feat(aircoding): AirCoding V2 baseline — deterministic multi-agent architecture

Forked from OpenCode v1.17.4 with multi-agent system:
- 5 agents: aircoding, scheduler, worker, architect, reviewer
- Deterministic DAG scheduling engine (coordinator_tick)
- Tool whitelists as hard enforcement
- AirCoding validation plugin
- V1 requirements: C4 docs, ADR, AGENTS.md, debug-log.md
- Design documents in docs/

packages/llm/test/continuation-scenarios.ts

@@ -0,0 +1,104 @@
+import { LLM, Message, ToolCallPart, ToolDefinition, ToolResultPart, type ContentPart, type Model } from "../src"
+
+export const basicContinuation = ["system", "user-text", "assistant-text", "user-follow-up"] as const
+export const toolContinuation = ["tool-call", "tool-result"] as const
+export const reasoningContinuation = ["assistant-reasoning", "encrypted-reasoning"] as const
+export const mediaContinuation = ["user-image"] as const
+export const maximalContinuation = [
+  ...basicContinuation,
+  ...toolContinuation,
+  ...reasoningContinuation,
+  ...mediaContinuation,
+] as const
+
+export type ContinuationFeature = (typeof maximalContinuation)[number]
+
+export const nativeOpenAIResponsesContinuation = [
+  ...basicContinuation,
+  ...toolContinuation,
+  "encrypted-reasoning",
+  ...mediaContinuation,
+] as const satisfies ReadonlyArray<ContinuationFeature>
+
+export const nativeAnthropicMessagesContinuation = [
+  ...basicContinuation,
+  ...toolContinuation,
+  "assistant-reasoning",
+  ...mediaContinuation,
+] as const satisfies ReadonlyArray<ContinuationFeature>
+
+export const continuationTool = ToolDefinition.make({
+  name: "get_weather",
+  description: "Get current weather for a city.",
+  inputSchema: {
+    type: "object",
+    properties: { city: { type: "string" } },
+    required: ["city"],
+    additionalProperties: false,
+  },
+})
+
+export function continuationRequest(input: {
+  readonly id: string
+  readonly model: Model
+  readonly features: ReadonlyArray<ContinuationFeature>
+  readonly image?: string
+}) {
+  const features = new Set(input.features)
+  const messages = []
+  const firstUser: ContentPart[] = []
+  const firstAssistant: ContentPart[] = []
+
+  if (features.has("user-text")) firstUser.push({ type: "text", text: "What is shown here?" })
+  if (features.has("user-image"))
+    firstUser.push({ type: "media", mediaType: "image/png", data: input.image ?? "AAECAw==" })
+  if (firstUser.length > 0) messages.push(Message.user(firstUser))
+
+  if (features.has("assistant-reasoning"))
+    firstAssistant.push({
+      type: "reasoning",
+      text: "I inspected the previous turn.",
+      providerMetadata: { anthropic: { signature: "sig_continuation_1" } },
+    })
+  if (features.has("encrypted-reasoning"))
+    firstAssistant.push({
+      type: "reasoning",
+      text: "I inspected the previous turn.",
+      providerMetadata: {
+        openai: {
+          itemId: "rs_continuation_1",
+          reasoningEncryptedContent: "encrypted-continuation-state",
+        },
+      },
+    })
+  if (features.has("assistant-text")) firstAssistant.push({ type: "text", text: "It shows a small test image." })
+  if (firstAssistant.length > 0) messages.push(Message.assistant(firstAssistant))
+
+  if (features.has("tool-call")) {
+    messages.push(Message.user("Check the weather in Paris before continuing."))
+    messages.push(
+      Message.assistant([ToolCallPart.make({ id: "call_weather_1", name: "get_weather", input: { city: "Paris" } })]),
+    )
+  }
+  if (features.has("tool-result")) {
+    messages.push(
+      Message.tool(ToolResultPart.make({ id: "call_weather_1", name: "get_weather", result: { temperature: 22 } })),
+    )
+    if (features.has("assistant-text")) messages.push(Message.assistant("Paris is 22 degrees."))
+  }
+  if (features.has("user-follow-up"))
+    messages.push(Message.user("Continue from this conversation in one short sentence."))
+
+  return LLM.request({
+    id: input.id,
+    model: input.model,
+    system: features.has("system") ? "You are concise. Continue from the provided history." : undefined,
+    messages,
+    tools: features.has("tool-call") ? [continuationTool] : [],
+    cache: "none",
+    providerOptions: features.has("encrypted-reasoning")
+      ? { openai: { store: false, include: ["reasoning.encrypted_content"], reasoningSummary: "auto" } }
+      : undefined,
+    generation: { maxTokens: 80, temperature: 0 },
+  })
+}