Which AI agent framework to use？Dify? LangChain? AutoGen? 9 Top used framework explained!

Developing AI agents involves selecting the right framework to match your project’s requirements, whether they are basic tasks, secondary development, or enterprise-level integrations. This guide explores several frameworks — Coze, Dify, FastGPT, MetaGPT, AutoGen, Spring AI, Swarm, LangChain, and LangGraph — to provide detailed insights into their workings and use cases.

1. Coze

Coze is a lightweight and user-friendly framework tailored for building simple AI agents with minimal overhead. It focuses on accessibility and rapid deployment for developers and non-developers alike.

How It Works:

• Simplicity: Coze provides an intuitive interface where users can set up agents without deep programming knowledge.

• Pre-Built Templates: It includes a variety of pre-configured agent templates for tasks like customer service, chatbot interactions, and basic data retrieval.

• Plug-and-Play Components: Developers can integrate existing AI models, like OpenAI’s GPT, to quickly create functional agents.

Use Case:

Ideal for C-end users (Consumers) or individuals needing a no-fuss solution for basic automation tasks.

Benefits:

• Ease of Use: Coze offers a user-friendly interface suitable for non-technical users. Agent functionality is rich. Supports workflows.
• Quick Setup: Pre-built templates accelerate the development process.
• Community: Coze has a rich plugin system and a lot of template avaliable in the market.

Disadvantages:

• Limited Scalability: Not designed for large-scale enterprise applications.
• Lack of RAG Features: RAG scenario support is relatively weak.
• Lack support for most models: Supports a small number of online models. (No Ollama, etc.)
• Not Open-Sourced: Coze is not currently open-source. It is a product under ByteDance. Coze has a rich plugin system and ease of use, but compared to Dify, its customizability may be somewhat limited.

2. Dify

Dify is an open-source platform that supports local private deployment. This means that users can customize it according to their needs and deploy it on their own servers, thereby better controlling data security and privacy. Dify’s open-source community is active and provides a wealth of resources and support. (Personally I would prefer Dify over FastGPT and Coze from my experience building simple AI workflow as it is more customizable)

How It Works:

• Minimal Configuration: Developers can use simple APIs to build agents without extensive setup.
• Modular Design: Tasks can be divided into modules, allowing developers to focus on specific functionalities.
• Integration Capabilities: Dify supports integration with third-party APIs and tools to extend its functionality.

Use Case:

Perfect for individual developers or small teams. Oriented towards enterprise users, suitable for programmers.

Benefits:

• Simple & Free Deployment: Dify supports deployment with Docker, which means you can easily have your own private Dify, supporting your privacy while keeping everything totally free.
• External API Integration: Dify allows seamless connection with external services, providing flexibility for developers.
• No-Code Development: Simplifies the creation process for users with limited programming knowledge.

• Support RAG: Dify actually have a nice RAG support (User can import their Notion as private knowledge base). It can also provide multiple option of RAG such as chunking method, indexing method but it didn’t support Open-source embedding model (Only OpenAI).

• Wide support for models: Supports all(or 99.99%) of large language models, such as Claude3, OpenAI, and also supports local open-source models.

• Community Support: As one of the most famous open-source LLM-related project (58k Star), there are a lot of people that are actively involved in the community by providing tutorial, merging pull request and handling issues, which means you can easily get support from them.

• A Range of Tools Integration: In its workflow, Dify had a range of difference tools needed for all kinds of service such as searching. You may even customize your own tools !

Disadvantages:

• Scalability Constraints: Less suited for large-scale enterprise projects.
• Limited Advanced Features: Focuses on simplicity, potentially limiting functionality for complex applications.
• Does not support multiple Agents: Although Dify supports workflow, it have poor support to multi-agent development.

3. FastGPT

FastGPT is an open-source platform that provides out-of-the-box data processing, model calling, and visualization capabilities. It can also be used to orchestrate workflows through Flow, thereby realizing complex question-and-answer scenarios. It is known for its powerful knowledge base system which is better than Dify or Coze.

How It Works:

• Domain-Specific AI Assistants: Create chatbots tailored to specific industries or knowledge areas by training models with your own documents or Q&A data.
• Automated Data Preprocessing: Streamline your workflow with automated text preprocessing, vectorization, and Q&A segmentation, saving you time and increasing efficiency.
• Visual Workflow Orchestration: Design complex AI workflows using an intuitive drag-and-drop interface, integrating tasks like database queries and inventory checks.

• Debugging Tools: Refine your models with comprehensive debugging features.

Use Case:

Best for applications that requires a Knowledge Base function. Oriented towards enterprise users, suitable for programmers.

Benefits:

• High Performance on Knowledge base: Optimized for low latency and precise retrieval for the Knowledge Base
• Great RAG support: FastGPT would actually be skilled at circumstances where RAG is used comparing with Coze or Dify, providing fast and accurate retrieval and more option for index model and file reading model.

Disadvantages:

• Lack of Support for some models: FastGPT support its model through OneAPI, it supports common large models but lacks some support for common used Ollama or Deepseek.
• Limited Versatility: While FastGPT excels at its core functions, it may not be suitable for more complex or nuanced queries.

4. MetaGPT

MetaGPT offers advanced capabilities for building multi-agent systems by encoding human workflows into standardized operating procedures (SOPs). This allows agents to collaborate efficiently on complex tasks.

How It Works:

• SOP Integration: Developers define SOPs as prompt sequences, enabling agents to emulate human-like workflows.
• Role Assignment: Tasks are broken down, and agents are assigned specific roles (e.g., researcher, analyst) to collaborate.
• Error Reduction: Intermediate results are validated by agents with domain-specific expertise, ensuring higher accuracy.
• Assembly Line Paradigm: MetaGPT operates like an assembly line, where agents complete subtasks sequentially.

Use Case:

Ideal for complex projects requiring task decomposition and multi-agent collaboration, such as research workflows or technical writing.

Benefits:

• Workflow Automation: Streamlines complex task execution through SOPs.
• Error Reduction: Validation steps improve output quality.

Disadvantages:

• Setup Complexity: Requires detailed planning and configuration.
• Overhead: High resource usage for multi-agent systems.

5. AutoGen

AutoGen is an open-source framework that is created by Microsoft that enables multiple agents to interact and converse with one another to achieve tasks collaboratively.

How It Works:

• Conversational Agents: Agents are designed to communicate with each other, sharing insights and solutions.
• Customizable Roles: Developers can define agent roles, such as planner, executor, or verifier.
• Multi-Modal Support: AutoGen can integrate with various tools, APIs, and data sources for enhanced functionality.
• Modes of Operation: Includes combinations of LLMs, human inputs, and external tools.

Use Case:

Useful for creating dynamic, adaptable AI systems in fields like project management, automation pipelines, or collaborative creative work.

Benefits:

• Collaboration: Agents can work together dynamically.
• Customizability: Highly adaptable to specific use cases.

Disadvantages:

• Development Complexity: Requires expertise in configuring multi-agent interactions.
• Resource Intensive: Demands significant computational resources.

6. Spring AI (Alibaba)

Spring AI is Alibaba’s enterprise-grade framework designed for integrating AI capabilities into Java large-scale applications and ecosystems. It’s official Page is here. Java development takes up to 42% of all the programmer, but currently most model only support Python, so Spring AI is unique on its compatibility to Java.

How It Works:

• Enterprise Integration: Provides seamless integration with existing enterprise systems, including CRMs, ERPs, and databases.
• Scalability: Handles large workloads and concurrent requests, making it suitable for enterprise environments.
• Security Features: Ensures robust data security and compliance with industry standards.
• Toolkits: Offers pre-built toolkits for common enterprise use cases, such as gateway, message queue, configuration center

Use Case:

An excellent choice for enterprises looking to incorporate AI into their operations while ensuring scalability and security.

Benefits:

• Enterprise-Ready: Tailored for large-scale applications.
• Robust Security: Meets industry compliance standards.
• Support for Java developer: While most other framework like OpenAI’s Swarm, Langchain and AutoGen mainly supports Python, SpringAI provides support for Java.

Disadvantages:

• High Costs: May be expensive for smaller businesses.
• Complex Setup: Requires significant initial configuration.

7. Swarm (OpenAI)

Swarm, developed by OpenAI, addresses some of these challenges by introducing a lightweight, stateless design that simplifies agent interactions. It employs concepts like “routines” and “handoffs” to guide agents through collaborative tasks, allowing for seamless transitions between specialized agents. This approach is particularly beneficial for applications requiring multiple, specialized capabilities to work together, such as customer service systems where initial contact is managed by one agent before passing on specific queries to others.

How It Works:

• Task-Specific Agents: Developers create agents designed for specific tasks, such as data summarization or sentiment analysis.
• Orchestration Tools: Swarm includes tools for managing and coordinating the activities of multiple agents.
• Focus on Modularity: Encourages building small, reusable components that can be combined to form larger workflows.
• Routines and Handoffs: These features guide agents through collaborative tasks, enabling smooth transitions between specialized agents.

Use Case:

Suitable for small-scale projects, educational experiments, or proof-of-concept demonstrations.

Benefits:

• Recent Innovation: As a newly announced framework, Swarm incorporates cutting-edge methodologies.
• Modularity: Supports efficient task division and orchestration.
• Flexible Collaboration: Routines allow for seamless agent transitions in multi-agent tasks.

Disadvantages:

• Immaturity: Swarm lacks extensive documentation and community support due to its recent release.
• Development Overhead: Stateless design may require custom memory solutions for complex applications.
• Stateless Design: its stateless design may not be ideal for complex decision-making tasks that require contextual memory
• Memory Management: There is a lack of built-in memory management, which requires developers to implement their own solutions

8. LangChain & LangGraph: The Ultimate AI Framework Combination

LangGraph and LangChain together offer an integrated, powerful framework for developing enterprise-level AI agents. They combine stateful interactions, multi-agent coordination, and seamless integrations to handle complex workflows that other frameworks often struggle with.

LangGraph Features:

• Stateful Interactions: Keeps track of context and agent state, enabling continuous and persistent workflows, essential for complex decision-making tasks.
• Multi-Agent Coordination: Supports collaboration between multiple agents, ideal for sophisticated tasks that require different types of expertise or actions.
• Graph-Based Workflows: Uses graph structures to model dynamic processes, allowing complex dependencies and flexible task execution.
• Integration with LangChain: LangGraph’s interaction with LangChain’s modular structure adds additional flexibility, enabling more diverse AI applications.

LangChain Features:

• Modular Integration: Simplifies the connection of language models with APIs, databases, and other data sources, reducing the need for custom solutions.
• Rapid Prototyping: Accelerates development with pre-built tools, making it easier to build, test, and deploy AI applications quickly.
• Chain-Based Approach: Ensures that workflows are linear and easy to manage, which is particularly useful for developers aiming for simplicity and clarity.
• Pre-Built Tools: A library of pre-configured tools that connect AI models to various data sources and services, reducing time spent on integration.

Comparative Insights:

• Task Complexity: LangGraph is perfect for enterprise tasks requiring multi-agent collaboration, state persistence, and complex decision-making flows. LangChain, however, excels in integrating external services and language models into a coherent system, ideal for tasks with straightforward, but extensive integrations.
• Development Experience: LangGraph provides a highly structured framework suited for large teams working on intricate, multi-agent projects. While LangChain is more flexible and quicker for prototyping, it can demand more time when managing complex interactions and dependencies.
• Community & Support: LangChain has a robust community with extensive documentation, tutorials, and plugins, ensuring developers can find resources easily. LangGraph, being relatively newer, might have fewer resources but benefits significantly from its tight integration with LangChain’s established ecosystem.

Why LangGraph & LangChain Outperform Other Frameworks:

• Comprehensive & Scalable Solution: The combined strength of LangGraph and LangChain gives developers a complete solution for managing complex agent interactions while integrating data sources, language models, and APIs. Other frameworks might excel in one area (e.g., API integration or state management), but struggle with the other.
• Rapid Development Without Sacrificing Complexity: While other frameworks may be fast to implement simple tasks, they often slow down as complexity grows. LangGraph and LangChain both prioritize rapid prototyping, while ensuring the flexibility to scale up for larger, more intricate systems.
• Dynamic and Flexible Workflows: LangChain’s chain-based approach makes it easier to design linear tasks, while LangGraph’s graph-based structure and multi-agent capabilities handle more dynamic and complex systems. Together, they can cover a wide range of use cases, from simple, single-agent tasks to complex, multi-agent interactions.
• Error Recovery & Persistent State: LangGraph’s stateful interactions are key for applications that require context over time. Other frameworks may not offer persistence, meaning developers have to build their own error recovery or state management systems, adding unnecessary complexity. LangGraph simplifies this process by integrating state management out of the box.
• Extensive Pre-Built Tools: LangChain provides a large collection of pre-built tools and integrations for language models, databases, APIs, and more. This toolset saves significant development time compared to frameworks that leave these integrations up to the developer.
• Advanced Customization and Control: LangGraph allows for deep customization of workflows, agent interactions, and control flow. This is especially useful for enterprise projects where unique workflows and agent behaviors are often required. Other frameworks may lack this level of customization, making them less suited for highly specialized tasks.
• Unified Ecosystem: The integration between LangGraph and LangChain creates a cohesive ecosystem where both components work in harmony, reducing friction and making development more efficient. Unlike separate tools that need to be manually connected, this combination simplifies the overall process of building and scaling AI systems.

The LangGraph and LangChain combination provides a unique, comprehensive, and scalable solution for developing sophisticated AI systems. Their stateful multi-agent coordination, rapid prototyping capabilities, and seamless integration with external tools and APIs make them the top choice for enterprise-level applications. With a strong community, extensive pre-built tools, and unmatched flexibility, LangGraph and LangChain deliver more than what other frameworks can offer on their own.

Conclusion

Choosing the right AI agent framework depends on your project’s needs. Here’s a quick breakdown:

Simple workflow and function (Nothing Complicated)

• For Simplicity and Quick Setup: If you need a simple solution for building basic AI agents without much technical complexity, Coze is a great option. It’s lightweight and easy to use, with pre-built templates and a plug-and-play approach.
• For Customization: If you want more control over your project, Dify is a strong choice. It allows local deployment, supports many models, and have great extensibility.
• For Knowledge Base and RAG Support: If your focus is on developing powerful knowledge bases and supporting retrieval-augmented generation (RAG) workflows, FastGPT is the best fit. It offers strong knowledge base support and fast retrieval, though its versatility is limited.

Suitable for quickly meeting needs and can be used for secondary development.

• For Complex, Multi-Agent Projects: If your project involves multiple agents collaborating on tasks, MetaGPT or AutoGen are the best choices. Both offer powerful multi-agent capabilities but come with higher setup complexity and resource requirements.

Enterprise-level Framework

• For Enterprise-Level Applications with Java: If you’re working on large-scale, enterprise-grade applications that require seamless integration with Java, Spring AI (Alibaba) is the best option. It’s designed for enterprise use and provide easy support for Java.
• For Task-Specific and Modular Systems: Swarm is ideal for small-scale, modular systems with specialized agents. It offers innovative task management but lacks mature community support and may require custom memory solutions.
• For Advanced and Scalable AI Systems: If you need a flexible, scalable solution for complex, enterprise-level AI projects, LangGraph & LangChain provide the most comprehensive and customizable framework. They excel at multi-agent coordination, state persistence, and integrations with external tools.

Disclaimer: This article is based on my personal experience and insights gathered from the programming community. It was written shortly about 3 weeks after Swarm was released. Please note that the information presented here may evolve as these frameworks continue to develop and gain wider usage. Always consider consulting additional resources and documentation for the most up-to-date and comprehensive details.