Multi-Agent Frameworks

Microsoft's framework for building multi-agent conversational AI systems. Enables creation of customizable agents that can converse with each other and humans to accomplish complex tasks.

Open-source framework for studying autonomous and communicative AI agents. Enables multi-agent role-playing scenarios where AI user and AI assistant agents collaborate to complete tasks.

Virtual software company powered by multiple intelligent agents with roles like CEO, CTO, programmer, and tester. Collaboratively designs, develops, tests, and documents software from natural language requests.

Library from LangChain for building stateful, multi-actor applications with LLMs. Uses graph-based state machines to coordinate complex agent workflows with persistence and human-in-the-loop capabilities.

Data framework for building LLM applications that connects custom data sources to large language models. Provides tools for indexing, retrieval, and agent-based reasoning over your data.

Multi-agent framework that simulates a software company with different AI roles like Product Manager, Architect, Engineer, and QA. Takes a one-line requirement and outputs complete software projects.

Experimental educational framework from OpenAI for exploring lightweight multi-agent orchestration. Focuses on agent handoffs and routines for building ergonomic multi-agent systems.

Framework for building AI assistants and agents with memory, knowledge, and tools. Provides pre-built agent templates and easy integration with various LLM providers and data sources.

Microsoft's open-source SDK for integrating LLMs into applications. Provides a unified interface for AI orchestration with support for plugins, planners, and memory across C#, Python, and Java.