Three issues surfaced in review of the previous commit:
1. **Veria — Medium**: ``litellm_params`` carries a nested
``litellm_embedding_config`` dict (auto-resolved from the model
registry on create / update) which itself holds ``api_key`` /
``aws_*`` / ``vertex_credentials``. The previous redactor only
inspected top-level keys, so the nested values passed through
unredacted. Recurse into nested dicts.
2. **Greptile — P2**: when ``litellm_params`` is a JSON-serialized
string (the in-memory registry occasionally stores it that way), the
previous redactor silently no-op'd via the ``isinstance(..., dict)``
guard and echoed the raw payload back. Now: parse, redact, re-serialize.
If the string is not valid JSON, replace it with the redaction
sentinel rather than echo it.
3. **mypy** flagged ``_redact_sensitive_litellm_params``'s
``Optional[Dict[str, Any]]`` signature as incompatible with the
``object``-typed call site. Widened to ``Any -> Any`` to reflect the
actual contract (the function now handles dict / str / None / other).
Also fixes a related test regression in
``test_remove_sensitive_info_from_deployment_with_excluded_keys``: the
``"credentials"`` plural addition to ``SensitiveDataMasker`` defaults
caused the first call (without ``excluded_keys``) to mutate the input
dict's ``litellm_credentials_name`` to a masked value. The second call
(with ``excluded_keys``) then saw the already-masked value rather than
the original. Construct fresh input for each call.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two architectural extensions to the credential-redaction in the previous
commit:
1. ``/vector_store/update`` had two gaps:
- No per-store access control. Any authenticated principal that
passed the premium-feature gate could mutate *any* vector store,
including stores belonging to other teams.
- The response returned the full DB row including ``litellm_params``,
so the caller could read another team's persisted provider
credentials by submitting a no-op metadata change.
Mirror the access-control check ``/vector_store/info`` already
performs (``_check_vector_store_access`` against the existing row),
redact ``litellm_params`` in the response, and add an
``except HTTPException: raise`` guard so the 403/404 responses don't
get rewritten as 500 by the catch-all.
2. ``SensitiveDataMasker``'s default ``sensitive_patterns`` set used
segment-exact matching, so ``credential`` matched ``vertex_credential``
but not ``vertex_credentials`` (the actual Vertex field name). The
previous commit worked around this with a per-call extension; this
commit puts the plural in the upstream defaults so every caller
(Redis config dump, MCP debug headers, cache routes, ...) gets the
correct behavior. The local override in
``vector_store_endpoints/management_endpoints.py`` is removed.
Also updates ``test_excluded_keys_exact_match`` which relied on
``credentials`` *not* being a sensitive pattern to demonstrate
case-sensitive ``excluded_keys`` matching. The intent of the test
(case-sensitive match) is preserved; the assertion now reflects that
when ``excluded_keys`` fails to apply (wrong case), the field falls
through to standard pattern-based masking instead of being passed
through unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
/simplify pass:
- Remove the single-call-site ``_redact_vector_store`` wrapper. Inline
the two-line redaction at its only caller in ``list_vector_stores``;
``get_vector_store_info`` was already calling the inner helper directly.
- Inherit ``SensitiveDataMasker``'s default sensitive-key set instead of
duplicating the 12-element list, then add only the plural
``credentials`` extension. Won't drift if upstream defaults change.
- Trim the over-explained docstring on ``_redact_sensitive_litellm_params``
to a one-paragraph summary; the WHY (credential-leakage class) belongs
in the commit message, not in every consumer's IDE tooltip.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
``LiteLLM_ManagedVectorStore.litellm_params`` carries the upstream provider
credential — OpenAI ``api_key``, AWS ``aws_access_key_id`` /
``aws_secret_access_key``, GCP ``vertex_credentials``, etc. ``GET
/vector_store/list`` and ``POST /vector_store/info`` return these
verbatim to any authenticated principal. Because both routes are in
``openai_routes``, ``RouteChecks.is_llm_api_route`` short-circuits the
standard role gate, so even read-only users and narrowly-scoped keys can
read every stored credential.
Replace credential-bearing values with the ``REDACTED_BY_LITELM``
sentinel in both responses while preserving non-secret keys
(``api_base``, ``region``, ``model``, ``api_version``) so callers can
still see *which* upstream is configured. Detection reuses
``SensitiveDataMasker.is_sensitive_key`` with the default heuristics
plus the plural ``credentials`` pattern (covers Vertex's
``vertex_credentials`` field, which the singular ``credential`` pattern
misses on segment-exact matching).
Applied at:
- ``list_vector_stores`` (``GET /vector_store/list``,
``GET /v1/vector_store/list``)
- ``get_vector_store_info`` (``POST /vector_store/info``), both the
in-memory-registry path and the prisma-DB fallback
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two related changes to how the master-key auth path interacts with
downstream consumers of UserAPIKeyAuth.api_key:
1. The master-key auth branch in user_api_key_auth.py now sets
`valid_token.api_key` to a stable alias
(`LITELLM_PROXY_MASTER_KEY_ALIAS = "litellm_proxy_master_key"`) instead
of the raw master key. Downstream consumers — spend logging,
Prometheus metrics, audit trails, rate limiting, cost tracking — now
receive the alias instead of the master key (which they would
previously hash and propagate). Neither the raw master key nor its
hash flows past the auth layer.
2. `_is_master_key` in spend_tracking_utils.py is reduced to a strict
raw-only constant-time comparison. The hashed form is no longer
considered equivalent.
Side effects:
- The two hash-detection blocks in `get_logging_payload` are removed.
They were re-detecting the master key per spend-log write to swap in
the alias; that detection happens once at the auth layer now.
- The `disable_adding_master_key_hash_to_db` general setting becomes a
no-op. Operators can remove it from their config; existing config is
still accepted.
- Operator dashboards that filter Prometheus metrics by the master-key
hash will need to switch to the `api_key="litellm_proxy_master_key"`
label.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Store search tool allowlists only on object permissions, wire auth/management/UI flows to object_permission.search_tools, and remove legacy team-metadata search credential code and tests.
Made-with: Cursor
Greptile review on #26756 (P2): if `attempt_db_reconnect` itself raises
(e.g. lock cancellation, timer error, unexpected internal failure), the
original `httpx.ReadError` / transport error was lost — `failure_handler`
and `db_exceptions` alerts then logged the reconnect exception instead of
the actual DB transport problem, masking the root cause.
Wrap the reconnect call in a try/except. On reconnect failure, re-raise
the *original* `first_exc` and chain the reconnect error as `__cause__`
so it remains visible for debuggability without becoming the primary
exception observers see.
Adds `test_call_with_db_reconnect_retry_preserves_original_error_when_reconnect_raises`
asserting (a) the propagated exception is the original transport error
and (b) the reconnect exception is attached as `__cause__`.
Two related fixes layered on top of the existing reconnect plumbing:
1. Restore reconnect-and-retry on `PrismaClient.get_generic_data` (issue
#25143). 1.83.x lost the transport-reconnect-and-retry-once branch that
1.82.6 had on this method, so transient `httpx.ReadError` flaps now
surface immediately as `db_exceptions` alerts. `_update_config_from_db`
fans out four concurrent `get_generic_data` reads, so a single transport
blip used to mark four alerts and a stale config window.
Adds `call_with_db_reconnect_retry` to `litellm/proxy/db/exception_handler.py`
— a single canonical "try DB read, on transport error reconnect once and
retry once" wrapper. Mirrors the inline pattern in
`auth_checks._fetch_key_object_from_db_with_reconnect` so we have one
implementation rather than three drifting copies, and gives future read
paths a clean opt-in.
2. Fix the `_engine_confirmed_dead` flag-reset bug in
`_run_reconnect_cycle`. The flag was cleared before `_do_heavy_reconnect()`
ran, so any failure inside the heavy reconnect (timeout, missing
DATABASE_URL, recreate failure) left the flag False — and the next
attempt could silently demote to the lightweight path even though the
engine was genuinely dead. Move the reset into the success branch so the
flag stays True across heavy-reconnect failures and the next attempt
re-enters the heavy branch.
Tests:
- `tests/test_litellm/proxy/db/test_exception_handler_reconnect_retry.py`
(new) — 9 tests covering the helper's contract: happy path, retry on
transport error, no retry on data-layer errors, propagation when reconnect
fails, propagation after second transport error, `hasattr` guard for
partial mocks, fresh-coroutine-per-call invariant, explicit timeout
override, default timeouts read off the prisma_client.
- `tests/test_litellm/proxy/db/test_prisma_self_heal.py` — adds:
- `test_get_generic_data_retries_on_transport_error_for_config_table`
- `test_get_generic_data_propagates_when_reconnect_fails`
- `test_engine_confirmed_dead_persists_across_failed_heavy_reconnect`
(regression test for the flag-reset bug).
All 16 self-heal tests + 9 helper tests + 535 auth/exception-handler tests
pass locally.
- server.py: drop the redundant server_id append in
_get_filtered_mcp_servers_from_mcp_server_names. iter_known_server_prefixes
already yields server_id unconditionally, so the manual append (and its
misleading comment) was a no-op duplicate.
- utils.py: rewrite the SHORT_MCP_TOOL_PREFIX docstring to accurately
describe the collision behaviour. The previous wording said collisions
were 'cosmetic only', but a natural-hash collision IS a routing-correctness
issue, which is precisely why we already added _assign_unique_short_prefix
to rehash deterministically. The new comment cross-references that path.
- utils.py: restrict the first character of the short prefix to [A-Za-z]
via a 52-char alphabet for position 0 only. The remaining two positions
still use the full base62 alphabet. This keeps prefixes valid identifiers
on every backend and gives 52*62*62 = 199_888 distinct prefixes (still
comfortably more than any realistic deployment).
- tests: add coverage proving the first character of the prefix is always
alphabetic across many server_ids and rehash attempts.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Two MCP servers can natural-hash to the same three-character base62
prefix. With 62**3 = 238_328 slots the birthday bound is ~488 servers
for 50% collision probability, so a single proxy hosting more than
~100 MCP servers has a non-trivial chance of seeing a collision in
practice — and a collision means tool names from two different servers
share a routing key, causing silent mis-routing.
Mitigation:
- compute_short_server_prefix(server_id, attempt=N) folds an attempt
counter into the SHA-256 seed, so rehashes are deterministic and
produce a fresh three-char prefix space per attempt.
- New MCPServer.short_prefix field caches the resolved (post-dedup)
prefix on the model so it stays stable across the process lifetime.
- MCPServerManager._assign_unique_short_prefix walks attempts 0..N
until it finds a prefix not already used by another server in the
combined registry. Logs an INFO line when a rehash happens so
operators have a breadcrumb if it ever does.
- Wired into every registration path: load_servers_from_config,
add_server, update_server, reload_servers_from_database. The
database reload path also carries the previously-resolved prefix
forward so reloads don't churn it.
- get_server_prefix prefers the cached short_prefix when set, so the
resolved value (not the raw natural hash) is used everywhere.
- iter_known_server_prefixes yields the cached short_prefix too, so
reverse-lookup tolerance covers the rehashed form.
No-op when LITELLM_USE_SHORT_MCP_TOOL_PREFIX is disabled — the field
stays None and behaviour is unchanged.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Adds LITELLM_USE_SHORT_MCP_TOOL_PREFIX. When enabled, tool / prompt /
resource / resource-template names emitted from MCP servers are prefixed
with a deterministic three-character base62 ID derived from the server's
server_id (SHA-256 → base62) instead of the (potentially long)
alias / server_name. This keeps namespaced tool names well under the
60-character upper bound enforced by some model APIs while still letting
us distinguish MCP-routed tools from local tools.
Behavioural notes:
- Default off — when the env var is unset, the long-prefix behaviour
is unchanged. The plan is to flip the default in a future release
and remove the gate after a deprecation window.
- Prefix derivation is deterministic, so it is stable across processes,
workers and restarts without any persistence layer.
- Reverse-lookup is tolerant: _create_prefixed_tools registers every
known prefix form (alias / server_name / server_id / short ID) in
the routing map and _get_mcp_server_from_tool_name resolves any of
them. Old clients holding cached long-prefixed names continue to
route correctly even after the flag is enabled.
- _get_allowed_mcp_servers_from_mcp_server_names accepts the short
prefix in /mcp/{server_name}-style URLs.
- The OpenAPI tool-listing path now filters by the active server
prefix instead of server.name so spec-backed servers benefit too.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
- Updated instances of DualCache to UserApiKeyCache across multiple files to enhance cache handling for user API keys.
- Adjusted cache retrieval and storage methods to ensure proper serialization and deserialization of cached objects.
- Introduced a new UserApiKeyCache class to streamline caching logic and improve type safety.
- Updated relevant tests to reflect changes in caching behavior and ensure compatibility with the new cache implementation.
Allow search requests to resolve provider credentials from request metadata, team metadata, and default team settings with clear precedence, and expose this flow in proxy docs/UI with regression tests.
Made-with: Cursor
Previously the normalize_callback_names call only ran when the existing
litellm_settings DB row already had a success_callback key. On the very
first write (no row yet, or row missing the key), incoming mixed-case
values like ["SQS", "sQs"] persisted as-is. delete_callback (lowercase
lookup) then could not find them, and a follow-up /config/update would
union normalized incoming with mixed-case stored entries, producing
duplicates.
Always normalize incoming success_callback before merging, and dedupe
both the standalone first-write case and the union-with-existing case.
Adds test_success_callback_normalized_on_first_write covering the
no-existing-row path; the existing union test still passes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The endpoint loaded the full merged YAML+DB config and re-saved every
top-level section to LiteLLM_Config rows via save_config(), so a UI toggle
of one field persisted unrelated YAML state to DB as a side effect. It
also rejected every request when store_model_in_db was False — including
the request that would flip the flag to True (chicken-and-egg).
Replace save_config with targeted per-section upserts: read the existing
litellm_config row, merge in the request, upsert just that row. Sections
the caller did not send are not touched. Drop the blanket
store_model_in_db guard — the endpoint already requires prisma_client,
and the startup-side override at proxy_server.py:6491 picks up
general_settings.store_model_in_db=True from the DB on next restart.
Adds a CLI flag (`--timeout_worker_healthcheck`, env `TIMEOUT_WORKER_HEALTHCHECK`)
that forwards to uvicorn's `timeout_worker_healthcheck` Config kwarg (added in
uvicorn 0.37.0). Lets operators raise the supervisor's worker-ping timeout above
the default 5s when triaging workers being killed and respawned under load.
The helper introspects `uvicorn.Config.__init__` and only sets the kwarg if
supported, otherwise prints a warning - so the existing uvicorn>=0.32.1,<1.0.0
floor pin is unaffected. Gunicorn and Hypercorn paths are unchanged (the uvicorn
supervisor isn't running there); the value is also not passed to the helper at
all on those paths so the "uvicorn too old" warning never fires spuriously.
* Use auth key name if there are no app id in in headers or in extra_data
* use key alias instead of key name
* Fix
* last priority key alias
* Fix
* Add tests
* [Feat] Day-0 support for GPT-5.5 and GPT-5.5 Pro (#26449)
* feat(openai): day-0 support for GPT-5.5 and GPT-5.5 Pro
Add pricing + capability entries for the new GPT-5.5 family launched by
OpenAI on 2026-04-24:
- gpt-5.5 / gpt-5.5-2026-04-23 (chat): $5/$30/$0.50 per 1M
input/output/cached input
- gpt-5.5-pro / gpt-5.5-pro-2026-04-23 (responses-only): $60/$360/$6
per 1M input/output/cached input
Other fees (long-context >272k, flex, batches, priority, cache
discounts) follow the same ratios as GPT-5.4, with context window
retained at 1.05M input / 128K output.
No transformation / classifier code changes are required:
OpenAIGPT5Config.is_model_gpt_5_4_plus_model() already matches 5.5+ via
numeric version parsing, and model registration is driven from the
JSON. The existing responses-API bridge for tools + reasoning_effort
(litellm/main.py:970) already covers gpt-5.5-pro.
Tests:
- GPT5_MODELS regression list now covers gpt-5.5-pro and dated variants
- New test_generic_cost_per_token_gpt55_pro cost-calc test
- Updated test_generic_cost_per_token_gpt55 for long-context fields
* fix(openai): mirror reasoning_effort flags onto gpt-5.5 dated variants
gpt-5.5-2026-04-23 and gpt-5.5-pro-2026-04-23 were missing the
supports_none_reasoning_effort, supports_xhigh_reasoning_effort, and
supports_minimal_reasoning_effort flags that their non-dated
counterparts define. Reasoning-effort routing in OpenAIGPT5Config is
fully capability-driven from these JSON flags — since an absent flag
is treated as False for opt-in levels (xhigh), users pinning to a
dated snapshot would silently lose xhigh support and diverge from the
base alias on logprobs + flexible temperature handling.
Copy the flags onto both dated variants so every dated snapshot
inherits the base model's reasoning-effort capability profile.
Adds a parametrized regression test that asserts
supports_{none,minimal,xhigh}_reasoning_effort parity between each
dated variant and its non-dated counterpart, preventing future drift
when new snapshots are added.
* [Feat] Add azure/gpt-5.5 + azure/gpt-5.5-pro entries (+ dated variants) (#26361)
* 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.
* Use sanitize deep copy style to replace deepcopy usage
* Added test checking error is not happening anymore
* Added warning log when json copy failed
* Reduce to one change
* Fix spaces
---------
Co-authored-by: Ido Lavi <ido@noma.security>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: TomAlon <tom@noma.security>
* fix(proxy): invoke post-call guardrails on pass-through endpoint responses (#20270)
Wire post_call_success_hook into non-streaming pass-through response path,
gated on explicit guardrail config (opt-in only, no backwards-compat break).
- Call post_call_success_hook after reading non-streaming response body
- Build enriched hook_data with guardrails metadata and litellm_logging_obj
at call site (avoids mutation of _parsed_body which is shared by logging)
- Handle ModifyResponseException with provider-agnostic error envelope,
post_call_failure_hook, and defensive try/except
- Strip stale content-length when guardrail modifies response body
- Move ModifyResponseException to litellm.exceptions to break cyclic import;
re-export from custom_guardrail for backwards compat
- Add call_type fallback in UnifiedLLMGuardrails for pass-through endpoints
using CallTypes.pass_through.value enum
* test: add unit tests for pass-through post-call guardrails
5 tests covering the post-call guardrail invocation on pass-through endpoints:
- post_call_success_hook fires when guardrails configured
- post_call_success_hook skipped when no guardrails (backwards compat)
- ModifyResponseException returns 200 with provider-agnostic error
- UnifiedLLMGuardrails resolves call_type from logging_obj for pass-through
- ModifyResponseException re-export from custom_guardrail stays in sync
* 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>
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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
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Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* 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.
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).
