Global Retry Budget Tracker Connector for AI agents.

It forces agents to check a central credit pool before retrying a failed task. By assigning a cost to errors like rate limits, it stops agents from retrying too many times and hitting your API limits repeatedly.

Yes, as long as your client is MCP-compatible, such as Claude, Cursor, or Windsurf, you can use this to manage your agentic workflows.

You can check the per-agent usage breakdown to see exactly how many credits each individual agent has consumed during the current session.

Yes, you can define a custom cost mapping so that a simple timeout costs less than a more expensive rate limit error.

Once the budget is exhausted, the agents will be prevented from attempting further retries, effectively breaking any infinite loops that were consuming resources.

You can simply trigger a reset to clear all current usage data and start the session over with a fresh pool of credits.

The system uses a credit-based model. You define a total_budget and a cost_map. When an agent encounters an error, check_and_deduct_retry checks if the remaining budget covers the cost of that specific error_type. If it does, the cost is deducted and the retry is permitted.

Yes. The check_and_deduct_retry capability tracks expenditure per agent_id, and you can view the full breakdown using get_session_status.

Once the budget is exhausted, should_retry will return false, effectively blocking any further retry attempts until the budget is reset.