PR #4579 added a `reasoning` field to the "show your reasoning step by
step" fixture so aimock would emit response.reasoning_summary_* deltas
for the OpenAI Responses API path. Side effect: aimock's Chat
Completions handler also emits non-standard `reasoning_content` deltas
(DeepSeek/Qwen-style) ahead of the role/content chunks. Many
integrations' OpenAI client adapters don't expect those deltas and
either hang or fail to parse the stream — manifesting as "assistant
did not respond within 30000ms" across most reasoning cells in
production.
Restore the original content-only fixture. The langgraph-python /
langgraph-fastapi agent fixes from #4579 still work against real
OpenAI (gpt-5-mini + Responses API streams real reasoning summaries),
but the aimock-driven path no longer exercises the role-reasoning
render — keyword-only assertion in the d5 probe handles that.
The agentic-chat-reasoning and reasoning-default-render cells in
langgraph-python and langgraph-fastapi were configured with
gpt-4o-mini + use_responses_api=False, which never produces AG-UI
REASONING_MESSAGE_* events: gpt-4o-mini is not a reasoning model and
the Chat Completions API does not surface reasoning summary items at
all. The frontend's reasoningMessage slot was rendering nothing,
even though the cells were billed as "reasoning" demos.
- Switch both reasoning agents to gpt-5-mini (override via
OPENAI_REASONING_MODEL) routed through the Responses API with
reasoning={"effort":"medium","summary":"detailed"} so the model's
chain of thought streams as content blocks that @ag-ui/langgraph
translates into REASONING_MESSAGE_* events.
- Update the aimock d5-all.json and harness reasoning-display.json
fixtures to include a "reasoning" field so aimock emits
response.reasoning_summary_text.delta SSE events deterministically
in CI without hitting a real LLM.
- Add a "Show reasoning" useConfigureSuggestions pill on both
reasoning demo pages so the user can trigger the fixture-matched
prompt with one click.
- Tighten the d5-reasoning-display probe: it now also asserts a
reasoning-role message rendered via [data-testid="reasoning-block"]
or [data-message-role="reasoning"], so a plain text response
containing the word "reasoning" no longer falsely passes.
- Un-skip the three streaming reasoning-block tests in
langgraph-python's agentic-chat-reasoning.spec.ts and add a
suggestion-pill test; expand the reasoning-default-render spec to
cover the default reasoning slot.
- Update the langgraph-python QA doc to describe the new model +
Responses API setup and the suggestion-pill flow.
Add D5 voice test that exercises sample-audio transcription via aimock.
Infrastructure: voice in D5 feature type registry + mapping, skipFill
support in conversation runner (9 new tests), inputValue forwarding
in e2e-deep Page wrappers, aimock transcription fixture, tool-free
weather fallback fixture for agents without tools. Verified locally:
D5 suite passes green on langgraph-python (60.4s).
agent-config: drop the AgentConfigLangGraphAgent subclass and use plain
LangGraphAgent. The subclass repacked CopilotKit provider properties
into forwardedProps.config.configurable.properties so the Python graph
could read them via RunnableConfig.configurable.properties — but
@ag-ui/langgraph@0.0.31 builds the LangGraph SDK request as
{ ..., config, context: { ...input.context, ...config.configurable } }
which merges configurable INTO context. LangGraph 0.6.0+ then rejects
with HTTP 400 'Cannot specify both configurable and context' on every
chat round-trip. Net effect: chat sent the user message, runtime 400'd,
no assistant response ever rendered. Removing the subclass unbreaks
the round-trip; the Python agent falls back to its DEFAULT_* constants
so the demo's frontend toggles no longer steer the system prompt
(known regression, tracked separately pending @ag-ui/langgraph fix
that decouples context from configurable).
byoc:
- D5 probe now sends the 'Sales dashboard' pill prompt (matches the
fixtures added in main:f0a89b843 in feature-parity.json) instead of
the previous generic 'render a byoc hashbrown' prompt that had no
matching JSON-shaped fixture. Removed the now-obsolete byoc.json D5
fixture file and regenerated the d5-all.json bundle (52 -> 50
fixtures).
- Added data-testid='copilot-assistant-message' + data-message-role=
'assistant' to the byoc-hashbrown and byoc-json-render renderer
wrapper divs. The CopilotChat default assistantMessage slot includes
these markers; overriding the slot with a custom JSON-rendering
component dropped them, so the e2e-deep conversation runner's
settle-detection cascade (which counts these selectors) never saw
the response and timed out at 30s. Re-attaching the markers is a
purely additive change that doesn't affect the renderers'
behavior.
- D5 byoc assertion now waits for [data-testid='metric-card'] AND a
chart (bar-chart or pie-chart) to render — a structural check on
the BYOC contract output, not a transcript-keyword check that the
custom renderer would never produce.
E2E status: 31/31 passing locally against
./bin/showcase up langgraph-python aimock with this branch's bundle.
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.
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.
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.
Alem Tuzlak