headroom-ai 0.2.9

The Context Optimization Layer for LLM Applications - Cut costs by 50-90%

Headroom

The Context Optimization Layer for LLM Applications

Cut your LLM costs by 50-90% without losing accuracy

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Why Headroom?

• Zero code changes - works as a transparent proxy
• 50-90% cost savings - verified on real workloads
• Reversible compression - LLM retrieves original data via CCR
• Content-aware - code, logs, JSON each handled optimally
• Provider caching - automatic prefix optimization for cache hits
• Persistent memory - remember across conversations with zero-latency extraction
• Framework native - LangChain, Agno, MCP, agents supported

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Headroom vs Alternatives

Approach	Token Reduction	Accuracy	Reversible	Latency
Headroom	50-90%	No loss	Yes (CCR)	~1-5ms
Truncation	Variable	Data loss	No	~0ms
Summarization	60-80%	Lossy	No	~500ms+
No optimization	0%	Full	N/A	0ms

Headroom wins because it intelligently selects relevant content while keeping a retrieval path to the original data.

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30-Second Quickstart

Option 1: Proxy (Zero Code Changes)

pip install "headroom-ai[proxy]"
headroom proxy --port 8787

Point your tools at the proxy:

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

# Any OpenAI-compatible client
OPENAI_BASE_URL=http://localhost:8787/v1 cursor

Option 2: LangChain Integration

pip install "headroom-ai[langchain]"

from langchain_openai import ChatOpenAI
from headroom.integrations import HeadroomChatModel

# Wrap your model - that's it!
llm = HeadroomChatModel(ChatOpenAI(model="gpt-4o"))

# Use exactly like before
response = llm.invoke("Hello!")

See the full LangChain Integration Guide for memory, retrievers, agents, and more.

Option 3: Agno Integration

pip install "headroom-ai[agno]"

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from headroom.integrations.agno import HeadroomAgnoModel

# Wrap your model - that's it!
model = HeadroomAgnoModel(OpenAIChat(id="gpt-4o"))
agent = Agent(model=model)

# Use exactly like before
response = agent.run("Hello!")

# Check savings
print(f"Tokens saved: {model.total_tokens_saved}")

See the full Agno Integration Guide for hooks, multi-provider support, and more.

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Framework Integrations

Framework	Integration	Docs
LangChain	HeadroomChatModel, memory, retrievers, agents	Guide
Agno	HeadroomAgnoModel, hooks, multi-provider	Guide
MCP	Tool output compression for Claude	Guide
Any OpenAI Client	Proxy server	Guide

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Features

Feature	Description	Docs
Memory	Persistent memory across conversations (zero-latency inline extraction)	Memory
Universal Compression	ML-based content detection + structure-preserving compression	Compression
SmartCrusher	Compresses JSON tool outputs statistically	Transforms
CacheAligner	Stabilizes prefixes for provider caching	Transforms
RollingWindow	Manages context limits without breaking tools	Transforms
CCR	Reversible compression with automatic retrieval	CCR Guide
LangChain	Memory, retrievers, agents, streaming	LangChain
Agno	Agent framework integration with hooks	Agno
Text Utilities	Opt-in compression for search/logs	Text Compression
LLMLingua-2	ML-based 20x compression (opt-in)	LLMLingua
Code-Aware	AST-based code compression (tree-sitter)	Transforms

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Performance

Scenario	Before	After	Savings
Search results (1000 items)	45,000 tokens	4,500 tokens	90%
Log analysis (500 entries)	22,000 tokens	3,300 tokens	85%
Long conversation (50 turns)	80,000 tokens	32,000 tokens	60%
Agent with tools (10 calls)	100,000 tokens	15,000 tokens	85%

Overhead: ~1-5ms per request

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Providers

Provider	Token Counting	Cache Optimization
OpenAI	tiktoken (exact)	Automatic prefix caching
Anthropic	Official API	cache_control blocks
Google	Official API	Context caching
Cohere	Official API	-
Mistral	Official tokenizer	-

New models auto-supported via naming pattern detection.

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Safety Guarantees

• Never removes human content - user/assistant messages preserved
• Never breaks tool ordering - tool calls and responses stay paired
• Parse failures are no-ops - malformed content passes through unchanged
• Compression is reversible - LLM retrieves original data via CCR

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Installation

pip install headroom-ai              # SDK only
pip install "headroom-ai[proxy]"     # Proxy server
pip install "headroom-ai[langchain]" # LangChain integration
pip install "headroom-ai[agno]"      # Agno agent framework
pip install "headroom-ai[code]"      # AST-based code compression
pip install "headroom-ai[llmlingua]" # ML-based compression
pip install "headroom-ai[all]"       # Everything

Requirements: Python 3.10+

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Documentation

Guide	Description
Memory Guide	Persistent memory for LLMs
Compression Guide	Universal compression with ML detection
LangChain Integration	Full LangChain support
Agno Integration	Full Agno agent framework support
SDK Guide	Fine-grained control
Proxy Guide	Production deployment
Configuration	All options
CCR Guide	Reversible compression
Metrics	Monitoring
Troubleshooting	Common issues

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Who's Using Headroom?

Add your project here! Open a PR or start a discussion.

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Contributing

git clone https://github.com/chopratejas/headroom.git
cd headroom
pip install -e ".[dev]"
pytest

See CONTRIBUTING.md for details.

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License

Apache License 2.0 - see LICENSE.

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