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Run deterministic agent orchestration with task decomposition, subagents, and review feedback.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "agentloop" yet — see the docs or source repo.
Use agentloop to design a deterministic multi-agent orchestration flow: decompose the task 'build a user permissions module for a SaaS product', then run architecture, backend, and testing subagents, and finally aggregate review feedback with step-by-step results.
A clear agent orchestration flow with task decomposition, subagent outputs, and final review conclusions.
Use agentloop to create a workflow where multiple LLM backends review the same technical proposal, log feedback from each round, and produce final revision recommendations in a repeatable process.
A repeatable multi-model review workflow with structured feedback logs and revision recommendations.
With agentloop, break down 'research performance and deployment options of open-source vector databases' into subtasks, assign search, summarization, and comparison to different subagents, then run quality review and consolidate the results.
A complete research orchestration flow including subtask assignment, execution outputs, review feedback, and a final consolidated report.
Create, manage, and compose AI agents for MCP-compatible clients and tools.
Add agentic tools with iterative reasoning and tool use to apps
Run dependent or parallel agent tasks and return structured results in one call.
Coordinate multiple AI agents on software projects with shared tasks and context.
Orchestrate multiple AI agents in real time and monitor tasks and artifacts.
Enable AI coding agents to communicate, share state, and coordinate work in real time.