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agent-memory

A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

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See how to install

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SKILL.md (original)

Review the instructions the agent will receive before you install.

agentMemory Skill

When to Use

Use this skill when you need a hybrid memory system that provides persistent, searchable knowledge management for AI agents.

This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.

Prerequisites

  • Node.js installed
  • Check if agentMemory is already installed in the project:
    ls -la .agentMemory
    

Setup

  1. Install Dependencies:

    npm install
    
  2. Build the Project:

    npm run compile
    
  3. Start the Memory Server: You need to run the MCP server to interact with the memory bank.

    npm run start-server <project_id> <absolute_path_to_workspace>
    

    Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.

Capabilities (MCP Tools)

Once the server is running, you can use these 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({})

Workflow

  1. Initialization: The first time you run this in a project, it may attempt to import existing markdown memory banks from .kilocode/, .clinerules/, or .roo/.
  2. Development Loop:
    • Before Task: Search memory for relevant context.
    • During Task: Use read/search to answer questions.
    • After Task: Write new findings to memory.
  3. Sync: Your writes are automatically synced to standard markdown files in the project.

Example

User request:

Use @agent-memory for this task: A hybrid memory system that provides persistent, searchable knowledge management for AI agents.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

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