PostgMem is a .NET-based MCP server that enables AI agents to store, search, and retrieve memories via vector embeddings in PostgreSQL with pgvector extension.
PostgMem is a .NET-based MCP server that enables AI agents to store, search, and retrieve memories via vector embeddings in PostgreSQL with pgvector extension.
PostgMem provides a comprehensive solution for managing memories in AI applications. It allows structured memory storage with vector embeddings, enabling semantic search and retrieval through similarity matching. Integrating with PostgreSQL and the pgvector extension, it supports efficient queries, filtering by tags, and storing diverse memory types. Built on .NET technology, it offers easy MCP protocol compatibility for seamless integration with AI agents and services, making it suitable for applications requiring dynamic memory management and fast retrieval.
Who will use PostgMem?
AI developers
Data scientists
Researchers
AI application architects
How to use the PostgMem?
Step1: Setup PostgreSQL with pgvector extension installed
Step2: Configure environment variables with database connection details
Step3: Run the PostgMem application
Step4: Use MCP Store, Search, Get, or Delete tools for memory management
PostgMem's Core Features & Benefits
The Core Features
Store memory with vectors and metadata
Retrieve memory by ID
Semantic search with similarity threshold
Filter memories with tags
The Benefits
Efficient vector similarity search
Easy integration with AI workflows
Structured and flexible memory management
PostgMem's Main Use Cases & Applications
AI agent context management
Semantic content retrieval
Memory-driven AI personalization
Research data storage and analysis
FAQs of PostgMem
What is PostgMem?
What technologies does PostgMem use?
How do I set up PostgMem?
Can I filter memories by tags?
What types of memories can be stored?
How does semantic search work?
Is programming knowledge required to use PostgMem?