Why this benchmark matters
Choosing the right foundation for your AI projects isn’t just about grabbing the newest library—it’s about matching the tool’s strengths to the problems you need to solve. This benchmark pits LangGraph, a framework built for stateful, long‑running LLM agents, against Dify, a low‑code platform that lets you stitch together visual workflows, RAG pipelines and chatbots with minimal code. By laying out their core attributes side by side, the comparison helps you see where each solution shines and where it may fall short.
What to look for
When you skim the table, keep these angles in mind:
- Scope and use cases – Are you building a multi‑agent system that needs durable execution and fine‑grained control (LangGraph), or do you want a drag‑and‑drop interface for rapid prototyping of bots and internal tools (Dify)?
- Feature depth – Notice LangGraph’s focus on token‑by‑token streaming, time‑travel debugging and integration with LangSmith, versus Dify’s visual builder, plugin marketplace and built‑in LLMOps monitoring.
- Integration flexibility – Both support the major LLM providers, but Dify also lists a broader range of local and cloud models, plus a host of database back‑ends.
- Deployment choices – LangGraph can run on its own cloud platform or be containerised, while Dify offers a SaaS option as well as self‑hosted Docker/Helm setups.
- Community and ecosystem – Evaluate the size and activity of each community; LangGraph has enterprise adopters like Klarna, whereas Dify boasts a large developer base and a bustling Discord.
- Observability tools – Look at the built‑in tracing and monitoring capabilities, which are crucial when you need to debug complex agent interactions or keep an eye on production LLM usage.
By scanning these pillars, you’ll be able to decide which platform aligns best with your technical constraints, team expertise, and long‑term goals. The table that follows gives you the raw data; this intro tells you why the numbers matter.
| Feature | LangGraph | Dify |
|---|---|---|
| Category | AI/ML framework – stateful LLM agent orchestration | AI development platform – low‑code visual workflow builder |
| Description | Controllable cognitive architecture for building, managing, and deploying long‑running, stateful LLM agents and multi‑agent workflows. | Open‑source, no‑code/low‑code platform for building, deploying and scaling LLM‑powered applications with visual workflows, RAG pipelines, agents and observability. |
| License | MIT | Dify Open Source License (based on Apache 2.0) |
| Year Released | 2023 | 2023 |
| Open‑Source Status | Yes | Yes |
| Primary Use Cases | Agent orchestration, multi‑agent systems, stateful workflows, tool usage, human‑in‑the‑loop, long‑running tasks. | Chatbots, Q&A bots, knowledge assistants, content generation, AI agents, internal tools, customer support, document analysis. |
| Key Features | Durable execution, human‑in‑the‑loop, comprehensive memory, token‑by‑token streaming, modular primitives, customizable control flows, debugging with LangSmith, production deployment via LangGraph Platform, MLflow tracing, token usage tracking, time‑travel and sub‑graph visualisation. | Drag‑and‑drop workflow builder, RAG pipelines, AI agents with tool calling, prompt IDE, backend‑as‑a‑service APIs, plugin marketplace, LLMOps monitoring, multi‑LLM support, enterprise‑grade security, scalable infrastructure. |
| Supported LLMs / Integrations | OpenAI, Anthropic, Mistral, Gemini, LangChain Google GenAI, plus LangChain ecosystem and MLflow. | OpenAI, Anthropic, Azure OpenAI, Mistral, Llama 3, vLLM, local models via Ollama, any OpenAI‑compatible API, dozens of others; integrations via MCP, HTTP APIs, Milvus, PG Vector, Redis, TiDB, external tools. |
| Programming Language(s) | Python | JavaScript/TypeScript (frontend), Node.js/Python (backend Docker images) |
| Deployment / Hosting Options | LangGraph Platform, LangGraph Cloud, local serve, containerised deployments. | Dify Cloud (SaaS) or self‑hosted via Docker‑Compose, Kubernetes, AWS, Alibaba Cloud, Azure, Helm charts. |
| Community & Adoption | Companies like Klarna, Replit, Elastic; active forum, tutorials, docs, examples. | ~180 000 developers, 70 000+ GitHub stars; active Discord and GitHub discussions. |
| Notable Clients / Users | Klarna, Replit, Elastic. | Volvo Cars, Ricoh, biomedicine firms, other large enterprises. |
| Observability / Debugging Tools | LangSmith visualisation, trace graphs, runtime metrics, token usage tracking. | LLMOps logs, performance dashboards, Langfuse integration, tracing, metrics. |
| Installation / Getting Started | pip install -U langgraph | Docker‑Compose (or Helm) deployment; see Dify documentation for quick start. |
So, which one fits your next project?
- LangGraph is for you if you need fine‑grained control over stateful LLM agents, want a programmable “cognitive architecture” that can run long‑running, multi‑agent workflows, and are comfortable working directly in Python. It shines when you want to embed custom control flows, debug step‑by‑step with LangSmith, or integrate tightly with the broader LangChain ecosystem.
- Dify is for you if you prefer a visual, low‑code environment that lets you stitch together RAG pipelines, chatbots, or AI‑augmented tools without writing much code. It’s ideal for teams that value drag‑and‑drop workflow building, quick deployment to SaaS or Docker, and built‑in observability for LLMOps.
The choice matters because it shapes how you’ll spend your time:
- With LangGraph, you’ll invest effort in writing and orchestrating Python code, gaining maximum flexibility and deep debugging capabilities, but you’ll also need to manage the surrounding infrastructure yourself.
- With Dify, you’ll trade some low‑level control for speed of prototyping and a ready‑made UI, letting you focus on product features while the platform handles hosting, scaling, and monitoring.
Pick the tool that aligns with your team’s skill set, the complexity of the agent logic you need, and how much you want the platform to handle for you.
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