Agent Nexus offers a modular architecture for designing, configuring, and running interconnected AI agents that collaborate to solve complex tasks. Developers can register agents dynamically, customize behavior through Python modules, and define communication pipelines via simple YAML configurations. The built-in message router ensures reliable inter-agent data flow, while integrated logging and monitoring tools help track performance and debug workflows. With support for popular AI libraries like OpenAI and Hugging Face, Agent Nexus simplifies the integration of diverse models. Whether prototyping research experiments, building automated customer service assistants, or simulating multi-agent environments, Agent Nexus streamlines development and testing of collaborative AI systems, from academic research to commercial deployments.

AIKitchen provides a developer-friendly Python toolkit enabling you to compose AI agents as modular building blocks. At its core, it offers pipeline definitions with stages for input preprocessing, LLM invocation, tool execution, and memory retrieval. Integrations with popular LLM providers allow flexibility, while built-in memory stores track conversational context. Developers can embed custom tasks, leverage retrieval-augmented generation for knowledge access, and gather standardized metrics to monitor performance. The framework also includes workflow orchestration capabilities, supporting sequential and conditional flows across multiple agents. With its plugin architecture, AIKitchen streamlines end-to-end agent development—from prototyping research ideas to deploying scalable digital workers in production environments.

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.

Evolving Agents provides a genetic programming–based framework for constructing and evolving modular AI agents. Users assemble agent architectures from interchangeable components, define environment simulations and fitness metrics, then run evolutionary cycles to automatically generate improved agent behaviors. The library includes tools for mutation, crossover, population management, and evolution monitoring, allowing researchers and developers to prototype, test, and refine autonomous agents in diverse simulated environments.