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0700b1e54e
* refactor(ui): extract the MCP server edit save payload into a pure builder `handleSave` built the update payload inline across 276 lines, spreading `...restValues` straight off a mounted-only `onFinish`. That makes the payload a function of which fields happen to be mounted, and it leaves no seam to test the shape without rendering the whole edit form. Move the payload construction into `editServerPayload.ts` as `buildEditServerPayload(values, ui)`, a pure function over the submitted values plus the nine pieces of component state the handler reads. Failures become values rather than early returns with a toast: the six error branches are a tagged union that `editPayloadErrorMessage` maps back to the exact strings shown today, via an exhaustive switch. `handleSave` keeps the network call, the OAuth token persistence and its own try/catch. This is a move, not a rewrite. To prove that, `editServerPayload.differential.test.ts` holds a baseline machine-extracted from the pre-refactor function body by line range, with the failure branches converted by exact string replacement. The generator refuses to emit unless the slice is still present verbatim in the source, every conversion matches exactly once, no toast call survives, and a deliberately corrupted probe still trips that check. 59 scenarios run both implementations and compare the payload object, its key order, and its serialised bytes, so a re-ordering that leaves values untouched is caught too. The duplicate local `AUTH_TYPES_REQUIRING_CREDENTIALS` is dropped in favour of the identical exported list, and `reduceStaticHeaders` is shared with the create side. Both were verified equal before reuse. Behaviour is unchanged. The 466 pre-existing tests in the directory pass unedited. * refactor(ui): type the MCP edit payload builder instead of Record<string, any> The extraction created a new public signature, so it should carry a real contract. buildEditServerPayload now takes EditServerFormValues and returns EditServerPayload, both declaring every field the builder actually reads and writes, with an unknown-valued index signature for the keys the form passes straight through. handleSave is annotated too, so antd's untyped onFinish value is narrowed once at the boundary rather than travelling as any. Fields that arrive from the store with their own runtime validation (static_headers, env_vars, credentials) stay unknown rather than being given a narrower declared type the form does not actually guarantee. Values are not run through a parser: the payload's serialised key order is part of the contract this module exists to hold, and rebuilding the object would reorder it. The credentials assignment moves from two post-hoc mutations to a single resolved entry, which keeps the payload readonly end to end and lands the key in the same position in all four branches. Behaviour is unchanged. The 59 differential scenarios still match the frozen pre-extraction body on object, Object.keys order and JSON.stringify bytes, and the mcp-servers suite is 525/525 across all 30 files. Three tsc probes confirm the new types have teeth: a wrong payload assignment, a misspelled field read and an invalid value each fail the type check. * feat(ui): add mounted-field projections for the MCP server form graph antd's onFinish reports exactly the fields mounted at submit time, and both MCP server payload builders spread that object straight through. react-hook-form with shouldUnregister false hands back the whole store instead, so a port needs the mount set written out explicitly before any JSX moves. This adds mountedEditFieldNames / mountedCreateFieldNames as pure functions over the form values, plus the projections that apply them, covering all 22 gates across the graph's 89 named bindings. No JSX changes, nothing imports them yet. Three behaviours are probed against antd 5.29.3 rather than assumed, and the tests pin all three: - a mounted-but-unset field is EMITTED as a key holding undefined, at the root and inside credentials, so the projection emits rather than omits - a Form.List row is NOT projected down to its mounted sub-fields, so env_vars rows pass through whole; filtering them would drop per-user values - the two roots disagree on more than StdioConfiguration: edit gates url on a deny-list while create uses an allow-list, and create additionally gates the whole auth section on a non-empty transport * refactor(ui): port the create key form off antd Form onto react-hook-form antd hands onFinish exactly the fields mounted at submit time, so a collapsed section contributes nothing to the request while the values typed into it survive for re-expansion. react-hook-form reaches only one of those two behaviours per shouldUnregister setting, so the store is kept intact and projected down to the mounted set through an explicit mount registry. MountedFormField carries the rest of the Form.Item contract the payload depends on: defaults taken from each field's own declaration rather than a blanket empty value, and help text that replaces the rule message instead of sitting beside it. The 60-case submit differential runs unedited against the port, joined by cases for the writers outside the submit path, mounted-set validation, Enter to submit, and switch coercion. * test(ui): pin the mounted-field sets by membership, not array order Two credential assertions compared the returned array with toStrictEqual against a literal in source order, so they failed when two names were swapped inside the source array even though the projected payload was unchanged. Key order is not observable in the payload, so those two assertions rejected a refactor that changes nothing a caller can see. Route both through the same sorted() helper the other fifteen set assertions already use. Membership keeps its teeth: deleting any one of the eighty emitted field names still fails the suite, while reordering two of them now passes. * docs(ui): state the mounted projection's static-name limit at its export The registry counts by name and the projection emits flat keys, so a Form.List row and its per-row sub-fields, whose names are generated at runtime, are never in the mounted set and go missing from the payload. That is silent and it is correct for every static field around it, so the contract belongs where the next consumer reads it. * refactor(ui): cut the mounted field's explanatory comments to the contract limit The mechanism the projection uses and the reason a helped field hides its rule message are both derivable from the code, so they belong in the pull request rather than in two places. What survives is the one thing no reader can derive: that a runtime-generated name is silently absent from the payload. * refactor(ui): drop the doc comment from MountedFormField The static-name constraint it described moves to the PR description, where it is not a second place to keep in sync with the code. * fix(ui): rebuild nested and list paths in the mounted-field projection projectMountedValues emitted one flat key per registered name, so a field registered under a dotted path produced a literal "credentials.client_id" key instead of a nested credentials object, and a field-array row produced "env_vars.0.name" instead of a row. Both are silent. buildEditServerPayload destructures credentials and passes it to buildCredentials, which returns undefined for a non-object, so every credential drops out of the payload while the code compiles and the existing suites pass. reduceStaticHeaders loses its rows the same way. Split each registered name on "." and rebuild the value, treating a numeric segment as an array index so field-array rows come back as arrays rather than objects keyed by digits. Nested credentials and both field-array sites are the same defect and take the same fix. * fix(ui): make mounted-field nesting opt-in via array names The first cut split every registered name on ".", which diverges from antd. antd's getNamePath is toArray, so a string name is a one-element path and "a.b" is stored as a literal flat key; only an array name nests. check_openapi_schema registers names taken from a live /openapi.json at runtime, so the field set is an input rather than source. A spec property containing a dot would have silently nested under the unconditional split and changed that payload with nothing failing. Accept string | readonly string[]. An array nests, with a numeric segment as an array index, which is what the credential paths and the field-array rows already use. A string stays one literal key. The registry keys by the joined path but projects from the original shape, so the two cannot drift. |
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🚅 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 sk-1234"} # LiteLLM 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 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"
}
}
}
}
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:
- 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 emails ✉️ ishaan@berri.ai / krrish@berri.ai