The open retrieval layer for AI coding agents. Indexes code, docs, legal, research, data — 22 parsers (incl. EPUB, DOCX, ODT), FTS5 + semantic search, knowledge graph. Serves surgical context via MCP. Open source, local, free.
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Retrieve The Forgotten Memory
Index everything in your project — code, docs, PDFs, legal texts, research, data — and your agent finds the right context instantly. No hallucinations. No cloud. No API costs.
Free · Local · Open Source · MIT
Your AI agent is flying blind.
It greps through thousands of files, misses the doc that answers the question, invents modules that don't exist, forgets what you decided last session. The bigger the project, the worse it gets. You've added a smarter model. It didn't help. Because the bottleneck isn't intelligence — it's retrieval.
Code indexers (Augment, Sourcegraph, Cursor) only see code. But your project isn't just code. It's specs, PRs, architecture decisions, research papers, PDFs, regulations, vault notes — the context your agent needs to stop guessing.
I was writing a French tax article (~50 pages of regulatory text, cross-references between code articles, case law, administrative doctrine). Claude Code kept grep-ing the same directories in loops, running out of