★ 3781 Python Apache-2.0 sse 更新 43分钟前

Headroom

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.

GitHub 官网

安装配置

暂未识别到可直接复制的 MCP 配置，请查看 GitHub README。后台管理员可以补充配置。

README 摘要

``` ██╗ ██╗███████╗ █████╗ ██████╗ ██████╗ ██████╗ ██████╗ ███╗ ███╗ ██║ ██║██╔════╝██╔══██╗██╔══██╗██╔══██╗██╔═══██╗██╔═══██╗████╗ ████║ ███████║█████╗ ███████║██║ ██║██████╔╝██║ ██║██║ ██║██╔████╔██║ ██╔══██║██╔══╝ ██╔══██║██║ ██║██╔══██╗██║ ██║██║ ██║██║╚██╔╝██║ ██║ ██║███████╗██║ ██║██████╔╝██║ ██║╚██████╔╝╚██████╔╝██║ ╚═╝ ██║ ╚═╝ ╚═╝╚══════╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚═╝ ╚═╝ The context compression layer for AI agents ``` 60–95% fewer tokens · library · proxy · MCP · 6 algorithms · local-first · reversible Docs · Install · Proof · Agents · Discord · llms.txt AI agents / LLMs: read /llms.txt here, or fetch the live index / full docs blob. --- > 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. 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) ``` 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](https://headroom-docs.vercel.app/docs/architecture) · [CCR reversible compression](https://headroom-docs.vercel.app/docs/ccr) · [Kompress-base model card](https://huggingface.co/chopratejas/kompress-base) ## Get started (60 seconds) ```bash # 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 ``` Gran...

Headroom

安装配置

README 摘要

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