Studio
Drag, connect, and run. Design multi-step agent workflows on a canvas instead of wiring SDKs and glue code.
Choose a credit plan. Credits are a universal balance across every Developers Digest app, so nothing is stranded in one tool.
One workflow wraps the underlying models. No API keys, no SDK setup, and the credit cost is always shown before you commit.
Export web-ready output and move on. Everything you make keeps its full recipe, so you can reproduce or iterate later.
Every agent is a graph of nodes and edges on a canvas. The logic is visible at a glance, so you and anyone you share it with can understand the flow without reading code.
Drop in LLM steps, your connected tools, branches, and loops. Mix models within one flow and route each step to whatever handles it best.
Execute a flow and watch data move through the graph live. Every node shows its input and output, so debugging means clicking the step that went wrong.
Swap a model, tweak a prompt, or reroute a branch and rerun from any node. The canvas keeps prior results so you only re-execute what changed.
Publish a flow as a reusable agent with its own trigger: run it on demand, on a schedule, or from an event. The canvas is the source of truth either way.
Designing and editing is free. Runs are metered by the universal Developers Digest credit balance shared with chat, images, voice, and more.
Agent Builder is a visual tool for building AI agents. You design workflows as a graph of nodes on a canvas, connecting models, tools, connectors, and logic, then run and debug the whole flow before shipping it as a reusable agent.
No. Flows are built by dragging nodes and connecting them. If you want code, optional script nodes let you drop in custom logic, but everything from prompts to branching works without it.
Flows can mix multiple LLMs in one graph and call your connected tools like Gmail and Slack, plus logic nodes for branching and loops. Each step routes to whatever handles it best.
Every node records its input and output for each run. You click the step that misbehaved, see exactly what it received and produced, fix it, and rerun from that node without repeating the rest of the flow.
A behind-the-scenes walkthrough of building and deploying AI agents fast, plus the map of what to learn and where to go next.
Read postLoop patterns, state management, and error recovery for the multi-step flows you assemble on the canvas, so a five-step demo holds up at scale.
Read postTyped gates, validated evidence, and controlled transitions. Why agent processes want a real graph, not a prompt checklist.
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