refactor(copilot): use Pydantic models and match/case in customize_agent

Addresses review feedback from ntindle:

1. Use typed parameters instead of kwargs.get():
   - Added CustomizeAgentInput Pydantic model with field_validator for stripping strings
   - Tool now uses params = CustomizeAgentInput(**kwargs) pattern

2. Use match/case for cleaner pattern matching:
   - Extracted response handling to _handle_customization_result method
   - Uses match result_type: case 'error' | 'clarifying_questions' | _

3. Improved code organization:
   - Split monolithic _execute into smaller focused methods
   - _handle_customization_result for response type handling
   - _save_or_preview_agent for final save/preview logic

autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.api.features.store.exceptions import AgentNotFoundError
@@ -27,6 +29,23 @@ from .models import (
 logger = logging.getLogger(__name__)
 
 
+class CustomizeAgentInput(BaseModel):
+    """Input parameters for the customize_agent tool."""
+
+    agent_id: str = ""
+    modifications: str = ""
+    context: str = ""
+    save: bool = True
+
+    @field_validator("agent_id", "modifications", "context", mode="before")
+    @classmethod
+    def strip_strings(cls, v: Any) -> str:
+        """Strip whitespace from string fields."""
+        if isinstance(v, str):
+            return v.strip()
+        return v if v is not None else ""
+
+
 class CustomizeAgentTool(BaseTool):
     """Tool for customizing marketplace/template agents using natural language."""
 
@@ -92,7 +111,7 @@ class CustomizeAgentTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
     ) -> ToolResponseBase:
         """Execute the customize_agent tool.
 
@@ -102,20 +121,17 @@ class CustomizeAgentTool(BaseTool):
         3. Call customize_template with the modification request
         4. Preview or save based on the save parameter
         """
-        agent_id = kwargs.get("agent_id", "").strip()
+        params = CustomizeAgentInput(**kwargs)
-        modifications = kwargs.get("modifications", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        if not agent_id:
+        if not params.agent_id:
             return ErrorResponse(
                 message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
                 error="missing_agent_id",
                 session_id=session_id,
             )
 
-        if not modifications:
+        if not params.modifications:
             return ErrorResponse(
                 message="Please describe how you want to customize this agent.",
                 error="missing_modifications",
@@ -123,11 +139,11 @@ class CustomizeAgentTool(BaseTool):
             )
 
         # Parse agent_id in format "creator/slug"
-        parts = [p.strip() for p in agent_id.split("/")]
+        parts = params.agent_id.split("/")
         if len(parts) != 2 or not parts[0] or not parts[1]:
             return ErrorResponse(
                 message=(
-                    f"Invalid agent ID format: '{agent_id}'. "
+                    f"Invalid agent ID format: '{params.agent_id}'. "
                     "Expected format is 'creator/agent-name' "
                     "(e.g., 'autogpt/newsletter-writer')."
                 ),
@@ -145,14 +161,14 @@ class CustomizeAgentTool(BaseTool):
         except AgentNotFoundError:
             return ErrorResponse(
                 message=(
-                    f"Could not find marketplace agent '{agent_id}'. "
+                    f"Could not find marketplace agent '{params.agent_id}'. "
                     "Please check the agent ID and try again."
                 ),
                 error="agent_not_found",
                 session_id=session_id,
             )
         except Exception as e:
-            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
+            logger.error(f"Error fetching marketplace agent {params.agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the marketplace agent. Please try again.",
                 error="fetch_error",
@@ -162,7 +178,7 @@ class CustomizeAgentTool(BaseTool):
         if not agent_details.store_listing_version_id:
             return ErrorResponse(
                 message=(
-                    f"The agent '{agent_id}' does not have an available version. "
+                    f"The agent '{params.agent_id}' does not have an available version. "
                     "Please try a different agent."
                 ),
                 error="no_version_available",
@@ -174,7 +190,7 @@ class CustomizeAgentTool(BaseTool):
             graph = await store_db.get_agent(agent_details.store_listing_version_id)
             template_agent = graph_to_json(graph)
         except Exception as e:
-            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
+            logger.error(f"Error fetching agent graph for {params.agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the agent configuration. Please try again.",
                 error="graph_fetch_error",
@@ -185,8 +201,8 @@ class CustomizeAgentTool(BaseTool):
         try:
             result = await customize_template(
                 template_agent=template_agent,
-                modification_request=modifications,
+                modification_request=params.modifications,
-                context=context,
+                context=params.context,
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -198,7 +214,7 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
         except Exception as e:
-            logger.error(f"Error calling customize_template for {agent_id}: {e}")
+            logger.error(f"Error calling customize_template for {params.agent_id}: {e}")
             return ErrorResponse(
                 message=(
                     "Failed to customize the agent due to a service error. "
@@ -219,55 +235,25 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Handle error response
+        # Handle response using match/case for cleaner pattern matching
-        if isinstance(result, dict) and result.get("type") == "error":
+        return await self._handle_customization_result(
-            error_msg = result.get("error", "Unknown error")
+            result=result,
-            error_type = result.get("error_type", "unknown")
+            params=params,
-            user_message = get_user_message_for_error(
+            agent_details=agent_details,
-                error_type,
+            user_id=user_id,
-                operation="customize the agent",
+            session_id=session_id,
-                llm_parse_message=(
+        )
-                    "The AI had trouble customizing the agent. "
-                    "Please try again or simplify your request."
-                ),
-                validation_message=(
-                    "The customized agent failed validation. "
-                    "Please try rephrasing your request."
-                ),
-                error_details=error_msg,
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"customization_failed:{error_type}",
-                session_id=session_id,
-            )
 
-        # Handle clarifying questions
+    async def _handle_customization_result(
-        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
+        self,
-            questions = result.get("questions") or []
+        result: dict[str, Any],
-            if not isinstance(questions, list):
+        params: CustomizeAgentInput,
-                logger.error(
+        agent_details: Any,
-                    f"Unexpected clarifying questions format: {type(questions)}"
+        user_id: str | None,
-                )
+        session_id: str | None,
-                questions = []
+    ) -> ToolResponseBase:
-            return ClarificationNeededResponse(
+        """Handle the result from customize_template using pattern matching."""
-                message=(
+        # Ensure result is a dict
-                    "I need some more information to customize this agent. "
-                    "Please answer the following questions:"
-                ),
-                questions=[
-                    ClarifyingQuestion(
-                        question=q.get("question", ""),
-                        keyword=q.get("keyword", ""),
-                        example=q.get("example"),
-                    )
-                    for q in questions
-                    if isinstance(q, dict)
-                ],
-                session_id=session_id,
-            )
-
-        # Result should be the customized agent JSON
         if not isinstance(result, dict):
             logger.error(f"Unexpected customize_template response type: {type(result)}")
             return ErrorResponse(
@@ -276,8 +262,77 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        customized_agent = result
+        result_type = result.get("type")
 
+        match result_type:
+            case "error":
+                error_msg = result.get("error", "Unknown error")
+                error_type = result.get("error_type", "unknown")
+                user_message = get_user_message_for_error(
+                    error_type,
+                    operation="customize the agent",
+                    llm_parse_message=(
+                        "The AI had trouble customizing the agent. "
+                        "Please try again or simplify your request."
+                    ),
+                    validation_message=(
+                        "The customized agent failed validation. "
+                        "Please try rephrasing your request."
+                    ),
+                    error_details=error_msg,
+                )
+                return ErrorResponse(
+                    message=user_message,
+                    error=f"customization_failed:{error_type}",
+                    session_id=session_id,
+                )
+
+            case "clarifying_questions":
+                questions_data = result.get("questions") or []
+                if not isinstance(questions_data, list):
+                    logger.error(
+                        f"Unexpected clarifying questions format: {type(questions_data)}"
+                    )
+                    questions_data = []
+
+                questions = [
+                    ClarifyingQuestion(
+                        question=q.get("question", "") if isinstance(q, dict) else "",
+                        keyword=q.get("keyword", "") if isinstance(q, dict) else "",
+                        example=q.get("example") if isinstance(q, dict) else None,
+                    )
+                    for q in questions_data
+                    if isinstance(q, dict)
+                ]
+
+                return ClarificationNeededResponse(
+                    message=(
+                        "I need some more information to customize this agent. "
+                        "Please answer the following questions:"
+                    ),
+                    questions=questions,
+                    session_id=session_id,
+                )
+
+            case _:
+                # Default case: result is the customized agent JSON
+                return await self._save_or_preview_agent(
+                    customized_agent=result,
+                    params=params,
+                    agent_details=agent_details,
+                    user_id=user_id,
+                    session_id=session_id,
+                )
+
+    async def _save_or_preview_agent(
+        self,
+        customized_agent: dict[str, Any],
+        params: CustomizeAgentInput,
+        agent_details: Any,
+        user_id: str | None,
+        session_id: str | None,
+    ) -> ToolResponseBase:
+        """Save or preview the customized agent based on params.save."""
         agent_name = customized_agent.get(
             "name", f"Customized {agent_details.agent_name}"
         )
@@ -287,7 +342,7 @@ class CustomizeAgentTool(BaseTool):
         node_count = len(nodes) if isinstance(nodes, list) else 0
         link_count = len(links) if isinstance(links, list) else 0
 
-        if not save:
+        if not params.save:
             return AgentPreviewResponse(
                 message=(
                     f"I've customized the agent '{agent_details.agent_name}'. "

autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/route.ts

@@ -1,17 +1,6 @@
 import { OAuthPopupResultMessage } from "./types";
 import { NextResponse } from "next/server";
 
-/**
- * Safely encode a value as JSON for embedding in a script tag.
- * Escapes characters that could break out of the script context to prevent XSS.
- */
-function safeJsonStringify(value: unknown): string {
-  return JSON.stringify(value)
-    .replace(/</g, "\\u003c")
-    .replace(/>/g, "\\u003e")
-    .replace(/&/g, "\\u0026");
-}
-
 // This route is intended to be used as the callback for integration OAuth flows,
 // controlled by the CredentialsInput component. The CredentialsInput opens the login
 // page in a pop-up window, which then redirects to this route to close the loop.
@@ -34,13 +23,12 @@ export async function GET(request: Request) {
   console.debug("Sending message to opener:", message);
 
   // Return a response with the message as JSON and a script to close the window
-  // Use safeJsonStringify to prevent XSS by escaping <, >, and & characters
   return new NextResponse(
     `
     <html>
       <body>
         <script>
-          window.opener.postMessage(${safeJsonStringify(message)});
+          window.opener.postMessage(${JSON.stringify(message)});
           window.close();
         </script>
       </body>

autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts

@@ -26,20 +26,8 @@ export function buildCopilotChatUrl(prompt: string): string {
 
 export function getQuickActions(): string[] {
   return [
-    "I don't know where to start, just ask me stuff",
+    "Show me what I can automate",
-    "I do the same thing every week and it's killing me",
+    "Design a custom workflow",
-    "Help me find where I'm wasting my time",
+    "Help me with content creation",
   ];
 }
-
-export function getInputPlaceholder(width?: number) {
-  if (!width) return "What's your role and what eats up most of your day?";
-
-  if (width < 500) {
-    return "I'm a chef and I hate...";
-  }
-  if (width <= 1080) {
-    return "What's your role and what eats up most of your day?";
-  }
-  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
-}

autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx

@@ -6,9 +6,7 @@ import { Text } from "@/components/atoms/Text/Text";
 import { Chat } from "@/components/contextual/Chat/Chat";
 import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { useEffect, useState } from "react";
 import { useCopilotStore } from "./copilot-page-store";
-import { getInputPlaceholder } from "./helpers";
 import { useCopilotPage } from "./useCopilotPage";
 
 export default function CopilotPage() {
@@ -16,25 +14,8 @@ export default function CopilotPage() {
   const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
   const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
   const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
-
-  const [inputPlaceholder, setInputPlaceholder] = useState(
-    getInputPlaceholder(),
-  );
-
-  useEffect(() => {
-    const handleResize = () => {
-      setInputPlaceholder(getInputPlaceholder(window.innerWidth));
-    };
-
-    handleResize();
-
-    window.addEventListener("resize", handleResize);
-    return () => window.removeEventListener("resize", handleResize);
-  }, []);
-
   const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
     state;
-
   const {
     handleQuickAction,
     startChatWithPrompt,
@@ -92,7 +73,7 @@ export default function CopilotPage() {
   }
 
   return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
+    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-6 py-10">
       <div className="w-full text-center">
         {isLoading ? (
           <div className="mx-auto max-w-2xl">
@@ -109,25 +90,25 @@ export default function CopilotPage() {
           </div>
         ) : (
           <>
-            <div className="mx-auto max-w-3xl">
+            <div className="mx-auto max-w-2xl">
               <Text
                 variant="h3"
-                className="mb-1 !text-[1.375rem] text-zinc-700"
+                className="mb-3 !text-[1.375rem] text-zinc-700"
               >
                 Hey, <span className="text-violet-600">{greetingName}</span>
               </Text>
               <Text variant="h3" className="mb-8 !font-normal">
-                Tell me about your work — I&apos;ll find what to automate.
+                What do you want to automate?
               </Text>
 
               <div className="mb-6">
                 <ChatInput
                   onSend={startChatWithPrompt}
-                  placeholder={inputPlaceholder}
+                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
                 />
               </div>
             </div>
-            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+            <div className="flex flex-nowrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
               {quickActions.map((action) => (
                 <Button
                   key={action}
@@ -135,7 +116,7 @@ export default function CopilotPage() {
                   variant="outline"
                   size="small"
                   onClick={() => handleQuickAction(action)}
-                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
+                  className="h-auto shrink-0 border-zinc-600 !px-4 !py-2 text-[1rem] text-zinc-600"
                 >
                   {action}
                 </Button>

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx

@@ -2,6 +2,7 @@ import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessi
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { cn } from "@/lib/utils";
 import { GlobeHemisphereEastIcon } from "@phosphor-icons/react";
 import { useEffect } from "react";
@@ -55,6 +56,10 @@ export function ChatContainer({
     onStreamingChange?.(isStreaming);
   }, [isStreaming, onStreamingChange]);
 
+  const breakpoint = useBreakpoint();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
   return (
     <div
       className={cn(
@@ -122,7 +127,11 @@ export function ChatContainer({
           disabled={isStreaming || !sessionId}
           isStreaming={isStreaming}
           onStop={stopStreaming}
-          placeholder="What else can I help with?"
+          placeholder={
+            isMobile
+              ? "You can search or just ask"
+              : 'You can search or just ask — e.g. "create a blog post outline"'
+          }
         />
       </div>
     </div>

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx

@@ -74,20 +74,19 @@ export function ChatInput({
             hasMultipleLines ? "rounded-xlarge" : "rounded-full",
           )}
         >
-          {!value && !isRecording && (
-            <div
-              className="pointer-events-none absolute inset-0 top-0.5 flex items-center justify-start pl-14 text-[1rem] text-zinc-400"
-              aria-hidden="true"
-            >
-              {isTranscribing ? "Transcribing..." : placeholder}
-            </div>
-          )}
           <textarea
             id={inputId}
             aria-label="Chat message input"
             value={value}
             onChange={handleChange}
             onKeyDown={handleKeyDown}
+            placeholder={
+              isTranscribing
+                ? "Transcribing..."
+                : isRecording
+                  ? ""
+                  : placeholder
+            }
             disabled={isInputDisabled}
             rows={1}
             className={cn(
@@ -123,14 +122,13 @@ export function ChatInput({
               size="icon"
               aria-label={isRecording ? "Stop recording" : "Start recording"}
               onClick={toggleRecording}
-              disabled={disabled || isTranscribing || isStreaming}
+              disabled={disabled || isTranscribing}
               className={cn(
                 isRecording
                   ? "animate-pulse border-red-500 bg-red-500 text-white hover:border-red-600 hover:bg-red-600"
                   : isTranscribing
                     ? "border-zinc-300 bg-zinc-100 text-zinc-400"
                     : "border-zinc-300 bg-white text-zinc-500 hover:border-zinc-400 hover:bg-zinc-50 hover:text-zinc-700",
-                isStreaming && "opacity-40",
               )}
             >
               {isTranscribing ? (

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx

@@ -38,8 +38,8 @@ export function AudioWaveform({
     // Create audio context and analyser
     const audioContext = new AudioContext();
     const analyser = audioContext.createAnalyser();
-    analyser.fftSize = 256;
+    analyser.fftSize = 512;
-    analyser.smoothingTimeConstant = 0.3;
+    analyser.smoothingTimeConstant = 0.8;
 
     // Connect the stream to the analyser
     const source = audioContext.createMediaStreamSource(stream);
@@ -73,11 +73,10 @@ export function AudioWaveform({
           maxAmplitude = Math.max(maxAmplitude, amplitude);
         }
 
-        // Normalize amplitude (0-128 range) to 0-1
+        // Map amplitude (0-128) to bar height
-        const normalized = maxAmplitude / 128;
+        const normalized = (maxAmplitude / 128) * 255;
-        // Apply sensitivity boost (multiply by 4) and use sqrt curve to amplify quiet sounds
+        const height =
-        const boosted = Math.min(1, Math.sqrt(normalized) * 4);
+          minBarHeight + (normalized / 255) * (maxBarHeight - minBarHeight);
-        const height = minBarHeight + boosted * (maxBarHeight - minBarHeight);
         newBars.push(height);
       }
 

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts

@@ -224,7 +224,7 @@ export function useVoiceRecording({
     [value, isTranscribing, toggleRecording, baseHandleKeyDown],
   );
 
-  const showMicButton = isSupported;
+  const showMicButton = isSupported && !isStreaming;
   const isInputDisabled = disabled || isStreaming || isTranscribing;
 
   // Cleanup on unmount