* 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.
* feat(proxy): add /v1/memory CRUD endpoints with user/team scoping
New LiteLLM_MemoryTable stores user/team-scoped key/value entries with
optional JSON metadata. Value is a String (LLM-readable text) and metadata
is an optional Json? envelope, matching the Letta + mem0 hybrid model so
future structured fields can be added without a schema migration.
Endpoints:
POST /v1/memory - create
GET /v1/memory - list (caller-scoped; admins see all)
GET /v1/memory/{key} - fetch one
PUT /v1/memory/{key} - upsert
DELETE /v1/memory/{key} - delete
Non-admin callers cannot set a user_id/team_id other than their own.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy/memory): omit metadata field when None on create
Prisma's Python client rejects `metadata=None` on a `Json?` field with
"A value is required but not set" — the field must be omitted from the
`data` dict entirely to store SQL NULL. Build the create payload
conditionally in both `create_memory` and the PUT-create branch of
`upsert_memory`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(ui): add Memory page to view/manage /v1/memory entries
Adds a new "Memory" sidebar item under Tools so users can see what their
agents have stored. Lists all memories visible to the caller (scoped by
the backend), with a key-search filter, preview column, scope tags, and
view/edit/delete actions. Create modal accepts optional JSON metadata.
- networking.tsx: fetchMemoryList / createMemory / updateMemory / deleteMemory
wired to the /v1/memory CRUD endpoints.
- MemoryView + MemoryEditModal: new antd-based components (per CLAUDE.md:
use antd for new UI, not tremor).
- page.tsx + leftnav.tsx: wire the "memory" route + sidebar entry.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(memory): add key_prefix filter + promote Memory to AI GATEWAY nav
Backend:
- GET /v1/memory now accepts `key_prefix` for Redis-style namespace
scans (e.g. `?key_prefix=user:`). When both `key` and `key_prefix`
are passed, `key_prefix` wins.
- Prefix filter sits under the visibility filter in the Prisma where
clause, so it can never leak rows across user/team scopes.
- New tests: prefix match, and cross-scope isolation (another user's
`user:*` rows must not appear in the caller's results).
UI:
- Memory moved from a Tools submenu to a top-level AI GATEWAY item
(alongside Agents, MCP Servers, Skills) — it's an API primitive,
not a tool-management surface.
- Search box now drives prefix search, matching the Redis mental
model ("type the namespace, see everything under it").
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): enforce unique key per scope by using NULLS NOT DISTINCT
The unique constraint `(key, user_id, team_id)` on LiteLLM_MemoryTable
silently allowed duplicates when user_id or team_id was NULL, because
Postgres treats every NULL as distinct by default (ANSI semantics). A
caller with no team_id could POST the same key three times and get
three rows.
Migration:
1. Dedupe existing rows, keeping the most recent per (key, user_id,
team_id), using `IS NOT DISTINCT FROM` so NULL == NULL.
2. Drop the old unique index.
3. Recreate it with `NULLS NOT DISTINCT` (Postgres 15+).
No code change: POST already returns 409 on unique-violation error
messages — it just wasn't firing before because the constraint didn't
catch the NULL-team case.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): make key globally unique, 409 on any duplicate
Switches from the compound unique `(key, user_id, team_id)` to a simple
`key @unique`. The compound form silently allowed duplicates when
user_id or team_id was NULL (Postgres treats each NULL as distinct), so
callers could POST the same key repeatedly. Globally-unique key means
one row per key, period — any duplicate create → 409.
- schema.prisma (×3): `key String @unique`, drop `@@unique(...)`.
- initial add_memory_table migration: unique index on (key) only.
- Remove the now-unused follow-up NULLS NOT DISTINCT migration.
- Endpoint error message simplified ("already exists" — no "for this scope").
- Test fake's create() now enforces global key uniqueness.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(ui/memory): full-width layout + user/teams-style columns
- Add `w-full` to the MemoryView outer div so the page fills the
flex-flex-1 container (was collapsing to intrinsic width).
- Replace the combined "Scope" column with separate User ID / Team ID
columns, matching the layout of the Users / Teams pages: ID, Name,
Preview, User ID, Team ID, Updated, Actions.
- IDs render with a truncated mono label + copy-to-clipboard button,
same pattern as view_users.
- Detail drawer now shows Memory ID / User ID / Team ID as separate
fields instead of stacked color tags.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(ui/memory): use clean MCP-style ID pill, drop copy icons
The ID / User ID / Team ID columns showed a mono text blob with a
copy-to-clipboard icon next to each value — too busy compared to the
MCP Servers page. Swap the renderer for MCP's pill style:
- Truncated mono ID inside a blue Tailwind pill
(`font-mono text-blue-600 bg-blue-50 ... rounded-md border`).
- No copy icon. Full ID surfaces via tooltip.
- ID column is a button that opens the detail drawer on click;
user/team ID pills are static (not clickable).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): address greptile review feedback
Addresses 5 greptile findings (3/5 → higher confidence target):
1. Identity-less orphan rows (P1): non-admin callers with no user_id AND
no team_id could create rows that the visibility filter would never
match again. Now rejected up front with 400 — caller must authenticate
with a scoped key or act as PROXY_ADMIN.
2. Upsert race returning 500 (P1): PUT's check-then-create isn't atomic;
a concurrent writer could slip a row in between the 404-check and the
create call. Now catch unique-violation on create, re-read, and fall
through to update — PUT stays idempotent. If the conflicting row
belongs to a different scope, surface a 409 instead of 500.
3. PUT-create scope inconsistency (P2): PUT's create branch always used
the caller's own user_id/team_id, so admins couldn't bootstrap rows
scoped elsewhere via PUT (only POST). Now PUT-create calls the shared
`_resolve_scope()` helper, matching POST semantics.
4. Stale schema comment (P2): schema said "Keyed by (key, user_id,
team_id)" but `key` is globally unique. Updated all three schema
copies to reflect the actual design.
5. UI silently truncated at 200 (P2): MemoryView fetched pageSize=200
with no load-more. Swapped to real server-side pagination driven by
`data.total`; page size is now 50 and the pager is a real AntD
control.
Also extracts a shared `_resolve_scope()` helper and `_is_unique_violation()`
from create_memory so POST and PUT don't drift on the scope/error logic.
Tests: +3 new (identity-less 400, PUT admin bootstrap, PUT race →
update), 18/18 pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): typed Prisma error + explicit-null metadata on PUT
Two more greptile threads from the last review:
- Unique-violation detection was string-matching "Unique"/"UniqueViolation"
in the exception message, fragile across Prisma/driver versions. Now
check the typed error `code == "P2002"` first, with string fallback.
- PUT could not distinguish "metadata omitted" from "metadata: null" —
both parsed as `None`, so callers had no way to clear stored metadata.
Switch to Pydantic v2's `model_fields_set` to tell which fields the
caller actually sent; explicit null now clears the column.
New tests:
- explicit null clears metadata
- omitted metadata preserves existing value
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(ui/memory): send explicit null when user clears metadata
Addresses the remaining P1 from the last greptile review:
When the edit modal's metadata textarea was cleared and saved,
`metadataParsed` stayed `undefined`, `JSON.stringify` dropped the key
entirely, and the backend's `model_fields_set` guard therefore left
the stored metadata untouched — UI showed success but nothing changed.
Now: empty textarea on edit → send explicit `null` so the backend
sees `metadata` in `model_fields_set` and clears the column.
Empty textarea on create still maps to `undefined` (field omitted)
to avoid Prisma's `Json? = None` quirk on insert.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(ui/memory): preserve slashes in key path encoding
The backend route `/v1/memory/{key:path}` supports keys with slashes,
but `encodeURIComponent` encoded `/` as `%2F`. Some proxies (nginx
default, CloudFlare, AWS ALB) reject or re-decode `%2F` mid-flight,
so UI update/delete calls on slash-containing keys could fail or
silently misroute.
New helper `encodeMemoryKeyForPath` splits by `/`, URL-encodes each
segment, then rejoins with literal `/`. Every other unsafe char
(spaces, `?`, `#`, `%`) stays encoded per-segment; slashes stay as
path delimiters, matching what the `:path` converter expects.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(ui/memory): drop misleading client-side column sorters
With server-side pagination, client sorters on `key` and `updated_at`
only reorder the current page while pretending to sort the full
dataset — users would see "sorted by name" but only the visible 50
rows would actually be sorted.
Remove the sorters. The backend already returns rows in
`updated_at DESC` order (sensible default for a memory view), and
users can narrow the result with the key-prefix filter.
Greptile also flagged missing `@@map` on the new model as a
"consistency" issue, but only 1 of 59 tables in this repo uses
`@@map` — the dominant pattern is to rely on Prisma's default
(model name == table name). Skipping that finding as a
false-positive on convention.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): compose visibility + key filters via explicit AND
Greptile P1 (filter-fragility): `where.update(vis)` was semantically
correct today, but dict-merging by key meant any future visibility
filter that grew a new top-level "OR" would silently clobber the
existing key filter.
Compose explicitly instead:
where = {"AND": [key_filter, vis]}
Applied to both `list_memory` and `_find_memory_for_caller`. When
either side is empty (admin has no visibility filter; list has no
key filter), skip the wrapper and use the non-empty side directly
to keep the generated SQL clean.
Test fake's `_matches` now understands top-level `AND` too.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* refactor(ui/memory): wrap write helpers with react-query useMutation
Previously the Memory view read via `useQuery` but called the raw
create/update/delete fetch helpers directly in handlers, tracking
loading state with a local `submitting` flag and invalidating state
via `refetch()`. That mixes two concerns:
- it skips react-query's mutation state (isPending / isError / isSuccess)
- `refetch()` only retouches the currently-mounted query instance, not
other cached pages, so navigating back to an older page could show
stale rows
Switch the three write paths to `useMutation`:
- `createMutation`, `updateMutation`, `deleteMutation` — each owns
the mutation fn, success toast, and error toast.
- Success handlers invalidate the whole `["memoryList", ...]` prefix
via `queryClient.invalidateQueries`, so every cached page refetches
(pagination + filter-aware).
- Refresh button now invalidates instead of `refetch()`, keeping all
behavior consistent.
- handleSave/handleDelete become thin adapters that call `.mutateAsync`;
their errors are swallowed locally since the mutation's onError has
already surfaced the toast.
Also tightened the edit modal's key-field tooltip to reflect the
actual global-unique semantics (was "Unique per user/team scope").
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): close cross-user write gap + sanitize 500 errors (Veria)
Addresses two Veria findings:
**High — cross-user memory tampering via team membership.** The
visibility filter uses an OR (`user_id == caller OR team_id == caller`)
so team members can SEE each other's team-scoped rows. That's
intentional for list/get. But because PUT/DELETE used the same filter
to find the target row, any team member could overwrite or delete a
teammate's *personal* row whenever both `user_id` and `team_id` were
stamped on it — broader visibility was being silently treated as
broader authority.
New `_assert_write_access(row, caller)` enforces ownership for
mutations. Non-admin rules:
- The row's `user_id` must match the caller (personal ownership), OR
- The row has no `user_id` and its `team_id` matches the caller's
team (a "pure team row" intended for shared writes).
Admins bypass the check. The same gate runs in PUT (both regular
and post-race-recovery branches) and DELETE.
**Medium — DB internals leaked through 500 detail.** Every `except`
block was raising `HTTPException(500, detail=str(e))`, which surfaces
Prisma error strings (table/column names, host:port, error class
names) to API callers. New `_internal_error()` helper logs the real
exception server-side and returns a generic, caller-safe `detail`.
Applied to create, list, upsert (general fallthrough), and delete.
Also tightened the race-recovery 409 message to drop the "in a
different scope" wording — the caller never needs to know whose
scope it lives in.
Tests (+5):
- teammate cannot overwrite personal row → 403
- teammate cannot delete personal row → 403
- teammate CAN modify pure team row (no user_id stamped) → 200
- admin bypasses write-auth → 200
- 500 response never echoes Prisma internals (table/host/class names)
25/25 unit tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(memory): require team admin to modify pure team rows
Tightens the write-authorization rule for "pure team rows" (rows with
no user_id stamped, only team_id) to match the pattern used by
team-management endpoints (`_is_user_team_admin` + `_is_user_org_admin_for_team`):
- Plain team members can READ team rows via the OR visibility filter
(intentional, unchanged).
- Only PROXY_ADMIN, team admins of the row's team_id, or org admins
for the team's organization may MODIFY them. Plain members get 403.
`_assert_write_access` is now async and takes the prisma_client so it
can fetch the team and run the existing `_is_user_team_admin` /
`_is_user_org_admin_for_team` helpers from
`litellm.proxy.management_endpoints.common_utils`. The org-admin path
is best-effort: it calls `get_user_object`, which depends on the
proxy_server module being initialized, so any exception there is
treated as "not an org admin" rather than crashing the request.
Tests:
- team admin can modify pure team row → 200
- plain team member cannot modify pure team row → 403
- plain team member cannot delete pure team row → 403
Updates the test fake to add a tiny `litellm_teamtable.find_unique`
implementation and a `_make_team(team_id, admin_user_ids=[...])`
helper.
27/27 unit tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix: mypy + UI page-metadata sync for memory page
Two CI failures:
1. mypy: `_find_memory_for_caller` had `key_filter` inferred as
`dict[str, str]` (literal type) and the conditional `{"AND": [key_filter, vis]}`
returned `dict[str, list[...]]`, so the join site failed
`dict-item` typing. Annotate both intermediates as `dict` so mypy
widens the value type.
2. UI test (`page_utils.test.ts > should have descriptions for all
pages`): every leftnav entry must have a description in
`page_metadata.ts`, and `memory` was missing. Added a one-line
description, matching the style of neighboring entries.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* [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.
* fix(schema): close LiteLLM_MemoryTable model brace dropped during merge
The rebase against `litellm_internal_staging` (which added
`LiteLLM_AdaptiveRouterState` / `LiteLLM_AdaptiveRouterSession`) left
the closing brace of `LiteLLM_MemoryTable` missing in all three
schema copies — the next model declaration ended up parsed as a field
of the memory table, surfacing as the CI prisma error:
error: This line is not a valid field or attribute definition.
--> schema.prisma:1250
|
1249 | // Per-(router, request_type, model) Beta posterior for the adaptive router.
1250 | model LiteLLM_AdaptiveRouterState {
Add the missing `}` (and the standard blank line) after the memory
table's `@@index([team_id])` in `schema.prisma`,
`litellm/proxy/schema.prisma`, and
`litellm-proxy-extras/litellm_proxy_extras/schema.prisma`.
`prisma generate --schema litellm/proxy/schema.prisma` now runs clean;
27/27 memory unit tests pass.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-authored-by: Mateo Wang <277851410+mateo-berri@users.noreply.github.com>
* feat: add gpt-5.5 to model cost map
Add gpt-5.5 entry with pricing from OpenAI flagship page:
input $5/1M, cached input $0.50/1M, output $30/1M, 272K context.
* test: add gpt-5.5 coverage for model cost map and gpt-5 routing
- Add gpt-5.5 to GPT5_MODELS parametrized list so both OpenAIGPT5Config
and AzureOpenAIGPT5Config routing tests cover the new model.
- Add test_generic_cost_per_token_gpt55 verifying the new entry's
cost-map values ($5/$0.50/$30 per 1M) and that generic_cost_per_token
returns the expected prompt/completion costs.
* fix(model-info): include reasoning effort support fields in get_model_info
_get_model_info_helper constructs ModelInfoBase explicitly but never
reads supports_xhigh/minimal/none_reasoning_effort from the cost map
JSON. Add the three fields so get_model_info() returns them correctly.
Also add supports_minimal_reasoning_effort to the ModelInfo TypedDict
(xhigh and none were already declared, minimal was missing).
* fix(model-registry): add missing reasoning effort fields for claude 4.6/4.7
Claude Opus 4.7 supports max reasoning effort (above xhigh).
The field was present for Opus 4.6 but missing for all Opus 4.7
entries (base, dated, Bedrock, Vertex AI, Azure AI).
All Claude 4.6/4.7 models (Opus 4.6, Sonnet 4.6, Opus 4.7) support
minimal reasoning effort via adaptive thinking. Add the field to all
provider variants.
* fix(adapter): map output_config.effort to reasoning_effort (#25079)
Anthropic's adaptive thinking (thinking.type="adaptive") and
output_config.effort were silently dropped when translating to
OpenAI format, resulting in no reasoning_effort on the outgoing
request.
Adapter changes (format translation):
- adapters/transformation.py: add "adaptive" branch to
translate_anthropic_thinking_to_reasoning_effort(); pass through
output_config.effort as-is in _translate_thinking_to_openai();
add "output_config" to translatable_anthropic_params
- adapters/handler.py: extract output_config from extra_kwargs into
request_data so it reaches the translation layer
- responses_adapters/transformation.py: add "adaptive" branch and
output_config param to translate_thinking_to_reasoning()
Handler changes (model-aware normalization):
- utils.py: add normalize_reasoning_effort_value() that uses
get_model_info() to map "max" → "xhigh"/"high" and
"minimal" → "minimal"/"low" based on model capabilities
- adapters/handler.py: call normalization before responses routing
- responses_adapters/handler.py: call normalization after translation
Relates to BerriAI/litellm#25079
* test(reasoning-effort): add tests for effort capability fields and normalize logic
Test coverage for:
- get_model_info returning supports_minimal/max_reasoning_effort fields
- JSON registry entries for claude 4.6/4.7 across all providers
- normalize_reasoning_effort_value degradation chains and exception fallback
- Adapter translation of adaptive thinking + output_config.effort
* fix: forward custom_llm_provider to normalize_reasoning_effort_value in responses adapter
- Validate max effort like xhigh: Opus 4.6/4.7 id patterns or supports_max_reasoning_effort
- Set supports_max_reasoning_effort on claude-opus-4-7 entries in model cost JSON
- Update tests and add test_max_effort_accepted_for_opus_47
Made-with: Cursor
* add moonshot/kimi-k2.6 to model registry
* add moonshot/kimi-k2.6 to backup model registry
* add tests for moonshot/kimi-k2.6 model registry
* fix moonshot/kimi-k2.6 pricing and add reasoning support
* fix moonshot/kimi-k2.6 pricing and add reasoning support in backup
* update kimi-k2.6 tests: fix pricing, add tool_choice and reasoning checks
* fix: load kimi-k2.6 registry tests from local backup instead of remote cost map
* Add supported providers to prompt caching doc
* Move Z.ai / GLM to cache_control marker list
* Mark xAI models as supporting prompt caching
* Narrow xAI prompt caching flag to models with documented cache pricing
* Add prompt caching flag to grok-4, grok-4-0709, grok-4-latest
---------
Co-authored-by: Michael Riad Zaky <michaelr@Michaels-MacBook-Air.local>
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes#25538
* test(proxy): add tests for _get_openapi_url
---------
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
* feat(prometheus): add api_provider label to spend metric (#25693)
* feat(prometheus): add api_provider label to spend metric
Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).
The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.
* add api_provider to requests metric + add test
Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
existing pattern in test_prometheus_labels.py
* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)
* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call
* refactor: remove unused BaseTranslation._ensure_litellm_metadata
* refactor: module level imports for ensure_litellm_metadata and CodeQL
* fix: update based off of Codex comment
* revert: undo usage of `_guardrail_litellm_metadata`
* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)
* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)
When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.
This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
synthetic tool calls, finish_reason stays "tool_calls" instead of
"stop". Callers (like the OpenAI SDK) misinterpret this as a pending
tool invocation.
json_schema requests with an explicit schema are unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* fix(utils): allowed_openai_params must not forward unset params as None
`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:
AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'
Reproducer (from #25697):
allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
body = {"chat_template_kwargs": {"enable_thinking": False}}
Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.
Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.
Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.
Fixes#25697
---------
Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
- streaming_iterator.py: adopted main's more defensive version of the
tool-arg queueing check (.get() instead of [], isinstance guard) —
same logic, same behavior, lower crash surface
- model_prices_and_context_window.json + backup: combined staging's
search_context_cost_per_query fields (PR #24372) with main's new
supports_service_tier field — both are independent additions to the
same Gemini model entries
- test_streaming_handler.py: kept Azure streaming regression test
(PR #24354) and added main's two new Gemini legacy vertex
finish_reason normalization tests
- test_gemini_batch_embeddings.py: kept staging's unsupported-params
filtering tests (PR #24370) and added main's index/order test
Resolved conflicts:
- streaming_handler.py: combined role check (PR #24354, Azure streaming)
with reasoning_items check (new in main) — both are independent OR
conditions in is_chunk_non_empty()
- CI/CD: accepted main's versions throughout
- Redis tests migrated to CircleCI (PR #25354): removed enable-redis
from GH Actions workflows
- E2E UI tests restructured (PR #25365): simplified CircleCI job
- Coverage via Codecov added to all GH Actions unit test workflows
- Deleted test-litellm-matrix.yml and test-proxy-e2e-azure-batches.yml
(removed in main)
Add Baseten Model API pricing entries for Nemotron, GLM, Kimi, GPT OSS, and DeepSeek models with validated model slugs. Include a focused regression test to assert provider and per-token pricing values.
Made-with: Cursor
* fix(vertex_ai): support pluggable (executable) credential_source for WIF auth (#24700)
The WIF credential dispatch in load_auth() only handled identity_pool and
aws credential types. When credential_source.executable was present (used
for Azure Managed Identity via Workload Identity Federation), it fell
through to identity_pool.Credentials which rejected it with MalformedError.
Add dispatch to google.auth.pluggable.Credentials for executable-type
credential sources, following the same pattern as the existing identity_pool
and aws helpers.
Fixes authentication for Azure Container Apps → GCP Vertex AI via WIF
with executable credential sources.
* feat(logging): add component and logger fields to JSON logs for 3rd p… (#24447)
* feat(logging): add component and logger fields to JSON logs for 3rd party filtering
* Let user-supplied extra fields win over auto-generated component/logger, tighten test assertions
* Feat - Add organization into the metrics metadata for org_id & org_alias (#24440)
* Add org_id and org_alias label names to Prometheus metric definitions
* Add user_api_key_org_alias to StandardLoggingUserAPIKeyMetadata
* Populate user_api_key_org_alias in pre-call metadata
* Pass org_id and org_alias into per-request Prometheus metric labels
* Add test for org labels on per-request Prometheus metrics
* chore: resolve test mockdata
* Address review: populate org_alias from DB view, add feature flag, use .get() for org metadata
* Add org labels to failure path and verify flag behavior in test
* Fix test: build flag-off enum_values without org fields
* Gate org labels behind feature flag in get_labels() instead of static metric lists
* Scope org label injection to metrics that carry team context, remove orphaned budget label defs, add test teardown
* Use explicit metric allowlist for org label injection instead of team heuristic
* Fix duplicate org label guard, move _org_label_metrics to class constant
* Reset custom_prometheus_metadata_labels after duplicate label assertion
* fix: emit org labels by default, remove flag, fix missing org_alias in all metadata paths
* fix: emit org labels by default, no opt-in flag required
* fix: write org_alias to metadata unconditionally in proxy_server.py
* fix: 429s from batch creation being converted to 500 (#24703)
* add us gov models (#24660)
* add us gov models
* added max tokens
* Litellm dev 04 02 2026 p1 (#25052)
* fix: replace hardcoded url
* fix: Anthropic web search cost not tracked for Chat Completions
The ModelResponse branch in response_object_includes_web_search_call()
only checked url_citation annotations and prompt_tokens_details, missing
Anthropic's server_tool_use.web_search_requests field. This caused
_handle_web_search_cost() to never fire for Anthropic Claude models.
Also routes vertex_ai/claude-* models to the Anthropic cost calculator
instead of the Gemini one, since Claude on Vertex uses the same
server_tool_use billing structure as the direct Anthropic API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* fix(anthropic): pass logging_obj to client.post for litellm_overhead_time_ms (#24071)
When LITELLM_DETAILED_TIMING=true, litellm_overhead_time_ms was null for
Anthropic because the handler did not pass logging_obj to client.post(),
so track_llm_api_timing could not set llm_api_duration_ms. Pass
logging_obj=logging_obj at all four post() call sites (make_call,
make_sync_call, acompletion, completion). Add test to ensure make_call
passes logging_obj to client.post.
Made-with: Cursor
* sap - add additional parameters for grounding
- additional parameter for grounding added for the sap provider
* sap - fix models
* (sap) add filtering, masking, translation SAP GEN AI Hub modules
* (sap) add tests and docs for new SAP modules
* (sap) add support of multiple modules config
* (sap) code refactoring
* (sap) rename file
* test(): add safeguard tests
* (sap) update tests
* (sap) update docs, solve merge conflict in transformation.py
* (sap) linter fix
* (sap) Align embedding request transformation with current API
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) mock commit
* (sap) run black formater
* (sap) add literals to models, add negative tests, fix test for tool transformation
* (sap) fix formating
* (sap) fix models
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) commit for rerun bot review
* (sap) minor improve
* (sap) fix after bot review
* (sap) lint fix
* docs(sap): update documentation
* fix(sap): change creds priority
* fix(sap): change creds priority
* fix(sap): fix sap creds unit test
* fix(sap): linter fix
* fix(sap): linter fix
* linter fix
* (sap) update logic of fetching creds, add additional tests
* (sap) clean up code
* (sap) fix after review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) add a possibility to put the service key by both variants
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) update test
* (sap) update service key resolve function
* (sap) run black formater
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix validate credentials, add negative tests for credential fetching
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) fix after bot review
* (sap) lint fix
* (sap) lint fix
* feat: support service_tier in gemini
* chore: add a service_tier field mapping from openai to gemini
* fix: use x-gemini-service-tier header in response
* docs: add service_tier to gemini docs
* chore: add defaut/standard mapping, and some tests
* chore: tidying up some case insensitivity
* chore: remove unnecessary guard
* fix: remove redundant test file
* fix: handle 'auto' case-insensitively
* fix: return service_tier on final steamed chunk
* chore: black
* feat: enable supports_service_tier to gemini models
* Fix get_standard_logging_metadata tests
* Fix test_get_model_info_bedrock_models
* Fix test_get_model_info_bedrock_models
* Fix remaining tests
* Fix mypy issues
* Fix tests
* Fix merge conflicts
* Fix code qa
* Fix code qa
* Fix code qa
* Fix greptile review
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Josh <36064836+J-Byron@users.noreply.github.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Alperen Kömürcü <alperen.koemuercue@sap.com>
Co-authored-by: Vasilisa Parshikova <vasilisa.parshikova@sap.com>
Co-authored-by: Lin Xu <lin.xu03@sap.com>
Co-authored-by: Mark McDonald <macd@google.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* fix: expose reasoning effort fields in get_model_info and add together_ai/gpt-oss-120b
- litellm/utils.py: pass supports_none_reasoning_effort and
supports_xhigh_reasoning_effort through _get_model_info_helper so
get_model_info() returns them (previously silently dropped). Fixes#25096.
- model_prices_and_context_window.json: add together_ai/openai/gpt-oss-120b
with supports_reasoning: true so reasoning_effort is accepted for this
model without requiring drop_params. Fixes#25132.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: consolidate duplicate together_ai/openai/gpt-oss-120b entry and sync backup file
* fix: link commit to GitHub account for CLA verification
---------
Co-authored-by: Austin Varga <austin@knowmi.ai>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(pricing): add unversioned vertex_ai/claude-haiku-4-5 entry
Missing unversioned entry causes cost tracking to return $0.00 for
all requests using vertex_ai/claude-haiku-4-5. All other Vertex AI
Claude models have both versioned and unversioned entries.
* fix(router): skip misleading tags error when no candidates (e.g. cooldown)
Return early from get_deployments_for_tag when healthy_deployments is empty so
tag-based routing does not raise no_deployments_with_tag_routing after cooldown
filters all deployments. Adds regression test.
Made-with: Cursor
* feat(oci): add embedding support and update model catalog
- Add OCIEmbeddingConfig for OCI GenAI embedding models
- Add 16 new chat models (Cohere, Meta Llama, xAI Grok, Google Gemini)
- Add 8 embedding models (Cohere embed v3.0, v4.0)
- Update documentation with embedding examples
- Update pricing for all new models
* test(oci): add unit tests for OCI embedding support
- 17 unit tests covering OCIEmbeddingConfig
- Tests for URL generation, param mapping, request/response transform
- Tests for model pricing JSON completeness
* style(oci): format with black and ruff
* fix(oci): correct embedding request body format
OCI embedText API expects inputs, truncate, and inputType at the
top level of the request body, not nested under embedTextDetails.
Fixed transformation and updated tests accordingly.
Verified with real OCI API: 3/3 embedding models working.
* docs: clarify tag routing early return and test intent
Made-with: Cursor
* fix(oci): address code review findings from Greptile
- P1: Fix signing URL mismatch with custom api_base by accepting
api_base parameter in transform_embedding_request
- P2: Remove encoding_format from supported params (OCI does not
support it, was silently dropped)
- P2: Raise ValueError for token-array inputs instead of silently
converting to string representation
- Add test for token-list rejection
* fix(mcp): add STS AssumeRole support for MCP SigV4 authentication
MCPSigV4Auth only supported static AWS credentials or the boto3 default
credential chain. Production Kubernetes environments typically authenticate
via IAM role assumption (sts:AssumeRole), which was not possible.
Add aws_role_name and aws_session_name parameters to the MCP SigV4 auth
stack. When aws_role_name is provided, MCPSigV4Auth calls sts:AssumeRole
to obtain temporary credentials before signing requests. Explicit keys,
if also provided, are used as the source identity for the STS call;
otherwise ambient credentials (pod role, instance profile) are used.
* fix: stop logging credential values and add missing redaction patterns
Replaces raw credential values in debug/error log messages with
boolean presence checks or type names. Adds PEM block, GCP token,
JWT, SAS token, and service-account blob patterns to the redaction
filter. Fixes private_key pattern to capture full PEM blocks instead
of stopping at the first whitespace.
Addresses: Vertex AI credential JSON (including RSA private key)
being logged to stderr on health check failures.
* fix: log only field names for UserAPIKeyAuth, not full object
* style: apply black formatting to experimental_mcp_client/client.py
* style: fix black/isort formatting and mypy error in proxy_server.py
- Fix black formatting in experimental_mcp_client/client.py (done in prev commit)
- Fix black/isort formatting in key_management_endpoints.py, proxy_server.py, transformation.py
- Fix mypy: iterate over optional list safely (access_group_ids or []) in proxy_server.py
* fix(test): patch check_migration.verbose_logger directly to fix xdist ordering issue
When test_proxy_cli.py tests run before test_check_migration.py in the same
xdist worker, litellm.proxy.db.check_migration is already in sys.modules.
Patching litellm._logging.verbose_logger has no effect on the already-bound
reference. Patch the correct target (check_migration.verbose_logger) and
import the module before patching so the order doesn't matter.
* fix(mypy): make api_base Optional in PydanticAIProviderConfig to match base class signature
---------
Co-authored-by: Ihsan Soydemir <soydemir.ihsan@gmail.com>
Co-authored-by: Milan <milan@berri.ai>
Co-authored-by: Daniel Gandolfi <danielgandolfi@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: user <70670632+stuxf@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* Litellm ishaan april1 (#25103)
* fix(proxy): enforce upperbound key params on key/update and add custom_key_update hook
The /key/update endpoint did not enforce upperbound_key_generate_params,
allowing users to bypass configured limits (tpm_limit, rpm_limit,
max_budget, duration, budget_duration) by updating an existing key
instead of generating a new one.
Extract the upperbound enforcement logic from _common_key_generation_helper()
into a standalone _enforce_upperbound_key_params() function and call it from
both the generate and update paths. For updates, None values are skipped
(not filled with defaults) since they mean "don't change this field".
Also adds a custom_key_update config option and user_custom_key_update global,
mirroring the existing custom_key_generate pattern, so custom key validation
logic can fire during key updates as well.
* fix(proxy): invoke custom_key_update hook in bulk update path
The user_custom_key_update hook was only called in update_key_fn
(single key update) but not in _process_single_key_update (bulk
update path), allowing custom validation to be bypassed via the
/key/update/bulk endpoint. Mirror the hook invocation in both paths.
* fix(proxy): pass UpdateKeyRequest to hook in bulk path, not BulkUpdateKeyRequestItem
Move the custom_key_update hook invocation to after UpdateKeyRequest
is constructed so the hook receives the same type in both single and
bulk update paths. Previously the bulk path passed
BulkUpdateKeyRequestItem (5 fields only), which would cause
AttributeError for hooks accessing fields like tpm_limit or models.
* fix(bedrock): promote cache usage to message_delta for Claude Code (#24850)
Ensure Bedrock/Anthropic-compatible streaming exposes cache usage where Claude Code reads it by promoting message_stop usage onto message_delta and preserving usage fields in fake-streamed message_delta events.
Made-with: Cursor
* fix(search): Support self-hosted Firecrawl response format in search transform (#24866)
The `transform_search_response` method only handled Firecrawl Cloud (v2)
response format where `data` is a dict with `web`/`news` keys. Self-hosted
Firecrawl (v1) returns `data` as a flat list of result objects, causing an
`AttributeError: 'list' object has no attribute 'get'`.
Detect the response format by checking if `data` is a list (self-hosted)
or dict (cloud) and handle both cases.
Cloud format: {"data": {"web": [...], "news": [...]}}
Self-hosted: {"success": true, "data": [{"url": "...", "title": "...", ...}]}
Co-authored-by: Synergy <synergyoclaw@gmail.com>
* feat: add environment and user tracking to prompt management (#24855)
* feat: add environment and user tracking to prompt management
- Add environment (development/staging/production) and created_by columns to LiteLLM_PromptTable
- Update unique constraint to [prompt_id, version, environment]
- All CRUD endpoints support environment filtering and user tracking
- Redesigned prompt detail page with environment tabs and version history
- UI: environment filter on list page, environment selector in editor
- 8 new tests for environment and user tracking
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: Black formatting and add environments to PromptInfoResponse TypeScript type
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: address Greptile review findings
- P1: delete_prompt scopes in-memory cleanup to environment when provided
- P2: dotprompt_content parsed directly regardless of environment flag
- P2: use distinct for environments query
- P2: fix double-fetch on initial mount in prompt_info.tsx
- fix: remove unsupported select kwarg from find_many
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: address remaining Greptile review comments
- Remove unused useCallback import (index.tsx)
- Remove unused ENV_COLORS variable (prompt_info.tsx)
- P1: in-memory fallback in get_prompt_versions now respects environment filter
- P1: reset selectedEnv when promptId changes to avoid stale state
- Cyclic imports are pre-existing pattern, not introduced by this PR
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: scope patch_prompt to environment using primary key
- Add environment query param to patch_prompt endpoint
- Look up target row by composite key (prompt_id + version + environment)
- Update by primary key (id) to target exactly one row
- Fixes Greptile finding: patch with multiple environments no longer ambiguous
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* fix: use actual start_time for failed request spend logs (#24906)
async_post_call_failure_hook set both start_time and end_time to
datetime.now(), making all failed requests show duration=0. Use the
actual start_time from litellm_logging_obj instead, so spend logs
reflect the real request duration on timeout and other failures.
Fixes#24888
* feat(bedrock): add nova canvas image edit support (#24869)
* feat(bedrock): add nova canvas image edit support
* fix(bedrock): support PathLike inputs for nova image edit
* chore: sync schema.prisma copies from root
* fix(mypy): correct type-ignore code for delta_usage arg-type
* fix(mypy): cast status_code to str, suppress intentional str yield
* fix(lint): extract _create_content_block_chunks to fix PLR0915
* fix(lint): extract helpers to fix PLR0915 in prompt endpoints
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: redhelix <amin.lalji@gmail.com>
Co-authored-by: Synergy <synergyoclaw@gmail.com>
Co-authored-by: Talha Anwar <37379131+talhaanwarch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: madhu19991 <madhu@thunkai.com>
Co-authored-by: Srikanth @adobe <devarakondasrikanth@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
* fix(test): update model armor streaming test to handle string or int error code
---------
Co-authored-by: michelligabriele <gabriele.michelli@icloud.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: redhelix <amin.lalji@gmail.com>
Co-authored-by: Synergy <synergyoclaw@gmail.com>
Co-authored-by: Talha Anwar <37379131+talhaanwarch@users.noreply.github.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: madhu19991 <madhu@thunkai.com>
Co-authored-by: Srikanth @adobe <devarakondasrikanth@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
These models include the full 1M token context at standard pricing with no 2x surcharge above 200k tokens.
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
