Follow-up to #3971. aimock supports fixture schema validation at startup via
--validate-on-load, but it's opt-in. The showcase Dockerfile did not pass
the flag, so fixtures with unrecognized response keys (e.g. "text" instead
of "content") loaded silently and only failed at request time with HTTP 500.
That's what crashed crewai-crews on startup.
Changes:
- showcase/aimock/Dockerfile: pass --validate-on-load so broken fixtures
fail the container boot, not individual requests.
- showcase/scripts/__tests__/aimock-fixtures.test.ts: new vitest spec that
loads feature-parity.json and smoke.json via @copilotkit/aimock's
loadFixtureFile + validateFixtures and asserts zero errors. Runs as part
of the existing showcase-validate CI workflow.
- showcase/scripts/package.json: add @copilotkit/aimock dependency for the
validator import.
Verified red-green: with the pre-#3971 broken "text" fixtures, validateFixtures
flags 5 errors; post-#3971 it returns zero. Docker red-green: container with
an intentionally broken fixture fails to start with "Validation failed: 1
error(s)" and non-zero exit.
The showcase aimock feature-parity fixture had five entries that used the
unrecognized field "text" instead of "content" in the response body.
aimock's /v1/chat/completions handler validates the response shape through
discriminators (isTextResponse, isToolCallResponse, etc.) and when none
match, returns 500 "Fixture response did not match any known type".
When CrewAI's ChatWithCrewFlow.__init__ calls generate_input_description_with_ai,
it sends a prompt containing "Reporting Analyst" / "reports" text (from the
LatestAiDevelopment crew definition). aimock substring-matches this against
the "report" fixture, which had the broken "text" shape, and returned 500.
CrewAI treats that as an InternalServerError, the crewai-crews container
crashes during module import (before uvicorn binds), Railway healthchecks
fail, and the deployment rolls back to the previous ACTIVE build.
Fix:
- Convert the 5 fixtures with "text" (plan, steps, mars, dashboard, report)
to the valid "content" field so they return 200 OK.
- Add a targeted fixture that matches CrewAI's exact startup prompt prefix
("Based on the following context, write a concise") and returns a clean
text description. This catches generate_input_description_with_ai and
generate_crew_description_with_ai before they can fall through to the
more generic fixtures, providing a more predictable response for CrewAI
startup.
Verified locally against @copilotkit/aimock@1.10.0:
- Before: the CrewAI-style prompt returns 500 with the exact error message
seen in Railway logs.
- After: returns 200 with a valid chat.completion envelope.
Note: CrewAI's blocking LLM call at module import remains fragile (any
aimock hiccup crashes the container before it can bind a port). That is
an upstream issue and will be tracked separately.
Jordan Ritter