Both BYOC demo pills were getting eaten by older generic substring
fixtures ("dashboard", "revenue by category as a pie chart", "monthly
expenses as a bar chart") — the json-render pills came back as foreign
tool calls (render_pie_chart) and the hashbrown sales pill came back as
the "Working on your request..." placeholder, leaving both renderers
empty.
Add a dedicated fixture per pill keyed on the full pill prompt sentence,
placed before the conflicting generic rules so first-fixture-wins lands
on the right one. Each fixture returns the exact content shape the
demo's renderer expects:
byoc-json-render -> {root, elements} flat spec for <Renderer />
byoc-hashbrown -> {ui:[{<tag>:{props:{...}}}]} for useJsonParser
Removes two pre-existing duplicates that had been added at the bottom of
the file but never matched because the conflicting generic rules came
first.
Beautiful Chat suggestion clicks looped forever because feature-parity.json
tool-calling fixtures lacked an `id` and a paired `toolCallId` followup.
After the agent ran the tool and re-prompted aimock, the same userMessage
substring matched again and the same toolCall was returned indefinitely.
Added explicit ids to 10 broken fixtures (pieChart, barChart, render_*_chart,
scheduleTime, search_flights, toggleTheme) and 11 paired toolCallId
followups returning content summaries — same convention the file already
uses for show_card, weather, etc.
Beautiful Chat layout also showed a black/white split and a broken logo on
the 8 integrations using the full ExampleLayout pattern (crewai-crews,
langgraph-fastapi, langgraph-python, langgraph-typescript, mastra,
ms-agent-dotnet, ms-agent-python, pydantic-ai). Two issues:
1. globals.css hardcoded `body { background: #fafaf9 }` and never defined
the brand tokens (--background, --foreground, --card, --primary, …) that
the layout, mode-toggle, todo card/column, and chart components reference
via Tailwind 4 arbitrary values. ThemeProvider was also adding `dark` to
<html> from system preference, so CopilotKit's chat went dark while body
stayed cream.
2. example-layout/index.tsx renders <img src="/copilotkit-logo.svg" /> but
the file did not exist in any integration's public/.
Added the full token set (light + dark) under :root and :root.dark/.dark,
registered the Tailwind 4 dark variant, switched body to var(--background)
/var(--foreground), and copied copilotkit-logo.svg + copilotkit-logo-mark.svg
into each integration's public/ from examples/integrations/langgraph-python.
Production showcase-aimock was running a week-old image because fixture
file changes in showcase/aimock/ did not trigger a CI rebuild. This adds
showcase-aimock to the Build & Deploy workflow matrix so it auto-deploys
on merge, creates a thin Dockerfile that bakes fixture files into the
image, documents the local-vs-production parity requirement in
docker-compose.local.yml, and adds an aimock fixture deployment section
to the RUNBOOK.
Add 3 fixtures for the independent LLM calls made by each sub-agent
tool (research_agent, writing_agent, critique_agent) so the demo works
without --proxy-only mode. Interleave them with supervisor fixtures for
readability. Rebuild d5-all.json bundle.
aimock v1.16.0 ships native turnIndex matching — counts assistant
messages in the request's message array instead of relying on
toolCallId in the last tool message. Stateless, concurrent-safe,
fixture-ordering-insensitive.
Removes all toolCallId match criteria from 9 D5 fixture files (8
source + 1 bundle), adds turnIndex values, and reorders fixtures
to natural 0→N reading order.
aimock sequenceIndex counter is server-global, causing fixture match
failures when multiple integrations run concurrently. Replace with
stateless toolCallId matching.
agent-config — empty stream
The /demos/agent-config runtime returned RUN_ERROR on every send. The
existing route wrapper repacked the provider's properties into
forwardedProps.config.configurable.properties so the agent could read
them via RunnableConfig["configurable"]["properties"]. LangGraph 1.x
(deployed langgraph 1.1.x / langgraph-api 0.7.x) now rejects any run
that sends configurable with "Cannot specify both configurable and
context. Prefer setting context alone." because it auto-injects an
empty context and refuses both channels at once.
Switch to LangGraph 1.x's context API: route wrapper repacks user props
onto forwardedProps.context instead, the Python graph defines
context_schema=AgentConfigContext and reads via the
Runtime[AgentConfigContext] parameter, and RunnableConfig is no longer
consulted. Unit tests updated to pass the flat context dict directly.
Add context to RESERVED_FORWARDED_PROPS_KEYS so a caller that
explicitly sets forwardedProps.context is preserved (and merged with
provider-supplied properties) instead of being treated as a user prop
and double-wrapped.
multimodal — PDF "doesn't work"
The multimodal pipeline (legacy-shape rewrite shim → ag-ui-langgraph
converter → _PdfFlattenMiddleware running pypdf) is fine; sample.pdf
extraction shows up correctly in the run state snapshot. The visible
breakage is the aimock fixture file: aimock matches userMessage as a
substring, and the generic "hi" fixture lives early enough to swallow
any prompt containing "this" — including "What is in this PDF?" and
"What is in this image?" — short-circuiting before the request reaches
real OpenAI and returning a generic "I'm your showcase assistant"
greeting that never references the attachment.
Add two more-specific fixtures ("this PDF", "this image") ahead of the
"hi" entry so multimodal prompts return responses grounded in the
bundled samples. The "hi" fixture stays in place so the several E2E
specs that fill literal "hi" still match it.
Aimock's Gemini adapter synthesizes tool_call_ids (call_gemini_<name>_N)
that don't match the D5 fixture IDs (call_d5_<name>_001). This breaks
ALL two-leg and multi-leg fixtures for google-adk and any future Gemini
backend because Gemini's functionResponse format has no id field.
sequenceIndex counts per-userMessage matches regardless of provider,
making fixtures provider-agnostic.
Also adds missing generate_haiku fixtures to d5-all.json (were in
gen-ui-custom.json but never bundled) and fixes hitl-text-input
fixture arg name→attendee to match frontend schema.
Documents the D5 e2e-deep Chromium memory envelope (4 services x 2
features = 8 concurrent contexts, ~2.4GB peak) and the two tuning
knobs operators should adjust if OOM occurs.
D5 probes hit real LLMs instead of fixtures because the 8 D5 fixture
files in showcase/ops/fixtures/d5/ were never loaded into aimock.
Railway fetches fixtures from GitHub raw URLs at boot — bundle all
D5 fixtures into d5-all.json and document the updated startCommand
with the new URL appearing before feature-parity.json for match
precedence.
Add 20 new aimock fixtures covering all D5 e2e-deep probe prompts:
- 9 toolCallId second-leg fixtures for tool-call round-trips
- 11 new userMessage fixtures for multi-turn and feature-specific flows
- Explicit tool call IDs on existing weather/pie-chart fixtures
- Correct Alice ordering to prevent substring collision
Without these fixtures, D5 probes hit real OpenAI (401 with test key)
or loop infinitely on tool calls with no second-leg response.
The aimock-fixture-coverage probe scans byoc-json-render.spec.ts and was
flagging two prompts without matching feature-parity.json fixtures:
- "Break down revenue by category as a pie chart"
- "Show me monthly expenses as a bar chart"
Existing pie/bar fixtures used substrings ("revenue distribution by
category", "expenses by category") that don't appear in these prompts,
so the substring matcher returned no hits.
Added two new fixtures with substrings that DO appear in the prompts
("revenue by category as a pie chart", "monthly expenses as a bar
chart") so the coverage probe stays green and aimock returns
deterministic chart data when the BYOC json-render specs run against
the sidecar.
Adds two feature-parity.json fixtures for the langgraph-python
byoc-json-render E2E spec prompts that the
aimock-fixture-coverage probe flagged as uncovered:
- "Break down revenue by category as a pie chart"
- "Show me monthly expenses as a bar chart"
Responses mirror the worked examples baked into the
byoc_json_render_agent system prompt verbatim, so the fixture
output matches what the live LLM would emit (a single
@json-render/react flat-spec JSON object) and the spec
assertions on [data-testid="pie-chart"] /
[data-testid="bar-chart"] resolve deterministically when the
suite runs against the aimock sidecar.
Wire CopilotChat AttachmentsConfig (image + PDF, inline base64) into a
dedicated /demos/multimodal cell backed by a vision-capable LangGraph
agent (gpt-4o). Scoped to its own runtime route so the vision cost
stays on this demo.
- Dedicated /api/copilotkit-multimodal route registering the new
multimodal graph under the multimodal-demo slug
- src/agents/multimodal_agent.py with an AgentMiddleware that flattens
PDF content parts to text server-side via pypdf
- Try with sample image / PDF buttons that drive CopilotChats own
hidden file input via DataTransfer + change event, so the sample and
paperclip paths exercise the same useAttachments pipeline
- public/demo-files/ placeholder — sample.png / sample.pdf binaries
are produced by the user at the end of the Wave 2b rollout
- Manifest + registry + constraints + docs-links wired; normalized
multi-modal -> multimodal in constraints to match the feature
registry id
- QA checklist + Playwright E2E spec (not run yet — pending deploy)
- aimock fixtures for sample prompts so CI runs are deterministic
- Extend the check-binaries hook allowlist to cover the shell-docs and
shell-dojo demo-content.json mirrors (same size as shell/src/data,
already allowed) so regenerated derived data can be committed
9 new userMessage->toolCall/content fixtures in feature-parity.json
for langgraph-python demos. New aimock-fixture-coverage.test.ts
enforces per-spec fixture presence — every E2E spec prompt must
have a matching aimock fixture or be explicitly skipped.
Tighten pie-chart fixture match to avoid beautiful-chat collision:
match specific gen-ui-tool-based prompt text instead of generic
substring that also hits beautiful-chat suggestion pills.
Railway now pulls ghcr.io/copilotkit/aimock:latest directly
via a startCommand that fetches fixtures from GitHub raw URLs.
The wrapper Dockerfile that copied fixtures into the image is
no longer needed.
Also updates README.md to reflect the new architecture:
- References Railway startCommand instead of Dockerfile
- Removes the Dockerfile entry from the fixtures listing
- Updates related workflow descriptions
Substring-match fixtures (pie chart, bar chart, schedule, trip, etc.)
cross-fired across demos with different tool surfaces and returned tool
names the target agent never registered, causing demos to render nothing
in prod when aimock handles traffic.
Fixture changes (showcase/aimock/feature-parity.json):
- Replace generic pie-chart / bar-chart matches with per-suggestion
specific phrases so gen-ui-tool-based gets render_pie_chart /
render_bar_chart directly and beautiful-chat gets pieChart / barChart
with real data (skipping the query_data two-step that caused the
infinite loop on re-matching prompts).
- Narrow schedule+meeting to the Beautiful Chat 30-minute prompt
returning scheduleTime.
- Narrow flight+fly to flights-from-SFO-to-JFK.
- Narrow background to sunset-themed-gradient.
- Remove trip, sales, pipeline, todo: substring-false-firing across
unrelated demos; interrupt/A2UI demos fall through to real LLM.
Guardrail (showcase/scripts/validate-fixture-tool-surface.ts):
- Pure validate() cross-references every fixture's tool-call name
against the tool surface of each demo whose suggestion prompt contains
the fixture's match substring. Loud failure when the fixture returns a
tool the demo's agent does not register.
- CLI walks packages/ collecting suggestions from page.tsx + hooks/,
frontend tools from useComponent / useHumanInTheLoop / useFrontendTool
/ useRenderTool / useDefaultRenderTool, and backend tools via route.ts
agentId->graphId map + langgraph.json graph->file + @tool decorators.
- 7 vitest cases written TDD-first covering the drift detection,
content-only fixtures, case-insensitivity, and multi-tool responses.
- Current state: 33 fixtures x 191 demos, no drift. Counterfactual
(reverting the pie-chart fix) correctly flags gen-ui-tool-based and
declarative-gen-ui.
Also fixes a separate runtime bug in the langgraph-python package
Dockerfile: WORKDIR /app left /app owned by root; the app user could
not create the .langgraph_api cache dir LangGraph's in-memory runtime
needs, so the agent crashed on boot. Added a non-recursive chown
app:app /app (preserves the original perf intent of the explicit
--chown on COPY, which avoided a recursive chown).
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.
Alem Tuzlak