Autogen vs Langchain

Autogen and Langchain are both popular ml inference & agents tools. This page compares their internal architecture, technology stack, data flow patterns, and system behavior — based on automated structural analysis of their source code. They share 1 technologies including asyncio.

microsoft/autogen

55,687

Stars

Python

Language

Components

1.3

Connectivity

langchain-ai/langchain

131,015

Stars

Python

Language

Components

0.4

Connectivity

Technology Stack

Shared Technologies

asyncio

Only in Autogen

fastapi react openai sdk anthropic sdk system.text.json typescript loguru

Only in Langchain

python pydantic threading tenacity pip

Architecture Layers

Autogen (4 layers)

Core Agent Framework

Base agent classes and conversation logic in both Python and .NET

Model Integrations

LLM client configurations for OpenAI, Azure OpenAI, Anthropic, LM Studio

AutoGen Studio Web App

FastAPI backend and React frontend for no-code agent building

Extensions & Tools

MCP (Model Context Protocol) integrations and additional capabilities

Langchain (5 layers)

Core Layer

Base abstractions and interfaces without third-party dependencies

Classic LangChain

Main framework package with high-level agent orchestration

Integration Partners

Third-party service integrations organized by provider

Specialized Tools

Text splitters, model profiles, and testing utilities

API Management

Deprecation handling, beta features, and backward compatibility

Data Flow

Autogen (7 stages)

User Input
Agent Processing
LLM Integration
Response Generation
Middleware Chain
Agent Handoff
Output Delivery

Langchain (5 stages)

Agent Planning
Tool Execution
Observation Processing
Response Generation
History Storage

System Behavior

Dimension	Autogen	Langchain
Data Pools	0	2
Feedback Loops	0	2
Delays	0	2
Control Points	0	3

Code Patterns

Unique to Autogen

agent pattern middleware pipeline configuration objects json polymorphism fastapi + react

Unique to Langchain

dynamic import system callback chain pattern abstract base classes deprecation management security by default

When to Choose

Choose Autogen when you need

Unique tech: fastapi, react, openai sdk
More detailed pipeline (7 stages)
Tighter integration between components

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Choose Langchain when you need

Unique tech: python, pydantic, threading
Streamlined pipeline (5 stages)
Loosely coupled, more modular

View full analysis →

Frequently Asked Questions

What are the main differences between Autogen and Langchain?

Autogen has 10 components with a connectivity ratio of 1.3, while Langchain has 10 components with a ratio of 0.4. They share 1 technologies but differ in 12 others.

Should I use Autogen or Langchain?

Choose Autogen if you need: Unique tech: fastapi, react, openai sdk; More detailed pipeline (7 stages). Choose Langchain if you need: Unique tech: python, pydantic, threading; Streamlined pipeline (5 stages).

How does the architecture of Autogen compare to Langchain?

Autogen is organized into 4 architecture layers with a 7-stage data pipeline. Langchain has 5 layers with a 5-stage pipeline.

What technology does Autogen use that Langchain doesn't?

Autogen uniquely uses: fastapi, react, openai sdk, anthropic sdk, system.text.json. Langchain uniquely uses: python, pydantic, threading, tenacity, pip.

Explore the interactive analysis

See the full architecture maps, code patterns, and dependency graphs.

Autogen Langchain

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Compared on March 25, 2026 by CodeSea. Written by Karolina Sarna.