Open-Source AI Agent Frameworks

As the demand for intelligent, autonomous applications grows, developers are increasingly turning to open-source AI agent frameworks to build, scale, and customize their own multi-agent systems. These frameworks empower back-end engineers to create agents that collaborate, reason, and adapt in real time—paving the way for more flexible, secure, and intelligent applications. In this article, I’ll highlight five powerful frameworks—LangChain, CrewAI, AutoGen, Semantic Kernel, and ModelScope-Agent—that are shaping the future of AI development. Open-Source AI Agent Frameworks LangChain / LangGraph Overview: LangChain is a framework for developing applications powered by language models. LangGraph extends LangChain by enabling the construction of multi-agent systems with memory and streaming capabilities. Supports building agents with complex workflows. Integration with various LLMs and tools. Repository: LangChain GitHub CrewAI Overview: An open-source framework designed for orchestrating multi-agent systems, allowing agents to collaborate on tasks through defined roles and shared goals. Facilitates intelligent teamwork among agents. Suitable for scenarios requiring collaborative problem-solving. Repository: CrewAI GitHub AutoGen Overview: A framework for building large language model (LLM) applications using multi-agent conversations. Supports complex workflows involving multiple agents. Facilitates the creation of autonomous agents that can plan and execute tasks. Repository: AutoGen GitHub Semantic Kernel Overview: Developed by Microsoft, Semantic Kernel is an open-source SDK that allows integration of LLMs with conventional programming languages like C# and Python. Combines AI services with traditional programming. Supports planning, memory, and connectors to various data sources. Repository: Semantic Kernel GitHub ModelScope-Agent Overview: A customizable agent framework based on open-source LLMs, designed for real-world applications. Supports tool-use abilities, memory control, and integration with various APIs. Facilitates building intelligent assistants and autonomous agents. Repository: ModelScope-Agent GitHub

May 4, 2025 - 05:28

As the demand for intelligent, autonomous applications grows, developers are increasingly turning to open-source AI agent frameworks to build, scale, and customize their own multi-agent systems. These frameworks empower back-end engineers to create agents that collaborate, reason, and adapt in real time—paving the way for more flexible, secure, and intelligent applications. In this article, I’ll highlight five powerful frameworks—LangChain, CrewAI, AutoGen, Semantic Kernel, and ModelScope-Agent—that are shaping the future of AI development.

LangChain / LangGraph

Overview: LangChain is a framework for developing applications powered by language models. LangGraph extends LangChain by enabling the construction of multi-agent systems with memory and streaming capabilities.

Supports building agents with complex workflows.
Integration with various LLMs and tools.

Repository: LangChain GitHub

CrewAI

Overview: An open-source framework designed for orchestrating multi-agent systems, allowing agents to collaborate on tasks through defined roles and shared goals.

Facilitates intelligent teamwork among agents.
Suitable for scenarios requiring collaborative problem-solving.

Repository: CrewAI GitHub

AutoGen

Overview: A framework for building large language model (LLM) applications using multi-agent conversations.

Supports complex workflows involving multiple agents.
Facilitates the creation of autonomous agents that can plan and execute tasks.

Repository: AutoGen GitHub

Semantic Kernel

Overview: Developed by Microsoft, Semantic Kernel is an open-source SDK that allows integration of LLMs with conventional programming languages like C# and Python.

Combines AI services with traditional programming.
Supports planning, memory, and connectors to various data sources.

Repository: Semantic Kernel GitHub

ModelScope-Agent

Overview: A customizable agent framework based on open-source LLMs, designed for real-world applications.

Supports tool-use abilities, memory control, and integration with various APIs.
Facilitates building intelligent assistants and autonomous agents.

Repository: ModelScope-Agent GitHub