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Secure AI virtual data roomsFlowise
Flowise is a visual, low-code platform for building AI agents and workflows without writing code. It integrates with LLMs like OpenAI and supports RAG, multi-agent systems, and chatbots through a drag-and-drop interface.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | FlowiseAI/Flowise |
| Owner | FlowiseAI |
| Primary language | TypeScript |
| License | Other — Requires review (not clearly OSI) |
| Stars | 54.4k |
| Forks | 24.7k |
| Open issues | 980 |
| Latest release | [email protected] (2026-06-25) |
| Last updated | 2026-07-06 |
| Source | https://github.com/FlowiseAI/Flowise |
What Flowise is
TypeScript-based monorepo (server, React UI, component library) exposing REST APIs for agent orchestration. Built on LangChain, supports pluggable LLM integrations, vector databases, and custom node components. Deployable via Docker or self-hosted on major cloud platforms.
Get the Flowise source
Clone the repository and explore it locally.
git clone https://github.com/FlowiseAI/Flowise.gitcd Flowise# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Node.js >= 20.0.0 required; deployment via npm, Docker, or pnpm monorepo build (heap size tuning may be needed for large builds).
- Environment configuration critical: review VITE_PORT and PORT setup in packages/ui and packages/server before production deployment.
- Third-party LLM API keys and vector DB credentials must be managed securely; no secrets management strategy documented in excerpt.
- Scale considerations unknown; 54K stars and active forks suggest adoption but no benchmarks or performance SLAs provided.
- Monorepository structure means updates to server, UI, or components ship together; atomicity and rollback strategy should be planned.
When to avoid it — and what to weigh
- Complex Stateful Workflows Requiring Custom Logic — Use cases needing deeply custom state machines, complex branching logic, or heavy conditional reasoning often outgrow the visual node paradigm.
- High-Security, Compliance-Locked Environments — Regulated industries (healthcare, finance) with strict data residency, audit trails, or encryption mandates should perform security review; no security posture claimed in available data.
- Production Systems with SLA Requirements — 980 open issues and active development signal ongoing maturity work. Mission-critical systems require proven uptime guarantees and vendor support not evident from OSS project.
- Teams Without Node.js/TypeScript Expertise — While no-code UI lowers entry barriers, extending or troubleshooting requires familiarity with Node.js backend and React frontend; limited support for other stacks.
License & commercial use
Licensed under Apache License Version 2.0 (permissive OSI license). Source code is open; redistribution and modification permitted with attribution.
Apache 2.0 is a permissive license compatible with commercial use, derivative works, and closed-source extensions. However, no commercial support, SLA, or indemnification terms are evident from the OSS repository. Organizations integrating Flowise commercially should: (1) review their legal requirements, (2) evaluate vendor support options (commercial Flowise Cloud is offered separately), (3) maintain internal fork management if heavily customized.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, penetration test results, or threat model provided in available data. Key risk areas to review independently: API authentication/authorization, credential storage (LLM keys, DB passwords), input validation on node parameters, and data residency in self-hosted environments. Network exposure of port 3000 in default setup should be gated behind reverse proxy/firewall in production.
Alternatives to consider
LangChain / LangSmith
Direct programmatic access to agentic workflows; lower visual overhead but higher dev effort. Better for teams with strong Python/TypeScript skills.
Make.com / Zapier
General-purpose no-code workflow automation with broader SaaS integrations. Trade-off: less LLM-native, closed ecosystem, hosted-only.
OpenAI GPTs / Assistants API
First-party OpenAI native agents. Simpler for single-provider use cases but less flexible for multi-provider or on-premises requirements.
Build on Flowise with DEV.co software developers
Flowise reduces dev cycles for agent prototyping and RAG systems. Start free, deploy self-hosted, integrate your LLMs. Explore docs or deploy on your infrastructure today.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
Flowise FAQ
Can I run Flowise entirely on-premises without cloud vendor lock-in?
Is there a commercial support or SLA option?
What LLM providers are supported?
Can I extend Flowise with custom nodes?
Software developers & web developers for hire
Flowise rarely ships on its own. As a software development company, DEV.co handles the surrounding work — data modeling, service integration, deployment, and the ai frameworks glue code that production actually requires.
Ready to build AI agents visually?
Flowise reduces dev cycles for agent prototyping and RAG systems. Start free, deploy self-hosted, integrate your LLMs. Explore docs or deploy on your infrastructure today.