Headroom

The Context Optimization Layer for LLM Applications

Tool outputs are 70-95% redundant boilerplate. Headroom compresses that away.

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Demo

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Does It Actually Work? A Real Test

The setup: 100 production log entries. One critical error buried at position 67.

BEFORE: 100 log entries (18,952 chars) - click to expand

[
  {"timestamp": "2024-12-15T00:00:00Z", "level": "INFO", "service": "api-gateway", "message": "Request processed successfully - latency=50ms", "request_id": "req-000000", "status_code": 200},
  {"timestamp": "2024-12-15T01:01:00Z", "level": "INFO", "service": "user-service", "message": "Request processed successfully - latency=51ms", "request_id": "req-000001", "status_code": 200},
  {"timestamp": "2024-12-15T02:02:00Z", "level": "INFO", "service": "inventory", "message": "Request processed successfully - latency=52ms", "request_id": "req-000002", "status_code": 200},
  // ... 64 more INFO entries ...
  {"timestamp": "2024-12-15T03:47:23Z", "level": "FATAL", "service": "payment-gateway", "message": "Connection pool exhausted", "error_code": "PG-5523", "resolution": "Increase max_connections to 500 in config/database.yml", "affected_transactions": 1847},
  // ... 32 more INFO entries ...
]

AFTER: Headroom compresses to 6 entries (1,155 chars):

[
  {"timestamp": "2024-12-15T00:00:00Z", "level": "INFO", "service": "api-gateway", ...},
  {"timestamp": "2024-12-15T01:01:00Z", "level": "INFO", "service": "user-service", ...},
  {"timestamp": "2024-12-15T02:02:00Z", "level": "INFO", "service": "inventory", ...},
  {"timestamp": "2024-12-15T03:47:23Z", "level": "FATAL", "service": "payment-gateway", "error_code": "PG-5523", "resolution": "Increase max_connections to 500 in config/database.yml", "affected_transactions": 1847},
  {"timestamp": "2024-12-15T02:38:00Z", "level": "INFO", "service": "inventory", ...},
  {"timestamp": "2024-12-15T03:39:00Z", "level": "INFO", "service": "auth", ...}
]

What happened: First 3 items + the FATAL error + last 2 items. The critical error at position 67 was automatically preserved.

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The question we asked Claude: "What caused the outage? What's the error code? What's the fix?"

	Baseline	Headroom
Input tokens	10,144	1,260
Correct answers	4/4	4/4

Both responses: "payment-gateway service, error PG-5523, fix: Increase max_connections to 500, 1,847 transactions affected"

87.6% fewer tokens. Same answer.

Run it yourself: python examples/needle_in_haystack_test.py

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Multi-Tool Agent Test: Real Function Calling

The setup: An Agno agent with 4 tools (GitHub Issues, ArXiv Papers, Code Search, Database Logs) investigating a memory leak. Total tool output: 62,323 chars (~15,580 tokens).

from agno.agent import Agent
from agno.models.anthropic import Claude
from headroom.integrations.agno import HeadroomAgnoModel

# Wrap your model - that's it!
base_model = Claude(id="claude-sonnet-4-20250514")
model = HeadroomAgnoModel(wrapped_model=base_model)

agent = Agent(model=model, tools=[search_github, search_arxiv, search_code, query_db])
response = agent.run("Investigate the memory leak and recommend a fix")

Results with Claude Sonnet:

	Baseline	Headroom
Tokens sent to API	15,662	6,100
API requests	2	2
Tool calls	4	4
Duration	26.5s	27.0s

76.3% fewer tokens. Same comprehensive answer.

Both found: Issue #42 (memory leak), the cleanup_worker() fix, OutOfMemoryError logs (7.8GB/8GB, 847 threads), and relevant research papers.

Run it yourself: python examples/multi_tool_agent_test.py

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How It Works

Headroom optimizes LLM context before it hits the provider — without changing your agent logic or tools.

flowchart LR
  User["Your App"]
  Entry["Headroom"]
  Transform["Context<br/>Optimization"]
  LLM["LLM Provider"]
  Response["Response"]

  User --> Entry --> Transform --> LLM --> Response

Inside Headroom

flowchart TB

subgraph Pipeline["Transform Pipeline"]
  CA["Cache Aligner<br/><i>Stabilizes dynamic tokens</i>"]
  SC["Smart Crusher<br/><i>Removes redundant tool output</i>"]
  CM["Context Manager<br/><i>Fits token budget</i>"]
  CA --> SC --> CM
end

subgraph CCR["CCR: Compress-Cache-Retrieve"]
  Store[("Compressed<br/>Store")]
  Tool["Retrieve Tool"]
  Tool <--> Store
end

LLM["LLM Provider"]

CM --> LLM
SC -. "Stores originals" .-> Store
LLM -. "Requests full context<br/>if needed" .-> Tool

Headroom never throws data away. It compresses aggressively and retrieves precisely.

What actually happens

1. Headroom intercepts context — Tool outputs, logs, search results, and intermediate agent steps.

2. Dynamic content is stabilized — Timestamps, UUIDs, request IDs are normalized so prompts cache cleanly.

3. Low-signal content is removed — Repetitive or redundant data is crushed, not truncated.

4. Original data is preserved — Full content is stored separately and retrieved only if the LLM asks.

5. Provider caches finally work — Headroom aligns prompts so OpenAI, Anthropic, and Google caches actually hit.

For deep technical details, see Architecture Documentation.

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Why Headroom?

• Zero code changes - works as a transparent proxy
• 47-92% savings - depends on your workload (tool-heavy = more savings)
• Image compression - 40-90% reduction via trained ML router (OpenAI, Anthropic, Google)
• Reversible compression - LLM retrieves original data via CCR
• Content-aware - code, logs, JSON, images each handled optimally
• Provider caching - automatic prefix optimization for cache hits
• Framework native - LangChain, Agno, MCP, agents supported

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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
Image Compression	40-90% token reduction for images via trained ML router	Image Compression
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
Evals Framework	Prove compression preserves accuracy (12+ datasets)	Evals

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Evaluation Framework: Prove It Works

Skeptical? Good. We built a comprehensive evaluation framework to prove compression preserves accuracy.

# Install evals
pip install "headroom-ai[evals]"

# Quick sanity check (5 samples)
python -m headroom.evals quick

# Run on real datasets
python -m headroom.evals benchmark --dataset hotpotqa -n 100

How Evals Work

Original Context ───► LLM ───► Response A
                                   │
Compressed Context ─► LLM ───► Response B
                                   │
                    Compare A vs B │
                    ─────────────────
                    F1 Score: 0.95
                    Semantic Similarity: 0.97
                    Ground Truth Match: ✓
                    ─────────────────
                    PASS: Accuracy preserved

Available Datasets (12+)

Category	Datasets
RAG	HotpotQA, Natural Questions, TriviaQA, MS MARCO, SQuAD
Long Context	LongBench (4K-128K tokens), NarrativeQA
Tool Use	BFCL (function calling), ToolBench, Built-in samples
Code	CodeSearchNet, HumanEval

CI Integration

# GitHub Actions
- name: Run Compression Evals
  run: python -m headroom.evals quick -n 20
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}

Exit code 0 if accuracy ≥ 90%, 1 otherwise.

See the full Evals Documentation for datasets, metrics, and programmatic API.

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Verified Performance

These numbers are from actual API calls, not estimates:

Scenario	Before	After	Savings	Verified
Code search (100 results)	17,765 tokens	1,408 tokens	92%	Claude Sonnet
SRE incident debugging	65,694 tokens	5,118 tokens	92%	GPT-4o
Codebase exploration	78,502 tokens	41,254 tokens	47%	GPT-4o
GitHub issue triage	54,174 tokens	14,761 tokens	73%	GPT-4o

Overhead: ~1-5ms compression latency

When savings are highest: Tool-heavy workloads (search, logs, database queries) When savings are lowest: Conversation-heavy workloads with minimal tool use

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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[evals]"     # Evaluation 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
Evals Framework	Prove compression preserves accuracy
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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