Signet: Open-Source Memory Layer for AI Coding Agents Hits 80% F1 on LoCoMo

Signet is an open-source memory system designed for AI coding agents like Claude Code, OpenCode, OpenClaw, and Codex. It addresses memory management by separating memory extraction from the agent's conversational flow.

Performance and Approach

The system recently achieved 80% F1 on the LoCoMo benchmark, which evaluates long-term conversational memory. For comparison, standard RAG scores around 41% on this benchmark, GPT-4 with full context scores 32%, and the human ceiling is 87.9%.

Unlike approaches that give agents a "remember" tool, Signet flips the architecture:

• Memories are extracted after each session by a separate LLM pipeline—no tool calls during conversation
• Relevant context is injected before each prompt—the agent doesn't search for what it needs, it just has it

The approach is analogous to human memory where information surfaces automatically rather than requiring explicit queries.

Technical Implementation

Everything runs locally using SQLite on your machine with no cloud dependencies, working offline. The same agent memory persists across different coding tools. Setup requires one install command and runs in a few minutes. The project is Apache 2.0 licensed.

Future Development

The team is working on a per-user predictive memory model that learns your patterns and anticipates what context you'll need before you ask. This model will be trained locally with weights staying on your machine.