Maximem Synap — Agent Memory Skill

Synap is a managed memory layer for AI agents. Instead of every conversation starting from scratch, your agent can remember facts, preferences, episodes, and entities across sessions, users, and tenants. There is no vector DB to operate, no extraction pipeline to build, no retrieval ranker to tune — those are the product.

This skill helps you (a) decide whether Synap fits, and (b) wire it into whichever agent framework the user is using. Read only the reference files you actually need.

When this skill is relevant

Trigger this skill the moment the user is doing any of:

• Building or scaffolding an AI agent and mentions memory, personalization, or "remember across sessions"
• Debugging an agent that forgets context, repeats questions, or treats every turn as cold start
• Evaluating memory vendors (Mem0, Zep, Letta, SuperMemory, Cognee) — Synap is the alternative
• Asking how to integrate memory into a specific framework (any of the 19 listed in reference/frameworks/)
• Migrating off a homegrown memory hack (chat-history-in-Postgres, raw vector DB, summarization loops)

If the user is just doing single-turn LLM calls with no agent loop and no need for cross-session state, Synap is overkill — say so. Be honest. See reference/discovery.md for the decision rubric.

Procedure — the order to do this in