* fix(memory): jsonify metadata before Prisma writes on /v1/memory
The POST/PUT memory endpoints handed bare dicts (and bare `None`) to
prisma-client-python for the `Json?` `metadata` column, which the client
rejects with `MissingRequiredValueError` / `DataError: metadata should
be of any of the following types: NullableJsonNullValueInput, Json`.
Both the create and upsert paths now route writes through the existing
`jsonify_object` helper used elsewhere in the proxy for `Json?` columns
(e.g. `LiteLLM_VerificationToken.budget_limits`), and omit metadata
when None so the column defaults to SQL NULL via the schema.
Explicit `metadata: null` on PUT is now a no-op for the column to match
how the rest of the proxy handles nullable JSON fields (no
`JsonNull`/`DbNull` sentinel exists in prisma-client-python — see
RobertCraigie/prisma-client-py#714). A payload with only `metadata: null`
returns 400 instead of a misleading 200.
Made-with: Cursor
* fix(memory): JSON-encode non-dict metadata before Prisma writes
`jsonify_object` only stringifies dict values, so list-shaped metadata
still hit Prisma as raw Python objects and triggered the same
DataError this PR is meant to fix. `metadata` is typed `Optional[Any]`
so list payloads are valid input. Replace `jsonify_object` with a
local `_serialize_metadata_for_prisma` helper that always `json.dumps`
non-string values, applied at all three write sites
(POST create, PUT update, PUT-create). Adds regression tests for
list metadata on each path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): always json.dumps metadata, not just non-strings
The str-passthrough in `_serialize_metadata_for_prisma` left plain
Python strings (e.g. `metadata: "hello"`) unencoded — Postgres `jsonb`
rejects bare-word strings as invalid JSON, reproducing the same
DataError this PR is meant to fix. Always `json.dumps` regardless of
input type so all `Optional[Any]` shapes (dict, list, scalar, str)
become valid JSON. Adds a regression test for plain-string metadata.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): encode explicit metadata:null as JSON null to clear field
prisma-client-python has no JsonNull/DbNull sentinel for writing a
true SQL NULL on `Json?` columns (RobertCraigie/prisma-client-py#714),
so an earlier iteration of this PR treated `PUT {"metadata": null}`
as a no-op. That doesn't match the natural caller expectation that
explicit-null clears the field.
Encode it as the JSON literal `null` instead — stored as Postgres
`jsonb 'null'`, which prisma deserializes back to Python `None` on
read. Subsequent reads return `metadata: null`, so the field is
effectively cleared from the caller's perspective. Strict SQL NULL
remains unreachable via the typed client and would require raw SQL.
Also clean up stale `jsonify_object` references in test mock comments
(replaced by `_serialize_metadata_for_prisma`).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(memory ui): use shared DeleteResourceModal for memory deletion
Swap the imperative `Modal.confirm` in MemoryView for the shared
`DeleteResourceModal`, so memory deletion matches the rest of the
dashboard: type-to-confirm guard on the key, in-flight loading state
on the OK button, cancel disabled while the request is pending, and
the modal stays open on error so the user can retry.
Made-with: Cursor
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): jsonify metadata before Prisma writes on /v1/memory
The POST/PUT memory endpoints handed bare dicts (and bare `None`) to
prisma-client-python for the `Json?` `metadata` column, which the client
rejects with `MissingRequiredValueError` / `DataError: metadata should
be of any of the following types: NullableJsonNullValueInput, Json`.
Both the create and upsert paths now route writes through the existing
`jsonify_object` helper used elsewhere in the proxy for `Json?` columns
(e.g. `LiteLLM_VerificationToken.budget_limits`), and omit metadata
when None so the column defaults to SQL NULL via the schema.
Explicit `metadata: null` on PUT is now a no-op for the column to match
how the rest of the proxy handles nullable JSON fields (no
`JsonNull`/`DbNull` sentinel exists in prisma-client-python — see
RobertCraigie/prisma-client-py#714). A payload with only `metadata: null`
returns 400 instead of a misleading 200.
Made-with: Cursor
* fix(memory): JSON-encode non-dict metadata before Prisma writes
`jsonify_object` only stringifies dict values, so list-shaped metadata
still hit Prisma as raw Python objects and triggered the same
DataError this PR is meant to fix. `metadata` is typed `Optional[Any]`
so list payloads are valid input. Replace `jsonify_object` with a
local `_serialize_metadata_for_prisma` helper that always `json.dumps`
non-string values, applied at all three write sites
(POST create, PUT update, PUT-create). Adds regression tests for
list metadata on each path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): always json.dumps metadata, not just non-strings
The str-passthrough in `_serialize_metadata_for_prisma` left plain
Python strings (e.g. `metadata: "hello"`) unencoded — Postgres `jsonb`
rejects bare-word strings as invalid JSON, reproducing the same
DataError this PR is meant to fix. Always `json.dumps` regardless of
input type so all `Optional[Any]` shapes (dict, list, scalar, str)
become valid JSON. Adds a regression test for plain-string metadata.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Code review noted the previous test reimplemented the proxy's
try/except/finally around post_call_success_hook, so it would not catch a
regression that re-introduced the duplicate-log bug in the production code
path. Extract the gating logic into
`ProxyBaseLLMRequestProcessing._flush_deferred_async_logging` so tests
exercise the production helper directly.
The proxy finally block becomes a single call to the helper. Tests now
invoke the helper itself and additionally assert (via inspect.getsource)
that base_process_llm_request continues to delegate to the helper rather
than inlining the gate.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When a non-streaming request had any post-call guardrail registered, the
proxy deferred the async-success logging closure until after
post_call_success_hook ran. The finally block fired that closure even when
the hook raised — the propagating HTTPException then routed through
post_call_failure_hook → _handle_logging_proxy_only_error, which writes its
own failure spend log via async_failure_handler. The result was two spend
log rows per blocked request: one Success exposing the blocked LLM response
and one Failure. Reproduces with both pre and post bedrock guardrails
configured for a team when the post-call OUTPUT scan blocks the response.
Gate the deferred closure on _exception_raised so the failure path remains
the single source of truth for blocked requests.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(guardrails): apply team-level guardrails alongside global policy guardrails
Two bugs prevented team-direct guardrails from being automatically applied
when using a team-scoped API key:
1. Auth caching: `valid_token.team_metadata` was never refreshed from the
freshly-fetched team object at the "Check 6" step in
`_user_api_key_auth_builder`. Guardrails added to a team after the key
was first cached were therefore invisible to `move_guardrails_to_metadata`.
Fix: propagate `_team_obj.metadata` → `valid_token.team_metadata` after
every "Check 6" team fetch (user_api_key_auth.py).
2. Guardrail execution: `get_guardrail_from_metadata` checked
`data["litellm_metadata"]` before `data["metadata"]`. When a request
carried a non-empty `litellm_metadata` without a "guardrails" key, the
merged guardrail list written to `data["metadata"]` by
`move_guardrails_to_metadata` was shadowed and the guardrail received an
empty requested-guardrails list (custom_guardrail.py).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix merge conflict
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Mirrors the existing api_base handling in _load_credentials_from_config():
when a vector_store_config references litellm_credential_name and the stored
credential does not itself define aws_sts_endpoint or aws_web_identity_token,
any caller-supplied value on those keys is removed. Keeps endpoint and
identity fields aligned with the credential definition.
* fix(bedrock guardrail): dedupe post-call log when only post_call is configured
When a Bedrock guardrail runs with only post_call configured, the post-call
trace section showed the same guardrail twice (one entry per parallel
INPUT/OUTPUT API call). Add skip_logging param to make_bedrock_api_request
and pass it for the INPUT scan so the OUTPUT scan stands as the single
canonical post_call log entry. INPUT exceptions still propagate, so the
input-side blocking behavior is preserved.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(bedrock guardrail): post_call only scans OUTPUT, not INPUT
post_call is the response-validation hook by definition — input scanning
belongs to pre_call / during_call. The previous code ran an extra INPUT
scan in post_call when no pre/during hook was configured, which produced
a duplicate "post-call" entry in the trace and was semantically wrong
for a "post-call" event.
Drops the should_validate_input branch and parallel asyncio.gather in both
async_post_call_success_hook and async_post_call_streaming_iterator_hook
in favor of a single OUTPUT scan. Reverts the now-unneeded skip_logging
parameter on make_bedrock_api_request introduced in the previous commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Fall back to email match when looking up the caller in
members_with_roles — email-onboarded members may have user_id=None on the
stored entry, which caused a false 404 for valid members. (P1)
- Replace 3 raw Prisma queries with get_team_object / get_team_membership /
get_user_object so the endpoint reuses the cache + retry layer the rest of
the proxy uses. (P2)
- Allow internal_user role to reach /team/{team_id}/members/me by adding the
route to LiteLLMRoutes.self_managed_routes (the handler already enforces
member-of-team access).
- Return null from the UI fetch on 404 instead of throwing, so a proxy admin
who isn't a team member sees the existing empty state rather than an error
string in the always-visible tab. (P2)
- Move the fetch out of networking.tsx into a colocated React Query hook
(useMyTeamMember) next to MyUserTab; TeamInfo now passes only teamId.
- Tooltip + empty-state copy on Model Scope: drop "(all team models)"
parenthetical and the redundant tooltip line.
- Tests: build real LiteLLM_TeamMembership / LiteLLM_BudgetTableFull
fixtures (with created_at) so the Pydantic Union resolves to the Full
variant; add an assertion that budget_reset_at survives end-to-end; add a
test for the email-only member match path.
* feat(azure): add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants)
Azure variants of OpenAI's GPT-5.5 family. Microsoft has not yet
shipped GPT-5.5 on Azure OpenAI (latest GA on the Foundry models page
is GPT-5.4 as of 2026-04-24), but adding the entries day-0 mirrors the
established precedent for azure/gpt-5.4* (which were in the cost map
before the Azure rollout) so cost tracking and capability flags work
the moment customers deploy.
Schema follows the existing azure/gpt-5.4* shape:
- Same base/long-context pricing as openai/gpt-5.5*: $5/$30 chat,
$60/$360 pro per 1M, with priority tier 2x base
- Azure variants drop the flex/batches keys (Azure has no flex tier)
but keep priority pricing, matching gpt-5.4* precedent
- mode=chat for the thinking model, mode=responses for pro
reasoning_effort capability flags mirror the OpenAI variants exactly
since Azure proxies the same API contract: minimal rejection on both
chat and pro, low/none rejection on pro. Once #26456 (which sets
supports_low_reasoning_effort + minimal=false on openai/gpt-5.5*)
lands, OpenAI and Azure flag profiles align.
Tests pin entry presence + pricing for all four Azure variants and
verify the live-API-derived reasoning_effort flags.
* test: register supports_low_reasoning_effort in cost-map JSON schema
azure/gpt-5.5-pro and azure/gpt-5.5-pro-2026-04-23 added in this branch
carry supports_low_reasoning_effort=false. The strict
'additionalProperties: false' schema in
test_aaamodel_prices_and_context_window_json_is_valid rejected the new
key. Register it alongside the other supports_*_reasoning_effort
entries.
Note: the runtime side of this flag (code that reads it) lands in
#26456. Until that PR merges the flag is inert for both Azure and
OpenAI pro entries, but having the schema accept it lets cost-map
tests pass on either merge order.
Adds a new "My User" tab on the team detail page (between Overview and
Virtual Keys) so non-admin team members can see their own spend, budget,
budget reset date, rate limits, model scope, and team role.
Backend
- New `GET /team/{team_id}/members/me` endpoint that resolves the caller
from the API key and returns only their own LiteLLM_TeamMembership row
plus minimal team context (alias, role, email). Returns 404 if the
caller is not a member of the team. Avoids exposing other members'
data, which would happen if we filtered `/team/info` client-side.
- New `TeamMemberInfoResponse` Pydantic model.
Frontend
- New `MyUserTab` component (antd) — read-only summary cards.
- New `teamMemberMeCall` helper in networking.tsx.
- Tab is visible to all team members (including non-admins).
When litellm_credential_name resolves to no values (name not registered
in litellm.credential_list), bail out before touching the resolved
config. Prevents stripping a caller-supplied api_base in the no-match
case.
Mirrors the existing vertex_credentials handling for the newer
vertex_ai_credentials field, and extends the dynamic masker's sensitive
pattern set to recognize the plural form so other plural-named credential
fields are also covered.
When a vector_store config references a stored credential by name,
overwrite caller-supplied values on the same keys with the credential
values, and drop api_base from the resolved config when the credential
does not define one. Keeps the api_key / api_base pair consistent with
the credential definition.
Bring `/server/oauth/{server_id}/authorize`, `/token`, and `/register`
in line with `fetch_mcp_server`: the helper that resolves the server now
also applies the per-caller access policy. Admin-view callers are
unrestricted; non-admins must have the server in their allowed-servers
set; servers resolved from the admin-only `/server/oauth/session`
temporary cache reject non-admins.
dimensions=1 was silently dropped before #24415 wired the param through
to Vertex. Now that it's forwarded, Vertex rejects it (valid values:
128, 256, 512, 1408). Switch the test to a valid dimension.
Add a new CI workflow that rejects pull requests from forks when they:
- Modify uv.lock (any change at all)
- Add new dependencies to any pyproject.toml file (root, litellm-proxy-extras, enterprise)
Security properties:
- Uses pull_request (not pull_request_target) so no secrets are exposed
- All action refs pinned to full SHA hashes
- persist-credentials: false on all checkouts
- permissions: {} (no GitHub token permissions)
- No user-controlled input in run: blocks (no script injection)
- Proper TOML parsing via stdlib tomllib (not regex on raw text)
- Only triggers when dependency files are actually changed (paths filter)
Internal PRs (from branches in the canonical repo) skip the job entirely.
Co-authored-by: Krrish Dholakia <krrish-berri-2@users.noreply.github.com>
Sync CI configs with upstream/litellm_internal_staging:
- Migrate from Poetry to uv (PR #25007)
- Pull in zizmor security fixes for workflows
- Add isolated unit test workflows + reusable bases
- Drop redundant matrix workflow and azure-batches workflow
- Drop .circleci/requirements.txt (replaced by uv lockfile)
After ``return_exceptions=True``, gather results are typed
``Any | BaseException``. Mypy could not narrow through
``None if isinstance(x, HTTPException) else x`` ternaries, leaving
BaseException in the type passed to ``common_checks``.
Switch the narrowing checks to ``BaseException`` (HTTPException is the
only one still possible after authz failures were re-raised) and add
explicit ``Optional[...]`` annotations so the typed objects flow into
``common_checks``. No behavior change.
The asyncio.gather in `_run_centralized_common_checks` ran with
`return_exceptions=False` and a single bare `except HTTPException`
arm, so an HTTPException from any one fetch (the realistic case is a
404 from `get_team_object` when a token references a deleted team)
zeroed out the user, end-user, project, and global-spend contexts in
addition to falling back the team object. That silently skipped the
user budget, end-user budget, and project enforcement passes inside
`common_checks` for the unrelated contexts that had actually fetched
fine.
Switch to `return_exceptions=True` and apply per-fetch fallback
(matches the pre-refactor per-fetch try/except pattern in the builder):
- ProxyException / BudgetExceededError still propagate as authz failures.
- HTTPException on the team fetch reconstructs from the token; on the
other fetches it nulls only that one context.
- Successful fetches always reach `common_checks` intact.
Adds two unit tests covering the team-404 and user-404 cases to lock
the per-fetch isolation in. Drops the inaccurate `PROXY_ADMIN tokens
short-circuit` claim from the docstring — admin tokens still flow
through `common_checks`; admin status is only honored where the
underlying check exempts it.
