Wren is a Python-based AI agent framework designed to help developers create, manage, and deploy autonomous agents. It provides abstractions for defining tools (APIs or functions), memory stores for context retention, and orchestration logic to handle multi-step reasoning. With Wren, you can rapidly prototype chatbots, task automation scripts, and research assistants by composing LLM calls, registering custom tools, and persisting conversation history. Its modular design and callback capabilities make it easy to extend and integrate with existing applications.

CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.

Embabel Agent provides a structured approach for building AI agents in Node.js and browser environments. Developers define tools—such as HTTP fetchers, database connectors, or custom functions—and configure agent behaviors through simple JSON or JavaScript classes. The framework maintains conversation history, routes queries to the appropriate tool, and supports plugin extensions. Embabel Agent is ideal for creating chatbots with dynamic capabilities, automated assistants that interact with multiple APIs, and research prototypes that require on-the-fly orchestration of AI calls.