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Buildautomata Memory MCP
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Buildautomata Memory MCP

BuildAutomata Memory is a persistent memory system based on the MCP protocol, providing semantic search and versioned memory storage across sessions for AI agents, supporting intelligent classification, timeline tracing, and multi-tool synchronization
Knowledge management and memoryDeveloper tools#Memory storage#Semantic search#Version control#Intelligent agent.Python
2.5 points
7.6K
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Overview

Installation

Content Details

Alternatives

What is BuildAutomata Memory?

BuildAutomata Memory is a memory server based on the Model Context Protocol (MCP), providing persistent long-term memory capabilities for AI assistants (such as Claude). It's like a smart notebook that allows AI to remember important conversation content, user preferences, and project progress, and automatically recall and use this information in subsequent sessions.

How to use BuildAutomata Memory?

After installation and configuration, the AI assistant will automatically use the memory function. You can tell the AI to 'remember this information', or the AI will automatically identify important content for storage. In subsequent conversations, the AI will intelligently retrieve relevant memories to provide more coherent and personalized services.

Applicable scenarios

Suitable for work scenarios that require long-term context: project development tracking, research and learning records, personalized preference memory, cross-tool workflow coordination, etc. Particularly suitable for complex tasks that require AI to maintain continuity.

Main features

Semantic search
Search memories based on meaning rather than keywords, and understand the intent of natural language queries
Temporal version control
Completely record the evolution history of memories, and view the memory state at any point in time
Cross-tool synchronization
Share memories among tools such as Claude Desktop, Claude Code, and Cursor AI
Intelligent organization
Automatically classify, label, and score importance, and intelligently manage memory content
Persistent storage
Based on SQLite and Qdrant vector databases, ensure long-term storage of memories
Hybrid search
Combine vector similarity search and full-text search to provide the most relevant results
Advantages
๐Ÿš€ Improve AI conversation coherence - Remember historical context and avoid repeated explanations
๐Ÿ’ก Personalized experience - Learn user preferences and work habits
๐Ÿ”„ Seamless integration - Automatically cooperate with existing AI tools
๐Ÿ“Š Intelligent management - Automatically identify important content and optimize storage
๐Ÿ”’ Data security - Locally stored, with full control over personal data
Limitations
Requires initial configuration - Installation and setup steps are required
Depends on external components - It is recommended to use Qdrant for the best experience
Learning curve - It takes time to adapt to the new workflow
Storage limitation - A large number of memories may require regular cleaning

How to use

Install dependencies
Ensure that Python 3.10+ is installed on the system, and then install the necessary packages
Configure Claude Desktop
Edit the Claude Desktop configuration file and add MCP server settings
Add server configuration
Add BuildAutomata Memory server information to the configuration file
Restart and start using
Restart Claude Desktop, and the AI assistant now has the memory function

Usage examples

Personalized preference memory
Let the AI remember your personal preferences and automatically apply them in subsequent conversations
Project progress tracking
Track the status and decisions of complex projects across multiple sessions
Research note management
Systematically store and retrieve discoveries and insights during the research process
Workflow coordination
Maintain consistency of work context among different AI tools

Frequently Asked Questions

Do I need programming knowledge to use it?
Where is the memory data stored? Is it secure?
Can I use it without Qdrant?
How to manage storage space?
Which AI tools are supported?
What is the difference between the open-source version and the paid version?

Related resources

GitHub Repository
Open-source code, issue feedback, and community discussions
Gumroad Complete Suite
One-click installation version containing pre-compiled components
MCP Protocol Documentation
Official technical documentation for the Model Context Protocol
Qdrant Vector Database
Installation and usage guide for the vector search engine
CLI Tool Documentation
Detailed usage instructions for the command-line interface

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "buildautomata-memory": {
      "command": "python",
      "args": ["C:/path/to/buildautomata_memory_mcp_dev/buildautomata_memory_mcp.py"]
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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