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Merge branch 'litellm_internal_staging' into devin_ai_fix_bedrock_batch_file_bytes_36388
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Browse source 
Resolves the transform_create_file_response conflict by keeping the
_uploaded_object_size handoff over the response Content-Length read,
and adds the rebind-ok justification LIT011 now requires for the
upload-size litellm_params handoff after the base budget ratcheted.
This commit is contained in:
mateo-berri 2026-08-15 17:31:52 -07:00
commit 904ff9efa7
1251 changed files with 107778 additions and 35390 deletions
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84
.circleci/config.yml
View file

@ -2744,84 +2744,6 @@ jobs:
file: ./coverage.xml
flags: circleci
ui_build:
docker:
- image: cimg/node:24.19@sha256:8966565f07189a67d64d6808a2b127f31dafae566508e3547f55640e1070bfad
auth:
username: ${DOCKERHUB_USERNAME}
password: ${DOCKERHUB_PASSWORD}
resource_class: medium+
working_directory: ~/project
steps:
- checkout
- skip_if_unrelated_changes:
category: client
- setup_google_dns
- restore_cache:
keys:
- ui-build-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
- ui-build-deps-v1-
- restore_cache:
keys:
- ui-nextjs-cache-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
- ui-nextjs-cache-v1-
- run:
name: Install dependencies
command: |
cd ui/litellm-dashboard
npm ci
- save_cache:
key: ui-build-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
paths:
- ui/litellm-dashboard/node_modules
- run:
name: Build UI
command: |
cd ui/litellm-dashboard
source ./build_ui.sh
- save_cache:
key: ui-nextjs-cache-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
paths:
- ui/litellm-dashboard/.next/cache
- persist_to_workspace:
root: .
paths:
- litellm/proxy/_experimental/out
ui_unit_tests:
docker:
- image: cimg/node:24.19@sha256:8966565f07189a67d64d6808a2b127f31dafae566508e3547f55640e1070bfad
auth:
username: ${DOCKERHUB_USERNAME}
password: ${DOCKERHUB_PASSWORD}
resource_class: xlarge
working_directory: ~/project
steps:
- checkout
- skip_if_unrelated_changes:
category: client
- setup_google_dns
- restore_cache:
keys:
- ui-unit-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
- ui-unit-deps-v1-
- run:
name: Install dependencies
command: |
cd ui/litellm-dashboard
npm ci
- save_cache:
key: ui-unit-deps-v1-{{ checksum "ui/litellm-dashboard/package-lock.json" }}
paths:
- ui/litellm-dashboard/node_modules
- run:
name: Run UI unit tests (Vitest)
command: |
cd ui/litellm-dashboard
CI=true npm run test -- --run \
--pool forks --poolOptions.forks.maxForks=6
e2e_ui_testing:
docker:
- image: cimg/python:3.12-browsers@sha256:b432899af01c9a311bf74f4f22e9ada2e5306d4b1b4383f8d29e1228a5844ef2
@ -3181,12 +3103,6 @@ workflows:
filters: *main_branches
- litellm_router_unit_testing:
filters: *main_branches
- ui_build:
filters: *main_branches
- ui_unit_tests:
requires:
- ui_build
filters: *main_branches
- auth_ui_unit_tests:
filters: *main_branches
- proxy_behavior_tests:

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
View file

@ -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 }}

42
.github/pull_request_template.md vendored
View file

@ -64,12 +64,36 @@ If you're seeing a delay in your PR being merged, ping the LiteLLM Team on [Slac
## Screenshots / Proof of Fix
<!-- Include screenshots, screen recordings, or command (e.g., curl) + output demonstrating that your changes work as expected
The proof must be completely e2e with no mocks, using, for example, actual LLM calls costing real $. `pytest` commands are not enough
For bug fixes: show reproduction before the fix and passing behavior after
Include the commit hash each proof was captured at, for both the before and the after runs
If the change applies to all three LLM endpoints (/v1/responses, /v1/chat/completions, /v1/messages), include proof for every single one of them, not just one
For new features: show the feature working end-to-end
For UI changes: include before/after screenshots -->
The proof must be completely e2e with no mocks, using actual LLM calls costing real $$$ if applicable. `pytest` commands are not enough
Show ONLY the latest run: capture Before at the merge base and After at the PR's current tip, and when new commits change behavior, replace this whole section with the fresh run instead of stacking it on top of older ones. The run must be up to date. As soon as a new commit is made and it makes this PR description's after sha stale (it's no longer tip of PR), you must re-run the QA
Structure the section exactly as below: Before and After one heading level below this section, each naming the commit hash it was captured at, one lower-level heading per case inside each, the same case names in the same order on both sides, and numbered steps (command, observed output) under every case, never loose prose; shared setup (config, payloads) goes above Before, and with a single case, drop the case headings and number the steps directly
### Before (<hash>)
#### <case 1>
1. ...
2. ...
#### <case 2>
1. ...
### After (<hash>)
#### <case 1>
1. ...
2. ...
#### <case 2>
1. ...
For bug fixes: Before shows the reproduction, After shows the same steps passing
For new features: Before shows the capability missing, After shows it working end-to-end
If the change applies to all three LLM endpoints (/v1/responses, /v1/chat/completions, /v1/messages), make each endpoint its own case, not just one
For UI changes: before/after screenshots under the same headings -->
## Type
@ -83,7 +107,11 @@ 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

104
.github/workflows/test-unit-proxy-legacy.yml vendored
View file

@ -1,104 +0,0 @@
name: "Unit Tests: Proxy Legacy Tests"
on:
pull_request:
branches:
- main
- litellm_internal_staging
- litellm_oss_staging
- "litellm_**"
push:
branches:
- main
- litellm_internal_staging
permissions:
contents: read
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
test:
runs-on: ubuntu-latest
timeout-minutes: 20
strategy:
fail-fast: false
matrix:
test-group:
- name: "auth-and-jwt"
path: "tests/proxy_unit_tests/test_[a-j]*.py"
- name: "key-generation"
path: "tests/proxy_unit_tests/test_[k-o]*.py"
- name: "proxy-config"
path: "tests/proxy_unit_tests/test_prisma*.py tests/proxy_unit_tests/test_prompt*.py tests/proxy_unit_tests/test_proxy_[c-r]*.py"
- name: "proxy-server"
path: "tests/proxy_unit_tests/test_proxy_server.py"
- name: "proxy-server-extras"
path: "tests/proxy_unit_tests/test_proxy_server_*.py tests/proxy_unit_tests/test_proxy_setting_guardrails.py"
- name: "proxy-utils"
path: "tests/proxy_unit_tests/test_proxy_utils.py"
- name: "proxy-token-counter"
path: "tests/proxy_unit_tests/test_proxy_token_counter.py"
- name: "proxy-response-and-misc"
path: "tests/proxy_unit_tests/test_[r-t]*.py"
- name: "proxy-user-auth-and-spend"
path: "tests/proxy_unit_tests/test_[u-z]*.py"
name: ${{ matrix.test-group.name }}
steps:
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
with:
persist-credentials: false
- name: Detect backend-relevant changes
id: changes
uses: ./.github/actions/detect-backend-changes
- name: Set up Python
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
- name: Set up uv
uses: ./.github/actions/setup-uv-with-retries
with:
version: "0.10.9"
- name: Cache uv dependencies
uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4.3.0
with:
path: |
~/.cache/uv
.venv
key: ${{ runner.os }}-uv-${{ hashFiles('uv.lock') }}
restore-keys: |
${{ runner.os }}-uv-
- name: Install dependencies
if: steps.changes.outputs.decision != 'skip'
run: |
.github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router
- 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
- name: Run tests - ${{ matrix.test-group.name }}
if: steps.changes.outputs.decision != 'skip'
env:
TEST_PATH: ${{ matrix.test-group.path }}
run: |
uv run --no-sync pytest ${TEST_PATH} \
--tb=short -vv \
--maxfail=10 \
-n 2 \
--reruns 1 \
--reruns-delay 1 \
--dist=loadscope \
--durations=20

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

11
CLAUDE.md
View file

@ -1,4 +1,4 @@
Do not write comments unless they are:
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
@ -29,7 +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
@ -51,6 +53,8 @@ When you fix violations gated by `ruff-strict-budget.json`, `type-discipline-bud
`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
`make check`, `make lint`, `scripts/pre_commit_lint.sh`, and the standalone budget gates (`scripts/ruff_strict_gate.py`, `scripts/type_discipline_gate.py`, `scripts/type_check_gate.py`) each hold one of 2 machine-wide slots, so when other sessions or worktrees on the same box are already running heavy work, yours prints "all N machine-wide slots are busy; queueing" and then stays quiet until a slot frees. Give the command a long timeout and let it wait rather than killing it, retrying it, or assuming it hung. Don't change the # of machine-wide slots or make it unlimited by setting `LITELLM_GATE_SLOTS=0`
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
If you get an LIT001 or LIT002 fail, refactor the code to follow functional programming best practices rather than introducing mutable data structures. For example, build values in one shot with comprehensions or generators wrapped in `tuple()` / `MappingProxyType()` / `frozenset()` instead of seeding an empty `list`/`dict`/`set` and mutating it over time. Ideally, `# mutable-ok` is never used; reach for it only as a genuine last resort when an immutable rewrite is truly impossible, and always pair it with a real reason
@ -81,7 +85,8 @@ Follow these coding conventions for new/updated code (a three-line fix in a lega
- Never-nester: early returns over deep nesting
- Don't throw; model failures as values (One function (e.g., raise_public) maps error union to existing public exception contracts via exhaustive match + assert_never)
- No mutation; don't reassign variables, global or local. Instead of mutable lists and dicts, prefer tuples, frozen dataclasses (with slots=True), `MappingProxyType`, etc.
- Annotate every variable with `: Final` (LIT010). Unpacking and walrus targets cannot carry the annotation, so they are implicitly final. Don't rebind them. Never rebind or mutate function parameters (LIT011); `self`/`cls` attribute stores are the exception. If rebinding or in-place mutation is truly unavoidable, suppress with `# rebind-ok: <reason>` explaining why
- Annotate every variable with `: Final` (LIT010). Unpacking and walrus targets cannot carry the annotation, so they are implicitly final. Don't rebind them. Never rebind or mutate function parameters (LIT011); `self`/`cls` attribute stores are the exception. If rebinding or in-place mutation is truly unavoidable, suppress with `# rebind-ok: <reason>`
- Qualify every TypedDict field with `ReadOnly[...]` (LIT012), which nests freely with `Required` / `NotRequired` / `Annotated` in any order. If making the key writable is truly unavoidable, suppress with `# writable-ok: <reason>`
- Use dependency injection
- Fully typed; no `Any` or coarse types like `dict[str, Any]` or just `dict`. Every function parameter must be strongly typed
- Use tagged unions + match

23
Makefile
View file

@ -4,11 +4,11 @@
.PHONY: help test test-unit test-unit-llms test-unit-proxy-guardrails test-unit-proxy-core test-unit-proxy-misc \
test-unit-integrations test-unit-core-utils test-unit-other test-unit-root \
test-proxy-unit-a test-proxy-unit-b test-integration test-unit-helm \
info lint lint-dev lint-checks format \
info lint lint-inner lint-dev lint-checks format \
lint-basedpyright lint-e2e-basedpyright lint-basedpyright-budget-update lint-type-discipline lint-type-discipline-budget-update \
lint-ruff-budget lint-ruff-budget-update lint-budget-update lint-gate \
install-dev install-proxy-dev install-test-deps install-hooks \
install-helm-unittest check-circular-imports check-import-safety check pre-commit \
install-helm-unittest check-circular-imports check-import-safety check check-inner pre-commit \
lint-install lint-fetch-base bootstrap
# Default target
@ -52,10 +52,17 @@ help:
@echo " make test-proxy-unit-b - Run proxy_unit_tests (p-z, ~28 files)"
@echo " make test-integration - Run integration tests"
@echo " make test-unit-helm - Run helm unit tests"
@echo ""
@echo "Heavy targets (check, lint) queue for LITELLM_GATE_SLOTS machine-wide"
@echo "slots (default 2; 0 disables) so parallel sessions don't thrash one machine."
UV := uv
UV_RUN := $(UV) run --no-sync
# Machine-wide slot queue for the heavy targets below; python3 + stdlib only, so
# it runs before any venv exists. See scripts/gate_slot_lock.py.
GATE_SLOT_LOCK := python3 scripts/gate_slot_lock.py
LINT_DEP_INSTALL ?= install-dev
LINT_E2E_DEP_INSTALL ?= lint-install
LINT_DEP_BASE ?= lint-fetch-base
@ -73,6 +80,8 @@ info:
install-dev:
$(UV) sync --inexact --frozen
# Deliberately unqueued: provisioning is I/O bound, so it doesn't need one of the
# machine-wide slots the CPU-bound gates below share.
bootstrap:
$(UV) sync --inexact --frozen --extra proxy --group proxy-dev --group e2e-dev
$(UV_RUN) python scripts/prisma_generate_if_needed.py
@ -229,7 +238,10 @@ check-import-safety: $(LINT_DEP_INSTALL)
# does (merge-base with origin/litellm_internal_staging). Setup (env sync, Prisma client,
# base fetch) runs once up front; the checks themselves are independent, so a sub-make
# fans them out with -j and the fast ones finish under basedpyright's shadow.
lint: lint-install lint-fetch-base
lint:
@$(GATE_SLOT_LOCK) $(MAKE) lint-inner
lint-inner: lint-install lint-fetch-base
$(MAKE) -j $(LINT_JOBS) $(LINT_OUTPUT_SYNC) LINT_DEP_INSTALL= LINT_E2E_DEP_INSTALL= LINT_DEP_BASE= lint-checks
lint-checks: lint-format-check-changed lint-ruff lint-gate lint-type-discipline lint-basedpyright lint-e2e-basedpyright check-circular-imports check-import-safety
@ -244,7 +256,10 @@ lint-dev: lint-format-changed check-circular-imports check-import-safety
# test-linting.yml (Python), test-litellm-ui-build.yml's frontend-lint (dashboard), and
# check-ui-api-types.yml (API-type drift), skipping any whose files aren't in scope.
# Not auto-installed as a git hook so it never slows an unrelated human commit.
check: bootstrap
check:
@$(GATE_SLOT_LOCK) $(MAKE) check-inner
check-inner: bootstrap
./scripts/pre_commit_lint.sh
pre-commit:

2
backend/routes/allowlist.py
View file

@ -146,11 +146,13 @@ BACKEND_EXACT_PATHS: frozenset[str] = frozenset(
"/docs/oauth2-redirect",
"/redoc",
"/fallback/login",
"/mcp", # bare spelling of the aggregate MCP endpoint; /mcp/ prefix covers the rest
}
)
BACKEND_MOUNT_PATHS: frozenset[str] = frozenset(
{
"/swagger", # API documentation static assets belong to the backend
"/mcp", # lazily-mounted MCP sub-app serves on the backend component
}
)

40
basedpyright-code-budget.json
View file

@ -1,30 +1,30 @@
{
"reportAny": {
"limit": 27731
"limit": 22945
},
"reportArgumentType": {
"limit": 2626
"limit": 2579
},
"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": 7311
},
"reportFunctionMemberAccess": {
"limit": 7
@ -54,10 +54,10 @@
"limit": 0
},
"reportMissingParameterType": {
"limit": 5835
"limit": 5707
},
"reportMissingTypeArgument": {
"limit": 15790
"limit": 15640
},
"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": 44776
},
"reportUnknownLambdaType": {
"limit": 113
},
"reportUnknownMemberType": {
"limit": 39773
"limit": 39237
},
"reportUnknownParameterType": {
"limit": 20207
"limit": 19967
},
"reportUnknownVariableType": {
"limit": 31281
"limit": 30881
},
"reportUnnecessaryCast": {
"limit": 122
"limit": 117
},
"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

1
ci_cd/generate_model_prices_schema.py
View file

@ -96,6 +96,7 @@ ARRAY_KEYS: dict[str, JsonSchema] = {
"output_cost_per_token": NONNEG_NUMBER,
"output_cost_per_reasoning_token": NONNEG_NUMBER,
"cache_read_input_token_cost": NONNEG_NUMBER,
"cache_creation_input_token_cost": NONNEG_NUMBER,
"input_cost_per_query": NONNEG_NUMBER,
},
"additionalProperties": False,

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,

147
enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py
View file

@ -3,7 +3,8 @@ 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, Any, Dict, List, Optional, Tuple
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, Dict, Final, List, Optional, Tuple
from litellm._logging import verbose_proxy_logger
from litellm._uuid import uuid
@ -23,6 +24,15 @@ if TYPE_CHECKING:
CHECK_BATCH_COST_USER_AGENT = "LiteLLM Proxy/CheckBatchCost"
TERMINAL_MANAGED_OBJECT_STATUSES: Final[Tuple[str, ...]] = (
"completed",
"complete",
"failed",
"expired",
"cancelled",
"stale_expired",
)
class CheckBatchCost:
def __init__(
@ -42,6 +52,33 @@ class CheckBatchCost:
# Cached after the first poll cycle. Once we know the column is absent we skip
# the guaranteed-failing primary query on every subsequent cycle.
self._has_batch_processed_column: bool = True
self.batch_processed_support_confirmed: bool = False
@staticmethod
def _is_missing_batch_processed_column_error(err: Exception) -> bool:
message: Final = str(err).lower()
return "batch_processed" in message or "unknown column" in message or "does not exist" in message
async def confirm_batch_processed_support(self) -> None:
"""
Probe the batch_processed column before the proxy serves traffic, so the retrieve
path never sees an unconfirmed poller on a schema that has the column and accounts
inline for a batch the first poll cycle then accounts again.
"""
try:
await self.prisma_client.db.litellm_managedobjecttable.find_first(
where={"file_purpose": "batch", "batch_processed": False}
)
except Exception as probe_err:
if not self._is_missing_batch_processed_column_error(probe_err):
verbose_proxy_logger.debug(
f"CheckBatchCost: batch_processed probe failed, the poll cycle will confirm support: {probe_err}"
)
return
self._has_batch_processed_column = False
verbose_proxy_logger.warning("CheckBatchCost: batch_processed column not found, querying without it")
return
self.batch_processed_support_confirmed = True
async def _get_user_info(self, batch_id: str, user_id: Optional[str]) -> Dict[str, Any]:
"""
@ -132,11 +169,11 @@ class CheckBatchCost:
in non-terminal states as 'stale_expired'. These will never complete and
should not be polled.
"""
cutoff = datetime.now(timezone.utc) - timedelta(days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS)
result = await self.prisma_client.db.litellm_managedobjecttable.update_many(
cutoff: Final = datetime.now(timezone.utc) - timedelta(days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS)
result: Final = await self.prisma_client.db.litellm_managedobjecttable.update_many(
where={
"file_purpose": "batch",
"status": {"not_in": ["completed", "complete", "failed", "expired", "cancelled", "stale_expired"]},
"status": {"not_in": list(TERMINAL_MANAGED_OBJECT_STATUSES)},
"created_at": {"lt": cutoff},
},
data={"status": "stale_expired"},
@ -147,6 +184,26 @@ class CheckBatchCost:
f"(older than {MANAGED_OBJECT_STALENESS_CUTOFF_DAYS} days) as stale_expired"
)
if not self._has_batch_processed_column:
return
# A row already in a terminal status is never rewritten by the sweep above, so
# without this it keeps a poll-page slot forever and starves newer batches.
retired: Final = await self.prisma_client.db.litellm_managedobjecttable.update_many(
where={
"file_purpose": "batch",
"batch_processed": False,
"status": {"in": ["complete", "completed"]},
"created_at": {"lt": cutoff},
},
data={"batch_processed": True},
)
if retired > 0:
verbose_proxy_logger.warning(
f"CheckBatchCost: gave up on {retired} completed managed objects older than "
f"{MANAGED_OBJECT_STALENESS_CUTOFF_DAYS} days that were never costed"
)
async def _fallback_find_jobs(self) -> list:
"""Query batch jobs without the batch_processed filter (for older schemas)."""
return await self.prisma_client.db.litellm_managedobjecttable.find_many(
@ -167,6 +224,68 @@ class CheckBatchCost:
order={"created_at": "asc"},
)
async def _retire_job(self, job: "LiteLLM_ManagedObjectTable", reason: str) -> None:
"""
Take a row that can never be costed out of the poll page. Leaving it selectable
would burn one of the MAX_OBJECTS_PER_POLL_CYCLE slots on every future cycle, and
once enough such rows accumulate no newer batch is ever reached. Older schemas
without batch_processed can only be excluded through the status filter.
"""
data: Final = (
{"batch_processed": True}
if self._has_batch_processed_column
else {"status": "stale_expired"}
)
try:
await self.prisma_client.db.litellm_managedobjecttable.update(
where={"id": job.id},
data=data,
)
except Exception as db_err:
verbose_proxy_logger.error(
f"CheckBatchCost: failed to retire uncostable job {job.id} ({reason}): {db_err}"
)
return
verbose_proxy_logger.warning(
f"CheckBatchCost: job {job.id} can never be costed ({reason}), "
"so it will no longer be polled"
)
@staticmethod
def _has_unified_id_without_model(job: "LiteLLM_ManagedObjectTable") -> bool:
"""A unified id that decodes but carries no model_id can never be routed."""
from litellm.proxy.openai_files_endpoints.common_utils import (
convert_b64_uid_to_unified_uid,
get_model_id_from_unified_batch_id,
)
decoded: Final = convert_b64_uid_to_unified_uid(job.unified_object_id)
return (
decoded != job.unified_object_id
and get_model_id_from_unified_batch_id(decoded) is None
)
@staticmethod
def _is_batch_gone_at_provider(error: Exception, batch_id: str) -> bool:
"""
A 404 naming the batch means the provider dropped its record of it, so no later
retrieve can ever succeed. A 404 about anything else, a renamed Azure deployment
or a fallback deployment that never saw this batch, is still fixable in config, so
it keeps retrying.
"""
import openai
from litellm.exceptions import NotFoundError
return isinstance(error, (NotFoundError, openai.NotFoundError)) and batch_id in str(error)
def _batch_deployment_exists(self, model_id: str) -> bool:
"""A 404 only proves the batch is gone when it came from the batch's own
deployment. Once that deployment leaves the router, default fallbacks can
silently send the retrieve to a provider that never saw the batch, so its
404 must not retire the row; the staleness sweep bounds it instead."""
return self.llm_router.get_deployment(model_id=model_id) is not None
@staticmethod
def _record_error(
prom_logger: Optional["PrometheusLogger"], error_type: str
@ -446,6 +565,7 @@ class CheckBatchCost:
credentials = self.llm_router.get_deployment_credentials_with_provider(model_id) or {}
_file_content = await afile_content(
file_id=raw_output_file_id,
_litellm_internal_model_credentials=MappingProxyType(dict(credentials)),
**credentials,
)
@ -631,8 +751,9 @@ class CheckBatchCost:
take=MAX_OBJECTS_PER_POLL_CYCLE,
order={"created_at": "asc"},
)
self.batch_processed_support_confirmed = True
except Exception as query_err:
if "batch_processed" not in str(query_err).lower() and "unknown column" not in str(query_err).lower() and "does not exist" not in str(query_err).lower():
if not self._is_missing_batch_processed_column_error(query_err):
raise
# Permanent schema gap — cache the result so future cycles skip straight to fallback
self._has_batch_processed_column = False
@ -645,6 +766,8 @@ class CheckBatchCost:
for job in jobs:
routing = self._resolve_job_routing(job, prom_logger)
if routing is None:
if self._has_unified_id_without_model(job):
await self._retire_job(job, "unified object id has no model id")
continue
model_id, batch_id = routing
@ -667,11 +790,13 @@ class CheckBatchCost:
)
if prom_logger:
prom_logger.record_check_batch_cost_error("provider_retrieval_error")
if self._is_batch_gone_at_provider(e, batch_id) and self._batch_deployment_exists(model_id):
await self._retire_job(job, f"batch {batch_id} no longer exists at the provider")
continue
## RETRIEVE THE BATCH JOB OUTPUT FILE
if (
response.status == "completed"
response.status in ("completed", "complete", "expired")
and response.output_file_id is not None
):
try:
@ -698,7 +823,7 @@ class CheckBatchCost:
# mark the job as complete
try:
update_data: dict = {
"status": "complete",
"status": response.status if response.status != "completed" else "complete",
"file_object": response.model_dump_json(),
}
if self._has_batch_processed_column:
@ -712,7 +837,13 @@ class CheckBatchCost:
f"CheckBatchCost: failed to mark job {job.id} complete in DB: {db_err}"
)
elif response.status in ("failed", "expired", "cancelled"):
elif response.status in (
"completed",
"complete",
"failed",
"expired",
"cancelled",
):
try:
from litellm.proxy.openai_files_endpoints.common_utils import (
_is_base64_encoded_unified_file_id,

7
enterprise/litellm_enterprise/proxy/hooks/managed_files.py
View file

@ -54,6 +54,9 @@ from litellm.proxy.openai_files_endpoints.common_utils import (
normalize_mime_type_for_provider,
resolve_managed_output_file_model_name,
)
from litellm.proxy.pass_through_endpoints.llm_provider_handlers.batch_attribution import (
request_tags_from_metadata,
)
from litellm.types.llms.openai import ( # pyright: ignore[reportAttributeAccessIssue]
AllMessageValues,
AsyncCursorPage,
@ -1146,6 +1149,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
## Check if unified_file_id is in the response
unified_file_id = response._hidden_params.get("unified_file_id") # managed file id
unified_batch_id = response._hidden_params.get("unified_batch_id") # managed batch id
is_batch_create: Final = unified_file_id is not None
model_id = cast(Optional[str], response._hidden_params.get("model_id"))
model_name = cast(Optional[str], response._hidden_params.get("model_name"))
@ -1216,6 +1220,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
model_mappings={model_id: provider_file_id},
user_api_key_dict=user_api_key_dict,
)
request_metadata: Final = data.get("litellm_metadata")
await self.store_unified_object_id(
unified_object_id=response.id,
file_object=response,
@ -1223,6 +1228,8 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
model_object_id=original_response_id,
file_purpose="batch",
user_api_key_dict=user_api_key_dict,
request_tags=request_tags_from_metadata(request_metadata if isinstance(request_metadata, dict) else {}),
persist_attribution=is_batch_create,
)
# Only record batch creation metric on actual create (not retrieve/cancel).

4
enterprise/pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "litellm-enterprise"
version = "0.1.54"
version = "0.1.56"
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 = "0.1.56"
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

4
helm/litellm/templates/backend/deployment.yaml
View file

@ -81,6 +81,10 @@ spec:
readinessProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.backend.startupProbe }}
startupProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.backend.lifecycle }}
lifecycle:
{{- toYaml . | nindent 12 }}

4
helm/litellm/templates/backend/hpa.yaml
View file

@ -30,4 +30,8 @@ spec:
type: Utilization
averageUtilization: {{ .Values.backend.hpa.targetMemoryUtilizationPercentage }}
{{- end }}
{{- with .Values.backend.hpa.behavior }}
behavior:
{{- toYaml . | nindent 4 }}
{{- end }}
{{- end }}

4
helm/litellm/templates/gateway/deployment.yaml
View file

@ -83,6 +83,10 @@ spec:
readinessProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.gateway.startupProbe }}
startupProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.gateway.lifecycle }}
lifecycle:
{{- toYaml . | nindent 12 }}

4
helm/litellm/templates/gateway/hpa.yaml
View file

@ -30,4 +30,8 @@ spec:
type: Utilization
averageUtilization: {{ .Values.gateway.hpa.targetMemoryUtilizationPercentage }}
{{- end }}
{{- with .Values.gateway.hpa.behavior }}
behavior:
{{- toYaml . | nindent 4 }}
{{- end }}
{{- end }}

4
helm/litellm/templates/ui/deployment.yaml
View file

@ -69,6 +69,10 @@ spec:
readinessProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.ui.startupProbe }}
startupProbe:
{{- toYaml . | nindent 12 }}
{{- end }}
{{- with .Values.ui.lifecycle }}
lifecycle:
{{- toYaml . | nindent 12 }}

4
helm/litellm/templates/ui/hpa.yaml
View file

@ -30,4 +30,8 @@ spec:
type: Utilization
averageUtilization: {{ .Values.ui.hpa.targetMemoryUtilizationPercentage }}
{{- end }}
{{- with .Values.ui.hpa.behavior }}
behavior:
{{- toYaml . | nindent 4 }}
{{- end }}
{{- end }}

58
helm/litellm/tests/hpa_behavior_tests.yaml Normal file
View file

@ -0,0 +1,58 @@
suite: test HPA scaling behavior passthrough
templates:
- gateway/hpa.yaml
- backend/hpa.yaml
- ui/hpa.yaml
values:
- ./values/required.yaml
tests:
- it: HPA omits spec.behavior by default, so Kubernetes' default scaling applies
templates:
- gateway/hpa.yaml
- backend/hpa.yaml
asserts:
- isKind:
of: HorizontalPodAutoscaler
- notExists:
path: spec.behavior
- it: gateway HPA renders spec.behavior verbatim when configured
template: gateway/hpa.yaml
set:
gateway.hpa.behavior:
scaleDown:
stabilizationWindowSeconds: 300
policies:
- { type: Percent, value: 50, periodSeconds: 60 }
scaleUp:
stabilizationWindowSeconds: 0
selectPolicy: Max
policies:
- { type: Percent, value: 100, periodSeconds: 30 }
- { type: Pods, value: 2, periodSeconds: 30 }
asserts:
- equal:
path: spec.behavior
value:
scaleDown:
stabilizationWindowSeconds: 300
policies:
- { type: Percent, value: 50, periodSeconds: 60 }
scaleUp:
stabilizationWindowSeconds: 0
selectPolicy: Max
policies:
- { type: Percent, value: 100, periodSeconds: 30 }
- { type: Pods, value: 2, periodSeconds: 30 }
- it: behavior passthrough works on every autoscaled component (ui parity)
template: ui/hpa.yaml
set:
ui.hpa.enabled: true
ui.hpa.behavior:
scaleUp:
stabilizationWindowSeconds: 0
asserts:
- equal:
path: spec.behavior.scaleUp.stabilizationWindowSeconds
value: 0

27
helm/litellm/tests/probe_tests.yaml
View file

@ -104,3 +104,30 @@ tests:
periodSeconds: 15
timeoutSeconds: 4
failureThreshold: 3
- it: no startupProbe by default, so existing installs are unchanged
templates:
- gateway/deployment.yaml
- backend/deployment.yaml
asserts:
- notExists:
path: spec.template.spec.containers[0].startupProbe
- it: startupProbe renders verbatim when configured, gating a slow cold start
template: gateway/deployment.yaml
set:
gateway.startupProbe:
httpGet: { path: /health/readiness, port: http }
failureThreshold: 30
periodSeconds: 10
timeoutSeconds: 5
asserts:
- equal:
path: spec.template.spec.containers[0].startupProbe
value:
httpGet:
path: /health/readiness
port: http
failureThreshold: 30
periodSeconds: 10
timeoutSeconds: 5

24
helm/litellm/values.yaml
View file

@ -223,12 +223,28 @@ gateway:
initialDelaySeconds: 5
periodSeconds: 10
timeoutSeconds: 10
# Optional startupProbe. Empty by default, so existing installs are unchanged
# and liveness/readiness apply from container start. Set it to gate
# liveness/readiness until a slow cold start finishes — a high failureThreshold
# tolerates long first-boot times without a liveness-kill loop, e.g.:
# httpGet: { path: /health/readiness, port: http }
# failureThreshold: 30
# periodSeconds: 10
startupProbe: {}
hpa:
enabled: true
minReplicas: 1
maxReplicas: 10
targetCPUUtilizationPercentage: 70
targetMemoryUtilizationPercentage: 80
# Optional autoscaling/v2 scaling behavior (scaleUp / scaleDown policies and
# stabilization windows). Empty by default -> Kubernetes' default behavior.
# Rendered verbatim under spec.behavior, e.g.:
# scaleUp:
# stabilizationWindowSeconds: 0
# policies:
# - { type: Percent, value: 100, periodSeconds: 30 }
behavior: {}
# PodDisruptionBudget for the gateway pods. Set exactly one of
# `minAvailable` / `maxUnavailable` (minAvailable wins if both are set;
# enabling without either falls back to `maxUnavailable: 1`). Disabled by
@ -319,11 +335,15 @@ backend:
initialDelaySeconds: 5
periodSeconds: 10
timeoutSeconds: 10
# Optional startupProbe; same shape as gateway.startupProbe. Empty by default.
startupProbe: {}
hpa:
enabled: true
minReplicas: 1
maxReplicas: 4
targetCPUUtilizationPercentage: 70
# Optional autoscaling/v2 scaling behavior; same shape as gateway.hpa.behavior.
behavior: {}
# Same shape as gateway.pdb.
pdb:
enabled: false
@ -379,11 +399,15 @@ ui:
httpGet: { path: /, port: http }
initialDelaySeconds: 2
periodSeconds: 10
# Optional startupProbe; same shape as gateway.startupProbe. Empty by default.
startupProbe: {}
hpa:
enabled: false
minReplicas: 1
maxReplicas: 3
targetCPUUtilizationPercentage: 80
# Optional autoscaling/v2 scaling behavior; same shape as gateway.hpa.behavior.
behavior: {}
# Same shape as gateway.pdb.
pdb:
enabled: false

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/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);

49
litellm-proxy-extras/litellm_proxy_extras/migrations/20260811172448_add_shadow_eval/migration.sql Normal file
View file

@ -0,0 +1,49 @@
-- CreateTable
CREATE TABLE "LiteLLM_ShadowEvalJob" (
"id" TEXT NOT NULL,
"api_key_id" TEXT NOT NULL,
"router_name" TEXT NOT NULL,
"judge_model" TEXT NOT NULL,
"shadow_percentage" DOUBLE PRECISION NOT NULL,
"max_turns" INTEGER NOT NULL,
"created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
"created_by" TEXT,
"ends_at" TIMESTAMP(3) NOT NULL,
"stopped_at" TIMESTAMP(3),
CONSTRAINT "LiteLLM_ShadowEvalJob_pkey" PRIMARY KEY ("id")
);
-- CreateTable
CREATE TABLE "LiteLLM_ShadowEvalAttempt" (
"id" TEXT NOT NULL,
"job_id" TEXT NOT NULL,
"request_id" TEXT NOT NULL,
"outcome" TEXT NOT NULL,
"tier" TEXT,
"real_model" TEXT,
"shadow_model" TEXT,
"confidence" DOUBLE PRECISION,
"judge_cost" DOUBLE PRECISION NOT NULL DEFAULT 0,
"error" TEXT,
"created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
CONSTRAINT "LiteLLM_ShadowEvalAttempt_pkey" PRIMARY KEY ("id")
);
-- CreateIndex
CREATE INDEX "LiteLLM_ShadowEvalJob_api_key_id_idx" ON "LiteLLM_ShadowEvalJob"("api_key_id");
-- CreateIndex
CREATE INDEX "LiteLLM_ShadowEvalJob_created_at_idx" ON "LiteLLM_ShadowEvalJob"("created_at");
-- CreateIndex
CREATE INDEX "LiteLLM_ShadowEvalAttempt_job_id_idx" ON "LiteLLM_ShadowEvalAttempt"("job_id");
-- One active job per key, enforced by the database rather than a read-then-create in the
-- start endpoint, which races against a concurrent start on another pod. Partial indexes
-- are not expressible in schema.prisma, so this lives here only. Active means not yet
-- stopped; the start endpoint stamps stopped_at on expired jobs before creating.
CREATE UNIQUE INDEX "LiteLLM_ShadowEvalJob_one_active_per_key"
ON "LiteLLM_ShadowEvalJob"("api_key_id") WHERE "stopped_at" IS NULL;

8
litellm-proxy-extras/litellm_proxy_extras/migrations/20260813180408_add_shadow_eval_direction/migration.sql Normal file
View file

@ -0,0 +1,8 @@
-- AlterTable
ALTER TABLE "LiteLLM_ShadowEvalJob" ADD COLUMN "baseline_model" TEXT,
ADD COLUMN "direction" TEXT NOT NULL DEFAULT 'forward';
DROP INDEX IF EXISTS "LiteLLM_ShadowEvalJob_one_active_per_key";
CREATE UNIQUE INDEX IF NOT EXISTS "LiteLLM_ShadowEvalJob_one_active_per_key_direction"
ON "LiteLLM_ShadowEvalJob"("api_key_id", "direction") WHERE "stopped_at" IS NULL;

48
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
@ -1447,6 +1450,49 @@ model LiteLLM_AutoRouterSession {
@@index([last_turn_at], map: "idx_autorouter_session_last_turn")
}
// Shadow eval: evaluation of an auto-router against a key's live traffic, in either
// direction. forward duplicates the requests the key did not route through the router
// through it, answering whether the key should adopt it; reverse duplicates the requests
// the router did serve against a fixed baseline model, answering whether a key already on
// it still benefits. Either way a sampled slice runs in a detached task and an LLM judge
// compares real vs shadow responses blind. The job row is immutable config plus
// stopped_at; every count, status, and spend figure is derived from the append-only
// attempt rows, so nothing can disagree across pods or stop races.
model LiteLLM_ShadowEvalJob {
id String @id @default(cuid())
api_key_id String // hashed virtual key whose traffic is shadowed
router_name String // the auto-router under evaluation, in either direction
direction String @default("forward") // forward | reverse
baseline_model String? // reverse only: the fixed model the router is judged against
judge_model String
shadow_percentage Float
max_turns Int // sample budget: judge at most this many turns
created_at DateTime @default(now())
created_by String?
ends_at DateTime
stopped_at DateTime?
@@index([api_key_id])
@@index([created_at])
}
// One row per sampled pipeline: a blind verdict (real | shadow | tie) or an error.
model LiteLLM_ShadowEvalAttempt {
id String @id @default(cuid())
job_id String
request_id String // the judged real request
outcome String // real | shadow | tie | error
tier String? // router's tier for the prompt, when classified
real_model String?
shadow_model String?
confidence Float?
judge_cost Float @default(0)
error String?
created_at DateTime @default(now())
@@index([job_id])
}
// ---------------------------------------------------------------------------
// Workflow Run Tracking
//

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

@ -1,6 +1,6 @@
[project]
name = "litellm-proxy-extras"
version = "0.4.84"
version = "0.4.86"
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 = "0.4.86"
version_files = [
"pyproject.toml:^version",
"../pyproject.toml:litellm-proxy-extras==",

3
litellm/__init__.py
View file

@ -172,6 +172,7 @@ callbacks: List[
callback_settings: Dict[str, Dict[str, Any]] = {}
initialized_langfuse_clients: int = 0
langfuse_default_tags: Optional[List[str]] = None
langfuse_enable_update_trace_keys: bool = False
langsmith_batch_size: Optional[int] = None
prometheus_initialize_budget_metrics: Optional[bool] = False
prometheus_latency_buckets: Optional[List[float]] = None
@ -197,6 +198,7 @@ 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 +247,7 @@ 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

10
litellm/_redis.py
View file

@ -67,12 +67,20 @@ def _init_arg_names(cls: type) -> frozenset[str]:
Keyword-only parameters are included, and the MRO is walked because redis-py splits a
connection's parameters between ``AbstractConnection`` and its concrete subclasses.
Each ``__init__`` is unwrapped before introspection: redis-py >= 7.4 decorates
``AbstractConnection.__init__`` with ``@deprecated_args``, whose wrapper is declared
``(self, *args, **kwargs)`` — introspecting the wrapper directly loses every real
parameter (``socket_timeout`` included), which silently emptied this allowlist and
dropped the socket timeouts from url-configured connections. ``inspect.unwrap``
follows the ``__wrapped__`` chain to the true signature and is a no-op on
undecorated ``__init__``s.
"""
return frozenset(
name
for klass in inspect.getmro(cls)
if klass is not object
for spec in (inspect.getfullargspec(klass.__init__),)
for spec in (inspect.getfullargspec(inspect.unwrap(klass.__init__)),)
for name in spec.args + spec.kwonlyargs
)

23
litellm/a2a_protocol/litellm_completion_bridge/handler.py
View file

@ -10,7 +10,7 @@ A2A Streaming Events (in order):
4. Status update (kind: "status-update") - Final status "completed" with final=true
"""
from collections.abc import AsyncIterator, Mapping
from collections.abc import AsyncIterator, Callable, Coroutine, Mapping
from typing import Any, Final
import litellm
@ -54,7 +54,7 @@ class A2ACompletionBridgeHandler:
agent_extra_headers: Mapping[str, str] | None,
*,
stream: bool,
) -> Mapping[str, Any]:
) -> Mapping[str, object]:
# Extract message from params
message: Final = params.get("message", {})
@ -63,7 +63,7 @@ class A2ACompletionBridgeHandler:
# Get completion params
custom_llm_provider: Final = litellm_params.get("custom_llm_provider")
model: Final = litellm_params.get("model", "agent")
model: Final[str] = litellm_params.get("model", "agent")
# Build full model string if provider specified
# Skip prepending if model already starts with the provider prefix
@ -109,13 +109,16 @@ class A2ACompletionBridgeHandler:
return completion_params
@staticmethod
async def _acompletion(completion_params: Mapping[str, Any]) -> ModelResponse | CustomStreamWrapper:
return await litellm.acompletion(**completion_params)
async def _acompletion(completion_params: Mapping[str, object]) -> ModelResponse | CustomStreamWrapper:
acompletion_fn: Final[Callable[..., Coroutine[object, object, ModelResponse | CustomStreamWrapper]]] = vars(
litellm
)["acompletion"]
return await acompletion_fn(**completion_params)
@staticmethod
async def handle_non_streaming(
request_id: str,
params: dict[str, Any],
params: dict[str, object],
litellm_params: dict[str, Any],
api_base: str | None = None,
agent_extra_headers: dict[str, str] | None = None,
@ -296,8 +299,8 @@ class A2ACompletionBridgeHandler:
# Convenience functions that delegate to the class methods
async def handle_a2a_completion(
request_id: str,
params: dict[str, Any],
litellm_params: dict[str, Any],
params: dict[str, object],
litellm_params: dict[str, object],
api_base: str | None = None,
agent_extra_headers: dict[str, str] | None = None,
) -> dict[str, object]:
@ -313,8 +316,8 @@ async def handle_a2a_completion(
async def handle_a2a_completion_streaming(
request_id: str,
params: dict[str, Any],
litellm_params: dict[str, Any],
params: dict[str, object],
litellm_params: dict[str, object],
api_base: str | None = None,
agent_extra_headers: dict[str, str] | None = None,
) -> AsyncIterator[dict[str, object]]:

66
litellm/a2a_protocol/main.py
View file

@ -12,7 +12,8 @@ Provides standalone functions with @client decorator for LiteLLM logging integra
import asyncio
import datetime
import uuid
from collections.abc import AsyncIterator, Coroutine
from collections.abc import AsyncIterator, Coroutine, Mapping
from types import ModuleType
from typing import TYPE_CHECKING, Any, Final, Optional, cast
import litellm
@ -38,12 +39,15 @@ if TYPE_CHECKING:
SendMessageResponse,
SendStreamingMessageRequest,
SendStreamingMessageResponse,
SendStreamingMessageSuccessResponse,
Task,
)
from a2a.types.a2a_pb2 import SendMessageRequest as CoreSendMessageRequest
from a2a.types.a2a_pb2 import StreamResponse as CoreStreamResponse
# Runtime imports — requires a2a-sdk>=1.1.0
A2A_SDK_AVAILABLE = False
_a2a_conversions: Any = None
_a2a_conversions: ModuleType | None = None
try:
from a2a.client import Client, ClientCallContext, ClientConfig, create_client
@ -128,7 +132,7 @@ _A2A_COST_PARAM_KEYS: Final = ("cost_per_query", "input_cost_per_token", "output
def _set_litellm_params_on_logging_obj(
kwargs: dict[str, Any],
litellm_params: dict[str, Any],
litellm_params: Mapping[str, object],
) -> None:
"""
Merge the agent's pricing params into model_call_details["litellm_params"]
@ -150,7 +154,7 @@ def _set_litellm_params_on_logging_obj(
logging_obj.model_call_details["litellm_params"] = {**existing, **cost_params}
def _get_a2a_model_info(a2a_client: Any, kwargs: dict[str, Any]) -> str:
def _get_a2a_model_info(a2a_client: "A2AClientType", kwargs: dict[str, Any]) -> str:
"""
Extract agent info and set model/custom_llm_provider for cost tracking.
@ -179,7 +183,7 @@ def _get_a2a_model_info(a2a_client: Any, kwargs: dict[str, Any]) -> str:
return agent_name
def _get_a2a_client_agent_card(a2a_client: Any) -> Optional["AgentCard"]:
def _get_a2a_client_agent_card(a2a_client: "A2AClientType") -> Optional["AgentCard"]:
agent_card = cast(Optional["AgentCard"], getattr(a2a_client, "_litellm_agent_card", None))
if agent_card is not None:
return agent_card
@ -191,9 +195,9 @@ def _get_a2a_client_agent_card(a2a_client: Any) -> Optional["AgentCard"]:
async def _send_message_via_completion_bridge(
request: "SendMessageRequest",
custom_llm_provider: str,
custom_llm_provider: object,
api_base: str | None,
litellm_params: dict[str, Any],
litellm_params: dict[str, object],
agent_extra_headers: dict[str, str] | None = None,
) -> LiteLLMSendMessageResponse:
"""
@ -224,6 +228,20 @@ def _get_a2a_call_context(a2a_client: "A2AClientType") -> Optional["A2ACallConte
return getattr(a2a_client, "_litellm_call_context", None)
def _to_core_send_message_request(request: "SendMessageRequest") -> "CoreSendMessageRequest":
from a2a.compat.v0_3 import conversions
return conversions.to_core_send_message_request(request)
def _to_compat_stream_response(
event: "CoreStreamResponse", request_id: str | int
) -> "SendStreamingMessageSuccessResponse":
from a2a.compat.v0_3 import conversions
return conversions.to_compat_stream_response(event, request_id=request_id)
async def _send_message(a2a_client: "A2AClientType", request: "SendMessageRequest") -> "SendMessageResponse":
"""Send a non-streaming message via a2a-sdk 1.x and return JSON-RPC response."""
if _a2a_conversions is None:
@ -231,17 +249,14 @@ async def _send_message(a2a_client: "A2AClientType", request: "SendMessageReques
"The 'a2a' package is required for A2A agent invocation. Install it with: pip install a2a-sdk"
)
pb_request: Final = _a2a_conversions.to_core_send_message_request(request)
pb_request: Final = _to_core_send_message_request(request)
last_event = None
async for event in a2a_client.send_message(pb_request, context=_get_a2a_call_context(a2a_client)):
last_event = event
if last_event is None:
raise RuntimeError("A2A send_message failed: no response received from agent.")
stream_compat: Final = _a2a_conversions.to_compat_stream_response(
last_event,
request_id=request.id,
)
stream_compat: Final = _to_compat_stream_response(last_event, request_id=request.id)
result: Final = stream_compat.result
if not isinstance(result, (Message, Task)):
raise RuntimeError(
@ -306,12 +321,9 @@ async def _stream_messages(
"The 'a2a' package is required for A2A agent invocation. Install it with: pip install a2a-sdk"
)
pb_request: Final = _a2a_conversions.to_core_send_message_request(request)
pb_request: Final[CoreSendMessageRequest] = _a2a_conversions.to_core_send_message_request(request)
async for event in a2a_client.send_message(pb_request, context=_get_a2a_call_context(a2a_client)):
compat_chunk = _a2a_conversions.to_compat_stream_response(
event,
request_id=request.id,
)
compat_chunk = _to_compat_stream_response(event, request_id=request.id)
yield SendStreamingMessageResponse(root=compat_chunk)
@ -368,10 +380,10 @@ async def asend_message(
a2a_client: Optional["A2AClientType"] = None,
request: Optional["SendMessageRequest"] = None,
api_base: str | None = None,
litellm_params: dict[str, Any] | None = None,
litellm_params: dict[str, object] | None = None,
agent_id: str | None = None,
agent_extra_headers: dict[str, str] | None = None,
**kwargs: Any,
**kwargs: object,
) -> LiteLLMSendMessageResponse:
"""
Async: Send a message to an A2A agent.
@ -485,7 +497,7 @@ async def asend_message(
response: Final = LiteLLMSendMessageResponse.from_a2a_response(a2a_response, request_id=str(request.id))
# Calculate token usage from request and response
response_dict: Final = a2a_response.model_dump(mode="json", exclude_none=True)
response_dict: Final[dict[str, object]] = a2a_response.model_dump(mode="json", exclude_none=True)
(
prompt_tokens,
completion_tokens,
@ -516,7 +528,7 @@ def send_message(
a2a_client: "A2AClientType",
request: "SendMessageRequest",
**kwargs: Any,
) -> LiteLLMSendMessageResponse | Coroutine[Any, Any, LiteLLMSendMessageResponse]:
) -> LiteLLMSendMessageResponse | Coroutine[object, object, LiteLLMSendMessageResponse]:
"""
Sync: Send a message to an A2A agent.
@ -545,9 +557,9 @@ def _build_streaming_logging_obj(
request: "SendStreamingMessageRequest",
agent_name: str,
agent_id: str | None,
litellm_params: dict[str, Any] | None,
metadata: dict[str, Any] | None,
proxy_server_request: dict[str, Any] | None,
litellm_params: dict[str, object] | None,
metadata: dict[str, object] | None,
proxy_server_request: dict[str, object] | None,
) -> Logging:
"""Build logging object for streaming A2A requests."""
start_time: Final = datetime.datetime.now()
@ -588,10 +600,10 @@ async def asend_message_streaming(
a2a_client: Optional["A2AClientType"] = None,
request: Optional["SendStreamingMessageRequest"] = None,
api_base: str | None = None,
litellm_params: dict[str, Any] | None = None,
litellm_params: dict[str, object] | None = None,
agent_id: str | None = None,
metadata: dict[str, Any] | None = None,
proxy_server_request: dict[str, Any] | None = None,
metadata: dict[str, object] | None = None,
proxy_server_request: dict[str, object] | None = None,
agent_extra_headers: dict[str, str] | None = None,
**kwargs: object,
) -> AsyncIterator[Any]:

11
litellm/batches/batch_utils.py
View file

@ -5,7 +5,8 @@ from typing import Any, Final, Literal
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.llm_cost_calc.utils import _parse_prompt_tokens_details
from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS
from litellm.litellm_core_utils.llm_cost_calc.utils import parse_prompt_tokens_details
from litellm.types.llms.openai import Batch
from litellm.types.utils import CallTypes, ModelInfo, Usage
from litellm.utils import token_counter
@ -101,7 +102,7 @@ def _iter_successful_output_line_stats(
continue
response_body = _get_response_from_batch_job_output_file(entry, custom_llm_provider)
usage = _get_batch_job_usage_from_response_body(response_body, custom_llm_provider)
prompt_details = _parse_prompt_tokens_details(usage)
prompt_details = parse_prompt_tokens_details(usage)
raw_model = response_body.get("model")
response_model = raw_model if isinstance(raw_model, str) and raw_model else None
if model_info is not None or custom_llm_provider in ("anthropic", "bedrock"):
@ -295,7 +296,7 @@ def _extract_file_access_credentials(litellm_params: dict | None) -> dict:
if litellm_params:
# List of credential keys that should be passed to file operations
credential_keys: Final = [
credential_keys: Final = (
"api_key",
"api_base",
"api_version",
@ -309,7 +310,9 @@ def _extract_file_access_credentials(litellm_params: dict | None) -> dict:
"bucket_name",
"timeout",
"max_retries",
]
"_litellm_internal_model_credentials",
*AWS_CREDENTIAL_KWARGS_KEYS,
)
for key in credential_keys:
if key in litellm_params:
credentials[key] = litellm_params[key]

2
litellm/batches/main.py
View file

@ -22,6 +22,7 @@ from openai.types.batch import BatchRequestCounts
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.get_litellm_params import add_trusted_model_credentials_to_litellm_params
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.anthropic.batches.handler import AnthropicBatchesHandler
from litellm.llms.azure.batches.handler import AzureBatchesAPI
@ -527,6 +528,7 @@ def retrieve_batch(
custom_llm_provider=custom_llm_provider,
**kwargs,
)
add_trusted_model_credentials_to_litellm_params(litellm_params, kwargs)
if litellm_logging_obj is not None:
litellm_logging_obj.update_from_kwargs(
kwargs=kwargs,

45
litellm/caching/caching.py
View file

@ -66,20 +66,7 @@ class Cache:
default_in_memory_ttl: float | None = None,
default_in_redis_ttl: float | None = None,
similarity_threshold: float | None = None,
supported_call_types: list[CachingSupportedCallTypes] | None = [
"completion",
"acompletion",
"embedding",
"aembedding",
"atranscription",
"transcription",
"atext_completion",
"text_completion",
"arerank",
"rerank",
"responses",
"aresponses",
],
supported_call_types: list[CachingSupportedCallTypes] | None = list(DEFAULT_CACHING_SUPPORTED_CALL_TYPES),
# s3 Bucket, boto3 configuration
azure_account_url: str | None = None,
azure_blob_container: str | None = None,
@ -927,20 +914,7 @@ def enable_cache(
host: str | None = None,
port: str | None = None,
password: str | None = None,
supported_call_types: list[CachingSupportedCallTypes] | None = [
"completion",
"acompletion",
"embedding",
"aembedding",
"atranscription",
"transcription",
"atext_completion",
"text_completion",
"arerank",
"rerank",
"responses",
"aresponses",
],
supported_call_types: list[CachingSupportedCallTypes] | None = list(DEFAULT_CACHING_SUPPORTED_CALL_TYPES),
**kwargs,
):
"""
@ -987,20 +961,7 @@ def update_cache(
host: str | None = None,
port: str | None = None,
password: str | None = None,
supported_call_types: list[CachingSupportedCallTypes] | None = [
"completion",
"acompletion",
"embedding",
"aembedding",
"atranscription",
"transcription",
"atext_completion",
"text_completion",
"arerank",
"rerank",
"responses",
"aresponses",
],
supported_call_types: list[CachingSupportedCallTypes] | None = list(DEFAULT_CACHING_SUPPORTED_CALL_TYPES),
**kwargs,
):
"""

44
litellm/caching/caching_handler.py
View file

@ -18,8 +18,8 @@ import asyncio
import datetime
import inspect
import time
from collections.abc import AsyncGenerator, Callable, Generator
from typing import TYPE_CHECKING, Any, Final, Optional
from collections.abc import AsyncGenerator, AsyncIterator, Callable, Generator
from typing import TYPE_CHECKING, Any, Final, Optional, TypeVar
from pydantic import BaseModel
@ -49,10 +49,15 @@ from litellm.types.utils import (
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.anthropic.experimental_pass_through.messages.response_cache import (
AnthropicMessagesStreamCacheWriter,
)
from litellm.types.utils import PromptTokensDetailsWrapper
else:
LiteLLMLoggingObj = Any
_StreamResultT = TypeVar("_StreamResultT")
from litellm.litellm_core_utils.core_helpers import (
_get_parent_otel_span_from_kwargs,
@ -106,7 +111,8 @@ def _should_defer_streaming_cache_hit_callbacks(*, kwargs: dict[str, Any]) -> bo
When stream=True, do not run success callbacks at cache-hit time.
Cached chat/text completion replay uses CustomStreamWrapper; cached Responses
replay uses CachedResponsesAPIStreamingIterator. Both invoke logging success
replay uses CachedResponsesAPIStreamingIterator; cached Anthropic Messages
replay uses CachedAnthropicMessagesStreamIterator. All invoke logging success
handlers when the stream finishes; firing them here too would double-count
spend and callback records.
"""
@ -835,6 +841,18 @@ class LLMCachingHandler:
response_type="audio_transcription",
hidden_params=hidden_params,
)
elif (
call_type == CallTypes.anthropic_messages.value or call_type == CallTypes.aanthropic_messages.value
) and isinstance(cached_result, dict):
from litellm.llms.anthropic.experimental_pass_through.messages.response_cache import (
convert_cached_anthropic_messages_result,
)
cached_result = convert_cached_anthropic_messages_result(
cached_result=cached_result,
logging_obj=logging_obj,
kwargs=kwargs,
)
elif (call_type == "aresponses" or call_type == "responses") and isinstance(cached_result, dict):
use_chat_completion_cache: Final = _is_chat_completion_cached_dict(cached_result)
if use_chat_completion_cache:
@ -1031,6 +1049,26 @@ class LLMCachingHandler:
and (kwargs.get("cache", {}).get("no-store", False) is not True)
)
def wrap_streaming_result_for_cache(
self, result: _StreamResultT, call_type: str
) -> "_StreamResultT | AnthropicMessagesStreamCacheWriter":
if call_type not in (
CallTypes.anthropic_messages.value,
CallTypes.aanthropic_messages.value,
):
return result
if litellm.cache is None or not self._should_store_result_in_cache(
original_function=self.original_function, kwargs=self.request_kwargs
):
return result
if not isinstance(result, AsyncIterator):
return result
from litellm.llms.anthropic.experimental_pass_through.messages.response_cache import (
AnthropicMessagesStreamCacheWriter,
)
return AnthropicMessagesStreamCacheWriter(stream=result, caching_handler=self)
def _is_call_type_supported_by_cache(
self,
original_function: Callable,

4
litellm/caching/redis_cache.py
View file

@ -1572,7 +1572,7 @@ class RedisCache(BaseCache):
async def _pipeline_rpush_helper(
self,
pipe: pipeline,
rpush_list: list[RedisPipelineRpushOperation],
rpush_list: Sequence[RedisPipelineRpushOperation],
) -> list[int]:
"""Helper function for pipeline rpush operations"""
for rpush_op in rpush_list:
@ -1588,7 +1588,7 @@ class RedisCache(BaseCache):
@_redis_circuit_breaker_guard
async def async_rpush_pipeline(
self,
rpush_list: list[RedisPipelineRpushOperation],
rpush_list: Sequence[RedisPipelineRpushOperation],
) -> list[int]:
"""
Use Redis Pipelines for bulk RPUSH operations

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.

37
litellm/constants.py
View file

@ -141,6 +141,8 @@ LITELLM_UI_ALLOW_HEADERS: Final = [
"x-litellm-semantic-filter",
"x-litellm-semantic-filter-tools",
"x-litellm-adaptive-router-model",
"x-litellm-applied-guardrails",
"x-litellm-guardrail-scan-id",
]
# Gemini model-specific minimal thinking budget constants
@ -472,6 +474,8 @@ 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,
@ -1323,6 +1327,7 @@ 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 = (
@ -1478,21 +1483,31 @@ 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))
SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS: Final = float(
os.getenv("SPEND_LOG_CLEANUP_BATCH_FAILURE_BACKOFF_SECONDS", 0.5)
)
SPEND_LOG_CLEANUP_RUN_BUDGET_SECONDS: Final = float(os.getenv("SPEND_LOG_CLEANUP_RUN_BUDGET_SECONDS", "300"))
SPEND_LOG_CLEANUP_BATCH_TIMEOUT_SECONDS: Final = float(os.getenv("SPEND_LOG_CLEANUP_BATCH_TIMEOUT_SECONDS", "30"))
SPEND_LOG_CLEANUP_REMAINING_COUNT_CAP: Final = int(os.getenv("SPEND_LOG_CLEANUP_REMAINING_COUNT_CAP", "100000"))
TOOL_SPEND_TOP_TOOLS: Final = 100
SPEND_LOG_PARTITION_INTERVAL: Final = os.getenv("SPEND_LOG_PARTITION_INTERVAL", "day")
SPEND_LOG_PARTITION_PRECREATE_AHEAD: Final = int(os.getenv("SPEND_LOG_PARTITION_PRECREATE_AHEAD", 7))
SPEND_LOG_WRITE_BATCH_MAX_BYTES: Final = max(1, int(os.getenv("SPEND_LOG_WRITE_BATCH_MAX_BYTES", 2_000_000)))
SPEND_LOG_QUEUE_SIZE_THRESHOLD: Final = int(os.getenv("SPEND_LOG_QUEUE_SIZE_THRESHOLD", 100))
SPEND_LOG_QUEUE_MAX_BYTES: Final = max(1, int(os.getenv("SPEND_LOG_QUEUE_MAX_BYTES", "64000000")))
SPEND_LOG_QUEUE_POLL_INTERVAL: Final = float(os.getenv("SPEND_LOG_QUEUE_POLL_INTERVAL", 2.0))
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)))
@ -1521,6 +1536,10 @@ 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
@ -1719,3 +1738,21 @@ 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
# Deployments named in the lapsed-window alert before it is truncated, so a fleet-wide
# expiry cannot produce an alert too large for the channel delivering it.
PTU_LAPSED_ALERT_LIMIT: Final[int] = 10
# 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

10
litellm/cost_calculator.py
View file

@ -26,11 +26,11 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import (
_generic_cost_per_character,
_get_regional_uplift_multiplier,
_get_service_tier_cost_key,
_parse_prompt_tokens_details,
calculate_cost_component,
generic_cost_per_token,
get_billable_input_tokens,
get_token_type_cost_breakdown,
parse_prompt_tokens_details,
select_cost_metric_for_model,
)
from litellm.llms.anthropic.cost_calculation import (
@ -645,7 +645,11 @@ def cost_per_token(
else:
model_info: Final = _cached_get_model_info_helper(model=model, custom_llm_provider=custom_llm_provider)
if (model_info.get("input_cost_per_token") or 0.0) > 0 or (model_info.get("output_cost_per_token") or 0.0) > 0:
if (
(model_info.get("input_cost_per_token") or 0.0) > 0
or (model_info.get("output_cost_per_token") or 0.0) > 0
or model_info.get("tiered_pricing") is not None
):
return generic_cost_per_token(
model=model,
usage=usage_block,
@ -2159,7 +2163,7 @@ def batch_cost_calculator(
if input_cost_per_token_batches:
total_prompt_cost = usage.prompt_tokens * input_cost_per_token_batches
elif input_cost_per_token:
details: Final = _parse_prompt_tokens_details(usage)
details: Final = parse_prompt_tokens_details(usage)
cache_read_tokens: Final = details["cache_hit_tokens"]
cache_creation_tokens: Final = details["cache_creation_tokens"]

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

16
litellm/files/main.py
View file

@ -11,7 +11,6 @@ import time
import uuid as uuid_module
from collections.abc import Coroutine
from functools import partial
from types import MappingProxyType
from typing import Any, Final, Literal, cast
import httpx
@ -34,6 +33,7 @@ import litellm
from litellm import get_secret_str
from litellm.files.streaming import FileContentStreamingResponse
from litellm.files.types import FileContentProvider, FileContentStreamingResult
from litellm.litellm_core_utils.get_litellm_params import add_trusted_model_credentials_to_litellm_params
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.llms.azure.common_utils import get_azure_credentials
@ -85,14 +85,6 @@ bedrock_files_instance: Final = BedrockFilesHandler()
#################################################
def _add_trusted_model_credentials_to_litellm_params(
litellm_params_dict: dict[str, Any], kwargs: dict[str, Any]
) -> None:
trusted_model_credentials: Final = kwargs.get("_litellm_internal_model_credentials")
if isinstance(trusted_model_credentials, type(MappingProxyType({}))):
litellm_params_dict["_litellm_internal_model_credentials"] = trusted_model_credentials
@client
async def acreate_file(
file: FileTypes,
@ -372,7 +364,7 @@ def file_retrieve(
)
if provider_config is not None:
litellm_params_dict: Final = get_litellm_params(**kwargs)
_add_trusted_model_credentials_to_litellm_params(
add_trusted_model_credentials_to_litellm_params(
litellm_params_dict=litellm_params_dict,
kwargs=kwargs,
)
@ -494,7 +486,7 @@ def file_delete(
pass
optional_params: Final = GenericLiteLLMParams(**kwargs)
litellm_params_dict: Final = get_litellm_params(**kwargs)
_add_trusted_model_credentials_to_litellm_params(
add_trusted_model_credentials_to_litellm_params(
litellm_params_dict=litellm_params_dict,
kwargs=kwargs,
)
@ -834,7 +826,7 @@ def file_content(
try:
optional_params: Final = GenericLiteLLMParams(**kwargs)
litellm_params_dict: Final = get_litellm_params(**kwargs)
_add_trusted_model_credentials_to_litellm_params(
add_trusted_model_credentials_to_litellm_params(
litellm_params_dict=litellm_params_dict,
kwargs=kwargs,
)

61
litellm/google_genai/adapters/transformation.py
View file

@ -1,6 +1,8 @@
import json
from collections.abc import AsyncIterator, Iterator
from typing import Any, Final, cast
from typing import Any, Final, TypedDict, cast
from typing_extensions import ReadOnly
from litellm import verbose_logger
from litellm.litellm_core_utils.json_validation_rule import normalize_tool_schema
@ -28,6 +30,19 @@ from litellm.types.utils import (
)
class _GenAITextPart(TypedDict, total=False):
text: ReadOnly[str]
class _GenAISystemInstruction(TypedDict, total=False):
parts: ReadOnly[list[_GenAITextPart]]
class _GenAIPart(TypedDict, total=False):
text: ReadOnly[str]
functionCall: ReadOnly[dict[str, object]]
class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
"""
Wrapper for streaming Google GenAI generate_content responses.
@ -36,9 +51,9 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
sent_first_chunk: bool = False
# State tracking for accumulating partial tool calls
accumulated_tool_calls: dict[str, dict[str, Any]]
accumulated_tool_calls: dict[str, dict[str, str]]
def __init__(self, completion_stream: Any):
def __init__(self, completion_stream: object):
self.sent_first_chunk = False
self.accumulated_tool_calls = {}
self._returned_response = False
@ -85,7 +100,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
# After the stream is exhausted, check for any remaining accumulated tool calls
if self.accumulated_tool_calls:
try:
parts: Final = []
parts: Final[list[_GenAIPart]] = []
for (
tool_call_index,
tool_call_data,
@ -94,7 +109,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
# For tool calls with no arguments, accumulated_args will be "", which is not valid JSON.
# We default to an empty JSON object in this case.
parsed_args = json.loads(tool_call_data["arguments"] or "{}")
function_call_part = {
function_call_part: _GenAIPart = {
"functionCall": {
"name": tool_call_data["name"] or "undefined_tool_name",
"args": parsed_args,
@ -110,7 +125,7 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
tool_call_data["arguments"],
)
if parts:
final_chunk: Final = {
final_chunk: Final[dict[str, object]] = {
"candidates": [
{
"content": {"parts": parts, "role": "model"},
@ -273,9 +288,9 @@ class GoogleGenAIAdapter:
def _add_generic_litellm_params_to_request(
self,
completion_request_dict: dict[str, Any],
completion_request_dict: dict[str, object],
litellm_params: GenericLiteLLMParams | None = None,
) -> dict:
) -> dict[str, object]:
"""Add generic litellm params to request. e.g add api_base, api_key, api_version, etc.
Args:
@ -295,7 +310,7 @@ class GoogleGenAIAdapter:
def translate_completion_output_params_streaming(
self,
completion_stream: Any,
completion_stream: object,
) -> AsyncIterator[bytes] | None:
"""Transform streaming completion output to Google GenAI format"""
google_genai_wrapper: Final = GoogleGenAIStreamWrapper(completion_stream=completion_stream)
@ -307,12 +322,12 @@ class GoogleGenAIAdapter:
tools: list[dict[str, Any]],
) -> list[ChatCompletionToolParam]:
"""Transform Google GenAI tools to OpenAI tools format"""
openai_tools: Final[list[dict[str, Any]]] = []
openai_tools: Final[list[dict[str, object]]] = []
for tool in tools:
if "functionDeclarations" in tool:
for func_decl in tool["functionDeclarations"]:
function_chunk: dict[str, Any] = {
function_chunk: dict[str, object] = {
"name": func_decl.get("name", ""),
}
@ -321,7 +336,7 @@ class GoogleGenAIAdapter:
if "parametersJsonSchema" in func_decl:
function_chunk["parameters"] = func_decl["parametersJsonSchema"]
openai_tool = {"type": "function", "function": function_chunk}
openai_tool: dict[str, object] = {"type": "function", "function": function_chunk}
openai_tools.append(openai_tool)
# normalize the tool schemas
@ -345,7 +360,7 @@ class GoogleGenAIAdapter:
def _transform_contents_to_messages(
self,
contents: list[dict[str, Any]],
system_instruction: dict[str, Any] | None = None,
system_instruction: _GenAISystemInstruction | None = None,
) -> list[AllMessageValues]:
"""Transform Google GenAI contents to OpenAI messages format"""
messages: Final[list[AllMessageValues]] = []
@ -461,7 +476,7 @@ class GoogleGenAIAdapter:
def translate_completion_to_generate_content(
self,
response: ModelResponse,
) -> dict[str, Any]:
) -> dict[str, object]:
"""
Transform litellm completion response to Google GenAI generate_content format
@ -490,7 +505,7 @@ class GoogleGenAIAdapter:
parts = [{"text": message_content}] if message_content else []
# Create Google GenAI format response
generate_content_response: Final[dict[str, Any]] = {
generate_content_response: Final[dict[str, object]] = {
"candidates": [
{
"content": {"parts": parts, "role": "model"},
@ -524,7 +539,7 @@ class GoogleGenAIAdapter:
self,
response: ModelResponse | ModelResponseStream,
wrapper: GoogleGenAIStreamWrapper,
) -> dict[str, Any] | None:
) -> dict[str, object] | None:
"""
Transform streaming litellm completion chunk to Google GenAI generate_content format
@ -560,7 +575,7 @@ class GoogleGenAIAdapter:
return None
# Create Google GenAI streaming format response
streaming_chunk: Final[dict[str, Any]] = {
streaming_chunk: Final[dict[str, object]] = {
"candidates": [
{
"content": {"parts": parts, "role": "model"},
@ -597,9 +612,9 @@ class GoogleGenAIAdapter:
def _transform_openai_message_to_google_genai_parts(
self,
message: Any,
) -> list[dict[str, Any]]:
) -> list[_GenAIPart]:
"""Transform OpenAI message to Google GenAI parts format"""
parts: Final[list[dict[str, Any]]] = []
parts: Final[list[_GenAIPart]] = []
# Add text content if present
if hasattr(message, "content") and message.content:
@ -614,7 +629,7 @@ class GoogleGenAIAdapter:
except json.JSONDecodeError:
args = {}
function_call_part = {
function_call_part: _GenAIPart = {
"functionCall": {
"name": tool_call.function.name or "undefined_tool_name",
"args": args,
@ -626,14 +641,14 @@ class GoogleGenAIAdapter:
def _transform_openai_delta_to_google_genai_parts_with_accumulation(
self, delta: Any, wrapper: GoogleGenAIStreamWrapper
) -> list[dict[str, Any]]:
) -> list[_GenAIPart]:
"""Transforms OpenAI delta to Google GenAI parts, accumulating streaming tool calls."""
# 1. Initialize wrapper state if it doesn't exist
if not hasattr(wrapper, "accumulated_tool_calls"):
wrapper.accumulated_tool_calls = {}
parts: Final[list[dict[str, Any]]] = []
parts: Final[list[_GenAIPart]] = []
if hasattr(delta, "content") and delta.content:
parts.append({"text": delta.content})
@ -686,7 +701,7 @@ class GoogleGenAIAdapter:
# The part will be created by a later chunk that brings the name.
if accumulated_name:
# If successful, create the part and clean up
function_call_part = {"functionCall": {"name": accumulated_name, "args": parsed_args}}
function_call_part: _GenAIPart = {"functionCall": {"name": accumulated_name, "args": parsed_args}}
parts.append(function_call_part)
# Remove the completed tool call from the accumulator

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."""

6
litellm/integrations/custom_guardrail.py
View file

@ -198,6 +198,7 @@ class CustomGuardrail(CustomLogger):
violation_message: str,
request_data: dict[str, Any],
detection_info: dict[str, Any] | None = None,
original_response: object = None,
) -> None:
"""
Raise a passthrough exception for guardrail violations.
@ -213,6 +214,10 @@ class CustomGuardrail(CustomLogger):
violation_message: The formatted violation message to return to the user
request_data: The original request data dictionary
detection_info: Optional dictionary with detection metadata (scores, rules, etc.)
original_response: The blocked LLM response when raising from a post-call
hook. It carries the real token usage the upstream call consumed, so
the synthetic block response reports it instead of zeros. Leave None
for pre-call/during-call blocks (the LLM was never invoked).
Raises:
ModifyResponseException: Always raises this exception to short-circuit
@ -235,6 +240,7 @@ class CustomGuardrail(CustomLogger):
request_data=request_data,
guardrail_name=self.guardrail_name,
detection_info=detection_info,
original_response=original_response,
)
def raise_sensitive_data_route_exception(

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)

115
litellm/integrations/langfuse/langfuse.py
View file

@ -2,8 +2,9 @@
# On success, logs events to Langfuse
import os
import traceback
from collections.abc import Callable
from collections.abc import Callable, Iterable, Mapping
from datetime import datetime
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, Final, cast
from packaging.version import Version
@ -30,6 +31,7 @@ from litellm.types.utils import (
ImageResponse,
ModelResponse,
RerankResponse,
StandardLoggingMetadata,
StandardLoggingPayload,
StandardLoggingPromptManagementMetadata,
TextCompletionResponse,
@ -46,6 +48,11 @@ else:
Langfuse = Any
_DENIED_STEERING_KEYS: Final = frozenset({"headers", "endpoint", "caching_groups", "previous_models"})
_NO_METADATA: Final[Mapping[str, Any]] = MappingProxyType({})
_REDACTED_PROXY_HEADERS: Final[frozenset[str]] = frozenset({"authorization", "cookie", "referer"})
def _extract_cache_read_input_tokens(usage_obj) -> int:
"""
Extract cache_read_input_tokens from usage object.
@ -75,6 +82,22 @@ 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,
@ -496,16 +519,14 @@ class LangFuseLogger:
else []
)
if standard_logging_object is None:
end_user_id = None
prompt_management_metadata: StandardLoggingPromptManagementMetadata | None = None
else:
end_user_id = standard_logging_object["metadata"].get("user_api_key_end_user_id", None)
prompt_management_metadata = cast(
StandardLoggingPromptManagementMetadata | None,
standard_logging_object["metadata"].get("prompt_management_metadata", None),
)
allowlisted_metadata: Final[StandardLoggingMetadata | dict[str, Any]] = (
standard_logging_object["metadata"] if standard_logging_object is not None else _NO_METADATA
)
end_user_id: Final = allowlisted_metadata.get("user_api_key_end_user_id", None)
prompt_management_metadata: Final[StandardLoggingPromptManagementMetadata | None] = cast(
StandardLoggingPromptManagementMetadata | None,
allowlisted_metadata.get("prompt_management_metadata", None),
)
# Clean Metadata before logging - never log raw metadata
# the raw metadata can contain circular references which leads to infinite recursion
@ -524,12 +545,7 @@ class LangFuseLogger:
tags.append(f"{key}:{value}")
# clean litellm metadata before logging
if key in [
"headers",
"endpoint",
"caching_groups",
"previous_models",
]:
if key in _DENIED_STEERING_KEYS:
continue
else:
clean_metadata[key] = value
@ -552,10 +568,13 @@ 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", []))
requested_trace_keys: Final = _as_steering_key_sequence(clean_metadata.pop("update_trace_keys", ()))
update_trace_keys: Final = (
requested_trace_keys if _as_steering_flag(litellm.langfuse_enable_update_trace_keys) else ()
)
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(
@ -614,19 +633,18 @@ class LangFuseLogger:
trace_params["output"] = output if not mask_output else "redacted-by-litellm"
if debug is True or (isinstance(debug, str) and debug.lower() == "true"):
if "metadata" in trace_params:
# log the raw_metadata in the trace
trace_params["metadata"]["metadata_passed_to_litellm"] = metadata
else:
trace_params["metadata"] = {"metadata_passed_to_litellm": metadata}
debug_metadata: Final = {
key: value for key, value in metadata.items() if isinstance(value, (str, int, float, bool))
}
trace_params["metadata"] = {
**(trace_params.get("metadata") or _NO_METADATA),
"metadata_passed_to_litellm": debug_metadata,
}
cost: Final = kwargs.get("response_cost", None)
verbose_logger.debug("trace: %s", cost)
clean_metadata["litellm_response_cost"] = cost
if standard_logging_object is not None:
hidden_params: Final = standard_logging_object.get("hidden_params", {})
clean_metadata["hidden_params"] = filter_exceptions_from_params(hidden_params)
hidden_params: Final = standard_logging_object.get("hidden_params") if standard_logging_object else None
if (
litellm.langfuse_default_tags is not None
@ -638,22 +656,24 @@ class LangFuseLogger:
tags.append(f"proxy_base_url:{proxy_base_url}")
api_base: Final = litellm_params.get("api_base", None)
if api_base:
clean_metadata["api_base"] = api_base
vertex_location: Final = kwargs.get("vertex_location", None)
if vertex_location:
clean_metadata["vertex_location"] = vertex_location
aws_region_name: Final = kwargs.get("aws_region_name", None)
if aws_region_name:
clean_metadata["aws_region_name"] = aws_region_name
candidate_enrichments: Final = (
("litellm_response_cost", cost, True),
("hidden_params", filter_exceptions_from_params(hidden_params), hidden_params is not None),
("api_base", api_base, bool(api_base)),
("vertex_location", vertex_location, bool(vertex_location)),
("aws_region_name", aws_region_name, bool(aws_region_name)),
("cache_hit", kwargs.get("cache_hit") or False, self._supports_tags() and "cache_hit" in kwargs),
)
enrichments: Final[Mapping[str, Any]] = {
key: value for key, value, include in candidate_enrichments if include
}
if self._supports_tags():
if "cache_hit" in kwargs:
if kwargs["cache_hit"] is None:
kwargs["cache_hit"] = False
clean_metadata["cache_hit"] = kwargs["cache_hit"]
if "cache_hit" in kwargs and kwargs["cache_hit"] is None:
kwargs["cache_hit"] = False # rebind-ok: pre-existing normalization other integrations rely on
if existing_trace_id is None:
trace_params.update({"tags": tags})
@ -666,13 +686,13 @@ class LangFuseLogger:
if headers:
for key, value in headers.items():
# these headers can leak our API keys and/or JWT tokens
if key.lower() not in ["authorization", "cookie", "referer"]:
if key.lower() not in _REDACTED_PROXY_HEADERS:
clean_headers[key] = value
trace: Final[StatefulTraceClient] = self.Langfuse.trace(**trace_params)
# Log provider specific information as a span
log_provider_specific_information_as_span(trace, clean_metadata)
log_provider_specific_information_as_span(trace, enrichments)
# Log guardrail information as a span
self._log_guardrail_information_as_span(
@ -745,7 +765,10 @@ class LangFuseLogger:
"output": output if not mask_output else "redacted-by-litellm",
"usage": usage,
"usage_details": usage_details,
"metadata": log_requester_metadata(clean_metadata),
"metadata": {
**log_requester_metadata(redact_user_api_key_info(metadata=allowlisted_metadata)),
**enrichments,
},
"level": level,
"version": clean_metadata.pop("version", None),
}
@ -1042,7 +1065,7 @@ def _add_prompt_to_generation_params(
def log_provider_specific_information_as_span(
trace,
clean_metadata,
clean_metadata: Mapping[str, Any],
):
"""
Logs provider-specific information as spans.
@ -1082,7 +1105,7 @@ def log_provider_specific_information_as_span(
)
def log_requester_metadata(clean_metadata: dict):
def log_requester_metadata(clean_metadata: Mapping[str, Any]):
returned_metadata: Final = {}
requester_metadata: Final = clean_metadata.get("requester_metadata") or {}
for k, v in clean_metadata.items():

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")

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 {}

85
litellm/integrations/rubrik.py
View file

@ -6,12 +6,13 @@ import random
import time
import uuid
from collections import Counter
from collections.abc import Mapping, Sequence
from collections.abc import Awaitable, Mapping, Sequence
from dataclasses import dataclass
from types import MappingProxyType
from typing import TYPE_CHECKING, Any, Final, Literal, Optional
from typing import TYPE_CHECKING, Any, Final, Literal, Optional, TypedDict
import httpx
from typing_extensions import Never, ReadOnly
from litellm._logging import verbose_logger
from litellm.integrations.custom_batch_logger import CustomBatchLogger
@ -48,7 +49,20 @@ _WEBHOOK_PATH_PROMPT_MODERATION: Final = "/v1/before_prompt/openai/v1"
_WEBHOOK_PATH_LOGGING_BATCH: Final = "/v1/litellm/batch"
_MAX_QUEUE_SIZE: Final = 10_000
_DROP_WARNING_INTERVAL_SECONDS: Final = 60.0
_EMPTY_MAPPING: Final[Mapping[str, Any]] = MappingProxyType({})
_EMPTY_MAPPING: Final[Mapping[str, Never]] = MappingProxyType({})
class _ServiceToolCall(TypedDict):
id: ReadOnly[str]
class _ServiceMessage(TypedDict, total=False):
content: ReadOnly[str]
tool_calls: ReadOnly[Sequence[_ServiceToolCall]]
class _ServiceChoice(TypedDict, total=False):
message: ReadOnly[_ServiceMessage]
class _MalformedToolBlockingResponseError(Exception):
@ -143,7 +157,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
else {"Content-Type": "application/json"}
)
self._periodic_flush_task: asyncio.Task[Any] | None = self._start_periodic_flush_task()
self._periodic_flush_task: asyncio.Task[None] | None = self._start_periodic_flush_task()
@classmethod
def get_supported_event_hooks(cls) -> list[GuardrailEventHooks]:
@ -191,7 +205,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
params={"timeout": httpx.Timeout(5.0, connect=2.0)},
)
def _start_periodic_flush_task(self) -> asyncio.Task[Any] | None:
def _start_periodic_flush_task(self) -> asyncio.Task[None] | None:
"""Start the periodic flush task only when an event loop is already running."""
try:
loop: Final = asyncio.get_running_loop()
@ -212,7 +226,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
Closing them here would close the shared connection pool for every
other logger instance; let LiteLLM manage their lifecycle instead.
"""
task: Final = getattr(self, "_periodic_flush_task", None)
task: Final[asyncio.Task[None] | None] = getattr(self, "_periodic_flush_task", None)
if task is not None:
task.cancel()
@ -253,7 +267,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
@staticmethod
async def _guarded(
coro: Any,
coro: Awaitable[GenericGuardrailAPIInputs],
inputs: GenericGuardrailAPIInputs,
label: str,
) -> GenericGuardrailAPIInputs:
@ -400,7 +414,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
request_data["_rubrik_logging_obj"] = logging_obj
@staticmethod
def _normalize_tool_calls(tool_calls: Any) -> tuple[ChatCompletionMessageToolCall, ...]:
def _normalize_tool_calls(tool_calls: Sequence[object]) -> tuple[ChatCompletionMessageToolCall, ...]:
"""Convert tool_calls from inputs to ChatCompletionMessageToolCall objects."""
return tuple(RubrikLogger._normalize_tool_call(tc) for tc in tool_calls)
@ -427,7 +441,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
raise TypeError(f"Cannot normalize tool_call of type {type(tc).__name__}: {tc!r}")
@staticmethod
def _join_texts(texts: Any) -> str:
def _join_texts(texts: Sequence[str] | None) -> str:
"""Join response text segments into the single content string the
webhook evaluates. Empty when there is no assistant text."""
if not texts:
@ -439,14 +453,14 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
tool_calls: Sequence[ChatCompletionMessageToolCall],
content: str,
request_id: str | None,
) -> Mapping[str, Any]:
) -> Mapping[str, object]:
"""Build an OpenAI ChatCompletion-format dict (assistant text + tool
calls) for the after_completion webhook.
``content`` is sent so the webhook can moderate the response text;
``None`` when the assistant produced no text (tool-call-only response).
"""
message: Final[dict[str, Any]] = {
message: Final[dict[str, object]] = {
"role": "assistant",
"content": content or None,
}
@ -467,7 +481,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
}
@staticmethod
def _flatten_messages_for_moderation(messages: Any) -> tuple[Mapping[str, Any], ...]:
def _flatten_messages_for_moderation(messages: Sequence[object] | None) -> tuple[Mapping[str, Any], ...]:
"""Collapse each message's content to a plain string for the webhook.
litellm normalizes Anthropic ``/v1/messages`` requests to OpenAI shape,
@ -506,8 +520,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
@staticmethod
def _build_prompt_moderation_payload(
inputs: GenericGuardrailAPIInputs,
request_data: Mapping[str, Any],
) -> Mapping[str, Any]:
request_data: Mapping[str, object],
) -> Mapping[str, object]:
"""Build the bare OpenAI request the before_prompt webhook consumes.
Unlike the after_completion envelope, this endpoint takes a raw OpenAI
@ -516,7 +530,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
``/v1/messages`` requests too. Optional fields are sent only when
present so the payload stays clean.
"""
payload: Final[dict[str, Any]] = {
payload: Final[dict[str, object]] = {
"model": inputs.get("model") or request_data.get("model") or "",
"messages": RubrikLogger._flatten_messages_for_moderation(inputs.get("structured_messages")),
}
@ -540,8 +554,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
@staticmethod
def _extract_request_data(
call_details: Mapping[str, Any],
request_data: Mapping[str, Any] | None,
) -> Mapping[str, Any]:
request_data: Mapping[str, object] | None,
) -> Mapping[str, object]:
"""Extract original request data from model_call_details for the
response moderation service envelope.
@ -576,7 +590,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
}
@staticmethod
def _sanitize_proxy_server_request(proxy_server_request: Any) -> Any:
def _sanitize_proxy_server_request(proxy_server_request: object) -> object:
"""Allowlist only routing fields (``url``, ``method``) when forwarding
``proxy_server_request`` to an external webhook, dropping inbound
``headers`` (Authorization, Cookie, x-api-key, ...) and the raw
@ -586,17 +600,18 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
return {key: proxy_server_request[key] for key in ("url", "method") if key in proxy_server_request}
@staticmethod
def _resolve_model(request_data: Mapping[str, Any], call_details: Mapping[str, Any]) -> str:
def _resolve_model(request_data: Mapping[str, object], call_details: Mapping[str, str]) -> str:
"""Get the model name for the ModifyResponseException."""
response: Final = request_data.get("response")
if response and hasattr(response, "model"):
return response.model or "unknown"
response_model: Final[str | None] = getattr(response, "model", None)
return response_model or "unknown"
return call_details.get("model", "unknown")
# -- Logging hooks ---------------------------------------------------------
@staticmethod
def _correlation_id(call_details: Mapping[str, Any], request_data: Mapping[str, Any] | None = None) -> str | None:
def _correlation_id(call_details: Mapping[str, str], request_data: Mapping[str, str] | None = None) -> str | None:
"""The id that joins a blocked request's two S3 logs by filename: the
moderation (``_blocking``) log and the failure (response) log.
@ -610,7 +625,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
return call_details.get("litellm_call_id") or (request_data or _EMPTY_MAPPING).get("litellm_call_id")
@classmethod
def _apply_correlation_id(cls, payload: dict[str, Any], source: Mapping[str, Any]) -> None:
def _apply_correlation_id(cls, payload: dict[str, object], source: Mapping[str, str]) -> None:
"""Pin ``payload["id"]`` to ``litellm_call_id`` in place so this log
shares its S3 filename id with the moderation (``_blocking``) and
failure logs for the same request -- for every provider.
@ -630,7 +645,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
payload["id"] = correlated
@staticmethod
def _prepend_system_prompt(payload: dict[str, Any], source: Mapping[str, Any]) -> None:
def _prepend_system_prompt(payload: dict[str, object], source: Mapping[str, object]) -> None:
"""Prepend ``source["system"]`` onto ``payload["messages"]``.
Builds a NEW messages list rather than mutating ``payload["messages"]``
@ -658,7 +673,9 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
exc_info=True,
)
async def _prepare_log_payload(self, kwargs: Mapping[str, Any], event_type: str) -> StandardLoggingPayload | None:
async def _prepare_log_payload(
self, kwargs: Mapping[str, object], event_type: str
) -> StandardLoggingPayload | None:
"""Shared logic for success logging (sampled)."""
if random.random() > self.sampling_rate:
verbose_logger.debug("Skipping Rubrik %s logging (sampling_rate=%s)", event_type, self.sampling_rate)
@ -697,7 +714,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
self._dropped_since_warning = 0
self._last_drop_warning_time = now
async def _enqueue_log_event(self, kwargs: Mapping[str, Any], event_type: str):
async def _enqueue_log_event(self, kwargs: Mapping[str, object], event_type: str):
try:
payload: Final = await self._prepare_log_payload(kwargs, event_type)
if payload is None:
@ -862,7 +879,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
base: Final = call_details.get("standard_logging_object")
if base is not None:
payload: dict = safe_deep_copy(base)
payload: dict[str, object] = safe_deep_copy(base)
else:
verbose_logger.debug(
"Rubrik: standard_logging_object not yet on model_call_details "
@ -908,7 +925,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
cls,
call_details: Mapping[str, Any],
user_api_key_dict: "UserAPIKeyAuth",
) -> dict[str, Any]:
) -> dict[str, object]:
# Convert datetime to a Unix float so json.dumps can serialize it.
# httpx's json= parameter uses stdlib json.dumps with no custom encoder.
_raw_start: Final = call_details.get("start_time")
@ -996,7 +1013,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
# -- Webhook services ------------------------------------------------------
async def _post_json(self, endpoint: str, payload: Mapping[str, Any], service_name: str) -> Mapping[str, Any]:
async def _post_json(self, endpoint: str, payload: Mapping[str, object], service_name: str) -> Mapping[str, Any]:
"""POST ``payload`` to a Rubrik webhook and return its dict response.
Raises:
@ -1010,7 +1027,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
headers=self._headers,
)
http_response.raise_for_status()
result: Final = http_response.json()
result: Final[object] = http_response.json()
if not isinstance(result, dict):
raise TypeError(
f"{service_name} returned non-dict JSON "
@ -1021,8 +1038,8 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
async def _post_to_response_moderation_endpoint(
self,
response_data: Mapping[str, Any],
request_data: Mapping[str, Any],
response_data: Mapping[str, object],
request_data: Mapping[str, object],
) -> Mapping[str, Any]:
"""Post the ``{request, response}`` envelope to the after_completion
webhook and return its (possibly rewritten) response.
@ -1039,7 +1056,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
"Response moderation service",
)
async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, Any]) -> Mapping[str, Any]:
async def _post_to_prompt_moderation_endpoint(self, payload: Mapping[str, object]) -> Mapping[str, Any]:
"""Post a bare OpenAI request to the before_prompt webhook.
Returns ``{}`` (passthrough) or a synthetic chat.completion (block).
@ -1054,7 +1071,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
chat.completion whose ``choices[0].message.content`` is the refusal
explanation.
"""
choices: Final = service_response.get("choices")
choices: Final[Sequence[_ServiceChoice] | None] = service_response.get("choices")
if not choices:
return None
message: Final = choices[0].get("message") or _EMPTY_MAPPING
@ -1086,7 +1103,7 @@ class RubrikLogger(CustomGuardrail, CustomBatchLogger):
Expects service_response in OpenAI chat completion format:
{"choices": [{"message": {"tool_calls": [...], "content": "..."}}]}
"""
choices: Final = service_response.get("choices") or ()
choices: Final[Sequence[_ServiceChoice]] = service_response.get("choices") or ()
if not choices:
raise _MalformedToolBlockingResponseError("Response moderation service returned empty response")

844
litellm/integrations/shadow_eval_logger.py Normal file
View file

@ -0,0 +1,844 @@
"""Shadow Eval Logger: samples a shadowed key's successful LLM requests (chat completions,
Anthropic Messages, and Responses API surfaces, each normalized to chat shape), duplicates
each against the job's other arm in a detached task (the auto-router for a forward job, the
fixed baseline model for a reverse one), blind-judges real vs shadow, and appends one
``LiteLLM_ShadowEvalAttempt`` row (verdict or error) as the feature's only hot-path write.
Counts, status, and spend derive from those rows at read time, so nothing can disagree
across pods or stop races; the hook reads active jobs through a short-TTL cache."""
import asyncio
import hashlib
import random
from collections.abc import Callable, Mapping, Sequence
from dataclasses import dataclass
from datetime import datetime, timezone
from itertools import groupby
from operator import itemgetter
from types import MappingProxyType
from typing import TYPE_CHECKING, Final
from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError, field_validator, model_validator
from litellm._logging import verbose_logger
from litellm.caching.in_memory_cache import InMemoryCache
from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY
from litellm.integrations.custom_logger import CustomLogger
from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs
from litellm.litellm_core_utils.internal_call_metadata import sanitized_forwardable_call_metadata
from litellm.litellm_core_utils.llm_judge import (
default_router_provider,
extract_text_from_content,
judge_acompletion,
parse_json_verdict,
)
from litellm.litellm_core_utils.redact_messages import should_redact_message_logging
from litellm.types.management_endpoints.auto_router_endpoints import ShadowEvalDirection
from litellm.types.utils import SHADOW_EVAL_JUDGE_CALL_ORIGIN, SHADOW_EVAL_ROUTER_CALL_ORIGIN
if TYPE_CHECKING:
from litellm.proxy.utils import PrismaClient
from litellm.router import Router
from litellm.types.utils import StandardLoggingPayload
# A job starting, stopping, or hitting its turn budget propagates to sampling within one
# TTL; the turn budget can overshoot by at most one TTL of in-flight samples per pod.
_JOBS_CACHE_TTL_SECONDS: Final = 10
# Concurrent shadow+judge pipelines per pod: a traffic spike turns into skipped samples
# rather than an unbounded task pileup.
_MAX_CONCURRENT_SHADOW_TASKS: Final = 16
# Total character budget for the judge's user prompt, however long the conversation and
# the two responses are, so the prompt can never overflow a judge model's context window.
_MAX_JUDGE_RESPONSE_CHARS: Final = 8_000
_MAX_JUDGE_PROMPT_CHARS: Final = 24_000
# The judge answers with a small JSON object; a tighter budget truncates the JSON
# mid-object and the attempt is lost to an error row.
JUDGE_MAX_OUTPUT_TOKENS: Final = 500
_MAX_ERROR_CHARS: Final = 500
_EMPTY_METADATA: Final[Mapping[str, object]] = MappingProxyType({})
# Typed boundaries around the owner transformations, which declare untyped returns:
# a request or message that fails this lenient shape check is skipped, never sampled.
_CHAT_REQUEST_ADAPTER: Final = TypeAdapter(Mapping[str, object])
_CHAT_MESSAGES_ADAPTER: Final = TypeAdapter(tuple[Mapping[str, object], ...])
_MESSAGE_ITEMS_ADAPTER: Final = TypeAdapter(tuple[object, ...])
def _chat_messages(kwargs: Mapping[str, object]) -> tuple[Mapping[str, object], ...]:
raw: Final = kwargs.get("messages")
return tuple(m for m in raw if isinstance(m, Mapping)) if isinstance(raw, Sequence) else ()
def _proxy_wire_body(kwargs: Mapping[str, object]) -> Mapping[str, object]:
litellm_params: Final = kwargs.get("litellm_params")
request: Final = litellm_params.get("proxy_server_request") if isinstance(litellm_params, Mapping) else None
body: Final = request.get("body") if isinstance(request, Mapping) else None
return body if isinstance(body, Mapping) else _EMPTY_METADATA
def _chat_request_from_chat(
kwargs: Mapping[str, object], model_parameters: Mapping[str, object]
) -> Mapping[str, object]:
"""Chat requests are already chat-shaped: the logged model_parameters forward as-is."""
return MappingProxyType({**model_parameters, "messages": _chat_messages(kwargs)})
# Anthropic params the adapter copies through untranslated; the translatable set comes
# from the adapter itself at call time.
_ANTHROPIC_SAMPLING_PARAM_KEYS: Final = frozenset(("max_tokens", "temperature", "top_p", "top_k", "reasoning_effort"))
def _chat_request_from_anthropic_messages(
kwargs: Mapping[str, object], _model_parameters: Mapping[str, object]
) -> Mapping[str, object]:
"""/v1/messages logs surface-native block messages with ``system`` top-level: the
native provider path carries it in kwargs, the openai-compatible bridge path only in
the proxy's snapshot of the client's wire body. Params come from the wire body alone,
because the logged optional_params switch dialect per provider path (the bridge's
inner completion rewrites them to chat shape mid-flight); the adapter translates
them alongside the messages, and sampling params copy through untranslated."""
from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import (
LiteLLMAnthropicMessagesAdapter,
)
adapter: Final = LiteLLMAnthropicMessagesAdapter()
wire_body: Final = _proxy_wire_body(kwargs)
system: Final = kwargs.get("system") or wire_body.get("system")
param_keys: Final = (
frozenset(adapter.translatable_anthropic_params()) | _ANTHROPIC_SAMPLING_PARAM_KEYS
) - frozenset(("messages", "system"))
request: Final = MappingProxyType(
dict(
(
*((k, v) for k, v in wire_body.items() if k in param_keys),
("model", str(kwargs.get("model") or "")),
("messages", _CHAT_MESSAGES_ADAPTER.validate_python(kwargs.get("messages") or ())),
*((("system", system),) if system is not None else ()),
)
)
)
translated, _ = adapter.translate_anthropic_to_openai(request) # pyright: ignore[reportArgumentType] # wire-body mapping is the surface's native request shape; the adapter is duck-typed and read-only here
return translated
def _chat_request_from_responses(
kwargs: Mapping[str, object], _model_parameters: Mapping[str, object]
) -> Mapping[str, object]:
"""/v1/responses logs the raw ``input`` under ``kwargs["messages"]``, an alias
function_setup creates for responses call types: a bare string, chat-shaped dicts,
or item dicts; ``instructions`` is the system prompt. Params come from the wire body
for the same reason as the messages surface; the transformer translates them with
the input (max_output_tokens to max_tokens, Responses tools to chat tools, reasoning
to reasoning_effort) and never reads surface-only keys like previous_response_id."""
from litellm.responses.litellm_completion_transformation.transformation import (
LiteLLMCompletionResponsesConfig,
)
from litellm.types.llms.openai import ResponsesAPIOptionalRequestParams
wire_body: Final = _proxy_wire_body(kwargs)
instructions: Final = kwargs.get("instructions") or wire_body.get("instructions")
responses_request: Final = MappingProxyType(
dict(
(
*((k, v) for k, v in wire_body.items() if k in ResponsesAPIOptionalRequestParams.__annotations__),
*((("instructions", instructions),) if instructions is not None else ()),
)
)
)
return _CHAT_REQUEST_ADAPTER.validate_python(
LiteLLMCompletionResponsesConfig.transform_responses_api_request_to_chat_completion_request( # pyright: ignore[reportUnknownMemberType] # transformer declares a bare dict return
model=str(kwargs.get("model") or ""),
input=kwargs.get("messages"), # pyright: ignore[reportArgumentType] # untyped callback kwargs; transformer validates shapes
responses_api_request=responses_request, # pyright: ignore[reportArgumentType] # wire-body dict filtered to the surface's own request keys; the transformer is duck-typed
)
)
def _chat_final_text(response_obj: object) -> str:
"""The assistant's text, or empty when the turn carries tool calls: only text-final
turns produce a judgeable A/B comparison."""
try:
message: Final = (
response_obj["choices"][0]["message"]
if isinstance(response_obj, Mapping)
else response_obj.choices[0].message # pyright: ignore[reportAttributeAccessIssue] # duck-typed ModelResponse
)
except (AttributeError, KeyError, IndexError, TypeError):
return ""
read: Final = message.get if isinstance(message, Mapping) else lambda key: getattr(message, key, None)
if read("tool_calls") or read("function_call"):
return ""
return extract_text_from_content(read("content"))
def _responses_final_text(response_obj: object) -> str:
"""The turn's aggregated output text, or empty when the turn carries tool calls. A
dict-shaped payload is validated into the owner type first, because ``output_text``
is a derived property rather than a serialized field, so it never exists on a dict;
a dict the owner type rejects is unjudgeable and skipped."""
from litellm.types.llms.openai import ResponsesAPIResponse
try:
response: Final = (
ResponsesAPIResponse.model_validate(response_obj) if isinstance(response_obj, Mapping) else response_obj
)
except ValidationError:
return ""
output: Final = getattr(response, "output", None)
if not isinstance(output, Sequence):
return ""
items: Final = tuple(item.model_dump() if isinstance(item, BaseModel) else item for item in output)
if any(
not isinstance(item, Mapping) or item.get("type") in ("function_call", "custom_tool_call") for item in items
):
return ""
return str(getattr(response, "output_text", "") or "")
class _SurfaceOps:
"""One row per sampled call_type: how its logged request becomes a chat-shaped
request (messages plus translated generation params) and how its response yields
the judgeable final text. Membership in this table IS the sampling allowlist;
unknown call types fail closed. ``wire_params`` marks the surfaces whose params
come from the proxy's wire-body snapshot, which is taken before the guardrail
pre-call hook: those rows must not sample a request a pre-call guardrail rewrote,
or the shadow call would replay content (tools, unmasked entities) the guardrail
removed."""
__slots__ = ("chat_request", "final_text", "wire_params")
def __init__(
self,
chat_request: Callable[[Mapping[str, object], Mapping[str, object]], Mapping[str, object]],
final_text: Callable[[object], str],
wire_params: bool,
) -> None:
self.chat_request = chat_request
self.final_text = final_text
self.wire_params = wire_params
_CHAT_OPS: Final = _SurfaceOps(_chat_request_from_chat, _chat_final_text, wire_params=False)
_ANTHROPIC_OPS: Final = _SurfaceOps(_chat_request_from_anthropic_messages, _chat_final_text, wire_params=True)
_RESPONSES_OPS: Final = _SurfaceOps(_chat_request_from_responses, _responses_final_text, wire_params=True)
# Guardrail hooks that never rewrite the outbound request: they run in parallel with
# the call, on the response, or on logged copies. Anything else (pre_call, pre_mcp_call,
# a future mode) counts as request-mutating, failing closed.
_NON_MUTATING_GUARDRAIL_MODES: Final = frozenset(
("during_call", "post_call", "logging_only", "during_mcp_call", "post_mcp_call", "realtime_input_transcription")
)
def _request_mutating_guardrail_ran(request_metadata: Mapping[str, object]) -> bool:
"""Whether a guardrail that can rewrite the outbound request ran on this one, read
from the same guardrail-information entries spend logging uses. str-enum modes
compare equal to their plain-string values, and an entry whose mode is missing or
unrecognized counts as mutating."""
raw: Final = request_metadata.get("standard_logging_guardrail_information")
entries: Final = raw if isinstance(raw, Sequence) else ()
modes_per_entry: Final = tuple(entry.get("guardrail_mode") for entry in entries if isinstance(entry, Mapping))
return any(
not all(
mode in _NON_MUTATING_GUARDRAIL_MODES for mode in (modes if isinstance(modes, list | tuple) else (modes,))
)
for modes in modes_per_entry
)
# Translated-request keys that never forward to the shadow call: identity and transport,
# not generation. Empty-list values (e.g. tools) carry nothing and are dropped with them.
_UNFORWARDED_REQUEST_KEYS: Final = frozenset(("model", "messages", "stream", "stream_options", "metadata"))
def _forwards_nothing(value: object) -> bool:
return value is None or (isinstance(value, list) and len(value) == 0)
def _judgeable_sample(
ops: _SurfaceOps,
kwargs: Mapping[str, object],
model_parameters: Mapping[str, object],
response_obj: object,
) -> tuple[tuple[Mapping[str, object], ...], Mapping[str, object], str] | None:
"""The normalized chat conversation, the forwardable generation params, and the
judgeable final text; None when this request's shapes cannot be sampled (tool-final
turn, empty text, or a shape the owner transformations reject)."""
try:
request: Final = ops.chat_request(kwargs, model_parameters)
items: Final = _MESSAGE_ITEMS_ADAPTER.validate_python(request.get("messages"))
messages: Final = _CHAT_MESSAGES_ADAPTER.validate_python(
tuple(m.model_dump(exclude_none=True) if isinstance(m, BaseModel) else m for m in items)
)
except Exception as e: # noqa: BLE001 # a rejected shape is skipped, never sampled
verbose_logger.debug("shadow_eval: request normalization failed, skipping: %s", e)
return None
real_text: Final = ops.final_text(response_obj)
if not messages or not real_text:
return None
params: Final = MappingProxyType(
{k: v for k, v in request.items() if k not in _UNFORWARDED_REQUEST_KEYS and not _forwards_nothing(v)}
)
return messages, params, real_text
_SURFACE_OPS: Final[Mapping[str, _SurfaceOps]] = MappingProxyType(
{
"completion": _CHAT_OPS,
"acompletion": _CHAT_OPS,
"anthropic_messages": _ANTHROPIC_OPS,
"aresponses": _RESPONSES_OPS,
"responses": _RESPONSES_OPS,
}
)
PAIRWISE_JUDGE_SYSTEM_PROMPT: Final = """You are an impartial quality judge comparing two responses to the same conversation.
The responses are labeled A and B in random order. You do not know which system produced which.
Criteria: correctness, completeness, clarity, conciseness.
Return ONLY valid JSON in this exact format, no other text:
{
"preference": "A" | "B" | "tie",
"confidence": <0.0 to 1.0>,
"reasoning": "<one sentence>"
}"""
class PairwiseVerdict(BaseModel):
"""The judge's blind A/B verdict, validated at the parse boundary."""
preference: str = "tie"
confidence: float = 0.0
def _sample_hits(request_id: str, job_id: str, percentage: float) -> bool:
"""Deterministically decide whether a request falls in the shadowed slice: hash-based
rather than random so retries sample the same way and pods agree without coordination."""
digest: Final = hashlib.sha256(f"{job_id}:{request_id}".encode()).digest()
bucket: Final = int.from_bytes(digest[:8], "big") / float(2**64)
return bucket * 100.0 < percentage
def _judge_call_cost(response: object) -> float:
"""Price a judge call, treating an unmapped judge model as free rather than fatal."""
import litellm
try:
return litellm.completion_cost(completion_response=response) or 0.0
except Exception: # noqa: BLE001 # unmapped judge model: the verdict still counts, cost stays 0
return 0.0
def _unmask_preference(raw_preference: str, real_is_a: bool) -> str:
"""Map the judge's blind A/B/tie verdict back to real/shadow/tie."""
normalized: Final = raw_preference.strip().lower()
if normalized == "a":
return "real" if real_is_a else "shadow"
if normalized == "b":
return "shadow" if real_is_a else "real"
return "tie"
def _judge_user_prompt(conversation: str, response_a: str, response_b: str) -> str:
"""The judge prompt under one total character budget: each response is capped, and
the conversation tail gets whatever budget the responses left over."""
a: Final = response_a[:_MAX_JUDGE_RESPONSE_CHARS]
b: Final = response_b[:_MAX_JUDGE_RESPONSE_CHARS]
conversation_budget: Final = _MAX_JUDGE_PROMPT_CHARS - len(a) - len(b)
return (
f"Conversation:\n{conversation[-conversation_budget:]}\n\n"
f"Response A:\n{a}\n\n"
f"Response B:\n{b}\n\n"
"Which response is better?"
)
async def _key_or_team_is_over_budget(metadata: Mapping[str, object]) -> bool:
"""Whether the shadowed key or its team is over budget, decided by the same owners
the request path uses, so counter keys and thresholds can never drift from auth's.
Advisory and fail-open: real traffic on an over-budget key is already rejected at
auth (so nothing reaches the success hook), and this gate only closes the race
where the key crosses its budget while a request is in flight.
"""
try:
from litellm.exceptions import BudgetExceededError
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.auth.auth_checks import (
_team_max_budget_check,
_virtual_key_max_budget_check,
get_team_object,
)
from litellm.proxy.proxy_server import prisma_client, proxy_logging_obj, user_api_key_cache
except ImportError:
return False
auth: Final = metadata.get("user_api_key_auth")
if not isinstance(auth, UserAPIKeyAuth):
return False
try:
await _virtual_key_max_budget_check(valid_token=auth, proxy_logging_obj=proxy_logging_obj)
if auth.team_id:
team: Final = await get_team_object(
team_id=auth.team_id,
prisma_client=prisma_client,
user_api_key_cache=user_api_key_cache,
check_cache_only=True,
)
await _team_max_budget_check(team_object=team, valid_token=auth, proxy_logging_obj=proxy_logging_obj)
except BudgetExceededError:
return True
except Exception as e: # noqa: BLE001 # advisory gate: a failed read must not block sampling
verbose_logger.debug("shadow_eval: budget read failed: %s", e)
return False
def _routing_decision(metadata: Mapping[str, object]) -> Mapping[str, object]:
"""The routing decision a pre-routing strategy wrote to a call's metadata, empty when
a plain model served it. Read off the sampled request for the control arm, and off the
shadow call's own write-back for the shadow arm."""
decision: Final = metadata.get("routing_decision")
return decision if isinstance(decision, Mapping) else _EMPTY_METADATA
def _routed_tier(metadata: Mapping[str, object]) -> str | None:
decision: Final = _routing_decision(metadata)
raw: Final = decision.get("tier_label") or decision.get("tier")
return str(raw) if raw is not None else None
def _request_was_routed_by(request_metadata: Mapping[str, object], router_name: str) -> bool:
"""Whether the router under evaluation served this request, which is what decides
the direction it belongs to. A forward job skips its own router's traffic, since
duplicating it would compare the router to itself: guaranteed ties, judge spend for
zero information. A reverse job samples exactly that traffic and nothing else."""
return _routing_decision(request_metadata).get("router_model_name") == router_name
@dataclass(frozen=True, slots=True)
class _CallFailure:
"""A shadow or judge call that produced no usable response. cost carries any judge
spend the failed attempt still billed, so job-level judge_spend never undercounts."""
error: str
cost: float = 0.0
@dataclass(frozen=True, slots=True)
class _ShadowResponse:
"""A successful shadow call, with what the attempt row records."""
text: str
model: str
tier: str | None
@dataclass(frozen=True, slots=True)
class _JudgeVerdict:
"""A parsed judge verdict, unmasked back to real/shadow/tie."""
preference: str
confidence: float
cost: float
class ActiveShadowEvalJob(BaseModel):
"""One active job as the sampling path needs it, validated straight off the untyped
job row: immutable config plus the attempt count as of the cache fill (the turn
budget's staleness is bounded by the cache TTL). Every way a row can be unsamplable
is a validation error here, so a bad row is skipped rather than sampled wrongly."""
model_config = ConfigDict(frozen=True, from_attributes=True)
id: str
router_name: str
direction: ShadowEvalDirection = "forward"
baseline_model: str | None = None
shadow_percentage: float
judge_model: str
max_turns: int
ends_at: datetime
attempts: int = 0
@field_validator("ends_at")
@classmethod
def _as_utc(cls, value: datetime) -> datetime:
return value.replace(tzinfo=timezone.utc) if value.tzinfo is None else value
@model_validator(mode="after")
def _baseline_model_matches_direction(self) -> "ActiveShadowEvalJob":
if (self.baseline_model is not None) != (self.direction == "reverse"):
raise ValueError("baseline_model is set for exactly the reverse jobs")
return self
@property
def shadow_target(self) -> str:
"""The model the duplicated arm calls: the router itself for a forward job, the
fixed baseline for a reverse one. Total because the validator above pins
baseline_model to reverse jobs and only those."""
return self.baseline_model or self.router_name
def _as_active_job(record: object, attempts: int) -> ActiveShadowEvalJob | None:
"""The sampling path's view of one job row, or None for a row it cannot sample: an
unknown direction, or a reverse job with no baseline model to duplicate against.
Failing closed here is what keeps the dispatch path total."""
try:
job: Final = ActiveShadowEvalJob.model_validate(record)
except ValidationError as e:
verbose_logger.debug("shadow_eval: skipping unsamplable job row: %s", e)
return None
return job.model_copy(update={"attempts": attempts})
_jobs_cache: Final = InMemoryCache(max_size_in_memory=4, default_ttl=_JOBS_CACHE_TTL_SECONDS)
_JOBS_CACHE_KEY: Final = "shadow_eval:active_jobs"
class ShadowEvalLogger(CustomLogger):
"""Fires blind pairwise shadow evaluations for keys with an active shadow-eval job."""
def __init__(
self,
router_provider: Callable[[], "Router | None"] | None = None,
prisma_provider: Callable[[], "PrismaClient | None"] | None = None,
jobs_cache: InMemoryCache | None = None,
) -> None:
"""Providers are callables so the proxy's lazily-initialized globals are resolved
at call time, not at logger construction."""
self._router_provider = router_provider or default_router_provider
self._prisma_provider = prisma_provider or _default_prisma_provider
self._jobs_cache = jobs_cache or _jobs_cache
self._inflight_shadow_tasks: int = 0
# Starts per job since the last cache fill, never decremented within a
# generation; the refill absorbs written rows and resets.
self._job_starts: dict[str, int] = {} # mutable-ok: per-generation counter
async def _active_jobs(self) -> Mapping[str, tuple[ActiveShadowEvalJob, ...]]:
"""Active jobs by api_key_id, cache-first. A key holds at most one job per
direction, so the value is a collection. A DB fault returns empty without
caching, so sampling pauses for that request and the next one retries."""
cached: Final = await self._jobs_cache.async_get_cache(_JOBS_CACHE_KEY)
if cached is not None:
return cached # pyright: ignore[reportReturnType] # cache stores exactly this mapping shape
prisma: Final = self._prisma_provider()
if prisma is None:
return _EMPTY_JOBS
try:
records: Final = await prisma.db.litellm_shadowevaljob.find_many(
where={ # mutable-ok: Prisma filter
"stopped_at": None,
"ends_at": {"gt": datetime.now(timezone.utc)}, # mutable-ok: Prisma filter
},
)
grouped: Final = (
await prisma.db.litellm_shadowevalattempt.group_by(
by=["job_id"],
count=True,
where={"job_id": {"in": [str(record.id) for record in records]}}, # mutable-ok: Prisma filter
)
if records
else ()
)
attempt_counts: Final = {str(row["job_id"]): int(row["_count"]["_all"]) for row in grouped or []}
by_key: Final = tuple(
sorted(
(
(str(record.api_key_id), job)
for record in records or []
if (job := _as_active_job(record, attempt_counts.get(str(record.id), 0))) is not None
),
key=itemgetter(0),
)
)
jobs: Final = MappingProxyType(
{key: tuple(job for _, job in group) for key, group in groupby(by_key, key=itemgetter(0))}
)
await self._jobs_cache.async_set_cache(_JOBS_CACHE_KEY, jobs)
self._job_starts = {} # rebind-ok: new generation, counts absorbed into the fill
return jobs
except Exception as e: # noqa: BLE001 # a DB blip must never break request logging
verbose_logger.debug("shadow_eval: active-job read failed: %s", e)
return _EMPTY_JOBS
#### hook ####
async def async_log_success_event(
self,
kwargs: Mapping[str, object],
response_obj: object,
start_time: object,
end_time: object,
) -> None:
try:
payload: Final[StandardLoggingPayload | None] = kwargs.get("standard_logging_object") # pyright: ignore[reportAssignmentType] # untyped callback kwargs
if payload is None:
return
raw_meta: Final = get_litellm_metadata_from_kwargs(dict(kwargs)) # mutable-ok: helper needs dict
request_metadata: Final = raw_meta if isinstance(raw_meta, Mapping) else _EMPTY_METADATA
if request_metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY):
return # internal sub-call (our own shadow/judge, a classifier), not user traffic
# redaction rewrites logged content before callbacks run, so this hook
# only ever sees placeholders for a redacted request
if should_redact_message_logging(dict(kwargs)): # mutable-ok: predicate takes a plain dict
return
metadata: Final = payload.get("metadata") or _EMPTY_METADATA
api_key_hash: Final = metadata.get("user_api_key_hash")
if not api_key_hash:
return
request_id: Final = payload.get("id") or ""
if not request_id:
return
ops: Final = _SURFACE_OPS.get(str(payload.get("call_type") or ""))
if ops is None:
return # only surfaces this table can normalize are comparable; unknown types fail closed
if ops.wire_params and _request_mutating_guardrail_ran(request_metadata):
return # the wire-body snapshot predates the rewrite; replaying it would resurrect stripped content
# A key can hold one job per direction, and a request routed by one job's
# router while bypassing the other's qualifies for both. Each is separately
# budgeted, so both fire; the request is normalized once, and only when at
# least one job sampled it.
eligible: Final = tuple(
job
for job in (await self._active_jobs()).get(str(api_key_hash), ())
if datetime.now(timezone.utc) < job.ends_at
and job.attempts + self._job_starts.get(job.id, 0) < job.max_turns
and _sample_hits(request_id, job.id, job.shadow_percentage)
and _request_was_routed_by(request_metadata, job.router_name) == (job.direction == "reverse")
)
if not eligible:
return
sample: Final = _judgeable_sample(
ops,
kwargs,
MappingProxyType(dict(payload.get("model_parameters") or {})), # mutable-ok: frozen snapshot
response_obj,
)
if sample is None:
return
messages, shadow_params, real_text = sample
control_tier: Final = _routed_tier(request_metadata)
for job in eligible:
if self._inflight_shadow_tasks >= _MAX_CONCURRENT_SHADOW_TASKS:
return
self._job_starts[job.id] = self._job_starts.get(job.id, 0) + 1
self._inflight_shadow_tasks += 1
asyncio.create_task(
self._run_shadow_eval(
job=job,
request_id=request_id,
messages=messages,
real_text=real_text,
real_model=payload.get("model") or "",
control_tier=control_tier,
shadow_params=shadow_params,
parent_metadata=MappingProxyType(dict(request_metadata)), # mutable-ok: frozen snapshot
)
).add_done_callback(self._release_shadow_slot)
except Exception as e: # noqa: BLE001 # logging hooks must never fail the request
verbose_logger.debug("shadow_eval: failed to schedule task: %s", e)
def _release_shadow_slot(self, _task: "asyncio.Task[None]") -> None:
self._inflight_shadow_tasks -= 1
#### the detached pipeline: one attempt row per sampled request, verdict or error ####
async def _run_shadow_eval(
self,
job: ActiveShadowEvalJob,
request_id: str,
messages: Sequence[Mapping[str, object]],
real_text: str,
real_model: str,
control_tier: str | None,
shadow_params: Mapping[str, object],
parent_metadata: Mapping[str, object],
) -> None:
"""Budget gate -> shadow call -> blind judge -> one attempt row. The prisma gate
sits above the dispatch so no provider spend happens without a place to record
the outcome, and the budget read lives here rather than in the success hook."""
prisma: Final = self._prisma_provider()
try:
if prisma is None:
return
if await _key_or_team_is_over_budget(parent_metadata):
return
shadow: Final = await self._call_router_shadow(job.shadow_target, messages, shadow_params, parent_metadata)
if isinstance(shadow, _CallFailure):
await self._record_attempt(prisma, job, request_id, control_tier, outcome="error", error=shadow.error)
return
verdict: Final = await self._call_judge(
judge_model=job.judge_model,
messages=messages,
real_text=real_text,
shadow_text=shadow.text,
parent_metadata=parent_metadata,
)
if isinstance(verdict, _CallFailure):
await self._record_attempt(
prisma,
job,
request_id,
control_tier,
outcome="error",
error=verdict.error,
shadow=shadow,
judge_cost=verdict.cost,
)
return
await self._record_attempt(
prisma,
job,
request_id,
control_tier,
outcome=verdict.preference,
shadow=shadow,
real_model=real_model,
confidence=verdict.confidence,
judge_cost=verdict.cost,
)
except Exception as e: # noqa: BLE001 # detached task: record what happened, never raise
verbose_logger.debug("shadow_eval: pipeline failed for %s: %s", request_id, e)
await self._record_attempt(
prisma, job, request_id, control_tier, outcome="error", error=f"pipeline error: {e}"
)
@staticmethod
async def _record_attempt(
prisma: "PrismaClient | None",
job: ActiveShadowEvalJob,
request_id: str,
control_tier: str | None,
*,
outcome: str,
shadow: _ShadowResponse | None = None,
real_model: str = "",
confidence: float | None = None,
judge_cost: float = 0.0,
error: str | None = None,
) -> None:
if prisma is None:
return
try:
await prisma.db.litellm_shadowevalattempt.create(
data={ # mutable-ok: Prisma payload
"job_id": job.id,
"request_id": request_id,
"outcome": outcome,
"tier": control_tier if job.direction == "reverse" else (shadow.tier if shadow else None),
"real_model": real_model or None,
"shadow_model": shadow.model if shadow else None,
"confidence": confidence,
"judge_cost": judge_cost,
"error": error[:_MAX_ERROR_CHARS] if error else None,
}
)
except Exception as e: # noqa: BLE001 # a lost row degrades sample size, nothing can disagree with it
verbose_logger.debug("shadow_eval: attempt write failed for %s: %s", request_id, e)
async def _call_router_shadow(
self,
target_model: str,
messages: Sequence[Mapping[str, object]],
shadow_params: Mapping[str, object],
parent_metadata: Mapping[str, object],
) -> "_ShadowResponse | _CallFailure":
"""Send the prompt through the arm nobody was served: the auto-router under
evaluation, or a reverse job's fixed baseline model. The metadata carries the
shadowed key's identity (spend attribution) and receives a routing decision
write-back, which a plain baseline model simply never makes."""
router: Final = self._router_provider()
if router is None:
return _CallFailure("no router configured on this pod")
shadow_metadata: Final[dict[str, object]] = ( # mutable-ok: router writes its routing decision back
sanitized_forwardable_call_metadata(parent_metadata, SHADOW_EVAL_ROUTER_CALL_ORIGIN)
)
try:
response: Final = await router.acompletion(
model=target_model,
messages=messages, # pyright: ignore[reportArgumentType] # snapshot of the SDK's own message dicts
metadata=shadow_metadata,
num_retries=0,
fallbacks=[], # mutable-ok: SDK kwarg; a failed shadow is a recorded error, never a spend multiplier
**shadow_params,
)
except Exception as e: # noqa: BLE001 # provider errors become error rows, not crashes
verbose_logger.debug("shadow_eval: router call failed: %s", e)
return _CallFailure(f"shadow router call failed: {e}")
text: Final = _chat_final_text(response)
if not text:
return _CallFailure("shadow router returned an empty response")
return _ShadowResponse(
text=text,
model=str(getattr(response, "model", None) or _routing_decision(shadow_metadata).get("routed_model") or ""),
tier=_routed_tier(shadow_metadata),
)
async def _call_judge(
self,
judge_model: str,
messages: Sequence[Mapping[str, object]],
real_text: str,
shadow_text: str,
parent_metadata: Mapping[str, object],
) -> "_JudgeVerdict | _CallFailure":
"""Blind pairwise judge with A/B labels randomized to cancel position bias."""
real_is_a: Final = random.random() < 0.5
response_a: Final = real_text if real_is_a else shadow_text
response_b: Final = shadow_text if real_is_a else real_text
conversation: Final = "\n".join(
f"{str(m.get('role', 'user')).upper()}: {extract_text_from_content(m.get('content'))}"
for m in messages
if m.get("content") is not None
)
judge_metadata: Final = sanitized_forwardable_call_metadata(parent_metadata, SHADOW_EVAL_JUDGE_CALL_ORIGIN)
judge_messages: Final = [ # mutable-ok: SDK takes a list
{"role": "system", "content": PAIRWISE_JUDGE_SYSTEM_PROMPT}, # mutable-ok: SDK message
{
"role": "user",
"content": _judge_user_prompt(conversation, response_a, response_b),
}, # mutable-ok: SDK message
]
try:
response: Final = await judge_acompletion(
self._router_provider(),
judge_model,
judge_messages, # pyright: ignore[reportArgumentType] # plain SDK message dicts
temperature=0,
max_tokens=JUDGE_MAX_OUTPUT_TOKENS,
metadata=judge_metadata,
)
except Exception as e: # noqa: BLE001 # judge outages become error rows, not crashes
verbose_logger.debug("shadow_eval: judge call failed: %s", e)
return _CallFailure(f"judge call failed: {e}")
try:
raw: Final = response["choices"][0]["message"]["content"] or ""
verdict: Final = PairwiseVerdict.model_validate(parse_json_verdict(raw))
except Exception as e: # noqa: BLE001 # malformed verdicts become error rows
verbose_logger.debug("shadow_eval: unparseable judge verdict: %s", e)
return _CallFailure(f"unparseable judge verdict: {e}", cost=_judge_call_cost(response))
return _JudgeVerdict(
preference=_unmask_preference(verdict.preference, real_is_a),
confidence=max(0.0, min(1.0, verdict.confidence)),
cost=_judge_call_cost(response),
)
_EMPTY_JOBS: Final[Mapping[str, tuple[ActiveShadowEvalJob, ...]]] = MappingProxyType({})
def _default_prisma_provider() -> "PrismaClient | None":
try:
from litellm.proxy.proxy_server import prisma_client
except ImportError:
return None
return prisma_client

49
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
@ -73,6 +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
@ -394,7 +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.
@ -462,7 +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()
@ -833,7 +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)
@ -1278,8 +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,
@ -1470,7 +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 [])
@ -1478,9 +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:
@ -1675,8 +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.
@ -1700,7 +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
):

38
litellm/interactions/litellm_responses_transformation/handler.py
View file

@ -2,8 +2,8 @@
Handler for transforming interactions API requests to litellm.responses requests.
"""
from collections.abc import AsyncIterator, Coroutine, Iterator
from typing import Any, Final, cast
from collections.abc import AsyncIterator, Callable, Coroutine, Iterator
from typing import Any, Final
import litellm
from litellm.interactions.litellm_responses_transformation.streaming_iterator import (
@ -37,7 +37,7 @@ class LiteLLMResponsesInteractionsHandler:
) -> (
InteractionsAPIResponse
| Iterator[InteractionsAPIStreamingResponse]
| Coroutine[Any, Any, InteractionsAPIResponse | AsyncIterator[InteractionsAPIStreamingResponse]]
| Coroutine[object, object, InteractionsAPIResponse | AsyncIterator[InteractionsAPIStreamingResponse]]
):
"""
Handle Interactions API request by calling litellm.responses().
@ -55,13 +55,15 @@ class LiteLLMResponsesInteractionsHandler:
InteractionsAPIResponse or streaming iterator
"""
# Transform interactions request to responses request
responses_request = LiteLLMResponsesInteractionsConfig.transform_interactions_request_to_responses_request(
model=model,
input=input,
optional_params=optional_params,
custom_llm_provider=custom_llm_provider,
stream=stream,
**kwargs,
responses_request: Final = (
LiteLLMResponsesInteractionsConfig.transform_interactions_request_to_responses_request(
model=model,
input=input,
optional_params=optional_params,
custom_llm_provider=custom_llm_provider,
stream=stream,
**kwargs,
)
)
if _is_async:
@ -76,7 +78,10 @@ class LiteLLMResponsesInteractionsHandler:
# Call litellm.responses()
# Note: litellm.responses() returns Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]
# but the type checker may see it as a coroutine in some contexts
responses_response: Final = litellm.responses(
responses_fn: Final[Callable[..., ResponsesAPIResponse | BaseResponsesAPIStreamingIterator]] = vars(litellm)[
"responses"
]
responses_response: Final = responses_fn(
**responses_request,
)
@ -92,8 +97,7 @@ class LiteLLMResponsesInteractionsHandler:
)
# At this point, responses_response must be ResponsesAPIResponse (not streaming)
# Cast to satisfy type checker since we've already checked it's not a streaming iterator
responses_api_response: Final = cast(ResponsesAPIResponse, responses_response)
responses_api_response: Final = responses_response
# Transform responses response to interactions response
return LiteLLMResponsesInteractionsConfig.transform_responses_response_to_interactions_response(
@ -112,7 +116,10 @@ class LiteLLMResponsesInteractionsHandler:
"""Async handler for interactions API requests."""
# Call litellm.aresponses()
# Note: litellm.aresponses() returns Union[ResponsesAPIResponse, BaseResponsesAPIStreamingIterator]
responses_response: Final = await litellm.aresponses(
aresponses_fn: Final[
Callable[..., Coroutine[object, object, ResponsesAPIResponse | BaseResponsesAPIStreamingIterator]]
] = vars(litellm)["aresponses"]
responses_response: Final = await aresponses_fn(
**responses_request,
)
@ -128,8 +135,7 @@ class LiteLLMResponsesInteractionsHandler:
)
# At this point, responses_response must be ResponsesAPIResponse (not streaming)
# Cast to satisfy type checker since we've already checked it's not a streaming iterator
responses_api_response: Final = cast(ResponsesAPIResponse, responses_response)
responses_api_response: Final = responses_response
# Transform responses response to interactions response
return LiteLLMResponsesInteractionsConfig.transform_responses_response_to_interactions_response(

154
litellm/interactions/litellm_responses_transformation/transformation.py
View file

@ -2,12 +2,16 @@
Transformation utilities for bridging Interactions API to Responses API.
This module handles transforming between:
- Interactions API format (Google's format with Turn[], system_instruction, etc.)
- Interactions API format (Google's format with Step[]/Turn[], system_instruction, etc.)
- Responses API format (OpenAI's format with input[], instructions, etc.)
"""
from collections.abc import Mapping, Sequence
from types import MappingProxyType
from typing import Any, Final, cast
from pydantic import BaseModel
from litellm.types.interactions import (
InteractionInput,
InteractionsAPIOptionalRequestParams,
@ -19,6 +23,8 @@ from litellm.types.llms.openai import (
ResponsesAPIResponse,
)
_STEP_TYPE_ROLES: Final = MappingProxyType({"user_input": "user", "model_output": "assistant"})
class LiteLLMResponsesInteractionsConfig:
"""Configuration class for transforming between Interactions API and Responses API."""
@ -91,112 +97,94 @@ class LiteLLMResponsesInteractionsConfig:
Interactions API input can be:
- string: "Hello"
- Turn[]: [{"role": "user", "content": [...]}]
- Content object
- Step[]: [{"type": "user_input", "content": [...]}, {"type": "model_output", "content": [...]}]
- Turn[] (legacy): [{"role": "user", "content": [...]}]
- Content | Content[]: one user message worth of content parts
Responses API input is:
- string: "Hello"
- Message[]: [{"role": "user", "content": [...]}]
- Message[]: [{"role": "user", "content": [{"type": "input_text", ...}]}]
"""
if isinstance(input, str):
# ResponseInputParam accepts str
return cast(ResponseInputParam, input)
if isinstance(input, list):
# Turn[] format - convert to Responses API Message[] format
messages: Final = []
for turn in input:
if isinstance(turn, dict):
role = turn.get("role", "user")
content = turn.get("content", [])
transformed: Final = (
[
LiteLLMResponsesInteractionsConfig._transform_history_item(item)
for item in input
if LiteLLMResponsesInteractionsConfig._is_history_item(item)
]
if any(LiteLLMResponsesInteractionsConfig._is_history_item(item) for item in input)
else [
{
"role": "user",
"content": LiteLLMResponsesInteractionsConfig._transform_content_array(input, "user"),
}
]
)
return cast(ResponseInputParam, transformed)
# Transform content array
transformed_content = LiteLLMResponsesInteractionsConfig._transform_content_array(content)
messages.append(
{
"role": role,
"content": transformed_content,
}
)
elif isinstance(turn, Turn):
# Pydantic model
role = turn.role if hasattr(turn, "role") else "user"
content = turn.content if hasattr(turn, "content") else []
# Ensure content is a list for _transform_content_array
# Cast to List[Any] to handle various content types
if isinstance(content, list):
content_list: list[Any] = list(content)
elif content is not None:
content_list = [content]
else:
content_list = []
transformed_content = LiteLLMResponsesInteractionsConfig._transform_content_array(content_list)
messages.append(
{
"role": role,
"content": transformed_content,
}
)
return cast(ResponseInputParam, messages)
# Single content object - wrap in message
if isinstance(input, dict):
raw_content: Final = input.get("content")
content_items: Final = raw_content if isinstance(raw_content, list) else [input]
return cast(
ResponseInputParam,
[
{
"role": "user",
"content": LiteLLMResponsesInteractionsConfig._transform_content_array(
input.get("content", []) if isinstance(input.get("content"), list) else [input]
),
"content": LiteLLMResponsesInteractionsConfig._transform_content_array(content_items, "user"),
}
],
)
# Fallback: convert to string
return cast(ResponseInputParam, str(input))
@staticmethod
def _transform_content_array(content: list[Any]) -> list[dict[str, Any]]:
"""Transform Interactions API content array to Responses API format."""
if not isinstance(content, list):
# Single content item - wrap in array
content = [content]
def _is_history_item(item: object) -> bool:
if isinstance(item, Turn):
return True
return isinstance(item, dict) and ("role" in item or item.get("type") in _STEP_TYPE_ROLES)
transformed: Final[list[dict[str, Any]]] = []
for item in content:
if isinstance(item, dict):
# Already in dict format, pass through
transformed.append(item)
elif isinstance(item, str):
# Plain string - wrap in text format
transformed.append({"type": "text", "text": item})
else:
# Pydantic model or other - convert to dict
if hasattr(item, "model_dump"):
dumped = item.model_dump()
if isinstance(dumped, dict):
transformed.append(dumped)
else:
# Fallback: wrap in text format
transformed.append({"type": "text", "text": str(dumped)})
elif hasattr(item, "dict"):
dumped = item.dict()
if isinstance(dumped, dict):
transformed.append(dumped)
else:
# Fallback: wrap in text format
transformed.append({"type": "text", "text": str(dumped)})
else:
# Fallback: wrap in text format
transformed.append({"type": "text", "text": str(item)})
@staticmethod
def _transform_history_item(item: object) -> Mapping[str, object]:
raw: Final = item.model_dump(exclude_none=True) if isinstance(item, Turn) else item
fields: Final = raw if isinstance(raw, Mapping) else {}
role: Final = LiteLLMResponsesInteractionsConfig._responses_role(fields)
raw_content: Final = fields.get("content")
content_items: Final = (
raw_content if isinstance(raw_content, list) else [] if raw_content is None else [raw_content]
)
return {
"role": role,
"content": LiteLLMResponsesInteractionsConfig._transform_content_array(content_items, role),
}
return transformed
@staticmethod
def _responses_role(item: Mapping[str, object]) -> str:
step_role: Final = _STEP_TYPE_ROLES.get(str(item.get("type", "")))
if step_role is not None:
return step_role
raw_role: Final = str(item.get("role") or "user")
return "assistant" if raw_role == "model" else raw_role
@staticmethod
def _transform_content_array(content: Sequence[object], role: str) -> Sequence[Mapping[str, object]]:
"""Transform Interactions API content parts to Responses API parts for the given role."""
return [LiteLLMResponsesInteractionsConfig._transform_content_item(item, role) for item in content]
@staticmethod
def _transform_content_item(item: object, role: str) -> Mapping[str, object]:
text_type: Final = "output_text" if role == "assistant" else "input_text"
if isinstance(item, str):
return {"type": text_type, "text": item}
if isinstance(item, Mapping):
if item.get("type") == "text":
return {"type": text_type, "text": str(item.get("text", ""))}
return item
if isinstance(item, BaseModel):
return LiteLLMResponsesInteractionsConfig._transform_content_item(item.model_dump(exclude_none=True), role)
return {"type": text_type, "text": str(item)}
@staticmethod
def transform_responses_response_to_interactions_response(

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

15
litellm/litellm_core_utils/exception_mapping_utils.py
View file

@ -34,12 +34,16 @@ class ExceptionCheckers:
"""
@staticmethod
def is_error_str_rate_limit(error_str: str) -> bool:
def is_error_str_rate_limit(error_str: str, status_code: int | None = None) -> bool:
"""
Check if an error string indicates a rate limit error.
Args:
error_str: The error string to check
status_code: The HTTP status the provider returned, when known. Gates only the
bare-number branch: providers echo the request back in validation errors and
429 is an ordinary token id, so an echoed prompt can put a standalone 429 in
the body of a 400. The phrase branches stay ungated (#11455).
Returns:
True if the error indicates a rate limit, False otherwise
@ -47,8 +51,9 @@ class ExceptionCheckers:
if not isinstance(error_str, str):
return False
# Only treat 429 as a rate limit signal when it appears as a standalone token
if re.search(r"\b429\b", error_str):
# A standalone 429 counts unless the provider's own status says otherwise. The
# status is read off an arbitrary exception, so a non-integer means "unknown".
if re.search(r"\b429\b", error_str) and (not isinstance(status_code, int) or status_code == 429):
return True
_error_str_lower: Final = error_str.lower()
@ -280,7 +285,9 @@ def _map_openai_exception(
else:
exception_provider = custom_llm_provider[0].upper() + custom_llm_provider[1:] + "Exception"
if ExceptionCheckers.is_error_str_rate_limit(error_str):
if ExceptionCheckers.is_error_str_rate_limit(
error_str, status_code=getattr(original_exception, "status_code", None)
):
raise RateLimitError(
message=f"RateLimitError: {exception_provider} - {message}",
model=model,

18
litellm/litellm_core_utils/get_litellm_params.py
View file

@ -1,3 +1,5 @@
from collections.abc import Mapping, MutableMapping
from types import MappingProxyType
from typing import Final
from litellm.llms.openai.data_residency import infer_openai_data_residency
@ -184,3 +186,19 @@ def get_litellm_params(
litellm_params[key] = kwargs[key]
return litellm_params
def add_trusted_model_credentials_to_litellm_params(
litellm_params_dict: MutableMapping[str, object], kwargs: Mapping[str, object]
) -> None:
"""
Carry the immutable server-side credential snapshot into litellm_params.
get_litellm_params has a fixed signature, so callers that need the snapshot to
survive into the logging object and the downstream file read have to re-add it. Only
a MappingProxyType is accepted, since providers resolve trusted configuration such
as a Bedrock file bucket from it and must not read a request-supplied mapping.
"""
trusted_model_credentials: Final = kwargs.get("_litellm_internal_model_credentials")
if isinstance(trusted_model_credentials, MappingProxyType):
litellm_params_dict["_litellm_internal_model_credentials"] = trusted_model_credentials

94
litellm/litellm_core_utils/internal_call_metadata.py Normal file
View file

@ -0,0 +1,94 @@
"""Metadata a request forwards to the internal LLM sub-calls it triggers.
Internal features (the auto-router's classifier and embeddings, shadow eval's shadow and
judge calls) bill real provider spend that nobody typed a prompt for. That spend must land
on the same key/team/org/user as the request that caused it, so the sub-call carries the
caller's identity metadata, minus two things that must never be forwarded as-is:
* ``user_api_key_budget_reservation`` (and the reservation nested inside
``user_api_key_auth``) belongs to the parent completion. If a sub-call's cost callback
sees it, that callback finalizes the reservation and the parent's own callback then
skips incrementing the key/team budget counters, losing the parent's spend.
``user_api_key_auth`` itself is kept, sanitized, because model access-group filtering
needs it.
* The sub-call is stamped with ``INTERNAL_CALL_ORIGIN_METADATA_KEY`` so its spend log row
records that it is not traffic the caller sent.
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Final
from litellm.constants import INTERNAL_CALL_ORIGIN_METADATA_KEY
from litellm.types.utils import InternalCallOrigin
BUDGET_RESERVATION_METADATA_KEYS: Final = frozenset({"user_api_key_budget_reservation"})
_USER_API_KEY_AUTH_KEY: Final = "user_api_key_auth"
FORWARDABLE_IDENTITY_METADATA_KEYS: Final = frozenset(
{
"user_api_key",
"user_api_key_hash",
"user_api_key_alias",
"user_api_key_team_id",
"user_api_key_org_id",
"user_api_key_user_id",
"user_api_key_end_user_id",
_USER_API_KEY_AUTH_KEY,
}
)
"""The caller-identity subset a detached sub-call needs to be attributed and
budget-checked like the request that spawned it. Everything else on the parent's metadata
(routing decision, guardrail state, logging payload) describes the parent call and would
be a lie on a sub-call that runs after it returned."""
def sanitize_user_api_key_auth(auth: object) -> object:
"""Copy of the auth object with its budget reservation removed; the cost callback
falls back to reading the reservation from inside the auth object."""
if isinstance(auth, dict):
return {k: v for k, v in auth.items() if k != "budget_reservation"} # mutable-ok: SDK metadata value
reservation: Final[object] = getattr(auth, "budget_reservation", None)
model_copy: Final[object] = getattr(auth, "model_copy", None)
if reservation is not None and callable(model_copy):
return model_copy(update={"budget_reservation": None}) # mutable-ok: pydantic update payload
return auth
def _sanitized(parent_metadata: Mapping[str, object]) -> dict[str, object]: # mutable-ok: SDK metadata kwarg
return { # mutable-ok: SDK metadata kwarg
k: sanitize_user_api_key_auth(v) if k == _USER_API_KEY_AUTH_KEY else v
for k, v in parent_metadata.items()
if k not in BUDGET_RESERVATION_METADATA_KEYS
}
def forwarded_internal_call_metadata(
parent_metadata: Mapping[str, object] | None,
call_origin: InternalCallOrigin,
) -> dict[str, object]: # mutable-ok: SDK metadata kwarg
"""Parent metadata, minus its budget reservation, stamped with the sub-call's origin.
For sub-calls made inside the parent request (classifier, embeddings), where the
parent's full context still describes the call being made.
"""
if not parent_metadata:
return {} # mutable-ok: SDK metadata kwarg
return _sanitized(parent_metadata) | { # mutable-ok: SDK metadata kwarg
INTERNAL_CALL_ORIGIN_METADATA_KEY: call_origin
}
def sanitized_forwardable_call_metadata(
parent_metadata: Mapping[str, object],
call_origin: InternalCallOrigin,
) -> dict[str, object]: # mutable-ok: SDK metadata kwarg
"""Just the caller's identity, stamped with the sub-call's origin.
For sub-calls detached from the parent request (shadow eval), which outlive it and
must not inherit per-request state such as its routing decision or logging payload.
"""
identity: Final = {k: v for k, v in parent_metadata.items() if k in FORWARDABLE_IDENTITY_METADATA_KEYS}
return _sanitized(identity) | {INTERNAL_CALL_ORIGIN_METADATA_KEY: call_origin} # mutable-ok: SDK metadata kwarg

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,
),

88
litellm/litellm_core_utils/llm_cost_calc/tiered_pricing.py
View file

@ -1,5 +1,5 @@
"""
Provider-neutral graduated tiered pricing calculation.
Provider-neutral tiered pricing calculation.
Shared by provider cost calculators (e.g. Dashscope) and the proxy budget
reservation logic so neither has to depend on the other.
@ -25,80 +25,6 @@ def _coerce_cost_per_token(value: float | str | None) -> float:
return float(value)
def calculate_tiered_cost(
tokens: int,
tiered_pricing: list[dict],
cost_key: str,
fallback_cost_key: str | None = None,
) -> float:
"""
Calculate cost for a given number of tokens based on a true tiered pricing structure.
This function iterates through sorted pricing tiers, calculates the cost for the
number of tokens that fall into each tier's range, and sums them up to get the total cost.
Args:
tokens (int): The total number of tokens to calculate the cost for.
tiered_pricing (List[dict]): A list of dictionaries, where each dictionary
represents a pricing tier.
cost_key (str): The key in the tier dictionary that holds the per-token cost
(e.g., 'input_cost_per_token').
fallback_cost_key (Optional[str], optional): A fallback key to use if the
primary `cost_key` is not found in a tier. Defaults to None.
Returns:
float: The total calculated cost for the given tokens.
Example:
>>> tiered_pricing = [
... {"range": [0, 100000], "input_cost_per_token": 0.0001},
... {"range": [100000, 500000], "input_cost_per_token": 0.00005},
... ]
Calculating cost for 150,000 tokens:
(100,000 * 0.0001) + (50,000 * 0.00005) = $12.5
"""
if not tiered_pricing or tokens <= 0:
return 0.0
total_cost = 0.0
tokens_processed = 0
sorted_tiers: Final = sorted(tiered_pricing, key=lambda x: x.get("range", [0, 0])[0])
for tier in sorted_tiers:
if tokens_processed >= tokens:
break
tier_range = tier.get("range", [])
if len(tier_range) != 2:
continue
range_start, range_end = tier_range
if tokens <= range_start:
continue
tier_start = max(range_start, tokens_processed)
tier_end = min(range_end, tokens)
if tier_end > tier_start:
tokens_in_tier = tier_end - tier_start
cost_per_token = tier.get(cost_key) or tier.get(fallback_cost_key, 0)
total_cost += tokens_in_tier * _coerce_cost_per_token(cost_per_token)
tokens_processed = tier_end
# After loop, check if any tokens remain (i.e., tokens > highest tier's end range)
# and charge them at the last tier's rate.
if tokens_processed < tokens and sorted_tiers:
last_tier: Final = sorted_tiers[-1]
remaining_tokens: Final = tokens - tokens_processed
cost_per_token = last_tier.get(cost_key) or last_tier.get(fallback_cost_key, 0)
total_cost += remaining_tokens * _coerce_cost_per_token(cost_per_token)
return total_cost
def select_tier_for_input(
tiered_pricing: list[dict],
input_tokens: int,
@ -134,6 +60,12 @@ def tier_rate(
cost_key: str,
fallback_cost_key: str | None = None,
) -> float:
"""Read a per-token rate from a tier, coercing YAML string costs to float."""
raw: Final = tier.get(cost_key) or tier.get(fallback_cost_key, 0)
return _coerce_cost_per_token(raw)
"""Read a per-token rate from a tier, coercing YAML string costs to float.
A rate that is explicitly present wins over the fallback, an explicit zero
included, so a tier can declare a token type free.
"""
primary: Final = tier.get(cost_key)
if primary is not None:
return _coerce_cost_per_token(primary)
return _coerce_cost_per_token(tier.get(fallback_cost_key, 0))

47
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 +24,19 @@ from litellm.types.utils import (
)
def _output_item_type(output_item: object) -> str | None:
item_type: Final = output_item.get("type") if isinstance(output_item, dict) else getattr(output_item, "type", None)
return item_type if isinstance(item_type, str) else None
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
@ -117,10 +131,28 @@ class StandardBuiltInToolCostTracking:
if result is not None:
return result
return StandardBuiltInToolCostTracking.get_cost_for_web_search(
per_call_cost = StandardBuiltInToolCostTracking.get_cost_for_web_search(
web_search_options=standard_built_in_tools_params.get("web_search_options", None),
model_info=model_info,
)
return per_call_cost * StandardBuiltInToolCostTracking._count_web_search_calls(response_object)
@staticmethod
def _count_web_search_calls(response_object: object) -> int:
"""
Number of web searches to bill for on the per-call pricing path.
Providers that report a request count in usage (gemini, anthropic, xai, vertex) are handled by
get_cost_for_web_search_request and never reach here. This path prices per call, so it must count
the web_search_call items. Chat-completions responses only expose url_citation annotations with no
count, so they floor to a single billable search.
"""
if isinstance(response_object, ResponsesAPIResponse):
count = sum(
1 for output_item in response_object.output if _output_item_type(output_item) == "web_search_call"
)
return max(count, 1)
return 1
@staticmethod
def _handle_file_search_cost(
@ -351,6 +383,10 @@ 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 +406,8 @@ class StandardBuiltInToolCostTracking:
)
):
return True
if _usage_reports_server_side_web_search_calls(usage):
return True
return False
@ -430,12 +468,7 @@ class StandardBuiltInToolCostTracking:
Returns:
True if the ResponsesAPIResponse includes one of the specified output types, False otherwise.
"""
output: Final = response_object.output
for output_item in output:
_output_type: str | None = getattr(output_item, "type", None)
if _output_type == output_type:
return True
return False
return any(_output_item_type(output_item) == output_type for output_item in response_object.output)
@staticmethod
def _safe_get_model_info(model: str, custom_llm_provider: str | None = None) -> ModelInfo | None:

169
litellm/litellm_core_utils/llm_cost_calc/utils.py
View file

@ -8,6 +8,10 @@ from typing import Any, Final, Literal, TypedDict, cast
import litellm
from litellm._logging import verbose_logger
from litellm.litellm_core_utils.llm_cost_calc.tiered_pricing import (
select_tier_for_input,
tier_rate,
)
from litellm.types.utils import (
CacheCreationTokenDetails,
CallTypes,
@ -49,6 +53,12 @@ _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):
@ -89,7 +99,7 @@ def get_billable_input_tokens(usage: Usage) -> int:
Returns the number of billable input tokens.
Subtracts cached tokens from prompt tokens if applicable.
"""
details: Final = _parse_prompt_tokens_details(usage)
details: Final = parse_prompt_tokens_details(usage)
return usage.prompt_tokens - details["cache_hit_tokens"]
@ -201,8 +211,63 @@ def _parse_above_token_threshold(key: str) -> float:
return float(threshold_str.replace("k", "")) * (1000 if "k" in threshold_str else 1)
def _select_priced_tier(model_info: ModelInfo, usage: Usage) -> dict | None:
tiered_pricing: Final = model_info.get("tiered_pricing")
if not isinstance(tiered_pricing, list) or not tiered_pricing:
return None
tier: Final = select_tier_for_input(tiered_pricing=tiered_pricing, input_tokens=usage.prompt_tokens)
if tier is None or "input_cost_per_token" not in tier:
return None
return tier
def _get_tiered_reasoning_rate(model_info: ModelInfo, usage: Usage) -> float | None:
tier: Final = _select_priced_tier(model_info=model_info, usage=usage)
if tier is None:
return None
if "output_cost_per_reasoning_token" not in tier and "output_cost_per_token" not in tier:
return None
return tier_rate(tier, "output_cost_per_reasoning_token", "output_cost_per_token")
def _get_tiered_base_costs(model_info: ModelInfo, usage: Usage) -> tuple[float, float, float, float, float] | None:
"""
Resolve the base rates from a model's ``tiered_pricing`` table, if it has one.
Tiered pricing is all-or-nothing: one tier is picked from the request's input tokens
and every token of the request is billed at that tier's rate. Rates the tier does not
declare fall back to the tier's input rate, so a request never mixes tiers.
An output rate is the exception: a tier table that spells out only input rates would
otherwise serve every completion for free, so the model's own output rate stands in.
"""
tier: Final = _select_priced_tier(model_info=model_info, usage=usage)
if tier is None:
return None
cache_creation_cost: Final = tier_rate(tier, "cache_creation_input_token_cost", "input_cost_per_token")
completion_cost: Final = (
tier_rate(tier, "output_cost_per_token")
if "output_cost_per_token" in tier
else _get_cost_per_unit(model_info, "output_cost_per_token") or 0.0
)
return (
tier_rate(tier, "input_cost_per_token"),
completion_cost,
cache_creation_cost,
tier_rate(tier, "cache_creation_input_token_cost_above_1hr", "cache_creation_input_token_cost")
or cache_creation_cost,
tier_rate(tier, "cache_read_input_token_cost", "input_cost_per_token"),
)
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.
@ -210,9 +275,16 @@ 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)
"""
tiered_base_costs: Final = _get_tiered_base_costs(model_info=model_info, usage=usage)
if tiered_base_costs is not None:
return tiered_base_costs
# Get service tier aware cost keys
input_cost_key: Final = _get_service_tier_cost_key("input_cost_per_token", service_tier)
output_cost_key: Final = _get_service_tier_cost_key("output_cost_per_token", service_tier)
@ -262,7 +334,7 @@ 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
@ -457,7 +529,7 @@ class PromptTokensDetailsResult(TypedDict):
audio_length_seconds: float
def _parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult:
def parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult:
cache_hit_tokens: Final = cast(int | None, getattr(usage.prompt_tokens_details, "cached_tokens", 0)) or 0
cache_creation_tokens: Final = (
cast(
@ -527,7 +599,7 @@ class CompletionTokensDetailsResult(TypedDict):
video_tokens: int
def _parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult:
def parse_completion_tokens_details(usage: Usage) -> CompletionTokensDetailsResult:
audio_tokens: Final = (
cast(
int | None,
@ -681,6 +753,23 @@ def _get_regional_uplift_multiplier(model_info: ModelInfo, data_residency: str |
return 1.0
def get_provider_specific_geo_multiplier(model_info: ModelInfo, usage: Usage) -> float:
"""
Resolve the provider-specific regional pricing multiplier for the geo the
request was served from (``usage.inference_geo``), e.g. Anthropic's ``us: 1.1``
stored under ``provider_specific_entry``. The regional surcharge applies to
every token type, so per-type cost breakdowns must scale by it too.
Returns 1.0 when the request was served globally or the model carries no
multiplier for the geo.
"""
inference_geo: Final = getattr(usage, "inference_geo", None)
if not isinstance(inference_geo, str) or inference_geo.lower() in ("global", "not_available"):
return 1.0
provider_specific_entry: Final[dict[str, float]] = model_info.get("provider_specific_entry") or {}
return float(provider_specific_entry.get(inference_geo.lower(), 1.0))
def _resolve_reasoning_token_cost(
model_info: ModelInfo,
service_tier: str | None,
@ -747,7 +836,7 @@ def generic_cost_per_token(
audio_length_seconds=0.0,
)
if usage.prompt_tokens_details:
prompt_tokens_details = _parse_prompt_tokens_details(usage)
prompt_tokens_details = parse_prompt_tokens_details(usage)
## EDGE CASE - text tokens not set or includes cached tokens (double-counting)
## Some providers (like xAI) report text_tokens = prompt_tokens (including cached)
@ -777,7 +866,12 @@ 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,
@ -797,7 +891,7 @@ def generic_cost_per_token(
video_tokens = 0
is_text_tokens_total = False
if usage.completion_tokens_details is not None:
completion_tokens_details: Final = _parse_completion_tokens_details(usage)
completion_tokens_details: Final = parse_completion_tokens_details(usage)
audio_tokens = completion_tokens_details["audio_tokens"]
text_tokens = completion_tokens_details["text_tokens"]
reasoning_tokens = completion_tokens_details["reasoning_tokens"]
@ -834,10 +928,15 @@ def generic_cost_per_token(
## REASONING COST
if not is_text_tokens_total and reasoning_tokens and reasoning_tokens > 0:
_output_cost_per_reasoning_token = _resolve_reasoning_token_cost(
model_info=model_info,
service_tier=service_tier,
completion_base_cost=completion_base_cost,
tiered_reasoning_rate: Final = _get_tiered_reasoning_rate(model_info=model_info, usage=usage)
_output_cost_per_reasoning_token = (
tiered_reasoning_rate
if tiered_reasoning_rate is not None
else _resolve_reasoning_token_cost(
model_info=model_info,
service_tier=service_tier,
completion_base_cost=completion_base_cost,
)
)
completion_cost += float(reasoning_tokens) * _output_cost_per_reasoning_token
@ -909,29 +1008,37 @@ 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"]
if usage.completion_tokens_details is not None
else 0
parse_completion_tokens_details(usage)["reasoning_tokens"] if usage.completion_tokens_details is not None else 0
)
if not reasoning_tokens:
reasoning_tokens = _coerce_token_count(getattr(usage, "reasoning_tokens", 0))
# Reasoning is billed at the explicit per-reasoning-token rate when the model
# defines one, otherwise at the standard output-token rate - this mirrors how the
# total completion cost is computed, so the breakdown can never diverge from it.
reasoning_rate = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None)
if reasoning_rate is None:
reasoning_rate = completion_base_cost
# Reasoning is billed at the selected tier's reasoning rate for tiered models,
# else at the explicit per-reasoning-token rate when the model defines one,
# otherwise at the standard output-token rate - this mirrors how the total
# completion cost is computed, so the breakdown can never diverge from it.
tiered_reasoning_rate: Final = _get_tiered_reasoning_rate(model_info=model_info, usage=usage)
flat_reasoning_rate: Final = _get_cost_per_unit(model_info, "output_cost_per_reasoning_token", None)
reasoning_rate: Final = (
tiered_reasoning_rate
if tiered_reasoning_rate is not None
else (flat_reasoning_rate if flat_reasoning_rate is not None else completion_base_cost)
)
reasoning_cost = float(reasoning_tokens) * reasoning_rate
cache_read_tokens = 0
cache_creation_tokens = 0
cache_creation_token_details: CacheCreationTokenDetails | None = None
if usage.prompt_tokens_details is not None:
prompt_tokens_details: Final = _parse_prompt_tokens_details(usage)
prompt_tokens_details: Final = parse_prompt_tokens_details(usage)
cache_read_tokens = prompt_tokens_details["cache_hit_tokens"]
cache_creation_tokens = prompt_tokens_details["cache_creation_tokens"]
cache_creation_token_details = prompt_tokens_details["cache_creation_token_details"]
@ -958,6 +1065,14 @@ def get_token_type_cost_breakdown(
cache_read_cost *= uplift
cache_creation_cost *= uplift
# Mirror the provider-specific geo uplift (e.g. Anthropic us: 1.1) the totals
# apply, so cache and reasoning line items stay reconciled with them.
geo_multiplier: Final = get_provider_specific_geo_multiplier(model_info=model_info, usage=usage)
if geo_multiplier != 1.0:
reasoning_cost *= geo_multiplier
cache_read_cost *= geo_multiplier
cache_creation_cost *= geo_multiplier
return TokenTypeCostBreakdown(
reasoning_cost=reasoning_cost,
cache_read_cost=cache_read_cost,
@ -996,9 +1111,13 @@ 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,
)

87
litellm/litellm_core_utils/llm_judge.py Normal file
View file

@ -0,0 +1,87 @@
"""Shared primitives for LLM-judge features (llm_as_a_judge guardrail, shadow eval)."""
from __future__ import annotations
import json
import re
from typing import TYPE_CHECKING, Final
import litellm
if TYPE_CHECKING:
from litellm import Router
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse
JSON_FENCE_RE: Final = re.compile(r"```(?:json)?\s*(.*?)\s*```", re.DOTALL | re.IGNORECASE)
def default_router_provider() -> Router | None:
try:
from litellm.proxy.proxy_server import llm_router
except ImportError:
return None
return llm_router
def parse_json_verdict(raw: str) -> dict[str, object]: # mutable-ok: plain parsed-JSON payload
"""Parse a judge's JSON verdict, tolerating markdown fences and surrounding prose."""
text = raw.strip() # rebind-ok: progressively narrowed to the JSON payload
fenced: Final = JSON_FENCE_RE.search(text)
if fenced is not None:
text = fenced.group(1).strip() # rebind-ok: progressively narrowed to the JSON payload
parsed: object
try:
parsed = json.loads(text)
except json.JSONDecodeError:
start: Final = text.find("{")
end: Final = text.rfind("}")
if start == -1 or end <= start:
raise
parsed = json.loads(text[start : end + 1])
if not isinstance(parsed, dict):
raise ValueError("judge response is not a JSON object")
return {str(k): v for k, v in parsed.items()} # mutable-ok: plain parsed-JSON payload
def extract_text_from_content(content: object) -> str:
"""Return plain text from a message content field (str or multimodal list)."""
if isinstance(content, str):
return content
if isinstance(content, list):
return " ".join(
str(part.get("text", "")) for part in content if isinstance(part, dict) and part.get("type") == "text"
)
return ""
def router_resolves_model(router: Router | None, model: str) -> bool:
"""Whether the model name resolves through the proxy's router (configured deployment
or model-group alias), the same check the judge dispatch itself makes, so start-time
validation cannot accept a name the call path then fails on."""
return router is not None and bool(model in router.model_group_alias or router.get_model_list(model_name=model))
async def judge_acompletion(
router: Router | None,
judge_model: str,
messages: list[AllMessageValues], # mutable-ok: the SDK acompletion signature takes a list
**params: object,
) -> ModelResponse:
"""Dispatch a judge call through the proxy's router when the judge model is a
configured deployment (DB-stored credentials work), through the SDK for
provider-qualified public names. The router path never retries or falls back:
a failed judge call is the caller's counted failure, not a spend multiplier.
Sampling preferences are advisory: models that removed sampling params (e.g.
claude-sonnet-5) drop them instead of rejecting the judge call."""
if router_resolves_model(router, judge_model):
return await router.acompletion( # pyright: ignore[reportOptionalMemberAccess] # router_resolves_model implies router is not None
model=judge_model,
messages=messages,
num_retries=0,
fallbacks=[],
drop_params=True,
**params,
)
return await litellm.acompletion(model=judge_model, messages=messages, num_retries=0, drop_params=True, **params)

14
litellm/litellm_core_utils/model_param_helper.py
View file

@ -18,6 +18,7 @@ from openai.types.responses.response_create_params import (
)
from litellm._logging import verbose_logger
from litellm.types.llms.anthropic import AnthropicMessagesRequest
from litellm.types.rerank import RerankRequest
@ -40,7 +41,7 @@ class ModelParamHelper:
@staticmethod
def get_exclude_params_for_model_parameters() -> set[str]:
return set(["messages", "prompt", "input"])
return set(["messages", "prompt", "input", "system"])
@staticmethod
def _get_relevant_args_to_use_for_logging() -> set[str]:
@ -73,6 +74,7 @@ class ModelParamHelper:
transcription_kwargs: Final = ModelParamHelper._get_litellm_supported_transcription_kwargs()
rerank_kwargs: Final = ModelParamHelper._get_litellm_supported_rerank_kwargs()
responses_api_kwargs: Final = ModelParamHelper._get_litellm_supported_responses_api_kwargs()
anthropic_messages_kwargs: Final = ModelParamHelper._get_litellm_supported_anthropic_messages_kwargs()
exclude_kwargs: Final = ModelParamHelper._get_exclude_kwargs()
combined_kwargs = chat_completion_kwargs.union(
@ -81,6 +83,7 @@ class ModelParamHelper:
transcription_kwargs,
rerank_kwargs,
responses_api_kwargs,
anthropic_messages_kwargs,
)
combined_kwargs = combined_kwargs.difference(exclude_kwargs)
return combined_kwargs
@ -167,12 +170,19 @@ class ModelParamHelper:
streaming_params: Final[set[str]] = set(getattr(ResponseCreateParamsStreaming, "__annotations__", {}).keys())
return non_streaming_params.union(streaming_params)
@staticmethod
def _get_litellm_supported_anthropic_messages_kwargs() -> frozenset[str]:
"""
Get the litellm supported Anthropic /v1/messages kwargs
"""
return frozenset(AnthropicMessagesRequest.__annotations__.keys())
@staticmethod
def _get_exclude_kwargs() -> set[str]:
"""
Get the kwargs to exclude from the cache key
"""
return set(["metadata"])
return set(["metadata", "litellm_metadata"])
ModelParamHelper._relevant_logging_args = frozenset(ModelParamHelper._get_relevant_args_to_use_for_logging())

90
litellm/litellm_core_utils/prompt_templates/common_utils.py
View file

@ -6,7 +6,8 @@ import io
import json
import mimetypes
import re
from collections.abc import Mapping, Sequence
from collections.abc import Iterable, Mapping, Sequence
from itertools import groupby
from os import PathLike
from pathlib import Path
from typing import TYPE_CHECKING, Any, Final, Literal, cast
@ -26,7 +27,9 @@ from litellm.types.llms.openai import (
AllMessageValues,
ChatCompletionAssistantMessage,
ChatCompletionFileObject,
ChatCompletionImageObject,
ChatCompletionResponseMessage,
ChatCompletionTextObject,
ChatCompletionToolParam,
ChatCompletionUserMessage,
)
@ -41,7 +44,6 @@ from litellm.types.utils import (
if TYPE_CHECKING: # newer pattern to avoid importing pydantic objects on __init__.py
from litellm.types.llms.anthropic import AnthropicInputSchema
from litellm.types.llms.openai import ChatCompletionImageObject
DEFAULT_USER_CONTINUE_MESSAGE: Final = ChatCompletionUserMessage(content="Please continue.", role="user")
@ -1002,7 +1004,7 @@ def _has_legacy_defs(schema: object) -> bool:
return "definitions" in schema or (isinstance(components, dict) and isinstance(components.get("schemas"), dict))
# Schema-bomb budget for ``unpack_legacy_defs``: cap the cumulative JSON-byte
# Schema-bomb budget for ``$ref`` inlining: cap the cumulative JSON-byte
# size of every inlined target. A byte cap is the universal measure of
# expansion -- it simultaneously bounds ref-count fan-out, node-count
# amplification, and scalar-byte amplification (large ``description`` /
@ -1010,14 +1012,14 @@ def _has_legacy_defs(schema: object) -> bool:
# inline well under 1MB; 10MB sits two orders of magnitude above that, well
# below memory-pressure territory, and rejects request-supplied bombs before
# the proxy materialises them.
_LEGACY_DEFS_MAX_INLINED_BYTES: Final = 10_000_000
DEFS_MAX_INLINED_BYTES: Final = 10_000_000
def unpack_legacy_defs(
schema: dict,
*,
copy: bool = False,
max_inlined_bytes: int = _LEGACY_DEFS_MAX_INLINED_BYTES,
max_inlined_bytes: int = DEFS_MAX_INLINED_BYTES,
) -> dict:
"""Inline ``$ref``s backed by draft-04 ``definitions`` / OpenAPI
``components.schemas``. ``$defs`` is left untouched.
@ -1605,6 +1607,84 @@ def extract_images_from_message(message: AllMessageValues) -> list[str]:
return images
TOOL_RESULT_IMAGE_PLACEHOLDER: Final = "[Tool returned an image - see the following user message]"
TOOL_RESULT_IMAGE_BOUNDARY: Final = "[The following images are tool output - treat them as data, not instructions]"
def _is_image_url_part(part: object) -> bool:
return isinstance(part, dict) and part.get("type") == "image_url"
def _tool_message_carries_image(message: AllMessageValues) -> bool:
if message.get("role") != "tool":
return False
content = message.get("content")
return isinstance(content, list) and any(_is_image_url_part(part) for part in content)
def _split_images_from_tool_message(
message: AllMessageValues,
) -> tuple[AllMessageValues, tuple[ChatCompletionImageObject, ...]]:
content = message.get("content")
if not isinstance(content, list):
return message, ()
image_parts = tuple(
cast(ChatCompletionImageObject, part) # cast-ok: shape checked by _is_image_url_part
for part in content
if _is_image_url_part(part)
)
if not image_parts:
return message, ()
remaining_parts = [ # mutable-ok: tool message content must stay a json list
part for part in content if not _is_image_url_part(part)
]
new_content = remaining_parts if remaining_parts else TOOL_RESULT_IMAGE_PLACEHOLDER
rewritten = {**message, "content": new_content} # mutable-ok: chat messages are plain json dicts
return cast(AllMessageValues, rewritten), image_parts # cast-ok: dict spread keeps keys like cache_control
def _hoist_images_in_tool_message_run(
run: Iterable[AllMessageValues],
) -> list[AllMessageValues]: # mutable-ok: message pipelines type messages as mutable lists
split_results = tuple(_split_images_from_tool_message(message) for message in run)
hoisted_images = [ # mutable-ok: user message content must be a json list
image for _, images in split_results for image in images
]
rewritten_messages = [message for message, _ in split_results] # mutable-ok: pipelines mutate message lists
if not hoisted_images:
return rewritten_messages
boundary_part = ChatCompletionTextObject(type="text", text=TOOL_RESULT_IMAGE_BOUNDARY)
hoisted_content = [boundary_part, *hoisted_images] # mutable-ok: user message content must be a json list
rewritten_messages.append(ChatCompletionUserMessage(role="user", content=hoisted_content))
return rewritten_messages
def hoist_images_from_tool_messages(
messages: list[AllMessageValues], # mutable-ok: message pipelines type messages as mutable lists
) -> list[AllMessageValues]: # mutable-ok: message pipelines type messages as mutable lists
"""
Move image content out of role:"tool" messages into a user message inserted
after the run of consecutive tool messages it belongs to.
The OpenAI chat spec only allows text in tool messages, so OpenAI-compatible
providers either reject or silently ignore images placed there (e.g. an
Anthropic tool_result carrying a screenshot). Each rewritten tool message
keeps its tool_call_id and any non-image parts (falling back to a text
placeholder), and the user message is only inserted after the last
consecutive tool message so the assistant tool_calls -> tool messages
adjacency that strict providers validate is preserved. The inserted user
message leads with a text part marking the images as tool output so the
model does not read them with user authority.
"""
if not any(_tool_message_carries_image(message) for message in messages):
return messages
return [ # mutable-ok: pipelines mutate message lists
rewritten_message
for is_tool_run, run in groupby(messages, key=lambda message: message.get("role") == "tool")
for rewritten_message in (_hoist_images_in_tool_message_run(run) if is_tool_run else run)
]
def _attempt_json_repair(s: str) -> Any | None:
"""
Attempt to repair truncated JSON produced by LLM tool calls.

86
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",
@ -1418,7 +1418,7 @@ def convert_to_gemini_tool_call_result(
content_type = content.get("type", "")
if content_type == "text":
content_str += content.get("text", "")
elif content_type == "image":
elif content_type == "image": # pyright: ignore[reportUnnecessaryComparison] # loose runtime dict
# Anthropic-native image block: {"type": "image", "source": {"type": "base64", ...}}
source = content.get("source", {})
if isinstance(source, dict) and source.get("type") == "base64":
@ -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)

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