Last year I was running a LangGraph agent in production. It failed on step 4 of 6 nd by the time I opened the logs, the state was gone. I had no idea what the agent was holding in memory at step 3, what tool call it made, or what the model actually returned. I reran it and it passed. I had no idea why.

That kept happening and I started adding print statements everywhere. Then I started dumping state to files manually but at some point I had more debugging code than actual agent code and it still wasn't giving me what I needed.

So I built ai-agent-vcr. It records the full execution state at every step inputs, outputs, tool calls, memory, model responses Under 5ms overhead. When something breaks you rewind to the exact step, look at what the agent was holding, edit it if you want nd resume from there without rerunning everything before it. The name is a bad VHS joke.

The thing I kept getting wrong before I built this I assumed most agent bugs were model bugs. They are not. The model did exactly what you told it to given what was in context.

The problem is that by step 4 the context has drifted from what you expected, and without a record of how it got there you are just guessing.

Works with LangGraph and CrewAI and Traces are plain JSONL, git-friendly, no cloud required.

Still early and rough around the edges. Would genuinely like to hear from anyone who has hit the same wall.