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Documentation Index

Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt

Use this file to discover all available pages before exploring further.

Learn how to integrate Maxim observability and online evaluation with your LiteLLM Proxy in just one line of configuration.

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Prerequisites

Install the required Python packages: 
pip install litellm[proxy]>=1.30.0 maxim-py==3.4.16 python-dotenv>=0.21.1

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Project Layout

.
├── config.yml
├── maxim_proxy_tracer.py
├── requirements.txt
├── .env
└── (optional) Dockerfile & docker-compose.yml

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1. Define the Tracer

Create a file maxim_proxy_tracer.py next to your proxy entrypoint:
maxim_proxy_tracer.py
from maxim.logger.litellm_proxy import MaximLiteLLMProxyTracer

# This single object wires up all LiteLLM traffic to Maxim
litellm_handler = MaximLiteLLMProxyTracer()

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2. Update config.yml

Point LiteLLM’s callback at your tracer:
config.yml
litellm_settings:
  callbacks: maxim_proxy_tracer.litellm_handler
(Your existing model_list and general_settings remain unchanged.)

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3. Configure Environment Variables

Add the following to a .env file or export in your shell: 
OPENAI_API_KEY=…       # API key for LiteLLM
MAXIM_API_KEY=…        # API key for Maxim ingestion
MAXIM_LOG_REPO_ID=…    # ID of your Maxim log repository

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4. Run the Proxy Locally

You can start the proxy directly via the LiteLLM CLI: 
litellm --port 8000 --config config.yml

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5. Run with Docker Compose

If you prefer Docker, use the provided Dockerfile and docker-compose.yml: 
docker-compose up -d
  • Port: 8000
  • Health check: GET /health
  • Logs: streamed to proxy_logs.log
That’s it—no additional code changes required. Every request through your LiteLLM Proxy will now be traced, logged, and evaluated in Maxim.