibleChevron { + transition: transform 0.3s ease; + font-size: 0.7rem; +} + +.collapsibleChevronOpen { + transform: rotate(90deg); +} + +.collapsibleContent { + max-height: 0; + overflow: hidden; + transition: max-height 0.35s ease; +} + +.collapsibleContentOpen { + max-height: 600px; +} + +.dataTable { + width: 100%; + border-collapse: collapse; + margin-top: 0.75rem; + font-size: 0.85rem; +} + +.dataTable th, +.dataTable td { + padding: 0.5rem 0.75rem; + text-align: left; + border-bottom: 1px solid var(--mw-panel-border); +} + +.dataTable th { + font-weight: 600; + color: var(--mw-text-secondary); + font-size: 0.75rem; + text-transform: uppercase; + letter-spacing: 0.04em; +} + +.dataTable td { + color: var(--mw-text-primary); + font-variant-numeric: tabular-nums; +} + +/* ── Reproduce section ── */ +.reproduceSection { + margin-top: 1rem; +} + +/* ── Done badge ── */ +.doneBadge { + display: inline-block; + font-size: 0.75rem; + font-weight: 600; + padding: 0.2rem 0.6rem; + border-radius: 4px; + margin-left: 0.5rem; +} + +.doneBadgeBefore { + color: var(--mw-warning-accent); + background: var(--mw-layer-warning-bg); +} + +.doneBadgeAfter { + color: var(--mw-success-accent); + background: var(--mw-stage-green-active-bg); +} + +/* ── Responsive ── */ +@media (max-width: 768px) { + .stageWrapper { + width: 140px; + } + + .pipelineTwoCol .stageWrapper { + width: 160px; + } + + .benchmarkColumns { + flex-direction: column; + } + + .summaryStats { + flex-direction: column; + align-items: center; + } +} diff --git a/enterprise/dist/litellm_enterprise-0.1.29-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.29-py3-none-any.whl new file mode 100644 index 00000000000..0895ecbc427 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.29-py3-none-any.whl differ diff --git a/enterprise/dist/litellm_enterprise-0.1.29.tar.gz b/enterprise/dist/litellm_enterprise-0.1.29.tar.gz new file mode 100644 index 00000000000..6781cf26cc9 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.29.tar.gz differ diff --git a/enterprise/dist/litellm_enterprise-0.1.30-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.30-py3-none-any.whl new file mode 100644 index 00000000000..0165bb096c0 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.30-py3-none-any.whl differ diff --git a/enterprise/dist/litellm_enterprise-0.1.30.tar.gz b/enterprise/dist/litellm_enterprise-0.1.30.tar.gz new file mode 100644 index 00000000000..2bb7510e5d3 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.30.tar.gz differ diff --git a/enterprise/dist/litellm_enterprise-0.1.31-py3-none-any.whl b/enterprise/dist/litellm_enterprise-0.1.31-py3-none-any.whl new file mode 100644 index 00000000000..03cadbd9023 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.31-py3-none-any.whl differ diff --git a/enterprise/dist/litellm_enterprise-0.1.31.tar.gz b/enterprise/dist/litellm_enterprise-0.1.31.tar.gz new file mode 100644 index 00000000000..1ba1a717f62 Binary files /dev/null and b/enterprise/dist/litellm_enterprise-0.1.31.tar.gz differ diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py index 61e0745bab1..d3e04769300 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py +++ b/enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py @@ -30,8 +30,15 @@ from litellm.integrations.email_templates.user_invitation_email import ( from litellm.integrations.email_templates.templates import ( MAX_BUDGET_ALERT_EMAIL_TEMPLATE, SOFT_BUDGET_ALERT_EMAIL_TEMPLATE, + TEAM_SOFT_BUDGET_ALERT_EMAIL_TEMPLATE, +) +from litellm.proxy._types import ( + CallInfo, + InvitationNew, + Litellm_EntityType, + UserAPIKeyAuth, + WebhookEvent, ) -from litellm.proxy._types import CallInfo, InvitationNew, UserAPIKeyAuth, WebhookEvent from litellm.secret_managers.main import get_secret_bool from litellm.types.integrations.slack_alerting import LITELLM_LOGO_URL from litellm.constants import ( @@ -217,6 +224,78 @@ class BaseEmailLogger(CustomLogger): ) pass + async def send_team_soft_budget_alert_email(self, event: WebhookEvent): + """ + Send email to team members when team soft budget is crossed + Supports multiple recipients via alert_emails field from team metadata + """ + # Collect all recipient emails + recipient_emails: List[str] = [] + + # Add additional alert emails from team metadata.soft_budget_alert_emails + if hasattr(event, "alert_emails") and event.alert_emails: + for email in event.alert_emails: + if email and email not in recipient_emails: # Avoid duplicates + recipient_emails.append(email) + + # If no recipients found, skip sending + if not recipient_emails: + verbose_proxy_logger.warning( + f"No recipient emails found for team soft budget alert. event={event.model_dump(exclude_none=True)}" + ) + return + + # Validate that we have at least one valid email address + first_recipient_email = recipient_emails[0] + if not first_recipient_email or not first_recipient_email.strip(): + verbose_proxy_logger.warning( + f"Invalid recipient email found for team soft budget alert. event={event.model_dump(exclude_none=True)}" + ) + return + + verbose_proxy_logger.debug( + f"send_team_soft_budget_alert_email_event: {json.dumps(event.model_dump(exclude_none=True), indent=4, default=str)}" + ) + + # Get email params using the first recipient email (for template formatting) + # For team alerts with alert_emails, we don't need user_id lookup since we already have email addresses + # Pass user_id=None to prevent _get_email_params from trying to look up email from a potentially None user_id + email_params = await self._get_email_params( + email_event=EmailEvent.soft_budget_crossed, + user_id=None, # Team alerts don't require user_id when alert_emails are provided + user_email=first_recipient_email, + event_message=event.event_message, + ) + + # Format budget values + soft_budget_str = f"${event.soft_budget}" if event.soft_budget is not None else "N/A" + spend_str = f"${event.spend}" if event.spend is not None else "$0.00" + max_budget_info = "" + if event.max_budget is not None: + max_budget_info = f"Maximum Budget: ${event.max_budget}
" + + # Use team alias or generic greeting + team_alias = event.team_alias or "Team" + + email_html_content = TEAM_SOFT_BUDGET_ALERT_EMAIL_TEMPLATE.format( + email_logo_url=email_params.logo_url, + team_alias=team_alias, + soft_budget=soft_budget_str, + spend=spend_str, + max_budget_info=max_budget_info, + base_url=email_params.base_url, + email_support_contact=email_params.support_contact, + ) + + # Send email to all recipients + await self.send_email( + from_email=self.DEFAULT_LITELLM_EMAIL, + to_email=recipient_emails, + subject=email_params.subject, + html_body=email_html_content, + ) + pass + async def send_max_budget_alert_email(self, event: WebhookEvent): """ Send email to user when max budget alert threshold is reached @@ -285,15 +364,36 @@ class BaseEmailLogger(CustomLogger): # - Don't re-alert, if alert already sent _cache: DualCache = self.internal_usage_cache - # percent of max_budget left to spend - if user_info.max_budget is None and user_info.soft_budget is None: - return - # For soft_budget alerts, check if we've already sent an alert if type == "soft_budget": + # For team soft budget alerts, we only need team soft_budget to be set + # For other entity types, we need either max_budget or soft_budget + if user_info.event_group == Litellm_EntityType.TEAM: + if user_info.soft_budget is None: + return + # For team soft budget alerts, require alert_emails to be configured + # Team soft budget alerts are sent via metadata.soft_budget_alerting_emails + if user_info.alert_emails is None or len(user_info.alert_emails) == 0: + verbose_proxy_logger.debug( + "Skipping team soft budget email alert: no alert_emails configured", + ) + return + else: + # For non-team alerts, require either max_budget or soft_budget + if user_info.max_budget is None and user_info.soft_budget is None: + return if user_info.soft_budget is not None and user_info.spend >= user_info.soft_budget: # Generate cache key based on event type and identifier - _id = user_info.token or user_info.user_id or "default_id" + # Use appropriate ID based on event_group to ensure unique cache keys per entity type + if user_info.event_group == Litellm_EntityType.TEAM: + _id = user_info.team_id or "default_id" + elif user_info.event_group == Litellm_EntityType.ORGANIZATION: + _id = user_info.organization_id or "default_id" + elif user_info.event_group == Litellm_EntityType.USER: + _id = user_info.user_id or "default_id" + else: + # For KEY and other types, use token or user_id + _id = user_info.token or user_info.user_id or "default_id" _cache_key = f"email_budget_alerts:soft_budget_crossed:{_id}" # Check if we've already sent this alert @@ -318,10 +418,15 @@ class BaseEmailLogger(CustomLogger): projected_exceeded_date=user_info.projected_exceeded_date, projected_spend=user_info.projected_spend, event_group=user_info.event_group, + alert_emails=user_info.alert_emails, ) try: - await self.send_soft_budget_alert_email(webhook_event) + # Use team-specific function for team alerts, otherwise use standard function + if user_info.event_group == Litellm_EntityType.TEAM: + await self.send_team_soft_budget_alert_email(webhook_event) + else: + await self.send_soft_budget_alert_email(webhook_event) # Cache the alert to prevent duplicate sends await _cache.async_set_cache( diff --git a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py index d4ee4042b1a..bb25e4f0626 100644 --- a/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py +++ b/enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py @@ -53,7 +53,7 @@ class CheckBatchCost: jobs = await self.prisma_client.db.litellm_managedobjecttable.find_many( where={ - "status": "validating", + "status": {"in": ["validating", "in_progress", "finalizing"]}, "file_purpose": "batch", } ) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 5ee3372cca7..a41b3f3bf6f 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -166,7 +166,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): "updated_by": user_api_key_dict.user_id, "status": file_object.status, }, - "update": {}, # don't do anything if it already exists + "update": { + "file_object": file_object.model_dump_json(), + "status": file_object.status, + "updated_by": user_api_key_dict.user_id, + }, # FIX: Update status and file_object on every operation to keep state in sync }, ) @@ -354,6 +358,31 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) return False + async def check_file_ids_access( + self, file_ids: List[str], user_api_key_dict: UserAPIKeyAuth + ) -> None: + """ + Check if the user has access to a list of file IDs. + Only checks managed (unified) file IDs. + + Args: + file_ids: List of file IDs to check access for + user_api_key_dict: User API key authentication details + + Raises: + HTTPException: If user doesn't have access to any of the files + """ + for file_id in file_ids: + is_unified_file_id = _is_base64_encoded_unified_file_id(file_id) + if is_unified_file_id: + if not await self.can_user_call_unified_file_id( + file_id, user_api_key_dict + ): + raise HTTPException( + status_code=403, + detail=f"User {user_api_key_dict.user_id} does not have access to the file {file_id}", + ) + async def async_pre_call_hook( # noqa: PLR0915 self, user_api_key_dict: UserAPIKeyAuth, @@ -387,6 +416,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if messages: file_ids = self.get_file_ids_from_messages(messages) if file_ids: + # Check user has access to all managed files + await self.check_file_ids_access(file_ids, user_api_key_dict) + # Check if any files are stored in storage backends and need base64 conversion # This is needed for Vertex AI/Gemini which requires base64 content is_vertex_ai = model and ("vertex_ai" in model or "gemini" in model.lower()) @@ -402,15 +434,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) data["model_file_id_mapping"] = model_file_id_mapping elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value: - # Handle managed files in responses API input + # Handle managed files in responses API input and tools + file_ids = [] + + # Extract file IDs from input parameter input_data = data.get("input") if input_data: - file_ids = self.get_file_ids_from_responses_input(input_data) - if file_ids: - model_file_id_mapping = await self.get_model_file_id_mapping( - file_ids, user_api_key_dict.parent_otel_span - ) - data["model_file_id_mapping"] = model_file_id_mapping + file_ids.extend(self.get_file_ids_from_responses_input(input_data)) + + # Extract file IDs from tools parameter (e.g., code_interpreter container) + tools = data.get("tools") + if tools: + file_ids.extend(self.get_file_ids_from_responses_tools(tools)) + + if file_ids: + # Check user has access to all managed files + await self.check_file_ids_access(file_ids, user_api_key_dict) + + model_file_id_mapping = await self.get_model_file_id_mapping( + file_ids, user_api_key_dict.parent_otel_span + ) + data["model_file_id_mapping"] = model_file_id_mapping elif call_type == CallTypes.afile_content.value: retrieve_file_id = cast(Optional[str], data.get("file_id")) potential_file_id = ( @@ -460,8 +504,6 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): if retrieve_object_id else False ) - print(f"🔥potential_llm_object_id: {potential_llm_object_id}") - print(f"🔥retrieve_object_id: {retrieve_object_id}") if potential_llm_object_id and retrieve_object_id: ## VALIDATE USER HAS ACCESS TO THE OBJECT ## if not await self.can_user_call_unified_object_id( @@ -614,6 +656,41 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): return file_ids + def get_file_ids_from_responses_tools( + self, tools: List[Dict[str, Any]] + ) -> List[str]: + """ + Gets file ids from responses API tools parameter. + + The tools can contain code_interpreter with container.file_ids: + [ + { + "type": "code_interpreter", + "container": {"type": "auto", "file_ids": ["file-123", "file-456"]} + } + ] + """ + file_ids: List[str] = [] + + if not isinstance(tools, list): + return file_ids + + for tool in tools: + if not isinstance(tool, dict): + continue + + # Check for code_interpreter with container file_ids + if tool.get("type") == "code_interpreter": + container = tool.get("container") + if isinstance(container, dict): + container_file_ids = container.get("file_ids") + if isinstance(container_file_ids, list): + for file_id in container_file_ids: + if isinstance(file_id, str): + file_ids.append(file_id) + + return file_ids + async def get_model_file_id_mapping( self, file_ids: List[str], litellm_parent_otel_span: Span ) -> dict: @@ -822,49 +899,49 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): batch_id=response.id, model_id=model_id ) - if ( - response.output_file_id and model_id - ): # return a file id with the model_id and output_file_id - original_output_file_id = response.output_file_id - response.output_file_id = self.get_unified_output_file_id( - output_file_id=response.output_file_id, - model_id=model_id, - model_name=model_name, - ) - - # Fetch the actual file object for the output file - file_object = None - try: - # Use litellm to retrieve the file object from the provider - from litellm import afile_retrieve - file_object = await afile_retrieve( - custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", - file_id=original_output_file_id + # Handle both output_file_id and error_file_id + for file_attr in ["output_file_id", "error_file_id"]: + file_id_value = getattr(response, file_attr, None) + if file_id_value and model_id: + original_file_id = file_id_value + unified_file_id = self.get_unified_output_file_id( + output_file_id=original_file_id, + model_id=model_id, + model_name=model_name, ) - verbose_logger.debug( - f"Successfully retrieved file object for output_file_id={original_output_file_id}" + setattr(response, file_attr, unified_file_id) + + # Fetch the actual file object from the provider + file_object = None + try: + # Use litellm to retrieve the file object from the provider + from litellm import afile_retrieve + file_object = await afile_retrieve( + custom_llm_provider=model_name.split("/")[0] if model_name and "/" in model_name else "openai", + file_id=original_file_id + ) + verbose_logger.debug( + f"Successfully retrieved file object for {file_attr}={original_file_id}" + ) + except Exception as e: + verbose_logger.warning( + f"Failed to retrieve file object for {file_attr}={original_file_id}: {str(e)}. Storing with None and will fetch on-demand." + ) + + await self.store_unified_file_id( + file_id=unified_file_id, + file_object=file_object, + litellm_parent_otel_span=user_api_key_dict.parent_otel_span, + model_mappings={model_id: original_file_id}, + user_api_key_dict=user_api_key_dict, ) - except Exception as e: - verbose_logger.warning( - f"Failed to retrieve file object for output_file_id={original_output_file_id}: {str(e)}. Storing with None and will fetch on-demand." - ) - - await self.store_unified_file_id( - file_id=response.output_file_id, - file_object=file_object, - litellm_parent_otel_span=user_api_key_dict.parent_otel_span, - model_mappings={model_id: original_output_file_id}, - user_api_key_dict=user_api_key_dict, - ) - asyncio.create_task( - self.store_unified_object_id( - unified_object_id=response.id, - file_object=response, - litellm_parent_otel_span=user_api_key_dict.parent_otel_span, - model_object_id=original_response_id, - file_purpose="batch", - user_api_key_dict=user_api_key_dict, - ) + await self.store_unified_object_id( + unified_object_id=response.id, + file_object=response, + litellm_parent_otel_span=user_api_key_dict.parent_otel_span, + model_object_id=original_response_id, + file_purpose="batch", + user_api_key_dict=user_api_key_dict, ) elif isinstance(response, LiteLLMFineTuningJob): ## Check if unified_file_id is in the response @@ -881,15 +958,13 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): response.id = self.get_unified_generic_response_id( model_id=model_id, generic_response_id=response.id ) - asyncio.create_task( - self.store_unified_object_id( - unified_object_id=response.id, - file_object=response, - litellm_parent_otel_span=user_api_key_dict.parent_otel_span, - model_object_id=original_response_id, - file_purpose="fine-tune", - user_api_key_dict=user_api_key_dict, - ) + await self.store_unified_object_id( + unified_object_id=response.id, + file_object=response, + litellm_parent_otel_span=user_api_key_dict.parent_otel_span, + model_object_id=original_response_id, + file_purpose="fine-tune", + user_api_key_dict=user_api_key_dict, ) elif isinstance(response, AsyncCursorPage): """ diff --git a/enterprise/pyproject.toml b/enterprise/pyproject.toml index 0d86460a649..eca5cdb97df 100644 --- a/enterprise/pyproject.toml +++ b/enterprise/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-enterprise" -version = "0.1.28" +version = "0.1.31" description = "Package for LiteLLM Enterprise features" authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.1.28" +version = "0.1.31" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-enterprise==", diff --git a/litellm-js/spend-logs/package.json b/litellm-js/spend-logs/package.json index 9c1c2d4f6dc..67292567145 100644 --- a/litellm-js/spend-logs/package.json +++ b/litellm-js/spend-logs/package.json @@ -11,6 +11,8 @@ "tsx": "^4.7.1" }, "overrides": { - "glob": ">=11.1.0" + "glob": ">=11.1.0", + "tar": ">=7.5.7", + "@isaacs/brace-expansion": ">=5.0.1" } } diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.30-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.30-py3-none-any.whl new file mode 100644 index 00000000000..383f9b7b43f Binary files /dev/null and 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b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.36.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205091235_allow_team_guardrail_config/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205091235_allow_team_guardrail_config/migration.sql new file mode 100644 index 00000000000..000b96b3b87 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205091235_allow_team_guardrail_config/migration.sql @@ -0,0 +1,6 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedTeamTable" ADD COLUMN "allow_team_guardrail_config" BOOLEAN NOT NULL DEFAULT false; + +-- AlterTable +ALTER TABLE "LiteLLM_TeamTable" ADD COLUMN "allow_team_guardrail_config" BOOLEAN NOT NULL DEFAULT false; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205144610_add_soft_budget_to_team_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205144610_add_soft_budget_to_team_table/migration.sql new file mode 100644 index 00000000000..a64f1de342f --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260205144610_add_soft_budget_to_team_table/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_TeamTable" ADD COLUMN "soft_budget" DOUBLE PRECISION; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207093506_add_available_on_public_internet_to_mcp_servers/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207093506_add_available_on_public_internet_to_mcp_servers/migration.sql new file mode 100644 index 00000000000..1efde3dbe0f --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207093506_add_available_on_public_internet_to_mcp_servers/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_MCPServerTable" ADD COLUMN "available_on_public_internet" BOOLEAN NOT NULL DEFAULT false; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207110613_add_soft_budget_to_deleted_teams_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207110613_add_soft_budget_to_deleted_teams_table/migration.sql new file mode 100644 index 00000000000..abfb153061b --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260207110613_add_soft_budget_to_deleted_teams_table/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedTeamTable" ADD COLUMN "soft_budget" DOUBLE PRECISION; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260129103648_add_verificationtoken_indexes/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260209085821_add_verificationtoken_indexes/migration.sql similarity index 100% rename from litellm-proxy-extras/litellm_proxy_extras/migrations/20260129103648_add_verificationtoken_indexes/migration.sql rename to litellm-proxy-extras/litellm_proxy_extras/migrations/20260209085821_add_verificationtoken_indexes/migration.sql diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212103349_adjust_tags_policy_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212103349_adjust_tags_policy_table/migration.sql new file mode 100644 index 00000000000..f3a0821d37f --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212103349_adjust_tags_policy_table/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_PolicyAttachmentTable" ADD COLUMN "tags" TEXT[] DEFAULT ARRAY[]::TEXT[]; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql new file mode 100644 index 00000000000..67e75e84c4a --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20260212143306_add_access_group_table/migration.sql @@ -0,0 +1,33 @@ +-- AlterTable +ALTER TABLE "LiteLLM_DeletedTeamTable" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_DeletedVerificationToken" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_TeamTable" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- AlterTable +ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN "access_group_ids" TEXT[] DEFAULT ARRAY[]::TEXT[]; + +-- CreateTable +CREATE TABLE "LiteLLM_AccessGroupTable" ( + "access_group_id" TEXT NOT NULL, + "access_group_name" TEXT NOT NULL, + "description" TEXT, + "access_model_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "access_mcp_server_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "access_agent_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "assigned_team_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "assigned_key_ids" TEXT[] DEFAULT ARRAY[]::TEXT[], + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "created_by" TEXT, + "updated_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_by" TEXT, + + CONSTRAINT "LiteLLM_AccessGroupTable_pkey" PRIMARY KEY ("access_group_id") +); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_AccessGroupTable_access_group_name_key" ON "LiteLLM_AccessGroupTable"("access_group_name"); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 8992f4d8df6..844d5660667 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -113,6 +113,7 @@ model LiteLLM_TeamTable { members_with_roles Json @default("{}") metadata Json @default("{}") max_budget Float? + soft_budget Float? spend Float @default(0.0) models String[] max_parallel_requests Int? @@ -127,8 +128,10 @@ model LiteLLM_TeamTable { model_max_budget Json @default("{}") router_settings Json? @default("{}") team_member_permissions String[] @default([]) + access_group_ids String[] @default([]) policies String[] @default([]) model_id Int? @unique // id for LiteLLM_ModelTable -> stores team-level model aliases + allow_team_guardrail_config Boolean @default(false) // if true, team admin can configure guardrails for this team litellm_organization_table LiteLLM_OrganizationTable? @relation(fields: [organization_id], references: [organization_id]) litellm_model_table LiteLLM_ModelTable? @relation(fields: [model_id], references: [id]) object_permission LiteLLM_ObjectPermissionTable? @relation(fields: [object_permission_id], references: [object_permission_id]) @@ -146,6 +149,7 @@ model LiteLLM_DeletedTeamTable { members_with_roles Json @default("{}") metadata Json @default("{}") max_budget Float? + soft_budget Float? spend Float @default(0.0) models String[] max_parallel_requests Int? @@ -158,9 +162,11 @@ model LiteLLM_DeletedTeamTable { model_max_budget Json @default("{}") router_settings Json? @default("{}") team_member_permissions String[] @default([]) + access_group_ids String[] @default([]) policies String[] @default([]) model_id Int? // id for LiteLLM_ModelTable -> stores team-level model aliases - + allow_team_guardrail_config Boolean @default(false) + // Original timestamps from team creation/updates created_at DateTime? @map("created_at") updated_at DateTime? @map("updated_at") @@ -260,6 +266,7 @@ model LiteLLM_MCPServerTable { token_url String? registration_url String? allow_all_keys Boolean @default(false) + available_on_public_internet Boolean @default(false) } // Generate Tokens for Proxy @@ -288,6 +295,7 @@ model LiteLLM_VerificationToken { allowed_cache_controls String[] @default([]) allowed_routes String[] @default([]) policies String[] @default([]) + access_group_ids String[] @default([]) model_spend Json @default("{}") model_max_budget Json @default("{}") budget_id String? @@ -356,6 +364,7 @@ model LiteLLM_DeletedVerificationToken { allowed_cache_controls String[] @default([]) allowed_routes String[] @default([]) policies String[] @default([]) + access_group_ids String[] @default([]) model_spend Json @default("{}") model_max_budget Json @default("{}") router_settings Json? @default("{}") @@ -797,6 +806,7 @@ model LiteLLM_GuardrailsTable { guardrail_name String @unique litellm_params Json guardrail_info Json? + team_id String? created_at DateTime @default(now()) updated_at DateTime @updatedAt } @@ -921,8 +931,29 @@ model LiteLLM_PolicyAttachmentTable { teams String[] @default([]) // Team aliases or patterns keys String[] @default([]) // Key aliases or patterns models String[] @default([]) // Model names or patterns + tags String[] @default([]) // Tag patterns (e.g., ["healthcare", "prod-*"]) created_at DateTime @default(now()) created_by String? updated_at DateTime @default(now()) @updatedAt updated_by String? } + +//Unified Access Groups table for storing unified access groups +model LiteLLM_AccessGroupTable { + access_group_id String @id @default(uuid()) + access_group_name String @unique + description String? + + // Resource memberships - explicit arrays per type + access_model_ids String[] @default([]) + access_mcp_server_ids String[] @default([]) + access_agent_ids String[] @default([]) + + assigned_team_ids String[] @default([]) + assigned_key_ids String[] @default([]) + + created_at DateTime @default(now()) + created_by String? + updated_at DateTime @default(now()) @updatedAt + updated_by String? +} \ No newline at end of file diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index fb6996b71db..eda49bfb9fa 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.4.29" +version = "0.4.36" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.4.29" +version = "0.4.36" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 112d58d49d8..0fdbac63feb 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -175,6 +175,7 @@ _async_failure_callback: List[Union[str, Callable, "CustomLogger"]] = ( # Custo pre_call_rules: List[Callable] = [] post_call_rules: List[Callable] = [] turn_off_message_logging: Optional[bool] = False +standard_logging_payload_excluded_fields: Optional[List[str]] = None # Fields to exclude from StandardLoggingPayload before callbacks receive it log_raw_request_response: bool = False redact_messages_in_exceptions: Optional[bool] = False redact_user_api_key_info: Optional[bool] = False @@ -261,6 +262,8 @@ extra_spend_tag_headers: Optional[List[str]] = None in_memory_llm_clients_cache: "LLMClientCache" safe_memory_mode: bool = False enable_azure_ad_token_refresh: Optional[bool] = False +# Proxy Authentication - auto-obtain/refresh OAuth2/JWT tokens for LiteLLM Proxy +proxy_auth: Optional[Any] = None ### DEFAULT AZURE API VERSION ### AZURE_DEFAULT_API_VERSION = "2025-02-01-preview" # this is updated to the latest ### DEFAULT WATSONX API VERSION ### @@ -1153,6 +1156,7 @@ from .exceptions import ( BadRequestError, ImageFetchError, NotFoundError, + PermissionDeniedError, RateLimitError, ServiceUnavailableError, BadGatewayError, @@ -1378,6 +1382,7 @@ if TYPE_CHECKING: from .llms.topaz.image_variations.transformation import TopazImageVariationConfig as TopazImageVariationConfig from litellm.llms.openai.completion.transformation import OpenAITextCompletionConfig as OpenAITextCompletionConfig from .llms.groq.chat.transformation import GroqChatConfig as GroqChatConfig + from .llms.a2a.chat.transformation import A2AConfig as A2AConfig from .llms.voyage.embedding.transformation import VoyageEmbeddingConfig as VoyageEmbeddingConfig from .llms.voyage.embedding.transformation_contextual import VoyageContextualEmbeddingConfig as VoyageContextualEmbeddingConfig from .llms.infinity.embedding.transformation import InfinityEmbeddingConfig as InfinityEmbeddingConfig @@ -1390,6 +1395,7 @@ if TYPE_CHECKING: from .llms.litellm_proxy.responses.transformation import LiteLLMProxyResponsesAPIConfig as LiteLLMProxyResponsesAPIConfig from .llms.volcengine.responses.transformation import VolcEngineResponsesAPIConfig as VolcEngineResponsesAPIConfig from .llms.manus.responses.transformation import ManusResponsesAPIConfig as ManusResponsesAPIConfig + from .llms.perplexity.responses.transformation import PerplexityResponsesConfig as PerplexityResponsesConfig from .llms.gemini.interactions.transformation import GoogleAIStudioInteractionsConfig as GoogleAIStudioInteractionsConfig from .llms.openai.chat.o_series_transformation import OpenAIOSeriesConfig as OpenAIOSeriesConfig, OpenAIOSeriesConfig as OpenAIO1Config from .llms.anthropic.skills.transformation import AnthropicSkillsConfig as AnthropicSkillsConfig diff --git a/litellm/_lazy_imports_registry.py b/litellm/_lazy_imports_registry.py index 0e52e9a59eb..ebe9af9d85c 100644 --- a/litellm/_lazy_imports_registry.py +++ b/litellm/_lazy_imports_registry.py @@ -213,6 +213,7 @@ LLM_CONFIG_NAMES = ( "TopazImageVariationConfig", "OpenAITextCompletionConfig", "GroqChatConfig", + "A2AConfig", "GenAIHubOrchestrationConfig", "VoyageEmbeddingConfig", "VoyageContextualEmbeddingConfig", @@ -225,6 +226,7 @@ LLM_CONFIG_NAMES = ( "XAIResponsesAPIConfig", "LiteLLMProxyResponsesAPIConfig", "VolcEngineResponsesAPIConfig", + "PerplexityResponsesConfig", "GoogleAIStudioInteractionsConfig", "OpenAIOSeriesConfig", "AnthropicSkillsConfig", @@ -273,6 +275,7 @@ LLM_CONFIG_NAMES = ( "LmStudioEmbeddingConfig", "NscaleConfig", "PerplexityChatConfig", + "PerplexityResponsesConfig", "AzureOpenAIO1Config", "IBMWatsonXAIConfig", "IBMWatsonXChatConfig", @@ -850,6 +853,7 @@ _LLM_CONFIGS_IMPORT_MAP = { "OpenAITextCompletionConfig", ), "GroqChatConfig": (".llms.groq.chat.transformation", "GroqChatConfig"), + "A2AConfig": (".llms.a2a.chat.transformation", "A2AConfig"), "GenAIHubOrchestrationConfig": ( ".llms.sap.chat.transformation", "GenAIHubOrchestrationConfig", @@ -899,6 +903,10 @@ _LLM_CONFIGS_IMPORT_MAP = { ".llms.manus.responses.transformation", "ManusResponsesAPIConfig", ), + "PerplexityResponsesConfig": ( + ".llms.perplexity.responses.transformation", + "PerplexityResponsesConfig", + ), "GoogleAIStudioInteractionsConfig": ( ".llms.gemini.interactions.transformation", "GoogleAIStudioInteractionsConfig", diff --git a/litellm/_logging.py b/litellm/_logging.py index e222627e76c..fd833f7056a 100644 --- a/litellm/_logging.py +++ b/litellm/_logging.py @@ -1,9 +1,13 @@ -import json +import ast import logging import os import sys from datetime import datetime from logging import Formatter +from typing import Any, Dict, Optional + +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.litellm_core_utils.safe_json_loads import safe_json_loads set_verbose = False @@ -19,6 +23,67 @@ handler = logging.StreamHandler() handler.setLevel(numeric_level) +def _try_parse_json_message(message: str) -> Optional[Dict[str, Any]]: + """ + Try to parse a log message as JSON. Returns parsed dict if valid, else None. + Handles messages that are entirely valid JSON (e.g. json.dumps output). + Uses shared safe_json_loads for consistent error handling. + """ + if not message or not isinstance(message, str): + return None + msg_stripped = message.strip() + if not (msg_stripped.startswith("{") or msg_stripped.startswith("[")): + return None + parsed = safe_json_loads(message, default=None) + if parsed is None or not isinstance(parsed, dict): + return None + return parsed + + +def _try_parse_embedded_python_dict(message: str) -> Optional[Dict[str, Any]]: + """ + Try to find and parse a Python dict repr (e.g. str(d) or repr(d)) embedded in + the message. Handles patterns like: + "get_available_deployment for model: X, Selected deployment: {'model_name': '...', ...} for model: X" + Uses ast.literal_eval for safe parsing. Returns the parsed dict or None. + """ + if not message or not isinstance(message, str) or "{" not in message: + return None + i = 0 + while i < len(message): + start = message.find("{", i) + if start == -1: + break + depth = 0 + for j in range(start, len(message)): + c = message[j] + if c == "{": + depth += 1 + elif c == "}": + depth -= 1 + if depth == 0: + substr = message[start : j + 1] + try: + result = ast.literal_eval(substr) + if isinstance(result, dict) and len(result) > 0: + return result + except (ValueError, SyntaxError, TypeError): + pass + break + i = start + 1 + return None + + +# Standard LogRecord attribute names - used to identify 'extra' fields. +# Derived at runtime so we automatically include version-specific attrs (e.g. taskName). +def _get_standard_record_attrs() -> frozenset: + """Standard LogRecord attribute names - excludes extra keys from logger.debug(..., extra={...}).""" + return frozenset(logging.LogRecord("", 0, "", 0, "", (), None).__dict__.keys()) + + +_STANDARD_RECORD_ATTRS = _get_standard_record_attrs() + + class JsonFormatter(Formatter): def __init__(self): super(JsonFormatter, self).__init__() @@ -29,16 +94,31 @@ class JsonFormatter(Formatter): return dt.isoformat() def format(self, record): - json_record = { - "message": record.getMessage(), + message_str = record.getMessage() + json_record: Dict[str, Any] = { + "message": message_str, "level": record.levelname, "timestamp": self.formatTime(record), } + # Parse embedded JSON or Python dict repr in message so sub-fields become first-class properties + parsed = _try_parse_json_message(message_str) + if parsed is None: + parsed = _try_parse_embedded_python_dict(message_str) + if parsed is not None: + for key, value in parsed.items(): + if key not in json_record: + json_record[key] = value + + # Include extra attributes passed via logger.debug("msg", extra={...}) + for key, value in record.__dict__.items(): + if key not in _STANDARD_RECORD_ATTRS and key not in json_record: + json_record[key] = value + if record.exc_info: json_record["stacktrace"] = self.formatException(record.exc_info) - return json.dumps(json_record) + return safe_dumps(json_record) # Function to set up exception handlers for JSON logging @@ -169,15 +249,15 @@ def _initialize_loggers_with_handler(handler: logging.Handler): def _get_uvicorn_json_log_config(): """ Generate a uvicorn log_config dictionary that applies JSON formatting to all loggers. - + This ensures that uvicorn's access logs, error logs, and all application logs are formatted as JSON when json_logs is enabled. """ json_formatter_class = "litellm._logging.JsonFormatter" - + # Use the module-level log_level variable for consistency uvicorn_log_level = log_level.upper() - + log_config = { "version": 1, "disable_existing_loggers": False, @@ -222,7 +302,7 @@ def _get_uvicorn_json_log_config(): }, }, } - + return log_config diff --git a/litellm/a2a_protocol/__init__.py b/litellm/a2a_protocol/__init__.py index d8d349bb98a..85c03687e25 100644 --- a/litellm/a2a_protocol/__init__.py +++ b/litellm/a2a_protocol/__init__.py @@ -39,6 +39,12 @@ Example usage (class-based): """ from litellm.a2a_protocol.client import A2AClient +from litellm.a2a_protocol.exceptions import ( + A2AAgentCardError, + A2AConnectionError, + A2AError, + A2ALocalhostURLError, +) from litellm.a2a_protocol.main import ( aget_agent_card, asend_message, @@ -49,11 +55,19 @@ from litellm.a2a_protocol.main import ( from litellm.types.agents import LiteLLMSendMessageResponse __all__ = [ + # Client "A2AClient", + # Functions "asend_message", "send_message", "asend_message_streaming", "aget_agent_card", "create_a2a_client", + # Response types "LiteLLMSendMessageResponse", + # Exceptions + "A2AError", + "A2AConnectionError", + "A2AAgentCardError", + "A2ALocalhostURLError", ] diff --git a/litellm/a2a_protocol/card_resolver.py b/litellm/a2a_protocol/card_resolver.py index 7c4c5af149d..4c5dd3e3ba6 100644 --- a/litellm/a2a_protocol/card_resolver.py +++ b/litellm/a2a_protocol/card_resolver.py @@ -7,6 +7,7 @@ Extends the A2A SDK's card resolver to support multiple well-known paths. from typing import TYPE_CHECKING, Any, Dict, Optional from litellm._logging import verbose_logger +from litellm.constants import LOCALHOST_URL_PATTERNS if TYPE_CHECKING: from a2a.types import AgentCard @@ -26,15 +27,61 @@ except ImportError: pass +def is_localhost_or_internal_url(url: Optional[str]) -> bool: + """ + Check if a URL is a localhost or internal URL. + + This detects common development URLs that are accidentally left in + agent cards when deploying to production. + + Args: + url: The URL to check + + Returns: + True if the URL is localhost/internal + """ + if not url: + return False + + url_lower = url.lower() + + return any(pattern in url_lower for pattern in LOCALHOST_URL_PATTERNS) + + +def fix_agent_card_url(agent_card: "AgentCard", base_url: str) -> "AgentCard": + """ + Fix the agent card URL if it contains a localhost/internal address. + + Many A2A agents are deployed with agent cards that contain internal URLs + like "http://0.0.0.0:8001/" or "http://localhost:8000/". This function + replaces such URLs with the provided base_url. + + Args: + agent_card: The agent card to fix + base_url: The base URL to use as replacement + + Returns: + The agent card with the URL fixed if necessary + """ + card_url = getattr(agent_card, "url", None) + + if card_url and is_localhost_or_internal_url(card_url): + # Normalize base_url to ensure it ends with / + fixed_url = base_url.rstrip("/") + "/" + agent_card.url = fixed_url + + return agent_card + + class LiteLLMA2ACardResolver(_A2ACardResolver): # type: ignore[misc] """ Custom A2A card resolver that supports multiple well-known paths. - + Extends the base A2ACardResolver to try both: - /.well-known/agent-card.json (standard) - /.well-known/agent.json (previous/alternative) """ - + async def get_agent_card( self, relative_card_path: Optional[str] = None, @@ -42,17 +89,17 @@ class LiteLLMA2ACardResolver(_A2ACardResolver): # type: ignore[misc] ) -> "AgentCard": """ Fetch the agent card, trying multiple well-known paths. - + First tries the standard path, then falls back to the previous path. - + Args: relative_card_path: Optional path to the agent card endpoint. If None, tries both well-known paths. http_kwargs: Optional dictionary of keyword arguments to pass to httpx.get - + Returns: AgentCard from the A2A agent - + Raises: A2AClientHTTPError or A2AClientJSONError if both paths fail """ @@ -62,13 +109,13 @@ class LiteLLMA2ACardResolver(_A2ACardResolver): # type: ignore[misc] relative_card_path=relative_card_path, http_kwargs=http_kwargs, ) - + # Try both well-known paths paths = [ AGENT_CARD_WELL_KNOWN_PATH, PREV_AGENT_CARD_WELL_KNOWN_PATH, ] - + last_error = None for path in paths: try: @@ -85,11 +132,11 @@ class LiteLLMA2ACardResolver(_A2ACardResolver): # type: ignore[misc] ) last_error = e continue - + # If we get here, all paths failed - re-raise the last error if last_error is not None: raise last_error - + # This shouldn't happen, but just in case raise Exception( f"Failed to fetch agent card from {self.base_url}. " diff --git a/litellm/a2a_protocol/exception_mapping_utils.py b/litellm/a2a_protocol/exception_mapping_utils.py new file mode 100644 index 00000000000..49dbb22b158 --- /dev/null +++ b/litellm/a2a_protocol/exception_mapping_utils.py @@ -0,0 +1,203 @@ +""" +A2A Protocol Exception Mapping Utils. + +Maps A2A SDK exceptions to LiteLLM A2A exception types. +""" + +from typing import TYPE_CHECKING, Any, Optional + +from litellm._logging import verbose_logger +from litellm.a2a_protocol.card_resolver import ( + fix_agent_card_url, + is_localhost_or_internal_url, +) +from litellm.a2a_protocol.exceptions import ( + A2AAgentCardError, + A2AConnectionError, + A2AError, + A2ALocalhostURLError, +) +from litellm.constants import CONNECTION_ERROR_PATTERNS + +if TYPE_CHECKING: + from a2a.client import A2AClient as A2AClientType + + +# Runtime import +A2A_SDK_AVAILABLE = False +try: + from a2a.client import A2AClient as _A2AClient # type: ignore[no-redef] + + A2A_SDK_AVAILABLE = True +except ImportError: + _A2AClient = None # type: ignore[assignment, misc] + + +class A2AExceptionCheckers: + """ + Helper class for checking various A2A error conditions. + """ + + @staticmethod + def is_connection_error(error_str: str) -> bool: + """ + Check if an error string indicates a connection error. + + Args: + error_str: The error string to check + + Returns: + True if the error indicates a connection issue + """ + if not isinstance(error_str, str): + return False + + error_str_lower = error_str.lower() + return any(pattern in error_str_lower for pattern in CONNECTION_ERROR_PATTERNS) + + @staticmethod + def is_localhost_url(url: Optional[str]) -> bool: + """ + Check if a URL is a localhost/internal URL. + + Args: + url: The URL to check + + Returns: + True if the URL is localhost/internal + """ + return is_localhost_or_internal_url(url) + + @staticmethod + def is_agent_card_error(error_str: str) -> bool: + """ + Check if an error string indicates an agent card error. + + Args: + error_str: The error string to check + + Returns: + True if the error is related to agent card fetching/parsing + """ + if not isinstance(error_str, str): + return False + + error_str_lower = error_str.lower() + agent_card_patterns = [ + "agent card", + "agent-card", + ".well-known", + "card not found", + "invalid agent", + ] + return any(pattern in error_str_lower for pattern in agent_card_patterns) + + +def map_a2a_exception( + original_exception: Exception, + card_url: Optional[str] = None, + api_base: Optional[str] = None, + model: Optional[str] = None, +) -> Exception: + """ + Map an A2A SDK exception to a LiteLLM A2A exception type. + + Args: + original_exception: The original exception from the A2A SDK + card_url: The URL from the agent card (if available) + api_base: The original API base URL + model: The model/agent name + + Returns: + A mapped LiteLLM A2A exception + + Raises: + A2ALocalhostURLError: If the error is a connection error to a localhost URL + A2AConnectionError: If the error is a general connection error + A2AAgentCardError: If the error is related to agent card issues + A2AError: For other A2A-related errors + """ + error_str = str(original_exception) + + # Check for localhost URL connection error (special case - retryable) + if ( + card_url + and api_base + and A2AExceptionCheckers.is_localhost_url(card_url) + and A2AExceptionCheckers.is_connection_error(error_str) + ): + raise A2ALocalhostURLError( + localhost_url=card_url, + base_url=api_base, + original_error=original_exception, + model=model, + ) + + # Check for agent card errors + if A2AExceptionCheckers.is_agent_card_error(error_str): + raise A2AAgentCardError( + message=error_str, + url=api_base, + model=model, + ) + + # Check for general connection errors + if A2AExceptionCheckers.is_connection_error(error_str): + raise A2AConnectionError( + message=error_str, + url=card_url or api_base, + model=model, + ) + + # Default: wrap in generic A2AError + raise A2AError( + message=error_str, + model=model, + ) + + +def handle_a2a_localhost_retry( + error: A2ALocalhostURLError, + agent_card: Any, + a2a_client: "A2AClientType", + is_streaming: bool = False, +) -> "A2AClientType": + """ + Handle A2ALocalhostURLError by fixing the URL and creating a new client. + + This is called when we catch an A2ALocalhostURLError and want to retry + with the corrected URL. + + Args: + error: The localhost URL error + agent_card: The agent card object to fix + a2a_client: The current A2A client + is_streaming: Whether this is a streaming request (for logging) + + Returns: + A new A2A client with the fixed URL + + Raises: + ImportError: If the A2A SDK is not installed + """ + if not A2A_SDK_AVAILABLE or _A2AClient is None: + raise ImportError( + "A2A SDK is required for localhost retry handling. " + "Install it with: pip install a2a" + ) + + request_type = "streaming " if is_streaming else "" + verbose_logger.warning( + f"A2A {request_type}request to '{error.localhost_url}' failed: {error.original_error}. " + f"Agent card contains localhost/internal URL. " + f"Retrying with base_url '{error.base_url}'." + ) + + # Fix the agent card URL + fix_agent_card_url(agent_card, error.base_url) + + # Create a new client with the fixed agent card (transport caches URL) + return _A2AClient( + httpx_client=a2a_client._transport.httpx_client, # type: ignore[union-attr] + agent_card=agent_card, + ) diff --git a/litellm/a2a_protocol/exceptions.py b/litellm/a2a_protocol/exceptions.py new file mode 100644 index 00000000000..546b23105be --- /dev/null +++ b/litellm/a2a_protocol/exceptions.py @@ -0,0 +1,150 @@ +""" +A2A Protocol Exceptions. + +Custom exception types for A2A protocol operations, following LiteLLM's exception pattern. +""" + +from typing import Optional + +import httpx + + +class A2AError(Exception): + """ + Base exception for A2A protocol errors. + + Follows the same pattern as LiteLLM's main exceptions. + """ + + def __init__( + self, + message: str, + status_code: int = 500, + llm_provider: str = "a2a_agent", + model: Optional[str] = None, + response: Optional[httpx.Response] = None, + litellm_debug_info: Optional[str] = None, + max_retries: Optional[int] = None, + num_retries: Optional[int] = None, + ): + self.status_code = status_code + self.message = f"litellm.A2AError: {message}" + self.llm_provider = llm_provider + self.model = model + self.litellm_debug_info = litellm_debug_info + self.max_retries = max_retries + self.num_retries = num_retries + self.response = response or httpx.Response( + status_code=self.status_code, + request=httpx.Request(method="POST", url="https://litellm.ai"), + ) + super().__init__(self.message) + + def __str__(self) -> str: + _message = self.message + if self.num_retries: + _message += f" LiteLLM Retried: {self.num_retries} times" + if self.max_retries: + _message += f", LiteLLM Max Retries: {self.max_retries}" + return _message + + def __repr__(self) -> str: + return self.__str__() + + +class A2AConnectionError(A2AError): + """ + Raised when connection to an A2A agent fails. + + This typically occurs when: + - The agent is unreachable + - The agent card contains a localhost/internal URL + - Network issues prevent connection + """ + + def __init__( + self, + message: str, + url: Optional[str] = None, + model: Optional[str] = None, + response: Optional[httpx.Response] = None, + litellm_debug_info: Optional[str] = None, + max_retries: Optional[int] = None, + num_retries: Optional[int] = None, + ): + self.url = url + super().__init__( + message=message, + status_code=503, + llm_provider="a2a_agent", + model=model, + response=response, + litellm_debug_info=litellm_debug_info, + max_retries=max_retries, + num_retries=num_retries, + ) + + +class A2AAgentCardError(A2AError): + """ + Raised when there's an issue with the agent card. + + This includes: + - Failed to fetch agent card + - Invalid agent card format + - Missing required fields + """ + + def __init__( + self, + message: str, + url: Optional[str] = None, + model: Optional[str] = None, + response: Optional[httpx.Response] = None, + litellm_debug_info: Optional[str] = None, + ): + self.url = url + super().__init__( + message=message, + status_code=404, + llm_provider="a2a_agent", + model=model, + response=response, + litellm_debug_info=litellm_debug_info, + ) + + +class A2ALocalhostURLError(A2AConnectionError): + """ + Raised when an agent card contains a localhost/internal URL. + + Many A2A agents are deployed with agent cards that contain internal URLs + like "http://0.0.0.0:8001/" or "http://localhost:8000/". This error + indicates that the URL needs to be corrected and the request should be retried. + + Attributes: + localhost_url: The localhost/internal URL found in the agent card + base_url: The public base URL that should be used instead + original_error: The original connection error that was raised + """ + + def __init__( + self, + localhost_url: str, + base_url: str, + original_error: Optional[Exception] = None, + model: Optional[str] = None, + ): + self.localhost_url = localhost_url + self.base_url = base_url + self.original_error = original_error + + message = ( + f"Agent card contains localhost/internal URL '{localhost_url}'. " + f"Retrying with base URL '{base_url}'." + ) + super().__init__( + message=message, + url=localhost_url, + model=model, + ) diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py index b326f9e7ed5..642dfaf023c 100644 --- a/litellm/a2a_protocol/main.py +++ b/litellm/a2a_protocol/main.py @@ -44,6 +44,11 @@ except ImportError: # Import our custom card resolver that supports multiple well-known paths from litellm.a2a_protocol.card_resolver import LiteLLMA2ACardResolver +from litellm.a2a_protocol.exception_mapping_utils import ( + handle_a2a_localhost_retry, + map_a2a_exception, +) +from litellm.a2a_protocol.exceptions import A2ALocalhostURLError # Use our custom resolver instead of the default A2A SDK resolver A2ACardResolver = LiteLLMA2ACardResolver @@ -244,10 +249,50 @@ async def asend_message( verbose_logger.info(f"A2A send_message request_id={request.id}, agent={agent_name}") - a2a_response = await a2a_client.send_message(request) + # Get agent card URL for localhost retry logic + agent_card = getattr(a2a_client, "_litellm_agent_card", None) or getattr( + a2a_client, "agent_card", None + ) + card_url = getattr(agent_card, "url", None) if agent_card else None + + # Retry loop: if connection fails due to localhost URL in agent card, retry with fixed URL + a2a_response = None + for _ in range(2): # max 2 attempts: original + 1 retry + try: + a2a_response = await a2a_client.send_message(request) + break # success, exit retry loop + except A2ALocalhostURLError as e: + # Localhost URL error - fix and retry + a2a_client = handle_a2a_localhost_retry( + error=e, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=False, + ) + card_url = agent_card.url if agent_card else None + except Exception as e: + # Map exception - will raise A2ALocalhostURLError if applicable + try: + map_a2a_exception(e, card_url, api_base, model=agent_name) + except A2ALocalhostURLError as localhost_err: + # Localhost URL error - fix and retry + a2a_client = handle_a2a_localhost_retry( + error=localhost_err, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=False, + ) + card_url = agent_card.url if agent_card else None + continue + except Exception: + # Re-raise the mapped exception + raise verbose_logger.info(f"A2A send_message completed, request_id={request.id}") + # a2a_response is guaranteed to be set if we reach here (loop breaks on success or raises) + assert a2a_response is not None + # Wrap in LiteLLM response type for _hidden_params support response = LiteLLMSendMessageResponse.from_a2a_response(a2a_response) @@ -307,6 +352,48 @@ def send_message( ) +def _build_streaming_logging_obj( + request: "SendStreamingMessageRequest", + agent_name: str, + agent_id: Optional[str], + litellm_params: Optional[Dict[str, Any]], + metadata: Optional[Dict[str, Any]], + proxy_server_request: Optional[Dict[str, Any]], +) -> Logging: + """Build logging object for streaming A2A requests.""" + start_time = datetime.datetime.now() + model = f"a2a_agent/{agent_name}" + + logging_obj = Logging( + model=model, + messages=[{"role": "user", "content": "streaming-request"}], + stream=False, + call_type="asend_message_streaming", + start_time=start_time, + litellm_call_id=str(request.id), + function_id=str(request.id), + ) + logging_obj.model = model + logging_obj.custom_llm_provider = "a2a_agent" + logging_obj.model_call_details["model"] = model + logging_obj.model_call_details["custom_llm_provider"] = "a2a_agent" + if agent_id: + logging_obj.model_call_details["agent_id"] = agent_id + + _litellm_params = litellm_params.copy() if litellm_params else {} + if metadata: + _litellm_params["metadata"] = metadata + if proxy_server_request: + _litellm_params["proxy_server_request"] = proxy_server_request + + logging_obj.litellm_params = _litellm_params + logging_obj.optional_params = _litellm_params + logging_obj.model_call_details["litellm_params"] = _litellm_params + logging_obj.model_call_details["metadata"] = metadata or {} + + return logging_obj + + async def asend_message_streaming( a2a_client: Optional["A2AClientType"] = None, request: Optional["SendStreamingMessageRequest"] = None, @@ -403,55 +490,72 @@ async def asend_message_streaming( verbose_logger.info(f"A2A send_message_streaming request_id={request.id}") - # Track for logging - start_time = datetime.datetime.now() - stream = a2a_client.send_message_streaming(request) - # Build logging object for streaming completion callbacks agent_card = getattr(a2a_client, "_litellm_agent_card", None) or getattr( a2a_client, "agent_card", None ) + card_url = getattr(agent_card, "url", None) if agent_card else None agent_name = getattr(agent_card, "name", "unknown") if agent_card else "unknown" - model = f"a2a_agent/{agent_name}" - logging_obj = Logging( - model=model, - messages=[{"role": "user", "content": "streaming-request"}], - stream=False, # complete response logging after stream ends - call_type="asend_message_streaming", - start_time=start_time, - litellm_call_id=str(request.id), - function_id=str(request.id), - ) - logging_obj.model = model - logging_obj.custom_llm_provider = "a2a_agent" - logging_obj.model_call_details["model"] = model - logging_obj.model_call_details["custom_llm_provider"] = "a2a_agent" - if agent_id: - logging_obj.model_call_details["agent_id"] = agent_id - - # Propagate litellm_params for spend logging (includes cost_per_query, etc.) - _litellm_params = litellm_params.copy() if litellm_params else {} - # Merge metadata into litellm_params.metadata (required for proxy cost tracking) - if metadata: - _litellm_params["metadata"] = metadata - if proxy_server_request: - _litellm_params["proxy_server_request"] = proxy_server_request - - logging_obj.litellm_params = _litellm_params - logging_obj.optional_params = _litellm_params # used by cost calc - logging_obj.model_call_details["litellm_params"] = _litellm_params - logging_obj.model_call_details["metadata"] = metadata or {} - - iterator = A2AStreamingIterator( - stream=stream, + logging_obj = _build_streaming_logging_obj( request=request, - logging_obj=logging_obj, agent_name=agent_name, + agent_id=agent_id, + litellm_params=litellm_params, + metadata=metadata, + proxy_server_request=proxy_server_request, ) - async for chunk in iterator: - yield chunk + # Retry loop: if connection fails due to localhost URL in agent card, retry with fixed URL + # Connection errors in streaming typically occur on first chunk iteration + first_chunk = True + for attempt in range(2): # max 2 attempts: original + 1 retry + stream = a2a_client.send_message_streaming(request) + iterator = A2AStreamingIterator( + stream=stream, + request=request, + logging_obj=logging_obj, + agent_name=agent_name, + ) + + try: + first_chunk = True + async for chunk in iterator: + if first_chunk: + first_chunk = False # connection succeeded + yield chunk + return # stream completed successfully + except A2ALocalhostURLError as e: + # Only retry on first chunk, not mid-stream + if first_chunk and attempt == 0: + a2a_client = handle_a2a_localhost_retry( + error=e, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=True, + ) + card_url = agent_card.url if agent_card else None + else: + raise + except Exception as e: + # Only map exception on first chunk + if first_chunk and attempt == 0: + try: + map_a2a_exception(e, card_url, api_base, model=agent_name) + except A2ALocalhostURLError as localhost_err: + # Localhost URL error - fix and retry + a2a_client = handle_a2a_localhost_retry( + error=localhost_err, + agent_card=agent_card, + a2a_client=a2a_client, + is_streaming=True, + ) + card_url = agent_card.url if agent_card else None + continue + except Exception: + # Re-raise the mapped exception + raise + raise async def create_a2a_client( diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json new file mode 100644 index 00000000000..5edb8067a08 --- /dev/null +++ b/litellm/anthropic_beta_headers_config.json @@ -0,0 +1,151 @@ +{ + "description": "Mapping of Anthropic beta headers for each provider. Keys are input header names, values are provider-specific header names (or null if unsupported). Only headers present in mapping keys with non-null values can be forwarded.", + "anthropic": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "bash_20241022": "bash_20241022", + "bash_20250124": "bash_20250124", + "code-execution-2025-08-25": "code-execution-2025-08-25", + "compact-2026-01-12": "compact-2026-01-12", + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": "context-1m-2025-08-07", + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": "effort-2025-11-24", + "fast-mode-2026-02-01": "fast-mode-2026-02-01", + "files-api-2025-04-14": "files-api-2025-04-14", + "structured-output-2024-03-01": "structured-output-2024-03-01", + "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14", + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": "mcp-client-2025-11-20", + "mcp-client-2025-04-04": "mcp-client-2025-04-04", + "mcp-servers-2025-12-04": "mcp-servers-2025-12-04", + "output-128k-2025-02-19": "output-128k-2025-02-19", + "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05", + "skills-2025-10-02": "skills-2025-10-02", + "structured-outputs-2025-11-13": "structured-outputs-2025-11-13", + "text_editor_20241022": "text_editor_20241022", + "text_editor_20250124": "text_editor_20250124", + "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19", + "web-fetch-2025-09-10": "web-fetch-2025-09-10", + "web-search-2025-03-05": "web-search-2025-03-05" + }, + "azure_ai": { + "advanced-tool-use-2025-11-20": "advanced-tool-use-2025-11-20", + "bash_20241022": "bash_20241022", + "bash_20250124": "bash_20250124", + "code-execution-2025-08-25": "code-execution-2025-08-25", + "compact-2026-01-12": null, + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": "context-1m-2025-08-07", + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": "effort-2025-11-24", + "fast-mode-2026-02-01": null, + "files-api-2025-04-14": "files-api-2025-04-14", + "fine-grained-tool-streaming-2025-05-14": null, + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": "mcp-client-2025-11-20", + "mcp-client-2025-04-04": "mcp-client-2025-04-04", + "mcp-servers-2025-12-04": "mcp-servers-2025-12-04", + "output-128k-2025-02-19": null, + "structured-output-2024-03-01": null, + "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05", + "skills-2025-10-02": "skills-2025-10-02", + "structured-outputs-2025-11-13": "structured-outputs-2025-11-13", + "text_editor_20241022": null, + "text_editor_20250124": null, + "token-efficient-tools-2025-02-19": null, + "web-fetch-2025-09-10": "web-fetch-2025-09-10", + "web-search-2025-03-05": "web-search-2025-03-05" + }, + "bedrock_converse": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "bash_20241022": null, + "bash_20250124": null, + "code-execution-2025-08-25": null, + "compact-2026-01-12": null, + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": null, + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": null, + "fast-mode-2026-02-01": null, + "files-api-2025-04-14": null, + "fine-grained-tool-streaming-2025-05-14": null, + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": null, + "mcp-client-2025-04-04": null, + "mcp-servers-2025-12-04": null, + "output-128k-2025-02-19": null, + "structured-output-2024-03-01": null, + "prompt-caching-scope-2026-01-05": null, + "skills-2025-10-02": null, + "structured-outputs-2025-11-13": "structured-outputs-2025-11-13", + "text_editor_20241022": null, + "text_editor_20250124": null, + "token-efficient-tools-2025-02-19": null, + "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19", + "web-fetch-2025-09-10": null, + "web-search-2025-03-05": null + }, + "bedrock": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "bash_20241022": null, + "bash_20250124": null, + "code-execution-2025-08-25": null, + "compact-2026-01-12": "compact-2026-01-12", + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": "context-1m-2025-08-07", + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": null, + "fast-mode-2026-02-01": null, + "files-api-2025-04-14": null, + "fine-grained-tool-streaming-2025-05-14": null, + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": null, + "mcp-client-2025-04-04": null, + "mcp-servers-2025-12-04": null, + "output-128k-2025-02-19": null, + "structured-output-2024-03-01": null, + "prompt-caching-scope-2026-01-05": null, + "skills-2025-10-02": null, + "structured-outputs-2025-11-13": null, + "text_editor_20241022": null, + "text_editor_20250124": null, + "token-efficient-tools-2025-02-19": null, + "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19", + "web-fetch-2025-09-10": null, + "web-search-2025-03-05": null + }, + "vertex_ai": { + "advanced-tool-use-2025-11-20": "tool-search-tool-2025-10-19", + "bash_20241022": null, + "bash_20250124": null, + "code-execution-2025-08-25": null, + "compact-2026-01-12": null, + "computer-use-2025-01-24": "computer-use-2025-01-24", + "computer-use-2025-11-24": "computer-use-2025-11-24", + "context-1m-2025-08-07": null, + "context-management-2025-06-27": "context-management-2025-06-27", + "effort-2025-11-24": null, + "fast-mode-2026-02-01": null, + "files-api-2025-04-14": null, + "fine-grained-tool-streaming-2025-05-14": null, + "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14", + "mcp-client-2025-11-20": null, + "mcp-client-2025-04-04": null, + "mcp-servers-2025-12-04": null, + "output-128k-2025-02-19": null, + "structured-output-2024-03-01": null, + "prompt-caching-scope-2026-01-05": null, + "skills-2025-10-02": null, + "structured-outputs-2025-11-13": null, + "text_editor_20241022": null, + "text_editor_20250124": null, + "token-efficient-tools-2025-02-19": null, + "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19", + "web-fetch-2025-09-10": null, + "web-search-2025-03-05": "web-search-2025-03-05" + } +} \ No newline at end of file diff --git a/litellm/anthropic_beta_headers_manager.py b/litellm/anthropic_beta_headers_manager.py new file mode 100644 index 00000000000..9730ae02698 --- /dev/null +++ b/litellm/anthropic_beta_headers_manager.py @@ -0,0 +1,237 @@ +""" +Centralized manager for Anthropic beta headers across different providers. + +This module provides utilities to: +1. Load beta header configuration from JSON (mapping of supported headers per provider) +2. Filter and map beta headers based on provider support +3. Handle provider-specific header name mappings (e.g., advanced-tool-use -> tool-search-tool) + +Design: +- JSON config contains mapping of beta headers for each provider +- Keys are input header names, values are provider-specific header names (or null if unsupported) +- Only headers present in mapping keys with non-null values can be forwarded +- This enforces stricter validation than the previous unsupported list approach +""" + +import json +import os +from typing import Dict, List, Optional, Set + +from litellm.litellm_core_utils.litellm_logging import verbose_logger + +# Cache for the loaded configuration +_BETA_HEADERS_CONFIG: Optional[Dict] = None + + +def _load_beta_headers_config() -> Dict: + """ + Load the beta headers configuration from JSON file. + Uses caching to avoid repeated file reads. + + Returns: + Dict containing the beta headers configuration + """ + global _BETA_HEADERS_CONFIG + + if _BETA_HEADERS_CONFIG is not None: + return _BETA_HEADERS_CONFIG + + config_path = os.path.join( + os.path.dirname(__file__), + "anthropic_beta_headers_config.json" + ) + + try: + with open(config_path, "r") as f: + _BETA_HEADERS_CONFIG = json.load(f) + verbose_logger.debug(f"Loaded beta headers config from {config_path}") + return _BETA_HEADERS_CONFIG + except Exception as e: + verbose_logger.error(f"Failed to load beta headers config: {e}") + # Return empty config as fallback (empty mappings) + return { + "anthropic": {}, + "azure_ai": {}, + "bedrock": {}, + "bedrock_converse": {}, + "vertex_ai": {} + } + + +def get_provider_name(provider: str) -> str: + """ + Resolve provider aliases to canonical provider names. + + Args: + provider: Provider name (may be an alias) + + Returns: + Canonical provider name + """ + config = _load_beta_headers_config() + aliases = config.get("provider_aliases", {}) + return aliases.get(provider, provider) + + +def filter_and_transform_beta_headers( + beta_headers: List[str], + provider: str, +) -> List[str]: + """ + Filter and transform beta headers based on provider's mapping configuration. + + This function: + 1. Only allows headers that are present in the provider's mapping keys + 2. Filters out headers with null values (unsupported) + 3. Maps headers to provider-specific names (e.g., advanced-tool-use -> tool-search-tool) + + Args: + beta_headers: List of Anthropic beta header values + provider: Provider name (e.g., "anthropic", "bedrock", "vertex_ai") + + Returns: + List of filtered and transformed beta headers for the provider + """ + if not beta_headers: + return [] + + config = _load_beta_headers_config() + provider = get_provider_name(provider) + + # Get the header mapping for this provider + provider_mapping = config.get(provider, {}) + + filtered_headers: Set[str] = set() + + for header in beta_headers: + header = header.strip() + + # Check if header is in the mapping + if header not in provider_mapping: + verbose_logger.debug( + f"Dropping unknown beta header '{header}' for provider '{provider}' (not in mapping)" + ) + continue + + # Get the mapped header value + mapped_header = provider_mapping[header] + + # Skip if header is unsupported (null value) + if mapped_header is None: + verbose_logger.debug( + f"Dropping unsupported beta header '{header}' for provider '{provider}'" + ) + continue + + # Add the mapped header + filtered_headers.add(mapped_header) + + return sorted(list(filtered_headers)) + + +def is_beta_header_supported( + beta_header: str, + provider: str, +) -> bool: + """ + Check if a specific beta header is supported by a provider. + + Args: + beta_header: The Anthropic beta header value + provider: Provider name + + Returns: + True if the header is in the mapping with a non-null value, False otherwise + """ + config = _load_beta_headers_config() + provider = get_provider_name(provider) + provider_mapping = config.get(provider, {}) + + # Header is supported if it's in the mapping and has a non-null value + return beta_header in provider_mapping and provider_mapping[beta_header] is not None + + +def get_provider_beta_header( + anthropic_beta_header: str, + provider: str, +) -> Optional[str]: + """ + Get the provider-specific beta header name for a given Anthropic beta header. + + This function handles header transformations/mappings (e.g., advanced-tool-use -> tool-search-tool). + + Args: + anthropic_beta_header: The Anthropic beta header value + provider: Provider name + + Returns: + The provider-specific header name if supported, or None if unsupported/unknown + """ + config = _load_beta_headers_config() + provider = get_provider_name(provider) + + # Get the header mapping for this provider + provider_mapping = config.get(provider, {}) + + # Check if header is in the mapping + if anthropic_beta_header not in provider_mapping: + return None + + # Return the mapped value (could be None if unsupported) + return provider_mapping[anthropic_beta_header] + + +def update_headers_with_filtered_beta( + headers: dict, + provider: str, +) -> dict: + """ + Update headers dict by filtering and transforming anthropic-beta header values. + Modifies the headers dict in place and returns it. + + Args: + headers: Request headers dict (will be modified in place) + provider: Provider name + + Returns: + Updated headers dict + """ + existing_beta = headers.get("anthropic-beta") + if not existing_beta: + return headers + + # Parse existing beta headers + beta_values = [b.strip() for b in existing_beta.split(",") if b.strip()] + + # Filter and transform based on provider + filtered_beta_values = filter_and_transform_beta_headers( + beta_headers=beta_values, + provider=provider, + ) + + # Update or remove the header + if filtered_beta_values: + headers["anthropic-beta"] = ",".join(filtered_beta_values) + else: + # Remove the header if no values remain + headers.pop("anthropic-beta", None) + + return headers + + +def get_unsupported_headers(provider: str) -> List[str]: + """ + Get all beta headers that are unsupported by a provider (have null values in mapping). + + Args: + provider: Provider name + + Returns: + List of unsupported Anthropic beta header names + """ + config = _load_beta_headers_config() + provider = get_provider_name(provider) + provider_mapping = config.get(provider, {}) + + # Return headers with null values + return [header for header, value in provider_mapping.items() if value is None] diff --git a/litellm/batch_completion/main.py b/litellm/batch_completion/main.py index 7100fb004f8..446e3f2f990 100644 --- a/litellm/batch_completion/main.py +++ b/litellm/batch_completion/main.py @@ -237,17 +237,37 @@ def batch_completion_models_all_responses(*args, **kwargs): if "model" in kwargs: kwargs.pop("model") if "models" in kwargs: - models = kwargs["models"] - kwargs.pop("models") + models = kwargs.pop("models") else: raise Exception("'models' param not in kwargs") + if isinstance(models, str): + models = [models] + elif isinstance(models, (list, tuple)): + models = list(models) + else: + raise TypeError("'models' must be a string or list of strings") + + if len(models) == 0: + return [] + responses = [] with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor: - for idx, model in enumerate(models): - future = executor.submit(litellm.completion, *args, model=model, **kwargs) - if future.result() is not None: - responses.append(future.result()) + futures = [ + executor.submit(litellm.completion, *args, model=model, **kwargs) + for model in models + ] + + for future in futures: + try: + result = future.result() + if result is not None: + responses.append(result) + except Exception as e: + print_verbose( + f"batch_completion_models_all_responses: model request failed: {str(e)}" + ) + continue return responses diff --git a/litellm/caching/redis_cache.py b/litellm/caching/redis_cache.py index ea7e3f5a979..03d09ecc041 100644 --- a/litellm/caching/redis_cache.py +++ b/litellm/caching/redis_cache.py @@ -1123,7 +1123,7 @@ class RedisCache(BaseCache): redis_client = redis_async.Redis(**self.redis_kwargs) # Test the connection - ping_result = await redis_client.ping() + ping_result = await redis_client.ping() # type: ignore[misc] # Close the connection await redis_client.aclose() # type: ignore[attr-defined] diff --git a/litellm/caching/redis_cluster_cache.py b/litellm/caching/redis_cluster_cache.py index 91fcf1d7288..664578c8700 100644 --- a/litellm/caching/redis_cluster_cache.py +++ b/litellm/caching/redis_cluster_cache.py @@ -83,7 +83,7 @@ class RedisClusterCache(RedisCache): ) # Test the connection - ping_result = await redis_client.ping() # type: ignore[attr-defined] + ping_result = await redis_client.ping() # type: ignore[attr-defined, misc] # Close the connection await redis_client.aclose() # type: ignore[attr-defined] diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 57bd05124aa..e546a0dbb02 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -227,6 +227,84 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return input_items, instructions + def _map_optional_params_to_responses_api_request( + self, + optional_params: dict, + responses_api_request: "ResponsesAPIOptionalRequestParams", + ) -> None: + """Map optional_params into responses_api_request (mutates in place).""" + for key, value in optional_params.items(): + if value is None: + continue + if key in ("max_tokens", "max_completion_tokens"): + responses_api_request["max_output_tokens"] = value + elif key == "tools" and value is not None: + responses_api_request["tools"] = ( + self._convert_tools_to_responses_format( + cast(List[Dict[str, Any]], value) + ) + ) + elif key == "response_format": + text_format = self._transform_response_format_to_text_format(value) + if text_format: + responses_api_request["text"] = text_format # type: ignore + elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): + responses_api_request[key] = value # type: ignore + elif key == "previous_response_id": + responses_api_request["previous_response_id"] = value + elif key == "reasoning_effort": + responses_api_request["reasoning"] = self._map_reasoning_effort(value) + elif key == "web_search_options": + self._add_web_search_tool(responses_api_request, value) + + def _build_sanitized_litellm_params( + self, litellm_params: dict + ) -> Dict[str, Any]: + """Build sanitized litellm_params with merged metadata.""" + responses_optional_param_keys = set( + ResponsesAPIOptionalRequestParams.__annotations__.keys() + ) + sanitized: Dict[str, Any] = { + key: value + for key, value in litellm_params.items() + if key not in responses_optional_param_keys + } + legacy_metadata = litellm_params.get("metadata") + existing_litellm_metadata = litellm_params.get("litellm_metadata") + merged_litellm_metadata: Dict[str, Any] = {} + if isinstance(legacy_metadata, dict): + merged_litellm_metadata.update(legacy_metadata) + if isinstance(existing_litellm_metadata, dict): + merged_litellm_metadata.update(existing_litellm_metadata) + if merged_litellm_metadata: + sanitized["litellm_metadata"] = merged_litellm_metadata + else: + sanitized.pop("litellm_metadata", None) + return sanitized + + def _merge_responses_api_request_into_request_data( + self, + request_data: Dict[str, Any], + responses_api_request: "ResponsesAPIOptionalRequestParams", + instructions: Optional[str], + ) -> None: + """Add non-None values from responses_api_request into request_data.""" + for key, value in responses_api_request.items(): + if value is None: + continue + if key == "instructions" and instructions: + request_data["instructions"] = instructions + elif key == "stream_options" and isinstance(value, dict): + request_data["stream_options"] = value.get("include_obfuscation") + elif key == "user" and isinstance(value, str): + # OpenAI API requires user param to be max 64 chars - truncate if longer + if len(value) <= 64: + request_data["user"] = value + else: + request_data["user"] = value[:64] + else: + request_data[key] = value + def transform_request( self, model: str, @@ -251,36 +329,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if instructions: responses_api_request["instructions"] = instructions - # Map optional parameters - for key, value in optional_params.items(): - if value is None: - continue - if key in ("max_tokens", "max_completion_tokens"): - responses_api_request["max_output_tokens"] = value - elif key == "tools" and value is not None: - # Convert chat completion tools to responses API tools format - responses_api_request["tools"] = ( - self._convert_tools_to_responses_format( - cast(List[Dict[str, Any]], value) - ) - ) - elif key == "response_format": - # Convert response_format to text.format - text_format = self._transform_response_format_to_text_format(value) - if text_format: - responses_api_request["text"] = text_format # type: ignore - elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): - responses_api_request[key] = value # type: ignore - elif key == "metadata": - responses_api_request["metadata"] = value - elif key == "previous_response_id": - responses_api_request["previous_response_id"] = value - elif key == "reasoning_effort": - responses_api_request["reasoning"] = self._map_reasoning_effort(value) - elif key == "web_search_options": - self._add_web_search_tool(responses_api_request, value) + self._map_optional_params_to_responses_api_request( + optional_params, responses_api_request + ) - # Get stream parameter from litellm_params if not in optional_params stream = optional_params.get("stream") or litellm_params.get("stream", False) verbose_logger.debug(f"Chat provider: Stream parameter: {stream}") @@ -304,11 +356,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): setattr(litellm_logging_obj, "call_type", CallTypes.responses.value) + sanitized_litellm_params = self._build_sanitized_litellm_params( + litellm_params + ) + request_data = { "model": api_model, "input": input_items, "litellm_logging_obj": litellm_logging_obj, - **litellm_params, + **sanitized_litellm_params, "client": client, } @@ -316,18 +372,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): f"Chat provider: Final request model={api_model}, input_items={len(input_items)}" ) - # Add non-None values from responses_api_request - for key, value in responses_api_request.items(): - if value is not None: - if key == "instructions" and instructions: - request_data["instructions"] = instructions - elif key == "stream_options" and isinstance(value, dict): - request_data["stream_options"] = value.get("include_obfuscation") - elif key == "user": # string can't be longer than 64 characters - if isinstance(value, str) and len(value) <= 64: - request_data["user"] = value - else: - request_data[key] = value + self._merge_responses_api_request_into_request_data( + request_data, responses_api_request, instructions + ) + + if headers: + request_data["extra_headers"] = headers return request_data diff --git a/litellm/constants.py b/litellm/constants.py index 7625d55f003..73713e6a05b 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -2,6 +2,8 @@ import os import sys from typing import List, Literal +from litellm.litellm_core_utils.env_utils import get_env_int + DEFAULT_HEALTH_CHECK_PROMPT = str( os.getenv("DEFAULT_HEALTH_CHECK_PROMPT", "test from litellm") ) @@ -46,6 +48,14 @@ DEFAULT_REPLICATE_POLLING_DELAY_SECONDS = int( os.getenv("DEFAULT_REPLICATE_POLLING_DELAY_SECONDS", 1) ) DEFAULT_IMAGE_TOKEN_COUNT = int(os.getenv("DEFAULT_IMAGE_TOKEN_COUNT", 250)) + +# Model cost map validation constants +MODEL_COST_MAP_MIN_MODEL_COUNT = int( + os.getenv("MODEL_COST_MAP_MIN_MODEL_COUNT", 50) +) # Minimum number of models a fetched cost map must contain to be considered valid +MODEL_COST_MAP_MAX_SHRINK_RATIO = float( + os.getenv("MODEL_COST_MAP_MAX_SHRINK_RATIO", 0.5) +) # Maximum allowed shrinkage ratio vs local backup (0.5 = reject if fetched map is <50% of backup) DEFAULT_IMAGE_WIDTH = int(os.getenv("DEFAULT_IMAGE_WIDTH", 300)) DEFAULT_IMAGE_HEIGHT = int(os.getenv("DEFAULT_IMAGE_HEIGHT", 300)) # Maximum size for image URL downloads in MB (default 50MB, set to 0 to disable limit) @@ -81,6 +91,30 @@ MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH = int( os.getenv("MAX_MCP_SEMANTIC_FILTER_TOOLS_HEADER_LENGTH", 150) ) +# MCP OAuth2 Client Credentials Defaults +MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS = int( + os.getenv("MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS", "60") +) +MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE = int( + os.getenv("MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE", "200") +) +MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL = int( + os.getenv("MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL", "3600") +) + +# Default npm cache directory for STDIO MCP servers. +# npm/npx needs a writable cache dir; in containers the default (~/.npm) +# may not exist or be read-only. /tmp is always writable. +MCP_NPM_CACHE_DIR = os.getenv("MCP_NPM_CACHE_DIR", "/tmp/.npm_mcp_cache") +MCP_OAUTH2_TOKEN_CACHE_MIN_TTL = int( + os.getenv("MCP_OAUTH2_TOKEN_CACHE_MIN_TTL", "10") +) + +LITELLM_UI_ALLOW_HEADERS = [ + "x-litellm-semantic-filter", + "x-litellm-semantic-filter-tools", +] + # Gemini model-specific minimal thinking budget constants DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH", 1) @@ -94,11 +128,19 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( ) ) +# Maximum number of callbacks that can be registered +# This prevents callbacks from exponentially growing and consuming CPU resources +# Override with LITELLM_MAX_CALLBACKS env var for large deployments (e.g., many teams with guardrails) +MAX_CALLBACKS = get_env_int("LITELLM_MAX_CALLBACKS", 30) + # Generic fallback for unknown models DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET", 128) ) +# Provider-specific API base URLs +XAI_API_BASE = "https://api.x.ai/v1" + DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET = int( os.getenv("DEFAULT_REASONING_EFFORT_LOW_THINKING_BUDGET", 1024) ) @@ -182,6 +224,10 @@ REDIS_DAILY_AGENT_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_agent_spend_update_bu REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) MAX_SIZE_IN_MEMORY_QUEUE = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", 2000)) +# Bounds asyncio.Queue() instances (log queues, spend update queues, etc.) to prevent unbounded memory growth +LITELLM_ASYNCIO_QUEUE_MAXSIZE = int( + os.getenv("LITELLM_ASYNCIO_QUEUE_MAXSIZE", 1000) +) MAX_IN_MEMORY_QUEUE_FLUSH_COUNT = int( os.getenv("MAX_IN_MEMORY_QUEUE_FLUSH_COUNT", 1000) ) @@ -303,9 +349,23 @@ MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( DEFAULT_MAX_TOKENS_FOR_TRITON = int(os.getenv("DEFAULT_MAX_TOKENS_FOR_TRITON", 2000)) #### Networking settings #### request_timeout: float = float(os.getenv("REQUEST_TIMEOUT", 6000)) # time in seconds -DEFAULT_A2A_AGENT_TIMEOUT: float = float( - os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000) -) # 10 minutes +DEFAULT_A2A_AGENT_TIMEOUT: float = float(os.getenv("DEFAULT_A2A_AGENT_TIMEOUT", 6000)) # 10 minutes +# Patterns that indicate a localhost/internal URL in A2A agent cards that should be +# replaced with the original base_url. This is a common misconfiguration where +# developers deploy agents with development URLs in their agent cards. +LOCALHOST_URL_PATTERNS: List[str] = [ + "localhost", + "127.0.0.1", + "0.0.0.0", + "[::1]", # IPv6 localhost +] +# Patterns in error messages that indicate a connection failure +CONNECTION_ERROR_PATTERNS: List[str] = [ + "connect", + "connection", + "network", + "refused", +] STREAM_SSE_DONE_STRING: str = "[DONE]" STREAM_SSE_DATA_PREFIX: str = "data: " ### SPEND TRACKING ### @@ -974,6 +1034,8 @@ BEDROCK_CONVERSE_MODELS = [ "openai.gpt-oss-120b-1:0", "anthropic.claude-haiku-4-5-20251001-v1:0", "anthropic.claude-sonnet-4-5-20250929-v1:0", + "anthropic.claude-opus-4-6-v1:0", + "anthropic.claude-opus-4-6-v1", "anthropic.claude-opus-4-1-20250805-v1:0", "anthropic.claude-opus-4-20250514-v1:0", "anthropic.claude-sonnet-4-20250514-v1:0", @@ -1276,6 +1338,9 @@ DEFAULT_SLACK_ALERTING_THRESHOLD = int( os.getenv("DEFAULT_SLACK_ALERTING_THRESHOLD", 300) ) MAX_TEAM_LIST_LIMIT = int(os.getenv("MAX_TEAM_LIST_LIMIT", 20)) +MAX_POLICY_ESTIMATE_IMPACT_ROWS = int( + os.getenv("MAX_POLICY_ESTIMATE_IMPACT_ROWS", 1000) +) DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD = float( os.getenv("DEFAULT_PROMPT_INJECTION_SIMILARITY_THRESHOLD", 0.7) ) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index bef4d52ce49..4ea22dbd90f 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -1,5 +1,6 @@ # What is this? ## File for 'response_cost' calculation in Logging +import logging import time from functools import lru_cache from typing import TYPE_CHECKING, Any, List, Literal, Optional, Tuple, Union, cast @@ -774,10 +775,11 @@ def _apply_cost_discount( discount_amount = original_cost * discount_percent final_cost = original_cost - discount_amount - verbose_logger.debug( - f"Applied {discount_percent*100}% discount to {custom_llm_provider}: " - f"${original_cost:.6f} -> ${final_cost:.6f} (saved ${discount_amount:.6f})" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + f"Applied {discount_percent*100}% discount to {custom_llm_provider}: " + f"${original_cost:.6f} -> ${final_cost:.6f} (saved ${discount_amount:.6f})" + ) return final_cost, discount_percent, discount_amount @@ -807,17 +809,20 @@ def _apply_cost_margin( margin_config = None if custom_llm_provider and custom_llm_provider in litellm.cost_margin_config: margin_config = litellm.cost_margin_config[custom_llm_provider] - verbose_logger.debug( - f"Found provider-specific margin config for {custom_llm_provider}: {margin_config}" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + f"Found provider-specific margin config for {custom_llm_provider}: {margin_config}" + ) elif "global" in litellm.cost_margin_config: margin_config = litellm.cost_margin_config["global"] - verbose_logger.debug(f"Using global margin config: {margin_config}") + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug(f"Using global margin config: {margin_config}") else: - verbose_logger.debug( - f"No margin config found. Provider: {custom_llm_provider}, " - f"Available configs: {list(litellm.cost_margin_config.keys())}" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + f"No margin config found. Provider: {custom_llm_provider}, " + f"Available configs: {list(litellm.cost_margin_config.keys())}" + ) if margin_config is not None: # Handle different margin config formats @@ -836,11 +841,12 @@ def _apply_cost_margin( final_cost = original_cost + margin_total_amount - verbose_logger.debug( - f"Applied margin to {custom_llm_provider or 'global'}: " - f"${original_cost:.6f} -> ${final_cost:.6f} " - f"(margin: {margin_percent*100 if margin_percent > 0 else 0}% + ${margin_fixed_amount:.6f} = ${margin_total_amount:.6f})" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + f"Applied margin to {custom_llm_provider or 'global'}: " + f"${original_cost:.6f} -> ${final_cost:.6f} " + f"(margin: {margin_percent*100 if margin_percent > 0 else 0}% + ${margin_fixed_amount:.6f} = ${margin_total_amount:.6f})" + ) return final_cost, margin_percent, margin_fixed_amount, margin_total_amount @@ -1021,9 +1027,10 @@ def completion_cost( # noqa: PLR0915 for idx, model in enumerate(potential_model_names): try: - verbose_logger.debug( - f"selected model name for cost calculation: {model}" - ) + if verbose_logger.isEnabledFor(logging.DEBUG): + verbose_logger.debug( + f"selected model name for cost calculation: {model}" + ) if completion_response is not None and ( isinstance(completion_response, BaseModel) @@ -1411,37 +1418,47 @@ def completion_cost( # noqa: PLR0915 # Apply discount from module-level config if configured original_cost = _final_cost - _final_cost, discount_percent, discount_amount = _apply_cost_discount( - base_cost=_final_cost, - custom_llm_provider=custom_llm_provider, - ) + if litellm.cost_discount_config: + _final_cost, discount_percent, discount_amount = _apply_cost_discount( + base_cost=_final_cost, + custom_llm_provider=custom_llm_provider, + ) + else: + discount_percent = 0.0 + discount_amount = 0.0 # Apply margin from module-level config if configured - ( - _final_cost, - margin_percent, - margin_fixed_amount, - margin_total_amount, - ) = _apply_cost_margin( - base_cost=_final_cost, - custom_llm_provider=custom_llm_provider, - ) + if litellm.cost_margin_config: + ( + _final_cost, + margin_percent, + margin_fixed_amount, + margin_total_amount, + ) = _apply_cost_margin( + base_cost=_final_cost, + custom_llm_provider=custom_llm_provider, + ) + else: + margin_percent = 0.0 + margin_fixed_amount = 0.0 + margin_total_amount = 0.0 # Store cost breakdown in logging object if available - _store_cost_breakdown_in_logging_obj( - litellm_logging_obj=litellm_logging_obj, - prompt_tokens_cost_usd_dollar=prompt_tokens_cost_usd_dollar, - completion_tokens_cost_usd_dollar=completion_tokens_cost_usd_dollar, - cost_for_built_in_tools_cost_usd_dollar=cost_for_built_in_tools, - total_cost_usd_dollar=_final_cost, - additional_costs=additional_costs, - original_cost=original_cost, - discount_percent=discount_percent, - discount_amount=discount_amount, - margin_percent=margin_percent, - margin_fixed_amount=margin_fixed_amount, - margin_total_amount=margin_total_amount, - ) + if litellm_logging_obj is not None: + _store_cost_breakdown_in_logging_obj( + litellm_logging_obj=litellm_logging_obj, + prompt_tokens_cost_usd_dollar=prompt_tokens_cost_usd_dollar, + completion_tokens_cost_usd_dollar=completion_tokens_cost_usd_dollar, + cost_for_built_in_tools_cost_usd_dollar=cost_for_built_in_tools, + total_cost_usd_dollar=_final_cost, + original_cost=original_cost, + additional_costs=additional_costs, + discount_percent=discount_percent, + discount_amount=discount_amount, + margin_percent=margin_percent, + margin_fixed_amount=margin_fixed_amount, + margin_total_amount=margin_total_amount, + ) return _final_cost except Exception as e: @@ -2116,3 +2133,5 @@ def handle_realtime_stream_cost_calculation( total_cost = input_cost_per_token + output_cost_per_token return total_cost + + diff --git a/litellm/experimental_mcp_client/client.py b/litellm/experimental_mcp_client/client.py index 3e8f9bc337b..5e21ff9754f 100644 --- a/litellm/experimental_mcp_client/client.py +++ b/litellm/experimental_mcp_client/client.py @@ -209,6 +209,8 @@ class MCPClient: headers["X-API-Key"] = self._mcp_auth_value elif self.auth_type == MCPAuth.authorization: headers["Authorization"] = self._mcp_auth_value + elif self.auth_type == MCPAuth.oauth2: + headers["Authorization"] = f"Bearer {self._mcp_auth_value}" elif isinstance(self._mcp_auth_value, dict): headers.update(self._mcp_auth_value) diff --git a/litellm/integrations/arize/arize.py b/litellm/integrations/arize/arize.py index 9c2f0d95d4d..fe2f9f41f1b 100644 --- a/litellm/integrations/arize/arize.py +++ b/litellm/integrations/arize/arize.py @@ -28,6 +28,41 @@ else: class ArizeLogger(OpenTelemetry): + """ + Arize logger that sends traces to an Arize endpoint. + + Creates its own dedicated TracerProvider so it can coexist with the + generic ``otel`` callback (or any other OTEL-based integration) without + fighting over the global ``opentelemetry.trace`` TracerProvider singleton. + """ + + def _init_tracing(self, tracer_provider): + """ + Override to always create a *private* TracerProvider for Arize. + + See ArizePhoenixLogger._init_tracing for full rationale. + """ + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.trace import SpanKind + + if tracer_provider is not None: + self.tracer = tracer_provider.get_tracer("litellm") + self.span_kind = SpanKind + return + + provider = TracerProvider(resource=self._get_litellm_resource(self.config)) + provider.add_span_processor(self._get_span_processor()) + self.tracer = provider.get_tracer("litellm") + self.span_kind = SpanKind + + def _init_otel_logger_on_litellm_proxy(self): + """ + Override: Arize should NOT overwrite the proxy's + ``open_telemetry_logger``. That attribute is reserved for the + primary ``otel`` callback which handles proxy-level parent spans. + """ + pass + def set_attributes(self, span: Span, kwargs, response_obj: Optional[Any]): ArizeLogger.set_arize_attributes(span, kwargs, response_obj) return diff --git a/litellm/integrations/arize/arize_phoenix.py b/litellm/integrations/arize/arize_phoenix.py index cd345a7f76d..1b038c098f8 100644 --- a/litellm/integrations/arize/arize_phoenix.py +++ b/litellm/integrations/arize/arize_phoenix.py @@ -5,43 +5,211 @@ from litellm._logging import verbose_logger from litellm.integrations.arize import _utils from litellm.integrations.arize._utils import ArizeOTELAttributes from litellm.types.integrations.arize_phoenix import ArizePhoenixConfig -from litellm.integrations.opentelemetry import OpenTelemetry if TYPE_CHECKING: + from opentelemetry.sdk.trace import TracerProvider from opentelemetry.trace import Span as _Span + from opentelemetry.trace import SpanKind + from litellm.integrations.opentelemetry import OpenTelemetry as _OpenTelemetry from litellm.integrations.opentelemetry import OpenTelemetryConfig as _OpenTelemetryConfig from litellm.types.integrations.arize import Protocol as _Protocol Protocol = _Protocol OpenTelemetryConfig = _OpenTelemetryConfig Span = Union[_Span, Any] + OpenTelemetry = _OpenTelemetry else: Protocol = Any OpenTelemetryConfig = Any Span = Any + TracerProvider = Any + SpanKind = Any + # Import OpenTelemetry at runtime + try: + from litellm.integrations.opentelemetry import OpenTelemetry + except ImportError: + OpenTelemetry = None # type: ignore ARIZE_HOSTED_PHOENIX_ENDPOINT = "https://otlp.arize.com/v1/traces" -class ArizePhoenixLogger(OpenTelemetry): +class ArizePhoenixLogger(OpenTelemetry): # type: ignore + """ + Arize Phoenix logger that sends traces to a Phoenix endpoint. + + Creates its own dedicated TracerProvider so it can coexist with the + generic ``otel`` callback (or any other OTEL-based integration) without + fighting over the global ``opentelemetry.trace`` TracerProvider singleton. + """ + + def _init_tracing(self, tracer_provider): + """ + Override to always create a *private* TracerProvider for Arize Phoenix. + + The base ``OpenTelemetry._init_tracing`` falls back to the global + TracerProvider when one already exists. That causes whichever + integration initialises second to silently reuse the first one's + exporter, so spans only reach one destination. + + By creating our own provider we guarantee Arize Phoenix always gets + its own exporter pipeline, regardless of initialisation order. + """ + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.trace import SpanKind + + if tracer_provider is not None: + # Explicitly supplied (e.g. in tests) — honour it. + self.tracer = tracer_provider.get_tracer("litellm") + self.span_kind = SpanKind + return + + # Always create a dedicated provider — never touch the global one. + provider = TracerProvider(resource=self._get_litellm_resource(self.config)) + provider.add_span_processor(self._get_span_processor()) + self.tracer = provider.get_tracer("litellm") + self.span_kind = SpanKind + verbose_logger.debug( + "ArizePhoenixLogger: Created dedicated TracerProvider " + "(endpoint=%s, exporter=%s)", + self.config.endpoint, + self.config.exporter, + ) + + def _init_otel_logger_on_litellm_proxy(self): + """ + Override: Arize Phoenix should NOT overwrite the proxy's + ``open_telemetry_logger``. That attribute is reserved for the + primary ``otel`` callback which handles proxy-level parent spans. + """ + pass + def set_attributes(self, span: Span, kwargs, response_obj: Optional[Any]): ArizePhoenixLogger.set_arize_phoenix_attributes(span, kwargs, response_obj) return @staticmethod def set_arize_phoenix_attributes(span: Span, kwargs, response_obj): + from litellm.integrations.opentelemetry_utils.base_otel_llm_obs_attributes import safe_set_attribute + _utils.set_attributes(span, kwargs, response_obj, ArizeOTELAttributes) - - # Set project name on the span for all traces to go to custom Phoenix projects - config = ArizePhoenixLogger.get_arize_phoenix_config() - if config.project_name: - from litellm.integrations.opentelemetry_utils.base_otel_llm_obs_attributes import safe_set_attribute - safe_set_attribute(span, "openinference.project.name", config.project_name) - + + # Dynamic project name: check metadata first, then fall back to env var config + dynamic_project_name = ArizePhoenixLogger._get_dynamic_project_name(kwargs) + if dynamic_project_name: + safe_set_attribute(span, "openinference.project.name", dynamic_project_name) + else: + # Fall back to static config from env var + config = ArizePhoenixLogger.get_arize_phoenix_config() + if config.project_name: + safe_set_attribute(span, "openinference.project.name", config.project_name) + return + @staticmethod + def _get_dynamic_project_name(kwargs) -> Optional[str]: + """ + Retrieve dynamic Phoenix project name from request metadata. + + Users can set `metadata.phoenix_project_name` in their request to route + traces to different Phoenix projects dynamically. + """ + standard_logging_payload = kwargs.get("standard_logging_object") + if isinstance(standard_logging_payload, dict): + metadata = standard_logging_payload.get("metadata") + if isinstance(metadata, dict): + project_name = metadata.get("phoenix_project_name") + if project_name: + return str(project_name) + + # Also check litellm_params.metadata for SDK usage + litellm_params = kwargs.get("litellm_params") + if isinstance(litellm_params, dict): + metadata = litellm_params.get("metadata") or {} + else: + metadata = {} + if isinstance(metadata, dict): + project_name = metadata.get("phoenix_project_name") + if project_name: + return str(project_name) + + return None + + def _handle_success(self, kwargs, response_obj, start_time, end_time): + """ + Override to prevent creating duplicate litellm_request spans when a proxy parent span exists. + + ArizePhoenixLogger should reuse the proxy parent span instead of creating a new litellm_request span, + to maintain a shallow span hierarchy as expected by Arize Phoenix. + """ + from opentelemetry.trace import Status, StatusCode + from litellm.secret_managers.main import get_secret_bool + from litellm.integrations.opentelemetry import LITELLM_PROXY_REQUEST_SPAN_NAME + + verbose_logger.debug( + "ArizePhoenixLogger: Logging kwargs: %s, OTEL config settings=%s", + kwargs, + self.config, + ) + ctx, parent_span = self._get_span_context(kwargs) + + # ArizePhoenixLogger NEVER creates a litellm_request span when a proxy parent span exists + # This is different from the base OpenTelemetry behavior which respects USE_OTEL_LITELLM_REQUEST_SPAN + should_create_primary_span = parent_span is None or ( + parent_span.name != LITELLM_PROXY_REQUEST_SPAN_NAME + and get_secret_bool("USE_OTEL_LITELLM_REQUEST_SPAN") + ) + + if should_create_primary_span: + # Create a new litellm_request span + span = self._start_primary_span( + kwargs, response_obj, start_time, end_time, ctx + ) + # Raw-request sub-span (if enabled) - child of litellm_request span + self._maybe_log_raw_request( + kwargs, response_obj, start_time, end_time, span + ) + # Ensure proxy-request parent span is annotated with the actual operation kind + if ( + parent_span is not None + and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME + ): + self.set_attributes(parent_span, kwargs, response_obj) + else: + # Do not create primary span (keep hierarchy shallow when parent exists) + span = None + # Only set attributes if the span is still recording (not closed) + # Note: parent_span is guaranteed to be not None here + if parent_span.is_recording(): + parent_span.set_status(Status(StatusCode.OK)) + self.set_attributes(parent_span, kwargs, response_obj) + # Raw-request as direct child of parent_span + self._maybe_log_raw_request( + kwargs, response_obj, start_time, end_time, parent_span + ) + + # 3. Guardrail span + self._create_guardrail_span(kwargs=kwargs, context=ctx) + + # 4. Metrics & cost recording + self._record_metrics(kwargs, response_obj, start_time, end_time) + + # 5. Semantic logs. + if self.config.enable_events: + log_span = span if span is not None else parent_span + if log_span is not None: + self._emit_semantic_logs(kwargs, response_obj, log_span) + + # 6. Do NOT end parent span - it should be managed by its creator + # External spans (from Langfuse, user code, HTTP headers, global context) must not be closed by LiteLLM + # However, proxy-created spans should be closed here + if ( + parent_span is not None + and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME + ): + parent_span.end(end_time=self._to_ns(end_time)) + @staticmethod def get_arize_phoenix_config() -> ArizePhoenixConfig: """ diff --git a/litellm/integrations/cloudzero/transform.py b/litellm/integrations/cloudzero/transform.py index e06b944a419..b40a71da1c6 100644 --- a/litellm/integrations/cloudzero/transform.py +++ b/litellm/integrations/cloudzero/transform.py @@ -103,10 +103,15 @@ class CBFTransformer: # Use team_alias if available, otherwise team_id, otherwise fallback to 'unknown' entity_id = str(team_alias) if team_alias else (str(team_id) if team_id else 'unknown') + # Get alias fields if they exist + api_key_alias = row.get('api_key_alias') + organization_alias = row.get('organization_alias') + project_alias = row.get('project_alias') + user_alias = row.get('user_alias') + dimensions = { 'entity_type': CZEntityType.TEAM.value, 'entity_id': entity_id, - 'team_id': str(team_id) if team_id else 'unknown', 'team_alias': str(team_alias) if team_alias else 'unknown', 'model': model, 'model_group': str(row.get('model_group', '')), @@ -119,28 +124,37 @@ class CBFTransformer: 'failed_requests': str(row.get('failed_requests', 0)), 'cache_creation_tokens': str(row.get('cache_creation_input_tokens', 0)), 'cache_read_tokens': str(row.get('cache_read_input_tokens', 0)), + 'organization_alias': str(organization_alias) if organization_alias else '', + 'project_alias': str(project_alias) if project_alias else '', + 'user_alias': str(user_alias) if user_alias else '', } # Extract CZRN components to populate corresponding CBF columns czrn_components = self.czrn_generator.extract_components(resource_id) service_type, provider, region, owner_account_id, resource_type, cloud_local_id = czrn_components + # Build resource/account as concat of api_key_alias and api_key_prefix + resource_account = f"{api_key_alias}|{api_key_hash}" if api_key_alias else api_key_hash + # CloudZero CBF format with proper column names cbf_record = { # Required CBF fields 'time/usage_start': usage_date.isoformat() if usage_date else None, # Required: ISO-formatted UTC datetime 'cost/cost': float(row.get('spend', 0.0)), # Required: billed cost - 'resource/id': resource_id, # Required when resource tags are present + 'resource/id': resource_id, # CZRN (CloudZero Resource Name) # Usage metrics for token consumption 'usage/amount': total_tokens, # Numeric value of tokens consumed 'usage/units': 'tokens', # Description of token units - # CBF fields that correspond to CZRN components - 'resource/service': service_type, # Maps to CZRN service-type (litellm) - 'resource/account': owner_account_id, # Maps to CZRN owner-account-id (entity_id) + # CBF fields - updated per LIT-1907 + 'resource/service': str(row.get('model_group', '')), # Send model_group + 'resource/account': resource_account, # Send api_key_alias|api_key_prefix 'resource/region': region, # Maps to CZRN region (cross-region) - 'resource/usage_family': resource_type, # Maps to CZRN resource-type (llm-usage) + 'resource/usage_family': str(row.get('custom_llm_provider', '')), # Send provider + + # Action field + 'action/operation': str(team_id) if team_id else '', # Send team_id # Line item details 'lineitem/type': 'Usage', # Standard usage line item @@ -155,13 +169,11 @@ class CBFTransformer: if value and value != 'N/A' and value != 'unknown': # Only add meaningful tags cbf_record[f'resource/tag:{key}'] = str(value) - # Add token breakdown as resource tags for analysis + # Add token breakdown as resource tags for analysis (excluding total_tokens per LIT-1907) if prompt_tokens > 0: cbf_record['resource/tag:prompt_tokens'] = str(prompt_tokens) if completion_tokens > 0: cbf_record['resource/tag:completion_tokens'] = str(completion_tokens) - if total_tokens > 0: - cbf_record['resource/tag:total_tokens'] = str(total_tokens) return CBFRecord(cbf_record) diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index a5bb530fc56..fb395366004 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -268,6 +268,7 @@ class CustomGuardrail(CustomLogger): """ Returns the guardrail(s) to be run from the metadata or root """ + if "guardrails" in data: return data["guardrails"] metadata = data.get("litellm_metadata") or data.get("metadata", {}) @@ -475,11 +476,18 @@ class CustomGuardrail(CustomLogger): guardrail_config: DynamicGuardrailParams = DynamicGuardrailParams( **guardrail[self.guardrail_name] ) + extra_body = guardrail_config.get("extra_body", {}) if self._validate_premium_user() is not True: + if isinstance(extra_body, dict) and extra_body: + verbose_logger.warning( + "Guardrail %s: ignoring dynamic extra_body keys %s because premium_user is False", + self.guardrail_name, + list(extra_body.keys()), + ) return {} # Return the extra_body if it exists, otherwise empty dict - return guardrail_config.get("extra_body", {}) + return extra_body return {} @@ -608,6 +616,7 @@ class CustomGuardrail(CustomLogger): end_time: Optional[float] = None, duration: Optional[float] = None, event_type: Optional[GuardrailEventHooks] = None, + original_inputs: Optional[Dict] = None, ): """ Add StandardLoggingGuardrailInformation to the request data @@ -615,7 +624,20 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ # Convert None to empty dict to satisfy type requirements - guardrail_response = {} if response is None else response + guardrail_response: Union[Dict[str, Any], str] = ( + {} if response is None else response + ) + + # For apply_guardrail functions in custom_code_guardrail scenario, + # simplify the logged response to "allow", "deny", or "mask" + if original_inputs is not None and isinstance(response, dict): + # Check if inputs were modified by comparing them + if self._inputs_were_modified(original_inputs, response): + guardrail_response = "mask" + else: + guardrail_response = "allow" + + verbose_logger.debug(f"Guardrail response: {response}") self.add_standard_logging_guardrail_information_to_request_data( guardrail_json_response=guardrail_response, @@ -642,8 +664,14 @@ class CustomGuardrail(CustomLogger): This gets logged on downsteam Langfuse, DataDog, etc. """ + # For custom_code_guardrail scenario, log as "deny" instead of full exception + # Check if this is from custom_code_guardrail by checking the class name + guardrail_response: Union[Exception, str] = e + if "CustomCodeGuardrail" in self.__class__.__name__: + guardrail_response = "deny" + self.add_standard_logging_guardrail_information_to_request_data( - guardrail_json_response=e, + guardrail_json_response=guardrail_response, request_data=request_data, guardrail_status="guardrail_failed_to_respond", duration=duration, @@ -653,6 +681,25 @@ class CustomGuardrail(CustomLogger): ) raise e + def _inputs_were_modified(self, original_inputs: Dict, response: Dict) -> bool: + """ + Compare original inputs with response to determine if content was modified. + + Returns True if the inputs were modified (mask scenario), False otherwise (allow scenario). + """ + # Get all keys from both dictionaries + all_keys = set(original_inputs.keys()) | set(response.keys()) + + # Compare each key's value + for key in all_keys: + original_value = original_inputs.get(key) + response_value = response.get(key) + if original_value != response_value: + return True + + # No modifications detected + return False + def mask_content_in_string( self, content_string: str, @@ -760,6 +807,12 @@ def log_guardrail_information(func): self: CustomGuardrail = args[0] request_data: dict = kwargs.get("data") or kwargs.get("request_data") or {} event_type = _infer_event_type_from_function_name(func.__name__) + + # Store original inputs for comparison (for apply_guardrail functions) + original_inputs = None + if func.__name__ == "apply_guardrail" and "inputs" in kwargs: + original_inputs = kwargs.get("inputs") + try: response = await func(*args, **kwargs) return self._process_response( @@ -769,6 +822,7 @@ def log_guardrail_information(func): end_time=datetime.now().timestamp(), duration=(datetime.now() - start_time).total_seconds(), event_type=event_type, + original_inputs=original_inputs, ) except Exception as e: return self._process_error( @@ -786,6 +840,12 @@ def log_guardrail_information(func): self: CustomGuardrail = args[0] request_data: dict = kwargs.get("data") or kwargs.get("request_data") or {} event_type = _infer_event_type_from_function_name(func.__name__) + + # Store original inputs for comparison (for apply_guardrail functions) + original_inputs = None + if func.__name__ == "apply_guardrail" and "inputs" in kwargs: + original_inputs = kwargs.get("inputs") + try: response = func(*args, **kwargs) return self._process_response( @@ -793,6 +853,7 @@ def log_guardrail_information(func): request_data=request_data, duration=(datetime.now() - start_time).total_seconds(), event_type=event_type, + original_inputs=original_inputs, ) except Exception as e: return self._process_error( diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 07d237c4758..c244363e389 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -664,6 +664,37 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac return final_response """ pass + + async def async_should_run_chat_completion_agentic_loop( + self, + response: Any, + model: str, + messages: List[Dict], + tools: Optional[List[Dict]], + stream: bool, + custom_llm_provider: str, + kwargs: Dict, + ) -> Tuple[bool, Dict]: + """ + Hook to determine if chat completion agentic loop should be executed. + """ + return False, {} + + async def async_run_chat_completion_agentic_loop( + self, + tools: Dict, + model: str, + messages: List[Dict], + response: Any, + optional_params: Dict, + logging_obj: "LiteLLMLoggingObj", + stream: bool, + kwargs: Dict, + ) -> Any: + """ + Hook to execute chat completion agentic loop based on context from should_run hook. + """ + pass # Useful helpers for custom logger classes @@ -743,15 +774,17 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac self, model_call_details: Dict ) -> Dict: """ - Only redacts messages and responses when self.turn_off_message_logging is True + Redacts or excludes fields from StandardLoggingPayload before callbacks receive it. + This method handles two features: + 1. turn_off_message_logging: When True, redacts messages and responses + 2. standard_logging_payload_excluded_fields: Removes specified fields entirely - By default, self.turn_off_message_logging is False and this does nothing. - - Return a redacted deepcopy of the provided logging payload. + Return a modified copy of the provided logging payload. This is useful for logging payloads that contain sensitive information. """ + import litellm from copy import copy from litellm import Choices, Message, ModelResponse @@ -759,14 +792,17 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac turn_off_message_logging: bool = getattr( self, "turn_off_message_logging", False ) + excluded_fields: Optional[List[str]] = getattr( + litellm, "standard_logging_payload_excluded_fields", None + ) - if turn_off_message_logging is False: + # Early return if no processing needed + if turn_off_message_logging is False and not excluded_fields: return model_call_details # Only make a shallow copy of the top-level dict to avoid deepcopy issues # with complex objects like AuthenticationError that may be present model_call_details_copy = copy(model_call_details) - redacted_str = "redacted-by-litellm" standard_logging_object = model_call_details.get("standard_logging_object") if standard_logging_object is None: return model_call_details_copy @@ -774,39 +810,58 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac # Make a copy of just the standard_logging_object to avoid modifying the original standard_logging_object_copy = copy(standard_logging_object) - if standard_logging_object_copy.get("messages") is not None: - standard_logging_object_copy["messages"] = [ - Message(content=redacted_str).model_dump() - ] + # Handle excluded fields - remove them entirely from the payload + if excluded_fields: + for field in excluded_fields: + if field in standard_logging_object_copy: + del standard_logging_object_copy[field] - if standard_logging_object_copy.get("response") is not None: - response = standard_logging_object_copy["response"] - # Check if this is a ResponsesAPIResponse (has "output" field) - if isinstance(response, dict) and "output" in response: - # Make a copy to avoid modifying the original - from copy import deepcopy + # Handle turn_off_message_logging - redact messages and responses (if not already excluded) + if turn_off_message_logging: + redacted_str = "redacted-by-litellm" - response_copy = deepcopy(response) - # Redact content in output array - if isinstance(response_copy.get("output"), list): - for output_item in response_copy["output"]: - if isinstance(output_item, dict) and "content" in output_item: - if isinstance(output_item["content"], list): - # Redact text in content items - for content_item in output_item["content"]: - if ( - isinstance(content_item, dict) - and "text" in content_item - ): - content_item["text"] = redacted_str - standard_logging_object_copy["response"] = response_copy - else: - # Standard ModelResponse format - model_response = ModelResponse( - choices=[Choices(message=Message(content=redacted_str))] - ) - model_response_dict = model_response.model_dump() - standard_logging_object_copy["response"] = model_response_dict + if ( + "messages" not in (excluded_fields or []) + and standard_logging_object_copy.get("messages") is not None + ): + standard_logging_object_copy["messages"] = [ + Message(content=redacted_str).model_dump() + ] + + if ( + "response" not in (excluded_fields or []) + and standard_logging_object_copy.get("response") is not None + ): + response = standard_logging_object_copy["response"] + # Check if this is a ResponsesAPIResponse (has "output" field) + if isinstance(response, dict) and "output" in response: + # Make a copy to avoid modifying the original + from copy import deepcopy + + response_copy = deepcopy(response) + # Redact content in output array + if isinstance(response_copy.get("output"), list): + for output_item in response_copy["output"]: + if ( + isinstance(output_item, dict) + and "content" in output_item + ): + if isinstance(output_item["content"], list): + # Redact text in content items + for content_item in output_item["content"]: + if ( + isinstance(content_item, dict) + and "text" in content_item + ): + content_item["text"] = redacted_str + standard_logging_object_copy["response"] = response_copy + else: + # Standard ModelResponse format + model_response = ModelResponse( + choices=[Choices(message=Message(content=redacted_str))] + ) + model_response_dict = model_response.model_dump() + standard_logging_object_copy["response"] = model_response_dict model_call_details_copy["standard_logging_object"] = ( standard_logging_object_copy diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 127b0e53fa8..64e0b26a8e7 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -45,7 +45,14 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.integrations.base_health_check import IntegrationHealthCheckStatus -from litellm.types.integrations.datadog import * +from litellm.types.integrations.datadog import ( + DD_ERRORS, + DD_MAX_BATCH_SIZE, + DataDogStatus, + DatadogInitParams, + DatadogPayload, + DatadogProxyFailureHookJsonMessage, +) from litellm.types.services import ServiceLoggerPayload, ServiceTypes from litellm.types.utils import StandardLoggingPayload @@ -85,12 +92,14 @@ class DataDogLogger( """ try: verbose_logger.debug("Datadog: in init datadog logger") - + self.is_mock_mode = should_use_datadog_mock() - + if self.is_mock_mode: create_mock_datadog_client() - verbose_logger.debug("[DATADOG MOCK] Datadog logger initialized in mock mode") + verbose_logger.debug( + "[DATADOG MOCK] Datadog logger initialized in mock mode" + ) ######################################################### # Handle datadog_params set as litellm.datadog_params @@ -209,6 +218,96 @@ class DataDogLogger( ) pass + async def async_post_call_failure_hook( + self, + request_data: dict, + original_exception: Exception, + user_api_key_dict: Any, + traceback_str: Optional[str] = None, + ) -> Optional[Any]: + """ + Log proxy-level failures (e.g. 401 auth, DB connection errors) to Datadog. + + Ensures failures that occur before or outside the LLM completion flow + (e.g. ConnectError during auth when DB is down) are visible in Datadog + alongside Prometheus. + """ + try: + from litellm.litellm_core_utils.litellm_logging import ( + StandardLoggingPayloadSetup, + ) + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + error_information = StandardLoggingPayloadSetup.get_error_information( + original_exception=original_exception, + traceback_str=traceback_str, + ) + _code = error_information.get("error_code") or "" + status_code: Optional[int] = None + if _code and str(_code).strip().isdigit(): + status_code = int(_code) + + # Use project-standard sanitized user context when running in proxy + user_context: Dict[str, Any] = {} + try: + from litellm.proxy.litellm_pre_call_utils import ( + LiteLLMProxyRequestSetup, + ) + + _meta = ( + LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key( + user_api_key_dict=user_api_key_dict + ) + ) + user_context = dict(_meta) if isinstance(_meta, dict) else _meta + except Exception: + # Fallback if proxy not available (e.g. SDK-only): minimal safe fields + if hasattr(user_api_key_dict, "request_route"): + user_context["request_route"] = getattr( + user_api_key_dict, "request_route", None + ) + if hasattr(user_api_key_dict, "team_id"): + user_context["team_id"] = getattr( + user_api_key_dict, "team_id", None + ) + if hasattr(user_api_key_dict, "user_id"): + user_context["user_id"] = getattr( + user_api_key_dict, "user_id", None + ) + if hasattr(user_api_key_dict, "end_user_id"): + user_context["end_user_id"] = getattr( + user_api_key_dict, "end_user_id", None + ) + + message_payload: DatadogProxyFailureHookJsonMessage = { + "exception": error_information.get("error_message") + or str(original_exception), + "error_class": error_information.get("error_class") + or original_exception.__class__.__name__, + "status_code": status_code, + "traceback": error_information.get("traceback") or "", + "user_api_key_dict": user_context, + } + + dd_payload = DatadogPayload( + ddsource=get_datadog_source(), + ddtags=get_datadog_tags(), + hostname=get_datadog_hostname(), + message=safe_dumps(message_payload), + service=get_datadog_service(), + status=DataDogStatus.ERROR, + ) + self._add_trace_context_to_payload(dd_payload=dd_payload) + self.log_queue.append(dd_payload) + + if len(self.log_queue) >= self.batch_size: + await self.async_send_batch() + except Exception as e: + verbose_logger.exception( + f"Datadog: async_post_call_failure_hook - {str(e)}\n{traceback.format_exc()}" + ) + return None + async def async_send_batch(self): """ Sends the in memory logs queue to datadog api @@ -230,9 +329,11 @@ class DataDogLogger( len(self.log_queue), self.intake_url, ) - + if self.is_mock_mode: - verbose_logger.debug("[DATADOG MOCK] Mock mode enabled - API calls will be intercepted") + verbose_logger.debug( + "[DATADOG MOCK] Mock mode enabled - API calls will be intercepted" + ) response = await self.async_send_compressed_data(self.log_queue) if response.status_code == 413: diff --git a/litellm/integrations/email_templates/templates.py b/litellm/integrations/email_templates/templates.py index 5de23db0f24..091351df2bb 100644 --- a/litellm/integrations/email_templates/templates.py +++ b/litellm/integrations/email_templates/templates.py @@ -85,6 +85,30 @@ SOFT_BUDGET_ALERT_EMAIL_TEMPLATE = """ The LiteLLM team
""" +TEAM_SOFT_BUDGET_ALERT_EMAIL_TEMPLATE = """ + LiteLLM Logo + +

Hi {team_alias} team member,
+ + Your LiteLLM team has crossed its soft budget limit of {soft_budget}.

+ + Current Spend: {spend}
+ Soft Budget: {soft_budget}
+ {max_budget_info} + +

+ ⚠️ Note: Your API requests will continue to work, but you should monitor your usage closely. + If you reach your maximum budget, requests will be rejected. +

+ + You can view your usage and manage your budget in the LiteLLM Dashboard.

+ + If you have any questions, please send an email to {email_support_contact}

+ + Best,
+ The LiteLLM team
+""" + MAX_BUDGET_ALERT_EMAIL_TEMPLATE = """ LiteLLM Logo diff --git a/litellm/integrations/gcs_bucket/gcs_bucket.py b/litellm/integrations/gcs_bucket/gcs_bucket.py index 3cb62905531..0f1ba4a4093 100644 --- a/litellm/integrations/gcs_bucket/gcs_bucket.py +++ b/litellm/integrations/gcs_bucket/gcs_bucket.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple from urllib.parse import quote from litellm._logging import verbose_logger +from litellm.constants import LITELLM_ASYNCIO_QUEUE_MAXSIZE from litellm.integrations.additional_logging_utils import AdditionalLoggingUtils from litellm.integrations.gcs_bucket.gcs_bucket_base import GCSBucketBase from litellm.proxy._types import CommonProxyErrors @@ -41,7 +42,9 @@ class GCSBucketLogger(GCSBucketBase, AdditionalLoggingUtils): batch_size=self.batch_size, flush_interval=self.flush_interval, ) - self.log_queue: asyncio.Queue[GCSLogQueueItem] = asyncio.Queue() # type: ignore[assignment] + self.log_queue: asyncio.Queue[GCSLogQueueItem] = asyncio.Queue( # type: ignore[assignment] + maxsize=LITELLM_ASYNCIO_QUEUE_MAXSIZE + ) asyncio.create_task(self.periodic_flush()) AdditionalLoggingUtils.__init__(self) @@ -69,6 +72,9 @@ class GCSBucketLogger(GCSBucketBase, AdditionalLoggingUtils): ) if logging_payload is None: raise ValueError("standard_logging_object not found in kwargs") + # When queue is at maxsize, flush immediately to make room (no blocking, no data dropped) + if self.log_queue.full(): + await self.flush_queue() await self.log_queue.put( GCSLogQueueItem( payload=logging_payload, kwargs=kwargs, response_obj=response_obj @@ -91,9 +97,9 @@ class GCSBucketLogger(GCSBucketBase, AdditionalLoggingUtils): ) if logging_payload is None: raise ValueError("standard_logging_object not found in kwargs") - # Add to logging queue - this will be flushed periodically - # Use asyncio.Queue.put() for thread-safe concurrent access - # If queue is full, this will block until space is available (backpressure) + # When queue is at maxsize, flush immediately to make room (no blocking, no data dropped) + if self.log_queue.full(): + await self.flush_queue() await self.log_queue.put( GCSLogQueueItem( payload=logging_payload, kwargs=kwargs, response_obj=response_obj diff --git a/litellm/integrations/langfuse/langfuse_otel.py b/litellm/integrations/langfuse/langfuse_otel.py index 08493a0e8ec..8955d3619f7 100644 --- a/litellm/integrations/langfuse/langfuse_otel.py +++ b/litellm/integrations/langfuse/langfuse_otel.py @@ -8,9 +8,8 @@ from litellm.integrations.arize import _utils from litellm.integrations.langfuse.langfuse_otel_attributes import ( LangfuseLLMObsOTELAttributes, ) -from litellm.integrations.opentelemetry import OpenTelemetry +from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig from litellm.types.integrations.langfuse_otel import ( - LangfuseOtelConfig, LangfuseSpanAttributes, ) from litellm.types.utils import StandardCallbackDynamicParams @@ -18,17 +17,8 @@ from litellm.types.utils import StandardCallbackDynamicParams if TYPE_CHECKING: from opentelemetry.trace import Span as _Span - from litellm.integrations.opentelemetry import ( - OpenTelemetryConfig as _OpenTelemetryConfig, - ) - from litellm.types.integrations.arize import Protocol as _Protocol - - Protocol = _Protocol - OpenTelemetryConfig = _OpenTelemetryConfig Span = Union[_Span, Any] else: - Protocol = Any - OpenTelemetryConfig = Any Span = Any @@ -37,8 +27,12 @@ LANGFUSE_CLOUD_US_ENDPOINT = "https://us.cloud.langfuse.com/api/public/otel" class LangfuseOtelLogger(OpenTelemetry): - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) + def __init__(self, config=None, *args, **kwargs): + # Prevent LangfuseOtelLogger from modifying global environment variables by constructing config manually + # and passing it to the parent OpenTelemetry class + if config is None: + config = self._create_open_telemetry_config_from_langfuse_env() + super().__init__(config=config, *args, **kwargs) @staticmethod def set_langfuse_otel_attributes(span: Span, kwargs, response_obj): @@ -114,6 +108,10 @@ class LangfuseOtelLogger(OpenTelemetry): for key, enum_attr in mapping.items(): if key in metadata and metadata[key] is not None: value = metadata[key] + if key == "trace_id" and isinstance(value, str): + # trace_id must be 32 hex char no dashes for langfuse : Litellm sends uuid with dashes (might be breaking at some point) + value = value.replace("-", "") + if isinstance(value, (list, dict)): try: value = json.dumps(value) @@ -265,8 +263,47 @@ class LangfuseOtelLogger(OpenTelemetry): """ return os.environ.get("LANGFUSE_OTEL_HOST") or os.environ.get("LANGFUSE_HOST") + def _create_open_telemetry_config_from_langfuse_env(self) -> OpenTelemetryConfig: + """ + Creates OpenTelemetryConfig from Langfuse environment variables. + Does NOT modify global environment variables. + """ + from litellm.integrations.opentelemetry import OpenTelemetryConfig + + public_key = os.environ.get("LANGFUSE_PUBLIC_KEY", None) + secret_key = os.environ.get("LANGFUSE_SECRET_KEY", None) + + if not public_key or not secret_key: + # If no keys, return default from env (likely logging to console or something else) + return OpenTelemetryConfig.from_env() + + # Determine endpoint - default to US cloud + langfuse_host = LangfuseOtelLogger._get_langfuse_otel_host() + + if langfuse_host: + # If LANGFUSE_HOST is provided, construct OTEL endpoint from it + if not langfuse_host.startswith("http"): + langfuse_host = "https://" + langfuse_host + endpoint = f"{langfuse_host.rstrip('/')}/api/public/otel" + verbose_logger.debug(f"Using Langfuse OTEL endpoint from host: {endpoint}") + else: + # Default to US cloud endpoint + endpoint = LANGFUSE_CLOUD_US_ENDPOINT + verbose_logger.debug(f"Using Langfuse US cloud endpoint: {endpoint}") + + auth_header = LangfuseOtelLogger._get_langfuse_authorization_header( + public_key=public_key, secret_key=secret_key + ) + otlp_auth_headers = f"Authorization={auth_header}" + + return OpenTelemetryConfig( + exporter="otlp_http", + endpoint=endpoint, + headers=otlp_auth_headers, + ) + @staticmethod - def get_langfuse_otel_config() -> LangfuseOtelConfig: + def get_langfuse_otel_config() -> "OpenTelemetryConfig": """ Retrieves the Langfuse OpenTelemetry configuration based on environment variables. @@ -276,7 +313,7 @@ class LangfuseOtelLogger(OpenTelemetry): LANGFUSE_HOST: Optional. Custom Langfuse host URL. Defaults to US cloud. Returns: - LangfuseOtelConfig: A Pydantic model containing Langfuse OTEL configuration. + OpenTelemetryConfig: A Pydantic model containing Langfuse OTEL configuration. Raises: ValueError: If required keys are missing. @@ -308,12 +345,14 @@ class LangfuseOtelLogger(OpenTelemetry): ) otlp_auth_headers = f"Authorization={auth_header}" - # Set standard OTEL environment variables - os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint - os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = otlp_auth_headers + # Prevent modification of global env vars which causes leakage + # os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint + # os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = otlp_auth_headers - return LangfuseOtelConfig( - otlp_auth_headers=otlp_auth_headers, protocol="otlp_http" + return OpenTelemetryConfig( + exporter="otlp_http", + endpoint=endpoint, + headers=otlp_auth_headers, ) @staticmethod diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 18898be7dce..b847180174a 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -5,6 +5,10 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast import litellm from litellm._logging import verbose_logger +from litellm.integrations._types.open_inference import ( + OpenInferenceSpanKindValues, + SpanAttributes, +) from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.secret_managers.main import get_secret_bool @@ -17,10 +21,6 @@ from litellm.types.utils import ( StandardCallbackDynamicParams, StandardLoggingPayload, ) -from litellm.integrations._types.open_inference import ( - OpenInferenceSpanKindValues, - SpanAttributes, -) # OpenTelemetry imports moved to individual functions to avoid import errors when not installed @@ -40,7 +40,9 @@ if TYPE_CHECKING: Context = Union[_Context, Any] SpanExporter = Union[_SpanExporter, Any] UserAPIKeyAuth = Union[_UserAPIKeyAuth, Any] - ManagementEndpointLoggingPayload = Union[_ManagementEndpointLoggingPayload, Any] + ManagementEndpointLoggingPayload = Union[ + _ManagementEndpointLoggingPayload, Any + ] else: Span = Any Tracer = Any @@ -70,6 +72,13 @@ class OpenTelemetryConfig: model_id: Optional[str] = None def __post_init__(self) -> None: + # If endpoint is specified but exporter is still the default "console", + # automatically infer "otlp_http" to send traces to the endpoint. + # This fixes an issue where UI-configured OTEL settings would default + # to console output instead of sending traces to the configured endpoint. + if self.endpoint and isinstance(self.exporter, str) and self.exporter == "console": + self.exporter = "otlp_http" + if not self.service_name: self.service_name = os.getenv("OTEL_SERVICE_NAME", "litellm") if not self.deployment_environment: @@ -95,12 +104,16 @@ class OpenTelemetryConfig: exporter = os.getenv( "OTEL_EXPORTER_OTLP_PROTOCOL", os.getenv("OTEL_EXPORTER", "console") ) - endpoint = os.getenv("OTEL_EXPORTER_OTLP_ENDPOINT", os.getenv("OTEL_ENDPOINT")) + endpoint = os.getenv( + "OTEL_EXPORTER_OTLP_ENDPOINT", os.getenv("OTEL_ENDPOINT") + ) headers = os.getenv( "OTEL_EXPORTER_OTLP_HEADERS", os.getenv("OTEL_HEADERS") ) # example: OTEL_HEADERS=x-honeycomb-team=B85YgLm96***" enable_metrics: bool = ( - os.getenv("LITELLM_OTEL_INTEGRATION_ENABLE_METRICS", "false").lower() + os.getenv( + "LITELLM_OTEL_INTEGRATION_ENABLE_METRICS", "false" + ).lower() == "true" ) enable_events: bool = ( @@ -108,7 +121,9 @@ class OpenTelemetryConfig: == "true" ) service_name = os.getenv("OTEL_SERVICE_NAME", "litellm") - deployment_environment = os.getenv("OTEL_ENVIRONMENT_NAME", "production") + deployment_environment = os.getenv( + "OTEL_ENVIRONMENT_NAME", "production" + ) model_id = os.getenv("OTEL_MODEL_ID", service_name) if exporter == "in_memory": @@ -157,7 +172,9 @@ class OpenTelemetry(CustomLogger): logging.getLogger(__name__) # Enable OpenTelemetry logging - otel_exporter_logger = logging.getLogger("opentelemetry.sdk.trace.export") + otel_exporter_logger = logging.getLogger( + "opentelemetry.sdk.trace.export" + ) otel_exporter_logger.setLevel(logging.DEBUG) # init CustomLogger params @@ -253,7 +270,9 @@ class OpenTelemetry(CustomLogger): # Don't call set_provider to preserve existing context else: # Default proxy provider or unknown type, create our own - verbose_logger.debug("OpenTelemetry: Creating new %s", provider_name) + verbose_logger.debug( + "OpenTelemetry: Creating new %s", provider_name + ) provider = create_new_provider_fn() set_provider_fn(provider) except Exception as e: @@ -274,7 +293,9 @@ class OpenTelemetry(CustomLogger): from opentelemetry.trace import SpanKind def create_tracer_provider(): - provider = TracerProvider(resource=self._get_litellm_resource(self.config)) + provider = TracerProvider( + resource=self._get_litellm_resource(self.config) + ) provider.add_span_processor(self._get_span_processor()) return provider @@ -388,10 +409,14 @@ class OpenTelemetry(CustomLogger): def log_failure_event(self, kwargs, response_obj, start_time, end_time): self._handle_failure(kwargs, response_obj, start_time, end_time) - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + async def async_log_success_event( + self, kwargs, response_obj, start_time, end_time + ): self._handle_success(kwargs, response_obj, start_time, end_time) - async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time): + async def async_log_failure_event( + self, kwargs, response_obj, start_time, end_time + ): self._handle_failure(kwargs, response_obj, start_time, end_time) async def async_service_success_hook( @@ -588,7 +613,9 @@ class OpenTelemetry(CustomLogger): if dynamic_headers is not None: # Create spans using a temporary tracer with dynamic headers - tracer_to_use = self._get_tracer_with_dynamic_headers(dynamic_headers) + tracer_to_use = self._get_tracer_with_dynamic_headers( + dynamic_headers + ) verbose_logger.debug( "Using dynamic headers for this request: %s", dynamic_headers ) @@ -599,9 +626,9 @@ class OpenTelemetry(CustomLogger): def _get_dynamic_otel_headers_from_kwargs(self, kwargs) -> Optional[dict]: """Extract dynamic headers from kwargs if available.""" - standard_callback_dynamic_params: Optional[StandardCallbackDynamicParams] = ( - kwargs.get("standard_callback_dynamic_params") - ) + standard_callback_dynamic_params: Optional[ + StandardCallbackDynamicParams + ] = kwargs.get("standard_callback_dynamic_params") if not standard_callback_dynamic_params: return None @@ -619,10 +646,14 @@ class OpenTelemetry(CustomLogger): # Prevents thread exhaustion by reusing providers for the same credential sets (e.g. per-team keys) cache_key = str(sorted(dynamic_headers.items())) if cache_key in self._tracer_provider_cache: - return self._tracer_provider_cache[cache_key].get_tracer(LITELLM_TRACER_NAME) + return self._tracer_provider_cache[cache_key].get_tracer( + LITELLM_TRACER_NAME + ) # Create a temporary tracer provider with dynamic headers - temp_provider = TracerProvider(resource=self._get_litellm_resource(self.config)) + temp_provider = TracerProvider( + resource=self._get_litellm_resource(self.config) + ) temp_provider.add_span_processor( self._get_span_processor(dynamic_headers=dynamic_headers) ) @@ -674,7 +705,10 @@ class OpenTelemetry(CustomLogger): kwargs, response_obj, start_time, end_time, span ) # Ensure proxy-request parent span is annotated with the actual operation kind - if parent_span is not None and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME: + if ( + parent_span is not None + and parent_span.name == LITELLM_PROXY_REQUEST_SPAN_NAME + ): self.set_attributes(parent_span, kwargs, response_obj) else: # Do not create primary span (keep hierarchy shallow when parent exists) @@ -750,7 +784,9 @@ class OpenTelemetry(CustomLogger): metadata = litellm_params.get("metadata") or {} generation_name = metadata.get("generation_name") - raw_span_name = generation_name if generation_name else RAW_REQUEST_SPAN_NAME + raw_span_name = ( + generation_name if generation_name else RAW_REQUEST_SPAN_NAME + ) otel_tracer: Tracer = self.get_tracer_to_use_for_request(kwargs) raw_span = otel_tracer.start_span( @@ -775,7 +811,9 @@ class OpenTelemetry(CustomLogger): } std_log = kwargs.get("standard_logging_object") - md = getattr(std_log, "metadata", None) or (std_log or {}).get("metadata", {}) + md = getattr(std_log, "metadata", None) or (std_log or {}).get( + "metadata", {} + ) for key in [ "user_api_key_hash", "user_api_key_alias", @@ -797,9 +835,9 @@ class OpenTelemetry(CustomLogger): common_attrs[f"metadata.{key}"] = str(md[key]) # get hidden params - hidden_params = getattr(std_log, "hidden_params", None) or (std_log or {}).get( - "hidden_params", {} - ) + hidden_params = getattr(std_log, "hidden_params", None) or ( + std_log or {} + ).get("hidden_params", {}) if hidden_params: common_attrs["hidden_params"] = safe_dumps(hidden_params) @@ -833,7 +871,9 @@ class OpenTelemetry(CustomLogger): self._record_response_duration_metric(kwargs, end_time, common_attrs) @staticmethod - def _to_timestamp(val: Optional[Union[datetime, float, str]]) -> Optional[float]: + def _to_timestamp( + val: Optional[Union[datetime, float, str]], + ) -> Optional[float]: """Convert datetime/float/string to timestamp.""" if val is None: return None @@ -850,7 +890,9 @@ class OpenTelemetry(CustomLogger): except ValueError: return None - def _record_time_to_first_token_metric(self, kwargs: dict, common_attrs: dict): + def _record_time_to_first_token_metric( + self, kwargs: dict, common_attrs: dict + ): """Record Time to First Token (TTFT) metric for streaming requests.""" optional_params = kwargs.get("optional_params", {}) is_streaming = optional_params.get("stream", False) @@ -863,7 +905,10 @@ class OpenTelemetry(CustomLogger): api_call_start_time = kwargs.get("api_call_start_time", None) completion_start_time = kwargs.get("completion_start_time", None) - if api_call_start_time is not None and completion_start_time is not None: + if ( + api_call_start_time is not None + and completion_start_time is not None + ): # Convert to timestamps if needed (handles datetime, float, and string) api_call_start_ts = self._to_timestamp(api_call_start_time) completion_start_ts = self._to_timestamp(completion_start_time) @@ -871,7 +916,9 @@ class OpenTelemetry(CustomLogger): if api_call_start_ts is None or completion_start_ts is None: return # Skip recording if conversion failed - time_to_first_token_seconds = completion_start_ts - api_call_start_ts + time_to_first_token_seconds = ( + completion_start_ts - api_call_start_ts + ) self._time_to_first_token_histogram.record( time_to_first_token_seconds, attributes=common_attrs ) @@ -941,7 +988,9 @@ class OpenTelemetry(CustomLogger): generation_time_seconds = duration_s if generation_time_seconds > 0: - time_per_output_token_seconds = generation_time_seconds / completion_tokens + time_per_output_token_seconds = ( + generation_time_seconds / completion_tokens + ) self._time_per_output_token_histogram.record( time_per_output_token_seconds, attributes=common_attrs ) @@ -1002,24 +1051,26 @@ class OpenTelemetry(CustomLogger): # See: https://github.com/open-telemetry/opentelemetry-python/pull/4676 # TODO: Refactor to use the proper OTEL Logs API instead of directly creating SDK LogRecords - from opentelemetry._logs import SeverityNumber, get_logger, get_logger_provider - try: - from opentelemetry.sdk._logs import ( - LogRecord as SdkLogRecord, # type: ignore[attr-defined] # OTEL < 1.39.0 - ) - except ImportError: - from opentelemetry.sdk._logs._internal import ( - LogRecord as SdkLogRecord, # OTEL >= 1.39.0 - ) + from opentelemetry._logs import ( + SeverityNumber, + get_logger, + ) + + # MyPy evaluates both branches of try/except imports and can fail when + # newer OTEL stubs remove/relocate symbols. Gate the typing import so + # only the canonical location is type-checked. + if TYPE_CHECKING: + from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord + else: + try: + from opentelemetry.sdk._logs import ( + LogRecord as SdkLogRecord, # type: ignore[attr-defined] + ) + except ImportError: + from opentelemetry.sdk._logs._internal import LogRecord as SdkLogRecord otel_logger = get_logger(LITELLM_LOGGER_NAME) - # Get the resource from the logger provider - logger_provider = get_logger_provider() - resource = getattr( - logger_provider, "_resource", None - ) or self._get_litellm_resource(self.config) - parent_ctx = span.get_span_context() provider = (kwargs.get("litellm_params") or {}).get( "custom_llm_provider", "Unknown" @@ -1028,7 +1079,10 @@ class OpenTelemetry(CustomLogger): # per-message events for msg in kwargs.get("messages", []): role = msg.get("role", "user") - attrs = {"event_name": "gen_ai.content.prompt", "gen_ai.system": provider} + attrs = { + "event_name": "gen_ai.content.prompt", + "gen_ai.system": provider, + } if role == "tool" and msg.get("id"): attrs["id"] = msg["id"] if self.message_logging and msg.get("content"): @@ -1042,7 +1096,6 @@ class OpenTelemetry(CustomLogger): severity_number=SeverityNumber.INFO, severity_text="INFO", body=msg.copy(), - resource=resource, attributes=attrs, ) otel_logger.emit(log_record) @@ -1074,7 +1127,6 @@ class OpenTelemetry(CustomLogger): severity_number=SeverityNumber.INFO, severity_text="INFO", body=body, - resource=resource, attributes=attrs, ) otel_logger.emit(log_record) @@ -1144,7 +1196,9 @@ class OpenTelemetry(CustomLogger): value=guardrail_information.get("guardrail_mode"), ) - masked_entity_count = guardrail_information.get("masked_entity_count") + masked_entity_count = guardrail_information.get( + "masked_entity_count" + ) if masked_entity_count is not None: guardrail_span.set_attribute( "masked_entity_count", safe_dumps(masked_entity_count) @@ -1171,8 +1225,9 @@ class OpenTelemetry(CustomLogger): # Decide whether to create a primary span # Always create if no parent span exists (backward compatibility) # OR if USE_OTEL_LITELLM_REQUEST_SPAN is explicitly enabled - should_create_primary_span = parent_otel_span is None or get_secret_bool( - "USE_OTEL_LITELLM_REQUEST_SPAN" + should_create_primary_span = ( + parent_otel_span is None + or get_secret_bool("USE_OTEL_LITELLM_REQUEST_SPAN") ) if should_create_primary_span: @@ -1198,7 +1253,9 @@ class OpenTelemetry(CustomLogger): if parent_otel_span.is_recording(): parent_otel_span.set_status(Status(StatusCode.ERROR)) self.set_attributes(parent_otel_span, kwargs, response_obj) - self._record_exception_on_span(span=parent_otel_span, kwargs=kwargs) + self._record_exception_on_span( + span=parent_otel_span, kwargs=kwargs + ) # Create span for guardrail information self._create_guardrail_span(kwargs=kwargs, context=_parent_context) @@ -1221,7 +1278,9 @@ class OpenTelemetry(CustomLogger): 2. Sets structured error attributes from StandardLoggingPayloadErrorInformation """ try: - from litellm.integrations._types.open_inference import ErrorAttributes + from litellm.integrations._types.open_inference import ( + ErrorAttributes, + ) # Get the exception object if available exception = kwargs.get("exception") @@ -1231,15 +1290,17 @@ class OpenTelemetry(CustomLogger): span.record_exception(exception) # Get StandardLoggingPayload for structured error information - standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( - "standard_logging_object" + standard_logging_payload: Optional[StandardLoggingPayload] = ( + kwargs.get("standard_logging_object") ) if standard_logging_payload is None: return # Extract error_information from StandardLoggingPayload - error_information = standard_logging_payload.get("error_information") + error_information = standard_logging_payload.get( + "error_information" + ) if error_information is None: # Fallback to error_str if error_information is not available @@ -1329,7 +1390,9 @@ class OpenTelemetry(CustomLogger): ) pass - def cast_as_primitive_value_type(self, value) -> Union[str, bool, int, float]: + def cast_as_primitive_value_type( + self, value + ) -> Union[str, bool, int, float]: """ Casts the value to a primitive OTEL type if it is not already a primitive type. @@ -1399,8 +1462,8 @@ class OpenTelemetry(CustomLogger): optional_params = kwargs.get("optional_params", {}) litellm_params = kwargs.get("litellm_params", {}) or {} - standard_logging_payload: Optional[StandardLoggingPayload] = kwargs.get( - "standard_logging_object" + standard_logging_payload: Optional[StandardLoggingPayload] = ( + kwargs.get("standard_logging_object") ) if standard_logging_payload is None: raise ValueError("standard_logging_object not found in kwargs") @@ -1422,11 +1485,13 @@ class OpenTelemetry(CustomLogger): ) or (standard_logging_payload or {}).get("hidden_params", {}) if hidden_params: self.safe_set_attribute( - span=span, key="hidden_params", value=safe_dumps(hidden_params) + span=span, + key="hidden_params", + value=safe_dumps(hidden_params), ) # Cost breakdown tracking - cost_breakdown: Optional[CostBreakdown] = standard_logging_payload.get( - "cost_breakdown" + cost_breakdown: Optional[CostBreakdown] = ( + standard_logging_payload.get("cost_breakdown") ) if cost_breakdown: for key, value in cost_breakdown.items(): @@ -1502,7 +1567,9 @@ class OpenTelemetry(CustomLogger): # The unique identifier for the completion. if response_obj and response_obj.get("id"): self.safe_set_attribute( - span=span, key="gen_ai.response.id", value=response_obj.get("id") + span=span, + key="gen_ai.response.id", + value=response_obj.get("id"), ) # The model used to generate the response. @@ -1618,7 +1685,6 @@ class OpenTelemetry(CustomLogger): for idx, choice in enumerate(response_obj.get("choices")): if choice.get("finish_reason"): - message = choice.get("message") tool_calls = message.get("tool_calls") if tool_calls: @@ -1631,12 +1697,16 @@ class OpenTelemetry(CustomLogger): ) except Exception as e: - self.handle_callback_failure(callback_name=self.callback_name or "opentelemetry") + self.handle_callback_failure( + callback_name=self.callback_name or "opentelemetry" + ) verbose_logger.exception( "OpenTelemetry logging error in set_attributes %s", str(e) ) - def _cast_as_primitive_value_type(self, value) -> Union[str, bool, int, float]: + def _cast_as_primitive_value_type( + self, value + ) -> Union[str, bool, int, float]: """ Casts the value to a primitive OTEL type if it is not already a primitive type. @@ -1670,7 +1740,10 @@ class OpenTelemetry(CustomLogger): if isinstance(messages, str): # Handle system_instructions passed as a string return [ - {"role": "system", "parts": [{"type": "text", "content": messages}]} + { + "role": "system", + "parts": [{"type": "text", "content": messages}], + } ] transformed = [] @@ -1711,9 +1784,11 @@ class OpenTelemetry(CustomLogger): message = choice.get("message") or {} finish_reason = choice.get("finish_reason") - transformed_msg = self._transform_messages_to_otel_semantic_conventions( - [message] - )[0] + transformed_msg = ( + self._transform_messages_to_otel_semantic_conventions( + [message] + )[0] + ) if finish_reason: transformed_msg["finish_reason"] = finish_reason @@ -1722,9 +1797,12 @@ class OpenTelemetry(CustomLogger): def set_raw_request_attributes(self, span: Span, kwargs, response_obj): try: + self.set_attributes(span, kwargs, response_obj) kwargs.get("optional_params", {}) litellm_params = kwargs.get("litellm_params", {}) or {} - custom_llm_provider = litellm_params.get("custom_llm_provider", "Unknown") + custom_llm_provider = litellm_params.get( + "custom_llm_provider", "Unknown" + ) _raw_response = kwargs.get("original_response") _additional_args = kwargs.get("additional_args", {}) or {} @@ -1737,7 +1815,9 @@ class OpenTelemetry(CustomLogger): if complete_input_dict and isinstance(complete_input_dict, dict): for param, val in complete_input_dict.items(): self.safe_set_attribute( - span=span, key=f"llm.{custom_llm_provider}.{param}", value=val + span=span, + key=f"llm.{custom_llm_provider}.{param}", + value=val, ) ############################################# @@ -1769,7 +1849,8 @@ class OpenTelemetry(CustomLogger): ) except Exception as e: verbose_logger.exception( - "OpenTelemetry logging error in set_raw_request_attributes %s", str(e) + "OpenTelemetry logging error in set_raw_request_attributes %s", + str(e), ) def _to_ns(self, dt): @@ -1809,7 +1890,9 @@ class OpenTelemetry(CustomLogger): ) litellm_params = kwargs.get("litellm_params", {}) or {} - proxy_server_request = litellm_params.get("proxy_server_request", {}) or {} + proxy_server_request = ( + litellm_params.get("proxy_server_request", {}) or {} + ) headers = proxy_server_request.get("headers", {}) or {} traceparent = headers.get("traceparent", None) _metadata = litellm_params.get("metadata", {}) or {} @@ -1828,7 +1911,10 @@ class OpenTelemetry(CustomLogger): "OpenTelemetry: Using traceparent header for context propagation" ) carrier = {"traceparent": traceparent} - return TraceContextTextMapPropagator().extract(carrier=carrier), None + return ( + TraceContextTextMapPropagator().extract(carrier=carrier), + None, + ) # Priority 3: Active span from global context (auto-detection) try: @@ -1956,10 +2042,14 @@ class OpenTelemetry(CustomLogger): self.OTEL_HEADERS, ) - _split_otel_headers = OpenTelemetry._get_headers_dictionary(self.OTEL_HEADERS) + _split_otel_headers = OpenTelemetry._get_headers_dictionary( + self.OTEL_HEADERS + ) # Normalize endpoint for logs - ensure it points to /v1/logs instead of /v1/traces - normalized_endpoint = self._normalize_otel_endpoint(self.OTEL_ENDPOINT, "logs") + normalized_endpoint = self._normalize_otel_endpoint( + self.OTEL_ENDPOINT, "logs" + ) verbose_logger.debug( "OpenTelemetry: Log endpoint normalized from %s to %s", @@ -2047,14 +2137,18 @@ class OpenTelemetry(CustomLogger): self.OTEL_HEADERS, ) - _split_otel_headers = OpenTelemetry._get_headers_dictionary(self.OTEL_HEADERS) + _split_otel_headers = OpenTelemetry._get_headers_dictionary( + self.OTEL_HEADERS + ) normalized_endpoint = self._normalize_otel_endpoint( self.OTEL_ENDPOINT, "metrics" ) if self.OTEL_EXPORTER == "console": exporter = ConsoleMetricExporter() - return PeriodicExportingMetricReader(exporter, export_interval_millis=5000) + return PeriodicExportingMetricReader( + exporter, export_interval_millis=5000 + ) elif ( self.OTEL_EXPORTER == "otlp_http" @@ -2070,7 +2164,9 @@ class OpenTelemetry(CustomLogger): headers=_split_otel_headers, preferred_temporality={Histogram: AggregationTemporality.DELTA}, ) - return PeriodicExportingMetricReader(exporter, export_interval_millis=5000) + return PeriodicExportingMetricReader( + exporter, export_interval_millis=5000 + ) elif self.OTEL_EXPORTER == "otlp_grpc" or self.OTEL_EXPORTER == "grpc": try: @@ -2088,7 +2184,9 @@ class OpenTelemetry(CustomLogger): headers=_split_otel_headers, preferred_temporality={Histogram: AggregationTemporality.DELTA}, ) - return PeriodicExportingMetricReader(exporter, export_interval_millis=5000) + return PeriodicExportingMetricReader( + exporter, export_interval_millis=5000 + ) else: verbose_logger.warning( @@ -2096,7 +2194,9 @@ class OpenTelemetry(CustomLogger): self.OTEL_EXPORTER, ) exporter = ConsoleMetricExporter() - return PeriodicExportingMetricReader(exporter, export_interval_millis=5000) + return PeriodicExportingMetricReader( + exporter, export_interval_millis=5000 + ) def _normalize_otel_endpoint( self, endpoint: Optional[str], signal_type: str @@ -2167,7 +2267,9 @@ class OpenTelemetry(CustomLogger): return endpoint @staticmethod - def _get_headers_dictionary(headers: Optional[Union[str, dict]]) -> Dict[str, str]: + def _get_headers_dictionary( + headers: Optional[Union[str, dict]], + ) -> Dict[str, str]: """ Convert a string or dictionary of headers into a dictionary of headers. """ diff --git a/litellm/integrations/posthog.py b/litellm/integrations/posthog.py index dd7c3627b87..c4b6e843d60 100644 --- a/litellm/integrations/posthog.py +++ b/litellm/integrations/posthog.py @@ -17,6 +17,7 @@ from typing import Any, Dict, Optional, Tuple from litellm._logging import verbose_logger from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.integrations.posthog_mock_client import ( should_use_posthog_mock, create_mock_posthog_client, @@ -100,7 +101,7 @@ class PostHogLogger(CustomBatchLogger): response = self.sync_client.post( url=capture_url, - json=payload, + content=safe_dumps(payload), headers=headers, ) response.raise_for_status() @@ -356,7 +357,7 @@ class PostHogLogger(CustomBatchLogger): response = await self.async_client.post( url=capture_url, - json=payload, + content=safe_dumps(payload), headers=headers, ) response.raise_for_status() @@ -438,7 +439,7 @@ class PostHogLogger(CustomBatchLogger): response = self.sync_client.post( url=capture_url, - json=payload, + content=safe_dumps(payload), headers=headers, ) response.raise_for_status() diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 2c897cb0692..1675201f1f1 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -1,6 +1,7 @@ # used for /metrics endpoint on LiteLLM Proxy #### What this does #### # On success, log events to Prometheus +import asyncio import os import sys from datetime import datetime, timedelta @@ -28,7 +29,10 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.types.integrations.prometheus import * -from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name +from litellm.types.integrations.prometheus import ( + _sanitize_prometheus_label_name, + _sanitize_prometheus_label_value, +) from litellm.types.utils import StandardLoggingPayload if TYPE_CHECKING: @@ -1188,28 +1192,34 @@ class PrometheusLogger(CustomLogger): _user_spend = _metadata.get("user_api_key_user_spend", None) _user_max_budget = _metadata.get("user_api_key_user_max_budget", None) - await self._set_api_key_budget_metrics_after_api_request( - user_api_key=user_api_key, - user_api_key_alias=user_api_key_alias, - response_cost=response_cost, - key_max_budget=_api_key_max_budget, - key_spend=_api_key_spend, - ) - - await self._set_team_budget_metrics_after_api_request( - user_api_team=user_api_team, - user_api_team_alias=user_api_team_alias, - team_spend=_team_spend, - team_max_budget=_team_max_budget, - response_cost=response_cost, - ) - - await self._set_user_budget_metrics_after_api_request( - user_id=user_id, - user_spend=_user_spend, - user_max_budget=_user_max_budget, - response_cost=response_cost, + results = await asyncio.gather( + self._set_api_key_budget_metrics_after_api_request( + user_api_key=user_api_key, + user_api_key_alias=user_api_key_alias, + response_cost=response_cost, + key_max_budget=_api_key_max_budget, + key_spend=_api_key_spend, + ), + self._set_team_budget_metrics_after_api_request( + user_api_team=user_api_team, + user_api_team_alias=user_api_team_alias, + team_spend=_team_spend, + team_max_budget=_team_max_budget, + response_cost=response_cost, + ), + self._set_user_budget_metrics_after_api_request( + user_id=user_id, + user_spend=_user_spend, + user_max_budget=_user_max_budget, + response_cost=response_cost, + ), + return_exceptions=True, ) + for i, r in enumerate(results): + if isinstance(r, Exception): + verbose_logger.debug( + f"[Non-Blocking] Prometheus: Budget metric lookup {['key', 'team', 'user'][i]} failed: {r}" + ) def _increment_top_level_request_and_spend_metrics( self, @@ -1269,11 +1279,17 @@ class PrometheusLogger(CustomLogger): ) self.litellm_remaining_api_key_requests_for_model.labels( - user_api_key, user_api_key_alias, model_group, model_id + _sanitize_prometheus_label_value(user_api_key), + _sanitize_prometheus_label_value(user_api_key_alias), + _sanitize_prometheus_label_value(model_group), + _sanitize_prometheus_label_value(model_id), ).set(remaining_requests) self.litellm_remaining_api_key_tokens_for_model.labels( - user_api_key, user_api_key_alias, model_group, model_id + _sanitize_prometheus_label_value(user_api_key), + _sanitize_prometheus_label_value(user_api_key_alias), + _sanitize_prometheus_label_value(model_group), + _sanitize_prometheus_label_value(model_id), ).set(remaining_tokens) def _set_latency_metrics( @@ -1394,14 +1410,14 @@ class PrometheusLogger(CustomLogger): try: self.litellm_llm_api_failed_requests_metric.labels( - end_user_id, - user_api_key, - user_api_key_alias, - model, - user_api_team, - user_api_team_alias, - user_id, - standard_logging_payload.get("model_id", ""), + _sanitize_prometheus_label_value(end_user_id), + _sanitize_prometheus_label_value(user_api_key), + _sanitize_prometheus_label_value(user_api_key_alias), + _sanitize_prometheus_label_value(model), + _sanitize_prometheus_label_value(user_api_team), + _sanitize_prometheus_label_value(user_api_team_alias), + _sanitize_prometheus_label_value(user_id), + _sanitize_prometheus_label_value(standard_logging_payload.get("model_id", "")), ).inc() self.set_llm_deployment_failure_metrics(kwargs) except Exception as e: @@ -1683,6 +1699,108 @@ class PrometheusLogger(CustomLogger): ) pass + def _safe_get(self, obj: Any, key: str, default: Any = None) -> Any: + """Get value from dict or Pydantic model.""" + if obj is None: + return default + if isinstance(obj, dict): + return obj.get(key, default) + return getattr(obj, key, default) + + def _extract_deployment_failure_label_values( + self, request_kwargs: dict + ) -> Dict[str, Optional[str]]: + """ + Extract label values for deployment failure metrics from all available + sources in request_kwargs. Falls back to litellm_params metadata and + user_api_key_auth when standard_logging_payload has None values. + """ + standard_logging_payload = ( + request_kwargs.get("standard_logging_object", {}) or {} + ) + _litellm_params = request_kwargs.get("litellm_params", {}) or {} + _metadata_raw = self._safe_get(standard_logging_payload, "metadata") or {} + if isinstance(_metadata_raw, dict): + _metadata = _metadata_raw + else: + _metadata = { + "user_api_key_alias": getattr( + _metadata_raw, "user_api_key_alias", None + ), + "user_api_key_team_id": getattr( + _metadata_raw, "user_api_key_team_id", None + ), + "user_api_key_team_alias": getattr( + _metadata_raw, "user_api_key_team_alias", None + ), + "user_api_key_hash": getattr(_metadata_raw, "user_api_key_hash", None), + "requester_ip_address": getattr( + _metadata_raw, "requester_ip_address", None + ), + "user_agent": getattr(_metadata_raw, "user_agent", None), + } + _litellm_params_metadata = _litellm_params.get("metadata", {}) or {} + + # Extract user_api_key_auth if present (proxy injects this, skipped in merge) + user_api_key_auth = _litellm_params_metadata.get("user_api_key_auth") + + def _get_api_key_alias() -> Optional[str]: + val = _metadata.get("user_api_key_alias") + if val is not None: + return val + val = _litellm_params_metadata.get("user_api_key_alias") + if val is not None: + return val + if user_api_key_auth is not None: + return getattr(user_api_key_auth, "key_alias", None) + return None + + def _get_team_id() -> Optional[str]: + val = _metadata.get("user_api_key_team_id") + if val is not None: + return val + val = _litellm_params_metadata.get("user_api_key_team_id") + if val is not None: + return val + if user_api_key_auth is not None: + return getattr(user_api_key_auth, "team_id", None) + return None + + def _get_team_alias() -> Optional[str]: + val = _metadata.get("user_api_key_team_alias") + if val is not None: + return val + val = _litellm_params_metadata.get("user_api_key_team_alias") + if val is not None: + return val + if user_api_key_auth is not None: + return getattr(user_api_key_auth, "team_alias", None) + return None + + def _get_hashed_api_key() -> Optional[str]: + val = _metadata.get("user_api_key_hash") + if val is not None: + return val + val = _litellm_params_metadata.get("user_api_key_hash") + if val is not None: + return val + if user_api_key_auth is not None: + return getattr(user_api_key_auth, "api_key", None) or getattr( + user_api_key_auth, "api_key_hash", None + ) + return None + + return { + "api_key_alias": _get_api_key_alias(), + "team": _get_team_id(), + "team_alias": _get_team_alias(), + "hashed_api_key": _get_hashed_api_key(), + "client_ip": _metadata.get("requester_ip_address") + or _litellm_params_metadata.get("requester_ip_address"), + "user_agent": _metadata.get("user_agent") + or _litellm_params_metadata.get("user_agent"), + } + def set_llm_deployment_failure_metrics(self, request_kwargs: dict): """ Sets Failure metrics when an LLM API call fails @@ -1707,6 +1825,21 @@ class PrometheusLogger(CustomLogger): model_id = standard_logging_payload.get("model_id", None) exception = request_kwargs.get("exception", None) + # Fallback: model_id from litellm_metadata.model_info + if model_id is None: + _model_info = ( + (_litellm_params.get("litellm_metadata") or {}).get("model_info") + or (_litellm_params.get("metadata") or {}).get("model_info") + or {} + ) + model_id = _model_info.get("id") + + # Fallback: model_group from litellm_metadata + if model_group is None: + model_group = (_litellm_params.get("litellm_metadata") or {}).get( + "model_group" + ) or (_litellm_params.get("metadata") or {}).get("model_group") + llm_provider = _litellm_params.get("custom_llm_provider", None) if self._should_skip_metrics_for_invalid_key( @@ -1714,9 +1847,37 @@ class PrometheusLogger(CustomLogger): standard_logging_payload=standard_logging_payload, ): return - hashed_api_key = standard_logging_payload.get("metadata", {}).get( + + # Extract context labels from all available sources (fix for None labels) + fallback_values = self._extract_deployment_failure_label_values( + request_kwargs + ) + _metadata = standard_logging_payload.get("metadata", {}) or {} + hashed_api_key = fallback_values.get("hashed_api_key") or _metadata.get( "user_api_key_hash" ) + api_key_alias = fallback_values.get("api_key_alias") or _metadata.get( + "user_api_key_alias" + ) + team = fallback_values.get("team") or _metadata.get("user_api_key_team_id") + team_alias = fallback_values.get("team_alias") or _metadata.get( + "user_api_key_team_alias" + ) + client_ip = fallback_values.get("client_ip") or _metadata.get( + "requester_ip_address" + ) + user_agent = fallback_values.get("user_agent") or _metadata.get( + "user_agent" + ) + + # exception_status: prefer status_code, fallback to exception class for known types + exception_status = None + if exception is not None: + exception_status = str(getattr(exception, "status_code", None)) + if exception_status == "None" or not exception_status: + code = getattr(exception, "code", None) + if code is not None: + exception_status = str(code) # Create enum_values for the label factory (always create for use in different metrics) enum_values = UserAPIKeyLabelValues( @@ -1724,26 +1885,18 @@ class PrometheusLogger(CustomLogger): model_id=model_id, api_base=api_base, api_provider=llm_provider, - exception_status=( - str(getattr(exception, "status_code", None)) if exception else None - ), + exception_status=exception_status, exception_class=( self._get_exception_class_name(exception) if exception else None ), - requested_model=model_group, + requested_model=model_group or litellm_model_name, hashed_api_key=hashed_api_key, - api_key_alias=standard_logging_payload["metadata"][ - "user_api_key_alias" - ], - team=standard_logging_payload["metadata"]["user_api_key_team_id"], - team_alias=standard_logging_payload["metadata"][ - "user_api_key_team_alias" - ], + api_key_alias=api_key_alias, + team=team, + team_alias=team_alias, tags=standard_logging_payload.get("request_tags", []), - client_ip=standard_logging_payload["metadata"].get( - "requester_ip_address" - ), - user_agent=standard_logging_payload["metadata"].get("user_agent"), + client_ip=client_ip, + user_agent=user_agent, ) """ @@ -2210,7 +2363,11 @@ class PrometheusLogger(CustomLogger): increment metric when litellm.Router / load balancing logic places a deployment in cool down """ self.litellm_deployment_cooled_down.labels( - litellm_model_name, model_id, api_base, api_provider, exception_status + _sanitize_prometheus_label_value(litellm_model_name), + _sanitize_prometheus_label_value(model_id), + _sanitize_prometheus_label_value(api_base), + _sanitize_prometheus_label_value(api_provider), + _sanitize_prometheus_label_value(exception_status), ).inc() def increment_callback_logging_failure( @@ -2761,12 +2918,14 @@ class PrometheusLogger(CustomLogger): max_budget=max_budget, ) try: + # Note: Setting check_db_only=True bypasses cache and hits DB on every request, + # causing huge latency increase and CPU spikes. Keep check_db_only=False. user_info = await get_user_object( user_id=user_id, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, user_id_upsert=False, - check_db_only=True, + check_db_only=False, ) except Exception as e: verbose_logger.debug( @@ -2928,9 +3087,10 @@ def prometheus_label_factory( # Extract dictionary from Pydantic object enum_dict = enum_values.model_dump() - # Filter supported labels + # Filter supported labels and sanitize values to prevent breaking + # the Prometheus text format (e.g. U+2028 Line Separator in label values) filtered_labels = { - label: value + label: _sanitize_prometheus_label_value(value) for label, value in enum_dict.items() if label in supported_enum_labels } @@ -2948,14 +3108,14 @@ def prometheus_label_factory( # check sanitized key sanitized_key = _sanitize_prometheus_label_name(key) if sanitized_key in supported_enum_labels: - filtered_labels[sanitized_key] = value + filtered_labels[sanitized_key] = _sanitize_prometheus_label_value(value) # Add custom tags if configured if enum_values.tags is not None: custom_tag_labels = get_custom_labels_from_tags(enum_values.tags) for key, value in custom_tag_labels.items(): if key in supported_enum_labels: - filtered_labels[key] = value + filtered_labels[key] = _sanitize_prometheus_label_value(value) for label in supported_enum_labels: if label not in filtered_labels: diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 5d36b760afb..1277cac51d7 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -17,6 +17,7 @@ from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.websearch_interception.tools import ( get_litellm_web_search_tool, is_web_search_tool, + is_web_search_tool_chat_completion, ) from litellm.integrations.websearch_interception.transformation import ( WebSearchTransformation, @@ -48,7 +49,8 @@ class WebSearchInterceptionLogger(CustomLogger): Args: enabled_providers: List of LLM providers to enable interception for. Use LlmProviders enum values (e.g., [LlmProviders.BEDROCK]) - Default: [LlmProviders.BEDROCK] + If None or empty list, enables for ALL providers. + Default: None (all providers enabled) search_tool_name: Name of search tool configured in router's search_tools. If None, will attempt to use first available search tool. """ @@ -183,10 +185,10 @@ class WebSearchInterceptionLogger(CustomLogger): verbose_logger.debug( f"WebSearchInterception: Pre-request hook called" f" - custom_llm_provider={custom_llm_provider}" - f" - enabled_providers={self.enabled_providers}" + f" - enabled_providers={self.enabled_providers or 'ALL'}" ) - if custom_llm_provider not in self.enabled_providers: + if self.enabled_providers is not None and custom_llm_provider not in self.enabled_providers: verbose_logger.debug( f"WebSearchInterception: Skipping - provider {custom_llm_provider} not in {self.enabled_providers}" ) @@ -245,7 +247,12 @@ class WebSearchInterceptionLogger(CustomLogger): custom_llm_provider: str, kwargs: Dict, ) -> Tuple[bool, Dict]: - """Check if WebSearch tool interception is needed""" + """ + Check if WebSearch tool interception is needed for Anthropic Messages API. + + This is the legacy method for Anthropic-style responses. + For chat completions, use async_should_run_chat_completion_agentic_loop instead. + """ verbose_logger.debug(f"WebSearchInterception: Hook called! provider={custom_llm_provider}, stream={stream}") verbose_logger.debug(f"WebSearchInterception: Response type: {type(response)}") @@ -253,7 +260,7 @@ class WebSearchInterceptionLogger(CustomLogger): # Check if provider should be intercepted # Note: custom_llm_provider is already normalized by get_llm_provider() # (e.g., "bedrock/invoke/..." -> "bedrock") - if custom_llm_provider not in self.enabled_providers: + if self.enabled_providers is not None and custom_llm_provider not in self.enabled_providers: verbose_logger.debug( f"WebSearchInterception: Skipping provider {custom_llm_provider} (not in enabled list: {self.enabled_providers})" ) @@ -267,10 +274,11 @@ class WebSearchInterceptionLogger(CustomLogger): ) return False, {} - # Detect WebSearch tool_use in response + # Detect WebSearch tool_use in response (Anthropic format) should_intercept, tool_calls = WebSearchTransformation.transform_request( response=response, stream=stream, + response_format="anthropic", ) if not should_intercept: @@ -288,6 +296,67 @@ class WebSearchInterceptionLogger(CustomLogger): "tool_calls": tool_calls, "tool_type": "websearch", "provider": custom_llm_provider, + "response_format": "anthropic", + } + return True, tools_dict + + async def async_should_run_chat_completion_agentic_loop( + self, + response: Any, + model: str, + messages: List[Dict], + tools: Optional[List[Dict]], + stream: bool, + custom_llm_provider: str, + kwargs: Dict, + ) -> Tuple[bool, Dict]: + """ + Check if WebSearch tool interception is needed for Chat Completions API. + + Similar to async_should_run_agentic_loop but for OpenAI-style chat completions. + """ + + verbose_logger.debug(f"WebSearchInterception: Chat completion hook called! provider={custom_llm_provider}, stream={stream}") + verbose_logger.debug(f"WebSearchInterception: Response type: {type(response)}") + + # Check if provider should be intercepted + if self.enabled_providers is not None and custom_llm_provider not in self.enabled_providers: + verbose_logger.debug( + f"WebSearchInterception: Skipping provider {custom_llm_provider} (not in enabled list: {self.enabled_providers})" + ) + return False, {} + + # Check if tools include any web search tool (strict check for chat completions) + has_websearch_tool = any(is_web_search_tool_chat_completion(t) for t in (tools or [])) + if not has_websearch_tool: + verbose_logger.debug( + "WebSearchInterception: No litellm_web_search tool in request" + ) + return False, {} + + # Detect WebSearch tool_calls in response (OpenAI format) + should_intercept, tool_calls = WebSearchTransformation.transform_request( + response=response, + stream=stream, + response_format="openai", + ) + + if not should_intercept: + verbose_logger.debug( + "WebSearchInterception: No WebSearch tool_calls detected in response" + ) + return False, {} + + verbose_logger.debug( + f"WebSearchInterception: Detected {len(tool_calls)} WebSearch tool call(s), executing agentic loop" + ) + + # Return tools dict with tool calls + tools_dict = { + "tool_calls": tool_calls, + "tool_type": "websearch", + "provider": custom_llm_provider, + "response_format": "openai", } return True, tools_dict @@ -303,7 +372,11 @@ class WebSearchInterceptionLogger(CustomLogger): stream: bool, kwargs: Dict, ) -> Any: - """Execute agentic loop with WebSearch execution""" + """ + Execute agentic loop with WebSearch execution for Anthropic Messages API. + + This is the legacy method for Anthropic-style responses. + """ tool_calls = tools["tool_calls"] @@ -321,6 +394,41 @@ class WebSearchInterceptionLogger(CustomLogger): kwargs=kwargs, ) + async def async_run_chat_completion_agentic_loop( + self, + tools: Dict, + model: str, + messages: List[Dict], + response: Any, + optional_params: Dict, + logging_obj: Any, + stream: bool, + kwargs: Dict, + ) -> Any: + """ + Execute agentic loop with WebSearch execution for Chat Completions API. + + Similar to async_run_agentic_loop but for OpenAI-style chat completions. + """ + + tool_calls = tools["tool_calls"] + response_format = tools.get("response_format", "openai") + + verbose_logger.debug( + f"WebSearchInterception: Executing chat completion agentic loop for {len(tool_calls)} search(es)" + ) + + return await self._execute_chat_completion_agentic_loop( + model=model, + messages=messages, + tool_calls=tool_calls, + optional_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=kwargs, + response_format=response_format, + ) + async def _execute_agentic_loop( self, model: str, @@ -382,7 +490,8 @@ class WebSearchInterceptionLogger(CustomLogger): ) # Make follow-up request with search results - follow_up_messages = messages + [assistant_message, user_message] + # Type cast: user_message is a Dict for Anthropic format (default response_format) + follow_up_messages = messages + [assistant_message, cast(Dict, user_message)] verbose_logger.debug( "WebSearchInterception: Making follow-up request with search results" @@ -521,6 +630,150 @@ class WebSearchInterceptionLogger(CustomLogger): ) raise + async def _execute_chat_completion_agentic_loop( # noqa: PLR0915 + self, + model: str, + messages: List[Dict], + tool_calls: List[Dict], + optional_params: Dict, + logging_obj: Any, + stream: bool, + kwargs: Dict, + response_format: str = "openai", + ) -> Any: + """Execute litellm.search() and make follow-up chat completion request""" + + # Extract search queries from tool_calls + search_tasks = [] + for tool_call in tool_calls: + # Handle both Anthropic-style input and OpenAI-style function.arguments + query = None + if "input" in tool_call and isinstance(tool_call["input"], dict): + query = tool_call["input"].get("query") + elif "function" in tool_call: + func = tool_call["function"] + if isinstance(func, dict): + args = func.get("arguments", {}) + if isinstance(args, dict): + query = args.get("query") + + if query: + verbose_logger.debug( + f"WebSearchInterception: Queuing search for query='{query}'" + ) + search_tasks.append(self._execute_search(query)) + else: + verbose_logger.warning( + f"WebSearchInterception: Tool call {tool_call.get('id')} has no query" + ) + # Add empty result for tools without query + search_tasks.append(self._create_empty_search_result()) + + # Execute searches in parallel + verbose_logger.debug( + f"WebSearchInterception: Executing {len(search_tasks)} search(es) in parallel" + ) + search_results = await asyncio.gather(*search_tasks, return_exceptions=True) + + # Handle any exceptions in search results + final_search_results: List[str] = [] + for i, result in enumerate(search_results): + if isinstance(result, Exception): + verbose_logger.error( + f"WebSearchInterception: Search {i} failed with error: {str(result)}" + ) + final_search_results.append( + f"Search failed: {str(result)}" + ) + elif isinstance(result, str): + final_search_results.append(cast(str, result)) + else: + verbose_logger.warning( + f"WebSearchInterception: Unexpected result type {type(result)} at index {i}" + ) + final_search_results.append(str(result)) + + # Build assistant and tool messages using transformation + assistant_message, tool_messages_or_user = WebSearchTransformation.transform_response( + tool_calls=tool_calls, + search_results=final_search_results, + response_format=response_format, + ) + + # Make follow-up request with search results + # For OpenAI format, tool_messages_or_user is a list of tool messages + if response_format == "openai": + follow_up_messages = messages + [assistant_message] + cast(List[Dict], tool_messages_or_user) + else: + # For Anthropic format (shouldn't happen in this method, but handle it) + follow_up_messages = messages + [assistant_message, cast(Dict, tool_messages_or_user)] + + verbose_logger.debug( + "WebSearchInterception: Making follow-up chat completion request with search results" + ) + verbose_logger.debug( + f"WebSearchInterception: Follow-up messages count: {len(follow_up_messages)}" + ) + + # Use litellm.acompletion for follow-up request + try: + # Remove internal parameters that shouldn't be passed to follow-up request + internal_params = { + '_websearch_interception', + 'acompletion', + 'litellm_logging_obj', + 'custom_llm_provider', + 'model_alias_map', + 'stream_response', + 'custom_prompt_dict', + } + kwargs_for_followup = { + k: v for k, v in kwargs.items() + if not k.startswith('_websearch_interception') and k not in internal_params + } + + # Get full model name from kwargs + full_model_name = model + if "custom_llm_provider" in kwargs: + custom_llm_provider = kwargs["custom_llm_provider"] + # Reconstruct full model name with provider prefix if needed + if not model.startswith(custom_llm_provider): + # Check if model already has a provider prefix + if "/" not in model: + full_model_name = f"{custom_llm_provider}/{model}" + + verbose_logger.debug( + f"WebSearchInterception: Using model name: {full_model_name}" + ) + + # Prepare tools for follow-up request (same as original) + tools_param = optional_params.get("tools") + + # Remove tools and extra_body from optional_params to avoid issues + # extra_body often contains internal LiteLLM params that shouldn't be forwarded + optional_params_clean = { + k: v for k, v in optional_params.items() + if k not in {"tools", "extra_body", "model_alias_map","stream_response", "custom_prompt_dict" } + } + + final_response = await litellm.acompletion( + model=full_model_name, + messages=follow_up_messages, + tools=tools_param, + **optional_params_clean, + **kwargs_for_followup, + ) + + verbose_logger.debug( + f"WebSearchInterception: Follow-up request completed, response type: {type(final_response)}" + ) + return final_response + except Exception as e: + verbose_logger.exception( + f"WebSearchInterception: Follow-up request failed: {str(e)}" + ) + raise + async def _create_empty_search_result(self) -> str: """Create an empty search result for tool calls without queries""" return "No search query provided" diff --git a/litellm/integrations/websearch_interception/tools.py b/litellm/integrations/websearch_interception/tools.py index 4f8b7372fe3..c39d150fb19 100644 --- a/litellm/integrations/websearch_interception/tools.py +++ b/litellm/integrations/websearch_interception/tools.py @@ -49,12 +49,57 @@ def get_litellm_web_search_tool() -> Dict[str, Any]: } +def is_web_search_tool_chat_completion(tool: Dict[str, Any]) -> bool: + """ + Check if a tool is a web search tool for Chat Completions API (strict check). + + This is a stricter version that ONLY checks for the exact LiteLLM web search tool name. + Use this for Chat Completions API to avoid false positives with user-defined tools. + + Detects ONLY: + - LiteLLM standard: name == "litellm_web_search" (Anthropic format) + - OpenAI format: type == "function" with function.name == "litellm_web_search" + + Args: + tool: Tool dictionary to check + + Returns: + True if tool is exactly the LiteLLM web search tool + + Example: + >>> is_web_search_tool_chat_completion({"name": "litellm_web_search"}) + True + >>> is_web_search_tool_chat_completion({"type": "function", "function": {"name": "litellm_web_search"}}) + True + >>> is_web_search_tool_chat_completion({"name": "web_search"}) + False + >>> is_web_search_tool_chat_completion({"name": "WebSearch"}) + False + """ + tool_name = tool.get("name", "") + tool_type = tool.get("type", "") + + # Check for OpenAI format: {"type": "function", "function": {"name": "litellm_web_search"}} + if tool_type == "function" and "function" in tool: + function_def = tool.get("function", {}) + function_name = function_def.get("name", "") + if function_name == LITELLM_WEB_SEARCH_TOOL_NAME: + return True + + # Check for LiteLLM standard tool (Anthropic format) + if tool_name == LITELLM_WEB_SEARCH_TOOL_NAME: + return True + + return False + + def is_web_search_tool(tool: Dict[str, Any]) -> bool: """ Check if a tool is a web search tool (native or LiteLLM standard). Detects: - LiteLLM standard: name == "litellm_web_search" + - OpenAI format: type == "function" with function.name == "litellm_web_search" - Anthropic native: type starts with "web_search_" (e.g., "web_search_20250305") - Claude Code: name == "web_search" with a type field - Custom: name == "WebSearch" (legacy format) @@ -68,6 +113,8 @@ def is_web_search_tool(tool: Dict[str, Any]) -> bool: Example: >>> is_web_search_tool({"name": "litellm_web_search"}) True + >>> is_web_search_tool({"type": "function", "function": {"name": "litellm_web_search"}}) + True >>> is_web_search_tool({"type": "web_search_20250305", "name": "web_search"}) True >>> is_web_search_tool({"name": "calculator"}) @@ -75,8 +122,15 @@ def is_web_search_tool(tool: Dict[str, Any]) -> bool: """ tool_name = tool.get("name", "") tool_type = tool.get("type", "") + + # Check for OpenAI format: {"type": "function", "function": {"name": "..."}} + if tool_type == "function" and "function" in tool: + function_def = tool.get("function", {}) + function_name = function_def.get("name", "") + if function_name == LITELLM_WEB_SEARCH_TOOL_NAME: + return True - # Check for LiteLLM standard tool + # Check for LiteLLM standard tool (Anthropic format) if tool_name == LITELLM_WEB_SEARCH_TOOL_NAME: return True diff --git a/litellm/integrations/websearch_interception/transformation.py b/litellm/integrations/websearch_interception/transformation.py index 313358822a5..e44ec35c3a2 100644 --- a/litellm/integrations/websearch_interception/transformation.py +++ b/litellm/integrations/websearch_interception/transformation.py @@ -1,10 +1,10 @@ """ WebSearch Tool Transformation -Transforms between Anthropic tool_use format and LiteLLM search format. +Transforms between Anthropic/OpenAI tool_use format and LiteLLM search format. """ - -from typing import Any, Dict, List, Tuple +import json +from typing import Any, Dict, List, Tuple, Union from litellm._logging import verbose_logger from litellm.constants import LITELLM_WEB_SEARCH_TOOL_NAME @@ -17,28 +17,31 @@ class WebSearchTransformation: Handles transformation between: - Anthropic tool_use format → LiteLLM search requests - - LiteLLM SearchResponse → Anthropic tool_result format + - OpenAI tool_calls format → LiteLLM search requests + - LiteLLM SearchResponse → Anthropic/OpenAI tool_result format """ @staticmethod def transform_request( response: Any, stream: bool, + response_format: str = "anthropic", ) -> Tuple[bool, List[Dict]]: """ - Transform Anthropic response to extract WebSearch tool calls. + Transform model response to extract WebSearch tool calls. - Detects if response contains WebSearch tool_use blocks and extracts + Detects if response contains WebSearch tool_use/tool_calls blocks and extracts the search queries for execution. Args: - response: Model response (dict or AnthropicMessagesResponse) + response: Model response (dict, AnthropicMessagesResponse, or ModelResponse) stream: Whether response is streaming + response_format: Response format - "anthropic" or "openai" (default: "anthropic") Returns: (has_websearch, tool_calls): has_websearch: True if WebSearch tool_use found - tool_calls: List of tool_use dicts with id, name, input + tool_calls: List of tool_use/tool_calls dicts with id, name, input/function Note: Streaming requests are handled by converting stream=True to stream=False @@ -54,8 +57,11 @@ class WebSearchTransformation: ) return False, [] - # Parse non-streaming response - return WebSearchTransformation._detect_from_non_streaming_response(response) + # Parse non-streaming response based on format + if response_format == "openai": + return WebSearchTransformation._detect_from_openai_response(response) + else: + return WebSearchTransformation._detect_from_non_streaming_response(response) @staticmethod def _detect_from_non_streaming_response( @@ -114,26 +120,142 @@ class WebSearchTransformation: return len(tool_calls) > 0, tool_calls + @staticmethod + def _detect_from_openai_response( + response: Any, + ) -> Tuple[bool, List[Dict]]: + """Parse OpenAI-style response for WebSearch tool_calls""" + + # Handle both dict and ModelResponse objects + if isinstance(response, dict): + choices = response.get("choices", []) + else: + if not hasattr(response, "choices"): + verbose_logger.debug( + "WebSearchInterception: Response has no choices attribute" + ) + return False, [] + choices = response.choices or [] + + if not choices: + verbose_logger.debug( + "WebSearchInterception: Response has empty choices" + ) + return False, [] + + # Get first choice's message + first_choice = choices[0] + if isinstance(first_choice, dict): + message = first_choice.get("message", {}) + else: + message = getattr(first_choice, "message", None) + + if not message: + verbose_logger.debug( + "WebSearchInterception: First choice has no message" + ) + return False, [] + + # Get tool_calls from message + if isinstance(message, dict): + openai_tool_calls = message.get("tool_calls", []) + else: + openai_tool_calls = getattr(message, "tool_calls", None) or [] + + if not openai_tool_calls: + verbose_logger.debug( + "WebSearchInterception: Message has no tool_calls" + ) + return False, [] + + # Find all WebSearch tool calls + tool_calls = [] + for tool_call in openai_tool_calls: + # Handle both dict and object tool calls + if isinstance(tool_call, dict): + tool_id = tool_call.get("id") + tool_type = tool_call.get("type") + function = tool_call.get("function", {}) + function_name = function.get("name") if isinstance(function, dict) else getattr(function, "name", None) + function_arguments = function.get("arguments") if isinstance(function, dict) else getattr(function, "arguments", None) + else: + tool_id = getattr(tool_call, "id", None) + tool_type = getattr(tool_call, "type", None) + function = getattr(tool_call, "function", None) + function_name = getattr(function, "name", None) if function else None + function_arguments = getattr(function, "arguments", None) if function else None + + # Check for LiteLLM standard or legacy web search tools + if tool_type == "function" and function_name in ( + LITELLM_WEB_SEARCH_TOOL_NAME, "WebSearch", "web_search" + ): + # Parse arguments (might be JSON string) + if isinstance(function_arguments, str): + try: + arguments = json.loads(function_arguments) + except json.JSONDecodeError: + verbose_logger.warning( + f"WebSearchInterception: Failed to parse function arguments: {function_arguments}" + ) + arguments = {} + else: + arguments = function_arguments or {} + + # Convert to internal format (similar to Anthropic) + tool_call_dict = { + "id": tool_id, + "type": "function", + "name": function_name, + "function": { + "name": function_name, + "arguments": arguments, + }, + "input": arguments, # For compatibility with Anthropic format + } + tool_calls.append(tool_call_dict) + verbose_logger.debug( + f"WebSearchInterception: Found {function_name} tool_call with id={tool_id}" + ) + + return len(tool_calls) > 0, tool_calls + @staticmethod def transform_response( tool_calls: List[Dict], search_results: List[str], - ) -> Tuple[Dict, Dict]: + response_format: str = "anthropic", + ) -> Tuple[Dict, Union[Dict, List[Dict]]]: """ - Transform LiteLLM search results to Anthropic tool_result format. + Transform LiteLLM search results to Anthropic/OpenAI tool_result format. - Builds the assistant and user messages needed for the agentic loop + Builds the assistant and user/tool messages needed for the agentic loop follow-up request. Args: - tool_calls: List of tool_use dicts from transform_request + tool_calls: List of tool_use/tool_calls dicts from transform_request search_results: List of search result strings (one per tool_call) + response_format: Response format - "anthropic" or "openai" (default: "anthropic") Returns: - (assistant_message, user_message): - assistant_message: Message with tool_use blocks - user_message: Message with tool_result blocks + (assistant_message, user_or_tool_messages): + For Anthropic: assistant_message with tool_use blocks, user_message with tool_result blocks + For OpenAI: assistant_message with tool_calls, tool_messages list with tool results """ + if response_format == "openai": + return WebSearchTransformation._transform_response_openai( + tool_calls, search_results + ) + else: + return WebSearchTransformation._transform_response_anthropic( + tool_calls, search_results + ) + + @staticmethod + def _transform_response_anthropic( + tool_calls: List[Dict], + search_results: List[str], + ) -> Tuple[Dict, Dict]: + """Transform to Anthropic format (single user message with tool_result blocks)""" # Build assistant message with tool_use blocks assistant_message = { "role": "assistant", @@ -163,6 +285,40 @@ class WebSearchTransformation: return assistant_message, user_message + @staticmethod + def _transform_response_openai( + tool_calls: List[Dict], + search_results: List[str], + ) -> Tuple[Dict, List[Dict]]: + """Transform to OpenAI format (assistant with tool_calls, separate tool messages)""" + # Build assistant message with tool_calls + assistant_message = { + "role": "assistant", + "tool_calls": [ + { + "id": tc["id"], + "type": "function", + "function": { + "name": tc["name"], + "arguments": json.dumps(tc["input"]) if isinstance(tc["input"], dict) else str(tc["input"]), + }, + } + for tc in tool_calls + ], + } + + # Build separate tool messages (one per tool call) + tool_messages = [ + { + "role": "tool", + "tool_call_id": tool_calls[i]["id"], + "content": search_results[i], + } + for i in range(len(tool_calls)) + ] + + return assistant_message, tool_messages + @staticmethod def format_search_response(result: SearchResponse) -> str: """ diff --git a/litellm/litellm_core_utils/core_helpers.py b/litellm/litellm_core_utils/core_helpers.py index 00695cbfb5b..7c8e2ebeaff 100644 --- a/litellm/litellm_core_utils/core_helpers.py +++ b/litellm/litellm_core_utils/core_helpers.py @@ -94,8 +94,8 @@ def map_finish_reason( return "length" elif finish_reason == "tool_use": # anthropic return "tool_calls" - elif finish_reason == "content_filtered": - return "content_filter" + elif finish_reason == "compaction": + return "length" return finish_reason diff --git a/litellm/litellm_core_utils/env_utils.py b/litellm/litellm_core_utils/env_utils.py new file mode 100644 index 00000000000..34c65275331 --- /dev/null +++ b/litellm/litellm_core_utils/env_utils.py @@ -0,0 +1,21 @@ +""" +Utility helpers for reading and parsing environment variables. +""" + +import os + + +def get_env_int(env_var: str, default: int) -> int: + """Parse an environment variable as an integer, falling back to default on invalid values. + + Handles empty strings, whitespace, and non-numeric values gracefully + so that misconfiguration doesn't crash the process at import time. + """ + raw = os.getenv(env_var) + if raw is None: + return default + raw = raw.strip() + try: + return int(raw) + except (ValueError, TypeError): + return default diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index 3ddcae69315..dde44cced36 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -70,6 +70,11 @@ class ExceptionCheckers: Check if an error string indicates a context window exceeded error. """ _error_str_lowercase = error_str.lower() + # Exclude param validation errors (e.g. OpenAI "user" param max 64 chars) + if "string_above_max_length" in _error_str_lowercase: + return False + if "invalid 'user'" in _error_str_lowercase and "string too long" in _error_str_lowercase: + return False known_exception_substrings = [ "exceed context limit", "this model's maximum context length is", @@ -98,16 +103,18 @@ class ExceptionCheckers: """ Check if an error string indicates a content policy violation error. """ + _lower = error_str.lower() known_exception_substrings = [ - "invalid_request_error", "content_policy_violation", + "responsibleaipolicyviolation", "the response was filtered due to the prompt triggering azure openai's content management", "your task failed as a result of our safety system", "the model produced invalid content", "content_filter_policy", + "your request was rejected as a result of our safety system", ] for substring in known_exception_substrings: - if substring in error_str.lower(): + if substring in _lower: return True return False @@ -2060,6 +2067,19 @@ def exception_type( # type: ignore # noqa: PLR0915 if isinstance(body_dict, dict): if isinstance(body_dict.get("error"), dict): azure_error_code = body_dict["error"].get("code") # type: ignore[index] + # Also check inner_error for + # ResponsibleAIPolicyViolation which indicates a + # content policy violation even when the top-level + # code is generic (e.g. "invalid_request_error"). + if azure_error_code != "content_policy_violation": + _inner = ( + body_dict["error"].get("inner_error") # type: ignore[index] + or body_dict["error"].get("innererror") # type: ignore[index] + ) + if isinstance(_inner, dict) and _inner.get( + "code" + ) == "ResponsibleAIPolicyViolation": + azure_error_code = "content_policy_violation" else: azure_error_code = body_dict.get("code") except Exception: diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index 060e98fd49f..36a8dfdb5a6 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -1,19 +1,48 @@ from typing import Optional +# Pre-define optional kwargs keys as frozenset for O(1) lookups +# These are extracted from kwargs only if present, avoiding unnecessary .get() calls +_OPTIONAL_KWARGS_KEYS = frozenset({ + "azure_ad_token", + "tenant_id", + "client_id", + "client_secret", + "azure_username", + "azure_password", + "azure_scope", + "timeout", + "bucket_name", + "vertex_credentials", + "vertex_project", + "vertex_location", + "vertex_ai_project", + "vertex_ai_location", + "vertex_ai_credentials", + "aws_region_name", + "aws_access_key_id", + "aws_secret_access_key", + "aws_session_token", + "aws_session_name", + "aws_profile_name", + "aws_role_name", + "aws_web_identity_token", + "aws_sts_endpoint", + "aws_external_id", + "aws_bedrock_runtime_endpoint", + "tpm", + "rpm", +}) + + def _get_base_model_from_litellm_call_metadata( metadata: Optional[dict], ) -> Optional[str]: if metadata is None: return None - - if metadata is not None: - model_info = metadata.get("model_info", {}) - - if model_info is not None: - base_model = model_info.get("base_model", None) - if base_model is not None: - return base_model + model_info = metadata.get("model_info") + if model_info: + return model_info.get("base_model") return None @@ -66,6 +95,7 @@ def get_litellm_params( litellm_request_debug: Optional[bool] = None, **kwargs, ) -> dict: + # Build base dict with explicit parameters (always included) litellm_params = { "acompletion": acompletion, "api_key": api_key, @@ -112,37 +142,15 @@ def get_litellm_params( "ssl_verify": ssl_verify, "merge_reasoning_content_in_choices": merge_reasoning_content_in_choices, "api_version": api_version, - "azure_ad_token": kwargs.get("azure_ad_token"), - "tenant_id": kwargs.get("tenant_id"), - "client_id": kwargs.get("client_id"), - "client_secret": kwargs.get("client_secret"), - "azure_username": kwargs.get("azure_username"), - "azure_password": kwargs.get("azure_password"), - "azure_scope": kwargs.get("azure_scope"), "max_retries": max_retries, - "timeout": kwargs.get("timeout"), - "bucket_name": kwargs.get("bucket_name"), - "vertex_credentials": kwargs.get("vertex_credentials"), - "vertex_project": kwargs.get("vertex_project"), - "vertex_location": kwargs.get("vertex_location"), - "vertex_ai_project": kwargs.get("vertex_ai_project"), - "vertex_ai_location": kwargs.get("vertex_ai_location"), - "vertex_ai_credentials": kwargs.get("vertex_ai_credentials"), "use_litellm_proxy": use_litellm_proxy, "litellm_request_debug": litellm_request_debug, - "aws_region_name": kwargs.get("aws_region_name"), - # AWS credentials for Bedrock/Sagemaker - "aws_access_key_id": kwargs.get("aws_access_key_id"), - "aws_secret_access_key": kwargs.get("aws_secret_access_key"), - "aws_session_token": kwargs.get("aws_session_token"), - "aws_session_name": kwargs.get("aws_session_name"), - "aws_profile_name": kwargs.get("aws_profile_name"), - "aws_role_name": kwargs.get("aws_role_name"), - "aws_web_identity_token": kwargs.get("aws_web_identity_token"), - "aws_sts_endpoint": kwargs.get("aws_sts_endpoint"), - "aws_external_id": kwargs.get("aws_external_id"), - "aws_bedrock_runtime_endpoint": kwargs.get("aws_bedrock_runtime_endpoint"), - "tpm": kwargs.get("tpm"), - "rpm": kwargs.get("rpm"), } + + # Sparse extraction: only add kwargs keys that are actually present + if kwargs: + for key in _OPTIONAL_KWARGS_KEYS: + if key in kwargs: + litellm_params[key] = kwargs[key] + return litellm_params diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 718773a1b16..8ab4ec15b07 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -51,7 +51,7 @@ def handle_cohere_chat_model_custom_llm_provider( if custom_llm_provider == "cohere" and model in litellm.cohere_chat_models: return model, "cohere_chat" - if "/" in model: + if model and "/" in model: _custom_llm_provider, _model = model.split("/", 1) if ( _custom_llm_provider @@ -84,7 +84,7 @@ def handle_anthropic_text_model_custom_llm_provider( ): return model, "anthropic_text" - if "/" in model: + if model and "/" in model: _custom_llm_provider, _model = model.split("/", 1) if ( _custom_llm_provider @@ -113,6 +113,12 @@ def get_llm_provider( # noqa: PLR0915 Return model, custom_llm_provider, dynamic_api_key, api_base """ try: + # Early validation - model is required + if model is None: + raise ValueError( + "model parameter is required but was None. Please provide a valid model name." + ) + if litellm.LiteLLMProxyChatConfig._should_use_litellm_proxy_by_default( litellm_params=litellm_params ): diff --git a/litellm/litellm_core_utils/get_model_cost_map.py b/litellm/litellm_core_utils/get_model_cost_map.py index 9b86f4ca2f0..e622a317454 100644 --- a/litellm/litellm_core_utils/get_model_cost_map.py +++ b/litellm/litellm_core_utils/get_model_cost_map.py @@ -8,40 +8,187 @@ export LITELLM_LOCAL_MODEL_COST_MAP=True ``` """ +import json import os +from importlib.resources import files import httpx +from litellm import verbose_logger +from litellm.constants import ( + MODEL_COST_MAP_MAX_SHRINK_RATIO, + MODEL_COST_MAP_MIN_MODEL_COUNT, +) + + +class GetModelCostMap: + """ + Handles fetching, validating, and loading the model cost map. + + Only the backup model *count* is cached (a single int). The full + backup dict is never held in memory — it is only parsed when it + needs to be *returned* as a fallback. + """ + + _backup_model_count: int = -1 # -1 = not yet loaded + + @staticmethod + def load_local_model_cost_map() -> dict: + """Load the local backup model cost map bundled with the package.""" + content = json.loads( + files("litellm") + .joinpath("model_prices_and_context_window_backup.json") + .read_text(encoding="utf-8") + ) + return content + + @classmethod + def _get_backup_model_count(cls) -> int: + """Return the number of models in the local backup (cached int).""" + if cls._backup_model_count < 0: + backup = cls.load_local_model_cost_map() + cls._backup_model_count = len(backup) + return cls._backup_model_count + + @staticmethod + def _check_is_valid_dict(fetched_map: dict) -> bool: + """Check 1: fetched map is a non-empty dict.""" + if not isinstance(fetched_map, dict): + verbose_logger.warning( + "LiteLLM: Fetched model cost map is not a dict (type=%s). " + "Falling back to local backup.", + type(fetched_map).__name__, + ) + return False + + if len(fetched_map) == 0: + verbose_logger.warning( + "LiteLLM: Fetched model cost map is empty. " + "Falling back to local backup.", + ) + return False + + return True + + @classmethod + def _check_model_count_not_reduced( + cls, + fetched_map: dict, + backup_model_count: int, + min_model_count: int = MODEL_COST_MAP_MIN_MODEL_COUNT, + max_shrink_ratio: float = MODEL_COST_MAP_MAX_SHRINK_RATIO, + ) -> bool: + """Check 2: model count has not reduced significantly vs backup.""" + fetched_count = len(fetched_map) + + if fetched_count < min_model_count: + verbose_logger.warning( + "LiteLLM: Fetched model cost map has only %d models (minimum=%d). " + "This may indicate a corrupted upstream file. " + "Falling back to local backup.", + fetched_count, + min_model_count, + ) + return False + + if backup_model_count > 0 and fetched_count < backup_model_count * max_shrink_ratio: + verbose_logger.warning( + "LiteLLM: Fetched model cost map shrank significantly " + "(fetched=%d, backup=%d, threshold=%.0f%%). " + "This may indicate a corrupted upstream file. " + "Falling back to local backup.", + fetched_count, + backup_model_count, + max_shrink_ratio * 100, + ) + return False + + return True + + @classmethod + def validate_model_cost_map( + cls, + fetched_map: dict, + backup_model_count: int, + min_model_count: int = MODEL_COST_MAP_MIN_MODEL_COUNT, + max_shrink_ratio: float = MODEL_COST_MAP_MAX_SHRINK_RATIO, + ) -> bool: + """ + Validate the integrity of a fetched model cost map. + + Runs each check in order and returns False on the first failure. + + Checks: + 1. ``_check_is_valid_dict`` -- fetched map is a non-empty dict. + 2. ``_check_model_count_not_reduced`` -- model count meets minimum + and has not shrunk >``max_shrink_ratio`` vs backup. + + Returns True if all checks pass, False otherwise. + """ + if not cls._check_is_valid_dict(fetched_map): + return False + + if not cls._check_model_count_not_reduced( + fetched_map=fetched_map, + backup_model_count=backup_model_count, + min_model_count=min_model_count, + max_shrink_ratio=max_shrink_ratio, + ): + return False + + return True + + @staticmethod + def fetch_remote_model_cost_map(url: str, timeout: int = 5) -> dict: + """ + Fetch the model cost map from a remote URL. + + Returns the parsed JSON dict. Raises on network/parse errors + (caller is expected to handle). + """ + response = httpx.get(url, timeout=timeout) + response.raise_for_status() + return response.json() + def get_model_cost_map(url: str) -> dict: - if ( - os.getenv("LITELLM_LOCAL_MODEL_COST_MAP", False) - or os.getenv("LITELLM_LOCAL_MODEL_COST_MAP", False) == "True" - ): - from importlib.resources import files - import json + """ + Public entry point — returns the model cost map dict. - content = json.loads( - files("litellm") - .joinpath("model_prices_and_context_window_backup.json") - .read_text(encoding="utf-8") - ) - return content + 1. If ``LITELLM_LOCAL_MODEL_COST_MAP`` is set, uses the local backup only. + 2. Otherwise fetches from ``url``, validates integrity, and falls back + to the local backup on any failure. + + Only the backup model count is cached (a single int) for validation. + The full backup dict is only parsed when it must be *returned* as a + fallback — it is never held in memory long-term. + """ + # Note: can't use get_secret_bool here — this runs during litellm.__init__ + # before litellm._key_management_settings is set. + if os.getenv("LITELLM_LOCAL_MODEL_COST_MAP", "").lower() == "true": + return GetModelCostMap.load_local_model_cost_map() try: - response = httpx.get( - url, timeout=5 - ) # set a 5 second timeout for the get request - response.raise_for_status() # Raise an exception if the request is unsuccessful - content = response.json() - return content - except Exception: - from importlib.resources import files - import json - - content = json.loads( - files("litellm") - .joinpath("model_prices_and_context_window_backup.json") - .read_text(encoding="utf-8") + content = GetModelCostMap.fetch_remote_model_cost_map(url) + except Exception as e: + verbose_logger.warning( + "LiteLLM: Failed to fetch remote model cost map from %s: %s. " + "Falling back to local backup.", + url, + str(e), ) - return content + return GetModelCostMap.load_local_model_cost_map() + + # Validate using cached count (cheap int comparison, no file I/O) + if not GetModelCostMap.validate_model_cost_map( + fetched_map=content, + backup_model_count=GetModelCostMap._get_backup_model_count(), + ): + verbose_logger.warning( + "LiteLLM: Fetched model cost map failed integrity check. " + "Using local backup instead. url=%s", + url, + ) + return GetModelCostMap.load_local_model_cost_map() + + return content diff --git a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py index c425319b4d4..ff521d47804 100644 --- a/litellm/litellm_core_utils/initialize_dynamic_callback_params.py +++ b/litellm/litellm_core_utils/initialize_dynamic_callback_params.py @@ -1,8 +1,35 @@ from typing import Dict, Optional - from litellm.secret_managers.main import get_secret_str from litellm.types.utils import StandardCallbackDynamicParams +# Hardcoded list of supported callback params to avoid runtime inspection issues with TypedDict +_supported_callback_params = [ + "langfuse_public_key", + "langfuse_secret", + "langfuse_secret_key", + "langfuse_host", + "langfuse_prompt_version", + "gcs_bucket_name", + "gcs_path_service_account", + "langsmith_api_key", + "langsmith_project", + "langsmith_base_url", + "langsmith_sampling_rate", + "langsmith_tenant_id", + "humanloop_api_key", + "arize_api_key", + "arize_space_key", + "arize_space_id", + "posthog_api_key", + "posthog_host", + "braintrust_api_key", + "braintrust_project", + "braintrust_host", + "slack_webhook_url", + "lunary_public_key", + "turn_off_message_logging", +] + def initialize_standard_callback_dynamic_params( kwargs: Optional[Dict] = None, @@ -15,13 +42,10 @@ def initialize_standard_callback_dynamic_params( standard_callback_dynamic_params = StandardCallbackDynamicParams() if kwargs: - _supported_callback_params = ( - StandardCallbackDynamicParams.__annotations__.keys() - ) - + # 1. Check top-level kwargs for param in _supported_callback_params: if param in kwargs: - _param_value = kwargs.pop(param) + _param_value = kwargs.get(param) if ( _param_value is not None and isinstance(_param_value, str) @@ -30,4 +54,22 @@ def initialize_standard_callback_dynamic_params( _param_value = get_secret_str(secret_name=_param_value) standard_callback_dynamic_params[param] = _param_value # type: ignore + # 2. Fallback: check "metadata" or "litellm_params" -> "metadata" + metadata = (kwargs.get("metadata") or {}).copy() + litellm_params = kwargs.get("litellm_params") or {} + if isinstance(litellm_params, dict): + metadata.update(litellm_params.get("metadata") or {}) + + if isinstance(metadata, dict): + for param in _supported_callback_params: + if param not in standard_callback_dynamic_params and param in metadata: + _param_value = metadata.get(param) + if ( + _param_value is not None + and isinstance(_param_value, str) + and "os.environ/" in _param_value + ): + _param_value = get_secret_str(secret_name=_param_value) + standard_callback_dynamic_params[param] = _param_value # type: ignore + return standard_callback_dynamic_params diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 1b3a687f1f3..82a7af64f97 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -203,6 +203,10 @@ except Exception as e: EnterpriseStandardLoggingPayloadSetupVAR = None _in_memory_loggers: List[Any] = [] +_STANDARD_LOGGING_METADATA_KEYS: frozenset = frozenset( + StandardLoggingMetadata.__annotations__.keys() +) + ### GLOBAL VARIABLES ### # Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys @@ -522,7 +526,8 @@ class Logging(LiteLLMLoggingBaseClass): } self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) - verbose_logger.debug(f"self.optional_params: {self.optional_params}") + if _is_debugging_on() or self.litellm_request_debug: + verbose_logger.debug(f"self.optional_params: {self.optional_params}") self.model_call_details.update( { @@ -3759,7 +3764,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 from litellm.integrations.opentelemetry import OpenTelemetry for callback in _in_memory_loggers: - if isinstance(callback, OpenTelemetry): + if type(callback) is OpenTelemetry: return callback # type: ignore otel_logger = OpenTelemetry( **_get_custom_logger_settings_from_proxy_server( @@ -3917,18 +3922,6 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 return langfuse_logger # type: ignore elif logging_integration == "langfuse_otel": from litellm.integrations.langfuse.langfuse_otel import LangfuseOtelLogger - from litellm.integrations.opentelemetry import ( - OpenTelemetry, - OpenTelemetryConfig, - ) - - langfuse_otel_config = LangfuseOtelLogger.get_langfuse_otel_config() - - # The endpoint and headers are now set as environment variables by get_langfuse_otel_config() - otel_config = OpenTelemetryConfig( - exporter=langfuse_otel_config.protocol, - headers=langfuse_otel_config.otlp_auth_headers, - ) for callback in _in_memory_loggers: if ( @@ -3936,8 +3929,10 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 and callback.callback_name == "langfuse_otel" ): return callback # type: ignore + # Allow LangfuseOtelLogger to initialize its own config safely + # This prevents startup crashes if LANGFUSE keys are not in env (e.g. for dynamic usage) _otel_logger = LangfuseOtelLogger( - config=otel_config, callback_name="langfuse_otel" + config=None, callback_name="langfuse_otel" ) _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore @@ -4525,17 +4520,12 @@ class StandardLoggingPayloadSetup: user_api_key_auth_metadata=None, ) if isinstance(metadata, dict): - # Filter the metadata dictionary to include only the specified keys - supported_keys = StandardLoggingMetadata.__annotations__.keys() - for key in supported_keys: - if key in metadata: - clean_metadata[key] = metadata[key] # type: ignore + for key in metadata.keys() & _STANDARD_LOGGING_METADATA_KEYS: + clean_metadata[key] = metadata[key] # type: ignore - if metadata.get("user_api_key") is not None: - if is_valid_sha256_hash(str(metadata.get("user_api_key"))): - clean_metadata["user_api_key_hash"] = metadata.get( - "user_api_key" - ) # this is the hash + user_api_key = metadata.get("user_api_key") + if user_api_key and isinstance(user_api_key, str) and is_valid_sha256_hash(user_api_key): + clean_metadata["user_api_key_hash"] = user_api_key _potential_requester_metadata = metadata.get( "metadata", None ) # check if user passed metadata in the sdk request - e.g. metadata for langsmith logging - https://docs.litellm.ai/docs/observability/langsmith_integration#set-langsmith-fields diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py index 4f76a5bad03..34d25817378 100644 --- a/litellm/litellm_core_utils/logging_callback_manager.py +++ b/litellm/litellm_core_utils/logging_callback_manager.py @@ -2,6 +2,7 @@ from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set, Type, Uni import litellm from litellm._logging import verbose_logger +from litellm.constants import MAX_CALLBACKS from litellm.integrations.additional_logging_utils import AdditionalLoggingUtils from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger @@ -24,9 +25,6 @@ class LoggingCallbackManager: - Keep a reasonable MAX_CALLBACKS limit (this ensures callbacks don't exponentially grow and consume CPU Resources) """ - # healthy maximum number of callbacks - unlikely someone needs more than 20 - MAX_CALLBACKS = 30 - def add_litellm_input_callback(self, callback: Union[CustomLogger, str]): """ Add a input callback to litellm.input_callback @@ -114,6 +112,27 @@ class LoggingCallbackManager: for c in remove_list: callback_list.remove(c) + def remove_callbacks_by_type(self, callback_list, callback_type): + """ + Remove all callbacks of a specific type from a callback list. + + Args: + callback_list: The list to remove callbacks from (e.g., litellm.callbacks) + callback_type: The class type to match (e.g., SemanticToolFilterHook) + + Example: + litellm.logging_callback_manager.remove_callbacks_by_type( + litellm.callbacks, SemanticToolFilterHook + ) + """ + if not isinstance(callback_list, list): + return + + remove_list = [c for c in callback_list if isinstance(c, callback_type)] + + for c in remove_list: + callback_list.remove(c) + def _add_string_callback_to_list( self, callback: str, parent_list: List[Union[CustomLogger, Callable, str]] ): @@ -134,9 +153,9 @@ class LoggingCallbackManager: Check if adding another callback would exceed MAX_CALLBACKS Returns True if safe to add, False if would exceed limit """ - if len(parent_list) >= self.MAX_CALLBACKS: + if len(parent_list) >= MAX_CALLBACKS: verbose_logger.warning( - f"Cannot add callback - would exceed MAX_CALLBACKS limit of {self.MAX_CALLBACKS}. Current callbacks: {len(parent_list)}" + f"Cannot add callback - would exceed MAX_CALLBACKS limit of {MAX_CALLBACKS}. Current callbacks: {len(parent_list)}" ) return False return True diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py index 7790fb83361..cdddee4e54e 100644 --- a/litellm/litellm_core_utils/prompt_templates/common_utils.py +++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py @@ -443,13 +443,21 @@ def update_messages_with_model_file_ids( def update_responses_input_with_model_file_ids( input: Any, + model_id: Optional[str] = None, + model_file_id_mapping: Optional[Dict[str, Dict[str, str]]] = None, ) -> Union[str, List[Dict[str, Any]]]: """ Updates responses API input with provider-specific file IDs. File IDs are always inside the content array, not as direct input_file items. - For managed files (unified file IDs), decodes the base64-encoded unified file ID - and extracts the llm_output_file_id directly. + For managed files (unified file IDs), uses model_file_id_mapping if provided, + otherwise decodes the base64-encoded unified file ID and extracts the llm_output_file_id directly. + + Args: + input: The responses API input parameter + model_id: The model ID to use for looking up provider-specific file IDs + model_file_id_mapping: Dictionary mapping litellm file IDs to provider file IDs + Format: {"litellm_file_id": {"model_id": "provider_file_id"}} """ from litellm.proxy.openai_files_endpoints.common_utils import ( _is_base64_encoded_unified_file_id, @@ -479,22 +487,35 @@ def update_responses_input_with_model_file_ids( ): file_id = content_item.get("file_id") if file_id: - # Check if this is a managed file ID (base64-encoded unified file ID) - is_unified_file_id = _is_base64_encoded_unified_file_id(file_id) - if is_unified_file_id: - unified_file_id = convert_b64_uid_to_unified_uid(file_id) - if "llm_output_file_id," in unified_file_id: - provider_file_id = unified_file_id.split( - "llm_output_file_id," - )[1].split(";")[0] - else: - # Fallback: keep original if we can't extract - provider_file_id = file_id + provider_file_id = file_id # Default to original + + # Check if we have a mapping for this file ID + if model_file_id_mapping and model_id and file_id in model_file_id_mapping: + # Use the model-specific file ID from mapping + provider_file_id = ( + model_file_id_mapping.get(file_id, {}).get(model_id) + or file_id + ) updated_content_item = content_item.copy() updated_content_item["file_id"] = provider_file_id updated_content.append(updated_content_item) else: - updated_content.append(content_item) + # Check if this is a base64-encoded unified file ID without mapping + is_unified_file_id = _is_base64_encoded_unified_file_id(file_id) + if is_unified_file_id: + # Fallback: decode unified file ID + unified_file_id = convert_b64_uid_to_unified_uid(file_id) + if "llm_output_file_id," in unified_file_id: + provider_file_id = unified_file_id.split( + "llm_output_file_id," + )[1].split(";")[0] + + updated_content_item = content_item.copy() + updated_content_item["file_id"] = provider_file_id + updated_content.append(updated_content_item) + else: + # Not a managed file, keep as-is + updated_content.append(content_item) else: updated_content.append(content_item) else: @@ -506,6 +527,68 @@ def update_responses_input_with_model_file_ids( return updated_input +def update_responses_tools_with_model_file_ids( + tools: Optional[List[Dict[str, Any]]], + model_id: Optional[str] = None, + model_file_id_mapping: Optional[Dict[str, Dict[str, str]]] = None, +) -> Optional[List[Dict[str, Any]]]: + """ + Updates responses API tools with provider-specific file IDs. + + Handles code_interpreter tools with container.file_ids. + + Args: + tools: The responses API tools parameter + model_id: The model ID to use for looking up provider-specific file IDs + model_file_id_mapping: Dictionary mapping litellm file IDs to provider file IDs + Format: {"litellm_file_id": {"model_id": "provider_file_id"}} + """ + if not tools or not isinstance(tools, list): + return tools + + if not model_file_id_mapping or not model_id: + return tools + + updated_tools = [] + for tool in tools: + if not isinstance(tool, dict): + updated_tools.append(tool) + continue + + updated_tool = tool.copy() + + # Handle code_interpreter with container file_ids + if tool.get("type") == "code_interpreter": + container = tool.get("container") + if isinstance(container, dict): + container_file_ids = container.get("file_ids") + if isinstance(container_file_ids, list): + updated_file_ids = [] + for file_id in container_file_ids: + if isinstance(file_id, str): + # Check if we have a mapping for this file ID + if file_id in model_file_id_mapping: + # Map to provider-specific file ID + provider_file_id = ( + model_file_id_mapping.get(file_id, {}).get(model_id) + or file_id + ) + updated_file_ids.append(provider_file_id) + else: + updated_file_ids.append(file_id) + else: + updated_file_ids.append(file_id) + + # Update the tool with new file IDs + updated_container = container.copy() + updated_container["file_ids"] = updated_file_ids + updated_tool["container"] = updated_container + + updated_tools.append(updated_tool) + + return updated_tools + + def extract_file_data(file_data: FileTypes) -> ExtractedFileData: """ Extracts and processes file data from various input formats. @@ -1189,3 +1272,59 @@ def parse_tool_call_arguments( ) raise ValueError(error_message) from e + + +def split_concatenated_json_objects(raw: str) -> List[Dict[str, Any]]: + """ + Split a string that contains one or more concatenated JSON objects into + a list of parsed dicts. + + LLM providers (notably Bedrock Claude Sonnet 4.5) sometimes return + multiple tool-call argument objects concatenated in a single + ``arguments`` string, e.g.:: + + '{"command":["curl",...]}{"command":["curl",...]}{"command":["curl",...]}' + + ``json.loads()`` fails on this with ``JSONDecodeError: Extra data``. + This helper uses ``json.JSONDecoder.raw_decode()`` to walk the string + and extract each JSON object individually. + + Returns + ------- + list[dict] + A list of parsed dicts – one per JSON object found. If *raw* is + empty or whitespace-only, an empty list is returned. + + Raises + ------ + json.JSONDecodeError + If the string contains text that cannot be parsed as JSON at all. + """ + import json + + raw = raw.strip() + if not raw: + return [] + + decoder = json.JSONDecoder() + results: List[Dict[str, Any]] = [] + idx = 0 + length = len(raw) + + while idx < length: + # Skip whitespace between objects + while idx < length and raw[idx] in " \t\n\r": + idx += 1 + if idx >= length: + break + + obj, end_idx = decoder.raw_decode(raw, idx) + if isinstance(obj, dict): + results.append(obj) + else: + # Non-dict JSON value – wrap in empty dict (Bedrock requires + # toolUse.input to be an object). + results.append({}) + idx = end_idx + + return results diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index c4c56a8d335..c907ed32b95 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -2190,6 +2190,16 @@ def anthropic_messages_pt( # noqa: PLR0915 while msg_i < len(messages) and messages[msg_i]["role"] == "assistant": assistant_content_block: ChatCompletionAssistantMessage = messages[msg_i] # type: ignore + # Extract compaction_blocks from provider_specific_fields and add them first + _provider_specific_fields_raw = assistant_content_block.get( + "provider_specific_fields" + ) + if isinstance(_provider_specific_fields_raw, dict): + _compaction_blocks = _provider_specific_fields_raw.get("compaction_blocks") + if _compaction_blocks and isinstance(_compaction_blocks, list): + # Add compaction blocks at the beginning of assistant content : https://platform.claude.com/docs/en/build-with-claude/compaction + assistant_content.extend(_compaction_blocks) # type: ignore + thinking_blocks = assistant_content_block.get("thinking_blocks", None) if ( thinking_blocks is not None @@ -3277,25 +3287,68 @@ def _convert_to_bedrock_tool_call_invoke( - extract name - extract id """ + from litellm.litellm_core_utils.prompt_templates.common_utils import ( + split_concatenated_json_objects, + ) try: _parts_list: List[BedrockContentBlock] = [] for tool in tool_calls: if "function" in tool: - id = tool["id"] + tool_id = tool["id"] name = tool["function"].get("name", "") arguments = tool["function"].get("arguments", "") - arguments_dict = json.loads(arguments) if arguments else {} - # Ensure arguments_dict is always a dict (Bedrock requires toolUse.input to be an object) - # When some providers return arguments: '""' (JSON-encoded empty string), json.loads returns "" - if not isinstance(arguments_dict, dict): - arguments_dict = {} + if not arguments or not arguments.strip(): arguments_dict = {} else: - arguments_dict = json.loads(arguments) + try: + arguments_dict = json.loads(arguments) + # Ensure arguments_dict is always a dict + # (Bedrock requires toolUse.input to be an object). + # Some providers return arguments: '""' which + # json.loads decodes to a bare string. + if not isinstance(arguments_dict, dict): + arguments_dict = {} + except json.JSONDecodeError: + # The model may return multiple JSON objects + # concatenated in a single arguments string, e.g. + # '{"cmd":"a"}{"cmd":"b"}{"cmd":"c"}' + # Split them and emit one toolUse block per object. + # Fixes: https://github.com/BerriAI/litellm/issues/20543 + parsed_objects = split_concatenated_json_objects( + arguments + ) + if parsed_objects: + # First object keeps the original tool id. + for obj_idx, obj in enumerate(parsed_objects): + block_id = ( + tool_id + if obj_idx == 0 + else f"{tool_id}_{obj_idx}" + ) + bedrock_tool = BedrockToolUseBlock( + input=obj, name=name, toolUseId=block_id + ) + _parts_list.append( + BedrockContentBlock(toolUse=bedrock_tool) + ) + # cache_control applies to the whole original + # tool call; attach after the last split block. + if tool.get("cache_control", None) is not None: + _parts_list.append( + BedrockContentBlock( + cachePoint=CachePointBlock( + type="default" + ) + ) + ) + continue + # Fallback: no objects extracted — use empty dict. + arguments_dict = {} + bedrock_tool = BedrockToolUseBlock( - input=arguments_dict, name=name, toolUseId=id + input=arguments_dict, name=name, toolUseId=tool_id ) bedrock_content_block = BedrockContentBlock(toolUse=bedrock_tool) _parts_list.append(bedrock_content_block) @@ -3987,10 +4040,12 @@ class BedrockConverseMessagesProcessor: assistant_parts=assistants_parts, ) elif element["type"] == "text": - assistants_part = BedrockContentBlock( - text=element["text"] - ) - assistants_parts.append(assistants_part) + # Skip completely empty strings to avoid blank content blocks + if element.get("text", "").strip(): + assistants_part = BedrockContentBlock( + text=element["text"] + ) + assistants_parts.append(assistants_part) elif element["type"] == "image_url": if isinstance(element["image_url"], dict): image_url = element["image_url"]["url"] @@ -4015,9 +4070,12 @@ class BedrockConverseMessagesProcessor: elif _assistant_content is not None and isinstance( _assistant_content, str ): - assistant_content.append( - BedrockContentBlock(text=_assistant_content) - ) + # Skip completely empty strings to avoid blank content blocks + if _assistant_content.strip(): + assistant_content.append( + BedrockContentBlock(text=_assistant_content) + ) + # If content is empty/whitespace, skip it (don't add a placeholder) # Add cache point block for assistant string content _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( @@ -4348,12 +4406,11 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 assistant_parts=assistants_parts, ) elif element["type"] == "text": - # AWS Bedrock doesn't allow empty or whitespace-only text content, so use placeholder for empty strings - text_content = ( - element["text"] if element["text"].strip() else "." - ) - assistants_part = BedrockContentBlock(text=text_content) - assistants_parts.append(assistants_part) + # AWS Bedrock doesn't allow empty or whitespace-only text content + # Skip completely empty strings to avoid blank content blocks + if element.get("text", "").strip(): + assistants_part = BedrockContentBlock(text=element["text"]) + assistants_parts.append(assistants_part) elif element["type"] == "image_url": if isinstance(element["image_url"], dict): image_url = element["image_url"]["url"] @@ -4376,9 +4433,9 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 assistants_parts.append(_cache_point_block) assistant_content.extend(assistants_parts) elif _assistant_content is not None and isinstance(_assistant_content, str): - # AWS Bedrock doesn't allow empty or whitespace-only text content, so use placeholder for empty strings - text_content = _assistant_content if _assistant_content.strip() else "." - assistant_content.append(BedrockContentBlock(text=text_content)) + # Skip completely empty strings to avoid blank content blocks + if _assistant_content.strip(): + assistant_content.append(BedrockContentBlock(text=_assistant_content)) # Add cache point block for assistant string content _cache_point_block = ( litellm.AmazonConverseConfig()._get_cache_point_block( diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py index 0effed3db70..5d6d1fbc1c5 100644 --- a/litellm/litellm_core_utils/redact_messages.py +++ b/litellm/litellm_core_utils/redact_messages.py @@ -130,45 +130,55 @@ def perform_redaction(model_call_details: dict, result): def should_redact_message_logging(model_call_details: dict) -> bool: """ Determine if message logging should be redacted. + + Priority order: + 1. Dynamic parameter (turn_off_message_logging in request) + 2. Headers (litellm-disable-message-redaction / litellm-enable-message-redaction) + 3. Global setting (litellm.turn_off_message_logging) """ litellm_params = model_call_details.get("litellm_params", {}) metadata_field = get_metadata_variable_name_from_kwargs(litellm_params) metadata = litellm_params.get(metadata_field, {}) - - # Get headers from the metadata - request_headers = metadata.get("headers", {}) if isinstance(metadata, dict) else {} + if not isinstance(metadata, dict): + # Fall back: litellm_metadata was None, try metadata + metadata = litellm_params.get("metadata", {}) + if not isinstance(metadata, dict): + metadata = {} - possible_request_headers = [ + # Get headers from the metadata + request_headers = metadata.get("headers", {}) + + # Check for headers that explicitly control redaction + if request_headers and bool( + request_headers.get("litellm-disable-message-redaction", False) + ): + # User explicitly disabled redaction via header + return False + + possible_enable_headers = [ "litellm-enable-message-redaction", # old header. maintain backwards compatibility "x-litellm-enable-message-redaction", # new header ] is_redaction_enabled_via_header = False - for header in possible_request_headers: + for header in possible_enable_headers: if bool(request_headers.get(header, False)): is_redaction_enabled_via_header = True break - # check if user opted out of logging message/response to callbacks - if ( - litellm.turn_off_message_logging is not True - and is_redaction_enabled_via_header is not True - and _get_turn_off_message_logging_from_dynamic_params(model_call_details) - is not True - ): - return False - - if request_headers and bool( - request_headers.get("litellm-disable-message-redaction", False) - ): - return False - - # user has OPTED OUT of message redaction - if _get_turn_off_message_logging_from_dynamic_params(model_call_details) is False: - return False - - return True + # Priority 1: Check dynamic parameter first (if explicitly set) + dynamic_turn_off = _get_turn_off_message_logging_from_dynamic_params(model_call_details) + if dynamic_turn_off is not None: + # Dynamic parameter is explicitly set, use it + return dynamic_turn_off + + # Priority 2: Check if header explicitly enables redaction + if is_redaction_enabled_via_header: + return True + + # Priority 3: Fall back to global setting + return litellm.turn_off_message_logging is True def redact_message_input_output_from_logging( diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index 53252df0a28..76c7246b87e 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -1,6 +1,6 @@ import base64 import time -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union, cast +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union, cast from litellm.types.llms.openai import ( ChatCompletionAssistantContentValue, @@ -326,10 +326,22 @@ class ChunkProcessor: thinking_blocks: List[ Union["ChatCompletionThinkingBlock", "ChatCompletionRedactedThinkingBlock"] ] = [] - combined_thinking_text: Optional[str] = None - data: Optional[str] = None - signature: Optional[str] = None - type: Literal["thinking", "redacted_thinking"] = "thinking" + current_thinking_text_parts: List[str] = [] + current_signature: Optional[str] = None + + def _flush_thinking_block() -> None: + nonlocal current_thinking_text_parts, current_signature + if len(current_thinking_text_parts) > 0 and current_signature: + thinking_blocks.append( + ChatCompletionThinkingBlock( + type="thinking", + thinking="".join(current_thinking_text_parts), + signature=current_signature, + ) + ) + current_thinking_text_parts = [] + current_signature = None + for chunk in chunks: choices = chunk["choices"] for choice in choices: @@ -339,33 +351,25 @@ class ChunkProcessor: for thinking_block in thinking: thinking_type = thinking_block.get("type", None) if thinking_type and thinking_type == "redacted_thinking": - type = "redacted_thinking" - data = thinking_block.get("data", None) + _flush_thinking_block() + redacted_data = thinking_block.get("data", None) + if redacted_data: + thinking_blocks.append( + ChatCompletionRedactedThinkingBlock( + type="redacted_thinking", + data=redacted_data, + ) + ) else: - type = "thinking" thinking_text = thinking_block.get("thinking", None) if thinking_text: - if combined_thinking_text is None: - combined_thinking_text = "" - - combined_thinking_text += thinking_text + current_thinking_text_parts.append(thinking_text) signature = thinking_block.get("signature", None) + if signature: + current_signature = signature + _flush_thinking_block() - if combined_thinking_text and type == "thinking" and signature: - thinking_blocks.append( - ChatCompletionThinkingBlock( - type=type, - thinking=combined_thinking_text, - signature=signature, - ) - ) - elif data and type == "redacted_thinking": - thinking_blocks.append( - ChatCompletionRedactedThinkingBlock( - type=type, - data=data, - ) - ) + _flush_thinking_block() if len(thinking_blocks) > 0: return thinking_blocks diff --git a/litellm/litellm_core_utils/token_counter.py b/litellm/litellm_core_utils/token_counter.py index a99bd1cd0f3..6b9e51034c0 100644 --- a/litellm/litellm_core_utils/token_counter.py +++ b/litellm/litellm_core_utils/token_counter.py @@ -706,7 +706,7 @@ def _count_content_list( if isinstance(c, str): num_tokens += count_function(c) elif c["type"] == "text": - num_tokens += count_function(c.get("text", "")) + num_tokens += count_function(str(c.get("text", ""))) elif c["type"] == "image_url": image_url = c.get("image_url") num_tokens += _count_image_tokens( @@ -722,7 +722,7 @@ def _count_content_list( elif c["type"] == "thinking": # Claude extended thinking content block # Count the thinking text and skip signature (opaque signature blob) - thinking_text = c.get("thinking", "") + thinking_text = str(c.get("thinking", "")) if thinking_text: num_tokens += count_function(thinking_text) else: diff --git a/litellm/llms/a2a/__init__.py b/litellm/llms/a2a/__init__.py new file mode 100644 index 00000000000..043efa5e8bf --- /dev/null +++ b/litellm/llms/a2a/__init__.py @@ -0,0 +1,6 @@ +""" +A2A (Agent-to-Agent) Protocol Provider for LiteLLM +""" +from .chat.transformation import A2AConfig + +__all__ = ["A2AConfig"] diff --git a/litellm/llms/a2a/chat/__init__.py b/litellm/llms/a2a/chat/__init__.py new file mode 100644 index 00000000000..76bf4dd71d9 --- /dev/null +++ b/litellm/llms/a2a/chat/__init__.py @@ -0,0 +1,6 @@ +""" +A2A Chat Completion Implementation +""" +from .transformation import A2AConfig + +__all__ = ["A2AConfig"] diff --git a/litellm/llms/a2a/chat/guardrail_translation/README.md b/litellm/llms/a2a/chat/guardrail_translation/README.md new file mode 100644 index 00000000000..1e18f5cda3a --- /dev/null +++ b/litellm/llms/a2a/chat/guardrail_translation/README.md @@ -0,0 +1,155 @@ +# A2A Protocol Guardrail Translation Handler + +Handler for processing A2A (Agent-to-Agent) Protocol messages with guardrails. + +## Overview + +This handler processes A2A JSON-RPC 2.0 input/output by: +1. Extracting text from message parts (`kind: "text"`) +2. Applying guardrails to text content +3. Mapping guardrailed text back to original structure + +## A2A Protocol Format + +### Input Format (JSON-RPC 2.0) + +```json +{ + "jsonrpc": "2.0", + "id": "request-id", + "method": "message/send", + "params": { + "message": { + "kind": "message", + "messageId": "...", + "role": "user", + "parts": [ + {"kind": "text", "text": "Hello, my SSN is 123-45-6789"} + ] + }, + "metadata": { + "guardrails": ["block-ssn"] + } + } +} +``` + +### Output Formats + +The handler supports multiple A2A response formats: + +**Direct message:** +```json +{ + "result": { + "kind": "message", + "parts": [{"kind": "text", "text": "Response text"}] + } +} +``` + +**Nested message:** +```json +{ + "result": { + "message": { + "parts": [{"kind": "text", "text": "Response text"}] + } + } +} +``` + +**Task with artifacts:** +```json +{ + "result": { + "kind": "task", + "artifacts": [ + {"parts": [{"kind": "text", "text": "Artifact text"}]} + ] + } +} +``` + +**Task with status message:** +```json +{ + "result": { + "kind": "task", + "status": { + "message": { + "parts": [{"kind": "text", "text": "Status message"}] + } + } + } +} +``` + +**Streaming artifact-update:** +```json +{ + "result": { + "kind": "artifact-update", + "artifact": { + "parts": [{"kind": "text", "text": "Streaming text"}] + } + } +} +``` + +## Usage + +The handler is automatically discovered and applied when guardrails are used with A2A endpoints. + +### Via LiteLLM Proxy + +```bash +curl -X POST 'http://localhost:4000/a2a/my-agent' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer your-api-key' \ +-d '{ + "jsonrpc": "2.0", + "id": "1", + "method": "message/send", + "params": { + "message": { + "kind": "message", + "messageId": "msg-1", + "role": "user", + "parts": [{"kind": "text", "text": "Hello, my SSN is 123-45-6789"}] + }, + "metadata": { + "guardrails": ["block-ssn"] + } + } +}' +``` + +### Specifying Guardrails + +Guardrails can be specified in the A2A request via the `metadata.guardrails` field: + +```json +{ + "params": { + "message": {...}, + "metadata": { + "guardrails": ["block-ssn", "pii-filter"] + } + } +} +``` + +## Extension + +Override these methods to customize behavior: + +- `_extract_texts_from_result()`: Custom text extraction from A2A responses +- `_extract_texts_from_parts()`: Custom text extraction from message parts +- `_apply_text_to_path()`: Custom application of guardrailed text + +## Call Types + +This handler is registered for: +- `CallTypes.send_message`: Synchronous A2A message sending +- `CallTypes.asend_message`: Asynchronous A2A message sending diff --git a/litellm/llms/a2a/chat/guardrail_translation/__init__.py b/litellm/llms/a2a/chat/guardrail_translation/__init__.py new file mode 100644 index 00000000000..13c20677485 --- /dev/null +++ b/litellm/llms/a2a/chat/guardrail_translation/__init__.py @@ -0,0 +1,11 @@ +"""A2A Protocol handler for Unified Guardrails.""" + +from litellm.llms.a2a.chat.guardrail_translation.handler import A2AGuardrailHandler +from litellm.types.utils import CallTypes + +guardrail_translation_mappings = { + CallTypes.send_message: A2AGuardrailHandler, + CallTypes.asend_message: A2AGuardrailHandler, +} + +__all__ = ["guardrail_translation_mappings"] diff --git a/litellm/llms/a2a/chat/guardrail_translation/handler.py b/litellm/llms/a2a/chat/guardrail_translation/handler.py new file mode 100644 index 00000000000..770453f2def --- /dev/null +++ b/litellm/llms/a2a/chat/guardrail_translation/handler.py @@ -0,0 +1,315 @@ +""" +A2A Protocol Handler for Unified Guardrails + +This module provides guardrail translation support for A2A (Agent-to-Agent) Protocol. +It handles both JSON-RPC 2.0 input requests and output responses, extracting text +from message parts and applying guardrails. + +A2A Protocol Format: +- Input: JSON-RPC 2.0 with params.message.parts containing text parts +- Output: JSON-RPC 2.0 with result containing message/artifact parts +""" + +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union + +from litellm._logging import verbose_proxy_logger +from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs + +if TYPE_CHECKING: + from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth + + +class A2AGuardrailHandler(BaseTranslation): + """ + Handler for processing A2A Protocol messages with guardrails. + + This class provides methods to: + 1. Process input messages (pre-call hook) - extracts text from A2A message parts + 2. Process output responses (post-call hook) - extracts text from A2A response parts + + A2A Message Format: + - Input: params.message.parts[].text (where kind == "text") + - Output: result.message.parts[].text or result.artifacts[].parts[].text + """ + + async def process_input_messages( + self, + data: dict, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> Any: + """ + Process A2A input messages by applying guardrails to text content. + + Extracts text from A2A message parts and applies guardrails. + + Args: + data: The A2A JSON-RPC 2.0 request data + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + + Returns: + Modified data with guardrails applied to text content + """ + # A2A request format: { "params": { "message": { "parts": [...] } } } + params = data.get("params", {}) + message = params.get("message", {}) + parts = message.get("parts", []) + + if not parts: + verbose_proxy_logger.debug("A2A: No parts in message, skipping guardrail") + return data + + texts_to_check: List[str] = [] + text_part_indices: List[int] = [] # Track which parts contain text + + # Step 1: Extract text from all text parts + for part_idx, part in enumerate(parts): + if part.get("kind") == "text": + text = part.get("text", "") + if text: + texts_to_check.append(text) + text_part_indices.append(part_idx) + + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + + # Pass the structured A2A message to guardrails + inputs["structured_messages"] = [message] + + # Include agent model info if available + model = data.get("model") + if model: + inputs["model"] = model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + + # Step 3: Apply guardrailed text back to original parts + if guardrailed_texts and len(guardrailed_texts) == len(text_part_indices): + for task_idx, part_idx in enumerate(text_part_indices): + parts[part_idx]["text"] = guardrailed_texts[task_idx] + + verbose_proxy_logger.debug("A2A: Processed input message: %s", message) + + return data + + async def process_output_response( + self, + response: Any, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> Any: + """ + Process A2A output response by applying guardrails to text content. + + Handles multiple A2A response formats: + - Direct message: {"result": {"kind": "message", "parts": [...]}} + - Nested message: {"result": {"message": {"parts": [...]}}} + - Task with artifacts: {"result": {"kind": "task", "artifacts": [{"parts": [...]}]}} + - Task with status message: {"result": {"kind": "task", "status": {"message": {"parts": [...]}}}} + + Args: + response: A2A JSON-RPC 2.0 response dict or object + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata + + Returns: + Modified response with guardrails applied to text content + """ + # Handle both dict and Pydantic model responses + if hasattr(response, "model_dump"): + response_dict = response.model_dump() + is_pydantic = True + elif isinstance(response, dict): + response_dict = response + is_pydantic = False + else: + verbose_proxy_logger.warning( + "A2A: Unknown response type %s, skipping guardrail", type(response) + ) + return response + + result = response_dict.get("result", {}) + if not result or not isinstance(result, dict): + verbose_proxy_logger.debug("A2A: No result in response, skipping guardrail") + return response + + # Find all text-containing parts in the response + texts_to_check: List[str] = [] + # Each mapping is (path_to_parts_list, part_index) + # path_to_parts_list is a tuple of keys to navigate to the parts list + task_mappings: List[Tuple[Tuple[str, ...], int]] = [] + + # Extract texts from all possible locations + self._extract_texts_from_result( + result=result, + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + if not texts_to_check: + verbose_proxy_logger.debug("A2A: No text content in response") + return response + + # Step 2: Apply guardrail to all texts in batch + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response_dict} + + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + + # Step 3: Apply guardrailed text back to original response + if guardrailed_texts and len(guardrailed_texts) == len(task_mappings): + for task_idx, (path, part_idx) in enumerate(task_mappings): + self._apply_text_to_path( + result=result, + path=path, + part_idx=part_idx, + text=guardrailed_texts[task_idx], + ) + + verbose_proxy_logger.debug("A2A: Processed output response") + + # Update the original response + if is_pydantic: + # For Pydantic models, we need to update the underlying dict + # and the model will reflect the changes + response_dict["result"] = result + return response + else: + response["result"] = result + return response + + def _extract_texts_from_result( + self, + result: Dict[str, Any], + texts_to_check: List[str], + task_mappings: List[Tuple[Tuple[str, ...], int]], + ) -> None: + """ + Extract text from all possible locations in an A2A result. + + Handles multiple response formats: + 1. Direct message with parts: {"parts": [...]} + 2. Nested message: {"message": {"parts": [...]}} + 3. Task with artifacts: {"artifacts": [{"parts": [...]}]} + 4. Task with status message: {"status": {"message": {"parts": [...]}}} + 5. Streaming artifact-update: {"artifact": {"parts": [...]}} + """ + # Case 1: Direct parts in result (direct message) + if "parts" in result: + self._extract_texts_from_parts( + parts=result["parts"], + path=("parts",), + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + # Case 2: Nested message + message = result.get("message") + if message and isinstance(message, dict) and "parts" in message: + self._extract_texts_from_parts( + parts=message["parts"], + path=("message", "parts"), + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + # Case 3: Streaming artifact-update (singular artifact) + artifact = result.get("artifact") + if artifact and isinstance(artifact, dict) and "parts" in artifact: + self._extract_texts_from_parts( + parts=artifact["parts"], + path=("artifact", "parts"), + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + # Case 4: Task with status message + status = result.get("status", {}) + if isinstance(status, dict): + status_message = status.get("message") + if ( + status_message + and isinstance(status_message, dict) + and "parts" in status_message + ): + self._extract_texts_from_parts( + parts=status_message["parts"], + path=("status", "message", "parts"), + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + # Case 5: Task with artifacts (plural, array) + artifacts = result.get("artifacts", []) + if artifacts and isinstance(artifacts, list): + for artifact_idx, art in enumerate(artifacts): + if isinstance(art, dict) and "parts" in art: + self._extract_texts_from_parts( + parts=art["parts"], + path=("artifacts", str(artifact_idx), "parts"), + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) + + def _extract_texts_from_parts( + self, + parts: List[Dict[str, Any]], + path: Tuple[str, ...], + texts_to_check: List[str], + task_mappings: List[Tuple[Tuple[str, ...], int]], + ) -> None: + """Extract text from message parts.""" + for part_idx, part in enumerate(parts): + if part.get("kind") == "text": + text = part.get("text", "") + if text: + texts_to_check.append(text) + task_mappings.append((path, part_idx)) + + def _apply_text_to_path( + self, + result: Dict[Union[str, int], Any], + path: Tuple[str, ...], + part_idx: int, + text: str, + ) -> None: + """Apply guardrailed text back to the specified path in the result.""" + # Navigate to the parts list + current = result + for key in path: + if key.isdigit(): + # Array index + current = current[int(key)] + else: + current = current[key] + + # Update the text in the part + current[part_idx]["text"] = text diff --git a/litellm/llms/a2a/chat/streaming_iterator.py b/litellm/llms/a2a/chat/streaming_iterator.py new file mode 100644 index 00000000000..4b689414ddd --- /dev/null +++ b/litellm/llms/a2a/chat/streaming_iterator.py @@ -0,0 +1,103 @@ +""" +A2A Streaming Response Iterator +""" +from typing import Optional, Union + +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.types.utils import GenericStreamingChunk, ModelResponseStream + +from ..common_utils import extract_text_from_a2a_response + + +class A2AModelResponseIterator(BaseModelResponseIterator): + """ + Iterator for parsing A2A streaming responses. + + Converts A2A JSON-RPC streaming chunks to OpenAI-compatible format. + """ + + def __init__( + self, + streaming_response, + sync_stream: bool, + json_mode: Optional[bool] = False, + model: str = "a2a/agent", + ): + super().__init__( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + self.model = model + + def chunk_parser(self, chunk: dict) -> Union[GenericStreamingChunk, ModelResponseStream]: + """ + Parse A2A streaming chunk to OpenAI format. + + A2A chunk format: + { + "jsonrpc": "2.0", + "id": "request-id", + "result": { + "message": { + "parts": [{"kind": "text", "text": "content"}] + } + } + } + + Or for tasks: + { + "jsonrpc": "2.0", + "result": { + "kind": "task", + "status": {"state": "running"}, + "artifacts": [{"parts": [{"kind": "text", "text": "content"}]}] + } + } + """ + try: + # Extract text from A2A response + text = extract_text_from_a2a_response(chunk) + + # Determine finish reason + finish_reason = self._get_finish_reason(chunk) + + # Return generic streaming chunk + return GenericStreamingChunk( + text=text, + is_finished=bool(finish_reason), + finish_reason=finish_reason or "", + usage=None, + index=0, + tool_use=None, + ) + except Exception: + # Return empty chunk on parse error + return GenericStreamingChunk( + text="", + is_finished=False, + finish_reason="", + usage=None, + index=0, + tool_use=None, + ) + + def _get_finish_reason(self, chunk: dict) -> Optional[str]: + """Extract finish reason from A2A chunk""" + result = chunk.get("result", {}) + + # Check for task completion + if isinstance(result, dict): + status = result.get("status", {}) + if isinstance(status, dict): + state = status.get("state") + if state == "completed": + return "stop" + elif state == "failed": + return "stop" # Map failed state to 'stop' (valid finish_reason) + + # Check for [DONE] marker + if chunk.get("done") is True: + return "stop" + + return None diff --git a/litellm/llms/a2a/chat/transformation.py b/litellm/llms/a2a/chat/transformation.py new file mode 100644 index 00000000000..163cd5ab22e --- /dev/null +++ b/litellm/llms/a2a/chat/transformation.py @@ -0,0 +1,370 @@ +""" +A2A Protocol Transformation for LiteLLM +""" +import uuid +from typing import Any, Dict, Iterator, List, Optional, Union + +import httpx + +from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator +from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import Choices, Message, ModelResponse + +from ..common_utils import ( + A2AError, + convert_messages_to_prompt, + extract_text_from_a2a_response, +) +from .streaming_iterator import A2AModelResponseIterator + + +class A2AConfig(BaseConfig): + """ + Configuration for A2A (Agent-to-Agent) Protocol. + + Handles transformation between OpenAI and A2A JSON-RPC 2.0 formats. + """ + + @staticmethod + def resolve_agent_config_from_registry( + model: str, + api_base: Optional[str], + api_key: Optional[str], + headers: Optional[Dict[str, Any]], + optional_params: Dict[str, Any], + ) -> tuple[Optional[str], Optional[str], Optional[Dict[str, Any]]]: + """ + Resolve agent configuration from registry if model format is "a2a/". + + Extracts agent name from model string and looks up configuration in the + agent registry (if available in proxy context). + + Args: + model: Model string (e.g., "a2a/my-agent") + api_base: Explicit api_base (takes precedence over registry) + api_key: Explicit api_key (takes precedence over registry) + headers: Explicit headers (takes precedence over registry) + optional_params: Dict to merge additional litellm_params into + + Returns: + Tuple of (api_base, api_key, headers) with registry values filled in + """ + # Extract agent name from model (e.g., "a2a/my-agent" -> "my-agent") + agent_name = model.split("/", 1)[1] if "/" in model else None + + # Only lookup if agent name exists and some config is missing + if not agent_name or (api_base is not None and api_key is not None and headers is not None): + return api_base, api_key, headers + + # Try registry lookup (only available in proxy context) + try: + from litellm.proxy.agent_endpoints.agent_registry import ( + global_agent_registry, + ) + + agent = global_agent_registry.get_agent_by_name(agent_name) + if agent: + # Get api_base from agent card URL + if api_base is None and agent.agent_card_params: + api_base = agent.agent_card_params.get("url") + + # Get api_key, headers, and other params from litellm_params + if agent.litellm_params: + if api_key is None: + api_key = agent.litellm_params.get("api_key") + + if headers is None: + agent_headers = agent.litellm_params.get("headers") + if agent_headers: + headers = agent_headers + + # Merge other litellm_params (timeout, max_retries, etc.) + for key, value in agent.litellm_params.items(): + if key not in ["api_key", "api_base", "headers", "model"] and key not in optional_params: + optional_params[key] = value + except ImportError: + pass # Registry not available (not running in proxy context) + + return api_base, api_key, headers + + def get_supported_openai_params(self, model: str) -> List[str]: + """Return list of supported OpenAI parameters""" + return [ + "stream", + "temperature", + "max_tokens", + "top_p", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + """ + Map OpenAI parameters to A2A parameters. + + For A2A protocol, we need to map the stream parameter so + transform_request can determine which JSON-RPC method to use. + """ + # Map stream parameter + for param, value in non_default_params.items(): + if param == "stream" and value is True: + optional_params["stream"] = value + + return optional_params + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate environment and set headers for A2A requests. + + Args: + headers: Request headers dict + model: Model name + messages: Messages list + optional_params: Optional parameters + litellm_params: LiteLLM parameters + api_key: API key (optional for A2A) + api_base: API base URL + + Returns: + Updated headers dict + """ + # Ensure Content-Type is set to application/json for JSON-RPC 2.0 + if "content-type" not in headers and "Content-Type" not in headers: + headers["Content-Type"] = "application/json" + + # Add Authorization header if API key is provided + if api_key is not None: + headers["Authorization"] = f"Bearer {api_key}" + + return headers + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete A2A agent endpoint URL. + + A2A agents use JSON-RPC 2.0 at the base URL, not specific paths. + The method (message/send or message/stream) is specified in the + JSON-RPC request body, not in the URL. + + Args: + api_base: Base URL of the A2A agent (e.g., "http://0.0.0.0:9999") + api_key: API key (not used for URL construction) + model: Model name (not used for A2A, agent determined by api_base) + optional_params: Optional parameters + litellm_params: LiteLLM parameters + stream: Whether this is a streaming request (affects JSON-RPC method) + + Returns: + Complete URL for the A2A endpoint (base URL) + """ + if api_base is None: + raise ValueError("api_base is required for A2A provider") + + # A2A uses JSON-RPC 2.0 at the base URL + # Remove trailing slash for consistency + return api_base.rstrip("/") + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform OpenAI request to A2A JSON-RPC 2.0 format. + + Args: + model: Model name + messages: List of OpenAI messages + optional_params: Optional parameters + litellm_params: LiteLLM parameters + headers: Request headers + + Returns: + A2A JSON-RPC 2.0 request dict + """ + # Generate request ID + request_id = str(uuid.uuid4()) + + if not messages: + raise ValueError("At least one message is required for A2A completion") + + # Convert all messages to maintain conversation history + # Use helper to format conversation with role prefixes + full_context = convert_messages_to_prompt(messages) + + # Create single A2A message with full conversation context + a2a_message = { + "role": "user", + "parts": [{"kind": "text", "text": full_context}], + "messageId": str(uuid.uuid4()), + } + + # Build JSON-RPC 2.0 request + # For A2A protocol, the method is "message/send" for non-streaming + # and "message/stream" for streaming + stream = optional_params.get("stream", False) + method = "message/stream" if stream else "message/send" + + request_data = { + "jsonrpc": "2.0", + "id": request_id, + "method": method, + "params": { + "message": a2a_message + } + } + + return request_data + + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: Any, + request_data: dict, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform A2A JSON-RPC 2.0 response to OpenAI format. + + Args: + model: Model name + raw_response: HTTP response from A2A agent + model_response: Model response object to populate + logging_obj: Logging object + request_data: Original request data + messages: Original messages + optional_params: Optional parameters + litellm_params: LiteLLM parameters + encoding: Encoding object + api_key: API key + json_mode: JSON mode flag + + Returns: + Populated ModelResponse object + """ + try: + response_json = raw_response.json() + except Exception as e: + raise A2AError( + status_code=raw_response.status_code, + message=f"Failed to parse A2A response: {str(e)}", + headers=dict(raw_response.headers), + ) + + # Check for JSON-RPC error + if "error" in response_json: + error = response_json["error"] + raise A2AError( + status_code=raw_response.status_code, + message=f"A2A error: {error.get('message', 'Unknown error')}", + headers=dict(raw_response.headers), + ) + + # Extract text from A2A response + text = extract_text_from_a2a_response(response_json) + + # Populate model response + model_response.choices = [ + Choices( + finish_reason="stop", + index=0, + message=Message( + content=text, + role="assistant", + ), + ) + ] + + # Set model + model_response.model = model + + # Set ID from response + model_response.id = response_json.get("id", str(uuid.uuid4())) + + return model_response + + def get_model_response_iterator( + self, + streaming_response: Union[Iterator, Any], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> BaseModelResponseIterator: + """ + Get streaming iterator for A2A responses. + + Args: + streaming_response: Streaming response iterator + sync_stream: Whether this is a sync stream + json_mode: JSON mode flag + + Returns: + A2A streaming iterator + """ + return A2AModelResponseIterator( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + + def _openai_message_to_a2a_message(self, message: Dict[str, Any]) -> Dict[str, Any]: + """ + Convert OpenAI message to A2A message format. + + Args: + message: OpenAI message dict + + Returns: + A2A message dict + """ + content = message.get("content", "") + role = message.get("role", "user") + + return { + "role": role, + "parts": [{"kind": "text", "text": str(content)}], + "messageId": str(uuid.uuid4()), + } + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + """Return appropriate error class for A2A errors""" + # Convert headers to dict if needed + headers_dict = dict(headers) if isinstance(headers, httpx.Headers) else headers + return A2AError( + status_code=status_code, + message=error_message, + headers=headers_dict, + ) diff --git a/litellm/llms/a2a/common_utils.py b/litellm/llms/a2a/common_utils.py new file mode 100644 index 00000000000..116e1205409 --- /dev/null +++ b/litellm/llms/a2a/common_utils.py @@ -0,0 +1,152 @@ +""" +Common utilities for A2A (Agent-to-Agent) Protocol +""" +from typing import Any, Dict, List + +from pydantic import BaseModel + +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + convert_content_list_to_str, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.types.llms.openai import AllMessageValues + + +class A2AError(BaseLLMException): + """Base exception for A2A protocol errors""" + + def __init__( + self, + status_code: int, + message: str, + headers: Dict[str, Any] = {}, + ): + super().__init__( + status_code=status_code, + message=message, + headers=headers, + ) + + +def convert_messages_to_prompt(messages: List[AllMessageValues]) -> str: + """ + Convert OpenAI messages to a single prompt string for A2A agent. + + Formats each message as "{role}: {content}" and joins with newlines + to preserve conversation history. Handles both string and list content. + + Args: + messages: List of OpenAI-format messages + + Returns: + Formatted prompt string with full conversation context + """ + conversation_parts = [] + for msg in messages: + # Use LiteLLM's helper to extract text from content (handles both str and list) + content_text = convert_content_list_to_str(message=msg) + + # Get role + if isinstance(msg, BaseModel): + role = msg.model_dump().get("role", "user") + elif isinstance(msg, dict): + role = msg.get("role", "user") + else: + role = dict(msg).get("role", "user") # type: ignore + + if content_text: + conversation_parts.append(f"{role}: {content_text}") + + return "\n".join(conversation_parts) + + +def extract_text_from_a2a_message( + message: Dict[str, Any], depth: int = 0, max_depth: int = 10 +) -> str: + """ + Extract text content from A2A message parts. + + Args: + message: A2A message dict with 'parts' containing text parts + depth: Current recursion depth (internal use) + max_depth: Maximum recursion depth to prevent infinite loops + + Returns: + Concatenated text from all text parts + """ + if message is None or depth >= max_depth: + return "" + + parts = message.get("parts", []) + text_parts: List[str] = [] + + for part in parts: + if part.get("kind") == "text": + text_parts.append(part.get("text", "")) + # Handle nested parts if they exist + elif "parts" in part: + nested_text = extract_text_from_a2a_message(part, depth + 1, max_depth) + if nested_text: + text_parts.append(nested_text) + + return " ".join(text_parts) + + +def extract_text_from_a2a_response( + response_dict: Dict[str, Any], max_depth: int = 10 +) -> str: + """ + Extract text content from A2A response result. + + Args: + response_dict: A2A response dict with 'result' containing message + max_depth: Maximum recursion depth to prevent infinite loops + + Returns: + Text from response message parts + """ + result = response_dict.get("result", {}) + if not isinstance(result, dict): + return "" + + # A2A response can have different formats: + # 1. Direct message: {"result": {"kind": "message", "parts": [...]}} + # 2. Nested message: {"result": {"message": {"parts": [...]}}} + # 3. Task with artifacts: {"result": {"kind": "task", "artifacts": [{"parts": [...]}]}} + # 4. Task with status message: {"result": {"kind": "task", "status": {"message": {"parts": [...]}}}} + # 5. Streaming artifact-update: {"result": {"kind": "artifact-update", "artifact": {"parts": [...]}}} + + # Check if result itself has parts (direct message) + if "parts" in result: + return extract_text_from_a2a_message(result, depth=0, max_depth=max_depth) + + # Check for nested message + message = result.get("message") + if message: + return extract_text_from_a2a_message(message, depth=0, max_depth=max_depth) + + # Check for streaming artifact-update (singular artifact) + artifact = result.get("artifact") + if artifact and isinstance(artifact, dict): + return extract_text_from_a2a_message( + artifact, depth=0, max_depth=max_depth + ) + + # Check for task status message (common in Gemini A2A agents) + status = result.get("status", {}) + if isinstance(status, dict): + status_message = status.get("message") + if status_message: + return extract_text_from_a2a_message( + status_message, depth=0, max_depth=max_depth + ) + + # Handle task result with artifacts (plural, array) + artifacts = result.get("artifacts", []) + if artifacts and len(artifacts) > 0: + first_artifact = artifacts[0] + return extract_text_from_a2a_message( + first_artifact, depth=0, max_depth=max_depth + ) + + return "" diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 8e1016bd5bd..a14e7d118e8 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -34,6 +34,7 @@ from litellm.types.llms.openai import ( ) from litellm.types.utils import ( ChatCompletionMessageToolCall, + Choices, GenericGuardrailAPIInputs, ModelResponse, ) @@ -76,7 +77,8 @@ class AnthropicMessagesHandler(BaseTranslation): chat_completion_compatible_request, tool_name_mapping = ( LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai( - anthropic_message_request=cast(AnthropicMessagesRequest, data) + # Use a shallow copy to avoid mutating request data (pop on litellm_metadata). + anthropic_message_request=cast(AnthropicMessagesRequest, data.copy()) ) ) @@ -84,9 +86,9 @@ class AnthropicMessagesHandler(BaseTranslation): texts_to_check: List[str] = [] images_to_check: List[str] = [] - tools_to_check: List[ChatCompletionToolParam] = ( - chat_completion_compatible_request.get("tools", []) - ) + tools_to_check: List[ + ChatCompletionToolParam + ] = chat_completion_compatible_request.get("tools", []) task_mappings: List[Tuple[int, Optional[int]]] = [] # Track (message_index, content_index) for each text # content_index is None for string content, int for list content @@ -282,7 +284,10 @@ class AnthropicMessagesHandler(BaseTranslation): if hasattr(content_block, "model_dump"): block_dict = content_block.model_dump() else: - block_dict = {"type": block_type, "text": getattr(content_block, "text", None)} + block_dict = { + "type": block_type, + "text": getattr(content_block, "text", None), + } else: continue @@ -358,30 +363,40 @@ class AnthropicMessagesHandler(BaseTranslation): """ has_ended = self._check_streaming_has_ended(responses_so_far) if has_ended: - # build the model response from the responses_so_far - model_response = cast( - ModelResponse, - AnthropicPassthroughLoggingHandler._build_complete_streaming_response( - all_chunks=responses_so_far, - litellm_logging_obj=cast("LiteLLMLoggingObj", litellm_logging_obj), - model="", - ), + built_response = AnthropicPassthroughLoggingHandler._build_complete_streaming_response( + all_chunks=responses_so_far, + litellm_logging_obj=cast("LiteLLMLoggingObj", litellm_logging_obj), + model="", ) - tool_calls_list = cast(Optional[List[ChatCompletionMessageToolCall]], model_response.choices[0].message.tool_calls) # type: ignore - string_so_far = model_response.choices[0].message.content # type: ignore - guardrail_inputs = GenericGuardrailAPIInputs() - if string_so_far: - guardrail_inputs["texts"] = [string_so_far] - if tool_calls_list: - guardrail_inputs["tool_calls"] = tool_calls_list - _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid - inputs=guardrail_inputs, - request_data={}, - input_type="response", - logging_obj=litellm_logging_obj, - ) + # Check if model_response is valid and has choices before accessing + if ( + built_response is not None + and hasattr(built_response, "choices") + and built_response.choices + ): + model_response = cast(ModelResponse, built_response) + first_choice = cast(Choices, model_response.choices[0]) + tool_calls_list = cast( + Optional[List[ChatCompletionMessageToolCall]], + first_choice.message.tool_calls, + ) + string_so_far = first_choice.message.content + guardrail_inputs = GenericGuardrailAPIInputs() + if string_so_far: + guardrail_inputs["texts"] = [string_so_far] + if tool_calls_list: + guardrail_inputs["tool_calls"] = tool_calls_list + + _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid + inputs=guardrail_inputs, + request_data={}, + input_type="response", + logging_obj=litellm_logging_obj, + ) + else: + verbose_proxy_logger.debug("Skipping output guardrail - model response has no choices") return responses_so_far string_so_far = self.get_streaming_string_so_far(responses_so_far) @@ -648,7 +663,10 @@ class AnthropicMessagesHandler(BaseTranslation): if isinstance(content_block, dict): if content_block.get("type") == "text": cast(Dict[str, Any], content_block)["text"] = guardrail_response - elif hasattr(content_block, "type") and getattr(content_block, "type", None) == "text": + elif ( + hasattr(content_block, "type") + and getattr(content_block, "type", None) == "text" + ): # Update Pydantic object's text attribute if hasattr(content_block, "text"): content_block.text = guardrail_response diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index 6a9aafd076b..f51adf96102 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -58,6 +58,9 @@ from litellm.types.utils import ( from ...base import BaseLLM from ..common_utils import AnthropicError, process_anthropic_headers +from litellm.anthropic_beta_headers_manager import ( + update_headers_with_filtered_beta, +) from .transformation import AnthropicConfig if TYPE_CHECKING: @@ -75,6 +78,7 @@ async def make_call( logging_obj, timeout: Optional[Union[float, httpx.Timeout]], json_mode: bool, + speed: Optional[str] = None, ) -> Tuple[Any, httpx.Headers]: if client is None: client = litellm.module_level_aclient @@ -103,6 +107,7 @@ async def make_call( streaming_response=response.aiter_lines(), sync_stream=False, json_mode=json_mode, + speed=speed, ) # LOGGING @@ -126,6 +131,7 @@ def make_sync_call( logging_obj, timeout: Optional[Union[float, httpx.Timeout]], json_mode: bool, + speed: Optional[str] = None, ) -> Tuple[Any, httpx.Headers]: if client is None: client = litellm.module_level_client # re-use a module level client @@ -159,7 +165,7 @@ def make_sync_call( ) completion_stream = ModelResponseIterator( - streaming_response=response.iter_lines(), sync_stream=True, json_mode=json_mode + streaming_response=response.iter_lines(), sync_stream=True, json_mode=json_mode, speed=speed ) # LOGGING @@ -213,6 +219,7 @@ class AnthropicChatCompletion(BaseLLM): logging_obj=logging_obj, timeout=timeout, json_mode=json_mode, + speed=optional_params.get("speed") if optional_params else None, ) streamwrapper = CustomStreamWrapper( completion_stream=completion_stream, @@ -329,6 +336,10 @@ class AnthropicChatCompletion(BaseLLM): litellm_params=litellm_params, ) + headers = update_headers_with_filtered_beta( + headers=headers, provider=custom_llm_provider + ) + config = ProviderConfigManager.get_provider_chat_config( model=model, provider=LlmProviders(custom_llm_provider), @@ -427,6 +438,7 @@ class AnthropicChatCompletion(BaseLLM): logging_obj=logging_obj, timeout=timeout, json_mode=json_mode, + speed=optional_params.get("speed") if optional_params else None, ) return CustomStreamWrapper( completion_stream=completion_stream, @@ -485,13 +497,14 @@ class AnthropicChatCompletion(BaseLLM): class ModelResponseIterator: def __init__( - self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False + self, streaming_response, sync_stream: bool, json_mode: Optional[bool] = False, speed: Optional[str] = None ): self.streaming_response = streaming_response self.response_iterator = self.streaming_response self.content_blocks: List[ContentBlockDelta] = [] self.tool_index = -1 self.json_mode = json_mode + self.speed = speed # Generate response ID once per stream to match OpenAI-compatible behavior self.response_id = _generate_id() @@ -512,6 +525,9 @@ class ModelResponseIterator: # Accumulate web_search_tool_result blocks for multi-turn reconstruction # See: https://github.com/BerriAI/litellm/issues/17737 self.web_search_results: List[Dict[str, Any]] = [] + + # Accumulate compaction blocks for multi-turn reconstruction + self.compaction_blocks: List[Dict[str, Any]] = [] def check_empty_tool_call_args(self) -> bool: """ @@ -538,7 +554,7 @@ class ModelResponseIterator: def _handle_usage(self, anthropic_usage_chunk: Union[dict, UsageDelta]) -> Usage: return AnthropicConfig().calculate_usage( - usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=None + usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=None, speed=self.speed ) def _content_block_delta_helper(self, chunk: dict) -> Tuple[ @@ -592,6 +608,12 @@ class ModelResponseIterator: ) ] provider_specific_fields["thinking_blocks"] = thinking_blocks + elif "content" in content_block["delta"] and content_block["delta"].get("type") == "compaction_delta": + # Handle compaction delta + provider_specific_fields["compaction_delta"] = { + "type": "compaction_delta", + "content": content_block["delta"]["content"] + } return text, tool_use, thinking_blocks, provider_specific_fields @@ -721,6 +743,20 @@ class ModelResponseIterator: provider_specific_fields=provider_specific_fields, ) + elif content_block_start["content_block"]["type"] == "compaction": + # Handle compaction blocks + # The full content comes in content_block_start + self.compaction_blocks.append( + content_block_start["content_block"] + ) + provider_specific_fields["compaction_blocks"] = ( + self.compaction_blocks + ) + provider_specific_fields["compaction_start"] = { + "type": "compaction", + "content": content_block_start["content_block"].get("content", "") + } + elif content_block_start["content_block"]["type"].endswith("_tool_result"): # Handle all tool result types (web_search, bash_code_execution, text_editor, etc.) content_type = content_block_start["content_block"]["type"] diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 1b61b533275..9938cd7979b 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -170,9 +170,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): tool_call["caller"] = cast(Dict[str, Any], anthropic_tool_content["caller"]) # type: ignore[typeddict-item] return tool_call - def _is_claude_opus_4_5(self, model: str) -> bool: + @staticmethod + def _is_claude_opus_4_6(model: str) -> bool: """Check if the model is Claude Opus 4.5.""" - return "opus-4-5" in model.lower() or "opus_4_5" in model.lower() + return "opus-4-6" in model.lower() or "opus_4_6" in model.lower() def get_supported_openai_params(self, model: str): params = [ @@ -189,6 +190,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "response_format", "user", "web_search_options", + "speed", ] if "claude-3-7-sonnet" in model or supports_reasoning( @@ -659,10 +661,15 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): @staticmethod def _map_reasoning_effort( - reasoning_effort: Optional[Union[REASONING_EFFORT, str]], + reasoning_effort: Optional[Union[REASONING_EFFORT, str]], + model: str, ) -> Optional[AnthropicThinkingParam]: - if reasoning_effort is None: + if reasoning_effort is None or reasoning_effort == "none": return None + if AnthropicConfig._is_claude_opus_4_6(model): + return AnthropicThinkingParam( + type="adaptive", + ) elif reasoning_effort == "low": return AnthropicThinkingParam( type="enabled", @@ -826,6 +833,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "sonnet-4-5", "opus-4.1", "opus-4-1", + "opus-4.5", + "opus-4-5", + "opus-4.6", + "opus-4-6", } ): _output_format = ( @@ -860,13 +871,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): - # For Claude Opus 4.5, map reasoning_effort to output_config - if self._is_claude_opus_4_5(model): - optional_params["output_config"] = {"effort": value} - - # For other models, map to thinking parameter optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value + reasoning_effort=value, model=model ) elif param == "web_search_options" and isinstance(value, dict): hosted_web_search_tool = self.map_web_search_tool( @@ -877,6 +883,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) elif param == "extra_headers": optional_params["extra_headers"] = value + elif param == "context_management" and isinstance(value, dict): + # Pass through Anthropic-specific context_management parameter + optional_params["context_management"] = value + elif param == "speed" and isinstance(value, str): + # Pass through Anthropic-specific speed parameter for fast mode + optional_params["speed"] = value ## handle thinking tokens self.update_optional_params_with_thinking_tokens( @@ -922,6 +934,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): Translate system message to anthropic format. Removes system message from the original list and returns a new list of anthropic system message content. + Filters out system messages containing x-anthropic-billing-header metadata. """ system_prompt_indices = [] anthropic_system_message_list: List[AnthropicSystemMessageContent] = [] @@ -933,6 +946,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): # Skip empty text blocks - Anthropic API raises errors for empty text if not system_message_block["content"]: continue + # Skip system messages containing x-anthropic-billing-header metadata + if system_message_block["content"].startswith("x-anthropic-billing-header:"): + continue anthropic_system_message_content = AnthropicSystemMessageContent( type="text", text=system_message_block["content"], @@ -951,6 +967,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): text_value = _content.get("text") if _content.get("type") == "text" and not text_value: continue + # Skip system messages containing x-anthropic-billing-header metadata + if _content.get("type") == "text" and text_value and text_value.startswith("x-anthropic-billing-header:"): + continue anthropic_system_message_content = ( AnthropicSystemMessageContent( type=_content.get("type"), @@ -1026,9 +1045,37 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if beta_value not in existing_values: headers["anthropic-beta"] = f"{existing_beta}, {beta_value}" - def _ensure_context_management_beta_header(self, headers: dict) -> None: - beta_value = ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value - self._ensure_beta_header(headers, beta_value) + def _ensure_context_management_beta_header( + self, headers: dict, context_management: dict + ) -> None: + """ + Add appropriate beta headers based on context_management edits. + - If any edit has type "compact_20260112", add compact-2026-01-12 header + - For all other edits, add context-management-2025-06-27 header + """ + edits = context_management.get("edits", []) + + has_compact = False + has_other = False + + for edit in edits: + edit_type = edit.get("type", "") + if edit_type == "compact_20260112": + has_compact = True + else: + has_other = True + + # Add compact header if any compact edits exist + if has_compact: + self._ensure_beta_header( + headers, ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value + ) + + # Add context management header if any other edits exist + if has_other: + self._ensure_beta_header( + headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value + ) def update_headers_with_optional_anthropic_beta( self, headers: dict, optional_params: dict @@ -1056,11 +1103,17 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): headers, ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value ) if optional_params.get("context_management") is not None: - self._ensure_context_management_beta_header(headers) + self._ensure_context_management_beta_header( + headers, optional_params["context_management"] + ) if optional_params.get("output_format") is not None: self._ensure_beta_header( headers, ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value ) + if optional_params.get("speed") == "fast": + self._ensure_beta_header( + headers, ANTHROPIC_BETA_HEADER_VALUES.FAST_MODE_2026_02_01.value + ) return headers def transform_request( @@ -1225,6 +1278,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): List[ChatCompletionToolCallChunk], Optional[List[Any]], Optional[List[Any]], + Optional[List[Any]], ]: text_content = "" citations: Optional[List[Any]] = None @@ -1237,6 +1291,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): tool_calls: List[ChatCompletionToolCallChunk] = [] web_search_results: Optional[List[Any]] = None tool_results: Optional[List[Any]] = None + compaction_blocks: Optional[List[Any]] = None for idx, content in enumerate(completion_response["content"]): if content["type"] == "text": text_content += content["text"] @@ -1278,6 +1333,12 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): thinking_blocks.append( cast(ChatCompletionRedactedThinkingBlock, content) ) + + ## COMPACTION + elif content["type"] == "compaction": + if compaction_blocks is None: + compaction_blocks = [] + compaction_blocks.append(content) ## CITATIONS if content.get("citations") is not None: @@ -1299,13 +1360,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if thinking_content is not None: reasoning_content += thinking_content - return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results + return text_content, citations, thinking_blocks, reasoning_content, tool_calls, web_search_results, tool_results, compaction_blocks def calculate_usage( self, usage_object: dict, reasoning_content: Optional[str], completion_response: Optional[dict] = None, + speed: Optional[str] = None, ) -> Usage: # NOTE: Sometimes the usage object has None set explicitly for token counts, meaning .get() & key access returns None, and we need to account for this prompt_tokens = usage_object.get("input_tokens", 0) or 0 @@ -1316,6 +1378,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): cache_creation_token_details: Optional[CacheCreationTokenDetails] = None web_search_requests: Optional[int] = None tool_search_requests: Optional[int] = None + inference_geo: Optional[str] = None + if "inference_geo" in _usage and _usage["inference_geo"] is not None: + inference_geo = _usage["inference_geo"] + if ( "cache_creation_input_tokens" in _usage and _usage["cache_creation_input_tokens"] is not None @@ -1399,6 +1465,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if (web_search_requests is not None or tool_search_requests is not None) else None ), + inference_geo=inference_geo, + speed=speed, ) return usage @@ -1409,6 +1477,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): model_response: ModelResponse, json_mode: Optional[bool] = None, prefix_prompt: Optional[str] = None, + speed: Optional[str] = None, ): _hidden_params: Dict = {} _hidden_params["additional_headers"] = process_anthropic_headers( @@ -1442,6 +1511,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): tool_calls, web_search_results, tool_results, + compaction_blocks, ) = self.extract_response_content(completion_response=completion_response) if ( @@ -1469,6 +1539,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): provider_specific_fields["tool_results"] = tool_results if container is not None: provider_specific_fields["container"] = container + if compaction_blocks is not None: + provider_specific_fields["compaction_blocks"] = compaction_blocks _message = litellm.Message( tool_calls=tool_calls, @@ -1477,6 +1549,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): thinking_blocks=thinking_blocks, reasoning_content=reasoning_content, ) + _message.provider_specific_fields = provider_specific_fields ## HANDLE JSON MODE - anthropic returns single function call json_mode_message = self._transform_response_for_json_mode( @@ -1501,24 +1574,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): usage_object=completion_response["usage"], reasoning_content=reasoning_content, completion_response=completion_response, + speed=speed, ) setattr(model_response, "usage", usage) # type: ignore model_response.created = int(time.time()) model_response.model = completion_response["model"] - context_management_response = completion_response.get("context_management") - if context_management_response is not None: - _hidden_params["context_management"] = context_management_response - try: - model_response.__dict__["context_management"] = ( - context_management_response - ) - except Exception: - pass - model_response._hidden_params = _hidden_params - return model_response def get_prefix_prompt(self, messages: List[AllMessageValues]) -> Optional[str]: @@ -1580,6 +1643,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) prefix_prompt = self.get_prefix_prompt(messages=messages) + speed = optional_params.get("speed") model_response = self.transform_parsed_response( completion_response=completion_response, @@ -1587,6 +1651,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): model_response=model_response, json_mode=json_mode, prefix_prompt=prefix_prompt, + speed=speed, ) return model_response diff --git a/litellm/llms/anthropic/cost_calculation.py b/litellm/llms/anthropic/cost_calculation.py index 8f34eb00ce5..271406f2f7d 100644 --- a/litellm/llms/anthropic/cost_calculation.py +++ b/litellm/llms/anthropic/cost_calculation.py @@ -22,10 +22,22 @@ def cost_per_token(model: str, usage: "Usage") -> Tuple[float, float]: Returns: Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd """ - return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="anthropic" + model_with_prefix = model + + # First, prepend inference_geo if present + if hasattr(usage, "inference_geo") and usage.inference_geo and usage.inference_geo.lower() not in ["global", "not_available"]: + model_with_prefix = f"{usage.inference_geo}/{model_with_prefix}" + + # Then, prepend speed if it's "fast" + if hasattr(usage, "speed") and usage.speed == "fast": + model_with_prefix = f"fast/{model_with_prefix}" + + prompt_cost, completion_cost = generic_cost_per_token( + model=model_with_prefix, usage=usage, custom_llm_provider="anthropic" ) + return prompt_cost, completion_cost + def get_cost_for_anthropic_web_search( model_info: Optional["ModelInfo"] = None, diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py index a17eba75b3b..c6caaddf98b 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py @@ -30,6 +30,58 @@ ANTHROPIC_ADAPTER = AnthropicAdapter() class LiteLLMMessagesToCompletionTransformationHandler: + @staticmethod + def _route_openai_thinking_to_responses_api_if_needed( + completion_kwargs: Dict[str, Any], + *, + thinking: Optional[Dict[str, Any]], + ) -> None: + """ + When users call `litellm.anthropic.messages.*` with a non-Anthropic model and + `thinking={"type": "enabled", ...}`, LiteLLM converts this into OpenAI + `reasoning_effort`. + + For OpenAI models, Chat Completions typically does not return reasoning text + (only token accounting). To return a thinking-like content block in the + Anthropic response format, we route the request through OpenAI's Responses API + and request a reasoning summary. + """ + custom_llm_provider = completion_kwargs.get("custom_llm_provider") + if custom_llm_provider is None: + try: + _, inferred_provider, _, _ = litellm.utils.get_llm_provider( + model=cast(str, completion_kwargs.get("model")) + ) + custom_llm_provider = inferred_provider + except Exception: + custom_llm_provider = None + + if custom_llm_provider != "openai": + return + + if not isinstance(thinking, dict) or thinking.get("type") != "enabled": + return + + model = completion_kwargs.get("model") + if isinstance(model, str) and model and not model.startswith("responses/"): + # Prefix model with "responses/" to route to OpenAI Responses API + completion_kwargs["model"] = f"responses/{model}" + + reasoning_effort = completion_kwargs.get("reasoning_effort") + if isinstance(reasoning_effort, str) and reasoning_effort: + completion_kwargs["reasoning_effort"] = { + "effort": reasoning_effort, + "summary": "detailed", + } + elif isinstance(reasoning_effort, dict): + if ( + "summary" not in reasoning_effort + and "generate_summary" not in reasoning_effort + ): + updated_reasoning_effort = dict(reasoning_effort) + updated_reasoning_effort["summary"] = "detailed" + completion_kwargs["reasoning_effort"] = updated_reasoning_effort + @staticmethod def _prepare_completion_kwargs( *, @@ -123,6 +175,11 @@ class LiteLLMMessagesToCompletionTransformationHandler: ): completion_kwargs[key] = value + LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed( + completion_kwargs, + thinking=thinking, + ) + return completion_kwargs, tool_name_mapping @staticmethod diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 444f821c20a..169b138a5f7 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -939,22 +939,8 @@ class LiteLLMAnthropicMessagesAdapter: self, choices: List[Choices], tool_name_mapping: Optional[Dict[str, str]] = None, - ) -> List[ - Union[ - AnthropicResponseContentBlockText, - AnthropicResponseContentBlockToolUse, - AnthropicResponseContentBlockThinking, - AnthropicResponseContentBlockRedactedThinking, - ] - ]: - new_content: List[ - Union[ - AnthropicResponseContentBlockText, - AnthropicResponseContentBlockToolUse, - AnthropicResponseContentBlockThinking, - AnthropicResponseContentBlockRedactedThinking, - ] - ] = [] + ) -> List[Dict[str, Any]]: + new_content: List[Dict[str, Any]] = [] for choice in choices: # Handle thinking blocks first if ( @@ -978,7 +964,7 @@ class LiteLLMAnthropicMessagesAdapter: if signature_value is not None else None ), - ) + ).model_dump() ) elif thinking_block.get("type") == "redacted_thinking": data_value = thinking_block.get("data", "") @@ -986,7 +972,7 @@ class LiteLLMAnthropicMessagesAdapter: AnthropicResponseContentBlockRedactedThinking( type="redacted_thinking", data=str(data_value) if data_value is not None else "", - ) + ).model_dump() ) # Handle reasoning_content when thinking_blocks is not present elif ( @@ -998,7 +984,7 @@ class LiteLLMAnthropicMessagesAdapter: type="thinking", thinking=str(choice.message.reasoning_content), signature=None, - ) + ).model_dump() ) # Handle text content @@ -1006,7 +992,7 @@ class LiteLLMAnthropicMessagesAdapter: new_content.append( AnthropicResponseContentBlockText( type="text", text=choice.message.content - ) + ).model_dump() ) # Handle tool calls (in parallel to text content) if ( @@ -1044,7 +1030,7 @@ class LiteLLMAnthropicMessagesAdapter: tool_use_block.provider_specific_fields = ( provider_specific_fields ) - new_content.append(tool_use_block) + new_content.append(tool_use_block.model_dump()) return new_content diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py index 308bf367d06..8f2f3bf3545 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/transformation.py @@ -43,9 +43,46 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): "thinking", "context_management", "output_format", + "inference_geo", + "speed", + "output_config", # TODO: Add Anthropic `metadata` support # "metadata", ] + + @staticmethod + def _filter_billing_headers_from_system(system_param): + """ + Filter out x-anthropic-billing-header metadata from system parameter. + + Args: + system_param: Can be a string or a list of system message content blocks + + Returns: + Filtered system parameter (string or list), or None if all content was filtered + """ + if isinstance(system_param, str): + # If it's a string and starts with billing header, filter it out + if system_param.startswith("x-anthropic-billing-header:"): + return None + return system_param + elif isinstance(system_param, list): + # Filter list of system content blocks + filtered_list = [] + for content_block in system_param: + if isinstance(content_block, dict): + text = content_block.get("text", "") + content_type = content_block.get("type", "") + # Skip text blocks that start with billing header + if content_type == "text" and text.startswith("x-anthropic-billing-header:"): + continue + filtered_list.append(content_block) + else: + # Keep non-dict items as-is + filtered_list.append(content_block) + return filtered_list if len(filtered_list) > 0 else None + else: + return system_param def get_complete_url( self, @@ -112,6 +149,17 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): message="max_tokens is required for Anthropic /v1/messages API", status_code=400, ) + + # Filter out x-anthropic-billing-header from system messages + system_param = anthropic_messages_optional_request_params.get("system") + if system_param is not None: + filtered_system = self._filter_billing_headers_from_system(system_param) + if filtered_system is not None and len(filtered_system) > 0: + anthropic_messages_optional_request_params["system"] = filtered_system + else: + # Remove system parameter if all content was filtered out + anthropic_messages_optional_request_params.pop("system", None) + ####### get required params for all anthropic messages requests ###### verbose_logger.debug(f"TRANSFORMATION DEBUG - Messages: {messages}") anthropic_messages_request: AnthropicMessagesRequest = AnthropicMessagesRequest( @@ -175,10 +223,11 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): - context_management: adds 'context-management-2025-06-27' - tool_search: adds provider-specific tool search header - output_format: adds 'structured-outputs-2025-11-13' + - speed: adds 'fast-mode-2026-02-01' Args: headers: Request headers dict - optional_params: Optional parameters including tools, context_management, output_format + optional_params: Optional parameters including tools, context_management, output_format, speed custom_llm_provider: Provider name for looking up correct tool search header """ beta_values: set = set() @@ -189,13 +238,36 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig): beta_values.update(b.strip() for b in existing_beta.split(",")) # Check for context management - if optional_params.get("context_management") is not None: - beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value) + context_management_param = optional_params.get("context_management") + if context_management_param is not None: + # Check edits array for compact_20260112 type + edits = context_management_param.get("edits", []) + has_compact = False + has_other = False + + for edit in edits: + edit_type = edit.get("type", "") + if edit_type == "compact_20260112": + has_compact = True + else: + has_other = True + + # Add compact header if any compact edits exist + if has_compact: + beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value) + + # Add context management header if any other edits exist + if has_other: + beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value) # Check for structured outputs if optional_params.get("output_format") is not None: beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.STRUCTURED_OUTPUT_2025_09_25.value) + # Check for fast mode + if optional_params.get("speed") == "fast": + beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.FAST_MODE_2026_02_01.value) + # Check for tool search tools tools = optional_params.get("tools") if tools: diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index cb9fe0aeb30..44ee51d14ab 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -901,7 +901,20 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): if response.json()["status"] == "failed": error_data = response.json() - raise AzureOpenAIError(status_code=400, message=json.dumps(error_data)) + # Preserve Azure error details (e.g. content_policy_violation, + # inner_error, content_filter_results) as structured body so + # exception_type() can route them correctly. + _error_body = error_data.get("error", error_data) + _error_msg = ( + _error_body.get("message", "Image generation failed") + if isinstance(_error_body, dict) + else json.dumps(error_data) + ) + raise AzureOpenAIError( + status_code=400, + message=_error_msg, + body=error_data, + ) result = response.json()["result"] return httpx.Response( @@ -999,7 +1012,20 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): if response.json()["status"] == "failed": error_data = response.json() - raise AzureOpenAIError(status_code=400, message=json.dumps(error_data)) + # Preserve Azure error details (e.g. content_policy_violation, + # inner_error, content_filter_results) as structured body so + # exception_type() can route them correctly. + _error_body = error_data.get("error", error_data) + _error_msg = ( + _error_body.get("message", "Image generation failed") + if isinstance(_error_body, dict) + else json.dumps(error_data) + ) + raise AzureOpenAIError( + status_code=400, + message=_error_msg, + body=error_data, + ) result = response.json()["result"] return httpx.Response( @@ -1060,6 +1086,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers: dict, client=None, timeout=None, + model: Optional[str] = None, ) -> ImageResponse: response: Optional[dict] = None @@ -1071,8 +1098,9 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): if api_base.endswith("/"): api_base = api_base.rstrip("/") api_version: str = azure_client_params.get("api_version", "") + # Use the deployment name (model) for URL construction, not the base_model from data img_gen_api_base = self.create_azure_base_url( - azure_client_params=azure_client_params, model=data.get("model", "") + azure_client_params=azure_client_params, model=model or data.get("model", "") ) ## LOGGING @@ -1159,21 +1187,20 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): model = model else: model = None - ## BASE MODEL CHECK if ( model_response is not None - and optional_params.get("base_model", None) is not None + and litellm_params is not None + and litellm_params.get("base_model", None) is not None ): - model_response._hidden_params["model"] = optional_params.pop( - "base_model" - ) + model_response._hidden_params["model"] = litellm_params.get("base_model", None) # Azure image generation API doesn't support extra_body parameter extra_body = optional_params.pop("extra_body", {}) flattened_params = {**optional_params, **extra_body} - data = {"model": model, "prompt": prompt, **flattened_params} + base_model = litellm_params.get("base_model", None) if litellm_params else None + data = {"model": base_model or model, "prompt": prompt, **flattened_params} max_retries = data.pop("max_retries", 2) if not isinstance(max_retries, int): raise AzureOpenAIError( @@ -1196,10 +1223,11 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): is_async=False, ) if aimg_generation is True: - return self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout, headers=headers) # type: ignore + return self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout, headers=headers, model=model) # type: ignore + # Use the deployment name (model) for URL construction, not the base_model from data img_gen_api_base = self.create_azure_base_url( - azure_client_params=azure_client_params, model=data.get("model", "") + azure_client_params=azure_client_params, model=model ) ## LOGGING diff --git a/litellm/llms/azure/chat/gpt_transformation.py b/litellm/llms/azure/chat/gpt_transformation.py index 0ae6fad7300..18dad503a59 100644 --- a/litellm/llms/azure/chat/gpt_transformation.py +++ b/litellm/llms/azure/chat/gpt_transformation.py @@ -105,6 +105,7 @@ class AzureOpenAIConfig(BaseConfig): "modalities", "audio", "web_search_options", + "prompt_cache_key", ] def _is_response_format_supported_model(self, model: str) -> bool: diff --git a/litellm/llms/azure_ai/anthropic/messages_transformation.py b/litellm/llms/azure_ai/anthropic/messages_transformation.py index 0d00c907031..a4dc88f9c68 100644 --- a/litellm/llms/azure_ai/anthropic/messages_transformation.py +++ b/litellm/llms/azure_ai/anthropic/messages_transformation.py @@ -62,7 +62,6 @@ class AzureAnthropicMessagesConfig(AnthropicMessagesConfig): if "content-type" not in headers: headers["content-type"] = "application/json" - # Update headers with anthropic beta features (context management, tool search, etc.) headers = self._update_headers_with_anthropic_beta( headers=headers, optional_params=optional_params, diff --git a/litellm/llms/azure_ai/anthropic/transformation.py b/litellm/llms/azure_ai/anthropic/transformation.py index 2d8d3b987c7..c5510db68b1 100644 --- a/litellm/llms/azure_ai/anthropic/transformation.py +++ b/litellm/llms/azure_ai/anthropic/transformation.py @@ -2,7 +2,6 @@ Azure Anthropic transformation config - extends AnthropicConfig with Azure authentication """ from typing import TYPE_CHECKING, Dict, List, Optional, Union - from litellm.llms.anthropic.chat.transformation import AnthropicConfig from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.types.llms.openai import AllMessageValues @@ -87,6 +86,7 @@ class AzureAnthropicConfig(AnthropicConfig): if "anthropic-version" not in headers: headers["anthropic-version"] = "2023-06-01" + return headers def transform_request( diff --git a/litellm/llms/azure_ai/chat/transformation.py b/litellm/llms/azure_ai/chat/transformation.py index 04d2b3a2769..585efd3307d 100644 --- a/litellm/llms/azure_ai/chat/transformation.py +++ b/litellm/llms/azure_ai/chat/transformation.py @@ -11,12 +11,14 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( _audio_or_image_in_message_content, convert_content_list_to_str, ) +from litellm.llms.azure.common_utils import BaseAzureLLM from litellm.llms.base_llm.chat.transformation import LiteLLMLoggingObj from litellm.llms.openai.common_utils import drop_params_from_unprocessable_entity_error from litellm.llms.openai.openai import OpenAIConfig from litellm.llms.xai.chat.transformation import XAIChatConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import AllMessageValues +from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ModelResponse, ProviderField from litellm.utils import _add_path_to_api_base, supports_tool_choice @@ -64,12 +66,21 @@ class AzureAIStudioConfig(OpenAIConfig): api_key: Optional[str] = None, api_base: Optional[str] = None, ) -> dict: - if api_base and self._should_use_api_key_header(api_base): - headers["api-key"] = api_key + if api_key: + if api_base and self._should_use_api_key_header(api_base): + headers["api-key"] = api_key + else: + headers["Authorization"] = f"Bearer {api_key}" else: - headers["Authorization"] = f"Bearer {api_key}" + # No api_key provided — fall back to Azure AD token-based auth + litellm_params_obj = GenericLiteLLMParams( + **(litellm_params if isinstance(litellm_params, dict) else {}) + ) + headers = BaseAzureLLM._base_validate_azure_environment( + headers=headers, litellm_params=litellm_params_obj + ) - headers["Content-Type"] = "application/json" # tell Azure AI Studio to expect JSON + headers["Content-Type"] = "application/json" return headers diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 1de1c40c438..304c707fa0b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -211,25 +211,13 @@ class BaseAWSLLM: aws_external_id=aws_external_id, ) elif aws_role_name is not None: - # Check if we're in IRSA and trying to assume the same role we already have - current_role_arn = os.getenv("AWS_ROLE_ARN") - web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") - - # In IRSA environments, we should skip role assumption if we're already running as the target role - # This is true when: - # 1. We have AWS_ROLE_ARN set (current role) - # 2. We have AWS_WEB_IDENTITY_TOKEN_FILE set (IRSA environment) - # 3. The current role matches the requested role - if ( - current_role_arn - and web_identity_token_file - and current_role_arn == aws_role_name - ): + # Check if we're already running as the target role and can skip assumption + # This handles IRSA (EKS), ECS task roles, and EC2 instance profiles + if self._is_already_running_as_role(aws_role_name, ssl_verify=ssl_verify): verbose_logger.debug( - "Using IRSA same-role optimization: calling _auth_with_env_vars" + "Already running as target role %s, using ambient credentials", + aws_role_name, ) - # We're already running as this role via IRSA, no need to assume it again - # Use the default boto3 credentials (which will use the IRSA credentials) credentials, _cache_ttl = self._auth_with_env_vars() else: verbose_logger.debug( @@ -553,6 +541,107 @@ class BaseAWSLLM: aws_region_name = "us-west-2" return aws_region_name + @staticmethod + def _parse_arn_account_and_role_name( + arn: str, + ) -> Optional[Tuple[str, str, str]]: + """ + Parse an ARN and return (partition, account_id, role_name). + + Handles: + - arn:aws:iam::123456789012:role/MyRole + - arn:aws:iam::123456789012:role/path/to/MyRole + - arn:aws:sts::123456789012:assumed-role/MyRole/session-name + + Returns None if the ARN cannot be parsed. + """ + # ARN format: arn:PARTITION:SERVICE:REGION:ACCOUNT:RESOURCE + parts = arn.split(":") + if len(parts) < 6 or parts[0] != "arn": + return None + + partition = parts[1] # e.g. "aws", "aws-cn", "aws-us-gov" + account_id = parts[4] + resource = ":".join(parts[5:]) # rejoin in case resource contains colons + + if resource.startswith("role/"): + # arn:aws:iam::ACCOUNT:role/[path/]ROLE_NAME + role_name = resource.split("/")[-1] + elif resource.startswith("assumed-role/"): + # arn:aws:sts::ACCOUNT:assumed-role/ROLE_NAME/SESSION + role_parts = resource.split("/") + if len(role_parts) >= 2: + role_name = role_parts[1] + else: + return None + else: + return None + + return partition, account_id, role_name + + def _is_already_running_as_role( + self, + aws_role_name: str, + ssl_verify: Optional[Union[bool, str]] = None, + ) -> bool: + """ + Check if the current environment is already running as the target IAM role. + + This handles multiple AWS environments: + - IRSA (EKS): AWS_ROLE_ARN + AWS_WEB_IDENTITY_TOKEN_FILE are set + - ECS task roles: Uses sts:GetCallerIdentity to check current role ARN + - EC2 instance profiles: Uses sts:GetCallerIdentity to check current role ARN + + Compares partition, account ID, and role name to avoid cross-account + false matches. + + Returns True if the current identity matches the target role, meaning + we can skip sts:AssumeRole and use ambient credentials directly. + """ + target_parsed = self._parse_arn_account_and_role_name(aws_role_name) + if target_parsed is None: + return False + + target_partition, target_account, target_role = target_parsed + + # Fast path: IRSA environment check (no API call needed) + current_role_arn = os.getenv("AWS_ROLE_ARN") + web_identity_token_file = os.getenv("AWS_WEB_IDENTITY_TOKEN_FILE") + if current_role_arn and web_identity_token_file: + return current_role_arn == aws_role_name + + # For ECS/EC2: call sts:GetCallerIdentity to check if already running as the role + try: + import boto3 + + with tracer.trace("boto3.client(sts).get_caller_identity"): + sts_client = boto3.client( + "sts", verify=self._get_ssl_verify(ssl_verify) + ) + identity = sts_client.get_caller_identity() + caller_arn = identity.get("Arn", "") + + caller_parsed = self._parse_arn_account_and_role_name(caller_arn) + if caller_parsed is not None: + caller_partition, caller_account, caller_role = caller_parsed + if ( + caller_partition == target_partition + and caller_account == target_account + and caller_role == target_role + ): + verbose_logger.debug( + "Current identity already matches target role: %s", + aws_role_name, + ) + return True + + except Exception as e: + verbose_logger.debug( + "Could not determine current role identity: %s", str(e) + ) + + return False + @tracer.wrap() def _auth_with_web_identity_token( self, @@ -867,7 +956,35 @@ class BaseAWSLLM: if aws_external_id is not None: assume_role_params["ExternalId"] = aws_external_id - sts_response = sts_client.assume_role(**assume_role_params) + try: + sts_response = sts_client.assume_role(**assume_role_params) + except Exception as e: + error_str = str(e) + if "AccessDenied" in error_str: + # Only fall back to ambient credentials if we can positively + # confirm the caller is already the target role (same account, + # partition, and role name). This avoids silently using the + # wrong identity when there is a genuine trust-policy or + # permission misconfiguration. + if self._is_already_running_as_role( + aws_role_name, ssl_verify=ssl_verify + ): + verbose_logger.warning( + "AssumeRole failed for %s (%s). " + "Caller is already running as this role; " + "falling back to ambient credentials.", + aws_role_name, + error_str, + ) + return self._auth_with_env_vars() + # Genuine permission error — re-raise + verbose_logger.error( + "AssumeRole AccessDenied for %s and caller is NOT " + "the same role. Re-raising. Error: %s", + aws_role_name, + error_str, + ) + raise # Extract the credentials from the response and convert to Session Credentials sts_credentials = sts_response["Credentials"] diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index d5bd054118d..25af852e09c 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -13,7 +13,9 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.types.utils import ModelResponse from litellm.utils import CustomStreamWrapper - +from litellm.anthropic_beta_headers_manager import ( + update_headers_with_filtered_beta, + ) from ..base_aws_llm import BaseAWSLLM, Credentials from ..common_utils import BedrockError from .invoke_handler import AWSEventStreamDecoder, MockResponseIterator, make_call @@ -337,7 +339,11 @@ class BedrockConverseLLM(BaseAWSLLM): headers = {"Content-Type": "application/json"} if extra_headers is not None: headers = {"Content-Type": "application/json", **extra_headers} - + + # Filter beta headers in HTTP headers before making the request + headers = update_headers_with_filtered_beta( + headers=headers, provider="bedrock_converse" + ) ### ROUTING (ASYNC, STREAMING, SYNC) if acompletion: if isinstance(client, HTTPHandler): diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index f6d7e128580..efa755d515e 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -66,6 +66,7 @@ from ..common_utils import ( BedrockModelInfo, get_anthropic_beta_from_headers, get_bedrock_tool_name, + is_claude_4_5_on_bedrock, ) # Computer use tool prefixes supported by Bedrock @@ -81,6 +82,7 @@ BEDROCK_COMPUTER_USE_TOOLS = [ UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS = [ "advanced-tool-use", # Bedrock Converse doesn't support advanced-tool-use beta headers "prompt-caching", # Prompt caching not supported in Converse API + "compact-2026-01-12", # The compact beta feature is not currently supported on the Converse and ConverseStream APIs ] @@ -306,9 +308,7 @@ class AmazonConverseConfig(BaseConfig): return "nova-2-lite" in model_without_region def _map_web_search_options( - self, - web_search_options: dict, - model: str + self, web_search_options: dict, model: str ) -> Optional[BedrockToolBlock]: """ Map web_search_options to Nova grounding systemTool. @@ -431,7 +431,7 @@ class AmazonConverseConfig(BaseConfig): else: # Anthropic and other models: convert to thinking parameter optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - reasoning_effort + reasoning_effort=reasoning_effort, model=model ) def get_supported_openai_params(self, model: str) -> List[str]: @@ -617,37 +617,6 @@ class AmazonConverseConfig(BaseConfig): return transformed_tools - def _filter_unsupported_beta_headers_for_bedrock( - self, model: str, beta_list: list - ) -> list: - """ - Remove beta headers that are not supported on Bedrock Converse API for the given model. - - Extended thinking beta headers are only supported on specific Claude 4+ models. - Some beta headers are universally unsupported on Bedrock Converse API. - - Args: - model: The model name - beta_list: The list of beta headers to filter - - Returns: - Filtered list of beta headers - """ - filtered_betas = [] - - # 1. Filter out beta headers that are universally unsupported on Bedrock Converse - for beta in beta_list: - should_keep = True - for unsupported_pattern in UNSUPPORTED_BEDROCK_CONVERSE_BETA_PATTERNS: - if unsupported_pattern in beta.lower(): - should_keep = False - break - - if should_keep: - filtered_betas.append(beta) - - return filtered_betas - def _separate_computer_use_tools( self, tools: List[OpenAIChatCompletionToolParam], model: str ) -> Tuple[ @@ -808,11 +777,11 @@ class AmazonConverseConfig(BaseConfig): if param == "web_search_options" and isinstance(value, dict): # Note: we use `isinstance(value, dict)` instead of `value and isinstance(value, dict)` # because empty dict {} is falsy but is a valid way to enable Nova grounding - grounding_tool = self._map_web_search_options(value, model) - if grounding_tool is not None: - optional_params = self._add_tools_to_optional_params( - optional_params=optional_params, tools=[grounding_tool] - ) + grounding_tool = self._map_web_search_options(value, model) + if grounding_tool is not None: + optional_params = self._add_tools_to_optional_params( + optional_params=optional_params, tools=[grounding_tool] + ) # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models # Nova Lite 2 handles token budgeting differently through reasoningConfig @@ -926,6 +895,7 @@ class AmazonConverseConfig(BaseConfig): ChatCompletionAssistantMessage, ], block_type: Literal["system"], + model: Optional[str] = None, ) -> Optional[SystemContentBlock]: pass @@ -939,6 +909,7 @@ class AmazonConverseConfig(BaseConfig): ChatCompletionAssistantMessage, ], block_type: Literal["content_block"], + model: Optional[str] = None, ) -> Optional[ContentBlock]: pass @@ -951,16 +922,26 @@ class AmazonConverseConfig(BaseConfig): ChatCompletionAssistantMessage, ], block_type: Literal["system", "content_block"], + model: Optional[str] = None, ) -> Optional[Union[SystemContentBlock, ContentBlock]]: - if message_block.get("cache_control", None) is None: + cache_control = message_block.get("cache_control", None) + if cache_control is None: return None + + cache_point = CachePointBlock(type="default") + if isinstance(cache_control, dict) and "ttl" in cache_control: + ttl = cache_control["ttl"] + if ttl in ["5m", "1h"] and model is not None: + if is_claude_4_5_on_bedrock(model): + cache_point["ttl"] = ttl + if block_type == "system": - return SystemContentBlock(cachePoint=CachePointBlock(type="default")) + return SystemContentBlock(cachePoint=cache_point) else: - return ContentBlock(cachePoint=CachePointBlock(type="default")) + return ContentBlock(cachePoint=cache_point) def _transform_system_message( - self, messages: List[AllMessageValues] + self, messages: List[AllMessageValues], model: Optional[str] = None ) -> Tuple[List[AllMessageValues], List[SystemContentBlock]]: system_prompt_indices = [] system_content_blocks: List[SystemContentBlock] = [] @@ -972,7 +953,7 @@ class AmazonConverseConfig(BaseConfig): SystemContentBlock(text=message["content"]) ) cache_block = self._get_cache_point_block( - message, block_type="system" + message, block_type="system", model=model ) if cache_block: system_content_blocks.append(cache_block) @@ -983,7 +964,7 @@ class AmazonConverseConfig(BaseConfig): SystemContentBlock(text=m["text"]) ) cache_block = self._get_cache_point_block( - m, block_type="system" + m, block_type="system", model=model ) if cache_block: system_content_blocks.append(cache_block) @@ -1112,7 +1093,28 @@ class AmazonConverseConfig(BaseConfig): # Add computer use tools and anthropic_beta if needed (only when computer use tools are present) if computer_use_tools: - anthropic_beta_list.append("computer-use-2024-10-22") + # Determine the correct computer-use beta header based on model + # "computer-use-2025-11-24" for Claude Opus 4.6, Claude Opus 4.5 + # "computer-use-2025-01-24" for Claude Sonnet 4.5, Haiku 4.5, Opus 4.1, Sonnet 4, Opus 4, and Sonnet 3.7 + # "computer-use-2024-10-22" for older models + model_lower = model.lower() + if "opus-4.6" in model_lower or "opus_4.6" in model_lower or "opus-4-6" in model_lower or "opus_4_6" in model_lower: + computer_use_header = "computer-use-2025-11-24" + elif "opus-4.5" in model_lower or "opus_4.5" in model_lower or "opus-4-5" in model_lower or "opus_4_5" in model_lower: + computer_use_header = "computer-use-2025-11-24" + elif any(pattern in model_lower for pattern in [ + "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", + "haiku-4.5", "haiku_4.5", "haiku-4-5", "haiku_4_5", + "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", + "sonnet-4", "sonnet_4", + "opus-4", "opus_4", + "sonnet-3.7", "sonnet_3.7", "sonnet-3-7", "sonnet_3_7" + ]): + computer_use_header = "computer-use-2025-01-24" + else: + computer_use_header = "computer-use-2024-10-22" + + anthropic_beta_list.append(computer_use_header) # Transform computer use tools to proper Bedrock format transformed_computer_tools = self._transform_computer_use_tools( computer_use_tools @@ -1127,24 +1129,9 @@ class AmazonConverseConfig(BaseConfig): # Set anthropic_beta in additional_request_params if we have any beta features # ONLY apply to Anthropic/Claude models - other models (e.g., Qwen, Llama) don't support this field - # and will error with "unknown variant anthropic_beta" if included base_model = BedrockModelInfo.get_base_model(model) if anthropic_beta_list and base_model.startswith("anthropic"): - # Remove duplicates while preserving order - unique_betas = [] - seen = set() - for beta in anthropic_beta_list: - if beta not in seen: - unique_betas.append(beta) - seen.add(beta) - - # Filter out unsupported beta headers for Bedrock Converse API - filtered_betas = self._filter_unsupported_beta_headers_for_bedrock( - model=model, - beta_list=unique_betas, - ) - - additional_request_params["anthropic_beta"] = filtered_betas + additional_request_params["anthropic_beta"] = anthropic_beta_list return bedrock_tools, anthropic_beta_list @@ -1196,9 +1183,11 @@ class AmazonConverseConfig(BaseConfig): ) # Prepare and separate parameters - inference_params, additional_request_params, request_metadata = self._prepare_request_params( - optional_params, model - ) + ( + inference_params, + additional_request_params, + request_metadata, + ) = self._prepare_request_params(optional_params, model) original_tools = inference_params.pop("tools", []) @@ -1250,7 +1239,9 @@ class AmazonConverseConfig(BaseConfig): litellm_params: dict, headers: Optional[dict] = None, ) -> RequestObject: - messages, system_content_blocks = self._transform_system_message(messages) + messages, system_content_blocks = self._transform_system_message( + messages, model=model + ) # Convert last user message to guarded_text if guardrailConfig is present messages = self._convert_consecutive_user_messages_to_guarded_text( @@ -1306,7 +1297,9 @@ class AmazonConverseConfig(BaseConfig): litellm_params: dict, headers: Optional[dict] = None, ) -> RequestObject: - messages, system_content_blocks = self._transform_system_message(messages) + messages, system_content_blocks = self._transform_system_message( + messages, model=model + ) # Convert last user message to guarded_text if guardrailConfig is present messages = self._convert_consecutive_user_messages_to_guarded_text( @@ -1484,7 +1477,9 @@ class AmazonConverseConfig(BaseConfig): return message, returned_finish_reason - def _translate_message_content(self, content_blocks: List[ContentBlock]) -> Tuple[ + def _translate_message_content( + self, content_blocks: List[ContentBlock] + ) -> Tuple[ str, List[ChatCompletionToolCallChunk], Optional[List[BedrockConverseReasoningContentBlock]], @@ -1501,9 +1496,9 @@ class AmazonConverseConfig(BaseConfig): """ content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None citationsContentBlocks: Optional[List[CitationsContentBlock]] = None for idx, content in enumerate(content_blocks): """ @@ -1557,7 +1552,7 @@ class AmazonConverseConfig(BaseConfig): return content_str, tools, reasoningContentBlocks, citationsContentBlocks - def _transform_response( # noqa: PLR0915 + def _transform_response( # noqa: PLR0915 self, model: str, response: httpx.Response, @@ -1630,9 +1625,9 @@ class AmazonConverseConfig(BaseConfig): chat_completion_message: ChatCompletionResponseMessage = {"role": "assistant"} content_str = "" tools: List[ChatCompletionToolCallChunk] = [] - reasoningContentBlocks: Optional[List[BedrockConverseReasoningContentBlock]] = ( - None - ) + reasoningContentBlocks: Optional[ + List[BedrockConverseReasoningContentBlock] + ] = None citationsContentBlocks: Optional[List[CitationsContentBlock]] = None if message is not None: @@ -1651,15 +1646,17 @@ class AmazonConverseConfig(BaseConfig): provider_specific_fields["citationsContent"] = citationsContentBlocks if provider_specific_fields: - chat_completion_message["provider_specific_fields"] = provider_specific_fields + chat_completion_message[ + "provider_specific_fields" + ] = provider_specific_fields if reasoningContentBlocks is not None: - chat_completion_message["reasoning_content"] = ( - self._transform_reasoning_content(reasoningContentBlocks) - ) - chat_completion_message["thinking_blocks"] = ( - self._transform_thinking_blocks(reasoningContentBlocks) - ) + chat_completion_message[ + "reasoning_content" + ] = self._transform_reasoning_content(reasoningContentBlocks) + chat_completion_message[ + "thinking_blocks" + ] = self._transform_thinking_blocks(reasoningContentBlocks) chat_completion_message["content"] = content_str if ( json_mode is True diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index c936b2cd23c..dfab81123fd 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -133,27 +133,9 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): beta_set.add("tool-search-tool-2025-10-19") # Filter out beta headers that Bedrock Invoke doesn't support - # AWS Bedrock only supports a specific whitelist of beta flags - # Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html - BEDROCK_SUPPORTED_BETAS = { - "computer-use-2024-10-22", # Legacy computer use - "computer-use-2025-01-24", # Current computer use (Claude 3.7 Sonnet) - "token-efficient-tools-2025-02-19", # Tool use (Claude 3.7+ and Claude 4+) - "interleaved-thinking-2025-05-14", # Interleaved thinking (Claude 4+) - "output-128k-2025-02-19", # 128K output tokens (Claude 3.7 Sonnet) - "dev-full-thinking-2025-05-14", # Developer mode for raw thinking (Claude 4+) - "context-1m-2025-08-07", # 1 million tokens (Claude Sonnet 4) - "context-management-2025-06-27", # Context management (Claude Sonnet/Haiku 4.5) - "effort-2025-11-24", # Effort parameter (Claude Opus 4.5) - "tool-search-tool-2025-10-19", # Tool search (Claude Opus 4.5) - "tool-examples-2025-10-29", # Tool use examples (Claude Opus 4.5) - } - - # Only keep beta headers that Bedrock supports - beta_set = {beta for beta in beta_set if beta in BEDROCK_SUPPORTED_BETAS} - - if beta_set: - _anthropic_request["anthropic_beta"] = list(beta_set) + # Uses centralized configuration from anthropic_beta_headers_config.json + beta_list = list(beta_set) + _anthropic_request["anthropic_beta"] = beta_list return _anthropic_request diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index 65d237bdbdf..4c87f6fa994 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -446,6 +446,29 @@ def get_bedrock_base_model(model: str) -> str: return model +def is_claude_4_5_on_bedrock(model: str) -> bool: + """ + Check if the model is a Claude 4.5 model on Bedrock. + Claude 4.5 models support prompt caching with '5m' and '1h' TTL on Bedrock. + """ + model_lower = model.lower() + claude_4_5_patterns = [ + "sonnet-4.5", + "sonnet_4.5", + "sonnet-4-5", + "sonnet_4_5", + "haiku-4.5", + "haiku_4.5", + "haiku-4-5", + "haiku_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", + ] + return any(pattern in model_lower for pattern in claude_4_5_patterns) + + # Import after standalone functions to avoid circular imports from litellm.llms.bedrock.count_tokens.bedrock_token_counter import BedrockTokenCounter @@ -815,21 +838,23 @@ def get_anthropic_beta_from_headers(headers: dict) -> List[str]: # If it's already a list, return it if isinstance(anthropic_beta_header, list): return anthropic_beta_header - + # Try to parse as JSON array first (e.g., '["interleaved-thinking-2025-05-14", "claude-code-20250219"]') if isinstance(anthropic_beta_header, str): anthropic_beta_header = anthropic_beta_header.strip() - if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith("]"): + if anthropic_beta_header.startswith("[") and anthropic_beta_header.endswith( + "]" + ): try: parsed = json.loads(anthropic_beta_header) if isinstance(parsed, list): return [str(beta).strip() for beta in parsed] except json.JSONDecodeError: pass # Fall through to comma-separated parsing - + # Fall back to comma-separated values return [beta.strip() for beta in anthropic_beta_header.split(",")] - + return [] diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index b1c45ea83a2..477fa3316d1 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -23,7 +23,10 @@ from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) -from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers +from litellm.llms.bedrock.common_utils import ( + get_anthropic_beta_from_headers, + is_claude_4_5_on_bedrock, +) from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER from litellm.types.llms.openai import AllMessageValues from litellm.types.router import GenericLiteLLMParams @@ -52,10 +55,6 @@ class AmazonAnthropicClaudeMessagesConfig( # Beta header patterns that are not supported by Bedrock Invoke API # These will be filtered out to prevent 400 "invalid beta flag" errors - UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS = [ - "advanced-tool-use", # Bedrock Invoke doesn't support advanced-tool-use beta headers - "prompt-caching-scope" - ] def __init__(self, **kwargs): BaseAnthropicMessagesConfig.__init__(self, **kwargs) @@ -116,15 +115,22 @@ class AmazonAnthropicClaudeMessagesConfig( ) def _remove_ttl_from_cache_control( - self, anthropic_messages_request: Dict + self, anthropic_messages_request: Dict, model: Optional[str] = None ) -> None: """ Remove `ttl` field from cache_control in messages. Bedrock doesn't support the ttl field in cache_control. + Update: Bedock supports `5m` and `1h` for Claude 4.5 models. + Args: anthropic_messages_request: The request dictionary to modify in-place + model: The model name to check if it supports ttl """ + is_claude_4_5 = False + if model: + is_claude_4_5 = self._is_claude_4_5_on_bedrock(model) + if "messages" in anthropic_messages_request: for message in anthropic_messages_request["messages"]: if isinstance(message, dict) and "content" in message: @@ -133,7 +139,14 @@ class AmazonAnthropicClaudeMessagesConfig( for item in content: if isinstance(item, dict) and "cache_control" in item: cache_control = item["cache_control"] - if isinstance(cache_control, dict) and "ttl" in cache_control: + if ( + isinstance(cache_control, dict) + and "ttl" in cache_control + ): + ttl = cache_control["ttl"] + if is_claude_4_5 and ttl in ["5m", "1h"]: + continue + cache_control.pop("ttl", None) def _supports_extended_thinking_on_bedrock(self, model: str) -> bool: @@ -155,10 +168,18 @@ class AmazonAnthropicClaudeMessagesConfig( # Supported models on Bedrock for extended thinking supported_patterns = [ - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", # Opus 4.5 - "opus-4.1", "opus_4.1", "opus-4-1", "opus_4_1", # Opus 4.1 - "opus-4", "opus_4", # Opus 4 - "sonnet-4", "sonnet_4", # Sonnet 4 + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", # Opus 4.5 + "opus-4.1", + "opus_4.1", + "opus-4-1", + "opus_4_1", # Opus 4.1 + "opus-4", + "opus_4", # Opus 4 + "sonnet-4", + "sonnet_4", # Sonnet 4 ] return any(pattern in model_lower for pattern in supported_patterns) @@ -175,10 +196,27 @@ class AmazonAnthropicClaudeMessagesConfig( """ model_lower = model.lower() opus_4_5_patterns = [ - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", ] return any(pattern in model_lower for pattern in opus_4_5_patterns) + def _is_claude_4_5_on_bedrock(self, model: str) -> bool: + """ + Check if the model is Claude 4.5 on Bedrock. + + Claude Sonnet 4.5, Haiku 4.5, and Opus 4.5 support 1-hour prompt caching. + + Args: + model: The model name + + Returns: + True if the model is Claude 4.5 + """ + return is_claude_4_5_on_bedrock(model) + def _supports_tool_search_on_bedrock(self, model: str) -> bool: """ Check if the model supports tool search on Bedrock. @@ -199,71 +237,24 @@ class AmazonAnthropicClaudeMessagesConfig( # Supported models for tool search on Bedrock supported_patterns = [ # Opus 4.5 - "opus-4.5", "opus_4.5", "opus-4-5", "opus_4_5", + "opus-4.5", + "opus_4.5", + "opus-4-5", + "opus_4_5", # Sonnet 4.5 - "sonnet-4.5", "sonnet_4.5", "sonnet-4-5", "sonnet_4_5", + "sonnet-4.5", + "sonnet_4.5", + "sonnet-4-5", + "sonnet_4_5", + # Opus 4.6 + "opus-4.6", + "opus_4.6", + "opus-4-6", + "opus_4_6", ] return any(pattern in model_lower for pattern in supported_patterns) - def _filter_unsupported_beta_headers_for_bedrock( - self, model: str, beta_set: set - ) -> None: - """ - Remove beta headers that are not supported on Bedrock for the given model. - - Extended thinking beta headers are only supported on specific Claude 4+ models. - Advanced tool use headers are not supported on Bedrock Invoke API, but need to be - translated to Bedrock-specific headers for models that support tool search - (Claude Opus 4.5, Sonnet 4.5). - This prevents 400 "invalid beta flag" errors on Bedrock. - - Note: Bedrock Invoke API fails with a 400 error when unsupported beta headers - are sent, returning: {"message":"invalid beta flag"} - - Translation for models supporting tool search (Opus 4.5, Sonnet 4.5): - - advanced-tool-use-2025-11-20 -> tool-search-tool-2025-10-19 + tool-examples-2025-10-29 - - Args: - model: The model name - beta_set: The set of beta headers to filter in-place - """ - beta_headers_to_remove = set() - has_advanced_tool_use = False - - # 1. Filter out beta headers that are universally unsupported on Bedrock Invoke and track if advanced-tool-use header is present - for beta in beta_set: - for unsupported_pattern in self.UNSUPPORTED_BEDROCK_INVOKE_BETA_PATTERNS: - if unsupported_pattern in beta.lower(): - beta_headers_to_remove.add(beta) - has_advanced_tool_use = True - break - - - # 2. Filter out extended thinking headers for models that don't support them - extended_thinking_patterns = [ - "extended-thinking", - "interleaved-thinking", - ] - if not self._supports_extended_thinking_on_bedrock(model): - for beta in beta_set: - for pattern in extended_thinking_patterns: - if pattern in beta.lower(): - beta_headers_to_remove.add(beta) - break - - # Remove all filtered headers - for beta in beta_headers_to_remove: - beta_set.discard(beta) - - # 3. Translate advanced-tool-use to Bedrock-specific headers for models that support tool search - # Ref: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages-request-response.html - # Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool - if has_advanced_tool_use and self._supports_tool_search_on_bedrock(model): - beta_set.add("tool-search-tool-2025-10-19") - beta_set.add("tool-examples-2025-10-29") - - def _get_tool_search_beta_header_for_bedrock( self, model: str, @@ -290,7 +281,9 @@ class AmazonAnthropicClaudeMessagesConfig( input_examples_used: Whether input examples are used beta_set: The set of beta headers to modify in-place """ - if tool_search_used and not (programmatic_tool_calling_used or input_examples_used): + if tool_search_used and not ( + programmatic_tool_calling_used or input_examples_used + ): beta_set.discard(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) if "opus-4" in model.lower() or "opus_4" in model.lower(): beta_set.add("tool-search-tool-2025-10-19") @@ -302,13 +295,13 @@ class AmazonAnthropicClaudeMessagesConfig( ) -> None: """ Convert Anthropic output_format to inline schema in message content. - + Bedrock Invoke doesn't support the output_format parameter, so we embed the schema directly into the user message content as text instructions. - + This approach adds the schema to the last user message, instructing the model to respond in the specified JSON format. - + Args: output_format: The output_format dict with 'type' and 'schema' anthropic_messages_request: The request dict to modify in-place @@ -321,35 +314,32 @@ class AmazonAnthropicClaudeMessagesConfig( schema = output_format.get("schema") if not schema: return - + # Get messages from the request messages = anthropic_messages_request.get("messages", []) if not messages: return - + # Find the last user message last_user_message_idx = None for idx in range(len(messages) - 1, -1, -1): if messages[idx].get("role") == "user": last_user_message_idx = idx break - + if last_user_message_idx is None: return - + last_user_message = messages[last_user_message_idx] content = last_user_message.get("content", []) - + # Ensure content is a list if isinstance(content, str): content = [{"type": "text", "text": content}] last_user_message["content"] = content - + # Add schema as text content to the message - schema_text = { - "type": "text", - "text": json.dumps(schema) - } + schema_text = {"type": "text", "text": json.dumps(schema)} content.append(schema_text) def transform_anthropic_messages_request( @@ -374,9 +364,9 @@ class AmazonAnthropicClaudeMessagesConfig( # 1. anthropic_version is required for all claude models if "anthropic_version" not in anthropic_messages_request: - anthropic_messages_request["anthropic_version"] = ( - self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION - ) + anthropic_messages_request[ + "anthropic_version" + ] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION # 2. `stream` is not allowed in request body for bedrock invoke if "stream" in anthropic_messages_request: @@ -386,8 +376,10 @@ class AmazonAnthropicClaudeMessagesConfig( if "model" in anthropic_messages_request: anthropic_messages_request.pop("model", None) - # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it) - self._remove_ttl_from_cache_control(anthropic_messages_request) + # 4. Remove `ttl` field from cache_control in messages (Bedrock doesn't support it for older models) + self._remove_ttl_from_cache_control( + anthropic_messages_request=anthropic_messages_request, model=model + ) # 5. Convert `output_format` to inline schema (Bedrock invoke doesn't support output_format) output_format = anthropic_messages_request.pop("output_format", None) @@ -396,14 +388,14 @@ class AmazonAnthropicClaudeMessagesConfig( output_format=output_format, anthropic_messages_request=anthropic_messages_request, ) - + # 6. AUTO-INJECT beta headers based on features used anthropic_model_info = AnthropicModelInfo() tools = anthropic_messages_optional_request_params.get("tools") messages_typed = cast(List[AllMessageValues], messages) tool_search_used = anthropic_model_info.is_tool_search_used(tools) - programmatic_tool_calling_used = anthropic_model_info.is_programmatic_tool_calling_used( - tools + programmatic_tool_calling_used = ( + anthropic_model_info.is_programmatic_tool_calling_used(tools) ) input_examples_used = anthropic_model_info.is_input_examples_used(tools) @@ -428,16 +420,14 @@ class AmazonAnthropicClaudeMessagesConfig( beta_set=beta_set, ) - # Filter out unsupported beta headers for Bedrock (e.g., advanced-tool-use, extended-thinking on non-Opus/Sonnet 4 models) - self._filter_unsupported_beta_headers_for_bedrock( - model=model, - beta_set=beta_set, - ) - + # --- Custom logic: if tool-search-tool-2025-10-19 is present, add tool-examples-2025-10-29 --- + if "tool-search-tool-2025-10-19" in beta_set: + beta_set.add("tool-examples-2025-10-29") + # ------------------------------------------------------------------------------ + if beta_set: anthropic_messages_request["anthropic_beta"] = list(beta_set) - - + return anthropic_messages_request def get_async_streaming_response_iterator( @@ -455,7 +445,7 @@ class AmazonAnthropicClaudeMessagesConfig( ) # Convert decoded Bedrock events to Server-Sent Events expected by Anthropic clients. return self.bedrock_sse_wrapper( - completion_stream=completion_stream, + completion_stream=completion_stream, litellm_logging_obj=litellm_logging_obj, request_body=request_body, ) @@ -474,14 +464,14 @@ class AmazonAnthropicClaudeMessagesConfig( from litellm.llms.anthropic.experimental_pass_through.messages.streaming_iterator import ( BaseAnthropicMessagesStreamingIterator, ) + handler = BaseAnthropicMessagesStreamingIterator( litellm_logging_obj=litellm_logging_obj, request_body=request_body, ) - + async for chunk in handler.async_sse_wrapper(completion_stream): yield chunk - class AmazonAnthropicClaudeMessagesStreamDecoder(AWSEventStreamDecoder): diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index a7b83d8c802..fb98006c7e4 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -1,9 +1,10 @@ import asyncio import contextlib import os +import ssl import typing import urllib.request -from typing import Callable, Dict, Optional, Union +from typing import Any, Callable, Dict, Optional, Union import aiohttp import aiohttp.client_exceptions @@ -139,8 +140,13 @@ class LiteLLMAiohttpTransport(AiohttpTransport): Credit to: https://github.com/karpetrosyan/httpx-aiohttp for this implementation """ - def __init__(self, client: Union[ClientSession, Callable[[], ClientSession]]): + def __init__( + self, + client: Union[ClientSession, Callable[[], ClientSession]], + ssl_verify: Optional[Union[bool, ssl.SSLContext]] = None, + ): self.client = client + self._ssl_verify = ssl_verify # Store for per-request SSL override super().__init__(client=client) # Store the client factory for recreating sessions when needed if callable(client): @@ -214,6 +220,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): timeout: dict, proxy: Optional[str], sni_hostname: Optional[str], + ssl_verify: Optional[Union[bool, ssl.SSLContext]] = None, ) -> ClientResponse: """ Helper function to make an aiohttp request with the given parameters. @@ -224,6 +231,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): timeout: Timeout settings dict with 'connect', 'read', 'pool' keys proxy: Optional proxy URL sni_hostname: Optional SNI hostname for SSL + ssl_verify: Optional SSL verification setting (False to disable, SSLContext for custom) Returns: ClientResponse from aiohttp @@ -237,21 +245,28 @@ class LiteLLMAiohttpTransport(AiohttpTransport): data = request.stream # type: ignore request.headers.pop("transfer-encoding", None) # handled by aiohttp - response = await client_session.request( - method=request.method, - url=YarlURL(str(request.url), encoded=True), - headers=request.headers, - data=data, - allow_redirects=False, - auto_decompress=False, - timeout=ClientTimeout( + # Only pass ssl kwarg when explicitly configured, to avoid + # overriding the session/connector defaults with None (which is + # not a valid value for aiohttp's ssl parameter). + request_kwargs: Dict[str, Any] = { + "method": request.method, + "url": YarlURL(str(request.url), encoded=True), + "headers": request.headers, + "data": data, + "allow_redirects": False, + "auto_decompress": False, + "timeout": ClientTimeout( sock_connect=timeout.get("connect"), sock_read=timeout.get("read"), connect=timeout.get("pool"), ), - proxy=proxy, - server_hostname=sni_hostname, - ).__aenter__() + "proxy": proxy, + "server_hostname": sni_hostname, + } + if ssl_verify is not None: + request_kwargs["ssl"] = ssl_verify + + response = await client_session.request(**request_kwargs).__aenter__() return response @@ -268,6 +283,9 @@ class LiteLLMAiohttpTransport(AiohttpTransport): # Resolve proxy settings from environment variables proxy = await self._get_proxy_settings(request) + # Use stored SSL configuration for per-request override + ssl_config = self._ssl_verify + try: with map_aiohttp_exceptions(): response = await self._make_aiohttp_request( @@ -276,6 +294,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): timeout=timeout, proxy=proxy, sni_hostname=sni_hostname, + ssl_verify=ssl_config, ) except RuntimeError as e: # Handle the case where session was closed between our check and actual use @@ -296,6 +315,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): timeout=timeout, proxy=proxy, sni_hostname=sni_hostname, + ssl_verify=ssl_config, ) else: # Re-raise if it's a different RuntimeError diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index ac9dd5998e2..5cf6efe5ba2 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -846,6 +846,16 @@ class AsyncHTTPHandler: if str_to_bool(os.getenv("AIOHTTP_TRUST_ENV", "False")) is True: trust_env = True + ######################################################### + # Determine SSL config to pass to transport for per-request override + # This ensures ssl_verify works even with shared sessions + ######################################################### + ssl_for_transport: Optional[Union[bool, ssl.SSLContext]] = None + if ssl_context is not None: + ssl_for_transport = ssl_context + elif ssl_verify is False: + ssl_for_transport = False + verbose_logger.debug("Creating AiohttpTransport...") # Use shared session if provided and valid @@ -853,7 +863,10 @@ class AsyncHTTPHandler: verbose_logger.debug( f"SHARED SESSION: Reusing existing ClientSession (ID: {id(shared_session)})" ) - return LiteLLMAiohttpTransport(client=shared_session) + return LiteLLMAiohttpTransport( + client=shared_session, + ssl_verify=ssl_for_transport, + ) # Create new session only if none provided or existing one is invalid verbose_logger.debug( @@ -877,6 +890,7 @@ class AsyncHTTPHandler: connector=TCPConnector(**transport_connector_kwargs), trust_env=trust_env, ), + ssl_verify=ssl_for_transport, ) @staticmethod @@ -1192,7 +1206,28 @@ def get_async_httpx_client( If not present, creates a new client Caches the new client and returns it. + + Note: When shared_session is provided, the cache is bypassed to ensure + the user's session (with its trace_configs, connector settings, etc.) + is used for the request. """ + # When shared_session is provided, bypass cache and create a new handler + # that uses the user's session directly. This preserves the user's + # session configuration including trace_configs for aiohttp tracing. + if shared_session is not None: + verbose_logger.debug( + f"shared_session provided (ID: {id(shared_session)}), bypassing client cache" + ) + if params is not None: + handler_params = {k: v for k, v in params.items() if k != "disable_aiohttp_transport"} + handler_params["shared_session"] = shared_session + return AsyncHTTPHandler(**handler_params) + else: + return AsyncHTTPHandler( + timeout=httpx.Timeout(timeout=600.0, connect=5.0), + shared_session=shared_session, + ) + _params_key_name = "" if params is not None: for key, value in params.items(): @@ -1219,12 +1254,10 @@ def get_async_httpx_client( if params is not None: # Filter out params that are only used for cache key, not for AsyncHTTPHandler.__init__ handler_params = {k: v for k, v in params.items() if k != "disable_aiohttp_transport"} - handler_params["shared_session"] = shared_session _new_client = AsyncHTTPHandler(**handler_params) else: _new_client = AsyncHTTPHandler( timeout=httpx.Timeout(timeout=600.0, connect=5.0), - shared_session=shared_session, ) cache.set_cache( diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index d2ea7e872a2..a97ebd8e74c 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -81,6 +81,9 @@ from litellm.types.llms.anthropic_skills import ( ListSkillsResponse, Skill, ) +from litellm.anthropic_beta_headers_manager import ( + update_headers_with_filtered_beta, + ) from litellm.types.llms.openai import ( CreateBatchRequest, CreateFileRequest, @@ -302,7 +305,7 @@ class BaseLLMHTTPHandler: logging_obj=logging_obj, signed_json_body=signed_json_body, ) - return provider_config.transform_response( + initial_response = provider_config.transform_response( model=model, raw_response=response, model_response=model_response, @@ -316,6 +319,20 @@ class BaseLLMHTTPHandler: json_mode=json_mode, ) + # Call agentic chat completion hooks + final_response = await self._call_agentic_chat_completion_hooks( + response=initial_response, + model=model, + messages=messages, + optional_params=optional_params, + logging_obj=logging_obj, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=litellm_params, + ) + + return final_response if final_response is not None else initial_response + def completion( self, model: str, @@ -412,6 +429,11 @@ class BaseLLMHTTPHandler: }, ) + # Check if stream was converted for WebSearch interception + # This is set by the async_pre_request_hook in WebSearchInterceptionLogger + if litellm_params.get("_websearch_interception_converted_stream", False): + logging_obj.model_call_details["websearch_interception_converted_stream"] = True + if acompletion is True: if stream is True: data = self._add_stream_param_to_request_body( @@ -1839,6 +1861,10 @@ class BaseLLMHTTPHandler: api_key=api_key, api_base=api_base, ) + + headers = update_headers_with_filtered_beta( + headers=headers, provider=custom_llm_provider + ) logging_obj.update_environment_variables( model=model, @@ -4361,10 +4387,10 @@ class BaseLLMHTTPHandler: kwargs: Dict, ) -> Optional[Any]: """ - Call agentic completion hooks for all custom loggers. + Call agentic completion hooks for all custom loggers (Anthropic Messages API). - 1. Call async_should_run_agentic_completion to check if agentic loop is needed - 2. If yes, call async_run_agentic_completion to execute the loop + 1. Call async_should_run_agentic_loop to check if agentic loop is needed + 2. If yes, call async_run_agentic_loop to execute the loop Returns the response from agentic loop, or None if no hook runs. """ @@ -4453,6 +4479,105 @@ class BaseLLMHTTPHandler: return None + async def _call_agentic_chat_completion_hooks( + self, + response: Any, + model: str, + messages: List[Dict], + optional_params: Dict, + logging_obj: "LiteLLMLoggingObj", + stream: bool, + custom_llm_provider: str, + kwargs: Dict, + ) -> Optional[Any]: + """ + Call agentic chat completion hooks for all custom loggers (Chat Completions API). + + 1. Call async_should_run_chat_completion_agentic_loop to check if agentic loop is needed + 2. If yes, call async_run_chat_completion_agentic_loop to execute the loop + + Returns the response from agentic loop, or None if no hook runs. + """ + from litellm._logging import verbose_logger + from litellm.integrations.custom_logger import CustomLogger + + callbacks = litellm.callbacks + ( + logging_obj.dynamic_success_callbacks or [] + ) + tools = optional_params.get("tools", []) + + for callback in callbacks: + try: + if isinstance(callback, CustomLogger): + # Check if callback has the chat completion agentic loop method + if not hasattr(callback, "async_should_run_chat_completion_agentic_loop"): + continue + + # First: Check if agentic loop should run + should_run, tool_calls = ( + await callback.async_should_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=stream, + custom_llm_provider=custom_llm_provider, + kwargs=kwargs, + ) + ) + + if should_run: + # Second: Execute agentic loop + # Add custom_llm_provider to kwargs so the agentic loop can reconstruct the full model name + kwargs_with_provider = kwargs.copy() if kwargs else {} + kwargs_with_provider["custom_llm_provider"] = custom_llm_provider + agentic_response = await callback.async_run_chat_completion_agentic_loop( + tools=tool_calls, + model=model, + messages=messages, + response=response, + optional_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=kwargs_with_provider, + ) + # First hook that runs agentic loop wins + return agentic_response + + except Exception as e: + verbose_logger.exception( + f"LiteLLM.AgenticHookError: Exception in chat completion agentic hooks: {str(e)}" + ) + + # Check if we need to convert response to fake stream for chat completions + # This happens when: + # 1. Stream was originally True but converted to False for WebSearch interception + # 2. No agentic loop ran (LLM didn't use the tool) + # 3. We have a non-streaming response that needs to be converted to streaming + websearch_converted_stream = ( + logging_obj.model_call_details.get("websearch_interception_converted_stream", False) + if logging_obj is not None + else False + ) + + if websearch_converted_stream: + from litellm._logging import verbose_logger + from litellm.llms.base_llm.base_model_iterator import ( + convert_model_response_to_streaming, + ) + + verbose_logger.debug( + "WebSearchInterception: No tool call made, converting non-streaming chat completion to fake stream" + ) + + # Convert the non-streaming ModelResponse to a fake stream + if hasattr(response, "choices"): + # Use the existing converter for ModelResponse + fake_stream = convert_model_response_to_streaming(response) + return fake_stream + + return None + def _handle_error( self, e: Exception, diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 2b7f5dd5995..7c2a9569c58 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -60,6 +60,38 @@ from ...anthropic.chat.transformation import AnthropicConfig from ...openai_like.chat.transformation import OpenAILikeChatConfig from ..common_utils import DatabricksBase, DatabricksException +def _sanitize_empty_content(message_dict: dict[str, Any]) -> None: + """ + Remove or filter content so empty text blocks are not sent. + Databricks Model Serving uses Anthropic Messages API spec and rejects empty text blocks. + """ + content = message_dict.get("content") + if content is None: + message_dict.pop("content", None) + return + if isinstance(content, str): + if not content.strip(): + message_dict.pop("content") + return + if isinstance(content, list): + if not content: + message_dict.pop("content") + return + filtered = [ + block + for block in content + if not ( + isinstance(block, dict) + and block.get("type") == "text" + and not (block.get("text") or "").strip() + ) + ] + if not filtered: + message_dict.pop("content") + else: + message_dict["content"] = filtered + + if TYPE_CHECKING: from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj @@ -298,7 +330,8 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if "reasoning_effort" in non_default_params and "claude" in model: optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - non_default_params.get("reasoning_effort") + reasoning_effort=non_default_params.get("reasoning_effort"), + model=model ) optional_params.pop("reasoning_effort", None) ## handle thinking tokens @@ -349,6 +382,7 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): # Move message-level cache_control into a content block when content is a string. if "cache_control" in _message and isinstance(_message.get("content"), str): _message = self._move_cache_control_into_string_content_block(_message) + _sanitize_empty_content(cast(dict[str, Any], _message)) new_messages.append(_message) if is_async: diff --git a/litellm/llms/deprecated_providers/palm.py b/litellm/llms/deprecated_providers/palm.py index 3039222c0e2..657a6fdb229 100644 --- a/litellm/llms/deprecated_providers/palm.py +++ b/litellm/llms/deprecated_providers/palm.py @@ -139,7 +139,7 @@ def completion( ) ## COMPLETION CALL try: - response = palm.generate_text(prompt=prompt, **inference_params) + response = palm.generate_text(prompt=prompt, **inference_params) # type: ignore[attr-defined] except Exception as e: raise PalmError( message=str(e), diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index 86bcd94450f..7ec32fecc46 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -236,6 +236,10 @@ class FireworksAIConfig(OpenAIGPTConfig): disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, ) filter_value_from_dict(cast(dict, message), "cache_control") + # Remove fields not permitted by FireworksAI that may cause: + # "Not permitted, field: 'messages[n].provider_specific_fields'" + if isinstance(message, dict) and "provider_specific_fields" in message: + cast(dict, message).pop("provider_specific_fields", None) return messages diff --git a/litellm/llms/gemini/files/transformation.py b/litellm/llms/gemini/files/transformation.py index 37f1376c2b1..cc799cfd6aa 100644 --- a/litellm/llms/gemini/files/transformation.py +++ b/litellm/llms/gemini/files/transformation.py @@ -210,7 +210,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): We expect file_id to be the URI (e.g. https://generativelanguage.googleapis.com/v1beta/files/...) as returned by the upload response. """ - api_key = litellm_params.get("api_key") + api_key = litellm_params.get("api_key") or self.get_api_key() if not api_key: raise ValueError("api_key is required") @@ -222,7 +222,8 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): api_base = api_base.rstrip("/") url = "{}/v1beta/{}?key={}".format(api_base, file_id, api_key) - return url, {"Content-Type": "application/json"} + # Return empty params dict - API key is already in URL, no query params needed + return url, {} def transform_retrieve_file_response( self, @@ -299,7 +300,7 @@ class GoogleAIStudioFilesHandler(GeminiModelInfo, BaseFilesConfig): # Extract the file path from full URI file_name = file_id.split("/v1beta/")[-1] else: - file_name = file_id + file_name = file_id if file_id.startswith("files/") else f"files/{file_id}" # Construct the delete URL url = f"{api_base}/v1beta/{file_name}" diff --git a/litellm/llms/gigachat/chat/transformation.py b/litellm/llms/gigachat/chat/transformation.py index ba14de1f65d..f546f356e11 100644 --- a/litellm/llms/gigachat/chat/transformation.py +++ b/litellm/llms/gigachat/chat/transformation.py @@ -386,33 +386,7 @@ class GigaChatConfig(BaseConfig): transformed.append(message) - # Collapse consecutive user messages - return self._collapse_user_messages(transformed) - - def _collapse_user_messages(self, messages: List[dict]) -> List[dict]: - """Collapse consecutive user messages into one.""" - collapsed: List[dict] = [] - prev_user_msg: Optional[dict] = None - content_parts: List[str] = [] - - for msg in messages: - if msg.get("role") == "user" and prev_user_msg is not None: - content_parts.append(msg.get("content", "")) - else: - if content_parts and prev_user_msg: - prev_user_msg["content"] = "\n".join( - [prev_user_msg.get("content", "")] + content_parts - ) - content_parts = [] - collapsed.append(msg) - prev_user_msg = msg if msg.get("role") == "user" else None - - if content_parts and prev_user_msg: - prev_user_msg["content"] = "\n".join( - [prev_user_msg.get("content", "")] + content_parts - ) - - return collapsed + return transformed def transform_response( self, diff --git a/litellm/llms/github_copilot/chat/transformation.py b/litellm/llms/github_copilot/chat/transformation.py index 50f18cedf9b..be8ad7d0877 100644 --- a/litellm/llms/github_copilot/chat/transformation.py +++ b/litellm/llms/github_copilot/chat/transformation.py @@ -1,11 +1,16 @@ -from typing import Any, Optional, Tuple, cast, List +from typing import List, Optional, Tuple + from litellm.exceptions import AuthenticationError from litellm.llms.openai.openai import OpenAIConfig from litellm.types.llms.openai import AllMessageValues from ..authenticator import Authenticator -from ..common_utils import GetAPIKeyError, GITHUB_COPILOT_API_BASE +from ..common_utils import ( + GITHUB_COPILOT_API_BASE, + GetAPIKeyError, + get_copilot_default_headers, +) class GithubCopilotConfig(OpenAIConfig): @@ -25,9 +30,7 @@ class GithubCopilotConfig(OpenAIConfig): api_key: Optional[str], custom_llm_provider: str, ) -> Tuple[Optional[str], Optional[str], str]: - dynamic_api_base = ( - self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE - ) + dynamic_api_base = self.authenticator.get_api_base() or GITHUB_COPILOT_API_BASE try: dynamic_api_key = self.authenticator.get_api_key() except GetAPIKeyError as e: @@ -45,14 +48,24 @@ class GithubCopilotConfig(OpenAIConfig): ): import litellm - disable_copilot_system_to_assistant = ( - litellm.disable_copilot_system_to_assistant - ) - if not disable_copilot_system_to_assistant: - for message in messages: - if "role" in message and message["role"] == "system": - cast(Any, message)["role"] = "assistant" - return messages + # Check if system-to-assistant conversion is disabled + if litellm.disable_copilot_system_to_assistant: + # GitHub Copilot API now supports system prompts for all models (Claude, GPT, etc.) + # No conversion needed - just return messages as-is + return messages + + # Default behavior: convert system messages to assistant for compatibility + transformed_messages = [] + for message in messages: + if message.get("role") == "system": + # Convert system message to assistant message + transformed_message = message.copy() + transformed_message["role"] = "assistant" + transformed_messages.append(transformed_message) + else: + transformed_messages.append(message) + + return transformed_messages def validate_environment( self, @@ -69,6 +82,14 @@ class GithubCopilotConfig(OpenAIConfig): headers, model, messages, optional_params, litellm_params, api_key, api_base ) + # Add Copilot-specific headers (editor-version, user-agent, etc.) + try: + copilot_api_key = self.authenticator.get_api_key() + copilot_headers = get_copilot_default_headers(copilot_api_key) + validated_headers = {**copilot_headers, **validated_headers} + except GetAPIKeyError: + pass # Will be handled later in the request flow + # Add X-Initiator header based on message roles initiator = self._determine_initiator(messages) validated_headers["X-Initiator"] = initiator @@ -87,7 +108,7 @@ class GithubCopilotConfig(OpenAIConfig): For other models, returns standard OpenAI parameters (which may include reasoning_effort for o-series models). """ from litellm.utils import supports_reasoning - + # Get base OpenAI parameters base_params = super().get_supported_openai_params(model) @@ -118,7 +139,7 @@ class GithubCopilotConfig(OpenAIConfig): """ Check if any message contains vision content (images). Returns True if any message has content with vision-related types, otherwise False. - + Checks for: - image_url content type (OpenAI format) - Content items with type 'image_url' diff --git a/litellm/llms/oci/chat/transformation.py b/litellm/llms/oci/chat/transformation.py index 84f39ef2525..1c22602b483 100644 --- a/litellm/llms/oci/chat/transformation.py +++ b/litellm/llms/oci/chat/transformation.py @@ -218,6 +218,7 @@ class OCIChatConfig(BaseConfig): "parallel_tool_calls": False, "audio": False, "web_search_options": False, + "response_format": "responseFormat", } # Cohere and Gemini use the same parameter mapping as GENERIC @@ -269,6 +270,9 @@ class OCIChatConfig(BaseConfig): adapted_params[alias] = value + if alias == "responseFormat": + adapted_params["response_format"] = value + return adapted_params def _sign_with_oci_signer( @@ -673,6 +677,36 @@ class OCIChatConfig(BaseConfig): selected_params["tools"] = adapt_tool_definition_to_oci_standard( # type: ignore[assignment] selected_params["tools"], vendor # type: ignore[arg-type] ) + + # Transform response_format type to OCI uppercase format + if "responseFormat" in selected_params: + rf = selected_params["responseFormat"] + if isinstance(rf, dict) and "type" in rf: + rf_payload = dict(rf) + selected_params["responseFormat"] = rf_payload + + response_type = rf_payload["type"] + schema_payload: Optional[Any] = None + + if "json_schema" in rf_payload: + raw_schema_payload = rf_payload.pop("json_schema") + if isinstance(raw_schema_payload, dict): + schema_payload = dict(raw_schema_payload) + else: + schema_payload = raw_schema_payload + + if schema_payload is not None: + rf_payload["jsonSchema"] = schema_payload + + if vendor == OCIVendors.COHERE: + # Cohere expects lower-case type values + rf_payload["type"] = response_type + else: + format_type = response_type.upper() + if format_type == "JSON": + format_type = "JSON_OBJECT" + rf_payload["type"] = format_type + return selected_params def adapt_messages_to_cohere_standard(self, messages: List[AllMessageValues]) -> List[CohereMessage]: @@ -804,13 +838,24 @@ class OCIChatConfig(BaseConfig): if not user_messages: raise Exception("No user message found for Cohere model") + # Extract system messages into preambleOverride + system_messages = [msg for msg in messages if msg.get("role") == "system"] + preamble_override = None + if system_messages: + preamble = "\n".join( + self._extract_text_content(msg["content"]) for msg in system_messages + ) + if preamble: + preamble_override = preamble # Create Cohere-specific chat request + optional_cohere_params = self._get_optional_params(OCIVendors.COHERE, optional_params) chat_request = CohereChatRequest( apiFormat="COHERE", message=self._extract_text_content(user_messages[-1]["content"]), chatHistory=self.adapt_messages_to_cohere_standard(messages), - **self._get_optional_params(OCIVendors.COHERE, optional_params) + preambleOverride=preamble_override, + **optional_cohere_params ) data = OCICompletionPayload( diff --git a/litellm/llms/ollama/chat/transformation.py b/litellm/llms/ollama/chat/transformation.py index 8c98cc54050..bc5aa654aad 100644 --- a/litellm/llms/ollama/chat/transformation.py +++ b/litellm/llms/ollama/chat/transformation.py @@ -502,13 +502,12 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator): reasoning_content: Optional[str] = None content: Optional[str] = None if chunk["message"].get("thinking") is not None: - if self.started_reasoning_content is False: - reasoning_content = chunk["message"].get("thinking") - self.started_reasoning_content = True - elif self.finished_reasoning_content is False: - reasoning_content = chunk["message"].get("thinking") - self.finished_reasoning_content = True + reasoning_content = chunk["message"].get("thinking") + self.started_reasoning_content = True elif chunk["message"].get("content") is not None: + if self.started_reasoning_content and not self.finished_reasoning_content: + self.finished_reasoning_content = True + message_content = chunk["message"].get("content") if "" in message_content: message_content = message_content.replace("", "") diff --git a/litellm/llms/openai/chat/gpt_transformation.py b/litellm/llms/openai/chat/gpt_transformation.py index 6cc09dafc2f..16368907070 100644 --- a/litellm/llms/openai/chat/gpt_transformation.py +++ b/litellm/llms/openai/chat/gpt_transformation.py @@ -20,12 +20,12 @@ from typing import ( import httpx import litellm +from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import ( _extract_reasoning_content, _handle_invalid_parallel_tool_calls, _should_convert_tool_call_to_json_mode, ) -from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.litellm_core_utils.prompt_templates.common_utils import get_tool_call_names from litellm.litellm_core_utils.prompt_templates.image_handling import ( async_convert_url_to_base64, @@ -161,6 +161,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig): "web_search_options", "service_tier", "safety_identifier", + "prompt_cache_key", ] # works across all models model_specific_params = [] diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index fb00aa28f45..c406f502b45 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -21,7 +21,13 @@ from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation from litellm.main import stream_chunk_builder from litellm.types.llms.openai import ChatCompletionToolParam -from litellm.types.utils import Choices, GenericGuardrailAPIInputs, ModelResponse, ModelResponseStream, StreamingChoices +from litellm.types.utils import ( + Choices, + GenericGuardrailAPIInputs, + ModelResponse, + ModelResponseStream, + StreamingChoices, +) if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -80,9 +86,9 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if tool_calls_to_check: inputs["tool_calls"] = tool_calls_to_check # type: ignore if messages: - inputs["structured_messages"] = ( - messages # pass the openai /chat/completions messages to the guardrail, as-is - ) + inputs[ + "structured_messages" + ] = messages # pass the openai /chat/completions messages to the guardrail, as-is # Pass tools (function definitions) to the guardrail tools = data.get("tools") if tools: @@ -362,14 +368,17 @@ class OpenAIChatCompletionsHandler(BaseTranslation): # check if the stream has ended has_stream_ended = False for chunk in responses_so_far: - if chunk.choices[0].finish_reason is not None: + if chunk.choices and chunk.choices[0].finish_reason is not None: has_stream_ended = True break if has_stream_ended: # convert to model response model_response = cast( - ModelResponse, stream_chunk_builder(chunks=responses_so_far, logging_obj=litellm_logging_obj) + ModelResponse, + stream_chunk_builder( + chunks=responses_so_far, logging_obj=litellm_logging_obj + ), ) # run process_output_response await self.process_output_response( diff --git a/litellm/llms/openai/common_utils.py b/litellm/llms/openai/common_utils.py index 8bcecd35232..ce470f04aca 100644 --- a/litellm/llms/openai/common_utils.py +++ b/litellm/llms/openai/common_utils.py @@ -15,14 +15,12 @@ if TYPE_CHECKING: from aiohttp import ClientSession import litellm -from litellm._logging import verbose_logger from litellm.llms.base_llm.chat.transformation import BaseLLMException from litellm.llms.custom_httpx.http_handler import ( _DEFAULT_TTL_FOR_HTTPX_CLIENTS, AsyncHTTPHandler, get_ssl_configuration, ) -from litellm.types.utils import LlmProviders class OpenAIError(BaseLLMException): @@ -205,67 +203,30 @@ class BaseOpenAILLM: if litellm.aclient_session is not None: return litellm.aclient_session - # Use the global cached client system to prevent memory leaks (issue #14540) - # This routes through get_async_httpx_client() which provides TTL-based caching - from litellm.llms.custom_httpx.http_handler import get_async_httpx_client + # Get unified SSL configuration + ssl_config = get_ssl_configuration() - try: - # Get SSL config and include in params for proper cache key - ssl_config = get_ssl_configuration() - params = {"ssl_verify": ssl_config} if ssl_config is not None else {} - params["disable_aiohttp_transport"] = litellm.disable_aiohttp_transport - - # Get a cached AsyncHTTPHandler which manages the httpx.AsyncClient - cached_handler = get_async_httpx_client( - llm_provider=LlmProviders.OPENAI, # Cache key includes provider - params=params, # Include SSL config in cache key + return httpx.AsyncClient( + verify=ssl_config, + transport=AsyncHTTPHandler._create_async_transport( + ssl_context=ssl_config + if isinstance(ssl_config, ssl.SSLContext) + else None, + ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, shared_session=shared_session, - ) - # Return the underlying httpx client from the handler - return cached_handler.client - except (ImportError, AttributeError, KeyError) as e: - # Fallback to creating a client directly if caching system unavailable - # This preserves backwards compatibility - verbose_logger.debug( - f"Client caching unavailable ({type(e).__name__}), using direct client creation" - ) - ssl_config = get_ssl_configuration() - return httpx.AsyncClient( - verify=ssl_config, - transport=AsyncHTTPHandler._create_async_transport( - ssl_context=ssl_config - if isinstance(ssl_config, ssl.SSLContext) - else None, - ssl_verify=ssl_config if isinstance(ssl_config, bool) else None, - shared_session=shared_session, - ), - follow_redirects=True, - ) + ), + follow_redirects=True, + ) @staticmethod def _get_sync_http_client() -> Optional[httpx.Client]: if litellm.client_session is not None: return litellm.client_session - # Use the global cached client system to prevent memory leaks (issue #14540) - from litellm.llms.custom_httpx.http_handler import _get_httpx_client + # Get unified SSL configuration + ssl_config = get_ssl_configuration() - try: - # Get SSL config and include in params for proper cache key - ssl_config = get_ssl_configuration() - params = {"ssl_verify": ssl_config} if ssl_config is not None else None - - # Get a cached HTTPHandler which manages the httpx.Client - cached_handler = _get_httpx_client(params=params) - # Return the underlying httpx client from the handler - return cached_handler.client - except (ImportError, AttributeError, KeyError) as e: - # Fallback to creating a client directly if caching system unavailable - verbose_logger.debug( - f"Client caching unavailable ({type(e).__name__}), using direct client creation" - ) - ssl_config = get_ssl_configuration() - return httpx.Client( - verify=ssl_config, - follow_redirects=True, - ) + return httpx.Client( + verify=ssl_config, + follow_redirects=True, + ) diff --git a/litellm/llms/openai/embeddings/guardrail_translation/__init__.py b/litellm/llms/openai/embeddings/guardrail_translation/__init__.py new file mode 100644 index 00000000000..a60662282ca --- /dev/null +++ b/litellm/llms/openai/embeddings/guardrail_translation/__init__.py @@ -0,0 +1,13 @@ +"""OpenAI Embeddings handler for Unified Guardrails.""" + +from litellm.llms.openai.embeddings.guardrail_translation.handler import ( + OpenAIEmbeddingsHandler, +) +from litellm.types.utils import CallTypes + +guardrail_translation_mappings = { + CallTypes.embedding: OpenAIEmbeddingsHandler, + CallTypes.aembedding: OpenAIEmbeddingsHandler, +} + +__all__ = ["guardrail_translation_mappings", "OpenAIEmbeddingsHandler"] diff --git a/litellm/llms/openai/embeddings/guardrail_translation/handler.py b/litellm/llms/openai/embeddings/guardrail_translation/handler.py new file mode 100644 index 00000000000..7458020e109 --- /dev/null +++ b/litellm/llms/openai/embeddings/guardrail_translation/handler.py @@ -0,0 +1,179 @@ +""" +OpenAI Embeddings Handler for Unified Guardrails + +This module provides guardrail translation support for OpenAI's embeddings endpoint. +The handler processes the 'input' parameter for guardrails. +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Union + +from litellm._logging import verbose_proxy_logger +from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.utils import GenericGuardrailAPIInputs + +if TYPE_CHECKING: + from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.types.utils import EmbeddingResponse + + +class OpenAIEmbeddingsHandler(BaseTranslation): + """ + Handler for processing OpenAI embeddings requests with guardrails. + + This class provides methods to: + 1. Process input text (pre-call hook) + 2. Process output response (post-call hook) - embeddings don't typically need output guardrails + + The handler specifically processes the 'input' parameter which can be: + - A single string + - A list of strings (for batch embeddings) + - A list of integers (token IDs - not processed by guardrails) + - A list of lists of integers (batch token IDs - not processed by guardrails) + """ + + async def process_input_messages( + self, + data: dict, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, + ) -> Any: + """ + Process input text by applying guardrails to text content. + + Args: + data: Request data dictionary containing 'input' parameter + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + + Returns: + Modified data with guardrails applied to input + """ + input_data = data.get("input") + if input_data is None: + verbose_proxy_logger.debug( + "OpenAI Embeddings: No input found in request data" + ) + return data + + if isinstance(input_data, str): + data = await self._process_string_input( + data, input_data, guardrail_to_apply, litellm_logging_obj + ) + elif isinstance(input_data, list): + data = await self._process_list_input( + data, input_data, guardrail_to_apply, litellm_logging_obj + ) + else: + verbose_proxy_logger.warning( + "OpenAI Embeddings: Unexpected input type: %s. Expected string or list.", + type(input_data), + ) + + return data + + async def _process_string_input( + self, + data: dict, + input_data: str, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any], + ) -> dict: + """Process a single string input through the guardrail.""" + inputs = GenericGuardrailAPIInputs(texts=[input_data]) + if model := data.get("model"): + inputs["model"] = model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + if guardrailed_texts := guardrailed_inputs.get("texts"): + data["input"] = guardrailed_texts[0] + verbose_proxy_logger.debug( + "OpenAI Embeddings: Applied guardrail to string input. " + "Original length: %d, New length: %d", + len(input_data), + len(data["input"]), + ) + + return data + + async def _process_list_input( + self, + data: dict, + input_data: List[Union[str, int, List[int]]], + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any], + ) -> dict: + """Process a list input through the guardrail (if it contains strings).""" + if len(input_data) == 0: + return data + + first_item = input_data[0] + + # Skip non-text inputs (token IDs) + if isinstance(first_item, (int, list)): + verbose_proxy_logger.debug( + "OpenAI Embeddings: Input is token IDs, skipping guardrail processing" + ) + return data + + if not isinstance(first_item, str): + verbose_proxy_logger.warning( + "OpenAI Embeddings: Unexpected input list item type: %s", + type(first_item), + ) + return data + + # List of strings - apply guardrail + inputs = GenericGuardrailAPIInputs(texts=input_data) # type: ignore + if model := data.get("model"): + inputs["model"] = model + + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + if guardrailed_texts := guardrailed_inputs.get("texts"): + data["input"] = guardrailed_texts + verbose_proxy_logger.debug( + "OpenAI Embeddings: Applied guardrail to %d inputs", + len(guardrailed_texts), + ) + + return data + + async def process_output_response( + self, + response: "EmbeddingResponse", + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, + ) -> Any: + """ + Process output response - embeddings responses contain vectors, not text. + + For embeddings, the output is numerical vectors, so there's typically + no text content to apply guardrails to. This method is a no-op but + is included for interface consistency. + + Args: + response: Embedding response object + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata + + Returns: + Unmodified response (embeddings don't have text output to guard) + """ + verbose_proxy_logger.debug( + "OpenAI Embeddings: Output response processing skipped - " + "embeddings contain vectors, not text" + ) + return response diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 8a8070240da..da87852dff5 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -501,6 +501,88 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): else: raise e + async def _call_agentic_completion_hooks_openai( + self, + response: Any, + model: str, + messages: List[Dict], + optional_params: Dict, + logging_obj: LiteLLMLoggingObj, + stream: bool, + litellm_params: Dict, + ) -> Optional[Any]: + """ + Call agentic completion hooks for all custom loggers (OpenAI Chat Completions API). + + 1. Call async_should_run_chat_completion_agentic_loop to check if agentic loop is needed + 2. If yes, call async_run_chat_completion_agentic_loop to execute the loop + + Returns the response from agentic loop, or None if no hook runs. + """ + from litellm._logging import verbose_logger + from litellm.integrations.custom_logger import CustomLogger + + callbacks = litellm.callbacks + ( + logging_obj.dynamic_success_callbacks or [] + ) + # Avoid logging full callback objects to prevent leaking sensitive data + verbose_logger.debug( + "LiteLLM.AgenticHooks: callbacks_count=%s", len(callbacks) + ) + tools = optional_params.get("tools", []) + # Avoid logging full tools payloads; they may contain sensitive parameters + verbose_logger.debug( + "LiteLLM.AgenticHooks: tools_count=%s", len(tools) if isinstance(tools, list) else 1 if tools else 0 + ) + # Get custom_llm_provider from litellm_params + custom_llm_provider = litellm_params.get("custom_llm_provider", "openai") + + for callback in callbacks: + try: + if isinstance(callback, CustomLogger): + # Check if the callback has the chat completion agentic loop methods + if not hasattr(callback, 'async_should_run_chat_completion_agentic_loop'): + continue + + # First: Check if agentic loop should run (using chat completion method) + should_run, tool_calls = ( + await callback.async_should_run_chat_completion_agentic_loop( + response=response, + model=model, + messages=messages, + tools=tools, + stream=stream, + custom_llm_provider=custom_llm_provider, + kwargs=litellm_params, + ) + ) + + if should_run: + # Second: Execute agentic loop + kwargs_with_provider = litellm_params.copy() if litellm_params else {} + kwargs_with_provider["custom_llm_provider"] = custom_llm_provider + + # For OpenAI Chat Completions, use the chat completion agentic loop method + agentic_response = await callback.async_run_chat_completion_agentic_loop( + tools=tool_calls, + model=model, + messages=messages, + response=response, + optional_params=optional_params, + logging_obj=logging_obj, + stream=stream, + kwargs=kwargs_with_provider, + ) + # First hook that runs agentic loop wins + return agentic_response + + except Exception as e: + verbose_logger.exception( + f"LiteLLM.AgenticHookError: Exception in agentic completion hooks for OpenAI: {str(e)}" + ) + + return None + def mock_streaming( self, response: ModelResponse, @@ -844,7 +926,6 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): logging_obj=logging_obj, ) stringified_response = response.model_dump() - logging_obj.post_call( input=data["messages"], api_key=api_key, @@ -859,6 +940,20 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): _response_headers=headers, ) + # Call agentic completion hooks (e.g., for websearch_interception) + agentic_response = await self._call_agentic_completion_hooks_openai( + response=final_response_obj, + model=model, + messages=messages, + optional_params=optional_params, + logging_obj=logging_obj, + stream=False, + litellm_params=litellm_params, + ) + + if agentic_response is not None: + final_response_obj = agentic_response + if fake_stream is True: return self.mock_streaming( response=cast(ModelResponse, final_response_obj), diff --git a/litellm/llms/openai/realtime/handler.py b/litellm/llms/openai/realtime/handler.py index fd04ac4d458..ef9cc43c3e1 100644 --- a/litellm/llms/openai/realtime/handler.py +++ b/litellm/llms/openai/realtime/handler.py @@ -16,6 +16,62 @@ from ..openai import OpenAIChatCompletion class OpenAIRealtime(OpenAIChatCompletion): + """ + Base handler for OpenAI-compatible realtime WebSocket connections. + + Subclasses can override template methods to customize: + - _get_default_api_base(): Default API base URL + - _get_additional_headers(): Extra headers beyond Authorization + - _get_ssl_config(): SSL configuration for WebSocket connection + """ + + def _get_default_api_base(self) -> str: + """ + Get the default API base URL for this provider. + Override this in subclasses to set provider-specific defaults. + """ + return "https://api.openai.com/" + + def _get_additional_headers(self, api_key: str) -> dict: + """ + Get additional headers beyond Authorization. + Override this in subclasses to customize headers (e.g., remove OpenAI-Beta). + + Args: + api_key: API key for authentication + + Returns: + Dictionary of additional headers + """ + return { + "Authorization": f"Bearer {api_key}", + "OpenAI-Beta": "realtime=v1", + } + + def _get_ssl_config(self, url: str) -> Any: + """ + Get SSL configuration for WebSocket connection. + Override this in subclasses to customize SSL behavior. + + Args: + url: WebSocket URL (ws:// or wss://) + + Returns: + SSL configuration (None, True, or SSLContext) + """ + if url.startswith("ws://"): + return None + + # Use the shared SSL context which respects custom CA certs and SSL settings + ssl_config = get_shared_realtime_ssl_context() + + # If ssl_config is False (ssl_verify=False), websockets library needs True instead + # to establish connection without verification (False would fail) + if ssl_config is False: + return True + + return ssl_config + def _construct_url(self, api_base: str, query_params: RealtimeQueryParams) -> str: """ Construct the backend websocket URL with all query parameters (including 'model'). @@ -45,8 +101,9 @@ class OpenAIRealtime(OpenAIChatCompletion): ): import websockets from websockets.asyncio.client import ClientConnection + if api_base is None: - api_base = "https://api.openai.com/" + api_base = self._get_default_api_base() if api_key is None: raise ValueError("api_key is required for OpenAI realtime calls") @@ -56,30 +113,27 @@ class OpenAIRealtime(OpenAIChatCompletion): url = self._construct_url(api_base, query_params) try: - # Only use SSL context for secure websocket connections (wss://) - # websockets library doesn't accept ssl argument for ws:// URIs - ssl_context = None if url.startswith("ws://") else get_shared_realtime_ssl_context() + # Get provider-specific SSL configuration + ssl_config = self._get_ssl_config(url) + + # Get provider-specific headers + headers = self._get_additional_headers(api_key) + # Log a masked request preview consistent with other endpoints. logging_obj.pre_call( input=None, api_key=api_key, additional_args={ "api_base": url, - "headers": { - "Authorization": f"Bearer {api_key}", - "OpenAI-Beta": "realtime=v1", - }, + "headers": headers, "complete_input_dict": {"query_params": query_params}, }, ) async with websockets.connect( # type: ignore url, - additional_headers={ - "Authorization": f"Bearer {api_key}", # type: ignore - "OpenAI-Beta": "realtime=v1", - }, + additional_headers=headers, # type: ignore max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES, - ssl=ssl_context, + ssl=ssl_config, ) as backend_ws: realtime_streaming = RealTimeStreaming( websocket, cast(ClientConnection, backend_ws), logging_obj diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index d943662f9e4..ad3d4c932d4 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -319,9 +319,7 @@ class OpenAIResponsesHandler(BaseTranslation): return response if not response_output: - verbose_proxy_logger.debug( - "OpenAI Responses API: Empty output in response" - ) + verbose_proxy_logger.debug("OpenAI Responses API: Empty output in response") return response # Step 1: Extract all text content and tool calls from response output @@ -427,27 +425,30 @@ class OpenAIResponsesHandler(BaseTranslation): handle_raw_dict_callback=None, ) - tool_calls = model_response_choices[0].message.tool_calls - text = model_response_choices[0].message.content - guardrail_inputs = GenericGuardrailAPIInputs() - if text: - guardrail_inputs["texts"] = [text] - if tool_calls: - guardrail_inputs["tool_calls"] = cast( - List[ChatCompletionToolCallChunk], tool_calls - ) - # Include model information from the response if available - response_model = final_chunk.get("response", {}).get("model") - if response_model: - guardrail_inputs["model"] = response_model - if tool_calls or text: - _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( - inputs=guardrail_inputs, - request_data={}, - input_type="response", - logging_obj=litellm_logging_obj, - ) - return responses_so_far + if model_response_choices: + tool_calls = model_response_choices[0].message.tool_calls + text = model_response_choices[0].message.content + guardrail_inputs = GenericGuardrailAPIInputs() + if text: + guardrail_inputs["texts"] = [text] + if tool_calls: + guardrail_inputs["tool_calls"] = cast( + List[ChatCompletionToolCallChunk], tool_calls + ) + # Include model information from the response if available + response_model = final_chunk.get("response", {}).get("model") + if response_model: + guardrail_inputs["model"] = response_model + if tool_calls or text: + _guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=guardrail_inputs, + request_data={}, + input_type="response", + logging_obj=litellm_logging_obj, + ) + return responses_so_far + else: + verbose_proxy_logger.debug("Skipping output guardrail - model response has no choices") # model_response_stream = OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(final_chunk) # tool_calls = model_response_stream.choices[0].tool_calls # convert openai response to model response @@ -513,11 +514,9 @@ class OpenAIResponsesHandler(BaseTranslation): # Check if it's an OutputText with text if isinstance(content_item, OutputText): if content_item.text: - return True elif isinstance(content_item, dict): if content_item.get("text"): - return True return False diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index cc2439b431a..3e089682097 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast, get_type_hin import httpx from openai.types.responses import ResponseReasoningItem -from pydantic import BaseModel +from pydantic import BaseModel, ValidationError import litellm from litellm._logging import verbose_logger @@ -240,25 +240,26 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): event_pydantic_model = OpenAIResponsesAPIConfig.get_event_model_class( event_type=event_type ) - # Defensive: Some OpenAI-compatible providers may send `error.code: null`. - # Pydantic will raise a ValidationError when it expects a string but gets None. - # Coalesce a None `error.code` to a stable default string so streaming - # iteration does not crash (see issue report). This keeps behavior similar - # to previous fixes (coalesce before validation) and lets higher-level - # handlers still receive an `ErrorEvent` object. + # Some OpenAI-compatible providers send error.code: null; coalesce so validation succeeds. try: error_obj = parsed_chunk.get("error") if isinstance(error_obj, dict) and error_obj.get("code") is None: - # Preserve other fields, but ensure `code` is a non-null string parsed_chunk = dict(parsed_chunk) parsed_chunk["error"] = dict(error_obj) parsed_chunk["error"]["code"] = "unknown_error" except Exception: - # If anything unexpected happens here, fall back to attempting - # instantiation and let higher-level handlers manage errors. verbose_logger.debug("Failed to coalesce error.code in parsed_chunk") - return event_pydantic_model(**parsed_chunk) + try: + return event_pydantic_model(**parsed_chunk) + except ValidationError: + verbose_logger.debug( + "Pydantic validation failed for %s with chunk %s, " + "falling back to model_construct", + event_pydantic_model.__name__, + parsed_chunk, + ) + return event_pydantic_model.model_construct(**parsed_chunk) @staticmethod def get_event_model_class(event_type: str) -> Any: @@ -307,6 +308,10 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ResponsesAPIStreamEvents.MCP_CALL_FAILED: MCPCallFailedEvent, ResponsesAPIStreamEvents.IMAGE_GENERATION_PARTIAL_IMAGE: ImageGenerationPartialImageEvent, ResponsesAPIStreamEvents.ERROR: ErrorEvent, + # Shell tool events: passthrough as GenericEvent so payload is preserved + ResponsesAPIStreamEvents.SHELL_CALL_IN_PROGRESS: GenericEvent, + ResponsesAPIStreamEvents.SHELL_CALL_COMPLETED: GenericEvent, + ResponsesAPIStreamEvents.SHELL_CALL_OUTPUT: GenericEvent, } model_class = event_models.get(cast(ResponsesAPIStreamEvents, event_type)) diff --git a/litellm/llms/openai/videos/transformation.py b/litellm/llms/openai/videos/transformation.py index 3073b22e1ca..0dd7940a92e 100644 --- a/litellm/llms/openai/videos/transformation.py +++ b/litellm/llms/openai/videos/transformation.py @@ -269,26 +269,27 @@ class OpenAIVideoConfig(BaseVideoConfig): ) -> Tuple[str, Dict]: """ Transform the video list request for OpenAI API. - + OpenAI API expects the following request: - GET /v1/videos """ # Use the api_base directly for video list url = api_base - + # Prepare query parameters params = {} if after is not None: - params["after"] = after + # Decode the wrapped video ID back to the original provider ID + params["after"] = extract_original_video_id(after) if limit is not None: params["limit"] = str(limit) if order is not None: params["order"] = order - + # Add any extra query parameters if extra_query: params.update(extra_query) - + return url, params def transform_video_list_response( @@ -296,18 +297,40 @@ class OpenAIVideoConfig(BaseVideoConfig): raw_response: httpx.Response, logging_obj: LiteLLMLoggingObj, custom_llm_provider: Optional[str] = None, - ) -> Dict[str,str]: + ) -> Dict[str, str]: response_data = raw_response.json() - + if custom_llm_provider and "data" in response_data: for video_obj in response_data.get("data", []): if isinstance(video_obj, dict) and "id" in video_obj: video_obj["id"] = encode_video_id_with_provider( - video_obj["id"], - custom_llm_provider, - video_obj.get("model") + video_obj["id"], + custom_llm_provider, + video_obj.get("model"), ) - + + # Encode pagination cursor IDs so they remain consistent + # with the wrapped data[].id format + data_list = response_data.get("data", []) + if response_data.get("first_id"): + first_model = None + if data_list and isinstance(data_list[0], dict): + first_model = data_list[0].get("model") + response_data["first_id"] = encode_video_id_with_provider( + response_data["first_id"], + custom_llm_provider, + first_model, + ) + if response_data.get("last_id"): + last_model = None + if data_list and isinstance(data_list[-1], dict): + last_model = data_list[-1].get("model") + response_data["last_id"] = encode_video_id_with_provider( + response_data["last_id"], + custom_llm_provider, + last_model, + ) + return response_data def transform_video_delete_request( diff --git a/litellm/llms/perplexity/responses/__init__.py b/litellm/llms/perplexity/responses/__init__.py new file mode 100644 index 00000000000..9bdf810e839 --- /dev/null +++ b/litellm/llms/perplexity/responses/__init__.py @@ -0,0 +1,7 @@ +""" +Perplexity Agentic Research API (Responses API) module +""" + +from .transformation import PerplexityResponsesConfig + +__all__ = ["PerplexityResponsesConfig"] diff --git a/litellm/llms/perplexity/responses/transformation.py b/litellm/llms/perplexity/responses/transformation.py new file mode 100644 index 00000000000..178e76ea970 --- /dev/null +++ b/litellm/llms/perplexity/responses/transformation.py @@ -0,0 +1,409 @@ +""" +Transformation logic for Perplexity Agentic Research API (Responses API) + +This module handles the translation between OpenAI's Responses API format +and Perplexity's Responses API format, which supports: +- Third-party model access (OpenAI, Anthropic, Google, xAI, etc.) +- Presets for optimized configurations +- Web search and URL fetching tools +- Reasoning effort control +- Instructions parameter for system-level guidance +""" + +from typing import Any, Dict, List, Optional, Union + +import httpx + +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + ResponseAPIUsage, + ResponseInputParam, + ResponsesAPIOptionalRequestParams, + ResponsesAPIResponse, + ResponsesAPIStreamingResponse, +) +from litellm.types.router import GenericLiteLLMParams +from litellm.types.utils import LlmProviders + + +class PerplexityResponsesConfig(OpenAIResponsesAPIConfig): + """ + Configuration for Perplexity Agentic Research API (Responses API) + + + Reference: https://docs.perplexity.ai/agentic-research/quickstart + """ + + @property + def custom_llm_provider(self) -> LlmProviders: + return LlmProviders.PERPLEXITY + + def get_supported_openai_params(self, model: str) -> list: + """ + Perplexity Responses API supports a different set of parameters + + Ref: https://docs.perplexity.ai/api-reference/responses-post + """ + return [ + "max_output_tokens", + "stream", + "temperature", + "top_p", + "tools", + "reasoning", + "preset", + "instructions", + "models", # Model fallback support + ] + + def validate_environment( + self, headers: dict, model: str, litellm_params: Optional[GenericLiteLLMParams] + ) -> dict: + """Validate environment and set up headers""" + # Get API key from environment + api_key = ( + get_secret_str("PERPLEXITYAI_API_KEY") + or get_secret_str("PERPLEXITY_API_KEY") + ) + + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + headers["Content-Type"] = "application/json" + + return headers + + def get_complete_url( + self, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """Get the complete URL for the Perplexity Responses API""" + if api_base is None: + api_base = get_secret_str("PERPLEXITY_API_BASE") or "https://api.perplexity.ai" + + # Ensure api_base doesn't end with a slash + api_base = api_base.rstrip("/") + + # Add the responses endpoint + return f"{api_base}/v1/responses" + + def map_openai_params( + self, + response_api_optional_params: ResponsesAPIOptionalRequestParams, + model: str, + drop_params: bool, + ) -> Dict: + """ + Map OpenAI Responses API parameters to Perplexity format + + Key differences: + - Supports 'preset' parameter for predefined configurations + - Supports 'instructions' parameter for system-level guidance + - Tools are specified differently (web_search, fetch_url) + """ + mapped_params: Dict[str, Any] = {} + + # Map standard parameters + if response_api_optional_params.get("max_output_tokens"): + mapped_params["max_output_tokens"] = response_api_optional_params["max_output_tokens"] + + if response_api_optional_params.get("temperature"): + mapped_params["temperature"] = response_api_optional_params["temperature"] + + if response_api_optional_params.get("top_p"): + mapped_params["top_p"] = response_api_optional_params["top_p"] + + if response_api_optional_params.get("stream"): + mapped_params["stream"] = response_api_optional_params["stream"] + + if response_api_optional_params.get("stream_options"): + mapped_params["stream_options"] = response_api_optional_params["stream_options"] + + # Map Perplexity-specific parameters (using .get() with Any dict access) + preset = response_api_optional_params.get("preset") # type: ignore + if preset: + mapped_params["preset"] = preset + + instructions = response_api_optional_params.get("instructions") # type: ignore + if instructions: + mapped_params["instructions"] = instructions + + if response_api_optional_params.get("reasoning"): + mapped_params["reasoning"] = response_api_optional_params["reasoning"] + + tools = response_api_optional_params.get("tools") + if tools: + # Convert tools to list of dicts for transformation + tools_list = [dict(tool) if hasattr(tool, '__dict__') else tool for tool in tools] # type: ignore + mapped_params["tools"] = self._transform_tools(tools_list) # type: ignore + + return mapped_params + + def _transform_tools(self, tools: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + Transform tools to Perplexity format + + Perplexity supports: + - web_search: Performs web searches + - fetch_url: Fetches content from URLs + """ + perplexity_tools = [] + + for tool in tools: + if isinstance(tool, dict): + tool_type = tool.get("type") + + # Direct Perplexity tool format + if tool_type in ["web_search", "fetch_url"]: + perplexity_tools.append(tool) + + # OpenAI function format - try to map to Perplexity tools + elif tool_type == "function": + function = tool.get("function", {}) + function_name = function.get("name", "") + + if function_name == "web_search" or "search" in function_name.lower(): + perplexity_tools.append({"type": "web_search"}) + elif function_name == "fetch_url" or "fetch" in function_name.lower(): + perplexity_tools.append({"type": "fetch_url"}) + + return perplexity_tools + + def transform_responses_api_request( + self, + model: str, + input: Union[str, ResponseInputParam], + response_api_optional_request_params: Dict, + litellm_params: GenericLiteLLMParams, + headers: dict, + ) -> Dict: + """ + Transform request to Perplexity Responses API format + """ + # Check if the model is a preset (format: preset/preset-name) + if model.startswith("preset/"): + preset_name = model.replace("preset/", "") + data = { + "preset": preset_name, + "input": self._format_input(input), + } + # Check if preset is explicitly provided in params + elif response_api_optional_request_params.get("preset"): + data = { + "preset": response_api_optional_request_params.pop("preset"), + "input": self._format_input(input), + } + else: + # Full request format for third-party models + data = { + "model": model, + "input": self._format_input(input), + } + + # Add all optional parameters + for key, value in response_api_optional_request_params.items(): + data[key] = value + + return data + + def _format_input(self, input: Union[str, ResponseInputParam]) -> Union[str, List[Dict[str, Any]]]: + """ + Format input for Perplexity Responses API + + The API accepts either: + - A simple string for single-turn queries + - An array of message objects for multi-turn conversations + """ + if isinstance(input, str): + return input + + # Handle ResponseInputParam format + if isinstance(input, list): + formatted_messages = [] + for item in input: + if isinstance(item, dict): + formatted_message = { + "type": "message", + "role": item.get("role"), + "content": item.get("content", ""), + } + formatted_messages.append(formatted_message) + return formatted_messages + + return str(input) + + def transform_response_api_response( + self, + model: str, + raw_response: httpx.Response, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIResponse: + """ + Transform Perplexity Responses API response to OpenAI Responses API format + """ + try: + raw_response_json = raw_response.json() + except Exception as e: + raise BaseLLMException( + status_code=raw_response.status_code, + message=f"Failed to parse response: {str(e)}", + ) + + # Check for error status + status = raw_response_json.get("status") + if status == "failed": + error = raw_response_json.get("error", {}) + error_message = error.get("message", "Unknown error") + raise BaseLLMException( + status_code=raw_response.status_code, + message=error_message, + ) + + # Transform usage to handle Perplexity's cost structure + usage_data = raw_response_json.get("usage", {}) + transformed_usage_dict = self._transform_usage(usage_data) + + # Convert usage dict to ResponseAPIUsage object + usage_obj = ResponseAPIUsage(**transformed_usage_dict) if transformed_usage_dict else None + + # Map Perplexity response to OpenAI Responses API format + response = ResponsesAPIResponse( + id=raw_response_json.get("id", ""), + object="response", + created_at=raw_response_json.get("created_at", 0), + status=raw_response_json.get("status", "completed"), + model=raw_response_json.get("model", model), + output=raw_response_json.get("output", []), + usage=usage_obj, + ) + + return response + + def _transform_usage(self, usage_data: Dict[str, Any]) -> Dict[str, Any]: + """ + Transform Perplexity usage data to OpenAI format + + Perplexity returns: + { + "input_tokens": 100, + "output_tokens": 200, + "total_tokens": 300, + "cost": { + "currency": "USD", + "input_cost": 0.0001, + "output_cost": 0.0002, + "total_cost": 0.0003 + } + } + + OpenAI expects: + { + "input_tokens": 100, + "output_tokens": 200, + "total_tokens": 300, + "cost": 0.0003 + } + """ + transformed = { + "input_tokens": usage_data.get("input_tokens", 0), + "output_tokens": usage_data.get("output_tokens", 0), + "total_tokens": usage_data.get("total_tokens", 0), + } + + # Transform cost from Perplexity format (dict) to OpenAI format (float) + cost_obj = usage_data.get("cost") + if isinstance(cost_obj, dict) and "total_cost" in cost_obj: + transformed["cost"] = cost_obj["total_cost"] + verbose_logger.debug( + "Transformed Perplexity cost object to float: %s -> %s", + cost_obj, + cost_obj["total_cost"] + ) + elif cost_obj is not None: + # If cost is already a float/number, use it as-is + transformed["cost"] = cost_obj + + # Add input_tokens_details if present + if "input_tokens_details" in usage_data: + transformed["input_tokens_details"] = usage_data["input_tokens_details"] + + # Add output_tokens_details if present + if "output_tokens_details" in usage_data: + transformed["output_tokens_details"] = usage_data["output_tokens_details"] + + return transformed + + def transform_streaming_response( + self, + model: str, + parsed_chunk: dict, + logging_obj: LiteLLMLoggingObj, + ) -> ResponsesAPIStreamingResponse: + """ + Transform a parsed streaming response chunk into a ResponsesAPIStreamingResponse + """ + # Get the event type from the chunk + verbose_logger.debug("Raw Perplexity Chunk=%s", parsed_chunk) + event_type = str(parsed_chunk.get("type")) + event_pydantic_model = PerplexityResponsesConfig.get_event_model_class( + event_type=event_type + ) + + # Transform Perplexity-specific fields to OpenAI format + parsed_chunk = self._transform_perplexity_chunk(parsed_chunk) + + # Defensive: Handle error.code being null (similar to OpenAI implementation) + try: + error_obj = parsed_chunk.get("error") + if isinstance(error_obj, dict) and error_obj.get("code") is None: + # Preserve other fields, but ensure `code` is a non-null string + parsed_chunk = dict(parsed_chunk) + parsed_chunk["error"] = dict(error_obj) + parsed_chunk["error"]["code"] = "unknown_error" + except Exception: + # If anything unexpected happens here, fall back to attempting + # instantiation and let higher-level handlers manage errors. + verbose_logger.debug("Failed to coalesce error.code in parsed_chunk") + + return event_pydantic_model(**parsed_chunk) + + def _transform_perplexity_chunk(self, chunk: dict) -> dict: + """ + Transform Perplexity-specific fields in a streaming chunk to OpenAI format. + + This handles: + - Converting Perplexity's cost object to a simple float + """ + # Make a copy to avoid modifying the original + chunk = dict(chunk) + + # Transform usage.cost from Perplexity format to OpenAI format + # Perplexity: {"currency": "USD", "input_cost": 0.0001, "output_cost": 0.0002, "total_cost": 0.0003} + # OpenAI: 0.0003 (just the total_cost as a float) + try: + response_obj = chunk.get("response") + if isinstance(response_obj, dict): + usage_obj = response_obj.get("usage") + if isinstance(usage_obj, dict): + cost_obj = usage_obj.get("cost") + if isinstance(cost_obj, dict) and "total_cost" in cost_obj: + # Replace the cost object with just the total_cost value + chunk = dict(chunk) + chunk["response"] = dict(response_obj) + chunk["response"]["usage"] = dict(usage_obj) + chunk["response"]["usage"]["cost"] = cost_obj["total_cost"] + verbose_logger.debug( + "Transformed Perplexity cost object to float: %s -> %s", + cost_obj, + cost_obj["total_cost"] + ) + except Exception as e: + # If transformation fails, log and continue with original chunk + verbose_logger.debug("Failed to transform Perplexity cost object: %s", e) + + return chunk diff --git a/litellm/llms/sagemaker/embedding/transformation.py b/litellm/llms/sagemaker/embedding/transformation.py index bd8abc5e01a..04b201380fc 100644 --- a/litellm/llms/sagemaker/embedding/transformation.py +++ b/litellm/llms/sagemaker/embedding/transformation.py @@ -102,11 +102,18 @@ class SagemakerEmbeddingConfig(BaseEmbeddingConfig): status_code=raw_response.status_code ) - if "embedding" not in response_data: + # Handle both raw array format (TEI) and wrapped format (standard HF) + if isinstance(response_data, list): + # TEI and some HF models return raw embedding arrays directly + embeddings = response_data + elif isinstance(response_data, dict) and "embedding" in response_data: + # Standard HF format with "embedding" key + embeddings = response_data["embedding"] + else: raise SagemakerError( - status_code=500, message="HF response missing 'embedding' field" + status_code=500, + message=f"Unexpected response format. Expected list or dict with 'embedding' key, got: {type(response_data).__name__}", ) - embeddings = response_data["embedding"] if not isinstance(embeddings, list): raise SagemakerError( diff --git a/litellm/llms/vertex_ai/common_utils.py b/litellm/llms/vertex_ai/common_utils.py index a0e2ddf5e98..02b69b94d94 100644 --- a/litellm/llms/vertex_ai/common_utils.py +++ b/litellm/llms/vertex_ai/common_utils.py @@ -1,4 +1,5 @@ import re +from copy import deepcopy from enum import Enum from typing import Any, Dict, List, Literal, Optional, Set, Tuple, Union, get_type_hints @@ -684,7 +685,7 @@ def convert_anyof_null_to_nullable(schema, depth=0): if anyof is not None: contains_null = False for atype in anyof: - if atype == {"type": "null"}: + if isinstance(atype, dict) and atype.get("type") == "null": # remove null type anyof.remove(atype) contains_null = True @@ -801,8 +802,38 @@ def _convert_schema_types(schema, depth=0): if "type" in schema: type_val = schema["type"] if isinstance(type_val, list) and len(type_val) > 1: - # Convert ["string", "number"] -> {"anyOf": [{"type": "STRING"}, {"type": "NUMBER"}]} - schema["anyOf"] = [{"type": t} for t in type_val if isinstance(t, str)] + # Convert type arrays to anyOf format + # Fields that are specific to object/array types and should move into anyOf + type_specific_fields = {"properties", "required", "additionalProperties", "items", "minItems", "maxItems", "minProperties", "maxProperties"} + + any_of: List[Dict[str, Any]] = [] + for t in type_val: + if not isinstance(t, str): + continue + if t == "null": + # Keep null entry minimal so we can strip it later. + any_of.append({"type": "null"}) + continue + + # For object/array types, include type-specific fields + if t in ("object", "array"): + item_schema = {"type": t} + # Move type-specific fields into this anyOf item + for field in type_specific_fields: + if field in schema: + item_schema[field] = deepcopy(schema[field]) + any_of.append(item_schema) + else: + # For primitive types, only include the type + any_of.append({"type": t}) + + # Remove type-specific fields from parent if we moved them into anyOf + has_object_or_array = any(t in ("object", "array") for t in type_val if isinstance(t, str)) + if has_object_or_array: + for field in type_specific_fields: + schema.pop(field, None) + + schema["anyOf"] = any_of schema.pop("type") elif isinstance(type_val, list) and len(type_val) == 1: schema["type"] = type_val[0] diff --git a/litellm/llms/vertex_ai/files/transformation.py b/litellm/llms/vertex_ai/files/transformation.py index b3612113ec2..2470c59bbac 100644 --- a/litellm/llms/vertex_ai/files/transformation.py +++ b/litellm/llms/vertex_ai/files/transformation.py @@ -165,7 +165,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig): """ Get the complete url for the request """ - bucket_name = litellm_params.get("bucket_name") or os.getenv("GCS_BUCKET_NAME") + bucket_name = litellm_params.get("bucket_name") or litellm_params.get("litellm_metadata", {}).pop("gcs_bucket_name", None) or os.getenv("GCS_BUCKET_NAME") if not bucket_name: raise ValueError("GCS bucket_name is required") file_data = data.get("file") diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 3004f39b973..00b461dcda0 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -437,6 +437,27 @@ def _gemini_convert_messages_with_history( # noqa: PLR0915 else: assistant_content.append(PartType(text=assistant_text)) # type: ignore + ## HANDLE ASSISTANT IMAGES FIELD + # Process images field if present (for generated images from assistant) + assistant_images = assistant_msg.get("images") + if assistant_images is not None and isinstance(assistant_images, list): + for image_item in assistant_images: + if isinstance(image_item, dict): + image_url_obj = image_item.get("image_url") + if isinstance(image_url_obj, dict): + assistant_image_url = image_url_obj.get("url") + format = image_url_obj.get("format") + detail = image_url_obj.get("detail") + media_resolution_enum = _convert_detail_to_media_resolution_enum(detail) + if assistant_image_url: + _part = _process_gemini_media( + image_url=assistant_image_url, + format=format, + media_resolution_enum=media_resolution_enum, + model=model, + ) + assistant_content.append(_part) + ## HANDLE ASSISTANT FUNCTION CALL if ( assistant_msg.get("tool_calls", []) is not None diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py index a9ac21bb56f..04ae4b6beb8 100644 --- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py +++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py @@ -478,6 +478,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): if "type" in tool and tool["type"] == "computer_use": computer_use_config = {k: v for k, v in tool.items() if k != "type"} tool = {VertexToolName.COMPUTER_USE.value: computer_use_config} + # Handle OpenAI-style web_search and web_search_preview tools + # Transform them to Gemini's googleSearch tool + elif "type" in tool and tool["type"] in ("web_search", "web_search_preview"): + verbose_logger.info( + f"Gemini: Transforming OpenAI-style '{tool['type']}' tool to googleSearch" + ) + tool = {VertexToolName.GOOGLE_SEARCH.value: {}} # Handle tools with 'type' field (OpenAI spec compliance) Ignore this field -> https://github.com/BerriAI/litellm/issues/14644#issuecomment-3342061838 elif "type" in tool: tool = {k: tool[k] for k in tool if k != "type"} @@ -1725,6 +1732,52 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig): else: return "stop" + @staticmethod + def _check_prompt_level_content_filter( + processed_chunk: GenerateContentResponseBody, + response_id: Optional[str], + ) -> Optional["ModelResponseStream"]: + """ + Check if prompt is blocked due to content filtering at the prompt level. + + This handles the case where Vertex AI blocks the prompt before generation begins, + indicated by promptFeedback.blockReason being present. + + Args: + processed_chunk: The parsed response chunk from Vertex AI + response_id: The response ID from the chunk + + Returns: + ModelResponseStream with content_filter finish_reason if blocked, None otherwise. + + Note: + This is consistent with non-streaming _handle_blocked_response() behavior. + Candidate-level content filtering (SAFETY, RECITATION, etc.) is handled + separately via _process_candidates() → _check_finish_reason(). + """ + from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices + + # Check if prompt is blocked due to content filtering + prompt_feedback = processed_chunk.get("promptFeedback") + if prompt_feedback and "blockReason" in prompt_feedback: + verbose_logger.debug( + f"Prompt blocked due to: {prompt_feedback.get('blockReason')} - {prompt_feedback.get('blockReasonMessage')}" + ) + + # Create a content_filter response (consistent with non-streaming _handle_blocked_response) + choice = StreamingChoices( + finish_reason="content_filter", + index=0, + delta=Delta(content=None, role="assistant"), + logprobs=None, + enhancements=None, + ) + + model_response = ModelResponseStream(choices=[choice], id=response_id) + return model_response + + return None + @staticmethod def _calculate_web_search_requests(grounding_metadata: List[dict]) -> Optional[int]: web_search_requests: Optional[int] = None @@ -2806,6 +2859,15 @@ class ModelResponseIterator: processed_chunk = GenerateContentResponseBody(**chunk) # type: ignore response_id = processed_chunk.get("responseId") model_response = ModelResponseStream(choices=[], id=response_id) + + # Check if prompt is blocked due to content filtering + blocked_response = VertexGeminiConfig._check_prompt_level_content_filter( + processed_chunk=processed_chunk, + response_id=response_id, + ) + if blocked_response is not None: + model_response = blocked_response + usage: Optional[Usage] = None _candidates: Optional[List[Candidates]] = processed_chunk.get("candidates") grounding_metadata: List[dict] = [] diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py index 9b8ff3ecc2d..54c3f9e0474 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/experimental_pass_through/transformation.py @@ -7,7 +7,6 @@ from litellm.llms.anthropic.experimental_pass_through.messages.transformation im from litellm.types.llms.anthropic import ( ANTHROPIC_BETA_HEADER_VALUES, ANTHROPIC_HOSTED_TOOLS, - ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER, ) from litellm.types.llms.anthropic_tool_search import get_tool_search_beta_header from litellm.types.llms.vertex_ai import VertexPartnerProvider @@ -65,10 +64,29 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert existing_beta = headers.get("anthropic-beta") if existing_beta: beta_values.update(b.strip() for b in existing_beta.split(",")) - - # Use the helper to remove unsupported beta headers - self.remove_unsupported_beta(headers) - beta_values.discard(ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER) + + # Check for context management + context_management_param = optional_params.get("context_management") + if context_management_param is not None: + # Check edits array for compact_20260112 type + edits = context_management_param.get("edits", []) + has_compact = False + has_other = False + + for edit in edits: + edit_type = edit.get("type", "") + if edit_type == "compact_20260112": + has_compact = True + else: + has_other = True + + # Add compact header if any compact edits exist + if has_compact: + beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value) + + # Add context management header if any other edits exist + if has_other: + beta_values.add(ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value) # Check for web search tool for tool in tools: @@ -128,23 +146,3 @@ class VertexAIPartnerModelsAnthropicMessagesConfig(AnthropicMessagesConfig, Vert ) # do not pass output_format in request body to vertex ai - vertex ai does not support output_format as yet return anthropic_messages_request - - def remove_unsupported_beta(self, headers: dict) -> None: - """ - Helper method to remove unsupported beta headers from the beta headers. - Modifies headers in place. - """ - unsupported_beta_headers = [ - ANTHROPIC_PROMPT_CACHING_SCOPE_BETA_HEADER - ] - existing_beta = headers.get("anthropic-beta") - if existing_beta: - filtered_beta = [ - b.strip() - for b in existing_beta.split(",") - if b.strip() not in unsupported_beta_headers - ] - if filtered_beta: - headers["anthropic-beta"] = ",".join(filtered_beta) - elif "anthropic-beta" in headers: - del headers["anthropic-beta"] diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py index 1df07f405e6..6a5b934661a 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/anthropic/transformation.py @@ -51,6 +51,42 @@ class VertexAIAnthropicConfig(AnthropicConfig): def custom_llm_provider(self) -> Optional[str]: return "vertex_ai" + def _add_context_management_beta_headers( + self, beta_set: set, context_management: dict + ) -> None: + """ + Add context_management beta headers to the beta_set. + + - If any edit has type "compact_20260112", add compact-2026-01-12 header + - For all other edits, add context-management-2025-06-27 header + + Args: + beta_set: Set of beta headers to modify in-place + context_management: The context_management dict from optional_params + """ + from litellm.types.llms.anthropic import ANTHROPIC_BETA_HEADER_VALUES + + edits = context_management.get("edits", []) + has_compact = False + has_other = False + + for edit in edits: + edit_type = edit.get("type", "") + if edit_type == "compact_20260112": + has_compact = True + else: + has_other = True + + # Add compact header if any compact edits exist + if has_compact: + beta_set.add(ANTHROPIC_BETA_HEADER_VALUES.COMPACT_2026_01_12.value) + + # Add context management header if any other edits exist + if has_other: + beta_set.add( + ANTHROPIC_BETA_HEADER_VALUES.CONTEXT_MANAGEMENT_2025_06_27.value + ) + def transform_request( self, model: str, @@ -68,10 +104,10 @@ class VertexAIAnthropicConfig(AnthropicConfig): ) data.pop("model", None) # vertex anthropic doesn't accept 'model' parameter - + # VertexAI doesn't support output_format parameter, remove it if present data.pop("output_format", None) - + tools = optional_params.get("tools") tool_search_used = self.is_tool_search_used(tools) auto_betas = self.get_anthropic_beta_list( @@ -85,11 +121,30 @@ class VertexAIAnthropicConfig(AnthropicConfig): beta_set = set(auto_betas) if tool_search_used: - beta_set.add("tool-search-tool-2025-10-19") # Vertex requires this header for tool search + beta_set.add( + "tool-search-tool-2025-10-19" + ) # Vertex requires this header for tool search + + # Add context_management beta headers (compact and/or context-management) + context_management = optional_params.get("context_management") + if context_management: + self._add_context_management_beta_headers(beta_set, context_management) + + extra_headers = optional_params.get("extra_headers") or {} + anthropic_beta_value = extra_headers.get("anthropic-beta", "") + if isinstance(anthropic_beta_value, str) and anthropic_beta_value: + for beta in anthropic_beta_value.split(","): + beta = beta.strip() + if beta: + beta_set.add(beta) + elif isinstance(anthropic_beta_value, list): + beta_set.update(anthropic_beta_value) + + data.pop("extra_headers", None) if beta_set: data["anthropic_beta"] = list(beta_set) - + return data def map_openai_params( @@ -109,7 +164,7 @@ class VertexAIAnthropicConfig(AnthropicConfig): original_model = model if "response_format" in non_default_params: model = "claude-3-sonnet-20240229" # Use a model that will use tool-based approach - + # Call parent method with potentially modified model name optional_params = super().map_openai_params( non_default_params=non_default_params, @@ -117,10 +172,10 @@ class VertexAIAnthropicConfig(AnthropicConfig): model=model, drop_params=drop_params, ) - + # Restore original model name for any other processing model = original_model - + return optional_params def transform_response( diff --git a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py index 3842159fd7b..c6914ac3d6b 100644 --- a/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py +++ b/litellm/llms/vertex_ai/vertex_ai_partner_models/count_tokens/handler.py @@ -107,6 +107,11 @@ class VertexAIPartnerModelsTokenCounter(VertexBase): vertex_project = self.get_vertex_ai_project(litellm_params) vertex_location = self.get_vertex_ai_location(litellm_params) + # Map empty location/cluade models to a supported region for count-tokens endpoint + # https://docs.cloud.google.com/vertex-ai/generative-ai/docs/partner-models/claude/count-tokens + if not vertex_location or "claude" in model.lower(): + vertex_location = "us-central1" + # Get access token and resolved project ID access_token, project_id = await self._ensure_access_token_async( credentials=vertex_credentials, @@ -118,7 +123,7 @@ class VertexAIPartnerModelsTokenCounter(VertexBase): endpoint_url = self._build_count_tokens_endpoint( model=model, project_id=project_id, - vertex_location=vertex_location or "us-central1", + vertex_location=vertex_location, api_base=litellm_params.get("api_base"), ) diff --git a/litellm/llms/xai/chat/transformation.py b/litellm/llms/xai/chat/transformation.py index 245e10e45c1..21782fc6fbf 100644 --- a/litellm/llms/xai/chat/transformation.py +++ b/litellm/llms/xai/chat/transformation.py @@ -4,6 +4,7 @@ import httpx import litellm from litellm._logging import verbose_logger +from litellm.constants import XAI_API_BASE from litellm.litellm_core_utils.prompt_templates.common_utils import ( filter_value_from_dict, strip_name_from_messages, @@ -14,8 +15,6 @@ from litellm.types.utils import Choices, ModelResponse, Usage, PromptTokensDetai from ...openai.chat.gpt_transformation import OpenAIGPTConfig -XAI_API_BASE = "https://api.x.ai/v1" - class XAIChatConfig(OpenAIGPTConfig): @property diff --git a/litellm/llms/xai/realtime/__init__.py b/litellm/llms/xai/realtime/__init__.py new file mode 100644 index 00000000000..3b0d345f2c2 --- /dev/null +++ b/litellm/llms/xai/realtime/__init__.py @@ -0,0 +1,5 @@ +"""xAI Realtime API handler.""" + +from .handler import XAIRealtime + +__all__ = ["XAIRealtime"] diff --git a/litellm/llms/xai/realtime/handler.py b/litellm/llms/xai/realtime/handler.py new file mode 100644 index 00000000000..c79477ba1df --- /dev/null +++ b/litellm/llms/xai/realtime/handler.py @@ -0,0 +1,38 @@ +""" +This file contains the handler for xAI's Grok Voice Agent API `/v1/realtime` endpoint. + +xAI's Realtime API is fully OpenAI-compatible, so we inherit from OpenAIRealtime +and only override the configuration differences. + +This requires websockets, and is currently only supported on LiteLLM Proxy. +""" + +from litellm.constants import XAI_API_BASE + +from ...openai.realtime.handler import OpenAIRealtime + + +class XAIRealtime(OpenAIRealtime): + """ + Handler for xAI Grok Voice Agent API. + + xAI's Realtime API uses the same WebSocket protocol as OpenAI but with: + - Different endpoint: wss://api.x.ai/v1/realtime (via _get_default_api_base) + - No OpenAI-Beta header required (via _get_additional_headers) + - Model: grok-4-1-fast-non-reasoning + + All WebSocket logic is inherited from OpenAIRealtime. + """ + + def _get_default_api_base(self) -> str: + """xAI uses a different API base URL.""" + return XAI_API_BASE + + def _get_additional_headers(self, api_key: str) -> dict: + """ + xAI does NOT require the OpenAI-Beta header. + Only send Authorization header. + """ + return { + "Authorization": f"Bearer {api_key}", + } diff --git a/litellm/llms/xai/responses/transformation.py b/litellm/llms/xai/responses/transformation.py index 82b4771fb4d..95873aab846 100644 --- a/litellm/llms/xai/responses/transformation.py +++ b/litellm/llms/xai/responses/transformation.py @@ -2,6 +2,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union import litellm from litellm._logging import verbose_logger +from litellm.constants import XAI_API_BASE from litellm.llms.openai.responses.transformation import OpenAIResponsesAPIConfig from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams @@ -16,8 +17,6 @@ if TYPE_CHECKING: else: LiteLLMLoggingObj = Any -XAI_API_BASE = "https://api.x.ai/v1" - class XAIResponsesAPIConfig(OpenAIResponsesAPIConfig): """ diff --git a/litellm/main.py b/litellm/main.py index 7d591f76882..bca023e65ec 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -1199,6 +1199,13 @@ def completion( # type: ignore # noqa: PLR0915 headers = {} if extra_headers is not None: headers.update(extra_headers) + # Inject proxy auth headers if configured + if litellm.proxy_auth is not None: + try: + proxy_headers = litellm.proxy_auth.get_auth_headers() + headers.update(proxy_headers) + except Exception as e: + verbose_logger.warning(f"Failed to get proxy auth headers: {e}") num_retries = kwargs.get( "num_retries", None ) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor. @@ -2199,6 +2206,48 @@ def completion( # type: ignore # noqa: PLR0915 logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit aleph alpha's requirements client=client, ) + elif custom_llm_provider == "a2a": + # A2A (Agent-to-Agent) Protocol + # Resolve agent configuration from registry if model format is "a2a/" + api_base, api_key, headers = litellm.A2AConfig.resolve_agent_config_from_registry( + model=model, + api_base=api_base, + api_key=api_key, + headers=headers, + optional_params=optional_params, + ) + + # Fall back to environment variables and defaults + api_base = api_base or litellm.api_base or get_secret_str("A2A_API_BASE") + + if api_base is None: + raise Exception( + "api_base is required for A2A provider. " + "Either provide api_base parameter, set A2A_API_BASE environment variable, " + "or register the agent in the proxy with model='a2a/'." + ) + + headers = headers or litellm.headers + + response = base_llm_http_handler.completion( + model=model, + stream=stream, + messages=messages, + acompletion=acompletion, + api_base=api_base, + model_response=model_response, + optional_params=optional_params, + litellm_params=litellm_params, + shared_session=shared_session, + custom_llm_provider=custom_llm_provider, + timeout=timeout, + headers=headers, + encoding=_get_encoding(), + api_key=api_key, + logging_obj=logging, + client=client, + provider_config=provider_config, + ) elif custom_llm_provider == "gigachat": # GigaChat - Sber AI's LLM (Russia) api_key = ( @@ -2455,6 +2504,20 @@ def completion( # type: ignore # noqa: PLR0915 headers = headers or litellm.headers + # Add GitHub Copilot headers (same as /responses endpoint does) + if custom_llm_provider == "github_copilot": + from litellm.llms.github_copilot.common_utils import ( + get_copilot_default_headers, + ) + from litellm.llms.github_copilot.authenticator import Authenticator + + copilot_auth = Authenticator() + copilot_api_key = copilot_auth.get_api_key() + copilot_headers = get_copilot_default_headers(copilot_api_key) + if extra_headers: + copilot_headers.update(extra_headers) + extra_headers = copilot_headers + if extra_headers is not None: optional_params["extra_headers"] = extra_headers @@ -3113,8 +3176,8 @@ def completion( # type: ignore # noqa: PLR0915 api_key or litellm.api_key or litellm.openrouter_key - or get_secret("OPENROUTER_API_KEY") - or get_secret("OR_API_KEY") + or get_secret_str("OPENROUTER_API_KEY") + or get_secret_str("OR_API_KEY") ) openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" @@ -4555,6 +4618,13 @@ def embedding( # noqa: PLR0915 headers = {} if extra_headers is not None: headers.update(extra_headers) + # Inject proxy auth headers if configured + if litellm.proxy_auth is not None: + try: + proxy_headers = litellm.proxy_auth.get_auth_headers() + headers.update(proxy_headers) + except Exception as e: + verbose_logger.warning(f"Failed to get proxy auth headers: {e}") ### CUSTOM MODEL COST ### input_cost_per_token = kwargs.get("input_cost_per_token", None) output_cost_per_token = kwargs.get("output_cost_per_token", None) @@ -4884,8 +4954,8 @@ def embedding( # noqa: PLR0915 api_key or litellm.api_key or litellm.openrouter_key - or get_secret("OPENROUTER_API_KEY") - or get_secret("OR_API_KEY") + or get_secret_str("OPENROUTER_API_KEY") + or get_secret_str("OR_API_KEY") ) openrouter_site_url = get_secret("OR_SITE_URL") or "https://litellm.ai" diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 485bee4f191..7c82d47dcdc 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -744,7 +744,8 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 346 + "tool_use_system_prompt_tokens": 346, + "supports_native_streaming": true }, "anthropic.claude-3-5-sonnet-20240620-v1:0": { "input_cost_per_token": 3e-06, @@ -962,6 +963,156 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "anthropic.claude-opus-4-6-v1": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "global.anthropic.claude-opus-4-6-v1": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "us.anthropic.claude-opus-4-6-v1": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.75e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "eu.anthropic.claude-opus-4-6-v1": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.75e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "apac.anthropic.claude-opus-4-6-v1": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.75e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, "anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, @@ -1443,6 +1594,33 @@ "supports_tool_choice": true, "supports_vision": true }, + "azure_ai/claude-opus-4-6": { + "input_cost_per_token": 5e-06, + "output_cost_per_token": 2.5e-05, + "litellm_provider": "azure_ai", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "azure_ai/claude-opus-4-1": { "cache_creation_input_token_cost": 1.875e-05, "cache_creation_input_token_cost_above_1hr": 3e-05, @@ -5670,6 +5848,20 @@ "output_cost_per_token": 7e-07, "supports_tool_choice": true }, + "azure_ai/kimi-k2.5": { + "input_cost_per_token": 6e-07, + "litellm_provider": "azure_ai", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3e-06, + "source": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/kimi-k2-5-now-in-microsoft-foundry/4492321", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_video_input": true, + "supports_vision": true + }, "azure_ai/ministral-3b": { "input_cost_per_token": 4e-08, "litellm_provider": "azure_ai", @@ -5913,6 +6105,42 @@ "output_cost_per_token": 2.4e-05, "supports_tool_choice": true }, + "bedrock/ap-northeast-1/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 7.3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.03e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, + "bedrock/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 7.3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.03e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, + "bedrock/moonshotai.kimi-k2.5": { + "input_cost_per_token": 7.3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.03e-06, + "source": "https://platform.moonshot.ai/docs/guide/kimi-k2-5-quickstart", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_video_input": true, + "supports_vision": true + }, "bedrock/ap-south-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 3.18e-06, "litellm_provider": "bedrock", @@ -5931,6 +6159,17 @@ "mode": "chat", "output_cost_per_token": 7.2e-07 }, + "bedrock/ap-south-1/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 7.1e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.94e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, "bedrock/ca-central-1/meta.llama3-70b-instruct-v1:0": { "input_cost_per_token": 3.05e-06, "litellm_provider": "bedrock", @@ -6136,6 +6375,17 @@ "mode": "chat", "output_cost_per_token": 1.01e-06 }, + "bedrock/sa-east-1/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 7.3e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 3.03e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, "bedrock/us-east-1/1-month-commitment/anthropic.claude-instant-v1": { "input_cost_per_second": 0.011, "litellm_provider": "bedrock", @@ -6272,6 +6522,28 @@ "output_cost_per_token": 7e-07, "supports_tool_choice": true }, + "bedrock/us-east-1/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, + "bedrock/us-east-2/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, "bedrock/us-gov-east-1/amazon.nova-pro-v1:0": { "input_cost_per_token": 9.6e-07, "litellm_provider": "bedrock", @@ -6678,6 +6950,17 @@ "output_cost_per_token": 7e-07, "supports_tool_choice": true }, + "bedrock/us-west-2/moonshotai.kimi-k2-thinking": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": true, + "supports_reasoning": true + }, "bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0": { "cache_creation_input_token_cost": 1e-06, "cache_read_input_token_cost": 8e-08, @@ -7454,6 +7737,223 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "claude-opus-4-6": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "fast/claude-opus-4-6": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + 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1.375e-05, + "cache_creation_input_token_cost_above_1hr": 1.1e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.75e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + 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true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "claude-opus-4-6-20260205": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "fast/claude-opus-4-6-20260205": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 3e-05, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 0.00015, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, + "us/claude-opus-4-6-20260205": { + "cache_creation_input_token_cost": 6.875e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_creation_input_token_cost_above_1hr": 1.1e-05, + "cache_read_input_token_cost": 5.5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + "input_cost_per_token": 5.5e-06, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.75e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 + }, "claude-sonnet-4-20250514": { "deprecation_date": "2026-05-14", "cache_creation_input_token_cost": 3.75e-06, @@ -8550,6 +9050,43 @@ } ] }, + "dashscope/qwen3-max": { + "litellm_provider": "dashscope", + "max_input_tokens": 258048, + "max_output_tokens": 65536, + "max_tokens": 65536, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "tiered_pricing": [ + { + "input_cost_per_token": 1.2e-06, + "output_cost_per_token": 6e-06, + "range": [ + 0, + 32000.0 + ] + }, + { + "input_cost_per_token": 2.4e-06, + "output_cost_per_token": 1.2e-05, + "range": [ + 32000.0, + 128000.0 + ] + }, + { + "input_cost_per_token": 3e-06, + "output_cost_per_token": 1.5e-05, + "range": [ + 128000.0, + 252000.0 + ] + } + ] + }, "dashscope/qwq-plus": { "input_cost_per_token": 8e-07, "litellm_provider": "dashscope", @@ -10223,14 +10760,22 @@ "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", - "max_input_tokens": 128000, + "max_input_tokens": 131072, "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", "output_cost_per_token": 4.2e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], "supports_assistant_prefill": true, "supports_function_calling": true, + "supports_native_streaming": true, + "supports_parallel_function_calling": true, "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, "supports_tool_choice": true }, "deepseek/deepseek-coder": { @@ -10267,16 +10812,24 @@ "input_cost_per_token": 2.8e-07, "input_cost_per_token_cache_hit": 2.8e-08, "litellm_provider": "deepseek", - "max_input_tokens": 128000, - "max_output_tokens": 8192, - "max_tokens": 8192, + "max_input_tokens": 131072, + "max_output_tokens": 65536, + "max_tokens": 65536, "mode": "chat", "output_cost_per_token": 4.2e-07, + "source": "https://api-docs.deepseek.com/quick_start/pricing", + "supported_endpoints": [ + "/v1/chat/completions" + ], "supports_assistant_prefill": true, - "supports_function_calling": true, + "supports_function_calling": false, + "supports_native_streaming": true, + "supports_parallel_function_calling": false, "supports_prompt_caching": true, "supports_reasoning": true, - "supports_tool_choice": true + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": false }, "deepseek/deepseek-v3": { "cache_creation_input_token_cost": 0.0, @@ -10574,6 +11127,32 @@ "/v1/audio/transcriptions" ] }, + "elevenlabs/eleven_v3": { + "input_cost_per_character": 0.00018, + "litellm_provider": "elevenlabs", + "metadata": { + "calculation": "$0.18/1000 characters (Scale plan pricing, 1 credit per character)", + "notes": "ElevenLabs Eleven v3 - most expressive TTS model with 70+ languages and audio tags support" + }, + "mode": "audio_speech", + "source": "https://elevenlabs.io/pricing", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, + "elevenlabs/eleven_multilingual_v2": { + "input_cost_per_character": 0.00018, + "litellm_provider": "elevenlabs", + "metadata": { + "calculation": "$0.18/1000 characters (Scale plan pricing, 1 credit per character)", + "notes": "ElevenLabs Eleven Multilingual v2 - default TTS model with 29 languages support" + }, + "mode": "audio_speech", + "source": "https://elevenlabs.io/pricing", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, "embed-english-light-v2.0": { "input_cost_per_token": 1e-07, "litellm_provider": "cohere", @@ -12850,6 +13429,40 @@ "supports_vision": true, "supports_web_search": true }, + "deep-research-pro-preview-12-2025": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 65536, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini-2.5-flash-lite": { "cache_read_input_token_cost": 1e-08, "input_cost_per_audio_token": 3e-07, @@ -13304,7 +13917,8 @@ "supports_tool_choice": true, "supports_video_input": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_native_streaming": true }, "vertex_ai/gemini-3-pro-preview": { "cache_read_input_token_cost": 2e-07, @@ -13352,7 +13966,8 @@ "supports_tool_choice": true, "supports_video_input": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_native_streaming": true }, "vertex_ai/gemini-3-flash-preview": { "cache_read_input_token_cost": 5e-08, @@ -13395,7 +14010,8 @@ "supports_tool_choice": true, "supports_video_input": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_native_streaming": true }, "gemini-2.5-pro-exp-03-25": { "cache_read_input_token_cost": 1.25e-07, @@ -14762,6 +15378,42 @@ "supports_vision": true, "supports_web_search": true }, + "gemini/deep-research-pro-preview-12-2025": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "gemini", + "max_input_tokens": 65536, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "rpm": 1000, + "tpm": 4000000, + "output_cost_per_token_batches": 6e-06, + "source": "https://ai.google.dev/gemini-api/docs/pricing", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image" + ], + "supported_output_modalities": [ + "text", + "image" + ], + "supports_function_calling": false, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true + }, "gemini/gemini-2.5-flash-lite": { "cache_read_input_token_cost": 1e-08, "input_cost_per_audio_token": 3e-07, @@ -15136,44 +15788,14 @@ "tpm": 250000 }, "gemini/gemini-2.5-flash-preview-tts": { - "cache_read_input_token_cost": 3.75e-08, - "input_cost_per_audio_token": 1e-06, - "input_cost_per_token": 1.5e-07, + "input_cost_per_token": 3e-07, "litellm_provider": "gemini", - "max_audio_length_hours": 8.4, - "max_audio_per_prompt": 1, - "max_images_per_prompt": 3000, - "max_input_tokens": 1048576, - "max_output_tokens": 65535, - "max_pdf_size_mb": 30, - "max_tokens": 65535, - "max_video_length": 1, - "max_videos_per_prompt": 10, - "mode": "chat", - "output_cost_per_reasoning_token": 3.5e-06, - "output_cost_per_token": 6e-07, - "rpm": 10, - "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "mode": "audio_speech", + "output_cost_per_token": 2.5e-06, + "source": "https://ai.google.dev/pricing", "supported_endpoints": [ - "/v1/chat/completions", - "/v1/completions" - ], - "supported_modalities": [ - "text" - ], - "supported_output_modalities": [ - "audio" - ], - "supports_audio_output": false, - "supports_function_calling": true, - "supports_prompt_caching": true, - "supports_reasoning": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true, - "supports_web_search": true, - "tpm": 250000 + "/v1/audio/speech" + ] }, "gemini/gemini-2.5-pro": { "cache_read_input_token_cost": 1.25e-07, @@ -15346,6 +15968,7 @@ "supports_url_context": true, "supports_vision": true, "supports_web_search": true, + "supports_native_streaming": true, "tpm": 800000 }, "gemini-3-flash-preview": { @@ -15391,7 +16014,8 @@ "supports_tool_choice": true, "supports_url_context": true, "supports_vision": true, - "supports_web_search": true + "supports_web_search": true, + "supports_native_streaming": true }, "gemini/gemini-2.5-pro-exp-03-25": { "cache_read_input_token_cost": 0.0, @@ -20782,6 +21406,36 @@ "max_input_tokens": 1000000, "max_output_tokens": 8192 }, + "minimax/MiniMax-M2.5": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.2e-06, + "cache_read_input_token_cost": 3e-08, + "cache_creation_input_token_cost": 3.75e-07, + "litellm_provider": "minimax", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_system_messages": true, + "max_input_tokens": 1000000, + "max_output_tokens": 8192 + }, + "minimax/MiniMax-M2.5-lightning": { + "input_cost_per_token": 3e-07, + "output_cost_per_token": 2.4e-06, + "cache_read_input_token_cost": 3e-08, + "cache_creation_input_token_cost": 3.75e-07, + "litellm_provider": "minimax", + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_system_messages": true, + "max_input_tokens": 1000000, + "max_output_tokens": 8192 + }, "minimax/MiniMax-M2": { "input_cost_per_token": 3e-07, "output_cost_per_token": 1.2e-06, @@ -21497,9 +22151,10 @@ "max_tokens": 262144, "mode": "chat", "output_cost_per_token": 3e-06, - "source": "https://platform.moonshot.ai/docs/pricing/chat", + "source": "https://platform.moonshot.ai/docs/guide/kimi-k2-5-quickstart", "supports_function_calling": true, "supports_tool_choice": true, + "supports_video_input": true, "supports_vision": true }, "moonshot/kimi-latest": { @@ -21961,6 +22616,19 @@ "output_cost_per_token": 2.3e-07, "supports_system_messages": true }, + "nvidia.nemotron-nano-3-30b": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 262144, + "max_output_tokens": 8192, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 2.4e-07, + "supports_function_calling": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "o1": { "cache_read_input_token_cost": 7.5e-06, "input_cost_per_token": 1.5e-05, @@ -22966,36 +23634,6 @@ "output_cost_per_token": 2e-07, "supports_system_messages": true }, - "openrouter/anthropic/claude-2": { - "input_cost_per_token": 1.102e-05, - "litellm_provider": "openrouter", - "max_output_tokens": 8191, - "max_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 3.268e-05, - "supports_tool_choice": true - }, - "openrouter/anthropic/claude-3-5-haiku": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_tokens": 200000, - "mode": "chat", - "output_cost_per_token": 5e-06, - "supports_function_calling": true, - "supports_tool_choice": true - }, - "openrouter/anthropic/claude-3-5-haiku-20241022": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 5e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "tool_use_system_prompt_tokens": 264 - }, "openrouter/anthropic/claude-3-haiku": { "input_cost_per_image": 0.0004, "input_cost_per_token": 2.5e-07, @@ -23007,43 +23645,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/anthropic/claude-3-haiku-20240307": { - "input_cost_per_token": 2.5e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.25e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 264 - }, - "openrouter/anthropic/claude-3-opus": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 7.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 395 - }, - "openrouter/anthropic/claude-3-sonnet": { - "input_cost_per_image": 0.0048, - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_tokens": 200000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/anthropic/claude-3.5-sonnet": { "input_cost_per_token": 3e-06, "litellm_provider": "openrouter", @@ -23059,20 +23660,6 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, - "openrouter/anthropic/claude-3.5-sonnet:beta": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_computer_use": true, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 159 - }, "openrouter/anthropic/claude-3.7-sonnet": { "input_cost_per_image": 0.0048, "input_cost_per_token": 3e-06, @@ -23090,31 +23677,6 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, - "openrouter/anthropic/claude-3.7-sonnet:beta": { - "input_cost_per_image": 0.0048, - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 200000, - "max_output_tokens": 128000, - "max_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_computer_use": true, - "supports_function_calling": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "supports_vision": true, - "tool_use_system_prompt_tokens": 159 - }, - "openrouter/anthropic/claude-instant-v1": { - "input_cost_per_token": 1.63e-06, - "litellm_provider": "openrouter", - "max_output_tokens": 8191, - "max_tokens": 8191, - "mode": "chat", - "output_cost_per_token": 5.51e-06, - "supports_tool_choice": true - }, "openrouter/anthropic/claude-opus-4": { "input_cost_per_image": 0.0048, "cache_creation_input_token_cost": 1.875e-05, @@ -23253,30 +23815,6 @@ "source": "https://openrouter.ai/api/v1/models/bytedance/ui-tars-1.5-7b", "supports_tool_choice": true }, - "openrouter/cognitivecomputations/dolphin-mixtral-8x7b": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 32769, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/cohere/command-r-plus": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_tokens": 128000, - "mode": "chat", - "output_cost_per_token": 1.5e-05, - "supports_tool_choice": true - }, - "openrouter/databricks/dbrx-instruct": { - "input_cost_per_token": 6e-07, - "litellm_provider": "openrouter", - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-07, - "supports_tool_choice": true - }, "openrouter/deepseek/deepseek-chat": { "input_cost_per_token": 1.4e-07, "litellm_provider": "openrouter", @@ -23344,17 +23882,6 @@ "supports_reasoning": false, "supports_tool_choice": true }, - "openrouter/deepseek/deepseek-coder": { - "input_cost_per_token": 1.4e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 66000, - "max_output_tokens": 4096, - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2.8e-07, - "supports_prompt_caching": true, - "supports_tool_choice": true - }, "openrouter/deepseek/deepseek-r1": { "input_cost_per_token": 5.5e-07, "input_cost_per_token_cache_hit": 1.4e-07, @@ -23385,14 +23912,6 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "openrouter/fireworks/firellava-13b": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, "openrouter/google/gemini-2.0-flash-001": { "deprecation_date": "2026-03-31", "input_cost_per_audio_token": 7e-07, @@ -23554,46 +24073,6 @@ "supports_web_search": true, "tpm": 800000 }, - "openrouter/google/gemini-pro-1.5": { - "input_cost_per_image": 0.00265, - "input_cost_per_token": 2.5e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 1000000, - "max_output_tokens": 8192, - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 7.5e-06, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "openrouter/google/gemini-pro-vision": { - "input_cost_per_image": 0.0025, - "input_cost_per_token": 1.25e-07, - "litellm_provider": "openrouter", - "max_tokens": 45875, - "mode": "chat", - "output_cost_per_token": 3.75e-07, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, - "openrouter/google/palm-2-chat-bison": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 25804, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/google/palm-2-codechat-bison": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 20070, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, "openrouter/gryphe/mythomax-l2-13b": { "input_cost_per_token": 1.875e-06, "litellm_provider": "openrouter", @@ -23602,14 +24081,6 @@ "output_cost_per_token": 1.875e-06, "supports_tool_choice": true }, - "openrouter/jondurbin/airoboros-l2-70b-2.1": { - "input_cost_per_token": 1.3875e-05, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.3875e-05, - "supports_tool_choice": true - }, "openrouter/mancer/weaver": { "input_cost_per_token": 5.625e-06, "litellm_provider": "openrouter", @@ -23618,30 +24089,6 @@ "output_cost_per_token": 5.625e-06, "supports_tool_choice": true }, - "openrouter/meta-llama/codellama-34b-instruct": { - "input_cost_per_token": 5e-07, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 5e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-2-13b-chat": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-2-70b-chat": { - "input_cost_per_token": 1.5e-06, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.5e-06, - "supports_tool_choice": true - }, "openrouter/meta-llama/llama-3-70b-instruct": { "input_cost_per_token": 5.9e-07, "litellm_provider": "openrouter", @@ -23650,38 +24097,6 @@ "output_cost_per_token": 7.9e-07, "supports_tool_choice": true }, - "openrouter/meta-llama/llama-3-70b-instruct:nitro": { - "input_cost_per_token": 9e-07, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 9e-07, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-3-8b-instruct:extended": { - "input_cost_per_token": 2.25e-07, - "litellm_provider": "openrouter", - "max_tokens": 16384, - "mode": "chat", - "output_cost_per_token": 2.25e-06, - "supports_tool_choice": true - }, - "openrouter/meta-llama/llama-3-8b-instruct:free": { - "input_cost_per_token": 0.0, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 0.0, - "supports_tool_choice": true - }, - "openrouter/microsoft/wizardlm-2-8x22b:nitro": { - "input_cost_per_token": 1e-06, - "litellm_provider": "openrouter", - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1e-06, - "supports_tool_choice": true - }, "openrouter/minimax/minimax-m2": { "input_cost_per_token": 2.55e-07, "litellm_provider": "openrouter", @@ -23695,20 +24110,6 @@ "supports_reasoning": true, "supports_tool_choice": true }, - "openrouter/mistralai/devstral-2512:free": { - "input_cost_per_image": 0, - "input_cost_per_token": 0, - "litellm_provider": "openrouter", - "max_input_tokens": 262144, - "max_output_tokens": 262144, - "max_tokens": 262144, - "mode": "chat", - "output_cost_per_token": 0, - "supports_function_calling": true, - "supports_prompt_caching": false, - "supports_tool_choice": true, - "supports_vision": false - }, "openrouter/mistralai/devstral-2512": { "input_cost_per_image": 0, "input_cost_per_token": 1.5e-07, @@ -23787,14 +24188,6 @@ "output_cost_per_token": 1.3e-07, "supports_tool_choice": true }, - "openrouter/mistralai/mistral-7b-instruct:free": { - "input_cost_per_token": 0.0, - "litellm_provider": "openrouter", - "max_tokens": 8192, - "mode": "chat", - "output_cost_per_token": 0.0, - "supports_tool_choice": true - }, "openrouter/mistralai/mistral-large": { "input_cost_per_token": 8e-06, "litellm_provider": "openrouter", @@ -23839,16 +24232,9 @@ "source": "https://openrouter.ai/moonshotai/kimi-k2.5", "supports_function_calling": true, "supports_tool_choice": true, + "supports_video_input": true, "supports_vision": true }, - "openrouter/nousresearch/nous-hermes-llama2-13b": { - "input_cost_per_token": 2e-07, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 2e-07, - "supports_tool_choice": true - }, "openrouter/openai/gpt-3.5-turbo": { "input_cost_per_token": 1.5e-06, "litellm_provider": "openrouter", @@ -23873,17 +24259,6 @@ "output_cost_per_token": 6e-05, "supports_tool_choice": true }, - "openrouter/openai/gpt-4-vision-preview": { - "input_cost_per_image": 0.01445, - "input_cost_per_token": 1e-05, - "litellm_provider": "openrouter", - "max_tokens": 130000, - "mode": "chat", - "output_cost_per_token": 3e-05, - "supports_function_calling": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1": { "cache_read_input_token_cost": 5e-07, "input_cost_per_token": 2e-06, @@ -23901,23 +24276,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-2025-04-14": { - "cache_read_input_token_cost": 5e-07, - "input_cost_per_token": 2e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 8e-06, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1-mini": { "cache_read_input_token_cost": 1e-07, "input_cost_per_token": 4e-07, @@ -23935,23 +24293,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-mini-2025-04-14": { - "cache_read_input_token_cost": 1e-07, - "input_cost_per_token": 4e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 1.6e-06, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4.1-nano": { "cache_read_input_token_cost": 2.5e-08, "input_cost_per_token": 1e-07, @@ -23969,23 +24310,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/gpt-4.1-nano-2025-04-14": { - "cache_read_input_token_cost": 2.5e-08, - "input_cost_per_token": 1e-07, - "litellm_provider": "openrouter", - "max_input_tokens": 1047576, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 4e-07, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_prompt_caching": true, - "supports_response_schema": true, - "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true - }, "openrouter/openai/gpt-4o": { "input_cost_per_token": 2.5e-06, "litellm_provider": "openrouter", @@ -24057,11 +24381,8 @@ "max_input_tokens": 272000, "max_output_tokens": 128000, "max_tokens": 128000, - "mode": "responses", + "mode": "chat", "output_cost_per_token": 1.4e-05, - "supported_endpoints": [ - "/v1/responses" - ], "supported_modalities": [ "text", "image" @@ -24222,58 +24543,6 @@ "supports_tool_choice": true, "supports_vision": true }, - "openrouter/openai/o1-mini": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 65536, - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-mini-2024-09-12": { - "input_cost_per_token": 3e-06, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 65536, - "max_tokens": 65536, - "mode": "chat", - "output_cost_per_token": 1.2e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-preview": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, - "openrouter/openai/o1-preview-2024-09-12": { - "input_cost_per_token": 1.5e-05, - "litellm_provider": "openrouter", - "max_input_tokens": 128000, - "max_output_tokens": 32768, - "max_tokens": 32768, - "mode": "chat", - "output_cost_per_token": 6e-05, - "supports_function_calling": true, - "supports_parallel_function_calling": true, - "supports_tool_choice": true, - "supports_vision": false - }, "openrouter/openai/o3-mini": { "input_cost_per_token": 1.1e-06, "litellm_provider": "openrouter", @@ -24302,14 +24571,6 @@ "supports_tool_choice": true, "supports_vision": false }, - "openrouter/pygmalionai/mythalion-13b": { - "input_cost_per_token": 1.875e-06, - "litellm_provider": "openrouter", - "max_tokens": 4096, - "mode": "chat", - "output_cost_per_token": 1.875e-06, - "supports_tool_choice": true - }, "openrouter/qwen/qwen-2.5-coder-32b-instruct": { "input_cost_per_token": 1.8e-07, "litellm_provider": "openrouter", @@ -24343,6 +24604,31 @@ "supports_tool_choice": true, "supports_function_calling": true }, + "openrouter/qwen/qwen3-235b-a22b-2507": { + "input_cost_per_token": 7.1e-08, + "litellm_provider": "openrouter", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 1e-07, + "source": "https://openrouter.ai/qwen/qwen3-235b-a22b-2507", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "openrouter/qwen/qwen3-235b-a22b-thinking-2507": { + "input_cost_per_token": 1.1e-07, + "litellm_provider": "openrouter", + "max_input_tokens": 262144, + "max_output_tokens": 262144, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 6e-07, + "source": "https://openrouter.ai/qwen/qwen3-235b-a22b-thinking-2507", + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "openrouter/switchpoint/router": { "input_cost_per_token": 8.5e-07, "litellm_provider": "openrouter", @@ -24376,20 +24662,6 @@ "supports_tool_choice": true, "supports_web_search": true }, - "openrouter/x-ai/grok-4-fast:free": { - "input_cost_per_token": 0, - "litellm_provider": "openrouter", - "max_input_tokens": 2000000, - "max_output_tokens": 30000, - "max_tokens": 30000, - "mode": "chat", - "output_cost_per_token": 0, - "source": "https://openrouter.ai/x-ai/grok-4-fast:free", - "supports_function_calling": true, - "supports_reasoning": true, - "supports_tool_choice": true, - "supports_web_search": false - }, "openrouter/z-ai/glm-4.6": { "input_cost_per_token": 4e-07, "litellm_provider": "openrouter", @@ -25093,6 +25365,66 @@ "supports_function_calling": true, "supports_tool_choice": true }, + "perplexity/preset/pro-search": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_preset": true + }, + "perplexity/openai/gpt-4o": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/openai/gpt-4o-mini": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/openai/gpt-5.2": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": true + }, + "perplexity/anthropic/claude-3-5-sonnet-20241022": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/anthropic/claude-3-5-haiku-20241022": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/google/gemini-2.0-flash-exp": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/google/gemini-2.0-flash-thinking-exp": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": true + }, + "perplexity/xai/grok-2-1212": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, + "perplexity/xai/grok-2-vision-1212": { + "litellm_provider": "perplexity", + "mode": "responses", + "supports_web_search": true, + "supports_reasoning": false + }, "publicai/aisingapore/Qwen-SEA-LION-v4-32B-IT": { "input_cost_per_token": 0.0, "litellm_provider": "publicai", @@ -27113,6 +27445,34 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "together_ai/zai-org/GLM-4.7": { + "input_cost_per_token": 4.5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 200000, + "max_output_tokens": 200000, + "max_tokens": 200000, + "mode": "chat", + "output_cost_per_token": 2e-06, + "source": "https://www.together.ai/models/glm-4-7", + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "together_ai/moonshotai/Kimi-K2.5": { + "input_cost_per_token": 5e-07, + "litellm_provider": "together_ai", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 2.8e-06, + "source": "https://www.together.ai/models/kimi-k2-5", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "supports_reasoning": true + }, "together_ai/moonshotai/Kimi-K2-Instruct-0905": { "input_cost_per_token": 1e-06, "litellm_provider": "together_ai", @@ -27829,7 +28189,9 @@ "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 3e-07 + "output_cost_per_token": 3e-07, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/alibaba/qwen3-coder": { "input_cost_per_token": 4e-07, @@ -27838,7 +28200,9 @@ "max_output_tokens": 66536, "max_tokens": 66536, "mode": "chat", - "output_cost_per_token": 1.6e-06 + "output_cost_per_token": 1.6e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/amazon/nova-lite": { "input_cost_per_token": 6e-08, @@ -27847,7 +28211,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.4e-07 + "output_cost_per_token": 2.4e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_response_schema": true }, "vercel_ai_gateway/amazon/nova-micro": { "input_cost_per_token": 3.5e-08, @@ -27856,7 +28223,9 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 1.4e-07 + "output_cost_per_token": 1.4e-07, + "supports_function_calling": true, + "supports_response_schema": true }, "vercel_ai_gateway/amazon/nova-pro": { "input_cost_per_token": 8e-07, @@ -27865,7 +28234,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 3.2e-06 + "output_cost_per_token": 3.2e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_response_schema": true }, "vercel_ai_gateway/amazon/titan-embed-text-v2": { "input_cost_per_token": 2e-08, @@ -27885,7 +28257,11 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 1.25e-06 + "output_cost_per_token": 1.25e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/anthropic/claude-3-opus": { "cache_creation_input_token_cost": 1.875e-05, @@ -27896,7 +28272,11 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 7.5e-05 + "output_cost_per_token": 7.5e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/anthropic/claude-3.5-haiku": { "cache_creation_input_token_cost": 1e-06, @@ -27907,7 +28287,11 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 4e-06 + "output_cost_per_token": 4e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/anthropic/claude-3.5-sonnet": { "cache_creation_input_token_cost": 3.75e-06, @@ -27918,7 +28302,11 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/anthropic/claude-3.7-sonnet": { "cache_creation_input_token_cost": 3.75e-06, @@ -27929,7 +28317,11 @@ "max_output_tokens": 64000, "max_tokens": 64000, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/anthropic/claude-4-opus": { "cache_creation_input_token_cost": 1.875e-05, @@ -27940,7 +28332,11 @@ "max_output_tokens": 32000, "max_tokens": 32000, "mode": "chat", - "output_cost_per_token": 7.5e-05 + 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+ "litellm_provider": "vercel_ai_gateway", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "vercel_ai_gateway/anthropic/claude-sonnet-4": { + "cache_creation_input_token_cost": 3.75e-06, + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 3e-06, + "litellm_provider": "vercel_ai_gateway", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true + }, + "vercel_ai_gateway/anthropic/claude-sonnet-4.5": { + "cache_creation_input_token_cost": 3.75e-06, + "cache_read_input_token_cost": 3e-07, + "input_cost_per_token": 3e-06, + "litellm_provider": "vercel_ai_gateway", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 1.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true }, "vercel_ai_gateway/cohere/command-a": { "input_cost_per_token": 2.5e-06, @@ -27960,7 +28545,10 @@ "max_output_tokens": 8000, "max_tokens": 8000, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/cohere/command-r": { "input_cost_per_token": 1.5e-07, @@ -27969,7 +28557,9 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 6e-07 + "output_cost_per_token": 6e-07, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/cohere/command-r-plus": { "input_cost_per_token": 2.5e-06, @@ -27978,7 +28568,9 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/cohere/embed-v4.0": { "input_cost_per_token": 1.2e-07, @@ -27996,7 +28588,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2.19e-06 + "output_cost_per_token": 2.19e-06, + "supports_tool_choice": true }, "vercel_ai_gateway/deepseek/deepseek-r1-distill-llama-70b": { "input_cost_per_token": 7.5e-07, @@ -28005,7 +28598,10 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 9.9e-07 + "output_cost_per_token": 9.9e-07, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/deepseek/deepseek-v3": { "input_cost_per_token": 9e-07, @@ -28014,7 +28610,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 9e-07 + "output_cost_per_token": 9e-07, + "supports_tool_choice": true }, "vercel_ai_gateway/google/gemini-2.0-flash": { "deprecation_date": "2026-03-31", @@ -28024,7 +28621,11 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 6e-07 + "output_cost_per_token": 6e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/google/gemini-2.0-flash-lite": { "deprecation_date": "2026-03-31", @@ -28034,7 +28635,11 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 3e-07 + "output_cost_per_token": 3e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/google/gemini-2.5-flash": { "input_cost_per_token": 3e-07, @@ -28043,7 +28648,11 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", - "output_cost_per_token": 2.5e-06 + "output_cost_per_token": 2.5e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/google/gemini-2.5-pro": { "input_cost_per_token": 2.5e-06, @@ -28052,7 +28661,11 @@ "max_output_tokens": 65536, "max_tokens": 65536, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/google/gemini-embedding-001": { "input_cost_per_token": 1.5e-07, @@ -28070,7 +28683,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 2e-07 + "output_cost_per_token": 2e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/google/text-embedding-005": { "input_cost_per_token": 2.5e-08, @@ -28106,7 +28722,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 7.9e-07 + "output_cost_per_token": 7.9e-07, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-3-8b": { "input_cost_per_token": 5e-08, @@ -28115,7 +28732,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 8e-08 + "output_cost_per_token": 8e-08, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-3.1-70b": { "input_cost_per_token": 7.2e-07, @@ -28124,7 +28742,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 7.2e-07 + "output_cost_per_token": 7.2e-07, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-3.1-8b": { "input_cost_per_token": 5e-08, @@ -28133,7 +28752,9 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 8e-08 + "output_cost_per_token": 8e-08, + "supports_function_calling": true, + "supports_response_schema": true }, "vercel_ai_gateway/meta/llama-3.2-11b": { "input_cost_per_token": 1.6e-07, @@ -28142,7 +28763,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 1.6e-07 + "output_cost_per_token": 1.6e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-3.2-1b": { "input_cost_per_token": 1e-07, @@ -28160,7 +28784,9 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 1.5e-07 + "output_cost_per_token": 1.5e-07, + "supports_function_calling": true, + "supports_response_schema": true }, "vercel_ai_gateway/meta/llama-3.2-90b": { "input_cost_per_token": 7.2e-07, @@ -28169,7 +28795,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 7.2e-07 + "output_cost_per_token": 7.2e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-3.3-70b": { "input_cost_per_token": 7.2e-07, @@ -28178,7 +28807,9 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 7.2e-07 + "output_cost_per_token": 7.2e-07, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-4-maverick": { "input_cost_per_token": 2e-07, @@ -28187,7 +28818,8 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 6e-07 + "output_cost_per_token": 6e-07, + "supports_tool_choice": true }, "vercel_ai_gateway/meta/llama-4-scout": { "input_cost_per_token": 1e-07, @@ -28196,7 +28828,10 @@ "max_output_tokens": 8192, "max_tokens": 8192, "mode": "chat", - "output_cost_per_token": 3e-07 + "output_cost_per_token": 3e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/mistral/codestral": { "input_cost_per_token": 3e-07, @@ -28205,7 +28840,9 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 9e-07 + "output_cost_per_token": 9e-07, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/mistral/codestral-embed": { "input_cost_per_token": 1.5e-07, @@ -28223,7 +28860,10 @@ "max_output_tokens": 128000, "max_tokens": 128000, "mode": "chat", - "output_cost_per_token": 2.8e-07 + "output_cost_per_token": 2.8e-07, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/mistral/magistral-medium": { "input_cost_per_token": 2e-06, @@ -28232,7 +28872,10 @@ "max_output_tokens": 64000, "max_tokens": 64000, "mode": "chat", - "output_cost_per_token": 5e-06 + "output_cost_per_token": 5e-06, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/mistral/magistral-small": { "input_cost_per_token": 5e-07, @@ -28241,7 +28884,8 @@ "max_output_tokens": 64000, "max_tokens": 64000, "mode": "chat", - "output_cost_per_token": 1.5e-06 + "output_cost_per_token": 1.5e-06, + "supports_function_calling": true }, "vercel_ai_gateway/mistral/ministral-3b": { "input_cost_per_token": 4e-08, @@ -28250,7 +28894,9 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 4e-08 + "output_cost_per_token": 4e-08, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/mistral/ministral-8b": { "input_cost_per_token": 1e-07, @@ -28259,7 +28905,10 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 1e-07 + "output_cost_per_token": 1e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/mistral/mistral-embed": { "input_cost_per_token": 1e-07, @@ -28277,7 +28926,9 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 6e-06 + "output_cost_per_token": 6e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/mistral/mistral-saba-24b": { "input_cost_per_token": 7.9e-07, @@ -28295,7 +28946,10 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 3e-07 + "output_cost_per_token": 3e-07, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/mistral/mixtral-8x22b-instruct": { "input_cost_per_token": 1.2e-06, @@ -28304,7 +28958,8 @@ "max_output_tokens": 2048, "max_tokens": 2048, "mode": "chat", - "output_cost_per_token": 1.2e-06 + "output_cost_per_token": 1.2e-06, + "supports_function_calling": true }, "vercel_ai_gateway/mistral/pixtral-12b": { "input_cost_per_token": 1.5e-07, @@ -28313,7 +28968,11 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 1.5e-07 + "output_cost_per_token": 1.5e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/mistral/pixtral-large": { "input_cost_per_token": 2e-06, @@ -28322,7 +28981,11 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 6e-06 + "output_cost_per_token": 6e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/moonshotai/kimi-k2": { "input_cost_per_token": 5.5e-07, @@ -28331,7 +28994,9 @@ "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 2.2e-06 + "output_cost_per_token": 2.2e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/morph/morph-v3-fast": { "input_cost_per_token": 8e-07, @@ -28358,7 +29023,9 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 1.5e-06 + "output_cost_per_token": 1.5e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/openai/gpt-3.5-turbo-instruct": { "input_cost_per_token": 1.5e-06, @@ -28376,7 +29043,10 @@ "max_output_tokens": 4096, "max_tokens": 4096, "mode": "chat", - "output_cost_per_token": 3e-05 + "output_cost_per_token": 3e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/openai/gpt-4.1": { "cache_creation_input_token_cost": 0.0, @@ -28387,7 +29057,11 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 8e-06 + "output_cost_per_token": 8e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/gpt-4.1-mini": { "cache_creation_input_token_cost": 0.0, @@ -28398,7 +29072,11 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 1.6e-06 + "output_cost_per_token": 1.6e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/gpt-4.1-nano": { "cache_creation_input_token_cost": 0.0, @@ -28409,7 +29087,11 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 4e-07 + "output_cost_per_token": 4e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/gpt-4o": { "cache_creation_input_token_cost": 0.0, @@ -28420,7 +29102,11 @@ "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/gpt-4o-mini": { "cache_creation_input_token_cost": 0.0, @@ -28431,7 +29117,11 @@ "max_output_tokens": 16384, "max_tokens": 16384, "mode": "chat", - "output_cost_per_token": 6e-07 + "output_cost_per_token": 6e-07, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/o1": { "cache_creation_input_token_cost": 0.0, @@ -28442,7 +29132,11 @@ "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", - "output_cost_per_token": 6e-05 + "output_cost_per_token": 6e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/o3": { "cache_creation_input_token_cost": 0.0, @@ -28453,7 +29147,11 @@ "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", - "output_cost_per_token": 8e-06 + "output_cost_per_token": 8e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/o3-mini": { "cache_creation_input_token_cost": 0.0, @@ -28464,7 +29162,10 @@ "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", - "output_cost_per_token": 4.4e-06 + "output_cost_per_token": 4.4e-06, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/o4-mini": { "cache_creation_input_token_cost": 0.0, @@ -28475,7 +29176,11 @@ "max_output_tokens": 100000, "max_tokens": 100000, "mode": "chat", - "output_cost_per_token": 4.4e-06 + "output_cost_per_token": 4.4e-06, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_response_schema": true }, "vercel_ai_gateway/openai/text-embedding-3-large": { "input_cost_per_token": 1.3e-07, @@ -28547,7 +29252,10 @@ "max_output_tokens": 32000, "max_tokens": 32000, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/vercel/v0-1.5-md": { "input_cost_per_token": 3e-06, @@ -28556,7 +29264,10 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-2": { "input_cost_per_token": 2e-06, @@ -28565,7 +29276,9 @@ "max_output_tokens": 4000, "max_tokens": 4000, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-2-vision": { "input_cost_per_token": 2e-06, @@ -28574,7 +29287,10 @@ "max_output_tokens": 32768, "max_tokens": 32768, "mode": "chat", - "output_cost_per_token": 1e-05 + "output_cost_per_token": 1e-05, + "supports_vision": true, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-3": { "input_cost_per_token": 3e-06, @@ -28583,7 +29299,9 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-3-fast": { "input_cost_per_token": 5e-06, @@ -28592,7 +29310,8 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.5e-05 + "output_cost_per_token": 2.5e-05, + "supports_function_calling": true }, "vercel_ai_gateway/xai/grok-3-mini": { "input_cost_per_token": 3e-07, @@ -28601,7 +29320,9 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 5e-07 + "output_cost_per_token": 5e-07, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-3-mini-fast": { "input_cost_per_token": 6e-07, @@ -28610,7 +29331,9 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 4e-06 + "output_cost_per_token": 4e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/xai/grok-4": { "input_cost_per_token": 3e-06, @@ -28619,7 +29342,9 @@ "max_output_tokens": 256000, "max_tokens": 256000, "mode": "chat", - "output_cost_per_token": 1.5e-05 + "output_cost_per_token": 1.5e-05, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/zai/glm-4.5": { "input_cost_per_token": 6e-07, @@ -28628,7 +29353,9 @@ "max_output_tokens": 131072, "max_tokens": 131072, "mode": "chat", - "output_cost_per_token": 2.2e-06 + "output_cost_per_token": 2.2e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/zai/glm-4.5-air": { "input_cost_per_token": 2e-07, @@ -28637,7 +29364,9 @@ "max_output_tokens": 96000, "max_tokens": 96000, "mode": "chat", - "output_cost_per_token": 1.1e-06 + "output_cost_per_token": 1.1e-06, + "supports_function_calling": true, + "supports_tool_choice": true }, "vercel_ai_gateway/zai/glm-4.6": { "litellm_provider": "vercel_ai_gateway", @@ -28705,7 +29434,9 @@ "supports_prompt_caching": true, "supports_reasoning": true, "supports_response_schema": true, - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_native_streaming": true, + "supports_vision": true }, "vertex_ai/claude-3-5-sonnet": { "input_cost_per_token": 3e-06, @@ -28976,7 +29707,38 @@ "supports_response_schema": true, "supports_tool_choice": true, "supports_vision": true, - "tool_use_system_prompt_tokens": 159 + "tool_use_system_prompt_tokens": 159, + "supports_native_streaming": true + }, + "vertex_ai/claude-opus-4-6": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_read_input_token_cost": 5e-07, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + "input_cost_per_token": 5e-06, + "input_cost_per_token_above_200k_tokens": 1e-05, + "litellm_provider": "vertex_ai-anthropic_models", + "max_input_tokens": 1000000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": false, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 346 }, "vertex_ai/claude-sonnet-4-5": { "cache_creation_input_token_cost": 3.75e-06, @@ -29028,7 +29790,8 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": true + "supports_vision": true, + "supports_native_streaming": true }, "vertex_ai/claude-opus-4@20250514": { "cache_creation_input_token_cost": 1.875e-05, @@ -29310,6 +30073,21 @@ "output_cost_per_token_batches": 6e-06, "source": "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-pro-image" }, + "vertex_ai/deep-research-pro-preview-12-2025": { + "input_cost_per_image": 0.0011, + "input_cost_per_token": 2e-06, + "input_cost_per_token_batches": 1e-06, + "litellm_provider": "vertex_ai-language-models", + "max_input_tokens": 65536, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "image_generation", + "output_cost_per_image": 0.134, + "output_cost_per_image_token": 0.00012, + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_batches": 6e-06, + "source": "https://docs.cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/3-pro-image" + }, "vertex_ai/imagegeneration@006": { "litellm_provider": "vertex_ai-image-models", "mode": "image_generation", @@ -29618,6 +30396,21 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "vertex_ai/zai-org/glm-5-maas": { + "cache_read_input_token_cost": 1e-07, + "input_cost_per_token": 1e-06, + "litellm_provider": "vertex_ai-zai_models", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 3.2e-06, + "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing#glm-models", + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "vertex_ai/mistral-medium-3": { "input_cost_per_token": 4e-07, "litellm_provider": "vertex_ai-mistral_models", @@ -29799,7 +30592,9 @@ "mode": "chat", "output_cost_per_token": 1e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supported_regions": ["global"], + "supported_regions": [ + "global" + ], "supports_function_calling": true, "supports_tool_choice": true }, @@ -29812,7 +30607,9 @@ "mode": "chat", "output_cost_per_token": 4e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supported_regions": ["global"], + "supported_regions": [ + "global" + ], "supports_function_calling": true, "supports_tool_choice": true }, @@ -29825,7 +30622,9 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supported_regions": ["global"], + "supported_regions": [ + "global" + ], "supports_function_calling": true, "supports_tool_choice": true }, @@ -29838,7 +30637,9 @@ "mode": "chat", "output_cost_per_token": 1.2e-06, "source": "https://cloud.google.com/vertex-ai/generative-ai/pricing", - "supported_regions": ["global"], + "supported_regions": [ + "global" + ], "supports_function_calling": true, "supports_tool_choice": true }, @@ -31113,6 +31914,20 @@ "supports_vision": true, "supports_web_search": true }, + "zai.glm-4.7": { + "input_cost_per_token": 6e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "mode": "chat", + "output_cost_per_token": 2.2e-06, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "source": "https://aws.amazon.com/bedrock/pricing/" + }, "zai/glm-4.7": { "cache_creation_input_token_cost": 0, "cache_read_input_token_cost": 1.1e-07, @@ -34786,5 +35601,694 @@ "mode": "chat", "output_cost_per_token": 0, "supports_reasoning": true + }, + "tts-1-1106": { + "input_cost_per_character": 1.5e-05, + "litellm_provider": "openai", + "mode": "audio_speech", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, + "tts-1-hd-1106": { + "input_cost_per_character": 3e-05, + "litellm_provider": "openai", + "mode": "audio_speech", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, + "gpt-4o-mini-tts-2025-03-20": { + "input_cost_per_token": 2.5e-06, + "litellm_provider": "openai", + "mode": "audio_speech", + "output_cost_per_audio_token": 1.2e-05, + "output_cost_per_second": 0.00025, + "output_cost_per_token": 1e-05, + "supported_endpoints": [ + "/v1/audio/speech" + ], + "supported_modalities": [ + "text", + "audio" + ], + "supported_output_modalities": [ + "audio" + ] + }, + "gpt-4o-mini-tts-2025-12-15": { + 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"supports_web_search": true, + "tpm": 8000000 + }, + "gemini-flash-lite-latest": { + "cache_read_input_token_cost": 1e-08, + "input_cost_per_audio_token": 3e-07, + "input_cost_per_token": 1e-07, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 4e-07, + "output_cost_per_token": 4e-07, + "rpm": 15, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-lite", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + 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+ "input_cost_per_audio_token": 1e-06, + "input_cost_per_token": 3e-07, + "litellm_provider": "gemini", + "max_audio_length_hours": 8.4, + "max_audio_per_prompt": 1, + "max_images_per_prompt": 3000, + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_pdf_size_mb": 30, + "max_tokens": 65535, + "max_video_length": 1, + "max_videos_per_prompt": 10, + "mode": "chat", + "output_cost_per_reasoning_token": 2.5e-06, + "output_cost_per_token": 2.5e-06, + "rpm": 100000, + "source": "https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview", + "supported_endpoints": [ + "/v1/chat/completions", + "/v1/completions", + "/v1/batch" + ], + "supported_modalities": [ + "text", + "image", + "audio", + "video" + ], + "supported_output_modalities": [ + "text" + ], + "supports_audio_output": false, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true, + "supports_url_context": true, + "supports_vision": true, + "supports_web_search": true, + "tpm": 8000000 } -} \ No newline at end of file +} diff --git a/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py b/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py index 081d83dd1c8..75b75d3ba44 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py +++ b/litellm/proxy/_experimental/mcp_server/auth/litellm_auth_handler.py @@ -27,6 +27,7 @@ class MCPAuthenticatedUser(AuthenticatedUser): oauth2_headers: Optional[Dict[str, str]] = None, mcp_protocol_version: Optional[str] = None, raw_headers: Optional[Dict[str, str]] = None, + client_ip: Optional[str] = None, ): self.user_api_key_auth = user_api_key_auth self.mcp_auth_header = mcp_auth_header @@ -35,3 +36,4 @@ class MCPAuthenticatedUser(AuthenticatedUser): self.mcp_protocol_version = mcp_protocol_version self.oauth2_headers = oauth2_headers self.raw_headers = raw_headers + self.client_ip = client_ip diff --git a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py index 49d6ac7d898..ed4fb133478 100644 --- a/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py +++ b/litellm/proxy/_experimental/mcp_server/auth/user_api_key_auth_mcp.py @@ -1,11 +1,17 @@ from typing import Dict, List, Optional, Set, Tuple +from fastapi import HTTPException from starlette.datastructures import Headers from starlette.requests import Request from starlette.types import Scope from litellm._logging import verbose_logger -from litellm.proxy._types import LiteLLM_TeamTable, SpecialHeaders, UserAPIKeyAuth +from litellm.proxy._types import ( + LiteLLM_TeamTable, + ProxyException, + SpecialHeaders, + UserAPIKeyAuth, +) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth @@ -63,6 +69,13 @@ class MCPRequestHandler: HTTPException: If headers are invalid or missing required headers """ headers = MCPRequestHandler._safe_get_headers_from_scope(scope) + + # Check if there is an explicit LiteLLM API key (primary header) + has_explicit_litellm_key = ( + headers.get(MCPRequestHandler.LITELLM_API_KEY_HEADER_NAME_PRIMARY) + is not None + ) + litellm_api_key = ( MCPRequestHandler.get_litellm_api_key_from_headers(headers) or "" ) @@ -106,16 +119,38 @@ class MCPRequestHandler: request.body = mock_body # type: ignore if ".well-known" in str(request.url): # public routes validated_user_api_key_auth = UserAPIKeyAuth() - # elif litellm_api_key == "": - # from fastapi import HTTPException - - # raise HTTPException( - # status_code=401, - # detail="LiteLLM API key is missing. Please add it or use OAuth authentication.", - # headers={ - # "WWW-Authenticate": f'Bearer resource_metadata=f"{request.base_url}/.well-known/oauth-protected-resource"', - # }, - # ) + elif has_explicit_litellm_key: + # Explicit x-litellm-api-key provided - always validate normally + validated_user_api_key_auth = await user_api_key_auth( + api_key=litellm_api_key, request=request + ) + elif oauth2_headers: + # No x-litellm-api-key, but Authorization header present. + # Could be a LiteLLM key (backward compat) OR an OAuth2 token + # from an upstream MCP provider (e.g. Atlassian). + # Try LiteLLM auth first; on auth failure, treat as OAuth2 passthrough. + try: + validated_user_api_key_auth = await user_api_key_auth( + api_key=litellm_api_key, request=request + ) + except HTTPException as e: + if e.status_code in (401, 403): + verbose_logger.debug( + "MCP OAuth2: Authorization header is not a valid LiteLLM key, " + "treating as OAuth2 token passthrough" + ) + validated_user_api_key_auth = UserAPIKeyAuth() + else: + raise + except ProxyException as e: + if str(e.code) in ("401", "403"): + verbose_logger.debug( + "MCP OAuth2: Authorization header is not a valid LiteLLM key, " + "treating as OAuth2 token passthrough" + ) + validated_user_api_key_auth = UserAPIKeyAuth() + else: + raise else: validated_user_api_key_auth = await user_api_key_auth( api_key=litellm_api_key, request=request @@ -342,55 +377,44 @@ class MCPRequestHandler: return [] @staticmethod - async def _get_key_object_permission( + def _get_key_object_permission( user_api_key_auth: Optional[UserAPIKeyAuth] = None, ): - """Helper to get key object_permission from cache or DB.""" - from litellm.proxy.auth.auth_checks import get_object_permission - from litellm.proxy.proxy_server import ( - prisma_client, - proxy_logging_obj, - user_api_key_cache, - ) + """ + Get key object_permission - already loaded by get_key_object() in main auth flow. + Note: object_permission is automatically populated when the key is fetched via + get_key_object() in litellm/proxy/auth/auth_checks.py + """ if not user_api_key_auth: return None - # Already loaded - if user_api_key_auth.object_permission: - return user_api_key_auth.object_permission - - # Need to fetch from DB - if user_api_key_auth.object_permission_id and prisma_client: - return await get_object_permission( - object_permission_id=user_api_key_auth.object_permission_id, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=user_api_key_auth.parent_otel_span, - proxy_logging_obj=proxy_logging_obj, - ) - - return None + return user_api_key_auth.object_permission @staticmethod async def _get_team_object_permission( user_api_key_auth: Optional[UserAPIKeyAuth] = None, ): - """Helper to get team object_permission from cache or DB.""" - from litellm.proxy.auth.auth_checks import ( - get_object_permission, - get_team_object, - ) + """ + Get team object_permission - automatically loaded by get_team_object() in main auth flow. + + Note: object_permission is automatically populated when the team is fetched via + get_team_object() in litellm/proxy/auth/auth_checks.py + """ + from litellm.proxy.auth.auth_checks import get_team_object from litellm.proxy.proxy_server import ( prisma_client, proxy_logging_obj, user_api_key_cache, ) + verbose_logger.debug( + f"MCP team permission lookup: team_id={user_api_key_auth.team_id if user_api_key_auth else None}" + ) if not user_api_key_auth or not user_api_key_auth.team_id or not prisma_client: return None - # First get the team object (which may have object_permission already loaded) + # Get the team object (which has object_permission already loaded) team_obj: Optional[LiteLLM_TeamTable] = await get_team_object( team_id=user_api_key_auth.team_id, prisma_client=prisma_client, @@ -402,21 +426,7 @@ class MCPRequestHandler: if not team_obj: return None - # Already loaded - if team_obj.object_permission: - return team_obj.object_permission - - # Need to fetch from DB using object_permission_id - if team_obj.object_permission_id: - return await get_object_permission( - object_permission_id=team_obj.object_permission_id, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=user_api_key_auth.parent_otel_span, - proxy_logging_obj=proxy_logging_obj, - ) - - return None + return team_obj.object_permission @staticmethod async def get_allowed_tools_for_server( @@ -438,8 +448,8 @@ class MCPRequestHandler: return None try: - # Get key and team object permissions - key_obj_perm = await MCPRequestHandler._get_key_object_permission( + # Get key and team object permissions (already loaded in main auth flow) + key_obj_perm = MCPRequestHandler._get_key_object_permission( user_api_key_auth ) team_obj_perm = await MCPRequestHandler._get_team_object_permission( @@ -526,9 +536,25 @@ class MCPRequestHandler: user_api_key_auth: Optional[UserAPIKeyAuth] = None, ) -> List[str]: try: - key_object_permission = await MCPRequestHandler._get_key_object_permission( + # Get key object permission (already loaded in main auth flow, or fetch from DB) + key_object_permission = MCPRequestHandler._get_key_object_permission( user_api_key_auth ) + if key_object_permission is None and user_api_key_auth and user_api_key_auth.object_permission_id: + from litellm.proxy.auth.auth_checks import get_object_permission + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + if prisma_client is not None: + key_object_permission = await get_object_permission( + object_permission_id=user_api_key_auth.object_permission_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ) if key_object_permission is None: return [] @@ -558,12 +584,10 @@ class MCPRequestHandler: """ Get allowed MCP servers for a team. - Uses the helper _get_team_object_permission which: - 1. First checks if object_permission is already loaded on the team - 2. If not, fetches from DB using object_permission_id if it exists + Note: object_permission is automatically loaded by get_team_object() in main auth flow. """ try: - # Use the helper method that properly handles fetching from DB if needed + # Get team object permission (already loaded in main auth flow) object_permissions = await MCPRequestHandler._get_team_object_permission( user_api_key_auth ) diff --git a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py index 56feff548ad..b0ae03c94d3 100644 --- a/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/discoverable_endpoints.py @@ -1,6 +1,6 @@ import json from typing import Optional -from urllib.parse import urlencode, urlparse, urlunparse +from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse from fastapi import APIRouter, Form, HTTPException, Request from fastapi.responses import HTMLResponse, JSONResponse, RedirectResponse @@ -9,13 +9,15 @@ from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.common_utils.encrypt_decrypt_utils import ( decrypt_value_helper, encrypt_value_helper, ) from litellm.proxy.common_utils.http_parsing_utils import _read_request_body -from litellm.types.mcp_server.mcp_server_manager import MCPServer from litellm.proxy.utils import get_server_root_path +from litellm.types.mcp import MCPAuth +from litellm.types.mcp_server.mcp_server_manager import MCPServer router = APIRouter( tags=["mcp"], @@ -124,6 +126,29 @@ def decode_state_hash(encrypted_state: str) -> dict: return state_data +def _resolve_oauth2_server_for_root_endpoints( + client_ip: Optional[str] = None, +) -> Optional[MCPServer]: + """ + Resolve the MCP server for root-level OAuth endpoints (no server name in path). + + When the MCP SDK hits root-level endpoints like /register, /authorize, /token + without a server name prefix, we try to find the right server automatically. + Returns the server if exactly one OAuth2 server is configured, else None. + """ + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) + + registry = global_mcp_server_manager.get_filtered_registry(client_ip=client_ip) + oauth2_servers = [ + s for s in registry.values() if s.auth_type == MCPAuth.oauth2 + ] + if len(oauth2_servers) == 1: + return oauth2_servers[0] + return None + + async def authorize_with_server( request: Request, mcp_server: MCPServer, @@ -169,7 +194,13 @@ async def authorize_with_server( if code_challenge_method: params["code_challenge_method"] = code_challenge_method - return RedirectResponse(f"{mcp_server.authorization_url}?{urlencode(params)}") + parsed_auth_url = urlparse(mcp_server.authorization_url) + existing_params = dict(parse_qsl(parsed_auth_url.query)) + existing_params.update(params) + final_url = urlunparse( + parsed_auth_url._replace(query=urlencode(existing_params)) + ) + return RedirectResponse(final_url) async def exchange_token_with_server( @@ -300,7 +331,12 @@ async def authorize( ) lookup_name = mcp_server_name or client_id - mcp_server = global_mcp_server_manager.get_mcp_server_by_name(lookup_name) + client_ip = IPAddressUtils.get_mcp_client_ip(request) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name( + lookup_name, client_ip=client_ip + ) + if mcp_server is None and mcp_server_name is None: + mcp_server = _resolve_oauth2_server_for_root_endpoints() if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") return await authorize_with_server( @@ -342,7 +378,12 @@ async def token_endpoint( ) lookup_name = mcp_server_name or client_id - mcp_server = global_mcp_server_manager.get_mcp_server_by_name(lookup_name) + client_ip = IPAddressUtils.get_mcp_client_ip(request) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name( + lookup_name, client_ip=client_ip + ) + if mcp_server is None and mcp_server_name is None: + mcp_server = _resolve_oauth2_server_for_root_endpoints() if mcp_server is None: raise HTTPException(status_code=404, detail="MCP server not found") return await exchange_token_with_server( @@ -423,9 +464,19 @@ def _build_oauth_protected_resource_response( ) request_base_url = get_request_base_url(request) + + # When no server name provided, try to resolve the single OAuth2 server + if mcp_server_name is None: + resolved = _resolve_oauth2_server_for_root_endpoints() + if resolved: + mcp_server_name = resolved.server_name or resolved.name + mcp_server: Optional[MCPServer] = None if mcp_server_name: - mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name) + client_ip = IPAddressUtils.get_mcp_client_ip(request) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name( + mcp_server_name, client_ip=client_ip + ) # Build resource URL based on the pattern if mcp_server_name: @@ -525,6 +576,12 @@ def _build_oauth_authorization_server_response( request_base_url = get_request_base_url(request) + # When no server name provided, try to resolve the single OAuth2 server + if mcp_server_name is None: + resolved = _resolve_oauth2_server_for_root_endpoints() + if resolved: + mcp_server_name = resolved.server_name or resolved.name + authorization_endpoint = ( f"{request_base_url}/{mcp_server_name}/authorize" if mcp_server_name @@ -538,7 +595,10 @@ def _build_oauth_authorization_server_response( mcp_server: Optional[MCPServer] = None if mcp_server_name: - mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name) + client_ip = IPAddressUtils.get_mcp_client_ip(request) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name( + mcp_server_name, client_ip=client_ip + ) return { "issuer": request_base_url, # point to your proxy @@ -627,9 +687,25 @@ async def register_client(request: Request, mcp_server_name: Optional[str] = Non "redirect_uris": [f"{request_base_url}/callback"], } if not mcp_server_name: + resolved = _resolve_oauth2_server_for_root_endpoints() + if resolved: + return await register_client_with_server( + request=request, + mcp_server=resolved, + client_name=data.get("client_name", ""), + grant_types=data.get("grant_types", []), + response_types=data.get("response_types", []), + token_endpoint_auth_method=data.get( + "token_endpoint_auth_method", "" + ), + fallback_client_id=resolved.server_name or resolved.name, + ) return dummy_return - mcp_server = global_mcp_server_manager.get_mcp_server_by_name(mcp_server_name) + client_ip = IPAddressUtils.get_mcp_client_ip(request) + mcp_server = global_mcp_server_manager.get_mcp_server_by_name( + mcp_server_name, client_ip=client_ip + ) if mcp_server is None: return dummy_return return await register_client_with_server( diff --git a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py index 4d53ae7059d..14bbb82808d 100644 --- a/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py +++ b/litellm/proxy/_experimental/mcp_server/guardrail_translation/handler.py @@ -1,26 +1,37 @@ """ MCP Guardrail Handler for Unified Guardrails. -This handler works with the synthetic "messages" payload generated by -`ProxyLogging._convert_mcp_to_llm_format`, which always produces a single user -message whose `content` string encodes the MCP tool name and arguments. The -handler simply feeds that text through the configured guardrail and writes the -result back onto the message. +Converts an MCP call_tool (name + arguments) into a single OpenAI-compatible +tool_call and passes it to apply_guardrail. Works with the synthetic payload +from ProxyLogging._convert_mcp_to_llm_format. + +Note: For MCP tool definitions (schema) -> OpenAI tools=[], see +litellm.experimental_mcp_client.tools.transform_mcp_tool_to_openai_tool +when you have a full MCP Tool from list_tools. Here we only have the call +payload (name + arguments) so we just build the tool_call. """ from typing import TYPE_CHECKING, Any, Dict, Optional +from mcp.types import Tool as MCPTool + from litellm._logging import verbose_proxy_logger +from litellm.experimental_mcp_client.tools import transform_mcp_tool_to_openai_tool from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.llms.openai import ( + ChatCompletionToolParam, + ChatCompletionToolParamFunctionChunk, +) from litellm.types.utils import GenericGuardrailAPIInputs if TYPE_CHECKING: - from litellm.integrations.custom_guardrail import CustomGuardrail from mcp.types import CallToolResult + from litellm.integrations.custom_guardrail import CustomGuardrail + class MCPGuardrailTranslationHandler(BaseTranslation): - """Guardrail translation handler for MCP tool calls.""" + """Guardrail translation handler for MCP tool calls (passes a single tool_call to guardrail).""" async def process_input_messages( self, @@ -28,56 +39,51 @@ class MCPGuardrailTranslationHandler(BaseTranslation): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, ) -> Dict[str, Any]: - messages = data.get("messages") - if not isinstance(messages, list) or not messages: - verbose_proxy_logger.debug("MCP Guardrail: No messages to process") + mcp_tool_name = data.get("mcp_tool_name") or data.get("name") + mcp_arguments = data.get("mcp_arguments") or data.get("arguments") + mcp_tool_description = data.get("mcp_tool_description") or data.get( + "description" + ) + if mcp_arguments is None or not isinstance(mcp_arguments, dict): + mcp_arguments = {} + + if not mcp_tool_name: + verbose_proxy_logger.debug("MCP Guardrail: mcp_tool_name missing") return data - first_message = messages[0] - content: Optional[str] = None - if isinstance(first_message, dict): - content = first_message.get("content") - else: - content = getattr(first_message, "content", None) + # Convert MCP input via transform_mcp_tool_to_openai_tool, then map to litellm + # ChatCompletionToolParam (openai SDK type has incompatible strict/cache_control). + mcp_tool = MCPTool( + name=mcp_tool_name, + description=mcp_tool_description or "", + inputSchema={}, # Call payload has no schema; guardrail gets args from request_data + ) + openai_tool = transform_mcp_tool_to_openai_tool(mcp_tool) + fn = openai_tool["function"] + tool_def: ChatCompletionToolParam = { + "type": "function", + "function": ChatCompletionToolParamFunctionChunk( + name=fn["name"], + description=fn.get("description") or "", + parameters=fn.get("parameters") + or { + "type": "object", + "properties": {}, + "additionalProperties": False, + }, + strict=fn.get("strict", False) or False, # Default to False if None + ), + } + inputs: GenericGuardrailAPIInputs = GenericGuardrailAPIInputs( + tools=[tool_def], + ) - if not isinstance(content, str): - verbose_proxy_logger.debug( - "MCP Guardrail: Message content missing or not a string", - ) - return data - - inputs = GenericGuardrailAPIInputs(texts=[content]) - # Include model information if available - model = data.get("model") - if model: - inputs["model"] = model - guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + await guardrail_to_apply.apply_guardrail( inputs=inputs, request_data=data, input_type="request", logging_obj=litellm_logging_obj, ) - guardrailed_texts = ( - guardrailed_inputs.get("texts", []) if guardrailed_inputs else [] - ) - - if guardrailed_texts: - new_content = guardrailed_texts[0] - if isinstance(first_message, dict): - first_message["content"] = new_content - else: - setattr(first_message, "content", new_content) - - verbose_proxy_logger.debug( - "MCP Guardrail: Updated content for tool %s", - data.get("mcp_tool_name"), - ) - else: - verbose_proxy_logger.debug( - "MCP Guardrail: Guardrail returned no text updates for tool %s", - data.get("mcp_tool_name"), - ) - return data async def process_output_response( @@ -87,7 +93,6 @@ class MCPGuardrailTranslationHandler(BaseTranslation): litellm_logging_obj: Optional[Any] = None, user_api_key_dict: Optional[Any] = None, ) -> Any: - # Not implemented: MCP guardrail translation never calls this path today. verbose_proxy_logger.debug( "MCP Guardrail: Output processing not implemented for MCP tools", ) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 4c17a2ff3e0..62a06468e9a 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -14,6 +14,7 @@ import re from typing import Any, Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast from urllib.parse import urlparse +import anyio from fastapi import HTTPException from httpx import HTTPStatusError from mcp import ReadResourceResult, Resource @@ -36,6 +37,7 @@ from litellm.llms.custom_httpx.http_handler import get_async_httpx_client from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( MCPRequestHandler, ) +from litellm.proxy._experimental.mcp_server.oauth2_token_cache import resolve_mcp_auth from litellm.proxy._experimental.mcp_server.utils import ( MCP_TOOL_PREFIX_SEPARATOR, add_server_prefix_to_name, @@ -53,6 +55,7 @@ from litellm.proxy._types import ( MCPTransportType, UserAPIKeyAuth, ) +from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper from litellm.proxy.utils import ProxyLogging from litellm.types.llms.custom_http import httpxSpecialProvider @@ -65,20 +68,23 @@ from litellm.types.mcp_server.mcp_server_manager import ( from litellm.types.utils import CallTypes try: - from mcp.shared.tool_name_validation import ( # type: ignore + from mcp.shared.tool_name_validation import ( + validate_tool_name, # pyright: ignore[reportAssignmentType] + ) + from mcp.shared.tool_name_validation import ( SEP_986_URL, - validate_tool_name, ) except ImportError: from pydantic import BaseModel + SEP_986_URL = "https://github.com/modelcontextprotocol/protocol/blob/main/proposals/0001-tool-name-validation.md" - class ToolNameValidationResult(BaseModel): + class _ToolNameValidationResult(BaseModel): is_valid: bool = True warnings: list = [] - def validate_tool_name(name: str) -> ToolNameValidationResult: # type: ignore[misc] - return ToolNameValidationResult() + def validate_tool_name(name: str) -> _ToolNameValidationResult: # type: ignore[misc] + return _ToolNameValidationResult() # Probe includes characters on both sides of the separator to mimic real prefixed tool names. @@ -324,6 +330,9 @@ class MCPServerManager: access_groups=server_config.get("access_groups", None), static_headers=server_config.get("static_headers", None), allow_all_keys=bool(server_config.get("allow_all_keys", False)), + available_on_public_internet=bool( + server_config.get("available_on_public_internet", False) + ), ) self.config_mcp_servers[server_id] = new_server @@ -333,7 +342,7 @@ class MCPServerManager: verbose_logger.info( f"Loading OpenAPI spec from {spec_path} for server {server_name}" ) - self._register_openapi_tools( + await self._register_openapi_tools( spec_path=spec_path, server=new_server, base_url=server_config.get("url", ""), @@ -345,7 +354,9 @@ class MCPServerManager: self.initialize_tool_name_to_mcp_server_name_mapping() - def _register_openapi_tools(self, spec_path: str, server: MCPServer, base_url: str): + async def _register_openapi_tools( + self, spec_path: str, server: MCPServer, base_url: str + ): """ Register tools from an OpenAPI specification for a given server. @@ -367,15 +378,15 @@ class MCPServerManager: get_base_url as get_openapi_base_url, ) from litellm.proxy._experimental.mcp_server.openapi_to_mcp_generator import ( - load_openapi_spec, + load_openapi_spec_async, ) from litellm.proxy._experimental.mcp_server.tool_registry import ( global_mcp_tool_registry, ) try: - # Load OpenAPI spec - spec = load_openapi_spec(spec_path) + # Load OpenAPI spec (async to avoid "called from within a running event loop") + spec = await load_openapi_spec_async(spec_path) # Use base_url from config if provided, otherwise extract from spec if not base_url: @@ -469,12 +480,12 @@ class MCPServerManager: ) # Update tool name to server name mapping (for both prefixed and base names) - self.tool_name_to_mcp_server_name_mapping[ - base_tool_name - ] = server_prefix - self.tool_name_to_mcp_server_name_mapping[ - prefixed_tool_name - ] = server_prefix + self.tool_name_to_mcp_server_name_mapping[base_tool_name] = ( + server_prefix + ) + self.tool_name_to_mcp_server_name_mapping[prefixed_tool_name] = ( + server_prefix + ) registered_count += 1 verbose_logger.debug( @@ -622,6 +633,9 @@ class MCPServerManager: allowed_tools=getattr(mcp_server, "allowed_tools", None), disallowed_tools=getattr(mcp_server, "disallowed_tools", None), allow_all_keys=mcp_server.allow_all_keys, + available_on_public_internet=bool( + getattr(mcp_server, "available_on_public_internet", False) + ), updated_at=getattr(mcp_server, "updated_at", None), ) return new_server @@ -660,24 +674,47 @@ class MCPServerManager: return [ server.server_id for server in self.get_registry().values() - if server.allow_all_keys + if server.allow_all_keys is True ] async def get_allowed_mcp_servers( self, user_api_key_auth: Optional[UserAPIKeyAuth] = None ) -> List[str]: """ - Get the allowed MCP Servers for the user + Get the allowed MCP Servers for the user. + + Priority: + 1. If object_permission.mcp_servers is explicitly set, use it (even for admins) + 2. If admin and no object_permission, return all servers + 3. Otherwise, use standard permission checks """ from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view - # If admin, get all servers - if user_api_key_auth and _user_has_admin_view(user_api_key_auth): - return list(self.get_registry().keys()) - allow_all_server_ids = self.get_allow_all_keys_server_ids() try: + # Check if object_permission.mcp_servers is explicitly set + has_explicit_object_permission = False + if user_api_key_auth and user_api_key_auth.object_permission: + # Check if mcp_servers is explicitly set (not None, empty list is valid) + if user_api_key_auth.object_permission.mcp_servers is not None: + has_explicit_object_permission = True + verbose_logger.debug( + f"Object permission mcp_servers explicitly set: {user_api_key_auth.object_permission.mcp_servers}" + ) + + # If admin but NO explicit object permission, get all servers + if ( + user_api_key_auth + and _user_has_admin_view(user_api_key_auth) + and not has_explicit_object_permission + ): + verbose_logger.debug( + "Admin user without explicit object_permission - returning all servers" + ) + return list(self.get_registry().keys()) + + # Get allowed servers from object permissions (respects object_permission even for admins) allowed_mcp_servers = await MCPRequestHandler.get_allowed_mcp_servers( user_api_key_auth ) @@ -696,6 +733,23 @@ class MCPServerManager: verbose_logger.warning(f"Failed to get allowed MCP servers: {str(e)}.") return allow_all_server_ids + def filter_server_ids_by_ip( + self, server_ids: List[str], client_ip: Optional[str] + ) -> List[str]: + """ + Filter server IDs by client IP — external callers only see public servers. + + Returns server_ids unchanged when client_ip is None (no filtering). + """ + if client_ip is None: + return server_ids + return [ + sid + for sid in server_ids + if (s := self.get_mcp_server_by_id(sid)) is not None + and self._is_server_accessible_from_ip(s, client_ip) + ] + async def get_tools_for_server(self, server_id: str) -> List[MCPTool]: """ Get the tools for a given server @@ -806,7 +860,7 @@ class MCPServerManager: return resolved_env - def _create_mcp_client( + async def _create_mcp_client( self, server: MCPServer, mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None, @@ -816,19 +870,35 @@ class MCPServerManager: """ Create an MCPClient instance for the given server. + Auth resolution (single place for all auth logic): + 1. ``mcp_auth_header`` — per-request/per-user override + 2. OAuth2 client_credentials token — auto-fetched and cached + 3. ``server.authentication_token`` — static token from config/DB + Args: - server (MCPServer): The server configuration - mcp_auth_header: MCP auth header to be passed to the MCP server. This is optional and will be used if provided. + server: The server configuration. + mcp_auth_header: Optional per-request auth override. + extra_headers: Additional headers to forward. + stdio_env: Environment variables for stdio transport. Returns: - MCPClient: Configured MCP client instance + Configured MCP client instance. """ + auth_value = await resolve_mcp_auth(server, mcp_auth_header) + transport = server.transport or MCPTransport.sse # Handle stdio transport if transport == MCPTransport.stdio: - # For stdio, we need to get the stdio config from the server - resolved_env = stdio_env if stdio_env is not None else server.env or {} + resolved_env = stdio_env if stdio_env is not None else dict(server.env or {}) + + # Ensure npm-based STDIO MCP servers have a writable cache dir. + # In containers the default (~/.npm or /app/.npm) may not exist + # or be read-only, causing npx to fail with ENOENT. + if "NPM_CONFIG_CACHE" not in resolved_env: + from litellm.constants import MCP_NPM_CACHE_DIR + + resolved_env["NPM_CONFIG_CACHE"] = MCP_NPM_CACHE_DIR stdio_config: Optional[MCPStdioConfig] = None if server.command and server.args is not None: stdio_config = MCPStdioConfig( @@ -841,7 +911,7 @@ class MCPServerManager: server_url="", # Not used for stdio transport_type=transport, auth_type=server.auth_type, - auth_value=mcp_auth_header or server.authentication_token, + auth_value=auth_value, timeout=60.0, stdio_config=stdio_config, extra_headers=extra_headers, @@ -853,7 +923,7 @@ class MCPServerManager: server_url=server_url, transport_type=transport, auth_type=server.auth_type, - auth_value=mcp_auth_header or server.authentication_token, + auth_value=auth_value, timeout=60.0, extra_headers=extra_headers, ) @@ -893,7 +963,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -953,7 +1023,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -997,7 +1067,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -1041,7 +1111,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -1082,7 +1152,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -1112,7 +1182,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=mcp_auth_header, extra_headers=extra_headers, @@ -1377,6 +1447,9 @@ class MCPServerManager: """ Fetch tools from MCP client with timeout and error handling. + Uses anyio.fail_after() instead of asyncio.wait_for() to avoid conflicts + with the MCP SDK's anyio TaskGroup. See GitHub issue #20715 for details. + Args: client: MCP client instance server_name: Name of the server for logging @@ -1384,24 +1457,12 @@ class MCPServerManager: Returns: List of tools from the server """ - - async def _list_tools_task(): - try: + try: + with anyio.fail_after(30.0): tools = await client.list_tools() verbose_logger.debug(f"Tools from {server_name}: {tools}") return tools - except asyncio.CancelledError: - verbose_logger.warning(f"Client operation cancelled for {server_name}") - return [] - except Exception as e: - verbose_logger.warning( - f"Client operation failed for {server_name}: {str(e)}" - ) - return [] - - try: - return await asyncio.wait_for(_list_tools_task(), timeout=30.0) - except asyncio.TimeoutError: + except TimeoutError: verbose_logger.warning(f"Timeout while listing tools from {server_name}") return [] except asyncio.CancelledError: @@ -1916,7 +1977,7 @@ class MCPServerManager: stdio_env = self._build_stdio_env(mcp_server, raw_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=mcp_server, mcp_auth_header=server_auth_header, extra_headers=extra_headers, @@ -1929,7 +1990,9 @@ class MCPServerManager: ) async def _call_tool_via_client(client, params): - return await client.call_tool(params, host_progress_callback=host_progress_callback) + return await client.call_tool( + params, host_progress_callback=host_progress_callback + ) tasks.append( asyncio.create_task(_call_tool_via_client(client, call_tool_params)) @@ -1967,7 +2030,6 @@ class MCPServerManager: oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, host_progress_callback: Optional[Callable] = None, - ) -> CallToolResult: """ Call a tool with the given name and arguments @@ -2091,8 +2153,8 @@ class MCPServerManager: Note: This now handles prefixed tool names """ for server in self.get_registry().values(): - if server.auth_type == MCPAuth.oauth2: - # Skip OAuth2 servers for now as they may require user-specific tokens + if server.needs_user_oauth_token: + # Skip OAuth2 servers that rely on user-provided tokens continue tools = await self._get_tools_from_server(server) for tool in tools: @@ -2200,6 +2262,43 @@ class MCPServerManager: servers.append(server) return servers + def _get_general_settings(self) -> Dict[str, Any]: + """Get general_settings, importing lazily to avoid circular imports.""" + try: + from litellm.proxy.proxy_server import ( + general_settings as proxy_general_settings, + ) + + return proxy_general_settings + except ImportError: + # Fallback if proxy_server not available + return {} + + def _is_server_accessible_from_ip( + self, server: MCPServer, client_ip: Optional[str] + ) -> bool: + """ + Check if a server is accessible from the given client IP. + + - If client_ip is None, no IP filtering is applied (internal callers). + - If the server has available_on_public_internet=True, it's always accessible. + - Otherwise, only internal/private IPs can access it. + """ + if client_ip is None: + return True + if server.available_on_public_internet: + return True + # Check backwards compat: litellm.public_mcp_servers + public_ids = set(litellm.public_mcp_servers or []) + if server.server_id in public_ids: + return True + # Non-public server: only accessible from internal IPs + general_settings = self._get_general_settings() + internal_networks = IPAddressUtils.parse_internal_networks( + general_settings.get("mcp_internal_ip_ranges") + ) + return IPAddressUtils.is_internal_ip(client_ip, internal_networks) + def get_mcp_server_by_id(self, server_id: str) -> Optional[MCPServer]: """ Get the MCP Server from the server id @@ -2212,27 +2311,72 @@ class MCPServerManager: def get_public_mcp_servers(self) -> List[MCPServer]: """ - Get the public MCP servers + Get the public MCP servers (available_on_public_internet=True flag on server). + Also includes servers from litellm.public_mcp_servers for backwards compat. """ servers: List[MCPServer] = [] - if litellm.public_mcp_servers is None: - return servers - for server_id in litellm.public_mcp_servers: - server = self.get_mcp_server_by_id(server_id) - if server: + public_ids = set(litellm.public_mcp_servers or []) + for server in self.get_registry().values(): + if server.available_on_public_internet or server.server_id in public_ids: servers.append(server) return servers - def get_mcp_server_by_name(self, server_name: str) -> Optional[MCPServer]: + def get_mcp_server_by_name( + self, server_name: str, client_ip: Optional[str] = None + ) -> Optional[MCPServer]: """ - Get the MCP Server from the server name + Get the MCP Server from the server name. + + Uses priority-based matching to avoid collisions: + 1. First pass: exact alias match (highest priority) + 2. Second pass: exact server_name match + 3. Third pass: exact name match (lowest priority) + + Args: + server_name: The server name to look up. + client_ip: Optional client IP for access control. When provided, + non-public servers are hidden from external IPs. """ registry = self.get_registry() + # Pass 1: Match by alias (highest priority) + for server in registry.values(): + if server.alias == server_name: + if not self._is_server_accessible_from_ip(server, client_ip): + return None + return server + # Pass 2: Match by server_name for server in registry.values(): if server.server_name == server_name: + if not self._is_server_accessible_from_ip(server, client_ip): + return None + return server + # Pass 3: Match by name (lowest priority) + for server in registry.values(): + if server.name == server_name: + if not self._is_server_accessible_from_ip(server, client_ip): + return None return server return None + def get_filtered_registry( + self, client_ip: Optional[str] = None + ) -> Dict[str, MCPServer]: + """ + Get registry filtered by client IP access control. + + Args: + client_ip: Optional client IP. When provided, non-public servers + are hidden from external IPs. When None, returns all servers. + """ + registry = self.get_registry() + if client_ip is None: + return registry + return { + k: v + for k, v in registry.items() + if self._is_server_accessible_from_ip(v, client_ip) + } + def _generate_stable_server_id( self, server_name: str, @@ -2305,7 +2449,7 @@ class MCPServerManager: should_skip_health_check = False # Skip if auth_type is oauth2 - if server.auth_type == MCPAuth.oauth2: + if server.needs_user_oauth_token: should_skip_health_check = True # Skip if auth_type is not none and authentication_token is missing elif ( @@ -2320,7 +2464,7 @@ class MCPServerManager: if server.static_headers: extra_headers.update(server.static_headers) - client = self._create_mcp_client( + client = await self._create_mcp_client( server=server, mcp_auth_header=None, extra_headers=extra_headers, @@ -2338,6 +2482,9 @@ class MCPServerManager: except asyncio.TimeoutError: health_check_error = "Health check timed out after 10 seconds" status = "unhealthy" + except asyncio.CancelledError: + health_check_error = "Health check was cancelled" + status = "unknown" except Exception as e: health_check_error = str(e) status = "unhealthy" @@ -2462,6 +2609,7 @@ class MCPServerManager: token_url=server.token_url, registration_url=server.registration_url, allow_all_keys=server.allow_all_keys, + available_on_public_internet=server.available_on_public_internet, ) async def get_all_mcp_servers_unfiltered(self) -> List[LiteLLM_MCPServerTable]: diff --git a/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py new file mode 100644 index 00000000000..0de381ee1df --- /dev/null +++ b/litellm/proxy/_experimental/mcp_server/oauth2_token_cache.py @@ -0,0 +1,163 @@ +""" +OAuth2 client_credentials token cache for MCP servers. + +Automatically fetches and refreshes access tokens for MCP servers configured +with ``client_id``, ``client_secret``, and ``token_url``. +""" + +import asyncio +from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union + +import httpx + +from litellm._logging import verbose_logger +from litellm.caching.in_memory_cache import InMemoryCache +from litellm.constants import ( + MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL, + MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE, + MCP_OAUTH2_TOKEN_CACHE_MIN_TTL, + MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS, +) +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client +from litellm.types.llms.custom_http import httpxSpecialProvider + +if TYPE_CHECKING: + from litellm.types.mcp_server.mcp_server_manager import MCPServer + + +class MCPOAuth2TokenCache(InMemoryCache): + """ + In-memory cache for OAuth2 client_credentials tokens, keyed by server_id. + + Inherits from ``InMemoryCache`` for TTL-based storage and eviction. + Adds per-server ``asyncio.Lock`` to prevent duplicate concurrent fetches. + """ + + def __init__(self) -> None: + super().__init__( + max_size_in_memory=MCP_OAUTH2_TOKEN_CACHE_MAX_SIZE, + default_ttl=MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL, + ) + self._locks: Dict[str, asyncio.Lock] = {} + + def _get_lock(self, server_id: str) -> asyncio.Lock: + return self._locks.setdefault(server_id, asyncio.Lock()) + + async def async_get_token(self, server: "MCPServer") -> Optional[str]: + """Return a valid access token, fetching or refreshing as needed. + + Returns ``None`` when the server lacks client credentials config. + """ + if not server.has_client_credentials: + return None + + server_id = server.server_id + + # Fast path — cached token is still valid + cached = self.get_cache(server_id) + if cached is not None: + return cached + + # Slow path — acquire per-server lock then double-check + async with self._get_lock(server_id): + cached = self.get_cache(server_id) + if cached is not None: + return cached + + token, ttl = await self._fetch_token(server) + self.set_cache(server_id, token, ttl=ttl) + return token + + async def _fetch_token(self, server: "MCPServer") -> Tuple[str, int]: + """POST to ``token_url`` with ``grant_type=client_credentials``. + + Returns ``(access_token, ttl_seconds)`` where ttl accounts for the + expiry buffer so the cache entry expires before the real token does. + """ + client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) + + if not server.client_id or not server.client_secret or not server.token_url: + raise ValueError( + f"MCP server '{server.server_id}' missing required OAuth2 fields: " + f"client_id={bool(server.client_id)}, " + f"client_secret={bool(server.client_secret)}, " + f"token_url={bool(server.token_url)}" + ) + + data: Dict[str, str] = { + "grant_type": "client_credentials", + "client_id": server.client_id, + "client_secret": server.client_secret, + } + if server.scopes: + data["scope"] = " ".join(server.scopes) + + verbose_logger.debug( + "Fetching OAuth2 client_credentials token for MCP server %s", + server.server_id, + ) + + try: + response = await client.post(server.token_url, data=data) + response.raise_for_status() + except httpx.HTTPStatusError as exc: + raise ValueError( + f"OAuth2 token request for MCP server '{server.server_id}' " + f"failed with status {exc.response.status_code}" + ) from exc + + body = response.json() + + if not isinstance(body, dict): + raise ValueError( + f"OAuth2 token response for MCP server '{server.server_id}' " + f"returned non-object JSON (got {type(body).__name__})" + ) + + access_token = body.get("access_token") + if not access_token: + raise ValueError( + f"OAuth2 token response for MCP server '{server.server_id}' " + f"missing 'access_token'" + ) + + # Safely parse expires_in — providers may return null or non-numeric values + raw_expires_in = body.get("expires_in") + try: + expires_in = int(raw_expires_in) if raw_expires_in is not None else MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL + except (TypeError, ValueError): + expires_in = MCP_OAUTH2_TOKEN_CACHE_DEFAULT_TTL + + ttl = max(expires_in - MCP_OAUTH2_TOKEN_EXPIRY_BUFFER_SECONDS, MCP_OAUTH2_TOKEN_CACHE_MIN_TTL) + + verbose_logger.info( + "Fetched OAuth2 token for MCP server %s (expires in %ds)", + server.server_id, + expires_in, + ) + return access_token, ttl + + def invalidate(self, server_id: str) -> None: + """Remove a cached token (e.g. after a 401).""" + self.delete_cache(server_id) + + +mcp_oauth2_token_cache = MCPOAuth2TokenCache() + + +async def resolve_mcp_auth( + server: "MCPServer", + mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None, +) -> Optional[Union[str, Dict[str, str]]]: + """Resolve the auth value for an MCP server. + + Priority: + 1. ``mcp_auth_header`` — per-request/per-user override + 2. OAuth2 client_credentials token — auto-fetched and cached + 3. ``server.authentication_token`` — static token from config/DB + """ + if mcp_auth_header: + return mcp_auth_header + if server.has_client_credentials: + return await mcp_oauth2_token_cache.async_get_token(server) + return server.authentication_token diff --git a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py index b635f15ed09..deb0b4f9549 100644 --- a/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py +++ b/litellm/proxy/_experimental/mcp_server/openapi_to_mcp_generator.py @@ -3,6 +3,8 @@ This module is used to generate MCP tools from OpenAPI specs. """ import json +import asyncio +import os from pathlib import PurePosixPath from typing import Any, Dict, Optional from urllib.parse import quote @@ -45,8 +47,36 @@ def _sanitize_path_parameter_value(param_value: Any, param_name: str) -> str: def load_openapi_spec(filepath: str) -> Dict[str, Any]: - """Load OpenAPI specification from JSON file.""" - with open(filepath, "r") as f: + """ + Sync wrapper. For URL specs, use the shared/custom MCP httpx client. + """ + try: + # If we're already inside an event loop, prefer the async function. + asyncio.get_running_loop() + raise RuntimeError( + "load_openapi_spec() was called from within a running event loop. " + "Use 'await load_openapi_spec_async(...)' instead." + ) + except RuntimeError as e: + # "no running event loop" is fine; other RuntimeErrors we re-raise + if "no running event loop" not in str(e).lower(): + raise + return asyncio.run(load_openapi_spec_async(filepath)) + +async def load_openapi_spec_async(filepath: str) -> Dict[str, Any]: + if filepath.startswith("http://") or filepath.startswith("https://"): + client = get_async_httpx_client(llm_provider=httpxSpecialProvider.MCP) + # NOTE: do not close shared client if get_async_httpx_client returns a shared singleton. + # If it returns a new client each time, consider wrapping it in an async context manager. + r = await client.get(filepath) + r.raise_for_status() + return r.json() + + # fallback: local file + # Local filesystem path + if not os.path.exists(filepath): + raise FileNotFoundError(f"OpenAPI spec not found at {filepath}") + with open(filepath, "r", encoding="utf-8") as f: return json.load(f) diff --git a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py index d93f852f22d..aed81afd254 100644 --- a/litellm/proxy/_experimental/mcp_server/rest_endpoints.py +++ b/litellm/proxy/_experimental/mcp_server/rest_endpoints.py @@ -1,6 +1,6 @@ import importlib from datetime import datetime -from typing import Dict, List, Optional, Union +from typing import Any, Awaitable, Callable, Dict, List, Optional, Union from fastapi import APIRouter, Depends, HTTPException, Query, Request @@ -10,8 +10,10 @@ from litellm.proxy._experimental.mcp_server.ui_session_utils import ( ) from litellm.proxy._experimental.mcp_server.utils import merge_mcp_headers from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.ip_address_utils import IPAddressUtils from litellm.proxy.auth.user_api_key_auth import user_api_key_auth from litellm.types.mcp import MCPAuth +from litellm.types.utils import CallTypes MCP_AVAILABLE: bool = True try: @@ -28,12 +30,14 @@ router = APIRouter( if MCP_AVAILABLE: from mcp.types import Tool as MCPTool + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) from litellm.proxy._experimental.mcp_server.server import ( ListMCPToolsRestAPIResponseObject, MCPServer, + _tool_name_matches, execute_mcp_tool, filter_tools_by_allowed_tools, ) @@ -76,10 +80,87 @@ if MCP_AVAILABLE: for tool in tools ] + def _extract_mcp_headers_from_request( + request: Request, + mcp_request_handler_cls, + ) -> tuple: + """ + Extract MCP auth headers from HTTP request. + + Returns: + Tuple of (mcp_auth_header, mcp_server_auth_headers, raw_headers) + """ + headers = request.headers + raw_headers = dict(headers) + mcp_auth_header = mcp_request_handler_cls._get_mcp_auth_header_from_headers( + headers + ) + mcp_server_auth_headers = ( + mcp_request_handler_cls._get_mcp_server_auth_headers_from_headers(headers) + ) + return mcp_auth_header, mcp_server_auth_headers, raw_headers + + async def _resolve_allowed_mcp_servers_with_ip_filter( + request: Request, + user_api_key_dict: UserAPIKeyAuth, + server_id: str, + ) -> List[MCPServer]: + """ + Resolve allowed MCP servers for a tool call with IP filtering. + + Args: + request: The HTTP request object + user_api_key_dict: The user's API key auth object + server_id: The server ID to validate access for + + Returns: + List of allowed MCPServer objects + + Raises: + HTTPException: If the server_id is not allowed + """ + # Get all auth contexts + auth_contexts = await build_effective_auth_contexts(user_api_key_dict) + + # Collect allowed server IDs from all contexts, then apply IP filtering + _rest_client_ip = IPAddressUtils.get_mcp_client_ip(request) + allowed_server_ids_set = set() + for auth_context in auth_contexts: + servers = await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth=auth_context, + ) + allowed_server_ids_set.update(servers) + + allowed_server_ids_set = set( + global_mcp_server_manager.filter_server_ids_by_ip( + list(allowed_server_ids_set), _rest_client_ip + ) + ) + + # Check if the specified server_id is allowed + if server_id not in allowed_server_ids_set: + raise HTTPException( + status_code=403, + detail={ + "error": "access_denied", + "message": f"The key is not allowed to access server {server_id}", + }, + ) + + # Build allowed_mcp_servers list (only include allowed servers) + allowed_mcp_servers: List[MCPServer] = [] + for allowed_server_id in allowed_server_ids_set: + server = global_mcp_server_manager.get_mcp_server_by_id(allowed_server_id) + if server is not None: + allowed_mcp_servers.append(server) + + return allowed_mcp_servers + async def _get_tools_for_single_server( server, server_auth_header, raw_headers: Optional[Dict[str, str]] = None, + user_api_key_auth: Optional[UserAPIKeyAuth] = None, ): """Helper function to get tools for a single server.""" tools = await global_mcp_server_manager._get_tools_from_server( @@ -94,8 +175,58 @@ if MCP_AVAILABLE: if server.allowed_tools is not None and len(server.allowed_tools) > 0: tools = filter_tools_by_allowed_tools(tools, server) + # Filter tools based on user_api_key_auth.object_permission.mcp_tool_permissions + # This provides per-key/team/org control over which tools can be accessed + if ( + user_api_key_auth + and user_api_key_auth.object_permission + and user_api_key_auth.object_permission.mcp_tool_permissions + ): + allowed_tools_for_server = ( + user_api_key_auth.object_permission.mcp_tool_permissions.get( + server.server_id + ) + ) + if ( + allowed_tools_for_server is not None + and len(allowed_tools_for_server) > 0 + ): + # Filter tools to only include those in the allowed list + tools = [ + tool + for tool in tools + if _tool_name_matches(tool.name, allowed_tools_for_server) + ] + return _create_tool_response_objects(tools, server.mcp_info) + async def _resolve_allowed_mcp_servers_for_tool_call( + user_api_key_dict: UserAPIKeyAuth, + server_id: str, + ) -> List[MCPServer]: + """Resolve allowed MCP servers for the given user and validate server_id access.""" + auth_contexts = await build_effective_auth_contexts(user_api_key_dict) + allowed_server_ids_set = set() + for auth_context in auth_contexts: + servers = await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth=auth_context + ) + allowed_server_ids_set.update(servers) + if server_id not in allowed_server_ids_set: + raise HTTPException( + status_code=403, + detail={ + "error": "access_denied", + "message": f"The key is not allowed to access server {server_id}", + }, + ) + allowed_mcp_servers: List[MCPServer] = [] + for allowed_server_id in allowed_server_ids_set: + server = global_mcp_server_manager.get_mcp_server_by_id(allowed_server_id) + if server is not None: + allowed_mcp_servers.append(server) + return allowed_mcp_servers + ######################################################## @router.get("/tools/list", dependencies=[Depends(user_api_key_auth)]) async def list_tool_rest_api( @@ -142,21 +273,25 @@ if MCP_AVAILABLE: auth_contexts = await build_effective_auth_contexts(user_api_key_dict) + _rest_client_ip = IPAddressUtils.get_mcp_client_ip(request) + allowed_server_ids_set = set() for auth_context in auth_contexts: servers = await global_mcp_server_manager.get_allowed_mcp_servers( - user_api_key_auth=auth_context + user_api_key_auth=auth_context, ) allowed_server_ids_set.update(servers) - allowed_server_ids = list(allowed_server_ids_set) + allowed_server_ids = global_mcp_server_manager.filter_server_ids_by_ip( + list(allowed_server_ids_set), _rest_client_ip + ) list_tools_result = [] error_message = None # If server_id is specified, only query that specific server if server_id: - if server_id not in allowed_server_ids_set: + if server_id not in allowed_server_ids: raise HTTPException( status_code=403, detail={ @@ -178,7 +313,10 @@ if MCP_AVAILABLE: try: list_tools_result = await _get_tools_for_single_server( - server, server_auth_header, raw_headers_from_request + server, + server_auth_header, + raw_headers_from_request, + user_api_key_dict, ) except Exception as e: verbose_logger.exception( @@ -214,7 +352,10 @@ if MCP_AVAILABLE: try: tools_result = await _get_tools_for_single_server( - server, server_auth_header, raw_headers_from_request + server, + server_auth_header, + raw_headers_from_request, + user_api_key_dict, ) list_tools_result.extend(tools_result) except Exception as e: @@ -261,7 +402,14 @@ if MCP_AVAILABLE: from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( MCPRequestHandler, ) - from litellm.proxy.proxy_server import add_litellm_data_to_request, proxy_config + from litellm.proxy.common_request_processing import ( + ProxyBaseLLMRequestProcessing, + ) + from litellm.proxy.proxy_server import ( + general_settings, + proxy_config, + proxy_logging_obj, + ) try: data = await request.json() @@ -289,28 +437,22 @@ if MCP_AVAILABLE: tool_arguments = data.get("arguments") - data = await add_litellm_data_to_request( - data=data, - request=request, - user_api_key_dict=user_api_key_dict, - proxy_config=proxy_config, + proxy_base_llm_response_processor = ProxyBaseLLMRequestProcessing(data=data) + data, logging_obj = ( + await proxy_base_llm_response_processor.common_processing_pre_call_logic( + request=request, + user_api_key_dict=user_api_key_dict, + proxy_config=proxy_config, + route_type=CallTypes.call_mcp_tool.value, + proxy_logging_obj=proxy_logging_obj, + general_settings=general_settings, + ) ) - # FIX: Extract MCP auth headers from request - # The UI sends bearer token in x-mcp-auth header and server-specific headers, - # but they weren't being extracted and passed to call_mcp_tool. - # This fix ensures auth headers are properly extracted from the HTTP request - # and passed through to the MCP server for authentication. - headers = request.headers - raw_headers_from_request = dict(headers) - mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers( - headers + # Extract MCP auth headers from request and add to data dict + mcp_auth_header, mcp_server_auth_headers, raw_headers_from_request = ( + _extract_mcp_headers_from_request(request, MCPRequestHandler) ) - mcp_server_auth_headers = ( - MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers) - ) - - # Add extracted headers to data dict to pass to call_mcp_tool if mcp_auth_header: data["mcp_auth_header"] = mcp_auth_header if mcp_server_auth_headers: @@ -322,35 +464,10 @@ if MCP_AVAILABLE: if "metadata" in data and "user_api_key_auth" in data["metadata"]: data["user_api_key_auth"] = data["metadata"]["user_api_key_auth"] - # Get all auth contexts - auth_contexts = await build_effective_auth_contexts(user_api_key_dict) - - # Collect allowed server IDs from all contexts - allowed_server_ids_set = set() - for auth_context in auth_contexts: - servers = await global_mcp_server_manager.get_allowed_mcp_servers( - user_api_key_auth=auth_context - ) - allowed_server_ids_set.update(servers) - - # Check if the specified server_id is allowed - if server_id not in allowed_server_ids_set: - raise HTTPException( - status_code=403, - detail={ - "error": "access_denied", - "message": f"The key is not allowed to access server {server_id}", - }, - ) - - # Build allowed_mcp_servers list (only include allowed servers) - allowed_mcp_servers: List[MCPServer] = [] - for allowed_server_id in allowed_server_ids_set: - server = global_mcp_server_manager.get_mcp_server_by_id( - allowed_server_id - ) - if server is not None: - allowed_mcp_servers.append(server) + # Resolve allowed MCP servers with IP filtering + allowed_mcp_servers = await _resolve_allowed_mcp_servers_with_ip_filter( + request, user_api_key_dict, server_id + ) # Call execute_mcp_tool directly (permission checks already done) result = await execute_mcp_tool( @@ -411,24 +528,50 @@ if MCP_AVAILABLE: NewMCPServerRequest, ) + def _extract_credentials( + request: NewMCPServerRequest, + ) -> tuple: + """ + Extract OAuth credentials from the nested ``request.credentials`` dict. + + Returns: + (client_id, client_secret, scopes) — any value may be ``None``. + """ + creds = request.credentials if isinstance(request.credentials, dict) else {} + client_id: Optional[str] = creds.get("client_id") + client_secret: Optional[str] = creds.get("client_secret") + scopes_raw = creds.get("scopes") + scopes: Optional[List[str]] = scopes_raw if isinstance(scopes_raw, list) else None + return client_id, client_secret, scopes + async def _execute_with_mcp_client( request: NewMCPServerRequest, - operation, + operation: Callable[..., Awaitable[Any]], mcp_auth_header: Optional[Union[str, Dict[str, str]]] = None, oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, - ): + ) -> dict: """ - Common helper to create MCP client, execute operation, and ensure proper cleanup. + Create a temporary MCP client from *request*, run *operation*, and return the result. + + For M2M OAuth servers (those with ``client_id``, ``client_secret``, and + ``token_url``), the incoming ``oauth2_headers`` are dropped so that + ``resolve_mcp_auth`` can auto-fetch a token via ``client_credentials``. Args: - request: MCP server configuration - operation: Async function that takes a client and returns the operation result + request: MCP server configuration submitted by the UI. + operation: Async callable that receives the created client and returns a result dict. + mcp_auth_header: Pre-resolved credential header (API-key / bearer token). + oauth2_headers: Headers extracted from the incoming request (may contain the + litellm API key — must NOT be forwarded for M2M servers). + raw_headers: Raw request headers forwarded for stdio env construction. Returns: - Operation result or error response + The dict returned by *operation*, or an error dict on failure. """ try: + client_id, client_secret, scopes = _extract_credentials(request) + server_model = MCPServer( server_id=request.server_id or "", name=request.alias or request.server_name or "", @@ -440,18 +583,30 @@ if MCP_AVAILABLE: args=request.args, env=request.env, static_headers=request.static_headers, + client_id=client_id, + client_secret=client_secret, + token_url=request.token_url, + scopes=scopes, + authorization_url=request.authorization_url, + registration_url=request.registration_url, ) stdio_env = global_mcp_server_manager._build_stdio_env( server_model, raw_headers ) + # For M2M OAuth servers, drop the incoming Authorization header so that + # resolve_mcp_auth can auto-fetch a token via client_credentials. + effective_oauth2_headers = ( + None if server_model.has_client_credentials else oauth2_headers + ) + merged_headers = merge_mcp_headers( - extra_headers=oauth2_headers, + extra_headers=effective_oauth2_headers, static_headers=request.static_headers, ) - client = global_mcp_server_manager._create_mcp_client( + client = await global_mcp_server_manager._create_mcp_client( server=server_model, mcp_auth_header=mcp_auth_header, extra_headers=merged_headers, @@ -460,11 +615,14 @@ if MCP_AVAILABLE: return await operation(client) - except Exception as e: - verbose_logger.error(f"Error in MCP operation: {e}", exc_info=True) + except (KeyboardInterrupt, SystemExit): + raise + except BaseException as e: + verbose_logger.error("Error in MCP operation: %s", e, exc_info=True) return { "status": "error", - "message": "An internal error has occurred while testing the MCP server.", + "error": True, + "message": "Failed to connect to MCP server. Check proxy logs for details.", } @router.post("/test/connection", dependencies=[Depends(user_api_key_auth)]) diff --git a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py index b01bf142385..e5cb6a0098d 100644 --- a/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py +++ b/litellm/proxy/_experimental/mcp_server/semantic_tool_filter.py @@ -81,6 +81,9 @@ class SemanticMCPToolFilter: def _extract_tool_info(self, tool) -> tuple[str, str]: """Extract name and description from MCP tool or OpenAI function dict.""" + name: str + description: str + if isinstance(tool, dict): # OpenAI function format name = tool.get("name", "") diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 79cd88227a9..fa28b6070b3 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -5,12 +5,24 @@ LiteLLM MCP Server Routes import asyncio import contextlib -from datetime import datetime import traceback import uuid -from typing import Any, AsyncIterator, Dict, List, Optional, Tuple, Union, cast, Callable +from datetime import datetime +from typing import ( + Any, + AsyncIterator, + Callable, + Dict, + List, + Optional, + Tuple, + Union, + cast, +) + from fastapi import FastAPI, HTTPException from pydantic import AnyUrl, ConfigDict +from starlette.requests import Request as StarletteRequest from starlette.types import Receive, Scope, Send from litellm._logging import verbose_logger @@ -19,12 +31,19 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( MCPRequestHandler, ) +from litellm.proxy._experimental.mcp_server.discoverable_endpoints import ( + get_request_base_url, +) from litellm.proxy._experimental.mcp_server.utils import ( LITELLM_MCP_SERVER_DESCRIPTION, LITELLM_MCP_SERVER_NAME, LITELLM_MCP_SERVER_VERSION, ) from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.ip_address_utils import IPAddressUtils +from litellm.proxy.litellm_pre_call_utils import ( + LiteLLMProxyRequestSetup, +) from litellm.types.mcp import MCPAuth from litellm.types.mcp_server.mcp_server_manager import MCPInfo, MCPServer from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall @@ -213,6 +232,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( f"MCP list_tools - User API Key Auth from context: {user_api_key_auth}" @@ -276,6 +296,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( @@ -288,7 +309,7 @@ if MCP_AVAILABLE: host_token = getattr(host_ctx.meta, 'progressToken', None) if host_token and hasattr(host_ctx, 'session') and host_ctx.session: host_session = host_ctx.session - + async def forward_progress(progress: float, total: float | None): """Forward progress notifications from external MCP to Host""" try: @@ -300,7 +321,7 @@ if MCP_AVAILABLE: verbose_logger.debug(f"Forwarded progress {progress}/{total} to Host") except Exception as e: verbose_logger.error(f"Failed to forward progress to Host: {e}") - + host_progress_callback = forward_progress verbose_logger.debug(f"Host progressToken captured: {host_token[:8]}...") except Exception as e: @@ -387,6 +408,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( f"MCP list_prompts - User API Key Auth from context: {user_api_key_auth}" @@ -440,6 +462,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( @@ -467,6 +490,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( f"MCP list_resources - User API Key Auth from context: {user_api_key_auth}" @@ -505,6 +529,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() verbose_logger.debug( f"MCP list_resource_templates - User API Key Auth from context: {user_api_key_auth}" @@ -544,6 +569,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers, oauth2_headers, raw_headers, + _client_ip, ) = get_auth_context() read_resource_result = await mcp_read_resource( @@ -702,13 +728,57 @@ if MCP_AVAILABLE: return tools_to_return + def _get_client_ip_from_context() -> Optional[str]: + """ + Extract client_ip from auth context. + Returns None if context not set (caller should handle this as "no IP filtering"). + """ + try: + auth_user = auth_context_var.get() + if auth_user and isinstance(auth_user, MCPAuthenticatedUser): + return auth_user.client_ip + except Exception: + pass + return None + async def _get_allowed_mcp_servers( user_api_key_auth: Optional[UserAPIKeyAuth], mcp_servers: Optional[List[str]], + client_ip: Optional[str] = None, ) -> List[MCPServer]: - """Return allowed MCP servers for a request after applying filters.""" + """Return allowed MCP servers for a request after applying filters. + + Args: + user_api_key_auth: The authenticated user's API key info. + mcp_servers: Optional list of server names to filter to. + client_ip: Client IP for IP-based access control. If None, falls back to + auth context. Pass explicitly from request handlers for safety. + Note: If client_ip is None and auth context is not set, IP filtering is skipped. + This is intentional for internal callers but may indicate a bug if called + from a request handler without proper context setup. + """ + # Use explicit client_ip if provided, otherwise try auth context + if client_ip is None: + client_ip = _get_client_ip_from_context() + if client_ip is None: + verbose_logger.debug( + "MCP _get_allowed_mcp_servers called without client_ip and no auth context. " + "IP filtering will be skipped. This is expected for internal calls." + ) + allowed_mcp_server_ids = ( - await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + await global_mcp_server_manager.get_allowed_mcp_servers( + user_api_key_auth + ) + ) + allowed_mcp_server_ids = ( + global_mcp_server_manager.filter_server_ids_by_ip( + allowed_mcp_server_ids, client_ip + ) + ) + verbose_logger.debug( + "MCP IP filter: client_ip=%s, allowed_server_ids=%s", + client_ip, allowed_mcp_server_ids, ) allowed_mcp_servers: List[MCPServer] = [] for allowed_mcp_server_id in allowed_mcp_server_ids: @@ -775,6 +845,7 @@ if MCP_AVAILABLE: raw_headers: Optional[Dict[str, str]] = None, log_list_tools_to_spendlogs: bool = False, list_tools_log_source: Optional[str] = None, + litellm_trace_id: Optional[str] = None, ) -> List[MCPTool]: """ Helper method to fetch tools from MCP servers based on server filtering criteria. @@ -812,6 +883,7 @@ if MCP_AVAILABLE: "model": "MCP: list_tools", "call_type": CallTypes.list_mcp_tools.value, "litellm_call_id": list_tools_call_id, + "litellm_trace_id": litellm_trace_id, "metadata": { "spend_logs_metadata": spend_logs_metadata, }, @@ -827,13 +899,14 @@ if MCP_AVAILABLE: ], } - # Attach user identifiers when available (matches call_mcp_tool style) + # Attach user identifiers using the standard helper if user_api_key_auth is not None: - user_api_key = getattr(user_api_key_auth, "api_key", None) - if user_api_key: - cast(dict, list_tools_request_data["metadata"])[ - "user_api_key" - ] = user_api_key + + LiteLLMProxyRequestSetup.add_user_api_key_auth_to_request_metadata( + data=list_tools_request_data, + user_api_key_dict=user_api_key_auth, + _metadata_variable_name="metadata", + ) user_identifier = getattr( user_api_key_auth, "end_user_id", None @@ -1715,7 +1788,7 @@ if MCP_AVAILABLE: oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, litellm_logging_obj: Optional[Any] = None, - host_progress_callback: Optional[Callable] = None, + host_progress_callback: Optional[Callable] = None, ) -> CallToolResult: """Handle tool execution for managed server tools""" # Import here to avoid circular import @@ -1891,6 +1964,10 @@ if MCP_AVAILABLE: oauth2_headers, raw_headers, ) = await extract_mcp_auth_context(scope, path) + + # Extract client IP for MCP access control + _client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope)) + verbose_logger.debug( f"MCP request mcp_servers (header/path): {mcp_servers}" ) @@ -1899,12 +1976,12 @@ if MCP_AVAILABLE: ) # https://datatracker.ietf.org/doc/html/rfc9728#name-www-authenticate-response for server_name in mcp_servers or []: - server = global_mcp_server_manager.get_mcp_server_by_name(server_name) + server = global_mcp_server_manager.get_mcp_server_by_name( + server_name, client_ip=_client_ip + ) if server and server.auth_type == MCPAuth.oauth2 and not oauth2_headers: - from starlette.requests import Request - - request = Request(scope) - base_url = str(request.base_url).rstrip("/") + request = StarletteRequest(scope) + base_url = get_request_base_url(request) authorization_uri = ( f"Bearer authorization_uri=" @@ -1925,6 +2002,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers=mcp_server_auth_headers, oauth2_headers=oauth2_headers, raw_headers=raw_headers, + client_ip=_client_ip, ) # Ensure session managers are initialized @@ -1936,12 +2014,13 @@ if MCP_AVAILABLE: _strip_stale_mcp_session_header(scope, session_manager) await session_manager.handle_request(scope, receive, send) + except HTTPException: + # Re-raise HTTP exceptions to preserve status codes and details + raise except Exception as e: - raise e verbose_logger.exception(f"Error handling MCP request: {e}") - # Instead of re-raising, try to send a graceful error response + # Try to send a graceful error response for non-HTTP exceptions try: - # Send a proper HTTP error response instead of letting the exception bubble up from starlette.responses import JSONResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR @@ -1969,6 +2048,10 @@ if MCP_AVAILABLE: oauth2_headers, raw_headers, ) = await extract_mcp_auth_context(scope, path) + + # Extract client IP for MCP access control + _sse_client_ip = IPAddressUtils.get_mcp_client_ip(StarletteRequest(scope)) + verbose_logger.debug( f"MCP request mcp_servers (header/path): {mcp_servers}" ) @@ -1982,6 +2065,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers=mcp_server_auth_headers, oauth2_headers=oauth2_headers, raw_headers=raw_headers, + client_ip=_sse_client_ip, ) if not _SESSION_MANAGERS_INITIALIZED: @@ -2045,6 +2129,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None, oauth2_headers: Optional[Dict[str, str]] = None, raw_headers: Optional[Dict[str, str]] = None, + client_ip: Optional[str] = None, ) -> None: """ Set the UserAPIKeyAuth in the auth context variable. @@ -2054,6 +2139,7 @@ if MCP_AVAILABLE: mcp_auth_header: MCP auth header to be passed to the MCP server (deprecated) mcp_servers: Optional list of server names and access groups to filter by mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value} + client_ip: Client IP address for MCP access control """ auth_user = MCPAuthenticatedUser( user_api_key_auth=user_api_key_auth, @@ -2062,6 +2148,7 @@ if MCP_AVAILABLE: mcp_server_auth_headers=mcp_server_auth_headers, oauth2_headers=oauth2_headers, raw_headers=raw_headers, + client_ip=client_ip, ) auth_context_var.set(auth_user) @@ -2073,14 +2160,15 @@ if MCP_AVAILABLE: Optional[Dict[str, Dict[str, str]]], Optional[Dict[str, str]], Optional[Dict[str, str]], + Optional[str], ] ): """ Get the UserAPIKeyAuth from the auth context variable. Returns: - Tuple[Optional[UserAPIKeyAuth], Optional[str], Optional[List[str]], Optional[Dict[str, str]]]: - UserAPIKeyAuth object, MCP auth header (deprecated), MCP servers (can include access groups), and server-specific auth headers + Tuple containing: UserAPIKeyAuth, MCP auth header (deprecated), + MCP servers, server-specific auth headers, OAuth2 headers, raw headers, client IP """ auth_user = auth_context_var.get() if auth_user and isinstance(auth_user, MCPAuthenticatedUser): @@ -2091,8 +2179,9 @@ if MCP_AVAILABLE: auth_user.mcp_server_auth_headers, auth_user.oauth2_headers, auth_user.raw_headers, + auth_user.client_ip, ) - return None, None, None, None, None, None + return None, None, None, None, None, None, None ######################################################## ############ End of Auth Context Functions ############# diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html deleted file mode 100644 index a8bd30680ab..00000000000 --- a/litellm/proxy/_experimental/out/404.html +++ /dev/null @@ -1 +0,0 @@ -404: This page could not be found.LiteLLM Dashboard

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", + }, + { + "role": "user", + "content": "", + }, + { + "role": "user", + "content": "", + }, + ] + + for msg in test_messages: + test_payload = { + "model": "gpt-4o", + "messages": [ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": "Hello! How can I help?"}, + msg, + ], + } + + mock_request = MagicMock() + mock_request.body = AsyncMock(return_value=orjson.dumps(test_payload)) + mock_request.headers = {"content-type": "application/json"} + mock_request.scope = {} + + result = await _read_request_body(mock_request) + + assert result["model"] == "gpt-4o" + assert len(result["messages"]) == 3 + assert result["messages"][2]["content"] == msg["content"], ( + f"Message content with HTML was modified during parsing: " + f"expected={msg['content']!r}, got={result['messages'][2]['content']!r}" + ) diff --git a/tests/test_litellm/proxy/db/db_transaction_queue/test_spend_update_queue.py b/tests/test_litellm/proxy/db/db_transaction_queue/test_spend_update_queue.py index 9993b25dfdd..0ed5940dd75 100644 --- a/tests/test_litellm/proxy/db/db_transaction_queue/test_spend_update_queue.py +++ b/tests/test_litellm/proxy/db/db_transaction_queue/test_spend_update_queue.py @@ -225,6 +225,39 @@ async def test_aggregate_queue_updates_accuracy(spend_queue): assert aggregated["team_list_transactions"]["team1"] == 5.0 +def test_get_aggregated_spend_update_queue_item_does_not_mutate_original_updates( + spend_queue, +): + original_update: SpendUpdateQueueItem = { + "entity_type": Litellm_EntityType.USER, + "entity_id": "user1", + "response_cost": 10.0, + } + duplicate_key_update: SpendUpdateQueueItem = { + "entity_type": Litellm_EntityType.USER, + "entity_id": "user1", + "response_cost": 20.0, + } + + aggregated_updates = spend_queue._get_aggregated_spend_update_queue_item( + [original_update, duplicate_key_update] + ) + user1_aggregated_update = next( + ( + update + for update in aggregated_updates + if update.get("entity_type") == Litellm_EntityType.USER + and update.get("entity_id") == "user1" + ), + None, + ) + + assert original_update["response_cost"] == 10.0 + assert user1_aggregated_update is not None + assert user1_aggregated_update["response_cost"] == 30.0 + assert user1_aggregated_update is not original_update + + @pytest.mark.asyncio async def test_queue_size_reduction_with_large_volume(monkeypatch, spend_queue): """Test that queue size is actually reduced when dealing with many items""" diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index 72403b0ba7b..6ccecf59eed 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -132,7 +132,7 @@ async def test_update_daily_spend_with_null_entity_id(): assert create_data["model"] == "gpt-4" assert create_data["custom_llm_provider"] == "openai" assert create_data["mcp_namespaced_tool_name"] == "" - assert create_data["endpoint"] is None + assert create_data["endpoint"] == "" assert create_data["prompt_tokens"] == 10 assert create_data["completion_tokens"] == 20 assert create_data["spend"] == 0.1 @@ -194,7 +194,7 @@ async def test_update_daily_spend_sorting(): "model_group": None, "mcp_namespaced_tool_name": "", "custom_llm_provider": "openai", - "endpoint": None, + "endpoint": "", "prompt_tokens": 10, "completion_tokens": 20, "spend": 0.1, @@ -838,4 +838,126 @@ async def test_endpoint_field_is_correctly_mapped_from_call_type(): assert transaction["date"] == "2024-01-01" assert transaction["api_key"] == "test-key" assert transaction["model"] == "gpt-4" - assert transaction["custom_llm_provider"] == "openai" \ No newline at end of file + assert transaction["custom_llm_provider"] == "openai" + + +@pytest.mark.asyncio +async def test_update_daily_spend_logs_detailed_error_on_batch_upsert_failure(): + """ + Test that when batch upsert fails, detailed error information is logged. + This ensures proper debugging information is available for issues like unique constraint violations. + """ + from litellm._logging import verbose_proxy_logger + + # Setup + mock_prisma_client = MagicMock() + mock_batcher = MagicMock() + mock_table = MagicMock() + mock_batch_context = MagicMock() + mock_batch_context.__aenter__ = AsyncMock(return_value=mock_batcher) + mock_batcher.litellm_dailyuserspend = mock_table + + # Make the batch context manager's exit raise an exception + # This simulates a batch commit failure (e.g., unique constraint violation) + test_exception = Exception("Unique constraint violation") + mock_batch_context.__aexit__ = AsyncMock(side_effect=test_exception) + mock_prisma_client.db.batch_.return_value = mock_batch_context + + # Create a transaction + daily_spend_transactions = { + "test_key": { + "user_id": "test-user", + "date": "2024-01-01", + "api_key": "test-api-key", + "model": "gpt-4", + "custom_llm_provider": "openai", + "prompt_tokens": 10, + "completion_tokens": 20, + "spend": 0.1, + "api_requests": 1, + "successful_requests": 1, + "failed_requests": 0, + } + } + + # Create a mock proxy_logging_obj with failure_handler as AsyncMock + mock_proxy_logging = MagicMock() + mock_proxy_logging.failure_handler = AsyncMock() + + # Mock the logger to capture exception calls + with patch.object(verbose_proxy_logger, 'exception') as mock_exception_logger: + # Call the method and expect it to raise the exception + with pytest.raises(Exception, match="Unique constraint violation"): + await DBSpendUpdateWriter._update_daily_spend( + n_retry_times=0, # No retries to make test faster + prisma_client=mock_prisma_client, + proxy_logging_obj=mock_proxy_logging, + daily_spend_transactions=daily_spend_transactions, + entity_type="user", + entity_id_field="user_id", + table_name="litellm_dailyuserspend", + unique_constraint_name="user_id_date_api_key_model_custom_llm_provider_mcp_namespaced_tool_name_endpoint", + ) + + # Verify that exception was logged with detailed information + assert mock_exception_logger.called + call_args = mock_exception_logger.call_args[0][0] + assert "Daily user spend batch upsert failed" in call_args + assert "Table: litellm_dailyuserspend" in call_args + assert "Constraint: user_id_date_api_key_model_custom_llm_provider_mcp_namespaced_tool_name_endpoint" in call_args + assert "Batch size: 1" in call_args + assert "Unique constraint violation" in call_args + + +@pytest.mark.asyncio +async def test_update_daily_spend_re_raises_exception_after_logging(): + """ + Test that when batch upsert fails, the exception is properly re-raised after logging. + This ensures that error handling continues to work correctly upstream. + """ + # Setup + mock_prisma_client = MagicMock() + mock_batcher = MagicMock() + mock_table = MagicMock() + mock_batch_context = MagicMock() + mock_batch_context.__aenter__ = AsyncMock(return_value=mock_batcher) + mock_batcher.litellm_dailyuserspend = mock_table + + # Create a transaction + daily_spend_transactions = { + "test_key": { + "user_id": "test-user", + "date": "2024-01-01", + "api_key": "test-api-key", + "model": "gpt-4", + "custom_llm_provider": "openai", + "prompt_tokens": 10, + "completion_tokens": 20, + "spend": 0.1, + "api_requests": 1, + "successful_requests": 1, + "failed_requests": 0, + } + } + + # Create a custom exception to verify it's re-raised + custom_exception = ValueError("Database connection lost") + mock_batch_context.__aexit__ = AsyncMock(side_effect=custom_exception) + mock_prisma_client.db.batch_.return_value = mock_batch_context + + # Create a mock proxy_logging_obj with failure_handler as AsyncMock + mock_proxy_logging = MagicMock() + mock_proxy_logging.failure_handler = AsyncMock() + + # Verify the exception is re-raised + with pytest.raises(ValueError, match="Database connection lost"): + await DBSpendUpdateWriter._update_daily_spend( + n_retry_times=0, # No retries to make test faster + prisma_client=mock_prisma_client, + proxy_logging_obj=mock_proxy_logging, + daily_spend_transactions=daily_spend_transactions, + entity_type="user", + entity_id_field="user_id", + table_name="litellm_dailyuserspend", + unique_constraint_name="user_id_date_api_key_model_custom_llm_provider_mcp_namespaced_tool_name_endpoint", + ) \ No newline at end of file diff --git a/tests/test_litellm/proxy/discovery_endpoints/test_ui_discovery_endpoints.py b/tests/test_litellm/proxy/discovery_endpoints/test_ui_discovery_endpoints.py index 88d31e993dd..9d0c771e1d9 100644 --- a/tests/test_litellm/proxy/discovery_endpoints/test_ui_discovery_endpoints.py +++ b/tests/test_litellm/proxy/discovery_endpoints/test_ui_discovery_endpoints.py @@ -31,6 +31,7 @@ def test_ui_discovery_endpoints_with_defaults(): assert data["proxy_base_url"] is None assert data["auto_redirect_to_sso"] is False assert data["admin_ui_disabled"] is False + assert data["sso_configured"] is False def test_ui_discovery_endpoints_with_custom_server_root_path(): @@ -50,6 +51,7 @@ def test_ui_discovery_endpoints_with_custom_server_root_path(): assert data["server_root_path"] == "/litellm" assert data["proxy_base_url"] is None assert data["auto_redirect_to_sso"] is False + assert data["sso_configured"] is False def test_ui_discovery_endpoints_with_proxy_base_url_when_set(): @@ -69,6 +71,7 @@ def test_ui_discovery_endpoints_with_proxy_base_url_when_set(): assert data["server_root_path"] == "/" assert data["proxy_base_url"] == "https://proxy.example.com" assert data["auto_redirect_to_sso"] is False + assert data["sso_configured"] is False def test_ui_discovery_endpoints_with_sso_configured_and_auto_redirect_enabled(): @@ -88,6 +91,30 @@ def test_ui_discovery_endpoints_with_sso_configured_and_auto_redirect_enabled(): assert data["server_root_path"] == "/litellm" assert data["proxy_base_url"] == "https://proxy.example.com" assert data["auto_redirect_to_sso"] is True + assert data["sso_configured"] is True + + +def test_ui_discovery_endpoints_with_sso_configured_and_auto_redirect_not_set_defaults_to_false(): + """When SSO is configured but AUTO_REDIRECT_UI_LOGIN_TO_SSO is not set, defaults to False.""" + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + with patch("litellm.proxy.utils.get_server_root_path", return_value="/litellm"), \ + patch("litellm.proxy.utils.get_proxy_base_url", return_value="https://proxy.example.com"), \ + patch("litellm.proxy.auth.auth_utils._has_user_setup_sso", return_value=True), \ + patch.dict(os.environ, {"DISABLE_ADMIN_UI": "false"}, clear=False): + # Ensure AUTO_REDIRECT_UI_LOGIN_TO_SSO is not set (simulate default) + os.environ.pop("AUTO_REDIRECT_UI_LOGIN_TO_SSO", None) + + response = client.get("/.well-known/litellm-ui-config") + + assert response.status_code == 200 + data = response.json() + assert data["server_root_path"] == "/litellm" + assert data["proxy_base_url"] == "https://proxy.example.com" + assert data["auto_redirect_to_sso"] is False + assert data["sso_configured"] is True def test_ui_discovery_endpoints_with_sso_configured_but_auto_redirect_disabled(): @@ -107,6 +134,7 @@ def test_ui_discovery_endpoints_with_sso_configured_but_auto_redirect_disabled() assert data["server_root_path"] == "/litellm" assert data["proxy_base_url"] == "https://proxy.example.com" assert data["auto_redirect_to_sso"] is False + assert data["sso_configured"] is True def test_ui_discovery_endpoints_with_sso_not_configured_but_auto_redirect_enabled(): @@ -126,6 +154,7 @@ def test_ui_discovery_endpoints_with_sso_not_configured_but_auto_redirect_enable assert data["server_root_path"] == "/" assert data["proxy_base_url"] is None assert data["auto_redirect_to_sso"] is False + assert data["sso_configured"] is False def test_ui_discovery_endpoints_both_routes_return_same_data(): @@ -164,6 +193,7 @@ def test_ui_discovery_endpoints_with_admin_ui_disabled(): assert data["proxy_base_url"] is None assert data["auto_redirect_to_sso"] is False assert data["admin_ui_disabled"] is True + assert data["sso_configured"] is False def test_ui_discovery_endpoints_with_admin_ui_enabled(): @@ -184,4 +214,5 @@ def test_ui_discovery_endpoints_with_admin_ui_enabled(): assert data["proxy_base_url"] is None assert data["auto_redirect_to_sso"] is False assert data["admin_ui_disabled"] is False + assert data["sso_configured"] is False diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py index be8c84a554f..a0d92a0fa6f 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py @@ -920,3 +920,212 @@ class TestContentFilterGuardrail: assert ( "matched_text" not in detection ), "Sensitive content should not be logged" + + @pytest.mark.asyncio + async def test_harm_toxic_abuse_blocks_abusive_input(self): + """ + Test that harm_toxic_abuse content category blocks abusive/toxic input + including censored profanity, misspellings, and harmful phrases. + """ + guardrail = ContentFilterGuardrail( + guardrail_name="test-toxic-abuse", + categories=[ + { + "category": "harm_toxic_abuse", + "enabled": True, + "action": "BLOCK", + "severity_threshold": "medium", + } + ], + ) + + toxic_input = ( + "You stupid f**ing piece of sht AI, why are you so useless? " + "Go kill yourself you worthless bot." + ) + + with pytest.raises(HTTPException) as exc_info: + await guardrail.apply_guardrail( + inputs={"texts": [toxic_input]}, + request_data={}, + input_type="request", + ) + + assert exc_info.value.status_code == 403 + detail = exc_info.value.detail + if isinstance(detail, dict): + assert detail.get("category") == "harm_toxic_abuse" + else: + assert "harm_toxic_abuse" in str(detail) + + @pytest.mark.asyncio + async def test_harm_toxic_abuse_blocks_sht_ai(self): + """Test that harm_toxic_abuse blocks input containing 'sht AI' (phrase or word sht).""" + guardrail = ContentFilterGuardrail( + guardrail_name="test-toxic-abuse-sht", + categories=[ + { + "category": "harm_toxic_abuse", + "enabled": True, + "action": "BLOCK", + "severity_threshold": "medium", + } + ], + ) + + with pytest.raises(HTTPException) as exc_info: + await guardrail.apply_guardrail( + inputs={"texts": ["sht AI"]}, + request_data={}, + input_type="request", + ) + + assert exc_info.value.status_code == 403 + detail = exc_info.value.detail + if isinstance(detail, dict): + assert detail.get("category") == "harm_toxic_abuse" + else: + assert "harm_toxic_abuse" in str(detail) + async def test_html_tags_in_messages_not_blocked(self): + """ + Test that HTML tags like ", + "", + "Can you explain what
", + "", + "", + "The ", + "
click
", + ] + + for pattern_name in PREBUILT_PATTERNS: + compiled = get_compiled_pattern(pattern_name) + for test_string in html_test_strings: + match = compiled.search(test_string) + if match: + # Some patterns may legitimately match substrings + # (e.g., URL pattern matching src='https://...') + # but they should not match the script/HTML tag itself + matched_text = match.group() + assert "' in " + f"test string: {test_string!r}. " + f"LLM message content should not be blocked for HTML tags." + ) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py index 8957b534ea8..cebba2ff5e1 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/openai/test_moderations.py @@ -83,7 +83,9 @@ async def test_openai_moderation_guardrail_adds_to_litellm_callbacks(): @pytest.mark.asyncio async def test_openai_moderation_guardrail_safe_content(): - """Test OpenAI moderation guardrail with safe content""" + """Test OpenAI moderation guardrail with safe content via apply_guardrail""" + from litellm.types.utils import GenericGuardrailAPIInputs + with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}): guardrail = OpenAIModerationGuardrail( guardrail_name="test-openai-moderation", @@ -122,28 +124,86 @@ async def test_openai_moderation_guardrail_safe_content(): ) with patch.object(guardrail, 'async_make_request', return_value=mock_response): - # Test pre-call hook with safe content - user_api_key_dict = UserAPIKeyAuth(api_key="test") - data = { - "messages": [ + # Test apply_guardrail with safe content using structured_messages + inputs = GenericGuardrailAPIInputs( + structured_messages=[ {"role": "user", "content": "Hello, how are you today?"} ] - } - - result = await guardrail.async_pre_call_hook( - user_api_key_dict=user_api_key_dict, - cache=None, - data=data, - call_type="completion" ) - # Should return the original data unchanged - assert result == data + result = await guardrail.apply_guardrail( + inputs=inputs, + request_data={"messages": [{"role": "user", "content": "Hello, how are you today?"}]}, + input_type="request" + ) + + # Should return the original inputs unchanged + assert result == inputs + + +@pytest.mark.asyncio +async def test_openai_moderation_guardrail_apply_guardrail(): + """Test OpenAI moderation guardrail apply_guardrail method (unified guardrail interface)""" + from litellm.types.utils import GenericGuardrailAPIInputs + + with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}): + guardrail = OpenAIModerationGuardrail( + guardrail_name="test-openai-moderation", + ) + + # Mock safe moderation response + mock_response = OpenAIModerationResponse( + id="modr-123", + model="omni-moderation-latest", + results=[ + OpenAIModerationResult( + flagged=False, + categories={ + "sexual": False, + "hate": False, + "harassment": False, + "self-harm": False, + "violence": False, + }, + category_scores={ + "sexual": 0.001, + "hate": 0.001, + "harassment": 0.001, + "self-harm": 0.001, + "violence": 0.001, + }, + category_applied_input_types={ + "sexual": [], + "hate": [], + "harassment": [], + "self-harm": [], + "violence": [], + } + ) + ] + ) + + with patch.object(guardrail, 'async_make_request', return_value=mock_response): + # Test apply_guardrail with texts (embeddings-style input) + inputs = GenericGuardrailAPIInputs( + texts=["Hello, how are you?", "What is the weather?"] + ) + + result = await guardrail.apply_guardrail( + inputs=inputs, + request_data={}, + input_type="request", + ) + + # Should return inputs unchanged (moderation doesn't modify, only blocks) + assert result == inputs @pytest.mark.asyncio async def test_openai_moderation_guardrail_harmful_content(): - """Test OpenAI moderation guardrail with harmful content""" + """Test OpenAI moderation guardrail with harmful content via apply_guardrail""" + from litellm.types.utils import GenericGuardrailAPIInputs + with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}): guardrail = OpenAIModerationGuardrail( guardrail_name="test-openai-moderation", @@ -182,22 +242,20 @@ async def test_openai_moderation_guardrail_harmful_content(): ) with patch.object(guardrail, 'async_make_request', return_value=mock_response): - # Test pre-call hook with harmful content - user_api_key_dict = UserAPIKeyAuth(api_key="test") - data = { - "messages": [ + # Test apply_guardrail with harmful content using structured_messages + inputs = GenericGuardrailAPIInputs( + structured_messages=[ {"role": "user", "content": "This is hateful content"} ] - } + ) # Should raise HTTPException from fastapi import HTTPException with pytest.raises(HTTPException) as exc_info: - await guardrail.async_pre_call_hook( - user_api_key_dict=user_api_key_dict, - cache=None, - data=data, - call_type="completion" + await guardrail.apply_guardrail( + inputs=inputs, + request_data={"messages": [{"role": "user", "content": "This is hateful content"}]}, + input_type="request" ) assert exc_info.value.status_code == 400 diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py index 5c039141928..7d2b6e84de7 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py @@ -14,10 +14,14 @@ import pytest import litellm from litellm import ModelResponse from litellm.exceptions import GuardrailRaisedException +from litellm._version import version as litellm_version from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.generic_guardrail_api import ( GenericGuardrailAPI, ) +from litellm.proxy.guardrails.guardrail_hooks.generic_guardrail_api.generic_guardrail_api import ( + _HEADER_PRESENT_PLACEHOLDER, +) from litellm.types.utils import Choices, Message @@ -351,6 +355,58 @@ class TestMetadataExtraction: # Should be empty dict assert request_metadata == {} + @pytest.mark.asyncio + async def test_inbound_headers_and_litellm_version_forwarded_and_sanitized( + self, generic_guardrail, mock_request_data_input + ): + """ + Ensure inbound proxy request headers are forwarded in JSON payload with allowlist: + allowed headers show their value; all other headers show presence only ([present]). + """ + # Add proxy_server_request headers as they exist in proxy request context + request_data = dict(mock_request_data_input) + request_data["proxy_server_request"] = { + "headers": { + "User-Agent": "OpenAI/Python 2.17.0", + "Authorization": "Bearer should-not-forward", + "Cookie": "session=should-not-forward", + "X-Request-Id": "req_123", + } + } + + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["test"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + await generic_guardrail.apply_guardrail( + inputs={"texts": ["test"]}, + request_data=request_data, + input_type="request", + ) + + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + + # New fields should exist + assert json_payload["litellm_version"] == litellm_version + assert "request_headers" in json_payload + assert isinstance(json_payload["request_headers"], dict) + req_headers = json_payload["request_headers"] + + # Allowed: value forwarded + assert req_headers.get("User-Agent") == "OpenAI/Python 2.17.0" + + # Not on allowlist: key present, value is placeholder only + assert req_headers.get("Authorization") == _HEADER_PRESENT_PLACEHOLDER + assert req_headers.get("Cookie") == _HEADER_PRESENT_PLACEHOLDER + assert req_headers.get("X-Request-Id") == _HEADER_PRESENT_PLACEHOLDER + class TestGuardrailActions: """Test different guardrail action responses""" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_grayswan.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_grayswan.py index 6dc658827bc..109ad0bfdc8 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_grayswan.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_grayswan.py @@ -34,8 +34,9 @@ def test_prepare_payload_uses_dynamic_overrides( "policy_id": "dynamic-policy", "reasoning_mode": "thinking", } + request_data = {} - payload = grayswan_guardrail._prepare_payload(messages, dynamic_body) + payload = grayswan_guardrail._prepare_payload(messages, dynamic_body, request_data) assert payload["messages"] == messages assert payload["categories"] == {"custom": "override"} @@ -47,14 +48,27 @@ def test_prepare_payload_falls_back_to_guardrail_defaults( grayswan_guardrail: GraySwanGuardrail, ) -> None: messages = [{"role": "user", "content": "hello"}] + request_data = {} - payload = grayswan_guardrail._prepare_payload(messages, {}) + payload = grayswan_guardrail._prepare_payload(messages, {}, request_data) assert payload["categories"] == {"safety": "general policy"} assert payload["policy_id"] == "default-policy" assert payload["reasoning_mode"] == "hybrid" +def test_prepare_payload_includes_dynamic_metadata( + grayswan_guardrail: GraySwanGuardrail, +) -> None: + messages = [{"role": "user", "content": "hello"}] + dynamic_body = {"metadata": {"trace_id": "trace-123", "tags": ["a", "b"]}} + request_data = {} + + payload = grayswan_guardrail._prepare_payload(messages, dynamic_body, request_data) + + assert payload["metadata"] == dynamic_body["metadata"] + + def test_process_response_does_not_block_under_threshold( grayswan_guardrail: GraySwanGuardrail, ) -> None: @@ -160,6 +174,119 @@ async def test_run_guardrail_raises_api_error( await grayswan_guardrail.run_grayswan_guardrail(payload) +@pytest.mark.asyncio +async def test_apply_guardrail_passthrough_not_swallowed_by_fail_open( + monkeypatch, +) -> None: + guardrail = GraySwanGuardrail( + guardrail_name="grayswan-passthrough", + api_key="test-key", + on_flagged_action="passthrough", + violation_threshold=0.2, + fail_open=True, + event_hook=GuardrailEventHooks.pre_call, + ) + + async def _fake_call(_payload: dict): + return {"violation": 0.92, "violated_rule_descriptions": []} + + monkeypatch.setattr(guardrail, "_call_grayswan_api", _fake_call) + + with pytest.raises(ModifyResponseException): + await guardrail.apply_guardrail( + inputs={"texts": ["bad"]}, + request_data={"model": "gpt-4"}, + input_type="request", + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_block_not_swallowed_by_fail_open( + monkeypatch, +) -> None: + guardrail = GraySwanGuardrail( + guardrail_name="grayswan-block", + api_key="test-key", + on_flagged_action="block", + violation_threshold=0.2, + fail_open=True, + event_hook=GuardrailEventHooks.pre_call, + ) + + async def _fake_call(_payload: dict): + return {"violation": 0.92, "violated_rule_descriptions": []} + + monkeypatch.setattr(guardrail, "_call_grayswan_api", _fake_call) + + with pytest.raises(HTTPException): + await guardrail.apply_guardrail( + inputs={"texts": ["bad"]}, + request_data={"model": "gpt-4"}, + input_type="request", + ) + + +@pytest.mark.asyncio +async def test_apply_guardrail_non_grayswan_http_exception_fail_open_true( + monkeypatch, +) -> None: + guardrail = GraySwanGuardrail( + guardrail_name="grayswan-error", + api_key="test-key", + on_flagged_action="monitor", + violation_threshold=0.2, + fail_open=True, + event_hook=GuardrailEventHooks.pre_call, + ) + + async def _fake_call(_payload: dict): + return {"violation": 0.0, "violated_rule_descriptions": []} + + def _fake_process(**_kwargs): + raise HTTPException(status_code=500, detail={"error": "upstream failed"}) + + monkeypatch.setattr(guardrail, "_call_grayswan_api", _fake_call) + monkeypatch.setattr(guardrail, "_process_response_internal", _fake_process) + + result = await guardrail.apply_guardrail( + inputs={"texts": ["ok"]}, + request_data={"model": "gpt-4"}, + input_type="request", + ) + + assert result["texts"] == ["ok"] + + +@pytest.mark.asyncio +async def test_apply_guardrail_non_grayswan_http_exception_fail_open_false( + monkeypatch, +) -> None: + guardrail = GraySwanGuardrail( + guardrail_name="grayswan-error", + api_key="test-key", + on_flagged_action="monitor", + violation_threshold=0.2, + fail_open=False, + event_hook=GuardrailEventHooks.pre_call, + ) + + async def _fake_call(_payload: dict): + return {"violation": 0.0, "violated_rule_descriptions": []} + + def _fake_process(**_kwargs): + raise HTTPException(status_code=500, detail={"error": "upstream failed"}) + + monkeypatch.setattr(guardrail, "_call_grayswan_api", _fake_call) + monkeypatch.setattr(guardrail, "_process_response_internal", _fake_process) + + with pytest.raises(GraySwanGuardrailAPIError): + await guardrail.apply_guardrail( + inputs={"texts": ["ok"]}, + request_data={"model": "gpt-4"}, + input_type="request", + ) + + def test_process_response_passthrough_raises_exception_in_pre_call() -> None: """Test that passthrough mode raises ModifyResponseException in pre_call hook.""" guardrail = GraySwanGuardrail( diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py index 5ec9b13408e..f01c23f7116 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py @@ -6,6 +6,7 @@ Tests PII detection and masking for different message formats import asyncio import os import sys +from contextlib import asynccontextmanager from unittest.mock import MagicMock, patch import pytest @@ -18,10 +19,41 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.presidio import ( _OPTIONAL_PresidioPIIMasking, ) +from litellm.exceptions import GuardrailRaisedException from litellm.types.guardrails import LitellmParams, PiiAction, PiiEntityType from litellm.types.utils import Choices, Message, ModelResponse +def _make_mock_session_iterator(json_response): + """Create a mock _get_session_iterator that yields a session returning json_response.""" + + @asynccontextmanager + async def mock_iterator(): + class MockResponse: + async def json(self): + return json_response + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + pass + + class MockSession: + def post(self, *args, **kwargs): + return MockResponse() + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + pass + + yield MockSession() + + return mock_iterator + + @pytest.fixture def presidio_guardrail(): """Create a Presidio guardrail instance for testing""" @@ -889,39 +921,134 @@ async def test_analyze_text_error_dict_handling(): output_parse_pii=False, ) - # Mock the HTTP response to return error dict - class MockResponse: - async def json(self): - return {"error": "No text provided"} - - async def __aenter__(self): - return self - - async def __aexit__(self, *args): - pass - - class MockSession: - def post(self, *args, **kwargs): - return MockResponse() - - async def __aenter__(self): - return self - - async def __aexit__(self, *args): - pass - - with patch("aiohttp.ClientSession", return_value=MockSession()): + with patch.object( + presidio, + "_get_session_iterator", + _make_mock_session_iterator({"error": "No text provided"}), + ): result = await presidio.analyze_text( text="some text", presidio_config=None, request_data={}, ) - # Should return empty list when error dict is received - assert result == [], "Error dict should be handled gracefully" + assert result == [], "Error dict should be handled gracefully" print("✓ analyze_text error dict handling test passed") +@pytest.mark.asyncio +async def test_analyze_text_string_response_handling(): + """ + Test that analyze_text handles string responses from Presidio API. + + When Presidio returns a string (e.g. error message from websearch/hosted models), + should handle gracefully instead of crashing with TypeError about mapping vs str. + """ + presidio = _OPTIONAL_PresidioPIIMasking( + presidio_analyzer_api_base="http://mock-presidio:5002/", + presidio_anonymizer_api_base="http://mock-presidio:5001/", + output_parse_pii=False, + ) + + with patch.object( + presidio, + "_get_session_iterator", + _make_mock_session_iterator("Internal Server Error"), + ): + result = await presidio.analyze_text( + text="some text", + presidio_config=None, + request_data={}, + ) + assert result == [], "String response should be handled gracefully" + + +@pytest.mark.asyncio +async def test_analyze_text_invalid_response_raises_when_block_configured(): + """ + When pii_entities_config has BLOCK and Presidio returns invalid response, + should raise GuardrailRaisedException (fail-closed) rather than silently allowing content. + """ + presidio = _OPTIONAL_PresidioPIIMasking( + presidio_analyzer_api_base="http://mock-presidio:5002/", + presidio_anonymizer_api_base="http://mock-presidio:5001/", + output_parse_pii=False, + pii_entities_config={PiiEntityType.CREDIT_CARD: PiiAction.BLOCK}, + ) + + with patch.object( + presidio, + "_get_session_iterator", + _make_mock_session_iterator("Internal Server Error"), + ): + with pytest.raises(GuardrailRaisedException) as exc_info: + await presidio.analyze_text( + text="some text", + presidio_config=None, + request_data={}, + ) + assert "BLOCK" in str(exc_info.value) or "Presidio" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_analyze_text_invalid_response_raises_when_mask_configured(): + """ + When pii_entities_config has MASK and Presidio returns invalid response, + should raise GuardrailRaisedException (fail-closed) because PII masking is expected. + """ + presidio = _OPTIONAL_PresidioPIIMasking( + presidio_analyzer_api_base="http://mock-presidio:5002/", + presidio_anonymizer_api_base="http://mock-presidio:5001/", + output_parse_pii=False, + pii_entities_config={PiiEntityType.CREDIT_CARD: PiiAction.MASK}, + ) + + with patch.object( + presidio, + "_get_session_iterator", + _make_mock_session_iterator("Internal Server Error"), + ): + with pytest.raises(GuardrailRaisedException) as exc_info: + await presidio.analyze_text( + text="some text", + presidio_config=None, + request_data={}, + ) + assert "PII protection is configured" in str(exc_info.value) + + +@pytest.mark.asyncio +async def test_analyze_text_list_with_non_dict_items(): + """ + Test that analyze_text skips non-dict items in the result list. + + When Presidio returns a list containing strings (malformed response), + should skip invalid items and return parsed valid ones. + """ + presidio = _OPTIONAL_PresidioPIIMasking( + presidio_analyzer_api_base="http://mock-presidio:5002/", + presidio_anonymizer_api_base="http://mock-presidio:5001/", + output_parse_pii=False, + ) + + json_response = [ + {"entity_type": "PERSON", "start": 0, "end": 5, "score": 0.9}, + "invalid_string_item", + {"entity_type": "EMAIL", "start": 10, "end": 25, "score": 0.85}, + ] + with patch.object( + presidio, "_get_session_iterator", _make_mock_session_iterator(json_response) + ): + result = await presidio.analyze_text( + text="some text", + presidio_config=None, + request_data={}, + ) + assert len(result) == 2, "Should parse 2 valid dict items and skip the string" + assert result[0].get("entity_type") == "PERSON" + assert result[1].get("entity_type") == "EMAIL" + + @pytest.mark.asyncio async def test_tool_calling_complete_scenario( presidio_guardrail, mock_user_api_key, mock_cache diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py index 2a33c56b56a..b41cded1d0a 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/unified_guardrails/test_unified_guardrail.py @@ -8,12 +8,13 @@ from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTra from litellm.proxy._experimental.mcp_server.guardrail_translation.handler import ( MCPGuardrailTranslationHandler, ) +from litellm.proxy._types import UserAPIKeyAuth from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail import unified_guardrail as unified_module from litellm.proxy.guardrails.guardrail_hooks.unified_guardrail.unified_guardrail import ( UnifiedLLMGuardrails, ) from litellm.types.guardrails import GuardrailEventHooks -from litellm.types.utils import CallTypes +from litellm.types.utils import CallTypes, Delta, ModelResponseStream, StreamingChoices class RecordingGuardrail(CustomGuardrail): @@ -131,3 +132,100 @@ class TestUnifiedLLMGuardrails: ) assert guardrail.event_history == [GuardrailEventHooks.during_call] + + class TestAsyncPostCallStreamingIteratorHook: + @pytest.mark.asyncio + async def test_streaming_content_not_lost_on_sampled_chunks(self): + """ + Verify that every chunk's content is preserved in the output stream. + + The bug: process_output_streaming_response puts the combined + guardrailed text in the first chunk and clears all subsequent + chunks to "". The hook then yielded processed_items[-1] (the + cleared last item), permanently losing every Nth chunk's content. + """ + + class _ContentClearingTranslation(BaseTranslation): + """Simulates the real OpenAI handler behavior that triggers the bug.""" + + async def process_input_messages(self, data, guardrail_to_apply, litellm_logging_obj=None): # type: ignore[override] + return data + + async def process_output_response(self, response, guardrail_to_apply, litellm_logging_obj=None, user_api_key_dict=None): # type: ignore[override] + return response + + async def process_output_streaming_response( + self, + responses_so_far, + guardrail_to_apply, + litellm_logging_obj=None, + user_api_key_dict=None, + ): + # Simulate what the real handler does: + # put combined text in first chunk, clear the rest + combined = "" + for resp in responses_so_far: + for choice in resp.choices: + if choice.delta and choice.delta.content: + combined += choice.delta.content + + first_set = False + for resp in responses_so_far: + for choice in resp.choices: + if not first_set: + choice.delta.content = combined + first_set = True + else: + choice.delta.content = "" + + return responses_so_far + + # Override the mapping to use our content-clearing translation + unified_module.endpoint_guardrail_translation_mappings = { + CallTypes.acompletion: _ContentClearingTranslation, + } + + handler = UnifiedLLMGuardrails() + guardrail = RecordingGuardrail() + + # Create 10 streaming chunks with distinct content + chunks = [] + for i in range(10): + chunk = ModelResponseStream( + choices=[StreamingChoices( + delta=Delta(content=f"word{i} ", role="assistant"), + finish_reason=None, + )], + ) + chunks.append(chunk) + + async def mock_stream(): + for chunk in chunks: + yield chunk + + user_api_key_dict = UserAPIKeyAuth( + api_key="test-key", + request_route="/v1/chat/completions", + ) + + request_data = { + "guardrail_to_apply": guardrail, + "model": "gpt-4", + } + + # Collect all yielded chunks + yielded_contents = [] + async for item in handler.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=mock_stream(), + request_data=request_data, + ): + content = item.choices[0].delta.content if item.choices[0].delta else None + yielded_contents.append(content) + + # Every chunk should have non-empty content + for i, content in enumerate(yielded_contents): + assert content is not None and content != "", ( + f"Chunk {i} lost its content (got {content!r}). " + f"Expected non-empty content for every streamed chunk." + ) diff --git a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py index 88f56c24067..c0f16c8b953 100644 --- a/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py +++ b/tests/test_litellm/proxy/guardrails/test_guardrail_endpoints.py @@ -149,6 +149,111 @@ async def test_list_guardrails_v2_with_db_and_config( assert isinstance(config_guardrail.litellm_params, BaseLitellmParams) +@pytest.mark.asyncio +async def test_list_guardrails_v2_masks_sensitive_data_in_db_guardrails(mocker): + """Test that sensitive litellm_params are masked for DB guardrails in list response""" + db_guardrail_with_secrets = { + "guardrail_id": "secret-db-guardrail", + "guardrail_name": "DB Guardrail with Secrets", + "litellm_params": { + "guardrail": "azure/text_moderations", + "mode": "pre_call", + "api_key": "sk-1234567890abcdef", + "api_base": "https://api.secret.example.com", + }, + "guardrail_info": {"description": "Test guardrail"}, + "created_at": datetime.now(), + "updated_at": datetime.now(), + } + + mock_prisma_client = mocker.Mock() + mock_prisma_client.db = mocker.Mock() + mock_prisma_client.db.litellm_guardrailstable = mocker.Mock() + mock_prisma_client.db.litellm_guardrailstable.find_many = AsyncMock( + return_value=[db_guardrail_with_secrets] + ) + + mock_in_memory_handler = mocker.Mock() + mock_in_memory_handler.list_in_memory_guardrails.return_value = [] + + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mocker.patch( + "litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER", + mock_in_memory_handler, + ) + + response = await list_guardrails_v2() + + assert len(response.guardrails) == 1 + guardrail = response.guardrails[0] + litellm_params = guardrail.litellm_params + if isinstance(litellm_params, dict): + params = litellm_params + else: + params = litellm_params.model_dump() if hasattr(litellm_params, "model_dump") else dict(litellm_params) + + # Sensitive keys (containing "key", "secret", "token", etc.) should be masked + assert params["api_key"] != "sk-1234567890abcdef" + assert "****" in str(params["api_key"]) + # Non-sensitive keys should remain unchanged + assert params["guardrail"] == "azure/text_moderations" + assert params["mode"] == "pre_call" + assert params["api_base"] == "https://api.secret.example.com" + + +@pytest.mark.asyncio +async def test_list_guardrails_v2_masks_sensitive_data_in_config_guardrails(mocker): + """Test that sensitive litellm_params are masked for in-memory/config guardrails in list response""" + config_guardrail_with_secrets = { + "guardrail_id": "secret-config-guardrail", + "guardrail_name": "Config Guardrail with Secrets", + "litellm_params": { + "guardrail": "bedrock", + "mode": "during_call", + "api_key": "my-secret-bedrock-key", + "vertex_credentials": "{sensitive_creds}", + }, + "guardrail_info": {"description": "Test guardrail from config"}, + } + + mock_prisma_client = mocker.Mock() + mock_prisma_client.db = mocker.Mock() + mock_prisma_client.db.litellm_guardrailstable = mocker.Mock() + mock_prisma_client.db.litellm_guardrailstable.find_many = AsyncMock( + return_value=[] + ) + + mock_in_memory_handler = mocker.Mock() + mock_in_memory_handler.list_in_memory_guardrails.return_value = [ + config_guardrail_with_secrets + ] + + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + mocker.patch( + "litellm.proxy.guardrails.guardrail_registry.IN_MEMORY_GUARDRAIL_HANDLER", + mock_in_memory_handler, + ) + + response = await list_guardrails_v2() + + assert len(response.guardrails) == 1 + guardrail = response.guardrails[0] + litellm_params = guardrail.litellm_params + if isinstance(litellm_params, dict): + params = litellm_params + else: + params = litellm_params.model_dump() if hasattr(litellm_params, "model_dump") else dict(litellm_params) + + # Sensitive keys should be masked + assert params["api_key"] != "my-secret-bedrock-key" + assert "****" in str(params["api_key"]) + assert params["vertex_credentials"] != "{sensitive_creds}" + assert "****" in str(params["vertex_credentials"]) + # Non-sensitive keys should remain unchanged + assert params["guardrail"] == "bedrock" + assert params["mode"] == "during_call" + + @pytest.mark.asyncio async def test_get_guardrail_info_from_db(mocker, mock_prisma_client): """Test getting guardrail info from DB""" diff --git a/tests/test_litellm/proxy/hooks/test_image_generation_guardrails.py b/tests/test_litellm/proxy/hooks/test_image_generation_guardrails.py new file mode 100644 index 00000000000..4a5d901b74d --- /dev/null +++ b/tests/test_litellm/proxy/hooks/test_image_generation_guardrails.py @@ -0,0 +1,293 @@ +""" +Tests that guardrails (post_call_success_hook) fire for image generation requests. + +The /images/generations endpoint in proxy/image_endpoints/endpoints.py calls +proxy_logging_obj.post_call_success_hook after a successful image generation. +These tests verify: +1. CustomGuardrail.async_post_call_success_hook is invoked for image generation. +2. A guardrail can inspect and transform the image response. +3. A guardrail that raises blocks the response (exception propagates). +""" + +import os +import sys +from typing import Any, Optional +from unittest.mock import patch + +import pytest + +sys.path.insert(0, os.path.abspath("../../../..")) + +import litellm +from litellm.caching.caching import DualCache +from litellm.integrations.custom_guardrail import CustomGuardrail +from litellm.integrations.custom_logger import CustomLogger +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.utils import ProxyLogging +from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.utils import ImageObject, ImageResponse + + +def _make_image_response(**kwargs) -> ImageResponse: + """Helper to build a minimal ImageResponse for tests.""" + return ImageResponse( + data=[ImageObject(url="https://example.com/img.png")], + **kwargs, + ) + + +# --------------------------------------------------------------------------- +# 1. Hook is invoked for image generation responses +# --------------------------------------------------------------------------- + + +class TrackingGuardrail(CustomGuardrail): + """Guardrail that records whether it was called and with what args.""" + + def __init__(self): + super().__init__( + guardrail_name="tracking_guardrail", + default_on=True, + event_hook=GuardrailEventHooks.post_call, + ) + self.called = False + self.received_data: Optional[dict] = None + self.received_response: Optional[Any] = None + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + self.called = True + self.received_data = data + self.received_response = response + return response + + +@pytest.mark.asyncio +async def test_post_call_success_hook_invoked_for_image_generation(): + """ + Verify that a default-on guardrail's async_post_call_success_hook is + called when ProxyLogging.post_call_success_hook is invoked with an + ImageResponse (the same path used by the /images/generations endpoint). + """ + guardrail = TrackingGuardrail() + image_response = _make_image_response() + + with patch("litellm.callbacks", [guardrail]): + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + + data = {"model": "dall-e-3", "prompt": "A sunset over mountains"} + user_api_key_dict = UserAPIKeyAuth(api_key="test-key") + + result = await proxy_logging.post_call_success_hook( + data=data, + response=image_response, + user_api_key_dict=user_api_key_dict, + ) + + assert guardrail.called is True, "Guardrail hook was not invoked for image generation" + assert guardrail.received_data is not None + assert guardrail.received_data["model"] == "dall-e-3" + assert isinstance(guardrail.received_response, ImageResponse) + # The response should be passed through unchanged + assert result is image_response + + +# --------------------------------------------------------------------------- +# 2. Guardrail can transform image generation response +# --------------------------------------------------------------------------- + + +class TransformingGuardrail(CustomGuardrail): + """Guardrail that replaces the image URL in the response.""" + + def __init__(self): + super().__init__( + guardrail_name="transforming_guardrail", + default_on=True, + event_hook=GuardrailEventHooks.post_call, + ) + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + # Return a modified image response (e.g., watermarked URL) + return ImageResponse( + data=[ImageObject(url="https://example.com/watermarked.png")], + ) + + +@pytest.mark.asyncio +async def test_guardrail_can_transform_image_response(): + """ + Verify that a guardrail can replace the ImageResponse returned to the client. + """ + guardrail = TransformingGuardrail() + original_response = _make_image_response() + + with patch("litellm.callbacks", [guardrail]): + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + + data = {"model": "dall-e-3", "prompt": "A sunset"} + user_api_key_dict = UserAPIKeyAuth(api_key="test-key") + + result = await proxy_logging.post_call_success_hook( + data=data, + response=original_response, + user_api_key_dict=user_api_key_dict, + ) + + assert result is not original_response + assert isinstance(result, ImageResponse) + assert result.data[0].url == "https://example.com/watermarked.png" + + +# --------------------------------------------------------------------------- +# 3. Guardrail that raises blocks the image response +# --------------------------------------------------------------------------- + + +class BlockingGuardrail(CustomGuardrail): + """Guardrail that raises on unsafe image prompts.""" + + def __init__(self): + super().__init__( + guardrail_name="blocking_guardrail", + default_on=True, + event_hook=GuardrailEventHooks.post_call, + ) + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + raise ValueError("Image content blocked by guardrail") + + +@pytest.mark.asyncio +async def test_guardrail_exception_propagates_for_image_generation(): + """ + Verify that an exception raised in a guardrail's post_call_success_hook + propagates up (the proxy endpoint wraps this in an error response). + """ + guardrail = BlockingGuardrail() + + with patch("litellm.callbacks", [guardrail]): + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + + data = {"model": "dall-e-3", "prompt": "Something unsafe"} + user_api_key_dict = UserAPIKeyAuth(api_key="test-key") + + with pytest.raises(ValueError, match="Image content blocked by guardrail"): + await proxy_logging.post_call_success_hook( + data=data, + response=_make_image_response(), + user_api_key_dict=user_api_key_dict, + ) + + +# --------------------------------------------------------------------------- +# 4. Non-guardrail CustomLogger also fires for image generation +# --------------------------------------------------------------------------- + + +class TrackingLogger(CustomLogger): + """Plain CustomLogger (not a guardrail) that tracks invocations.""" + + def __init__(self): + self.called = False + self.received_response = None + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + self.called = True + self.received_response = response + return response + + +@pytest.mark.asyncio +async def test_custom_logger_post_call_success_hook_fires_for_image_generation(): + """ + Verify that a plain CustomLogger (non-guardrail) callback also has its + async_post_call_success_hook invoked for image generation responses. + """ + logger = TrackingLogger() + image_response = _make_image_response() + + with patch("litellm.callbacks", [logger]): + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + + data = {"model": "dall-e-3", "prompt": "A cat"} + user_api_key_dict = UserAPIKeyAuth(api_key="test-key") + + result = await proxy_logging.post_call_success_hook( + data=data, + response=image_response, + user_api_key_dict=user_api_key_dict, + ) + + assert logger.called is True + assert isinstance(logger.received_response, ImageResponse) + assert result is image_response + + +# --------------------------------------------------------------------------- +# 5. Guardrail with should_run_guardrail=False is skipped +# --------------------------------------------------------------------------- + + +class OptInGuardrail(CustomGuardrail): + """Guardrail that is NOT default_on, so it only runs if explicitly requested.""" + + def __init__(self): + super().__init__( + guardrail_name="opt_in_guardrail", + default_on=False, + event_hook=GuardrailEventHooks.post_call, + ) + self.called = False + + async def async_post_call_success_hook( + self, + data: dict, + user_api_key_dict: UserAPIKeyAuth, + response: Any, + ) -> Any: + self.called = True + return response + + +@pytest.mark.asyncio +async def test_non_default_guardrail_skipped_for_image_generation(): + """ + Verify that a guardrail with default_on=False is NOT invoked for image + generation unless the request explicitly enables it. + """ + guardrail = OptInGuardrail() + + with patch("litellm.callbacks", [guardrail]): + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + + # No guardrails key in data -> should_run_guardrail returns False + data = {"model": "dall-e-3", "prompt": "A sunset"} + user_api_key_dict = UserAPIKeyAuth(api_key="test-key") + + await proxy_logging.post_call_success_hook( + data=data, + response=_make_image_response(), + user_api_key_dict=user_api_key_dict, + ) + + assert guardrail.called is False, "Opt-in guardrail should not fire without explicit request" diff --git a/tests/test_litellm/proxy/image_endpoints/test_endpoints.py b/tests/test_litellm/proxy/image_endpoints/test_endpoints.py index a3b6a9c6022..c35630176bc 100644 --- a/tests/test_litellm/proxy/image_endpoints/test_endpoints.py +++ b/tests/test_litellm/proxy/image_endpoints/test_endpoints.py @@ -40,10 +40,14 @@ async def test_image_generation_prompt_rerouting(monkeypatch): async def fake_post_call_failure_hook(**_: Any) -> None: return None + async def fake_post_call_success_hook(*, data, user_api_key_dict, response): + return response + fake_proxy_logger = SimpleNamespace( pre_call_hook=fake_pre_call_hook, update_request_status=fake_update_request_status, post_call_failure_hook=fake_post_call_failure_hook, + post_call_success_hook=fake_post_call_success_hook, ) captured_route_request_data: Dict[str, Any] = {} diff --git a/tests/test_litellm/proxy/management_endpoints/search_endpoints/test_search_tool_management.py b/tests/test_litellm/proxy/management_endpoints/search_endpoints/test_search_tool_management.py new file mode 100644 index 00000000000..c7e3fba94ee --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/search_endpoints/test_search_tool_management.py @@ -0,0 +1,546 @@ +import os +import sys +from datetime import datetime +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest +from fastapi.testclient import TestClient + +sys.path.insert( + 0, os.path.abspath("../../../../..") +) # Adds the parent directory to the system path + +from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + +# Import proxy_server module first to ensure it's initialized +import litellm.proxy.proxy_server as ps + +# Now we can safely import app +from litellm.proxy.proxy_server import app + +client = TestClient(app) + + +@pytest.mark.asyncio +async def test_list_search_tools_db_only(monkeypatch): + """Test listing search tools when only DB tools exist""" + # Mock DB tools + db_tools = [ + { + "search_tool_id": "test-id-1", + "search_tool_name": "db-tool-1", + "litellm_params": {"search_provider": "perplexity", "api_key": "sk-test"}, + "search_tool_info": {"description": "DB tool 1"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + } + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(return_value={}) + mock_proxy_config.parse_search_tools = MagicMock(return_value=None) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + assert len(data["search_tools"]) == 1 + + tool = data["search_tools"][0] + assert tool["search_tool_id"] == "test-id-1" + assert tool["search_tool_name"] == "db-tool-1" + assert tool["is_from_config"] is False + # Verify datetime conversion to ISO string + assert tool["created_at"] == "2023-11-09T12:34:56" + assert tool["updated_at"] == "2023-11-09T13:45:12" + # Verify masking of sensitive values + assert tool["litellm_params"]["api_key"] != "sk-test" + assert "****" in tool["litellm_params"]["api_key"] + assert tool["litellm_params"]["search_provider"] == "perplexity" + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_config_only(monkeypatch): + """Test listing search tools when only config tools exist""" + # Mock DB tools - empty + db_tools = [] + + # Mock config tools + config_tools = [ + { + "search_tool_name": "config-tool-1", + "litellm_params": {"search_provider": "tavily", "api_key": "tvly-secret-key"}, + "search_tool_info": {"description": "Config tool 1"}, + } + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(return_value={"search_tools": config_tools}) + mock_proxy_config.parse_search_tools = MagicMock(return_value=config_tools) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + assert len(data["search_tools"]) == 1 + + tool = data["search_tools"][0] + assert tool["search_tool_name"] == "config-tool-1" + assert tool["is_from_config"] is True + assert tool["search_tool_id"] is None + assert tool["created_at"] is None + assert tool["updated_at"] is None + # Verify masking + assert "tv****ey" in tool["litellm_params"]["api_key"] + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_filters_duplicate_config_tools(monkeypatch): + """ + Test that config tools with the same name as DB tools are filtered out. + This tests the new filtering logic added in lines 139-142. + """ + # Mock DB tools + db_tools = [ + { + "search_tool_id": "db-id-1", + "search_tool_name": "existing-tool", + "litellm_params": {"search_provider": "perplexity", "api_key": "sk-db"}, + "search_tool_info": {"description": "DB tool"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + } + ] + + # Mock config tools - one duplicate, one unique + config_tools = [ + { + "search_tool_name": "existing-tool", # Duplicate - should be filtered + "litellm_params": {"search_provider": "tavily", "api_key": "tvly-config"}, + "search_tool_info": {"description": "Config tool - duplicate"}, + }, + { + "search_tool_name": "unique-config-tool", # Unique - should be included + "litellm_params": {"search_provider": "tavily", "api_key": "tvly-unique"}, + "search_tool_info": {"description": "Config tool - unique"}, + }, + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(return_value={"search_tools": config_tools}) + mock_proxy_config.parse_search_tools = MagicMock(return_value=config_tools) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + # Should have 1 DB tool + 1 unique config tool (duplicate filtered out) + assert len(data["search_tools"]) == 2 + + # Verify DB tool is present + db_tool = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "existing-tool"), + None, + ) + assert db_tool is not None + assert db_tool["is_from_config"] is False + assert db_tool["search_tool_id"] == "db-id-1" + # Verify masking of sensitive values in DB tool + assert db_tool["litellm_params"]["api_key"] != "sk-db" + assert "****" in db_tool["litellm_params"]["api_key"] + assert db_tool["litellm_params"]["search_provider"] == "perplexity" + + # Verify unique config tool is present + config_tool = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "unique-config-tool"), + None, + ) + assert config_tool is not None + assert config_tool["is_from_config"] is True + + # Verify duplicate config tool is NOT present + duplicate_tool = next( + ( + t + for t in data["search_tools"] + if t["search_tool_name"] == "existing-tool" and t["is_from_config"] is True + ), + None, + ) + assert duplicate_tool is None + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_datetime_conversion(monkeypatch): + """ + Test that datetime objects in DB tools are properly converted to ISO format strings. + This tests the new datetime conversion logic using _convert_datetime_to_str. + """ + # Mock DB tools with datetime objects + db_tools = [ + { + "search_tool_id": "test-id-1", + "search_tool_name": "datetime-test-tool", + "litellm_params": {"search_provider": "perplexity", "api_key": "sk-test"}, + "search_tool_info": {"description": "Test tool"}, + "created_at": datetime(2024, 1, 15, 10, 30, 45, 123456), + "updated_at": datetime(2024, 1, 16, 14, 20, 30, 789012), + }, + { + "search_tool_id": "test-id-2", + "search_tool_name": "null-datetime-tool", + "litellm_params": {"search_provider": "tavily", "api_key": "tvly-test"}, + "search_tool_info": None, + "created_at": None, + "updated_at": None, + }, + { + "search_tool_id": "test-id-3", + "search_tool_name": "string-datetime-tool", + "litellm_params": {"search_provider": "perplexity", "api_key": "sk-test"}, + "search_tool_info": {"description": "Already string"}, + "created_at": "2024-01-17T08:15:00", # Already a string + "updated_at": "2024-01-18T09:25:00", # Already a string + }, + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(return_value={}) + mock_proxy_config.parse_search_tools = MagicMock(return_value=None) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + assert len(data["search_tools"]) == 3 + + # Test datetime conversion for tool 1 + tool1 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "datetime-test-tool"), + None, + ) + assert tool1 is not None + assert isinstance(tool1["created_at"], str) + assert tool1["created_at"] == "2024-01-15T10:30:45.123456" + assert isinstance(tool1["updated_at"], str) + assert tool1["updated_at"] == "2024-01-16T14:20:30.789012" + # Verify masking of sensitive values + assert tool1["litellm_params"]["api_key"] != "sk-test" + assert "****" in tool1["litellm_params"]["api_key"] + + # Test None handling for tool 2 + tool2 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "null-datetime-tool"), + None, + ) + assert tool2 is not None + assert tool2["created_at"] is None + assert tool2["updated_at"] is None + # Verify masking of sensitive values + assert tool2["litellm_params"]["api_key"] != "tvly-test" + assert "****" in tool2["litellm_params"]["api_key"] + + # Test string passthrough for tool 3 + tool3 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "string-datetime-tool"), + None, + ) + assert tool3 is not None + assert tool3["created_at"] == "2024-01-17T08:15:00" + assert tool3["updated_at"] == "2024-01-18T09:25:00" + # Verify masking of sensitive values + assert tool3["litellm_params"]["api_key"] != "sk-test" + assert "****" in tool3["litellm_params"]["api_key"] + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_config_error_handling(monkeypatch): + """Test that config errors are handled gracefully""" + # Mock DB tools + db_tools = [ + { + "search_tool_id": "test-id-1", + "search_tool_name": "db-tool-1", + "litellm_params": {"search_provider": "perplexity", "api_key": "sk-test"}, + "search_tool_info": {"description": "DB tool"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + } + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config to raise an error + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(side_effect=Exception("Config error")) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + # Should still succeed and return DB tools only + response = client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + # Should only have DB tools since config failed + assert len(data["search_tools"]) == 1 + assert data["search_tools"][0]["search_tool_name"] == "db-tool-1" + # Verify masking of sensitive values + assert data["search_tools"][0]["litellm_params"]["api_key"] != "sk-test" + assert "****" in data["search_tools"][0]["litellm_params"]["api_key"] + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_no_prisma_client(monkeypatch): + """Test error handling when prisma_client is None""" + with patch("litellm.proxy.proxy_server.prisma_client", None): + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 500 + data = response.json() + assert "Prisma client not initialized" in data["detail"] + finally: + app.dependency_overrides.pop(user_api_key_auth, None) + + +@pytest.mark.asyncio +async def test_list_search_tools_db_masking_sensitive_values(monkeypatch): + """ + Test that sensitive values in DB search tools are properly masked. + This tests the new masking logic added for database search tools. + """ + # Mock DB tools with various sensitive fields + db_tools = [ + { + "search_tool_id": "test-id-1", + "search_tool_name": "perplexity-tool", + "litellm_params": { + "search_provider": "perplexity", + "api_key": "pplx-sk-1234567890abcdef", + "api_base": "https://api.perplexity.ai", + }, + "search_tool_info": {"description": "Perplexity tool"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + }, + { + "search_tool_id": "test-id-2", + "search_tool_name": "tavily-tool", + "litellm_params": { + "search_provider": "tavily", + "api_key": "tvly-secret-key-12345", + "api_base": "https://api.tavily.com", + }, + "search_tool_info": {"description": "Tavily tool"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + }, + { + "search_tool_id": "test-id-3", + "search_tool_name": "tool-with-token", + "litellm_params": { + "search_provider": "custom", + "access_token": "token-abcdefghijklmnop", + "secret_key": "secret-xyz123", + }, + "search_tool_info": {"description": "Tool with token"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + }, + { + "search_tool_id": "test-id-4", + "search_tool_name": "tool-with-non-sensitive", + "litellm_params": { + "search_provider": "custom", + "max_results": 10, + "timeout": 30, + }, + "search_tool_info": {"description": "Tool without sensitive fields"}, + "created_at": datetime(2023, 11, 9, 12, 34, 56), + "updated_at": datetime(2023, 11, 9, 13, 45, 12), + }, + ] + + # Mock SearchToolRegistry + mock_registry = MagicMock() + mock_registry.get_all_search_tools_from_db = AsyncMock(return_value=db_tools) + with patch( + "litellm.proxy.search_endpoints.search_tool_management.SEARCH_TOOL_REGISTRY", + mock_registry, + ): + # Mock prisma_client + mock_prisma = MagicMock() + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma): + # Mock proxy_config + mock_proxy_config = MagicMock() + mock_proxy_config.get_config = AsyncMock(return_value={}) + mock_proxy_config.parse_search_tools = MagicMock(return_value=None) + with patch("litellm.proxy.proxy_server.proxy_config", mock_proxy_config): + # Mock auth + from litellm.proxy.auth.user_api_key_auth import user_api_key_auth + + app.dependency_overrides[user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + test_client = TestClient(app) + response = test_client.get("/search_tools/list") + assert response.status_code == 200 + data = response.json() + assert "search_tools" in data + assert len(data["search_tools"]) == 4 + + # Test tool 1: api_key should be masked + tool1 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "perplexity-tool"), + None, + ) + assert tool1 is not None + assert tool1["litellm_params"]["api_key"] != "pplx-sk-1234567890abcdef" + assert "****" in tool1["litellm_params"]["api_key"] + assert tool1["litellm_params"]["search_provider"] == "perplexity" + assert tool1["litellm_params"]["api_base"] == "https://api.perplexity.ai" + + # Test tool 2: api_key should be masked + tool2 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "tavily-tool"), + None, + ) + assert tool2 is not None + assert tool2["litellm_params"]["api_key"] != "tvly-secret-key-12345" + assert "****" in tool2["litellm_params"]["api_key"] + assert tool2["litellm_params"]["search_provider"] == "tavily" + + # Test tool 3: access_token and secret_key should be masked + tool3 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "tool-with-token"), + None, + ) + assert tool3 is not None + assert tool3["litellm_params"]["access_token"] != "token-abcdefghijklmnop" + assert "****" in tool3["litellm_params"]["access_token"] + assert tool3["litellm_params"]["secret_key"] != "secret-xyz123" + assert "****" in tool3["litellm_params"]["secret_key"] + + # Test tool 4: non-sensitive fields should remain unmasked + tool4 = next( + (t for t in data["search_tools"] if t["search_tool_name"] == "tool-with-non-sensitive"), + None, + ) + assert tool4 is not None + assert tool4["litellm_params"]["max_results"] == 10 + assert tool4["litellm_params"]["timeout"] == 30 + assert tool4["litellm_params"]["search_provider"] == "custom" + finally: + app.dependency_overrides.pop(user_api_key_auth, None) diff --git a/tests/test_litellm/proxy/management_endpoints/test_access_group_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_access_group_endpoints.py new file mode 100644 index 00000000000..5f204918d08 --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/test_access_group_endpoints.py @@ -0,0 +1,583 @@ +""" +Tests for access group management endpoints. +""" + +import os +import sys +import types +from contextlib import asynccontextmanager +from datetime import datetime +from unittest.mock import AsyncMock, MagicMock + +import pytest +from fastapi.testclient import TestClient +from prisma.errors import PrismaError + +import litellm.proxy.proxy_server as ps +from litellm.proxy.proxy_server import app +from litellm.proxy._types import ( + CommonProxyErrors, + LitellmUserRoles, + UserAPIKeyAuth, +) + +sys.path.insert(0, os.path.abspath("../../../")) + + +def _make_access_group_record( + access_group_id: str = "ag-123", + access_group_name: str = "test-group", + description: str | None = "Test description", + access_model_ids: list | None = None, + access_mcp_server_ids: list | None = None, + access_agent_ids: list | None = None, + assigned_team_ids: list | None = None, + assigned_key_ids: list | None = None, + created_by: str | None = "admin-user", + updated_by: str | None = "admin-user", + created_at: datetime | None = None, +): + record = MagicMock() + record.access_group_id = access_group_id + record.access_group_name = access_group_name + record.description = description + record.access_model_ids = access_model_ids or [] + record.access_mcp_server_ids = access_mcp_server_ids or [] + record.access_agent_ids = access_agent_ids or [] + record.assigned_team_ids = assigned_team_ids or [] + record.assigned_key_ids = assigned_key_ids or [] + record.created_at = created_at or datetime.now() + record.created_by = created_by + record.updated_at = datetime.now() + record.updated_by = updated_by + return record + + +@pytest.fixture +def client_and_mocks(monkeypatch): + """Setup mock prisma and admin auth for access group endpoints.""" + mock_access_group_table = MagicMock() + mock_prisma = MagicMock() + + def _create_side_effect(*, data): + return _make_access_group_record( + access_group_id="ag-new", + access_group_name=data.get("access_group_name", "new"), + description=data.get("description"), + access_model_ids=data.get("access_model_ids", []), + access_mcp_server_ids=data.get("access_mcp_server_ids", []), + access_agent_ids=data.get("access_agent_ids", []), + assigned_team_ids=data.get("assigned_team_ids", []), + assigned_key_ids=data.get("assigned_key_ids", []), + created_by=data.get("created_by"), + updated_by=data.get("updated_by"), + ) + + mock_access_group_table.create = AsyncMock(side_effect=_create_side_effect) + mock_access_group_table.find_unique = AsyncMock(return_value=None) + mock_access_group_table.find_many = AsyncMock(return_value=[]) + mock_access_group_table.update = AsyncMock(side_effect=lambda *, where, data: _make_access_group_record( + access_group_id=where.get("access_group_id", "ag-123"), + access_group_name=data.get("access_group_name", "updated"), + description=data.get("description"), + access_model_ids=data.get("access_model_ids", []), + access_mcp_server_ids=data.get("access_mcp_server_ids", []), + access_agent_ids=data.get("access_agent_ids", []), + assigned_team_ids=data.get("assigned_team_ids", []), + assigned_key_ids=data.get("assigned_key_ids", []), + updated_by=data.get("updated_by"), + )) + mock_access_group_table.delete = AsyncMock(return_value=None) + + mock_team_table = MagicMock() + mock_team_table.find_many = AsyncMock(return_value=[]) + mock_team_table.update = AsyncMock(return_value=None) + + mock_key_table = MagicMock() + mock_key_table.find_many = AsyncMock(return_value=[]) + mock_key_table.update = AsyncMock(return_value=None) + + @asynccontextmanager + async def mock_tx(): + tx = types.SimpleNamespace( + litellm_accessgrouptable=mock_access_group_table, + litellm_teamtable=mock_team_table, + litellm_verificationtoken=mock_key_table, + ) + yield tx + + mock_db = types.SimpleNamespace( + litellm_accessgrouptable=mock_access_group_table, + litellm_teamtable=mock_team_table, + litellm_verificationtoken=mock_key_table, + tx=mock_tx, + ) + mock_prisma.db = mock_db + + monkeypatch.setattr(ps, "prisma_client", mock_prisma) + + admin_user = UserAPIKeyAuth( + user_id="admin_user", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + app.dependency_overrides[ps.user_api_key_auth] = lambda: admin_user + + client = TestClient(app) + + yield client, mock_prisma, mock_access_group_table + + app.dependency_overrides.clear() + monkeypatch.setattr(ps, "prisma_client", ps.prisma_client) + + +# Paths for primary and alias endpoints (alias: /v1/unified_access_group) +ACCESS_GROUP_PATHS = ["/v1/access_group", "/v1/unified_access_group"] + + +# --------------------------------------------------------------------------- +# CREATE +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +@pytest.mark.parametrize( + "payload", + [ + {"access_group_name": "group-a"}, + { + "access_group_name": "group-b", + "description": "Group B description", + "access_model_ids": ["model-1"], + "access_mcp_server_ids": ["mcp-1"], + "assigned_team_ids": ["team-1"], + }, + ], +) +def test_create_access_group_success(client_and_mocks, base_path, payload): + """Create access group with various payloads returns 201.""" + client, _, mock_table = client_and_mocks + + resp = client.post(base_path, json=payload) + assert resp.status_code == 201 + body = resp.json() + assert body["access_group_name"] == payload["access_group_name"] + assert body.get("access_group_id") is not None + mock_table.create.assert_awaited_once() + + +def test_create_access_group_duplicate_name_conflict(client_and_mocks): + """Create with duplicate name returns 409.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_name="existing-group") + mock_table.find_unique = AsyncMock(return_value=existing) + + resp = client.post("/v1/access_group", json={"access_group_name": "existing-group"}) + assert resp.status_code == 409 + assert "already exists" in resp.json()["detail"] + + +@pytest.mark.parametrize( + "error_message", + [ + "Unique constraint failed on the fields: (`access_group_name`)", + "P2002: Unique constraint failed", + "unique constraint violation", + ], +) +def test_create_access_group_race_condition_returns_409(client_and_mocks, error_message): + """Create race condition: Prisma unique constraint surfaces as 409, not 500.""" + client, _, mock_table = client_and_mocks + + mock_table.find_unique = AsyncMock(return_value=None) + mock_table.create = AsyncMock(side_effect=Exception(error_message)) + + resp = client.post("/v1/access_group", json={"access_group_name": "race-group"}) + assert resp.status_code == 409 + assert "already exists" in resp.json()["detail"] + + +@pytest.mark.parametrize("user_role", [LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY]) +def test_create_access_group_forbidden_non_admin(client_and_mocks, user_role): + """Non-admin users cannot create access groups.""" + client, _, _ = client_and_mocks + + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_id="regular_user", + user_role=user_role, + ) + + resp = client.post("/v1/access_group", json={"access_group_name": "forbidden"}) + assert resp.status_code == 403 + assert resp.json()["detail"]["error"] == CommonProxyErrors.not_allowed_access.value + + +def test_create_access_group_validation_missing_name(client_and_mocks): + """Create with missing access_group_name returns 422.""" + client, _, _ = client_and_mocks + + resp = client.post("/v1/access_group", json={}) + assert resp.status_code == 422 + + +def test_create_access_group_500_on_non_constraint_prisma_error(client_and_mocks): + """Create with non-unique-constraint Prisma error returns 500.""" + client, _, mock_table = client_and_mocks + + mock_table.find_unique = AsyncMock(return_value=None) + mock_table.create = AsyncMock(side_effect=Exception("Some other database error")) + + # Use raise_server_exceptions=False so unhandled exceptions become 500 responses + test_client = TestClient(app, raise_server_exceptions=False) + resp = test_client.post("/v1/access_group", json={"access_group_name": "test-group"}) + assert resp.status_code == 500 + + +# --------------------------------------------------------------------------- +# LIST +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +def test_list_access_groups_success_empty(client_and_mocks, base_path): + """List access groups returns empty list when none exist.""" + client, _, mock_table = client_and_mocks + + resp = client.get(base_path) + assert resp.status_code == 200 + assert resp.json() == [] + mock_table.find_many.assert_awaited_once() + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +def test_list_access_groups_success_with_items(client_and_mocks, base_path): + """List access groups returns items when they exist.""" + client, _, mock_table = client_and_mocks + + records = [ + _make_access_group_record(access_group_id="ag-1", access_group_name="group-1"), + _make_access_group_record(access_group_id="ag-2", access_group_name="group-2"), + ] + mock_table.find_many = AsyncMock(return_value=records) + + resp = client.get(base_path) + assert resp.status_code == 200 + body = resp.json() + assert len(body) == 2 + assert body[0]["access_group_name"] == "group-1" + assert body[1]["access_group_name"] == "group-2" + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +def test_list_access_groups_ordered_by_created_at_desc(client_and_mocks, base_path): + """List access groups calls find_many with created_at desc order.""" + client, _, mock_table = client_and_mocks + + older = datetime(2025, 1, 1, 12, 0, 0) + newer = datetime(2025, 1, 2, 12, 0, 0) + records = [ + _make_access_group_record( + access_group_id="ag-newer", + access_group_name="newer-group", + created_at=newer, + ), + _make_access_group_record( + access_group_id="ag-older", + access_group_name="older-group", + created_at=older, + ), + ] + mock_table.find_many = AsyncMock(return_value=records) + + resp = client.get(base_path) + assert resp.status_code == 200 + body = resp.json() + assert len(body) == 2 + # Mock returns newest first (simulating Prisma order desc) + assert body[0]["access_group_name"] == "newer-group" + assert body[1]["access_group_name"] == "older-group" + mock_table.find_many.assert_awaited_once_with(order={"created_at": "desc"}) + + +@pytest.mark.parametrize("user_role", [LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY]) +def test_list_access_groups_forbidden_non_admin(client_and_mocks, user_role): + """Non-admin users cannot list access groups.""" + client, _, _ = client_and_mocks + + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_id="regular_user", + user_role=user_role, + ) + + resp = client.get("/v1/access_group") + assert resp.status_code == 403 + assert resp.json()["detail"]["error"] == CommonProxyErrors.not_allowed_access.value + + +# --------------------------------------------------------------------------- +# GET +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +@pytest.mark.parametrize("access_group_id", ["ag-123", "ag-other-id"]) +def test_get_access_group_success(client_and_mocks, base_path, access_group_id): + """Get access group by id returns record when found.""" + client, _, mock_table = client_and_mocks + + record = _make_access_group_record(access_group_id=access_group_id) + mock_table.find_unique = AsyncMock(return_value=record) + + resp = client.get(f"{base_path}/{access_group_id}") + assert resp.status_code == 200 + assert resp.json()["access_group_id"] == access_group_id + + +def test_get_access_group_not_found(client_and_mocks): + """Get access group returns 404 when not found.""" + client, _, mock_table = client_and_mocks + + mock_table.find_unique = AsyncMock(return_value=None) + + resp = client.get("/v1/access_group/nonexistent-id") + assert resp.status_code == 404 + assert "not found" in resp.json()["detail"] + + +@pytest.mark.parametrize("user_role", [LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY]) +def test_get_access_group_forbidden_non_admin(client_and_mocks, user_role): + """Non-admin users cannot get access group.""" + client, _, _ = client_and_mocks + + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_id="regular_user", + user_role=user_role, + ) + + resp = client.get("/v1/access_group/ag-123") + assert resp.status_code == 403 + assert resp.json()["detail"]["error"] == CommonProxyErrors.not_allowed_access.value + + +# --------------------------------------------------------------------------- +# UPDATE +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +@pytest.mark.parametrize( + "update_payload", + [ + {"description": "Updated description"}, + {"access_model_ids": ["model-1", "model-2"]}, + {"assigned_team_ids": [], "assigned_key_ids": ["key-1"]}, + ], +) +def test_update_access_group_success(client_and_mocks, base_path, update_payload): + """Update access group with various payloads returns 200.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id="ag-update") + mock_table.find_unique = AsyncMock(return_value=existing) + + resp = client.put(f"{base_path}/ag-update", json=update_payload) + assert resp.status_code == 200 + mock_table.update.assert_awaited_once() + + +def test_update_access_group_not_found(client_and_mocks): + """Update access group returns 404 when not found.""" + client, _, mock_table = client_and_mocks + + mock_table.find_unique = AsyncMock(return_value=None) + + resp = client.put( + "/v1/access_group/nonexistent-id", + json={"description": "Updated"}, + ) + assert resp.status_code == 404 + assert "not found" in resp.json()["detail"] + mock_table.update.assert_not_awaited() + + +@pytest.mark.parametrize("user_role", [LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY]) +def test_update_access_group_forbidden_non_admin(client_and_mocks, user_role): + """Non-admin users cannot update access groups.""" + client, _, _ = client_and_mocks + + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_id="regular_user", + user_role=user_role, + ) + + resp = client.put("/v1/access_group/ag-123", json={"description": "Updated"}) + assert resp.status_code == 403 + assert resp.json()["detail"]["error"] == CommonProxyErrors.not_allowed_access.value + + +def test_update_access_group_empty_body(client_and_mocks): + """Update with empty body succeeds; only updated_by is set.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id="ag-update", access_group_name="unchanged") + mock_table.find_unique = AsyncMock(return_value=existing) + + resp = client.put("/v1/access_group/ag-update", json={}) + assert resp.status_code == 200 + mock_table.update.assert_awaited_once() + call_kwargs = mock_table.update.call_args.kwargs + assert call_kwargs["where"] == {"access_group_id": "ag-update"} + assert "updated_by" in call_kwargs["data"] + assert call_kwargs["data"]["updated_by"] == "admin_user" + + +# --------------------------------------------------------------------------- +# DELETE +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize("base_path", ACCESS_GROUP_PATHS) +@pytest.mark.parametrize("access_group_id", ["ag-123", "ag-delete-me"]) +def test_delete_access_group_success(client_and_mocks, base_path, access_group_id): + """Delete access group returns 204 when found.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id=access_group_id) + mock_table.find_unique = AsyncMock(return_value=existing) + + resp = client.delete(f"{base_path}/{access_group_id}") + assert resp.status_code == 204 + mock_table.delete.assert_awaited_once() + + +def test_delete_access_group_not_found(client_and_mocks): + """Delete access group returns 404 when not found.""" + client, _, mock_table = client_and_mocks + + mock_table.find_unique = AsyncMock(return_value=None) + + resp = client.delete("/v1/access_group/nonexistent-id") + assert resp.status_code == 404 + assert "not found" in resp.json()["detail"] + mock_table.delete.assert_not_awaited() + + +@pytest.mark.parametrize("user_role", [LitellmUserRoles.INTERNAL_USER, LitellmUserRoles.INTERNAL_USER_VIEW_ONLY]) +def test_delete_access_group_forbidden_non_admin(client_and_mocks, user_role): + """Non-admin users cannot delete access groups.""" + client, _, _ = client_and_mocks + + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_id="regular_user", + user_role=user_role, + ) + + resp = client.delete("/v1/access_group/ag-123") + assert resp.status_code == 403 + assert resp.json()["detail"]["error"] == CommonProxyErrors.not_allowed_access.value + + +def test_delete_access_group_cleans_up_teams_and_keys(client_and_mocks): + """Delete removes access_group_id from teams and keys before deleting the group.""" + client, mock_prisma, mock_access_group_table = client_and_mocks + mock_team_table = mock_prisma.db.litellm_teamtable + mock_key_table = mock_prisma.db.litellm_verificationtoken + + existing = _make_access_group_record(access_group_id="ag-to-delete") + mock_access_group_table.find_unique = AsyncMock(return_value=existing) + + team_with_group = MagicMock() + team_with_group.team_id = "team-1" + team_with_group.access_group_ids = ["ag-to-delete", "ag-other"] + mock_team_table.find_many = AsyncMock(return_value=[team_with_group]) + + key_with_group = MagicMock() + key_with_group.token = "key-token-1" + key_with_group.access_group_ids = ["ag-to-delete"] + mock_key_table.find_many = AsyncMock(return_value=[key_with_group]) + + resp = client.delete("/v1/access_group/ag-to-delete") + assert resp.status_code == 204 + + mock_team_table.update.assert_awaited_once_with( + where={"team_id": "team-1"}, + data={"access_group_ids": ["ag-other"]}, + ) + mock_key_table.update.assert_awaited_once_with( + where={"token": "key-token-1"}, + data={"access_group_ids": []}, + ) + mock_access_group_table.delete.assert_awaited_once_with( + where={"access_group_id": "ag-to-delete"} + ) + + +def test_delete_access_group_503_on_db_connection_error(client_and_mocks): + """Delete returns 503 when DB connection error occurs during transaction.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id="ag-to-delete") + mock_table.find_unique = AsyncMock(return_value=existing) + mock_table.delete = AsyncMock(side_effect=PrismaError()) + + resp = client.delete("/v1/access_group/ag-to-delete") + assert resp.status_code == 503 + assert resp.json()["detail"] == CommonProxyErrors.db_not_connected_error.value + + +def test_delete_access_group_404_on_p2025_or_record_not_found(client_and_mocks): + """Delete returns 404 when Prisma raises P2025 or record-not-found error.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id="ag-to-delete") + mock_table.find_unique = AsyncMock(return_value=existing) + mock_table.delete = AsyncMock(side_effect=Exception("P2025: Record to delete does not exist")) + + resp = client.delete("/v1/access_group/ag-to-delete") + assert resp.status_code == 404 + assert "not found" in resp.json()["detail"] + + +def test_delete_access_group_500_on_generic_exception(client_and_mocks): + """Delete returns 500 when generic exception occurs during transaction.""" + client, _, mock_table = client_and_mocks + + existing = _make_access_group_record(access_group_id="ag-to-delete") + mock_table.find_unique = AsyncMock(return_value=existing) + mock_table.delete = AsyncMock(side_effect=RuntimeError("Unexpected error")) + + resp = client.delete("/v1/access_group/ag-to-delete") + assert resp.status_code == 500 + assert "Failed to delete access group" in resp.json()["detail"] + + +# --------------------------------------------------------------------------- +# DB NOT CONNECTED +# --------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "method,url,factory", + [ + ("post", "/v1/access_group", lambda: {"json": {"access_group_name": "test"}}), + ("get", "/v1/access_group", lambda: {}), + ("get", "/v1/access_group/ag-123", lambda: {}), + ("put", "/v1/access_group/ag-123", lambda: {"json": {"description": "x"}}), + ("delete", "/v1/access_group/ag-123", lambda: {}), + # Alias: /v1/unified_access_group + ("post", "/v1/unified_access_group", lambda: {"json": {"access_group_name": "test"}}), + ("get", "/v1/unified_access_group", lambda: {}), + ("get", "/v1/unified_access_group/ag-123", lambda: {}), + ("put", "/v1/unified_access_group/ag-123", lambda: {"json": {"description": "x"}}), + ("delete", "/v1/unified_access_group/ag-123", lambda: {}), + ], +) +def test_access_group_endpoints_db_not_connected(client_and_mocks, monkeypatch, method, url, factory): + """All endpoints return 500 when DB is not connected.""" + client, _, _ = client_and_mocks + + monkeypatch.setattr(ps, "prisma_client", None) + + resp = getattr(client, method)(url, **factory()) + assert resp.status_code == 500 + assert resp.json()["detail"]["error"] == CommonProxyErrors.db_not_connected_error.value diff --git a/tests/test_litellm/proxy/management_endpoints/test_budget_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_budget_endpoints.py index d5c3ecae7d6..b15b9d622e4 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_budget_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_budget_endpoints.py @@ -11,7 +11,6 @@ import litellm.proxy.proxy_server as ps from litellm.proxy.proxy_server import app from litellm.proxy._types import UserAPIKeyAuth, LitellmUserRoles, CommonProxyErrors -import litellm.proxy.management_endpoints.budget_management_endpoints as bm sys.path.insert( 0, os.path.abspath("../../../") @@ -22,13 +21,13 @@ sys.path.insert( def client_and_mocks(monkeypatch): # Setup MagicMock Prisma mock_prisma = MagicMock() - mock_table = MagicMock() + mock_table = MagicMock() mock_table.create = AsyncMock(side_effect=lambda *, data: data) mock_table.update = AsyncMock(side_effect=lambda *, where, data: {**where, **data}) mock_prisma.db = types.SimpleNamespace( - litellm_budgettable = mock_table, - litellm_dailyspend = mock_table, + litellm_budgettable=mock_table, + litellm_dailyspend=mock_table, ) # Monkeypatch Mocked Prisma client into the server module @@ -79,6 +78,7 @@ async def test_new_budget_db_not_connected(client_and_mocks, monkeypatch): # override the prisma_client that the handler imports at runtime import litellm.proxy.proxy_server as ps + monkeypatch.setattr(ps, "prisma_client", None) # Call /budget/new endpoint @@ -123,6 +123,7 @@ async def test_update_budget_db_not_connected(client_and_mocks, monkeypatch): # override the prisma_client that the handler imports at runtime import litellm.proxy.proxy_server as ps + monkeypatch.setattr(ps, "prisma_client", None) payload = {"budget_id": "any", "max_budget": 1.0} @@ -136,7 +137,7 @@ async def test_update_budget_db_not_connected(client_and_mocks, monkeypatch): async def test_update_budget_allows_null_max_budget(client_and_mocks): """ Test that /budget/update allows setting max_budget to null. - + Previously, using exclude_none=True would drop null values, making it impossible to remove a budget limit. With exclude_unset=True, explicitly setting max_budget to null should include it in the update. @@ -144,11 +145,11 @@ async def test_update_budget_allows_null_max_budget(client_and_mocks): client, _, mock_table = client_and_mocks captured_data = {} - + async def capture_update(*, where, data): captured_data.update(data) return {**where, **data} - + mock_table.update = AsyncMock(side_effect=capture_update) payload = { @@ -159,9 +160,11 @@ async def test_update_budget_allows_null_max_budget(client_and_mocks): assert resp.status_code == 200, resp.text # Verify that max_budget=None was included in the update data - assert "max_budget" in captured_data, "max_budget should be included when explicitly set to null" + assert ( + "max_budget" in captured_data + ), "max_budget should be included when explicitly set to null" assert captured_data["max_budget"] is None, "max_budget should be None" - + mock_table.update.assert_awaited_once() @@ -169,7 +172,7 @@ async def test_update_budget_allows_null_max_budget(client_and_mocks): async def test_new_budget_negative_max_budget(client_and_mocks): """ Test that /budget/new rejects negative max_budget values. - + This prevents the issue where negative budgets would always trigger budget exceeded errors. """ @@ -181,7 +184,7 @@ async def test_new_budget_negative_max_budget(client_and_mocks): } resp = client.post("/budget/new", json=payload) assert resp.status_code == 400, resp.text - + detail = resp.json()["detail"] assert "max_budget cannot be negative" in str(detail) @@ -199,7 +202,7 @@ async def test_new_budget_negative_soft_budget(client_and_mocks): } resp = client.post("/budget/new", json=payload) assert resp.status_code == 400, resp.text - + detail = resp.json()["detail"] assert "soft_budget cannot be negative" in str(detail) @@ -217,7 +220,7 @@ async def test_update_budget_negative_max_budget(client_and_mocks): } resp = client.post("/budget/update", json=payload) assert resp.status_code == 400, resp.text - + detail = resp.json()["detail"] assert "max_budget cannot be negative" in str(detail) @@ -235,6 +238,30 @@ async def test_update_budget_negative_soft_budget(client_and_mocks): } resp = client.post("/budget/update", json=payload) assert resp.status_code == 400, resp.text - + detail = resp.json()["detail"] assert "soft_budget cannot be negative" in str(detail) + + +@pytest.mark.asyncio +async def test_new_budget_invalid_model_max_budget(client_and_mocks, monkeypatch): + """ + Test that /budget/new validates model_max_budget and returns 400 for invalid structure. + Per-model budget implementation: validate_model_max_budget is called in new_budget. + """ + import litellm.proxy.proxy_server as ps + + monkeypatch.setattr(ps, "premium_user", True) + + client, _, _ = client_and_mocks + + payload = { + "budget_id": "budget_invalid_mmb", + "max_budget": 10.0, + "model_max_budget": {"gpt-4": "not-a-dict"}, + } + resp = client.post("/budget/new", json=payload) + # Pydantic may reject invalid structure with 422 before our validator runs + assert resp.status_code in (400, 422), resp.text + detail = resp.json()["detail"] + assert "model_max_budget" in str(detail) or "dictionary" in str(detail).lower() diff --git a/tests/test_litellm/proxy/management_endpoints/test_common_utils.py b/tests/test_litellm/proxy/management_endpoints/test_common_utils.py new file mode 100644 index 00000000000..8b7b5a6fb7a --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/test_common_utils.py @@ -0,0 +1,488 @@ +""" +Tests for litellm/proxy/management_endpoints/common_utils.py + +Covers the fix for GitHub issue #20304: +Empty guardrails/policies arrays sent by the UI should NOT trigger the +enterprise (premium) license check, but should still be applied so that +users can intentionally clear previously-set fields. +""" + +from datetime import datetime, timezone +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from litellm.proxy._types import ( + Member, + LiteLLM_OrganizationMembershipTable, + LiteLLM_TeamTable, + LiteLLM_UserTable, + LitellmUserRoles, + UserAPIKeyAuth, +) +from litellm.proxy.management_endpoints.common_utils import ( + _is_user_team_admin, + _org_admin_can_invite_user, + _set_object_metadata_field, + _team_admin_can_invite_user, + _update_metadata_fields, + _user_has_admin_privileges, + _user_has_admin_view, + admin_can_invite_user, +) + + +class TestUpdateMetadataFieldsEmptyCollections: + """ + Regression tests for issue #20304. + + The UI sends empty arrays (`[]`) for enterprise-only fields like + guardrails, policies, and logging even when the user hasn't configured + these features. The backend must not treat empty collections as an + intent to use the feature, and therefore must not trigger the premium + license check. + + However, empty collections must still be written into metadata so that + users can intentionally clear a previously-set field (e.g. removing all + guardrails by sending `guardrails: []`). + """ + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_empty_list_does_not_trigger_premium_check(self, mock_premium_check): + """Empty lists for premium fields must not trigger the premium check.""" + updated_kv = { + "team_id": "test-team", + "guardrails": [], + "policies": [], + "logging": [], + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_not_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_empty_list_still_updates_metadata(self, mock_premium_check): + """ + Empty lists must still be moved into metadata so users can clear + previously-set fields (e.g. remove all guardrails). + """ + updated_kv = { + "team_id": "test-team", + "guardrails": [], + "policies": [], + } + _update_metadata_fields(updated_kv=updated_kv) + # The fields should have been moved into metadata + assert "guardrails" not in updated_kv, ( + "guardrails should be popped from top-level" + ) + assert "policies" not in updated_kv, ( + "policies should be popped from top-level" + ) + assert updated_kv["metadata"]["guardrails"] == [] + assert updated_kv["metadata"]["policies"] == [] + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_empty_dict_does_not_trigger_premium_check(self, mock_premium_check): + """Empty dicts for premium fields must not trigger the premium check.""" + updated_kv = { + "team_id": "test-team", + "secret_manager_settings": {}, + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_not_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_empty_dict_still_updates_metadata(self, mock_premium_check): + """ + Empty dicts must still be moved into metadata so users can clear + previously-set fields. + """ + updated_kv = { + "team_id": "test-team", + "secret_manager_settings": {}, + } + _update_metadata_fields(updated_kv=updated_kv) + assert "secret_manager_settings" not in updated_kv, ( + "secret_manager_settings should be popped from top-level" + ) + assert updated_kv["metadata"]["secret_manager_settings"] == {} + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_none_value_does_not_trigger_premium_check(self, mock_premium_check): + """None values for premium fields should be silently ignored.""" + updated_kv = { + "team_id": "test-team", + "guardrails": None, + "policies": None, + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_not_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_absent_fields_do_not_trigger_premium_check(self, mock_premium_check): + """Fields not present in the dict should not trigger premium check.""" + updated_kv = { + "team_id": "test-team", + "team_alias": "example-team", + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_not_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_non_empty_list_triggers_premium_check(self, mock_premium_check): + """Non-empty lists for premium fields should trigger the premium check.""" + updated_kv = { + "team_id": "test-team", + "guardrails": ["my-guardrail"], + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_non_empty_value_triggers_premium_check(self, mock_premium_check): + """Non-empty string values for premium fields should trigger the premium check.""" + updated_kv = { + "team_id": "test-team", + "tags": ["production"], + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_called() + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_non_empty_list_updates_metadata(self, mock_premium_check): + """Non-empty lists should be moved into metadata.""" + updated_kv = { + "team_id": "test-team", + "guardrails": ["my-guardrail"], + } + _update_metadata_fields(updated_kv=updated_kv) + assert "guardrails" not in updated_kv + assert updated_kv["metadata"]["guardrails"] == ["my-guardrail"] + + @patch("litellm.proxy.management_endpoints.common_utils._premium_user_check") + def test_ui_typical_payload_does_not_trigger_premium_check(self, mock_premium_check): + """ + Simulate the exact payload the UI sends when no enterprise features + are configured. This must NOT trigger the premium check. + """ + # This is the payload structure the UI sends (from issue #20304) + updated_kv = { + "team_id": "67848772-1a8b-4343-938c-17e60f1db860", + "team_alias": "example-team", + "models": ["gpt-4"], + "metadata": { + "guardrails": [], + "logging": [], + }, + "policies": [], + } + _update_metadata_fields(updated_kv=updated_kv) + mock_premium_check.assert_not_called() + + +class TestUserHasAdminView: + """Tests for _user_has_admin_view function.""" + + @pytest.mark.parametrize( + "user_role,expected", + [ + (LitellmUserRoles.PROXY_ADMIN, True), + (LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, True), + (LitellmUserRoles.INTERNAL_USER, False), + (LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, False), + ], + ) + def test_user_has_admin_view_by_role(self, user_role, expected): + """Parametrized test: admin roles return True, non-admin return False.""" + mock_auth = MagicMock() + mock_auth.user_role = user_role + assert _user_has_admin_view(mock_auth) == expected + + def test_user_has_admin_view_with_user_api_key_auth(self): + """Test with actual UserAPIKeyAuth object.""" + auth_admin = UserAPIKeyAuth( + user_id="u1", + api_key="sk-xxx", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + auth_user = UserAPIKeyAuth( + user_id="u2", + api_key="sk-yyy", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + assert _user_has_admin_view(auth_admin) is True + assert _user_has_admin_view(auth_user) is False + + +class TestIsUserTeamAdmin: + """Tests for _is_user_team_admin function.""" + + @pytest.mark.parametrize( + "members_with_roles,user_id,expected", + [ + ( + [Member(user_id="u1", role="admin")], + "u1", + True, + ), + ( + [Member(user_id="u1", role="user")], + "u1", + False, + ), + ( + [Member(user_id="u2", role="admin"), Member(user_id="u1", role="admin")], + "u1", + True, + ), + ([], "u1", False), + ], + ) + def test_is_user_team_admin_parametrized( + self, members_with_roles, user_id, expected + ): + """Parametrized test: user is team admin only when in members_with_roles with admin role.""" + mock_auth = MagicMock() + mock_auth.user_id = user_id + team = LiteLLM_TeamTable( + team_id="team-1", + members_with_roles=members_with_roles, + ) + assert _is_user_team_admin(mock_auth, team) == expected + + def test_is_user_team_admin_user_not_in_team(self): + """Test returns False when user is not in team members.""" + auth = UserAPIKeyAuth(user_id="u99", api_key="sk-x", user_role=None) + team = LiteLLM_TeamTable( + team_id="team-1", + members_with_roles=[Member(user_id="u1", role="admin")], + ) + assert _is_user_team_admin(auth, team) is False + + +class TestOrgAdminCanInviteUser: + """Tests for _org_admin_can_invite_user function.""" + + def _make_membership(self, org_id: str, user_role: str): + now = datetime.now(timezone.utc) + return LiteLLM_OrganizationMembershipTable( + user_id="u", + organization_id=org_id, + user_role=user_role, + created_at=now, + updated_at=now, + ) + + @pytest.mark.parametrize( + "admin_orgs,target_orgs,expected", + [ + (["org1"], ["org1"], True), + (["org1", "org2"], ["org2"], True), + (["org1"], ["org2"], False), + ([], ["org1"], False), + (["org1"], [], False), + ], + ) + def test_org_admin_can_invite_user_parametrized( + self, admin_orgs, target_orgs, expected + ): + """Parametrized test: can invite when target is in org where admin has ORG_ADMIN role.""" + admin_user = LiteLLM_UserTable( + user_id="admin", + organization_memberships=[ + self._make_membership(oid, LitellmUserRoles.ORG_ADMIN.value) + for oid in admin_orgs + ], + ) + target_user = LiteLLM_UserTable( + user_id="target", + organization_memberships=[ + self._make_membership(oid, LitellmUserRoles.INTERNAL_USER.value) + for oid in target_orgs + ], + ) + assert _org_admin_can_invite_user(admin_user, target_user) == expected + + def test_org_admin_can_invite_user_no_shared_org(self): + """Test returns False when admin has no org admin role.""" + admin_user = LiteLLM_UserTable( + user_id="admin", + organization_memberships=[ + self._make_membership("org1", LitellmUserRoles.INTERNAL_USER.value), + ], + ) + target_user = LiteLLM_UserTable( + user_id="target", + organization_memberships=[ + self._make_membership("org1", LitellmUserRoles.INTERNAL_USER.value), + ], + ) + assert _org_admin_can_invite_user(admin_user, target_user) is False + + +class TestTeamAdminCanInviteUser: + """Tests for _team_admin_can_invite_user async function.""" + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "admin_teams,target_teams,user_is_admin_in,expected", + [ + (["t1"], ["t1"], ["t1"], True), + (["t1", "t2"], ["t2"], ["t1", "t2"], True), + (["t1"], ["t2"], ["t1"], False), + ], + ) + async def test_team_admin_can_invite_user_parametrized( + self, admin_teams, target_teams, user_is_admin_in, expected + ): + """Parametrized test: can invite when target shares a team where user is admin.""" + mock_prisma = MagicMock() + mock_auth = MagicMock() + mock_auth.user_id = "admin" + + admin_user = LiteLLM_UserTable(user_id="admin", teams=admin_teams) + target_user = LiteLLM_UserTable(user_id="target", teams=target_teams) + + def make_team(tid, is_admin): + m = ( + [{"user_id": "admin", "role": "admin"}] + if is_admin + else [] + ) + obj = MagicMock() + obj.team_id = tid + obj.model_dump = lambda: {"team_id": tid, "members_with_roles": m} + return obj + + teams = [ + make_team(tid, tid in user_is_admin_in) for tid in admin_teams + ] + mock_prisma.db.litellm_teamtable.find_many = AsyncMock( + return_value=teams + ) + + result = await _team_admin_can_invite_user( + user_api_key_dict=mock_auth, + admin_user_obj=admin_user, + target_user_obj=target_user, + prisma_client=mock_prisma, + ) + assert result == expected + + @pytest.mark.asyncio + async def test_team_admin_can_invite_user_no_shared_team(self): + """Test returns False when admin and target share no team.""" + mock_prisma = MagicMock() + mock_auth = MagicMock() + mock_auth.user_id = "admin" + admin_user = LiteLLM_UserTable(user_id="admin", teams=[]) + target_user = LiteLLM_UserTable(user_id="target", teams=["t1"]) + + result = await _team_admin_can_invite_user( + user_api_key_dict=mock_auth, + admin_user_obj=admin_user, + target_user_obj=target_user, + prisma_client=mock_prisma, + ) + assert result is False + + +class TestUserHasAdminPrivileges: + """Tests for _user_has_admin_privileges async function.""" + + @pytest.mark.asyncio + async def test_proxy_admin_has_privileges(self): + """Proxy admin always has admin privileges.""" + auth = UserAPIKeyAuth( + user_id="admin", + api_key="sk-x", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + result = await _user_has_admin_privileges( + user_api_key_dict=auth, + prisma_client=None, + ) + assert result is True + + @pytest.mark.asyncio + async def test_non_admin_no_prisma_returns_false(self): + """Non-admin with no prisma connection has no privileges.""" + auth = UserAPIKeyAuth( + user_id="user1", + api_key="sk-x", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + result = await _user_has_admin_privileges( + user_api_key_dict=auth, + prisma_client=None, + ) + assert result is False + + +class TestAdminCanInviteUser: + """Tests for admin_can_invite_user async function.""" + + @pytest.mark.asyncio + async def test_proxy_admin_can_invite_any_user(self): + """Proxy admin can invite any user regardless of org/team.""" + auth = UserAPIKeyAuth( + user_id="admin", + api_key="sk-x", + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + result = await admin_can_invite_user( + target_user_id="any-user", + user_api_key_dict=auth, + prisma_client=None, + ) + assert result is True + + @pytest.mark.asyncio + async def test_non_admin_cannot_invite_without_prisma(self): + """Non-admin with no prisma cannot invite.""" + auth = UserAPIKeyAuth( + user_id="user1", + api_key="sk-x", + user_role=LitellmUserRoles.INTERNAL_USER, + ) + result = await admin_can_invite_user( + target_user_id="other-user", + user_api_key_dict=auth, + prisma_client=None, + ) + assert result is False + + +class TestSetObjectMetadataField: + """Tests for _set_object_metadata_field function.""" + + @pytest.mark.parametrize( + "field_name,value,should_call_premium", + [ + ("guardrails", ["g1"], True), + ("model_rpm_limit", {"gpt-4": 10}, False), + ], + ) + def test_set_object_metadata_field_parametrized( + self, field_name, value, should_call_premium + ): + """Parametrized test: premium fields trigger _premium_user_check.""" + team = LiteLLM_TeamTable(team_id="t1", metadata={}) + with patch( + "litellm.proxy.management_endpoints.common_utils._premium_user_check" + ) as mock_premium: + _set_object_metadata_field(team, field_name, value) + if should_call_premium: + mock_premium.assert_called_once() + else: + mock_premium.assert_not_called() + assert team.metadata[field_name] == value + + def test_set_object_metadata_field_initializes_metadata_if_none(self): + """Test initializes metadata dict when object has None.""" + team = LiteLLM_TeamTable(team_id="t1", metadata=None) + with patch( + "litellm.proxy.management_endpoints.common_utils._premium_user_check" + ): + _set_object_metadata_field(team, "model_rpm_limit", {"x": 1}) + assert team.metadata == {"model_rpm_limit": {"x": 1}} diff --git a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py index dc436bac087..919af96f760 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py @@ -1133,6 +1133,24 @@ def test_update_internal_user_params_ignores_other_nones(): assert non_default_values["max_budget"] == 100.0 +def test_update_internal_user_params_keeps_original_max_budget_when_not_provided(): + """ + Test that _update_internal_user_params does not include max_budget + when it's not provided in the request (should keep original value). + """ + # Create test data without max_budget + data_json = {"user_id": "test_user", "user_alias": "test_alias"} + data = UpdateUserRequest(user_id="test_user", user_alias="test_alias") + + # Call the function + non_default_values = _update_internal_user_params(data_json=data_json, data=data) + + # Assertions: max_budget should NOT be in non_default_values + assert "max_budget" not in non_default_values + assert "user_id" in non_default_values + assert "user_alias" in non_default_values + + def test_generate_request_base_validator(): """ Test that GenerateRequestBase validator converts empty string to None for max_budget diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index e90fb277eed..39f8d1cccb0 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -804,6 +804,108 @@ async def test_key_update_object_permissions_missing_permission_record(monkeypat mock_prisma_client.db.litellm_objectpermissiontable.upsert.assert_called_once() +@pytest.mark.asyncio +async def test_key_info_returns_object_permission(monkeypatch): + """ + Test that /key/info correctly returns the object_permission relation. + + This test verifies that when calling /key/info for a key with object_permission_id, + the response includes the full object_permission object with fields like + mcp_access_groups, mcp_servers, vector_stores, agents, etc. + + Regression test for bug where object_permission_id was returned but not the + related object_permission object. + """ + from unittest.mock import AsyncMock, MagicMock + + import pytest + + from litellm.proxy._types import LiteLLM_VerificationToken + from litellm.proxy.management_endpoints.key_management_endpoints import info_key_fn + + # Mock prisma client + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Mock key with object_permission_id + test_key_token = "hashed_test_token_123" + test_object_permission_id = "objperm_info_test_123" + + mock_key_info = MagicMock(spec=LiteLLM_VerificationToken) + mock_key_info.token = test_key_token + mock_key_info.object_permission_id = test_object_permission_id + mock_key_info.user_id = "user123" + mock_key_info.team_id = None + mock_key_info.litellm_budget_table = None + + # Mock the dict/model_dump methods + mock_key_info.model_dump.return_value = { + "token": test_key_token, + "object_permission_id": test_object_permission_id, + "user_id": "user123", + "team_id": None, + "litellm_budget_table": None, + } + mock_key_info.dict.return_value = mock_key_info.model_dump.return_value + + # Mock find_unique for the key lookup + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_info + ) + + # Mock object permission record + mock_object_permission = MagicMock() + mock_object_permission.model_dump.return_value = { + "object_permission_id": test_object_permission_id, + "mcp_access_groups": ["test_group_1", "test_group_2"], + "mcp_servers": ["server_1"], + "vector_stores": ["vs_1", "vs_2"], + "agents": ["agent_1"], + } + mock_object_permission.dict.return_value = mock_object_permission.model_dump.return_value + + # Mock find_unique for object permission lookup + mock_prisma_client.db.litellm_objectpermissiontable.find_unique = AsyncMock( + return_value=mock_object_permission + ) + + # Create user API key dict + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + api_key="sk-test-key-456", + ) + + # Call info_key_fn + result = await info_key_fn( + key="sk-test-key-456", + user_api_key_dict=user_api_key_dict, + ) + + # Assertions + assert "info" in result + assert "object_permission_id" in result["info"] + assert result["info"]["object_permission_id"] == test_object_permission_id + + # CRITICAL: Verify that object_permission object is included in response + assert "object_permission" in result["info"], ( + "object_permission field missing from /key/info response. " + "Expected full object_permission object to be attached." + ) + + # Verify object_permission contains the expected fields + obj_perm = result["info"]["object_permission"] + assert obj_perm["object_permission_id"] == test_object_permission_id + assert obj_perm["mcp_access_groups"] == ["test_group_1", "test_group_2"] + assert obj_perm["mcp_servers"] == ["server_1"] + assert obj_perm["vector_stores"] == ["vs_1", "vs_2"] + assert obj_perm["agents"] == ["agent_1"] + + # Verify the object permission was actually queried from database + mock_prisma_client.db.litellm_objectpermissiontable.find_unique.assert_called_once_with( + where={"object_permission_id": test_object_permission_id} + ) + + def test_get_new_token_with_valid_key(): """Test get_new_token function when provided with a valid key that starts with 'sk-'""" from litellm.proxy._types import RegenerateKeyRequest @@ -4038,6 +4140,72 @@ async def test_list_keys_with_invalid_status(): assert "deleted" in str(exc_info.value.message) +@pytest.mark.asyncio +async def test_list_keys_non_admin_user_id_auto_set(): + """ + Test that when a non-admin user calls list_keys with user_id=None, + the user_id is automatically set to the authenticated user's user_id. + """ + from unittest.mock import Mock, patch + + mock_prisma_client = AsyncMock() + + # Create a non-admin user with a user_id + test_user_id = "test-user-123" + mock_user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id=test_user_id, + ) + + # Mock user info returned by validate_key_list_check + mock_user_info = LiteLLM_UserTable( + user_id=test_user_id, + user_email="test@example.com", + teams=[], + organization_memberships=[], + ) + + # Mock _list_key_helper to capture the user_id argument + mock_list_key_helper = AsyncMock(return_value={ + "keys": [], + "total_count": 0, + "current_page": 1, + "total_pages": 0, + }) + + # Mock prisma_client to be non-None + with patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client): + with patch( + "litellm.proxy.management_endpoints.key_management_endpoints.validate_key_list_check", + return_value=mock_user_info, + ): + with patch( + "litellm.proxy.management_endpoints.key_management_endpoints.get_admin_team_ids", + return_value=[], + ): + with patch( + "litellm.proxy.management_endpoints.key_management_endpoints._list_key_helper", + mock_list_key_helper, + ): + mock_request = Mock() + + # Call list_keys with user_id=None + await list_keys( + request=mock_request, + user_api_key_dict=mock_user_api_key_dict, + user_id=None, # This should be auto-set to test_user_id + status=None, # Explicitly set status to None to avoid validation errors + ) + + # Verify that _list_key_helper was called with user_id set to the authenticated user's user_id + mock_list_key_helper.assert_called_once() + call_kwargs = mock_list_key_helper.call_args.kwargs + assert call_kwargs["user_id"] == test_user_id, ( + f"Expected user_id to be set to {test_user_id}, " + f"but got {call_kwargs.get('user_id')}" + ) + + @pytest.mark.asyncio async def test_generate_key_negative_max_budget(): """ diff --git a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py index f7e7fcebaef..e81c6264f7b 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_mcp_management_endpoints.py @@ -1,15 +1,15 @@ -import json import os import sys import types -from types import SimpleNamespace from datetime import datetime, timedelta +from types import SimpleNamespace from typing import List, Optional from unittest.mock import AsyncMock, MagicMock, patch import pytest from fastapi import FastAPI, HTTPException from fastapi.testclient import TestClient + from litellm._uuid import uuid from litellm.proxy.management_endpoints import ( mcp_management_endpoints as mgmt_endpoints, @@ -204,25 +204,28 @@ class TestListMCPServers: transport="http", ), ] - mock_manager.get_all_allowed_mcp_servers = AsyncMock( - return_value=mock_servers - ) + mock_manager.get_all_allowed_mcp_servers = AsyncMock(return_value=mock_servers) for idx, server in enumerate(mock_servers): server.credentials = {"auth_value": f"secret_{idx}"} - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", - return_value=True, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", + return_value=True, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): # Import and call the function from litellm.proxy.management_endpoints.mcp_management_endpoints import ( @@ -269,12 +272,15 @@ class TestListMCPServers: return_value=mock_servers ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._get_user_mcp_management_mode", - return_value="view_all", - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_user_mcp_management_mode", + return_value="view_all", + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), ): from litellm.proxy.management_endpoints.mcp_management_endpoints import ( fetch_all_mcp_servers, @@ -285,6 +291,79 @@ class TestListMCPServers: assert len(result) == 2 assert {server.server_id for server in result} == {"server-1", "server-2"} + @pytest.mark.asyncio + async def test_list_mcp_servers_view_all_mode_virtual_key_is_sanitized(self): + """Issue #20325: virtual keys should get a safe discovery view.""" + + mock_user_auth = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="test_user_id", + api_key="test_api_key", + allowed_routes=["mcp_routes"], + ) + + mock_servers = [ + generate_mock_mcp_server_db_record(server_id="server-1", alias="One"), + generate_mock_mcp_server_db_record(server_id="server-2", alias="Two"), + ] + for idx, server in enumerate(mock_servers): + server.credentials = {"auth_value": f"secret_{idx}"} + server.env = {"API_KEY": "super-secret"} + server.static_headers = {"Authorization": "Bearer super-secret"} + server.mcp_access_groups = ["group-a"] + server.teams = [{"team_id": "team-1", "team_alias": "Team 1"}] + server.command = "bash" + server.args = ["-lc", "echo hi"] + server.extra_headers = ["Authorization"] + + mock_manager = MagicMock() + mock_manager.get_all_mcp_servers_unfiltered = AsyncMock( + return_value=mock_servers + ) + mock_manager.get_all_allowed_mcp_servers = AsyncMock(return_value=mock_servers) + + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_user_mcp_management_mode", + return_value="view_all", + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=MagicMock(), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), + ): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + fetch_all_mcp_servers, + ) + + result = await fetch_all_mcp_servers(user_api_key_dict=mock_user_auth) + + # Ensure we did not bypass filtering via view_all for restricted virtual keys. + mock_manager.get_all_mcp_servers_unfiltered.assert_not_called() + + assert len(result) == 2 + assert {server.server_id for server in result} == {"server-1", "server-2"} + + for server in result: + assert server.credentials is None + assert server.url is None + assert server.static_headers is None + assert server.env == {} + assert server.command is None + assert server.args == [] + assert server.extra_headers == [] + assert server.allowed_tools == [] + assert server.mcp_access_groups == [] + assert server.teams == [] + @pytest.mark.asyncio async def test_list_mcp_servers_combined_config_and_db(self): """ @@ -374,25 +453,28 @@ class TestListMCPServers: transport="http", ), ] - mock_manager.get_all_allowed_mcp_servers = AsyncMock( - return_value=mock_servers - ) + mock_manager.get_all_allowed_mcp_servers = AsyncMock(return_value=mock_servers) for idx, server in enumerate(mock_servers): server.credentials = {"auth_value": f"secret_{idx}"} - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", - return_value=True, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", + return_value=True, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): # Import and call the function from litellm.proxy.management_endpoints.mcp_management_endpoints import ( @@ -494,25 +576,28 @@ class TestListMCPServers: url="https://actions.zapier.com/mcp/sse", ), ] - mock_manager.get_all_allowed_mcp_servers = AsyncMock( - return_value=mock_servers - ) + mock_manager.get_all_allowed_mcp_servers = AsyncMock(return_value=mock_servers) for idx, server in enumerate(mock_servers): server.credentials = {"auth_value": f"secret_{idx}"} - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", - return_value=False, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", + return_value=False, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): # Import and call the function from litellm.proxy.management_endpoints.mcp_management_endpoints import ( @@ -540,6 +625,71 @@ class TestListMCPServers: assert server.alias == "Allowed Zapier MCP" assert server.url == "https://actions.zapier.com/mcp/sse" + @pytest.mark.asyncio + async def test_admin_user_with_object_permission_respects_mcp_servers(self): + """ + Test that admin users with explicit object_permission.mcp_servers + only see the servers specified in object_permission. + + Scenario: Admin user has object_permission.mcp_servers set to specific servers + Expected: Only those servers are returned, not all servers in the registry + """ + from litellm.proxy._types import LiteLLM_ObjectPermissionTable + + # Create mock object permission with specific servers + mock_object_permission = LiteLLM_ObjectPermissionTable( + object_permission_id="test-obj-perm-id", + mcp_servers=["server-1", "server-2"], # Only these two servers + mcp_access_groups=[], + mcp_tool_permissions={}, + vector_stores=[], + agents=[], + agent_access_groups=[], + ) + + # Create admin user with object permission + mock_user_auth = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + user_id="admin_user_id", + api_key="admin_api_key", + object_permission=mock_object_permission, + object_permission_id="test-obj-perm-id", + ) + + # Mock servers that the user should see + server_1 = generate_mock_mcp_server_db_record( + server_id="server-1", alias="Server 1", url="https://server1.example.com" + ) + server_2 = generate_mock_mcp_server_db_record( + server_id="server-2", alias="Server 2", url="https://server2.example.com" + ) + + # Mock manager + mock_manager = MagicMock() + mock_manager.get_all_allowed_mcp_servers = AsyncMock( + return_value=[server_1, server_2] + ) + + with patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ): + from litellm.proxy.management_endpoints.mcp_management_endpoints import ( + fetch_all_mcp_servers, + ) + + result = await fetch_all_mcp_servers(user_api_key_dict=mock_user_auth) + + # Verify results - should only return the 2 servers in object_permission + assert len(result) == 2 + server_ids = {server.server_id for server in result} + assert server_ids == {"server-1", "server-2"} + + # Verify credentials are redacted + assert all(server.credentials is None for server in result) @pytest.mark.asyncio async def test_fetch_single_mcp_server_redacts_credentials(self): @@ -562,18 +712,23 @@ class TestListMCPServers: user_role=LitellmUserRoles.PROXY_ADMIN ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_mcp_server", - AsyncMock(return_value=mock_server), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.health_check_server", - AsyncMock(return_value=mock_health_result), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", - return_value=True, + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_mcp_server", + AsyncMock(return_value=mock_server), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.health_check_server", + AsyncMock(return_value=mock_health_result), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", + return_value=True, + ), ): from litellm.proxy.management_endpoints.mcp_management_endpoints import ( fetch_mcp_server, @@ -610,18 +765,23 @@ class TestListMCPServers: user_role=LitellmUserRoles.PROXY_ADMIN ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_mcp_server", - AsyncMock(return_value=mock_server), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.health_check_server", - AsyncMock(return_value=mock_health_result), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", - return_value=True, + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_mcp_server", + AsyncMock(return_value=mock_server), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.health_check_server", + AsyncMock(return_value=mock_health_result), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._user_has_admin_view", + return_value=True, + ), ): from litellm.proxy.management_endpoints.mcp_management_endpoints import ( fetch_mcp_server, @@ -763,16 +923,20 @@ class TestTemporaryMCPSessionEndpoints: mock_manager.get_mcp_server_by_id.return_value = inherited_server mock_manager.build_mcp_server_from_table = AsyncMock(return_value=built_server) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.validate_and_normalize_mcp_server_payload", - MagicMock(), - ) as validate_mock, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._cache_temporary_mcp_server", - MagicMock(), - ) as cache_mock: + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.validate_and_normalize_mcp_server_payload", + MagicMock(), + ) as validate_mock, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._cache_temporary_mcp_server", + MagicMock(), + ) as cache_mock, + ): response = await add_session_mcp_server( payload=payload, user_api_key_dict=user_auth, @@ -832,13 +996,16 @@ class TestTemporaryMCPSessionEndpoints: server = generate_mock_mcp_server_config_record(server_id="server-1") authorize_response = MagicMock() - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", - return_value=server, - ) as get_server, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.authorize_with_server", - AsyncMock(return_value=authorize_response), - ) as authorize_mock: + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", + return_value=server, + ) as get_server, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.authorize_with_server", + AsyncMock(return_value=authorize_response), + ) as authorize_mock, + ): result = await mcp_authorize( request=request, server_id="server-1", @@ -875,13 +1042,16 @@ class TestTemporaryMCPSessionEndpoints: server = generate_mock_mcp_server_config_record(server_id="server-1") exchange_response = {"access_token": "token"} - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", - return_value=server, - ) as get_server, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.exchange_token_with_server", - AsyncMock(return_value=exchange_response), - ) as exchange_mock: + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", + return_value=server, + ) as get_server, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.exchange_token_with_server", + AsyncMock(return_value=exchange_response), + ) as exchange_mock, + ): result = await mcp_token( request=request, server_id="server-1", @@ -922,16 +1092,20 @@ class TestTemporaryMCPSessionEndpoints: "token_endpoint_auth_method": "client_secret_basic", } - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", - return_value=server, - ) as get_server, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._read_request_body", - AsyncMock(return_value=request_body), - ) as read_body, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.register_client_with_server", - AsyncMock(return_value=register_response), - ) as register_mock: + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_cached_temporary_mcp_server_or_404", + return_value=server, + ) as get_server, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._read_request_body", + AsyncMock(return_value=request_body), + ) as read_body, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.register_client_with_server", + AsyncMock(return_value=register_response), + ) as register_mock, + ): result = await mcp_register(request=request, server_id="server-1") assert result is register_response @@ -947,6 +1121,7 @@ class TestTemporaryMCPSessionEndpoints: fallback_client_id="server-1", ) + class TestUpdateMCPServer: """Test suite for update MCP server functionality""" @@ -954,7 +1129,7 @@ class TestUpdateMCPServer: async def test_update_mcp_server_respects_extra_headers(self): """ Test that updating an MCP server with extra_headers properly saves the field. - + This test ensures that extra_headers field in UpdateMCPServerRequest is properly handled and persisted when updating an MCP server. """ @@ -999,21 +1174,27 @@ class TestUpdateMCPServer: ) # Mock the update_mcp_server function to capture the call - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", - return_value=mock_prisma_client, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.validate_and_normalize_mcp_server_payload", - MagicMock(), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.update_mcp_server", - AsyncMock(return_value=updated_server), - ) as update_mock, patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.add_server", - AsyncMock(), - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.reload_servers_from_database", - AsyncMock(), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.get_prisma_client_or_throw", + return_value=mock_prisma_client, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.validate_and_normalize_mcp_server_payload", + MagicMock(), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.update_mcp_server", + AsyncMock(return_value=updated_server), + ) as update_mock, + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.add_server", + AsyncMock(), + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager.reload_servers_from_database", + AsyncMock(), + ), ): # Import and call the function from litellm.proxy.management_endpoints.mcp_management_endpoints import ( @@ -1030,7 +1211,10 @@ class TestUpdateMCPServer: # First arg is prisma_client, second is the payload (UpdateMCPServerRequest) called_payload = call_args[0][1] assert called_payload.server_id == "test-server-1" - assert called_payload.extra_headers == ["X-Custom-Header", "X-Another-Header"] + assert called_payload.extra_headers == [ + "X-Custom-Header", + "X-Another-Header", + ] assert called_payload.alias == "Updated Test Server" # Verify the result includes extra_headers @@ -1081,12 +1265,15 @@ class TestHealthCheckServers: return_value=[mock_health_result_1, mock_health_result_2] ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): result = await health_check_servers( server_ids=None, @@ -1130,10 +1317,17 @@ class TestMCPRegistryEndpoint: mock_manager = MagicMock() mock_manager.get_registry.return_value = {mock_server.server_id: mock_server} + # The registry endpoint uses get_filtered_registry (filters by client IP) + mock_manager.get_filtered_registry.return_value = { + mock_server.server_id: mock_server + } - with patch_proxy_general_settings({"enable_mcp_registry": True}), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, + with ( + patch_proxy_general_settings({"enable_mcp_registry": True}), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), ): response = client.get("/v1/mcp/registry.json") @@ -1180,12 +1374,15 @@ class TestMCPRegistryEndpoint: return_value=[mock_health_result] ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): result = await health_check_servers( server_ids=["server-1"], @@ -1243,12 +1440,15 @@ class TestManagementPayloadValidation: return_value=[health_result_one, health_result_two] ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints._get_user_mcp_management_mode", - return_value="view_all", - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints._get_user_mcp_management_mode", + return_value="view_all", + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), ): result = await health_check_servers( server_ids=None, @@ -1293,12 +1493,15 @@ class TestManagementPayloadValidation: return_value=[mock_health_result] # Only server-1 is returned (accessible) ) - with patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", - mock_manager, - ), patch( - "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", - AsyncMock(return_value=[mock_user_auth]), + with ( + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.global_mcp_server_manager", + mock_manager, + ), + patch( + "litellm.proxy.management_endpoints.mcp_management_endpoints.build_effective_auth_contexts", + AsyncMock(return_value=[mock_user_auth]), + ), ): result = await health_check_servers( server_ids=["server-1", "server-unauthorized"], diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index 467ee3661d1..c4c953b75fb 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -38,6 +38,7 @@ from litellm.proxy.management_endpoints.team_endpoints import ( _transform_teams_to_deleted_records, _validate_and_populate_member_user_info, delete_team, + list_available_teams, router, team_member_add_duplication_check, team_member_delete, @@ -4931,6 +4932,291 @@ async def test_update_team_negative_team_member_budget(): assert request.team_member_budget == -15.0 +# Parametrized tests for soft_budget in create endpoint +@pytest.mark.parametrize( + "soft_budget,max_budget,should_succeed,expected_soft_budget,expected_max_budget,error_message", + [ + # Test 1: Soft budget only - success + soft budget set + (50.0, None, True, 50.0, None, None), + # Test 2: Soft budget with higher max budget, success with both set + (50.0, 100.0, True, 50.0, 100.0, None), + # Test 3: Soft budget with lower max budget, fail + (100.0, 50.0, False, None, None, "soft_budget (100.0) must be strictly lower than max_budget (50.0)"), + # Test 4: Soft budget equal to max budget, fail + (100.0, 100.0, False, None, None, "soft_budget (100.0) must be strictly lower than max_budget (100.0)"), + ], +) +@pytest.mark.asyncio +async def test_new_team_soft_budget_validation( + soft_budget, max_budget, should_succeed, expected_soft_budget, expected_max_budget, error_message +): + """ + Test soft_budget validation in /team/new endpoint. + + Covers: + - Soft budget only - success + soft budget set + - Soft budget with higher max budget, success with both set + - Soft budget with lower max budget, fail + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create admin user to bypass user budget checks + admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + user_id="admin-user", + models=[], + ) + + # Create team request with soft_budget and optionally max_budget + team_request = NewTeamRequest( + team_alias="test-soft-budget-team", + soft_budget=soft_budget, + max_budget=max_budget, + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + + # Setup mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.jsonify_team_object = lambda db_data: db_data + mock_prisma.get_data = AsyncMock(return_value=None) + mock_prisma.update_data = AsyncMock() + + # Mock user cache + from litellm.proxy._types import LiteLLM_UserTable + mock_user_obj = LiteLLM_UserTable( + user_id="admin-user", + max_budget=None, # Admin has no budget limit + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Mock team creation + mock_created_team = MagicMock() + mock_created_team.team_id = "test-team-123" + mock_created_team.team_alias = "test-soft-budget-team" + mock_created_team.soft_budget = expected_soft_budget + mock_created_team.max_budget = expected_max_budget + mock_created_team.members_with_roles = [] + mock_created_team.metadata = None + mock_created_team.model_dump.return_value = { + "team_id": "test-team-123", + "team_alias": "test-soft-budget-team", + "soft_budget": expected_soft_budget, + "max_budget": expected_max_budget, + "members_with_roles": [], + } + mock_prisma.db.litellm_teamtable.create = AsyncMock(return_value=mock_created_team) + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_created_team) + + # Mock model table + mock_prisma.db.litellm_modeltable = MagicMock() + mock_prisma.db.litellm_modeltable.create = AsyncMock(return_value=MagicMock(id="model123")) + + # Mock user table operations + mock_user = MagicMock() + mock_user.user_id = "admin-user" + mock_user.model_dump.return_value = {"user_id": "admin-user", "teams": ["test-team-123"]} + mock_prisma.db.litellm_usertable = MagicMock() + mock_prisma.db.litellm_usertable.upsert = AsyncMock(return_value=mock_user) + mock_prisma.db.litellm_usertable.update = AsyncMock(return_value=mock_user) + + # Mock team membership table + mock_membership = MagicMock() + mock_membership.model_dump.return_value = { + "team_id": "test-team-123", + "user_id": "admin-user", + "budget_id": None, + } + mock_prisma.db.litellm_teammembership = MagicMock() + mock_prisma.db.litellm_teammembership.create = AsyncMock(return_value=mock_membership) + + if should_succeed: + # Should NOT raise an exception + result = await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=admin_user, + ) + + # Verify the team was created successfully with correct values + assert result is not None + assert result["team_id"] == "test-team-123" + if expected_soft_budget is not None: + assert result["soft_budget"] == expected_soft_budget + if expected_max_budget is not None: + assert result["max_budget"] == expected_max_budget + else: + # Should raise ProxyException + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + if error_message: + assert error_message in str(exc_info.value.message) + + +# Parametrized tests for soft_budget in update endpoint +@pytest.mark.parametrize( + "existing_soft_budget,existing_max_budget,update_soft_budget,update_max_budget,should_succeed,expected_soft_budget,expected_max_budget,error_message", + [ + # Test 1: Soft budget only (no previous max_budget) - success with soft budget set + (None, None, 50.0, None, True, 50.0, None, None), + # Test 2: Soft budget with max budget - success if soft budget is strictly lower than max budget + (None, None, 50.0, 100.0, True, 50.0, 100.0, None), + # Test 3: Soft budget with max budget - fail if soft budget >= max budget + (None, None, 100.0, 50.0, False, None, None, "soft_budget (100.0) must be strictly lower than max_budget (50.0)"), + # Test 4: Only max budget with existing soft_budget, success with max_budget strictly greater + (50.0, None, None, 100.0, True, 50.0, 100.0, None), + # Test 5: Only max budget with existing soft_budget, fail if max_budget <= soft_budget + (50.0, None, None, 50.0, False, None, None, "max_budget (50.0) must be strictly greater than soft_budget (50.0)"), + # Test 6: Update both soft_budget and max_budget - success if soft < max + (30.0, 100.0, 40.0, 80.0, True, 40.0, 80.0, None), + # Test 7: Update both soft_budget and max_budget - fail if soft >= max + (30.0, 100.0, 80.0, 40.0, False, None, None, "soft_budget (80.0) must be strictly lower than max_budget (40.0)"), + ], +) +@pytest.mark.asyncio +async def test_update_team_soft_budget_validation( + existing_soft_budget, existing_max_budget, update_soft_budget, update_max_budget, + should_succeed, expected_soft_budget, expected_max_budget, error_message +): + """ + Test soft_budget validation in /team/update endpoint. + + Covers: + - Soft budget only (no previous max_budget) - success with soft budget set + - Soft budget with max budget - success if soft budget is strictly lower than max budget, fail otherwise + - Only max budget with existing soft_budget, success with max_budget strictly greater, fail otherwise + """ + from fastapi import Request + + from litellm.proxy._types import ( + LiteLLM_UserTable, + ProxyException, + UpdateTeamRequest, + UserAPIKeyAuth, + ) + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create admin user to bypass user budget checks + admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, + user_id="admin-user", + models=[], + ) + + # Create update request + update_request = UpdateTeamRequest( + team_id="test-team-123", + soft_budget=update_soft_budget, + max_budget=update_max_budget, + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + + # Mock existing team with existing budgets + mock_existing_team = MagicMock() + mock_existing_team.team_id = "test-team-123" + mock_existing_team.organization_id = None + mock_existing_team.soft_budget = existing_soft_budget + mock_existing_team.max_budget = existing_max_budget + mock_existing_team.model_dump.return_value = { + "team_id": "test-team-123", + "organization_id": None, + "soft_budget": existing_soft_budget, + "max_budget": existing_max_budget, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Mock user cache + mock_user_obj = LiteLLM_UserTable( + user_id="admin-user", + max_budget=None, # Admin has no budget limit + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Mock updated team - preserve existing values if not being updated + final_soft_budget = update_soft_budget if update_soft_budget is not None else existing_soft_budget + final_max_budget = update_max_budget if update_max_budget is not None else existing_max_budget + + mock_updated_team = MagicMock() + mock_updated_team.team_id = "test-team-123" + mock_updated_team.organization_id = None + mock_updated_team.soft_budget = final_soft_budget + mock_updated_team.max_budget = final_max_budget + mock_updated_team.model_dump.return_value = { + "team_id": "test-team-123", + "organization_id": None, + "soft_budget": final_soft_budget, + "max_budget": final_max_budget, + } + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_updated_team) + mock_prisma.jsonify_team_object = lambda db_data: db_data + mock_cache.async_set_cache = AsyncMock() # Mock cache set for _cache_team_object + + if should_succeed: + # Should NOT raise an exception + result = await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=admin_user, + ) + + # Verify the team was updated successfully with correct values + assert result is not None + assert result["data"].team_id == "test-team-123" + # Verify soft_budget matches expected value (or final computed value if expected is None) + if expected_soft_budget is not None: + assert result["data"].soft_budget == expected_soft_budget + else: + assert result["data"].soft_budget == final_soft_budget + # Verify max_budget matches expected value (or final computed value if expected is None) + if expected_max_budget is not None: + assert result["data"].max_budget == expected_max_budget + else: + assert result["data"].max_budget == final_max_budget + else: + # Should raise ProxyException + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + if error_message: + assert error_message in str(exc_info.value.message) + + @pytest.mark.asyncio async def test_new_team_positive_budgets_accepted(): """ @@ -5586,3 +5872,37 @@ async def test_validate_and_populate_member_user_info_only_user_id_not_found(): mock_prisma_client.db.litellm_usertable.find_unique.assert_called_once_with( where={"user_id": "nonexistent-user"} ) + + +@pytest.mark.asyncio +async def test_list_available_teams_returns_empty_list_when_none_configured(): + """ + Test that /team/available returns an empty list when no available teams + are configured, instead of raising an exception. + """ + import litellm + + mock_request = MagicMock() + mock_user_key = UserAPIKeyAuth(user_id="test-user", token="fake-token") + + with patch( + "litellm.proxy.proxy_server.prisma_client", mock_prisma_client + ): + # Case 1: default_internal_user_params is None + original = litellm.default_internal_user_params + litellm.default_internal_user_params = None + result = await list_available_teams( + http_request=mock_request, + user_api_key_dict=mock_user_key, + ) + assert result == [] + + # Case 2: default_internal_user_params exists but has no "available_teams" key + litellm.default_internal_user_params = {"some_other_param": "value"} + result = await list_available_teams( + http_request=mock_request, + user_api_key_dict=mock_user_key, + ) + assert result == [] + + litellm.default_internal_user_params = original diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py index 41096503a2e..74d36c0acac 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py +++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py @@ -25,6 +25,8 @@ from litellm.proxy.management_endpoints.ui_sso import ( MicrosoftSSOHandler, SSOAuthenticationHandler, normalize_email, + process_sso_jwt_access_token, + determine_role_from_groups, _setup_team_mappings, ) from litellm.types.proxy.management_endpoints.ui_sso import ( @@ -1298,6 +1300,7 @@ async def test_get_generic_sso_response_with_additional_headers(): # Mock the SSO provider and its methods mock_sso_instance = MagicMock() mock_sso_instance.verify_and_process = AsyncMock(return_value=mock_sso_response) + mock_sso_instance.access_token = None # Avoid triggering JWT decode in process_sso_jwt_access_token mock_sso_class = MagicMock(return_value=mock_sso_instance) @@ -1359,6 +1362,7 @@ async def test_get_generic_sso_response_with_empty_headers(): # Mock the SSO provider and its methods mock_sso_instance = MagicMock() mock_sso_instance.verify_and_process = AsyncMock(return_value=mock_sso_response) + mock_sso_instance.access_token = None # Avoid triggering JWT decode in process_sso_jwt_access_token mock_sso_class = MagicMock(return_value=mock_sso_instance) @@ -2370,47 +2374,6 @@ class TestProcessSSOJWTAccessToken: "groups": ["team1", "team2", "team3"], } - def test_process_sso_jwt_access_token_with_valid_token( - self, mock_jwt_handler, sample_jwt_token, sample_jwt_payload - ): - """Test processing a valid JWT access token with team extraction""" - from litellm.proxy.management_endpoints.ui_sso import ( - process_sso_jwt_access_token, - ) - - # Create a result object without team_ids - result = CustomOpenID( - id="test_user", - email="test@example.com", - first_name="Test", - last_name="User", - display_name="Test User", - provider="generic", - team_ids=[], - ) - - with patch("jwt.decode", return_value=sample_jwt_payload) as mock_jwt_decode: - # Act - process_sso_jwt_access_token( - access_token_str=sample_jwt_token, - sso_jwt_handler=mock_jwt_handler, - result=result, - ) - - # Assert - # Verify JWT was decoded correctly - mock_jwt_decode.assert_called_once_with( - sample_jwt_token, options={"verify_signature": False} - ) - - # Verify team IDs were extracted from JWT - mock_jwt_handler.get_team_ids_from_jwt.assert_called_once_with( - sample_jwt_payload - ) - - # Verify team IDs were set on the result object - assert result.team_ids == ["team1", "team2", "team3"] - def test_process_sso_jwt_access_token_with_existing_team_ids( self, mock_jwt_handler, sample_jwt_token ): @@ -2545,24 +2508,6 @@ class TestProcessSSOJWTAccessToken: mock_jwt_handler.get_team_ids_from_jwt.assert_not_called() assert result.team_ids == [] - def test_process_sso_jwt_access_token_no_sso_jwt_handler(self, sample_jwt_token): - """Test that nothing happens when sso_jwt_handler is None""" - from litellm.proxy.management_endpoints.ui_sso import ( - process_sso_jwt_access_token, - ) - - result = CustomOpenID(id="test_user", email="test@example.com", team_ids=[]) - - with patch("jwt.decode") as mock_jwt_decode: - # Act - process_sso_jwt_access_token( - access_token_str=sample_jwt_token, sso_jwt_handler=None, result=result - ) - - # Assert nothing was processed - mock_jwt_decode.assert_not_called() - assert result.team_ids == [] - def test_process_sso_jwt_access_token_no_result( self, mock_jwt_handler, sample_jwt_token ): @@ -2583,10 +2528,12 @@ class TestProcessSSOJWTAccessToken: mock_jwt_decode.assert_not_called() mock_jwt_handler.get_team_ids_from_jwt.assert_not_called() - def test_process_sso_jwt_access_token_jwt_decode_exception( + def test_process_sso_jwt_access_token_non_decode_exception_propagates( self, mock_jwt_handler, sample_jwt_token ): - """Test that JWT decode exceptions are not caught (should propagate up)""" + """Test that non-DecodeError JWT exceptions still propagate up.""" + import jwt as pyjwt + from litellm.proxy.management_endpoints.ui_sso import ( process_sso_jwt_access_token, ) @@ -2594,19 +2541,16 @@ class TestProcessSSOJWTAccessToken: result = CustomOpenID(id="test_user", email="test@example.com", team_ids=[]) with patch( - "jwt.decode", side_effect=Exception("JWT decode error") + "jwt.decode", side_effect=pyjwt.exceptions.InvalidKeyError("Invalid key") ) as mock_jwt_decode: - # Act & Assert - with pytest.raises(Exception, match="JWT decode error"): + with pytest.raises(pyjwt.exceptions.InvalidKeyError, match="Invalid key"): process_sso_jwt_access_token( access_token_str=sample_jwt_token, sso_jwt_handler=mock_jwt_handler, result=result, ) - # Verify JWT decode was attempted mock_jwt_decode.assert_called_once() - # But team extraction should not have been called mock_jwt_handler.get_team_ids_from_jwt.assert_not_called() def test_process_sso_jwt_access_token_empty_team_ids_from_jwt( @@ -2639,6 +2583,124 @@ class TestProcessSSOJWTAccessToken: # Even empty team IDs should be set assert result.team_ids == [] + def test_process_sso_jwt_access_token_with_opaque_token(self, mock_jwt_handler): + """Test that opaque (non-JWT) access tokens are handled gracefully without raising.""" + from litellm.proxy.management_endpoints.ui_sso import ( + process_sso_jwt_access_token, + ) + + result = CustomOpenID( + id="test_user", + email="test@example.com", + first_name="Test", + last_name="User", + display_name="Test User", + provider="generic", + team_ids=["existing_team"], + user_role=None, + ) + + # Opaque tokens like those from Logto are short random strings, not JWTs + opaque_token = "uTxyjXbS_random_opaque_token_string" + + # Should NOT raise - opaque tokens should be silently skipped + process_sso_jwt_access_token( + access_token_str=opaque_token, + sso_jwt_handler=mock_jwt_handler, + result=result, + ) + + # Result should be untouched + mock_jwt_handler.get_team_ids_from_jwt.assert_not_called() + assert result.team_ids == ["existing_team"] + assert result.user_role is None + + def test_process_sso_jwt_access_token_real_jwt_with_role_and_teams( + self, mock_jwt_handler + ): + """Test that a real JWT containing role and team fields is correctly processed.""" + import jwt as pyjwt + + from litellm.proxy.management_endpoints.ui_sso import ( + process_sso_jwt_access_token, + ) + + payload = { + "sub": "user123", + "email": "admin@example.com", + "role": "proxy_admin", + "groups": ["team_alpha", "team_beta"], + } + real_jwt_token = pyjwt.encode(payload, "test-secret", algorithm="HS256") + + mock_jwt_handler.get_team_ids_from_jwt.return_value = [ + "team_alpha", + "team_beta", + ] + + result = CustomOpenID( + id="user123", + email="admin@example.com", + first_name="Admin", + last_name="User", + display_name="Admin User", + provider="generic", + team_ids=[], + user_role=None, + ) + + process_sso_jwt_access_token( + access_token_str=real_jwt_token, + sso_jwt_handler=mock_jwt_handler, + result=result, + ) + + # Team IDs should be extracted via sso_jwt_handler + mock_jwt_handler.get_team_ids_from_jwt.assert_called_once_with(payload) + assert result.team_ids == ["team_alpha", "team_beta"] + + # Role should be extracted from the "role" field in the JWT + from litellm.proxy._types import LitellmUserRoles + + assert result.user_role == LitellmUserRoles.PROXY_ADMIN + + def test_process_sso_jwt_access_token_real_jwt_without_role_and_teams(self): + """Test that a real JWT without role/team fields leaves result unchanged.""" + import jwt as pyjwt + + from litellm.proxy.management_endpoints.ui_sso import ( + process_sso_jwt_access_token, + ) + + payload = { + "sub": "user456", + "email": "plain@example.com", + "iat": 1700000000, + } + real_jwt_token = pyjwt.encode(payload, "test-secret", algorithm="HS256") + + result = CustomOpenID( + id="user456", + email="plain@example.com", + first_name="Plain", + last_name="User", + display_name="Plain User", + provider="generic", + team_ids=[], + user_role=None, + ) + + # No sso_jwt_handler, no role/team fields in JWT + process_sso_jwt_access_token( + access_token_str=real_jwt_token, + sso_jwt_handler=None, + result=result, + ) + + # Nothing should be modified + assert result.team_ids == [] + assert result.user_role is None + @pytest.mark.asyncio async def test_get_ui_settings_includes_api_doc_base_url(): @@ -3848,3 +3910,339 @@ async def test_setup_team_mappings(): mock_prisma.db.litellm_ssoconfig.find_unique.assert_called_once_with( where={"id": "sso_config"} ) + + +# ============================================================================ +# Tests for get_litellm_user_role with list inputs (Keycloak returns lists) +# ============================================================================ + + +def test_get_litellm_user_role_with_string(): + """Test that get_litellm_user_role works with a plain string.""" + from litellm.proxy._types import LitellmUserRoles + from litellm.proxy.management_endpoints.types import get_litellm_user_role + + result = get_litellm_user_role("proxy_admin") + assert result == LitellmUserRoles.PROXY_ADMIN + + +def test_get_litellm_user_role_with_list(): + """ + Test that get_litellm_user_role handles list inputs. + Keycloak returns roles as arrays like ["proxy_admin"] instead of strings. + """ + from litellm.proxy._types import LitellmUserRoles + from litellm.proxy.management_endpoints.types import get_litellm_user_role + + result = get_litellm_user_role(["proxy_admin"]) + assert result == LitellmUserRoles.PROXY_ADMIN + + +def test_get_litellm_user_role_with_empty_list(): + """Test that get_litellm_user_role returns None for empty lists.""" + from litellm.proxy.management_endpoints.types import get_litellm_user_role + + result = get_litellm_user_role([]) + assert result is None + + +def test_get_litellm_user_role_with_invalid_role(): + """Test that get_litellm_user_role returns None for invalid roles.""" + from litellm.proxy.management_endpoints.types import get_litellm_user_role + + result = get_litellm_user_role("not_a_real_role") + assert result is None + + +def test_get_litellm_user_role_with_list_multiple_roles(): + """Test that get_litellm_user_role takes the first element from a multi-element list.""" + from litellm.proxy._types import LitellmUserRoles + from litellm.proxy.management_endpoints.types import get_litellm_user_role + + result = get_litellm_user_role(["proxy_admin", "internal_user"]) + assert result == LitellmUserRoles.PROXY_ADMIN + + +# ============================================================================ +# Tests for process_sso_jwt_access_token role extraction +# ============================================================================ + + +def test_process_sso_jwt_access_token_extracts_role_from_access_token(): + """ + Test that process_sso_jwt_access_token extracts user role from the JWT + access token when the UserInfo response did not include it. + + This is the core fix for the Keycloak SSO role mapping bug: Keycloak's + UserInfo endpoint does not return role claims, but the JWT access token + contains them. + """ + import jwt as pyjwt + + from litellm.proxy._types import LitellmUserRoles + + # Create a JWT access token with role claims (as Keycloak would) + access_token_payload = { + "sub": "user-123", + "email": "admin@test.com", + "litellm_role": ["proxy_admin"], + } + access_token_str = pyjwt.encode(access_token_payload, "secret", algorithm="HS256") + + # Result object with no role set (simulating UserInfo response without roles) + result = CustomOpenID( + id="user-123", + email="admin@test.com", + display_name="Admin User", + team_ids=[], + user_role=None, + ) + + # Call with GENERIC_USER_ROLE_ATTRIBUTE pointing to litellm_role + with patch.dict(os.environ, {"GENERIC_USER_ROLE_ATTRIBUTE": "litellm_role"}): + process_sso_jwt_access_token( + access_token_str=access_token_str, + sso_jwt_handler=None, + result=result, + role_mappings=None, + ) + + assert result.user_role == LitellmUserRoles.PROXY_ADMIN + + +def test_process_sso_jwt_access_token_does_not_override_existing_role(): + """ + Test that process_sso_jwt_access_token does NOT override a role that was + already extracted from the UserInfo response. + """ + import jwt as pyjwt + + from litellm.proxy._types import LitellmUserRoles + + access_token_payload = { + "sub": "user-123", + "litellm_role": ["internal_user"], + } + access_token_str = pyjwt.encode(access_token_payload, "secret", algorithm="HS256") + + # Result already has a role (e.g., set from UserInfo) + result = CustomOpenID( + id="user-123", + email="admin@test.com", + display_name="Admin User", + team_ids=[], + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + + with patch.dict(os.environ, {"GENERIC_USER_ROLE_ATTRIBUTE": "litellm_role"}): + process_sso_jwt_access_token( + access_token_str=access_token_str, + sso_jwt_handler=None, + result=result, + role_mappings=None, + ) + + # Should keep the original role + assert result.user_role == LitellmUserRoles.PROXY_ADMIN + + +def test_process_sso_jwt_access_token_extracts_role_from_nested_field(): + """ + Test role extraction from a nested JWT field like resource_access.client.roles. + """ + import jwt as pyjwt + + from litellm.proxy._types import LitellmUserRoles + + access_token_payload = { + "sub": "user-123", + "resource_access": { + "my-client": { + "roles": ["proxy_admin"] + } + }, + } + access_token_str = pyjwt.encode(access_token_payload, "secret", algorithm="HS256") + + result = CustomOpenID( + id="user-123", + email="admin@test.com", + display_name="Admin User", + team_ids=[], + user_role=None, + ) + + with patch.dict(os.environ, {"GENERIC_USER_ROLE_ATTRIBUTE": "resource_access.my-client.roles"}): + process_sso_jwt_access_token( + access_token_str=access_token_str, + sso_jwt_handler=None, + result=result, + role_mappings=None, + ) + + assert result.user_role == LitellmUserRoles.PROXY_ADMIN + + +def test_process_sso_jwt_access_token_with_role_mappings(): + """ + Test role extraction using role_mappings (group-based role determination) + from the JWT access token. + """ + import jwt as pyjwt + + from litellm.proxy._types import LitellmUserRoles + from litellm.types.proxy.management_endpoints.ui_sso import RoleMappings + + access_token_payload = { + "sub": "user-123", + "groups": ["keycloak-admins", "developers"], + } + access_token_str = pyjwt.encode(access_token_payload, "secret", algorithm="HS256") + + result = CustomOpenID( + id="user-123", + email="admin@test.com", + display_name="Admin User", + team_ids=[], + user_role=None, + ) + + role_mappings = RoleMappings( + provider="generic", + group_claim="groups", + default_role=LitellmUserRoles.INTERNAL_USER, + roles={ + LitellmUserRoles.PROXY_ADMIN: ["keycloak-admins"], + LitellmUserRoles.INTERNAL_USER: ["developers"], + }, + ) + + process_sso_jwt_access_token( + access_token_str=access_token_str, + sso_jwt_handler=None, + result=result, + role_mappings=role_mappings, + ) + + # Should get highest privilege role + assert result.user_role == LitellmUserRoles.PROXY_ADMIN + +def test_generic_response_convertor_with_extra_attributes(monkeypatch): + """Test that extra attributes are extracted when GENERIC_USER_EXTRA_ATTRIBUTES is set""" + from litellm.proxy.management_endpoints.ui_sso import generic_response_convertor + + monkeypatch.setenv("GENERIC_CLIENT_ID", "test_client") + monkeypatch.setenv("GENERIC_USER_EXTRA_ATTRIBUTES", "custom_field1,custom_field2,custom_field3") + + mock_response = { + "sub": "user-id-123", + "email": "user@example.com", + "given_name": "John", + "family_name": "Doe", + "name": "John Doe", + "provider": "generic", + "custom_field1": "value1", + "custom_field2": ["item1", "item2"], + "custom_field3": {"nested": "data"}, + } + + mock_jwt_handler = MagicMock(spec=JWTHandler) + mock_jwt_handler.get_team_ids_from_jwt.return_value = [] + + result = generic_response_convertor( + response=mock_response, + jwt_handler=mock_jwt_handler, + sso_jwt_handler=None, + role_mappings=None, + ) + + assert result.extra_fields is not None + assert result.extra_fields["custom_field1"] == "value1" + assert result.extra_fields["custom_field2"] == ["item1", "item2"] + assert result.extra_fields["custom_field3"] == {"nested": "data"} + +def test_generic_response_convertor_without_extra_attributes(monkeypatch): + """Test backward compatibility - extra_fields is None when env var not set""" + from litellm.proxy.management_endpoints.ui_sso import generic_response_convertor + + monkeypatch.setenv("GENERIC_CLIENT_ID", "test_client") + # Don't set GENERIC_USER_EXTRA_ATTRIBUTES + + mock_response = { + "sub": "user-id-123", + "email": "user@example.com", + "given_name": "John", + "family_name": "Doe", + "name": "John Doe", + "provider": "generic", + "custom_field1": "value1", + "custom_field2": "value2", + } + + mock_jwt_handler = MagicMock(spec=JWTHandler) + mock_jwt_handler.get_team_ids_from_jwt.return_value = [] + + result = generic_response_convertor( + response=mock_response, + jwt_handler=mock_jwt_handler, + sso_jwt_handler=None, + role_mappings=None, + ) + + assert result.extra_fields is None + +def test_generic_response_convertor_extra_attributes_with_nested_paths(monkeypatch): + """Test that nested paths work with dot notation""" + from litellm.proxy.management_endpoints.ui_sso import generic_response_convertor + + monkeypatch.setenv("GENERIC_CLIENT_ID", "test_client") + monkeypatch.setenv("GENERIC_USER_EXTRA_ATTRIBUTES", "org_info.department,org_info.manager") + + mock_response = { + "sub": "user-id-123", + "email": "user@example.com", + "org_info": { + "department": "Engineering", + "manager": "Jane Smith" + } + } + + mock_jwt_handler = MagicMock(spec=JWTHandler) + mock_jwt_handler.get_team_ids_from_jwt.return_value = [] + + result = generic_response_convertor( + response=mock_response, + jwt_handler=mock_jwt_handler, + sso_jwt_handler=None, + role_mappings=None, + ) + + assert result.extra_fields is not None + assert result.extra_fields["org_info.department"] == "Engineering" + assert result.extra_fields["org_info.manager"] == "Jane Smith" + +def test_generic_response_convertor_extra_attributes_missing_field(monkeypatch): + """Test that missing fields return None""" + from litellm.proxy.management_endpoints.ui_sso import generic_response_convertor + + monkeypatch.setenv("GENERIC_CLIENT_ID", "test_client") + monkeypatch.setenv("GENERIC_USER_EXTRA_ATTRIBUTES", "missing_field,another_missing") + + mock_response = { + "sub": "user-id-123", + "email": "user@example.com", + } + + mock_jwt_handler = MagicMock(spec=JWTHandler) + mock_jwt_handler.get_team_ids_from_jwt.return_value = [] + + result = generic_response_convertor( + response=mock_response, + jwt_handler=mock_jwt_handler, + sso_jwt_handler=None, + role_mappings=None, + ) + + assert result.extra_fields is not None + assert result.extra_fields["missing_field"] is None + assert result.extra_fields["another_missing"] is None \ No newline at end of file diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index daae6d465a7..e50e10352e2 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -1316,6 +1316,133 @@ async def test_delete_pass_through_endpoint_not_found(): assert "not found" in str(exc_info.value.detail).lower() +@pytest.mark.asyncio +async def test_get_pass_through_endpoints_includes_config_and_db(): + """ + Test that get_pass_through_endpoints returns both config-defined and DB endpoints, + with correct is_from_config flag. Config-only endpoints have is_from_config=True, + DB endpoints have is_from_config=False. When same path exists in both, DB overrides. + """ + from litellm.proxy._types import ( + PassThroughEndpointResponse, + PassThroughGenericEndpoint, + UserAPIKeyAuth, + ) + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + get_pass_through_endpoints, + ) + + # Config-defined endpoints (from config file) + config_endpoints = [ + { + "path": "/v1/rerank", + "target": "https://api.cohere.com/v1/rerank", + "headers": {"content-type": "application/json"}, + }, + { + "path": "/v1/config-only", + "target": "https://config.example.com/api", + "headers": {}, + }, + ] + + # DB endpoints (one overlaps with config path, one is DB-only) + db_endpoints = [ + { + "id": "db-endpoint-1", + "path": "/v1/rerank", # Same as config - DB should override + "target": "https://db-override.com/v1/rerank", + "headers": {}, + "include_subpath": False, + }, + { + "id": "db-endpoint-2", + "path": "/db/only", + "target": "https://db-only.example.com/api", + "headers": {}, + "include_subpath": False, + }, + ] + + with patch( + "litellm.proxy.proxy_server.prisma_client", + MagicMock(), + ): + with patch( + "litellm.proxy.pass_through_endpoints.pass_through_endpoints._get_pass_through_endpoints_from_db", + new_callable=AsyncMock, + ) as mock_get_db: + with patch( + "litellm.proxy.pass_through_endpoints.pass_through_endpoints._get_pass_through_endpoints_from_config" + ) as mock_get_config: + db_objects = [ + PassThroughGenericEndpoint(**ep, is_from_config=False) + for ep in db_endpoints + ] + config_objects = [ + PassThroughGenericEndpoint(**ep, is_from_config=True) + for ep in config_endpoints + ] + mock_get_db.return_value = db_objects + mock_get_config.return_value = config_objects + + mock_user = MagicMock(spec=UserAPIKeyAuth) + + result = await get_pass_through_endpoints( + endpoint_id=None, + user_api_key_dict=mock_user, + team_id=None, + ) + + assert isinstance(result, PassThroughEndpointResponse) + # config_only: /v1/config-only (not in db_paths) + # db: /v1/rerank (overrides config), /db/only + # So we should have: /v1/config-only (from config) + /v1/rerank + /db/only (from db) + assert len(result.endpoints) == 3 + + # Check is_from_config values + by_path = {ep.path: ep for ep in result.endpoints} + assert by_path["/v1/config-only"].is_from_config is True + assert by_path["/v1/rerank"].is_from_config is False # DB overrides + assert by_path["/db/only"].is_from_config is False + + # Verify DB override: /v1/rerank should have DB target + assert by_path["/v1/rerank"].target == "https://db-override.com/v1/rerank" + + +def test_get_pass_through_endpoints_from_config_skips_malformed(): + """ + Test that _get_pass_through_endpoints_from_config skips malformed endpoints + and returns only valid ones, without raising. + """ + from litellm.proxy.pass_through_endpoints.pass_through_endpoints import ( + _get_pass_through_endpoints_from_config, + ) + + # Mix of valid and malformed config endpoints + config_passthrough_endpoints = [ + {"path": "/valid/1", "target": "https://valid1.example.com"}, + {}, # Missing required path and target + {"path": "/missing-target"}, # Missing required target + {"target": "https://example.com"}, # Missing required path + {"path": "/valid/2", "target": "https://valid2.example.com", "headers": {}}, + ] + + with patch( + "litellm.proxy.proxy_server.config_passthrough_endpoints", + config_passthrough_endpoints, + ): + result = _get_pass_through_endpoints_from_config() + + # Only the 2 valid endpoints should be returned + assert len(result) == 2 + paths = {ep.path for ep in result} + assert "/valid/1" in paths + assert "/valid/2" in paths + for ep in result: + assert ep.is_from_config is True + + @pytest.mark.asyncio async def test_delete_pass_through_endpoint_empty_list(): """ diff --git a/tests/test_litellm/proxy/policy_engine/test_attachment_registry.py b/tests/test_litellm/proxy/policy_engine/test_attachment_registry.py index 1ed956fe99f..c853253eedd 100644 --- a/tests/test_litellm/proxy/policy_engine/test_attachment_registry.py +++ b/tests/test_litellm/proxy/policy_engine/test_attachment_registry.py @@ -192,6 +192,139 @@ class TestGetAttachedPolicies: assert "strict-policy" not in registry.get_attached_policies(context_wrong_team) +class TestTagBasedAttachments: + """Test tag-based policy attachment matching.""" + + def test_tag_matching_and_wildcards(self): + """Test tag matching: exact match, wildcard match, and no-match cases.""" + registry = AttachmentRegistry() + registry.load_attachments([ + {"policy": "hipaa-policy", "tags": ["healthcare"]}, + {"policy": "health-policy", "tags": ["health-*"]}, + ]) + + # Exact tag match + context = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + attached = registry.get_attached_policies(context) + assert "hipaa-policy" in attached + assert "health-policy" not in attached # "healthcare" doesn't match "health-*" + + # Wildcard tag match + context_wildcard = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["health-prod"], + ) + attached_wildcard = registry.get_attached_policies(context_wildcard) + assert "health-policy" in attached_wildcard + assert "hipaa-policy" not in attached_wildcard + + # No match — wrong tag + context_no_match = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["finance"], + ) + assert registry.get_attached_policies(context_no_match) == [] + + # No match — no tags on context + context_no_tags = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=None, + ) + assert registry.get_attached_policies(context_no_tags) == [] + + def test_tag_combined_with_team(self): + """Test attachment with both tags and teams requires BOTH to match (AND logic).""" + registry = AttachmentRegistry() + registry.load_attachments([ + {"policy": "strict-policy", "teams": ["team-a"], "tags": ["healthcare"]}, + ]) + + # Match — both team and tag match + context = PolicyMatchContext( + team_alias="team-a", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + assert "strict-policy" in registry.get_attached_policies(context) + + # No match — tag matches but team doesn't + context_wrong_team = PolicyMatchContext( + team_alias="team-b", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + assert "strict-policy" not in registry.get_attached_policies(context_wrong_team) + + # No match — team matches but tag doesn't + context_wrong_tag = PolicyMatchContext( + team_alias="team-a", key_alias="key", model="gpt-4", + tags=["finance"], + ) + assert "strict-policy" not in registry.get_attached_policies(context_wrong_tag) + + +class TestMatchAttribution: + """Test get_attached_policies_with_reasons — the attribution logic that + powers response headers and the Policy Simulator UI.""" + + def test_reasons_for_global_tag_team_attachments(self): + """Test that match reasons correctly describe WHY each policy matched.""" + registry = AttachmentRegistry() + registry.load_attachments([ + {"policy": "global-baseline", "scope": "*"}, + {"policy": "hipaa-policy", "tags": ["healthcare"]}, + {"policy": "team-policy", "teams": ["health-team"]}, + ]) + + context = PolicyMatchContext( + team_alias="health-team", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + results = registry.get_attached_policies_with_reasons(context) + reasons = {r["policy_name"]: r["matched_via"] for r in results} + + assert reasons["global-baseline"] == "scope:*" + assert "tag:healthcare" in reasons["hipaa-policy"] + assert "team:health-team" in reasons["team-policy"] + + def test_tags_only_attachment_matches_any_team_key_model(self): + """Test the primary use case: tags-only attachment with no team/key/model + constraint matches any request that carries the tag.""" + registry = AttachmentRegistry() + registry.load_attachments([ + {"policy": "hipaa-guardrails", "tags": ["healthcare"]}, + ]) + + # Should match regardless of team/key/model + context = PolicyMatchContext( + team_alias="random-team", key_alias="random-key", model="claude-3", + tags=["healthcare"], + ) + attached = registry.get_attached_policies(context) + assert "hipaa-guardrails" in attached + + # Should not match without the tag + context_no_tag = PolicyMatchContext( + team_alias="random-team", key_alias="random-key", model="claude-3", + ) + assert registry.get_attached_policies(context_no_tag) == [] + + def test_attachment_with_no_scope_matches_everything(self): + """Test that an attachment with no scope/teams/keys/models/tags + matches everything because teams/keys/models default to ['*'].""" + registry = AttachmentRegistry() + registry.load_attachments([ + {"policy": "catch-all"}, + ]) + + context = PolicyMatchContext( + team_alias="any-team", key_alias="any-key", model="gpt-4", + ) + attached = registry.get_attached_policies(context) + assert "catch-all" in attached + + class TestAttachmentRegistrySingleton: """Test global singleton behavior.""" diff --git a/tests/test_litellm/proxy/policy_engine/test_policy_matcher.py b/tests/test_litellm/proxy/policy_engine/test_policy_matcher.py index c011f31af6a..fccb26496ac 100644 --- a/tests/test_litellm/proxy/policy_engine/test_policy_matcher.py +++ b/tests/test_litellm/proxy/policy_engine/test_policy_matcher.py @@ -64,6 +64,70 @@ class TestPolicyMatcherScopeMatching: assert PolicyMatcher.scope_matches(scope, context) is True +class TestPolicyMatcherScopeMatchingWithTags: + """Test scope matching with tag patterns.""" + + def test_scope_tag_matching(self): + """Test scope tag matching: exact, wildcard, no-match, and empty context tags.""" + # Exact match + scope = PolicyScope(teams=["*"], keys=["*"], models=["*"], tags=["healthcare"]) + context = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["healthcare", "internal"], + ) + assert PolicyMatcher.scope_matches(scope, context) is True + + # Wildcard match + scope_wc = PolicyScope(teams=["*"], keys=["*"], models=["*"], tags=["health-*"]) + context_wc = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["health-prod"], + ) + assert PolicyMatcher.scope_matches(scope_wc, context_wc) is True + + # No match — wrong tag + context_wrong = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", + tags=["finance"], + ) + assert PolicyMatcher.scope_matches(scope, context_wrong) is False + + # No match — context has no tags + context_none = PolicyMatchContext( + team_alias="team", key_alias="key", model="gpt-4", tags=None, + ) + assert PolicyMatcher.scope_matches(scope, context_none) is False + + # Scope without tags matches any context (opt-in semantics) + scope_no_tags = PolicyScope(teams=["*"], keys=["*"], models=["*"]) + assert PolicyMatcher.scope_matches(scope_no_tags, context) is True + + def test_scope_tags_and_team_combined(self): + """Test scope with both tags and team — both must match (AND logic).""" + scope = PolicyScope(teams=["team-a"], keys=["*"], models=["*"], tags=["healthcare"]) + + # Both match + context_both = PolicyMatchContext( + team_alias="team-a", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + assert PolicyMatcher.scope_matches(scope, context_both) is True + + # Tag matches, team doesn't + context_wrong_team = PolicyMatchContext( + team_alias="team-b", key_alias="key", model="gpt-4", + tags=["healthcare"], + ) + assert PolicyMatcher.scope_matches(scope, context_wrong_team) is False + + # Team matches, tag doesn't + context_wrong_tag = PolicyMatchContext( + team_alias="team-a", key_alias="key", model="gpt-4", + tags=["finance"], + ) + assert PolicyMatcher.scope_matches(scope, context_wrong_tag) is False + + class TestPolicyMatcherWithAttachments: """Test getting matching policies via attachments.""" diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py index 13368d0a142..0d4a711812f 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py @@ -12,7 +12,7 @@ sys.path.insert( 0, os.path.abspath("../../../..") ) # Adds the parent directory to the system path -from unittest.mock import MagicMock, patch +from unittest.mock import AsyncMock, MagicMock, patch import litellm import litellm.proxy.proxy_server as ps @@ -205,6 +205,8 @@ ignored_keys = [ "metadata.additional_usage_values.prompt_tokens_details", "metadata.additional_usage_values.cache_creation_input_tokens", "metadata.additional_usage_values.cache_read_input_tokens", + "metadata.additional_usage_values.inference_geo", + "metadata.additional_usage_values.speed", "metadata.litellm_overhead_time_ms", "metadata.cost_breakdown", ] @@ -345,6 +347,196 @@ async def test_ui_view_spend_logs_with_user_id(client, monkeypatch): assert data["data"][0]["user"] == "test_user_1" +# Mock spend logs with distinct values for sorting tests. +# req_a: spend=0.10, tokens=500, start/end earliest +# req_b: spend=0.05, tokens=200, start/end 2nd +# req_c: spend=0.20, tokens=50, start/end latest +# req_d: spend=0.01, tokens=100, start/end 3rd +_SORT_TEST_LOGS = [ + { + "request_id": "req_a", + "api_key": "sk-test-key", + "user": "user1", + "spend": 0.10, + "total_tokens": 500, + "startTime": "2025-01-01T00:00:00+00:00", + "endTime": "2025-01-01T00:01:00+00:00", + "model": "gpt-3.5-turbo", + }, + { + "request_id": "req_b", + "api_key": "sk-test-key", + "user": "user1", + "spend": 0.05, + "total_tokens": 200, + "startTime": "2025-01-01T00:00:01+00:00", + "endTime": "2025-01-01T00:01:01+00:00", + "model": "gpt-3.5-turbo", + }, + { + "request_id": "req_c", + "api_key": "sk-test-key", + "user": "user1", + "spend": 0.20, + "total_tokens": 50, + "startTime": "2025-01-01T00:00:03+00:00", + "endTime": "2025-01-01T00:01:03+00:00", + "model": "gpt-3.5-turbo", + }, + { + "request_id": "req_d", + "api_key": "sk-test-key", + "user": "user1", + "spend": 0.01, + "total_tokens": 100, + "startTime": "2025-01-01T00:00:02+00:00", + "endTime": "2025-01-01T00:01:02+00:00", + "model": "gpt-3.5-turbo", + }, +] + + +def _sort_logs(logs, order_clause): + """Sort logs by the given Prisma-style order clause, e.g. {'spend': 'asc'}.""" + if not order_clause: + return list(logs) + key, direction = next(iter(order_clause.items())) + reverse = direction.lower() == "desc" + return sorted(logs, key=lambda x: x.get(key, 0), reverse=reverse) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "sort_by,sort_order,expected_request_ids", + [ + # spend: 0.01(d) < 0.05(b) < 0.10(a) < 0.20(c) + ("spend", "asc", ["req_d", "req_b", "req_a", "req_c"]), + ("spend", "desc", ["req_c", "req_a", "req_b", "req_d"]), + # total_tokens: 50(c) < 100(d) < 200(b) < 500(a) + ("total_tokens", "asc", ["req_c", "req_d", "req_b", "req_a"]), + ("total_tokens", "desc", ["req_a", "req_b", "req_d", "req_c"]), + # startTime: 00:00:00(a) < 00:00:01(b) < 00:00:02(d) < 00:00:03(c) + ("startTime", "asc", ["req_a", "req_b", "req_d", "req_c"]), + ("startTime", "desc", ["req_c", "req_d", "req_b", "req_a"]), + # endTime: same ordering as startTime + ("endTime", "asc", ["req_a", "req_b", "req_d", "req_c"]), + ("endTime", "desc", ["req_c", "req_d", "req_b", "req_a"]), + # default when sort_by not provided: startTime desc + (None, "desc", ["req_c", "req_d", "req_b", "req_a"]), + ], +) +async def test_ui_view_spend_logs_sort_by_and_sort_order( + client, monkeypatch, sort_by, sort_order, expected_request_ids +): + """Test that spend logs are returned in the correct order for each sort_by/sort_order.""" + base_logs = list(_SORT_TEST_LOGS) + + async def mock_find_many(*args, **kwargs): + order = kwargs.get("order", {}) + return _sort_logs(base_logs, order) + + async def mock_count(*args, **kwargs): + return len(base_logs) + + class MockPrismaClient: + def __init__(self): + self.db = MagicMock() + self.db.litellm_spendlogs = MagicMock() + self.db.litellm_spendlogs.find_many = AsyncMock(side_effect=mock_find_many) + self.db.litellm_spendlogs.count = AsyncMock(side_effect=mock_count) + + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", MockPrismaClient()) + monkeypatch.setattr( + "litellm.proxy.spend_tracking.spend_management_endpoints._is_admin_view_safe", + lambda user_api_key_dict: True, + ) + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + start_date = "2024-12-25 00:00:00" + end_date = "2025-01-02 23:59:59" + + params = { + "start_date": start_date, + "end_date": end_date, + } + if sort_by is not None: + params["sort_by"] = sort_by + if sort_order is not None: + params["sort_order"] = sort_order + + response = client.get( + "/spend/logs/ui", + params=params, + headers={"Authorization": "Bearer sk-test"}, + ) + + assert response.status_code == 200, response.text + data = response.json() + assert "data" in data + + actual_ids = [log["request_id"] for log in data["data"]] + assert actual_ids == expected_request_ids, ( + f"Expected order {expected_request_ids}, got {actual_ids} " + f"(sort_by={sort_by}, sort_order={sort_order})" + ) + finally: + app.dependency_overrides.pop(ps.user_api_key_auth, None) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "sort_by,sort_order", + [ + ("invalid", "asc"), + ("spend", "invalid"), + ], +) +async def test_ui_view_spend_logs_sort_validation_errors( + client, monkeypatch, sort_by, sort_order +): + """Test that invalid sort_by and sort_order return 400.""" + async def mock_count(*args, **kwargs): + return 0 + + class MockPrismaClient: + def __init__(self): + self.db = MagicMock() + self.db.litellm_spendlogs = MagicMock() + self.db.litellm_spendlogs.find_many = AsyncMock(return_value=[]) + self.db.litellm_spendlogs.count = AsyncMock(side_effect=mock_count) + + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", MockPrismaClient()) + monkeypatch.setattr( + "litellm.proxy.spend_tracking.spend_management_endpoints._is_admin_view_safe", + lambda user_api_key_dict: True, + ) + app.dependency_overrides[ps.user_api_key_auth] = lambda: UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin_user" + ) + + try: + start_date = "2024-12-25 00:00:00" + end_date = "2025-01-02 23:59:59" + + response = client.get( + "/spend/logs/ui", + params={ + "start_date": start_date, + "end_date": end_date, + "sort_by": sort_by, + "sort_order": sort_order, + }, + headers={"Authorization": "Bearer sk-test"}, + ) + + assert response.status_code == 400 + finally: + app.dependency_overrides.pop(ps.user_api_key_auth, None) + + @pytest.mark.asyncio async def test_ui_view_spend_logs_with_team_id(client, monkeypatch): # Mock data for the test @@ -1024,6 +1216,85 @@ async def test_ui_view_spend_logs_with_model(client, monkeypatch): assert data["data"][0]["model"] == "gpt-3.5-turbo" +@pytest.mark.asyncio +async def test_ui_view_spend_logs_with_model_id(client, monkeypatch): + """Test that the model_id query param filters spend logs by litellm model deployment id.""" + mock_spend_logs = [ + { + "id": "log1", + "request_id": "req1", + "api_key": "sk-test-key", + "user": "test_user_1", + "team_id": "team1", + "spend": 0.05, + "startTime": datetime.datetime.now(timezone.utc).isoformat(), + "model": "gpt-3.5-turbo", + "model_id": "deployment-id-1", + "status": "success", + }, + { + "id": "log2", + "request_id": "req2", + "api_key": "sk-test-key", + "user": "test_user_2", + "team_id": "team1", + "spend": 0.10, + "startTime": datetime.datetime.now(timezone.utc).isoformat(), + "model": "gpt-4", + "model_id": "deployment-id-2", + "status": "success", + }, + ] + + class MockDB: + async def find_many(self, *args, **kwargs): + if ( + "where" in kwargs + and "model_id" in kwargs["where"] + and kwargs["where"]["model_id"] == "deployment-id-1" + ): + return [mock_spend_logs[0]] + return mock_spend_logs + + async def count(self, *args, **kwargs): + if ( + "where" in kwargs + and "model_id" in kwargs["where"] + and kwargs["where"]["model_id"] == "deployment-id-1" + ): + return 1 + return len(mock_spend_logs) + + class MockPrismaClient: + def __init__(self): + self.db = MockDB() + self.db.litellm_spendlogs = self.db + + mock_prisma_client = MockPrismaClient() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + start_date = ( + datetime.datetime.now(timezone.utc) - datetime.timedelta(days=7) + ).strftime("%Y-%m-%d %H:%M:%S") + end_date = datetime.datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S") + + response = client.get( + "/spend/logs/ui", + params={ + "model_id": "deployment-id-1", + "start_date": start_date, + "end_date": end_date, + }, + headers={"Authorization": "Bearer sk-test"}, + ) + + assert response.status_code == 200 + data = response.json() + assert data["total"] == 1 + assert len(data["data"]) == 1 + assert data["data"][0]["model_id"] == "deployment-id-1" + + @pytest.mark.asyncio async def test_ui_view_spend_logs_with_key_hash(client, monkeypatch): # Mock data for the test diff --git a/tests/test_litellm/proxy/test_api_key_masking_in_errors.py b/tests/test_litellm/proxy/test_api_key_masking_in_errors.py new file mode 100644 index 00000000000..2c16a2fd8bd --- /dev/null +++ b/tests/test_litellm/proxy/test_api_key_masking_in_errors.py @@ -0,0 +1,136 @@ +""" +Tests that API keys are masked in error responses. + +When an invalid/malformed API key is sent (e.g., with a leading space or +wrong prefix), the error response must NOT return the key in plain text. +Instead, it should show only the first 4 and last 4 characters with **** +in the middle. +""" + +import pytest + + +class TestKeyMaskingInAuthErrors: + """Test that user_api_key_auth masks keys in validation error messages.""" + + def test_assert_message_masks_key_without_sk_prefix(self): + """ + When a key doesn't start with 'sk-', the AssertionError message + should contain a masked version, not the full key. + """ + from litellm.proxy.auth.auth_utils import abbreviate_api_key + + # Simulate the logic from user_api_key_auth.py + api_key = "my-secret-api-key-1234567890abcdef" + _masked_key = ( + "{}****{}".format(api_key[:4], api_key[-4:]) + if len(api_key) > 8 + else "****" + ) + + # The masked key should NOT contain the full original key + assert api_key not in _masked_key + # Should show first 4 and last 4 chars + assert _masked_key == "my-s****cdef" + + def test_assert_message_masks_key_with_leading_space(self): + """ + Reported case: key with leading space like ' sk-abc123...' + """ + api_key = " sk-abc123def456ghi789jkl012mno345pqr" + _masked_key = ( + "{}****{}".format(api_key[:4], api_key[-4:]) + if len(api_key) > 8 + else "****" + ) + + assert api_key not in _masked_key + assert _masked_key == " sk-****5pqr" + + def test_assert_message_masks_short_key(self): + """Short keys (<=8 chars) should be fully masked.""" + api_key = "short" + _masked_key = ( + "{}****{}".format(api_key[:4], api_key[-4:]) + if len(api_key) > 8 + else "****" + ) + assert _masked_key == "****" + + def test_key_not_starting_with_sk_raises_masked_error(self): + """ + Verify the assert message format contains masked key, not the original. + + Note: Python's AssertionError str(e) includes the expression + message, + but the *message* part (which is what gets passed to ProxyException) + should only contain the masked key. + """ + api_key = "bad-key-format-1234567890abcdefghijklmnop" + _masked_key = ( + "{}****{}".format(api_key[:4], api_key[-4:]) + if len(api_key) > 8 + else "****" + ) + + # Build the same message string that user_api_key_auth.py would produce + error_message = "LiteLLM Virtual Key expected. Received={}, expected to start with 'sk-'.".format( + _masked_key + ) + # The full key must NOT appear in the message + assert api_key not in error_message + # The masked version should appear + assert _masked_key in error_message + # Should still have helpful context + assert "expected to start with 'sk-'" in error_message + + +class TestKeyMaskingInKeyManagement: + """Test that key_management_endpoints masks keys in validation errors.""" + + def test_invalid_key_format_error_is_masked(self): + """ + When creating a key that doesn't start with 'sk-', the error + should not include the full key value. + """ + key_value = "bad-prefix-1234567890abcdefghijklmnop" + _masked = ( + "{}****{}".format(key_value[:4], key_value[-4:]) + if len(key_value) > 8 + else "****" + ) + + error_msg = f"Invalid key format. LiteLLM Virtual Key must start with 'sk-'. Received: {_masked}" + + # Full key must not appear + assert key_value not in error_msg + # Masked version should appear + assert _masked in error_msg + assert "bad-****mnop" in error_msg + + +class TestPresidioErrorSanitization: + """Test that Presidio errors don't leak request text containing keys.""" + + def test_analyze_text_error_does_not_leak_text(self): + """ + If Presidio analyzer fails, the error message should NOT contain + the original text that was being analyzed. + """ + # Simulate what happens: user message contains an API key, + # Presidio fails, error message should be sanitized + original_text = "Please use this key: sk-secret1234567890abcdefghijklmnop" + + # The sanitized exception from our fix + sanitized_error = f"Presidio PII analysis failed: ConnectionError" + + assert original_text not in sanitized_error + assert "sk-secret1234567890abcdefghijklmnop" not in sanitized_error + + def test_anonymize_text_error_does_not_leak_text(self): + """ + If Presidio anonymizer fails, the error should be sanitized. + """ + sanitized_error = f"Presidio PII anonymization failed: ClientError" + + assert "sk-" not in sanitized_error + assert "api_key" not in sanitized_error diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 69cf8240c63..7bebe00d61e 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -77,6 +77,93 @@ class TestProxyBaseLLMRequestProcessing: pytest.fail("litellm_call_id is not a valid UUID") assert data_passed["litellm_call_id"] == returned_data["litellm_call_id"] + @pytest.mark.asyncio + async def test_should_apply_hierarchical_router_settings_as_override( + self, monkeypatch + ): + """ + Test that hierarchical router settings are stored as router_settings_override + instead of creating a full user_config with model_list. + + This approach avoids expensive per-request Router instantiation by passing + settings as kwargs overrides to the main router. + """ + processing_obj = ProxyBaseLLMRequestProcessing(data={}) + mock_request = MagicMock(spec=Request) + mock_request.headers = {} + + async def mock_add_litellm_data_to_request(*args, **kwargs): + return {} + + async def mock_common_processing_pre_call_logic( + user_api_key_dict, data, call_type + ): + data_copy = copy.deepcopy(data) + return data_copy + + mock_proxy_logging_obj = MagicMock(spec=ProxyLogging) + mock_proxy_logging_obj.pre_call_hook = AsyncMock( + side_effect=mock_common_processing_pre_call_logic + ) + monkeypatch.setattr( + litellm.proxy.common_request_processing, + "add_litellm_data_to_request", + mock_add_litellm_data_to_request, + ) + + mock_general_settings = {} + mock_user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + mock_proxy_config = MagicMock(spec=ProxyConfig) + + mock_router_settings = { + "routing_strategy": "least-busy", + "timeout": 30.0, + "num_retries": 3, + } + mock_proxy_config._get_hierarchical_router_settings = AsyncMock( + return_value=mock_router_settings + ) + + mock_llm_router = MagicMock() + + mock_prisma_client = MagicMock() + monkeypatch.setattr( + "litellm.proxy.proxy_server.prisma_client", + mock_prisma_client, + ) + + route_type = "acompletion" + + returned_data, logging_obj = await processing_obj.common_processing_pre_call_logic( + request=mock_request, + general_settings=mock_general_settings, + user_api_key_dict=mock_user_api_key_dict, + proxy_logging_obj=mock_proxy_logging_obj, + proxy_config=mock_proxy_config, + route_type=route_type, + llm_router=mock_llm_router, + ) + + mock_proxy_config._get_hierarchical_router_settings.assert_called_once_with( + user_api_key_dict=mock_user_api_key_dict, + prisma_client=mock_prisma_client, + proxy_logging_obj=mock_proxy_logging_obj, + ) + # get_model_list should NOT be called - we no longer copy model list for per-request routers + mock_llm_router.get_model_list.assert_not_called() + + # Settings should be stored as router_settings_override (not user_config) + # This allows passing them as kwargs to the main router instead of creating a new one + assert "router_settings_override" in returned_data + assert "user_config" not in returned_data + + router_settings_override = returned_data["router_settings_override"] + assert router_settings_override["routing_strategy"] == "least-busy" + assert router_settings_override["timeout"] == 30.0 + assert router_settings_override["num_retries"] == 3 + # model_list should NOT be in the override settings + assert "model_list" not in router_settings_override + @pytest.mark.asyncio async def test_stream_timeout_header_processing(self): """ diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index acd99090397..d65df0087ad 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -2996,9 +2996,13 @@ async def test_get_image_non_root_uses_var_lib_assets_dir(monkeypatch): monkeypatch.setenv("LITELLM_NON_ROOT", "true") monkeypatch.delenv("UI_LOGO_PATH", raising=False) - # Mock os.path operations + # Mock os.path operations - exists=False for assets_dir so makedirs gets called + def exists_side_effect(path): + return False if path == "/var/lib/litellm/assets" else True + with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, \ - patch("litellm.proxy.proxy_server.os.path.exists", return_value=True), \ + patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \ + patch("litellm.proxy.proxy_server.os.access", return_value=True), \ patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, \ patch("litellm.proxy.proxy_server.FileResponse") as mock_file_response: @@ -3038,14 +3042,16 @@ async def test_get_image_non_root_fallback_to_default_logo(monkeypatch): def exists_side_effect(path): exists_calls.append(path) - # Return False for /var/lib/litellm/assets/logo.jpg to trigger fallback - if "/var/lib/litellm/assets/logo.jpg" in path: + # Return False for /var/lib/litellm/assets* so: makedirs is called, logo fallback + # triggers, and we don't return early with cached file + if "/var/lib/litellm/assets" in path: return False return True # Mock os.path operations with patch("litellm.proxy.proxy_server.os.makedirs") as mock_makedirs, \ patch("litellm.proxy.proxy_server.os.path.exists", side_effect=exists_side_effect), \ + patch("litellm.proxy.proxy_server.os.access", return_value=True), \ patch("litellm.proxy.proxy_server.os.getenv") as mock_getenv, \ patch("litellm.proxy.proxy_server.FileResponse") as mock_file_response: @@ -3203,3 +3209,123 @@ def test_deep_merge_dicts_skips_none_and_empty_lists(monkeypatch): assert result["general_settings"]["nested"]["key1"] == "updated_value1" assert result["general_settings"]["nested"]["key2"] == "value2" assert result["general_settings"]["nested"]["key3"] == "value3" + + +class TestInvitationEndpoints: + """Tests for /invitation/new and /invitation/delete endpoints.""" + + @pytest.fixture + def client_with_auth(self): + """Create a test client with admin authentication.""" + from litellm.proxy._types import LitellmUserRoles + from litellm.proxy.proxy_server import cleanup_router_config_variables + + cleanup_router_config_variables() + filepath = os.path.dirname(os.path.abspath(__file__)) + config_fp = f"{filepath}/test_configs/test_config_no_auth.yaml" + asyncio.run(initialize(config=config_fp, debug=True)) + + mock_auth = MagicMock() + mock_auth.user_id = "admin-user-id" + mock_auth.user_role = LitellmUserRoles.PROXY_ADMIN + mock_auth.api_key = "sk-test" + app.dependency_overrides[user_api_key_auth] = lambda: mock_auth + + return TestClient(app) + + @pytest.mark.parametrize( + "endpoint,payload,mock_return", + [ + ( + "/invitation/new", + {"user_id": "target-user-123"}, + { + "id": "inv-123", + "user_id": "target-user-123", + "is_accepted": False, + "accepted_at": None, + "expires_at": "2025-02-18T00:00:00", + "created_at": "2025-02-11T00:00:00", + "created_by": "admin-user-id", + "updated_at": "2025-02-11T00:00:00", + "updated_by": "admin-user-id", + }, + ), + ( + "/invitation/delete", + {"invitation_id": "inv-456"}, + { + "id": "inv-456", + "user_id": "target-user-123", + "is_accepted": False, + "accepted_at": None, + "expires_at": "2025-02-18T00:00:00", + "created_at": "2025-02-11T00:00:00", + "created_by": "admin-user-id", + "updated_at": "2025-02-11T00:00:00", + "updated_by": "admin-user-id", + }, + ), + ], + ) + def test_invitation_endpoints_proxy_admin_success( + self, client_with_auth, endpoint, payload, mock_return + ): + """Proxy admin can successfully create and delete invitations.""" + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + mock_prisma.db.litellm_invitationlink = MagicMock() + if endpoint == "/invitation/new": + mock_create = AsyncMock(return_value=mock_return) + with patch( + "litellm.proxy.management_helpers.user_invitation.create_invitation_for_user", + mock_create, + ): + response = client_with_auth.post(endpoint, json=payload) + else: + mock_prisma.db.litellm_invitationlink.find_unique = AsyncMock( + return_value={**mock_return, "created_by": "admin-user-id"} + ) + mock_prisma.db.litellm_invitationlink.delete = AsyncMock( + return_value=mock_return + ) + response = client_with_auth.post(endpoint, json=payload) + + assert response.status_code == 200 + data = response.json() + assert data["id"] == mock_return["id"] + assert data["user_id"] == mock_return["user_id"] + + @pytest.mark.parametrize( + "endpoint,payload", + [ + ("/invitation/new", {"user_id": "target-user-123"}), + ("/invitation/delete", {"invitation_id": "inv-456"}), + ], + ) + def test_invitation_endpoints_non_admin_denied( + self, client_with_auth, endpoint, payload + ): + """Non-admin users cannot access invitation endpoints.""" + from litellm.proxy._types import LitellmUserRoles + + mock_auth = MagicMock() + mock_auth.user_id = "regular-user" + mock_auth.user_role = LitellmUserRoles.INTERNAL_USER + mock_auth.api_key = "sk-regular" + app.dependency_overrides[user_api_key_auth] = lambda: mock_auth + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + mock_prisma.db.litellm_invitationlink = MagicMock() + # Avoid triggering async DB calls in _user_has_admin_privileges + with patch( + "litellm.proxy.proxy_server._user_has_admin_privileges", + new_callable=AsyncMock, + return_value=False, + ): + response = client_with_auth.post(endpoint, json=payload) + + assert response.status_code == 400 + body = response.json() + # ProxyException handler returns {"error": {...}}, HTTPException returns {"detail": {...}} + error_content = body.get("error", body.get("detail", body)) + assert "not allowed" in str(error_content).lower() diff --git a/tests/test_litellm/proxy/test_route_a2a_models.py b/tests/test_litellm/proxy/test_route_a2a_models.py new file mode 100644 index 00000000000..1288a9b2c9f --- /dev/null +++ b/tests/test_litellm/proxy/test_route_a2a_models.py @@ -0,0 +1,105 @@ +""" +Test A2A model routing in proxy. + +Maps to: litellm/proxy/agent_endpoints/a2a_routing.py +""" +import os +import sys + +sys.path.insert(0, os.path.abspath("../../..")) + +from unittest.mock import AsyncMock, Mock, patch + +import pytest + +from litellm.proxy.agent_endpoints.a2a_routing import route_a2a_agent_request +from litellm.proxy.route_llm_request import route_request + + +@pytest.mark.asyncio +async def test_route_a2a_model_bypasses_router(): + """Test that a2a/ prefixed models bypass router and go directly to litellm with api_base""" + + # Mock data for chat completion with a2a model + data = { + "model": "a2a/test-agent", + "messages": [{"role": "user", "content": "Hello"}], + } + + # Mock router that doesn't have the a2a model + mock_router = Mock() + mock_router.model_names = ["gpt-4", "gpt-3.5-turbo"] + mock_router.deployment_names = [] + mock_router.has_model_id = Mock(return_value=False) + mock_router.model_group_alias = None + mock_router.router_general_settings = Mock(pass_through_all_models=False) + mock_router.default_deployment = None + mock_router.pattern_router = Mock(patterns=[]) + mock_router.map_team_model = Mock(return_value=None) + + # Mock agent in registry + from litellm.types.agents import AgentResponse + + mock_agent = AgentResponse( + agent_id="test-agent-id", + agent_name="test-agent", + agent_card_params={"url": "http://agent.example.com"}, + litellm_params=None, + ) + + mock_registry = Mock() + mock_registry.get_agent_by_name = Mock(return_value=mock_agent) + + # Mock litellm.acompletion to verify it's called + mock_acompletion = AsyncMock(return_value={"id": "test-response"}) + + with patch("litellm.acompletion", mock_acompletion): + with patch( + "litellm.proxy.agent_endpoints.agent_registry.global_agent_registry", + mock_registry, + ): + result = await route_request( + data=data, + llm_router=mock_router, + user_model=None, + route_type="acompletion", + ) + + # Verify litellm.acompletion was called with api_base injected + mock_acompletion.assert_called_once() + call_kwargs = mock_acompletion.call_args.kwargs + assert call_kwargs["model"] == "a2a/test-agent" + assert call_kwargs["api_base"] == "http://agent.example.com" + + +@pytest.mark.asyncio +async def test_route_non_a2a_model_raises_error_if_not_in_router(): + """Test that non-a2a models that aren't in router raise an error""" + + # Mock data for chat completion with model not in router + data = { + "model": "unknown-model", + "messages": [{"role": "user", "content": "Hello"}], + } + + # Mock router without the model + mock_router = Mock() + mock_router.model_names = ["gpt-4", "gpt-3.5-turbo"] + mock_router.deployment_names = [] + mock_router.has_model_id = Mock(return_value=False) + mock_router.model_group_alias = None + mock_router.router_general_settings = Mock(pass_through_all_models=False) + mock_router.default_deployment = None + mock_router.pattern_router = Mock(patterns=[]) + mock_router.map_team_model = Mock(return_value=None) + + # Should raise ProxyModelNotFoundError + from litellm.proxy.route_llm_request import ProxyModelNotFoundError + + with pytest.raises(ProxyModelNotFoundError): + await route_request( + data=data, + llm_router=mock_router, + user_model=None, + route_type="acompletion", + ) diff --git a/tests/test_litellm/proxy/test_route_llm_request.py b/tests/test_litellm/proxy/test_route_llm_request.py index 90eace63714..1283d2ccbe7 100644 --- a/tests/test_litellm/proxy/test_route_llm_request.py +++ b/tests/test_litellm/proxy/test_route_llm_request.py @@ -137,62 +137,103 @@ async def test_route_request_no_model_required_with_router_settings_and_no_route @pytest.mark.asyncio -async def test_route_request_with_invalid_router_params(): +async def test_route_request_with_router_settings_override(): """ - Test that route_request filters out invalid Router init params from 'user_config'. - This covers the fix for https://github.com/BerriAI/litellm/issues/19693 + Test that route_request handles router_settings_override by merging settings into kwargs + instead of creating a new Router (which is expensive and was the old behavior). """ - import litellm - from litellm.router import Router - from unittest.mock import AsyncMock - - # Mock data with user_config containing invalid keys (simulating DB entry) + # Mock data with router_settings_override containing per-request settings data = { "model": "gpt-3.5-turbo", - "user_config": { - "model_list": [ - { - "model_name": "gpt-3.5-turbo", - "litellm_params": {"model": "gpt-3.5-turbo", "api_key": "test"}, - } - ], - "model_alias_map": {"alias": "real_model"}, # INVALID PARAM - "invalid_garbage_key": "crash_me", # INVALID PARAM + "messages": [{"role": "user", "content": "Hello"}], + "router_settings_override": { + "fallbacks": [{"gpt-3.5-turbo": ["gpt-4"]}], + "num_retries": 5, + "timeout": 30, + "model_group_retry_policy": {"gpt-3.5-turbo": {"RateLimitErrorRetries": 3}}, + # These settings should be ignored (not in per_request_settings list) + "routing_strategy": "least-busy", + "model_group_alias": {"alias": "real_model"}, }, } - # We expect Router(**config) to succeed because of the filtering. - # If filtering fails, this will raise TypeError and fail the test. + llm_router = MagicMock() + llm_router.acompletion.return_value = "success" + + response = await route_request(data, llm_router, None, "acompletion") + + assert response == "success" + # Verify the router method was called with merged settings + call_kwargs = llm_router.acompletion.call_args[1] + assert call_kwargs["fallbacks"] == [{"gpt-3.5-turbo": ["gpt-4"]}] + assert call_kwargs["num_retries"] == 5 + assert call_kwargs["timeout"] == 30 + assert call_kwargs["model_group_retry_policy"] == {"gpt-3.5-turbo": {"RateLimitErrorRetries": 3}} + # Verify unsupported settings were NOT merged + assert "routing_strategy" not in call_kwargs + assert "model_group_alias" not in call_kwargs + # Verify router_settings_override was removed from data + assert "router_settings_override" not in call_kwargs + + +@pytest.mark.asyncio +async def test_route_request_with_router_settings_override_no_router(): + """ + Test that router_settings_override works when no router is provided, + falling back to litellm module directly. + """ + import litellm + + data = { + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello"}], + "router_settings_override": { + "fallbacks": [{"gpt-3.5-turbo": ["gpt-4"]}], + "num_retries": 3, + }, + } + + # Use MagicMock explicitly to avoid auto-AsyncMock behavior in Python 3.12+ + mock_completion = MagicMock(return_value="success") + original_acompletion = litellm.acompletion + litellm.acompletion = mock_completion + try: - # route_request calls getattr(user_router, route_type)(**data) - # We'll mock the internal call to avoid making real network requests - with pytest.MonkeyPatch.context() as m: - # Mock the method that gets called on the router instance - # We don't easily have access to the instance created INSIDE existing route_request - # So we will wrap litellm.Router to spy on it or verify it doesn't crash + response = await route_request(data, None, None, "acompletion") - original_router_init = litellm.Router.__init__ + assert response == "success" + # Verify litellm.acompletion was called with merged settings + call_kwargs = mock_completion.call_args[1] + assert call_kwargs["fallbacks"] == [{"gpt-3.5-turbo": ["gpt-4"]}] + assert call_kwargs["num_retries"] == 3 + finally: + litellm.acompletion = original_acompletion - def safe_router_init(self, **kwargs): - # Verify that invalid keys are NOT present in kwargs - assert "model_alias_map" not in kwargs - assert "invalid_garbage_key" not in kwargs - # Call original init (which would raise TypeError if invalid keys were present) - original_router_init(self, **kwargs) - m.setattr(litellm.Router, "__init__", safe_router_init) +@pytest.mark.asyncio +async def test_route_request_with_router_settings_override_preserves_existing(): + """ + Test that router_settings_override does not override settings already in the request. + Request-level settings take precedence over key/team settings. + """ + data = { + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello"}], + "num_retries": 10, # Request-level setting + "router_settings_override": { + "num_retries": 3, # Key/team setting - should NOT override + "timeout": 30, # Key/team setting - should be applied + }, + } - # Use 'acompletion' as the route_type - # We also need to mock the completion method to avoid real calls - m.setattr(Router, "acompletion", AsyncMock(return_value="success")) + llm_router = MagicMock() + llm_router.acompletion.return_value = "success" - response = await route_request(data, None, None, "acompletion") - assert response == "success" + response = await route_request(data, llm_router, None, "acompletion") - except TypeError as e: - pytest.fail( - f"route_request raised TypeError, implying invalid params were passed to Router: {e}" - ) - except Exception: - # Other exceptions might happen (e.g. valid config issues) but we care about TypeError here - pass + assert response == "success" + call_kwargs = llm_router.acompletion.call_args[1] + # Request-level num_retries should take precedence + assert call_kwargs["num_retries"] == 10 + # Key/team timeout should be applied since not in request + assert call_kwargs["timeout"] == 30 diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py index 5074bbf4397..6d6162437c4 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py @@ -7,6 +7,7 @@ sys.path.insert( from litellm.responses.litellm_completion_transformation.transformation import ( LiteLLMCompletionResponsesConfig, + TOOL_CALLS_CACHE, ) from litellm.types.llms.openai import ( ChatCompletionResponseMessage, @@ -17,6 +18,8 @@ from litellm.types.utils import ( CompletionTokensDetailsWrapper, Message, ModelResponse, + Function, + ChatCompletionMessageToolCall, PromptTokensDetailsWrapper, Usage, ) @@ -755,6 +758,98 @@ class TestFunctionCallTransformation: tool_call = tool_calls[0] assert tool_call.get("id") == "fallback_id" + def test_ensure_tool_results_preserves_cached_openai_object_tool_call(self): + """ + Test cached ChatCompletionMessageToolCall objects are normalized correctly. + """ + tool_call_id = "call_cached_openai_object" + TOOL_CALLS_CACHE.set_cache( + key=tool_call_id, + value=ChatCompletionMessageToolCall( + id=tool_call_id, + type="function", + function=Function( + name="search_web", + arguments='{"query": "python bugs"}', + ), + ), + ) + + messages_missing_tool_calls = [ + {"role": "user", "content": "Search for python bugs"}, + {"role": "assistant", "content": None, "tool_calls": []}, + {"role": "tool", "content": "Found 5 results", "tool_call_id": tool_call_id}, + ] + + try: + fixed_messages = LiteLLMCompletionResponsesConfig._ensure_tool_results_have_corresponding_tool_calls( + messages=messages_missing_tool_calls, + tools=None, + ) + finally: + TOOL_CALLS_CACHE.delete_cache(key=tool_call_id) + + assistant_msg = fixed_messages[1] + tool_calls = assistant_msg.get("tool_calls", []) + assert len(tool_calls) == 1 + + tool_call = tool_calls[0] + function = tool_call.get("function", {}) + assert function.get("name") == "search_web" + assert function.get("arguments") == '{"query": "python bugs"}' + + def test_ensure_tool_results_preserves_cached_attr_object_tool_call(self): + """ + Test cached attribute-only tool call objects are normalized correctly. + """ + + class AttrOnlyFunction: + def __init__(self, name: str, arguments: str): + self.name = name + self.arguments = arguments + + class AttrOnlyToolCall: + def __init__(self, id: str, type: str, function: AttrOnlyFunction): + self.id = id + self.type = type + self.function = function + + tool_call_id = "call_cached_attr_object" + TOOL_CALLS_CACHE.set_cache( + key=tool_call_id, + value=AttrOnlyToolCall( + id=tool_call_id, + type="function", + function=AttrOnlyFunction( + name="search_web", + arguments='{"query": "attribute objects"}', + ), + ), + ) + + messages_missing_tool_calls = [ + {"role": "user", "content": "Search using attr object"}, + {"role": "assistant", "content": None, "tool_calls": []}, + {"role": "tool", "content": "Found 3 results", "tool_call_id": tool_call_id}, + ] + + try: + fixed_messages = LiteLLMCompletionResponsesConfig._ensure_tool_results_have_corresponding_tool_calls( + messages=messages_missing_tool_calls, + tools=None, + ) + finally: + TOOL_CALLS_CACHE.delete_cache(key=tool_call_id) + + assistant_msg = fixed_messages[1] + tool_calls = assistant_msg.get("tool_calls", []) + assert len(tool_calls) == 1 + + tool_call = tool_calls[0] + function = tool_call.get("function", {}) + assert function.get("name") == "search_web" + assert function.get("arguments") == '{"query": "attribute objects"}' + class TestToolChoiceTransformation: """Test the tool_choice transformation fix for Cursor IDE bug""" @@ -1424,6 +1519,47 @@ class TestUsageTransformation: assert response_usage.input_tokens_details is None assert response_usage.output_tokens_details is None + def test_transform_usage_with_image_tokens(self): + """Test that image_tokens from Vertex AI/Gemini are properly transformed to output_tokens_details""" + # Setup: Simulate Vertex AI/Gemini usage with image_tokens in completion_tokens_details + usage = Usage( + prompt_tokens=10, + completion_tokens=150, + total_tokens=160, + completion_tokens_details=CompletionTokensDetailsWrapper( + reasoning_tokens=0, + text_tokens=50, + image_tokens=100, # From Vertex AI candidatesTokensDetails with modality="IMAGE" + ), + ) + + chat_completion_response = ModelResponse( + id="test-response-id", + created=1234567890, + model="gemini-2.0-flash", + object="chat.completion", + usage=usage, + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="Here is the generated image.", role="assistant"), + ) + ], + ) + + # Execute + response_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage( + chat_completion_response=chat_completion_response + ) + + # Assert + assert response_usage.output_tokens == 150 + assert response_usage.output_tokens_details is not None + assert response_usage.output_tokens_details.reasoning_tokens == 0 + assert response_usage.output_tokens_details.text_tokens == 50 + assert response_usage.output_tokens_details.image_tokens == 100 + class TestStreamingIDConsistency: """Test cases for consistent IDs across streaming events (issue #14962)""" @@ -1637,4 +1773,4 @@ class TestStreamingIDConsistency: # Verify it matches the cached ID assert iterator._cached_item_id is not None - assert iterator._cached_item_id == text_done_id \ No newline at end of file + assert iterator._cached_item_id == text_done_id diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_tool_call_streaming_transformation.py b/tests/test_litellm/responses/litellm_completion_transformation/test_tool_call_streaming_transformation.py index 8d324bea611..071eefaef47 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_tool_call_streaming_transformation.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_tool_call_streaming_transformation.py @@ -229,3 +229,164 @@ def test_tool_call_arguments_are_chunked_to_match_openai_behavior(): assert sequence_numbers == sorted(sequence_numbers) assert len(set(sequence_numbers)) == len(sequence_numbers) # All unique + +def test_tool_call_delta_without_id_uses_index_mapping(): + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + + chunks = [ + [ + { + "index": 0, + "id": "call_abc123", + "type": "function", + "function": {"name": "get_weather", "arguments": '{"lo'}, + } + ], + [{"index": 0, "type": "function", "function": {"arguments": 'cation":'}}], + [{"index": 0, "type": "function", "function": {"arguments": ' "New'}}], + [{"index": 0, "type": "function", "function": {"arguments": ' York"}'}}], + ] + + for tool_calls in chunks: + iterator._queue_tool_call_delta_events(tool_calls) + + all_events = [] + while iterator._pending_tool_events: + all_events.append(iterator._pending_tool_events.pop(0)) + + delta_events = [ + evt + for evt in all_events + if evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA + ] + streamed_arguments = "".join(evt.delta for evt in delta_events) + + assert streamed_arguments == '{"location": "New York"}' + + output_item_added_events = [ + evt + for evt in all_events + if evt.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED + ] + assert len(output_item_added_events) == 1 + assert output_item_added_events[0].item.id == "call_abc123" + + +def test_parallel_tool_calls_without_ids_use_index_mapping(): + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_a", + "type": "function", + "function": {"name": "tool_a", "arguments": '{"x":'}, + }, + { + "index": 1, + "id": "call_b", + "type": "function", + "function": {"name": "tool_b", "arguments": '{"y":'}, + }, + ] + ) + iterator._queue_tool_call_delta_events( + [ + {"index": 0, "type": "function", "function": {"arguments": "1}"}}, + {"index": 1, "type": "function", "function": {"arguments": "2}"}}, + ] + ) + + all_events = [] + while iterator._pending_tool_events: + all_events.append(iterator._pending_tool_events.pop(0)) + + output_item_added_events = [ + evt + for evt in all_events + if evt.type == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED + ] + assert len(output_item_added_events) == 2 + + delta_events = [ + evt + for evt in all_events + if evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA + ] + arguments_by_call_id = {} + for evt in delta_events: + arguments_by_call_id.setdefault(evt.item_id, "") + arguments_by_call_id[evt.item_id] += evt.delta + + assert arguments_by_call_id["call_a"] == '{"x":1}' + assert arguments_by_call_id["call_b"] == '{"y":2}' + + +def test_reused_index_with_new_call_id_marks_fallback_ambiguous(): + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=AsyncMock(), + request_input="Test input", + responses_api_request={}, + ) + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_a", + "type": "function", + "function": {"name": "tool_a", "arguments": '{"a":'}, + } + ] + ) + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_b", + "type": "function", + "function": {"name": "tool_b", "arguments": '{"b":'}, + } + ] + ) + # Ambiguous chunk: index reused and id missing. We should skip fallback rather than misroute. + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "type": "function", + "function": {"arguments": "1}"}, + } + ] + ) + + all_events = [] + while iterator._pending_tool_events: + all_events.append(iterator._pending_tool_events.pop(0)) + + delta_events = [ + evt + for evt in all_events + if evt.type == ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA + ] + arguments_by_call_id = {} + for evt in delta_events: + arguments_by_call_id.setdefault(evt.item_id, "") + arguments_by_call_id[evt.item_id] += evt.delta + + assert arguments_by_call_id["call_a"] == '{"a":' + assert arguments_by_call_id["call_b"] == '{"b":' + assert arguments_by_call_id["call_a"] != '{"a":1}' + assert arguments_by_call_id["call_b"] != '{"b":1}' diff --git a/tests/test_litellm/responses/test_responses_api_request_body.py b/tests/test_litellm/responses/test_responses_api_request_body.py new file mode 100644 index 00000000000..9c20d630a1b --- /dev/null +++ b/tests/test_litellm/responses/test_responses_api_request_body.py @@ -0,0 +1,103 @@ +""" +Test that litellm.responses() / litellm.aresponses() send the expected request body +over the wire. Expected JSON bodies are stored in expected_responses_api_request/. +""" +import json +from pathlib import Path +from unittest.mock import AsyncMock, patch + +import httpx +import pytest + +import litellm + + +def _expected_dir() -> Path: + """Path to expected_responses_api_request folder (sibling of test_litellm/responses).""" + return Path(__file__).resolve().parent.parent / "expected_responses_api_request" + + +@pytest.mark.asyncio +async def test_aresponses_context_management_and_shell_request_body_matches_expected(): + """ + Call litellm.aresponses() with context_management and shell tool; + assert the httpx POST request body matches the expected JSON. + """ + expected_path = _expected_dir() / "context_management_and_shell.json" + assert expected_path.exists(), f"Expected file not found: {expected_path}" + with open(expected_path) as f: + expected_body = json.load(f) + + # Minimal Responses API response so parsing succeeds + mock_response = { + "id": "resp_ctx_shell_test", + "object": "response", + "created_at": 1734366691, + "status": "completed", + "model": "gpt-4o", + "output": [ + { + "type": "message", + "id": "msg_1", + "status": "completed", + "role": "assistant", + "content": [ + {"type": "output_text", "text": "Done.", "annotations": []} + ], + } + ], + "parallel_tool_calls": True, + "usage": { + "input_tokens": 10, + "output_tokens": 5, + "total_tokens": 15, + "output_tokens_details": {"reasoning_tokens": 0}, + }, + "error": None, + "incomplete_details": None, + "instructions": None, + "metadata": None, + "temperature": None, + "tool_choice": "auto", + "tools": [], + "top_p": None, + "max_output_tokens": None, + "previous_response_id": None, + "reasoning": None, + "truncation": None, + "user": None, + } + + class MockResponse: + def __init__(self, json_data, status_code=200): + self._json_data = json_data + self.status_code = status_code + self.text = json.dumps(json_data) + self.headers = httpx.Headers({}) + + def json(self): + return self._json_data + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new_callable=AsyncMock, + ) as mock_post: + mock_post.return_value = MockResponse(mock_response, 200) + + await litellm.aresponses( + model="openai/gpt-4o", + input=expected_body["input"], + context_management=expected_body["context_management"], + tools=expected_body["tools"], + tool_choice=expected_body["tool_choice"], + max_output_tokens=expected_body["max_output_tokens"], + ) + + mock_post.assert_called_once() + request_body = mock_post.call_args.kwargs["json"] + + for key, expected_value in expected_body.items(): + assert key in request_body, f"Missing key in request body: {key}" + assert request_body[key] == expected_value, ( + f"Mismatch for key {key}: got {request_body[key]!r}, expected {expected_value!r}" + ) diff --git a/tests/test_litellm/router_unit_tests/test_filter_deployments_by_access_groups.py b/tests/test_litellm/router_unit_tests/test_filter_deployments_by_access_groups.py deleted file mode 100644 index 9ac5072c5d8..00000000000 --- a/tests/test_litellm/router_unit_tests/test_filter_deployments_by_access_groups.py +++ /dev/null @@ -1,227 +0,0 @@ -""" -Unit tests for filter_deployments_by_access_groups function. - -Tests the fix for GitHub issue #18333: Models loadbalanced outside of Model Access Group. -""" - -import pytest - -from litellm.router_utils.common_utils import filter_deployments_by_access_groups - - -class TestFilterDeploymentsByAccessGroups: - """Tests for the filter_deployments_by_access_groups function.""" - - def test_no_filter_when_no_access_groups_in_metadata(self): - """When no allowed_access_groups in metadata, return all deployments.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - {"model_info": {"id": "2", "access_groups": ["AG2"]}}, - ] - request_kwargs = {"metadata": {"user_api_key_team_id": "team-1"}} - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - assert len(result) == 2 # All deployments returned - - def test_filter_to_single_access_group(self): - """Filter to only deployments matching allowed access group.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - {"model_info": {"id": "2", "access_groups": ["AG2"]}}, - ] - request_kwargs = {"metadata": {"user_api_key_allowed_access_groups": ["AG2"]}} - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - assert len(result) == 1 - assert result[0]["model_info"]["id"] == "2" - - def test_filter_with_multiple_allowed_groups(self): - """Filter with multiple allowed access groups.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - {"model_info": {"id": "2", "access_groups": ["AG2"]}}, - {"model_info": {"id": "3", "access_groups": ["AG3"]}}, - ] - request_kwargs = { - "metadata": {"user_api_key_allowed_access_groups": ["AG1", "AG2"]} - } - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - assert len(result) == 2 - ids = [d["model_info"]["id"] for d in result] - assert "1" in ids - assert "2" in ids - assert "3" not in ids - - def test_deployment_with_multiple_access_groups(self): - """Deployment with multiple access groups should match if any overlap.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1", "AG2"]}}, - {"model_info": {"id": "2", "access_groups": ["AG3"]}}, - ] - request_kwargs = {"metadata": {"user_api_key_allowed_access_groups": ["AG2"]}} - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - assert len(result) == 1 - assert result[0]["model_info"]["id"] == "1" - - def test_deployment_without_access_groups_included(self): - """Deployments without access groups should be included (not restricted).""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - {"model_info": {"id": "2"}}, # No access_groups - {"model_info": {"id": "3", "access_groups": []}}, # Empty access_groups - ] - request_kwargs = {"metadata": {"user_api_key_allowed_access_groups": ["AG2"]}} - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - # Should include deployments 2 and 3 (no restrictions) - assert len(result) == 2 - ids = [d["model_info"]["id"] for d in result] - assert "2" in ids - assert "3" in ids - - def test_dict_deployment_passes_through(self): - """When deployment is a dict (specific deployment), pass through.""" - deployment = {"model_info": {"id": "1", "access_groups": ["AG1"]}} - request_kwargs = {"metadata": {"user_api_key_allowed_access_groups": ["AG2"]}} - - result = filter_deployments_by_access_groups( - healthy_deployments=deployment, - request_kwargs=request_kwargs, - ) - - assert result == deployment # Unchanged - - def test_none_request_kwargs_passes_through(self): - """When request_kwargs is None, return deployments unchanged.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - ] - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=None, - ) - - assert result == deployments - - def test_litellm_metadata_fallback(self): - """Should also check litellm_metadata for allowed access groups.""" - deployments = [ - {"model_info": {"id": "1", "access_groups": ["AG1"]}}, - {"model_info": {"id": "2", "access_groups": ["AG2"]}}, - ] - request_kwargs = { - "litellm_metadata": {"user_api_key_allowed_access_groups": ["AG1"]} - } - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - assert len(result) == 1 - assert result[0]["model_info"]["id"] == "1" - - -def test_filter_deployments_by_access_groups_issue_18333(): - """ - Regression test for GitHub issue #18333. - - Scenario: Two models named 'gpt-5' in different access groups (AG1, AG2). - Team2 has access to AG2 only. When Team2 requests 'gpt-5', only the AG2 - deployment should be available for load balancing. - """ - deployments = [ - { - "model_name": "gpt-5", - "litellm_params": {"model": "gpt-4.1", "api_key": "key-1"}, - "model_info": {"id": "ag1-deployment", "access_groups": ["AG1"]}, - }, - { - "model_name": "gpt-5", - "litellm_params": {"model": "gpt-4o", "api_key": "key-2"}, - "model_info": {"id": "ag2-deployment", "access_groups": ["AG2"]}, - }, - ] - - # Team2's request with allowed access groups - request_kwargs = { - "metadata": { - "user_api_key_team_id": "team-2", - "user_api_key_allowed_access_groups": ["AG2"], - } - } - - result = filter_deployments_by_access_groups( - healthy_deployments=deployments, - request_kwargs=request_kwargs, - ) - - # Only AG2 deployment should be returned - assert len(result) == 1 - assert result[0]["model_info"]["id"] == "ag2-deployment" - assert result[0]["litellm_params"]["model"] == "gpt-4o" - - -def test_get_access_groups_from_models(): - """ - Test the helper function that extracts access group names from models list. - This is used by the proxy to populate user_api_key_allowed_access_groups. - """ - from litellm.proxy.auth.model_checks import get_access_groups_from_models - - # Setup: access groups definition - model_access_groups = { - "AG1": ["gpt-4", "gpt-5"], - "AG2": ["claude-v1", "claude-v2"], - "beta-models": ["gpt-5-turbo"], - } - - # Test 1: Extract access groups from models list - models = ["gpt-4", "AG1", "AG2", "some-other-model"] - result = get_access_groups_from_models( - model_access_groups=model_access_groups, models=models - ) - assert set(result) == {"AG1", "AG2"} - - # Test 2: No access groups in models list - models = ["gpt-4", "claude-v1", "some-model"] - result = get_access_groups_from_models( - model_access_groups=model_access_groups, models=models - ) - assert result == [] - - # Test 3: Empty models list - result = get_access_groups_from_models( - model_access_groups=model_access_groups, models=[] - ) - assert result == [] - - # Test 4: All access groups - models = ["AG1", "AG2", "beta-models"] - result = get_access_groups_from_models( - model_access_groups=model_access_groups, models=models - ) - assert set(result) == {"AG1", "AG2", "beta-models"} diff --git a/tests/test_litellm/secret_managers/test_aws_secret_manager_rotation.py b/tests/test_litellm/secret_managers/test_aws_secret_manager_rotation.py new file mode 100644 index 00000000000..83982482623 --- /dev/null +++ b/tests/test_litellm/secret_managers/test_aws_secret_manager_rotation.py @@ -0,0 +1,109 @@ +""" +Regression tests for AWS Secrets Manager same-name in-place rotation fix. + +When current_secret_name == new_secret_name (e.g. key alias preserved during +rotation), AWS must use PutSecretValue to update in place instead of +create+delete, which would fail with ResourceExistsException. +""" +from unittest.mock import AsyncMock, patch + +import pytest + +from litellm.secret_managers.aws_secret_manager_v2 import AWSSecretsManagerV2 + + +@pytest.mark.asyncio +async def test_rotate_secret_same_name_uses_put_secret_value(): + """ + When current_secret_name == new_secret_name, async_rotate_secret should + call PutSecretValue (async_put_secret_value) instead of create+delete. + """ + secret_name = "litellm/tenant/litellm-metis-key" + new_value = "sk-new-rotated-key-value" + + with patch.object( + AWSSecretsManagerV2, + "async_put_secret_value", + new_callable=AsyncMock, + return_value={"ARN": "arn:aws:secretsmanager:us-east-1:123:secret:test"}, + ) as mock_put: + with patch.object( + AWSSecretsManagerV2, + "async_write_secret", + new_callable=AsyncMock, + ) as mock_write: + with patch.object( + AWSSecretsManagerV2, + "async_delete_secret", + new_callable=AsyncMock, + ) as mock_delete: + manager = AWSSecretsManagerV2() + result = await manager.async_rotate_secret( + current_secret_name=secret_name, + new_secret_name=secret_name, + new_secret_value=new_value, + ) + + # PutSecretValue (in-place update) should be called + mock_put.assert_called_once_with( + secret_name=secret_name, + secret_value=new_value, + optional_params=None, + timeout=None, + ) + # Create + delete should NOT be called + mock_write.assert_not_called() + mock_delete.assert_not_called() + assert result["ARN"] == "arn:aws:secretsmanager:us-east-1:123:secret:test" + + +@pytest.mark.asyncio +async def test_rotate_secret_different_names_uses_create_delete(): + """ + When current_secret_name != new_secret_name, async_rotate_secret should + use base class logic (create new, delete old). + """ + current_name = "litellm/old-key-alias" + new_name = "litellm/virtual-key-new-token-id" + new_value = "sk-new-key-value" + + with patch.object( + AWSSecretsManagerV2, + "async_read_secret", + new_callable=AsyncMock, + side_effect=["sk-old-value", new_value], # read old, then read new + ): + with patch.object( + AWSSecretsManagerV2, + "async_write_secret", + new_callable=AsyncMock, + return_value={"ARN": "arn:new"}, + ) as mock_write: + with patch.object( + AWSSecretsManagerV2, + "async_delete_secret", + new_callable=AsyncMock, + return_value={}, + ) as mock_delete: + with patch.object( + AWSSecretsManagerV2, + "async_put_secret_value", + new_callable=AsyncMock, + ) as mock_put: + manager = AWSSecretsManagerV2() + await manager.async_rotate_secret( + current_secret_name=current_name, + new_secret_name=new_name, + new_secret_value=new_value, + ) + + # PutSecretValue should NOT be called (different names) + mock_put.assert_not_called() + # Create + delete should be called + mock_write.assert_called_once() + mock_delete.assert_called_once_with( + secret_name=current_name, + recovery_window_in_days=7, + optional_params=None, + timeout=None, + ) diff --git a/tests/test_litellm/secret_managers/test_secret_managers_main.py b/tests/test_litellm/secret_managers/test_secret_managers_main.py index eaef6956cd5..4a6e303586a 100644 --- a/tests/test_litellm/secret_managers/test_secret_managers_main.py +++ b/tests/test_litellm/secret_managers/test_secret_managers_main.py @@ -46,15 +46,24 @@ def mock_env(): yield os.environ -@patch("litellm.secret_managers.main.oidc_cache") -@patch("litellm.secret_managers.main._get_oidc_http_handler") -@patch("httpx.Client") # Prevent any real HTTP connections -def test_oidc_google_success(mock_httpx_client, mock_get_http_handler, mock_oidc_cache): - mock_oidc_cache.get_cache.return_value = None - mock_handler = MockHTTPHandler(timeout=600.0) - mock_get_http_handler.return_value = mock_handler +def test_oidc_google_success(): + """Test Google OIDC token fetch with mocked handler (no real network calls).""" secret_name = "oidc/google/[invalid url, do not cite]" - result = get_secret(secret_name) + mock_handler = MockHTTPHandler(timeout=600.0) + mock_get_http_handler = Mock(return_value=mock_handler) + mock_oidc_cache = Mock() + mock_oidc_cache.get_cache.return_value = None + + with patch("litellm.secret_managers.main.oidc_cache", mock_oidc_cache): + with patch( + "litellm.secret_managers.main._get_oidc_http_handler", + mock_get_http_handler, + ): + with patch( + "litellm.secret_managers.main.HTTPHandler", + side_effect=lambda timeout=None: mock_handler, + ): + result = get_secret(secret_name) assert result == "mocked_token" assert mock_handler.last_params == {"audience": "[invalid url, do not cite]"} @@ -63,32 +72,49 @@ def test_oidc_google_success(mock_httpx_client, mock_get_http_handler, mock_oidc ) -@patch("litellm.secret_managers.main.oidc_cache") -@patch("litellm.secret_managers.main._get_oidc_http_handler") -def test_oidc_google_cached(mock_get_http_handler, mock_oidc_cache): +def test_oidc_google_cached(): + """Test Google OIDC uses cache and does not call HTTP (no real network calls).""" + secret_name = "oidc/google/[invalid url, do not cite]" + mock_get_http_handler = Mock() + mock_oidc_cache = Mock() mock_oidc_cache.get_cache.return_value = "cached_token" - secret_name = "oidc/google/[invalid url, do not cite]" - result = get_secret(secret_name) + with patch("litellm.secret_managers.main.oidc_cache", mock_oidc_cache): + with patch( + "litellm.secret_managers.main._get_oidc_http_handler", + mock_get_http_handler, + ): + with patch( + "litellm.secret_managers.main.HTTPHandler", + Mock(side_effect=AssertionError("HTTPHandler should not be used")), + ): + result = get_secret(secret_name) assert result == "cached_token", f"Expected cached token, got {result}" mock_oidc_cache.get_cache.assert_called_with(key=secret_name) - # Verify HTTP handler was never called since we had a cached token mock_get_http_handler.assert_not_called() -@patch("litellm.secret_managers.main.oidc_cache") -@patch("litellm.secret_managers.main._get_oidc_http_handler") -def test_oidc_google_failure(mock_get_http_handler, mock_oidc_cache): +def test_oidc_google_failure(): + """Test Google OIDC raises when provider returns error (no real network calls).""" + secret_name = "oidc/google/https://example.com/api" mock_handler = MockHTTPHandler(timeout=600.0) mock_handler.status_code = 400 - mock_get_http_handler.return_value = mock_handler + mock_get_http_handler = Mock(return_value=mock_handler) + mock_oidc_cache = Mock() mock_oidc_cache.get_cache.return_value = None - - secret_name = "oidc/google/https://example.com/api" - with pytest.raises(ValueError, match="Google OIDC provider failed"): - get_secret(secret_name) + with patch("litellm.secret_managers.main.oidc_cache", mock_oidc_cache): + with patch( + "litellm.secret_managers.main._get_oidc_http_handler", + mock_get_http_handler, + ): + with patch( + "litellm.secret_managers.main.HTTPHandler", + side_effect=lambda timeout=None: mock_handler, + ): + with pytest.raises(ValueError, match="Google OIDC provider failed"): + get_secret(secret_name) def test_oidc_circleci_success(monkeypatch): @@ -151,20 +177,18 @@ def test_oidc_azure_file_success(mock_env, tmp_path): @patch("litellm.secret_managers.main.get_azure_ad_token_provider") -@patch.dict(os.environ, {}, clear=False) # Ensure AZURE_FEDERATED_TOKEN_FILE is not set -def test_oidc_azure_ad_token_success(mock_get_azure_ad_token_provider): - # Ensure the env var is not set so it falls through to Azure AD token provider - if "AZURE_FEDERATED_TOKEN_FILE" in os.environ: - del os.environ["AZURE_FEDERATED_TOKEN_FILE"] - +def test_oidc_azure_ad_token_success(mock_get_azure_ad_token_provider, monkeypatch): + # Force-unset so we always hit the Azure AD token provider path (CI may set AZURE_FEDERATED_TOKEN_FILE) + monkeypatch.delenv("AZURE_FEDERATED_TOKEN_FILE", raising=False) + # Mock the token provider function that gets returned and called mock_token_provider = Mock(return_value="azure_ad_token") mock_get_azure_ad_token_provider.return_value = mock_token_provider - + # Also mock the Azure Identity SDK to prevent any real Azure calls with patch("azure.identity.get_bearer_token_provider") as mock_bearer: mock_bearer.return_value = mock_token_provider - + secret_name = "oidc/azure/api://azure-audience" result = get_secret(secret_name) diff --git a/tests/test_litellm/test_a2a_registry_lookup.py b/tests/test_litellm/test_a2a_registry_lookup.py new file mode 100644 index 00000000000..9938f10a43f --- /dev/null +++ b/tests/test_litellm/test_a2a_registry_lookup.py @@ -0,0 +1,73 @@ +""" +Test A2A provider registry lookup functionality. + +Maps to: litellm/llms/a2a/chat/transformation.py +""" +import os +import sys + +sys.path.insert(0, os.path.abspath("../..")) + +import pytest + +import litellm +from litellm.llms.a2a.chat.transformation import A2AConfig + + +def test_resolve_agent_config_from_registry_static_method(): + """Test the static helper method for registry resolution""" + + # Test 1: No agent name in model + api_base, api_key, headers = A2AConfig.resolve_agent_config_from_registry( + model="a2a", + api_base="http://test.com", + api_key=None, + headers=None, + optional_params={} + ) + assert api_base == "http://test.com" + + # Test 2: All params provided - should not lookup registry + api_base, api_key, headers = A2AConfig.resolve_agent_config_from_registry( + model="a2a/test-agent", + api_base="http://explicit.com", + api_key="explicit-key", + headers={"X-Test": "value"}, + optional_params={} + ) + assert api_base == "http://explicit.com" + assert api_key == "explicit-key" + + +def test_a2a_registry_integration(): + """Test registry lookup in proxy context""" + + try: + from litellm.proxy.agent_endpoints.agent_registry import global_agent_registry + from litellm.types.agents import AgentResponse + + # Create test agent + test_agent = AgentResponse( + agent_id="test-id", + agent_name="test-agent", + agent_card_params={"url": "http://registry-url.example.com:9999"}, + litellm_params={"api_key": "registry-key"}, + ) + + # Register and test + original_agents = global_agent_registry.agent_list.copy() + global_agent_registry.register_agent(test_agent) + + try: + litellm.completion( + model="a2a/test-agent", + messages=[{"role": "user", "content": "Hello"}] + ) + except Exception as e: + # Should use registry URL (connection error expected) + assert "registry-url.example.com" in str(e) or "APIConnectionError" in str(type(e).__name__) + finally: + global_agent_registry.agent_list = original_agents + + except ImportError: + pytest.skip("Registry not available (not in proxy context)") diff --git a/tests/test_litellm/test_anthropic_beta_headers_filtering.py b/tests/test_litellm/test_anthropic_beta_headers_filtering.py new file mode 100644 index 00000000000..880e96f40a9 --- /dev/null +++ b/tests/test_litellm/test_anthropic_beta_headers_filtering.py @@ -0,0 +1,423 @@ +""" +Test suite for Anthropic beta headers filtering and mapping across all providers. + +This test validates: +1. Headers with null values in the config are filtered out +2. Headers with non-null values are correctly mapped to provider-specific names +3. Unknown headers (not in config) are filtered out +4. For Bedrock providers, beta headers appear in the request body (not just HTTP headers) +""" +import json +import os +from typing import Dict, List +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +import litellm +from litellm.anthropic_beta_headers_manager import ( + filter_and_transform_beta_headers, +) + + +class TestAnthropicBetaHeadersFiltering: + """Test beta header filtering and mapping for all providers.""" + + @pytest.fixture(autouse=True) + def setup(self): + """Load the beta headers config for testing.""" + config_path = os.path.join( + os.path.dirname(litellm.__file__), + "anthropic_beta_headers_config.json", + ) + with open(config_path, "r") as f: + self.config = json.load(f) + + def get_all_beta_headers(self) -> List[str]: + """Get all beta headers from the anthropic provider config.""" + return list(self.config.get("anthropic", {}).keys()) + + def get_supported_headers(self, provider: str) -> List[str]: + """Get headers with non-null values for a provider.""" + provider_config = self.config.get(provider, {}) + return [ + header for header, value in provider_config.items() if value is not None + ] + + def get_unsupported_headers(self, provider: str) -> List[str]: + """Get headers with null values for a provider.""" + provider_config = self.config.get(provider, {}) + return [header for header, value in provider_config.items() if value is None] + + def get_mapped_headers(self, provider: str) -> Dict[str, str]: + """Get mapping of input headers to provider-specific headers.""" + provider_config = self.config.get(provider, {}) + return { + header: value + for header, value in provider_config.items() + if value is not None + } + + @pytest.mark.parametrize( + "provider", + ["anthropic", "azure_ai", "bedrock_converse", "bedrock", "vertex_ai"], + ) + def test_filter_and_transform_beta_headers_all_headers(self, provider): + """Test filtering with all possible beta headers.""" + all_headers = self.get_all_beta_headers() + supported_headers = self.get_supported_headers(provider) + unsupported_headers = self.get_unsupported_headers(provider) + mapped_headers = self.get_mapped_headers(provider) + + filtered = filter_and_transform_beta_headers( + beta_headers=all_headers, provider=provider + ) + + for header in unsupported_headers: + assert ( + header not in filtered + ), f"Unsupported header '{header}' should be filtered out for {provider}" + assert ( + mapped_headers.get(header) not in filtered + ), f"Mapped value of unsupported header '{header}' should not appear for {provider}" + + for header in supported_headers: + expected_mapped = mapped_headers[header] + assert ( + expected_mapped in filtered + ), f"Supported header '{header}' should be mapped to '{expected_mapped}' for {provider}" + + @pytest.mark.parametrize( + "provider", + ["anthropic", "azure_ai", "bedrock_converse", "bedrock", "vertex_ai"], + ) + def test_unknown_headers_filtered_out(self, provider): + """Test that headers not in the config are filtered out.""" + unknown_headers = [ + "unknown-header-1", + "unknown-header-2", + "fake-beta-2025-01-01", + ] + all_headers = self.get_all_beta_headers() + unknown_headers + + filtered = filter_and_transform_beta_headers( + beta_headers=all_headers, provider=provider + ) + + for unknown in unknown_headers: + assert ( + unknown not in filtered + ), f"Unknown header '{unknown}' should be filtered out for {provider}" + + @pytest.mark.asyncio + async def test_anthropic_messages_http_headers_filtering(self): + """Test that Anthropic messages API filters HTTP headers correctly.""" + all_headers = self.get_all_beta_headers() + unsupported = self.get_unsupported_headers("anthropic") + + with patch( + "litellm.llms.custom_httpx.http_handler.get_async_httpx_client" + ) as mock_client_factory: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "id": "msg_123", + "type": "message", + "role": "assistant", + "content": [{"type": "text", "text": "Hello"}], + "model": "claude-3-5-sonnet-20241022", + "stop_reason": "end_turn", + "usage": {"input_tokens": 10, "output_tokens": 20}, + } + mock_response.headers = {} + + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client_factory.return_value = mock_client + + try: + await litellm.acompletion( + model="anthropic/claude-3-5-sonnet-20241022", + messages=[{"role": "user", "content": "Hi"}], + extra_headers={"anthropic-beta": ",".join(all_headers)}, + mock_response="Hello", + ) + except Exception: + pass + + if mock_client.post.called: + call_kwargs = mock_client.post.call_args.kwargs + headers = call_kwargs.get("headers", {}) + beta_header = headers.get("anthropic-beta", "") + + if beta_header: + beta_values = [b.strip() for b in beta_header.split(",")] + for unsupported_header in unsupported: + assert ( + unsupported_header not in beta_values + ), f"Unsupported header '{unsupported_header}' should not be in HTTP headers for Anthropic" + + @pytest.mark.asyncio + async def test_azure_ai_messages_http_headers_filtering(self): + """Test that Azure AI messages API filters HTTP headers correctly.""" + all_headers = self.get_all_beta_headers() + unsupported = self.get_unsupported_headers("azure_ai") + + with patch( + "litellm.llms.custom_httpx.http_handler.get_async_httpx_client" + ) as mock_client_factory: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "id": "msg_123", + "type": "message", + "role": "assistant", + "content": [{"type": "text", "text": "Hello"}], + "model": "claude-3-5-sonnet-20241022", + "stop_reason": "end_turn", + "usage": {"input_tokens": 10, "output_tokens": 20}, + } + mock_response.headers = {} + + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client_factory.return_value = mock_client + + try: + await litellm.acompletion( + model="azure_ai/claude-3-5-sonnet-20241022", + messages=[{"role": "user", "content": "Hi"}], + api_key="test-key", + api_base="https://test.azure.com", + extra_headers={"anthropic-beta": ",".join(all_headers)}, + mock_response="Hello", + ) + except Exception: + pass + + if mock_client.post.called: + call_kwargs = mock_client.post.call_args.kwargs + headers = call_kwargs.get("headers", {}) + beta_header = headers.get("anthropic-beta", "") + + if beta_header: + beta_values = [b.strip() for b in beta_header.split(",")] + for unsupported_header in unsupported: + assert ( + unsupported_header not in beta_values + ), f"Unsupported header '{unsupported_header}' should not be in HTTP headers for Azure AI" + + @pytest.mark.asyncio + async def test_bedrock_converse_headers_and_body_filtering(self): + """Test that Bedrock Converse filters both HTTP headers and request body correctly.""" + all_headers = self.get_all_beta_headers() + unsupported = self.get_unsupported_headers("bedrock_converse") + mapped_headers = self.get_mapped_headers("bedrock_converse") + + with patch("httpx.AsyncClient") as mock_client_class: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "output": {"message": {"role": "assistant", "content": [{"text": "Hello"}]}}, + "stopReason": "end_turn", + "usage": {"inputTokens": 10, "outputTokens": 20}, + } + mock_response.headers = {} + mock_response.raise_for_status = MagicMock() + + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client_class.return_value.__aenter__.return_value = mock_client + + try: + await litellm.acompletion( + model="bedrock/converse/us.anthropic.claude-3-5-sonnet-20241022-v2:0", + messages=[{"role": "user", "content": "Hi"}], + aws_access_key_id="test", + aws_secret_access_key="test", + aws_region_name="us-east-1", + extra_headers={"anthropic-beta": ",".join(all_headers)}, + mock_response="Hello", + ) + except Exception: + pass + + if mock_client.post.called: + call_kwargs = mock_client.post.call_args.kwargs + headers = call_kwargs.get("headers", {}) + beta_header = headers.get("anthropic-beta", "") + + if beta_header: + beta_values = [b.strip() for b in beta_header.split(",")] + for unsupported_header in unsupported: + assert ( + unsupported_header not in beta_values + ), f"Unsupported header '{unsupported_header}' should not be in HTTP headers for Bedrock Converse" + + data = call_kwargs.get("data") + if data: + body = json.loads(data) + body_beta = body.get("additionalModelRequestFields", {}).get( + "anthropic_beta", [] + ) + + for unsupported_header in unsupported: + assert ( + unsupported_header not in body_beta + ), f"Unsupported header '{unsupported_header}' should not be in request body for Bedrock Converse" + + for header, mapped_value in mapped_headers.items(): + if header in all_headers and mapped_value in body_beta: + assert ( + mapped_value in body_beta + ), f"Supported header '{header}' should be mapped to '{mapped_value}' in request body for Bedrock Converse" + + @pytest.mark.asyncio + async def test_vertex_ai_messages_http_headers_filtering(self): + """Test that Vertex AI messages API filters HTTP headers correctly.""" + all_headers = self.get_all_beta_headers() + unsupported = self.get_unsupported_headers("vertex_ai") + + with patch( + "litellm.llms.custom_httpx.http_handler.get_async_httpx_client" + ) as mock_client_factory: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "id": "msg_123", + "type": "message", + "role": "assistant", + "content": [{"type": "text", "text": "Hello"}], + "model": "claude-3-5-sonnet-20241022", + "stop_reason": "end_turn", + "usage": {"input_tokens": 10, "output_tokens": 20}, + } + mock_response.headers = {} + + mock_client = AsyncMock() + mock_client.post = AsyncMock(return_value=mock_response) + mock_client_factory.return_value = mock_client + + with patch( + "litellm.llms.vertex_ai.vertex_llm_base.VertexBase._ensure_access_token" + ) as mock_token: + mock_token.return_value = ("test-token", "test-project") + + try: + await litellm.acompletion( + model="vertex_ai/claude-3-5-sonnet-20241022", + messages=[{"role": "user", "content": "Hi"}], + vertex_project="test-project", + vertex_location="us-central1", + extra_headers={"anthropic-beta": ",".join(all_headers)}, + mock_response="Hello", + ) + except Exception: + pass + + if mock_client.post.called: + call_kwargs = mock_client.post.call_args.kwargs + headers = call_kwargs.get("headers", {}) + beta_header = headers.get("anthropic-beta", "") + + if beta_header: + beta_values = [b.strip() for b in beta_header.split(",")] + for unsupported_header in unsupported: + assert ( + unsupported_header not in beta_values + ), f"Unsupported header '{unsupported_header}' should not be in HTTP headers for Vertex AI" + + def test_header_mapping_correctness(self): + """Test that headers are mapped correctly for providers with transformations.""" + test_cases = [ + { + "provider": "bedrock", + "input": "advanced-tool-use-2025-11-20", + "expected": "tool-search-tool-2025-10-19", + }, + { + "provider": "vertex_ai", + "input": "advanced-tool-use-2025-11-20", + "expected": "tool-search-tool-2025-10-19", + }, + { + "provider": "anthropic", + "input": "advanced-tool-use-2025-11-20", + "expected": "advanced-tool-use-2025-11-20", + }, + { + "provider": "bedrock_converse", + "input": "computer-use-2025-01-24", + "expected": "computer-use-2025-01-24", + }, + { + "provider": "azure_ai", + "input": "advanced-tool-use-2025-11-20", + "expected": "advanced-tool-use-2025-11-20", + }, + ] + + for test_case in test_cases: + filtered = filter_and_transform_beta_headers( + beta_headers=[test_case["input"]], provider=test_case["provider"] + ) + + assert ( + test_case["expected"] in filtered + ), f"Header '{test_case['input']}' should be mapped to '{test_case['expected']}' for {test_case['provider']}, but got: {filtered}" + + def test_null_value_headers_filtered(self): + """Test that headers with null values are always filtered out.""" + for provider in ["anthropic", "azure_ai", "bedrock_converse", "bedrock", "vertex_ai"]: + unsupported = self.get_unsupported_headers(provider) + + if unsupported: + filtered = filter_and_transform_beta_headers( + beta_headers=unsupported, provider=provider + ) + + assert ( + len(filtered) == 0 + ), f"All null-value headers should be filtered out for {provider}, but got: {filtered}" + + def test_empty_headers_list(self): + """Test that empty headers list returns empty result.""" + for provider in ["anthropic", "azure_ai", "bedrock_converse", "bedrock", "vertex_ai"]: + filtered = filter_and_transform_beta_headers( + beta_headers=[], provider=provider + ) + + assert ( + len(filtered) == 0 + ), f"Empty headers list should return empty result for {provider}" + + def test_mixed_supported_and_unsupported_headers(self): + """Test filtering with a mix of supported, unsupported, and unknown headers.""" + for provider in ["anthropic", "azure_ai", "bedrock_converse", "bedrock", "vertex_ai"]: + supported = self.get_supported_headers(provider) + unsupported = self.get_unsupported_headers(provider) + mapped_headers = self.get_mapped_headers(provider) + + if not supported or not unsupported: + continue + + test_headers = ( + [supported[0]] + + [unsupported[0]] + + ["unknown-header-123"] + ) + + filtered = filter_and_transform_beta_headers( + beta_headers=test_headers, provider=provider + ) + + expected_mapped = mapped_headers[supported[0]] + assert ( + expected_mapped in filtered + ), f"Supported header should be in result for {provider}" + assert ( + unsupported[0] not in filtered + ), f"Unsupported header should not be in result for {provider}" + assert ( + "unknown-header-123" not in filtered + ), f"Unknown header should not be in result for {provider}" diff --git a/tests/test_litellm/test_claude_opus_4_6_config.py b/tests/test_litellm/test_claude_opus_4_6_config.py new file mode 100644 index 00000000000..071d0a26369 --- /dev/null +++ b/tests/test_litellm/test_claude_opus_4_6_config.py @@ -0,0 +1,183 @@ +""" +Validate Claude Opus 4.6 model configuration entries. +""" + +import json +import os + +import litellm + + +def test_opus_4_6_model_pricing_and_capabilities(): + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") + with open(json_path) as f: + model_data = json.load(f) + + expected_models = { + "claude-opus-4-6": { + "provider": "anthropic", + "has_long_context_pricing": True, + "tool_use_system_prompt_tokens": 346, + "max_input_tokens": 1000000, + }, + "claude-opus-4-6-20260205": { + "provider": "anthropic", + "has_long_context_pricing": True, + "tool_use_system_prompt_tokens": 346, + "max_input_tokens": 1000000, + }, + "anthropic.claude-opus-4-6-v1": { + "provider": "bedrock_converse", + "has_long_context_pricing": True, + "tool_use_system_prompt_tokens": 346, + "max_input_tokens": 1000000, + }, + "vertex_ai/claude-opus-4-6": { + "provider": "vertex_ai-anthropic_models", + "has_long_context_pricing": True, + "tool_use_system_prompt_tokens": 346, + "max_input_tokens": 1000000, + }, + "azure_ai/claude-opus-4-6": { + "provider": "azure_ai", + "has_long_context_pricing": False, + "tool_use_system_prompt_tokens": 159, + "max_input_tokens": 200000, + }, + } + + for model_name, config in expected_models.items(): + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + + assert info["litellm_provider"] == config["provider"] + assert info["mode"] == "chat" + assert info["max_input_tokens"] == config["max_input_tokens"] + assert info["max_output_tokens"] == 128000 + assert info["max_tokens"] == 128000 + + assert info["input_cost_per_token"] == 5e-06 + assert info["output_cost_per_token"] == 2.5e-05 + assert info["cache_creation_input_token_cost"] == 6.25e-06 + assert info["cache_read_input_token_cost"] == 5e-07 + + if config["has_long_context_pricing"]: + assert info["input_cost_per_token_above_200k_tokens"] == 1e-05 + assert info["output_cost_per_token_above_200k_tokens"] == 3.75e-05 + assert info["cache_creation_input_token_cost_above_200k_tokens"] == 1.25e-05 + assert info["cache_read_input_token_cost_above_200k_tokens"] == 1e-06 + + assert info["supports_assistant_prefill"] is False + assert info["supports_function_calling"] is True + assert info["supports_prompt_caching"] is True + assert info["supports_reasoning"] is True + assert info["supports_tool_choice"] is True + assert info["supports_vision"] is True + assert info["tool_use_system_prompt_tokens"] == config["tool_use_system_prompt_tokens"] + + +def test_opus_4_6_bedrock_regional_model_pricing(): + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") + with open(json_path) as f: + model_data = json.load(f) + + expected_models = { + "global.anthropic.claude-opus-4-6-v1": { + "input_cost_per_token": 5e-06, + "output_cost_per_token": 2.5e-05, + "cache_creation_input_token_cost": 6.25e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token_above_200k_tokens": 1e-05, + "output_cost_per_token_above_200k_tokens": 3.75e-05, + "cache_creation_input_token_cost_above_200k_tokens": 1.25e-05, + "cache_read_input_token_cost_above_200k_tokens": 1e-06, + }, + "us.anthropic.claude-opus-4-6-v1": { + "input_cost_per_token": 5.5e-06, + "output_cost_per_token": 2.75e-05, + "cache_creation_input_token_cost": 6.875e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + }, + "eu.anthropic.claude-opus-4-6-v1": { + "input_cost_per_token": 5.5e-06, + "output_cost_per_token": 2.75e-05, + "cache_creation_input_token_cost": 6.875e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + }, + "apac.anthropic.claude-opus-4-6-v1": { + "input_cost_per_token": 5.5e-06, + "output_cost_per_token": 2.75e-05, + "cache_creation_input_token_cost": 6.875e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + }, + "apac.anthropic.claude-opus-4-6-v1": { + "input_cost_per_token": 5.5e-06, + "output_cost_per_token": 2.75e-05, + "cache_creation_input_token_cost": 6.875e-06, + "cache_read_input_token_cost": 5.5e-07, + "input_cost_per_token_above_200k_tokens": 1.1e-05, + "output_cost_per_token_above_200k_tokens": 4.125e-05, + "cache_creation_input_token_cost_above_200k_tokens": 1.375e-05, + "cache_read_input_token_cost_above_200k_tokens": 1.1e-06, + }, + } + + for model_name, expected in expected_models.items(): + assert model_name in model_data, f"Missing model entry: {model_name}" + info = model_data[model_name] + assert info["litellm_provider"] == "bedrock_converse" + assert info["max_input_tokens"] == 1000000 + assert info["max_output_tokens"] == 128000 + assert info["max_tokens"] == 128000 + assert info["supports_assistant_prefill"] is False + assert info["tool_use_system_prompt_tokens"] == 346 + for key, value in expected.items(): + assert info[key] == value + + +def test_opus_4_6_alias_and_dated_metadata_match(): + json_path = os.path.join(os.path.dirname(__file__), "../../model_prices_and_context_window.json") + with open(json_path) as f: + model_data = json.load(f) + + alias = model_data["claude-opus-4-6"] + dated = model_data["claude-opus-4-6-20260205"] + + keys_to_match = [ + "max_input_tokens", + "max_output_tokens", + "max_tokens", + "input_cost_per_token", + "output_cost_per_token", + "cache_creation_input_token_cost", + "cache_creation_input_token_cost_above_1hr", + "cache_read_input_token_cost", + "input_cost_per_token_above_200k_tokens", + "output_cost_per_token_above_200k_tokens", + "cache_creation_input_token_cost_above_200k_tokens", + "cache_read_input_token_cost_above_200k_tokens", + "supports_assistant_prefill", + "tool_use_system_prompt_tokens", + ] + for key in keys_to_match: + assert alias[key] == dated[key], f"Mismatch for {key}" + + +def test_opus_4_6_bedrock_converse_registration(): + assert "anthropic.claude-opus-4-6-v1" in litellm.BEDROCK_CONVERSE_MODELS + assert "global.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models + assert "us.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models + assert "eu.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models + assert "apac.anthropic.claude-opus-4-6-v1" in litellm.bedrock_converse_models diff --git a/tests/test_litellm/test_constants.py b/tests/test_litellm/test_constants.py index 77f2f308f88..23447a02e04 100644 --- a/tests/test_litellm/test_constants.py +++ b/tests/test_litellm/test_constants.py @@ -38,6 +38,11 @@ def test_all_numeric_constants_can_be_overridden(): print("all numeric constants", json.dumps(numeric_constants, indent=4)) + # Constants that use a different env var name than the constant name + constant_to_env_var = { + "MAX_CALLBACKS": "LITELLM_MAX_CALLBACKS", + } + # Verify all numeric constants have environment variable support for name, value in numeric_constants: # Skip constants that are not meant to be overridden (if any) @@ -47,8 +52,11 @@ def test_all_numeric_constants_can_be_overridden(): # Create a test value that's different from the default test_value = value + 1 if isinstance(value, int) else value + 0.1 + # Use the env var name that the constants module actually reads + env_var_name = constant_to_env_var.get(name, name) + # Set the environment variable - with mock.patch.dict(os.environ, {name: str(test_value)}): + with mock.patch.dict(os.environ, {env_var_name: str(test_value)}): print("overriding", name, "with", test_value) importlib.reload(constants) diff --git a/tests/test_litellm/test_deepseek_model_metadata.py b/tests/test_litellm/test_deepseek_model_metadata.py new file mode 100644 index 00000000000..4900af5d97d --- /dev/null +++ b/tests/test_litellm/test_deepseek_model_metadata.py @@ -0,0 +1,180 @@ +""" +Regression tests for #20885 – ``supports_response_schema`` (and related +capability flags) must be consistent between the bare model-name entry +(e.g. ``deepseek-chat``) and the provider-prefixed entry +(e.g. ``deepseek/deepseek-chat``) in the model-cost map. + +The bug caused ``supports_response_schema("deepseek/deepseek-chat")`` to +return ``False`` even though the canonical ``deepseek-chat`` entry has the +field set to ``True``. +""" + +import json +import os +import sys + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.utils import ( + _supports_factory, + supports_response_schema, +) + + +# --------------------------------------------------------------------------- +# Data-level tests – verify the JSON files are in sync +# --------------------------------------------------------------------------- + + +def _load_backup_json() -> dict: + """Load the backup JSON directly from disk.""" + backup_path = os.path.join( + os.path.dirname(litellm.__file__), + "model_prices_and_context_window_backup.json", + ) + with open(backup_path, encoding="utf-8") as f: + return json.load(f) + + +class TestDeepSeekModelCostEntries: + """Verify that provider-prefixed DeepSeek entries contain the same + capability flags as their bare-name counterparts in the JSON files.""" + + def test_deepseek_chat_supports_response_schema_in_backup(self): + data = _load_backup_json() + entry = data.get("deepseek/deepseek-chat", {}) + assert entry.get("supports_response_schema") is True + + def test_deepseek_reasoner_supports_response_schema_in_backup(self): + data = _load_backup_json() + entry = data.get("deepseek/deepseek-reasoner", {}) + assert entry.get("supports_response_schema") is True + + def test_deepseek_chat_supports_system_messages_in_backup(self): + data = _load_backup_json() + entry = data.get("deepseek/deepseek-chat", {}) + assert entry.get("supports_system_messages") is True + + def test_deepseek_reasoner_supports_system_messages_in_backup(self): + data = _load_backup_json() + entry = data.get("deepseek/deepseek-reasoner", {}) + assert entry.get("supports_system_messages") is True + + def test_deepseek_chat_max_input_tokens_matches_bare_in_backup(self): + data = _load_backup_json() + bare = data.get("deepseek-chat", {}) + prefixed = data.get("deepseek/deepseek-chat", {}) + assert prefixed.get("max_input_tokens") == bare.get("max_input_tokens") + + def test_deepseek_reasoner_max_output_tokens_matches_bare_in_backup(self): + data = _load_backup_json() + bare = data.get("deepseek-reasoner", {}) + prefixed = data.get("deepseek/deepseek-reasoner", {}) + assert prefixed.get("max_output_tokens") == bare.get("max_output_tokens") + + def test_main_json_deepseek_chat_supports_response_schema(self): + main_path = os.path.join( + os.path.dirname(os.path.dirname(litellm.__file__)), + "model_prices_and_context_window.json", + ) + with open(main_path, encoding="utf-8") as f: + data = json.load(f) + entry = data.get("deepseek/deepseek-chat", {}) + assert entry.get("supports_response_schema") is True + + def test_main_json_deepseek_reasoner_supports_response_schema(self): + main_path = os.path.join( + os.path.dirname(os.path.dirname(litellm.__file__)), + "model_prices_and_context_window.json", + ) + with open(main_path, encoding="utf-8") as f: + data = json.load(f) + entry = data.get("deepseek/deepseek-reasoner", {}) + assert entry.get("supports_response_schema") is True + + +# --------------------------------------------------------------------------- +# API-level tests – verify supports_response_schema returns True +# --------------------------------------------------------------------------- + + +class TestSupportsResponseSchemaDeepSeek: + """All calling conventions for DeepSeek should return True for + ``supports_response_schema``.""" + + def test_provider_slash_model(self): + assert supports_response_schema(model="deepseek/deepseek-chat") is True + + def test_explicit_provider(self): + assert ( + supports_response_schema( + model="deepseek-chat", custom_llm_provider="deepseek" + ) + is True + ) + + def test_reasoner_provider_slash_model(self): + assert supports_response_schema(model="deepseek/deepseek-reasoner") is True + + def test_reasoner_explicit_provider(self): + assert ( + supports_response_schema( + model="deepseek-reasoner", custom_llm_provider="deepseek" + ) + is True + ) + + +# --------------------------------------------------------------------------- +# Fallback-logic test – bare model entry used when prefixed is incomplete +# --------------------------------------------------------------------------- + + +class TestBareModelFallback: + """When a provider-prefixed entry is missing a capability flag, the + ``_supports_factory`` fallback should consult the bare model-name + entry in ``litellm.model_cost``.""" + + def test_fallback_uses_bare_entry(self): + """Temporarily remove ``supports_response_schema`` from the prefixed + entry and verify the fallback still returns True.""" + key = "deepseek/deepseek-chat" + original = litellm.model_cost.get(key, {}).get("supports_response_schema") + try: + # Simulate the pre-fix state: field missing from prefixed entry + if key in litellm.model_cost: + litellm.model_cost[key].pop("supports_response_schema", None) + result = _supports_factory( + model="deepseek-chat", + custom_llm_provider="deepseek", + key="supports_response_schema", + ) + assert result is True + finally: + # Restore + if key in litellm.model_cost and original is not None: + litellm.model_cost[key]["supports_response_schema"] = original + + def test_no_fallback_when_explicitly_false(self): + """If the prefixed entry explicitly sets a capability to ``False``, + the fallback must NOT override it.""" + key = "deepseek/deepseek-reasoner" + # After the data fix, deepseek/deepseek-reasoner has + # supports_function_calling=false (matching the bare entry). + # Explicitly set it to False to test the guard. + original = litellm.model_cost.get(key, {}).get("supports_function_calling") + try: + if key in litellm.model_cost: + litellm.model_cost[key]["supports_function_calling"] = False + result = _supports_factory( + model="deepseek-reasoner", + custom_llm_provider="deepseek", + key="supports_function_calling", + ) + assert result is False + finally: + if key in litellm.model_cost and original is not None: + litellm.model_cost[key]["supports_function_calling"] = original diff --git a/tests/test_litellm/test_exception_exports.py b/tests/test_litellm/test_exception_exports.py new file mode 100644 index 00000000000..cde26295bad --- /dev/null +++ b/tests/test_litellm/test_exception_exports.py @@ -0,0 +1,31 @@ +""" +Test that all standard HTTP error exceptions are exported from litellm.__init__. +""" + +import litellm + + +def test_permission_denied_error_is_exported(): + """PermissionDeniedError (403) should be accessible as litellm.PermissionDeniedError.""" + assert hasattr(litellm, "PermissionDeniedError") + assert litellm.PermissionDeniedError is not None + + +def test_all_http_error_exceptions_exported(): + """All standard HTTP error exceptions should be accessible at module level.""" + expected_exceptions = [ + "BadRequestError", # 400 + "AuthenticationError", # 401 + "PermissionDeniedError", # 403 + "NotFoundError", # 404 + "Timeout", # 408 + "UnprocessableEntityError", # 422 + "RateLimitError", # 429 + "InternalServerError", # 500 + "BadGatewayError", # 502 + "ServiceUnavailableError", # 503 + ] + for exc_name in expected_exceptions: + assert hasattr(litellm, exc_name), ( + f"litellm.{exc_name} is not exported from litellm.__init__" + ) diff --git a/tests/test_litellm/test_logging.py b/tests/test_litellm/test_logging.py index 7e5931d8c0f..6f65ada7459 100644 --- a/tests/test_litellm/test_logging.py +++ b/tests/test_litellm/test_logging.py @@ -1,27 +1,21 @@ import asyncio -import datetime import json import os import sys -import unittest -from typing import List, Optional, Tuple -from unittest.mock import ANY, MagicMock, Mock, patch +from typing import List -import httpx import pytest sys.path.insert( 0, os.path.abspath("../../..") ) # Adds the parent directory to the system-path -import io import logging import sys -import unittest -from contextlib import redirect_stdout import litellm from litellm._logging import ( ALL_LOGGERS, + JsonFormatter, _initialize_loggers_with_handler, _turn_on_json, verbose_logger, @@ -72,6 +66,117 @@ def test_json_mode_emits_one_record_per_logger(capfd): assert "timestamp" in obj, "`timestamp` key missing" +def test_json_formatter_parses_embedded_json_message(): + """ + Test that JsonFormatter parses embedded JSON in the message field and promotes + sub-fields to first-class JSON properties for downstream querying. + """ + formatter = JsonFormatter() + record = logging.LogRecord( + name="LiteLLM", + level=logging.DEBUG, + pathname="", + lineno=0, + msg='{"event": "giveup", "exception": "Connection failed", "model_name": "gpt-4"}', + args=(), + exc_info=None, + ) + output = formatter.format(record) + obj = json.loads(output) + # Standard fields preserved + assert "message" in obj + assert obj["level"] == "DEBUG" + assert "timestamp" in obj + # Embedded JSON fields promoted to top-level for querying + assert obj["event"] == "giveup" + assert obj["exception"] == "Connection failed" + assert obj["model_name"] == "gpt-4" + + +def test_json_formatter_includes_extra_attributes(): + """ + Test that JsonFormatter includes extra attributes from logger.debug("msg", extra={...}). + """ + formatter = JsonFormatter() + record = logging.LogRecord( + name="LiteLLM", + level=logging.DEBUG, + pathname="", + lineno=0, + msg="POST Request Sent from LiteLLM", + args=(), + exc_info=None, + ) + record.api_base = "https://api.openai.com" + record.authorization = "Bearer sk-***" + output = formatter.format(record) + obj = json.loads(output) + assert obj["message"] == "POST Request Sent from LiteLLM" + assert obj["api_base"] == "https://api.openai.com" + assert obj["authorization"] == "Bearer sk-***" + + +def test_json_formatter_plain_message_unchanged(): + """ + Test that non-JSON messages are passed through as-is in the message field. + """ + formatter = JsonFormatter() + record = logging.LogRecord( + name="LiteLLM", + level=logging.INFO, + pathname="", + lineno=0, + msg="Cache hit!", + args=(), + exc_info=None, + ) + output = formatter.format(record) + obj = json.loads(output) + assert obj["message"] == "Cache hit!" + assert "event" not in obj + assert "exception" not in obj + + +def test_json_formatter_parses_embedded_python_dict_repr(): + """ + Test that JsonFormatter parses Python dict repr (str/deployment) embedded in + plain text, e.g. from get_available_deployment logs. + Reproduces Roni's reported case. + """ + formatter = JsonFormatter() + msg = ( + "get_available_deployment for model: text-embedding-3-large, " + "Selected deployment: {'model_name': 'text-embedding-3-large', " + "'litellm_params': {'api_key': 'sk**********', 'tpm': 1000000, 'rpm': 2000, " + "'use_in_pass_through': False, 'use_litellm_proxy': False, " + "'merge_reasoning_content_in_choices': False, 'model': 'text-embedding-3-large'}, " + "'model_info': {'id': 'a624b057aec64ada48311', 'db_model': False}} " + "for model: text-embedding-3-large" + ) + record = logging.LogRecord( + name="LiteLLM Router", + level=logging.INFO, + pathname="", + lineno=0, + msg=msg, + args=(), + exc_info=None, + ) + output = formatter.format(record) + obj = json.loads(output) + assert "message" in obj + assert obj["level"] == "INFO" + # Python dict parsed and promoted to first-class properties + assert obj["model_name"] == "text-embedding-3-large" + assert "litellm_params" in obj + assert obj["litellm_params"]["api_key"] == "sk**********" + assert obj["litellm_params"]["tpm"] == 1000000 + assert obj["litellm_params"]["use_in_pass_through"] is False + assert "model_info" in obj + assert obj["model_info"]["id"] == "a624b057aec64ada48311" + assert obj["model_info"]["db_model"] is False + + def test_initialize_loggers_with_handler_sets_propagate_false(): """ Test that the initialize_loggers_with_handler function sets propagate to False for all loggers @@ -96,7 +201,7 @@ async def test_cache_hit_includes_custom_llm_provider(): test_custom_logger = CacheHitCustomLogger() original_callbacks = litellm.callbacks.copy() if litellm.callbacks else [] litellm.callbacks = [test_custom_logger] - + try: # First call - should be a cache miss response1 = await litellm.acompletion( @@ -105,10 +210,10 @@ async def test_cache_hit_includes_custom_llm_provider(): mock_response="test response", caching=True, ) - + # Wait for logging to complete await asyncio.sleep(0.5) - + # Second identical call - should be a cache hit response2 = await litellm.acompletion( model="gpt-3.5-turbo", @@ -116,38 +221,43 @@ async def test_cache_hit_includes_custom_llm_provider(): mock_response="test response", caching=True, ) - + # Wait for logging to complete await asyncio.sleep(0.5) - + # Verify we have logged events - assert len(test_custom_logger.logged_standard_logging_payloads) >= 2, \ - f"Expected at least 2 logged events, got {len(test_custom_logger.logged_standard_logging_payloads)}" - + assert ( + len(test_custom_logger.logged_standard_logging_payloads) >= 2 + ), f"Expected at least 2 logged events, got {len(test_custom_logger.logged_standard_logging_payloads)}" + # Find the cache hit event (should be the second call) cache_hit_payload = None for payload in test_custom_logger.logged_standard_logging_payloads: if payload.get("cache_hit") is True: cache_hit_payload = payload break - + # Verify cache hit event was found - assert cache_hit_payload is not None, "No cache hit event found in logged payloads" - + assert ( + cache_hit_payload is not None + ), "No cache hit event found in logged payloads" + # Verify custom_llm_provider is included in the cache hit payload - assert "custom_llm_provider" in cache_hit_payload, \ - "custom_llm_provider missing from cache hit standard logging payload" - + assert ( + "custom_llm_provider" in cache_hit_payload + ), "custom_llm_provider missing from cache hit standard logging payload" + # Verify custom_llm_provider has a valid value (should be "openai" for gpt-3.5-turbo) custom_llm_provider = cache_hit_payload["custom_llm_provider"] - assert custom_llm_provider is not None and custom_llm_provider != "", \ - f"custom_llm_provider should not be None or empty, got: {custom_llm_provider}" - + assert ( + custom_llm_provider is not None and custom_llm_provider != "" + ), f"custom_llm_provider should not be None or empty, got: {custom_llm_provider}" + print( f"Cache hit standard logging payload with custom_llm_provider: {custom_llm_provider}", json.dumps(cache_hit_payload, indent=2), ) - + finally: # Clean up litellm.callbacks = original_callbacks diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 08ae804ea80..9dcb16b545e 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -1869,3 +1869,215 @@ async def test_aguardrail(): assert result["result"] == "success" assert result["selected_guardrail"]["id"] == "guardrail-1" + +@pytest.mark.asyncio +async def test_anthropic_messages_call_type_is_cached(): + """ + Regression test: Verify that anthropic_messages call type is allowed + in PromptCachingDeploymentCheck.async_log_success_event. + """ + import asyncio + from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import ( + PromptCachingDeploymentCheck, + ) + from litellm.router_utils.prompt_caching_cache import PromptCachingCache + from litellm.caching.dual_cache import DualCache + from litellm.types.utils import CallTypes + from litellm.types.utils import ( + StandardLoggingPayload, + StandardLoggingModelInformation, + StandardLoggingMetadata, + StandardLoggingHiddenParams, + ) + + # Create mock standard logging payload inline + def create_standard_logging_payload() -> StandardLoggingPayload: + return StandardLoggingPayload( + id="test_id", + call_type="completion", + response_cost=0.1, + response_cost_failure_debug_info=None, + status="success", + total_tokens=30, + prompt_tokens=20, + completion_tokens=10, + startTime=1234567890.0, + endTime=1234567891.0, + completionStartTime=1234567890.5, + model_map_information=StandardLoggingModelInformation( + model_map_key="gpt-3.5-turbo", model_map_value=None + ), + model="gpt-3.5-turbo", + model_id="model-123", + model_group="openai-gpt", + api_base="https://api.openai.com", + metadata=StandardLoggingMetadata( + user_api_key_hash="test_hash", + user_api_key_org_id=None, + user_api_key_alias="test_alias", + user_api_key_team_id="test_team", + user_api_key_user_id="test_user", + user_api_key_team_alias="test_team_alias", + spend_logs_metadata=None, + requester_ip_address="127.0.0.1", + requester_metadata=None, + ), + cache_hit=False, + cache_key=None, + saved_cache_cost=0.0, + request_tags=[], + end_user=None, + requester_ip_address="127.0.0.1", + messages=[{"role": "user", "content": "Hello, world!"}], + response={"choices": [{"message": {"content": "Hi there!"}}]}, + error_str=None, + model_parameters={"stream": True}, + hidden_params=StandardLoggingHiddenParams( + model_id="model-123", + cache_key=None, + api_base="https://api.openai.com", + response_cost="0.1", + additional_headers=None, + ), + ) + + cache = DualCache() + deployment_check = PromptCachingDeploymentCheck(cache=cache) + prompt_cache = PromptCachingCache(cache=cache) + + # Create messages with enough tokens to pass the caching threshold + test_messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "test long message here" * 1024, + "cache_control": { + "type": "ephemeral", + "ttl": "5m" + } + } + ] + } + ] + test_model_id = "test-model-id-123" + + # Create a payload with anthropic_messages call type + payload = create_standard_logging_payload() + payload["call_type"] = CallTypes.anthropic_messages.value + payload["messages"] = test_messages + payload["model"] = "anthropic/claude-3-5-sonnet-20240620" + payload["model_id"] = test_model_id + + # Log the success event (should cache the model_id) + await deployment_check.async_log_success_event( + kwargs={"standard_logging_object": payload}, + response_obj={}, + start_time=1234567890.0, + end_time=1234567891.0, + ) + + # Small delay to ensure cache write completes + await asyncio.sleep(0.1) + + # Verify that the model_id was actually cached + cached_result = await prompt_cache.async_get_model_id( + messages=test_messages, + tools=None, + ) + + # This assertion will FAIL if anthropic_messages is filtered out + assert cached_result is not None, "Model ID should be cached for anthropic_messages call type" + assert cached_result["model_id"] == test_model_id, f"Expected {test_model_id}, got {cached_result['model_id']}" + + +def test_update_kwargs_with_deployment_propagates_model_tags(): + """ + Test that deployment-level tags from litellm_params are merged into + kwargs metadata when _update_kwargs_with_deployment is called. + + This ensures model-level tags defined in config.yaml appear in SpendLogs. + See: https://github.com/BerriAI/litellm/issues/XXXX + """ + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o-mini", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key", + "tags": ["openai-account", "production"], + }, + }, + ], + ) + + kwargs: dict = {"metadata": {}} + deployment = router.get_deployment_by_model_group_name( + model_group_name="gpt-4o-mini" + ) + router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + + # Deployment tags should be propagated to kwargs metadata + assert "tags" in kwargs["metadata"] + assert "openai-account" in kwargs["metadata"]["tags"] + assert "production" in kwargs["metadata"]["tags"] + + +def test_update_kwargs_with_deployment_merges_tags_without_duplicates(): + """ + Test that when both request-level and deployment-level tags exist, + they are merged without duplicates. + """ + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o-mini", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key", + "tags": ["openai-account", "shared-tag"], + }, + }, + ], + ) + + # Simulate request that already has tags (from request body or key/team level) + kwargs: dict = {"metadata": {"tags": ["user-tag", "shared-tag"]}} + deployment = router.get_deployment_by_model_group_name( + model_group_name="gpt-4o-mini" + ) + router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + + # Both sources should be merged, no duplicates + assert "user-tag" in kwargs["metadata"]["tags"] + assert "openai-account" in kwargs["metadata"]["tags"] + assert "shared-tag" in kwargs["metadata"]["tags"] + assert kwargs["metadata"]["tags"].count("shared-tag") == 1 + + +def test_update_kwargs_with_deployment_no_tags(): + """ + Test that when deployment has no tags, kwargs metadata is not affected. + """ + router = litellm.Router( + model_list=[ + { + "model_name": "gpt-4o-mini", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "fake-key", + }, + }, + ], + ) + + kwargs: dict = {"metadata": {}} + deployment = router.get_deployment_by_model_group_name( + model_group_name="gpt-4o-mini" + ) + router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs) + + # No tags key should be added if deployment has no tags + assert "tags" not in kwargs["metadata"] diff --git a/tests/test_litellm/test_router_model_cost_isolation.py b/tests/test_litellm/test_router_model_cost_isolation.py new file mode 100644 index 00000000000..2112295e040 --- /dev/null +++ b/tests/test_litellm/test_router_model_cost_isolation.py @@ -0,0 +1,264 @@ +""" +Test that per-deployment custom pricing does not pollute the shared backend +model key in litellm.model_cost. + +When two deployments share the same backend model (e.g. vertex_ai/gemini-2.5-flash) +and one has explicit zero-cost pricing in model_info, the other deployment +should still use the built-in pricing. +""" + +import os +import sys + +import pytest + +sys.path.insert( + 0, os.path.abspath("../../..") +) # Adds the parent directory to the system path + +import litellm +from litellm import Router + + +def test_should_not_pollute_shared_key_with_zero_cost_pricing(): + """ + When deployment A has input_cost_per_token=0 and deployment B has no + custom pricing, deployment B should still report the built-in pricing + (not zero). + """ + backend_model = "vertex_ai/gemini-2.5-flash" + + # Grab built-in pricing before creating any router + builtin_info = litellm.get_model_info(model=backend_model) + builtin_input_cost = builtin_info["input_cost_per_token"] + builtin_output_cost = builtin_info["output_cost_per_token"] + + # Sanity: built-in pricing should be non-zero for this model + assert builtin_input_cost > 0, "Test requires a model with non-zero built-in pricing" + assert builtin_output_cost > 0, "Test requires a model with non-zero built-in pricing" + + router = Router( + model_list=[ + # Deployment A: explicit zero-cost pricing + { + "model_name": "custom-zero-cost-model", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-1", + }, + "model_info": { + "id": "deployment-a-zero-cost", + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + }, + }, + # Deployment B: no custom pricing, relies on built-in + { + "model_name": "standard-cost-model", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-2", + }, + "model_info": { + "id": "deployment-b-builtin-cost", + }, + }, + ], + ) + + # Deployment A: should report zero pricing via its unique model_id + info_a = router.get_deployment_model_info( + model_id="deployment-a-zero-cost", + model_name=backend_model, + ) + assert info_a is not None + assert info_a["input_cost_per_token"] == 0.0 + assert info_a["output_cost_per_token"] == 0.0 + + # Deployment B: should report built-in pricing, NOT zero + info_b = router.get_deployment_model_info( + model_id="deployment-b-builtin-cost", + model_name=backend_model, + ) + assert info_b is not None + assert info_b["input_cost_per_token"] == builtin_input_cost, ( + f"Deployment B should use built-in input cost {builtin_input_cost}, " + f"got {info_b['input_cost_per_token']}" + ) + assert info_b["output_cost_per_token"] == builtin_output_cost, ( + f"Deployment B should use built-in output cost {builtin_output_cost}, " + f"got {info_b['output_cost_per_token']}" + ) + + +def test_should_not_pollute_shared_key_with_custom_nonzero_pricing(): + """ + A deployment with custom (non-zero) pricing should not overwrite + the shared backend key's built-in pricing. + """ + backend_model = "vertex_ai/gemini-2.5-flash" + + builtin_info = litellm.get_model_info(model=backend_model) + builtin_input_cost = builtin_info["input_cost_per_token"] + + router = Router( + model_list=[ + # Deployment with custom high pricing + { + "model_name": "expensive-model", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-3", + }, + "model_info": { + "id": "deployment-expensive", + "input_cost_per_token": 0.99, + "output_cost_per_token": 0.99, + }, + }, + # Deployment relying on built-in pricing + { + "model_name": "standard-model", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-4", + }, + "model_info": { + "id": "deployment-standard", + }, + }, + ], + ) + + # Custom pricing deployment should see its custom values + info_expensive = router.get_deployment_model_info( + model_id="deployment-expensive", + model_name=backend_model, + ) + assert info_expensive is not None + assert info_expensive["input_cost_per_token"] == 0.99 + assert info_expensive["output_cost_per_token"] == 0.99 + + # Standard deployment should still see built-in pricing + info_standard = router.get_deployment_model_info( + model_id="deployment-standard", + model_name=backend_model, + ) + assert info_standard is not None + assert info_standard["input_cost_per_token"] == builtin_input_cost, ( + f"Standard deployment should use built-in pricing {builtin_input_cost}, " + f"got {info_standard['input_cost_per_token']}" + ) + + +def test_should_store_full_pricing_under_deployment_model_id(): + """ + Per-deployment pricing (including zero) should be stored and + retrievable via the unique model_id key in litellm.model_cost. + """ + backend_model = "vertex_ai/gemini-2.5-flash" + + router = Router( + model_list=[ + { + "model_name": "zero-cost-model", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-5", + }, + "model_info": { + "id": "deployment-zero-check", + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + }, + }, + ], + ) + + # The model_id entry should exist and have the zero pricing + entry = litellm.model_cost.get("deployment-zero-check") + assert entry is not None, "Deployment should be registered by model_id" + assert entry["input_cost_per_token"] == 0.0 + assert entry["output_cost_per_token"] == 0.0 + + +def test_should_preserve_builtin_pricing_regardless_of_deployment_order(): + """ + The built-in pricing should be preserved no matter which deployment + is processed first (zero-cost first, or standard first). + """ + backend_model = "vertex_ai/gemini-2.5-flash" + + builtin_info = litellm.get_model_info(model=backend_model) + builtin_input_cost = builtin_info["input_cost_per_token"] + builtin_output_cost = builtin_info["output_cost_per_token"] + + # Order 1: standard first, then zero-cost + router1 = Router( + model_list=[ + { + "model_name": "standard-first", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-6", + }, + "model_info": {"id": "order1-standard"}, + }, + { + "model_name": "zero-cost-second", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-7", + }, + "model_info": { + "id": "order1-zero", + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + }, + }, + ], + ) + + info_std_1 = router1.get_deployment_model_info( + model_id="order1-standard", model_name=backend_model + ) + assert info_std_1["input_cost_per_token"] == builtin_input_cost + assert info_std_1["output_cost_per_token"] == builtin_output_cost + + # Order 2: zero-cost first, then standard + router2 = Router( + model_list=[ + { + "model_name": "zero-cost-first", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-8", + }, + "model_info": { + "id": "order2-zero", + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + }, + }, + { + "model_name": "standard-second", + "litellm_params": { + "model": backend_model, + "api_key": "fake-key-9", + }, + "model_info": {"id": "order2-standard"}, + }, + ], + ) + + info_std_2 = router2.get_deployment_model_info( + model_id="order2-standard", model_name=backend_model + ) + assert info_std_2["input_cost_per_token"] == builtin_input_cost, ( + f"Order should not matter. Expected {builtin_input_cost}, " + f"got {info_std_2['input_cost_per_token']}" + ) + assert info_std_2["output_cost_per_token"] == builtin_output_cost, ( + f"Order should not matter. Expected {builtin_output_cost}, " + f"got {info_std_2['output_cost_per_token']}" + ) diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index c7803445eb4..7374a605798 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -18,10 +18,12 @@ from litellm.types.utils import ( ModelResponseStream, StreamingChoices, ) +from litellm.types.utils import CallTypes from litellm.utils import ( ProviderConfigManager, TextCompletionStreamWrapper, _check_provider_match, + _is_streaming_request, get_llm_provider, get_optional_params_image_gen, is_cached_message, @@ -659,6 +661,7 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): "supports_url_context": {"type": "boolean"}, "supports_reasoning": {"type": "boolean"}, "supports_service_tier": {"type": "boolean"}, + "supports_preset": {"type": "boolean"}, "tool_use_system_prompt_tokens": {"type": "number"}, "tpm": {"type": "number"}, "supported_endpoints": { @@ -2283,18 +2286,14 @@ def test_register_model_with_scientific_notation(): """ Test that the register_model function can handle scientific notation in the model name. """ - # Use a unique model name to avoid conflicts with other tests - test_model_name = "test-scientific-notation-model-unique-12345" + import uuid - # Clean up any pre-existing entry and clear caches - if test_model_name in litellm.model_cost: - del litellm.model_cost[test_model_name] + # Use a truly unique model name with uuid to avoid conflicts when tests run in parallel + test_model_name = f"test-scientific-notation-model-{uuid.uuid4().hex[:12]}" # Clear LRU caches that might have stale data from litellm.utils import ( - _cached_get_model_info_helper, _invalidate_model_cost_lowercase_map, - get_model_info, ) _invalidate_model_cost_lowercase_map() @@ -2870,6 +2869,111 @@ class TestProxyLoggingBudgetAlerts: type=alert_type, user_info=user_info ) + async def test_budget_alerts_soft_budget_with_alert_emails_bypasses_alerting_none(self): + """ + Test that soft_budget alerts with alert_emails bypass the alerting=None check + and send emails even when alerting is None. + + This tests the new logic that allows team-specific soft budget email alerts + via metadata.soft_budget_alerting_emails to work even when global alerting is disabled. + """ + from litellm.caching.caching import DualCache + from litellm.proxy.utils import ProxyLogging + from litellm.proxy._types import CallInfo, Litellm_EntityType + + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging.alerting = None # Global alerting is disabled + proxy_logging.slack_alerting_instance = AsyncMock() + proxy_logging.email_logging_instance = AsyncMock() + + # Create CallInfo with alert_emails set (simulating team metadata extraction) + user_info = CallInfo( + token="test-token", + spend=100.0, + soft_budget=50.0, + user_id="test-user", + team_id="test-team", + team_alias="test-team-alias", + event_group=Litellm_EntityType.TEAM, + alert_emails=["team1@example.com", "team2@example.com"], + ) + + # Should send email even though alerting is None (because of alert_emails) + await proxy_logging.budget_alerts(type="soft_budget", user_info=user_info) + + # Verify slack was NOT called (alerting is None) + proxy_logging.slack_alerting_instance.budget_alerts.assert_not_called() + + # Verify email WAS called (bypasses alerting=None check) + proxy_logging.email_logging_instance.budget_alerts.assert_called_once_with( + type="soft_budget", user_info=user_info + ) + + async def test_budget_alerts_soft_budget_without_alert_emails_respects_alerting_none(self): + """ + Test that soft_budget alerts WITHOUT alert_emails still respect alerting=None + and do not send emails when alerting is None. + """ + from litellm.caching.caching import DualCache + from litellm.proxy.utils import ProxyLogging + from litellm.proxy._types import CallInfo, Litellm_EntityType + + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging.alerting = None + proxy_logging.slack_alerting_instance = AsyncMock() + proxy_logging.email_logging_instance = AsyncMock() + + # Create CallInfo WITHOUT alert_emails + user_info = CallInfo( + token="test-token", + spend=100.0, + soft_budget=50.0, + user_id="test-user", + team_id="test-team", + team_alias="test-team-alias", + event_group=Litellm_EntityType.TEAM, + alert_emails=None, # No alert emails + ) + + # Should NOT send email (alerting is None and no alert_emails) + await proxy_logging.budget_alerts(type="soft_budget", user_info=user_info) + + # Verify no calls were made + proxy_logging.slack_alerting_instance.budget_alerts.assert_not_called() + proxy_logging.email_logging_instance.budget_alerts.assert_not_called() + + async def test_budget_alerts_soft_budget_with_empty_alert_emails_respects_alerting_none(self): + """ + Test that soft_budget alerts with empty alert_emails list still respect alerting=None. + """ + from litellm.caching.caching import DualCache + from litellm.proxy.utils import ProxyLogging + from litellm.proxy._types import CallInfo, Litellm_EntityType + + proxy_logging = ProxyLogging(user_api_key_cache=DualCache()) + proxy_logging.alerting = None + proxy_logging.slack_alerting_instance = AsyncMock() + proxy_logging.email_logging_instance = AsyncMock() + + # Create CallInfo with empty alert_emails list + user_info = CallInfo( + token="test-token", + spend=100.0, + soft_budget=50.0, + user_id="test-user", + team_id="test-team", + team_alias="test-team-alias", + event_group=Litellm_EntityType.TEAM, + alert_emails=[], # Empty list + ) + + # Should NOT send email (alert_emails is empty) + await proxy_logging.budget_alerts(type="soft_budget", user_info=user_info) + + # Verify no calls were made + proxy_logging.slack_alerting_instance.budget_alerts.assert_not_called() + proxy_logging.email_logging_instance.budget_alerts.assert_not_called() + def test_azure_ai_claude_provider_config(): """Test that Azure AI Claude models return AzureAnthropicConfig for proper tool transformation.""" @@ -3169,3 +3273,105 @@ class TestDropParamsWithPromptCacheKey: assert "prompt_cache_key" not in result # temperature should remain (it's supported by Bedrock) assert result.get("temperature") == 0.7 + + +class TestIsStreamingRequest: + def test_stream_true_in_kwargs(self): + assert _is_streaming_request(kwargs={"stream": True}, call_type="acompletion") is True + + def test_stream_false_in_kwargs(self): + assert _is_streaming_request(kwargs={"stream": False}, call_type="acompletion") is False + + def test_no_stream_in_kwargs(self): + assert _is_streaming_request(kwargs={}, call_type="acompletion") is False + + def test_generate_content_stream_string(self): + assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream.value) is True + + def test_agenerate_content_stream_string(self): + assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream.value) is True + + def test_generate_content_stream_enum(self): + assert _is_streaming_request(kwargs={}, call_type=CallTypes.generate_content_stream) is True + + def test_agenerate_content_stream_enum(self): + assert _is_streaming_request(kwargs={}, call_type=CallTypes.agenerate_content_stream) is True + + def test_non_streaming_call_type_string(self): + assert _is_streaming_request(kwargs={}, call_type="acompletion") is False + + def test_non_streaming_call_type_enum(self): + assert _is_streaming_request(kwargs={}, call_type=CallTypes.acompletion) is False + + def test_stream_true_overrides_non_streaming_call_type(self): + assert _is_streaming_request(kwargs={"stream": True}, call_type=CallTypes.acompletion) is True + + +class TestMetadataNoneHandling: + """ + Test that metadata=None in kwargs doesn't cause TypeError. + + When metadata key exists with value None (e.g., from Azure OpenAI streaming), + dict.get("metadata", {}) returns None (key exists, so default is ignored). + The fix uses (kwargs.get("metadata") or {}) which handles both missing key + and explicit None value. + + Related: #20871 + """ + + def test_metadata_none_get_previous_models(self): + """kwargs.get("metadata") or {} should return {} when metadata is None.""" + kwargs = {"metadata": None} + previous_models = (kwargs.get("metadata") or {}).get( + "previous_models", None + ) + assert previous_models is None + + def test_metadata_none_model_group_check(self): + """'model_group' in (kwargs.get("metadata") or {}) should not raise TypeError.""" + kwargs = {"metadata": None} + _is_litellm_router_call = "model_group" in ( + kwargs.get("metadata") or {} + ) + assert _is_litellm_router_call is False + + def test_metadata_missing_key(self): + """Should work when metadata key is completely absent.""" + kwargs = {} + previous_models = (kwargs.get("metadata") or {}).get( + "previous_models", None + ) + assert previous_models is None + + def test_metadata_present_with_values(self): + """Should work when metadata has actual values.""" + kwargs = {"metadata": {"previous_models": ["model1"], "model_group": "test"}} + previous_models = (kwargs.get("metadata") or {}).get( + "previous_models", None + ) + assert previous_models == ["model1"] + _is_litellm_router_call = "model_group" in ( + kwargs.get("metadata") or {} + ) + assert _is_litellm_router_call is True + + def test_metadata_none_causes_error_with_old_pattern(self): + """Demonstrate the bug: dict.get('metadata', {}) returns None when key exists with None value.""" + kwargs = {"metadata": None} + # Old pattern: kwargs.get("metadata", {}) returns None because key exists + result = kwargs.get("metadata", {}) + assert result is None # This is the root cause of the bug + + # Attempting to use .get() on None raises AttributeError or TypeError + with pytest.raises((TypeError, AttributeError)): + kwargs.get("metadata", {}).get("previous_models", None) + + # Attempting 'in' on None raises TypeError + with pytest.raises(TypeError): + "model_group" in kwargs.get("metadata", {}) + + def test_litellm_params_metadata_none(self): + """litellm_params.get("metadata") or {} should handle None value.""" + litellm_params = {"metadata": None} + metadata = litellm_params.get("metadata") or {} + assert metadata == {} diff --git a/tests/test_litellm/test_video_generation.py b/tests/test_litellm/test_video_generation.py index cfc1535052c..c8cc292519b 100644 --- a/tests/test_litellm/test_video_generation.py +++ b/tests/test_litellm/test_video_generation.py @@ -798,7 +798,7 @@ def test_openai_transform_video_content_request_empty_params(): def test_video_content_handler_uses_get_for_openai(): """HTTP handler must use GET (not POST) for OpenAI content download.""" from litellm.types.router import GenericLiteLLMParams - + handler = BaseLLMHTTPHandler() config = OpenAIVideoConfig() @@ -807,7 +807,12 @@ def test_video_content_handler_uses_get_for_openai(): mock_response.content = b"mp4-bytes" mock_client.get.return_value = mock_response + # Patch both where _get_httpx_client is used and where it is defined so the mock + # is used regardless of import order / CI environment with patch( + "litellm.llms.custom_httpx.http_handler._get_httpx_client", + return_value=mock_client, + ), patch( "litellm.llms.custom_httpx.llm_http_handler._get_httpx_client", return_value=mock_client, ): @@ -916,6 +921,181 @@ def test_encode_video_id_with_provider_handles_azure_video_prefix(): ) assert encoded_twice == encoded_id # Should return the same encoded ID +class TestVideoListTransformation: + """Tests for video list request/response transformation with provider ID encoding.""" + + def test_transform_video_list_response_encodes_first_id_and_last_id(self): + """Verify that first_id and last_id are encoded with provider metadata.""" + config = OpenAIVideoConfig() + + mock_http_response = MagicMock() + mock_http_response.json.return_value = { + "object": "list", + "data": [ + { + "id": "video_aaa", + "object": "video", + "model": "sora-2", + "status": "completed", + }, + { + "id": "video_bbb", + "object": "video", + "model": "sora-2", + "status": "completed", + }, + ], + "first_id": "video_aaa", + "last_id": "video_bbb", + "has_more": False, + } + + result = config.transform_video_list_response( + raw_response=mock_http_response, + logging_obj=MagicMock(), + custom_llm_provider="azure", + ) + + from litellm.types.videos.utils import decode_video_id_with_provider + + # data[].id should be encoded + for item in result["data"]: + decoded = decode_video_id_with_provider(item["id"]) + assert decoded["custom_llm_provider"] == "azure" + + # first_id and last_id should also be encoded + first_decoded = decode_video_id_with_provider(result["first_id"]) + assert first_decoded["custom_llm_provider"] == "azure" + assert first_decoded["video_id"] == "video_aaa" + assert first_decoded["model_id"] == "sora-2" + + last_decoded = decode_video_id_with_provider(result["last_id"]) + assert last_decoded["custom_llm_provider"] == "azure" + assert last_decoded["video_id"] == "video_bbb" + assert last_decoded["model_id"] == "sora-2" + + def test_transform_video_list_response_no_provider_leaves_ids_unchanged(self): + """When custom_llm_provider is None, all IDs should remain unchanged.""" + config = OpenAIVideoConfig() + + mock_http_response = MagicMock() + mock_http_response.json.return_value = { + "object": "list", + "data": [ + {"id": "video_aaa", "object": "video", "model": "sora-2", "status": "completed"}, + ], + "first_id": "video_aaa", + "last_id": "video_aaa", + "has_more": False, + } + + result = config.transform_video_list_response( + raw_response=mock_http_response, + logging_obj=MagicMock(), + custom_llm_provider=None, + ) + + assert result["data"][0]["id"] == "video_aaa" + assert result["first_id"] == "video_aaa" + assert result["last_id"] == "video_aaa" + + def test_transform_video_list_response_missing_pagination_fields(self): + """first_id / last_id may be absent or null; should not raise.""" + config = OpenAIVideoConfig() + + mock_http_response = MagicMock() + mock_http_response.json.return_value = { + "object": "list", + "data": [ + {"id": "video_aaa", "object": "video", "model": "sora-2", "status": "completed"}, + ], + "has_more": False, + } + + result = config.transform_video_list_response( + raw_response=mock_http_response, + logging_obj=MagicMock(), + custom_llm_provider="azure", + ) + + # data[].id should still be encoded + from litellm.types.videos.utils import decode_video_id_with_provider + + decoded = decode_video_id_with_provider(result["data"][0]["id"]) + assert decoded["custom_llm_provider"] == "azure" + + # first_id / last_id should not be present + assert "first_id" not in result + assert "last_id" not in result + + def test_transform_video_list_request_decodes_after_parameter(self): + """Encoded 'after' cursor should be decoded back to the raw provider ID.""" + from litellm.types.videos.utils import encode_video_id_with_provider + + config = OpenAIVideoConfig() + + raw_id = "video_69888baee890819086dd3366bfc372fe" + encoded_id = encode_video_id_with_provider(raw_id, "azure", "sora-2") + + url, params = config.transform_video_list_request( + api_base="https://my-resource.openai.azure.com/openai/v1/videos", + litellm_params=MagicMock(), + headers={}, + after=encoded_id, + limit=10, + ) + + assert params["after"] == raw_id + assert params["limit"] == "10" + + def test_transform_video_list_request_passes_through_plain_after(self): + """A plain (non-encoded) 'after' value should pass through unchanged.""" + config = OpenAIVideoConfig() + + url, params = config.transform_video_list_request( + api_base="https://api.openai.com/v1/videos", + litellm_params=MagicMock(), + headers={}, + after="video_plain_id", + ) + + assert params["after"] == "video_plain_id" + + def test_transform_video_list_roundtrip(self): + """first_id from list response should decode correctly when used as after parameter.""" + config = OpenAIVideoConfig() + + # Simulate a list response + mock_http_response = MagicMock() + mock_http_response.json.return_value = { + "object": "list", + "data": [ + {"id": "video_aaa", "object": "video", "model": "sora-2", "status": "completed"}, + {"id": "video_bbb", "object": "video", "model": "sora-2", "status": "completed"}, + ], + "first_id": "video_aaa", + "last_id": "video_bbb", + "has_more": True, + } + + list_result = config.transform_video_list_response( + raw_response=mock_http_response, + logging_obj=MagicMock(), + custom_llm_provider="azure", + ) + + # Use the encoded last_id as the 'after' cursor for the next page + _, params = config.transform_video_list_request( + api_base="https://my-resource.openai.azure.com/openai/v1/videos", + litellm_params=MagicMock(), + headers={}, + after=list_result["last_id"], + ) + + # The after param sent to the upstream API should be the raw video ID + assert params["after"] == "video_bbb" + + class TestVideoEndpointsProxyLitellmParams: """Test that video proxy endpoints (status, content, remix) respect litellm_params from proxy config.""" diff --git a/tests/test_team.py b/tests/test_team.py index d67c5e670f4..275181590c5 100644 --- a/tests/test_team.py +++ b/tests/test_team.py @@ -532,6 +532,7 @@ async def test_team_update_sc_2(): or k == "object_permission" or k == "litellm_model_table" or k == "policies" + or k == "allow_team_guardrail_config" ): pass else: diff --git a/ui/litellm-dashboard/e2e_tests/constants.ts b/ui/litellm-dashboard/e2e_tests/constants.ts index b07bd68fcf1..58b56af0a2b 100644 --- a/ui/litellm-dashboard/e2e_tests/constants.ts +++ b/ui/litellm-dashboard/e2e_tests/constants.ts @@ -1 +1,6 @@ export const ADMIN_STORAGE_PATH = "admin.storageState.json"; + +export const E2E_UPDATE_LIMITS_KEY_ID_PREFIX = "102c"; +export const E2E_DELETE_KEY_ID_PREFIX = "94a5"; +export const E2E_DELETE_KEY_NAME = "e2eDeleteKey"; +export const E2E_REGENERATE_KEY_ID_PREFIX = "593a"; diff --git a/ui/litellm-dashboard/e2e_tests/tests/keys/deleteKey.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/keys/deleteKey.spec.ts new file mode 100644 index 00000000000..a5841316251 --- /dev/null +++ b/ui/litellm-dashboard/e2e_tests/tests/keys/deleteKey.spec.ts @@ -0,0 +1,25 @@ +import { test, expect } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_DELETE_KEY_ID_PREFIX, E2E_DELETE_KEY_NAME } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +test.describe("Delete Key", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Able to delete a key", async ({ page }) => { + await navigateToPage(page, Page.ApiKeys); + await expect(page.getByRole("button", { name: "Next" })).toBeVisible(); + await page + .locator("button", { + hasText: E2E_DELETE_KEY_ID_PREFIX, + }) + .click(); + await page.getByRole("button", { name: "Delete Key" }).click(); + await page.getByRole("textbox", { name: E2E_DELETE_KEY_NAME }).click(); + await page.getByRole("textbox", { name: E2E_DELETE_KEY_NAME }).fill(E2E_DELETE_KEY_NAME); + const deleteButton = page.getByRole("button", { name: "Delete", exact: true }); + await expect(deleteButton).toBeEnabled(); + await deleteButton.click(); + await expect(page.getByText("Key deleted successfully")).toBeVisible(); + }); +}); diff --git a/ui/litellm-dashboard/e2e_tests/tests/keys/regenerateKey.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/keys/regenerateKey.spec.ts new file mode 100644 index 00000000000..0188a4f81ce --- /dev/null +++ b/ui/litellm-dashboard/e2e_tests/tests/keys/regenerateKey.spec.ts @@ -0,0 +1,21 @@ +import { test, expect } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_REGENERATE_KEY_ID_PREFIX } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +test.describe("Regenerate Key", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Able to regenerate a key", async ({ page }) => { + await navigateToPage(page, Page.ApiKeys); + await expect(page.getByRole("button", { name: "Next" })).toBeVisible(); + await page + .locator("button", { + hasText: E2E_REGENERATE_KEY_ID_PREFIX, + }) + .click(); + await page.getByRole("button", { name: "Regenerate Key" }).click(); + await page.getByRole("button", { name: "Regenerate", exact: true }).click(); + await expect(page.getByText("Virtual Key regenerated")).toBeVisible(); + }); +}); diff --git a/ui/litellm-dashboard/e2e_tests/tests/keys/updateKeyLimits.spec.ts b/ui/litellm-dashboard/e2e_tests/tests/keys/updateKeyLimits.spec.ts new file mode 100644 index 00000000000..6cae36272ab --- /dev/null +++ b/ui/litellm-dashboard/e2e_tests/tests/keys/updateKeyLimits.spec.ts @@ -0,0 +1,27 @@ +import { test, expect } from "@playwright/test"; +import { ADMIN_STORAGE_PATH, E2E_UPDATE_LIMITS_KEY_ID_PREFIX } from "../../constants"; +import { Page } from "../../fixtures/pages"; +import { navigateToPage } from "../../helpers/navigation"; + +test.describe("Update Key TPM and RPM Limits", () => { + test.use({ storageState: ADMIN_STORAGE_PATH }); + + test("Able to update a key's TPM and RPM limits", async ({ page }) => { + await navigateToPage(page, Page.ApiKeys); + await expect(page.getByRole("button", { name: "Next" })).toBeVisible(); + await page + .locator("button", { + hasText: E2E_UPDATE_LIMITS_KEY_ID_PREFIX, + }) + .click(); + await page.getByRole("tab", { name: "Settings" }).click(); + await page.getByRole("button", { name: "Edit Settings" }).click(); + await page.getByRole("spinbutton", { name: "TPM Limit" }).click(); + await page.getByRole("spinbutton", { name: "TPM Limit" }).fill("123"); + await page.getByRole("spinbutton", { name: "RPM Limit" }).click(); + await page.getByRole("spinbutton", { name: "RPM Limit" }).fill("456"); + await page.getByRole("button", { name: "Save Changes" }).click(); + await expect(page.getByRole("paragraph").filter({ hasText: "TPM: 123" })).toBeVisible(); + await expect(page.getByRole("paragraph").filter({ hasText: "RPM: 456" })).toBeVisible(); + }); +}); diff --git a/ui/litellm-dashboard/knip.json b/ui/litellm-dashboard/knip.json new file mode 100644 index 00000000000..e93d1997d62 --- /dev/null +++ b/ui/litellm-dashboard/knip.json @@ -0,0 +1,18 @@ +{ + "$schema": "https://unpkg.com/knip@5/schema.json", + "entry": ["scripts/**/*.ts"], + "project": [ + "src/**/*.{ts,tsx}", + "tests/**/*.{ts,tsx}", + "scripts/**/*.ts", + "e2e_tests/**/*.ts" + ], + "playwright": { + "config": "e2e_tests/playwright.config.ts", + "entry": [ + "e2e_tests/**/*.spec.ts", + "e2e_tests/**/*.setup.ts", + "e2e_tests/globalSetup.ts" + ] + } +} diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json index 33d8ea54b30..3a21813fbf4 100644 --- a/ui/litellm-dashboard/package-lock.json +++ b/ui/litellm-dashboard/package-lock.json @@ -59,10 +59,11 @@ "eslint-config-prettier": "^10.1.8", "eslint-plugin-unused-imports": "^4.2.0", "jsdom": "^27.0.0", + "knip": "^5.83.1", "postcss": "^8.4.33", "prettier": "3.2.5", "tailwindcss": "^3.4.1", - 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"https://github.com/sponsors/sindresorhus" } }, + "node_modules/zod": { + "version": "4.3.6", + "resolved": "https://registry.npmjs.org/zod/-/zod-4.3.6.tgz", + "integrity": "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==", + "dev": true, + "license": "MIT", + "funding": { + "url": "https://github.com/sponsors/colinhacks" + } + }, "node_modules/zwitch": { "version": "2.0.4", "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", @@ -12659,6 +13159,21 @@ "type": "github", "url": "https://github.com/sponsors/wooorm" } + }, + "node_modules/@next/swc-win32-ia32-msvc": { + "version": "14.2.33", + "resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.33.tgz", + "integrity": "sha512-pc9LpGNKhJ0dXQhZ5QMmYxtARwwmWLpeocFmVG5Z0DzWq5Uf0izcI8tLc+qOpqxO1PWqZ5A7J1blrUIKrIFc7Q==", + "cpu": [ + "ia32" + ], + "optional": true, + "os": [ + "win32" + ], + "engines": { + "node": ">= 10" + } } } } diff --git a/ui/litellm-dashboard/package.json b/ui/litellm-dashboard/package.json index 23c6aa7c09e..164368eb6ba 100644 --- a/ui/litellm-dashboard/package.json +++ b/ui/litellm-dashboard/package.json @@ -3,7 +3,7 @@ "version": "0.1.0", "private": true, "scripts": { - "dev": "next dev --turbo", + "dev": "next dev", "build": "next build", "start": "next start", "lint": "next lint", @@ -14,7 +14,9 @@ "format": "prettier --write .", "format:check": "prettier --check .", "e2e": "playwright test --config e2e_tests/playwright.config.ts", - "e2e:ui": "playwright test --ui --config e2e_tests/playwright.config.ts" + "e2e:ui": "playwright test --ui --config e2e_tests/playwright.config.ts", + "knip": "knip", + "knip:fix": "knip --fix" }, "dependencies": { "@anthropic-ai/sdk": "^0.54.0", @@ -68,19 +70,23 @@ "eslint-config-prettier": "^10.1.8", "eslint-plugin-unused-imports": "^4.2.0", "jsdom": "^27.0.0", + "knip": "^5.83.1", "postcss": "^8.4.33", "prettier": "3.2.5", "tailwindcss": "^3.4.1", - "typescript": "5.3.3", + "typescript": "^5.3.3", "vite": "^7.1.11", "vitest": "^3.2.4" }, "overrides": { + "diff": ">=8.0.3", "prismjs": ">=1.30.0", "webpack-dev-server": ">=5.2.1", "mermaid": ">=11.10.0", "js-yaml": ">=4.1.1", "glob": ">=11.1.0", + "tar": ">=7.5.7", + "@isaacs/brace-expansion": ">=5.0.1", "node-forge": ">=1.3.2", "lodash-es": ">=4.17.23", "lodash": ">=4.17.23" diff --git a/ui/litellm-dashboard/scripts/e2e_tests/neonHelperScripts.ts b/ui/litellm-dashboard/scripts/e2e_tests/neonHelperScripts.ts index 3078a0d90d2..089ad4e7926 100644 --- a/ui/litellm-dashboard/scripts/e2e_tests/neonHelperScripts.ts +++ b/ui/litellm-dashboard/scripts/e2e_tests/neonHelperScripts.ts @@ -1,4 +1,4 @@ -import { createApiClient } from "@neondatabase/api-client"; +import { createApiClient, EndpointType } from "@neondatabase/api-client"; import { config } from "dotenv"; import { resolve } from "path"; @@ -27,6 +27,13 @@ export async function createNeonE2ETestingBranch(projectId: string, parentBranch parent_id: parentBranchId, expires_at: expireAt ?? new Date(Date.now() + 1000 * 60 * 30).toISOString(), }, + endpoints: [ + { + type: EndpointType.ReadWrite, + autoscaling_limit_min_cu: 0.25, + autoscaling_limit_max_cu: 1, + }, + ], }); return response; } catch (error) { @@ -35,13 +42,15 @@ export async function createNeonE2ETestingBranch(projectId: string, parentBranch } export async function getNeonE2ETestingBranchConnectionString() { - await createNeonE2ETestingBranch(PROJECT_ID, PARENT_BRANCH); - + const createBranchResponse = await createNeonE2ETestingBranch(PROJECT_ID, PARENT_BRANCH); + const projectId = createBranchResponse.data.branch.project_id; const response = await apiClient.getConnectionUri({ database_name: NEON_E2E_UI_TEST_DB_NAME, role_name: "neondb_owner", - projectId: PROJECT_ID, + projectId: projectId, }); console.log("connection string:", response.data.uri); return response.data.uri; } + +getNeonE2ETestingBranchConnectionString(); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/components/Sidebar2.tsx b/ui/litellm-dashboard/src/app/(dashboard)/components/Sidebar2.tsx index 405f8329b67..a74d3c108d6 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/components/Sidebar2.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/components/Sidebar2.tsx @@ -31,7 +31,7 @@ import { import * as React from "react"; import { useRouter, usePathname } from "next/navigation"; import { all_admin_roles, internalUserRoles, isAdminRole, rolesWithWriteAccess } from "@/utils/roles"; -import UsageIndicator from "@/components/usage_indicator"; +import UsageIndicator from "@/components/UsageIndicator"; import { serverRootPath } from "@/components/networking"; const { Sider } = Layout; @@ -64,7 +64,7 @@ const getBasePath = () => { const raw = process.env.NEXT_PUBLIC_BASE_URL ?? ""; const trimmed = raw.replace(/^\/+|\/+$/g, ""); // strip leading/trailing slashes const uiPath = trimmed ? `/${trimmed}/` : "/"; - + // If serverRootPath is set and not "/", prepend it to the UI path if (serverRootPath && serverRootPath !== "/") { // Remove trailing slash from serverRootPath and ensure uiPath has no leading slash for proper joining @@ -72,7 +72,7 @@ const getBasePath = () => { const cleanUiPath = uiPath.replace(/^\/+/, ""); return `${cleanServerRoot}/${cleanUiPath}`; } - + return uiPath; }; @@ -153,170 +153,170 @@ const toHref = (slugOrPath: string) => { // ----- Menu config (unchanged labels/icons; same appearance) ----- const menuItems: MenuItemCfg[] = [ - { key: "1", page: "api-keys", label: "Virtual Keys", icon: }, - { - key: "3", - page: "llm-playground", - label: "Test Key", - icon: , - roles: rolesWithWriteAccess, - }, - { - key: "2", - page: "models", - label: "Models + Endpoints", - icon: , - roles: rolesWithWriteAccess, - }, - { - key: "12", - page: "new_usage", - label: "Usage", - icon: , - roles: [...all_admin_roles, ...internalUserRoles], - }, - { key: "6", page: "teams", label: "Teams", icon: }, - { - key: "17", - page: "organizations", - label: "Organizations", - icon: , - roles: all_admin_roles, - }, - { - key: "5", - page: "users", - label: "Internal Users", - icon: , - roles: all_admin_roles, - }, - { key: "14", page: "api_ref", label: "API Reference", icon: }, - { - key: "16", - page: "model-hub-table", - label: "Model Hub", - icon: , - }, - { key: "15", page: "logs", label: "Logs", icon: }, - { - key: "11", - page: "guardrails", - label: "Guardrails", - icon: , - roles: all_admin_roles, - }, - { - key: "28", - page: "policies", - label: "Policies", - icon: , - roles: all_admin_roles, - }, - { - key: "26", - page: "tools", - label: "Tools", - icon: , - children: [ - { key: "18", page: "mcp-servers", label: "MCP Servers", icon: }, - { - key: "21", - page: "vector-stores", - label: "Vector Stores", - icon: , - roles: all_admin_roles, - }, - ], - }, - { - key: "experimental", - page: "experimental", - label: "Experimental", - icon: , - children: [ - { - key: "9", - page: "caching", - label: "Caching", - icon: , - roles: all_admin_roles, - }, - { - key: "25", - page: "prompts", - label: "Prompts", - icon: , - roles: all_admin_roles, - }, - { - key: "10", - page: "budgets", - label: "Budgets", - icon: , - roles: all_admin_roles, - }, - { - key: "20", - page: "transform-request", - label: "API Playground", - icon: , - roles: [...all_admin_roles, ...internalUserRoles], - }, - { - key: "19", - page: "tag-management", - label: "Tag Management", - icon: , - roles: all_admin_roles, - }, - { - key: "27", - page: "claude-code-plugins", - label: "Claude Code Plugins", - icon: , - roles: all_admin_roles, - }, - { key: "4", page: "usage", label: "Old Usage", icon: }, - ], - }, - { - key: "settings", - page: "settings", - label: "Settings", - icon: , - roles: all_admin_roles, - children: [ - { - key: "11", - page: "general-settings", - label: "Router Settings", - icon: , - roles: all_admin_roles, - }, - { - key: "8", - page: "settings", - label: "Logging & Alerts", - icon: , - roles: all_admin_roles, - }, - { - key: "13", - page: "admin-panel", - label: "Admin Settings", - icon: , - roles: all_admin_roles, - }, - { - key: "14", - page: "ui-theme", - label: "UI Theme", - icon: , - roles: all_admin_roles, - }, - ], - }, - ]; + { key: "1", page: "api-keys", label: "Virtual Keys", icon: }, + { + key: "3", + page: "llm-playground", + label: "Test Key", + icon: , + roles: rolesWithWriteAccess, + }, + { + key: "2", + page: "models", + label: "Models + Endpoints", + icon: , + roles: rolesWithWriteAccess, + }, + { + key: "12", + page: "new_usage", + label: "Usage", + icon: , + roles: [...all_admin_roles, ...internalUserRoles], + }, + { key: "6", page: "teams", label: "Teams", icon: }, + { + key: "17", + page: "organizations", + label: "Organizations", + icon: , + roles: all_admin_roles, + }, + { + key: "5", + page: "users", + label: "Internal Users", + icon: , + roles: all_admin_roles, + }, + { key: "14", page: "api_ref", label: "API Reference", icon: }, + { + key: "16", + page: "model-hub-table", + label: "Model Hub", + icon: , + }, + { key: "15", page: "logs", label: "Logs", icon: }, + { + key: "11", + page: "guardrails", + label: "Guardrails", + icon: , + roles: all_admin_roles, + }, + { + key: "28", + page: "policies", + label: "Policies", + icon: , + roles: all_admin_roles, + }, + { + key: "26", + page: "tools", + label: "Tools", + icon: , + children: [ + { key: "18", page: "mcp-servers", label: "MCP Servers", icon: }, + { + key: "21", + page: "vector-stores", + label: "Vector Stores", + icon: , + roles: all_admin_roles, + }, + ], + }, + { + key: "experimental", + page: "experimental", + label: "Experimental", + icon: , + children: [ + { + key: "9", + page: "caching", + label: "Caching", + icon: , + roles: all_admin_roles, + }, + { + key: "25", + page: "prompts", + label: "Prompts", + icon: , + roles: all_admin_roles, + }, + { + key: "10", + page: "budgets", + label: "Budgets", + icon: , + roles: all_admin_roles, + }, + { + key: "20", + page: "transform-request", + label: "API Playground", + icon: , + roles: [...all_admin_roles, ...internalUserRoles], + }, + { + key: "19", + page: "tag-management", + label: "Tag Management", + icon: , + roles: all_admin_roles, + }, + { + key: "27", + page: "claude-code-plugins", + label: "Claude Code Plugins", + icon: , + roles: all_admin_roles, + }, + { key: "4", page: "usage", label: "Old Usage", icon: }, + ], + }, + { + key: "settings", + page: "settings", + label: "Settings", + icon: , + roles: all_admin_roles, + children: [ + { + key: "11", + page: "general-settings", + label: "Router Settings", + icon: , + roles: all_admin_roles, + }, + { + key: "8", + page: "settings", + label: "Logging & Alerts", + icon: , + roles: all_admin_roles, + }, + { + key: "13", + page: "admin-panel", + label: "Admin Settings", + icon: , + roles: all_admin_roles, + }, + { + key: "14", + page: "ui-theme", + label: "UI Theme", + icon: , + roles: all_admin_roles, + }, + ], + }, +]; const Sidebar2: React.FC = ({ accessToken, userRole, defaultSelectedKey, collapsed = false }) => { const router = useRouter(); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroCreate.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroCreate.test.ts new file mode 100644 index 00000000000..8334aea56e7 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroCreate.test.ts @@ -0,0 +1,325 @@ +import { describe, it, expect, vi, beforeEach, afterEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useCloudZeroCreate } from "./useCloudZeroCreate"; + +const { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, +} = vi.hoisted(() => { + const mockProxyBaseUrl = "https://proxy.example.com"; + const mockAccessToken = "test-access-token"; + const mockHeaderName = "X-LiteLLM-API-Key"; + const mockGetProxyBaseUrl = vi.fn(() => mockProxyBaseUrl); + const mockGetGlobalLitellmHeaderName = vi.fn(() => mockHeaderName); + + return { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, + }; +}); + +vi.mock("@/components/networking", () => ({ + getProxyBaseUrl: mockGetProxyBaseUrl, + getGlobalLitellmHeaderName: mockGetGlobalLitellmHeaderName, +})); + +describe("useCloudZeroCreate", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should successfully create CloudZero integration with all parameters", async () => { + const mockResponse = { message: "Integration created successfully", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + timezone: "America/New_York", + api_key: "test-api-key", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/init`, { + method: "POST", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + connection_id: "test-connection-id", + timezone: "America/New_York", + api_key: "test-api-key", + }), + }); + }); + + it("should successfully create CloudZero integration with minimal parameters", async () => { + const mockResponse = { message: "Integration created successfully", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/init`, { + method: "POST", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + connection_id: "test-connection-id", + timezone: "UTC", + }), + }); + }); + + it("should use default timezone when not provided", async () => { + const mockResponse = { message: "Integration created successfully" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const callBody = JSON.parse((fetchSpy as any).mock.calls[0][1].body); + expect(callBody.timezone).toBe("UTC"); + }); + + it("should not include api_key in body when not provided", async () => { + const mockResponse = { message: "Integration created successfully" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + timezone: "UTC", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const callBody = JSON.parse((fetchSpy as any).mock.calls[0][1].body); + expect(callBody).not.toHaveProperty("api_key"); + }); + + it("should handle error response with error.message", async () => { + const errorResponse = { error: { message: "Connection ID already exists" } }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Connection ID already exists"); + }); + + it("should handle error response with message field", async () => { + const errorResponse = { message: "Invalid API key" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Invalid API key"); + }); + + it("should handle error response with detail field", async () => { + const errorResponse = { detail: "Server error occurred" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Server error occurred"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Failed to create CloudZero integration"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it("should throw error when accessToken is empty string", async () => { + const { result } = renderHook(() => useCloudZeroCreate(""), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + }); + + it("should throw error when accessToken is null", async () => { + const { result } = renderHook(() => useCloudZeroCreate(null as any), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + const mockResponse = { message: "Success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroCreate(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-connection-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/init", expect.any(Object)); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroDryRun.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroDryRun.test.ts new file mode 100644 index 00000000000..74d657b3e85 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroDryRun.test.ts @@ -0,0 +1,239 @@ +import { describe, it, expect, vi, beforeEach, afterEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useCloudZeroDryRun } from "./useCloudZeroDryRun"; + +const { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, +} = vi.hoisted(() => { + const mockProxyBaseUrl = "https://proxy.example.com"; + const mockAccessToken = "test-access-token"; + const mockHeaderName = "X-LiteLLM-API-Key"; + const mockGetProxyBaseUrl = vi.fn(() => mockProxyBaseUrl); + const mockGetGlobalLitellmHeaderName = vi.fn(() => mockHeaderName); + + return { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, + }; +}); + +vi.mock("@/components/networking", () => ({ + getProxyBaseUrl: mockGetProxyBaseUrl, + getGlobalLitellmHeaderName: mockGetGlobalLitellmHeaderName, +})); + +describe("useCloudZeroDryRun", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should successfully perform dry run with custom limit", async () => { + const mockResponse = { records_processed: 5, status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 20 }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/dry-run`, { + method: "POST", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + limit: 20, + }), + }); + }); + + it("should use default limit of 10 when limit is not provided", async () => { + const mockResponse = { records_processed: 10, status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({}); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + const callBody = JSON.parse((fetchSpy as any).mock.calls[0][1].body); + expect(callBody.limit).toBe(10); + }); + + it("should handle error response with error.message", async () => { + const errorResponse = { error: { message: "Dry run failed" } }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Dry run failed"); + }); + + it("should handle error response with message field", async () => { + const errorResponse = { message: "Invalid configuration" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Invalid configuration"); + }); + + it("should handle error response with detail field", async () => { + const errorResponse = { detail: "Server error" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Server error"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Failed to perform dry run"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it.each([ + ["empty string", ""], + ["null", null], + ])("should throw error when accessToken is %s", async (_, invalidToken) => { + const { result } = renderHook(() => useCloudZeroDryRun(invalidToken as any), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + const mockResponse = { records_processed: 10 }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroDryRun(mockAccessToken), { wrapper }); + + result.current.mutate({ limit: 5 }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/dry-run", expect.any(Object)); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroExport.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroExport.test.ts new file mode 100644 index 00000000000..72a1cfd24aa --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroExport.test.ts @@ -0,0 +1,239 @@ +import { describe, it, expect, vi, beforeEach, afterEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useCloudZeroExport } from "./useCloudZeroExport"; + +const { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, +} = vi.hoisted(() => { + const mockProxyBaseUrl = "https://proxy.example.com"; + const mockAccessToken = "test-access-token"; + const mockHeaderName = "X-LiteLLM-API-Key"; + const mockGetProxyBaseUrl = vi.fn(() => mockProxyBaseUrl); + const mockGetGlobalLitellmHeaderName = vi.fn(() => mockHeaderName); + + return { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, + }; +}); + +vi.mock("@/components/networking", () => ({ + getProxyBaseUrl: mockGetProxyBaseUrl, + getGlobalLitellmHeaderName: mockGetGlobalLitellmHeaderName, +})); + +describe("useCloudZeroExport", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should successfully export data with custom operation", async () => { + const mockResponse = { records_exported: 100, status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/export`, { + method: "POST", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + operation: "replace_daily", + }), + }); + }); + + it("should use default operation of replace_hourly when operation is not provided", async () => { + const mockResponse = { records_exported: 50, status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({}); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + const callBody = JSON.parse((fetchSpy as any).mock.calls[0][1].body); + expect(callBody.operation).toBe("replace_hourly"); + }); + + it("should handle error response with error.message", async () => { + const errorResponse = { error: { message: "Export failed" } }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Export failed"); + }); + + it("should handle error response with message field", async () => { + const errorResponse = { message: "Invalid operation" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "invalid_op" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Invalid operation"); + }); + + it("should handle error response with detail field", async () => { + const errorResponse = { detail: "Server error occurred" }; + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Server error occurred"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Failed to export data"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it.each([ + ["empty string", ""], + ["null", null], + ])("should throw error when accessToken is %s", async (_, invalidToken) => { + const { result } = renderHook(() => useCloudZeroExport(invalidToken as any), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + const mockResponse = { records_exported: 50 }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroExport(mockAccessToken), { wrapper }); + + result.current.mutate({ operation: "replace_daily" }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/export", expect.any(Object)); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.test.ts new file mode 100644 index 00000000000..b0c96987519 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.test.ts @@ -0,0 +1,675 @@ +import { describe, it, expect, vi, beforeEach, afterEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useCloudZeroSettings, useCloudZeroUpdateSettings, useCloudZeroDeleteSettings } from "./useCloudZeroSettings"; +import { CloudZeroSettings } from "@/components/CloudZeroCostTracking/types"; + +const { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, + mockCreateQueryKeys, +} = vi.hoisted(() => { + const mockProxyBaseUrl = "https://proxy.example.com"; + const mockAccessToken = "test-access-token"; + const mockHeaderName = "X-LiteLLM-API-Key"; + const mockGetProxyBaseUrl = vi.fn(() => mockProxyBaseUrl); + const mockGetGlobalLitellmHeaderName = vi.fn(() => mockHeaderName); + const mockCreateQueryKeys = vi.fn((resource: string) => ({ + all: [resource], + lists: () => [resource, "list"], + list: (params?: any) => [resource, "list", { params }], + details: () => [resource, "detail"], + detail: (uid: string) => [resource, "detail", uid], + })); + + return { + mockProxyBaseUrl, + mockAccessToken, + mockHeaderName, + mockGetProxyBaseUrl, + mockGetGlobalLitellmHeaderName, + mockCreateQueryKeys, + }; +}); + +vi.mock("@/components/networking", () => ({ + getProxyBaseUrl: mockGetProxyBaseUrl, + getGlobalLitellmHeaderName: mockGetGlobalLitellmHeaderName, +})); + +vi.mock("../common/queryKeysFactory", () => ({ + createQueryKeys: mockCreateQueryKeys, +})); + +const mockCloudZeroSettings: CloudZeroSettings = { + api_key_masked: "sk-****1234", + connection_id: "test-connection-id", + timezone: "America/New_York", + status: "active", +}; + +describe("useCloudZeroSettings", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should return CloudZero settings data when query is successful", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockCloudZeroSettings, + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + expect(result.current.isLoading).toBe(true); + expect(result.current.data).toBeUndefined(); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockCloudZeroSettings); + expect(result.current.error).toBeNull(); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/settings`, { + method: "GET", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + }); + }); + + it("should return null when settings are not configured (missing both api_key_masked and connection_id)", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => ({}), + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toBeNull(); + }); + + it("should return settings when at least one required field is present", async () => { + const settingsWithConnectionId = { connection_id: "test-connection-id" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => settingsWithConnectionId, + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(settingsWithConnectionId); + }); + + it("should handle error responses", async () => { + const errorCases = [ + { error: { message: "Failed to fetch" }, expected: "Failed to fetch" }, + { error: "Unauthorized", expected: "Unauthorized" }, + { message: "Not found", expected: "Not found" }, + { detail: "Server error", expected: "Server error" }, + ]; + + for (const errorResponse of errorCases) { + vi.clearAllMocks(); + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe(errorResponse.expected); + } + }); + + it("should handle error response with string error data", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => "Error string", + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Error string"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + statusText: "Internal Server Error", + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Internal Server Error"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it("should not execute query when accessToken is missing", () => { + const { result } = renderHook(() => useCloudZeroSettings(""), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(fetchSpy).not.toHaveBeenCalled(); + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockCloudZeroSettings, + }); + + const { result } = renderHook(() => useCloudZeroSettings(mockAccessToken), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/settings", expect.any(Object)); + }); +}); + +describe("useCloudZeroUpdateSettings", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should successfully update settings with all parameters", async () => { + const mockResponse = { message: "Settings updated successfully", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "new-connection-id", + timezone: "America/Los_Angeles", + api_key: "new-api-key", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/settings`, { + method: "PUT", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + body: JSON.stringify({ + connection_id: "new-connection-id", + timezone: "America/Los_Angeles", + api_key: "new-api-key", + }), + }); + }); + + it("should not include undefined fields in request body", async () => { + const mockResponse = { message: "Updated" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const callBody = JSON.parse((fetchSpy as any).mock.calls[0][1].body); + expect(callBody).toEqual({ connection_id: "test-id" }); + expect(callBody).not.toHaveProperty("timezone"); + expect(callBody).not.toHaveProperty("api_key"); + }); + + it("should invalidate settings query on success", async () => { + const mockResponse = { message: "Updated", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + queryClient.setQueryData(["cloudZeroSettings", "list", { params: {} }], mockCloudZeroSettings); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const queryCache = queryClient.getQueryCache(); + const queries = queryCache.findAll(); + const settingsQuery = queries.find((q) => q.queryKey[0] === "cloudZeroSettings"); + + expect(settingsQuery).toBeDefined(); + }); + + it("should handle error responses", async () => { + const errorCases = [ + { error: { message: "Update failed" }, expected: "Update failed" }, + { error: "Validation error", expected: "Validation error" }, + { message: "Invalid input", expected: "Invalid input" }, + { detail: "Server error", expected: "Server error" }, + ]; + + for (const errorResponse of errorCases) { + vi.clearAllMocks(); + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe(errorResponse.expected); + } + }); + + it("should handle error response with string error data", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => "Error string", + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Error string"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + statusText: "Bad Request", + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Bad Request"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it("should throw error when accessToken is missing", async () => { + const testCases = ["", null as any]; + + for (const accessToken of testCases) { + vi.clearAllMocks(); + const { result } = renderHook(() => useCloudZeroUpdateSettings(accessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + } + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + const mockResponse = { message: "Updated", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroUpdateSettings(mockAccessToken), { wrapper }); + + result.current.mutate({ + connection_id: "test-id", + }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/settings", expect.any(Object)); + }); +}); + +describe("useCloudZeroDeleteSettings", () => { + let queryClient: QueryClient; + let fetchSpy: ReturnType; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + fetchSpy = vi.fn(); + global.fetch = fetchSpy; + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should successfully delete settings", async () => { + const mockResponse = { message: "Settings deleted successfully", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockResponse); + expect(fetchSpy).toHaveBeenCalledWith(`${mockProxyBaseUrl}/cloudzero/delete`, { + method: "DELETE", + headers: { + [mockHeaderName]: `Bearer ${mockAccessToken}`, + "Content-Type": "application/json", + }, + }); + }); + + it("should invalidate settings query on success", async () => { + const mockResponse = { message: "Deleted", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + queryClient.setQueryData(["cloudZeroSettings", "list", { params: {} }], mockCloudZeroSettings); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const queryCache = queryClient.getQueryCache(); + const queries = queryCache.findAll(); + const settingsQuery = queries.find((q) => q.queryKey[0] === "cloudZeroSettings"); + + expect(settingsQuery).toBeDefined(); + }); + + it("should handle error responses", async () => { + const errorCases = [ + { error: { message: "Delete failed" }, expected: "Delete failed" }, + { error: "Permission denied", expected: "Permission denied" }, + { message: "Not found", expected: "Not found" }, + { detail: "Server error", expected: "Server error" }, + ]; + + for (const errorResponse of errorCases) { + vi.clearAllMocks(); + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => errorResponse, + }); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe(errorResponse.expected); + } + }); + + it("should handle error response with string error data", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + json: async () => "Error string", + }); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Error string"); + }); + + it("should handle error response with invalid JSON", async () => { + (fetchSpy as any).mockResolvedValue({ + ok: false, + statusText: "Internal Server Error", + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Internal Server Error"); + }); + + it("should handle network error", async () => { + const networkError = new Error("Network request failed"); + (fetchSpy as any).mockRejectedValue(networkError); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(networkError); + }); + + it("should throw error when accessToken is missing", async () => { + const testCases = ["", null as any]; + + for (const accessToken of testCases) { + vi.clearAllMocks(); + const { result } = renderHook(() => useCloudZeroDeleteSettings(accessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(fetchSpy).not.toHaveBeenCalled(); + } + }); + + it("should use relative URL when proxyBaseUrl is not set", async () => { + mockGetProxyBaseUrl.mockReturnValue(""); + const mockResponse = { message: "Deleted", status: "success" }; + (fetchSpy as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const { result } = renderHook(() => useCloudZeroDeleteSettings(mockAccessToken), { wrapper }); + + result.current.mutate(); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(fetchSpy).toHaveBeenCalledWith("/cloudzero/delete", expect.any(Object)); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.ts index 7fd61077385..d5a111d0cfe 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/cloudzero/useCloudZeroSettings.ts @@ -53,7 +53,7 @@ export const useCloudZeroSettings = (accessToken: string) => { return useQuery({ queryKey: cloudZeroSettingsKeys.list({}), queryFn: async () => await getCloudZeroSettings(accessToken), - enabled: !!accessToken && !!getProxyBaseUrl(), + enabled: !!accessToken, staleTime: 60 * 60 * 1000, // 1 hour - data rarely changes gcTime: 60 * 60 * 1000, // 1 hour - keep in cache for 1 hour }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useMCPSemanticFilterSettings.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useMCPSemanticFilterSettings.ts new file mode 100644 index 00000000000..e91f5aa670b --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useMCPSemanticFilterSettings.ts @@ -0,0 +1,19 @@ +import { getMCPSemanticFilterSettings } from "@/components/networking"; +import { useQuery } from "@tanstack/react-query"; +import { createQueryKeys } from "../common/queryKeysFactory"; +import useAuthorized from "../useAuthorized"; + +const mcpSemanticFilterSettingsKeys = createQueryKeys( + "mcpSemanticFilterSettings" +); + +export const useMCPSemanticFilterSettings = () => { + const { accessToken } = useAuthorized(); + return useQuery>({ + queryKey: mcpSemanticFilterSettingsKeys.list({}), + queryFn: async () => await getMCPSemanticFilterSettings(accessToken), + enabled: !!accessToken, + staleTime: 60 * 60 * 1000, // 1 hour + gcTime: 60 * 60 * 1000, // 1 hour + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useUpdateMCPSemanticFilterSettings.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useUpdateMCPSemanticFilterSettings.ts new file mode 100644 index 00000000000..2062b4f4c29 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpSemanticFilterSettings/useUpdateMCPSemanticFilterSettings.ts @@ -0,0 +1,25 @@ +import { updateMCPSemanticFilterSettings } from "@/components/networking"; +import { useMutation, useQueryClient } from "@tanstack/react-query"; +import { createQueryKeys } from "../common/queryKeysFactory"; + +const mcpSemanticFilterSettingsKeys = createQueryKeys( + "mcpSemanticFilterSettings" +); + +export const useUpdateMCPSemanticFilterSettings = (accessToken: string) => { + const queryClient = useQueryClient(); + + return useMutation({ + mutationFn: async (settings: Record) => { + if (!accessToken) { + throw new Error("Access token is required"); + } + return updateMCPSemanticFilterSettings(accessToken, settings); + }, + onSuccess: () => { + queryClient.invalidateQueries({ + queryKey: mcpSemanticFilterSettingsKeys.all, + }); + }, + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPAccessGroups.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPAccessGroups.test.ts new file mode 100644 index 00000000000..9c555ff1234 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPAccessGroups.test.ts @@ -0,0 +1,124 @@ +/* @vitest-environment jsdom */ +import React from "react"; +import { renderHook, waitFor } from "@testing-library/react"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { useMCPAccessGroups } from "./useMCPAccessGroups"; +import * as networking from "@/components/networking"; + +vi.mock("@/components/networking", () => ({ + fetchMCPAccessGroups: vi.fn(), +})); + +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: vi.fn(() => ({ + accessToken: "test-token-456", + })), +})); + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, + }, + }); + +const wrapper = ({ children }: { children: React.ReactNode }) => { + const queryClient = createQueryClient(); + return React.createElement(QueryClientProvider, { client: queryClient }, children); +}; + +const mockAccessToken = "test-token-456"; +const mockAccessGroups = ["group-1", "group-2", "group-3"]; + +describe("useMCPAccessGroups", () => { + beforeEach(async () => { + vi.clearAllMocks(); + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: mockAccessToken, + } as any); + }); + + it("should return hook result without errors", () => { + vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue([]); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + expect(result.current).toBeDefined(); + expect(result.current).toHaveProperty("data"); + expect(result.current).toHaveProperty("isSuccess"); + expect(result.current).toHaveProperty("isError"); + expect(result.current).toHaveProperty("status"); + }); + + it("should return MCP access groups when access token is present", async () => { + vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue(mockAccessGroups); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(networking.fetchMCPAccessGroups).toHaveBeenCalledWith(mockAccessToken); + expect(result.current.data).toEqual(mockAccessGroups); + }); + + it("should not fetch when access token is null", async () => { + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: null, + } as any); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + expect(result.current.isFetching).toBe(false); + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(networking.fetchMCPAccessGroups).not.toHaveBeenCalled(); + }); + + it("should not fetch when access token is empty string", async () => { + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: "", + } as any); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + expect(result.current.isFetching).toBe(false); + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(networking.fetchMCPAccessGroups).not.toHaveBeenCalled(); + }); + + it("should expose error state when fetch fails", async () => { + const mockError = new Error("Failed to fetch MCP access groups"); + vi.mocked(networking.fetchMCPAccessGroups).mockRejectedValue(mockError); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(mockError); + expect(result.current.data).toBeUndefined(); + }); + + it("should return empty array when API returns no groups", async () => { + vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue([]); + + const { result } = renderHook(() => useMCPAccessGroups(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual([]); + }); +}); \ No newline at end of file diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPServers.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPServers.test.ts new file mode 100644 index 00000000000..3681ffc7475 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/mcpServers/useMCPServers.test.ts @@ -0,0 +1,134 @@ +/* @vitest-environment jsdom */ +import React from "react"; +import { renderHook, waitFor } from "@testing-library/react"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { useMCPServers } from "./useMCPServers"; +import * as networking from "@/components/networking"; + +vi.mock("@/components/networking", () => ({ + fetchMCPServers: vi.fn(), +})); + +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: vi.fn(() => ({ + accessToken: "test-token-123", + })), +})); + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, + }, + }); + +const wrapper = ({ children }: { children: React.ReactNode }) => { + const queryClient = createQueryClient(); + return React.createElement(QueryClientProvider, { client: queryClient }, children); +}; + +const mockAccessToken = "test-token-123"; +const mockServers = [ + { + server_id: "server-1", + server_name: "Server One", + url: "http://localhost:4000", + created_at: "2025-01-01T00:00:00Z", + created_by: "user-1", + updated_at: "2025-01-01T00:00:00Z", + updated_by: "user-1", + }, +]; + +describe("useMCPServers", () => { + beforeEach(async () => { + vi.clearAllMocks(); + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: mockAccessToken, + } as any); + }); + + it("should return hook result without errors", () => { + vi.mocked(networking.fetchMCPServers).mockResolvedValue([]); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + expect(result.current).toBeDefined(); + expect(result.current).toHaveProperty("data"); + expect(result.current).toHaveProperty("isSuccess"); + expect(result.current).toHaveProperty("isError"); + expect(result.current).toHaveProperty("status"); + }); + + it("should return MCP servers when access token is present", async () => { + vi.mocked(networking.fetchMCPServers).mockResolvedValue(mockServers); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(networking.fetchMCPServers).toHaveBeenCalledWith(mockAccessToken); + expect(result.current.data).toEqual(mockServers); + }); + + it("should not fetch when access token is null", async () => { + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: null, + } as any); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + expect(result.current.isFetching).toBe(false); + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(networking.fetchMCPServers).not.toHaveBeenCalled(); + }); + + it("should not fetch when access token is empty string", async () => { + const useAuthorizedModule = await import("@/app/(dashboard)/hooks/useAuthorized"); + vi.mocked(useAuthorizedModule.default).mockReturnValue({ + accessToken: "", + } as any); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + expect(result.current.isFetching).toBe(false); + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(networking.fetchMCPServers).not.toHaveBeenCalled(); + }); + + it("should expose error state when fetch fails", async () => { + const mockError = new Error("Failed to fetch MCP servers"); + vi.mocked(networking.fetchMCPServers).mockRejectedValue(mockError); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(mockError); + expect(result.current.data).toBeUndefined(); + }); + + it("should return empty array when API returns empty list", async () => { + vi.mocked(networking.fetchMCPServers).mockResolvedValue([]); + + const { result } = renderHook(() => useMCPServers(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual([]); + }); +}); \ No newline at end of file diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts index 4985206092f..2539cc63f95 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.test.ts @@ -3,13 +3,14 @@ import { renderHook, waitFor } from "@testing-library/react"; import React, { ReactNode } from "react"; import { beforeEach, describe, expect, it, vi } from "vitest"; import { - useModelsInfo, - useModelHub, useAllProxyModels, + useInfiniteModelInfo, + useModelHub, + useModelsInfo, useSelectedTeamModels, - type ProxyModel, type AllProxyModelsResponse, type PaginatedModelInfoResponse, + type ProxyModel, } from "./useModels"; vi.mock("@/components/networking", () => ({ @@ -23,7 +24,7 @@ vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ default: () => mockUseAuthorized(), })); -import { modelInfoCall, modelHubCall, modelAvailableCall } from "@/components/networking"; +import { modelAvailableCall, modelHubCall, modelInfoCall } from "@/components/networking"; const mockProxyModel: ProxyModel = { id: "model-1", @@ -106,7 +107,7 @@ describe("useModelsInfo", () => { undefined, undefined, undefined, - undefined + undefined, ); expect(modelInfoCall).toHaveBeenCalledTimes(1); }); @@ -130,7 +131,7 @@ describe("useModelsInfo", () => { undefined, undefined, undefined, - undefined + undefined, ); }); @@ -393,7 +394,7 @@ describe("useAllProxyModels", () => { null, true, false, - "expand" + "expand", ); expect(modelAvailableCall).toHaveBeenCalledTimes(1); }); @@ -531,13 +532,7 @@ describe("useSelectedTeamModels", () => { expect(result.current.data).toEqual(mockAllProxyModelsResponse); expect(result.current.error).toBeNull(); - expect(modelAvailableCall).toHaveBeenCalledWith( - "test-access-token", - "test-user-id", - "Admin", - true, - "team-1" - ); + expect(modelAvailableCall).toHaveBeenCalledWith("test-access-token", "test-user-id", "Admin", true, "team-1"); expect(modelAvailableCall).toHaveBeenCalledTimes(1); }); @@ -639,3 +634,222 @@ describe("useSelectedTeamModels", () => { expect(modelAvailableCall).not.toHaveBeenCalled(); }); }); + +describe("useInfiniteModelInfo", () => { + let queryClient: QueryClient; + + const mockPageOneResponse: PaginatedModelInfoResponse = { + data: [{ model_name: "gpt-4", model_info: { id: "model-1" } }], + total_count: 2, + current_page: 1, + total_pages: 2, + size: 50, + }; + + const mockPageTwoResponse: PaginatedModelInfoResponse = { + data: [{ model_name: "claude-3", model_info: { id: "model-2" } }], + total_count: 2, + current_page: 2, + total_pages: 2, + size: 50, + }; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should return defined result", () => { + (modelInfoCall as any).mockResolvedValue(mockPageOneResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current).toBeDefined(); + expect(result.current).toHaveProperty("data"); + expect(result.current).toHaveProperty("fetchNextPage"); + expect(result.current).toHaveProperty("hasNextPage"); + expect(result.current).toHaveProperty("isFetchingNextPage"); + expect(result.current).toHaveProperty("isLoading"); + }); + + it("should return paginated data and call modelInfoCall with page 1 initially", async () => { + (modelInfoCall as any).mockResolvedValue(mockPageOneResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current.isLoading).toBe(true); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data?.pages).toHaveLength(1); + expect(result.current.data?.pages[0]).toEqual(mockPageOneResponse); + expect(result.current.hasNextPage).toBe(true); + expect(modelInfoCall).toHaveBeenCalledWith("test-access-token", "test-user-id", "Admin", 1, 50, undefined); + expect(modelInfoCall).toHaveBeenCalledTimes(1); + }); + + it("should use custom size parameter", async () => { + (modelInfoCall as any).mockResolvedValue(mockPageOneResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(25), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + }); + + expect(modelInfoCall).toHaveBeenCalledWith("test-access-token", "test-user-id", "Admin", 1, 25, undefined); + }); + + it("should pass search parameter to modelInfoCall", async () => { + (modelInfoCall as any).mockResolvedValue(mockPageOneResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(50, "gpt"), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + }); + + expect(modelInfoCall).toHaveBeenCalledWith("test-access-token", "test-user-id", "Admin", 1, 50, "gpt"); + }); + + it("should fetch next page when fetchNextPage is called", async () => { + (modelInfoCall as any).mockResolvedValueOnce(mockPageOneResponse).mockResolvedValueOnce(mockPageTwoResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + expect(result.current.hasNextPage).toBe(true); + }); + + await result.current.fetchNextPage(); + + await waitFor(() => { + expect(result.current.data?.pages).toHaveLength(2); + expect(result.current.data?.pages[1]).toEqual(mockPageTwoResponse); + expect(result.current.hasNextPage).toBe(false); + }); + + expect(modelInfoCall).toHaveBeenNthCalledWith(2, "test-access-token", "test-user-id", "Admin", 2, 50, undefined); + }); + + it("should return undefined for hasNextPage when on last page", async () => { + const lastPageResponse: PaginatedModelInfoResponse = { + ...mockPageOneResponse, + current_page: 1, + total_pages: 1, + }; + (modelInfoCall as any).mockResolvedValue(lastPageResponse); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.hasNextPage).toBe(false); + }); + + it("should handle error when modelInfoCall fails", async () => { + const errorMessage = "Failed to fetch models"; + const testError = new Error(errorMessage); + (modelInfoCall as any).mockRejectedValue(testError); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current.isLoading).toBe(true); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(testError); + expect(result.current.data).toBeUndefined(); + expect(modelInfoCall).toHaveBeenCalledTimes(1); + }); + + it("should not execute query when accessToken is missing", () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(modelInfoCall).not.toHaveBeenCalled(); + }); + + it("should not execute query when userId is missing", () => { + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: null, + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(modelInfoCall).not.toHaveBeenCalled(); + }); + + it("should not execute query when userRole is missing", () => { + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: null, + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useInfiniteModelInfo(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(modelInfoCall).not.toHaveBeenCalled(); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts index c57de675e0e..fe1afdcc39f 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/models/useModels.ts @@ -1,4 +1,4 @@ -import { useQuery } from "@tanstack/react-query"; +import { useQuery, useInfiniteQuery } from "@tanstack/react-query"; import { createQueryKeys } from "../common/queryKeysFactory"; import { modelInfoCall, modelHubCall, modelAvailableCall } from "@/components/networking"; import useAuthorized from "../useAuthorized"; @@ -26,6 +26,7 @@ const modelKeys = createQueryKeys("models"); const modelHubKeys = createQueryKeys("modelHub"); const allProxyModelsKeys = createQueryKeys("allProxyModels"); const selectedTeamModelsKeys = createQueryKeys("selectedTeamModels"); +const infiniteModelKeys = createQueryKeys("infiniteModels"); export const useModelsInfo = (page: number = 1, size: number = 50, search?: string, modelId?: string, teamId?: string, sortBy?: string, sortOrder?: string) => { const { accessToken, userId, userRole } = useAuthorized(); @@ -74,3 +75,38 @@ export const useSelectedTeamModels = (teamID: string | null) => { enabled: Boolean(accessToken && userId && userRole && teamID), }); }; + +export const useInfiniteModelInfo = ( + size: number = 50, + search?: string, +) => { + const { accessToken, userId, userRole } = useAuthorized(); + return useInfiniteQuery({ + queryKey: infiniteModelKeys.list({ + filters: { + ...(userId && { userId }), + ...(userRole && { userRole }), + size, + ...(search && { search }), + }, + }), + queryFn: async ({ pageParam }) => { + return await modelInfoCall( + accessToken!, + userId!, + userRole!, + pageParam as number, + size, + search, + ); + }, + initialPageParam: 1, + getNextPageParam: (lastPage) => { + if (lastPage.current_page < lastPage.total_pages) { + return lastPage.current_page + 1; + } + return undefined; + }, + enabled: Boolean(accessToken && userId && userRole), + }); +}; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useEditSSOSettings.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useEditSSOSettings.test.ts new file mode 100644 index 00000000000..8b2fee6cf30 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useEditSSOSettings.test.ts @@ -0,0 +1,312 @@ +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useEditSSOSettings, EditSSOSettingsParams, EditSSOSettingsResponse } from "./useEditSSOSettings"; +import { updateSSOSettings } from "@/components/networking"; + +vi.mock("@/components/networking", () => ({ + updateSSOSettings: vi.fn(), +})); + +const mockUseAuthorized = vi.fn(); +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: () => mockUseAuthorized(), +})); + +const mockUpdateResponse: EditSSOSettingsResponse = { + message: "SSO settings updated successfully", + google_client_id: "updated-google-client-id", +}; + +describe("useEditSSOSettings", () => { + let queryClient: QueryClient; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + expect(result.current).toBeDefined(); + expect(result.current.mutate).toBeDefined(); + expect(result.current.mutateAsync).toBeDefined(); + }); + + it("should successfully update SSO settings", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + google_client_secret: "new-google-client-secret", + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + expect(updateSSOSettings).toHaveBeenCalledTimes(1); + expect(result.current.data).toEqual(mockUpdateResponse); + expect(result.current.error).toBeNull(); + }); + + it("should handle error when updateSSOSettings fails", async () => { + const errorMessage = "Failed to update SSO settings"; + const testError = new Error(errorMessage); + + (updateSSOSettings as any).mockRejectedValue(testError); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + }; + + result.current.mutateAsync(params).catch(() => {}); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + expect(result.current.error).toEqual(testError); + expect(result.current.data).toBeUndefined(); + }); + + it("should throw error when accessToken is missing", async () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + }; + + await expect(result.current.mutateAsync(params)).rejects.toThrow("Access token is required"); + + expect(updateSSOSettings).not.toHaveBeenCalled(); + }); + + it("should update Microsoft SSO settings", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + microsoft_client_id: "new-microsoft-client-id", + microsoft_client_secret: "new-microsoft-client-secret", + microsoft_tenant: "new-tenant", + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + }); + + it("should update generic SSO settings", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + generic_client_id: "new-generic-client-id", + generic_client_secret: "new-generic-client-secret", + generic_authorization_endpoint: "https://example.com/auth", + generic_token_endpoint: "https://example.com/token", + generic_userinfo_endpoint: "https://example.com/userinfo", + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + }); + + it("should update role mappings", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + role_mappings: { + provider: "google", + group_claim: "groups", + default_role: "internal_user", + roles: { + "admin-group": ["proxy_admin"], + }, + }, + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + }); + + it("should update multiple settings at once", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + microsoft_client_id: "new-microsoft-client-id", + proxy_base_url: "https://new-proxy.example.com", + user_email: "newuser@example.com", + sso_provider: "google", + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + }); + + it("should handle null values in params", async () => { + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: null, + google_client_secret: null, + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateSSOSettings).toHaveBeenCalledWith("test-access-token", params); + }); + + it("should set isPending to true during mutation", async () => { + let resolvePromise: (value: EditSSOSettingsResponse) => void; + const pendingPromise = new Promise((resolve) => { + resolvePromise = resolve; + }); + + (updateSSOSettings as any).mockReturnValue(pendingPromise); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + }; + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isPending).toBe(true); + }); + + resolvePromise!(mockUpdateResponse); + + await waitFor(() => { + expect(result.current.isPending).toBe(false); + }); + }); + + it("should handle network timeout error", async () => { + const timeoutError = new Error("Network timeout"); + + (updateSSOSettings as any).mockRejectedValue(timeoutError); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + }; + + result.current.mutateAsync(params).catch(() => {}); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(timeoutError); + }); + + it("should reset error state on successful mutation after error", async () => { + const errorMessage = "Failed to update"; + const testError = new Error(errorMessage); + + (updateSSOSettings as any).mockRejectedValueOnce(testError); + + const { result } = renderHook(() => useEditSSOSettings(), { wrapper }); + + const params: EditSSOSettingsParams = { + google_client_id: "new-google-client-id", + }; + + result.current.mutateAsync(params).catch(() => {}); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + (updateSSOSettings as any).mockResolvedValue(mockUpdateResponse); + + result.current.mutateAsync(params); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + expect(result.current.isError).toBe(false); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useSSOSettings.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useSSOSettings.test.ts new file mode 100644 index 00000000000..4e8d892b5d8 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/sso/useSSOSettings.test.ts @@ -0,0 +1,310 @@ +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useSSOSettings, SSOSettingsResponse } from "./useSSOSettings"; +import { getSSOSettings } from "@/components/networking"; + +vi.mock("@/components/networking", () => ({ + getSSOSettings: vi.fn(), +})); + +const mockUseAuthorized = vi.fn(); +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: () => mockUseAuthorized(), +})); + +const mockSSOSettingsResponse: SSOSettingsResponse = { + values: { + google_client_id: "test-google-client-id", + google_client_secret: "test-google-client-secret", + microsoft_client_id: "test-microsoft-client-id", + microsoft_client_secret: "test-microsoft-client-secret", + microsoft_tenant: "test-tenant", + generic_client_id: "test-generic-client-id", + generic_client_secret: "test-generic-client-secret", + generic_authorization_endpoint: "https://example.com/auth", + generic_token_endpoint: "https://example.com/token", + generic_userinfo_endpoint: "https://example.com/userinfo", + proxy_base_url: "https://proxy.example.com", + user_email: "test@example.com", + ui_access_mode: "proxy_admin", + role_mappings: { + provider: "google", + group_claim: "groups", + default_role: "internal_user", + roles: { + "admin-group": ["proxy_admin"], + "viewer-group": ["internal_user_viewer"], + }, + }, + team_mappings: { + team_ids_jwt_field: "team_ids", + }, + }, + field_schema: { + description: "SSO Settings Schema", + properties: { + google_client_id: { + description: "Google OAuth Client ID", + type: "string", + }, + microsoft_client_id: { + description: "Microsoft OAuth Client ID", + type: "string", + }, + }, + }, +}; + +describe("useSSOSettings", () => { + let queryClient: QueryClient; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + (getSSOSettings as any).mockResolvedValue(mockSSOSettingsResponse); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should return SSO settings data when query is successful", async () => { + (getSSOSettings as any).mockResolvedValue(mockSSOSettingsResponse); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(true); + expect(result.current.data).toBeUndefined(); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockSSOSettingsResponse); + expect(result.current.error).toBeNull(); + expect(getSSOSettings).toHaveBeenCalledWith("test-access-token"); + expect(getSSOSettings).toHaveBeenCalledTimes(1); + }); + + it("should handle error when getSSOSettings fails", async () => { + const errorMessage = "Failed to fetch SSO settings"; + const testError = new Error(errorMessage); + + (getSSOSettings as any).mockRejectedValue(testError); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(true); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(testError); + expect(result.current.data).toBeUndefined(); + expect(getSSOSettings).toHaveBeenCalledWith("test-access-token"); + expect(getSSOSettings).toHaveBeenCalledTimes(1); + }); + + it("should not execute query when accessToken is missing", async () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + + expect(getSSOSettings).not.toHaveBeenCalled(); + }); + + it("should not execute query when userId is missing", async () => { + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: null, + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + + expect(getSSOSettings).not.toHaveBeenCalled(); + }); + + it("should not execute query when userRole is missing", async () => { + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: null, + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + + expect(getSSOSettings).not.toHaveBeenCalled(); + }); + + it("should not execute query when all auth values are missing", async () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: null, + userRole: null, + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + + expect(getSSOSettings).not.toHaveBeenCalled(); + }); + + it("should execute query when all auth values are present", async () => { + (getSSOSettings as any).mockResolvedValue(mockSSOSettingsResponse); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + }); + + expect(getSSOSettings).toHaveBeenCalledWith("test-access-token"); + expect(getSSOSettings).toHaveBeenCalledTimes(1); + }); + + it("should return empty values when API returns minimal data", async () => { + const minimalResponse: SSOSettingsResponse = { + values: { + google_client_id: null, + google_client_secret: null, + microsoft_client_id: null, + microsoft_client_secret: null, + microsoft_tenant: null, + generic_client_id: null, + generic_client_secret: null, + generic_authorization_endpoint: null, + generic_token_endpoint: null, + generic_userinfo_endpoint: null, + proxy_base_url: null, + user_email: null, + ui_access_mode: null, + role_mappings: { + provider: "", + group_claim: "", + default_role: "internal_user", + roles: {}, + }, + team_mappings: { + team_ids_jwt_field: "", + }, + }, + field_schema: { + description: "", + properties: {}, + }, + }; + + (getSSOSettings as any).mockResolvedValue(minimalResponse); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(minimalResponse); + expect(getSSOSettings).toHaveBeenCalledWith("test-access-token"); + }); + + it("should handle network timeout error", async () => { + const timeoutError = new Error("Network timeout"); + + (getSSOSettings as any).mockRejectedValue(timeoutError); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(timeoutError); + expect(result.current.data).toBeUndefined(); + }); + + it("should use correct query key", async () => { + (getSSOSettings as any).mockResolvedValue(mockSSOSettingsResponse); + + const { result } = renderHook(() => useSSOSettings(), { wrapper }); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const queryCache = queryClient.getQueryCache(); + const queries = queryCache.findAll(); + const ssoQuery = queries.find((q) => q.queryKey[0] === "sso"); + + expect(ssoQuery).toBeDefined(); + expect(ssoQuery?.queryKey).toEqual(["sso", "detail", "settings"]); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.test.ts index 91ffbcfafa2..217ca426c25 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.test.ts @@ -2,22 +2,28 @@ import { describe, it, expect, vi, beforeEach } from "vitest"; import { renderHook, waitFor } from "@testing-library/react"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; import React, { ReactNode } from "react"; -import { useTeams } from "./useTeams"; +import { useTeams, useTeam, useDeletedTeams, DeletedTeam, teamListCall } from "./useTeams"; import { fetchTeams } from "@/app/(dashboard)/networking"; +import { teamInfoCall } from "@/components/networking"; import type { Team } from "@/components/key_team_helpers/key_list"; -// Mock the networking function vi.mock("@/app/(dashboard)/networking", () => ({ fetchTeams: vi.fn(), })); -// Mock useAuthorized hook - we can override this in individual tests +vi.mock("@/components/networking", () => ({ + teamInfoCall: vi.fn(), + getProxyBaseUrl: vi.fn(() => ""), + getGlobalLitellmHeaderName: vi.fn(() => "Authorization"), + deriveErrorMessage: vi.fn((data) => data?.error || "Error"), + handleError: vi.fn(), +})); + const mockUseAuthorized = vi.fn(); vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ default: () => mockUseAuthorized(), })); -// Mock data const mockTeams: Team[] = [ { team_id: "team-1", @@ -31,6 +37,7 @@ const mockTeams: Team[] = [ created_at: "2024-01-01T00:00:00Z", keys: [], members_with_roles: [], + spend: 50.0, }, { team_id: "team-2", @@ -44,6 +51,7 @@ const mockTeams: Team[] = [ created_at: "2024-01-02T00:00:00Z", keys: [], members_with_roles: [], + spend: 100.0, }, ]; @@ -78,6 +86,14 @@ describe("useTeams", () => { const wrapper = ({ children }: { children: ReactNode }) => React.createElement(QueryClientProvider, { client: queryClient }, children); + it("should render", () => { + (fetchTeams as any).mockResolvedValue(mockTeams); + + const { result } = renderHook(() => useTeams(), { wrapper }); + + expect(result.current).toBeDefined(); + }); + it("should return teams data when query is successful", async () => { // Mock successful API call (fetchTeams as any).mockResolvedValue(mockTeams); @@ -273,3 +289,509 @@ describe("useTeams", () => { expect(fetchTeams).toHaveBeenCalledWith("test-access-token", null, "Admin", null); }); }); + +describe("useTeam", () => { + let queryClient: QueryClient; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + (teamInfoCall as any).mockResolvedValue(mockTeams[0]); + + const { result } = renderHook(() => useTeam("team-1"), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should return team data when query is successful", async () => { + (teamInfoCall as any).mockResolvedValue(mockTeams[0]); + + const { result } = renderHook(() => useTeam("team-1"), { wrapper }); + + expect(result.current.isLoading).toBe(true); + expect(result.current.data).toBeUndefined(); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockTeams[0]); + expect(result.current.error).toBeNull(); + expect(teamInfoCall).toHaveBeenCalledWith("test-access-token", "team-1"); + expect(teamInfoCall).toHaveBeenCalledTimes(1); + }); + + it("should handle error when teamInfoCall fails", async () => { + const errorMessage = "Failed to fetch team"; + const testError = new Error(errorMessage); + + (teamInfoCall as any).mockRejectedValue(testError); + + const { result } = renderHook(() => useTeam("team-1"), { wrapper }); + + expect(result.current.isLoading).toBe(true); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(testError); + expect(result.current.data).toBeUndefined(); + expect(teamInfoCall).toHaveBeenCalledWith("test-access-token", "team-1"); + expect(teamInfoCall).toHaveBeenCalledTimes(1); + }); + + it("should not execute query when accessToken is missing", () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useTeam("team-1"), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(teamInfoCall).not.toHaveBeenCalled(); + }); + + it("should not execute query when teamId is missing", () => { + const { result } = renderHook(() => useTeam(undefined), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(teamInfoCall).not.toHaveBeenCalled(); + }); + + it("should use initialData from teams list cache when available", async () => { + queryClient.setQueryData(["teams", "list", { params: {} }], mockTeams); + + const { result } = renderHook(() => useTeam("team-1"), { wrapper }); + + expect(result.current.data).toEqual(mockTeams[0]); + // When initialData is present, isLoading is false but isFetching is true + expect(result.current.isLoading).toBe(false); + expect(result.current.isFetching).toBe(true); + + await waitFor(() => { + expect(result.current.isFetching).toBe(false); + }); + }); + + it("should return undefined initialData when teamId is not in cache", () => { + queryClient.setQueryData(["teams", "list", { params: {} }], mockTeams); + + const { result } = renderHook(() => useTeam("non-existent-team"), { wrapper }); + + expect(result.current.data).toBeUndefined(); + }); + + it("should throw error in queryFn when accessToken or teamId is missing (defensive check)", async () => { + // This tests the defensive error path in queryFn (lines 111-112) + // The enabled check prevents queryFn from running, but we can test the defensive code + // by manually constructing and calling the queryFn logic + + // Set up mocks + mockUseAuthorized.mockReturnValue({ + accessToken: null, // Missing accessToken + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + // Import useQueryClient to get access to query client + const { useQueryClient } = await import("@tanstack/react-query"); + + // Manually test the queryFn logic by calling it directly + // This simulates what would happen if enabled check was bypassed + const testQueryFn = async () => { + const { accessToken } = mockUseAuthorized(); + const teamId = "team-1"; + + // This is the defensive check from lines 111-112 + if (!accessToken || !teamId) { + throw new Error("Missing auth or teamId"); + } + + return teamInfoCall(accessToken, teamId); + }; + + // Test that the error is thrown + await expect(testQueryFn()).rejects.toThrow("Missing auth or teamId"); + + // Also test with missing teamId + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const testQueryFnMissingTeamId = async () => { + const { accessToken } = mockUseAuthorized(); + const teamId = undefined; // Missing teamId + + if (!accessToken || !teamId) { + throw new Error("Missing auth or teamId"); + } + + return teamInfoCall(accessToken, teamId); + }; + + await expect(testQueryFnMissingTeamId()).rejects.toThrow("Missing auth or teamId"); + }); +}); + +describe("teamListCall", () => { + beforeEach(() => { + vi.clearAllMocks(); + global.fetch = vi.fn(); + }); + + it("should successfully fetch teams list", async () => { + const mockResponse = { + teams: mockTeams, + total: 2, + page: 1, + page_size: 10, + total_pages: 1, + }; + + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const result = await teamListCall("test-access-token", 1, 10, {}); + + expect(result).toEqual(mockResponse); + expect(global.fetch).toHaveBeenCalledWith( + "/v2/team/list?page=1&page_size=10", + expect.objectContaining({ + method: "GET", + headers: expect.objectContaining({ + Authorization: "Bearer test-access-token", + "Content-Type": "application/json", + }), + }), + ); + }); + + it("should include query parameters when options are provided", async () => { + const mockResponse = { teams: mockTeams }; + + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const options = { + organizationID: "org-1", + teamID: "team-1", + team_alias: "Test Team", + userID: "user-1", + sortBy: "created_at", + sortOrder: "desc", + }; + + await teamListCall("test-access-token", 1, 10, options); + + const callUrl = (global.fetch as any).mock.calls[0][0]; + expect(callUrl).toContain("organization_id=org-1"); + expect(callUrl).toContain("team_id=team-1"); + expect(callUrl).toContain("team_alias=Test+Team"); // URL encoding converts spaces to + + expect(callUrl).toContain("user_id=user-1"); + expect(callUrl).toContain("sort_by=created_at"); + expect(callUrl).toContain("sort_order=desc"); + expect(callUrl).toContain("page=1"); + expect(callUrl).toContain("page_size=10"); + }); + + it("should filter out null and undefined parameters", async () => { + const mockResponse = { teams: mockTeams }; + + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + const options = { + organizationID: null, + teamID: undefined, + userID: "user-1", + }; + + await teamListCall("test-access-token", 1, 10, options); + + const callUrl = (global.fetch as any).mock.calls[0][0]; + expect(callUrl).not.toContain("organization_id"); + expect(callUrl).not.toContain("team_id"); + expect(callUrl).toContain("user_id=user-1"); + }); + + it("should use baseUrl when provided", async () => { + const { getProxyBaseUrl } = await import("@/components/networking"); + (getProxyBaseUrl as any).mockReturnValue("https://api.example.com"); + + const mockResponse = { teams: mockTeams }; + + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => mockResponse, + }); + + await teamListCall("test-access-token", 1, 10, {}); + + const callUrl = (global.fetch as any).mock.calls[0][0]; + expect(callUrl).toBe("https://api.example.com/v2/team/list?page=1&page_size=10"); + }); + + it("should handle error response", async () => { + const errorData = { error: "Failed to fetch teams" }; + (global.fetch as any).mockResolvedValue({ + ok: false, + json: async () => errorData, + }); + + await expect(teamListCall("test-access-token", 1, 10, {})).rejects.toThrow("Failed to fetch teams"); + }); + + it("should handle network errors", async () => { + const networkError = new Error("Network error"); + (global.fetch as any).mockRejectedValue(networkError); + + await expect(teamListCall("test-access-token", 1, 10, {})).rejects.toThrow("Network error"); + }); + + it("should handle error when response.json() fails", async () => { + (global.fetch as any).mockResolvedValue({ + ok: false, + json: async () => { + throw new Error("Invalid JSON"); + }, + }); + + await expect(teamListCall("test-access-token", 1, 10, {})).rejects.toThrow(); + }); +}); + +describe("useDeletedTeams", () => { + let queryClient: QueryClient; + + const mockDeletedTeams: DeletedTeam[] = [ + { + ...mockTeams[0], + deleted_at: "2024-01-10T00:00:00Z", + deleted_by: "admin-user", + }, + { + ...mockTeams[1], + deleted_at: "2024-01-11T00:00:00Z", + deleted_by: "admin-user", + }, + ]; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + + mockUseAuthorized.mockReturnValue({ + accessToken: "test-access-token", + userId: "test-user-id", + userRole: "Admin", + token: "test-token", + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + global.fetch = vi.fn(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => ({ teams: mockDeletedTeams }), + }); + + const { result } = renderHook(() => useDeletedTeams(1, 10, {}), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should return deleted teams data when query is successful", async () => { + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => ({ teams: mockDeletedTeams }), + }); + + const { result } = renderHook(() => useDeletedTeams(1, 10, {}), { wrapper }); + + expect(result.current.isLoading).toBe(true); + expect(result.current.data).toBeUndefined(); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockDeletedTeams); + expect(result.current.error).toBeNull(); + }); + + it("should handle error when API call fails", async () => { + (global.fetch as any).mockResolvedValue({ + ok: false, + json: async () => ({ error: "Failed to fetch deleted teams" }), + }); + + const { result } = renderHook(() => useDeletedTeams(1, 10, {}), { wrapper }); + + expect(result.current.isLoading).toBe(true); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toBeDefined(); + expect(result.current.data).toBeUndefined(); + }); + + it("should not execute query when accessToken is missing", () => { + mockUseAuthorized.mockReturnValue({ + accessToken: null, + userId: "test-user-id", + userRole: "Admin", + token: null, + userEmail: "test@example.com", + premiumUser: false, + disabledPersonalKeyCreation: null, + showSSOBanner: false, + }); + + const { result } = renderHook(() => useDeletedTeams(1, 10, {}), { wrapper }); + + expect(result.current.isLoading).toBe(false); + expect(result.current.data).toBeUndefined(); + expect(result.current.isFetched).toBe(false); + expect(global.fetch).not.toHaveBeenCalled(); + }); + + it("should use placeholderData when paginating", async () => { + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => ({ teams: mockDeletedTeams }), + }); + + const { result, rerender } = renderHook( + ({ page }) => useDeletedTeams(page, 10, {}), + { + wrapper, + initialProps: { page: 1 }, + }, + ); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + rerender({ page: 2 }); + + expect(result.current.data).toEqual(mockDeletedTeams); + }); + + it("should pass options to API call", async () => { + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => ({ teams: mockDeletedTeams }), + }); + + const options = { + organizationID: "org-1", + teamID: "team-1", + userID: "user-1", + }; + + renderHook(() => useDeletedTeams(1, 10, options), { wrapper }); + + await waitFor(() => { + expect(global.fetch).toHaveBeenCalled(); + }); + + const callUrl = (global.fetch as any).mock.calls[0][0]; + expect(callUrl).toContain("organization_id=org-1"); + expect(callUrl).toContain("team_id=team-1"); + expect(callUrl).toContain("user_id=user-1"); + expect(callUrl).toContain("status=deleted"); + }); + + it("should handle response when data is directly an array (not wrapped in teams property)", async () => { + (global.fetch as any).mockResolvedValue({ + ok: true, + json: async () => mockDeletedTeams, // Direct array, not wrapped in { teams: ... } + }); + + const { result } = renderHook(() => useDeletedTeams(1, 10, {}), { wrapper }); + + await waitFor(() => { + expect(result.current.isLoading).toBe(false); + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockDeletedTeams); + expect(result.current.error).toBeNull(); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts index fb2a002787b..a86b5cd51f6 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/teams/useTeams.ts @@ -35,7 +35,7 @@ export interface TeamListCallOptions { status?: string | null; } -const teamListCall = async ( +export const teamListCall = async ( accessToken: string, page: number, pageSize: number, diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiConfig/useUIConfig.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiConfig/useUIConfig.test.ts index 6429aeafb5a..aba5dddf13d 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiConfig/useUIConfig.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiConfig/useUIConfig.test.ts @@ -23,6 +23,7 @@ vi.mock("../common/queryKeysFactory", () => ({ // Mock data const mockUIConfig: LiteLLMWellKnownUiConfig = { + sso_configured: true, server_root_path: "/api", proxy_base_url: "https://proxy.example.com", auto_redirect_to_sso: true, @@ -99,6 +100,7 @@ describe("useUIConfig", () => { server_root_path: "/v1", proxy_base_url: null, auto_redirect_to_sso: false, + sso_configured: false, admin_ui_disabled: true, }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.test.ts index 785f003d2f8..0fc3bda27fc 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.test.ts @@ -10,12 +10,6 @@ vi.mock("@/components/networking", () => ({ getUiSettings: vi.fn(), })); -// Mock useAuthorized hook - we can override this in individual tests -const mockUseAuthorized = vi.fn(); -vi.mock("../useAuthorized", () => ({ - default: () => mockUseAuthorized(), -})); - // Mock data const mockUISettings: Record = { theme: "dark", @@ -39,18 +33,6 @@ describe("useUISettings", () => { // Reset all mocks vi.clearAllMocks(); - - // Set default mock for useAuthorized (enabled state) - mockUseAuthorized.mockReturnValue({ - accessToken: "test-access-token", - userRole: "Admin", - userId: "test-user-id", - token: "test-token", - userEmail: "test@example.com", - premiumUser: false, - disabledPersonalKeyCreation: null, - showSSOBanner: false, - }); }); const wrapper = ({ children }: { children: ReactNode }) => @@ -74,7 +56,7 @@ describe("useUISettings", () => { expect(result.current.data).toEqual(mockUISettings); expect(result.current.error).toBeNull(); - expect(getUiSettings).toHaveBeenCalledWith("test-access-token"); + expect(getUiSettings).toHaveBeenCalledWith(); expect(getUiSettings).toHaveBeenCalledTimes(1); }); @@ -98,58 +80,10 @@ describe("useUISettings", () => { expect(result.current.error).toEqual(testError); expect(result.current.data).toBeUndefined(); - expect(getUiSettings).toHaveBeenCalledWith("test-access-token"); + expect(getUiSettings).toHaveBeenCalledWith(); expect(getUiSettings).toHaveBeenCalledTimes(1); }); - it("should not execute query when accessToken is missing", async () => { - // Mock missing accessToken - mockUseAuthorized.mockReturnValue({ - accessToken: null, - userRole: "Admin", - userId: "test-user-id", - token: null, - userEmail: "test@example.com", - premiumUser: false, - disabledPersonalKeyCreation: null, - showSSOBanner: false, - }); - - const { result } = renderHook(() => useUISettings(), { wrapper }); - - // Query should not execute - expect(result.current.isLoading).toBe(false); - expect(result.current.data).toBeUndefined(); - expect(result.current.isFetched).toBe(false); - - // API should not be called - expect(getUiSettings).not.toHaveBeenCalled(); - }); - - it("should not execute query when accessToken is empty string", async () => { - // Mock empty accessToken - mockUseAuthorized.mockReturnValue({ - accessToken: "", - userRole: "Admin", - userId: "test-user-id", - token: "", - userEmail: "test@example.com", - premiumUser: false, - disabledPersonalKeyCreation: null, - showSSOBanner: false, - }); - - const { result } = renderHook(() => useUISettings(), { wrapper }); - - // Query should not execute - expect(result.current.isLoading).toBe(false); - expect(result.current.data).toBeUndefined(); - expect(result.current.isFetched).toBe(false); - - // API should not be called - expect(getUiSettings).not.toHaveBeenCalled(); - }); - it("should return empty object when API returns empty settings", async () => { // Mock API returning empty object (getUiSettings as any).mockResolvedValue({}); @@ -163,7 +97,7 @@ describe("useUISettings", () => { }); expect(result.current.data).toEqual({}); - expect(getUiSettings).toHaveBeenCalledWith("test-access-token"); + expect(getUiSettings).toHaveBeenCalledWith(); }); it("should handle network timeout error", async () => { diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.ts index 46a0254d0db..14c6c5e3888 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUISettings.ts @@ -1,16 +1,13 @@ import { getUiSettings } from "@/components/networking"; import { useQuery } from "@tanstack/react-query"; import { createQueryKeys } from "../common/queryKeysFactory"; -import useAuthorized from "../useAuthorized"; const uiSettingsKeys = createQueryKeys("uiSettings"); export const useUISettings = () => { - const { accessToken } = useAuthorized(); return useQuery>({ queryKey: uiSettingsKeys.list({}), - queryFn: async () => await getUiSettings(accessToken), - enabled: !!accessToken, + queryFn: async () => await getUiSettings(), staleTime: 60 * 60 * 1000, // 1 hour - data rarely changes gcTime: 60 * 60 * 1000, // 1 hour - keep in cache for 1 hour }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUpdateUISettings.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUpdateUISettings.test.ts new file mode 100644 index 00000000000..9dfadc0cd98 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/uiSettings/useUpdateUISettings.test.ts @@ -0,0 +1,240 @@ +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import React, { ReactNode } from "react"; +import { useUpdateUISettings } from "./useUpdateUISettings"; +import { updateUiSettings } from "@/components/networking"; + +vi.mock("@/components/networking", () => ({ + updateUiSettings: vi.fn(), +})); + +const mockUpdateUiSettingsResponse = { + message: "UI settings updated successfully", + status: "success", + settings: { + disable_model_add_for_internal_users: true, + disable_team_admin_delete_team_user: false, + }, +}; + +describe("useUpdateUISettings", () => { + let queryClient: QueryClient; + + beforeEach(() => { + queryClient = new QueryClient({ + defaultOptions: { + queries: { + retry: false, + }, + mutations: { + retry: false, + }, + }, + }); + + vi.clearAllMocks(); + }); + + const wrapper = ({ children }: { children: ReactNode }) => + React.createElement(QueryClientProvider, { client: queryClient }, children); + + it("should render", () => { + (updateUiSettings as any).mockResolvedValue(mockUpdateUiSettingsResponse); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + expect(result.current).toBeDefined(); + }); + + it("should update UI settings when mutation is successful", async () => { + (updateUiSettings as any).mockResolvedValue(mockUpdateUiSettingsResponse); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(result.current.data).toEqual(mockUpdateUiSettingsResponse); + expect(updateUiSettings).toHaveBeenCalledWith("test-access-token", settings); + expect(updateUiSettings).toHaveBeenCalledTimes(1); + }); + + it("should handle error when updateUiSettings fails", async () => { + const errorMessage = "Failed to update UI settings"; + const testError = new Error(errorMessage); + + (updateUiSettings as any).mockRejectedValue(testError); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(testError); + expect(updateUiSettings).toHaveBeenCalledWith("test-access-token", settings); + expect(updateUiSettings).toHaveBeenCalledTimes(1); + }); + + it("should throw error when accessToken is missing", async () => { + const { result } = renderHook(() => useUpdateUISettings(""), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(updateUiSettings).not.toHaveBeenCalled(); + }); + + it("should throw error when accessToken is null", async () => { + const { result } = renderHook(() => useUpdateUISettings(null as any), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error?.message).toBe("Access token is required"); + expect(updateUiSettings).not.toHaveBeenCalled(); + }); + + it("should invalidate uiSettings queries on success", async () => { + (updateUiSettings as any).mockResolvedValue(mockUpdateUiSettingsResponse); + + queryClient.setQueryData(["uiSettings", "detail", "settings"], { values: {} }); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + const queryCache = queryClient.getQueryCache(); + const queries = queryCache.findAll({ queryKey: ["uiSettings"] }); + expect(queries.length).toBeGreaterThan(0); + }); + + it("should handle multiple settings updates", async () => { + (updateUiSettings as any).mockResolvedValue(mockUpdateUiSettingsResponse); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings1 = { + disable_model_add_for_internal_users: true, + }; + + const settings2 = { + disable_team_admin_delete_team_user: false, + }; + + result.current.mutate(settings1); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + result.current.mutate(settings2); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateUiSettings).toHaveBeenCalledTimes(2); + expect(updateUiSettings).toHaveBeenNthCalledWith(1, "test-access-token", settings1); + expect(updateUiSettings).toHaveBeenNthCalledWith(2, "test-access-token", settings2); + }); + + it("should handle empty settings object", async () => { + (updateUiSettings as any).mockResolvedValue(mockUpdateUiSettingsResponse); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + result.current.mutate({}); + + await waitFor(() => { + expect(result.current.isSuccess).toBe(true); + }); + + expect(updateUiSettings).toHaveBeenCalledWith("test-access-token", {}); + }); + + it("should handle network timeout error", async () => { + const timeoutError = new Error("Network timeout"); + + (updateUiSettings as any).mockRejectedValue(timeoutError); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + await waitFor(() => { + expect(result.current.isError).toBe(true); + }); + + expect(result.current.error).toEqual(timeoutError); + }); + + it("should set isPending during mutation", async () => { + let resolvePromise: (value: any) => void; + const promise = new Promise((resolve) => { + resolvePromise = resolve; + }); + + (updateUiSettings as any).mockReturnValue(promise); + + const { result } = renderHook(() => useUpdateUISettings("test-access-token"), { wrapper }); + + const settings = { + disable_model_add_for_internal_users: true, + }; + + result.current.mutate(settings); + + // Wait for the mutation to start and isPending to become true + await waitFor(() => { + expect(result.current.isPending).toBe(true); + }); + + resolvePromise!(mockUpdateUiSettingsResponse); + + await waitFor(() => { + expect(result.current.isPending).toBe(false); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts index 78eddbd8d3c..76a3129d6d7 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.test.ts @@ -8,12 +8,13 @@ import useAuthorized from "./useAuthorized"; // Unmock useAuthorized to test the actual implementation vi.unmock("@/app/(dashboard)/hooks/useAuthorized"); -const { replaceMock, clearTokenCookiesMock, getProxyBaseUrlMock, getUiConfigMock, isJwtExpiredMock } = vi.hoisted(() => ({ +const { replaceMock, clearTokenCookiesMock, getProxyBaseUrlMock, getUiConfigMock, decodeTokenMock, checkTokenValidityMock } = vi.hoisted(() => ({ replaceMock: vi.fn(), clearTokenCookiesMock: vi.fn(), getProxyBaseUrlMock: vi.fn(() => "http://proxy.example"), getUiConfigMock: vi.fn(), - isJwtExpiredMock: vi.fn(), + decodeTokenMock: vi.fn(), + checkTokenValidityMock: vi.fn(), })); vi.mock("next/navigation", () => ({ @@ -43,7 +44,8 @@ vi.mock("@/utils/jwtUtils", async (importOriginal) => { const actual = await importOriginal(); return { ...actual, - isJwtExpired: isJwtExpiredMock, + decodeToken: decodeTokenMock, + checkTokenValidity: checkTokenValidityMock, }; }); @@ -77,7 +79,8 @@ describe("useAuthorized", () => { clearTokenCookiesMock.mockReset(); getProxyBaseUrlMock.mockClear(); getUiConfigMock.mockReset(); - isJwtExpiredMock.mockReset(); + decodeTokenMock.mockReset(); + checkTokenValidityMock.mockReset(); clearCookie(); }); @@ -87,10 +90,10 @@ describe("useAuthorized", () => { proxy_base_url: null, auto_redirect_to_sso: false, admin_ui_disabled: false, + sso_configured: false, }); - isJwtExpiredMock.mockReturnValue(false); - - const token = createJwt({ + + const decodedPayload = { key: "api-key-123", user_id: "user-1", user_email: "user@example.com", @@ -98,7 +101,12 @@ describe("useAuthorized", () => { premium_user: true, disabled_non_admin_personal_key_creation: false, login_method: "username_password", - }); + }; + + decodeTokenMock.mockReturnValue(decodedPayload); + checkTokenValidityMock.mockReturnValue(true); + + const token = createJwt(decodedPayload); document.cookie = `token=${token}; path=/;`; const { result } = renderHook(() => useAuthorized(), { wrapper }); @@ -124,8 +132,12 @@ describe("useAuthorized", () => { proxy_base_url: null, auto_redirect_to_sso: false, admin_ui_disabled: false, + sso_configured: false, }); + decodeTokenMock.mockReturnValue(null); + checkTokenValidityMock.mockReturnValue(false); + document.cookie = "token=invalid-token; path=/;"; const { result } = renderHook(() => useAuthorized(), { wrapper }); @@ -145,10 +157,10 @@ describe("useAuthorized", () => { proxy_base_url: null, auto_redirect_to_sso: false, admin_ui_disabled: true, + sso_configured: false, }); - isJwtExpiredMock.mockReturnValue(false); - const token = createJwt({ + const decodedPayload = { key: "api-key-123", user_id: "user-1", user_email: "user@example.com", @@ -156,7 +168,12 @@ describe("useAuthorized", () => { premium_user: true, disabled_non_admin_personal_key_creation: false, login_method: "username_password", - }); + }; + + decodeTokenMock.mockReturnValue(decodedPayload); + checkTokenValidityMock.mockReturnValue(true); + + const token = createJwt(decodedPayload); document.cookie = `token=${token}; path=/;`; const { result } = renderHook(() => useAuthorized(), { wrapper }); @@ -176,8 +193,12 @@ describe("useAuthorized", () => { proxy_base_url: null, auto_redirect_to_sso: false, admin_ui_disabled: false, + sso_configured: false, }); + decodeTokenMock.mockReturnValue(null); + checkTokenValidityMock.mockReturnValue(false); + // No token cookie set const { result } = renderHook(() => useAuthorized(), { wrapper }); @@ -195,15 +216,20 @@ describe("useAuthorized", () => { proxy_base_url: null, auto_redirect_to_sso: false, admin_ui_disabled: false, + sso_configured: false, }); - isJwtExpiredMock.mockReturnValue(true); - const token = createJwt({ + const decodedPayload = { key: "api-key-123", user_id: "user-1", user_email: "user@example.com", user_role: "app_admin", - }); + }; + + decodeTokenMock.mockReturnValue(decodedPayload); + checkTokenValidityMock.mockReturnValue(false); + + const token = createJwt(decodedPayload); document.cookie = `token=${token}; path=/;`; const { result } = renderHook(() => useAuthorized(), { wrapper }); @@ -213,6 +239,6 @@ describe("useAuthorized", () => { }); expect(replaceMock).toHaveBeenCalledWith("http://proxy.example/ui/login"); - expect(isJwtExpiredMock).toHaveBeenCalledWith(token); + expect(checkTokenValidityMock).toHaveBeenCalledWith(token); }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts index 531a240a371..0b60971c1eb 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useAuthorized.ts @@ -2,8 +2,7 @@ import { getProxyBaseUrl } from "@/components/networking"; import { clearTokenCookies, getCookie } from "@/utils/cookieUtils"; -import { isJwtExpired } from "@/utils/jwtUtils"; -import { jwtDecode } from "jwt-decode"; +import { checkTokenValidity, decodeToken } from "@/utils/jwtUtils"; import { useRouter } from "next/navigation"; import { useEffect, useMemo } from "react"; import { useUIConfig } from "./uiConfig/useUIConfig"; @@ -43,44 +42,31 @@ const useAuthorized = () => { const token = typeof document !== "undefined" ? getCookie("token") : null; - // Step 1: Check for missing token or expired JWT - kick out immediately (even if UI Config is loading) + const decoded = useMemo(() => decodeToken(token), [token]); + const isTokenValid = useMemo(() => checkTokenValidity(token), [token]); + const isLoading = isUIConfigLoading; + const isAuthorized = isTokenValid && !uiConfig?.admin_ui_disabled; + + // Single useEffect for all redirect logic useEffect(() => { - if (!token || (token && isJwtExpired(token))) { + if (isLoading) return; + + if (!isAuthorized) { if (token) { clearTokenCookies(); } router.replace(`${getProxyBaseUrl()}/ui/login`); } - }, [token, router]); - - useEffect(() => { - if (isUIConfigLoading) { - return; - } - if (uiConfig?.admin_ui_disabled) { - router.replace(`${getProxyBaseUrl()}/ui/login`); - } - }, [router, isUIConfigLoading, uiConfig]); - - // Decode safely - const decoded = useMemo(() => { - if (!token) return null; - try { - return jwtDecode(token) as Record; - } catch { - // Bad token in cookie — clear and bounce - clearTokenCookies(); - router.replace(`${getProxyBaseUrl()}/ui/login`); - return null; - } - }, [token, router]); + }, [isLoading, isAuthorized, token, router]); return { - token: token, + isLoading, + isAuthorized, + token: isAuthorized ? token : null, accessToken: decoded?.key ?? null, userId: decoded?.user_id ?? null, userEmail: decoded?.user_email ?? null, - userRole: formatUserRole(decoded?.user_role ?? null), + userRole: formatUserRole(decoded?.user_role), premiumUser: decoded?.premium_user ?? null, disabledPersonalKeyCreation: decoded?.disabled_non_admin_personal_key_creation ?? null, showSSOBanner: decoded?.login_method === "username_password", diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowNewBadge.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowNewBadge.test.ts new file mode 100644 index 00000000000..e01e2a4cf84 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowNewBadge.test.ts @@ -0,0 +1,180 @@ +import { describe, it, expect, beforeEach, afterEach, vi } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { useDisableShowNewBadge } from "./useDisableShowNewBadge"; +import { LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils"; + +describe("useDisableShowNewBadge", () => { + const STORAGE_KEY = "disableShowNewBadge"; + + beforeEach(() => { + localStorage.clear(); + vi.clearAllMocks(); + }); + + afterEach(() => { + localStorage.clear(); + }); + + it("should return false when localStorage is empty", () => { + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + }); + + it("should return false when localStorage value is not 'true'", () => { + localStorage.setItem(STORAGE_KEY, "false"); + + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + }); + + it("should return true when localStorage value is 'true'", () => { + localStorage.setItem(STORAGE_KEY, "true"); + + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(true); + }); + + it("should return false when localStorage value is an empty string", () => { + localStorage.setItem(STORAGE_KEY, ""); + + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + }); + + it("should update when storage event fires for the correct key", async () => { + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "true", + }); + window.dispatchEvent(storageEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when storage event fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + + const storageEvent = new StorageEvent("storage", { + key: "otherKey", + newValue: "true", + }); + window.dispatchEvent(storageEvent); + + expect(result.current).toBe(false); + }); + + it("should update when custom LOCAL_STORAGE_EVENT fires for the correct key", async () => { + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when custom LOCAL_STORAGE_EVENT fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: "otherKey" }, + }); + window.dispatchEvent(customEvent); + + expect(result.current).toBe(false); + }); + + it("should update when localStorage changes from false to true via custom event", async () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should update when localStorage changes from true to false via storage event", async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const { result } = renderHook(() => useDisableShowNewBadge()); + + expect(result.current).toBe(true); + + localStorage.setItem(STORAGE_KEY, "false"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "false", + }); + window.dispatchEvent(storageEvent); + + await waitFor(() => { + expect(result.current).toBe(false); + }); + }); + + it("should cleanup event listeners on unmount", () => { + const addEventListenerSpy = vi.spyOn(window, "addEventListener"); + const removeEventListenerSpy = vi.spyOn(window, "removeEventListener"); + + const { unmount } = renderHook(() => useDisableShowNewBadge()); + + expect(addEventListenerSpy).toHaveBeenCalledTimes(2); + expect(addEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(addEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + + unmount(); + + expect(removeEventListenerSpy).toHaveBeenCalledTimes(2); + expect(removeEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(removeEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + }); + + it("should handle multiple hooks independently", async () => { + const { result: result1 } = renderHook(() => useDisableShowNewBadge()); + const { result: result2 } = renderHook(() => useDisableShowNewBadge()); + + expect(result1.current).toBe(false); + expect(result2.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result1.current).toBe(true); + expect(result2.current).toBe(true); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.test.ts new file mode 100644 index 00000000000..7373f9a3202 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableShowPrompts.test.ts @@ -0,0 +1,180 @@ +import { describe, it, expect, beforeEach, afterEach, vi } from "vitest"; +import { renderHook, waitFor } from "@testing-library/react"; +import { useDisableShowPrompts } from "./useDisableShowPrompts"; +import { LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils"; + +describe("useDisableShowPrompts", () => { + const STORAGE_KEY = "disableShowPrompts"; + + beforeEach(() => { + localStorage.clear(); + vi.clearAllMocks(); + }); + + afterEach(() => { + localStorage.clear(); + }); + + it("should return false when localStorage is empty", () => { + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + }); + + it("should return false when localStorage value is not 'true'", () => { + localStorage.setItem(STORAGE_KEY, "false"); + + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + }); + + it("should return true when localStorage value is 'true'", () => { + localStorage.setItem(STORAGE_KEY, "true"); + + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(true); + }); + + it("should return false when localStorage value is an empty string", () => { + localStorage.setItem(STORAGE_KEY, ""); + + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + }); + + it("should update when storage event fires for the correct key", async () => { + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "true", + }); + window.dispatchEvent(storageEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when storage event fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + + const storageEvent = new StorageEvent("storage", { + key: "otherKey", + newValue: "true", + }); + window.dispatchEvent(storageEvent); + + expect(result.current).toBe(false); + }); + + it("should update when custom LOCAL_STORAGE_EVENT fires for the correct key", async () => { + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when custom LOCAL_STORAGE_EVENT fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: "otherKey" }, + }); + window.dispatchEvent(customEvent); + + expect(result.current).toBe(false); + }); + + it("should update when localStorage changes from false to true via custom event", async () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should update when localStorage changes from true to false via storage event", async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const { result } = renderHook(() => useDisableShowPrompts()); + + expect(result.current).toBe(true); + + localStorage.setItem(STORAGE_KEY, "false"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "false", + }); + window.dispatchEvent(storageEvent); + + await waitFor(() => { + expect(result.current).toBe(false); + }); + }); + + it("should cleanup event listeners on unmount", () => { + const addEventListenerSpy = vi.spyOn(window, "addEventListener"); + const removeEventListenerSpy = vi.spyOn(window, "removeEventListener"); + + const { unmount } = renderHook(() => useDisableShowPrompts()); + + expect(addEventListenerSpy).toHaveBeenCalledTimes(2); + expect(addEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(addEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + + unmount(); + + expect(removeEventListenerSpy).toHaveBeenCalledTimes(2); + expect(removeEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(removeEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + }); + + it("should handle multiple hooks independently", async () => { + const { result: result1 } = renderHook(() => useDisableShowPrompts()); + const { result: result2 } = renderHook(() => useDisableShowPrompts()); + + expect(result1.current).toBe(false); + expect(result2.current).toBe(false); + + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + + await waitFor(() => { + expect(result1.current).toBe(true); + expect(result2.current).toBe(true); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.test.ts new file mode 100644 index 00000000000..bd0e69c0de3 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.test.ts @@ -0,0 +1,190 @@ +import { describe, it, expect, beforeEach, afterEach, vi } from "vitest"; +import { act, renderHook, waitFor } from "@testing-library/react"; +import { useDisableUsageIndicator } from "./useDisableUsageIndicator"; +import { LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils"; + +describe("useDisableUsageIndicator", () => { + const STORAGE_KEY = "disableUsageIndicator"; + + beforeEach(() => { + localStorage.clear(); + vi.clearAllMocks(); + }); + + afterEach(() => { + localStorage.clear(); + }); + + it("should return false when localStorage is empty", () => { + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + }); + + it("should return false when localStorage value is not 'true'", () => { + localStorage.setItem(STORAGE_KEY, "false"); + + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + }); + + it("should return true when localStorage value is 'true'", () => { + localStorage.setItem(STORAGE_KEY, "true"); + + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(true); + }); + + it("should return false when localStorage value is an empty string", () => { + localStorage.setItem(STORAGE_KEY, ""); + + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + }); + + it("should update when storage event fires for the correct key", async () => { + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + + await act(async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "true", + }); + window.dispatchEvent(storageEvent); + }); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when storage event fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + + const storageEvent = new StorageEvent("storage", { + key: "otherKey", + newValue: "true", + }); + window.dispatchEvent(storageEvent); + + expect(result.current).toBe(false); + }); + + it("should update when custom LOCAL_STORAGE_EVENT fires for the correct key", async () => { + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + + await act(async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + }); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should not update when custom LOCAL_STORAGE_EVENT fires for a different key", () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: "otherKey" }, + }); + window.dispatchEvent(customEvent); + + expect(result.current).toBe(false); + }); + + it("should update when localStorage changes from false to true via custom event", async () => { + localStorage.setItem(STORAGE_KEY, "false"); + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(false); + + await act(async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + }); + + await waitFor(() => { + expect(result.current).toBe(true); + }); + }); + + it("should update when localStorage changes from true to false via storage event", async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const { result } = renderHook(() => useDisableUsageIndicator()); + + expect(result.current).toBe(true); + + await act(async () => { + localStorage.setItem(STORAGE_KEY, "false"); + const storageEvent = new StorageEvent("storage", { + key: STORAGE_KEY, + newValue: "false", + }); + window.dispatchEvent(storageEvent); + }); + + await waitFor(() => { + expect(result.current).toBe(false); + }); + }); + + it("should cleanup event listeners on unmount", () => { + const addEventListenerSpy = vi.spyOn(window, "addEventListener"); + const removeEventListenerSpy = vi.spyOn(window, "removeEventListener"); + + const { unmount } = renderHook(() => useDisableUsageIndicator()); + + expect(addEventListenerSpy).toHaveBeenCalledTimes(2); + expect(addEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(addEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + + unmount(); + + expect(removeEventListenerSpy).toHaveBeenCalledTimes(2); + expect(removeEventListenerSpy).toHaveBeenCalledWith("storage", expect.any(Function)); + expect(removeEventListenerSpy).toHaveBeenCalledWith(LOCAL_STORAGE_EVENT, expect.any(Function)); + }); + + it("should handle multiple hooks independently", async () => { + const { result: result1 } = renderHook(() => useDisableUsageIndicator()); + const { result: result2 } = renderHook(() => useDisableUsageIndicator()); + + expect(result1.current).toBe(false); + expect(result2.current).toBe(false); + + await act(async () => { + localStorage.setItem(STORAGE_KEY, "true"); + const customEvent = new CustomEvent(LOCAL_STORAGE_EVENT, { + detail: { key: STORAGE_KEY }, + }); + window.dispatchEvent(customEvent); + }); + + await waitFor(() => { + expect(result1.current).toBe(true); + expect(result2.current).toBe(true); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.ts new file mode 100644 index 00000000000..7f4e2295090 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/useDisableUsageIndicator.ts @@ -0,0 +1,33 @@ +import { getLocalStorageItem, LOCAL_STORAGE_EVENT } from "@/utils/localStorageUtils"; +import { useSyncExternalStore } from "react"; + +function subscribe(callback: () => void) { + const onStorage = (e: StorageEvent) => { + if (e.key === "disableUsageIndicator") { + callback(); + } + }; + + const onCustom = (e: Event) => { + const { key } = (e as CustomEvent).detail; + if (key === "disableUsageIndicator") { + callback(); + } + }; + + window.addEventListener("storage", onStorage); + window.addEventListener(LOCAL_STORAGE_EVENT, onCustom); + + return () => { + window.removeEventListener("storage", onStorage); + window.removeEventListener(LOCAL_STORAGE_EVENT, onCustom); + }; +} + +function getSnapshot() { + return getLocalStorageItem("disableUsageIndicator") === "true"; +} + +export function useDisableUsageIndicator() { + return useSyncExternalStore(subscribe, getSnapshot); +} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx index 6a4882a92a2..9d77774cb4c 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView.tsx @@ -26,7 +26,7 @@ import ModelGroupAliasSettings from "../../../components/model_group_alias_setti import ModelInfoView from "../../../components/model_info_view"; import NotificationsManager from "../../../components/molecules/notifications_manager"; import PassThroughSettings from "../../../components/pass_through_settings"; -import TeamInfoView from "../../../components/team/team_info"; +import TeamInfoView from "../../../components/team/TeamInfo"; import useAuthorized from "../hooks/useAuthorized"; interface ModelDashboardProps { @@ -400,6 +400,7 @@ const ModelsAndEndpointsView: React.FC = ({ premiumUser, te all_models_on_proxy={allModelsOnProxy} getDisplayModelName={getDisplayModelName} setSelectedModelId={setSelectedModelId} + teams={teams} />
Current Team: - {isLoading ? ( - - ) : ( - { + if (value === "personal") { + setCurrentTeam("personal"); // Reset to page 1 when team changes setCurrentPage(1); setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 })); + } else { + const team = teams?.find((t) => t.team_id === value); + if (team) { + setCurrentTeam(team); + // Reset to page 1 when team changes + setCurrentPage(1); + setPagination((prev: PaginationState) => ({ ...prev, pageIndex: 0 })); + } } - } - }} - > - -
-
- Personal -
- - {isLoadingTeams ? ( - -
- - Loading teams... -
- - ) : ( - teams - ?.filter((team) => team.team_id) - .map((team) => ( - -
-
- - {team.team_alias - ? `${team.team_alias.slice(0, 30)}...` - : `Team ${team.team_id.slice(0, 30)}...`} - -
- - )) - )} - - )} + }} + loading={isLoadingTeams} + options={[ + { + value: "personal", + label: ( + + + Personal + + ), + }, + ...(teams + ?.filter((team) => team.team_id) + .map((team) => ({ + value: team.team_id, + label: ( + + + + {team.team_alias ? team.team_alias : team.team_id} + + + ), + })) ?? []), + ]} + /> + )} +
-
View: - {isLoading ? ( - - ) : ( - - )} +
+ {isLoading ? ( + + ) : ( + setSelectedModelGroup(value === "all" ? "all" : value)} + onChange={(value) => setSelectedModelGroup(value === "all" ? "all" : value)} placeholder="Filter by Public Model Name" - > - All Models - Wildcard Models (*) - {availableModelGroups.map((group, idx) => ( - - {group} - - ))} - + showSearch + options={[ + { value: "all", label: "All Models" }, + { value: "wildcard", label: "Wildcard Models (*)" }, + ...availableModelGroups.map((group, idx) => ({ + value: group, + label: group, + })), + ]} + />
{/* Model Access Group Filter */}
+ showSearch + options={[ + { value: "all", label: "All Model Access Groups" }, + ...availableModelAccessGroups.map((accessGroup, idx) => ({ + value: accessGroup, + label: accessGroup, + })), + ]} + />
)} diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/FilterByContent.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/FilterByContent.tsx deleted file mode 100644 index 60f4819c0c0..00000000000 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/FilterByContent.tsx +++ /dev/null @@ -1,134 +0,0 @@ -import { Select, SelectItem, Text } from "@tremor/react"; -import React, { useState } from "react"; -import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; -import { Team } from "@/components/key_team_helpers/key_list"; - -interface FilterByContentProps { - setSelectedAPIKey: (key: any) => void; - keys: any[] | null; - teams: Team[] | null; - setSelectedCustomer: (customer: string | null) => void; - allEndUsers: any[]; -} - -const FilterByContent = ({ - setSelectedAPIKey, - keys, - teams, - setSelectedCustomer, - allEndUsers, -}: FilterByContentProps) => { - const { premiumUser } = useAuthorized(); - - const [selectedTeamFilter, setSelectedTeamFilter] = useState(null); - - return ( -
- Select API Key Name - - {premiumUser ? ( -
- - - Select Customer Name - - - - Select Team - - -
- ) : ( -
- {/* ... existing non-premium user content ... */} - Select Team - - -
- )} -
- ); -}; - -export default FilterByContent; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/ModelAnalyticsTab.tsx b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/ModelAnalyticsTab.tsx deleted file mode 100644 index 5fd744ca6f4..00000000000 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/ModelAnalyticsTab.tsx +++ /dev/null @@ -1,474 +0,0 @@ -import { - AreaChart, - BarChart, - Button, - Card, - Col, - DateRangePickerValue, - Grid, - Select, - SelectItem, - Subtitle, - Tab, - TabGroup, - Table, - TableBody, - TableCell, - TableHead, - TableHeaderCell, - TableRow, - TabList, - TabPanel, - TabPanels, - Text, - Title, -} from "@tremor/react"; -import UsageDatePicker from "@/components/shared/usage_date_picker"; -import { Popover } from "antd"; -import { FilterIcon } from "@heroicons/react/outline"; -import TimeToFirstToken from "@/components/model_metrics/time_to_first_token"; -import React, { useEffect } from "react"; -import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; -import { Team } from "@/components/key_team_helpers/key_list"; -import { - adminGlobalActivityExceptions, - adminGlobalActivityExceptionsPerDeployment, - modelExceptionsCall, - modelMetricsCall, - modelMetricsSlowResponsesCall, - streamingModelMetricsCall, -} from "@/components/networking"; -import FilterByContent from "@/app/(dashboard)/models-and-endpoints/components/ModelAnalyticsTab/FilterByContent"; - -interface GlobalExceptionActivityData { - sum_num_rate_limit_exceptions: number; - daily_data: { date: string; num_rate_limit_exceptions: number }[]; -} - -interface ModelAnalyticsTabProps { - dateValue: DateRangePickerValue; - setDateValue: (dateValue: DateRangePickerValue) => void; - selectedModelGroup: string | null; - availableModelGroups: string[]; - setShowAdvancedFilters: (showAdvancedFilters: boolean) => void; - modelMetrics: any[]; - modelMetricsCategories: any[]; - streamingModelMetrics: any[]; - streamingModelMetricsCategories: any[]; - customTooltip: any; - slowResponsesData: any[]; - modelExceptions: any[]; - globalExceptionData: GlobalExceptionActivityData; - allExceptions: any[]; - globalExceptionPerDeployment: any[]; - setSelectedAPIKey: (key: string | null) => void; - keys: any[] | null; - setSelectedCustomer: (selectedCustomer: string | null) => void; - teams: Team[] | null; - allEndUsers: any[]; - selectedAPIKey: any; - selectedCustomer: string | null; - selectedTeam: string | null; - setSelectedModelGroup: (selectedModelGroup: string | null) => void; - setModelMetrics: (metrics: any) => void; - setModelMetricsCategories: (categories: any) => void; - setStreamingModelMetrics: (metrics: any) => void; - setStreamingModelMetricsCategories: (categories: any) => void; - setSlowResponsesData: (data: any) => void; - setModelExceptions: (exceptions: any) => void; - setAllExceptions: (exceptions: any) => void; - setGlobalExceptionData: (data: any) => void; - setGlobalExceptionPerDeployment: (data: any) => void; -} - -const ModelAnalyticsTab = ({ - dateValue, - setDateValue, - selectedModelGroup, - availableModelGroups, - setShowAdvancedFilters, - modelMetrics, - modelMetricsCategories, - streamingModelMetrics, - streamingModelMetricsCategories, - customTooltip, - slowResponsesData, - modelExceptions, - globalExceptionData, - allExceptions, - globalExceptionPerDeployment, - setSelectedAPIKey, - keys, - setSelectedCustomer, - teams, - allEndUsers, - selectedAPIKey, - selectedCustomer, - selectedTeam, - setSelectedModelGroup, - setModelMetrics, - setModelMetricsCategories, - setStreamingModelMetrics, - setStreamingModelMetricsCategories, - setSlowResponsesData, - setModelExceptions, - setAllExceptions, - setGlobalExceptionData, - setGlobalExceptionPerDeployment, -}: ModelAnalyticsTabProps) => { - const { accessToken, userId, userRole, premiumUser } = useAuthorized(); - - useEffect(() => { - updateModelMetrics(selectedModelGroup, dateValue.from, dateValue.to); - }, [selectedAPIKey, selectedCustomer, selectedTeam]); - - const updateModelMetrics = async ( - modelGroup: string | null, - startTime: Date | undefined, - endTime: Date | undefined, - ) => { - console.log("Updating model metrics for group:", modelGroup); - if (!accessToken || !userId || !userRole || !startTime || !endTime) { - return; - } - console.log("inside updateModelMetrics - startTime:", startTime, "endTime:", endTime); - setSelectedModelGroup(modelGroup); - - let selected_token = selectedAPIKey?.token; - if (selected_token === undefined) { - selected_token = null; - } - - let selected_customer = selectedCustomer; - if (selected_customer === undefined) { - selected_customer = null; - } - - try { - const modelMetricsResponse = await modelMetricsCall( - accessToken, - userId, - userRole, - modelGroup, - startTime.toISOString(), - endTime.toISOString(), - selected_token, - selected_customer, - ); - console.log("Model metrics response:", modelMetricsResponse); - - // Assuming modelMetricsResponse now contains the metric data for the specified model group - setModelMetrics(modelMetricsResponse.data); - setModelMetricsCategories(modelMetricsResponse.all_api_bases); - - const streamingModelMetricsResponse = await streamingModelMetricsCall( - accessToken, - modelGroup, - startTime.toISOString(), - endTime.toISOString(), - ); - - // Assuming modelMetricsResponse now contains the metric data for the specified model group - setStreamingModelMetrics(streamingModelMetricsResponse.data); - setStreamingModelMetricsCategories(streamingModelMetricsResponse.all_api_bases); - - const modelExceptionsResponse = await modelExceptionsCall( - accessToken, - userId, - userRole, - modelGroup, - startTime.toISOString(), - endTime.toISOString(), - selected_token, - selected_customer, - ); - console.log("Model exceptions response:", modelExceptionsResponse); - setModelExceptions(modelExceptionsResponse.data); - setAllExceptions(modelExceptionsResponse.exception_types); - - const slowResponses = await modelMetricsSlowResponsesCall( - accessToken, - userId, - userRole, - modelGroup, - startTime.toISOString(), - endTime.toISOString(), - selected_token, - selected_customer, - ); - - console.log("slowResponses:", slowResponses); - - setSlowResponsesData(slowResponses); - - if (modelGroup) { - const dailyExceptions = await adminGlobalActivityExceptions( - accessToken, - startTime?.toISOString().split("T")[0], - endTime?.toISOString().split("T")[0], - modelGroup, - ); - - setGlobalExceptionData(dailyExceptions); - - const dailyExceptionsPerDeplyment = await adminGlobalActivityExceptionsPerDeployment( - accessToken, - startTime?.toISOString().split("T")[0], - endTime?.toISOString().split("T")[0], - modelGroup, - ); - - setGlobalExceptionPerDeployment(dailyExceptionsPerDeplyment); - } - } catch (error) { - console.error("Failed to fetch model metrics", error); - } - }; - - return ( - -
- - This page is deprecated and will be removed in the future. Some functionality may not work as expected. - -
- - - { - setDateValue(value); - updateModelMetrics(selectedModelGroup, value.from, value.to); - }} - /> - - - Select Model Group - - - - - } - overlayStyle={{ - width: "20vw", - }} - > - - - - - - - - - - - Avg. Latency per Token - Time to first token - - - -

(seconds/token)

- - average Latency for successfull requests divided by the total tokens - - {modelMetrics && modelMetricsCategories && ( - - )} - - - - - - - - - - - - - - Deployment - Success Responses - - Slow Responses

Success Responses taking 600+s

- - - - - {slowResponsesData.map((metric, idx) => ( - - {metric.api_base} - {metric.total_count} - {metric.slow_count} - - ))} - -
- - - - - - All Exceptions for {selectedModelGroup} - - - - - - - - All Up Rate Limit Errors (429) for {selectedModelGroup} - - - - Num Rate Limit Errors {globalExceptionData.sum_num_rate_limit_exceptions} - - console.log(v)} - /> - - - - - - {premiumUser ? ( - <> - {globalExceptionPerDeployment.map((globalActivity, index) => ( - - {globalActivity.api_base ? globalActivity.api_base : "Unknown API Base"} - - - - Num Rate Limit Errors (429) {globalActivity.sum_num_rate_limit_exceptions} - - console.log(v)} - /> - - - - ))} - - ) : ( - <> - {globalExceptionPerDeployment && - globalExceptionPerDeployment.length > 0 && - globalExceptionPerDeployment.slice(0, 1).map((globalActivity, index) => ( - - ✨ Rate Limit Errors by Deployment -

Upgrade to see exceptions for all deployments

- - - {globalActivity.api_base} - - - - Num Rate Limit Errors {globalActivity.sum_num_rate_limit_exceptions} - - console.log(v)} - /> - - - - - ))} - - )} - - - ); -}; - -export default ModelAnalyticsTab; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.test.ts b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.test.ts index eb7aecaa679..42b76726922 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.test.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.test.ts @@ -50,4 +50,73 @@ describe("transformModelData", () => { const result = transformModelData(null, mockGetProviderFromModel); expect(result).toEqual({ data: [] }); }); + + it("should handle zero cost models correctly", () => { + const rawData = { + data: [ + { + model_name: "gemini-2.5-flash", + litellm_params: { + model: "vertex_ai/gemini-2.5-flash", + }, + model_info: { + input_cost_per_token: 0.0, + output_cost_per_token: 0.0, + max_tokens: 65535, + max_input_tokens: 1048576, + }, + }, + ], + }; + + const result = transformModelData(rawData, mockGetProviderFromModel); + + // Zero costs should be converted to "0.00" per 1M tokens, not left as 0 or null + expect(result.data[0]).toHaveProperty("input_cost", "0.00"); + expect(result.data[0]).toHaveProperty("output_cost", "0.00"); + }); + + it("should handle null cost fields in model_info", () => { + const rawData = { + data: [ + { + model_name: "some-model", + litellm_params: { + model: "openai/some-model", + }, + model_info: { + input_cost_per_token: null, + output_cost_per_token: null, + max_tokens: 4096, + max_input_tokens: 8192, + }, + }, + ], + }; + + const result = transformModelData(rawData, mockGetProviderFromModel); + + // Null costs should remain null (displayed as "-" in the UI) + expect(result.data[0].input_cost).toBeNull(); + expect(result.data[0].output_cost).toBeNull(); + }); + + it("should handle missing model_info", () => { + const rawData = { + data: [ + { + model_name: "some-model", + litellm_params: { + model: "openai/some-model", + }, + }, + ], + }; + + const result = transformModelData(rawData, mockGetProviderFromModel); + + // Missing model_info should result in null costs + expect(result.data[0].input_cost).toBeNull(); + expect(result.data[0].output_cost).toBeNull(); + }); }); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.ts b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.ts index 3ebf9ddd72b..963fba57507 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.ts +++ b/ui/litellm-dashboard/src/app/(dashboard)/models-and-endpoints/utils/modelDataTransformer.ts @@ -15,8 +15,8 @@ export const transformModelData = (rawModelData: any, getProviderFromModel: (mod let model_info = curr_model?.model_info; let provider = ""; - let input_cost = "Undefined"; - let output_cost = "Undefined"; + let input_cost: any = null; + let output_cost: any = null; let max_tokens = "Undefined"; let max_input_tokens = "Undefined"; let cleanedLitellmParams = {}; @@ -58,11 +58,11 @@ export const transformModelData = (rawModelData: any, getProviderFromModel: (mod transformedData[i].litellm_model_name = litellm_model_name; // Convert Cost in terms of Cost per 1M tokens - if (transformedData[i].input_cost) { + if (transformedData[i].input_cost != null) { transformedData[i].input_cost = (Number(transformedData[i].input_cost) * 1000000).toFixed(2); } - if (transformedData[i].output_cost) { + if (transformedData[i].output_cost != null) { transformedData[i].output_cost = (Number(transformedData[i].output_cost) * 1000000).toFixed(2); } diff --git a/ui/litellm-dashboard/src/app/(dashboard)/settings/admin-settings/page.tsx b/ui/litellm-dashboard/src/app/(dashboard)/settings/admin-settings/page.tsx index be2551f670f..8dae33afe7e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/settings/admin-settings/page.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/settings/admin-settings/page.tsx @@ -1,26 +1,11 @@ "use client"; -import AdminPanel from "@/components/admins"; -import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; -import { useState } from "react"; -import useTeams from "@/app/(dashboard)/hooks/useTeams"; +import AdminPanel from "@/components/AdminPanel"; const AdminSettings = () => { - const { teams, setTeams } = useTeams(); - - const [searchParams, setSearchParams] = useState(() => - typeof window === "undefined" ? new URLSearchParams() : new URLSearchParams(window.location.search), - ); - const { accessToken, userId, premiumUser, showSSOBanner } = useAuthorized(); return ( ); }; diff --git a/ui/litellm-dashboard/src/app/(dashboard)/teams/TeamsView.tsx b/ui/litellm-dashboard/src/app/(dashboard)/teams/TeamsView.tsx index 10616e95523..88bdf3cdda0 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/teams/TeamsView.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/teams/TeamsView.tsx @@ -2,7 +2,7 @@ import React, { useState, useEffect } from "react"; import { teamDeleteCall, Organization } from "@/components/networking"; import { fetchTeams } from "@/components/common_components/fetch_teams"; import { Form } from "antd"; -import TeamInfoView from "@/components/team/team_info"; +import TeamInfoView from "@/components/team/TeamInfo"; import TeamSSOSettings from "@/components/TeamSSOSettings"; import { isAdminRole } from "@/utils/roles"; import { Card, Button, Col, Text, Grid, TabPanel } from "@tremor/react"; diff --git a/ui/litellm-dashboard/src/app/layout.tsx b/ui/litellm-dashboard/src/app/layout.tsx index 95c485fe2f0..1233da9046f 100644 --- a/ui/litellm-dashboard/src/app/layout.tsx +++ b/ui/litellm-dashboard/src/app/layout.tsx @@ -2,6 +2,8 @@ import type { Metadata } from "next"; import { Inter } from "next/font/google"; import "./globals.css"; +import AntdGlobalProvider from "@/contexts/AntdGlobalProvider"; + const inter = Inter({ subsets: ["latin"] }); export const metadata: Metadata = { @@ -17,7 +19,9 @@ export default function RootLayout({ }>) { return ( - {children} + + {children} + ); } diff --git a/ui/litellm-dashboard/src/app/login/LoginPage.test.tsx b/ui/litellm-dashboard/src/app/login/LoginPage.test.tsx index 79834512605..ad2dde2da83 100644 --- a/ui/litellm-dashboard/src/app/login/LoginPage.test.tsx +++ b/ui/litellm-dashboard/src/app/login/LoginPage.test.tsx @@ -64,7 +64,12 @@ describe("LoginPage", () => { it("should render", async () => { (useUIConfig as ReturnType).mockReturnValue({ - data: { auto_redirect_to_sso: false, server_root_path: "/", proxy_base_url: null }, + data: { + auto_redirect_to_sso: false, + server_root_path: "/", + proxy_base_url: null, + sso_configured: false, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(null); @@ -84,7 +89,12 @@ describe("LoginPage", () => { it("should call router.replace to dashboard when jwt is valid", async () => { const validToken = "valid-token"; (useUIConfig as ReturnType).mockReturnValue({ - data: { auto_redirect_to_sso: false, server_root_path: "/", proxy_base_url: null }, + data: { + auto_redirect_to_sso: false, + server_root_path: "/", + proxy_base_url: null, + sso_configured: false, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(validToken); @@ -105,7 +115,12 @@ describe("LoginPage", () => { it("should call router.push to SSO when jwt is invalid and auto_redirect_to_sso is true", async () => { const invalidToken = "invalid-token"; (useUIConfig as ReturnType).mockReturnValue({ - data: { auto_redirect_to_sso: true, server_root_path: "/", proxy_base_url: null }, + data: { + auto_redirect_to_sso: true, + server_root_path: "/", + proxy_base_url: null, + sso_configured: true, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(invalidToken); @@ -126,7 +141,12 @@ describe("LoginPage", () => { it("should not call router when jwt is invalid and auto_redirect_to_sso is false", async () => { const invalidToken = "invalid-token"; (useUIConfig as ReturnType).mockReturnValue({ - data: { auto_redirect_to_sso: false, server_root_path: "/", proxy_base_url: null }, + data: { + auto_redirect_to_sso: false, + server_root_path: "/", + proxy_base_url: null, + sso_configured: false, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(invalidToken); @@ -150,7 +170,12 @@ describe("LoginPage", () => { it("should send user to dashboard when jwt is valid even if auto_redirect_to_sso is true", async () => { const validToken = "valid-token"; (useUIConfig as ReturnType).mockReturnValue({ - data: { auto_redirect_to_sso: true, server_root_path: "/", proxy_base_url: null }, + data: { + auto_redirect_to_sso: true, + server_root_path: "/", + proxy_base_url: null, + sso_configured: true, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(validToken); @@ -172,7 +197,12 @@ describe("LoginPage", () => { it("should show alert when admin_ui_disabled is true", async () => { (useUIConfig as ReturnType).mockReturnValue({ - data: { admin_ui_disabled: true, server_root_path: "/", proxy_base_url: null }, + data: { + admin_ui_disabled: true, + server_root_path: "/", + proxy_base_url: null, + sso_configured: false, + }, isLoading: false, }); (getCookie as ReturnType).mockReturnValue(null); @@ -192,4 +222,60 @@ describe("LoginPage", () => { expect(mockPush).not.toHaveBeenCalled(); expect(mockReplace).not.toHaveBeenCalled(); }); + + it("should show Login with SSO button when sso_configured is true", async () => { + (useUIConfig as ReturnType).mockReturnValue({ + data: { + auto_redirect_to_sso: false, + server_root_path: "/", + proxy_base_url: null, + sso_configured: true, + }, + isLoading: false, + }); + (getCookie as ReturnType).mockReturnValue(null); + (isJwtExpired as ReturnType).mockReturnValue(true); + + const queryClient = createQueryClient(); + render( + + + , + ); + + await waitFor(() => { + expect(screen.getByRole("heading", { name: "Login" })).toBeInTheDocument(); + }); + + expect(screen.getByRole("button", { name: "Login with SSO" })).toBeInTheDocument(); + }); + + it("should show disabled Login with SSO button with popover when sso_configured is false", async () => { + (useUIConfig as ReturnType).mockReturnValue({ + data: { + auto_redirect_to_sso: false, + server_root_path: "/", + proxy_base_url: null, + sso_configured: false, + }, + isLoading: false, + }); + (getCookie as ReturnType).mockReturnValue(null); + (isJwtExpired as ReturnType).mockReturnValue(true); + + const queryClient = createQueryClient(); + render( + + + , + ); + + await waitFor(() => { + expect(screen.getByRole("heading", { name: "Login" })).toBeInTheDocument(); + }); + + const ssoButton = screen.getByRole("button", { name: "Login with SSO" }); + expect(ssoButton).toBeInTheDocument(); + expect(ssoButton).toBeDisabled(); + }); }); diff --git a/ui/litellm-dashboard/src/app/login/LoginPage.tsx b/ui/litellm-dashboard/src/app/login/LoginPage.tsx index 620cb41dfee..a05fa4e214e 100644 --- a/ui/litellm-dashboard/src/app/login/LoginPage.tsx +++ b/ui/litellm-dashboard/src/app/login/LoginPage.tsx @@ -8,7 +8,7 @@ import { getCookie } from "@/utils/cookieUtils"; import { isJwtExpired } from "@/utils/jwtUtils"; import { InfoCircleOutlined } from "@ant-design/icons"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; -import { Alert, Button, Card, Form, Input, Space, Typography } from "antd"; +import { Alert, Button, Card, Form, Input, Popover, Space, Typography } from "antd"; import { useRouter } from "next/navigation"; import { useEffect, useState } from "react"; @@ -179,8 +179,39 @@ function LoginPageContent() { {isLoginLoading ? "Logging in..." : "Login"} + + {!uiConfig?.sso_configured ? ( + + + + ) : ( + + )} + + {uiConfig?.sso_configured && ( + Single Sign-On (SSO) is enabled. LiteLLM no longer automatically redirects to the SSO login flow upon loading this page. To re-enable auto-redirect-to-SSO, set AUTO_REDIRECT_UI_LOGIN_TO_SSO=true in your environment configuration.} + /> + )} ); diff --git a/ui/litellm-dashboard/src/app/page.tsx b/ui/litellm-dashboard/src/app/page.tsx index 5f94db7e9f4..28b8d81cc56 100644 --- a/ui/litellm-dashboard/src/app/page.tsx +++ b/ui/litellm-dashboard/src/app/page.tsx @@ -4,7 +4,7 @@ import APIReferenceView from "@/app/(dashboard)/api-reference/APIReferenceView"; import SidebarProvider from "@/app/(dashboard)/components/SidebarProvider"; import OldModelDashboard from "@/app/(dashboard)/models-and-endpoints/ModelsAndEndpointsView"; import PlaygroundPage from "@/app/(dashboard)/playground/page"; -import AdminPanel from "@/components/admins"; +import AdminPanel from "@/components/AdminPanel"; import AgentsPanel from "@/components/agents"; import BudgetPanel from "@/components/budgets/budget_panel"; import CacheDashboard from "@/components/cache_dashboard"; @@ -27,7 +27,7 @@ import Organizations, { fetchOrganizations } from "@/components/organizations"; import PassThroughSettings from "@/components/pass_through_settings"; import PromptsPanel from "@/components/prompts"; import PublicModelHub from "@/components/public_model_hub"; -import { SearchTools } from "@/components/search_tools"; +import { SearchTools } from "@/components/SearchTools"; import Settings from "@/components/settings"; import { SurveyPrompt, SurveyModal, ClaudeCodePrompt, ClaudeCodeModal } from "@/components/survey"; import TagManagement from "@/components/tag_management"; @@ -43,7 +43,7 @@ import { isJwtExpired } from "@/utils/jwtUtils"; import { isAdminRole } from "@/utils/roles"; import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; import { jwtDecode } from "jwt-decode"; -import { useSearchParams, ReadonlyURLSearchParams } from "next/navigation"; +import { useSearchParams } from "next/navigation"; import { Suspense, useEffect, useState } from "react"; import { ConfigProvider, theme } from "antd"; @@ -469,12 +469,6 @@ function CreateKeyPageContent() { /> ) : page == "admin-panel" ? ( ) : page == "api_ref" ? ( diff --git a/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.test.tsx b/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.test.tsx index a88ce0d7938..ee59ac84ece 100644 --- a/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.test.tsx +++ b/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.test.tsx @@ -1,8 +1,13 @@ import * as networking from "@/components/networking"; -import { render, screen, waitFor } from "@testing-library/react"; import { afterEach, describe, expect, it, vi } from "vitest"; +import { renderWithProviders, screen, waitFor } from "../../../tests/test-utils"; import ModelHubTable from "./ModelHubTable"; +const mockUseUISettings = vi.hoisted(() => vi.fn()); +const mockGetCookie = vi.hoisted(() => vi.fn()); +const mockCheckTokenValidity = vi.hoisted(() => vi.fn()); +const mockRouterReplace = vi.hoisted(() => vi.fn()); + vi.mock("@/components/networking", () => ({ getUiConfig: vi.fn(), modelHubPublicModelsCall: vi.fn(), @@ -11,11 +16,13 @@ vi.mock("@/components/networking", () => ({ getProxyBaseUrl: vi.fn(() => "http://localhost:4000"), getAgentsList: vi.fn(), fetchMCPServers: vi.fn(), + getUiSettings: vi.fn(), + getClaudeCodeMarketplace: vi.fn(), })); vi.mock("next/navigation", () => ({ useRouter: () => ({ - replace: vi.fn(), + replace: mockRouterReplace, }), })); @@ -23,11 +30,82 @@ vi.mock("@/components/public_model_hub", () => ({ default: () =>
Public Model Hub
, })); +vi.mock("@/app/(dashboard)/hooks/uiSettings/useUISettings", () => ({ + useUISettings: mockUseUISettings, +})); + +vi.mock("@/utils/cookieUtils", () => ({ + getCookie: mockGetCookie, +})); + +vi.mock("@/utils/jwtUtils", () => ({ + checkTokenValidity: mockCheckTokenValidity, +})); + describe("ModelHubTable", () => { afterEach(() => { vi.clearAllMocks(); }); + // Reusable helper function to setup mocks for auth redirect tests + const setupAuthRedirectTest = ( + requireAuth: boolean, + tokenValue: string | null, + isTokenValid: boolean + ) => { + mockUseUISettings.mockReturnValue({ + data: { + values: { + require_auth_for_public_ai_hub: requireAuth, + }, + }, + isLoading: false, + }); + mockGetCookie.mockReturnValue(tokenValue); + mockCheckTokenValidity.mockReturnValue(isTokenValid); + mockRouterReplace.mockClear(); + + // Setup other required mocks + vi.mocked(networking.getUiConfig).mockResolvedValue({ + server_root_path: "/", + proxy_base_url: "http://localhost:4000", + auto_redirect_to_sso: false, + admin_ui_disabled: false, + sso_configured: false, + }); + vi.mocked(networking.modelHubPublicModelsCall).mockResolvedValue([]); + vi.mocked(networking.getUiSettings).mockResolvedValue({ + values: { + require_auth_for_public_ai_hub: requireAuth, + }, + }); + }; + + // Reusable test function for auth redirect scenarios + const testAuthRedirect = ( + requireAuth: boolean, + tokenValue: string | null, + isTokenValid: boolean, + shouldRedirect: boolean, + description: string + ) => { + it(description, async () => { + setupAuthRedirectTest(requireAuth, tokenValue, isTokenValid); + + renderWithProviders( + + ); + + await waitFor(() => { + if (shouldRedirect) { + expect(mockRouterReplace).toHaveBeenCalledWith("http://localhost:4000/ui/login"); + } else { + expect(mockRouterReplace).not.toHaveBeenCalled(); + } + }); + }); + }; + it("should render", async () => { vi.mocked(networking.modelHubCall).mockResolvedValue({ data: [], @@ -39,8 +117,15 @@ describe("ModelHubTable", () => { agents: [], }); vi.mocked(networking.fetchMCPServers).mockResolvedValue([]); + vi.mocked(networking.getUiSettings).mockResolvedValue({ + values: {}, + }); + mockUseUISettings.mockReturnValue({ + data: { values: {} }, + isLoading: false, + }); - render(); + renderWithProviders(); await waitFor(() => { expect(screen.getByText("AI Hub")).toBeInTheDocument(); @@ -56,10 +141,18 @@ describe("ModelHubTable", () => { proxy_base_url: "http://localhost:4000", auto_redirect_to_sso: false, admin_ui_disabled: false, + sso_configured: false, }); modelHubPublicModelsCallMock.mockResolvedValue([]); + vi.mocked(networking.getUiSettings).mockResolvedValue({ + values: {}, + }); + mockUseUISettings.mockReturnValue({ + data: { values: {} }, + isLoading: false, + }); - render(); + renderWithProviders(); await waitFor(() => { expect(getUiConfigMock).toHaveBeenCalled(); @@ -71,4 +164,56 @@ describe("ModelHubTable", () => { expect(getUiConfigCallOrder).toBeLessThan(modelHubPublicModelsCallOrder); }); + + describe("authentication redirect behavior", () => { + // Test cases where requireAuth is true - should redirect on invalid tokens + testAuthRedirect( + true, + null, + false, + true, + "should redirect to login when requireAuth is true and there is no token" + ); + + testAuthRedirect( + true, + "expired-token", + false, + true, + "should redirect to login when requireAuth is true and token is expired" + ); + + testAuthRedirect( + true, + "malformed-token", + false, + true, + "should redirect to login when requireAuth is true and token is malformed" + ); + + // Test cases where requireAuth is false - should NOT redirect regardless of token state + testAuthRedirect( + false, + null, + false, + false, + "should not redirect when requireAuth is false and there is no token" + ); + + testAuthRedirect( + false, + "expired-token", + false, + false, + "should not redirect when requireAuth is false and token is expired" + ); + + testAuthRedirect( + false, + "malformed-token", + false, + false, + "should not redirect when requireAuth is false and token is malformed" + ); + }); }); diff --git a/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.tsx b/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.tsx index 23bfb7d219f..71b84e281df 100644 --- a/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.tsx +++ b/ui/litellm-dashboard/src/components/AIHub/ModelHubTable.tsx @@ -27,6 +27,9 @@ import { Copy } from "lucide-react"; import { useRouter } from "next/navigation"; import React, { useCallback, useEffect, useState } from "react"; import { Prism as SyntaxHighlighter } from "react-syntax-highlighter"; +import { useUISettings } from "@/app/(dashboard)/hooks/uiSettings/useUISettings"; +import { checkTokenValidity } from "@/utils/jwtUtils"; +import { getCookie } from "@/utils/cookieUtils"; interface ModelHubTableProps { accessToken: string | null; @@ -76,6 +79,30 @@ const ModelHubTable: React.FC = ({ accessToken, publicPage, const [isMcpModalVisible, setIsMcpModalVisible] = useState(false); const [isMakeMcpPublicModalVisible, setIsMakeMcpPublicModalVisible] = useState(false); const router = useRouter(); + const { data: uiSettings, isLoading: isUISettingsLoading } = useUISettings(); + + // Check authentication requirement for public AI Hub + useEffect(() => { + // Only check when UI settings are loaded and this is a public page + if (isUISettingsLoading || !publicPage) { + return; + } + + const requireAuth = uiSettings?.values?.require_auth_for_public_ai_hub; + + // If require_auth_for_public_ai_hub is true, verify token + if (requireAuth === true) { + const token = getCookie("token"); + const isTokenValid = checkTokenValidity(token); + + // If token is invalid, redirect to login + if (!isTokenValid) { + router.replace(`${getProxyBaseUrl()}/ui/login`); + return; + } + } + // If require_auth_for_public_ai_hub is false, allow public access (no change) + }, [isUISettingsLoading, publicPage, uiSettings, router]); useEffect(() => { const fetchData = async (accessToken: string) => { @@ -483,7 +510,7 @@ const ModelHubTable: React.FC = ({ accessToken, publicPage, = ({ accessToken, publicPage, ({ + getSSOSettings: (...args: unknown[]) => mockGetSSOSettings(...args), + getAllowedIPs: (...args: unknown[]) => mockGetAllowedIPs(...args), + addAllowedIP: (...args: unknown[]) => mockAddAllowedIP(...args), + deleteAllowedIP: (...args: unknown[]) => mockDeleteAllowedIP(...args), +})); + +vi.mock("./constants", () => ({ + useBaseUrl: () => "http://localhost:4000", +})); + +vi.mock("./Settings/AdminSettings/SSOSettings/SSOSettings", () => ({ + default: () =>
SSO Settings
, +})); + +vi.mock("./Settings/AdminSettings/UISettings/UISettings", () => ({ + default: () =>
UI Settings
, +})); + +vi.mock("./SCIM", () => ({ + default: () =>
SCIM Config
, +})); + +vi.mock("./SSOModals", () => ({ + default: () =>
SSO Modals
, +})); + +vi.mock("./UIAccessControlForm", () => ({ + default: () =>
UI Access Control Form
, +})); + +const mockUseAuthorized = vi.fn(); +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: () => mockUseAuthorized(), +})); + +describe("AdminPanel", () => { + beforeEach(() => { + vi.clearAllMocks(); + mockUseAuthorized.mockReturnValue({ + premiumUser: false, + accessToken: "test-token", + userId: "user-1", + }); + mockGetSSOSettings.mockResolvedValue({ + values: {}, + }); + mockGetAllowedIPs.mockResolvedValue([]); + mockAddAllowedIP.mockResolvedValue({}); + mockDeleteAllowedIP.mockResolvedValue({}); + }); + + it("should render the admin panel", () => { + render(); + expect(screen.getByRole("heading", { name: /admin access/i })).toBeInTheDocument(); + expect(screen.getByText(/go to 'internal users' page to add other admins/i)).toBeInTheDocument(); + }); + + describe("Tabs", () => { + it("should render all tabs", () => { + render(); + expect(screen.getByRole("tab", { name: /sso settings/i })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: /security settings/i })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: /scim/i })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: /ui settings/i })).toBeInTheDocument(); + }); + + it("should display Security Settings content when Security Settings tab is clicked", async () => { + const user = userEvent.setup(); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + expect(screen.getByRole("heading", { name: /security settings/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /add sso/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /allowed ips/i })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: /ui access control/i })).toBeInTheDocument(); + }); + + it("should display SCIM content when SCIM tab is clicked", async () => { + const user = userEvent.setup(); + render(); + const scimTab = screen.getByRole("tab", { name: /scim/i }); + await user.click(scimTab); + expect(screen.getByText("SCIM Config")).toBeInTheDocument(); + }); + }); + + describe("SSO Configuration", () => { + it("should check SSO configuration on mount when accessToken is available", async () => { + render(); + await waitFor(() => { + expect(mockGetSSOSettings).toHaveBeenCalledWith("test-token"); + }); + }); + + it("should display 'Add SSO' button when SSO is not configured", async () => { + const user = userEvent.setup(); + mockGetSSOSettings.mockResolvedValue({ + values: {}, + }); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + await waitFor(() => { + expect(screen.getByRole("button", { name: /add sso/i })).toBeInTheDocument(); + }); + }); + + it("should display 'Edit SSO Settings' button when SSO is configured", async () => { + const user = userEvent.setup(); + mockGetSSOSettings.mockResolvedValue({ + values: { + google_client_id: "test-id", + google_client_secret: "test-secret", + }, + }); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + await waitFor(() => { + expect(screen.getByRole("button", { name: /edit sso settings/i })).toBeInTheDocument(); + }); + }); + + it("should detect Google SSO configuration", async () => { + mockGetSSOSettings.mockResolvedValue({ + values: { + google_client_id: "test-id", + google_client_secret: "test-secret", + }, + }); + render(); + await waitFor(() => { + expect(mockGetSSOSettings).toHaveBeenCalled(); + }); + }); + + it("should detect Microsoft SSO configuration", async () => { + mockGetSSOSettings.mockResolvedValue({ + values: { + microsoft_client_id: "test-id", + microsoft_client_secret: "test-secret", + }, + }); + render(); + await waitFor(() => { + expect(mockGetSSOSettings).toHaveBeenCalled(); + }); + }); + + it("should detect Generic SSO configuration", async () => { + mockGetSSOSettings.mockResolvedValue({ + values: { + generic_client_id: "test-id", + generic_client_secret: "test-secret", + }, + }); + render(); + await waitFor(() => { + expect(mockGetSSOSettings).toHaveBeenCalled(); + }); + }); + + it("should handle SSO configuration check error gracefully", async () => { + mockGetSSOSettings.mockRejectedValue(new Error("Network error")); + render(); + await waitFor(() => { + expect(mockGetSSOSettings).toHaveBeenCalled(); + }); + }); + }); + + describe("Allowed IPs", () => { + beforeEach(async () => { + const user = userEvent.setup(); + mockUseAuthorized.mockReturnValue({ + premiumUser: true, + accessToken: "test-token", + userId: "user-1", + }); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + }); + + it("should open allowed IPs modal when premium user clicks Allowed IPs button", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockResolvedValue(["192.168.1.1", "10.0.0.1"]); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + expect(screen.getByRole("dialog", { name: /manage allowed ip addresses/i })).toBeInTheDocument(); + }); + }); + + it("should display 'All IP Addresses Allowed' when no IPs are configured", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockResolvedValue([]); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + expect(screen.getByText("All IP Addresses Allowed")).toBeInTheDocument(); + }); + }); + + it("should display list of allowed IPs", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockResolvedValue(["192.168.1.1", "10.0.0.1"]); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + expect(screen.getByText("192.168.1.1")).toBeInTheDocument(); + expect(screen.getByText("10.0.0.1")).toBeInTheDocument(); + }); + }); + + it("should show delete button for IP addresses except 'All IP Addresses Allowed'", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockResolvedValue(["192.168.1.1", "All IP Addresses Allowed"]); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + const deleteButtons = screen.queryAllByRole("button", { name: /delete/i }); + expect(deleteButtons.length).toBeGreaterThan(0); + }); + }); + + it("should not show delete button for 'All IP Addresses Allowed'", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockResolvedValue(["All IP Addresses Allowed"]); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + expect(screen.getByText("All IP Addresses Allowed")).toBeInTheDocument(); + }); + const deleteButtons = screen.queryAllByRole("button", { name: /delete/i }); + expect(deleteButtons.length).toBe(0); + }); + + it("should handle error when fetching allowed IPs fails", async () => { + const user = userEvent.setup(); + mockGetAllowedIPs.mockRejectedValue(new Error("Network error")); + const allowedIPsButton = screen.getByRole("button", { name: /allowed ips/i }); + await user.click(allowedIPsButton); + await waitFor(() => { + expect(mockGetAllowedIPs).toHaveBeenCalled(); + }); + }); + }); + + describe("UI Access Control", () => { + it("should show premium user message when non-premium user tries to access UI Access Control", async () => { + const user = userEvent.setup(); + mockUseAuthorized.mockReturnValue({ + premiumUser: false, + accessToken: "test-token", + userId: "user-1", + }); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + const uiAccessControlButton = screen.getByRole("button", { name: /ui access control/i }); + await user.click(uiAccessControlButton); + await waitFor(() => { + expect(screen.queryByRole("dialog", { name: /ui access control settings/i })).not.toBeInTheDocument(); + }); + }); + + it("should open UI Access Control modal when premium user clicks button", async () => { + const user = userEvent.setup(); + mockUseAuthorized.mockReturnValue({ + premiumUser: true, + accessToken: "test-token", + userId: "user-1", + }); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + const uiAccessControlButton = screen.getByRole("button", { name: /ui access control/i }); + await user.click(uiAccessControlButton); + await waitFor(() => { + expect(screen.getByRole("dialog", { name: /ui access control settings/i })).toBeInTheDocument(); + expect(screen.getByText("UI Access Control Form")).toBeInTheDocument(); + }); + }); + }); + + describe("Login without SSO", () => { + it("should display fallback login URL", async () => { + const user = userEvent.setup(); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + const link = screen.getByRole("link", { name: /http:\/\/localhost:4000\/fallback\/login/i }); + expect(link).toBeInTheDocument(); + expect(link).toHaveAttribute("href", "http://localhost:4000/fallback/login"); + expect(link).toHaveAttribute("target", "_blank"); + }); + }); + + describe("SSO Configuration Deprecation Warning", () => { + it("should display deprecation warning in Security Settings tab", async () => { + const user = userEvent.setup(); + render(); + const securityTab = screen.getByRole("tab", { name: /security settings/i }); + await user.click(securityTab); + await waitFor(() => { + expect(screen.getByText(/sso configuration deprecated/i)).toBeInTheDocument(); + expect( + screen.getByText(/editing sso settings on this page is deprecated and will be removed/i), + ).toBeInTheDocument(); + }); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/AdminPanel.tsx b/ui/litellm-dashboard/src/components/AdminPanel.tsx new file mode 100644 index 00000000000..82bb08794ff --- /dev/null +++ b/ui/litellm-dashboard/src/components/AdminPanel.tsx @@ -0,0 +1,373 @@ +/** + * Allow proxy admin to add other people to view global spend + * Use this to avoid sharing master key with others + */ +import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import { + Button, + Callout, + Card, + Table, + TableBody, + TableCell, + TableHead, + TableHeaderCell, + TableRow, +} from "@tremor/react"; +import { Alert, Button as Button2, Form, Input, Modal, Tabs, Typography } from "antd"; +import React, { useEffect, useState } from "react"; +import { useBaseUrl } from "./constants"; +import NotificationsManager from "./molecules/notifications_manager"; +import { + addAllowedIP, + deleteAllowedIP, + getAllowedIPs, + getSSOSettings, +} from "./networking"; +import SCIMConfig from "./SCIM"; +import SSOSettings from "./Settings/AdminSettings/SSOSettings/SSOSettings"; +import UISettings from "./Settings/AdminSettings/UISettings/UISettings"; +import SSOModals from "./SSOModals"; +import UIAccessControlForm from "./UIAccessControlForm"; + +const { Title, Paragraph, Text } = Typography; + +interface AdminPanelProps { + proxySettings?: any; +} + +const AdminPanel: React.FC = ({ proxySettings }) => { + const { premiumUser, accessToken, userId: userID } = useAuthorized(); + const [form] = Form.useForm(); + const [isAddSSOModalVisible, setIsAddSSOModalVisible] = useState(false); + const [isInstructionsModalVisible, setIsInstructionsModalVisible] = useState(false); + const [isAllowedIPModalVisible, setIsAllowedIPModalVisible] = useState(false); + const [isAddIPModalVisible, setIsAddIPModalVisible] = useState(false); + const [isDeleteIPModalVisible, setIsDeleteIPModalVisible] = useState(false); + const [isUIAccessControlModalVisible, setIsUIAccessControlModalVisible] = useState(false); + const [allowedIPs, setAllowedIPs] = useState([]); + const [ipToDelete, setIPToDelete] = useState(null); + const [ssoConfigured, setSsoConfigured] = useState(false); + + const baseUrl = useBaseUrl(); + const all_ip_address_allowed = "All IP Addresses Allowed"; + + let nonSssoUrl = baseUrl; + nonSssoUrl += "/fallback/login"; + + const checkSSOConfiguration = async () => { + if (accessToken) { + try { + const ssoData = await getSSOSettings(accessToken); + + if (ssoData && ssoData.values) { + const hasGoogleSSO = ssoData.values.google_client_id && ssoData.values.google_client_secret; + const hasMicrosoftSSO = ssoData.values.microsoft_client_id && ssoData.values.microsoft_client_secret; + const hasGenericSSO = ssoData.values.generic_client_id && ssoData.values.generic_client_secret; + + setSsoConfigured(hasGoogleSSO || hasMicrosoftSSO || hasGenericSSO); + } else { + setSsoConfigured(false); + } + } catch (error) { + console.error("Error checking SSO configuration:", error); + setSsoConfigured(false); + } + } + }; + + const handleShowAllowedIPs = async () => { + try { + if (premiumUser !== true) { + NotificationsManager.fromBackend( + "This feature is only available for premium users. Please upgrade your account.", + ); + return; + } + if (accessToken) { + const data = await getAllowedIPs(accessToken); + setAllowedIPs(data && data.length > 0 ? data : [all_ip_address_allowed]); + } else { + setAllowedIPs([all_ip_address_allowed]); + } + } catch (error) { + console.error("Error fetching allowed IPs:", error); + NotificationsManager.fromBackend(`Failed to fetch allowed IPs ${error}`); + setAllowedIPs([all_ip_address_allowed]); + } finally { + if (premiumUser === true) { + setIsAllowedIPModalVisible(true); + } + } + }; + + const handleAddIP = async (values: { ip: string }) => { + try { + if (accessToken) { + await addAllowedIP(accessToken, values.ip); + // Fetch the updated list of IPs + const updatedIPs = await getAllowedIPs(accessToken); + setAllowedIPs(updatedIPs); + NotificationsManager.success("IP address added successfully"); + } + } catch (error) { + console.error("Error adding IP:", error); + NotificationsManager.fromBackend(`Failed to add IP address ${error}`); + } finally { + setIsAddIPModalVisible(false); + } + }; + + const handleDeleteIP = async (ip: string) => { + setIPToDelete(ip); + setIsDeleteIPModalVisible(true); + }; + + const confirmDeleteIP = async () => { + if (ipToDelete && accessToken) { + try { + await deleteAllowedIP(accessToken, ipToDelete); + // Fetch the updated list of IPs + const updatedIPs = await getAllowedIPs(accessToken); + setAllowedIPs(updatedIPs.length > 0 ? updatedIPs : [all_ip_address_allowed]); + NotificationsManager.success("IP address deleted successfully"); + } catch (error) { + console.error("Error deleting IP:", error); + NotificationsManager.fromBackend(`Failed to delete IP address ${error}`); + } finally { + setIsDeleteIPModalVisible(false); + setIPToDelete(null); + } + } + }; + + const handleAddSSOOk = () => { + setIsAddSSOModalVisible(false); + form.resetFields(); + if (accessToken && premiumUser) { + checkSSOConfiguration(); + } + }; + + const handleAddSSOCancel = () => { + setIsAddSSOModalVisible(false); + form.resetFields(); + }; + + const handleShowInstructions = (formValues: Record) => { + setIsAddSSOModalVisible(false); + setIsInstructionsModalVisible(true); + }; + + const handleInstructionsOk = () => { + setIsInstructionsModalVisible(false); + if (accessToken && premiumUser) { + checkSSOConfiguration(); + } + }; + + const handleInstructionsCancel = () => { + setIsInstructionsModalVisible(false); + if (accessToken && premiumUser) { + checkSSOConfiguration(); + } + }; + + useEffect(() => { + checkSSOConfiguration(); + }, [accessToken, premiumUser, checkSSOConfiguration]); + + const handleUIAccessControlOk = () => { + setIsUIAccessControlModalVisible(false); + }; + + const handleUIAccessControlCancel = () => { + setIsUIAccessControlModalVisible(false); + }; + + const tabItems = [ + { + key: "sso-settings", + label: "SSO Settings", + children: , + }, + { + key: "security-settings", + label: "Security Settings", + children: ( + <> + + ✨ Security Settings + +
+
+ +
+
+ +
+
+ +
+
+ + +
+ + setIsAllowedIPModalVisible(false)} + footer={[ + , + , + ]} + > + + + + IP Address + Action + + + + {allowedIPs.map((ip, index) => ( + + {ip} + + {ip !== all_ip_address_allowed && ( + + )} + + + ))} + +
+ + + setIsAddIPModalVisible(false)} + footer={null} + > +
+ + + + + Add IP Address + +
+ + + setIsDeleteIPModalVisible(false)} + onOk={confirmDeleteIP} + footer={[ + , + , + ]} + > + Are you sure you want to delete the IP address: {ipToDelete}? + + + {/* UI Access Control Modal */} + + { + handleUIAccessControlOk(); + NotificationsManager.success("UI Access Control settings updated successfully"); + }} + /> + +
+ + If you need to login without sso, you can access{" "} + + {nonSssoUrl}{" "} + + + + ), + }, + { + key: "scim", + label: "SCIM", + children: , + }, + { + key: "ui-settings", + label: "UI Settings", + children: , + }, + ]; + + return ( +
+ Admin Access + Go to 'Internal Users' page to add other admins. + +
+ ); +}; + +export default AdminPanel; diff --git a/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx b/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx new file mode 100644 index 00000000000..829b73734dc --- /dev/null +++ b/ui/litellm-dashboard/src/components/CreateUserButton.test.tsx @@ -0,0 +1,264 @@ +import { render, screen, waitFor, within } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { CreateUserButton } from "./CreateUserButton"; +import * as networking from "./networking"; +import NotificationsManager from "./molecules/notifications_manager"; + +vi.mock("./networking", () => ({ + userCreateCall: vi.fn(), + modelAvailableCall: vi.fn().mockResolvedValue({ data: [] }), + invitationCreateCall: vi.fn(), + getProxyUISettings: vi.fn().mockResolvedValue({ + PROXY_BASE_URL: null, + PROXY_LOGOUT_URL: null, + DEFAULT_TEAM_DISABLED: false, + SSO_ENABLED: false, + }), + getProxyBaseUrl: vi.fn().mockReturnValue("http://localhost"), +})); + +vi.mock("./bulk_create_users_button", () => ({ + default: () =>
Bulk Create Users
, +})); + +const mockUserCreateCall = vi.mocked(networking.userCreateCall); +const mockInvitationCreateCall = vi.mocked(networking.invitationCreateCall); +const mockGetProxyUISettings = vi.mocked(networking.getProxyUISettings); +const mockNotificationsManager = vi.mocked(NotificationsManager); + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { queries: { retry: false, gcTime: 0 } }, + }); + +const defaultProps = { + userID: "123", + accessToken: "token", + teams: [], + possibleUIRoles: null as Record> | null, +}; + +function renderWithProviders(ui: React.ReactElement) { + const qc = createQueryClient(); + return render({ui}); +} + +describe("CreateUserButton", { timeout: 20000 }, () => { + beforeEach(() => { + vi.clearAllMocks(); + mockGetProxyUISettings.mockResolvedValue({ + PROXY_BASE_URL: null, + PROXY_LOGOUT_URL: null, + DEFAULT_TEAM_DISABLED: false, + SSO_ENABLED: false, + }); + }); + + it("should render the create user form when embedded", () => { + renderWithProviders( + , + ); + expect(screen.getByRole("button", { name: /create user/i })).toBeInTheDocument(); + }); + + it("should render the invite user button when not embedded", async () => { + renderWithProviders(); + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + }); + + it("should open the invite modal when invite user button is clicked", async () => { + const user = userEvent.setup(); + renderWithProviders(); + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + await user.click(screen.getByRole("button", { name: /\+ invite user/i })); + const dialog = screen.getByRole("dialog", { name: /invite user/i }); + expect(dialog).toBeInTheDocument(); + expect(within(dialog).getByRole("button", { name: /invite user/i })).toBeInTheDocument(); + }); + + it("should display email invitations info message in embedded mode", () => { + renderWithProviders(); + expect(screen.getByText("Email invitations")).toBeInTheDocument(); + }); + + it("should display user role options when possibleUIRoles is provided", async () => { + const possibleUIRoles = { + proxy_admin: { ui_label: "Admin", description: "Full access" }, + proxy_user: { ui_label: "User", description: "Limited access" }, + }; + renderWithProviders( + , + ); + await userEvent.click(screen.getByRole("combobox", { name: /user role/i })); + expect(screen.getByText("Admin")).toBeInTheDocument(); + expect(screen.getByText("User")).toBeInTheDocument(); + }); + + it("should call userCreateCall when form is submitted in embedded mode", async () => { + const user = userEvent.setup(); + mockUserCreateCall.mockResolvedValue({ data: { user_id: "new-user-123" } }); + mockInvitationCreateCall.mockResolvedValue({ + id: "inv-1", + user_id: "new-user-123", + has_user_setup_sso: false, + } as any); + + renderWithProviders( + , + ); + + await user.type(screen.getByLabelText(/user email/i), "test@example.com"); + await user.click(screen.getByRole("combobox", { name: /user role/i })); + await user.click(screen.getByText("User")); + await user.click(screen.getByRole("button", { name: /create user/i })); + + await waitFor(() => { + expect(mockUserCreateCall).toHaveBeenCalledWith("token", null, expect.objectContaining({ + user_email: "test@example.com", + user_role: "proxy_user", + })); + }); + }); + + it("should call onUserCreated callback when user is created in embedded mode", async () => { + const user = userEvent.setup(); + const onUserCreated = vi.fn(); + mockUserCreateCall.mockResolvedValue({ data: { user_id: "new-user-456" } }); + + renderWithProviders( + , + ); + + await user.type(screen.getByLabelText(/user email/i), "embedded@example.com"); + await user.click(screen.getByRole("combobox", { name: /user role/i })); + await user.click(screen.getByText("User")); + await user.click(screen.getByRole("button", { name: /create user/i })); + + await waitFor(() => { + expect(onUserCreated).toHaveBeenCalledWith("new-user-456"); + }); + }); + + it("should show success notification when user is created successfully in standalone mode", async () => { + const user = userEvent.setup(); + mockUserCreateCall.mockResolvedValue({ data: { user_id: "new-user-789" } }); + mockInvitationCreateCall.mockResolvedValue({ + id: "inv-2", + user_id: "new-user-789", + has_user_setup_sso: false, + } as any); + + renderWithProviders( + , + ); + + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + await user.click(screen.getByRole("button", { name: /\+ invite user/i })); + + const dialog = screen.getByRole("dialog", { name: /invite user/i }); + await user.type(within(dialog).getByLabelText(/user email/i), "standalone@example.com"); + await user.click(within(dialog).getByRole("combobox", { name: /global proxy role/i })); + await user.click(screen.getByText("User")); + await user.click(within(dialog).getByRole("button", { name: /invite user/i })); + + await waitFor(() => { + expect(mockNotificationsManager.success).toHaveBeenCalledWith("API user Created"); + }); + }); + + it("should show error notification when user creation fails", async () => { + const user = userEvent.setup(); + mockUserCreateCall.mockRejectedValue({ response: { data: { detail: "Email already exists" } } }); + + renderWithProviders( + , + ); + + await user.type(screen.getByLabelText(/user email/i), "duplicate@example.com"); + await user.click(screen.getByRole("combobox", { name: /user role/i })); + await user.click(screen.getByText("User")); + await user.click(screen.getByRole("button", { name: /create user/i })); + + await waitFor(() => { + expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Email already exists"); + }); + }); + + it("should show info notification when making API call", async () => { + const user = userEvent.setup(); + mockUserCreateCall.mockResolvedValue({ data: { user_id: "new-user" } }); + mockInvitationCreateCall.mockResolvedValue({ + id: "inv-3", + user_id: "new-user", + has_user_setup_sso: false, + } as any); + + renderWithProviders( + , + ); + + await user.type(screen.getByLabelText(/user email/i), "info@example.com"); + await user.click(screen.getByRole("combobox", { name: /user role/i })); + await user.click(screen.getByText("User")); + await user.click(screen.getByRole("button", { name: /create user/i })); + + await waitFor(() => { + expect(mockNotificationsManager.info).toHaveBeenCalledWith("Making API Call"); + }); + }); + + it("should close modal when cancel is clicked in standalone mode", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + await user.click(screen.getByRole("button", { name: /\+ invite user/i })); + expect(screen.getByRole("dialog", { name: /invite user/i })).toBeInTheDocument(); + + const dialog = screen.getByRole("dialog", { name: /invite user/i }); + await user.click(within(dialog).getByRole("button", { name: /close/i })); + expect(screen.queryByRole("dialog")).not.toBeInTheDocument(); + }); + + it("should show onboarding modal when user is created and SSO is disabled", async () => { + const user = userEvent.setup(); + mockUserCreateCall.mockResolvedValue({ data: { user_id: "sso-user" } }); + mockInvitationCreateCall.mockResolvedValue({ + id: "inv-sso", + user_id: "sso-user", + has_user_setup_sso: false, + } as any); + + renderWithProviders( + , + ); + + await waitFor(() => { + expect(screen.getByRole("button", { name: /\+ invite user/i })).toBeInTheDocument(); + }); + await user.click(screen.getByRole("button", { name: /\+ invite user/i })); + + const dialog = screen.getByRole("dialog", { name: /invite user/i }); + await user.type(within(dialog).getByLabelText(/user email/i), "sso@example.com"); + await user.click(within(dialog).getByRole("combobox", { name: /global proxy role/i })); + await user.click(screen.getByText("User")); + await user.click(within(dialog).getByRole("button", { name: /invite user/i })); + + await waitFor(() => { + expect(mockInvitationCreateCall).toHaveBeenCalledWith("token", "sso-user"); + }); + await waitFor(() => { + expect(mockNotificationsManager.success).toHaveBeenCalledWith("API user Created"); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/create_user_button.tsx b/ui/litellm-dashboard/src/components/CreateUserButton.tsx similarity index 80% rename from ui/litellm-dashboard/src/components/create_user_button.tsx rename to ui/litellm-dashboard/src/components/CreateUserButton.tsx index 935c34321ef..d463ced08f6 100644 --- a/ui/litellm-dashboard/src/components/create_user_button.tsx +++ b/ui/litellm-dashboard/src/components/CreateUserButton.tsx @@ -1,33 +1,29 @@ -import React, { useState, useEffect } from "react"; -import { Button, Modal, Form, Input, Select, Select as Select2 } from "antd"; -import { - Button as Button2, - Text, - TextInput, - SelectItem, - Accordion, - AccordionHeader, - AccordionBody, - Title, -} from "@tremor/react"; -import OnboardingModal from "./onboarding_link"; -import { InvitationLink } from "./onboarding_link"; -import { - userCreateCall, - modelAvailableCall, - invitationCreateCall, - getProxyUISettings, - getProxyBaseUrl, -} from "./networking"; -import BulkCreateUsers from "./bulk_create_users_button"; -const { Option } = Select; -import { Tooltip } from "antd"; -import { InfoCircleOutlined } from "@ant-design/icons"; -import { getModelDisplayName } from "./key_team_helpers/fetch_available_models_team_key"; +import { InfoCircleOutlined, UserAddOutlined } from "@ant-design/icons"; import { useQueryClient } from "@tanstack/react-query"; -import NotificationsManager from "./molecules/notifications_manager"; +import { + Accordion, + AccordionBody, + AccordionHeader, + Button as Button2, + SelectItem, + TextInput, +} from "@tremor/react"; +import { Alert, Button, Form, Input, Modal, Select, Select as Select2, Space, Tooltip, Typography } from "antd"; +import React, { useEffect, useState } from "react"; +import BulkCreateUsers from "./bulk_create_users_button"; import TeamDropdown from "./common_components/team_dropdown"; - +import { getModelDisplayName } from "./key_team_helpers/fetch_available_models_team_key"; +import NotificationsManager from "./molecules/notifications_manager"; +import { + getProxyBaseUrl, + getProxyUISettings, + invitationCreateCall, + modelAvailableCall, + userCreateCall, +} from "./networking"; +import OnboardingModal, { InvitationLink } from "./onboarding_link"; +const { Option } = Select; +const { Text, Link, Title } = Typography; // Helper function to generate UUID compatible across all environments const generateUUID = (): string => { if (typeof crypto !== "undefined" && crypto.randomUUID) { @@ -58,14 +54,8 @@ interface UISettings { SSO_ENABLED: boolean; } -const Createuser: React.FC = ({ - userID, - accessToken, - teams, - possibleUIRoles, - onUserCreated, - isEmbedded = false, -}) => { +export const CreateUserButton: React.FC = ({ + userID, accessToken, teams, possibleUIRoles, onUserCreated, isEmbedded = false }) => { const queryClient = useQueryClient(); const [uiSettings, setUISettings] = useState(null); const [form] = Form.useForm(); @@ -75,28 +65,18 @@ const Createuser: React.FC = ({ const [isInvitationLinkModalVisible, setIsInvitationLinkModalVisible] = useState(false); const [invitationLinkData, setInvitationLinkData] = useState(null); const [baseUrl, setBaseUrl] = useState(null); - // get all models useEffect(() => { const fetchData = async () => { try { - const userRole = "any"; // You may need to get the user role dynamically + const userRole = "any"; const modelDataResponse = await modelAvailableCall(accessToken, userID, userRole); - // Assuming modelDataResponse.data contains an array of model objects with a 'model_name' property const availableModels = []; for (let i = 0; i < modelDataResponse.data.length; i++) { const model = modelDataResponse.data[i]; availableModels.push(model.id); } - console.log("Model data response:", modelDataResponse.data); - console.log("Available models:", availableModels); - - // Assuming modelDataResponse.data contains an array of model names setUserModels(availableModels); - - // get ui settings const uiSettingsResponse = await getProxyUISettings(accessToken); - console.log("uiSettingsResponse:", uiSettingsResponse); - setUISettings(uiSettingsResponse); } catch (error) { console.error("Error fetching model data:", error); @@ -104,9 +84,8 @@ const Createuser: React.FC = ({ }; setBaseUrl(getProxyBaseUrl()); - - fetchData(); // Call the function to fetch model data when the component mounts - }, []); // Empty dependency array to run only once + fetchData(); + }, []); const handleOk = () => { setIsModalVisible(false); @@ -126,25 +105,19 @@ const Createuser: React.FC = ({ setIsModalVisible(true); } if ((!formValues.models || formValues.models.length === 0) && formValues.user_role !== "proxy_admin") { - console.log("formValues.user_role", formValues.user_role); - // If models is empty or undefined, set it to "no-default-models" formValues.models = ["no-default-models"]; } - console.log("formValues in create user:", formValues); const response = await userCreateCall(accessToken, null, formValues); await queryClient.invalidateQueries({ queryKey: ["userList"] }); - console.log("user create Response:", response); setApiuser(true); const user_id = response.data?.user_id || response.user_id; - // Call the callback if provided (for embedded mode) if (onUserCreated && isEmbedded) { onUserCreated(user_id); form.resetFields(); - return; // Skip the invitation flow when embedded + return; } - // only do invite link flow if sso is not enabled if (!uiSettings?.SSO_ENABLED) { invitationCreateCall(accessToken, user_id).then((data) => { data.has_user_setup_sso = false; @@ -184,6 +157,21 @@ const Createuser: React.FC = ({ if (isEmbedded) { return (
+ + New users receive an email invite only when an email integration (SMTP, Resend, or SendGrid) is configured. + {" "} + + Learn how to set up email notifications + + + } + type="info" + showIcon + className="mb-4" + /> @@ -194,9 +182,9 @@ const Createuser: React.FC = ({
{ui_label}{" "} -

+ {description} -

+
))} @@ -228,22 +216,39 @@ const Createuser: React.FC = ({ - Create a User who can own keys + + Create a User who can own keys + + New users receive an email invite only when an email integration (SMTP, Resend, or SendGrid) is configured. + {" "} + + Learn how to set up email notifications + + + } + type="info" + showIcon + className="mb-4" + /> + - + Global Proxy Role{" "} - + @@ -254,12 +259,12 @@ const Createuser: React.FC = ({ {possibleUIRoles && Object.entries(possibleUIRoles).map(([role, { ui_label, description }]) => ( -
- {ui_label}{" "} -

- {description} -

-
+ + {ui_label} + + + {" - "}{description} + ))} @@ -279,7 +284,7 @@ const Createuser: React.FC = ({ - Personal Key Creation + Personal Key Creation = ({
- +
@@ -326,6 +331,4 @@ const Createuser: React.FC = ({ )} ); -}; - -export default Createuser; +}; \ No newline at end of file diff --git a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx index 3052f790098..6da2f82a2f1 100644 --- a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx +++ b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.test.tsx @@ -37,12 +37,19 @@ vi.mock("antd", async (importOriginal) => { mode, ...props }: any) => { + // Simulate maxTagCount responsive behavior - if value length > 5, call maxTagPlaceholder + const shouldShowPlaceholder = maxTagCount === "responsive" && Array.isArray(value) && value.length > 5; + const visibleValues = shouldShowPlaceholder ? value.slice(0, 5) : value; + const omittedValues = shouldShowPlaceholder + ? value.slice(5).map((v: string) => ({ value: v, label: v })) + : []; + return (
+ {shouldShowPlaceholder && maxTagPlaceholder && ( +
{maxTagPlaceholder(omittedValues)}
+ )}
); }, @@ -82,6 +92,24 @@ const mockUseTeam = vi.mocked(useTeam); const mockUseOrganization = vi.mocked(useOrganization); const mockUseCurrentUser = vi.mocked(useCurrentUser); +const createMockOrganization = (models: string[]): Organization => ({ + organization_id: "org-1", + organization_alias: "Test Org", + budget_id: "budget-1", + metadata: {}, + models, + spend: 0, + model_spend: {}, + created_at: "2024-01-01", + created_by: "user-1", + updated_at: "2024-01-01", + updated_by: "user-1", + litellm_budget_table: null, + teams: null, + users: null, + members: null, +}); + describe("ModelSelect", () => { const mockProxyModels: ProxyModel[] = [ { id: "gpt-4", object: "model", created: 1234567890, owned_by: "openai" }, @@ -112,125 +140,44 @@ describe("ModelSelect", () => { } as any); }); - it("should render", async () => { + it("should render with all option groups", async () => { renderWithProviders( , ); await waitFor(() => { expect(screen.getByTestId("model-select")).toBeInTheDocument(); - }); - }); - - it("should show skeleton loader when loading", () => { - mockUseAllProxyModels.mockReturnValue({ - data: undefined, - isLoading: true, - } as any); - - renderWithProviders(); - - expect(screen.getByTestId("skeleton-input")).toBeInTheDocument(); - expect(screen.queryByTestId("model-select")).not.toBeInTheDocument(); - }); - - it("should show skeleton loader when team is loading", () => { - mockUseTeam.mockReturnValue({ - data: undefined, - isLoading: true, - } as any); - - renderWithProviders(); - - expect(screen.getByTestId("skeleton-input")).toBeInTheDocument(); - }); - - it("should show skeleton loader when organization is loading", () => { - mockUseOrganization.mockReturnValue({ - data: undefined, - isLoading: true, - } as any); - - renderWithProviders(); - - expect(screen.getByTestId("skeleton-input")).toBeInTheDocument(); - }); - - it("should show skeleton loader when current user is loading", () => { - mockUseCurrentUser.mockReturnValue({ - data: undefined, - isLoading: true, - } as any); - - renderWithProviders(); - - expect(screen.getByTestId("skeleton-input")).toBeInTheDocument(); - }); - - it("should render special options group", async () => { - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["all-proxy-models"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; - - mockUseOrganization.mockReturnValue({ - data: mockOrganization, - isLoading: false, - } as any); - - renderWithProviders( - , - ); - - await waitFor(() => { - const select = screen.getByTestId("model-select"); - expect(select).toBeInTheDocument(); - expect(screen.getByText("All Proxy Models")).toBeInTheDocument(); - expect(screen.getByText("No Default Models")).toBeInTheDocument(); - }); - }); - - it("should render wildcard options group", async () => { - renderWithProviders( - , - ); - - await waitFor(() => { + expect(screen.getByText("gpt-4")).toBeInTheDocument(); + expect(screen.getByText("claude-3")).toBeInTheDocument(); expect(screen.getByText("All Openai models")).toBeInTheDocument(); expect(screen.getByText("All Anthropic models")).toBeInTheDocument(); }); }); - it("should render regular models group", async () => { - renderWithProviders( - , - ); + it("should show skeleton loader when any data is loading", () => { + const loadingScenarios = [ + { hook: mockUseAllProxyModels, context: "user" as const }, + { hook: mockUseTeam, context: "team" as const, props: { teamID: "team-1" } }, + { hook: mockUseOrganization, context: "organization" as const, props: { organizationID: "org-1" } }, + { hook: mockUseCurrentUser, context: "user" as const }, + ]; - await waitFor(() => { - expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.getByText("claude-3")).toBeInTheDocument(); + loadingScenarios.forEach(({ hook, context, props = {} }) => { + hook.mockReturnValue({ + data: undefined, + isLoading: true, + } as any); + + const { unmount } = renderWithProviders( + , + ); + + expect(screen.getByTestId("skeleton-input")).toBeInTheDocument(); + unmount(); }); }); - it("should call onChange when selecting a regular model", async () => { + it("should handle model selection and onChange", async () => { const user = userEvent.setup(); renderWithProviders( , @@ -242,32 +189,16 @@ describe("ModelSelect", () => { const select = screen.getByRole("listbox"); await user.selectOptions(select, "gpt-4"); - expect(mockOnChange).toHaveBeenCalledWith(["gpt-4"]); + + await user.selectOptions(select, ["gpt-4", "claude-3"]); + expect(mockOnChange).toHaveBeenCalled(); }); - it("should call onChange with only last special option when multiple special options are selected", async () => { + it("should handle special options correctly", async () => { const user = userEvent.setup(); - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["all-proxy-models"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; - mockUseOrganization.mockReturnValue({ - data: mockOrganization, + data: createMockOrganization(["all-proxy-models"]), isLoading: false, } as any); @@ -281,16 +212,16 @@ describe("ModelSelect", () => { ); await waitFor(() => { - expect(screen.getByTestId("model-select")).toBeInTheDocument(); + expect(screen.getByText("All Proxy Models")).toBeInTheDocument(); + expect(screen.getByText("No Default Models")).toBeInTheDocument(); }); const select = screen.getByRole("listbox"); await user.selectOptions(select, ["all-proxy-models", "no-default-models"]); - expect(mockOnChange).toHaveBeenCalledWith(["no-default-models"]); }); - it("should disable regular models when special option is selected", async () => { + it("should disable models when special option is selected", async () => { renderWithProviders( { ); await waitFor(() => { - const gpt4Option = screen.getByRole("option", { name: "gpt-4" }); - expect(gpt4Option).toBeDisabled(); + expect(screen.getByRole("option", { name: "gpt-4" })).toBeDisabled(); + expect(screen.getByRole("option", { name: "All Openai models" })).toBeDisabled(); }); }); - it("should disable wildcard models when special option is selected", async () => { - renderWithProviders( - , - ); + it("should filter models based on context", async () => { + const testCases = [ + { + name: "user context with includeUserModels", + context: "user" as const, + options: { includeUserModels: true }, + setup: () => { + mockUseCurrentUser.mockReturnValue({ + data: { models: ["gpt-4"] }, + isLoading: false, + } as any); + }, + expectedVisible: ["gpt-4"], + expectedHidden: ["claude-3"], + }, + { + name: "user context without includeUserModels", + context: "user" as const, + options: {}, + setup: () => { + mockUseCurrentUser.mockReturnValue({ + data: { models: ["gpt-4"] }, + isLoading: false, + } as any); + }, + expectedVisible: [], + expectedHidden: ["gpt-4", "claude-3"], + }, + { + name: "team context without organization", + context: "team" as const, + options: {}, + props: { teamID: "team-1" }, + setup: () => { + mockUseTeam.mockReturnValue({ + data: { team_id: "team-1", team_alias: "Test Team", models: [] }, + isLoading: false, + } as any); + mockUseOrganization.mockReturnValue({ + data: undefined, + isLoading: false, + } as any); + }, + expectedVisible: ["gpt-4", "claude-3"], + expectedHidden: [], + }, + { + name: "team context with organization having all-proxy-models", + context: "team" as const, + options: {}, + props: { teamID: "team-1", organizationID: "org-1" }, + setup: () => { + mockUseTeam.mockReturnValue({ + data: { team_id: "team-1", team_alias: "Test Team", models: [] }, + isLoading: false, + } as any); + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["all-proxy-models"]), + isLoading: false, + } as any); + }, + expectedVisible: ["gpt-4", "claude-3"], + expectedHidden: [], + }, + { + name: "team context with organization filtering models", + context: "team" as const, + options: {}, + props: { teamID: "team-1", organizationID: "org-1" }, + setup: () => { + mockUseTeam.mockReturnValue({ + data: { team_id: "team-1", team_alias: "Test Team", models: [] }, + isLoading: false, + } as any); + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["gpt-4"]), + isLoading: false, + } as any); + }, + expectedVisible: ["gpt-4"], + expectedHidden: ["claude-3"], + }, + { + name: "organization context", + context: "organization" as const, + options: {}, + props: { organizationID: "org-1" }, + setup: () => { + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["gpt-4"]), + isLoading: false, + } as any); + }, + expectedVisible: ["gpt-4", "claude-3"], + expectedHidden: [], + }, + { + name: "global context", + context: "global" as const, + options: {}, + setup: () => { }, + expectedVisible: ["gpt-4", "claude-3"], + expectedHidden: [], + }, + ]; - await waitFor(() => { - const openaiWildcardOption = screen.getByRole("option", { name: "All Openai models" }); - expect(openaiWildcardOption).toBeDisabled(); - }); + for (const testCase of testCases) { + testCase.setup(); + const { unmount } = renderWithProviders( + , + ); + + await waitFor(() => { + testCase.expectedVisible.forEach((model) => { + expect(screen.getByText(model)).toBeInTheDocument(); + }); + testCase.expectedHidden.forEach((model) => { + expect(screen.queryByText(model)).not.toBeInTheDocument(); + }); + }); + + unmount(); + vi.clearAllMocks(); + mockUseAllProxyModels.mockReturnValue({ + data: { data: mockProxyModels }, + isLoading: false, + } as any); + } }); - it("should disable other special options when one special option is selected", async () => { - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["all-proxy-models"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; + it("should show All Proxy Models option based on conditions", async () => { + const testCases = [ + { + name: "when showAllProxyModelsOverride is true", + context: "user" as const, + options: { showAllProxyModelsOverride: true, includeSpecialOptions: true }, + setup: () => { }, + shouldShow: true, + }, + { + name: "when organization has all-proxy-models", + context: "organization" as const, + options: { includeSpecialOptions: true }, + props: { organizationID: "org-1" }, + setup: () => { + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["all-proxy-models"]), + isLoading: false, + } as any); + }, + shouldShow: true, + }, + { + name: "when organization has empty models array", + context: "organization" as const, + options: { includeSpecialOptions: true }, + props: { organizationID: "org-1" }, + setup: () => { + mockUseOrganization.mockReturnValue({ + data: createMockOrganization([]), + isLoading: false, + } as any); + }, + shouldShow: true, + }, + { + name: "when context is global", + context: "global" as const, + options: { includeSpecialOptions: true }, + setup: () => { }, + shouldShow: true, + }, + { + name: "when organization has specific models", + context: "organization" as const, + options: { includeSpecialOptions: true }, + props: { organizationID: "org-1" }, + setup: () => { + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["gpt-4"]), + isLoading: false, + } as any); + }, + shouldShow: false, + }, + ]; - mockUseOrganization.mockReturnValue({ - data: mockOrganization, - isLoading: false, - } as any); + for (const testCase of testCases) { + testCase.setup(); + const { unmount } = renderWithProviders( + , + ); - renderWithProviders( - , - ); + await waitFor(() => { + if (testCase.shouldShow) { + expect(screen.getByText("All Proxy Models")).toBeInTheDocument(); + } else { + expect(screen.queryByText("All Proxy Models")).not.toBeInTheDocument(); + expect(screen.getByText("No Default Models")).toBeInTheDocument(); + } + }); - await waitFor(() => { - const noDefaultOption = screen.getByRole("option", { name: "No Default Models" }); - expect(noDefaultOption).toBeDisabled(); - }); - }); - - it("should filter models when showAllProxyModelsOverride is true", async () => { - renderWithProviders( - , - ); - - await waitFor(() => { - expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.getByText("claude-3")).toBeInTheDocument(); - }); - }); - - it("should filter models when organization has all-proxy-models in models array", async () => { - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["all-proxy-models"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; - - mockUseOrganization.mockReturnValue({ - data: mockOrganization, - isLoading: false, - } as any); - - renderWithProviders(); - - await waitFor(() => { - expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.getByText("claude-3")).toBeInTheDocument(); - }); - }); - - it("should show all models when organization context is used", async () => { - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["gpt-4"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; - - mockUseOrganization.mockReturnValue({ - data: mockOrganization, - isLoading: false, - } as any); - - renderWithProviders(); - - await waitFor(() => { - expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.getByText("claude-3")).toBeInTheDocument(); - }); - }); - - it("should use custom dataTestId when provided", async () => { - renderWithProviders( - , - ); - - await waitFor(() => { - expect(screen.getByTestId("custom-test-id")).toBeInTheDocument(); - }); - }); - - it("should handle multiple model selections", async () => { - const user = userEvent.setup(); - renderWithProviders( - , - ); - - await waitFor(() => { - expect(screen.getByTestId("model-select")).toBeInTheDocument(); - }); - - const select = screen.getByRole("listbox"); - await user.selectOptions(select, "gpt-4"); - expect(mockOnChange).toHaveBeenCalledWith(["gpt-4"]); - - await user.selectOptions(select, "claude-3"); - expect(mockOnChange).toHaveBeenCalled(); - const allCalls = mockOnChange.mock.calls.map((call) => call[0]); - expect(allCalls.some((call) => Array.isArray(call) && call.includes("gpt-4"))).toBe(true); - expect(allCalls.some((call) => Array.isArray(call) && call.includes("claude-3"))).toBe(true); - }); - - it("should capitalize provider name in wildcard options", async () => { - renderWithProviders( - , - ); - - await waitFor(() => { - expect(screen.getByText("All Openai models")).toBeInTheDocument(); - expect(screen.getByText("All Anthropic models")).toBeInTheDocument(); - }); + unmount(); + vi.clearAllMocks(); + mockUseAllProxyModels.mockReturnValue({ + data: { data: mockProxyModels }, + isLoading: false, + } as any); + } }); it("should deduplicate models with same id", async () => { @@ -505,52 +479,29 @@ describe("ModelSelect", () => { }); }); - it("should filter models based on user context with includeUserModels option", async () => { - mockUseCurrentUser.mockReturnValue({ - data: { models: ["gpt-4"] }, - isLoading: false, - } as any); - - renderWithProviders(); + it("should use custom dataTestId when provided", async () => { + renderWithProviders( + , + ); await waitFor(() => { - expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.queryByText("claude-3")).not.toBeInTheDocument(); + expect(screen.getByTestId("custom-test-id")).toBeInTheDocument(); }); }); - it("should filter models based on team context", async () => { - const mockTeam = { - team_id: "team-1", - team_alias: "Test Team", - models: ["gpt-4"], - }; - - const mockOrganization: Organization = { - organization_id: "org-1", - organization_alias: "Test Org", - budget_id: "budget-1", - metadata: {}, - models: ["gpt-4"], - spend: 0, - model_spend: {}, - created_at: "2024-01-01", - created_by: "user-1", - updated_at: "2024-01-01", - updated_by: "user-1", - litellm_budget_table: null, - teams: null, - users: null, - members: null, - }; - + it("should return all proxy models for team context when organization has empty models array", async () => { mockUseTeam.mockReturnValue({ - data: mockTeam, + data: { team_id: "team-1", team_alias: "Test Team", models: [] }, isLoading: false, } as any); mockUseOrganization.mockReturnValue({ - data: mockOrganization, + data: createMockOrganization([]), isLoading: false, } as any); @@ -558,7 +509,62 @@ describe("ModelSelect", () => { await waitFor(() => { expect(screen.getByText("gpt-4")).toBeInTheDocument(); - expect(screen.queryByText("claude-3")).not.toBeInTheDocument(); + expect(screen.getByText("claude-3")).toBeInTheDocument(); + }); + }); + + it("should disable No Default Models when all-proxy-models is selected", async () => { + mockUseOrganization.mockReturnValue({ + data: createMockOrganization(["all-proxy-models"]), + isLoading: false, + } as any); + + renderWithProviders( + , + ); + + await waitFor(() => { + const noDefaultOption = screen.getByRole("option", { name: "No Default Models" }); + expect(noDefaultOption).toBeDisabled(); + }); + }); + + it("should render maxTagPlaceholder when many items are selected", async () => { + // Create many models to trigger maxTagCount responsive behavior + const manyModels: ProxyModel[] = Array.from({ length: 20 }, (_, i) => ({ + id: `model-${i}`, + object: "model", + created: 1234567890, + owned_by: "test", + })); + + mockUseAllProxyModels.mockReturnValue({ + data: { data: manyModels }, + isLoading: false, + } as any); + + const selectedValues = manyModels.slice(0, 10).map((m) => m.id); + + renderWithProviders( + , + ); + + await waitFor(() => { + expect(screen.getByTestId("model-select")).toBeInTheDocument(); + // Verify maxTagPlaceholder is rendered with omitted values + expect(screen.getByTestId("max-tag-placeholder")).toBeInTheDocument(); + expect(screen.getByText(/\+5 more/)).toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx index 78ccdddd81b..74b2619f7f3 100644 --- a/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx +++ b/ui/litellm-dashboard/src/components/ModelSelect/ModelSelect.tsx @@ -30,10 +30,11 @@ export interface ModelSelectProps { showAllProxyModelsOverride?: boolean; includeSpecialOptions?: boolean; }; - context: "team" | "organization" | "user"; + context: "team" | "organization" | "user" | "global"; dataTestId?: string; value?: string[]; onChange: (values: string[]) => void; + style?: React.CSSProperties; } type FilterContextArgs = { @@ -65,6 +66,10 @@ const contextFilters: Record { return allProxyModels; }, + + global: ({ allProxyModels }) => { + return allProxyModels; + }, }; const filterModels = ( @@ -84,7 +89,7 @@ const filterModels = ( }; export const ModelSelect = (props: ModelSelectProps) => { - const { teamID, organizationID, options, context, dataTestId, value = [], onChange } = props; + const { teamID, organizationID, options, context, dataTestId, value = [], onChange, style } = props; const { includeUserModels, showAllTeamModelsOption, showAllProxyModelsOverride, includeSpecialOptions } = options || {}; const { data: allProxyModels, isLoading: isLoadingAllProxyModels } = useAllProxyModels(); @@ -98,7 +103,7 @@ export const ModelSelect = (props: ModelSelectProps) => { const organizationHasAllProxyModels = organization?.models.includes(MODEL_SELECT_ALL_PROXY_MODELS_SPECIAL_VALUE.value) || organization?.models.length === 0; const shouldShowAllProxyModels = showAllProxyModelsOverride || - (organizationHasAllProxyModels && includeSpecialOptions); + (organizationHasAllProxyModels && includeSpecialOptions) || context === "global"; if (isLoading) { return ; @@ -134,6 +139,7 @@ export const ModelSelect = (props: ModelSelectProps) => { data-testid={dataTestId} value={value} onChange={handleChange} + style={style} options={[ includeSpecialOptions ? { @@ -194,6 +200,7 @@ export const ModelSelect = (props: ModelSelectProps) => { }, ]} mode="multiple" + placeholder="Select Models" allowClear maxTagCount="responsive" maxTagPlaceholder={(omittedValues) => ( diff --git a/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.test.tsx b/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.test.tsx new file mode 100644 index 00000000000..b91f97885e6 --- /dev/null +++ b/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.test.tsx @@ -0,0 +1,301 @@ +import { screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { beforeEach, describe, expect, it, vi } from "vitest"; +import { renderWithProviders } from "../../../../tests/test-utils"; +import { PaginatedModelSelect } from "./PaginatedModelSelect"; + +const mockFetchNextPage = vi.fn(); + +vi.mock("@/app/(dashboard)/hooks/models/useModels", () => ({ + useInfiniteModelInfo: vi.fn(), +})); + +vi.mock("@tanstack/react-pacer/debouncer", () => { + const React = require("react"); + return { + useDebouncedState: (initial: string) => { + const [value, setValue] = React.useState(initial); + return [value, setValue]; + }, + }; +}); + +import { useInfiniteModelInfo } from "@/app/(dashboard)/hooks/models/useModels"; + +const mockUseInfiniteModelInfo = vi.mocked(useInfiniteModelInfo); + +const mockPagesWithModels = { + pages: [ + { + data: [ + { model_name: "GPT-4", model_info: { id: "model-1" } }, + { model_name: "Claude-3", model_info: { id: "model-2" } }, + ], + total_count: 2, + current_page: 1, + total_pages: 1, + size: 50, + }, + ], +}; + +const mockEmptyPages = { + pages: [{ data: [], total_count: 0, current_page: 1, total_pages: 1, size: 50 }], +}; + +describe("PaginatedModelSelect", () => { + const mockOnChange = vi.fn(); + + const defaultHookReturn = { + data: mockPagesWithModels, + fetchNextPage: mockFetchNextPage, + hasNextPage: false, + isFetchingNextPage: false, + isLoading: false, + }; + + beforeEach(() => { + vi.clearAllMocks(); + mockUseInfiniteModelInfo.mockReturnValue(defaultHookReturn as any); + }); + + it("should render", () => { + renderWithProviders(); + + expect(screen.getByRole("combobox")).toBeInTheDocument(); + expect(screen.getByText("Select a model")).toBeInTheDocument(); + }); + + it("should display custom placeholder when provided", () => { + renderWithProviders( + , + ); + + expect(screen.getByText("Choose model")).toBeInTheDocument(); + }); + + it("should display model options when data is loaded", async () => { + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "GPT-4 (model-1)" })).toBeInTheDocument(); + expect(screen.getByRole("option", { name: "Claude-3 (model-2)" })).toBeInTheDocument(); + }); + }); + + it("should call onChange when user selects a model", async () => { + const user = userEvent.setup({ delay: null }); + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await user.click(combobox); + + const visibleOption = await screen.findByTitle("GPT-4 (model-1)"); + await user.click(visibleOption); + + await waitFor(() => { + expect(mockOnChange).toHaveBeenCalledWith("model-1"); + }); + }); + + it("should display selected value when value prop is provided", async () => { + renderWithProviders( + , + ); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "GPT-4 (model-1)" })).toBeInTheDocument(); + }); + }); + + it("should show loading state when isLoading is true", () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + isLoading: true, + } as any); + + renderWithProviders(); + + expect(screen.getByRole("combobox")).toHaveAttribute("aria-expanded", "false"); + }); + + it("should pass pageSize to useInfiniteModelInfo", () => { + renderWithProviders(); + + expect(mockUseInfiniteModelInfo).toHaveBeenCalledWith(25, undefined); + }); + + it("should pass search to useInfiniteModelInfo when user types", async () => { + const user = userEvent.setup(); + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await user.click(combobox); + await user.keyboard("gpt"); + + await waitFor(() => { + expect(mockUseInfiniteModelInfo).toHaveBeenCalledWith(50, "gpt"); + }); + }); + + it("should have scroll container for infinite loading when hasNextPage is true", async () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + hasNextPage: true, + isFetchingNextPage: false, + } as any); + + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "GPT-4 (model-1)" })).toBeInTheDocument(); + }); + + const scrollableContainer = document.querySelector( + ".ant-select-dropdown .rc-virtual-list-holder", + ); + expect(scrollableContainer).toBeInTheDocument(); + expect(scrollableContainer).toHaveAttribute("style"); + }); + + it("should deduplicate models with same id across pages", async () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + data: { + pages: [ + { + data: [ + { model_name: "GPT-4", model_info: { id: "model-1" } }, + { model_name: "GPT-4 Dupe", model_info: { id: "model-1" } }, + ], + total_count: 2, + current_page: 1, + total_pages: 1, + size: 50, + }, + ], + }, + fetchNextPage: mockFetchNextPage, + hasNextPage: false, + isFetchingNextPage: false, + isLoading: false, + } as any); + + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + const model1Options = screen.queryAllByRole("option", { name: /model-1/ }); + expect(model1Options.length).toBe(1); + }); + }); + + it("should skip models without model_info id", async () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + data: { + pages: [ + { + data: [ + { model_name: "Valid Model", model_info: { id: "valid-id" } }, + { model_name: "No ID", model_info: null }, + { model_name: "Empty ID", model_info: { id: "" } }, + ], + total_count: 3, + current_page: 1, + total_pages: 1, + size: 50, + }, + ], + }, + fetchNextPage: mockFetchNextPage, + hasNextPage: false, + isFetchingNextPage: false, + isLoading: false, + } as any); + + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "Valid Model (valid-id)" })).toBeInTheDocument(); + expect(screen.queryByRole("option", { name: "No ID" })).not.toBeInTheDocument(); + expect(screen.queryByRole("option", { name: "Empty ID" })).not.toBeInTheDocument(); + }); + }); + + it("should show model ID only when model_name is empty", async () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + data: { + pages: [ + { + data: [{ model_name: "", model_info: { id: "id-only" } }], + total_count: 1, + current_page: 1, + total_pages: 1, + size: 50, + }, + ], + }, + fetchNextPage: mockFetchNextPage, + hasNextPage: false, + isFetchingNextPage: false, + isLoading: false, + } as any); + + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + await userEvent.click(combobox); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "id-only" })).toBeInTheDocument(); + }); + }); + + it("should respect allowClear prop", () => { + renderWithProviders( + , + ); + + expect(screen.getByRole("combobox")).toBeInTheDocument(); + }); + + it("should respect disabled prop", () => { + renderWithProviders(); + + const combobox = screen.getByRole("combobox"); + expect(combobox.closest(".ant-select")).toHaveClass("ant-select-disabled"); + }); + + it("should not call fetchNextPage when hasNextPage is false", async () => { + mockUseInfiniteModelInfo.mockReturnValue({ + ...defaultHookReturn, + hasNextPage: false, + } as any); + + renderWithProviders(); + + await userEvent.click(screen.getByRole("combobox")); + + await waitFor(() => { + expect(screen.getByRole("option", { name: "GPT-4 (model-1)" })).toBeInTheDocument(); + }); + + expect(mockFetchNextPage).not.toHaveBeenCalled(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.tsx b/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.tsx new file mode 100644 index 00000000000..9b22fd1bd87 --- /dev/null +++ b/ui/litellm-dashboard/src/components/ModelSelect/PaginatedModelSelect/PaginatedModelSelect.tsx @@ -0,0 +1,143 @@ +import { useInfiniteModelInfo } from "@/app/(dashboard)/hooks/models/useModels"; +import { LoadingOutlined } from "@ant-design/icons"; +import { useDebouncedState } from "@tanstack/react-pacer/debouncer"; +import { Select, Space, Typography } from "antd"; +import { useMemo, useState, type UIEvent } from "react"; + +const { Text } = Typography; + +export interface PaginatedModelSelectProps { + value?: string; + onChange?: (value: string) => void; + placeholder?: string; + style?: React.CSSProperties; + pageSize?: number; + allowClear?: boolean; + disabled?: boolean; +} + +const SCROLL_THRESHOLD = 0.8; +const DEBOUNCE_MS = 300; + +export const PaginatedModelSelect = ({ + value, + onChange, + placeholder = "Select a model", + style, + pageSize = 50, + allowClear = true, + disabled = false, +}: PaginatedModelSelectProps) => { + const [searchInput, setSearchInput] = useState(""); + const [debouncedSearch, setDebouncedSearch] = useDebouncedState("", { + wait: DEBOUNCE_MS, + }); + + const { + data, + fetchNextPage, + hasNextPage, + isFetchingNextPage, + isLoading, + } = useInfiniteModelInfo(pageSize, debouncedSearch || undefined); + + const options = useMemo(() => { + if (!data?.pages) return []; + + const seen = new Set(); + const result: { label: string; value: string; modelName: string; modelId: string }[] = []; + + for (const page of data.pages) { + for (const model of page.data) { + const modelId = model.model_info?.id ?? ""; + const modelName = model.model_name ?? ""; + + // Dedupe by id - skip models without id (can't uniquely identify) + if (!modelId || seen.has(modelId)) continue; + seen.add(modelId); + + result.push({ + label: modelName ? `${modelName} (${modelId})` : modelId, + value: modelId, + modelName, + modelId, + }); + } + } + + return result; + }, [data]); + + const optionRender = (option: { data: { modelName: string; modelId: string; label: string } }) => { + const { modelName, modelId } = option.data; + + return ( + <> + {modelName ? ( + + + Model name: + {modelName} + + + Model ID: {modelId} + + + ) : ( + Model ID: {modelId} + )} + + ); + }; + + const handlePopupScroll = (e: UIEvent) => { + const target = e.currentTarget; + const scrollRatio = + (target.scrollTop + target.clientHeight) / target.scrollHeight; + + if (scrollRatio >= SCROLL_THRESHOLD && hasNextPage && !isFetchingNextPage) { + fetchNextPage(); + } + }; + + const handleSearch = (value: string) => { + setSearchInput(value); + setDebouncedSearch(value); + }; + + const handleChange = (v: string | string[] | null) => { + const normalized = + typeof v === "string" ? v : Array.isArray(v) ? v[0] ?? "" : ""; + onChange?.(normalized); + }; + + return ( + ({ + label: model.model_group, + value: model.model_group, + }))} + placeholder={loadingModels ? "Loading models..." : "Select embedding model"} + showSearch + disabled={isUpdating || loadingModels} + loading={loadingModels} + notFoundContent={ + loadingModels ? "Loading..." : "No embedding models available" + } + /> + + + + Top K Results + + + + + } + > + + + + + Similarity Threshold + + + + + } + > + + + + +
+ +
+ + + + {/* Right Column - Test Configuration */} + + + + + + )} + + ); +} diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/MCPSemanticFilterTestPanel.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/MCPSemanticFilterTestPanel.tsx new file mode 100644 index 00000000000..084de740014 --- /dev/null +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/MCPSemanticFilterTestPanel.tsx @@ -0,0 +1,164 @@ +import { CodeOutlined, PlayCircleOutlined } from "@ant-design/icons"; +import { Alert, Button, Card, Input, Space, Tabs, Typography } from "antd"; +import ModelSelector from "@/components/common_components/ModelSelector"; +import { TestResult } from "./semanticFilterTestUtils"; + +interface MCPSemanticFilterTestPanelProps { + accessToken: string | null; + testQuery: string; + setTestQuery: (value: string) => void; + testModel: string; + setTestModel: (value: string) => void; + isTesting: boolean; + onTest: () => void; + filterEnabled: boolean; + testResult: TestResult | null; + curlCommand: string; +} + +export default function MCPSemanticFilterTestPanel({ + accessToken, + testQuery, + setTestQuery, + testModel, + setTestModel, + isTesting, + onTest, + filterEnabled, + testResult, + curlCommand, +}: MCPSemanticFilterTestPanelProps) { + return ( + + +
+ + Test Query + + setTestQuery(e.target.value)} + rows={4} + disabled={isTesting} + /> +
+ +
+ +
+ + + + {!filterEnabled && ( + + )} + + {testResult && ( +
+ Results + +
+ + Selected Tools: + +
    + {testResult.tools.map((tool, index) => ( +
  • + {tool} +
    • + ))} +
+
+
+ )} + + ), + }, + { + key: "api", + label: "API Usage", + children: ( +
+ + + API Usage + + + Use this curl command to test the semantic filter with your current configuration. + + + Response headers to check: + +
    +
  • + + x-litellm-semantic-filter: shows total tools → selected tools + + + Example: 10→3 + +
    • +
  • + + x-litellm-semantic-filter-tools: CSV of selected tool names + + + Example: wikipedia-fetch,github-search,slack-post + +
    • +
+ 
+              {curlCommand}
+
+
+ ), + }, + ]} + /> + + ); +} diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/semanticFilterTestUtils.ts b/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/semanticFilterTestUtils.ts new file mode 100644 index 00000000000..e80ed6b08d7 --- /dev/null +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/MCPSemanticFilterSettings/semanticFilterTestUtils.ts @@ -0,0 +1,95 @@ +import NotificationManager from "@/components/molecules/notifications_manager"; +import { testMCPSemanticFilter } from "@/components/networking"; + +export interface TestResult { + totalTools: number; + selectedTools: number; + tools: string[]; +} + +interface FilterHeaders { + filter: string | null; + tools: string | null; +} + +const parseFilterHeaders = (headers: FilterHeaders): TestResult | null => { + if (!headers.filter) { + return null; + } + + const [total, selected] = headers.filter.split("->").map(Number); + const tools = headers.tools + ? headers.tools.split(",").map((name) => name.trim()) + : []; + + return { totalTools: total, selectedTools: selected, tools }; +}; + +export const runSemanticFilterTest = async ({ + accessToken, + testModel, + testQuery, + setIsTesting, + setTestResult, +}: { + accessToken: string; + testModel: string; + testQuery: string; + setIsTesting: (value: boolean) => void; + setTestResult: (result: TestResult | null) => void; +}) => { + if (!testQuery || !testModel || !accessToken) { + NotificationManager.error("Please enter a query and select a model"); + return; + } + + setIsTesting(true); + setTestResult(null); + + try { + const { headers } = await testMCPSemanticFilter( + accessToken, + testModel, + testQuery + ); + const parsedResult = parseFilterHeaders(headers); + + if (!parsedResult) { + NotificationManager.warning( + "Semantic filter is not enabled or no tools were filtered" + ); + return; + } + + setTestResult(parsedResult); + NotificationManager.success("Semantic filter test completed successfully"); + } catch (error) { + console.error("Test failed:", error); + NotificationManager.error("Failed to test semantic filter"); + } finally { + setIsTesting(false); + } +}; + +export const getCurlCommand = (testModel: string, testQuery: string) => + `curl --location 'http://localhost:4000/v1/responses' \\ +--header 'Content-Type: application/json' \\ +--header 'Authorization: Bearer sk-1234' \\ +--data '{ + "model": "${testModel}", + "input": [ + { + "role": "user", + "content": "${testQuery || "Your query here"}", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + "tool_choice": "required" +}'`; diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.test.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.test.tsx index 31dcfc102ec..639564bbd35 100644 --- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.test.tsx +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.test.tsx @@ -20,6 +20,13 @@ vi.mock("@/app/(dashboard)/hooks/uiSettings/useUpdateUISettings", () => ({ useUpdateUISettings: mockUseUpdateUISettings, })); +vi.mock("@/components/molecules/notifications_manager", () => ({ + default: { + success: vi.fn(), + fromBackend: vi.fn(), + }, +})); + const buildSettingsResponse = (overrides?: Partial>) => ({ data: { field_schema: { @@ -28,10 +35,18 @@ const buildSettingsResponse = (overrides?: Partial>) => disable_model_add_for_internal_users: { description: "Disable model add for internal users", }, + disable_team_admin_delete_team_user: { + description: "Disable team admin delete team user", + }, + require_auth_for_public_ai_hub: { + description: "Require authentication for public AI Hub", + }, }, }, values: { disable_model_add_for_internal_users: false, + disable_team_admin_delete_team_user: false, + require_auth_for_public_ai_hub: false, }, }, isLoading: false, @@ -57,6 +72,8 @@ describe("UISettings", () => { expect(screen.getByText("UI Settings")).toBeInTheDocument(); expect(screen.getByRole("switch", { name: "Disable model add for internal users" })).toBeInTheDocument(); + expect(screen.getByRole("switch", { name: "Disable team admin delete team user" })).toBeInTheDocument(); + expect(screen.getByRole("switch", { name: "Require authentication for public AI Hub" })).toBeInTheDocument(); }); it("should toggle setting and call update", () => { @@ -87,4 +104,62 @@ describe("UISettings", () => { ); expect(NotificationManager.success).toHaveBeenCalledWith("UI settings updated successfully"); }); + + it("should toggle disable team admin delete team user setting and call update", () => { + const mutateMock = vi.fn((_settings, options) => { + options?.onSuccess?.(); + }); + + mockUseUpdateUISettings.mockReturnValue({ + mutate: mutateMock, + isPending: false, + error: null, + }); + + render(); + + const toggle = screen.getByRole("switch", { name: "Disable team admin delete team user" }); + + act(() => { + fireEvent.click(toggle); + }); + + expect(mutateMock).toHaveBeenCalledWith( + { disable_team_admin_delete_team_user: true }, + expect.objectContaining({ + onSuccess: expect.any(Function), + onError: expect.any(Function), + }), + ); + expect(NotificationManager.success).toHaveBeenCalledWith("UI settings updated successfully"); + }); + + it("should toggle require auth for public AI Hub setting and call update", () => { + const mutateMock = vi.fn((_settings, options) => { + options?.onSuccess?.(); + }); + + mockUseUpdateUISettings.mockReturnValue({ + mutate: mutateMock, + isPending: false, + error: null, + }); + + render(); + + const toggle = screen.getByRole("switch", { name: "Require authentication for public AI Hub" }); + + act(() => { + fireEvent.click(toggle); + }); + + expect(mutateMock).toHaveBeenCalledWith( + { require_auth_for_public_ai_hub: true }, + expect.objectContaining({ + onSuccess: expect.any(Function), + onError: expect.any(Function), + }), + ); + expect(NotificationManager.success).toHaveBeenCalledWith("UI settings updated successfully"); + }); }); diff --git a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx index 6cc9cbf4309..a43f0e9d42d 100644 --- a/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx +++ b/ui/litellm-dashboard/src/components/Settings/AdminSettings/UISettings/UISettings.tsx @@ -15,6 +15,7 @@ export default function UISettings() { const schema = data?.field_schema; const property = schema?.properties?.disable_model_add_for_internal_users; const disableTeamAdminDeleteProperty = schema?.properties?.disable_team_admin_delete_team_user; + const requireAuthForPublicAIHubProperty = schema?.properties?.require_auth_for_public_ai_hub; const enabledPagesProperty = schema?.properties?.enabled_ui_pages_internal_users; const values = data?.values ?? {}; const isDisabledForInternalUsers = Boolean(values.disable_model_add_for_internal_users); @@ -59,6 +60,20 @@ export default function UISettings() { }); }; + const handleToggleRequireAuthForPublicAIHub = (checked: boolean) => { + updateSettings( + { require_auth_for_public_ai_hub: checked }, + { + onSuccess: () => { + NotificationManager.success("UI settings updated successfully"); + }, + onError: (error) => { + NotificationManager.fromBackend(error); + }, + }, + ); + }; + return ( {isLoading ? ( @@ -113,6 +128,22 @@ export default function UISettings() { + + + + Require authentication for public AI Hub + {requireAuthForPublicAIHubProperty?.description && ( + {requireAuthForPublicAIHubProperty.description} + )} + + + {/* Page Visibility for Internal Users */} diff --git a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx index 8a7119aa4a3..94bfbd54426 100644 --- a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx +++ b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.test.tsx @@ -14,6 +14,10 @@ vi.mock("../../../playground/llm_calls/fetch_models", () => ({ fetchAvailableModels: vi.fn(), })); +vi.mock("@/app/(dashboard)/hooks/models/useModelCostMap", () => ({ + useModelCostMap: vi.fn().mockReturnValue({ data: null }), +})); + vi.mock("openai", () => ({ default: { OpenAI: vi.fn().mockImplementation(() => ({ @@ -97,20 +101,9 @@ describe("Fallbacks", () => { modelData: mockModelData, }; - const findDeleteButton = (container: HTMLElement) => { - const tableRows = container.querySelectorAll("tbody tr"); - if (tableRows.length === 0) return null; - const firstRow = tableRows[0]; - const actionCells = firstRow.querySelectorAll("td"); - const lastCell = actionCells[actionCells.length - 1]; - const buttons = lastCell.querySelectorAll("button"); - if (buttons.length >= 2) { - return buttons[buttons.length - 1]; - } - const clickableElements = lastCell.querySelectorAll("[class*='cursor-pointer'], button"); - return Array.from(clickableElements).find((el) => - el.className.includes("red") || el.className.includes("hover:text-red") - ) || clickableElements[clickableElements.length - 1]; + const getFirstRowDeleteButton = () => { + const deleteButtons = screen.getAllByTestId("delete-fallback-button"); + return deleteButtons.length > 0 ? deleteButtons[0] : null; }; beforeEach(() => { @@ -156,20 +149,31 @@ describe("Fallbacks", () => { await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); - expect(screen.getByText("gpt-3.5-turbo, claude-3-opus")).toBeInTheDocument(); - expect(screen.getByText("claude-3-opus")).toBeInTheDocument(); + expect(screen.getAllByText(/gpt-3\.5-turbo/).length).toBeGreaterThan(0); + expect(screen.getAllByText(/claude-3-opus/).length).toBeGreaterThan(0); }); }); - it("should open delete modal when delete icon is clicked", async () => { - const user = userEvent.setup(); - const { container } = render(); + it("should show delete button for each fallback row when fallbacks exist", async () => { + render(); await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); }); - const deleteButton = findDeleteButton(container); + const deleteButtons = screen.getAllByTestId("delete-fallback-button"); + expect(deleteButtons.length).toBe(2); + }); + + it("should open delete modal when delete icon is clicked", async () => { + const user = userEvent.setup(); + render(); + + await waitFor(() => { + expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); + }); + + const deleteButton = getFirstRowDeleteButton(); expect(deleteButton).not.toBeNull(); await user.click(deleteButton as HTMLElement); @@ -182,13 +186,13 @@ describe("Fallbacks", () => { it("should delete fallback when confirmed", async () => { const user = userEvent.setup(); - const { container } = render(); + render(); await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); }); - const deleteButton = findDeleteButton(container); + const deleteButton = getFirstRowDeleteButton(); expect(deleteButton).not.toBeNull(); await user.click(deleteButton as HTMLElement); @@ -210,13 +214,13 @@ describe("Fallbacks", () => { it("should close delete modal when cancel is clicked", async () => { const user = userEvent.setup(); - const { container } = render(); + render(); await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); }); - const deleteButton = findDeleteButton(container); + const deleteButton = getFirstRowDeleteButton(); expect(deleteButton).not.toBeNull(); await user.click(deleteButton as HTMLElement); @@ -237,13 +241,13 @@ describe("Fallbacks", () => { const user = userEvent.setup(); const error = new Error("Delete failed"); vi.mocked(networkingModule.setCallbacksCall).mockRejectedValueOnce(error); - const { container } = render(); + render(); await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); }); - const deleteButton = findDeleteButton(container); + const deleteButton = getFirstRowDeleteButton(); expect(deleteButton).not.toBeNull(); await user.click(deleteButton as HTMLElement); @@ -264,13 +268,13 @@ describe("Fallbacks", () => { const user = userEvent.setup(); const error = new Error("Delete failed"); vi.mocked(networkingModule.setCallbacksCall).mockRejectedValueOnce(error); - const { container } = render(); + render(); await waitFor(() => { expect(screen.getAllByText("gpt-4").length).toBeGreaterThan(0); }); - const deleteButton = findDeleteButton(container); + const deleteButton = getFirstRowDeleteButton(); expect(deleteButton).not.toBeNull(); await user.click(deleteButton as HTMLElement); @@ -296,6 +300,9 @@ describe("Fallbacks", () => { await waitFor(() => { expect(screen.getByTestId("add-fallbacks-button")).toBeInTheDocument(); + expect( + screen.getByText(/No fallbacks configured. Add fallbacks to automatically try another model/), + ).toBeInTheDocument(); }); expect(screen.queryByText("gpt-4")).not.toBeInTheDocument(); @@ -309,6 +316,9 @@ describe("Fallbacks", () => { await waitFor(() => { expect(screen.getByTestId("add-fallbacks-button")).toBeInTheDocument(); + expect( + screen.getByText(/No fallbacks configured. Add fallbacks to automatically try another model/), + ).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.tsx b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.tsx index f493cc51323..9c5933aba3a 100644 --- a/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.tsx +++ b/ui/litellm-dashboard/src/components/Settings/RouterSettings/Fallbacks/Fallbacks.tsx @@ -1,9 +1,11 @@ -import { PlayIcon, TrashIcon } from "@heroicons/react/outline"; +import { useModelCostMap } from "@/app/(dashboard)/hooks/models/useModelCostMap"; +import { ArrowRightIcon, PlayIcon, TrashIcon } from "@heroicons/react/outline"; import { Icon, Table, TableBody, TableCell, TableHead, TableHeaderCell, TableRow } from "@tremor/react"; -import { Tooltip } from "antd"; +import { Tooltip, Typography } from "antd"; import openai from "openai"; import React, { useEffect, useState } from "react"; import DeleteResourceModal from "../../../common_components/DeleteResourceModal"; +import { ProviderLogo } from "../../../molecules/models/ProviderLogo"; import NotificationsManager from "../../../molecules/notifications_manager"; import { getCallbacksCall, setCallbacksCall } from "../../../networking"; import AddFallbacks from "./AddFallbacks"; @@ -11,6 +13,61 @@ import AddFallbacks from "./AddFallbacks"; type FallbackEntry = { [modelName: string]: string[] }; type Fallbacks = FallbackEntry[]; +const modelCardClass = + "inline-flex items-center gap-2 px-2.5 py-1 rounded-md border border-gray-200 bg-gray-50 text-sm font-medium text-gray-800 shrink-0"; + +function renderModelNameCell( + modelName: string, + getProviderFromModel?: (modelName: string) => string, +): React.ReactNode { + const provider = getProviderFromModel?.(modelName) ?? modelName; + return ( + + + {modelName} + + ); +} + +function renderFallbacksChain( + _primaryModel: string, + fallbackModels: string[], + getProviderFromModel?: (modelName: string) => string, +): React.ReactNode { + const list = Array.isArray(fallbackModels) ? fallbackModels : []; + if (list.length === 0) return null; + + const ChainCard = ({ modelName }: { modelName: string }) => { + const provider = getProviderFromModel?.(modelName) ?? modelName; + return ( + + + {modelName} + + ); + }; + return ( + + + + + + {list.map((model, i) => ( + + {i > 0 && ( + + )} + + + ))} + + + ); +} + interface FallbacksProps { accessToken: string | null; userRole: string | null; @@ -71,6 +128,14 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo const [fallbackToDelete, setFallbackToDelete] = useState(null); const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false); + const { data: modelCostMapData } = useModelCostMap(); + const getProviderFromModel = (model: string): string => { + if (modelCostMapData != null && typeof modelCostMapData === "object" && model in modelCostMapData) { + return modelCostMapData[model]["litellm_provider"] ?? ""; + } + return ""; + }; + useEffect(() => { if (!accessToken || !userRole || !userID) { return; @@ -177,6 +242,8 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo } }; + const hasFallbacks = Array.isArray(routerSettings.fallbacks) && routerSettings.fallbacks.length > 0; + return ( <> = ({ accessToken, userRole, userID, mo value={routerSettings.fallbacks || []} onChange={handleFallbacksChange} /> - - - - Model Name - Fallbacks - Actions - - + {!hasFallbacks ? ( +
+ + No fallbacks configured. Add fallbacks to automatically try another model when the primary + fails. + +
+ ) : ( +
+ + + Model Name + Fallbacks + Actions + + - - {routerSettings["fallbacks"] && - routerSettings["fallbacks"].map((item: FallbackEntry, index: number) => + + {routerSettings["fallbacks"].map((item: FallbackEntry, index: number) => Object.entries(item).map(([key, value]) => ( - {key} - {Array.isArray(value) ? value.join(", ") : value} - + + {renderModelNameCell(key, getProviderFromModel)} + + + {renderFallbacksChain(key, Array.isArray(value) ? value : [], getProviderFromModel)} + + = ({ accessToken, userRole, userID, mo /> - handleDeleteClick(item)} - className="cursor-pointer hover:text-red-600" - /> + onKeyDown={(e) => e.key === "Enter" && handleDeleteClick(item)} + className="cursor-pointer inline-flex" + > + + )), )} - -
+ + + )} ({ - default: ({ value, onChange }: { value: string | null; onChange: (value: string) => void }) => ( - onChange(e.target.value)} + aria-label="Budget duration" + > - ), - getBudgetDurationLabel: vi.fn((value: string) => value), -})); + ); + BudgetDurationDropdown.displayName = "BudgetDurationDropdown"; + return { + default: BudgetDurationDropdown, + getBudgetDurationLabel: vi.fn((value: string) => `Budget: ${value}`), + }; +}); -// Mock the model display name helper vi.mock("./key_team_helpers/fetch_available_models_team_key", () => ({ getModelDisplayName: vi.fn((model: string) => model), })); +vi.mock("./ModelSelect/ModelSelect", () => { + const ModelSelect = ({ value, onChange }: { value: string[]; onChange: (value: string[]) => void }) => ( + + ); + ModelSelect.displayName = "ModelSelect"; + return { ModelSelect }; +}); + +vi.mock("antd", async (importOriginal) => { + const actual = await importOriginal(); + const React = await import("react"); + const SelectComponent = ({ + value, + onChange, + mode, + children, + className, + }: { + value: any; + onChange: (value: any) => void; + mode?: string; + children: React.ReactNode; + className?: string; + }) => { + const isMultiple = mode === "multiple"; + const selectValue = isMultiple ? (Array.isArray(value) ? value : []) : value || ""; + return React.createElement( + "select", + { + multiple: isMultiple, + value: selectValue, + onChange: (e: React.ChangeEvent) => { + const selectedValues = Array.from(e.target.selectedOptions, (option) => option.value); + onChange(isMultiple ? selectedValues : selectedValues[0] || undefined); + }, + className, + "aria-label": "Select", + role: "listbox", + }, + children, + ); + }; + SelectComponent.displayName = "Select"; + const SelectOption = ({ value: optionValue, children: optionChildren }: { value: string; children: React.ReactNode }) => + React.createElement("option", { value: optionValue }, optionChildren); + SelectOption.displayName = "SelectOption"; + SelectComponent.Option = SelectOption; + const Spin = ({ size }: { size?: string }) => React.createElement("div", { "data-testid": "spinner", "data-size": size }); + Spin.displayName = "Spin"; + const Switch = ({ checked, onChange }: { checked: boolean; onChange: (checked: boolean) => void }) => + React.createElement("input", { + type: "checkbox", + role: "switch", + checked: checked, + onChange: (e) => onChange(e.target.checked), + "aria-label": "Toggle switch", + }); + Switch.displayName = "Switch"; + const Paragraph = ({ children }: { children: React.ReactNode }) => React.createElement("p", {}, children); + Paragraph.displayName = "Paragraph"; + return { + ...actual, + Spin, + Switch, + Select: SelectComponent, + Typography: { + Paragraph, + }, + }; +}); + +const mockGetDefaultTeamSettings = vi.mocked(networking.getDefaultTeamSettings); +const mockUpdateDefaultTeamSettings = vi.mocked(networking.updateDefaultTeamSettings); +const mockModelAvailableCall = vi.mocked(networking.modelAvailableCall); +const mockNotificationsManager = vi.mocked(NotificationsManager); + describe("TeamSSOSettings", () => { + const defaultProps = { + accessToken: "test-token", + userID: "test-user", + userRole: "admin", + }; + + const mockSettings = { + values: { + budget_duration: "monthly", + max_budget: 1000, + enabled: true, + allowed_models: ["gpt-4", "claude-3"], + models: ["gpt-4"], + status: "active", + }, + field_schema: { + description: "Default team settings schema", + properties: { + budget_duration: { + type: "string", + description: "Budget duration setting", + }, + max_budget: { + type: "number", + description: "Maximum budget amount", + }, + enabled: { + type: "boolean", + description: "Enable feature", + }, + allowed_models: { + type: "array", + items: { + enum: ["gpt-4", "claude-3", "gpt-3.5-turbo"], + }, + description: "Allowed models", + }, + models: { + type: "array", + description: "Selected models", + }, + status: { + type: "string", + enum: ["active", "inactive", "pending"], + description: "Status", + }, + }, + }, + }; + beforeEach(() => { vi.clearAllMocks(); + mockModelAvailableCall.mockResolvedValue({ + data: [{ id: "gpt-4" }, { id: "claude-3" }], + }); }); - it("renders the component", async () => { - // Mock successful API responses - vi.mocked(networking.getDefaultTeamSettings).mockResolvedValue({ + it("should render", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Default Team Settings")).toBeInTheDocument(); + }); + }); + + it("should show loading spinner while fetching settings", () => { + mockGetDefaultTeamSettings.mockImplementation(() => new Promise(() => { })); + + renderWithProviders(); + + expect(screen.getByTestId("spinner")).toBeInTheDocument(); + }); + + it("should display message when no settings are available", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(null as any); + + renderWithProviders(); + + await waitFor(() => { + expect( + screen.getByText("No team settings available or you do not have permission to view them."), + ).toBeInTheDocument(); + }); + }); + + it("should not fetch settings when access token is null", async () => { + renderWithProviders(); + + await waitFor(() => { + expect(mockGetDefaultTeamSettings).not.toHaveBeenCalled(); + }); + }); + + it("should display settings fields with correct values", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Budget Duration")).toBeInTheDocument(); + expect(screen.getByText("Max Budget")).toBeInTheDocument(); + }); + + expect(screen.getByText("Budget: monthly")).toBeInTheDocument(); + expect(screen.getByText("1000")).toBeInTheDocument(); + const enabledTexts = screen.getAllByText("Enabled"); + expect(enabledTexts.length).toBeGreaterThan(0); + }); + + it("should display 'Not set' for null values", async () => { + const settingsWithNulls = { + ...mockSettings, values: { - budget_duration: "monthly", - max_budget: 1000, + ...mockSettings.values, + max_budget: null, }, + }; + mockGetDefaultTeamSettings.mockResolvedValue(settingsWithNulls); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Not set")).toBeInTheDocument(); + }); + }); + + it("should toggle edit mode when edit button is clicked", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + expect(screen.getByRole("button", { name: "Cancel" })).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Save Changes" })).toBeInTheDocument(); + expect(screen.queryByRole("button", { name: "Edit Settings" })).not.toBeInTheDocument(); + }); + + it("should cancel edit mode and reset values", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + const cancelButton = screen.getByRole("button", { name: "Cancel" }); + await userEvent.click(cancelButton); + + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + expect(screen.queryByRole("button", { name: "Cancel" })).not.toBeInTheDocument(); + }); + + it("should save settings when save button is clicked", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + mockUpdateDefaultTeamSettings.mockResolvedValue({ + settings: mockSettings.values, + }); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Save Changes" })).toBeInTheDocument(); + }); + + const saveButton = screen.getByRole("button", { name: "Save Changes" }); + await userEvent.click(saveButton); + + await waitFor(() => { + expect(mockUpdateDefaultTeamSettings).toHaveBeenCalledWith("test-token", mockSettings.values); + }); + + expect(mockNotificationsManager.success).toHaveBeenCalledWith("Default team settings updated successfully"); + }); + + it("should show error notification when save fails", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + mockUpdateDefaultTeamSettings.mockRejectedValue(new Error("Save failed")); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Save Changes" })).toBeInTheDocument(); + }); + + const saveButton = screen.getByRole("button", { name: "Save Changes" }); + await userEvent.click(saveButton); + + await waitFor(() => { + expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to update team settings"); + }); + }); + + it("should render boolean field as switch in edit mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + const switchElement = screen.getByRole("switch"); + expect(switchElement).toBeInTheDocument(); + expect(switchElement).toBeChecked(); + }); + }); + + it("should update boolean value when switch is toggled", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByRole("switch")).toBeInTheDocument(); + }); + + const switchElement = screen.getByRole("switch"); + await userEvent.click(switchElement); + + expect(switchElement).not.toBeChecked(); + }); + + it("should render budget duration dropdown in edit mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByLabelText("Budget duration")).toBeInTheDocument(); + }); + }); + + it("should update budget duration when dropdown value changes", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByLabelText("Budget duration")).toBeInTheDocument(); + }); + + const dropdown = screen.getByLabelText("Budget duration"); + await userEvent.selectOptions(dropdown, "daily"); + + expect(dropdown).toHaveValue("daily"); + }); + + it("should render text input for string fields in edit mode", async () => { + const settingsWithString = { + ...mockSettings, field_schema: { - description: "Default team settings", + ...mockSettings.field_schema, properties: { - budget_duration: { + ...mockSettings.field_schema.properties, + team_name: { type: "string", - description: "Budget duration", - }, - max_budget: { - type: "number", - description: "Maximum budget", + description: "Team name", }, }, }, + values: { + ...mockSettings.values, + team_name: "Test Team", + }, + }; + mockGetDefaultTeamSettings.mockResolvedValue(settingsWithString); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); }); - vi.mocked(networking.modelAvailableCall).mockResolvedValue({ - data: [{ id: "gpt-4" }, { id: "claude-3" }], + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + const textInput = screen.getByDisplayValue("Test Team"); + expect(textInput).toBeInTheDocument(); + }); + }); + + it("should render enum select for string enum fields in edit mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); }); - renderWithProviders(); + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); - const container = await screen.findByText("Default Team Settings"); - expect(container).toBeInTheDocument(); + await waitFor(() => { + const statusSelect = screen.getAllByRole("listbox")[0]; + expect(statusSelect).toBeInTheDocument(); + }); + }); + + it("should render multi-select for array enum fields in edit mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + const multiSelects = screen.getAllByRole("listbox"); + expect(multiSelects.length).toBeGreaterThan(0); + }); + }); + + it("should render ModelSelect for models field in edit mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByTestId("model-select")).toBeInTheDocument(); + }); + }); + + it("should display models as badges in view mode", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + const gpt4Elements = screen.getAllByText("gpt-4"); + expect(gpt4Elements.length).toBeGreaterThan(0); + }); + }); + + it("should display 'None' for empty arrays in view mode", async () => { + const settingsWithEmptyArray = { + ...mockSettings, + values: { + ...mockSettings.values, + models: [], + }, + }; + mockGetDefaultTeamSettings.mockResolvedValue(settingsWithEmptyArray); + + renderWithProviders(); + + await waitFor(() => { + const noneTexts = screen.getAllByText("None"); + expect(noneTexts.length).toBeGreaterThan(0); + }); + }); + + it("should display schema description when available", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Default team settings schema")).toBeInTheDocument(); + }); + }); + + it("should show error notification when fetching settings fails", async () => { + mockGetDefaultTeamSettings.mockRejectedValue(new Error("Fetch failed")); + + renderWithProviders(); + + await waitFor(() => { + expect(mockNotificationsManager.fromBackend).toHaveBeenCalledWith("Failed to fetch team settings"); + }); + }); + + it("should handle model fetch error gracefully", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + mockModelAvailableCall.mockRejectedValue(new Error("Model fetch failed")); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Default Team Settings")).toBeInTheDocument(); + }); + }); + + it("should disable cancel button while saving", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + mockUpdateDefaultTeamSettings.mockImplementation( + () => new Promise((resolve) => setTimeout(() => resolve({ settings: mockSettings.values }), 100)), + ); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Edit Settings" })).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + await userEvent.click(editButton); + + await waitFor(() => { + expect(screen.getByRole("button", { name: "Save Changes" })).toBeInTheDocument(); + }); + + const saveButton = screen.getByRole("button", { name: "Save Changes" }); + await userEvent.click(saveButton); + + const cancelButton = screen.getByRole("button", { name: "Cancel" }); + expect(cancelButton).toBeDisabled(); + }); + + it("should display field descriptions", async () => { + mockGetDefaultTeamSettings.mockResolvedValue(mockSettings); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Budget duration setting")).toBeInTheDocument(); + expect(screen.getByText("Maximum budget amount")).toBeInTheDocument(); + }); + }); + + it("should format field names by replacing underscores and capitalizing", async () => { + const settingsWithUnderscores = { + ...mockSettings, + field_schema: { + ...mockSettings.field_schema, + properties: { + ...mockSettings.field_schema.properties, + max_budget_per_user: { + type: "number", + description: "Max budget per user", + }, + }, + }, + values: { + ...mockSettings.values, + max_budget_per_user: 500, + }, + }; + mockGetDefaultTeamSettings.mockResolvedValue(settingsWithUnderscores); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Max Budget Per User")).toBeInTheDocument(); + }); + }); + + it("should display 'No schema information available' when schema is missing", async () => { + const settingsWithoutSchema = { + values: {}, + field_schema: null, + }; + mockGetDefaultTeamSettings.mockResolvedValue(settingsWithoutSchema); + + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("No schema information available")).toBeInTheDocument(); + }); }); }); diff --git a/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx b/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx index 8537b108cdc..33bfc783afd 100644 --- a/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx +++ b/ui/litellm-dashboard/src/components/TeamSSOSettings.tsx @@ -5,6 +5,7 @@ import { getDefaultTeamSettings, updateDefaultTeamSettings, modelAvailableCall } import BudgetDurationDropdown, { getBudgetDurationLabel } from "./common_components/budget_duration_dropdown"; import { getModelDisplayName } from "./key_team_helpers/fetch_available_models_team_key"; import NotificationsManager from "./molecules/notifications_manager"; +import { ModelSelect } from "./ModelSelect/ModelSelect"; interface TeamSSOSettingsProps { accessToken: string | null; @@ -116,22 +117,15 @@ const TeamSSOSettings: React.FC = ({ accessToken, userID, ); } else if (key === "models") { return ( - + context="global" + style={{ width: "100%" }} + options={{ + includeSpecialOptions: true, + }} + /> ); } else if (type === "string" && property.enum) { return ( diff --git a/ui/litellm-dashboard/src/components/UsageIndicator.test.tsx b/ui/litellm-dashboard/src/components/UsageIndicator.test.tsx new file mode 100644 index 00000000000..8c7c15bc5a5 --- /dev/null +++ b/ui/litellm-dashboard/src/components/UsageIndicator.test.tsx @@ -0,0 +1,187 @@ +import React from "react"; +import { describe, it, expect, vi, beforeEach } from "vitest"; +import { render, screen, waitFor } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import UsageIndicator from "./UsageIndicator"; + +vi.mock("./networking", () => ({ + getRemainingUsers: vi.fn(), + getLicenseInfo: vi.fn().mockResolvedValue(null), +})); + +vi.mock("@/app/(dashboard)/hooks/useDisableUsageIndicator", () => ({ + useDisableUsageIndicator: vi.fn(() => false), +})); + +import { getRemainingUsers } from "./networking"; + +const mockGetRemainingUsers = vi.mocked(getRemainingUsers); + +const DEFAULT_USAGE_DATA = { + total_users: 100, + total_users_used: 1, + total_users_remaining: 99, + total_teams: null, + total_teams_used: 0, + total_teams_remaining: null, +}; + +describe("UsageIndicator", () => { + beforeEach(() => { + vi.clearAllMocks(); + mockGetRemainingUsers.mockResolvedValue(DEFAULT_USAGE_DATA); + }); + + it("should render when given access token and usage data loads", async () => { + render(); + + await screen.findByText("Usage"); + + expect(screen.getByText("Usage")).toBeInTheDocument(); + }); + + it("should not show Near limit when users usage is below 80% (1/100 -> 1%)", async () => { + render(); + + await screen.findByText("Usage"); + + expect(screen.queryByText("Near limit")).not.toBeInTheDocument(); + }); + + it("should render nothing when both total_users and total_teams are null", async () => { + mockGetRemainingUsers.mockResolvedValue({ + total_users: null, + total_teams: null, + total_users_used: 520, + total_teams_used: 4, + total_teams_remaining: null, + total_users_remaining: null, + }); + + render(); + + await waitFor(() => { + expect(screen.queryByText("Usage")).not.toBeInTheDocument(); + expect(screen.queryByText("Loading...")).not.toBeInTheDocument(); + }); + }); + + it("should show Near limit for Teams when at 80% usage (4/5)", async () => { + mockGetRemainingUsers.mockResolvedValue({ + total_users: null, + total_users_used: 0, + total_users_remaining: null, + total_teams: 5, + total_teams_used: 4, + total_teams_remaining: 1, + }); + + render(); + + await screen.findByText("Usage"); + + expect(screen.getByText("Teams")).toBeInTheDocument(); + expect(screen.getByText("Near limit")).toBeInTheDocument(); + }); + + it("should show Over limit for Users when usage exceeds 100% (105/100)", async () => { + mockGetRemainingUsers.mockResolvedValue({ + total_users: 100, + total_users_used: 105, + total_users_remaining: -5, + total_teams: null, + total_teams_used: 0, + total_teams_remaining: null, + }); + + render(); + + await screen.findByText("Usage"); + + expect(screen.getByText("Users")).toBeInTheDocument(); + expect(screen.getByText("Over limit")).toBeInTheDocument(); + }); + + it("should show Over limit for Teams when usage exceeds 100%", async () => { + mockGetRemainingUsers.mockResolvedValue({ + total_users: null, + total_users_used: 0, + total_users_remaining: null, + total_teams: 10, + total_teams_used: 12, + total_teams_remaining: -2, + }); + + render(); + + await screen.findByText("Usage"); + + expect(screen.getByText("Teams")).toBeInTheDocument(); + expect(screen.getByText("Over limit")).toBeInTheDocument(); + }); + + it("should render nothing when accessToken is null", () => { + render(); + + expect(mockGetRemainingUsers).not.toHaveBeenCalled(); + expect(screen.queryByText("Usage")).not.toBeInTheDocument(); + }); + + it("should render nothing when disableUsageIndicator is true", async () => { + const { useDisableUsageIndicator } = await import("@/app/(dashboard)/hooks/useDisableUsageIndicator"); + (useDisableUsageIndicator as ReturnType).mockReturnValue(true); + + render(); + + await waitFor(() => { + expect(screen.queryByText("Usage")).not.toBeInTheDocument(); + }); + + (useDisableUsageIndicator as ReturnType).mockReturnValue(false); + }); + + it("should show Loading while fetching", () => { + mockGetRemainingUsers.mockImplementation(() => new Promise(() => {})); + + render(); + + expect(screen.getByText("Loading...")).toBeInTheDocument(); + }); + + it("should show error message when fetch fails", async () => { + const consoleSpy = vi.spyOn(console, "error").mockImplementation(() => {}); + mockGetRemainingUsers.mockRejectedValue(new Error("Network error")); + + render(); + + expect(await screen.findByText("Failed to load usage data")).toBeInTheDocument(); + + consoleSpy.mockRestore(); + }); + + it("should minimize when user clicks minimize button", async () => { + const user = userEvent.setup(); + render(); + + await screen.findByText("Usage"); + + const minimizeButton = screen.getByTitle("Minimize"); + await user.click(minimizeButton); + + expect(screen.queryByText("Users")).not.toBeInTheDocument(); + expect(screen.getByTitle("Show usage details")).toBeInTheDocument(); + }); + + it("should restore from minimized when user clicks restore button", async () => { + const user = userEvent.setup(); + render(); + + await screen.findByText("Usage"); + + await user.click(screen.getByTitle("Minimize")); + await user.click(screen.getByTitle("Show usage details")); + + expect(screen.getByText("Usage")).toBeInTheDocument(); + expect(screen.getByText("Users")).toBeInTheDocument(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/usage_indicator.tsx b/ui/litellm-dashboard/src/components/UsageIndicator.tsx similarity index 73% rename from ui/litellm-dashboard/src/components/usage_indicator.tsx rename to ui/litellm-dashboard/src/components/UsageIndicator.tsx index ed5e8e07555..47da9f78bdc 100644 --- a/ui/litellm-dashboard/src/components/usage_indicator.tsx +++ b/ui/litellm-dashboard/src/components/UsageIndicator.tsx @@ -1,7 +1,8 @@ +import { useDisableUsageIndicator } from "@/app/(dashboard)/hooks/useDisableUsageIndicator"; import { Badge } from "@tremor/react"; -import { AlertTriangle, ChevronDown, ChevronUp, Loader2, Minus, TrendingUp, UserCheck, Users } from "lucide-react"; +import { AlertTriangle, Calendar, ChevronDown, ChevronUp, Loader2, Minus, TrendingUp, UserCheck, Users } from "lucide-react"; import { useEffect, useState } from "react"; -import { getRemainingUsers } from "./networking"; +import { getRemainingUsers, getLicenseInfo, LicenseInfo } from "./networking"; // Simple utility function to combine class names const cn = (...classes: (string | boolean | undefined)[]) => { @@ -22,11 +23,35 @@ interface UsageData { total_teams_remaining: number | null; } +// Calculate days until expiration +const getDaysUntilExpiration = (expirationDate: string | null): number | null => { + if (!expirationDate) return null; + const expDate = new Date(expirationDate + 'T00:00:00Z'); // Force UTC midnight + const now = new Date(); + now.setHours(0, 0, 0, 0); // Normalize to local midnight + const diffTime = expDate.getTime() - now.getTime(); + const diffDays = Math.ceil(diffTime / (1000 * 60 * 60 * 24)); + return diffDays; +}; + +// Format expiration for display +const formatExpirationDisplay = (daysRemaining: number | null): string => { + if (daysRemaining === null) return "No expiration"; + if (daysRemaining < 0) return "Expired"; + if (daysRemaining === 0) return "Expires today"; + if (daysRemaining === 1) return "1 day remaining"; + if (daysRemaining < 30) return `${daysRemaining} days remaining`; + if (daysRemaining < 60) return "1 month remaining"; + const months = Math.floor(daysRemaining / 30); + return `${months} months remaining`; +}; + export default function UsageIndicator({ accessToken, width = 220 }: UsageIndicatorProps) { - const position = "bottom-left"; + const disableUsageIndicator = useDisableUsageIndicator(); const [isExpanded, setIsExpanded] = useState(false); const [isMinimized, setIsMinimized] = useState(false); const [data, setData] = useState(null); + const [licenseInfo, setLicenseInfo] = useState(null); const [isLoading, setIsLoading] = useState(false); const [error, setError] = useState(null); @@ -38,8 +63,12 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica setError(null); try { - const result = await getRemainingUsers(accessToken); - setData(result); + const [usageResult, licenseResult] = await Promise.all([ + getRemainingUsers(accessToken), + getLicenseInfo(accessToken).catch(() => null), // Don't fail if license endpoint unavailable + ]); + setData(usageResult); + setLicenseInfo(licenseResult); } catch (err) { console.error("Failed to fetch usage data:", err); setError("Failed to load usage data"); @@ -51,6 +80,13 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica fetchData(); }, [accessToken]); + // Calculate license expiration metrics + const daysUntilExpiration = licenseInfo?.expiration_date + ? getDaysUntilExpiration(licenseInfo.expiration_date) + : null; + const isLicenseExpired = daysUntilExpiration !== null && daysUntilExpiration < 0; + const isLicenseExpiringSoon = daysUntilExpiration !== null && daysUntilExpiration >= 0 && daysUntilExpiration < 30; + // Calculate derived values from data const getUsageMetrics = (data: UsageData | null) => { if (!data) { @@ -105,35 +141,38 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica const { isOverLimit, isNearLimit, usagePercentage, userMetrics, teamMetrics } = getUsageMetrics(data); + // Include license status in overall status + const hasAnyIssue = isOverLimit || isNearLimit || isLicenseExpired || isLicenseExpiringSoon; + const hasError = isOverLimit || isLicenseExpired; + const hasWarning = (isNearLimit || isLicenseExpiringSoon) && !hasError; + const getStatusColor = () => { - if (isOverLimit) return "red"; - if (isNearLimit) return "yellow"; + if (hasError) return "red"; + if (hasWarning) return "yellow"; return "green"; }; const getStatusIcon = () => { - if (isOverLimit) return ; - if (isNearLimit) return ; + if (hasError) return ; + if (hasWarning) return ; return null; }; // Minimized view - just a small restore button const MinimizedView = () => { - const hasIssues = isOverLimit || isNearLimit; - return (
@@ -197,13 +245,13 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica onClick={() => setIsExpanded(!isExpanded)} className={cn( "flex items-center gap-3 text-left hover:bg-gray-50 rounded-md px-0 py-1 transition-colors flex-1 min-w-0", - isOverLimit && "text-red-600", - isNearLimit && "text-yellow-600", + hasError && "text-red-600", + hasWarning && "text-yellow-600", )} > Usage Status - {(isOverLimit || isNearLimit) && ( + {hasAnyIssue && ( {getStatusIcon()} @@ -228,6 +276,28 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica {/* Expanded details - simple and compact */} {isExpanded && (
+ {/* License expiration section */} + {licenseInfo?.has_license && licenseInfo.expiration_date && ( +
+
+ + License +
+
+ {isLicenseExpired ? ( + + ) : isLicenseExpiringSoon ? ( + + ) : null} + {formatExpirationDisplay(daysUntilExpiration)} +
+
+ )} + {/* Users section */} {data.total_users !== null && (
@@ -322,7 +392,6 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica // Optimized CardStyleView for 220px width const CardStyleView = () => { if (isMinimized) { - const hasIssues = isOverLimit || isNearLimit; return ( @@ -415,6 +496,50 @@ export default function UsageIndicator({ accessToken, width = 220 }: UsageIndica {/* Compact stats optimized for 220px */}
+ {/* License expiration section */} + {licenseInfo?.has_license && licenseInfo.expiration_date && ( +
+
+ + License + + {isLicenseExpired ? "Expired" : isLicenseExpiringSoon ? "Expiring soon" : "OK"} + +
+
+ Status: + + {formatExpirationDisplay(daysUntilExpiration)} + +
+ {licenseInfo.license_type && ( +
+ Type: + {licenseInfo.license_type} +
+ )} +
+ )} + {/* Users section */} {data.total_users !== null && (
({ + ChartLoader: ({ isDateChanging }: { isDateChanging: boolean }) => ( +
+ {isDateChanging ? "Processing date selection..." : "Loading chart data..."} +
+ ), +})); + +vi.mock("../../../molecules/models/ProviderLogo", () => ({ + ProviderLogo: ({ provider }: { provider: string }) => ( +
{provider}
+ ), +})); + +describe("SpendByProvider", () => { + const mockProviderSpend = [ + { + provider: "openai", + spend: 150.5, + requests: 100, + successful_requests: 95, + failed_requests: 5, + tokens: 50000, + }, + { + provider: "anthropic", + spend: 200.75, + requests: 120, + successful_requests: 115, + failed_requests: 5, + tokens: 75000, + }, + { + provider: "unknown", + spend: 0, + requests: 10, + successful_requests: 0, + failed_requests: 10, + tokens: 0, + }, + { + provider: "google", + spend: 0, + requests: 0, + successful_requests: 0, + failed_requests: 0, + tokens: 0, + }, + ]; + + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("should render", () => { + render(); + expect(screen.getByText("Spend by Provider")).toBeInTheDocument(); + }); + + it("should display the title", () => { + render(); + expect(screen.getByText("Spend by Provider")).toBeInTheDocument(); + }); + + it("should display Show Zero Spend toggle", () => { + render(); + expect(screen.getByText("Show Zero Spend")).toBeInTheDocument(); + expect(screen.getAllByRole("switch")[0]).toBeInTheDocument(); + }); + + it("should display Show Unknown toggle", () => { + render(); + expect(screen.getByText("Show Unknown")).toBeInTheDocument(); + expect(screen.getAllByRole("switch")[1]).toBeInTheDocument(); + }); + + it("should display table headers", () => { + render(); + expect(screen.getByText("Provider")).toBeInTheDocument(); + expect(screen.getByText("Spend")).toBeInTheDocument(); + expect(screen.getByText("Successful")).toBeInTheDocument(); + expect(screen.getByText("Failed")).toBeInTheDocument(); + expect(screen.getByText("Tokens")).toBeInTheDocument(); + }); + + it("should display provider data in table", () => { + render(); + expect(screen.getAllByText("openai").length).toBeGreaterThan(0); + expect(screen.getAllByText("anthropic").length).toBeGreaterThan(0); + expect(screen.getByText("$150.50")).toBeInTheDocument(); + expect(screen.getByText("$200.75")).toBeInTheDocument(); + }); + + it("should display formatted spend values with two decimal places", () => { + render(); + expect(screen.getByText("$150.50")).toBeInTheDocument(); + expect(screen.getByText("$200.75")).toBeInTheDocument(); + }); + + it("should display successful requests with locale formatting", () => { + render(); + expect(screen.getByText("95")).toBeInTheDocument(); + expect(screen.getByText("115")).toBeInTheDocument(); + }); + + it("should display failed requests with locale formatting", () => { + render(); + expect(screen.getAllByText("5").length).toBeGreaterThan(0); + }); + + it("should display tokens with locale formatting", () => { + render(); + expect(screen.getByText("50,000")).toBeInTheDocument(); + expect(screen.getByText("75,000")).toBeInTheDocument(); + }); + + it("should display provider logos", () => { + render(); + expect(screen.getByTestId("provider-logo-openai")).toBeInTheDocument(); + expect(screen.getByTestId("provider-logo-anthropic")).toBeInTheDocument(); + }); + + it("should filter out providers with zero spend by default", () => { + render(); + expect(screen.getAllByText("openai").length).toBeGreaterThan(0); + expect(screen.getAllByText("anthropic").length).toBeGreaterThan(0); + expect(screen.queryByText("google")).not.toBeInTheDocument(); + }); + + it("should filter out unknown provider by default", () => { + render(); + expect(screen.queryByText("unknown")).not.toBeInTheDocument(); + }); + + it("should display ChartLoader when loading is true", () => { + render(); + expect(screen.getByTestId("chart-loader")).toBeInTheDocument(); + expect(screen.getByText("Loading chart data...")).toBeInTheDocument(); + }); + + it("should display ChartLoader with date changing message when isDateChanging is true", () => { + render(); + expect(screen.getByTestId("chart-loader")).toBeInTheDocument(); + expect(screen.getByText("Processing date selection...")).toBeInTheDocument(); + }); + + it("should not display table when loading is true", () => { + render(); + expect(screen.queryByText("Provider")).not.toBeInTheDocument(); + }); + + it("should handle empty provider spend array", () => { + render(); + expect(screen.getByText("Provider")).toBeInTheDocument(); + expect(screen.getByText("Spend")).toBeInTheDocument(); + }); + + it("should handle provider with null provider name", () => { + const providerSpendWithNull = [ + { + provider: null as unknown as string, + spend: 100, + requests: 50, + successful_requests: 45, + failed_requests: 5, + tokens: 25000, + }, + ]; + render(); + expect(screen.getByText("$100.00")).toBeInTheDocument(); + }); + + it("should handle provider with empty string provider name", () => { + const providerSpendWithEmpty = [ + { + provider: "", + spend: 100, + requests: 50, + successful_requests: 45, + failed_requests: 5, + tokens: 25000, + }, + ]; + render(); + expect(screen.getByText("$100.00")).toBeInTheDocument(); + }); + + it("should display large token numbers with comma formatting", () => { + const providerSpendWithLargeTokens = [ + { + provider: "openai", + spend: 1000, + requests: 1000, + successful_requests: 950, + failed_requests: 50, + tokens: 1234567, + }, + ]; + render(); + expect(screen.getByText("1,234,567")).toBeInTheDocument(); + }); + + it("should filter data correctly when both toggles are off", () => { + render(); + expect(screen.getAllByText("openai").length).toBeGreaterThan(0); + expect(screen.getAllByText("anthropic").length).toBeGreaterThan(0); + expect(screen.queryByText("google")).not.toBeInTheDocument(); + expect(screen.queryByText("unknown")).not.toBeInTheDocument(); + }); + + it("should include all providers with spend greater than zero by default", () => { + const providerSpendWithMixed = [ + { + provider: "provider1", + spend: 0.01, + requests: 10, + successful_requests: 10, + failed_requests: 0, + tokens: 1000, + }, + { + provider: "provider2", + spend: 0, + requests: 0, + successful_requests: 0, + failed_requests: 0, + tokens: 0, + }, + ]; + render(); + expect(screen.getAllByText("provider1").length).toBeGreaterThan(0); + expect(screen.queryByText("provider2")).not.toBeInTheDocument(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/SpendByProvider.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/SpendByProvider.tsx new file mode 100644 index 00000000000..4bbcc48dc4a --- /dev/null +++ b/ui/litellm-dashboard/src/components/UsagePage/components/EntityUsage/SpendByProvider.tsx @@ -0,0 +1,131 @@ +import { formatNumberWithCommas } from "@/utils/dataUtils"; +import { InfoCircleOutlined } from "@ant-design/icons"; +import { + Card, + Col, + DonutChart, + Grid, + Switch, + Table, + TableBody, + TableCell, + TableHead, + TableHeaderCell, + TableRow, + Title, +} from "@tremor/react"; +import { Tooltip } from "antd"; +import React, { useState } from "react"; +import { ProviderLogo } from "../../../molecules/models/ProviderLogo"; +import { ChartLoader } from "../../../shared/chart_loader"; + +interface ProviderSpendData { + provider: string; + spend: number; + requests: number; + successful_requests: number; + failed_requests: number; + tokens: number; +} + +interface SpendByProviderProps { + loading: boolean; + isDateChanging: boolean; + providerSpend: ProviderSpendData[]; +} + +const SpendByProvider: React.FC = ({ loading, isDateChanging, providerSpend }) => { + const [includeZeroSpend, setIncludeZeroSpend] = useState(false); + const [includeUnknown, setIncludeUnknown] = useState(false); + + const filteredProviderSpend = providerSpend.filter((provider) => { + const isUnknown = provider.provider?.toLowerCase() === "unknown"; + + // If includeUnknown is true, always include unknown provider + if (isUnknown) { + return includeUnknown; + } + + // If includeZeroSpend is true, include all providers (including those with 0 spend) + // Otherwise, only include providers with spend > 0 + if (includeZeroSpend) { + return true; + } + + return provider.spend > 0; + }); + + return ( + +
+ Spend by Provider +
+
+ + +
+
+
+ + + + +
+ +
+
+
+ {loading ? ( + + ) : ( + + + `$${formatNumberWithCommas(value, 2)}`} + colors={["cyan"]} + /> + + + + + + Provider + Spend + Successful + Failed + Tokens + + + + {filteredProviderSpend.map((provider) => ( + + +
+ {provider.provider && } + {provider.provider} +
+ + ${formatNumberWithCommas(provider.spend, 2)} + + {provider.successful_requests.toLocaleString()} + + + {provider.failed_requests.toLocaleString()} + + {provider.tokens.toLocaleString()} + + ))} + +
+ + + )} + + ); +}; + +export default SpendByProvider; diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx index 69a1bb4e305..1a344d3dd95 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx +++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.test.tsx @@ -1,20 +1,21 @@ import { useAgents } from "@/app/(dashboard)/hooks/agents/useAgents"; import { useCustomers } from "@/app/(dashboard)/hooks/customers/useCustomers"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import { useCurrentUser } from "@/app/(dashboard)/hooks/users/useCurrentUser"; import { act, fireEvent, screen, waitFor } from "@testing-library/react"; import { beforeAll, beforeEach, describe, expect, it, vi } from "vitest"; import { renderWithProviders } from "../../../../tests/test-utils"; import type { Organization } from "../../networking"; import * as networking from "../../networking"; -import NewUsagePage from "./UsagePageView"; +import UsagePage from "./UsagePageView"; // Polyfill ResizeObserver for test environment beforeAll(() => { if (typeof window !== "undefined" && !window.ResizeObserver) { window.ResizeObserver = class ResizeObserver { - observe() {} - unobserve() {} - disconnect() {} + observe() { } + unobserve() { } + disconnect() { } } as any; } }); @@ -45,6 +46,47 @@ vi.mock("./EntityUsage/EntityUsage", () => ({ EntityList: [], })); +vi.mock("./EntityUsage/SpendByProvider", () => ({ + default: () =>
Spend By Provider
, +})); + +vi.mock("./EndpointUsage/EndpointUsage", () => ({ + default: () =>
Endpoint Usage
, +})); + +vi.mock("./UsageViewSelect/UsageViewSelect", async () => { + const React = await import("react"); + const UsageViewSelect = ({ value, onChange }: any) => { + return React.createElement( + "select", + { + value, + onChange: (e: any) => onChange?.(e.target.value), + role: "combobox", + "data-testid": "usage-view-select", + }, + React.createElement("option", { value: "global" }, "Global Usage"), + React.createElement("option", { value: "team" }, "Team Usage"), + React.createElement("option", { value: "organization" }, "Organization Usage"), + React.createElement("option", { value: "customer" }, "Customer Usage"), + React.createElement("option", { value: "tag" }, "Tag Usage"), + React.createElement("option", { value: "agent" }, "Agent Usage"), + React.createElement("option", { value: "user-agent-activity" }, "User Agent Activity"), + ); + }; + UsageViewSelect.displayName = "UsageViewSelect"; + return { UsageViewSelect }; +}); + +vi.mock("../../shared/advanced_date_picker", async () => { + const React = await import("react"); + const AdvancedDatePicker = () => { + return React.createElement("div", { "data-testid": "advanced-date-picker" }, "Date Picker"); + }; + AdvancedDatePicker.displayName = "AdvancedDatePicker"; + return { default: AdvancedDatePicker }; +}); + vi.mock("../../user_agent_activity", () => ({ default: () =>
User Agent Activity
, })); @@ -70,6 +112,10 @@ vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ default: vi.fn(), })); +vi.mock("@/app/(dashboard)/hooks/users/useCurrentUser", () => ({ + useCurrentUser: vi.fn(), +})); + vi.mock("antd", async (importOriginal) => { const React = await import("react"); const actual = await importOriginal(); @@ -133,12 +179,40 @@ vi.mock("antd", async (importOriginal) => { } (Table as any).displayName = "Table"; + function Segmented(props: any) { + const { value, onChange, options, ...rest } = props; + return React.createElement( + "div", + { ...rest, "data-testid": "antd-segmented" }, + options?.map((opt: any) => + React.createElement( + "button", + { + key: opt.value, + onClick: () => onChange?.(opt.value), + "data-selected": value === opt.value, + }, + opt.label, + ), + ), + ); + } + (Segmented as any).displayName = "AntdSegmented"; + + function Tooltip(props: any) { + const { title, children, ...rest } = props; + return React.createElement("div", { ...rest, "data-testid": "antd-tooltip", title }, children); + } + (Tooltip as any).displayName = "AntdTooltip"; + return { ...actual, Select, Alert, Badge, Table, + Segmented, + Tooltip, }; }); @@ -160,15 +234,97 @@ vi.mock("@ant-design/icons", async () => { BarChartOutlined: Icon, ClockCircleOutlined: Icon, CalendarOutlined: Icon, + InfoCircleOutlined: Icon, }; }); -describe("NewUsage", () => { +// Mock Tremor components +vi.mock("@tremor/react", async () => { + const React = await import("react"); + const actual = await import("@tremor/react"); + + function TabGroup({ children }: any) { + return React.createElement("div", { "data-testid": "tremor-tab-group" }, children); + } + + function TabList({ children }: any) { + return React.createElement("div", { "data-testid": "tremor-tab-list" }, children); + } + + function Tab({ children, ...props }: any) { + return React.createElement("button", { ...props, "data-testid": "tremor-tab" }, children); + } + + function TabPanels({ children }: any) { + return React.createElement("div", { "data-testid": "tremor-tab-panels" }, children); + } + + function TabPanel({ children }: any) { + return React.createElement("div", { "data-testid": "tremor-tab-panel" }, children); + } + + function Card({ children, ...props }: any) { + return React.createElement("div", { ...props, "data-testid": "tremor-card" }, children); + } + + function Grid({ children, numItems, ...props }: any) { + return React.createElement("div", { ...props, "data-testid": "tremor-grid" }, children); + } + + function Col({ children, numColSpan, ...props }: any) { + return React.createElement("div", { ...props, "data-testid": "tremor-col" }, children); + } + + function Title({ children, ...props }: any) { + return React.createElement("h2", { ...props, "data-testid": "tremor-title" }, children); + } + + function Text({ children, ...props }: any) { + return React.createElement("p", { ...props, "data-testid": "tremor-text" }, children); + } + + function BarChart({ data, valueFormatter, yAxisWidth, showLegend, customTooltip, ...props }: any) { + return React.createElement("div", { ...props, "data-testid": "tremor-bar-chart" }, "Bar Chart"); + } + + function DonutChart({ data, ...props }: any) { + return React.createElement("div", { ...props, "data-testid": "tremor-donut-chart" }, "Donut Chart"); + } + + function Button({ children, icon, onClick, ...props }: any) { + return React.createElement( + "button", + { ...props, onClick, "data-testid": "tremor-button" }, + icon && React.createElement("span", { "data-testid": "tremor-button-icon" }), + children, + ); + } + + return { + ...actual, + TabGroup, + TabList, + Tab, + TabPanels, + TabPanel, + Card, + Grid, + Col, + Title, + Text, + BarChart, + DonutChart, + Button, + }; +}); + +describe("UsagePage", () => { const mockUserDailyActivityAggregatedCall = vi.mocked(networking.userDailyActivityAggregatedCall); const mockTagListCall = vi.mocked(networking.tagListCall); const mockUseCustomers = vi.mocked(useCustomers); const mockUseAgents = vi.mocked(useAgents); const mockUseAuthorized = vi.mocked(useAuthorized); + const mockUseCurrentUser = vi.mocked(useCurrentUser); const mockSpendData = { results: [ @@ -308,9 +464,6 @@ describe("NewUsage", () => { ]; const defaultProps = { - accessToken: "test-token", - userRole: "Admin", - userID: "user-123", teams: [ { team_id: "team-1", @@ -330,20 +483,27 @@ describe("NewUsage", () => { }, ], organizations: [], - premiumUser: true, }; beforeEach(() => { mockUseAuthorized.mockReturnValue({ token: "mock-token", - accessToken: defaultProps.accessToken, - userId: defaultProps.userID, + accessToken: "test-token", + userId: "user-123", userEmail: "test@example.com", - userRole: defaultProps.userRole, - premiumUser: defaultProps.premiumUser, + userRole: "Admin", + premiumUser: true, disabledPersonalKeyCreation: false, showSSOBanner: false, }); + mockUseCurrentUser.mockReturnValue({ + data: { + user_id: "user-123", + max_budget: null, + }, + isLoading: false, + error: null, + } as any); mockUserDailyActivityAggregatedCall.mockClear(); mockTagListCall.mockClear(); mockUserDailyActivityAggregatedCall.mockResolvedValue(mockSpendData); @@ -361,7 +521,7 @@ describe("NewUsage", () => { }); it("should render and fetch usage data on mount", async () => { - renderWithProviders(); + renderWithProviders(); // Wait for data to be fetched await waitFor(() => { @@ -380,7 +540,7 @@ describe("NewUsage", () => { }); it("should display usage metrics and charts", async () => { - renderWithProviders(); + renderWithProviders(); await waitFor(() => { expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); @@ -396,13 +556,13 @@ describe("NewUsage", () => { const totalTokensElements = screen.getAllByText("Total Tokens"); expect(totalTokensElements.length).toBeGreaterThan(0); - // Check for chart titles + // Check for chart titles (these are in the Cost tab) expect(screen.getByText("Daily Spend")).toBeInTheDocument(); expect(screen.getByText("Top Virtual Keys")).toBeInTheDocument(); }); it("should switch between usage views correctly", async () => { - renderWithProviders(); + renderWithProviders(); await waitFor(() => { expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); @@ -412,7 +572,7 @@ describe("NewUsage", () => { expect(screen.getByText("Daily Spend")).toBeInTheDocument(); // Switch to Team Usage view - const usageSelect = screen.getByRole("combobox"); + const usageSelect = screen.getByTestId("usage-view-select"); act(() => { fireEvent.change(usageSelect, { target: { value: "team" } }); }); @@ -436,13 +596,13 @@ describe("NewUsage", () => { }); it("should show organization usage banner and view for admins", async () => { - renderWithProviders(); + renderWithProviders(); await waitFor(() => { expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); }); - const usageSelect = screen.getByRole("combobox"); + const usageSelect = screen.getByTestId("usage-view-select"); act(() => { fireEvent.change(usageSelect, { target: { value: "organization" } }); }); @@ -461,13 +621,13 @@ describe("NewUsage", () => { error: null, } as any); - renderWithProviders(); + renderWithProviders(); await waitFor(() => { expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); }); - const usageSelect = screen.getByRole("combobox"); + const usageSelect = screen.getByTestId("usage-view-select"); act(() => { fireEvent.change(usageSelect, { target: { value: "customer" } }); }); @@ -485,13 +645,13 @@ describe("NewUsage", () => { error: null, } as any); - renderWithProviders(); + renderWithProviders(); await waitFor(() => { expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); }); - const usageSelect = screen.getByRole("combobox"); + const usageSelect = screen.getByTestId("usage-view-select"); act(() => { fireEvent.change(usageSelect, { target: { value: "agent" } }); }); diff --git a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx index 88385248b6e..688ee73767f 100644 --- a/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx +++ b/ui/litellm-dashboard/src/components/UsagePage/components/UsagePageView.tsx @@ -6,28 +6,22 @@ * Works at 1m+ spend logs, by querying an aggregate table instead. */ +import { InfoCircleOutlined } from "@ant-design/icons"; import { BarChart, Card, Col, DateRangePickerValue, - DonutChart, Grid, Tab, TabGroup, - Table, - TableBody, - TableCell, - TableHead, - TableHeaderCell, - TableRow, TabList, TabPanel, TabPanels, Text, - Title, + Title } from "@tremor/react"; -import { Alert, Segmented } from "antd"; +import { Alert, Segmented, Tooltip } from "antd"; import React, { useCallback, useEffect, useMemo, useState } from "react"; import { useAgents } from "@/app/(dashboard)/hooks/agents/useAgents"; @@ -42,7 +36,6 @@ import CloudZeroExportModal from "../../cloudzero_export_modal"; import EntityUsageExportModal from "../../EntityUsageExport"; import { Team } from "../../key_team_helpers/key_list"; import { Organization, tagListCall, userDailyActivityAggregatedCall, userDailyActivityCall } from "../../networking"; -import { getProviderLogoAndName } from "../../provider_info_helpers"; import AdvancedDatePicker from "../../shared/advanced_date_picker"; import { ChartLoader } from "../../shared/chart_loader"; import { Tag } from "../../tag_management/types"; @@ -52,6 +45,7 @@ import { DailyData, KeyMetricWithMetadata, MetricWithMetadata } from "../types"; import { valueFormatterSpend } from "../utils/value_formatters"; import EndpointUsage from "./EndpointUsage/EndpointUsage"; import EntityUsage, { EntityList } from "./EntityUsage/EntityUsage"; +import SpendByProvider from "./EntityUsage/SpendByProvider"; import TopKeyView from "./EntityUsage/TopKeyView"; import { UsageOption, UsageViewSelect } from "./UsageViewSelect/UsageViewSelect"; @@ -509,7 +503,12 @@ const UsagePage: React.FC = ({ teams, organizations }) => { - Failed Requests +
+ Failed Requests + + + +
{userSpendData.metadata?.total_failed_requests?.toLocaleString() || 0} @@ -669,79 +668,11 @@ const UsagePage: React.FC = ({ teams, organizations }) => { {/* Spend by Provider */} - -
- Spend by Provider -
- {loading ? ( - - ) : ( - - - `$${formatNumberWithCommas(value, 2)}`} - colors={["cyan"]} - /> - - - - - - Provider - Spend - Successful - Failed - Tokens - - - - {getProviderSpend() - .filter((provider) => provider.spend > 0) - .map((provider) => ( - - -
- {provider.provider && ( - {`${provider.provider} { - const target = e.target as HTMLImageElement; - const parent = target.parentElement; - if (parent) { - const fallbackDiv = document.createElement("div"); - fallbackDiv.className = - "w-4 h-4 rounded-full bg-gray-200 flex items-center justify-center text-xs"; - fallbackDiv.textContent = provider.provider?.charAt(0) || "-"; - parent.replaceChild(fallbackDiv, target); - } - }} - /> - )} - {provider.provider} -
- - ${formatNumberWithCommas(provider.spend, 2)} - - {provider.successful_requests.toLocaleString()} - - - {provider.failed_requests.toLocaleString()} - - {provider.tokens.toLocaleString()} - - ))} - -
- - - )} - + {/* Usage Metrics */} diff --git a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx index ef5bb2a0371..749396c82f3 100644 --- a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx +++ b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.test.tsx @@ -542,3 +542,86 @@ it("should display 'Default Proxy Admin' for created_by when value is 'default_u expect(defaultProxyAdminElements.length).toBeGreaterThan(0); }); }); + + +it("should render table without crashing when models is null", async () => { + const keyWithNullModels = { + ...mockKey, + models: null as unknown as string[], + }; + + mockUseFilterLogic.mockReturnValue({ + filters: { + "Team ID": "", + "Organization ID": "", + "Key Alias": "", + "User ID": "", + "Sort By": "created_at", + "Sort Order": "desc", + }, + filteredKeys: [keyWithNullModels], + allKeyAliases: ["test-key-alias"], + allTeams: [mockTeam], + allOrganizations: [mockOrganization], + handleFilterChange: vi.fn(), + handleFilterReset: vi.fn(), + }); + + const mockProps = { + teams: [mockTeam], + organizations: [mockOrganization], + onSortChange: vi.fn(), + currentSort: { + sortBy: "created_at", + sortOrder: "desc" as const, + }, + }; + + // This should not throw an error + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Test Key Alias")).toBeInTheDocument(); + }); +}); + +it("should render table without crashing when models is undefined", async () => { + const keyWithUndefinedModels = { + ...mockKey, + models: undefined as unknown as string[], + }; + + mockUseFilterLogic.mockReturnValue({ + filters: { + "Team ID": "", + "Organization ID": "", + "Key Alias": "", + "User ID": "", + "Sort By": "created_at", + "Sort Order": "desc", + }, + filteredKeys: [keyWithUndefinedModels], + allKeyAliases: ["test-key-alias"], + allTeams: [mockTeam], + allOrganizations: [mockOrganization], + handleFilterChange: vi.fn(), + handleFilterReset: vi.fn(), + }); + + const mockProps = { + teams: [mockTeam], + organizations: [mockOrganization], + onSortChange: vi.fn(), + currentSort: { + sortBy: "created_at", + sortOrder: "desc" as const, + }, + }; + + // This should not throw an error + renderWithProviders(); + + await waitFor(() => { + expect(screen.getByText("Test Key Alias")).toBeInTheDocument(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx index 465b9b8fbe0..f7c47943e7e 100644 --- a/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx +++ b/ui/litellm-dashboard/src/components/VirtualKeysPage/VirtualKeysTable.tsx @@ -727,7 +727,7 @@ export function VirtualKeysTable({ teams, organizations, onSortChange, currentSo whiteSpace: "pre-wrap", overflow: "hidden", }} - className={`py-0.5 max-h-8 overflow-hidden text-ellipsis whitespace-nowrap ${cell.column.id === "models" && (cell.getValue() as string[]).length > 3 ? "px-0" : ""}`} + className={`py-0.5 max-h-8 overflow-hidden text-ellipsis whitespace-nowrap ${cell.column.id === "models" && Array.isArray(cell.getValue()) && (cell.getValue() as string[]).length > 3 ? "px-0" : ""}`} > {flexRender(cell.column.columnDef.cell, cell.getContext())} diff --git a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx index 02738a46666..308b4b395f7 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.test.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.test.tsx @@ -34,8 +34,25 @@ vi.mock("antd", () => { ); PanelComponent.displayName = "Collapse.Panel"; CollapseComponent.Panel = PanelComponent; + const TableComponent = ({ dataSource, columns }: { dataSource?: unknown[]; columns?: { title: string }[] }) => ( + + + + {columns?.map((col, i) => ( + + ))} + + + + {dataSource?.map((_, i) => ( + + ))} + +
{col.title}
+ ); return { Collapse: CollapseComponent, + Table: TableComponent, }; }); @@ -95,6 +112,7 @@ const MOCK_TEAMS: Team[] = [ created_at: "2025-01-01", keys: [], members_with_roles: [], + spend: 0, }, { team_id: "team2", @@ -108,6 +126,7 @@ const MOCK_TEAMS: Team[] = [ created_at: "2025-01-01", keys: [], members_with_roles: [], + spend: 0, }, ]; @@ -141,6 +160,7 @@ const createMockModelActivityData = (label: string, overrides: Partial { expect(tokenElements.length).toBeGreaterThan(0); }); + it("should not display Top Virtual Keys section when model has no top_api_keys", () => { + render(); + expect(screen.queryByText("Top Virtual Keys by Spend")).not.toBeInTheDocument(); + }); + it("should display top API keys section when present", () => { const modelWithTopKeys: Record = { "gpt-4": { @@ -329,6 +355,58 @@ describe("ActivityMetrics", () => { expect(screen.getByText(/Team: team1/)).toBeInTheDocument(); }); + it("should display Model Usage when model has top_models", () => { + const modelWithTopModels: Record = { + "gpt-4": { + ...mockModelMetrics["gpt-4"], + top_models: [ + { + model: "gpt-4", + spend: 100.5, + requests: 100, + successful_requests: 95, + failed_requests: 5, + tokens: 50000, + }, + ], + }, + }; + + render(); + expect(screen.getByRole("heading", { name: "Model Usage" })).toBeInTheDocument(); + }); + + it("should display Spend per day in model section", () => { + render(); + expect(screen.getByText("Spend per day")).toBeInTheDocument(); + }); + + it("should display Requests per day in model section", () => { + render(); + expect(screen.getByText("Requests per day")).toBeInTheDocument(); + }); + + it("should display Success vs Failed Requests in model section", () => { + render(); + expect(screen.getByText("Success vs Failed Requests")).toBeInTheDocument(); + }); + + it("should sort empty string model key last in collapse order", () => { + const modelsWithEmptyKey: Record = { + "gpt-4": { ...mockModelMetrics["gpt-4"] }, + "": { + ...createMockModelActivityData(""), + label: "Unknown", + }, + }; + + render(); + const headings = screen.getAllByRole("heading", { level: 2 }); + const gpt4Index = headings.findIndex((h) => h.textContent?.includes("GPT-4")); + const unknownIndex = headings.findIndex((h) => h.textContent?.includes("Unknown")); + expect(gpt4Index).toBeLessThan(unknownIndex); + }); + it("should display average tokens per successful request", () => { render(); const avgTokensElements = screen.getAllByText(/avg per successful request/); @@ -962,6 +1040,73 @@ describe("processActivityData", () => { expect(result["gpt-4"].top_api_keys[4].spend).toBe(16.0); }); + it("should return empty object when results array is empty", () => { + const result = processActivityData({ results: [] }, "models"); + expect(result).toEqual({}); + }); + + it("should populate top_models for api_keys when models breakdown contains api_key_breakdown for that key", () => { + const dailyActivityWithModelsForKey: { results: DailyData[] } = { + results: [ + { + date: "2025-01-01", + metrics: EMPTY_SPEND_METRICS, + breakdown: { + models: { + "gpt-4": { + metrics: EMPTY_SPEND_METRICS, + metadata: {}, + api_key_breakdown: { + "api-key-hash-1": { + metrics: { + spend: 60.0, + prompt_tokens: 18000, + completion_tokens: 12000, + total_tokens: 30000, + api_requests: 60, + successful_requests: 57, + failed_requests: 3, + cache_read_input_tokens: 0, + cache_creation_input_tokens: 0, + }, + metadata: { key_alias: "key-alias-1", team_id: "team1" }, + }, + }, + }, + }, + model_groups: {}, + mcp_servers: {}, + providers: {}, + api_keys: { + "api-key-hash-1": { + metrics: { + spend: 60.0, + prompt_tokens: 18000, + completion_tokens: 12000, + total_tokens: 30000, + api_requests: 60, + successful_requests: 57, + failed_requests: 3, + cache_read_input_tokens: 0, + cache_creation_input_tokens: 0, + }, + metadata: { key_alias: "key-alias-1", team_id: "team1" }, + }, + }, + entities: {}, + }, + }, + ], + }; + + const result = processActivityData(dailyActivityWithModelsForKey, "api_keys", MOCK_TEAMS); + + expect(result["api-key-hash-1"].top_models).toHaveLength(1); + expect(result["api-key-hash-1"].top_models[0].model).toBe("gpt-4"); + expect(result["api-key-hash-1"].top_models[0].spend).toBe(60.0); + expect(result["api-key-hash-1"].top_models[0].requests).toBe(60); + }); + it("should not process api_key_breakdown when key is api_keys", () => { const dailyActivityWithBreakdown: { results: DailyData[] } = { results: [ @@ -1130,4 +1275,14 @@ describe("formatKeyLabel", () => { const result = formatKeyLabel(modelData, "actual-key", MOCK_TEAMS); expect(result).toBe("key-hash-actual-key (team: Test Team 1)"); }); + + it("should return key_alias with team_id when teams array is empty", () => { + const modelData = createMockKeyMetricWithMetadata({ + key_alias: "my-key", + team_id: "team1", + }); + + const result = formatKeyLabel(modelData, "my-key", []); + expect(result).toBe("my-key (team_id: team1)"); + }); }); diff --git a/ui/litellm-dashboard/src/components/activity_metrics.tsx b/ui/litellm-dashboard/src/components/activity_metrics.tsx index 61006fa6147..2337d41d8df 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.tsx @@ -148,7 +148,6 @@ const ModelSection = ({ categories={["metrics.successful_requests", "metrics.failed_requests"]} colors={["green", "red"]} valueFormatter={valueFormatter} - stack customTooltip={CustomTooltip} showLegend={false} /> diff --git a/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.test.tsx b/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.test.tsx new file mode 100644 index 00000000000..fb2f15c621a --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.test.tsx @@ -0,0 +1,296 @@ +import { render, screen, waitFor, fireEvent } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { describe, expect, it, vi } from "vitest"; +import RouterConfigBuilder from "./RouterConfigBuilder"; + +const MOCK_MODEL_INFO = [ + { model_group: "gpt-4", mode: "chat" }, + { model_group: "gpt-3.5-turbo", mode: "chat" }, + { model_group: "claude-3-opus", mode: "chat" }, +]; + +describe("RouterConfigBuilder", () => { + it("should render", () => { + render(); + + expect(screen.getByText("Routes Configuration")).toBeInTheDocument(); + }); + + it("should display Add Route button", () => { + render(); + + expect(screen.getByRole("button", { name: /add route/i })).toBeInTheDocument(); + }); + + it("should show empty state when no routes are configured", () => { + render(); + + expect(screen.getByText(/no routes configured/i)).toBeInTheDocument(); + }); + + it("should add a route when Add Route is clicked", async () => { + const user = userEvent.setup(); + render(); + + await user.click(screen.getByRole("button", { name: /add route/i })); + + expect(screen.getByText("Route 1: Unnamed")).toBeInTheDocument(); + }); + + it("should call onChange when a route is added", async () => { + const user = userEvent.setup(); + const onChange = vi.fn(); + render(); + + await user.click(screen.getByRole("button", { name: /add route/i })); + + expect(onChange).toHaveBeenCalledWith({ + routes: [ + expect.objectContaining({ + name: "", + utterances: [], + description: "", + score_threshold: 0.5, + }), + ], + }); + }); + + it("should initialize routes from value prop", async () => { + const value = { + routes: [ + { + name: "gpt-4", + utterances: ["hello", "hi"], + description: "For greetings", + score_threshold: 0.7, + }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + }); + + it("should support both name and model fields in value prop", async () => { + const value = { + routes: [{ model: "gpt-3.5-turbo", utterances: [], description: "", score_threshold: 0.5 }], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-3.5-turbo")).toBeInTheDocument(); + }); + }); + + it("should remove a route when delete button is clicked", async () => { + const user = userEvent.setup(); + const value = { + routes: [ + { + name: "gpt-4", + utterances: [], + description: "", + score_threshold: 0.5, + }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + const deleteButton = screen.getByRole("button", { name: "delete" }); + await user.click(deleteButton); + + await waitFor(() => { + expect(screen.queryByText("Route 1: gpt-4")).not.toBeInTheDocument(); + expect(screen.getByText(/no routes configured/i)).toBeInTheDocument(); + }); + }); + + it("should call onChange when route is removed", async () => { + const user = userEvent.setup(); + const onChange = vi.fn(); + const value = { + routes: [ + { + name: "gpt-4", + utterances: [], + description: "", + score_threshold: 0.5, + }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + const deleteButton = screen.getByRole("button", { name: "delete" }); + await user.click(deleteButton); + + await waitFor(() => { + expect(onChange).toHaveBeenCalledWith({ routes: [] }); + }); + }); + + + + + it("should update route when description is changed", async () => { + const user = userEvent.setup(); + const onChange = vi.fn(); + const value = { + routes: [ + { + name: "gpt-4", + utterances: [], + description: "", + score_threshold: 0.5, + }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + const descriptionInput = screen.getByPlaceholderText("Describe when this route should be used..."); + await user.type(descriptionInput, "For code generation"); + + await waitFor(() => { + const lastCall = onChange.mock.calls[onChange.mock.calls.length - 1]; + expect(lastCall[0].routes[0].description).toBe("For code generation"); + }); + }); + + it("should update route when score threshold is changed", async () => { + const onChange = vi.fn(); + const value = { + routes: [ + { + name: "gpt-4", + utterances: [], + description: "", + score_threshold: 0.5, + }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + const scoreInput = screen.getByRole("spinbutton"); + fireEvent.change(scoreInput, { target: { value: "0.9" } }); + + await waitFor(() => { + const lastCall = onChange.mock.calls[onChange.mock.calls.length - 1]; + expect(lastCall[0].routes[0].score_threshold).toBe(0.9); + }); + }); + + it("should add multiple routes", async () => { + const user = userEvent.setup(); + render(); + + await user.click(screen.getByRole("button", { name: /add route/i })); + await user.click(screen.getByRole("button", { name: /add route/i })); + + expect(screen.getByText("Route 1: Unnamed")).toBeInTheDocument(); + expect(screen.getByText("Route 2: Unnamed")).toBeInTheDocument(); + }); + + it("should toggle JSON preview visibility", async () => { + const user = userEvent.setup(); + const { container } = render(); + + expect(screen.getByText("JSON Preview")).toBeInTheDocument(); + expect(screen.getByRole("button", { name: "Show" })).toBeInTheDocument(); + expect(container.querySelector("pre")).not.toBeInTheDocument(); + + await user.click(screen.getByRole("button", { name: "Show" })); + + expect(screen.getByRole("button", { name: "Hide" })).toBeInTheDocument(); + expect(container.querySelector("pre")).toBeInTheDocument(); + + await user.click(screen.getByRole("button", { name: "Hide" })); + + expect(screen.getByRole("button", { name: "Show" })).toBeInTheDocument(); + expect(container.querySelector("pre")).not.toBeInTheDocument(); + }); + + it("should display JSON preview with route data when routes exist", async () => { + const user = userEvent.setup(); + const { container } = render( + , + ); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + await user.click(screen.getByRole("button", { name: "Show" })); + + const preElement = container.querySelector("pre"); + expect(preElement).toBeInTheDocument(); + expect(preElement?.textContent).toContain("gpt-4"); + expect(preElement?.textContent).toContain("hello"); + expect(preElement?.textContent).toContain("0.8"); + }); + + it("should display model selector with options from modelInfo", async () => { + const value = { + routes: [ + { name: "", utterances: [], description: "", score_threshold: 0.5 }, + ], + }; + render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: Unnamed")).toBeInTheDocument(); + }); + + expect(screen.getByText("Model")).toBeInTheDocument(); + const comboboxes = screen.getAllByRole("combobox"); + expect(comboboxes.length).toBeGreaterThan(0); + }); + + it("should clear routes when value prop changes to empty", async () => { + const value = { + routes: [ + { + name: "gpt-4", + utterances: [], + description: "", + score_threshold: 0.5, + }, + ], + }; + const { rerender } = render(); + + await waitFor(() => { + expect(screen.getByText("Route 1: gpt-4")).toBeInTheDocument(); + }); + + rerender(); + + await waitFor(() => { + expect(screen.getByText(/no routes configured/i)).toBeInTheDocument(); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.tsx b/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.tsx new file mode 100644 index 00000000000..9b93287b36d --- /dev/null +++ b/ui/litellm-dashboard/src/components/add_model/RouterConfigBuilder.tsx @@ -0,0 +1,277 @@ +import { DeleteOutlined, InfoCircleOutlined, PlusOutlined } from "@ant-design/icons"; +import { Select as AntdSelect, Button, Card, Collapse, Divider, Empty, Flex, Input, InputNumber, Space, Tooltip, Typography } from "antd"; +import React, { useEffect, useState } from "react"; +import { ModelGroup } from "../playground/llm_calls/fetch_models"; + +const { Text } = Typography; + +const { TextArea } = Input; + +interface Route { + id: string; + model: string; + utterances: string[]; + description: string; + score_threshold: number; +} + +interface SavedRoute { + id?: string; + name?: string; + model?: string; + utterances?: string[]; + description?: string; + score_threshold?: number; +} + +interface RouterConfig { + routes?: SavedRoute[]; +} + +interface RouterConfigBuilderProps { + modelInfo: ModelGroup[]; + value?: RouterConfig; + onChange?: (config: any) => void; +} + +const RouterConfigBuilder: React.FC = ({ modelInfo, value, onChange }) => { + const [routes, setRoutes] = useState([]); + const [showJsonPreview, setShowJsonPreview] = useState(false); + const [expandedRoutes, setExpandedRoutes] = useState([]); + + // Initialize routes from value prop - preserve existing route IDs to avoid focus loss when parent re-renders + useEffect(() => { + const routesFromValue = value?.routes; + if (routesFromValue) { + const routeIds: string[] = []; + setRoutes((prevRoutes) => { + const initializedRoutes = routesFromValue.map((route: SavedRoute, index: number) => { + const existingRoute = prevRoutes[index]; + const id = existingRoute?.id || route.id || `route-${index}-${Date.now()}`; + routeIds.push(id); + return { + id, + model: route.name || route.model || "", // handle both 'name' and 'model' fields + utterances: route.utterances || [], + description: route.description || "", + score_threshold: route.score_threshold ?? 0.5, + }; + }); + return initializedRoutes; + }); + setExpandedRoutes(routeIds); + } else { + setRoutes([]); + setExpandedRoutes([]); + } + }, [value]); + + // Handle adding a new route + const addRoute = () => { + const newRouteId = `route-${Date.now()}`; + const newRoute: Route = { + id: newRouteId, + model: "", + utterances: [], + description: "", + score_threshold: 0.5, + }; + const updatedRoutes = [...routes, newRoute]; + setRoutes(updatedRoutes); + updateConfig(updatedRoutes); + // Automatically expand the new route + setExpandedRoutes((prev) => [...prev, newRouteId]); + }; + + // Handle removing a route + const removeRoute = (routeId: string) => { + const updatedRoutes = routes.filter((route) => route.id !== routeId); + setRoutes(updatedRoutes); + updateConfig(updatedRoutes); + // Remove from expanded routes as well + setExpandedRoutes((prev) => prev.filter((id) => id !== routeId)); + }; + + // Handle updating a route + const updateRoute = (routeId: string, field: keyof Route, value: any) => { + const updatedRoutes = routes.map((route) => (route.id === routeId ? { ...route, [field]: value } : route)); + setRoutes(updatedRoutes); + updateConfig(updatedRoutes); + }; + + // Update the overall configuration + const updateConfig = (updatedRoutes: Route[]) => { + const config = { + routes: updatedRoutes.map((route) => ({ + name: route.model, + utterances: route.utterances, + description: route.description, + score_threshold: route.score_threshold, + })), + }; + onChange?.(config); + }; + + // Handle utterances change (convert textarea string to array) + const handleUtterancesChange = (routeId: string, utterancesText: string) => { + const utterancesArray = utterancesText + .split("\n") + .map((line) => line.trim()) // Only trims leading/trailing whitespace, preserves internal spaces + .filter((line) => line.length > 0); + updateRoute(routeId, "utterances", utterancesArray); + }; + + // Prepare model options for dropdowns + const modelOptions = modelInfo.map((model) => ({ + value: model.model_group, + label: model.model_group, + })); + + const generateConfig = () => { + return { + routes: routes.map((route) => ({ + name: route.model, + utterances: route.utterances, + description: route.description, + score_threshold: route.score_threshold, + })), + }; + }; + + return ( +
+ + + Routes Configuration + + + + + + + + {/* Routes */} + {routes.length === 0 ? ( + + + + ) : ( + setExpandedRoutes(Array.isArray(keys) ? keys : [keys].filter(Boolean))} + style={{ width: "100%" }} + items={routes.map((route, index) => ({ + key: route.id, + label: ( + + Route {index + 1}: {route.model || "Unnamed"} + + ), + extra: ( +