May 2026 monthly summary for dapr/dapr-agents focusing on delivering a robust Human-in-the-Loop (HITL) workflow for the durable agent, elevating governance, reliability, and test coverage. The work centered on introducing a hook-based HITL runtime, expanding configurations and schemas, and validating changes with comprehensive tests and examples. Dependency upgrades and stability hardening were performed to ensure long-term maintainability and performance.

April 2026: Implemented Usage Data Extraction Enhancement in pydantic/pydantic-ai. Refactored usage extraction to leverage RequestUsage.extract(), improving processing, structuring of usage data, and ensuring provider-specific details are captured. This enhances data quality for analytics and billing readiness, and reduces duplication by centralizing extraction logic. No major bugs reported this month; focus was on delivering a robust data pipeline and laying groundwork for downstream integrations.

March 2026: Implemented and hardened purge workflow data management for dapr/dapr-agents, delivering a safe data cleanup mechanism for workflow state and long-term memory. The feature reduces data buildup and improves governance for individual workflow instances, with enhanced robustness and error handling to prevent cleanup failures from blocking progress. Also refined tests to cover failure paths and reviewed changes for reliability and maintainability across the agent/orchestrator stack.