headroom vs hippocampus
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.; hippocampus: Hippocampus is an open-source, self-hosted MCP memory server that provides persistent, cross-platform memory for AI assistants. Deploy once and connect to multiple AI platforms such as Claude, ChatGPT, Gemini, Cursor, and Perplexity, enabling them to share and recall context seamlessly. It uses a knowledge graph with local embeddings and encrypts the database at rest.
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- Your agent's problem is missing or outdated docs, not too much context: a docs server fits better.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.
Store and recall personal preferences across different AI assistants
Side-by-Side Comparison
headroomhippocampusFeatures
headroomhippocampusUse Cases
headroomhippocampusBest For
headroomhippocampusFAQ
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