AgentMemory: Giving Autonomous AI Agents Long-Term Recall

A lightweight, vector-based memory management system that allows your autonomous agents to remember past interactions and learn over time.

The Missing Piece for Autonomous Agents: Memory

One of the biggest limitations of current Large Language Models is their stateless nature. Once the context window fills up, the agent forgets earlier conversations. AgentMemory solves this by introducing a robust, vector-based episodic memory system for AI agents.

A neural network dynamically writing to and retrieving from a high-dimensional vector database.

Core Features

AgentMemory seamlessly integrates with existing frameworks (like LangChain or AutoGPT) to provide persistent storage. It automatically embeds conversational turns and system events into a vector database (like Chroma or Pinecone).

Implementation Example

import agentmemory
# Initialize memory storage
memory = agentmemory.init()
# Save an event
agentmemory.create_memory("user", "My favorite coding language is Python.")
# Retrieve context
context = agentmemory.search_memory("What language should I use?")