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
10/36
Guided Assistant
Dify
14/36
Adaptive Collaborator
LangChain
Dimension Breakdown (0-4 each)
Action Capability
Dify
1
LangChain
3
Autonomy
Dify
1
LangChain
2
Planning
Dify
1
LangChain
2
Adaptation
Dify
0
LangChain
1
State & Memory
Dify
1
LangChain
1
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
Choose Dify when the priority is quickly operating and publishing a complete AI application through a managed or self-hosted platform, with document knowledge bases, workspace collaboration, runtime visibility, and web/API delivery already part of the product. Choose LangChain when your team wants to implement the application directly in Python and needs composable building blocks for bespoke agents, retrieval pipelines, tools, and external integrations—particularly if you plan to control agent orchestration with LangGraph and use LangSmith during debugging and evaluation.
Choose Dify if...
- +Choose Dify if you want a ready-to-configure platform for shipping chatbot, text-generation, agent, chatflow, or workflow applications as branded web apps or APIs, rather than assembling the application UI and runtime entirely in code.
- +Choose Dify if your team needs built-in knowledge-base operations such as document upload and managed knowledge storage for retrieval-enabled applications.
- +Choose Dify if you need a choice between using a hosted cloud workspace and self-hosting the same platform for tighter infrastructure control.
- +Choose Dify if non-engineering collaborators need workspace roles, team collaboration, app logs, runtime-data analysis, and branding controls around deployed AI apps.
Choose LangChain if...
- +Choose LangChain if you are building inside a Python codebase and need a modular framework to compose your own model calls, embeddings, vector stores, retrievers, tools, and external-system integrations.
- +Choose LangChain if your application requires highly custom multi-step agent behavior, especially when you want to use LangGraph for more explicit and controllable orchestration.
- +Choose LangChain if provider portability is primarily a code-architecture concern: its interoperable interfaces let you swap models, retrieval components, and integrations while retaining application logic.
- +Choose LangChain if you want developer-oriented debugging, evaluation, and deployment support through the LangSmith ecosystem as you harden a custom agent workflow.