agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
- License 许可
- MIT
- 最近更新
- 2026-03-28
- Stars
- ★ 0
- Forks
- 2
01 前言 Front Matter
- name
- agent-memory-mcp
- author
- Amit Rathiesh
- description
- A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
03 SKILL.md 正文
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
-
Clone the Repository: Clone the
agentMemoryproject into your agent's workspace or a parallel directory:git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory -
Install Dependencies:
cd .agent/skills/agent-memory npm install npm run compile -
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
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