dag-replay-debugger

DAG replay debugging enables state inspection of completed nodes to locate where a decision or reasoning branch diverged. You review inputs, prompts, outputs, evaluator scores, context entries, timing, and cost to pinpoint the exact failure point in a multi-node agent pipeline.

Checkpoint replay supports replaying a run from any completed node with options to keep inputs, edit upstream outputs, swap skills, or change model tiers. This lets you evaluate alternate outcomes deterministically without rerunning the entire pipeline from scratch.

Execution diffing compares original and replayed traces side-by-side to spot missing outputs, model drift, routing mistakes, or cost and quality tradeoffs. This side-by-side comparison reveals exactly where and why two runs diverged for root-cause analysis.

Time-travel debugging for DAG executions provides post-mortem analysis by operating against stored execution traces and checkpoints. It exposes node-level state including reasoning traces and chain-of-thought, enabling engineers to inspect why a node made a particular decision after a run completes.

Yes, this replay debugger operates against stored execution traces and checkpoints from completed DAG runs. You need prior execution data to inspect inputs, prompts, outputs, and reasoning traces, or to perform modified replay and execution diffing for root-cause analysis.

Use replay debugging when a full rerun is costly and you need to isolate a divergence point. It supports editing upstream outputs, swapping skills, or changing model tiers at specific nodes to validate fixes deterministically, avoiding the overhead of rerunning the entire multi-node pipeline.