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Add keyless snapshot refresh mode

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Tianyi Cui
2026-07-10 00:48:27 +08:00
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examples/acp-agent/README.md
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@@ -33,7 +33,7 @@ The editor sets each session's `cwd` to the project it opens; both the agent's b
## Snapshot tests (record-once / replay-deterministic)
This example is the home of the harness's **snapshot tests** — they boot this server as a real subprocess, drive it with a deterministic input script, and diff its normalized output against committed golden files. The model is made deterministic by `@deepseek-ai/dsh-llm-replay`, a function/namespace plugin that installs an `llm/stream` waterfall listener and short-circuits it, serving model streams reconstructed from a recorded **session JSONL** fixture (`<scenario>/session.jsonl`) — so replay needs no API key. The fixture IS the persisted session log: its `assistant/chunk` events carry every `StreamChunk`, so grouping them by `(turn, step)` reconstructs each `stream()` call (one model call per loop step). Recording is therefore "run the real agent once and harvest the `.jsonl`". The two failure modes not expressible as logged chunks — a pure throw before any chunk, and cancel/hang — use an optional `<scenario>/replay.override.json` sidecar (a `ReplayEntry[]` that replaces the derived script). A scenario that needs the agent to operate on existing files ships an optional `<scenario>/workspace/` directory — the harness copies its contents into the temp cwd before the run (see `workspace-edit`). See [the ACP snapshot tests RFC](../../docs/rfc/implemented/testing/2026-06-19-acp-snapshot-tests.md) for the full design.
This example is the home of the harness's **snapshot tests** — they boot this server as a real subprocess, drive it with a deterministic input script, and diff its normalized output against committed golden files. The model is made deterministic by `@deepseek-ai/dsh-llm-replay`, a function/namespace plugin that installs an `llm/stream` waterfall listener and short-circuits it, serving model streams reconstructed from a recorded **session JSONL** fixture (`<scenario>/session.jsonl`) — so replay needs no API key. The fixture IS the persisted session log: its `assistant/chunk` events carry every `StreamChunk`, so grouping them by `(turn, step)` reconstructs each `stream()` call (one model call per loop step). Recording is therefore "run the real agent once and harvest the `.jsonl`"; use `pnpm run test:snapshot:record` when the model transcript itself should change, and `pnpm run test:snapshot:refresh` when the committed model transcript is still the right mock input and only the current replay output/goldens need to be rewritten. The two failure modes not expressible as logged chunks — a pure throw before any chunk, and cancel/hang — use an optional `<scenario>/replay.override.json` sidecar (a `ReplayEntry[]` that replaces the derived script). A scenario that needs the agent to operate on existing files ships an optional `<scenario>/workspace/` directory — the harness copies its contents into the temp cwd before the run (see `workspace-edit`). See [the ACP snapshot tests RFC](../../docs/rfc/implemented/testing/2026-06-19-acp-snapshot-tests.md) for the full design.
## MVP limitations