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
Pick **Dify** when you want to build and run a production-ready LLM app (chatbot/agent/workflows) with a built-in Knowledge base (document upload), app publishing as a web app or API, and operational tooling like app logs/runtime analysis—especially if you need team collaboration and managed or self-hosted deployment. Pick **LangChain** when you want a developer-centric Python “agent engineering platform” to assemble your own multi-step workflows by composing interoperable components (models/tools/retrieval) and extending the system with LangGraph and LangSmith for more controllable orchestration, debugging, and evaluation.
Choose Dify if...
- +Choose **Dify** if you want a **managed (cloud) or self-hosted “AI app platform”** where you can build and operate production LLM apps via **chatbot / text generator / agent / chatflow / workflow** constructs, including a **document-based Knowledge base** (document upload + knowledge storage) and **app logs/runtime data analysis** for monitoring.
- +Choose **Dify** if your priority is **shipping an AI feature as a web app or API** with less custom engineering—Dify supports **publishing apps** and includes **role management** and **workspace-based collaboration** for teams (plus a free sandbox with **200 message credits**).
- +Choose **Dify** if you need **one place to compare and switch among multiple model providers** (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) while keeping the app/workflow design consistent.
- +Choose **Dify** if your team values **workflow-based agent apps** with built-in operational visibility (logs/runtime analysis) and collaboration, rather than assembling components in code.
Choose LangChain if...
- +Choose **LangChain** if you’re a developer building **custom agent behaviors and multi-step workflows in Python**, where you want a **modular framework** to assemble model calls, tools, retrieval, and integrations into your own architecture.
- +Choose **LangChain** if you want the flexibility to **swap model providers while keeping application logic stable**, using the framework’s interoperable interfaces for models/embeddings/vector stores/retrievers and broad third-party integrations.
- +Choose **LangChain** if you’re already investing in the broader LangChain ecosystem for **more controlled orchestration and engineering rigor**—specifically **LangGraph** for orchestration and **LangSmith** for debugging/evaluation/deployment support.
- +Choose **LangChain** if you prefer a **self-hosted, code-first approach** (framework installation + importing into your codebase) over a packaged end-user app platform, and you expect to tailor the agent workflow beyond what a no/low-code app builder would provide.