AI Agent Workflow Builder

Why Visualize AI Agent Workflows?

Modern AI agents are no longer single-turn chatbots. They plan multi-step tasks, call external tools, loop until goals are met, delegate to sub-agents, and recover from errors. Frameworks like LangChain, LangGraph, CrewAI, OpenAI Agents SDK, AutoGen, and Vercel AI SDK each have their own abstractions — but the underlying pattern is always a graph of steps connected by data flow and control flow.

When you try to build these systems straight in code, it's easy to lose track of the bigger picture. A visual workflow makes it immediately obvious where branches happen, what tools are invoked where, and which steps depend on which. Our workflow builder lets you sketch that structure first, then implement from a diagram that already exists.

Node Types Supported

The builder provides node types that map directly to common agent framework primitives:

LLM node — a model call with system prompt, user input, and model selection (GPT-4, Claude, Gemini, Llama).
Tool node — external function calls (web search, code execution, database query, HTTP request, custom tools).
Conditional node — branching logic based on LLM output, classification, or explicit rules.
Loop node — iterative execution with exit conditions (e.g., "keep searching until confidence > 0.8").
Agent node — a sub-agent with its own workflow, used for delegation in multi-agent systems.
Memory node — persistent context storage (vector DB, session memory, structured state).
Human-in-the-loop — approval gates where a human reviews before the workflow proceeds.
Input/Output nodes — entry points (user message, API webhook, cron trigger) and exit points (response, write to DB, send email).

Common Workflow Patterns

Most agentic workflows fall into a handful of recurring shapes. Recognizing them helps you design faster:

Prompt → Tool → Response

Classic retrieval-augmented generation (RAG). User asks a question, LLM decides what to look up, tool fetches data, LLM composes the answer.

ReAct Loop

The agent reasons, acts (calls a tool), observes the result, then loops. Used heavily for research and multi-step problem-solving.

Plan-and-Execute

One LLM generates a plan (ordered steps), a second executes each step, a third verifies. Great for complex multi-stage tasks.

Multi-Agent Orchestration

A supervisor agent routes tasks to specialist agents (researcher, coder, reviewer). Used in CrewAI, AutoGen, and LangGraph patterns.

Router Pattern

A classifier LLM routes the request to the right downstream chain (e.g., technical support vs. billing vs. sales). Cheap first step, specialized second step.

Reflection

The agent produces an answer, then a second LLM pass critiques it, then a third revises based on the critique. Boosts output quality significantly.

Who This Is For

AI engineers prototyping agents — sketch the flow before writing LangGraph nodes or LangChain chains. Catches architectural issues before you commit to code.
Technical PMs — document how an AI product actually works for stakeholders, customer success, and incident response runbooks.
Solo founders building AI apps — plan a complex workflow visually, then hand the diagram to an engineer (or Cursor / Claude Code) for implementation.
ML and data teams — visualize multi-step inference pipelines, especially when mixing traditional ML models with LLMs.
Educators and authors — explain agent concepts clearly in blog posts, tutorials, courses, and talks.

Frequently Asked Questions

Does the workflow builder execute the agent or just diagram it?

Today it's a design tool — you build the diagram and implement the agent in your framework of choice. Execution of diagrams directly is on our roadmap.

Can I export the workflow to code?

You can export the structure as JSON. From there it's straightforward to map to LangGraph, Vercel AI SDK, OpenAI Agents SDK, or your own orchestration code. We're working on direct code export for common frameworks.

Does it work with LangChain/LangGraph concepts?

Yes — the node types (LLM, tool, conditional, loop, state) map cleanly to LangGraph nodes and edges. Same for CrewAI tasks and crews, OpenAI Agents SDK, and AutoGen conversation patterns.

How does this compare to Flowise or n8n?

Flowise and n8n are execution platforms — you build AND run workflows in them. Our tool focuses on design: clean diagrams for documentation, planning, and handoff. Use us to plan; use them (or your own code) to run.