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
Pick Dify when you need a production-oriented AI app platform that teams can publish as a web app/API with built-in knowledge-base document handling, app logs/runtime analysis, and workspace-based collaboration—starting from a sandbox and scaling via plan-based limits. Pick LangChain when you want to engineer bespoke agent workflows in Python by composing models, retrievers, and external tools with a modular architecture, ideally alongside LangGraph (orchestration control) and LangSmith (debugging/evaluation) in a fully self-hosted setup.
Choose Dify if...
- +Choose Dify if you want a managed (cloud) or self-hosted *application platform* to build and ship production LLM apps like chatbots, agent apps, and “chatflow/workflow” apps—with publishing as a web app or API, plus built-in app logs/runtime data analysis and team collaboration via workspaces.
- +Choose Dify if your team needs an opinionated RAG/knowledge-base experience: document upload with defined knowledge storage limits, centralized “knowledge base” for retrieval, and model-provider selection in one place (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate).
- +Choose Dify if you want to move from prototyping to governed team usage: the free sandbox (200 message credits) to validate an app, then paid plans that increase message credits, knowledge-document limits, workflow/API usage, and (on Team) unlimited log history.
Choose LangChain if...
- +Choose LangChain if you’re building a custom agent/LLM system in code and want a modular Python framework to wire together models, embeddings, retrievers/vector stores, and tool integrations into multi-step workflows.
- +Choose LangChain if you want deeper developer control over orchestration and debugging by pairing it with the surrounding ecosystem: use LangGraph for more controllable agent orchestration and LangSmith for debugging, evaluation, and deployment workflows.
- +Choose LangChain if you prefer a fully self-hosted, open-source “agent engineering” approach where your application logic lives in your codebase (MIT-licensed library), making it easier to swap model providers while keeping your workflow components stable.