* fix(websearch): restore snippet text in native web_search_tool_result blocks (LIT-5315)
The build_web_search_tool_result_block method copied url/title/page_age but
hardcoded encrypted_content to empty string, never reading SearchResult.snippet.
This left every native block content-free, forcing clients to web_fetch each
result to recover evidence—the reported symptom.
The Anthropic spec carries page text only in encrypted_content (an opaque
server-issued blob we cannot mint), so snippet is emitted as an additive key
alongside the spec fields. encrypted_content stays empty rather than holding
plaintext, which would assert encryption semantics that don't hold.
The anthropic SDK's BaseModel sets extra='allow', so the additive snippet key
survives SDK parsing. litellm has no typed model for web_search_result at all,
so nothing drops it internally. Turn-2 replay behavior is unaffected: the
empty encrypted_content already exists today.
Tests:
- Updated test_shape_with_results to assert snippet present
- Added test_snippet_carried_for_every_result to cover multi-result ordering
- Added test_missing_snippet_degrades_to_empty_string for edge case
- Mutation check: reverting source-only yields 3 test failures, restored to 117 passed
Fixes: LIT-5315
Co-Authored-By: Claude <noreply@anthropic.com>
* fix(websearch): make synthesized web_search blocks replayable by native clients
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(websearch): flatten a resultless replayed search block so Bedrock accepts the next turn
The flatten added for LIT-5315 bails when the replayed web_search_tool_result
carries an empty content list, but that is exactly what the interceptor emits
when a search legitimately returns nothing and when a search raises. The block
survived into the outbound body, Bedrock rejected the tag, and the conversation
died on the following turn just as it did before the flatten existed.
An empty content list has no encrypted_content to respect and no evidence to
preserve, so it flattens safely, and its paired server_tool_use goes with it.
The rendered text now says so explicitly rather than emitting a bare header.
Adds the multi-turn replay coverage that existed nowhere: the outbound Bedrock
invoke body is asserted free of both block types, parametrized over the
results-present and resultless cases, and built from the interceptor's own
builder so the fixture cannot drift from what it emits.
Resolves LIT-5320
* test(websearch): pin flatten idempotency for the agentic-loop re-entry
The agentic loop re-enters the same /v1/messages entry point for its follow-up
call and hands it the original client history, so the flatten runs again over
already-flattened messages once per iteration. Bedrock always takes that path,
since its config reports web search as natively handled and the short-circuit
is skipped.
A pass that appended the rendered text instead of replacing the block would
duplicate the evidence on every iteration and re-ship the unsupported tag, and
no existing single-pass test sees it. Mutation checked: keeping the original
block alongside the rendered text fails this test on its own.
---------
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
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.
Guardrails silently skipped three surfaces on the Anthropic Messages
path, so an agent loop driven by /v1/messages ran unguarded:
- The Anthropic input translation never walked tool_result blocks, so
content returned by a local tool (a curl, a file read, an MCP call)
reached the model unscanned in both the string and list content
shapes, images inside a tool_result included.
- tool_permission only understood ModelResponse, so an Anthropic
non-streaming response or a raw SSE stream carrying tool_use blocks
passed through with no rule ever evaluated.
- ContentFilterGuardrail scanned inputs["texts"] but never
inputs["tool_calls"], so the arguments a model proposes for a tool
call went unchecked.
Tool call arguments are parsed as JSON before filtering so a MASK
action rewrites the value and leaves the payload valid JSON; non-JSON
arguments fall back to scanning the raw string. Denied tool_use blocks
are dropped from the Anthropic content array and replaced with a text
block, and stop_reason resets to end_turn when nothing tool-shaped
survives.
* fix(anthropic): split mixed reasoning stream chunks
* style: use builtin generic annotation
* fix(anthropic): split mixed stream chunks by payload kind
The mixed-chunk split cleared only the fields it knew about on each
deep-copied piece, so any other payload riding the chunk survived on
both pieces: tool_calls were emitted as two tool_use blocks with the
same id, thinking_blocks on the text piece emitted duplicated thinking
into a text block while dropping the answer text, and chunks whose
reasoning arrived only as thinking_blocks never split at all
Rebuild each piece's delta from scratch with exactly one payload kind
(reasoning, text, tool calls), ordered to match native Anthropic block
order. Fresh Delta construction keeps unset attributes deleted, which
matters because the translators branch on hasattr, and prevents future
Delta fields from riding along on every piece
* fix(anthropic): keep continuation and multi-choice chunks unsplit, emit signature-less thinking once
Adversarial verification against the merge-base found three shapes where
the payload-kind split changed behavior beyond its target: a mixed chunk
carrying a tool argument continuation was torn into a truncated block
plus a fabricated one, a multi-choice chunk lost its secondary choices'
payload, and a signature-less thinking_blocks piece inherited the
non-empty block start body so accumulators collected the thinking twice
Continuation and multi-choice chunks now pass through the splitter
untouched, matching the merge-base byte for byte, and signature-less
thinking_blocks pieces are normalized to reasoning_content so the block
start opens empty and the thinking text is emitted exactly once
---------
Co-authored-by: Napuh <naamanynadiemas@gmail.com>
* fix(router): don't cool down parent deployment on advisor sub-call failure
Advisor orchestration issues a sub-call to a different provider/credentials than the selected deployment. When that sub-call fails (e.g. a 401 because no advisor API key is configured), the exception propagates up and the router's deployment_callback_on_failure attributes it to the healthy parent deployment's model_info.id, cooling it down and rejecting unrelated callers to the same model group.
Tag advisor sub-call failures on the exception and skip cooldown for them in deployment_callback_on_failure. The exception is tagged rather than wrapped so its type is preserved and retry/fallback classification and the client-facing error are unchanged. Genuine executor/deployment failures are untagged and still cool down as before.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(router): tag advisor orchestration failures via provider-neutral util
Address review on LIT-4565: move the cooldown-exemption marker into
litellm/router_utils/cooldown_handlers.py so the router imports it at
module top instead of an in-function anthropic import, and extend the
exemption to AdvisorMaxIterationsError so a max-iterations orchestration
failure no longer cools down the healthy executor deployment.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: shivam <shivam@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
translate_thinking_for_model duplicated the same summary/auto_summary
wrapping logic as _translate_thinking_to_openai without the
disabled-thinking guard, so it could still wrap "none" into an
{effort, summary} dict when reasoning_auto_summary is enabled (caught
by Cursor Bugbot). Extract the wrapping rule into one shared
_apply_reasoning_summary_wrapping helper used by both call sites so
this invariant can't drift apart again.
Codecov flagged the empty-list early-return in
_translate_stop_sequences_to_openai as an uncovered line in the diff —
add a regression test asserting stop_sequences=[] does not set
new_kwargs["stop"].
Guard against reasoning_auto_summary wrapping "none" into a dict when
thinking is disabled — there's no reasoning trace to summarize, and
non-Claude providers (e.g. Fireworks) expect reasoning_effort as a
plain string.
Claude Code's auto-mode classifier sends stop_sequences and thinking:
{type: disabled} on /v1/messages. The Anthropic adapter passed
stop_sequences through unchanged instead of mapping it to OpenAI's stop,
which Fireworks' OpenAI-compatible endpoint rejects with HTTP 400. It also
dropped disabled thinking instead of mapping it to reasoning_effort: none,
so the model spent its output budget on reasoning it was told to skip.
Resolves LIT-4798
* fix(guardrails): add /v1/messages support for Straiker plugin
- Pass prepared response data to Anthropic Messages streaming post-call hooks (litellm/llms/anthropic/chat/guardrail_translation/handler.py)
- Normalize Straiker request, tool, finish-reason, and mode fields across Chat Completions, Messages, and Responses APIs
* fix(guardrails): gate cross-surface message resolution and cover streaming request data
Resolve request messages only for surfaces that have a mapped translation
handler. The unguarded fallback tried every registered handler in turn, which
raised AttributeError out of the guardrail's error handling on list-shaped
`input` bodies, and synthesized a chat message that was never sent for bodies
it happened to parse.
Prepare request data on the mid-stream Anthropic branch as well, matching the
terminal branch and the OpenAI handler, so guardrails that scan before
end-of-stream still receive identity metadata.
Read usage from Anthropic dict responses so non-streaming /v1/messages reports
token counts instead of null.
Add regression coverage for the streaming request data on both the terminal and
mid-stream branches; reverting either now fails.
---------
Co-authored-by: cs-mehta <chandra@straiker.ai>
* fix(anthropic): strip uniqueItems + other unsupported array/object constraints from output_format schema
Anthropic's structured outputs (`output_format`) validate the JSON schema
against a strict subset and reject cross-element / count constraints that a
constrained-decoding grammar cannot enforce, returning a 400
`invalid_request_error`.
`filter_anthropic_output_schema` already stripped the numeric / string /
item-count constraints (minimum, maximum, exclusiveMinimum/Maximum, minLength,
maxLength, minItems, maxItems) but still let these through:
- uniqueItems
- contains / minContains / maxContains
- minProperties / maxProperties
so a request using them fails with e.g. "output_format.schema: For 'array'
type, property 'uniqueItems' is not supported".
This is provider-visible: newer Claude models on the native `output_format`
path (e.g. `azure_ai`) 400, while `vertex_ai` is unaffected because it is
forced onto the permissive tool-use path (#18625 / #19201).
Add the missing keywords to the unsupported-field set and the description map,
and skip the advisory description note for a disabled boolean constraint
(`uniqueItems: false`) so it isn't misdescribed as required.
* fix(anthropic): serialize contains sub-schema in output_format advisory note
Address Greptile review: the `contains` advisory note previously discarded the
sub-schema, so the description only said an item must match "a schema" without
saying which. It now serializes the sub-schema as JSON (e.g. "array must
contain an item matching: {\"type\": \"integer\", \"const\": 1}"), matching the
other stripped constraints which carry their value. Sub-schema (dict/list)
values are json.dumps'd; scalar constraints are unchanged.
* style(anthropic): apply ruff format to output_format filter change
* style(test): ruff format anthropic schema filter tests
* test(anthropic): cover output_format array/object constraint filtering in test_litellm tree
Mirrors the schema-filter tests under tests/test_litellm/ so the coverage
job exercises the new uniqueItems/contains/min-maxProperties handling and the
uniqueItems: false branch.
---------
Co-authored-by: Darien Kindlund <darien@kindlund.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* 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>
The /v1/messages handler resolved only the auth object and the trace id, so tool
listing and tool execution ran without the caller's MCP auth headers. That fails
quietly rather than loudly: the tool still executes, just with no credentials, so
every server behind interactive OAuth, a bearer token or per-user env vars returns
nothing while the model reports it has no access. Only a no-auth server looks
healthy, which is exactly what the first proof used.
Threading the missing arguments would have left the real problem in place. Each
gateway surface rebuilds the same context by hand (responses/main.py twice,
chat_completions_handler, mcp_streaming_iterator), which is why a new surface
drops fields; this adds a fifth that dropped six of eight. Resolve it once into a
frozen MCPRequestContext and have the handlers take that, so a field cannot be
forgotten at a call site. chat_completions_handler now uses it too, and the
resolver reads user_api_key_auth from both metadata keys because
LITELLM_METADATA_ROUTES carry it in litellm_metadata while chat uses metadata.
Also stop the loop when every tool call was skipped. tool_results is empty then,
and the tool_result message built from it has empty content, which Anthropic
rejects; the caller saw a 400 from mid-loop instead of the model's own answer.
Tests pin both: dropping the headers from either listing or execution fails, and
so does removing the empty-results guard.
MCP tool calling worked on /v1/chat/completions and /v1/responses but not on
/v1/messages. Those are the only two surfaces with an MCP gateway entry point,
so a litellm_proxy MCP reference reached Anthropic verbatim inside tools and the
API rejected the request with "Input tag 'mcp' found using 'type' does not match
any of the expected tags". The playground never surfaced this because it dropped
the reference before sending, and disabled the MCP selector for the endpoint.
Add the third entry point in anthropic_messages_handler, ahead of the provider
branch so it covers the native path and both bridges from one place. The gateway
expands the reference against the caller's own credentials and access control,
which is the whole point of routing it through litellm rather than handing the
url to the provider.
/v1/messages needs Anthropic's own tool shape, so transform_mcp_tool_to_anthropic_tool
joins the OpenAI chat and Responses transforms alongside it. The tool loop speaks
tool_use and tool_result rather than OpenAI tool_calls, and reuses the existing
FakeAnthropicMessagesStreamIterator to re-stream the result, the same pattern the
websearch interception already uses on this route. Argument extraction moves into
the shared extractor: an Anthropic tool_use block carries its arguments under
`input`, and reading only `arguments` failed silently, executing the tool with
every argument dropped.
On the frontend the request builder declared selectedMCPTools and never read it,
so no tools key was ever sent. Wire it through a shared block builder and add the
endpoint to MCP_SUPPORTED_ENDPOINTS, which is what greys the selector out.
Resolves LIT-4517
Resolves LIT-4518
* feat(guardrails): add Compresr guardrail for query-aware context compression
Adds a first-class guardrail that compresses bulky message content (tool
outputs, RAG chunks, search results) through the Compresr API before the
request reaches the LLM, via the apply_guardrail / structured_messages hook
so it covers /chat/completions, /v1/messages, and /v1/responses (the latter
through the texts channel, mirrored only when the replacement is
unambiguous; anything ambiguous is left uncompressed).
Distinct from whole-conversation compressors:
- Query-aware: each message is compressed against the intent that produced
it (a tool output against its originating tool call's name + arguments,
resolved via tool_call_id; otherwise the last user message).
- Recoverable: each compressed message carries a hash marker and the request
gains a compresr_retrieve tool, so the model can pull the original content
back through the agentic loop when the compressed version is not enough.
Originals are cached in-process, scoped to the caller's virtual-key hash
plus the request's litellm_call_id, with a TTL and a per-call byte cap;
recovery is skipped when no caller scope is available so one caller can
never read another's originals. The store is per-process, so multi-worker
deployments need sticky routing (or enable_retrieval=false).
Fail-closed by default (fail_open configurable), SSRF-validated api_base
(alternate IP-literal encodings included), cross-tenant-isolated recovery
store, and upstream errors redacted from client-facing responses. The
outbound client follows redirects and re-resolves DNS per request, so the
api_base host/IP checks are defense-in-depth, not a full SSRF guarantee;
this is documented as a known limitation. Requests where nothing was
actually compressed are returned untouched (same object identity) so
handlers skip the write-back. Auto-discovered via the guardrail_hooks
registry.
* fix(guardrails): cap Compresr recovery store total memory
The recovery store bounded bytes per call and entry count, but had no
aggregate cap: 256 tracked call ids at the 10 MiB per-call default could
retain ~2.5 GiB per worker. A flood of requests with distinct
x-litellm-call-id values and large compressible tool outputs could
exhaust a shared proxy worker.
Add a global byte budget (_MAX_TOTAL_STORE_BYTES, 256 MiB) across all
entries. A running total is maintained on every insert/eviction so the
cap is enforced without re-encoding the whole store on the request path;
oldest entries are evicted once the budget is exceeded, always keeping
the most-recent entry so recovery still works for the request populating
the store. +2 regression tests.
* fix(guardrails): gate and bound Compresr recovery loop
Two hardening fixes to the compresr_retrieve agentic loop:
1. Only run the loop when a retrieve call resolves to recovery state this
guardrail actually created for the request. Previously the gate checked
only that the caller-supplied tool list contained a compresr_retrieve
function and that the model emitted a call, so a caller could define
their own same-named tool and force an extra provider round-trip with
nothing to recover. The plan now returns run_agentic_loop=False when no
requested hash resolves.
2. Bound the follow-up against retrieval amplification: each distinct hash
is expanded at most once (repeats get a short marker) and at most
_MAX_RETRIEVALS_PER_LOOP calls are honored, so prompting the model to
call compresr_retrieve many times with the same marker cannot balloon
the follow-up. _retrieve_original now returns None on miss.
+3 regression tests; two existing security tests updated to assert the
stronger veto behavior (forged/cross-tenant hashes now stop the loop
entirely instead of returning a not-found follow-up).
* fix(guardrails): warn when Compresr recovery is skipped without auth scope
When enable_retrieval is on (the default) but the proxy has no per-key
auth, the request has no caller scope, so recovery is silently disabled:
content is compressed but the compresr_retrieve tool is never injected and
the originals are dropped, with no runtime indication. Emit a one-shot
call-time warning so operators can see recovery is being suppressed and
configure virtual-key auth. +1 regression test.
* style(guardrails): tighten Compresr guardrail comments
Condense the verbose multi-line inline comments and the api_base docstring
to concise form. No behavior change.
* fix(guardrails): keep injected tool on Responses API + bound recovery markers by byte cap
Two fixes for reviewer-flagged defects in the Compresr guardrail:
- Responses API: _merge_tools_after_guardrail iterated only over the
request's original tools, dropping any tool a guardrail appended (the
compresr_retrieve recovery tool) whenever the request already had tools.
Keep the appended tools so recovery works on /v1/responses.
- Recovery markers: markers + originals were built for every compressed
target before the per-call byte cap trimmed the store, so an evicted
original left a marker the model could never retrieve. Attach recovery
only while the store (existing entries under the same key + this call's
originals) stays within the cap, so a shipped marker is always retrievable
-- including on a later turn that reuses the store key.
Adds regression tests for both paths.
* refactor(guardrails): extract _existing_originals to keep apply_guardrail under the complexity gate
The byte-cap fix added a branch to apply_guardrail, tipping it past the
C901 complexity ceiling. Move the store lookup into a small helper; no
behavior change.
* fix(guardrails): harden Compresr SSRF blocklist, re-arm no-scope warning, tolerate odd tool shapes
* fix(guardrails): rerun input guardrails on Compresr retrieval follow-up
* chore: remove unrelated deepkeep files committed by mistake
---------
Co-authored-by: charafkamel <charafkamel@live.com>
An empty upstream delta (e.g. Bedrock Converse's empty reasoning delta
mid-thinking-block) falls through the translate fallback as
text_delta {"text": ""} at the open thinking block's index, crashing
Anthropic SDK clients like Claude Code with "Content block is not a
text block". Payload-less deltas carry no information, so never emit
them.
* fix(anthropic/passthrough): drop temperature and cap thinking budget when downgrading adaptive thinking for pre-4.6 models
* test(anthropic/passthrough): use sufficient max_tokens for reasoning_effort thinking mapping
* fix(anthropic/passthrough): drop incompatible temperature when downgrading adaptive thinking for pre-4.6 models
Narrow the fix to the temperature reconciliation; the reasoning_effort
budget cap is reverted because the live translation grid relies on
budget_tokens >= max_tokens to reject unsupported effort tiers
(xhigh/max) on budget-mode models, so capping turned those 400s into
200s.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(anthropic-messages): send bare-string tool_choice to Responses API, propagate router-alias litellm_params
The Anthropic /v1/messages -> Responses API adapter always wrapped
tool_choice in an object ({"type": "auto"}, {"type": "required"}), but
the Responses API's tool_choice schema for these cases is a bare
string ("auto"/"required"/"none"). Sending the object shape to an
OpenAI-compatible backend (e.g. vLLM) fails Pydantic validation with a
400. The "none" case also fell through to "auto" instead of mapping to
"none".
Separately, litellm_params configured directly on a router-alias
deployment (auto_router/complexity_router, adaptive_router,
quality_router, or semantic auto_router) - e.g.
cache_control_injection_points, drop_params - were silently dropped
for every request through that alias. async_pre_routing_hook swaps
`model` from the alias name to the selected tier/route's model before
the deployment lookup runs, so the outbound call only ever merged in
the tier deployment's own litellm_params, never the alias's. Register
non-routing-config litellm_params from the alias deployment and apply
them to the request whenever a pre-routing hook substitutes the model.
* fix: satisfy ruff-strict-budget UP006 and router coverage checker
Use builtin dict[...] generics instead of typing.Dict for the two new
annotations introduced in the previous commit, since they pushed
UP006 over the codebase ceiling in ruff-strict-budget.json. Add a
direct unit test for _register_pre_routing_alias_overrides so the
text-based router_code_coverage.py checker sees it exercised by name.
* fix(router): replace alias-param denylist with a tight allowlist
_PRE_ROUTING_ALIAS_RESERVED_PARAMS excluded router-init-only keys from
the alias's litellm_params before forwarding the rest as request
kwargs, but GenericLiteLLMParams also holds deployment-management
fields (tpm, rpm, weight, tags, max_budget, budget_duration,
use_in_pass_through, litellm_credential_name, ...) on the same object.
Any of those left off the denylist would get silently forwarded as if
they were request kwargs.
Replace the denylist with a tight allowlist of exactly the two
request-shaping params this feature exists for - drop_params and
cache_control_injection_points - so unrelated management fields never
reach the outbound call regardless of what else GenericLiteLLMParams
grows to hold.
* fix(router): re-register adaptive-alias overrides on set_model_list reload
set_model_list() unconditionally clears pre_routing_alias_overrides on
every call (e.g. /config/reload), but _finalize_adaptive_router_if_configured()
skips rebuilding an AdaptiveRouter whose model_name already exists in
self.adaptive_routers - so _register_pre_routing_alias_overrides() never
ran again for an auto_router/adaptive_router alias after a reload,
silently dropping its drop_params/cache_control_injection_points.
Build the Deployment unconditionally and re-register its overrides even
on the skip-existing-router path; only the (expensive) AdaptiveRouter
construction itself stays skipped.
* style: ruff format after merging litellm_internal_staging
* fix(router): drop the alias-param allowlist, exclude only model
Per review discussion: instead of a router.py-local allowlist of exactly
which litellm_params an alias (auto_router/complexity_router,
adaptive_router, quality_router, semantic auto_router) can forward to
the request it routes, _register_pre_routing_alias_overrides now
forwards everything except `model` (the alias marker itself, e.g.
auto_router/complexity_router, never a real provider model).
Router-init-only fields (complexity_router_config,
complexity_router_default_model, auto_router_config,
auto_router_config_path, auto_router_default_model,
auto_router_embedding_model, adaptive_router_config,
adaptive_router_default_model, quality_router_config,
quality_router_default_model) now flow into request_kwargs unfiltered
too. That's safe because litellm.completion()/acompletion() already
strips anything in litellm.types.utils.all_litellm_params before
building the provider request - added these 10 keys there, alongside
the deployment-management fields (tpm, rpm, weight, ...) already listed.
Verified live: without that addition, complexity_router_config lands in
extra_body and ships raw to the provider; with it, it's stripped.
This moves the "which fields aren't real LLM params" list from a
router.py-local allowlist to the single existing global list every
completion() call already depends on, instead of maintaining two.
* refactor(router): look up alias litellm_params on demand instead of caching them
_register_pre_routing_alias_overrides cached each alias's litellm_params
into self.pre_routing_alias_overrides at deployment-init time, which
required keeping that cache in sync with set_model_list() reloads - the
exact bug the previous adaptive-router-reload fix was patching around
(AdaptiveRouter survives a reload, but the cache didn't always get
refreshed to match).
Delete the cache and the registration method entirely. async_pre_routing_hook
now looks up the alias's own litellm_params directly from self.model_list
via self.model_name_to_deployment_indices at request time, the same
model_list that's already correctly rebuilt on every set_model_list()
call. No second piece of state to invalidate, so the reload staleness
bug class isn't possible anymore, and it's less code than before.
Clients that pass thinking={"type": "adaptive"} directly (not via the
reasoning_effort alias) on the /chat/completions interface had it forwarded
unmodified to pre-4.6 Anthropic models, which reject the shape. Mirrors the
translation already applied on the native /v1/messages passthrough (#32867):
translate to legacy thinking={type: enabled, budget_tokens}, capped below
max_tokens, dropping thinking when max_tokens can't fit even the minimum
budget. Hoists the shared budget-capping helper onto AnthropicConfig so both
paths use one implementation.
* fix(anthropic): translate adaptive thinking/effort to pre-4.6 model support
AnthropicMessagesConfig now reshapes the 4.6+ adaptive-thinking interface
(thinking:{type:adaptive} + output_config:{effort:...}) to whatever the routed
model supports. Thinking-capable non-adaptive models (e.g. Haiku 4.5, Sonnet 4.5)
get the effort translated to a legacy thinking budget_tokens. Models with no
reasoning support have thinking/effort dropped under drop_params. And because
adaptive thinking carries no budget while the legacy form must satisfy Anthropic's
max_tokens > budget_tokens rule, the translated budget is capped below max_tokens,
dropping thinking when max_tokens can't fit the minimum budget. 4.6+ models pass
through untouched.
This matters because clients like Claude Code speak native Anthropic /v1/messages
and send the adaptive interface unconditionally, regardless of the routed model.
The native passthrough previously only capability-gated the OpenAI-style
reasoning_effort alias and forwarded native output_config/adaptive thinking raw, so
a pre-4.6 model rejected it with "This model does not support the effort parameter"
and the request failed. Claude Code already gets drop_params auto-set, so its
requests now succeed.
* test(anthropic): gate undersized-max_tokens thinking drop on drop_params; add edge tests
Addresses review feedback on the max_tokens-too-small branch. Previously a
thinking-capable model whose max_tokens could not fit the minimum thinking budget
had thinking silently dropped regardless of drop_params, while a residual
output_config field in the same call still raised when drop_params was off. Gate
both consistently on drop_params: raise a clear error (naming max_tokens for the
undersized case) when drop_params is off, drop otherwise. Claude Code gets
drop_params auto-set, so it still succeeds.
Adds tests for the undersized-max_tokens raise, the residual output_config raise,
and the no-adaptive-interface passthrough on a non-adaptive model.
* fix(anthropic): make adaptive-effort translation silent to avoid breaking provider strip contracts
The previous raise-when-not-drop_params behavior broke existing bedrock and vertex
messages tests: those providers already silently strip unsupported output_config
for pre-4.6 models (issue #22797) with no drop_params required, and the shared
parent transform raising pre-empted that. It also conflicted with the goal of
keeping requests working rather than failing them.
Make the reshape silent: translate effort to legacy thinking for thinking-capable
models, drop thinking for non-reasoning models, and remove only the consumed effort
key from output_config, leaving any residual (e.g. format) for provider subclasses
(bedrock/vertex) to handle. No raise, no drop_params gating. This also resolves the
review note about inconsistent drop_params handling by making every path uniform.
Updates the tests to assert the silent behavior and residual output_config
preservation.
* fix(anthropic): handle output_config-capable but non-adaptive models (Opus 4.5)
Greptile caught a real bug: the early-return guard treated supports_output_config
as equivalent to supporting adaptive thinking. Claude Opus 4.5 advertises
supports_output_config (it accepts output_config.effort) but is not adaptive, so it
rejects thinking:{type:adaptive} with "adaptive thinking is not supported on this
model". The guard early-returned for Opus 4.5 and forwarded the adaptive thinking
block raw, reproducing the exact failure the fix is meant to prevent.
thinking:{type:adaptive} and output_config.effort are independent capabilities.
Only early-return for adaptive-thinking models. For a model that supports
output_config.effort but is not adaptive, keep the native effort and drop only the
unsupported adaptive thinking block. Verified live against Opus 4.5: the Claude Code
payload now returns 200 instead of 400.
Adds regression tests for Opus 4.5 with and without adaptive thinking.
* fix(anthropic): translate adaptive thinking for effort-capable pre-4.6 models
Claude Opus 4.5 advertises supports_output_config but not adaptive thinking,
so the early-return guard forwarded thinking.type=adaptive raw and Anthropic
rejected it. The guard now only skips true adaptive models; effort-only
requests on effort-capable models still pass through untouched. The
_map_reasoning_effort call is wrapped to surface unrecognized effort values
as a clean 400, matching _translate_reasoning_effort_to_anthropic
* fix(anthropic): fall back to legacy thinking when effort level unsupported
Opus 4.5 accepts output_config.effort but only low/medium/high; Claude Code
defaults to xhigh on newer models, so preserving that level raw gets rejected
by Anthropic. Gate the native-effort passthrough on _validate_effort_for_model
and fall through to the budget translation for unsupported levels
* fix(anthropic): keep effort-only requests untouched for provider normalization
The xhigh fall-through consumed effort-only requests on effort-capable
models, breaking bedrock invoke's own normalization which clamps xhigh to
the model's ceiling after the base transform runs
(test_bedrock_messages_normalizes_output_config_effort_for_opus). Restrict
the fall-through to requests that carry adaptive thinking; effort-only
requests pass through so provider subclasses keep owning level clamping
---------
Co-authored-by: Abhimanyu Kapur <38531241+akapur99@users.noreply.github.com>
vertex_ai/claude-opus-4-8@default (and sibling @default models) were
misclassified as non-adaptive because _model_map_lookup_candidates only
stripped provider prefixes but never the @<suffix> portion. The lookup
produced candidates like ["vertex_ai/claude-opus-4-8@default",
"claude-opus-4-8@default"], neither of which exists in model_cost, so
_is_adaptive_thinking_model returned False. LiteLLM then sent
thinking.type=enabled to a @default Vertex AI endpoint that requires
thinking.type=adaptive, resulting in a 400.
_strip_version_suffix now removes @<suffix> from each candidate,
adding the bare model name (e.g. "claude-opus-4-8") to the lookup
chain. Also adds supports_adaptive_thinking: true to the three
@default model_cost entries that were missing it as belt-and-suspenders.
Co-authored-by: Deepanshu <deepanshu.lulla@alpha-sense.com>
* fix(batches): price anthropic passthrough message batches correctly in batch cost job
Anthropic message batches created via the /anthropic passthrough were never
cost tracked. The CheckBatchCost job fetched batch results from the Files API
(POST /v1/files/msgbatch_.../content), which Anthropic rejects with "File id
must have file_ prefix"; the error response was silently wrapped as file
content, parsed as zero successful rows, logged as a $0 aretrieve_batch spend
row, and the job was marked batch_processed=true so the $0 was permanent.
Route msgbatch_ file ids to GET /v1/messages/batches/{id}/results in the
anthropic files transformation, raise on HTTP error status in
retrieve_file_content instead of returning the error body as content, parse
Anthropic's results JSONL shape (result.type == "succeeded",
result.message.usage with cache creation/read tokens) in batch_utils, price
cache creation tokens at cache_creation_input_token_cost in the batch cost
fallback (50% batch discount preserved for base input, cache reads, cache
writes, and output), and leave the managed object row unprocessed when cost
tracking fails so a later poll retries instead of permanently recording $0.
* fix(batches): carry cache token details into aggregated anthropic batch usage
* fix(bedrock): emit SSE error event when invoke Messages stream ends without message_stop
* fix(bedrock): tighten stream-terminal detection to avoid false positives and double errors
The bytes branch of _is_message_stop_chunk used a plain substring match,
so a content_block_delta whose partial_json contained the literal text
message_stop would look like a real terminal event and suppress the
synthetic incomplete-stream error. Match the SSE event header line
instead.
Also treat a provider-emitted error event as terminal so a stream that
ends with an upstream error is not followed by a second, contradictory
synthetic incomplete-stream error.
* test(bedrock): lock in that the synthetic truncation error event is excluded from logged chunks
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix(anthropic): require caller api_key and SSRF-validate api_base in advisor tool
The advisor_20260301 interceptor honored a caller-supplied api_base once
allow_client_side_credentials was enabled, even without a caller-supplied
api_key. AnthropicModelInfo.get_auth_header() then fell back to the proxy's
own ANTHROPIC_API_KEY/ANTHROPIC_AUTH_TOKEN, so the server's real credentials
plus the conversation history got sent to a caller-chosen destination
_resolve_advisor_credentials() now only honors api_base alongside a
non-empty caller-supplied api_key, requires the https scheme, and validates
api_base via validate_url() before use, mirroring check_complete_credentials
in auth_utils.py. https is required because validate_url only DNS-pins the
connection for http; for https with TLS verification on it returns the URL
unchanged and relies on certificate validation to block DNS rebinding
* fix(anthropic): also reject advisor api_base when ssl_verify is disabled
validate_url only DNS-pins the connection for http, or for https with
litellm.ssl_verify disabled; the previous https-only check missed the
ssl_verify=False case, where validate_url's rewritten URL was still being
discarded, per Greptile's review of this PR. Reject api_base outright when
ssl_verify is False so the discarded rewrite can no longer matter
* fix(anthropic): keep context_management working when drop_params is enabled
drop_params (proxy-wide or per-request) silently disabled the in-gateway
context_management polyfill on the /v1/messages -> chat completions adapter
path, even though context_management is a LiteLLM-supported param (native on
Anthropic, polyfilled elsewhere). Gate the polyfill on an explicit
additional_drop_params: ["context_management"] opt-out instead, which also
makes that escape hatch actually work on the adapter path.
* test(anthropic): cover sync adapter polyfill gate for global drop_params and additional_drop_params
* feat(messages): passthrough /v1/messages to native endpoints via supported_endpoints
The unified /v1/messages proxy endpoint always translated inbound Anthropic
requests down to /v1/chat/completions (or the Responses API for openai) when the
deployment's provider lacked a native Anthropic-messages config, dropping
Anthropic-only features like cache_control and thinking. Some customers run
OpenAI-compatible servers (self-hosted vLLM, DeepSeek's Anthropic endpoint, etc.)
that also natively expose /v1/messages and want the raw Anthropic payload
forwarded untranslated, while keeping provider openai so /v1/chat/completions to
the same deployment stays native.
Opt in per deployment via model_info.supported_endpoints containing
/v1/messages. When present, the gate routes to a generic, provider-agnostic
OpenAILikeAnthropicMessagesConfig that POSTs the Anthropic payload to
{api_base}/v1/messages with Bearer auth, instead of translating. Default
behavior is unchanged. Generalizes and supersedes the hosted_vllm-only,
env-var-toggled PR #28745.
* fix(messages): preserve standard-cased caller headers in native passthrough
The OpenAI-like Anthropic passthrough config only checked for lowercase header
names before injecting Bearer auth, anthropic-version, and content-type
defaults. A caller sending standard-cased Authorization, Anthropic-Version, or
Content-Type was treated as missing those headers, so LiteLLM added duplicate
lowercase variants and overwrote the caller's credential/version at the HTTP
layer. Header presence is now checked case-insensitively and the merge no longer
mutates the caller dict.
Also moves the feature docs out of the main repo (docs live in litellm-docs).
* fix(openai_like/messages): delegate to parent transform and inject anthropic-beta headers
The passthrough config bypassed the parent transform and skipped header beta injection. Both gaps cause native /v1/messages features (context management, advisor tool, fast mode, structured outputs, reasoning_effort, advisor stripping) to silently degrade on opted-in deployments. Reuse the parent's pipeline and call _update_headers_with_anthropic_beta after merging defaults
* fix: normalize anthropic-beta header key case before beta injection
* style: collapse anthropic-beta header normalization to single line
ruff format --check requires the comprehension on one line (it fits within
the 120 char limit); fixes the lint job failure on the bugbot autofix commit
* fix(messages): forward anthropic-beta to native passthrough upstream
The shared anthropic_messages HTTP handler ran update_headers_with_filtered_beta
with the deployment's custom_llm_provider after validate. For the native
/v1/messages passthrough that provider is openai, which has no beta-header
mapping, so every anthropic-beta value (caller-supplied or feature-derived for
speed/context_management/etc.) was stripped to empty before the upstream
request, breaking beta passthrough to the Anthropic-compatible endpoint.
Beta filtering only makes sense on cross-provider translation paths where the
upstream cannot understand Anthropic betas. Gate it on a new
should_filter_anthropic_beta_headers() that defaults to True (bedrock, vertex_ai,
native anthropic unchanged) and is overridden to False by
OpenAILikeAnthropicMessagesConfig, whose upstream is a native Anthropic endpoint,
so betas pass through verbatim.
* chore: remove accidentally committed local QA logs and config
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
* fix(passthrough): drop top-level additional_drop_params on /v1/messages
On the Anthropic Messages pass-through path, additional_drop_params only
stripped nested dotted paths, so plain top-level keys like `thinking` and
`context_management` were forwarded to the provider. Bedrock rejects these
with "Extra inputs are not permitted", returning a 400 to Claude App/CLI
even when the user configured `additional_drop_params: ["thinking"]`.
delete_nested_value already handles plain top-level fields, so route every
drop param through it and remove the nested-only filter. Fixes#25931.
* fix(passthrough): drop thinking for bedrock inference-profile ARNs on /v1/messages
Opaque Bedrock Application Inference Profile ARNs contain neither "anthropic"
nor "claude", so is_anthropic_claude_model returned False and the thinking
param was rewritten to reasoning_effort before additional_drop_params ran.
That made additional_drop_params: ["thinking"] a no-op for the converse-ARN
form, and the Bedrock Converse transform re-expanded reasoning_effort back into
additionalModelRequestFields.thinking, so the request 400'd.
Extend the thinking-translation gates to also accept bedrock ARNs via the
existing is_bedrock_arn_model helper, mirroring the cache_control path, so
thinking is preserved as thinking and additional_drop_params can drop it.
* test(batches): add 1:1 test file scaffold for batches component paths
Co-authored-by: Cursor <cursoragent@cursor.com>
* Add harness test for create batch endpoint
* Add retrieve endpoint harness tests
* Add list endpoint harness tests
* Add cancel endpoint harness tests
* Add cancel endpoint harness tests
* Add test for litellm/batches/main.py
* Add test for litellm/tests/test_litellm/batches/test_batch_utils.py
* Add handler and transformation tests for all providers
* Fix: run batches tests in cicd
* fix(tests): remove azure/__init__.py that shadowed azure namespace package
Adding __init__.py to tests/test_litellm/llms/azure/ caused pytest to
insert tests/test_litellm/llms/ into sys.path[0], making our empty
azure/ dir shadow the real azure-identity namespace package. Any test
that patched azure.identity.* would then fail with AttributeError.
* style(tests): apply ruff format to test_batch_utils.py
Base migrated the formatter from black to ruff format (#31317); reformat the
batches scaffold test file to match.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
