Langflow vs Dify
Two of the top agent platforms, side by side: score, setup, license, activity and what each review found.
Langflow
Visual flow builder that deploys agents as APIs or MCP servers
Dify
Visual LLM app platform with workflows, RAG pipeline, agents and APIs
| What we compare | Langflow | Dify |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 87, widely used | 93, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | 93, healthy |
| Easy to run | 33, some setup | 33, some setup |
| Agent-ready | 70, partly | 45, minimal |
| Facts from GitHub and the README | ||
| Stars | 155.5k | 158.1k |
| License | MIT (permissive) | custom license (read the license) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
Langflow
Langflow is a Python 3.10 to 3.14 visual builder (uv pip install langflow, or the langflowai/langflow Docker image on port 7860) for agents and LLM workflows. Every component is editable Python, flows run in an interactive playground, and a finished flow can be served as an API, exported as JSON for Python apps or exposed as an MCP server. Multi-agent orchestration and LangSmith or LangFuse tracing are built in.
Who it is for: Developers prototyping agent flows who want Python under the hood
Strengths
- Any flow becomes an API endpoint or an MCP server for MCP clients
- Component source is Python you can edit inside the builder
- One container on port 7860; no other service in the quick start
- MIT license; desktop builds for Windows and macOS
Weaknesses
- README names no model providers, vector stores or resource needs
- No root Dockerfile or compose file; container config lives in the docs
- Enterprise-ready claim is not detailed in the README
- no GPU
- Docker + Compose
- port 7860
Dify
Dify is an LLM app platform started with Docker Compose (dashboard on port 80) that needs 2 CPU cores and 4 GiB RAM. One canvas covers visual workflows, a prompt IDE, a RAG pipeline that ingests PDFs and PPTs, sandboxed agents using Marketplace tools, MCP servers or your own APIs, plus LLMOps tracing via Opik, Langfuse or Arize Phoenix. Hundreds of models work, including OpenAI-compatible endpoints.
Who it is for: Product teams building LLM apps and RAG workflows without heavy code
Strengths
- Workflow, RAG, agents, prompt IDE and model management in one canvas
- Hundreds of models: GPT, Mistral, Llama3 and any OpenAI-compatible API
- Observability through Opik, Langfuse and Arize Phoenix
- Every feature is exposed through an API (backend-as-a-service)
Weaknesses
- Dify Open Source License adds conditions on top of Apache 2.0
- SSO, RBAC and support SLAs are reserved for Dify Enterprise
- Minimum 2 CPU cores and 4 GiB RAM for the Compose stack
- Dashboard binds to port 80 by default
- RAM ≥ 4 GB
- no GPU
- Docker + Compose
- Models: OpenAI GPT, Mistral, Llama 3, OpenAI-compatible APIs, dozens of inference providers
- port 80