The original check `"gpt-5-chat" not in model` already correctly
classifies all current gpt-5 variants (including gpt-5.3-chat and
gpt-5.1-chat, which do NOT contain the substring "gpt-5-chat"). This
change replaces it with an explicit `startswith("gpt-5-chat")` prefix
test on the provider-prefix-stripped model name.
The new check is functionally equivalent for all existing model names
but makes the classification boundary unambiguous and forward-safe:
future model names that might contain "gpt-5-chat" as an interior
substring won't accidentally be excluded from the GPT-5 reasoning path.
Also moves the new regression test from tests/ root to
tests/test_litellm/llms/openai/ so it is included in `make test-unit`.
The useEffect that re-fetches logs on sort/page/time changes:
useEffect(() => {
if (hasBackendFilters && accessToken) {
performSearch(filters, currentPage);
}
}, [sortBy, sortOrder, currentPage, startTime, endTime, isCustomDate]);
intentionally omits `filters` and `hasBackendFilters` from its dep array
to avoid double-fetches when a filter is applied. The side-effect is a
stale-closure bug: the effect captures `filters` and `hasBackendFilters`
from the render where its deps last changed, not from the render where
the user selected, e.g., a Key Alias.
Reproduce: set Key Alias → results appear correctly → change page or
sort → the effect fires with the OLD `filters` snapshot (no key_alias)
→ API request is sent without the filter → table shows unfiltered data.
Fix: store the latest `filters` and `hasBackendFilters` in refs that are
kept in sync on every render. The sort/page/time effect reads from the
refs instead of the closure so it always uses the current filter state
without altering the dep array.
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4 (1M context) <noreply@anthropic.com>
When backend filters (e.g. Key Alias) are active on the Request Logs
page, the manual Fetch button called logs.refetch() which re-runs the
main TanStack Query. That query does not carry backend-only filter
params such as key_alias, so the button had two problems:
1. It fired a redundant API request without the active filters.
2. It did not refresh the filtered result set — backendFilteredLogs
stayed frozen at the last debounce-triggered fetch.
Fix: expose refetchWithFilters() from useLogFilterLogic and route the
Fetch button through it when hasBackendFilters is true. This cancels
any in-flight debounce and calls performSearch with the current filter
state, keeping all active filters intact.
Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4 (1M context) <noreply@anthropic.com>
* fix(anthropic): handle tool_choice type 'none' in messages API
* test(anthropic): add regression test for tool_choice type 'none'
---------
Co-authored-by: BillionClaw <267901332+BillionClaw@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
When reasoning_auto_summary is enabled (via litellm_settings or env var),
automatically set thinking.display="summarized" on native /v1/messages
requests. This ensures thinking content is returned in the response
instead of being omitted (the default on Claude 4.7+).
Only applies when thinking is enabled (type != "disabled").
The existing reasoning_auto_summary flag already handles the
/v1/responses path (summary="detailed") and the chat/completions
adapter path — this extends coverage to the native messages handler.
`get_file_ids_from_messages` and `update_messages_with_model_file_ids`
assume every content block with `type: "file"` has a nested `file` dict in
the OpenAI Chat Completions shape. That assumption is too strong: `type:
"file"` is a public content-block discriminator and several real producers
emit blocks that use it without the OpenAI `file` sub-dict. For example,
LangChain v1's `_normalize_messages` rewrites OpenAI file blocks into
`{"type":"file","id":"...","base64":"...","mime_type":"...","extras":{}}`
before they reach LiteLLM.
`AnthropicConfig.validate_environment` calls both helpers unconditionally
on every Anthropic (and Anthropic-via-Vertex) request, so any such block
raises `KeyError: 'file'` which the Vertex partner layer then wraps as a
`500 InternalServerError` before the LLM is even contacted.
This patch switches both helpers from `c["file"]` to a defensive
`c.get("file")` + dict check. When the block does not match the OpenAI
shape there is no file_id to extract or remap, so we skip it and leave
the block untouched for the downstream provider transformer to handle.
Adds 5 regression tests covering the LangChain v1 shape, the OpenAI
happy path, mixed shapes in one message, `file` set to a non-dict value,
and the remap path for non-OpenAI blocks.
Related to #24503, which proposed raising `BadRequestError` in the same
spots. For these two discovery functions specifically, the skip semantics
is strictly more permissive: well-formed OpenAI blocks still yield their
file_id, and legitimate non-OpenAI blocks stop crashing the request.
* fix(model-info): include reasoning effort support fields in get_model_info
_get_model_info_helper constructs ModelInfoBase explicitly but never
reads supports_xhigh/minimal/none_reasoning_effort from the cost map
JSON. Add the three fields so get_model_info() returns them correctly.
Also add supports_minimal_reasoning_effort to the ModelInfo TypedDict
(xhigh and none were already declared, minimal was missing).
* fix(model-registry): add missing reasoning effort fields for claude 4.6/4.7
Claude Opus 4.7 supports max reasoning effort (above xhigh).
The field was present for Opus 4.6 but missing for all Opus 4.7
entries (base, dated, Bedrock, Vertex AI, Azure AI).
All Claude 4.6/4.7 models (Opus 4.6, Sonnet 4.6, Opus 4.7) support
minimal reasoning effort via adaptive thinking. Add the field to all
provider variants.
* fix(adapter): map output_config.effort to reasoning_effort (#25079)
Anthropic's adaptive thinking (thinking.type="adaptive") and
output_config.effort were silently dropped when translating to
OpenAI format, resulting in no reasoning_effort on the outgoing
request.
Adapter changes (format translation):
- adapters/transformation.py: add "adaptive" branch to
translate_anthropic_thinking_to_reasoning_effort(); pass through
output_config.effort as-is in _translate_thinking_to_openai();
add "output_config" to translatable_anthropic_params
- adapters/handler.py: extract output_config from extra_kwargs into
request_data so it reaches the translation layer
- responses_adapters/transformation.py: add "adaptive" branch and
output_config param to translate_thinking_to_reasoning()
Handler changes (model-aware normalization):
- utils.py: add normalize_reasoning_effort_value() that uses
get_model_info() to map "max" → "xhigh"/"high" and
"minimal" → "minimal"/"low" based on model capabilities
- adapters/handler.py: call normalization before responses routing
- responses_adapters/handler.py: call normalization after translation
Relates to BerriAI/litellm#25079
* test(reasoning-effort): add tests for effort capability fields and normalize logic
Test coverage for:
- get_model_info returning supports_minimal/max_reasoning_effort fields
- JSON registry entries for claude 4.6/4.7 across all providers
- normalize_reasoning_effort_value degradation chains and exception fallback
- Adapter translation of adaptive thinking + output_config.effort
* fix: forward custom_llm_provider to normalize_reasoning_effort_value in responses adapter
* fix(mcp_semantic_tool_filter): match canonical tools that arrive with
a client-side namespace prefix.
`SemanticMCPToolFilter._get_tools_by_names` matched by exact equality
between the canonical name stored in the router
(`<server><MCP_TOOL_PREFIX_SEPARATOR><tool>`) and the name in the
incoming `tools[]` list. MCP clients such as opencode wrap every tool
name with their own additive alias prefix
(`<client_alias>_<canonical>`), so the two never matched, the filter
dropped every tool to zero, and the proxy forwarded `tools: []` with
`tool_choice: auto` — which strict upstream providers reject with a 400.
The fix adds anchored suffix matching with a separator check: the
canonical must form the complete tail of the incoming name and be
preceded by `_` or `-`. Exact matches still win over suffix matches,
incoming tools are returned at most once, and the original tool object
is passed through unchanged so the client-facing name survives for
tool-call round-trips.
Seven unit tests in a new TestGetToolsByNames class cover exact
match, underscore- and dash-prefixed variants, non-separator-anchored
suffixes (which must not match), exact-wins-over-prefixed precedence,
deduplication when two canonicals suffix-match the same incoming tool,
and ordering-follows-router-output.
Fixes#26078
* review: strengthen the suffix-fallback tie-breaker and the
deduplication regression test (Greptile comments on #26117)
- test_same_tool_not_returned_twice now passes two distinct canonicals
("read_file" and "file") that both suffix-match the same incoming
tool, rather than the same canonical twice, so the assertion
actually exercises the used_ids dedup path instead of the
duplicate-input-list path.
- The suffix fallback in _get_tools_by_names now prefers the shortest
incoming name that still qualifies under the separator-anchored
match. In the one-prefix-per-client opencode scenario this is a
no-op, but in multi-namespace configurations the shortest qualifying
name is the least-wrapped one and is the most defensible deterministic
choice, replacing the dict-insertion-order fallback.
- Adds test_suffix_fallback_prefers_shortest_candidate covering the
new tie-breaker directly.
Still 15 tests passing locally (was 14).
* review(#26117): gate suffix-matching on canonical containing MCP_TOOL_PREFIX_SEPARATOR
@krrish-berri-2 flagged a possible collision in the suffix fallback:
a local user function whose name happens to end in a bare canonical
substring (e.g. my_firecrawl_scrape vs canonical firecrawl_scrape)
would be spuriously selected.
Server-registered MCP tools are always emitted as
<server_name><MCP_TOOL_PREFIX_SEPARATOR><tool_name> via
add_server_prefix_to_name, so a canonical without the separator is
not a namespaced MCP tool and does not warrant suffix matching.
Added that guard to _name_matches_canonical with a regression test
(test_does_not_collide_with_local_function_on_unprefixed_canonical)
that reproduces the collision before the fix and is pinned after.
Pre-existing TestGetToolsByNames fixtures that relied on bare
canonicals (get_weather, search, read_file, write/delete/read) were
switched to realistic server-prefixed ones so they continue to
exercise the suffix-fallback path under the new guard. The opencode
scenario (client prefix on already-server-prefixed canonical) is
unchanged.
---------
Co-authored-by: sakenuGOD <sakenuGOD@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
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Two changes, both test-only:
- Configure the aiohttp session with TCPConnector(force_close=True) and an
explicit ClientTimeout(total=30, connect=10). Prevents reuse of idle TCP
connections that the proxy/kernel may have closed during the long window
between setup POSTs and the later poll loop, and surfaces a blocked proxy
event loop quickly instead of hanging on aiohttp's 5-minute default.
- In poll_key_spend_until, catch aiohttp.ClientError and asyncio.TimeoutError
around the single /key/info call. A transient transport hiccup now logs and
retries on the next tick instead of failing the entire polling loop.
Addresses the ConnectionTimeoutError observed on the first /key/info call
after the 20 chat completions.
The daily queue parameter types on _restore_spend_updates_to_in_memory_queues
were narrowed to specific subtypes (DailyUserSpendTransaction, etc), but
the caller passes Dict[str, BaseDailySpendTransaction] — the return type
of flush_and_get_aggregated_daily_spend_update_transactions. Widen the
parameters to the base type.
Also replace dynamic TypedDict key lookup (which returned object) with
explicit literal-keyed get() calls so mypy can type-narrow each field.
Replace recursive `_walk` helper with a stack-based traversal so the
recursive_detector CI check passes without adding to the ignore list,
and avoid Python recursion limits on deeply nested payloads.
Made-with: Cursor
store_in_memory_spend_updates_in_redis drained the in-memory queues
into local variables before the rpush pipeline. If rpush raised (cloud
Redis hiccup, timeout, connection blip), those already-drained
transactions were garbage-collected with the scheduler job, silently
losing all spend aggregated during that tick.
Wrap the rpush in try/except. On failure, re-enqueue the aggregated
transactions into their respective in-memory queues so the next
scheduler tick retries.
Add a unit test that seeds real queues, simulates an rpush failure,
and asserts the transactions land back in-memory.
Replace the calibration step (one request + 10-minute poll) with an
independent ground truth computed from response usage via
litellm.cost_per_token. All N requests are made up front, so a single
dropped Redis write no longer kills the test.
Add /health/readiness checks at test start and on poll timeout so the
failure message surfaces proxy state (db, cache) instead of "calibration
timed out".
Set PROXY_BATCH_WRITE_AT=2 in the spend tracking CI job to shorten the
scheduler flush window.
Principle: GHA handles work that doesn't need external API keys; CCI
stays for integration tests that hit real API endpoints.
Four CCI jobs moved to new or extended GHA workflows:
1. check_code_and_doc_quality (was 25 runs: ruff + import-safety +
21 code_coverage_tests + 3 documentation_tests + circular-imports).
- The 21 tests/code_coverage_tests/*.py scripts and the 3
tests/documentation_tests/*.py scripts run in the new
.github/workflows/test-code-quality.yml workflow.
- ruff, import-safety, and circular-imports were already run by
.github/workflows/test-linting.yml — no new migration needed.
- The 3 documentation_tests scripts read
docs/my-website/docs/proxy/config_settings.md. Since docs have
moved to BerriAI/litellm-docs, the GHA workflow checks out that
repo and symlinks docs/my-website -> the checkout so the
existing hardcoded paths resolve without touching the scripts.
The stale local docs/my-website/ copy in this repo will be
removed in a separate PR.
2. semgrep (custom-rule SAST against .semgrep/rules).
- New .github/workflows/test-semgrep.yml.
3. installing_litellm_on_python + installing_litellm_on_python_3_13
(pip install compat checks on Python 3.12 and 3.13).
- New .github/workflows/test-install-litellm.yml as a matrix job.
- 3.12 run also verifies litellm_enterprise import; 3.13 run
skips that check (matches previous CCI behavior).
- installing_litellm_on_python_v2_migration_resolver stays in CCI
because it requires a postgres service.
CCI .circleci/config.yml: -112 lines, 4 jobs and their workflow refs
removed.
Restore guardrail spend/UI event_type wiring, request_data on streaming
OUTPUT paths, and centralized match redaction after the upstream revert.
Made-with: Cursor
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Follow-up cleanup after an independent review pass surfaced a few
loose ends:
- Delete a 6x-duplicated filter block in litellm_mapped_tests_proxy_part2
(same kind of copy-paste residue we fixed earlier in
langfuse_logging_unit_tests).
- Delete the empty "Install Semgrep" run step in the semgrep job — the
command body was empty because semgrep is installed on-demand via
uv tool run in the next step.
- Standardize machine-executor image: one job was on ubuntu-2204:2023.10.1
while build_docker_database_image was already on ubuntu-2204:2024.04.1.
Bumped everything to 2024.04.1.
- Remove the legacy "version: 2" inside the workflows: block — CircleCI
2.1 top-level already declares the version.
- Drop `{{ checksum ".circleci/config.yml" }}` from cache keys (13 sites).
It was busting the cache on every unrelated config edit; the uv.lock
checksum alone is the right dependency cache key.
- Add partial-restore fallbacks to every restore_cache with a single
templated key (10 sites). Jobs now fall back to the latest cache with
a matching prefix if the exact uv.lock hash isn't cached yet.
Net: -14 lines.
Docker-executor jobs:
- Consolidate base images on cimg/python:3.12. Jobs previously on
3.11 (26 jobs), 3.9 (1 historical: upload-coverage), and an
incidental 3.13.1 (litellm_assistants_api_testing) now use 3.12.
- installing_litellm_on_python_3_13 keeps cimg/python:3.13.1 as its
explicit "latest Python supported" install-check matrix job.
Machine-executor jobs:
- Delete the miniconda install step from 10 jobs. uv now manages
Python directly: uv sync --python 3.12 auto-downloads a
python-build-standalone interpreter if the ubuntu-2204 base
image's default python doesn't match.
- Remove 37 "if [ -f conda.sh ]; then conda activate myenv" wrappers
and 2 unconditional conda activate blocks left behind from the
conda days.
- proxy_build_from_pip_tests keeps its 3.13 target (it was
conda create -n myenv python=3.13) via uv sync --python 3.13.
Net: -301 lines.
Add a start_postgres command parameterized on db_name (default
circle_test) that runs the postgres-db container and waits for port
5432 to accept connections. Replace all 11 inline docker run /
wait_for_service blocks with a single - start_postgres call.
The helm chart test overrides db_name to litellm_test; everything
else uses the default.
One of the 11 sites previously used a bespoke pg_isready loop instead
of wait_for_service; it now goes through the same TCP-probe path
everyone else uses, which is sufficient for test ordering purposes.
Net: -112 lines.
Add a single install_uv command in the commands: section that encodes
the uv version (0.10.9) and its SHA256 in one place, then replace all
42 inline curl|sha256|install blocks across every job that needs uv.
setup_litellm_test_deps now calls install_uv too, so the shared
test-dep bootstrap goes through the same path.
Bumping uv version or SHA is now a one-line change instead of 43.
Net: -203 lines.
- Remove mypy_linting job (GHA test-linting.yml already runs this)
- Remove three redundant "Install curl" apt-get steps (curl is
already present on the ubuntu-2204 machine image and used
successfully earlier in each affected job)
- Dedupe langfuse_logging_unit_tests filter block (6x copy of the
same two branch filters collapsed to 1)
- Pin all docker image references by @sha256 digest so builds stay
reproducible when upstream tags are updated:
cimg/python:3.9, 3.11, 3.12, 3.12-browsers, 3.13.1, cimg/node:20.19,
cimg/postgres:16.0, and postgres:14 used via docker run
Net: -62 lines, 49 image references pinned.
* add moonshot/kimi-k2.6 to model registry
* add moonshot/kimi-k2.6 to backup model registry
* add tests for moonshot/kimi-k2.6 model registry
* fix moonshot/kimi-k2.6 pricing and add reasoning support
* fix moonshot/kimi-k2.6 pricing and add reasoning support in backup
* update kimi-k2.6 tests: fix pricing, add tool_choice and reasoning checks
* fix: load kimi-k2.6 registry tests from local backup instead of remote cost map
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