AG2

Ouroboros is a spec-driven multi-agent framework that shifts from traditional prompting to specification-driven development, supporting multi-agent collaboration, MCP tool integration, and automated workflow orchestration for building high-quality agent systems.

DeepResearchAgent is a hierarchical multi-agent system designed for deep research tasks and general-purpose problem solving, using a top-level planning agent to coordinate specialized sub-agents for automated task decomposition and efficient cross-domain execution.

Bee Agent Framework is a production-ready AI agent development framework supporting both Python and TypeScript, offering multi-modal agent building, tool integration, and observability capabilities for rapid production deployment.

Lightweight AI agent framework with built-in memory, tool calling, and tree-of-thought reasoning, supporting multi-agent collaboration and self-learning, compatible with OpenAI, DeepSeek, Qwen, and other major LLMs with MCP/SSE protocol integration.