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
11/36
Guided Assistant
Dify
10/36
Guided Assistant
LangChain
Dimension Breakdown (0-4 each)
Action Capability
Dify
1
LangChain
2
Autonomy
Dify
1
LangChain
1
Planning
Dify
1
LangChain
1
Adaptation
Dify
1
LangChain
0
State & Memory
Dify
1
LangChain
0
Reliability
Dify
1
LangChain
0
Interoperability
Dify
1
LangChain
2
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 a managed (or self-hosted) platform to design chatflows/workflow-based agent apps with a document knowledge base, then observe runtime behavior via logs and publish directly as a web app/API—ideal for teams optimizing for faster productionization and collaboration. Pick LangChain when you want to build and control agent behavior in your own Python codebase, composing interoperable components for tools and retrieval, and especially when you’ll lean on the wider ecosystem (LangGraph for orchestration control and LangSmith for debugging/evaluation) to harden prototypes for production.
Choose Dify if...
- +Choose Dify if you want a production-style AI app platform where you build “chatflows” and “workflow” agent apps, attach a document-based knowledge base, and then publish the result as a web app or API—without having to assemble everything yourself in code.
- +Choose Dify if your team values operational features like app logs/runtime data analysis and workspace-based collaboration/role management, plus the option to run it via a cloud sandbox or self-host it for tighter infrastructure control.
- +Choose Dify if you need a single interface to compare and switch among multiple model providers (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) while keeping the app logic focused on the app/agent workflow and integrations rather than custom framework wiring.
- +Choose Dify for “app-building” workflows where you’d prefer managing knowledge storage limits, observability, and publishing from the platform—especially when you want to move from a free sandbox (200 message credits) to a paid team workspace.
Choose LangChain if...
- +Choose LangChain if you’re a developer building custom LLM/agent logic in Python and want a modular “agent engineering platform” to connect models, tools, retrieval components, and external systems into multi-step workflows.
- +Choose LangChain if you need deeper control over orchestration by using LangGraph for more controllable agent orchestration, and you want LangSmith support for debugging, evaluation, and deployment workflows around your code.
- +Choose LangChain if you want an open-source, self-hosted framework that’s meant to be embedded into your own codebase (install via pip/uv) so you can harden prototypes by iterating on components rather than using a higher-level app builder.
- +Choose LangChain if you’re integrating retrieval (embeddings, vector stores, retrievers) and want to keep application logic stable while swapping model providers via the framework’s interoperable interfaces and third-party integrations.