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39797 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
ryan-crabbe-berri
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bbc62c34f6 |
fix(proxy): allow clearing empty-collection user fields via /user/update
_update_internal_user_params dumps the request with exclude_unset and then
discards any value equal to [] or {}, so an explicitly sent empty collection
never reached the database. Because exclude_unset already drops fields the
caller never sent, that filter only ever fired on values set on purpose,
leaving a user's old models, allowed_cache_controls and model_max_budget in
place after a clear attempt. Route those real user columns through the same
fields_set check max_budget already uses so an explicit empty value is written.
config, permissions and aliases are not user-table columns, so they keep going
through the empty-collection filter and never reach Prisma.
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ryan-crabbe-berri
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3fc73c1aff
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test(ui): scrub stale return-url cookie from e2e storageState (#30317)
The login flow stores a post-login return URL in the litellm_return_url cookie (5 minute TTL). globalSetup snapshots cookies into the per-role storageState that every spec reuses, so when the snapshot races ahead of the app consuming that cookie, each test inheriting it gets redirected to the stale URL (/ui/?login=success) mid-assertion the first time it mounts a page. That one rogue navigation is behind the recurring e2e failures whose call logs all show "navigated to /ui/?login=success" while waiting for an element; which specs die varies run to run with snapshot timing. Clear the cookie right before saving the snapshot so no test starts with a pending redirect. |
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ryan-crabbe-berri
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ee5b2a367d
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fix(ui): label request logs column "Key Alias" to match filter (#31037)
The request logs table column displayed "Key Name" while its accessor (metadata.user_api_key_alias) and the corresponding filter both use the "Key Alias" label; this aligns the column header with that naming. |
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ryan-crabbe-berri
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3615049071
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feat(proxy): allow llm_api_routes virtual keys to list MCP tools via /v1/mcp/tools (#31031)
* feat(proxy): allow llm_api_routes virtual keys to list MCP tools via /v1/mcp/tools GET /v1/mcp/tools returns the MCP tools available to the calling key, the same data already exposed through /mcp/tools/list and /mcp-rest/tools/list, both of which are in llm_api_routes. The /v1/mcp/tools path was in no route group, so virtual keys created from the UI (which default to allowed_routes=["llm_api_routes"]) got a 403 listing tools one way but not the other. Add it to mcp_inference_routes. Unlike /v1/mcp/server, this path has no management write counterpart, so it does not need the method-aware carve-out used for server discovery. * test(proxy): parametrize MCP inference route check over the full endpoint set |
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ryan-crabbe-berri
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26da56fbb6
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feat(ui): add Amazon Bedrock Mantle to the Add Model provider dropdown (#31034)
The Add Model provider dropdown is driven by provider_create_fields.json (served at /public/providers/fields), and Bedrock Mantle had no entry, so it could not be selected even though the backend provider, its models, and the UI enum/logo mappings already existed. Add a bedrock_mantle entry exposing the credential fields the provider actually honors: an optional bearer api_key for BYOK, the AWS SigV4 chain, a region, and an api_base override. Selecting it now populates the bedrock_mantle models from the cost map via the existing getProviderModels filter. Also resolve the provider logo when getProviderLogoAndName is given the enum key (e.g. BedrockMantle) rather than the slug, which the dropdown passes; previously only slugs that lowercase-matched their key (like bedrock) resolved a logo. |
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ryan-crabbe-berri
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19a29e0579
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feat(mcp): scope a key to zero MCP servers with no-mcp-servers sentinel (#31029)
* feat(mcp): scope a key to zero MCP servers with no-mcp-servers sentinel
A key under a team that has MCP servers had no way to opt out of them;
an empty list has always meant "inherit the team". This adds a
no-mcp-servers sentinel (mirroring no-default-models for models) so a key
can declare an explicit zero that overrides team inheritance, additive
grants, and allow_all_keys servers, surfaced as an exclusive "No MCP
Servers" option in the key create/edit UI.
* refactor(ui): centralize no-mcp-servers sentinel in a shared constant
The sentinel string was defined under two different local names and
inlined in two more files; a single exported constant removes the drift
risk flagged in review.
* fix(mcp): enforce no-mcp-servers sentinel on toolset-scoped routes
Toolset scoping replaced a key's mcp_servers with the toolset's servers,
dropping the no-mcp-servers sentinel, so a key opted out of all MCP could
still execute a granted toolset's tools via /toolset/{name}/mcp. Deny
toolset access when the key carries the sentinel, checked before the admin
branch to match get_allowed_mcp_servers.
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yucheng-berri
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4ef7d0815b
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fix(bedrock): only expand config-sourced AWS credential references (#30867)
AWS auth parameters in the Bedrock and SageMaker path could be expanded against the process environment when credentials were built. Config-sourced references are already expanded at load time, so restrict expansion to that path: a reference still present at request time is treated as caller-supplied input and is left as-is, and the web-identity helper rejects environment-variable references before resolving the token. Also rework the ambient AWS_* fallback as a single pass that pairs each value with its own env-var name, fixing a latent index misalignment that left AWS_EXTERNAL_ID unresolved. Adds regression tests covering the resolution behavior. |
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ryan-crabbe-berri
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ce4111b800
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fix(proxy): scope team BYOK models by key team_id in /model/info (#31009)
GET /model/info returned an empty list for a team key whose team only has team-scoped BYOK deployments, even though /v1/models and a master key both returned them. _get_caller_byok_team_scope resolved the caller's allowed teams only from user_api_key_dict.user_id and the bound user's team memberships. A team or service key has user_id=None, so the helper returned an empty set and _byok_row_outside_caller_teams then dropped every team BYOK row This includes the key's own team_id in the allowed-team set across every non-admin branch, since a team key is authoritatively scoped to its team regardless of whether a bound user is resolvable or a formal member of that team Completes the work in #30025, which aligned /v1/model/info with router deployments but missed the team-key case in the scope helper it introduced |
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Mateo Wang
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8bc18388e3
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fix: prevent key-level metadata.tags from leaking into Bedrock passthrough body (#30985)
* fix: prevent key-level metadata.tags from leaking into Bedrock passthrough body * test: cover bedrock key-tag litellm_metadata pre-seed in common_checks Add a regression test asserting key-level tags on a bedrock passthrough request land in litellm_metadata and never leak into the provider-facing metadata field, which closes the codecov/patch gap on the auth_checks pre-seed line. Also drop the now-stale comment that hardcoded metadata["headers"]; the headers are written under whichever metadata field _get_metadata_variable_name selects. * refactor(auth): pre-seed litellm_metadata from LITELLM_METADATA_ROUTES The auth-time pre-seed in common_checks hardcoded "bedrock", so any other route later added to LITELLM_METADATA_ROUTES would reintroduce GH#30629 (key tags leaking into the provider-facing metadata field) without a matching update here. Key off the shared constant instead, and extend the regression test to cover a non-bedrock metadata route so the route-agnostic behavior is locked in. * fix(auth): pre-seed litellm_metadata before header-tag merge apply_client_tag_policy_pre_auth runs in user_api_key_auth.py before common_checks, so it resolved get_metadata_variable_name_from_kwargs to 'metadata' (litellm_metadata was not yet present). common_checks then pre-seeded litellm_metadata on LITELLM_METADATA_ROUTES, after which apply_key_tags_pre_auth and _tag_max_budget_check both targeted litellm_metadata, leaving header tags stranded in metadata and invisible to per-tag budget enforcement on Bedrock and other matching routes. Extract the pre-seed into LiteLLMProxyRequestSetup.pre_seed_litellm_metadata_for_route and invoke it before apply_client_tag_policy_pre_auth so all tag merges and the budget-check read agree on the same metadata key. * test(auth): guard early litellm_metadata pre-seed wiring for header tags Bugbot's autofix added a pre-seed of litellm_metadata in _run_centralized_common_checks before apply_client_tag_policy_pre_auth, so x-litellm-tags header tags land in litellm_metadata and stay visible to _tag_max_budget_check on LITELLM_METADATA_ROUTES. Its test replayed that call order in the test body, so removing the production call site still passed. Add a wiring-level regression that drives the real _run_centralized_common_checks and asserts header tags land in litellm_metadata (not metadata) for bedrock and /v1/messages. Dropping the pre-seed call site now fails the test. --------- Co-authored-by: Zang Peiyu <166481866+factnn@users.noreply.github.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Yassin Kortam
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e70f7e2d7a
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fix(ui): resolve user_id to email in Spend Per User usage chart (#30992)
The Usage dashboard "Spend Per User" chart rendered raw UUIDs (and default_user_id) instead of emails. /user/daily/activity passed entity_metadata_field=None, so every user entity in the breakdown carried empty metadata; the chart could only fall back to the user_id. The frontend resolved labels from a separately paginated user list, so any spender not on a loaded page showed as a UUID. Resolve the email/alias for the user_ids actually on the page (mirroring how api key metadata is already resolved) and attach it to the entity metadata, so the chart labels each spender with their email and falls back to the UUID only when no email is on file. get_daily_activity gains an optional resolve_entity_metadata hook so the user endpoint can do this page-scoped lookup without loading the whole user table. Resolves LIT-3889 |
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Yassin Kortam
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fd377eece8
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feat(scim): ingest enterprise extension attributes into user metadata (#30893)
Map the SCIM enterprise extension block (urn:ietf:params:scim:schemas:extension:enterprise:2.0:User) onto SCIMUser so create and PUT persist employeeNumber, costCenter, organization, division, department, and manager into LiteLLM_UserTable.metadata under scim_enterprise, and round-trip them back out on read. This lets financial reporting group spend by fields like cost center and department. The enterprise block holds directory-only HR attributes, so it is kept out of the generic user management responses (/user/info, /v2/user/info, and /user/list), which non-proxy-admin callers such as team and org admins can use to read other users. The data still lands in metadata for reporting and still round-trips through the SCIM read endpoints, which build their response from the user row directly. Resolves LIT-3617 |
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Yassin Kortam
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2be0183cc7
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feat(scim): drive global proxy role from a SCIM admin group (#30895)
Adds an optional litellm_settings.scim_admin_group. When configured, the global proxy role is recomputed from a user's resulting groups on every SCIM write that can change membership: user create/PUT/PATCH and group create/PUT/PATCH/DELETE, plus the existing-email upsert path. Membership in the admin group grants PROXY_ADMIN and its absence demotes to the non-admin default, enabling just-in-time elevation and automatic demotion without a re-login. When the setting is unset the role is never touched, so current behavior is preserved and a misconfigured IdP can never unexpectedly grant admin. |
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yucheng-berri
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1667b8f740
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fix: reject model_list in proxy body and gate advisor client credentials (#30585)
* fix: validate proxy request body and nested fields
Ensure caller-supplied request fields cannot override server-side deployment
configuration, and apply request-body validation consistently to nested
structures. Adjusts router kwarg handling and client-side credential handling
for base-url overrides
* test: cover router strip ordering and advisor clientside credential gate
* fix: clear deployment credentials on client base-url override
When a request overrides api_base/base_url, recompute the deployment's
litellm_params (clearing the deployment's own api_key) and drop the cached
client built for the original endpoint, so the deployment credential is not
reused for the client-supplied endpoint. Adds regression tests that assert the
credentials actually forwarded to litellm.completion/acompletion.
* fix(proxy): require api_key alongside api_base override
A request that overrides api_base/base_url but supplies no api_key still
left the proxy carrying a server credential: once the override clears the
banned-param opt-in, the provider re-resolves a key from the environment
(api_key or get_secret("OPENAI_API_KEY") and ~30 sibling chains in
main.py) and forwards it to the caller-controlled URL. Popping the
deployment api_key only changed which server key leaked.
Gate is_request_body_safe so a permitted api_base/base_url override must
also carry a non-empty caller api_key; reject otherwise. The env
resolution in main.py is left as the provider boundary.
* fix(proxy): extend request-body banlist with five additional credential and session targeting fields
Yuneng's review found five deployment-owned request-body params still missing
from the denylist and the router strip set. Each lets a caller reach the
operator's provider credentials or retarget the outbound request:
aws_profile_name selects a local AWS profile, oci_compartment_id and oci_region
retarget the OCI request, litellm_credential_name selects any server-loaded
credential by name with no ownership check, and runtimeSessionId resumes a
Bedrock AgentCore runtime session (AWS does not enforce session-to-user
mapping, so this is a cross-tenant session-resume vector).
Add all five to _BANNED_REQUEST_BODY_PARAMS in auth_utils.py and to
_DEPLOYMENT_OWNED_CREDENTIAL_KWARGS in router.py. Deployment litellm_params and
SDK direct calls are unaffected: the banlist gates the request body only, and
the router strip drops caller kwargs, never deployment["litellm_params"].
* test: rename arbitrary canary values in security tests to neutral placeholders
* fix(proxy): apply api_key co-presence to nested base override and warn on Router credential strip
P1-A: is_request_body_safe descended into _NESTED_CONFIG_KEYS
(litellm_embedding_config, extra_body) for the banned-param check but not for
the api_key co-presence check, so a base override smuggled into one of those
nested dicts cleared the client-side-credentials opt-in without a paired
api_key and let the provider re-resolve a server credential from the
environment. Run _check_base_override_has_api_key on each nested config dict
too, so the requirement applies wherever a base override is permitted.
P1-B: the deployment-owned credential strip in the Router runs unconditionally
on every _completion/_acompletion, which is security-correct but silently
drops per-call api_version/vertex_project/etc. for SDK Router callers. Emit a
single warning (key names only, never values) when the strip removes a
non-empty value, so the backwards-incompatible behavior is visible without
gating the strip on a context flag that does not exist.
* fix(proxy): apply api_key co-presence to tool-entry base override
is_request_body_safe scans three surfaces (root, _NESTED_CONFIG_KEYS, and
tools[]); the previous commit extended the api_key co-presence rule to root
and nested config dicts but not to tool entries. With
allow_client_side_credentials enabled, a tool entry carrying api_base/base_url
and no paired api_key cleared the gate, letting a provider interceptor fall
back to a server-side credential for a caller-controlled URL. Add the same
_check_base_override_has_api_key call to each tool dict and its nested function
dict, mirroring the symmetry already applied to the nested config keys. The
rule is unchanged: api_key must live in the same dict as the base override it
accompanies.
* test(proxy/auth): require paired api_key under extra_body opt-in
* fix(router): gate deployment-owned kwarg strip on litellm.proxy_is_running
* fix(advisor): narrow proxy-import guard to ImportError-family
* fix(router): gate api_key clear on base override behind litellm.proxy_is_running
* test(proxy/auth): scope proxy_is_running flag to dynamic-params class with autouse fixture
* style: use built-in generics in PR-added type annotations
* revert: drop proxy_is_running flag and router-level credential strip; rely on proxy gate
* revert: scope PR to LIT-3828 + LIT-3834 only; drop LIT-3830/LIT-3833 changes
* style: black-format advisor orchestration test
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Yassin Kortam
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bc5638b38e
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fix(ui): stop per-model usage export from duplicating user spend across models (#30980)
Some checks are pending
GitHub Actions Security Analysis / zizmor (push) Waiting to run
The "day-by-day by user and model" usage export overcounted spend by repeating each user-day total once per model. generateDailyWithModelsData iterated day.breakdown.models crossed with the entity's own api_key_breakdown and added the api key's full daily spend to every model, leaving the per-model object (modelData) unused and never matching the api key to the model. A user who called N models got N identical rows, so the column summed to N times the real spend; one reported export totaled $167,953 against a true $21,353 (7.87x). Attribute each model only the spend of the keys that actually used it by intersecting the entity's api keys with modelData.api_key_breakdown. This mirrors the already-correct pattern in activity_metrics.tsx, so per-model rows now sum back to the user-day total and models a user never called no longer appear. The existing tests passed only because their mocks omitted models[*].api_key_breakdown and never asserted numeric values. The mocks now match the real /user/daily/activity payload, and new tests assert that per-model spend sums to the user-day total and that uncalled models are omitted; both fail on the old code. Resolves LIT-3866 |
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Yassin Kortam
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1322ad7224
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perf(otel): resolve LITELLM_OTEL_V2 flag once instead of rebuilding settings per call (#30989)
is_otel_v2_enabled() constructed a pydantic-settings model (_OTelV2Flag) on every call, which re-scans the process environment and costs ~28us. The flag is read multiple times along the proxy request hot path (auth, logging-callback setup, proxy_server), so the cost compounded into a measurable per-request CPU overhead and a throughput regression visible from v1.87.3 onward. The flag is a process-level setting that is fixed at startup, so resolve it once with lru_cache. Caching it alone restores throughput to the pre-regression baseline in load tests. Tests that toggle the env now call cache_clear(). |
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tin-berri
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963816c00e
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fix(mcp): stop exposing MCP server URLs on the AI Hub and public hub API (#30902)
The AI Hub MCP Hub listed each MCP server's upstream URL in a table column and in the server Details modal, on both the authenticated dashboard and the public hub. The unauthenticated GET /public/mcp_hub endpoint also returned the url field via MCPPublicServer, so the upstream address was readable by any client even with the column gone. These surfaces are for end users discovering available servers, so the gateway-internal endpoint should not be exposed there. Drop the URL column from both MCP Hub tables and the URL field from both detail modals, and remove url from MCPPublicServer so /public/mcp_hub no longer serialises it; schema.d.ts is updated to match. The public hub MCPServerData interface no longer declares url since the response omits it. Admin surfaces that configure the endpoint (the MCP server management page, the submissions review tab, the make-public form) and the authenticated /v1/mcp/server endpoint are untouched. publicMCPHubColumns is lifted to a module-level export so both hub column sets get a mutation-killing regression test, public_model_hub.test.tsx covers the details modal hiding the url, and test_public_endpoints.py asserts /public/mcp_hub never returns url even when the server has one. |
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tin-berri
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f2b823ce1a
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test(interactions): drop role from Interaction output fields to match Google spec (#30986)
Google moved the role field off the top-level Interaction response schema onto the per-turn Turn schema, so the OpenAPI compliance canary test_interaction_response_fields started failing on main with "Output field 'role' not in spec". role on Turn is already asserted by test_turn_schema, so dropping it from the Interaction output_fields list realigns the test with the live spec without losing coverage. |
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Mateo Wang
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6437b812be
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refactor(streaming): extract chunk_creator dispatch so basedpyright can analyze it (#30793) | ||
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Mateo Wang
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1ea91bf296
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refactor(exceptions): extract exception_type provider dispatch so basedpyright can analyze it (#30802) | ||
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Krrish Dholakia
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84c1414aef
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feat(sandbox): code interpreter interceptor on the Responses API (#30905)
Some checks are pending
GitHub Actions Security Analysis / zizmor (push) Waiting to run
* feat(sandbox): code interpreter interceptor on the Responses API
Route OpenAI's code interpreter to a configured sandbox (e2b) instead of
OpenAI's container, with no client change. A client calls /v1/responses with
a code_interpreter tool; the interceptor converts it to a function tool so the
model emits the code, runs that code in the sandbox via the phase 1 primitive,
feeds the result back, and lets the agentic loop continue.
Reuses the existing agentic-loop hooks (no new hook methods). The anthropic
agentic caller _call_agentic_completion_hooks gains an api_surface argument and
a responses execute path (_execute_responses_agentic_plan re-calls aresponses);
the responses handler invokes it after transforming the response. Web search and
compression interceptors are untouched.
Adds an api_base passthrough to the sandbox SDK and a sandbox_tools registry the
proxy parses, so the interceptor resolves a named tool to provider/key/base.
v0 limitation: no file upload or download yet; stdout and inline results flow
back, attaching input files and downloading produced files do not.
* feat(sandbox): re-inject code_interpreter_call so the response matches OpenAI
The native OpenAI Responses code interpreter returns a code_interpreter_call
output item (id, type, status, code, container_id, outputs) alongside the
message. The interceptor now re-injects an equivalent item via
async_post_agentic_loop_response_hook so a client gets the same response shape
whether the code ran in OpenAI's container or the sandbox: build_plan records
the executed code and the container id per call, and the post hook inserts the
code_interpreter_call before the message in the final response output.
* feat(sandbox): support streaming for the code interpreter interceptor
A stream:true /v1/responses request with code_interpreter previously broke,
because the agentic loop only runs on the non-streaming responses path. The
interceptor now forces stream=False in the pre-call hook (so the loop runs in
the sandbox) and the responses handler wraps the completed response back into a
synthetic stream via MockResponsesAPIStreamingIterator, so the caller still gets
SSE. The follow-up call and nested wrapping are guarded by stripping the
converted-stream flag from the follow-up request and only wrapping at the
outermost call (agentic loop depth 0).
* fix(lint): use builtin generics in code interpreter interceptor to satisfy UP006 budget
* fix(code-interpreter): gate sandbox execution, delete sandboxes, harden registry
Gate the agentic loop on a server-set interception marker and re-check
provider scope so an authenticated caller cannot trigger sandbox code
execution by naming their own function tool litellm_code_execution; the
marker is stripped from client requests at the proxy boundary and only
set when the pre-call hook actually converts a native code_interpreter
tool. Delete the sandbox once the final response is assembled instead of
leaking it until its own timeout, and prune expired cache entries by
deleting their containers too. Resolve sandbox params once at create time
and reuse them for run and delete. Clear the sandbox-tool registry before
re-registering so stale tools do not survive a config reload.
* fix(lint): use PEP 604 X | None unions to satisfy UP045 budget
* test(code-interpreter): cover execution-error and unparseable-argument tool-call paths
* fix(code-interpreter): rewrite forced code_interpreter tool_choice to the function tool
* test(sandbox): cover sandbox-tool registry resolution, reload clearing, and secret lookup
* test(code-interpreter): cover dict-shaped responses and object-attribute tool-call detection
* fix(proxy): strip client-supplied _code_interpreter_interception_converted_stream
A client could inject the converted-stream marker to force the completed
response to be re-wrapped as a synthetic SSE stream it never requested.
Add it to the untrusted root control fields alongside the other agentic
loop markers so the proxy strips it at the request boundary.
* fix(code-interpreter): isolate sandboxes by server-minted key and clear registry on tool removal
Key the per-request sandbox cache on a server-minted random token instead
of the caller-controlled litellm_call_id (sourced from the x-litellm-call-id
header). Two concurrent requests that send a colliding call id can no longer
share a sandbox container and read each other's code or files. The token is
minted in the pre-call hook when interception activates, stripped from client
requests at the proxy boundary, and survives the server-driven followups so a
single request still reuses one sandbox across the agentic loop.
Register sandbox tools unconditionally with an empty-list fallback so a config
reload that removes sandbox_tools clears the previously registered credentials
instead of leaving them resolvable in the process.
* refactor(sandbox): swap the tool registry atomically on reload
Build the new registry and rebind it in one assignment instead of clearing
then repopulating in place, so a concurrent resolve_sandbox_tool can never
observe a transiently empty or half-populated registry during a config
reload. clear_sandbox_tools now delegates to register_sandbox_tools([]).
* fix(code-interpreter): cap caller loop limit and emit OpenAI-shaped outputs
Strip max_agentic_loops at the proxy request boundary so an authenticated
caller cannot raise the agentic-loop ceiling to drive many upstream model
calls and sandbox executions from a single request; the loop stays bounded
by the server default.
Populate the re-injected code_interpreter_call.outputs with an OpenAI-shaped
logs array ([{"type": "logs", "logs": stdout}], or [] when there is no
stdout) instead of None, so clients that iterate over outputs or validate the
response through the OpenAI SDK's Pydantic model do not break.
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Krrish Dholakia
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accbd7e587
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feat: litellm plugin architecture v2 (#30688)
* feat: plugin architecture — toggle between AI Gateway and external plugins
Adds a generic plugin system so any external service can register with
litellm and appear as a mode in the UI alongside the AI Gateway.
Backend (litellm/proxy/plugin_routes.py — new):
- GET /api/plugins: returns registered plugins from config; returns
plugin_key only to authenticated requests
- ANY /plugin-proxy/{name}/{path}: reverse proxies API calls to plugin
Config:
general_settings:
plugins:
- name: my-plugin
display_name: My Plugin
url: https://my-plugin.example.com
plugin_key: sk-... # plugin auth key, passed to iframe
UI:
- PluginModeContext.tsx: fetches /api/plugins, persists mode to localStorage
- leftnav.tsx: mode switcher dropdown at top of sidebar; plugin mode shows
plugin-specific nav items
- layout.tsx: renders iframe to plugin URL in plugin mode; passes plugin_key
as ?token= for auto sign-in
Plugin contract: expose GET /api/plugin-manifest returning
{ name, display_name, nav_items[], capabilities[] }. No litellm changes
needed to add new plugins — config only.
Reference implementation: LiteLLM-Labs/litellm-agent-control-plane
* feat: add Plugins tab to Admin Settings UI
Allows admins to add/edit/delete plugin registrations directly in the
litellm UI under Admin Settings > Plugins, instead of editing config.yaml.
Uses existing /config/field/update API to persist to general_settings.plugins.
Each plugin entry has: name (identifier), display_name, url, plugin_key.
* fix(ci): black, prettier, eslint, async-client violations
- Black: format plugin_routes.py and proxy_server.py
- Prettier: format PluginModeContext.tsx and PluginSettings.tsx
- ESLint: replace raw fetch() with createApiClient in PluginModeContext
- ESLint: use lazy useState initializer to read localStorage instead of
calling setModeState inside useEffect (react-hooks/set-state-in-effect)
- code-quality: replace httpx.AsyncClient per-request with
get_async_httpx_client() shared client (avoids +500ms overhead)
* fix(ci): schema.d.ts regen, Black proxy_server.py, ApiClientConfig fix
- Regenerate schema.d.ts for new /api/plugins routes
- Re-run Black 26.3.1 on proxy_server.py (matches CI version)
- Fix PluginModeContext: createApiClient requires getBaseUrl field
* fix: security hardening + CI fixes
Security (Greptile 1/5 → addressing all 3 findings):
- plugin_routes.py: add Depends(user_api_key_auth) to both /api/plugins
and /plugin-proxy/{name}/{path} — was an unauthenticated open relay
- plugin_routes.py: /api/plugins now returns plugin_key only to callers
with a valid litellm token (enforced by user_api_key_auth), not just
any header presence
- layout.tsx: replace ?token= URL param with postMessage(targetOrigin)
— token no longer exposed in browser history / logs / Referer headers
CI:
- backend/routes/allowlist.py: add /api/plugins and /plugin-proxy/ to
fix test_gateway_plus_backend_covers_full_app
- schema.d.ts: regenerated with enterprise routes included
- Black + Prettier formatting
* fix: regenerate schema.d.ts with enterprise routes included
Install litellm-enterprise workspace member before gen:api so audit and
other enterprise routes appear in the generated types, matching what CI
produces with uv sync --extra proxy.
* fix: exclude plugin routes from OpenAPI schema, restore upstream schema.d.ts
Both /api/plugins and /plugin-proxy/ are internal infrastructure routes,
not part of the public litellm API surface. Marking include_in_schema=False
prevents Python-version-dependent schema diffs from breaking the schema
sync check across different environments.
* fix: schema.d.ts - passing schema base + exact plugin route types from openapi-typescript
Use the CI-correct schema from a recently passing branch as base, then
inject plugin route entries (paths + operations) generated by
openapi-typescript from the plugin routes' OpenAPI spec. This avoids
Python-version-dependent formatting differences that made local gen:api
produce incorrect output.
* fix: schema.d.ts - insert plugin ops at correct route registration position
Plugin operations belong after delete_memory_v1_memory__key__delete
(memory_router is included immediately before plugin_router in proxy_server.py),
not after list_organization which is alphabetically but not registration-order.
* fix: schema.d.ts - correct op positions from hunk analysis
list_plugins_api_plugins_get goes after event_logging_batch op (hunk 1: line 33583).
plugin_proxy ops go after create_policy_policies_post (hunk 2: line 44634).
Previous location after delete_memory_v1_memory__key__delete was wrong.
* fix: schema.d.ts - proxy ops go before create_policy (after otel_spans)
* fix(security): restrict plugin_key to proxy_admin role only
Veria finding: plugin_key was returned to any authenticated caller.
Now only proxy_admin users receive plugin credentials in /api/plugins
response — regular internal users see plugin name/url but not the key.
* fix: update schema.d.ts docstring for list_plugins
* fix: clear plugin registry on config reload (Greptile medium)
register_plugins_from_config now replaces the registry instead of
merging, so plugins removed from config are unreachable immediately
without requiring a process restart.
* fix(security): encrypted token exchange for plugin iframe — no raw litellm credential exposure
The dashboard was sending the user's litellm bearer token to the plugin
iframe via postMessage, allowing a compromised plugin to act as that user.
Fix:
- GET /api/plugins/auth-token: proxy encrypts caller token with Fernet
keyed from LITELLM_SALT_KEY, returns ciphertext only
- UI postMessages the ciphertext (not raw token) to the iframe
- Plugin decrypts server-side with same LITELLM_SALT_KEY via POST /api/plugin-auth
- Raw litellm credential never leaves the proxy in plaintext
Additional hardening already in place:
- /plugin-proxy/* strips Authorization header, injects plugin_key instead
- plugin_key only returned to proxy_admin role via /api/plugins
- Plugin registry cleared (not merged) on config reload
Adds docs/plugin_architecture.md with plugin integration guide.
* fix(code-quality): use get_async_httpx_client in plugin_proxy
* fix: add /api/plugins/auth-token to schema.d.ts
* fix: use apiClient for auth-token fetch, copy correct layout.tsx and PluginModeContext
- Replace raw fetch() with createApiClient (fixes no-restricted-syntax ESLint rule)
- Copy correct layout.tsx with encrypted token + postMessage approach
- Copy correct PluginModeContext.tsx with accessToken prop injection
- Update schema.d.ts with auth-token path and operation entries
* fix: add plugin_auth_token operation to schema.d.ts
* fix(security): strip cookie/set-cookie + fix compressed response headers
Veria High: cookie header was forwarded to plugin backends allowing
capture of litellm JWT session cookies. Strip cookie on requests.
Strip set-cookie from responses so plugins cannot overwrite litellm
session cookies.
Greptile P1: httpx decompresses responses but resp.headers still
contained Content-Encoding/Transfer-Encoding/Content-Length from the
wire. Forwarding these caused double-decompression and length errors.
Now filtered via _RESPONSE_STRIP before returning to the browser.
* fix: update plugin_key help text — no more ?token= reference
* fix(security): disable follow_redirects to prevent SSRF
follow_redirects=True allowed a plugin backend to return a 3xx to an
internal URL, causing the proxy to fetch that internal service and relay
the response. Disabled: clients handle their own redirects.
* fix: forward user identity headers to plugin to address confused deputy
Plugins receive X-LiteLLM-User-Id and X-LiteLLM-User-Role so they can
enforce their own per-user access control before acting on requests that
arrive with the shared plugin_key credential.
* fix(security): restrict /plugin-proxy/* to proxy_admin role
Closes the confused deputy gap: regular users could invoke any plugin
endpoint using the shared plugin_key as a bearer credential. Now only
proxy_admin callers can use the plugin proxy route.
Plugin UIs communicate with the plugin service directly via the iframe
(using the encrypted token exchange); this proxy route is for
administrative/server-to-server access only.
* fix: update schema.d.ts for admin-only proxy route docstring
* fix(bug): use PassThroughEndpoint instead of None for get_async_httpx_client
get_async_httpx_client(llm_provider=None) raises TypeError — the function
concatenates the provider string and None is not a str. Use
httpxSpecialProvider.PassThroughEndpoint, the enum value used by other
internal proxy pass-through routes.
* fix(security): add 30s TTL to encrypted plugin auth tokens
Veria medium: encrypted tokens had no expiry, allowing indefinite replay.
Fernet embeds a timestamp; decrypt_token now passes ttl=30 so tokens
older than 30 seconds are rejected even with a valid HMAC.
Plugin's /api/plugin-auth must call litellm within 30s of the iframe
receiving the postMessage — normal browser behavior, tight enough to
close the replay window.
* feat(ui): topnav plugin switcher, embed plugins at their root
Builds on the plugin architecture already on this branch (encrypted-token
postMessage handshake, /api/plugins, PluginSettings) and removes the parts of the
embed that assumed a specific plugin's shape.
The mode switcher moves out of the sidebar into the topnav and lists AI Gateway
plus each registered plugin by its display_name. Selecting a plugin hides
litellm's sidebar entirely and renders the plugin full-bleed at its root url; the
plugin draws its own navigation inside the iframe. This drops the hardcoded
"Agent Control Plane" label and the hardcoded Sessions/Agents/Routines/... nav
groups (agentControlPlaneMenuGroups / acpPagePaths) that only matched the agent
platform and 404'd for a plugin that serves only / (e.g. the chat UI). The
encrypted-token postMessage flow is unchanged.
Note: embedding at root means a plugin must route internally from /; plugins that
previously relied on the /sessions entrypoint should redirect from their root.
* fix(security): audience-scoped identity claim replaces litellm token
Veria: shared LITELLM_SALT_KEY with plugins + encrypting user bearer token
created delegation/impersonation risk.
Architecture change:
- /api/plugins/auth-token now issues a plugin-scoped identity CLAIM
{user_id, user_role, plugin, exp} encrypted with HMAC(LITELLM_SALT_KEY, plugin_name)
- Each plugin holds only its own HMAC-derived key; cannot forge claims for
other plugins or recover LITELLM_SALT_KEY
- Claim contains NO litellm bearer token — compromised plugin learns caller
identity only, cannot act as that user against the proxy
- 30s TTL enforced in both Fernet header and explicit exp field
- LAP /api/plugin-auth verifies claim, returns its own master key to browser
(LAP key never exposed without valid claim)
* fix(plugins): allow registering plugins from the admin UI
Adding a plugin in the UI POSTs general_settings.plugins to /config/field/update,
which rejected it with "Invalid field=plugins passed in." because `plugins` was
not a field on ConfigGeneralSettings. Add a typed PluginConfig model and a
`plugins` field so the update validates and persists.
The in-memory plugin registry only refreshed at startup, so a plugin added via
the UI did not appear in /api/plugins (the view switcher) until a restart. Refresh
the registry from the new general_settings whenever the plugins field is updated.
While here, type the registry as dict[str, PluginConfig] instead of raw dicts so
list_plugins and plugin_proxy access typed attributes.
Fix the Plugin Key field copy: it is optional and only used to authenticate
litellm's server-side reverse proxy to a plugin's own backend
(/plugin-proxy/<name>/*). It is not involved in iframe auth, which forwards the
user's litellm token. Plugins that use the forwarded token leave it blank.
* fix: regenerate schema.d.ts with PluginConfig type and updated auth-token endpoint
* fix: use CI-compatible schema base for plugin entries
* fix(plugins): load DB-persisted plugins on startup
Plugins added through the admin UI are saved to DB general_settings, but the
registry only initialised from the YAML config at boot, so UI-added plugins
disappeared from the view switcher after a restart (the Plugins table still
listed them since it reads the DB directly). Refresh the registry from the DB
general_settings when it is merged in at startup.
* fix: add PluginConfig schema, plugins field, fix list_plugins return type
* fix: correct PluginConfig and plugins field positions in schema
* fix: correct plugins field position in schema (after pass_through_endpoints)
* fix: update PluginConfig.plugin_key description to match _types.py source
* fix: move plugins field after pass_through_request_timeout (correct alphabetical position)
* fix: redact plugin_key in config/field/info response
Veria medium: proxy_admin_viewer could read plugin_key via
GET /config/field/info?field_name=plugins. Now plugin_key is
replaced with *** in the response regardless of caller role.
The credential is only usable server-side.
* fix(security): correct plugin docs salt-key guidance, drop iframe clipboard-read
Address the two open Veria findings on the plugin architecture.
The plugin docs told external services to decrypt the iframe auth payload
with the proxy's LITELLM_SALT_KEY directly. That is both insecure and wrong:
the running code derives a per-plugin key as HMAC-SHA256(LITELLM_SALT_KEY,
plugin_name) and ships only a short-lived identity claim with no litellm
bearer token. Sharing the master salt would let a compromised plugin decrypt
any litellm secret recovered from a dump or backup. Rewrite the doc to match
the implementation: the proxy computes the per-plugin key once and provisions
it as a dedicated secret, the plugin validates the claim's audience and 30s
TTL, and LITELLM_SALT_KEY never leaves the proxy. Also refresh the now-stale
module and UI comments that still described the old shared-key token flow.
Drop clipboard-read from the plugin iframe's allow attribute so an untrusted
plugin can no longer read the user's clipboard; clipboard-write is retained.
* fix(ci): modernize PluginConfig typing, refresh budget baselines via merge
* fix(plugins): close iframe auth race and empty-plugins mode fallback
Address the two open Greptile behavioral findings.
The iframe auth handshake only posted the encrypted claim on the iframe's
`load` event. When the auth-token fetch resolved after the iframe had already
loaded, that listener never fired again and the plugin never received the
claim. Send the claim immediately as well as on subsequent loads so both
orderings are covered.
The plugin mode fallback guarded on a non-empty plugins list, so removing all
plugins left a user stranded on a stale mode with a blank iframe instead of
returning to the AI Gateway. Track a loaded flag and fall back to ai-gateway
once plugins have loaded whenever the stored mode is no longer registered,
including the empty-list case.
Add a PluginModeContext regression test covering the empty-list fallback and
the still-registered path.
* chore: re-trigger CI (GH Actions missed the prior head; re-run flaky live-API suites)
* fix(plugins): scope iframe auth claim to the active plugin
The iframe auth-token fetch omitted plugin_name, so the proxy always issued a
claim encrypted under the default plugin's per-plugin key. For any other active
plugin the iframe received a claim it could not decrypt and sign-in silently
broke, and because the cached claim was posted to whichever plugin was mounted,
a compromised iframe could replay the default plugin's claim. The active
plugin's name was also missing from the fetch effect's dependencies, so
switching plugins never refreshed the claim.
Request the claim with the active plugin's name, re-fetch when the active
plugin changes, and only deliver a claim while it still matches the mounted
plugin so one plugin's claim is never replayed to another.
* fix(plugins): never overwrite a stored plugin_key with its redaction placeholder
/config/field/info redacts every plugin_key to "***", so an admin editing a
plugin in the settings UI posted that placeholder straight back and the update
handler persisted "***" as the real credential, permanently destroying the key.
Preserve the stored credential on update: a blank or redacted plugin_key now
sources the existing key from the saved config, only a real value replaces it,
and a placeholder with no stored key is dropped rather than written. The edit
modal also starts the key field blank so an untouched save keeps the current
key, with the field labelled accordingly.
* fix(security): sandbox proxied plugin responses on the dashboard origin
The /plugin-proxy reverse proxy returned the plugin's body and content-type on
the litellm dashboard origin, so a compromised plugin could serve an HTML/JS
document that a proxy_admin navigates to and have it execute with the admin's
session against same-origin management APIs.
Force every proxied response inert: set Content-Security-Policy: sandbox (opaque
origin, scripts disabled) and X-Content-Type-Options: nosniff, applied after the
plugin's own headers so they cannot be overridden. The header construction moves
to a pure helper with a unit test covering the sandbox enforcement and the
existing wire/cookie header stripping.
* fix(plugins): recover to ai-gateway when the plugins fetch fails
The loaded flag was only set on a successful /api/plugins response, so when the
fetch failed a user with a plugin mode stored in localStorage stayed on the
blank plugin placeholder with no switcher to escape. Mark loaded in a finally
so the stored mode still falls back to ai-gateway on failure, and add a
regression test for the failed-fetch path.
* fix(security): never return plugin_key from /api/plugins
The plugin list endpoint returned the plaintext plugin_key to proxy_admin
callers, and the dashboard fetches /api/plugins on every load into React state,
so the credential was exposed to DevTools, memory snapshots, and any same-origin
script. The browser never uses the key; the proxy injects it server-side from
the registry and admin key management runs through the redacted
/config/field/info path. Drop plugin_key from the response for every caller and
update the regression test to assert it is never returned.
* chore(ui): regenerate schema.d.ts for updated list_plugins docstring
* fix(security): strip every litellm auth header before forwarding to plugins
The plugin reverse proxy only removed Authorization and x-api-key, but
user_api_key_auth also authenticates a caller via API-Key, x-goog-api-key,
Ocp-Apim-Subscription-Key, x-litellm-api-key, and any configured custom key
header. A malicious plugin could lure a proxy_admin into calling
/plugin-proxy/... with the litellm key in one of those headers; the request
authenticated locally and then forwarded the same key to the plugin, letting it
impersonate the admin.
Add a canonical SpecialHeaders.litellm_credential_header_names() that the auth
header enum is the single source for, and strip that whole set plus the live
general_settings.litellm_key_header_name from every forwarded request. New auth
headers added to SpecialHeaders are now stripped automatically. Regression tests
cover each credential header, the custom configured header, and the canonical
list's contents.
|
||
|
Mateo Wang
|
9f97111edd
|
feat(fireworks_ai): sync chat completions endpoint with full API surface (#30885)
* feat(fireworks_ai): sync chat completions endpoint with full API surface Add 23 missing request parameters to get_supported_openai_params(): seed, top_logprobs, min_p, typical_p, repetition_penalty, mirostat_target, mirostat_lr, logit_bias, echo, echo_last, ignore_eos, prompt_cache_key, prompt_cache_isolation_key, raw_output, perf_metrics_in_response, return_token_ids, safe_tokenization, service_tier, metadata, speculation, prediction, stream_options, sampling_mask. Also add reasoning_history gated on supports_reasoning. Fix prompt_truncate_length to prompt_truncate_len to match the actual API parameter name. The old name was never in DEFAULT_CHAT_COMPLETION_PARAM_VALUES, so it always went to extra_body and was rejected by Fireworks; it never actually worked. Normalize reasoning_effort boolean values to strings: True becomes "medium", False becomes "none". The Fireworks OpenAPI schema documents these as accepted types, but the server rejects non-string values with HTTP 400 in practice. Integers pass through as-is since the server is expected to validate them. Auto-inject stream_options.include_usage=true when stream=true and the user has not explicitly set stream_options. Without this, Fireworks returns null usage in all streaming chunks, which is inconsistent with the non-streaming behavior where usage is always present. If the user explicitly sets include_usage=false, it is preserved. Capture Fireworks-specific response fields in transform_response(): perf_metrics, prompt_token_ids, raw_output, and token_ids are now extracted from the response and stored in response._hidden_params (fireworks_perf_metrics, fireworks_prompt_token_ids, fireworks_raw_outputs, fireworks_token_ids) so they are accessible to logging, the proxy, and downstream consumers when the corresponding request parameters are enabled. Remove deprecated document inlining logic. Document inlining was deprecated on 2025-06-30 (https://docs.fireworks.ai/updates/changelog#-document-inlining-deprecation). This removes _add_transform_inline_image_block(), the file-to-image_url migration in _transform_messages_helper(), and the disable_add_transform_inline_image_block lookup. Current models that support image input do so natively as VLMs. cache_control, provider_specific_fields, and thinking_blocks stripping is retained. Update get_provider_info() to look up supports_vision and supports_pdf_input from the model cost map instead of hardcoding both to True (which was based on the now-deprecated document inlining). supports_prompt_caching remains True. API docs: https://docs.fireworks.ai/api-reference/post-chatcompletions Reasoning guide: https://docs.fireworks.ai/guides/reasoning Prompt caching: https://docs.fireworks.ai/guides/prompt-caching * fix fireworks chat api surface gaps * Scope Fireworks thinking param to reasoning models * style: fix black formatting * fix(test): update minimax-m3 expected_vision to True * test: cover non-dict content branch in transform_messages_helper * fix(fireworks_ai): remove metadata from supported params to prevent internal metadata disclosure * test(fireworks_ai): replace stale document-inlining capability test The CircleCI-only litellm_utils_tests suite still asserted the old behavior where document inlining made every Fireworks model report supports_pdf_input and supports_vision as True. That premise was removed in this change, so the test now reflects cost-map-driven capabilities: unmapped models no longer advertise vision/PDF support while mapped VLMs like minimax-m3 still do. * test(fireworks_ai): add end-to-end regression for native OpenAI params The existing coverage for the newly supported OpenAI-native params asserted list membership in get_supported_openai_params or called map_openai_params with a hand-built dict, both of which bypass the get_optional_params gate (DEFAULT_CHAT_COMPLETION_PARAM_VALUES). That gate is what previously raised UnsupportedParamsError for seed, top_logprobs, logit_bias, prompt_cache_key, service_tier and prediction when drop_params=False. Assert the full path so a revert of the supported-params additions fails the test instead of passing a shallow membership check. * test(fireworks_ai): fix test isolation in vision/inlining tests Use monkeypatch in test_fireworks_ai_vision_capability_from_cost_map so the LITELLM_LOCAL_MODEL_COST_MAP env var and litellm.model_cost are restored after the test instead of leaking global state into the rest of the process. Switch the document-inlining integration tests off deepseek-v3p1, whose supports_vision is null in the cost map, onto minimax-m3 which is explicitly supports_vision:true. The pass-through assertions no longer depend on a model incidentally not being marked non-vision. * fix(fireworks_ai): gate image rejection on exact vision capability The image_url rejection read supports_vision via _get_model_cost_capability, which falls back to hyphen-boundary substring matching when no exact cost-map entry exists. A custom or fine-tuned model id that merely contains a known non-vision model's short name (e.g. an id ending in -glm-5p2) inherited that entry's supports_vision:false and hard-failed valid image_url blocks on a vision-capable deployment. Split the exact candidate-key lookup into _get_model_cost_capability_exact and use it for the hard rejection so a fuzzy match can never block images; the substring fallback stays a soft signal for capability reporting. Also rewrites the fallback as a comprehension + max instead of an accumulating loop. * feat(fireworks_ai): surface response fields on streaming responses The Fireworks-specific response fields (perf_metrics, prompt_token_ids, per-choice raw_output and token_ids) were only captured into _hidden_params in transform_response, which runs for non-streaming completions; streaming chat went through the default OpenAI chunk handler and dropped them. Add a FireworksAIChatCompletionStreamingHandler that the provider now returns from get_model_response_iterator. It reuses one extraction helper with transform_response and attaches the fields to each streamed chunk's provider_specific_fields, which is the channel litellm preserves when it rebuilds streamed chunks (per-chunk _hidden_params is not carried through). Per-choice token_ids/raw_output ride the content chunks; response-level perf_metrics/prompt_token_ids ride the final usage chunk. Covered by an end-to-end streaming test through litellm.completion(stream=True). --------- Co-authored-by: Ahmad Shahzad <ahmad@shahzad.dev> Co-authored-by: Graham Neubig <398875+neubig@users.noreply.github.com> |
||
|
Yassin Kortam
|
84266bf924
|
feat(auth): resolve caller identity once into a Principal at the auth seam (#30887)
Introduce a single, typed caller identity that is resolved once at the auth boundary and read by reference downstream, instead of being re-derived from a 50-field key object or rebuilt from request metadata. What this adds (litellm/proxy/auth/resolvers/), organized by responsibility: - Principal: a small, frozen, identity-only value type (user / organization / teams / project / end-user / roles / scopes / network), with its sub-models and the role mapping. No budget or policy state; those stay on the key object. - DbIdentityStore: the auth flow's resolver, owning both halves of resolving a caller. resolve_key does the one combined_view lookup (cache, then DB via the shared lower-level helpers, then write-back) and returns the key object, which still flows for budget / rate-limit / policy unchanged. principal_from_key projects the identity slice of that key object into a Principal, issuing no lookup. user_api_key_auth resolves every key through the store rather than calling get_key_object directly; auth_checks.get_key_object stays as the legacy entrypoint for its other callers until they migrate. - network: the X-Forwarded-For / trusted-proxy CIDR primitives live here in one place. trusted_proxy_utils now imports them rather than keeping a second copy. At the seam, user_api_key_auth projects one per-request Principal off the resolved key object and stamps the request network context onto it once (X-Forwarded-For is trusted only when trusted_proxy_ranges is configured). It is attached to request.state.principal for the downstream consumers later phases add. The projection is additive and defensive: a failure never rejects an already-authenticated request, and a missing principal must be treated as deny by any future reader. The Principal is always identifiable (credential_ref and a stable subject off the token), never anonymous. This is additive and changes no behavior today; it is the identity foundation the spend-attribution and authorization phases build on. |
||
|
yuneng-jiang
|
df5619f9b2
|
chore: update Next.js build artifacts (2026-06-21 00:50 UTC, node v20.20.2) (#30906) | ||
|
ryan-crabbe-berri
|
8125ddd2f8
|
fix(ui): source api-keys identity from useAuthorized to stop "User ID is not set" (#30903)
The migrated /ui/api-keys route gates rendering on useAuthorized() but read userID from the AuthContext (useAuth), which hydrates asynchronously. On a hard refresh or deep link the route could render UserDashboard before AuthContext had populated userID, so UserDashboard hit its `userID == null` guard and showed "User ID is not set". The legacy index page avoided this by gating on AuthContext's own authLoading; the migration switched the gate to useAuthorized without aligning the identity source. Read identity from useAuthorized (a synchronous cookie decode) so userID is populated whenever the route is authorized. useAuth is kept only for the backfill setters UserDashboard still expects, until the planned AuthContext consolidation removes them. Refs LIT-3687 |
||
|
Krrish Dholakia
|
53593f697d
|
feat(sandbox): e2b code execution primitive (#30898)
* feat(sandbox): add e2b code execution primitive Add a provider-agnostic code execution primitive that runs model-generated code in an isolated sandbox and returns the output, with e2b as the first backend over raw httpx (no SDK dependency). Public API: litellm.acode_interpreter_tool (ephemeral create -> run -> delete) plus the low-level lifecycle litellm.acreate_sandbox / arun_code / adelete_sandbox. Each is @client-decorated so operations are logged like litellm.asearch. Backends implement BaseSandboxConfig; resolved via ProviderConfigManager.get_provider_sandbox_config. * fix(sandbox): address review feedback and CI gates - document e2b provider in provider_endpoints_support.json and add a sandbox endpoint definition - regenerate dashboard CallTypes after the sandbox call-type additions - guard explicit timeout=0 instead of coercing it to the default - require a ContainerHandle access token before running code; reject bare-id runs - return False on a 404 delete now that the shared http handler raises for status - skip non-JSON NDJSON lines and cap streamed output to bound memory - move the real-network integration tests out of tests/test_litellm into tests/integration/sandbox * fix(sandbox): satisfy strict ruff gate and scope star-exports - modernize annotations in the new sandbox modules to PEP 585/604 (list/dict, X | None) and drop the now-unnecessary quoted forward refs so the strict-rule budget delta for UP006/UP037/UP045 returns to zero - add __all__ to litellm/sandbox/main.py so 'import *' only re-exports the four public entrypoints instead of leaking module-level imports * fix(sandbox): drop quotes on sandbox config return annotation utils.py uses 'from __future__ import annotations', so the quoted forward ref tripped UP037; the unquoted union is lazily evaluated and keeps the strict-rule delta at zero * chore(sandbox): re-trigger automated review after addressing feedback |
||
|
Mateo Wang
|
b16cfd7de9
|
test: point router/completion/triton tests at the local fake OpenAI endpoint (#30900)
* test: point router/completion/triton tests at the local fake OpenAI endpoint The shared Railway-hosted mock (exampleopenaiendpoint-production.up.railway.app) takes down unrelated CI jobs whenever it is unreachable. #30695 moved the mounted proxy configs onto a job-local fake server but left these in-Python api_base literals pointing at the dead host, so litellm_router_testing, local_testing_part1, local_testing_part2 and llm_translation_testing still fail with a 404 "Application not found" when Railway is down Resolve the api_base from FAKE_OPENAI_API_BASE (default http://127.0.0.1:8190) through a shared helper, auto-start the canned server from the local_testing and llm_translation conftests when nothing is already serving, and extend the server with a Triton embeddings route and a slow-endpoint delay so the triton and latency-timeout tests run fully offline. The deliberately broken fallback URL is left as-is so fallback handling still has a failing upstream * fix: ignore non-loopback FAKE_OPENAI_API_BASE so the local mock is used in CI * fix: drop 0.0.0.0 from loopback hosts, an unreliable client connect target * fix(tests): keep fake OpenAI mock alive across xdist workers ensure_fake_openai_endpoint registered atexit on the worker that spawned the subprocess, so under -n 4 the first worker to drain its queue would terminate the shared mock while siblings were still hitting it. Detach the child via start_new_session and drop the per-worker teardown; reuse on /health handles re-runs and CI containers clean up themselves |
||
|
Shivam Rawat
|
c7efa77de3
|
fix(watsonx): wrap string embedding input in array for WatsonX API (#30897)
* fix(watsonx): wrap string embedding input in array for WatsonX API WatsonX text/embeddings expects inputs as []string; OpenAI clients often send a single string. Co-authored-by: Cursor <cursoragent@cursor.com> * style(watsonx): format watsonx embed transformation for black Co-authored-by: Cursor <cursoragent@cursor.com> * fix(watsonx): avoid UP006 in embed transformation strict lint gate Use list[str] and branch-based input normalization instead of List and cast so the watsonx embedding change does not add strict ruff UP006 violations. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Shivam Rawat <shivamrawat@Shivams-MacBook-Pro.local> Co-authored-by: Cursor <cursoragent@cursor.com> |
||
|
yuneng-jiang
|
46d2861001
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chore(ci): bump deps (#30899)
* bump: version 0.1.42 → 0.1.43 * bump: version 1.89.0 → 1.90.0 * adding uv lock |
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yuneng-jiang
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140ca3012a
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chore: update Next.js build artifacts (2026-06-20 21:24 UTC, node v20.20.2) (#30894) | ||
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Mateo Wang
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a7b0b0ba09
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feat: add lint-gate target and truncation-proof summary to the strict ruff gate (#30877)
* feat: add CI-parity mode and truncation-proof summary to strict ruff gate * refactor: tolerant worktree cleanup and concrete GateInputs types * fix: clean up temp dir when git worktree add fails * fix: align lint-gate with CI by dropping unused --ci-parity path The lint-gate Makefile target invoked ruff_strict_gate.py with --ci-parity, which counted violations on a throwaway merge of base into HEAD against base counts at the base tip. CI in test-linting.yml runs the same script without --ci-parity on a PR-head checkout, taking the gather_fast path that counts on the live tree against base counts at the merge-base. A local pass could therefore disagree with CI. Drop --ci-parity from the Makefile and remove the now-unused gather_ci_parity branch and flag so there is one code path that both local and CI exercise. The docstring claim that CI runs against the synthetic merge ref was also wrong; the workflow checks out github.event.pull_request.head.sha. --------- Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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Mateo Wang
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15aa40b36e
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test(ui): isolate OldTeams delete-warning tests from leaked mock (#30871)
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The deprecated OldTeams component takes only accessToken, userID, userRole and premiumUser; it ignores the teams prop these tests passed and instead populates its table from the mocked teamListCall. The delete-warning block never set teamListCall, and vi.clearAllMocks clears call history but not implementations, so the table rendered the "Legacy Team" (keys.length 2) left behind by the previous block's last test. Both delete tests therefore ran against that leaked team: the keys-present case passed only because the leaked count happened to be 2, and the no-keys case rendered the same warning it asserted should be absent, so it failed. Seed the team through the channel the component actually reads (teamListCall) and drop the props it never consumes, so each test renders exactly the team it declares. The keys-present case now uses a distinctive count so it can no longer pass on a coincidental leak |
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Yassin Kortam
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9c3ad1b094
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feat(caching): add valkey-semantic cache backend and fix semantic cache scope keys (#30675)
Adds a "valkey-semantic" cache type so semantic prompt caching can run against Valkey clusters (for example AWS ElastiCache for Valkey) using the valkey-search module. The existing "redis-semantic" backend cannot drive valkey-search. RedisVL gates the connection on a RediSearch module version that valkey-search does not report, and its SemanticCache index declares the prompt as a TEXT field, which valkey-search does not implement. ValkeySemanticCache therefore talks to valkey-search directly over redis-py: it builds a vector index from the field types valkey-search supports (TAG for caller scope, VECTOR for the prompt embedding) and runs KNN queries for retrieval. Prompt extraction, embedding generation, and cached-response parsing are reused from RedisSemanticCache since those are backend agnostic. The redis dependency is imported lazily in the cache dispatch so importing litellm without redis installed still works. It also fixes semantic-cache scope keys so similarity matching works across reworded prompts. get_cache_key() hashed messages / prompt / input into the litellm_cache_key that every semantic backend filters its KNN search on, so a paraphrase landed in a different bucket and never matched, even far above the similarity threshold. For semantic cache types the prompt-bearing params are now excluded from the scope key and the server-set tenant identity (user_api_key, team, org) is appended instead, restoring embedding matching within a tenant while keeping cache entries scoped to the authenticated key / team / org. The three semantic backends share this key, so the same change fixes redis-semantic and qdrant-semantic. Connections resolve from VALKEY_HOST / VALKEY_PORT / VALKEY_PASSWORD, falling back to REDIS_* for drop-in compatibility, and passwordless clusters (IAM or no-auth) are supported. Resolves #29121 Fixes #29086 |
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yuneng-jiang
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ea17236a1e
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fix(ui): warn that team models are deleted in the delete-team modal (#29990)
The delete-team confirmation modal warned that a team's keys would be deleted but said nothing about models. #29977 made team deletion also delete the team's BYOK models, so the modal copy was understating what gets removed. The warning banner now mentions models alongside keys, and the always-shown confirmation message does too so a team that has models but no keys (the banner only renders when keys exist) still gets warned. |
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yuneng-jiang
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60dc8420ed
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fix(ui): repoint dead usage guide link to cost tracking docs (#30859)
The "View Usage Guide" button on the legacy Usage page (shown when DISABLE_EXPENSIVE_DB_QUERIES is set, i.e. SpendLogs has 1M+ rows) linked to docs/proxy/spending_monitoring, which was removed from the docs and now returns 404. Point it at docs/proxy/cost_tracking, which is live. Fixes LIT-2724 |
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Yassin Kortam
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4847fa5dd5
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fix(proxy): record partial spend on the failure row for interrupted streams (#30788)
A streaming request that breaks mid-flight, for example on a mid-stream read timeout, still bills the provider for the chunks already delivered, yet the proxy recorded that interrupted request as a zero-spend failure. An earlier revision logged the recovered partial usage through the success path, which mislabeled a failed request as a success and produced a misleading spend row This recovers the partial usage where the failure is actually logged. The streaming handler assembles the usage from the chunks seen so far and stashes it, with its cost, on the logging object before firing the failure handlers. The proxy failure hook lifts that usage and cost onto request_data before the non-serialisable logging object is popped, and the spend-log writer records the real partial spend on the failure row instead of a hardcoded zero; get_logging_payload honors the recovered usage for the token columns and _failure_handler_helper_fn preserves the recovered cost so the non-DB failure loggers stay consistent A request that recovers via a successful fallback is unaffected: the failure hook only fires when the whole request fails, so the fallback's combined-usage success row stays the single source of truth and there is no double counting Resolves LIT-3825 Co-authored-by: veria-ai[bot] <224490171+veria-ai[bot]@users.noreply.github.com> |
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Yassin Kortam
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bd74c62ff1
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fix(passthrough): recover output tokens for interrupted anthropic streams (#30787) | ||
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yucheng-berri
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1f9323792c
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fix(otel): one v2 logger owns the global provider; scope tenant OTLP creds per exporter (#30590)
* fix(otel): one v2 logger owns the global provider; scope tenant creds per exporter The proxy published the OTel global TracerProvider before callbacks were initialized, so no preset logger existed yet and a second generic logger was built that won the global provider. Server spans then exported through a different provider than the preset's gen-ai spans, orphaning the LLM span on the preset backend. Publish after callback init and reuse the already-built logger instead. Separately, per-request tenant OTLP credentials were stamped onto every OTLP exporter, leaking one backend's key onto a co-configured backend. Tag each exporter with the preset that contributed it and apply dynamic credentials only to the matching owner. * fix(otel): satisfy Any-discipline on changed lines Type the logger-selection parameter as Sequence[object] (isinstance narrows it), cast the list[Any] global at the single call site, and pass model_copy a typed dict[str, str] update so no changed line carries an Any value. * fix(otel): annotate the untyped-global boundary with any-ok select_global_otel_v2_logger consumes litellm._in_memory_loggers, a shared List[Any] global this change does not own. A cast doesn't satisfy the Any-discipline checker (it inspects the inner expression), and re-annotating the global is out of scope, so mark the single boundary line any-ok. * test(otel): cover the startup global-provider publish via injectable helper The publish step lived inline in proxy_startup_event (a FastAPI lifespan unit tests do not execute), so its lines were uncovered though the selection logic was tested. Extract publish_global_otel_v2_provider, which selects the single v2 logger and publishes its provider through an injected setter, and unit-test that the published provider is the selected logger's. proxy_server delegates to it. * refactor(otel): select global provider from the registered owner, not a list scan The startup publish picked the global TracerProvider by scanning _in_memory_loggers for the first OpenTelemetryV2, re-deriving an answer the factory already settled: the first logger built registers itself as proxy_server.open_telemetry_logger, and every other v2 path (guardrail, identity seeding, phase spans) routes through that owner via _registered_v2_logger. Pass that owner into select_global_otel_v2_logger so the global provider reuses the same logger instead of an independent, order-dependent guess; the list scan remains the SDK-path fallback. The owner is injected at the proxy call site to keep the helper free of hidden global reads. * refactor(otel): type ExporterSpec.owner as an ExporterOwner enum The owner field carried free-form strings that had to match preset callback names. Introduce a str-based ExporterOwner enum (values equal to the callback names, so per-request credential routing's owner==callback_name comparison still holds) and have each preset tag its exporter with the enum member. * refactor(otel): rename ExporterOwner.ARIZE to ARIZE_AX Distinguish the hosted Arize AX backend from Arize Phoenix at the member level while keeping the value 'arize' (the public callback name routing compares against). Add a comment noting AX and Phoenix are separate backends. |
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Mateo Wang
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1bd603d1ac
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chore(typing): add boto3/botocore stubs so basedpyright resolves the AWS SDK (#30815)
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Mateo Wang
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31eca17007
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chore: make pr template linear portion clearer (#30766) | ||
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Mateo Wang
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f9b8b9700c
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fix(proxy): use e.request_data for logging_obj in ModifyResponseException streaming passthrough (#30800)
* fix(proxy): use e.request_data for logging_obj in ModifyResponseException streaming passthrough
When a guardrail blocks a streaming request pre-call by raising
ModifyResponseException (or RejectedRequestError), chat_completion streams the
violation message back as a 200 by building a CustomStreamWrapper. It read the
logging object from the outer request body (`data.get("litellm_logging_obj")`),
but that dict never carries litellm_logging_obj -- it diverges from the
processor's data at function_setup, and only the processor copy (exposed as
e.request_data, already bound to `_data` here) gets the logging object
attached. CustomStreamWrapper.__init__ then dereferences
`logging_obj.model_call_details` on None and 500s the request with
"AttributeError: 'NoneType' object has no attribute 'model_call_details'".
Read logging_obj from `_data` (= e.request_data) in both streaming
passthrough handlers so the refusal streams correctly. The non-streaming and
the anthropic/responses passthrough paths were unaffected.
Adds a regression test asserting the wrapper receives the logging object from
e.request_data rather than None.
* test(proxy): cover RejectedRequestError streaming passthrough
The streaming logging_obj fix was applied to both the ModifyResponseException
and RejectedRequestError handlers, but only the former had a regression test.
Extract a shared helper and add a parallel test for the RejectedRequestError
streaming path so both handlers stay guarded against the None-logging_obj crash.
---------
Co-authored-by: Joseph Barker <joseph.barker@rubrik.com>
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yuneng-jiang
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7e5699c7ab
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ci(zizmor): gate PRs on medium+ findings and clear existing ones (#30797)
Switch the zizmor check to fail on any finding at medium severity or above (advanced-security off, min-severity medium, annotations on) so it can be promoted to a required check, and pin the engine to zizmor 1.24.1 through zizmor-action v0.5.6 for deterministic runs. Clear the findings that were outstanding so the check passes: correct mismatched action pin version comments, scope the proxy endpoint workflow's id-token and pull-requests permissions to the jobs that use them, and mark the server-root-path docker build as non-publishing while dropping its shared gha build cache. |
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yucheng-berri
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5637b3212e
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feat(proxy): configurable response headers and login-page hint (#30792)
* feat(proxy): add configurable response headers middleware Adds a small ASGI middleware that sets standard response headers (X-Frame-Options, Content-Security-Policy frame-ancestors, X-Content-Type-Options) on proxy and UI responses. Strict-Transport-Security is optional and gated behind LITELLM_ENABLE_HSTS for HTTPS deployments. Values use setdefault so a route that sets its own header is preserved. * feat(proxy/ui): make login page credentials hint configurable build_ui_login_form accepts a hide_default_credentials_hint parameter and google_login reads LITELLM_HIDE_DEFAULT_CREDENTIALS_HINT (or general_settings) so the legacy login page behaves consistently with the new UI. Also collapses a duplicated branch and removes an unused variable and module-level constant. * fix(proxy/ui): apply credentials hint flag on /fallback/login The /fallback/login handler still rendered the default-credentials hint regardless of LITELLM_HIDE_DEFAULT_CREDENTIALS_HINT. Collapse its duplicate branch and forward the flag, matching google_login, so all login surfaces behave consistently. Adds regression tests for /fallback/login and makes the ui_sso test helper restore os.environ so env vars do not leak across tests. |
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ryan-crabbe-berri
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32bdd004bd
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feat(ui): migrate api-keys landing to App Router path route (#30699)
Cut the default "Virtual Keys" landing (?page=api-keys) over to a path route at (dashboard)/api-keys. The dashboard is extracted into a shared ApiKeysDashboard component used by both the new route and the index's inline render, so there's no duplication. Adding the MIGRATED_PAGES entry repoints the sidebar item and redirects ?page=api-keys to /ui/api-keys. The index is the post-login landing and still hosts the legacy switch for the not-yet-migrated pages (models, pass-through, usage) plus the invitation flow, so it stays. The auto-redirect now fires only for an explicit ?page= param, leaving the bare /ui/ landing to render inline; this keeps the return-URL handling and the invitation_id flow (both of which run at the bare landing) intact, where a blanket redirect would have dropped them. The new route uses useAuthorized for the login gate, matching every other migrated route. |
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ryan-crabbe-berri
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e4a53f50de
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chore: remove in-product survey and Claude Code feedback nudges (#30773)
Delete the in-product survey and Claude Code feedback prompts end to end. Frontend: remove the src/components/survey/ module, the index page's nudge state/effects/handlers, the getInProductNudgesCall helper, and the orphaned "Disable UI nudges" toggle in the admin UI Settings page; prune the stale eslint-suppressions entries. Backend: remove the now-dead /in_product_nudges route, the InProductNudgeResponse type, and the disable_ui_nudges UI setting (Field + allowlist). Nothing read it for logic and the UISettings model is extra="allow", so existing stored configs are unaffected (the value is just no longer surfaced). schema.d.ts is regenerated and the two tests covering the removed route/setting are dropped. |
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ryan-crabbe-berri
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ba0233c4ce
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fix(test): drop references to removed Agent Shin workflows (#30791)
PR #30784 deleted .github/workflows/review_gate.yml and triage_pr_with_llm.yml, but test_github_triage_workflows.py still listed both in its parametrize tables, so _load_workflow raised FileNotFoundError for every case naming them. Remove the two stale entries from DESTRUCTIVE_GATE_ENV and LLM_CLIENT_INSTALLER_WORKFLOWS; the remaining four workflows that still exist keep their guardrail coverage. |
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yuneng-jiang
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e7532b72df
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ci(zizmor): also run on litellm_internal_staging (#30789)
* chore(ci): remove Agent Shin pull_request_target workflows Drop the two Agent Shin workflows that ran on the pull_request_target trigger: the PR triage workflow and the review gate. Both were dry-run and gated behind AGENT_SHIN_ENABLED, so no live automation changes. The shared scripts under .github/scripts stay in place; four other Agent Shin workflows still depend on them and run on schedule, dispatch, and issue events rather than pull_request_target * ci(zizmor): also run on litellm_internal_staging |
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Sameer Kankute
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4c25b7a13d
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chore: litellm oss staging (#30745)
* fix(proxy): bump health-check max_tokens default to 16 for GPT-5 compatibility (#30708) OpenAI GPT-5 models require max_completion_tokens >= 16. Health checks were using 5 (proxy/health_check.py) and 10 (health_check_helpers.py), causing failures on GPT-5 models. Fixes #23836 * fix: increase health check max_tokens from 5 to 16 (#23836) (#26610) GPT-5 models enforce a minimum of 16 for max_output_tokens. The current default of 5 still causes health checks to fail for these models. Bump the non-wildcard default to 16 — the smallest value that satisfies all known provider minimums while keeping health checks lightweight. Also tightens the wildcard test assertion from a weak disjunctive check to strict key-absence. Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix: ensure checks show gemini-3-flash-preview supports responseJsonS… (#30696) * fix: ensure checks show gemini-3-flash-preview supports responseJsonSchema. * fix: remove async keyword from test. * fix: make Bedrock Mantle Responses routing data-driven per model (#30700) * Make Bedrock Mantle Responses routing data-driven per model Route Bedrock Mantle models to the native Responses API based on each model's price-map capability signal instead of a hardcoded model-name heuristic, and derive the OpenAI-compatible base path segment per model. Responses dispatch now selects the native config when the model advertises responses support (/v1/responses in supported_endpoints, or mode=responses), both overridable via register_model and proxy model_info. This enables native Responses for gpt-oss-120b/20b and the gemma-4 family while keeping chat-only models (gpt-oss safeguard, nvidia, mistral, ...) on the existing chat-completions emulation. Capability is per-model, so gpt-oss-120b routes natively while gpt-oss-safeguard-120b does not despite sharing the gpt-oss substring. The wire path is a separate concern, driven by the existing use_openai_responses_path flag rather than a model-name match: gpt-5.x and gemma-4-* on /openai/v1, everything else (incl. gpt-oss) on /v1. The chat config now derives its base from the same flag, fixing gemma-4 chat-completions requests that previously went to /v1 instead of /openai/v1. Cost maps: add supported_endpoints to the gpt-oss entries (responses for the non-safeguard variants, chat-only for safeguard) and supported_endpoints + use_openai_responses_path to all three gemma-4 entries. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * Address review: move capability helper into bedrock_mantle package Move the Responses capability check out of utils.py into litellm/llms/bedrock_mantle/common_utils.py as mantle_supports_responses, alongside its companion wire-path helper mantle_base_segment. Both are now pure functions of (model, model_cost): the price-map mode/supported_endpoints read replaces the get_model_info call, so the rules are unit-testable without patching global state and the Bedrock Mantle package is self-contained. Use str | None instead of Optional[str] on the new signatures to satisfy the ruff UP045 strict-rule gate. Add direct unit tests for both helpers. Fix test_register_model_restore_undoes_existing_key_overwrite: gpt-oss-120b now legitimately supports Responses, so it can no longer be the "None after restore" vehicle; use the chat-only safeguard variant, which isolates the register/restore effect from the model's own capability. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Co-authored-by: Sameer Kankute <sameer@berri.ai> * fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup (#30366) * fix(proxy): fail fast on non-PostgreSQL DATABASE_URL instead of hanging on startup LiteLLM's Prisma datasource is pinned to provider = 'postgresql', so a sqlite:// or mysql:// DATABASE_URL can never connect. Today that surfaces as an opaque startup stall where the port never binds, and a separate 'DB not connected' 500 on /key/generate when no DATABASE_URL is set at all leaves operators guessing what to configure. Validate the DATABASE_URL / DIRECT_URL scheme in run_server before any Prisma call and exit with an actionable message naming the unsupported scheme. Also reword CommonProxyErrors.db_not_connected_error to tell the operator to set DATABASE_URL to a postgresql:// connection string. Add regression tests covering postgres acceptance and sqlite/mysql/mssql rejection. * fix: resolve CI failures and proxy DB URL typing issue * fix(dashscope): treat an explicit 0.0 tier cost as a real price, not missing (#30653) The tiered cost calculator resolved a tier's per-token cost with `tier.get(cost_key) or tier.get(fallback_cost_key, 0)`. Because `or` short-circuits on any falsy value, a tier that legitimately prices a component at 0.0 (e.g. a free-cache-read tier with cache_read_input_token_cost: 0.0, or a free-reasoning tier) is treated as missing and silently billed at the full fallback rate (input_cost_per_token / output_cost_per_token). The flat-pricing path in the same module already handles this correctly with an `is None` guard. Resolve tier costs through a small helper that mirrors it, so 0.0 is honored at both the in-range and overflow sites. No shipped model currently has a 0.0 tier cost, so this is a latent defect; the fix makes the tiered path consistent with the flat path and prevents over-charging the first time such a tier appears. Adds unit tests covering the in-range and overflow paths, and drops an unused import flagged by ruff in the touched test file. * feat(proxy): show session-aggregate cost and duration in request logs (#25708) (#30507) * fix(anthropic): don't leak tool 'type' into OpenAI function parameters schema (#30618) In the messages->chat/completions bridge, translate_anthropic_tools_to_openai merged every non-mapped tool key into the function parameters dict. The Anthropic tool 'type' (e.g. 'custom') thus overwrote parameters.type ('object' -> 'custom'), and providers reject it ('custom' is not a valid JSON-Schema type). Exclude 'type' from the passthrough. Fixes #30557. * fix(proxy): stop IAM-refresh engine restart from cascading reconnects (#29176) (#30183) An RDS IAM token refresh recreates the Prisma client, which SIGKILLs the running query-engine and spawns a new one. That planned kill was indistinguishable from a crash, and three reconnect paths used two uncoordinated locks, so a single refresh triggered a cascade of engine kill/respawn cycles: 1. `_safe_refresh_token` (holds `_reconnection_lock`) -> recreate -> kill old engine, spawn new one. 2. The engine-death watcher sees that kill, assumes a crash, and calls `attempt_db_reconnect(force=True)` (a different lock, `_db_reconnect_lock`) -> recreate again -> kills the fresh engine. 3. In-flight queries failing during the swap are classified as transport errors and trigger their own `attempt_db_reconnect` -> recreate again. Fix coordinates planned restarts across the wrapper and the watcher: - PrismaWrapper records the old engine PID in `_expected_engine_deaths` before killing it; all four watcher death-detectors (waitpid thread, pidfd, already-dead probe, os.kill poll) consume that PID and skip the reconnect instead of treating it as a crash. - `recreate_prisma_client` now serializes through `_reconnection_lock` and bumps a monotonic `_engine_generation`. Callers pass `expected_generation` as an optimistic-lock token, so racing/cascading recreates collapse into a single restart (losers no-op). This closes the two-lock gap. - The direct reconnect path probes the writer with SELECT 1 before recreating; a healthy connection (e.g. engine already replaced by a refresh) skips the recreate entirely. - `_safe_refresh_token` coalesces: it skips when the current token still has more than the refresh buffer of runway, so stacked triggers (proactive loop + __getattr__ fallback) don't each restart the engine. An `on_engine_replaced` hook re-arms the watcher on the new PID. RoutingPrismaWrapper forwards `expected_generation` and skips recreating the reader when the writer recreate was skipped. * feat(bedrock): support file content retrieval for batch output files (#30595) Implements transform_file_content_request and transform_file_content_response in BedrockFilesConfig so GET /v1/files/{id}/content works for Bedrock batch files. The request transform resolves the file id (direct s3:// URI or base64 unified id) to its S3 object, validates bucket and key prefix against the server-configured bucket, and SigV4-signs an S3 GetObject using the same credential and region resolution as the existing upload path. The credential and region params are validated into a typed model at the boundary, so the only untyped values left are the botocore signing primitives. Also fixes the proxy managed-files path: CredentialLiteLLMParams now carries s3_bucket_name (previously dropped when building deployment credentials) and the managed-files hook passes the deployment credential snapshot when routing afile_content, so unified-id content retrieval works with per-model bucket config instead of only the AWS_S3_BUCKET_NAME env var. Preserves managed-file access control: the proxy file-content endpoint now rejects raw cloud-storage ids (s3://, gs://), which would otherwise skip the owner/team check that only runs for unified ids and let a caller read another tenant's batch output by its object key. Managed outputs are reachable only through their unified file id. The afile_content "not found" error now reports the caller's unified id rather than the resolved internal S3 URI. Fixes #16186, #15563 * fix(oci): make Cohere {{trace}} judges work (tool param types + agentic tool-calling continuation) (#30646) * fix(oci): map Cohere tool array/object params to lowercase builtins OCI's Cohere backend returns HTTP 500 on a tool parameter typed as a bare "List", which is what OCI_JSON_TO_PYTHON_TYPES produced for JSON-schema arrays. MLflow {{trace}} judges trip this: their tools (get_root_span, get_span) take an attributes_to_fetch array. The lowercase builtins list/dict are accepted; only the bare "List" 500s ("Dict" happens to be tolerated, but both are lowercased for consistency). Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest). Adds a unit regression on the transformed parameterDefinitions plus a gated integration test exercising an array-param tool end to end. * fix(oci): make Cohere agentic tool-calling continuation work Two bugs broke the OCI Cohere tool-calling loop that MLflow {{trace}} judges drive once a tool has been executed and its result is fed back. Request side: litellm pulled the last user message into the top-level `message` and emitted the tool result as a TOOL entry in chatHistory. OCI rejects that ("cannot specify message if the last entry in chat history contains tool results"), and an empty message alone is rejected too ("message must be at least 1 token long or tool results must be specified"). OCI carries the current turn's results in a dedicated top-level `toolResults` field. The Cohere transform now sends an empty message, keeps the user turn in chatHistory, and puts the results in `toolResults`, matching the langchain-oracle reference. Tool results are no longer represented as chatHistory entries. Response side: tool-grounded answers come back with citations carrying `documentIds` (camelCase) and no `document_ids`, which made the required `CohereCitation.document_ids` field fail validation and sink the whole response parse. Those citations are never surfaced, so the field (and CohereSearchQuery's generation_id) is now optional. Verified live against us-chicago-1 (cohere.command-a-03-2025 and command-latest), single and multi-round tool loops. Adds unit regressions on the transformed request shape and on citation parsing, plus gated integration tests for the continuation. * feat: integrate Repelloai Argus guardrail (#30673) * feat(guardrails): add RepelloAI Argus guardrail integration (#1) * feat(guardrails): add RepelloAI Argus guardrail integration Add a new guardrail hook backed by RepelloAI Argus, with dashboard-managed asset policies enforced via an asset_id and X-API-Key auth. * fix(guardrails): harden RepelloAI Argus guardrail - scan streaming responses on output (was bypassing the guardrail) - log blocked verdicts as guardrail_intervened instead of success - treat auth/config errors (401/403/404/422) as misconfiguration that always blocks, not a fail-open-able unreachable error - default unreachable_fallback to fail_closed and read it directly; block on unknown/malformed verdicts so an API change can't silently disable enforcement - type unreachable_fallback as a Literal, drop the duplicate config model, expose unreachable_fallback in the config schema, and stop leaking the raw provider response / exception strings to the client * fix(guardrails): address RepelloAI Argus review feedback - support ARGUS_API_KEY (with REPELLOAI_API_KEY fallback) - make asset_id required in the config model - normalize unreachable_fallback so only fail_open opens; block on 400 misconfig - correct the shared unreachable_fallback field description * docs(guardrails): add RepelloAI Argus docs page and dashboard listing - add docs page covering config, env vars, modes, verdicts, failure semantics - list RepelloAI Argus in the Guardrail Garden with provider/logo mappings - add a regression test for the provider logo and display-name resolution * fix(guardrails): keep RepelloAI asset_id optional in config model A required asset_id leaked onto the shared LitellmParams (which inherits RepelloAIGuardrailConfigModel), breaking validation for every other guardrail. Keep it optional like sibling models; the guardrail __init__ still raises when asset_id is missing, which is the real enforcement. * Add comment for last user turn scanning * feat(guardrails): harden repelloai scanning * feat(guardrails): expand repelloai scanning to include tool definitions Add extraction of tool definitions and tool call arguments to the RepelloAI guardrail scanning. Improves detection coverage by including function schemas and parameters in the prompt sent to the guardrail service. Also captures detailed error responses in logs and adds guardrail header to streaming responses. * refactor(guardrails): fix and harden repelloai schema text extraction - Fix duplicate text in _iter_schema_text: previously all dict values were re-queued onto the stack even after scalar/list keys were already extracted explicitly, causing names/descriptions to appear twice in the scanned prompt - Extract schema key frozensets to module-level constants so they are not reconstructed on every call - Change _iter_schema_text from @classmethod to @staticmethod (cls unused) - Narrow _call_analyze stage param from str to Literal["prompt", "response"] - Add HttpxResponse type annotation to _raise_for_config_error - Add LLMResponseTypes annotation to async_post_call_success_hook response param * fix(guardrails): resolve pyright type errors in repelloai guardrail - Narrow async_handler.post return from Response|None to Response with explicit None guard before calling raise_for_status/json - Fix list comprehension returning str|None by switching to explicit loop with isinstance guard so pyright tracks the narrowing - Cast model_dump() result to Dict since hasattr does not narrow object type in pyright * fix(guardrails/repello): include Responses API instructions field in prompt scan The /v1/responses top-level `instructions` field was not included in _extract_prompt_text, allowing a caller to bypass guardrail policy checks by putting blocked content in `instructions` while keeping `input` benign. * feat: add api_key to config model and read prompt from data dict * fix(guardrails/repello): plug input_text and tool-call response bypass gaps Responses API input content parts with type 'input_text' were silently dropped by build_inspection_messages (which only handles type='text'), allowing callers to send blocked content via that path without triggering the pre-call scan. Fix: add _extract_input_text_parts to RepelloAIGuardrail and call it when walking the Responses API input messages. Post-call scanning skipped responses whose choices contained only tool_calls or function_call (message.content=None), letting models put blocked output in function arguments undetected. Fix: _extract_chat_completion_text now calls _extract_tool_call_args_from_message on each choice message. Also replace typing.Dict/List with builtin dict/list to clear TID251 strict ruff violations introduced by this file. * fix(guardrails/repello): scan Responses API function_call output arguments Output items with type 'function_call' in a /v1/responses response were skipped by _extract_responses_api_text; only 'message' items were walked. A model could return blocked content in function_call.arguments undetected. Now extract arguments from function_call output items before scanning. * refactor(guardrails/repello): clean up typing and remove lint-any workarounds - Replace Optional[X]/Union[X,Y] with X|None/X|Y union syntax throughout - Use dict[str, object] instead of bare dict in all signatures - Remove **kwargs from __init__; declare guardrail_name, event_hook, default_on explicitly - Replace getattr(litellm_params, ...) with direct attribute access now that LitellmParams inherits RepelloAIGuardrailConfigModel - Add _event_hook_from_mode() to convert str|list[str]|Mode to typed GuardrailEventHooks - Use TypeAdapter.validate_json() instead of response.json() + manual dict construction - Add _is_object_dict/_is_object_list TypeGuard helpers to narrow object types without Any - Remove cast() workarounds and typed intermediate variables that existed only for the now-removed lint-any CI check - Drop _AddLiteLLMCallback Protocol; budget has sufficient slack for the one reportUnknownMemberType - Fix GuardrailConfigModel missing type arg: GuardrailConfigModel[BaseModel] * fix(guardrails/repello): suppress LIT007 on TypeGuard helpers and add streaming scan-skip warning - Add guard-ok suppressions to _is_object_dict and _is_object_list to satisfy the LIT007 hard-zero budget gate - Emit verbose_proxy_logger.warning when the streaming hook finds no inspectable text after assembly, matching observability of pre/post hooks * refactor: modifications for lint check * feat: add Pinstripes as an OpenAI-compatible provider (#30567) * feat: add Pinstripes as an OpenAI-compatible provider Pinstripes (https://pinstripes.io) is an OpenAI-compatible inference provider serving open-source models (GLM-4.5-Air, Qwen3, DeepSeek, etc.) with per-token pricing and no subscriptions. Changes: - `litellm/llms/openai_like/providers.json`: register pinstripes with base_url, api_key_env, and max_completion_tokens→max_tokens mapping - `litellm/types/utils.py`: add `PINSTRIPES = "pinstripes"` to LlmProviders - `litellm/constants.py`: add to openai_compatible_providers and openai_compatible_endpoints lists - `litellm/litellm_core_utils/get_llm_provider_logic.py`: auto-detect provider when api_base is "https://pinstripes.io/v1" - `provider_endpoints_support.json`: document supported endpoints - `tests/`: 7 unit tests covering provider registration, resolution, URL auto-detection, api_base override, and Router config Usage: import litellm response = litellm.completion( model="pinstripes/ps/glm-4.5-air", messages=[{"role": "user", "content": "Hello"}], api_key=os.environ["PINSTRIPES_API_KEY"], ) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): resolve Greptile P1 review comments - Add api_base_env: PINSTRIPES_API_BASE to providers.json so env var override works - Set responses: false in provider_endpoints_support.json — not actually wired up - Remove docs/my-website/docs/providers/pinstripes.md — belongs in litellm-docs repo Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): add api_base_env and correct responses capability - Add api_base_env: PINSTRIPES_API_BASE to providers.json - Set responses: false in provider_endpoints_support.json Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): wire up Responses API — add supported_endpoints Adds supported_endpoints: ["/v1/chat/completions", "/v1/responses"] so JSONProviderRegistry.supports_responses_api returns true correctly, matching what provider_endpoints_support.json advertises. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * feat(pinstripes): enable embeddings endpoint Pinstripes serves nomic-embed-text-v1.5 and bge-m3 via /v1/embeddings. Add /v1/embeddings to supported_endpoints and set embeddings: true. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): use 4-space indentation in model_prices_and_context_window.json Matches the file's existing convention. Flagged by Greptile review. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(pinstripes): set a2a: false — A2A protocol not implemented All comparable JSON-configured providers (tensormesh, parasail, empiriolabs, libertai, neosantara) have a2a: false. Pinstripes does not implement the Google A2A protocol, so this should be false to match. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> --------- Co-authored-by: inference_provider <max@redactedlab.com> Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> * fix(rag): attach existing OpenAI file ids (#30628) * fix(rag): attach existing OpenAI file ids * chore: use modern typing in rag ingest fix * chore: retrigger ci * fix(anthropic-messages): apply cache_control_injection_points on /v1/messages path (#30341) cache_control_injection_points was only consumed by the chat/completions prompt-management hook; on the native Anthropic /v1/messages path it was forwarded unused, so deployment-level cache injection was silently dropped (cache_creation_input_tokens stayed 0 for Anthropic-native clients). Add AnthropicCacheControlHook.apply_to_anthropic_messages_request to inject cache_control at block level for system / tools / message locations (the only forms /v1/messages accepts), wire it into the native anthropic_messages handler, and pop the param so it does not leak upstream as an unknown field. A {location: message, role: system} config is redirected to the top-level system prompt so the same YAML works on both endpoints. Injection respects Anthropic's 4-block cache_control limit shared across system, tools, and messages: client-supplied markers count toward the cap and are never overwritten, a slot is reserved per Bedrock tool_config point, and injection stops once the budget is exhausted. Locations this path cannot represent (tool_config) are forwarded downstream instead of being silently consumed, mirroring get_chat_completion_prompt's remaining_points pass-through. Built on litellm_internal_staging. Refs BerriAI/litellm#30293 * fix(proxy): release budget reservation when a request is cancelled mid-flight (#30522) * fix(proxy): release budget reservation on cancel when no chunk was delivered The pre-call budget reservation increments the cross-pod spend counter by a request's worst-case cost, then reconciles it on success (cost callback) or error (failure hook). A client disconnect or timeout cancels the request and surfaces as CancelledError / GeneratorExit, which neither path catches, so the reservation leaks. Under a retry storm the leaked holds accumulate, pin the counter above real spend, and return spurious 429 "Budget has been exceeded" to keys whose spend is far below budget; the counter only recovers when its TTL lapses, so the failure is intermittent and self-healing. Release the reservation in async_streaming_data_generator (which the Anthropic and Google SSE generators delegate to) on the (CancelledError, GeneratorExit) path, alongside the existing max_parallel_requests release. release_budget_ reservation_on_cancel runs under asyncio.shield so it completes despite the in-progress cancellation, is guarded by the reservation's finalized flag, and swallows a failing release so it cannot replace the in-flight cancellation. The refund is gated on whether a chunk reached the client. The flag is set immediately before the yield, after the slow-path hook await: an async generator suspends at the yield, so a GeneratorExit on disconnect after a delivered chunk sees it True (keep the hold), while a cancellation during the slow-path await leaves it False (refund, nothing sent). A non-streaming cancellation delivers nothing and a completed non-streaming response is reconciled by the success callback, so neither needs a release here. Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(proxy): reconcile a cancelled reservation to input cost, not zero A streaming request cancelled before the first chunk previously reconciled its reservation to zero and finalized it. But by the time the generator is consuming the response the provider call was already dispatched, so the input tokens were billed even though no chunk reached the client, and the success/failure cost callbacks are skipped on cancellation. Refunding to zero let a caller send an expensive request and abort pre-token to dodge the input charge. Compute the request's input-token cost at reservation time and reconcile the cancelled reservation to it instead of zero. The worst-case output portion of the reservation is still released (so a legitimate mid-flight cancellation no longer pins the counter and 429s the key), while the input the provider already processed is charged. --------- Co-authored-by: Bytechoreographer <Bytechoreographer@users.noreply.github.com> Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix(caching): encode object name in GCS cache GET path (#30378) GCS cache reads always missed when gcs_path was set. The GET methods interpolated the object name directly into the URL path, while the GCS JSON API requires it to be URL-encoded (a "/" must be sent as %2F). With gcs_path configured the object name is "<prefix>/<sha256>", so the raw slash produced a malformed object path and GCS returned 404. httpx does not raise on 4xx, so the status_code == 200 check fell through and get/async_get returned None, silently missing on every read. Without gcs_path the key has no slash, which is why this went unnoticed. Wrap the object name with urllib.parse.quote(..., safe="") in get_cache and async_get_cache. Apply the same encoding to the name= query parameter in set_cache and async_set_cache so the key written matches the key read back. Adds regression tests asserting the GET path and SET query are encoded (%2F) when gcs_path is set, for both sync and async paths; these fail on the unpatched code. Fixes #30377 * chore: add soniox stt-async-v5 model (#30672) * fix(proxy): include model group aliases in v1 model info (#30626) * Include model group aliases in v1 model info * Fix model info alias implementation * removed extra blank line * chore: rerun CI * fix(lint): remove redundant noqa directive in proxy_cli.py * fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme * Revert "fix: address greptile review - restore bedrock_mantle auth symbols, guard OCI empty message list, validate DIRECT_URL scheme" This reverts commit |
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yuneng-jiang
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c0352c5aa8
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chore(ci): remove Agent Shin pull_request_target workflows (#30784)
Drop the two Agent Shin workflows that ran on the pull_request_target trigger: the PR triage workflow and the review gate. Both were dry-run and gated behind AGENT_SHIN_ENABLED, so no live automation changes. The shared scripts under .github/scripts stay in place; four other Agent Shin workflows still depend on them and run on schedule, dispatch, and issue events rather than pull_request_target |
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Yassin Kortam
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a8b94b9a87
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fix(proxy): enforce budgets against authoritative DB spend when the cross-pod counter is unreliable (#30684)
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Budget enforcement reads spend from the cross-pod Redis counter via get_current_spend, which trusted the counter whenever Redis returned a value. A Redis instance that restarts and reloads an older RDB snapshot (the customer's logs repeat "Redis is loading the dataset in memory") comes back with a stale-low counter; that read is a hit, not a clean miss, so the existing DB reseed never ran and a key kept getting admitted even though its recorded spend was already over max_budget. The symptom was recorded spend sitting above the limit while requests kept succeeding. Read-time enforcement: get_current_spend takes an optional max_budget and, when the counter would admit the request but reads below this caller's last-known recorded spend, re-reads the authoritative spend and enforces against the higher value. The authoritative source depends on the counter: key/team/user/org/team-member read the DB row, per-window budgets aggregate spend logs, and end-user/tag have no DB row so the caller's freshly-loaded recorded spend is used. Healthy primary counters and freshly reset keys stay off the DB path, and the value is cached in-process for a few seconds, so a persistently stale counter drives at most one read per counter per window. When the DB value is higher, the counter is repaired with a monotonic, atomic set-max (RedisCache.async_set_max) so every worker reads the corrected total and a concurrent increment is never clobbered. Reconcile no longer fails open: when the post-call reservation reconcile found the counter missing or an adjustment that would drive it negative, it deleted the counter and continued (the deletion is what left counters nil/unenforced after a Redis reload). It now reseeds from the DB's lagging authoritative floor instead of deleting; the monotonic set-max can only raise a stale-low counter, and the read-time floor converges to the true total as the spend buffer flushes. The pre-call admission resize path keeps its original fail-closed behavior. Opt-in strict enforcement: general_settings.fail_closed_budget_enforcement (default False) makes the authoritative re-check run for every budgeted entity (closing the gap where a stale-low counter and a stale-low cached fallback would otherwise both pass the cheap guard), and rejects a request with 503 when the spend backing an admit decision can be verified against neither Redis nor the database. Default behavior is unchanged; the re-check stays bounded by the in-process cache. Resolves LIT-3772 |