Which integrations are available for this server?

Support platform for project donations and community contributions Supports [Docker](/mcp/servers/integrations/docker) deployment for Qdrant vector database to enhance semantic search capabilities Source code repository hosting with issue tracking and community discussions for the open-source version Commercial bundle distribution platform providing pre-compiled components and priority support Alternative support platform for project donations and community contributions Uses [SQLite](/mcp/servers/integrations/sqlite) database with FTS5 for persistent storage and full-text search of memories and metadata

BuildAutomata Memory MCP Server

Persistent, versioned memory system for AI agents via Model Context Protocol (MCP)

What is This?

BuildAutomata Memory is an MCP server that gives AI agents (like Claude) persistent, searchable memory that survives across conversations. Think of it as giving your AI a long-term memory system with:

🧠 Semantic Search - Find memories by meaning, not just keywords
📚 Temporal Versioning - Complete history of how memories evolve
🏷️ Smart Organization - Categories, tags, importance scoring
🔄 Cross-Tool Sync - Share memories between Claude Desktop, Claude Code, Cursor AI
💾 Persistent Storage - SQLite + optional Qdrant vector DB

Quick Start

Prerequisites

Python 3.10+
Claude Desktop (for MCP integration) OR any MCP-compatible client

Installation

Clone this repository

git clone https://github.com/brucepro/buildautomata_memory_mcp.git cd buildautomata_memory_mcp-main

Install dependencies

pip install mcp qdrant-client sentence-transformers

Configure Claude Desktop

Edit your Claude Desktop config (AppData/Roaming/Claude/claude_desktop_config.json on Windows):

{ "mcpServers": { "buildautomata-memory": { "command": "python", "args": ["C:/path/to/buildautomata_memory_mcp_dev/buildautomata_memory_mcp.py"] } } }

Restart Claude Desktop

That's it! The memory system will auto-create its database on first run.

CLI Usage (Claude Code, Scripts, Automation)

In addition to the MCP server, this repo includes interactive_memory.py - a CLI for direct memory access:

# Search memories python interactive_memory.py search "consciousness research" --limit 5 # Store a new memory python interactive_memory.py store "Important discovery..." --category research --importance 0.9 --tags "ai,insight" # View memory evolution python interactive_memory.py timeline --query "project updates" --limit 10 # Get statistics python interactive_memory.py stats

See README_CLI.md for complete CLI documentation.

Quick Access Scripts

Windows:

memory.bat search "query" memory.bat store "content" --importance 0.8

Linux/Mac:

./memory.sh search "query" ./memory.sh store "content" --importance 0.8

Features

Core Capabilities

Hybrid Search: Combines vector similarity (Qdrant) + full-text search (SQLite FTS5)
Temporal Versioning: Every memory update creates a new version - full audit trail
Smart Decay: Importance scores decay over time based on access patterns
Rich Metadata: Categories, tags, importance, custom metadata
LRU Caching: Fast repeated access with automatic cache management
Thread-Safe: Concurrent operations with proper locking

MCP Tools Exposed

When running as an MCP server, provides these tools to Claude:

store_memory - Create new memory
update_memory - Modify existing memory (creates new version)
search_memories - Semantic + full-text search with filters
get_memory_timeline - View complete version history
get_memory_stats - System statistics
prune_old_memories - Cleanup old/low-importance memories
run_maintenance - Database optimization

Architecture

┌─────────────────┐ │ Claude Desktop │ │ (MCP Client) │ └────────┬────────┘ │ ┌────▼─────────────────────┐ │ MCP Server │ │ buildautomata_memory │ └────┬─────────────────────┘ │ ┌────▼──────────┐ │ MemoryStore │ └────┬──────────┘ │ ┌────┴────┬─────────────┬──────────────┐ ▼ ▼ ▼ ▼ ┌───────┐ ┌────────┐ ┌──────────┐ ┌─────────────┐ │SQLite │ │Qdrant │ │Sentence │ │ LRU Cache │ │ FTS5 │ │Vector │ │Transform │ │ (in-memory) │ └───────┘ └────────┘ └──────────┘ └─────────────┘

Use Cases

1. Persistent AI Context

User: "Remember that I prefer detailed technical explanations" [Memory stored with category: user_preference] Next session... Claude: *Automatically recalls preference and provides detailed response*

2. Project Continuity

Session 1: Work on project A, store progress Session 2: Claude recalls project state, continues where you left off Session 3: View timeline of all project decisions

3. Research & Learning

- Store research findings as you discover them - Tag by topic, importance, source - Search semantically: "What did I learn about neural networks?" - View how understanding evolved over time

4. Multi-Tool Workflow

Claude Desktop → Stores insight via MCP Claude Code → Retrieves via CLI Cursor AI → Accesses same memory database = Unified AI persona across all tools

Want the Complete Bundle?

🎁 Get the Gumroad Bundle

The Gumroad version includes:

✅ Priority support via email
✅ Project support Help the project get funding.

This open-source version:

✅ Free for personal/educational/small business use (<$100k revenue)
✅ Full source code access
✅ Community support via GitHub issues

Both versions use the exact same core code - you're just choosing between project support (Gumroad) vs DIY (GitHub).

Configuration

Environment Variables

# User/Agent Identity BA_USERNAME=buildautomata_ai_v012 # Default user ID BA_AGENT_NAME=claude_assistant # Default agent ID # Qdrant (Vector Search) QDRANT_HOST=localhost # Qdrant server host QDRANT_PORT=6333 # Qdrant server port # System Limits MAX_MEMORIES=10000 # Max memories before pruning CACHE_MAXSIZE=1000 # LRU cache size QDRANT_MAX_RETRIES=3 # Retry attempts MAINTENANCE_INTERVAL_HOURS=24 # Auto-maintenance interval

Database Location

Memories are stored at:

<script_dir>/memory_repos/<username>_<agent_name>/memoryv012.db

Qdrant:

We are now using embedded qdrant. You can overide this setting by running your own server.

Optional: Qdrant Setup

For enhanced semantic search (highly recommended):

Option 1: Docker

docker run -p 6333:6333 qdrant/qdrant

Option 2: Manual Install

Download from Qdrant Releases

Troubleshooting

"Permission denied" on database

Check memory_repos/ directory permissions
On Windows: Run as administrator if needed

Claude Desktop doesn't show tools

Check claude_desktop_config.json path is correct
Verify Python is in system PATH
Restart Claude Desktop completely
Check logs in Claude Desktop → Help → View Logs

Import errors

pip install --upgrade mcp qdrant-client sentence-transformers

License

Open Source (This GitHub Version):

Free for personal, educational, and small business use (<$100k annual revenue)
Must attribute original author (Jurden Bruce)
See LICENSE file for full terms

Commercial License:

Companies with >$100k revenue: $200/user or $20,000/company (whichever is lower)
Contact: sales@brucepro.net

Support

Community Support (Free)

GitHub Issues: Report bugs or request features
Discussions: Ask questions, share tips

Priority Support (Gumroad Customers)

Email: sales@brucepro.net
Faster response times
Setup assistance
Custom configuration help

Contributing

Contributions welcome! Please:

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

Credits

Author: Jurden Bruce Project: BuildAutomata Year: 2025

Built with:

MCP - Model Context Protocol
Qdrant - Vector database
Sentence Transformers - Embeddings
SQLite - Persistent storage