amberfork

Point at a failing AI-agent run. amberfork aligns it against a known-good run, finds the exact step where they diverged, and shows what changed. Local, deterministic, no account.

cargo install amberfork
amberfork demo        # a bundled sample pair — see a fork with zero setup, no files

Then point it at your own traces:

amberfork diff bad.json --against good.json    # exits 1 on a fork; --json for machines

Traces are plain JSON — a run is an ordered list of steps (llm / tool / agent / other, each with a name and inputs/outputs). The format is deliberately forgiving and documented in docs/trace-format.md; converting your agent framework's log into it is typically a ~50-line script.

Why trust it

• Deterministic. Lexical/tf-idf alignment — same inputs, same answer, no model call, no API key, nothing leaves your machine.
• Measured. On the pre-registered benchmark (sealed test split, n=35), the engine localizes the injected fork within 3 steps 91% of the time vs 49% for the best positional baseline. Protocol, splits, and every number: BENCHMARK.md.
• Honest output. Absorbed noise (retries, re-orders) is shown as such; the fork ships with a confidence, and --json exposes the full alignment for machines.

Full documentation, benchmark reproduction, and the engineering notebook live in the repository.