Local Memory System for AI Coding Tools Extracts 2,600+ Facts from Conversation Logs

A developer has built a local memory system for AI coding tools that runs entirely on their machine, addressing the problem of re-explaining context in new sessions. The system works with Claude Code, Factory.ai, and Codex CLI, writing all conversation logs to a single SQLite database.

How It Works

The system uses several automated processes:

• A cron job runs every 15 minutes to ingest conversation logs into SQLite
• Hourly, it generates vector embeddings and extracts structured facts using a local LLM (the developer runs Nemotron 3 Super on a DGX Spark via ollama)
• Every new Claude Code session starts with a memory-context.md file auto-injected via CLAUDE.md
• Mid-session, Claude can search the full history via MCP tools including keyword search, semantic search, fact lookup, and entity graph exploration

Usage Statistics

After a few months of normal use:

• 13,000+ messages indexed across 400+ sessions
• 2,600+ facts extracted (preferences, decisions, error/solution pairs, tool patterns)
• 330+ entities tracked (libraries, services, languages with mention counts)
• 40 MB database size

The entity graph tracks usage patterns like "you've used pytest 45 times, playwright 20 times, jest 3 times" based on actual usage data.

Features and Limitations

The system includes a browser-based UI for searching, curating facts, and previewing what gets injected into context, plus a CLI tool and slash commands. It's not plug-and-play—users need to set up cron jobs, configure MCP, and optionally run ollama. The developer notes this is their first open source project and welcomes feedback on architecture, fact extraction approach, MCP tool design, and Python/project structure improvements.