Engram-go: Persistent memory for AI agents

Engram-go: Persistent Memory for AI Agents

Engram-go is a high-performance, persistent memory solution designed specifically for AI agents. Built with Go and leveraging PostgreSQL with the pgvector extension, it provides a robust and efficient way for your AI applications to store and retrieve information over time. This tool addresses the critical need for agents to maintain context and learn from past interactions, enabling more sophisticated and stateful AI behaviors.

What Engram-go Does

Engram-go acts as a dedicated memory store for your AI agents. It allows agents to save relevant data, such as conversation history, learned facts, user preferences, or task progress, and then efficiently query this data when needed. By providing a persistent layer, it ensures that agents don't lose their state between sessions or restarts, facilitating continuous learning and improved decision-making. The integration with PostgreSQL and pgvector enables powerful semantic search capabilities, allowing agents to find information based on meaning rather than just keywords.

Key Features

• Lightweight and Fast: With a container size of approximately 10MB and a cold start time of around 200ms, Engram-go minimizes resource overhead and ensures quick access to memory.
• Go-based: Developed in Go, it offers excellent performance, concurrency, and ease of integration for Go-based AI projects.
• PostgreSQL + pgvector: Utilizes the power and reliability of PostgreSQL for data storage, combined with the pgvector extension for efficient vector embeddings and similarity search.
• Persistent Storage: Ensures that agent memory is saved and available across application restarts.
• Scalable: Built on a robust database, it can scale to handle growing memory requirements.

Who Engram-go is For

Engram-go is an ideal solution for AI developers building agents that require persistent state and the ability to learn from past interactions. This includes developers working on:

• Chatbots and Conversational Agents: To maintain conversation history and user context.
• Personal Assistants: For remembering user preferences and past requests.
• Autonomous Agents: To store learned behaviors, environmental observations, and task progress.
• AI-powered Applications: Where long-term memory and context are crucial for functionality.

If you are developing AI agents that need to remember, learn, and act intelligently over time, Engram-go provides the foundational memory infrastructure you need.