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Orchestrate parallel AI workers to decompose, review, and continue complex tasks.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "random-agent" yet — see the docs or source repo.
Decompose “research pricing, core features, and user feedback for five competitors” into parallel subtasks, assign them to different workers, then consolidate results, auto-review conflicting findings, and generate next-step recommendations.
A structured report with task decomposition, worker outputs, review conclusions, and next research recommendations.
For the issue “API response has become slow,” create multiple workers to inspect logs, recent commits, performance bottlenecks, and test coverage in parallel; automatically summarize findings, identify the most likely causes, and generate follow-up investigation tasks.
An orchestrated troubleshooting result including parallel analyses, priority assessment, and next-round execution tasks.
For “publishing an AI tool review article,” assign different workers to research, outline, draft, and fact-check; after completion, automatically review overall quality and generate a revision checklist.
A content production package with stage outputs, overall review feedback, and an actionable revision task list.
Manage AI agent tasks in real time with human-in-the-loop collaboration.
Use multi-agent chat and multiple models to handle complex workflows.
Turn goals into task DAGs and orchestrate TypeScript-native multi-agent workflows.
Orchestrate multi-agent workflows with parallel tasks, pipelines, scheduling, and peer review.
Launch parallel autonomous AI agents from one MCP client for complex tasks.
Enable AI agents to read, write, and evolve memory across apps.