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headroom

chopratejas/headroom 

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                  The context compression layer for AI agents

60–95% fewer tokens · library · proxy · MCP · 6 algorithms · local-first · reversible

CI codecov PyPI npm Model: Kompress-base License: Apache 2.0 Docs

Docs · Install · Proof · Agents · Discord · llms.txt

AI agents / LLMs: read /llms.txt here, or fetch the live index / full docs blob.

chopratejas%2Fheadroom | Trendshift

Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. Same answers, fraction of the tokens.

Headroom in action
Live: 10,144 → 1,260 tokens — same FATAL found.

What it does

  • Library — compress(messages) in Python or TypeScript, inline in any app
  • Proxy — headroom proxy --port 8787, zero code changes, any language
  • Agent wrap — headroom wrap claude|codex|cursor|aider|copilot in one command
  • MCP server — headroom_compress, headroom_retrieve, headroom_stats for any MCP client
  • Cross-agent memory — shared store across Claude, Codex, Gemini, auto-dedup
  • headroom learn — mines failed sessions, writes corrections to CLAUDE.md / AGENTS.md
  • Reversible (CCR) — originals never deleted; LLM retrieves on demand

How it works (30 seconds) 

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 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 · …)
  • ContentRouter — detects content type, selects the right compressor
  • SmartCrusher / CodeCompressor / Kompress-base — compress JSON, AST, or prose
  • CacheAligner — stabilizes prefixes so provider KV caches actually hit
  • CCR — stores originals locally; LLM calls headroom_retrieve if it needs them

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

Get started (60 seconds) 

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# 1 — Install
pip install "headroom-ai[all]"          # Python
npm install headroom-ai                 # Node / TypeScript

# 2 — Pick your mode
headroom wrap claude                    # wrap a coding agent
headroom proxy --port 8787              # drop-in proxy, zero code changes
# or: from headroom import compress      # inline library

# 3 — See the savings
headroom stats

Granular extras: [proxy], [mcp], [ml], [agno], [langchain], [evals]. Requires Python 3.10+.

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 · Full benchmarks & methodology

Agent compatibility matrix

Agent headroom wrap Notes
Claude Code ● --memory · --code-graph
Codex ● shares memory with Claude
Cursor ● prints config — paste once
Aider ● starts proxy + launches
Copilot CLI ● starts proxy + launches
OpenClaw ● installs as ContextEngine plugin

Any OpenAI-compatible client works via headroom proxy. MCP-native: headroom mcp install.

When to use · When to skip

Great fit if you…

  • run AI coding agents daily and want savings without changing your code
  • work across multiple agents and want shared memory
  • need reversible compression — originals always retrievable via CCR

Skip it if you…

  • only use a single provider’s native compaction and don’t need cross-agent memory
  • work in a sandboxed environment where local processes can’t run
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.
Pipeline internals

Headroom 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 do the work: CacheAligner, ContentRouter, SmartCrusher, CodeCompressor, Kompress-base, IntelligentContext / RollingWindow.
  • Pipeline extensions observe or customize lifecycle stages via on_pipeline_event(...).
  • Compression hooks sit alongside the canonical lifecycle as an additional extension seam.
  • Proxy extensions remain the server/app integration seam for ASGI middleware, routes, and startup policy.

Provider and tool-specific behavior lives 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 delegate provider-specific env shaping, API target normalization, backend selection, and transport dispatch.

Install 

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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+.

Using pipx? Choose a supported interpreter explicitly:

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pipx install --python python3.13 "headroom-ai[all]"

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

headroom learn

headroom learn in action

headroom learn — mines failed sessions, writes corrections to CLAUDE.md / AGENTS.md / GEMINI.md.

Documentation

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

Compared to

Headroom runs locally, covers every content type, 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
lean-ctx CLI commands, MCP tools, editor rules CLI wrapper · MCP 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 --short, scoped ls, summarized installers. Huge thanks to the RTK team; their tool is a first-class part of our stack, and Headroom compresses everything downstream of it. Headroom can also use lean-ctx as the selected CLI context tool; set HEADROOM_CONTEXT_TOOL=lean-ctx before running headroom wrap ....

Contributing 

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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.

Community

License

Apache 2.0 — see LICENSE.

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