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:

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

2. Install Dependencies:

   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:

   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.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

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

# 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:

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.

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.