headroom/wiki/sdk.md

SDK Guide

The Headroom SDK wraps your existing LLM client to add compression and optimization transparently.

Installation

pip install headroom-ai openai

Quick Start

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 the original client
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Hello!"},
    ],
)

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

Tool Output Compression

Real savings happen with tool outputs. Here's where Headroom shines:

import json

# Conversation with large tool output
messages = [
    {"role": "user", "content": "Search for Python tutorials"},
    {
        "role": "assistant",
        "content": None,
        "tool_calls": [
            {
                "id": "call_123",
                "type": "function",
                "function": {"name": "search", "arguments": '{"q": "python"}'},
            }
        ],
    },
    {
        "role": "tool",
        "tool_call_id": "call_123",
        "content": json.dumps(
            {"results": [{"title": f"Tutorial {i}", "score": 100 - i} for i in range(500)]}
        ),
    },
    {"role": "user", "content": "What are the top 3?"},
]

# Headroom compresses 500 results to ~15, keeping highest-scoring items
response = client.chat.completions.create(model="gpt-4o-mini", messages=messages)

# Check savings
stats = client.get_stats()
print(f"Tokens saved: {stats['session']['tokens_saved_total']}")
# Typical output: "Tokens saved: 3500"

Supported Providers

OpenAI

from headroom import HeadroomClient, OpenAIProvider
from openai import OpenAI

client = HeadroomClient(
    original_client=OpenAI(),
    provider=OpenAIProvider(),
)

Anthropic

from headroom import HeadroomClient, AnthropicProvider
from anthropic import Anthropic

client = HeadroomClient(
    original_client=Anthropic(),
    provider=AnthropicProvider(),
)

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)

Google

from headroom import HeadroomClient, GoogleProvider
import google.generativeai as genai

client = HeadroomClient(
    original_client=genai,
    provider=GoogleProvider(),
)

Check Stats

# Session stats (no database query)
stats = client.get_stats()
print(stats)
# {
#   "session": {"requests_total": 10, "tokens_saved_total": 5000, ...},
#   "config": {"mode": "optimize", "provider": "openai", ...},
#   "transforms": {"smart_crusher_enabled": True, ...}
# }

Validate Setup

result = client.validate_setup()
if not result["valid"]:
    print("Setup issues:", result["issues"])

Modes

Optimize (Default)

Applies all safe transforms:

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

Audit

Observes and logs without modifying:

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

Simulate

Returns a plan without making the API call:

plan = client.chat.completions.simulate(
    model="gpt-4o",
    messages=large_conversation,
)

print(f"Would save {plan.tokens_saved} tokens")
print(f"Transforms: {plan.transforms}")

Per-Request Overrides

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    # Override mode for this request
    headroom_mode="audit",
    # Reserve more tokens for output
    headroom_output_buffer_tokens=8000,
    # Keep last N turns
    headroom_keep_turns=5,
)

Enable Logging

import logging

logging.basicConfig(level=logging.INFO)

# Now you'll see:
# INFO:headroom.transforms.pipeline:Pipeline complete: 45000 -> 4500 tokens
# INFO:headroom.transforms.smart_crusher:SmartCrusher: kept 15 of 1000 items

Streaming

Streaming works transparently:

stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

Error Handling

from headroom import (
    HeadroomClient,
    HeadroomError,
    ConfigurationError,
    ProviderError,
)

try:
    response = client.chat.completions.create(...)
except ConfigurationError as e:
    print(f"Config issue: {e}")
except ProviderError as e:
    print(f"Provider issue: {e}")
except HeadroomError as e:
    print(f"Headroom error: {e}")

Historical Metrics

Query stored metrics:

from datetime import datetime, timedelta

metrics = client.get_metrics(
    start_time=datetime.utcnow() - timedelta(hours=1),
    limit=100,
)

for m in metrics:
    print(f"{m.timestamp}: {m.tokens_input_before} -> {m.tokens_input_after}")

Advanced Configuration

See Configuration for full options:

client = HeadroomClient(
    original_client=OpenAI(),
    provider=OpenAIProvider(),
    default_mode="optimize",
    enable_cache_optimizer=True,
    enable_semantic_cache=False,
    model_context_limits={
        "gpt-4o": 128000,
        "gpt-4o-mini": 128000,
    },
)

Comparison with Proxy

Aspect	SDK	Proxy
Setup	Wrap client	Point URL
Control	Fine-grained	Global
Metrics	In-process	Centralized
Best for	Custom apps	Existing tools

Use the SDK when you need fine-grained control. Use the proxy for existing tools like Claude Code, Cursor, etc.