* 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.
The original check `"gpt-5-chat" not in model` already correctly
classifies all current gpt-5 variants (including gpt-5.3-chat and
gpt-5.1-chat, which do NOT contain the substring "gpt-5-chat"). This
change replaces it with an explicit `startswith("gpt-5-chat")` prefix
test on the provider-prefix-stripped model name.
The new check is functionally equivalent for all existing model names
but makes the classification boundary unambiguous and forward-safe:
future model names that might contain "gpt-5-chat" as an interior
substring won't accidentally be excluded from the GPT-5 reasoning path.
Also moves the new regression test from tests/ root to
tests/test_litellm/llms/openai/ so it is included in `make test-unit`.
* fix(anthropic): handle tool_choice type 'none' in messages API
* test(anthropic): add regression test for tool_choice type 'none'
---------
Co-authored-by: BillionClaw <267901332+BillionClaw@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
When reasoning_auto_summary is enabled (via litellm_settings or env var),
automatically set thinking.display="summarized" on native /v1/messages
requests. This ensures thinking content is returned in the response
instead of being omitted (the default on Claude 4.7+).
Only applies when thinking is enabled (type != "disabled").
The existing reasoning_auto_summary flag already handles the
/v1/responses path (summary="detailed") and the chat/completions
adapter path — this extends coverage to the native messages handler.
`get_file_ids_from_messages` and `update_messages_with_model_file_ids`
assume every content block with `type: "file"` has a nested `file` dict in
the OpenAI Chat Completions shape. That assumption is too strong: `type:
"file"` is a public content-block discriminator and several real producers
emit blocks that use it without the OpenAI `file` sub-dict. For example,
LangChain v1's `_normalize_messages` rewrites OpenAI file blocks into
`{"type":"file","id":"...","base64":"...","mime_type":"...","extras":{}}`
before they reach LiteLLM.
`AnthropicConfig.validate_environment` calls both helpers unconditionally
on every Anthropic (and Anthropic-via-Vertex) request, so any such block
raises `KeyError: 'file'` which the Vertex partner layer then wraps as a
`500 InternalServerError` before the LLM is even contacted.
This patch switches both helpers from `c["file"]` to a defensive
`c.get("file")` + dict check. When the block does not match the OpenAI
shape there is no file_id to extract or remap, so we skip it and leave
the block untouched for the downstream provider transformer to handle.
Adds 5 regression tests covering the LangChain v1 shape, the OpenAI
happy path, mixed shapes in one message, `file` set to a non-dict value,
and the remap path for non-OpenAI blocks.
Related to #24503, which proposed raising `BadRequestError` in the same
spots. For these two discovery functions specifically, the skip semantics
is strictly more permissive: well-formed OpenAI blocks still yield their
file_id, and legitimate non-OpenAI blocks stop crashing the request.
* 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
* fix(mcp_semantic_tool_filter): match canonical tools that arrive with
a client-side namespace prefix.
`SemanticMCPToolFilter._get_tools_by_names` matched by exact equality
between the canonical name stored in the router
(`<server><MCP_TOOL_PREFIX_SEPARATOR><tool>`) and the name in the
incoming `tools[]` list. MCP clients such as opencode wrap every tool
name with their own additive alias prefix
(`<client_alias>_<canonical>`), so the two never matched, the filter
dropped every tool to zero, and the proxy forwarded `tools: []` with
`tool_choice: auto` — which strict upstream providers reject with a 400.
The fix adds anchored suffix matching with a separator check: the
canonical must form the complete tail of the incoming name and be
preceded by `_` or `-`. Exact matches still win over suffix matches,
incoming tools are returned at most once, and the original tool object
is passed through unchanged so the client-facing name survives for
tool-call round-trips.
Seven unit tests in a new TestGetToolsByNames class cover exact
match, underscore- and dash-prefixed variants, non-separator-anchored
suffixes (which must not match), exact-wins-over-prefixed precedence,
deduplication when two canonicals suffix-match the same incoming tool,
and ordering-follows-router-output.
Fixes#26078
* review: strengthen the suffix-fallback tie-breaker and the
deduplication regression test (Greptile comments on #26117)
- test_same_tool_not_returned_twice now passes two distinct canonicals
("read_file" and "file") that both suffix-match the same incoming
tool, rather than the same canonical twice, so the assertion
actually exercises the used_ids dedup path instead of the
duplicate-input-list path.
- The suffix fallback in _get_tools_by_names now prefers the shortest
incoming name that still qualifies under the separator-anchored
match. In the one-prefix-per-client opencode scenario this is a
no-op, but in multi-namespace configurations the shortest qualifying
name is the least-wrapped one and is the most defensible deterministic
choice, replacing the dict-insertion-order fallback.
- Adds test_suffix_fallback_prefers_shortest_candidate covering the
new tie-breaker directly.
Still 15 tests passing locally (was 14).
* review(#26117): gate suffix-matching on canonical containing MCP_TOOL_PREFIX_SEPARATOR
@krrish-berri-2 flagged a possible collision in the suffix fallback:
a local user function whose name happens to end in a bare canonical
substring (e.g. my_firecrawl_scrape vs canonical firecrawl_scrape)
would be spuriously selected.
Server-registered MCP tools are always emitted as
<server_name><MCP_TOOL_PREFIX_SEPARATOR><tool_name> via
add_server_prefix_to_name, so a canonical without the separator is
not a namespaced MCP tool and does not warrant suffix matching.
Added that guard to _name_matches_canonical with a regression test
(test_does_not_collide_with_local_function_on_unprefixed_canonical)
that reproduces the collision before the fix and is pinned after.
Pre-existing TestGetToolsByNames fixtures that relied on bare
canonicals (get_weather, search, read_file, write/delete/read) were
switched to realistic server-prefixed ones so they continue to
exercise the suffix-fallback path under the new guard. The opencode
scenario (client prefix on already-server-prefixed canonical) is
unchanged.
---------
Co-authored-by: sakenuGOD <sakenuGOD@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
store_in_memory_spend_updates_in_redis drained the in-memory queues
into local variables before the rpush pipeline. If rpush raised (cloud
Redis hiccup, timeout, connection blip), those already-drained
transactions were garbage-collected with the scheduler job, silently
losing all spend aggregated during that tick.
Wrap the rpush in try/except. On failure, re-enqueue the aggregated
transactions into their respective in-memory queues so the next
scheduler tick retries.
Add a unit test that seeds real queues, simulates an rpush failure,
and asserts the transactions land back in-memory.
Restore guardrail spend/UI event_type wiring, request_data on streaming
OUTPUT paths, and centralized match redaction after the upstream revert.
Made-with: Cursor
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* 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
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P1 review: adaptive_router.py had a top-level import of
AdaptiveRouterUpdateQueue from litellm.proxy.db, which broke the
SDK/proxy boundary that every other router strategy respects. No
other router_strategy module imports from litellm.proxy at module
level.
The queue only depends on litellm._logging — it never needed to
live under litellm.proxy. Moved:
litellm/proxy/db/db_transaction_queue/adaptive_router_update_queue.py
→ litellm/router_strategy/adaptive_router/update_queue.py
tests/test_litellm/proxy/db/db_transaction_queue/
test_adaptive_router_update_queue.py
→ tests/test_litellm/router_strategy/adaptive_router/test_update_queue.py
Also switched the queue's logger from verbose_proxy_logger to
verbose_router_logger to match the new module's ownership.
P2 review: drop unused constant STAGNATION_JACCARD_EXACT from
config.py — it was defined but never referenced.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Previously, members added to a team without an explicit per-member budget were
all linked to the same `litellm_budgettable` row referenced by the team's
`metadata.team_member_budget_id`. Updating one member's budget via
`/team/member_update` mutated the shared row and silently changed every other
member's budget too.
Now both write paths produce a private, per-member budget:
- `add_new_member` clones the team's default budget into a fresh row when a
member is added without `max_budget_in_team`/`allowed_models`. If no team
default exists, the membership is created with no budget.
- `_upsert_budget_and_membership` detects when an existing membership still
points at the team's default budget id and clones-on-write, relinking the
membership to the new private budget before applying the update.
- `team_member_update` reads `team_member_budget_id` from team metadata and
passes it through so the helper can make this distinction.
Adds unit tests for clone-on-write, in-place update of a private budget, and
the no-default-no-budget add path.
Made-with: Cursor
P1: start the adaptive-router flusher loop unconditionally at proxy boot
instead of gating on 'adaptive_routers is non-empty'. Adaptive routers
added via /config/reload after boot now have their queues drained.
State is lazy-loaded per router on first flush tick (new _state_loaded
flag on AdaptiveRouter) so hot-reloaded routers still get their
persisted priors.
P2: _finalize_adaptive_router_if_configured now prunes stale
AdaptiveRouterPostCallHook callbacks from every litellm callback list
before registering new ones. Without this, every Router replacement
left the old hooks wired up in litellm.callbacks and double-fired
signal recording for every request. Uses
logging_callback_manager.remove_callbacks_by_type (same pattern as the
semantic tool filter).
CI fixes:
- black --check failure: reformatted litellm/router.py
- schema migration diff: aligned @@index with the explicit index name
('idx_adaptive_router_session_activity') from the original migration
by adding 'map:' to all three schema.prisma copies. No new migration
needed.
Tests: 1 new covering the prune-on-hot-reload path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Mark last_updated_at (AdaptiveRouterState) and last_activity_at
(AdaptiveRouterSession) with @updatedAt so Prisma refreshes the
timestamps on every write. Without this the fields stayed frozen at
INSERT time and the last_activity_at index was misleading for any
future TTL/eviction logic. Applied to all three schema.prisma copies;
no migration SQL change needed (Prisma @updatedAt is a client-side
annotation that doesn't touch DDL).
- get_state_snapshot: report cell.total_samples instead of alpha+beta
for the 'samples' field. The previous value inflated every cell by
the COLD_START_MASS prior (e.g. showed 10.0 before any real traffic
arrived), which confused operators reading /adaptive_router/.../state.
Updated docs + the snapshot test to match.
Also fixes two pre-existing merge-break syntax errors in router.py
(missing ')' on the AdaptiveRouter TYPE_CHECKING import; truncated
async_pre_routing_hook dispatch call for the adaptive router branch)
that were masking the rest of the file from the interpreter.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Existing tests pinned exact kwargs on `PrismaManager.setup_database`,
but the opt-in v2 resolver added `use_v2_resolver=False` to every call.
Update the three assertions to reflect the new signature.
Fixes:
- TestHealthAppFactory::test_use_prisma_db_push_flag_behavior
- TestHealthAppFactory::test_startup_fails_when_db_setup_fails
Adds total_spend column to LiteLLM_TeamMembership that accumulates
continuously and is not zeroed by the budget cycle reset job. This
enables UI surfaces to distinguish current-cycle spend (the existing
spend column, which resets) from lifetime spend per team member.
Also exposes budget_reset_at on LiteLLM_BudgetTable so /team/info
callers can see when a member's budget window next resets. The field
was already stored in the DB but stripped by the response Pydantic
model.
Includes regression tests that:
- Guard the reset job against ever writing total_spend: 0
- Verify the spend writer increments both spend and total_spend in
one UPDATE statement.
Two fail-safes for the /v1/messages → Bedrock Invoke pass-through so new
Anthropic-only extensions Claude Code starts sending can't reach Bedrock
and trigger a 400 "Extra inputs are not permitted":
1. Top-level body fields are filtered to a typed allowlist. New
`BedrockInvokeAnthropicMessagesRequest` TypedDict (in
`litellm/types/llms/bedrock.py`) captures the Bedrock Invoke Anthropic
Messages body schema; the runtime allowlist is derived from its
`__annotations__` so the type and the filter can't drift. Anchored to
the AWS reference page in docstrings + transform comment. An
exact-set test pins the resolved allowlist so any future edit forces
conscious review.
Drops context_management, output_config, speed, mcp_servers,
container, inference_geo, internal litellm_metadata, and any future
Anthropic addition. output_format stays as an active inline-schema
conversion (not just a strip).
2. The anthropic-beta header list is filtered + transformed against the
bedrock mapping for ALL betas, not just auto-injected ones. The
previous code union'd user-provided betas back in unfiltered, so a
client on a new Anthropic-direct beta (e.g. advisor-tool-…,
context-management-…) could still pin the request to fail. In a proxy
context the client can't know the backend is Bedrock; the provider
mapping is authoritative. User-provided drops are logged at WARNING
so intentional overrides leave a breadcrumb.
Updates one existing test that happened to assert on the old buggy
pass-through (it used output-128k-2025-02-19, which is null in the
bedrock mapping and would 400 at runtime); rewrote it against a
bedrock-supported beta.
Scope: messages/invoke only. The same user-beta bypass exists in
chat/invoke but that's a different code path with different
user-expectation trade-offs — follow-up.
VertexAIImagenImageEditConfig.get_complete_url was resolving vertex_project
and vertex_location only from env vars and global settings, ignoring
litellm_params. Users supplying project/location exclusively via YAML
config would get a ValueError or wrong URL even after auth headers were fixed.
Mirrors the pattern already used by VertexAIGeminiImageEditConfig and
image_generation counterpart (safe_get_vertex_ai_project/location).
Also fixes api_key type hint in MockImageEditConfig (str -> Optional[str])
and adds a test covering get_complete_url credential resolution.
Made-with: Cursor
Adds three test cases to prevent regression of the Vertex AI image_edit
credentials bug:
1. test_validate_environment_signature_includes_litellm_params: ensures
all image-edit configs accept litellm_params (contract for the handler)
2. test_vertex_gemini_image_edit_reads_credentials_from_litellm_params:
verifies Gemini config reads from litellm_params first
3. test_vertex_imagen_image_edit_reads_credentials_from_litellm_params:
verifies Imagen config reads from litellm_params first
These tests catch if the fix is accidentally reverted or if new image-edit
configs are added without the litellm_params parameter.
Made-with: Cursor
When aimage_edit or image_edit was called with Vertex AI Gemini/Imagen models
via YAML-style config (vertex_project / vertex_credentials in proxy YAML),
the credentials were dropped during handler-to-config plumbing, causing
fallback to Application Default Credentials and DefaultCredentialsError.
Root cause: image_edit_handler and async_image_edit_handler did not pass
litellm_params to validate_environment, unlike image_generation_handler.
Fixes:
1. Widen BaseImageEditConfig.validate_environment signature to accept
litellm_params and api_base (optional kwargs).
2. Forward dict(litellm_params) and litellm_params.api_base from both
sync and async image_edit handlers to validate_environment.
3. Update VertexAIImagenImageEditConfig.validate_environment to read
vertex_ai_project/vertex_ai_credentials from litellm_params first,
matching Gemini config pattern (secondary latent bug fix).
4. Widen all image-edit config override signatures to match base.
Made-with: Cursor
Add regression tests that mock make_bedrock_api_request and verify
input_type=request uses source=INPUT with user messages, and
input_type=response uses source=OUTPUT with synthetic ModelResponse.
Made-with: Cursor
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- _owner_cache now opportunistically sweeps expired entries past
_OWNER_CACHE_SWEEP_THRESHOLD live entries. Previously sessions that never
came back piled up forever.
- flush_session_to_db strips session_id/router_name/model_name from the update
payload. Prisma rejects writes to @@id fields.
- record_turn no longer persists last_user_content / last_assistant_content /
tool_call_history / pending_tool_calls. Those are needed only in-memory for
the next turn's signal detection; writing user prompts and tool payloads to
the DB would store PII for every conversation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- SessionState now carries clean_credit_awarded + last_processed_turn (matching
the DB schema). Satisfaction only fires once per session AND only after
MIN_TURNS_FOR_CLEAN_CREDIT turns of context — early "thanks" no longer
inflates alpha.
- _detect_failure no longer treats empty content as failure. Many tools
legitimately return empty output (zero-result searches, silent bash);
penalizing those corrupted the bandit posterior. Only is_error fires now.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
