{"repo":"headroomlabs-ai/headroom","free":true,"listed":false,"github":"https://github.com/headroomlabs-ai/headroom","clone":"git clone https://github.com/headroomlabs-ai/headroom.git","description":"Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.","language":"Python","stars":66522,"topics":["agent","ai","anthropic","claude-code","compression","context-engineering","context-window","cursor","fastapi","langchain","llm","mcp","openai","prompt-engineering","proxy","python","rag","token-optimization","tokens","typescript"],"license":"Apache-2.0","category":"dev_tool","readme_excerpt":"
\n  ██╗  ██╗███████╗ █████╗ ██████╗ ██████╗  ██████╗  ██████╗ ███╗   ███╗\n  ██║  ██║██╔════╝██╔══██╗██╔══██╗██╔══██╗██╔═══██╗██╔═══██╗████╗ ████║\n  ███████║█████╗  ███████║██║  ██║██████╔╝██║   ██║██║   ██║██╔████╔██║\n  ██╔══██║██╔══╝  ██╔══██║██║  ██║██╔══██╗██║   ██║██║   ██║██║╚██╔╝██║\n  ██║  ██║███████╗██║  ██║██████╔╝██║  ██║╚██████╔╝╚██████╔╝██║ ╚═╝ ██║\n  ╚═╝  ╚═╝╚══════╝╚═╝  ╚═╝╚═════╝ ╚═╝  ╚═╝ ╚═════╝  ╚═════╝ ╚═╝     ╚═╝\n              The context compression layer for AI agents\n
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60–95% fewer tokens (for JSON data), 15-20% fewer tokens (for coding agents) · library · proxy · MCP · content-aware compressors · local-first · reversible

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\n \"CI\"\n \"codecov\"\n \"PyPI\"\n \"npm\"\n \"Model:\n \"License:\n \"Docs\"\n

\n\n\n\n

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

\n\n

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

\n\n---\n

\"chopratejas%2Fheadroom

\n\nHeadroom 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.\n\n

\n \"Headroom\n
Live: 10,144 → 1,260 tokens — same FATAL found.\n

\n\n## What it does\n\n- **Library** — `compress(messages)` in Python or TypeScript, inline in any app\n- **Proxy** — `headroom proxy --port 8787`, zero code changes, any language\n- **Agent wrap** — `headroom wrap claude|codex|grok|copilot|cursor|aider|opencode|cline|continue|goose|openhands|openclaw|vibe|omp|zcode` in one command; undo with `headroom unwrap `\n- **MCP server** — `headroom_compress`, `headroom_retrieve`, `headroom_stats` for any MCP client\n- **Cross-agent memory** — shared store across Claude, Codex, Gemini, Grok, auto-dedup\n- **`headroom learn`** — mines failed sessions, writes corrections to `CLAUDE.local.md` (default, gitignored) or `CLAUDE.md` / `AGENTS.md` / `GEMINI.md` / `GROK.md`\n- **Output token reduction** — trims what the model *writes back* (not just what you send): drops ceremony/restated code and skips deep \"thinking\" on routine steps. See [Output token reduction](#output-token-reduction-cut-what-the-model-writes-back).\n- **Reversible (CCR)** — originals are cached for retrieval on demand\n\n## How it works (30 seconds)\n\n```\n Your agent / app\n (Claude Code, Cursor, Codex, LangChain, Agno, Strands, your own code…)\n │ prompts · tool outputs · logs · RAG results · files\n ▼\n ┌────────────────────────────────────────────────────┐\n │ Headroom (runs locally — your data stays here) │\n │ ──────────────────────────────────────────────── │\n │ CacheAligner → ContentRouter → CCR │\n │ ├─ SmartCrusher (JSON) │\n │ ├─ CodeCompressor (AST) │\n │ └─ Kompress-v2-base (text, HF) │\n │ │\n │ Cross-agent memory · headroom learn · MCP │\n └────────────────────────────────────────────────────┘\n │ compressed prompt + retrieval tool\n ▼\n LLM provider (Anthropic · OpenAI · Bedrock · …)\n```\n\n- **ContentRouter** — detects content type, selects the right compressor\n- **SmartCrusher / CodeCompressor / Kompress-v2-base** — compress JSON, AST, or prose\n- **CacheAligner** - detects and warns about volatile content that can bust provider KV cache prefixes; never rewrites prompts\n- **CCR** — stores originals locally; LLM calls `headroom_retrieve` if it needs them\n\n→ [Architecture](https://headroom-docs.vercel.app/docs/architecture) · [CCR reversible compression](https://headroom-docs.vercel.app/docs/ccr) · [Kompress-v2-base model card](https://huggingface.co/chopratejas/kompress-v2-base)\n\n## Get started (60 seconds)\n\n```bash\n# 1 — Install\nuv tool install --python 3.13 \"headroom-ai[all]\" # CLI as a global tool in a self-contained virtual env\npip install \"headroom-ai[all]\" # Python — ships the `headroom` CLI\nnpm install headroom-ai # TypeScript SDK only — no `headroom` CLI\n\n# 2 — Pick your mode (the `headroom` commands below come from the uv or pip install)\nheadroom deploy # turnkey local deployment + agent config\nheadroom wrap claude # wrap a coding agent\nheadroom proxy --port 8787 # drop-in proxy, zero code changes\n# or: from headroom import compress # inline library\n\n# 3 — Verify setup and see the savings\nheadroom doctor # health check — confirms routing is working\nheadroom perf\nheadroom dashboard # live savings dashboard (proxy must be running)\n```\n\nTo use headroom, it is recommended you launch a wrapped agent session each time so that all necessary setup is completed. When wrapping a coding agent, headroom starts a local proxy, installs **Serena** for semantic code navigation, and launches a coding agent session configured to proxy requests through headroom.\n\nSerena is registered at **user scope** (for Claude Code, in `~/.claude.json`), so it stays available in your other projects until you run `headroom unwrap`. To skip it entirely, wrap with `--code-memory none`.\n\nThe `headroom` CLI ships **only** via the PyPI package. The npm `headroom-ai` is the TypeScript SDK — a library you import (`import { compress } from 'headroom-ai'`), not a CLI, so it provides no `headroom` command.\n\nGranular extras: `[proxy]`, `[mcp]`, `[ml]`, `[code]`, `[memory]`, `[vector]` (optional HNSW backend — needs a C++ toolchain, not in `[all]`), `[relevance]`, `[image]`, `[agno]`, `[langchain]`, `[evals]`, `[pytorch-mps]` (Apple-GPU memory-embedder offload — set `HEADROOM_EMBEDDER_RUNTIME=pytorch_mps`). Requires **Python 3.10+**.\n\n### Codex / global install\n\nIf Codex or another MCP client cannot inherit a shell `PATH` reliably, install Headroom as a persistent uv tool and point the client at the absolute binary path:\n\n```bash\nuv tool install \"headroom-ai[all]\"\ncommand -v headroom\n```\n\nThen use the returned path in MCP config:\n\n```toml\n[mcp_servers.headroom]\ncommand = \"/absolute/path/from/command-v/headroom\"\nargs = [\"mcp\", \"serve\"]\n```\n\n`command = \"headroom\"` only works when the client starts with a `PATH` that already includes the uv tool directory.\n\n## Proof\n\n**Savings on real agent workloads:**\n\n| Workload | Before | After | Savings |\n|-------------------------------|-------:|-------:|--------:|\n| Code search (100 results) | 17,765 | 1,408 | **92%** |\n| SRE incident debugging | 65,694 | 5,118 | **92%** |\n| GitHub issue triage | 54,174 | 14,761 | **73%** |\n| Codebase exploration | 78,502 | 41,254 | **47%** |\n\n**Accuracy preserved on standard benchmarks:**\n\n| Benchmark | Category | N | Baseline | Headroom | Delta |\n|------------|----------|----:|---------:|---------:|------------|\n| GSM8K | Math | 100 | 0.870 | 0.870 | **±0.000** |\n| TruthfulQA | Factual | 100 | 0.530 | 0.560 | **+0.030** |\n| SQuAD v2 | QA | 100 | — | **97%** | 19% compression |\n| BFCL | Tools | 100 | — | **97%** | 32% compression |\n\nReproduce: `python -m headroom.evals suite --tier 1` · [Full benchmarks & methodology](https://headroom-docs.vercel.app/docs/benchmarks)\n\n## Output token reduction (cut what the model writes back)\n\nEverything above shrinks the prompt you **send**. But you also pay for every\ntoken the model **writes back** — and on Opus-class models output costs 5× input.\nA lot of that output is waste: \"Great, let me…\" preambles, re-printing code you\njust showed it, and deep \"thinking\" on routine steps like reading a file.\n\nHeadroom can trim that too, from the proxy, without you changing any code:\n\n- **Verbosity steering** — appends a short \"be terse, don't restate context\"\n note to the end of the system prompt (so your prompt cache still hits).\n- **Effort routing** — when a turn is just the model resuming after a tool result\n (a file read, a passing test), it dials the model's thinking effort down. New\n questions and errors keep full effort.\n\nApplies to Anthropic `/v1/messages` **and** OpenAI-compatible 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