domains / families / agent-tooling / agent-memory

agent-memory

combined

A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

When to use

• **Starting a session**: Load relevant context from memory
• **After conversations**: Store important facts
• **After failures**: Record lessons learned
• **Meeting new people/projects**: Track as entities
• Use this skill when you need a hybrid memory system that provides persistent, searchable knowledge management for AI agents. This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs wi…

When not to use

• Use this skill only when the task clearly matches its upstream source and local project context.
• Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
• Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

Skill page

Conflict

The two copies describe different agent-memory systems with different installation, APIs, and workflows. The first is an MCP-based Node.js server (agentMemory) with memory_search/memory_write/memory_read/memory_stats tools, a review-and-approve setup, and markdown sync. The second is a Python AgentMemory class installed via clawdhub with remember/learn/recall/get_lessons/track_entity methods and a SQLite database. Neither is a shortened or wrapped version of the other, so crowning one would hide a real alternative.