# Proxy Server Documentation

The Headroom proxy server is a production-ready HTTP server that applies context optimization to all requests passing through it.

## Starting the Proxy

```bash
# Basic usage
headroom proxy

# Custom port
headroom proxy --port 8080

# With all options
headroom proxy \
  --host 0.0.0.0 \
  --port 8787 \
  --log-file /var/log/headroom.jsonl \
  --budget 100.0
```

## Command Line Options

| Option | Default | Description |
|--------|---------|-------------|
| `--host` | `127.0.0.1` | Host to bind to |
| `--port` | `8787` | Port to bind to |
| `--no-optimize` | `false` | Disable optimization (passthrough mode) |
| `--no-cache` | `false` | Disable semantic caching |
| `--no-rate-limit` | `false` | Disable rate limiting |
| `--log-file` | None | Path to JSONL log file |
| `--budget` | None | Daily budget limit in USD |

## API Endpoints

### Health Check

```bash
curl http://localhost:8787/health
```

Response:
```json
{
  "status": "healthy",
  "optimize": true,
  "stats": {
    "total_requests": 42,
    "tokens_saved": 15000,
    "savings_percent": 45.2
  }
}
```

### Detailed Statistics

```bash
curl http://localhost:8787/stats
```

### Prometheus Metrics

```bash
curl http://localhost:8787/metrics
```

### LLM APIs

The proxy supports both Anthropic and OpenAI API formats:

```bash
# Anthropic format
POST /v1/messages

# OpenAI format
POST /v1/chat/completions
```

## Using with Claude Code

```bash
# Start proxy
headroom proxy --port 8787

# In another terminal
ANTHROPIC_BASE_URL=http://localhost:8787 claude
```

## Using with Cursor

1. Start the proxy: `headroom proxy`
2. In Cursor settings, set the base URL to `http://localhost:8787`

## Using with OpenAI SDK

```python
from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8787/v1",
    api_key="your-api-key",  # Still needed for upstream
)
```

## Features

### Semantic Caching

The proxy caches responses for repeated queries:

- LRU eviction with configurable max entries
- TTL-based expiration
- Cache key based on message content hash

### Rate Limiting

Token bucket rate limiting protects against runaway costs:

- Configurable requests per minute
- Configurable tokens per minute
- Per-API-key tracking

### Cost Tracking

Track spending and enforce budgets:

- Real-time cost estimation
- Budget periods: hourly, daily, monthly
- Automatic request rejection when over budget

### Prometheus Metrics

Export metrics for monitoring:

```
headroom_requests_total
headroom_tokens_saved_total
headroom_cost_usd_total
headroom_latency_ms_sum
```

## Configuration via Environment

```bash
export HEADROOM_HOST=0.0.0.0
export HEADROOM_PORT=8787
export HEADROOM_BUDGET=100.0
headroom proxy
```

## Running in Production

For production deployments:

```bash
# Use a process manager
pip install gunicorn

# Run with gunicorn
gunicorn headroom.proxy.server:app \
  --workers 4 \
  --bind 0.0.0.0:8787 \
  --worker-class uvicorn.workers.UvicornWorker
```

Or with Docker:

```dockerfile
FROM python:3.11-slim
RUN pip install headroom[proxy]
EXPOSE 8787
CMD ["headroom", "proxy", "--host", "0.0.0.0"]
```