llmrouter

# LLM Router

An intelligent proxy that classifies incoming requests by complexity and routes them to appropriate LLM models. Use cheaper/faster models for simple tasks and reserve expensive models for complex ones.

**Works with [OpenClaw](https://github.com/openclaw/openclaw)** to reduce token usage and API costs by routing simple requests to smaller models.

**Status:** Tested with Anthropic, OpenAI, Google Gemini, Kimi/Moonshot, and Ollama.

## Quick Start

### Prerequisites

1. **Python 3.10+** with pip
2. **Ollama** (optional - only if using local classification)
3. **Anthropic API key** or Claude Code OAuth token (or other provider key)

### Setup

```bash
# Clone if not already present
git clone https://github.com/alexrudloff/llmrouter.git
cd llmrouter

# Create virtual environment (required on modern Python)
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Pull classifier model (if using local classification)
ollama pull qwen2.5:3b

# Copy and customize config
cp config.yaml.example config.yaml
# Edit config.yaml with your API key and model preferences
```

### Verify Installation

```bash
# Start the server
source venv/bin/activate
python server.py

# In another terminal, test health endpoint
curl http://localhost:4001/health
# Should return: {"status": "ok", ...}
```

### Start the Server

```bash
python server.py
```

Options:
- `--port PORT` - Port to listen on (default: 4001)
- `--host HOST` - Host to bind (default: 127.0.0.1)
- `--config PATH` - Config file path (default: config.yaml)
- `--log` - Enable verbose logging
- `--openclaw` - Enable OpenClaw compatibility (rewrites model name in system prompt)

## Configuration

Edit `config.yaml` to customize:

### Model Routing

```yaml
# Anthropic routing
models:
  super_easy: "anthropic:claude-haiku-4-5-20251001"
  easy: "anthropic:claude-haiku-4-5-20251001"
  medium: "anthropic:claude-sonnet-4-20250514"
  hard: "anthropic:claude-opus-4-20250514"...