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* Add LangSmith mock client support - Create langsmith_mock_client.py following GCS and Langfuse patterns - Add mock mode detection via LANGSMITH_MOCK environment variable - Intercept LangSmith API calls via AsyncHTTPHandler.post patching - Add verbose logging throughout mock implementation - Update LangsmithLogger to initialize mock client when mock mode enabled - Supports configurable mock latency via LANGSMITH_MOCK_LATENCY_MS * Add Datadog mock client support - Create datadog_mock_client.py following GCS, Langfuse, and LangSmith patterns - Add mock mode detection via DATADOG_MOCK environment variable - Intercept Datadog API calls via AsyncHTTPHandler.post and httpx.Client.post patching - Add verbose logging throughout mock implementation - Update DataDogLogger and DataDogLLMObsLogger to initialize mock client when mock mode enabled - Supports both async and sync logging paths - Supports configurable mock latency via DATADOG_MOCK_LATENCY_MS * refactor: consolidate mock client logic into factory pattern - Create mock_client_factory.py to centralize common mock HTTP client logic - Refactor GCS, Langfuse, LangSmith, and Datadog mock clients to use factory - Improve GET/DELETE mock accuracy for GCS (return valid StandardLoggingPayload) - Fix DELETE mock to return empty body (204 No Content) instead of JSON - Reduce code duplication across integration mock clients * feat: add PostHog mock client support - Create posthog_mock_client.py using factory pattern - Integrate mock client into PostHogLogger with mock mode detection - Add verbose logging for mock mode initialization and batch operations - Enable mock mode via POSTHOG_MOCK environment variable * Add Helicone mock client support - Created helicone_mock_client.py using factory pattern (similar to GCS) - Integrated mock mode detection and initialization in HeliconeLogger - Mock client patches HTTPHandler.post to intercept Helicone API calls - Uses factory pattern for should_use_mock and MockResponse utilities - Custom HTTPHandler.post patching required since HTTPHandler uses self.client.send() * Add mock support for Braintrust integration and extend mock client factory - Add braintrust_mock_client.py with mock HTTP client for Braintrust integration testing - Integrate mock client into BraintrustLogger with mock mode detection - Refactor Helicone mock client to fully utilize factory's HTTPHandler.post patching - Extend mock_client_factory to support patching HTTPHandler.post for sync calls - Enable endpoint-specific mock responses for Braintrust (/project vs /project_logs) - All mock clients now properly handle both async (AsyncHTTPHandler) and sync (HTTPHandler) calls * Fix linter errors: remove unused imports and suppress complexity warning - Remove unused imports from gcs_bucket_mock_client.py (httpx, json, timedelta, Dict, Optional) - Remove unused Callable import from mock_client_factory.py - Add noqa comment to suppress PLR0915 complexity warning for create_mock_client_factory function * Document mock environment variables for PostHog, Helicone, Braintrust, Datadog, and Langsmith integrations - Add POSTHOG_MOCK and POSTHOG_MOCK_LATENCY_MS documentation - Add HELICONE_MOCK and HELICONE_MOCK_LATENCY_MS documentation - Add BRAINTRUST_MOCK and BRAINTRUST_MOCK_LATENCY_MS documentation - Add DATADOG_MOCK and DATADOG_MOCK_LATENCY_MS documentation - Add LANGSMITH_MOCK and LANGSMITH_MOCK_LATENCY_MS documentation All mock env vars follow the same pattern: enable mock mode for integration testing by intercepting API calls and returning mock responses without making actual network calls. * Fix security issue |
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🚅 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.