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Agent Memory

Free

Persistent memory management for AI agents.

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Free · Opens the source repo

What Agent Memory does

The Agent Memory skill provides a robust memory management system designed for AI agents, enabling them to maintain a persistent and searchable knowledge base. This skill operates as an MCP server, allowing for efficient reading, writing, and searching of long-term memories related to architecture, patterns, and decisions. By automatically syncing with project documentation, it helps ensure that critical information is readily accessible, enhancing the efficiency of AI-driven workflows.

Users can leverage three primary capabilities: memory_search, memory_write, and memory_read. The memory_search function allows users to query memories based on specific criteria, such as type or tags, making it easy to retrieve relevant information. The memory_write function enables users to document new knowledge or decisions, ensuring that insights are captured for future reference. Meanwhile, memory_read provides a straightforward way to retrieve specific memory content by key, facilitating quick access to previously recorded information.

In addition to these core functionalities, the skill includes a standalone dashboard for visualizing memory usage, which can be accessed via a local server. This dashboard provides analytics on memory utilization, helping users monitor and optimize their knowledge management practices. Overall, the Agent Memory skill is particularly suited for developers and designers who require a structured approach to managing complex information within their AI projects.

When to use it

Use this skill when you need to maintain a searchable memory bank for your AI agent, especially in complex projects where documentation is crucial.

When not to use it

Avoid using this skill for tasks outside the scope of memory management, such as real-time data processing or environment-specific validations.

What you can build with it

Documenting Architecture Decisions

Use the `memory_write` function to record key architecture decisions, ensuring they are easily retrievable later.

Searching for Patterns

Utilize the `memory_search` capability to quickly find design patterns relevant to your current project.

Monitoring Memory Usage

Access the dashboard to visualize and analyze memory usage, helping you optimize your knowledge management strategy.

How to install Agent Memory

View source

1. Install with the skills CLI

npx skills add sickn33/agentic-awesome-skills/agent-memory-mcp --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by sickn33

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.

Frequently asked questions about Agent Memory

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