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
11/36
Guided Assistant
Dify
10/36
Guided Assistant
LangChain
Dimension Breakdown (0-4 each)
Action Capability
Dify
1
LangChain
2
Autonomy
Dify
1
LangChain
1
Planning
Dify
1
LangChain
1
Adaptation
Dify
1
LangChain
0
State & Memory
Dify
1
LangChain
0
Reliability
Dify
1
LangChain
0
Interoperability
Dify
1
LangChain
2
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 getting a team-operated LLM app into users’ hands: it provides a cloud or self-hosted app platform with knowledge bases, workflow-oriented app types, publishing to web/API, and operational collaboration controls. Choose LangChain when the priority is maximum developer control inside a Python codebase: it is better suited to custom agent logic and composable integrations, especially when combined with LangGraph and LangSmith for orchestration and engineering workflows.
Choose Dify if...
- +Choose Dify if you want a platform that can turn an AI chatbot, text generator, agent, chatflow, or workflow into a publishable web app or API without assembling the full application layer yourself.
- +Choose Dify if your team needs built-in knowledge-base document upload/storage, app logs, runtime-data analysis, workspace collaboration, role management, and web-app branding controls in the same product.
- +Choose Dify if you need the choice of a managed cloud sandbox for quick trials and a self-hosted deployment option for tighter infrastructure control.
- +Choose Dify if you want to compare and use providers such as OpenAI, Anthropic, Azure OpenAI, Hugging Face, Replicate, and Llama 2 from an application-building platform.
Choose LangChain if...
- +Choose LangChain if you are building the AI application directly in Python and need modular, code-level composition of model calls, embeddings, vector stores, retrievers, tools, and external systems.
- +Choose LangChain if your agent requires bespoke multi-step behavior—such as calling internal services or third-party tools—and you want to define that logic as interoperable components rather than configure it in an app platform.
- +Choose LangChain if you need to keep application logic portable while swapping model providers, retrieval back ends, or other integrations.
- +Choose LangChain if you want to pair your code with LangGraph for more controllable agent orchestration and LangSmith for debugging, evaluation, and deployment support.