CogniRepo
mcp-name: io.github.ashlesh-t/cognirepo
Persistent memory and context for any AI tool. Not a chatbot — infrastructure.
lookup_symbol returns file:line very quickly — grep takes 2–8 seconds. On Python repos ≥ 15K LOC, CogniRepo cuts AI coding agent token usage by 50–80% compared to raw file reads — benchmarked on Flask, FastAPI, Celery, and Ansible (1,800+ files). Works with Claude Code, Cursor, and Gemini CLI. Fully offline. No API keys required for indexing or any of the 34 MCP tools.
What it does
Every AI conversation starts from zero. Claude, Cursor, Gemini — none of them remember what you fixed yesterday, which files relate to which features, or what decisions were made last sprint. CogniRepo fixes that.
It sits between your codebase and any AI tool, providing:
- Semantic memory — FAISS vector store with sentence-transformer embeddings. Store decisions, docs, architecture notes. Retrieve them with natural language.
- Episodic log — append-only event journal. Know what happened before that error.
- Knowledge graph — NetworkX DiGraph linking functions, classes, files, imports, inheritance chains, call relationships, and concepts. All queryable.
- AST reverse index — O(1) symbol lookup across your entire codebase in any supported language.
- User behavior profiling — tracks how you prompt so Claude adapts its response style without you having to re-explain preferences every session.
- Error tracking — records errors with prevention hints so Claude avoids repeating the same mistake across sessions.
- Session history — persists conversation exchanges so any session can resume where the last one ended.
- Architectural summaries — auto-generated on first init; built entirely from the local AST index (no API key needed). File → directory → repo summary tree, embedded into FAISS for semantic search.
- Multi-model orchestration — classify query complexity → build context → route to the right model. Claude for deep reasoning, Gemini Flash for quick lookups. All automatic.
