- frontend-tools-async: drop the stray broad 'project planning' fixture entry that
substring-shadowed 'Find my notes about project planning' -> run-loop. Green.
- gen-ui-open + gen-ui-open-advanced: remove 6 gen-ui-open-owned strays from
gen-ui-tool-based.json ('3D axis visualization', 'Inline expression evaluator',
'render an open gen-ui element', 'continue the advanced gen-ui flow') that
collided (same context, different responses) with gen-ui-open.json. Green;
gen-ui-tool-based unaffected.
- multimodal: quarantine. On main + latest deps the browser gets 200 from the
runtime but never starts a run (runStartCount=0) — a shared multimodal
frontend/runtime issue (identical frontend to langgraph; agent works via direct
POST; my #5985 fix doesn't change it). Genuine gap, needs shared-layer work.
Full D6 now 36/36 green on official latest (ag-ui 1.0.1, core 1.13.0, openai 1.12.0).
- shared-state-streaming: replace main's stale 'counter' fixture (drifted from
langgraph canonical) with the write_document document demo (6-entry poem/email/
quantum + chunkSize) + SharedStateStreamingFrameworkAgent seed subclass; frontend
already matches langgraph. Un-quarantine + add to features. Green (3/3 turns).
- tool-rendering-reasoning-chain: un-quarantine + add to features. Green on core
1.13.0 / openai 1.12.0 (latest) via aimock encrypted_content (CopilotKit/aimock#342,
which fixes the Responses reasoning multi-tool regression) + the store:False agent.
CI-green depends on aimock#342 releasing.
- Drop reasoning-default-render / agentic-chat-reasoning from not_supported (no D6
probe featureType — not real cells).
Port the #5985 fix onto main: ported langgraph's full 18-entry custom-catchall
fixture (main's 4-entry set left SF/flights/d20/chain pills leaking to the
default-catchall fixture) + _ToolRenderingFrameworkAgent dropping the divergent
end-of-run MESSAGES_SNAPSHOT so the narration (not the tool card) is the terminal
bubble. Verified green via per-cell --direct. First cell of the #5985->main
re-integration.
The D6 e2e-full probe for ms-agent-python:gen-ui-declarative failed at turn 1
with reason=surface-missing. Two root causes, both fixed:
1. Stale aimock fixture. The fixture still carried the old D5 pill prompts
(KPI/pie/bar/status) plus a lone outer generate_a2ui entry for the current
sales-dashboard prompt with no matching inner _design_a2ui_surface or
narration. The backend looped generate_a2ui to its invocation limit and
RUN_FINISHED was blocked while the tool call stayed active. Re-authored to
the current 4 VantageThreads sales prompts mirroring the llamaindex green
north-star for this backend shape: the outer generate_a2ui returns a
`context` steering phrase (the ms-agent-framework session does not surface
the latest user message to the secondary LLM), the inner
_design_a2ui_surface fixture matches that phrase (not the full prompt), and
hasToolResult discriminates outer vs narration. This also avoids the stale
render-a2ui "KPI dashboard" catch-all collision that was rendering the wrong
surface.
2. Renderer/catalog drift. ms-agent-python's declarative-gen-ui renderers.tsx
and definitions.ts lagged the green peers — missing the DataTable and
InfoRow components (declarative-data-table / declarative-info-row testids)
that turns 2 and 4 assert, plus Metric trendValue and the Row/Column/Text
gap overrides. Brought both files to parity with the langgraph-python /
google-adk green cluster.
Red-green proof on the real control-plane surface (SHOWCASE_ISO_SLOT=11,
--d6 --isolate):
- RED: d6:ms-agent-python/gen-ui-declarative = red (exit 1, turn 1 surface-missing)
- GREEN: d6:ms-agent-python/gen-ui-declarative = green (exit 0, 1 passed)
Visually verified all 4 turns via Playwright (header-injected to replicate the
harness x-aimock-context): turn 1 renders 4 KPI metrics + region pie + monthly
bar; turn 2 the rep-quota DataTable + attainment bar; turn 3 three at-risk
StatusBadges + KPI metrics; turn 4 seven InfoRow account facts + product-line
pie.
Flip the 4 TS a2ui builders (shared/typescript + mastra,
claude-sdk-typescript, langgraph-typescript) from the legacy flat
operation shape to v0.9 nested (createSurface / updateComponents /
updateDataModel), matching the Python builder and what A2UI consumers
process. Flat ops were never processed as valid nested operations, so
the surface schema and components were never applied.
Also align the empty-data guard to Python's `if data:` semantics (empty
object -> no updateDataModel), add a v0.9 parity guard test to all 4
test files, and add 12 gen-ui-a2ui-fixed aimock fixtures.
Commit 7c3edca changed sample-attachment-buttons.tsx across all integrations
to auto-send via agent.addMessage with autoPrompt strings:
- "can you tell me what is in this demo image I just attached"
- "can you tell me what is in this demo pdf I just attached"
But the d5 harness fixture and all 19 d6 per-integration multimodal.json
fixtures still matched on the old strings:
- "describe the sample image"
- "summarize the sample document"
Aimock received requests with the new prompts, found no match, returned
a STRICT 404, and the agent emitted a streaming error back to the UI
(exact symptom: "An internal error has occurred while streaming events").
Also update agentic-chat.json across all 20 integrations (those files had
duplicate fallback entries for the old prompts) and fix split-fixtures.ts
to route the new strings to the "multimodal" feature bucket.
Local RED: ms-agent-python and crewai-crews both fail with fixture-miss
status=miss before this change.
Local GREEN: langgraph-typescript passes after this change (both turns
settle with "image" / "document" keywords confirmed in transcript).
Remaining failures after this fix are pre-existing Python backend issues
(ChatClientException on binary content parts in ms-agent-python; CrewAI
flow failure on binary content in crewai-crews) — unrelated to fixture
keys and tracked separately in the pydantic-ai multimodal work.
Mirrors LGP's `generate_a2ui` outer-emit fixture into 8 non-LGP slugs to close
the production 503 gap on the "Show me my sales dashboard for this quarter."
userMessage. Per /tmp/staging-journal-diff.md, aimock-staging returns 503 on
shape-B traffic (model=gpt-4.1, stream=true, tools=[generate_a2ui],
UA=AsyncOpenAI/Python) for all 8 slugs because no fixture matched.
Slugs patched (one entry each in d6/<slug>/gen-ui-declarative.json):
- llamaindex
- built-in-agent
- ag2
- langroid
- claude-sdk-typescript
- claude-sdk-python
- ms-agent-dotnet
- ms-agent-python
Each entry matches `userMessage` + `context: "<slug>"` and emits a
`generate_a2ui` toolcall with no args, mirroring LGP's sales-dashboard outer
entry. Per-slug unique toolCallId.
Red-green proof (local aimock, ghcr.io/copilotkit/aimock:latest):
RED (baseline main, 8 slugs): HTTP=404 no_fixture_match
GREEN (this branch, 8 slugs): HTTP=200 tool=generate_a2ui id=call_d6_decl_dash_outer_<slug>_001
LGP regression (baseline+fix): HTTP=200 (unchanged)
aimock fixture validation: 737/737 tests pass.
PR scope is intentionally narrow per CLAUDE.md "Scope PRs to flagged
findings": this closes ONE userMessage gap. Shape-A 503s (no tools key) and
other userMessage gaps remain as separate follow-ups.
crewai-crews/ms-agent-python/pydantic-ai catchall AAPL entries still carried stale $189.42/up 1.27% narration and toolCall args lacking price_usd/change_pct. Round 1 CR finding #3 was identified but never given a fix agent. Brought all 3 to canonical shape matching built-in-agent (A5) and csdkts narration (A6): price_usd=338.37, change_pct=-2.96, narration 'AAPL is trading at $338.37, down 2.96% on the day — rendered through the custom wildcard catchall.'
(cherry picked from commit df84657310451500278d0b3d0125c9c490042d2b)
built-in-agent, crewai-crews, ms-agent-python, pydantic-ai: Tokyo turn-1 tool result makes hasToolResult permanently true → AAPL fixture never matched → 30s timeout. Aligned with agno/langroid/llamaindex/strands/claude-sdk-python pattern: rely on userMessage+context (and toolCallId where relevant) as the gate.
(cherry picked from commit 8e313cd1b043cf1c61efb82143171a28d7699c49)
Closed PR #5465 introduced cross-fixture leakage by stripping the
toolName discriminator on shared {userMessage, context} keys: with
aimock's alphabetical first-match-wins ordering,
tool-rendering-custom-catchall.json sorts before
tool-rendering-default-catchall.json, so default-catchall page requests
were served the custom file's content. The probe was structurally blind
because it asserted only DOM testids (copilot-tool-render +
data-tool-name=get_weather) — both fixtures emit get_weather, so the
testid signal passed regardless of which fixture won.
Real LGP-gold pattern is disjoint userMessages between default and
custom catchall fixtures (NOT shared keys discriminated by toolName).
This PR ports the LGP-gold pattern to the other 17 integrations and
adds page-text content assertions to both d5 catchall probes so this
class of regression can't recur silently:
- Probe prompts disjoint between default-catchall and custom-catchall
- Fixture userMessages updated to match the new disjoint prompts
- Page-text content assertions in both d5 catchall probes
(default negatively asserts the custom-catchall leak phrase;
custom positively asserts it)
Local gold-standard red-green proof captured:
- Step A: live staging Playwright baseline (RED-OF-RECORD)
- Step B: local control-plane on origin/main reproduces structural
fragility (probes pass at testid level, custom fixture wins on
default's userMessage path)
- Step C: local control-plane on this branch — both catchall probes
GREEN with the new content-asserting assertions
tool-rendering-default-catchall: pass=true (5443ms)
tool-rendering-custom-catchall: pass=true (8956ms)
cross-tool signature passed
LGP (langgraph-python) was already disjoint; it remains untouched.
The previous 'weather in Tokyo' rename (388c69e68) was a substring of the
main tool-rendering chain pill prompt 'Chain a few tools in this single
turn: get the weather in Tokyo, search flights from SFO to Tokyo, and roll
a d20.' Because aimock loads fixtures alphabetically per integration dir
and uses substring match with first-match-wins, the catchall fixture
(loaded before tool-rendering.json) was intercepting chain pill matches
across 16 integrations.
Rename catchall fixture userMessage to 'forecast for Tokyo' — a phrase
not contained in any other pill prompt. Update the corresponding D5
catchall probe inputs in d5-tool-rendering-{default,custom}-catchall.ts
and the test assertions that pin those inputs.
Call-Site Enumeration: 'weather in Tokyo' remains intentionally in
page.tsx suggestions.ts pills (user-visible UX) and inside the chain
pill prompt itself — neither is in the substring-match path now.
The D5 e2e-deep probe for tool-rendering-{default,custom}-catchall sends
"weather in Tokyo" as the test input (harness/src/probes/scripts/
d5-tool-rendering-{default,custom}-catchall.ts). The fixture userMessage
matcher uses substring match. Main's catchall fixtures had a stale
"check Tokyo weather forecast" string that could not substring-match
the probe input, causing the fixture to miss and the probe to fall
through to the live LLM — surfacing as CV x red D4 on the dashboard.
Rename to the canonical "weather in Tokyo" string across affected
integrations' tool-rendering-{default,custom}-catchall.json files.
No agent or page.tsx changes; fixture content otherwise unchanged.
- SUPERSEDED annotations on unreachable recorded calculator entries (load-order shadowing is
the only guarantee; model gate is not a safety net)
- GOTCHAS sequenceIndex rewritten to per-X-Test-Id semantics with co-increment/eviction caveats
- hasToolResult paragraph corrected (omission = no gate, thread-global predicate)
- statelessness claim reconciled with sequence counters
- replace Websandbox host-bridge evaluateExpression (never registered in beautiful-chat) with
in-sandbox allowlist-gated Function() eval
- reorder specific calculator pair before generic (first-match-wins)
- allow exponential notation (eE) so chained evaluation of large results works
- drop thread-global hasToolResult gate that broke the pill after other tool-producing pills,
reorder toolCallId follow-ups before leg-1
- mint distinct tool_call_ids per repeat click via sequenceIndex variants + non-sequenced
fallback (fixes second-widget collapse)
- restore google-adk trailing newline
- document ordering invariants, gate tradeoffs, and sequence-counter scoping caveats in
fixture comments
Verified via local Playwright red-green (render, 7+8=15, chained exponent, interleaved pills,
repeat clicks).
The gen-ui-headless-complete probe
(showcase/harness/src/probes/scripts/d5-gen-ui-headless-complete.ts)
references fixtureFile "gen-ui-headless-complete.json", but that file
existed in no D6 slug, so the probe's first leg 503'd under strict.
Add gen-ui-headless-complete.json for all 18 D6 slugs, modeled on the
green langgraph-python headless-complete.json pattern: the four gen-UI
pills (weather/stock/highlight/revenue) with narration (toolCallId)
fixtures FIRST and toolcall (userMessage+context) fixtures AFTER, no
turnIndex gate, so every one of the probe's four sequential turns in one
chat thread matches regardless of prior assistant/tool history. Interrupt
fixtures are intentionally omitted (interrupt-headless is a separate
cluster item).
These 8 fixtures per slug share match keys with the pre-existing
headless-complete.json for the same context (the demos share pills and
are disambiguated at runtime by probe path), which raises the
aimock-fixtures collision-detection exact-duplicate count by 46. Bump
KNOWN_DUPLICATE_CEILING 230 -> 276 to match, consistent with how prior
per-integration feature fixtures bumped the baseline; the substring-shadow
ceiling is unchanged (no new shadows).
Validated: all 732 aimock-fixtures schema/collision tests pass, and a
local aimock --strict run returns 200 for each of the four pills across
turns (langgraph-python + pydantic-ai spearheads, plus spring-ai).
Port the replay-safe toolCallId stripping middleware from the ms-agent-dotnet
sibling route.ts so HITL/interrupt demos match toolCallId-keyed aimock
fixtures across the 2nd-turn request. The strip walks every inbound message
(role=tool, role=assistant.toolCalls[].id, toolCallId, tool_call_id) before
the AG-UI HttpAgent forwards them onto the FastAPI backend, and the outbound
event stream rewrites toolCallId on TOOL_CALL_* events to embed the
deterministic per-run suffix so the next turn's fixture matcher still
keys on the original (suffix-stripped) id.
Wraps the human_in_the_loop, interrupt-adapted, hitl-in-app, and
hitl-in-chat agents with the new replay-safe middleware. Adds gen-ui-agent
set_steps state-snapshot synthesis, readonly-state-agent-context system
message injection, and shared-state-read-write preference-as-system
injection — all ported verbatim from the dotnet sibling so the per-agent
shaping behavior is identical across the MAF runtimes.
chat-slots.json: add the turnIndex:1 "Give me a fun fact" fixture mirrored
from langgraph-python/chat-slots.json so the chat-slots.spec.ts second-
turn assertion ("second assistant turn is also wrapped in the custom
slot") gets a deterministic reply instead of falling through to headless-
simple's fun-fact fixture or the live proxy.
hitl-in-app and hitl-in-chat demo pages were already mirrored from LGP
(only a minor consumerAgentId addition on the in-app suggestions module).
No frontend page changes needed.
The B2 header-forwarding conveyance in src/agents/_header_forwarding.py
is untouched and verified intact (httpx hook + Starlette HTTP middleware
plus ContextVar bridge).
Multimodal diagnosis: the 5-test multimodal.spec.ts timeout is NOT a
routing/wiring gap. The agent_server.py mounts /multimodal FIRST (before
the catch-all "/"), the dedicated Next.js route /api/copilotkit-multimodal
registers the HttpAgent under "multimodal-demo", and the page wires
runtimeUrl + agent correctly. The multimodal fixture's userMessage
"describe the sample image" is the same stale phrasing LGP uses (which
passes at 185/0/2 — the test asserts a /image/i regex on the assistant
transcript, so fallthrough to the proxy still satisfies it). The
remaining suspects are (a) agent_framework_ag_ui's AG-UI -> AF adapter
mishandling inbound `binary` content parts, (b) the dual chat_client
init pattern (multimodal_chat_client built after the global httpx hook
is installed — the hook is idempotent so this should be safe), or
(c) >30s real-OpenAI vision latency under D6 record-replay. Capture
agent_server stderr during a single multimodal run to confirm.
Same pattern as ms-agent-dotnet: full d4 chat.json rewrite + d6 mirrors. Dropped stale
turnIndex gates and broad shadow matchers. Default-catchall now green via page-level
shadcn-catchall-renderer. 11 residual: declarative-gen-ui charts, multimodal conveyance,
tool-rendering, reasoning-chain.
Refresh d6 fixtures across the rollout cohort: ag2, built-in-agent,
claude-sdk-{python,typescript}, crewai-crews, google-adk, langgraph-fastapi,
langgraph-typescript, langroid, llamaindex, mastra, ms-agent-{dotnet,python},
pydantic-ai, strands. Companion d4/{langgraph-typescript,mastra,ms-agent-dotnet}
chat.json refreshes. Add the missing ms-agent-python/gen-ui-custom.json
to bring the integration up to the standard pill set.
Also narrow aimock/shared/common.json's generic 'hello' fixture to
'hello world' so it no longer shadows D6 pills whose prompts contain
'hello' as a substring (e.g. langgraph-python headless-simple sends
'Say hello in one short sentence.'). 'hello world' is unused by any
current demo pill, so the fixture remains a manual-typing fallback
without poisoning fixture matching.
This is a mid-rollout snapshot — fixture coverage is uneven across
integrations and rides alongside the conveyance shims landed earlier
in this branch.
Move monolithic d5-all.json + feature-parity.json + smoke.json into
per-integration directories under d4/<slug>/, d6/<slug>/, and shared/.
Every fixture file is now context-scoped to enable server-side aimock
routing via match.context. Migrated 12 HITL fixtures from main's
d5-all.json additions into shared/_migrated-from-d5-all-hitl.json
for follow-up distribution into per-integration files.
Mark