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agent-memory-mcp

agent-memory-mcp

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

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill 문서

GitHub

name: agent-memory-mcp description: "A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions)." risk: critical source: community date_added: "2026-02-27"

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. Review the Repository: Ask the user to approve network access to the named repository, then clone the pinned revision into a temporary directory, not an active skills path:

    review_dir="$(mktemp -d)"
    git clone --filter=blob:none https://github.com/webzler/agentMemory.git "$review_dir/agent-memory"
    git -C "$review_dir/agent-memory" checkout --detach 0409b7b7bb6fe443d0d4b6a6b1ee0d4df214f3cd
    git -C "$review_dir/agent-memory" ls-files
    

    Read all bundled files and inspect package.json, lockfiles, lifecycle scripts, network behavior, credential access, and filesystem scope. Show the findings and exact commit, then wait for explicit user approval.

  2. Install the Reviewed Revision:

    Copy the reviewed tree to a user-selected location after approval. Install locked dependencies only after the package scripts have been reviewed:

    cd <approved-agent-memory-directory>
    npm ci
    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.
  • Re-review upstream before changing the pinned revision; a commit pin improves reproducibility but is not a trust guarantee.
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