Merge branch 'dev' into fix/code-review-cosmetics-zamil

Resolved conflict in run_block.py by keeping dev's COPILOT_EXCLUDED_BLOCK_TYPES check.
fix: address CodeRabbit review bugs
2026-02-11 15:25:16 -05:00 · 2026-02-09 08:03:44 +00:00 · 2026-02-04 09:17:44 +00:00 · 2026-02-04 09:05:18 +00:00 · 2026-02-04 08:54:27 +00:00 · 2026-02-04 03:33:15 +00:00
219 changed files with 9710 additions and 13339 deletions
--- a/.github/workflows/claude-ci-failure-auto-fix.yml
+++ b/.github/workflows/claude-ci-failure-auto-fix.yml
@@ -22,7 +22,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.workflow_run.head_branch }}
          fetch-depth: 0
--- a/.github/workflows/claude-dependabot.yml
+++ b/.github/workflows/claude-dependabot.yml
@@ -30,7 +30,7 @@ jobs:
      actions: read # Required for CI access
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1

--- a/.github/workflows/claude.yml
+++ b/.github/workflows/claude.yml
@@ -40,7 +40,7 @@ jobs:
      actions: read # Required for CI access
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1

--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -58,7 +58,7 @@ jobs:
        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
    steps:
    - name: Checkout repository
-      uses: actions/checkout@v6
+      uses: actions/checkout@v4

    # Initializes the CodeQL tools for scanning.
    - name: Initialize CodeQL
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -27,7 +27,7 @@ jobs:
    # If you do not check out your code, Copilot will do this for you.
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          submodules: true
--- a/.github/workflows/docs-block-sync.yml
+++ b/.github/workflows/docs-block-sync.yml
@@ -23,7 +23,7 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1

--- a/.github/workflows/docs-claude-review.yml
+++ b/.github/workflows/docs-claude-review.yml
@@ -23,7 +23,7 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0

--- a/.github/workflows/docs-enhance.yml
+++ b/.github/workflows/docs-enhance.yml
@@ -28,7 +28,7 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1

--- a/.github/workflows/platform-autogpt-deploy-dev.yaml
+++ b/.github/workflows/platform-autogpt-deploy-dev.yaml
@@ -25,7 +25,7 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.inputs.git_ref || github.ref_name }}

@@ -52,7 +52,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DEPLOY_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
--- a/.github/workflows/platform-autogpt-deploy-prod.yml
+++ b/.github/workflows/platform-autogpt-deploy-prod.yml
@@ -17,7 +17,7 @@ jobs:

    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.ref_name || 'master' }}

@@ -45,7 +45,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DEPLOY_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
--- a/.github/workflows/platform-backend-ci.yml
+++ b/.github/workflows/platform-backend-ci.yml
@@ -68,7 +68,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          submodules: true
--- a/.github/workflows/platform-dev-deploy-event-dispatcher.yml
+++ b/.github/workflows/platform-dev-deploy-event-dispatcher.yml
@@ -82,7 +82,7 @@ jobs:
          
      - name: Dispatch Deploy Event
        if: steps.check_status.outputs.should_deploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -110,7 +110,7 @@ jobs:

      - name: Dispatch Undeploy Event (from comment)
        if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -168,7 +168,7 @@ jobs:
          github.event_name == 'pull_request' &&
          github.event.action == 'closed' &&
          steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
--- a/.github/workflows/platform-frontend-ci.yml
+++ b/.github/workflows/platform-frontend-ci.yml
@@ -31,7 +31,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4

      - name: Check for component changes
        uses: dorny/paths-filter@v3
@@ -71,7 +71,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4

      - name: Set up Node.js
        uses: actions/setup-node@v6
@@ -107,7 +107,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0

@@ -148,7 +148,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive

@@ -277,7 +277,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive

--- a/.github/workflows/platform-fullstack-ci.yml
+++ b/.github/workflows/platform-fullstack-ci.yml
@@ -29,7 +29,7 @@ jobs:

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4

      - name: Set up Node.js
        uses: actions/setup-node@v6
@@ -56,14 +56,14 @@ jobs:
        run: pnpm install --frozen-lockfile

  types:
-    runs-on: big-boi
+    runs-on: ubuntu-latest
    needs: setup
    strategy:
      fail-fast: false

    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive

@@ -85,7 +85,7 @@ jobs:

      - name: Run docker compose
        run: |
-          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d

      - name: Restore dependencies cache
        uses: actions/cache@v5
--- a/.github/workflows/repo-workflow-checker.yml
+++ b/.github/workflows/repo-workflow-checker.yml
@@ -11,7 +11,7 @@ jobs:
    steps:
      # - name: Wait some time for all actions to start
      #   run: sleep 30
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        # with:
          # fetch-depth: 0
      - name: Set up Python
--- a/autogpt_platform/autogpt_libs/poetry.lock
+++ b/autogpt_platform/autogpt_libs/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.

 [[package]]
 name = "annotated-doc"
@@ -67,7 +67,7 @@ description = "Backport of asyncio.Runner, a context manager that controls event
 optional = false
 python-versions = "<3.11,>=3.8"
 groups = ["dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
    {file = "backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5"},
    {file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
@@ -326,118 +326,100 @@ files = [

 [[package]]
 name = "coverage"
-version = "7.13.4"
+version = "7.10.5"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+    {file = "coverage-7.10.5-py3-none-any.whl", hash = "sha256:0be24d35e4db1d23d0db5c0f6a74a962e2ec83c426b5cac09f4234aadef38e4a"},
+    {file = "coverage-7.10.5.tar.gz", hash = "sha256:f2e57716a78bc3ae80b2207be0709a3b2b63b9f2dcf9740ee6ac03588a2015b6"},
 ]

 [package.dependencies]
@@ -541,7 +523,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
    {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
    {file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
@@ -2180,23 +2162,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]

 [[package]]
 name = "pytest-cov"
-version = "7.0.0"
+version = "6.2.1"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "pytest_cov-7.0.0-py3-none-any.whl", hash = "sha256:3b8e9558b16cc1479da72058bdecf8073661c7f57f7d3c5f22a1c23507f2d861"},
-    {file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
+    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
+    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
 ]

 [package.dependencies]
-coverage = {version = ">=7.10.6", extras = ["toml"]}
+coverage = {version = ">=7.5", extras = ["toml"]}
 pluggy = ">=1.2"
-pytest = ">=7"
+pytest = ">=6.2.5"

 [package.extras]
-testing = ["process-tests", "pytest-xdist", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]

 [[package]]
 name = "pytest-mock"
@@ -2563,7 +2545,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
    {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
    {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -2911,4 +2893,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"
+content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"
--- a/autogpt_platform/autogpt_libs/pyproject.toml
+++ b/autogpt_platform/autogpt_libs/pyproject.toml
@@ -26,7 +26,7 @@ pyright = "^1.1.408"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.3.0"
 pytest-mock = "^3.15.1"
-pytest-cov = "^7.0.0"
+pytest-cov = "^6.2.1"
 ruff = "^0.15.0"

 [build-system]
--- a/autogpt_platform/backend/backend/api/features/chat/config.py
+++ b/autogpt_platform/backend/backend/api/features/chat/config.py
@@ -93,12 +93,6 @@ class ChatConfig(BaseSettings):
        description="Name of the prompt in Langfuse to fetch",
    )

-    # Extended thinking configuration for Claude models
-    thinking_enabled: bool = Field(
-        default=True,
-        description="Enable adaptive thinking for Claude models via OpenRouter",
-    )
-
    @field_validator("api_key", mode="before")
    @classmethod
    def get_api_key(cls, v):
--- a/autogpt_platform/backend/backend/api/features/chat/db.py
+++ b/autogpt_platform/backend/backend/api/features/chat/db.py
@@ -45,7 +45,10 @@ async def create_chat_session(
        successfulAgentRuns=SafeJson({}),
        successfulAgentSchedules=SafeJson({}),
    )
-    return await PrismaChatSession.prisma().create(data=data)
+    return await PrismaChatSession.prisma().create(
+        data=data,
+        include={"Messages": True},
+    )


 async def update_chat_session(
--- a/autogpt_platform/backend/backend/api/features/chat/response_model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/response_model.py
@@ -10,8 +10,6 @@ from typing import Any

 from pydantic import BaseModel, Field

-from backend.util.json import dumps as json_dumps
-

 class ResponseType(str, Enum):
    """Types of streaming responses following AI SDK protocol."""
@@ -20,10 +18,6 @@ class ResponseType(str, Enum):
    START = "start"
    FINISH = "finish"

-    # Step lifecycle (one LLM API call within a message)
-    START_STEP = "start-step"
-    FINISH_STEP = "finish-step"
-
    # Text streaming
    TEXT_START = "text-start"
    TEXT_DELTA = "text-delta"
@@ -63,16 +57,6 @@ class StreamStart(StreamBaseResponse):
        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
    )

-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
-        data: dict[str, Any] = {
-            "type": self.type.value,
-            "messageId": self.messageId,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-

 class StreamFinish(StreamBaseResponse):
    """End of message/stream."""
@@ -80,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
    type: ResponseType = ResponseType.FINISH


-class StreamStartStep(StreamBaseResponse):
-    """Start of a step (one LLM API call within a message).
-
-    The AI SDK uses this to add a step-start boundary to message.parts,
-    enabling visual separation between multiple LLM calls in a single message.
-    """
-
-    type: ResponseType = ResponseType.START_STEP
-
-
-class StreamFinishStep(StreamBaseResponse):
-    """End of a step (one LLM API call within a message).
-
-    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
-    so the next LLM call in a tool-call continuation starts with clean state.
-    """
-
-    type: ResponseType = ResponseType.FINISH_STEP
-
-
 # ========== Text Streaming ==========


@@ -153,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
    type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
    toolCallId: str = Field(..., description="Tool call ID this responds to")
    output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
    toolName: str | None = Field(
        default=None, description="Name of the tool that was executed"
    )
@@ -161,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
        default=True, description="Whether the tool execution succeeded"
    )

-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-spec fields."""
-        import json
-
-        data = {
-            "type": self.type.value,
-            "toolCallId": self.toolCallId,
-            "output": self.output,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-

 # ========== Other ==========

@@ -195,18 +148,6 @@ class StreamError(StreamBaseResponse):
        default=None, description="Additional error details"
    )

-    def to_sse(self) -> str:
-        """Convert to SSE format, only emitting fields required by AI SDK protocol.
-
-        The AI SDK uses z.strictObject({type, errorText}) which rejects
-        any extra fields like `code` or `details`.
-        """
-        data = {
-            "type": self.type.value,
-            "errorText": self.errorText,
-        }
-        return f"data: {json_dumps(data)}\n\n"
-

 class StreamHeartbeat(StreamBaseResponse):
    """Heartbeat to keep SSE connection alive during long-running operations.
--- a/autogpt_platform/backend/backend/api/features/chat/routes.py
+++ b/autogpt_platform/backend/backend/api/features/chat/routes.py
@@ -6,7 +6,7 @@ from collections.abc import AsyncGenerator
 from typing import Annotated

 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel

@@ -17,29 +17,7 @@ from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-from .response_model import StreamFinish, StreamHeartbeat
-from .tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart

 config = ChatConfig()

@@ -288,36 +266,12 @@ async def stream_chat_post(

    """
    import asyncio
-    import time
-
-    stream_start_time = time.perf_counter()
-    log_meta = {"component": "ChatStream", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
-        f"user={user_id}, message_len={len(request.message)}",
-        extra={"json_fields": log_meta},
-    )

    session = await _validate_and_get_session(session_id, user_id)
-    logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
-            }
-        },
-    )

    # Create a task in the stream registry for reconnection support
    task_id = str(uuid_module.uuid4())
    operation_id = str(uuid_module.uuid4())
-    log_meta["task_id"] = task_id
-
-    task_create_start = time.perf_counter()
    await stream_registry.create_task(
        task_id=task_id,
        session_id=session_id,
@@ -326,28 +280,14 @@ async def stream_chat_post(
        tool_name="chat",
        operation_id=operation_id,
    )
-    logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
-            }
-        },
-    )

    # Background task that runs the AI generation independently of SSE connection
    async def run_ai_generation():
-        import time as time_module
-
-        gen_start_time = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
-            extra={"json_fields": log_meta},
-        )
-        first_chunk_time, ttfc = None, None
-        chunk_count = 0
        try:
+            # Emit a start event with task_id for reconnection
+            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
+            await stream_registry.publish_chunk(task_id, start_chunk)
+
            async for chunk in chat_service.stream_chat_completion(
                session_id,
                request.message,
@@ -355,79 +295,25 @@ async def stream_chat_post(
                user_id=user_id,
                session=session,  # Pass pre-fetched session to avoid double-fetch
                context=request.context,
-                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
            ):
-                chunk_count += 1
-                if first_chunk_time is None:
-                    first_chunk_time = time_module.perf_counter()
-                    ttfc = first_chunk_time - gen_start_time
-                    logger.info(
-                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
-                        extra={
-                            "json_fields": {
-                                **log_meta,
-                                "chunk_type": type(chunk).__name__,
-                                "time_to_first_chunk_ms": ttfc * 1000,
-                            }
-                        },
-                    )
                # Write to Redis (subscribers will receive via XREAD)
                await stream_registry.publish_chunk(task_id, chunk)

-            gen_end_time = time_module.perf_counter()
-            total_time = (gen_end_time - gen_start_time) * 1000
-            logger.info(
-                f"[TIMING] run_ai_generation FINISHED in {total_time/1000:.1f}s; "
-                f"task={task_id}, session={session_id}, "
-                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "time_to_first_chunk_ms": (
-                            ttfc * 1000 if ttfc is not None else None
-                        ),
-                        "n_chunks": chunk_count,
-                    }
-                },
-            )
+            # Mark task as completed
            await stream_registry.mark_task_completed(task_id, "completed")
        except Exception as e:
-            elapsed = time_module.perf_counter() - gen_start_time
            logger.error(
-                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": elapsed * 1000,
-                        "error": str(e),
-                    }
-                },
+                f"Error in background AI generation for session {session_id}: {e}"
            )
            await stream_registry.mark_task_completed(task_id, "failed")

    # Start the AI generation in a background task
    bg_task = asyncio.create_task(run_ai_generation())
    await stream_registry.set_task_asyncio_task(task_id, bg_task)
-    setup_time = (time.perf_counter() - stream_start_time) * 1000
-    logger.info(
-        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )

    # SSE endpoint that subscribes to the task's stream
    async def event_generator() -> AsyncGenerator[str, None]:
-        import time as time_module
-
-        event_gen_start = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
-            f"user={user_id}",
-            extra={"json_fields": log_meta},
-        )
        subscriber_queue = None
-        first_chunk_yielded = False
-        chunks_yielded = 0
        try:
            # Subscribe to the task stream (this replays existing messages + live updates)
            subscriber_queue = await stream_registry.subscribe_to_task(
@@ -442,70 +328,22 @@ async def stream_chat_post(
                return

            # Read from the subscriber queue and yield to SSE
-            logger.info(
-                "[TIMING] Starting to read from subscriber_queue",
-                extra={"json_fields": log_meta},
-            )
            while True:
                try:
                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    chunks_yielded += 1
-
-                    if not first_chunk_yielded:
-                        first_chunk_yielded = True
-                        elapsed = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
-                            f"type={type(chunk).__name__}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunk_type": type(chunk).__name__,
-                                    "elapsed_ms": elapsed * 1000,
-                                }
-                            },
-                        )
-
                    yield chunk.to_sse()

                    # Check for finish signal
                    if isinstance(chunk, StreamFinish):
-                        total_time = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
-                            f"n_chunks={chunks_yielded}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunks_yielded": chunks_yielded,
-                                    "total_time_ms": total_time * 1000,
-                                }
-                            },
-                        )
                        break
                except asyncio.TimeoutError:
+                    # Send heartbeat to keep connection alive
                    yield StreamHeartbeat().to_sse()

        except GeneratorExit:
-            logger.info(
-                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "chunks_yielded": chunks_yielded,
-                        "reason": "client_disconnect",
-                    }
-                },
-            )
            pass  # Client disconnected - background task continues
        except Exception as e:
-            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
-            logger.error(
-                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
-                extra={
-                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
-                },
-            )
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
        finally:
            # Unsubscribe when client disconnects or stream ends to prevent resource leak
            if subscriber_queue is not None:
@@ -519,18 +357,6 @@ async def stream_chat_post(
                        exc_info=True,
                    )
            # AI SDK protocol termination - always yield even if unsubscribe fails
-            total_time = time_module.perf_counter() - event_gen_start
-            logger.info(
-                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
-                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time * 1000,
-                        "chunks_yielded": chunks_yielded,
-                    }
-                },
-            )
            yield "data: [DONE]\n\n"

    return StreamingResponse(
@@ -548,90 +374,63 @@ async def stream_chat_post(
@router.get(
    "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
    session_id: str,
+    message: Annotated[str, Query(min_length=1, max_length=10000)],
    user_id: str | None = Depends(auth.get_user_id),
+    is_user_message: bool = Query(default=True),
 ):
    """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).

-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
-    Checks for an active (in-progress) task on the session and either replays
-    the full SSE stream or returns 204 No Content if nothing is running.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+      - Text fragments as they are generated
+      - Tool call UI elements (if invoked)
+      - Tool execution results

    Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
+        message: The user's new message to process.
        user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
    Returns:
-        StreamingResponse (SSE) when an active stream exists,
-        or 204 No Content when there is nothing to resume.
+        StreamingResponse: SSE-formatted response chunks.
+
    """
-    import asyncio
-
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-
-    if not active_task:
-        return Response(status_code=204)
-
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
-        user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
-    )
-
-    if subscriber_queue is None:
-        return Response(status_code=204)
+    session = await _validate_and_get_session(session_id, user_id)

    async def event_generator() -> AsyncGenerator[str, None]:
        chunk_count = 0
        first_chunk_type: str | None = None
-        try:
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    if chunk_count < 3:
-                        logger.info(
-                            "Resume stream chunk",
-                            extra={
-                                "session_id": session_id,
-                                "chunk_type": str(chunk.type),
-                            },
-                        )
-                    if not first_chunk_type:
-                        first_chunk_type = str(chunk.type)
-                    chunk_count += 1
-                    yield chunk.to_sse()
-
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-        except GeneratorExit:
-            pass
-        except Exception as e:
-            logger.error(f"Error in resume stream for session {session_id}: {e}")
-        finally:
-            try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            message,
+            is_user_message=is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+        ):
+            if chunk_count < 3:
+                logger.info(
+                    "Chat stream chunk",
+                    extra={
+                        "session_id": session_id,
+                        "chunk_type": str(chunk.type),
+                    },
                )
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            logger.info(
-                "Resume stream completed",
-                extra={
-                    "session_id": session_id,
-                    "n_chunks": chunk_count,
-                    "first_chunk_type": first_chunk_type,
-                },
-            )
-            yield "data: [DONE]\n\n"
+            if not first_chunk_type:
+                first_chunk_type = str(chunk.type)
+            chunk_count += 1
+            yield chunk.to_sse()
+        logger.info(
+            "Chat stream completed",
+            extra={
+                "session_id": session_id,
+                "chunk_count": chunk_count,
+                "first_chunk_type": first_chunk_type,
+            },
+        )
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"

    return StreamingResponse(
        event_generator(),
@@ -639,8 +438,8 @@ async def resume_session_stream(
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
        },
    )

@@ -952,42 +751,3 @@ async def health_check() -> dict:
        "service": "chat",
        "version": "0.1.0",
    }
-
-
-# ========== Schema Export (for OpenAPI / Orval codegen) ==========
-
-ToolResponseUnion = (
-    AgentsFoundResponse
-    | NoResultsResponse
-    | AgentDetailsResponse
-    | SetupRequirementsResponse
-    | ExecutionStartedResponse
-    | NeedLoginResponse
-    | ErrorResponse
-    | InputValidationErrorResponse
-    | AgentOutputResponse
-    | UnderstandingUpdatedResponse
-    | AgentPreviewResponse
-    | AgentSavedResponse
-    | ClarificationNeededResponse
-    | BlockListResponse
-    | BlockOutputResponse
-    | DocSearchResultsResponse
-    | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
-)
-
-
-@router.get(
-    "/schema/tool-responses",
-    response_model=ToolResponseUnion,
-    include_in_schema=True,
-    summary="[Dummy] Tool response type export for codegen",
-    description="This endpoint is not meant to be called. It exists solely to "
-    "expose tool response models in the OpenAPI schema for frontend codegen.",
-)
-async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
-    """Never called at runtime. Exists only so Orval generates TS types."""
-    raise HTTPException(status_code=501, detail="Schema-only endpoint")
--- a/autogpt_platform/backend/backend/api/features/chat/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/service.py
@@ -1,12 +1,15 @@
 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

@@ -52,10 +55,8 @@ from .response_model import (
    StreamBaseResponse,
    StreamError,
    StreamFinish,
-    StreamFinishStep,
    StreamHeartbeat,
    StreamStart,
-    StreamStartStep,
    StreamTextDelta,
    StreamTextEnd,
    StreamTextStart,
@@ -353,10 +354,6 @@ async def stream_chat_completion(
    retry_count: int = 0,
    session: ChatSession | None = None,
    context: dict[str, str] | None = None,  # {url: str, content: str}
-    _continuation_message_id: (
-        str | None
-    ) = None,  # Internal: reuse message ID for tool call continuations
-    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
    """Main entry point for streaming chat completions with database handling.

@@ -377,45 +374,21 @@ async def stream_chat_completion(
        ValueError: If max_context_messages is exceeded

    """
-    completion_start = time.monotonic()
-
-    # Build log metadata for structured logging
-    log_meta = {"component": "ChatService", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
    logger.info(
-        f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
-        f"message_len={len(message) if message else 0}, is_user={is_user_message}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "message_len": len(message) if message else 0,
-                "is_user_message": is_user_message,
-            }
-        },
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
    )

    # Only fetch from Redis if session not provided (initial call)
    if session is None:
-        fetch_start = time.monotonic()
        session = await get_chat_session(session_id, user_id)
-        fetch_time = (time.monotonic() - fetch_start) * 1000
        logger.info(
-            f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
-            f"n_messages={len(session.messages) if session else 0}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "duration_ms": fetch_time,
-                    "n_messages": len(session.messages) if session else 0,
-                }
-            },
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
+            f"message_count={len(session.messages) if session else 0}"
        )
    else:
        logger.info(
-            f"[TIMING] Using provided session, messages={len(session.messages)}",
-            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
+            f"Using provided session object: {session.session_id}, "
+            f"message_count={len(session.messages)}"
        )

    if not session:
@@ -436,25 +409,17 @@ async def stream_chat_completion(

        # Track user message in PostHog
        if is_user_message:
-            posthog_start = time.monotonic()
            track_user_message(
                user_id=user_id,
                session_id=session_id,
                message_length=len(message),
            )
-            posthog_time = (time.monotonic() - posthog_start) * 1000
-            logger.info(
-                f"[TIMING] track_user_message took {posthog_time:.1f}ms",
-                extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
-            )

-    upsert_start = time.monotonic()
-    session = await upsert_chat_session(session)
-    upsert_time = (time.monotonic() - upsert_start) * 1000
    logger.info(
-        f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
+        f"message_count={len(session.messages)}"
    )
+    session = await upsert_chat_session(session)
    assert session, "Session not found"

    # Generate title for new sessions on first user message (non-blocking)
@@ -492,13 +457,7 @@ async def stream_chat_completion(
            asyncio.create_task(_update_title())

    # Build system prompt with business understanding
-    prompt_start = time.monotonic()
    system_prompt, understanding = await _build_system_prompt(user_id)
-    prompt_time = (time.monotonic() - prompt_start) * 1000
-    logger.info(
-        f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
-    )

    # Initialize variables for streaming
    assistant_response = ChatMessage(
@@ -521,29 +480,13 @@ async def stream_chat_completion(
    should_retry = False

    # Generate unique IDs for AI SDK protocol
-    import uuid as uuid_module
-
-    is_continuation = _continuation_message_id is not None
-    message_id = _continuation_message_id or str(uuid_module.uuid4())
+    message_id = str(uuid_module.uuid4())
    text_block_id = str(uuid_module.uuid4())

-    # Only yield message start for the initial call, not for continuations.
-    setup_time = (time.monotonic() - completion_start) * 1000
-    logger.info(
-        f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
-    if not is_continuation:
-        yield StreamStart(messageId=message_id, taskId=_task_id)
-
-    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
-    yield StreamStartStep()
+    # Yield message start
+    yield StreamStart(messageId=message_id)

    try:
-        logger.info(
-            "[TIMING] Calling _stream_chat_chunks",
-            extra={"json_fields": log_meta},
-        )
        async for chunk in _stream_chat_chunks(
            session=session,
            tools=tools,
@@ -643,10 +586,6 @@ async def stream_chat_completion(
                    )
                yield chunk
            elif isinstance(chunk, StreamFinish):
-                if has_done_tool_call:
-                    # Tool calls happened — close the step but don't send message-level finish.
-                    # The continuation will open a new step, and finish will come at the end.
-                    yield StreamFinishStep()
                if not has_done_tool_call:
                    # Emit text-end before finish if we received text but haven't closed it
                    if has_received_text and not text_streaming_ended:
@@ -678,8 +617,6 @@ async def stream_chat_completion(
                            has_saved_assistant_message = True

                    has_yielded_end = True
-                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
-                    yield StreamFinishStep()
                    yield chunk
            elif isinstance(chunk, StreamError):
                has_yielded_error = True
@@ -729,10 +666,6 @@ async def stream_chat_completion(
            logger.info(
                f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
            )
-            # Close the current step before retrying so the recursive call's
-            # StreamStartStep doesn't produce unbalanced step events.
-            if not has_yielded_end:
-                yield StreamFinishStep()
            should_retry = True
        else:
            # Non-retryable error or max retries exceeded
@@ -768,7 +701,6 @@ async def stream_chat_completion(
                error_response = StreamError(errorText=error_message)
                yield error_response
            if not has_yielded_end:
-                yield StreamFinishStep()
                yield StreamFinish()
            return

@@ -783,8 +715,6 @@ async def stream_chat_completion(
            retry_count=retry_count + 1,
            session=session,
            context=context,
-            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
-            _task_id=_task_id,
        ):
            yield chunk
        return  # Exit after retry to avoid double-saving in finally block
@@ -854,8 +784,6 @@ async def stream_chat_completion(
            session=session,  # Pass session object to avoid Redis refetch
            context=context,
            tool_call_response=str(tool_response_messages),
-            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
-            _task_id=_task_id,
        ):
            yield chunk

@@ -912,10 +840,6 @@ 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:
@@ -966,21 +890,9 @@ async def _stream_chat_chunks(
        SSE formatted JSON response objects

    """
-    import time as time_module
-
-    stream_chunks_start = time_module.perf_counter()
    model = config.model

-    # Build log metadata for structured logging
-    log_meta = {"component": "ChatService", "session_id": session.session_id}
-    if session.user_id:
-        log_meta["user_id"] = session.user_id
-
-    logger.info(
-        f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
-        f"user={session.user_id}, n_messages={len(session.messages)}",
-        extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
-    )
+    logger.info("Starting pure chat stream")

    messages = session.to_openai_messages()
    if system_prompt:
@@ -991,18 +903,12 @@ async def _stream_chat_chunks(
        messages = [system_message] + messages

    # Apply context window management
-    context_start = time_module.perf_counter()
    context_result = await _manage_context_window(
        messages=messages,
        model=model,
        api_key=config.api_key,
        base_url=config.base_url,
    )
-    context_time = (time_module.perf_counter() - context_start) * 1000
-    logger.info(
-        f"[TIMING] _manage_context_window took {context_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": context_time}},
-    )

    if context_result.error:
        if "System prompt dropped" in context_result.error:
@@ -1037,19 +943,9 @@ async def _stream_chat_chunks(

        while retry_count <= MAX_RETRIES:
            try:
-                elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
-                retry_info = (
-                    f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
-                )
                logger.info(
-                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "elapsed_ms": elapsed,
-                            "retry_count": retry_count,
-                        }
-                    },
+                    f"Creating OpenAI chat completion stream..."
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
                )

                # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1066,11 +962,6 @@ async def _stream_chat_chunks(
                        :128
                    ]  # OpenRouter limit

-                # Enable adaptive thinking for Anthropic models via OpenRouter
-                if config.thinking_enabled and "anthropic" in model.lower():
-                    extra_body["reasoning"] = {"enabled": True}
-
-                api_call_start = time_module.perf_counter()
                stream = await client.chat.completions.create(
                    model=model,
                    messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1080,11 +971,6 @@ async def _stream_chat_chunks(
                    stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
                    extra_body=extra_body,
                )
-                api_init_time = (time_module.perf_counter() - api_call_start) * 1000
-                logger.info(
-                    f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
-                    extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
-                )

                # Variables to accumulate tool calls
                tool_calls: list[dict[str, Any]] = []
@@ -1095,13 +981,10 @@ async def _stream_chat_chunks(

                # Track if we've started the text block
                text_started = False
-                first_content_chunk = True
-                chunk_count = 0

                # Process the stream
                chunk: ChatCompletionChunk
                async for chunk in stream:
-                    chunk_count += 1
                    if chunk.usage:
                        yield StreamUsage(
                            promptTokens=chunk.usage.prompt_tokens,
@@ -1124,23 +1007,6 @@ async def _stream_chat_chunks(
                            if not text_started and text_block_id:
                                yield StreamTextStart(id=text_block_id)
                                text_started = True
-                            # Log timing for first content chunk
-                            if first_content_chunk:
-                                first_content_chunk = False
-                                ttfc = (
-                                    time_module.perf_counter() - api_call_start
-                                ) * 1000
-                                logger.info(
-                                    f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
-                                    f"(since API call), n_chunks={chunk_count}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "time_to_first_chunk_ms": ttfc,
-                                            "n_chunks": chunk_count,
-                                        }
-                                    },
-                                )
                            # Stream the text delta
                            text_response = StreamTextDelta(
                                id=text_block_id or "",
@@ -1197,21 +1063,7 @@ async def _stream_chat_chunks(
                                        toolName=tool_calls[idx]["function"]["name"],
                                    )
                                    emitted_start_for_idx.add(idx)
-                stream_duration = time_module.perf_counter() - api_call_start
-                logger.info(
-                    f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
-                    f"duration={stream_duration:.2f}s, "
-                    f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "stream_duration_ms": stream_duration * 1000,
-                            "finish_reason": finish_reason,
-                            "n_chunks": chunk_count,
-                            "n_tool_calls": len(tool_calls),
-                        }
-                    },
-                )
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")

                # Yield all accumulated tool calls after the stream is complete
                # This ensures all tool call arguments have been fully received
@@ -1231,12 +1083,6 @@ async def _stream_chat_chunks(
                        # Re-raise to trigger retry logic in the parent function
                        raise

-                total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
-                logger.info(
-                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time/1000:.1f}s; "
-                    f"session={session.session_id}, user={session.user_id}",
-                    extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-                )
                yield StreamFinish()
                return
            except Exception as e:
@@ -1304,8 +1150,6 @@ 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"]

@@ -1716,7 +1560,6 @@ async def _execute_long_running_tool_with_streaming(
            task_id,
            StreamError(errorText=str(e)),
        )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())

        await _update_pending_operation(
@@ -1833,10 +1676,6 @@ async def _generate_llm_continuation(
        if session_id:
            extra_body["session_id"] = session_id[:128]

-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
        retry_count = 0
        last_error: Exception | None = None
        response = None
@@ -1931,8 +1770,6 @@ 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)
@@ -1967,22 +1804,13 @@ async def _generate_llm_continuation_with_streaming(
        if session_id:
            extra_body["session_id"] = session_id[:128]

-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
        # 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())

        # Publish start event
        await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
-        await stream_registry.publish_chunk(task_id, StreamStartStep())
        await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))

        # Stream the response
@@ -2006,7 +1834,6 @@ async def _generate_llm_continuation_with_streaming(

        # Publish end events
        await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())

        if assistant_content:
            # Reload session from DB to avoid race condition with user messages
@@ -2048,5 +1875,4 @@ async def _generate_llm_continuation_with_streaming(
            task_id,
            StreamError(errorText=f"Failed to generate response: {e}"),
        )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())
--- a/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
+++ b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
@@ -104,24 +104,6 @@ async def create_task(
    Returns:
        The created ActiveTask instance (metadata only)
    """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata for structured logging
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "session_id": session_id,
-    }
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
-        extra={"json_fields": log_meta},
-    )
-
    task = ActiveTask(
        task_id=task_id,
        session_id=session_id,
@@ -132,18 +114,10 @@ async def create_task(
    )

    # Store metadata in Redis
-    redis_start = time.perf_counter()
    redis = await get_redis_async()
-    redis_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] get_redis_async took {redis_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
-    )
-
    meta_key = _get_task_meta_key(task_id)
    op_key = _get_operation_mapping_key(operation_id)

-    hset_start = time.perf_counter()
    await redis.hset(  # type: ignore[misc]
        meta_key,
        mapping={
@@ -157,22 +131,12 @@ async def create_task(
            "created_at": task.created_at.isoformat(),
        },
    )
-    hset_time = (time.perf_counter() - hset_start) * 1000
-    logger.info(
-        f"[TIMING] redis.hset took {hset_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
-    )
-
    await redis.expire(meta_key, config.stream_ttl)

    # Create operation_id -> task_id mapping for webhook lookups
    await redis.set(op_key, task_id, ex=config.stream_ttl)

-    total_time = (time.perf_counter() - start_time) * 1000
-    logger.info(
-        f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
-        extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-    )
+    logger.debug(f"Created task {task_id} for session {session_id}")

    return task

@@ -192,60 +156,26 @@ async def publish_chunk(
    Returns:
        The Redis Stream message ID
    """
-    import time
-
-    start_time = time.perf_counter()
-    chunk_type = type(chunk).__name__
    chunk_json = chunk.model_dump_json()
    message_id = "0-0"

-    # Build log metadata
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "chunk_type": chunk_type,
-    }
-
    try:
        redis = await get_redis_async()
        stream_key = _get_task_stream_key(task_id)

        # Write to Redis Stream for persistence and real-time delivery
-        xadd_start = time.perf_counter()
        raw_id = await redis.xadd(
            stream_key,
            {"data": chunk_json},
            maxlen=config.stream_max_length,
        )
-        xadd_time = (time.perf_counter() - xadd_start) * 1000
        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()

        # Set TTL on stream to match task metadata TTL
        await redis.expire(stream_key, config.stream_ttl)
-
-        total_time = (time.perf_counter() - start_time) * 1000
-        # Only log timing for significant chunks or slow operations
-        if (
-            chunk_type
-            in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
-            or total_time > 50
-        ):
-            logger.info(
-                f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "xadd_time_ms": xadd_time,
-                        "message_id": message_id,
-                    }
-                },
-            )
    except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
        logger.error(
-            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
+            f"Failed to publish chunk for task {task_id}: {e}",
            exc_info=True,
        )

@@ -270,61 +200,24 @@ async def subscribe_to_task(
        An asyncio Queue that will receive stream chunks, or None if task not found
        or user doesn't have access
    """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata
-    log_meta = {"component": "StreamRegistry", "task_id": task_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
-        extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
-    )
-
-    redis_start = time.perf_counter()
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-    hgetall_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
-    )

    if not meta:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "reason": "task_not_found",
-                }
-            },
-        )
+        logger.debug(f"Task {task_id} not found in Redis")
        return None

    # Note: Redis client uses decode_responses=True, so keys are strings
    task_status = meta.get("status", "")
    task_user_id = meta.get("user_id", "") or None
-    log_meta["session_id"] = meta.get("session_id", "")

    # Validate ownership - if task has an owner, requester must match
    if task_user_id:
        if user_id != task_user_id:
            logger.warning(
-                f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "task_owner": task_user_id,
-                        "reason": "access_denied",
-                    }
-                },
+                f"User {user_id} denied access to task {task_id} "
+                f"owned by {task_user_id}"
            )
            return None

@@ -332,19 +225,7 @@ async def subscribe_to_task(
    stream_key = _get_task_stream_key(task_id)

    # Step 1: Replay messages from Redis Stream
-    xread_start = time.perf_counter()
    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
-    xread_time = (time.perf_counter() - xread_start) * 1000
-    logger.info(
-        f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": xread_time,
-                "task_status": task_status,
-            }
-        },
-    )

    replayed_count = 0
    replay_last_id = last_message_id
@@ -363,48 +244,19 @@ async def subscribe_to_task(
                    except Exception as e:
                        logger.warning(f"Failed to replay message: {e}")

-    logger.info(
-        f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "n_messages_replayed": replayed_count,
-                "replay_last_id": replay_last_id,
-            }
-        },
-    )
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")

    # Step 2: If task is still running, start stream listener for live updates
    if task_status == "running":
-        logger.info(
-            "[TIMING] Task still running, starting _stream_listener",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
        listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
+            _stream_listener(task_id, subscriber_queue, replay_last_id)
        )
        # Track listener task for cleanup on unsubscribe
        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
    else:
        # Task is completed/failed - add finish marker
-        logger.info(
-            f"[TIMING] Task already {task_status}, adding StreamFinish",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
        await subscriber_queue.put(StreamFinish())

-    total_time = (time.perf_counter() - start_time) * 1000
-    logger.info(
-        f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
-        f"n_messages_replayed={replayed_count}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "total_time_ms": total_time,
-                "n_messages_replayed": replayed_count,
-            }
-        },
-    )
    return subscriber_queue


@@ -412,7 +264,6 @@ async def _stream_listener(
    task_id: str,
    subscriber_queue: asyncio.Queue[StreamBaseResponse],
    last_replayed_id: str,
-    log_meta: dict | None = None,
 ) -> None:
    """Listen to Redis Stream for new messages using blocking XREAD.

@@ -423,27 +274,10 @@ async def _stream_listener(
        task_id: Task ID to listen for
        subscriber_queue: Queue to deliver messages to
        last_replayed_id: Last message ID from replay (continue from here)
-        log_meta: Structured logging metadata
    """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Use provided log_meta or build minimal one
-    if log_meta is None:
-        log_meta = {"component": "StreamRegistry", "task_id": task_id}
-
-    logger.info(
-        f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
-        extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
-    )
-
    queue_id = id(subscriber_queue)
    # Track the last successfully delivered message ID for recovery hints
    last_delivered_id = last_replayed_id
-    messages_delivered = 0
-    first_message_time = None
-    xread_count = 0

    try:
        redis = await get_redis_async()
@@ -453,39 +287,9 @@ async def _stream_listener(
        while True:
            # Block for up to 30 seconds waiting for new messages
            # This allows periodic checking if task is still running
-            xread_start = time.perf_counter()
-            xread_count += 1
            messages = await redis.xread(
                {stream_key: current_id}, block=30000, count=100
            )
-            xread_time = (time.perf_counter() - xread_start) * 1000
-
-            if messages:
-                msg_count = sum(len(msgs) for _, msgs in messages)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "n_messages": msg_count,
-                            "duration_ms": xread_time,
-                        }
-                    },
-                )
-            elif xread_time > 1000:
-                # Only log timeouts (30s blocking)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "duration_ms": xread_time,
-                            "reason": "timeout",
-                        }
-                    },
-                )

            if not messages:
                # Timeout - check if task is still running
@@ -522,30 +326,10 @@ async def _stream_listener(
                                )
                                # Update last delivered ID on successful delivery
                                last_delivered_id = current_id
-                                messages_delivered += 1
-                                if first_message_time is None:
-                                    first_message_time = time.perf_counter()
-                                    elapsed = (first_message_time - start_time) * 1000
-                                    logger.info(
-                                        f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
-                                        extra={
-                                            "json_fields": {
-                                                **log_meta,
-                                                "elapsed_ms": elapsed,
-                                                "chunk_type": type(chunk).__name__,
-                                            }
-                                        },
-                                    )
                            except asyncio.TimeoutError:
                                logger.warning(
-                                    f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "timeout_s": QUEUE_PUT_TIMEOUT,
-                                            "reason": "queue_full",
-                                        }
-                                    },
+                                    f"Subscriber queue full for task {task_id}, "
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
                                )
                                # Send overflow error with recovery info
                                try:
@@ -567,44 +351,15 @@ async def _stream_listener(

                            # Stop listening on finish
                            if isinstance(chunk, StreamFinish):
-                                total_time = (time.perf_counter() - start_time) * 1000
-                                logger.info(
-                                    f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "total_time_ms": total_time,
-                                            "messages_delivered": messages_delivered,
-                                        }
-                                    },
-                                )
                                return
                    except Exception as e:
-                        logger.warning(
-                            f"Error processing stream message: {e}",
-                            extra={"json_fields": {**log_meta, "error": str(e)}},
-                        )
+                        logger.warning(f"Error processing stream message: {e}")

    except asyncio.CancelledError:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "messages_delivered": messages_delivered,
-                    "reason": "cancelled",
-                }
-            },
-        )
+        logger.debug(f"Stream listener cancelled for task {task_id}")
        raise  # Re-raise to propagate cancellation
    except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.error(
-            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
-        )
+        logger.error(f"Stream listener error for task {task_id}: {e}")
        # On error, send finish to unblock subscriber
        try:
            await asyncio.wait_for(
@@ -613,24 +368,10 @@ async def _stream_listener(
            )
        except (asyncio.TimeoutError, asyncio.QueueFull):
            logger.warning(
-                "Could not deliver finish event after error",
-                extra={"json_fields": log_meta},
+                f"Could not deliver finish event for task {task_id} after error"
            )
    finally:
        # Clean up listener task mapping on exit
-        total_time = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
-            f"delivered={messages_delivered}, xread_count={xread_count}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "total_time_ms": total_time,
-                    "messages_delivered": messages_delivered,
-                    "xread_count": xread_count,
-                }
-            },
-        )
        _listener_tasks.pop(queue_id, None)


@@ -857,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
        ResponseType,
        StreamError,
        StreamFinish,
-        StreamFinishStep,
        StreamHeartbeat,
        StreamStart,
-        StreamStartStep,
        StreamTextDelta,
        StreamTextEnd,
        StreamTextStart,
@@ -874,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
    type_to_class: dict[str, type[StreamBaseResponse]] = {
        ResponseType.START.value: StreamStart,
        ResponseType.FINISH.value: StreamFinish,
-        ResponseType.START_STEP.value: StreamStartStep,
-        ResponseType.FINISH_STEP.value: StreamFinishStep,
        ResponseType.TEXT_START.value: StreamTextStart,
        ResponseType.TEXT_DELTA.value: StreamTextDelta,
        ResponseType.TEXT_END.value: StreamTextEnd,
--- a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py
@@ -3,6 +3,8 @@
 import logging
 from typing import Any

+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession

 from .agent_generator import (
@@ -28,6 +30,26 @@ 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."""

@@ -85,7 +107,7 @@ class CreateAgentTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Execute the create_agent tool.

@@ -94,16 +116,14 @@ class CreateAgentTool(BaseTool):
        2. Generate agent JSON (external service handles fixing and validation)
        3. Preview or save based on the save parameter
        """
-        description = kwargs.get("description", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
+        params = CreateAgentInput(**kwargs)
        session_id = session.session_id if session else None

-        # Extract async processing params (passed by long-running tool handler)
+        # Extract async processing params
        operation_id = kwargs.get("_operation_id")
        task_id = kwargs.get("_task_id")

-        if not description:
+        if not params.description:
            return ErrorResponse(
                message="Please provide a description of what the agent should do.",
                error="Missing description parameter",
@@ -115,7 +135,7 @@ class CreateAgentTool(BaseTool):
            try:
                library_agents = await get_all_relevant_agents_for_generation(
                    user_id=user_id,
-                    search_query=description,
+                    search_query=params.description,
                    include_marketplace=True,
                )
                logger.debug(
@@ -126,7 +146,7 @@ class CreateAgentTool(BaseTool):

        try:
            decomposition_result = await decompose_goal(
-                description, context, library_agents
+                params.description, params.context, library_agents
            )
        except AgentGeneratorNotConfiguredError:
            return ErrorResponse(
@@ -142,7 +162,7 @@ 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": description[:100]},
+                details={"description": params.description[:100]},
                session_id=session_id,
            )

@@ -158,7 +178,7 @@ class CreateAgentTool(BaseTool):
                message=user_message,
                error=f"decomposition_failed:{error_type}",
                details={
-                    "description": description[:100],
+                    "description": params.description[:100],
                    "service_error": error_msg,
                    "error_type": error_type,
                },
@@ -244,7 +264,7 @@ 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": description[:100]},
+                details={"description": params.description[:100]},
                session_id=session_id,
            )

@@ -266,7 +286,7 @@ class CreateAgentTool(BaseTool):
                message=user_message,
                error=f"generation_failed:{error_type}",
                details={
-                    "description": description[:100],
+                    "description": params.description[:100],
                    "service_error": error_msg,
                    "error_type": error_type,
                },
@@ -291,7 +311,7 @@ class CreateAgentTool(BaseTool):
        node_count = len(agent_json.get("nodes", []))
        link_count = len(agent_json.get("links", []))

-        if not save:
+        if not params.save:
            return AgentPreviewResponse(
                message=(
                    f"I've generated an agent called '{agent_name}' with {node_count} blocks. "
--- a/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py
@@ -3,6 +3,8 @@
 import logging
 from typing import Any

+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.api.features.store.exceptions import AgentNotFoundError
@@ -27,6 +29,23 @@ from .models import (
 logger = logging.getLogger(__name__)


+class CustomizeAgentInput(BaseModel):
+    """Input parameters for the customize_agent tool."""
+
+    agent_id: str = ""
+    modifications: str = ""
+    context: str = ""
+    save: bool = True
+
+    @field_validator("agent_id", "modifications", "context", mode="before")
+    @classmethod
+    def strip_strings(cls, v: Any) -> str:
+        """Strip whitespace from string fields."""
+        if isinstance(v, str):
+            return v.strip()
+        return v if v is not None else ""
+
+
 class CustomizeAgentTool(BaseTool):
    """Tool for customizing marketplace/template agents using natural language."""

@@ -92,7 +111,7 @@ class CustomizeAgentTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Execute the customize_agent tool.

@@ -102,20 +121,17 @@ class CustomizeAgentTool(BaseTool):
        3. Call customize_template with the modification request
        4. Preview or save based on the save parameter
        """
-        agent_id = kwargs.get("agent_id", "").strip()
-        modifications = kwargs.get("modifications", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
+        params = CustomizeAgentInput(**kwargs)
        session_id = session.session_id if session else None

-        if not agent_id:
+        if not params.agent_id:
            return ErrorResponse(
                message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
                error="missing_agent_id",
                session_id=session_id,
            )

-        if not modifications:
+        if not params.modifications:
            return ErrorResponse(
                message="Please describe how you want to customize this agent.",
                error="missing_modifications",
@@ -123,11 +139,11 @@ class CustomizeAgentTool(BaseTool):
            )

        # Parse agent_id in format "creator/slug"
-        parts = [p.strip() for p in agent_id.split("/")]
+        parts = [p.strip() for p in params.agent_id.split("/")]
        if len(parts) != 2 or not parts[0] or not parts[1]:
            return ErrorResponse(
                message=(
-                    f"Invalid agent ID format: '{agent_id}'. "
+                    f"Invalid agent ID format: '{params.agent_id}'. "
                    "Expected format is 'creator/agent-name' "
                    "(e.g., 'autogpt/newsletter-writer')."
                ),
@@ -145,14 +161,14 @@ class CustomizeAgentTool(BaseTool):
        except AgentNotFoundError:
            return ErrorResponse(
                message=(
-                    f"Could not find marketplace agent '{agent_id}'. "
+                    f"Could not find marketplace agent '{params.agent_id}'. "
                    "Please check the agent ID and try again."
                ),
                error="agent_not_found",
                session_id=session_id,
            )
        except Exception as e:
-            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
+            logger.error(f"Error fetching marketplace agent {params.agent_id}: {e}")
            return ErrorResponse(
                message="Failed to fetch the marketplace agent. Please try again.",
                error="fetch_error",
@@ -162,7 +178,7 @@ class CustomizeAgentTool(BaseTool):
        if not agent_details.store_listing_version_id:
            return ErrorResponse(
                message=(
-                    f"The agent '{agent_id}' does not have an available version. "
+                    f"The agent '{params.agent_id}' does not have an available version. "
                    "Please try a different agent."
                ),
                error="no_version_available",
@@ -174,7 +190,7 @@ class CustomizeAgentTool(BaseTool):
            graph = await store_db.get_agent(agent_details.store_listing_version_id)
            template_agent = graph_to_json(graph)
        except Exception as e:
-            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
+            logger.error(f"Error fetching agent graph for {params.agent_id}: {e}")
            return ErrorResponse(
                message="Failed to fetch the agent configuration. Please try again.",
                error="graph_fetch_error",
@@ -185,8 +201,8 @@ class CustomizeAgentTool(BaseTool):
        try:
            result = await customize_template(
                template_agent=template_agent,
-                modification_request=modifications,
-                context=context,
+                modification_request=params.modifications,
+                context=params.context,
            )
        except AgentGeneratorNotConfiguredError:
            return ErrorResponse(
@@ -198,7 +214,7 @@ class CustomizeAgentTool(BaseTool):
                session_id=session_id,
            )
        except Exception as e:
-            logger.error(f"Error calling customize_template for {agent_id}: {e}")
+            logger.error(f"Error calling customize_template for {params.agent_id}: {e}")
            return ErrorResponse(
                message=(
                    "Failed to customize the agent due to a service error. "
@@ -219,55 +235,25 @@ class CustomizeAgentTool(BaseTool):
                session_id=session_id,
            )

-        # Handle error response
-        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,
-            )
+        # 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 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
+    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
        if not isinstance(result, dict):
            logger.error(f"Unexpected customize_template response type: {type(result)}")
            return ErrorResponse(
@@ -276,8 +262,77 @@ class CustomizeAgentTool(BaseTool):
                session_id=session_id,
            )

-        customized_agent = result
+        result_type = result.get("type")

+        match result_type:
+            case "error":
+                error_msg = result.get("error", "Unknown error")
+                error_type = result.get("error_type", "unknown")
+                user_message = get_user_message_for_error(
+                    error_type,
+                    operation="customize the agent",
+                    llm_parse_message=(
+                        "The AI had trouble customizing the agent. "
+                        "Please try again or simplify your request."
+                    ),
+                    validation_message=(
+                        "The customized agent failed validation. "
+                        "Please try rephrasing your request."
+                    ),
+                    error_details=error_msg,
+                )
+                return ErrorResponse(
+                    message=user_message,
+                    error=f"customization_failed:{error_type}",
+                    session_id=session_id,
+                )
+
+            case "clarifying_questions":
+                questions_data = result.get("questions") or []
+                if not isinstance(questions_data, list):
+                    logger.error(
+                        f"Unexpected clarifying questions format: {type(questions_data)}"
+                    )
+                    questions_data = []
+
+                questions = [
+                    ClarifyingQuestion(
+                        question=q.get("question", "") if isinstance(q, dict) else "",
+                        keyword=q.get("keyword", "") if isinstance(q, dict) else "",
+                        example=q.get("example") if isinstance(q, dict) else None,
+                    )
+                    for q in questions_data
+                    if isinstance(q, dict)
+                ]
+
+                return ClarificationNeededResponse(
+                    message=(
+                        "I need some more information to customize this agent. "
+                        "Please answer the following questions:"
+                    ),
+                    questions=questions,
+                    session_id=session_id,
+                )
+
+            case _:
+                # Default case: result is the customized agent JSON
+                return await self._save_or_preview_agent(
+                    customized_agent=result,
+                    params=params,
+                    agent_details=agent_details,
+                    user_id=user_id,
+                    session_id=session_id,
+                )
+
+    async def _save_or_preview_agent(
+        self,
+        customized_agent: dict[str, Any],
+        params: CustomizeAgentInput,
+        agent_details: Any,
+        user_id: str | None,
+        session_id: str | None,
+    ) -> ToolResponseBase:
+        """Save or preview the customized agent based on params.save."""
        agent_name = customized_agent.get(
            "name", f"Customized {agent_details.agent_name}"
        )
@@ -287,7 +342,7 @@ class CustomizeAgentTool(BaseTool):
        node_count = len(nodes) if isinstance(nodes, list) else 0
        link_count = len(links) if isinstance(links, list) else 0

-        if not save:
+        if not params.save:
            return AgentPreviewResponse(
                message=(
                    f"I've customized the agent '{agent_details.agent_name}'. "
--- a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py
@@ -3,6 +3,8 @@
 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 (
@@ -27,6 +29,20 @@ 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."""

@@ -90,7 +106,7 @@ class EditAgentTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Execute the edit_agent tool.

@@ -99,35 +115,32 @@ class EditAgentTool(BaseTool):
        2. Generate updated agent (external service handles fixing and validation)
        3. Preview or save based on the save parameter
        """
-        agent_id = kwargs.get("agent_id", "").strip()
-        changes = kwargs.get("changes", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
+        params = EditAgentInput(**kwargs)
        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 agent_id:
+        if not params.agent_id:
            return ErrorResponse(
                message="Please provide the agent ID to edit.",
                error="Missing agent_id parameter",
                session_id=session_id,
            )

-        if not changes:
+        if not params.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(agent_id, user_id)
+        current_agent = await get_agent_as_json(params.agent_id, user_id)

        if current_agent is None:
            return ErrorResponse(
-                message=f"Could not find agent with ID '{agent_id}' in your library.",
+                message=f"Could not find agent '{params.agent_id}' in your library.",
                error="agent_not_found",
                session_id=session_id,
            )
@@ -138,7 +151,7 @@ class EditAgentTool(BaseTool):
                graph_id = current_agent.get("id")
                library_agents = await get_all_relevant_agents_for_generation(
                    user_id=user_id,
-                    search_query=changes,
+                    search_query=params.changes,
                    exclude_graph_id=graph_id,
                    include_marketplace=True,
                )
@@ -148,9 +161,11 @@ class EditAgentTool(BaseTool):
            except Exception as e:
                logger.warning(f"Failed to fetch library agents: {e}")

-        update_request = changes
-        if context:
-            update_request = f"{changes}\n\nAdditional context:\n{context}"
+        update_request = params.changes
+        if params.context:
+            update_request = (
+                f"{params.changes}\n\nAdditional context:\n{params.context}"
+            )

        try:
            result = await generate_agent_patch(
@@ -174,7 +189,7 @@ 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": agent_id, "changes": changes[:100]},
+                details={"agent_id": params.agent_id, "changes": params.changes[:100]},
                session_id=session_id,
            )

@@ -206,8 +221,8 @@ class EditAgentTool(BaseTool):
                message=user_message,
                error=f"update_generation_failed:{error_type}",
                details={
-                    "agent_id": agent_id,
-                    "changes": changes[:100],
+                    "agent_id": params.agent_id,
+                    "changes": params.changes[:100],
                    "service_error": error_msg,
                    "error_type": error_type,
                },
@@ -239,7 +254,7 @@ class EditAgentTool(BaseTool):
        node_count = len(updated_agent.get("nodes", []))
        link_count = len(updated_agent.get("links", []))

-        if not save:
+        if not params.save:
            return AgentPreviewResponse(
                message=(
                    f"I've updated the agent. "
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_agent.py
@@ -2,6 +2,8 @@

 from typing import Any

+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession

 from .agent_search import search_agents
@@ -9,6 +11,18 @@ 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."""

@@ -36,10 +50,11 @@ class FindAgentTool(BaseTool):
        }

    async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
+        self, user_id: str | None, session: ChatSession, **kwargs: Any
    ) -> ToolResponseBase:
+        params = FindAgentInput(**kwargs)
        return await search_agents(
-            query=kwargs.get("query", "").strip(),
+            query=params.query,
            source="marketplace",
            session_id=session.session_id,
            user_id=user_id,
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
@@ -2,6 +2,7 @@ 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
@@ -40,6 +41,18 @@ COPILOT_EXCLUDED_BLOCK_IDS = {
 }


+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."""

@@ -81,24 +94,24 @@ class FindBlockTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Search for blocks matching the query.

        Args:
            user_id: User ID (required)
            session: Chat session
-            query: Search query
+            **kwargs: Tool parameters

        Returns:
            BlockListResponse: List of matching blocks
            NoResultsResponse: No blocks found
            ErrorResponse: Error message
        """
-        query = kwargs.get("query", "").strip()
+        params = FindBlockInput(**kwargs)
        session_id = session.session_id

-        if not query:
+        if not params.query:
            return ErrorResponse(
                message="Please provide a search query",
                session_id=session_id,
@@ -107,7 +120,7 @@ class FindBlockTool(BaseTool):
        try:
            # Search for blocks using hybrid search
            results, total = await unified_hybrid_search(
-                query=query,
+                query=params.query,
                content_types=[ContentType.BLOCK],
                page=1,
                page_size=_OVERFETCH_PAGE_SIZE,
@@ -115,7 +128,7 @@ class FindBlockTool(BaseTool):

            if not results:
                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
+                    message=f"No blocks found for '{params.query}'",
                    suggestions=[
                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
                        "Check spelling of technical terms",
@@ -217,7 +230,7 @@ class FindBlockTool(BaseTool):

            if not blocks:
                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
+                    message=f"No blocks found for '{params.query}'",
                    suggestions=[
                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
                    ],
@@ -226,13 +239,13 @@ class FindBlockTool(BaseTool):

            return BlockListResponse(
                message=(
-                    f"Found {len(blocks)} block(s) matching '{query}'. "
+                    f"Found {len(blocks)} block(s) matching '{params.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=query,
+                query=params.query,
                session_id=session_id,
            )

--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_library_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_library_agent.py
@@ -2,6 +2,8 @@

 from typing import Any

+from pydantic import BaseModel, field_validator
+
 from backend.api.features.chat.model import ChatSession

 from .agent_search import search_agents
@@ -9,6 +11,15 @@ 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."""

@@ -42,10 +53,11 @@ class FindLibraryAgentTool(BaseTool):
        return True

    async def _execute(
-        self, user_id: str | None, session: ChatSession, **kwargs
+        self, user_id: str | None, session: ChatSession, **kwargs: Any
    ) -> ToolResponseBase:
+        params = FindLibraryAgentInput(**kwargs)
        return await search_agents(
-            query=kwargs.get("query", "").strip(),
+            query=params.query,
            source="library",
            session_id=session.session_id,
            user_id=user_id,
--- a/autogpt_platform/backend/backend/api/features/chat/tools/get_doc_page.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/get_doc_page.py
@@ -4,6 +4,8 @@ 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 (
@@ -18,6 +20,18 @@ 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."""

@@ -75,23 +89,23 @@ class GetDocPageTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Fetch full content of a documentation page.

        Args:
            user_id: User ID (not required for docs)
            session: Chat session
-            path: Path to the documentation file
+            **kwargs: Tool parameters

        Returns:
            DocPageResponse: Full document content
            ErrorResponse: Error message
        """
-        path = kwargs.get("path", "").strip()
+        params = GetDocPageInput(**kwargs)
        session_id = session.session_id if session else None

-        if not path:
+        if not params.path:
            return ErrorResponse(
                message="Please provide a documentation path.",
                error="Missing path parameter",
@@ -99,7 +113,7 @@ class GetDocPageTool(BaseTool):
            )

        # Sanitize path to prevent directory traversal
-        if ".." in path or path.startswith("/"):
+        if ".." in params.path or params.path.startswith("/"):
            return ErrorResponse(
                message="Invalid documentation path.",
                error="invalid_path",
@@ -107,11 +121,11 @@ class GetDocPageTool(BaseTool):
            )

        docs_root = self._get_docs_root()
-        full_path = docs_root / path
+        full_path = docs_root / params.path

        if not full_path.exists():
            return ErrorResponse(
-                message=f"Documentation page not found: {path}",
+                message=f"Documentation page not found: {params.path}",
                error="not_found",
                session_id=session_id,
            )
@@ -128,19 +142,19 @@ class GetDocPageTool(BaseTool):

        try:
            content = full_path.read_text(encoding="utf-8")
-            title = self._extract_title(content, path)
+            title = self._extract_title(content, params.path)

            return DocPageResponse(
                message=f"Retrieved documentation page: {title}",
                title=title,
-                path=path,
+                path=params.path,
                content=content,
-                doc_url=self._make_doc_url(path),
+                doc_url=self._make_doc_url(params.path),
                session_id=session_id,
            )

        except Exception as e:
-            logger.error(f"Failed to read documentation page {path}: {e}")
+            logger.error(f"Failed to read documentation page {params.path}: {e}")
            return ErrorResponse(
                message=f"Failed to read documentation page: {str(e)}",
                error="read_failed",
--- a/autogpt_platform/backend/backend/api/features/chat/tools/helpers.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/helpers.py
@@ -1,29 +0,0 @@
-"""Shared helpers for chat tools."""
-
-from typing import Any
-
-
-def get_inputs_from_schema(
-    input_schema: dict[str, Any],
-    exclude_fields: set[str] | None = None,
-) -> list[dict[str, Any]]:
-    """Extract input field info from JSON schema."""
-    if not isinstance(input_schema, dict):
-        return []
-
-    exclude = exclude_fields or set()
-    properties = input_schema.get("properties", {})
-    required = set(input_schema.get("required", []))
-
-    return [
-        {
-            "name": name,
-            "title": schema.get("title", name),
-            "type": schema.get("type", "string"),
-            "description": schema.get("description", ""),
-            "required": name in required,
-            "default": schema.get("default"),
-        }
-        for name, schema in properties.items()
-        if name not in exclude
-    ]
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
@@ -24,7 +24,6 @@ from backend.util.timezone_utils import (
 )

 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
    AgentDetails,
    AgentDetailsResponse,
@@ -262,7 +261,7 @@ class RunAgentTool(BaseTool):
                        ),
                        requirements={
                            "credentials": requirements_creds_list,
-                            "inputs": get_inputs_from_schema(graph.input_schema),
+                            "inputs": self._get_inputs_list(graph.input_schema),
                            "execution_modes": self._get_execution_modes(graph),
                        },
                    ),
@@ -370,6 +369,22 @@ class RunAgentTool(BaseTool):
                session_id=session_id,
            )

+    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
+        """Extract inputs list from schema."""
+        inputs_list = []
+        if isinstance(input_schema, dict) and "properties" in input_schema:
+            for field_name, field_schema in input_schema["properties"].items():
+                inputs_list.append(
+                    {
+                        "name": field_name,
+                        "title": field_schema.get("title", field_name),
+                        "type": field_schema.get("type", "string"),
+                        "description": field_schema.get("description", ""),
+                        "required": field_name in input_schema.get("required", []),
+                    }
+                )
+        return inputs_list
+
    def _get_execution_modes(self, graph: GraphModel) -> list[str]:
        """Get available execution modes for the graph."""
        trigger_info = graph.trigger_setup_info
@@ -383,7 +398,7 @@ class RunAgentTool(BaseTool):
        suffix: str,
    ) -> str:
        """Build a message describing available inputs for an agent."""
-        inputs_list = get_inputs_from_schema(graph.input_schema)
+        inputs_list = self._get_inputs_list(graph.input_schema)
        required_names = [i["name"] for i in inputs_list if i["required"]]
        optional_names = [i["name"] for i in inputs_list if not i["required"]]

--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
@@ -5,6 +5,7 @@ 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
@@ -12,15 +13,14 @@ from backend.api.features.chat.tools.find_block import (
    COPILOT_EXCLUDED_BLOCK_IDS,
    COPILOT_EXCLUDED_BLOCK_TYPES,
 )
-from backend.data.block import AnyBlockSchema, get_block
+from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError

 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
    BlockOutputResponse,
    ErrorResponse,
@@ -29,14 +29,30 @@ from .models import (
    ToolResponseBase,
    UserReadiness,
 )
-from .utils import (
-    build_missing_credentials_from_field_info,
-    match_credentials_to_requirements,
-)
+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."""

@@ -81,41 +97,118 @@ class RunBlockTool(BaseTool):
    def requires_auth(self) -> bool:
        return True

+    async def _check_block_credentials(
+        self,
+        user_id: str,
+        block: Any,
+        input_data: dict[str, Any] | None = None,
+    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
+        """
+        Check if user has required credentials for a block.
+
+        Args:
+            user_id: User ID
+            block: Block to check credentials for
+            input_data: Input data for the block (used to determine provider via discriminator)
+
+        Returns:
+            tuple[matched_credentials, missing_credentials]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+        input_data = input_data or {}
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            effective_field_info = field_info
+            if field_info.discriminator and field_info.discriminator_mapping:
+                # Get discriminator from input, falling back to schema default
+                discriminator_value = input_data.get(field_info.discriminator)
+                if discriminator_value is None:
+                    field = block.input_schema.model_fields.get(
+                        field_info.discriminator
+                    )
+                    if field and field.default is not PydanticUndefined:
+                        discriminator_value = field.default
+
+                if (
+                    discriminator_value
+                    and discriminator_value in field_info.discriminator_mapping
+                ):
+                    effective_field_info = field_info.discriminate(discriminator_value)
+                    logger.debug(
+                        f"Discriminated provider for {field_name}: "
+                        f"{discriminator_value} -> {effective_field_info.provider}"
+                    )
+
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in effective_field_info.provider
+                    and cred.type in effective_field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(effective_field_info.provider), "unknown")
+                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
    async def _execute(
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Execute a block with the given input data.

        Args:
            user_id: User ID (required)
            session: Chat session
-            block_id: Block UUID to execute
-            input_data: Input values for the block
+            **kwargs: Tool parameters

        Returns:
            BlockOutputResponse: Block execution outputs
            SetupRequirementsResponse: Missing credentials
            ErrorResponse: Error message
        """
-        block_id = kwargs.get("block_id", "").strip()
-        input_data = kwargs.get("input_data", {})
+        params = RunBlockInput(**kwargs)
        session_id = session.session_id

-        if not block_id:
+        if not params.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",
@@ -123,15 +216,15 @@ class RunBlockTool(BaseTool):
            )

        # Get the block
-        block = get_block(block_id)
+        block = get_block(params.block_id)
        if not block:
            return ErrorResponse(
-                message=f"Block '{block_id}' not found",
+                message=f"Block '{params.block_id}' not found",
                session_id=session_id,
            )
        if block.disabled:
            return ErrorResponse(
-                message=f"Block '{block_id}' is disabled",
+                message=f"Block '{params.block_id}' is disabled",
                session_id=session_id,
            )

@@ -151,8 +244,8 @@ class RunBlockTool(BaseTool):
        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")

        creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = (
-            await self._resolve_block_credentials(user_id, block, input_data)
+        matched_credentials, missing_credentials = await self._check_block_credentials(
+            user_id, block, params.input_data
        )

        if missing_credentials:
@@ -170,7 +263,7 @@ class RunBlockTool(BaseTool):
                ),
                session_id=session_id,
                setup_info=SetupInfo(
-                    agent_id=block_id,
+                    agent_id=params.block_id,
                    agent_name=block.name,
                    user_readiness=UserReadiness(
                        has_all_credentials=False,
@@ -199,7 +292,7 @@ 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-{block_id}"
+            synthetic_node_id = f"copilot-node-{params.block_id}"
            synthetic_node_exec_id = (
                f"copilot-{session.session_id}-{uuid.uuid4().hex[:8]}"
            )
@@ -234,8 +327,8 @@ class RunBlockTool(BaseTool):

            for field_name, cred_meta in matched_credentials.items():
                # Inject metadata into input_data (for validation)
-                if field_name not in input_data:
-                    input_data[field_name] = cred_meta.model_dump()
+                if field_name not in params.input_data:
+                    params.input_data[field_name] = cred_meta.model_dump()

                # Fetch actual credentials and pass as kwargs (for execution)
                actual_credentials = await creds_manager.get(
@@ -252,14 +345,14 @@ 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(
-                input_data,
+                params.input_data,
                **exec_kwargs,
            ):
                outputs[output_name].append(output_data)

            return BlockOutputResponse(
                message=f"Block '{block.name}' executed successfully",
-                block_id=block_id,
+                block_id=params.block_id,
                block_name=block.name,
                outputs=dict(outputs),
                success=True,
@@ -281,75 +374,29 @@ class RunBlockTool(BaseTool):
                session_id=session_id,
            )

-    async def _resolve_block_credentials(
-        self,
-        user_id: str,
-        block: AnyBlockSchema,
-        input_data: dict[str, Any] | None = None,
-    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-        """
-        Resolve credentials for a block by matching user's available credentials.
-
-        Args:
-            user_id: User ID
-            block: Block to resolve credentials for
-            input_data: Input data for the block (used to determine provider via discriminator)
-
-        Returns:
-            tuple of (matched_credentials, missing_credentials) - matched credentials
-            are used for block execution, missing ones indicate setup requirements.
-        """
-        input_data = input_data or {}
-        requirements = self._resolve_discriminated_credentials(block, input_data)
-
-        if not requirements:
-            return {}, []
-
-        return await match_credentials_to_requirements(user_id, requirements)
-
-    def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
        """Extract non-credential inputs from block schema."""
+        inputs_list = []
        schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-        return get_inputs_from_schema(schema, exclude_fields=credentials_fields)

-    def _resolve_discriminated_credentials(
-        self,
-        block: AnyBlockSchema,
-        input_data: dict[str, Any],
-    ) -> dict[str, CredentialsFieldInfo]:
-        """Resolve credential requirements, applying discriminator logic where needed."""
-        credentials_fields_info = block.input_schema.get_credentials_fields_info()
-        if not credentials_fields_info:
-            return {}
+        for field_name, field_schema in properties.items():
+            # Skip credential fields
+            if field_name in credentials_fields:
+                continue

-        resolved: dict[str, CredentialsFieldInfo] = {}
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )

-        for field_name, field_info in credentials_fields_info.items():
-            effective_field_info = field_info
-
-            if field_info.discriminator and field_info.discriminator_mapping:
-                discriminator_value = input_data.get(field_info.discriminator)
-                if discriminator_value is None:
-                    field = block.input_schema.model_fields.get(
-                        field_info.discriminator
-                    )
-                    if field and field.default is not PydanticUndefined:
-                        discriminator_value = field.default
-
-                if (
-                    discriminator_value
-                    and discriminator_value in field_info.discriminator_mapping
-                ):
-                    effective_field_info = field_info.discriminate(discriminator_value)
-                    # For host-scoped credentials, add the discriminator value
-                    # (e.g., URL) so _credential_is_for_host can match it
-                    effective_field_info.discriminator_values.add(discriminator_value)
-                    logger.debug(
-                        f"Discriminated provider for {field_name}: "
-                        f"{discriminator_value} -> {effective_field_info.provider}"
-                    )
-
-            resolved[field_name] = effective_field_info
-
-        return resolved
+        return inputs_list
--- a/autogpt_platform/backend/backend/api/features/chat/tools/search_docs.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/search_docs.py
@@ -4,6 +4,7 @@ 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
@@ -28,6 +29,18 @@ 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."""

@@ -91,24 +104,24 @@ class SearchDocsTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
        """Search documentation and return relevant sections.

        Args:
            user_id: User ID (not required for docs)
            session: Chat session
-            query: Search query
+            **kwargs: Tool parameters

        Returns:
            DocSearchResultsResponse: List of matching documentation sections
            NoResultsResponse: No results found
            ErrorResponse: Error message
        """
-        query = kwargs.get("query", "").strip()
+        params = SearchDocsInput(**kwargs)
        session_id = session.session_id if session else None

-        if not query:
+        if not params.query:
            return ErrorResponse(
                message="Please provide a search query.",
                error="Missing query parameter",
@@ -118,7 +131,7 @@ class SearchDocsTool(BaseTool):
        try:
            # Search using hybrid search for DOCUMENTATION content type only
            results, total = await unified_hybrid_search(
-                query=query,
+                query=params.query,
                content_types=[ContentType.DOCUMENTATION],
                page=1,
                page_size=MAX_RESULTS * 2,  # Fetch extra for deduplication
@@ -127,7 +140,7 @@ class SearchDocsTool(BaseTool):

            if not results:
                return NoResultsResponse(
-                    message=f"No documentation found for '{query}'.",
+                    message=f"No documentation found for '{params.query}'.",
                    suggestions=[
                        "Try different keywords",
                        "Use more general terms",
@@ -162,7 +175,7 @@ class SearchDocsTool(BaseTool):

            if not deduplicated:
                return NoResultsResponse(
-                    message=f"No documentation found for '{query}'.",
+                    message=f"No documentation found for '{params.query}'.",
                    suggestions=[
                        "Try different keywords",
                        "Use more general terms",
@@ -195,7 +208,7 @@ class SearchDocsTool(BaseTool):
                message=f"Found {len(doc_results)} relevant documentation sections.",
                results=doc_results,
                count=len(doc_results),
-                query=query,
+                query=params.query,
                session_id=session_id,
            )

--- a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
@@ -8,7 +8,6 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
-    Credentials,
    CredentialsFieldInfo,
    CredentialsMetaInput,
    HostScopedCredentials,
@@ -224,99 +223,6 @@ async def get_or_create_library_agent(
    return library_agents[0]


-async def match_credentials_to_requirements(
-    user_id: str,
-    requirements: dict[str, CredentialsFieldInfo],
-) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-    """
-    Match user's credentials against a dictionary of credential requirements.
-
-    This is the core matching logic shared by both graph and block credential matching.
-    """
-    matched: dict[str, CredentialsMetaInput] = {}
-    missing: list[CredentialsMetaInput] = []
-
-    if not requirements:
-        return matched, missing
-
-    available_creds = await get_user_credentials(user_id)
-
-    for field_name, field_info in requirements.items():
-        matching_cred = find_matching_credential(available_creds, field_info)
-
-        if matching_cred:
-            try:
-                matched[field_name] = create_credential_meta_from_match(matching_cred)
-            except Exception as e:
-                logger.error(
-                    f"Failed to create CredentialsMetaInput for field '{field_name}': "
-                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
-                    f"credential_id={matching_cred.id}",
-                    exc_info=True,
-                )
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=f"{field_name} (validation failed: {e})",
-                    )
-                )
-        else:
-            provider = next(iter(field_info.provider), "unknown")
-            cred_type = next(iter(field_info.supported_types), "api_key")
-            missing.append(
-                CredentialsMetaInput(
-                    id=field_name,
-                    provider=provider,  # type: ignore
-                    type=cred_type,  # type: ignore
-                    title=field_name.replace("_", " ").title(),
-                )
-            )
-
-    return matched, missing
-
-
-async def get_user_credentials(user_id: str) -> list[Credentials]:
-    """Get all available credentials for a user."""
-    creds_manager = IntegrationCredentialsManager()
-    return await creds_manager.store.get_all_creds(user_id)
-
-
-def find_matching_credential(
-    available_creds: list[Credentials],
-    field_info: CredentialsFieldInfo,
-) -> Credentials | None:
-    """Find a credential that matches the required provider, type, scopes, and host."""
-    for cred in available_creds:
-        if cred.provider not in field_info.provider:
-            continue
-        if cred.type not in field_info.supported_types:
-            continue
-        if cred.type == "oauth2" and not _credential_has_required_scopes(
-            cred, field_info
-        ):
-            continue
-        if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
-            continue
-        return cred
-    return None
-
-
-def create_credential_meta_from_match(
-    matching_cred: Credentials,
-) -> CredentialsMetaInput:
-    """Create a CredentialsMetaInput from a matched credential."""
-    return CredentialsMetaInput(
-        id=matching_cred.id,
-        provider=matching_cred.provider,  # type: ignore
-        type=matching_cred.type,
-        title=matching_cred.title,
-    )
-
-
 async def match_user_credentials_to_graph(
    user_id: str,
    graph: GraphModel,
@@ -425,6 +331,8 @@ def _credential_has_required_scopes(
    # If no scopes are required, any credential matches
    if not requirements.required_scopes:
        return True
+
+    # Check that credential scopes are a superset of required scopes
    return set(credential.scopes).issuperset(requirements.required_scopes)


--- a/autogpt_platform/backend/backend/api/features/chat/tools/workspace_files.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/workspace_files.py
@@ -2,9 +2,9 @@

 import base64
 import logging
-from typing import Any, Optional
+from typing import Any

-from pydantic import BaseModel
+from pydantic import BaseModel, field_validator

 from backend.api.features.chat.model import ChatSession
 from backend.data.workspace import get_or_create_workspace
@@ -78,6 +78,65 @@ 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."""

@@ -131,8 +190,9 @@ class ListWorkspaceFilesTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
+        params = ListWorkspaceFilesInput(**kwargs)
        session_id = session.session_id

        if not user_id:
@@ -141,9 +201,7 @@ class ListWorkspaceFilesTool(BaseTool):
                session_id=session_id,
            )

-        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)
+        limit = min(params.limit, 100)

        try:
            workspace = await get_or_create_workspace(user_id)
@@ -151,13 +209,13 @@ class ListWorkspaceFilesTool(BaseTool):
            manager = WorkspaceManager(user_id, workspace.id, session_id)

            files = await manager.list_files(
-                path=path_prefix,
+                path=params.path_prefix,
                limit=limit,
-                include_all_sessions=include_all_sessions,
+                include_all_sessions=params.include_all_sessions,
            )
            total = await manager.get_file_count(
-                path=path_prefix,
-                include_all_sessions=include_all_sessions,
+                path=params.path_prefix,
+                include_all_sessions=params.include_all_sessions,
            )

            file_infos = [
@@ -171,7 +229,9 @@ class ListWorkspaceFilesTool(BaseTool):
                for f in files
            ]

-            scope_msg = "all sessions" if include_all_sessions else "current session"
+            scope_msg = (
+                "all sessions" if params.include_all_sessions else "current session"
+            )
            return WorkspaceFileListResponse(
                files=file_infos,
                total_count=total,
@@ -259,8 +319,9 @@ class ReadWorkspaceFileTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
+        params = ReadWorkspaceFileInput(**kwargs)
        session_id = session.session_id

        if not user_id:
@@ -269,11 +330,7 @@ class ReadWorkspaceFileTool(BaseTool):
                session_id=session_id,
            )

-        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:
+        if not params.file_id and not params.path:
            return ErrorResponse(
                message="Please provide either file_id or path",
                session_id=session_id,
@@ -285,21 +342,21 @@ class ReadWorkspaceFileTool(BaseTool):
            manager = WorkspaceManager(user_id, workspace.id, session_id)

            # Get file info
-            if file_id:
-                file_info = await manager.get_file_info(file_id)
+            if params.file_id:
+                file_info = await manager.get_file_info(params.file_id)
                if file_info is None:
                    return ErrorResponse(
-                        message=f"File not found: {file_id}",
+                        message=f"File not found: {params.file_id}",
                        session_id=session_id,
                    )
-                target_file_id = file_id
+                target_file_id = params.file_id
            else:
                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
+                assert params.path is not None
+                file_info = await manager.get_file_info_by_path(params.path)
                if file_info is None:
                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
+                        message=f"File not found at path: {params.path}",
                        session_id=session_id,
                    )
                target_file_id = file_info.id
@@ -309,7 +366,7 @@ 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 force_download_url:
+            if is_small_file and is_text_file and not params.force_download_url:
                content = await manager.read_file_by_id(target_file_id)
                content_b64 = base64.b64encode(content).decode("utf-8")

@@ -429,8 +486,9 @@ class WriteWorkspaceFileTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
+        params = WriteWorkspaceFileInput(**kwargs)
        session_id = session.session_id

        if not user_id:
@@ -439,19 +497,13 @@ class WriteWorkspaceFileTool(BaseTool):
                session_id=session_id,
            )

-        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:
+        if not params.filename:
            return ErrorResponse(
                message="Please provide a filename",
                session_id=session_id,
            )

-        if not content_b64:
+        if not params.content_base64:
            return ErrorResponse(
                message="Please provide content_base64",
                session_id=session_id,
@@ -459,7 +511,7 @@ class WriteWorkspaceFileTool(BaseTool):

        # Decode content
        try:
-            content = base64.b64decode(content_b64)
+            content = base64.b64decode(params.content_base64)
        except Exception:
            return ErrorResponse(
                message="Invalid base64-encoded content",
@@ -476,7 +528,7 @@ class WriteWorkspaceFileTool(BaseTool):

        try:
            # Virus scan
-            await scan_content_safe(content, filename=filename)
+            await scan_content_safe(content, filename=params.filename)

            workspace = await get_or_create_workspace(user_id)
            # Pass session_id for session-scoped file access
@@ -484,10 +536,10 @@ class WriteWorkspaceFileTool(BaseTool):

            file_record = await manager.write_file(
                content=content,
-                filename=filename,
-                path=path,
-                mime_type=mime_type,
-                overwrite=overwrite,
+                filename=params.filename,
+                path=params.path,
+                mime_type=params.mime_type,
+                overwrite=params.overwrite,
            )

            return WorkspaceWriteResponse(
@@ -557,8 +609,9 @@ class DeleteWorkspaceFileTool(BaseTool):
        self,
        user_id: str | None,
        session: ChatSession,
-        **kwargs,
+        **kwargs: Any,
    ) -> ToolResponseBase:
+        params = DeleteWorkspaceFileInput(**kwargs)
        session_id = session.session_id

        if not user_id:
@@ -567,10 +620,7 @@ class DeleteWorkspaceFileTool(BaseTool):
                session_id=session_id,
            )

-        file_id: Optional[str] = kwargs.get("file_id")
-        path: Optional[str] = kwargs.get("path")
-
-        if not file_id and not path:
+        if not params.file_id and not params.path:
            return ErrorResponse(
                message="Please provide either file_id or path",
                session_id=session_id,
@@ -583,15 +633,15 @@ class DeleteWorkspaceFileTool(BaseTool):

            # Determine the file_id to delete
            target_file_id: str
-            if file_id:
-                target_file_id = file_id
+            if params.file_id:
+                target_file_id = params.file_id
            else:
                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
+                assert params.path is not None
+                file_info = await manager.get_file_info_by_path(params.path)
                if file_info is None:
                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
+                        message=f"File not found at path: {params.path}",
                        session_id=session_id,
                    )
                target_file_id = file_info.id
--- a/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
+++ b/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.

 import logging
 import re
-import time
 from dataclasses import dataclass
 from typing import Any, Literal

@@ -363,11 +362,7 @@ async def unified_hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """

-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)

    total = results[0]["total_count"] if results else 0
    # Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """

-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)

    total = results[0]["total_count"] if results else 0

@@ -727,87 +718,6 @@ async def hybrid_search_simple(
    return await hybrid_search(query=query, page=page, page_size=page_size)


-# ============================================================================
-# Diagnostics
-# ============================================================================
-
-# Rate limit: only log vector error diagnostics once per this interval
-_VECTOR_DIAG_INTERVAL_SECONDS = 60
-_last_vector_diag_time: float = 0
-
-
-async def _log_vector_error_diagnostics(error: Exception) -> None:
-    """Log diagnostic info when 'type vector does not exist' error occurs.
-
-    Note: Diagnostic queries use query_raw_with_schema which may run on a different
-    pooled connection than the one that failed. Session-level search_path can differ,
-    so these diagnostics show cluster-wide state, not necessarily the failed session.
-
-    Includes rate limiting to avoid log spam - only logs once per minute.
-    Caller should re-raise the error after calling this function.
-    """
-    global _last_vector_diag_time
-
-    # Check if this is the vector type error
-    error_str = str(error).lower()
-    if not (
-        "type" in error_str and "vector" in error_str and "does not exist" in error_str
-    ):
-        return
-
-    # Rate limit: only log once per interval
-    now = time.time()
-    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
-        return
-    _last_vector_diag_time = now
-
-    try:
-        diagnostics: dict[str, object] = {}
-
-        try:
-            search_path_result = await query_raw_with_schema("SHOW search_path")
-            diagnostics["search_path"] = search_path_result
-        except Exception as e:
-            diagnostics["search_path"] = f"Error: {e}"
-
-        try:
-            schema_result = await query_raw_with_schema("SELECT current_schema()")
-            diagnostics["current_schema"] = schema_result
-        except Exception as e:
-            diagnostics["current_schema"] = f"Error: {e}"
-
-        try:
-            user_result = await query_raw_with_schema(
-                "SELECT current_user, session_user, current_database()"
-            )
-            diagnostics["user_info"] = user_result
-        except Exception as e:
-            diagnostics["user_info"] = f"Error: {e}"
-
-        try:
-            # Check pgvector extension installation (cluster-wide, stable info)
-            ext_result = await query_raw_with_schema(
-                "SELECT extname, extversion, nspname as schema "
-                "FROM pg_extension e "
-                "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                "WHERE extname = 'vector'"
-            )
-            diagnostics["pgvector_extension"] = ext_result
-        except Exception as e:
-            diagnostics["pgvector_extension"] = f"Error: {e}"
-
-        logger.error(
-            f"Vector type error diagnostics:\n"
-            f"  Error: {error}\n"
-            f"  search_path: {diagnostics.get('search_path')}\n"
-            f"  current_schema: {diagnostics.get('current_schema')}\n"
-            f"  user_info: {diagnostics.get('user_info')}\n"
-            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
-        )
-    except Exception as diag_error:
-        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
-
-
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights
--- a/autogpt_platform/backend/backend/blocks/claude_code.py
+++ b/autogpt_platform/backend/backend/blocks/claude_code.py
@@ -1,10 +1,10 @@
 import json
 import shlex
 import uuid
-from typing import TYPE_CHECKING, Literal, Optional
+from typing import Literal, Optional

 from e2b import AsyncSandbox as BaseAsyncSandbox
-from pydantic import SecretStr
+from pydantic import BaseModel, SecretStr

 from backend.data.block import (
    Block,
@@ -20,13 +20,6 @@ from backend.data.model import (
    SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.sandbox_files import (
-    SandboxFileOutput,
-    extract_and_store_sandbox_files,
-)
-
-if TYPE_CHECKING:
-    from backend.executor.utils import ExecutionContext


 class ClaudeCodeExecutionError(Exception):
@@ -181,15 +174,22 @@ class ClaudeCodeBlock(Block):
            advanced=True,
        )

+    class FileOutput(BaseModel):
+        """A file extracted from the sandbox."""
+
+        path: str
+        relative_path: str  # Path relative to working directory (for GitHub, etc.)
+        name: str
+        content: str
+
    class Output(BlockSchemaOutput):
        response: str = SchemaField(
            description="The output/response from Claude Code execution"
        )
-        files: list[SandboxFileOutput] = SchemaField(
+        files: list["ClaudeCodeBlock.FileOutput"] = SchemaField(
            description=(
                "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."
+                "Each file has 'path', 'relative_path', 'name', and 'content' fields."
            )
        )
        conversation_history: str = SchemaField(
@@ -252,7 +252,6 @@ class ClaudeCodeBlock(Block):
                            "relative_path": "index.html",
                            "name": "index.html",
                            "content": "<html>Hello World</html>",
-                            "workspace_ref": None,
                        }
                    ],
                ),
@@ -268,12 +267,11 @@ class ClaudeCodeBlock(Block):
                "execute_claude_code": lambda *args, **kwargs: (
                    "Created index.html with hello world content",  # response
                    [
-                        SandboxFileOutput(
+                        ClaudeCodeBlock.FileOutput(
                            path="/home/user/index.html",
                            relative_path="index.html",
                            name="index.html",
                            content="<html>Hello World</html>",
-                            workspace_ref=None,
                        )
                    ],  # files
                    "User: Create a hello world HTML file\n"
@@ -296,8 +294,7 @@ class ClaudeCodeBlock(Block):
        existing_sandbox_id: str,
        conversation_history: str,
        dispose_sandbox: bool,
-        execution_context: "ExecutionContext",
-    ) -> tuple[str, list[SandboxFileOutput], str, str, str]:
+    ) -> tuple[str, list["ClaudeCodeBlock.FileOutput"], str, str, str]:
        """
        Execute Claude Code in an E2B sandbox.

@@ -452,18 +449,14 @@ class ClaudeCodeBlock(Block):
                else:
                    new_conversation_history = turn_entry

-            # Extract files created/modified during this run and store to workspace
-            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,
+            # Extract files created/modified during this run
+            files = await self._extract_files(
+                sandbox, working_directory, start_timestamp
            )

            return (
                response,
-                sandbox_files,  # Already SandboxFileOutput objects
+                files,
                new_conversation_history,
                current_session_id,
                sandbox_id,
@@ -478,6 +471,140 @@ class ClaudeCodeBlock(Block):
            if dispose_sandbox and sandbox:
                await sandbox.kill()

+    async def _extract_files(
+        self,
+        sandbox: BaseAsyncSandbox,
+        working_directory: str,
+        since_timestamp: str | None = None,
+    ) -> list["ClaudeCodeBlock.FileOutput"]:
+        """
+        Extract text files created/modified during this Claude Code execution.
+
+        Args:
+            sandbox: The E2B sandbox instance
+            working_directory: Directory to search for files
+            since_timestamp: ISO timestamp - only return files modified after this time
+
+        Returns:
+            List of FileOutput objects with path, relative_path, name, and content
+        """
+        files: list[ClaudeCodeBlock.FileOutput] = []
+
+        # Text file extensions we can safely read as text
+        text_extensions = {
+            ".txt",
+            ".md",
+            ".html",
+            ".htm",
+            ".css",
+            ".js",
+            ".ts",
+            ".jsx",
+            ".tsx",
+            ".json",
+            ".xml",
+            ".yaml",
+            ".yml",
+            ".toml",
+            ".ini",
+            ".cfg",
+            ".conf",
+            ".py",
+            ".rb",
+            ".php",
+            ".java",
+            ".c",
+            ".cpp",
+            ".h",
+            ".hpp",
+            ".cs",
+            ".go",
+            ".rs",
+            ".swift",
+            ".kt",
+            ".scala",
+            ".sh",
+            ".bash",
+            ".zsh",
+            ".sql",
+            ".graphql",
+            ".env",
+            ".gitignore",
+            ".dockerfile",
+            "Dockerfile",
+            ".vue",
+            ".svelte",
+            ".astro",
+            ".mdx",
+            ".rst",
+            ".tex",
+            ".csv",
+            ".log",
+        }
+
+        try:
+            # List files recursively using find command
+            # Exclude node_modules and .git directories, but allow hidden files
+            # like .env and .gitignore (they're filtered by text_extensions later)
+            # Filter by timestamp to only get files created/modified during this run
+            safe_working_dir = shlex.quote(working_directory)
+            timestamp_filter = ""
+            if since_timestamp:
+                timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
+            find_result = await sandbox.commands.run(
+                f"find {safe_working_dir} -type f "
+                f"{timestamp_filter}"
+                f"-not -path '*/node_modules/*' "
+                f"-not -path '*/.git/*' "
+                f"2>/dev/null"
+            )
+
+            if find_result.stdout:
+                for file_path in find_result.stdout.strip().split("\n"):
+                    if not file_path:
+                        continue
+
+                    # Check if it's a text file we can read
+                    is_text = any(
+                        file_path.endswith(ext) for ext in text_extensions
+                    ) or file_path.endswith("Dockerfile")
+
+                    if is_text:
+                        try:
+                            content = await sandbox.files.read(file_path)
+                            # Handle bytes or string
+                            if isinstance(content, bytes):
+                                content = content.decode("utf-8", errors="replace")
+
+                            # Extract filename from path
+                            file_name = file_path.split("/")[-1]
+
+                            # Calculate relative path by stripping working directory
+                            relative_path = file_path
+                            if file_path.startswith(working_directory):
+                                relative_path = file_path[len(working_directory) :]
+                                # Remove leading slash if present
+                                if relative_path.startswith("/"):
+                                    relative_path = relative_path[1:]
+
+                            files.append(
+                                ClaudeCodeBlock.FileOutput(
+                                    path=file_path,
+                                    relative_path=relative_path,
+                                    name=file_name,
+                                    content=content,
+                                )
+                            )
+                        except Exception:
+                            # Skip files that can't be read
+                            pass
+
+        except Exception:
+            # If file extraction fails, return empty results
+            pass
+
+        return files
+
    def _escape_prompt(self, prompt: str) -> str:
        """Escape the prompt for safe shell execution."""
        # Use single quotes and escape any single quotes in the prompt
@@ -490,7 +617,6 @@ class ClaudeCodeBlock(Block):
        *,
        e2b_credentials: APIKeyCredentials,
        anthropic_credentials: APIKeyCredentials,
-        execution_context: "ExecutionContext",
        **kwargs,
    ) -> BlockOutput:
        try:
@@ -511,7 +637,6 @@ class ClaudeCodeBlock(Block):
                existing_sandbox_id=input_data.sandbox_id,
                conversation_history=input_data.conversation_history,
                dispose_sandbox=input_data.dispose_sandbox,
-                execution_context=execution_context,
            )

            yield "response", response
--- a/autogpt_platform/backend/backend/blocks/code_executor.py
+++ b/autogpt_platform/backend/backend/blocks/code_executor.py
@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Literal, Optional
+from typing import Any, Literal, Optional

 from e2b_code_interpreter import AsyncSandbox
 from e2b_code_interpreter import Result as E2BExecutionResult
@@ -20,13 +20,6 @@ from backend.data.model import (
    SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.sandbox_files import (
-    SandboxFileOutput,
-    extract_and_store_sandbox_files,
-)
-
-if TYPE_CHECKING:
-    from backend.executor.utils import ExecutionContext

 TEST_CREDENTIALS = APIKeyCredentials(
    id="01234567-89ab-cdef-0123-456789abcdef",
@@ -92,9 +85,6 @@ class CodeExecutionResult(MainCodeExecutionResult):
 class BaseE2BExecutorMixin:
    """Shared implementation methods for E2B executor blocks."""

-    # Default output directory for file extraction
-    OUTPUT_DIR = "/output"
-
    async def execute_code(
        self,
        api_key: str,
@@ -105,21 +95,14 @@ class BaseE2BExecutorMixin:
        timeout: Optional[int] = None,
        sandbox_id: Optional[str] = None,
        dispose_sandbox: bool = False,
-        execution_context: Optional["ExecutionContext"] = None,
-        extract_files: bool = False,
    ):
        """
        Unified code execution method that handles all three use cases:
        1. Create new sandbox and execute (ExecuteCodeBlock)
        2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
        3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
-
-        Args:
-            extract_files: If True and execution_context provided, extract files from
-                           /output directory and store to workspace.
        """  # noqa
        sandbox = None
-        files: list[SandboxFileOutput] = []
        try:
            if sandbox_id:
                # Connect to existing sandbox (ExecuteCodeStepBlock case)
@@ -131,9 +114,6 @@ class BaseE2BExecutorMixin:
                sandbox = await AsyncSandbox.create(
                    api_key=api_key, template=template_id, timeout=timeout
                )
-                # Create /output directory for file extraction
-                if extract_files:
-                    await sandbox.commands.run(f"mkdir -p {self.OUTPUT_DIR}")
                if setup_commands:
                    for cmd in setup_commands:
                        await sandbox.commands.run(cmd)
@@ -153,24 +133,7 @@ class BaseE2BExecutorMixin:
            stdout_logs = "".join(execution.logs.stdout)
            stderr_logs = "".join(execution.logs.stderr)

-            # Extract files from /output if requested
-            if extract_files and execution_context:
-                files = await extract_and_store_sandbox_files(
-                    sandbox=sandbox,
-                    working_directory=self.OUTPUT_DIR,
-                    execution_context=execution_context,
-                    since_timestamp=None,  # Get all files in /output
-                    text_only=False,  # Include binary files too
-                )
-
-            return (
-                results,
-                text_output,
-                stdout_logs,
-                stderr_logs,
-                sandbox.sandbox_id,
-                files,
-            )
+            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
        finally:
            # Dispose of sandbox if requested to reduce usage costs
            if dispose_sandbox and sandbox:
@@ -275,12 +238,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
            description="Standard output logs from execution"
        )
        stderr_logs: str = SchemaField(description="Standard error logs from execution")
-        files: list[SandboxFileOutput] = SchemaField(
-            description=(
-                "Files written to /output directory during execution. "
-                "Each file has path, name, content, and workspace_ref (if stored)."
-            ),
-        )

    def __init__(self):
        super().__init__(
@@ -302,30 +259,23 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                ("results", []),
                ("response", "Hello World"),
                ("stdout_logs", "Hello World\n"),
-                ("files", []),
            ],
            test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox, execution_context, extract_files: (  # noqa
+                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: (  # noqa
                    [],  # results
                    "Hello World",  # text_output
                    "Hello World\n",  # stdout_logs
                    "",  # stderr_logs
                    "sandbox_id",  # sandbox_id
-                    [],  # files
                ),
            },
        )

    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: "ExecutionContext",
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
    ) -> BlockOutput:
        try:
-            results, text_output, stdout, stderr, _, files = await self.execute_code(
+            results, text_output, stdout, stderr, _ = await self.execute_code(
                api_key=credentials.api_key.get_secret_value(),
                code=input_data.code,
                language=input_data.language,
@@ -333,8 +283,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                setup_commands=input_data.setup_commands,
                timeout=input_data.timeout,
                dispose_sandbox=input_data.dispose_sandbox,
-                execution_context=execution_context,
-                extract_files=True,
            )

            # Determine result object shape & filter out empty formats
@@ -348,8 +296,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                yield "stdout_logs", stdout
            if stderr:
                yield "stderr_logs", stderr
-            # Always yield files (empty list if none)
-            yield "files", [f.model_dump() for f in files]
        except Exception as e:
            yield "error", str(e)

@@ -447,7 +393,6 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
                    "Hello World\n",  # stdout_logs
                    "",  # stderr_logs
                    "sandbox_id",  # sandbox_id
-                    [],  # files
                ),
            },
        )
@@ -456,7 +401,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
    ) -> BlockOutput:
        try:
-            _, text_output, stdout, stderr, sandbox_id, _ = await self.execute_code(
+            _, text_output, stdout, stderr, sandbox_id = await self.execute_code(
                api_key=credentials.api_key.get_secret_value(),
                code=input_data.setup_code,
                language=input_data.language,
@@ -555,7 +500,6 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
                    "Hello World\n",  # stdout_logs
                    "",  # stderr_logs
                    sandbox_id,  # sandbox_id
-                    [],  # files
                ),
            },
        )
@@ -564,7 +508,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
    ) -> BlockOutput:
        try:
-            results, text_output, stdout, stderr, _, _ = await self.execute_code(
+            results, text_output, stdout, stderr, _ = await self.execute_code(
                api_key=credentials.api_key.get_secret_value(),
                code=input_data.step_code,
                language=input_data.language,
--- a/autogpt_platform/backend/backend/blocks/human_in_the_loop.py
+++ b/autogpt_platform/backend/backend/blocks/human_in_the_loop.py
@@ -21,71 +21,43 @@ logger = logging.getLogger(__name__)

 class HumanInTheLoopBlock(Block):
    """
-    Pauses execution and waits for human approval or rejection of the data.
+    This block pauses execution and waits for human approval or modification of the data.

-    When executed, this block creates a pending review entry and sets the node execution
-    status to REVIEW. The execution remains paused until a human user either approves
-    or rejects the data.
+    When executed, it creates a pending review entry and sets the node execution status
+    to REVIEW. The execution will remain paused until a human user either:
+    - Approves the data (with or without modifications)
+    - Rejects the data

-    **How it works:**
-    - The input data is presented to a human reviewer
-    - The reviewer can approve or reject (and optionally modify the data if editable)
-    - On approval: the data flows out through the `approved_data` output pin
-    - On rejection: the data flows out through the `rejected_data` output pin
-
-    **Important:** The output pins yield the actual data itself, NOT status strings.
-    The approval/rejection decision determines WHICH output pin fires, not the value.
-    You do NOT need to compare the output to "APPROVED" or "REJECTED" - simply connect
-    downstream blocks to the appropriate output pin for each case.
-
-    **Example usage:**
-    - Connect `approved_data` → next step in your workflow (data was approved)
-    - Connect `rejected_data` → error handling or notification (data was rejected)
+    This is useful for workflows that require human validation or intervention before
+    proceeding to the next steps.
    """

    class Input(BlockSchemaInput):
-        data: Any = SchemaField(
-            description="The data to be reviewed by a human user. "
-            "This exact data will be passed through to either approved_data or "
-            "rejected_data output based on the reviewer's decision."
-        )
+        data: Any = SchemaField(description="The data to be reviewed by a human user")
        name: str = SchemaField(
-            description="A descriptive name for what this data represents. "
-            "This helps the reviewer understand what they are reviewing.",
+            description="A descriptive name for what this data represents",
        )
        editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data before "
-            "approving or rejecting it",
+            description="Whether the human reviewer can edit the data",
            default=True,
            advanced=True,
        )

    class Output(BlockSchemaOutput):
        approved_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer APPROVES it. "
-            "The value is the actual data itself (not a status string like 'APPROVED'). "
-            "If the reviewer edited the data, this contains the modified version. "
-            "Connect downstream blocks here for the 'approved' workflow path."
+            description="The data when approved (may be modified by reviewer)"
        )
        rejected_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer REJECTS it. "
-            "The value is the actual data itself (not a status string like 'REJECTED'). "
-            "If the reviewer edited the data, this contains the modified version. "
-            "Connect downstream blocks here for the 'rejected' workflow path."
+            description="The data when rejected (may be modified by reviewer)"
        )
        review_message: str = SchemaField(
-            description="Optional message provided by the reviewer explaining their "
-            "decision. Only outputs when the reviewer provides a message; "
-            "this pin does not fire if no message was given.",
-            default="",
+            description="Any message provided by the reviewer", default=""
        )

    def __init__(self):
        super().__init__(
            id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution for human review. Data flows through "
-            "approved_data or rejected_data output based on the reviewer's decision. "
-            "Outputs contain the actual data, not status strings.",
+            description="Pause execution and wait for human approval or modification of data",
            categories={BlockCategory.BASIC},
            input_schema=HumanInTheLoopBlock.Input,
            output_schema=HumanInTheLoopBlock.Output,
--- a/autogpt_platform/backend/backend/data/graph.py
+++ b/autogpt_platform/backend/backend/data/graph.py
@@ -743,11 +743,6 @@ class GraphModel(Graph, GraphMeta):
                # For invalid blocks, we still raise immediately as this is a structural issue
                raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")

-            if block.disabled:
-                raise ValueError(
-                    f"Block {node.block_id} is disabled and cannot be used in graphs"
-                )
-
            node_input_mask = (
                nodes_input_masks.get(node.id, {}) if nodes_input_masks else {}
            )
--- a/autogpt_platform/backend/backend/data/rabbitmq.py
+++ b/autogpt_platform/backend/backend/data/rabbitmq.py
@@ -1,4 +1,3 @@
-import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -226,10 +225,6 @@ class SyncRabbitMQ(RabbitMQBase):
 class AsyncRabbitMQ(RabbitMQBase):
    """Asynchronous RabbitMQ client"""

-    def __init__(self, config: RabbitMQConfig):
-        super().__init__(config)
-        self._reconnect_lock: asyncio.Lock | None = None
-
    @property
    def is_connected(self) -> bool:
        return bool(self._connection and not self._connection.is_closed)
@@ -240,17 +235,7 @@ class AsyncRabbitMQ(RabbitMQBase):

    @conn_retry("AsyncRabbitMQ", "Acquiring async connection")
    async def connect(self):
-        if self.is_connected and self._channel and not self._channel.is_closed:
-            return
-
-        if (
-            self.is_connected
-            and self._connection
-            and (self._channel is None or self._channel.is_closed)
-        ):
-            self._channel = await self._connection.channel()
-            await self._channel.set_qos(prefetch_count=1)
-            await self.declare_infrastructure()
+        if self.is_connected:
            return

        self._connection = await aio_pika.connect_robust(
@@ -306,46 +291,24 @@ class AsyncRabbitMQ(RabbitMQBase):
                    exchange, routing_key=queue.routing_key or queue.name
                )

-    @property
-    def _lock(self) -> asyncio.Lock:
-        if self._reconnect_lock is None:
-            self._reconnect_lock = asyncio.Lock()
-        return self._reconnect_lock
-
-    async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
-        """Get a valid channel, reconnecting if the current one is stale.
-
-        Uses a lock to prevent concurrent reconnection attempts from racing.
-        """
-        if self.is_ready:
-            return self._channel  # type: ignore  # is_ready guarantees non-None
-
-        async with self._lock:
-            # Double-check after acquiring lock
-            if self.is_ready:
-                return self._channel  # type: ignore
-
-            self._channel = None
-            await self.connect()
-
-            if self._channel is None:
-                raise RuntimeError("Channel should be established after connect")
-
-            return self._channel
-
-    async def _publish_once(
+    @func_retry
+    async def publish_message(
        self,
        routing_key: str,
        message: str,
        exchange: Optional[Exchange] = None,
        persistent: bool = True,
    ) -> None:
-        channel = await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")

        if exchange:
-            exchange_obj = await channel.get_exchange(exchange.name)
+            exchange_obj = await self._channel.get_exchange(exchange.name)
        else:
-            exchange_obj = channel.default_exchange
+            exchange_obj = self._channel.default_exchange

        await exchange_obj.publish(
            aio_pika.Message(
@@ -359,23 +322,9 @@ class AsyncRabbitMQ(RabbitMQBase):
            routing_key=routing_key,
        )

-    @func_retry
-    async def publish_message(
-        self,
-        routing_key: str,
-        message: str,
-        exchange: Optional[Exchange] = None,
-        persistent: bool = True,
-    ) -> None:
-        try:
-            await self._publish_once(routing_key, message, exchange, persistent)
-        except aio_pika.exceptions.ChannelInvalidStateError:
-            logger.warning(
-                "RabbitMQ channel invalid, forcing reconnect and retrying publish"
-            )
-            async with self._lock:
-                self._channel = None
-            await self._publish_once(routing_key, message, exchange, persistent)
-
    async def get_channel(self) -> aio_pika.abc.AbstractChannel:
-        return await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")
+        return self._channel
--- a/autogpt_platform/backend/backend/executor/manager.py
+++ b/autogpt_platform/backend/backend/executor/manager.py
@@ -213,9 +213,6 @@ async def execute_node(
        block_name=node_block.name,
    )

-    if node_block.disabled:
-        raise ValueError(f"Block {node_block.id} is disabled and cannot be executed")
-
    # Sanity check: validate the execution input.
    input_data, error = validate_exec(node, data.inputs, resolve_input=False)
    if input_data is None:
--- a/autogpt_platform/backend/backend/util/prompt.py
+++ b/autogpt_platform/backend/backend/util/prompt.py
@@ -364,44 +364,6 @@ def _remove_orphan_tool_responses(
    return result


-def validate_and_remove_orphan_tool_responses(
-    messages: list[dict],
-    log_warning: bool = True,
-) -> list[dict]:
-    """
-    Validate tool_call/tool_response pairs and remove orphaned responses.
-
-    Scans messages in order, tracking all tool_call IDs. Any tool response
-    referencing an ID not seen in a preceding message is considered orphaned
-    and removed. This prevents API errors like Anthropic's "unexpected tool_use_id".
-
-    Args:
-        messages: List of messages to validate (OpenAI or Anthropic format)
-        log_warning: Whether to log a warning when orphans are found
-
-    Returns:
-        A new list with orphaned tool responses removed
-    """
-    available_ids: set[str] = set()
-    orphan_ids: set[str] = set()
-
-    for msg in messages:
-        available_ids |= _extract_tool_call_ids_from_message(msg)
-        for resp_id in _extract_tool_response_ids_from_message(msg):
-            if resp_id not in available_ids:
-                orphan_ids.add(resp_id)
-
-    if not orphan_ids:
-        return messages
-
-    if log_warning:
-        logger.warning(
-            f"Removing {len(orphan_ids)} orphan tool response(s): {orphan_ids}"
-        )
-
-    return _remove_orphan_tool_responses(messages, orphan_ids)
-
-
 def _ensure_tool_pairs_intact(
    recent_messages: list[dict],
    all_messages: list[dict],
@@ -761,13 +723,6 @@ async def compress_context(

    # Filter out any None values that may have been introduced
    final_msgs: list[dict] = [m for m in msgs if m is not None]
-
-    # ---- STEP 6: Final tool-pair validation ---------------------------------
-    # After all compression steps, verify that every tool response has a
-    # matching tool_call in a preceding assistant message. Remove orphans
-    # to prevent API errors (e.g., Anthropic's "unexpected tool_use_id").
-    final_msgs = validate_and_remove_orphan_tool_responses(final_msgs)
-
    final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
    error = None
    if final_count + reserve > target_tokens:
--- a/autogpt_platform/backend/backend/util/sandbox_files.py
+++ b/autogpt_platform/backend/backend/util/sandbox_files.py
@@ -1,288 +0,0 @@
-"""
-Shared utilities for extracting and storing files from E2B sandboxes.
-
-This module provides common file extraction and workspace storage functionality
-for blocks that run code in E2B sandboxes (Claude Code, Code Executor, etc.).
-"""
-
-import logging
-import shlex
-from dataclasses import dataclass
-from typing import TYPE_CHECKING
-
-from pydantic import BaseModel
-
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
-
-if TYPE_CHECKING:
-    from e2b import AsyncSandbox as BaseAsyncSandbox
-
-    from backend.executor.utils import ExecutionContext
-
-logger = logging.getLogger(__name__)
-
-# Text file extensions that can be safely read and stored as text
-TEXT_EXTENSIONS = {
-    ".txt",
-    ".md",
-    ".html",
-    ".htm",
-    ".css",
-    ".js",
-    ".ts",
-    ".jsx",
-    ".tsx",
-    ".json",
-    ".xml",
-    ".yaml",
-    ".yml",
-    ".toml",
-    ".ini",
-    ".cfg",
-    ".conf",
-    ".py",
-    ".rb",
-    ".php",
-    ".java",
-    ".c",
-    ".cpp",
-    ".h",
-    ".hpp",
-    ".cs",
-    ".go",
-    ".rs",
-    ".swift",
-    ".kt",
-    ".scala",
-    ".sh",
-    ".bash",
-    ".zsh",
-    ".sql",
-    ".graphql",
-    ".env",
-    ".gitignore",
-    ".dockerfile",
-    "Dockerfile",
-    ".vue",
-    ".svelte",
-    ".astro",
-    ".mdx",
-    ".rst",
-    ".tex",
-    ".csv",
-    ".log",
-}
-
-
-class SandboxFileOutput(BaseModel):
-    """A file extracted from a sandbox and optionally stored in workspace."""
-
-    path: str
-    """Full path in the sandbox."""
-
-    relative_path: str
-    """Path relative to the working directory."""
-
-    name: str
-    """Filename only."""
-
-    content: str
-    """File content as text (for backward compatibility)."""
-
-    workspace_ref: str | None = None
-    """Workspace reference (workspace://{id}#mime) if stored, None otherwise."""
-
-
-@dataclass
-class ExtractedFile:
-    """Internal representation of an extracted file before storage."""
-
-    path: str
-    relative_path: str
-    name: str
-    content: bytes
-    is_text: bool
-
-
-async def extract_sandbox_files(
-    sandbox: "BaseAsyncSandbox",
-    working_directory: str,
-    since_timestamp: str | None = None,
-    text_only: bool = True,
-) -> list[ExtractedFile]:
-    """
-    Extract files from an E2B sandbox.
-
-    Args:
-        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.
-
-    Returns:
-        List of ExtractedFile objects with path, content, and metadata
-    """
-    files: list[ExtractedFile] = []
-
-    try:
-        # Build find command
-        safe_working_dir = shlex.quote(working_directory)
-        timestamp_filter = ""
-        if since_timestamp:
-            timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
-
-        find_result = await sandbox.commands.run(
-            f"find {safe_working_dir} -type f "
-            f"{timestamp_filter}"
-            f"-not -path '*/node_modules/*' "
-            f"-not -path '*/.git/*' "
-            f"2>/dev/null"
-        )
-
-        if not find_result.stdout:
-            return files
-
-        for file_path in find_result.stdout.strip().split("\n"):
-            if not file_path:
-                continue
-
-            # Check if it's a text file
-            is_text = any(file_path.endswith(ext) for ext in TEXT_EXTENSIONS)
-
-            # Skip non-text files if text_only mode
-            if text_only and not is_text:
-                continue
-
-            try:
-                # Read file content as bytes
-                content = await sandbox.files.read(file_path, format="bytes")
-                if isinstance(content, str):
-                    content = content.encode("utf-8")
-                elif isinstance(content, bytearray):
-                    content = bytes(content)
-
-                # Extract filename from path
-                file_name = file_path.split("/")[-1]
-
-                # Calculate relative path
-                relative_path = file_path
-                if file_path.startswith(working_directory):
-                    relative_path = file_path[len(working_directory) :]
-                    if relative_path.startswith("/"):
-                        relative_path = relative_path[1:]
-
-                files.append(
-                    ExtractedFile(
-                        path=file_path,
-                        relative_path=relative_path,
-                        name=file_name,
-                        content=content,
-                        is_text=is_text,
-                    )
-                )
-            except Exception as e:
-                logger.debug(f"Failed to read file {file_path}: {e}")
-                continue
-
-    except Exception as e:
-        logger.warning(f"File extraction failed: {e}")
-
-    return files
-
-
-async def store_sandbox_files(
-    extracted_files: list[ExtractedFile],
-    execution_context: "ExecutionContext",
-) -> list[SandboxFileOutput]:
-    """
-    Store extracted sandbox files to workspace and return output objects.
-
-    Args:
-        extracted_files: List of files extracted from sandbox
-        execution_context: Execution context for workspace storage
-
-    Returns:
-        List of SandboxFileOutput objects with workspace refs
-    """
-    outputs: list[SandboxFileOutput] = []
-
-    for file in extracted_files:
-        # Decode content for text files (for backward compat content field)
-        if file.is_text:
-            try:
-                content_str = file.content.decode("utf-8", errors="replace")
-            except Exception:
-                content_str = ""
-        else:
-            content_str = f"[Binary file: {len(file.content)} bytes]"
-
-        # Try to store in workspace
-        workspace_ref: str | None = None
-        try:
-            # Convert bytes to data URI for store_media_file
-            import base64
-            import mimetypes
-
-            mime_type = mimetypes.guess_type(file.name)[0] or "application/octet-stream"
-            data_uri = (
-                f"data:{mime_type};base64,{base64.b64encode(file.content).decode()}"
-            )
-
-            result = await store_media_file(
-                file=MediaFileType(data_uri),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            # Result is workspace://... or data:... depending on context
-            if result.startswith("workspace://"):
-                workspace_ref = result
-        except Exception as e:
-            logger.warning(f"Failed to store file {file.name} to workspace: {e}")
-            # For binary files, fall back to data URI to prevent data loss
-            if not file.is_text:
-                content_str = data_uri
-
-        outputs.append(
-            SandboxFileOutput(
-                path=file.path,
-                relative_path=file.relative_path,
-                name=file.name,
-                content=content_str,
-                workspace_ref=workspace_ref,
-            )
-        )
-
-    return outputs
-
-
-async def extract_and_store_sandbox_files(
-    sandbox: "BaseAsyncSandbox",
-    working_directory: str,
-    execution_context: "ExecutionContext",
-    since_timestamp: str | None = None,
-    text_only: bool = True,
-) -> list[SandboxFileOutput]:
-    """
-    Extract files from sandbox and store them in workspace.
-
-    This is the main entry point combining extraction and storage.
-
-    Args:
-        sandbox: The E2B sandbox instance
-        working_directory: Directory to search for files
-        execution_context: Execution context for workspace storage
-        since_timestamp: ISO timestamp - only return files modified after this time
-        text_only: If True, only extract text files
-
-    Returns:
-        List of SandboxFileOutput objects with content and workspace refs
-    """
-    extracted = await extract_sandbox_files(
-        sandbox=sandbox,
-        working_directory=working_directory,
-        since_timestamp=since_timestamp,
-        text_only=text_only,
-    )
-
-    return await store_sandbox_files(extracted, execution_context)
--- a/autogpt_platform/backend/poetry.lock
+++ b/autogpt_platform/backend/poetry.lock
@@ -46,14 +46,14 @@ pycares = ">=4.9.0,<5"

 [[package]]
 name = "aiofiles"
-version = "25.1.0"
+version = "24.1.0"
 description = "File support for asyncio."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695"},
-    {file = "aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2"},
+    {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
+    {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
 ]

 [[package]]
@@ -1382,14 +1382,14 @@ tzdata = "*"

 [[package]]
 name = "fastapi"
-version = "0.128.6"
+version = "0.128.5"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "fastapi-0.128.6-py3-none-any.whl", hash = "sha256:bb1c1ef87d6086a7132d0ab60869d6f1ee67283b20fbf84ec0003bd335099509"},
-    {file = "fastapi-0.128.6.tar.gz", hash = "sha256:0cb3946557e792d731b26a42b04912f16367e3c3135ea8290f620e234f2b604f"},
+    {file = "fastapi-0.128.5-py3-none-any.whl", hash = "sha256:bceec0de8aa6564599c5bcc0593b0d287703562c848271fca8546fd2c87bf4dd"},
+    {file = "fastapi-0.128.5.tar.gz", hash = "sha256:a7173579fc162d6471e3c6fbd9a4b7610c7a3b367bcacf6c4f90d5d022cab711"},
 ]

 [package.dependencies]
@@ -3078,14 +3078,14 @@ type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]

 [[package]]
 name = "langfuse"
-version = "3.14.1"
+version = "3.13.0"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "langfuse-3.14.1-py3-none-any.whl", hash = "sha256:17bed605dbfc9947cbd1738a715f6d27c1b80b6da9f2946586171958fa5820d0"},
-    {file = "langfuse-3.14.1.tar.gz", hash = "sha256:404a6104cd29353d7829aa417ec46565b04917e5599afdda96c5b0865f4bc991"},
+    {file = "langfuse-3.13.0-py3-none-any.whl", hash = "sha256:71912ddac1cc831a65df895eae538a556f564c094ae51473e747426e9ded1a9d"},
+    {file = "langfuse-3.13.0.tar.gz", hash = "sha256:dacea8111ca4442e97dbfec4f8d676cf9709b35357a26e468f8887b95de0012f"},
 ]

 [package.dependencies]
@@ -8440,4 +8440,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "c06e96ad49388ba7a46786e9ea55ea2c1a57408e15613237b4bee40a592a12af"
+content-hash = "14686ee0e2dc446a75d0db145b08dc410dc31c357e25085bb0f9b0174711c4b1"
--- a/autogpt_platform/backend/pyproject.toml
+++ b/autogpt_platform/backend/pyproject.toml
@@ -21,7 +21,7 @@ cryptography = "^46.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
 elevenlabs = "^1.50.0"
-fastapi = "^0.128.6"
+fastapi = "^0.128.5"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
 google-api-python-client = "^2.177.0"
@@ -34,7 +34,7 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^3.14.1"
+langfuse = "^3.11.0"
 launchdarkly-server-sdk = "^9.14.1"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"
@@ -76,7 +76,7 @@ yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"
-aiofiles = "^25.1.0"
+aiofiles = "^24.1.0"
 tiktoken = "^0.12.0"
 aioclamd = "^1.0.0"
 setuptools = "^80.9.0"
--- a/autogpt_platform/frontend/Dockerfile
+++ b/autogpt_platform/frontend/Dockerfile
@@ -25,12 +25,8 @@ RUN if [ -f .env.production ]; then \
      cp .env.default .env; \
    fi
 RUN pnpm run generate:api
-# Disable source-map generation in Docker builds to halve webpack memory usage.
-# Source maps are only useful when SENTRY_AUTH_TOKEN is set (Vercel deploys);
-# the Docker image never uploads them, so generating them just wastes RAM.
-ENV NEXT_PUBLIC_SOURCEMAPS="false"
 # In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
-RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=8192" pnpm build; else NODE_OPTIONS="--max-old-space-size=8192" pnpm build; fi
+RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi

 # Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
 FROM node:21-alpine AS prod
--- a/autogpt_platform/frontend/next.config.mjs
+++ b/autogpt_platform/frontend/next.config.mjs
@@ -1,12 +1,8 @@
 import { withSentryConfig } from "@sentry/nextjs";

-// Allow Docker builds to skip source-map generation (halves memory usage).
-// Defaults to true so Vercel/local builds are unaffected.
-const enableSourceMaps = process.env.NEXT_PUBLIC_SOURCEMAPS !== "false";
-
 /** @type {import('next').NextConfig} */
 const nextConfig = {
-  productionBrowserSourceMaps: enableSourceMaps,
+  productionBrowserSourceMaps: true,
  // Externalize OpenTelemetry packages to fix Turbopack HMR issues
  serverExternalPackages: [
    "@opentelemetry/instrumentation",
@@ -18,37 +14,9 @@ const nextConfig = {
    serverActions: {
      bodySizeLimit: "256mb",
    },
+    // Increase body size limit for API routes (file uploads) - 256MB to match backend limit
+    proxyClientMaxBodySize: "256mb",
    middlewareClientMaxBodySize: "256mb",
-    // Limit parallel webpack workers to reduce peak memory during builds.
-    cpus: 2,
-  },
-  // Work around cssnano "Invalid array length" bug in Next.js's bundled
-  // cssnano-simple comment parser when processing very large CSS chunks.
-  // CSS is still bundled correctly; gzip handles most of the size savings anyway.
-  webpack: (config, { dev }) => {
-    if (!dev) {
-      // Next.js adds CssMinimizerPlugin internally (after user config), so we
-      // can't filter it from config.plugins. Instead, intercept the webpack
-      // compilation hooks and replace the buggy plugin's tap with a no-op.
-      config.plugins.push({
-        apply(compiler) {
-          compiler.hooks.compilation.tap(
-            "DisableCssMinimizer",
-            (compilation) => {
-              compilation.hooks.processAssets.intercept({
-                register: (tap) => {
-                  if (tap.name === "CssMinimizerPlugin") {
-                    return { ...tap, fn: async () => {} };
-                  }
-                  return tap;
-                },
-              });
-            },
-          );
-        },
-      });
-    }
-    return config;
  },
  images: {
    domains: [
@@ -86,16 +54,9 @@ const nextConfig = {
  transpilePackages: ["geist"],
 };

-// Only run the Sentry webpack plugin when we can actually upload source maps
-// (i.e. on Vercel with SENTRY_AUTH_TOKEN set). The Sentry *runtime* SDK
-// (imported in app code) still captures errors without the plugin.
-// Skipping the plugin saves ~1 GB of peak memory during `next build`.
-const skipSentryPlugin =
-  process.env.NODE_ENV !== "production" ||
-  !enableSourceMaps ||
-  !process.env.SENTRY_AUTH_TOKEN;
+const isDevelopmentBuild = process.env.NODE_ENV !== "production";

-export default skipSentryPlugin
+export default isDevelopmentBuild
  ? nextConfig
  : withSentryConfig(nextConfig, {
      // For all available options, see:
@@ -135,7 +96,7 @@ export default skipSentryPlugin

      // This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
      sourcemaps: {
-        disable: !enableSourceMaps,
+        disable: false,
        assets: [".next/**/*.js", ".next/**/*.js.map"],
        ignore: ["**/node_modules/**"],
        deleteSourcemapsAfterUpload: false, // Source is public anyway :)
--- a/autogpt_platform/frontend/package.json
+++ b/autogpt_platform/frontend/package.json
@@ -7,7 +7,7 @@
  },
  "scripts": {
    "dev": "pnpm run generate:api:force && next dev --turbo",
-    "build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
+    "build": "next build",
    "start": "next start",
    "start:standalone": "cd .next/standalone && node server.js",
    "lint": "next lint && prettier --check .",
@@ -30,7 +30,6 @@
    "defaults"
  ],
  "dependencies": {
-    "@ai-sdk/react": "3.0.61",
    "@faker-js/faker": "10.0.0",
    "@hookform/resolvers": "5.2.2",
    "@next/third-parties": "15.4.6",
@@ -61,10 +60,6 @@
    "@rjsf/utils": "6.1.2",
    "@rjsf/validator-ajv8": "6.1.2",
    "@sentry/nextjs": "10.27.0",
-    "@streamdown/cjk": "1.0.1",
-    "@streamdown/code": "1.0.1",
-    "@streamdown/math": "1.0.1",
-    "@streamdown/mermaid": "1.0.1",
    "@supabase/ssr": "0.7.0",
    "@supabase/supabase-js": "2.78.0",
    "@tanstack/react-query": "5.90.6",
@@ -73,7 +68,6 @@
    "@vercel/analytics": "1.5.0",
    "@vercel/speed-insights": "1.2.0",
    "@xyflow/react": "12.9.2",
-    "ai": "6.0.59",
    "boring-avatars": "1.11.2",
    "class-variance-authority": "0.7.1",
    "clsx": "2.1.1",
@@ -93,6 +87,7 @@
    "launchdarkly-react-client-sdk": "3.9.0",
    "lodash": "4.17.21",
    "lucide-react": "0.552.0",
+    "moment": "2.30.1",
    "next": "15.4.10",
    "next-themes": "0.4.6",
    "nuqs": "2.7.2",
@@ -117,11 +112,9 @@
    "remark-math": "6.0.0",
    "shepherd.js": "14.5.1",
    "sonner": "2.0.7",
-    "streamdown": "2.1.0",
    "tailwind-merge": "2.6.0",
    "tailwind-scrollbar": "3.1.0",
    "tailwindcss-animate": "1.0.7",
-    "use-stick-to-bottom": "1.1.2",
    "uuid": "11.1.0",
    "vaul": "1.1.2",
    "zod": "3.25.76",
@@ -179,8 +172,7 @@
  },
  "pnpm": {
    "overrides": {
-      "@opentelemetry/instrumentation": "0.209.0",
-      "lodash-es": "4.17.23"
+      "@opentelemetry/instrumentation": "0.209.0"
    }
  },
  "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"
--- a/autogpt_platform/frontend/pnpm-lock.yaml
+++ b/autogpt_platform/frontend/pnpm-lock.yaml
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts
@@ -1,4 +1,4 @@
-import debounce from "lodash/debounce";
+import { debounce } from "lodash";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { getQueryClient } from "@/lib/react-query/queryClient";
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx
@@ -70,10 +70,10 @@ export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
          {children}
        </div>
        {canScrollLeft && (
-          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-background via-background/80 to-background/0" />
+          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-white via-white/80 to-white/0" />
        )}
        {canScrollRight && (
-          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-background via-background/80 to-background/0" />
+          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-white via-white/80 to-white/0" />
        )}
        {canScrollLeft && (
          <button
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx
@@ -1,80 +0,0 @@
-"use client";
-
-import { SidebarProvider } from "@/components/ui/sidebar";
-import { ChatContainer } from "./components/ChatContainer/ChatContainer";
-import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
-import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
-import { MobileHeader } from "./components/MobileHeader/MobileHeader";
-import { ScaleLoader } from "./components/ScaleLoader/ScaleLoader";
-import { useCopilotPage } from "./useCopilotPage";
-
-export function CopilotPage() {
-  const {
-    sessionId,
-    messages,
-    status,
-    error,
-    stop,
-    createSession,
-    onSend,
-    isLoadingSession,
-    isCreatingSession,
-    isUserLoading,
-    isLoggedIn,
-    // Mobile drawer
-    isMobile,
-    isDrawerOpen,
-    sessions,
-    isLoadingSessions,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleSelectSession,
-    handleNewChat,
-  } = useCopilotPage();
-
-  if (isUserLoading || !isLoggedIn) {
-    return (
-      <div className="fixed inset-0 z-50 flex items-center justify-center bg-[#f8f8f9]">
-        <ScaleLoader className="text-neutral-400" />
-      </div>
-    );
-  }
-
-  return (
-    <SidebarProvider
-      defaultOpen={true}
-      className="h-[calc(100vh-72px)] min-h-0"
-    >
-      {!isMobile && <ChatSidebar />}
-      <div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
-        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
-        <div className="flex-1 overflow-hidden">
-          <ChatContainer
-            messages={messages}
-            status={status}
-            error={error}
-            sessionId={sessionId}
-            isLoadingSession={isLoadingSession}
-            isCreatingSession={isCreatingSession}
-            onCreateSession={createSession}
-            onSend={onSend}
-            onStop={stop}
-          />
-        </div>
-      </div>
-      {isMobile && (
-        <MobileDrawer
-          isOpen={isDrawerOpen}
-          sessions={sessions}
-          currentSessionId={sessionId}
-          isLoading={isLoadingSessions}
-          onSelectSession={handleSelectSession}
-          onNewChat={handleNewChat}
-          onClose={handleCloseDrawer}
-          onOpenChange={handleDrawerOpenChange}
-        />
-      )}
-    </SidebarProvider>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx
@@ -1,74 +0,0 @@
-"use client";
-import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
-import { UIDataTypes, UIMessage, UITools } from "ai";
-import { LayoutGroup, motion } from "framer-motion";
-import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
-import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
-import { EmptySession } from "../EmptySession/EmptySession";
-
-export interface ChatContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  sessionId: string | null;
-  isLoadingSession: boolean;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-  onStop: () => void;
-}
-export const ChatContainer = ({
-  messages,
-  status,
-  error,
-  sessionId,
-  isLoadingSession,
-  isCreatingSession,
-  onCreateSession,
-  onSend,
-  onStop,
-}: ChatContainerProps) => {
-  const inputLayoutId = "copilot-2-chat-input";
-
-  return (
-    <CopilotChatActionsProvider onSend={onSend}>
-      <LayoutGroup id="copilot-2-chat-layout">
-        <div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
-          {sessionId ? (
-            <div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
-              <ChatMessagesContainer
-                messages={messages}
-                status={status}
-                error={error}
-                isLoading={isLoadingSession}
-              />
-              <motion.div
-                initial={{ opacity: 0 }}
-                animate={{ opacity: 1 }}
-                transition={{ duration: 0.3 }}
-                className="relative px-3 pb-2 pt-2"
-              >
-                <div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
-                <ChatInput
-                  inputId="chat-input-session"
-                  onSend={onSend}
-                  disabled={status === "streaming"}
-                  isStreaming={status === "streaming"}
-                  onStop={onStop}
-                  placeholder="What else can I help with?"
-                />
-              </motion.div>
-            </div>
-          ) : (
-            <EmptySession
-              inputLayoutId={inputLayoutId}
-              isCreatingSession={isCreatingSession}
-              onCreateSession={onCreateSession}
-              onSend={onSend}
-            />
-          )}
-        </div>
-      </LayoutGroup>
-    </CopilotChatActionsProvider>
-  );
-};
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx
@@ -1,294 +0,0 @@
-import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
-import {
-  Conversation,
-  ConversationContent,
-  ConversationScrollButton,
-} from "@/components/ai-elements/conversation";
-import {
-  Message,
-  MessageContent,
-  MessageResponse,
-} from "@/components/ai-elements/message";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { toast } from "@/components/molecules/Toast/use-toast";
-import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
-import { useEffect, useRef, useState } from "react";
-import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
-import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
-import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
-import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
-import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
-import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
-import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
-import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
-
-// ---------------------------------------------------------------------------
-// Workspace media support
-// ---------------------------------------------------------------------------
-
-/**
- * Resolve workspace:// URLs in markdown text to proxy download URLs.
- * Detects MIME type from the hash fragment (e.g. workspace://id#video/mp4)
- * and prefixes the alt text with "video:" so the custom img component can
- * render a <video> element instead.
- */
-function resolveWorkspaceUrls(text: string): string {
-  return text.replace(
-    /!\[([^\]]*)\]\(workspace:\/\/([^)#\s]+)(?:#([^)\s]*))?\)/g,
-    (_match, alt: string, fileId: string, mimeHint?: string) => {
-      const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
-      const url = `/api/proxy${apiPath}`;
-      if (mimeHint?.startsWith("video/")) {
-        return `![video:${alt || "Video"}](${url})`;
-      }
-      return `![${alt || "Image"}](${url})`;
-    },
-  );
-}
-
-/**
- * Custom img component for Streamdown that renders <video> elements
- * for workspace video files (detected via "video:" alt-text prefix).
- * Falls back to <video> when an <img> fails to load for workspace files.
- */
-function WorkspaceMediaImage(props: React.JSX.IntrinsicElements["img"]) {
-  const { src, alt, ...rest } = props;
-  const [imgFailed, setImgFailed] = useState(false);
-  const isWorkspace = src?.includes("/workspace/files/") ?? false;
-
-  if (!src) return null;
-
-  if (alt?.startsWith("video:") || (imgFailed && isWorkspace)) {
-    return (
-      <span className="my-2 inline-block">
-        <video
-          controls
-          className="h-auto max-w-full rounded-md border border-zinc-200"
-          preload="metadata"
-        >
-          <source src={src} />
-          Your browser does not support the video tag.
-        </video>
-      </span>
-    );
-  }
-
-  return (
-    // eslint-disable-next-line @next/next/no-img-element
-    <img
-      src={src}
-      alt={alt || "Image"}
-      className="h-auto max-w-full rounded-md border border-zinc-200"
-      loading="lazy"
-      onError={() => {
-        if (isWorkspace) setImgFailed(true);
-      }}
-      {...rest}
-    />
-  );
-}
-
-/** Stable components override for Streamdown (avoids re-creating on every render). */
-const STREAMDOWN_COMPONENTS = { img: WorkspaceMediaImage };
-
-const THINKING_PHRASES = [
-  "Thinking...",
-  "Considering this...",
-  "Working through this...",
-  "Analyzing your request...",
-  "Reasoning...",
-  "Looking into it...",
-  "Processing your request...",
-  "Mulling this over...",
-  "Piecing it together...",
-  "On it...",
-];
-
-function getRandomPhrase() {
-  return THINKING_PHRASES[Math.floor(Math.random() * THINKING_PHRASES.length)];
-}
-
-interface ChatMessagesContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  isLoading: boolean;
-}
-
-export const ChatMessagesContainer = ({
-  messages,
-  status,
-  error,
-  isLoading,
-}: ChatMessagesContainerProps) => {
-  const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
-  const lastToastTimeRef = useRef(0);
-
-  useEffect(() => {
-    if (status === "submitted") {
-      setThinkingPhrase(getRandomPhrase());
-    }
-  }, [status]);
-
-  // Show a toast when a new error occurs, debounced to avoid spam
-  useEffect(() => {
-    if (!error) return;
-    const now = Date.now();
-    if (now - lastToastTimeRef.current < 3_000) return;
-    lastToastTimeRef.current = now;
-    toast({
-      variant: "destructive",
-      title: "Something went wrong",
-      description:
-        "The assistant encountered an error. Please try sending your message again.",
-    });
-  }, [error]);
-
-  const lastMessage = messages[messages.length - 1];
-  const lastAssistantHasVisibleContent =
-    lastMessage?.role === "assistant" &&
-    lastMessage.parts.some(
-      (p) =>
-        (p.type === "text" && p.text.trim().length > 0) ||
-        p.type.startsWith("tool-"),
-    );
-
-  const showThinking =
-    status === "submitted" ||
-    (status === "streaming" && !lastAssistantHasVisibleContent);
-
-  return (
-    <Conversation className="min-h-0 flex-1">
-      <ConversationContent className="flex min-h-screen flex-1 flex-col gap-6 px-3 py-6">
-        {isLoading && messages.length === 0 && (
-          <div className="flex min-h-full flex-1 items-center justify-center">
-            <LoadingSpinner className="text-neutral-600" />
-          </div>
-        )}
-        {messages.map((message, messageIndex) => {
-          const isLastAssistant =
-            messageIndex === messages.length - 1 &&
-            message.role === "assistant";
-          const messageHasVisibleContent = message.parts.some(
-            (p) =>
-              (p.type === "text" && p.text.trim().length > 0) ||
-              p.type.startsWith("tool-"),
-          );
-
-          return (
-            <Message from={message.role} key={message.id}>
-              <MessageContent
-                className={
-                  "text-[1rem] leading-relaxed " +
-                  "group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
-                  "group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
-                }
-              >
-                {message.parts.map((part, i) => {
-                  switch (part.type) {
-                    case "text":
-                      return (
-                        <MessageResponse
-                          key={`${message.id}-${i}`}
-                          components={STREAMDOWN_COMPONENTS}
-                        >
-                          {resolveWorkspaceUrls(part.text)}
-                        </MessageResponse>
-                      );
-                    case "tool-find_block":
-                      return (
-                        <FindBlocksTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-find_agent":
-                    case "tool-find_library_agent":
-                      return (
-                        <FindAgentsTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-search_docs":
-                    case "tool-get_doc_page":
-                      return (
-                        <SearchDocsTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-run_block":
-                      return (
-                        <RunBlockTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-run_agent":
-                    case "tool-schedule_agent":
-                      return (
-                        <RunAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-create_agent":
-                      return (
-                        <CreateAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-edit_agent":
-                      return (
-                        <EditAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-view_agent_output":
-                      return (
-                        <ViewAgentOutputTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    default:
-                      return null;
-                  }
-                })}
-                {isLastAssistant &&
-                  !messageHasVisibleContent &&
-                  showThinking && (
-                    <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                      {thinkingPhrase}
-                    </span>
-                  )}
-              </MessageContent>
-            </Message>
-          );
-        })}
-        {showThinking && lastMessage?.role !== "assistant" && (
-          <Message from="assistant">
-            <MessageContent className="text-[1rem] leading-relaxed">
-              <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                {thinkingPhrase}
-              </span>
-            </MessageContent>
-          </Message>
-        )}
-        {error && (
-          <div className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
-            <p className="font-medium">Something went wrong</p>
-            <p className="mt-1 text-red-600">
-              The assistant encountered an error. Please try sending your
-              message again.
-            </p>
-          </div>
-        )}
-      </ConversationContent>
-      <ConversationScrollButton />
-    </Conversation>
-  );
-};
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx
@@ -1,188 +0,0 @@
-"use client";
-import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
-import { Button } from "@/components/atoms/Button/Button";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-import {
-  Sidebar,
-  SidebarContent,
-  SidebarFooter,
-  SidebarHeader,
-  SidebarTrigger,
-  useSidebar,
-} from "@/components/ui/sidebar";
-import { cn } from "@/lib/utils";
-import { PlusCircleIcon, PlusIcon } from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { parseAsString, useQueryState } from "nuqs";
-
-export function ChatSidebar() {
-  const { state } = useSidebar();
-  const isCollapsed = state === "collapsed";
-  const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
-
-  const { data: sessionsResponse, isLoading: isLoadingSessions } =
-    useGetV2ListSessions({ limit: 50 });
-
-  const sessions =
-    sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
-
-  function handleNewChat() {
-    setSessionId(null);
-  }
-
-  function handleSelectSession(id: string) {
-    setSessionId(id);
-  }
-
-  function formatDate(dateString: string) {
-    const date = new Date(dateString);
-    const now = new Date();
-    const diffMs = now.getTime() - date.getTime();
-    const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-    if (diffDays === 0) return "Today";
-    if (diffDays === 1) return "Yesterday";
-    if (diffDays < 7) return `${diffDays} days ago`;
-
-    const day = date.getDate();
-    const ordinal =
-      day % 10 === 1 && day !== 11
-        ? "st"
-        : day % 10 === 2 && day !== 12
-          ? "nd"
-          : day % 10 === 3 && day !== 13
-            ? "rd"
-            : "th";
-    const month = date.toLocaleDateString("en-US", { month: "short" });
-    const year = date.getFullYear();
-
-    return `${day}${ordinal} ${month} ${year}`;
-  }
-
-  return (
-    <Sidebar
-      variant="inset"
-      collapsible="icon"
-      className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
-    >
-      {isCollapsed && (
-        <SidebarHeader
-          className={cn(
-            "flex",
-            isCollapsed
-              ? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
-              : "flex-row items-center justify-between",
-          )}
-        >
-          <motion.div
-            key={isCollapsed ? "header-collapsed" : "header-expanded"}
-            className="flex flex-col items-center gap-3 pt-4"
-            initial={{ opacity: 0, filter: "blur(3px)" }}
-            animate={{ opacity: 1, filter: "blur(0px)" }}
-            transition={{ type: "spring", bounce: 0.2 }}
-          >
-            <div className="flex flex-col items-center gap-2">
-              <SidebarTrigger />
-              <Button
-                variant="ghost"
-                onClick={handleNewChat}
-                style={{ minWidth: "auto", width: "auto" }}
-              >
-                <PlusCircleIcon className="!size-5" />
-                <span className="sr-only">New Chat</span>
-              </Button>
-            </div>
-          </motion.div>
-        </SidebarHeader>
-      )}
-      <SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.1 }}
-            className="flex items-center justify-between px-3"
-          >
-            <Text variant="h3" size="body-medium">
-              Your chats
-            </Text>
-            <div className="relative left-6">
-              <SidebarTrigger />
-            </div>
-          </motion.div>
-        )}
-
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.15 }}
-            className="mt-4 flex flex-col gap-1"
-          >
-            {isLoadingSessions ? (
-              <div className="flex min-h-[30rem] items-center justify-center py-4">
-                <LoadingSpinner size="small" className="text-neutral-600" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => handleSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === sessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === sessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || `Untitled chat`}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </motion.div>
-        )}
-      </SidebarContent>
-      {!isCollapsed && sessionId && (
-        <SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.2 }}
-          >
-            <Button
-              variant="primary"
-              size="small"
-              onClick={handleNewChat}
-              className="w-full"
-              leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
-            >
-              New Chat
-            </Button>
-          </motion.div>
-        </SidebarFooter>
-      )}
-    </Sidebar>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx
@@ -1,16 +0,0 @@
-"use client";
-
-import { CopilotChatActionsContext } from "./useCopilotChatActions";
-
-interface Props {
-  onSend: (message: string) => void | Promise<void>;
-  children: React.ReactNode;
-}
-
-export function CopilotChatActionsProvider({ onSend, children }: Props) {
-  return (
-    <CopilotChatActionsContext.Provider value={{ onSend }}>
-      {children}
-    </CopilotChatActionsContext.Provider>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts
@@ -1,23 +0,0 @@
-"use client";
-
-import { createContext, useContext } from "react";
-
-interface CopilotChatActions {
-  onSend: (message: string) => void | Promise<void>;
-}
-
-const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
-  null,
-);
-
-export function useCopilotChatActions(): CopilotChatActions {
-  const ctx = useContext(CopilotChatActionsContext);
-  if (!ctx) {
-    throw new Error(
-      "useCopilotChatActions must be used within CopilotChatActionsProvider",
-    );
-  }
-  return ctx;
-}
-
-export { CopilotChatActionsContext };
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx
@@ -0,0 +1,99 @@
+"use client";
+
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+import { Text } from "@/components/atoms/Text/Text";
+import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
+import type { ReactNode } from "react";
+import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
+import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
+import { MobileHeader } from "./components/MobileHeader/MobileHeader";
+import { useCopilotShell } from "./useCopilotShell";
+
+interface Props {
+  children: ReactNode;
+}
+
+export function CopilotShell({ children }: Props) {
+  const {
+    isMobile,
+    isDrawerOpen,
+    isLoading,
+    isCreatingSession,
+    isLoggedIn,
+    hasActiveSession,
+    sessions,
+    currentSessionId,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChatClick,
+    handleSessionClick,
+    hasNextPage,
+    isFetchingNextPage,
+    fetchNextPage,
+  } = useCopilotShell();
+
+  if (!isLoggedIn) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <ChatLoader />
+      </div>
+    );
+  }
+
+  return (
+    <div
+      className="flex overflow-hidden bg-[#EFEFF0]"
+      style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
+    >
+      {!isMobile && (
+        <DesktopSidebar
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSessionClick}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClick}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+
+      <div className="relative flex min-h-0 flex-1 flex-col">
+        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
+        <div className="flex min-h-0 flex-1 flex-col">
+          {isCreatingSession ? (
+            <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
+              <div className="flex flex-col items-center gap-4">
+                <ChatLoader />
+                <Text variant="body" className="text-zinc-500">
+                  Creating your chat...
+                </Text>
+              </div>
+            </div>
+          ) : (
+            children
+          )}
+        </div>
+      </div>
+
+      {isMobile && (
+        <MobileDrawer
+          isOpen={isDrawerOpen}
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSessionClick}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClick}
+          onClose={handleCloseDrawer}
+          onOpenChange={handleDrawerOpenChange}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+    </div>
+  );
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx
@@ -0,0 +1,70 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { Plus } from "@phosphor-icons/react";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  hasActiveSession: boolean;
+}
+
+export function DesktopSidebar({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  hasActiveSession,
+}: Props) {
+  return (
+    <aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
+      <div className="shrink-0 px-6 py-4">
+        <Text variant="h3" size="body-medium">
+          Your chats
+        </Text>
+      </div>
+      <div
+        className={cn(
+          "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+          scrollbarStyles,
+        )}
+      >
+        <SessionsList
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={onSelectSession}
+          onFetchNextPage={onFetchNextPage}
+        />
+      </div>
+      {hasActiveSession && (
+        <div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+          <Button
+            variant="primary"
+            size="small"
+            onClick={onNewChat}
+            className="w-full"
+            leftIcon={<Plus width="1rem" height="1rem" />}
+          >
+            New Chat
+          </Button>
+        </div>
+      )}
+    </aside>
+  );
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx
@@ -0,0 +1,91 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { PlusIcon, X } from "@phosphor-icons/react";
+import { Drawer } from "vaul";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  isOpen: boolean;
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  onClose: () => void;
+  onOpenChange: (open: boolean) => void;
+  hasActiveSession: boolean;
+}
+
+export function MobileDrawer({
+  isOpen,
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  onClose,
+  onOpenChange,
+  hasActiveSession,
+}: Props) {
+  return (
+    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
+      <Drawer.Portal>
+        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
+        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
+          <div className="shrink-0 border-b border-zinc-200 p-4">
+            <div className="flex items-center justify-between">
+              <Drawer.Title className="text-lg font-semibold text-zinc-800">
+                Your chats
+              </Drawer.Title>
+              <Button
+                variant="icon"
+                size="icon"
+                aria-label="Close sessions"
+                onClick={onClose}
+              >
+                <X width="1.25rem" height="1.25rem" />
+              </Button>
+            </div>
+          </div>
+          <div
+            className={cn(
+              "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+              scrollbarStyles,
+            )}
+          >
+            <SessionsList
+              sessions={sessions}
+              currentSessionId={currentSessionId}
+              isLoading={isLoading}
+              hasNextPage={hasNextPage}
+              isFetchingNextPage={isFetchingNextPage}
+              onSelectSession={onSelectSession}
+              onFetchNextPage={onFetchNextPage}
+            />
+          </div>
+          {hasActiveSession && (
+            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+              <Button
+                variant="primary"
+                size="small"
+                onClick={onNewChat}
+                className="w-full"
+                leftIcon={<PlusIcon width="1rem" height="1rem" />}
+              >
+                New Chat
+              </Button>
+            </div>
+          )}
+        </Drawer.Content>
+      </Drawer.Portal>
+    </Drawer.Root>
+  );
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts
@@ -0,0 +1,24 @@
+import { useState } from "react";
+
+export function useMobileDrawer() {
+  const [isDrawerOpen, setIsDrawerOpen] = useState(false);
+
+  const handleOpenDrawer = () => {
+    setIsDrawerOpen(true);
+  };
+
+  const handleCloseDrawer = () => {
+    setIsDrawerOpen(false);
+  };
+
+  const handleDrawerOpenChange = (open: boolean) => {
+    setIsDrawerOpen(open);
+  };
+
+  return {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  };
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx
@@ -0,0 +1,80 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
+import { Text } from "@/components/atoms/Text/Text";
+import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
+import { cn } from "@/lib/utils";
+import { getSessionTitle } from "../../helpers";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+}
+
+export function SessionsList({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+}: Props) {
+  if (isLoading) {
+    return (
+      <div className="space-y-1">
+        {Array.from({ length: 5 }).map((_, i) => (
+          <div key={i} className="rounded-lg px-3 py-2.5">
+            <Skeleton className="h-5 w-full" />
+          </div>
+        ))}
+      </div>
+    );
+  }
+
+  if (sessions.length === 0) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <Text variant="body" className="text-zinc-500">
+          You don&apos;t have previous chats
+        </Text>
+      </div>
+    );
+  }
+
+  return (
+    <InfiniteList
+      items={sessions}
+      hasMore={hasNextPage}
+      isFetchingMore={isFetchingNextPage}
+      onEndReached={onFetchNextPage}
+      className="space-y-1"
+      renderItem={(session) => {
+        const isActive = session.id === currentSessionId;
+        return (
+          <button
+            onClick={() => onSelectSession(session.id)}
+            className={cn(
+              "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
+              isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
+            )}
+          >
+            <Text
+              variant="body"
+              className={cn(
+                "font-normal",
+                isActive ? "text-zinc-600" : "text-zinc-800",
+              )}
+            >
+              {getSessionTitle(session)}
+            </Text>
+          </button>
+        );
+      }}
+    />
+  );
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts
@@ -0,0 +1,91 @@
+import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { okData } from "@/app/api/helpers";
+import { useEffect, useState } from "react";
+
+const PAGE_SIZE = 50;
+
+export interface UseSessionsPaginationArgs {
+  enabled: boolean;
+}
+
+export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
+  const [offset, setOffset] = useState(0);
+
+  const [accumulatedSessions, setAccumulatedSessions] = useState<
+    SessionSummaryResponse[]
+  >([]);
+
+  const [totalCount, setTotalCount] = useState<number | null>(null);
+
+  const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
+    { limit: PAGE_SIZE, offset },
+    {
+      query: {
+        enabled: enabled && offset >= 0,
+      },
+    },
+  );
+
+  useEffect(() => {
+    const responseData = okData(data);
+    if (responseData) {
+      const newSessions = responseData.sessions;
+      const total = responseData.total;
+      setTotalCount(total);
+
+      if (offset === 0) {
+        setAccumulatedSessions(newSessions);
+      } else {
+        setAccumulatedSessions((prev) => [...prev, ...newSessions]);
+      }
+    } else if (!enabled) {
+      setAccumulatedSessions([]);
+      setTotalCount(null);
+    }
+  }, [data, offset, enabled]);
+
+  const hasNextPage =
+    totalCount !== null && accumulatedSessions.length < totalCount;
+
+  const areAllSessionsLoaded =
+    totalCount !== null &&
+    accumulatedSessions.length >= totalCount &&
+    !isFetching &&
+    !isLoading;
+
+  useEffect(() => {
+    if (
+      hasNextPage &&
+      !isFetching &&
+      !isLoading &&
+      !isError &&
+      totalCount !== null
+    ) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  }, [hasNextPage, isFetching, isLoading, isError, totalCount]);
+
+  const fetchNextPage = () => {
+    if (hasNextPage && !isFetching) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  };
+
+  const reset = () => {
+    // Only reset the offset - keep existing sessions visible during refetch
+    // The effect will replace sessions when new data arrives at offset 0
+    setOffset(0);
+  };
+
+  return {
+    sessions: accumulatedSessions,
+    isLoading,
+    isFetching,
+    hasNextPage,
+    areAllSessionsLoaded,
+    totalCount,
+    fetchNextPage,
+    reset,
+  };
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts
@@ -0,0 +1,106 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { format, formatDistanceToNow, isToday } from "date-fns";
+
+export function convertSessionDetailToSummary(session: SessionDetailResponse) {
+  return {
+    id: session.id,
+    created_at: session.created_at,
+    updated_at: session.updated_at,
+    title: undefined,
+  };
+}
+
+export function filterVisibleSessions(sessions: SessionSummaryResponse[]) {
+  const fiveMinutesAgo = Date.now() - 5 * 60 * 1000;
+  return sessions.filter((session) => {
+    const hasBeenUpdated = session.updated_at !== session.created_at;
+
+    if (hasBeenUpdated) return true;
+
+    const isRecentlyCreated =
+      new Date(session.created_at).getTime() > fiveMinutesAgo;
+
+    return isRecentlyCreated;
+  });
+}
+
+export function getSessionTitle(session: SessionSummaryResponse) {
+  if (session.title) return session.title;
+
+  const isNewSession = session.updated_at === session.created_at;
+
+  if (isNewSession) {
+    const createdDate = new Date(session.created_at);
+    if (isToday(createdDate)) {
+      return "Today";
+    }
+    return format(createdDate, "MMM d, yyyy");
+  }
+
+  return "Untitled Chat";
+}
+
+export function getSessionUpdatedLabel(session: SessionSummaryResponse) {
+  if (!session.updated_at) return "";
+  return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
+}
+
+export function mergeCurrentSessionIntoList(
+  accumulatedSessions: SessionSummaryResponse[],
+  currentSessionId: string | null,
+  currentSessionData: SessionDetailResponse | null | undefined,
+  recentlyCreatedSessions?: Map<string, SessionSummaryResponse>,
+) {
+  const filteredSessions: SessionSummaryResponse[] = [];
+  const addedIds = new Set<string>();
+
+  if (accumulatedSessions.length > 0) {
+    const visibleSessions = filterVisibleSessions(accumulatedSessions);
+
+    if (currentSessionId) {
+      const currentInAll = accumulatedSessions.find(
+        (s) => s.id === currentSessionId,
+      );
+      if (currentInAll) {
+        const isInVisible = visibleSessions.some(
+          (s) => s.id === currentSessionId,
+        );
+        if (!isInVisible) {
+          filteredSessions.push(currentInAll);
+          addedIds.add(currentInAll.id);
+        }
+      }
+    }
+
+    for (const session of visibleSessions) {
+      if (!addedIds.has(session.id)) {
+        filteredSessions.push(session);
+        addedIds.add(session.id);
+      }
+    }
+  }
+
+  if (currentSessionId && currentSessionData) {
+    if (!addedIds.has(currentSessionId)) {
+      const summarySession = convertSessionDetailToSummary(currentSessionData);
+      filteredSessions.unshift(summarySession);
+      addedIds.add(currentSessionId);
+    }
+  }
+
+  if (recentlyCreatedSessions) {
+    for (const [sessionId, sessionData] of recentlyCreatedSessions) {
+      if (!addedIds.has(sessionId)) {
+        filteredSessions.unshift(sessionData);
+        addedIds.add(sessionId);
+      }
+    }
+  }
+
+  return filteredSessions;
+}
+
+export function getCurrentSessionId(searchParams: URLSearchParams) {
+  return searchParams.get("sessionId");
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
@@ -0,0 +1,124 @@
+"use client";
+
+import {
+  getGetV2GetSessionQueryKey,
+  getGetV2ListSessionsQueryKey,
+  useGetV2GetSession,
+} from "@/app/api/__generated__/endpoints/chat/chat";
+import { okData } from "@/app/api/helpers";
+import { useChatStore } from "@/components/contextual/Chat/chat-store";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useQueryClient } from "@tanstack/react-query";
+import { usePathname, useSearchParams } from "next/navigation";
+import { useCopilotStore } from "../../copilot-page-store";
+import { useCopilotSessionId } from "../../useCopilotSessionId";
+import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
+import { getCurrentSessionId } from "./helpers";
+import { useShellSessionList } from "./useShellSessionList";
+
+export function useCopilotShell() {
+  const pathname = usePathname();
+  const searchParams = useSearchParams();
+  const queryClient = useQueryClient();
+  const breakpoint = useBreakpoint();
+  const { isLoggedIn } = useSupabase();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
+  const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
+
+  const isOnHomepage = pathname === "/copilot";
+  const paramSessionId = searchParams.get("sessionId");
+
+  const {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  } = useMobileDrawer();
+
+  const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
+
+  const currentSessionId = getCurrentSessionId(searchParams);
+
+  const { data: currentSessionData } = useGetV2GetSession(
+    currentSessionId || "",
+    {
+      query: {
+        enabled: !!currentSessionId,
+        select: okData,
+      },
+    },
+  );
+
+  const {
+    sessions,
+    isLoading,
+    isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    resetPagination,
+    recentlyCreatedSessionsRef,
+  } = useShellSessionList({
+    paginationEnabled,
+    currentSessionId,
+    currentSessionData,
+    isOnHomepage,
+    paramSessionId,
+  });
+
+  const stopStream = useChatStore((s) => s.stopStream);
+  const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
+
+  function handleSessionClick(sessionId: string) {
+    if (sessionId === currentSessionId) return;
+
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
+    if (recentlyCreatedSessionsRef.current.has(sessionId)) {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2GetSessionQueryKey(sessionId),
+      });
+    }
+    setUrlSessionId(sessionId, { shallow: false });
+    if (isMobile) handleCloseDrawer();
+  }
+
+  function handleNewChatClick() {
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
+    resetPagination();
+    queryClient.invalidateQueries({
+      queryKey: getGetV2ListSessionsQueryKey(),
+    });
+    setUrlSessionId(null, { shallow: false });
+    if (isMobile) handleCloseDrawer();
+  }
+
+  return {
+    isMobile,
+    isDrawerOpen,
+    isLoggedIn,
+    hasActiveSession:
+      Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
+    isLoading: isLoading || isCreatingSession,
+    isCreatingSession,
+    sessions,
+    currentSessionId: urlSessionId,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChatClick,
+    handleSessionClick,
+    hasNextPage,
+    isFetchingNextPage: isSessionsFetching,
+    fetchNextPage,
+  };
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts
@@ -0,0 +1,113 @@
+import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { useChatStore } from "@/components/contextual/Chat/chat-store";
+import { useQueryClient } from "@tanstack/react-query";
+import { useEffect, useMemo, useRef } from "react";
+import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
+import {
+  convertSessionDetailToSummary,
+  filterVisibleSessions,
+  mergeCurrentSessionIntoList,
+} from "./helpers";
+
+interface UseShellSessionListArgs {
+  paginationEnabled: boolean;
+  currentSessionId: string | null;
+  currentSessionData: SessionDetailResponse | null | undefined;
+  isOnHomepage: boolean;
+  paramSessionId: string | null;
+}
+
+export function useShellSessionList({
+  paginationEnabled,
+  currentSessionId,
+  currentSessionData,
+  isOnHomepage,
+  paramSessionId,
+}: UseShellSessionListArgs) {
+  const queryClient = useQueryClient();
+  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
+
+  const {
+    sessions: accumulatedSessions,
+    isLoading: isSessionsLoading,
+    isFetching: isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    reset: resetPagination,
+  } = useSessionsPagination({
+    enabled: paginationEnabled,
+  });
+
+  const recentlyCreatedSessionsRef = useRef<
+    Map<string, SessionSummaryResponse>
+  >(new Map());
+
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId) {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    }
+  }, [isOnHomepage, paramSessionId, queryClient]);
+
+  useEffect(() => {
+    if (currentSessionId && currentSessionData) {
+      const isNewSession =
+        currentSessionData.updated_at === currentSessionData.created_at;
+      const isNotInAccumulated = !accumulatedSessions.some(
+        (s) => s.id === currentSessionId,
+      );
+      if (isNewSession || isNotInAccumulated) {
+        const summary = convertSessionDetailToSummary(currentSessionData);
+        recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
+      }
+    }
+  }, [currentSessionId, currentSessionData, accumulatedSessions]);
+
+  useEffect(() => {
+    for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
+      if (accumulatedSessions.some((s) => s.id === sessionId)) {
+        recentlyCreatedSessionsRef.current.delete(sessionId);
+      }
+    }
+  }, [accumulatedSessions]);
+
+  useEffect(() => {
+    const unsubscribe = onStreamComplete(() => {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    });
+    return unsubscribe;
+  }, [onStreamComplete, queryClient]);
+
+  const sessions = useMemo(
+    () =>
+      mergeCurrentSessionIntoList(
+        accumulatedSessions,
+        currentSessionId,
+        currentSessionData,
+        recentlyCreatedSessionsRef.current,
+      ),
+    [accumulatedSessions, currentSessionId, currentSessionData],
+  );
+
+  const visibleSessions = useMemo(
+    () => filterVisibleSessions(sessions),
+    [sessions],
+  );
+
+  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
+
+  return {
+    sessions: visibleSessions,
+    isLoading,
+    isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    resetPagination,
+    recentlyCreatedSessionsRef,
+  };
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx
@@ -1,111 +0,0 @@
-"use client";
-
-import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { SpinnerGapIcon } from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { useEffect, useState } from "react";
-import {
-  getGreetingName,
-  getInputPlaceholder,
-  getQuickActions,
-} from "./helpers";
-
-interface Props {
-  inputLayoutId: string;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-}
-
-export function EmptySession({
-  inputLayoutId,
-  isCreatingSession,
-  onSend,
-}: Props) {
-  const { user } = useSupabase();
-  const greetingName = getGreetingName(user);
-  const quickActions = getQuickActions();
-  const [loadingAction, setLoadingAction] = useState<string | null>(null);
-  const [inputPlaceholder, setInputPlaceholder] = useState(
-    getInputPlaceholder(),
-  );
-
-  useEffect(() => {
-    setInputPlaceholder(getInputPlaceholder(window.innerWidth));
-  }, [window.innerWidth]);
-
-  async function handleQuickActionClick(action: string) {
-    if (isCreatingSession || loadingAction) return;
-
-    setLoadingAction(action);
-    try {
-      await onSend(action);
-    } finally {
-      setLoadingAction(null);
-    }
-  }
-
-  return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-0 py-5 md:px-6 md:py-10">
-      <motion.div
-        className="w-full max-w-3xl text-center"
-        initial={{ opacity: 0 }}
-        animate={{ opacity: 1 }}
-        transition={{ duration: 0.3 }}
-      >
-        <div className="mx-auto max-w-3xl">
-          <Text variant="h3" 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">
-            Tell me about your work — I&apos;ll find what to automate.
-          </Text>
-
-          <div className="mb-6">
-            <motion.div
-              layoutId={inputLayoutId}
-              transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
-              className="w-full px-2"
-            >
-              <ChatInput
-                inputId="chat-input-empty"
-                onSend={onSend}
-                disabled={isCreatingSession}
-                placeholder={inputPlaceholder}
-                className="w-full"
-              />
-            </motion.div>
-          </div>
-        </div>
-
-        <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-          {quickActions.map((action) => (
-            <Button
-              key={action}
-              type="button"
-              variant="outline"
-              size="small"
-              onClick={() => void handleQuickActionClick(action)}
-              disabled={isCreatingSession || loadingAction !== null}
-              aria-busy={loadingAction === action}
-              leftIcon={
-                loadingAction === action ? (
-                  <SpinnerGapIcon
-                    className="h-4 w-4 animate-spin"
-                    weight="bold"
-                  />
-                ) : null
-              }
-              className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
-            >
-              {action}
-            </Button>
-          ))}
-        </div>
-      </motion.div>
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx
@@ -1,140 +0,0 @@
-import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { scrollbarStyles } from "@/components/styles/scrollbars";
-import { cn } from "@/lib/utils";
-import { PlusIcon, SpinnerGapIcon, X } from "@phosphor-icons/react";
-import { Drawer } from "vaul";
-
-interface Props {
-  isOpen: boolean;
-  sessions: SessionSummaryResponse[];
-  currentSessionId: string | null;
-  isLoading: boolean;
-  onSelectSession: (sessionId: string) => void;
-  onNewChat: () => void;
-  onClose: () => void;
-  onOpenChange: (open: boolean) => void;
-}
-
-function formatDate(dateString: string) {
-  const date = new Date(dateString);
-  const now = new Date();
-  const diffMs = now.getTime() - date.getTime();
-  const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-  if (diffDays === 0) return "Today";
-  if (diffDays === 1) return "Yesterday";
-  if (diffDays < 7) return `${diffDays} days ago`;
-
-  const day = date.getDate();
-  const ordinal =
-    day % 10 === 1 && day !== 11
-      ? "st"
-      : day % 10 === 2 && day !== 12
-        ? "nd"
-        : day % 10 === 3 && day !== 13
-          ? "rd"
-          : "th";
-  const month = date.toLocaleDateString("en-US", { month: "short" });
-  const year = date.getFullYear();
-
-  return `${day}${ordinal} ${month} ${year}`;
-}
-
-export function MobileDrawer({
-  isOpen,
-  sessions,
-  currentSessionId,
-  isLoading,
-  onSelectSession,
-  onNewChat,
-  onClose,
-  onOpenChange,
-}: Props) {
-  return (
-    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
-      <Drawer.Portal>
-        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
-        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
-          <div className="shrink-0 border-b border-zinc-200 px-4 py-2">
-            <div className="flex items-center justify-between">
-              <Drawer.Title className="text-lg font-semibold text-zinc-800">
-                Your chats
-              </Drawer.Title>
-              <Button
-                variant="icon"
-                size="icon"
-                aria-label="Close sessions"
-                onClick={onClose}
-              >
-                <X width="1rem" height="1rem" />
-              </Button>
-            </div>
-          </div>
-          <div
-            className={cn(
-              "flex min-h-0 flex-1 flex-col gap-1 overflow-y-auto px-3 py-3",
-              scrollbarStyles,
-            )}
-          >
-            {isLoading ? (
-              <div className="flex items-center justify-center py-4">
-                <SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => onSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === currentSessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === currentSessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || "Untitled chat"}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </div>
-          {currentSessionId && (
-            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-              <Button
-                variant="primary"
-                size="small"
-                onClick={onNewChat}
-                className="w-full"
-                leftIcon={<PlusIcon width="1rem" height="1rem" />}
-              >
-                New Chat
-              </Button>
-            </div>
-          )}
-        </Drawer.Content>
-      </Drawer.Portal>
-    </Drawer.Root>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx
@@ -1,54 +0,0 @@
-import { cn } from "@/lib/utils";
-import { AnimatePresence, motion } from "framer-motion";
-
-interface Props {
-  text: string;
-  className?: string;
-}
-
-export function MorphingTextAnimation({ text, className }: Props) {
-  const letters = text.split("");
-
-  return (
-    <div className={cn(className)}>
-      <AnimatePresence mode="popLayout" initial={false}>
-        <motion.div key={text} className="whitespace-nowrap">
-          <motion.span className="inline-flex overflow-hidden">
-            {letters.map((char, index) => (
-              <motion.span
-                key={`${text}-${index}`}
-                initial={{
-                  opacity: 0,
-                  y: 8,
-                  rotateX: "80deg",
-                  filter: "blur(6px)",
-                }}
-                animate={{
-                  opacity: 1,
-                  y: 0,
-                  rotateX: "0deg",
-                  filter: "blur(0px)",
-                }}
-                exit={{
-                  opacity: 0,
-                  y: -8,
-                  rotateX: "-80deg",
-                  filter: "blur(6px)",
-                }}
-                style={{ willChange: "transform" }}
-                transition={{
-                  delay: 0.015 * index,
-                  type: "spring",
-                  bounce: 0.5,
-                }}
-                className="inline-block"
-              >
-                {char === " " ? "\u00A0" : char}
-              </motion.span>
-            ))}
-          </motion.span>
-        </motion.div>
-      </AnimatePresence>
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css
@@ -1,69 +0,0 @@
-.loader {
-  position: relative;
-  animation: rotate 1s infinite;
-}
-
-.loader::before,
-.loader::after {
-  border-radius: 50%;
-  content: "";
-  display: block;
-  /* 40% of container size */
-  height: 40%;
-  width: 40%;
-}
-
-.loader::before {
-  animation: ball1 1s infinite;
-  background-color: #a1a1aa; /* zinc-400 */
-  box-shadow: calc(var(--spacing)) 0 0 #18181b; /* zinc-900 */
-  margin-bottom: calc(var(--gap));
-}
-
-.loader::after {
-  animation: ball2 1s infinite;
-  background-color: #18181b; /* zinc-900 */
-  box-shadow: calc(var(--spacing)) 0 0 #a1a1aa; /* zinc-400 */
-}
-
-@keyframes rotate {
-  0% {
-    transform: rotate(0deg) scale(0.8);
-  }
-  50% {
-    transform: rotate(360deg) scale(1.2);
-  }
-  100% {
-    transform: rotate(720deg) scale(0.8);
-  }
-}
-
-@keyframes ball1 {
-  0% {
-    box-shadow: calc(var(--spacing)) 0 0 #18181b;
-  }
-  50% {
-    box-shadow: 0 0 0 #18181b;
-    margin-bottom: 0;
-    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
-  }
-  100% {
-    box-shadow: calc(var(--spacing)) 0 0 #18181b;
-    margin-bottom: calc(var(--gap));
-  }
-}
-
-@keyframes ball2 {
-  0% {
-    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
-  }
-  50% {
-    box-shadow: 0 0 0 #a1a1aa;
-    margin-top: calc(var(--ball-size) * -1);
-    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
-  }
-  100% {
-    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
-    margin-top: 0;
-  }
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx
@@ -1,28 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./OrbitLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function OrbitLoader({ size = 24, className }: Props) {
-  const ballSize = Math.round(size * 0.4);
-  const spacing = Math.round(size * 0.6);
-  const gap = Math.round(size * 0.2);
-
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={
-        {
-          width: size,
-          height: size,
-          "--ball-size": `${ballSize}px`,
-          "--spacing": `${spacing}px`,
-          "--gap": `${gap}px`,
-        } as React.CSSProperties
-      }
-    />
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx
@@ -1,26 +0,0 @@
-import { cn } from "@/lib/utils";
-
-interface Props {
-  value: number;
-  label?: string;
-  className?: string;
-}
-
-export function ProgressBar({ value, label, className }: Props) {
-  const clamped = Math.min(100, Math.max(0, value));
-
-  return (
-    <div className={cn("flex flex-col gap-1.5", className)}>
-      <div className="flex items-center justify-between text-xs text-neutral-500">
-        <span>{label ?? "Working on it..."}</span>
-        <span>{Math.round(clamped)}%</span>
-      </div>
-      <div className="h-2 w-full overflow-hidden rounded-full bg-neutral-200">
-        <div
-          className="h-full rounded-full bg-neutral-900 transition-[width] duration-300 ease-out"
-          style={{ width: `${clamped}%` }}
-        />
-      </div>
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css
@@ -1,34 +0,0 @@
-.loader {
-  position: relative;
-  display: inline-block;
-  flex-shrink: 0;
-}
-
-.loader::before,
-.loader::after {
-  content: "";
-  box-sizing: border-box;
-  width: 100%;
-  height: 100%;
-  border-radius: 50%;
-  background: currentColor;
-  position: absolute;
-  left: 0;
-  top: 0;
-  animation: ripple 2s linear infinite;
-}
-
-.loader::after {
-  animation-delay: 1s;
-}
-
-@keyframes ripple {
-  0% {
-    transform: scale(0);
-    opacity: 1;
-  }
-  100% {
-    transform: scale(1);
-    opacity: 0;
-  }
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx
@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./PulseLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function PulseLoader({ size = 24, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.module.css
@@ -1,35 +0,0 @@
-.loader {
-  width: 48px;
-  height: 48px;
-  display: inline-block;
-  position: relative;
-}
-
-.loader::after,
-.loader::before {
-  content: "";
-  box-sizing: border-box;
-  width: 100%;
-  height: 100%;
-  border-radius: 50%;
-  background: currentColor;
-  position: absolute;
-  left: 0;
-  top: 0;
-  animation: animloader 2s linear infinite;
-}
-
-.loader::after {
-  animation-delay: 1s;
-}
-
-@keyframes animloader {
-  0% {
-    transform: scale(0);
-    opacity: 1;
-  }
-  100% {
-    transform: scale(1);
-    opacity: 0;
-  }
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.tsx
@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./ScaleLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function ScaleLoader({ size = 48, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css
@@ -1,57 +0,0 @@
-.loader {
-  position: relative;
-  display: inline-block;
-  flex-shrink: 0;
-  transform: rotateZ(45deg);
-  perspective: 1000px;
-  border-radius: 50%;
-  color: currentColor;
-}
-
-.loader::before,
-.loader::after {
-  content: "";
-  display: block;
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: inherit;
-  height: inherit;
-  border-radius: 50%;
-  transform: rotateX(70deg);
-  animation: spin 1s linear infinite;
-}
-
-.loader::after {
-  color: var(--spinner-accent, #a855f7);
-  transform: rotateY(70deg);
-  animation-delay: 0.4s;
-}
-
-@keyframes spin {
-  0%,
-  100% {
-    box-shadow: 0.2em 0 0 0 currentColor;
-  }
-  12% {
-    box-shadow: 0.2em 0.2em 0 0 currentColor;
-  }
-  25% {
-    box-shadow: 0 0.2em 0 0 currentColor;
-  }
-  37% {
-    box-shadow: -0.2em 0.2em 0 0 currentColor;
-  }
-  50% {
-    box-shadow: -0.2em 0 0 0 currentColor;
-  }
-  62% {
-    box-shadow: -0.2em -0.2em 0 0 currentColor;
-  }
-  75% {
-    box-shadow: 0 -0.2em 0 0 currentColor;
-  }
-  87% {
-    box-shadow: 0.2em -0.2em 0 0 currentColor;
-  }
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx
@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./SpinnerLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function SpinnerLoader({ size = 24, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx
@@ -1,235 +0,0 @@
-import { Link } from "@/components/atoms/Link/Link";
-import { Text } from "@/components/atoms/Text/Text";
-import { cn } from "@/lib/utils";
-
-/* ------------------------------------------------------------------ */
-/*  Layout                                                            */
-/* ------------------------------------------------------------------ */
-
-export function ContentGrid({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return <div className={cn("grid gap-2", className)}>{children}</div>;
-}
-
-/* ------------------------------------------------------------------ */
-/*  Card                                                              */
-/* ------------------------------------------------------------------ */
-
-export function ContentCard({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <div
-      className={cn(
-        "min-w-0 rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
-        className,
-      )}
-    >
-      <div className="rounded-lg bg-neutral-100 p-3">{children}</div>
-    </div>
-  );
-}
-
-/** Flex row with a left content area (`children`) and an optional right‑side `action`. */
-export function ContentCardHeader({
-  children,
-  action,
-  className,
-}: {
-  children: React.ReactNode;
-  action?: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <div className={cn("flex items-start justify-between gap-2", className)}>
-      <div className="min-w-0">{children}</div>
-      {action}
-    </div>
-  );
-}
-
-export function ContentCardTitle({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="body-medium"
-      className={cn("truncate text-zinc-800", className)}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentCardSubtitle({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="small"
-      className={cn("mt-0.5 truncate font-mono text-zinc-800", className)}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentCardDescription({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="body" className={cn("mt-2 text-zinc-800", className)}>
-      {children}
-    </Text>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Text                                                              */
-/* ------------------------------------------------------------------ */
-
-export function ContentMessage({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="body" className={cn("text-zinc-800", className)}>
-      {children}
-    </Text>
-  );
-}
-
-export function ContentHint({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="small" className={cn("text-neutral-500", className)}>
-      {children}
-    </Text>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Code / data                                                       */
-/* ------------------------------------------------------------------ */
-
-export function ContentCodeBlock({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <pre
-      className={cn(
-        "whitespace-pre-wrap rounded-lg border bg-black p-3 text-xs text-neutral-200",
-        className,
-      )}
-    >
-      {children}
-    </pre>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Inline elements                                                   */
-/* ------------------------------------------------------------------ */
-
-export function ContentBadge({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="small"
-      as="span"
-      className={cn(
-        "shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-zinc-800",
-        className,
-      )}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentLink({
-  href,
-  children,
-  className,
-  ...rest
-}: Omit<React.ComponentProps<typeof Link>, "className"> & {
-  className?: string;
-}) {
-  return (
-    <Link
-      variant="primary"
-      isExternal
-      href={href}
-      className={cn("shrink-0 text-xs text-purple-500", className)}
-      {...rest}
-    >
-      {children}
-    </Link>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Lists                                                             */
-/* ------------------------------------------------------------------ */
-
-export function ContentSuggestionsList({
-  items,
-  max = 5,
-  className,
-}: {
-  items: string[];
-  max?: number;
-  className?: string;
-}) {
-  if (items.length === 0) return null;
-  return (
-    <ul
-      className={cn(
-        "mt-2 list-disc space-y-1 pl-5 font-sans text-[0.75rem] leading-[1.125rem] text-zinc-800",
-        className,
-      )}
-    >
-      {items.slice(0, max).map((s) => (
-        <li key={s}>{s}</li>
-      ))}
-    </ul>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx
@@ -1,102 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { CaretDownIcon } from "@phosphor-icons/react";
-import { AnimatePresence, motion, useReducedMotion } from "framer-motion";
-import { useId } from "react";
-import { useToolAccordion } from "./useToolAccordion";
-
-interface Props {
-  icon: React.ReactNode;
-  title: React.ReactNode;
-  titleClassName?: string;
-  description?: React.ReactNode;
-  children: React.ReactNode;
-  className?: string;
-  defaultExpanded?: boolean;
-  expanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-export function ToolAccordion({
-  icon,
-  title,
-  titleClassName,
-  description,
-  children,
-  className,
-  defaultExpanded,
-  expanded,
-  onExpandedChange,
-}: Props) {
-  const shouldReduceMotion = useReducedMotion();
-  const contentId = useId();
-  const { isExpanded, toggle } = useToolAccordion({
-    expanded,
-    defaultExpanded,
-    onExpandedChange,
-  });
-
-  return (
-    <div
-      className={cn(
-        "mt-2 w-full rounded-lg border border-slate-200 bg-slate-100 px-3 py-2",
-        className,
-      )}
-    >
-      <button
-        type="button"
-        aria-expanded={isExpanded}
-        aria-controls={contentId}
-        onClick={toggle}
-        className="flex w-full items-center justify-between gap-3 py-1 text-left"
-      >
-        <div className="flex min-w-0 items-center gap-3">
-          <span className="flex shrink-0 items-center text-gray-800">
-            {icon}
-          </span>
-          <div className="min-w-0">
-            <p
-              className={cn(
-                "truncate text-sm font-medium text-gray-800",
-                titleClassName,
-              )}
-            >
-              {title}
-            </p>
-            {description && (
-              <p className="truncate text-xs text-slate-800">{description}</p>
-            )}
-          </div>
-        </div>
-        <CaretDownIcon
-          className={cn(
-            "h-4 w-4 shrink-0 text-slate-500 transition-transform",
-            isExpanded && "rotate-180",
-          )}
-          weight="bold"
-        />
-      </button>
-
-      <AnimatePresence initial={false}>
-        {isExpanded && (
-          <motion.div
-            id={contentId}
-            initial={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
-            exit={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            transition={
-              shouldReduceMotion
-                ? { duration: 0 }
-                : { type: "spring", bounce: 0.35, duration: 0.55 }
-            }
-            className="overflow-hidden"
-            style={{ willChange: "height, opacity, filter" }}
-          >
-            <div className="pb-2 pt-3">{children}</div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts
@@ -1,32 +0,0 @@
-import { useState } from "react";
-
-interface UseToolAccordionOptions {
-  expanded?: boolean;
-  defaultExpanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-interface UseToolAccordionResult {
-  isExpanded: boolean;
-  toggle: () => void;
-}
-
-export function useToolAccordion({
-  expanded,
-  defaultExpanded = false,
-  onExpandedChange,
-}: UseToolAccordionOptions): UseToolAccordionResult {
-  const [uncontrolledExpanded, setUncontrolledExpanded] =
-    useState(defaultExpanded);
-
-  const isControlled = typeof expanded === "boolean";
-  const isExpanded = isControlled ? expanded : uncontrolledExpanded;
-
-  function toggle() {
-    const next = !isExpanded;
-    if (!isControlled) setUncontrolledExpanded(next);
-    onExpandedChange?.(next);
-  }
-
-  return { isExpanded, toggle };
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts
@@ -0,0 +1,56 @@
+"use client";
+
+import { create } from "zustand";
+
+interface CopilotStoreState {
+  isStreaming: boolean;
+  isSwitchingSession: boolean;
+  isCreatingSession: boolean;
+  isInterruptModalOpen: boolean;
+  pendingAction: (() => void) | null;
+}
+
+interface CopilotStoreActions {
+  setIsStreaming: (isStreaming: boolean) => void;
+  setIsSwitchingSession: (isSwitchingSession: boolean) => void;
+  setIsCreatingSession: (isCreating: boolean) => void;
+  openInterruptModal: (onConfirm: () => void) => void;
+  confirmInterrupt: () => void;
+  cancelInterrupt: () => void;
+}
+
+type CopilotStore = CopilotStoreState & CopilotStoreActions;
+
+export const useCopilotStore = create<CopilotStore>((set, get) => ({
+  isStreaming: false,
+  isSwitchingSession: false,
+  isCreatingSession: false,
+  isInterruptModalOpen: false,
+  pendingAction: null,
+
+  setIsStreaming(isStreaming) {
+    set({ isStreaming });
+  },
+
+  setIsSwitchingSession(isSwitchingSession) {
+    set({ isSwitchingSession });
+  },
+
+  setIsCreatingSession(isCreatingSession) {
+    set({ isCreatingSession });
+  },
+
+  openInterruptModal(onConfirm) {
+    set({ isInterruptModalOpen: true, pendingAction: onConfirm });
+  },
+
+  confirmInterrupt() {
+    const { pendingAction } = get();
+    set({ isInterruptModalOpen: false, pendingAction: null });
+    if (pendingAction) pendingAction();
+  },
+
+  cancelInterrupt() {
+    set({ isInterruptModalOpen: false, pendingAction: null });
+  },
+}));
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/helpers.ts
@@ -1,26 +1,6 @@
-import { User } from "@supabase/supabase-js";
+import type { User } from "@supabase/supabase-js";

-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...'";
-}
-
-export function getQuickActions() {
-  return [
-    "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 getGreetingName(user?: User | null) {
+export function getGreetingName(user?: User | null): string {
  if (!user) return "there";
  const metadata = user.user_metadata as Record<string, unknown> | undefined;
  const fullName = metadata?.full_name;
@@ -36,3 +16,30 @@ export function getGreetingName(user?: User | null) {
  }
  return "there";
 }
+
+export function buildCopilotChatUrl(prompt: string): string {
+  const trimmed = prompt.trim();
+  if (!trimmed) return "/copilot/chat";
+  const encoded = encodeURIComponent(trimmed);
+  return `/copilot/chat?prompt=${encoded}`;
+}
+
+export function getQuickActions(): string[] {
+  return [
+    "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...'";
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts
@@ -1,128 +0,0 @@
-import type { UIMessage, UIDataTypes, UITools } from "ai";
-
-interface SessionChatMessage {
-  role: string;
-  content: string | null;
-  tool_call_id: string | null;
-  tool_calls: unknown[] | null;
-}
-
-function coerceSessionChatMessages(
-  rawMessages: unknown[],
-): SessionChatMessage[] {
-  return rawMessages
-    .map((m) => {
-      if (!m || typeof m !== "object") return null;
-      const msg = m as Record<string, unknown>;
-
-      const role = typeof msg.role === "string" ? msg.role : null;
-      if (!role) return null;
-
-      return {
-        role,
-        content:
-          typeof msg.content === "string"
-            ? msg.content
-            : msg.content == null
-              ? null
-              : String(msg.content),
-        tool_call_id:
-          typeof msg.tool_call_id === "string"
-            ? msg.tool_call_id
-            : msg.tool_call_id == null
-              ? null
-              : String(msg.tool_call_id),
-        tool_calls: Array.isArray(msg.tool_calls) ? msg.tool_calls : null,
-      };
-    })
-    .filter((m): m is SessionChatMessage => m !== null);
-}
-
-function safeJsonParse(value: string): unknown {
-  try {
-    return JSON.parse(value) as unknown;
-  } catch {
-    return value;
-  }
-}
-
-function toToolInput(rawArguments: unknown): unknown {
-  if (typeof rawArguments === "string") {
-    const trimmed = rawArguments.trim();
-    return trimmed ? safeJsonParse(trimmed) : {};
-  }
-  if (rawArguments && typeof rawArguments === "object") return rawArguments;
-  return {};
-}
-
-export function convertChatSessionMessagesToUiMessages(
-  sessionId: string,
-  rawMessages: unknown[],
-): UIMessage<unknown, UIDataTypes, UITools>[] {
-  const messages = coerceSessionChatMessages(rawMessages);
-  const toolOutputsByCallId = new Map<string, unknown>();
-
-  for (const msg of messages) {
-    if (msg.role !== "tool") continue;
-    if (!msg.tool_call_id) continue;
-    if (msg.content == null) continue;
-    toolOutputsByCallId.set(msg.tool_call_id, msg.content);
-  }
-
-  const uiMessages: UIMessage<unknown, UIDataTypes, UITools>[] = [];
-
-  messages.forEach((msg, index) => {
-    if (msg.role === "tool") return;
-    if (msg.role !== "user" && msg.role !== "assistant") return;
-
-    const parts: UIMessage<unknown, UIDataTypes, UITools>["parts"] = [];
-
-    if (typeof msg.content === "string" && msg.content.trim()) {
-      parts.push({ type: "text", text: msg.content, state: "done" });
-    }
-
-    if (msg.role === "assistant" && Array.isArray(msg.tool_calls)) {
-      for (const rawToolCall of msg.tool_calls) {
-        if (!rawToolCall || typeof rawToolCall !== "object") continue;
-        const toolCall = rawToolCall as {
-          id?: unknown;
-          function?: { name?: unknown; arguments?: unknown };
-        };
-
-        const toolCallId = String(toolCall.id ?? "").trim();
-        const toolName = String(toolCall.function?.name ?? "").trim();
-        if (!toolCallId || !toolName) continue;
-
-        const input = toToolInput(toolCall.function?.arguments);
-        const output = toolOutputsByCallId.get(toolCallId);
-
-        if (output !== undefined) {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "output-available",
-            input,
-            output: typeof output === "string" ? safeJsonParse(output) : output,
-          });
-        } else {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "input-available",
-            input,
-          });
-        }
-      }
-    }
-
-    if (parts.length === 0) return;
-
-    uiMessages.push({
-      id: `${sessionId}-${index}`,
-      role: msg.role,
-      parts,
-    });
-  });
-
-  return uiMessages;
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
@@ -0,0 +1,13 @@
+"use client";
+import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
+import { Flag } from "@/services/feature-flags/use-get-flag";
+import { type ReactNode } from "react";
+import { CopilotShell } from "./components/CopilotShell/CopilotShell";
+
+export default function CopilotLayout({ children }: { children: ReactNode }) {
+  return (
+    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
+      <CopilotShell>{children}</CopilotShell>
+    </FeatureFlagPage>
+  );
+}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
@@ -1,13 +1,149 @@
 "use client";

-import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
-import { Flag } from "@/services/feature-flags/use-get-flag";
-import { CopilotPage } from "./CopilotPage";
+import { Button } from "@/components/atoms/Button/Button";
+import { Skeleton } from "@/components/atoms/Skeleton/Skeleton";
+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() {
+  const { state, handlers } = useCopilotPage();
+  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,
+    handleSessionNotFound,
+    handleStreamingChange,
+  } = handlers;
+
+  if (hasSession) {
+    return (
+      <div className="flex h-full flex-col">
+        <Chat
+          className="flex-1"
+          initialPrompt={initialPrompt}
+          onSessionNotFound={handleSessionNotFound}
+          onStreamingChange={handleStreamingChange}
+        />
+        <Dialog
+          title="Interrupt current chat?"
+          styling={{ maxWidth: 300, width: "100%" }}
+          controlled={{
+            isOpen: isInterruptModalOpen,
+            set: (open) => {
+              if (!open) cancelInterrupt();
+            },
+          }}
+          onClose={cancelInterrupt}
+        >
+          <Dialog.Content>
+            <div className="flex flex-col gap-4">
+              <Text variant="body">
+                The current chat response will be interrupted. Are you sure you
+                want to continue?
+              </Text>
+              <Dialog.Footer>
+                <Button
+                  type="button"
+                  variant="outline"
+                  onClick={cancelInterrupt}
+                >
+                  Cancel
+                </Button>
+                <Button
+                  type="button"
+                  variant="primary"
+                  onClick={confirmInterrupt}
+                >
+                  Continue
+                </Button>
+              </Dialog.Footer>
+            </div>
+          </Dialog.Content>
+        </Dialog>
+      </div>
+    );
+  }

-export default function Page() {
  return (
-    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
-      <CopilotPage />
-    </FeatureFlagPage>
+    <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">
+            <Skeleton className="mx-auto mb-3 h-8 w-64" />
+            <Skeleton className="mx-auto mb-8 h-6 w-80" />
+            <div className="mb-8">
+              <Skeleton className="mx-auto h-14 w-full rounded-lg" />
+            </div>
+            <div className="flex flex-wrap items-center justify-center gap-3">
+              {Array.from({ length: 4 }).map((_, i) => (
+                <Skeleton key={i} className="h-9 w-48 rounded-md" />
+              ))}
+            </div>
+          </div>
+        ) : (
+          <>
+            <div className="mx-auto max-w-3xl">
+              <Text
+                variant="h3"
+                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">
+                Tell me about your work — I&apos;ll find what to automate.
+              </Text>
+
+              <div className="mb-6">
+                <ChatInput
+                  onSend={startChatWithPrompt}
+                  placeholder={inputPlaceholder}
+                />
+              </div>
+            </div>
+            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+              {quickActions.map((action) => (
+                <Button
+                  key={action}
+                  type="button"
+                  variant="outline"
+                  size="small"
+                  onClick={() => handleQuickAction(action)}
+                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
+                >
+                  {action}
+                </Button>
+              ))}
+            </div>
+          </>
+        )}
+      </div>
+    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx
@@ -1,234 +0,0 @@
-"use client";
-
-import { WarningDiamondIcon } from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
-import {
-  ContentCardDescription,
-  ContentCodeBlock,
-  ContentGrid,
-  ContentHint,
-  ContentLink,
-  ContentMessage,
-} from "../../components/ToolAccordion/AccordionContent";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
-import {
-  ClarificationQuestionsCard,
-  ClarifyingQuestion,
-} from "./components/ClarificationQuestionsCard";
-import {
-  AccordionIcon,
-  formatMaybeJson,
-  getAnimationText,
-  getCreateAgentToolOutput,
-  isAgentPreviewOutput,
-  isAgentSavedOutput,
-  isClarificationNeededOutput,
-  isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
-  ToolIcon,
-  truncateText,
-  type CreateAgentToolOutput,
-} from "./helpers";
-
-export interface CreateAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: CreateAgentToolPart;
-}
-
-function getAccordionMeta(output: CreateAgentToolOutput) {
-  const icon = <AccordionIcon />;
-
-  if (isAgentSavedOutput(output)) {
-    return { icon, title: output.agent_name };
-  }
-  if (isAgentPreviewOutput(output)) {
-    return {
-      icon,
-      title: output.agent_name,
-      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
-    };
-  }
-  if (isClarificationNeededOutput(output)) {
-    const questions = output.questions ?? [];
-    return {
-      icon,
-      title: "Needs clarification",
-      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
-      expanded: true,
-    };
-  }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return {
-      icon,
-      title: "Creating agent, this may take a few minutes. Sit back and relax.",
-    };
-  }
-  return {
-    icon: (
-      <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
-    ),
-    title: "Error",
-    titleClassName: "text-red-500",
-  };
-}
-
-export function CreateAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getCreateAgentToolOutput(part);
-
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-
-  const isOperating =
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output));
-
-  const progress = useAsymptoticProgress(isOperating);
-
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
-
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    const questions =
-      output && isClarificationNeededOutput(output)
-        ? (output.questions ?? [])
-        : [];
-
-    const contextMessage = questions
-      .map((q) => {
-        const answer = answers[q.keyword] || "";
-        return `> ${q.question}\n\n${answer}`;
-      })
-      .join("\n\n");
-
-    onSend(
-      `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isOperating && (
-            <ContentGrid>
-              <ProgressBar value={progress} className="max-w-[280px]" />
-              <ContentHint>
-                This could take a few minutes, grab a coffee ☕
-              </ContentHint>
-            </ContentGrid>
-          )}
-
-          {isAgentSavedOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              <div className="flex flex-wrap gap-2">
-                <ContentLink href={output.library_agent_link}>
-                  Open in library
-                </ContentLink>
-                <ContentLink href={output.agent_page_link}>
-                  Open in builder
-                </ContentLink>
-              </div>
-              <ContentCodeBlock>
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isAgentPreviewOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.description?.trim() && (
-                <ContentCardDescription>
-                  {output.description}
-                </ContentCardDescription>
-              )}
-              <ContentCodeBlock>
-                {truncateText(formatMaybeJson(output.agent_json), 1600)}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isClarificationNeededOutput(output) && (
-            <ClarificationQuestionsCard
-              questions={(output.questions ?? []).map((q) => {
-                const item: ClarifyingQuestion = {
-                  question: q.question,
-                  keyword: q.keyword,
-                };
-                const example =
-                  typeof q.example === "string" && q.example.trim()
-                    ? q.example.trim()
-                    : null;
-                if (example) item.example = example;
-                return item;
-              })}
-              message={output.message}
-              onSubmitAnswers={handleClarificationAnswers}
-            />
-          )}
-
-          {isErrorOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.error && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.error)}
-                </ContentCodeBlock>
-              )}
-              {output.details && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.details)}
-                </ContentCodeBlock>
-              )}
-            </ContentGrid>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx
@@ -1,186 +0,0 @@
-import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
-import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
-import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-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 {
-  PlusCircleIcon,
-  PlusIcon,
-  WarningDiamondIcon,
-} from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
-
-export type CreateAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
-  | AgentPreviewResponse
-  | AgentSavedResponse
-  | ClarificationNeededResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): CreateAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
-      type === ResponseType.agent_preview ||
-      type === ResponseType.agent_saved ||
-      type === ResponseType.clarification_needed ||
-      type === ResponseType.error
-    ) {
-      return output as CreateAgentToolOutput;
-    }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
-    if ("agent_json" in output && "agent_name" in output)
-      return output as AgentPreviewResponse;
-    if ("agent_id" in output && "library_agent_id" in output)
-      return output as AgentSavedResponse;
-    if ("questions" in output) return output as ClarificationNeededResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getCreateAgentToolOutput(
-  part: unknown,
-): CreateAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isOperationStartedOutput(
-  output: CreateAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: CreateAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: CreateAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
-export function isAgentPreviewOutput(
-  output: CreateAgentToolOutput,
-): output is AgentPreviewResponse {
-  return output.type === ResponseType.agent_preview || "agent_json" in output;
-}
-
-export function isAgentSavedOutput(
-  output: CreateAgentToolOutput,
-): output is AgentSavedResponse {
-  return (
-    output.type === ResponseType.agent_saved || "agent_page_link" in output
-  );
-}
-
-export function isClarificationNeededOutput(
-  output: CreateAgentToolOutput,
-): output is ClarificationNeededResponse {
-  return (
-    output.type === ResponseType.clarification_needed || "questions" in output
-  );
-}
-
-export function isErrorOutput(
-  output: CreateAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return "Creating a new agent";
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return "Creating a new agent";
-      if (isOperationStartedOutput(output)) return "Agent creation started";
-      if (isOperationPendingOutput(output)) return "Agent creation in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent creation already in progress";
-      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
-      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
-      if (isClarificationNeededOutput(output)) return "Needs clarification";
-      return "Error creating agent";
-    }
-    case "output-error":
-      return "Error creating agent";
-    default:
-      return "Creating a new agent";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  if (isError) {
-    return (
-      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
-    );
-  }
-  if (isStreaming) {
-    return <OrbitLoader size={24} />;
-  }
-  return <PlusIcon size={14} weight="regular" className="text-neutral-400" />;
-}
-
-export function AccordionIcon() {
-  return <PlusCircleIcon size={32} weight="light" />;
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}
-
-export function truncateText(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx
@@ -1,231 +0,0 @@
-"use client";
-
-import { WarningDiamondIcon } from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
-import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
-import {
-  ContentCardDescription,
-  ContentCodeBlock,
-  ContentGrid,
-  ContentHint,
-  ContentLink,
-  ContentMessage,
-} from "../../components/ToolAccordion/AccordionContent";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
-import {
-  ClarificationQuestionsCard,
-  ClarifyingQuestion,
-} from "../CreateAgent/components/ClarificationQuestionsCard";
-import {
-  AccordionIcon,
-  formatMaybeJson,
-  getAnimationText,
-  getEditAgentToolOutput,
-  isAgentPreviewOutput,
-  isAgentSavedOutput,
-  isClarificationNeededOutput,
-  isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
-  ToolIcon,
-  truncateText,
-  type EditAgentToolOutput,
-} from "./helpers";
-
-export interface EditAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: EditAgentToolPart;
-}
-
-function getAccordionMeta(output: EditAgentToolOutput): {
-  icon: React.ReactNode;
-  title: string;
-  titleClassName?: string;
-  description?: string;
-} {
-  const icon = <AccordionIcon />;
-
-  if (isAgentSavedOutput(output)) {
-    return { icon, title: output.agent_name };
-  }
-  if (isAgentPreviewOutput(output)) {
-    return {
-      icon,
-      title: output.agent_name,
-      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
-    };
-  }
-  if (isClarificationNeededOutput(output)) {
-    const questions = output.questions ?? [];
-    return {
-      icon,
-      title: "Needs clarification",
-      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
-    };
-  }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return { icon: <OrbitLoader size={32} />, title: "Editing agent" };
-  }
-  return {
-    icon: (
-      <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
-    ),
-    title: "Error",
-    titleClassName: "text-red-500",
-  };
-}
-
-export function EditAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getEditAgentToolOutput(part);
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-  const isOperating =
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output));
-  const progress = useAsymptoticProgress(isOperating);
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
-
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    const questions =
-      output && isClarificationNeededOutput(output)
-        ? (output.questions ?? [])
-        : [];
-
-    const contextMessage = questions
-      .map((q) => {
-        const answer = answers[q.keyword] || "";
-        return `> ${q.question}\n\n${answer}`;
-      })
-      .join("\n\n");
-
-    onSend(
-      `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with editing the agent.`,
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isOperating && (
-            <ContentGrid>
-              <ProgressBar value={progress} className="max-w-[280px]" />
-              <ContentHint>
-                This could take a few minutes, grab a coffee ☕
-              </ContentHint>
-            </ContentGrid>
-          )}
-
-          {isAgentSavedOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              <div className="flex flex-wrap gap-2">
-                <ContentLink href={output.library_agent_link}>
-                  Open in library
-                </ContentLink>
-                <ContentLink href={output.agent_page_link}>
-                  Open in builder
-                </ContentLink>
-              </div>
-              <ContentCodeBlock>
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isAgentPreviewOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.description?.trim() && (
-                <ContentCardDescription>
-                  {output.description}
-                </ContentCardDescription>
-              )}
-              <ContentCodeBlock>
-                {truncateText(formatMaybeJson(output.agent_json), 1600)}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isClarificationNeededOutput(output) && (
-            <ClarificationQuestionsCard
-              questions={(output.questions ?? []).map((q) => {
-                const item: ClarifyingQuestion = {
-                  question: q.question,
-                  keyword: q.keyword,
-                };
-                const example =
-                  typeof q.example === "string" && q.example.trim()
-                    ? q.example.trim()
-                    : null;
-                if (example) item.example = example;
-                return item;
-              })}
-              message={output.message}
-              onSubmitAnswers={handleClarificationAnswers}
-            />
-          )}
-
-          {isErrorOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.error && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.error)}
-                </ContentCodeBlock>
-              )}
-              {output.details && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.details)}
-                </ContentCodeBlock>
-              )}
-            </ContentGrid>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx
@@ -1,188 +0,0 @@
-import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
-import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
-import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-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 {
-  NotePencilIcon,
-  PencilLineIcon,
-  WarningDiamondIcon,
-} from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
-
-export type EditAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
-  | AgentPreviewResponse
-  | AgentSavedResponse
-  | ClarificationNeededResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): EditAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
-      type === ResponseType.agent_preview ||
-      type === ResponseType.agent_saved ||
-      type === ResponseType.clarification_needed ||
-      type === ResponseType.error
-    ) {
-      return output as EditAgentToolOutput;
-    }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
-    if ("agent_json" in output && "agent_name" in output)
-      return output as AgentPreviewResponse;
-    if ("agent_id" in output && "library_agent_id" in output)
-      return output as AgentSavedResponse;
-    if ("questions" in output) return output as ClarificationNeededResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getEditAgentToolOutput(
-  part: unknown,
-): EditAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isOperationStartedOutput(
-  output: EditAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: EditAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: EditAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
-export function isAgentPreviewOutput(
-  output: EditAgentToolOutput,
-): output is AgentPreviewResponse {
-  return output.type === ResponseType.agent_preview || "agent_json" in output;
-}
-
-export function isAgentSavedOutput(
-  output: EditAgentToolOutput,
-): output is AgentSavedResponse {
-  return (
-    output.type === ResponseType.agent_saved || "agent_page_link" in output
-  );
-}
-
-export function isClarificationNeededOutput(
-  output: EditAgentToolOutput,
-): output is ClarificationNeededResponse {
-  return (
-    output.type === ResponseType.clarification_needed || "questions" in output
-  );
-}
-
-export function isErrorOutput(
-  output: EditAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return "Editing the agent";
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return "Editing the agent";
-      if (isOperationStartedOutput(output)) return "Agent update started";
-      if (isOperationPendingOutput(output)) return "Agent update in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent update already in progress";
-      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
-      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
-      if (isClarificationNeededOutput(output)) return "Needs clarification";
-      return "Error editing agent";
-    }
-    case "output-error":
-      return "Error editing agent";
-    default:
-      return "Editing the agent";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  if (isError) {
-    return (
-      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
-    );
-  }
-  if (isStreaming) {
-    return <OrbitLoader size={24} />;
-  }
-  return (
-    <PencilLineIcon size={14} weight="regular" className="text-neutral-400" />
-  );
-}
-
-export function AccordionIcon() {
-  return <NotePencilIcon size={32} weight="light" />;
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}
-
-export function truncateText(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/FindAgents.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/FindAgents.tsx
@@ -1,127 +0,0 @@
-"use client";
-
-import { ToolUIPart } from "ai";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import {
-  ContentBadge,
-  ContentCard,
-  ContentCardDescription,
-  ContentCardHeader,
-  ContentCardTitle,
-  ContentGrid,
-  ContentLink,
-} from "../../components/ToolAccordion/AccordionContent";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import {
-  AccordionIcon,
-  getAgentHref,
-  getAnimationText,
-  getFindAgentsOutput,
-  getSourceLabelFromToolType,
-  isAgentsFoundOutput,
-  isErrorOutput,
-  ToolIcon,
-} from "./helpers";
-
-export interface FindAgentsToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: FindAgentsToolPart;
-}
-
-export function FindAgentsTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const output = getFindAgentsOutput(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-
-  const query =
-    typeof part.input === "object" && part.input !== null
-      ? String((part.input as { query?: unknown }).query ?? "").trim()
-      : "";
-
-  const agentsFoundOutput =
-    part.state === "output-available" && output && isAgentsFoundOutput(output)
-      ? output
-      : null;
-
-  const hasAgents =
-    !!agentsFoundOutput &&
-    agentsFoundOutput.agents.length > 0 &&
-    (typeof agentsFoundOutput.count !== "number" ||
-      agentsFoundOutput.count > 0);
-  const totalCount = agentsFoundOutput ? agentsFoundOutput.count : 0;
-  const { source } = getSourceLabelFromToolType(part.type);
-  const scopeText =
-    source === "library"
-      ? "in your library"
-      : source === "marketplace"
-        ? "in marketplace"
-        : "";
-  const accordionDescription = `Found ${totalCount}${scopeText ? ` ${scopeText}` : ""}${
-    query ? ` for "${query}"` : ""
-  }`;
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon
-          toolType={part.type}
-          isStreaming={isStreaming}
-          isError={isError}
-        />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasAgents && agentsFoundOutput && (
-        <ToolAccordion
-          icon={<AccordionIcon toolType={part.type} />}
-          title="Agent results"
-          description={accordionDescription}
-        >
-          <ContentGrid className="sm:grid-cols-2">
-            {agentsFoundOutput.agents.map((agent) => {
-              const href = getAgentHref(agent);
-              const agentSource =
-                agent.source === "library"
-                  ? "Library"
-                  : agent.source === "marketplace"
-                    ? "Marketplace"
-                    : null;
-              return (
-                <ContentCard key={agent.id}>
-                  <ContentCardHeader
-                    action={
-                      href ? <ContentLink href={href}>Open</ContentLink> : null
-                    }
-                  >
-                    <div className="flex items-center gap-2">
-                      <ContentCardTitle>{agent.name}</ContentCardTitle>
-                      {agentSource && (
-                        <ContentBadge>{agentSource}</ContentBadge>
-                      )}
-                    </div>
-                    <ContentCardDescription className="mt-1 line-clamp-2">
-                      {agent.description}
-                    </ContentCardDescription>
-                  </ContentCardHeader>
-                </ContentCard>
-              );
-            })}
-          </ContentGrid>
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/helpers.tsx
@@ -1,187 +0,0 @@
-import type { AgentInfo } from "@/app/api/__generated__/models/agentInfo";
-import type { AgentsFoundResponse } from "@/app/api/__generated__/models/agentsFoundResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import {
-  FolderOpenIcon,
-  MagnifyingGlassIcon,
-  SquaresFourIcon,
-  StorefrontIcon,
-} from "@phosphor-icons/react";
-import { ToolUIPart } from "ai";
-
-export interface FindAgentInput {
-  query: string;
-}
-
-export type FindAgentsOutput =
-  | AgentsFoundResponse
-  | NoResultsResponse
-  | ErrorResponse;
-
-export type FindAgentsToolType =
-  | "tool-find_agent"
-  | "tool-find_library_agent"
-  | (string & {});
-
-function parseOutput(output: unknown): FindAgentsOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.agents_found ||
-      type === ResponseType.no_results ||
-      type === ResponseType.error
-    ) {
-      return output as FindAgentsOutput;
-    }
-    if ("agents" in output && "count" in output)
-      return output as AgentsFoundResponse;
-    if ("suggestions" in output && !("error" in output))
-      return output as NoResultsResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getFindAgentsOutput(part: unknown): FindAgentsOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isAgentsFoundOutput(
-  output: FindAgentsOutput,
-): output is AgentsFoundResponse {
-  return output.type === ResponseType.agents_found || "agents" in output;
-}
-
-export function isNoResultsOutput(
-  output: FindAgentsOutput,
-): output is NoResultsResponse {
-  return (
-    output.type === ResponseType.no_results ||
-    ("suggestions" in output && !("error" in output))
-  );
-}
-
-export function isErrorOutput(
-  output: FindAgentsOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getSourceLabelFromToolType(toolType?: FindAgentsToolType): {
-  source: "marketplace" | "library" | "unknown";
-  label: string;
-} {
-  if (toolType === "tool-find_library_agent") {
-    return { source: "library", label: "Library" };
-  }
-  if (toolType === "tool-find_agent") {
-    return { source: "marketplace", label: "Marketplace" };
-  }
-  return { source: "unknown", label: "Agents" };
-}
-
-export function getAnimationText(part: {
-  type?: FindAgentsToolType;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  const { source } = getSourceLabelFromToolType(part.type);
-  const query = (part.input as FindAgentInput | undefined)?.query?.trim();
-
-  // Action phrase matching legacy ToolCallMessage
-  const actionPhrase =
-    source === "library"
-      ? "Looking for library agents"
-      : "Looking for agents in the marketplace";
-
-  const queryText = query ? ` matching "${query}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `${actionPhrase}${queryText}`;
-
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) {
-        return `${actionPhrase}${queryText}`;
-      }
-      if (isNoResultsOutput(output)) {
-        return `No agents found${queryText}`;
-      }
-      if (isAgentsFoundOutput(output)) {
-        const count = output.count ?? output.agents?.length ?? 0;
-        return `Found ${count} agent${count === 1 ? "" : "s"}${queryText}`;
-      }
-      if (isErrorOutput(output)) {
-        return `Error finding agents${queryText}`;
-      }
-      return `${actionPhrase}${queryText}`;
-    }
-
-    case "output-error":
-      return `Error finding agents${queryText}`;
-
-    default:
-      return actionPhrase;
-  }
-}
-
-export function getAgentHref(agent: AgentInfo): string | null {
-  if (agent.source === "library") {
-    return `/library/agents/${encodeURIComponent(agent.id)}`;
-  }
-
-  const [creator, slug, ...rest] = agent.id.split("/");
-  if (!creator || !slug || rest.length > 0) return null;
-  return `/marketplace/agent/${encodeURIComponent(creator)}/${encodeURIComponent(slug)}`;
-}
-
-export function ToolIcon({
-  toolType,
-  isStreaming,
-  isError,
-}: {
-  toolType?: FindAgentsToolType;
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  const { source } = getSourceLabelFromToolType(toolType);
-  const IconComponent =
-    source === "library" ? MagnifyingGlassIcon : SquaresFourIcon;
-
-  return (
-    <IconComponent
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function AccordionIcon({ toolType }: { toolType?: FindAgentsToolType }) {
-  const { source } = getSourceLabelFromToolType(toolType);
-  const IconComponent = source === "library" ? FolderOpenIcon : StorefrontIcon;
-  return <IconComponent size={32} weight="light" />;
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/FindBlocks.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/FindBlocks.tsx
@@ -1,92 +0,0 @@
-"use client";
-
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import {
-  ContentCard,
-  ContentCardDescription,
-  ContentCardTitle,
-} from "../../components/ToolAccordion/AccordionContent";
-import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
-import type { BlockInfoSummary } from "@/app/api/__generated__/models/blockInfoSummary";
-import { ToolUIPart } from "ai";
-import { HorizontalScroll } from "@/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll";
-import {
-  AccordionIcon,
-  getAnimationText,
-  parseOutput,
-  ToolIcon,
-} from "./helpers";
-
-export interface FindBlockInput {
-  query: string;
-}
-
-export type FindBlockOutput = BlockListResponse;
-
-export interface FindBlockToolPart {
-  type: string;
-  toolName?: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: FindBlockInput | unknown;
-  output?: string | FindBlockOutput | unknown;
-  title?: string;
-}
-
-interface Props {
-  part: FindBlockToolPart;
-}
-
-function BlockCard({ block }: { block: BlockInfoSummary }) {
-  return (
-    <ContentCard className="w-48 shrink-0">
-      <ContentCardTitle>{block.name}</ContentCardTitle>
-      <ContentCardDescription className="mt-1 line-clamp-2">
-        {block.description}
-      </ContentCardDescription>
-    </ContentCard>
-  );
-}
-
-export function FindBlocksTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError = part.state === "output-error";
-
-  const parsed =
-    part.state === "output-available" ? parseOutput(part.output) : null;
-  const hasBlocks = !!parsed && parsed.blocks.length > 0;
-
-  const query = (part.input as FindBlockInput | undefined)?.query?.trim();
-  const accordionDescription = parsed
-    ? `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${query ? ` for "${query}"` : ""}`
-    : undefined;
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasBlocks && parsed && (
-        <ToolAccordion
-          icon={<AccordionIcon />}
-          title="Block results"
-          description={accordionDescription}
-        >
-          <HorizontalScroll dependencyList={[parsed.blocks.length]}>
-            {parsed.blocks.map((block) => (
-              <BlockCard key={block.id} block={block} />
-            ))}
-          </HorizontalScroll>
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/helpers.tsx
@@ -1,75 +0,0 @@
-import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import { CubeIcon, PackageIcon } from "@phosphor-icons/react";
-import { FindBlockInput, FindBlockToolPart } from "./FindBlocks";
-
-export function parseOutput(output: unknown): BlockListResponse | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (type === ResponseType.block_list || "blocks" in output) {
-      return output as BlockListResponse;
-    }
-  }
-  return null;
-}
-
-export function getAnimationText(part: FindBlockToolPart): string {
-  const query = (part.input as FindBlockInput | undefined)?.query?.trim();
-  const queryText = query ? ` matching "${query}"` : "";
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `Searching for blocks${queryText}`;
-
-    case "output-available": {
-      const parsed = parseOutput(part.output);
-      if (parsed) {
-        return `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${queryText}`;
-      }
-      return `Searching for blocks${queryText}`;
-    }
-
-    case "output-error":
-      return `Error finding blocks${queryText}`;
-
-    default:
-      return "Searching for blocks";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  return (
-    <PackageIcon
-      size={14}
-      weight="regular"
-      className={
-        isError
-          ? "text-red-500"
-          : isStreaming
-            ? "text-neutral-500"
-            : "text-neutral-400"
-      }
-    />
-  );
-}
-
-export function AccordionIcon() {
-  return <CubeIcon size={32} weight="light" />;
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx
@@ -1,86 +0,0 @@
-"use client";
-
-import type { ToolUIPart } from "ai";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { ContentMessage } from "../../components/ToolAccordion/AccordionContent";
-import {
-  getAccordionMeta,
-  getAnimationText,
-  getRunAgentToolOutput,
-  isRunAgentAgentDetailsOutput,
-  isRunAgentErrorOutput,
-  isRunAgentExecutionStartedOutput,
-  isRunAgentNeedLoginOutput,
-  isRunAgentSetupRequirementsOutput,
-  ToolIcon,
-} from "./helpers";
-import { ExecutionStartedCard } from "./components/ExecutionStartedCard/ExecutionStartedCard";
-import { AgentDetailsCard } from "./components/AgentDetailsCard/AgentDetailsCard";
-import { SetupRequirementsCard } from "./components/SetupRequirementsCard/SetupRequirementsCard";
-import { ErrorCard } from "./components/ErrorCard/ErrorCard";
-
-export interface RunAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: RunAgentToolPart;
-}
-
-export function RunAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getRunAgentToolOutput(part);
-  const isError =
-    part.state === "output-error" ||
-    (!!output && isRunAgentErrorOutput(output));
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isRunAgentExecutionStartedOutput(output) ||
-      isRunAgentAgentDetailsOutput(output) ||
-      isRunAgentSetupRequirementsOutput(output) ||
-      isRunAgentNeedLoginOutput(output) ||
-      isRunAgentErrorOutput(output));
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isRunAgentExecutionStartedOutput(output) && (
-            <ExecutionStartedCard output={output} />
-          )}
-
-          {isRunAgentAgentDetailsOutput(output) && (
-            <AgentDetailsCard output={output} />
-          )}
-
-          {isRunAgentSetupRequirementsOutput(output) && (
-            <SetupRequirementsCard output={output} />
-          )}
-
-          {isRunAgentNeedLoginOutput(output) && (
-            <ContentMessage>{output.message}</ContentMessage>
-          )}
-
-          {isRunAgentErrorOutput(output) && <ErrorCard output={output} />}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/AgentDetailsCard.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/AgentDetailsCard.tsx
@@ -1,116 +0,0 @@
-"use client";
-
-import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
-import { AnimatePresence, motion } from "framer-motion";
-import { useState } from "react";
-import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import { ContentMessage } from "../../../../components/ToolAccordion/AccordionContent";
-import { buildInputSchema } from "./helpers";
-
-interface Props {
-  output: AgentDetailsResponse;
-}
-
-export function AgentDetailsCard({ output }: Props) {
-  const { onSend } = useCopilotChatActions();
-  const [showInputForm, setShowInputForm] = useState(false);
-  const [inputValues, setInputValues] = useState<Record<string, unknown>>({});
-
-  function handleRunWithExamples() {
-    onSend(
-      `Run the agent "${output.agent.name}" with placeholder/example values so I can test it.`,
-    );
-  }
-
-  function handleRunWithInputs() {
-    const nonEmpty = Object.fromEntries(
-      Object.entries(inputValues).filter(
-        ([, v]) => v !== undefined && v !== null && v !== "",
-      ),
-    );
-    onSend(
-      `Run the agent "${output.agent.name}" with these inputs: ${JSON.stringify(nonEmpty, null, 2)}`,
-    );
-    setShowInputForm(false);
-    setInputValues({});
-  }
-
-  return (
-    <div className="grid gap-2">
-      <ContentMessage>
-        Run this agent with example values or your own inputs.
-      </ContentMessage>
-
-      <div className="flex gap-2 pt-4">
-        <Button size="small" className="w-fit" onClick={handleRunWithExamples}>
-          Run with example values
-        </Button>
-        <Button
-          variant="outline"
-          size="small"
-          className="w-fit"
-          onClick={() => setShowInputForm((prev) => !prev)}
-        >
-          Run with my inputs
-        </Button>
-      </div>
-
-      <AnimatePresence initial={false}>
-        {showInputForm && buildInputSchema(output.agent.inputs) && (
-          <motion.div
-            initial={{ height: 0, opacity: 0, filter: "blur(6px)" }}
-            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
-            exit={{ height: 0, opacity: 0, filter: "blur(6px)" }}
-            transition={{
-              height: { type: "spring", bounce: 0.15, duration: 0.5 },
-              opacity: { duration: 0.25 },
-              filter: { duration: 0.2 },
-            }}
-            className="overflow-hidden"
-            style={{ willChange: "height, opacity, filter" }}
-          >
-            <div className="mt-4 rounded-2xl border bg-background p-3 pt-4">
-              <Text variant="body-medium">Enter your inputs</Text>
-              <FormRenderer
-                jsonSchema={buildInputSchema(output.agent.inputs)!}
-                handleChange={(v) => setInputValues(v.formData ?? {})}
-                uiSchema={{
-                  "ui:submitButtonOptions": { norender: true },
-                }}
-                initialValues={inputValues}
-                formContext={{
-                  showHandles: false,
-                  size: "small",
-                }}
-              />
-              <div className="-mt-8 flex gap-2">
-                <Button
-                  variant="primary"
-                  size="small"
-                  className="w-fit"
-                  onClick={handleRunWithInputs}
-                >
-                  Run
-                </Button>
-                <Button
-                  variant="secondary"
-                  size="small"
-                  className="w-fit"
-                  onClick={() => {
-                    setShowInputForm(false);
-                    setInputValues({});
-                  }}
-                >
-                  Cancel
-                </Button>
-              </div>
-            </div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Otto	92c74fec19	Merge branch 'dev' into fix/code-review-cosmetics-zamil Resolved conflict in run_block.py by keeping dev's COPILOT_EXCLUDED_BLOCK_TYPES check.	2026-02-09 08:03:44 +00:00
Otto	a093d57ed2	fix: address CodeRabbit review bugs - customize_agent.py: Strip whitespace from split parts of agent_id - edit_agent.py: Use model_config instead of deprecated class Config - edit_agent.py: Fix undefined agent_id/changes → params.agent_id/params.changes - find_library_agent.py: Remove docstrings per coding guidelines - get_doc_page.py: Fix undefined path → params.path - run_block.py: Fix undefined block_id → params.block_id - workspace_files.py: Fix undefined include_all_sessions → params.include_all_sessions	2026-02-04 09:17:44 +00:00
Otto	6692f39cbd	refactor(copilot): add Pydantic input models to all tools Convert all CoPilot tools from kwargs.get() pattern to Pydantic models: Tools updated: - find_agent.py: FindAgentInput - find_library_agent.py: FindLibraryAgentInput - find_block.py: FindBlockInput - search_docs.py: SearchDocsInput - get_doc_page.py: GetDocPageInput - create_agent.py: CreateAgentInput - edit_agent.py: EditAgentInput - run_block.py: RunBlockInput - workspace_files.py: 4 input models (List/Read/Write/Delete) Benefits: - Type safety with automatic validation - Consistent string stripping via field_validators - Better IDE support and error messages - Cleaner _execute methods using params object Addresses ntindle review feedback about kwargs pattern.	2026-02-04 09:05:18 +00:00
Otto	aeba28266c	refactor(copilot): use Pydantic models and match/case in customize_agent Addresses review feedback from ntindle on PR #11943: 1. Use typed parameters instead of kwargs.get(): - Added CustomizeAgentInput Pydantic model with field_validator - Tool now uses params = CustomizeAgentInput(**kwargs) pattern 2. Use match/case for cleaner pattern matching: - Extracted response handling to _handle_customization_result method - Uses match result_type: case 'error' \| 'clarifying_questions' \| _ 3. Improved code organization: - Split monolithic _execute into smaller focused methods - _handle_customization_result for response type handling - _save_or_preview_agent for final save/preview logic	2026-02-04 08:54:27 +00:00
Otto	6d8c83c039	refactor(backend): move local imports to module level in chat service Addresses review feedback from PRs #11937, #11856: - Move uuid import to top level (was duplicated in 3 functions) - Move compress_context import to top level - Remove redundant local imports for cast and ChatCompletionMessageParam (already imported at module level) Refs: - https://github.com/Significant-Gravitas/AutoGPT/pull/11937#discussion_r2761107861 - https://github.com/Significant-Gravitas/AutoGPT/pull/11856#discussion_r2761558008 - https://github.com/Significant-Gravitas/AutoGPT/pull/11856#discussion_r2761559661	2026-02-04 03:33:15 +00:00