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 the fastest path to shipping production-ready LLM functionality (document/RAG knowledge base, workflow/agent chatflows, and published web/API endpoints) with logs and team/workspace collaboration, and you’re fine using a platform that supports multiple model providers plus cloud or self-hosted deployment. Pick LangChain when you want maximum flexibility as a developer to engineer agents and retrieval workflows in Python by wiring models/tools/retrievers together, especially if you’ll pair it with LangGraph for orchestration and LangSmith for debugging/evaluation in a self-hosted, code-centric setup.
Choose Dify if...
- +Choose Dify if you want a managed, “app-building” platform to publish working LLM apps as a **web app or API**, with **knowledge-base document upload/storage**, built-in **app logs/runtime data analysis**, and **workflow/agent chatflows** you can operate as a production service.
- +Choose Dify if your team needs a **hybrid cloud or self-hosted deployment** option plus collaboration controls like **workspaces, role management, and team member limits**, rather than building orchestration from scratch in code.
- +Choose Dify if you want an opinionated way to run **RAG + multi-model-provider comparisons** (OpenAI, Anthropic, Llama 2, Azure OpenAI, Hugging Face, Replicate) in one place without wiring everything together manually.
- +Choose Dify if you value a quick start path like the **free sandbox (200 message credits)** and then scaling to plan-based throughput/workspace limits for teams (Professional/Team tiers).
Choose LangChain if...
- +Choose LangChain if you’re a developer building a **custom agent engineering stack** and want a Python framework to compose **multi-step workflows** from modular components (models, tools, retrievers/vector stores) rather than deploying finished “apps” in a platform UI.
- +Choose LangChain if you want to stay in a code-first workflow where you can swap model providers while keeping your application logic stable, using its **interoperable interfaces** for models/embeddings/retrievers.
- +Choose LangChain if you plan to orchestrate agents with **LangGraph** and use **LangSmith** for debugging, evaluation, and deployment support—LangChain is positioned as the underlying component layer in that ecosystem.
- +Choose LangChain if you prefer fully self-hosted control as an **open-source MIT-licensed** library you install into your project (pip/uv) rather than a Dify-style cloud/self-hosted app platform.