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Introduction to and selection of 12 Agent intelligent body development frameworks | Tool comparison analysis and selection suggestions!

Written by

Silas Grey

Updated on:June-30th-2025

This article is compiled from the content of teacher Jiutian's open class. According to the teacher's detailed explanation, the current large model Agent development framework ecology is very rich, mainly divided into the following categories:

1. Classification and technical in-depth analysis of Agent development framework

1. Low-code framework: the gateway to agile development

Representative tools : Coze (ByteDance), dify (domestic open source), LangFlow (LangChain ecosystem)
Technical features :

Visual orchestration: supports drag-and-drop workflow design (such as Coze's node-based editor), built-in preset templates (customer service bot, data analysis assistant, etc.), suitable for users with no programming knowledge to quickly verify ideas.
Cloud-native integration: Coze directly connects to cloud storage and provisioning, so developers do not need to build their own infrastructure.
Limitations: It is difficult to access custom tools and implement complex logic. It is also difficult to match the actual needs of the enterprise without secondary development.

2. Native basic framework: the core of lightweight development

Core capabilities : Based on large model native function calling (such as DeepSeek-V3's Tool Use)
Technical implementation :

Call tools directly through OpenAI compatible APIs without the need for intermediate encapsulation.

Example code snippet (DeepSeek-V3):

response = client.chat.completions.create(
    model = "deepseek-chat" ,
    messages = [{ "role" :  "user" ,  "content" :  "Query Beijing weather and generate charts" }],
    tools=[search_tool, plot_tool] # Register tool list
)

Applicable scenarios : Simple tool chain, which can realize basic Agent functions without complex framework, suitable for lightweight requirements or learning underlying principles.

3. Code framework: the cornerstone of project implementation

LangChain :

Modular design: Reusability is achieved through abstract layers such as Chain , Agent , and Memory .
Typical application: Building a question-answering system with historical memory (such as ConversationalRetrievalChain ).

LangGraph :

Graph computing engine: Use StateGraph to define nodes (Agent/Tool) and edges (flow logic).
Advantageous scenarios: Multi-stage review process (such as user needs → solution generation → compliance check → result output).

LlamaIndex :

Data enhancement: Supports RAG (retrieval enhanced generation) and structured data query (SQL conversion).
Latest progress: Deeply integrated with the LangChain ecosystem (such as LlamaIndexTool ).

4. Multi-Agent Framework: Decoupling Solution for Complex Systems

AutoGen (Microsoft) :

Role-based design: built-in UserProxyAgent (user agent), AssistantAgent (execution AI), GroupChat (multi-agent session).
Experimental data: In complex tasks (such as academic paper analysis), multi-agent collaboration reduces the error rate by 40% compared to single-agent collaboration ( Source: Microsoft Research ).

CrewAI :

Task pipeline: Atomic operations are defined through Task , and Crew orchestrates task dependencies.
Special features: Supports Tools priority scheduling and resource competition resolution (such as multiple agents competing for GPU).

5. Popular integration projects: rapid implementation of vertical scenarios

MetaGPT :

Standardized process: simulate the division of roles in a software company (product manager → engineer → tester).
Open source data: More than 90% of simple Python script generation has been automated (GitHub case).

ChatDev :

Field focus: A full-process framework optimized for intelligent agent development (requirements analysis → code generation → test deployment).

2. Framework Selection Strategy: From Theory to Implementation

1. Learning Path

beginner :

Start with a low-code platform (such as Coze) or native function calling
Getting started tools: Coze (build a customer service bot in 1 day) + DeepSeek-V3 native API (weather query demo).
Key goal: Understand the core logic of the agent (perception → planning → execution → feedback).

Advanced Development :

Turn to the advanced framework and learn modular design
Core framework: LangChain (implementing a question-answering system with memory) + LangGraph (building an e-commerce order processing pipeline).

Complex Systems

Try a multi-agent framework (such as CrewAI) or refer to the design concept of AutoGen

2. Key considerations

Model capability dependence : As you mentioned, the strong agent capabilities of DeepSeek-V3 and GPT-4 can reduce the complexity of the framework, while weak models need to rely on the engineering compensation of the framework (such as ReAct mode).
Development efficiency vs flexibility : low-code tools are fast but limited, while code frameworks are flexible but have a high learning cost.

3. Quantitative evaluation model for technology selection

Dimensions	Low-code framework	Native API	LangChain	Multi-Agent Framework
Development speed (1-5)	5	4	3	2
Flexibility (1-5)	2	3	4	5
Suitable for team size	<5 people	<10 people	5-20 people	>20 people
Typical business scenarios	MVP Validation	Lightweight tools	Enterprise Applications	Complex Systems

3. Technical breakthroughs in DeepSeek-V3 Agent capabilities

The weather query + drawing case demonstrated by Teacher Jiutian reflects the key advantages of the current strong base model:

1. The underlying principle of autonomous process orchestration

Dynamic DAG Engine :

Based on LLM's implicit process control, it automatically generates task dependency graphs similar to Apache Airflow.
Example: In a weather query task, the model autonomously decides to call the search API first and then trigger the drawing tool.

2. Implementation details of the fault-tolerance mechanism

Multi-level Fallback strategy :

First failure: automatic retry (such as changing the search keyword)
Second failure: switch tools (e.g. use WolframAlpha instead of regular search engines)
Eventually fail: return a structured error code (such as ERROR::FONT_NOT_FOUND )

3. Performance comparison data (DeepSeek-V3 vs GPT-4)

index	DeepSeek-V3	GPT-4
Tool call accuracy	92.3%	94.1%
Complex task completion time	8.2s	7.5s
Adaptability to Chinese scenes	excellent	good

4. Enterprise-level implementation plan

For developers who want to reproduce the function calling example you demonstrated:

Environment preparation : Install the DeepSeek-V3 SDK and ensure tool calling permissions (such as network search and Python execution).
Tool registration : clearly define the input/output format of each tool (such as the return structure of the search API).
Task decomposition prompts : No complex template is required, but clear task objectives are required (such as "obtain Beijing weather and draw a map").
Error handling : As in the example, the model automatically detects garbled characters and tries to repair them. Developers can design a fallback mechanism for the tool in advance.

5. Technology Trends and Frontier Directions

1. Deep integration of base model and framework

Framework function sinking :

LangChain's Tool calling logic is gradually being natively supported by large models (such as GPT-4's parallel function calling )
Framework role change: from process control center to auxiliary optimizer (prompt engineering enhancement, distributed scheduling)

2. The explosion of vertical frameworks

Industry Cases :

Medical field: Clara Agent integrates DICOM medical image analysis tool chain
Financial field: FinAgent has built-in 400+ financial data APIs

Technical features :

Domain-specific tool registration center
Industry compliance automatic checking module

3. Counterattack strategy for small models

Distillation compensation technology :

Use the big model to generate synthetic data to train a dedicated small model
Solution to improve tool calling accuracy:

Dynamic CoT (Chain-of-Thought) hint injection
Tool Description Vectorized Retrieval (Enhanced Understanding via RAG)