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 want to ship production-ready LLM apps as web apps/APIs with a platform workflow + knowledge base, logs/runtime analysis, and team collaboration, with the option to run it in cloud or self-hosted; it’s especially strong if you want to iterate quickly from the free sandbox into governed higher-throughput plans. Pick LangChain when you need a developer-first, open-source Python “agent engineering” foundation where you assemble and control your own agents/workflows by wiring models, retrievers, tools, and integrations—typically alongside LangGraph (orchestration control) and LangSmith (debug/eval/deploy).
Choose Dify if...
- +Choose Dify if you want a managed “AI app platform” experience that goes beyond code—building chatbot/text/agent/chatflow/workflow apps with a built-in knowledge base (document upload + stored knowledge), and publishing those apps as a web app or an API.
- +Choose Dify if your team needs operational features like app logs/runtime data analysis plus workspace-based collaboration (multiple members, role management, branding customization), with a clear path from a free sandbox (200 message credits) to higher paid throughput.
- +Choose Dify if you prefer a no/low-code workflow approach for production LLM apps and want hybrid deployment (cloud or self-hosted) with support for multiple model providers (OpenAI, Anthropic, Azure OpenAI, Hugging Face, Replicate, etc.) in one place.
- +Choose Dify if you want to compare and switch among model providers while also relying on platform-level guardrails around app/workflow usage limits, knowledge document limits, and log retention—rather than managing all those concerns yourself.
Choose LangChain if...
- +Choose LangChain if you’re a developer building custom agents and LLM-powered applications in Python and want a modular framework to compose model calls, tools, retrieval, and multi-step workflows directly in your codebase.
- +Choose LangChain if you want deeper control over orchestration by pairing it with LangGraph (for more controllable agent orchestration) and you’ll use LangSmith for debugging, evaluation, and deployment support.
- +Choose LangChain if you need to “engineer” agent workflows (swapping model providers while keeping your application logic stable) and you expect to integrate with your own external systems via the ecosystem of tools/data-source integrations.
- +Choose LangChain if you’re optimizing for an open-source, self-hosted development workflow (installable via pip) where you manage deployment and architecture rather than relying on an end-user app platform.