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A planner agent breaks your request into tasks, then assigns each task to a specialist agent with a clear role. The planner creates a small mission list, delegates in parallel, then combines everything into one final answer.

Sub-agents run concurrently, each focused on one objective. The system waits for all outcomes, then combines them. Three independent tasks launch together, then the final answer is assembled from all three outputs.

lmkgpt exposes an API endpoint so external systems can trigger the same multi-agent workflow. A product can submit a prompt server-to-server and receive structured outputs plus the final summary.

Progress is streamed from server to page continuously, so updates appear as agents produce results. Agent statuses and text stream in increments, not as a single delayed payload.

The app keeps the planning steps, per-agent objectives, and raw outputs visible for review. You can verify that the final answer matches what the agents actually produced.

The system prefers one provider first, then falls back when errors are retryable. Each agent can recover independently, so one timeout does not collapse the whole run.