Memory MCP Integration LoRA

[codelion]

🚀 New Recipe Proposal: Memory MCP Integration LoRA

Problem Statement

Local models (especially smaller ones like Qwen2.5-Coder-0.5B, Gemma-3-1B) lack the ability to effectively interact with the Memory MCP Server for persistent knowledge graph-based memory management. This prevents them from maintaining context across conversations and building user-specific knowledge over time.

Proposed Solution

Create a specialized LoRA adapter that teaches models to:

• Interact with the Memory MCP Server's knowledge graph structure
• Generate appropriate tool calls for entity/relation/observation management
• Maintain persistent memory across chat sessions
• Query and update the knowledge graph effectively

Key Objectives

1. MCP Protocol Mastery: Train models to use Memory MCP tools correctly (create_entities, create_relations, add_observations, read_graph, etc.)
2. Knowledge Graph Understanding: Teach structured thinking about entities, relations, and observations
3. Memory Strategy: Implement effective patterns for when to store, update, or retrieve memories
4. Context Persistence: Enable cross-session memory recall and updates

Training Methodology

Following Ellora's philosophy of self-supervised data generation:

1. Synthetic Conversation Generation: Use Magpie-style approach to generate diverse conversational scenarios requiring memory
2. Memory Operation Sequences: Create training data with proper MCP tool calling patterns
3. Graph State Tracking: Include examples of building and querying knowledge graphs
4. GRPO Training: Use preference learning to optimize memory usage patterns

Expected Dataset Structure

{
  "conversations": [
    {
      "user": "My name is Alice and I work at Anthropic",
      "assistant_thinking": "Need to create entities for user and organization, then relate them",
      "mcp_calls": [
        {"tool": "create_entities", "params": {"entities": [{"name": "Alice", "type": "person"}]}},
        {"tool": "create_entities", "params": {"entities": [{"name": "Anthropic", "type": "organization"}]}},
        {"tool": "create_relations", "params": {"relations": [{"from": "Alice", "to": "Anthropic", "type": "works_at"}]}}
      ]
    }
  ]
}

Technical Approach

1. Base Models: Start with Qwen2.5-Coder-0.5B-Instruct and Gemma-3-1B-IT
2. LoRA Configuration: r=16, alpha=32, targeting attention layers
3. Training Framework: Unsloth for memory efficiency + PEFT
4. Evaluation: Test on memory recall accuracy, graph construction quality, and cross-session persistence

Success Metrics

• Memory Recall Rate: >85% accurate retrieval of stored information
• Graph Construction: Proper entity/relation/observation structure in >90% cases
• Tool Usage Accuracy: >95% correct MCP tool call formatting
• Cross-Session Persistence: Successfully maintains context across 5+ conversation sessions

Deliverables

1. Training notebook: Ellora_Recipe_6_Memory_MCP_Integration_LoRA.ipynb
2. Trained adapters on HuggingFace Hub
3. Evaluation benchmarks for memory operations
4. Documentation and usage examples

Why This Recipe?

• Ecosystem Integration: Bridges local models with MCP infrastructure
• Practical Value: Enables persistent memory for chatbots and assistants
• No External Dependencies: Uses self-generated training data
• Wide Applicability: Any model can gain memory capabilities

Related Work

• Memory MCP Server: https://github.com/modelcontextprotocol/servers/tree/main/src/memory
• MCP Protocol: https://modelcontextprotocol.io/
• Similar to Recipe #3 (Tool Calling) but specialized for memory operations

Next Steps

1. Design the synthetic data generation pipeline
2. Create memory operation scenarios
3. Implement GRPO training with memory-specific rewards
4. Benchmark against Claude's native MCP integration

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This recipe would extend Ellora's collection with a crucial capability for building stateful AI applications with local models. The Memory MCP integration would allow even small models to maintain long-term context and build personalized knowledge graphs.

cc: @codelion