Improve pilot chat live traces and remove mock placeholders

Frontend/server/agent/crmAgent.ts

@@ -30,6 +30,17 @@ export type AgentReply = {
   dbWrites?: Array<{ kind: string; detail: string }>;
 };
 
+export type AgentTraceEvent = {
+  text: string;
+  toolRun?: {
+    name: string;
+    status: "ok" | "error";
+    input: string;
+    output: string;
+    at: string;
+  };
+};
+
 function normalize(s: string) {
   return s.trim().toLowerCase();
 }
@@ -81,7 +92,12 @@ export async function runCrmAgent(userText: string): Promise<AgentReply> {
 }
 
 export async function runCrmAgentFor(
-  input: { teamId: string; userId: string; userText: string },
+  input: {
+    teamId: string;
+    userId: string;
+    userText: string;
+    onTrace?: (event: AgentTraceEvent) => Promise<void> | void;
+  },
 ): Promise<AgentReply> {
   const mode = (process.env.CF_AGENT_MODE ?? "langgraph").toLowerCase();
   const llmApiKey =

Frontend/server/agent/langgraphCrmAgent.ts

@@ -1,5 +1,5 @@
 import { randomUUID } from "node:crypto";
-import type { AgentReply } from "./crmAgent";
+import type { AgentReply, AgentTraceEvent } from "./crmAgent";
 import { prisma } from "../utils/prisma";
 import { ensureDataset } from "../dataset/exporter";
 import { createReactAgent } from "@langchain/langgraph/prebuilt";
@@ -322,6 +322,7 @@ export async function runLangGraphCrmAgentFor(input: {
   teamId: string;
   userId: string;
   userText: string;
+  onTrace?: (event: AgentTraceEvent) => Promise<void> | void;
 }): Promise<AgentReply> {
   const genericApiKey =
     process.env.LLM_API_KEY ||
@@ -388,6 +389,15 @@ export async function runLangGraphCrmAgentFor(input: {
   const toolRuns: NonNullable<AgentReply["toolRuns"]> = [];
   const pendingChanges: PendingChange[] = [];
 
+  async function emitTrace(event: AgentTraceEvent) {
+    if (!input.onTrace) return;
+    try {
+      await input.onTrace(event);
+    } catch {
+      // Trace transport errors must not break the agent response.
+    }
+  }
+
   function compact(value: unknown, max = 240) {
     const text = typeof value === "string" ? value : JSON.stringify(value);
     if (!text) return "";
@@ -510,6 +520,7 @@ export async function runLangGraphCrmAgentFor(input: {
       const toolName = `crm:${raw.action}`;
       const startedAt = new Date().toISOString();
       toolsUsed.push(toolName);
+      await emitTrace({ text: `Tool started: ${toolName}` });
 
       const executeAction = async () => {
         if (raw.action === "get_snapshot") {
@@ -753,21 +764,31 @@ export async function runLangGraphCrmAgentFor(input: {
 
       try {
         const result = await executeAction();
-        toolRuns.push({
+        const run = {
           name: toolName,
           status: "ok",
           input: compact(raw),
           output: compact(result),
           at: startedAt,
+        } as const;
+        toolRuns.push(run);
+        await emitTrace({
+          text: `Tool finished: ${toolName}`,
+          toolRun: run,
         });
         return result;
       } catch (error: any) {
-        toolRuns.push({
+        const run = {
           name: toolName,
           status: "error",
           input: compact(raw),
           output: compact(error?.message || String(error)),
           at: startedAt,
+        } as const;
+        toolRuns.push(run);
+        await emitTrace({
+          text: `Tool failed: ${toolName}`,
+          toolRun: run,
         });
         throw error;
       }
@@ -796,14 +817,19 @@ export async function runLangGraphCrmAgentFor(input: {
       : {}),
   });
 
-  const agent = createReactAgent({
-    llm: model,
-    tools: [crmTool],
-    responseFormat: z.object({
-      answer: z.string().describe("Final assistant answer for the user."),
-      plan: z.array(z.string()).min(1).max(10).describe("Short plan (3-8 steps)."),
-    }),
-  });
+  const agent = useGigachat
+    ? createReactAgent({
+        llm: model,
+        tools: [crmTool],
+      })
+    : createReactAgent({
+        llm: model,
+        tools: [crmTool],
+        responseFormat: z.object({
+          answer: z.string().describe("Final assistant answer for the user."),
+          plan: z.array(z.string()).min(1).max(10).describe("Short plan (3-8 steps)."),
+        }),
+      });
 
   const system = [
     "You are Pilot, a CRM assistant.",
@@ -831,13 +857,34 @@ export async function runLangGraphCrmAgentFor(input: {
   );
 
   const structured = res?.structuredResponse as { answer?: string; plan?: string[] } | undefined;
-  const text = structured?.answer?.trim() || "Готово.";
-  const plan = Array.isArray(structured?.plan) ? structured!.plan : ["Собрать данные", "Сформировать ответ"];
+  const fallbackText = (() => {
+    const messages = Array.isArray(res?.messages) ? res.messages : [];
+    for (let i = messages.length - 1; i >= 0; i -= 1) {
+      const msg = messages[i];
+      const type = String(msg?.type ?? "").toLowerCase();
+      if (type !== "ai") continue;
+      const content = msg?.content;
+      if (typeof content === "string" && content.trim()) return content.trim();
+      if (Array.isArray(content)) {
+        const text = content
+          .map((part: any) => (typeof part?.text === "string" ? part.text : ""))
+          .filter(Boolean)
+          .join("\n")
+          .trim();
+        if (text) return text;
+      }
+    }
+    return "";
+  })();
+  const text = structured?.answer?.trim() || fallbackText || "Готово.";
+  const plan = Array.isArray(structured?.plan) && structured.plan.length
+    ? structured.plan
+    : ["Собрать данные", "Сформировать ответ"];
 
   return {
     text,
     plan,
-    thinking: plan,
+    thinking: [],
     tools: toolsUsed,
     toolRuns,
     dbWrites: dbWrites.length ? dbWrites : undefined,