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c7ab9adde5
litellm/provider_endpoints_support.json
Sameer Kankute c7ab9adde5
Litellm oss staging 030626 (#29578) 
* Fix incorrect agent API request example payload structure (#29556)

* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs (#29427)

* fix(otel): add litellm_metadata fallback in _get_span_context and _end_proxy_span_from_kwargs

On /v1/messages and other LITELLM_METADATA_ROUTES, the parent OTel span
is stored in litellm_params['litellm_metadata'] instead of
litellm_params['metadata']. When the request body contains a native
'metadata' field (e.g. Anthropic's {"user_id": "..."}),
litellm_params['metadata'] gets overwritten and the parent span is lost,
producing orphan root spans with a different trace_id.

Add fallback checks to litellm_metadata in:
- _get_span_context(): so child spans find the correct parent
- _end_proxy_span_from_kwargs(): so the proxy span gets closed

Fixes: https://github.com/BerriAI/litellm/issues/27934

* test(otel): tighten assertions per Greptile review

- test_span_context_metadata_takes_priority: assert litellm_metadata
  span is never accessed, proving metadata takes priority
- test_span_context_no_parent_when_neither_has_span: assert both ctx
  and detected_span are None

---------

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* fix: remove premature end-user budget check from get_end_user_object (#29420)

* fix(proxy): remove premature end-user budget check from get_end_user_object

Problem:
- `_check_end_user_budget()` was called inside `get_end_user_object()`
- This caused budget checks to run BEFORE `skip_budget_checks` could be evaluated
- Zero-cost models (e.g., local vLLM) were incorrectly blocked when
  end-users exceeded their budget, even though they should bypass budget checks

Solution:
- Remove `_check_end_user_budget()` calls from `get_end_user_object()`
- Budget enforcement now happens exclusively in `common_checks()` where
  `skip_budget_checks` context is available
- `get_end_user_object()` keeps `route` as optional in function parameter for backwards compatibility and future implementation.

* refactor(tests): update budget enforcement tests to reflect changes in get_end_user_object

- test_get_end_user_object() verifies data fetching
- test_check_end_user_budget() verifies enforcement
- test_budget_enforcement_blocks_over_budget_users() integrates _check_end_user_budget()
- test_resolve_end_user_reraises_budget_exceeded() is now test_resolve_end_user since no budget exceeded is thrown in get_end_user_object()

* Gemini /images/generate and /images/edits billing fixes + add support for size and aspect ratio params (#29534)

* Fix Gemini image config mapping

* Address Gemini image config review

* Format Gemini image generation transform

* Fix Gemini image token usage logging

* Share Gemini image request helpers

* Fix Gemini Imagen model routing

* Fixes as per self code review

* Fixes per internal code review

* Stop gating Imagen imageSize forwarding

* Document Gemini image size mapping source

* chore: retrigger lint

* Clarify Gemini candidate count precedence

* Add Inception provider (#29522)

* add inception as provider (chat, fim)

* linting

* seperate test suite for chat and fim

* fix test coverage

* fix: model hub custom pricing model info (#29293)

* Opik user auth key metadata extractors (#28397)

* fix: enhance Opik metadata extraction to include user API key auth context fixed after refactoring to extractor logic

* test: add unit tests for OPik metadata extraction logic

* fix: enhance extract_opik_metadata function to prioritize metadata sources for improved accuracy

* fix(ci): clarified comments and edited unit tests

* test: add unit tests for OPik metadata extraction with auth and requester overrides

* fix(ui): replace fixed favicon.ico with current api get /get_favicon (#29532)

Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>

* fix(vertex/gemini): keep tool_call reference when a text-only assistant message follows (#29561)

`_gemini_convert_messages_with_history` tracks `last_message_with_tool_calls`
so a following tool result can be matched back to its tool call. The assignment
was inside a branch guarded by
`assistant_msg.get("tool_calls", []) is not None`, which is also True for a
text-only assistant message (an empty list is not None). As a result, an
assistant message with no tool calls that appears between a tool call and its
tool result overwrote the reference, and conversion failed with:

    Exception: Missing corresponding tool call for tool response message.

This shape is common: a model emits a short narration/assistant message after a
tool call before the tool result is appended.

Only update `last_message_with_tool_calls` when the assistant message actually
carries tool_calls (or a function_call). Adds a regression test.

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models (#28572)

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575)

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505)

When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.

For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.

Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.

New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.

* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg

Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.

Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.

Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).

* chore: trigger shin-agent re-eval on retargeted staging base

* chore: trigger shin-agent re-eval against updated Greptile state

* Add 1-hour cache write pricing for EU/AU/JP Bedrock Anthropic models

The 1-hour prompt-cache write tier
(`cache_creation_input_token_cost_above_1hr`) was added to the
us./global. variants of the Claude 4.5/4.6/4.7 family on Bedrock, but
the eu./au./jp. cross-region inference profiles were left without it.
AWS Bedrock pricing applies the same +10% regional premium across all
geo profiles, so eu./au./jp. should carry the same 1-hour rates as
us. (1.6x the 5-minute regional rate).

Without these fields, cost tracking on EU/AU/JP Bedrock 1-hour-TTL
prompt caching falls back to the 5-minute write rate and undercounts
spend by ~60% for European, Australian, and Japanese tenants.

Adds the 1-hour tier (and Sonnet 4.5's long-context >200K tier where
AWS publishes one) to 14 regional Bedrock entries in both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:

  - eu./au.   Opus 4.6     ($11.00 / MTok)
  - eu./au.   Opus 4.7     ($11.00 / MTok)
  - eu./au./jp. Sonnet 4.6 ($6.60 / MTok)
  - eu./au./jp. Sonnet 4.5 ($6.60 / MTok regular, $13.20 / MTok LC)
  - eu./au./jp. Haiku 4.5  ($2.20 / MTok)

Also extends `tests/test_litellm/test_bedrock_anthropic_1hr_cache_pricing.py`
with a `REGIONAL_EXPECTED` parametrized block covering all 13 new
entries plus the existing 1.6x ratio invariant.

Note: `eu.anthropic.claude-opus-4-5-20251101-v1:0` carries the
wrong 5m rate today (base 6.25e-06 instead of regional 6.875e-06),
which would break the 1.6x ratio check. It is intentionally left out
of this PR so the scope stays "1-hour cache tier addition" — a
separate follow-up should correct the EU 5m rates for Opus 4.5.

---------

Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* Add 1-hour cache write pricing tier for Vertex AI Anthropic models (#28569)

* fix(thinking): handle None thinking param in is_thinking_enabled (#28598)

Squash-merged by litellm-agent from Terrajlz's PR.

* feat(helm): support tpl rendering in podAnnotations (#28609)

Squash-merged by litellm-agent from devauxbr's PR.

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505) (#28575)

* Forward custom_llm_provider through the Responses API bridge (Fixes #28505)

When a Chat Completions request to a GPT-5.4+ model contains both
`tools` and `reasoning_effort`, `completion()` auto-routes through
`responses_api_bridge`. The bridge handler called
`litellm.responses()` / `litellm.aresponses()` without forwarding the
already-resolved `custom_llm_provider`, so the downstream call
re-invoked `get_llm_provider()` with `custom_llm_provider=None` and
stripped a second provider prefix from a `provider/provider/model`
deployment string.

For a deployment configured as `openai/openai/openai/gpt-5.5`,
the bridge flow sent `openai/gpt-5.5` to the upstream API instead of
the correct `openai/openai/gpt-5.5`. Upstream APIs that enforce
model-name allow-lists rejected this as `key_model_access_denied`.

Fix: pass the locally-resolved `custom_llm_provider` into both the
sync `responses()` and async `aresponses()` calls so the downstream
`_resolve_model_provider_for_responses` sees an explicit provider
and skips the second prefix-strip.

New regression test
`tests/test_litellm/completion_extras/test_responses_bridge_provider_propagation.py`
pins both call sites: each must forward `custom_llm_provider`.

* fix(28505): set custom_llm_provider on request_data instead of as duplicate kwarg

Greptile flagged that the previous patch passed custom_llm_provider as an
explicit kwarg to responses()/aresponses() while request_data already
carried it via the spread of sanitized_litellm_params, which would raise
TypeError: got multiple values for keyword argument on every real bridge
call.

Switches to assigning request_data['custom_llm_provider'] before the call
so the resolved provider wins over whatever sanitized_litellm_params spread
in, without duplicating the kwarg.

Updates the regression test to seed request_data with a sentinel
custom_llm_provider so it actually exercises the overwrite path (the
previous test mocked transform_request with a minimal dict and never hit
the conflict).

* chore: trigger shin-agent re-eval on retargeted staging base

* chore: trigger shin-agent re-eval against updated Greptile state

* Add 1-hour cache write pricing tier for Vertex AI Anthropic models

GCP Vertex AI publishes a separate 1-hour cache write column for the
Claude family (1.6x the 5-minute write rate, matching the documented
Bedrock ratio). LiteLLM's Vertex AI Anthropic entries only carry the
5-minute tier, so any request that uses `cache_control: {"ttl": "1h"}`
on Vertex AI Claude is undercounted in cost tracking by ~60%.

The runtime side already supports the 1-hour tier — `VertexAIAnthropicConfig`
extends `AnthropicConfig`, populating `ephemeral_1h_input_tokens`, and
`_calculate_cache_creation_cost` reads `cache_creation_input_token_cost_above_1hr`.
Only the price registry was missing data.

Adds the field to 19 vertex_ai/claude-* entries across both
`model_prices_and_context_window.json` and the bundled
`model_prices_and_context_window_backup.json`:

  - Haiku 4.5 ($1.25 -> $2.00 / MTok)
  - Sonnet 3.7 / 4 / 4.5 / 4.6 ($3.75 -> $6.00 / MTok)
  - Opus 4.5 / 4.6 / 4.7 ($6.25 -> $10.00 / MTok)
  - Opus 4 / 4.1 ($18.75 -> $30.00 / MTok)

Adds `tests/test_litellm/test_vertex_anthropic_1hr_cache_pricing.py`
mirroring the Bedrock equivalent — pins each (5m, 1h) pair per model
and asserts the 1.6x ratio across the family.

Fixes #27781.

---------

Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>

* Fix Gemini multimodal function responses (#29325)

Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>

* address greptile review: add _transform_image_usage method and model-map supports_image_size flag

- Add _transform_image_usage instance method to GoogleImageGenConfig that
  delegates to transform_gemini_image_usage, fixing the regression test
- Replace hardcoded "2.5-flash" string check in supports_gemini_image_size
  with a get_model_info lookup on supports_image_size (default true)
- Add supports_image_size: false to all gemini-2.5-flash model entries in
  model_prices_and_context_window.json so capability is controlled via the
  model map rather than embedded in code

* fix test failures: schema validation, mypy type, model info plumbing, pricing test

- Add supports_image_size to ModelInfoBase TypedDict so get_model_info surfaces it
- Pass supports_image_size through _get_model_info_helper constructor call
- Fix supports_gemini_image_size to use value is not False (None means unset, defaults to True)
- Add supports_image_size to JSON schema in test_aaamodel_prices_and_context_window_json_is_valid
- Correct gemini-3.1-flash-lite pricing assertions in test to match JSON values

* Add Azure AI Kimi K2.6 metadata (#27052)

* Add Azure AI Kimi K2.6 metadata

* Scope Kimi metadata test cost map setup

* fall back to substring check for models not in model_prices_and_context_window.json

Models like gemini-2.5-flash-image-preview are not in the pricing JSON,
so get_model_info raises. Fall back to "2.5-flash" not in model when the
JSON has no explicit supports_image_size entry for the model.

* fix(inception): don't forward global litellm.api_key to Inception FIM

Match the Inception chat config: resolve only an Inception-specific key
(param, litellm.inception_key, or INCEPTION_API_KEY) for the text-completion
FIM path. The global litellm.api_key (often an OpenAI key) was both leaking
to api.inceptionlabs.ai and taking precedence over the configured Inception
key when set.

* fix(auth): enforce end-user budget on custom-auth path that skips common_checks

get_end_user_object() no longer raises BudgetExceededError, so custom-auth
deployments with custom_auth_run_common_checks unset (which skip the
centralized common_checks gate) stopped enforcing the end-user budget,
letting an over-budget end user keep making requests. Re-enforce the
budget in _run_post_custom_auth_checks on that path.

---------

Signed-off-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Isha <72744901+IshaMeera@users.noreply.github.com>
Co-authored-by: aneeshsangvikar <aneeshsangvikar@fiddler.ai>
Co-authored-by: shin-berri <shin-laptop@berri.ai>
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Aneesh-Fiddler <aneeshfiddler@gmail.com>
Co-authored-by: Suleiman Elkhoury <108065141+suleimanelkhoury@users.noreply.github.com>
Co-authored-by: Dmitriy Alergant <93501479+DmitriyAlergant@users.noreply.github.com>
Co-authored-by: Yanis Miraoui <yanis.miraoui19@imperial.ac.uk>
Co-authored-by: Lovro Seder <vrovro@gmail.com>
Co-authored-by: Thomas Mildner <12685945+Thomas-Mildner@users.noreply.github.com>
Co-authored-by: José Luis Di Biase <josx@interorganic.com.ar>
Co-authored-by: Lai Quang Huy <64073540+1qh@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Filippo Menghi <113345637+Cyberfilo@users.noreply.github.com>
Co-authored-by: Terrajlz <info@jouleselectrictech.com>
Co-authored-by: Bruno Devaux <devaux.br@gmail.com>
Co-authored-by: ZHONG Ziwen <67355585+zzw-math@users.noreply.github.com>
Co-authored-by: Emerson Gomes <emerson.gomes@thalesgroup.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
2026-06-03 11:01:51 -07:00

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{
"_comment": "This file defines which endpoints are supported by each LiteLLM provider",
"_schema": {
"provider_slug": {
"display_name": "Display name shown in README (e.g., 'OpenAI (`openai`)')",
"url": "Link to provider documentation",
"endpoints": {
"chat_completions": "Supports /chat/completions endpoint",
"messages": "Supports /messages endpoint (Anthropic format)",
"responses": "Supports /responses endpoint (OpenAI/Anthropic unified)",
"embeddings": "Supports /embeddings endpoint",
"image_generations": "Supports /image/generations endpoint",
"audio_transcriptions": "Supports /audio/transcriptions endpoint",
"audio_speech": "Supports /audio/speech endpoint",
"moderations": "Supports /moderations endpoint",
"batches": "Supports /batches endpoint",
"rerank": "Supports /rerank endpoint",
"ocr": "Supports /ocr endpoint",
"search": "Supports /search endpoint",
"skills": "Supports /skills endpoint",
"interactions": "Supports /interactions endpoint (Google AI Interactions API)",
"a2a_(Agent Gateway)": "Supports /a2a/{agent}/message/send endpoint (A2A Protocol)",
"container": "Supports OpenAI's /containers endpoint",
"container_file": "Supports OpenAI's /containers/{id}/files endpoint",
"compact": "Supports /responses/compact endpoint",
"files": "Supports /files endpoint for file operations",
"image_edits": "Supports /images/edits endpoint for image editing",
"vector_stores_create": "Supports creating a new vector store via /vector_stores endpoint",
"vector_stores_search": "Supports searching a vector store via /vector_stores/{id}/search endpoint",
"video_generations": "Supports /videos/generations endpoint for video generation"
}
}
},
"providers": {
"a2a": {
"display_name": "A2A (Agent-to-Agent) (`a2a`)",
"url": "https://docs.litellm.ai/docs/providers/a2a",
"endpoints": {
"chat_completions": true,
"messages": false,
"responses": false,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false
}
},
"abliteration": {
"display_name": "Abliteration (`abliteration`)",
"url": "https://docs.litellm.ai/docs/providers/abliteration",
"endpoints": {
"chat_completions": true,
"messages": false,
"responses": false,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false
}
},
"aiml": {
"display_name": "AI/ML API (`aiml`)",
"url": "https://docs.litellm.ai/docs/providers/aiml",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": true,
"image_generations": true,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"ai21": {
"display_name": "AI21 (`ai21`)",
"url": "https://docs.litellm.ai/docs/providers/ai21",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"ai21_chat": {
"display_name": "AI21 Chat (`ai21_chat`)",
"url": "https://docs.litellm.ai/docs/providers/ai21",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"amazon_nova": {
"display_name": "Amazon Nova (`amazon_nova`)",
"url": "https://docs.litellm.ai/docs/providers/amazon_nova",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"anthropic": {
"display_name": "Anthropic (`anthropic`)",
"url": "https://docs.litellm.ai/docs/providers/anthropic",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": true,
"rerank": false,
"skills": true,
"a2a": true,
"interactions": true,
"count_tokens": true
}
},
"anthropic_text": {
"display_name": "Anthropic Text (`anthropic_text`)",
"url": "https://docs.litellm.ai/docs/providers/anthropic",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": true,
"rerank": false,
"skills": true,
"a2a": true,
"interactions": true
}
},
"apertis": {
"display_name": "Apertis (`apertis`)",
"endpoints": {
"chat_completions": true,
"messages": false,
"responses": false,
"embeddings": true,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false
}
},
"aihubmix": {
"display_name": "AIHubMix (`aihubmix`)",
"url": "https://docs.litellm.ai/docs/providers/aihubmix",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": true,
"image_generations": true,
"audio_transcriptions": true,
"audio_speech": true,
"moderations": true,
"batches": false,
"rerank": true,
"a2a": false
}
},
"assemblyai": {
"display_name": "AssemblyAI (`assemblyai`)",
"url": "https://docs.litellm.ai/docs/pass_through/assembly_ai",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": true,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"auto_router": {
"display_name": "Auto Router (`auto_router`)",
"url": "https://docs.litellm.ai/docs/proxy/auto_routing",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": true,
"interactions": true
}
},
"bedrock": {
"display_name": "AWS - Bedrock (`bedrock`)",
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"display_name": "Google Interactions API",
"leftnav_label": "/interactions",
"provider_json_field": "interactions",
"url": "https://docs.litellm.ai/docs/interactions",
"bridges_to_chat_completion": true
},
"mcp": {
"docs_label": "mcp",
"display_name": "Model Context Protocol (MCP)",
"leftnav_label": "/mcp",
"provider_json_field": "mcp",
"url": "https://docs.litellm.ai/docs/mcp"
},
"moderation": {
"docs_label": "moderation",
"display_name": "OpenAI Moderations API",
"leftnav_label": "/moderations",
"provider_json_field": "moderations",
"url": "https://docs.litellm.ai/docs/moderation"
},
"ocr": {
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"display_name": "Mistral OCR API",
"leftnav_label": "/ocr",
"provider_json_field": "ocr",
"url": "https://docs.litellm.ai/docs/ocr"
},
"rag_ingest": {
"docs_label": "rag_ingest",
"display_name": "RAG Ingest API",
"leftnav_label": "/rag/ingest",
"provider_json_field": "rag_ingest",
"url": "https://docs.litellm.ai/docs/rag_ingest"
},
"rag_query": {
"docs_label": "rag_query",
"display_name": "RAG Query API",
"leftnav_label": "/rag/query",
"provider_json_field": "rag_query",
"url": "https://docs.litellm.ai/docs/rag_query"
},
"realtime": {
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"display_name": "OpenAI Realtime API",
"leftnav_label": "/realtime",
"provider_json_field": "realtime",
"url": "https://docs.litellm.ai/docs/realtime"
},
"rerank": {
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"display_name": "Cohere Rerank API",
"leftnav_label": "/rerank",
"provider_json_field": "rerank",
"url": "https://docs.litellm.ai/docs/rerank"
},
"responses": {
"docs_label": "response_api",
"display_name": "OpenAI Responses API",
"leftnav_label": "/responses",
"provider_json_field": "responses",
"url": "https://docs.litellm.ai/docs/response_api",
"bridges_to_chat_completion": true
},
"response_api_compact": {
"docs_label": "response_api_compact",
"display_name": "OpenAI Responses API",
"leftnav_label": "/responses",
"provider_json_field": "compact",
"url": "https://docs.litellm.ai/docs/response_api"
},
"search": {
"docs_label": "search",
"display_name": "Search API",
"leftnav_label": "/search",
"provider_json_field": "search",
"url": "https://docs.litellm.ai/docs/search"
},
"skills": {
"docs_label": "skills",
"display_name": "Anthropic Skills API",
"leftnav_label": "/skills",
"provider_json_field": "skills",
"url": "https://docs.litellm.ai/docs/skills"
},
"text_completion": {
"docs_label": "text_completion",
"display_name": "OpenAI Completions API",
"leftnav_label": "/completions",
"provider_json_field": "text_completion",
"url": "https://docs.litellm.ai/docs/text_completion",
"bridges_to_chat_completion": true
},
"text_to_speech": {
"docs_label": "text_to_speech",
"display_name": "OpenAI Text-to-Speech API",
"leftnav_label": "/audio/speech",
"provider_json_field": "audio_speech",
"url": "https://docs.litellm.ai/docs/text_to_speech"
},
"vector_store_files": {
"docs_label": "vector_store_files",
"display_name": "OpenAI Vector Store Files API",
"leftnav_label": "/vector_stores/files",
"provider_json_field": "vector_store_files",
"url": "https://docs.litellm.ai/docs/vector_store_files"
},
"vector_stores_create": {
"docs_label": "vector_stores_create",
"display_name": "OpenAI Vector Stores Create API",
"leftnav_label": "/vector_stores/create",
"provider_json_field": "vector_stores_create",
"url": "https://docs.litellm.ai/docs/vector_stores/create"
},
"vector_stores_search": {
"docs_label": "vector_stores_search",
"display_name": "OpenAI Vector Stores Search API",
"leftnav_label": "/vector_stores/search",
"provider_json_field": "vector_stores_search",
"url": "https://docs.litellm.ai/docs/vector_stores/search"
},
"videos": {
"docs_label": "videos",
"display_name": "OpenAI Videos API",
"leftnav_label": "/videos",
"provider_json_field": "video_generations",
"url": "https://docs.litellm.ai/docs/videos"
}
}
}
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