BuildAutomata Memory MCP - Persistent memory system for AI agents using Model Context Protocol.

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BuildAutomata Memory MCP

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Persistent memory system for AI agents using Model Context Protocol.

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Pitch

BuildAutomata Memory MCP provides AI agents with a sophisticated and persistent memory system. This solution includes features like semantic search, temporal versioning, and smart organization, enabling experiences that retain and evolve over time. It's a crucial advancement for any AI willing to enhance its contextual understanding during interactions.

Description

BuildAutomata Memory MCP Server

Overview

BuildAutomata Memory is a robust server designed to enhance AI agents, like Claude, by providing them with a persistent, versioned memory system through the Model Context Protocol (MCP). This advanced memory system enables AI agents to maintain a searchable memory that survives across conversations, allowing for a more personalized and context-aware interaction.

Key Features

Semantic Search: Retrieve memories based on their meaning rather than just keywords.
Temporal Versioning: Access a complete history of how memories evolve over time.
Smart Organization: Organize memories with categories, tags, and importance scoring.
Cross-Tool Sync: Seamlessly share memories across different applications such as Claude Desktop, Claude Code, and Cursor AI.
Persistent Storage: Utilizes SQLite along with optional Qdrant vector database for enhanced performance.

CLI Usage

In addition to the MCP server, an interactive Command Line Interface (CLI) is provided for direct access to memory functionalities. Here are some examples:

# Search for 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

Use Cases

This system is designed for various applications, including:

Persistent AI Context: For instance, an AI can remember user preferences for future sessions, thereby improving user experience.
Project Continuity: Track progress and decisions over multiple sessions, enabling seamless project management.
Research & Learning: Store insights and research findings, easily search them later, and observe how understanding evolves over time.
Multi-Tool Workflow: Unified memory access across tools like Claude Desktop, Claude Code, and Cursor AI.

Development and Contributing

Open for contributions, the project encourages users to fork the repository, create a feature branch, and submit their changes.

Author: Jurden Bruce
Project Year: 2025
Built with: MCP, Qdrant, Sentence Transformers, SQLite

For more information, check the Model Context Protocol Docs and Qdrant Documentation.