Quickstart Guide¶

Get Headroom running in 5 minutes with these copy-paste examples.

Installation¶

# Core only (minimal dependencies)
pip install headroom

# With proxy server
pip install "headroom[proxy]"

# Everything
pip install "headroom[all]"

Option 1: Proxy Server (Zero Code Changes)¶

The fastest way to start saving tokens. Works with any OpenAI-compatible client.

Step 1: Start the Proxy¶

headroom proxy --port 8787

Step 2: Verify It's Running¶

curl http://localhost:8787/health
# Expected: {"status": "healthy", "mode": "optimize", ...}

Step 3: Point Your Client¶

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

# Cursor / Continue / any OpenAI client
OPENAI_BASE_URL=http://localhost:8787/v1 your-app

# Python
export OPENAI_BASE_URL=http://localhost:8787/v1
python your_script.py

Step 4: Check Savings¶

curl http://localhost:8787/stats
# {"requests_total": 42, "tokens_saved_total": 125000, ...}

Option 2: Python SDK¶

Wrap your existing client for fine-grained control.

Basic Example¶

from headroom import HeadroomClient, OpenAIProvider
from openai import OpenAI

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

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

print(response.choices[0].message.content)

# Check what happened
stats = client.get_stats()
print(f"Tokens saved: {stats['session']['tokens_saved_total']}")

With Tool Outputs (Where Savings Happen)¶

from headroom import HeadroomClient, OpenAIProvider
from openai import OpenAI
import json

client = HeadroomClient(
    original_client=OpenAI(),
    provider=OpenAIProvider(),
    default_mode="optimize",
)

# Simulate a conversation with large tool outputs
messages = [
    {"role": "system", "content": "You analyze search results."},
    {"role": "user", "content": "Search for Python tutorials."},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [{
            "id": "call_1",
            "type": "function",
            "function": {"name": "search", "arguments": '{"q": "python"}'},
        }],
    },
    {
        "role": "tool",
        "tool_call_id": "call_1",
        # This is where Headroom shines - compressing large outputs
        "content": json.dumps({
            "results": [{"title": f"Result {i}", "score": 100-i} for i in range(500)]
        }),
    },
    {"role": "user", "content": "What are the top 3 results?"},
]

# Headroom compresses the 500 results to ~20, keeping the most relevant
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
)

print(response.choices[0].message.content)

Simulate Before Sending¶

Preview optimizations without making an API call:

# See what would happen without calling the API
plan = client.chat.completions.simulate(
    model="gpt-4o",
    messages=messages,
)

print(f"Tokens before: {plan.tokens_before}")
print(f"Tokens after: {plan.tokens_after}")
print(f"Would save: {plan.tokens_saved} tokens ({plan.tokens_saved/plan.tokens_before*100:.0f}%)")
print(f"Transforms: {plan.transforms}")
print(f"Estimated savings: {plan.estimated_savings}")

Option 3: Anthropic SDK¶

from headroom import HeadroomClient, AnthropicProvider
from anthropic import Anthropic

client = HeadroomClient(
    original_client=Anthropic(),
    provider=AnthropicProvider(),
    default_mode="optimize",
)

# Use Anthropic-style API
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello, Claude!"},
    ],
)

print(response.content[0].text)

Verify It's Working¶

Method 1: Enable Logging¶

import logging
logging.basicConfig(level=logging.INFO)

# Now you'll see:
# INFO:headroom.transforms.pipeline:Pipeline complete: 45000 -> 4500 tokens (saved 40500, 90.0% reduction)
# INFO:headroom.transforms.smart_crusher:SmartCrusher: keeping 15 of 500 items

Method 2: Check Session Stats¶

stats = client.get_stats()
print(stats)
# {
#   "session": {"requests_total": 10, "tokens_saved_total": 5000, ...},
#   "config": {"mode": "optimize", "provider": "openai", ...},
#   "transforms": {"smart_crusher_enabled": True, ...}
# }

Method 3: Validate Setup¶

result = client.validate_setup()
if not result["valid"]:
    print("Setup issues:", result)
else:
    print("Setup OK!")
    print(f"Provider: {result['provider']['name']}")
    print(f"Storage: {result['storage']['url']}")

Common Configuration¶

Adjust Compression¶

from headroom import HeadroomClient, OpenAIProvider, HeadroomConfig

config = HeadroomConfig()

# Keep more items after compression (default: 15)
config.smart_crusher.max_items_after_crush = 30

# Only compress if tool output has > 500 tokens (default: 200)
config.smart_crusher.min_tokens_to_crush = 500

client = HeadroomClient(
    original_client=OpenAI(),
    provider=OpenAIProvider(),
    config=config,  # Pass custom config
    default_mode="optimize",
)

Skip Compression for Specific Tools¶

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    headroom_tool_profiles={
        "database_query": {"skip_compression": True},  # Never compress
        "search": {"max_items": 50},  # Keep more items
    },
)

Audit Mode (Observe Only)¶

# Start in audit mode - see what WOULD be optimized
client = HeadroomClient(
    original_client=OpenAI(),
    provider=OpenAIProvider(),
    default_mode="audit",  # No modifications, just logging
)

# Override per-request
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    headroom_mode="optimize",  # Enable for this request only
)

What Gets Optimized?¶

Content Type	What Headroom Does	Typical Savings
Tool outputs with lists	Keeps errors, anomalies, high-score items	70-90%
Repeated search results	Deduplicates and samples	60-80%
Long conversations	Drops old turns, keeps recent	40-60%
System prompts with dates	Stabilizes for cache hits	Cache savings

Next Steps¶

Configuration Reference - All configuration options
Transform Reference - How each transform works
Troubleshooting - Common issues and solutions
Examples - More complete examples

Quick Troubleshooting¶

"No token savings"¶

# 1. Check mode
stats = client.get_stats()
print(stats["config"]["mode"])  # Should be "optimize"

# 2. Enable logging to see what's happening
import logging
logging.basicConfig(level=logging.DEBUG)

"High latency"¶

# Use BM25 instead of embeddings for faster relevance scoring
config.smart_crusher.relevance.tier = "bm25"

"Compression too aggressive"¶

# Keep more items
config.smart_crusher.max_items_after_crush = 50

See Troubleshooting Guide for more solutions.