For agents whose top-level behavior is a pre-defined graph.

Desired features:

• Max depth / max loop iterations to avoid infinite loops
• Easy forking & joining
  • join on all tasks in the workflow
  • create a semaphore, add tasks, check if all tasks are done
• “Pause” for user input
• Read the output of any parent step, by name or by traversing graph
• Allow returning a status (e.g. expected failure) return value, and optionally a mutation handle to call exactly once and args for it (if you want more than the state in the graph journal)

Sample syntax

• In the s.doWhatTheLLMSaid (brain) mutation, it does addChild for all LLM tool calls / other actions, waiting for them to finish before continuing. The whole loop could be run from a brain mutation.
• In the future, we could add inline mutation functions, since .define is registering a mutation & can get called as the step, with a cursor pointing to the step it should execute & with what context.

import { WorkflowManager, workflowBrain, chain, loop, parallel } from "@convex-dev/workflow"; const workflow = new WorkflowManager(components.workflow, { workpool: components.workpool, // you can share it between agents }); // for conciseness const s = internal.mySteps; export const support = workflow.define({ name: "supportOrchestrator", args: { userId: v.id("users"), initialComplaint: v.string() }, steps: chain() .mutation(s.createThread) // args are typed as workflow initial args .parallel([ chain().action(s.investigateUserHistory), chain().action(s.detectProductOutage) ]) .loop({ maxIterations: 10, steps: chain() .action(s.askLLMWhatToDo) .mutation(s.doWhatTheLLMSaid), // this step dispatches children shouldContinue: s.askLLMToContinue }) .action(s.askLLMForNextStep) .mutation(s.updateStatusAndDispatch) ) });