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milan-berri
0fb710400f
* fix(spend_counter): seed Redis counter via SET NX to prevent cross-pod double-seed Symptom ------- Customers on multi-pod deployments see team `spend` jump to ~2x (or N x the pod count) shortly after a Redis cache miss / TTL expiry, triggering spurious "Budget Crossed" alerts and blocked requests until the value is manually reset. Root cause ---------- `SpendCounterReseed.coalesced` warmed the primary spend counter by calling `redis.async_increment(key, value=db_spend, refresh_ttl=True)`, which lowers to Redis `INCRBYFLOAT`. That is additive, not idempotent. The per-counter `asyncio.Lock` only coalesces seeders inside one process. With N pods sharing one Redis, on a cold key (cold start, TTL expiry, manual delete) every pod independently passes its lock + Redis re-check, reads the same `db_spend`, and issues `INCRBYFLOAT db_spend`. Final value: N x db_spend. Fix --- Use `redis.async_set_cache(key, value=db_spend, nx=True)` for the seed. SET NX is atomic across pods: exactly one writer initializes the key; losers read the winner's value via `async_get_cache`. This is the same idiom already used by `coalesced_window` in the same file, so the two seed paths are now consistent. Per-request deltas continue to use `INCRBYFLOAT` (correct - additive behaviour is what we want for increments, not for initial seed). Verification ------------ Live two-process repro against the same Postgres + Redis (DB spend = 506): Unpatched: 4/4 runs -> Redis counter = ~1012 (~2 x db_spend) Patched: 12/12 runs -> Redis counter = ~506 Unit tests (`test_proxy_server.py`): - New `test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed` patches `_get_lock` to return a fresh lock per caller (otherwise the per-process lock masks the race), races two `coalesced` calls, and asserts final = 506 with exactly one of two SET NX attempts winning. - 4 existing tests updated for the new seed contract (SET NX for the seed, INCRBYFLOAT only for the per-request delta). - Full `spend_counter or reseed or budget` slice: 22 passed. Co-authored-by: Cursor <cursoragent@cursor.com> * test(spend_counter): make SET NX mock atomic so loser branch is exercised Greptile flagged that `redis_set_cache` in test_primary_spend_counter_redis_concurrent_seed_does_not_double_seed placed `await asyncio.sleep(0)` AFTER the NX membership check. Both concurrent tasks observed an empty `redis_store`, passed the guard, and both returned True - so the loser branch (else: read back winner's value) was never exercised. Fix the mock to model real atomic Redis SET NX: - Yield BEFORE the membership check so two concurrent callers interleave the way real SET NX does (first to resume runs check + write atomically and wins; second resumes after the key exists and loses). - Track set_cache return values; assert sorted([loser, winner]) so we know exactly one task wins and one loses. - Track async_get_cache calls that happen AFTER at least one SET NX has completed; assert at least one such read - that is the loser-path fallback (`current_value = float(cached)` when seeded is False). Verified by temporarily reverting the mock to the old order: the test now fails with `expected exactly one SET NX winner and one loser, got [True, True]`, exactly the failure mode Greptile described. No production code change. Co-authored-by: Cursor <cursoragent@cursor.com> * test(spend_counter): mock async_set_cache to populate redis_store in concurrent read+write test `test_concurrent_read_and_write_paths_share_one_db_query` mocks `async_increment` to populate the in-memory `redis_store`, but did not mock `async_set_cache`. After the SET-NX seed change in `coalesced()`, the seed step writes via `async_set_cache(nx=True)` (default AsyncMock, no `redis_store` write), so the simulated Redis stays empty after the first reseed. The second `get_current_spend` then sees a clean Redis miss, re-enters the DB read path, and the test fails with `expected 1 DB query, got 2`. Fix: add a `redis_set_cache` side_effect that updates `redis_store` on `nx=True` (and rejects when the key already exists), matching the pattern used by the four sibling tests fixed in this branch's first commit. Pre-existing assertions are unchanged. Full `tests/test_litellm/proxy/test_proxy_server.py`: 158 passed. Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Cursor <cursoragent@cursor.com>
🚅 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
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"
}
}
}
}
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.
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
uv sync --all-extras --group proxy-dev uv run prisma generateprisma 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
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