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 you need a team-friendly, production app platform with knowledge bases, workflow/agent building, app publishing (web/API), and operational visibility (logs/runtime analysis)—go with Dify; it’s especially strong when you want the same AI app wired to multiple model providers with minimal engineering overhead. If instead you’re building bespoke agent behavior in Python and want fine-grained control over how components are chained—using LangChain as the wiring layer, then optionally LangGraph/LangSmith for orchestration control and debugging/evaluation—choose LangChain for its code-first, modular agent engineering approach.
Choose Dify if...
- +Choose Dify if you want a managed (or self-hosted) *application platform* to build production-ready chatbots/agent apps and “chatflows”/“workflow apps,” including document-based knowledge bases, app publishing as a web app or API, and built-in app logs/runtime data analysis for operating the system.
- +Choose Dify if your team benefits from collaboration and operations features—workspace-based team limits/role management, collaboration in shared workspaces, plus branding customization for published web apps—rather than building everything inside your own codebase.
- +Choose Dify if you want a fast way to compare and run the same app across multiple model providers (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) without reengineering your agent logic for each provider.
- +Choose Dify if you want a hybrid path: start in the free sandbox (200 message credits) and then move into paid workspaces for higher usage and more team/workspace capacity.
Choose LangChain if...
- +Choose LangChain if you’re a developer building *custom agent workflows in code* (Python) and want a modular “agent engineering” approach to connect models, tools, retrieval, and external systems into multi-step chains.
- +Choose LangChain if you want maximum flexibility over orchestration and lifecycle tooling by pairing with LangGraph for more controllable agent orchestration and LangSmith for debugging, evaluation, and deployment support.
- +Choose LangChain if you’re planning to swap model providers while keeping application logic stable, since its design emphasizes interoperable interfaces (models/embeddings/vector stores/retrievers) and third-party integrations.
- +Choose LangChain if you prefer a purely self-hosted/open-source developer framework (install via pip/uv, MIT-licensed) rather than using a finished end-user app platform for publishing and observability.