Every authorization decision now flows through one audit record - the compliance
trail enterprise auth needs and the biggest gap called out in the design review.
- audit.py: a frozen AuthzDecision (decision, subject, domain, obj, action, route,
reason, auth_method) and record(), which logs the decision and fans out to any
registered sinks (DB / SIEM). Sinks are isolated: a failing sink is logged but
never affects the request outcome.
- The authorizer records allow, deny, and loud-open for every governed and
ungoverned control-plane route; the entry point records the model-call (call)
decision on the inference path. auth_method is threaded through so the record
shows how the principal authenticated.
- register_sink / reset_sinks exported for wiring a durable sink.
Fully typed (no new Any), mypy clean on 22 files, 134 tests green, still imports
with casbin/authlib absent.
The decision core used Any where it was over-cautious about coupling. Replace it
with real types (under TYPE_CHECKING, so the framework-free core stays import-
light) and small Protocols:
- identity is UserAPIKeyAuth end to end (authenticators, entry, principal,
context, stages); the authenticator chain returns it, not Any
- SupportsEnforce types the authorizer's engine param; CasbinRuleRow / PolicyDB /
PolicyAdminDB type the dynamic Prisma casbin-rule access (read and write),
narrowed with one explicit cast at the adapter boundary
- AuthContext deps are PrismaClient / UserApiKeyCache / ProxyLogging / Span
- loaders, settings, key sets, role conversion all carry their real types
The only Any left is Dict[str, Any] for genuinely arbitrary JSON (decoded JWT
claims, RFC 7662 introspection responses, request bodies) - that is the honest
type, not a gap. Verified: mypy clean on all 21 files, zero bare Any annotations,
129 tests green, still imports with casbin/authlib absent.
Pure move (git mv, history preserved): the flat 17-file package hid the
architecture behind an alphabetical wall. Group by request phase so the module
is self-documenting:
authn/ authenticators, jwt_claims, jwt_verifier, oauth2_introspection
authz/ enforcer + model.conf, authorizer, route_map, policy_store, policy_admin
stages/ enrichment, end_user, budgets
(top) entry, context, principal, management_endpoints, __init__
No content changes beyond rewired relative imports; tests moved to mirror the
layout. Behavior is identical — the same 129 tests pass, mypy/black/ruff clean,
and the package still imports with casbin/authlib absent.
Closes the hierarchy-budget gap: team, organization, and global caps live in v1's
common_checks (not the pre-call hooks), which auth_v2 does not run, so they were
unenforced under v2. enforce_hierarchy_budgets calls the exact same functions v1
uses - _team_max_budget_check, _organization_max_budget_check, and
get_global_proxy_spend + _global_proxy_budget_check - so there is one budget
implementation with two callers (true single authority), with the correct
spend-counter conventions and no edit to v1's path. Wired into the inference
branch for all login types; a breach surfaces as the same 429 ProxyException v1
raises.
Verified in-process: a team over budget is blocked (BudgetExceededError), under
budget is allowed, and a teamless identity is a no-op. Real-counter accuracy
across pods remains a live check (see INTEGRATION.md).
Drives the real max_budget_limiter pre-call hook with an enrichment-shaped
identity (teamless JWT/OAuth: no key, user-level budget filled from the user
row), mocking only the spend counter, to confirm the enrichment -> hook chain:
over budget -> 429, under budget -> allowed, no user budget -> no personal
enforcement, and a team member's personal budget is skipped (documenting that
team users are governed by the team budget, the item-4 gap). Real-counter
accuracy is the one piece still needing a live proxy.
In-process integration test with TestClient and an in-memory Prisma stand-in,
exercising the real request path without a running proxy or provider keys (the
allow/deny decision happens at auth time, before any model call):
- flag dispatch -> user_api_key_auth -> v2 entry -> authenticator chain ->
casbin -> 403/200
- master key resolves to proxy_admin and passes a governed route
- a non-admin with no grant is denied; granting a role and assigning it (live
policy CRUD) then unlocks the route, proving the cache reset on write
- the _require_admin gate rejects a non-admin over the wire
- model calls require the `call` permission: denied without a grant, allowed
after granting call on gpt-* and assigning it
This is the wiring that unit tests can't cover, verified in CI.
auth_v2 now seeds request-identity Baggage by calling seed_request_identity at the
auth boundary in each entry branch (model included for inference), the same
SDK-free seeder v1 uses. It propagates team/key/user identity to every downstream
route span and no-ops when the OTel SDK is absent or V2 isn't active, so it's safe
to call unconditionally.
This is the established OTel v2 mechanism, so the earlier identity_span_attributes
helper (and its module/tests) was speculative and is removed. Only live
verification of the exported spans remains; see INTEGRATION.md.
The policy CRUD endpoints were gated by _require_admin but it had no test. Add
deny/allow coverage: only PROXY_ADMIN may edit policies; view-only admins, every
other role, and no role are rejected with 403. This is the guard that stops a
non-admin from editing the casbin policy store, so a silent regression here would
be a privilege escalation.
Virtual keys arrive fully populated via get_key_object; master/JWT/OAuth logins
returned a thin identity, so the existing pre-call budget/limit hooks read None
and enforced nothing for them. enrich_identity copies the user/team budget+limit
fields 1:1 from the user/team rows into the identity's distinct user_*/team_*
slots, filling only unset fields (never overriding an already-resolved value).
Wired into the inference path for non-virtual-key logins, with get_user_object /
get_team_object injected as loaders so the mapping is unit-tested without a DB.
Additive by construction: these logins enforce nothing today, so it cannot
regress existing behavior. The exact enforcement still needs a live rate-limit
check before it is trusted; see INTEGRATION.md.
Adds the first two pipeline stages that consume RequestAuthContext instead of
the auth gate doing their work:
- end_user.resolve_end_user: extracts the customer (reusing the existing
request-body/header logic) and records it on the context via attach_end_user.
Validation is dependency-injected so it is testable without a DB. Wired into
the inference path so the context carries the end-user for spend attribution.
- telemetry.identity_span_attributes: returns OTel attributes for the request's
identity, for the route span (OTel v2) to set. Telemetry reads the context
rather than auth seeding the span; absent fields are omitted.
Both are pure/injectable and unit-tested; the contract stays the single source
of truth. Type-safe (mypy), formatted, linted.
Introduces the single, type-safe object every post-auth stage reads instead of
poking at request.state with untyped attribute access. auth_v2 populates it once
(identity, principal, auth_method, route, end_user_id) after authn+authz and
publishes it via set_auth_context; budget/limit hooks, the end-user resolver, and
the telemetry span consume it through get_auth_context / try_get_auth_context.
- RequestAuthContext is a frozen dataclass; attach_end_user replaces rather than
mutates, so one stage can't clobber another's view
- identity is annotated under TYPE_CHECKING so the contract is statically typed
without coupling the decoupled core to the heavy litellm import
- authenticators now advertise an AuthMethod and the chain returns a typed
AuthResult, so how a request authenticated is recorded for telemetry
- context is set in every branch (control, model-call, loud-open)
Verified type-safe with mypy. This is the linchpin for moving budgets, end-user
resolution, and telemetry out of the auth gate into their own stages.
Calling a model is now the `call` action on the `model:<id>` object, decided by
the same casbin role engine as everything else. Grant it via a role (wildcard
objects like model:gpt-*, or g2 groups) or directly to a key/user subject. The
legacy key.models list and access-group expansion are no longer consulted, so
with no grant a key can call nothing (clean-slate default-deny).
Removes the separate data-plane predicate (data_plane.py and its conf/tests);
the inference path now runs the same enforcer the control plane uses, extracted
into a shared _build_enforcer helper. Adds `call` to the valid policy actions.
Note: this puts a casbin check on the inference hot path. It builds the enforcer
per request from the short-TTL policy snapshot; caching a long-lived in-memory
enforcer with cross-pod invalidation is the next step before this is enabled at
real traffic.
The inference gate now allows a model when the key lists an access-group name
the model belongs to, mirroring v1 model_in_access_group. The group lookup is
resolved from the router in the entry point and injected into can_call_model,
which stays a pure predicate. Closes the access-group parity gap flagged when
the data plane moved off casbin; name, wildcard, and sentinel matching are
unchanged.
Credentials are REST-style: the verb is the HTTP method (POST /credentials is
create, GET is list) and the id is in the path, which the verb-in-path RPC
matcher cannot express. Adds method-aware, prefix-capable matching for these
resources and governs the credentials admin surface.
Fixes a latent bypass while wiring this: authorize() re-matched the route
without the method, so any REST route would have resolved to None inside
authorize() and been loud-opened even when the entry point had classified it as
governed. The method is now threaded through. Path-param ids are not extracted
yet, so credential objects stay at "credential:*" (per-id policies a follow-up).
Governs the collection-level management operations (register/list/health/
submissions) for MCP servers and guardrails, mapped to read/write with
wildcard objects since they are not per-id. Runtime guardrail verbs
(apply_guardrail, test_custom_code) remain loud-open as data/runtime concerns,
not management.
Extends the control-plane route map to the remaining clean-CRUD management
resources, following the established read/write/delete/manage pattern with
per-resource ids. These routes were loud-open before, so this only tightens
coverage. Non-uniform surfaces (guardrails, mcp servers, credentials) stay
deferred because their verbs don't map cleanly onto the four actions.
Also corrects two stale comments that still described the data plane as casbin
ABAC after it became a plain predicate.
A token shaped like a JWT (non sk-, two dots) was claimed by the JWT
authenticator even when no jwks_uri was configured, producing a 500 from the
settings loader. It now only claims JWT-shaped tokens when JWT auth is actually
configured, so an unconfigured deployment ends the chain in a clean 401.
The inference-path model check ran through a casbin enforcer whose matcher was a
trivial membership test (unrestricted || requested in allowed). On the hot path
that is pure overhead and indirection; casbin earns its keep on the control
plane (roles, deny-override, domains), not here. Replaces it with a direct
predicate and removes data_plane.conf.
Also closes a parity gap with v1: the gate now honors wildcard patterns
(e.g. bedrock/*, openai/*) via v1's is_model_allowed_by_pattern semantics, where
before it only matched exact names and would over-deny wildcard model lists.
Access-group expansion remains a tracked follow-up.
The JWT verifier used authlib's default decoder, which permits HMAC alongside
RSA/EC. With a JWKS of public keys that is the RS256->HS256 confusion setup: an
attacker signs HS256 using the public key as the HMAC secret. authlib happens to
reject this today via key-type checks, but relying on that is fragile and breaks
the moment a symmetric key enters the key set. Pinning the accepted algorithms
to an asymmetric allowlist closes the class outright.
Adds a regression test that forges an HS256 token from the JWKS public key and
asserts it is rejected.
Adds tenancy via domain-scoped role assignment (casbin g3): a role can be
granted globally (g) or only within a domain (g3), e.g. admin within team:eng
but nowhere else. The matcher accepts either, so existing global assignments are
unchanged. The policy store buckets g/g2/g3 separately and the assignment
endpoint takes an optional domain.
Adds litellm/proxy/auth/v2/README.md documenting configuration, the policy
admin API, and a live verification runbook.
Tests cover domain-scoped vs global role resolution, coexistence of both, the
g3 split in the policy store, and g3 assignment rule construction.
Completes the authn surface from the plan with an RFC 7662 introspection node
for opaque bearer tokens. Dispatched only for tokens that are neither virtual
keys nor JWTs, and only when an introspection endpoint is configured, so
unconfigured deployments fall through to a clean 401 rather than calling out.
The introspection response parsing is a framework-free core: a token is valid
only when explicitly active=true, the subject claim is required, and OAuth
scopes map to a litellm role via an explicit scope->role map (unmapped scopes
grant no role). The HTTP introspection call is isolated from that core.
Tests cover inactive/missing-active rejection, missing subject, and scope
(string and list) to role mapping.
Expands control-plane governance to keys, users, and organizations (read/write/
delete), mirroring models and teams. Membership changes (team/member_add,
organization/member_add, etc.) become the `manage` action on their resource.
Adds casbin resource grouping (g2) so a policy can grant on a named group of
resource ids rather than one row per id; this is where litellm access groups
map in. The matcher prefers a group match and falls back to keyMatch, so
direct/wildcard object matching is unchanged when no g2 rules exist. The policy
store now splits g2 rows out from g groupings and the enforcer loads them under
the g2 role manager.
Tests cover grouped vs ungrouped access, preserved direct matching, the g2/g
split, and governance of the new resources and the manage action.
Brings inference under casbin too, via the hybrid design: control-plane access
stays RBAC policy rows, data-plane access (can this principal call this model)
is a casbin ABAC matcher over an attribute carried on the already-loaded key, so
the hot path reads no policy store and writes no per-key policy rows.
Inference routes (chat/completions, completions, embeddings, responses) now
authenticate and check the requested model against the principal's allowed-model
attribute. Semantics match v1: an empty models list, "*", "all-proxy-models" and
"all-team-models" are unrestricted; otherwise the model must be in the list.
Tests cover the allow/deny/unrestricted matrix and inference-route detection.
Adds endpoints to manage casbin policies and role assignments without raw DB
inserts: add/remove a permission, add/remove a role assignment, and list rules
under /auth/v2/policy/*. Requests go through a validated, normalized core
(actions limited to read/write/delete/manage, effects to allow/deny, role and
object strings normalized) so malformed rules can't reach the policy table.
The policy surface governs itself: those routes are a "policy" resource in the
route map, so only a role permitted to manage policy (the bootstrap proxy_admin
role) can edit them, with an explicit proxy-admin check as defense in depth.
Writes reset the policy snapshot cache so changes take effect immediately.
Tests cover rule validation/normalization and the rule<->row conversion.
Adds a JWT node to the authenticator chain, dispatched on credential shape
(3-part token, not a sk- key). authlib owns the crypto: JWKS fetch with TTL
caching, kid-based key selection, signature verification, and exp/iss/aud
validation. A thin framework-free layer maps verified claims to an identity
using configurable claim names and an explicit upstream-role to litellm-role
map; unmapped role values are dropped rather than trusted.
JWT settings come from general_settings.auth_v2_jwt (or AUTH_V2_JWKS_URI etc.).
Verification failures normalize to 401 so callers never branch on authlib's
internal exception types.
Tests cover signature tampering, expiry, wrong audience/issuer, unknown signer,
and garbage input on the verifier, plus claim extraction and role mapping.
Adds a master-key authenticator ahead of the virtual-key node so the proxy admin
can authenticate under auth_v2 (slice 1 was virtual-key only, which locked the
master key out). Match is a constant-time exact compare; the raw key never
propagates downstream, a stable alias stands in for it.
Extends casbin governance to the team management plane (/team/new, /team/update,
/team/delete, /team/info) mirroring models. Team membership/permission routes
(member_add, etc.) stay loud-open, deferred with the recursive `manage` action.
Tests cover the master-key exact-match deny path (near-match, non-string, empty,
unconfigured) and team route governance.
Introduces auth_v2 as a clean-slate, flag-gated auth path (general_settings
auth_version: v2). When on, the existing auth is bypassed entirely and requests
flow through a new authenticator chain plus a casbin authorization engine.
Slice 1 scope:
- Entry point fork in user_api_key_auth; v1 untouched when the flag is off
- Authenticator chain with a virtual-key node resolving identity via the
existing key store (reuses get_key_object; no parallel identity storage)
- casbin engine: RBAC policy rows for the control plane, role bridged from the
key's existing user_role; per-resource-id objects supported
- Governs the model-deployment management plane only (/model/new, /model/update,
/model/delete, /model/info); every other route is loud-open and logs a warning
so unprotected surfaces are never silent
- Policies/groupings stored in LiteLLM_CasbinRule, loaded on cold routes with a
short snapshot cache; a bootstrap policy keeps proxy_admin fully authorized
- Decision core (enforcer, route map, principal, authorizer, policy store) holds
no framework imports, so it is unit-testable in isolation
Tests cover the allow/deny matrix, deny-override, domain scoping, per-id
granularity, loud-open behavior, and policy loading.
Data plane (inference-time model access) and additional resources/mechanisms are
deferred to later slices. casbin governs everything eventually via ABAC matchers
over cached attributes; this slice lays the control-plane foundation.
In Pydantic v2, Optional[T] without a default is a required field. Any
row with budget_id=null triggered a validation error and returned 401.
Co-authored-by: Florent Chenebault <florent.chenebault@lifen.fr>
* fix(azure): apply api_version fallback chain to image edit URL
`AzureImageEditConfig.get_complete_url` only read `api_version` from
`litellm_params`. When callers configured it via `litellm.api_version`
or `AZURE_API_VERSION`, the constructed URL had no `?api-version=` and
Azure responded `404 Resource not found`.
Apply the same fallback chain the Azure chat path already uses in
`common_utils.py`:
litellm_params > litellm.api_version > AZURE_API_VERSION env >
litellm.AZURE_DEFAULT_API_VERSION
Adds 5 unit tests pinning each layer of the chain plus a regression
guard for `api_base` that already carries `?api-version=`.
* feat(mcp): core sampling and elicitation flow with security hardening
- Add sampling_handler.py: full MCP sampling/createMessage flow with
model selection (hint-based + priority-based), auth enforcement,
budget checks, route restriction gates, and tag policy pre-auth
- Add elicitation_handler.py: MCP elicitation/create relay with
downstream client capability detection
- Wire sampling/elicitation callbacks in mcp_server_manager.py
gated behind allow_sampling/allow_elicitation config flags
- Add allow_sampling/allow_elicitation fields to MCPServer type
- Fix session lock deadlock: skip lock for JSON-RPC response POSTs
(elicitation/sampling replies) with truncated-body heuristic
- Extend client.py with sampling_callback and elicitation_callback
- Security: RouteChecks gate, tag-budget bypass fix, x-forwarded-for
spoofing fix, Latin-1 header encoding guard
- Add 4 new test modules (model access, priority selection, request
builder, tool conversion) + update existing MCP tests
* fix(security): run pre-call guardrails before MCP sampling acompletion
Without this, an upstream MCP server with allow_sampling enabled could
send prompts that bypass every guardrail (content filtering, PII
redaction, prompt-injection detection) configured on /chat/completions.
- Call proxy_logging_obj.pre_call_hook(call_type='acompletion') before
llm_router.acompletion so guardrails fire for sampling sub-calls
- Add HTTPException to the re-raise list so guardrail rejections
propagate correctly instead of being swallowed as generic errors
* feat(bedrock_mantle): add Responses API support (/openai/v1/responses) (#29490)
* feat(bedrock_mantle): add Responses API transformation config
* test(bedrock_mantle): cover trailing-slash api_base normalization
* feat(bedrock_mantle): export BedrockMantleResponsesAPIConfig
* feat(bedrock_mantle): register gpt-5.x Responses config (gpt-oss unchanged)
* feat(bedrock_mantle): add gpt-5.5/gpt-5.4 Responses price-map entries
* refactor(bedrock_mantle): exclude gpt-oss instead of allow-listing gpt-5 for Responses routing
Frontier OpenAI models on Bedrock Mantle are Responses-only on /openai/v1/responses;
gpt-oss is the legacy family that also speaks chat-completions. Gate by excluding
gpt-oss (which keeps its chat-completions emulation) and defaulting everything else
to the native Responses config, so future frontier models (gpt-6, etc.) route
correctly without a code change. Verified against the live us-east-2 Mantle endpoint:
gpt-oss 400s on /openai/v1/responses while gpt-5.5 400s on both standard paths.
* test(bedrock_mantle): cover supports_native_websocket opt-out
Closes the one uncovered line flagged by codecov on the Responses config.
The assertion documents that Mantle Responses has no realtime/websocket
transport, so realtime routing must not attempt a socket it cannot serve.
* fix(bedrock_mantle): route file_search through emulation instead of forwarding to Mantle
BedrockMantleResponsesAPIConfig inherited supports_native_file_search()
-> True from OpenAIResponsesAPIConfig but never overrode it. Mantle has no
OpenAI vector stores, so a forwarded file_search tool is rejected with a
400 (verified upstream: Tool type 'file_search' is not supported). Opting
out, like the existing supports_native_websocket override, routes the tool
through LiteLLM's file_search emulation instead.
* fix(bedrock_mantle): only route openai.gpt frontier models to Responses
The previous gate excluded gpt-oss and routed every other model to the
native Responses config. But on Mantle only the OpenAI gpt frontier models
(gpt-5.x) are served on /openai/v1/responses; gpt-oss and the non-OpenAI
families (nvidia, mistral, google, zai, ...) are chat-completions only and
400 on that path. Allow-list the openai.gpt- family (excluding gpt-oss)
instead, so chat-only models fall through to the chat-completions emulation.
Verified against the live us-east-2 endpoint: nvidia.nemotron-nano-9b-v2
returns 400 on /openai/v1/responses and 200 on /v1/chat/completions.
* feat(custom_llm): allow streaming/astreaming to yield ModelResponseStream (#27580)
* fix(custom_llm): allow streaming/astreaming to yield ModelResponseStream directly
* fix(streaming): enhance ModelResponseStream handling for custom LLM providers
* fix(streaming): strip finish_reason from content chunks and ensure tool_calls are preserved
* fix(streaming): add type ignore for finish_reason assignment in CustomStreamWrapper
* fix(proxy): strip stack trace from HTTP 503 responses (CWE-209) (#28330)
* fix(proxy/cwe-209): strip Python traceback from HTTP 503 error responses
The /cache/ping endpoint included a full Python traceback in its 503 error
response body (inside the ProxyException message), leaking internal file
paths, line numbers, and call stacks to any caller. Two MCP route handlers
in proxy_server.py similarly interpolated str(e) into "Internal server
error" detail strings.
Fix: log the traceback server-side via verbose_proxy_logger.exception()
and omit it from the ProxyException payload / HTTPException detail returned
to clients. Tests updated to assert no "traceback" keyword or frame paths
appear in the 503 body, with a new dedicated regression test.
CWE-209: Generation of Error Message Containing Sensitive Information.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(proxy/cwe-209): apply Greptile P2 fixes and add MCP exception-path tests
Greptile 4/5 review identified two remaining gaps and Codecov reported
0% coverage on the two MCP handler exception branches:
1. caching_routes.py — str(e) in "Service Unhealthy ({str(e)})" could
still leak Redis hostnames/IPs; replaced with static "Service Unhealthy".
HTTPException is now re-raised before the generic handler so the
"cache not initialized" 503 still reaches callers with its detail.
Removed the redundant str(e) arg from verbose_proxy_logger.exception()
(exception() already appends the traceback automatically).
2. tests — two new unit tests cover the exception paths in
dynamic_mcp_route and toolset_mcp_route that were previously at 0%:
- test_dynamic_mcp_route_unexpected_exception_returns_500_without_traceback
- test_toolset_mcp_route_unexpected_exception_returns_500_without_traceback
All 25 tests pass (9 caching + 16 MCP).
CWE-209: Generation of Error Message Containing Sensitive Information.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* test(caching_routes): restore precise assertion in test_cache_ping_no_cache_initialized
The assertion was weakened to `"Cache not initialized" in str(data)`, which
matches the raw string of the entire response dict and would pass even if the
error moved to an unexpected field or changed structure.
Restore a targeted check on the parsed response: assert the exact string in
the correct field `data["detail"]`, matching FastAPI's HTTPException
serialisation format {"detail": "<message>"}.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* test(caching_routes): restore precise assertion and add CWE-209 no-cache path test
The assertion in test_cache_ping_no_cache_initialized was weakened to
`"Cache not initialized" in str(data)`, which matched against the raw string
representation of the entire response dict. This would pass silently even if
the error message moved to an unexpected field or the structure changed.
Restore a targeted assertion on the parsed field:
assert data["detail"] == "Cache not initialized. litellm.cache is None"
matching FastAPI's HTTPException serialisation format exactly.
Add test_cache_ping_no_cache_does_not_expose_internals to show the code path
is still working correctly after the CWE-209 fix: verifies that the HTTPException
is re-raised as-is (no traceback, no source paths), and asserts the complete
response structure is exactly {"detail": "Cache not initialized. litellm.cache is None"}.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(caching_routes): restore ProxyException envelope for null-cache 503
The except HTTPException: raise guard (added in the CWE-209 fix) caused
the null-cache HTTPException to escape as FastAPI's {"detail": "..."} shape
instead of the {"error": {...}} ProxyException envelope that callers expect.
Move the null-cache guard before the try block and raise ProxyException
directly so the response structure is consistent with all other /cache/ping
503s, and the except HTTPException: raise guard is only reachable by
unexpected downstream HTTPExceptions.
Update the two no-cache tests to assert the correct ProxyException envelope.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update utils.py (#26609)
* feat(pricing): add Snowflake Cortex REST API model pricing (#26612)
* feat(pricing): add Snowflake Cortex REST API model pricing
## Summary
Adds pricing and context window information for 20+ Snowflake Cortex REST API models to `model_prices_and_context_window.json`.
## What's included
- **7 Claude models** (sonnet-4-5, sonnet-4-6, 4-sonnet, 4-opus, haiku-4-5, 3-7-sonnet, 3-5-sonnet) — with prompt caching rates
- **4 OpenAI models** (gpt-4.1, gpt-5, gpt-5-mini, gpt-5-nano) — with prompt caching rates
- **5 Llama models** (3.1-8b, 3.1-70b, 3.1-405b, 3.3-70b, 4-maverick)
- **1 DeepSeek model** (deepseek-r1)
- **1 Mistral model** (mistral-large2)
- **1 Snowflake model** (snowflake-llama-3.3-70b)
- **2 Embedding models** (arctic-embed-l-v2.0, arctic-embed-m-v2.0)
Each entry includes `input_cost_per_token`, `output_cost_per_token`, `cache_read_input_token_cost` (where applicable), `max_input_tokens`, `max_output_tokens`, and capability flags (`supports_function_calling`, `supports_vision`, `supports_prompt_caching`, `supports_reasoning`).
## Pricing source
All prices are in USD per token, sourced from the official [Snowflake Service Consumption Table](https://www.snowflake.com/legal-files/CreditConsumptionTable.pdf) — Tables 6(b) (REST API with Prompt Caching) and 6(c) (REST API).
## Context
The existing `snowflake/` provider has zero model entries in the pricing JSON, which means LiteLLM cannot track costs for Snowflake Cortex calls. This PR fills that gap.
## Related
- Existing provider: `litellm/llms/snowflake/`
- Cortex REST API docs: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api
* Update model_prices_and_context_window.json
Fix the JSON parsing error
* Update model_prices_and_context_window.json
Removed the duplicate entry
* fix(utils): copy extra_body before adding unknown params to prevent model config mutation (#29620)
Fixes#29615. In add_provider_specific_params_to_optional_params, the line:
extra_body = passed_params.pop("extra_body", None) or {}
returns the original dict reference when extra_body is non-empty (truthy).
Subsequent writes like extra_body[k] = passed_params[k] then mutate the
shared model config object held by the router, poisoning /model/info and
all subsequent requests for that deployment.
The or {} short-circuit creates a new dict only when extra_body is falsy
(None or {}), which is why the bug does not reproduce with extra_body: {}.
Fix: wrap in dict() so we always work on a fresh shallow copy.
* fix(vertex_ai): Bake tool_choice into Gemini CachedContent body to prevent silent drop (#29097)
* fix(vertex_ai): bake tool_choice into Gemini CachedContent body to prevent silent drop
* address greptile feedback on tool_choice cache test
* adds test that uses ToolConfig(functionCallingConfig=FunctionCallingConfig(mode=ANY)) instead of a dict literal, mirroring what map_tool_choice_values actually produce
* fix(gemini/veo): move image from parameters into instances[0] (#29501)
* fix(gemini/veo): move image from parameters into instances[0]
Veo's predictLongRunning schema puts image (and prompt) on the
instances element; parameters is for aspectRatio/durationSeconds/etc.
The Gemini path was leaving image in params_copy, so it ended up
nested under parameters and the API silently ignored it.
The Vertex path already builds the instance dict explicitly, so this
just aligns the Gemini path with it.
Fixes#29498
* address greptile: unconditional pop + BytesIO test
- Pop `image` from params_copy unconditionally so it never reaches
GeminiVideoGenerationParameters even when None, removing implicit
reliance on Pydantic's extra-field-ignore.
- Add test_transform_video_create_request_image_filelike_goes_to_instance
covering the BytesIO path (_convert_image_to_gemini_format) — round-trips
the base64 to confirm encoding.
- Add test_transform_video_create_request_image_none_is_dropped covering
the new None branch.
* fix(huggingface): handle special token text in embedding usage (#29660)
* fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params (#29655)
* fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params
ToolPermissionGuardrail builds self.rules and the compiled target/pattern
maps only in __init__. The base update_in_memory_litellm_params re-sets raw
attributes via setattr but never rebuilds those maps, so a guardrail updated
in place (PUT /guardrails, or the immediate in-memory sync) keeps enforcing
the construction-time rules until it is reinitialized (PATCH path, periodic
DB poll, or restart).
Extract the compile step into _load_rules and override
update_in_memory_litellm_params to rebuild from it (dict- and model-safe),
re-normalizing default_action / on_disallowed_action. Mirrors the existing
PresidioGuardrail override of the same method. Adds regression tests.
Fixes#29592.
* fix(guardrails): handle dict params in ToolPermissionGuardrail in-memory update
Delegate to super() only for LitellmParams input (the base setattr loop is
model-only); apply the raw-dict case inline. Fixes the mypy arg-type error
and makes the recompile work when the proxy passes the raw DB dict.
* fix(guardrails): preserve tool-permission rules on a partial in-memory update
A partial update (e.g. a LitellmParams whose rules field is None) ran through
the generic setattr, which set self.rules to None, and the recompile was
skipped, leaving the guardrail with no rules. Snapshot the previous rules and
restore them when the update carries no rules; an explicit empty list still
clears them. Adds a regression test for the rules-absent case.
Addresses the Greptile review note on #29655.
* fix(bedrock): stop base_model label from stripping tools/tool_choice (#29621)
* fix(bedrock): stop base_model label from stripping tools/tool_choice
A Router/proxy Bedrock deployment whose model_info.base_model is a friendly
label (e.g. claude-haiku-4-5) silently lost tools/tool_choice: the outgoing
Converse request was built without toolConfig, so the model behaved as if no
tools were provided. Worked in v1.84.0, regressed in v1.85.0, and with
drop_params=true it failed silently.
Two changes compound into the bug. completion() passed model_info.base_model
as the model argument to get_optional_params, so the real Bedrock model id
never reached supported-param resolution; and get_supported_openai_params
resolved the provider config's params from base_model or model, letting the
label fully replace the real model. For Bedrock the label resolves to no tool
support, so tools/tool_choice were dropped before transformation.
completion() now keeps model as the real deployment model and threads the
resolved base_model (kwarg or model_info) through separately, and
get_supported_openai_params treats base_model as additive: it returns the
union of the params supported by model and by base_model. A hint can only add
capabilities, never strip ones the real model already exposes, which also
preserves the original base_model behavior from #27717 and Azure's base_model
driven model-type detection.
Fixes#29618
* test(main): make base_model param test robust to new parametrize cases
Restore an explicit per-case expected_model_param literal instead of
hardcoding the gemini id, so a future case with a different model can't
produce a misleading assertion failure.
* fix(fireworks_ai): pass response_format json_schema through unchanged (#29606)
FireworksAIConfig.map_openai_params was rewriting the OpenAI strict
`{type: json_schema, json_schema: {name, strict, schema}}` shape into
`{type: json_object, schema: ...}` before sending to Fireworks, dropping
`strict` and `name` and changing the `type`. Per Fireworks' docs json_object
means "force any valid JSON output (no specific schema)", so the schema
constraint was effectively dropped and grammar-guided decoding never ran;
model output silently violated the schema.
The rewrite landed in #7085 (Dec 2024) when Fireworks did not yet accept
native json_schema. Fireworks accepts the OpenAI strict shape natively now,
so the rewrite has become a regression.
Removes the rewrite. Passes response_format through unchanged. Updates the
existing test_map_response_format to assert pass-through. Adds focused
regression tests in tests/test_litellm/ covering preservation of type,
strict, name, and schema body, plus that json_object alone still works.
* fix(types): import Required from typing_extensions in gemini types
* style: reformat sampling_handler.py for py312 black compat
* refactor(mcp-sampling): extract helpers to fix PLR0915 too-many-statements in handle_sampling_create_message
* fix(proxy-server): add explicit ProxyLogging type annotation to proxy_logging_obj to fix mypy inference
* fix(mcp-sampling): suppress mypy assignment error on ImportError fallback for proxy_logging_obj
* fix(test): use .value when comparing LlmProviders enum against string in test_default_api_base
* fix(test): iterate LlmProviders enum in test_default_api_base to avoid str pollution from custom provider registration
litellm.provider_list is a mutable global initialized to list(LlmProviders) but custom_llm_setup() appends plain provider strings to it. When a test_custom_llm.py test runs first in the same xdist worker, provider_list contains a str and calling .value on it raises AttributeError. Iterate the immutable LlmProviders enum instead, which is deterministic and what the check intends.
* fix(mcp): depth-aware JSON-RPC response detection and neutral speed-priority fallback
Replace the flat substring check in the truncated-body routing path with a
top-level-key scan so a JSON-RPC response whose result payload nests a
"method" field is still detected as a response and skips the session lock,
removing a deadlock against the in-flight tool call awaiting it.
Drop the inverse max_output_tokens speed proxy when no model exposes
output_tokens_per_second; context-window size does not track latency, so a
neutral score avoids biasing speedPriority toward the smallest-context model.
* fix(guardrails): make ToolPermission rule reload atomic on invalid regex
_load_rules appended each rule to self.rules before compiling its regex, so an
invalid pattern raised mid-loop after the bad rule was already live but without
a _compiled_rule_targets entry. _matches_regex reads a missing compiled target
as a None pattern and returns True, turning the bad rule into a match-all that
silently applies its decision to every tool. Via update_in_memory_litellm_params
(PUT /guardrails) this corrupted the live guardrail.
Build the parsed rules and compiled maps into locals and swap them in only after
every regex compiles, and restore the previous ruleset if a live update is
rejected, so an invalid regex now fails the update without leaving the guardrail
enforcing a broken policy.
* test(mcp): cover sampling conversion, model resolution, and elicitation relay paths
The MCP sampling and elicitation handlers shipped with partial test
coverage, leaving the response-to-MCP conversion, the model resolution
fallback chain, completion-kwargs assembly, guardrail routing, and the
entire elicitation relay untested. That pulled the PR's diff (patch)
coverage below the codecov threshold even though overall project
coverage rose.
Add focused unit tests for _convert_openai_response_to_mcp_result,
_convert_mcp_tools_to_openai, _convert_mcp_tool_choice_to_openai, image
and audio content conversion, the hint-matching and fallback branches of
_resolve_model_from_preferences, _build_completion_kwargs, the router and
guardrail-rejection paths of _run_guardrails_and_call_llm, the
handle_sampling_create_message success and error-propagation flows, the
marker-hoisting fallback for tool content on unexpected roles, and the
elicitation form/url/generic relay together with its decline paths
---------
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: lengkejun <lengkejun@xd.com>
Co-authored-by: Yug <yugborana000@gmail.com>
Co-authored-by: Kent <72616338+kingdoooo@users.noreply.github.com>
Co-authored-by: tanmay958 <53569547+tanmay958@users.noreply.github.com>
Co-authored-by: DrishnaTrivedi <142084770+DrishnaTrivedi@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Navnit Shukla <Navnit.shukla25@gmail.com>
Co-authored-by: PRABHU KIRAN VANDRANKI <72809214+VANDRANKI@users.noreply.github.com>
Co-authored-by: Adrian Lopez <109683617+adriangomez24@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: JooHo Lee <96564470+BWAAEEEK@users.noreply.github.com>
Co-authored-by: Dinesh Girbide <85330597+Dinesh-Girbide@users.noreply.github.com>
Co-authored-by: cloudwiz <22098246+andrey-dubnik@users.noreply.github.com>
Co-authored-by: Ahmad Khan <ahmadkhan2508@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(proxy): match passthrough registry routes bare-to-bare with SERVER_ROOT_PATH
After #28547, get_request_route strips the deployment prefix while registry
lookup still re-inflated stored paths via SERVER_ROOT_PATH, causing 404s
under paths like /llmproxy/ml. Compare normalized bare routes in both
is_registered_pass_through_route and get_registered_pass_through_route.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(proxy): patch utils.get_server_root_path in passthrough auth tests
After removing get_server_root_path from pass_through_endpoints, route
and JWT tests must mock litellm.proxy.utils where normalization reads it.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Streaming responses from the proxy (/chat/completions, /v1/messages,
/v1/responses, assistants) all return through create_response() but never
sent the headers that tell an intermediary reverse proxy not to buffer the
SSE stream. nginx with the default proxy_buffering, k8s ingress-nginx, and
Envoy/Istio sidecars therefore hold the whole stream and release it in one
batch, which looks like a broken/buffered stream to the client even though
litellm is yielding chunks incrementally.
Add Cache-Control: no-cache and X-Accel-Buffering: no to every
StreamingResponse create_response() returns, matching what the proxy already
does for its own usage/policy SSE endpoints. Fixes#28384.
#29612 exempts UI/CLI session tokens from the key budget ceiling when they
create a team key, keyed on data.team_id. That value is read after the
default_key_generate_params loop can populate team_id, so on deployments that
set default_key_generate_params.team_id a request the caller did not scope to a
team is treated as a team key and skips the ceiling. Capture _requested_team_id
before defaults run and key the exemption off it, mirroring how
_requested_max_budget is already captured. Requests the caller did not scope to a
team keep the ceiling.
Non-admin users creating a team key through the UI were rejected with
"max_budget cannot exceed the caller's own max_budget (0.25)". The request is
authenticated by a UI/CLI session token whose max_budget is the per-session chat
spend cap (max_ui_session_budget, default $0.25), and the delegated-authority
budget ceiling (GHSA-q775-qw9r-2r4g) treated that cap as a delegation limit.
Skip the ceiling only when a session token creates a team key (data.team_id set);
that key's spend is bounded by the team budget at request time. Personal keys and
every other non-admin caller keep the ceiling, so a session token cannot mint an
arbitrary-budget personal key.
* fix duplicate cost callbacks for anthropic streaming pass-through
Two bugs caused _PROXY_track_cost_callback to see stream=True +
complete_streaming_response=None on every streaming pass-through request,
making the dedup guard in dispatch_success_handlers permanently inactive:
1. pass_through_endpoints.py created the Logging object with stream=False
for all requests. _is_assembled_stream_success short-circuits on
self.stream is not True, so has_dispatched_final_stream_success was
never set and any second dispatch went through unchecked.
Fix: set logging_obj.stream = True after stream detection.
2. _create_anthropic_response_logging_payload set complete_streaming_response
inside the try block after litellm.completion_cost(), so a pricing error
caused an early return without setting it on model_call_details.
Fix: set complete_streaming_response before the try block.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix stream
* add stream to logging obj
* test(pass_through): give mock logging object a real model_call_details dict
The anthropic passthrough logging payload now records the assembled
response on model_call_details before cost calculation, which requires
model_call_details to support item assignment. In production it is always
a dict; the existing unit test stubbed the logging object with a bare Mock
whose attribute is not subscriptable, so the new assignment raised
TypeError. Use a real dict to match the production logging object.
* test(pass_through): cover streaming logging-obj stream flag
The streaming branch of pass_through_request that marks the logging object
as streaming (logging_obj.stream and model_call_details["stream"]) had no
unit coverage, so the patch coverage gate flagged it. Add a regression test
that drives a streaming pass-through request through pass_through_request and
asserts the logging object is flagged as a stream before dispatch.
* test(pass_through): cover SSE-response stream flag fallback branch
The auto-detected streaming branch of pass_through_request (when a request
that was not flagged as streaming returns a text/event-stream response) sets
logging_obj.stream and model_call_details["stream"] but had no unit coverage,
so the codecov patch gate failed at 60%. Drive a non-streaming pass-through
request whose upstream response is SSE through pass_through_request and assert
the logging object is flagged as a stream before dispatch.
* fix(pass_through): gate complete_streaming_response on stream flag
perform_redaction only scrubs complete_streaming_response when
model_call_details["stream"] is True. Setting it unconditionally for
non-streaming Anthropic pass-through responses left the assembled
response unredacted in model_call_details, which is handed to logging
callbacks as kwargs when message logging is disabled. Only record it for
actual streaming responses so redaction always applies.
---------
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Add support for websocket via codex
* Add model alias and creds support
* fix: skip cost tracking for WS session wrapper call types
The @client decorator on _aresponses_websocket fires async_success_handler
with result=None after the session ends. This triggered cost tracking errors
because standard_logging_object is never built for None results.
Per-turn costs are correctly tracked by individual litellm.aresponses calls
inside the session. The outer session-level logging obj should not attempt
cost tracking.
Fix: skip _aresponses_websocket and _arealtime call types in deployment_callback_on_success,
RouterBudgetLimiting.async_log_success_event, and _PROXY_track_cost_callback.
* fix: address Greptile review comments
Fix JSON injection: use json.dumps instead of f-string interpolation for model name in WS body.
Add 30s timeout for first WS frame to prevent unbounded connection resource tie-up.
Restore per-event model override in streaming_iterator; fall back to connection-level model when event omits it.
Strengthen regression test: inject alias into kwargs via _update_kwargs_with_deployment mock so the test would fail on un-fixed code.
* fix: handle nested response.create format in first-frame model extraction
When ?model= is omitted, the first WS frame can carry the model in either flat
format (first_event["model"]) or nested format (first_event["response"]["model"]).
The flat-only check would silently reject clients using the nested wire format.
Mirrors the same two-format logic in _build_base_call_kwargs.
* fix: don't force connection-level custom_llm_provider on per-event model overrides
If a client sends a different model per response.create turn, litellm needs to
re-resolve the provider from that model string. Forcing the connection-level
custom_llm_provider would silently route the request to the wrong backend.
Only inject custom_llm_provider when the per-event model matches the
connection-level model.
* refactor: extract WS model extraction into testable function
Pull the flat/nested model extraction into _extract_model_from_first_ws_event
so tests import and exercise the real function rather than a copy.
* fix: compare providers not full model strings in _inject_credentials
The model == self.model guard was too strict: same-provider model variants
(e.g., vertex_ai/gemini-2.0 -> vertex_ai/gemini-1.5 on one connection) would
lose custom_llm_provider, breaking routing when a custom api_base is in use.
Compare the provider extracted by get_llm_provider instead, so same-provider
variants still inherit the connection-level provider while cross-provider
overrides let litellm re-resolve.
* style: black formatting
* refactor: extract first-frame model resolution to fix PLR0915 (too many statements)
* Fix responses WebSocket first-frame validation
* fix: classify WS first-frame read errors and clarify cost-skip log
Distinguish client disconnects from server errors when reading the
responses WebSocket first frame, make the cost-tracking skip log message
accurate for session wrappers (which do carry a model), and resolve the
connection-level provider once per session instead of on every
response.create event.
* test: cover WS first-frame read errors and same-provider credential injection
Adds regression tests for the still-uncovered responses WebSocket paths:
the timeout, invalid-JSON and missing-model branches of
_read_ws_model_from_first_frame, plus the provider comparison in
ManagedResponsesWebSocketHandler._same_provider and _inject_credentials
(same-provider model variants keep the connection provider; cross-provider
models re-resolve).
* fix(responses-ws): fall back to explicit custom_llm_provider when connection model is unresolvable
When a WebSocket session is opened with a custom deployment alias that litellm
cannot resolve to a provider, _connection_provider was None, so _same_provider
returned False for every resolvable per-event model and the connection-level
custom_llm_provider was dropped. Use the explicitly-set custom_llm_provider as
the connection provider in that case so same-provider per-event models still
inherit it while genuinely cross-provider models continue to re-resolve.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(anthropic/adapter): open thinking block for reasoning_content-only streaming chunks
The /v1/messages streaming content-block classifier (_translate_streaming_openai_chunk_to_anthropic_content_block) only recognized thinking_blocks. OpenAI-compatible reasoning backends (vLLM/SGLang reasoning parsers: DeepSeek-R1, Qwen3, gpt-oss, ...) populate reasoning_content with thinking_blocks=None, so the classifier fell through to a text block. The delta translator already emits thinking_delta for reasoning_content, so those deltas landed inside a text block and Anthropic streaming clients (Claude Code, SDK .stream()) silently dropped the chain-of-thought.
Mirror the reasoning_content fallback already present in the non-stream translator and the streaming delta translator so the classifier opens a thinking block. Adds a focused regression test.
* fix(anthropic/adapter): reach reasoning_content branch when thinking_blocks attr is absent
Delta deletes the thinking_blocks attribute when unset, so the prior nested check was unreachable for reasoning-only chunks (vLLM/SGLang). Make it a sibling elif so the content block is classified as thinking.
* test(proxy): stop component-allowlist test leaking DATABASE_URL into xdist peers
The component-allowlist test pins throwaway DATABASE_URL/LITELLM_MASTER_KEY
values at import time via os.environ so importing proxy_server doesn't need a
live database. Those values persisted for the whole pytest-xdist worker, so a
sibling test sharing the worker (test_key_rotation_e2e's DB-backed E2E case)
saw the leaked sqlite DATABASE_URL, treated it as an available database instead
of skipping, and the Prisma engine rejected the non-postgres URL (P1012 ->
httpx.ConnectError). Restore the prior environment after the import so the
throwaway values never escape the module.
---------
Co-authored-by: Tai An <antai12232931@outlook.com>
* Fix error code and context id injection bug
* Add support for all A2A methods
* Add logging
* address greptile review: relay upstream JSON-RPC errors, move _PASCAL_TO_WIRE to module level, add error path tests
* fix(a2a): run pre_call_hook for tasks/resubscribe SSE path to enforce guardrails
tasks/resubscribe was returning the raw SSE stream without calling proxy_logging_obj.pre_call_hook, silently bypassing any guardrails configured on the agent. This patch calls pre_call_hook before streaming begins and wires post_call_failure_hook into the SSE generator so errors are logged. Adds a regression test verifying the hook is called.
* fix(a2a): use get_async_httpx_client instead of creating httpx clients per request
Creating httpx.AsyncClient instances per-request adds ~500ms latency. Switch _forward_jsonrpc and _forward_jsonrpc_sse to use the shared client from get_async_httpx_client(httpxSpecialProvider.A2A).
* fix(a2a): forward caller identity headers on task ops; validate push notification URL
Two security fixes for task management methods:
1. All task operations (tasks/get, tasks/list, tasks/cancel, tasks/resubscribe, push notification config methods) now forward X-LiteLLM-User-Id and X-LiteLLM-Team-Id headers to the upstream agent, so the agent can scope task access to the authenticated caller.
2. tasks/pushNotificationConfig/set validates the callback URL before forwarding: requires HTTPS and rejects private/loopback/reserved IP ranges and localhost hostnames to prevent SSRF.
* Fix A2A task hook and push URL handling
* fix(a2a): fix mypy type errors for request_id and header_name dict key types
* Fix A2A request id and params forwarding
* Forward trace IDs for A2A task calls
* fix(a2a): strip client-forwarded X-LiteLLM-* headers before applying authenticated identity
A client could send x-a2a-<agent>-x-litellm-user-id in their request and have it forwarded to the upstream agent as an authenticated identity header. Fix: sanitize any X-LiteLLM-* headers from agent_extra_headers before merging, then apply the authenticated identity headers last so they always override client-supplied values.
* Fix A2A SSE fallback JSON-RPC error code
* Fix A2A SSE error id backfill
* fix(a2a): validate both push notification url fields to close SSRF bypass
* fix(a2a): widen request_id annotation to match JSON-RPC id call sites
* fix(a2a): run post-call streaming hook for tasks/resubscribe so agent guardrails apply
tasks/resubscribe returned the raw upstream SSE stream without routing events
through the post-call streaming hook, so output guardrails configured on the
agent were silently skipped for streaming task subscriptions while every other
task method and message/stream applied them. Parse upstream JSON-RPC SSE events
and feed them through async_streaming_data_generator, matching message/stream,
so guardrails inspect the streamed task content. Adds a regression test that
fails when the streamed events bypass the guardrail hook.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* Fix incorrect agent API request example payload structure (#29556)
* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427)
* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs
On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span
is stored in litellm_params['litellm_metadata'] instead of
litellm_params['metadata']. When the request body contains a native
'metadata' field (e.g. Anthropic's {"user_id": "..."}),
litellm_params['metadata'] gets overwritten and the parent span is lost,
producing orphan root spans with a different trace_id.
Add fallback checks to litellm_metadata in:
- _get_span_context(): so child spans find the correct parent
- _end_proxy_span_from_kwargs(): so the proxy span gets closed
Fixes: https://github.com/BerriAI/litellm/issues/27934
* test(otel): tighten assertions per Greptile review
- test_span_context_metadata_takes_priority: assert litellm_metadata
span is never accessed, proving metadata takes priority
- test_span_context_no_parent_when_neither_has_span: assert both ctx
and detected_span are None
---------
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix: remove premature end-user budget check from get_end_user_object (#29420)
* fix(proxy): remove premature end-user budget check from get_end_user_object
Problem:
- `_check_end_user_budget()` was called inside `get_end_user_object()`
- This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated
- Zero-cost models (e.g., local vLLM) were incorrectly blocked when
end-users exceeded their budget, even though they should bypass budget checks
Solution:
- Remove `_check_end_user_budget()` calls from `get_end_user_object()`
- Budget enforcement now happens exclusively in `common_checks()` where
`skip_budget_checks` context is available
- `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation.
* refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object
- test_get_end_user_object() verifies data fetching
- test_check_end_user_budget() verifies enforcement
- test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget()
- test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object()
* Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534)
* Fix Gemini image config mapping
* Address Gemini image config review
* Format Gemini image generation transform
* Fix Gemini image token usage logging
* Share Gemini image request helpers
* Fix Gemini Imagen model routing
* Fixes as per self code review
* Fixes per internal code review
* Stop gating Imagen imageSize forwarding
* Document Gemini image size mapping source
* chore: retrigger lint
* Clarify Gemini candidate count precedence
* Add Inception provider (#29522)
* add inception as provider (chat, fim)
* linting
* seperate test suite for chat and fim
* fix test coverage
* fix: model hub custom pricing model info (#29293)
* Opik user auth key metadata extractors (#28397)
* fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic
* test: add unit tests for OPik metadata extraction logic
* fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy
* fix(ci): clarified comments and edited unit tests
* test: add unit tests for OPik metadata extraction with auth and requester overrides
* fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532)
Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
* fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561)
`_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls`
so a following tool result can be matched back to its tool call. The assignment
was inside a branch guarded by
`assistant_msg.get("tool_calls", []) is not None`, which is also True for a
text-only assistant message (an empty list is not None). As a result, an
assistant message with no tool calls that appears between a tool call and its
tool result overwrote the reference, and conversion failed with:
Exception: Missing corresponding tool call for tool response message.
This shape is common: a model emits a short narration/assistant message after a
tool call before the tool result is appended.
Only update `last_message_with_tool_calls` when the assistant message actually
carries tool_calls (or a function_call). Adds a regression test.
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572)
* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)
Squash-merged by litellm-agent from Terrajlz's PR.
* feat(helm): support tpl rendering in podAnnotations (#28609)
Squash-merged by litellm-agent from devauxbr's PR.
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505) (#28575)
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505)
When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.
For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.
Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.
New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.
* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg
Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.
Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.
Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).
* chore: trigger shin-agent re-eval on retargeted staging base
* chore: trigger shin-agent re-eval against updated Greptile state
* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models
The 1-hour prompt-cache write tier
(`cache_creation_input_token_cost_above_1hr`) was added to the
us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but
the eu./au./jp. cross-region inference profiles were left without it.
AWS Bedrock pricing applies the same +10% regional premium across all
geo profiles, so eu./au./jp. should carry the same 1-hour rates as
us. (1.6x the 5-minute regional rate).
Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL
prompt caching falls back to the 5-minute write rate and undercounts
spend by ~60% for European, Australian, and Japanese tenants.
Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where
AWS publishes one) to 14 regional Bedrock entries in both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:
- eu./au. Opus 4.6 ($11.00 / MTok)
- eu./au. Opus 4.7 ($11.00 / MTok)
- eu./au./jp. Sonnet 4.6 ($6.60 / MTok)
- eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC)
- eu./au./jp. Haiku 4.5 ($2.20 / MTok)
Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py`
with a `REGIONAL_EXPECTED` parametrized block covering all 13 new
entries plus the existing 1.6x ratio invariant.
Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the
wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06),
which would break the 1.6x ratio check. It is intentionally left out
of this PR so the scope stays "1-hour cache tier addition" — a
separate follow-up should correct the EU 5m rates for Opus 4.5.
---------
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569)
* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)
Squash-merged by litellm-agent from Terrajlz's PR.
* feat(helm): support tpl rendering in podAnnotations (#28609)
Squash-merged by litellm-agent from devauxbr's PR.
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505) (#28575)
* Forward custom_llm_provider through the Responses API bridge (Fixes#28505)
When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.
For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.
Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.
New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.
* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg
Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.
Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.
Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).
* chore: trigger shin-agent re-eval on retargeted staging base
* chore: trigger shin-agent re-eval against updated Greptile state
* Add 1-hour cache write pricing tier for Vertex AI Anthropic models
GCP Vertex AI publishes a separate 1-hour cache write column for the
Claude family (1.6x the 5-minute write rate, matching the documented
Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the
5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}`
on Vertex AI Claude is undercounted in cost tracking by ~60%.
The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig`
extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and
`_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`.
Only the price registry was missing data.
Adds the field to 19 vertex_ai/claude-* entries across both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:
- Haiku 4.5 ($1.25 -> $2.00 / MTok)
- Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok)
- Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok)
- Opus 4 / 4.1 ($18.75 -> $30.00 / MTok)
Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py`
mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model
and asserts the 1.6x ratio across the family.
Fixes#27781.
---------
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* Fix Gemini multimodal function responses (#29325)
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* address greptile review: add _transform_image_usage method and model-map supports_image_size flag
- Add _transform_image_usage instance method to GoogleImageGenConfig that
delegates to transform_gemini_image_usage, fixing the regression test
- Replace hardcoded "2.5-flash" string check in supports_gemini_image_size
with a get_model_info lookup on supports_image_size (default true)
- Add supports_image_size: false to all gemini-2.5-flash model entries in
model_prices_and_context_window.json so capability is controlled via the
model map rather than embedded in code
* fix test failures: schema validation, mypy type, model info plumbing, pricing test
- Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it
- Pass supports_image_size through _get_model_info_helper constructor call
- Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True)
- Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid
- Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values
* Add Azure AI Kimi K2.6 metadata (#27052)
* Add Azure AI Kimi K2.6 metadata
* Scope Kimi metadata test cost map setup
* fall back to substring check for models not in model_prices_and_context_window.json
Models like gemini-2.5-flash-image-preview are not in the pricing JSON,
so get_model_info raises. Fall back to "2.5-flash" not in model when the
JSON has no explicit supports_image_size entry for the model.
* fix(inception): don't forward global litellm.api_key to Inception FIM
Match the Inception chat config: resolve only an Inception-specific key
(param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion
FIM path. The global litellm.api_key (often an OpenAI key) was both leaking
to api.inceptionlabs.ai and taking precedence over the configured Inception
key when set.
* fix(auth): enforce end-user budget on custom-auth path that skips common_checks
get_end_user_object() no longer raises BudgetExceededError, so custom-auth
deployments with custom_auth_run_common_checks unset (which skip the
centralized common_checks gate) stopped enforcing the end-user budget,
letting an over-budget end user keep making requests. Re-enforce the
budget in _run_post_custom_auth_checks on that path.
---------
Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com>
Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com>
Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com>
Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk>
Co-authored-by: Lovro Seder <vrovro@gmail.com>
Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com>
Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(key_generate): allow team members to create keys on org-scoped teams
When a virtual key is created for a team, enterprise logic inherits the
team's organization_id onto the key (add_team_organization_id). Since the
VERIA-55 org-IDOR fix, /key/generate then required the caller to be an
explicit LiteLLM_OrganizationMembership member of that org, returning
403 "Caller is not a member of organization_id=<uuid>". Admins normally
only add users to teams (not orgs), so self-serve key creation regressed
for any user on an org-scoped team (regression since v1.84.0-rc.1).
Skip the org-membership check when organization_id was inherited from the
key's team (organization_id == team_table.organization_id). Team-level
authorization already gates this path, so team membership is sufficient.
The membership check still runs when a caller assigns an organization_id
that did not come from the key's team, preserving the IDOR protection.
Adds regression tests covering both the team-inherited (allowed) and
foreign-org (still blocked) cases.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(key_generate): cover mismatched team org IDOR path on generate
Add test_generate_key_foreign_org_with_mismatched_team_still_enforces_membership
for the case where a team is present but request organization_id differs from
team_table.organization_id. Enterprise inheritance is no-op'd in the test so
the guard is exercised directly; membership validation must still run.
Addresses Greptile review on #29310.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(auth): let internal users view search tools
Internal users could not see search tools in the UI even when an admin
created them, while vector stores were visible. The Search Tools page
rendered but its list calls 403'd because the read routes were not in
internal_user_routes. Grant internal users read-only access to the
listing and provider routes; create/update/delete stay admin-only.
Resolves LIT-3150
* fix(auth): scope /search_tools/list to caller-allowed tools
Adding the read routes to internal_user_routes let any internal user
call /search_tools/list, which returned every configured tool's id,
provider, api_base, and metadata regardless of the caller's
object_permission.search_tools allowlist. The api_key was masked, so
this was metadata disclosure rather than a credential leak, but it
ignored the key/team scoping that /search already enforces.
Filter the listing through the same can_key_call_search_tool /
can_team_call_search_tool checks (exposed as a boolean
can_user_view_search_tool), so non-admin callers only see tools they
may invoke; admins still see all. Mirrors how /vector_store/list
scopes results.
* fix(types): resolve mypy errors in list_search_tools
The config and DB build loops reused one loop variable, so mypy pinned
it to the config element type (SearchToolTypedDict); its .get() calls
returned object and the DB element (SearchTool) failed the reuse
assignment. Give each loop its own variable so each gets its real type,
and coerce the config tool's SearchToolInfoTypedDict to a plain dict to
match SearchToolInfoResponse.search_tool_info.
* test(proxy/utils): pin ProxyLogging behavior
Add behavior-pinning tests for the ProxyLogging cluster in
litellm/proxy/utils.py under tests/test_litellm/proxy/utils/proxy_logging/.
Covers InternalUsageCache, _CallbackCapabilities, top-of-file helpers
(print_verbose, _get_email_logger_class, _accepts_litellm_call_info,
_enrich_http_exception_with_guardrail_context), the full ProxyLogging
class (lifecycle, MCP-LLM bridging, capability probes, guardrail
pipeline, pre/during/post/streaming hooks, alerting), plus the
bottom-of-region helpers (on_backoff, jsonify_object, _lookup_deprecated_key).
Each pinned symbol has happy-path and error-path coverage; happy paths
use direct dict-equality with three or more keys (or HiddenParams /
Pydantic model_validate where the surface is a Pydantic shape). The
subdirectory carries a local _pin_check.py and _coverage_check.py that
enforce the gate without surfacing numeric thresholds in CI logs.
Wires tests/test_litellm/proxy/utils into the existing test-path block
in .github/workflows/test-unit-proxy-endpoints.yml.
* test(proxy/utils): drop unused mock_httpx_client fixture
Declared in conftest.py but never referenced by any test. Removing
the dead fixture per Greptile P2 feedback.
* test(proxy/utils): drop local-only gate scripts from PR
_pin_check.py and _coverage_check.py are local stopping signals (not
wired into CI, consume a gitignored .pin_list.txt). They served their
purpose telling the engineer when to stop writing tests; the pytest
suite is the artifact that belongs in the repo.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* test(proxy/utils): pin PrismaClient and spend-update behavior
PR2 of the litellm/proxy/utils.py behavior-pinning plan
(https://www.notion.so/37343b8acdab81f68f39f66915f62bcf). Adds
tests/test_litellm/proxy/utils/prisma_and_spend/, with happy + error
pins for every symbol in the PR2 list: the config-param cache,
PrismaClient lifecycle/data ops/engine watcher/reconnect/health
clusters, the user-row cache and SMTP helper, password/token helpers,
ProxyUpdateSpend, and the module-level spend functions.
Tests run against fully-mocked Prisma stacks (patched ``Prisma`` /
``PrismaWrapper`` at fixture setup), with a fake SMTP transport and a
clock-driven asyncio.sleep for the monitor loop, so unit runs need no
DB or network. ``_pin_check.py`` enforces happy + error coverage for
every symbol; ``_coverage_check.py`` filters branch + line coverage to
the PR2 source range (lines 2,668-5,541) and prints PASS / FAIL with
no numbers. Workflow shard ``tests/test_litellm/proxy/utils`` is added
to the existing proxy-endpoints job.
* test(proxy/utils): commit pin list and drop dead exclusion line
Addresses Greptile review feedback on PR #29488:
- Check in ``.pin_list.txt`` (force-added, overriding the repo-wide
``.gitignore`` rule) so reviewers can reproduce the ``_pin_check.py``
PASS shown in the PR description without first regenerating the
file from Notion.
- Remove the unreachable ``_harness_smoke_test.py`` continue in
``_pin_check.py``: the surrounding ``test_*.py`` glob already
excludes underscore-prefixed files; rephrase the docstring instead.
* test(proxy/utils): shift PR2 coverage line range by +1 after merge
``litellm_internal_staging`` added one line in ``ProxyLogging`` at
``utils.py:645`` (PR1 territory, before the PR2 region). Bump the
``_PR2_LINE_START`` / ``_PR2_LINE_END`` constants accordingly so the
coverage gate keeps scoring the same source region after the merge.
* test(proxy/utils): drop committed pin-list and gate scripts
``_pin_check.py``, ``_coverage_check.py``, and ``.pin_list.txt`` are
local-only stopping signals: no workflow or pytest collection invokes
them, so committing them adds rot risk (line-range drift in the
coverage check, pin-list staleness) without any enforcement upside.
The pin-list contract lives in the Notion plan; the tests themselves
are the durable artifact.
---------
Co-authored-by: Claude <noreply@anthropic.com>
* test(proxy/utils): pin bottom-of-file helper behavior
Pin current behavior of the bottom-of-file pure-function helpers in
litellm/proxy/utils.py (projection, team config, time helpers,
guardrail merge, error helpers, URL/path helpers, premium gate, model
access, and misc DB/API-key helpers).
Adds tests/test_litellm/proxy/utils/helpers/ with one happy + one error
test per pinned symbol; folds the prior single-test
tests/test_litellm/proxy/test_utils.py into test_url_helpers.py and
deletes the old file. _pin_check.py and _coverage_check.py serve as
local stopping gates.
Adds tests/test_litellm/proxy/utils to the existing test-path block in
.github/workflows/test-unit-proxy-endpoints.yml.
Plan: https://www.notion.so/37343b8acdab81f68f39f66915f62bcf
Pin list: https://www.notion.so/37343b8acdab8150acdbf40e5756869f
* test(proxy/utils): apply greptile fixes to behavior-pinning gates
Address findings from the sibling PR1/PR2 greptile reviews that also
apply to this PR:
- Commit pin_list.txt alongside the gate script (was previously a
gitignored .pin_list.txt fetched from Notion). The gate is now
reproducible without out-of-band setup.
- Resolve the coverage region by locating the first pinned symbol's
def line in litellm/proxy/utils.py at runtime, instead of hardcoded
line numbers that drift when lines above shift.
- Word-boundary the pin reference check so pins like update_spend do
not falsely match update_spend_logs_job.
- Drop the dead _harness_smoke_test.py exclusion; the test_*.py glob
already filters underscore-prefixed files.
* test(proxy/utils): drop local-only stopping-signal scripts
Remove _pin_check.py, _coverage_check.py, and pin_list.txt. These were
dev-time tooling for knowing when test authoring was done; they are
not wired into CI and the test files themselves are the merge artifact.
---------
Co-authored-by: Claude <noreply@anthropic.com>
Auth failures on management endpoints such as team/list and
organization/list (invalid or expired keys) were raised as
ProxyException, whose __str__ returned an empty string, so the OTEL
SERVER span recorded an error with no message. ProxyException now
stringifies to its message, get_error_information prefers the explicit
.message attribute, and the proxy exception handlers stamp a consistent
error.type, error.code and error.message on the span
Resolves LIT-3515
* fix(auth): preserve 401 status for expired JWTs in OTel traces
Expired JWT access tokens raised a generic Exception with no status
code attached. Because the codeless exception was logged to OTel via
post_call_failure_hook before auth_exception_handler re-wrapped it as
ProxyException(401), the OTel span never set http.response.status_code
and trace viewers displayed it as a generic 500. Clients still got a
401 back, so traces and actual responses diverged.
Raise ProxyException(code=401, type=expired_key) directly at the source
in both JWT decode paths so the 401 is consistent across the client
response and the OTel http.response.status_code attribute, matching how
virtual-key expirations are handled.
* fix(auth): preserve 401 for expired JWTs on issuer-scoped path
The issuer-scoped JWT path (_auth_jwt_with_issuer) still raised a generic
Exception on expiry, surfacing as a 500 in client responses and OTel traces.
Raise ProxyException with expired_key/401 there too, matching auth_jwt, and
add a regression test exercising the issuer path end-to-end
