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* fix(mcp): OAuth2 chat connect - tools fetch, auth flow, and status fixes - schema.prisma: add missing MCP table fields (approval_status, submitted_by, submitted_at, reviewed_at, review_notes) to prevent destructive migrations - rest_endpoints.py: inject user OAuth token via extra_headers for OAuth2 servers so tools list is populated; add server name->UUID resolution so MCPConnectPicker name lookups work - mcp_registry.json: fix Atlassian defaults (transport: http, url: .../v1/mcp) - ChatPage.tsx: read mcpOauthReturn param to init sidebarView="apps" on OAuth return, clean up param after mount - MCPAppsPanel.tsx: auto-add OAuth2 servers to selectedServers when credential detected; onConnect also enables server for chat; disconnect removes from selectedServers - mcp_servers.tsx: sort servers by created_at DESC - useUserMcpOAuthFlow.tsx: append mcpOauthReturn=apps to return URL so Apps panel is mounted on return * address greptile review feedback (greploop iteration 1) * fix(mcp): inject stored OAuth2 token when fetching tools via /responses API When a user has connected an OAuth2 MCP server (e.g. Atlassian) and then uses the /responses endpoint with that server, tool listing was failing because the stored per-user OAuth token was never injected. Two fixes: 1. server.py: add _get_user_oauth_extra_headers_from_db() helper; call it in _get_tools_from_mcp_servers when oauth2_headers is None for an OAuth2 server, falling back to the user's stored token in LiteLLM_MCPUserCredentials 2. litellm_proxy_mcp_handler.py: also intercept MCP tools whose server_url matches */mcp/<server_name> (e.g. http://localhost:4000/mcp/atlassian_test) by rewriting them to litellm_proxy/mcp/<server_name> so they go through the internal handler (and get the OAuth token injected) instead of being forwarded to OpenAI raw where localhost is unreachable * address greptile review feedback (greploop iteration 2) * test(mcp): add unit test for OAuth2 token injection in _get_tools_from_mcp_servers Verifies that when _get_tools_from_mcp_servers is called for an OAuth2 MCP server without oauth2_headers in the request, the implementation: - calls _prefetch_oauth_creds_for_user once (not per-server) to avoid N+1 queries - passes the stored token as extra_headers={"Authorization": "Bearer ..."} to _get_tools_from_server so the upstream OAuth2 MCP server authenticates correctly * address greptile review feedback (greploop iteration 3) * address greptile review feedback (greploop iteration 4) * address greptile review feedback (greploop iteration 5) * redesign credentials table to use Tremor table layout matching Keys page * fix: /server/oauth authorize 422 - make client_id optional, fall back to real DB server * fix: mcp_token client_id optional, resolve from server record * fix: look up real server by UUID (get_mcp_server_by_id) before falling back to name * Update litellm/responses/mcp/litellm_proxy_mcp_handler.py Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> * fix: address greptile feedback - client_id guards, dict spread, helper refactor, tests - mcp_management_endpoints: raise 400 when resolved_client_id is empty in mcp_authorize and mcp_token instead of forwarding "" to upstream - litellm_proxy_mcp_handler: use {**tool, "server_url": ...} spread instead of dict(tool) + mutation for shallow copy safety - rest_endpoints: extract _oauth2_server_ids set comprehension to a named _get_oauth2_server_ids() helper for clarity; add Set to typing imports - test_rest_endpoints: add tests for name→UUID resolution path, access-denied when resolved UUID not in allowed list, and OAuth2 user token injection for single-server requests; fix fake_get_tools signature to accept extra_headers kwarg --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
🚅 LiteLLM
Call 100+ LLMs in OpenAI format. [Bedrock, Azure, OpenAI, VertexAI, Anthropic, Groq, etc.]
LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier
Use LiteLLM for
LLMs - Call 100+ LLMs (Python SDK + AI Gateway)
All Supported Endpoints - /chat/completions, /responses, /embeddings, /images, /audio, /batches, /rerank, /a2a, /messages and more.
Python SDK
pip install litellm
from litellm import completion
import os
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
# OpenAI
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])
# Anthropic
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}])
AI Gateway (Proxy Server)
Getting Started - E2E Tutorial - Setup virtual keys, make your first request
pip install 'litellm[proxy]'
litellm --model gpt-4o
import openai
client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000")
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
Agents - Invoke A2A Agents (Python SDK + AI Gateway)
Supported Providers - LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore, Pydantic AI
Python SDK - A2A Protocol
from litellm.a2a_protocol import A2AClient
from a2a.types import SendMessageRequest, MessageSendParams
from uuid import uuid4
client = A2AClient(base_url="http://localhost:10001")
request = SendMessageRequest(
id=str(uuid4()),
params=MessageSendParams(
message={
"role": "user",
"parts": [{"kind": "text", "text": "Hello!"}],
"messageId": uuid4().hex,
}
)
)
response = await client.send_message(request)
AI Gateway (Proxy Server)
Step 1. Add your Agent to the AI Gateway
Step 2. Call Agent via A2A SDK
from a2a.client import A2ACardResolver, A2AClient
from a2a.types import MessageSendParams, SendMessageRequest
from uuid import uuid4
import httpx
base_url = "http://localhost:4000/a2a/my-agent" # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer sk-1234"} # LiteLLM Virtual Key
async with httpx.AsyncClient(headers=headers) as httpx_client:
resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url)
agent_card = await resolver.get_agent_card()
client = A2AClient(httpx_client=httpx_client, agent_card=agent_card)
request = SendMessageRequest(
id=str(uuid4()),
params=MessageSendParams(
message={
"role": "user",
"parts": [{"kind": "text", "text": "Hello!"}],
"messageId": uuid4().hex,
}
)
)
response = await client.send_message(request)
MCP Tools - Connect MCP servers to any LLM (Python SDK + AI Gateway)
Python SDK - MCP Bridge
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from litellm import experimental_mcp_client
import litellm
server_params = StdioServerParameters(command="python", args=["mcp_server.py"])
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
# Load MCP tools in OpenAI format
tools = await experimental_mcp_client.load_mcp_tools(session=session, format="openai")
# Use with any LiteLLM model
response = await litellm.acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "What's 3 + 5?"}],
tools=tools
)
AI Gateway - MCP Gateway
Step 1. Add your MCP Server to the AI Gateway
Step 2. Call MCP tools via /chat/completions
curl -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"model": "gpt-4o",
"messages": [{"role": "user", "content": "Summarize the latest open PR"}],
"tools": [{
"type": "mcp",
"server_url": "litellm_proxy/mcp/github",
"server_label": "github_mcp",
"require_approval": "never"
}]
}'
Use with Cursor IDE
{
"mcpServers": {
"LiteLLM": {
"url": "http://localhost:4000/mcp/",
"headers": {
"x-litellm-api-key": "Bearer sk-1234"
}
}
}
}
How to use LiteLLM
You can use LiteLLM through either the Proxy Server or Python SDK. Both gives you a unified interface to access multiple LLMs (100+ LLMs). Choose the option that best fits your needs:
| LiteLLM AI Gateway | LiteLLM Python SDK | |
|---|---|---|
| Use Case | Central service (LLM Gateway) to access multiple LLMs | Use LiteLLM directly in your Python code |
| Who Uses It? | Gen AI Enablement / ML Platform Teams | Developers building LLM projects |
| Key Features | Centralized API gateway with authentication and authorization, multi-tenant cost tracking and spend management per project/user, per-project customization (logging, guardrails, caching), virtual keys for secure access control, admin dashboard UI for monitoring and management | Direct Python library integration in your codebase, Router with retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router, application-level load balancing and cost tracking, exception handling with OpenAI-compatible errors, observability callbacks (Lunary, MLflow, Langfuse, etc.) |
LiteLLM Performance: 8ms P95 latency at 1k RPS (See benchmarks here)
Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers
Stable Release: Use docker images with the -stable tag. These have undergone 12 hour load tests, before being published. More information about the release cycle here
Support for more providers. Missing a provider or LLM Platform, raise a feature request.
OSS Adopters
Netflix |
Supported Providers (Website Supported Models | Docs)
Run in Developer mode
Services
- Setup .env file in root
- Run dependant services
docker-compose up db prometheus
Backend
- (In root) create virtual environment
python -m venv .venv - Activate virtual environment
source .venv/bin/activate - Install dependencies
pip install -e ".[all]" pip install prismaprisma generate- Start proxy backend
python litellm/proxy/proxy_cli.py
Frontend
- Navigate to
ui/litellm-dashboard - Install dependencies
npm install - Run
npm run devto start the dashboard
Enterprise
For companies that need better security, user management and professional support
This covers:
- ✅ Features under the LiteLLM Commercial License:
- ✅ Feature Prioritization
- ✅ Custom Integrations
- ✅ Professional Support - Dedicated discord + slack
- ✅ Custom SLAs
- ✅ Secure access with Single Sign-On
Contributing
We welcome contributions to LiteLLM! Whether you're fixing bugs, adding features, or improving documentation, we appreciate your help.
Quick Start for Contributors
This requires poetry to be installed.
git clone https://github.com/BerriAI/litellm.git
cd litellm
make install-dev # Install development dependencies
make format # Format your code
make lint # Run all linting checks
make test-unit # Run unit tests
make format-check # Check formatting only
For detailed contributing guidelines, see CONTRIBUTING.md.
Code Quality / Linting
LiteLLM follows the Google Python Style Guide.
Our automated checks include:
- Black for code formatting
- Ruff for linting and code quality
- MyPy for type checking
- Circular import detection
- Import safety checks
All these checks must pass before your PR can be merged.
Support / talk with founders
- Schedule Demo 👋
- Community Discord 💭
- Community Slack 💭
- Our numbers 📞 +1 (770) 8783-106 / +1 (412) 618-6238
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai
Why did we build this
- Need for simplicity: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI and Cohere.