agent-memory-mcp

name	agent-memory-mcp
description	A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Agent Memory Skill

This skill provides a persistent, searchable memory bank that automatically syncs with project documentation. It runs as an MCP server to allow reading/writing/searching of long-term memories.

Prerequisites

• Node.js (v18+)

Setup

1. Clone the Repository: Clone the agentMemory project into your agent's workspace or a parallel directory:

   git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
   

2. Install Dependencies:

   cd .agent/skills/agent-memory
   npm install
   npm run compile
   

3. Start the MCP Server: Use the helper script to activate the memory bank for your current project:

   npm run start-server <project_id> <absolute_path_to_target_workspace>
   

   Example for current directory:

   npm run start-server my-project $(pwd)
   

Capabilities (MCP Tools)

memory_search

Search for memories by query, type, or tags.

• Args: query (string), type? (string), tags? (string[])
• Usage: "Find all authentication patterns" -> memory_search({ query: "authentication", type: "pattern" })

memory_write

Record new knowledge or decisions.

• Args: key (string), type (string), content (string), tags? (string[])
• Usage: "Save this architecture decision" -> memory_write({ key: "auth-v1", type: "decision", content: "..." })

memory_read

Retrieve specific memory content by key.

• Args: key (string)
• Usage: "Get the auth design" -> memory_read({ key: "auth-v1" })

memory_stats

View analytics on memory usage.

• Usage: "Show memory statistics" -> memory_stats({})

Dashboard

This skill includes a standalone dashboard to visualize memory usage.

npm run start-dashboard <absolute_path_to_target_workspace>

Access at: http://localhost:3333

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.