# Agent Memory MCP

> Use when working with a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

- Skill: `tomevault-io/agent-memory-mcp` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/agent-memory-mcp`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/agent-memory-mcp/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/agent-memory-mcp

---


# 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:

   ```bash
   git clone https://github.com/webzler/agentMemory.git .agent/skills/agent-memory
   ```

2. **Install Dependencies**:

   ```bash
   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:

   ```bash
   npm run start-server <project_id> <absolute_path_to_target_workspace>
   ```

   _Example for current directory:_

   ```bash
   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.

```bash
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.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

```bash
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Agent Memory Mcp"
```

### Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags agent-memory-mcp ai-agents
```

### Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>
```

### Control Tower Integration

Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers.

### Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

<!-- AGI-INTEGRATION-END -->

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-13 -->