* refactor(proxy): inject gateway activity through a typed repository
* ci: keep dependency tests in their existing shard
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(guardrails): record which policy pipeline steps and deployment-level hooks ran
* fix(guardrails): name only the request pipeline steps that ran in x-litellm-applied-guardrails (#49263)
* fix(guardrails): name only the request pipeline steps that ran in x-litellm-applied-guardrails
* test(guardrails): integration cells for request pipeline steps in x-litellm-applied-guardrails
* fix(guardrails): keep the agent cells on the shared proxy and shorten a long suppression line
* refactor(proxy): inject database repositories through per-repository dependencies
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(proxy): model Prisma client cleanup
* test(rust): include NativeDatabase in bridge API surface
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): give database shutdown one lifespan owner
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(proxy): log database cleanup errors and bound shutdown waits
Database.aclose now logs a failure in either cleanup step instead of raising
out of the lifespan exit, which matches how main handled stop errors and keeps
setup_prisma_client from raising out of cleanup after a failed connect
Shutdown waits for an in-flight reconnect for a fixed five seconds instead of
the reconnect timeout plus grace, and joins engine wait threads with a fixed
limit instead of indefinitely. The threads are daemon threads, so a thread
still alive after the limit cannot keep the process from exiting
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Moves the page-authored English on /policies, /tool-policies and
/guardrails-monitor into react-i18next t() calls backed by en.json, and
adds a small data test for en.json. The rendered English is unchanged.
* fix(batches): cost an unmanaged gs:// batch through the deployment that created it
The create now stores the router deployment id as model_id inside the
managed object row's file_object JSON and CheckBatchCost reads it before
falling back to the first deployment matching the base model in the
gs:// path. Rows without the field keep the old lookup
* fix(batches): tolerate a non-string or malformed file_object when reading the stored deployment
* fix(batches): read the stored creating deployment from the writer, not a lagging read replica
* fix(batches): resolve the writer database once so the type gate count does not grow
* fix(batches): decode a dict-shaped file_object when reading the stored deployment, accumulate wire samples, annotate test locals
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(sso): create the team_id_jwt_field team on UI SSO login when team_id_upsert is set
* fix(sso): keep the upserted team_id_jwt_field team out of result.team_ids so restricted_sso_group and the access-token group fallback are unchanged
* fix(sso): only upsert the team_id_jwt_field team on UI SSO login when enable_jwt_auth is on
The API JWT path only reaches team_id_upsert inside the enable_jwt_auth branch, so a deployment
with a leftover litellm_jwtauth block and JWT auth off never created teams. Gate the UI SSO upsert
the same way
* fix(sso): read enable_jwt_auth through the proxy_server module so the basedpyright gate stays at its ceiling
* fix(responses): run the guardrails a Responses WebSocket frame selects
* fix(responses): name the field a malformed frame guardrail selection got wrong
A frame whose guardrails or guardrail_config has the wrong shape now gets
an error event naming the field, instead of pydantic's raw validation dump
* fix(responses): accept every guardrail selection shape a Responses WebSocket frame sends, and run during_call guardrails
A frame can pick guardrails as a list of names or name-to-config dicts, inside metadata or litellm_metadata, with policies validated as a list of strings. during_call guardrails now run before the turn goes upstream, and selection fields sent next to a nested response are rejected instead of dropped
* fix(proxy): keep a key's configured guardrails when policies add guardrails
A policy's guardrails were merged with dict.fromkeys, so a configured guardrail entry ({name: {extra_body}}) in key metadata raised unhashable type: 'dict' and every request naming policies failed with a 500 on HTTP and on the Responses WebSocket. The merge now keeps existing entries and skips policy guardrails already selected by name. Also types the WebSocket test guardrail's hooks.
* fix(responses): run streaming-scoped guardrails on socket turns, keep guardrail fields on blocks, merge guardrail lists by name
* fix(responses): check frame guardrail picks against the team permission, honor picks beside a nested response, prefer a guardrail entry with settings
* style(responses): format the frame guard and give the pass-through guardrail test an enterprise license
* fix(proxy): keep pass-through guardrail settings, gate frame policy picks, skip moderation-only hooks on socket turns
* fix(responses): keep string metadata guardrail values as client metadata on socket turns
* fix(responses): ask no team permission for string metadata named like a guardrail pick on socket turns
* fix(proxy): socket guardrail picks read the key's team metadata when the proxy has no database
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Carry the Z.AI catalog fix from #40088 and move its regression into the mapped unit test file.
Co-authored-by: Chaitanya Laxman <chaitanyalaxman118@gmail.com>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(ui): cover separate Meta Model API provider onboarding
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): add Meta Model API provider onboarding
Co-authored-by: lostmartian <sahilgangurde08@gmail.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(ui): align Meta coverage with provider keys and listed registry
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): show Meta model placeholder
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: lostmartian <sahilgangurde08@gmail.com>
Seven integration tests fail on main for reasons that are not product bugs. Two are order or race bugs in the tests, one reads rows other tests wrote, one passed on an error body, and three still expect behavior that #48644, #39864 and #39540 changed on purpose
* fix(proxy): raise live budget counters for failure spend rows
A stream that breaks mid-flight writes its recovered cost to the spend log, but the failure hook
had already released the reservation and never touched the live counters, so an over-budget key
kept passing until the counter was next repaired from the database
Every charged failure row with a cost now raises the same counters the success path raises. The
error waits at most a second for them, so a hung Redis cannot hold it back
Fixes#35568
* test(proxy): cover failure spend counters across budgets, endpoints and outages
Integration cells for team, user, end-user, tag and window budgets,
concurrent streams, billed retries, native /v1/messages, Bedrock guardrail
cost, a Redis stall on two workers and disable_spend_updates
* test(proxy): check stalled failure counters and use the real spend writer
- The Redis stall cell now reads the burst key's live counter and requires at least the full failure spend
- Unit tests drop the charging writer double, bare dicts, list mutation and wall-clock timestamps
- Partial-stream test models are frozen
* test(proxy): move the stalled counter bound reason into the assert message
---------
Co-authored-by: yucheng-berri <293926326+yucheng-berri@users.noreply.github.com>
* fix(proxy): apply key and team router_settings to /v1/images/generations
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* style(proxy): format router settings helper
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): resolve key model_group_alias once on image generations
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(openrouter): list OpenRouter's live catalog for openrouter/* when check_provider_endpoint is on
* fix(openrouter): list models off the event loop and keep only chat-callable catalog ids
The model-list builders are sync, and with check_provider_endpoint on they call OpenRouter's catalog with a 10 second timeout and no caching of failures. They ran on the event loop inside /v1/models, /model/info, /model_group/info and the per-user listing, so one slow catalog froze every request on the worker. Those calls now run in a worker thread with asyncio.to_thread
File, batch and RAG credential lookups only need each name's deployment, and a wildcard that is itself a deployment sits in the list ahead of its expansions and resolves its own credentials. get_complete_model_list takes expand_deployed_wildcard_routes, which that lookup sets to False, so a deployed wildcard never fetches its provider catalog there. A key wildcard wider than any deployment, like anthropic/* over a deployed anthropic/claude-*, still expands so it finds the matching deployment's credentials. Every other caller keeps the default
The pattern router's model lookup tolerates a pattern removed between its two reads of the pattern table, which a deployment delete on the event loop can now do while a listing runs in a thread
Listing now reads only OpenRouter's models/user catalog. OpenRouter's own /models list carries no embedding models, the OpenAI-format /v1/models has no mode field so chat pickers cannot tell embeddings apart, /model_group/info labels them mode chat, and one failing catalog emptied the whole list. Ids ending in :batch are dropped because chat completions answers 404 for them, while :free and every other variant stay. An api_base that already ends in /chat/completions, which chat accepts as is, has that suffix stripped before the catalog route is joined
* fix(proxy): keep model listing off the shared thread pool and out of credential lookups
Listings run on their own thread pool so many slow catalogs cannot starve
the default executor that chat and Responses calls share. File and batch
credential lookups match deployed names against a key's wildcard grants
instead of listing any provider catalog. The token counter keeps OpenRouter
deployments on their previous path, and a catch-all "*" over provider/*
lists bare ids that route back through it
* test(proxy): give the off-loop listing tests a generous deadlock guard and typed ids
* fix(proxy): keep routed models in wildcard model info, skip key-only OpenRouter catalogs, cache failed listings briefly
* fix(utils): cache failed model listings under their own key so a late failure never hides a good listing
* fix(proxy): remember only timed-out provider listings so a provider that failed fast is listed again
* fix(proxy): ask each provider catalog once per worker, keep deployed names out of catch-all ids, and match file credentials by name
Listings that start together for one deployment now wait for the first catalog request. A catch-all wildcard no longer lists a name a deployment serves by name. A key wildcard only finds file and batch credentials on deployments named under it, so a bare name keeps the default credentials as before.
* fix(proxy): keep the listing failure log line the same for timed-out listings
* refactor(proxy): fold the cached listing lookup back into the provider listing function
* fix(proxy): list each model id once and keep a named deployment's row over a wildcard's
* fix(proxy): drop a wildcard's model info row when named deployments serve every name it lists
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): persist sent model_info fields on POST /model/update
POST /model/update rebuilt litellm_params from the request but dropped the
request's model_info, so changing base_model (or mode, tier, etc.) on an
existing DB model was silently ignored. Clients that only use this endpoint,
like the Terraform provider, could never fix a deployment's base_model
without deleting and recreating it.
Persist the model_info fields the caller actually sent, on top of the stored
ones. ModelInfo defaults never overwrite stored values, server-owned, team,
PTU and pricing fields stay on their dedicated write paths, and values echoed
from a /model/info response are not pinned.
* fix(proxy): keep echoed and blank model_info values out of POST /model/update writes
Blank strings from typed clients no longer overwrite stored values. Values echoed from
/model/info (the stored base model's cost map entry and discovered limits) are not
pinned, and an echoed catalog value resets a differing stored override like PATCH does.
direct_access and access_via_team_ids stay display-only. The tests stop patching the
auth check class and give the team lookup a real row instead.
* fix(proxy): lock the model row and skip unchanged model_info on POST /model/update
* fix(proxy): treat only keyed /model/update bodies as echoes and write the stored model_info as saved
A typed model_info value that matched the cost map, or the previous base model's entry,
was dropped and could erase a stored override. Echo detection now follows PATCH, only a
body carrying the /model/info key counts, compared against the base model the row has
after the write. The locked write patches the stored JSON instead of dumping the hydrated
Deployment, so litellm_params pricing is no longer copied into model_info. Tests run the
real Router with discovery over a mock upstream
* docs(terraform): note that model_info updates now take effect and overwrite values set outside Terraform
* test: expect POST /model/update to save the cache point flag, and wrap long test lines
test_x06 pinned the old contract, where POST /model/update answered 200 and ignored model_info.
This PR makes POST save model_info the way PATCH does, so x06 now asserts what its PATCH sibling
x01 asserts, the stored flag flips to true and cache points come back on.
* fix(proxy): answer 400 when POST /model/update gets an access_groups that is not a list of strings
* fix(proxy): read discovered model info through the router registry entry point
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(keys): allow assigning a project to an unassigned key on /key/update
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): suppress LIT002 on project assignment error payloads
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): format project assignment error detail
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(keys): assert accepted project assignment flows into update payload
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): drop stale LIT002 suppressions removed by main
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(keys): mock writer_db team lookup used by check_db_only path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(keys): write project assignment only while key row is still unassigned
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): satisfy type gate on guarded project assignment write
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(keys): default soft-budget update path to unguarded write
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(keys): run guarded project assignment in one writer transaction
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(keys): include object permission link in assignment guard
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(keys): return permission-linked payload without mutating the update dict
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): fix type errors and stale noqa after main merge
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): use Sequence in assignment guard model annotation
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): type update fields as a mapping in the key update path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* chore(keys): place cast reason on the cast call
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(keys): honor explicit null max_budget when assigning a project
* test(keys): assert explicit null max_budget flows into update payload
* fix(keys): match NULL models column in guarded project assignment
* fix(keys): authorize caller before project lookup on /key/update
* fix(keys): drop explicit null object_permission before project assignment write
---------
Co-authored-by: jesus <jesus@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost): bill the margin once on rebuilt streams and streamed Responses through the chat bridge
The last usage chunk and the Responses terminal event carry litellm's bill in usage.cost, and two readers took that bill for the upstream's own charge and margined it again: completion_cost on a stream_chunk_builder result, and stamp_responses_usage_cost on the chat bridge. The stamp now keeps the upstream's usage.cost under the hidden upstream_usage_cost key, the chunk builder carries it onto the rebuilt response, and the Responses stamp prices that saved figure
* fix(cost): hand the Responses stamp the upstream charge through upstream_usage_cost only, never the response-cost header
response_cost_calculator returns the response-cost header as the final bill without the margin, so a priced copy carrying the upstream's raw usage.cost there billed the client the unmargined figure. The hidden upstream_usage_cost key already feeds billed_usage_cost, which margins it once. The Responses test now prices through a real Logging object instead of a mocked calculator
* fix(cost): leave stamp_responses_usage_cost as on main
The Responses chat bridge copies the inner stream's final usage chunk, so the wrapper stamp keeping upstream_usage_cost already prices it once. Changing the Responses stamp was not needed for that and re-priced xai MCP rounds whose usage.cost is already litellm's bill (0.00512 for 0.00312 in test_xai_stream_auto_execute_bills_each_round_at_its_own_price_with_one_fixed_margin)
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test: bedrock claude prompt caching on /v1/messages reports and charges cache tokens
* ci: re-run after ci-coverage and assert-shard-coverage were cancelled by the GitHub Actions incident
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): normalize empty function_call_output to an empty tool result
* test(responses): preserve empty tool outputs through guardrails
---------
Co-authored-by: Tin <tin@berri.ai>
* feat(ui): show ai&'s name and logo in the dashboard
The Add Model form already lists ai& from its provider metadata, but the dashboard had no entry for it, so the provider showed no logo and model tables printed the raw aiand slug. Adds ai& to the dashboard provider list with its official mark, plated in dark mode so the red and black mark keep their colours, and the aiand/openai/gpt-oss-120b model placeholder
* fix(aiand): retire deepseek-v4-flash, glm-5.2 and kimi-k2.7-code
ai& retired these three models on 2026-10-27 and https://api.aiand.com/v1/models now lists ten. The rows keep their prices with a deprecation_date, so earlier spend still prices, and aiand/* stops listing them, the same way baseten handles retired rows. The other ten rows already match the live prices, context windows and output limits
* feat(aiand): add deepseek-v4.1-flash-1017
ai& launches deepseek-ai/deepseek-v4.1-flash-1017 on 2026-11-02. It is served on https://staging-api.aiand.com/v1/models with the same price, context window, output limit and capabilities as deepseek-v4.1-flash, so its row copies that one
* fix(aiand): retire deepseek-v4.1-flash
ai& retires deepseek-ai/deepseek-v4.1-flash on 2026-10-30, now that deepseek-v4.1-flash-1017 is live in production on https://api.aiand.com/v1/models with the same price, limits and capabilities. Its row keeps its prices with a deprecation_date, like the other three retired ai& rows
* fix(aiand): retire deepseek-v4.1-flash on 2026-11-06
https://api.aiand.com/v1/models now gives deepseek-v4.1-flash a deprecation_date of 2026-11-05T15:00Z, which is 2026-11-06 00:00 in Japan, so its row uses 2026-11-06 instead of 2026-10-30 and aiand/* keeps listing it until then. The wildcard test now leaves a model out only once its retirement date has passed
* test(aiand): derive retired models from the rows instead of pinning dates
The wildcard test now reads each row's deprecation_date, so it holds whichever dates ai& sets, and the test that only restated the JSON dates is gone
* fix(aiand): retire models on their UTC date while the proxy runs, and add muse-spark-open
The wildcard listing now checks each row's deprecation_date when it expands, against the UTC date the field
documents, so a model drops on its day without a restart. deepseek-v4.1-flash now carries 2026-11-05, the
UTC day ai& retires it, and meta/muse-spark-open gets its price row from ai&'s live /v1/models
* fix(aiand): hide retired models only from bundled listings, including the wildcard health check
* fix(aiand): share the UTC retirement check with Together AI and the health check sort
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): evict a deleted key from every worker's auth cache
/key/delete dropped the key object only from the in-memory cache of the worker
that served the call. Auth serves key objects cache-first, so every other
worker, and the Redis copy when enable_redis_auth_cache is on, kept
authenticating the deleted key until user_api_key_cache_ttl expired. /key/update
already evicts and broadcasts through evict_and_broadcast, delete now does the
same for the tokens it deletes
* fix(proxy): evict only the deleted rows and broadcast them in one batch
Scope the cross-worker eviction on /key/delete to the rows the caller was allowed to delete and send it through delete_cache_key_objects, so a caller-supplied name can no longer drop another team's cached entries on every worker
* refactor(proxy): drop the comment above the delete eviction call
* fix(proxy): run the usage cache eviction beside the auth eviction so a hung Redis costs one wait
* test(proxy): wait on the broadcast instead of a sleep and type the peer eviction tests
* test(proxy): wait for the peer to apply the broadcast instead of polling its cache
* test(proxy): cover hashed keys and aliases when deleting a key warmed on a peer
* fix(key_management): evict every requested key on delete, including keys whose row is already gone
A batch delete that names a live key and a key whose row was removed outside the API evicted only the live key, so the gone key kept authenticating from every worker's cache. Every requested key hash is evicted now, while raw non-key cache keys are still left alone
* test(key_management): split the gone-row eviction assertion into one comprehension per worker
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* perf(streaming): stop raising AttributeErrors inside pydantic on every streamed chunk
Every later plain chunk on openai and azure streams raised and caught 12 AttributeErrors inside pydantic. Assigning an undeclared extra goes through a class lookup that raises in the model metaclass and is never memoized, and the hidden_params getter went through the private attribute fallback
set_extra_attribute writes the extra and fields_set directly when a cached per-class check says pydantic would do the same, and falls back to setattr otherwise. The hidden_params getter reads the private storage directly. Emitted chunk JSON is unchanged
* fix(streaming): match pydantic's own extra-field bookkeeping on every supported version
Take the direct extra write only when the installed pydantic itself adds an assigned extra to
model_fields_set (2.13 and later) and the class defines no attribute of that name, so pydantic
2.11, 2.12 and 2.14 see the same model state as a plain setattr. A deleted _hidden_params raises
AttributeError again, and the mutable test models carry frozen-ok reasons.
* fix(streaming): keep the normal write for models that do not allow extra fields
openai's construct gives every model an extras dict, so a forbid model built that way still needs
setattr's ValueError instead of a silent extra write.
* test(streaming): cover a ClassVar declared without a value in the extra-field parity test
* fix(streaming): type the streamed choice's logprobs as the parsed value main now stores
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(model_prices): add EmpirioLabs model rows from the provider catalog
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(model_prices): align EmpirioLabs rows with the registry endpoint vocabulary and schema
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(model_prices): absorb Gemini long-prompt batch rates and Azure GPT-6 Astra long-context flex rates
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(model_prices): drop EmpirioLabs rows from the registry audit
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(passthrough): log the URL model and declared provider on Gemini and Vertex failures
Pass-through failures on Gemini and Vertex routes carry the model only in the
URL, so the failure spend row and the Prometheus failure counter logged an
empty model. The failure payload now falls back to the model parsed from the
URL on those routes, and the Prometheus provider label honors the provider the
request declares before inferring one from the model name
* fix(passthrough): keep model and provider on mid-stream and Vertex failure logs
Build the failure payload from the URL at both streamed relay sites so a
200 stream that dies mid-body still logs the model, derive vertex_ai for
Vertex routes that never declare a provider, and have the Vertex logging
handler reuse get_vertex_model_id_from_url instead of its own regex
* fix(passthrough): bound the Prometheus provider label and name Vertex failures like the success handler
Declared providers only reach the api_provider label when they name a
registered provider, otherwise the label falls back to inference.
Vertex failure rows now use the success handler's model naming
(vertex_ai/ prefix on rawPredict and streamRawPredict, vertex_ai/search_api
on :search) and its host-based provider detection, and the detected-stream
failure sites are covered by tests.
* fix(passthrough): name Google failures from the URL on untracked methods too
A Gemini or Vertex pass-through failure on a method the success handler does
not track (countTokens, for one) still logged model "" and no provider,
although the model sits in the URL. The failure payload now falls back to the
Google AI host for the provider and reads /models/<name> from the URL, so
countTokens 400s name their model and provider like generateContent ones do.
Non-Google hosts keep the tracked-route rule and still log nothing extra.
* fix(passthrough): trust the declared Google provider and Vertex-shaped paths on custom bases
The failure path only named the Google provider from the URL when the
method was one the success handler tracks or the host was a Google API
host, so a Gemini countTokens failure behind a custom GEMINI_API_BASE
(or a Vertex one behind a custom base) still logged an empty model. A
route that declares gemini or vertex_ai now wins outright, and a URL
whose path carries /projects/<p>/locations/<l>/ counts as Vertex on any
host, while a tracked method on a non-Google host no longer passes as
Vertex.
* fix(pass_through): stop the URL model parser at the query string so a configured key never lands in a spend row's model
* test(pass_through): run the URL-model failure tests through the real ProxyLogging with a recording callback
* fix(passthrough): scope declared provider to pass-through failures and match Google hosts only
Prometheus trusts the resolved provider as before and reads the request-declared
provider only on pass-through failures. Vertex attribution matches Google hosts,
strips operation poll suffixes, prefixes vertex_ai/ only for rawPredict, reads the
URL path only, covers Vertex Live WebSocket failures, and labels a router alias
failure with the alias the client requested
* fix(passthrough): attribute failures from the URL only on built-in Google routes and keep other routes' metric labels
A user-defined pass-through route to a Google host keeps its empty model and provider, since its URL names an upstream model rather than a LiteLLM-managed one. Prometheus failure labels read the route's provider and model_group only for gemini and vertex_ai, so Azure, OpenRouter, Bedrock and other pass-through routes keep their earlier labels. The Gemini URL parser drops the query string, Google host matching needs a dot or dash boundary, and the Vertex Claude prefix follows alt=sse like the success handler.
* fix(passthrough): log the URL model on guardrail blocks and the named model on Vertex Live failures
A pre-call guardrail block on a pass-through route now builds its failure
payload the same way upstream failures do, so the model and provider read
from the URL reach spend logs and metrics. Vertex Live websocket failures
(connect errors, upstream rejections, and the credential failure) now carry
the model from the ?model= query parameter under the vertex_ai provider
* fix(passthrough): mark guardrail-block and Vertex Live credential failures as pass-through calls
Prometheus reads a failure's declared provider only on pass-through calls, so
without call_type it fell back to guessing from the model name. That labeled a
guardrail block on the Gemini route as vertex_ai and the Live credential
failure as None
* fix(passthrough): let the route name a failure's provider and give guardrail blocks their own spend row
* fix(pass-through): keep URL model attribution off routes that name another provider
A built-in route that names a non-Google provider, like /openai pointed at
Gemini's OpenAI-compatible host, no longer takes its failure model from the
Google URL. A guardrail block response now returns the x-litellm-call-id of
its spend row.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(vertex_ai): forward and auto-add the compact-2026-09-04 beta on Claude routes
* test(vertex_ai): prove the compact-2026-09-04 beta reaches the wire through both Claude handlers
* fix(vertex_ai): move a client-sent body compact-2026-09-04 beta to the request header
* refactor(vertex_ai): build the Claude beta set without mutation and inject the client in the messages wire test
* fix(vertex_ai): keep client body betas in the body and answer 400 on a malformed anthropic_beta
* fix(rust): allow the compact-2026-09-04 beta on vertex_ai in the Rust beta table
The beta header config now maps compact-2026-09-04 for vertex_ai, so the Rust
table that mirrors it must stop rejecting that beta there, and the Rust Vertex
messages route adds it for a compaction request like the Python route does.
* fix(vertex_ai): keep client body betas out of the Vertex body next to litellm betas, honor drop_params, and send the compaction beta on count_tokens
Litellm's own betas replace a client's body anthropic_beta again, as on main, so a body beta
Vertex rejects no longer turns a 200 into a 400. A malformed beta drops under drop_params and
names its field otherwise. inline-tools-2026-09-15 joins compact-2026-09-04 as header-only, since
Vertex rejects both in the body. Vertex count_tokens sends compact-2026-09-04 when the request
carries compaction.
* fix(logging): log and bill Anthropic messages compaction responses on the Rust route
The Rust route hands the served Anthropic body to the logger, which validates it
against AnthropicResponse. That model had no compaction content block and no
compaction stop reason, so a compaction reply raised and wrote no spend row.
* fix(vertex_ai,azure_ai): forward compact-2026-09-04 to Azure AI Foundry, merge Vertex body betas, and redact compaction summaries
Azure AI Foundry needs the compact-2026-09-04 header for compaction, a replayed compaction block, and count tokens, so the beta map, the native compaction providers, and the Rust beta table now allow it there. Vertex keeps a client's body betas next to the ones litellm adds, as the Anthropic route does, and turn_off_message_logging now redacts compaction summaries too
* fix(vertex_ai,azure_ai): auto-add thinking display and tool change betas, redact streamed compaction summaries
Folds the header changes from #44103 into this PR. Vertex AI and Azure AI Claude
routes now add thinking-display-updates-2026-08-18 when thinking.display is
"updates" and mid-conversation-tool-changes-2026-07-01 when a tool_addition
block is present, matching the Anthropic and Bedrock routes.
With turn_off_message_logging on, compaction summaries from streamed messages
and streamed chat completions are now redacted in spend logs and callback
payloads, like the non-streamed response already was.
* fix(vertex_ai,azure_ai,rust): add tool change betas on count tokens and Rust messages, bill Rust server tool replies
Vertex and Azure count tokens now add mid-conversation-tool-changes-2026-07-01 when the history carries a tool_addition or tool_removal block, through one shared helper with the compaction beta.
The Rust /v1/messages route adds thinking-display-updates-2026-08-18 and mid-conversation-tool-changes-2026-07-01 when a request uses them, on the providers that accept each beta.
Rust-route logging parses the served body with the Anthropic response parser, so web_search, pause_turn, mcp_tool_use and container_upload replies get a spend row.
Legacy enabled thinking keeps display updates on anthropic too, and the Vertex transform reads drop_params through drops_unsupported_params.
* style(rust): rewrap beta map test line with cargo fmt
* test(rust): expect the display beta on Databricks, which main's beta list now accepts
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(vllm): send the deployment api_key on /vllm passthrough calls
VLLMModelInfo.get_api_key always returned None, so /vllm/{endpoint} calls to a router
model reached the vLLM server with no auth header even when the deployment had an
api_key, and a server started with --api-key answered 401 (#23443).
Return the deployment key (or VLLM_API_KEY) and send it as Authorization: Bearer, the
header vllm serve --api-key checks. x-api-key was never actually sent before, since the
key was always None.
* fix(vllm): send the pass-through key as a bearer on both /vllm routes
vllm serve --api-key only reads Authorization: Bearer, so a keyed vLLM
answered 401 on both pass-through routes. Send the key as a bearer and
keep x-api-key for gateways that read it. Router models send their own
deployment key and never fall back to VLLM_API_KEY, which belongs to
the /vllm route's own server.
* fix(vllm): send only the bearer on router models and stop doubling /v1 on /vllm
Router models now send the same Authorization: Bearer header /v1/chat/completions sends for that deployment, without the x-api-key copy that route never sent. The VLLM_API_BASE route keeps x-api-key next to the bearer, as it sent it before. A router model whose api_base already ends in /v1 no longer gets /v1/v1 on /vllm/v1 paths.
* fix(vllm): send no placeholder key from keyless router models and stop doubling /v1 on the VLLM_API_BASE route
* fix(vllm): send VLLM_API_KEY when a router model with no api_base is forwarded to VLLM_API_BASE
* fix(vllm): keep a router model's own key on the VLLM_API_BASE server and resolve an oidc VLLM_API_KEY
* fix(vllm): treat an empty key as no key so pass-through sends no empty bearer
* fix(vllm): send no bearer for a Google oidc key and match VLLM_API_BASE across a trailing slash
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(logging): warn when drop_params drops a requested reasoning level on the remaining paths
* fix(logging): keep guardrail and token-count reads of a request from logging drop warnings
Only the real Anthropic-messages adapter request reports a dropped thinking budget. Guardrail,
shadow-eval and token-count translations of the same request now drop it silently, so one request
logs one warning. The 422 retry also no longer hashes each loc entry, so a list inside loc cannot
raise TypeError.
* fix(bedrock): keep the DeepSeek R1 Converse reasoning_effort drop at debug level
Only allowed_openai_params reaches that branch, and it puts reasoning_effort back after the provider mapping, so a warning there claimed a drop that never happened
* fix(logging): warn on Gemini non-integer budget drops and keep the auto-router baseline silent
Gemini 2.x now logs the drop of a non-integer thinking budget. The auto-router
savings baseline builds a body it never sends, so it no longer warns. Logged
values are capped with reprlib, Azure AI marks only its drop_params retry as
gated, and Bedrock Chat Completions names the model without its route prefix.
* fix(logging): warn on the Gemini 3, Magistral, Ollama, Fireworks and DeepSeek ARN reasoning drops
Gemini 3 and later now log a dropped thinking budget, Magistral logs the
level it swaps for a reasoning prompt, Ollama logs a level it maps to
think off, Fireworks logs chat_template_kwargs effort keys dropped for a
model the map does not list as reasoning, and DeepSeek on Bedrock logs a
drop when the model is addressed by an ARN that advertises the param.
* fix(logging): warn on the remaining Ollama, DeepSeek and Tencent reasoning drops
The ollama/ completion route now shares the chat route's think mapping, so
a level it turns off logs the same warning. DeepSeek warns when it drops
thinking.budget_tokens, and Tencent warns when an adaptive-only model gets a
non-integer budget. Magistral no longer warns on low, medium, high or
enabled thinking, the values its reasoning prompt stands in for. The Gemini 3
budget warning no longer claims the model uses its default level.
* fix(reasoning): warn when Claude drops a non-string reasoning_effort
Claude chat and native /v1/messages dropped a reasoning_effort like 5 or
{"effort": 5} with no log line. Both now log one warn-only WARNING. A
Magistral thinking.type sent as a list no longer raises TypeError.
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
