copilotkit--copilotkit/showcase/harness/fixtures/d5

D5 Multi-Turn aimock Fixtures

Nine feature-type fixtures used by the D5 (complex interact) probes. D5 runs the D6 driver showcase/harness/src/probes/drivers/d6-all-pills.ts as "D6 take-one" (the former separate e2e-deep.ts driver was deleted), scoped to representative pills against the LangGraph Python (LGP) showcase as the reference implementation.

What "multi-turn" means in aimock

aimock's match model is single-shot, not conversation-aware: each fixture has match criteria + one response, and the first fixture to match wins on every incoming chat-completions request. There is no "session" abstraction.

Multi-turn behavior is therefore expressed as multiple sibling fixtures in the same file, each of which matches a different point in the conversation:

• Turn 1 user message (greeting / first ask) — matched via userMessage substring. Substring match is case-sensitive (see dist/router.js in the aimock package — text.includes(match.userMessage)), so prefer fragments that are stable across capitalization (e.g. "favorite color" rather than "What is my favorite color").
• Turn 2 user message — matched via a different userMessage substring that doesn't collide with turn 1.
• Mid-loop re-invocations after a tool call — matched by toolCallId on the last role: "tool" message. Never match these by userMessage, because the user message has not changed between the request that emitted the tool call and the request that carries the tool result back. Matching by userMessage would re-match the original tool-call fixture and create an infinite loop. (See skills/write-fixtures/SKILL.md in the aimock repo, "Why predicate, not userMessage?" — the JSON fixture format substitutes toolCallId for the predicate form used in the TS API.)
• Order matters: toolCallId-routed fixtures must appear above their corresponding userMessage-routed first-leg fixture in the file. aimock iterates top-to-bottom and uses first-match-wins; if the userMessage fixture appears first it will keep re-matching even after a tool result is appended (since the user message itself has not changed), and the toolCallId fixture will never fire.

tool-rendering, shared-state, hitl-approve-deny, hitl-text-input, hitl-steps, gen-ui-headless, gen-ui-custom, and mcp-subagents all rely on this toolCallId-routed pattern. agentic-chat is purely text — no tools — so it uses three plain userMessage substring matches.

Per-feature-type-against-LGP-only

These fixtures are recorded once, against LGP, and replayed across all 17 integrations via aimock's first-match-wins fixture pool. We accept that this elides integration-specific quirks (e.g. one integration may emit getWeather instead of get_weather, or chain tool calls in a different order). When that happens, D5 will report it as a test failure for that specific integration; we will then either (a) update the integration to bring it in line or (b) add a per-integration override fixture.

This trade — replay a single canonical fixture rather than re-record per integration — keeps the fleet of fixtures small (9 files, not 9×17 = 153), and keeps drift contained: when LGP changes, we re-record once, not 17 times.

How each fixture was constructed

These fixtures were hand-authored against the LGP source code as the reference, not captured via aimock --record. The showcase repo does not wire up aimock's record mode (see showcase/aimock/README.md § "Sync policy" — "Fixtures are hand-maintained. There is no automated capture, no scheduled re-recording...") so this set follows the same convention as showcase/aimock/feature-parity.json: read the agent source, decide what the expected tool calls / replies should be, write JSON.

For each feature type, the authoring inputs were:

Feature	LGP source files
agentic-chat	showcase/integrations/langgraph-python/src/agents/agentic_chat.py
tool-rendering	src/agents/tool_rendering_agent.py (get_weather) + src/app/demos/tool-rendering/weather-card.tsx
shared-state	src/agents/shared_state_read_write.py (set_notes tool, Preferences shared state) + src/app/demos/shared-state-read-write/{notes-card,preferences-card}.tsx
hitl-approve-deny	src/agents/hitl_in_app.py + src/app/demos/hitl-in-app/{page,approval-dialog}.tsx (request_user_approval frontend tool)
hitl-text-input	src/agents/hitl_in_chat_agent.py + src/app/demos/hitl-in-chat/{page,time-picker-card}.tsx (book_call HITL tool)
hitl-steps	src/agents/hitl_agent.py + src/app/demos/hitl/page.tsx (generate_task_steps frontend tool)
gen-ui-headless	src/app/demos/headless-simple/page.tsx (show_card useComponent) — backend agent is src/agents/main.py
gen-ui-custom	src/agents/gen_ui_tool_based.py + src/app/demos/gen-ui-tool-based/page.tsx (render_bar_chart / render_pie_chart)
mcp-subagents	src/agents/subagents.py + src/app/demos/subagents/{page,delegation-log}.tsx (research_agent / writing_agent / critique_agent)

A note on naming: the spec calls the eighth fixture mcp-subagents. LGP's canonical multi-agent demo is /demos/subagents (subagents-as-tools). /demos/mcp-apps exists separately and points at a public Excalidraw MCP server — that would require external network reachability at probe time, so we chose subagents as the realistic LGP fit. If a future D5 split needs both, add a second mcp-apps.json fixture and let the probe key on which demo it is exercising.

How to re-record (when LGP changes)

When an LGP agent changes its tool surface, prompt, or expected behavior, re-author the affected fixture by hand following the existing pattern:

1. Read the changed agent source in showcase/integrations/langgraph-python/src/agents/<name>.py.
2. Read the corresponding demo page in showcase/integrations/langgraph-python/src/app/demos/<id>/page.tsx to confirm what tool names the frontend registers / renders.
3. Update the fixture file in this directory:
   • Match user-typed prompts via userMessage substring (unique per turn).
   • Match agent loop re-invocations after a tool call via toolCallId on the id you assigned in the prior fixture's toolCalls[].id.
   • Keep tool-call argument shapes aligned with the agent's tool schema.
4. Validate the fixture loads cleanly:

   pnpm --filter @copilotkit/showcase-scripts test aimock-fixtures
   

   Note: the existing aimock-fixtures.test.ts discovers fixtures from showcase/aimock/, examples/integrations/*/fixtures/, and scripts/doc-tests/fixtures/ — it does not currently scan showcase/harness/fixtures/d5/. Either extend that test's globs in the same PR that lands the D5 driver, or run loadFixtureFile + validateFixtures from @copilotkit/aimock directly against this directory in a small one-off check.
5. Replay-verify each leg of the conversation against a booted aimock:

   npx @copilotkit/aimock --port 14010 --fixtures showcase/harness/fixtures/d5/<feature>.json --validate-on-load
   

   then issue chat-completions requests for each turn (turn 1 user message, turn 1 follow-up after tool result, turn 2 user message, ...) and assert the response shape matches what the fixture promises (text content or tool_calls). The set of 9 fixtures was bootstrapped this way at authoring time — 22 legs across 9 files, all replay-passing.
6. Once the D5 driver exists, run it against the LGP showcase locally with aimock pointed at the per-fixture file and confirm the full conversation short-circuits the live LLM (no requests should escape to the real provider).

If automated recording becomes worthwhile, the path is to wire aimock's --record mode into a periodic workflow that re-captures against real providers and diffs against checked-in fixtures — same idea sketched in showcase/aimock/README.md § "Drift risk".

Tradeoffs of the per-feature-type-against-LGP-only choice

Pros:

• 9 fixture files, not 153. Fewer files to keep in sync.
• LGP is the reference implementation by design — fixtures that match LGP's contract surface other integrations' divergences as legitimate parity failures rather than masking them with bespoke fixtures.
• One source of truth per feature.

Cons:

• Integrations whose tool names, argument shapes, or chaining behavior differ from LGP will fail D5 even when their behavior is locally correct.
• Authentic recorded behavior (real LLM streaming, real timing) is not captured — these are hand-authored. D5's parity-of-shape checks are still meaningful; latency-sensitive checks should rely on D6 (parity vs reference) with its own captured profile.

When per-integration overrides become necessary, place them at showcase/harness/fixtures/d5/<feature>.<integration>.json and load integration-specific fixtures ahead of the canonical one in the aimock fixture pool (first-match-wins).

Status of each fixture

Fixture	Status
agentic-chat.json	real (3 turns, no tools)
tool-rendering.json	real (1 turn, 1 tool call)
shared-state.json	real (2 user turns + 1 tool-routed leg)
hitl-approve-deny.json	real (1 turn, frontend HITL tool, approve path)
hitl-text-input.json	real (1 turn, frontend HITL tool, text/time picker)
hitl-steps.json	real (2 legs: toolCallId match + userMessage match)
gen-ui-headless.json	real (1 turn, show_card useComponent)
gen-ui-custom.json	real (1 turn, custom chart component)
mcp-subagents.json	real (1 turn, 3 chained sub-agent delegations)

None are marked pending — all nine are exercisable on LGP today against the agent source as it stands.