Only image-only unscoped content stays unrecorded; text or tool content
removed by scoping is recorded as not_run even when an image sits beside it.
Also keeps the type-discipline budget flat by returning the reason from the
helper and annotating the accumulator lists _extract_inputs requires.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The three guardrail translation handlers imported the exception from the proxy policy engine through a function-local import, which CodeQL flagged as a cyclic import. The exception and its helper now live next to the handlers in the shared guardrail translation utils, and the tests import it from there.
The Prompt Security modify-mode helper is also restructured into early-return TypedDict displays so the LIT002 budget stays at its limit
The not_run reason now says after message scoping only when the same messages carry text or tool calls without the skip flags applied. A request that is empty to begin with, whatever the flags, records no scannable content
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A request whose messages carry no scannable content at all, with no skip flag set, now records the neutral reason no scannable content instead of blaming configuration
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Images without text were never dispatched to guardrails before this change, so that gap is not a message scoping skip and must not get a not_run entry
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Usage tracking, compliance and the dashboard now treat both not_run (older spend logs) and skipped as unevaluated through a shared UNEVALUATED_GUARDRAIL_STATUSES set, so old records stop counting as passed. The skipped record is no longer written when the request carried images, since images without text were never dispatched to guardrails before this change and that gap is not a message-scoping skip
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The per-guardrail status a scoped-out evaluation records is now skipped, matching the
skip_*_in_guardrail settings that cause it. Request-level rollup still maps it to not_run
so the StandardLoggingPayload status contract is unchanged
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A guardrail that answers one rewritten text per message it saw no longer
matches the texts the Responses handler extracted once the request carries
instructions or tool items, so the rewrite was rejected with a 500. Spread
such an answer over the structured messages' text slots and write it back
through the structured path, have Prompt Security modify return
structured_messages directly, and give the chat completions pairing the same
named rejection instead of a silent misalignment when the counts differ.
* fix(openai): drop tool schema regex patterns OpenAI's validator cannot compile
OpenAI validates function tool parameters with jsonschema's format checker,
which compiles every pattern with Python re. Claude Code's Artifact tool ships
an ECMA-262 pattern with \p{..} Unicode property escapes, so any OpenAI target
behind /v1/messages, /v1/responses or /v1/chat/completions 400s with
"Invalid schema for function 'Artifact': '...' is not a 'regex'" for every
model family. Drop only the patterns Python re rejects, keep the rest, at the
same seams that already flatten top-level combinators.
* fix(openai): walk only schema positions, iteratively, and drop regexes for every openai deployment
Review round: the regex sanitizer now walks JSON Schema applicator positions
only (properties, items, prefixItems, combinators, $defs, additionalProperties
and the rest), so a pattern key inside default, examples, const or a vendor
extension is data and stays. It also drops patternProperties keys Python re
cannot compile, which OpenAI checks the same way. The walk is level-order and
rebuilt deepest level first instead of recursive, so the code-quality recursion
gate passes and there is no depth cap below what a JSON parser admits. On the
chat wire an openai deployment with a custom api_base now drops such regexes
too, since that base is usually a proxy in front of the same validator, while
the lossier combinator flattening stays limited to api.openai.com hosts.
Skipping the name write-back in either handler left every test green; a
guardrail that renames a tool call now has a regression test on both the
chat chunk path and the Anthropic SSE path
Bridged Responses streams give reasoning and message items output_index 0
and start function calls at 1, so keying rewrites by output_index rewrote
the wrong items. Rewrites now follow each function call's call_id through
the buffered item and argument events, refuse when an event cannot be
resolved to a rewritten call, and the refusal branches on all three
handlers get regression tests
A post_call pipeline guardrail that rewrites a streamed tool call (its
arguments or its name) now has that rewrite written back across the buffered
chunks on chat, Responses, and Messages streams, so the client receives the
rewritten tool call instead of the original. The chat handler rewrites the
first fragment of each tool-call index and blanks the rest, the Responses
handler syncs the function_call output items and their argument events, and
the Messages handler rewrites the tool_use content_block_start and
input_json_delta events in both dict and SSE-bytes chunks.
The delivers_ended_stream_text_rewrites flag becomes
delivers_ended_stream_rewrites, since the write-back now covers both text and
tool calls, and the executor only discards a tool-call rewrite on translations
without write-back or on a shape the translation refuses.
* fix(guardrails): skip streaming guardrail rounds that re-scan cleared output
Streaming guardrails scanned the finished answer twice at end of stream
whenever the chunk count landed on a multiple of the sampling rate, ran
sampled rounds whose payload was identical to the previous one, and on
/v1/messages could scan an empty text before the first content chunk.
Every redundant round is a paid guardrail provider call.
Each endpoint handler now exposes a scan key describing what a round
would hand to apply_guardrail (the text so far, plus tool calls once the
stream has ended), and the unified streaming hook skips a sampled or
end-of-stream round whose key equals the last scanned one or carries
nothing to scan yet. Rounds that carry tool calls are never skipped.
* test(guardrails): expect one end-of-stream scan when the terminal chunk is sampled
Update sampled cadence expectations and use tuple-backed scan state
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Brings the stack base up to origin/litellm_internal_staging. Staging's
get_streaming_string_so_far now joins delta-only Responses parts itself, so
the handler's separate delta joiner and the flag-gated delta scan go away
and the fallback keeps failing closed on a rewrite it cannot deliver. The
two tests that pinned the old flag-gated behaviour are dropped in favour of
staging's delta-assembly tests, and _process_ended_stream takes the same
UserAPIKeyAuth | None the typed process_output_response now expects
Chat streaming write-backs now match chunks by the choice's index field
instead of its list position, delivering rewrites to the right choice on
n>1 streams; an ended-stream rewrite on a multi-choice buffer fails
closed since stream_chunk_builder collapses the choices. The Responses
fallback joins output_text.delta events when delivery is expected, so a
delta-only buffer is guardrail-checked instead of released raw.
Replaces the metadata transfer approach: that block read top-level
litellm_metadata which guardrail info never populates, and the SLP
builder already reads the nested bucket it lands in, so it was dead
code and is reverted.
Real cause of missing evaluations: process_input_messages skips
apply_guardrail entirely when message scoping (skip_system_message,
skip_tool, scan_only_tool_results) leaves no scannable content, so
the guardrail shows up in applied_guardrails with no
guardrail_information entry. Now records a not_run entry unless the
guardrail records its own information.
A guardrail modify_response verdict on a streaming request only produced a
proper replacement on /v1/messages: the chat completions and Responses API
translations had no build_block_sse_chunks, so the ModifyResponseException
re-raised and surfaced as an in-stream 500 error frame (or a whole-request
500 in buffered mode) instead of the documented 200 replacement.
Implement build_block_sse_chunks for both OpenAI translations: chat emits a
content delta plus a finish_reason content_filter chunk with real usage;
Responses emits the typed event sequence (standalone via
build_synthetic_response_events pre-stream, or an output-item continuation
under the in-progress response id mid-stream) ending in response.completed.
Buffered streams governed by post_call policy pipelines now deliver text
rewrites back into the stream per surface (chat SSE, responses SSE,
anthropic messages SSE) instead of rejecting the request with a 400
upfront. Rewrites chain across pipeline steps; tool-call rewrites and
translations without stream write-back still withhold the stream.
OpenAI's chat completions API rejects tool_reference content parts in
role tool messages, so a mixed text plus reference tool result carried
through the Anthropic adapter turned a previously working request into
a 400 on chat-routed OpenAI and Azure deployments. Strip the reference
parts there, keeping a reference-only result as an empty-text tool
message so the preceding tool_call stays answered, mirroring the
Responses bridge skip.
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
A name bound twice keeps only the second binding. In `tests/` that is nearly
always a repeated import, harmless but misleading, and the same rule is what
catches the cases that are not harmless: a local that shadows an import the
module still calls, and a second `def test_x` that quietly replaces the first.
311 of the 344 sites were repeated imports and came out with ruff's own fix.
The remaining 33 needed a decision. Four modules imported a name they never
used because a local definition below already shadowed it. Two comprehensions
bound `call` over `unittest.mock.call`, which those modules import and use.
One test rebound the two module handles its nested reload closure had captured.
One class attribute shadowed an unused `status` import.
The load-test fixtures move to a conftest, which is how pytest is meant to share
them, so the test module no longer imports three fixture names it never calls.
The nine `prisma_client` parameters keep a narrow `noqa`: pytest resolves that
fixture by name before the body runs, so the parameter never shadows anything.
When the resolved deployment is provider openai and the model is GPT-5.6 or
newer, the cache control hook now writes prompt_cache_breakpoint on the
targeted content block and sets prompt_cache_options to explicit mode unless
the caller already passed one. The /v1/messages bridges carry the marker
through (the Responses bridge moves a marked system prompt into a developer
message, since top-level instructions cannot hold one). Breakpoint counting
and the stand-down check recognise both marker kinds, and client breakpoints
already present in messages are no longer subtracted from the cap twice.
Fixes#37509
Images nested inside an Anthropic `tool_result` block were dropped when the
request was adapted for an OpenAI-compatible provider, because the OpenAI tool
message shape only carried text. Hoist those images out of the tool result and
into a following user message so the model can still see them, and widen the
tool message content type to accept image parts.
Under scan_only_tool_results, legacy OpenAI function-role messages now count as tool results, and duplicate names among guardrail-returned tools keep only the first occurrence. CustomGuardrail.structured_messages_cover_full_request lets CrowdStrike AIDR declare that its writeback already rebuilds the whole conversation, so handlers install it as-is instead of merging it into the full message list a second time and duplicating out-of-scope rows. Lint budget ceilings ratchet down to match the tree
Gate the OpenAI handler's tools forwarding behind scan_only_tool_results,
matching the Anthropic handler, so a tool-results-only scan can no longer
evaluate or rewrite trusted function definitions.
When a guardrail returns a replacement structured_messages list, substitute
the returned messages back into the positions their scoped originals came
from instead of installing the scoped list as the whole conversation, so
out-of-scope messages (system prompt, prior turns) survive redaction on
both the OpenAI and Anthropic paths.
* feat(guardrails): add only_scan_new_messages for per-session incremental scanning
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(guardrails): use fixed TTL constant and revert unrelated test formatting
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(guardrails): run only_scan_new_messages in the unified apply_guardrail path
The initial wiring lived in BedrockGuardrail.async_pre_call_hook, but the proxy
routes Bedrock through the unified apply_guardrail interface, so the flag had no
effect live. Move incremental selection into apply_guardrail: filter the flat
texts list against per-session scanned hashes, skip the Bedrock call when nothing
is new, and mark hashes only after a successful (non-blocked) scan. Full-context
fallback is preserved when there is no session id, the cache is unavailable, or a
masking guardrail is configured.
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(guardrails): cover session-id fallbacks and mark_texts_scanned guards
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* fix(guardrails): fall back to full scan when incremental guardrail masks content, use shared cache
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(guardrails): cover generic agent multi-turn incremental scan
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(guardrails): cover incremental scan cache resolver fallbacks
Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(guardrails): cover flag interactions and /v1/messages incremental scan semantics
* feat(guardrails): make GUARDRAIL_SCANNED_MESSAGES_CACHE_TTL_SECONDS env configurable
* test(guardrails): prove skip_system/skip_tool are enforced upstream of incremental scan
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
Co-authored-by: Yucheng Zhu <yucheng@berri.ai>
* fix(streaming): surface in-body error payloads on OpenAI-compatible streams
vLLM and sglang return HTTP 200 streams whose SSE body carries the error,
e.g. data: {"error": {"message": "...", "code": 400}}. The OpenAI-compatible
chunk parser had no detection for this shape: since #23931 the payload parsed
into an empty chunk (choices=[]) and the stream ended silently with 200,
losing the provider's error and never attempting configured fallbacks.
Detect the payload in OpenAIChatCompletionStreamingHandler.chunk_parser and
raise OpenAIError with the upstream message and status code. The existing
mid-stream gate then applies: 4xx surface directly to the client, 5xx wrap
into MidStreamFallbackError so the router can run configured fallbacks.
Fixes#25492
* fix(streaming): serialize messageless error payloads as JSON
Address review feedback: an error dict without a message field now
serializes via json.dumps instead of Python dict repr
* fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes (#30089)
* fix(proxy): allow non-admin virtual keys to call GA Realtime WebRTC HTTP routes
Add the realtime WebRTC HTTP sub-routes (/realtime/client_secrets,
/realtime/calls and their /v1 + /openai/v1 variants) to
LiteLLMRoutes.openai_routes so is_llm_api_route() classifies them as
LLM API routes. Without this, non-admin virtual keys received
401 'Only proxy admin can be used to generate, delete, update info
for new keys/users/teams' when calling these endpoints.
Fixes#29923
* fix(proxy): validate session.model for realtime routes in model-access check
The GA Realtime WebRTC HTTP routes resolve the effective model from the
nested session.model (falling back to the top-level model), but the auth
layer's get_model_from_request() only extracted the top-level model. A
model-restricted virtual key could therefore place a disallowed model in
session.model, leave the top-level model unset, and skip can_key_call_model()
entirely - obtaining an ephemeral token for a model it is not allowed to use.
Extract session.model for the realtime client_secrets/calls routes so the
model-access check runs against the model the request will actually use.
Legitimate callers are unaffected; their permitted model still validates.
Relates to https://github.com/BerriAI/litellm/issues/29923
* fix(proxy): classify realtime transcription_sessions routes as LLM API routes
Add the GA Realtime WebRTC transcription_sessions HTTP routes to
openai_routes so is_llm_api_route() returns True for them, matching the
client_secrets and calls routes already fixed. These endpoints are
registered with user_api_key_auth in realtime_endpoints/endpoints.py, so
without this a non-admin virtual key calling
POST /v1/realtime/transcription_sessions would hit the admin-only 401
branch. Extends the regression test parametrization accordingly.
---------
Co-authored-by: habonlaci <4699494+habonlaci@users.noreply.github.com>
* feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models (#30272)
* feat(proxy): surface max_input_tokens/max_output_tokens on /v1/models
* fix(proxy): degrade /v1/models gracefully when model-group lookup fails
---------
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix: sort tiered token-cost thresholds numerically (#30375)
* fix: sort tiered token-cost thresholds numerically
_get_token_base_cost iterated input_cost_per_token_above_<N>_tokens keys with a
lexicographic sort, so for tiers whose thresholds have different digit lengths
(e.g. 90k vs 128k) a request crossing both was billed at the lower tier that
sorted first. Sort by the parsed numeric threshold instead, so the highest tier
the request actually crosses is applied.
* refactor: reuse _parse_above_token_threshold for inline threshold parse
---------
Co-authored-by: Eric (GabiDevFamily) <271972409+santino18727-debug@users.noreply.github.com>
* fix(openai): preserve cache_control for openai-compatible custom endpoints (#30387)
* fix(openai): preserve cache_control for openai-compatible custom endpoints
* fix(openai): use parsed hostname to detect real OpenAI for cache_control preservation
* fix(proxy): drain all daily-spend batches per flush cycle (#30281) (#30505)
* fix(types): prevent internal parallel_request_limiter fields from leaking to upstream providers (#30545)
* fix(types): add internal parallel_request_limiter fields to all_litellm_params to prevent forwarding to upstream providers
* test(types): add regression test for internal rate-limit fields in all_litellm_params
* fix(init): add bool type annotation to suppress_debug_info (#30531)
Module-level `suppress_debug_info = False` had no annotation, so strict
type checkers (e.g. ty) infer it as `Literal[False]`. Reassigning it to
`True` (as done in proxy_server.py and router.py) then fails with an
invalid-assignment error. Annotate it as `bool` to match every other
flag in this module.
* fix: coalesce null aggregates in update_metrics for no-spend keys (#29945)
* feat(team_endpoints): add query parameter `key_limit` to `/team/info` endpoint (#30006)
* feat(team_endpoints): Add query parameter key_limit to /team/info
* feat(team_endpoints): update schema.d.ts to include the new query parameter
* feat(team_endpoints): add tests for limitting key count in /team/info response
* feat(team_endpoints): Apply suggestions from greptile
* Set greater-than constraint on key-limit
* Fix type
* fix(router): release aiohttp connection when stream iteration ends abnormally (#30271)
* fix(router): release aiohttp connection when stream iteration ends abnormally
A streaming response that terminates with a mid-stream read timeout, a task
cancellation (client disconnect), or GeneratorExit never closed the underlying
aiohttp ClientResponse. aiohttp only auto-releases the connector slot at body
EOF, so each abnormally terminated stream permanently leaked one slot from the
shared TCPConnector pool. During a backend traffic spike the pool drains; once
exhausted every subsequent request to that host waits for a slot, times out
and surfaces as a 408, indefinitely, even after the backend recovers. Only a
proxy restart cleared the in-memory sessions, which matched the reported
symptom of a router stuck returning 408 for a healthy vLLM backend.
Close the response in a finally clause when iteration ends. On a fully read
response the connection was already released at EOF and close() is a no-op,
so keep-alive reuse for normal requests is unchanged.
Fixes#30192
* test(aiohttp): cover GeneratorExit path with a mock instead of a live socket
The previous slot-release test started a real aiohttp TCP server, which can
flake in offline CI and does not exercise this fix's code path directly.
Replace it with a dependency-injected mock that closes the stream generator
(GeneratorExit) and asserts the response is closed, covering the third
abnormal-exit path the finally block handles
* feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery (#30273)
* feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery
* refactor(proxy): move Anthropic model-list formatter into llms/anthropic/common_utils
* fix(proxy): make model_list request param optional for direct callers
* feat(dashscope): add Responses API support (#30286)
* feat(dashscope): add Responses API support
DashScope's OpenAI-compatible endpoint serves /responses, so register a
DashScopeResponsesAPIConfig that routes dashscope/* responses calls to
{api_base}/responses without rewriting the upstream model id, instead of
falling back to the chat-completions -> responses emulation pipeline.
Closes#29780
* feat(dashscope): mark responses API as not supporting native websocket
Matches the hosted_vllm/perplexity/openrouter responses configs, which all
override supports_native_websocket() to False since the OpenAI-compatible
endpoint has no native wss:// responses transport.
---------
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix(spend-logs): preserve error_message on ProxyException failures (#30381)
* fix(spend-logs): preserve error_message on ProxyException failures
`StandardLoggingPayloadSetup.get_error_information` used
`str(original_exception)` to populate the human-readable error message
stored in `spend_logs.metadata.error_information.error_message`.
`ProxyException` (litellm/proxy/_types.py:3453) sets `self.message` in
its constructor but does NOT call `super().__init__(message)` and does
NOT define `__str__`. As a result, `str(ProxyException(...))` returns
the empty string, and every auth/budget/quota rejection was landing
in spend_logs with `error_message=""` despite a fully populated
traceback.
Operator impact: dashboard "LLM Failure" rows became untriageable —
the only way to tell a 401 from a 429 was to manually unpack the
traceback JSON via psql. Burst failure patterns (e.g. a UI session
polling with a stale token) produced 20-30 indistinguishable
`error_code=401` rows per second.
Fix: prefer the `.message` attribute (set by ProxyException and every
litellm.exceptions.* class) over `str(exc)`. The `str(exc)` fallback
is retained for non-litellm exception types, preserving prior behavior.
Test plan:
- 2 new unit tests in tests/test_litellm/litellm_core_utils/
test_litellm_logging.py:
* test_get_error_information_prefers_message_attribute_over_str
* test_get_error_information_falls_back_to_str_when_no_message_attr
- Existing test_get_error_information_error_code_priority still passes
- End-to-end verified: bad-key 401 now stores full
"Authentication Error, Invalid proxy server token passed..."
message in spend_logs.metadata.error_information.error_message
* fix(spend-logs): preserve explicit empty .message + drop dead reference
Greptile P2 on #30381. The truthiness check `if message_attr:`
silently skipped an explicit empty-string `.message` and fell
through to `str(original_exception)`. For ProxyException-shaped
objects both produce empty, so the bug was latent; for other
exception types it would inject a different string into
error_information.error_message and corrupt the signal.
Use `is not None` so an empty string survives verbatim.
Also drop the stale `See e2e/cases/11.` comment reference — that
path does not exist anywhere in the repo and confuses future
readers.
Regression test added: an exception with `.message=""` and a
non-empty `super().__init__()` arg must yield error_message == "".
* ci: retrigger workflows after base branch change to litellm_internal_staging
* fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response (#30382)
* fix(anthropic): strip LiteLLM-injected total_tokens from /v1/messages response
The non-streaming /v1/messages response carries a LiteLLM-injected
usage.total_tokens = input_tokens + output_tokens that is not part of
the Anthropic API spec. This caused three problems:
1. Shape divergence with streaming on the same endpoint.
message_delta.usage in the SSE path never carries total_tokens.
Clients parsing both paths get two different schemas from one endpoint.
2. Shape divergence with upstream. Direct calls to
https://api.anthropic.com/v1/messages return no total_tokens field,
so clients using the official Anthropic SDK couldn't rely on it,
and clients that did rely on the LiteLLM-injected one broke when
bypassing the proxy.
3. Numerical misuse. total = input + output undercounts when
cache_read_input_tokens and cache_creation_input_tokens are
non-zero, because cache tokens are reported in their own fields.
A 100k-token cached prompt with 1 non-cache input token + 200
output tokens reports total_tokens = 201, off by ~99.8% from any
reasonable definition of "total."
Fix: add _strip_total_tokens_from_anthropic_response in
litellm/proxy/anthropic_endpoints/endpoints.py and invoke it in the
success path of anthropic_response right before returning. Only mutates
dict-shaped responses; streaming (which already lacks the field) is
left untouched.
spend_logs / Prometheus continue to compute total_tokens internally
for billing — this fix only strips the field from the wire response.
Scope: only the Anthropic passthrough endpoint /v1/messages. The
OpenAI-shape /v1/chat/completions is unaffected.
* fix(anthropic): gate total_tokens strip behind flag + handle Pydantic .usage
Two P1 greptile threads on #30382:
P1 — **Backwards-incompatible removal without a feature flag**
Stripping `usage.total_tokens` unconditionally breaks any client
currently reading the LiteLLM-shaped non-streaming /v1/messages
response. Per the codebase's policy (mirrors #30418), gate behind
a new flag.
- `litellm.strip_anthropic_total_tokens: bool = False` (default —
backward-compat: clients keep seeing total_tokens).
- Env override: `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS=true`.
- Docstring: planned to flip to True in a future major release;
opt in early.
P1 — **Silent no-op if `result` is a Pydantic model**
`base_process_llm_request` may return a Pydantic-style object
whose `.usage` is a plain dict (the most common shape — e.g.
objects wrapping raw upstream JSON). The original
`isinstance(response, dict)` guard skipped strip on those, so
`total_tokens` would still hit the wire. Helper now also reads
`getattr(response, "usage", None)` and strips when that's a dict.
Strongly-typed Pydantic `Usage` sub-models with required
`total_tokens` fields are still skipped — those impose type
constraints the helper doesn't try to subvert.
Tests:
- `test_strips_total_tokens_on_pydantic_model_with_dict_usage`
- `test_flag_defaults_off`
8/8 pass locally.
* fix(anthropic): drop env var for strip flag (docs CI)
Mirrors #30418's pattern (`expose_router_debug_in_errors: bool = True`,
no `os.getenv`). The `LITELLM_STRIP_ANTHROPIC_TOTAL_TOKENS` env var
introduced in the prior commit was flagged by
`tests/documentation_tests/test_env_keys.py` because the documentation
file `docs/my-website/docs/proxy/config_settings.md` lives in
`BerriAI/litellm-docs` (separate repo) and registering a new env key
requires a parallel docs PR — a friction we avoid here by exposing
the flag only as a Python attribute + `litellm_settings` config key,
both of which load through the existing proxy config plumbing without
needing the env-var registry to be updated.
No semantic change: default still False, behavior identical when set
via `litellm.strip_anthropic_total_tokens = True` or
`litellm_settings.strip_anthropic_total_tokens: true` in config.yaml.
Verified locally: env scan no longer surfaces the key; 8/8 tests pass.
* ci: retrigger workflows after base branch change to litellm_internal_staging
* fix(pricing): correct swapped input/output token costs for command-r7b-12-2024 (#30413)
* fix(pricing): correct swapped input/output token costs for command-r7b-12-2024
* test: resolve model prices JSON relative to test file for pip installs
* fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError (#30417)
* fix(exception-mapping): map Gemini upstream-error body code 429 to RateLimitError
Some Gemini-compatible gateways (e.g. new-api) wrap a 429 rate-limit
signal from upstream inside an HTTP 500/503 envelope, with the real
code only surfaced in the JSON body:
{"error":{"message":"...high demand...","type":"upstream_error",
"param":"","code":429}}
Previously LiteLLM only looked at the HTTP status and mapped this to
InternalServerError, which Router treats as non-retryable for many
configs — so users got hard 500s instead of fallback/retry.
Now the Gemini/Vertex exception mapper parses error.code from the body
and routes code 429 to RateLimitError before falling through to the
HTTP-status branches. Other body codes fall through unchanged.
Tests cover:
- new-api gateway's `code:429` payload now maps to RateLimitError
- Genuine 500-body responses stay InternalServerError
- Non-JSON body strings fall through to status-code mapping unchanged
* fix(exception-mapping): scope body-code 429 promotion to 5xx envelopes
Addresses greptile P1/P2 + @Sameerlite's review on #30417. The new
elif branch was firing for any HTTP status, so a gateway response of
HTTP 400 with body {"error":{"code":429,...}} would be incorrectly
promoted to RateLimitError (retryable) instead of falling through
to BadRequestError. Same trap for 401 -> AuthenticationError.
Scoped the body-code 429 check to `500 <= status_code < 600` —
covers 500/502/503/504 (gateways wrapping upstream 429 in any 5xx
envelope) without inviting the 4xx misclassification.
Tests: parametrized table now covers 5xx (500/502/503), 4xx (400/401),
and the existing fall-through cases, asserting each maps to the
exception type that matches the HTTP status code. 50/50 pass locally.
* ci: retrigger workflows after base branch change to litellm_internal_staging
* feat(router): add expose_router_debug_in_errors flag (default True) to redact internal model_group/fallback names (#30418)
* feat(router)!: redact internal model_group/fallback names from exception messages
The Router was unconditionally appending internal config names onto
exception.message:
- "Received Model Group=..."
- "Available Model Group Fallbacks=..."
- "No fallback model group found... Fallbacks={...}"
- "context_window_fallbacks={...}"
- Deployment-timeout messages including model_group
- Fallback failure detail listing fallback chain
ProxyException forwards .message verbatim to clients, so gateways were
leaking their model_name / fallback wiring in every failed call.
Fix: gate all five mutation sites on a new
`litellm.expose_router_debug_in_errors` flag (default False). Set to
True to restore upstream debug behavior for local debugging.
Why: matches the redaction posture this codebase already has for
upstream model identifiers (cf. _litellm_returned_model_name) and
removes the last common error-path leak of internal model_group names.
Breaking change marker (!): if anything parses "Received Model Group="
out of client error messages, flip the flag on or migrate to the
x-litellm-* response headers instead.
Tests: 7 cases covering each of the 5 redaction sites + the flag-on
inverse path, plus a "default off" sanity check.
* test(router): cover sites 1 + 3 of expose_router_debug_in_errors gate
Addresses Greptile / codecov feedback on #30418: patch coverage was
55.6% with 4 lines uncovered in litellm/router.py. The existing tests
exercised sites 2 (ContextWindowExceededError), 4 (no-fallback-found),
and 5 (Received Model Group) — both default and flag-on. Sites 1 and 3
were declared in the PR description as covered by "site 5 also fires"
but the gate body lines for each (the `e.message +=` inside the
`if litellm.expose_router_debug_in_errors:` branch) only execute when
the flag is on AND the specific exception path is taken, which neither
existing test triggered.
Added 4 new tests (default + flag-on × 2 sites):
- test_default_does_not_leak_deployment_timeout_debug
- test_flag_on_leaks_deployment_timeout_debug
- test_default_does_not_leak_content_policy_fallback_hint
- test_flag_on_leaks_content_policy_fallback_hint
Trigger details:
- Site 1 (litellm.Timeout in _acompletion) is reached via the
Router-supported `mock_timeout=True` + `timeout=0.001` kwargs on
`acompletion(...)`. Cannot embed a Timeout instance in model_list
because Router.__init__ deep-copies it and Timeout.__reduce__ does
not preserve the required positional args.
- Site 3 (ContentPolicyViolationError without content_policy_fallbacks
set, in async_function_with_fallbacks_common_utils) is reached by
passing a `mock_response=litellm.ContentPolicyViolationError(...)`
instance via the call-site kwarg — same deepcopy-avoidance reason.
11/11 tests pass locally. Patch coverage on litellm/router.py for this
PR's diff should now be 100%.
* chore(router): flip expose_router_debug_in_errors default to True
Addresses @Sameerlite's review on #30418 — maintain backward
compat on the wire. Redact becomes opt-in via setting the flag
to False; the historical behavior (leak internal model_group /
fallback wiring through exception messages) is preserved as the
default.
- litellm/__init__.py: default flipped to True, docstring rewritten
with deprecation note pointing at a future flip to False (redact
by default) in a major release.
- tests/test_litellm/test_router_exception_redaction.py: fixture
resets to True (was False); the "off" tests now explicitly set
False; the "default_leaks_*" tests rely on the fixture default.
test_flag_defaults_off -> test_flag_defaults_on.
- No router.py change needed; the gate keys off the same flag,
only the default changes.
- PR title no longer needs the breaking-change `!` marker — no
client sees a behavior change at default settings.
11/11 pass locally.
* ci: retrigger workflows after base branch change to litellm_internal_staging
* feat(guardrails): integrate Repelloai Argus guardrail (#30465)
* feat(guardrails): add RepelloAI Argus guardrail integration (#1)
* feat(guardrails): add RepelloAI Argus guardrail integration
Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed
asset policies enforced via an asset_id and X-API-Key auth.
* fix(guardrails): harden RepelloAI Argus guardrail
- scan streaming responses on output (was bypassing the guardrail)
- log blocked verdicts as guardrail_intervened instead of success
- treat auth/config errors (401/403/404/422) as misconfiguration that
always blocks, not a fail-open-able unreachable error
- default unreachable_fallback to fail_closed and read it directly;
block on unknown/malformed verdicts so an API change can't silently
disable enforcement
- type unreachable_fallback as a Literal, drop the duplicate config model,
expose unreachable_fallback in the config schema, and stop leaking the
raw provider response / exception strings to the client
* fix(guardrails): address RepelloAI Argus review feedback
- support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback)
- make asset_id required in the config model
- normalize unreachable_fallback so only fail_open opens; block on 400 misconfig
- correct the shared unreachable_fallback field description
* docs(guardrails): add RepelloAI Argus docs page and dashboard listing
- add docs page covering config, env vars, modes, verdicts, failure semantics
- list RepelloAI Argus in the Guardrail Garden with provider/logo mappings
- add a regression test for the provider logo and display-name resolution
* fix(guardrails): keep RepelloAI asset_id optional in config model
A required asset_id leaked onto the shared LitellmParams (which inherits
RepelloAIGuardrailConfigModel), breaking validation for every other
guardrail. Keep it optional like sibling models; the guardrail __init__
still raises when asset_id is missing, which is the real enforcement.
* Add comment for last user turn scanning
* feat(guardrails): harden repelloai scanning
* feat(guardrails): expand repelloai scanning to include tool definitions
Add extraction of tool definitions and tool call arguments to the RepelloAI
guardrail scanning. Improves detection coverage by including function schemas
and parameters in the prompt sent to the guardrail service. Also captures
detailed error responses in logs and adds guardrail header to streaming responses.
* refactor(guardrails): fix and harden repelloai schema text extraction
- Fix duplicate text in _iter_schema_text: previously all dict values were
re-queued onto the stack even after scalar/list keys were already extracted
explicitly, causing names/descriptions to appear twice in the scanned prompt
- Extract schema key frozensets to module-level constants so they are not
reconstructed on every call
- Change _iter_schema_text from @classmethod to @staticmethod (cls unused)
- Narrow _call_analyze stage param from str to Literal["prompt", "response"]
- Add HttpxResponse type annotation to _raise_for_config_error
- Add LLMResponseTypes annotation to async_post_call_success_hook response param
* fix(guardrails): resolve pyright type errors in repelloai guardrail
- Narrow async_handler.post return from Response|None to Response with
explicit None guard before calling raise_for_status/json
- Fix list comprehension returning str|None by switching to explicit loop
with isinstance guard so pyright tracks the narrowing
- Cast model_dump() result to Dict since hasattr does not narrow object
type in pyright
* fix(guardrails/repello): include Responses API instructions field in prompt scan
The /v1/responses top-level `instructions` field was not included in
_extract_prompt_text, allowing a caller to bypass guardrail policy checks
by putting blocked content in `instructions` while keeping `input` benign.
* feat: add api_key to config model and read prompt from data dict
* fix(guardrails/repello): plug input_text and tool-call response bypass gaps
Responses API input content parts with type 'input_text' were silently
dropped by build_inspection_messages (which only handles type='text'),
allowing callers to send blocked content via that path without triggering
the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail
and call it when walking the Responses API input messages.
Post-call scanning skipped responses whose choices contained only tool_calls
or function_call (message.content=None), letting models put blocked output in
function arguments undetected. Fix: _extract_chat_completion_text now calls
_extract_tool_call_args_from_message on each choice message.
Also replace typing.Dict/List with builtin dict/list to clear TID251 strict
ruff violations introduced by this file.
* fix(guardrails/repello): scan Responses API function_call output arguments
Output items with type 'function_call' in a /v1/responses response were
skipped by _extract_responses_api_text; only 'message' items were walked.
A model could return blocked content in function_call.arguments undetected.
Now extract arguments from function_call output items before scanning.
* fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients (#30486)
* fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients
When an Anthropic server-side tool (web_search, id `srvtoolu_...`) is used, its
result is carried in `provider_specific_fields.web_search_results` — PRs #17746
/ #17798 restore it for callers that round-trip provider_specific_fields. A
generic OpenAI client that does NOT preserve provider_specific_fields (e.g. Open
WebUI talking to a Vertex/Anthropic model over /chat/completions) drops it on
replay and instead sends back an assistant `tool_call` + a `tool` message both
keyed to the `srvtoolu_` id. The transform then produced a bare `server_tool_use`
(with no following *_tool_result) plus a user `tool_result` for the same id —
both invalid, so the next turn 400s:
messages.N.content.0: unexpected `tool_use_id` found in `tool_result` blocks:
srvtoolu_... Each `tool_result` block must have a corresponding `tool_use`
block in the previous message.
This is the commonly-reported vertex_ai symptom where Gemini works but Claude
400s on the 2nd turn of a web-search chat.
Fix (litellm/litellm_core_utils/prompt_templates/factory.py):
- convert_to_anthropic_tool_invoke: only emit a server_tool_use when its matching
*_tool_result is available to pair with it; otherwise skip it (a bare
server_tool_use is itself rejected).
- anthropic_messages_pt: drop a replayed `tool`/`function` message whose
tool_call_id starts with `srvtoolu_` (a server-executed tool produces no client
result; a user tool_result for it is invalid).
The existing reconstruction path (provider_specific_fields present, e.g. the
litellm SDK) is unchanged, as is regular client tool_use/tool_result.
Tests (tests/llm_translation/test_prompt_factory.py):
- update test_convert_to_anthropic_tool_invoke_server_tool ->
test_convert_to_anthropic_tool_invoke_server_tool_without_result_is_dropped
- add test_anthropic_messages_pt_generic_client_drops_orphan_server_tool
Follow-up to #17746 / #17798; addresses the generic-client (no
provider_specific_fields) case of #17737.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(anthropic): cover the srvtoolu_ round-trip fix in the test_litellm unit suite
The regression tests added in tests/llm_translation/test_prompt_factory.py aren't
run by the coverage CI job (it runs tests/test_litellm), so the new factory.py
branches showed as uncovered (codecov patch coverage). Add equivalent focused
tests in the unit suite so both new branches are exercised there:
- convert_to_anthropic_tool_invoke drops a srvtoolu_ server_tool_use when no
matching *_tool_result is available.
- anthropic_messages_pt drops the orphaned srvtoolu_ tool message a generic
OpenAI client replays.
Refs #17737
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(anthropic): cover the server_tool_use + result valid-pair path in unit suite
Covers the remaining patch-coverage lines codecov flagged: convert_to_anthropic_tool_invoke
emitting server_tool_use followed by its web_search_tool_result when the matching
result is present (the litellm-SDK round-trip path). Refs #17737
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* style(anthropic): flatten srvtoolu_ tool-message guard to a negated if
Addresses the Greptile style nit: replace the if-pass/else with a single negated
`if not (...)` guard around the tool_result append. Behavior unchanged. Refs #17737
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(proxy): require premium only when enabling premium metadata fields (#30285) (#30506)
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix(perplexity): stop double-billing reasoning tokens in manual cost fallback (#30488)
* fix(perplexity): stop double-billing reasoning tokens in manual cost fallback
When perplexity_cost_per_token cannot use the API-provided usage.cost.total_cost short-circuit and falls back to manual calculation, it multiplies the full usage.completion_tokens by output_cost_per_token and then adds reasoning_tokens * output_cost_per_reasoning_token on top. Per the OpenAI/Perplexity usage convention codified for the central path in PR #18607, completion_tokens already INCLUDES reasoning_tokens, so the manual fallback double-bills reasoning at both the output and reasoning rate.
Concrete impact on perplexity/sonar-deep-research (input 2e-6, output 8e-6, reasoning 3e-6): for the exact usage shape exercised by the live response fixture in tests/llm_translation/test_perplexity_reasoning.py (prompt_tokens=9, completion_tokens=20, reasoning_tokens=15) the current code charges 0.000223 vs the convention-correct 0.000103, a 2.165x overcharge. The bug is reachable whenever Perplexity omits the cost object (streaming chunks, fixture-driven paths, older API versions).
Subtracts reasoning_tokens (clamped at zero) from completion_tokens before applying the output rate, mirroring how dashscope/cost_calculator.py and the central generic_cost_per_token already handle it. Preserves the existing fallback behaviour when output_cost_per_reasoning_token is unset (all completion_tokens stay at the output rate).
Existing tests in tests/test_litellm/llms/perplexity/test_perplexity_cost_calculator.py asserted the buggy math and are updated to the convention-correct math. Adds a focused regression test using the exact usage shape from the live response fixture so this class of bug cannot be silently reintroduced.
* style(perplexity): drop redundant type annotation on else branch to satisfy mypy
mypy [no-redef] flagged 'completion_cost' as declared in both if and else arms; keeping the annotation only on the first declaration matches existing patterns in this file.
* fix(perplexity): update integration test expected costs for non-double-billed math
Three tests in test_perplexity_integration.py asserted the old buggy expectation
that reasoning_tokens are billed in addition to the full completion_tokens
count. After the fix in cost_per_token, reasoning_tokens are billed at the
reasoning rate and the remaining (completion_tokens - reasoning_tokens) at the
standard output rate, matching OpenAI/Perplexity convention (PR #18607).
Updates: test_end_to_end_cost_calculation_with_transformation,
test_main_cost_calculator_integration, test_high_volume_cost_calculation.
The high-volume sanity threshold drops to 0.25 to reflect the corrected total.
* fix(ui): use dynamic proxy base URL in MCP usage examples (#30487)
Replace hardcoded http://localhost:4000 with getProxyBaseUrl() in the
MCP server usage example and copy-to-clipboard snippet so the generated
configuration works for non-local deployments.
Fixes#30466
* feat: add missing UK PII entity types to Presidio guardrail (#30537)
* feat: add missing UK PII entity types to Presidio guardrail
Add UK_PASSPORT, UK_POSTCODE, and UK_VEHICLE_REGISTRATION to PiiEntityType enum and PII_ENTITY_CATEGORIES_MAP. These entity types are supported by Microsoft Presidio but were missing from litellm's type definitions, preventing users from configuring UK-specific PII detection.
* test: remove fragile hardcoded entity count test
Remove test_uk_category_entity_count which hardcodes len() == 5. The test_uk_entities_match_presidio_recognizers test already verifies exact set equality, making the count test redundant and fragile to future Presidio additions.
* style: apply Black formatting to match CI requirements
* fix: route volcengine (Doubao) tiered-pricing models to the tiered cost handler (#30357)
Volcengine (Doubao) models define `tiered_pricing` but no flat per-token cost, so cost_per_token fell through to generic_cost_per_token (which only reads flat costs) and tracked them at $0
Route custom_llm_provider == "volcengine" to the shared tiered-pricing handler in litellm/llms/dashscope/cost_calculator.py, which already computes graduated tier costs. Make that handler provider-agnostic by adding a custom_llm_provider argument (default "dashscope" preserves existing behavior) so get_model_info resolves the correct model map entry
Fixes#30346
* feat(mcp): make MCP gateway name and description configurable via env vars (#30473)
* feat(mcp): make MCP gateway name and description configurable via env vars
* Rename function _restore_env to _apply_env
* docs(mcp): document import-time capture of env-backed identity constants
Address Greptile review feedback: clarify that LITELLM_MCP_SERVER_NAME and
LITELLM_MCP_SERVER_DESCRIPTION are read once at import and require a module
reload to observe env changes after import.
Generated with AI assistance
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Yevhen Luhovtsov <yevhen.luhovtsov@intapp.com>
Co-authored-by: Claude <noreply@anthropic.com>
* fix(mcp): preserve native tools in semantic filter hook (#26650)
* fix(mcp): preserve native tools in semantic filter hook
The SemanticToolFilterHook.async_pre_call_hook passed ALL tools (MCP +
native) to filter_tools(), which only knows MCP-registered tool names.
Native tools silently failed the name match in _get_tools_by_names()
and were dropped from the request.
Fix: partition tools into native and MCP-registered before filtering.
Run the semantic filter only on MCP tools, then merge native tools
back unconditionally.
Changes:
- Robust _is_mcp_tool() using shape-based detection for OpenAI-format
dicts, safe regardless of future _extract_tool_info changes
- Single-pass partition loop (no double _is_mcp_tool calls)
- Preserve native tools in MCP expansion path (mixed requests)
- Track MCP expansion to prevent expanded tools bypassing filtering
- filter_stats reports MCP-only counts for accurate metrics
- Extracted _emit_filter_metadata() helper
- Skip spurious filter headers for all-native tool requests
Closes#26212
* remove stale docstring note referencing tools_expanded_from_mcp
* fix: handle Responses API name collision and preserve tool ordering
- Classify Responses API tools ({type: 'function', name: '...'}) as
native to prevent name collisions with MCP canonical names
- Preserve original request tool ordering using id()-based merge
instead of naive native+mcp concatenation
- Add 2 regression tests: name collision and ordering preservation
* style: apply black formatting
* fix(mcp): harden semantic filter — preserve all native tool formats, safe metadata access, graceful expansion failure, name-based merge
* lint: suppress PLR0915 on async_pre_call_hook (matches codebase convention)
* ci: retrigger checks after rebase onto litellm_internal_staging
* feat(fireworks): sync Fireworks AI model registry with current platform catalog (#30616)
Adds 12 new Fireworks serverless models and updates 3 existing entries in
model_prices_and_context_window.json and its bundled backup to match the
current Fireworks platform model list. New direct models: glm-5p2,
qwen3p7-plus, minimax-m3, minimax-m2p7, kimi-k2p7-code, kimi-k2p6,
deepseek-v4-pro, deepseek-v4-flash. New router endpoints: glm-5p1-fast,
kimi-k2p6-fast, kimi-k2p7-code-fast. Updated: glm-5p1, gpt-oss-120b, and
gpt-oss-20b now carry correct output token caps, cache-read pricing, and
explicit capability flags
max_tokens is set equal to max_output_tokens (not the full context window)
for models whose generation cap is below their context window. This avoids
the shared input+output budget path in get_modified_max_tokens, which would
otherwise let callers request output sizes the model cannot produce. The
same fix corrects the pre-existing glm-5p1, gpt-oss-120b, and gpt-oss-20b
entries that had max_tokens equal to the full context window
Short-form aliases (fireworks_ai/<model>) are added for every direct
accounts/fireworks/models/ entry so cost attribution works for callers
using bare model names. Router endpoints get short-form aliases too, and
transform_request now routes bare names ending in -fast to the
accounts/fireworks/routers/ path instead of defaulting every bare name to
models/. This keeps the kimi-k2p6-fast router from being misrouted to the
nonexistent models/kimi-k2p6-fast endpoint
kimi-k2p6-turbo is intentionally excluded; kimi-k2p6-fast is its
replacement. Context windows for deepseek-v4 and kimi models use the
power-of-two values (1048576 and 262144) published on the Fireworks model
pages, matching the convention already used by existing entries
Two regression tests in test_utils.py assert the exact per-token costs,
token limits, capability flags, and short-form-to-long-form equality for
all 15 models against both the main and backup cost maps. Two routing
tests in test_fireworks_ai_chat_transformation.py verify bare -fast names
route to routers/ and bare direct-model names route to models/
* fix(bedrock): handle role:"system" inside the messages array on /v1/messages (#29698) (#30443)
* feat(anthropic): hoist leading in-array system to top-level (helper)
* test(anthropic): cover _system_content_to_blocks edge cases; deepcopy cache_control
* test(anthropic): mid-conversation system normalization cases
* feat: add supports_mid_conversation_system flag to Claude Opus 4.8
Add supports_mid_conversation_system: true to all 9 claude-opus-4-8 cost-map
entries (Anthropic-native, Bedrock, Vertex, Azure AI) in both the root cost
map and the bundled package backup, since the runtime helper and tests read
the backup in local/offline mode.
Pin the mid-system passthrough regression test to the local cost map via the
existing local_model_cost_map fixture so it reads the branch-local flag rather
than the network-fetched main copy.
* fix(bedrock): normalize in-array system in /v1/messages handler (#29698)
Wire normalize_system_messages_for_anthropic into anthropic_messages_handler
so all Bedrock /v1/messages paths (Invoke / Mantle / ClaudePlatform /
Converse-bridge) hoist leading in-array system entries (and demote
mid-conversation ones on models lacking supports_mid_conversation_system) into
the top-level system field. The normalized messages/system are written back
into the local_vars snapshot the base_llm branch reads from, otherwise the
Invoke/Mantle fix would silently no-op.
Also fix the helper to resolve supports_mid_conversation_system through the
prefix-aware AnthropicModelInfo._supports_model_capability resolver. The raw
_supports_factory could not see the flag once get_llm_provider left the
invoke/ prefix on the model id, which would have wrongly demoted
mid-conversation system on a Bedrock invoke opus-4-8 path.
* fix(bedrock): resolve mid-conversation-system flag through mantle/invoke/converse route prefixes; drop unused param
* fix(types): widen system param to Union[str, List] for hoisted system blocks
* refactor(bedrock): drop dead local_vars messages writeback
* fix(bedrock/converse): translate in-array system in anthropic->openai adapter (#29698)
* fix(bedrock/converse): preserve cache_control on in-array system; test drop-empty
* fix(bedrock/converse): rename colliding local to satisfy mypy; test handler system-merge branches
* fix(types): register supports_mid_conversation_system in model-info schema
The cost-map JSON-schema validation test (test_aaamodel_prices_and_context_window_json_is_valid)
rejects unknown properties, so adding supports_mid_conversation_system to the opus-4-8
cost-map entries failed CI with 'Additional properties are not allowed'. Register the flag
in the INTENDED_SCHEMA allow-list and in the ProviderSpecificModelInfo TypedDict so it is a
typed, first-class capability flag alongside its peers (supports_output_config, etc.).
---------
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload (#28885)
* fix(bedrock/agentcore): optionally forward multimodal content blocks in InvokeAgentRuntime payload
By default the agentcore provider flattens the last message to a text-only
{"prompt": "..."} payload via convert_content_list_to_str, silently dropping
OpenAI multimodal blocks (image_url, file, input_audio, ...).
This adds an opt-in `forward_multimodal_content` litellm param. When truthy and
the last message's content is a list containing a non-text block, the original
OpenAI content list is forwarded verbatim under a new "content" field so an
attachment-aware AgentCore agent can read it. Default off keeps the payload
byte-identical to the legacy {"prompt": "..."} shape — existing agents are
unaffected.
The flag is read from optional_params (where other AgentCore params land) with a
litellm_params fallback, and accepts a bool or a config/env string ('true', '1', ...).
AgentCore Runtime is schemaless on the agent side — the agent's @app.entrypoint
parses arbitrary JSON up to 100 MB (per
https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-invoke-agent.html),
so this is a purely upstream change; no AgentCore-side schema is asserted.
* fix(bedrock/agentcore): shallow-copy forwarded multimodal content list
Address review feedback (Sameerlite): payload["content"] = last_content
aliased the caller's mutable messages[-1]["content"] list. Harmless today
because the payload is JSON-serialized immediately, but a latent footgun if
a future caller mutates the returned payload before serialization. Forward
list(last_content) so the payload owns its own list. Block dicts stay shared
on purpose — a deep copy would clone potentially large base64 media on the
request hot path, and the flagged risk was the shared list, not the blocks.
Update the passthrough tests to assert equality + distinct identity, and add
a regression test that mutating the payload list can't leak back into the
original message content.
* Revert "fix(mcp): preserve native tools in semantic filter hook (#26650)"
This reverts commit 438c825bd4.
* Revert "feat(guardrails): integrate Repelloai Argus guardrail (#30465)"
This reverts commit 54da7857f2.
* Revert "feat(dashscope): add Responses API support (#30286)"
This reverts commit 67662565e8.
* Revert "fix(bedrock): handle role:"system" inside the messages array on /v1/messages (#29698) (#30443)"
This reverts commit b8a8083308.
* Revert "fix(anthropic): drop orphaned server_tool_use on multi-turn replay from generic OpenAI clients (#30486)"
This reverts commit 6e9c0b0dd2.
* Revert "fix: route volcengine (Doubao) tiered-pricing models to the tiered cost handler (#30357)"
This reverts commit 172e302dab.
* Revert "feat(proxy): serve Anthropic-native /v1/models for Claude Code gateway discovery (#30273)"
This reverts commit 4e3188525e.
* fix: pass key_limit=None in team_member_update and patch model_cost in pricing test
team_member_update called team_info without key_limit, so the fastapi.Query
default object (not None) was passed through to get_data, which failed when
serializing it. Pass key_limit=None explicitly to avoid this.
test_get_model_info_costs patched litellm.model_cost from the local backup so
the assertion holds before the PR is merged and the remote main URL is updated.
* fix(security): validate resolved model in /realtime/client_secrets for non-transcription sessions (#30710)
Omitting both model and session.model caused the endpoint to default to
gpt-4o-realtime-preview without running can_key_call_resolved_model, so
any key could access that model regardless of its allowed-model list.
The transcription path already called can_key_call_resolved_model; this
adds the same call for the realtime path before returning.
* fix(lint): fix F821 undefined model_info and F841 unused metadata in create_model_info_response
* fix: black formatting and stub get_model_group_info in third team translation test
* fix: reformat utils.py with black 26.3.1 to match CI
* fix: replace Optional[X] with X | None to satisfy UP045 ruff strict gate
---------
Co-authored-by: Habon Laszlo <habonlaci@users.noreply.github.com>
Co-authored-by: habonlaci <4699494+habonlaci@users.noreply.github.com>
Co-authored-by: Armaan Sandhu <74664101+Ar-maan05@users.noreply.github.com>
Co-authored-by: santino18727-debug <santino18727@gmail.com>
Co-authored-by: Eric (GabiDevFamily) <271972409+santino18727-debug@users.noreply.github.com>
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Co-authored-by: jho1-godaddy <171078705+jho1-godaddy@users.noreply.github.com>
Co-authored-by: 安妮的心动录 <74543653+anneheartrecord@users.noreply.github.com>
Co-authored-by: Harshith Gujjeti <153299927+Harshxth@users.noreply.github.com>
Co-authored-by: Tomoya Tabuchi <t@tomoyat1.com>
Co-authored-by: Vedant Agarwal <43557509+Vedant-Agarwal@users.noreply.github.com>
Co-authored-by: Prathamesh Jadhav <55660103+lollinng@users.noreply.github.com>
Co-authored-by: songkuan-zheng <252822057+songkuan-zheng@users.noreply.github.com>
Co-authored-by: Kropiunig <48442031+Kropiunig@users.noreply.github.com>
Co-authored-by: Lavish Bansal <lavish.bansal619@gmail.com>
Co-authored-by: Shane Emmons <27679+semmons99@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Anuj ojha <ojhaanuj224@gmail.com>
Co-authored-by: Nahrin <nahrin@nahrinoda.com>
Co-authored-by: Nbouyaa <67773915+FadelT@users.noreply.github.com>
Co-authored-by: Vineeth Sai <vineethsai4444@gmail.com>
Co-authored-by: Eugene Lugovtsov <34510252+EugeneLugovtsov@users.noreply.github.com>
Co-authored-by: Yevhen Luhovtsov <yevhen.luhovtsov@intapp.com>
Co-authored-by: Ayush Shekhar <106994833+ayushh0110@users.noreply.github.com>
Co-authored-by: Ahmad Shahzad <107808273+shzdehmd@users.noreply.github.com>
Co-authored-by: Kent <72616338+kingdoooo@users.noreply.github.com>
Co-authored-by: Jón Levy <levy@apro.is>
