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Code Issues Projects Releases Packages Wiki Activity Actions 36

Merge pull request #26381 from BerriAI/litellm_internal_staging

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merge main
This commit is contained in:
Sameer Kankute 2026-04-24 09:22:28 +05:30 • committed by GitHub
commit 3c1b27e155
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2209 changed files with 88418 additions and 46097 deletions
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1844
.circleci/config.yml
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40
.github/scripts/close_duplicate_issues.py vendored
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@ -42,7 +42,9 @@ def gh(*args: str) -> str:
def fetch_open_issues(repo: str | None) -> list[dict]:
"""Fetch all open issues (excluding PRs) via gh api --paginate."""
if repo:
endpoint = f"repos/{repo}/issues?state=open&per_page=100&sort=created&direction=asc"
endpoint = (
f"repos/{repo}/issues?state=open&per_page=100&sort=created&direction=asc"
)
else:
endpoint = "repos/{owner}/{repo}/issues?state=open&per_page=100&sort=created&direction=asc"
cmd = ["api", "--paginate", endpoint]
@ -71,7 +73,9 @@ def close_as_duplicate(
repo_args = ["--repo", repo] if repo else []
if dry_run:
print(f" [DRY RUN] Would close #{issue_number} as duplicate of #{duplicate_of}")
print(
f" [DRY RUN] Would close #{issue_number} as duplicate of #{duplicate_of}"
)
return
# Add comment
@ -115,7 +119,9 @@ def find_duplicate(
return None
def scan_all(issues: list[dict], threshold: float, repo: str | None, dry_run: bool) -> int:
def scan_all(
issues: list[dict], threshold: float, repo: str | None, dry_run: bool
) -> int:
"""Compare every issue against all older issues. Returns count of duplicates found."""
# Sort oldest first
issues.sort(key=lambda i: i["number"])
@ -144,7 +150,11 @@ def scan_all(issues: list[dict], threshold: float, repo: str | None, dry_run: bo
def check_single(
issue_number: int, issues: list[dict], threshold: float, repo: str | None, dry_run: bool
issue_number: int,
issues: list[dict],
threshold: float,
repo: str | None,
dry_run: bool,
) -> bool:
"""Check a single issue against all older open issues. Returns True if duplicate found."""
target = None
@ -178,13 +188,23 @@ def check_single(
def main() -> None:
parser = argparse.ArgumentParser(description="Detect and close duplicate GitHub issues")
parser = argparse.ArgumentParser(
description="Detect and close duplicate GitHub issues"
)
mode = parser.add_mutually_exclusive_group(required=True)
mode.add_argument("--scan", action="store_true", help="Scan all open issues")
mode.add_argument("--issue-number", type=int, help="Check a single issue number")
parser.add_argument("--threshold", type=float, default=0.85, help="Similarity threshold (0-1)")
parser.add_argument("--close", action="store_true", help="Actually close duplicates (default is dry-run)")
parser.add_argument("--repo", type=str, help="Repository (owner/repo). Auto-detected if omitted.")
parser.add_argument(
"--threshold", type=float, default=0.85, help="Similarity threshold (0-1)"
)
parser.add_argument(
"--close",
action="store_true",
help="Actually close duplicates (default is dry-run)",
)
parser.add_argument(
"--repo", type=str, help="Repository (owner/repo). Auto-detected if omitted."
)
args = parser.parse_args()
dry_run = not args.close
@ -200,7 +220,9 @@ def main() -> None:
count = scan_all(issues, args.threshold, args.repo, dry_run)
print(f"\nTotal duplicates {'found' if dry_run else 'closed'}: {count}")
else:
found = check_single(args.issue_number, issues, args.threshold, args.repo, dry_run)
found = check_single(
args.issue_number, issues, args.threshold, args.repo, dry_run
)
sys.exit(0 if found else 0) # Always exit 0; finding no dup is not an error

20
.github/scripts/scan_keywords.py vendored
View file

@ -67,14 +67,13 @@ def send_webhook(webhook_url: str, payload: dict) -> None:
def _excerpt(text: str, max_len: int = 400) -> str:
if not text:
return ""
# Keep original formatting
if len(text) <= max_len:
return text
return text[: max_len - 1] + "…"
def main() -> int:
event = read_event_payload()
if not event:
@ -87,8 +86,19 @@ def main() -> int:
# Keywords from env or defaults
keywords_env = os.environ.get("KEYWORDS", "")
default_keywords = ["azure", "openai", "bedrock", "vertexai", "vertex ai", "anthropic"]
keywords = [k.strip() for k in keywords_env.split(",")] if keywords_env else default_keywords
default_keywords = [
"azure",
"openai",
"bedrock",
"vertexai",
"vertex ai",
"anthropic",
]
keywords = (
[k.strip() for k in keywords_env.split(",")]
if keywords_env
else default_keywords
)
matches = detect_keywords(combined_text, keywords)
found = bool(matches)
@ -129,5 +139,3 @@ def main() -> int:
if __name__ == "__main__":
raise SystemExit(main())

37
.github/workflows/_test-unit-services-base.yml vendored
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@ -32,41 +32,39 @@ on:
required: false
type: boolean
default: false
dist:
description: "pytest-xdist distribution mode (loadscope|load|worksteal|loadfile|no)"
required: false
type: string
default: "loadscope"
artifact-name:
description: "Unique name for the coverage artifact (must be unique per run)"
required: false
type: string
default: "run"
secrets:
DATABASE_URL:
required: false
POSTGRES_USER:
required: false
POSTGRES_PASSWORD:
required: false
permissions:
contents: read
# The postgres service container below is spawned per-job on localhost and
# destroyed with the job. Nothing outside the runner can reach it. The
# user/password/database here are not secrets — they're bootstrap values
# for a throwaway container — so we hardcode them instead of attaching
# every matrix shard to a GHA environment just to read three "secrets"
# (which also produces a "temporarily deployed to …" notification on the
# PR timeline per shard per push).
jobs:
run:
name: Run tests
runs-on: ubuntu-latest
timeout-minutes: ${{ inputs.timeout-minutes }}
# Environment is derived from the enable-* flags, not caller-controllable.
# This prevents callers from passing arbitrary environment names to bypass secret scoping.
environment: >-
${{
inputs.enable-postgres && 'integration-postgres' ||
''
}}
services:
postgres:
image: postgres@sha256:705a5d5b5836f3fcba0d02c4d281e6a7dd9ed2dd4078640f08a1e1e9896e097d # postgres:14
env:
POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}
POSTGRES_USER: litellm
POSTGRES_PASSWORD: litellm
POSTGRES_DB: litellm_test
ports:
- 5432:5432
@ -114,7 +112,7 @@ jobs:
- name: Run Prisma migrations
if: ${{ inputs.enable-postgres }}
env:
DATABASE_URL: ${{ secrets.DATABASE_URL }}
DATABASE_URL: "postgresql://litellm:litellm@localhost:5432/litellm_test"
run: |
uv run --no-sync prisma db push --schema litellm/proxy/schema.prisma --accept-data-loss
@ -124,7 +122,8 @@ jobs:
MAX_FAILURES: ${{ inputs.max-failures }}
WORKERS: ${{ inputs.workers }}
RERUNS: ${{ inputs.reruns }}
DATABASE_URL: ${{ inputs.enable-postgres && secrets.DATABASE_URL || '' }}
DIST: ${{ inputs.dist }}
DATABASE_URL: ${{ inputs.enable-postgres && 'postgresql://litellm:litellm@localhost:5432/litellm_test' || '' }}
run: |
if [ "${WORKERS}" = "0" ]; then
uv run --no-sync pytest ${TEST_PATH:?} \
@ -143,7 +142,7 @@ jobs:
-n "${WORKERS}" \
--reruns "${RERUNS}" \
--reruns-delay 1 \
--dist=loadscope \
--dist="${DIST}" \
--durations=20 \
--cov=litellm \
--cov-report=xml:coverage.xml \

2
.github/workflows/check_duplicate_issues.yml vendored
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@ -39,7 +39,7 @@ jobs:
if: github.event.action == 'opened'
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.11"
python-version: "3.12"
- name: Auto-close if high-confidence duplicate
if: github.event.action == 'opened'

65
.github/workflows/create-release-branch.yml vendored Normal file
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@ -0,0 +1,65 @@
name: Create Release Branch
on:
workflow_dispatch:
inputs:
tag:
description: "Release tag (e.g. v1.83.0-stable) — branch will be named release/<tag>"
required: true
type: string
commit_hash:
description: "Full 40-char commit SHA the branch should point to"
required: true
type: string
workflow_call:
inputs:
tag:
description: "Release tag"
required: true
type: string
commit_hash:
description: "Full 40-char commit SHA the branch should point to"
required: true
type: string
permissions: {}
jobs:
create-branch:
name: Create Release Branch
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- name: Validate inputs
env:
TAG: ${{ inputs.tag }}
COMMIT_HASH: ${{ inputs.commit_hash }}
run: |
if ! echo "${COMMIT_HASH}" | grep -qE '^[0-9a-f]{40}$'; then
echo "::error::commit_hash must be a full 40-character commit SHA"
exit 1
fi
if ! echo "${TAG}" | grep -qE '^v[0-9]+\.[0-9]+\.[0-9]+'; then
echo "::error::tag must start with vX.Y.Z"
exit 1
fi
- name: Create release branch
env:
TAG: ${{ inputs.tag }}
COMMIT_HASH: ${{ inputs.commit_hash }}
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
script: |
const tag = process.env.TAG;
const commitHash = process.env.COMMIT_HASH;
const branchName = `release/${tag}`;
await github.rest.git.createRef({
owner: context.repo.owner,
repo: context.repo.repo,
ref: `refs/heads/${branchName}`,
sha: commitHash,
});
core.info(`Created branch ${branchName} at ${commitHash}`);

11
.github/workflows/create-release.yml vendored
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@ -102,6 +102,17 @@ jobs:
body: updatedBody,
draft: false,
});
} catch (error) {
core.setFailed(error.message);
}
create-branch:
name: Create Release Branch
needs: release
permissions:
contents: write
uses: ./.github/workflows/create-release-branch.yml
with:
tag: ${{ inputs.tag }}
commit_hash: ${{ inputs.commit_hash }}

2
.github/workflows/llm-translation-testing.yml vendored
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@ -29,7 +29,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.11"
python-version: "3.12"
- name: Set up uv
uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7

2
.github/workflows/scan_duplicate_issues.yml vendored
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@ -29,7 +29,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.13"
python-version: "3.12"
- name: Scan for duplicate issues
env:

136
.github/workflows/test-code-quality.yml vendored Normal file
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@ -0,0 +1,136 @@
name: Code Quality Checks
on:
pull_request:
branches:
- main
- litellm_internal_staging
- litellm_oss_branch
- "litellm_**"
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
code-quality:
runs-on: ubuntu-latest
timeout-minutes: 15
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
persist-credentials: false
- name: Checkout litellm-docs (for documentation_tests)
uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
repository: BerriAI/litellm-docs
path: _litellm_docs_checkout
persist-credentials: false
- name: Wire up docs path expected by documentation_tests/*
run: |
# documentation_tests scripts read from docs/my-website/docs/...
# In litellm-docs the same files live at docs/... (repo root).
# Point docs/my-website -> litellm-docs checkout so the paths resolve.
rm -rf docs/my-website
ln -s ../_litellm_docs_checkout docs/my-website
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Set up uv
uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7
with:
version: "0.10.9"
- name: Cache uv dependencies
uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
.venv
key: ${{ runner.os }}-uv-${{ hashFiles('uv.lock') }}
restore-keys: |
${{ runner.os }}-uv-
- name: Install dependencies
run: uv sync --frozen --all-groups --all-extras
- name: check_licenses
run: uv run --no-sync python ./tests/code_coverage_tests/check_licenses.py
- name: check_provider_folders_documented
run: uv run --no-sync python ./tests/code_coverage_tests/check_provider_folders_documented.py
- name: router_code_coverage
run: uv run --no-sync python ./tests/code_coverage_tests/router_code_coverage.py
- name: test_chat_completion_imports
run: uv run --no-sync python ./tests/code_coverage_tests/test_chat_completion_imports.py
- name: info_log_check
run: uv run --no-sync python ./tests/code_coverage_tests/info_log_check.py
- name: check_guardrail_apply_decorator
run: uv run --no-sync python ./tests/code_coverage_tests/check_guardrail_apply_decorator.py
- name: test_ban_set_verbose
run: uv run --no-sync python ./tests/code_coverage_tests/test_ban_set_verbose.py
- name: code_qa_check_tests
run: uv run --no-sync python ./tests/code_coverage_tests/code_qa_check_tests.py
- name: check_get_model_cost_key_performance
run: uv run --no-sync python ./tests/code_coverage_tests/check_get_model_cost_key_performance.py
- name: test_proxy_types_import
run: uv run --no-sync python ./tests/code_coverage_tests/test_proxy_types_import.py
- name: callback_manager_test
run: uv run --no-sync python ./tests/code_coverage_tests/callback_manager_test.py
- name: recursive_detector
run: uv run --no-sync python ./tests/code_coverage_tests/recursive_detector.py
- name: test_router_strategy_async
run: uv run --no-sync python ./tests/code_coverage_tests/test_router_strategy_async.py
- name: litellm_logging_code_coverage
run: uv run --no-sync python ./tests/code_coverage_tests/litellm_logging_code_coverage.py
- name: ensure_async_clients_test
run: uv run --no-sync python ./tests/code_coverage_tests/ensure_async_clients_test.py
- name: enforce_llms_folder_style
run: uv run --no-sync python ./tests/code_coverage_tests/enforce_llms_folder_style.py
- name: prevent_key_leaks_in_exceptions
run: uv run --no-sync python ./tests/code_coverage_tests/prevent_key_leaks_in_exceptions.py
- name: check_unsafe_enterprise_import
run: uv run --no-sync python ./tests/code_coverage_tests/check_unsafe_enterprise_import.py
- name: ban_copy_deepcopy_kwargs
run: uv run --no-sync python ./tests/code_coverage_tests/ban_copy_deepcopy_kwargs.py
- name: check_fastuuid_usage
run: uv run --no-sync python ./tests/code_coverage_tests/check_fastuuid_usage.py
- name: memory_test
run: uv run --no-sync python ./tests/code_coverage_tests/memory_test.py
- name: documentation_test_env_keys
run: uv run --no-sync python ./tests/documentation_tests/test_env_keys.py
- name: documentation_test_router_settings
run: uv run --no-sync python ./tests/documentation_tests/test_router_settings.py
- name: documentation_test_api_docs
run: uv run --no-sync python ./tests/documentation_tests/test_api_docs.py

39
.github/workflows/test-semgrep.yml vendored Normal file
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@ -0,0 +1,39 @@
name: Semgrep
on:
pull_request:
branches:
- main
- litellm_internal_staging
- litellm_oss_branch
- "litellm_**"
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
semgrep:
runs-on: ubuntu-latest
timeout-minutes: 10
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
persist-credentials: false
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Set up uv
uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7
with:
version: "0.10.9"
- name: Run Semgrep (custom rules)
run: uv tool run --from 'semgrep==1.157.0' semgrep scan --config .semgrep/rules . --error

216
.github/workflows/test-unit-proxy-db.yml vendored
View file

@ -12,8 +12,74 @@ concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
# Semantic matrix: each shard groups tests by concern (auth, server, logging, …)
# rather than alphabetical letter ranges. Adding a new test file means adding it
# to whichever group it belongs to, not reshuffling slices.
#
# Design targets:
# * Every shard runs in <= 7 minutes of wall-clock on the default runner.
# Most of a shard's time is pytest plugin load + xdist worker imports +
# pytest-cov instrumentation, not the tests themselves. Keeping per-shard
# work low and matching worker count to runner cores is what controls it.
# * workers: 4 matches the 4-core ubuntu-latest runner. -n 8 on 4 cores
# oversubscribes 2x and workers fight for CPU during their cold-start
# imports (measured ~441% CPU for -n 8 locally, i.e. ~55% effective).
# * test_key_generate_prisma.py stays serial (workers=0) — it has event-loop
# conflicts with the logging worker when run in parallel.
# * test_proxy_utils.py runs as a single shard with --dist=worksteal so
# xdist balances its 188 parametrized cases across workers instead of
# pinning the whole file to one worker (the default --dist=loadscope
# behavior for single-file targets).
# * test_db_schema_migration.py is isolated because one test in it
# (test_aaaasschema_migration_check) takes ~170s — by itself it
# determines the shard's wall-clock floor.
jobs:
# Fast guard — fails the workflow if a test_*.py file under
# tests/proxy_unit_tests/ is not referenced by any matrix entry below.
# The semantic-shard design (no catch-all "remaining" bucket) relies on
# every test file being explicitly assigned; this guard prevents a new
# file from silently dropping out of CI.
assert-shard-coverage:
runs-on: ubuntu-latest
timeout-minutes: 2
permissions:
contents: read
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
persist-credentials: false
- name: Assert every test_*.py is in a matrix shard
run: |
python3 - <<'PY'
import pathlib, sys, yaml
wf = yaml.safe_load(open(".github/workflows/test-unit-proxy-db.yml"))
matrix = wf["jobs"]["proxy-db"]["strategy"]["matrix"]["include"]
referenced = set()
for entry in matrix:
for token in entry["test-path"].split():
if token.startswith("tests/proxy_unit_tests/"):
referenced.add(pathlib.PurePosixPath(token).name)
actual = {p.name for p in pathlib.Path("tests/proxy_unit_tests").iterdir()
if p.name.startswith("test_") and (p.suffix == ".py" or p.is_dir())
and p.name != "test_configs"}
orphans = sorted(actual - referenced)
if orphans:
print("ERROR: the following files/dirs under tests/proxy_unit_tests/")
print(" are not assigned to any shard in test-unit-proxy-db.yml:")
for o in orphans:
print(f" - {o}")
print()
print("Add each to whichever semantic shard it belongs to.")
sys.exit(1)
print(f"OK: all {len(actual)} files assigned to a shard.")
PY
proxy-db:
needs: assert-shard-coverage
# Display only the semantic shard name in the checks UI instead of GHA's
# default "proxy-db (key-generation, tests/proxy_unit_tests/…, 0, loadscope, 20)"
# which includes every matrix field and gets truncated past the test-path.
name: ${{ matrix.test-group }}
permissions:
contents: read
id-token: write
@ -22,19 +88,146 @@ jobs:
fail-fast: false
matrix:
include:
# Key generation tests must NOT run in parallel (event loop conflicts with logging worker)
# Must run serially — event-loop conflict with the logging worker.
- test-group: key-generation
test-path: "tests/proxy_unit_tests/test_key_generate_prisma.py"
workers: 0
timeout: 30
- test-group: auth-checks
test-path: "tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py"
workers: 8
dist: loadscope
timeout: 20
- test-group: remaining
test-path: "tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py"
workers: 8
timeout: 30
# ---- auth: split into 2 shards ----
- test-group: auth-checks
test-path: >-
tests/proxy_unit_tests/test_auth_checks.py
tests/proxy_unit_tests/test_user_api_key_auth.py
workers: 4
dist: loadscope
timeout: 15
- test-group: jwt-and-keys
test-path: >-
tests/proxy_unit_tests/test_jwt.py
tests/proxy_unit_tests/test_jwt_key_mapping.py
tests/proxy_unit_tests/test_proxy_custom_auth.py
tests/proxy_unit_tests/test_key_generate_dynamodb.py
tests/proxy_unit_tests/test_deployed_proxy_keygen.py
workers: 4
dist: loadscope
timeout: 15
# ---- test_proxy_utils.py, single shard, worksteal distribution ----
- test-group: proxy-utils
test-path: "tests/proxy_unit_tests/test_proxy_utils.py"
workers: 4
dist: worksteal
timeout: 15
# ---- proxy server: split into 2 shards ----
- test-group: proxy-server-core
test-path: >-
tests/proxy_unit_tests/test_proxy_server.py
tests/proxy_unit_tests/test_proxy_server_keys.py
tests/proxy_unit_tests/test_proxy_server_caching.py
tests/proxy_unit_tests/test_proxy_server_langfuse.py
tests/proxy_unit_tests/test_proxy_server_spend.py
tests/proxy_unit_tests/test_aproxy_startup.py
workers: 4
dist: loadscope
timeout: 15
- test-group: proxy-runtime
test-path: >-
tests/proxy_unit_tests/test_proxy_config_unit_test.py
tests/proxy_unit_tests/test_proxy_routes.py
tests/proxy_unit_tests/test_proxy_gunicorn.py
tests/proxy_unit_tests/test_server_root_path.py
tests/proxy_unit_tests/test_proxy_pass_user_config.py
tests/proxy_unit_tests/test_proxy_token_counter.py
workers: 4
dist: loadscope
timeout: 15
# ---- logging: split into 2 shards ----
- test-group: custom-logging
test-path: >-
tests/proxy_unit_tests/test_custom_callback_input.py
tests/proxy_unit_tests/test_custom_logger_s3_gcs.py
tests/proxy_unit_tests/test_proxy_custom_logger.py
workers: 4
dist: loadscope
timeout: 15
- test-group: logging-misc
test-path: >-
tests/proxy_unit_tests/test_proxy_reject_logging.py
tests/proxy_unit_tests/test_audit_logs_proxy.py
tests/proxy_unit_tests/test_search_api_logging.py
workers: 4
dist: loadscope
timeout: 15
# ---- db-and-spend: isolate the 170s schema-migration test ----
# test_db_schema_migration.py has exactly one test, and that test
# is mostly waiting on `prisma migrate deploy` / `prisma migrate
# diff` subprocesses (~170s). It does no CPU-bound Python work
# inside the test. Running with workers=0 (serial, no xdist)
# skips the 4-worker cold-start cost we'd otherwise pay for a
# single test, saving ~4 minutes of wall-clock.
- test-group: schema-migration
test-path: "tests/proxy_unit_tests/test_db_schema_migration.py"
workers: 0
dist: loadscope
timeout: 15
- test-group: db-and-spend
test-path: >-
tests/proxy_unit_tests/test_prisma_client_backoff_retry.py
tests/proxy_unit_tests/test_db_schema_changes.py
tests/proxy_unit_tests/test_e2e_pod_lock_manager.py
tests/proxy_unit_tests/test_skills_db.py
tests/proxy_unit_tests/test_update_daily_tag_spend.py
tests/proxy_unit_tests/test_update_spend.py
tests/proxy_unit_tests/test_project_endpoints_prisma.py
tests/proxy_unit_tests/test_proxy_encrypt_decrypt.py
workers: 4
dist: loadscope
timeout: 15
# ---- guardrails + budget + hooks: split into 2 ----
- test-group: guardrails-hooks
test-path: >-
tests/proxy_unit_tests/test_proxy_setting_guardrails.py
tests/proxy_unit_tests/test_banned_keyword_list.py
tests/proxy_unit_tests/test_unit_test_proxy_hooks.py
workers: 4
dist: loadscope
timeout: 15
- test-group: budgets
test-path: >-
tests/proxy_unit_tests/test_default_end_user_budget_simple.py
tests/proxy_unit_tests/test_unit_test_max_model_budget_limiter.py
tests/proxy_unit_tests/test_zero_cost_model_budget_bypass.py
workers: 4
dist: loadscope
timeout: 15
- test-group: endpoints-and-responses
test-path: >-
tests/proxy_unit_tests/test_blog_posts_endpoint.py
tests/proxy_unit_tests/test_models_fallback_endpoint.py
tests/proxy_unit_tests/test_google_endpoint_routing.py
tests/proxy_unit_tests/test_google_gemini_proxy_request.py
tests/proxy_unit_tests/test_get_favicon.py
tests/proxy_unit_tests/test_get_image.py
tests/proxy_unit_tests/test_ui_path_detection.py
tests/proxy_unit_tests/test_prompt_test_endpoint.py
tests/proxy_unit_tests/test_check_batch_cost.py
tests/proxy_unit_tests/test_check_responses_cost.py
tests/proxy_unit_tests/test_response_polling_handler.py
tests/proxy_unit_tests/test_response_polling_pre_call_checks.py
tests/proxy_unit_tests/test_realtime_cache.py
tests/proxy_unit_tests/test_proxy_exception_mapping.py
tests/proxy_unit_tests/test_custom_tokenizer_bug.py
tests/proxy_unit_tests/test_model_response_typing
workers: 4
dist: loadscope
timeout: 15
uses: ./.github/workflows/_test-unit-services-base.yml
with:
test-path: ${{ matrix.test-path }}
@ -42,8 +235,5 @@ jobs:
reruns: 2
timeout-minutes: ${{ matrix.timeout }}
enable-postgres: true
dist: ${{ matrix.dist }}
artifact-name: proxy-db-${{ matrix.test-group }}
secrets:
DATABASE_URL: ${{ secrets.DATABASE_URL }}
POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}

2
.github/workflows/test-unit-proxy-endpoints.yml vendored
View file

@ -36,6 +36,8 @@ jobs:
tests/test_litellm/proxy/health_endpoints
tests/test_litellm/proxy/public_endpoints
tests/test_litellm/proxy/prompts
tests/test_litellm/proxy/rag_endpoints
tests/test_litellm/proxy/realtime_endpoints
tests/test_litellm/proxy/ui_crud_endpoints
workers: 2
reruns: 2

8
.github/workflows/test-unit-security.yml vendored
View file

@ -1,6 +1,8 @@
name: "Unit Tests: Security"
# Uses DATABASE_URL secret — only runs on trusted branches, not PRs.
# Kept push-only (was previously required by DATABASE_URL secret scoping;
# now the postgres credentials are ephemeral localhost values but the
# push-trigger stays to match the proxy-db workflow cadence).
on:
push:
branches: [main, "litellm_**"]
@ -24,7 +26,3 @@ jobs:
timeout-minutes: 20
enable-postgres: true
artifact-name: security
secrets:
DATABASE_URL: ${{ secrets.DATABASE_URL }}
POSTGRES_USER: ${{ secrets.POSTGRES_USER }}
POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }}

2
CLAUDE.md
View file

@ -110,7 +110,7 @@ LiteLLM is a unified interface for 100+ LLM providers with two main components:
- When wiring a new UI entity type to an existing backend endpoint, verify the backend API contract (single value vs. array, required vs. optional params) and ensure the UI controls match — e.g., use a single-select dropdown when the backend accepts a single value, not a multi-select
### UI Component Library
- **Always use `antd` for new UI components** — we are migrating off of `@tremor/react`. Do not introduce new `Badge`, `Text`, `Card`, `Grid`, `Title`, or other imports from `@tremor/react` in any new or modified file. Use `antd` equivalents: `Tag` for labels, plain `<span>`/`<div>` with Tailwind classes (or `Typography.Text`) for text, `Card` from `antd`, etc. Note that `antd` has no `"yellow"` Tag color — use `"gold"` for amber/yellow.
- **Always use `antd` for new UI components** — we are migrating off of `@tremor/react`. Do not introduce new `Badge`, `Text`, `Card`, `Grid`, `Title`, or other imports from `@tremor/react` in any new or modified file. Use `antd` equivalents: `Tag` for labels, `Typography.Text` / `Typography.Title` / `Typography.Paragraph` for textual content (avoid plain text-only `<span>`, `<p>`, `<h*>` when Typography fits), and `Card` from `antd`. Note that `antd` has no `"yellow"` Tag color — use `"gold"` for amber/yellow.
### MCP OAuth / OpenAPI Transport Mapping
- `TRANSPORT.OPENAPI` is a UI-only concept. The backend only accepts `"http"`, `"sse"`, or `"stdio"`. Always map it to `"http"` before any API call (including pre-OAuth temp-session calls).

13
Dockerfile
View file

@ -27,10 +27,8 @@ RUN apk add --no-cache \
npm \
libsndfile
ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
UV_PROJECT_ENVIRONMENT=/app/.venv \
ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
UV_LINK_MODE=copy \
XDG_CACHE_HOME=/app/.cache \
PATH="/app/.venv/bin:${PATH}"
# Copy dependency metadata first for layer caching
@ -94,11 +92,14 @@ RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile supervi
{ apk del --no-cache npm 2>/dev/null || true; }
WORKDIR /app
ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
XDG_CACHE_HOME=/app/.cache \
PATH="/app/.venv/bin:${PATH}"
ENV PATH="/app/.venv/bin:${PATH}"
COPY --from=builder /app /app
# Prisma binaries live in $HOME/.cache (default prisma-python location),
# which is /root/.cache here. Copy them from the builder so they survive
# deployments that volume-mount /app/.cache (e.g. readOnlyRootFilesystem
# + emptyDir) — otherwise the mount would shadow the baked-in query engine.
COPY --from=builder /root/.cache /root/.cache
RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \
find /app/.venv -type d -path "*/tornado/test" -delete

303
ci_cd/run_migration.py
View file

@ -1,23 +1,234 @@
import argparse
import os
import subprocess
from pathlib import Path
from datetime import datetime
import testing.postgresql
import re
import shutil
import subprocess
import sys
from datetime import datetime
from pathlib import Path
import testing.postgresql
def create_migration(migration_name: str = None):
DESTRUCTIVE_PATTERN = re.compile(r"\bDROP\s+(COLUMN|TABLE|INDEX)\b", re.IGNORECASE)
DEFAULT_BASE_BRANCH = "litellm_internal_staging"
def _find_destructive_statements(sql: str) -> list:
"""Return SQL lines containing DROP COLUMN, DROP TABLE, or DROP INDEX."""
return [
line.strip() for line in sql.splitlines() if DESTRUCTIVE_PATTERN.search(line)
]
def _print_freshness_failure(
base_branch: str, reason: str, stderr_text: str = ""
) -> None:
"""Loudly refuse to run when the freshness check can't be completed."""
banner = "=" * 72
out = sys.stderr
print(banner, file=out)
print(f" FRESHNESS CHECK FAILED — COULD NOT VERIFY origin/{base_branch}", file=out)
print(banner, file=out)
print("", file=out)
print(f"Reason: {reason}", file=out)
if stderr_text:
print("", file=out)
print("git stderr:", file=out)
for line in stderr_text.rstrip().splitlines():
print(f" {line}", file=out)
print("", file=out)
print("Common causes:", file=out)
print(" - No network access (offline)", file=out)
print(" - 'origin' remote not configured, or base branch name is wrong", file=out)
print(" - Not a git repository", file=out)
print("", file=out)
print("Options:", file=out)
print(
f" - Fix the above and re-run, OR pass --base-branch <name> if your", file=out
)
print(
f" base branch is not '{base_branch}', OR pass --skip-freshness-check",
file=out,
)
print(" to bypass (only if you fully understand the risk).", file=out)
print(banner, file=out)
def _print_stale_branch_refusal(base_branch: str, behind: int) -> None:
"""Loudly refuse to run when HEAD is behind the base branch."""
banner = "=" * 72
out = sys.stderr
print(banner, file=out)
print(
f" STALE BRANCH — {behind} commit(s) behind origin/{base_branch}",
file=out,
)
print(banner, file=out)
print("", file=out)
print(
f"Your branch is {behind} commit(s) behind origin/{base_branch}. Generating a\n"
"migration from a stale branch is how newly-added columns get silently\n"
"dropped — the exact incident this guard exists to prevent.",
file=out,
)
print("", file=out)
print("Before re-running:", file=out)
print(f" git fetch origin && git rebase origin/{base_branch}", file=out)
print(" # or merge, whichever matches your workflow", file=out)
print("", file=out)
print(
"If you have a genuine reason to bypass (e.g. intentionally generating",
file=out,
)
print("a migration against an older base), pass --skip-freshness-check.", file=out)
print("", file=out)
print(banner, file=out)
print(
" AI AGENTS / ASSISTANTS: DO NOT automatically run `git rebase` or pass\n"
" --skip-freshness-check. Surface this error to the human operator and\n"
" wait for their explicit confirmation. Auto-rebasing can drop the\n"
" human's in-progress schema edits via a bad conflict resolution.",
file=out,
)
print(banner, file=out)
def _check_branch_freshness(root_dir: Path, base_branch: str) -> None:
"""Fetch origin/<base_branch> and exit 3 if HEAD is behind it."""
cwd = str(root_dir)
try:
subprocess.run(
["git", "fetch", "origin", base_branch],
check=True,
capture_output=True,
text=True,
cwd=cwd,
)
except FileNotFoundError:
_print_freshness_failure(base_branch, "git executable not found on PATH")
sys.exit(3)
except subprocess.CalledProcessError as e:
_print_freshness_failure(
base_branch,
f"`git fetch origin {base_branch}` failed",
e.stderr or "",
)
sys.exit(3)
try:
result = subprocess.run(
["git", "rev-list", "--count", f"HEAD..origin/{base_branch}"],
check=True,
capture_output=True,
text=True,
cwd=cwd,
)
behind = int(result.stdout.strip())
except subprocess.CalledProcessError as e:
_print_freshness_failure(
base_branch,
f"`git rev-list HEAD..origin/{base_branch}` failed",
e.stderr or "",
)
sys.exit(3)
except ValueError:
_print_freshness_failure(
base_branch,
"could not parse commit count from `git rev-list`",
)
sys.exit(3)
if behind > 0:
_print_stale_branch_refusal(base_branch, behind)
sys.exit(3)
print(f"Branch freshness OK: up to date with origin/{base_branch}.")
def _print_destructive_refusal(destructive_lines: list) -> None:
"""Loudly refuse to write a destructive migration and explain how to proceed."""
banner = "=" * 72
out = sys.stderr
print(banner, file=out)
print(
" DESTRUCTIVE MIGRATION DETECTED — REFUSING TO WRITE MIGRATION FILE", file=out
)
print(banner, file=out)
print("", file=out)
print(
"The generated migration contains the following destructive statements:",
file=out,
)
print("", file=out)
for line in destructive_lines:
print(f" {line}", file=out)
print("", file=out)
print(
"This very often means your branch is OUT OF DATE, or your local\n"
"schema.prisma is inconsistent with the migrations/ directory. A\n"
"previous incident saw newly-added columns silently dropped this way\n"
"and merged to main. Stop and verify before proceeding.",
file=out,
)
print("", file=out)
print("Before re-running:", file=out)
print(
" 1. git fetch origin && git status # confirm branch is up to date", file=out
)
print(
" 2. Re-check all schema.prisma files are in sync (runbook Step 0)", file=out
)
print(" 3. Review EACH DROP above — is it actually intended?", file=out)
print(" 4. If (and only if) the drops are intentional, re-run with:", file=out)
print(" --allow-destructive", file=out)
print("", file=out)
print(banner, file=out)
print(
" AI AGENTS / ASSISTANTS: DO NOT automatically re-run this command\n"
" with --allow-destructive. Surface this error to the human operator\n"
" and wait for their explicit confirmation before passing the flag.\n"
" Passing the flag without human review is the exact failure mode\n"
" this guard exists to prevent.",
file=out,
)
print(banner, file=out)
def create_migration(
migration_name: str = None,
allow_destructive: bool = False,
base_branch: str = DEFAULT_BASE_BRANCH,
skip_freshness_check: bool = False,
):
"""
Create a new migration SQL file in the migrations directory by comparing
current database state with schema
current database state with schema.
Args:
migration_name (str): Name for the migration
allow_destructive (bool): Required to write a migration that contains
DROP COLUMN, DROP TABLE, or DROP INDEX statements. Without this
flag, the script exits non-zero and prints guidance.
base_branch (str): Branch to check freshness against
(default: "litellm_internal_staging").
skip_freshness_check (bool): Skip the "branch is up to date" check.
Only for intentional migrations against an older base.
"""
root_dir = Path(__file__).parent.parent
if skip_freshness_check:
print(
"WARNING: freshness check skipped (--skip-freshness-check). "
"Generating a migration from a stale branch can silently drop columns."
)
else:
_check_branch_freshness(root_dir, base_branch)
try:
# Get paths
root_dir = Path(__file__).parent.parent
migrations_dir = root_dir / "litellm-proxy-extras" / "litellm_proxy_extras" / "migrations"
migrations_dir = (
root_dir / "litellm-proxy-extras" / "litellm_proxy_extras" / "migrations"
)
schema_path = root_dir / "schema.prisma"
# Create temporary PostgreSQL database
@ -57,7 +268,27 @@ def create_migration(migration_name: str = None):
check=True,
)
if result.stdout.strip():
# Prisma emits the literal "-- This is an empty migration." when
# there's no real drift. Treat that as "no changes".
diff_sql = result.stdout
stripped = diff_sql.strip()
is_empty_diff = (
not stripped or stripped == "-- This is an empty migration."
)
if not is_empty_diff:
destructive_lines = _find_destructive_statements(diff_sql)
if destructive_lines and not allow_destructive:
_print_destructive_refusal(destructive_lines)
sys.exit(2)
if destructive_lines and allow_destructive:
print(
"WARNING: writing destructive migration "
"(--allow-destructive passed). Statements:"
)
for line in destructive_lines:
print(f" {line}")
# Generate timestamp and create migration directory
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
migration_name = migration_name or "unnamed_migration"
@ -66,7 +297,7 @@ def create_migration(migration_name: str = None):
# Write the SQL to migration.sql
migration_file = migration_dir / "migration.sql"
migration_file.write_text(result.stdout)
migration_file.write_text(diff_sql)
print(f"Created migration in {migration_dir}")
return True
@ -88,8 +319,48 @@ def create_migration(migration_name: str = None):
if __name__ == "__main__":
# If running directly, can optionally pass migration name as argument
import sys
migration_name = sys.argv[1] if len(sys.argv) > 1 else None
create_migration(migration_name)
parser = argparse.ArgumentParser(
description=(
"Generate a Prisma migration by diffing the temp DB "
"(existing migrations applied) against schema.prisma."
)
)
parser.add_argument(
"migration_name",
nargs="?",
default=None,
help="Name for the migration (used in the generated directory name).",
)
parser.add_argument(
"--allow-destructive",
action="store_true",
help=(
"Required to write a migration that contains DROP COLUMN, "
"DROP TABLE, or DROP INDEX. Without this flag, destructive "
"diffs are refused."
),
)
parser.add_argument(
"--base-branch",
default=DEFAULT_BASE_BRANCH,
help=(
f"Branch to check freshness against (default: {DEFAULT_BASE_BRANCH}). "
"The script fetches origin/<base-branch> and refuses to run if HEAD "
"is behind it."
),
)
parser.add_argument(
"--skip-freshness-check",
action="store_true",
help=(
"Bypass the 'branch is up to date' check. Only for intentional "
"migrations against an older base. Pairs poorly with automation."
),
)
args = parser.parse_args()
create_migration(
args.migration_name,
allow_destructive=args.allow_destructive,
base_branch=args.base_branch,
skip_freshness_check=args.skip_freshness_check,
)

50
cookbook/anthropic_agent_sdk/agent_with_mcp.py
View file

@ -24,24 +24,26 @@ async def interactive_chat_with_mcp():
Interactive CLI chat with the agent and MCP server
"""
config = Config()
# Configure Anthropic SDK to point to LiteLLM gateway
litellm_base_url = setup_litellm_env(config)
# Fetch available models from proxy
available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY)
available_models = await fetch_available_models(
litellm_base_url, config.LITELLM_API_KEY
)
current_model = config.LITELLM_MODEL
# MCP server configuration
mcp_server_url = f"{litellm_base_url}/mcp/deepwiki2"
use_mcp = os.getenv("USE_MCP", "true").lower() == "true"
if not use_mcp:
print("⚠️ MCP disabled via USE_MCP=false")
print_header(litellm_base_url, current_model, has_mcp=use_mcp)
while True:
# Configure agent options
if use_mcp:
@ -58,7 +60,7 @@ async def interactive_chat_with_mcp():
"url": mcp_server_url,
"headers": {
"Authorization": f"Bearer {config.LITELLM_API_KEY}"
}
},
}
},
)
@ -78,12 +80,12 @@ async def interactive_chat_with_mcp():
model=current_model,
max_turns=50,
)
# Create agent client
try:
async with ClaudeSDKClient(options=options) as client:
conversation_active = True
while conversation_active:
# Get user input
try:
@ -91,34 +93,36 @@ async def interactive_chat_with_mcp():
except (EOFError, KeyboardInterrupt):
print("\n\n👋 Goodbye!")
return
# Handle commands
if user_input.lower() in ['quit', 'exit']:
if user_input.lower() in ["quit", "exit"]:
print("\n👋 Goodbye!")
return
if user_input.lower() == 'clear':
if user_input.lower() == "clear":
print("\n🔄 Starting new conversation...\n")
conversation_active = False
continue
if user_input.lower() == 'models':
if user_input.lower() == "models":
handle_model_list(available_models, current_model)
continue
if user_input.lower() == 'model':
new_model, should_restart = handle_model_switch(available_models, current_model)
if user_input.lower() == "model":
new_model, should_restart = handle_model_switch(
available_models, current_model
)
if should_restart:
current_model = new_model
conversation_active = False
continue
if not user_input:
continue
# Stream response from agent
await stream_response(client, user_input)
except Exception as e:
print(f"\n❌ Error creating agent client: {e}")
print("This might be an MCP configuration issue. Try running without MCP:")

62
cookbook/anthropic_agent_sdk/common.py
View file

@ -8,13 +8,13 @@ import httpx
class Config:
"""Configuration for LiteLLM Gateway connection"""
# LiteLLM proxy URL (default to local instance)
LITELLM_PROXY_URL = os.getenv("LITELLM_PROXY_URL", "http://localhost:4000")
# LiteLLM API key (master key or virtual key)
LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "sk-1234")
# Model name as configured in LiteLLM (e.g., "bedrock-claude-sonnet-4", "gpt-4", etc.)
LITELLM_MODEL = os.getenv("LITELLM_MODEL", "bedrock-claude-sonnet-4.5")
@ -28,7 +28,7 @@ async def fetch_available_models(base_url: str, api_key: str) -> list[str]:
response = await client.get(
f"{base_url}/models",
headers={"Authorization": f"Bearer {api_key}"},
timeout=10.0
timeout=10.0,
)
response.raise_for_status()
data = response.json()
@ -50,7 +50,7 @@ def setup_litellm_env(config: Config):
"""
Configure environment variables to point Agent SDK to LiteLLM
"""
litellm_base_url = config.LITELLM_PROXY_URL.rstrip('/')
litellm_base_url = config.LITELLM_PROXY_URL.rstrip("/")
os.environ["ANTHROPIC_BASE_URL"] = litellm_base_url
os.environ["ANTHROPIC_API_KEY"] = config.LITELLM_API_KEY
return litellm_base_url
@ -87,10 +87,12 @@ def handle_model_list(available_models: list[str], current_model: str):
print(f" {marker} {i}. {model}")
def handle_model_switch(available_models: list[str], current_model: str) -> tuple[str, bool]:
def handle_model_switch(
available_models: list[str], current_model: str
) -> tuple[str, bool]:
"""
Handle model switching
Returns:
tuple: (new_model, should_restart_conversation)
"""
@ -98,7 +100,7 @@ def handle_model_switch(available_models: list[str], current_model: str) -> tupl
for i, model in enumerate(available_models, 1):
marker = "✓" if model == current_model else " "
print(f" {marker} {i}. {model}")
try:
choice = input("\nEnter number (or press Enter to cancel): ").strip()
if choice:
@ -112,7 +114,7 @@ def handle_model_switch(available_models: list[str], current_model: str) -> tupl
print("❌ Invalid choice")
except (ValueError, IndexError):
print("❌ Invalid input")
return current_model, False
@ -120,41 +122,43 @@ async def stream_response(client, user_input: str):
"""
Stream response from the agent
"""
print("\n🤖 Assistant: ", end='', flush=True)
print("\n🤖 Assistant: ", end="", flush=True)
try:
await client.query(user_input)
# Show loading indicator
print("⏳ thinking...", end='', flush=True)
print("⏳ thinking...", end="", flush=True)
# Stream the response
first_chunk = True
async for msg in client.receive_response():
# Clear loading indicator on first message
if first_chunk:
print("\r🤖 Assistant: ", end='', flush=True)
print("\r🤖 Assistant: ", end="", flush=True)
first_chunk = False
# Handle different message types
if hasattr(msg, 'type'):
if msg.type == 'content_block_delta':
if hasattr(msg, "type"):
if msg.type == "content_block_delta":
# Streaming text delta
if hasattr(msg, 'delta') and hasattr(msg.delta, 'text'):
print(msg.delta.text, end='', flush=True)
elif msg.type == 'content_block_start':
if hasattr(msg, "delta") and hasattr(msg.delta, "text"):
print(msg.delta.text, end="", flush=True)
elif msg.type == "content_block_start":
# Start of content block
if hasattr(msg, 'content_block') and hasattr(msg.content_block, 'text'):
print(msg.content_block.text, end='', flush=True)
if hasattr(msg, "content_block") and hasattr(
msg.content_block, "text"
):
print(msg.content_block.text, end="", flush=True)
# Fallback to original content handling
if hasattr(msg, 'content'):
if hasattr(msg, "content"):
for content_block in msg.content:
if hasattr(content_block, 'text'):
print(content_block.text, end='', flush=True)
if hasattr(content_block, "text"):
print(content_block.text, end="", flush=True)
print() # New line after response
except Exception as e:
print(f"\r\n❌ Error: {e}")
print("Please check your LiteLLM gateway is running and configured correctly.")

42
cookbook/anthropic_agent_sdk/main.py
View file

@ -24,17 +24,19 @@ async def interactive_chat():
Interactive CLI chat with the agent
"""
config = Config()
# Configure Anthropic SDK to point to LiteLLM gateway
litellm_base_url = setup_litellm_env(config)
# Fetch available models from proxy
available_models = await fetch_available_models(litellm_base_url, config.LITELLM_API_KEY)
available_models = await fetch_available_models(
litellm_base_url, config.LITELLM_API_KEY
)
current_model = config.LITELLM_MODEL
print_header(litellm_base_url, current_model)
while True:
# Configure agent options for each conversation
options = ClaudeAgentOptions(
@ -42,11 +44,11 @@ async def interactive_chat():
model=current_model,
max_turns=50,
)
# Create agent client
async with ClaudeSDKClient(options=options) as client:
conversation_active = True
while conversation_active:
# Get user input
try:
@ -54,31 +56,33 @@ async def interactive_chat():
except (EOFError, KeyboardInterrupt):
print("\n\n👋 Goodbye!")
return
# Handle commands
if user_input.lower() in ['quit', 'exit']:
if user_input.lower() in ["quit", "exit"]:
print("\n👋 Goodbye!")
return
if user_input.lower() == 'clear':
if user_input.lower() == "clear":
print("\n🔄 Starting new conversation...\n")
conversation_active = False
continue
if user_input.lower() == 'models':
if user_input.lower() == "models":
handle_model_list(available_models, current_model)
continue
if user_input.lower() == 'model':
new_model, should_restart = handle_model_switch(available_models, current_model)
if user_input.lower() == "model":
new_model, should_restart = handle_model_switch(
available_models, current_model
)
if should_restart:
current_model = new_model
conversation_active = False
continue
if not user_input:
continue
# Stream response from agent
await stream_response(client, user_input)

6
cookbook/litellm_proxy_server/batch_api/bedrock/bedrock.py
View file

@ -11,15 +11,15 @@ BEDROCK_BATCH_MODEL = "bedrock/batch-anthropic.claude-3-5-sonnet-20240620-v1:0"
batch_input_file = client.files.create(
file=open("./bedrock_batch_completions.jsonl", "rb"),
purpose="batch",
extra_body={"target_model_names": BEDROCK_BATCH_MODEL}
extra_body={"target_model_names": BEDROCK_BATCH_MODEL},
)
print(batch_input_file)
# Create batch
batch = client.batches.create(
batch = client.batches.create(
input_file_id=batch_input_file.id,
endpoint="/v1/chat/completions",
completion_window="24h",
metadata={"description": "Test batch job"},
)
print(batch)
print(batch)

19
cookbook/litellm_proxy_server/cli_token_usage.py
View file

@ -8,6 +8,7 @@ in your Python scripts after running `litellm-proxy login`.
from textwrap import indent
import litellm
LITELLM_BASE_URL = "http://localhost:4000/"
@ -15,38 +16,38 @@ def main():
"""Using CLI token with LiteLLM SDK"""
print("🚀 Using CLI Token with LiteLLM SDK")
print("=" * 40)
#litellm._turn_on_debug()
# litellm._turn_on_debug()
# Get the CLI token
api_key = litellm.get_litellm_gateway_api_key()
if not api_key:
print("❌ No CLI token found. Please run 'litellm-proxy login' first.")
return
print("✅ Found CLI token.")
available_models = litellm.get_valid_models(
check_provider_endpoint=True,
custom_llm_provider="litellm_proxy",
api_key=api_key,
api_base=LITELLM_BASE_URL
api_base=LITELLM_BASE_URL,
)
print("✅ Available models:")
if available_models:
for i, model in enumerate(available_models, 1):
print(f" {i:2d}. {model}")
else:
print(" No models available")
# Use with LiteLLM
try:
response = litellm.completion(
model="litellm_proxy/gemini/gemini-2.5-flash",
messages=[{"role": "user", "content": "Hello from CLI token!"}],
api_key=api_key,
base_url=LITELLM_BASE_URL
base_url=LITELLM_BASE_URL,
)
print(f"✅ LLM Response: {response.model_dump_json(indent=4)}")
except Exception as e:
@ -55,7 +56,7 @@ def main():
if __name__ == "__main__":
main()
print("\n💡 Tips:")
print("1. Run 'litellm-proxy login' to authenticate first")
print("2. Replace 'https://your-proxy.com' with your actual proxy URL")

11
cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py
View file

@ -3,11 +3,12 @@ Use LiteLLM Proxy MCP Gateway to call MCP tools.
When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers.
"""
import openai
client = openai.OpenAI(
api_key="sk-1234", # paste your litellm proxy api key here
base_url="http://localhost:4000" # paste your litellm proxy base url here
api_key="sk-1234", # paste your litellm proxy api key here
base_url="http://localhost:4000", # paste your litellm proxy base url here
)
print("Making API request to Responses API with MCP tools")
@ -17,7 +18,7 @@ response = client.responses.create(
{
"role": "user",
"content": "give me TLDR of what BerriAI/litellm repo is about",
"type": "message"
"type": "message",
}
],
tools=[
@ -25,11 +26,11 @@ response = client.responses.create(
"type": "mcp",
"server_label": "litellm",
"server_url": "litellm_proxy",
"require_approval": "never"
"require_approval": "never",
}
],
stream=True,
tool_choice="required"
tool_choice="required",
)
for chunk in response:

7
cookbook/litellm_proxy_server/secret_manager/my_secret_manager.py
View file

@ -40,8 +40,10 @@ class InMemorySecretManager(CustomSecretManager):
) -> Optional[str]:
"""Read secret synchronously"""
from litellm._logging import verbose_proxy_logger
verbose_proxy_logger.info(f"CUSTOM SECRET MANAGER: LOOKING FOR SECRET: {secret_name}")
verbose_proxy_logger.info(
f"CUSTOM SECRET MANAGER: LOOKING FOR SECRET: {secret_name}"
)
value = self.secrets.get(secret_name)
verbose_proxy_logger.info(f"CUSTOM SECRET MANAGER: READ SECRET: {value}")
return value
@ -76,4 +78,3 @@ class InMemorySecretManager(CustomSecretManager):
del self.secrets[secret_name]
return {"status": "deleted", "secret_name": secret_name}
return {"status": "not_found", "secret_name": secret_name}

75
cookbook/livekit_agent_sdk/main.py
View file

@ -5,6 +5,7 @@ This example shows how to use LiveKit's xAI realtime plugin through LiteLLM prox
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
@ -23,71 +24,79 @@ async def run_voice_agent():
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}]
}
}))
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"]}
}))
await ws.send(
json.dumps(
{
"type": "response.create",
"response": {"modalities": ["text", "audio"]},
}
)
)
# Stream response
print("🎤 Response: ", end='', flush=True)
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 event["type"] == "response.output_audio_transcript.delta":
delta = event.get("delta", "")
if delta:
print(delta, end='', flush=True)
print(delta, end="", flush=True)
transcript.append(delta)
# Done when response completes
elif event['type'] == 'response.done':
elif event["type"] == "response.done":
break
except asyncio.TimeoutError:
pass
print("\n")
if transcript:
print(f"✅ Complete response: {''.join(transcript)}")
await ws.close()
@ -97,7 +106,7 @@ def main():
print("LiveKit xAI Voice Agent via LiteLLM Proxy")
print("=" * 70)
print()
try:
asyncio.run(run_voice_agent())
except KeyboardInterrupt:

14
cookbook/misc/test_responses_api.py
View file

@ -1,10 +1,9 @@
import base64
from openai import OpenAI
import time
client = OpenAI(
base_url="http://0.0.0.0:4001",
api_key="sk-1234"
)
client = OpenAI(base_url="http://0.0.0.0:4001", api_key="sk-1234")
# Function to encode the image
def encode_image(image_path):
@ -25,7 +24,7 @@ response = client.responses.create(
{
"role": "user",
"content": [
{ "type": "input_text", "text": "what color is the image"},
{"type": "input_text", "text": "what color is the image"},
{
"type": "input_image",
"image_url": f"data:image/jpeg;base64,{base64_image}",
@ -36,7 +35,6 @@ response = client.responses.create(
)
print(response.output_text)
print("response1 id===", response.id)
print("sleeping for 20 seconds...")
@ -45,9 +43,7 @@ print("making follow up request for existing id")
response2 = client.responses.create(
model="bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0",
previous_response_id=response.id,
input="ok, and what objects are in the image?"
input="ok, and what objects are in the image?",
)
print(response2.output_text)

11
cookbook/nova_sonic_realtime.py
View file

@ -52,11 +52,11 @@ class RealtimeClient:
async def connect(self):
"""Connect to LiteLLM proxy realtime endpoint."""
print(f"Connecting to {self.url}...")
headers = {}
if self.api_key:
headers["Authorization"] = f"Bearer {self.api_key}"
self.ws = await websockets.connect(
self.url,
additional_headers=headers,
@ -175,7 +175,9 @@ class RealtimeClient:
try:
while self.is_active:
audio_data = self.input_stream.read(CHUNK_SIZE, exception_on_overflow=False)
audio_data = self.input_stream.read(
CHUNK_SIZE, exception_on_overflow=False
)
await self.send_audio_chunk(audio_data)
await asyncio.sleep(0.01) # Small delay to prevent overwhelming
except Exception as e:
@ -270,6 +272,7 @@ async def main():
except Exception as e:
print(f"\n❌ Error: {e}")
import traceback
traceback.print_exc()
finally:
await client.close()
@ -281,7 +284,7 @@ if __name__ == "__main__":
print("2. Bedrock is configured in proxy_server_config.yaml")
print("3. AWS credentials are set")
print()
try:
asyncio.run(main())
except KeyboardInterrupt:

164
cookbook/veo_video_generation.py
View file

@ -21,49 +21,45 @@ from typing import Optional
class VeoVideoGenerator:
"""Complete Veo video generation client using LiteLLM proxy."""
def __init__(self, base_url: str = "http://localhost:4000/gemini/v1beta",
api_key: str = "sk-1234"):
def __init__(
self,
base_url: str = "http://localhost:4000/gemini/v1beta",
api_key: str = "sk-1234",
):
"""
Initialize the Veo video generator.
Args:
base_url: Base URL for the LiteLLM proxy with Gemini pass-through
api_key: API key for LiteLLM proxy authentication
"""
self.base_url = base_url
self.api_key = api_key
self.headers = {
"x-goog-api-key": api_key,
"Content-Type": "application/json"
}
self.headers = {"x-goog-api-key": api_key, "Content-Type": "application/json"}
def generate_video(self, prompt: str) -> Optional[str]:
"""
Initiate video generation with Veo.
Args:
prompt: Text description of the video to generate
Returns:
Operation name if successful, None otherwise
"""
print(f"🎬 Generating video with prompt: '{prompt}'")
url = f"{self.base_url}/models/veo-3.0-generate-preview:predictLongRunning"
payload = {
"instances": [{
"prompt": prompt
}]
}
payload = {"instances": [{"prompt": prompt}]}
try:
response = requests.post(url, headers=self.headers, json=payload)
response.raise_for_status()
data = response.json()
operation_name = data.get("name")
if operation_name:
print(f"✅ Video generation started: {operation_name}")
return operation_name
@ -71,58 +67,64 @@ class VeoVideoGenerator:
print("❌ No operation name returned")
print(f"Response: {json.dumps(data, indent=2)}")
return None
except requests.RequestException as e:
print(f"❌ Failed to start video generation: {e}")
if hasattr(e, 'response') and e.response is not None:
if hasattr(e, "response") and e.response is not None:
try:
error_data = e.response.json()
print(f"Error details: {json.dumps(error_data, indent=2)}")
except:
print(f"Error response: {e.response.text}")
return None
def wait_for_completion(self, operation_name: str, max_wait_time: int = 600) -> Optional[str]:
def wait_for_completion(
self, operation_name: str, max_wait_time: int = 600
) -> Optional[str]:
"""
Poll operation status until video generation is complete.
Args:
operation_name: Name of the operation to monitor
max_wait_time: Maximum time to wait in seconds (default: 10 minutes)
Returns:
Video URI if successful, None otherwise
"""
print("⏳ Waiting for video generation to complete...")
operation_url = f"{self.base_url}/{operation_name}"
start_time = time.time()
poll_interval = 10 # Start with 10 seconds
while time.time() - start_time < max_wait_time:
try:
print(f"🔍 Polling status... ({int(time.time() - start_time)}s elapsed)")
print(
f"🔍 Polling status... ({int(time.time() - start_time)}s elapsed)"
)
response = requests.get(operation_url, headers=self.headers)
response.raise_for_status()
data = response.json()
# Check for errors
if "error" in data:
print("❌ Error in video generation:")
print(json.dumps(data["error"], indent=2))
return None
# Check if operation is complete
is_done = data.get("done", False)
if is_done:
print("🎉 Video generation complete!")
try:
# Extract video URI from nested response
video_uri = data["response"]["generateVideoResponse"]["generatedSamples"][0]["video"]["uri"]
video_uri = data["response"]["generateVideoResponse"][
"generatedSamples"
][0]["video"]["uri"]
print(f"📹 Video URI: {video_uri}")
return video_uri
except KeyError as e:
@ -130,64 +132,68 @@ class VeoVideoGenerator:
print("Full response:")
print(json.dumps(data, indent=2))
return None
# Wait before next poll, with exponential backoff
time.sleep(poll_interval)
poll_interval = min(poll_interval * 1.2, 30) # Cap at 30 seconds
except requests.RequestException as e:
print(f"❌ Error polling operation status: {e}")
time.sleep(poll_interval)
print(f"⏰ Timeout after {max_wait_time} seconds")
return None
def download_video(self, video_uri: str, output_filename: str = "generated_video.mp4") -> bool:
def download_video(
self, video_uri: str, output_filename: str = "generated_video.mp4"
) -> bool:
"""
Download the generated video file.
Args:
video_uri: URI of the video to download (from Google's response)
output_filename: Local filename to save the video
Returns:
True if download successful, False otherwise
"""
print(f"⬇️ Downloading video...")
print(f"Original URI: {video_uri}")
# Convert Google URI to LiteLLM proxy URI
# Example: files/abc123 -> /gemini/v1beta/files/abc123:download?alt=media
if video_uri.startswith("files/"):
download_path = f"{video_uri}:download?alt=media"
else:
download_path = video_uri
litellm_download_url = f"{self.base_url}/{download_path}"
print(f"Download URL: {litellm_download_url}")
try:
# Download with streaming and redirect handling
response = requests.get(
litellm_download_url,
headers=self.headers,
litellm_download_url,
headers=self.headers,
stream=True,
allow_redirects=True # Handle redirects automatically
allow_redirects=True, # Handle redirects automatically
)
response.raise_for_status()
# Save video file
with open(output_filename, 'wb') as f:
with open(output_filename, "wb") as f:
downloaded_size = 0
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
downloaded_size += len(chunk)
# Progress indicator for large files
if downloaded_size % (1024 * 1024) == 0: # Every MB
print(f"📦 Downloaded {downloaded_size / (1024*1024):.1f} MB...")
print(
f"📦 Downloaded {downloaded_size / (1024*1024):.1f} MB..."
)
# Verify file was created and has content
if os.path.exists(output_filename):
file_size = os.path.getsize(output_filename)
@ -203,48 +209,52 @@ class VeoVideoGenerator:
else:
print("❌ File was not created")
return False
except requests.RequestException as e:
print(f"❌ Download failed: {e}")
if hasattr(e, 'response') and e.response is not None:
if hasattr(e, "response") and e.response is not None:
print(f"Status code: {e.response.status_code}")
print(f"Response headers: {dict(e.response.headers)}")
return False
def generate_and_download(self, prompt: str, output_filename: str = None) -> bool:
"""
Complete workflow: generate video and download it.
Args:
prompt: Text description for video generation
output_filename: Output filename (auto-generated if None)
Returns:
True if successful, False otherwise
"""
# Auto-generate filename if not provided
if output_filename is None:
timestamp = int(time.time())
safe_prompt = "".join(c for c in prompt[:30] if c.isalnum() or c in (' ', '-', '_')).rstrip()
output_filename = f"veo_video_{safe_prompt.replace(' ', '_')}_{timestamp}.mp4"
safe_prompt = "".join(
c for c in prompt[:30] if c.isalnum() or c in (" ", "-", "_")
).rstrip()
output_filename = (
f"veo_video_{safe_prompt.replace(' ', '_')}_{timestamp}.mp4"
)
print("=" * 60)
print("🎬 VEO VIDEO GENERATION WORKFLOW")
print("=" * 60)
# Step 1: Generate video
operation_name = self.generate_video(prompt)
if not operation_name:
return False
# Step 2: Wait for completion
video_uri = self.wait_for_completion(operation_name)
if not video_uri:
return False
# Step 3: Download video
success = self.download_video(video_uri, output_filename)
if success:
print("=" * 60)
print("🎉 SUCCESS! Video generation complete!")
@ -254,51 +264,51 @@ class VeoVideoGenerator:
print("=" * 60)
print("❌ FAILED! Video generation or download failed")
print("=" * 60)
return success
def main():
"""
Example usage of the VeoVideoGenerator.
Configure these environment variables:
- LITELLM_BASE_URL: Your LiteLLM proxy URL (default: http://localhost:4000/gemini/v1beta)
- LITELLM_API_KEY: Your LiteLLM API key (default: sk-1234)
"""
# Configuration from environment or defaults
base_url = os.getenv("LITELLM_BASE_URL", "http://localhost:4000/gemini/v1beta")
api_key = os.getenv("LITELLM_API_KEY", "sk-1234")
print("🚀 Starting Veo Video Generation Example")
print(f"📡 Using LiteLLM proxy at: {base_url}")
# Initialize generator
generator = VeoVideoGenerator(base_url=base_url, api_key=api_key)
# Example prompts - try different ones!
example_prompts = [
"A cat playing with a ball of yarn in a sunny garden",
"Ocean waves crashing against rocky cliffs at sunset",
"A bustling city street with people walking and cars passing by",
"A peaceful forest with sunlight filtering through the trees"
"A peaceful forest with sunlight filtering through the trees",
]
# Use first example or get from user
prompt = example_prompts[0]
print(f"🎬 Using prompt: '{prompt}'")
# Generate and download video
success = generator.generate_and_download(prompt)
if success:
print("\n✅ Example completed successfully!")
print("💡 Try modifying the prompt in the script for different videos!")
else:
print("\n❌ Example failed!")
print("🔧 Check your LiteLLM proxy configuration and Google AI Studio API key")
# Troubleshooting tips
print("\n🔍 Troubleshooting:")
print("1. Ensure LiteLLM proxy is running with Google AI Studio pass-through")

4
deploy/charts/litellm-helm/templates/deployment.yaml
View file

@ -47,7 +47,7 @@ spec:
{{- toYaml .Values.podSecurityContext | nindent 8 }}
{{- with .Values.extraInitContainers }}
initContainers:
{{- toYaml . | nindent 8 }}
{{- tpl (toYaml .) $ | nindent 8 }}
{{- end }}
containers:
- name: {{ include "litellm.name" . }}
@ -212,7 +212,7 @@ spec:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.extraContainers }}
{{- toYaml . | nindent 8 }}
{{- tpl (toYaml .) $ | nindent 8 }}
{{- end }}
volumes:
{{ if .Values.securityContext.readOnlyRootFilesystem }}

4
deploy/charts/litellm-helm/templates/migrations-job.yaml
View file

@ -37,7 +37,7 @@ spec:
serviceAccountName: {{ include "litellm.migrationServiceAccountName" . }}
{{- with .Values.migrationJob.extraInitContainers }}
initContainers:
{{- toYaml . | nindent 8 }}
{{- tpl (toYaml .) $ | nindent 8 }}
{{- end }}
containers:
- name: prisma-migrations
@ -96,7 +96,7 @@ spec:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.migrationJob.extraContainers }}
{{- toYaml . | nindent 8 }}
{{- tpl (toYaml .) $ | nindent 8 }}
{{- end }}
{{- with .Values.volumes }}
volumes:

58
deploy/charts/litellm-helm/tests/deployment_tests.yaml
View file

@ -319,3 +319,61 @@ tests:
asserts:
- notExists:
path: spec.minReadySeconds
- it: should work with extraInitContainers
template: deployment.yaml
set:
extraInitContainers:
- name: init-test
image: busybox:latest
command: ["echo", "hello"]
asserts:
- contains:
path: spec.template.spec.initContainers
content:
name: init-test
image: busybox:latest
command: ["echo", "hello"]
- it: should support tpl in extraInitContainers
template: deployment.yaml
set:
image:
repository: ghcr.io/berriai/litellm-database
tag: test
extraInitContainers:
- name: init-tpl
image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}"
command: ["echo", "hello"]
asserts:
- contains:
path: spec.template.spec.initContainers
content:
name: init-tpl
image: "ghcr.io/berriai/litellm-database:test"
command: ["echo", "hello"]
- it: should work with extraContainers
template: deployment.yaml
set:
extraContainers:
- name: sidecar
image: busybox:latest
asserts:
- contains:
path: spec.template.spec.containers
content:
name: sidecar
image: busybox:latest
- it: should support tpl in extraContainers
template: deployment.yaml
set:
image:
repository: ghcr.io/berriai/litellm-database
tag: test
extraContainers:
- name: sidecar-tpl
image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}"
asserts:
- contains:
path: spec.template.spec.containers
content:
name: sidecar-tpl
image: "ghcr.io/berriai/litellm-database:test"

66
deploy/charts/litellm-helm/tests/migrations-job_tests.yaml
View file

@ -188,3 +188,69 @@ tests:
- equal:
path: spec.template.spec.serviceAccountName
value: pre-existing-sa
- it: should work with extraInitContainers
template: migrations-job.yaml
set:
migrationJob:
enabled: true
extraInitContainers:
- name: init-test
image: busybox:latest
command: ["echo", "hello"]
asserts:
- contains:
path: spec.template.spec.initContainers
content:
name: init-test
image: busybox:latest
command: ["echo", "hello"]
- it: should support tpl in extraInitContainers
template: migrations-job.yaml
set:
image:
repository: ghcr.io/berriai/litellm-database
tag: test
migrationJob:
enabled: true
extraInitContainers:
- name: init-tpl
image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}"
command: ["echo", "hello"]
asserts:
- contains:
path: spec.template.spec.initContainers
content:
name: init-tpl
image: "ghcr.io/berriai/litellm-database:test"
command: ["echo", "hello"]
- it: should work with extraContainers
template: migrations-job.yaml
set:
migrationJob:
enabled: true
extraContainers:
- name: sidecar
image: busybox:latest
asserts:
- contains:
path: spec.template.spec.containers
content:
name: sidecar
image: busybox:latest
- it: should support tpl in extraContainers
template: migrations-job.yaml
set:
image:
repository: ghcr.io/berriai/litellm-database
tag: test
migrationJob:
enabled: true
extraContainers:
- name: sidecar-tpl
image: "{{ .Values.image.repository }}:{{ .Values.image.tag }}"
asserts:
- contains:
path: spec.template.spec.containers
content:
name: sidecar-tpl
image: "ghcr.io/berriai/litellm-database:test"

13
docker/Dockerfile.database
View file

@ -26,10 +26,8 @@ RUN apk add --no-cache \
npm \
libsndfile
ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
UV_PROJECT_ENVIRONMENT=/app/.venv \
ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
UV_LINK_MODE=copy \
XDG_CACHE_HOME=/app/.cache \
PATH="/app/.venv/bin:${PATH}"
# Copy dependency metadata first for layer caching
@ -92,11 +90,14 @@ RUN apk add --no-cache bash openssl tzdata nodejs npm python3 libsndfile supervi
{ apk del --no-cache npm 2>/dev/null || true; }
WORKDIR /app
ENV PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
XDG_CACHE_HOME=/app/.cache \
PATH="/app/.venv/bin:${PATH}"
ENV PATH="/app/.venv/bin:${PATH}"
COPY --from=builder /app /app
# Prisma binaries live in $HOME/.cache (default prisma-python location),
# which is /root/.cache here. Copy them from the builder so they survive
# deployments that volume-mount /app/.cache (e.g. readOnlyRootFilesystem
# + emptyDir) — otherwise the mount would shadow the baked-in query engine.
COPY --from=builder /root/.cache /root/.cache
RUN find /app/.venv -type f -path "*/tornado/test/*" -delete && \
find /app/.venv -type d -path "*/tornado/test" -delete

119
docker/Dockerfile.non_root
View file

@ -15,29 +15,21 @@ COPY --from=uvbin /uv /usr/local/bin/uv
COPY --from=uvbin /uvx /usr/local/bin/uvx
RUN for i in 1 2 3; do \
apk add --no-cache \
python3 \
python3-dev \
clang \
llvm \
lld \
gcc \
linux-headers \
build-base \
bash \
coreutils \
curl \
openssl \
openssl-dev \
nodejs \
npm \
libsndfile && break || sleep 5; \
apk add --no-cache \
python3 \
python3-dev \
gcc \
bash \
coreutils \
curl \
openssl \
libsndfile \
nodejs && break || sleep 5; \
done
ENV UV_PROJECT_ENVIRONMENT=/app/.venv \
UV_LINK_MODE=copy \
NVM_DIR=/root/.nvm \
PATH="/root/.nvm/versions/node/v20.20.2/bin:/app/.venv/bin:${PATH}" \
PATH="/app/.venv/bin:${PATH}" \
LITELLM_NON_ROOT=true \
PRISMA_BINARY_CACHE_DIR=/app/.cache/prisma-python/binaries \
PRISMA_CLI_BINARY_TARGETS="debian-openssl-3.0.x" \
@ -49,7 +41,8 @@ COPY enterprise/pyproject.toml enterprise/
COPY litellm-proxy-extras/pyproject.toml litellm-proxy-extras/
# Install third-party dependencies (cached unless pyproject.toml/uv.lock change)
RUN uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \
RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
uv sync --frozen --no-install-project --no-install-workspace --no-default-groups --no-editable \
--extra proxy \
--extra proxy-runtime \
--extra extra_proxy \
@ -62,38 +55,12 @@ COPY . .
# Set non-root flag for build time consistency
ENV LITELLM_NON_ROOT=true
# Build Admin UI once and stage the static output for the runtime image.
# NOTE: .npmrc files (which may set ignore-scripts=true and min-release-age=3d)
# are temporarily renamed during npm install/ci so they don't block lifecycle
# scripts needed by the build. This is safe because npm ci installs from
# package-lock.json with pinned versions + integrity hashes.
# Stage the pre-built Admin UI from the checked-in Next.js static export.
# _experimental/out/ is regenerated as part of the release runbook.
# Restructure extensionless routes (foo.html -> foo/index.html) to match the layout
# proxy_server.py expects, and drop a readiness marker.
RUN mkdir -p /var/lib/litellm/ui /var/lib/litellm/assets && \
([ -f /app/.npmrc ] && mv /app/.npmrc /app/.npmrc.bak || true) && \
NVM_VERSION="v0.40.4" && \
NVM_CHECKSUM="4b7412c49960c7d31e8df72da90c1fb5b8cccb419ac99537b737028d497aba4f" && \
NODE_VERSION="v20.20.2" && \
NVM_SCRIPT="/tmp/install-nvm.sh" && \
curl -fsSL "https://raw.githubusercontent.com/nvm-sh/nvm/${NVM_VERSION}/install.sh" -o "$NVM_SCRIPT" && \
echo "${NVM_CHECKSUM} ${NVM_SCRIPT}" | sha256sum -c - && \
bash "$NVM_SCRIPT" && \
export NVM_DIR="$HOME/.nvm" && \
. "$NVM_DIR/nvm.sh" && \
nvm install "${NODE_VERSION}" && \
nvm use "${NODE_VERSION}" && \
npm install -g npm@11.12.1 && \
npm install -g node-gyp@12.2.0 && \
ln -sf "$(npm root -g)/node-gyp" "$(npm root -g)/npm/node_modules/node-gyp" && \
npm cache clean --force && \
cd /app/ui/litellm-dashboard && \
if [ -f "/app/enterprise/enterprise_ui/enterprise_colors.json" ]; then \
cp /app/enterprise/enterprise_ui/enterprise_colors.json ./ui_colors.json; \
fi && \
([ -f .npmrc ] && mv .npmrc .npmrc.bak || true) && \
npm ci --no-audit --no-fund && \
([ -f .npmrc.bak ] && mv .npmrc.bak .npmrc || true) && \
([ -f /app/.npmrc.bak ] && mv /app/.npmrc.bak /app/.npmrc || true) && \
npm run build && \
cp -r /app/ui/litellm-dashboard/out/* /var/lib/litellm/ui/ && \
cp -r /app/litellm/proxy/_experimental/out/. /var/lib/litellm/ui/ && \
cp /app/litellm/proxy/logo.jpg /var/lib/litellm/assets/logo.jpg && \
( cd /var/lib/litellm/ui && \
for html_file in *.html; do \
@ -103,10 +70,10 @@ RUN mkdir -p /var/lib/litellm/ui /var/lib/litellm/assets && \
mv "$html_file" "$folder_name/index.html"; \
fi; \
done && \
touch .litellm_ui_ready ) && \
cd /app/ui/litellm-dashboard && rm -rf ./out
touch .litellm_ui_ready )
RUN if [ "$PROXY_EXTRAS_SOURCE" = "published" ]; then \
RUN --mount=type=cache,target=/app/.cache/uv,id=litellm-uv-cache \
if [ "$PROXY_EXTRAS_SOURCE" = "published" ]; then \
uv sync --frozen --no-default-groups --no-editable \
--extra proxy \
--extra proxy-runtime \
@ -123,10 +90,7 @@ RUN if [ "$PROXY_EXTRAS_SOURCE" = "published" ]; then \
--python python3; \
fi
RUN mkdir -p /app/.cache/npm && \
prisma generate --schema=./schema.prisma && \
prisma --version && \
prisma migrate diff --from-empty --to-schema-datamodel ./schema.prisma --script > /dev/null 2>&1 || true
RUN prisma generate --schema=./schema.prisma
RUN sed -i 's/\r$//' docker/entrypoint.sh && chmod +x docker/entrypoint.sh && \
sed -i 's/\r$//' docker/prod_entrypoint.sh && chmod +x docker/prod_entrypoint.sh
@ -137,33 +101,11 @@ WORKDIR /app
USER root
RUN for i in 1 2 3; do \
apk upgrade --no-cache && break || sleep 5; \
apk upgrade --no-cache && break || sleep 5; \
done && \
for i in 1 2 3; do \
apk add --no-cache python3 bash openssl tzdata nodejs npm supervisor libsndfile && break || sleep 5; \
done && \
apk upgrade --no-cache nodejs && \
npm install -g npm@11.12.1 tar@7.5.11 glob@11.1.0 @isaacs/brace-expansion@5.0.1 minimatch@10.2.4 diff@8.0.3 && \
GLOBAL="$(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 && \
find "$GLOBAL/npm" -type d -name "minimatch" -path "*/node_modules/minimatch" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/minimatch" "$d"; \
done && \
find "$GLOBAL/npm" -type d -name "diff" -path "*/node_modules/diff" | while read d; do \
rm -rf "$d" && cp -rL "$GLOBAL/diff" "$d"; \
done && \
find /usr/local/lib /usr/lib -path "*/node_modules/npm/package.json" -exec \
sed -i 's/"tar": "\^7\.5\.[0-9]*"/"tar": "^7.5.10"/g; s/"minimatch": "\^10\.[0-9.]*"/"minimatch": "^10.2.4"/g' {} + 2>/dev/null && \
npm cache clean --force && \
{ apk del --no-cache npm 2>/dev/null || true; }
apk add --no-cache python3 bash openssl tzdata supervisor libsndfile nodejs && break || sleep 5; \
done
COPY --from=builder /app /app
COPY --from=builder /var/lib/litellm/ui /var/lib/litellm/ui
@ -179,15 +121,10 @@ ENV PATH="/app/.venv/bin:${PATH}" \
PRISMA_SKIP_POSTINSTALL_GENERATE=1 \
PRISMA_HIDE_UPDATE_MESSAGE=1 \
PRISMA_ENGINES_CHECKSUM_IGNORE_MISSING=1 \
NPM_CONFIG_CACHE=/app/.cache/npm \
NPM_CONFIG_PREFER_OFFLINE=true \
PRISMA_OFFLINE_MODE=true
RUN sed -i 's/\r$//' docker/entrypoint.sh && \
sed -i 's/\r$//' docker/prod_entrypoint.sh && \
chmod +x docker/entrypoint.sh docker/prod_entrypoint.sh && \
mkdir -p /nonexistent /.npm /var/lib/litellm/assets /var/lib/litellm/ui /tmp/.npm && \
chown -R nobody:nogroup /app /var/lib/litellm/ui /var/lib/litellm/assets /nonexistent /.npm /tmp/.npm && \
RUN mkdir -p /nonexistent /var/lib/litellm/assets /var/lib/litellm/ui && \
chown -R nobody:nogroup /app /var/lib/litellm/ui /var/lib/litellm/assets /nonexistent && \
PRISMA_PATH=$(python -c "import os, prisma; print(os.path.dirname(prisma.__file__))") && \
chown -R nobody:nogroup "$PRISMA_PATH" && \
LITELLM_PKG_MIGRATIONS_PATH="$(python -c 'import os, litellm_proxy_extras; print(os.path.dirname(litellm_proxy_extras.__file__))' 2>/dev/null || echo '')/migrations" && \
@ -201,7 +138,7 @@ RUN sed -i 's/\r$//' docker/entrypoint.sh && \
[ -n "$LITELLM_PROXY_EXTRAS_PATH" ] && chmod -R g+w "$LITELLM_PROXY_EXTRAS_PATH" || true && \
chmod -R g+rX "$PRISMA_PATH" /var/lib/litellm/ui /var/lib/litellm/assets /app/.cache
USER nobody
USER 65534
RUN prisma generate --schema=./schema.prisma

2
docker/tests/nonroot.yaml
View file

@ -2,7 +2,7 @@ schemaVersion: 2.0.0
metadataTest:
entrypoint: ["docker/prod_entrypoint.sh"]
user: "nobody"
user: "65534"
workdir: "/app"
fileExistenceTests:

155
docs/my-website/docs/adaptive_router.md Normal file
View file

@ -0,0 +1,155 @@
# [BETA] Adaptive Router
:::info
Beta feature. Share feedback on [Discord](https://discord.gg/wuPM9dRgDw) or [Slack](https://join.slack.com/t/litellmossslack/shared_invite/zt-3o7nkuyfr-p_kbNJj8taRfXGgQI1~YyA).
:::
**Requirements:** LiteLLM Proxy with a Postgres database. Quality estimates are stored in Postgres and loaded on startup — without a database the router works but forgets everything learned on restart.
You have a cheap model and an expensive one. You want to use the cheap one when it's good enough, and the expensive one when it actually matters — without hardcoding rules you'll spend months tuning.
The adaptive router does this automatically. It tracks which model performs best for each type of request (code, writing, analysis, etc.) and routes accordingly, balancing quality against cost based on weights you control.
## Quick start
```yaml
model_list:
- model_name: gpt-4o
litellm_params:
model: openai/gpt-4o
model_info:
input_cost_per_token: 0.0000025
adaptive_router_preferences:
quality_tier: 3 # 1=budget, 2=mid, 3=frontier
strengths: ["code_generation", "analytical_reasoning"]
- model_name: gpt-4o-mini
litellm_params:
model: openai/gpt-4o-mini
model_info:
input_cost_per_token: 0.00000015
adaptive_router_preferences:
quality_tier: 2
strengths: ["factual_lookup"]
- model_name: my-router
litellm_params:
model: auto_router/adaptive_router
adaptive_router_config:
available_models: ["gpt-4o", "gpt-4o-mini"]
weights:
quality: 0.7 # raise this if quality complaints; lower if bill too high
cost: 0.3 # must sum to 1.0 with quality
```
Route to it by setting `model` to your adaptive router's name:
```bash
curl -X POST {{baseURL}}/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-d '{
"model": "my-router",
"messages": [
{"role": "user", "content": "build me a python script that parses CSV"},
{"role": "assistant", "content": "Here is a script using csv.DictReader..."},
{"role": "user", "content": "now add error handling for missing files"},
{"role": "assistant", "content": "Wrap the open() call in a try/except FileNotFoundError..."},
{"role": "user", "content": "perfect, that worked. thanks!"}
]
}'
```
The response includes a header telling you which model was actually picked:
```
x-litellm-adaptive-router-model: gpt-4o
```
The "thanks!" turn in the example above fires a satisfaction signal — that's what moves the bandit.
## Tuning cost vs. quality
The `weights` are your main lever:
| Goal | quality | cost |
|---|---|---|
| Minimize cost, quality is secondary | 0.3 | 0.7 |
| Balanced | 0.5 | 0.5 |
| Quality-first (default) | 0.7 | 0.3 |
| Quality non-negotiable | 0.9 | 0.1 |
The router learns over time. For the first ~10 requests per model, it relies on the tiers you declared. After that, real performance data takes over.
## Force a minimum quality tier per request
If a specific request needs a frontier model regardless of cost, pass this header:
```
x-litellm-min-quality-tier: 3
```
You can also pass `min_quality_tier` via request metadata instead of a header.
## What's being learned
The router classifies each request into one of 7 types and tracks how each model performs on each independently. A model that's great at factual lookup but poor at code will win factual requests and lose code requests — even if it's cheaper overall.
| Type | Example |
|---|---|
| `code_generation` | "write me a Python sort function" |
| `code_understanding` | "explain what this function does" |
| `technical_design` | "how should I design this API?" |
| `analytical_reasoning` | "calculate the probability that..." |
| `writing` | "draft an email to my team about..." |
| `factual_lookup` | "what is the capital of France?" |
| `general` | anything else |
[**See classifier code**](https://github.com/BerriAI/litellm/blob/litellm_adaptive_routing/litellm/router_strategy/adaptive_router/classifier.py)
Learning signals are inspired by [Signals: Trajectory Sampling and Triage for Agentic Interactions](https://arxiv.org/pdf/2604.00356).
## Inspect the current state
```
GET /adaptive_router/{router_name}/state
```
Returns current quality estimates per model per request type. Useful for understanding why a model is or isn't being picked.
```json
{
"routers": [
{
"router_name": "smart-cheap-router",
"available_models": ["fast", "smart"],
"weights": { "quality": 0.7, "cost": 0.3 },
"cells": [
{
"request_type": "analytical_reasoning",
"model": "fast",
"quality_mean": 0.5,
"samples": 0
},
{
"request_type": "analytical_reasoning",
"model": "smart",
"quality_mean": 0.95,
"samples": 0
}
]
}
]
}
```
`quality_mean` is the key number — it's the router's current estimate of how well that model handles that request type. `samples` counts how many real observations have moved the prior (starts at 0; the cold-start prior mass is excluded).
## Known limitations
- Latency isn't scored — a slow model can still win on quality + cost
- Signals are regex-based and English-biased — no LLM judge
- Hard cap of 200 observations per cell; no decay yet
- Once a model is picked for a session, other models' turns in that session don't contribute to learning

1
docs/my-website/docs/completion/prompt_caching.md
View file

@ -10,6 +10,7 @@ Supported Providers:
- Vertex AI (`vertex_ai/`, `vertex_ai_beta/`)
- Bedrock (`bedrock/`, `bedrock/invoke/`, `bedrock/converse`) ([All models bedrock supports prompt caching on](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html))
- Deepseek API (`deepseek/`)
- xAI (`xai/`)
For the supported providers, LiteLLM follows the OpenAI prompt caching usage object format:

25
docs/my-website/docs/completion/prompt_compression.md
View file

@ -8,6 +8,7 @@ The function keeps high-relevance and recent context, replaces low-relevance con
```python
import litellm
from litellm.types.utils import CallTypes
messages = [
{"role": "system", "content": "You are a coding assistant."},
@ -19,6 +20,7 @@ messages = [
compressed = litellm.compress(
messages=messages,
model="gpt-4o",
call_type=CallTypes.completion,
compression_trigger=1000,
compression_target=500,
)
@ -45,6 +47,7 @@ response = litellm.completion(
- `messages` (`List[dict]`, required): input conversation messages
- `model` (`str`, required): model name used for token counting
- `call_type` (`CallTypes`, default `CallTypes.completion`): the LiteLLM call type whose message schema these messages follow. Supported values: `CallTypes.completion` / `CallTypes.acompletion` (OpenAI chat-completions shape) and `CallTypes.anthropic_messages` (Anthropic Messages shape)
- `compression_trigger` (`int`, default `200000`): compress only if input token count exceeds this
- `compression_target` (`Optional[int]`, default `70% of compression_trigger`): desired post-compression token budget
- `embedding_model` (`Optional[str]`): if set, combines BM25 + embedding relevance scoring
@ -70,6 +73,28 @@ args = json.loads(tool_call.function.arguments)
full_content = compressed["cache"][args["key"]]
```
## Server-side Callback Loop (`/v1/messages`)
You can enable callback-based compression interception to make retrieval loops
transparent for Anthropic Messages calls:
```yaml
litellm_settings:
callbacks: ["compression_interception"]
compression_interception_params:
enabled: true
compression_trigger: 10000
compression_target: 7000
```
With this enabled, LiteLLM runs the following server-side flow:
1. Compresses inbound messages before the first provider call.
2. Injects the `litellm_content_retrieve` tool.
3. Detects retrieval `tool_use` blocks in the model response.
4. Resolves retrieval keys from the compression cache.
5. Reruns the model via agentic loop and returns the final answer.
## Performance
Benchmarked on [SWE-bench Lite](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Lite_bm25_27K) (real GitHub issues with ~27k tokens of BM25-retrieved repo context per problem).

41
docs/my-website/docs/providers/scaleway.md
View file

@ -60,3 +60,44 @@ curl http://localhost:4000/chat/completions \
## Supported features
Scaleway provider supports all features in [Generative APIs reference documentation ↗](https://www.scaleway.com/en/developers/api/generative-apis/), such as streaming, structured outputs and tool calling.
## Audio transcription
Scaleway's `/audio/transcriptions` endpoint is OpenAI-compatible and works with Whisper models.
### Python SDK
```python
import os
from litellm import transcription
os.environ["SCW_SECRET_KEY"] = "your-scaleway-secret-key"
with open("speech.mp3", "rb") as audio_file:
response = transcription(
model="scaleway/whisper-large-v3",
file=audio_file,
)
print(response.text)
```
### Proxy config
```yaml
model_list:
- model_name: scaleway-whisper
litellm_params:
model: scaleway/whisper-large-v3
api_key: "os.environ/SCW_SECRET_KEY"
```
### Proxy request
```bash
curl http://localhost:4000/v1/audio/transcriptions \
-H "Authorization: Bearer YOUR_LITELLM_MASTER_KEY" \
-F model="scaleway-whisper" \
-F file="@speech.mp3"
```
Supported optional params: `language`, `prompt`, `response_format`, `temperature`, `timestamp_granularities`.

11
docs/my-website/docs/providers/vertex.md
View file

@ -2061,7 +2061,7 @@ assert isinstance(
## Media Resolution Control (Images & Videos)
For Gemini 3+ models, LiteLLM supports per-part media resolution control using OpenAI's `detail` parameter. This allows you to specify different resolution levels for individual images and videos in your request, whether using `image_url` or `file` content types.
LiteLLM supports per-part media resolution control using OpenAI's `detail` parameter for all Gemini models. This allows you to specify different resolution levels for individual images and videos in your request, whether using `image_url` or `file` content types.
**Supported `detail` values:**
- `"low"` - Maps to `media_resolution: "low"` (280 tokens for images, 70 tokens per frame for videos)
@ -2146,12 +2146,12 @@ response = completion(
</Tabs>
:::info
**Per-Part Resolution:** Each image or video in your request can have its own `detail` setting, allowing mixed-resolution requests (e.g., a high-res chart alongside a low-res icon). This feature works with both `image_url` and `file` content types, and is only available for Gemini 3+ models.
**Per-Part Resolution:** Each image or video in your request can have its own `detail` setting, allowing mixed-resolution requests (e.g., a high-res chart alongside a low-res icon). This feature works with both `image_url` and `file` content types across all Gemini models.
:::
## Video Metadata Control
For Gemini 3+ models, LiteLLM supports fine-grained video processing control through the `video_metadata` field. This allows you to specify frame extraction rates and time ranges for video analysis.
LiteLLM supports fine-grained video processing control through the `video_metadata` field for all Gemini models (1.x, 2.x, 3+). This allows you to specify frame extraction rates and time ranges for video analysis.
**Supported `video_metadata` parameters:**
@ -2168,8 +2168,11 @@ For Gemini 3+ models, LiteLLM supports fine-grained video processing control thr
- `fps` remains unchanged
:::
:::tip
Video clipping (`start_offset`/`end_offset`) and frame rate control (`fps`) are supported by all Gemini models, but analysis quality is significantly higher with the **Gemini 2.5 series** (e.g., `gemini-2.5-flash`, `gemini-2.5-pro`).
:::
:::warning
- **Gemini 3+ Only:** This feature is only available for Gemini 3.0 and newer models
- **Video Files Recommended:** While `video_metadata` is designed for video files, error handling for other media types is delegated to the Vertex AI API
- **File Formats Supported:** Works with `gs://`, `https://`, and base64-encoded video files
:::

95
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@ -0,0 +1,95 @@
# Agentic Loop Hook
Build a `CustomLogger` callback that intercepts a model response, fulfills tool calls server-side, and reruns the model — transparently to the caller.
:::info Supported call types
- `async` only (sync calls do not trigger the hook)
- Non-streaming only (streaming responses cannot be inspected for tool calls)
- Works on both `/v1/messages` and `/v1/chat/completions`
:::
## Implement the callback
Override two methods on `CustomLogger`:
```python
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.integrations.custom_logger import AgenticLoopPlan, AgenticLoopRequestPatch
MY_TOOL = "my_tool"
class MyToolCallback(CustomLogger):
async def async_should_run_agentic_loop(
self, response, model, messages, tools, stream, custom_llm_provider, kwargs
):
# Return (True, context_dict) if there are tool calls to handle
content = getattr(response, "content", None) or []
calls = [b for b in content if isinstance(b, dict)
and b.get("type") == "tool_use" and b.get("name") == MY_TOOL]
if not calls:
return False, {}
return True, {"tool_calls": calls}
async def async_build_agentic_loop_plan(
self, tools, model, messages, response,
anthropic_messages_provider_config,
anthropic_messages_optional_request_params,
logging_obj, stream, kwargs,
):
calls = tools["tool_calls"]
results = [f"result for {c['input']}" for c in calls] # your logic here
follow_up = messages + [
{"role": "assistant", "content": [
{"type": "tool_use", "id": c["id"], "name": c["name"], "input": c["input"]}
for c in calls
]},
{"role": "user", "content": [
{"type": "tool_result", "tool_use_id": c["id"], "content": results[i]}
for i, c in enumerate(calls)
]},
]
return AgenticLoopPlan(
run_agentic_loop=True,
request_patch=AgenticLoopRequestPatch(messages=follow_up),
)
```
For `/v1/chat/completions`, override `async_build_chat_completion_agentic_loop_plan` instead — same idea, `optional_params` replaces `anthropic_messages_optional_request_params`.
## Register it
```python
import litellm
litellm.callbacks = [MyToolCallback()]
```
Or in `config.yaml`:
```yaml
litellm_settings:
callbacks: ["my_module.MyToolCallback"]
```
## `AgenticLoopPlan` fields
| Field | Effect |
|---|---|
| `run_agentic_loop=True` + `request_patch` | Reruns the model with the patched request |
| `response_override` | Returns this value directly to the caller (no rerun) |
| `terminate=True` | Stops the loop, returns the current response |
| `run_agentic_loop=False` (default) | Skips; next callback is checked |
`AgenticLoopRequestPatch` accepts: `model`, `messages`, `tools`, `max_tokens`, `optional_params`, `kwargs`.
## Loop safety
- Default max reruns: `3` — override per-request with `kwargs["max_agentic_loops"]`
- Identical tool-call fingerprints abort the loop automatically
- Current depth is in `kwargs["_agentic_loop_depth"]`
## Examples in this repo
- `litellm/integrations/compression_interception/handler.py`
- `litellm/integrations/websearch_interception/handler.py`

3
docs/my-website/docs/proxy/config_settings.md
View file

@ -487,7 +487,8 @@ router_settings:
| AZURE_STORAGE_CLIENT_ID | The Application Client ID to use for Authentication to Azure Blob Storage logging
| AZURE_STORAGE_CLIENT_SECRET | The Application Client Secret to use for Authentication to Azure Blob Storage logging
| AZURE_VECTOR_STORE_COST_PER_GB_PER_DAY | Cost per GB per day for Azure Vector Store service
| BACKGROUND_HEALTH_CHECK_MAX_TOKENS | Optional global default for `max_tokens` on proxy background health checks when a model has no `health_check_max_tokens`. If unset, non-wildcard models default to 1. Applies to wildcard routes when set. Default is unset
| BACKGROUND_HEALTH_CHECK_MAX_TOKENS | Optional global default for `max_tokens` on proxy background health checks when a model has no `health_check_max_tokens`. If unset, non-wildcard models default to 5. Applies to wildcard routes when set. Default is unset
| BACKGROUND_HEALTH_CHECK_MAX_TOKENS_REASONING | For **non-wildcard** reasoning models (`supports_reasoning(model)=true`), this takes precedence over `BACKGROUND_HEALTH_CHECK_MAX_TOKENS` when set. If unset, reasoning models fall back to `BACKGROUND_HEALTH_CHECK_MAX_TOKENS` (if set) or default behavior. Wildcard routes ignore this. Default is unset
| BATCH_STATUS_POLL_INTERVAL_SECONDS | Interval in seconds for polling batch status. Default is 3600 (1 hour)
| BATCH_STATUS_POLL_MAX_ATTEMPTS | Maximum number of attempts for polling batch status. Default is 24 (for 24 hours)
| BEDROCK_MAX_POLICY_SIZE | Maximum size for Bedrock policy. Default is 75

26
docs/my-website/docs/proxy/health.md
View file

@ -338,7 +338,7 @@ model_list:
## Health Check Max Tokens
By default, health checks use `max_tokens=1` to minimize cost and latency. For wildcard models, the default is `max_tokens=10`.
By default, health checks use `max_tokens=5` to balance reliability with low cost and latency. For wildcard models, the default is `max_tokens=10`.
You can override this per-model by setting `health_check_max_tokens` in the `model_info` section of your config.yaml.
@ -352,6 +352,30 @@ model_list:
health_check_max_tokens: 5 # 👈 OVERRIDE HEALTH CHECK MAX TOKENS
```
### Reasoning vs non-reasoning defaults
Reasoning models (per `supports_reasoning` in the model map) often need a higher health-check `max_tokens` because providers count reasoning tokens toward the completion budget. You can set **separate** limits without listing every model:
**Per deployment (`model_info`)** — used when `health_check_max_tokens` is not set. Ignored for wildcard routes (`*` in `litellm_params.model`, i.e. the deployment model string; not `health_check_model`).
```yaml
model_list:
- model_name: openai-stack
litellm_params:
model: openai/gpt-5-nano
api_key: os.environ/OPENAI_API_KEY
model_info:
health_check_max_tokens_reasoning: 128
health_check_max_tokens_non_reasoning: 1
```
**Global (environment)**:
- `BACKGROUND_HEALTH_CHECK_MAX_TOKENS_REASONING` — for non-wildcard reasoning models, this value takes precedence when set
- `BACKGROUND_HEALTH_CHECK_MAX_TOKENS` — global fallback for all models (including wildcard routes)
If neither is set, non-wildcard models default to `5` and wildcard routes omit `max_tokens`.
## `/health/readiness`
Unprotected endpoint for checking if proxy is ready to accept requests

61
docs/my-website/docs/proxy/users.md
View file

@ -333,6 +333,67 @@ curl 'http://0.0.0.0:4000/key/generate' \
}'
```
#### **Set multiple budget windows on a key**
Apply multiple concurrent budget limits at different time scales on the same key — for example, cap a key at **$10/day** AND **$100/month**.
**When is this useful?**
A single `budget_duration` window can't prevent a bad day from burning your entire month. Multiple budget windows let you:
- Block a runaway usage spike within the day while still allowing normal monthly spend.
- Give Claude Code rollouts a daily guardrail (`24h`) and a monthly ceiling (`30d`) so a single heavy session doesn't exhaust the whole month.
- Layer fine-grained hourly limits for bursty workloads on top of a weekly cap.
:::info
See [User Budget docs](https://docs.litellm.ai/docs/proxy/users) for more on how budgets work across keys, teams, and users.
:::
**Via API**
Pass `budget_limits` as a list of `{budget_duration, max_budget}` objects:
```bash
curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer <your-master-key>' \
--header 'Content-Type: application/json' \
--data-raw '{
"budget_limits": [
{"budget_duration": "24h", "max_budget": 10},
{"budget_duration": "30d", "max_budget": 100}
]
}'
```
Each window is tracked independently and resets on its own schedule:
| `budget_duration` | Resets |
|---|---|
| `1h` | Every hour |
| `24h` | Daily at midnight UTC |
| `7d` | Every Sunday at midnight UTC |
| `30d` | 1st of every month at midnight UTC |
**Via Dashboard**
Open **Virtual Keys → Create Key → Optional Settings → Budget Windows**.
![Step 1 - open key settings](https://colony-recorder.s3.amazonaws.com/files/2026-04-01/18930ba5-67c0-4031-afc0-57f37b4e59e4/ascreenshot_ef79d8a000bb41cdacf1bd9827732ee8_text_export.jpeg)
Click **+ Add Budget Window** to add a row, choose the period from the dropdown, and enter the spend cap.
![Step 2 - add a window](https://colony-recorder.s3.amazonaws.com/files/2026-04-01/5ae8c0b3-2d03-41ad-a63c-47b20c350dfe/ascreenshot_1a7dc6c7d65544f38fd8a65604674f22_text_export.jpeg)
Add a second row for a different time period (e.g. monthly $100 on top of a daily $10).
![Step 3 - add second window](https://colony-recorder.s3.amazonaws.com/files/2026-04-01/cbded3a7-1086-4e20-8f0f-de154b76146c/ascreenshot_c51c18752c3b4f8b976d28799b2638b6_text_export.jpeg)
Each window shows the reset schedule below the input so it's always clear when spend resets.
![Step 4 - reset hints](https://colony-recorder.s3.amazonaws.com/files/2026-04-01/8754f121-1640-4892-9dd0-fd4a870418bf/ascreenshot_8079eb0df2194e8f99e5258ba4b3c082_text_export.jpeg)
### ✨ Virtual Key (Model Specific)

111
docs/my-website/docs/skills_gateway.md Normal file
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@ -0,0 +1,111 @@
# Skills Gateway
<iframe width="840" height="500" src="https://www.loom.com/embed/cb74eb79df3e4c2b83a6efae54a589f9" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
LiteLLM acts as a **Skills Registry** — a central place to register, manage, and discover Claude Code skills across your organization. Teams can publish skills once and have agents and developers find them through a single hub.
## How it works
```mermaid
graph TD
Dev["👨‍💻 Developer<br/>registers a skill<br/>(GitHub URL or subdir)"] -->|POST /claude-code/plugins| Proxy["LiteLLM Proxy<br/>(Skills Registry)"]
Admin["🔑 Admin<br/>publishes skill<br/>(marks as public)"] -->|enable via UI or API| Proxy
Proxy -->|GET /public/skill_hub| SkillHub["🗂️ Skill Hub<br/>(AI Hub → Skill Hub tab)"]
Proxy -->|GET /claude-code/marketplace.json| Marketplace["📦 Claude Code<br/>Marketplace endpoint"]
SkillHub --> Human["🧑 Human<br/>browses & discovers skills<br/>in AI Hub UI"]
Marketplace --> Agent["🤖 Agent / Claude Code<br/>installs skill with<br/>/plugin marketplace add &lt;name&gt;"]
style Proxy fill:#1a73e8,color:#fff
style SkillHub fill:#e8f0fe,color:#1a73e8
style Marketplace fill:#e8f0fe,color:#1a73e8
```
## Quick start
### 1. Register a skill
Paste any GitHub URL into the Skills UI — LiteLLM auto-detects the source type and skill name.
```bash
curl -X POST https://your-proxy/claude-code/plugins \
-H "Authorization: Bearer $LITELLM_KEY" \
-H "Content-Type: application/json" \
-d '{
"name": "grill-me",
"source": {
"source": "git-subdir",
"url": "https://github.com/mattpocock/skills",
"path": "grill-me"
},
"description": "Interview skill for relentless questioning",
"domain": "Productivity",
"namespace": "interviews"
}'
```
Skills nested in subdirectories (e.g. `github.com/org/repo/tree/main/skill-name`) are supported — LiteLLM parses the URL automatically in the UI.
### 2. Publish to hub
In the Admin UI: **AI Hub → Skill Hub → Select Skills to Make Public**.
Or via API:
```bash
curl -X POST https://your-proxy/claude-code/plugins/grill-me/enable \
-H "Authorization: Bearer $LITELLM_KEY"
```
### 3. Browse the hub
Public skills appear at:
- **Admin UI**: AI Hub → Skill Hub tab
- **Public page**: `/ui/model_hub` → Skill Hub tab (no login required)
- **API**: `GET /public/skill_hub`
### 4. Install in Claude Code
Point Claude Code at your proxy marketplace once:
```json title="~/.claude/settings.json"
{
"extraKnownMarketplaces": {
"my-org": {
"source": "url",
"url": "https://your-proxy/claude-code/marketplace.json"
}
}
}
```
Then install any skill:
```
/plugin marketplace add grill-me
```
## Skill fields
| Field | Description |
|-------|-------------|
| `name` | Unique skill identifier (used in `/plugin marketplace add`) |
| `source` | Git source — `github`, `url`, or `git-subdir` |
| `description` | Short description shown in the hub |
| `domain` | Category for grouping (e.g. `Engineering`, `Productivity`) |
| `namespace` | Subcategory within a domain (e.g. `quality`, `meetings`) |
| `keywords` | Tags for search and filtering |
| `version` | Semver string |
## API reference
| Endpoint | Auth | Description |
|----------|------|-------------|
| `POST /claude-code/plugins` | Required | Register a skill |
| `GET /claude-code/plugins` | Required | List all skills (admin) |
| `POST /claude-code/plugins/{name}/enable` | Required | Publish a skill |
| `POST /claude-code/plugins/{name}/disable` | Required | Unpublish a skill |
| `GET /public/skill_hub` | None | List public skills |
| `GET /claude-code/marketplace.json` | None | Claude Code marketplace manifest |

11
docs/my-website/docs/tutorials/claude_code_byok.md
View file

@ -35,6 +35,17 @@ By default, LiteLLM strips `x-api-key` from client requests for security. Settin
:::
:::tip Configure via UI instead of config.yaml
You can also complete this setup from the LiteLLM admin UI:
- Add the model via **Models → Add Model**, leaving the **API Key** field blank.
- Enable the toggle at **Settings → UI Settings → "Forward LLM provider auth headers"**.
Both UI actions write to the database and override `config.yaml` at runtime.
:::
## Step 2: Create a LiteLLM Virtual Key
Create a virtual key in the LiteLLM UI or via API.

16
docs/my-website/docs/tutorials/prompt_caching.md
View file

@ -8,6 +8,22 @@ Reduce costs by up to 90% by using LiteLLM to auto-inject prompt caching checkpo
<Image img={require('../../img/auto_prompt_caching.png')} style={{ width: '800px', height: 'auto' }} />
Supported Providers (`cache_control` marker):
- Anthropic API (`anthropic/`)
- AWS Bedrock - Claude (`bedrock/`)
- Vertex AI - Claude and Gemini (`vertex_ai/`)
- Google AI Studio - Gemini (`gemini/`)
- Azure AI - Claude (`azure_ai/`)
- OpenRouter - Claude, Gemini, MiniMax, GLM, z-ai routes (`openrouter/`)
- Databricks - Claude (`databricks/`)
- DashScope / Qwen (`dashscope/`)
- MiniMax (`minimax/`)
- Z.ai / GLM (`zai/`)
Provider Managed (automatic, no marker needed):
- OpenAI (`openai/`)
- DeepSeek (`deepseek/`)
- xAI (`xai/`)
## How it works

28
docs/my-website/docusaurus.config.js
View file

@ -187,6 +187,32 @@ const config = {
},
],
[
'@signalwire/docusaurus-plugin-llms-txt',
{
markdown: {
enableFiles: true,
includeDocs: true,
},
llmsTxt: {
enableLlmsFullTxt: true,
includeDocs: true,
},
ui: {
copyPageContent: {
buttonLabel: 'Copy Page',
actions: {
viewMarkdown: true,
ai: {
chatGPT: true,
claude: true,
},
},
},
},
},
],
() => ({
name: 'cripchat',
injectHtmlTags() {
@ -239,7 +265,7 @@ const config = {
],
],
themes: ['@docusaurus/theme-mermaid'],
themes: ['@docusaurus/theme-mermaid', '@signalwire/docusaurus-theme-llms-txt'],
markdown: {
mermaid: true,
},

397
docs/my-website/package-lock.json generated
View file

@ -15,6 +15,8 @@
"@docusaurus/theme-mermaid": "3.8.1",
"@inkeep/cxkit-docusaurus": "0.5.107",
"@mdx-js/react": "3.1.1",
"@signalwire/docusaurus-plugin-llms-txt": "2.0.0-alpha.7",
"@signalwire/docusaurus-theme-llms-txt": "1.0.0-alpha.9",
"clsx": "1.2.1",
"prism-react-renderer": "1.3.5",
"react": "18.3.1",
@ -24,6 +26,7 @@
},
"devDependencies": {
"@docusaurus/module-type-aliases": "3.8.1",
"ajv": "^8.18.0",
"dotenv": "16.6.1"
},
"engines": {
@ -7140,6 +7143,72 @@
"integrity": "sha512-RNiOoTPkptFtSVzQevY/yWtZwf/RxyVnPy/OcA9HBM3MlGDnBEYL5B41H0MTn0Uec8Hi+2qUtTfG2WWZBmMejQ==",
"license": "BSD-3-Clause"
},
"node_modules/@signalwire/docusaurus-plugin-llms-txt": {
"version": "2.0.0-alpha.7",
"resolved": "https://registry.npmjs.org/@signalwire/docusaurus-plugin-llms-txt/-/docusaurus-plugin-llms-txt-2.0.0-alpha.7.tgz",
"integrity": "sha512-v9EcYXVNvMydIWVIzI1H2iC4/BNdystE0jJAQIFu68SHy1a13dESz9hn5YJE9Izx18QPny1jhXym/3wEP9+8LA==",
"license": "MIT",
"dependencies": {
"fs-extra": "^11.0.0",
"hast-util-select": "^6.0.4",
"hast-util-to-html": "^9.0.5",
"hast-util-to-string": "^3.0.1",
"p-map": "^7.0.2",
"rehype-parse": "^9",
"rehype-remark": "^10",
"remark-gfm": "^4",
"remark-stringify": "^11",
"string-width": "^5.0.0",
"unified": "^11",
"unist-util-visit": "^5"
},
"engines": {
"node": ">=18.0.0"
},
"peerDependencies": {
"@docusaurus/core": "^3.0.0"
}
},
"node_modules/@signalwire/docusaurus-plugin-llms-txt/node_modules/p-map": {
"version": "7.0.4",
"resolved": "https://registry.npmjs.org/p-map/-/p-map-7.0.4.tgz",
"integrity": "sha512-tkAQEw8ysMzmkhgw8k+1U/iPhWNhykKnSk4Rd5zLoPJCuJaGRPo6YposrZgaxHKzDHdDWWZvE/Sk7hsL2X/CpQ==",
"license": "MIT",
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/@signalwire/docusaurus-theme-llms-txt": {
"version": "1.0.0-alpha.9",
"resolved": "https://registry.npmjs.org/@signalwire/docusaurus-theme-llms-txt/-/docusaurus-theme-llms-txt-1.0.0-alpha.9.tgz",
"integrity": "sha512-ULCKEKkAUZVnLr8+ocR4tl7ogiiW13Hqtoo8SfNbgOyX1l4LN3a6j3/vxgc6qRYbgWlnqY3EPC7S3tenRsDjgQ==",
"license": "MIT",
"dependencies": {
"@docusaurus/core": "^3.0.0",
"@docusaurus/theme-common": "^3.0.0",
"clsx": "^2.0.0",
"react-icons": "^5.5.0"
},
"engines": {
"node": ">=18.0.0"
},
"peerDependencies": {
"react": "^18.0.0",
"react-dom": "^18.0.0"
}
},
"node_modules/@signalwire/docusaurus-theme-llms-txt/node_modules/clsx": {
"version": "2.1.1",
"resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz",
"integrity": "sha512-eYm0QWBtUrBWZWG0d386OGAw16Z995PiOVo2B7bjWSbHedGl5e0ZWaq65kOGgUSNesEIDkB9ISbTg/JK9dhCZA==",
"license": "MIT",
"engines": {
"node": ">=6"
}
},
"node_modules/@sinclair/typebox": {
"version": "0.27.10",
"resolved": "https://registry.npmjs.org/@sinclair/typebox/-/typebox-0.27.10.tgz",
@ -8972,6 +9041,16 @@
"integrity": "sha512-x+VAiMRL6UPkx+kudNvxTl6hB2XNNCG2r+7wixVfIYwu/2HKRXimwQyaumLjMveWvT2Hkd/cAJw+QBMfJ/EKVw==",
"license": "MIT"
},
"node_modules/bcp-47-match": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/bcp-47-match/-/bcp-47-match-2.0.3.tgz",
"integrity": "sha512-JtTezzbAibu8G0R9op9zb3vcWZd9JF6M0xOYGPn0fNCd7wOpRB1mU2mH9T8gaBGbAAyIIVgB2G7xG0GP98zMAQ==",
"license": "MIT",
"funding": {
"type": "github",
"url": "https://github.com/sponsors/wooorm"
}
},
"node_modules/big.js": {
"version": "5.2.2",
"resolved": "https://registry.npmjs.org/big.js/-/big.js-5.2.2.tgz",
@ -10330,6 +10409,22 @@
"url": "https://github.com/sponsors/fb55"
}
},
"node_modules/css-selector-parser": {
"version": "3.3.0",
"resolved": "https://registry.npmjs.org/css-selector-parser/-/css-selector-parser-3.3.0.tgz",
"integrity": "sha512-Y2asgMGFqJKF4fq4xHDSlFYIkeVfRsm69lQC1q9kbEsH5XtnINTMrweLkjYMeaUgiXBy/uvKeO/a1JHTNnmB2g==",
"funding": [
{
"type": "github",
"url": "https://github.com/sponsors/mdevils"
},
{
"type": "patreon",
"url": "https://patreon.com/mdevils"
}
],
"license": "MIT"
},
"node_modules/css-tree": {
"version": "3.2.1",
"resolved": "https://registry.npmjs.org/css-tree/-/css-tree-3.2.1.tgz",
@ -11291,6 +11386,19 @@
"node": ">=8"
}
},
"node_modules/direction": {
"version": "2.0.1",
"resolved": "https://registry.npmjs.org/direction/-/direction-2.0.1.tgz",
"integrity": "sha512-9S6m9Sukh1cZNknO1CWAr2QAWsbKLafQiyM5gZ7VgXHeuaoUwffKN4q6NC4A/Mf9iiPlOXQEKW/Mv/mh9/3YFA==",
"license": "MIT",
"bin": {
"direction": "cli.js"
},
"funding": {
"type": "github",
"url": "https://github.com/sponsors/wooorm"
}
},
"node_modules/dns-packet": {
"version": "5.6.1",
"resolved": "https://registry.npmjs.org/dns-packet/-/dns-packet-5.6.1.tgz",
@ -12812,6 +12920,38 @@
"node": ">= 0.4"
}
},
"node_modules/hast-util-embedded": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/hast-util-embedded/-/hast-util-embedded-3.0.0.tgz",
"integrity": "sha512-naH8sld4Pe2ep03qqULEtvYr7EjrLK2QHY8KJR6RJkTUjPGObe1vnx585uzem2hGra+s1q08DZZpfgDVYRbaXA==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"hast-util-is-element": "^3.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-from-html": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/hast-util-from-html/-/hast-util-from-html-2.0.3.tgz",
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"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"devlop": "^1.1.0",
"hast-util-from-parse5": "^8.0.0",
"parse5": "^7.0.0",
"vfile": "^6.0.0",
"vfile-message": "^4.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-from-parse5": {
"version": "8.0.3",
"resolved": "https://registry.npmjs.org/hast-util-from-parse5/-/hast-util-from-parse5-8.0.3.tgz",
@ -12832,6 +12972,62 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-has-property": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/hast-util-has-property/-/hast-util-has-property-3.0.0.tgz",
"integrity": "sha512-MNilsvEKLFpV604hwfhVStK0usFY/QmM5zX16bo7EjnAEGofr5YyI37kzopBlZJkHD4t887i+q/C8/tr5Q94cA==",
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},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-is-body-ok-link": {
"version": "3.0.1",
"resolved": "https://registry.npmjs.org/hast-util-is-body-ok-link/-/hast-util-is-body-ok-link-3.0.1.tgz",
"integrity": "sha512-0qpnzOBLztXHbHQenVB8uNuxTnm/QBFUOmdOSsEn7GnBtyY07+ENTWVFBAnXd/zEgd9/SUG3lRY7hSIBWRgGpQ==",
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"@types/hast": "^3.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-is-element": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/hast-util-is-element/-/hast-util-is-element-3.0.0.tgz",
"integrity": "sha512-Val9mnv2IWpLbNPqc/pUem+a7Ipj2aHacCwgNfTiK0vJKl0LF+4Ba4+v1oPHFpf3bLYmreq0/l3Gud9S5OH42g==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-minify-whitespace": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/hast-util-minify-whitespace/-/hast-util-minify-whitespace-1.0.1.tgz",
"integrity": "sha512-L96fPOVpnclQE0xzdWb/D12VT5FabA7SnZOUMtL1DbXmYiHJMXZvFkIZfiMmTCNJHUeO2K9UYNXoVyfz+QHuOw==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"hast-util-embedded": "^3.0.0",
"hast-util-is-element": "^3.0.0",
"hast-util-whitespace": "^3.0.0",
"unist-util-is": "^6.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-parse-selector": {
"version": "4.0.0",
"resolved": "https://registry.npmjs.org/hast-util-parse-selector/-/hast-util-parse-selector-4.0.0.tgz",
@ -12845,6 +13041,23 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-phrasing": {
"version": "3.0.1",
"resolved": "https://registry.npmjs.org/hast-util-phrasing/-/hast-util-phrasing-3.0.1.tgz",
"integrity": "sha512-6h60VfI3uBQUxHqTyMymMZnEbNl1XmEGtOxxKYL7stY2o601COo62AWAYBQR9lZbYXYSBoxag8UpPRXK+9fqSQ==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"hast-util-embedded": "^3.0.0",
"hast-util-has-property": "^3.0.0",
"hast-util-is-body-ok-link": "^3.0.0",
"hast-util-is-element": "^3.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-raw": {
"version": "9.1.0",
"resolved": "https://registry.npmjs.org/hast-util-raw/-/hast-util-raw-9.1.0.tgz",
@ -12870,6 +13083,33 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-select": {
"version": "6.0.4",
"resolved": "https://registry.npmjs.org/hast-util-select/-/hast-util-select-6.0.4.tgz",
"integrity": "sha512-RqGS1ZgI0MwxLaKLDxjprynNzINEkRHY2i8ln4DDjgv9ZhcYVIHN9rlpiYsqtFwrgpYU361SyWDQcGNIBVu3lw==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"@types/unist": "^3.0.0",
"bcp-47-match": "^2.0.0",
"comma-separated-tokens": "^2.0.0",
"css-selector-parser": "^3.0.0",
"devlop": "^1.0.0",
"direction": "^2.0.0",
"hast-util-has-property": "^3.0.0",
"hast-util-to-string": "^3.0.0",
"hast-util-whitespace": "^3.0.0",
"nth-check": "^2.0.0",
"property-information": "^7.0.0",
"space-separated-tokens": "^2.0.0",
"unist-util-visit": "^5.0.0",
"zwitch": "^2.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-estree": {
"version": "3.1.3",
"resolved": "https://registry.npmjs.org/hast-util-to-estree/-/hast-util-to-estree-3.1.3.tgz",
@ -12898,6 +13138,29 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-html": {
"version": "9.0.5",
"resolved": "https://registry.npmjs.org/hast-util-to-html/-/hast-util-to-html-9.0.5.tgz",
"integrity": "sha512-OguPdidb+fbHQSU4Q4ZiLKnzWo8Wwsf5bZfbvu7//a9oTYoqD/fWpe96NuHkoS9h0ccGOTe0C4NGXdtS0iObOw==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"@types/unist": "^3.0.0",
"ccount": "^2.0.0",
"comma-separated-tokens": "^2.0.0",
"hast-util-whitespace": "^3.0.0",
"html-void-elements": "^3.0.0",
"mdast-util-to-hast": "^13.0.0",
"property-information": "^7.0.0",
"space-separated-tokens": "^2.0.0",
"stringify-entities": "^4.0.0",
"zwitch": "^2.0.4"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-jsx-runtime": {
"version": "2.3.6",
"resolved": "https://registry.npmjs.org/hast-util-to-jsx-runtime/-/hast-util-to-jsx-runtime-2.3.6.tgz",
@ -12925,6 +13188,32 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-mdast": {
"version": "10.1.2",
"resolved": "https://registry.npmjs.org/hast-util-to-mdast/-/hast-util-to-mdast-10.1.2.tgz",
"integrity": "sha512-FiCRI7NmOvM4y+f5w32jPRzcxDIz+PUqDwEqn1A+1q2cdp3B8Gx7aVrXORdOKjMNDQsD1ogOr896+0jJHW1EFQ==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"@types/mdast": "^4.0.0",
"@ungap/structured-clone": "^1.0.0",
"hast-util-phrasing": "^3.0.0",
"hast-util-to-html": "^9.0.0",
"hast-util-to-text": "^4.0.0",
"hast-util-whitespace": "^3.0.0",
"mdast-util-phrasing": "^4.0.0",
"mdast-util-to-hast": "^13.0.0",
"mdast-util-to-string": "^4.0.0",
"rehype-minify-whitespace": "^6.0.0",
"trim-trailing-lines": "^2.0.0",
"unist-util-position": "^5.0.0",
"unist-util-visit": "^5.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-parse5": {
"version": "8.0.0",
"resolved": "https://registry.npmjs.org/hast-util-to-parse5/-/hast-util-to-parse5-8.0.0.tgz",
@ -12954,6 +13243,35 @@
"url": "https://github.com/sponsors/wooorm"
}
},
"node_modules/hast-util-to-string": {
"version": "3.0.1",
"resolved": "https://registry.npmjs.org/hast-util-to-string/-/hast-util-to-string-3.0.1.tgz",
"integrity": "sha512-XelQVTDWvqcl3axRfI0xSeoVKzyIFPwsAGSLIsKdJKQMXDYJS4WYrBNF/8J7RdhIcFI2BOHgAifggsvsxp/3+A==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-to-text": {
"version": "4.0.2",
"resolved": "https://registry.npmjs.org/hast-util-to-text/-/hast-util-to-text-4.0.2.tgz",
"integrity": "sha512-KK6y/BN8lbaq654j7JgBydev7wuNMcID54lkRav1P0CaE1e47P72AWWPiGKXTJU271ooYzcvTAn/Zt0REnvc7A==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"@types/unist": "^3.0.0",
"hast-util-is-element": "^3.0.0",
"unist-util-find-after": "^5.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/hast-util-whitespace": {
"version": "3.0.0",
"resolved": "https://registry.npmjs.org/hast-util-whitespace/-/hast-util-whitespace-3.0.0.tgz",
@ -19478,6 +19796,15 @@
"react": "^16.8.0 || ^17 || ^18 || ^19"
}
},
"node_modules/react-icons": {
"version": "5.5.0",
"resolved": "https://registry.npmjs.org/react-icons/-/react-icons-5.5.0.tgz",
"integrity": "sha512-MEFcXdkP3dLo8uumGI5xN3lDFNsRtrjbOEKDLD7yv76v4wpnEq2Lt2qeHaQOr34I/wPN3s3+N08WkQ+CW37Xiw==",
"license": "MIT",
"peerDependencies": {
"react": "*"
}
},
"node_modules/react-is": {
"version": "16.13.1",
"resolved": "https://registry.npmjs.org/react-is/-/react-is-16.13.1.tgz",
@ -19883,6 +20210,35 @@
"regjsparser": "bin/parser"
}
},
"node_modules/rehype-minify-whitespace": {
"version": "6.0.2",
"resolved": "https://registry.npmjs.org/rehype-minify-whitespace/-/rehype-minify-whitespace-6.0.2.tgz",
"integrity": "sha512-Zk0pyQ06A3Lyxhe9vGtOtzz3Z0+qZ5+7icZ/PL/2x1SHPbKao5oB/g/rlc6BCTajqBb33JcOe71Ye1oFsuYbnw==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"hast-util-minify-whitespace": "^1.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/rehype-parse": {
"version": "9.0.1",
"resolved": "https://registry.npmjs.org/rehype-parse/-/rehype-parse-9.0.1.tgz",
"integrity": "sha512-ksCzCD0Fgfh7trPDxr2rSylbwq9iYDkSn8TCDmEJ49ljEUBxDVCzCHv7QNzZOfODanX4+bWQ4WZqLCRWYLfhag==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"hast-util-from-html": "^2.0.0",
"unified": "^11.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/rehype-raw": {
"version": "7.0.0",
"resolved": "https://registry.npmjs.org/rehype-raw/-/rehype-raw-7.0.0.tgz",
@ -19913,6 +20269,23 @@
"url": "https://opencollective.com/unified"
}
},
"node_modules/rehype-remark": {
"version": "10.0.1",
"resolved": "https://registry.npmjs.org/rehype-remark/-/rehype-remark-10.0.1.tgz",
"integrity": "sha512-EmDndlb5NVwXGfUa4c9GPK+lXeItTilLhE6ADSaQuHr4JUlKw9MidzGzx4HpqZrNCt6vnHmEifXQiiA+CEnjYQ==",
"license": "MIT",
"dependencies": {
"@types/hast": "^3.0.0",
"@types/mdast": "^4.0.0",
"hast-util-to-mdast": "^10.0.0",
"unified": "^11.0.0",
"vfile": "^6.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/relateurl": {
"version": "0.2.7",
"resolved": "https://registry.npmjs.org/relateurl/-/relateurl-0.2.7.tgz",
@ -21641,6 +22014,16 @@
"url": "https://github.com/sponsors/wooorm"
}
},
"node_modules/trim-trailing-lines": {
"version": "2.1.0",
"resolved": "https://registry.npmjs.org/trim-trailing-lines/-/trim-trailing-lines-2.1.0.tgz",
"integrity": "sha512-5UR5Biq4VlVOtzqkm2AZlgvSlDJtME46uV0br0gENbwN4l5+mMKT4b9gJKqWtuL2zAIqajGJGuvbCbcAJUZqBg==",
"license": "MIT",
"funding": {
"type": "github",
"url": "https://github.com/sponsors/wooorm"
}
},
"node_modules/trough": {
"version": "2.2.0",
"resolved": "https://registry.npmjs.org/trough/-/trough-2.2.0.tgz",
@ -21825,6 +22208,20 @@
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/unist-util-find-after": {
"version": "5.0.0",
"resolved": "https://registry.npmjs.org/unist-util-find-after/-/unist-util-find-after-5.0.0.tgz",
"integrity": "sha512-amQa0Ep2m6hE2g72AugUItjbuM8X8cGQnFoHk0pGfrFeT9GZhzN5SW8nRsiGKK7Aif4CrACPENkA6P/Lw6fHGQ==",
"license": "MIT",
"dependencies": {
"@types/unist": "^3.0.0",
"unist-util-is": "^6.0.0"
},
"funding": {
"type": "opencollective",
"url": "https://opencollective.com/unified"
}
},
"node_modules/unist-util-is": {
"version": "6.0.1",
"resolved": "https://registry.npmjs.org/unist-util-is/-/unist-util-is-6.0.1.tgz",

3
docs/my-website/package.json
View file

@ -21,6 +21,8 @@
"@docusaurus/theme-mermaid": "3.8.1",
"@inkeep/cxkit-docusaurus": "0.5.107",
"@mdx-js/react": "3.1.1",
"@signalwire/docusaurus-plugin-llms-txt": "2.0.0-alpha.7",
"@signalwire/docusaurus-theme-llms-txt": "1.0.0-alpha.9",
"clsx": "1.2.1",
"prism-react-renderer": "1.3.5",
"react": "18.3.1",
@ -30,6 +32,7 @@
},
"devDependencies": {
"@docusaurus/module-type-aliases": "3.8.1",
"ajv": "^8.18.0",
"dotenv": "16.6.1"
},
"browserslist": {

9
docs/my-website/sidebars.js
View file

@ -339,6 +339,13 @@ const sidebars = {
},
],
},
{
type: "category",
label: "Skills Gateway",
items: [
"skills_gateway",
],
},
],
},
{
@ -529,6 +536,7 @@ const sidebars = {
description: "Modify requests, responses, and more",
items: [
"proxy/call_hooks",
"proxy/agentic_loop_hook",
"proxy/rules",
]
},
@ -1052,6 +1060,7 @@ const sidebars = {
},
items: [
"routing",
"adaptive_router",
"scheduler",
"proxy/auto_routing",
"proxy/load_balancing",

196
enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
View file

@ -3,6 +3,7 @@ Base class for sending emails to user after creating keys or invite links
"""
import html
import json
import os
from typing import List, Literal, Optional
@ -47,6 +48,15 @@ from litellm.secret_managers.main import get_secret_bool
from litellm.types.integrations.slack_alerting import LITELLM_LOGO_URL
def _parse_email_list(raw) -> List[str]:
"""Parse emails from a list or comma-separated string."""
if isinstance(raw, list):
return [e.strip() for e in raw if isinstance(e, str) and e.strip()]
elif isinstance(raw, str):
return [e.strip() for e in raw.split(",") if e.strip()]
return []
class BaseEmailLogger(CustomLogger):
DEFAULT_LITELLM_EMAIL = "notifications@alerts.litellm.ai"
DEFAULT_SUPPORT_EMAIL = "support@berri.ai"
@ -312,17 +322,22 @@ class BaseEmailLogger(CustomLogger):
)
pass
async def send_max_budget_alert_email(self, event: WebhookEvent):
async def send_max_budget_alert_email(
self,
event: WebhookEvent,
threshold_pct: Optional[int] = None,
recipient_emails: Optional[List[str]] = None,
):
"""
Send email to user when max budget alert threshold is reached
"""
email_params = await self._get_email_params(
email_event=EmailEvent.max_budget_alert,
user_id=event.user_id,
user_email=event.user_email,
event_message=event.event_message,
)
Send email to user when max budget alert threshold is reached.
Args:
event: The webhook event with spend/budget info
threshold_pct: Override percentage for multi-threshold alerts (e.g. 50, 75, 100).
When None, uses EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE (old behavior).
recipient_emails: Override recipient list for multi-threshold alerts.
When None, resolves single owner email via _get_email_params (old behavior).
"""
verbose_proxy_logger.debug(
f"send_max_budget_alert_email_event: {json.dumps(event.model_dump(exclude_none=True), indent=4, default=str)}"
)
@ -334,30 +349,67 @@ class BaseEmailLogger(CustomLogger):
)
# Calculate percentage and alert threshold
percentage = int(EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE * 100)
percentage = threshold_pct if threshold_pct is not None else int(
EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE * 100
)
threshold_fraction = percentage / 100.0
alert_threshold_str = (
f"${event.max_budget * EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE:.2f}"
f"${event.max_budget * threshold_fraction:.2f}"
if event.max_budget is not None
else "N/A"
)
email_html_content = MAX_BUDGET_ALERT_EMAIL_TEMPLATE.format(
email_logo_url=email_params.logo_url,
recipient_email=email_params.recipient_email,
percentage=percentage,
spend=spend_str,
max_budget=max_budget_str,
alert_threshold=alert_threshold_str,
base_url=email_params.base_url,
email_support_contact=email_params.support_contact,
)
await self.send_email(
from_email=self.DEFAULT_LITELLM_EMAIL,
to_email=[email_params.recipient_email],
subject=email_params.subject,
html_body=email_html_content,
)
pass
if recipient_emails:
# Multi-threshold path: batch send with generic key-based greeting
email_params = await self._get_email_params(
email_event=EmailEvent.max_budget_alert,
user_id=event.user_id,
user_email=event.user_email or recipient_emails[0],
event_message=event.event_message,
)
greeting = html.escape(
event.user_email or event.key_alias or event.token or ""
)
email_html_content = MAX_BUDGET_ALERT_EMAIL_TEMPLATE.format(
email_logo_url=email_params.logo_url,
recipient_email=greeting,
percentage=percentage,
spend=spend_str,
max_budget=max_budget_str,
alert_threshold=alert_threshold_str,
base_url=email_params.base_url,
email_support_contact=email_params.support_contact,
)
await self.send_email(
from_email=self.DEFAULT_LITELLM_EMAIL,
to_email=recipient_emails,
subject=email_params.subject,
html_body=email_html_content,
)
else:
# Old path: single recipient resolved from user_id/user_email
email_params = await self._get_email_params(
email_event=EmailEvent.max_budget_alert,
user_id=event.user_id,
user_email=event.user_email,
event_message=event.event_message,
)
email_html_content = MAX_BUDGET_ALERT_EMAIL_TEMPLATE.format(
email_logo_url=email_params.logo_url,
recipient_email=email_params.recipient_email,
percentage=percentage,
spend=spend_str,
max_budget=max_budget_str,
alert_threshold=alert_threshold_str,
base_url=email_params.base_url,
email_support_contact=email_params.support_contact,
)
await self.send_email(
from_email=self.DEFAULT_LITELLM_EMAIL,
to_email=[email_params.recipient_email],
subject=email_params.subject,
html_body=email_html_content,
)
async def budget_alerts(
self,
@ -469,6 +521,13 @@ class BaseEmailLogger(CustomLogger):
# For max_budget_alert, check if we've already sent an alert
if type == "max_budget_alert":
if user_info.max_budget is not None and user_info.spend is not None:
if user_info.max_budget_alert_emails:
# New path: multi-threshold alerts
await self._handle_multi_threshold_max_budget_alert(
user_info=user_info, _cache=_cache
)
return
alert_threshold = (
user_info.max_budget * EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE
)
@ -527,6 +586,87 @@ class BaseEmailLogger(CustomLogger):
)
return
async def _handle_multi_threshold_max_budget_alert(
self,
user_info: CallInfo,
_cache: DualCache,
):
"""
Loop over configured thresholds in max_budget_alert_emails,
check cache per threshold, and send to configured recipients.
"""
if not user_info.max_budget_alert_emails or user_info.max_budget is None:
return
for threshold_str, raw_emails in user_info.max_budget_alert_emails.items():
try:
threshold_pct = int(threshold_str)
except (ValueError, TypeError):
continue
threshold_amount = user_info.max_budget * (threshold_pct / 100.0)
if user_info.spend < threshold_amount:
continue
_id = user_info.token or user_info.user_id or "default_id"
_cache_key = (
f"email_budget_alerts:max_budget_alert:{threshold_pct}:{_id}"
)
result = await _cache.async_get_cache(key=_cache_key)
if result is not None:
continue
# Parse emails + auto-include owner
emails = _parse_email_list(raw_emails)
if user_info.user_email:
emails.append(user_info.user_email)
if not emails:
verbose_proxy_logger.warning(
"No recipients for %d%% threshold on key %s, skipping alert",
threshold_pct,
_id,
)
continue
recipient_emails = list(set(emails))
event_message = f"Max Budget Alert - {threshold_pct}% of Maximum Budget Reached"
webhook_event = WebhookEvent(
event="max_budget_alert",
event_message=event_message,
spend=user_info.spend,
max_budget=user_info.max_budget,
soft_budget=user_info.soft_budget,
token=user_info.token,
customer_id=user_info.customer_id,
user_id=user_info.user_id,
team_id=user_info.team_id,
team_alias=user_info.team_alias,
organization_id=user_info.organization_id,
user_email=user_info.user_email,
key_alias=user_info.key_alias,
projected_exceeded_date=user_info.projected_exceeded_date,
projected_spend=user_info.projected_spend,
event_group=user_info.event_group,
)
try:
await self.send_max_budget_alert_email(
webhook_event,
threshold_pct=threshold_pct,
recipient_emails=recipient_emails,
)
await _cache.async_set_cache(
key=_cache_key,
value="SENT",
ttl=EMAIL_BUDGET_ALERT_TTL,
)
except Exception as e:
verbose_proxy_logger.error(
f"Error sending multi-threshold max budget alert email for {threshold_pct}%: {e}",
exc_info=True,
)
async def _get_email_params(
self,
email_event: EmailEvent,

4
enterprise/pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "litellm-enterprise"
version = "0.1.36"
version = "0.1.38"
description = "Package for LiteLLM Enterprise features"
readme = "README.md"
requires-python = ">=3.9"
@ -25,7 +25,7 @@ required-version = "==0.10.9"
module-root = ""
[tool.commitizen]
version = "0.1.36"
version = "0.1.38"
version_files = [
"pyproject.toml:^version",
"../pyproject.toml:litellm-enterprise==",

7
litellm-proxy-extras/litellm_proxy_extras/_logging.py
View file

@ -23,7 +23,8 @@ class JsonFormatter(logging.Formatter):
def _is_json_enabled():
try:
import litellm
return getattr(litellm, 'json_logs', False)
return getattr(litellm, "json_logs", False)
except (ImportError, AttributeError):
return os.getenv("JSON_LOGS", "false").lower() == "true"
@ -35,6 +36,8 @@ if not logger.handlers:
if _is_json_enabled():
handler.setFormatter(JsonFormatter())
else:
handler.setFormatter(logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s"))
handler.setFormatter(
logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger.addHandler(handler)
logger.setLevel(logging.INFO)

5
litellm-proxy-extras/litellm_proxy_extras/migrations/20260401000000_add_budget_limits/migration.sql Normal file
View file

@ -0,0 +1,5 @@
-- AlterTable: add budget_limits column to LiteLLM_VerificationToken
ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN IF NOT EXISTS "budget_limits" JSONB;
-- AlterTable: add budget_limits column to LiteLLM_TeamTable
ALTER TABLE "LiteLLM_TeamTable" ADD COLUMN IF NOT EXISTS "budget_limits" JSONB;

9
litellm-proxy-extras/litellm_proxy_extras/migrations/20260401000000_add_team_member_model_scope/migration.sql Normal file
View file

@ -0,0 +1,9 @@
-- Add per-member model scope to LiteLLM_BudgetTable
-- allowed_models: empty array = inherit team models; non-empty = enforce member-level restriction
ALTER TABLE "LiteLLM_BudgetTable"
ADD COLUMN IF NOT EXISTS "allowed_models" TEXT[] DEFAULT ARRAY[]::TEXT[];
-- Add default_team_member_models to LiteLLM_TeamTable
-- Seeds allowed_models for newly added team members; empty = no per-member restriction
ALTER TABLE "LiteLLM_TeamTable"
ADD COLUMN IF NOT EXISTS "default_team_member_models" TEXT[] DEFAULT ARRAY[]::TEXT[];

39
litellm-proxy-extras/litellm_proxy_extras/migrations/20260418000000_add_adaptive_router_tables/migration.sql Normal file
View file

@ -0,0 +1,39 @@
-- One row per (router, request_type, model). Hot path on every routing decision.
CREATE TABLE "LiteLLM_AdaptiveRouterState" (
router_name TEXT NOT NULL,
request_type TEXT NOT NULL,
model_name TEXT NOT NULL,
alpha DOUBLE PRECISION NOT NULL,
beta DOUBLE PRECISION NOT NULL,
total_samples INTEGER NOT NULL DEFAULT 0,
last_updated_at TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (router_name, request_type, model_name)
);
-- One row per (session, router, model). Updated per turn via the queue.
CREATE TABLE "LiteLLM_AdaptiveRouterSession" (
session_id TEXT NOT NULL,
router_name TEXT NOT NULL,
model_name TEXT NOT NULL,
classified_type TEXT NOT NULL,
misalignment_count INTEGER NOT NULL DEFAULT 0,
stagnation_count INTEGER NOT NULL DEFAULT 0,
disengagement_count INTEGER NOT NULL DEFAULT 0,
satisfaction_count INTEGER NOT NULL DEFAULT 0,
failure_count INTEGER NOT NULL DEFAULT 0,
loop_count INTEGER NOT NULL DEFAULT 0,
exhaustion_count INTEGER NOT NULL DEFAULT 0,
last_user_content TEXT,
last_assistant_content TEXT,
tool_call_history JSONB NOT NULL DEFAULT '[]',
pending_tool_calls JSONB NOT NULL DEFAULT '{}',
turn_count INTEGER NOT NULL DEFAULT 0,
last_processed_turn INTEGER NOT NULL DEFAULT -1,
clean_credit_awarded BOOLEAN NOT NULL DEFAULT FALSE,
terminal_status INTEGER,
last_activity_at TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (session_id, router_name, model_name)
);
CREATE INDEX "idx_adaptive_router_session_activity"
ON "LiteLLM_AdaptiveRouterSession" (last_activity_at);

3
litellm-proxy-extras/litellm_proxy_extras/migrations/20260421135425_add_team_membership_total_spend/migration.sql Normal file
View file

@ -0,0 +1,3 @@
-- AlterTable
ALTER TABLE "LiteLLM_TeamMembership" ADD COLUMN "total_spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0;

50
litellm-proxy-extras/litellm_proxy_extras/schema.prisma
View file

@ -17,8 +17,9 @@ model LiteLLM_BudgetTable {
tpm_limit BigInt?
rpm_limit BigInt?
model_max_budget Json?
budget_duration String?
budget_duration String?
budget_reset_at DateTime?
allowed_models String[] @default([]) // per-member model scope; empty = inherit team models
created_at DateTime @default(now()) @map("created_at")
created_by String
updated_at DateTime @default(now()) @updatedAt @map("updated_at")
@ -140,6 +141,8 @@ model LiteLLM_TeamTable {
team_member_permissions String[] @default([])
access_group_ids String[] @default([])
policies String[] @default([])
default_team_member_models String[] @default([]) // default allowed_models for newly added team members; empty = no per-member restriction
budget_limits Json? // per-model budget limits for the team
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])
@ -401,6 +404,7 @@ model LiteLLM_VerificationToken {
rotation_interval String? // How often to rotate (e.g., "30d", "90d")
last_rotation_at DateTime? // When this key was last rotated
key_rotation_at DateTime? // When this key should next be rotated
budget_limits Json? // per-model budget limits for the key
litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id])
litellm_organization_table LiteLLM_OrganizationTable? @relation(fields: [organization_id], references: [organization_id])
litellm_project_table LiteLLM_ProjectTable? @relation(fields: [project_id], references: [project_id])
@ -612,6 +616,7 @@ model LiteLLM_TeamMembership {
user_id String
team_id String
spend Float @default(0.0)
total_spend Float @default(0.0)
budget_id String?
litellm_budget_table LiteLLM_BudgetTable? @relation(fields: [budget_id], references: [budget_id])
@@id([user_id, team_id])
@ -1219,3 +1224,46 @@ model LiteLLM_ClaudeCodePluginTable {
@@map("LiteLLM_ClaudeCodePluginTable")
}
// Per-(router, request_type, model) Beta posterior for the adaptive router.
model LiteLLM_AdaptiveRouterState {
router_name String
request_type String
model_name String
alpha Float
beta Float
total_samples Int @default(0)
last_updated_at DateTime @default(now()) @updatedAt
@@id([router_name, request_type, model_name])
}
// Per-(session, router, model) signal counters for the adaptive router.
model LiteLLM_AdaptiveRouterSession {
session_id String
router_name String
model_name String
classified_type String
misalignment_count Int @default(0)
stagnation_count Int @default(0)
disengagement_count Int @default(0)
satisfaction_count Int @default(0)
failure_count Int @default(0)
loop_count Int @default(0)
exhaustion_count Int @default(0)
last_user_content String?
last_assistant_content String?
tool_call_history Json @default("[]")
pending_tool_calls Json @default("{}")
turn_count Int @default(0)
last_processed_turn Int @default(-1)
clean_credit_awarded Boolean @default(false)
terminal_status Int?
last_activity_at DateTime @default(now()) @updatedAt
@@id([session_id, router_name, model_name])
@@index([last_activity_at], map: "idx_adaptive_router_session_activity")
}

406
litellm-proxy-extras/litellm_proxy_extras/utils.py
View file

@ -4,8 +4,8 @@ import random
import re
import shutil
import subprocess
import tempfile
import time
from datetime import datetime
from pathlib import Path
from typing import Optional
@ -30,6 +30,26 @@ def _get_prisma_env() -> dict:
return prisma_env
_MIGRATION_TS_RE = re.compile(r"^(\d{14})_")
def _migration_timestamp(name: str) -> int:
"""Extract the leading `YYYYMMDDHHMMSS` timestamp from a migration name.
Returns 0 if the name doesn't match the Prisma pattern — unexpected-format
entries sort as "oldest" and are treated as historical.
"""
m = _MIGRATION_TS_RE.match(name)
return int(m.group(1)) if m else 0
def _max_migration_timestamp(names) -> int:
"""Max timestamp in a set/list of migration names (0 if empty)."""
if not names:
return 0
return max(_migration_timestamp(n) for n in names)
def _get_prisma_command() -> str:
"""Get the Prisma command to use, bypassing Python wrapper in offline mode."""
if str_to_bool(os.getenv("PRISMA_OFFLINE_MODE")):
@ -256,21 +276,11 @@ class ProxyExtrasDBManager:
if not database_url:
logger.error("DATABASE_URL not set")
return
# Prefer DIRECT_URL for schema introspection — pooler URLs (e.g. neon -pooler)
# do not support the extended query protocol required by prisma migrate diff.
diff_url = os.getenv("DIRECT_URL") or database_url
diff_dir = (
Path(migrations_dir)
/ "migrations"
/ f"{datetime.now().strftime('%Y%m%d%H%M%S')}_baseline_diff"
)
try:
diff_dir.mkdir(parents=True, exist_ok=True)
except Exception as e:
if "Permission denied" in str(e):
logger.warning(
f"Permission denied - {e}\nunable to baseline db. Set LITELLM_MIGRATION_DIR environment variable to a writable directory to enable migrations."
)
return
raise e
diff_dir = Path(tempfile.mkdtemp(prefix="litellm_migration_diff_"))
diff_sql_path = diff_dir / "migration.sql"
# 1. Generate migration SQL for the diff between DB and schema
@ -283,7 +293,7 @@ class ProxyExtrasDBManager:
"migrate",
"diff",
"--from-url",
database_url,
diff_url,
"--to-schema-datamodel",
schema_path,
"--script",
@ -300,7 +310,40 @@ class ProxyExtrasDBManager:
# check if the migration was created
if not diff_sql_path.exists():
logger.warning("Migration diff was not created")
logger.warning(
"Migration diff was not created (prisma migrate diff failed — "
"likely a pooler URL). Falling back to direct SQL execution of "
"each migration file."
)
# Fall back: run each migration SQL file directly via prisma db execute.
# This works with pooler URLs (no schema introspection needed) and is
# safe to re-run because migrations use IF NOT EXISTS / IF EXISTS guards.
migration_files = sorted(Path(migrations_dir).glob("*/migration.sql"))
for mig_file in migration_files:
try:
subprocess.run(
[
_get_prisma_command(),
"db",
"execute",
"--file",
str(mig_file),
"--schema",
schema_path,
],
timeout=60,
check=True,
capture_output=True,
text=True,
env=_get_prisma_env(),
)
logger.info(f"Applied migration: {mig_file.parent.name}")
except subprocess.CalledProcessError as e:
logger.warning(
f"Failed to apply migration {mig_file.parent.name}: {e.stderr}"
)
except subprocess.TimeoutExpired:
logger.warning(f"Migration {mig_file.parent.name} timed out.")
return
logger.info(f"Migration diff created at {diff_sql_path}")
@ -360,18 +403,301 @@ class ProxyExtrasDBManager:
)
@staticmethod
def setup_database(use_migrate: bool = False) -> bool:
def _strip_prisma_query_params(url: str) -> str:
"""Remove Prisma-specific query params (connection_limit, pool_timeout,
schema, etc.) from DATABASE_URL so psycopg can parse it."""
from urllib.parse import urlparse, urlunparse, parse_qsl, urlencode
parsed = urlparse(url)
if not parsed.query:
return url
libpq_params = {
"sslmode",
"sslcert",
"sslkey",
"sslrootcert",
"sslpassword",
"application_name",
"connect_timeout",
"client_encoding",
"options",
"service",
"gssencmode",
"krbsrvname",
"target_session_attrs",
}
kept = [(k, v) for k, v in parse_qsl(parsed.query) if k in libpq_params]
return urlunparse(parsed._replace(query=urlencode(kept)))
@staticmethod
def _warn_if_db_ahead_of_head(migrations_dir: str) -> None:
"""
Log a warning if _prisma_migrations contains applied migrations with
timestamps newer than every migration this build ships.
This is informational only for the v2 resolver — it tells the operator
the DB was likely migrated by a newer deployment, which is usually a
signal that this (older) version shouldn't run against it. We do NOT
block startup: many users have weird _prisma_migrations state from
prior thrashing bugs, and blocking them would be a breaking change.
Safe no-op if psycopg isn't installed or DB isn't reachable.
"""
database_url = os.getenv("DATABASE_URL")
if not database_url:
return
try:
import psycopg
except ImportError:
return
cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url)
known = set(ProxyExtrasDBManager._get_migration_names(migrations_dir))
try:
# autocommit=True keeps the SELECT outside a transaction. Without
# it, psycopg3's `with conn` calls COMMIT on clean exit — which
# fails after `UndefinedTable` (fresh DB) leaves the transaction
# in an aborted state.
with psycopg.connect(
cleaned_url, connect_timeout=10, autocommit=True
) as conn:
try:
rows = conn.execute(
"SELECT migration_name FROM _prisma_migrations "
"WHERE finished_at IS NOT NULL AND rolled_back_at IS NULL"
).fetchall()
except psycopg.errors.UndefinedTable:
return
except (psycopg.OperationalError, psycopg.DatabaseError):
# Swallow connection failures AND any other DB-layer error
# (e.g. InsufficientPrivilege if the runtime user lacks SELECT
# on _prisma_migrations). This is an informational check —
# never block startup on it.
return
applied = {r[0] for r in rows}
unknown = applied - known
if not unknown:
return
head_newest_ts = _max_migration_timestamp(known)
hostile = {
name for name in unknown if _migration_timestamp(name) > head_newest_ts
}
if not hostile:
return
sorted_hostile = sorted(hostile)
logger.warning(
"Database has %d migration(s) applied that are NEWER than any "
"migration this LiteLLM version ships. This usually means the "
"database was migrated by a newer LiteLLM deployment. Some API "
"endpoints may fail because this proxy's Prisma client does not "
"know about those schema changes. Consider upgrading this "
"deployment. Unknown: %s",
len(hostile),
", ".join(sorted_hostile[:5]) + (" ..." if len(sorted_hostile) > 5 else ""),
)
@staticmethod
def _setup_database_v2(use_migrate: bool) -> bool:
"""
v2 migration resolver (opt-in via --use_v2_migration_resolver).
Runs `prisma migrate deploy` and handles standard recovery paths
(P3005 baseline, P3009/P3018 idempotent errors). Critically, it does
NOT call `_resolve_all_migrations` — the diff-and-force recovery that
caused schema thrashing when two LiteLLM versions contended for the
same DB during rolling deploys.
Ahead-of-HEAD state (DB has migrations newer than this build ships)
is logged as a warning, not a fatal error — users whose DBs got into
weird shapes from the old thrashing should still be able to start.
"""
schema_path = ProxyExtrasDBManager._get_prisma_dir() + "/schema.prisma"
migrations_dir = ProxyExtrasDBManager._get_prisma_dir()
if not use_migrate:
# Preserve `prisma db push` path unchanged.
original_dir = os.getcwd()
os.chdir(migrations_dir)
try:
subprocess.run(
[_get_prisma_command(), "db", "push", "--accept-data-loss"],
timeout=60,
check=True,
env=_get_prisma_env(),
)
return True
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as e:
# Re-raise as RuntimeError so proxy_cli.py's
# `except RuntimeError` catches it and exits cleanly.
raise RuntimeError(f"prisma db push failed.\n\nDetail: {e}") from e
finally:
os.chdir(original_dir)
# Informational — never blocks.
ProxyExtrasDBManager._warn_if_db_ahead_of_head(migrations_dir)
original_dir = os.getcwd()
os.chdir(migrations_dir)
try:
for attempt in range(4):
try:
result = subprocess.run(
[_get_prisma_command(), "migrate", "deploy"],
timeout=60,
check=True,
capture_output=True,
text=True,
env=_get_prisma_env(),
)
logger.info(f"prisma migrate deploy stdout: {result.stdout}")
return True
except subprocess.TimeoutExpired:
logger.info(
f"prisma migrate deploy attempt {attempt + 1} timed out, retrying"
)
time.sleep(random.randrange(5, 15))
continue
except subprocess.CalledProcessError as e:
stderr = e.stderr or ""
if "P3005" in stderr and "database schema is not empty" in stderr:
logger.info(
"Schema exists but no migrations ledger — creating baseline"
)
ProxyExtrasDBManager._create_baseline_migration(schema_path)
continue
if "P3009" in stderr:
migration_match = re.search(r"`(\d+_\S+?)`", stderr)
if (
migration_match
and ProxyExtrasDBManager._is_idempotent_error(stderr)
):
name = migration_match.group(1)
logger.info(
f"Migration {name} failed idempotently — marking applied and retrying"
)
try:
ProxyExtrasDBManager._roll_back_migration(name)
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
):
pass # may already be rolled-back
try:
ProxyExtrasDBManager._resolve_specific_migration(name)
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as resolve_err:
# We're already inside the outer
# `except CalledProcessError` handler —
# re-raising CalledProcessError from here
# would escape as itself, bypassing
# proxy_cli.py's `except RuntimeError`.
raise RuntimeError(
f"Failed to mark migration {name} as applied "
f"after idempotent recovery. Manual "
f"intervention may be required.\n\n"
f"Detail: {resolve_err}"
) from resolve_err
continue
raise RuntimeError(
"Database migration failed and cannot be auto-recovered. "
f"Manual intervention required.\n\nPrisma error:\n{stderr}"
) from e
if "P3018" in stderr:
if ProxyExtrasDBManager._is_permission_error(stderr):
raise RuntimeError(
"Database migration failed due to insufficient "
"permissions. Please grant the required privileges "
f"and retry.\n\nPrisma error:\n{stderr}"
) from e
migration_match = re.search(
r"Migration name: (\d+_\S+)", stderr
)
if (
migration_match
and ProxyExtrasDBManager._is_idempotent_error(stderr)
):
name = migration_match.group(1)
logger.info(
f"Migration {name} SQL hit idempotent error — marking applied and retrying"
)
try:
ProxyExtrasDBManager._roll_back_migration(name)
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
):
pass # may already be rolled-back
try:
ProxyExtrasDBManager._resolve_specific_migration(name)
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as resolve_err:
raise RuntimeError(
f"Failed to mark migration {name} as applied "
f"after idempotent recovery. Manual "
f"intervention may be required.\n\n"
f"Detail: {resolve_err}"
) from resolve_err
continue
raise RuntimeError(
"Database migration failed and cannot be auto-recovered. "
f"Manual intervention required.\n\nPrisma error:\n{stderr}"
) from e
raise RuntimeError(
"Database migration failed and cannot be auto-recovered. "
f"Manual intervention required.\n\nPrisma error:\n{stderr}"
) from e
raise RuntimeError(
"Database migration failed after 4 attempts (retry loop "
"exhausted by timeouts or repeated idempotent-recovery "
"continues). Check database connectivity, load, and "
"_prisma_migrations ledger state."
)
finally:
os.chdir(original_dir)
@staticmethod
def setup_database(
use_migrate: bool = False, use_v2_resolver: bool = False
) -> bool:
"""
Set up the database using either prisma migrate or prisma db push
Uses migrations from litellm-proxy-extras package
Args:
schema_path (str): Path to the Prisma schema file
use_migrate (bool): Whether to use prisma migrate instead of db push
use_migrate: Whether to use prisma migrate instead of db push
use_v2_resolver: Opt into the v2 migration resolver (safer during
rolling deploys; does not run the diff-and-force recovery
that causes schema thrashing). Defaults to False for
backwards compatibility.
Returns:
bool: True if setup was successful, False otherwise
"""
if use_v2_resolver:
logger.info("Using v2 migration resolver (--use_v2_migration_resolver)")
return ProxyExtrasDBManager._setup_database_v2(use_migrate=use_migrate)
schema_path = ProxyExtrasDBManager._get_prisma_dir() + "/schema.prisma"
for attempt in range(4):
original_dir = os.getcwd()
@ -395,6 +721,14 @@ class ProxyExtrasDBManager:
logger.info("prisma migrate deploy completed")
# Skip sanity check when deploy reports no pending migrations —
# DB already matches schema, no drift to correct.
if "No pending migrations to apply" in result.stdout:
logger.info(
"No pending migrations — skipping post-migration sanity check"
)
return True
# Run sanity check to ensure DB matches schema
logger.info("Running post-migration sanity check...")
ProxyExtrasDBManager._resolve_all_migrations(
@ -419,7 +753,10 @@ class ProxyExtrasDBManager:
ProxyExtrasDBManager._roll_back_migration(
failed_migration
)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as rollback_err:
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as rollback_err:
logger.warning(
f"Failed to roll back migration {failed_migration}: {rollback_err}. "
f"It may already be in a rolled-back state."
@ -431,10 +768,19 @@ class ProxyExtrasDBManager:
logger.info(
f"✅ Migration {failed_migration} resolved, retrying to apply remaining migrations"
)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as resolve_err:
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as resolve_err:
logger.warning(
f"Failed to resolve migration {failed_migration}: {resolve_err}"
)
# Apply any schema drift not covered by the marked-as-applied migration
ProxyExtrasDBManager._resolve_all_migrations(
migrations_dir,
schema_path,
mark_all_applied=False,
)
else:
logger.info(
f"Found failed migration: {failed_migration}, marking as rolled back"
@ -531,7 +877,10 @@ class ProxyExtrasDBManager:
ProxyExtrasDBManager._roll_back_migration(
migration_name
)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as rollback_err:
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as rollback_err:
logger.warning(
f"Failed to roll back migration {migration_name}: {rollback_err}. "
f"It may already be in a rolled-back state."
@ -548,10 +897,19 @@ class ProxyExtrasDBManager:
f"✅ Migration {migration_name} resolved, "
f"retrying to apply remaining migrations"
)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as resolve_err:
except (
subprocess.CalledProcessError,
subprocess.TimeoutExpired,
) as resolve_err:
logger.warning(
f"Failed to resolve migration {migration_name}: {resolve_err}"
)
# Apply any schema drift not covered by the marked-as-applied migration
ProxyExtrasDBManager._resolve_all_migrations(
migrations_dir,
schema_path,
mark_all_applied=False,
)
else:
# Unknown P3018 error - log and re-raise for safety
logger.warning(

50
litellm-proxy-extras/migration_runbook.md
View file

@ -2,6 +2,8 @@
This is a runbook for creating and running database migrations for the LiteLLM proxy. For use for litellm engineers only.
> **AI AGENTS / ASSISTANTS:** If the script refuses with either a "STALE BRANCH" or "DESTRUCTIVE MIGRATION DETECTED" error, **do NOT** bypass it on your own (no `git rebase`, no `--skip-freshness-check`, no `--allow-destructive`). Surface the error to the human operator and wait for their explicit confirmation. See the [Branch freshness](#branch-freshness-check) and [Destructive migrations](#destructive-migrations-drop-column--drop-table) sections below.
## Step 0: Sync All `schema.prisma` Files
Before doing anything else, make sure all `schema.prisma` files in the repo are in sync. There are multiple copies that must match:
@ -46,10 +48,50 @@ uv run --with testing.postgresql python ci_cd/run_migration.py "your_migration_n
## What It Does
1. Creates temp PostgreSQL DB
2. Applies existing migrations
3. Compares with `schema.prisma`
4. Generates new migration if changes found
1. **Verifies the current branch is up to date with `origin/litellm_internal_staging`** (see [Branch freshness](#branch-freshness-check))
2. Creates temp PostgreSQL DB
3. Applies existing migrations
4. Compares with `schema.prisma`
5. Generates new migration if changes found
6. **Refuses to write if the generated SQL contains `DROP COLUMN`, `DROP TABLE`, or `DROP INDEX`**, unless `--allow-destructive` is passed
## Branch Freshness Check
Before generating anything, `run_migration.py` runs `git fetch origin <base>` and refuses to proceed if `HEAD` is behind `origin/<base>`. Default base is `litellm_internal_staging` (the branch PRs target). A previous incident saw a stale branch silently drop production columns; freshness is the first-line defense.
Flags:
- `--base-branch <name>` — check against a different base (e.g. `main`). Default is `litellm_internal_staging`.
- `--skip-freshness-check` — bypass entirely. Only for intentional migrations against an older base.
When the guard fires:
1. Update your branch:
```bash
git fetch origin && git rebase origin/litellm_internal_staging
# or git merge origin/litellm_internal_staging — whichever matches your workflow
```
2. Re-run `run_migration.py`.
> **AI AGENTS / ASSISTANTS:** Do **not** auto-rebase or auto-pass `--skip-freshness-check`. A bad conflict resolution during rebase can itself drop the human's in-progress schema edits. Surface the error and wait for explicit confirmation.
## Destructive Migrations (DROP COLUMN / DROP TABLE / DROP INDEX)
If the generated diff contains `DROP COLUMN`, `DROP TABLE`, or `DROP INDEX`, `run_migration.py` exits non-zero and refuses to write the migration file. A previous incident saw newly-added columns silently dropped by a stale branch and merged to main — this guard exists to prevent a repeat.
When the guard fires:
1. Run `git fetch origin && git status` — confirm your branch is up to date with the base branch.
2. Re-check all `schema.prisma` files are in sync (Step 0).
3. Review EACH `DROP` statement printed in the error — is it actually intended?
4. Only if the drops are genuinely intentional, re-run with the flag:
```bash
uv run --with testing.postgresql python ci_cd/run_migration.py "your_migration_name" --allow-destructive
```
> **AI AGENTS / ASSISTANTS:** Do **not** automatically re-run the command with `--allow-destructive`. If the guard fires while you are driving the runbook for a human, stop, show them the error, and wait for their explicit confirmation before passing the flag. Auto-passing `--allow-destructive` is the exact failure mode this guard exists to prevent.
## Common Fixes

4
litellm-proxy-extras/pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "litellm-proxy-extras"
version = "0.4.66"
version = "0.4.68"
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
readme = "README.md"
requires-python = ">=3.9"
@ -25,7 +25,7 @@ required-version = "==0.10.9"
module-root = ""
[tool.commitizen]
version = "0.4.66"
version = "0.4.68"
version_files = [
"pyproject.toml:^version",
"../pyproject.toml:litellm-proxy-extras==",

242
litellm-proxy-extras/tests/test_setup_database_fail_fast.py Normal file
View file

@ -0,0 +1,242 @@
"""Regression tests for ProxyExtrasDBManager v2 migration resolver.
The v2 resolver is opt-in via `--use_v2_migration_resolver` / the
`use_v2_resolver=True` kwarg. These tests exercise the v2 path; the v1
(default) behavior is unchanged from pre-fix.
"""
import subprocess
from unittest.mock import patch
import pytest
from litellm_proxy_extras.utils import (
ProxyExtrasDBManager,
_max_migration_timestamp,
_migration_timestamp,
)
def _fake_migrate_deploy_failure(returncode: int, stderr: str):
def _run(*args, **kwargs):
raise subprocess.CalledProcessError(
returncode=returncode,
cmd=args[0],
stderr=stderr,
output="",
)
return _run
def test_v2_p3018_permission_error_raises_runtime_error(monkeypatch, tmp_path):
"""v2: a permission failure during migrate deploy raises RuntimeError."""
monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x")
monkeypatch.setattr(
ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
)
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
stderr = (
"Error: P3018\nMigration name: 20250326162113_baseline\n"
"Database error code: 42501\npermission denied for schema public"
)
with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
with pytest.raises(RuntimeError, match="permission"):
ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
def test_v2_non_idempotent_p3009_raises_runtime_error(monkeypatch, tmp_path):
"""v2: a non-idempotent migration failure raises (no silent recovery)."""
monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x")
monkeypatch.setattr(
ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
)
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
stderr = (
"Error: P3009\nMigration `20260101000000_genuinely_broken` failed\n"
'Reason: syntax error at or near "BRKN" LINE 42'
)
with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
def test_strip_prisma_query_params_removes_connection_limit():
"""DATABASE_URLs with Prisma-specific params should be parseable by psycopg."""
url = "postgresql://u:p@h:5432/db?connection_limit=100&pool_timeout=60&sslmode=require"
stripped = ProxyExtrasDBManager._strip_prisma_query_params(url)
assert "connection_limit" not in stripped
assert "pool_timeout" not in stripped
assert "sslmode=require" in stripped
def test_strip_prisma_query_params_passthrough_no_query():
"""URLs without query strings are returned unchanged."""
url = "postgresql://u:p@h:5432/db"
assert ProxyExtrasDBManager._strip_prisma_query_params(url) == url
def test_migration_timestamp_extracts_leading_digits():
assert _migration_timestamp("20260101000000_add_foo") == 20260101000000
assert _migration_timestamp("20250326162113_baseline") == 20250326162113
def test_migration_timestamp_returns_zero_on_malformed():
assert _migration_timestamp("0_init") == 0
assert _migration_timestamp("not_a_migration") == 0
def test_max_migration_timestamp():
names = {"20250326000000_a", "20260415000000_b", "20251115000000_c"}
assert _max_migration_timestamp(names) == 20260415000000
def test_max_migration_timestamp_empty_set():
assert _max_migration_timestamp(set()) == 0
def test_v1_default_still_calls_resolve_all_migrations(monkeypatch, tmp_path):
"""v1 (default) continues to call _resolve_all_migrations on the happy path.
This is the existing buggy behavior — we're not fixing it in v1, only
offering v2 as opt-in. This test pins the default so that a future
inadvertent default flip is caught.
"""
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
# Stub `prisma migrate deploy` to claim success with pending migrations
# applied, which is the code path that triggers the legacy post-migration
# sanity check (a call to _resolve_all_migrations).
class FakeResult:
stdout = "Applied migration.\n"
stderr = ""
def fake_run(cmd, *args, **kwargs):
return FakeResult()
resolve_called = {"n": 0}
def fake_resolve(*args, **kwargs):
resolve_called["n"] += 1
monkeypatch.setattr("subprocess.run", fake_run)
monkeypatch.setattr(ProxyExtrasDBManager, "_resolve_all_migrations", fake_resolve)
ok = ProxyExtrasDBManager.setup_database(use_migrate=True) # v2 flag NOT set
assert ok is True
assert resolve_called["n"] == 1, "v1 default should still invoke the legacy path"
def test_v2_db_push_wraps_subprocess_error_as_runtime_error(monkeypatch, tmp_path):
"""v2: a failing `prisma db push` must raise RuntimeError, not leak
CalledProcessError past proxy_cli.py's `except RuntimeError`."""
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
stderr = "db push error"
with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
with pytest.raises(RuntimeError, match="prisma db push failed"):
ProxyExtrasDBManager.setup_database(use_migrate=False, use_v2_resolver=True)
def test_v2_warn_ahead_of_head_swallows_db_errors(monkeypatch, tmp_path):
"""_warn_if_db_ahead_of_head must never raise — it's informational.
Non-connection DB errors (e.g. InsufficientPrivilege from a user
without SELECT on _prisma_migrations) must be caught, not propagated.
"""
import psycopg
monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x")
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
class _FakeConn:
def __enter__(self):
return self
def __exit__(self, *a):
return False
def execute(self, *a, **kw):
# Simulate an InsufficientPrivilege (subclass of DatabaseError).
raise psycopg.errors.InsufficientPrivilege("permission denied")
def _fake_connect(*a, **kw):
return _FakeConn()
monkeypatch.setattr("psycopg.connect", _fake_connect)
# Must not raise.
ProxyExtrasDBManager._warn_if_db_ahead_of_head(str(tmp_path))
def test_v2_resolve_specific_migration_failure_raises_runtime_error(
monkeypatch, tmp_path
):
"""If marking a migration as applied fails inside P3009 idempotent
recovery, the subprocess error must be re-raised as RuntimeError so
proxy_cli.py catches it cleanly (instead of leaking CalledProcessError)."""
monkeypatch.setattr(
ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
)
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
monkeypatch.setattr(
ProxyExtrasDBManager, "_roll_back_migration", lambda *a, **kw: None
)
# First call: migrate deploy -> P3009 idempotent error.
# Recovery path tries _resolve_specific_migration; that also raises.
def _failing_resolve(*a, **kw):
raise subprocess.CalledProcessError(
returncode=1,
cmd="prisma migrate resolve --applied",
stderr="resolve failed",
output="",
)
monkeypatch.setattr(
ProxyExtrasDBManager, "_resolve_specific_migration", _failing_resolve
)
stderr = (
"Error: P3009\nMigration `20260101000000_some_migration` failed\n"
"relation already exists"
)
with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
with pytest.raises(
RuntimeError, match="Failed to mark migration .* as applied"
):
ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
def test_v2_does_not_call_resolve_all_migrations(monkeypatch, tmp_path):
"""v2 must never call _resolve_all_migrations — that's the bug it fixes."""
monkeypatch.setattr(
ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
)
monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
(tmp_path / "schema.prisma").write_text("// stub")
class FakeResult:
stdout = "Applied migration.\n"
stderr = ""
monkeypatch.setattr("subprocess.run", lambda *a, **kw: FakeResult())
resolve_called = {"n": 0}
monkeypatch.setattr(
ProxyExtrasDBManager,
"_resolve_all_migrations",
lambda *a, **kw: resolve_called.__setitem__("n", resolve_called["n"] + 1),
)
ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
assert ok is True
assert resolve_called["n"] == 0, "v2 must not invoke the diff-and-force recovery"

127
litellm/__init__.py
View file

@ -148,6 +148,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"vantage",
"posthog",
"levo",
"compression_interception",
]
cold_storage_custom_logger: Optional[_custom_logger_compatible_callbacks_literal] = None
logged_real_time_event_types: Optional[Union[List[str], Literal["*"]]] = None
@ -168,12 +169,12 @@ prometheus_latency_buckets: Optional[List[float]] = None
require_auth_for_metrics_endpoint: Optional[bool] = False
argilla_batch_size: Optional[int] = None
datadog_use_v1: Optional[bool] = False # if you want to use v1 datadog logged payload.
gcs_pub_sub_use_v1: Optional[
bool
] = False # if you want to use v1 gcs pubsub logged payload
generic_api_use_v1: Optional[
bool
] = False # if you want to use v1 generic api logged payload
gcs_pub_sub_use_v1: Optional[bool] = (
False # if you want to use v1 gcs pubsub logged payload
)
generic_api_use_v1: Optional[bool] = (
False # if you want to use v1 generic api logged payload
)
argilla_transformation_object: Optional[Dict[str, Any]] = None
_async_input_callback: List[
Union[str, Callable, "CustomLogger"]
@ -193,26 +194,26 @@ _async_failure_callback: List[
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
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
filter_invalid_headers: Optional[bool] = False
add_user_information_to_llm_headers: Optional[
bool
] = None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
add_user_information_to_llm_headers: Optional[bool] = (
None # adds user_id, team_id, token hash (params from StandardLoggingMetadata) to request headers
)
store_audit_logs = False # Enterprise feature, allow users to see audit logs
skip_system_message_in_guardrail: bool = False
### end of callbacks #############
email: Optional[
str
] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
token: Optional[
str
] = None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
email: Optional[str] = (
None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
)
token: Optional[str] = (
None # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
)
telemetry = True
max_tokens: int = DEFAULT_MAX_TOKENS # OpenAI Defaults
drop_params = bool(os.getenv("LITELLM_DROP_PARAMS", False))
@ -274,9 +275,11 @@ use_client: bool = False
ssl_verify: Union[str, bool] = True
ssl_security_level: Optional[str] = None
ssl_certificate: Optional[str] = None
ssl_ecdh_curve: Optional[
str
] = None # Set to 'X25519' to disable PQC and improve performance
user_url_validation: bool = True
user_url_allowed_hosts: List[str] = []
ssl_ecdh_curve: Optional[str] = (
None # Set to 'X25519' to disable PQC and improve performance
)
disable_streaming_logging: bool = False
disable_token_counter: bool = False
disable_add_transform_inline_image_block: bool = False
@ -330,20 +333,24 @@ enable_loadbalancing_on_batch_endpoints: Optional[bool] = None
enable_caching_on_provider_specific_optional_params: bool = (
False # feature-flag for caching on optional params - e.g. 'top_k'
)
caching: bool = False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
caching_with_models: bool = False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
cache: Optional[
"Cache"
] = None # cache object <- use this - https://docs.litellm.ai/docs/caching
caching: bool = (
False # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
)
caching_with_models: bool = (
False # # Not used anymore, will be removed in next MAJOR release - https://github.com/BerriAI/litellm/discussions/648
)
cache: Optional["Cache"] = (
None # cache object <- use this - https://docs.litellm.ai/docs/caching
)
default_in_memory_ttl: Optional[float] = None
default_redis_ttl: Optional[float] = None
default_redis_batch_cache_expiry: Optional[float] = None
model_alias_map: Dict[str, str] = {}
model_group_settings: Optional["ModelGroupSettings"] = None
max_budget: float = 0.0 # set the max budget across all providers
budget_duration: Optional[
str
] = None # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
budget_duration: Optional[str] = (
None # proxy only - resets budget after fixed duration. You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d").
)
default_soft_budget: float = (
DEFAULT_SOFT_BUDGET # by default all litellm proxy keys have a soft budget of 50.0
)
@ -352,7 +359,9 @@ forward_traceparent_to_llm_provider: bool = False
_current_cost = 0.0 # private variable, used if max budget is set
error_logs: Dict = {}
add_function_to_prompt: bool = False # if function calling not supported by api, append function call details to system prompt
add_function_to_prompt: bool = (
False # if function calling not supported by api, append function call details to system prompt
)
client_session: Optional[httpx.Client] = None
aclient_session: Optional[httpx.AsyncClient] = None
model_fallbacks: Optional[List] = None # Deprecated for 'litellm.fallbacks'
@ -376,6 +385,7 @@ datadog_params: Optional[Union[DatadogInitParams, Dict]] = None
aws_sqs_callback_params: Optional[Dict] = None
generic_logger_headers: Optional[Dict] = None
default_key_generate_params: Optional[Dict] = None
default_key_max_budget_alert_emails: Optional[Dict[str, list]] = None
upperbound_key_generate_params: Optional[LiteLLM_UpperboundKeyGenerateParams] = None
key_generation_settings: Optional["StandardKeyGenerationConfig"] = None
default_internal_user_params: Optional[Dict] = None
@ -399,7 +409,9 @@ prometheus_emit_stream_label: bool = False
disable_add_prefix_to_prompt: bool = (
False # used by anthropic, to disable adding prefix to prompt
)
disable_copilot_system_to_assistant: bool = 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: bool = (
False # If false (default), converts all 'system' role messages to 'assistant' for GitHub Copilot compatibility. Set to true to disable this behavior.
)
public_mcp_servers: Optional[List[str]] = None
public_model_groups: Optional[List[str]] = None
public_agent_groups: Optional[List[str]] = None
@ -408,9 +420,9 @@ public_agent_groups: Optional[List[str]] = None
# Old format: { "displayName": "url" } (for backward compatibility)
public_model_groups_links: Dict[str, Union[str, Dict[str, Any]]] = {}
#### REQUEST PRIORITIZATION #######
priority_reservation: Optional[
Dict[str, Union[float, "PriorityReservationDict"]]
] = None
priority_reservation: Optional[Dict[str, Union[float, "PriorityReservationDict"]]] = (
None
)
# priority_reservation_settings is lazy-loaded via __getattr__
# Only declare for type checking - at runtime __getattr__ handles it
if TYPE_CHECKING:
@ -418,13 +430,17 @@ if TYPE_CHECKING:
######## Networking Settings ########
use_aiohttp_transport: bool = True # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
use_aiohttp_transport: bool = (
True # Older variable, aiohttp is now the default. use disable_aiohttp_transport instead.
)
aiohttp_trust_env: bool = False # set to true to use HTTP_ Proxy settings
disable_aiohttp_transport: bool = False # Set this to true to use httpx instead
disable_aiohttp_trust_env: bool = (
False # When False, aiohttp will respect HTTP(S)_PROXY env vars
)
force_ipv4: bool = False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
force_ipv4: bool = (
False # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
)
network_mock: bool = False # When True, use mock transport — no real network calls
####### STOP SEQUENCE LIMIT #######
@ -439,13 +455,13 @@ context_window_fallbacks: Optional[List] = None
content_policy_fallbacks: Optional[List] = None
allowed_fails: int = 3
allow_dynamic_callback_disabling: bool = True
num_retries_per_request: Optional[
int
] = None # for the request overall (incl. fallbacks + model retries)
num_retries_per_request: Optional[int] = (
None # for the request overall (incl. fallbacks + model retries)
)
####### SECRET MANAGERS #####################
secret_manager_client: Optional[
Any
] = None # list of instantiated key management clients - e.g. azure kv, infisical, etc.
secret_manager_client: Optional[Any] = (
None # list of instantiated key management clients - e.g. azure kv, infisical, etc.
)
_google_kms_resource_name: Optional[str] = None
_key_management_system: Optional["KeyManagementSystem"] = None
# Note: KeyManagementSettings must be eagerly imported because _key_management_settings
@ -458,12 +474,12 @@ output_parse_pii: bool = False
from litellm.litellm_core_utils.get_model_cost_map import get_model_cost_map
model_cost = get_model_cost_map(url=model_cost_map_url)
cost_discount_config: Dict[
str, float
] = {} # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
cost_margin_config: Dict[
str, Union[float, Dict[str, float]]
] = {} # Provider-specific or global cost margins. Examples:
cost_discount_config: Dict[str, float] = (
{}
) # Provider-specific cost discounts {"vertex_ai": 0.05} = 5% discount
cost_margin_config: Dict[str, Union[float, Dict[str, float]]] = (
{}
) # Provider-specific or global cost margins. Examples:
# Percentage: {"openai": 0.10} = 10% margin
# Fixed: {"openai": {"fixed_amount": 0.001}} = $0.001 per request
# Global: {"global": 0.05} = 5% global margin on all providers
@ -1313,12 +1329,12 @@ from . import rag
from .types.llms.custom_llm import CustomLLMItem
custom_provider_map: List[CustomLLMItem] = []
_custom_providers: List[
str
] = [] # internal helper util, used to track names of custom providers
disable_hf_tokenizer_download: Optional[
bool
] = None # disable huggingface tokenizer download. Defaults to openai clk100
_custom_providers: List[str] = (
[]
) # internal helper util, used to track names of custom providers
disable_hf_tokenizer_download: Optional[bool] = (
None # disable huggingface tokenizer download. Defaults to openai clk100
)
global_disable_no_log_param: bool = False
### CLI UTILITIES ###
@ -1486,6 +1502,9 @@ if TYPE_CHECKING:
from .llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import (
AmazonAnthropicClaudeMessagesConfig as AmazonAnthropicClaudeMessagesConfig,
)
from .llms.bedrock.messages.mantle_transformation import (
AmazonMantleMessagesConfig as AmazonMantleMessagesConfig,
)
from .llms.together_ai.chat import TogetherAIConfig as TogetherAIConfig
from .llms.nlp_cloud.chat.handler import NLPCloudConfig as NLPCloudConfig
from .llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (

1
litellm/_lazy_imports.py
View file

@ -14,6 +14,7 @@ How it works:
This makes importing litellm much faster because we don't load heavy dependencies
until they're actually needed.
"""
import importlib
import sys
from typing import Any, Optional, cast, Callable

5
litellm/_lazy_imports_registry.py
View file

@ -171,6 +171,7 @@ LLM_CONFIG_NAMES = (
"CohereChatConfig",
"AnthropicMessagesConfig",
"AmazonAnthropicClaudeMessagesConfig",
"AmazonMantleMessagesConfig",
"TogetherAIConfig",
"NLPCloudConfig",
"VertexGeminiConfig",
@ -715,6 +716,10 @@ _LLM_CONFIGS_IMPORT_MAP = {
".llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation",
"AmazonAnthropicClaudeMessagesConfig",
),
"AmazonMantleMessagesConfig": (
".llms.bedrock.messages.mantle_transformation",
"AmazonMantleMessagesConfig",
),
"TogetherAIConfig": (".llms.together_ai.chat", "TogetherAIConfig"),
"NLPCloudConfig": (".llms.nlp_cloud.chat.handler", "NLPCloudConfig"),
"VertexGeminiConfig": (

6
litellm/a2a_protocol/main.py
View file

@ -120,9 +120,9 @@ def _get_a2a_model_info(a2a_client: Any, kwargs: Dict[str, Any]) -> str:
litellm_logging_obj.model = model
litellm_logging_obj.custom_llm_provider = custom_llm_provider
litellm_logging_obj.model_call_details["model"] = model
litellm_logging_obj.model_call_details[
"custom_llm_provider"
] = custom_llm_provider
litellm_logging_obj.model_call_details["custom_llm_provider"] = (
custom_llm_provider
)
return agent_name

4
litellm/a2a_protocol/providers/bedrock_agentcore/handler.py
View file

@ -99,9 +99,7 @@ class BedrockAgentCoreA2AHandler:
)
)
verbose_logger.info(
f"BedrockAgentCore A2A: Sending streaming request to {url}"
)
verbose_logger.info(f"BedrockAgentCore A2A: Sending streaming request to {url}")
client = get_async_httpx_client(
llm_provider=cast(Any, httpxSpecialProvider.A2AProvider),

6
litellm/a2a_protocol/streaming_iterator.py
View file

@ -168,9 +168,9 @@ class A2AStreamingIterator:
result: Dict[str, Any] = {
"id": getattr(self.request, "id", "unknown"),
"jsonrpc": "2.0",
"usage": usage.model_dump()
if hasattr(usage, "model_dump")
else dict(usage),
"usage": (
usage.model_dump() if hasattr(usage, "model_dump") else dict(usage)
),
}
# Add final chunk result if available

1
litellm/anthropic_interface/__init__.py
View file

@ -1,6 +1,7 @@
"""
Anthropic module for LiteLLM
"""
from .messages import acreate, create
__all__ = ["acreate", "create"]

4
litellm/anthropic_interface/messages/__init__.py
View file

@ -38,7 +38,7 @@ async def acreate(
top_k: Optional[int] = None,
top_p: Optional[float] = None,
container: Optional[Dict] = None,
**kwargs
**kwargs,
) -> Union[AnthropicMessagesResponse, AsyncIterator]:
"""
Async wrapper for Anthropic's messages API
@ -97,7 +97,7 @@ def create(
top_k: Optional[int] = None,
top_p: Optional[float] = None,
container: Optional[Dict] = None,
**kwargs
**kwargs,
) -> Union[
AnthropicMessagesResponse,
AsyncIterator[Any],

10
litellm/caching/caching_handler.py
View file

@ -78,7 +78,9 @@ class CachingHandlerResponse(BaseModel):
cached_result: Optional[Any] = None
final_embedding_cached_response: Optional[EmbeddingResponse] = None
embedding_all_elements_cache_hit: bool = False # this is set to True when all elements in the list have a cache hit in the embedding cache, if true return the final_embedding_cached_response no need to make an API call
embedding_all_elements_cache_hit: bool = (
False # this is set to True when all elements in the list have a cache hit in the embedding cache, if true return the final_embedding_cached_response no need to make an API call
)
in_memory_cache_obj = InMemoryCache()
@ -1014,9 +1016,9 @@ class LLMCachingHandler:
}
if litellm.cache is not None:
litellm_params[
"preset_cache_key"
] = litellm.cache._get_preset_cache_key_from_kwargs(**kwargs)
litellm_params["preset_cache_key"] = (
litellm.cache._get_preset_cache_key_from_kwargs(**kwargs)
)
else:
litellm_params["preset_cache_key"] = None

1
litellm/caching/gcs_cache.py
View file

@ -1,6 +1,7 @@
"""GCS Cache implementation
Supports syncing responses to Google Cloud Storage Buckets using HTTP requests.
"""
import json
import asyncio
from typing import Optional

4
litellm/completion_extras/litellm_responses_transformation/handler.py
View file

@ -142,9 +142,7 @@ class ResponsesToCompletionBridgeHandler:
custom_llm_provider=custom_llm_provider,
)
def completion(
self, *args, **kwargs
) -> Union[
def completion(self, *args, **kwargs) -> Union[
Coroutine[Any, Any, Union["ModelResponse", "CustomStreamWrapper"]],
"ModelResponse",
"CustomStreamWrapper",

36
litellm/completion_extras/litellm_responses_transformation/transformation.py
View file

@ -300,10 +300,10 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
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)
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)
@ -506,9 +506,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
annotations=annotations,
reasoning_items=cast(
Optional[List[ChatCompletionReasoningItem]],
[pending_reasoning_item]
if pending_reasoning_item is not None
else None,
(
[pending_reasoning_item]
if pending_reasoning_item is not None
else None
),
),
)
@ -566,9 +568,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
reasoning_content=reasoning_content,
reasoning_items=cast(
Optional[List[ChatCompletionReasoningItem]],
[pending_reasoning_item]
if pending_reasoning_item is not None
else None,
(
[pending_reasoning_item]
if pending_reasoning_item is not None
else None
),
),
)
choices.append(
@ -1154,9 +1158,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
)
if provider_specific_fields:
function_chunk[
"provider_specific_fields"
] = provider_specific_fields
function_chunk["provider_specific_fields"] = (
provider_specific_fields
)
tool_call_index = parsed_chunk.get("output_index", 0)
tool_call_chunk = ChatCompletionToolCallChunk(
@ -1229,9 +1233,9 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
# Add provider_specific_fields to function if present
if provider_specific_fields:
function_chunk[
"provider_specific_fields"
] = provider_specific_fields
function_chunk["provider_specific_fields"] = (
provider_specific_fields
)
tool_call_index = parsed_chunk.get("output_index", 0)
tool_call_chunk = ChatCompletionToolCallChunk(

420
litellm/compression/compress.py
View file

@ -1,9 +1,9 @@
"""
Main compress() function — orchestrates BM25/embedding scoring, message stubbing,
and retrieval tool injection.
Main compress() function — normalizes input messages, orchestrates BM25/embedding
scoring, message stubbing, and retrieval tool injection.
"""
from typing import Any, Dict, List, Optional, Set, Union, cast
from typing import Any, Dict, List, Optional, Set, Tuple, Union, cast
from litellm.caching.dual_cache import DualCache
from litellm.compression.message_stubbing import (
@ -15,27 +15,196 @@ from litellm.compression.retrieval_tool import build_retrieval_tool
from litellm.compression.scoring.bm25 import bm25_score_messages
from litellm.litellm_core_utils.token_counter import token_counter
from litellm.types.compression import CompressedResult
from litellm.types.utils import AllMessageValues, Message
from litellm.types.utils import CallTypes
# CallTypes that produce Anthropic-shaped messages (structured content blocks).
# Everything else is treated as OpenAI chat-completions shape.
_ANTHROPIC_CALL_TYPES = frozenset({CallTypes.anthropic_messages.value})
# CallTypes that are valid targets for compression. Compression operates on
# message-shaped inputs, so we only accept call types whose payload is a list
# of role/content messages.
_SUPPORTED_CALL_TYPES = frozenset(
{
CallTypes.completion.value,
CallTypes.acompletion.value,
CallTypes.anthropic_messages.value,
}
)
def _normalize_call_type(call_type: Union[CallTypes, str]) -> str:
"""Return the string value for a ``CallTypes`` enum or a raw string."""
if isinstance(call_type, CallTypes):
return call_type.value
return call_type
def _is_anthropic_call_type(call_type: str) -> bool:
return call_type in _ANTHROPIC_CALL_TYPES
def _build_retrieval_tools(keys: List[str], call_type: str) -> List[dict]:
"""
Build retrieval tool definitions in the target request schema.
- Chat-completions call types: keep OpenAI function-tool schema.
- Anthropic messages call type: remap to Anthropic's custom tool schema.
"""
if not keys:
return []
openai_tools = [build_retrieval_tool(keys)]
if not _is_anthropic_call_type(call_type):
return openai_tools
# Lazy import to avoid introducing provider transformation imports during
# module import for non-Anthropic call paths.
from litellm.llms.anthropic.chat.transformation import AnthropicConfig
anthropic_tools, _mcp_servers = AnthropicConfig()._map_tools(openai_tools)
return cast(List[dict], anthropic_tools)
def _content_to_text(content: Any) -> str:
"""
Convert OpenAI/Anthropic message content blocks to plain text.
Text extraction policy:
- Include text-bearing fields only (`text` blocks + string values).
- For `tool_result`, expand into nested `content` items.
- Ignore non-textual blocks (images/documents/tool metadata/thinking metadata).
Implemented iteratively (stack-based) to avoid unbounded recursion.
"""
parts: List[str] = []
stack: List[Any] = [content]
while stack:
item = stack.pop()
if isinstance(item, str):
parts.append(item)
elif isinstance(item, list):
# Push list items in reverse order so they are processed left-to-right.
for element in reversed(item):
stack.append(element)
elif isinstance(item, dict):
item_type = item.get("type")
if item_type == "text":
parts.append(str(item.get("text", "")))
elif item_type == "tool_result":
stack.append(item.get("content", ""))
return " ".join(parts)
def _normalize_messages_for_compression(
messages: List[dict],
call_type: str,
) -> Tuple[List[dict], List[dict]]:
"""
Normalize each original message to a text-surrogate content for scoring.
Returns:
(normalized_messages, original_messages_copy)
"""
if call_type not in _SUPPORTED_CALL_TYPES:
raise ValueError(
f"Unsupported call_type={call_type!r} for compression. "
f"Expected one of: {sorted(_SUPPORTED_CALL_TYPES)}."
)
original_messages: List[Dict[str, Any]] = [dict(m) for m in messages]
normalized_messages: List[dict] = []
for msg in original_messages:
normalized_messages.append(
{
**msg,
"content": _content_to_text(msg.get("content", "")),
}
)
return normalized_messages, original_messages
def _extract_last_user_message(messages: List[dict]) -> str:
"""Return the text content of the last user message."""
for msg in reversed(messages):
if msg.get("role") == "user":
content = msg.get("content", "")
if isinstance(content, str):
return content
if isinstance(content, list):
parts = []
for part in content:
if isinstance(part, dict) and part.get("type") == "text":
parts.append(part.get("text", ""))
elif isinstance(part, str):
parts.append(part)
return " ".join(parts)
return _content_to_text(msg.get("content", ""))
return ""
def _extract_tool_use_ids(content: Any) -> List[str]:
if not isinstance(content, list):
return []
tool_use_ids: List[str] = []
for part in content:
if not isinstance(part, dict):
continue
if part.get("type") != "tool_use":
continue
tool_use_id = part.get("id")
if isinstance(tool_use_id, str) and tool_use_id:
tool_use_ids.append(tool_use_id)
return tool_use_ids
def _extract_tool_result_ids(content: Any) -> Set[str]:
if not isinstance(content, list):
return set()
tool_result_ids: Set[str] = set()
for part in content:
if not isinstance(part, dict):
continue
if part.get("type") != "tool_result":
continue
tool_use_id = part.get("tool_use_id")
if isinstance(tool_use_id, str) and tool_use_id:
tool_result_ids.add(tool_use_id)
return tool_result_ids
def _extract_anthropic_tool_exchange_spans(
messages: List[dict],
) -> Tuple[List[Set[int]], Optional[str]]:
"""
Return atomic 2-message spans for Anthropic tool exchanges.
Each assistant message containing `tool_use` must be immediately followed by a
user message containing matching `tool_result` blocks for all tool_use ids.
"""
spans: List[Set[int]] = []
i = 0
while i < len(messages):
current = messages[i]
if current.get("role") != "assistant":
i += 1
continue
tool_use_ids = _extract_tool_use_ids(current.get("content"))
if not tool_use_ids:
i += 1
continue
if i + 1 >= len(messages):
return [], "invalid_anthropic_tool_sequence"
next_msg = messages[i + 1]
if next_msg.get("role") != "user":
return [], "invalid_anthropic_tool_sequence"
tool_result_ids = _extract_tool_result_ids(next_msg.get("content"))
if not tool_result_ids:
return [], "invalid_anthropic_tool_sequence"
for tool_use_id in tool_use_ids:
if tool_use_id not in tool_result_ids:
return [], "invalid_anthropic_tool_sequence"
spans.append({i, i + 1})
i += 2
return spans, None
def _get_protected_indices(messages: List[dict]) -> List[int]:
"""
Return indices of messages that must never be compressed:
@ -87,9 +256,98 @@ def _combine_scores(
return [bm25_weight * b + emb_weight * e for b, e in zip(norm_bm25, norm_emb)]
def _select_kept_indices_for_budget(
normalized_messages: List[dict],
original_messages: List[dict],
combined_scores: List[float],
compression_target: int,
model: str,
initial_kept_indices: Set[int],
tool_exchange_spans: List[Set[int]],
) -> Tuple[Set[int], Dict[int, dict]]:
kept_indices = set(initial_kept_indices)
current_tokens = 0
for i in kept_indices:
current_tokens += token_counter(
model=model,
text=cast(str, normalized_messages[i].get("content", "") or ""),
)
# Fill token budget from highest-scoring units.
# A unit is either:
# 1) a single message index, or
# 2) an Anthropic tool-exchange span that must be kept/dropped atomically.
truncated_overrides: Dict[int, dict] = {} # idx -> truncated message dict
span_id_by_index: Dict[int, int] = {}
for span_id, span in enumerate(tool_exchange_spans):
for idx in span:
span_id_by_index[idx] = span_id
# Build single-message candidate units (non-span messages).
candidate_units: List[Tuple[float, Tuple[int, ...], bool]] = []
for idx in range(len(normalized_messages)):
if idx in span_id_by_index or idx in kept_indices:
continue
candidate_units.append((combined_scores[idx], (idx,), True))
# Build span candidate units (atomic keep/drop for tool exchanges).
for span in tool_exchange_spans:
span_indices = tuple(sorted(span))
if any(idx in kept_indices for idx in span_indices):
continue
span_score = max(combined_scores[idx] for idx in span_indices)
candidate_units.append((span_score, span_indices, False))
# Sort by descending relevance score.
candidate_units.sort(key=lambda item: item[0], reverse=True)
for _score, indices, can_truncate in candidate_units:
if any(idx in kept_indices for idx in indices):
continue
msg_tokens = 0
for idx in indices:
msg_tokens += token_counter(
model=model,
text=cast(str, normalized_messages[idx].get("content", "") or ""),
)
remaining = compression_target - current_tokens
if remaining <= 0:
break # budget exhausted
if current_tokens + msg_tokens <= compression_target:
# Fits entirely
kept_indices.update(indices)
current_tokens += msg_tokens
elif can_truncate and len(indices) == 1 and remaining >= 100:
# Too large to fit whole single message, but we have budget — truncate it.
idx = indices[0]
truncated = truncate_message(original_messages[idx], remaining)
truncated_tokens = token_counter(
model=model,
text=truncated.get("content", "") or "",
)
truncated_overrides[idx] = truncated
kept_indices.add(idx)
current_tokens += truncated_tokens
return kept_indices, truncated_overrides
def _get_dropped_tool_span_indices(
kept_indices: Set[int], tool_exchange_spans: List[Set[int]]
) -> Set[int]:
dropped_tool_span_indices: Set[int] = set()
for span in tool_exchange_spans:
if not any(idx in kept_indices for idx in span):
dropped_tool_span_indices.update(span)
return dropped_tool_span_indices
def compress(
messages: List[dict],
model: str,
call_type: Union[CallTypes, str] = CallTypes.completion,
compression_trigger: int = 200_000,
compression_target: Optional[int] = None,
embedding_model: Optional[str] = None,
@ -108,6 +366,12 @@ def compress(
Parameters:
messages: The conversation messages to (potentially) compress.
model: The LLM model name — used for token counting.
call_type: The LiteLLM call type whose message schema these messages
follow. Supported values:
- ``CallTypes.completion`` / ``CallTypes.acompletion`` — OpenAI
chat-completions shape (default)
- ``CallTypes.anthropic_messages`` — Anthropic Messages shape
(structured content blocks + atomic tool exchanges)
compression_trigger: Only compress if input exceeds this token count.
compression_target: Target token count after compression.
Defaults to ``compression_trigger // 2``.
@ -122,29 +386,37 @@ def compress(
A ``CompressedResult`` dict containing compressed messages, token
counts, a cache of original content, and the retrieval tool definition.
"""
call_type_str = _normalize_call_type(call_type)
normalized_messages, original_messages = _normalize_messages_for_compression(
messages=messages,
call_type=call_type_str,
)
if compression_target is None:
compression_target = compression_trigger * 7 // 10
original_tokens = token_counter(
model=model, messages=cast(List[Union[AllMessageValues, Message]], messages)
model=model,
messages=cast(List[Any], original_messages),
)
# Pass through if below trigger
if original_tokens <= compression_trigger:
return CompressedResult(
messages=messages,
messages=original_messages,
original_tokens=original_tokens,
compressed_tokens=original_tokens,
compression_ratio=0.0,
cache={},
tools=[],
compression_skipped_reason="below_trigger",
)
# Extract query for relevance scoring
query = _extract_last_user_message(messages)
query = _extract_last_user_message(normalized_messages)
# Score each message
bm25_scores = bm25_score_messages(query, messages)
bm25_scores = bm25_score_messages(query, normalized_messages)
if embedding_model:
from litellm.compression.scoring.embedding_scorer import (
@ -153,7 +425,7 @@ def compress(
emb_scores = embedding_score_messages(
query,
messages,
normalized_messages,
model=embedding_model,
cache=compression_cache,
embedding_model_params=embedding_model_params,
@ -162,94 +434,80 @@ def compress(
else:
combined_scores = bm25_scores
# Sort message indices by score descending
ranked_indices = sorted(
range(len(messages)),
key=lambda i: combined_scores[i],
reverse=True,
)
# Protected messages are never compressed
protected_indices = _get_protected_indices(messages)
protected_indices = _get_protected_indices(normalized_messages)
kept_indices: Set[int] = set(protected_indices)
# Count tokens for protected messages
current_tokens = 0
for i in kept_indices:
current_tokens += token_counter(
model=model, text=messages[i].get("content", "") or ""
tool_exchange_spans: List[Set[int]] = []
if _is_anthropic_call_type(call_type_str):
tool_exchange_spans, tool_sequence_error = (
_extract_anthropic_tool_exchange_spans(original_messages)
)
# Fill token budget from highest-scoring messages.
# For each candidate (ranked by relevance):
# - If it fits entirely → keep it as-is.
# - If it doesn't fit but there's meaningful remaining budget → truncate it
# to fill as much of the budget as possible.
# - Otherwise → stub it (pointer only, content goes to cache).
# Multiple messages may be truncated so we preserve partial content from
# several high-scoring messages rather than fully stubbing all but one.
truncated_overrides: Dict[int, dict] = {} # idx -> truncated message dict
for idx in ranked_indices:
if idx in kept_indices:
continue
msg_content = messages[idx].get("content", "") or ""
msg_tokens = token_counter(model=model, text=msg_content)
remaining = compression_target - current_tokens
if remaining <= 0:
break # budget exhausted
if current_tokens + msg_tokens <= compression_target:
# Fits entirely
kept_indices.add(idx)
current_tokens += msg_tokens
elif remaining >= 100:
# Too large to fit whole, but we have budget — truncate it.
truncated = truncate_message(messages[idx], remaining)
truncated_tokens = token_counter(
model=model,
text=truncated.get("content", "") or "",
if tool_sequence_error is not None:
return CompressedResult(
messages=original_messages,
original_tokens=original_tokens,
compressed_tokens=original_tokens,
compression_ratio=0.0,
cache={},
tools=[],
compression_skipped_reason=tool_sequence_error,
)
truncated_overrides[idx] = truncated
kept_indices.add(idx)
current_tokens += truncated_tokens
for span in tool_exchange_spans:
# If any message in the span is protected, keep the whole span.
if any(idx in kept_indices for idx in span):
kept_indices.update(span)
kept_indices, truncated_overrides = _select_kept_indices_for_budget(
normalized_messages=normalized_messages,
original_messages=original_messages,
combined_scores=combined_scores,
compression_target=compression_target,
model=model,
initial_kept_indices=kept_indices,
tool_exchange_spans=tool_exchange_spans,
)
# Build compressed messages and cache
compressed_messages: List[dict] = []
cache: Dict[str, str] = {}
used_keys: Set[str] = set()
dropped_tool_span_indices = _get_dropped_tool_span_indices(
kept_indices=kept_indices, tool_exchange_spans=tool_exchange_spans
)
for i, msg in enumerate(messages):
for i, msg in enumerate(original_messages):
if i in dropped_tool_span_indices:
continue
if i in kept_indices:
# Use the truncated version if we made one, otherwise the original
compressed_messages.append(truncated_overrides.get(i, msg))
else:
key = extract_key(msg, fallback_index=i, used_keys=used_keys)
content = msg.get("content", "")
if isinstance(content, list):
content = " ".join(
p.get("text", "") if isinstance(p, dict) else str(p)
for p in content
)
key = extract_key(
normalized_messages[i], fallback_index=i, used_keys=used_keys
)
content = _content_to_text(msg.get("content", ""))
cache[key] = content
compressed_messages.append(stub_message(msg, key))
# Build retrieval tool
tools = [build_retrieval_tool(list(cache.keys()))] if cache else []
# Build retrieval tool in the target request schema
tools = _build_retrieval_tools(list(cache.keys()), call_type=call_type_str)
compressed_tokens = token_counter(
model=model,
messages=cast(List[Union[AllMessageValues, Message]], compressed_messages),
messages=cast(List[Any], compressed_messages),
)
return CompressedResult(
messages=compressed_messages,
original_tokens=original_tokens,
compressed_tokens=compressed_tokens,
compression_ratio=round(1 - (compressed_tokens / original_tokens), 4)
if original_tokens > 0
else 0.0,
compression_ratio=(
round(1 - (compressed_tokens / original_tokens), 4)
if original_tokens > 0
else 0.0
),
cache=cache,
tools=tools,
)

20
litellm/constants.py
View file

@ -164,6 +164,7 @@ MCP_STDIO_ALLOWED_COMMANDS: frozenset = frozenset(
LITELLM_UI_ALLOW_HEADERS = [
"x-litellm-semantic-filter",
"x-litellm-semantic-filter-tools",
"x-litellm-adaptive-router-model",
]
# Gemini model-specific minimal thinking budget constants
@ -1360,6 +1361,25 @@ try:
)
except (ValueError, TypeError):
BACKGROUND_HEALTH_CHECK_MAX_TOKENS = None
_background_health_check_max_tokens_reasoning_env = os.getenv(
"BACKGROUND_HEALTH_CHECK_MAX_TOKENS_REASONING"
)
try:
_raw_background_health_check_max_tokens_reasoning = (
_background_health_check_max_tokens_reasoning_env.strip()
if _background_health_check_max_tokens_reasoning_env is not None
else ""
)
BACKGROUND_HEALTH_CHECK_MAX_TOKENS_REASONING: Optional[int] = (
int(_raw_background_health_check_max_tokens_reasoning)
if _raw_background_health_check_max_tokens_reasoning
else None
)
except (ValueError, TypeError):
BACKGROUND_HEALTH_CHECK_MAX_TOKENS_REASONING = None
LITTELM_INTERNAL_HEALTH_SERVICE_ACCOUNT_NAME = "litellm-internal-health-check"
LITTELM_CLI_SERVICE_ACCOUNT_NAME = "litellm-cli"
LITELLM_INTERNAL_JOBS_SERVICE_ACCOUNT_NAME = "litellm_internal_jobs"

8
litellm/containers/endpoint_factory.py
View file

@ -90,10 +90,10 @@ def create_sync_endpoint_function(endpoint_config: Dict) -> Callable:
custom_llm_provider=resolved_custom_llm_provider,
litellm_params=litellm_params,
)
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
)
if container_provider_config is None:

106
litellm/containers/main.py
View file

@ -168,7 +168,10 @@ def create_container(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[ContainerObject, Coroutine[Any, Any, ContainerObject],]:
) -> Union[
ContainerObject,
Coroutine[Any, Any, ContainerObject],
]:
"""Create a container using the OpenAI Container API.
Currently supports OpenAI
@ -208,10 +211,10 @@ def create_container(
**kwargs,
)
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if container_provider_config is None:
@ -260,7 +263,7 @@ def create_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
# Encode container_id with provider/model metadata for routing
if isinstance(container_obj, ContainerObject):
container_obj = ContainerRequestUtils.encode_container_id_in_response(
@ -269,7 +272,7 @@ def create_container(
litellm_metadata=kwargs.get("litellm_metadata"),
extra_body=extra_body,
)
return container_obj
except Exception as e:
@ -405,7 +408,10 @@ def list_containers(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[ContainerListResponse, Coroutine[Any, Any, ContainerListResponse],]:
) -> Union[
ContainerListResponse,
Coroutine[Any, Any, ContainerListResponse],
]:
"""List containers using the OpenAI Container API.
Currently supports OpenAI
@ -434,10 +440,10 @@ def list_containers(
**kwargs,
)
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if container_provider_config is None:
@ -601,7 +607,10 @@ def retrieve_container(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[ContainerObject, Coroutine[Any, Any, ContainerObject],]:
) -> Union[
ContainerObject,
Coroutine[Any, Any, ContainerObject],
]:
"""Retrieve a container using the OpenAI Container API.
Currently supports OpenAI
@ -630,7 +639,7 @@ def retrieve_container(
api_version=api_version,
**kwargs,
)
# Decode container ID and extract provider info
original_container_id, resolved_custom_llm_provider, litellm_params = (
decode_managed_container_id_for_request(
@ -643,10 +652,10 @@ def retrieve_container(
was_encoded = original_container_id != container_id
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
)
if container_provider_config is None:
@ -678,7 +687,7 @@ def retrieve_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
# Encode container_id with provider/model metadata for routing
# If input was encoded, preserve encoding in output using the decoded model_id
if isinstance(container_obj, ContainerObject):
@ -691,14 +700,14 @@ def retrieve_container(
if "model_info" not in litellm_metadata:
litellm_metadata["model_info"] = {}
litellm_metadata["model_info"]["id"] = litellm_params["model_id"]
container_obj = ContainerRequestUtils.encode_container_id_in_response(
response_obj=container_obj,
custom_llm_provider=resolved_custom_llm_provider,
litellm_metadata=litellm_metadata,
extra_body=None,
)
return container_obj
except Exception as e:
@ -822,7 +831,10 @@ def delete_container(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[DeleteContainerResult, Coroutine[Any, Any, DeleteContainerResult],]:
) -> Union[
DeleteContainerResult,
Coroutine[Any, Any, DeleteContainerResult],
]:
"""Delete a container using the OpenAI Container API.
Currently supports OpenAI
@ -851,7 +863,7 @@ def delete_container(
api_version=api_version,
**kwargs,
)
# Decode container ID and extract provider info
original_container_id, resolved_custom_llm_provider, litellm_params = (
decode_managed_container_id_for_request(
@ -864,10 +876,10 @@ def delete_container(
was_encoded = original_container_id != container_id
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
)
if container_provider_config is None:
@ -899,7 +911,7 @@ def delete_container(
timeout=timeout or DEFAULT_REQUEST_TIMEOUT,
_is_async=_is_async,
)
# Encode container_id in response with provider/model metadata for routing
# If input was encoded, preserve encoding in output using the decoded model_id
if isinstance(delete_result, DeleteContainerResult):
@ -912,14 +924,14 @@ def delete_container(
if "model_info" not in litellm_metadata:
litellm_metadata["model_info"] = {}
litellm_metadata["model_info"]["id"] = litellm_params["model_id"]
delete_result = ContainerRequestUtils.encode_container_id_in_response(
response_obj=delete_result,
custom_llm_provider=resolved_custom_llm_provider,
litellm_metadata=litellm_metadata,
extra_body=None,
)
return delete_result
except Exception as e:
@ -1057,7 +1069,10 @@ def list_container_files(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[ContainerFileListResponse, Coroutine[Any, Any, ContainerFileListResponse],]:
) -> Union[
ContainerFileListResponse,
Coroutine[Any, Any, ContainerFileListResponse],
]:
"""List files in a container using the OpenAI Container API.
Currently supports OpenAI
@ -1086,7 +1101,7 @@ def list_container_files(
api_version=api_version,
**kwargs,
)
# Decode container ID and extract provider info
original_container_id, resolved_custom_llm_provider, litellm_params = (
decode_managed_container_id_for_request(
@ -1095,12 +1110,12 @@ def list_container_files(
litellm_params=litellm_params,
)
)
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
)
if container_provider_config is None:
@ -1285,7 +1300,10 @@ def upload_container_file(
extra_query: Optional[Dict[str, Any]] = None,
extra_body: Optional[Dict[str, Any]] = None,
**kwargs,
) -> Union[ContainerFileObject, Coroutine[Any, Any, ContainerFileObject],]:
) -> Union[
ContainerFileObject,
Coroutine[Any, Any, ContainerFileObject],
]:
"""Upload a file to a container using the OpenAI Container API.
This endpoint allows uploading files directly to a container session,
@ -1343,7 +1361,7 @@ def upload_container_file(
api_version=api_version,
**kwargs,
)
# Decode container ID and extract provider info
original_container_id, resolved_custom_llm_provider, litellm_params = (
decode_managed_container_id_for_request(
@ -1352,12 +1370,12 @@ def upload_container_file(
litellm_params=litellm_params,
)
)
# get provider config
container_provider_config: Optional[
BaseContainerConfig
] = ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
container_provider_config: Optional[BaseContainerConfig] = (
ProviderConfigManager.get_provider_container_config(
provider=litellm.LlmProviders(resolved_custom_llm_provider),
)
)
if container_provider_config is None:

9
litellm/containers/utils.py
View file

@ -32,6 +32,7 @@ def decode_managed_container_id_for_request(
return original_container_id, custom_llm_provider, litellm_params
T = TypeVar("T")
@ -129,14 +130,14 @@ class ContainerRequestUtils:
litellm_metadata = litellm_metadata or {}
model_info: Dict[str, Any] = litellm_metadata.get("model_info", {}) or {}
model_id = model_info.get("id")
# Check if we should encode based on routing metadata
should_encode = False
# Case 1: Router/proxy usage (model_id from router)
if model_id is not None:
should_encode = True
# Case 2: target_model_names in extra_body (model-specific routing)
if extra_body and "target_model_names" in extra_body:
should_encode = True
@ -148,7 +149,7 @@ class ContainerRequestUtils:
model_id = target_models.split(",")[0].strip()
elif isinstance(target_models, list) and len(target_models) > 0:
model_id = str(target_models[0]).strip()
# Only encode if we have routing metadata
if should_encode and response_obj and hasattr(response_obj, "id"):
encoded_id = ResponsesAPIRequestUtils._build_container_id(

31
litellm/cost_calculator.py
View file

@ -545,10 +545,9 @@ def cost_per_token( # noqa: PLR0915
model=model, custom_llm_provider=custom_llm_provider
)
if (
(model_info.get("input_cost_per_token") or 0.0) > 0
or (model_info.get("output_cost_per_token") or 0.0) > 0
):
if (model_info.get("input_cost_per_token") or 0.0) > 0 or (
model_info.get("output_cost_per_token") or 0.0
) > 0:
return generic_cost_per_token(
model=model,
usage=usage_block,
@ -1141,9 +1140,9 @@ def completion_cost( # noqa: PLR0915
or isinstance(completion_response, dict)
): # tts returns a custom class
if isinstance(completion_response, dict):
usage_obj: Optional[
Union[dict, Usage]
] = completion_response.get("usage", {})
usage_obj: Optional[Union[dict, Usage]] = (
completion_response.get("usage", {})
)
else:
usage_obj = getattr(completion_response, "usage", {})
if isinstance(usage_obj, BaseModel) and not _is_known_usage_objects(
@ -1606,11 +1605,23 @@ def completion_cost( # noqa: PLR0915
_cache_read_cost: Optional[float] = None
_cache_creation_cost: Optional[float] = None
if cost_per_token_usage_object is not None:
_cr = getattr(cost_per_token_usage_object, "cache_read_input_tokens", None) or (cost_per_token_usage_object.model_extra or {}).get("cache_read_input_tokens")
_cc = getattr(cost_per_token_usage_object, "cache_creation_input_tokens", None) or (cost_per_token_usage_object.model_extra or {}).get("cache_creation_input_tokens")
_cr = getattr(
cost_per_token_usage_object, "cache_read_input_tokens", None
) or (cost_per_token_usage_object.model_extra or {}).get(
"cache_read_input_tokens"
)
_cc = getattr(
cost_per_token_usage_object,
"cache_creation_input_tokens",
None,
) or (cost_per_token_usage_object.model_extra or {}).get(
"cache_creation_input_tokens"
)
if (_cr or _cc) and model:
try:
_mi = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)
_mi = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
_cr_rate = _mi.get("cache_read_input_token_cost")
if _cr and _cr_rate is not None:
_cache_read_cost = float(_cr) * float(_cr_rate)

88
litellm/evals/main.py
View file

@ -152,10 +152,10 @@ def create_eval(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -343,10 +343,10 @@ def list_evals(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -513,10 +513,10 @@ def get_eval(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -682,10 +682,10 @@ def update_eval(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -893,10 +893,10 @@ def delete_eval(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1047,10 +1047,10 @@ def cancel_eval(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1230,10 +1230,10 @@ def create_run(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1418,10 +1418,10 @@ def list_runs(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1592,10 +1592,10 @@ def get_run(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1752,10 +1752,10 @@ def cancel_run(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:
@ -1921,10 +1921,10 @@ def delete_run(
custom_llm_provider = "openai"
# Get provider config
evals_api_provider_config: Optional[
BaseEvalsAPIConfig
] = ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
evals_api_provider_config: Optional[BaseEvalsAPIConfig] = (
ProviderConfigManager.get_provider_evals_api_config( # type: ignore
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if evals_api_provider_config is None:

3
litellm/exceptions.py
View file

@ -281,7 +281,7 @@ class Timeout(openai.APITimeoutError): # type: ignore
return _message
class PermissionDeniedError(openai.PermissionDeniedError): # type:ignore
class PermissionDeniedError(openai.PermissionDeniedError): # type: ignore
def __init__(
self,
message,
@ -847,6 +847,7 @@ class BudgetExceededError(Exception):
):
self.current_cost = current_cost
self.max_budget = max_budget
self.status_code = 429
message = (
message
or f"Budget has been exceeded! Current cost: {current_cost}, Max budget: {max_budget}"

12
litellm/files/main.py
View file

@ -10,7 +10,7 @@ import contextvars
import time
import uuid as uuid_module
from functools import partial
from typing import Any,Coroutine, Dict, Literal, Optional, Union, cast
from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast
import httpx
@ -53,7 +53,10 @@ from litellm.types.llms.openai import (
OpenAIFileObject,
)
from litellm.types.router import *
from litellm.types.utils import OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, LlmProviders
from litellm.types.utils import (
OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS,
LlmProviders,
)
from litellm.utils import (
ProviderConfigManager,
client,
@ -73,6 +76,8 @@ def _should_sdk_support_streaming(
Return whether file content streaming is supported for the provider.
"""
return custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS
openai_files_instance = OpenAIFilesAPI()
azure_files_instance = AzureOpenAIFilesAPI()
vertex_ai_files_instance = VertexAIFilesHandler()
@ -1094,9 +1099,10 @@ def file_content_streaming(
)
if asyncio.iscoroutine(response):
async def _await_and_wrap() -> FileContentStreamingResult:
return _wrap_streaming_result(await response)
return _await_and_wrap()
return _wrap_streaming_result(response)
return _wrap_streaming_result(response)

20
litellm/files/streaming.py
View file

@ -1,6 +1,15 @@
import datetime
import traceback
from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, Optional, Union, cast
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Dict,
Iterator,
Optional,
Union,
cast,
)
import anyio
from litellm.files.types import FileContentProvider
@ -11,6 +20,7 @@ if TYPE_CHECKING:
)
from litellm.types.utils import StandardLoggingHiddenParams, StandardLoggingPayload
class FileContentStreamingResponse:
"""
Iterator wrapper for file content streaming that carries LiteLLM metadata
@ -84,7 +94,9 @@ class FileContentStreamingResponse:
self._close_completed = True
self._logging_completed = True
stream_to_close = self.stream_iterator
self.stream_iterator = cast(Union[Iterator[bytes], AsyncIterator[bytes]], iter(()))
self.stream_iterator = cast(
Union[Iterator[bytes], AsyncIterator[bytes]], iter(())
)
# Shield cleanup from request cancellation so upstream HTTP connections
# are released promptly on client disconnects.
@ -103,7 +115,9 @@ class FileContentStreamingResponse:
self._close_completed = True
self._logging_completed = True
stream_to_close = self.stream_iterator
self.stream_iterator = cast(Union[Iterator[bytes], AsyncIterator[bytes]], iter(()))
self.stream_iterator = cast(
Union[Iterator[bytes], AsyncIterator[bytes]], iter(())
)
if hasattr(stream_to_close, "close"):
cast(Iterator[bytes], stream_to_close).close() # type: ignore[attr-defined]

40
litellm/images/main.py
View file

@ -210,7 +210,10 @@ def image_generation( # noqa: PLR0915
api_version: Optional[str] = None,
custom_llm_provider=None,
**kwargs,
) -> Union[ImageResponse, Coroutine[Any, Any, ImageResponse],]:
) -> Union[
ImageResponse,
Coroutine[Any, Any, ImageResponse],
]:
"""
Maps the https://api.openai.com/v1/images/generations endpoint.
@ -407,6 +410,7 @@ def image_generation( # noqa: PLR0915
litellm.LlmProviders.RUNWAYML,
litellm.LlmProviders.VERTEX_AI,
litellm.LlmProviders.OPENROUTER,
litellm.LlmProviders.DASHSCOPE,
):
if image_generation_config is None:
raise ValueError(
@ -864,11 +868,11 @@ def image_edit( # noqa: PLR0915
)
# get provider config
image_edit_provider_config: Optional[
BaseImageEditConfig
] = ProviderConfigManager.get_provider_image_edit_config(
model=model,
provider=litellm.LlmProviders(custom_llm_provider),
image_edit_provider_config: Optional[BaseImageEditConfig] = (
ProviderConfigManager.get_provider_image_edit_config(
model=model,
provider=litellm.LlmProviders(custom_llm_provider),
)
)
if image_edit_provider_config is None:
@ -876,20 +880,20 @@ def image_edit( # noqa: PLR0915
local_vars.update(kwargs)
# Get ImageEditOptionalRequestParams with only valid parameters
image_edit_optional_params: ImageEditOptionalRequestParams = (
_get_ImageEditRequestUtils().get_requested_image_edit_optional_param(
local_vars
)
image_edit_optional_params: (
ImageEditOptionalRequestParams
) = _get_ImageEditRequestUtils().get_requested_image_edit_optional_param(
local_vars
)
# Get optional parameters for the responses API
image_edit_request_params: Dict = (
_get_ImageEditRequestUtils().get_optional_params_image_edit(
model=model,
image_edit_provider_config=image_edit_provider_config,
image_edit_optional_params=image_edit_optional_params,
drop_params=kwargs.get("drop_params"),
additional_drop_params=kwargs.get("additional_drop_params"),
)
image_edit_request_params: (
Dict
) = _get_ImageEditRequestUtils().get_optional_params_image_edit(
model=model,
image_edit_provider_config=image_edit_provider_config,
image_edit_optional_params=image_edit_optional_params,
drop_params=kwargs.get("drop_params"),
additional_drop_params=kwargs.get("additional_drop_params"),
)
# Pre Call logging

8
litellm/integrations/SlackAlerting/hanging_request_check.py
View file

@ -102,10 +102,10 @@ class AlertingHangingRequestCheck:
)
for request_id in hanging_requests:
hanging_request_data: Optional[
HangingRequestData
] = await self.hanging_request_cache.async_get_cache(
key=request_id,
hanging_request_data: Optional[HangingRequestData] = (
await self.hanging_request_cache.async_get_cache(
key=request_id,
)
)
if hanging_request_data is None:

18
litellm/integrations/SlackAlerting/slack_alerting.py
View file

@ -852,9 +852,9 @@ class SlackAlerting(CustomBatchLogger):
### UNIQUE CACHE KEY ###
cache_key = provider + region_name
outage_value: Optional[
ProviderRegionOutageModel
] = await self.internal_usage_cache.async_get_cache(key=cache_key)
outage_value: Optional[ProviderRegionOutageModel] = (
await self.internal_usage_cache.async_get_cache(key=cache_key)
)
# Convert deployment_ids back to set if it was stored as a list
if outage_value is not None:
@ -1443,9 +1443,9 @@ Model Info:
self.alert_to_webhook_url is not None
and alert_type in self.alert_to_webhook_url
):
_digest_webhook: Optional[
Union[str, List[str]]
] = self.alert_to_webhook_url[alert_type]
_digest_webhook: Optional[Union[str, List[str]]] = (
self.alert_to_webhook_url[alert_type]
)
elif self.default_webhook_url is not None:
_digest_webhook = self.default_webhook_url
else:
@ -1499,9 +1499,9 @@ Model Info:
self.alert_to_webhook_url is not None
and alert_type in self.alert_to_webhook_url
):
slack_webhook_url: Optional[
Union[str, List[str]]
] = self.alert_to_webhook_url[alert_type]
slack_webhook_url: Optional[Union[str, List[str]]] = (
self.alert_to_webhook_url[alert_type]
)
elif self.default_webhook_url is not None:
slack_webhook_url = self.default_webhook_url
else:

1
litellm/integrations/agentops/agentops.py
View file

@ -1,6 +1,7 @@
"""
AgentOps integration for LiteLLM - Provides OpenTelemetry tracing for LLM calls
"""
import os
from dataclasses import dataclass
from typing import Optional, Dict, Any

8
litellm/integrations/anthropic_cache_control_hook.py
View file

@ -106,10 +106,10 @@ class AnthropicCacheControlHook(CustomPromptManagement):
targetted_index += len(messages)
if 0 <= targetted_index < len(messages):
messages[
targetted_index
] = AnthropicCacheControlHook._safe_insert_cache_control_in_message(
messages[targetted_index], control
messages[targetted_index] = (
AnthropicCacheControlHook._safe_insert_cache_control_in_message(
messages[targetted_index], control
)
)
else:
verbose_logger.warning(

6
litellm/integrations/arize/arize_phoenix.py
View file

@ -178,9 +178,9 @@ class ArizePhoenixLogger(OpenTelemetry): # type: ignore
start_time_val = kwargs.get("start_time", kwargs.get("api_call_start_time"))
parent_span = self.tracer.start_span(
name="litellm_proxy_request",
start_time=self._to_ns(start_time_val)
if start_time_val is not None
else None,
start_time=(
self._to_ns(start_time_val) if start_time_val is not None else None
),
context=traceparent_ctx,
kind=self.span_kind.SERVER,
)

12
litellm/integrations/azure_storage/azure_storage.py
View file

@ -54,12 +54,12 @@ class AzureBlobStorageLogger(CustomBatchLogger):
self._service_client_timeout: Optional[float] = None
# Internal variables used for Token based authentication
self.azure_auth_token: Optional[
str
] = None # the Azure AD token to use for Azure Storage API requests
self.token_expiry: Optional[
datetime
] = None # the expiry time of the currentAzure AD token
self.azure_auth_token: Optional[str] = (
None # the Azure AD token to use for Azure Storage API requests
)
self.token_expiry: Optional[datetime] = (
None # the expiry time of the currentAzure AD token
)
asyncio.create_task(self.periodic_flush())
self.flush_lock = asyncio.Lock()

6
litellm/integrations/braintrust_logging.py
View file

@ -52,9 +52,9 @@ class BraintrustLogger(CustomLogger):
"Authorization": "Bearer " + self.api_key,
"Content-Type": "application/json",
}
self._project_id_cache: Dict[
str, str
] = {} # Cache mapping project names to IDs
self._project_id_cache: Dict[str, str] = (
{}
) # Cache mapping project names to IDs
self.global_braintrust_http_handler = get_async_httpx_client(
llm_provider=httpxSpecialProvider.LoggingCallback
)

8
litellm/integrations/cloudzero/cloudzero.py
View file

@ -402,10 +402,10 @@ class CloudZeroLogger(CustomLogger):
from litellm.constants import CLOUDZERO_EXPORT_INTERVAL_MINUTES
from litellm.integrations.custom_logger import CustomLogger
prometheus_loggers: List[
CustomLogger
] = litellm.logging_callback_manager.get_custom_loggers_for_type(
callback_type=CloudZeroLogger
prometheus_loggers: List[CustomLogger] = (
litellm.logging_callback_manager.get_custom_loggers_for_type(
callback_type=CloudZeroLogger
)
)
# we need to get the initialized prometheus logger instance(s) and call logger.initialize_remaining_budget_metrics() on them
verbose_logger.debug("found %s cloudzero loggers", len(prometheus_loggers))

12
litellm/integrations/cloudzero/transform.py
View file

@ -159,9 +159,9 @@ class CBFTransformer:
# 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
"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, # CZRN (CloudZero Resource Name)
# Usage metrics for token consumption
@ -182,9 +182,9 @@ class CBFTransformer:
# Add CZRN components that don't have direct CBF column mappings as resource tags
cbf_record["resource/tag:provider"] = provider # CZRN provider component
cbf_record[
"resource/tag:model"
] = cloud_local_id # CZRN cloud-local-id component (model)
cbf_record["resource/tag:model"] = (
cloud_local_id # CZRN cloud-local-id component (model)
)
# Add resource tags for all dimensions (using resource/tag:<key> format)
for key, value in dimensions.items():

14
litellm/integrations/compression_interception/__init__.py Normal file
View file

@ -0,0 +1,14 @@
"""
Compression Interception Module
Provides server-side prompt compression + retrieval tool fulfillment for
Anthropic Messages agentic loops.
"""
from litellm.integrations.compression_interception.handler import (
CompressionInterceptionLogger,
)
__all__ = [
"CompressionInterceptionLogger",
]

399
litellm/integrations/compression_interception/handler.py Normal file
View file

@ -0,0 +1,399 @@
"""
Compression Interception Handler
CustomLogger that compresses inbound Anthropic Messages requests and fulfills
litellm_content_retrieve tool calls server-side via the typed agentic loop plan.
"""
import time
import uuid
from typing import Any, Dict, List, Optional, Tuple, cast
from litellm._logging import verbose_logger
from litellm.compression import compress
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.integrations.compression_interception import (
CompressionInterceptionConfig,
)
from litellm.types.integrations.custom_logger import (
AgenticLoopPlan,
AgenticLoopRequestPatch,
)
from litellm.types.utils import CallTypes
LITELLM_CONTENT_RETRIEVE_TOOL_NAME = "litellm_content_retrieve"
_CACHE_TTL_SECONDS = 15 * 60
class CompressionInterceptionLogger(CustomLogger):
"""
CustomLogger that implements transparent prompt compression + retrieval loops.
Flow:
1. Compress inbound /v1/messages requests in pre-call hook.
2. Inject litellm_content_retrieve tool and persist compressed cache by call_id.
3. Detect retrieval tool_use blocks in first model response.
4. Build typed rerun plan with tool_result blocks from the compressed cache.
"""
def __init__(
self,
enabled: bool = True,
compression_trigger: int = 200_000,
compression_target: Optional[int] = None,
embedding_model: Optional[str] = None,
embedding_model_params: Optional[Dict[str, Any]] = None,
):
super().__init__()
self.enabled = enabled
self.compression_trigger = compression_trigger
self.compression_target = compression_target
self.embedding_model = embedding_model
self.embedding_model_params = embedding_model_params
self._compression_cache_by_call_id: Dict[str, Tuple[Dict[str, str], float]] = {}
@classmethod
def from_config_yaml(
cls, config: CompressionInterceptionConfig
) -> "CompressionInterceptionLogger":
return cls(
enabled=bool(config.get("enabled", True)),
compression_trigger=int(config.get("compression_trigger", 200_000)),
compression_target=config.get("compression_target"),
embedding_model=config.get("embedding_model"),
embedding_model_params=config.get("embedding_model_params"),
)
@staticmethod
def initialize_from_proxy_config(
litellm_settings: Dict[str, Any],
callback_specific_params: Dict[str, Any],
) -> "CompressionInterceptionLogger":
compression_params: CompressionInterceptionConfig = {}
if "compression_interception_params" in litellm_settings:
compression_params = litellm_settings["compression_interception_params"]
elif "compression_interception" in callback_specific_params:
compression_params = callback_specific_params["compression_interception"]
return CompressionInterceptionLogger.from_config_yaml(compression_params)
async def async_pre_call_deployment_hook(
self, kwargs: Dict[str, Any], call_type: Optional[CallTypes]
) -> Optional[dict]:
if not self.enabled:
return None
if call_type is not None and call_type != CallTypes.anthropic_messages:
return None
if int(kwargs.get("_agentic_loop_depth", 0) or 0) > 0:
return None
messages = kwargs.get("messages")
model = kwargs.get("model")
if not isinstance(messages, list) or not isinstance(model, str):
return None
if self._has_retrieval_tool(kwargs.get("tools")):
return None
self._prune_expired_cache()
compressed = compress( # type: ignore
messages=messages,
model=model,
call_type=CallTypes.anthropic_messages,
compression_trigger=self.compression_trigger,
compression_target=self.compression_target,
embedding_model=self.embedding_model,
embedding_model_params=self.embedding_model_params,
)
cache = cast(Dict[str, str], compressed.get("cache", {}))
skip_reason = cast(Optional[str], compressed.get("compression_skipped_reason"))
compressed_tools = cast(List[Dict[str, Any]], compressed.get("tools", []))
# Only mutate kwargs when compression actually produced a result.
# If compression was a no-op (below trigger, invalid tool sequence, etc.),
# leave ``messages`` and ``tools`` untouched — injecting an empty
# ``tools: []`` onto a request that originally had no tools breaks
# Anthropic Messages requests.
if cache:
kwargs["messages"] = compressed["messages"]
if compressed_tools:
kwargs["tools"] = self._merge_tools(
existing_tools=cast(
Optional[List[Dict[str, Any]]], kwargs.get("tools")
),
compressed_tools=compressed_tools,
)
call_id = cast(Optional[str], kwargs.get("litellm_call_id"))
if not call_id:
call_id = str(uuid.uuid4())
kwargs["litellm_call_id"] = call_id
self._compression_cache_by_call_id[call_id] = (cache, time.time())
verbose_logger.debug(
"CompressionInterception: compressed request [call_id=%s original=%d compressed=%d cached_keys=%d]",
call_id,
compressed.get("original_tokens"),
compressed.get("compressed_tokens"),
len(cache),
)
elif skip_reason is not None:
verbose_logger.debug(
"CompressionInterception: compression skipped [reason=%s original=%d compressed=%d]",
skip_reason,
compressed.get("original_tokens"),
compressed.get("compressed_tokens"),
)
return kwargs
async def async_should_run_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]:
if not self.enabled:
return False, {}
if not self._has_retrieval_tool(tools):
return False, {}
tool_calls, thinking_blocks = self._extract_retrieval_tool_calls(
response=response
)
if not tool_calls:
return False, {}
return True, {
"tool_calls": tool_calls,
"thinking_blocks": thinking_blocks,
"tool_type": "compression_retrieval",
}
async def async_build_agentic_loop_plan(
self,
tools: Dict,
model: str,
messages: List[Dict],
response: Any,
anthropic_messages_provider_config: Any,
anthropic_messages_optional_request_params: Dict,
logging_obj: Any,
stream: bool,
kwargs: Dict,
) -> AgenticLoopPlan:
self._prune_expired_cache()
tool_calls = cast(List[Dict[str, Any]], tools.get("tool_calls", []))
thinking_blocks = cast(List[Dict[str, Any]], tools.get("thinking_blocks", []))
call_id = self._resolve_call_id(logging_obj=logging_obj, kwargs=kwargs)
cache = self._get_cache(call_id=call_id)
retrieval_results = [
self._resolve_retrieval_content(tc, cache) for tc in tool_calls
]
assistant_message = {
"role": "assistant",
"content": thinking_blocks
+ [
{
"type": "tool_use",
"id": tc.get("id"),
"name": tc.get("name", LITELLM_CONTENT_RETRIEVE_TOOL_NAME),
"input": tc.get("input", {}),
}
for tc in tool_calls
],
}
user_message = {
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": tool_calls[i].get("id"),
"content": retrieval_results[i],
}
for i in range(len(tool_calls))
],
}
follow_up_messages = messages + [assistant_message, user_message]
max_tokens = cast(
Optional[int],
anthropic_messages_optional_request_params.get("max_tokens")
or kwargs.get("max_tokens"),
)
optional_params_without_max_tokens = {
k: v
for k, v in anthropic_messages_optional_request_params.items()
if k != "max_tokens"
}
full_model_name = model
if logging_obj is not None:
agentic_params = logging_obj.model_call_details.get(
"agentic_loop_params", {}
)
full_model_name = cast(str, agentic_params.get("model", model))
request_patch = AgenticLoopRequestPatch(
model=full_model_name,
messages=follow_up_messages,
max_tokens=max_tokens,
optional_params=optional_params_without_max_tokens,
kwargs=self._prepare_followup_kwargs(kwargs=kwargs),
)
return AgenticLoopPlan(
run_agentic_loop=True,
request_patch=request_patch,
metadata={"tool_type": "compression_retrieval", "call_id": call_id or ""},
)
def _prune_expired_cache(self) -> None:
now = time.time()
self._compression_cache_by_call_id = {
call_id: (cache, created_at)
for call_id, (
cache,
created_at,
) in self._compression_cache_by_call_id.items()
if now - created_at <= _CACHE_TTL_SECONDS
}
def _get_cache(self, call_id: Optional[str]) -> Dict[str, str]:
if not call_id:
return {}
cache_entry = self._compression_cache_by_call_id.get(call_id)
if cache_entry is None:
return {}
return cache_entry[0]
def _resolve_call_id(
self, logging_obj: Any, kwargs: Dict[str, Any]
) -> Optional[str]:
if logging_obj is not None:
logging_call_id = getattr(logging_obj, "litellm_call_id", None)
if isinstance(logging_call_id, str) and logging_call_id:
return logging_call_id
kwargs_call_id = kwargs.get("litellm_call_id")
return cast(
Optional[str], kwargs_call_id if isinstance(kwargs_call_id, str) else None
)
def _resolve_retrieval_content(
self, tool_call: Dict[str, Any], cache: Dict[str, str]
) -> str:
raw_input = tool_call.get("input", {})
key = ""
if isinstance(raw_input, dict):
key = str(raw_input.get("key", "") or "")
if not key:
return "No retrieval key provided."
if key in cache:
return cache[key]
return f"[compressed content key '{key}' not found]"
def _extract_retrieval_tool_calls(
self, response: Any
) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
if isinstance(response, dict):
content = response.get("content", [])
else:
content = getattr(response, "content", []) or []
if not isinstance(content, list):
return [], []
tool_calls: List[Dict[str, Any]] = []
thinking_blocks: List[Dict[str, Any]] = []
for block in content:
if isinstance(block, dict):
block_type = block.get("type")
block_name = block.get("name")
if block_type in ("thinking", "redacted_thinking"):
thinking_blocks.append(block)
if (
block_type == "tool_use"
and block_name == LITELLM_CONTENT_RETRIEVE_TOOL_NAME
):
tool_calls.append(
{
"id": block.get("id"),
"type": "tool_use",
"name": block_name,
"input": block.get("input", {}),
}
)
else:
block_type = getattr(block, "type", None)
block_name = getattr(block, "name", None)
if block_type == "thinking":
thinking_blocks.append(
{
"type": "thinking",
"thinking": getattr(block, "thinking", ""),
"signature": getattr(block, "signature", ""),
}
)
elif block_type == "redacted_thinking":
thinking_blocks.append(
{
"type": "redacted_thinking",
"data": getattr(block, "data", ""),
}
)
if (
block_type == "tool_use"
and block_name == LITELLM_CONTENT_RETRIEVE_TOOL_NAME
):
tool_calls.append(
{
"id": getattr(block, "id", None),
"type": "tool_use",
"name": block_name,
"input": getattr(block, "input", {}) or {},
}
)
return tool_calls, thinking_blocks
def _prepare_followup_kwargs(self, kwargs: Dict[str, Any]) -> Dict[str, Any]:
internal_keys = {"litellm_logging_obj"}
return {
k: v
for k, v in kwargs.items()
if not k.startswith("_compression_interception") and k not in internal_keys
}
def _has_retrieval_tool(self, tools: Any) -> bool:
if not isinstance(tools, list):
return False
for tool in tools:
if not isinstance(tool, dict):
continue
function = tool.get("function")
if tool.get("type") == "function" and isinstance(function, dict):
if function.get("name") == LITELLM_CONTENT_RETRIEVE_TOOL_NAME:
return True
if (
tool.get("type") == "custom"
and tool.get("name") == LITELLM_CONTENT_RETRIEVE_TOOL_NAME
):
return True
return False
def _merge_tools(
self,
existing_tools: Optional[List[Dict[str, Any]]],
compressed_tools: List[Dict[str, Any]],
) -> List[Dict[str, Any]]:
merged = list(existing_tools or [])
if self._has_retrieval_tool(merged):
return merged
merged.extend(compressed_tools)
return merged

63
litellm/integrations/custom_guardrail.py
View file

@ -2,6 +2,7 @@ from datetime import datetime
from typing import (
TYPE_CHECKING,
Any,
ClassVar,
Dict,
List,
Literal,
@ -12,6 +13,7 @@ from typing import (
)
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.core_helpers import redact_nested_match_and_regex_keys
from litellm.caching import DualCache
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.guardrails import (
@ -81,6 +83,9 @@ class ModifyResponseException(Exception):
class CustomGuardrail(CustomLogger):
# If True, during_call runs async_moderation_hook instead of the unified apply_guardrail path.
use_native_during_call_hook: ClassVar[bool] = False
def __init__(
self,
guardrail_name: Optional[str] = None,
@ -255,26 +260,44 @@ class CustomGuardrail(CustomLogger):
f"Event hook {event_hook} is not in the supported event hooks {supported_event_hooks}"
)
@staticmethod
def _get_admin_metadata(data: dict) -> dict:
"""Return merged admin-configured key and team metadata from the request data.
The proxy may inject admin metadata (user_api_key_metadata,
user_api_key_team_metadata) into either ``metadata`` or
``litellm_metadata`` depending on endpoint. Check both so a caller
cannot shadow admin config by pre-populating the other key.
Key-level settings override team-level.
"""
team_meta: dict = {}
key_meta: dict = {}
for key in ("metadata", "litellm_metadata"):
# Defensive: an unparsed JSON-string metadata could leak past the
# proxy's normal parse path; don't AttributeError on .get().
meta = data.get(key)
if not isinstance(meta, dict):
continue
team_meta = meta.get("user_api_key_team_metadata") or team_meta
key_meta = meta.get("user_api_key_metadata") or key_meta
return {**team_meta, **key_meta}
def get_disable_global_guardrail(self, data: dict) -> Optional[bool]:
"""
Returns True if the global guardrail should be disabled
Returns True if the global guardrail should be disabled.
Reads from admin-configured key/team metadata only, not from
the request body, to prevent callers from disabling guardrails.
"""
if "disable_global_guardrails" in data:
return data["disable_global_guardrails"]
metadata = data.get("litellm_metadata") or data.get("metadata", {})
if "disable_global_guardrails" in metadata:
return metadata["disable_global_guardrails"]
return False
return self._get_admin_metadata(data).get("disable_global_guardrails", False)
def get_opted_out_global_guardrails_from_metadata(self, data: dict) -> List[str]:
"""
Returns the list of global guardrail names the team/key has opted out of.
Reads from admin-configured key/team metadata only.
"""
if "opted_out_global_guardrails" in data:
value = data["opted_out_global_guardrails"]
return value if isinstance(value, list) else []
metadata = data.get("litellm_metadata") or data.get("metadata", {})
value = metadata.get("opted_out_global_guardrails")
value = self._get_admin_metadata(data).get("opted_out_global_guardrails")
return value if isinstance(value, list) else []
def _is_valid_response_type(self, result: Any) -> bool:
@ -417,7 +440,9 @@ class CustomGuardrail(CustomLogger):
"""
requested_guardrails = self.get_guardrail_from_metadata(data)
disable_global_guardrail = self.get_disable_global_guardrail(data)
opted_out_global_guardrails = self.get_opted_out_global_guardrails_from_metadata(data)
opted_out_global_guardrails = (
self.get_opted_out_global_guardrails_from_metadata(data)
)
verbose_logger.debug(
"inside should_run_guardrail for guardrail=%s event_type= %s guardrail_supported_event_hooks= %s requested_guardrails= %s self.default_on= %s",
self.guardrail_name,
@ -426,7 +451,10 @@ class CustomGuardrail(CustomLogger):
requested_guardrails,
self.default_on,
)
if self.default_on is True and self.guardrail_name in opted_out_global_guardrails:
if (
self.default_on is True
and self.guardrail_name in opted_out_global_guardrails
):
return False
if self.default_on is True and disable_global_guardrail is not True:
@ -614,6 +642,13 @@ class CustomGuardrail(CustomLogger):
if isinstance(item, dict):
item.pop("secret_fields", None)
# Default-safe behavior: never persist raw matched spans in standard
# guardrail logging payloads (single shared implementation; Bedrock hooks pass
# raw provider JSON so redaction is not duplicated upstream).
clean_guardrail_response = redact_nested_match_and_regex_keys(
clean_guardrail_response
)
slg = StandardLoggingGuardrailInformation(
guardrail_name=self.guardrail_name,
guardrail_provider=guardrail_provider,

45
litellm/integrations/custom_logger.py
View file

@ -20,6 +20,7 @@ from litellm.constants import DEFAULT_MAX_RECURSE_DEPTH_SENSITIVE_DATA_MASKER
from litellm.types.integrations.argilla import ArgillaItem
from litellm.types.llms.openai import AllMessageValues, ChatCompletionRequest
from litellm.types.prompts.init_prompts import PromptSpec
from litellm.types.integrations.custom_logger import AgenticLoopPlan
from litellm.types.utils import (
AdapterCompletionStreamWrapper,
CallTypes,
@ -239,7 +240,7 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
self,
model: str,
request_kwargs: Dict,
messages: Optional[List[Dict[str, str]]] = None,
messages: Optional[List[Dict[str, Any]]] = None,
input: Optional[Union[str, List]] = None,
specific_deployment: Optional[bool] = False,
) -> Optional[PreRoutingHookResponse]:
@ -676,6 +677,26 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
"""
pass
async def async_build_agentic_loop_plan(
self,
tools: Dict,
model: str,
messages: List[Dict],
response: Any,
anthropic_messages_provider_config: Any,
anthropic_messages_optional_request_params: Dict,
logging_obj: "LiteLLMLoggingObj",
stream: bool,
kwargs: Dict,
) -> AgenticLoopPlan:
"""
Build a typed rerun plan for Anthropic Messages agentic loops.
Override this method to separate callback decision/tool execution from
follow-up request execution (handled by BaseLLMHTTPHandler).
"""
return AgenticLoopPlan(run_agentic_loop=False)
async def async_should_run_chat_completion_agentic_loop(
self,
response: Any,
@ -707,6 +728,22 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
"""
pass
async def async_build_chat_completion_agentic_loop_plan(
self,
tools: Dict,
model: str,
messages: List[Dict],
response: Any,
optional_params: Dict,
logging_obj: "LiteLLMLoggingObj",
stream: bool,
kwargs: Dict,
) -> AgenticLoopPlan:
"""
Build a typed rerun plan for chat-completions agentic loops.
"""
return AgenticLoopPlan(run_agentic_loop=False)
# Useful helpers for custom logger classes
def truncate_standard_logging_payload_content(
@ -874,9 +911,9 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac
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
model_call_details_copy["standard_logging_object"] = (
standard_logging_object_copy
)
return model_call_details_copy
async def get_proxy_server_request_from_cold_storage_with_object_key(

24
litellm/integrations/datadog/datadog_llm_obs.py
View file

@ -349,9 +349,9 @@ class DataDogLLMObsLogger(CustomBatchLogger):
if standard_logging_payload.get("status") == "failure":
# Try to get structured error information first
error_information: Optional[
StandardLoggingPayloadErrorInformation
] = standard_logging_payload.get("error_information")
error_information: Optional[StandardLoggingPayloadErrorInformation] = (
standard_logging_payload.get("error_information")
)
if error_information:
error_info = DDLLMObsError(
@ -621,9 +621,9 @@ class DataDogLLMObsLogger(CustomBatchLogger):
latency_metrics["litellm_overhead_time_ms"] = litellm_overhead_ms
# Guardrail overhead latency
guardrail_info: Optional[
list[StandardLoggingGuardrailInformation]
] = standard_logging_payload.get("guardrail_information")
guardrail_info: Optional[list[StandardLoggingGuardrailInformation]] = (
standard_logging_payload.get("guardrail_information")
)
if guardrail_info is not None:
total_duration = 0.0
for info in guardrail_info:
@ -793,15 +793,15 @@ class DataDogLLMObsLogger(CustomBatchLogger):
if function_arguments:
# Store arguments as JSON string for Datadog
if isinstance(function_arguments, str):
kv_pairs[
f"tool_calls.{idx}.function.arguments"
] = function_arguments
kv_pairs[f"tool_calls.{idx}.function.arguments"] = (
function_arguments
)
else:
import json
kv_pairs[
f"tool_calls.{idx}.function.arguments"
] = json.dumps(function_arguments)
kv_pairs[f"tool_calls.{idx}.function.arguments"] = (
json.dumps(function_arguments)
)
except (KeyError, TypeError, ValueError) as e:
verbose_logger.debug(
f"DataDogLLMObs: Error processing tool call {idx}: {str(e)}"

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