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 |
32-point evaluation
Agenticness
10/32
Guided Assistant
Dify
8/32
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
0
LangChain
1
State & Memory
Dify
2
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:** Sandbox plan with 200 message credits, 1 team workspace, 1 team member, 5 apps, 50 knowledge documents, and limited throughput.
- **Professional ($59/workspace/month):** 5,000 message credits/month, 3 team members, 50 apps, 500 knowledge documents, and higher limits for workflows and API usage.
- **Team ($159/workspace/month):** 10,000 message credits/month, 50 team members, 200 apps, 1,000 knowledge documents, and higher throughput plus unlimited log history.
- **Enterprise:** Pricing not publicly listed; contact sales.
LangChain
- **Free:** Open-source library under the MIT license
- **Pro:** Not publicly available for the core library
- **Enterprise:** Not publicly available from the README content
Analysis
Our Verdict
If you’re building something you want to deploy quickly as a **working AI app/API with document-based knowledge retrieval plus team collaboration and runtime logs**, pick **Dify**—it’s designed for app/workflow creation with publishing, observability, and a hybrid cloud/self-hosted approach. If instead you’re a developer aiming for a highly customized agent system where you’ll own the architecture in code—composing models/tools/retrievers and integrating orchestration/debugging via the LangGraph/LangSmith ecosystem—pick **LangChain**.
Choose Dify if...
- +Choose **Dify** if you want a managed (or self-hosted) **application-building platform** where you can create **chatbot, agent, chatflow, and workflow apps** with a built-in **knowledge base** (document upload/knowledge storage) and then **publish as a web app or API**.
- +Choose **Dify** if your team needs **production operations** out of the box—specifically **app logs/runtime data analysis**, plan-based limits for throughput, and **workspace-based collaboration/role management** for teams.
- +Choose **Dify** if you prefer a **low-code workflow/agent builder** and want a single place to compare and switch among **multiple model providers** (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) without restructuring application code.
Choose LangChain if...
- +Choose **LangChain** if you want a **developer framework** to assemble custom multi-step LLM workflows from modular components (models, embeddings, retrievers, tool integrations) and keep full control in your own codebase.
- +Choose **LangChain** if you’re pairing it with **LangGraph** for more controllable agent orchestration and **LangSmith** for debugging/evaluation/deployment support—i.e., you want an “agent engineering platform” rather than a finished app platform.
- +Choose **LangChain** if you need the flexibility to swap model providers while keeping your logic stable, and you’re comfortable building directly in **Python** (installing the library and composing components).