AgentWorkingMemory: A Local Memory System for AI Coding Agents

What AgentWorkingMemory Solves

AI coding agents like Claude Code lack persistent memory between sessions. Developers end up re-explaining architecture, database schemas, and previous decisions every time they start a new session, wasting time and context window space. While Claude Code offers some tools like --continue to resume conversations, auto-memory that saves notes to markdown files, and CLAUDE.md project documentation, these have limitations:

• --continue or --resume restores entire chat threads but consumes context window space and only works with one thread at a time
• Auto-memory loads the first 200 lines of MEMORY.md but lacks retrieval intelligence—it doesn't know which notes are relevant to current work
• Project docs like CLAUDE.md work for stable information but go stale quickly in fast-evolving projects

AgentWorkingMemory addresses these issues by accumulating knowledge across sessions, surfacing relevant context for current work, and improving over time without manual management.

How It Works

AWM runs entirely locally on your machine with no cloud dependencies. The system consists of:

• A SQLite database for storage
• Three local ML models (~124MB total, downloaded once and cached)
• A Node.js process

There's no server to run, no Docker container, and no background daemon. When you start Claude Code, it automatically spins up AWM through MCP (Model Context Protocol). When you close the session, it stops. Everything stays local—no cloud, no API keys, no data leaving your machine. For extra security, AWM supports bearer token authentication to lock down access to the memory API.

Setup and Usage

Installation requires two commands:

npm install -g agent-working-memory
awm setup --global

After restarting Claude Code, 14 memory tools appear automatically. The first session takes about 30 seconds while the ML models download (~124MB, cached after that). From that point on:

• The agent writes memories when it learns something important
• It recalls relevant memories when starting new work
• It checkpoints its state to recover after interruptions

The system was developed while rebuilding a 20-year-old codebase (~1.4 million lines) into a modern stack (~250K lines estimated), specifically for a membership management platform with 88 database tables and multi-sprint development using multiple AI agents in parallel.