headroom vs locus
headroom: Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM. It achieves the same answers with a fraction of the tokens.; locus: Locus provides persistent project-aware memory for AI coding tools, using three memory layers: structural, semantic, and episodic. It works with any MCP-compatible client and offers 14 tools and 3 resources for exploring, searching, and managing memory. Carbon Copy enables passive conversation capture for later recall.
TL;DR
- Long tool output, logs, JSON responses or RAG chunks are driving up cost or hurting answers.
- You want compression without code changes: run it as a local proxy, an MCP server, or by wrapping Claude Code, Codex or Cursor.
- Compression must run locally, with originals cached so the model can retrieve the full text.
- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.
- You cannot add a component that must keep running and may add latency.
- Your tasks cannot tolerate lossy compression: verify answer quality on your own work first.
Maintain project context across AI coding sessions to avoid losing architectural decisions and file structure awareness
Side-by-Side Comparison
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headroomlocusBest For
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