feat(FN-3581): honor runtime model precedence for assigned agents
This merge delivers five major feature clusters: a fully rebuilt dependency graph plugin with draggable nodes, position persistence, modular architecture, highlighting and selection states, toolbar navigation, and keyboard controls; a new roadmap plugin with domain store, ordering logic, and compreh Fusion-Task-Id: FN-3581
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Fusion
@@ -53,22 +53,100 @@ const ACTIVE_STATUSES = new Set(["planning", "researching", "executing", "finali
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* 1. Per-task modelProvider/modelId (both must be set)
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* 2. Project/global execution lane fallback
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*/
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function extractExecutorModelFromLog(entries: AgentLogEntry[]): { provider: string; modelId: string } | null {
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let result: { provider: string; modelId: string } | null = null;
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for (const entry of entries) {
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if (entry.agent !== "executor" || entry.type !== "text") continue;
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const match = entry.text.match(/^Executor using model: (.+?)\/(.+)$/);
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if (match) {
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result = { provider: match[1], modelId: match[2] };
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}
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}
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return result;
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}
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function extractReviewerModelFromLog(entries: AgentLogEntry[]): { provider: string; modelId: string } | null {
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let result: { provider: string; modelId: string } | null = null;
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for (const entry of entries) {
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if (entry.agent !== "reviewer" || entry.type !== "text") continue;
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const match = entry.text.match(/^Reviewer using model: (.+?)\/(.+)$/);
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if (match) {
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result = { provider: match[1], modelId: match[2] };
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}
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}
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return result;
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}
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function extractAssignedRuntimeModel(agent: Agent | null | undefined): ModelSelection {
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const runtimeConfig = (agent?.runtimeConfig ?? undefined) as Record<string, unknown> | undefined;
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const model = typeof runtimeConfig?.model === "string" ? runtimeConfig.model.trim() : "";
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if (model) {
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const slashIdx = model.indexOf("/");
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if (slashIdx > 0 && slashIdx < model.length - 1) {
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return {
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provider: model.slice(0, slashIdx),
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modelId: model.slice(slashIdx + 1),
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};
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}
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}
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const provider = typeof runtimeConfig?.modelProvider === "string" ? runtimeConfig.modelProvider.trim() : "";
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const modelId = typeof runtimeConfig?.modelId === "string" ? runtimeConfig.modelId.trim() : "";
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return {
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provider: provider || undefined,
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modelId: modelId || undefined,
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};
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}
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/**
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* Resolve the effective executor model following the engine's resolution order:
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* 1. Runtime executor model from agent log marker
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* 2. Assigned agent runtime model (active runs only)
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* 3. Per-task modelProvider/modelId override
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* 4. Project/global execution lane fallback
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*/
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function resolveEffectiveExecutor(
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task: Task | TaskDetail,
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logEntries: AgentLogEntry[],
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assignedAgent: Agent | null,
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settings?: Settings,
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): ModelSelection {
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const fromLog = extractExecutorModelFromLog(logEntries);
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if (fromLog) return fromLog;
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if (ACTIVE_STATUSES.has(task.status ?? "") || task.column === "in-progress") {
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const assignedModel = extractAssignedRuntimeModel(assignedAgent);
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if (assignedModel.provider && assignedModel.modelId) {
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return assignedModel;
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}
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}
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return resolveTaskExecutionModel(task, settings);
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}
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/**
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* Resolve the effective validator model following the engine's resolution order:
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* 1. Per-task validatorModelProvider/validatorModelId (both must be set)
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* 2. Project/global validator lane fallback
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* 1. Runtime reviewer model from agent log marker
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* 2. Assigned agent runtime model (active runs only)
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* 3. Per-task validatorModelProvider/validatorModelId override
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* 4. Project/global validator lane fallback
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*/
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function resolveEffectiveValidator(
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task: Task | TaskDetail,
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logEntries: AgentLogEntry[],
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assignedAgent: Agent | null,
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settings?: Settings,
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): ModelSelection {
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const fromLog = extractReviewerModelFromLog(logEntries);
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if (fromLog) return fromLog;
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if (ACTIVE_STATUSES.has(task.status ?? "") || task.column === "in-progress") {
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const assignedModel = extractAssignedRuntimeModel(assignedAgent);
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if (assignedModel.provider && assignedModel.modelId) {
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return assignedModel;
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}
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}
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return resolveTaskValidatorModel(task, settings);
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}
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@@ -1952,8 +2030,8 @@ export function TaskDetailContent({
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<AgentLogViewer
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entries={agentLogEntries}
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loading={agentLogLoading}
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executorModel={resolveEffectiveExecutor(task, settings)}
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validatorModel={resolveEffectiveValidator(task, settings)}
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executorModel={resolveEffectiveExecutor(task, agentLogEntries, assignedAgent, settings)}
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validatorModel={resolveEffectiveValidator(task, agentLogEntries, assignedAgent, settings)}
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planningModel={resolveEffectivePlanning(task, agentLogEntries, settings)}
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hasMore={agentLogHasMore}
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onLoadMore={loadMoreAgentLogs}
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@@ -478,6 +478,113 @@ describe("TaskDetailModal", () => {
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});
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});
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it("shows executor/reviewer models from runtime agent-log markers", async () => {
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const { fetchSettings } = await import("../../api");
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const { useAgentLogs } = await import("../../hooks/useAgentLogs");
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vi.mocked(fetchSettings).mockResolvedValueOnce({
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modelPresets: [],
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autoSelectModelPreset: false,
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defaultPresetBySize: {},
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defaultProvider: "anthropic",
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defaultModelId: "claude-sonnet-4-5",
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} as any);
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vi.mocked(useAgentLogs).mockReturnValue({
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entries: [
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{ timestamp: "2026-01-01T00:00:01Z", taskId: "FN-099", text: "Executor using model: openai/gpt-4o", type: "text" as const, agent: "executor" },
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{ timestamp: "2026-01-01T00:00:02Z", taskId: "FN-099", text: "Reviewer using model: google/gemini-2.5-pro", type: "text" as const, agent: "reviewer" },
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],
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loading: false,
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clear: vi.fn(),
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loadMore: vi.fn(async () => {}),
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hasMore: false,
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total: null,
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loadingMore: false,
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});
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const { container } = render(
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<TaskDetailModal
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task={makeTask({ prompt: "# Hello\n\nContent" })}
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onClose={noop}
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onMoveTask={noopMove}
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onDeleteTask={noopDelete}
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onMergeTask={noopMerge}
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onOpenDetail={noopOpenDetail}
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addToast={noop}
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/>,
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);
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fireEvent.click(screen.getByText("Logs"));
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fireEvent.click(screen.getByText("Agent Log"));
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await waitFor(() => {
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const header = container.querySelector("[data-testid='agent-log-model-header']");
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expect(header).toBeTruthy();
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});
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const expandButton = screen.getByTestId("agent-log-model-expand") as HTMLButtonElement;
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if (expandButton.getAttribute("aria-expanded") !== "true") {
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fireEvent.click(expandButton);
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}
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const header = container.querySelector("[data-testid='agent-log-model-header']") as HTMLElement;
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expect(header.textContent).toContain("openai/gpt-4o");
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expect(header.textContent).toContain("google/gemini-2.5-pro");
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});
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it("falls back to assigned-agent runtime model when no runtime marker exists", async () => {
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const { fetchSettings, fetchAgent } = await import("../../api");
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const { useAgentLogs } = await import("../../hooks/useAgentLogs");
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vi.mocked(fetchSettings).mockResolvedValueOnce({
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modelPresets: [],
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autoSelectModelPreset: false,
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defaultPresetBySize: {},
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defaultProvider: "anthropic",
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defaultModelId: "claude-sonnet-4-5",
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} as any);
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vi.mocked(fetchAgent).mockResolvedValueOnce({
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id: "agent-1",
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name: "Agent One",
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role: "executor",
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state: "active",
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runtimeConfig: { model: "openai/gpt-4.1" },
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} as any);
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vi.mocked(useAgentLogs).mockReturnValue({
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entries: [{ timestamp: "2026-01-01T00:00:00Z", taskId: "FN-099", text: "hello", type: "text" as const }],
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loading: false,
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clear: vi.fn(),
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loadMore: vi.fn(async () => {}),
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hasMore: false,
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total: null,
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loadingMore: false,
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});
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const { container } = render(
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<TaskDetailModal
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task={makeTask({ prompt: "# Hello\n\nContent", assignedAgentId: "agent-1", status: "executing", column: "in-progress" })}
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onClose={noop}
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onMoveTask={noopMove}
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onDeleteTask={noopDelete}
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onMergeTask={noopMerge}
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onOpenDetail={noopOpenDetail}
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addToast={noop}
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/>,
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);
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fireEvent.click(screen.getByText("Logs"));
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fireEvent.click(screen.getByText("Agent Log"));
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await waitFor(() => {
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const header = container.querySelector("[data-testid='agent-log-model-header']");
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expect(header).toBeTruthy();
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});
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const expandButton = screen.getByTestId("agent-log-model-expand") as HTMLButtonElement;
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if (expandButton.getAttribute("aria-expanded") !== "true") {
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fireEvent.click(expandButton);
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}
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const header = container.querySelector("[data-testid='agent-log-model-header']") as HTMLElement;
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expect(header.textContent).toContain("openai/gpt-4.1");
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});
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describe("step progress", () => {
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it("renders step progress section when steps exist", () => {
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const { container } = render(
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