Langflow vs vibe-trading
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
vibe-trading
Finance research agent for backtests, market data and broker connectors
Verdict
Langflow ranks higher, 81 to 77. Of the 5 score parts we measure for both, Langflow leads on 2, vibe-trading on 2 and 1 is even.
- AdoptionLangflow leads by 26 points, 96 to 70: a wide gap.
- FreshnessEven at 100.
- Maintenancevibe-trading leads by 8 points, 99 to 91.
- Easy to runvibe-trading leads by 17 points, 67 to 50: a wide gap.
- Agent-readyLangflow leads by 70 points, 70 to 0: 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 vibe-trading if you are
Quant researchers and traders who want an LLM agent for backtesting
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
| What we compare | Langflow | vibe-trading |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | 99, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 155.9k | 36.1k |
| 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
vibe-trading
Vibe-Trading is a Python agent (FastAPI backend, React 19 web UI, CLI, MCP) for trading research. It pulls market data across many exchanges, runs backtests and factor analysis, and checks figures in generated reports against tool evidence. It also connects to 18 brokers and sends scheduled reports through channels such as Email, Slack, Discord and Feishu.
Who it is for: Quant researchers and traders who want an LLM agent for backtesting
Strengths
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
- 18 broker connectors, several read-only, with paper/live permissions documented
- Active: v0.1.16 shipped 116 merged PRs, with near-daily fixes afterward
Weaknesses
- Narrow domain: finance research only, not a general agent builder
- Some broker features are unavailable, e.g. Robinhood options execution
- Data quality depends on public sources such as Stooq, which can block requests
- Frequent backtest-correctness fixes in the changelog; verify results independently
- no GPU
- Docker + Compose
- Needs LLM provider (OpenRouter is the default), Market data sources, Broker APIs (optional)
- Models: OpenRouter, Codex, Copilot, custom model endpoints
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