Auto-captures decision context from multi-agent workflows to preserve the 'why' behind every choice. Enables task traceability, reasoning retrieval, and continuous improvement across planning and implementation sessions.
Enables recording and analyzing AI agent execution traces, including event logging, metric computation, loop detection, and JSON export for debugging agent behavior.
Provides a persistent hierarchical task tree for LLM agents, enabling them to decompose work, track progress, record results, and handle failures outside the context window, with an optional web UI for monitoring and control.
