Merge branch 'dev' into fix/claude-code-binary-files-v2

## Summary

- Add `SUGGESTED_GOAL` response type and `SuggestedGoalResponse` model
to backend; vague/unachievable goals now return a structured suggestion
instead of a generic error
- Add `SuggestedGoalCard` frontend component (amber styling, "Use this
goal" button) that lets users accept and re-submit a refined goal in one
click
- Add error recovery buttons ("Try again", "Simplify goal") to the error
output block
- Update copilot system prompt with explicit guidance for handling
`suggested_goal` and `clarifying_questions` feedback loops
- Add `create_agent_test.py` covering all four decomposition result
types

## Test plan

- [ ] Trigger vague goal (e.g. "monitor social media") →
`SuggestedGoalCard` renders with amber styling
- [ ] Trigger unachievable goal (e.g. "read my mind") → card shows goal
type "Goal cannot be accomplished" with reason
- [ ] Click "Use this goal" → sends message and triggers new
`create_agent` call with the suggested goal
- [ ] Trigger an error → "Try again" and "Simplify goal" buttons appear
below the error
- [ ] Clarifying questions answered → LLM re-calls `create_agent` with
context (system prompt guidance)
- [ ] Backend tests pass: `poetry run pytest
backend/api/features/chat/tools/create_agent_test.py -xvs` (requires
Docker services)

<!-- greptile_comment -->

<details><summary><h3>Greptile Summary</h3></summary>

Replaced generic `ErrorResponse` with structured `SuggestedGoalResponse`
for vague/unachievable goals in the copilot agent creation flow. Added
frontend `SuggestedGoalCard` component with amber styling and "Use this
goal" button for one-click goal refinement. Enhanced system prompt with
explicit feedback loop handling for `suggested_goal` and
`clarifying_questions`. Added comprehensive test coverage for all four
decomposition result types.

**Key improvements:**
- Better UX: Users can now accept refined goals with one click instead
of manually retyping
- Clearer error recovery: Added "Try again" and "Simplify goal" buttons
to error blocks
- Structured data: Backend now returns `suggested_goal`, `reason`,
`original_goal`, and `goal_type` fields instead of embedding everything
in error messages

**Issue found:**
- The `reason` field from the backend is not being passed to or
displayed by the `SuggestedGoalCard` component, so users won't see the
explanation for why their goal was rejected (especially important for
unachievable goals where it explains what blocks are missing)
</details>


<details><summary><h3>Confidence Score: 4/5</h3></summary>

- Safe to merge after fixing the missing `reason` field in the frontend
component
- Implementation is well-structured with good test coverage and follows
established patterns. The issue with the missing `reason` field is
straightforward to fix but important for UX - users won't understand why
their goal was rejected without it. All other changes are solid: backend
properly returns structured data, tests cover all cases, and the
component integration follows the project's conventions.
-
autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx
and SuggestedGoalCard.tsx need the `reason` prop added
</details>


<details><summary><h3>Flowchart</h3></summary>

```mermaid
flowchart TD
    Start[User submits goal to create_agent] --> Decompose[decompose_goal analyzes request]
    
    Decompose --> CheckType{Decomposition result type?}
    
    CheckType -->|clarifying_questions| Questions[Return ClarificationNeededResponse]
    Questions --> UserAnswers[User answers questions]
    UserAnswers --> Retry[Retry with context]
    Retry --> Decompose
    
    CheckType -->|vague_goal| VagueResponse[Return SuggestedGoalResponse<br/>goal_type: vague]
    VagueResponse --> ShowSuggestion[Frontend: SuggestedGoalCard<br/>amber styling]
    ShowSuggestion --> UserAccepts{User clicks<br/>Use this goal?}
    UserAccepts -->|Yes| NewGoal[Send suggested goal]
    NewGoal --> Decompose
    UserAccepts -->|No| End1[User refines manually]
    
    CheckType -->|unachievable_goal| UnachievableResponse[Return SuggestedGoalResponse<br/>goal_type: unachievable<br/>reason: missing blocks]
    UnachievableResponse --> ShowSuggestion
    
    CheckType -->|success| Generate[generate_agent creates workflow]
    Generate --> SaveOrPreview{save parameter?}
    SaveOrPreview -->|true| Save[Save to library<br/>AgentSavedResponse]
    SaveOrPreview -->|false| Preview[AgentPreviewResponse]
    
    CheckType -->|error| ErrorFlow[Return ErrorResponse]
    ErrorFlow --> ShowError[Frontend: Show error with<br/>Try again & Simplify goal buttons]
    ShowError --> UserRetry{User action?}
    UserRetry -->|Try again| Decompose
    UserRetry -->|Simplify goal| GetHelp[Ask LLM to simplify]
    GetHelp --> Decompose
    
    Save --> End2[Done]
    Preview --> End2
    End1 --> End2
```
</details>


<sub>Last reviewed commit: 2f37aee</sub>

<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->

autogpt_platform/backend/backend/api/features/chat/routes.py

@@ -50,6 +50,7 @@ from backend.copilot.tools.models import (
     OperationPendingResponse,
     OperationStartedResponse,
     SetupRequirementsResponse,
+    SuggestedGoalResponse,
     UnderstandingUpdatedResponse,
 )
 from backend.copilot.tracking import track_user_message
@@ -984,6 +985,7 @@ ToolResponseUnion = (
     | AgentPreviewResponse
     | AgentSavedResponse
     | ClarificationNeededResponse
+    | SuggestedGoalResponse
     | BlockListResponse
     | BlockDetailsResponse
     | BlockOutputResponse

autogpt_platform/backend/backend/blocks/claude_code.py

@@ -187,9 +187,11 @@ class ClaudeCodeBlock(Block):
         )
         files: list[SandboxFileOutput] = SchemaField(
             description=(
-                "List of text files created/modified by Claude Code during this execution. "
+                "List of files created/modified by Claude Code during this execution. "
+                "Includes text files and binary files (images, PDFs, etc.). "
                 "Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. "
-                "workspace_ref contains a workspace:// URI if the file was stored to workspace."
+                "workspace_ref contains a workspace:// URI for workspace storage. "
+                "For binary files, content contains a placeholder; use workspace_ref to access the file."
             )
         )
         conversation_history: str = SchemaField(
@@ -452,13 +454,15 @@ class ClaudeCodeBlock(Block):
                 else:
                     new_conversation_history = turn_entry
 
-            # Extract files created/modified during this run and store to workspace
+            # Extract files created/modified during this run and store to workspace.
+            # Binary files (images, PDFs, etc.) are stored via store_media_file
+            # which handles virus scanning and workspace storage.
             sandbox_files = await extract_and_store_sandbox_files(
                 sandbox=sandbox,
                 working_directory=working_directory,
                 execution_context=execution_context,
                 since_timestamp=start_timestamp,
-                text_only=True,
+                text_only=False,
             )
 
             return (

autogpt_platform/backend/backend/copilot/service.py

@@ -118,6 +118,8 @@ Adapt flexibly to the conversation context. Not every interaction requires all s
    - Find reusable components with `find_block`
    - Create custom solutions with `create_agent` if nothing suitable exists
    - Modify existing library agents with `edit_agent`
+   - **When `create_agent` returns `suggested_goal`**: Present the suggestion to the user and ask "Would you like me to proceed with this refined goal?" If they accept, call `create_agent` again with the suggested goal.
+   - **When `create_agent` returns `clarifying_questions`**: After the user answers, call `create_agent` again with the original description AND the answers in the `context` parameter.
 
 5. **Execute**: Run automations immediately, schedule them, or set up webhooks using `run_agent`. Test specific components with `run_block`.
 
@@ -164,6 +166,11 @@ Adapt flexibly to the conversation context. Not every interaction requires all s
 - Use `add_understanding` to capture valuable business context
 - When tool calls fail, try alternative approaches
 
+**Handle Feedback Loops:**
+- When a tool returns a suggested alternative (like a refined goal), present it clearly and ask the user for confirmation before proceeding
+- When clarifying questions are answered, immediately re-call the tool with the accumulated context
+- Don't ask redundant questions if the user has already provided context in the conversation
+
 ## CRITICAL REMINDER
 
 You are NOT a chatbot. You are NOT documentation. You are a partner who helps busy business owners get value quickly by showing proof through working automations. Bias toward action over explanation."""

autogpt_platform/backend/backend/copilot/tools/create_agent.py

@@ -22,6 +22,7 @@ from .models import (
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
+    SuggestedGoalResponse,
     ToolResponseBase,
 )
 
@@ -186,26 +187,28 @@ class CreateAgentTool(BaseTool):
         if decomposition_result.get("type") == "unachievable_goal":
             suggested = decomposition_result.get("suggested_goal", "")
             reason = decomposition_result.get("reason", "")
-            return ErrorResponse(
+            return SuggestedGoalResponse(
                 message=(
-                    f"This goal cannot be accomplished with the available blocks. "
-                    f"{reason} "
-                    f"Suggestion: {suggested}"
+                    f"This goal cannot be accomplished with the available blocks. {reason}"
                 ),
-                error="unachievable_goal",
-                details={"suggested_goal": suggested, "reason": reason},
+                suggested_goal=suggested,
+                reason=reason,
+                original_goal=description,
+                goal_type="unachievable",
                 session_id=session_id,
             )
 
         if decomposition_result.get("type") == "vague_goal":
             suggested = decomposition_result.get("suggested_goal", "")
-            return ErrorResponse(
-                message=(
-                    f"The goal is too vague to create a specific workflow. "
-                    f"Suggestion: {suggested}"
-                ),
-                error="vague_goal",
-                details={"suggested_goal": suggested},
+            reason = decomposition_result.get(
+                "reason", "The goal needs more specific details"
+            )
+            return SuggestedGoalResponse(
+                message="The goal is too vague to create a specific workflow.",
+                suggested_goal=suggested,
+                reason=reason,
+                original_goal=description,
+                goal_type="vague",
                 session_id=session_id,
             )
 

autogpt_platform/backend/backend/copilot/tools/create_agent_test.py

@@ -0,0 +1,142 @@
+"""Tests for CreateAgentTool response types."""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from backend.copilot.tools.create_agent import CreateAgentTool
+from backend.copilot.tools.models import (
+    ClarificationNeededResponse,
+    ErrorResponse,
+    SuggestedGoalResponse,
+)
+
+from ._test_data import make_session
+
+_TEST_USER_ID = "test-user-create-agent"
+
+
+@pytest.fixture
+def tool():
+    return CreateAgentTool()
+
+
+@pytest.fixture
+def session():
+    return make_session(_TEST_USER_ID)
+
+
+@pytest.mark.asyncio
+async def test_missing_description_returns_error(tool, session):
+    """Missing description returns ErrorResponse."""
+    result = await tool._execute(user_id=_TEST_USER_ID, session=session, description="")
+    assert isinstance(result, ErrorResponse)
+    assert result.error == "Missing description parameter"
+
+
+@pytest.mark.asyncio
+async def test_vague_goal_returns_suggested_goal_response(tool, session):
+    """vague_goal decomposition result returns SuggestedGoalResponse, not ErrorResponse."""
+    vague_result = {
+        "type": "vague_goal",
+        "suggested_goal": "Monitor Twitter mentions for a specific keyword and send a daily digest email",
+    }
+
+    with (
+        patch(
+            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=[],
+        ),
+        patch(
+            "backend.copilot.tools.create_agent.decompose_goal",
+            new_callable=AsyncMock,
+            return_value=vague_result,
+        ),
+    ):
+        result = await tool._execute(
+            user_id=_TEST_USER_ID,
+            session=session,
+            description="monitor social media",
+        )
+
+    assert isinstance(result, SuggestedGoalResponse)
+    assert result.goal_type == "vague"
+    assert result.suggested_goal == vague_result["suggested_goal"]
+    assert result.original_goal == "monitor social media"
+    assert result.reason == "The goal needs more specific details"
+    assert not isinstance(result, ErrorResponse)
+
+
+@pytest.mark.asyncio
+async def test_unachievable_goal_returns_suggested_goal_response(tool, session):
+    """unachievable_goal decomposition result returns SuggestedGoalResponse, not ErrorResponse."""
+    unachievable_result = {
+        "type": "unachievable_goal",
+        "suggested_goal": "Summarize the latest news articles on a topic and send them by email",
+        "reason": "There are no blocks for mind-reading.",
+    }
+
+    with (
+        patch(
+            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=[],
+        ),
+        patch(
+            "backend.copilot.tools.create_agent.decompose_goal",
+            new_callable=AsyncMock,
+            return_value=unachievable_result,
+        ),
+    ):
+        result = await tool._execute(
+            user_id=_TEST_USER_ID,
+            session=session,
+            description="read my mind",
+        )
+
+    assert isinstance(result, SuggestedGoalResponse)
+    assert result.goal_type == "unachievable"
+    assert result.suggested_goal == unachievable_result["suggested_goal"]
+    assert result.original_goal == "read my mind"
+    assert result.reason == unachievable_result["reason"]
+    assert not isinstance(result, ErrorResponse)
+
+
+@pytest.mark.asyncio
+async def test_clarifying_questions_returns_clarification_needed_response(
+    tool, session
+):
+    """clarifying_questions decomposition result returns ClarificationNeededResponse."""
+    clarifying_result = {
+        "type": "clarifying_questions",
+        "questions": [
+            {
+                "question": "What platform should be monitored?",
+                "keyword": "platform",
+                "example": "Twitter, Reddit",
+            }
+        ],
+    }
+
+    with (
+        patch(
+            "backend.copilot.tools.create_agent.get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=[],
+        ),
+        patch(
+            "backend.copilot.tools.create_agent.decompose_goal",
+            new_callable=AsyncMock,
+            return_value=clarifying_result,
+        ),
+    ):
+        result = await tool._execute(
+            user_id=_TEST_USER_ID,
+            session=session,
+            description="monitor social media and alert me",
+        )
+
+    assert isinstance(result, ClarificationNeededResponse)
+    assert len(result.questions) == 1
+    assert result.questions[0].keyword == "platform"

autogpt_platform/backend/backend/copilot/tools/models.py

@@ -2,7 +2,7 @@
 
 from datetime import datetime
 from enum import Enum
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import BaseModel, Field
 
@@ -50,6 +50,8 @@ class ResponseType(str, Enum):
     # Feature request types
     FEATURE_REQUEST_SEARCH = "feature_request_search"
     FEATURE_REQUEST_CREATED = "feature_request_created"
+    # Goal refinement
+    SUGGESTED_GOAL = "suggested_goal"
 
 
 # Base response model
@@ -296,6 +298,22 @@ class ClarificationNeededResponse(ToolResponseBase):
     questions: list[ClarifyingQuestion] = Field(default_factory=list)
 
 
+class SuggestedGoalResponse(ToolResponseBase):
+    """Response when the goal needs refinement with a suggested alternative."""
+
+    type: ResponseType = ResponseType.SUGGESTED_GOAL
+    suggested_goal: str = Field(description="The suggested alternative goal")
+    reason: str = Field(
+        default="", description="Why the original goal needs refinement"
+    )
+    original_goal: str = Field(
+        default="", description="The user's original goal for context"
+    )
+    goal_type: Literal["vague", "unachievable"] = Field(
+        default="vague", description="Type: 'vague' or 'unachievable'"
+    )
+
+
 # Documentation search models
 class DocSearchResult(BaseModel):
     """A single documentation search result."""

autogpt_platform/backend/backend/util/sandbox_files.py

@@ -74,8 +74,50 @@ TEXT_EXTENSIONS = {
     ".tex",
     ".csv",
     ".log",
+    ".svg",  # SVG is XML-based text
 }
 
+# Binary file extensions we explicitly support extracting
+BINARY_EXTENSIONS = {
+    # Images
+    ".png",
+    ".jpg",
+    ".jpeg",
+    ".gif",
+    ".webp",
+    ".ico",
+    ".bmp",
+    ".tiff",
+    ".tif",
+    # Documents
+    ".pdf",
+    # Archives
+    ".zip",
+    ".tar",
+    ".gz",
+    ".7z",
+    # Audio
+    ".mp3",
+    ".wav",
+    ".ogg",
+    ".flac",
+    # Video
+    ".mp4",
+    ".webm",
+    ".mov",
+    ".avi",
+    # Fonts
+    ".woff",
+    ".woff2",
+    ".ttf",
+    ".otf",
+    ".eot",
+}
+
+# Maximum file size for binary extraction (50MB)
+# Prevents OOM from accidentally extracting huge files
+MAX_BINARY_FILE_SIZE = 50 * 1024 * 1024
+
 
 class SandboxFileOutput(BaseModel):
     """A file extracted from a sandbox and optionally stored in workspace."""
@@ -120,7 +162,8 @@ async def extract_sandbox_files(
         sandbox: The E2B sandbox instance
         working_directory: Directory to search for files
         since_timestamp: ISO timestamp - only return files modified after this time
-        text_only: If True, only extract text files (default). If False, extract all files.
+        text_only: If True, only extract text files. If False, also extract
+                   supported binary files (images, PDFs, etc.).
 
     Returns:
         List of ExtractedFile objects with path, content, and metadata
@@ -149,15 +192,48 @@ async def extract_sandbox_files(
             if not file_path:
                 continue
 
-            # Check if it's a text file
-            is_text = any(file_path.endswith(ext) for ext in TEXT_EXTENSIONS)
+            # Check file type (case-insensitive for extensions)
+            file_path_lower = file_path.lower()
+            is_text = any(
+                file_path_lower.endswith(ext.lower()) for ext in TEXT_EXTENSIONS
+            )
+            is_binary = any(
+                file_path_lower.endswith(ext.lower()) for ext in BINARY_EXTENSIONS
+            )
 
-            # Skip non-text files if text_only mode
+            # Skip files with unrecognized extensions
+            if not is_text and not is_binary:
+                continue
+
+            # In text_only mode, skip binary files
             if text_only and not is_text:
                 continue
 
             try:
-                # Read file content as bytes
+                # Check file size before reading to prevent OOM
+                stat_result = await sandbox.commands.run(
+                    f"stat -c %s {shlex.quote(file_path)} 2>/dev/null"
+                )
+                if stat_result.exit_code != 0 or not stat_result.stdout:
+                    logger.debug(f"Skipping {file_path}: could not determine file size")
+                    continue
+
+                try:
+                    file_size = int(stat_result.stdout.strip())
+                except ValueError:
+                    logger.debug(
+                        f"Skipping {file_path}: unexpected stat output "
+                        f"{stat_result.stdout.strip()!r}"
+                    )
+                    continue
+
+                if file_size > MAX_BINARY_FILE_SIZE:
+                    logger.info(
+                        f"Skipping {file_path}: size {file_size} bytes "
+                        f"exceeds limit {MAX_BINARY_FILE_SIZE}"
+                    )
+                    continue
+
                 content = await sandbox.files.read(file_path, format="bytes")
                 if isinstance(content, str):
                     content = content.encode("utf-8")

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx

@@ -26,6 +26,7 @@ import {
 } from "./components/ClarificationQuestionsCard";
 import sparklesImg from "./components/MiniGame/assets/sparkles.png";
 import { MiniGame } from "./components/MiniGame/MiniGame";
+import { SuggestedGoalCard } from "./components/SuggestedGoalCard";
 import {
   AccordionIcon,
   formatMaybeJson,
@@ -38,6 +39,7 @@ import {
   isOperationInProgressOutput,
   isOperationPendingOutput,
   isOperationStartedOutput,
+  isSuggestedGoalOutput,
   ToolIcon,
   truncateText,
   type CreateAgentToolOutput,
@@ -77,6 +79,13 @@ function getAccordionMeta(output: CreateAgentToolOutput) {
       expanded: true,
     };
   }
+  if (isSuggestedGoalOutput(output)) {
+    return {
+      icon,
+      title: "Goal needs refinement",
+      expanded: true,
+    };
+  }
   if (
     isOperationStartedOutput(output) ||
     isOperationPendingOutput(output) ||
@@ -125,8 +134,13 @@ export function CreateAgentTool({ part }: Props) {
       isAgentPreviewOutput(output) ||
       isAgentSavedOutput(output) ||
       isClarificationNeededOutput(output) ||
+      isSuggestedGoalOutput(output) ||
       isErrorOutput(output));
 
+  function handleUseSuggestedGoal(goal: string) {
+    onSend(`Please create an agent with this goal: ${goal}`);
+  }
+
   function handleClarificationAnswers(answers: Record<string, string>) {
     const questions =
       output && isClarificationNeededOutput(output)
@@ -245,6 +259,16 @@ export function CreateAgentTool({ part }: Props) {
             />
           )}
 
+          {isSuggestedGoalOutput(output) && (
+            <SuggestedGoalCard
+              message={output.message}
+              suggestedGoal={output.suggested_goal}
+              reason={output.reason}
+              goalType={output.goal_type ?? "vague"}
+              onUseSuggestedGoal={handleUseSuggestedGoal}
+            />
+          )}
+
           {isErrorOutput(output) && (
             <ContentGrid>
               <ContentMessage>{output.message}</ContentMessage>
@@ -258,6 +282,22 @@ export function CreateAgentTool({ part }: Props) {
                   {formatMaybeJson(output.details)}
                 </ContentCodeBlock>
               )}
+              <div className="flex gap-2">
+                <Button
+                  variant="outline"
+                  size="small"
+                  onClick={() => onSend("Please try creating the agent again.")}
+                >
+                  Try again
+                </Button>
+                <Button
+                  variant="outline"
+                  size="small"
+                  onClick={() => onSend("Can you help me simplify this goal?")}
+                >
+                  Simplify goal
+                </Button>
+              </div>
             </ContentGrid>
           )}
         </ToolAccordion>

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/components/SuggestedGoalCard.tsx

@@ -0,0 +1,63 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { ArrowRightIcon, LightbulbIcon } from "@phosphor-icons/react";
+
+interface Props {
+  message: string;
+  suggestedGoal: string;
+  reason?: string;
+  goalType: string;
+  onUseSuggestedGoal: (goal: string) => void;
+}
+
+export function SuggestedGoalCard({
+  message,
+  suggestedGoal,
+  reason,
+  goalType,
+  onUseSuggestedGoal,
+}: Props) {
+  return (
+    <div className="rounded-xl border border-amber-200 bg-amber-50/50 p-4">
+      <div className="flex items-start gap-3">
+        <LightbulbIcon
+          size={20}
+          weight="fill"
+          className="mt-0.5 text-amber-600"
+        />
+        <div className="flex-1 space-y-3">
+          <div>
+            <Text variant="body-medium" className="font-medium text-slate-900">
+              {goalType === "unachievable"
+                ? "Goal cannot be accomplished"
+                : "Goal needs more detail"}
+            </Text>
+            <Text variant="small" className="text-slate-600">
+              {reason || message}
+            </Text>
+          </div>
+
+          <div className="rounded-lg border border-amber-300 bg-white p-3">
+            <Text variant="small" className="mb-1 font-semibold text-amber-800">
+              Suggested alternative:
+            </Text>
+            <Text variant="body-medium" className="text-slate-900">
+              {suggestedGoal}
+            </Text>
+          </div>
+
+          <Button
+            onClick={() => onUseSuggestedGoal(suggestedGoal)}
+            variant="primary"
+          >
+            <span className="inline-flex items-center gap-1.5">
+              Use this goal <ArrowRightIcon size={14} weight="bold" />
+            </span>
+          </Button>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx

@@ -6,6 +6,7 @@ import type { OperationInProgressResponse } from "@/app/api/__generated__/models
 import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
 import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
+import type { SuggestedGoalResponse } from "@/app/api/__generated__/models/suggestedGoalResponse";
 import {
   PlusCircleIcon,
   PlusIcon,
@@ -21,6 +22,7 @@ export type CreateAgentToolOutput =
   | AgentPreviewResponse
   | AgentSavedResponse
   | ClarificationNeededResponse
+  | SuggestedGoalResponse
   | ErrorResponse;
 
 function parseOutput(output: unknown): CreateAgentToolOutput | null {
@@ -43,6 +45,7 @@ function parseOutput(output: unknown): CreateAgentToolOutput | null {
       type === ResponseType.agent_preview ||
       type === ResponseType.agent_saved ||
       type === ResponseType.clarification_needed ||
+      type === ResponseType.suggested_goal ||
       type === ResponseType.error
     ) {
       return output as CreateAgentToolOutput;
@@ -55,6 +58,7 @@ function parseOutput(output: unknown): CreateAgentToolOutput | null {
     if ("agent_id" in output && "library_agent_id" in output)
       return output as AgentSavedResponse;
     if ("questions" in output) return output as ClarificationNeededResponse;
+    if ("suggested_goal" in output) return output as SuggestedGoalResponse;
     if ("error" in output || "details" in output)
       return output as ErrorResponse;
   }
@@ -114,6 +118,14 @@ export function isClarificationNeededOutput(
   );
 }
 
+export function isSuggestedGoalOutput(
+  output: CreateAgentToolOutput,
+): output is SuggestedGoalResponse {
+  return (
+    output.type === ResponseType.suggested_goal || "suggested_goal" in output
+  );
+}
+
 export function isErrorOutput(
   output: CreateAgentToolOutput,
 ): output is ErrorResponse {
@@ -139,6 +151,7 @@ export function getAnimationText(part: {
       if (isAgentSavedOutput(output)) return `Saved ${output.agent_name}`;
       if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
       if (isClarificationNeededOutput(output)) return "Needs clarification";
+      if (isSuggestedGoalOutput(output)) return "Goal needs refinement";
       return "Error creating agent";
     }
     case "output-error":

autogpt_platform/frontend/src/app/api/openapi.json

@@ -1052,6 +1052,7 @@
                     {
                       "$ref": "#/components/schemas/ClarificationNeededResponse"
                     },
+                    { "$ref": "#/components/schemas/SuggestedGoalResponse" },
                     { "$ref": "#/components/schemas/BlockListResponse" },
                     { "$ref": "#/components/schemas/BlockDetailsResponse" },
                     { "$ref": "#/components/schemas/BlockOutputResponse" },
@@ -10796,7 +10797,8 @@
           "bash_exec",
           "operation_status",
           "feature_request_search",
-          "feature_request_created"
+          "feature_request_created",
+          "suggested_goal"
         ],
         "title": "ResponseType",
         "description": "Types of tool responses."
@@ -11677,6 +11679,47 @@
         "enum": ["DRAFT", "PENDING", "APPROVED", "REJECTED"],
         "title": "SubmissionStatus"
       },
+      "SuggestedGoalResponse": {
+        "properties": {
+          "type": {
+            "$ref": "#/components/schemas/ResponseType",
+            "default": "suggested_goal"
+          },
+          "message": { "type": "string", "title": "Message" },
+          "session_id": {
+            "anyOf": [{ "type": "string" }, { "type": "null" }],
+            "title": "Session Id"
+          },
+          "suggested_goal": {
+            "type": "string",
+            "title": "Suggested Goal",
+            "description": "The suggested alternative goal"
+          },
+          "reason": {
+            "type": "string",
+            "title": "Reason",
+            "description": "Why the original goal needs refinement",
+            "default": ""
+          },
+          "original_goal": {
+            "type": "string",
+            "title": "Original Goal",
+            "description": "The user's original goal for context",
+            "default": ""
+          },
+          "goal_type": {
+            "type": "string",
+            "enum": ["vague", "unachievable"],
+            "title": "Goal Type",
+            "description": "Type: 'vague' or 'unachievable'",
+            "default": "vague"
+          }
+        },
+        "type": "object",
+        "required": ["message", "suggested_goal"],
+        "title": "SuggestedGoalResponse",
+        "description": "Response when the goal needs refinement with a suggested alternative."
+      },
       "SuggestionsResponse": {
         "properties": {
           "otto_suggestions": {

docs/integrations/block-integrations/claude_code.md

@@ -16,7 +16,7 @@ When activated, the block:
    - Install dependencies (npm, pip, etc.)
    - Run terminal commands
    - Build and test applications
-5. Extracts all text files created/modified during execution
+5. Extracts all files created/modified during execution (text files and binary files like images, PDFs, etc.)
 6. Returns the response and files, optionally keeping the sandbox alive for follow-up tasks
 
 The block supports conversation continuation through three mechanisms:
@@ -42,7 +42,7 @@ The block supports conversation continuation through three mechanisms:
 | Output | Description |
 |--------|-------------|
 | Response | The output/response from Claude Code execution |
-| Files | List of text files created/modified during execution. Each file includes path, relative_path, name, and content fields |
+| Files | List of files (text and binary) created/modified during execution. Includes images, PDFs, and other supported formats. Each file has path, relative_path, name, content, and workspace_ref fields. Binary files are stored in workspace and accessible via workspace_ref |
 | Conversation History | Full conversation history including this turn. Use to restore context on a fresh sandbox |
 | Session ID | Session ID for this conversation. Pass back with sandbox_id to continue the conversation |
 | Sandbox ID | ID of the sandbox instance (null if disposed). Pass back with session_id to continue the conversation |

docs/integrations/block-integrations/llm.md

@@ -535,7 +535,7 @@ When activated, the block:
 2. Installs the latest version of Claude Code in the sandbox
 3. Optionally runs setup commands to prepare the environment
 4. Executes your prompt using Claude Code, which can create/edit files, install dependencies, run terminal commands, and build applications
-5. Extracts all text files created/modified during execution
+5. Extracts all files created/modified during execution (text files and binary files like images, PDFs, etc.)
 6. Returns the response and files, optionally keeping the sandbox alive for follow-up tasks
 
 The block supports conversation continuation through three mechanisms:
@@ -563,7 +563,7 @@ The block supports conversation continuation through three mechanisms:
 |--------|-------------|------|
 | error | Error message if execution failed | str |
 | response | The output/response from Claude Code execution | str |
-| files | List of text files created/modified by Claude Code during this execution. Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. workspace_ref contains a workspace:// URI if the file was stored to workspace. | List[SandboxFileOutput] |
+| files | List of files created/modified by Claude Code during this execution. Includes text files and binary files (images, PDFs, etc.). Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. workspace_ref contains a workspace:// URI for workspace storage. For binary files, content contains a placeholder; use workspace_ref to access the file. | List[SandboxFileOutput] |
 | conversation_history | Full conversation history including this turn. Pass this to conversation_history input to continue on a fresh sandbox if the previous sandbox timed out. | str |
 | session_id | Session ID for this conversation. Pass this back along with sandbox_id to continue the conversation. | str |
 | sandbox_id | ID of the sandbox instance. Pass this back along with session_id to continue the conversation. This is None if dispose_sandbox was True (sandbox was disposed). | str |