mirror of
https://github.com/tiennm99/litellm.git
synced 2026-07-20 04:18:42 +00:00
0afffe4366
* feat: multiple concurrent budget windows per API key and team (#24883) * feat(proxy): add BudgetLimitEntry type and wire budget_limits into key/team models * feat(schema): add budget_limits Json column to VerificationToken and TeamTable * feat(migrations): add migration for budget_limits column on keys and teams * feat(keys): initialize budget_limits windows with reset_at on key create/update * feat(teams): initialize budget_limits windows with reset_at on team create/update * feat(auth): add _virtual_key_multi_budget_check and _team_multi_budget_check * feat(auth): call multi-budget checks from common_checks for keys and teams * feat(proxy): increment per-window Redis spend counters after each request * feat(budget): reset individual budget windows on schedule via reset_budget_job * feat(ui): add hourly option to BudgetDurationDropdown * feat(ui): add budget_limits field to KeyResponse type * feat(ui): add Budget Windows editor to key edit view * feat(ui): add Budget Windows editor to create key form * fix(proxy): strip budget_limits=None before Prisma upsert to fix login 500 Prisma rejects nullable JSON fields (Json? without @default) when passed as Python None — it needs the field omitted entirely so the DB stores NULL via the column's nullable constraint. This was breaking /v2/login because the UI session key creation path hit the upsert with budget_limits=None. * ui(key-edit): use antd InputNumber+Button for budget windows, add reset hints * ui(create-key): use antd InputNumber+Button for budget windows, add reset hints * docs(users): add multiple budget windows section with API + dashboard walkthrough * fix: BudgetExceededError returns HTTP 429 instead of 400 - Add status_code=429 to BudgetExceededError class - auth_exception_handler hardcoded code=400 → code=429 * fix: no-op else branch in multi-budget auth checks causes KeyError - BudgetLimitEntry objects must be coerced via model_dump() not left as-is - Move _virtual_key_multi_budget_check into common_checks (was asymmetric with _team_multi_budget_check which already lived there) * fix: len() on JSON string returns char count not window count Guard with isinstance check + json.loads() before iterating per-window Redis counters in increment_spend_counters * fix: silent except:pass hides Redis reset failures in reset_budget_windows Log Redis counter reset failures as warnings so they are observable * test: add unit tests for multi-budget window enforcement 5 tests covering: no budget_limits passes, under budget passes, over hourly window raises 429, over monthly window raises 429, BudgetLimitEntry objects coerced without KeyError * fix: key per-window counters stable across reorders (duration key, not index) * fix: team+key per-window spend increments use duration key, not index * fix: budget window reset uses duration key; log failures instead of swallowing * refactor: extract BudgetWindowsEditor to shared component * refactor: key_edit_view imports BudgetWindowsEditor from shared component * refactor: create_key_button imports BudgetWindowsEditor from shared component --------- Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com> * fix(reset_budget_job): extract _reset_expired_window helper to fix PLR0915 too many statements * feat(skills): Skills Registry & Hub — register skills, browse in AI Hub, public skill hub (#25118) * feat(skills): add domain and namespace fields to plugin types * feat(skills): store and return domain/namespace inside manifest_json * feat(skills): add /public/skill_hub endpoint for unauthenticated access * feat(skills): whitelist /public/skill_hub from auth requirements * feat(skills): add domain, namespace to Plugin and RegisterPluginRequest types * feat(skills): smart URL parser — paste github URL, auto-detect source type and name * feat(skills): replace enable toggle with Public badge, make rows clickable * feat(skills): add skill detail view with Overview and How to Use tabs * feat(skills): add MakeSkillPublicForm modal for publishing skills to the hub * feat(skills): rename panel to Skills, wire in skill detail view on row click * feat(skills): add skill hub table columns — name, description, domain, source, status * feat(skills): add SkillHubDashboard with stats row, domain dropdown filter, and table * feat(skills): add Skill Hub tab to AI Hub with Select Skills to Make Public button * feat(skills): move Skills to top-level nav item directly under MCP Servers * feat(skills): add skillHubPublicCall and NEXT_PUBLIC_BASE_URL support * feat(skills): add Skill Hub tab to public AI Hub page * feat(skills): add skills page routing in main app router * feat(skills): add /skills page route * chore: update package-lock after npm install * docs(skills): add Skills Gateway doc page with mermaid architecture diagram * docs(skills): add Skills Gateway to sidebar under Agent & MCP Gateway * docs(skills): add loom walkthrough video to Skills Gateway doc * chore: fixes --------- Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com> Co-authored-by: Yuneng Jiang <yuneng@berri.ai>
🚅 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
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
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 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 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.