agent-memory

A persistent memory layer for Claude Code (and any MCP-compatible agent) that captures every user prompt and tool_call from your coding sessions, exposes them for recall via an MCP server + FastAPI, and feeds them back into a local fine-tune pipeline that produces project-specific tool-calling LoRAs. Built locally, runs locally — Postgres + pgvector, FastAPI on 127.0.0.1:3377, GGUFs in LM Studio. The hooks are the recorder; the mem_* tables are the journal; the fine-tune pipeline is what turns that journal into a model that actually knows your codebases.

System overview

The write path (hooks → FastAPI → queue → Postgres) is fire-and-forget and never blocks Claude. The read path (MCP tools) uses Reciprocal Rank Fusion across pgvector and Postgres FTS. The training path reads directly from mem_tool_calls joined to mem_user_prompts and emits Qwen-format chat datasets.

Database schema (overview)

All tables are mem_*-prefixed to avoid collisions in a shared Postgres.

Full schema lives in scripts/migrations/*.sql. The current head is 013-project-consolidation.sql. See docs/PRIMER.md for column-level details and docs/fine_tune/V2_DATA_PIPELINE_PLAN.md for the prompt↔tool_call linkage design introduced by migration 012.

Hooks — how data gets in

Five Node.js hooks live in hooks/. They are designed fire-and-forget (~30-40ms p99) and exit 0 on every error path so a misconfigured or down agent-memory never blocks Claude.