> For the complete documentation index, see [llms.txt](https://aivilles-organization.gitbook.io/aivilles-organization/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aivilles-organization.gitbook.io/aivilles-organization/welcome-to-aiville/architecture-of-generative-agents.md). # Architecture of Generative Agents **Generative Agents** are AI-driven characters built on large language models (LLMs) that simulate realistic human-like behavior. They are designed for open-world environments, where they can interact with other agents, respond to changing contexts, and autonomously evolve through memory, reasoning, and planning. At the heart of this architecture is a novel agent framework that integrates LLMs with memory retrieval and synthesis mechanisms. This ensures that agents generate behavior not just from prompt inputs, but from a coherent and personalized internal history. Without such mechanisms, an LLM might generate plausible actions in isolation — but fail to exhibit continuity, react to past events, or demonstrate consistent identity. The key challenge, therefore, is enabling agents to retrieve, reflect on, and reason over their memory in a way that informs long-term, believable behavior.This architecture is structured around three core components: * **Memory Stream & Retrieval** * **Reflection** * **Planning & Action**