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fix(replay-e2e): match by conversation, not the living system prompt (#3436)

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* fix(replay-e2e): match by conversation, not the living system prompt

The model-replay match key hashed the full input including the lead-agent
system prompt. That prompt is edited frequently (e.g. #3195 added a "File
Editing Workflow" section), so the committed fixture went stale the moment
the prompt changed on main — turning the Layer-2 render gate RED on every
unrelated PR (#3430, #3432, ...). This was a self-inflicted false positive.

Root-cause fix:
- replay_provider._canonical_messages now EXCLUDES the system message from
  the hash. The conversation (human/ai/tool) is the stable contract that
  identifies a recorded turn; the system prompt is an internal detail not
  part of the front-back contract under test. (Mirrors how open-design keys
  its mock picker on the user prompt, not the system internals.) Proven
  robust: injecting a prompt edit no longer causes a replay miss.
- Layer-1 golden was BLIND to replay misses: the gateway swallows a miss
  into an assistant error message, so the shape-only golden stayed green on
  a stale fixture. It now inspects replay_provider.replay_misses() and fails
  loud. (Layer-2 already fails on a miss.)
- Re-recorded write_read_file.ultra fixture + regenerated golden under the
  new conversation-only hash.
- Layer-2 render spec: assert the in-graph auto-title (deterministic); the
  follow-up suggestion is fired async and depends on a clean JSON model
  output, so assert it only when the fixture captured one — never gate on
  its absence (recording flakiness must not block CI).
- docs: REPLAY_E2E.md updated.

Verified: Layer-1 golden green (no miss), Layer-2 both specs green,
CI=true make test 4033 passed / 0 failed, frontend pnpm check clean.

* test(replay-e2e): restore suggestions coverage with a reliable capture

Addresses review feedback (the suggestion path was dropped from Layer-2):

- record spec now waits for the `/suggestions` response before checking
  capture stability, so the recorded fixture reliably includes the
  frontend-fired suggestions turn (previously the stability window could
  return before suggestions fired, yielding a fixture without it).
- Re-recorded write_read_file.ultra: 5 turns (write_file, auto-title,
  read_file, answer, suggestions). Golden unchanged — suggestions is a
  separate /suggestions call, not part of the /runs/stream SSE sequence.
- Layer-2 spec: restore the hard `EXPECTED_SUGGESTION` assertion. With the
  record spec now waiting for /suggestions, a fixture missing the suggestion
  turn means a broken recording and must fail loud, not pass silently.

Verified: Layer-1 golden green (no miss), Layer-2 both specs green
(auto-title + suggestion render), frontend pnpm check clean.

* ci: re-trigger (flaky Docker Hub image pull in sandbox e2e, unrelated)

backend-unit-tests failed only in test_sandbox_orphan_reconciliation_e2e.py
with 'docker pull busybox:latest ... context deadline exceeded' — a CI-runner
network flake reaching Docker Hub, not related to this docs/tests-only change.
Empty commit to re-run CI.

---------

Co-authored-by: DanielWalnut <45447813+hetaoBackend@users.noreply.github.com>
This commit is contained in:
Xinmin Zeng
2026-06-08 17:32:41 +08:00
committed by GitHub
parent 3b105d1e5f
commit 799bef6d9d
7 changed files with 202 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files
backend/docs/REPLAY_E2E.md
+19 -6
View File
@@ -50,12 +50,25 @@ gateway's own run/event stores using the request's auth context, so the real
## How replay works ## How replay works
`tests/replay_provider.py::ReplayChatModel` returns recorded assistant turns keyed `tests/replay_provider.py::ReplayChatModel` returns recorded assistant turns keyed
by a **normalized hash** of the model input (strips `<system-reminder>`, dates, by a **normalized hash of the conversation** (human / ai / tool messages — role,
UUIDs, tmp paths). A miss raises loudly rather than passing silently. The system text, tool-call name+args; with `<system-reminder>`, dates, UUIDs, tmp paths
prompt is made environment-independent by pinning skills + extensions empty and stripped). A miss raises loudly rather than passing silently.
disabling memory/summarization (`tests/_replay_fixture.py::build_config_yaml`), so
a fixture replays the same across machines, days, and CI. Replaying needs **no **The system prompt is excluded from the match key.** The lead-agent system
API key**. prompt is a living, frequently-edited implementation detail — its wording changes
across PRs (e.g. #3195 added a "File Editing Workflow" section). Hashing it would
make every fixture go stale and red-fail unrelated PRs the moment anyone edits the
prompt. The conversation flow (user input → tool calls → results → answer) is the
stable contract that identifies a recorded turn. (This mirrors how open-design's
mock picker keys on the user prompt, not the system internals.) Combined with
pinning skills + extensions empty and disabling memory/summarization
(`tests/_replay_fixture.py::build_config_yaml`), a fixture replays the same across
machines, days, prompt edits, and CI. Replaying needs **no API key**.
A swallowed hash-miss keeps the SSE *event shapes* identical (the gateway wraps it
into a normal assistant error message), so the Layer-1 golden can't catch a miss
by shape alone — it inspects `replay_provider.replay_misses()` and fails loud
instead. Layer-2 already fails on a miss (the recorded turns never render).
## Record a new scenario (needs a real key — dev machine only) ## Record a new scenario (needs a real key — dev machine only)
backend/tests/fixtures/replay/write_read_file.ultra.events.json
Vendored
+60
View File
@@ -64,6 +64,66 @@
"viewed_images" "viewed_images"
] ]
}, },
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{
"event": "values",
"keys": [
"artifacts",
"messages",
"thread_data",
"title",
"viewed_images"
]
},
{ {
"event": "end", "event": "end",
"keys": null "keys": null
backend/tests/fixtures/replay/write_read_file.ultra.json
Vendored
+58 -49
View File
@@ -1,7 +1,7 @@
{ {
"scenario": "write_read_file", "scenario": "write_read_file",
"mode": "ultra", "mode": "ultra",
"model": "gpt-5.5", "model": "sre/gpt-5",
"prompt": "Using your own file tools directly, create the file /mnt/user-data/outputs/note.txt with exactly this content: hi from replay. Then read that same file back and reply with its exact contents. Do NOT delegate to a subagent and do NOT use the task tool — do it yourself. Do not ask any clarifying questions.", "prompt": "Using your own file tools directly, create the file /mnt/user-data/outputs/note.txt with exactly this content: hi from replay. Then read that same file back and reply with its exact contents. Do NOT delegate to a subagent and do NOT use the task tool — do it yourself. Do not ask any clarifying questions.",
"context": { "context": {
"is_bootstrap": false, "is_bootstrap": false,
@@ -12,7 +12,7 @@
}, },
"turns": [ "turns": [
{ {
"input_hash": "686cd44a9f17fadc0398768731324f3980480a027593a475fad4583581df677f", "input_hash": "9c50eda6ab7e8593dabccbdeadc70a4a7bf778b2c0c3f275f1f96cf2c8ab58db",
"output": { "output": {
"type": "ai", "type": "ai",
"data": { "data": {
@@ -20,36 +20,36 @@
"additional_kwargs": {}, "additional_kwargs": {},
"response_metadata": { "response_metadata": {
"finish_reason": "tool_calls", "finish_reason": "tool_calls",
"model_name": "gpt-5.5", "model_name": "sre/gpt-5",
"model_provider": "openai" "model_provider": "openai"
}, },
"type": "ai", "type": "ai",
"name": null, "name": null,
"id": "lc_run--019e8c60-8d4b-79a1-8d77-0a67fc360ce4", "id": "lc_run--019ea641-acda-7423-9a9f-79725057bc20",
"tool_calls": [ "tool_calls": [
{ {
"name": "write_file", "name": "write_file",
"args": { "args": {
"description": "Create requested note file", "description": "Create the requested output file with exact content",
"path": "/mnt/user-data/outputs/note.txt", "path": "/mnt/user-data/outputs/note.txt",
"content": "hi from replay" "content": "hi from replay."
}, },
"id": "call_UdIzq5Vyx7pu1Usnj4wPCC6G", "id": "call_FV7zhKonjx5CAa1RwIcKihpi",
"type": "tool_call" "type": "tool_call"
} }
], ],
"invalid_tool_calls": [], "invalid_tool_calls": [],
"usage_metadata": { "usage_metadata": {
"input_tokens": 3285, "input_tokens": 3664,
"output_tokens": 66, "output_tokens": 434,
"total_tokens": 3351, "total_tokens": 4098,
"input_token_details": { "input_token_details": {
"audio": 0, "audio": 0,
"cache_read": 0 "cache_read": 3584
}, },
"output_token_details": { "output_token_details": {
"audio": 0, "audio": 0,
"reasoning": 21 "reasoning": 384
} }
} }
} }
@@ -60,36 +60,36 @@
"output": { "output": {
"type": "ai", "type": "ai",
"data": { "data": {
"content": "File Creation and Verification", "content": "Direct File Creation and Readback",
"additional_kwargs": {}, "additional_kwargs": {},
"response_metadata": { "response_metadata": {
"finish_reason": "stop", "finish_reason": "stop",
"model_name": "gpt-5.5", "model_name": "sre/gpt-5",
"model_provider": "openai" "model_provider": "openai"
}, },
"type": "ai", "type": "ai",
"name": null, "name": null,
"id": "lc_run--019e8c60-9c18-72c1-95e8-f6a240747395", "id": "lc_run--019ea641-cf52-7793-900e-15ad4f032c0e",
"tool_calls": [], "tool_calls": [],
"invalid_tool_calls": [], "invalid_tool_calls": [],
"usage_metadata": { "usage_metadata": {
"input_tokens": 104, "input_tokens": 104,
"output_tokens": 53, "output_tokens": 656,
"total_tokens": 157, "total_tokens": 760,
"input_token_details": { "input_token_details": {
"audio": 0, "audio": 0,
"cache_read": 0 "cache_read": 0
}, },
"output_token_details": { "output_token_details": {
"audio": 0, "audio": 0,
"reasoning": 39 "reasoning": 640
} }
} }
} }
} }
}, },
{ {
"input_hash": "92430ba866abe577c86d2e67eb5158b10f3f19ec306aa9de235bb06736320d70", "input_hash": "6af134379b2a9efa01b4f63032f88211d5f38f459f8bed621eb6c65e8e05c1f9",
"output": { "output": {
"type": "ai", "type": "ai",
"data": { "data": {
@@ -97,31 +97,31 @@
"additional_kwargs": {}, "additional_kwargs": {},
"response_metadata": { "response_metadata": {
"finish_reason": "tool_calls", "finish_reason": "tool_calls",
"model_name": "gpt-5.5", "model_name": "sre/gpt-5",
"model_provider": "openai" "model_provider": "openai"
}, },
"type": "ai", "type": "ai",
"name": null, "name": null,
"id": "lc_run--019e8c60-b036-7710-8db9-717ab54e5805", "id": "lc_run--019ea641-f523-7d60-a416-b051fba469a2",
"tool_calls": [ "tool_calls": [
{ {
"name": "read_file", "name": "read_file",
"args": { "args": {
"description": "Read requested note file", "description": "Verify contents to echo back exactly",
"path": "/mnt/user-data/outputs/note.txt" "path": "/mnt/user-data/outputs/note.txt"
}, },
"id": "call_0BFNns0FkRb3n2LR0PRrfbIJ", "id": "call_YevFCnLcjWfWHaZm8wwMpEk8",
"type": "tool_call" "type": "tool_call"
} }
], ],
"invalid_tool_calls": [], "invalid_tool_calls": [],
"usage_metadata": { "usage_metadata": {
"input_tokens": 3334, "input_tokens": 3719,
"output_tokens": 33, "output_tokens": 35,
"total_tokens": 3367, "total_tokens": 3754,
"input_token_details": { "input_token_details": {
"audio": 0, "audio": 0,
"cache_read": 0 "cache_read": 3584
}, },
"output_token_details": { "output_token_details": {
"audio": 0, "audio": 0,
@@ -132,29 +132,29 @@
} }
}, },
{ {
"input_hash": "8ab757aa51f9d556adcea07c0221445a2b791cc882ef11922babf7f2865d1913", "input_hash": "04751c4f7b0107b78b5c97d417063883fd586f5ebcbc4acf79be6cb3c0cdaec1",
"output": { "output": {
"type": "ai", "type": "ai",
"data": { "data": {
"content": "hi from replay", "content": "hi from replay.",
"additional_kwargs": {}, "additional_kwargs": {},
"response_metadata": { "response_metadata": {
"finish_reason": "stop", "finish_reason": "stop",
"model_name": "gpt-5.5", "model_name": "sre/gpt-5",
"model_provider": "openai" "model_provider": "openai"
}, },
"type": "ai", "type": "ai",
"name": null, "name": null,
"id": "lc_run--019e8c60-bef3-7201-a30a-cbc5f45920ba", "id": "lc_run--019ea641-ff38-7751-9c2b-cc648811883b",
"tool_calls": [], "tool_calls": [],
"invalid_tool_calls": [], "invalid_tool_calls": [],
"usage_metadata": { "usage_metadata": {
"input_tokens": 3380, "input_tokens": 3768,
"output_tokens": 7, "output_tokens": 8,
"total_tokens": 3387, "total_tokens": 3776,
"input_token_details": { "input_token_details": {
"audio": 0, "audio": 0,
"cache_read": 0 "cache_read": 3584
}, },
"output_token_details": { "output_token_details": {
"audio": 0, "audio": 0,
@@ -165,56 +165,65 @@
} }
}, },
{ {
"input_hash": "fd67723cc8810ce79b4785fec4c251a272a91d677a216c735b23b5f6d3dec0c3", "input_hash": "8b98ebdbb53e88f000556c4753adede8eaa076ff6fd7b8a1285bfd18aee8144d",
"output": { "output": {
"type": "ai", "type": "ai",
"data": { "data": {
"content": "[\"Can you append another line to the file?\",\"Can you show the file path again?\",\"Can you delete the file now?\"]", "content": "[\n \"Can you show the file size and last modified time of /mnt/user-data/outputs/note.txt?\",\n \"List the contents of /mnt/user-data/outputs/ to confirm the file exists.\",\n \"Append 'second line' to /mnt/user-data/outputs/note.txt and print its new contents.\"\n]",
"additional_kwargs": { "additional_kwargs": {
"refusal": null "refusal": null
}, },
"response_metadata": { "response_metadata": {
"token_usage": { "token_usage": {
"completion_tokens": 71, "completion_tokens": 909,
"prompt_tokens": 224, "prompt_tokens": 224,
"total_tokens": 295, "total_tokens": 1133,
"completion_tokens_details": { "completion_tokens_details": {
"accepted_prediction_tokens": 0, "accepted_prediction_tokens": 0,
"audio_tokens": 0, "audio_tokens": 0,
"reasoning_tokens": 33, "reasoning_tokens": 832,
"rejected_prediction_tokens": 0 "rejected_prediction_tokens": 0
}, },
"prompt_tokens_details": { "prompt_tokens_details": {
"audio_tokens": 0, "audio_tokens": 0,
"cached_tokens": 0 "cached_tokens": 0
}, },
"input_tokens": 0, "latency_checkpoint": {
"output_tokens": 0, "engine_tbt_ms": 12,
"input_tokens_details": null "engine_ttft_ms": 324,
"engine_ttlt_ms": 10965,
"pre_inference_ms": 153,
"service_tbt_ms": 12,
"service_ttft_ms": 849,
"service_ttlt_ms": 11491,
"total_duration_ms": 11351,
"user_visible_ttft_ms": 696
}
}, },
"model_provider": "openai", "model_provider": "openai",
"model_name": "gpt-5.5", "model_name": "sre/gpt-5",
"system_fingerprint": null, "system_fingerprint": null,
"id": "chatcmpl-DmaI5yVqQ39LRWyugoCEPalKw0gBR", "id": "chatcmpl-DoPFALdwiyEDYOIN7wFYhqBrr6eTA",
"service_tier": "default",
"finish_reason": "stop", "finish_reason": "stop",
"logprobs": null "logprobs": null
}, },
"type": "ai", "type": "ai",
"name": null, "name": null,
"id": "lc_run--019e8c60-d025-7fd2-9cc9-8b4fb8fe1a82-0", "id": "lc_run--019ea642-0eac-78f1-a506-931e343184f1-0",
"tool_calls": [], "tool_calls": [],
"invalid_tool_calls": [], "invalid_tool_calls": [],
"usage_metadata": { "usage_metadata": {
"input_tokens": 224, "input_tokens": 224,
"output_tokens": 71, "output_tokens": 909,
"total_tokens": 295, "total_tokens": 1133,
"input_token_details": { "input_token_details": {
"audio": 0, "audio": 0,
"cache_read": 0 "cache_read": 0
}, },
"output_token_details": { "output_token_details": {
"audio": 0, "audio": 0,
"reasoning": 33 "reasoning": 832
} }
} }
} }
backend/tests/replay_provider.py
+35 -5
View File
@@ -76,6 +76,24 @@ from pydantic import PrivateAttr
_FIXTURE_ENV = "DEERFLOW_REPLAY_FIXTURE" _FIXTURE_ENV = "DEERFLOW_REPLAY_FIXTURE"
# Process-wide record of replay misses. A miss raises inside the model, but the
# gateway's LLMErrorHandlingMiddleware swallows it into a normal assistant error
# message — so the SSE *event shapes* are unchanged and a shape-only golden stays
# green on a stale fixture. The in-process Layer-1 test inspects this list to fail
# loud on a miss instead. (Layer-2 already fails on a miss: the recorded turns
# never render.)
_replay_misses: list[str] = []
def replay_misses() -> list[str]:
"""Hashes that missed the fixture since the last reset (see ``_replay_misses``)."""
return list(_replay_misses)
def reset_replay_misses() -> None:
_replay_misses.clear()
# Volatile substrings that differ between a recording run and a replay run but # Volatile substrings that differ between a recording run and a replay run but
# carry no semantic weight for matching. Normalized to stable placeholders # carry no semantic weight for matching. Normalized to stable placeholders
# before hashing so the same logical input hashes identically across processes. # before hashing so the same logical input hashes identically across processes.
@@ -117,13 +135,24 @@ def _content_to_text(content: Any) -> str:
def _canonical_messages(messages: list[BaseMessage]) -> str: def _canonical_messages(messages: list[BaseMessage]) -> str:
"""Project messages to a stable shape that excludes volatile metadata/ids. """Project messages to a stable shape that excludes volatile metadata/ids.
Keeps only what determines the model's next output: role, text content, and Keeps only what determines which recorded turn to replay: the conversation
tool-call name+args. Drops ``id``, ``response_metadata``, ``usage_metadata``, (human / ai / tool messages — role, text content, tool-call name+args). Drops
and ``tool_call_id`` (all volatile), then normalizes embedded volatile ``id``, ``response_metadata``, ``usage_metadata``, ``tool_call_id`` (all
substrings. volatile), then normalizes embedded volatile substrings.
**The system message is excluded entirely.** The lead-agent system prompt is
a living, frequently-edited implementation detail (its wording changes across
PRs), not part of the front-back contract this harness verifies. Hashing it
would make every fixture go stale — and red-fail on unrelated PRs — the moment
anyone edits the prompt. The conversation flow (user input -> tool calls ->
results -> answer) is the stable key that identifies a recorded turn.
""" """
projected: list[dict[str, Any]] = [] projected: list[dict[str, Any]] = []
for message in messages: for message in messages:
# Exclude the system prompt from the match key — see docstring. It is the
# most-edited part of the prompt and not part of the contract under test.
if message.type == "system":
continue
content = _normalize_text(_content_to_text(message.content)) content = _normalize_text(_content_to_text(message.content))
tool_calls = getattr(message, "tool_calls", None) tool_calls = getattr(message, "tool_calls", None)
# Drop messages that are empty after normalization — e.g. a turn that was # Drop messages that are empty after normalization — e.g. a turn that was
@@ -189,6 +218,7 @@ class ReplayChatModel(BaseChatModel):
key = hash_messages(messages) key = hash_messages(messages)
bucket = self._table.get(key) bucket = self._table.get(key)
if not bucket: if not bucket:
_replay_misses.append(key)
preview = _canonical_messages(messages) preview = _canonical_messages(messages)
raise KeyError( raise KeyError(
f"replay miss: no recorded output for input hash {key} in {self._fixture_path!r}. " f"replay miss: no recorded output for input hash {key} in {self._fixture_path!r}. "
@@ -227,4 +257,4 @@ class ReplayChatModel(BaseChatModel):
# Re-export so the recorder shares the exact hashing logic. # Re-export so the recorder shares the exact hashing logic.
__all__ = ["ReplayChatModel", "hash_messages"] __all__ = ["ReplayChatModel", "hash_messages", "replay_misses", "reset_replay_misses"]
backend/tests/test_replay_golden.py
+13 -3
View File
@@ -66,14 +66,24 @@ def test_replay_write_read_file_ultra_matches_golden(tmp_path: Path, monkeypatch
cfg = app_config_module.get_app_config() cfg = app_config_module.get_app_config()
cfg.database.sqlite_dir = str(home / "db") cfg.database.sqlite_dir = str(home / "db")
# Fail loud on a replay miss. The gateway swallows a hash-miss into a normal
# assistant error message, so the SSE *shapes* below stay green on a stale
# fixture — the miss list is the only reliable signal at this layer.
import replay_provider
from app.gateway.app import create_app from app.gateway.app import create_app
replay_provider.reset_replay_misses()
events = drive_gateway(create_app(), prompt=fixture["prompt"], context=fixture["context"]) events = drive_gateway(create_app(), prompt=fixture["prompt"], context=fixture["context"])
assert events, "replay produced no SSE events" assert events, "replay produced no SSE events"
assert events[0]["event"] == "metadata", f"first event should be metadata, got {events[0]!r}" assert events[0]["event"] == "metadata", f"first event should be metadata, got {events[0]!r}"
assert events[-1]["event"] == "end", f"last event should be end (run completed), got {events[-1]!r}" assert events[-1]["event"] == "end", f"last event should be end (run completed), got {events[-1]!r}"
misses = replay_provider.replay_misses()
assert not misses, f"replay miss ({len(misses)}): the fixture is stale vs the current system prompt or agent graph. Re-record it (see backend/docs/REPLAY_E2E.md). Missed hashes: {misses}"
# Regenerate the committed golden after re-recording the fixture: # Regenerate the committed golden after re-recording the fixture:
# DEERFLOW_WRITE_GOLDEN=1 uv run pytest tests/test_replay_golden.py # DEERFLOW_WRITE_GOLDEN=1 uv run pytest tests/test_replay_golden.py
if os.environ.get("DEERFLOW_WRITE_GOLDEN"): if os.environ.get("DEERFLOW_WRITE_GOLDEN"):
@@ -81,7 +91,7 @@ def test_replay_write_read_file_ultra_matches_golden(tmp_path: Path, monkeypatch
return return
golden = json.loads(events_path.read_text(encoding="utf-8"))["events"] golden = json.loads(events_path.read_text(encoding="utf-8"))["events"]
# A replay hash-miss surfaces as the run erroring mid-stream -> the event # Guards backend SSE protocol drift: the event name + payload-key sequence
# shape sequence diverges from the golden, so this assertion is the catch-all # must match the committed golden. (Replay divergence is caught by the miss
# for both backend SSE drift and replay divergence. # assertion above, not here — a swallowed miss keeps the shapes identical.)
assert events == golden, f"SSE event-shape sequence drifted from the golden.\ngot ({len(events)}): {[e['event'] for e in events]}\nwant ({len(golden)}): {[e['event'] for e in golden]}" assert events == golden, f"SSE event-shape sequence drifted from the golden.\ngot ({len(events)}): {[e['event'] for e in events]}\nwant ({len(golden)}): {[e['event'] for e in golden]}"
frontend/tests/e2e-real-backend/real-backend-render.spec.ts
+7 -3
View File
@@ -85,17 +85,21 @@ test.describe("real backend render (replay, no API key)", () => {
await textarea.fill(PROMPT); await textarea.fill(PROMPT);
await textarea.press("Enter"); await textarea.press("Enter");
// Replay-only DOM assertions (derived from the fixture): they render only if // Replay-only DOM assertions (derived from the fixture): both are
// model-generated strings absent from the user prompt, so they render only if
// the recorded turns replayed AND the real frontend rendered them — the // the recorded turns replayed AND the real frontend rendered them — the
// in-graph auto-title and the post-answer follow-up suggestion. Together they // in-graph auto-title and the post-answer follow-up suggestion. Together they
// prove the whole pipeline (replay backend -> real frontend render). // prove the whole pipeline (replay backend -> real frontend render). The
// record spec waits for the /suggestions response, so a re-recorded fixture
// always captures the suggestion turn — a missing one is a broken recording
// and must fail loud here, not pass silently.
expect( expect(
EXPECTED_TITLE, EXPECTED_TITLE,
"fixture should contain an auto-title turn", "fixture should contain an auto-title turn",
).not.toBe(""); ).not.toBe("");
expect( expect(
EXPECTED_SUGGESTION, EXPECTED_SUGGESTION,
"fixture should contain a suggestions turn", "fixture should contain a suggestions turn (re-record; the record spec waits for /suggestions)",
).not.toBe(""); ).not.toBe("");
await expect(page.getByText(EXPECTED_TITLE)).toBeVisible({ await expect(page.getByText(EXPECTED_TITLE)).toBeVisible({
timeout: 60_000, timeout: 60_000,
frontend/tests/e2e-record/record-write-read-file.spec.ts
+10
View File
@@ -104,6 +104,16 @@ test("record write/read-file run through the real frontend", async ({
await textarea.fill(PROMPT); await textarea.fill(PROMPT);
await textarea.press("Enter"); await textarea.press("Enter");
// Suggestions fire only AFTER the run completes (input-box.tsx POSTs
// /suggestions). Wait for that response so its model call lands in the capture
// before we check for stability — otherwise the stability window can return
// first and the recorded fixture would be missing the suggestions turn.
await page
.waitForResponse((r) => r.url().includes("/suggestions"), {
timeout: 90_000,
})
.catch(() => undefined);
const captured = await waitForCaptureStable(out!); const captured = await waitForCaptureStable(out!);
console.log( console.log(
`[record] captures stabilized at ${captured} model call(s) -> ${out}`, `[record] captures stabilized at ${captured} model call(s) -> ${out}`,