Flowise
Build and self-host AI Agents through a visual interface.
Per-dimension scores and reasoning
Evidence shows the repository contains SECURITY.md, but it explicitly states the product is sunset and no longer accepts security vulnerability reports, which undermines trust. No specific implementation or documentation for least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution was found. Therefore all trust criteria score 0.
The repository includes package.json and README, but no clear evidence of self-consistency, dependency availability, or failure messages. No test results or error handling documentation. Therefore all reliability criteria score 0.
README describes target audience and usage scenarios, but no detailed capability boundaries, trigger precision, or environment fit. Therefore all adaptability criteria score 0.
README provides installation instructions and documentation links, but no clear evidence of information architecture, naming stability, examples/FAQ, known limitations, versioning changelog, or maintenance responsibility. License file exists but includes commercial license portions, and SECURITY.md declares the product is sunset. Therefore all convention criteria score 0.
README claims to build AI agents visually, but no evidence of output usability, marginal value, or cost-benefit. Therefore all effectiveness criteria score 0.
Claims in README lack traceable evidence, no cross-source corroboration, and no separation of facts and inferences. Therefore all verifiability criteria score 0.
- The product is officially sunset and no longer accepts security vulnerability reports; assess risks before use.
- The license includes commercial license portions; read carefully to understand usage restrictions.
- No documentation on security configuration or permission management; potential security risks.
What does this agent do, and when should you use it?
Flowise is an application for building AI Agents visually. Its monorepo identifies a Node backend, a React frontend, third-party node integrations, and Swagger UI API documentation generated from Express. After startup, the application is available locally at http://localhost:3000; development mode reloads code changes at http://localhost:8080. The repository documents global npm installation, Docker Compose, local Docker image builds, and several self-hosting deployment paths. It suits teams that want a graphical Agent-building environment while retaining control over deployment and source development.
Users build AI Agents through a visual interface. Running npx flowise start starts Flowise and exposes the application at http://localhost:3000. For source development, the server module in packages/server serves Node backend API logic, the ui module in packages/ui supplies the React frontend, components supplies third-party node integrations, and api-documentation generates Swagger UI API documentation from Express. Developers can set VITE_PORT in packages/ui, set PORT in packages/server, and run pnpm dev, which reloads code changes automatically.
- A product team that wants to assemble AI Agents graphically on a local machine can install the package and run it on localhost:3000.
- An operations team deploying an Agent-building application into existing infrastructure can use Docker Compose or build its own Docker image.
- A development team maintaining a separated frontend and backend codebase can work across the
server,ui, andcomponentsmodules. - An engineering team needing service API documentation can use the
api-documentationmodule, which generates Swagger UI from Express. - A developer validating interface and backend changes during implementation can configure ports and use
pnpm devwith automatic reloads.
What are this agent's strengths and limitations?
- Provides a visual interface for Agent construction rather than only a command-line or library interface.
- The monorepo separates Node backend logic, React UI, third-party node integrations, and generated API documentation.
- Offers both a quick npm start path and Docker Compose or Docker-image deployment paths.
- Documents self-hosting directions for AWS, Azure, GCP, DigitalOcean, and other listed platforms.
- Requires Node.js >= 20.0.0, so environments on older Node versions need an upgrade.
- Source development requires PNPM and separate
.envfiles for UI and server port configuration. - Builds can fail with JavaScript heap exhaustion and exit code 134, requiring manual
NODE_OPTIONS=--max-old-space-size=4096configuration. - Repository metadata says NOASSERTION while the README states Apache License 2.0, so adopters should verify the applicable license.
How do you install or deploy this agent?
Node.js >= 20.0.0 is required. Install with:
npm install -g flowiseThen start it with:
npx flowise startOpen http://localhost:3000. The supplied material does not list an account, API key, or other credential required to start.
How do you use this agent?
After global installation, run npx flowise start, open http://localhost:3000 in a browser, and begin building AI Agents through the interface. For source development, clone the repository and run pnpm install, pnpm build, and pnpm start. Development mode requires a .env file with VITE_PORT in packages/ui and a .env file with PORT in packages/server, followed by pnpm dev.
FAQ
Where do I access it after startup?
npx flowise start, open http://localhost:3000.Can it be deployed with Docker?
docker folder, copy .env.example to .env and run docker compose up -d; alternatively, build an image with docker build --no-cache -t flowise ..Are API keys or accounts required?
.env file in packages/server.What is the development URL?
pnpm dev workflow is available at http://localhost:8080 and reloads code changes automatically.