Signet: An Open-Source Local-First Memory Substrate for AI Agents

What Signet Solves

Most current AI agent memory systems operate like RAG (Retrieval-Augmented Generation): user message → search memory → retrieve results → answer. This works for explicit queries but breaks when relevant context is implicit.

Examples from the source:

• "Set up the database for the new service" should surface that PostgreSQL was already chosen
• "My transcript was denied, no record under my name" should surface that the user changed their name
• "What time should I set my alarm for my 8:30 meeting?" should surface commute time

The problem isn't storage - it's that these systems wait for the current message to contain enough query signal to retrieve the right past context.

How Signet Works

Signet handles memory outside the agent loop with this architecture:

• Preserves raw transcripts
• Distills sessions into structured memory
• Links entities, constraints, and relations into a graph
• Uses graph traversal + hybrid retrieval to build a candidate set
• Reranks candidates for prompt-time relevance
• Injects context before the next prompt starts

The agent isn't deciding what to save or when to search - it starts with context already available. This moves from query-dependent retrieval toward ambient recall.

Technical Details

Signet is:

• Local-first (SQLite + markdown)
• Inspectable and repairable
• Works across Claude Code, Codex, OpenCode, and OpenClaw

On LoCoMo, it currently achieves 87.5% answer accuracy with 100% Hit@10 retrieval on an 8-question sample. The developer notes this is a small sample but shows the approach is promising.