14f4c34c61
* fix(ci): stop five stale or flaky CI reds and retry CyberArk policy-load conflicts The Langfuse redaction unit test exports to a local OTLP capture instead of polling Langfuse Cloud through a recorded lookup. The passthrough worker-kill test only requires spend rows for requests the surviving worker served. The spend-routes sweep treats the intentional /spend/capture_rate 503 as expected. CyberArk retries a 409 policy load in Python, Rust and the e2e Conjur helper instead of reading it as "variable exists". The integration egress guard now matches the script's own cgroup, so it no longer blocks the CircleCI agent, which runs as the same user. * fix(ci): keep the policy-load backoff typed as float * fix(ci): retry CyberArk policy loads without blocking the event loop and tighten the worker-kill and Langfuse tests * fix(secrets): load CyberArk policy one request at a time per manager * test(secrets): pin that non-conflict CyberArk policy failures are not retried * test(unit): run tests/unit with only an allowlisted host environment CircleCI's unit job inherits every project env var, so real provider keys, REDIS_HOST, DATABASE_URL and AWS or Azure credentials reached tests that assume none are set. Locally, litellm's import-time load_dotenv did the same from any .env up the tree. The unit conftest now drops every variable outside a small allowlist and disables dotenv before litellm is imported. * test(e2e): name a failed search and the stuck batch status instead of misattributing them The websearch session test read an empty web_search_tool_result_error block as a successful search, so a failing search tool surfaced as a session billing bug. The batch cancellation timeout now reports the last status the proxy returned. * fix(ci): scrub the host environment per unit test instead of for the whole pytest process GHA shards run tests/unit next to other suites in one process, so the import-time scrub deleted MCP_TEST_PEER_PYTHON before tests/mcp_tests read it and the MCP upstream fell back to the SDK2 interpreter. The two websearch tests that called OpenAI and Perplexity live are removed: tests/unit no longer sees their keys. * fix(ci): scrub only the host variables present before litellm is imported The per-test scrub also deleted TIKTOKEN_CACHE_DIR, which litellm sets at import to its bundled encodings, so tokenizer paths tried to download them and hit the socket guard. The prisma setup test now passes its own database URL instead of reading one another test leaked into the process environment. * fix(ci): stop the order-dependent unit reds and settle logging tasks on their own queue LoggingWorker marked a task done on whichever queue was current when the callback finished, so a callback that outlived an event-loop change raised "task_done() called too many times" or undercounted the new loop's queue. It now settles the queue the task came from. The rest are test isolation fixes for failures that only appeared when another file ran first on the same xdist worker: a replaced user_api_key_cache, breaker metrics unregistered by prometheus tests, semantic_router's health-check filter on uvicorn.access, logging tasks carried over from bedrock tests, a Router-written model_cost entry, and a stray post captured by the langflow test. The token counter check now asserts bounded chunking instead of wall-clock time. * test(e2e/ui): wait for the logout redirect before visiting a protected page Logout revokes the session server-side before clearing cookies and navigating, so an immediate page.goto either ran with the cookie still set or was aborted by the logout redirect (net::ERR_ABORTED). * test(unit): restore the prometheus metrics config per test and settle logs carried from earlier tests in the a2a cost tests * test(router): pin the router clock in the usage counter tests so a minute rollover cannot empty the read * test(e2e/ui): wait for logout to clear the token cookie instead of for a login redirect * test(integration/mcp): answer the model-info probe another test's proxy sends to the model double |
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| .cargo | ||
| .circleci | ||
| .devcontainer | ||
| .githooks | ||
| .github | ||
| .semgrep/rules | ||
| backend | ||
| ci_cd | ||
| cookbook | ||
| db_scripts | ||
| docker | ||
| enterprise | ||
| examples | ||
| gateway | ||
| helm | ||
| litellm | ||
| litellm-proxy-extras | ||
| litellm-rust | ||
| migrations | ||
| packaging/homebrew | ||
| scripts | ||
| terraform | ||
| tests | ||
| ui | ||
| vscode-extension | ||
| .dockerignore | ||
| .env.example | ||
| .git-blame-ignore-revs | ||
| .gitattributes | ||
| .gitguardian.yaml | ||
| .gitignore | ||
| .grype.yaml | ||
| .npmrc | ||
| AGENTS.md | ||
| ARCHITECTURE.md | ||
| basedpyright-code-budget.json | ||
| codecov.yaml | ||
| CONTRIBUTING.md | ||
| cosign.pub | ||
| docker-compose.hardened.yml | ||
| docker-compose.yml | ||
| Dockerfile | ||
| GEMINI.md | ||
| LICENSE | ||
| license_cache.json | ||
| Makefile | ||
| mcp_servers.json | ||
| model_prices_and_context_window.json | ||
| model_prices_and_context_window.schema.json | ||
| osv-scanner.toml | ||
| package-lock.json | ||
| package.json | ||
| policy_templates.json | ||
| prometheus.yml | ||
| provider_endpoints_support.json | ||
| proxy_server_config.yaml | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| qa_sticky_session.sh | ||
| README.md | ||
| render.yaml | ||
| router_plugins.json | ||
| ruff-strict-budget.json | ||
| ruff-strict.toml | ||
| ruff-tests.toml | ||
| ruff.toml | ||
| rust-toolchain.toml | ||
| schema.prisma | ||
| security.md | ||
| taplo.toml | ||
| test-quality-budget.json | ||
| type-discipline-budget.json | ||
| uv.lock | ||
| whitelisted_bedrock_models.txt | ||
🚅 LiteLLM
LiteLLM AI Gateway
Open Source AI Gateway for 100+ LLMs. Self-hosted. Enterprise-ready. Call any LLM in OpenAI format.
LiteLLM Proxy Server (AI Gateway) | Hosted Proxy | Enterprise Tier | Website
What is LiteLLM
LiteLLM is an open source AI Gateway that gives you a single, unified interface to call 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure, and more — using the OpenAI format.
Use it as a Python SDK for direct library integration, or deploy the AI Gateway (Proxy Server) as a centralized service for your team or organization.
Jump to LiteLLM Proxy (LLM Gateway) Docs
Jump to Supported LLM Providers
Why LiteLLM
Managing LLM calls across providers gets complicated fast — different SDKs, auth patterns, request formats, and error types for every model. LiteLLM removes that friction:
- Unified API — one interface for 100+ LLMs, no provider-specific SDK juggling
- Drop-in OpenAI compatibility — swap providers without rewriting your code
- Production-ready gateway — virtual keys, spend tracking, guardrails, load balancing, and an admin dashboard out of the box
- 8ms P95 latency at 1k RPS (benchmarks)
OSS Adopters
Netflix |
Features
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
uv add 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
uv tool 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 — set protocolVersion to 1.0 or 0.3 per agent
Step 2. Call Agent via A2A SDK (requires a2a-sdk>=1.1.0)
import httpx
from a2a.client import A2ACardResolver, ClientConfig, ClientFactory
from a2a.types import Message, Part, Role, SendMessageRequest
from a2a.utils.constants import TransportProtocol
from uuid import uuid4
base_url = "http://localhost:4000/a2a/my-agent" # LiteLLM proxy + agent name
headers = {"Authorization": "Bearer <your-master-key>"} # LiteLLM master key or a virtual key
async with httpx.AsyncClient(headers=headers, timeout=60.0) as http_client:
resolver = A2ACardResolver(httpx_client=http_client, base_url=base_url)
agent_card = await resolver.get_agent_card()
config = ClientConfig(
httpx_client=http_client,
streaming=False,
supported_protocol_bindings=[TransportProtocol.JSONRPC, TransportProtocol.HTTP_JSON],
)
client = ClientFactory(config).create(agent_card)
request = SendMessageRequest(
message=Message(
message_id=uuid4().hex,
role=Role.ROLE_USER,
parts=[Part(text="Hello!")],
)
)
async for event in client.send_message(request):
populated = event.ListFields()
if populated and populated[0][0].name in ("message", "msg"):
print("".join(getattr(p, "text", "") or "" for p in populated[0][1].parts))
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 <your-master-key>' \
-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 <your-master-key>"
}
}
}
}
For MCP OAuth, an upstream may advertise dynamic client registration but refuse requests with HTTP 401 or 403. If the provider requires a pre-registered OAuth app, configure its credentials.client_id and, when required, credentials.client_secret on the MCP server. This skips dynamic registration in the gateway sign-in flow. The provider must approve the app for MCP access; reaching its authorization page does not establish that login or tool calls will succeed
Supported Providers (Website Supported Models | Docs)
Get Started
You can use LiteLLM through either the Proxy Server or Python SDK. Both give 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.) |
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.
Deploy on AWS or GCP with Terraform
Run the LiteLLM proxy as a production-ready componentized stack (gateway, backend, UI on separate services; managed Postgres + Redis + object store) using the published Terraform modules. Both modules are on the public Terraform Registry — no auth needed.
AWS — ECS Fargate + Aurora + ElastiCache + ALB
git clone https://github.com/BerriAI/litellm.git
cd litellm/terraform/litellm/aws/examples/default
cp terraform.tfvars.example terraform.tfvars # edit region/tenant/env
terraform init && terraform apply
Or call the module from your own root config:
# main.tf
terraform {
required_version = ">= 1.6.0"
required_providers {
aws = { source = "hashicorp/aws", version = "~> 5.60" }
}
}
provider "aws" {
region = "us-west-2"
}
module "litellm" {
source = "BerriAI/litellm/aws"
version = "~> 1.89"
region = "us-west-2"
azs = ["us-west-2a", "us-west-2b"]
tenant = "acme"
env = "prod"
# Production: provide an ACM cert. Without one, set allow_plaintext_alb = true
# (dev/trial only).
# acm_certificate_arn = "arn:aws:acm:us-west-2:111122223333:certificate/..."
allow_plaintext_alb = true
}
output "litellm_url" {
value = module.litellm.alb_dns_name
}
terraform init
terraform apply
Provider API keys live in AWS Secrets Manager; reference ARNs via gateway_extra_secrets. Full input list and architecture diagram on the registry page.
GCP — Cloud Run + Cloud SQL + Memorystore + HTTPS LB
Real 1-click. Opens Cloud Shell, clones this repo, and walks you through terraform apply via a built-in DeployStack tutorial — pick the project, the tutorial sets up the Artifact Registry remote repo, writes terraform.tfvars from your answers, and runs apply.
To call the module from your own config instead, Cloud Run can't pull from ghcr.io directly, so first set up a one-time Artifact Registry remote repo backed by GHCR:
gcloud artifacts repositories create litellm \
--location=us-central1 \
--repository-format=docker \
--mode=remote-repository \
--remote-docker-repo=https://ghcr.io \
--project=my-gcp-project
Then:
# main.tf
terraform {
required_version = ">= 1.6.0"
required_providers {
google = { source = "hashicorp/google", version = "~> 6.10" }
google-beta = { source = "hashicorp/google-beta", version = "~> 6.10" }
}
}
provider "google" { project = "my-gcp-project"; region = "us-central1" }
provider "google-beta" { project = "my-gcp-project"; region = "us-central1" }
module "litellm" {
source = "BerriAI/litellm/google"
version = "~> 1.89"
project_id = "my-gcp-project"
region = "us-central1"
tenant = "acme"
env = "prod"
# Replace my-gcp-project with your GCP project ID (same value as project_id above).
image_registry = "us-central1-docker.pkg.dev/my-gcp-project/litellm/berriai"
# Production: provide DNS already pointing at the LB IP for Google-managed certs.
# Without one, set allow_plaintext_lb = true (dev/trial only).
# lb_domains = ["proxy.example.com"]
allow_plaintext_lb = true
}
output "litellm_url" {
value = module.litellm.load_balancer_url
}
terraform init
terraform apply
Provider API keys live in Secret Manager; reference resource IDs (e.g. projects/my-gcp-project/secrets/openai-api-key) via gateway_extra_secrets. Full input list and architecture diagram on the registry page.
Both stacks include
- The full componentized split (gateway / backend / UI as independent services)
- Managed Postgres (writer + reader) and Redis
- Versioned object store for proxy state + file uploads
- An auto-generated
LITELLM_MASTER_KEYin your cloud's secret manager - A one-off migration job that runs
prisma migrate deploybefore the proxy starts - The same
proxy_configsurface as the Helm chart — pass YAML as a typed map
The Terraform modules live at terraform/litellm/aws/ and terraform/litellm/gcp/ in this repo; the registry entries are read-only mirrors updated on each release.
Run in Developer Mode
Services
- Setup .env file in root
- Run dependent services
docker-compose up db prometheus
Backend
- Run
make bootstrap - Start proxy backend:
uv run python litellm/proxy/proxy_cli.py
Frontend
- Navigate to
ui/litellm-dashboard(dependencies were already installed w/make bootstrap) - Start dashboard:
npm run dev
Verify Docker Image Signatures
All LiteLLM Docker images published to GHCR are signed with cosign. Every release is signed with the same key introduced in commit 0112e53.
Verify using the pinned commit hash (recommended):
A commit hash is cryptographically immutable, so this is the strongest way to ensure you are using the original signing key:
cosign verify \
--key https://raw.githubusercontent.com/BerriAI/litellm/0112e53046018d726492c814b3644b7d376029d0/cosign.pub \
ghcr.io/berriai/litellm:<release-tag>
Verify using a release tag (convenience):
Tags are protected in this repository and resolve to the same key. This option is easier to read but relies on tag protection rules:
cosign verify \
--key https://raw.githubusercontent.com/BerriAI/litellm/<release-tag>/cosign.pub \
ghcr.io/berriai/litellm:<release-tag>
Replace <release-tag> with the version you are deploying (e.g. v1.83.0-stable).
Enterprise
For companies that need better security, user management and professional support
Get an Enterprise License Talk to founders
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 uv 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.
📖 Contributing to documentation? The LiteLLM docs have moved to a separate repository: BerriAI/litellm-docs. Please open doc PRs there. Docs are served at docs.litellm.ai.
Code Quality / Linting
LiteLLM follows the Google Python Style Guide.
Our automated checks include:
- Ruff for formatting, linting, and code quality
- basedpyright 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 emails ✉️ ishaan@berri.ai / krrish@berri.ai