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 đź’¬
Dify
8/32
Guided Assistant đź’¬
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 want to build, operate, and publish LLM chatflows/agent/workflow apps with a built-in knowledge base (document upload + retrieval), app logs/runtime analysis, and collaboration/role management—plus hybrid cloud or self-hosted deployment and multi-provider model support from one UI/workflow builder. Pick LangChain when you want to engineer agents and LLM apps programmatically in Python with modular component composition for models/tools/retrieval, and especially when you’ll rely on LangGraph for tighter orchestration and LangSmith for evaluation/debugging—treating it as a developer framework rather than a turnkey app platform.
Choose Dify if...
- +Choose Dify if you want a managed “AI app platform” to build and publish production chatbots/agent apps and workflow-based systems (publish as a web app or API) with a built-in knowledge base for document upload + retrieval, plus app logs and runtime analysis for operating what you build.
- +Choose Dify if you need hybrid deployment (cloud service or self-hosted) and team collaboration features like workspace-based roles, web app branding customization, and higher plan limits for apps/workflows/API usage as you scale beyond a prototype.
- +Choose Dify if you want to compare and switch across multiple model providers (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) from one platform without changing your application logic, especially when you’re focused on shipping an app rather than coding an agent orchestration stack.
- +Choose Dify if you prefer a workflow/app builder approach (chatflow/workflow apps) and want to avoid writing a large amount of glue code for integrating models, retrieval, and external services—using the platform’s app, knowledge, and observability features instead.
Choose LangChain if...
- +Choose LangChain if you’re building custom agents and LLM applications in Python where you need a modular “agent engineering” framework to compose multi-step workflows from interoperable components (models, embeddings, retrievers/vector stores) and manage tool calls explicitly in code.
- +Choose LangChain if you need the freedom to harden your prototypes by swapping model providers while keeping application logic stable, using its integrated interfaces for models/embeddings/retrievers and third-party integrations.
- +Choose LangChain if you want deeper control over orchestration and debugging by pairing it with the broader Lang ecosystem—especially LangGraph for more controllable agent orchestration and LangSmith for debugging/evaluation/deployment support.
- +Choose LangChain if you’re comfortable with a self-hosted developer workflow (install via pip/uv, work in your codebase) and want an open-source MIT-licensed foundation rather than an end-to-end app publishing platform. (Also useful if you plan to leverage MCP support.)