Overview and comparative analysis of useful open source agent frameworks

With the rapid development of artificial intelligence technology, especially the increasing capabilities of large language models (LLMs), building intelligent agents that can autonomously perceive the environment, perform reasoning and planning, and execute tasks has become a hot research and application field. Open source agent frameworks provide developers with a series of tools and abstractions to simplify the agent building process. This article aims to analyze and compare the current popular open source agent frameworks, covering their main features, technology stacks, community support, flexibility, ease of use, scalability, application cases, license agreements, and advantages and disadvantages, so as to help users choose the right framework according to their needs.

Mainstream open source Agent framework

According to existing research data, currently popular open source Agent frameworks include: LangChain, AutoGen, Semantic Kernel, CrewAI, LlamaIndex Agents, Smolagents, dify, Haystack and SuperAGI. These frameworks have different focuses on design concepts, core functions and applicable scenarios, providing developers with a variety of choices.

Detailed analysis of the framework

- LangChain

●  Main features and functions:  LangChain is a comprehensive framework designed to help developers build applications using LLM. It provides modular components, simplifies the processing of complex workflows, and supports data awareness and agent capabilities. It connects different language model tasks through chains to achieve complex end-to-end applications. LangChain also integrates memory functions for maintaining context in multiple rounds of conversations.

●  Programming language and technology stack:  LangChain is mainly developed in Python and JavaScript, and can work with libraries such as TensorFlow, PyTorch, and Hugging Face. It supports integration with multiple LLM providers (such as OpenAI, Anthropic, Google) and various data sources and tools.

●  Community support and activity:  LangChain has a large and active community with over 100,000 stars on GitHub and thousands of contributors. The community provides rich documentation, tutorials, examples, and support channels.

●  Flexibility, ease of use, and scalability:  LangChain is known for its high flexibility and scalability, supporting custom models, tips, and chains. However, due to its modularity and wide range of functionality, the learning curve may be steep for beginners.

●  Application cases and actual projects:  LangChain is widely used to build chatbots, document summarization, code generation, question-answering systems, etc. Many companies, from startups to global enterprises, are using LangChain to build AI applications.

●  License agreement and commercial use restrictions:  LangChain adopts the MIT license, which allows commercial use, modification and distribution.

●  Summary of advantages and disadvantages:

○  Pros:  Feature-rich, extensive integration, highly flexible and extensible, strong community support, applicable to a variety of LLMs and data sources.

○  Cons:  Can have a steeper learning curve, documentation is sometimes inconsistent or outdated, can be too complex for simple tasks, has some criticism for use in production environments.

-AutoGen

●  Main features and functions:  AutoGen is an open source framework developed by Microsoft, focusing on building the next generation of LLM applications based on multi-agent dialogues, aiming to simplify the orchestration, automation and optimization of complex LLM workflows. It supports various dialogue modes, customizable agents, code execution, and human-machine collaboration.

●  Programming language and technology stack:  AutoGen is mainly developed in Python and C#. It integrates multiple LLMs (such as OpenAI, Azure OpenAI, Anthropic, Google, Mistral, IBM watsonx.ai) and tools. It supports code execution in local Shell, Docker, and Jupyter Kernel.

●  Community support and activity:  AutoGen has an active open source community with a large number of stars and contributors on GitHub, a Discord server, and weekly office hours.

●  Flexibility, ease of use, and extensibility:  AutoGen is very flexible in building complex multi-agent workflows, with customizable agents and conversation modes. AutoGen Studio lowers the entry barrier. Its architecture is well extensible, including Core, AgentChat, and Extensions APIs.

●  Application cases and actual projects:  AutoGen is used in code generation, data analysis, AI assistants, automated software development tasks, interactive learning environments, complex decision-making systems, and customer service applications.

●  License agreement and commercial use restrictions:  AutoGen adopts CC-BY-4.0 and MIT licenses, allowing commercial use.

●  Summary of advantages and disadvantages:

○  Advantages:  strong multi-agent orchestration capabilities, flexible dialogue mode, support for code execution, code-free GUI, and active community.

○  Disadvantages:  Steep learning curve for advanced features, may be less structured than other frameworks.

- Semantic Kernel

●  Main features and functions:  Semantic Kernel is a lightweight open source SDK designed to integrate AI models into existing applications, supporting C#, Python, and Java. It adopts a skill-based architecture, includes plug-ins and planners, provides enterprise-level reliability and security, as well as memory management and responsible AI functions.

●  Programming language and technology stack:  Semantic Kernel supports C#, Python, and Java. It integrates with multiple LLMs such as OpenAI, Azure OpenAI, Hugging Face, Amazon Bedrock, Google, Mistral, Ollama, ONNX, etc.

●  Community support and activity:  Semantic Kernel has an active community with a large number of stars and contributors on GitHub, as well as GitHub Discussions and regular office hours.

●  Flexibility, ease of use, and extensibility:  Semantic Kernel is designed for extensibility and is extended through plugins and custom functions. It provides clear prompt implementation. But since the integration method is different from other frameworks, there may be a certain learning curve.

●  Application cases and actual projects:  Semantic Kernel is used to build Copilot, automate business processes, enhance the AI ​​capabilities of existing applications, question-answering systems, and content generation, etc.

●  License agreement and commercial use restrictions:  Semantic Kernel adopts the MIT license, which allows commercial use.

●  Summary of advantages and disadvantages:

○  Advantages:  Supports .NET, clear prompt implementation, suitable for experimental and production environments, enterprise-level.

○  Disadvantages:  Need to frequently adapt to the rapidly evolving AI field, limited resources and documentation, and a learning curve.

- CrewAI

●  Main features and functions:  CrewAI is a framework for orchestrating role-playing AI agents ("crews"), emphasizing multi-agent collaboration, providing a flexible memory system and built-in error handling mechanism, and supporting parallel and hierarchical task execution.

●  Programming language and technology stack:  CrewAI is developed in Python. It supports connecting to various LLMs (OpenAI, Anthropic, Google, Mistral, IBM watsonx.ai) and RAG tools.

●  Community support and activity:  CrewAI has a growing and active community, with a large number of stars and contributors on GitHub and a dedicated community forum.

●  Flexibility, ease of use, and extensibility:  CrewAI focuses on building multi-agent systems with role-playing capabilities that are easy to use and provide high-level simplicity and fine-grained low-level control. It can be extended through custom tools and APIs.

●  Application cases and real projects:  CrewAI is used in stock market analysis, content creation, customer segmentation, lead scoring, marketing and sales strategy automation, travel agencies, simulation and internal automation, etc.

●  License agreement and commercial use restrictions:  CrewAI adopts the MIT license, which allows commercial use.

●  Summary of advantages and disadvantages:

○  Advantages:  Easy to use, focused on multi-agent collaboration, fast and flexible workflow building, production-ready.

○  Disadvantages:  Deeper customization can become complicated, mainly based on Python.

- LlamaIndex Agents

●  Main features and functions:  LlamaIndex Agents is a framework specifically designed for creating LLM applications that rely on external data sources. It provides Agent capabilities to better utilize data, supports various Agent types and tool usage, provides AgentWorkflow for building and orchestrating Agent systems, and integrates more than 160 data sources and more than 40 vector stores.1

●  Programming language and technology stack:  LlamaIndex Agents is developed in Python and TypeScript. It integrates with many LLMs (such as OpenAI, Llama, etc.) and data sources (API, PDF, Documentation, SQL, etc.).

●  Community support and activity:  LlamaIndex Agents has a large and active community with a large number of stars and contributors on GitHub and a Discord server.

●  Flexibility, ease of use, and extensibility:  LlamaIndex Agents is a highly flexible framework with unlimited levels of customization. It is easy to set up and use, especially for retrieval tasks. AgentWorkflow allows building custom Agent workflows.

●  Application cases and real projects:  LlamaIndex Agents are used for question answering of private documents, summarization of large knowledge bases, building professional search agents, context-enhanced research assistants, report generation, customer support, productivity assistants, coding assistants, etc.

●  License agreement and commercial use restrictions:  LlamaIndex Agents adopts the MIT license, which allows commercial use. LlamaCloud is a commercial knowledge management platform.

●  Summary of advantages and disadvantages:

○  Advantages:  strong data integration capability, high retrieval efficiency, production-ready, flexible and customizable.

○  Disadvantages:  The learning curve for beginners in AI Agent construction may be steep, and it focuses mainly on data retrieval.

-Smolagents

●  Main features and functions:  Smolagents is a lightweight library that builds AI Agents with minimal code, emphasizes "code agents" that write and execute Python code, supports traditional tool calls to Agents, shares tools through Hugging Face Hub, and securely executes code through E2B or Docker.1

●  Programming language and technology stack:  Smolagents is developed in Python.5 It integrates with any LLM (Hugging Face Hub, OpenAI, Anthropic via LiteLLM).

●  Community support and activity:  Smolagents has an active community and discussions on GitHub.

●  Flexibility, ease of use, and extensibility:  Smolagents is very simple to use with a low level of abstraction. The code-centric approach provides flexibility for complex logic to be extensible through custom tools.

●  Application cases and actual projects:  Smolagents is used to build conversational agents, automate code generation tasks, develop AI-driven tools, integrate AI capabilities, etc.

●  License agreement and commercial use restrictions:  Smolagents uses Apache License 2.0, which allows commercial use.

●  Summary of advantages and disadvantages:

○  Advantages:  Easy to use, Code Agent-centric, wide LLM compatibility, suitable for rapid prototyping.

○  Disadvantages:  Fewer built-in tools compared to larger frameworks, may encounter version issues.

- Dify

●  Main features and functions:  Dify is an open source LLMOps platform that combines backend as a service, visual prompt orchestration, built-in RAG engine, flexible AI Agent framework, intuitive low-code workflow and multi-model support

●  Programming language and technology stack:  Dify uses Python/Flask/PostgreSQL for the backend and Next.js for the frontend. It supports various LLMs (OpenAI, Claude, Llama2, Qwen, etc.) and vector databases.

●  Community support and activity:  Dify has a growing community with a large number of stars on GitHub and active presence on Discord and Reddit.

●  Flexibility, ease of use, and extensibility:  Dify makes building AI applications and workflows very simple and beginner-friendly through a no-code/low-code visual interface. It provides a flexible API for integration.208 For advanced workflows that require deep code control, its customization may be limited.

●  Application cases and actual projects:  Dify is used to build chatbots, AI assistants, content generation tools, knowledge management solutions, internal productivity applications, automated business workflows, customer support systems, etc.

●  License agreement and commercial use restrictions:  Dify uses a modified Apache License 2.0, which requires a commercial license for multi-tenant SaaS services.

●  Summary of advantages and disadvantages:

○  Advantages:  Beginner-friendly, fast development, visual interface, support for multiple models, built-in RAG.

○  Disadvantages:  Limited customization for advanced scenarios, some features rely on external APIs, local deployment requires Docker.

-Haystack

●  Main features and functions:  Haystack is an open source framework for building production-ready LLM applications, RAG pipelines, and search systems, with modular architecture, flexible pipeline design, integration with various model providers, high customizability, and data reproducibility.

●  Programming language and technology stack:  Haystack is mainly developed in Python. It is integrated with Hugging Face Transformers, OpenAI, Anthropic, Elasticsearch, Weaviate, Pinecone, etc.

●  Community support and activity:  Haystack has an active community, with a large number of stars and contributors on GitHub, and a Discord server that regularly hosts community exchange events.

●  Flexibility, ease of use, and extensibility:  Haystack is very flexible in building modular pipelines for various NLP tasks (semantic search, RAG, question-answering, conversational AI, content generation) The learning curve is moderate, especially for NLP novices Can be extended through custom components

●  Application cases and actual projects:  Haystack is used in RAG, document search, question and answer, conversational AI, content generation, doctor assistant, hospital customer service assistant, investment memo generator, etc.

●  License agreement and commercial use restrictions:  Haystack uses Apache License 2.0, which allows commercial use. Deepset Cloud is a commercial platform.

●  Summary of advantages and disadvantages:

○  Advantages:  Modular architecture, flexible pipeline, strong integration capabilities, production-ready, supports diverse data structures

○  Disadvantages:  Moderate learning curve, some features rely heavily on Elasticsearch, community support may not be as good as other large frameworks

-SuperAGI

●  Main features and functions:  SuperAGI is an open source autonomous AI Agent framework for developers, which supports building, managing and running autonomous agents, concurrent agents, tool integration, agent memory storage, GUI management, action console, multi-vector database, performance telemetry and optimized token usage

●  Programming language and technology stack:  SuperAGI is mainly developed in Python. It integrates various tools (GitHub, Jira, Slack, Twitter, Zapier, Google Search) and vector databases using LLM and LAM (Large Action Models).

●  Community support and activity:  SuperAGI has an active community with a large number of stars on GitHub, and is active on Discord, Twitter, and Reddit, and also organizes hackathons.

●  Flexibility, ease of use, and extensibility:  SuperAGI is a versatile framework for building autonomous agents with customizable workflows. It provides a GUI for easy management. It can be extended through tools and toolkits.

●  Application cases and real projects:  SuperAGI is used in sales, marketing, support, engineering, human resources, IT, digital worker automation, document processing, internal service desk, insurance underwriting, drug discovery, customer support, software development, fraud detection, network security, etc.

●  License agreement and commercial use restrictions:  SuperAGI adopts the MIT license, which allows commercial use. SuperAGI Cloud is a commercial platform.

●  Summary of advantages and disadvantages:

○  Advantages:  A versatile Agent framework that supports concurrent Agents, has extensive tool integration, provides a GUI management interface, and has an active community.

○  Disadvantages:  AGI infrastructure has a steep learning curve for beginners and can be resource intensive.

- LangGraph

●  Main features and functions:  LangGraph is a framework for orchestrating complex agent systems, graph-based workflows, support for loops and branches, state management, human-machine collaboration, first-class streaming support, integration with LangChain and LangSmith.

●  Programming language and technology stack:  LangGraph is mainly developed in Python and JavaScript. Built on LangChain, it integrates various LLMs and tools supported by LangChain

●  Community support and activity:  LangGraph benefits from the large LangChain community, and there are discussions on GitHub.

●  Flexibility, ease of use, and scalability:  LangGraph provides fine-grained control over agent workflows through a graph-based approach, supporting branches and loops for complex scenarios. Due to its graph-based paradigm, the learning curve is steeper. Seamless integration with LangChain and LangSmith enhances its scalability and observability

●  Application cases and real projects:  complex multi-step tasks, chatbots, agent systems, workflow automation, decision trees, compliance workflows, content generation pipelines, travel planning

●  License agreement and commercial use restrictions:  MIT license, commercial use is allowed. LangGraph Platform is a commercial product