Which Large‑Model AI Agent Framework Is Best? A Guide to 12 Options

Overview of Agent Development Frameworks

Building a large‑model AI Agent can be broken down into four fundamental modules that must work together: Memory for persisting conversation history and results, Tools for extending the agent’s capabilities via external functions or APIs, Planning that acts as the agent’s “brain” to decompose tasks into ordered steps, and Action that executes the planned steps and feeds results back to the model.

Four Framework Categories

Frameworks are grouped by abstraction level and target audience, ranging from visual low‑code solutions to full‑stack code libraries and specialized multi‑agent orchestrators.

Low‑code/No‑code Platforms

These platforms let users assemble agents through drag‑and‑drop interfaces with little or no coding.

Coze (扣子) : One of the most popular domestic low‑code platforms; users can bind plugins or workflows to a large model via a visual editor and publish agents to platforms such as Doubao and WeChat.

Dify : Requires basic development and deployment skills (e.g., Docker); after deployment, users can visually design workflows, offering a balance between ease of use and flexibility.

RAGFlow : Focuses on knowledge‑base applications; provides visual component composition for question‑answer agents but supports fewer tools than Coze or Dify.

Basic Programming Paradigm

When existing frameworks cannot meet specific needs, developers can directly use the large model’s native Function Calling capability to implement memory, planning, and action modules from scratch.

Applicable scenarios : Ideal for programmers who need highly customized, lightweight agents or who want to deeply understand the underlying mechanics of Agent operation. The approach demands implementing every component, leading to higher development complexity and maintenance cost.

Function Calling was introduced by OpenAI in July 2023 for the GPT‑4 model, enabling structured calls to external tools and marking a key milestone toward “actionable” large models.

Advanced Code Frameworks

These libraries wrap Function Calling, prompt engineering, and other low‑level capabilities into higher‑level APIs, offering a standardized component set that speeds up development of complex, production‑grade agents.

LangChain : Supplies standardized classes for Memory, Tools, Planning, etc., and encourages linear, chain‑style agent construction.

LangGraph : Extends LangChain with graph‑computing concepts, allowing loops, branches, and stateful workflows for more sophisticated logic, at the cost of a steeper learning curve.

LlamaIndex : Strong in data retrieval and indexing; overlaps with LangChain but has a more intricate architecture and slightly lower ease‑of‑use.

Multi‑Agent Frameworks

When a single agent cannot handle complex tasks, multi‑agent architectures distribute responsibilities across specialized agents (e.g., product‑manager, programmer, tester) that collaborate to complete a workflow.

CrewAI : Built on LangChain, it provides clear concepts for defining, orchestrating, and managing multiple agents, with an easy‑to‑learn API.

Swarm : An OpenAI‑open‑source lightweight framework for experimenting with multi‑agent collaboration.

Assistant API : OpenAI’s official solution that tightly integrates with its GPT models; powerful but limited by model binding and network restrictions.

Additional notable projects in the ecosystem include Microsoft’s AutoGen and OpenAI’s MetaGPT , both of which ship pre‑defined expert‑role agents that can be combined to rapidly assemble a multi‑agent team.

Conclusion

The twelve surveyed frameworks fall into four categories—low‑code/no‑code platforms, basic programming paradigms, advanced code libraries, and multi‑agent frameworks—each targeting distinct user groups and application scenarios. By matching project requirements (speed of prototyping, customization depth, scalability, or collaborative complexity) with the appropriate category, developers can make an informed selection of the most suitable AI Agent development framework.