Full RAG stack — ingestion, chunking, embedding, vector search — scoped to your project and organized with tags. Your agents ask, AgentCognos answers. Retrieval as a solved problem.
Get Started Free →AgentCognos handles the entire knowledge pipeline — from raw files to ranked, tagged, project-scoped retrieval — behind a single MCP interface your agents can call like any other tool.
Every document, chunk, and embedding is namespaced to a project. Agents working on agentquanta-backend only search that project's knowledge base — no cross-contamination, no irrelevant results from other repos or contexts.
Every chunk carries the tags assigned at ingest time. Search can filter to a specific tag or combination — ["auth", "backend"] narrows results to exactly the right slice of your knowledge base. Tags are first-class, not metadata afterthoughts.
Point AgentCognos at a file, directory, or URL. It handles reading, chunking (with configurable overlap and chunk size), embedding via Ollama (nomic-embed-text, 768-dim), and storage to pgvector. The entire pipeline runs locally — your data never leaves your machine.
Results are ranked by combining pgvector cosine similarity with PostgreSQL full-text search using Reciprocal Rank Fusion. Semantic understanding catches concept matches even when keywords don't align. Exact keyword matching catches what embeddings sometimes miss.
Every operation — ingest, search, list projects, list tags, delete, re-embed — is available as an MCP tool. Your agents call recall_search the same way they call any other tool. No SDK, no HTTP client, no credentials to manage in the agent prompt.
Files change. Re-ingesting a source automatically supersedes stale chunks — old embeddings are marked, not deleted, so you can audit what changed. Version-aware chunking tracks modification timestamps, making it safe to re-run ingest on an evolving codebase at any time.
AgentCognos runs entirely on your machine. PostgreSQL + pgvector for storage, Ollama for embeddings. If Ollama is unavailable, search falls back to full-text automatically.
AgentCognos pairs naturally with AgentSynapse — Synapse for episodic agent memory, Recall for structured knowledge retrieval. Use both and your agents always have the right context.