Chapter 6

A panoramic view of AI application frameworks

Understand at a glance LangChain、LlamaIndex、LangGraph、AutoGPT、MetaGPT Click each card to enter the corresponding detailed page and view the core capabilities and Code Map of the five major AI application frameworks.

At a glance: the core functions of the four major frameworks and the full Code Map overview

Original overview diagram (four major frameworks)

FRAMEWORK MAP

AI Frameworks

LangChainLlamaIndexLangGraphAutoGPTMetaGPT

The following sections will break down each framework’s core logic, code structure, core capabilities, and suitable scenarios one by one, and provide a comparison table and selection guide.

Core insight: a framework is not magic; it is division of labor

These frameworks solve problems at different layers of AI applications:LlamaIndex manages "knowledge input"，LangChain handles "workflow orchestration"，LangGraph manages "stateful complex flows"，AutoGPT handles "autonomous execution"，MetaGPT manages 'team collaboration'. It is not about one replacing the other; rather, each has its own role, and only together do they form a complete AI application system.

1. LangChain

· Toolbox + orchestration layer

AI application orchestration framework

Core logic

Chain (chained calls) + Agent (decides which tools to call), combining LLMs, tools, memory, and other capabilities into an application.

Code Map (core module)

chains/

Chained flow orchestration

agents/

Agent (decision-making + tool calling)

tools/

Tool integration layer

memory/

Memory management

prompts/

Prompt template

output_parsers/

Output parsing

retrievers/

Retrieval interface (connected to vector database)

Core Capabilities

Chain orchestration

Agent

Tool calling

Memory management

Prompt template

Suitable scenarios

AI application developmentWorkflow orchestrationAgent system construction

View LangChain Detailed explanation

2. AutoGPT

· Let AI do the work itself

Autonomous AI agent

Core logic

Goal → Plan → Execute → Reflect (loop). AI autonomously breaks down tasks, calls tools, and iterates continuously.

Core loop

ThinkingThought

ActionAction

ObserveObservation

MemoryMemory

Continuous loop

Code Map (core module)

autogpt/agent/

Core Agent cycle

autogpt/memory/

Short-term and long-term memory

autogpt/workspace/

File system (read/write)

autogpt/tools/

Toolset (tester/code execution, etc.)

autogpt/config/

Runtime parameters

autogpt/loop.py

Core loop entry point (key point)

Core Capabilities

Autonomous planning

Tool usage

Memory storage

Code execution

File operations

Suitable scenarios

Automatic task executionData collectionPersonal assistantPrototype validation

View AutoGPT Detailed explanation

3. MetaGPT

· AI Company Simulator

Multi-agent collaboration framework

Core logic

Multi-Agent + SOP (standard operating procedure). Let multiple AI roles collaborate according to a process to complete tasks.

Core process (example: software development process)

Requirements analysis

Architect

Design plan

Engineer

Implementation

Test acceptance

Deploy

Delivery and deployment

Code Map (core module)

metagpt/roles/

Role definitions (PM/Engineer/QA, etc.)

metagpt/actions/

Role behavior and capabilities

metagpt/environment/

Shared environment and context

metagpt/memory/

Team memory

metagpt/workflows/

Workflow orchestration

metagpt/team.py

Core of multi-agent collaboration

Core Capabilities

Role division

Collaboration communication

Task decomposition

Workflow orchestration

Code generation

Suitable scenarios

Project auto-generationTeam collaboration simulationComplex task pipeline

View MetaGPT Detailed explanation

4. LlamaIndex

· Feed knowledge to AI

Data connection and RAG engine

Core logic

Data → Index → Retrieve → Generate. Give the LLM your knowledge and improve Q&A accuracy.

Core process

Data source

Chunking

Vectorized Embedding

Retrieve search

Generate

Code Map (core module)

llama_index/readers/

Data loading (PDF/web pages/DB, etc.)

llama_index/nodes/

Text chunking (Node)

llama_index/indices/

Index structures (vector indexes, etc.)

llama_index/retrievers/

Retriever

llama_index/query_engine/

Query engine

llama_index/embeddings/

Vectorized model interface

Core Capabilities

Data integration

Index building

Vector search

Query engine

RAG enhancement

Suitable scenarios

Knowledge base Q&AEnterprise searchAI customer serviceDocument analysis

View LlamaIndex Detailed explanation

5. LangGraph

· Stateful · Multi-node · Cyclic execution

Graph-based Agent orchestration framework

Core logic

Graph (graph) + Node (node) + Edge (edge) + State (state). Orchestrate complex applications with a graph structure, supporting conditional branching, loops, and human intervention.

Detailed explanation of image preview(The four-in-one overview diagram is not included; provided separately)

FRAMEWORK MAP

LangGraph

Click the image or the “View LangGraph Details” below to enter the details page and view the high-resolution original image and section-by-section analysis.

Code Map (core module)

langgraph/graph/

Graph structure core (StateGraph, etc.)

langgraph/nodes/

Node definition (task unit)

langgraph/edges/

Branching and conditional jumps

langgraph/state/

Global state and persistence

langgraph/checkpoint/

Checkpointing and recovery

langgraph/prebuilt/

Preconfigured Agent template

Core Capabilities

Graph structure orchestration

State management

Loop / conditional branching

Multi-agent

Visual debugging

Suitable scenarios

Complex conversation systemMulti-agent collaborationWorkflow AutomationDecision-making and planning

View LangGraph Detailed explanation

Quick comparison of the five major frameworks

Framework	Core positioning	Core logic	Core Capabilities	Advantages	Weakness
LangChain	Application orchestration	Chained calls + Agent	Orchestration, tools, memory	Flexible and powerful, with a rich ecosystem	You need to design the workflow yourself
AutoGPT	Autonomous execution	Goal-driven + self-reflective execution	Plan and execute autonomously	High autonomy	Unstable, low controllability
MetaGPT	Multi-agent collaboration	Role division + SOP	Collaboration, process, generation	Standardization, engineering	The process is fixed and lacks flexibility
LlamaIndex	Data and knowledge retrieval	RAG retrieval-augmented generation	Data, indexes, retrieval	Strong data processing	Not responsible for execution logic
LangGraph	Diagrammatic agent orchestration	Graph + State + Edge	State, loops, multi-Agent	Flexible workflow, built-in visualization	Higher learning cost than LangChain

How to work together? (recommended combinations)

User requirements

LlamaIndexProvide knowledge

LangChainWorkflow orchestration

AutoGPTAutonomous execution

MetaGPTMulti-agent collaboration

Output results

Best-practice combination: LangChain + LlamaIndex + AutoGPT + MetaGPT

Knowledge more accurate

(RAG)

Execution is more automated

(Autonomous Agent)

Collaboration becomes more efficient

(Multi-agent)

The process is more flexible

(Orchestration control)

Selection guide

I want to quickly set up an application framework

LangChain

Want to build complex stateful / multi-agent workflows

LangGraph

Want AI to automatically execute tasks

AutoGPT

Want the AI team to collaborate to complete the project

MetaGPT

Want Q&A with local private data

LlamaIndex

Want an all-in-one AI platform

Use in combination

Learning outcomes

After completing this chapter, you will:

1Understand the core positioning differences among the four major frameworks: LangChain / AutoGPT / MetaGPT / LlamaIndex
2Able to read each framework's Code Map and know which modules in the source code are most critical
3Master the engineering implementation approaches for the four paradigms: RAG, Agent, Multi-Agent, and Workflow
4Able to choose based on business requirements rather than blindly chasing popular frameworks
5Understand how the four frameworks combine into a complete AI application system

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