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Supervise self-improving AI loops by mining sessions and optimizing workflows continuously.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "super-loop-mcp" yet — see the docs or source repo.
Analyze the last 20 agent run logs, identify repeated failure steps, inefficiencies, and auto-fixable patterns, then propose an improved loop workflow.
A recommendation report with failure patterns, root causes, and an optimized agent loop process.
Read past sessions, determine which task strategies had the highest success rates, and turn them into new default execution rules, continuing until I stop you.
Extracted high-success strategies, an updated rule set, and ongoing execution status updates.
Act as a supervisor for the current self-improving agent loop, immediately correct goal drift, repeated attempts, or quality drops, and log the reason for each correction.
A supervision log containing corrective actions, trigger conditions, and quality improvement notes.
Continuously mines and improves AI agent workflows for self-improving execution loops.
Find, adapt, and design reusable AI agent workflow loops.
Turn plain-English goals into verified, looped, observable IDE agent build runs.
Detects agent infinite loops and provides safeguards with recovery recommendations.
Run iterative AI coding tasks on repositories with quality, security, and approvals.
Orchestrate tasks as a DAG with validation loops and iterative fixes