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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.
Detect retry loops and iteration patterns to improve debugging and repair attempts.
Run deterministic agent orchestration with task decomposition, subagents, and review feedback.
Turn plain-English goals into verified, looped, observable IDE agent build runs.
Detects agent infinite loops and provides safeguards with recovery recommendations.
Add human approval checkpoints to AI agents with signed decision proof.
Helps AI agents learn from tasks, detect mistakes, and surface insights.