Langflow vs deer-flow
Two of the top projects in Agent platforms, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
Langflow
Visual builder for AI agents and workflows, exposed as API or MCP server
deer-flow
Agent harness that runs sub-agents, memory and sandboxed skills for long tasks
Verdict
Langflow ranks higher, 80 to 75. Of the 5 score parts we measure for both, Langflow leads on 2, deer-flow on none and 3 are even.
- AdoptionLangflow leads by 10 points, 92 to 82.
- FreshnessEven at 100.
- MaintenanceLangflow leads by 15 points, 91 to 76: a wide gap.
- Easy to runEven at 50.
- Agent-readyEven at 70.
Pick Langflow if you are
Developers building agent workflows visually and serving them as APIs/MCP tools
- Every flow can be called as a REST API or exposed as an MCP server
- Component source is editable in Python, so custom nodes need no fork
Pick deer-flow if you are
Engineers self-hosting a multi-agent harness for research and long-running tasks
- Interactive `make setup` wizard generates config.yaml and .env in about 2 minutes
- Web UI model registration for OpenAI, Anthropic, DeepSeek, Qwen, Doubao, Kimi, Zhipu, MiniMax
| What we compare | Langflow | deer-flow |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 92, widely used | 82, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | 76, fair |
| Easy to run | 50, easy | 50, easy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 155.7k | 83.9k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
Langflow
Langflow is a Python platform for assembling agents and workflows on a visual canvas, with Python source access to customize any component. Each flow can be served as an HTTP API, exported as JSON, or deployed as an MCP server so MCP clients can call it as a tool. It installs via uv/pip, Docker, or a Windows and macOS desktop app, and serves its UI on port 7860.
Who it is for: Developers building agent workflows visually and serving them as APIs/MCP tools
Strengths
- Every flow can be called as a REST API or exposed as an MCP server
- Component source is editable in Python, so custom nodes need no fork
- Built-in playground for step-by-step testing of flows
- Observability integrations with LangSmith and LangFuse; MIT license
Weaknesses
- Official releases require setting LANGFLOW_SUPERUSER_PASSWORD; dev branches default to auto-login
- Python 3.10–3.14 required for the pip install path
- README gives no RAM or hardware requirements
- Specific supported models and vector databases are not listed in the README
- Docker + Compose
- Needs Python 3.10–3.14, uv
- Models: major LLM providers, vector databases
- port 7860
deer-flow
DeerFlow 2.0 is a ByteDance agent harness where a lead agent delegates to sub-agents, backed by long-term memory, a sandboxed file system and extensible skills. It includes a web UI, a Gateway, MCP support, IM channels, scheduled tasks, a terminal UI and an embedded Python client. It is a ground-up rewrite that shares no code with v1.
Who it is for: Engineers self-hosting a multi-agent harness for research and long-running tasks
Strengths
- Interactive `make setup` wizard generates config.yaml and .env in about 2 minutes
- Web UI model registration for OpenAI, Anthropic, DeepSeek, Qwen, Doubao, Kimi, Zhipu, MiniMax
- Tracing via LangSmith, Langfuse and Monocle, plus `make doctor` and `make support-bundle`
- Many search and fetch backends: Brave, Tavily, Jina, Firecrawl, Crawl4AI, InfoQuest
Weaknesses
- README warns improper deployment carries security risks; Gateway admin equals code execution
- Local development needs Python 3.12+ and Node.js 22+
- README is dense with config detail; RAM and hardware needs are not stated
- v2 shares no code with v1; v1 users must stay on the 1.x branch
- no GPU
- Docker + Compose
- Needs LLM provider API key, Python 3.12+, Node.js 22+
- Models: OpenAI, Anthropic, DeepSeek, Qwen, Doubao
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