Step 1: Set up Flowise. You can use the cloud version or self-host the open-source edition. The visual canvas looks the same either way, so start wherever is most convenient for you.

Step 2: Create a new flow. Start a blank flow and drag in the first building block, typically a language model node. Choose your preferred LLM provider and set the model you want the agent to use.

Step 3: Connect tools and data. Add a vector store or document loader so the agent can answer from your own content. Then attach tool nodes such as web search or API actions so the agent can actually complete tasks.

Step 4: Wire up memory. Add a memory node so the agent remembers the conversation context. This makes multi-turn interactions feel natural instead of stateless.

Step 5: Test your agent. Use the built-in chat preview to try questions and see how the connected blocks behave. Iterate on the flow until responses are accurate and useful.

Step 6: Deploy and integrate. Expose your flow through the REST API or embed the chat widget into your website. Because Flowise is open source, you can host it fully on your own infrastructure.

With Flowise you can prototype agentic AI in hours rather than weeks. Start with a small flow, test it, and expand as you understand what your users need.