Langflow vs mindshub
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
mindshub
Agent workspace for knowledge work, with swappable open agent harnesses
Verdict
Langflow ranks higher, 81 to 78. Of the 3 score parts we measure for both, Langflow leads on 2, mindshub on none and 1 is even.
- AdoptionLangflow leads by 26 points, 95 to 69: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score mindshub on this; Langflow has 91.
- Easy to runWe do not score mindshub on this; Langflow has 50.
- Agent-readyLangflow leads by 30 points, 70 to 40: a wide gap.
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 mindshub if you are
Teams wanting a self-hostable agent workspace for research and automation
- Swappable open-source agent harnesses (Anton, Hermes) selectable from a dropdown
- Connectors for BigQuery, Postgres, Gmail, Drive, HubSpot, Notion and Linear with per-connection credentials
| What we compare | Langflow | mindshub |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 95, widely used | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 70, partly | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 155.7k | 39.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Oct 2026 | Apr 2026 |
| Language | Python | Makefile |
| Docker | Yes | Yes |
| GPU | Not stated | Not stated |
| arm64 or Apple Silicon | Mentioned | Not stated |
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
mindshub
Superproject that builds the MindsHub desktop (Electron) and web app, agent backend and data vault from git submodules. Agents handle research, reports, scheduled tasks and app or document artifacts, using the Anton (default) or Hermes harness and a model router across Claude, GPT, Gemini, DeepSeek, Qwen and Kimi. Also offered as a hosted web app and macOS/Windows installers.
Who it is for: Teams wanting a self-hostable agent workspace for research and automation
Strengths
- Swappable open-source agent harnesses (Anton, Hermes) selectable from a dropdown
- Connectors for BigQuery, Postgres, Gmail, Drive, HubSpot, Notion and Linear with per-connection credentials
- Cross-session memory, skill library and scheduled tasks included
- MIT license; deployable locally, in a VPC, on-prem or air-gapped
Weaknesses
- Linux has no installer; requires building from source with make and submodules
- Pro tier needed for all frontier models and private artifacts
- README gives no RAM, GPU or port requirements
- Bundled submodules carry their own licenses
- Compose
- Needs make, uv, Python 3.10-3.13, Electron
- Models: Claude, GPT, Gemini, DeepSeek, Qwen
More in Agent platforms
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.