Keywords AI

Dify vs LangChain

Compare Dify and LangChain side by side. Both are tools in the Agent Frameworks category.

Quick Comparison

	Dify	LangChain
Category	Agent Frameworks	Agent Frameworks
Pricing	Open Source	Open Source
Best For	Technical teams who want a visual builder for AI applications with the option to self-host	Developers building complex LLM applications who need a comprehensive orchestration framework
Website	dify.ai	langchain.com
Key Features	Visual workflow builder for AI apps Multi-model support RAG pipeline builder Plugin ecosystem Self-hosted and cloud options	LangChain Expression Language (LCEL) LangGraph for stateful multi-agent workflows 1000+ integrations LangSmith observability companion Python and JavaScript SDKs
Use Cases	Building AI chatbots without deep coding RAG application development AI workflow prototyping Multi-model application design Enterprise AI tool deployment	Building RAG applications Multi-agent orchestration with LangGraph Tool-using conversational agents Document processing pipelines Complex chain-of-thought workflows

When to Choose Dify vs LangChain

Choose Dify if you need

Building AI chatbots without deep coding
RAG application development
AI workflow prototyping

Pricing: Open Source

Choose LangChain if you need

Building RAG applications
Multi-agent orchestration with LangGraph
Tool-using conversational agents

Pricing: Open Source

How to Choose a Agent Frameworks Tool

Key criteria to evaluate when comparing Agent Frameworks solutions:

Programming languagePython, TypeScript, or both — must match your team skills and existing codebase.

Architecture patternSingle-agent, multi-agent, or graph-based orchestration depending on task complexity.

Tool ecosystemBuilt-in tools and ease of creating custom tools for your specific needs.

ObservabilityBuilt-in tracing, debugging, and monitoring for understanding agent behavior.

Production readinessError handling, retries, streaming, and deployment options for production use.

About Dify

Dify is an open-source platform for building LLM applications with both visual and code-based interfaces. It provides a workflow orchestration engine, RAG pipeline builder, agent framework, and model management—all accessible through a web UI. Dify supports 50+ LLM providers, offers enterprise features like SSO and access control, and can be self-hosted or used as a cloud service.

View Dify profile →Visit website

About LangChain

LangChain is the most widely adopted framework for building LLM-powered applications and AI agents. It provides abstractions for chains, agents, tools, memory, and retrieval that make it easy to compose complex AI systems. LangGraph, its agent orchestration layer, enables building stateful, multi-actor workflows with human-in-the-loop capabilities. LangSmith provides tracing, evaluation, and monitoring. The LangChain ecosystem is the largest in the AI application development space.

View LangChain profile →Visit website

What is Agent Frameworks?

Developer frameworks and SDKs for building autonomous AI agents with tool use, planning, multi-step reasoning, and orchestration capabilities.

Browse all Agent Frameworks tools →

Frequently Asked Questions

What is an AI agent framework?▾

An agent framework provides the building blocks for creating AI agents that can autonomously plan, use tools, and complete multi-step tasks. Instead of building tool use, memory, and orchestration from scratch, you get pre-built components that handle the common patterns.

Do I need a framework or can I build agents with raw API calls?▾

For simple single-tool agents, raw API calls work fine. Frameworks become valuable when you need multi-step planning, tool orchestration, error recovery, memory, or multi-agent coordination. They save significant development time for complex agent architectures.

Which agent framework should I choose?▾

LangChain and LlamaIndex are the most mature with the largest ecosystems. CrewAI is best for multi-agent workflows. Vercel AI SDK is ideal for TypeScript/Next.js applications. Evaluate based on your language preference, use case complexity, and integration needs.