Side-by-side comparison
Dify vs LangChain
vs
Side-by-side comparison based on our agenticness evaluation framework
At a glance
Quick Facts
| Feature | Dify | LangChain |
|---|---|---|
| Category | Agent Frameworks & Orchestration | Agent Frameworks & Orchestration |
| Deployment | Hybrid (cloud + self-hosted) | Self-hosted |
| Autonomy Level | Semi-autonomous | Copilot (human-in-loop) |
| Model Support | Multi-model | Multi-model |
| Open Source | Yes | Yes |
| MCP Support | -- | Yes |
| Team Support | Small team | Small team |
| Pricing Model | Free / open source | Free / open source |
| Interface | web, api | api, cli |
36-point evaluation
Agenticness
13/36
Guided Assistant
Dify
9/36
Guided Assistant
LangChain
Dimension Breakdown (0-4 each)
Action Capability
Dify
1
LangChain
2
Autonomy
Dify
1
LangChain
1
Planning
Dify
2
LangChain
1
Adaptation
Dify
1
LangChain
1
State & Memory
Dify
2
LangChain
0
Reliability
Dify
1
LangChain
0
Interoperability
Dify
1
LangChain
1
Safety
Dify
2
LangChain
1
Scores from our agenticness evaluation framework. Higher is more autonomous.
Features & Use Cases
Dify
Features
- Cloud-hosted and self-hosted deployment options
- Free sandbox with 200 message credits
- Supports OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, and Replicate
- Builds chatbot, text generator, agent, chatflow, and workflow apps
- Knowledge base with document upload and knowledge storage limits
- Publish apps as a web app or API
- App logs and runtime data analysis
- Role management and web app branding customization
Use Cases
- A developer prototyping an AI app with the free sandbox before moving to a paid workspace
- A small team building a production chatbot or workflow app with document retrieval
- A company that wants a self-hosted option for tighter infrastructure control
- A team that needs to publish AI functionality as an API or web app
- An organization that wants to compare model providers in one platform
LangChain
Features
- Python framework for building agents and LLM applications
- Interoperable interfaces for models, embeddings, vector stores, and retrievers
- Third-party integrations for data sources, tools, and model providers
- Modular component-based architecture for composing workflows
- Works with LangGraph for more controllable agent orchestration
- Integrates with LangSmith for debugging, evaluation, and deployment support
- Open-source MIT-licensed codebase
Use Cases
- Building custom AI agents that call tools and external systems
- Prototyping LLM applications before hardening them for production
- Connecting language models to retrieval and data-augmentation workflows
- Swapping model providers while keeping application logic stable
- Developing and debugging agent workflows alongside LangGraph and LangSmith
Pricing
Dify
- **Free / open source** — full functionality available at no cost.
LangChain
- **Free / open source** — full functionality available at no cost.
Analysis
Our Verdict
If your goal is to deliver an AI chatbot/agent/workflow product quickly with document-based knowledge retrieval, built-in logs/runtime analysis, and straightforward publishing as a web app or API (optionally with self-hosting for control), choose Dify. If your goal is to build and iterate custom agent logic in Python—wiring models/tools/retrieval into your own workflows with modular components and then hardening orchestration and evaluation via LangGraph and LangSmith—choose LangChain.
Choose Dify if...
- +Choose Dify if you want a production-minded *app platform* that lets you build chatbot, text generator, agent, and chatflow/workflow apps with a built-in knowledge base (document upload + knowledge storage), then publish them as a web app or API—without having to assemble everything in code.
- +Choose Dify if you care about operational features like app logs/runtime data analysis and team collaboration via workspaces/role management, plus a managed (cloud) option with a free sandbox for initial experimentation before moving to paid usage.
- +Choose Dify if your team wants to compare and route across multiple model providers (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) from a single place, or if you need a hybrid approach (cloud service or self-hosted) for infrastructure control.
- +Choose Dify if you’re specifically workflow/agent-app focused and want a semi-autonomous, workflow-based builder experience rather than building the orchestration layer yourself in a code framework.
Choose LangChain if...
- +Choose LangChain if you’re a developer team that wants an open-source Python framework to *compose* your own multi-step LLM/agent workflows by connecting models, tools, retrieval components, and external systems via interoperable interfaces.
- +Choose LangChain if you’re planning deeper orchestration and debugging by pairing it with LangGraph (for more controllable agent orchestration) and LangSmith (for debugging, evaluation, and deployment support) as part of your engineering workflow.
- +Choose LangChain if you want maximum flexibility to prototype and iterate in code—swapping model providers and assembling RAG/retrieval pipelines while keeping the application logic stable.
- +Choose LangChain if you prefer a self-hosted, developer-first foundation (pip/uv install, library-based development) rather than an end-user app-building platform with publishing/logs/collaboration features baked in.