chopratejas--headroom/wiki/getting-started.md

Getting Started with Headroom

This guide will help you get up and running with Headroom in under 5 minutes.

Installation

Python:

# Core package (minimal dependencies)
pip install headroom-ai

# With proxy server
pip install headroom-ai[proxy]

# With semantic relevance (for smarter compression)
pip install headroom-ai[relevance]

# Everything
pip install headroom-ai[all]

TypeScript / Node.js:

npm install headroom-ai

Docker-native:

curl -fsSL https://raw.githubusercontent.com/chopratejas/headroom/main/scripts/install.sh | bash

PowerShell:

irm https://raw.githubusercontent.com/chopratejas/headroom/main/scripts/install.ps1 | iex

See Docker-native install for wrapper behavior, compose usage, and host-integrated wrap flows.

If you want Headroom to stay up in the background and automatically serve supported tools, use Persistent Installs:

headroom install apply --preset persistent-service --providers auto

Quick Start: Proxy Mode (Recommended)

The easiest way to use Headroom is as a proxy server:

# Start the proxy
headroom proxy --port 8787

Then point your LLM client at it:

# Claude Code
ANTHROPIC_BASE_URL=http://localhost:8787 claude

# GitHub Copilot CLI (default Anthropic-style proxy route)
headroom wrap copilot -- --model claude-sonnet-4-20250514

# OpenAI-compatible clients
OPENAI_BASE_URL=http://localhost:8787/v1 your-app

That's it! All your requests now go through Headroom and get optimized automatically.

Quick Start: Python SDK

If you want programmatic control:

from headroom import HeadroomClient
from openai import OpenAI

# Create a wrapped client
client = HeadroomClient(
    original_client=OpenAI(),
    default_mode="optimize",
)

# Use exactly like the original
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
)

Modes

Audit Mode

Observe without modifying:

client = HeadroomClient(
    original_client=OpenAI(),
    default_mode="audit",
)
# Logs metrics but doesn't change requests

Optimize Mode

Apply transforms to reduce tokens:

client = HeadroomClient(
    original_client=OpenAI(),
    default_mode="optimize",
)
# Compresses tool outputs, aligns cache prefixes, etc.

Simulate Mode

Preview what optimizations would do:

plan = client.chat.completions.simulate(
    model="gpt-4o",
    messages=[...],
)
print(f"Would save {plan.tokens_saved} tokens")
print(f"Transforms: {plan.transforms_applied}")

Next Steps

• Proxy Server Documentation - Configure the proxy
• Transforms Reference - Understand each transform
• API Reference - Full API documentation