headroom-ai 0.21.4

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

Headroom

Compress everything your AI agent reads. Same answers, fraction of the tokens.

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Every tool call, log line, DB read, RAG chunk, and file your agent injects into a prompt is mostly boilerplate. Headroom strips the noise and keeps the signal — losslessly, locally, and without touching accuracy.

100 logs. One FATAL error buried at position 67. Both runs found it. Baseline 10,144 tokens → Headroom 1,260 tokens — 87% fewer, identical answer. python examples/needle_in_haystack_test.py

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Quick start

Works with Anthropic, OpenAI, Google, Bedrock, Vertex, Azure, OpenRouter, and 100+ models via LiteLLM.

Wrap your coding agent — one command:

pip install "headroom-ai[all]"

headroom wrap claude      # Claude Code
headroom wrap codex       # Codex
headroom wrap cursor      # Cursor
headroom wrap aider       # Aider
headroom wrap copilot     # GitHub Copilot CLI

Drop it into your own code — Python or TypeScript:

from headroom import compress

result = compress(messages, model="claude-sonnet-4-5")
response = client.messages.create(model="claude-sonnet-4-5", messages=result.messages)
print(f"Saved {result.tokens_saved} tokens ({result.compression_ratio:.0%})")

import { compress } from 'headroom-ai';
const result = await compress(messages, { model: 'gpt-4o' });

Or run it as a proxy — zero code changes, any language:

headroom proxy --port 8787
ANTHROPIC_BASE_URL=http://localhost:8787 your-app
OPENAI_BASE_URL=http://localhost:8787/v1 your-app

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Why Headroom

• Accuracy-preserving. GSM8K 0.870 → 0.870 (±0.000). TruthfulQA +0.030. SQuAD v2 and BFCL both 97% accuracy after compression. Validated on public OSS benchmarks you can rerun yourself.
• Runs on your machine. No cloud API, no data egress. Compression latency is milliseconds — faster end-to-end for Sonnet / Opus / GPT-4 class models than a hosted service round-trip.
• Kompress-base on HuggingFace. Our open-source text compressor, fine-tuned on real agentic traces — tool outputs, logs, RAG chunks, code. Install with pip install "headroom-ai[ml]".
• Cross-agent memory and learning. Claude Code saves a fact, Codex reads it back. headroom learn mines failed sessions and writes corrections straight to CLAUDE.md / AGENTS.md / GEMINI.md — reliability compounds over time.
• Reversible (CCR). Compression is not deletion. The model can always call headroom_retrieve to pull the original bytes. Nothing is thrown away.

Bundles the RTK binary for shell-output rewriting — full attribution below.

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How it fits

 Your agent / app
   (Claude Code, Cursor, Codex, LangChain, Agno, Strands, your own code…)
        │   prompts · tool outputs · logs · RAG results · files
        ▼
    ┌────────────────────────────────────────────────────┐
    │  Headroom   (runs locally — your data stays here)  │
    │  ───────────────────────────────────────────────   │
    │  CacheAligner  →  ContentRouter  →  CCR             │
    │                    ├─ SmartCrusher   (JSON)         │
    │                    ├─ CodeCompressor (AST)          │
    │                    └─ Kompress-base  (text, HF)     │
    │                                                     │
    │  Cross-agent memory  ·  headroom learn  ·  MCP      │
    └────────────────────────────────────────────────────┘
        │   compressed prompt  +  retrieval tool
        ▼
 LLM provider  (Anthropic · OpenAI · Bedrock · …)

→ Architecture · CCR reversible compression · Kompress-base model card

Canonical pipeline lifecycle

Headroom now exposes one stable request lifecycle across compress(), the SDK, and the proxy:

Setup → Pre-Start → Post-Start → Input Received → Input Cached → Input Routed → Input Compressed → Input Remembered → Pre-Send → Post-Send → Response Received

• Transforms still do the work: CacheAligner, ContentRouter, SmartCrusher, CodeCompressor, Kompress-base, IntelligentContext / RollingWindow.
• Pipeline extensions observe or customize those lifecycle stages via on_pipeline_event(...).
• Compression hooks still work and now sit alongside the canonical lifecycle instead of being the only extension seam.
• Proxy extensions remain the server/app integration seam for ASGI middleware, routes, and startup policy.

Provider slices

Provider and tool-specific behavior is being moved behind dedicated modules under headroom/providers/ so core orchestration stays focused on lifecycle, sequencing, and policy.

• CLI/tool slices: headroom/providers/claude, copilot, codex, openclaw
• Provider runtime slices: headroom/providers/claude, gemini, plus shared backend/runtime dispatch in headroom/providers/registry.py
• Core files stay orchestration-first: wrap.py, client.py, cli/proxy.py, and proxy/server.py now delegate provider-specific env shaping, API target normalization, backend selection, and transport dispatch instead of inlining those rules.

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Proof

Savings on real agent workloads:

Workload	Before	After	Savings
Code search (100 results)	17,765	1,408	92%
SRE incident debugging	65,694	5,118	92%
GitHub issue triage	54,174	14,761	73%
Codebase exploration	78,502	41,254	47%

Accuracy preserved on standard benchmarks:

Benchmark	Category	N	Baseline	Headroom	Delta
GSM8K	Math	100	0.870	0.870	±0.000
TruthfulQA	Factual	100	0.530	0.560	+0.030
SQuAD v2	QA	100	—	97%	19% compression
BFCL	Tools	100	—	97%	32% compression

Reproduce:

python -m headroom.evals suite --tier 1

Community, live:

60B+ tokens saved by the community in the last 20 days — live leaderboard →

→ Full benchmarks & methodology

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Built for coding agents

Agent	One-command wrap	Notes
Claude Code	headroom wrap claude	--memory for cross-agent memory, --code-graph for codebase intel
Codex	headroom wrap codex --memory	Shares the same memory store as Claude
Cursor	headroom wrap cursor	Prints Cursor config — paste once, done
Aider	headroom wrap aider	Starts proxy, launches Aider
Copilot CLI	headroom wrap copilot	Starts proxy, launches Copilot
OpenClaw	headroom wrap openclaw	Installs Headroom as ContextEngine plugin

MCP-native too — headroom mcp install exposes headroom_compress, headroom_retrieve, and headroom_stats to any MCP client.

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Integrations

Drop Headroom into any stack

Your setup	Hook in with
Any Python app	compress(messages, model=…)
Any TypeScript app	await compress(messages, { model })
Anthropic / OpenAI SDK	withHeadroom(new Anthropic()) · withHeadroom(new OpenAI())
Vercel AI SDK	wrapLanguageModel({ model, middleware: headroomMiddleware() })
LiteLLM	litellm.callbacks = [HeadroomCallback()]
LangChain	HeadroomChatModel(your_llm)
Agno	HeadroomAgnoModel(your_model)
Strands	Strands guide
ASGI apps	app.add_middleware(CompressionMiddleware)
Multi-agent	SharedContext().put / .get
MCP clients	headroom mcp install

What's inside

• SmartCrusher — universal JSON: arrays of dicts, nested objects, mixed types.
• CodeCompressor — AST-aware for Python, JS, Go, Rust, Java, C++.
• Kompress-base — our HuggingFace model, trained on agentic traces.
• Image compression — 40–90% reduction via trained ML router.
• CacheAligner — stabilizes prefixes so Anthropic/OpenAI KV caches actually hit.
• IntelligentContext — score-based context fitting with learned importance.
• CCR — reversible compression; LLM retrieves originals on demand.
• Cross-agent memory — shared store, agent provenance, auto-dedup.
• SharedContext — compressed context passing across multi-agent workflows.
• headroom learn — plugin-based failure mining for Claude, Codex, Gemini.

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Install

pip install "headroom-ai[all]"          # Python, everything
npm  install headroom-ai                # TypeScript / Node
docker pull ghcr.io/chopratejas/headroom:latest

Granular extras: [proxy], [mcp], [ml] (Kompress-base), [agno], [langchain], [evals]. Requires Python 3.10+.

→ Installation guide — Docker tags, persistent service, PowerShell, devcontainers.

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Documentation

Start here	Go deeper
Quickstart	Architecture
Proxy	How compression works
MCP tools	CCR — reversible compression
Memory	Cache optimization
Failure learning	Benchmarks
Configuration	Limitations

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Compared to

Headroom runs locally, covers every content type (not just CLI or text), works with every major framework, and is reversible.

	Scope	Deploy	Local	Reversible
Headroom	All context — tools, RAG, logs, files, history	Proxy · library · middleware · MCP	Yes	Yes
RTK	CLI command outputs	CLI wrapper	Yes	No
Compresr, Token Co.	Text sent to their API	Hosted API call	No	No
OpenAI Compaction	Conversation history	Provider-native	No	No

Attribution. Headroom ships with the excellent RTK binary for shell-output rewriting — git show → git show --short, noisy ls → scoped, chatty installers → summarized. Huge thanks to the RTK team; their tool is a first-class part of our stack, and Headroom compresses everything downstream of it.

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Contributing

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

Devcontainers in .devcontainer/ (default + memory-stack with Qdrant & Neo4j). See CONTRIBUTING.md.

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Community

• Live leaderboard — 60B+ tokens saved and counting.
• Discord — questions, feedback, war stories.
• Kompress-base on HuggingFace — the model behind our text compression.

License

Apache 2.0 — see LICENSE.