* fix(mcp): drop caller host and configured upstream headers from logged metadata
The synthetic request that carries MCP client headers into
add_litellm_data_to_request forwarded the caller's Host header, and
Request.url is built from it, so a caller chose the proxy_server_request
url and the metadata endpoint that every logging callback records.
_upstream_credential_headers also only knew the configured client side
auth header and the x-mcp- prefix family, so a header name declared in
mcp_servers.<name>.extra_headers reached logging metadata in cleartext.
Those names are admin chosen, so no prefix rule can recognize them; read
them off the server registry instead. The header is still forwarded
upstream, which is what extra_headers is for. authorization is left out
because clean_headers already strips it and claiming it here would move
authenticated_with_header on the oauth passthrough config.
The Responses bridge tests stub the server manager, so their fakes gain
the registry accessor the sanitizer now reads.
* fix(mcp): drop caller host from the sanitized header mapping too
The synthetic request stopped forwarding host, but the parallel sanitizer
did not, so a forged hostname still reached the guardrail payload and the
list_tools spend row. Drop it there as well.
Exempt the configured identity headers from the upstream credential set.
get_user_from_headers resolves end user attribution off the same request
this module reconstructs, and it only fills end_user_id when auth left it
unset, so claiming user_header_name or a user_header_mappings name would
lose attribution on the MCP paths that authenticate upstream.
Drop the isinstance guard on extra_headers entries: the field is typed
list[str], so the check is dead and basedpyright scores it.
* fix(mcp): accept a bare user_header_mappings entry when exempting identity headers
get_internal_user_header_from_mapping and get_customer_user_header_from_mapping
both normalize a single mapping to a one element list, and config_settings.md
documents the key as a dict. Iterating the bare form yields its keys instead,
so the exemption silently matched nothing and an identity header also named in
an MCP server's extra_headers was dropped after all.
* feat(search): add Nimble as a search provider
Adds `NimbleSearchConfig` so `search_provider: nimble` works across the SDK,
the proxy /v1/search endpoint, the Search Tools dashboard, and spend tracking.
Nimble's /v2/search already uses the Perplexity unified spec's parameter names,
so the request transform is close to a pass-through. `search_domain_filter`
splits into include_domains/exclude_domains on the spec's `-` prefix, `country`
is upper-cased to the ISO form Nimble documents, and everything else is
forwarded so focus, search_depth, time_range and the rest stay reachable. On the
response side, snippet prefers `content` and falls back to `description`, and a
malformed body raises an attributed error rather than reporting an empty search.
Also tightens `BaseSearchConfig.get_supported_perplexity_optional_params` to
return `frozenset[str]` instead of a bare mutable `set`, which every caller
already treats as read-only.
* fix(search): surface Nimble error bodies instead of empty results
Greptile flagged that a null or absent `results` degraded to a successful empty
search. A search with no hits comes back as `"results": []`, verified against the
live API, so the field is now required and anything else raises the attributed
schema error the other malformed bodies already take.
Also unwraps Nimble's second error envelope. Collection failures return
`{"success", "task_id", "message"}` rather than the `{"detail"}` shape validation
errors use, and only the latter was being read.
Drops comments that restated the adjacent code.
* docs(search): drop the Nimble param list from the transform docstring
It restated the vendor's API reference, which the module docstring already links,
and would go stale the moment Nimble adds a focus mode.
Shadow eval only answered "should this key adopt this auto-router". Once a key
is on the router it is invisible to the feature, because the sampling gate skips
any request the shadowed router already served, so post-adoption quality
regressions go unmeasured.
Reverse mode inverts the arms: sample the traffic the router did serve and
duplicate it against a fixed baseline_model, judged by the same blind pairwise
judge. Same job table, same attempt rows, same aggregates.
real_* stays the arm the caller was served and shadow_* the duplicated one, so
in reverse real_model is the router's pick and shadow_model is the baseline. The
active-job slot becomes one per (key, direction) so both directions can run at
once, and tier attribution in reverse reads the control request's routing
decision rather than the shadow call's write-back.
The MCP sub-app is attached with app.mount("/mcp", ...) and a Starlette
mount never matches its bare prefix, so POST /mcp fell through to the
router's redirect_slashes 307. Behind a TLS-terminating ingress whose
peer address is not in uvicorn's forwarded-allow-ips (default: loopback
only) the redirect Location is built from the socket scheme as http://,
and MCP clients strip the Authorization header on the cross-origin
follow, so reconnects fail with ECONNRESET right after a successful
OAuth flow. The redirect also fires before auth, so the bare spelling
never returns the RFC 9728 WWW-Authenticate challenge that OAuth
clients need to start the flow.
Add an explicit /mcp route beside the existing /toolset/{name}/mcp and
/{name}/mcp spellings, forwarding to handle_streamable_http_mcp with
the same scope rewrite those routes already use (path=/mcp,
_original_path preserved for OAuth challenge URL selection). When the
mcp package is unavailable the route 404s, matching what the bare
sub-app serves on /mcp/ in that state. /mcp/, /mcp/{server},
/{server}/mcp and /toolset/{name}/mcp spellings are unchanged; the
exact-match route and the mount have disjoint match sets so
registration order cannot matter.
Move the blocked-usage mapping for /v1/responses next to
blocked_response_usage in guardrail_translation utils, map bridged chat
prompt/completion tokens to Responses API input/output tokens, and let
raise_passthrough_exception attach the blocked response so post-call
guardrail blocks report real usage
Merge deployment model_info into a copy of the lru_cache'd get_model_info() dict and drop unset Nones, so Deployment's mirrored pricing defaults no longer overwrite built-in prices process-wide.
Fixes#36980
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Anthropic's Models API declares max_input_tokens and max_tokens as nullable, not
optional, and the live vendor endpoint returns both keys on every entry. The
merged Anthropic-native listing dropped either key whenever LiteLLM could not
resolve a limit, so a client validating against a nullable-but-required schema
saw a malformed entry for any model the cost map does not know.
OpenAI and Azure GPT-5.x answer a chat request whose output budget cannot fit a
single visible token with a 400, while the same models return a length-truncated
200 one or two tokens higher. Agents that probe a model with a hardcoded
max_tokens of 1 read that 400 as "model unavailable".
The four chat request helpers now recognise the provider's own sentence and hand
back the length-truncated response the provider gives at a slightly larger
budget: finish_reason "length", empty content, zero completion tokens. Any other
400 still raises. Streaming is covered by the same seam, and the caller's budget
is never raised on their behalf.
The provider bills the prompt it processed but sends no usage object with the
400, so the prompt tokens are estimated with the same token_counter every other
usage-less path uses. Reporting zero would let a caller send an arbitrarily
large prompt with max_tokens 1 and be charged nothing.
update_trace_keys lets a caller name which request metadata entries get copied
onto an existing trace, and the name is unrestricted. Sending
update_trace_keys: ["user_api_key_auth"] with existing_trace_id serializes the
resolved auth object, including the team callback credentials it carries, onto
the trace through Langfuse.trace(**trace_params). TraceBody is Extra.allow, so
an unexpected key ships rather than being dropped.
Any holder of a team key can do this and read the result in the destination the
team already logs to, so the feature is now inert unless an operator turns it on
with langfuse_enable_update_trace_keys.
This reverts commit ab2333b6c4.
Every Admin UI login mints its session key against the sentinel team_id
`litellm-dashboard`, and no LiteLLM_TeamTable row is ever created for it.
That lookup is therefore a provably-absent row on every UI request, which
#36837 turned into a hard refusal with no override, so the whole dashboard
404s.
Reverting restores the token-derived fallback. The model-access widening
#36837 closed is reopened and needs a re-land that exempts the UI sentinel
team.
cost_per_second treated a declared-but-zero output_cost_per_second as a real
rate, so the output branch claimed the call and the elif locked out
input_cost_per_second. Every transcription model shipping
output_cost_per_second 0.0 next to a real input rate billed $0, which covers
43 of the 55 per-second entries in the cost map: all 36 deepgram models, both
assemblyai, both elevenlabs scribe, both groq whisper and azure-stt. Custom
deployments pairing the two fields the same way billed $0 as well
Take the output branch only when that rate is actually billable, so a zero
falls through to the input rate. Entries that duplicate one rate into both
fields, whisper-1 among them, keep billing exactly what they bill today
`_query_first_with_cached_plan_fallback` recovers from Postgres's "cached
plan must not change result type" by recreating the Prisma client, which
drops both the server-side plans and the engine's client-side statement-name
cache. Since #30183 the shared reconnect path probes the writer with
`SELECT 1` first and skips the recreate when it answers, which is right for
the IAM token refresh it was added for and wrong here: the connection is
healthy, it is the session's prepared statements that are stale, so the probe
always passes and always vetoes the recreate. Callers now pass
`force_recreate` to skip that probe, and only the cached-plan fallback does.
Getting past the probe is not enough on its own. Both cooldown checks would
still skip the recreate for 15 seconds after any earlier reconnect, which
outlives the 10 second auth retry window, so a migration landing in that
window kept 503ing. `force=True` would fix that but would also let every
concurrent caller of the same burst kill the engine the first one just built.
The caller instead names the engine it observed before the query, and the
cooldown is waived only while that engine is still the live one, so the first
caller repairs the pool and the rest fall back to the normal cooldown.
That engine has to be the one the query actually ran on. `query_first` is a
top-level read, so with a read replica configured it is dispatched to the
reader and it is the reader's prepared statements that go stale, while
`writer_db` names a different engine with its own counter. The observation
and the cooldown comparison both go through `read_db`, added alongside
`writer_db` and backed by a `read_target` property on the routing wrapper
that `__getattr__` now dispatches through so the two cannot drift.
The observation carries the wrapper, not just its generation. `read_db`
resolves to the reader while it is available and to the writer once it is
not, and those counters are independent and both start at zero, so comparing
a bare number across that switch pits one engine's counter against another's.
Equal by coincidence waives the cooldown for an engine already replaced;
unequal gates a caller that needs the recreate. Identity settles it, and is
sound because the engine object is never re-pointed without the generation
also moving.
Three smaller holes on the way out. The waiver is withdrawn once a repair of
that same engine has been tried and failed, so a burst collapses onto one
attempt instead of each caller running its own recreate serially; the record
is keyed per engine rather than counted globally, so an unrelated reconnect
failure cannot suppress a stale reader's recovery and a writer failure cannot
evict the reader's record. And a forced recreate that the optimistic-lock
guard declines is no longer reported as a success on either the direct or the
heavy path, since the routing wrapper leaves the reader untouched in that
case; a decline is deliberately not counted as a failure, so the caller's own
backoff still gets its waiver on the next attempt.
A decline on the heavy path clears the dead-engine flag before raising. The
clear after the cycle is skipped by any raise, which is right for a failure
and wrong here, and the non-forced path already clears it on a decline, so
this restores that policy rather than inventing one. Stranding the flag would
route the next cycle back down the probe-free heavy branch, where the
refreshed generation matches and the recreate kills the healthy engine a
refresh just spawned, which is #29176.
Clearing that flag is necessary and not sufficient. The escalation check
re-arms it whenever the consecutive-failure count sits at the threshold, so a
decline that left the count alone sent the very next attempt back down the
same path. A decline is raised only at the generation guard, and the
generation moves only after a replacement connects, so a decline is proof
that a replacement succeeded and the count is reset on it.
Fixes#36418
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The two new `translate_tools_to_responses_api` calls carried
`# type: ignore[arg-type]`, which CLAUDE.md bans as LIT009: pyrightconfig.json
sets enableTypeIgnoreComments to false, so the comment silently does nothing and
the reportArgumentType error stands. Annotating the fixtures as
list[AllAnthropicToolsValues] makes both calls check clean with no suppression
at all.
MCP tool calls run their guardrails against a throwaway LLM-shaped dict
built by `ProxyLogging._convert_mcp_to_llm_format`, not against the dict
the tool call is logged from. `@log_guardrail_information` therefore
appended `standard_logging_guardrail_information` to that throwaway
dict's metadata bucket, where `get_standard_logging_object_payload`
never saw it, so the Guardrails Monitor reported zero evaluations and
zero blocks for all MCP traffic.
Thread the request's `litellm_logging_obj` into `pre_call_tool_check`
and `_create_during_hook_task` and bridge the guardrail records onto it:
- Seed `data["litellm_logging_obj"]`, which unified guardrails read and
pass into `apply_guardrail`.
- Call `_sync_guardrail_info_to_logging_obj` in a `finally`, which is
what native guardrails need and what makes the block path work: a
blocked call raises straight out of `pre_call_tool_check`, so the
record has to be attached before the exception leaves the frame.
Only the guardrail evaluation records are copied. The synthetic
request's messages and tool arguments are deliberately left behind --
they can carry end-user data and nothing in the monitor needs them.
In `call_mcp_tool`, flush the failure handlers before
`post_call_failure_hook` so the `status="failure"` standard logging
object exists when `_ProxyDBLogger.async_post_call_failure_hook` writes
the spend-log row the monitor's "Total Blocked" counts. Both handlers
gate on `should_run_logging("sync_failure")` / `("async_failure")` and
then mark it, so the `@client` wrapper's own post-raise logging is a
no-op and nothing is double-counted -- the same pattern
`_fire_mcp_tool_call_logging` already uses for `isError=True`.
Threaded through every MCP tool entry point: the managed-server path,
the local-OpenAPI registry path, the legacy registry fallback, and the
Responses API's `_execute_tool_calls`.
Translating Anthropic tools left the outbound function-tool `strict` unset,
which the Responses API does not read as non-strict. OpenAI's function-calling
docs say strict mode requires every field in `properties` to be marked
required, and with `strict` omitted the schema gets normalized to satisfy that
instead of being rejected. What users see is a tool whose `required` lists
every property, so models fill optional Anthropic tool arguments with empty
values. Send `strict` explicitly so an unset value stays non-strict and an
explicit `strict: true` still reaches the provider
On the Chat Completions adapter, `strict` was also missing from
`mapped_tool_params`, so a tool-level `strict` was merged into the OpenAI
function `parameters` schema (mutating the caller's `input_schema` along the
way) instead of being set on the function. Map it to `function.strict` and
leave it unset when the caller omits it, since Chat Completions already
defaults to non-strict
