Productivity

agent-memory-mcp

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill agent-memory-mcp
summary

Persistent, searchable memory system for agents to store and retrieve architectural decisions, patterns, and project knowledge.

  • Provides four core MCP tools: memory_search for querying by text/type/tags, memory_write to record decisions and patterns, memory_read to retrieve specific entries, and memory_stats for usage analytics
  • Runs as an MCP server that syncs with project documentation, enabling long-term knowledge retention across agent sessions
  • Includes a standalone dashboard (por
skill.md

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.

general reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    agent-memory-mcp is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Piyush G· Sep 9, 2024

    Keeps context tight: agent-memory-mcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Aug 8, 2024

    Registry listing for agent-memory-mcp matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Sakshi Patil· Jul 7, 2024

    agent-memory-mcp reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ganesh Mohane· Jun 6, 2024

    I recommend agent-memory-mcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· May 5, 2024

    Useful defaults in agent-memory-mcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Apr 4, 2024

    agent-memory-mcp has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Rahul Santra· Mar 3, 2024

    Solid pick for teams standardizing on skills: agent-memory-mcp is focused, and the summary matches what you get after install.

  • Pratham Ware· Feb 2, 2024

    We added agent-memory-mcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Jan 1, 2024

    agent-memory-mcp fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.