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Insight for: [Feature] Task retry and error recovery

Robust error handling and fault tolerance for multi-agent tasks. Specifically, configurable retry logic and error recovery strategies for failed LLM API calls.

Analyzed: Apr 2, 2026

This feature request for configurable retry logic and error recovery directly addresses a critical reliability concern for multi-agent systems in 'production environments.' The current 'aggressive' cascadeFailure() mechanism for transient LLM API errors (rate limits, timeouts) is impractical. Implementing retryPolicy with backoff strategies and distinguishing 'retryable vs non-retryable errors' is essential for building resilient AI applications. This enhancement positions the framework as more robust and enterprise-ready, reducing operational overhead and improving overall system stability. It acknowledges the inherent unreliability of external API dependencies and provides a necessary mechanism for graceful degradation and self-healing, crucial for market adoption in mission-critical use cases.

Task retry error recovery configurable retry logic failed tasks production environments LLM API calls rate limits timeouts transient errors cascadeFailure() downstream tasks retryPolicy maxRetries backoff (exponential, linear, fixed) retryableErrors task level agent prompt retry events observability acceptance criteria

GitHub Issue

Parent Entity

[Feature] Task retry and error recovery

State: Open