HMLR Go MCP Server

A Go implementation of the HMLR (Hierarchical Memory Lookup & Routing) memory system, exposed as an MCP (Model Context Protocol) server for integration with Claude Code and other MCP clients.

What is HMLR?

HMLR replaces traditional vector-only RAG systems with a structured, state-aware memory architecture that achieves perfect (1.00) faithfulness and context recall across adversarial benchmarks.

Key Features:

• 🧠 Temporal Resolution: Automatically handles conflicting facts across time
• 📋 Bridge Blocks: Organizes conversations by topic with smart routing
• 🎯 4 Routing Scenarios: Continuation, Resumption, New Topic, Topic Shift
• 🔍 Semantic Recall: Retrieves relevant memories even with zero keyword overlap
• 💾 XDG Compliant: Stores data in ~/.local/share/remember/

Quick Start

Prerequisites

• Go 1.23+ installed
• OpenAI API key (for embeddings and LLM features)

Installation

1. Clone the repository:

git clone <your-repo-url>
cd remember-standalone

1. Create .env file with your API keys:

cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

Example .env:

OPENAI_API_KEY=sk-proj-...
ANTHROPIC_API_KEY=sk-ant-...  # Optional, for future features
MEMORY_OPENAI_MODEL=gpt-4o-mini  # Optional, defaults to gpt-4o-mini

1. Build the server:

go build -o bin/hmlr-server ./cmd/server

1. Run the server:

./bin/hmlr-server

The server will start on stdio and wait for MCP protocol messages.

Configuration

Environment Variables

Required:

• OPENAI_API_KEY - Your OpenAI API key for embeddings and LLM features

Optional:

• MEMORY_OPENAI_MODEL - Chat model to use (default: gpt-4o-mini)
  • Options: gpt-4o-mini, gpt-4o, o1-mini, etc.
  • Affects metadata extraction, fact extraction, and user profile learning
  • gpt-4o-mini is recommended for best balance of speed and quality

Model Selection Guide:

• gpt-4o-mini: Recommended - Good balance of speed, quality, and cost (~$0.15/1M input tokens)
• gpt-4o: Highest quality, slower, more expensive (~$2.50/1M input tokens)
• o1-mini: Advanced reasoning capabilities (~$3/1M input tokens)

Usage with Claude Code

Add to your Claude Code MCP settings (~/.config/claude-code/mcp_settings.json):

{
  "mcpServers": {
    "remember": {
      "command": "/path/to/remember-standalone/bin/hmlr-server",
      "args": [],
      "env": {
        "OPENAI_API_KEY": "your-key-here",
        "MEMORY_OPENAI_MODEL": "gpt-4o-mini"
      }
    }
  }
}

MCP Tools

The server exposes 5 MCP tools:

1. store_conversation

Store a conversation turn in HMLR memory.

Input:

{
  "message": "What's the capital of France?",
  "context": "Optional additional context"
}

Output:

{
  "block_id": "block_20251206_143022",
  "turn_id": "turn_20251206_143022_abc123",
  "routing_scenario": "topic_continuation",
  "facts_extracted": 1
}

2. retrieve_memory

Search for relevant memories.

Input:

{
  "query": "What did we discuss about France?",
  "max_results": 5
}

Output:

{
  "memories": [
    {
      "block_id": "block_20251206_143022",
      "topic_label": "Geography",
      "relevance_score": 0.95,
      "summary": "Discussion about European capitals",
      "turns": [...]
    }
  ],
  "facts": [
    {
      "key": "capital_of_France",
      "value": "Paris",
      "confidence": 1.0
    }
  ]
}

3. list_active_topics

List all active conversation topics.

Output:

{
  "topics": [
    {
      "block_id": "block_20251206_143022",
      "topic_label": "Geography",
      "status": "ACTIVE",
      "turn_count": 3,
      "created_at": "2025-12-06T14:30:22Z"
    }
  ]
}

4. get_topic_history

Get full conversation history for a topic.

Input:

{
  "block_id": "block_20251206_143022"
}

5. get_user_profile

Get the user profile summary.

Output:

{
  "profile": {
    "preferences": {},
    "topics_of_interest": [],
    "last_updated": "2025-12-06T14:30:22Z"
  }
}

Architecture

HMLR Memory System
├── Governor         # Smart routing (4 scenarios)
├── ChunkEngine      # Hierarchical chunking (turn → paragraph → sentence)
├── Storage          # XDG-compliant file + SQLite storage
├── Bridge Blocks    # Topic-based conversation organization
└── MCP Server       # Stdio transport for Claude Code integration

Storage Layout

~/.local/share/remember/
├── bridge_blocks/
│   └── 2025-12-06/
│       ├── block_*.json         # Conversation topics
│       └── day_metadata.json
├── facts.db                      # SQLite fact database
├── embeddings/
│   └── *.json                    # Vector embeddings
└── user_profile.json             # Long-term user profile

Development

Running Tests

# Run all scenario tests (with REAL storage, no mocks!)
go test -v ./.scratch/

# Run specific scenario
go test -v ./.scratch/ -run TestScenario01

Project Structure

remember-standalone/
├── cmd/
│   └── server/           # Main entry point
├── internal/
│   ├── core/            # Governor, ChunkEngine
│   ├── storage/         # Storage implementation
│   ├── models/          # Data structures
│   └── mcp/             # MCP tools and handlers
├── .scratch/            # Scenario tests (not committed)
├── scenarios.jsonl      # Documented test scenarios
└── DESIGN.md           # Full architecture design

Scenario Testing

This project follows scenario-driven testing with zero mocks:

# All tests use REAL storage, REAL SQLite, REAL files
.scratch/scenario_01_store_retrieve_test.go  # Basic storage
.scratch/scenario_02_routing_test.go         # Governor routing
.scratch/scenario_03_chunking_test.go        # Hierarchical chunking

See scenarios.jsonl for documented test scenarios.

Implementation Status

✅ Phase 1: Foundation

• XDG storage initialization
• Bridge Block JSON storage
• SQLite facts database

✅ Phase 2: Core Components

• Governor with 4 routing scenarios
• ChunkEngine for hierarchical chunking
• FactScrubber with LLM-based extraction
• ContextHydrator for intelligent prompt assembly
• LatticeCrawler for vector-based candidate retrieval

✅ Phase 3: Background Agents

• Scribe agent for async user profile learning
• Goroutine-based async processing

✅ Phase 4: MCP Server

• Stdio transport
• All 5 MCP tools implemented
• .env loading for API keys

✅ Phase 5: Advanced Features

• Vector embeddings with OpenAI (text-embedding-3-small)
• LLM-based fact extraction (GPT-4o-mini)
• Semantic memory search with cosine similarity
• Dynamic user profiles with intelligent merging

Roadmap

• Integrate OpenAI embeddings (text-embedding-3-small)
• Implement FactScrubber with LLM
• Add vector-based semantic search
• Implement Scribe agent for user profiles
• Add ContextHydrator for prompt assembly
• Implement LatticeCrawler for memory retrieval
• RAGAS benchmark tests (3/3 passing at 1.00)
• Performance optimizations
• Port remaining RAGAS tests (7C, 8, 9, 12)

Contributing

This project uses:

• TDD - Write tests first, then implement
• Scenario Testing - Real dependencies, no mocks
• Subagent-Driven Development - Fresh context per task

See DESIGN.md for full architecture details.

License

MIT License - See LICENSE file

References

• HMLR Python Implementation
• MCP Specification
• Go MCP SDK