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name: agent-memory description: A hybrid memory system that provides persistent, searchable knowledge management for AI agents. risk: critical source: https://github.com/webzler/agentMemory/tree/main/ source_repo: webzler/agentMemory source_type: community date_added: 2026-07-01 license: MIT license_source: https://github.com/webzler/agentMemory/blob/main/LICENSE

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.