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Code Issues Projects Releases Packages Wiki Activity Actions 36

Merge remote-tracking branch 'berri/litellm_internal_staging' into litellm_playground_shadcn

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# Conflicts:
#	ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatUI.test.tsx
#	ui/litellm-dashboard/src/app/(dashboard)/playground/components/chat_ui/ChatUI.tsx
#	ui/litellm-dashboard/src/components/policies/PolicySelector.tsx
This commit is contained in:
mubashir1osmani 2026-08-13 11:28:42 -07:00
commit 136ef345da
1479 changed files with 65430 additions and 25083 deletions
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56
.github/ISSUE_TEMPLATE/bug_report.yml vendored
View file

@ -23,30 +23,56 @@ body:
label: What happened?
description: Also tell us, what did you expect to happen?
placeholder: Tell us what you see!
value: "A bug happened!"
validations:
required: true
- type: textarea
id: steps-to-reproduce
id: user-flow
attributes:
label: Steps to Reproduce
description: Please provide a numbered list of the exact steps to reproduce this bug (include a curl/python snippet to reproduce it). Number each step (1., 2., 3., ...) in the order you performed them.
label: User Flow
description: |
Two ordered lists, "Before a (hypothetical) fix" and "After a (hypothetical) fix", walking the same end user through the same task, written strictly from that user's seat. Every rule below applies.
- Describe the real application and the routes its users actually hit, not a generic scenario
- Lead each list with one plain sentence saying where the flow fails (before) or would succeed (after), then number the steps
- Every step is something the user does or observes: the HTTP method and full URL they hit, what they sent, and what visibly came back (status code, error text, the shape of an ID). UI steps name the page URL and what is on screen
- No LiteLLM internals: never name functions, files, DB tables, config classes, hooks, callbacks, or code paths. "The upload hands back an ID that looks like OpenAI's own `file-abc123` instead of the scrambled one the gateway returned" is right, "no managed-file row was registered" is wrong
- Keep the two lists step-for-step identical until they diverge, so the broken step is obvious
- If the bug has a security or authorization consequence, end each list with what another user can do that they shouldn't be able to, and what they could no longer do after a fix
placeholder: |
1. config.yaml file/ .env file/ etc.
2. Run the following code...
3. Observe the error...
value: |
1.
2.
3.
Before a (hypothetical) fix: a developer whose app streams chat completions gets no token counts back, so their cost dashboard reads zero
1. They send POST https://litellm-domain/v1/chat/completions with "stream": true and no stream_options
2. The last SSE chunk arrives with "usage": null, so their app records 0 prompt and 0 completion tokens
3. They open https://litellm-domain/ui/?page=logs and see the request logged at $0 spend
After a (hypothetical) fix: the same request comes back with real token counts, so the dashboard shows real spend
1. The proxy admin sets always_include_stream_usage: true and restarts the proxy
2. The developer sends the same POST https://litellm-domain/v1/chat/completions with "stream": true and no stream_options
3. The last SSE chunk now carries a usage object with real prompt and completion token counts
4. https://litellm-domain/ui/?page=logs shows that request at non-zero spend
validations:
required: true
- type: textarea
id: logs
id: proof-of-bug
attributes:
label: Relevant log output
description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
render: shell
label: Proof the bug occurs
description: |
The commands (e.g., curl) and their full output, screenshots, or a screen recording demonstrating that the bug happens. Every rule below applies.
- The proof must be completely e2e with no mocks, against a live proxy you ran yourself (e.g., `litellm --config config.yaml --detailed_debug` on localhost:4000), hitting real LLM provider APIs, costing real $ if needed, where the bug involves a provider call. `pytest` commands are not enough
- Show exactly what the end user sees or does, matching the User Flow above step for step
- Start with the config.yaml (or SDK setup) and any env vars the proxy ran with, then the exact version or commit hash the proof was captured at, so a maintainer can stand up the same proxy before running your commands. Keep the real values for env vars that aren't sensitive, they are often the reason the bug happens, and redact only the secrets: never paste a real API key, virtual key, database URL, or other credential, here or anywhere else in the issue
- If the bug applies to more than one of the LLM endpoints (/v1/responses, /v1/chat/completions, /v1/messages), include proof for every one of them, not just one
- For UI bugs: include screenshots and the page URLs you were on. Scrub keys and tokens out of screenshots too (for example, the virtual key is briefly shown in the panel right after you create a virtual key)
placeholder: |
Config / setup the proxy ran with:
Version or commit:
Commands and their full output:
validations:
required: true
- type: dropdown
id: component
attributes:

49
.github/ISSUE_TEMPLATE/feature_request.yml vendored
View file

@ -24,10 +24,53 @@ body:
validations:
required: true
- type: textarea
id: motivation
id: user-flow
attributes:
label: Motivation, pitch
description: Please outline the motivation for the proposal. Is your feature request related to a specific problem? e.g., "I'm working on X and would like Y to be possible". If this is related to another GitHub issue, please link here too.
label: User Flow
description: |
Two ordered lists, "Before this feature (today)" and "After this feature (ideal user flow)", walking the same end user through the same task, written strictly from that user's seat. Every rule below applies.
- Describe the real application and the routes its users actually hit, not a generic scenario. Link any related GitHub issue or provider API docs
- Lead each list with one plain sentence saying where the flow dead-ends today and what it would let them do instead, then number the steps
- Every step is something the user does or observes: the HTTP method and full URL they hit, what they sent, and what visibly came back (status code, error text, the shape of an ID). UI steps name the page URL and what is on screen
- No LiteLLM internals: never name functions, files, DB tables, config classes, hooks, callbacks, or code paths. Ask for the behavior you need, not the implementation you imagine
- Keep the two lists step-for-step identical until they diverge, so the missing capability is obvious
- "Before this feature" is also where you show the workaround you're living with, which is what tells us how badly this is needed
placeholder: |
Before this feature (today): a developer batching nightly summaries has no way to mark those calls as low priority, so they compete with live traffic for the same rate limit
1. They send POST https://litellm-domain/v1/chat/completions for 500 documents in a loop
2. Around document 120 they start getting 429s naming the rpm limit, and their user-facing chat app starts getting them too
3. Their workaround is a hand-rolled sleep between calls, which stretches the batch to 3 hours and still collides at peak
After this feature (ideal user flow): the same batch runs as background work that yields to live traffic
1. The developer sends the same POST with "service_tier": "flex"
2. Batch calls queue behind interactive ones instead of 429ing, and the response comes back with the tier it was served at
3. The live chat app keeps returning 200s throughout the batch
4. https://litellm-domain/ui/?page=logs shows the batch requests tagged with that tier
validations:
required: true
- type: textarea
id: how-far-you-got
attributes:
label: How far you got
description: |
Run as many steps of the "After this feature (ideal user flow)" list as you can against a live proxy you ran yourself (e.g., `litellm --config config.yaml --detailed_debug` on localhost:4000), then paste the commands (e.g., curl) and their full output, ending at the step that dead-ends. Every rule below applies.
- Say plainly what stopped you there, in user terms: the option you passed came back ignored, the response 400'd naming an unsupported field, there is no button on the page for it. This is what proves the feature is genuinely missing rather than undocumented
- No mocks. Where the flow involves a provider call, hit the real provider API, even if it costs real $. `pytest` commands are not enough
- Include the config.yaml (or SDK setup) and env vars the proxy ran with, plus the version or commit you were on. Keep the real values for env vars that aren't sensitive, and redact only the secrets: never paste a real API key, virtual key, database URL, or other credential, here or anywhere else in the issue
- If the provider already supports this, link their API docs and paste a direct call to them succeeding, so we can see the shape LiteLLM should be sending
- For UI asks: include screenshots of the page you got stuck on and its URL. Scrub keys and tokens out of screenshots too (for example, the virtual key is briefly shown in the panel right after you create a virtual key)
placeholder: |
Config / setup the proxy ran with:
Version or commit:
Commands and their full output, up to the step that dead-ends:
What stopped me there:
validations:
required: true
- type: dropdown

40
.github/actions/cache-prisma-binaries/action.yml vendored Normal file
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@ -0,0 +1,40 @@
name: "Cache Prisma binaries"
description: >-
Cache the Prisma CLI and engine binaries that `prisma generate` downloads, so
only the first job on a given prisma-client-py version pays for the download.
prisma-client-py shells out to `npm install prisma@<version>` whenever its
binary cache directory has no CLI entrypoint, which pulls ~85 MB of query and
schema engines over the network. That normally takes a few seconds, but it is
unbounded: one shard of a proxy-db run took 5m18s on that single step versus
3.8s on its eleven siblings, which pushed the job past its timeout and got a
fully passing test run cancelled.
Callers must not set PRISMA_BINARY_CACHE_DIR. The prisma-client-py default
(~/.cache/prisma-python/binaries/<prisma-version>/<engine-version>) is already
keyed by both versions, so a cache entry can never be served to a run that
expects different binaries.
runs:
using: composite
steps:
- name: Resolve prisma-client-py version
id: version
shell: bash
run: |
version="$(grep -A1 '^name = "prisma"$' uv.lock | sed -n 's/^version = "\(.*\)"$/\1/p' | head -1)"
if [ -z "${version}" ]; then
echo "could not resolve the prisma package version from uv.lock" >&2
exit 1
fi
echo "version=${version}" >> "$GITHUB_OUTPUT"
- name: Restore Prisma binaries
uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
# ~/.cache/prisma-python holds the npm install tree prisma-client-py
# drives; ~/.cache/prisma is where @prisma/engines stages its downloads.
path: |
~/.cache/prisma-python
~/.cache/prisma
key: ${{ runner.os }}-prisma-binaries-${{ steps.version.outputs.version }}

6
.github/pull_request_template.md vendored
View file

@ -83,7 +83,11 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac
🚄 Infrastructure
✅ Test
## Changes
## Caveats (if any)
<!-- Short bullet points, just like the TLDR: one line per bullet, roughly 10 words max
Call out known limitations, follow-up work, or anything a reviewer should watch out for
Leave this section empty if there are none -->
## QA runbook

29
.github/scripts/triage_with_llm.py vendored
View file

@ -582,7 +582,9 @@ def build_issue_prompt(*, title: str, body: str) -> str:
Commands whose external dependencies (LLM provider, DB,
network) are mocked or stubbed do NOT count.
Prose-only "steps to reproduce" with no run output, video, or
screenshot do NOT satisfy (1).
screenshot do NOT satisfy (1). An unfilled template scaffold
(bare headings such as "Version or commit:" with nothing under
them, empty numbered lists) counts as absent, not as evidence.
(2) Expected vs. actual behavior (`has_expected_vs_actual`).
FAIL the bug report if either (1) or (2) is missing. Do not bias
@ -595,6 +597,13 @@ def build_issue_prompt(*, title: str, body: str) -> str:
that it does not today).
- Motivation / use case with a concrete example (config, API call,
UI flow, or scenario showing what's blocked today).
- END-TO-END EVIDENCE OF THE DEAD-END (set
`has_dead_end_evidence=true` only when this is present): a video,
a screenshot, or the exact command(s) actually run paired with
their real output, showing the point where the flow stops today.
Mocked or stubbed dependencies do NOT count, and an unfilled
template scaffold (bare headings, empty numbered lists) counts as
absent.
For an issue that is neither a bug report nor a feature request (a
question, support request, or discussion), PASS as long as it has a
@ -608,6 +617,7 @@ def build_issue_prompt(*, title: str, body: str) -> str:
"has_repro": boolean,
"has_expected_vs_actual": boolean,
"has_motivation_example": boolean,
"has_dead_end_evidence": boolean,
"missing": ["plain-english strings naming what is missing"],
"explanation": "1-2 sentence reasoning for the team to skim"
}}
@ -705,6 +715,10 @@ _ISSUE_BUG_LABELS: tuple[tuple[str, str], ...] = (
)
_ISSUE_FEATURE_LABELS: tuple[tuple[str, str], ...] = (
("has_motivation_example", "Motivation and concrete example"),
(
"has_dead_end_evidence",
"End-to-end evidence of the dead-end (video, screenshot, or command + real output)",
),
)
@ -836,8 +850,11 @@ def format_issue_close_comment(verdict: dict) -> str:
"video, a screenshot, or the exact commands you ran with their real output / "
"traceback) plus expected vs. actual behavior. Written steps with no run output, "
"video, or screenshot don't count, and mocked or stubbed runs don't count.\n"
" - For **feature requests**: a concrete description of what should change, plus a "
"use case and example (config / API call / UI flow).\n"
" - For **feature requests**: a concrete description of what should change, a "
"use case and example (config / API call / UI flow), plus end-to-end evidence of "
"the dead-end (a video, a screenshot, or the exact commands you ran with their "
"real output showing where the flow stops today). Mocked or stubbed runs don't "
"count.\n"
"2. Comment `@agent-shin reconsider`. I'll re-run triage and reopen the issue if it "
"now meets the bar. (GitHub doesn't let external authors reopen an issue a maintainer "
"or bot closed, so the comment-based reconsider is the reliable path.)\n"
@ -943,8 +960,10 @@ def format_grace_warning_issue_comment(verdict: dict) -> str:
"screenshot, or the exact commands you ran with their real output / traceback) plus "
"expected vs. actual behavior. Written steps with no run output don't count, and "
"mocked or stubbed runs don't count.\n"
"- For **feature requests**: a concrete description of what should change, plus a use "
"case and example (config / API call / UI flow).\n"
"- For **feature requests**: a concrete description of what should change, a use "
"case and example (config / API call / UI flow), plus end-to-end evidence of the "
"dead-end (a video, a screenshot, or the exact commands you ran with their real "
"output showing where the flow stops today). Mocked or stubbed runs don't count.\n"
"\n"
"**If the issue does get auto-closed in 2 hours**, comment `@agent-shin reconsider` "
"and I'll re-evaluate. If it now meets the bar, I'll reopen the issue.\n"

34
.github/workflows/_test-unit-base.yml vendored
View file

@ -18,10 +18,25 @@ on:
type: number
default: 2
timeout-minutes:
description: "Job timeout in minutes"
description: >-
Timeout for the test step alone. Setup (checkout, dependency install,
Prisma client generation) gets its own allowance on top, so a slow
runner or a cold binary download can never cancel passing tests.
required: false
type: number
default: 20
job-timeout-minutes:
description: >-
Backstop for the whole job. Keep it >= `timeout-minutes` plus 35: 30 for
the per-step ceilings on the setup steps below, and 5 for the runner
overhead the job clock charges but no step owns (job init, step
transitions, post-job cleanup). That headroom is what makes the test
budget a floor rather than a hope, since setup cannot overrun into it
without failing its own step first. GitHub expressions have no
arithmetic, so the sum is passed in rather than computed.
required: false
type: number
default: 55
max-failures:
description: "Stop after this many failures"
required: false
@ -44,30 +59,35 @@ jobs:
run:
name: Run tests
runs-on: ubuntu-latest
timeout-minutes: ${{ inputs.timeout-minutes }}
timeout-minutes: ${{ inputs.job-timeout-minutes }}
outputs:
decision: ${{ steps.changes.outputs.decision }}
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
timeout-minutes: 3
with:
persist-credentials: false
- name: Detect backend-relevant changes
id: changes
timeout-minutes: 2
uses: ./.github/actions/detect-backend-changes
- name: Set up Python
timeout-minutes: 3
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Set up uv
timeout-minutes: 3
uses: ./.github/actions/setup-uv-with-retries
with:
version: "0.10.9"
- name: Cache uv dependencies
timeout-minutes: 5
uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
@ -79,18 +99,24 @@ jobs:
- name: Install dependencies
if: steps.changes.outputs.decision != 'skip'
timeout-minutes: 8
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml
- name: Cache Prisma binaries
if: steps.changes.outputs.decision != 'skip'
timeout-minutes: 3
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
if: steps.changes.outputs.decision != 'skip'
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
timeout-minutes: 3
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma
- name: Run tests
if: steps.changes.outputs.decision != 'skip'
timeout-minutes: ${{ inputs.timeout-minutes }}
env:
TEST_PATH: ${{ inputs.test-path }}
MAX_FAILURES: ${{ inputs.max-failures }}

6
.github/workflows/check-ui-api-types.yml vendored
View file

@ -71,10 +71,12 @@ jobs:
if: steps.changes.outputs.relevant == 'true'
run: .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router
- name: Cache Prisma binaries
if: steps.changes.outputs.relevant == 'true'
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
if: steps.changes.outputs.relevant == 'true'
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma
- name: Set up Node.js

5
.github/workflows/mutation-test.yml vendored
View file

@ -57,9 +57,10 @@ jobs:
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml
- name: Cache Prisma binaries
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma

5
.github/workflows/publish-basedpyright-base-counts.yml vendored
View file

@ -43,12 +43,13 @@ jobs:
with:
version: "0.10.9"
- name: Cache Prisma binaries
uses: ./.github/actions/cache-prisma-binaries
# The gate provisions its own measurement env (.venv-typecheck: a frozen
# uv sync of its canonical dependency groups plus a generated Prisma
# client), so no install step here can drift from what local runs measure.
- name: Emit basedpyright counts for HEAD
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
python scripts/type_check_gate.py --emit-counts-dir "$RUNNER_TEMP/basedpyright-counts"
counts_file=$(ls "$RUNNER_TEMP"/basedpyright-counts/basedpyright-counts-*.json)

6
.github/workflows/test-code-quality.yml vendored
View file

@ -65,6 +65,12 @@ jobs:
- name: check_provider_folders_documented
run: uv run --no-sync python ./tests/code_coverage_tests/check_provider_folders_documented.py
- name: check_prisma_binary_cache
run: uv run --no-sync python ./tests/code_coverage_tests/check_prisma_binary_cache.py
- name: check_workflow_startup_safety
run: uv run --no-sync python ./tests/code_coverage_tests/check_workflow_startup_safety.py
- name: router_code_coverage
run: uv run --no-sync python ./tests/code_coverage_tests/router_code_coverage.py

17
.github/workflows/test-linting.yml vendored
View file

@ -43,9 +43,10 @@ jobs:
BASE_SHA: ${{ github.event.pull_request.base.sha }}
HEAD_SHA: ${{ github.event.pull_request.head.sha }}
run: |
MERGE_BASE=$(gh api "repos/${{ github.repository }}/compare/${BASE_SHA}...${HEAD_SHA}?per_page=1" --jq '.merge_base_commit.sha')
retry() { "$@" || { sleep 15; "$@"; } || { sleep 30; "$@"; }; }
MERGE_BASE=$(retry gh api "repos/${{ github.repository }}/compare/${BASE_SHA}...${HEAD_SHA}?per_page=1" --jq '.merge_base_commit.sha')
test -n "$MERGE_BASE"
git fetch --no-tags --depth=1 origin "$MERGE_BASE"
retry git fetch --no-tags --depth=1 origin "$MERGE_BASE"
echo "GATE_BASE_SHA=$MERGE_BASE" >> "$GITHUB_ENV"
- name: Set up Python
@ -71,12 +72,13 @@ jobs:
run: |
uv sync --frozen --group proxy-dev --group e2e-dev
- name: Cache Prisma binaries
uses: ./.github/actions/cache-prisma-binaries
# basedpyright resolves Prisma's generated client (litellm/proxy/schema.prisma)
# only after `prisma generate` writes prisma/client.py et al. Without this the
# DB wrappers typed against the generated client would degrade to Unknown.
- name: Generate Prisma client
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma
@ -119,7 +121,6 @@ jobs:
- name: Check basedpyright budget (delta vs base)
env:
GH_TOKEN: ${{ github.token }}
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync python scripts/type_check_gate.py --base "$GATE_BASE_SHA"
@ -161,7 +162,8 @@ jobs:
env:
BASE_SHA: ${{ github.event.pull_request.base.sha }}
run: |
git fetch --no-tags --depth=1 origin "$BASE_SHA"
retry() { "$@" || { sleep 15; "$@"; } || { sleep 30; "$@"; }; }
retry git fetch --no-tags --depth=1 origin "$BASE_SHA"
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
@ -205,7 +207,8 @@ jobs:
GITGUARDIAN_API_KEY: ${{ secrets.GITGUARDIAN_API_KEY }}
run: |
if [ -n "$GITGUARDIAN_API_KEY" ]; then
git fetch --no-tags --unshallow origin
retry() { "$@" || { sleep 15; "$@"; } || { sleep 30; "$@"; }; }
retry git fetch --no-tags --unshallow origin
uv tool run --from 'ggshield==1.48.0' ggshield secret scan repo .
else
echo "GITGUARDIAN_API_KEY not set, skipping ggshield scan"

5
.github/workflows/test-litellm-ui-unit.yml vendored
View file

@ -42,6 +42,11 @@ jobs:
- name: Install dependencies
run: npm ci
- name: Run UI type tests (Vitest)
env:
CI: "true"
run: npm run test:types
- name: Run UI unit tests (Vitest)
env:
CI: "true"

54
.github/workflows/test-terraform-modules.yml vendored Normal file
View file

@ -0,0 +1,54 @@
name: Terraform Modules
on:
push:
paths:
- "terraform/litellm/aws/**"
- ".github/workflows/test-terraform-modules.yml"
pull_request:
branches:
- main
- litellm_internal_staging
- litellm_oss_staging
- "litellm_**"
paths:
- "terraform/litellm/aws/**"
- ".github/workflows/test-terraform-modules.yml"
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
aws-module:
name: fmt, validate, test (aws)
runs-on: ubuntu-latest
timeout-minutes: 15
defaults:
run:
working-directory: terraform/litellm/aws
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
persist-credentials: false
- uses: hashicorp/setup-terraform@b9cd54a3c349d3f38e8881555d616ced269862dd # v3.1.2
with:
terraform_version: 1.13.3
terraform_wrapper: false
- name: fmt
run: terraform fmt -recursive -check -diff
- name: init
run: terraform init -backend=false -input=false
- name: validate
run: terraform validate
# Plan-only, mock_provider-backed: no AWS credentials, no API calls.
- name: test
run: terraform test

5
.github/workflows/test-terraform-provider.yml vendored
View file

@ -92,9 +92,10 @@ jobs:
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router
- name: Cache Prisma binaries
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma

6
.github/workflows/test-unit-documentation.yml vendored
View file

@ -65,10 +65,12 @@ jobs:
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router
- name: Cache Prisma binaries
if: steps.changes.outputs.decision != 'skip'
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
if: steps.changes.outputs.decision != 'skip'
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma

6
.github/workflows/test-unit-proxy-db.yml vendored
View file

@ -28,6 +28,10 @@ concurrency:
# 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.
# * `timeout` bounds the pytest step only. Checkout, dependency install, and
# Prisma client generation draw on a separate allowance in the base
# workflow, so slow setup shows up as a slow job rather than as a
# cancelled shard whose tests were passing.
# * 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).
@ -131,8 +135,6 @@ jobs:
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

1
.github/workflows/test-unit-proxy-endpoints.yml vendored
View file

@ -76,4 +76,5 @@ jobs:
workers: 4
reruns: 2
timeout-minutes: 60
job-timeout-minutes: 95
artifact-name: proxy-server

6
.github/workflows/test-unit-proxy-legacy.yml vendored
View file

@ -82,10 +82,12 @@ jobs:
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router
- name: Cache Prisma binaries
if: steps.changes.outputs.decision != 'skip'
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
if: steps.changes.outputs.decision != 'skip'
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma

5
.github/workflows/weekly_load_anomaly.yml vendored
View file

@ -51,9 +51,10 @@ jobs:
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra proxy
- name: Cache Prisma binaries
uses: ./.github/actions/cache-prisma-binaries
- name: Generate Prisma client
env:
PRISMA_BINARY_CACHE_DIR: ${{ runner.temp }}/prisma-cache
run: |
uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma

20
CLAUDE.md
View file

@ -1,4 +1,12 @@
Do not write any comments (existing comments can stay) unless explicitly asked to in a user (not system) prompt
Do not write comments unless they are any of:
- absolutely necessary to explain some very complex business logic (in which case, keep it concise and clear)
- used as an input for tools to read and act on. For example:
- entries in `.git-blame-ignore-revs` saying which commit is excluded from git blame
- a lint or type checker suppression like `# mutable-ok` or `# pyright: ignore[reportArgumentType] # <reason>` when introducing a truly unavoidable violation
- a TODO or FIXME
- Not great to have those, but if it's unavoidable, make sure to include a strong, concise reason for why it's there or, better yet, link to a GitHub issue for the follow-up work
Explanation: The point of this rule is to keep out AI slop comments. AI writes way too many and way too verbose comments. Code comments are, in a way, a violation of DRY code. You must update logic in two locations to change the code, and "hard to change" is literally the definition of tech debt. We should instead aim to write code that is intuitive and clear, even at a glance, to the reader, being both easy to maintain and high performance
Don't assume that the existing code is correct or the right way of doing things / good coding patterns. In fact, there are a lot of bad coding practices, overly complex code, code smells, etc. If something doesn't look right, speak up. Feel free to break existing patterns or question weird existing code to make new code high quality, as in:
@ -9,7 +17,7 @@ Don't assume that the existing code is correct or the right way of doing things
- easy to maintain/change
- modern
In that order of importance
In descending order of importance
When adding new features, add meaningful tests. Don't add tests that don't check anything substantial and is there just to make the code coverage pass. Yes, code coverage is important, but I'd rather have no signal whether the code is working than tests that don't fail when code is broken. The goal is to have tests that would fail before the feature was added/if the code was mutated in a way that breaks the feature and succeed only when the feature is fully working. I should run mutation testing and see > 90% kill rate
@ -21,7 +29,9 @@ End-to-end tests belong in `tests/e2e/` and must follow the harness conventions
When creating PRs, don't set base to `main`. `litellm_internal_staging` is the default base branch and serves that purpose for both internal and external / OSS contributions
When writing a PR body, treat the comments and imperative instructions inside @.github/pull_request_template.md as rules to follow, not just layout. Agent harnesses may strip HTML comments from copies of that file injected into context, so read .github/pull_request_template.md from disk before writing a PR body to make sure you see every comment rule
When writing a PR body, treat the comments and imperative instructions inside .github/pull_request_template.md as rules to follow, not just layout. Agent harnesses may strip HTML comments from copies of that file injected into context, so read .github/pull_request_template.md from disk before writing a PR body to make sure you see every comment rule
Same applies for filing bug reports and feature requests, with .github/ISSUE_TEMPLATE/bug_report.yml and .github/ISSUE_TEMPLATE/feature_request.yml, respectively
If you're resolving a linear ticket, in the "## Linear ticket" section of the PR, say "Resolves LIT-1234", replacing "LIT-1234" with the actual ticket id that you're resolving. If you don't have the ticket id, don't make one up or search for it. Just leave the section blank
@ -41,9 +51,7 @@ Python max line length is 120, not 88
When you fix violations gated by `ruff-strict-budget.json`, `type-discipline-budget.json`, or `basedpyright-code-budget.json`, run `make lint-budget-update` and commit the lowered limits so the ceilings ratchet down instead of leaving stale headroom. It measures the working tree, so it must contain exactly the fixes you're committing
Run `make check` (formerly `make pre-commit`, which still works as an alias) before every commit, merge commits included. It runs the CI-gating lint scoped to your staged files, so stage everything you intend to commit first; it warns about changed files you left unstaged and names the checks that were skipped because of them. With nothing staged it instead checks the working tree's diff against the merge base with origin/litellm_internal_staging, which is how you predict the CI lint on an already-committed branch, e.g. right after a merge commit. Deleted files count toward which checks run (a deletion alone can turn CI red) in both modes
`make check` saves its complete output to a log file in .git (overwriting previous logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice
`make check` (f.k.a. `make pre-commit`, which still works identically as an alias) saves its complete output to a log file in .git (overwriting previous logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice
If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and pushing `reportAny` / `reportExplicitAny` closer to their basedpyright ceilings, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in

40
basedpyright-code-budget.json
View file

@ -1,30 +1,30 @@
{
"reportAny": {
"limit": 27731
"limit": 23914
},
"reportArgumentType": {
"limit": 2626
"limit": 2580
},
"reportAssignmentType": {
"limit": 329
"limit": 323
},
"reportAttributeAccessIssue": {
"limit": 514
"limit": 488
},
"reportCallIssue": {
"limit": 116
"limit": 114
},
"reportConstantRedefinition": {
"limit": 40
},
"reportDeprecated": {
"limit": 215
"limit": 213
},
"reportDuplicateImport": {
"limit": 19
},
"reportExplicitAny": {
"limit": 8807
"limit": 7573
},
"reportFunctionMemberAccess": {
"limit": 7
@ -54,10 +54,10 @@
"limit": 0
},
"reportMissingParameterType": {
"limit": 5835
"limit": 5719
},
"reportMissingTypeArgument": {
"limit": 15790
"limit": 15657
},
"reportMissingTypeStubs": {
"limit": 40
@ -72,7 +72,7 @@
"limit": 0
},
"reportOptionalMemberAccess": {
"limit": 1077
"limit": 1069
},
"reportOptionalOperand": {
"limit": 0
@ -90,7 +90,7 @@
"limit": 8
},
"reportReturnType": {
"limit": 217
"limit": 213
},
"reportTypedDictNotRequiredAccess": {
"limit": 26
@ -99,37 +99,37 @@
"limit": 0
},
"reportUnknownArgumentType": {
"limit": 45063
"limit": 44832
},
"reportUnknownLambdaType": {
"limit": 113
},
"reportUnknownMemberType": {
"limit": 39773
"limit": 39269
},
"reportUnknownParameterType": {
"limit": 20207
"limit": 19988
},
"reportUnknownVariableType": {
"limit": 31281
"limit": 30923
},
"reportUnnecessaryCast": {
"limit": 122
"limit": 118
},
"reportUnnecessaryComparison": {
"limit": 701
"limit": 699
},
"reportUnnecessaryContains": {
"limit": 5
},
"reportUnnecessaryIsInstance": {
"limit": 862
"limit": 853
},
"reportUntypedBaseClass": {
"limit": 0
},
"reportUntypedFunctionDecorator": {
"limit": 33
"limit": 27
},
"reportUnusedClass": {
"limit": 23
@ -138,7 +138,7 @@
"limit": 139
},
"reportUnusedImport": {
"limit": 555
"limit": 545
},
"reportUnusedVariable": {
"limit": 146

20
enterprise/litellm_enterprise/enterprise_callbacks/send_emails/base_email.py
View file

@ -99,6 +99,7 @@ class BaseEmailLogger(CustomLogger):
email_html_content = USER_INVITATION_EMAIL_TEMPLATE.format(
email_logo_url=email_params.logo_url,
recipient_email=email_params.recipient_email,
invitation_link=email_params.base_url,
base_url=email_params.base_url,
email_support_contact=email_params.support_contact,
email_footer=email_params.signature,
@ -826,10 +827,15 @@ class BaseEmailLogger(CustomLogger):
"""
# Early validation
if not user_id:
verbose_proxy_logger.debug("No user_id provided for invitation link")
verbose_proxy_logger.warning(
"No user_id provided for invitation link. Email will link to base URL instead of onboarding page"
)
return base_url
if not await self._is_prisma_client_available():
verbose_proxy_logger.warning(
"Prisma client not available. Email will link to base URL instead of onboarding page"
)
return base_url
# Wait for any concurrent invitation creation to complete
@ -839,11 +845,15 @@ class BaseEmailLogger(CustomLogger):
invitation = await self._get_or_create_invitation(user_id)
if not invitation:
verbose_proxy_logger.warning(
f"Failed to get/create invitation for user_id: {user_id}"
f"Failed to get/create invitation for user_id: {user_id}. Email will link to base URL instead of onboarding page"
)
return base_url
return self._construct_invitation_link(invitation.id, base_url)
invitation_link = self._construct_invitation_link(invitation.id, base_url)
verbose_proxy_logger.info(
f"Successfully created invitation link for user_id: {user_id}"
)
return invitation_link
async def _is_prisma_client_available(self) -> bool:
"""Check if Prisma client is available"""
@ -921,7 +931,9 @@ class BaseEmailLogger(CustomLogger):
# http://localhost:4000/ui/onboarding?invitation_id=7a096b3a-37c6-440f-9dd1-ba22e8043f6b
"""
return f"{base_url}/ui/onboarding?invitation_id={invitation_id}"
base_url = base_url.rstrip("/")
invitation_link = f"{base_url}/ui/onboarding?invitation_id={invitation_id}"
return invitation_link
async def send_email(
self,

77
enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py
View file

@ -3,7 +3,7 @@ Polls LiteLLM_ManagedObjectTable to check if the batch job is complete, and if t
"""
from datetime import datetime, timedelta, timezone
from typing import TYPE_CHECKING, List, Optional, Tuple
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
from litellm._logging import verbose_proxy_logger
from litellm._uuid import uuid
@ -43,11 +43,15 @@ class CheckBatchCost:
# the guaranteed-failing primary query on every subsequent cycle.
self._has_batch_processed_column: bool = True
async def _get_user_info(self, batch_id, user_id) -> dict:
async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]:
"""
Look up user email and key alias by user_id for enriching the S3 callback metadata.
Returns a dict with user_api_key_user_email and user_api_key_alias (both may be None).
Returns an empty dict when user_id is None: batches created by a team or service
account key carry no user id, and find_unique(where={"user_id": None}) raises.
"""
if not user_id:
return {}
try:
user_row = await self.prisma_client.db.litellm_usertable.find_unique(
where={"user_id": user_id}
@ -62,6 +66,66 @@ class CheckBatchCost:
verbose_proxy_logger.error(f"CheckBatchCost: could not look up user {user_id} for batch {batch_id}: {e}")
return {}
async def _get_key_alias(self, batch_id: str, api_key: str | None) -> str | None:
"""Resolve the creating virtual key's alias from its hashed token."""
if not api_key:
return None
try:
key_row = await self.prisma_client.db.litellm_verificationtoken.find_unique(
where={"token": api_key}
)
return getattr(key_row, "key_alias", None) if key_row is not None else None
except Exception as e:
verbose_proxy_logger.error(f"CheckBatchCost: could not look up key alias for batch {batch_id}: {e}")
return None
async def _get_team_alias(self, team_id: str | None) -> str | None:
"""Resolve a team's alias from its id."""
if not team_id:
return None
try:
team_row = await self.prisma_client.db.litellm_teamtable.find_unique(
where={"team_id": team_id}
)
return getattr(team_row, "team_alias", None) if team_row is not None else None
except Exception as e:
verbose_proxy_logger.error(f"CheckBatchCost: could not look up team alias for team {team_id}: {e}")
return None
async def _build_creator_attribution_metadata(
self, job: "LiteLLM_ManagedObjectTable", batch_id: str
) -> Dict[str, Any]:
"""
Rebuild the spend-tracking metadata for the key, team, and tags that created the
batch so the batch-cost spend log is attributed the same way a non-batch request
is. Rows created before api_key and request_tags were persisted carry only
created_by and team_id, and fall back to those. A named creating key owns
user_api_key_alias; when it has no alias, or the key has since been rotated or
deleted, the field keeps the creating user's alias that _get_user_info filled in,
because a resolvable name is more useful on the spend row than a null.
"""
api_key = getattr(job, "api_key", None)
team_id = getattr(job, "team_id", None)
request_tags = getattr(job, "request_tags", None)
metadata: Dict[str, Any] = {
"user_api_key_user_id": job.created_by,
"user_api_key": api_key,
"user_api_key_team_id": team_id,
**(await self._get_user_info(batch_id, job.created_by)),
}
key_alias = await self._get_key_alias(batch_id, api_key)
if key_alias is not None:
metadata["user_api_key_alias"] = key_alias
team_alias = await self._get_team_alias(team_id)
if team_alias is not None:
metadata["user_api_key_team_alias"] = team_alias
if isinstance(request_tags, list) and request_tags:
metadata["tags"] = [tag for tag in request_tags if isinstance(tag, str)]
return metadata
async def _cleanup_stale_managed_objects(self) -> None:
"""
Mark managed objects older than MANAGED_OBJECT_STALENESS_CUTOFF_DAYS days
@ -485,9 +549,6 @@ class CheckBatchCost:
function_id=str(uuid.uuid4()),
)
creator_user_id = job.created_by
user_info = await self._get_user_info(batch_id, job.created_by)
logging_obj.update_environment_variables(
litellm_params={
# set the user-agent header so that S3 callback consumers can easily identify CheckBatchCost callbacks
@ -496,11 +557,7 @@ class CheckBatchCost:
"user-agent": CHECK_BATCH_COST_USER_AGENT,
}
},
"metadata": {
"user_api_key_user_id": creator_user_id,
"user_api_key_team_id": getattr(job, "team_id", None),
**user_info,
},
"metadata": await self._build_creator_attribution_metadata(job, batch_id),
},
optional_params={},
)

66
enterprise/litellm_enterprise/proxy/hooks/managed_files.py
View file

@ -163,6 +163,8 @@ class _ManagedObjectTableActions(Protocol):
self, where: Mapping[str, str], data: Mapping[str, Mapping[str, object]]
) -> "PrismaManagedObjectRow": ...
async def update_many(self, where: Mapping[str, object], data: Mapping[str, object]) -> int: ...
class _CursorPageArgs(TypedDict, total=False):
cursor: Mapping[str, str]
@ -263,7 +265,24 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
model_object_id: str,
file_purpose: Literal["batch", "fine-tune", "response"],
user_api_key_dict: UserAPIKeyAuth,
request_tags: Sequence[str] | None = None,
persist_attribution: bool = False,
create_if_missing: bool = True,
) -> None:
"""Persist a managed object row, caching it and upserting it in the DB.
persist_attribution is set only by the batch create, which is the one caller
that can speak for the creator; it gates the api_key and request_tags columns
that CheckBatchCost bills against, so a later poll or retrieve of the same
batch cannot record itself as the paying key. Like created_by and team_id,
both are written only in the upsert create branch, never on update.
create_if_missing is cleared by callers that observe a batch they did not
create, such as a poll. They still refresh status and file_object, but a
row absent from the table is left absent rather than created with the
observer as its creator, because created_by and team_id are written from
whoever calls the create branch.
"""
verbose_logger.info(f"Storing LiteLLM Managed {file_purpose} object with id={unified_object_id} in cache")
litellm_managed_object = LiteLLM_ManagedObjectTable(
unified_object_id=unified_object_id,
@ -277,6 +296,29 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
litellm_parent_otel_span=litellm_parent_otel_span,
)
from prisma import Json
api_key = user_api_key_dict.api_key or None
attribution_columns = (
{
**({"api_key": api_key} if api_key is not None else {}),
**({"request_tags": Json(list(request_tags))} if request_tags else {}),
}
if persist_attribution
else {}
)
# FIX: Update status and file_object on every operation to keep state in sync
update_columns: Final = {
"file_object": file_object.model_dump_json(),
"status": file_object.status,
"updated_by": user_api_key_dict.user_id,
}
if not create_if_missing:
await _managed_object_table(self.prisma_client).update_many(
where={"unified_object_id": unified_object_id},
data=update_columns,
)
return
await _managed_object_table(self.prisma_client).upsert(
where={"unified_object_id": unified_object_id},
data={
@ -289,12 +331,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
"team_id": user_api_key_dict.team_id,
"updated_by": user_api_key_dict.user_id,
"status": file_object.status,
**attribution_columns,
},
"update": {
"file_object": file_object.model_dump_json(),
"status": file_object.status,
"updated_by": user_api_key_dict.user_id,
}, # FIX: Update status and file_object on every operation to keep state in sync
"update": update_columns,
},
)
@ -1248,10 +1287,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
user_created_file_ids = await self.get_user_created_file_ids(user_api_key_dict, file_ids)
## Filter the response to only include the files created by the user
response.data = user_created_file_ids # type: ignore
self._scope_list_page_cursors(response, user_created_file_ids)
return response
return response
return response
@staticmethod
def _scope_list_page_cursors(response: AsyncCursorPage, data: List[OpenAIFileObject]) -> None:
"""Rebuild ``first_id`` / ``last_id`` from the caller-scoped page.
The upstream cursors point at rows that were just filtered out, so
leaving them in place discloses other callers' file ids. ``has_more``
is always cleared because ``after`` is never forwarded upstream, so
no further page is reachable through the proxy.
"""
if hasattr(response, "first_id"):
response.first_id = data[0].id if data else None
if hasattr(response, "last_id"):
response.last_id = data[-1].id if data else None
if hasattr(response, "has_more"):
response.has_more = False
async def afile_retrieve(
self, file_id: str, litellm_parent_otel_span: Optional[Span], llm_router: Optional[Router] = None
) -> OpenAIFileObject:

4
enterprise/pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "litellm-enterprise"
version = "0.1.54"
version = "0.1.55"
description = "Package for LiteLLM Enterprise features"
readme = "README.md"
requires-python = ">=3.9"
@ -26,7 +26,7 @@ required-version = ">=0.10.9"
module-root = ""
[tool.commitizen]
version = "0.1.54"
version = "0.1.55"
version_files = [
"pyproject.toml:^version",
"../pyproject.toml:litellm-enterprise==",

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

@ -105,6 +105,10 @@ spec:
{{- toYaml . | nindent 8 }}
{{- end }}
restartPolicy: OnFailure
{{- with .Values.nodeSelector }}
nodeSelector:
{{- toYaml . | nindent 8 }}
{{- end }}
{{- with .Values.affinity }}
affinity:
{{- toYaml . | nindent 8 }}

24
helm/litellm-helm/tests/migrations-job_tests.yaml
View file

@ -290,3 +290,27 @@ tests:
value:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: true
- it: should schedule onto the same nodes as the gateway
template: migrations-job.yaml
set:
migrationJob:
enabled: true
nodeSelector:
karpenter.sh/nodepool: litellm-e2e
tolerations:
- key: workload
operator: Equal
value: litellm-e2e
effect: NoSchedule
asserts:
- equal:
path: spec.template.spec.nodeSelector
value:
karpenter.sh/nodepool: litellm-e2e
- equal:
path: spec.template.spec.tolerations
value:
- key: workload
operator: Equal
value: litellm-e2e
effect: NoSchedule

2
litellm-proxy-extras/litellm_proxy_extras/migrations/20260713010000_add_ptu_columns_to_daily_team_spend/migration.sql Normal file
View file

@ -0,0 +1,2 @@
-- AlterTable
ALTER TABLE "LiteLLM_DailyTeamSpend" ADD COLUMN IF NOT EXISTS "ptu_flat_cost" DOUBLE PRECISION NOT NULL DEFAULT 0.0;

5
litellm-proxy-extras/litellm_proxy_extras/migrations/20260730000000_add_api_key_and_request_tags_to_managed_object_table/migration.sql Normal file
View file

@ -0,0 +1,5 @@
-- Add api_key and request_tags columns to LiteLLM_ManagedObjectTable
-- Captured at batch-create time so CheckBatchCost can attribute batch-cost spend
-- back to the creating virtual key (and its tags) even when created_by is null.
ALTER TABLE "LiteLLM_ManagedObjectTable" ADD COLUMN IF NOT EXISTS "api_key" TEXT;
ALTER TABLE "LiteLLM_ManagedObjectTable" ADD COLUMN IF NOT EXISTS "request_tags" JSONB DEFAULT '[]';

5
litellm-proxy-extras/litellm_proxy_extras/migrations/20260810000000_add_verificationtoken_settings_updated_at/migration.sql Normal file
View file

@ -0,0 +1,5 @@
-- AlterTable
ALTER TABLE "LiteLLM_VerificationToken" ADD COLUMN IF NOT EXISTS "settings_updated_at" TIMESTAMP(3);
-- AlterTable
ALTER TABLE "LiteLLM_DeletedVerificationToken" ADD COLUMN IF NOT EXISTS "settings_updated_at" TIMESTAMP(3);

7
litellm-proxy-extras/litellm_proxy_extras/schema.prisma
View file

@ -30,7 +30,7 @@ model LiteLLM_BudgetTable {
end_users LiteLLM_EndUserTable[] // multiple end-users can have the same budget
tags LiteLLM_TagTable[] // multiple tags can have the same budget
team_membership LiteLLM_TeamMembership[] // budgets of Users within a Team
organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization
organization_membership LiteLLM_OrganizationMembership[] // budgets of Users within a Organization
}
// Models on proxy
@ -452,6 +452,7 @@ model LiteLLM_VerificationToken {
created_by String?
updated_at DateTime? @default(now()) @updatedAt @map("updated_at")
updated_by String?
settings_updated_at DateTime? @map("settings_updated_at")
last_active DateTime? // When this key was last used
rotation_count Int? @default(0) // Number of times key has been rotated
auto_rotate Boolean? @default(false) // Whether this key should be auto-rotated
@ -548,6 +549,7 @@ model LiteLLM_DeletedVerificationToken {
created_by String? // Original creator
updated_at DateTime? // Last update timestamp before deletion
updated_by String? // Last user who updated before deletion
settings_updated_at DateTime? // Last configuration change before deletion
last_active DateTime? // When this key was last used before deletion
rotation_count Int? @default(0)
auto_rotate Boolean? @default(false)
@ -893,6 +895,7 @@ model LiteLLM_DailyTeamSpend {
api_requests BigInt @default(0)
successful_requests BigInt @default(0)
failed_requests BigInt @default(0)
ptu_flat_cost Float @default(0.0)
created_at DateTime @default(now())
updated_at DateTime @updatedAt
@ -985,6 +988,8 @@ model LiteLLM_ManagedObjectTable { // for batches or finetuning jobs which use t
created_at DateTime @default(now())
created_by String?
team_id String?
api_key String?
request_tags Json? @default("[]")
updated_at DateTime @updatedAt
updated_by String?

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

@ -1,6 +1,6 @@
[project]
name = "litellm-proxy-extras"
version = "0.4.84"
version = "0.4.85"
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
readme = "README.md"
requires-python = ">=3.9"
@ -26,7 +26,7 @@ required-version = ">=0.10.9"
module-root = ""
[tool.commitizen]
version = "0.4.84"
version = "0.4.85"
version_files = [
"pyproject.toml:^version",
"../pyproject.toml:litellm-proxy-extras==",

2
litellm/__init__.py
View file

@ -197,6 +197,7 @@ standard_logging_payload_excluded_fields: Optional[List[str]] = (
None # Fields to exclude from StandardLoggingPayload before callbacks receive it
)
log_raw_request_response: bool = False
request_correlation_in_logs: bool = False
redact_messages_in_exceptions: Optional[bool] = False
redact_user_api_key_info: Optional[bool] = False
# When True (default — preserves historical behavior), the Router appends
@ -245,6 +246,7 @@ use_chat_completions_url_for_anthropic_messages: bool = bool(
# config.yaml.
strip_anthropic_total_tokens: bool = False
anthropic_sse_ping_interval_seconds: float = 15.0
sse_keepalive_ping_interval_seconds: float | None = None
route_all_chat_openai_to_responses: bool = (
os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true"
) # When True, routes all OpenAI /chat/completions requests through the Responses API bridge

107
litellm/_logging.py
View file

@ -1,4 +1,5 @@
import ast
import contextvars
import logging
import os
import sys
@ -6,12 +7,44 @@ from datetime import datetime
from logging import Formatter
from typing import Any, Final
import litellm
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
from litellm.litellm_core_utils.secret_redaction import redact_string
set_verbose = False
session_id_var: Final[contextvars.ContextVar[str]] = contextvars.ContextVar("session_id", default="")
trace_id_var: Final[contextvars.ContextVar[str]] = contextvars.ContextVar("trace_id", default="")
_MAX_CORRELATION_ID_LENGTH: Final = 256
def _sanitize_correlation_id(value: str) -> str:
"""Strip control characters, bound length, and redact credential-shaped
content before a caller-controlled trace_id/session_id (e.g.
litellm_session_id, x-litellm-trace-id) is stamped into log lines.
Without the first two, a caller could embed \\r/\\n or terminal escape
sequences to forge fake log entries, or submit an oversized value repeated
across every log line for the request. Without the redaction, a caller
could smuggle a real credential (e.g. an sk-... key) through this field:
CorrelationContextFilter stamps trace_id/session_id onto the record after
SecretRedactionFilter has already run, so those two fields never otherwise
pass through credential redaction.
"""
stripped: Final = "".join(ch for ch in value if ch.isprintable())
return _redact_string(stripped[:_MAX_CORRELATION_ID_LENGTH])
def set_session_id(session_id: str) -> "contextvars.Token[str]":
return session_id_var.set(_sanitize_correlation_id(session_id))
def set_trace_id(trace_id: str) -> "contextvars.Token[str]":
return trace_id_var.set(_sanitize_correlation_id(trace_id))
if set_verbose is True:
logging.warning(
"`litellm.set_verbose` is deprecated. Please set `os.environ['LITELLM_LOG'] = 'DEBUG'` for debug logs."
@ -77,6 +110,28 @@ class SecretRedactionFilter(logging.Filter):
_secret_filter: Final = SecretRedactionFilter()
class CorrelationContextFilter(logging.Filter):
"""Stamps each log record with the current request's trace_id and session_id from contextvars.
Works in tandem with JsonFormatter: the formatter's record.__dict__ loop picks up these
attributes as first-class JSON fields without any formatter-level code.
"""
def filter(self, record: logging.LogRecord) -> bool:
if not litellm.request_correlation_in_logs:
return True
trace_id: Final = trace_id_var.get()
if trace_id:
record.trace_id = trace_id # rebind-ok: stamping the LogRecord is the Filter interface's contract
session_id: Final = session_id_var.get()
if session_id:
record.session_id = session_id # rebind-ok: stamping the LogRecord is the Filter interface's contract
return True
_correlation_filter: Final = CorrelationContextFilter()
json_logs = bool(os.getenv("JSON_LOGS", False))
# Create a handler for the logger (you may need to adapt this based on your needs)
log_level: Final = os.getenv("LITELLM_LOG", "DEBUG")
@ -84,6 +139,7 @@ numeric_level: Final[str] = getattr(logging, log_level.upper())
handler: Final = logging.StreamHandler()
handler.setLevel(numeric_level)
handler.addFilter(_secret_filter)
handler.addFilter(_correlation_filter)
def _try_parse_json_message(message: str) -> dict[str, Any] | None:
@ -146,6 +202,11 @@ def _get_standard_record_attrs() -> frozenset:
_STANDARD_RECORD_ATTRS: Final = _get_standard_record_attrs()
# CorrelationContextFilter is the only legitimate source for these two JSON fields;
# see JsonFormatter.format() for why they're excluded from the generic message-content
# and extra-attribute promotion paths.
_RESERVED_CORRELATION_FIELDS: Final = frozenset(("trace_id", "session_id"))
class JsonFormatter(Formatter):
def __init__(self):
@ -164,13 +225,18 @@ class JsonFormatter(Formatter):
"timestamp": self.formatTime(record),
}
# Parse embedded JSON or Python dict repr in message so sub-fields become first-class properties
# Parse embedded JSON or Python dict repr in message so sub-fields become first-class properties.
# trace_id/session_id are excluded here unconditionally (not just "if not already
# set") - CorrelationContextFilter is the only legitimate source for these two
# fields, and a message that merely happens to parse as JSON/dict (e.g. a proxy
# log line dumping raw request headers) must never be able to claim them, even on
# a record the filter hasn't stamped yet (no correlation context active for it).
parsed = _try_parse_json_message(message_str)
if parsed is None:
parsed = _try_parse_embedded_python_dict(message_str)
if parsed is not None:
for key, value in parsed.items():
if key not in json_record:
if key not in json_record and key not in _RESERVED_CORRELATION_FIELDS:
json_record[key] = value
# Include extra attributes passed via logger.debug("msg", extra={...})
@ -178,6 +244,18 @@ class JsonFormatter(Formatter):
if key not in _STANDARD_RECORD_ATTRS and key not in json_record:
json_record[key] = value
# trace_id/session_id are reserved: CorrelationContextFilter is the only
# legitimate source for these two fields. Without this, a message string
# that happens to parse as JSON/dict (e.g. a proxy log line dumping raw
# request headers) with a "trace_id"/"session_id" key would have already
# claimed the key at the parsed-message step above, and the extra-attributes
# loop's "key not in json_record" guard would then skip the real value -
# letting a caller-supplied header spoof another request's correlation ids.
for reserved_key in _RESERVED_CORRELATION_FIELDS:
value = getattr(record, reserved_key, None)
if value:
json_record[reserved_key] = value
# Set component/logger only if not already supplied via extra={...}
if "component" not in json_record:
json_record["component"] = record.name
@ -190,12 +268,34 @@ class JsonFormatter(Formatter):
return safe_dumps(json_record)
class CorrelationPlainFormatter(logging.Formatter):
"""Appends trace_id/session_id to plain-text log lines stamped by CorrelationContextFilter.
Mirrors JsonFormatter's handling of these two fields so request_correlation_in_logs
behaves the same whether or not json_logs is enabled.
"""
def format(self, record: logging.LogRecord) -> str:
formatted: Final = super().format(record)
trace_id: Final = getattr(record, "trace_id", None)
session_id: Final = getattr(record, "session_id", None)
if not trace_id and not session_id:
return formatted
parts: Final = tuple(
p
for p in (f"trace_id={trace_id}" if trace_id else None, f"session_id={session_id}" if session_id else None)
if p
)
return f"{formatted} [{' '.join(parts)}]"
# Function to set up exception handlers for JSON logging
def _setup_json_exception_handlers(formatter):
# Create a handler with JSON formatting for exceptions
error_handler: Final = logging.StreamHandler()
error_handler.setFormatter(formatter)
error_handler.addFilter(_secret_filter)
error_handler.addFilter(_correlation_filter)
# Setup excepthook for uncaught exceptions
def json_excepthook(exc_type, exc_value, exc_traceback):
@ -243,7 +343,7 @@ if json_logs:
handler.setFormatter(JsonFormatter())
_setup_json_exception_handlers(JsonFormatter())
else:
formatter: Final = logging.Formatter(
formatter: Final = CorrelationPlainFormatter(
"\033[92m%(asctime)s - %(name)s:%(levelname)s\033[0m: %(filename)s:%(lineno)s - %(message)s",
datefmt="%H:%M:%S",
)
@ -346,6 +446,7 @@ def _initialize_loggers_with_handler(handler: logging.Handler):
- Prevents bubbling to parent/root (critical to prevent duplicate JSON logs)
"""
handler.addFilter(_secret_filter)
handler.addFilter(_correlation_filter)
for lg in _get_loggers_to_initialize():
lg.handlers.clear() # remove any existing handlers
lg.addHandler(handler) # add JSON formatter handler

28
litellm/completion_extras/litellm_responses_transformation/handler.py
View file

@ -22,6 +22,7 @@ class ResponsesToCompletionBridgeHandlerInputKwargs(TypedDict):
model_response: "ModelResponse"
logging_obj: "LiteLLMLoggingObj"
custom_llm_provider: str
encoding: object
class ResponsesToCompletionBridgeHandler:
@ -102,35 +103,37 @@ class ResponsesToCompletionBridgeHandler:
from litellm import LiteLLMLoggingObj
from litellm.types.utils import ModelResponse
model: Final = kwargs.get("model")
typed_kwargs: Final[dict[str, object]] = kwargs
model: Final = typed_kwargs.get("model")
if model is None or not isinstance(model, str):
raise ValueError("model is required")
custom_llm_provider: Final = kwargs.get("custom_llm_provider")
custom_llm_provider: Final = typed_kwargs.get("custom_llm_provider")
if custom_llm_provider is None or not isinstance(custom_llm_provider, str):
raise ValueError("custom_llm_provider is required")
messages: Final = kwargs.get("messages")
messages: Final = typed_kwargs.get("messages")
if messages is None or not isinstance(messages, list):
raise ValueError("messages is required")
optional_params: Final = kwargs.get("optional_params")
optional_params: Final = typed_kwargs.get("optional_params")
if optional_params is None or not isinstance(optional_params, dict):
raise ValueError("optional_params is required")
litellm_params: Final = kwargs.get("litellm_params")
litellm_params: Final = typed_kwargs.get("litellm_params")
if litellm_params is None or not isinstance(litellm_params, dict):
raise ValueError("litellm_params is required")
headers: Final = kwargs.get("headers")
headers: Final = typed_kwargs.get("headers")
if headers is None or not isinstance(headers, dict):
raise ValueError("headers is required")
model_response: Final = kwargs.get("model_response")
model_response: Final = typed_kwargs.get("model_response")
if model_response is None or not isinstance(model_response, ModelResponse):
raise ValueError("model_response is required")
logging_obj: Final = kwargs.get("logging_obj")
logging_obj: Final = typed_kwargs.get("logging_obj")
if logging_obj is None or not isinstance(logging_obj, LiteLLMLoggingObj):
raise ValueError("logging_obj is required")
@ -143,6 +146,7 @@ class ResponsesToCompletionBridgeHandler:
model_response=model_response,
logging_obj=logging_obj,
custom_llm_provider=custom_llm_provider,
encoding=typed_kwargs.get("encoding"),
)
def completion(
@ -205,7 +209,7 @@ class ResponsesToCompletionBridgeHandler:
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=kwargs.get("encoding"),
encoding=validated_kwargs["encoding"],
api_key=kwargs.get("api_key"),
json_mode=kwargs.get("json_mode"),
)
@ -230,7 +234,7 @@ class ResponsesToCompletionBridgeHandler:
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=kwargs.get("encoding"),
encoding=validated_kwargs["encoding"],
api_key=kwargs.get("api_key"),
json_mode=kwargs.get("json_mode"),
)
@ -303,7 +307,7 @@ class ResponsesToCompletionBridgeHandler:
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=kwargs.get("encoding"),
encoding=validated_kwargs["encoding"],
api_key=kwargs.get("api_key"),
json_mode=kwargs.get("json_mode"),
)
@ -328,7 +332,7 @@ class ResponsesToCompletionBridgeHandler:
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=kwargs.get("encoding"),
encoding=validated_kwargs["encoding"],
api_key=kwargs.get("api_key"),
json_mode=kwargs.get("json_mode"),
)

101
litellm/completion_extras/litellm_responses_transformation/transformation.py
View file

@ -4,8 +4,8 @@ Handler for transforming /chat/completions api requests to litellm.responses req
import json
import os
from collections.abc import AsyncIterator, Callable, Iterable, Iterator, Mapping
from typing import TYPE_CHECKING, Any, Final, Literal, Union, cast
from collections.abc import AsyncIterator, Callable, Iterable, Iterator, Mapping, Sequence
from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict, Union, cast
from openai.types.responses.custom_tool_param import CustomToolParam
from openai.types.responses.response_input_param import (
@ -45,6 +45,9 @@ from litellm.types.utils import GenericStreamingChunk, ModelResponseStream
if TYPE_CHECKING:
from openai.types.responses import ResponseInputImageParam
from openai.types.responses.response_text_config_param import (
ResponseTextConfigParam as ResponseText,
)
from pydantic import BaseModel
from litellm import LiteLLMLoggingObj, ModelResponse
@ -57,6 +60,19 @@ if TYPE_CHECKING:
ChatCompletionThinkingBlock,
OpenAIMessageContentListBlock,
)
from litellm.types.utils import Choices
class _ReasoningSummaryText(TypedDict):
type: str
text: str
class _BuiltReasoningItem(TypedDict):
type: Literal["reasoning"]
id: str
encrypted_content: str | None
summary: Sequence[_ReasoningSummaryText]
def _get_reasoning_items(
@ -72,13 +88,13 @@ def _get_reasoning_items(
def _build_reasoning_item(
item_id: str,
encrypted_content: str | None,
summary_raw: Any,
) -> dict[str, Any]:
summary_raw: Iterable[object] | None,
) -> _BuiltReasoningItem:
"""Build a ChatCompletionReasoningItem-shaped dict from raw response data.
Handles both pydantic objects (attribute access) and plain dicts.
"""
summary: Final[list[dict[str, Any]]] = []
summary: Final[list[_ReasoningSummaryText]] = []
for s in summary_raw or []:
if isinstance(s, dict):
summary.append({"type": s.get("type", "summary_text"), "text": s.get("text", "")})
@ -98,7 +114,7 @@ def _build_reasoning_item(
class _ChatToolCallDict(ChatCompletionToolCallChunk, total=False):
provider_specific_fields: Mapping[str, Any]
provider_specific_fields: Mapping[str, object]
def _tool_call_dict_from_output_item(item: Mapping[str, Any], index: int) -> _ChatToolCallDict:
@ -142,10 +158,10 @@ def _flat_responses_tool_choice(choice_type: str, name: str) -> ToolChoiceFuncti
def _reasoning_item_to_response_input(
r_item: ChatCompletionReasoningItem | dict[str, Any],
) -> dict[str, Any]:
r_item: ChatCompletionReasoningItem,
) -> dict[str, object]:
"""Convert a stored ChatCompletionReasoningItem back to a Responses API input item."""
r_input: Final[dict[str, Any]] = {
r_input: Final[dict[str, object]] = {
"type": "reasoning",
"id": r_item.get("id") or f"rs_{id(r_item)}",
# summary is always required by the Responses API, even when empty
@ -181,7 +197,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return _flat_responses_tool_choice(choice_type, nested_name)
return tool_choice
def _handle_raw_dict_response_item(self, item: dict[str, Any], index: int) -> tuple[Any | None, int]:
def _handle_raw_dict_response_item(self, item: dict[str, Any], index: int) -> tuple["Choices | None", int]:
"""
Handle raw dict response items from Responses API (e.g., GPT-5 Codex format).
@ -228,8 +244,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
def convert_chat_completion_messages_to_responses_api(
self, messages: list["AllMessageValues"]
) -> tuple[list[Any], str | None]:
input_items: Final[list[Any]] = []
) -> tuple[list[object], str | None]:
input_items: Final[list[object]] = []
instructions: str | None = None
custom_tool_call_ids: Final = frozenset(
tool_call["id"]
@ -270,7 +286,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
# Convert tool message to function call output format
# The Responses API expects 'output' to be a list with input_text/input_image types
# Using list format for consistency across text and multimodal content
tool_output: list[dict[str, Any]]
tool_output: list[dict[str, object]]
if content is None:
tool_output = []
elif isinstance(content, str):
@ -308,7 +324,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
function = tool_call.get("function")
custom = tool_call.get("custom")
if function:
input_tool_call: dict[str, Any] = {
input_tool_call: dict[str, object] = {
"type": "function_call",
"call_id": tool_call["id"],
}
@ -376,15 +392,15 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
elif key == "web_search_options":
self._add_web_search_tool(responses_api_request, value)
def _build_sanitized_litellm_params(self, litellm_params: dict) -> dict[str, Any]:
def _build_sanitized_litellm_params(self, litellm_params: dict) -> dict[str, object]:
"""Build sanitized litellm_params with merged metadata."""
responses_optional_param_keys: Final = set(ResponsesAPIOptionalRequestParams.__annotations__.keys())
sanitized: Final[dict[str, Any]] = {
sanitized: Final[dict[str, object]] = {
key: value for key, value in litellm_params.items() if key not in responses_optional_param_keys
}
legacy_metadata: Final = litellm_params.get("metadata")
existing_litellm_metadata: Final = litellm_params.get("litellm_metadata")
merged_litellm_metadata: Final[dict[str, Any]] = {}
merged_litellm_metadata: Final[dict[str, object]] = {}
if isinstance(legacy_metadata, dict):
merged_litellm_metadata.update(legacy_metadata)
if isinstance(existing_litellm_metadata, dict):
@ -424,7 +440,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
litellm_params: dict,
headers: dict,
litellm_logging_obj: "LiteLLMLoggingObj",
client: Any | None = None,
client: object | None = None,
) -> dict:
(
input_items,
@ -498,9 +514,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
@staticmethod
def _convert_response_output_to_choices(
output_items: list[Any],
handle_raw_dict_callback: Callable | None = None,
) -> list[Any]:
output_items: Sequence[object],
handle_raw_dict_callback: Callable[..., tuple["Choices | None", int]] | None = None,
) -> list["Choices"]:
"""
Convert Responses API output items to chat completion choices.
@ -529,11 +545,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
choices: Final[list[Choices]] = []
index = 0
reasoning_content: str | None = None
pending_reasoning_item: dict[str, Any] | None = None
pending_reasoning_item: _BuiltReasoningItem | None = None
# Collect all tool calls to put them in a single choice
# (Chat Completions API expects all tool calls in one message)
accumulated_tool_calls: Final[list[dict[str, Any]]] = []
accumulated_tool_calls: Final[list[Mapping[str, object]]] = []
tool_call_index = 0
for item in output_items:
@ -640,7 +656,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return choices
@classmethod
def _extract_output_from_completed_event(cls, parsed_chunk: dict[str, Any]) -> list[dict[str, Any]] | None:
def _extract_output_from_completed_event(cls, parsed_chunk: Mapping[str, object]) -> list[dict[str, object]] | None:
response_payload: Final = parsed_chunk.get("response")
if not isinstance(response_payload, dict):
return None
@ -650,12 +666,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return cast(list[dict[str, Any]], response_output)
@classmethod
def _recover_output_items_from_raw_sse(cls, raw_sse: str | None) -> list[dict[str, Any]]:
def _recover_output_items_from_raw_sse(cls, raw_sse: str | None) -> list[dict[str, object]]:
if not raw_sse or not isinstance(raw_sse, str):
return []
recovered_output_items: Final[dict[int, dict[str, Any]]] = {}
recovered_text_only_items: Final[dict[int, dict[str, Any]]] = {}
recovered_output_items: Final[dict[int, dict[str, object]]] = {}
recovered_text_only_items: Final[dict[int, dict[str, object]]] = {}
for chunk in raw_sse.splitlines():
parsed_chunk = parse_sse_json_chunk(chunk)
@ -690,7 +706,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
# but text-only items at indices without a matching OUTPUT_ITEM_DONE
# must still be preserved (e.g. multi-output responses where some
# indices only emitted OUTPUT_TEXT_DONE).
merged_items: Final[dict[int, dict[str, Any]]] = {**recovered_text_only_items}
merged_items: Final[dict[int, dict[str, object]]] = {**recovered_text_only_items}
merged_items.update(recovered_output_items)
if merged_items:
@ -699,7 +715,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return []
@classmethod
def _recover_output_items_from_logging(cls, logging_obj: "LiteLLMLoggingObj") -> list[dict[str, Any]]:
def _recover_output_items_from_logging(cls, logging_obj: "LiteLLMLoggingObj") -> list[dict[str, object]]:
model_call_details: Final = getattr(logging_obj, "model_call_details", {}) or {}
original_response: Final = model_call_details.get("original_response")
return cls._recover_output_items_from_raw_sse(original_response)
@ -714,7 +730,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
messages: list["AllMessageValues"],
optional_params: dict,
litellm_params: dict,
encoding: Any,
encoding: object,
api_key: str | None = None,
json_mode: bool | None = None,
) -> "ModelResponse":
@ -788,7 +804,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
) -> BaseModelResponseIterator:
return OpenAiResponsesToChatCompletionStreamIterator(streaming_response, sync_stream, json_mode)
def _convert_content_str_to_input_text(self, content: str, role: str) -> dict[str, Any]:
def _convert_content_str_to_input_text(self, content: str, role: str) -> dict[str, object]:
if role == "user" or role == "system" or role == "tool":
return {"type": "input_text", "text": content}
else:
@ -825,13 +841,13 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
def _convert_content_to_responses_format(
self,
content: str
| list[Any]
| list[object]
| Iterable[
Union["OpenAIMessageContentListBlock", "ChatCompletionThinkingBlock", "ChatCompletionRedactedThinkingBlock"]
]
| None,
role: str,
) -> list[dict[str, Any]]:
) -> list[dict[str, object]]:
"""Convert chat completion content to responses API format"""
from litellm.types.llms.openai import ChatCompletionImageObject
@ -973,7 +989,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
return optional_params
def _map_reasoning_effort(self, reasoning_effort: str | dict[str, Any]) -> Reasoning | None:
def _map_reasoning_effort(self, reasoning_effort: str | Reasoning) -> Reasoning | None:
# If dict is passed, convert it directly to Reasoning object
if isinstance(reasoning_effort, dict):
return Reasoning(**reasoning_effort)
@ -1006,7 +1022,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
def _add_web_search_tool(
self,
responses_api_request: ResponsesAPIOptionalRequestParams,
web_search_options: Any,
web_search_options: object,
) -> None:
"""
Add web search tool to responses API request.
@ -1024,14 +1040,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
tools = []
responses_api_request["tools"] = tools
web_search_tool: Final[dict[str, Any]] = {"type": "web_search"}
web_search_tool: Final[dict[str, object]] = {"type": "web_search"}
if isinstance(web_search_options, dict):
web_search_tool.update(web_search_options)
# Cast to Any to match the expected union type for tools list items
tools.append(cast(Any, web_search_tool))
def _transform_response_format_to_text_format(self, response_format: dict[str, Any] | Any) -> dict[str, Any] | None:
def _transform_response_format_to_text_format(self, response_format: object) -> "ResponseText | None":
"""
Transform Chat Completion response_format parameter to Responses API text.format parameter.
@ -1130,7 +1146,12 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
def __init__(self, streaming_response, sync_stream: bool, json_mode: bool | None = False):
def __init__(
self,
streaming_response: Union[Iterator[str], AsyncIterator[str], "ModelResponse", "BaseModel"],
sync_stream: bool,
json_mode: bool | None = False,
):
super().__init__(streaming_response, sync_stream, json_mode)
self._chat_completion_id: str | None = None
self._tool_call_index_map: dict[int, int] = {} # mutable-ok: per-stream accumulator state
@ -1387,7 +1408,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
finish_reason: Final = "tool_calls" if has_function_calls else "stop"
# Extract reasoning items with encrypted_content for round-tripping
completed_reasoning_items: list[dict[str, Any]] | None = None
completed_reasoning_items: list[_BuiltReasoningItem] | None = None
for item in output_items:
if not isinstance(item, dict) or item.get("type") != "reasoning":
continue
@ -1439,7 +1460,7 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
]
)
def chunk_parser(self, chunk: dict) -> "ModelResponseStream":
def chunk_parser(self, chunk: dict[str, object]) -> "ModelResponseStream":
"""
Parse a Responses API streaming chunk and convert to OpenAI format.

29
litellm/constants.py
View file

@ -472,6 +472,8 @@ EMAIL_BUDGET_ALERT_MAX_SPEND_ALERT_PERCENTAGE: Final = float(
### ANTHROPIC CONSTANTS ###
ANTHROPIC_TOKEN_COUNTING_BETA_VERSION = os.getenv("ANTHROPIC_TOKEN_COUNTING_BETA_VERSION", "token-counting-2024-11-01")
ANTHROPIC_SKILLS_API_BETA_VERSION: Final = "skills-2025-10-02"
ANTHROPIC_BATCHES_ROUTE: Final = "/v1/messages/batches"
VERTEX_BATCH_PREDICTION_JOBS_ROUTE: Final = "batchPredictionJobs"
ANTHROPIC_WEB_SEARCH_TOOL_MAX_USES: Final = {
"low": 1,
"medium": 5,
@ -1322,6 +1324,8 @@ X_LITELLM_DISABLE_CALLBACKS: Final = "x-litellm-disable-callbacks"
LITELLM_METADATA_FIELD: Final = "litellm_metadata"
OLD_LITELLM_METADATA_FIELD: Final = "metadata"
RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model_name"
SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl"
CONSUMED_REQUEST_TAGS_METADATA_KEY: Final = "_consumed_request_tags"
INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin"
LITELLM_TRUNCATED_PAYLOAD_FIELD: Final = "litellm_truncated"
LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE: Final = (
@ -1477,6 +1481,10 @@ CLOUDZERO_MAX_FETCHED_DATA_RECORDS: Final = int(os.getenv("CLOUDZERO_MAX_FETCHED
SPEND_LOG_CLEANUP_JOB_NAME: Final = "spend_log_cleanup"
KEY_ROTATION_JOB_NAME: Final = "litellm_key_rotation_job"
EXPIRED_UI_SESSION_KEY_CLEANUP_JOB_NAME: Final = "litellm_expired_ui_session_key_cleanup_job"
WEEKLY_SPEND_REPORT_JOB_ID: Final = "weekly_spend_report_job"
MONTHLY_SPEND_REPORT_JOB_ID: Final = "monthly_spend_report_job"
PROMETHEUS_FALLBACK_STATS_JOB_ID: Final = "prometheus_fallback_stats_job"
SLACK_DAILY_REPORT_LOCK_ID: Final = "slack_daily_report"
SPEND_LOG_RUN_LOOPS: Final = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500))
SPEND_LOG_CLEANUP_BATCH_SIZE: Final = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000))
SPEND_LOG_CLEANUP_MAX_CONSECUTIVE_BATCH_FAILURES = int(os.getenv("SPEND_LOG_CLEANUP_MAX_CONSECUTIVE_BATCH_FAILURES", 3))
@ -1492,6 +1500,8 @@ SPEND_LOG_QUEUE_POLL_INTERVAL: Final = float(os.getenv("SPEND_LOG_QUEUE_POLL_INT
SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE: Final = int(os.getenv("SPEND_COUNTER_RESEED_LOCKS_MAX_SIZE", 10000))
DEFAULT_CRON_JOB_LOCK_TTL_SECONDS: Final = int(os.getenv("DEFAULT_CRON_JOB_LOCK_TTL_SECONDS", 60)) # 1 minute
PROXY_BUDGET_RESCHEDULER_MIN_TIME: Final = int(os.getenv("PROXY_BUDGET_RESCHEDULER_MIN_TIME", 597))
RESET_BUDGET_JOB_BATCH_SIZE: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_BATCH_SIZE", "500")))
RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN: Final = max(1, int(os.getenv("RESET_BUDGET_JOB_MAX_CHUNKS_PER_RUN", "100")))
PROXY_BATCH_POLLING_INTERVAL: Final = int(os.getenv("PROXY_BATCH_POLLING_INTERVAL", 3600))
MAX_OBJECTS_PER_POLL_CYCLE: Final = max(1, int(os.getenv("MAX_OBJECTS_PER_POLL_CYCLE", 50)))
MANAGED_OBJECT_STALENESS_CUTOFF_DAYS: Final = max(1, int(os.getenv("MANAGED_OBJECT_STALENESS_CUTOFF_DAYS", 7)))
@ -1520,6 +1530,10 @@ APSCHEDULER_REPLACE_EXISTING: Final = os.getenv("APSCHEDULER_REPLACE_EXISTING",
"1",
] # always replace existing jobs
# Width of the window scheduled background jobs are spread across, so they do not all fire
# on one instant on every replica. Tunable per deployment via general_settings.
DEFAULT_STAGGER_WINDOW_SECONDS: Final = 300
# The number of tag entries are higher than number of user, team entries. This leads to a higher QPS.
# This will run tag spcific tasks at a later time to smooth QPS
DAILY_TAG_SPEND_BATCH_MULTIPLIER: Final = 2.3
@ -1718,3 +1732,18 @@ BROWSER_SECURITY_HEADERS: Final[frozenset[str]] = frozenset(
)
UNSAFE_PROXY_RESPONSE_HEADERS: Final[frozenset[str]] = HTTP_FRAMING_HEADERS | BROWSER_SECURITY_HEADERS
# PTU reservation rollup writes rows to LiteLLM_DailyTeamSpend with this
# sentinel api_key so PTU flat cost stays distinguishable from real per-request
# spend under the table's composite unique constraint.
PTU_SENTINEL_API_KEY: Final[str] = "__ptu_flat_cost__"
PTU_ROLLUP_JOB_ID: Final[str] = "ptu_flat_cost_rollup_job"
PTU_ROLLUP_LOCK_TTL_SECONDS: Final[int] = 900
# Furthest back the catch-up pass looks for unpriced PTU days when a deployment
# declares no ptu_effective_from, bounding the scan for an open-ended window.
PTU_ROLLUP_MAX_BACKFILL_DAYS: Final[int] = 90
# Slack allowed when deciding a sentinel row is stale. The row's updated_at and the
# run's cutoff are stamped by different hosts, so clock skew between them must not let
# one run delete a charge another just wrote. A stale row is hours old and a concurrent
# one is seconds old, so a few minutes separates them.
PTU_PRUNE_SKEW_GRACE_SECONDS: Final[int] = 300

150
litellm/evals/main.py
View file

@ -7,7 +7,7 @@ import asyncio
import contextvars
from collections.abc import Coroutine
from functools import partial
from typing import Any, Final
from typing import Final
import httpx
@ -21,8 +21,10 @@ from litellm.types.llms.openai_evals import (
CancelRunResponse,
CreateEvalRequest,
CreateRunRequest,
DataSourceConfig,
DeleteEvalResponse,
Eval,
GraderConfig,
ListEvalsParams,
ListEvalsResponse,
ListRunsParams,
@ -41,13 +43,13 @@ DEFAULT_OPENAI_API_BASE: Final = "https://api.openai.com"
@client
async def acreate_eval(
data_source_config: dict[str, Any],
testing_criteria: list[dict[str, Any]],
data_source_config: DataSourceConfig,
testing_criteria: list[GraderConfig],
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -110,17 +112,17 @@ async def acreate_eval(
@client
def create_eval(
data_source_config: dict[str, Any],
testing_criteria: list[dict[str, Any]],
data_source_config: DataSourceConfig,
testing_criteria: list[GraderConfig],
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> Eval | Coroutine[Any, Any, Eval]:
) -> Eval | Coroutine[object, object, Eval]:
"""
Create a new evaluation
@ -231,8 +233,8 @@ async def alist_evals(
before: str | None = None,
order: str | None = None,
order_by: str | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -300,12 +302,12 @@ def list_evals(
before: str | None = None,
order: str | None = None,
order_by: str | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> ListEvalsResponse | Coroutine[Any, Any, ListEvalsResponse]:
) -> ListEvalsResponse | Coroutine[object, object, ListEvalsResponse]:
"""
List all evaluations
@ -413,8 +415,8 @@ def list_evals(
@client
async def aget_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -470,12 +472,12 @@ async def aget_eval(
@client
def get_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> Eval | Coroutine[Any, Any, Eval]:
) -> Eval | Coroutine[object, object, Eval]:
"""
Get an evaluation by ID
@ -564,10 +566,10 @@ def get_eval(
async def aupdate_eval(
eval_id: str,
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -630,14 +632,14 @@ async def aupdate_eval(
def update_eval(
eval_id: str,
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> Eval | Coroutine[Any, Any, Eval]:
) -> Eval | Coroutine[object, object, Eval]:
"""
Update an evaluation
@ -783,8 +785,8 @@ def update_eval(
@client
async def adelete_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -840,12 +842,12 @@ async def adelete_eval(
@client
def delete_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> DeleteEvalResponse | Coroutine[Any, Any, DeleteEvalResponse]:
) -> DeleteEvalResponse | Coroutine[object, object, DeleteEvalResponse]:
"""
Delete an evaluation
@ -933,8 +935,8 @@ def delete_eval(
@client
async def acancel_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -990,12 +992,12 @@ async def acancel_eval(
@client
def cancel_eval(
eval_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> CancelEvalResponse | Coroutine[Any, Any, CancelEvalResponse]:
) -> CancelEvalResponse | Coroutine[object, object, CancelEvalResponse]:
"""
Cancel a running evaluation
@ -1092,12 +1094,12 @@ def cancel_eval(
@client
async def acreate_run(
eval_id: str,
data_source: dict[str, Any],
data_source: dict[str, object],
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -1161,16 +1163,16 @@ async def acreate_run(
@client
def create_run(
eval_id: str,
data_source: dict[str, Any],
data_source: dict[str, object],
name: str | None = None,
metadata: dict[str, Any] | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_body: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
extra_body: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> Run | Coroutine[Any, Any, Run]:
) -> Run | Coroutine[object, object, Run]:
"""
Create a new run for an evaluation
@ -1280,8 +1282,8 @@ async def alist_runs(
after: str | None = None,
before: str | None = None,
order: str | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -1349,12 +1351,12 @@ def list_runs(
after: str | None = None,
before: str | None = None,
order: str | None = None,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> ListRunsResponse | Coroutine[Any, Any, ListRunsResponse]:
) -> ListRunsResponse | Coroutine[object, object, ListRunsResponse]:
"""
List all runs for an evaluation
@ -1462,8 +1464,8 @@ def list_runs(
async def aget_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -1522,12 +1524,12 @@ async def aget_run(
def get_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> Run | Coroutine[Any, Any, Run]:
) -> Run | Coroutine[object, object, Run]:
"""
Get a specific run
@ -1618,8 +1620,8 @@ def get_run(
async def acancel_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -1678,12 +1680,12 @@ async def acancel_run(
def cancel_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> CancelRunResponse | Coroutine[Any, Any, CancelRunResponse]:
) -> CancelRunResponse | Coroutine[object, object, CancelRunResponse]:
"""
Cancel a running run
@ -1783,8 +1785,8 @@ def cancel_run(
async def adelete_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
@ -1843,12 +1845,12 @@ async def adelete_run(
def delete_run(
eval_id: str,
run_id: str,
extra_headers: dict[str, Any] | None = None,
extra_query: dict[str, Any] | None = None,
extra_headers: dict[str, object] | None = None,
extra_query: dict[str, object] | None = None,
timeout: float | httpx.Timeout | None = None,
custom_llm_provider: str | None = None,
**kwargs,
) -> RunDeleteResponse | Coroutine[Any, Any, RunDeleteResponse]:
) -> RunDeleteResponse | Coroutine[object, object, RunDeleteResponse]:
"""
Delete a run

46
litellm/experimental_mcp_client/client.py
View file

@ -6,10 +6,11 @@ import asyncio
import base64
import os
from collections.abc import Awaitable, Callable, Generator
from datetime import timedelta
from typing import Any, Final, TypeVar
import httpx
from mcp import ClientSession, ReadResourceResult, Resource, StdioServerParameters
from mcp import ClientSession, McpError, ReadResourceResult, Resource, StdioServerParameters
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client
@ -69,6 +70,29 @@ def _first_non_cancelled_cause(exc: BaseException) -> BaseException | None:
return None
_SDK_READ_TIMEOUT_CODE: Final = int(httpx.codes.REQUEST_TIMEOUT)
"""The code the MCP SDK puts on its own elapsed read timeout, an HTTP status in a field that
otherwise carries JSON-RPC error codes."""
def _as_read_timeout(exc: BaseException) -> TimeoutError | None:
"""The session read timeout elapsing, re-expressed as a ``TimeoutError``, or ``None``.
The SDK reports its own elapsed read timeout as ``McpError`` carrying an HTTP status code in a
field that otherwise holds JSON-RPC error codes, and it relays an upstream's JSON-RPC error
through that same class and field. The numeric code alone therefore cannot separate the two, and
an upstream answering with application code 408 would be reported as a gateway timeout it never
caused. The SDK raises its own from inside an ``except TimeoutError``, so the elapsed timeout is
on the context chain, while a relayed error is built from a received message and has no such
chain; that is the discriminator.
"""
if not isinstance(exc, McpError) or exc.error.code != _SDK_READ_TIMEOUT_CODE:
return None
if not isinstance(exc.__context__, TimeoutError):
return None
return TimeoutError(exc.error.message)
TSessionResult = TypeVar("TSessionResult")
@ -347,7 +371,14 @@ class MCPClient:
session_kwargs["elicitation_callback"] = self._elicitation_callback
if self._logging_callback is not None:
session_kwargs["logging_callback"] = self._logging_callback
session_ctx: Final = ClientSession(read_stream, write_stream, **session_kwargs)
# The SDK drops a response stream that ends without a JSON-RPC reply, so nothing else
# ever fails the request.
session_ctx: Final = ClientSession(
read_stream,
write_stream,
read_timeout_seconds=timedelta(seconds=self.timeout),
**session_kwargs,
)
session: Final = await session_ctx.__aenter__()
try:
init_result: Final = await session.initialize()
@ -390,7 +421,16 @@ class MCPClient:
self._last_initialize_instructions = None
transport_ctx, http_client = self._create_transport_context()
return await self._execute_session_operation(transport_ctx, operation)
except Exception:
except Exception as e:
read_timeout: Final = _as_read_timeout(e)
if read_timeout is not None:
verbose_logger.warning(
"MCP client timed out after %ss waiting for %s to answer; the server accepted the "
"request and ended its response stream without a JSON-RPC reply",
self.timeout,
self.server_url or "stdio",
)
raise read_timeout from e
_log: Final = verbose_logger.debug if quiet_on_error else verbose_logger.warning
_log("MCP client run_with_session failed for %s", self.server_url or "stdio")
raise

7
litellm/images/main.py
View file

@ -315,7 +315,12 @@ def image_generation(
or get_secret_str("AZURE_API_KEY")
)
azure_ad_token: Final = optional_params.pop("azure_ad_token", None) or get_secret_str("AZURE_AD_TOKEN")
azure_ad_token_param: Final = optional_params.pop("azure_ad_token", None)
azure_ad_token: Final = (
azure_ad_token_param
if isinstance(azure_ad_token_param, str) and azure_ad_token_param
else get_secret_str("AZURE_AD_TOKEN")
)
# Create azure_ad_token_provider from tenant_id, client_id, client_secret if not already provided
if azure_ad_token_provider is None:

75
litellm/integrations/SlackAlerting/slack_alerting.py
View file

@ -9,6 +9,7 @@ from datetime import timedelta
from typing import TYPE_CHECKING, Any, Final, Literal
from openai import APIError
from pydantic import TypeAdapter
import litellm
import litellm.litellm_core_utils
@ -16,7 +17,7 @@ import litellm.litellm_core_utils.litellm_logging
import litellm.types
from litellm._logging import verbose_logger, verbose_proxy_logger
from litellm.caching.caching import DualCache
from litellm.constants import HOURS_IN_A_DAY
from litellm.constants import HOURS_IN_A_DAY, SLACK_DAILY_REPORT_LOCK_ID
from litellm.integrations.custom_batch_logger import CustomBatchLogger
from litellm.integrations.SlackAlerting.budget_alert_types import get_budget_alert_type
from litellm.integrations.SlackAlerting.hanging_request_check import (
@ -33,10 +34,14 @@ from litellm.llms.custom_httpx.http_handler import (
from litellm.proxy._types import (
AlertType,
CallInfo,
InvitationModel,
InvitationNew,
Litellm_EntityType,
UserAPIKeyAuth,
VirtualKeyEvent,
WebhookEvent,
)
from litellm.repositories.table_repositories import InvitationLinkRepository
from litellm.repositories.team_repository import TeamRepository
from litellm.repositories.user_repository import UserRepository
from litellm.types.integrations.slack_alerting import *
@ -46,6 +51,7 @@ from .batching_handler import send_to_webhook, squash_payloads
from .utils import process_slack_alerting_variables
if TYPE_CHECKING:
from litellm.proxy.db.db_transaction_queue.pod_lock_manager import PodLockManager
from litellm.router import Router as _Router
Router = _Router
@ -1081,6 +1087,44 @@ Model Info:
if email_logo_url is not None or email_support_contact is not None:
raise ValueError(f"Trying to Customize Email Alerting\n {CommonProxyErrors.not_premium_user.value}")
async def _construct_user_invitation_link(self, recipient_user_id: str | None, base_url: str) -> str:
from litellm.proxy.management_helpers.user_invitation import (
create_invitation_for_user,
)
from litellm.proxy.proxy_server import prisma_client
if recipient_user_id is None or prisma_client is None:
return base_url
try:
existing_invitations: Final = TypeAdapter(list[InvitationModel]).validate_python(
await InvitationLinkRepository(prisma_client).table.find_many( # pyright: ignore[reportAny] # untyped prisma boundary (any-ok), result validated by TypeAdapter
where={"user_id": recipient_user_id}, # mutable-ok: prisma find_many requires a dict where filter
order={"created_at": "desc"}, # mutable-ok: prisma find_many requires a dict order arg
),
from_attributes=True,
)
invitation: Final = (
existing_invitations[0]
if existing_invitations
else TypeAdapter(InvitationModel).validate_python(
await create_invitation_for_user(
data=InvitationNew(user_id=recipient_user_id),
user_api_key_dict=UserAPIKeyAuth(user_id=recipient_user_id),
),
from_attributes=True,
)
)
except Exception as e: # noqa: BLE001 # best-effort link build; any DB/creation failure falls back to base_url
verbose_proxy_logger.error(
"Error creating invitation link for user_id %s: %s",
recipient_user_id,
str(e),
)
return base_url
return f"{base_url.rstrip('/')}/ui/onboarding?invitation_id={invitation.id}"
async def send_key_created_or_user_invited_email(self, webhook_event: WebhookEvent) -> bool:
try:
from litellm.proxy.utils import send_email
@ -1139,11 +1183,14 @@ Model Info:
team_row: Final = await TeamRepository(prisma_client).table.find_unique(where={"team_id": team_id})
if team_row is not None:
team_name = team_row.team_alias or "-"
invitation_link: Final = await self._construct_user_invitation_link(
recipient_user_id=recipient_user_id, base_url=base_url
)
email_html_content = USER_INVITED_EMAIL_TEMPLATE.format(
email_logo_url=email_logo_url,
recipient_email=recipient_email,
team_name=team_name,
base_url=base_url,
base_url=invitation_link,
email_support_contact=email_support_contact,
)
else:
@ -1530,7 +1577,11 @@ Model Info:
except Exception:
pass
async def _run_scheduler_helper(self, llm_router) -> bool:
async def _run_scheduler_helper(
self,
llm_router,
pod_lock_manager: "PodLockManager | None" = None,
) -> bool:
"""
Returns:
- True -> report sent
@ -1555,6 +1606,16 @@ Model Info:
interval_seconds: Final = self.alerting_args.daily_report_frequency
if current_time - report_sent >= interval_seconds:
if (
pod_lock_manager is not None
and (
await pod_lock_manager.acquire_lock(
cronjob_id=SLACK_DAILY_REPORT_LOCK_ID, ttl=interval_seconds, allow_reentrant=False
)
)
is False
):
return False
# Sneak in the reporting logic here
await self.send_daily_reports(router=llm_router)
# Also, don't forget to update the report_sent time after sending the report!
@ -1566,7 +1627,11 @@ Model Info:
return report_sent_bool
async def _run_scheduled_daily_report(self, llm_router: Any | None = None):
async def _run_scheduled_daily_report(
self,
llm_router: Any | None = None,
pod_lock_manager: "PodLockManager | None" = None,
):
"""
If 'daily_reports' enabled
@ -1579,7 +1644,7 @@ Model Info:
if "daily_reports" in self.alert_types:
while True:
await self._run_scheduler_helper(llm_router=llm_router)
await self._run_scheduler_helper(llm_router=llm_router, pod_lock_manager=pod_lock_manager)
interval = random.randint(
self.alerting_args.report_check_interval - 3,
self.alerting_args.report_check_interval + 3,

30
litellm/integrations/anthropic_cache_control_hook.py
View file

@ -382,19 +382,23 @@ class AnthropicCacheControlHook(CustomPromptManagement):
model: str,
custom_llm_provider: str | None,
tools: list | None = None,
enable_prompt_caching: bool | None = None,
) -> list[CacheControlInjectionPoint]:
"""Default breakpoints when ``litellm.enable_anthropic_prompt_caching`` is on.
Caches the system prompt and the trailing turn, so the stable prefix
(system + tools + history) is reused while the breakpoint advances with
the conversation. Returns [] (stand down) when the flag is off, the
provider does not consume cache_control breakpoints (only anthropic /
bedrock do), the model lacks prompt-caching support, or the request
already carries client-supplied cache_control.
``enable_prompt_caching`` is the per-request override (stamped from key
metadata by the proxy); True turns auto-injection on for this request
even when the global flag is off. Caches the system prompt and the
trailing turn, so the stable prefix (system + tools + history) is
reused while the breakpoint advances with the conversation. Returns []
(stand down) when neither flag is on, the provider does not consume
cache_control breakpoints (only anthropic / bedrock do), the model
lacks prompt-caching support, or the request already carries
client-supplied cache_control.
"""
import litellm
if litellm.enable_anthropic_prompt_caching is not True:
if litellm.enable_anthropic_prompt_caching is not True and enable_prompt_caching is not True:
return []
provider = custom_llm_provider
@ -433,6 +437,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
model: str,
custom_llm_provider: str | None,
tools: list | None = None,
enable_prompt_caching: bool | None = None,
) -> None:
"""For /chat/completions: resolve the injection points the request should carry.
@ -458,6 +463,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
model=model,
custom_llm_provider=custom_llm_provider,
tools=tools,
enable_prompt_caching=enable_prompt_caching,
)
if points:
non_default_params["cache_control_injection_points"] = points
@ -478,12 +484,17 @@ class AnthropicCacheControlHook(CustomPromptManagement):
judgment happens once per request; points a prior pass wrote back
carry the judged stamp and are never re-judged (see
``_should_stand_down``). When none are configured but
``litellm.enable_anthropic_prompt_caching`` is on, synthesize default
breakpoints for the native /v1/messages path. Pops the key from kwargs;
``litellm.enable_anthropic_prompt_caching`` or the per-request
``enable_prompt_caching`` kwarg (stamped from key metadata) is on,
synthesize default breakpoints for the native /v1/messages path. Pops
both keys from kwargs;
if remaining (non-message) points exist they are written back so
downstream transforms can handle them.
"""
typed_messages = cast(list[AllMessageValues], messages) # cast-ok: Anthropic-shaped dicts from v1/messages
enable_prompt_caching: Final = cast( # cast-ok: kwargs is untyped; key stamped as bool by the proxy
bool | None, kwargs.pop("enable_prompt_caching", None)
)
configured: Final = cast( # cast-ok: kwargs is untyped; this key only holds the documented injection-point list
list[CacheControlInjectionPoint] | None, kwargs.pop("cache_control_injection_points", None)
)
@ -497,6 +508,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
tools=tools,
model=model,
custom_llm_provider=custom_llm_provider,
enable_prompt_caching=enable_prompt_caching,
)
if not injection_points:
return messages, system

79
litellm/integrations/arize/_utils.py
View file

@ -1,4 +1,5 @@
import json
from collections.abc import Mapping
from typing import TYPE_CHECKING, Any, Final
from typing_extensions import override
@ -12,7 +13,7 @@ from litellm.litellm_core_utils.redact_messages import (
should_redact_message_logging,
)
from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
from litellm.types.utils import StandardLoggingPayload
from litellm.types.utils import CallTypes, StandardLoggingMCPToolCall, StandardLoggingPayload
if TYPE_CHECKING:
from opentelemetry.trace import Span
@ -22,6 +23,7 @@ from litellm.integrations._types.open_inference import (
ImageAttributes,
MessageAttributes,
MessageContentAttributes,
OpenInferenceMimeTypeValues,
OpenInferenceSpanKindValues,
SpanAttributes,
ToolCallAttributes,
@ -480,6 +482,7 @@ def set_attributes(span: "Span", kwargs, response_obj, attributes: type[BaseLLMO
response_obj_for_attrs,
slp,
)
_safe_emit("mcp tool attrs", _maybe_set_mcp_tool_attrs, span, kwargs, slp, response_obj_for_attrs)
def _sanitize_optional_params(optional_params: dict | None) -> dict:
@ -538,9 +541,12 @@ def _set_request_attributes(
if optional_params.get("user"):
safe_set_attribute(span, "llm.user", optional_params.get("user"))
if response_obj and response_obj.get("id"):
if not hasattr(response_obj, "get"):
return
if response_obj.get("id"):
safe_set_attribute(span, "llm.response.id", response_obj.get("id"))
if response_obj and response_obj.get("model"):
if response_obj.get("model"):
safe_set_attribute(span, "llm.response.model", response_obj.get("model"))
@ -588,6 +594,8 @@ def _coerce_response_obj_for_attrs(response_obj):
- dicts and Pydantic models that already expose `.get` are returned
unchanged (preserves all current behavior, including the Responses API
flow which relies on Pydantic attribute access).
- Pydantic models without `.get` (e.g. the MCP SDK's `CallToolResult`,
logged for `call_mcp_tool` spans) are dumped to a dict.
- `httpx.Response` and other text-only responses (passthrough routes)
are JSON-decoded so the standard extraction paths can read fields like
`id`, `model`, and `usage`. On failure the original object is returned
@ -595,6 +603,9 @@ def _coerce_response_obj_for_attrs(response_obj):
"""
if response_obj is None or hasattr(response_obj, "get"):
return response_obj
dumped: Final = _to_plain_dict(response_obj)
if isinstance(dumped, dict):
return dumped
text: Final = getattr(response_obj, "text", None)
if isinstance(text, str) and text:
try:
@ -1058,3 +1069,65 @@ def _parse_passthrough_response(raw_response_obj, coerced_response_obj, kwargs):
except Exception:
return None
return None
def _maybe_set_mcp_tool_attrs(
span: "Span",
kwargs: Mapping[str, object],
standard_logging_payload: StandardLoggingPayload | None,
coerced_response_obj: object,
) -> None:
"""Render `call_mcp_tool` spans as OpenInference TOOL spans.
MCP tool calls carry neither `messages` nor `choices`, so the generic
extraction paths leave Input/Output blank. The tool name and arguments live
in `metadata.mcp_tool_call_metadata`; the result is an MCP `CallToolResult`
whose `content` is a list of typed parts.
"""
if standard_logging_payload is None:
return
if standard_logging_payload.get("call_type") != CallTypes.call_mcp_tool.value:
return
metadata: Final = standard_logging_payload.get("metadata")
mcp_meta: Final[StandardLoggingMCPToolCall | None] = metadata.get("mcp_tool_call_metadata") if metadata else None
if mcp_meta is None:
return
tool_name: Final = mcp_meta.get("name") or mcp_meta.get("namespaced_tool_name")
if tool_name:
safe_set_attribute(span, SpanAttributes.TOOL_NAME, tool_name)
if should_redact_message_logging(kwargs): # pyright: ignore[reportArgumentType] # reads, never mutates
return
arguments: Final[object] = mcp_meta.get("arguments")
if arguments is not None:
safe_set_attribute(span, SpanAttributes.INPUT_VALUE, safe_dumps(arguments))
safe_set_attribute(span, SpanAttributes.INPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value)
_set_mcp_tool_output(span, coerced_response_obj)
def _has_only_text_parts(content: object) -> bool:
return not isinstance(content, list) or all(_coerce_text([part]) is not None for part in content)
def _set_mcp_tool_output(span: "Span", coerced_response_obj: object) -> None:
if not isinstance(coerced_response_obj, Mapping):
return
content: Final[object] = coerced_response_obj.get("content")
text: Final[str | None] = _coerce_text(content)
if text and _has_only_text_parts(content):
safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, text)
safe_set_attribute(span, SpanAttributes.OUTPUT_MIME_TYPE, OpenInferenceMimeTypeValues.TEXT.value)
return
structured: Final[object] = coerced_response_obj.get("structuredContent")
payload: Final[object] = content if content else structured if structured is not None else content
if payload is None:
return
safe_set_attribute(span, SpanAttributes.OUTPUT_VALUE, safe_dumps(payload))
safe_set_attribute(span, SpanAttributes.OUTPUT_MIME_TYPE, OpenInferenceMimeTypeValues.JSON.value)

3
litellm/integrations/braintrust_mock_client.py
View file

@ -10,6 +10,7 @@ Usage:
import os
import time
from collections.abc import AsyncIterable, Iterable
from typing import Final
from urllib.parse import urlparse
@ -84,7 +85,7 @@ def _mock_http_handler_post(
timeout=None,
stream=False,
files=None,
content=None,
content: str | bytes | Iterable[bytes] | AsyncIterable[bytes] | None = None,
logging_obj=None,
):
"""Monkey-patched HTTPHandler.post that intercepts Braintrust calls with endpoint-specific responses."""

2
litellm/integrations/email_templates/templates.py
View file

@ -54,7 +54,7 @@ USER_INVITED_EMAIL_TEMPLATE: Final = """
You were invited to use OpenAI Proxy API for team {team_name} <br /> <br />
<a href="{base_url}" style="display: inline-block; padding: 10px 20px; background-color: #87ceeb; color: #fff; text-decoration: none; border-radius: 20px;">Get Started here</a> <br /> <br />
<a href="{base_url}" style="display: inline-block; padding: 10px 20px; background-color: #87ceeb; color: #fff; text-decoration: none; border-radius: 20px;">Accept Invitation</a> <br /> <br />
If you have any questions, please send an email to {email_support_contact} <br /> <br />

2
litellm/integrations/email_templates/user_invitation_email.py
View file

@ -131,7 +131,7 @@ USER_INVITATION_EMAIL_TEMPLATE: Final = """
</div>
<div class="btn-container">
<a href="{base_url}" class="btn">Accept Invitation</a>
<a href="{invitation_link}" class="btn">Accept Invitation</a>
</div>
<div class="quickstart">

3
litellm/integrations/gcs_bucket/gcs_bucket_mock_client.py
View file

@ -9,6 +9,7 @@ Usage:
"""
import asyncio
from collections.abc import AsyncIterable, Iterable
from typing import Final
from litellm._logging import verbose_logger
@ -113,7 +114,7 @@ async def _mock_async_handler_delete(
headers=None,
timeout=None,
stream=False,
content=None,
content: str | bytes | Iterable[bytes] | AsyncIterable[bytes] | None = None,
):
"""Monkey-patched AsyncHTTPHandler.delete that intercepts GCS calls."""
# Only mock GCS API calls

19
litellm/integrations/generic_api/generic_api_callback.py
View file

@ -11,7 +11,7 @@ import json
import os
import re
import traceback
from typing import Any, Final, Literal
from typing import Final, Literal
import httpx
@ -158,7 +158,7 @@ class GenericAPILogger(CustomBatchLogger):
"endpoint not set for GenericAPILogger, GENERIC_LOGGER_ENDPOINT not found in environment variables"
)
self.headers: dict = self._get_headers(headers)
self.headers: dict[str, str] = self._get_headers(headers)
self.endpoint: str = endpoint
self.event_types: list[API_EVENT_TYPES] | None = event_types
self.callback_name: str | None = callback_name
@ -248,18 +248,15 @@ class GenericAPILogger(CustomBatchLogger):
await asyncio.sleep(delay)
async def _post_with_retries(self, data: str) -> httpx.Response:
post_kwargs: Final[dict[str, Any]] = {
"url": self.endpoint,
"headers": self.headers,
"data": data,
}
if self.timeout is not None:
post_kwargs["timeout"] = self.timeout
total_attempts: Final = self.max_retries + 1
for attempt in range(total_attempts):
try:
return await self.async_httpx_client.post(**post_kwargs)
return await self.async_httpx_client.post(
url=self.endpoint,
headers=self.headers,
data=data,
timeout=self.timeout,
)
except Exception as e:
is_last_attempt = attempt == self.max_retries
should_retry = self._should_retry_exception(e)

24
litellm/integrations/langfuse/langfuse.py
View file

@ -2,7 +2,7 @@
# On success, logs events to Langfuse
import os
import traceback
from collections.abc import Callable
from collections.abc import Callable, Iterable
from datetime import datetime
from typing import TYPE_CHECKING, Any, Final, cast
@ -75,6 +75,22 @@ def _extract_cache_read_input_tokens(usage_obj) -> int:
return cache_read_input_tokens
def _as_steering_flag(value: object) -> bool:
"""A string ``str_to_bool`` does not recognise falls back to its truthiness."""
if isinstance(value, str):
parsed: Final = str_to_bool(value)
return bool(value) if parsed is None else parsed
return bool(value)
def _as_steering_key_sequence(value: object) -> tuple[str, ...]:
if isinstance(value, str):
return tuple(key.strip() for key in value.split(",") if key.strip())
if isinstance(value, Iterable):
return tuple(str(key) for key in value)
return ()
def resolve_langfuse_credentials(
langfuse_public_key=None,
langfuse_secret=None,
@ -552,10 +568,10 @@ class LangFuseLogger:
# This allows continuing an existing trace while still returning the correct trace_id
if existing_trace_id is not None:
trace_id = existing_trace_id
update_trace_keys: Final = cast(list, clean_metadata.pop("update_trace_keys", []))
update_trace_keys: Final = _as_steering_key_sequence(clean_metadata.pop("update_trace_keys", ()))
debug: Final = clean_metadata.pop("debug_langfuse", None)
mask_input: Final = clean_metadata.pop("mask_input", False)
mask_output: Final = clean_metadata.pop("mask_output", False)
mask_input: Final = _as_steering_flag(clean_metadata.pop("mask_input", False))
mask_output: Final = _as_steering_flag(clean_metadata.pop("mask_output", False))
# Look for masking function in the dedicated location first (set by scrub_sensitive_keys_in_metadata)
# Fall back to metadata for backwards compatibility
masking_function: Final = litellm_params.get("_langfuse_masking_function") or clean_metadata.pop(

10
litellm/integrations/langfuse/langfuse_otel.py
View file

@ -90,7 +90,6 @@ class LangfuseOtelLogger(OpenTelemetry):
"generation_name": LangfuseSpanAttributes.GENERATION_NAME,
"generation_id": LangfuseSpanAttributes.GENERATION_ID,
"parent_observation_id": LangfuseSpanAttributes.PARENT_OBSERVATION_ID,
"version": LangfuseSpanAttributes.GENERATION_VERSION,
"mask_input": LangfuseSpanAttributes.MASK_INPUT,
"mask_output": LangfuseSpanAttributes.MASK_OUTPUT,
"trace_user_id": LangfuseSpanAttributes.TRACE_USER_ID,
@ -99,13 +98,18 @@ class LangfuseOtelLogger(OpenTelemetry):
"trace_name": LangfuseSpanAttributes.TRACE_NAME,
"trace_id": LangfuseSpanAttributes.TRACE_ID,
"trace_metadata": LangfuseSpanAttributes.TRACE_METADATA,
"trace_version": LangfuseSpanAttributes.TRACE_VERSION,
"trace_release": LangfuseSpanAttributes.TRACE_RELEASE,
"trace_release": LangfuseSpanAttributes.RELEASE,
"existing_trace_id": LangfuseSpanAttributes.EXISTING_TRACE_ID,
"update_trace_keys": LangfuseSpanAttributes.UPDATE_TRACE_KEYS,
"debug_langfuse": LangfuseSpanAttributes.DEBUG_LANGFUSE,
}
version: Final = (
metadata.get("trace_version") if metadata.get("trace_version") is not None else metadata.get("version")
)
if version is not None:
safe_set_attribute(span, LangfuseSpanAttributes.VERSION.value, version)
for key, enum_attr in mapping.items():
if key in metadata and metadata[key] is not None:
value = metadata[key]

5
litellm/integrations/mock_client_factory.py
View file

@ -8,6 +8,7 @@ making actual network calls.
import asyncio
import json
from collections.abc import AsyncIterable, Iterable
from dataclasses import dataclass
from datetime import timedelta
from typing import Final, cast
@ -140,7 +141,7 @@ def create_mock_client_factory(config: MockClientConfig):
stream=False,
logging_obj=None,
files=None,
content=None,
content: str | bytes | Iterable[bytes] | AsyncIterable[bytes] | None = None,
):
"""Monkey-patched AsyncHTTPHandler.post that intercepts API calls."""
if isinstance(url, str) and _is_mock_url(url):
@ -193,7 +194,7 @@ def create_mock_client_factory(config: MockClientConfig):
timeout=None,
stream=False,
files=None,
content=None,
content: str | bytes | Iterable[bytes] | AsyncIterable[bytes] | None = None,
logging_obj=None,
):
"""Monkey-patched HTTPHandler.post that intercepts API calls."""

64
litellm/integrations/opentelemetry.py
View file

@ -1,7 +1,8 @@
import os
from collections.abc import Mapping
from dataclasses import dataclass, field
from datetime import datetime
from typing import TYPE_CHECKING, Any, Final, cast
from typing import TYPE_CHECKING, Any, Final, TypedDict, cast
import litellm
from litellm._logging import verbose_logger
@ -37,9 +38,11 @@ from litellm.types.utils import (
# OpenTelemetry imports moved to individual functions to avoid import errors when not installed
if TYPE_CHECKING:
from opentelemetry.sdk.trace import TracerProvider as _SDKTracerProvider
from opentelemetry.sdk.trace.export import SpanExporter as _SpanExporter
from opentelemetry.trace import Context as _Context
from opentelemetry.trace import Span as _Span
from opentelemetry.trace import SpanKind as _SpanKind
from opentelemetry.trace import Tracer as _Tracer
from litellm.proxy._types import (
@ -61,6 +64,25 @@ else:
ManagementEndpointLoggingPayload = Any
Context = Any
class _StartSpanRequiredKwargs(TypedDict):
name: str
start_time: int
context: "Context | None"
class _StartSpanKwargs(_StartSpanRequiredKwargs, total=False):
kind: "_SpanKind"
class _UsageCompletionTokensView(TypedDict, total=False):
completion_tokens: int
class _ResponseWithUsageView(TypedDict, total=False):
usage: "_UsageCompletionTokensView | None"
LITELLM_TRACER_NAME: Final = os.getenv("OTEL_TRACER_NAME", "litellm")
LITELLM_METER_NAME: Final = os.getenv("LITELLM_METER_NAME", "litellm")
LITELLM_LOGGER_NAME: Final = os.getenv("LITELLM_LOGGER_NAME", "litellm")
@ -297,9 +319,9 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
config: OpenTelemetryConfig | None = None,
callback_name: str | None = None,
# injection points for testing
tracer_provider: Any | None = None,
logger_provider: Any | None = None,
meter_provider: Any | None = None,
tracer_provider: object | None = None,
logger_provider: object | None = None,
meter_provider: object | None = None,
**kwargs,
):
team_metadata_keys_override: Final = kwargs.pop("baggage_team_metadata_keys", None)
@ -325,7 +347,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
self.OTEL_EXPORTER = self.config.exporter
self.OTEL_ENDPOINT = self.config.endpoint
self.OTEL_HEADERS = self.config.headers
self._tracer_provider_cache: dict[str, Any] = {}
self._tracer_provider_cache: dict[str, _SDKTracerProvider] = {}
self._init_tracing(tracer_provider)
_debug_otel: Final = str(os.getenv("DEBUG_OTEL", "False")).lower()
@ -870,7 +892,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
def _emit_guardrail_spans_from_request_data(
self,
request_data: dict,
parent_span: Any | None,
parent_span: "Span | None",
) -> None:
"""Emit ``guardrail`` spans from the request's proxy-internal metadata bucket
(``standard_logging_guardrail_information``).
@ -896,7 +918,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
# kwargs["litellm_params"]["metadata"]["_otel_internal"]. Pass the
# SAME metadata dict the proxy populated so _handle_failure and
# this hook see the same dedupe markers.
kwargs: Final[dict[str, Any]] = {
kwargs: Final[dict[str, object]] = {
"litellm_params": {"metadata": metadata},
"standard_logging_object": {
"guardrail_information": guardrail_information,
@ -1257,13 +1279,13 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
response_obj,
start_time,
end_time,
context,
context: "Context | None",
):
from opentelemetry.trace import Status, StatusCode
otel_tracer: Final[Tracer] = self.get_tracer_to_use_for_request(kwargs)
span_kwargs: Final[dict[str, Any]] = {
span_kwargs: Final[_StartSpanKwargs] = {
"name": self._get_span_name(kwargs),
"start_time": self._to_ns(start_time),
"context": context,
@ -1454,7 +1476,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
) = _resolve_metric_attribute_filter(attributes)
self._metric_attr_filter_resolved = True
def _filter_metric_attributes(self, attrs: dict[str, Any]) -> dict[str, Any]:
def _filter_metric_attributes(self, attrs: dict[str, str]) -> dict[str, str]:
if not self._metric_attr_filter_resolved:
self._ensure_metric_attribute_filter()
if self._metric_attr_include is not None:
@ -1559,7 +1581,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
def _record_time_per_output_token_metric(
self,
kwargs: dict,
response_obj: Any | None,
response_obj: "_ResponseWithUsageView | None",
end_time: datetime,
duration_s: float,
common_attrs: dict,
@ -1775,10 +1797,10 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
@staticmethod
def _resolve_guardrail_context(
span: Any | None,
parent_span: Any | None,
fallback_ctx: Any | None,
) -> Any | None:
span: "Span | None",
parent_span: "Span | None",
fallback_ctx: "Context | None",
) -> "Context | None":
"""
Return a valid OTEL context for guardrail child spans so they are
never orphaned (Issue #5). Priority:
@ -1945,7 +1967,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
if should_create_primary_span:
# Span 1: Request sent to litellm SDK
otel_tracer: Final[Tracer] = self.get_tracer_to_use_for_request(kwargs)
span_kwargs: Final[dict[str, Any]] = {
span_kwargs: Final[_StartSpanKwargs] = {
"name": self._get_span_name(kwargs),
"start_time": self._to_ns(start_time),
"context": _parent_context,
@ -2131,10 +2153,10 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
@staticmethod
def _tool_calls_kv_pair(
tool_calls: list[ChatCompletionMessageToolCall],
) -> dict[str, Any]:
) -> dict[str, object]:
from litellm.proxy._types import SpanAttributes
kv_pairs: Final[dict[str, Any]] = {}
kv_pairs: Final[dict[str, object]] = {}
for idx, tool_call in enumerate(tool_calls):
_function = tool_call.get("function")
if not _function:
@ -2691,8 +2713,8 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
import json
try:
_raw_response = json.loads(_raw_response)
for param, val in _raw_response.items():
_parsed: Final[Mapping[str, object]] = json.loads(_raw_response)
for param, val in _parsed.items():
self.safe_set_attribute(
span=span,
key=f"llm.{custom_llm_provider}.{param}",
@ -2722,7 +2744,7 @@ class OpenTelemetry(OTELGenAISemconvMixin, CustomLogger):
return int(dt * 1e9)
return int(dt.timestamp() * 1e9)
def _get_span_name(self, kwargs):
def _get_span_name(self, kwargs) -> str:
litellm_params: Final = kwargs.get("litellm_params", {})
metadata: Final = litellm_params.get("metadata") or {}
generation_name: Final = metadata.get("generation_name")

20
litellm/integrations/otel/logger.py
View file

@ -33,6 +33,7 @@ from litellm.integrations.otel.model.payloads import (
is_mcp_list_tools,
is_mcp_tool_call,
)
from litellm.integrations.otel.model.semconv import Error
from litellm.integrations.otel.model.spans import SpanRole, span_role_for_service
from litellm.integrations.otel.model.utils import to_ns
from litellm.integrations.otel.plumbing.context import (
@ -634,18 +635,23 @@ class OpenTelemetryV2(CustomLogger):
"""Stamp the v2 error.* attributes on the FastAPI-owned SERVER span for a
failure that dies before any LLM-call span exists (malformed body, auth /
validation rejection). Called from the proxy's global exception handler via
``_close_dangling_otel_server_span``. The instrumentor still owns the span's
status and lifecycle, so this only decorates it — never sets status, never
ends it — and emits no exception event, matching v1's SERVER-span behavior
and avoiding a duplicate of the event ``async_post_call_failure_hook`` or
the ``auth`` phase span already records."""
``_close_dangling_otel_server_span``, which swallows the exception into a
``JSONResponse`` so the instrumentor never sees it and leaves the span
``UNSET``; the status is set here instead (v1 did the same from the handler)
so a failed request reads as failed and not merely as a span carrying an
error message. The instrumentor still owns the span's lifecycle, so this
never ends it. The exception event is recorded only when nothing stamped
this span already — ``async_post_call_failure_hook`` and the ``auth`` phase
span record their own, and a second event would duplicate it — while the
attributes are always restamped so ``error.code`` stays pinned to the real
response status."""
if span is None or not is_recordable_span(span):
return
already_stamped: Final = Error.TYPE in (getattr(span, "attributes", None) or ())
stamp_error(
span,
_span_error_from_exception(exception, status_code=status_code),
record_event=False,
set_status=False,
record_event=not already_stamped,
)
async def async_post_call_failure_hook(

8
litellm/integrations/otel/presets/langfuse.py
View file

@ -42,9 +42,7 @@ def langfuse_dynamic_headers(params: StandardCallbackDynamicParams) -> dict[str,
public_key: Final = params.get("langfuse_public_key")
secret_key: Final = params.get("langfuse_secret_key")
if public_key and secret_key:
return {
"Authorization": _V1Langfuse._get_langfuse_authorization_header(
public_key=public_key, secret_key=secret_key
)
}
return _V1Langfuse._build_langfuse_otel_headers(
_V1Langfuse._get_langfuse_authorization_header(public_key=public_key, secret_key=secret_key)
)
return {}

106
litellm/integrations/websearch_interception/handler.py
View file

@ -10,7 +10,7 @@ import asyncio
import math
import uuid
from collections.abc import AsyncIterator, Mapping, Sequence
from typing import TYPE_CHECKING, Any, Final, cast
from typing import TYPE_CHECKING, Any, Final, TypedDict, cast
import litellm
from litellm._logging import verbose_logger
@ -42,7 +42,12 @@ from litellm.types.integrations.websearch_interception import (
WebSearchInterceptionConfig,
)
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import AgenticLoopParams, CallTypes, LlmProviders
from litellm.types.utils import (
AgenticLoopParams,
CallTypes,
LlmProviders,
StandardLoggingUserAPIKeyMetadata,
)
from litellm.utils import ProviderConfigManager
if TYPE_CHECKING:
@ -68,6 +73,23 @@ WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY: Final = "_websearch_interception_emit_native_b
WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY: Final = "websearch_native_blocks"
class _PlanMetadataView(TypedDict):
websearch_native_blocks: Sequence[Mapping[str, object]] | None
class _AgenticLoopParamsView(TypedDict):
agentic_loop_params: AgenticLoopParams
class _WebSearchSettingsView(TypedDict):
websearch_interception_params: WebSearchInterceptionConfig
class _SearchToolConfig(TypedDict, total=False):
search_tool_name: str
litellm_params: Mapping[str, object] | None
class WebSearchInterceptionLogger(CustomLogger):
"""
CustomLogger that intercepts WebSearch tool calls for models that don't
@ -389,7 +411,7 @@ class WebSearchInterceptionLogger(CustomLogger):
return tool.get("name")
@classmethod
def _sync_forced_tool_choice(cls, tool_choice: Any, converted_tools: list[dict[str, object]]) -> object:
def _sync_forced_tool_choice(cls, tool_choice: object, converted_tools: Sequence[Mapping[str, object]]) -> object:
"""Repoint a forced ``tool_choice`` at ``litellm_web_search`` when it
names a web-search tool that was just converted away.
@ -457,7 +479,7 @@ class WebSearchInterceptionLogger(CustomLogger):
kwargs[WEBSEARCH_EMIT_NATIVE_BLOCKS_KEY] = True
# Convert native web search tools to LiteLLM standard
converted_tools: Final = []
converted_tools: Final[list[dict[str, object]]] = []
for tool in tools:
if is_web_search_tool(tool):
standard_tool = get_litellm_web_search_tool()
@ -828,7 +850,10 @@ class WebSearchInterceptionLogger(CustomLogger):
Anthropic-native clients (Claude Desktop, the Anthropic SDK) can
render citations / sources alongside the model's textual reply.
"""
native_blocks: Final = plan.metadata.get(WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY)
metadata_view: Final[_PlanMetadataView] = {
"websearch_native_blocks": plan.metadata.get(WEBSEARCH_NATIVE_BLOCKS_METADATA_KEY)
}
native_blocks: Final = metadata_view["websearch_native_blocks"]
if not native_blocks:
return response
return self._inject_native_blocks(response, native_blocks)
@ -1273,8 +1298,10 @@ class WebSearchInterceptionLogger(CustomLogger):
kwargs_for_followup: Final = self._prepare_followup_kwargs(kwargs)
if logging_obj is not None:
agentic_params: Final[AgenticLoopParams] = logging_obj.model_call_details.get("agentic_loop_params", {})
full_model_name = agentic_params.get("model", model)
agentic_view: Final[_AgenticLoopParamsView] = {
"agentic_loop_params": logging_obj.model_call_details.get("agentic_loop_params", {})
}
full_model_name = agentic_view["agentic_loop_params"].get("model", model)
verbose_logger.debug(
"WebSearchInterception: Built anthropic request patch [call_id=%s model=%s messages=%d searches=%d]",
_call_id,
@ -1318,6 +1345,7 @@ class WebSearchInterceptionLogger(CustomLogger):
search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router)
search_provider: str | None = None
search_litellm_params: dict[str, Any] = {}
search_tool_name: Final = self._selected_search_tool_name(search_tool=search_tool)
if search_tool is not None:
await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs)
search_litellm_params = dict(search_tool.get("litellm_params", {}) or {})
@ -1334,12 +1362,30 @@ class WebSearchInterceptionLogger(CustomLogger):
verbose_logger.debug(
"WebSearchInterception: Executing search for '%s' using provider '%s'", query, search_provider
)
user_api_key_auth: Final = self._get_user_api_key_auth_from_kwargs(kwargs)
search_metadata: Final = (
None
if user_api_key_auth is None
else self._build_search_request_metadata(
user_api_key_auth=user_api_key_auth,
search_tool_name=search_tool_name,
)
)
search_kwargs: Final = {
key: value
for key, value in search_litellm_params.items()
if key != "search_provider" and value is not None
}
result: Final = await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs)
result: Final = (
await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs)
if search_metadata is None
else await litellm.asearch(
query=query,
search_provider=search_provider,
litellm_metadata=search_metadata,
**search_kwargs,
)
)
# Format using transformation function
search_result_text: Final = WebSearchTransformation.format_search_response(result)
@ -1396,6 +1442,35 @@ class WebSearchInterceptionLogger(CustomLogger):
team_object=team_object,
)
@staticmethod
def _build_search_request_metadata(
user_api_key_auth: "UserAPIKeyAuth",
search_tool_name: str | None,
) -> Mapping[str, object]:
"""
Spend-tracking metadata for the intercepted search, so its provider cost is logged
and billed against the key/user/team that made the originating LLM request instead
of being dropped by the proxy's spend hook for lack of an owner.
"""
from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup
user_api_key_metadata: Final[StandardLoggingUserAPIKeyMetadata] = (
LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=user_api_key_auth)
)
return { # mutable-ok: litellm's metadata channel is a plain dict its logging path reads and enriches
**user_api_key_metadata,
"model_group": search_tool_name,
"user_api_key": user_api_key_auth.api_key,
"user_api_key_auth": user_api_key_auth,
}
@staticmethod
def _selected_search_tool_name(search_tool: Mapping[str, object] | None) -> str | None:
if search_tool is None:
return None
search_tool_name: Final = search_tool.get("search_tool_name")
return search_tool_name if isinstance(search_tool_name, str) and search_tool_name else None
@staticmethod
def _get_user_api_key_auth_from_kwargs(kwargs: Mapping[str, object] | None) -> "UserAPIKeyAuth | None":
if not kwargs:
@ -1417,7 +1492,7 @@ class WebSearchInterceptionLogger(CustomLogger):
return None
def _select_search_tool_from_router(self, llm_router: object) -> dict[str, Any] | None:
def _select_search_tool_from_router(self, llm_router: object) -> "_SearchToolConfig | None":
if llm_router is None or not hasattr(llm_router, "search_tools"):
return None
search_tools: Final = list(getattr(llm_router, "search_tools") or [])
@ -1425,9 +1500,9 @@ class WebSearchInterceptionLogger(CustomLogger):
def _select_search_tool_from_list(
self,
search_tools: list[dict[str, Any]],
search_tools: list[_SearchToolConfig],
source: str,
) -> dict[str, Any] | None:
) -> "_SearchToolConfig | None":
if self.search_tool_name:
matching_tools = [tool for tool in search_tools if tool.get("search_tool_name") == self.search_tool_name]
if matching_tools:
@ -1622,8 +1697,8 @@ class WebSearchInterceptionLogger(CustomLogger):
@staticmethod
def initialize_from_proxy_config(
litellm_settings: dict[str, Any],
callback_specific_params: dict[str, Any],
litellm_settings: Mapping[str, WebSearchInterceptionConfig],
callback_specific_params: Mapping[str, object],
) -> "WebSearchInterceptionLogger":
"""
Static method to initialize WebSearchInterceptionLogger from proxy config.
@ -1647,7 +1722,10 @@ class WebSearchInterceptionLogger(CustomLogger):
# Get websearch_interception_params from litellm_settings or callback_specific_params
websearch_params: WebSearchInterceptionConfig = {}
if "websearch_interception_params" in litellm_settings:
websearch_params = litellm_settings["websearch_interception_params"]
settings_view: Final[_WebSearchSettingsView] = {
"websearch_interception_params": litellm_settings["websearch_interception_params"]
}
websearch_params = settings_view["websearch_interception_params"]
elif "websearch_interception" in callback_specific_params and isinstance(
callback_specific_params["websearch_interception"], dict
):

3
litellm/litellm_core_utils/cli_token_utils.py
View file

@ -8,6 +8,7 @@ This module has no dependencies on proxy code and can be safely imported at the
import json
import os
import time
from collections.abc import Mapping
from pathlib import Path
from typing import Final
@ -71,7 +72,7 @@ def get_litellm_gateway_api_key(
return token_data["key"]
def is_cli_token_fresh(token_data: dict, buffer_hours: float = 0.1) -> bool:
def is_cli_token_fresh(token_data: Mapping[str, object], buffer_hours: float = 0.1) -> bool:
"""Check whether a cached CLI token (as stored in token.json) is still
within its expiration window. Used by `lite auth print-token` to fail
fast, without a network round trip, once the cached token is past

4
litellm/litellm_core_utils/core_helpers.py
View file

@ -1,7 +1,7 @@
# What is this?
## Helper utilities
import copy
from collections.abc import Iterable
from collections.abc import Iterable, Mapping
from typing import TYPE_CHECKING, Any, Final, Literal
import httpx
@ -181,7 +181,7 @@ def add_missing_spend_metadata_to_litellm_metadata(litellm_metadata: dict, metad
def get_metadata_variable_name_from_kwargs(
kwargs: dict,
kwargs: Mapping[str, object],
) -> Literal["metadata", "litellm_metadata"]:
"""
Helper to return what the "metadata" field should be called in the request data

303
litellm/litellm_core_utils/litellm_logging.py
View file

@ -10,7 +10,7 @@ import subprocess
import sys
import time
import traceback
from collections.abc import Callable
from collections.abc import Callable, Mapping, Sequence
from datetime import datetime as dt_object
from functools import lru_cache
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Union, cast
@ -25,7 +25,15 @@ from litellm import (
log_raw_request_response,
turn_off_message_logging,
)
from litellm._logging import _is_debugging_on, _redact_string, verbose_logger
from litellm._logging import (
_is_debugging_on,
_redact_string,
session_id_var,
set_session_id,
set_trace_id,
trace_id_var,
verbose_logger,
)
from litellm._uuid import uuid
from litellm.batches.batch_utils import _handle_completed_batch
from litellm.caching.caching import DualCache, InMemoryCache
@ -168,6 +176,9 @@ from .initialize_dynamic_callback_params import (
from .specialty_caches.dynamic_logging_cache import DynamicLoggingCache
if TYPE_CHECKING:
from mcp.types import EmbeddedResource, ImageContent, TextContent
from litellm.integrations.otel.logger import OpenTelemetryV2
from litellm.llms.base_llm.passthrough.transformation import BasePassthroughConfig
try:
from litellm_enterprise.enterprise_callbacks.callback_controls import (
@ -203,14 +214,30 @@ except Exception as e:
PagerDutyAlerting = CustomLogger
EnterpriseCallbackControls = None
EnterpriseStandardLoggingPayloadSetupVAR = None
_in_memory_loggers: Final[list[Any]] = []
if TYPE_CHECKING:
from litellm.integrations.generic_api.generic_api_callback import (
GenericAPILogger as _GenericAPILoggerCls,
)
_STANDARD_LOGGING_METADATA_KEYS: Final[frozenset] = frozenset(StandardLoggingMetadata.__annotations__.keys())
_GENERIC_API_LOGGER_CLS: Final = _GenericAPILoggerCls
_RESEND_EMAIL_LOGGER_FACTORY: Final = CustomLogger
_SENDGRID_EMAIL_LOGGER_FACTORY: Final = CustomLogger
_SMTP_EMAIL_LOGGER_FACTORY: Final = CustomLogger
_PAGERDUTY_ALERTING_FACTORY: Final = CustomLogger
else:
_GENERIC_API_LOGGER_CLS: Final = GenericAPILogger
_RESEND_EMAIL_LOGGER_FACTORY: Final = ResendEmailLogger
_SENDGRID_EMAIL_LOGGER_FACTORY: Final = SendGridEmailLogger
_SMTP_EMAIL_LOGGER_FACTORY: Final = SMTPEmailLogger
_PAGERDUTY_ALERTING_FACTORY: Final = PagerDutyAlerting
_in_memory_loggers: Final[list[CustomLogger]] = []
_STANDARD_LOGGING_METADATA_KEYS: Final[frozenset[str]] = frozenset(StandardLoggingMetadata.__annotations__.keys())
### GLOBAL VARIABLES ###
# Cache custom pricing keys as frozenset for O(1) lookups instead of looping through 49 keys
_CUSTOM_PRICING_KEYS: Final[frozenset] = frozenset(CustomPricingLiteLLMParams.model_fields.keys())
_CUSTOM_PRICING_KEYS: Final[frozenset[str]] = frozenset(CustomPricingLiteLLMParams.model_fields.keys())
sentry_sdk_instance = None
capture_exception = None
@ -313,6 +340,7 @@ class Logging(LiteLLMLoggingBaseClass):
applied_guardrails: list[str] | None = None,
kwargs: dict | None = None,
log_raw_request_response: bool = False,
supports_correlation_logging: bool = True,
):
_input: Final[str | None] = messages # save original value of messages
if messages is not None:
@ -338,6 +366,36 @@ class Logging(LiteLLMLoggingBaseClass):
self.call_type = call_type
self.litellm_call_id = litellm_call_id
self.litellm_trace_id: str = litellm_trace_id if litellm_trace_id else str(uuid.uuid4())
# Capture the pre-call *value* (not a contextvars.Token) so restoration works
# even if this attempt's own logging ends up dispatched onto a different
# asyncio Task/context (e.g. via asyncio.create_task or the logging worker) -
# a Token can only be reset in the exact Context where it was created.
self._pre_call_trace_id: str = trace_id_var.get()
self._pre_call_session_id: str = session_id_var.get()
_sid: Final = kwargs.get("litellm_session_id") if kwargs else None
self.litellm_session_id: str = str(_sid) if _sid else ""
# supports_correlation_logging is False for calls originating from the
# sync client entry point (wrapper() in utils.py): a plain OS thread
# has no per-call context isolation the way an asyncio Task does, and
# a thread pool's worker threads are recycled across unrelated
# requests, so stamping trace_id/session_id there risks one request's
# ids leaking into a different, later request on the same thread. Sync
# support is deferred to a follow-up PR with its own safe-restore
# mechanism; async calls (the proxy's only call path) are unaffected.
if supports_correlation_logging:
set_trace_id(self.litellm_trace_id)
set_session_id(self.litellm_session_id)
# set_trace_id()/set_session_id() sanitize (strip control chars, bound
# length) before storing, so the contextvar's actual value can differ
# from self.litellm_trace_id/litellm_session_id. Capture what was
# really stored - _restore_correlation_context_if_unclaimed() must
# compare against this, not the raw ids, or a caller-supplied id
# containing control characters/oversized input would never match
# and cleanup would be skipped forever.
self._own_trace_id: str = trace_id_var.get()
self._own_session_id: str = session_id_var.get()
self.function_id = function_id
self.streaming_chunks: list[Any] = [] # for generating complete stream response
self.sync_streaming_chunks: list[Any] = [] # for generating complete stream response
@ -1246,7 +1304,9 @@ class Logging(LiteLLMLoggingBaseClass):
verbose_logger.exception("LiteLLM.LoggingError: [Non-Blocking] Exception occurred while logging %s", e)
return response_obj
def _parse_post_mcp_call_hook_response(self, response: MCPPostCallResponseObject | None) -> Any:
def _parse_post_mcp_call_hook_response(
self, response: MCPPostCallResponseObject | None
) -> "Sequence[TextContent | ImageContent | EmbeddedResource] | None":
"""
Parse the response from the post_mcp_tool_call_hook
@ -1690,7 +1750,7 @@ class Logging(LiteLLMLoggingBaseClass):
self.completion_start_time = completion_start_time
self.model_call_details["completion_start_time"] = self.completion_start_time
def normalize_logging_result(self, result: Any) -> Any:
def normalize_logging_result(self, result: Any) -> object:
"""
Some endpoints return a different type of result than what is expected by the logging system.
This function is used to normalize the result to the expected type.
@ -1726,7 +1786,7 @@ class Logging(LiteLLMLoggingBaseClass):
)
return logging_result
def _merge_hidden_params_from_response_into_metadata(self, logging_result: Any) -> None:
def _merge_hidden_params_from_response_into_metadata(self, logging_result: object) -> None:
"""
Copy response._hidden_params into litellm_params.metadata['hidden_params'].
@ -1787,7 +1847,9 @@ class Logging(LiteLLMLoggingBaseClass):
if (standard_logging_payload := self.model_call_details.get("standard_logging_object")) is not None:
emit_standard_logging_payload(standard_logging_payload)
def _build_standard_logging_payload(self, init_response_obj: Any, start_time: Any, end_time: Any) -> Any:
def _build_standard_logging_payload(
self, init_response_obj: object, start_time: Any, end_time: Any
) -> StandardLoggingPayload | None:
"""Build StandardLoggingPayload and accumulate its construction time."""
_start: Final = time.time()
payload: Final = get_standard_logging_object_payload(
@ -1908,7 +1970,7 @@ class Logging(LiteLLMLoggingBaseClass):
def _is_recognized_call_type_for_logging(
self,
logging_result: Any,
logging_result: object,
):
"""
Returns True if the call type is recognized for logging (eg. ModelResponse, ModelResponseStream, etc.)
@ -1992,7 +2054,67 @@ class Logging(LiteLLMLoggingBaseClass):
if complete_streaming_response is not None:
await self.async_success_handler(result=complete_streaming_response)
def success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs):
def _restore_correlation_context(self) -> None:
"""Restore trace_id/session_id contextvars to their pre-call value.
Without this, a nested LiteLLM call sharing the same asyncio Task as an
outer request (e.g. a guardrail's own LLM-as-judge call, an MCP sampling
call) would leave the outer request's subsequent log lines stamped with
the nested call's trace_id/session_id instead of its own.
Uses a plain set() of the captured pre-call value rather than
contextvars.Token-based reset(), since this can end up called from a
different asyncio Task/context than __init__ ran in (e.g. the request
task's own wrapper() finally block, plus async_success_handler
dispatched separately via asyncio.create_task/the logging worker) -
reset() only works in the exact Context a Token was created in and
raises otherwise. Deliberately NOT idempotent/guarded: each distinct
Task that calls this needs its own restore to actually take effect in
that Task's view of the contextvars, so calling it multiple times
(once per Task involved in this attempt) is required, not just safe.
"""
set_trace_id(self._pre_call_trace_id)
set_session_id(self._pre_call_session_id)
def _restore_correlation_context_if_unclaimed(self) -> None:
"""Guarded variant for __del__-triggered cleanup only.
__del__ can fire arbitrarily late (delayed by cyclic GC, possibly
after the consuming Task/thread has already moved on to a different,
still-active call). Unconditionally restoring in that case would
stomp the active call's trace_id/session_id with this abandoned
stream's stale pre-call snapshot. Only restore if the contextvars
still hold the ids *this* call set - i.e. nothing has claimed them
since - so an unrelated active call is never overwritten.
"""
if trace_id_var.get() == self._own_trace_id and session_id_var.get() == self._own_session_id:
self._restore_correlation_context()
def success_handler(
self,
result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml)
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
cache_hit: bool | None = None,
**kwargs: Any, # kwargs-ok: forwarded to _success_handler_body
) -> None:
"""Restores trace_id/session_id contextvars once this attempt's own success
logging (including any nested calls its callbacks trigger) is fully done."""
try:
return self._success_handler_body(
result=result, start_time=start_time, end_time=end_time, cache_hit=cache_hit, **kwargs
)
finally:
self._restore_correlation_context()
def _success_handler_body(
self,
result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml)
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
cache_hit: bool | None = None,
**kwargs: Any, # kwargs-ok: forwarded from success_handler
) -> None:
verbose_logger.debug("Logging Details LiteLLM-Success Call: Cache_hit=%s", cache_hit)
if not self.should_run_logging(event_type="sync_success"): # prevent double logging
return
@ -2399,7 +2521,31 @@ class Logging(LiteLLMLoggingBaseClass):
e,
)
async def async_success_handler(self, result=None, start_time=None, end_time=None, cache_hit=None, **kwargs):
async def async_success_handler(
self,
result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml)
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
cache_hit: bool | None = None,
**kwargs: Any, # kwargs-ok: forwarded to _async_success_handler_body
) -> None:
"""Restores trace_id/session_id contextvars once this attempt's own success
logging (including any nested calls its callbacks trigger) is fully done."""
try:
return await self._async_success_handler_body(
result=result, start_time=start_time, end_time=end_time, cache_hit=cache_hit, **kwargs
)
finally:
self._restore_correlation_context()
async def _async_success_handler_body(
self,
result: Any = None, # heterogeneous response object; varies by call type (ANN401 ignored, see ruff-strict.toml)
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
cache_hit: bool | None = None,
**kwargs: Any, # kwargs-ok: forwarded from async_success_handler
) -> None:
"""
Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions.
"""
@ -2791,7 +2937,32 @@ class Logging(LiteLLMLoggingBaseClass):
kwargs=self.model_call_details,
)
def failure_handler(self, exception, traceback_exception, start_time=None, end_time=None):
def failure_handler(
self,
exception: Exception,
traceback_exception: str,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
) -> None:
"""Restores trace_id/session_id contextvars once this attempt's own failure
logging (including any nested calls its callbacks trigger) is fully done."""
try:
return self._failure_handler_body(
exception=exception,
traceback_exception=traceback_exception,
start_time=start_time,
end_time=end_time,
)
finally:
self._restore_correlation_context()
def _failure_handler_body(
self,
exception: Exception,
traceback_exception: str,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
) -> None:
verbose_logger.debug("Logging Details LiteLLM-Failure Call: %s", litellm.failure_callback)
if not self.should_run_logging(event_type="sync_failure"): # prevent double logging
return
@ -2960,7 +3131,32 @@ class Logging(LiteLLMLoggingBaseClass):
"LiteLLM.LoggingError: [Non-Blocking] Exception occurred while failure logging %s", e
)
async def async_failure_handler(self, exception, traceback_exception, start_time=None, end_time=None):
async def async_failure_handler(
self,
exception: Exception,
traceback_exception: str,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
) -> None:
"""Restores trace_id/session_id contextvars once this attempt's own failure
logging (including any nested calls its callbacks trigger) is fully done."""
try:
return await self._async_failure_handler_body(
exception=exception,
traceback_exception=traceback_exception,
start_time=start_time,
end_time=end_time,
)
finally:
self._restore_correlation_context()
async def _async_failure_handler_body(
self,
exception: Exception,
traceback_exception: str,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
) -> None:
"""
Implementing async callbacks, to handle asyncio event loop issues when custom integrations need to use async functions.
"""
@ -3266,7 +3462,7 @@ class Logging(LiteLLMLoggingBaseClass):
model=self.model,
messages=[],
logging_obj=self,
optional_params={},
optional_params=self.optional_params or {},
api_key="",
request_data={},
encoding=litellm.encoding,
@ -3287,6 +3483,7 @@ class Logging(LiteLLMLoggingBaseClass):
),
model_response=litellm.ModelResponse(),
json_mode=None,
speed=self.optional_params.get("speed") if self.optional_params else None,
)
return result
@ -4043,7 +4240,7 @@ def _init_custom_logger_compatible_class(
for callback in _in_memory_loggers:
if isinstance(callback, PagerDutyAlerting):
return callback
pagerduty_logger: Final = PagerDutyAlerting(**custom_logger_init_args)
pagerduty_logger: Final = _PAGERDUTY_ALERTING_FACTORY(**custom_logger_init_args)
_in_memory_loggers.append(pagerduty_logger)
return pagerduty_logger
elif logging_integration == "anthropic_cache_control_hook":
@ -4073,7 +4270,7 @@ def _init_custom_logger_compatible_class(
return _gcs_pubsub_logger
elif logging_integration == "generic_api":
for callback in _in_memory_loggers:
if isinstance(callback, GenericAPILogger):
if isinstance(callback, _GENERIC_API_LOGGER_CLS):
return callback
generic_api_logger: Final = GenericAPILogger()
_in_memory_loggers.append(generic_api_logger)
@ -4082,21 +4279,21 @@ def _init_custom_logger_compatible_class(
for callback in _in_memory_loggers:
if isinstance(callback, ResendEmailLogger):
return callback
resend_email_logger: Final = ResendEmailLogger()
resend_email_logger: Final = _RESEND_EMAIL_LOGGER_FACTORY()
_in_memory_loggers.append(resend_email_logger)
return resend_email_logger
elif logging_integration == "sendgrid_email":
for callback in _in_memory_loggers:
if isinstance(callback, SendGridEmailLogger):
return callback
sendgrid_email_logger: Final = SendGridEmailLogger()
sendgrid_email_logger: Final = _SENDGRID_EMAIL_LOGGER_FACTORY()
_in_memory_loggers.append(sendgrid_email_logger)
return sendgrid_email_logger
elif logging_integration == "smtp_email":
for callback in _in_memory_loggers:
if isinstance(callback, SMTPEmailLogger):
return callback
smtp_email_logger: Final = SMTPEmailLogger()
smtp_email_logger: Final = _SMTP_EMAIL_LOGGER_FACTORY()
_in_memory_loggers.append(smtp_email_logger)
return smtp_email_logger
elif logging_integration == "humanloop":
@ -4163,7 +4360,7 @@ def _init_custom_logger_compatible_class(
return None
def _maybe_construct_otel_v2(callback_name: str, _in_memory_loggers: list) -> Any | None:
def _maybe_construct_otel_v2(callback_name: str, _in_memory_loggers: list[CustomLogger]) -> "OpenTelemetryV2 | None":
"""If ``LITELLM_OTEL_V2`` is on, build (or reuse) a single ``OpenTelemetryV2``
instance configured via the preset for ``callback_name``.
@ -4194,7 +4391,7 @@ def _maybe_construct_otel_v2(callback_name: str, _in_memory_loggers: list) -> An
return v2_logger
def _maybe_auto_initialize_arize_phoenix(_in_memory_loggers: list) -> None:
def _maybe_auto_initialize_arize_phoenix(_in_memory_loggers: list[CustomLogger]) -> None:
"""
Auto-initialize ArizePhoenixLogger when Phoenix env vars are detected.
@ -4421,7 +4618,7 @@ def get_custom_logger_compatible_class(
return callback
elif logging_integration == "generic_api":
for callback in _in_memory_loggers:
if isinstance(callback, GenericAPILogger):
if isinstance(callback, _GENERIC_API_LOGGER_CLS):
return callback
elif logging_integration == "resend_email":
for callback in _in_memory_loggers:
@ -5061,33 +5258,61 @@ class StandardLoggingPayloadSetup:
return end_time_float - start_time_float
@staticmethod
def _get_standard_logging_payload_trace_id(
def get_standard_logging_payload_trace_id(
logging_obj: Logging,
litellm_params: dict,
litellm_params: Mapping[str, Any],
) -> str:
"""
Returns the `litellm_trace_id` for this request
This helps link sessions when multiple requests are made in a single session
Gated behind `litellm.request_correlation_in_logs`:
- Off (default): legacy behavior, preserved for backward compatibility -
`litellm_session_id` takes priority over `litellm_trace_id` since historically
this field doubled as the session-grouping field.
- On: `litellm_trace_id` takes priority - trace_id and session_id are independent,
see `get_standard_logging_payload_session_id` for session tracking.
"""
dynamic_litellm_session_id: Final = litellm_params.get("litellm_session_id")
dynamic_litellm_trace_id: Final = litellm_params.get("litellm_trace_id")
metadata: Final = litellm_params.get("metadata")
metadata_session_id: Final = metadata.get("session_id") if metadata else None
metadata_trace_id: Final = metadata.get("trace_id") if metadata else None
# Note: we recommend using `litellm_session_id` for session tracking
# `litellm_trace_id` is an internal litellm param
ordered_candidates: Final[tuple[Any, Any, Any, Any]] = (
(dynamic_litellm_trace_id, dynamic_litellm_session_id, metadata_trace_id, metadata_session_id)
if litellm.request_correlation_in_logs
else (dynamic_litellm_session_id, dynamic_litellm_trace_id, metadata_session_id, metadata_trace_id)
)
for candidate in ordered_candidates:
if candidate:
return str(candidate)
return logging_obj.litellm_trace_id
@staticmethod
def get_standard_logging_payload_session_id(
logging_obj: Logging,
litellm_params: Mapping[str, Any],
) -> str:
"""
Returns the end-user/conversation `litellm_session_id` for this request, independent of trace_id.
Only populated when `litellm.request_correlation_in_logs` is enabled - off by default
to avoid changing existing StandardLoggingPayload shape for callers who haven't opted in.
Unlike `get_standard_logging_payload_trace_id`, this never falls back to a generated
per-call trace id: it's empty when the caller never supplied a session id.
"""
if not litellm.request_correlation_in_logs:
return ""
dynamic_litellm_session_id: Final = litellm_params.get("litellm_session_id")
if dynamic_litellm_session_id:
return str(dynamic_litellm_session_id)
elif dynamic_litellm_trace_id:
return str(dynamic_litellm_trace_id)
# Fallback: use metadata.session_id or metadata.trace_id for call chaining
metadata: Final = litellm_params.get("metadata") or {}
metadata_session_id: Final = metadata.get("session_id")
metadata_trace_id: Final = metadata.get("trace_id")
metadata: Final = litellm_params.get("metadata")
metadata_session_id: Final = metadata.get("session_id") if metadata else None
if metadata_session_id:
return str(metadata_session_id)
if metadata_trace_id:
return str(metadata_trace_id)
return logging_obj.litellm_trace_id
return logging_obj.litellm_session_id
@staticmethod
def _get_user_agent_tags(proxy_server_request: dict) -> list[str] | None:
@ -5392,7 +5617,11 @@ def get_standard_logging_object_payload(
payload: Final[StandardLoggingPayload] = StandardLoggingPayload(
id=str(id),
litellm_call_id=kwargs.get("litellm_call_id") or litellm_params.get("litellm_call_id"),
trace_id=StandardLoggingPayloadSetup._get_standard_logging_payload_trace_id(
trace_id=StandardLoggingPayloadSetup.get_standard_logging_payload_trace_id(
logging_obj=logging_obj,
litellm_params=litellm_params,
),
session_id=StandardLoggingPayloadSetup.get_standard_logging_payload_session_id(
logging_obj=logging_obj,
litellm_params=litellm_params,
),

19
litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py
View file

@ -2,6 +2,7 @@
Helper utilities for tracking the cost of built-in tools.
"""
from collections.abc import Mapping
from typing import Any, Final, Literal
import litellm
@ -23,6 +24,14 @@ from litellm.types.utils import (
)
def _usage_reports_server_side_web_search_calls(usage: Usage) -> bool:
details: Final = getattr(usage, "server_side_tool_usage_details", None)
if not isinstance(details, Mapping):
return False
calls: Final = details.get("web_search_calls")
return isinstance(calls, int) and calls > 0
class StandardBuiltInToolCostTracking:
"""
Helper class for tracking the cost of built-in tools
@ -351,6 +360,10 @@ class StandardBuiltInToolCostTracking:
# and _handle_web_search_cost() is never called.
if hasattr(usage, "server_tool_use") and _get_web_search_requests(usage.server_tool_use) is not None:
return True
# xAI reports usage.server_side_tool_usage_details.web_search_calls; a searched
# answer with no url_citation annotations has no other chat-path signal
if _usage_reports_server_side_web_search_calls(usage):
return True
return False
elif isinstance(response_object, ResponsesAPIResponse):
# response api explicitly includes web_search_call in the output
@ -370,6 +383,8 @@ class StandardBuiltInToolCostTracking:
)
):
return True
if _usage_reports_server_side_web_search_calls(usage):
return True
return False
@ -432,7 +447,9 @@ class StandardBuiltInToolCostTracking:
"""
output: Final = response_object.output
for output_item in output:
_output_type: str | None = getattr(output_item, "type", None)
_output_type: str | None = (
output_item.get("type") if isinstance(output_item, dict) else getattr(output_item, "type", None)
)
if _output_type == output_type:
return True
return False

39
litellm/litellm_core_utils/llm_cost_calc/utils.py
View file

@ -49,6 +49,12 @@ _SERVICE_TIER_TO_COST_KEY_SUFFIX: Final[Mapping[str, str]] = MappingProxyType(
}
)
_INCLUSIVE_THRESHOLD_PROVIDERS: Final = frozenset({"xai"})
def _uses_inclusive_token_thresholds(custom_llm_provider: str | None) -> bool:
return custom_llm_provider in _INCLUSIVE_THRESHOLD_PROVIDERS
def _get_token_detail_value(details: object, key: str) -> int | None:
if isinstance(details, dict):
@ -202,7 +208,11 @@ def _parse_above_token_threshold(key: str) -> float:
def _get_token_base_cost(
model_info: ModelInfo, usage: Usage, service_tier: str | None = None
model_info: ModelInfo,
usage: Usage,
service_tier: str | None = None,
*,
threshold_is_inclusive: bool = False,
) -> tuple[float, float, float, float, float]:
"""
Return prompt cost, completion cost, and cache costs for a given model and usage.
@ -210,6 +220,9 @@ def _get_token_base_cost(
If input_tokens > threshold and `input_cost_per_token_above_[x]k_tokens` or `input_cost_per_token_above_[x]_tokens` is set,
then we use the corresponding threshold cost for all token types.
`threshold_is_inclusive` switches that comparison to >=, for providers such as xAI
that bill the higher tier once the prompt reaches the threshold.
Returns:
Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost)
"""
@ -262,7 +275,7 @@ def _get_token_base_cost(
# Handle both formats: _above_128k_tokens and _above_128_tokens
threshold_str = key.split("_above_")[1].split("_tokens")[0]
threshold = _parse_above_token_threshold(key)
if usage.prompt_tokens > threshold:
if usage.prompt_tokens > threshold or (threshold_is_inclusive and usage.prompt_tokens == threshold):
# Prefer a service_tier-specific above-threshold key when available,
# e.g. input_cost_per_token_priority_above_200k_tokens for Gemini
# ON_DEMAND_PRIORITY. Falls back to the standard key automatically
@ -777,7 +790,12 @@ def generic_cost_per_token(
cache_creation_cost,
cache_creation_cost_above_1hr,
cache_read_cost,
) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier)
) = _get_token_base_cost(
model_info=model_info,
usage=usage,
service_tier=service_tier,
threshold_is_inclusive=_uses_inclusive_token_thresholds(custom_llm_provider),
)
prompt_cost = _calculate_input_cost(
prompt_tokens_details=prompt_tokens_details,
@ -909,7 +927,12 @@ def get_token_type_cost_breakdown(
cache_creation_cost_rate,
cache_creation_cost_above_1hr_rate,
cache_read_cost_rate,
) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier)
) = _get_token_base_cost(
model_info=model_info,
usage=usage,
service_tier=service_tier,
threshold_is_inclusive=_uses_inclusive_token_thresholds(custom_llm_provider),
)
reasoning_tokens = (
_parse_completion_tokens_details(usage)["reasoning_tokens"]
@ -996,9 +1019,13 @@ def calculate_image_response_cost_from_usage(
input_tokens_details: Final = getattr(usage, "input_tokens_details", None)
prompt_tokens_details: PromptTokensDetailsWrapper | None = None
if input_tokens_details is not None:
# input_tokens_details may be a dict (e.g. OpenAI image edit responses)
# or an object; read it tolerantly like the output side below, so image
# input tokens are priced at input_cost_per_image_token instead of
# silently falling back to the text rate.
prompt_tokens_details = PromptTokensDetailsWrapper(
text_tokens=getattr(input_tokens_details, "text_tokens", None),
image_tokens=getattr(input_tokens_details, "image_tokens", None),
text_tokens=_get_token_detail_value(input_tokens_details, "text_tokens"),
image_tokens=_get_token_detail_value(input_tokens_details, "image_tokens"),
cached_tokens=0,
)

84
litellm/litellm_core_utils/prompt_templates/factory.py
View file

@ -549,7 +549,7 @@ def _fetch_and_extract_template(
return chat_template, bos_token, eos_token
async def ahf_chat_template(model: str, messages: list, chat_template: Any | None = None):
async def ahf_chat_template(model: str, messages: list, chat_template: str | None = None):
"""HuggingFace chat template (async version)"""
from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import (
_aget_chat_template_file,
@ -576,7 +576,7 @@ async def ahf_chat_template(model: str, messages: list, chat_template: Any | Non
)
def hf_chat_template(model: str, messages: list, chat_template: Any | None = None):
def hf_chat_template(model: str, messages: list, chat_template: str | None = None):
"""HuggingFace chat template (sync version)"""
from litellm.litellm_core_utils.prompt_templates.huggingface_template_handler import (
_get_chat_template_file,
@ -1130,7 +1130,7 @@ def convert_to_azure_openai_messages(
def infer_protocol_value(
value: Any,
value: object,
) -> Literal[
"string_value",
"number_value",
@ -1702,7 +1702,9 @@ def convert_function_to_anthropic_tool_invoke(
_name: Final = get_attribute_or_key(function_call, "name") or ""
_arguments: Final = get_attribute_or_key(function_call, "arguments")
tool_input = parse_tool_call_arguments(_arguments, tool_name=_name, context="Anthropic function to tool invoke")
tool_input: Final = parse_tool_call_arguments(
_arguments, tool_name=_name, context="Anthropic function to tool invoke"
)
anthropic_tool_invoke: Final = [
AnthropicMessagesToolUseParam(
@ -1764,7 +1766,7 @@ def convert_to_anthropic_tool_invoke(
Fixes: https://github.com/BerriAI/litellm/issues/17737
"""
anthropic_tool_invoke: Final[list[AnthropicMessagesToolUseParam | dict[str, Any]]] = []
anthropic_tool_invoke: Final[list[AnthropicMessagesToolUseParam | dict[str, object]]] = []
for tool in tool_calls:
if not get_attribute_or_key(tool, "type") == "function":
@ -1785,7 +1787,7 @@ def convert_to_anthropic_tool_invoke(
# Server tool IDs start with "srvtoolu_"
if tool_id.startswith("srvtoolu_"):
# Create server_tool_use block instead of tool_use
_anthropic_server_tool_use: dict[str, Any] = {
_anthropic_server_tool_use: dict[str, object] = {
"type": "server_tool_use",
"id": tool_id,
"name": tool_name,
@ -2177,7 +2179,7 @@ def _is_orphaned_tool_result(
return False
def _declared_tool_call_ids(message: Mapping[str, Any]) -> frozenset[str]:
def _declared_tool_call_ids(message: Mapping[str, object]) -> frozenset[str]:
tool_calls: Final = message.get("tool_calls")
if not isinstance(tool_calls, list):
return frozenset()
@ -2186,7 +2188,7 @@ def _declared_tool_call_ids(message: Mapping[str, Any]) -> frozenset[str]:
)
def group_tool_exchanges(messages: Sequence[Mapping[str, Any]]) -> tuple[tuple[int, ...], ...]:
def group_tool_exchanges(messages: Sequence[Mapping[str, object]]) -> tuple[tuple[int, ...], ...]:
"""Group message indices into tool exchanges: an assistant row that made
tool calls, together with the tool rows answering the ids it declared.
@ -2204,7 +2206,7 @@ def group_tool_exchanges(messages: Sequence[Mapping[str, Any]]) -> tuple[tuple[i
return tuple(_iter_tool_exchange_groups(messages))
def _iter_tool_exchange_groups(messages: Sequence[Mapping[str, Any]]) -> Iterator[tuple[int, ...]]:
def _iter_tool_exchange_groups(messages: Sequence[Mapping[str, object]]) -> Iterator[tuple[int, ...]]:
index = 0
while index < len(messages):
declared = _declared_tool_call_ids(messages[index])
@ -2409,7 +2411,7 @@ def anthropic_messages_pt(
# Convert ChatCompletionImageUrlObject to dict if needed
image_url_value = m["image_url"]
if isinstance(image_url_value, str):
image_url_input: str | dict[str, Any] = image_url_value
image_url_input: str | dict[str, object] = image_url_value
else:
# ChatCompletionImageUrlObject or dict case - convert to dict
image_url_input = {
@ -3179,7 +3181,7 @@ def _load_image_from_url(image_url):
try:
# Send a GET request to the image URL
client: Final = HTTPHandler(concurrent_limit=1)
response: Final = safe_get(client, image_url)
response: Final[httpx.Response] = safe_get(client, image_url)
response.raise_for_status() # Raise an exception for HTTP errors
# Check the response's content type to ensure it is an image
@ -3382,7 +3384,7 @@ class BedrockImageProcessor:
@staticmethod
def _post_call_image_processing(response: httpx.Response, image_url: str = "") -> tuple[str, str]:
# Check the response's content type to ensure it is an image
content_type = response.headers.get("content-type")
content_type: str | None = response.headers.get("content-type")
# Use helper function to infer content type with fallback logic
content_type = infer_content_type_from_url_and_content(
@ -3406,7 +3408,7 @@ class BedrockImageProcessor:
params={"concurrent_limit": 1},
)
# Send a GET request to the image URL
response: Final = await async_safe_get(client, image_url)
response: Final[httpx.Response] = await async_safe_get(client, image_url)
response.raise_for_status() # Raise an exception for HTTP errors
return BedrockImageProcessor._post_call_image_processing(response, image_url)
@ -3419,7 +3421,7 @@ class BedrockImageProcessor:
try:
client: Final = HTTPHandler(concurrent_limit=1)
# Send a GET request to the image URL
response: Final = safe_get(client, image_url)
response: Final[httpx.Response] = safe_get(client, image_url)
response.raise_for_status() # Raise an exception for HTTP errors
return BedrockImageProcessor._post_call_image_processing(response, image_url)
@ -3967,6 +3969,36 @@ def _rename_duplicate_bedrock_document_names(
return contents
BEDROCK_DOCUMENT_PLACEHOLDER_TEXT: Final = "."
def _with_text_when_document_only(message: BedrockMessageBlock) -> BedrockMessageBlock:
blocks: Final = message["content"]
needs_text: Final = (
message["role"] == "user"
and any("document" in block for block in blocks)
and all("text" not in block for block in blocks)
)
if not needs_text:
return message
placeholder: Final = BedrockContentBlock(text=BEDROCK_DOCUMENT_PLACEHOLDER_TEXT)
cut: Final = len(blocks) - 1 if "cachePoint" in blocks[-1] else len(blocks)
return BedrockMessageBlock(role="user", content=[*blocks[:cut], placeholder, *blocks[cut:]])
def _ensure_document_messages_have_text(
contents: list[BedrockMessageBlock],
) -> list[BedrockMessageBlock]:
"""
Bedrock Converse rejects any user message that carries a document block
without a sibling text block ("A text block must be included when using
documents"), e.g. Claude Code sends the PDF as a document-only user turn.
Inject a placeholder text block, kept ahead of a trailing cachePoint so
the caller's cache boundary stays the final block.
"""
return [_with_text_when_document_only(message) for message in contents]
def _sort_bedrock_assistant_content_blocks(
blocks: list[BedrockContentBlock],
) -> list[BedrockContentBlock]:
@ -4535,7 +4567,7 @@ class BedrockConverseMessagesProcessor:
llm_provider=llm_provider,
)
return _rename_duplicate_bedrock_document_names(contents)
return _ensure_document_messages_have_text(_rename_duplicate_bedrock_document_names(contents))
@staticmethod
def translate_thinking_blocks_to_reasoning_content_blocks(
@ -4911,7 +4943,7 @@ def _bedrock_converse_messages_pt(
llm_provider=llm_provider,
)
return _rename_duplicate_bedrock_document_names(contents)
return _ensure_document_messages_have_text(_rename_duplicate_bedrock_document_names(contents))
def make_valid_bedrock_tool_name(input_tool_name: str) -> str:
@ -5328,10 +5360,10 @@ def get_attribute_or_key(tool_or_function, attribute, default=None):
class NormalizedToolCall(TypedDict):
id: str | None
name: str | None
arguments: dict[str, Any]
arguments: dict[str, object]
def _parse_tool_call_arguments(raw: Any, tool_name: str | None, context: str) -> dict[str, Any]:
def _parse_tool_call_arguments(raw: Any, tool_name: str | None, context: str) -> dict[str, object]:
# Anthropic's tool_use blocks already carry a parsed dict in "input";
# chat completions and the Responses API carry a JSON string that may be
# truncated by the model, so route those through the repair-aware parser.
@ -5352,12 +5384,12 @@ def _parse_tool_call_arguments(raw: Any, tool_name: str | None, context: str) ->
def _tool_calls_from_chat_completion_response(
response: Any, include_all_choices: bool = False
response: object, include_all_choices: bool = False
) -> list[NormalizedToolCall]:
choices: Final = get_attribute_or_key(response, "choices", None)
if not (isinstance(choices, list) and choices):
return []
tool_calls: Final[list[Any]] = []
tool_calls: Final[list[object]] = []
for choice in choices if include_all_choices else choices[:1]:
message = get_attribute_or_key(choice, "message", None)
choice_tool_calls = get_attribute_or_key(message, "tool_calls", None) if message else None
@ -5383,7 +5415,7 @@ def _tool_calls_from_chat_completion_response(
return result
def _tool_calls_from_responses_api_response(response: Any) -> list[NormalizedToolCall]:
def _tool_calls_from_responses_api_response(response: object) -> list[NormalizedToolCall]:
output: Final = get_attribute_or_key(response, "output", None)
if not isinstance(output, list):
return []
@ -5406,7 +5438,7 @@ def _tool_calls_from_responses_api_response(response: Any) -> list[NormalizedToo
return result
def _tool_calls_from_anthropic_messages_response(response: Any) -> list[NormalizedToolCall]:
def _tool_calls_from_anthropic_messages_response(response: object) -> list[NormalizedToolCall]:
content: Final = get_attribute_or_key(response, "content", None)
if not isinstance(content, list):
return []
@ -5425,7 +5457,7 @@ def _tool_calls_from_anthropic_messages_response(response: Any) -> list[Normaliz
return result
def get_tool_calls_from_response(response: Any, include_all_choices: bool = False) -> list[NormalizedToolCall]:
def get_tool_calls_from_response(response: object, include_all_choices: bool = False) -> list[NormalizedToolCall]:
"""
Extract tool/function calls from a response object into a normalized
``{"id", "name", "arguments"}`` shape, regardless of which API surface
@ -5456,7 +5488,7 @@ def get_tool_calls_from_response(response: Any, include_all_choices: bool = Fals
return []
def has_tool_with_name(tools: Any, tool_name: str) -> bool:
def has_tool_with_name(tools: object, tool_name: str) -> bool:
"""
Check whether a tools list (as sent to an LLM) includes a tool with the
given name, regardless of shape: OpenAI-style function tools
@ -5482,9 +5514,9 @@ def has_tool_with_name(tools: Any, tool_name: str) -> bool:
def resolve_structured_messages(
messages: list[dict[str, Any]] | None,
messages: list[dict[str, object]] | None,
request_kwargs: dict[str, Any],
) -> list[dict[str, Any]] | None:
) -> list[dict[str, object]] | None:
"""
Normalize a request's messages to OpenAI-spec chat-completions shape,
regardless of which API surface produced them (chat completions,

41
litellm/litellm_core_utils/streaming_chunk_builder_utils.py
View file

@ -145,7 +145,7 @@ class ChunkProcessor:
if first_hidden_params.get("created_at"):
def _created_at(chunk: Any) -> int | float:
def _created_at(chunk: object) -> int | float:
if isinstance(chunk, dict):
params = chunk.get("_hidden_params", {})
else:
@ -158,7 +158,7 @@ class ChunkProcessor:
return chunks
def update_model_response_with_hidden_params(
self, model_response: ModelResponse, chunk: dict[str, Any] | None = None
self, model_response: ModelResponse, chunk: Mapping[str, dict[str, object]] | None = None
) -> ModelResponse:
if chunk is None:
return model_response
@ -176,7 +176,7 @@ class ChunkProcessor:
if not chunks:
return
model: Final = getattr(response, "model", None)
model: Final[str | None] = getattr(response, "model", None)
if not model:
return
@ -214,7 +214,7 @@ class ChunkProcessor:
)
@staticmethod
def _get_chunk_id(chunks: list[dict[str, Any]]) -> str:
def _get_chunk_id(chunks: Sequence[Mapping[str, str]]) -> str:
"""
Chunks:
[{"id": ""}, {"id": "1"}, {"id": "1"}]
@ -225,7 +225,7 @@ class ChunkProcessor:
return ""
@staticmethod
def _get_model_from_chunks(chunks: list[dict[str, Any]], first_chunk_model: str) -> str:
def _get_model_from_chunks(chunks: Sequence[Mapping[str, str]], first_chunk_model: str) -> str:
"""
Get the actual model from chunks, preferring a model that differs from the first chunk.
@ -803,8 +803,28 @@ class ChunkProcessor:
completion_tokens_details=completion_tokens_details,
prompt_tokens_details=prompt_tokens_details,
cost=cost,
inference_geo=self._last_provider_pricing_field(chunks, "inference_geo"),
speed=self._last_provider_pricing_field(chunks, "speed"),
)
def _last_provider_pricing_field(
self,
chunks: Sequence["_UsageBearingChunk | ModelResponse"],
field: str,
) -> str | None:
"""
Last value of a provider-specific usage field that changes pricing but is not a
declared ``Usage`` field, e.g. Anthropic's ``speed`` (fast mode multiplies
non-cache token cost) and ``inference_geo``.
"""
values: Final = [
value
for chunk in chunks
if (usage_chunk := self._extract_usage_chunk(chunk)) is not None
and isinstance(value := getattr(usage_chunk, field, None), str)
]
return values[-1] if values else None
@staticmethod
def _reset_anthropic_cursor_completion_tokens(
chunks: Sequence["_UsageBearingChunk | ModelResponse"],
@ -934,7 +954,16 @@ class ChunkProcessor:
# Return a new usage object with the new values
returned_usage = Usage(**returned_usage.model_dump())
provider_pricing_fields: Final = {
field: value
for field, value in (
("inference_geo", calculated_usage_per_chunk["inference_geo"]),
("speed", calculated_usage_per_chunk["speed"]),
)
if value is not None
}
returned_usage = Usage(**returned_usage.model_dump(), **provider_pricing_fields)
return returned_usage

204
litellm/litellm_core_utils/streaming_handler.py
View file

@ -6,13 +6,14 @@ import logging
import threading
import time
import traceback
from collections.abc import AsyncIterator, Callable, Iterator
from collections.abc import AsyncIterator, Callable, Iterator, Mapping, Sequence
from dataclasses import dataclass
from typing import Any, Final, NoReturn, TypeVar, cast
from typing import Any, Final, NoReturn, Protocol, TypeVar, cast
import anyio
import httpx
from pydantic import BaseModel
from typing_extensions import NotRequired, TypedDict
import litellm
from litellm import verbose_logger
@ -54,7 +55,7 @@ _SYNC_ITER_EXHAUSTED: Final = object()
_GCHUNK_FIELDS: Final[frozenset] = frozenset(GChunk.__annotations__)
def _next_sync_or_exhausted(it: Any) -> Any:
def _next_sync_or_exhausted(it: Any) -> object:
"""
Call next(it) from a thread and return _SYNC_ITER_EXHAUSTED on StopIteration.
@ -68,7 +69,7 @@ def _next_sync_or_exhausted(it: Any) -> Any:
return _SYNC_ITER_EXHAUSTED
def is_async_iterable(obj: Any) -> bool:
def is_async_iterable(obj: object) -> bool:
"""
Check if an object is an async iterable (can be used with 'async for').
@ -81,7 +82,7 @@ def is_async_iterable(obj: Any) -> bool:
return isinstance(obj, collections.abc.AsyncIterable)
def print_verbose(print_statement):
def print_verbose(print_statement: object):
try:
if litellm.set_verbose:
print(print_statement) # noqa: T201
@ -96,18 +97,70 @@ class _ProviderChunkParsed:
@dataclass(frozen=True, slots=True)
class _ProviderChunkEarlyReturn:
value: Any
value: "ModelResponseStream | None"
_ProviderChunkResult = _ProviderChunkParsed | _ProviderChunkEarlyReturn
class _PredibaseStreamData(TypedDict):
token: NotRequired[Mapping[str, str]]
details: Mapping[str, str]
generated_text: str | None
error: str | None
class _Ai21StreamData(TypedDict):
completions: Sequence[Mapping[str, Mapping[str, str]]]
class _MaritalkStreamData(TypedDict):
answer: str
class _NlpCloudStreamData(TypedDict):
generated_text: str
class _AlephAlphaStreamData(TypedDict):
completions: Sequence[Mapping[str, str]]
class _AzureStreamChoice(TypedDict):
delta: Mapping[str, str] | None
finish_reason: str | None
class _AzureStreamData(TypedDict):
choices: Sequence[_AzureStreamChoice]
class _BasetenModelOutput(TypedDict):
data: NotRequired[Sequence[str]]
class _BasetenStreamData(TypedDict):
token: NotRequired[Mapping[str, str]]
model_output: NotRequired["_BasetenModelOutput | str"]
completion: NotRequired[object]
class _DeltaDumpDict(TypedDict):
role: NotRequired[str | None]
tool_calls: NotRequired[Sequence[Mapping[str, object]]]
class _TextCompletionChoiceLike(Protocol):
text: str
finish_reason: str | None
class CustomStreamWrapper:
def __init__(
self,
completion_stream,
model,
logging_obj: Any,
logging_obj: LiteLLMLoggingObject,
custom_llm_provider: str | None = None,
stream_options=None,
make_call: Callable | None = None,
@ -186,7 +239,7 @@ class CustomStreamWrapper:
# Snapshot assumes self._hidden_params is populated from litellm_params
# at init and never mutated during the stream. If that ever changes,
# this cache must be removed.
self._base_hidden_params: dict[str, Any] = {
self._base_hidden_params: dict[str, object] = {
**self._hidden_params,
"response_cost": None,
}
@ -213,7 +266,75 @@ class CustomStreamWrapper:
def __aiter__(self) -> AsyncIterator["ModelResponseStream"]:
return self
def _restore_consumer_correlation_context(self, *, guarded: bool = False) -> None:
"""Restore trace_id/session_id in the *consuming* thread/task/context.
wrapper_async() deliberately skips restoring correlation context when
it returns a stream, so log lines emitted while the caller iterates it
still carry this call's ids (see request_correlation_in_logs).
wrapper() (the sync path) never stamps anything in the first place -
see Logging.__init__'s supports_correlation_logging - so this method
is an inert no-op for sync-created streams, harmless to call anyway
since the class is shared between __next__ and __anext__.
But the terminal success/failure handlers this stream dispatches to
finish the job run on a *different* Task/thread (asyncio.create_task,
threading.Thread, or the shared executor) - restoring there fixes up
that detached context, not the one actually running the caller's
`for`/`async for` loop. Call this at every point control genuinely
returns to that consuming context: natural exhaustion (StopIteration/
StopAsyncIteration), a raised failure, or explicit aclose(). Never let
this raise - it must not break the caller's actual stream handling.
guarded=True (only __del__ uses this) skips the restore unless the
contextvars still hold the ids this stream's own call set, so a
delayed finalizer never overwrites a different, still-active call
that has since taken over the same Task/thread's context.
"""
try:
logging_obj: Final = getattr(self, "logging_obj", None)
if logging_obj is None:
return
method_name: Final = (
"_restore_correlation_context_if_unclaimed" if guarded else "_restore_correlation_context"
)
restore: Final = getattr(logging_obj, method_name, None)
if restore is not None:
restore()
except Exception as restore_error: # noqa: BLE001 # best-effort cleanup; must not raise into the caller
verbose_logger.debug("could not restore correlation context: %s", restore_error)
def __del__(self) -> None:
"""Best-effort correlation-context cleanup for an abandoned async stream.
Only meaningfully applies to streams created by wrapper_async(): it
leaves contextvars "open" across the caller's iteration, so if the
caller never fully consumes the stream - stops early, drops the
reference, cancels it - none of the exit points
_restore_consumer_correlation_context() is called from ever run. For a
sync stream (wrapper()), this is a no-op in practice: wrapper() never
stamps trace_id/session_id for sync calls in the first place (see
Logging.__init__'s supports_correlation_logging), so there is nothing
for this to clean up.
This is a best-effort fallback, not a guarantee: __del__ timing is
unpredictable (delayed by cyclic GC, not guaranteed at interpreter
shutdown, and may run on a different thread), so this can only reduce
how long the leak persists, not eliminate it. That's an acceptable
trade specifically because its blast radius is bounded to the one
asyncio Task this stream's own call ran in - each async call has its
own copy of the contextvars, and Tasks (unlike a thread pool's worker
threads) are never recycled across requests, so a delayed or missed
cleanup here can never misattribute a *different* request's logs.
guarded=True additionally ensures it never clobbers a different,
still-active call's context within that same Task if this fires late.
"""
self._restore_consumer_correlation_context(guarded=True)
async def aclose(self):
# Restore the consumer's outer context only after the underlying
# provider stream's own close (and its diagnostic logging below, if
# closing fails) completes - not before - so those log lines still
# carry this closing stream's own trace_id/session_id.
if self.completion_stream is not None:
stream_to_close: Final = self.completion_stream
self.completion_stream = None
@ -233,6 +354,7 @@ class CustomStreamWrapper:
"CustomStreamWrapper.aclose: error closing completion_stream: %s",
e,
)
self._restore_consumer_correlation_context()
def check_send_stream_usage(self, stream_options: dict | None):
return stream_options is not None and stream_options.get("include_usage", False) is True
@ -347,7 +469,7 @@ class CustomStreamWrapper:
finish_reason = ""
print_verbose(f"chunk: {chunk}")
if chunk.startswith("data:"):
data_json: Final = json.loads(chunk[5:])
data_json: Final[_PredibaseStreamData] = json.loads(chunk[5:])
print_verbose(f"data json: {data_json}")
if "token" in data_json and "text" in data_json["token"]:
text = data_json["token"]["text"]
@ -377,7 +499,7 @@ class CustomStreamWrapper:
def handle_ai21_chunk(self, chunk): # fake streaming
chunk = chunk.decode("utf-8")
data_json: Final = json.loads(chunk)
data_json: Final[_Ai21StreamData] = json.loads(chunk)
try:
text: Final = data_json["completions"][0]["data"]["text"]
is_finished: Final = True
@ -392,7 +514,7 @@ class CustomStreamWrapper:
def handle_maritalk_chunk(self, chunk): # fake streaming
chunk = chunk.decode("utf-8")
data_json: Final = json.loads(chunk)
data_json: Final[_MaritalkStreamData] = json.loads(chunk)
try:
text: Final = data_json["answer"]
is_finished: Final = True
@ -413,7 +535,7 @@ class CustomStreamWrapper:
if self.model and "dolphin" in self.model:
chunk = self.process_chunk(chunk=chunk)
else:
data_json: Final = json.loads(chunk)
data_json: Final[_NlpCloudStreamData] = json.loads(chunk)
chunk = data_json["generated_text"]
text = chunk
if "[DONE]" in text:
@ -430,7 +552,7 @@ class CustomStreamWrapper:
def handle_aleph_alpha_chunk(self, chunk):
chunk = chunk.decode("utf-8")
data_json: Final = json.loads(chunk)
data_json: Final[_AlephAlphaStreamData] = json.loads(chunk)
try:
text: Final = data_json["completions"][0]["completion"]
is_finished: Final = True
@ -458,7 +580,7 @@ class CustomStreamWrapper:
"finish_reason": finish_reason,
}
elif chunk.startswith("data:"):
data_json: Final = json.loads(chunk[5:]) # chunk.startswith("data:"):
data_json: Final[_AzureStreamData] = json.loads(chunk[5:]) # chunk.startswith("data:"):
try:
if len(data_json["choices"]) > 0:
delta: Final = data_json["choices"][0]["delta"]
@ -547,7 +669,7 @@ class CustomStreamWrapper:
text = ""
is_finished = False
finish_reason = None
choices: Final = getattr(chunk, "choices", [])
choices: Final[Sequence[_TextCompletionChoiceLike]] = getattr(chunk, "choices", [])
if len(choices) > 0:
text = choices[0].text
if choices[0].finish_reason is not None:
@ -568,7 +690,7 @@ class CustomStreamWrapper:
is_finished = False
finish_reason = None
usage = None
choices: Final = getattr(chunk, "choices", [])
choices: Final[Sequence[_TextCompletionChoiceLike]] = getattr(chunk, "choices", [])
if len(choices) > 0:
text = choices[0].text
if choices[0].finish_reason is not None:
@ -585,12 +707,12 @@ class CustomStreamWrapper:
except Exception as e:
raise e
def handle_baseten_chunk(self, chunk):
def handle_baseten_chunk(self, chunk) -> str:
try:
chunk = chunk.decode("utf-8")
if len(chunk) > 0:
if chunk.startswith("data:"):
data_json = json.loads(chunk[5:])
data_json: _BasetenStreamData = json.loads(chunk[5:])
if "token" in data_json and "text" in data_json["token"]:
return data_json["token"]["text"]
else:
@ -1256,13 +1378,14 @@ class CustomStreamWrapper:
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if "usage" in response_obj is not None:
_codestral_usage: Final[Usage] = response_obj["usage"]
setattr(
model_response,
"usage",
litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
prompt_tokens=_codestral_usage.prompt_tokens,
completion_tokens=_codestral_usage.completion_tokens,
total_tokens=_codestral_usage.total_tokens,
),
)
elif self.custom_llm_provider == "azure_text":
@ -1405,7 +1528,7 @@ class CustomStreamWrapper:
is None
):
t.function.arguments = ""
_json_delta: Final = delta.model_dump()
_json_delta: Final[_DeltaDumpDict] = delta.model_dump()
if "role" not in _json_delta or _json_delta["role"] is None:
_json_delta["role"] = "assistant" # mistral's api returns role as None
if "tool_calls" in _json_delta and isinstance(_json_delta["tool_calls"], list):
@ -1675,7 +1798,7 @@ class CustomStreamWrapper:
usage.cost, copy it into _hidden_params so litellm's cost
calculator uses it instead of a token-based estimate.
"""
_usage: Final = getattr(response, "usage", None)
_usage: Final[Usage | None] = getattr(response, "usage", None)
if _usage is not None and hasattr(_usage, "cost") and _usage.cost is not None:
if "additional_headers" not in response._hidden_params:
response._hidden_params["additional_headers"] = {}
@ -1839,6 +1962,7 @@ class CustomStreamWrapper:
if self.sent_stream_usage is False and self.send_stream_usage is True:
self.sent_stream_usage = True
return response
self._restore_consumer_correlation_context()
raise # Re-raise StopIteration
else:
self.sent_last_chunk = True
@ -1852,6 +1976,19 @@ class CustomStreamWrapper:
processed_chunk,
cache_hit,
) # log response
# Deliberately do NOT restore context here even though
# completion_stream is already exhausted: this chunk is still
# real data belonging to this call, and the caller's own
# (application-level) log statements processing it run
# immediately after this return, in this same synchronous
# frame - restoring first would make those lines carry the
# wrong ids, which is exactly what leaving context open during
# iteration is meant to prevent (see
# _restore_consumer_correlation_context's docstring). A caller
# that keeps iterating gets cleaned up on its next __next__()
# call (immediate StopIteration, handled above); one that
# stops right here relies on aclose() or the best-effort
# __del__ guard instead.
return processed_chunk
except Exception as e:
traceback_exception: Final = traceback.format_exc()
@ -1879,8 +2016,12 @@ class CustomStreamWrapper:
cache_hit = False
if self.custom_llm_provider is not None and self.custom_llm_provider == "cached_response":
cache_hit = True
self._check_max_streaming_duration()
try:
# Inside the try (not before it) so a raised litellm.Timeout flows
# through the same except Exception -> _handle_stream_fallback_error
# path as every other failure, restoring the consumer's correlation
# context - a check before the try would bypass that entirely.
self._check_max_streaming_duration()
if self.completion_stream is None:
await self.fetch_stream()
@ -2083,10 +2224,17 @@ class CustomStreamWrapper:
)
)
self._restore_consumer_correlation_context()
raise StopAsyncIteration # Re-raise StopIteration
else:
self.sent_last_chunk = True
processed_chunk: Final = self.finish_reason_handler()
# see sync __next__'s sibling branch: deliberately do NOT restore
# here - this chunk is still this call's own data, and restoring
# before returning it would corrupt the caller's own log
# statements processing it. A caller that keeps iterating gets
# cleaned up on the next __anext__() call; one that stops here
# relies on aclose() or the best-effort __del__ guard.
return processed_chunk
def _log_stream_failure_and_raise(self, e: Exception) -> NoReturn:
@ -2138,7 +2286,12 @@ class CustomStreamWrapper:
"""
from litellm.exceptions import MidStreamFallbackError
# Map to OpenAI exception format
# Map to OpenAI exception format. Some providers' mappers (e.g.
# _map_anthropic_exception, _map_aleph_alpha_exception) synchronously
# log a debug diagnostic (the raw status code) as part of mapping -
# restore the consumer's outer context only after this completes, so
# that diagnostic log line still carries the failing stream's own
# trace_id/session_id instead of the consumer's (or an empty one).
if isinstance(e, OpenAIError):
mapped_exception: Exception = e
else:
@ -2152,6 +2305,7 @@ class CustomStreamWrapper:
)
except Exception as mapping_error:
mapped_exception = mapping_error
self._restore_consumer_correlation_context()
def _normalize_status_code(exc: Exception) -> int | None:
"""Best-effort status_code extraction."""

230
litellm/llms/anthropic/chat/guardrail_translation/handler.py
View file

@ -14,6 +14,7 @@ Pattern Overview:
import json
from collections.abc import Mapping
from copy import deepcopy
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Final, cast
@ -110,14 +111,10 @@ EMPTY_EXTRACTED_INPUT: Final = ExtractedInput(scanned=(), images=())
class AnthropicMessagesHandler(BaseTranslation):
"""
Handler for processing Anthropic messages with guardrails.
"""Process Anthropic messages with guardrails.
This class provides methods to:
1. Process input messages (pre-call hook)
2. Process output responses (post-call hook)
Methods can be overridden to customize behavior for different message formats.
In-sequence system entries are untrusted client input. This handler scans and preserves
them through guardrail rewrites; downstream provider handling is out of scope.
"""
def __init__(self):
@ -331,16 +328,30 @@ class AnthropicMessagesHandler(BaseTranslation):
skip_tool: Final = effective_skip_tool_message_for_guardrail(guardrail_to_apply)
scan_only_tool_results: Final = effective_scan_only_tool_results_for_guardrail(guardrail_to_apply)
chat_completion_compatible_request: Final = self._translate_to_openai(data)
# Exclude only the trusted top-level prompt. In-sequence system entries are untrusted
# and must stay aligned with texts_to_check for positional masking. When the top-level
# prompt is included, the pre-existing count mismatch disables positional masking.
translation_source: Final = { # mutable-ok: API message payload
key: value for key, value in data.items() if key != "system"
}
chat_completion_compatible_request: Final = self._translate_to_openai(translation_source)
full_structured_messages: Final = cast(
list[AllMessageValues],
chat_completion_compatible_request.get("messages", []),
)
has_midturn_system_message: Final = any(
str(message.get("role") or "").lower() == "system" for message in full_structured_messages
)
hoisted_system_message: Final = None if skip_system else self._hoisted_top_level_system_message(data)
if hoisted_system_message is not None:
full_structured_messages.insert(0, hoisted_system_message)
# skip_system already excluded the trusted top-level prompt (it is simply not hoisted);
# in-sequence system entries are untrusted and always stay in scope.
scoped_message_indices: Final = scoped_structured_message_indices(
full_structured_messages,
scan_only_tool_results=scan_only_tool_results,
skip_system=skip_system,
skip_system=False,
skip_tool=skip_tool,
)
structured_messages: Final = [full_structured_messages[index] for index in scoped_message_indices]
@ -422,6 +433,8 @@ class AnthropicMessagesHandler(BaseTranslation):
scoped_indices=scoped_message_indices,
guardrailed_scoped=guardrailed_structured_messages,
),
hoisted_system_message=hoisted_system_message,
preserve_system_messages=has_midturn_system_message,
)
else:
# Step 3: Map guardrail responses back to original message structure
@ -435,36 +448,150 @@ class AnthropicMessagesHandler(BaseTranslation):
return data
@staticmethod
def _write_back_structured_messages(data: dict, structured_messages: list) -> None:
"""Convert compressed structured_messages back to Anthropic format and write to data.
def _hoisted_top_level_system_message(
self, data: dict
) -> AllMessageValues | None: # mutable-ok: API message payload
"""Return the system message produced by translating the top-level prompt."""
system: Final = data.get("system")
if not system:
return None
probe: Final = self._translate_to_openai(
{ # mutable-ok: API message payload
"model": data.get("model") or "",
"messages": [], # mutable-ok: API message payload
"system": system,
}
)
hoisted: Final = probe.get("messages") or [] # mutable-ok: API message payload
return hoisted[0] if hoisted else None
``anthropic_messages_pt`` merges every run of consecutive user/tool rows
into a single message, so a turn carrying only tool results and the user
turn that follows it come back fused, and the request the model sees no
longer has the boundaries the client sent. Converting a row at a time
would keep them apart but breaks tool pairing: an assistant row whose
tool results sit outside its own call reads as an orphaned tool call,
and under ``modify_params`` the sanitizer answers it with a synthetic
"tool execution skipped" result and drops the real one. Converting each
assistant row together with the tool rows that answer it, and every
other row on its own, satisfies both.
"""
@staticmethod
def _openai_system_message_to_anthropic(
message: dict[str, Any],
) -> dict[str, Any] | None: # mutable-ok: API message payload
"""Convert an OpenAI system message to the client's Anthropic-shaped entry."""
content: Final = message.get("content")
if isinstance(content, str):
return (
{"role": "system", "content": content} if content else None # mutable-ok: API message payload
) # mutable-ok: API message payload
if not isinstance(content, list):
return None
blocks: Final[list[dict[str, Any]]] = [] # mutable-ok: API message payload
for block in content:
if not isinstance(block, dict) or block.get("type") != "text":
continue
text = block.get("text")
if not isinstance(text, str) or not text:
continue
anthropic_block: dict[str, Any] = { # mutable-ok: API message payload
"type": "text",
"text": text,
} # mutable-ok: API message payload
cache_control = block.get("cache_control")
if cache_control:
anthropic_block["cache_control"] = deepcopy(cache_control)
blocks.append(anthropic_block)
return (
{"role": "system", "content": blocks} if blocks else None # mutable-ok: API message payload
) # mutable-ok: API message payload
@staticmethod
def _is_hoisted_top_level_system(message: object, hoisted_system_message: object) -> bool:
"""Match the hoisted prompt by identity, or by value after serialization."""
if hoisted_system_message is None:
return False
if message is hoisted_system_message:
return True
return (
isinstance(message, dict) and isinstance(hoisted_system_message, dict) and message == hoisted_system_message
)
@staticmethod
def _is_system(message: object) -> bool:
"""Whether the row is an in-sequence system message."""
return isinstance(message, dict) and str(message.get("role") or "").lower() == "system"
@staticmethod
def _defer_systems_inside_tool_exchanges(
structured_messages: list, # mutable-ok: API message payload
) -> list:
"""Hold a system row until the tool exchange around it completes so the call/result pair converts together."""
from litellm.litellm_core_utils.prompt_templates.factory import group_tool_exchanges
non_system_positions: Final[list[int]] = [
index
for index, message in enumerate(structured_messages)
if not AnthropicMessagesHandler._is_system(message)
]
exchange_end_for_start: Final[dict[int, int]] = {
non_system_positions[group[0]]: non_system_positions[group[-1]]
for group in group_tool_exchanges([structured_messages[index] for index in non_system_positions])
if len(group) > 1
}
ordered: Final[list] = [] # mutable-ok: API message payload
deferred_systems: Final[list] = [] # mutable-ok: API message payload
open_exchange_end = -1 # rebind-ok: advances to the enclosing exchange's last index
for index, message in enumerate(structured_messages):
if AnthropicMessagesHandler._is_system(message) and index < open_exchange_end:
deferred_systems.append(message)
continue
open_exchange_end = exchange_end_for_start.get(index, open_exchange_end)
ordered.append(message)
if index >= open_exchange_end and deferred_systems:
ordered.extend(deferred_systems)
deferred_systems.clear()
ordered.extend(deferred_systems)
return ordered
@staticmethod
def _write_back_structured_messages(
data: dict, # mutable-ok: API message payload
structured_messages: list, # mutable-ok: API message payload
hoisted_system_message: object = None,
preserve_system_messages: bool = False,
) -> None:
"""Write a guardrail's structured-message rewrite back without losing corrections."""
from litellm.litellm_core_utils.prompt_templates.factory import (
anthropic_messages_pt,
group_tool_exchanges,
)
_is_system: Final = AnthropicMessagesHandler._is_system
model: Final = str(data.get("model") or "")
non_system: Final = [m for m in structured_messages if m.get("role") != "system"]
groups: Final = tuple([non_system[index] for index in group] for group in group_tool_exchanges(non_system)) or (
non_system,
)
converted: Final = [
message
for group in groups
for message in anthropic_messages_pt(messages=group, model=model, llm_provider="anthropic")
]
converted: Final[list] = [] # mutable-ok: API message payload
def _convert_run(run: list) -> None: # mutable-ok: API message payload
for group in group_tool_exchanges(run):
converted.extend(
anthropic_messages_pt(
messages=[run[index] for index in group], # mutable-ok: API message payload
model=model,
llm_provider="anthropic",
)
)
ordered: Final = AnthropicMessagesHandler._defer_systems_inside_tool_exchanges(structured_messages)
run: Final[list] = [] # mutable-ok: API message payload
hoisted_dropped = False # rebind-ok: flips once the hoisted prompt is dropped
for message in ordered:
if not _is_system(message):
run.append(message)
continue
_convert_run(run)
run.clear()
if not hoisted_dropped and AnthropicMessagesHandler._is_hoisted_top_level_system(
message, hoisted_system_message
):
hoisted_dropped = True
continue
if preserve_system_messages:
anthropic_system = AnthropicMessagesHandler._openai_system_message_to_anthropic(message)
if anthropic_system is not None:
converted.append(anthropic_system)
_convert_run(run)
if not any(not _is_system(message) for message in converted):
converted.extend(anthropic_messages_pt(messages=[], model=model, llm_provider="anthropic"))
for msg in converted:
content = msg.get("content")
if isinstance(content, list):
@ -473,6 +600,31 @@ class AnthropicMessagesHandler(BaseTranslation):
block.pop("cache_control", None)
data["messages"] = converted
@staticmethod
def _extract_midturn_system_text(
message: dict[str, Any], # mutable-ok: API message payload
msg_idx: int,
) -> ExtractedInput:
"""Match the adapter's filtering so positional guardrail write-back stays aligned."""
content: Final = message.get("content")
if isinstance(content, str):
if not content:
return EMPTY_EXTRACTED_INPUT
return ExtractedInput(scanned=(ScannedText(content, MessageContentTarget(msg_idx)),), images=())
if not isinstance(content, list):
return EMPTY_EXTRACTED_INPUT
return ExtractedInput(
scanned=tuple(
ScannedText(text_str, ContentBlockTextTarget(msg_idx, content_idx))
for content_idx, content_item in enumerate(content)
if isinstance(content_item, dict)
and content_item.get("type") == "text"
and isinstance(text_str := content_item.get("text"), str)
and text_str
),
images=(),
)
def extract_request_tool_names(self, data: dict) -> list[str]:
"""Extract tool names from Anthropic messages request (tools[].name)."""
names: Final[list[str]] = []
@ -490,11 +642,17 @@ class AnthropicMessagesHandler(BaseTranslation):
skip_tool_message: bool = False,
scan_only_tool_results: bool = False,
) -> ExtractedInput:
"""Extract text content and images from a message.
In-sequence system entries are scanned even when ``skip_system_message`` is set:
that flag covers only the trusted top-level prompt, which never appears here.
"""
Extract text content and images from a message.
"""
role: Final = str(message.get("role") or "").lower()
if (skip_system_message and role == "system") or (skip_tool_message and role == "tool"):
role: Final = str(message.get("role") or "")
if role == "system":
if scan_only_tool_results:
return EMPTY_EXTRACTED_INPUT
return cls._extract_midturn_system_text(message=message, msg_idx=msg_idx)
if skip_tool_message and role.lower() == "tool":
return EMPTY_EXTRACTED_INPUT
content: Final = message.get("content", None)

5
litellm/llms/anthropic/chat/handler.py
View file

@ -58,6 +58,7 @@ from ..common_utils import AnthropicError, process_anthropic_headers
from .transformation import ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY, AnthropicConfig
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
from litellm.llms.base_llm.chat.transformation import BaseConfig
@ -206,7 +207,7 @@ class AnthropicChatCompletion(BaseLLM):
client: AsyncHTTPHandler | None,
encoding,
api_key,
logging_obj,
logging_obj: "LiteLLMLoggingObj",
stream,
_is_function_call,
data: dict,
@ -324,7 +325,7 @@ class AnthropicChatCompletion(BaseLLM):
print_verbose: Callable,
encoding,
api_key,
logging_obj,
logging_obj: "LiteLLMLoggingObj",
optional_params: dict,
timeout: float | httpx.Timeout,
litellm_params: dict,

44
litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
View file

@ -1,12 +1,13 @@
from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping
from typing import (
TYPE_CHECKING,
Any,
Final,
TypeAlias,
cast,
)
from typing_extensions import TypedDict
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.asyncify import run_async_function
@ -39,6 +40,11 @@ _AnthropicSystem: TypeAlias = "str | list[dict[str, object]] | None"
_ContextManagementSpec: TypeAlias = "dict[str, object] | list[dict[str, object]] | None"
class _CompletionKwargs(TypedDict, total=False, extra_items=object):
model: str
custom_llm_provider: str
def _messages_have_compaction_block(messages: _AnthropicMessages) -> bool:
"""Return True when any message carries a ``compaction`` content block."""
for msg in messages:
@ -312,7 +318,7 @@ ANTHROPIC_ADAPTER: Final = AnthropicAdapter()
class LiteLLMMessagesToCompletionTransformationHandler:
@staticmethod
def _route_openai_thinking_to_responses_api_if_needed(
completion_kwargs: dict[str, Any],
completion_kwargs: _CompletionKwargs,
*,
thinking: Mapping[str, object] | None,
) -> None:
@ -377,7 +383,7 @@ class LiteLLMMessagesToCompletionTransformationHandler:
@staticmethod
def _normalize_reasoning_effort(
completion_kwargs: dict[str, Any],
completion_kwargs: _CompletionKwargs,
) -> None:
"""
Normalize reasoning_effort values based on target model capabilities.
@ -393,7 +399,7 @@ class LiteLLMMessagesToCompletionTransformationHandler:
if reasoning_effort is None:
return
model: Final = cast(str, completion_kwargs.get("model", ""))
model: Final = completion_kwargs.get("model", "")
custom_llm_provider: Final = completion_kwargs.get("custom_llm_provider")
if isinstance(reasoning_effort, str):
@ -417,19 +423,19 @@ class LiteLLMMessagesToCompletionTransformationHandler:
max_tokens: int,
messages: _AnthropicMessages,
model: str,
metadata: dict | None = None,
metadata: dict[str, object] | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: _AnthropicSystem = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
tools: list[dict] | None = None,
thinking: dict[str, object] | None = None,
tool_choice: dict[str, object] | None = None,
tools: list[dict[str, object]] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: dict[str, object] | None = None,
extra_kwargs: Mapping[str, object] | None = None,
) -> tuple[dict[str, Any], dict[str, str]]:
) -> tuple[_CompletionKwargs, dict[str, str]]:
"""Prepare kwargs for litellm.completion/acompletion.
Returns:
@ -486,7 +492,7 @@ class LiteLLMMessagesToCompletionTransformationHandler:
if openai_request is None:
raise ValueError("Failed to translate request to OpenAI format")
completion_kwargs: Final[dict[str, Any]] = dict(openai_request)
completion_kwargs: Final[_CompletionKwargs] = {**openai_request}
if stream:
completion_kwargs["stream"] = stream
@ -538,17 +544,17 @@ class LiteLLMMessagesToCompletionTransformationHandler:
max_tokens: int,
messages: _AnthropicMessages,
model: str,
metadata: dict | None = None,
metadata: dict[str, object] | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: str | None = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
thinking: dict[str, object] | None = None,
tool_choice: dict[str, object] | None = None,
tools: list[dict[str, object]] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: dict[str, object] | None = None,
**kwargs,
) -> AnthropicMessagesResponse | AsyncIterator[bytes] | Iterator[bytes]:
"""Handle non-Anthropic models asynchronously using the adapter"""
@ -625,17 +631,17 @@ class LiteLLMMessagesToCompletionTransformationHandler:
max_tokens: int,
messages: _AnthropicMessages,
model: str,
metadata: dict | None = None,
metadata: dict[str, object] | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: str | None = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
thinking: dict[str, object] | None = None,
tool_choice: dict[str, object] | None = None,
tools: list[dict[str, object]] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: dict[str, object] | None = None,
_is_async: bool = False,
**kwargs,
) -> (

49
litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py
View file

@ -74,12 +74,12 @@ from litellm.llms.anthropic.experimental_pass_through.context_management import
)
from litellm.types.llms.anthropic import (
ANTHROPIC_HOSTED_TOOLS,
AllAnthropicPassThroughMessageValues,
AllAnthropicToolsValues,
AnthopicMessagesAssistantMessageParam,
AnthropicFinishReason,
AnthropicMessagesRequest,
AnthropicMessagesSystemMessageParam,
AnthropicMessagesToolChoice,
AnthropicMessagesUserMessageParam,
AnthropicResponseContentBlockRedactedThinking,
AnthropicResponseContentBlockText,
AnthropicResponseContentBlockThinking,
@ -343,7 +343,7 @@ class LiteLLMAnthropicMessagesAdapter:
def translate_anthropic_messages_to_openai(
self,
messages: list[AnthropicMessagesUserMessageParam | AnthopicMessagesAssistantMessageParam],
messages: list[AllAnthropicPassThroughMessageValues],
model: str | None = None,
) -> list:
new_messages: Final[list[AllMessageValues]] = []
@ -351,6 +351,11 @@ class LiteLLMAnthropicMessagesAdapter:
user_message: ChatCompletionUserMessage | None = None
tool_message_list: list[ChatCompletionToolMessage] = []
new_user_content_list: list[ChatCompletionTextObject | ChatCompletionImageObject] = []
if m["role"] == "system":
system_message = self._translate_midturn_system_message_to_openai(m, model)
if system_message is not None:
new_messages.append(system_message)
continue
## USER MESSAGE ##
if m["role"] == "user":
## translate user message
@ -848,6 +853,29 @@ class LiteLLMAnthropicMessagesAdapter:
for def_schema in schema[key].values():
LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(def_schema)
def _translate_midturn_system_message_to_openai(
self,
message: AnthropicMessagesSystemMessageParam,
model: str | None,
) -> ChatCompletionSystemMessage | None:
"""Translate an in-sequence system entry without changing its role or position."""
content: Final = message.get("content")
if isinstance(content, str):
return ChatCompletionSystemMessage(role="system", content=content) if content else None
if not isinstance(content, list):
return None
text_parts: Final[list[ChatCompletionTextObject]] = [] # mutable-ok: API message payload
for block in content:
if not isinstance(block, dict) or block.get("type") != "text": # pyright: ignore[reportUnnecessaryIsInstance] # untrusted client payload
continue
text = block.get("text")
if not text:
continue
text_obj = ChatCompletionTextObject(type="text", text=text)
self._add_cache_control_if_applicable(block, text_obj, model)
text_parts.append(text_obj)
return ChatCompletionSystemMessage(role="system", content=text_parts) if text_parts else None
def _add_system_message_to_messages(
self,
new_messages: list[AllMessageValues],
@ -976,6 +1004,17 @@ class LiteLLMAnthropicMessagesAdapter:
model: Final = new_kwargs.get("model", "")
if self.is_anthropic_claude_model(model) or self.is_bedrock_arn_model(model):
new_kwargs["thinking"] = thinking
# Adaptive thinking without its effort tier makes Bedrock Converse
# return zero reasoning blocks, so forward output_config (minus
# `format`, already translated to response_format) for Bedrock
# targets only: other bridged providers reject the raw param, and
# get_llm_provider strips the `bedrock/` prefix before this runs.
if model.startswith(("bedrock/", "converse/", "invoke/")) or self.is_bedrock_arn_model(model):
claude_output_config: Final = anthropic_message_request.get("output_config")
if isinstance(claude_output_config, dict):
effort_config: Final = {k: v for k, v in claude_output_config.items() if k != "format"}
if effort_config:
new_kwargs["output_config"] = effort_config # rebind-ok: out-param store like thinking above
return
reasoning_effort = self.translate_anthropic_thinking_to_reasoning_effort(cast(dict[str, Any], thinking))
@ -1049,8 +1088,8 @@ class LiteLLMAnthropicMessagesAdapter:
tool_name_mapping: dict[str, str] = {}
## CONVERT ANTHROPIC MESSAGES TO OPENAI
messages_list: Final[list[AnthropicMessagesUserMessageParam | AnthopicMessagesAssistantMessageParam]] = cast(
list[AnthropicMessagesUserMessageParam | AnthopicMessagesAssistantMessageParam],
messages_list: Final[list[AllAnthropicPassThroughMessageValues]] = cast(
list[AllAnthropicPassThroughMessageValues],
anthropic_message_request["messages"],
)
new_messages = self.translate_anthropic_messages_to_openai(

36
litellm/llms/anthropic/experimental_pass_through/messages/mcp_handler.py
View file

@ -7,8 +7,8 @@ tool through a ``tool_use`` content block, and results are fed back as
``tool_result`` blocks in a user message.
"""
from collections.abc import AsyncIterator, Mapping, Sequence
from typing import Any, Final
from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping, Sequence
from typing import Any, Final, NamedTuple
from litellm._logging import verbose_logger
from litellm.responses.mcp.request_context import MCPRequestContext
@ -24,14 +24,18 @@ from litellm.types.llms.anthropic_messages.anthropic_response import (
MAX_MCP_TOOL_USE_ITERATIONS: Final = 10
def _get_response_content(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, Any]]:
class _AnthropicMessagesCall(NamedTuple):
fn: Callable[..., Awaitable[AnthropicMessagesResponse | Iterator[bytes] | AsyncIterator[object]]]
def _get_response_content(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, object]]:
content: Final = response.get("content")
if not isinstance(content, list):
return ()
return tuple(block for block in content if isinstance(block, dict))
def _extract_tool_use_blocks(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, Any]]:
def _extract_tool_use_blocks(response: AnthropicMessagesResponse) -> Sequence[Mapping[str, object]]:
"""Return the ``tool_use`` content blocks the model emitted."""
return tuple(block for block in _get_response_content(response) if block.get("type") == "tool_use")
@ -41,7 +45,7 @@ def _get_stop_reason(response: AnthropicMessagesResponse) -> str | None:
return stop_reason if isinstance(stop_reason, str) else None
def _build_tool_result_message(tool_results: Sequence[Mapping[str, Any]]) -> AnthropicMessagesUserMessageParam:
def _build_tool_result_message(tool_results: Sequence[Mapping[str, object]]) -> AnthropicMessagesUserMessageParam:
"""Turn executed tool results into the user message Anthropic expects."""
return AnthropicMessagesUserMessageParam(
role="user",
@ -58,11 +62,11 @@ def _build_tool_result_message(tool_results: Sequence[Mapping[str, Any]]) -> Ant
async def anthropic_messages_with_mcp(
max_tokens: int,
messages: Sequence[Mapping[str, Any]],
messages: Sequence[Mapping[str, object]],
model: str,
tools: Sequence[Mapping[str, Any]] | None = None,
tools: Sequence[Mapping[str, object]] | None = None,
**kwargs: Any, # kwargs-ok: forwarded verbatim to litellm.anthropic_messages, which owns the param contract
) -> AnthropicMessagesResponse | AsyncIterator[Any]:
) -> AnthropicMessagesResponse | Iterator[bytes] | AsyncIterator[object]:
"""
Expand litellm_proxy MCP references for `/v1/messages` and run the tool loop.
@ -81,7 +85,7 @@ async def anthropic_messages_with_mcp(
mcp_references, other_tools = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools)
if not mcp_references:
return await litellm.anthropic_messages(
return await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn(
max_tokens=max_tokens,
messages=list(messages),
model=model,
@ -114,7 +118,7 @@ async def anthropic_messages_with_mcp(
)
stream: Final = bool(kwargs.pop("stream", False))
base_call_args: Final[Mapping[str, Any]] = {
base_call_args: Final[Mapping[str, object]] = {
"max_tokens": max_tokens,
"model": model,
"tools": all_tools or None,
@ -123,10 +127,12 @@ async def anthropic_messages_with_mcp(
}
if not should_auto_execute:
return await litellm.anthropic_messages(messages=list(messages), stream=stream, **base_call_args)
return await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn(
messages=list(messages), stream=stream, **base_call_args
)
working_messages: Sequence[Mapping[str, Any]] = tuple(messages)
response: AnthropicMessagesResponse = await litellm.anthropic_messages(
working_messages: Sequence[Mapping[str, object]] = tuple(messages)
response: AnthropicMessagesResponse = await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn(
messages=list(working_messages), stream=False, **base_call_args
)
@ -161,7 +167,9 @@ async def anthropic_messages_with_mcp(
{"role": "assistant", "content": list(_get_response_content(response))},
_build_tool_result_message(tool_results),
)
response = await litellm.anthropic_messages(messages=list(working_messages), stream=False, **base_call_args)
response = await _AnthropicMessagesCall(fn=litellm.anthropic_messages).fn(
messages=list(working_messages), stream=False, **base_call_args
)
else:
verbose_logger.warning(
"MCP tool loop hit its %s iteration cap for model %s; returning the last response",

33
litellm/llms/anthropic/experimental_pass_through/responses_adapters/handler.py
View file

@ -8,7 +8,12 @@ from collections.abc import AsyncIterator, Coroutine
from typing import Any, Final
import litellm
from litellm.types.llms.anthropic import AnthropicMessagesRequest
from litellm.types.llms.anthropic import (
AllAnthropicToolsValues,
AnthropicMessagesRequest,
AnthropicOutputConfig,
AnthropicOutputSchema,
)
from litellm.types.llms.anthropic_messages.anthropic_response import (
AnthropicMessagesResponse,
)
@ -27,24 +32,24 @@ def _build_responses_kwargs(
model: str,
context_management: dict | None = None,
metadata: dict | None = None,
output_config: dict | None = None,
output_config: AnthropicOutputConfig | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: str | None = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
tools: list[dict] | None = None,
tools: list[AllAnthropicToolsValues | dict] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: AnthropicOutputSchema | None = None,
extra_kwargs: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""
Build the kwargs dict to pass directly to litellm.responses() / litellm.aresponses().
"""
# Build a typed AnthropicMessagesRequest for the adapter
request_data: Final[dict[str, Any]] = {
request_data: Final[AnthropicMessagesRequest] = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
@ -128,19 +133,19 @@ class LiteLLMMessagesToResponsesAPIHandler:
model: str,
context_management: dict | None = None,
metadata: dict | None = None,
output_config: dict | None = None,
output_config: AnthropicOutputConfig | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: str | None = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
tools: list[dict] | None = None,
tools: list[AllAnthropicToolsValues | dict] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: AnthropicOutputSchema | None = None,
**kwargs,
) -> AnthropicMessagesResponse | AsyncIterator:
) -> AnthropicMessagesResponse | AsyncIterator[bytes]:
responses_kwargs: Final = _build_responses_kwargs(
max_tokens=max_tokens,
messages=messages,
@ -179,23 +184,23 @@ class LiteLLMMessagesToResponsesAPIHandler:
model: str,
context_management: dict | None = None,
metadata: dict | None = None,
output_config: dict | None = None,
output_config: AnthropicOutputConfig | None = None,
stop_sequences: list[str] | None = None,
stream: bool | None = False,
system: str | None = None,
temperature: float | None = None,
thinking: dict | None = None,
tool_choice: dict | None = None,
tools: list[dict] | None = None,
tools: list[AllAnthropicToolsValues | dict] | None = None,
top_k: int | None = None,
top_p: float | None = None,
output_format: dict | None = None,
output_format: AnthropicOutputSchema | None = None,
_is_async: bool = False,
**kwargs,
) -> (
AnthropicMessagesResponse
| AsyncIterator[Any]
| Coroutine[Any, Any, AnthropicMessagesResponse | AsyncIterator[Any]]
| AsyncIterator[bytes]
| Coroutine[None, None, AnthropicMessagesResponse | AsyncIterator[bytes]]
):
if _is_async:
return LiteLLMMessagesToResponsesAPIHandler.async_anthropic_messages_handler(

39
litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
View file

@ -6,6 +6,7 @@ path used for OpenAI and Azure models.
"""
import json
from collections.abc import Iterable
from typing import Any, Final, cast
from litellm.litellm_core_utils.reasoning_effort_utils import (
@ -15,15 +16,15 @@ from litellm.llms.anthropic.experimental_pass_through.utils import (
is_reasoning_auto_summary_enabled,
)
from litellm.types.llms.anthropic import (
AllAnthropicPassThroughMessageValues,
AllAnthropicToolsValues,
AnthopicMessagesAssistantMessageParam,
AnthropicFinishReason,
AnthropicMessagesRequest,
AnthropicMessagesToolChoice,
AnthropicMessagesUserMessageParam,
AnthropicResponseContentBlockText,
AnthropicResponseContentBlockThinking,
AnthropicResponseContentBlockToolUse,
AnthropicSystemMessageContent,
)
from litellm.types.llms.anthropic_messages.anthropic_response import (
AnthropicMessagesResponse,
@ -72,14 +73,32 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
return source.get("url")
return None
@staticmethod
def _translate_midturn_system_content_to_responses(
content: str | Iterable[AnthropicSystemMessageContent],
) -> list[dict[str, str]]: # mutable-ok: API message payload
"""Convert in-sequence system content to Responses input-text parts."""
if isinstance(content, str):
return (
[{"type": "input_text", "text": content}] if content else [] # mutable-ok: API message payload
) # mutable-ok: API message payload
if not isinstance(content, list):
return [] # mutable-ok: API message payload
return [ # mutable-ok: API message payload
{"type": "input_text", "text": text} # mutable-ok: API message payload
for block in content
if isinstance(block, dict) and block.get("type") == "text" and (text := block.get("text")) # pyright: ignore[reportUnnecessaryIsInstance] # untrusted client payload
]
def translate_messages_to_responses_input(
self,
messages: list[AnthropicMessagesUserMessageParam | AnthopicMessagesAssistantMessageParam],
messages: list[AllAnthropicPassThroughMessageValues],
) -> list[dict[str, Any]]:
"""
Convert Anthropic messages list to Responses API `input` items.
Mapping:
system text -> message(role=system, input_text)
user text -> message(role=user, input_text)
user image -> message(role=user, input_image)
user tool_result -> function_call_output
@ -89,6 +108,18 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
input_items: Final[list[dict[str, Any]]] = []
for m in messages:
if m["role"] == "system":
system_parts = self._translate_midturn_system_content_to_responses(m.get("content"))
if system_parts:
input_items.append(
{ # mutable-ok: API message payload
"type": "message",
"role": "system",
"content": system_parts,
}
)
continue
role = m["role"]
content = m.get("content")
@ -300,7 +331,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
"""
model: Final[str] = anthropic_request["model"]
messages_list: Final = cast(
list[AnthropicMessagesUserMessageParam | AnthopicMessagesAssistantMessageParam],
list[AllAnthropicPassThroughMessageValues],
anthropic_request["messages"],
)

6
litellm/llms/azure/azure.py
View file

@ -228,7 +228,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
litellm_params=litellm_params,
)
data = {"model": None, "messages": messages, **optional_params}
data: dict[str, object] = {"model": None, "messages": messages, **optional_params}
elif litellm.AzureOpenAIGPT5Config.is_model_gpt_5_model(model=litellm_params.get("base_model") or model):
data = litellm.AzureOpenAIGPT5Config().transform_request(
model=model,
@ -482,12 +482,12 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM):
def streaming(
self,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: str | None,
api_version: str,
dynamic_params: bool,
data: dict,
data: dict[str, object],
model: str,
timeout: Any,
max_retries: int,

5
litellm/llms/azure/chat/o_series_handler.py
View file

@ -5,10 +5,11 @@ Written separately to handle faking streaming for o1 and o3 models.
"""
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, Optional
from typing import TYPE_CHECKING, Optional
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.types.utils import ModelResponse
from ...openai.openai import OpenAIChatCompletion
@ -25,7 +26,7 @@ class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion):
timeout: float | httpx.Timeout,
optional_params: dict,
litellm_params: dict,
logging_obj: Any,
logging_obj: LiteLLMLoggingObj,
model: str | None = None,
messages: list | None = None,
print_verbose: Callable | None = None,

9
litellm/llms/azure/completion/handler.py
View file

@ -3,6 +3,7 @@ from typing import Any, Final
from openai import AsyncAzureOpenAI, AzureOpenAI
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.prompt_templates.factory import prompt_factory
from litellm.utils import CustomStreamWrapper, ModelResponse, TextCompletionResponse
@ -39,9 +40,9 @@ class AzureTextCompletion(BaseAzureLLM):
azure_ad_token_provider: Callable | None,
print_verbose: Callable,
timeout,
logging_obj,
logging_obj: LiteLLMLoggingObj,
optional_params,
litellm_params,
litellm_params: dict[str, object],
logger_fn,
acompletion: bool = False,
headers: dict | None = None,
@ -246,7 +247,7 @@ class AzureTextCompletion(BaseAzureLLM):
def streaming(
self,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: str | None,
api_version: str,
@ -299,7 +300,7 @@ class AzureTextCompletion(BaseAzureLLM):
async def async_streaming(
self,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: str | None,
api_version: str,

3
litellm/llms/azure_ai/anthropic/handler.py
View file

@ -9,6 +9,7 @@ from typing import Final
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
@ -40,7 +41,7 @@ class AzureAnthropicChatCompletion(AnthropicChatCompletion):
print_verbose: Callable,
encoding,
api_key,
logging_obj,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
timeout: float | httpx.Timeout,
litellm_params: dict,

2
litellm/llms/azure_ai/chat/transformation.py
View file

@ -248,7 +248,7 @@ class AzureAIStudioConfig(OpenAIConfig):
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
encoding=encoding,
encoding=encoding if encoding is not None else None,
api_key=api_key,
json_mode=json_mode,
)

17
litellm/llms/bedrock/batches/transformation.py
View file

@ -28,7 +28,11 @@ from litellm.types.llms.openai import (
from litellm.types.utils import LiteLLMBatch, LlmProviders
from ..base_aws_llm import BaseAWSLLM
from ..common_utils import CommonBatchFilesUtils, resolve_s3_encryption_key_id
from ..common_utils import (
CommonBatchFilesUtils,
merge_bedrock_aws_request_params,
resolve_s3_encryption_key_id,
)
# Bedrock batch input files are uploaded as
# s3://bucket/litellm-bedrock-files-{model, ":" -> "-"}-{uuid4}.jsonl (see
@ -130,7 +134,8 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
Get the complete URL for Bedrock batch creation.
Bedrock batch jobs are created via the model invocation job API.
"""
aws_region_name: Final = self._get_aws_region_name(optional_params, model)
request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params)
aws_region_name: Final = self._get_aws_region_name(request_params, model)
# Bedrock model invocation job endpoint
# Format: https://bedrock.{region}.amazonaws.com/model-invocation-job
@ -232,14 +237,15 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
# For Bedrock, we need to return a pre-signed request with AWS auth headers
# Use common utility for AWS signing
request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params)
endpoint_url: Final = (
f"https://bedrock.{self._get_aws_region_name(optional_params, model)}.amazonaws.com/model-invocation-job"
f"https://bedrock.{self._get_aws_region_name(request_params, model)}.amazonaws.com/model-invocation-job"
)
signed_headers, signed_data = self.common_utils.sign_aws_request(
service_name="bedrock",
data=bedrock_request,
endpoint_url=endpoint_url,
optional_params=optional_params,
optional_params=request_params,
method="POST",
)
@ -387,11 +393,12 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig):
endpoint_url: Final = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}"
# Use common utility for AWS signing
request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params)
signed_headers, _ = self.common_utils.sign_aws_request(
service_name="bedrock",
data={}, # GET request has no body
endpoint_url=endpoint_url,
optional_params=optional_params,
optional_params=request_params,
method="GET",
)

2
litellm/llms/bedrock/chat/converse_handler.py
View file

@ -89,7 +89,7 @@ class BedrockConverseLLM(BaseAWSLLM):
model_response: ModelResponse,
timeout: float | httpx.Timeout | None,
encoding,
logging_obj,
logging_obj: LiteLLMLoggingObject,
stream,
optional_params: dict,
litellm_params: dict,

14
litellm/llms/bedrock/chat/converse_transformation.py
View file

@ -80,6 +80,7 @@ from ..common_utils import (
bedrock_converse_supports_parallel_tool_use_config,
get_anthropic_beta_from_headers,
get_bedrock_tool_name,
is_bedrock_application_inference_profile_arn,
is_claude_4_5_on_bedrock,
normalize_bedrock_opus_output_config_effort,
)
@ -514,6 +515,7 @@ class AmazonConverseConfig(BaseConfig):
supported_params.append("tool_choice")
supported_params.append("thinking")
supported_params.append("reasoning_effort")
supported_params.append("output_config")
# For nova imported models, also add web_search_options
if "nova" in model.lower():
supported_params.append("web_search_options")
@ -564,6 +566,7 @@ class AmazonConverseConfig(BaseConfig):
):
supported_params.append("thinking")
supported_params.append("reasoning_effort")
supported_params.append("output_config")
if base_model.startswith("anthropic"):
supported_params.append("context_management")
@ -919,6 +922,10 @@ class AmazonConverseConfig(BaseConfig):
self._handle_reasoning_effort_parameter(
model=model, reasoning_effort=value, optional_params=optional_params
)
elif param == "output_config" and isinstance(value, dict):
mapped_output_config = dict(value)
normalize_bedrock_opus_output_config_effort(model=model, output_config=mapped_output_config)
optional_params["output_config"] = mapped_output_config # rebind-ok: out-param store like siblings
elif param == "context_management" and isinstance(value, (dict, list)):
self._map_context_management_param(value, optional_params)
if param == "requestMetadata":
@ -1312,7 +1319,12 @@ class AmazonConverseConfig(BaseConfig):
additional_request_params = filter_exceptions_from_params(additional_request_params)
if anthropic_output_config is not None and isinstance(anthropic_output_config, dict):
if base_model.startswith("anthropic"):
# Application inference profile ARNs hide the underlying model, so the
# effort ceiling and capability gates below cannot run; forward
# verbatim (like ``thinking``) and let Bedrock enforce.
if is_bedrock_application_inference_profile_arn(model):
additional_request_params["output_config"] = anthropic_output_config
elif base_model.startswith("anthropic"):
if litellm.drop_params is True and not AnthropicConfig._model_supports_effort_param(model, "bedrock"):
litellm.verbose_logger.warning(
DROP_UNSUPPORTED_OUTPUT_CONFIG_WARNING,

38
litellm/llms/bedrock/common_utils.py
View file

@ -36,6 +36,44 @@ class BedrockError(BaseLLMException):
pass
_BEDROCK_AWS_AUTH_PARAMETER_KEYS: Final[tuple[str, ...]] = (
"aws_access_key_id",
"aws_secret_access_key",
"aws_session_token",
"aws_region_name",
"aws_session_name",
"aws_profile_name",
"aws_role_name",
"aws_web_identity_token",
"aws_sts_endpoint",
"aws_external_id",
)
def merge_bedrock_aws_request_params(
litellm_params: Mapping[str, Any],
optional_params: Mapping[str, Any],
) -> dict[str, Any]:
"""Merge deployment and request parameters without allowing auth escalation.
Deployment configuration is authoritative for AWS authentication. When a
deployment supplies static credentials, caller-supplied profile/role/token
selectors must not redirect signing to another identity available on the
server. Requests may still provide AWS credentials when the deployment has
no static credentials configured.
"""
request_params: Final = {**optional_params, **litellm_params} # mutable-ok: AWS helpers require a plain dict
has_static_deployment_credentials: Final = all(
isinstance(litellm_params.get(key), str) and bool(litellm_params.get(key))
for key in ("aws_access_key_id", "aws_secret_access_key", "aws_region_name")
)
if has_static_deployment_credentials:
for key in _BEDROCK_AWS_AUTH_PARAMETER_KEYS:
if key not in litellm_params:
request_params.pop(key, None)
return request_params
# Lazy import cache to avoid circular imports and performance impact
_get_model_info = None

22
litellm/llms/bedrock/files/transformation.py
View file

@ -54,7 +54,7 @@ from litellm.types.utils import ExtractedFileData, LlmProviders, SpecialEnums
from litellm.utils import get_llm_provider
from ..base_aws_llm import BaseAWSLLM
from ..common_utils import BedrockError, resolve_s3_encryption_key_id
from ..common_utils import BedrockError, merge_bedrock_aws_request_params, resolve_s3_encryption_key_id
# litellm_params key used to hand the SigV4-signed GET headers from
# `transform_file_content_request` to `validate_environment` (the only hook
@ -285,6 +285,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
"""
Get the complete S3 URL for the file upload request
"""
request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params)
bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME")
if not bucket_name:
raise ValueError(
@ -293,7 +294,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
bucket_name, object_prefix = split_configured_cloud_bucket_name(bucket_name)
s3_region_name: Final = litellm_params.get("s3_region_name") or optional_params.get("s3_region_name")
aws_region_name: Final = s3_region_name or self._get_aws_region_name(optional_params, model)
aws_region_name: Final = s3_region_name or self._get_aws_region_name(request_params, model)
file_data: Final = data.get("file")
purpose: Final = data.get("purpose")
@ -309,7 +310,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
# S3 endpoint URL format
s3_endpoint_url: Final = (
optional_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com"
request_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com"
).rstrip("/")
return f"{s3_endpoint_url}/{bucket_name}/{encoded_object_name}"
@ -843,20 +844,23 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig):
)
# s3_region_name always wins for S3 operations (same priority as in
# get_complete_file_url above). Overwrite aws_region_name unconditionally
# so the SigV4 region matches the URL region, avoiding SignatureDoesNotMatch.
# get_complete_file_url above). Overwrite aws_region_name unconditionally,
# after the deployment-credential merge, so the SigV4 region matches the
# URL region, avoiding SignatureDoesNotMatch.
merged_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params)
s3_region_name: Final = litellm_params.get("s3_region_name") or optional_params.get("s3_region_name")
if s3_region_name:
optional_params = {**optional_params, "aws_region_name": s3_region_name}
request_params: Final = (
{**merged_params, "aws_region_name": s3_region_name} if s3_region_name else merged_params
)
# Sign the request and return a pre-signed request object
signed_headers, signed_body = self._sign_s3_request(
content=file_content,
api_base=api_base,
optional_params=optional_params,
optional_params=request_params,
s3_encryption_key_id=resolve_s3_encryption_key_id(
litellm_params=litellm_params,
optional_params=optional_params,
optional_params=request_params,
),
)

76
litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py
View file

@ -67,6 +67,8 @@ class AmazonAnthropicClaudeMessagesConfig(
DEFAULT_BEDROCK_ANTHROPIC_API_VERSION = "bedrock-2023-05-31"
WEBSEARCH_INTERCEPTION_DOCS_URL = "https://docs.litellm.ai/docs/integrations/websearch_interception"
@property
def custom_llm_provider(self) -> str | None:
return "bedrock"
@ -370,8 +372,9 @@ class AmazonAnthropicClaudeMessagesConfig(
"""
Check if the model supports tool search on Bedrock.
On Amazon Bedrock, server-side tool search is supported on Claude Opus 4.5
and Claude Sonnet 4.5 with the tool-search-tool-2025-10-19 beta header.
The model map's ``supports_tool_search`` flag is authoritative when
``model`` resolves to an entry that sets it; the name patterns below
cover ids the map cannot resolve (ARNs, unlisted regional variants).
Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
@ -381,9 +384,12 @@ class AmazonAnthropicClaudeMessagesConfig(
Returns:
True if the model supports tool search on Bedrock
"""
catalog: Final = AnthropicModelInfo._get_provider_resolved_capability(model, "supports_tool_search", "bedrock")
if catalog is not None:
return catalog
model_lower: Final = model.lower()
# Supported models for tool search on Bedrock
supported_patterns: Final = [
# Opus 4.5
"opus-4.5",
@ -405,10 +411,16 @@ class AmazonAnthropicClaudeMessagesConfig(
"sonnet_4.6",
"sonnet-4-6",
"sonnet_4_6",
# NOTE: Opus 4.7 on Bedrock does not support server-side tool search
# as of launch (2026-04-16). Bedrock rejects the tool type with:
# "tool type 'tool_search_tool_..._20251119' is not supported for this model".
# Re-add the opus-4.7 patterns here once AWS announces support.
# Opus 4.7
"opus-4.7",
"opus_4.7",
"opus-4-7",
"opus_4_7",
# Haiku 4.5
"haiku-4.5",
"haiku_4.5",
"haiku-4-5",
"haiku_4_5",
]
return any(pattern in model_lower for pattern in supported_patterns)
@ -424,11 +436,10 @@ class AmazonAnthropicClaudeMessagesConfig(
"""
Adjust tool search beta header for Bedrock.
Bedrock requires a different beta header for tool search on Opus 4 models
when tool search is used without programmatic tool calling or input examples.
Note: On Amazon Bedrock, server-side tool search is only supported on Claude Opus 4
with the `tool-search-tool-2025-10-19` beta header.
Bedrock requires a different beta header for tool search than the
Anthropic API when tool search is used without programmatic tool
calling or input examples: `tool-search-tool-2025-10-19`, and only on
the models listed in `_supports_tool_search_on_bedrock`.
Ref: https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool
@ -572,6 +583,45 @@ class AmazonAnthropicClaudeMessagesConfig(
return filtered_betas
@staticmethod
def _reject_unsupported_web_search_tools(anthropic_messages_request: dict[str, object], model: str) -> None:
"""
Bedrock's Anthropic endpoints cannot execute Anthropic's server-side
``web_search_*`` tool; forwarding it returns an opaque
"The provided request is not valid" 400 from Bedrock. Fail fast with an
error that names the problem and the fix instead.
When web search interception is enabled
(``litellm_settings.callbacks: ["websearch_interception"]``), the tool
is converted to a regular function tool before this transform runs, so
this guard never fires.
"""
from litellm.integrations.websearch_interception.tools import (
is_anthropic_native_web_search_tool,
)
tools: Final = anthropic_messages_request.get("tools")
if not isinstance(tools, list):
return
web_search_tool: Final = next(
(t for t in tools if isinstance(t, dict) and is_anthropic_native_web_search_tool(t)),
None,
)
if web_search_tool is None:
return
raise litellm.BadRequestError(
message=(
f"Bedrock does not support Anthropic's server-side web search tool "
f"(tool type '{web_search_tool.get('type')}', model '{model}'). "
"To use web search with this model, enable LiteLLM's web search interception "
"so the proxy executes the search instead: "
f"{AmazonAnthropicClaudeMessagesConfig.WEBSEARCH_INTERCEPTION_DOCS_URL}. "
"Alternatively, remove the web_search tool from the request."
),
model=model,
llm_provider="bedrock",
)
def _strip_unsupported_bedrock_invoke_fields(
self,
anthropic_messages_request: dict,
@ -630,6 +680,8 @@ class AmazonAnthropicClaudeMessagesConfig(
############## BEDROCK Invoke SPECIFIC TRANSFORMATION ###
#########################################################
self._reject_unsupported_web_search_tools(anthropic_messages_request=anthropic_messages_request, model=model)
# 1. anthropic_version is required for all claude models
if "anthropic_version" not in anthropic_messages_request:
anthropic_messages_request["anthropic_version"] = self.DEFAULT_BEDROCK_ANTHROPIC_API_VERSION

4
litellm/llms/codestral/completion/handler.py
View file

@ -195,7 +195,7 @@ class CodestralTextCompletion:
print_verbose: Callable,
encoding,
api_key: str,
logging_obj,
logging_obj: LiteLLMLogging,
optional_params: dict,
timeout: float | httpx.Timeout,
acompletion=None,
@ -383,7 +383,7 @@ class CodestralTextCompletion:
print_verbose: Callable,
encoding,
api_key,
logging_obj,
logging_obj: LiteLLMLogging,
data: dict,
timeout: float | httpx.Timeout,
optional_params=None,

2
litellm/llms/custom_httpx/aiohttp_handler.py
View file

@ -221,7 +221,7 @@ class BaseLLMAIOHTTPHandler:
timeout=timeout,
stream=stream,
files=files,
content=content,
content=content if content is not None else None,
params=params,
)
except httpx.HTTPStatusError as e:

61
litellm/llms/custom_httpx/http_handler.py
View file

@ -7,9 +7,9 @@ import ssl
import sys
import threading
import time
from collections.abc import Callable, Mapping
from collections.abc import AsyncIterable, Callable, Iterable, Mapping
from http.cookiejar import CookieJar, DefaultCookiePolicy
from typing import TYPE_CHECKING, Any, Final, Optional
from typing import TYPE_CHECKING, Any, Final, Optional, TypeAlias, TypedDict
import certifi
import httpx
@ -62,8 +62,23 @@ except Exception:
# https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.TCPConnector
_AIOHTTP_SUPPORTS_SOCKET_FACTORY: Final = "socket_factory" in inspect.signature(TCPConnector.__init__).parameters
_AddrInfo: TypeAlias = tuple[int | socket.AddressFamily, int | socket.SocketKind, int, str, tuple[object, ...]]
def _build_aiohttp_keepalive_socket_factory() -> Callable[[tuple[Any, ...]], socket.socket] | None:
_RequestContent: TypeAlias = str | bytes | Iterable[bytes] | AsyncIterable[bytes]
class _TCPConnectorKwargs(TypedDict, total=False):
local_addr: tuple[str, int] | None
ssl: "ssl.SSLContext | bool"
keepalive_timeout: float
ttl_dns_cache: int
enable_cleanup_closed: bool
limit: int
limit_per_host: int
socket_factory: Callable[[_AddrInfo], socket.socket]
def _build_aiohttp_keepalive_socket_factory() -> Callable[[_AddrInfo], socket.socket] | None:
"""
Build a socket_factory that enables SO_KEEPALIVE on aiohttp TCP sockets.
@ -78,7 +93,7 @@ def _build_aiohttp_keepalive_socket_factory() -> Callable[[tuple[Any, ...]], soc
if not AIOHTTP_SO_KEEPALIVE or not _AIOHTTP_SUPPORTS_SOCKET_FACTORY:
return None
def factory(addr_info: tuple[Any, ...]) -> socket.socket:
def factory(addr_info: _AddrInfo) -> socket.socket:
family, type_, proto = addr_info[0], addr_info[1], addr_info[2]
sock: Final = socket.socket(family=family, type=type_, proto=proto)
sock.setblocking(False)
@ -163,8 +178,8 @@ _STREAMING_ERROR_BODY_READ_EXECUTOR: Final = concurrent.futures.ThreadPoolExecut
def _prepare_request_data_and_content(
data: dict | str | bytes | None = None,
content: Any = None,
) -> tuple[dict | Mapping | None, Any]:
content: _RequestContent | None = None,
) -> tuple[dict | Mapping | None, _RequestContent | None]:
"""
Helper function to route data/content parameters correctly for httpx requests
@ -528,7 +543,7 @@ class AsyncHTTPHandler:
def __init__(
self,
timeout: float | httpx.Timeout | None = None,
event_hooks: Mapping[str, list[Callable[..., Any]]] | None = None,
event_hooks: Mapping[str, list[Callable[..., object]]] | None = None,
concurrent_limit=None, # Kept for backward compatibility, but ignored (no limits)
client_alias: str | None = None, # name for client in logs
ssl_verify: VerifyTypes | None = None,
@ -566,7 +581,7 @@ class AsyncHTTPHandler:
def create_client(
self,
timeout: float | httpx.Timeout | None,
event_hooks: Mapping[str, list[Callable[..., Any]]] | None,
event_hooks: Mapping[str, list[Callable[..., object]]] | None,
ssl_verify: VerifyTypes | None = None,
shared_session: Optional["ClientSession"] = None,
) -> httpx.AsyncClient:
@ -648,7 +663,7 @@ class AsyncHTTPHandler:
stream: bool = False,
logging_obj: LiteLLMLoggingObject | None = None,
files: RequestFiles | None = None,
content: Any = None,
content: _RequestContent | None = None,
):
start_time: Final = time.time()
try:
@ -691,7 +706,7 @@ class AsyncHTTPHandler:
end_time: Final = time.time()
time_delta: Final = round(end_time - start_time, 3)
headers = {}
error_response: Final = getattr(e, "response", None)
error_response: Final[httpx.Response | None] = getattr(e, "response", None)
if error_response is not None:
for key, value in error_response.headers.items():
headers[f"response_headers-{key}"] = value
@ -716,7 +731,7 @@ class AsyncHTTPHandler:
headers: dict | None = None,
timeout: float | httpx.Timeout | None = None,
stream: bool = False,
content: Any = None,
content: _RequestContent | None = None,
):
try:
if timeout is None:
@ -755,7 +770,7 @@ class AsyncHTTPHandler:
await new_client.aclose()
except httpx.TimeoutException as e:
headers = {}
error_response: Final = getattr(e, "response", None)
error_response: Final[httpx.Response | None] = getattr(e, "response", None)
if error_response is not None:
for key, value in error_response.headers.items():
headers[f"response_headers-{key}"] = value
@ -780,7 +795,7 @@ class AsyncHTTPHandler:
headers: dict | None = None,
timeout: float | httpx.Timeout | None = None,
stream: bool = False,
content: Any = None,
content: _RequestContent | None = None,
):
try:
if timeout is None:
@ -819,7 +834,7 @@ class AsyncHTTPHandler:
await new_client.aclose()
except httpx.TimeoutException as e:
headers = {}
error_response: Final = getattr(e, "response", None)
error_response: Final[httpx.Response | None] = getattr(e, "response", None)
if error_response is not None:
for key, value in error_response.headers.items():
headers[f"response_headers-{key}"] = value
@ -844,7 +859,7 @@ class AsyncHTTPHandler:
headers: dict | None = None,
timeout: float | httpx.Timeout | None = None,
stream: bool = False,
content: Any = None,
content: _RequestContent | None = None,
):
try:
if timeout is None:
@ -895,7 +910,7 @@ class AsyncHTTPHandler:
params: dict | None = None,
headers: dict | None = None,
stream: bool = False,
content: Any = None,
content: _RequestContent | None = None,
):
"""
Making POST request for a single connection client.
@ -993,7 +1008,7 @@ class AsyncHTTPHandler:
def _get_ssl_connector_kwargs(
ssl_verify: bool | None = None,
ssl_context: ssl.SSLContext | None = None,
) -> dict[str, Any]:
) -> _TCPConnectorKwargs:
"""
Helper method to get SSL connector initialization arguments for aiohttp TCPConnector.
@ -1004,7 +1019,7 @@ class AsyncHTTPHandler:
Returns:
Dict with appropriate SSL configuration for TCPConnector
"""
connector_kwargs: Final[dict[str, Any]] = {
connector_kwargs: Final[_TCPConnectorKwargs] = {
"local_addr": ("0.0.0.0", 0) if litellm.force_ipv4 else None,
}
@ -1054,7 +1069,7 @@ class AsyncHTTPHandler:
verbose_logger.debug("Creating AiohttpTransport...")
transport_connector_kwargs: Final = {
transport_connector_kwargs: Final[_TCPConnectorKwargs] = {
"keepalive_timeout": AIOHTTP_KEEPALIVE_TIMEOUT,
"ttl_dns_cache": AIOHTTP_TTL_DNS_CACHE,
**connector_kwargs,
@ -1212,7 +1227,7 @@ class HTTPHandler:
stream: bool = False,
timeout: float | httpx.Timeout | None = None,
files: dict | RequestFiles | None = None,
content: Any = None,
content: _RequestContent | None = None,
logging_obj: LiteLLMLoggingObject | None = None,
):
try:
@ -1265,7 +1280,7 @@ class HTTPHandler:
headers: dict | None = None,
stream: bool = False,
timeout: float | httpx.Timeout | None = None,
content: Any = None,
content: _RequestContent | None = None,
):
try:
# Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix)
@ -1315,7 +1330,7 @@ class HTTPHandler:
headers: dict | None = None,
stream: bool = False,
timeout: float | httpx.Timeout | None = None,
content: Any = None,
content: _RequestContent | None = None,
):
try:
# Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix)
@ -1364,7 +1379,7 @@ class HTTPHandler:
headers: dict | None = None,
timeout: float | httpx.Timeout | None = None,
stream: bool = False,
content: Any = None,
content: _RequestContent | None = None,
):
try:
# Prepare data/content parameters to prevent httpx DeprecationWarning (memory leak fix)

64
litellm/llms/custom_httpx/llm_http_handler.py
View file

@ -5,7 +5,8 @@ import ssl
from collections.abc import AsyncIterator, Coroutine, Iterator, Mapping
from contextlib import asynccontextmanager
from functools import lru_cache
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypeVar, Union, cast, get_type_hints
from types import ModuleType
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict, TypeVar, Union, cast, get_type_hints
from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
import httpx
@ -148,6 +149,7 @@ from .http_handler import get_shared_realtime_ssl_context
if TYPE_CHECKING:
from aiohttp import ClientSession
from websockets.asyncio.client import ClientConnection
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
@ -176,6 +178,19 @@ else:
_ResponseT = TypeVar("_ResponseT")
class _DeleteRequestKwargs(TypedDict, total=False):
url: str
headers: dict[str, str]
timeout: float | httpx.Timeout | None
json: dict[str, object]
class _MediaUploadKwargs(TypedDict, total=False):
headers: dict[str, str]
content: Iterator[bytes] | AsyncIterator[bytes]
timeout: float | httpx.Timeout
def _google_genai_streaming_hidden_params(
*,
api_base: str,
@ -1413,7 +1428,7 @@ class BaseLLMHTTPHandler:
headers: dict[str, object] | None,
provider_config: BaseOCRConfig,
litellm_params: dict,
) -> tuple[dict[str, Any], str, dict[str, Any], None]:
) -> tuple[dict[str, object], str, dict[str, object], None]:
"""
Shared logic for preparing OCR requests.
Returns: (headers, complete_url, data, files)
@ -1479,7 +1494,7 @@ class BaseLLMHTTPHandler:
headers: dict[str, object] | None,
provider_config: BaseOCRConfig,
litellm_params: dict,
) -> tuple[dict[str, Any], str, dict[str, Any], None]:
) -> tuple[dict[str, object], str, dict[str, object], None]:
"""
Async version of _prepare_ocr_request for providers that need async transforms.
Returns: (headers, complete_url, data, files)
@ -2361,14 +2376,14 @@ class BaseLLMHTTPHandler:
model: str,
input: str | ResponseInputParam,
custom_llm_provider: str,
response_api_optional_request_params: dict[str, Any],
response_api_optional_request_params: dict[str, object],
litellm_params: GenericLiteLLMParams,
logging_obj: LiteLLMLoggingObj,
) -> tuple[
str,
str | ResponseInputParam,
str,
dict[str, Any],
dict[str, object],
GenericLiteLLMParams,
]:
if not _has_pre_call_deployment_hook(logging_obj):
@ -2894,7 +2909,7 @@ class BaseLLMHTTPHandler:
},
)
delete_kwargs: Final[dict[str, Any]] = {
delete_kwargs: Final[_DeleteRequestKwargs] = {
"url": url,
"headers": headers,
"timeout": timeout,
@ -2984,7 +2999,7 @@ class BaseLLMHTTPHandler:
},
)
delete_kwargs: Final[dict[str, Any]] = {
delete_kwargs: Final[_DeleteRequestKwargs] = {
"url": url,
"headers": headers,
"timeout": timeout,
@ -3725,7 +3740,7 @@ class BaseLLMHTTPHandler:
timeout: float | httpx.Timeout | None,
) -> httpx.Response:
headers: Final = {**base_headers, "Content-Type": content_type}
kwargs: Final[dict[str, Any]] = {
kwargs: Final[_MediaUploadKwargs] = {
"headers": headers,
"content": self._iter_in_blocks(body_stream.iter_bytes(), self._MEDIA_UPLOAD_BLOCK_SIZE),
}
@ -3762,7 +3777,7 @@ class BaseLLMHTTPHandler:
break
yield cast(bytes, block)
kwargs: Final[dict[str, Any]] = {"headers": headers, "content": _abody()}
kwargs: Final[_MediaUploadKwargs] = {"headers": headers, "content": _abody()}
if timeout is not None:
kwargs["timeout"] = timeout
resp: Final = await client.client.post(url, **kwargs)
@ -5242,7 +5257,7 @@ class BaseLLMHTTPHandler:
def _wrap_responses_response_as_fake_stream(
self,
result: Any,
result: ResponsesAPIResponse,
model: str,
responses_api_provider_config: BaseResponsesAPIConfig,
logging_obj: "LiteLLMLoggingObj",
@ -5365,7 +5380,7 @@ class BaseLLMHTTPHandler:
async def _call_agentic_completion_hooks(
self,
response: Any,
response: object,
model: str,
messages: list[dict],
anthropic_messages_provider_config: "BaseAnthropicMessagesConfig",
@ -5536,7 +5551,7 @@ class BaseLLMHTTPHandler:
async def _call_agentic_chat_completion_hooks(
self,
response: Any,
response: ModelResponse,
model: str,
messages: list[dict],
optional_params: dict,
@ -5760,14 +5775,14 @@ class BaseLLMHTTPHandler:
@staticmethod
async def _open_realtime_backend_ws(
websockets_module: Any,
websockets_module: ModuleType,
url: str,
headers: dict,
ssl_context: Any,
ssl_context: bool | str | ssl.SSLContext,
*,
open_timeout: float = 8.0,
max_attempts: int = 3,
) -> Any:
) -> "ClientConnection":
"""Open the backend realtime websocket, retrying a hung open handshake.
The upstream Live handshake (e.g. Gemini Live) intermittently hangs on
@ -5826,7 +5841,6 @@ class BaseLLMHTTPHandler:
query_params: RealtimeQueryParams | None = None,
):
import websockets
from websockets.asyncio.client import ClientConnection
url: Final = provider_config.get_complete_url(api_base, model, api_key)
headers = provider_config.validate_environment(
@ -5844,12 +5858,12 @@ class BaseLLMHTTPHandler:
ssl_context.verify_mode = ssl.CERT_NONE
backend_ws: Final = await self._open_realtime_backend_ws(websockets, url, headers, ssl_context)
async with backend_ws:
_request_data: Final[dict[str, Any]] = {}
_request_data: Final[dict[str, object]] = {}
if litellm_metadata:
_request_data["litellm_metadata"] = litellm_metadata
realtime_streaming: Final = RealTimeStreaming(
websocket,
cast(ClientConnection, backend_ws),
backend_ws,
logging_obj,
provider_config,
model,
@ -6008,7 +6022,7 @@ class BaseLLMHTTPHandler:
)
else:
url = provider_config.get_complete_url(api_base=api_base, model=model or "", api_version=api_version)
headers: dict[str, Any] = provider_config.validate_environment(
headers: dict[str, object] = provider_config.validate_environment(
headers={}, model=model or "", api_key=api_key
)
else:
@ -6079,7 +6093,7 @@ class BaseLLMHTTPHandler:
if provider_config is not None:
url = provider_config.get_realtime_calls_url(api_base=api_base, model=model or "", api_version=api_version)
headers: dict[str, Any] = provider_config.get_realtime_calls_headers(ephemeral_key=openai_ephemeral_key)
headers: dict[str, object] = provider_config.get_realtime_calls_headers(ephemeral_key=openai_ephemeral_key)
else:
url = f"{api_base.rstrip('/')}/v1/realtime/calls"
headers = {
@ -6247,7 +6261,7 @@ class BaseLLMHTTPHandler:
yield backend
async with _backend_connection() as backend_ws:
_request_data: Final[dict[str, Any]] = {}
_request_data: Final[dict[str, object]] = {}
if litellm_metadata:
_request_data["litellm_metadata"] = litellm_metadata
@ -9444,7 +9458,7 @@ class BaseLLMHTTPHandler:
litellm_params=dict(litellm_params),
extra_body=extra_body,
)
all_optional_params: Final[dict[str, Any]] = dict(litellm_params)
all_optional_params: Final[dict[str, object]] = dict(litellm_params)
all_optional_params.update(vector_store_search_optional_params or {})
headers, signed_json_body = vector_store_provider_config.sign_request(
headers=headers,
@ -9540,7 +9554,7 @@ class BaseLLMHTTPHandler:
extra_body=extra_body,
)
all_optional_params: Final[dict[str, Any]] = dict(litellm_params)
all_optional_params: Final[dict[str, object]] = dict(litellm_params)
all_optional_params.update(vector_store_search_optional_params or {})
headers, signed_json_body = vector_store_provider_config.sign_request(
@ -9860,7 +9874,7 @@ class BaseLLMHTTPHandler:
url: Final = api_base
params: Final[dict[str, Any]] = {}
params: Final[dict[str, object]] = {}
if after is not None:
params["after"] = after
if before is not None:
@ -9938,7 +9952,7 @@ class BaseLLMHTTPHandler:
url: Final = api_base
params: Final[dict[str, Any]] = {}
params: Final[dict[str, object]] = {}
if after is not None:
params["after"] = after
if before is not None:

2
litellm/llms/github_copilot/chat/transformation.py
View file

@ -277,7 +277,7 @@ class GithubCopilotConfig(OpenAIConfig):
messages: list[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: Any,
encoding: object,
api_key: str | None = None,
json_mode: bool | None = None,
) -> "ModelResponse":

120
litellm/llms/nvidia_nim/rerank/ranking_transformation.py
View file

@ -8,13 +8,23 @@ Reference: https://build.nvidia.com/nvidia/llama-3_2-nv-rerankqa-1b-v2/deploy
from typing import Final
import httpx
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig
from litellm.types.rerank import RerankResponse
class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
"""
Configuration for NVIDIA NIM models that use the /v1/ranking endpoint.
The native /v1/ranking request schema accepts only 'model', 'query',
'passages', and 'truncate' -- requests containing 'top_k' are rejected
with a 400 validation error. Cohere-compatible 'top_n' is therefore
applied client-side by truncating the converted response instead of
being forwarded to the endpoint.
Example:
curl -X "POST" 'https://ai.api.nvidia.com/v1/ranking' \
-H 'Accept: application/json' \
@ -27,6 +37,16 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
}'
"""
SUPPORTED_PASSAGE_FIELDS: tuple[str, ...] = ("text", "image")
def __init__(self) -> None:
super().__init__()
# top_n captured in transform_rerank_request and applied in
# transform_rerank_response. The provider config is instantiated
# per-request (see ProviderConfigManager.get_provider_rerank_config),
# so this does not leak across requests.
self._client_side_top_n: int | None = None
def _get_clean_model_name(self, model: str) -> str:
"""Strip 'nvidia_nim/' and 'ranking/' prefixes from model name."""
# First strip nvidia_nim/ prefix if present
@ -58,6 +78,47 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
return f"{api_base}/v1/ranking"
def map_cohere_rerank_params(
self,
non_default_params: dict | None, # mutable-ok: matches BaseRerankConfig's request contract
model: str,
drop_params: bool,
query: str,
documents: list[str | dict[str, object]], # mutable-ok: matches BaseRerankConfig's document contract
custom_llm_provider: str | None = None,
top_n: int | None = None,
rank_fields: list[str] | None = None, # mutable-ok: matches BaseRerankConfig's field contract
return_documents: bool | None = True,
max_chunks_per_doc: int | None = None,
max_tokens_per_doc: int | None = None,
instruction: str | None = None,
) -> dict: # mutable-ok: LiteLLM provider transforms return mutable request dictionaries
"""
Keep Cohere's top_n as-is instead of mapping it to top_k.
The native /v1/ranking endpoint rejects top_k, so top_n is applied
client-side after the response is converted.
"""
optional_params: Final = super().map_cohere_rerank_params(
non_default_params=non_default_params,
model=model,
drop_params=drop_params,
query=query,
documents=documents,
custom_llm_provider=custom_llm_provider,
top_n=None, # do not map top_n -> top_k for /v1/ranking
rank_fields=rank_fields,
return_documents=return_documents,
max_chunks_per_doc=max_chunks_per_doc,
max_tokens_per_doc=max_tokens_per_doc,
instruction=instruction,
)
# /v1/ranking rejects top_k even when passed as a provider-specific param
optional_params.pop("top_k", None)
if top_n is not None:
optional_params["top_n"] = top_n
return optional_params
def transform_rerank_request(
self,
model: str,
@ -67,11 +128,66 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig):
) -> dict:
"""
Transform request, using clean model name without 'ranking/' prefix.
top_n / top_k are stripped from the outgoing request: the native
/v1/ranking endpoint accepts only model, query, passages, and
truncate. top_n is stashed and applied client-side in
transform_rerank_response.
"""
top_n: Final = optional_rerank_params.get("top_n")
if top_n is not None:
if isinstance(top_n, bool) or not isinstance(top_n, int) or top_n < 1:
raise ValueError(f"top_n must be a positive integer, got: {top_n!r}")
self._client_side_top_n = top_n
clean_model: Final = self._get_clean_model_name(model)
filtered_params: Final = { # mutable-ok: the base transformer requires a mutable request dictionary
k: v for k, v in optional_rerank_params.items() if k not in ("top_n", "top_k")
}
return super().transform_rerank_request(
model=clean_model,
optional_rerank_params=optional_rerank_params,
optional_rerank_params=filtered_params,
headers=headers,
litellm_params=litellm_params,
)
def transform_rerank_response(
self,
model: str,
raw_response: httpx.Response,
model_response: RerankResponse,
logging_obj: LiteLLMLoggingObj,
api_key: str | None = None,
request_data: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract
optional_params: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract
litellm_params: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract
) -> RerankResponse:
"""
Convert the native ranking response, then apply top_n client-side.
/v1/ranking returns rankings sorted by relevance, but sort before
truncating in case a server returns them unsorted.
"""
resolved_request_data: Final = request_data or {} # mutable-ok: the base transformer requires a dictionary
resolved_optional_params: Final = optional_params or {} # mutable-ok: response options are keyed lookups
resolved_litellm_params: Final = litellm_params or {} # mutable-ok: the base transformer requires a dictionary
response: Final = super().transform_rerank_response(
model=model,
raw_response=raw_response,
model_response=model_response,
logging_obj=logging_obj,
api_key=api_key,
request_data=resolved_request_data,
optional_params=resolved_optional_params,
litellm_params=resolved_litellm_params,
)
top_n: Final = resolved_optional_params.get("top_n") or self._client_side_top_n
if top_n is not None and response.results is not None and len(response.results) > top_n:
response.results = sorted(
response.results,
key=lambda result: result["relevance_score"],
reverse=True,
)[:top_n]
return response

28
litellm/llms/nvidia_nim/rerank/transformation.py
View file

@ -21,8 +21,9 @@ class NvidiaNimQueryObject(TypedDict):
text: Required[str]
class NvidiaNimPassageObject(TypedDict):
text: Required[str]
class NvidiaNimPassageObject(TypedDict, total=False):
text: str
image: str
class NvidiaNimRerankRequest(TypedDict, total=False):
@ -53,6 +54,10 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
DEFAULT_NIM_RERANK_API_BASE = "https://ai.api.nvidia.com"
# The legacy retrieval rerank route accepts text passages only. The native
# ranking subclass expands this tuple for VL models that accept images.
SUPPORTED_PASSAGE_FIELDS: tuple[str, ...] = ("text",)
def __init__(self) -> None:
pass
@ -206,11 +211,17 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
if isinstance(doc, str):
passages.append({"text": doc})
elif isinstance(doc, dict):
# If document is already a dict, check if it has 'text' field
if "text" in doc:
passages.append({"text": doc["text"]})
# Preserve only the structured passage fields supported by the
# selected rerank route.
supported_fields: NvidiaNimPassageObject = {} # mutable-ok: assembling a request TypedDict
if "text" in self.SUPPORTED_PASSAGE_FIELDS and "text" in doc:
supported_fields["text"] = doc["text"]
if "image" in self.SUPPORTED_PASSAGE_FIELDS and "image" in doc:
supported_fields["image"] = doc["image"]
if supported_fields:
passages.append(supported_fields)
else:
# Otherwise, stringify the dict
# No supported fields - stringify the dict
import json
passages.append({"text": json.dumps(doc)})
@ -304,9 +315,10 @@ class NvidiaNimRerankConfig(BaseRerankConfig):
"relevance_score": ranking["logit"],
}
# Include document if it was in the original request
# Include document if it was in the original request.
# Image-only passages carry no 'text' field, so guard the lookup.
index: int = ranking["index"]
if index < len(original_passages):
if index < len(original_passages) and "text" in original_passages[index]:
result_item["document"] = {"text": original_passages[index]["text"]}
results.append(result_item)

23
litellm/llms/oci/chat/transformation.py
View file

@ -10,7 +10,7 @@ implement the LiteLLM BaseConfig interface. Heavy-lifting lives in:
"""
import json
from collections.abc import AsyncIterator, Iterator
from collections.abc import AsyncIterator, Callable, Iterator
from typing import TYPE_CHECKING, Any, Final
import httpx
@ -713,8 +713,25 @@ class OCIChatConfig(BaseConfig):
class OCIStreamWrapper(CustomStreamWrapper):
"""Custom stream wrapper that dispatches OCI SSE chunks to the correct handler."""
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
def __init__(
self,
completion_stream: object,
model: str,
logging_obj: LiteLLMLoggingObj,
custom_llm_provider: str | None = None,
stream_options: object = None,
make_call: Callable[..., object] | None = None,
_response_headers: dict[str, object] | None = None,
) -> None:
super().__init__(
completion_stream=completion_stream,
model=model,
logging_obj=logging_obj,
custom_llm_provider=custom_llm_provider,
stream_options=stream_options,
make_call=make_call,
_response_headers=_response_headers,
)
# Tracks whether any prior Cohere chunk in this stream has emitted
# tool calls. The Cohere handler uses this to decide whether the
# terminal consolidation chunk's tool calls are duplicates (suppress)

4
litellm/llms/openai/completion/handler.py
View file

@ -217,7 +217,7 @@ class OpenAITextCompletion(BaseLLM):
def streaming(
self,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_key: str,
data: dict,
headers: dict,
@ -274,7 +274,7 @@ class OpenAITextCompletion(BaseLLM):
async def async_streaming(
self,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_key: str,
data: dict,
headers: dict,

72
litellm/llms/openai/openai.py
View file

@ -1,6 +1,6 @@
import time
import types
from collections.abc import AsyncIterator, Callable, Coroutine, Iterable, Iterator
from collections.abc import AsyncIterator, Callable, Coroutine, Iterable, Iterator, Mapping
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, cast
from urllib.parse import urlparse
@ -61,16 +61,17 @@ class MistralEmbeddingConfig:
def __init__(
self,
) -> None:
locals_: Final = locals().copy()
locals_: Final[Mapping[str, object]] = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
config_attrs: Final[Mapping[str, object]] = cls.__dict__
return {
k: v
for k, v in cls.__dict__.items()
for k, v in config_attrs.items()
if not k.startswith("__")
and not isinstance(
v,
@ -153,7 +154,7 @@ class OpenAIConfig(BaseConfig):
top_p: int | None = None,
response_format: dict | None = None,
) -> None:
locals_: Final = locals().copy()
locals_: Final[Mapping[str, object]] = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@ -261,7 +262,7 @@ class OpenAIConfig(BaseConfig):
messages: list[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: Any,
encoding: object,
api_key: str | None = None,
json_mode: bool | None = None,
) -> ModelResponse:
@ -299,7 +300,7 @@ class OpenAIConfig(BaseConfig):
streaming_response: Iterator[str] | AsyncIterator[str] | ModelResponse,
sync_stream: bool,
json_mode: bool | None = False,
) -> Any:
) -> "OpenAIChatCompletionResponseIterator":
return OpenAIChatCompletionResponseIterator(
streaming_response=streaming_response,
sync_stream=sync_stream,
@ -478,14 +479,14 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
async def _call_agentic_completion_hooks_openai(
self,
response: Any,
response: object,
model: str,
messages: list[dict],
optional_params: dict,
logging_obj: LiteLLMLoggingObj,
stream: bool,
litellm_params: dict,
) -> Any | None:
) -> object | None:
"""
Call agentic completion hooks for all custom loggers (OpenAI Chat Completions API).
@ -536,7 +537,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
kwargs_with_provider["custom_llm_provider"] = custom_llm_provider
# For OpenAI Chat Completions, use the chat completion agentic loop method
agentic_response = await callback.async_run_chat_completion_agentic_loop(
agentic_response: object = await callback.async_run_chat_completion_agentic_loop(
tools=tool_calls,
model=model,
messages=messages,
@ -580,7 +581,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
timeout: float | httpx.Timeout,
optional_params: dict,
litellm_params: dict,
logging_obj: Any,
logging_obj: LiteLLMLoggingObj,
model: str | None = None,
messages: list | None = None,
print_verbose: Callable | None = None,
@ -1590,7 +1591,7 @@ class OpenAIFilesAPI(BaseLLM):
client: OpenAI | AsyncOpenAI | None = None,
_is_async: bool = False,
) -> OpenAI | AsyncOpenAI | None:
received_args: Final = locals()
received_args: Final[Mapping[str, object]] = locals()
openai_client: OpenAI | AsyncOpenAI | None = None
if client is None:
data: Final = {}
@ -1628,7 +1629,7 @@ class OpenAIFilesAPI(BaseLLM):
max_retries: int | None,
organization: str | None,
client: OpenAI | AsyncOpenAI | None = None,
) -> OpenAIFileObject | Coroutine[Any, Any, OpenAIFileObject]:
) -> OpenAIFileObject | Coroutine[None, None, OpenAIFileObject]:
openai_client: Final[OpenAI | AsyncOpenAI | None] = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@ -1670,7 +1671,7 @@ class OpenAIFilesAPI(BaseLLM):
max_retries: int | None,
organization: str | None,
client: OpenAI | AsyncOpenAI | None = None,
) -> HttpxBinaryResponseContent | Coroutine[Any, Any, HttpxBinaryResponseContent]:
) -> HttpxBinaryResponseContent | Coroutine[None, None, HttpxBinaryResponseContent]:
openai_client: Final[OpenAI | AsyncOpenAI | None] = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@ -1948,7 +1949,7 @@ class OpenAIBatchesAPI(BaseLLM):
client: OpenAI | AsyncOpenAI | None = None,
_is_async: bool = False,
) -> OpenAI | AsyncOpenAI | None:
received_args: Final = locals()
received_args: Final[Mapping[str, object]] = locals()
openai_client: OpenAI | AsyncOpenAI | None = None
if client is None:
data: Final = {}
@ -1986,7 +1987,7 @@ class OpenAIBatchesAPI(BaseLLM):
max_retries: int | None,
organization: str | None,
client: OpenAI | AsyncOpenAI | None = None,
) -> LiteLLMBatch | Coroutine[Any, Any, LiteLLMBatch]:
) -> LiteLLMBatch | Coroutine[None, None, LiteLLMBatch]:
openai_client: Final[OpenAI | AsyncOpenAI | None] = self.get_openai_client(
api_key=api_key,
api_base=api_base,
@ -2160,7 +2161,7 @@ class OpenAIAssistantsAPI(BaseLLM):
organization: str | None,
client: OpenAI | None = None,
) -> OpenAI:
received_args: Final = locals()
received_args: Final[Mapping[str, object]] = locals()
if client is None:
data: Final = {}
for k, v in received_args.items():
@ -2185,7 +2186,7 @@ class OpenAIAssistantsAPI(BaseLLM):
organization: str | None,
client: AsyncOpenAI | None = None,
) -> AsyncOpenAI:
received_args: Final = locals()
received_args: Final[Mapping[str, object]] = locals()
if client is None:
data: Final = {}
for k, v in received_args.items():
@ -2848,7 +2849,7 @@ class OpenAIAssistantsAPI(BaseLLM):
assistant_id: str,
additional_instructions: str | None,
instructions: str | None,
metadata: dict | None,
metadata: dict[str, str] | None,
model: str | None,
stream: bool | None,
tools: Iterable[AssistantToolParam] | None,
@ -2912,23 +2913,32 @@ class OpenAIAssistantsAPI(BaseLLM):
assistant_id: str,
additional_instructions: str | None,
instructions: str | None,
metadata: dict | None,
metadata: dict[str, str] | None,
model: str | None,
tools: Iterable[AssistantToolParam] | None,
event_handler: AssistantEventHandler | None,
) -> AssistantStreamManager[AssistantEventHandler]:
data: Final[dict[str, Any]] = {
"thread_id": thread_id,
"assistant_id": assistant_id,
"additional_instructions": additional_instructions,
"instructions": instructions,
"metadata": metadata,
"model": model,
"tools": tools,
}
runs_stream: Final = client.beta.threads.runs.stream
if event_handler is not None:
data["event_handler"] = event_handler
return client.beta.threads.runs.stream(**data)
return runs_stream(
thread_id=thread_id,
assistant_id=assistant_id,
additional_instructions=additional_instructions,
instructions=instructions,
metadata=metadata,
model=model,
tools=tools,
event_handler=event_handler,
)
return runs_stream(
thread_id=thread_id,
assistant_id=assistant_id,
additional_instructions=additional_instructions,
instructions=instructions,
metadata=metadata,
model=model,
tools=tools,
)
# fmt: off
@ -2984,7 +2994,7 @@ class OpenAIAssistantsAPI(BaseLLM):
assistant_id: str,
additional_instructions: str | None,
instructions: str | None,
metadata: dict | None,
metadata: dict[str, str] | None,
model: str | None,
stream: bool | None,
tools: Iterable[AssistantToolParam] | None,

10
litellm/llms/openai/responses/transformation.py
View file

@ -353,6 +353,16 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
)
return event_pydantic_model.model_construct(**parsed_chunk)
@staticmethod
def parse_terminal_response_from_stream_chunks(all_chunks: list[str]) -> ResponsesAPIResponse | None:
for chunk_str in reversed(all_chunks):
for event_model in (ResponseCompletedEvent, ResponseIncompleteEvent, ResponseFailedEvent):
try:
return event_model.model_validate_json(chunk_str.removeprefix("data: ")).response
except ValueError:
continue
return None
@staticmethod
def get_event_model_class(event_type: str) -> Any:
"""

5
litellm/llms/openai_like/chat/handler.py
View file

@ -12,6 +12,7 @@ import httpx
import litellm
from litellm import LlmProviders
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
from litellm.llms.databricks.streaming_utils import ModelResponseIterator
@ -112,7 +113,7 @@ class OpenAILikeChatHandler(OpenAILikeBase):
print_verbose: Callable,
encoding,
api_key,
logging_obj,
logging_obj: LiteLLMLoggingObj,
stream,
data: dict,
optional_params=None,
@ -214,7 +215,7 @@ class OpenAILikeChatHandler(OpenAILikeBase):
print_verbose: Callable,
encoding,
api_key: str | None,
logging_obj,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
acompletion=None,
litellm_params: dict = {},

5
litellm/llms/predibase/chat/handler.py
View file

@ -9,6 +9,7 @@ from typing import Final
import httpx
import litellm
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
get_async_httpx_client,
@ -59,7 +60,7 @@ class PredibaseChatCompletion:
print_verbose: Callable,
encoding,
api_key: str,
logging_obj,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
litellm_params: dict,
tenant_id: str,
@ -250,7 +251,7 @@ class PredibaseChatCompletion:
print_verbose: Callable,
encoding,
api_key,
logging_obj,
logging_obj: LiteLLMLoggingObj,
data: dict,
timeout: float | httpx.Timeout,
optional_params=None,

5
litellm/llms/replicate/chat/handler.py
View file

@ -6,6 +6,7 @@ from typing import Final
import litellm
from litellm.constants import REPLICATE_POLLING_DELAY_SECONDS
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
@ -128,7 +129,7 @@ def completion(
print_verbose: Callable,
optional_params: dict,
litellm_params: dict,
logging_obj,
logging_obj: LiteLLMLoggingObj,
api_key,
encoding,
custom_prompt_dict={},
@ -246,7 +247,7 @@ async def async_completion(
input_data,
api_key,
api_base,
logging_obj,
logging_obj: LiteLLMLoggingObj,
print_verbose,
headers: dict,
) -> ModelResponse | CustomStreamWrapper:

8
litellm/llms/sagemaker/completion/handler.py
View file

@ -8,6 +8,7 @@ import httpx
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.asyncify import asyncify
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
from litellm.llms.custom_httpx.http_handler import (
_get_httpx_client,
@ -138,7 +139,7 @@ class SagemakerLLM(BaseAWSLLM):
model_response: ModelResponse,
print_verbose: Callable,
encoding,
logging_obj,
logging_obj: LiteLLMLoggingObj,
optional_params: dict,
litellm_params: dict,
timeout: float | httpx.Timeout | None = None,
@ -431,17 +432,18 @@ class SagemakerLLM(BaseAWSLLM):
if not prepared_request.body:
raise ValueError("Prepared request body is empty")
stream_logging_obj: Final[LiteLLMLoggingObj] = logging_obj
completion_stream: Final = await self.make_async_call(
api_base=prepared_request.url,
headers=prepared_request.headers,
data=cast(str, prepared_request.body),
logging_obj=logging_obj,
logging_obj=stream_logging_obj,
)
streaming_response: Final = CustomStreamWrapper(
completion_stream=completion_stream,
model=model,
custom_llm_provider="sagemaker",
logging_obj=logging_obj,
logging_obj=stream_logging_obj,
)
# LOGGING

41
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
View file

@ -3,7 +3,7 @@
## Initial implementation - covers gemini + image gen calls
import json
import time
from collections.abc import Callable, Mapping
from collections.abc import Callable, Mapping, Sequence
from copy import deepcopy
from functools import partial
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, Union, cast
@ -208,7 +208,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
presence_penalty: float | None = None,
seed: int | None = None,
) -> None:
locals_: Final = locals().copy()
locals_: Final[Mapping[str, object]] = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@ -1427,7 +1427,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
@staticmethod
def _extract_server_side_tool_invocations(
parts: list[HttpxPartType],
) -> list[dict[str, Any]] | None:
) -> list[dict[str, object]] | None:
"""Extract server-side tool invocations (toolCall/toolResponse) from parts.
These are returned by Gemini when context circulation is enabled
@ -1438,15 +1438,15 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
Returns:
List of server-side invocation dicts if any found, None otherwise.
"""
invocations: Final[list[dict[str, Any]]] = []
invocations: Final[list[dict[str, object]]] = []
# Index toolCalls by id so we can pair them with responses
tool_calls_by_id: Final[dict[str, dict[str, Any]]] = {}
tool_responses_by_id: Final[dict[str, dict[str, Any]]] = {}
tool_calls_by_id: Final[dict[str, dict[str, object]]] = {}
tool_responses_by_id: Final[dict[str, dict[str, object]]] = {}
for part in parts:
if "toolCall" in part:
tc = part["toolCall"]
entry: dict[str, Any] = {
entry: dict[str, object] = {
"tool_type": tc.get("toolType"),
"id": tc.get("id"),
"args": tc.get("args"),
@ -1753,7 +1753,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
response_tokens_details: CompletionTokensDetailsWrapper | None = None
usage_metadata: Final = completion_response["usageMetadata"]
def _get_token_count(detail: Mapping[str, Any]) -> int:
def _get_token_count(detail: Mapping[str, object]) -> int:
raw_token_count: Final = detail.get("tokenCount", detail.get("token_count", 0))
return raw_token_count if isinstance(raw_token_count, int) else 0
@ -2068,7 +2068,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
)
@staticmethod
def _get_stream_chunk_attr(chunk: Any, field_name: str) -> Any:
def _get_stream_chunk_attr(chunk: object, field_name: str) -> object:
if isinstance(chunk, dict):
value = chunk.get(field_name)
if value is not None:
@ -2110,10 +2110,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
def apply_assembled_streaming_response_metadata(
self,
response: ModelResponse,
chunks: list[Any],
chunks: list[object],
) -> None:
for field_name in VERTEX_AI_PROVIDER_METADATA_FIELDS:
merged: list[Any] = []
merged: list[object] = []
for chunk in chunks:
value = VertexGeminiConfig._get_stream_chunk_attr(chunk, field_name)
if not value:
@ -2214,8 +2214,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
functions: ChatCompletionToolCallFunctionChunk | None = None
thinking_blocks: list[ChatCompletionThinkingBlock] | None = None
reasoning_content: str | None = None
thought_signatures: Any | None = None
server_side_tool_invocations: list[dict[str, Any]] | None = None
thought_signatures: Sequence[str] | None = None
server_side_tool_invocations: list[dict[str, object]] | None = None
for idx, candidate in enumerate(_candidates):
if "content" not in candidate:
@ -2370,7 +2370,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
messages: list[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: Any,
encoding: object,
api_key: str | None = None,
json_mode: bool | None = None,
) -> ModelResponse:
@ -2486,7 +2486,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
## ADD SERVICE TIER ##
if getattr(raw_response, "headers", None):
if service_tier := raw_response.headers.get("x-gemini-service-tier"):
service_tier: Final[str | None] = raw_response.headers.get("x-gemini-service-tier")
if service_tier:
if service_tier.lower() == "standard":
setattr(model_response, "service_tier", "default")
else:
@ -2660,7 +2661,7 @@ class VertexLLM(VertexBase):
print_verbose: Callable,
data: dict,
timeout: float | httpx.Timeout | None,
encoding,
encoding: object,
logging_obj,
stream,
optional_params: dict,
@ -2756,7 +2757,7 @@ class VertexLLM(VertexBase):
"vertex_ai", "vertex_ai_beta", "gemini"
], # if it's vertex_ai or gemini (google ai studio)
timeout: float | httpx.Timeout | None,
encoding,
encoding: object,
logging_obj,
stream,
optional_params: dict,
@ -2873,7 +2874,7 @@ class VertexLLM(VertexBase):
custom_llm_provider: Literal[
"vertex_ai", "vertex_ai_beta", "gemini"
], # if it's vertex_ai or gemini (google ai studio)
encoding,
encoding: object,
logging_obj,
optional_params: dict,
acompletion: bool,
@ -3122,7 +3123,7 @@ class ModelResponseIterator:
def _apply_stream_candidates(
self,
_candidates: list[Candidates],
model_response: Any,
model_response: "ModelResponseStream",
) -> tuple[list[dict], list[dict], list[dict], list[dict]]:
(
grounding_metadata,
@ -3200,7 +3201,7 @@ class ModelResponseIterator:
def _apply_stream_usage_metadata(
self,
processed_chunk: Any,
processed_chunk: GenerateContentResponseBody,
model_response: Any,
grounding_metadata: list[dict],
) -> Usage | None:

2
litellm/llms/vertex_ai/vertex_ai_non_gemini.py
View file

@ -28,7 +28,7 @@ class TextStreamer:
Fake streaming iterator for Vertex AI Model Garden calls
"""
def __init__(self, text):
def __init__(self, text: str):
self.text = text.split() # let's assume words as a streaming unit
self.index = 0

115
litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
View file

@ -14,6 +14,11 @@ from typing import Any, Final, cast
import httpx
from litellm.llms.base_llm.chat.transformation import BaseLLMException
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
_get_httpx_client,
)
from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse
@ -123,6 +128,79 @@ class VertexGemmaConfig(OpenAIGPTConfig):
return response_json["predictions"]
@staticmethod
def _sync_post(
client: HTTPHandler | httpx.Client | None,
api_base: str,
headers: dict[str, str], # mutable-ok: forwarded to post(headers: dict | None)
request_data: dict[str, Any], # mutable-ok: forwarded to post(json: dict | ...)
timeout: float | httpx.Timeout | None,
) -> httpx.Response:
if isinstance(client, HTTPHandler):
return client.post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
if isinstance(client, httpx.Client):
if timeout is None:
return client.post(
url=api_base,
headers=headers,
json=request_data,
)
return client.post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
return _get_httpx_client().post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
@staticmethod
async def _async_post(
client: AsyncHTTPHandler | httpx.AsyncClient | None,
api_base: str,
headers: dict[str, str], # mutable-ok: forwarded to post(headers: dict | None)
request_data: dict[str, Any], # mutable-ok: forwarded to post(json: dict | ...)
timeout: float | httpx.Timeout | None,
) -> httpx.Response:
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.types.utils import LlmProviders
if isinstance(client, AsyncHTTPHandler):
return await client.post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
if isinstance(client, httpx.AsyncClient):
if timeout is None:
return await client.post(
url=api_base,
headers=headers,
json=request_data,
)
return await client.post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
return await get_async_httpx_client(llm_provider=LlmProviders.VERTEX_AI).post(
url=api_base,
headers=headers,
json=request_data,
timeout=timeout,
)
def completion(
self,
model: str,
@ -137,7 +215,7 @@ class VertexGemmaConfig(OpenAIGPTConfig):
acompletion: bool,
litellm_params: dict,
logger_fn: Callable | None = None,
client: httpx.Client | None = None,
client: HTTPHandler | AsyncHTTPHandler | httpx.Client | httpx.AsyncClient | None = None,
timeout: float | httpx.Timeout | None = None,
encoding=None,
custom_llm_provider: str = "vertex_ai",
@ -147,6 +225,7 @@ class VertexGemmaConfig(OpenAIGPTConfig):
Supports both sync and async requests with fake streaming.
"""
if acompletion:
async_client = client if isinstance(client, (AsyncHTTPHandler, httpx.AsyncClient)) else None
return self._async_completion(
model=model,
messages=messages,
@ -157,10 +236,12 @@ class VertexGemmaConfig(OpenAIGPTConfig):
logging_obj=logging_obj,
optional_params=optional_params,
litellm_params=litellm_params,
client=async_client,
timeout=timeout,
encoding=encoding,
)
else:
sync_client = client if isinstance(client, (HTTPHandler, httpx.Client)) else None
return self._sync_completion(
model=model,
messages=messages,
@ -171,6 +252,7 @@ class VertexGemmaConfig(OpenAIGPTConfig):
logging_obj=logging_obj,
optional_params=optional_params,
litellm_params=litellm_params,
client=sync_client,
timeout=timeout,
encoding=encoding,
)
@ -186,11 +268,11 @@ class VertexGemmaConfig(OpenAIGPTConfig):
logging_obj: Any,
optional_params: dict,
litellm_params: dict,
timeout: float | httpx.Timeout | None,
encoding: Any,
client: HTTPHandler | httpx.Client | None = None,
timeout: float | httpx.Timeout | None = None,
encoding: Any = None,
):
"""Synchronous completion request"""
from litellm.llms.custom_httpx.http_handler import HTTPHandler
from litellm.utils import convert_to_model_response_object
# Check if streaming is requested (will be faked)
@ -222,11 +304,11 @@ class VertexGemmaConfig(OpenAIGPTConfig):
)
# Make the HTTP request
http_handler: Final = HTTPHandler(concurrent_limit=1)
response: Final = http_handler.post(
url=api_base,
response: Final = self._sync_post(
client=client,
api_base=api_base,
headers=headers,
json=request_data,
request_data=request_data,
timeout=timeout,
)
@ -276,12 +358,11 @@ class VertexGemmaConfig(OpenAIGPTConfig):
logging_obj: Any,
optional_params: dict,
litellm_params: dict,
timeout: float | httpx.Timeout | None,
encoding: Any,
client: AsyncHTTPHandler | httpx.AsyncClient | None = None,
timeout: float | httpx.Timeout | None = None,
encoding: Any = None,
):
"""Asynchronous completion request"""
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.types.utils import LlmProviders
from litellm.utils import convert_to_model_response_object
# Check if streaming is requested (will be faked)
@ -313,13 +394,11 @@ class VertexGemmaConfig(OpenAIGPTConfig):
)
# Make the HTTP request
http_handler: Final = get_async_httpx_client(
llm_provider=LlmProviders.VERTEX_AI,
)
response: Final = await http_handler.post(
url=api_base,
response: Final = await self._async_post(
client=client,
api_base=api_base,
headers=headers,
json=request_data,
request_data=request_data,
timeout=timeout,
)

31
litellm/llms/xai/chat/transformation.py
View file

@ -1,4 +1,4 @@
from collections.abc import AsyncIterator, Iterator
from collections.abc import AsyncIterator, Iterator, Mapping
from typing import Any, Final
import httpx
@ -12,13 +12,15 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
strip_name_from_messages,
)
from litellm.llms.xai.common_utils import XAIModelInfo
from litellm.llms.xai.cost_calculator import (
apply_server_side_tool_usage_details_to_usage,
)
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import (
Choices,
ModelResponse,
ModelResponseStream,
PromptTokensDetailsWrapper,
Usage,
)
@ -248,7 +250,7 @@ class XAIChatConfig(OpenAIGPTConfig):
XAI API returns empty string for finish_reason when using tools,
so we need to fix this after the standard OpenAI transformation.
Also handles X.AI web search usage tracking by extracting num_sources_used.
Also handles X.AI web search usage tracking.
"""
# First, let the parent class handle the standard transformation
@ -351,25 +353,20 @@ class XAIChatConfig(OpenAIGPTConfig):
def _enhance_usage_with_xai_web_search_fields(self, model_response: ModelResponse, raw_response_json: dict) -> None:
"""
Extract num_sources_used from X.AI response and map it to web_search_requests.
Copy usage.server_side_tool_usage_details from the provider usage block
onto model_response.usage for tool cost calculation.
"""
if not hasattr(model_response, "usage") or model_response.usage is None:
return
usage: Final[Usage] = model_response.usage
num_sources_used = None
response_usage: Final = raw_response_json.get("usage", {})
if isinstance(response_usage, dict) and "num_sources_used" in response_usage:
num_sources_used = response_usage.get("num_sources_used")
# Map num_sources_used to web_search_requests for cost detection
if num_sources_used is not None and num_sources_used > 0:
if usage.prompt_tokens_details is None:
usage.prompt_tokens_details = PromptTokensDetailsWrapper()
usage.prompt_tokens_details.web_search_requests = int(num_sources_used)
setattr(usage, "num_sources_used", int(num_sources_used))
verbose_logger.debug("X.AI web search sources used: %s", num_sources_used)
response_usage: Final = raw_response_json.get("usage")
if not isinstance(response_usage, dict):
return
details: Final = response_usage.get("server_side_tool_usage_details")
if isinstance(details, Mapping):
apply_server_side_tool_usage_details_to_usage(usage, details)
verbose_logger.debug("X.AI server_side_tool_usage_details: %s", details)
@staticmethod
def _normalize_openai_compatible_usage_totals(

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