Learn in one article: What are the large model agent development frameworks? What are their differences?

introduction

With the explosion of big model technology, AI Agent (intelligent agent) has gradually become the core carrier of landing applications. It can not only understand language, but also independently plan, call tools, and execute tasks, truly upgrading the big model from a "language model" to an "action assistant". However, faced with the numerous Agent development frameworks on the market, how should developers choose? This article sorts out  9 major mainstream frameworks , analyzes their core features and differences, and helps you quickly find the right tool!

1. Inventory of mainstream Agent development frameworks

1. LangChain

• Core positioning : Industry benchmark framework, supporting complex task chain design, and strong integration capabilities.
• Features : modular design (Chains, Agents, Memory), multi-model compatibility (OpenAI, HuggingFace), support for tool calls (search, database, etc.).
• Applicable scenarios : conversation assistant, document question and answer, multi-step task system.
• Disadvantages : steep learning curve, complex debugging, and reliance on community ecosystem updates.

2. AutoGen (Microsoft)

• Core positioning : Multi-agent collaboration framework that supports asynchronous communication and complex interactions.
• Features : modular expansion, built-in debugging tools, support for distributed deployment, and provision of the graphical interface Autogen Studio.
• Applicable scenarios : multi-role collaborative tasks (such as code generation, engineering management), research projects.
• Disadvantages : Still in the experimental stage, use with caution in production environments; dependent on the Microsoft ecosystem.

3. LlamaIndex

• Core positioning : Data-intensive agent development, focusing on document parsing and indexing.
• Features : Integrated knowledge management platform LlamaCloud, support for complex instruction optimization (LlamaParse), and rich ecological tools (LlamaHub).
• Applicable scenarios : knowledge base question and answer, chatbot, and rapid product deployment.
• Disadvantages : Weak decision-making ability, biased towards data layer support.

4. CrewAI

• Core positioning : role-based collaborative framework that imitates the division of labor in human teams.
• Features : preset role structure (such as coordinator, executor), dynamic task allocation, and conflict resolution mechanism.
• Applicable scenarios : automated writing, team task scheduling, and enterprise-level process management.
• Disadvantages : Limited flexibility in customizing processes and few community cases.

5. Semantic Kernel (Microsoft)

• Core positioning : Enterprise-level LLM application development, emphasizing security and integration.
• Features : Supports multi-language programming, seamless integration with Microsoft ecosystem, and high-performance reasoning optimization.
• Applicable scenarios : legal assistants, enterprise-level automation systems.

Other frameworks at a glance

• Qwen-Agent : Optimized by Alibaba Cloud, friendly to Chinese scenarios, but dependent on the Alibaba ecosystem.
• MetaGPT : Simulates the role collaboration of a software company, suitable for standardized process tasks, but with high token consumption.
• LangGraph : An extension of LangChain, focusing on stateful multi-agent systems, suitable for complex decision-making scenarios.
• Swarm : A lightweight multi-agent framework suitable for quick experiments, but with limited functionality.

2. Framework comparison: How to choose?

Selection suggestion :

• Seeking flexibility and ecology : choose LangChain.
• Multi-agent research : Choose AutoGen or LangGraph.
• Fast data application : Select LlamaIndex.
• Enterprise-level needs : Consider Semantic Kernel or CrewAI.

3. Future Trends: Four Core Modules of Agent System

Regardless of the framework you choose, a mature Agent system must include four major modules:

1. Memory : Context management and long-term memory (such as LangChain's ConversationBuffer).
2. Tools : External capability extension (such as search API, code executor).
3. Control : Task planning and decision-making mechanisms (such as the ReAct reasoning process).
4. Environment : Deployment and interaction scenarios (such as web pages and enterprise WeChat).

Conclusion

The essence of the competition among agent frameworks is a competition of "system engineering capabilities". Developers need to weigh flexibility, ecological support and deployment costs according to business needs. In the future, with breakthroughs in multimodal and autonomous decision-making technologies, agents will be more deeply integrated into the real world and become a true "digital workforce".