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feat(acp-example): per-scenario workspace/ seeding + a real file-edit scenario

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Establishes the standard way to give a snapshot scenario a non-empty starting
workspace: an optional `<scenario>/workspace/` directory whose contents the
harness copies into the temp cwd before the run (for both record and replay),
so the agent's bash tools see the seeded files. The cwd is normalized in the
goldens, so seeded paths stay stable.

The new `workspace-edit` scenario demonstrates the full read→write→verify cycle
on a seeded file: it ships `workspace/greeting.txt` ("hello"), prompts the agent
to append a WORLD line and cat it back. The recorded log captures the real bash
edits (`echo WORLD >> greeting.txt`, then `cat` showing `hello\nWORLD`), and it
replays deterministically with no key.

Also hardens runScenario teardown (Codex review): workspace seeding and spawn
now run inside the try whose finally removes both temp dirs, so a seeding/spawn
failure can't leak them. Documents the convention in the RFC + example README.
This commit is contained in:
Tianyi Cui
2026-06-19 10:01:42 +08:00
parent 679aaacfc4
commit 76fceae5b3
9 changed files with 972 additions and 65 deletions
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examples/acp-agent/README.md
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@@ -32,7 +32,7 @@ The editor sets each session's `cwd` to the project it opens; the agent's bash t
## 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 `src/llm-replay.ts`, 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). See [docs/rfc/implemented/2026-06-19-acp-snapshot-tests.md](../../docs/rfc/implemented/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 `src/llm-replay.ts`, 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 [docs/rfc/implemented/2026-06-19-acp-snapshot-tests.md](../../docs/rfc/implemented/2026-06-19-acp-snapshot-tests.md) for the full design.
## MVP limitations