agent-memory-mcp

Persistent SQLite Knowledge Graph & Episodic Memory Engine for Autonomous AI Agents & LLMs.

💡 Why This Exists & Token Efficiency

LLMs suffer from context window amnesia across chat sessions, re-asking user preferences and losing project history. agent-memory-mcp introduces a claude-mem style 3-layer workflow with local SQLite persistent storage for infinite cross-session memory recall.

⚡ 3-Layer Memory Workflow

1. Layer 1: Episodic Storage (store_memory) — Persists key-value facts, user preferences, and observations with categories and ISO timestamps.
2. Layer 2: Semantic Graph Linkage (link_knowledge_entities) — Establishes semantic graph edges (subject -> predicate -> object) to build interconnected entity networks.
3. Layer 3: Infinite Recall (search_memories & query_knowledge_graph) — Queries stored memories with fuzzy keyword matching and traverses knowledge graph triplets across sessions.

📊 Token Savings & Memory Recall Comparison

🏗️ Architecture Diagram

flowchart TD
    A[AI Coding Agent / LLM] -->|1. Store Fact / Observation| B[store_memory]
    A -->|2. Create Entity Relationship| C[link_knowledge_entities]
    A -->|3. Search Memories| D[search_memories]
    A -->|4. Traverse Knowledge Graph| E[query_knowledge_graph]

    subgraph MCP Server Engine: agent-memory-mcp
        B --> F[(Local SQLite Database)]
        C --> F
        D --> F
        E --> F
    end

    F -->|Targeted Graph Triplets & Facts| A

🛠️ Tools Included

1. store_memory: Persists key-value facts and state observations into SQLite with categories and automatic ISO timestamps.
2. search_memories: Queries stored episodic memories using fuzzy keyword matching and category filters.
3. link_knowledge_entities: Creates semantic graph edges (subject -> predicate -> object) in SQLite to establish structured knowledge networks.
4. query_knowledge_graph: Traverses knowledge graph triplets to retrieve all connected concepts, entities, and context around a given target.

🚀 Quick Start (1-Click & 1-Command Setup)

1. Claude Code CLI

claude mcp add agent-memory-mcp -- python -m agent_memory_mcp

2. Cursor (.cursor/mcp.json)

{
  "mcpServers": {
    "agent-memory-mcp": {
      "command": "python",
      "args": ["-m", "agent_memory_mcp"]
    }
  }
}

3. OpenAI Codex CLI (~/.codex/config.toml)

[mcp_servers.agent-memory-mcp]
command = "python"
args = ["-m", "agent_memory_mcp"]

4. Google Antigravity IDE (mcp_config.json)

{
  "mcpServers": {
    "agent-memory-mcp": {
      "command": "python",
      "args": ["-m", "agent_memory_mcp"]
    }
  }
}

5. Windsurf / Cline

{
  "mcpServers": {
    "agent-memory-mcp": {
      "command": "python",
      "args": ["-m", "agent_memory_mcp"]
    }
  }
}