支援的端點 | liteLLM

📄️ /assistants

OpenAI has deprecated the Assistants API. It will shut down on August 26, 2026.

📄️ /containers

Manage OpenAI code interpreter containers (sessions) for executing code in isolated environments.

📄️ /containers/files

Manage files within Code Interpreter containers. Files are created automatically when code interpreter generates outputs (charts, CSVs, images, etc.).

📄️ /converse

Call Bedrock's /converse endpoint through LiteLLM Proxy.

📄️ /evals

LiteLLM Proxy supports OpenAI's Evaluations (Evals) API, allowing you to create, manage, and run evaluations to measure model performance against defined testing criteria.

📄️ /generateContent

Use LiteLLM to call Google AI's generateContent endpoints for text generation, multimodal interactions, and streaming responses.

📄️ /guardrails/apply_guardrail

Use this endpoint to directly call a guardrail configured on your LiteLLM instance. This is useful when you have services that need to directly call a guardrail.

📄️ /invoke

Call Bedrock's /invoke endpoint through LiteLLM Proxy.

📄️ /v1beta/agents (Gemini Managed Agents)

LiteLLM now supports the Google AI Studio Managed Agents API. Create, manage, and run custom agents through LiteLLM.

CRUD endpoints for storing and retrieving user/team-scoped memory entries on the LiteLLM proxy. Use these to persist conversation context, agent memory, team playbooks, or any key-value data scoped to users and teams.

📄️ /images/edits

LiteLLM provides image editing functionality that maps to OpenAI's /images/edits API endpoint. Now supports both single and multiple image editing.

📄️ Image Generations

Overview

📄️ [BETA] Image Variations

OpenAI's /image/variations endpoint is now supported.

📄️ /videos

| Feature | Supported |

📄️ /vector_stores/\{vector_store_id\}/files

Vector store files represent the individual files that live inside a vector store.

📄️ /vector_stores - Create Vector Store

Create a vector store which can be used to store and search document chunks for retrieval-augmented generation (RAG) use cases.

📄️ /vector_stores/search - Search Vector Store

Search a vector store for relevant chunks based on a query and file attributes filter. This is useful for retrieval-augmented generation (RAG) use cases.