Headroom
The Context Optimization Layer for LLM Applications
Tool outputs are 70-95% redundant boilerplate. Headroom compresses that away.
---
## Demo
---
## 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
```json
[
{"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):
```json
[
{"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.
---
**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`
---
## 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).
```python
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`
---
## How It Works
> Headroom optimizes LLM context *before* it hits the provider —
> without changing your agent logic or tools.
```mermaid
flowchart LR
User["Your App"]
Entry["Headroom"]
Transform["Context
Optimization"]
LLM["LLM Provider"]
Response["Response"]
User --> Entry --> Transform --> LLM --> Response
```
### Inside Headroom
```mermaid
flowchart TB
subgraph Pipeline["Transform Pipeline"]
CA["Cache Aligner
Stabilizes dynamic tokens"]
SC["Smart Crusher
Removes redundant tool output"]
CM["Intelligent Context
Score-based token fitting"]
CA --> SC --> CM
end
subgraph CCR["CCR: Compress-Cache-Retrieve"]
Store[("Compressed
Store")]
Tool["Retrieve Tool"]
Tool <--> Store
end
LLM["LLM Provider"]
CM --> LLM
SC -. "Stores originals" .-> Store
LLM -. "Requests full context
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](docs/ARCHITECTURE.md).
---
## 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
---
## 30-Second Quickstart
### Option 1: Proxy (Zero Code Changes)
```bash
pip install "headroom-ai[proxy]"
headroom proxy --port 8787
```
Point your tools at the proxy:
```bash
# Claude Code
ANTHROPIC_BASE_URL=http://localhost:8787 claude
# Any OpenAI-compatible client
OPENAI_BASE_URL=http://localhost:8787/v1 cursor
```
**Using AWS Bedrock, Google Vertex, or Azure?** Route through Headroom:
```bash
# AWS Bedrock (uses your AWS credentials)
headroom proxy --backend bedrock --region us-west-2
ANTHROPIC_BASE_URL=http://localhost:8787 claude
# Google Vertex AI
headroom proxy --backend vertex_ai --region us-central1
# Azure OpenAI
headroom proxy --backend azure --region eastus
```
### Option 2: LangChain Integration
```bash
pip install "headroom-ai[langchain]"
```
```python
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](docs/langchain.md) for memory, retrievers, agents, and more.
### Option 3: Agno Integration
```bash
pip install "headroom-ai[agno]"
```
```python
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](docs/agno.md) for hooks, multi-provider support, and more.
---
## Framework Integrations
| Framework | Integration | Docs |
|-----------|-------------|------|
| **LangChain** | `HeadroomChatModel`, memory, retrievers, agents | [Guide](docs/langchain.md) |
| **Agno** | `HeadroomAgnoModel`, hooks, multi-provider | [Guide](docs/agno.md) |
| **MCP** | Tool output compression for Claude | [Guide](docs/ccr.md) |
| **Any OpenAI Client** | Proxy server | [Guide](docs/proxy.md) |
---
## Features
| Feature | Description | Docs |
|---------|-------------|------|
| **Image Compression** | 40-90% token reduction for images via trained ML router | [Image Compression](docs/image-compression.md) |
| **Memory** | Persistent memory across conversations (zero-latency inline extraction) | [Memory](docs/memory.md) |
| **Universal Compression** | ML-based content detection + structure-preserving compression | [Compression](docs/compression.md) |
| **SmartCrusher** | Compresses JSON tool outputs statistically | [Transforms](docs/transforms.md) |
| **CacheAligner** | Stabilizes prefixes for provider caching | [Transforms](docs/transforms.md) |
| **IntelligentContext** | Score-based context dropping with TOIN-learned importance | [Transforms](docs/transforms.md) |
| **CCR** | Reversible compression with automatic retrieval | [CCR Guide](docs/ccr.md) |
| **LangChain** | Memory, retrievers, agents, streaming | [LangChain](docs/langchain.md) |
| **Agno** | Agent framework integration with hooks | [Agno](docs/agno.md) |
| **Text Utilities** | Opt-in compression for search/logs | [Text Compression](docs/text-compression.md) |
| **LLMLingua-2** | ML-based 20x compression (opt-in) | [LLMLingua](docs/llmlingua.md) |
| **Code-Aware** | AST-based code compression (tree-sitter) | [Transforms](docs/transforms.md) |
| **Evals Framework** | Prove compression preserves accuracy (12+ datasets) | [Evals](headroom/evals/README.md) |
---
## Evaluation Framework: Prove It Works
Skeptical? Good. We built a comprehensive evaluation framework to **prove** compression preserves accuracy.
```bash
# 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
```yaml
# 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](headroom/evals/README.md) for datasets, metrics, and programmatic API.
---
## 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
---
## 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.
---
## 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
---
## Installation
```bash
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+
---
## Documentation
| Guide | Description |
|-------|-------------|
| [Memory Guide](docs/memory.md) | Persistent memory for LLMs |
| [Compression Guide](docs/compression.md) | Universal compression with ML detection |
| [Evals Framework](headroom/evals/README.md) | Prove compression preserves accuracy |
| [LangChain Integration](docs/langchain.md) | Full LangChain support |
| [Agno Integration](docs/agno.md) | Full Agno agent framework support |
| [SDK Guide](docs/sdk.md) | Fine-grained control |
| [Proxy Guide](docs/proxy.md) | Production deployment |
| [Configuration](docs/configuration.md) | All options |
| [CCR Guide](docs/ccr.md) | Reversible compression |
| [Metrics](docs/metrics.md) | Monitoring |
| [Troubleshooting](docs/troubleshooting.md) | Common issues |
---
## Who's Using Headroom?
> Add your project here! [Open a PR](https://github.com/chopratejas/headroom/pulls) or [start a discussion](https://github.com/chopratejas/headroom/discussions).
---
## Contributing
```bash
git clone https://github.com/chopratejas/headroom.git
cd headroom
pip install -e ".[dev]"
pytest
```
See [CONTRIBUTING.md](CONTRIBUTING.md) for details.
---
## License
Apache License 2.0 - see [LICENSE](LICENSE).
---
Built for the AI developer community