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Orchestrate local multi-agent workflows with gated lifecycle, handoffs, and host continuation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "GRACE Orchestrator MCP" yet — see the docs or source repo.
Use GRACE Orchestrator MCP to build a local multi-agent workflow: let a requirements agent break down the task, pass it to a coding agent for implementation, and then have a testing agent validate the result. Define gated conditions, handoff events, and host-level continuation strategies for failures.
A runnable multi-agent orchestration plan with stage definitions, gating rules, agent handoffs, and failure continuation logic.
Design a local research workflow with GRACE Orchestrator MCP: a retrieval agent gathers sources, a summarization agent extracts key points, and a review agent checks conclusion reliability. Configure lifecycle controls, agent handoff events, and continue only after human approval.
A research workflow orchestration setup describing agent roles, trigger conditions, human approval checkpoints, and continuation behavior.
Use GRACE Orchestrator MCP to orchestrate a local operations troubleshooting flow: after a monitoring agent detects an anomaly, hand the event to a diagnosis agent for analysis, then let a remediation agent generate recovery suggestions. Include stage gating, event handoffs, and host-level resume mechanisms.
An ops-focused orchestration plan covering anomaly triggers, agent collaboration chains, and resume-after-failure rules.
Orchestrate multi-model agents for plan-code-review-deliver workflows.
Orchestrate multiple MCP server tools for complex Python workflows with logic.
Enforce structured AI workflows with dependencies, quality gates, and validated outputs.
Connect AI agents to IBM OpenPages for GRC queries and workflow actions.
Orchestrate and manage multiple MCP services through one unified interface.
Quickly test MCP connections with a simple greeting response.