feat(agent): route chat through selected model

src/app/api/agent/chat/route.ts

@@ -2,6 +2,7 @@ import { apiError, apiJson } from "@/lib/api-handler";
 import { resolveApiAuth } from "@/lib/api-auth";
 import { getAssistantPreferences, resolveAssistantSystemPrompt } from "@/lib/assistant-preference";
 import { isLlmConfigured } from "@/lib/llm";
+import { listLlmModels, resolveAssistantModel } from "@/lib/llm/models";
 import { clientChatInputSchema } from "@/modules/agent/messages";
 import { encodeSseEvent } from "@/modules/agent/server/progress";
 import { runAgentChat } from "@/modules/agent/server/run";
@@ -31,6 +32,18 @@ export async function POST(request: Request) {
     return apiError(parsed.error.issues[0]?.message ?? "Validation error", 400);
   }
 
+  const modelList = await listLlmModels();
+  const modelResolution = resolveAssistantModel({
+    requestedModel: parsed.data.model,
+    savedModel: assistant.model,
+    fallbackModel: modelList.fallbackModel,
+    models: modelList.models,
+  });
+
+  if (!modelResolution.ok) {
+    return apiError(modelResolution.error, 400);
+  }
+
   if (parsed.data.stream) {
     const stream = new ReadableStream<Uint8Array>({
       async start(controller) {
@@ -44,6 +57,7 @@ export async function POST(request: Request) {
             messages: parsed.data.messages,
             request,
             systemPrompt,
+            model: modelResolution.model,
             onProgress: send,
           });
 
@@ -78,6 +92,7 @@ export async function POST(request: Request) {
       messages: parsed.data.messages,
       request,
       systemPrompt,
+      model: modelResolution.model,
     });
 
     return apiJson({

src/lib/llm/index.ts

@@ -13,8 +13,8 @@ export type {
 export { getLlmConfig, isLlmConfigured } from "./config";
 export { createMockLlmClient } from "./mock";
 
-export function createLlmClient(override?: LlmClient): LlmClient {
-  if (override) return override;
+export function createLlmClient(options?: { model?: string; override?: LlmClient }): LlmClient {
+  if (options?.override) return options.override;
 
   const config = getLlmConfig();
   if (config.provider === "mock" || !config.baseUrl) {
@@ -24,6 +24,6 @@ export function createLlmClient(override?: LlmClient): LlmClient {
   return createOpenAiCompatibleClient({
     baseUrl: config.baseUrl,
     apiKey: config.apiKey,
-    model: config.model,
+    model: options?.model ?? config.model,
   });
 }

src/modules/agent/messages.ts

@@ -1,4 +1,5 @@
 import { z } from "zod";
+import { isValidLlmModelId } from "@/lib/llm/models";
 
 export const clientChatAttachmentSchema = z.object({
   type: z.literal("image"),
@@ -11,8 +12,14 @@ export const clientChatMessageSchema = z.object({
   attachments: z.array(clientChatAttachmentSchema).max(3).optional(),
 });
 
+export const clientChatModelSchema = z
+  .string()
+  .trim()
+  .refine((value) => isValidLlmModelId(value), "Invalid assistant model");
+
 export const clientChatInputSchema = z.object({
   stream: z.boolean().optional(),
+  model: clientChatModelSchema.optional(),
   messages: z.array(clientChatMessageSchema).min(1).max(40),
 });
 

src/modules/agent/server/run.ts

@@ -45,11 +45,12 @@ export async function runAgentChat(options: {
   messages: ClientChatMessage[];
   request: Request;
   systemPrompt?: string;
+  model?: string;
   llm?: LlmClient;
   executeTool?: ToolExecutor;
   onProgress?: AgentProgressHandler;
 }): Promise<AgentChatResult> {
-  const llm = options.llm ?? createLlmClient();
+  const llm = options.llm ?? createLlmClient({ model: options.model });
   const executeTool = options.executeTool ?? createApiToolExecutor(options.request);
   const onProgress = options.onProgress;
   const systemPrompt = options.systemPrompt ?? AGENT_SYSTEM_PROMPT;

tests/unit/agent-chat.test.ts

@@ -84,3 +84,36 @@ describe("runAgentChat", () => {
     assert.ok(result.message.content.length > 0);
   });
 });
+
+it("passes a model override to the OpenAI-compatible client", async () => {
+  const originalBaseUrl = process.env.LLM_BASE_URL;
+  const originalModel = process.env.LLM_MODEL;
+  const originalProvider = process.env.LLM_PROVIDER;
+  const originalFetch = globalThis.fetch;
+  let requestBody: unknown = null;
+
+  process.env.LLM_BASE_URL = "https://llm.example.test/v1";
+  process.env.LLM_MODEL = "llama3.2";
+  delete process.env.LLM_PROVIDER;
+
+  globalThis.fetch = (async (_input: RequestInfo | URL, init?: RequestInit) => {
+    requestBody = JSON.parse(String(init?.body));
+    return Response.json({
+      choices: [{ message: { role: "assistant", content: "done" }, finish_reason: "stop" }],
+    });
+  }) as typeof fetch;
+
+  const { createLlmClient } = await import("../../src/lib/llm/index");
+  const client = createLlmClient({ model: "qwen2.5-coder" });
+  await client.chatCompletion({ messages: [{ role: "user", content: "hello" }] });
+
+  assert.equal((requestBody as { model?: string }).model, "qwen2.5-coder");
+
+  globalThis.fetch = originalFetch;
+  if (originalBaseUrl === undefined) delete process.env.LLM_BASE_URL;
+  else process.env.LLM_BASE_URL = originalBaseUrl;
+  if (originalModel === undefined) delete process.env.LLM_MODEL;
+  else process.env.LLM_MODEL = originalModel;
+  if (originalProvider === undefined) delete process.env.LLM_PROVIDER;
+  else process.env.LLM_PROVIDER = originalProvider;
+});

tests/unit/agent-messages.test.ts

@@ -25,4 +25,22 @@ describe("clientChatInputSchema", () => {
 
     assert.equal(parsed.success, false);
   });
+
+  it("accepts an optional model ID", () => {
+    const parsed = clientChatInputSchema.safeParse({
+      model: "qwen2.5-coder",
+      messages: [{ role: "user", content: "hello" }],
+    });
+
+    assert.equal(parsed.success, true);
+  });
+
+  it("rejects invalid model IDs", () => {
+    const parsed = clientChatInputSchema.safeParse({
+      model: "bad model",
+      messages: [{ role: "user", content: "hello" }],
+    });
+
+    assert.equal(parsed.success, false);
+  });
 });