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
13/36
Guided Assistant
Dify
9/36
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
1
LangChain
1
State & Memory
Dify
2
LangChain
0
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 / 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 an end-to-end AI app platform that helps teams build, deploy (web app/API), and operate production-ready chatbot/agent “chatflows” and document-backed knowledge workflows—with logs, runtime analysis, role management, and the option to run cloud or self-host. Pick LangChain when you want a developer-centric, open-source Python “agent engineering” framework to compose custom multi-step agent workflows in code, especially if you’ll pair it with LangGraph (orchestration) and LangSmith (debugging/evaluation/deployment) to harden your system.
Choose Dify if...
- +Choose Dify if you want a managed platform to build and *publish* production LLM apps (chatbots, text generators, agent/automation “chatflows” and “workflows”) as a **web app or API**, with built-in **knowledge base/document upload** and operational features like **app logs/runtime data analysis** and **role management** for team collaboration.
- +Choose Dify if your team prefers a hybrid workflow (cloud sandbox or **self-hosted**) and would benefit from **multiple model providers managed in one place** (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) plus workspace-based collaboration rather than writing the orchestration logic from scratch.
- +Choose Dify if you’re prioritizing faster iteration toward an end-user-facing app—starting from the **free sandbox (200 message credits)** and then moving to paid workspaces for higher usage, more workspaces, and teamwork—without building your own app shell, knowledge-store plumbing, and observability layer.
- +Choose Dify if you want collaboration and governance baked in early (team member limits, collaboration/workspaces, and role management) while still keeping the option to **self-host** for tighter infrastructure control.
Choose LangChain if...
- +Choose LangChain if you’re a developer building custom agent/workflow logic in code and want a **Python framework** to assemble multi-step behaviors by connecting models, tools, retrieval, and external systems through composable modules.
- +Choose LangChain if you plan to use an agent orchestration and debugging/evaluation toolchain alongside it—specifically **LangGraph** for more controllable orchestration and **LangSmith** for debugging, evaluation, and deployment support—so you can iterate on agent behavior with more engineering rigor than a finished app platform.
- +Choose LangChain if you need flexibility to swap model providers while keeping your application logic stable, thanks to its interoperable interfaces for models/embeddings/vector stores/retrievers and third-party integrations (rather than managing everything inside a separate app builder UI).