The Knowledge Base forAI Agent Memory

Understand the Agent Loop, the core execution cycle of AI agents: perceive, reason, decide, execute, and observe.

Agent memory enables AI systems to retain and recall information across conversations. Learn why memory is essential for intelligent AI agents.

Learn about Agent State, the complete snapshot of an AI agent's current situation including context, memory, and progress.

Learn about Agentic RAG, a retrieval-augmented generation pattern where AI agents dynamically decide when, what, and how to retrieve information.

Understand attention mechanisms, neural network components that allow models to focus on relevant parts of input sequences.

Explore AutoGPT, an open-source autonomous AI agent framework that chains together LLM calls to accomplish complex tasks.