* feat(azure_ai): add MAI-Image-2.5 image generation support
Route azure_ai MAI models to /mai/v1/images/generations and map OpenAI size to width/height for the serverless API.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(azure_ai): address MAI image generation review feedback
Validate unsupported size values, default width/height independently, add MAI-Image-2.5 pricing, and expand test coverage.
@greptileai
Co-authored-by: Cursor <cursoragent@cursor.com>
* feat(azure_ai): add MAI image edit and expand model cost map
Add MAI image edit support with usage normalization for Azure response format,
and register MAI-Image-2.5-Flash and MAI-Image-2e pricing in the model map.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(azure_ai): validate MAI edit size by consuming map iterator
Greptile: lazy map() never evaluated int() so values like 1024xabc passed through.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(azure_ai): normalize MAI usage in generation response handler
Apply normalize_mai_image_usage before building ImageResponse so token-based
cost calculation works when Azure returns num_output_tokens fields.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(azure_ai): narrow MAI edit size param type for mypy
Co-authored-by: Cursor <cursoragent@cursor.com>
* Fix Azure MAI image response handling
* Fix MAI image generation base model routing
* fix(azure_ai): preserve zero num_output_tokens in MAI usage normalization
* fix(azure_ai): wrap MAI generation response JSON parsing in error handling
* fix(azure_ai): build MAI image edit URL correctly for /mai/ root bases
* fix(azure_ai): build MAI image generation URL correctly for /mai/ root bases
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(azure): apply api_version fallback chain to image edit URL
`AzureImageEditConfig.get_complete_url` only read `api_version` from
`litellm_params`. When callers configured it via `litellm.api_version`
or `AZURE_API_VERSION`, the constructed URL had no `?api-version=` and
Azure responded `404 Resource not found`.
Apply the same fallback chain the Azure chat path already uses in
`common_utils.py`:
litellm_params > litellm.api_version > AZURE_API_VERSION env >
litellm.AZURE_DEFAULT_API_VERSION
Adds 5 unit tests pinning each layer of the chain plus a regression
guard for `api_base` that already carries `?api-version=`.
* feat(mcp): core sampling and elicitation flow with security hardening
- Add sampling_handler.py: full MCP sampling/createMessage flow with
model selection (hint-based + priority-based), auth enforcement,
budget checks, route restriction gates, and tag policy pre-auth
- Add elicitation_handler.py: MCP elicitation/create relay with
downstream client capability detection
- Wire sampling/elicitation callbacks in mcp_server_manager.py
gated behind allow_sampling/allow_elicitation config flags
- Add allow_sampling/allow_elicitation fields to MCPServer type
- Fix session lock deadlock: skip lock for JSON-RPC response POSTs
(elicitation/sampling replies) with truncated-body heuristic
- Extend client.py with sampling_callback and elicitation_callback
- Security: RouteChecks gate, tag-budget bypass fix, x-forwarded-for
spoofing fix, Latin-1 header encoding guard
- Add 4 new test modules (model access, priority selection, request
builder, tool conversion) + update existing MCP tests
* fix(security): run pre-call guardrails before MCP sampling acompletion
Without this, an upstream MCP server with allow_sampling enabled could
send prompts that bypass every guardrail (content filtering, PII
redaction, prompt-injection detection) configured on /chat/completions.
- Call proxy_logging_obj.pre_call_hook(call_type='acompletion') before
llm_router.acompletion so guardrails fire for sampling sub-calls
- Add HTTPException to the re-raise list so guardrail rejections
propagate correctly instead of being swallowed as generic errors
* feat(bedrock_mantle): add Responses API support (/openai/v1/responses) (#29490)
* feat(bedrock_mantle): add Responses API transformation config
* test(bedrock_mantle): cover trailing-slash api_base normalization
* feat(bedrock_mantle): export BedrockMantleResponsesAPIConfig
* feat(bedrock_mantle): register gpt-5.x Responses config (gpt-oss unchanged)
* feat(bedrock_mantle): add gpt-5.5/gpt-5.4 Responses price-map entries
* refactor(bedrock_mantle): exclude gpt-oss instead of allow-listing gpt-5 for Responses routing
Frontier OpenAI models on Bedrock Mantle are Responses-only on /openai/v1/responses;
gpt-oss is the legacy family that also speaks chat-completions. Gate by excluding
gpt-oss (which keeps its chat-completions emulation) and defaulting everything else
to the native Responses config, so future frontier models (gpt-6, etc.) route
correctly without a code change. Verified against the live us-east-2 Mantle endpoint:
gpt-oss 400s on /openai/v1/responses while gpt-5.5 400s on both standard paths.
* test(bedrock_mantle): cover supports_native_websocket opt-out
Closes the one uncovered line flagged by codecov on the Responses config.
The assertion documents that Mantle Responses has no realtime/websocket
transport, so realtime routing must not attempt a socket it cannot serve.
* fix(bedrock_mantle): route file_search through emulation instead of forwarding to Mantle
BedrockMantleResponsesAPIConfig inherited supports_native_file_search()
-> True from OpenAIResponsesAPIConfig but never overrode it. Mantle has no
OpenAI vector stores, so a forwarded file_search tool is rejected with a
400 (verified upstream: Tool type 'file_search' is not supported). Opting
out, like the existing supports_native_websocket override, routes the tool
through LiteLLM's file_search emulation instead.
* fix(bedrock_mantle): only route openai.gpt frontier models to Responses
The previous gate excluded gpt-oss and routed every other model to the
native Responses config. But on Mantle only the OpenAI gpt frontier models
(gpt-5.x) are served on /openai/v1/responses; gpt-oss and the non-OpenAI
families (nvidia, mistral, google, zai, ...) are chat-completions only and
400 on that path. Allow-list the openai.gpt- family (excluding gpt-oss)
instead, so chat-only models fall through to the chat-completions emulation.
Verified against the live us-east-2 endpoint: nvidia.nemotron-nano-9b-v2
returns 400 on /openai/v1/responses and 200 on /v1/chat/completions.
* feat(custom_llm): allow streaming/astreaming to yield ModelResponseStream (#27580)
* fix(custom_llm): allow streaming/astreaming to yield ModelResponseStream directly
* fix(streaming): enhance ModelResponseStream handling for custom LLM providers
* fix(streaming): strip finish_reason from content chunks and ensure tool_calls are preserved
* fix(streaming): add type ignore for finish_reason assignment in CustomStreamWrapper
* fix(proxy): strip stack trace from HTTP 503 responses (CWE-209) (#28330)
* fix(proxy/cwe-209): strip Python traceback from HTTP 503 error responses
The /cache/ping endpoint included a full Python traceback in its 503 error
response body (inside the ProxyException message), leaking internal file
paths, line numbers, and call stacks to any caller. Two MCP route handlers
in proxy_server.py similarly interpolated str(e) into "Internal server
error" detail strings.
Fix: log the traceback server-side via verbose_proxy_logger.exception()
and omit it from the ProxyException payload / HTTPException detail returned
to clients. Tests updated to assert no "traceback" keyword or frame paths
appear in the 503 body, with a new dedicated regression test.
CWE-209: Generation of Error Message Containing Sensitive Information.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(proxy/cwe-209): apply Greptile P2 fixes and add MCP exception-path tests
Greptile 4/5 review identified two remaining gaps and Codecov reported
0% coverage on the two MCP handler exception branches:
1. caching_routes.py — str(e) in "Service Unhealthy ({str(e)})" could
still leak Redis hostnames/IPs; replaced with static "Service Unhealthy".
HTTPException is now re-raised before the generic handler so the
"cache not initialized" 503 still reaches callers with its detail.
Removed the redundant str(e) arg from verbose_proxy_logger.exception()
(exception() already appends the traceback automatically).
2. tests — two new unit tests cover the exception paths in
dynamic_mcp_route and toolset_mcp_route that were previously at 0%:
- test_dynamic_mcp_route_unexpected_exception_returns_500_without_traceback
- test_toolset_mcp_route_unexpected_exception_returns_500_without_traceback
All 25 tests pass (9 caching + 16 MCP).
CWE-209: Generation of Error Message Containing Sensitive Information.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* test(caching_routes): restore precise assertion in test_cache_ping_no_cache_initialized
The assertion was weakened to `"Cache not initialized" in str(data)`, which
matches the raw string of the entire response dict and would pass even if the
error moved to an unexpected field or changed structure.
Restore a targeted check on the parsed response: assert the exact string in
the correct field `data["detail"]`, matching FastAPI's HTTPException
serialisation format {"detail": "<message>"}.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* test(caching_routes): restore precise assertion and add CWE-209 no-cache path test
The assertion in test_cache_ping_no_cache_initialized was weakened to
`"Cache not initialized" in str(data)`, which matched against the raw string
representation of the entire response dict. This would pass silently even if
the error message moved to an unexpected field or the structure changed.
Restore a targeted assertion on the parsed field:
assert data["detail"] == "Cache not initialized. litellm.cache is None"
matching FastAPI's HTTPException serialisation format exactly.
Add test_cache_ping_no_cache_does_not_expose_internals to show the code path
is still working correctly after the CWE-209 fix: verifies that the HTTPException
is re-raised as-is (no traceback, no source paths), and asserts the complete
response structure is exactly {"detail": "Cache not initialized. litellm.cache is None"}.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(caching_routes): restore ProxyException envelope for null-cache 503
The except HTTPException: raise guard (added in the CWE-209 fix) caused
the null-cache HTTPException to escape as FastAPI's {"detail": "..."} shape
instead of the {"error": {...}} ProxyException envelope that callers expect.
Move the null-cache guard before the try block and raise ProxyException
directly so the response structure is consistent with all other /cache/ping
503s, and the except HTTPException: raise guard is only reachable by
unexpected downstream HTTPExceptions.
Update the two no-cache tests to assert the correct ProxyException envelope.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update utils.py (#26609)
* feat(pricing): add Snowflake Cortex REST API model pricing (#26612)
* feat(pricing): add Snowflake Cortex REST API model pricing
## Summary
Adds pricing and context window information for 20+ Snowflake Cortex REST API models to `model_prices_and_context_window.json`.
## What's included
- **7 Claude models** (sonnet-4-5, sonnet-4-6, 4-sonnet, 4-opus, haiku-4-5, 3-7-sonnet, 3-5-sonnet) — with prompt caching rates
- **4 OpenAI models** (gpt-4.1, gpt-5, gpt-5-mini, gpt-5-nano) — with prompt caching rates
- **5 Llama models** (3.1-8b, 3.1-70b, 3.1-405b, 3.3-70b, 4-maverick)
- **1 DeepSeek model** (deepseek-r1)
- **1 Mistral model** (mistral-large2)
- **1 Snowflake model** (snowflake-llama-3.3-70b)
- **2 Embedding models** (arctic-embed-l-v2.0, arctic-embed-m-v2.0)
Each entry includes `input_cost_per_token`, `output_cost_per_token`, `cache_read_input_token_cost` (where applicable), `max_input_tokens`, `max_output_tokens`, and capability flags (`supports_function_calling`, `supports_vision`, `supports_prompt_caching`, `supports_reasoning`).
## Pricing source
All prices are in USD per token, sourced from the official [Snowflake Service Consumption Table](https://www.snowflake.com/legal-files/CreditConsumptionTable.pdf) — Tables 6(b) (REST API with Prompt Caching) and 6(c) (REST API).
## Context
The existing `snowflake/` provider has zero model entries in the pricing JSON, which means LiteLLM cannot track costs for Snowflake Cortex calls. This PR fills that gap.
## Related
- Existing provider: `litellm/llms/snowflake/`
- Cortex REST API docs: https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-rest-api
* Update model_prices_and_context_window.json
Fix the JSON parsing error
* Update model_prices_and_context_window.json
Removed the duplicate entry
* fix(utils): copy extra_body before adding unknown params to prevent model config mutation (#29620)
Fixes#29615. In add_provider_specific_params_to_optional_params, the line:
extra_body = passed_params.pop("extra_body", None) or {}
returns the original dict reference when extra_body is non-empty (truthy).
Subsequent writes like extra_body[k] = passed_params[k] then mutate the
shared model config object held by the router, poisoning /model/info and
all subsequent requests for that deployment.
The or {} short-circuit creates a new dict only when extra_body is falsy
(None or {}), which is why the bug does not reproduce with extra_body: {}.
Fix: wrap in dict() so we always work on a fresh shallow copy.
* fix(vertex_ai): Bake tool_choice into Gemini CachedContent body to prevent silent drop (#29097)
* fix(vertex_ai): bake tool_choice into Gemini CachedContent body to prevent silent drop
* address greptile feedback on tool_choice cache test
* adds test that uses ToolConfig(functionCallingConfig=FunctionCallingConfig(mode=ANY)) instead of a dict literal, mirroring what map_tool_choice_values actually produce
* fix(gemini/veo): move image from parameters into instances[0] (#29501)
* fix(gemini/veo): move image from parameters into instances[0]
Veo's predictLongRunning schema puts image (and prompt) on the
instances element; parameters is for aspectRatio/durationSeconds/etc.
The Gemini path was leaving image in params_copy, so it ended up
nested under parameters and the API silently ignored it.
The Vertex path already builds the instance dict explicitly, so this
just aligns the Gemini path with it.
Fixes#29498
* address greptile: unconditional pop + BytesIO test
- Pop `image` from params_copy unconditionally so it never reaches
GeminiVideoGenerationParameters even when None, removing implicit
reliance on Pydantic's extra-field-ignore.
- Add test_transform_video_create_request_image_filelike_goes_to_instance
covering the BytesIO path (_convert_image_to_gemini_format) — round-trips
the base64 to confirm encoding.
- Add test_transform_video_create_request_image_none_is_dropped covering
the new None branch.
* fix(huggingface): handle special token text in embedding usage (#29660)
* fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params (#29655)
* fix(guardrails): recompile ToolPermissionGuardrail rules on update_in_memory_litellm_params
ToolPermissionGuardrail builds self.rules and the compiled target/pattern
maps only in __init__. The base update_in_memory_litellm_params re-sets raw
attributes via setattr but never rebuilds those maps, so a guardrail updated
in place (PUT /guardrails, or the immediate in-memory sync) keeps enforcing
the construction-time rules until it is reinitialized (PATCH path, periodic
DB poll, or restart).
Extract the compile step into _load_rules and override
update_in_memory_litellm_params to rebuild from it (dict- and model-safe),
re-normalizing default_action / on_disallowed_action. Mirrors the existing
PresidioGuardrail override of the same method. Adds regression tests.
Fixes#29592.
* fix(guardrails): handle dict params in ToolPermissionGuardrail in-memory update
Delegate to super() only for LitellmParams input (the base setattr loop is
model-only); apply the raw-dict case inline. Fixes the mypy arg-type error
and makes the recompile work when the proxy passes the raw DB dict.
* fix(guardrails): preserve tool-permission rules on a partial in-memory update
A partial update (e.g. a LitellmParams whose rules field is None) ran through
the generic setattr, which set self.rules to None, and the recompile was
skipped, leaving the guardrail with no rules. Snapshot the previous rules and
restore them when the update carries no rules; an explicit empty list still
clears them. Adds a regression test for the rules-absent case.
Addresses the Greptile review note on #29655.
* fix(bedrock): stop base_model label from stripping tools/tool_choice (#29621)
* fix(bedrock): stop base_model label from stripping tools/tool_choice
A Router/proxy Bedrock deployment whose model_info.base_model is a friendly
label (e.g. claude-haiku-4-5) silently lost tools/tool_choice: the outgoing
Converse request was built without toolConfig, so the model behaved as if no
tools were provided. Worked in v1.84.0, regressed in v1.85.0, and with
drop_params=true it failed silently.
Two changes compound into the bug. completion() passed model_info.base_model
as the model argument to get_optional_params, so the real Bedrock model id
never reached supported-param resolution; and get_supported_openai_params
resolved the provider config's params from base_model or model, letting the
label fully replace the real model. For Bedrock the label resolves to no tool
support, so tools/tool_choice were dropped before transformation.
completion() now keeps model as the real deployment model and threads the
resolved base_model (kwarg or model_info) through separately, and
get_supported_openai_params treats base_model as additive: it returns the
union of the params supported by model and by base_model. A hint can only add
capabilities, never strip ones the real model already exposes, which also
preserves the original base_model behavior from #27717 and Azure's base_model
driven model-type detection.
Fixes#29618
* test(main): make base_model param test robust to new parametrize cases
Restore an explicit per-case expected_model_param literal instead of
hardcoding the gemini id, so a future case with a different model can't
produce a misleading assertion failure.
* fix(fireworks_ai): pass response_format json_schema through unchanged (#29606)
FireworksAIConfig.map_openai_params was rewriting the OpenAI strict
`{type: json_schema, json_schema: {name, strict, schema}}` shape into
`{type: json_object, schema: ...}` before sending to Fireworks, dropping
`strict` and `name` and changing the `type`. Per Fireworks' docs json_object
means "force any valid JSON output (no specific schema)", so the schema
constraint was effectively dropped and grammar-guided decoding never ran;
model output silently violated the schema.
The rewrite landed in #7085 (Dec 2024) when Fireworks did not yet accept
native json_schema. Fireworks accepts the OpenAI strict shape natively now,
so the rewrite has become a regression.
Removes the rewrite. Passes response_format through unchanged. Updates the
existing test_map_response_format to assert pass-through. Adds focused
regression tests in tests/test_litellm/ covering preservation of type,
strict, name, and schema body, plus that json_object alone still works.
* fix(types): import Required from typing_extensions in gemini types
* style: reformat sampling_handler.py for py312 black compat
* refactor(mcp-sampling): extract helpers to fix PLR0915 too-many-statements in handle_sampling_create_message
* fix(proxy-server): add explicit ProxyLogging type annotation to proxy_logging_obj to fix mypy inference
* fix(mcp-sampling): suppress mypy assignment error on ImportError fallback for proxy_logging_obj
* fix(test): use .value when comparing LlmProviders enum against string in test_default_api_base
* fix(test): iterate LlmProviders enum in test_default_api_base to avoid str pollution from custom provider registration
litellm.provider_list is a mutable global initialized to list(LlmProviders) but custom_llm_setup() appends plain provider strings to it. When a test_custom_llm.py test runs first in the same xdist worker, provider_list contains a str and calling .value on it raises AttributeError. Iterate the immutable LlmProviders enum instead, which is deterministic and what the check intends.
* fix(mcp): depth-aware JSON-RPC response detection and neutral speed-priority fallback
Replace the flat substring check in the truncated-body routing path with a
top-level-key scan so a JSON-RPC response whose result payload nests a
"method" field is still detected as a response and skips the session lock,
removing a deadlock against the in-flight tool call awaiting it.
Drop the inverse max_output_tokens speed proxy when no model exposes
output_tokens_per_second; context-window size does not track latency, so a
neutral score avoids biasing speedPriority toward the smallest-context model.
* fix(guardrails): make ToolPermission rule reload atomic on invalid regex
_load_rules appended each rule to self.rules before compiling its regex, so an
invalid pattern raised mid-loop after the bad rule was already live but without
a _compiled_rule_targets entry. _matches_regex reads a missing compiled target
as a None pattern and returns True, turning the bad rule into a match-all that
silently applies its decision to every tool. Via update_in_memory_litellm_params
(PUT /guardrails) this corrupted the live guardrail.
Build the parsed rules and compiled maps into locals and swap them in only after
every regex compiles, and restore the previous ruleset if a live update is
rejected, so an invalid regex now fails the update without leaving the guardrail
enforcing a broken policy.
* test(mcp): cover sampling conversion, model resolution, and elicitation relay paths
The MCP sampling and elicitation handlers shipped with partial test
coverage, leaving the response-to-MCP conversion, the model resolution
fallback chain, completion-kwargs assembly, guardrail routing, and the
entire elicitation relay untested. That pulled the PR's diff (patch)
coverage below the codecov threshold even though overall project
coverage rose.
Add focused unit tests for _convert_openai_response_to_mcp_result,
_convert_mcp_tools_to_openai, _convert_mcp_tool_choice_to_openai, image
and audio content conversion, the hint-matching and fallback branches of
_resolve_model_from_preferences, _build_completion_kwargs, the router and
guardrail-rejection paths of _run_guardrails_and_call_llm, the
handle_sampling_create_message success and error-propagation flows, the
marker-hoisting fallback for tool content on unexpected roles, and the
elicitation form/url/generic relay together with its decline paths
---------
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: lengkejun <lengkejun@xd.com>
Co-authored-by: Yug <yugborana000@gmail.com>
Co-authored-by: Kent <72616338+kingdoooo@users.noreply.github.com>
Co-authored-by: tanmay958 <53569547+tanmay958@users.noreply.github.com>
Co-authored-by: DrishnaTrivedi <142084770+DrishnaTrivedi@users.noreply.github.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: Navnit Shukla <Navnit.shukla25@gmail.com>
Co-authored-by: PRABHU KIRAN VANDRANKI <72809214+VANDRANKI@users.noreply.github.com>
Co-authored-by: Adrian Lopez <109683617+adriangomez24@users.noreply.github.com>
Co-authored-by: hcl <chenglunhu@gmail.com>
Co-authored-by: JooHo Lee <96564470+BWAAEEEK@users.noreply.github.com>
Co-authored-by: Dinesh Girbide <85330597+Dinesh-Girbide@users.noreply.github.com>
Co-authored-by: cloudwiz <22098246+andrey-dubnik@users.noreply.github.com>
Co-authored-by: Ahmad Khan <ahmadkhan2508@gmail.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(azure): preserve AD token refresh in v1 OpenAI client path
The /openai/v1/ code path (api_version in {"v1", "latest", "preview"})
constructs a plain OpenAI/AsyncOpenAI client, but only forwarded
`api_key` from `azure_client_params`. When `enable_azure_ad_token_refresh`
is set (or any AD-only auth), `api_key` is None and the client
constructor raised "The api_key client option must be set...", breaking
every Azure call with a v1 api_version.
The OpenAI SDK (>=2.20.0) accepts a callable for `api_key` and re-invokes
it on every request via `_refresh_api_key`, so we now forward
`azure_ad_token_provider` directly — preserving the per-request token
refresh behavior of the regular AzureOpenAI client and avoiding the
expiry hole that resolving the token once at client-creation time would
introduce. Static `azure_ad_token` strings fall through to `api_key`.
For the async path we wrap the sync provider returned by azure-identity
in an async function since AsyncOpenAI expects `Callable[[], Awaitable[str]]`.
Fixes#27945https://claude.ai/code/session_01UnzrDSFUUgp5T2wRoPMxq5
* fix(azure): offload sync token provider to thread in v1 async wrapper
* fix(azure): include AD credential identity in v1 client cache key
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix(anthropic): handle empty streaming tool calls (#28549)
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
* [Feature][Bug Fix] Decouple Azure OpenAI Deployment ID from model name via base_model to fix gpt5 model routing (#28490)
* feat(azure): decouple deployment ID from model name via base_model
Azure OpenAI deployments have arbitrary names (deployment IDs) that may
not match the underlying model. Previously, model-type detection
(o-series, gpt-5, etc.) relied on substring matching against the
deployment name, causing misrouted configs and rejected params when
deployment names were non-standard (e.g. 'my-deployment-id' for gpt-5.2).
This change extends the existing base_model field to drive model-type
detection, config selection, supported param resolution, and param
mapping throughout the Azure call path:
- _get_azure_config() uses base_model for is_o_series/is_gpt_5 checks
- get_provider_chat_config() threads base_model for Azure
- get_supported_openai_params() accepts and uses base_model
- get_optional_params() accepts base_model and passes it to all Azure
config method calls (get_supported_openai_params, map_openai_params)
- azure.py completion handler uses base_model for GPT-5 detection
- Config internal methods (e.g. is_model_gpt_5_2_model) now receive
base_model so features like logprobs are correctly enabled
Fully backward compatible - when base_model is unset, behavior is
identical. Existing o_series/ and gpt5_series/ prefix workarounds
continue to work.
Usage in proxy config:
model_list:
- model_name: my-gpt5
litellm_params:
model: azure/my-deployment-id
model_info:
base_model: azure/gpt-5.2
Fixes: non-standard deployment names like 'prefix-gpt-5.2' rejecting
logprobs/top_logprobs despite the underlying model supporting them.
* Addressing Greptile comments.
* gemini-3.1-flash-lite pricing (#27933)
* feat(model_prices): add gemini-3.1-flash-lite pricing with standard/batch/flex/priority tiers
* fix pricing
* add service tier
---------
Co-authored-by: shin-berri <shin-laptop@berri.ai>
* fix(openai-responses): strip Anthropic cache_control from Responses API requests (#28431)
Squash-merged by litellm-agent from cwang-otto's PR.
* Treat None litellm_provider as wildcard in _check_provider_match (#28523)
Squash-merged by litellm-agent from adityasingh2400's PR.
* fix greptile
* fix: use _azure_detection_model in default Azure branch of get_supported_openai_params
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix(openai-responses): strip cache_control on compact endpoint as well
Co-authored-by: Yassin Kortam <yassin@berri.ai>
---------
Co-authored-by: Felipe Garé <90070734+FelipeRodriguesGare@users.noreply.github.com>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: withomasmicrosoft <withomas@microsoft.com>
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: cwang-otto <chengxuan.wang@ottotheagent.com>
Co-authored-by: Aditya Singh <60082699+adityasingh2400@users.noreply.github.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Yassin Kortam <yassin@berri.ai>
* fix: strip Gemini thought-signature from tool_use.id in non-streaming path; example websearch config (#27873)
- adapters/transformation.py: mirror the streaming path and strip the
`__thought__<b64>` suffix off `tool_call.id` before building the
AnthropicResponseContentBlockToolUse. Base64's `+ / =` characters
violate Anthropic's `^[a-zA-Z0-9_-]+$` tool_use.id pattern, so when a
conversation that flowed through Gemini is later replayed to an
Anthropic-native provider (Bedrock or Anthropic API) the request 400s.
- example_config_yaml/websearch_interception_config.yaml: register the
interceptor under `callbacks:` not `success_callback:`. `success_callback`
does not run pre-request hooks, so the tool-conversion step never fires
on `/v1/messages` and the raw `web_search_20250305` tool is forwarded
to Bedrock, which 400s.
- adds a unit test pinning the non-streaming strip behavior and the
surviving `^[a-zA-Z0-9_-]+$` shape of the resulting id.
Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com>
* Fix/azure image edit auth header (#27863)
* fix(azure/image_edit): use api-key header instead of Authorization Bearer
Delegate `AzureImageEditConfig.validate_environment` to
`BaseAzureLLM._base_validate_azure_environment` so the image-edit route
follows the same auth resolution as every other Azure provider:
- prefer the Azure-native `api-key` header when an API key is available
- fall back to `Authorization: Bearer <azure_ad_token>` only for AAD auth
The previous implementation unconditionally set
`Authorization: Bearer <api_key>`, which is the OpenAI-direct convention
and is rejected by Azure OpenAI / APIM-fronted deployments with
`401 Access denied due to missing subscription key`.
Adds regression tests covering api_key kwarg, litellm_params.api_key, and
the AAD-token fallback path.
Co-authored-by: Cursor <cursoragent@cursor.com>
* docs(azure/image_edit): pin api-key precedence semantics + add regression test
Address review feedback that the move to
``BaseAzureLLM._base_validate_azure_environment`` changed the relative
priority of the positional ``api_key`` kwarg vs. ``litellm_params["api_key"]``.
The new behavior — ``litellm_params["api_key"]`` wins, positional only fills
in when ``litellm_params["api_key"]`` is empty — is intentional and matches
every other Azure ``validate_environment``: ``AzureVideosConfig`` uses the
exact same merge logic, while ``AzureVectorStoresConfig`` and
``AzureResponsesAPIConfig`` don't accept a positional ``api_key`` at all.
The old ``or`` chain (positional wins) was the outlier and was part of the
same OpenAI-vs-Azure convention drift that produced the original
``Authorization: Bearer`` bug.
The only production caller (``llm_http_handler.image_edit``) sources both
values from the same ``litellm_params.api_key``, so this change is
behaviorally a no-op there. Document the precedence in the docstring and
lock it in with an explicit test so future refactors can't quietly
re-invert it.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: Adam Kirstein <adam.kirstein@disney.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(azure/image_edit): expect api-key header instead of Authorization Bearer
PR #27863 fixed Azure image edit to use the Azure-native api-key header
instead of OpenAI's Authorization: Bearer convention, but did not update
test_azure_image_edit_litellm_sdk to match. The test still asserted
'Authorization' in headers, which now fails since the new code routes
through BaseAzureLLM._base_validate_azure_environment and emits
api-key when an api_key is provided.
Update the assertion to pin the correct Azure behavior: api-key header
present with the resolved key, and no Authorization header.
---------
Co-authored-by: oss-agent-shin <ext-agent-shin@berri.ai>
Co-authored-by: oss-agent-shin <279349115+oss-agent-shin@users.noreply.github.com>
Co-authored-by: Adam Kirstein <107421694+justalittleadam@users.noreply.github.com>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
Co-authored-by: Adam Kirstein <adam.kirstein@disney.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* feat(realtime): OpenAI Realtime GA support and beta compatibility
- Normalize beta-style session.update to GA for upstream OpenAI; optional GA→beta
event translation when client sends OpenAI-Beta: realtime=v1
- Default upstream WebSocket without OpenAI-Beta; forward header when client opts in
- Extend OpenAI realtime types for GA event names and conversation item shapes
- Relax LiteLLMRealtimeStreamLoggingObject.results to List[Any] for GA events
- Update proxy client_secrets fallback to omit beta header; dashboard RealtimePlayground
- Add unit tests for remap, translation, and beta header helper
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix results
* fix greptile
* Fix mypy issues
* Remove unused class constants _GA_TEXT_DELTA_TYPES and _GA_AUDIO_DELTA_TYPES
These frozensets were defined as class-level constants in realtime_streaming.py
but never referenced anywhere in the codebase. Removing dead code.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* fix(realtime): use GA-shaped session.update in guardrail injections
The guardrail VAD injection code sent a beta-style session.update with a
flat turn_detection field:
{"session": {"turn_detection": {"create_response": false}}}
When the upstream OpenAI backend operates in GA mode (no OpenAI-Beta
header forwarded), it requires the nested GA shape:
{"session": {"type": "realtime", "audio": {"input": {"turn_detection": {"create_response": false}}}}}
The _remap_beta_session_to_ga helper was only applied to client-
originated session.update messages in client_ack_messages. Internally-
generated session.updates (sent via _send_to_backend) in two paths:
- _handle_raw_backend_message (raw/no provider_config path, line 518)
- backend_to_client_send_messages provider_config path (line 481)
bypassed the remap, so GA upstreams ignored or rejected them, breaking
audio transcription guardrails for all non-beta clients.
Fix: add _make_disable_auto_response_message() helper that always emits
the correct GA-shaped session.update, and replace both injection sites
with it.
Update existing tests to assert the GA nested shape instead of the old
flat beta shape, and add a new unit test for the helper itself.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
* Log realtime session type
* Fix beta realtime session payloads
* Fix realtime audio format remapping edge case
* Fix Azure realtime beta session shape
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
- Add image_generation/http_utils.azure_deployment_image_generation_json_body; call
from azure.py (keeps AzureChatCompletion focused on chat).
- Rename finalize_image_edit_multipart_data to finalize_image_edit_request_data with
docstring covering multipart and JSON POST payloads (review feedback).
Co-authored-by: Cursor <cursoragent@cursor.com>
- Image generation tests patch HTTPHandler.post / get_async_httpx_client so
make_*_azure_httpx_request runs and wire json is asserted on call kwargs.
- Azure image edit: strip model in finalize_image_edit_multipart_data using the
same URL string the handler passes to POST (no second get_complete_url in
transform). BaseImageEditConfig default finalize is a no-op.
Co-authored-by: Cursor <cursoragent@cursor.com>
Azure OpenAI routes image gen/edit by deployment in the URL; sending the
deployment id in model breaks gpt-image-2 (invalid_value). Strip model from
JSON for deployments/.../images/generations and from multipart data for
.../images/edits. Non-deployment URLs (e.g. Azure AI FLUX) unchanged.
Fixes#26316.
Co-authored-by: Cursor <cursoragent@cursor.com>
service_tier (priority/flex) was not forwarded to generic_cost_per_token
for azure and azure_ai providers, so tier-specific pricing was ignored
and standard pricing was always returned. Other providers (openai,
bedrock, gemini, vertex_ai) already pass it correctly.
Azure GPT-5.4+ models now get the same auto-routing treatment as OpenAI
when both `reasoning_effort` and `tools` are used in `litellm.completion()`.
Previously, `reasoning_effort` was silently dropped for Azure; now the
request is bridged to the Responses API which supports both parameters.
Fixes#23914
Add avideo_create_character, avideo_get_character, avideo_edit, and avideo_extension
to the skip condition since Azure video calls don't use initialize_azure_sdk_client.
Tests now properly skip with expected behavior instead of failing:
- test_ensure_initialize_azure_sdk_client_always_used[avideo_create_character] ✓
- test_ensure_initialize_azure_sdk_client_always_used[avideo_get_character] ✓
- test_ensure_initialize_azure_sdk_client_always_used[avideo_edit] ✓
- test_ensure_initialize_azure_sdk_client_always_used[avideo_extension] ✓
Made-with: Cursor
- Add _get_effort_level() to extract effective effort from string or dict
- Use effective_effort for xhigh validation, tool-drop, sampling, temperature guards
- Preserve dict format when it has summary/generate_summary for Responses API
- Add tests: xhigh-dict validation, none-dict for tools/sampling/temperature
- Update tests: dict-with-summary now preserved (not normalized)
Made-with: Cursor
The aresponses_websocket CallType was recently added but not included
in the test exclusion list. It uses WebSocket passthrough (not Azure SDK
client initialization), so it correctly doesn't call
initialize_azure_sdk_client.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(image_generation): propagate extra_headers to OpenAI image generation
Add headers parameter to image_generation() and aimage_generation() methods
in OpenAI provider, and pass headers from images/main.py to ensure custom
headers like cf-aig-authorization are properly forwarded to the OpenAI API.
Aligns behavior with completion() method and Azure provider implementation.
* test(image_generation): add tests for extra_headers propagation
Verify that extra_headers are correctly forwarded to OpenAI's
images.generate() in both sync and async paths, and that they
are absent when not provided.
* Add Prometheus child_exit cleanup for gunicorn workers
When a gunicorn worker exits (e.g. from max_requests recycling), its
per-process prometheus .db files remain on disk. For gauges using
livesum/liveall mode, this means the dead worker's last-known values
persist as if the process were still alive. Wire gunicorn's child_exit
hook to call mark_process_dead() so live-tracking gauges accurately
reflect only running workers.
* docs: update AssemblyAI docs with Universal-3 Pro, Speech Understanding, and LLM Gateway (#21130)
* docs: update AssemblyAI docs with Universal-3 Pro, Speech Understanding, and LLM Gateway provider config
* feat: add AssemblyAI LLM Gateway as OpenAI-compatible provider
* fix(mcp): update test mocks to use renamed filter_server_ids_by_ip_with_info
Tests were mocking the old method name `filter_server_ids_by_ip` but production
code at server.py:774 calls `filter_server_ids_by_ip_with_info` which returns
a (server_ids, blocked_count) tuple. The unmocked method on AsyncMock returned
a coroutine, causing "cannot unpack non-iterable coroutine object" errors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(test): update realtime guardrail test assertions for voice violation behavior
Tests were asserting no response.create/conversation.item.create sent to
backend when guardrail blocks, but the implementation intentionally sends
these to have the LLM voice the guardrail violation message to the user.
Updated assertions to verify the correct guardrail flow:
- response.cancel is sent to stop any in-progress response
- conversation.item.create with violation message is injected
- response.create is sent to voice the violation
- original blocked content is NOT forwarded
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(bedrock): restore parallel_tool_calls mapping in map_openai_params
The revert in 8565c70e53 removed the parallel_tool_calls handling from
map_openai_params, and the subsequent fix d0445e1e33 only re-added the
transform_request consumption but forgot to re-add the map_openai_params
producer that sets _parallel_tool_use_config. This meant parallel_tool_calls
was silently ignored for all Bedrock models.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(test): update Azure pass-through test to mock litellm.completion
Commit 99c62ca40e removed "azure" from _RESPONSES_API_PROVIDERS,
routing Azure models through litellm.completion instead of
litellm.responses. The test was not updated to match, causing it
to assert against the wrong mock.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add in_flight_requests metric to /health/backlog + prometheus (#22319)
* feat: add in_flight_requests metric to /health/backlog + prometheus
* refactor: clean class with static methods, add tests, fix sentinel pattern
* docs: add in_flight_requests to prometheus metrics and latency troubleshooting
* fix(db): add missing migration for LiteLLM_ClaudeCodePluginTable
PR #22271 added the LiteLLM_ClaudeCodePluginTable model to
schema.prisma but did not include a corresponding migration file,
causing test_aaaasschema_migration_check to fail.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: update stale docstring to match guardrail voicing behavior
Addresses Greptile review feedback.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(caching): store background task references in LLMClientCache._remove_key to prevent unawaited coroutine warnings
Fixes#22128
* [Feat] Agent RBAC Permission Fix - Ensure Internal Users cannot create agents (#22329)
* fix: enforce RBAC on agent endpoints — block non-admin create/update/delete
- Add /v1/agents/{agent_id} to agent_routes so internal users can
access GET-by-ID (previously returned 403 due to missing route pattern)
- Add _check_agent_management_permission() guard to POST, PUT, PATCH,
DELETE agent endpoints — only PROXY_ADMIN may mutate agents
- Add user_api_key_dict param to delete_agent so the role check works
- Add comprehensive unit tests for RBAC enforcement across all roles
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix: mock prisma_client in internal user get-agent-by-id test
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* feat(ui): hide agent create/delete controls for non-admin users
Match MCP servers pattern: wrap '+ Add New Agent' button in
isAdmin conditional so internal users see a read-only agents view.
Delete buttons in card and table were already gated.
Update empty-state copy for non-admin users.
Add 7 Vitest tests covering role-based visibility.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix: Add PROXY_ADMIN role to system user for key rotation (#21896)
* fix: Add PROXY_ADMIN role to system user for key rotation
The key rotation worker was failing with 'You are not authorized to regenerate this key'
when rotating team keys. This was because the system user created by
get_litellm_internal_jobs_user_api_key_auth() was missing the user_role field.
Without user_role=PROXY_ADMIN, the system user couldn't bypass team permission checks
in can_team_member_execute_key_management_endpoint(), causing authorization failures
for team key rotation.
This fix adds user_role=LitellmUserRoles.PROXY_ADMIN to the system user, allowing
it to bypass team permission checks and successfully rotate keys for all teams.
* test: Add unit test for system user PROXY_ADMIN role
- Verify internal jobs system user has PROXY_ADMIN role
- Critical for key rotation to bypass team permission checks
- Regression test for PR #21896
* fix: populate user_id and user_info for admin users in /user/info (#22239)
* fix: populate user_id and user_info for admin users in /user/info endpoint
Fixes#22179
When admin users call /user/info without a user_id parameter, the endpoint
was returning null for both user_id and user_info fields. This broke
budgeting tooling that relies on /user/info to look up current budget and spend.
Changes:
- Modified _get_user_info_for_proxy_admin() to accept user_api_key_dict parameter
- Added logic to fetch admin's own user info from database
- Updated function to return admin's user_id and user_info instead of null
- Updated unit test to verify admin user_id is populated
The fix ensures admin users get their own user information just like regular users.
* test: make mock get_data signature match real method
- Updated MockPrismaClientDB.get_data() to accept all parameters that the real method accepts
- Makes mock more robust against future refactors
- Added datetime and Union imports
- Mock now returns None when user_id is not provided
* [Fix] Pass MCP auth headers from request into tool fetch for /v1/responses and chat completions (#22291)
* fixed dynamic auth for /responses with mcp
* fixed greptile concern
* fix(bedrock): filter internal json_tool_call when mixed with real tools
Fixes#18381: When using both tools and response_format with Bedrock
Converse API, LiteLLM internally adds json_tool_call to handle structured
output. Bedrock may return both this internal tool AND real user-defined
tools, breaking consumers like OpenAI Agents SDK.
Changes:
- Non-streaming: Added _filter_json_mode_tools() to handle 3 scenarios:
only json_tool_call (convert to content), mixed (filter it out), or
no json_tool_call (pass through)
- Streaming: Added json_mode tracking to AWSEventStreamDecoder to suppress
json_tool_call chunks and convert to text content
- Fixed optional_params.pop() mutation issue
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* refactor: extract duplicated JSON unwrapping into helper method
Addresses review comment from greptile-apps:
https://github.com/BerriAI/litellm/pull/21107#pullrequestreview-3796085353
Changes:
- Added `_unwrap_bedrock_properties()` helper method to eliminate code duplication
- Replaced two identical JSON unwrapping blocks (lines 1592-1601 and 1612-1620)
with calls to the new helper method
- Improves maintainability - single source of truth for Bedrock properties unwrapping logic
The helper method:
- Parses JSON string
- Checks for single "properties" key structure
- Unwraps and returns the properties value
- Returns original string if unwrapping not needed or parsing fails
No functional changes - pure refactoring.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* fix: use correct class name AmazonConverseConfig in helper method calls
Fixed MyPy errors where BedrockConverseConfig was used instead of
AmazonConverseConfig in the _unwrap_bedrock_properties() calls.
Errors:
- Line 1619: BedrockConverseConfig -> AmazonConverseConfig
- Line 1631: BedrockConverseConfig -> AmazonConverseConfig
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* fix: shorten guardrail benchmark result filenames for Windows long path support
Fixes#21941
The generated result filenames from _save_confusion_results contained
parentheses, dots, and full yaml filenames, producing paths that exceed
the Windows 260-char MAX_PATH limit. Rework the safe_label logic to
produce short {topic}_{method_abbrev} filenames (e.g. insults_cf.json)
while preserving the full label inside the JSON content.
Rename existing tracked result files to match the new naming convention.
* Update litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/guardrail_benchmarks/test_eval.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Remove Apache 2 license from SKILL.md (#22322)
* fix(mcp): default available_on_public_internet to true (#22331)
* fix(mcp): default available_on_public_internet to true
MCPs were defaulting to private (available_on_public_internet=false) which
was a breaking change. This reverts the default to public (true) across:
- Pydantic models (AddMCPServerRequest, UpdateMCPServerRequest, LiteLLM_MCPServerTable)
- Prisma schema @default
- mcp_server_manager.py YAML config + DB loading fallbacks
- UI form initialValue and setFieldValue defaults
* fix(ui): add forceRender to Collapse.Panel so toggle defaults render correctly
Ant Design's Collapse.Panel lazy-renders children by default. Without
forceRender, the Form.Item for 'Available on Public Internet' isn't
mounted when the useEffect fires form.setFieldValue, causing the Switch
to visually show OFF even though the intended default is true.
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix(mcp): update remaining schema copies and MCPServer type default to true
Missed in previous commit per Greptile review:
- schema.prisma (root)
- litellm-proxy-extras/litellm_proxy_extras/schema.prisma
- litellm/types/mcp_server/mcp_server_manager.py MCPServer class
* ui(mcp): reframe network access as 'Internal network only' restriction
Replace scary 'Available on Public Internet' toggle with 'Internal network only'
opt-in restriction. Toggle OFF (default) = all networks allowed. Toggle ON =
restricted to internal network only. Auth is always required either way.
- MCPPermissionManagement: new label/tooltip/description, invert display via
getValueProps/getValueFromEvent so underlying available_on_public_internet
value is unchanged
- mcp_server_view: 'Public' → 'All networks', 'Internal' → 'Internal only' (orange)
- mcp_server_columns: same badge updates
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* fix(jwt): OIDC discovery URLs, roles array handling, dot-notation error hints (#22336)
* fix(jwt): support OIDC discovery URLs, handle roles array, improve error hints
Three fixes for Azure AD JWT auth:
1. OIDC discovery URL support - JWT_PUBLIC_KEY_URL can now be set to
.well-known/openid-configuration endpoints. The proxy fetches the
discovery doc, extracts jwks_uri, and caches it.
2. Handle roles claim as array - when team_id_jwt_field points to a list
(e.g. AAD's "roles": ["team1"]), auto-unwrap the first element instead
of crashing with 'unhashable type: list'.
3. Better error hint for dot-notation indexing - when team_id_jwt_field is
set to "roles.0" or "roles[0]", the 401 error now explains to use
"roles" instead and that LiteLLM auto-unwraps lists.
* Add integration demo script for JWT auth fixes (OIDC discovery, array roles, dot-notation hints)
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Add demo_servers.py for manual JWT auth testing with mock JWKS/OIDC endpoints
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Add demo screenshots for PR comment
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Add integration test results with screenshots for PR review
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* address greptile review feedback (greploop iteration 1)
- fix: add HTTP status code check in _resolve_jwks_url before parsing JSON
- fix: remove misleading bracket-notation hint from debug log (get_nested_value does not support it)
* Update tests/test_litellm/proxy/auth/test_handle_jwt.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* remove demo scripts and assets
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* perf: streaming latency improvements — 4 targeted hot-path fixes (#22346)
* perf: raise aiohttp connection pool limits (300→1000, 50/host→500)
* perf: skip model_copy() on every chunk — only copy usage-bearing chunks
* perf: replace list+join O(n²) with str+= O(n) in async_data_generator
* perf: cache model-level guardrail lookup per request, not per chunk
* test: add comprehensive Vitest coverage for CostTrackingSettings
Add 88 tests across 9 test files for the CostTrackingSettings component directory:
- provider_display_helpers.test.ts: 9 tests for helper functions
- how_it_works.test.tsx: 9 tests for discount calculator component
- add_provider_form.test.tsx: 7 tests for provider form validation
- add_margin_form.test.tsx: 9 tests for margin form with type toggle
- provider_discount_table.test.tsx: 12 tests for table editing and interactions
- provider_margin_table.test.tsx: 13 tests for margin table with sorting
- use_discount_config.test.ts: 11 tests for discount hook logic
- use_margin_config.test.ts: 12 tests for margin hook logic
- cost_tracking_settings.test.tsx: 15 tests for main component and role-based rendering
All tests passing. Coverage includes form validation, user interactions, API calls, state management, and conditional rendering.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* [Feature] Key list endpoint: Add project_id and access_group_id filters
Add filtering capabilities to /key/list endpoint for project_id and access_group_id parameters. Both filters work globally across all visibility rules and stack with existing sort/pagination params. Added comprehensive unit tests for the new filters.
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
* [Feature] UI - Projects: Add Project Details page with Edit modal
- Add ProjectDetailsPage with header, details card, spend/budget progress,
model spend bar chart, keys placeholder, and team info card
- Refactor CreateProjectModal into base form pattern (ProjectBaseForm)
shared between Create and Edit flows
- Add EditProjectModal with pre-filled form data from backend
- Add useProjectDetails and useUpdateProject hooks
- Add duplicate key validation for model limits and metadata
- Wire project ID click in table to navigate to detail view
- Move pagination inline with search bar
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* Update ui/litellm-dashboard/src/components/Projects/ProjectModals/CreateProjectModal.tsx
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(azure): forward realtime_protocol from config and relax api_version check for GA path
The realtime_protocol parameter set in config.yaml litellm_params was
not reliably reaching the Azure realtime handler. Add fallback chain:
kwargs → litellm_params → LITELLM_AZURE_REALTIME_PROTOCOL env var → beta.
Also relax the api_version validation to only require it for the beta
protocol path, since the GA/v1 path does not use api_version in the URL.
Make protocol matching case-insensitive so 'ga', 'GA', 'v1', 'V1' all
work consistently. Fix _construct_url type signature to accept Optional
api_version.
Fixes#22127
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
---------
Co-authored-by: Zero Clover <zero@root.me>
Co-authored-by: Ryan Crabbe <rcrabbe@berkeley.edu>
Co-authored-by: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com>
Co-authored-by: Dylan Duan <dylan.duan@assemblyai.com>
Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Shivaang <shivaang.05@gmail.com>
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Shivam Rawat <161387515+shivamrawat1@users.noreply.github.com>
Co-authored-by: Brian Caswell <bcaswell@microsoft.com>
Co-authored-by: Brian Caswell <bcaswell@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: rasmi <rrelasmar@gmail.com>
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* fix:fix: prompt_cache_key OAI + Azure OpenAI
* test_prompt_cache_key_supported
* test_azure_openai_with_prompt_cache_key
* fix: remove unnecessary async from test_azure_openai_with_prompt_cache_key
Addresses Greptile feedback: litellm.completion() is synchronous, so
async def is unnecessary and would silently pass without running.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove unused filter_and_transform_beta_headers imports
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test_azure_openai_with_prompt_cache_key
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add opus 4.5 and 4.6 to use outout_format param
* generate poetry lock with 2.3.2 poetry
* restore poetry lock
* e2e tests, key delete, update tpm rpm, and regenerate
* Split e2e ui testing for browser
* new login with sso button in login page
* option to hide usage indicator
* fix(cloudzero): update CBF field mappings per LIT-1907 (#20906)
* fix(cloudzero): update CBF field mappings per LIT-1907
Phase 1 field updates for CloudZero integration:
ADD/UPDATE:
- resource/account: Send concat(api_key_alias, '|', api_key_prefix)
- resource/service: Send model_group instead of service_type
- resource/usage_family: Send provider instead of hardcoded 'llm-usage'
- action/operation: NEW - Send team_id
- resource/id: Send model name instead of CZRN
- resource/tag:organization_alias: Add if exists
- resource/tag:project_alias: Add if exists
- resource/tag:user_alias: Add if exists
REMOVE:
- resource/tag:total_tokens: Removed
- resource/tag:team_id: Removed (team_id now in action/operation)
Fixes LIT-1907
* Update litellm/integrations/cloudzero/transform.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: define api_key_alias variable, update CBFRecord docstring
- Fix F821 lint error: api_key_alias was used but not defined
- Update CBFRecord docstring to reflect LIT-1907 field mappings
- Remove unused Optional import
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Add banner notifying of breaking change
* Add semgrep & Fix OOMs (#20912)
* [Feat] Policies - Allow connecting Policies to Tags, Simulating Policies, Viewing how many keys, teams it applies on (#20904)
* init schema with TAGS
* ui: add policy test
* resolvePoliciesCall
* add_policy_sources_to_metadata + headers
* types Policy
* preview Impact
* def _describe_match_reason(
* match based on TAGs
* TestTagBasedAttachments
* test fixes
* add policy_resolve_router
* add_guardrails_from_policy_engine
* TestMatchAttribution
* refactor
* fix
* fix: address Greptile review feedback on policy resolve endpoints
- Track unnamed keys/teams as separate counts instead of inflating
affected_keys_count with duplicate "(unnamed key)" placeholders.
Added unnamed_keys_count and unnamed_teams_count to response.
- Push alias pattern matching to DB via _build_alias_where() which
converts exact patterns to Prisma "in" and suffix wildcards to
"startsWith" filters.
- Gate sync_policies_from_db/sync_attachments_from_db behind
force_sync query param (default false) to avoid 2 DB round-trips
on every /policies/resolve request.
- Remove worktree-only conftest.py that cleared sys.modules at import
time — no longer needed since code moved to main repo.
- Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: eliminate duplicate DB queries and fix header delimiter ambiguity
- Fetch teams table once in estimate_attachment_impact and reuse for
both tag-based and alias-based lookups (was querying teams twice when
both tag_patterns and team_patterns were provided).
- Convert tag/team filter functions from async DB queries to sync
filters that operate on pre-fetched data (_filter_keys_by_tags,
_filter_teams_by_tags).
- Fix comma ambiguity in x-litellm-policy-sources header: use '; '
as entry delimiter since matched_via values can contain commas.
- Use '+' as the within-value separator in matched_via reason strings
(e.g. "tag:healthcare+team:health-team") to avoid conflict with
header delimiters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update litellm/proxy/policy_engine/policy_resolve_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: type error & better error handling (#20689)
* [Docs] Add docs guide for using policies (#20914)
* init schema with TAGS
* ui: add policy test
* resolvePoliciesCall
* add_policy_sources_to_metadata + headers
* types Policy
* preview Impact
* def _describe_match_reason(
* match based on TAGs
* TestTagBasedAttachments
* test fixes
* add policy_resolve_router
* add_guardrails_from_policy_engine
* TestMatchAttribution
* refactor
* fix
* fix: address Greptile review feedback on policy resolve endpoints
- Track unnamed keys/teams as separate counts instead of inflating
affected_keys_count with duplicate "(unnamed key)" placeholders.
Added unnamed_keys_count and unnamed_teams_count to response.
- Push alias pattern matching to DB via _build_alias_where() which
converts exact patterns to Prisma "in" and suffix wildcards to
"startsWith" filters.
- Gate sync_policies_from_db/sync_attachments_from_db behind
force_sync query param (default false) to avoid 2 DB round-trips
on every /policies/resolve request.
- Remove worktree-only conftest.py that cleared sys.modules at import
time — no longer needed since code moved to main repo.
- Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: eliminate duplicate DB queries and fix header delimiter ambiguity
- Fetch teams table once in estimate_attachment_impact and reuse for
both tag-based and alias-based lookups (was querying teams twice when
both tag_patterns and team_patterns were provided).
- Convert tag/team filter functions from async DB queries to sync
filters that operate on pre-fetched data (_filter_keys_by_tags,
_filter_teams_by_tags).
- Fix comma ambiguity in x-litellm-policy-sources header: use '; '
as entry delimiter since matched_via values can contain commas.
- Use '+' as the within-value separator in matched_via reason strings
(e.g. "tag:healthcare+team:health-team") to avoid conflict with
header delimiters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* docs v1 guide with UI imgs
* docs fix
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add dashscope/qwen3-max model with tiered pricing (#20919)
Add support for Alibaba Cloud's Qwen3-Max model with:
- 258K input tokens, 65K output tokens
- Tiered pricing based on context window usage (0-32K, 32K-128K, 128K-252K)
- Function calling and tool choice support
- Reasoning capabilities enabled
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
* fix linting
* docs: add Greptile review requirement to PR template (#20762)
* fix(azure): preserve content_policy_violation error details from Azure OpenAI
Closes#20811
Azure OpenAI returns rich error payloads for content policy violations
(inner_error with ResponsibleAIPolicyViolation, content_filter_results,
revised_prompt). Previously these details were lost when:
1. The top-level error code was not "content_policy_violation" but the
inner_error.code was "ResponsibleAIPolicyViolation" -- the structured
check only examined the top-level code.
2. The DALL-E image generation polling path stringified the error JSON
into the message field instead of setting the structured body, making
it impossible for exception_type() to extract error details.
3. The string-based fallback detector used "invalid_request_error" as a
content-policy indicator, which is too broad and could misclassify
regular bad-request errors.
Changes:
- exception_mapping_utils.py: Check inner_error.code for
ResponsibleAIPolicyViolation when top-level code is not
content_policy_violation. Replace overly broad "invalid_request_error"
string match with specific Azure safety-system messages.
- azure.py: Set structured body on AzureOpenAIError in both async and
sync DALL-E polling paths so exception_type() can inspect error details.
- test_azure_exception_mapping.py: Add regression tests covering the
exact error payloads from issue #20811.
- Fix pre-existing lint: duplicate PerplexityResponsesConfig dict key,
unused RouteChecks top-level import.
---------
Co-authored-by: Kelvin Tran <kelvin-tran@users.noreply.github.com>
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: shin-bot-litellm <shin-bot-litellm@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Alexsander Hamir <alexsanderhamirgomesbaptista@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com>
Co-authored-by: ken <122603020@qq.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
The Azure Responses API uses a different schema (flattened) for tools compared to the standard OpenAI/Azure Chat Completions API (nested). This caused a `BadRequestError` when users passed standard tool definitions.
Changes:
- Implemented tool flattening logic in `AzureOpenAIResponsesAPIConfig.transform_responses_api_request`.
- Added comprehensive unit tests in test_azure_transformation.py to verify nested-to-flat transformation, pass-through of flat tools, and immutability.
- Ensures cross-provider compatibility for tool definitions.
Fixes#19523
* feat(azure): add support for Azure OpenAI v1 API
When api_version is 'v1', 'latest', or 'preview', use the standard
OpenAI client instead of AzureOpenAI client with base_url pointing
to /openai/v1/ endpoint.
This follows Microsoft's documentation for the new v1 API format:
https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#api-specs
Changes:
- Add OpenAI/AsyncOpenAI imports to common_utils.py and azure.py
- Modify get_azure_openai_client() to detect v1 API versions and
create appropriate client type
- Update isinstance checks and type hints to accept both client types
- Add unit tests for v1 API client creation
* fix(azure): fix MyPy type errors for v1 API support
- Add type: ignore for AsyncOpenAI constructor
- Update type hints in files/handler.py and batches/handler.py
- Add OpenAI/AsyncOpenAI to Union types for client parameters
- Update isinstance checks to include OpenAI/AsyncOpenAI
* fix(azure): update type hints in files and batches handlers for v1 API
Update async method signatures to accept Union[AsyncAzureOpenAI, AsyncOpenAI]
to fix mypy errors when using v1 API client.
* fix(azure): add logprobs support for Azure OpenAI GPT-5 models
Azure OpenAI GPT-5 models (including gpt-5.2) support logprobs
parameters, unlike OpenAI's GPT-5 reasoning models. This fix
overrides the parent class restriction to enable logprobs for Azure.
Changes:
- Override get_supported_openai_params() in AzureOpenAIGPT5Config
- Add "logprobs" and "top_logprobs" to supported params
- Add comprehensive tests for logprobs functionality
Testing:
- Verified with direct Azure API calls to gpt-5.2
- API version: 2025-01-01-preview
- Successfully returns logprobs data
Related: #7974, #4022
* refactor: restrict logprobs support to gpt-5.2 only
Only gpt-5.2 has been verified to support logprobs on Azure.
Other gpt-5 variants (gpt-5, gpt-5.1) have not been tested.
Changes:
- Add conditional check for is_model_gpt_5_2_model()
- Update tests to be specific to gpt-5.2
- Add negative tests for gpt-5 and gpt-5.1
- Update documentation to reflect gpt-5.2 specificity
* fix: handle none content
* fix: defensive check on none value
* Fix test failures: Azure OCR skip, None content handling, PublicAI JSON config
- Skip aocr/ocr call types in Azure test (they don't use Azure SDK client)
- Handle None content in Responses API transformation (skip message creation)
- Update PublicAI tests to use JSON-based configuration system
- Add None check in PublicAI test fixture to fix type error
