open-swe/ui/src/lib/agents/useModelOptions.ts

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feat: add Agents chat UI for cloud threads (#1323) * feat(ui): add Agents chat UI ported from open-swe-app Introduce a Cursor-style Agents surface separate from the dashboard, with ported chat/diff components and mock thread data until LangGraph APIs land. Co-authored-by: Cursor <cursoragent@cursor.com> * feat(dashboard): wire Agents UI to LangGraph thread APIs Add dashboard thread list/detail/run/message/stream endpoints with a LangGraph message adapter, dashboard OAuth auth for runs, and TanStack Query hooks replacing mock data. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(dashboard): single agent reply per turn in Agents UI Use UUID thread IDs LangGraph accepts, skip confirming_completion for dashboard threads, and merge adapter agent messages so duplicate bubbles do not render. Co-authored-by: Cursor <cursoragent@cursor.com> * feat(ui): polish Agents UI with floating prompt and layout cleanup Remove no-op chrome (git panel, headers, sidebar search), port CloudPromptBar from open-swe-app, and refine chat layout so messages scroll behind the input. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(agent): patch deepagents reducer for None messages on checkpoint replay LangGraph thread state could 500 when cancelled runs left messages as None. Apply the reducer guard before graph import, fall back to metadata in the dashboard API, and adjust Agents prompt bar layout. Co-authored-by: Cursor <cursoragent@cursor.com> * feat(ui): unify sidebar user menu and clean up Agents UI navigation Extract SidebarUserMenu so the dashboard and Agents sidebars render the same profile button, drop the redundant Agents nav row in favor of the existing Back to Agents link, add the open-swe logo header to the Agents sidebar, flatten the New Agent button, and cap the home screen run list to keep the prompt input in view. * feat(ui): resizable/collapsible sidebar shared across dashboard and Agents Add a useSidebarLayout hook + SidebarFrame wrapper so both sidebars share a persisted width (default 260px, drag to resize, 200-420 range) and a collapse toggle that hides the panel and surfaces a floating reopen button. Also adds a DELETE /threads/{id} endpoint and an X-on- hover thread delete control in the Agents sidebar. * feat(ui): instant user message and busy indicator on Agents transition Stash submitted prompts in sessionStorage, pre-populate the new thread detail cache, and merge pending prompts into the rendered message list so the Agents page renders the user bubble plus the existing thinking spinner immediately instead of flashing a skeleton and "Agent is starting" while the run boots. * feat(ui): token-stream agent replies in the Agents thread view Opt the LangGraph runs into messages-tuple streaming and forward those events through the existing SSE channel. The frontend now applies AIMessageChunk deltas directly to the cached thread (cancelling any in-flight refetch first so optimistic tokens are not clobbered) and keeps positional pending prompts so the user bubble stays in the right place while the agent streams its reply. * fix(dashboard): await threads.join_stream before iterating threads.join_stream is async def returning an AsyncIterator, so it must be awaited before async for. The SSE endpoint was raising TypeError: 'async for' requires an object with __aiter__ method, got coroutine on every connection. * fix(dashboard): drop messages-tuple stream_mode that broke thinking-mode tool turns Setting stream_mode=["values","messages-tuple","updates"] on runs.create forces langchain_anthropic into streaming, and on the second model call (after tool execution) its serialized thinking blocks come back malformed, so Anthropic rejects the request with 'messages.1.content.0.thinking.thinking: Field required'. Revert to the default stream_mode so claude-opus thinking + tool use runs to completion. The frontend keeps the messages-event handler in place as a no-op fallback for when streaming is re-enabled. * feat(agents): per-thread model picker wired through to the run Add optional model_id/effort to the create-thread and send-message request bodies, forward them as agent_model_id/agent_effort in the LangGraph run configurable, and record the resolved choice in thread metadata so the UI can show the model the run is actually using. get_agent now picks the per-thread override last (highest priority over team default + profile override) and falls back gracefully when it is absent or unsupported. The frontend prompt bar becomes a controlled component fed by a shared useModelOptions hook (options + profile -> defaultSelection). AgentsHome seeds the picker from the user's profile default; the thread view seeds from the thread's recorded model/effort and lets each follow-up retarget the run. * refactor(ui): align Agents prompt bar layout with open-swe-app PromptBar Drop the absolute-positioned send button, restore the original px-4 py-3.5 min-h-[106px] flex-col container, and move the model picker into a mt-auto pt-2 footer row so the placeholder text and the model selector share the same horizontal padding. * chore: fix lint/format CI failures Remove unused imports and reformat two files flagged by ruff. * fix(tests): stop messages-reducer patch tests from polluting the suite Restore agent modules after reducer patch tests and import LangSmithSandbox from agent.server in proxy refresh tests so isinstance checks stay valid. --------- Co-authored-by: open-swe[bot] <open-swe@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com>
2026-05-22 11:15:59 -07:00
import { useQuery } from "@tanstack/react-query";
import { api, type ModelOption } from "@/lib/api";
export interface ModelSelection {
modelId: string;
effort: string;
}
export interface ModelOptionsResult {
models: ModelOption[];
defaultSelection: ModelSelection | null;
isLoading: boolean;
}
export function useModelOptions(): ModelOptionsResult {
const optionsQuery = useQuery({
queryKey: ["dashboard", "options"],
queryFn: () => api.options(),
staleTime: 5 * 60_000,
});
const profileQuery = useQuery({
queryKey: ["dashboard", "profile"],
queryFn: () => api.profile(),
staleTime: 5 * 60_000,
});
const models = optionsQuery.data?.models ?? [];
const profile = profileQuery.data;
let defaultSelection: ModelSelection | null = null;
const profileModelId = profile?.default_model;
const profileEffort = profile?.reasoning_effort;
if (
profileModelId &&
profileEffort &&
models.some((m) => m.id === profileModelId && m.efforts.includes(profileEffort))
) {
defaultSelection = { modelId: profileModelId, effort: profileEffort };
} else if (models.length > 0) {
const first = models[0]!;
defaultSelection = { modelId: first.id, effort: first.default_effort };
}
return {
models,
defaultSelection,
isLoading: optionsQuery.isLoading || profileQuery.isLoading,
};
}
const EFFORT_LABELS: Record<string, string> = {
low: "Low",
medium: "Medium",
high: "High",
xhigh: "XHigh",
max: "Max",
};
export function formatModelSelection(
models: ModelOption[],
selection: ModelSelection | null,
): string {
if (!selection) return "Default";
const model = models.find((m) => m.id === selection.modelId);
const modelLabel = model?.label ?? selection.modelId;
const effortLabel = EFFORT_LABELS[selection.effort] ?? selection.effort;
return `${modelLabel} ${effortLabel}`;
}