fix(copilot): update homepage copy to focus on problem discovery (#11956)

## Summary
Update the CoPilot homepage to shift from "what do you want to
automate?" to "tell me about your problems." This lowers the barrier to
engagement by letting users describe their work frustrations instead of
requiring them to identify automations themselves.

## Changes
| Element | Before | After |
|---------|--------|-------|
| Headline | "What do you want to automate?" | "Tell me about your work
— I'll find what to automate." |
| Placeholder | "You can search or just ask - e.g. 'create a blog post
outline'" | "What's your role and what eats up most of your day? e.g.
'I'm a real estate agent and I hate...'" |
| Button 1 | "Show me what I can automate" | "I don't know where to
start, just ask me stuff" |
| Button 2 | "Design a custom workflow" | "I do the same thing every
week and it's killing me" |
| Button 3 | "Help me with content creation" | "Help me find where I'm
wasting my time" |
| Container | max-w-2xl | max-w-3xl |

> **Note on container width:** The `max-w-2xl` → `max-w-3xl` change is
just to keep the longer headline on one line. This works but may not be
the ideal solution — @lluis-xai should advise on the proper approach.

## Why This Matters
The current UX assumes users know what they want to automate. In
reality, most users know what frustrates them but can't identify
automations. The current screen blocks Otto from starting the discovery
conversation that leads to useful recommendations.

## Files Changed
- `autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx` —
headline, placeholder, container width
- `autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts` —
quick action button text

Resolves: [SECRT-1876](https://linear.app/autogpt/issue/SECRT-1876)

---------

Co-authored-by: Lluis Agusti <hi@llu.lu>

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

@@ -1,15 +1,12 @@
 import asyncio
 import logging
 import time
-import uuid as uuid_module
 from asyncio import CancelledError
 from collections.abc import AsyncGenerator
 from typing import TYPE_CHECKING, Any, cast
 
 import openai
 
-from backend.util.prompt import compress_context
-
 if TYPE_CHECKING:
     from backend.util.prompt import CompressResult
 
@@ -470,6 +467,8 @@ async def stream_chat_completion(
     should_retry = False
 
     # Generate unique IDs for AI SDK protocol
+    import uuid as uuid_module
+
     message_id = str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
@@ -827,6 +826,10 @@ async def _manage_context_window(
     Returns:
         CompressResult with compacted messages and metadata
     """
+    import openai
+
+    from backend.util.prompt import compress_context
+
     # Convert messages to dict format
     messages_dict = []
     for msg in messages:
@@ -1137,6 +1140,8 @@ async def _yield_tool_call(
         KeyError: If expected tool call fields are missing
         TypeError: If tool call structure is invalid
     """
+    import uuid as uuid_module
+
     tool_name = tool_calls[yield_idx]["function"]["name"]
     tool_call_id = tool_calls[yield_idx]["id"]
 
@@ -1757,6 +1762,8 @@ async def _generate_llm_continuation_with_streaming(
     after a tool result is saved. Chunks are published to the stream registry
     so reconnecting clients can receive them.
     """
+    import uuid as uuid_module
+
     try:
         # Load fresh session from DB (bypass cache to get the updated tool result)
         await invalidate_session_cache(session_id)
@@ -1792,6 +1799,10 @@ async def _generate_llm_continuation_with_streaming(
             extra_body["session_id"] = session_id[:128]
 
         # Make streaming LLM call (no tools - just text response)
+        from typing import cast
+
+        from openai.types.chat import ChatCompletionMessageParam
+
         # Generate unique IDs for AI SDK protocol
         message_id = str(uuid_module.uuid4())
         text_block_id = str(uuid_module.uuid4())

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from pydantic import BaseModel, field_validator
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -30,26 +28,6 @@ from .models import (
 logger = logging.getLogger(__name__)
 
 
-class CreateAgentInput(BaseModel):
-    """Input parameters for the create_agent tool."""
-
-    description: str = ""
-    context: str = ""
-    save: bool = True
-    # Internal async processing params (passed by long-running tool handler)
-    _operation_id: str | None = None
-    _task_id: str | None = None
-
-    @field_validator("description", "context", mode="before")
-    @classmethod
-    def strip_strings(cls, v: Any) -> str:
-        """Strip whitespace from string fields."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-    class Config:
-        extra = "allow"  # Allow _operation_id, _task_id from kwargs
-
-
 class CreateAgentTool(BaseTool):
     """Tool for creating agents from natural language descriptions."""
 
@@ -107,7 +85,7 @@ class CreateAgentTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Execute the create_agent tool.
 
@@ -116,14 +94,16 @@ class CreateAgentTool(BaseTool):
         2. Generate agent JSON (external service handles fixing and validation)
         3. Preview or save based on the save parameter
         """
-        params = CreateAgentInput(**kwargs)
+        description = kwargs.get("description", "").strip()
+        context = kwargs.get("context", "")
+        save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        # Extract async processing params
+        # Extract async processing params (passed by long-running tool handler)
         operation_id = kwargs.get("_operation_id")
         task_id = kwargs.get("_task_id")
 
-        if not params.description:
+        if not description:
             return ErrorResponse(
                 message="Please provide a description of what the agent should do.",
                 error="Missing description parameter",
@@ -135,7 +115,7 @@ class CreateAgentTool(BaseTool):
             try:
                 library_agents = await get_all_relevant_agents_for_generation(
                     user_id=user_id,
-                    search_query=params.description,
+                    search_query=description,
                     include_marketplace=True,
                 )
                 logger.debug(
@@ -146,7 +126,7 @@ class CreateAgentTool(BaseTool):
 
         try:
             decomposition_result = await decompose_goal(
-                params.description, params.context, library_agents
+                description, context, library_agents
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -162,7 +142,7 @@ class CreateAgentTool(BaseTool):
             return ErrorResponse(
                 message="Failed to analyze the goal. The agent generation service may be unavailable. Please try again.",
                 error="decomposition_failed",
-                details={"description": params.description[:100]},
+                details={"description": description[:100]},
                 session_id=session_id,
             )
 
@@ -178,7 +158,7 @@ class CreateAgentTool(BaseTool):
                 message=user_message,
                 error=f"decomposition_failed:{error_type}",
                 details={
-                    "description": params.description[:100],
+                    "description": description[:100],
                     "service_error": error_msg,
                     "error_type": error_type,
                 },
@@ -264,7 +244,7 @@ class CreateAgentTool(BaseTool):
             return ErrorResponse(
                 message="Failed to generate the agent. The agent generation service may be unavailable. Please try again.",
                 error="generation_failed",
-                details={"description": params.description[:100]},
+                details={"description": description[:100]},
                 session_id=session_id,
             )
 
@@ -286,7 +266,7 @@ class CreateAgentTool(BaseTool):
                 message=user_message,
                 error=f"generation_failed:{error_type}",
                 details={
-                    "description": params.description[:100],
+                    "description": description[:100],
                     "service_error": error_msg,
                     "error_type": error_type,
                 },
@@ -311,7 +291,7 @@ class CreateAgentTool(BaseTool):
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        if not params.save:
+        if not save:
             return AgentPreviewResponse(
                 message=(
                     f"I've generated an agent called '{agent_name}' with {node_count} blocks. "

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

@@ -3,8 +3,6 @@
 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
@@ -29,23 +27,6 @@ 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."""
 
@@ -111,7 +92,7 @@ class CustomizeAgentTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Execute the customize_agent tool.
 
@@ -121,17 +102,20 @@ class CustomizeAgentTool(BaseTool):
         3. Call customize_template with the modification request
         4. Preview or save based on the save parameter
         """
-        params = CustomizeAgentInput(**kwargs)
+        agent_id = kwargs.get("agent_id", "").strip()
+        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 params.agent_id:
+        if not 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 params.modifications:
+        if not modifications:
             return ErrorResponse(
                 message="Please describe how you want to customize this agent.",
                 error="missing_modifications",
@@ -139,11 +123,11 @@ class CustomizeAgentTool(BaseTool):
             )
 
         # Parse agent_id in format "creator/slug"
-        parts = [p.strip() for p in params.agent_id.split("/")]
+        parts = [p.strip() for p in agent_id.split("/")]
         if len(parts) != 2 or not parts[0] or not parts[1]:
             return ErrorResponse(
                 message=(
-                    f"Invalid agent ID format: '{params.agent_id}'. "
+                    f"Invalid agent ID format: '{agent_id}'. "
                     "Expected format is 'creator/agent-name' "
                     "(e.g., 'autogpt/newsletter-writer')."
                 ),
@@ -161,14 +145,14 @@ class CustomizeAgentTool(BaseTool):
         except AgentNotFoundError:
             return ErrorResponse(
                 message=(
-                    f"Could not find marketplace agent '{params.agent_id}'. "
+                    f"Could not find marketplace agent '{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 {params.agent_id}: {e}")
+            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the marketplace agent. Please try again.",
                 error="fetch_error",
@@ -178,7 +162,7 @@ class CustomizeAgentTool(BaseTool):
         if not agent_details.store_listing_version_id:
             return ErrorResponse(
                 message=(
-                    f"The agent '{params.agent_id}' does not have an available version. "
+                    f"The agent '{agent_id}' does not have an available version. "
                     "Please try a different agent."
                 ),
                 error="no_version_available",
@@ -190,7 +174,7 @@ 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 {params.agent_id}: {e}")
+            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
             return ErrorResponse(
                 message="Failed to fetch the agent configuration. Please try again.",
                 error="graph_fetch_error",
@@ -201,8 +185,8 @@ class CustomizeAgentTool(BaseTool):
         try:
             result = await customize_template(
                 template_agent=template_agent,
-                modification_request=params.modifications,
-                context=params.context,
+                modification_request=modifications,
+                context=context,
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -214,7 +198,7 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
         except Exception as e:
-            logger.error(f"Error calling customize_template for {params.agent_id}: {e}")
+            logger.error(f"Error calling customize_template for {agent_id}: {e}")
             return ErrorResponse(
                 message=(
                     "Failed to customize the agent due to a service error. "
@@ -235,25 +219,55 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Handle response using match/case for cleaner pattern matching
-        return await self._handle_customization_result(
-            result=result,
-            params=params,
-            agent_details=agent_details,
-            user_id=user_id,
-            session_id=session_id,
-        )
+        # Handle error response
+        if isinstance(result, dict) and result.get("type") == "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,
+            )
 
-    async def _handle_customization_result(
-        self,
-        result: dict[str, Any],
-        params: CustomizeAgentInput,
-        agent_details: Any,
-        user_id: str | None,
-        session_id: str | None,
-    ) -> ToolResponseBase:
-        """Handle the result from customize_template using pattern matching."""
-        # Ensure result is a dict
+        # Handle clarifying questions
+        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
+            questions = result.get("questions") or []
+            if not isinstance(questions, list):
+                logger.error(
+                    f"Unexpected clarifying questions format: {type(questions)}"
+                )
+                questions = []
+            return ClarificationNeededResponse(
+                message=(
+                    "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(
@@ -262,77 +276,8 @@ class CustomizeAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        result_type = result.get("type")
+        customized_agent = result
 
-        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}"
         )
@@ -342,7 +287,7 @@ 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 params.save:
+        if not save:
             return AgentPreviewResponse(
                 message=(
                     f"I've customized the agent '{agent_details.agent_name}'. "

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

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from pydantic import BaseModel, ConfigDict, field_validator
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -29,20 +27,6 @@ from .models import (
 logger = logging.getLogger(__name__)
 
 
-class EditAgentInput(BaseModel):
-    model_config = ConfigDict(extra="allow")
-
-    agent_id: str = ""
-    changes: str = ""
-    context: str = ""
-    save: bool = True
-
-    @field_validator("agent_id", "changes", "context", mode="before")
-    @classmethod
-    def strip_strings(cls, v: Any) -> str:
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class EditAgentTool(BaseTool):
     """Tool for editing existing agents using natural language."""
 
@@ -106,7 +90,7 @@ class EditAgentTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Execute the edit_agent tool.
 
@@ -115,32 +99,35 @@ class EditAgentTool(BaseTool):
         2. Generate updated agent (external service handles fixing and validation)
         3. Preview or save based on the save parameter
         """
-        params = EditAgentInput(**kwargs)
+        agent_id = kwargs.get("agent_id", "").strip()
+        changes = kwargs.get("changes", "").strip()
+        context = kwargs.get("context", "")
+        save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
         # Extract async processing params (passed by long-running tool handler)
         operation_id = kwargs.get("_operation_id")
         task_id = kwargs.get("_task_id")
 
-        if not params.agent_id:
+        if not agent_id:
             return ErrorResponse(
                 message="Please provide the agent ID to edit.",
                 error="Missing agent_id parameter",
                 session_id=session_id,
             )
 
-        if not params.changes:
+        if not changes:
             return ErrorResponse(
                 message="Please describe what changes you want to make.",
                 error="Missing changes parameter",
                 session_id=session_id,
             )
 
-        current_agent = await get_agent_as_json(params.agent_id, user_id)
+        current_agent = await get_agent_as_json(agent_id, user_id)
 
         if current_agent is None:
             return ErrorResponse(
-                message=f"Could not find agent '{params.agent_id}' in your library.",
+                message=f"Could not find agent with ID '{agent_id}' in your library.",
                 error="agent_not_found",
                 session_id=session_id,
             )
@@ -151,7 +138,7 @@ class EditAgentTool(BaseTool):
                 graph_id = current_agent.get("id")
                 library_agents = await get_all_relevant_agents_for_generation(
                     user_id=user_id,
-                    search_query=params.changes,
+                    search_query=changes,
                     exclude_graph_id=graph_id,
                     include_marketplace=True,
                 )
@@ -161,11 +148,9 @@ class EditAgentTool(BaseTool):
             except Exception as e:
                 logger.warning(f"Failed to fetch library agents: {e}")
 
-        update_request = params.changes
-        if params.context:
-            update_request = (
-                f"{params.changes}\n\nAdditional context:\n{params.context}"
-            )
+        update_request = changes
+        if context:
+            update_request = f"{changes}\n\nAdditional context:\n{context}"
 
         try:
             result = await generate_agent_patch(
@@ -189,7 +174,7 @@ class EditAgentTool(BaseTool):
             return ErrorResponse(
                 message="Failed to generate changes. The agent generation service may be unavailable or timed out. Please try again.",
                 error="update_generation_failed",
-                details={"agent_id": params.agent_id, "changes": params.changes[:100]},
+                details={"agent_id": agent_id, "changes": changes[:100]},
                 session_id=session_id,
             )
 
@@ -221,8 +206,8 @@ class EditAgentTool(BaseTool):
                 message=user_message,
                 error=f"update_generation_failed:{error_type}",
                 details={
-                    "agent_id": params.agent_id,
-                    "changes": params.changes[:100],
+                    "agent_id": agent_id,
+                    "changes": changes[:100],
                     "service_error": error_msg,
                     "error_type": error_type,
                 },
@@ -254,7 +239,7 @@ class EditAgentTool(BaseTool):
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        if not params.save:
+        if not save:
             return AgentPreviewResponse(
                 message=(
                     f"I've updated the agent. "

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from pydantic import BaseModel, field_validator
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -11,18 +9,6 @@ from .base import BaseTool
 from .models import ToolResponseBase
 
 
-class FindAgentInput(BaseModel):
-    """Input parameters for the find_agent tool."""
-
-    query: str = ""
-
-    @field_validator("query", mode="before")
-    @classmethod
-    def strip_string(cls, v: Any) -> str:
-        """Strip whitespace from query."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class FindAgentTool(BaseTool):
     """Tool for discovering agents from the marketplace."""
 
@@ -50,11 +36,10 @@ class FindAgentTool(BaseTool):
         }
 
     async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs: Any
+        self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:
-        params = FindAgentInput(**kwargs)
         return await search_agents(
-            query=params.query,
+            query=kwargs.get("query", "").strip(),
             source="marketplace",
             session_id=session.session_id,
             user_id=user_id,

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

@@ -2,7 +2,6 @@ import logging
 from typing import Any
 
 from prisma.enums import ContentType
-from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
@@ -19,18 +18,6 @@ from backend.data.block import get_block
 logger = logging.getLogger(__name__)
 
 
-class FindBlockInput(BaseModel):
-    """Input parameters for the find_block tool."""
-
-    query: str = ""
-
-    @field_validator("query", mode="before")
-    @classmethod
-    def strip_string(cls, v: Any) -> str:
-        """Strip whitespace from query."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class FindBlockTool(BaseTool):
     """Tool for searching available blocks."""
 
@@ -72,24 +59,24 @@ class FindBlockTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Search for blocks matching the query.
 
         Args:
             user_id: User ID (required)
             session: Chat session
-            **kwargs: Tool parameters
+            query: Search query
 
         Returns:
             BlockListResponse: List of matching blocks
             NoResultsResponse: No blocks found
             ErrorResponse: Error message
         """
-        params = FindBlockInput(**kwargs)
+        query = kwargs.get("query", "").strip()
         session_id = session.session_id
 
-        if not params.query:
+        if not query:
             return ErrorResponse(
                 message="Please provide a search query",
                 session_id=session_id,
@@ -98,7 +85,7 @@ class FindBlockTool(BaseTool):
         try:
             # Search for blocks using hybrid search
             results, total = await unified_hybrid_search(
-                query=params.query,
+                query=query,
                 content_types=[ContentType.BLOCK],
                 page=1,
                 page_size=10,
@@ -106,7 +93,7 @@ class FindBlockTool(BaseTool):
 
             if not results:
                 return NoResultsResponse(
-                    message=f"No blocks found for '{params.query}'",
+                    message=f"No blocks found for '{query}'",
                     suggestions=[
                         "Try broader keywords like 'email', 'http', 'text', 'ai'",
                         "Check spelling of technical terms",
@@ -178,7 +165,7 @@ class FindBlockTool(BaseTool):
 
             if not blocks:
                 return NoResultsResponse(
-                    message=f"No blocks found for '{params.query}'",
+                    message=f"No blocks found for '{query}'",
                     suggestions=[
                         "Try broader keywords like 'email', 'http', 'text', 'ai'",
                     ],
@@ -187,13 +174,13 @@ class FindBlockTool(BaseTool):
 
             return BlockListResponse(
                 message=(
-                    f"Found {len(blocks)} block(s) matching '{params.query}'. "
+                    f"Found {len(blocks)} block(s) matching '{query}'. "
                     "To execute a block, use run_block with the block's 'id' field "
                     "and provide 'input_data' matching the block's input_schema."
                 ),
                 blocks=blocks,
                 count=len(blocks),
-                query=params.query,
+                query=query,
                 session_id=session_id,
             )
 

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

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from pydantic import BaseModel, field_validator
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -11,15 +9,6 @@ from .base import BaseTool
 from .models import ToolResponseBase
 
 
-class FindLibraryAgentInput(BaseModel):
-    query: str = ""
-
-    @field_validator("query", mode="before")
-    @classmethod
-    def strip_string(cls, v: Any) -> str:
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class FindLibraryAgentTool(BaseTool):
     """Tool for searching agents in the user's library."""
 
@@ -53,11 +42,10 @@ class FindLibraryAgentTool(BaseTool):
         return True
 
     async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs: Any
+        self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:
-        params = FindLibraryAgentInput(**kwargs)
         return await search_agents(
-            query=params.query,
+            query=kwargs.get("query", "").strip(),
             source="library",
             session_id=session.session_id,
             user_id=user_id,

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

@@ -4,8 +4,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from pydantic import BaseModel, field_validator
-
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool
 from backend.api.features.chat.tools.models import (
@@ -20,18 +18,6 @@ logger = logging.getLogger(__name__)
 DOCS_BASE_URL = "https://docs.agpt.co"
 
 
-class GetDocPageInput(BaseModel):
-    """Input parameters for the get_doc_page tool."""
-
-    path: str = ""
-
-    @field_validator("path", mode="before")
-    @classmethod
-    def strip_string(cls, v: Any) -> str:
-        """Strip whitespace from path."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class GetDocPageTool(BaseTool):
     """Tool for fetching full content of a documentation page."""
 
@@ -89,23 +75,23 @@ class GetDocPageTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Fetch full content of a documentation page.
 
         Args:
             user_id: User ID (not required for docs)
             session: Chat session
-            **kwargs: Tool parameters
+            path: Path to the documentation file
 
         Returns:
             DocPageResponse: Full document content
             ErrorResponse: Error message
         """
-        params = GetDocPageInput(**kwargs)
+        path = kwargs.get("path", "").strip()
         session_id = session.session_id if session else None
 
-        if not params.path:
+        if not path:
             return ErrorResponse(
                 message="Please provide a documentation path.",
                 error="Missing path parameter",
@@ -113,7 +99,7 @@ class GetDocPageTool(BaseTool):
             )
 
         # Sanitize path to prevent directory traversal
-        if ".." in params.path or params.path.startswith("/"):
+        if ".." in path or path.startswith("/"):
             return ErrorResponse(
                 message="Invalid documentation path.",
                 error="invalid_path",
@@ -121,11 +107,11 @@ class GetDocPageTool(BaseTool):
             )
 
         docs_root = self._get_docs_root()
-        full_path = docs_root / params.path
+        full_path = docs_root / path
 
         if not full_path.exists():
             return ErrorResponse(
-                message=f"Documentation page not found: {params.path}",
+                message=f"Documentation page not found: {path}",
                 error="not_found",
                 session_id=session_id,
             )
@@ -142,19 +128,19 @@ class GetDocPageTool(BaseTool):
 
         try:
             content = full_path.read_text(encoding="utf-8")
-            title = self._extract_title(content, params.path)
+            title = self._extract_title(content, path)
 
             return DocPageResponse(
                 message=f"Retrieved documentation page: {title}",
                 title=title,
-                path=params.path,
+                path=path,
                 content=content,
-                doc_url=self._make_doc_url(params.path),
+                doc_url=self._make_doc_url(path),
                 session_id=session_id,
             )
 
         except Exception as e:
-            logger.error(f"Failed to read documentation page {params.path}: {e}")
+            logger.error(f"Failed to read documentation page {path}: {e}")
             return ErrorResponse(
                 message=f"Failed to read documentation page: {str(e)}",
                 error="read_failed",

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

@@ -5,7 +5,6 @@ import uuid
 from collections import defaultdict
 from typing import Any
 
-from pydantic import BaseModel, field_validator
 from pydantic_core import PydanticUndefined
 
 from backend.api.features.chat.model import ChatSession
@@ -30,25 +29,6 @@ from .utils import build_missing_credentials_from_field_info
 logger = logging.getLogger(__name__)
 
 
-class RunBlockInput(BaseModel):
-    """Input parameters for the run_block tool."""
-
-    block_id: str = ""
-    input_data: dict[str, Any] = {}
-
-    @field_validator("block_id", mode="before")
-    @classmethod
-    def strip_block_id(cls, v: Any) -> str:
-        """Strip whitespace from block_id."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-    @field_validator("input_data", mode="before")
-    @classmethod
-    def ensure_dict(cls, v: Any) -> dict[str, Any]:
-        """Ensure input_data is a dict."""
-        return v if isinstance(v, dict) else {}
-
-
 class RunBlockTool(BaseTool):
     """Tool for executing a block and returning its outputs."""
 
@@ -182,29 +162,37 @@ class RunBlockTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Execute a block with the given input data.
 
         Args:
             user_id: User ID (required)
             session: Chat session
-            **kwargs: Tool parameters
+            block_id: Block UUID to execute
+            input_data: Input values for the block
 
         Returns:
             BlockOutputResponse: Block execution outputs
             SetupRequirementsResponse: Missing credentials
             ErrorResponse: Error message
         """
-        params = RunBlockInput(**kwargs)
+        block_id = kwargs.get("block_id", "").strip()
+        input_data = kwargs.get("input_data", {})
         session_id = session.session_id
 
-        if not params.block_id:
+        if not block_id:
             return ErrorResponse(
                 message="Please provide a block_id",
                 session_id=session_id,
             )
 
+        if not isinstance(input_data, dict):
+            return ErrorResponse(
+                message="input_data must be an object",
+                session_id=session_id,
+            )
+
         if not user_id:
             return ErrorResponse(
                 message="Authentication required",
@@ -212,25 +200,23 @@ class RunBlockTool(BaseTool):
             )
 
         # Get the block
-        block = get_block(params.block_id)
+        block = get_block(block_id)
         if not block:
             return ErrorResponse(
-                message=f"Block '{params.block_id}' not found",
+                message=f"Block '{block_id}' not found",
                 session_id=session_id,
             )
         if block.disabled:
             return ErrorResponse(
-                message=f"Block '{params.block_id}' is disabled",
+                message=f"Block '{block_id}' is disabled",
                 session_id=session_id,
             )
 
-        logger.info(
-            f"Executing block {block.name} ({params.block_id}) for user {user_id}"
-        )
+        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()
         matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block, params.input_data
+            user_id, block, input_data
         )
 
         if missing_credentials:
@@ -248,7 +234,7 @@ class RunBlockTool(BaseTool):
                 ),
                 session_id=session_id,
                 setup_info=SetupInfo(
-                    agent_id=params.block_id,
+                    agent_id=block_id,
                     agent_name=block.name,
                     user_readiness=UserReadiness(
                         has_all_credentials=False,
@@ -277,7 +263,7 @@ class RunBlockTool(BaseTool):
             # - node_exec_id = unique per block execution
             synthetic_graph_id = f"copilot-session-{session.session_id}"
             synthetic_graph_exec_id = f"copilot-session-{session.session_id}"
-            synthetic_node_id = f"copilot-node-{params.block_id}"
+            synthetic_node_id = f"copilot-node-{block_id}"
             synthetic_node_exec_id = (
                 f"copilot-{session.session_id}-{uuid.uuid4().hex[:8]}"
             )
@@ -312,8 +298,8 @@ class RunBlockTool(BaseTool):
 
             for field_name, cred_meta in matched_credentials.items():
                 # Inject metadata into input_data (for validation)
-                if field_name not in params.input_data:
-                    params.input_data[field_name] = cred_meta.model_dump()
+                if field_name not in input_data:
+                    input_data[field_name] = cred_meta.model_dump()
 
                 # Fetch actual credentials and pass as kwargs (for execution)
                 actual_credentials = await creds_manager.get(
@@ -330,14 +316,14 @@ class RunBlockTool(BaseTool):
             # Execute the block and collect outputs
             outputs: dict[str, list[Any]] = defaultdict(list)
             async for output_name, output_data in block.execute(
-                params.input_data,
+                input_data,
                 **exec_kwargs,
             ):
                 outputs[output_name].append(output_data)
 
             return BlockOutputResponse(
                 message=f"Block '{block.name}' executed successfully",
-                block_id=params.block_id,
+                block_id=block_id,
                 block_name=block.name,
                 outputs=dict(outputs),
                 success=True,

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

@@ -4,7 +4,6 @@ import logging
 from typing import Any
 
 from prisma.enums import ContentType
-from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool
@@ -29,18 +28,6 @@ MAX_RESULTS = 5
 SNIPPET_LENGTH = 200
 
 
-class SearchDocsInput(BaseModel):
-    """Input parameters for the search_docs tool."""
-
-    query: str = ""
-
-    @field_validator("query", mode="before")
-    @classmethod
-    def strip_string(cls, v: Any) -> str:
-        """Strip whitespace from query."""
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-
 class SearchDocsTool(BaseTool):
     """Tool for searching AutoGPT platform documentation."""
 
@@ -104,24 +91,24 @@ class SearchDocsTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
         """Search documentation and return relevant sections.
 
         Args:
             user_id: User ID (not required for docs)
             session: Chat session
-            **kwargs: Tool parameters
+            query: Search query
 
         Returns:
             DocSearchResultsResponse: List of matching documentation sections
             NoResultsResponse: No results found
             ErrorResponse: Error message
         """
-        params = SearchDocsInput(**kwargs)
+        query = kwargs.get("query", "").strip()
         session_id = session.session_id if session else None
 
-        if not params.query:
+        if not query:
             return ErrorResponse(
                 message="Please provide a search query.",
                 error="Missing query parameter",
@@ -131,7 +118,7 @@ class SearchDocsTool(BaseTool):
         try:
             # Search using hybrid search for DOCUMENTATION content type only
             results, total = await unified_hybrid_search(
-                query=params.query,
+                query=query,
                 content_types=[ContentType.DOCUMENTATION],
                 page=1,
                 page_size=MAX_RESULTS * 2,  # Fetch extra for deduplication
@@ -140,7 +127,7 @@ class SearchDocsTool(BaseTool):
 
             if not results:
                 return NoResultsResponse(
-                    message=f"No documentation found for '{params.query}'.",
+                    message=f"No documentation found for '{query}'.",
                     suggestions=[
                         "Try different keywords",
                         "Use more general terms",
@@ -175,7 +162,7 @@ class SearchDocsTool(BaseTool):
 
             if not deduplicated:
                 return NoResultsResponse(
-                    message=f"No documentation found for '{params.query}'.",
+                    message=f"No documentation found for '{query}'.",
                     suggestions=[
                         "Try different keywords",
                         "Use more general terms",
@@ -208,7 +195,7 @@ class SearchDocsTool(BaseTool):
                 message=f"Found {len(doc_results)} relevant documentation sections.",
                 results=doc_results,
                 count=len(doc_results),
-                query=params.query,
+                query=query,
                 session_id=session_id,
             )
 

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

@@ -2,9 +2,9 @@
 
 import base64
 import logging
-from typing import Any
+from typing import Any, Optional
 
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel
 
 from backend.api.features.chat.model import ChatSession
 from backend.data.workspace import get_or_create_workspace
@@ -78,65 +78,6 @@ class WorkspaceDeleteResponse(ToolResponseBase):
     success: bool
 
 
-# Input models for workspace tools
-class ListWorkspaceFilesInput(BaseModel):
-    """Input parameters for list_workspace_files tool."""
-
-    path_prefix: str | None = None
-    limit: int = 50
-    include_all_sessions: bool = False
-
-    @field_validator("path_prefix", mode="before")
-    @classmethod
-    def strip_path(cls, v: Any) -> str | None:
-        return v.strip() if isinstance(v, str) else None
-
-
-class ReadWorkspaceFileInput(BaseModel):
-    """Input parameters for read_workspace_file tool."""
-
-    file_id: str | None = None
-    path: str | None = None
-    force_download_url: bool = False
-
-    @field_validator("file_id", "path", mode="before")
-    @classmethod
-    def strip_strings(cls, v: Any) -> str | None:
-        return v.strip() if isinstance(v, str) else None
-
-
-class WriteWorkspaceFileInput(BaseModel):
-    """Input parameters for write_workspace_file tool."""
-
-    filename: str = ""
-    content_base64: str = ""
-    path: str | None = None
-    mime_type: str | None = None
-    overwrite: bool = False
-
-    @field_validator("filename", "content_base64", mode="before")
-    @classmethod
-    def strip_required(cls, v: Any) -> str:
-        return v.strip() if isinstance(v, str) else (v if v is not None else "")
-
-    @field_validator("path", "mime_type", mode="before")
-    @classmethod
-    def strip_optional(cls, v: Any) -> str | None:
-        return v.strip() if isinstance(v, str) else None
-
-
-class DeleteWorkspaceFileInput(BaseModel):
-    """Input parameters for delete_workspace_file tool."""
-
-    file_id: str | None = None
-    path: str | None = None
-
-    @field_validator("file_id", "path", mode="before")
-    @classmethod
-    def strip_strings(cls, v: Any) -> str | None:
-        return v.strip() if isinstance(v, str) else None
-
-
 class ListWorkspaceFilesTool(BaseTool):
     """Tool for listing files in user's workspace."""
 
@@ -190,9 +131,8 @@ class ListWorkspaceFilesTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
-        params = ListWorkspaceFilesInput(**kwargs)
         session_id = session.session_id
 
         if not user_id:
@@ -201,7 +141,9 @@ class ListWorkspaceFilesTool(BaseTool):
                 session_id=session_id,
             )
 
-        limit = min(params.limit, 100)
+        path_prefix: Optional[str] = kwargs.get("path_prefix")
+        limit = min(kwargs.get("limit", 50), 100)
+        include_all_sessions: bool = kwargs.get("include_all_sessions", False)
 
         try:
             workspace = await get_or_create_workspace(user_id)
@@ -209,13 +151,13 @@ class ListWorkspaceFilesTool(BaseTool):
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 
             files = await manager.list_files(
-                path=params.path_prefix,
+                path=path_prefix,
                 limit=limit,
-                include_all_sessions=params.include_all_sessions,
+                include_all_sessions=include_all_sessions,
             )
             total = await manager.get_file_count(
-                path=params.path_prefix,
-                include_all_sessions=params.include_all_sessions,
+                path=path_prefix,
+                include_all_sessions=include_all_sessions,
             )
 
             file_infos = [
@@ -229,9 +171,7 @@ class ListWorkspaceFilesTool(BaseTool):
                 for f in files
             ]
 
-            scope_msg = (
-                "all sessions" if params.include_all_sessions else "current session"
-            )
+            scope_msg = "all sessions" if include_all_sessions else "current session"
             return WorkspaceFileListResponse(
                 files=file_infos,
                 total_count=total,
@@ -319,9 +259,8 @@ class ReadWorkspaceFileTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
-        params = ReadWorkspaceFileInput(**kwargs)
         session_id = session.session_id
 
         if not user_id:
@@ -330,7 +269,11 @@ class ReadWorkspaceFileTool(BaseTool):
                 session_id=session_id,
             )
 
-        if not params.file_id and not params.path:
+        file_id: Optional[str] = kwargs.get("file_id")
+        path: Optional[str] = kwargs.get("path")
+        force_download_url: bool = kwargs.get("force_download_url", False)
+
+        if not file_id and not path:
             return ErrorResponse(
                 message="Please provide either file_id or path",
                 session_id=session_id,
@@ -342,21 +285,21 @@ class ReadWorkspaceFileTool(BaseTool):
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 
             # Get file info
-            if params.file_id:
-                file_info = await manager.get_file_info(params.file_id)
+            if file_id:
+                file_info = await manager.get_file_info(file_id)
                 if file_info is None:
                     return ErrorResponse(
-                        message=f"File not found: {params.file_id}",
+                        message=f"File not found: {file_id}",
                         session_id=session_id,
                     )
-                target_file_id = params.file_id
+                target_file_id = file_id
             else:
                 # path is guaranteed to be non-None here due to the check above
-                assert params.path is not None
-                file_info = await manager.get_file_info_by_path(params.path)
+                assert path is not None
+                file_info = await manager.get_file_info_by_path(path)
                 if file_info is None:
                     return ErrorResponse(
-                        message=f"File not found at path: {params.path}",
+                        message=f"File not found at path: {path}",
                         session_id=session_id,
                     )
                 target_file_id = file_info.id
@@ -366,7 +309,7 @@ class ReadWorkspaceFileTool(BaseTool):
             is_text_file = self._is_text_mime_type(file_info.mimeType)
 
             # Return inline content for small text files (unless force_download_url)
-            if is_small_file and is_text_file and not params.force_download_url:
+            if is_small_file and is_text_file and not force_download_url:
                 content = await manager.read_file_by_id(target_file_id)
                 content_b64 = base64.b64encode(content).decode("utf-8")
 
@@ -486,9 +429,8 @@ class WriteWorkspaceFileTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
-        params = WriteWorkspaceFileInput(**kwargs)
         session_id = session.session_id
 
         if not user_id:
@@ -497,13 +439,19 @@ class WriteWorkspaceFileTool(BaseTool):
                 session_id=session_id,
             )
 
-        if not params.filename:
+        filename: str = kwargs.get("filename", "")
+        content_b64: str = kwargs.get("content_base64", "")
+        path: Optional[str] = kwargs.get("path")
+        mime_type: Optional[str] = kwargs.get("mime_type")
+        overwrite: bool = kwargs.get("overwrite", False)
+
+        if not filename:
             return ErrorResponse(
                 message="Please provide a filename",
                 session_id=session_id,
             )
 
-        if not params.content_base64:
+        if not content_b64:
             return ErrorResponse(
                 message="Please provide content_base64",
                 session_id=session_id,
@@ -511,7 +459,7 @@ class WriteWorkspaceFileTool(BaseTool):
 
         # Decode content
         try:
-            content = base64.b64decode(params.content_base64)
+            content = base64.b64decode(content_b64)
         except Exception:
             return ErrorResponse(
                 message="Invalid base64-encoded content",
@@ -528,7 +476,7 @@ class WriteWorkspaceFileTool(BaseTool):
 
         try:
             # Virus scan
-            await scan_content_safe(content, filename=params.filename)
+            await scan_content_safe(content, filename=filename)
 
             workspace = await get_or_create_workspace(user_id)
             # Pass session_id for session-scoped file access
@@ -536,10 +484,10 @@ class WriteWorkspaceFileTool(BaseTool):
 
             file_record = await manager.write_file(
                 content=content,
-                filename=params.filename,
-                path=params.path,
-                mime_type=params.mime_type,
-                overwrite=params.overwrite,
+                filename=filename,
+                path=path,
+                mime_type=mime_type,
+                overwrite=overwrite,
             )
 
             return WorkspaceWriteResponse(
@@ -609,9 +557,8 @@ class DeleteWorkspaceFileTool(BaseTool):
         self,
         user_id: str | None,
         session: ChatSession,
-        **kwargs: Any,
+        **kwargs,
     ) -> ToolResponseBase:
-        params = DeleteWorkspaceFileInput(**kwargs)
         session_id = session.session_id
 
         if not user_id:
@@ -620,7 +567,10 @@ class DeleteWorkspaceFileTool(BaseTool):
                 session_id=session_id,
             )
 
-        if not params.file_id and not params.path:
+        file_id: Optional[str] = kwargs.get("file_id")
+        path: Optional[str] = kwargs.get("path")
+
+        if not file_id and not path:
             return ErrorResponse(
                 message="Please provide either file_id or path",
                 session_id=session_id,
@@ -633,15 +583,15 @@ class DeleteWorkspaceFileTool(BaseTool):
 
             # Determine the file_id to delete
             target_file_id: str
-            if params.file_id:
-                target_file_id = params.file_id
+            if file_id:
+                target_file_id = file_id
             else:
                 # path is guaranteed to be non-None here due to the check above
-                assert params.path is not None
-                file_info = await manager.get_file_info_by_path(params.path)
+                assert path is not None
+                file_info = await manager.get_file_info_by_path(path)
                 if file_info is None:
                     return ErrorResponse(
-                        message=f"File not found at path: {params.path}",
+                        message=f"File not found at path: {path}",
                         session_id=session_id,
                     )
                 target_file_id = file_info.id

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

@@ -1,6 +1,17 @@
 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.
@@ -23,12 +34,13 @@ 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(${JSON.stringify(message)});
+          window.opener.postMessage(${safeJsonStringify(message)});
           window.close();
         </script>
       </body>

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

@@ -26,8 +26,20 @@ export function buildCopilotChatUrl(prompt: string): string {
 
 export function getQuickActions(): string[] {
   return [
-    "Show me what I can automate",
-    "Design a custom workflow",
-    "Help me with content creation",
+    "I don't know where to start, just ask me stuff",
+    "I do the same thing every week and it's killing me",
+    "Help me find where I'm wasting my time",
   ];
 }
+
+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

@@ -6,7 +6,9 @@ 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() {
@@ -14,8 +16,25 @@ 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,
@@ -73,7 +92,7 @@ export default function CopilotPage() {
   }
 
   return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-6 py-10">
+    <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="w-full text-center">
         {isLoading ? (
           <div className="mx-auto max-w-2xl">
@@ -90,25 +109,25 @@ export default function CopilotPage() {
           </div>
         ) : (
           <>
-            <div className="mx-auto max-w-2xl">
+            <div className="mx-auto max-w-3xl">
               <Text
                 variant="h3"
-                className="mb-3 !text-[1.375rem] text-zinc-700"
+                className="mb-1 !text-[1.375rem] text-zinc-700"
               >
                 Hey, <span className="text-violet-600">{greetingName}</span>
               </Text>
               <Text variant="h3" className="mb-8 !font-normal">
-                What do you want to automate?
+                Tell me about your work — I&apos;ll find what to automate.
               </Text>
 
               <div className="mb-6">
                 <ChatInput
                   onSend={startChatWithPrompt}
-                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
+                  placeholder={inputPlaceholder}
                 />
               </div>
             </div>
-            <div className="flex flex-nowrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+            <div className="flex flex-wrap 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}
@@ -116,7 +135,7 @@ export default function CopilotPage() {
                   variant="outline"
                   size="small"
                   onClick={() => handleQuickAction(action)}
-                  className="h-auto shrink-0 border-zinc-600 !px-4 !py-2 text-[1rem] text-zinc-600"
+                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
                 >
                   {action}
                 </Button>

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

@@ -2,7 +2,6 @@ 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";
@@ -56,10 +55,6 @@ export function ChatContainer({
     onStreamingChange?.(isStreaming);
   }, [isStreaming, onStreamingChange]);
 
-  const breakpoint = useBreakpoint();
-  const isMobile =
-    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
-
   return (
     <div
       className={cn(
@@ -127,11 +122,7 @@ export function ChatContainer({
           disabled={isStreaming || !sessionId}
           isStreaming={isStreaming}
           onStop={stopStreaming}
-          placeholder={
-            isMobile
-              ? "You can search or just ask"
-              : 'You can search or just ask — e.g. "create a blog post outline"'
-          }
+          placeholder="What else can I help with?"
         />
       </div>
     </div>

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

@@ -74,19 +74,20 @@ 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(
@@ -122,13 +123,14 @@ export function ChatInput({
               size="icon"
               aria-label={isRecording ? "Stop recording" : "Start recording"}
               onClick={toggleRecording}
-              disabled={disabled || isTranscribing}
+              disabled={disabled || isTranscribing || isStreaming}
               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 = 512;
-    analyser.smoothingTimeConstant = 0.8;
+    analyser.fftSize = 256;
+    analyser.smoothingTimeConstant = 0.3;
 
     // Connect the stream to the analyser
     const source = audioContext.createMediaStreamSource(stream);
@@ -73,10 +73,11 @@ export function AudioWaveform({
           maxAmplitude = Math.max(maxAmplitude, amplitude);
         }
 
-        // Map amplitude (0-128) to bar height
-        const normalized = (maxAmplitude / 128) * 255;
-        const height =
-          minBarHeight + (normalized / 255) * (maxBarHeight - minBarHeight);
+        // Normalize amplitude (0-128 range) to 0-1
+        const normalized = maxAmplitude / 128;
+        // Apply sensitivity boost (multiply by 4) and use sqrt curve to amplify quiet sounds
+        const boosted = Math.min(1, Math.sqrt(normalized) * 4);
+        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 && !isStreaming;
+  const showMicButton = isSupported;
   const isInputDisabled = disabled || isStreaming || isTranscribing;
 
   // Cleanup on unmount

autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx

@@ -15,7 +15,6 @@ import {
 import { cn } from "@/lib/utils";
 import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
-import { storage, Key as StorageKey } from "@/services/storage/local-storage";
 import { WalletIcon } from "@phosphor-icons/react";
 import { PopoverClose } from "@radix-ui/react-popover";
 import { X } from "lucide-react";
@@ -175,7 +174,6 @@ export function Wallet() {
   const [prevCredits, setPrevCredits] = useState<number | null>(credits);
   const [flash, setFlash] = useState(false);
   const [walletOpen, setWalletOpen] = useState(false);
-  const [lastSeenCredits, setLastSeenCredits] = useState<number | null>(null);
 
   const totalCount = useMemo(() => {
     return groups.reduce((acc, group) => acc + group.tasks.length, 0);
@@ -200,38 +198,6 @@ export function Wallet() {
     setCompletedCount(completed);
   }, [groups, state?.completedSteps]);
 
-  // Load last seen credits from localStorage once on mount
-  useEffect(() => {
-    const stored = storage.get(StorageKey.WALLET_LAST_SEEN_CREDITS);
-    if (stored !== undefined && stored !== null) {
-      const parsed = parseFloat(stored);
-      if (!Number.isNaN(parsed)) setLastSeenCredits(parsed);
-      else setLastSeenCredits(0);
-    } else {
-      setLastSeenCredits(0);
-    }
-  }, []);
-
-  // Auto-open once if never shown, otherwise open only when credits increase beyond last seen
-  useEffect(() => {
-    if (typeof credits !== "number") return;
-    // Open once for first-time users
-    if (state && state.walletShown === false) {
-      requestAnimationFrame(() => setWalletOpen(true));
-      // Mark as shown so it won't reopen on every reload
-      updateState({ walletShown: true });
-      return;
-    }
-    // Open if user gained more credits than last acknowledged
-    if (
-      lastSeenCredits !== null &&
-      credits > lastSeenCredits &&
-      walletOpen === false
-    ) {
-      requestAnimationFrame(() => setWalletOpen(true));
-    }
-  }, [credits, lastSeenCredits, state?.walletShown, updateState, walletOpen]);
-
   const onWalletOpen = useCallback(async () => {
     if (!state?.walletShown) {
       updateState({ walletShown: true });
@@ -324,19 +290,7 @@ export function Wallet() {
   if (credits === null || !state) return null;
 
   return (
-    <Popover
-      open={walletOpen}
-      onOpenChange={(open) => {
-        setWalletOpen(open);
-        if (!open) {
-          // Persist the latest acknowledged credits so we only auto-open on future gains
-          if (typeof credits === "number") {
-            storage.set(StorageKey.WALLET_LAST_SEEN_CREDITS, String(credits));
-            setLastSeenCredits(credits);
-          }
-        }
-      }}
-    >
+    <Popover open={walletOpen} onOpenChange={(open) => setWalletOpen(open)}>
       <PopoverTrigger asChild>
         <div className="relative inline-block">
           <button