mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-05 08:07:05 +00:00
Mirror of https://github.com/BerriAI/litellm.git
15404db3d0
* fix: resolve mypy type errors in hiddenlayer guardrail and transformation - Fix return type of apply_guardrail from str to GenericGuardrailAPIInputs - Add None checks for logging_obj before accessing attributes - Convert AllMessageValues to dict format for HiddenLayer API compatibility - Fix payload type annotation in _call_hiddenlayer - Ensure transformed_output always returns list[dict[str, Any]] in transformation.py * fix: use litellm_call_id as trace_id fallback in langfuse logging - Only use standard_logging_object.trace_id if explicitly set via litellm_session_id or litellm_trace_id params - Fallback to litellm_call_id when no explicit trace_id is provided (matches test expectation) - Return the trace_id we set instead of generation_client.trace_id for consistency - Add warning if langfuse modifies the trace_id to help debug potential issues Fixes test_logging_trace_id test failure where auto-generated UUID was used instead of litellm_call_id * fix: document envs * fix: handle None response in /spend/logs endpoint when no records found - Return empty list [] instead of [None] when spend_log is None - Prevents 500 errors when querying by request_id, api_key, or user_id with no matching records - Fixes test_chat_completion_bad_model_with_spend_logs test failure * fix: use standard_logging_object trace_id when available in langfuse logger - Fix trace_id selection logic to use standard_logging_object.trace_id when available - Previously only used standard_logging_object.trace_id if explicitly set via params - Now uses standard_logging_object.trace_id whenever it's present, matching test expectations - Falls back to litellm_call_id if no trace_id is found - Fixes test_log_langfuse_v2_uses_standard_trace_id_when_available test failure |
||
|---|---|---|
| .circleci | ||
| .devcontainer | ||
| .github | ||
| ci_cd | ||
| cookbook | ||
| db_scripts | ||
| deploy | ||
| dist | ||
| docker | ||
| docs/my-website | ||
| enterprise | ||
| litellm | ||
| litellm-js | ||
| litellm-proxy-extras | ||
| scripts | ||
| tests | ||
| ui/litellm-dashboard | ||
| .dockerignore | ||
| .env.example | ||
| .flake8 | ||
| .git-blame-ignore-revs | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| AGENTS.md | ||
| batch_small.jsonl | ||
| CLAUDE.md | ||
| codecov.yaml | ||
| CONTRIBUTING.md | ||
| docker-compose.yml | ||
| Dockerfile | ||
| document.txt | ||
| GEMINI.md | ||
| index.yaml | ||
| LICENSE | ||
| Makefile | ||
| mcp_servers.json | ||
| model_prices_and_context_window.json | ||
| package-lock.json | ||
| package.json | ||
| poetry.lock | ||
| prometheus.yml | ||
| provider_endpoints_support.json | ||
| proxy_server_config.yaml | ||
| pyproject.toml | ||
| pyrightconfig.json | ||
| README.md | ||
| render.yaml | ||
| requirements.txt | ||
| ruff.toml | ||
| schema.prisma | ||
| security.md | ||
🚅 LiteLLM
Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
LiteLLM Proxy Server (LLM Gateway) | Hosted Proxy | Enterprise Tier
LiteLLM manages:
- Translate inputs to provider's
completion,embedding, andimage_generationendpoints - Consistent output, text responses will always be available at
['choices'][0]['message']['content'] - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - Router
- Set Budgets & Rate limits per project, api key, model LiteLLM Proxy Server (LLM Gateway)
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.
Usage (Docs)
pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-key"
messages = [{ "content": "Hello, how are you?","role": "user"}]
# openai call
response = completion(model="openai/gpt-4o", messages=messages)
# anthropic call
response = completion(model="anthropic/claude-sonnet-4-20250514", messages=messages)
print(response)
Response (OpenAI Format)
{
"id": "chatcmpl-1214900a-6cdd-4148-b663-b5e2f642b4de",
"created": 1751494488,
"model": "claude-sonnet-4-20250514",
"object": "chat.completion",
"system_fingerprint": null,
"choices": [
{
"finish_reason": "stop",
"index": 0,
"message": {
"content": "Hello! I'm doing well, thank you for asking. I'm here and ready to help with whatever you'd like to discuss or work on. How are you doing today?",
"role": "assistant",
"tool_calls": null,
"function_call": null
}
}
],
"usage": {
"completion_tokens": 39,
"prompt_tokens": 13,
"total_tokens": 52,
"completion_tokens_details": null,
"prompt_tokens_details": {
"audio_tokens": null,
"cached_tokens": 0
},
"cache_creation_input_tokens": 0,
"cache_read_input_tokens": 0
}
}
Note: LiteLLM also supports the Responses API (
litellm.responses())
Call any model supported by a provider, with model=<provider_name>/<model_name>. There might be provider-specific details here, so refer to provider docs for more information
Async (Docs)
from litellm import acompletion
import asyncio
async def test_get_response():
user_message = "Hello, how are you?"
messages = [{"content": user_message, "role": "user"}]
response = await acompletion(model="openai/gpt-4o", messages=messages)
return response
response = asyncio.run(test_get_response())
print(response)
Streaming (Docs)
LiteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response.
Streaming is supported for all models (Bedrock, Huggingface, TogetherAI, Azure, OpenAI, etc.)
from litellm import completion
messages = [{"content": "Hello, how are you?", "role": "user"}]
# gpt-4o
response = completion(model="openai/gpt-4o", messages=messages, stream=True)
for part in response:
print(part.choices[0].delta.content or "")
# claude sonnet 4
response = completion('anthropic/claude-sonnet-4-20250514', messages, stream=True)
for part in response:
print(part)
Response chunk (OpenAI Format)
{
"id": "chatcmpl-fe575c37-5004-4926-ae5e-bfbc31f356ca",
"created": 1751494808,
"model": "claude-sonnet-4-20250514",
"object": "chat.completion.chunk",
"system_fingerprint": null,
"choices": [
{
"finish_reason": null,
"index": 0,
"delta": {
"provider_specific_fields": null,
"content": "Hello",
"role": "assistant",
"function_call": null,
"tool_calls": null,
"audio": null
},
"logprobs": null
}
],
"provider_specific_fields": null,
"stream_options": null,
"citations": null
}
Logging Observability (Docs)
LiteLLM exposes pre defined callbacks to send data to Lunary, MLflow, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack
from litellm import completion
## set env variables for logging tools (when using MLflow, no API key set up is required)
os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
os.environ["HELICONE_API_KEY"] = "your-helicone-auth-key"
os.environ["LANGFUSE_PUBLIC_KEY"] = ""
os.environ["LANGFUSE_SECRET_KEY"] = ""
os.environ["ATHINA_API_KEY"] = "your-athina-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-key"
# set callbacks
litellm.success_callback = ["lunary", "mlflow", "langfuse", "athina", "helicone"] # log input/output to lunary, langfuse, supabase, athina, helicone etc
#openai call
response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
LiteLLM Proxy Server (LLM Gateway) - (Docs)
Track spend + Load Balance across multiple projects
The proxy provides:
📖 Proxy Endpoints - Swagger Docs
Quick Start Proxy - CLI
pip install 'litellm[proxy]'
Step 1: Start litellm proxy
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:4000
Step 2: Make ChatCompletions Request to Proxy
Important
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
Proxy Key Management (Docs)
Connect the proxy with a Postgres DB to create proxy keys
# Get the code
git clone https://github.com/BerriAI/litellm
# Go to folder
cd litellm
# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env
# Add the litellm salt key - you cannot change this after adding a model
# It is used to encrypt / decrypt your LLM API Key credentials
# We recommend - https://1password.com/password-generator/
# password generator to get a random hash for litellm salt key
echo 'LITELLM_SALT_KEY="sk-1234"' >> .env
# Start
docker compose up
UI on /ui on your proxy server
Set budgets and rate limits across multiple projects
POST /key/generate
Request
curl 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data-raw '{"models": ["gpt-3.5-turbo", "gpt-4", "claude-2"], "duration": "20m","metadata": {"user": "ishaan@berri.ai", "team": "core-infra"}}'
Expected Response
{
"key": "sk-kdEXbIqZRwEeEiHwdg7sFA", # Bearer token
"expires": "2023-11-19T01:38:25.838000+00:00" # datetime object
}
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]" - Start proxy backend
python litellm/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.