DETERMINISTIC REPLAY
Same trace produces same replay behavior. Same final state after replay. Every time, guaranteed.
A local-first developer tool for recording, replaying, and testing AI agent behavior. Make agent execution deterministic, observable, and testable.
Python SDK, VS Code Extension, and CLI tools for complete agent observability.
Decorator-based recording. @acp.tool() and @acp.llm_wrapper capture every execution automatically.
Timeline view, state inspector, diff viewer, and run comparison for forensic debugging.
Assert on patterns, tool usage, and outcomes. Tests catch regressions without depending on exact text.
acp inspect, replay, test, and analyze. All commands work on recorded artifacts—no agent execution.
Same trace = identical execution. Replay never calls external APIs. Read-only, side-effect free.
Detects high step count, memory growth, repeated calls, and error rates automatically.
Same trace produces same replay behavior. Same final state after replay. Every time, guaranteed.
Any step can be inspected. Inputs, outputs, and state visible at every decision point.
Tests catch logic regressions. Tests don't depend on exact text. YAML-based definitions.
Tool highlights inefficiencies. Broken agent scenario exists. Tool explains why it broke.
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Agent Control Plane — Built with precision.