fix(copilot): merge split assistant messages to prevent Anthropic API errors

Long-running tool flows created two consecutive assistant messages (one
with text, one with tool_calls), causing Anthropic's API to reject the
request with "unexpected tool_use_id found in tool_result blocks".

- Add defensive merge of consecutive assistant messages in to_openai_messages()
- Fix _yield_tool_call to add tool_calls to existing assistant instead of creating new one
- Use extend instead of assign for accumulated_tool_calls to prevent overwrites

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -22,7 +22,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ github.event.workflow_run.head_branch }}
           fetch-depth: 0

.github/workflows/claude-dependabot.yml

@@ -30,7 +30,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 1
 

.github/workflows/claude.yml

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

.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@v4
+      uses: actions/checkout@v6
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL

.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@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           submodules: true

.github/workflows/docs-block-sync.yml

@@ -23,7 +23,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 1
 

.github/workflows/docs-claude-review.yml

@@ -23,7 +23,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 

.github/workflows/docs-enhance.yml

@@ -28,7 +28,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 1
 

.github/workflows/platform-autogpt-deploy-dev.yaml

@@ -25,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         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@v3
+        uses: peter-evans/repository-dispatch@v4
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

.github/workflows/platform-autogpt-deploy-prod.yml

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         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@v3
+        uses: peter-evans/repository-dispatch@v4
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

.github/workflows/platform-backend-ci.yml

@@ -68,7 +68,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
           submodules: true

.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@v3
+        uses: peter-evans/repository-dispatch@v4
         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@v3
+        uses: peter-evans/repository-dispatch@v4
         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@v3
+        uses: peter-evans/repository-dispatch@v4
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

.github/workflows/platform-frontend-ci.yml

@@ -31,7 +31,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Check for component changes
         uses: dorny/paths-filter@v3
@@ -71,7 +71,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Node.js
         uses: actions/setup-node@v6
@@ -107,7 +107,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
 
@@ -148,7 +148,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -277,7 +277,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 

.github/workflows/platform-fullstack-ci.yml

@@ -29,7 +29,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Node.js
         uses: actions/setup-node@v6
@@ -56,14 +56,14 @@ jobs:
         run: pnpm install --frozen-lockfile
 
   types:
-    runs-on: ubuntu-latest
+    runs-on: big-boi
     needs: setup
     strategy:
       fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           submodules: recursive
 
@@ -85,7 +85,7 @@ jobs:
 
       - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
+          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
 
       - name: Restore dependencies cache
         uses: actions/cache@v5

.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@v4
+      - uses: actions/checkout@v6
         # with:
           # fetch-depth: 0
       - name: Set up Python

autogpt_platform/autogpt_libs/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 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 == \"3.10\""
+markers = "python_version < \"3.11\""
 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"},
@@ -326,100 +326,118 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.10.5"
+version = "7.13.4"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["dev"]
 files = [
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-    {file = "coverage-7.10.5-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:73269df37883e02d460bee0cc16be90509faea1e3bd105d77360b512d5bb9c33"},
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+    {file = "coverage-7.13.4-py3-none-any.whl", hash = "sha256:1af1641e57cf7ba1bd67d677c9abdbcd6cc2ab7da3bca7fa1e2b7e50e65f2ad0"},
+    {file = "coverage-7.13.4.tar.gz", hash = "sha256:e5c8f6ed1e61a8b2dcdf31eb0b9bbf0130750ca79c1c49eb898e2ad86f5ccc91"},
 ]
 
 [package.dependencies]
@@ -523,7 +541,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
     {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
     {file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
@@ -2162,23 +2180,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
 [[package]]
 name = "pytest-cov"
-version = "6.2.1"
+version = "7.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
-    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
+    {file = "pytest_cov-7.0.0-py3-none-any.whl", hash = "sha256:3b8e9558b16cc1479da72058bdecf8073661c7f57f7d3c5f22a1c23507f2d861"},
+    {file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
 ]
 
 [package.dependencies]
-coverage = {version = ">=7.5", extras = ["toml"]}
+coverage = {version = ">=7.10.6", extras = ["toml"]}
 pluggy = ">=1.2"
-pytest = ">=6.2.5"
+pytest = ">=7"
 
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
+testing = ["process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "pytest-mock"
@@ -2545,7 +2563,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 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"},
@@ -2893,4 +2911,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"
+content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"

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 = "^6.2.1"
+pytest-cov = "^7.0.0"
 ruff = "^0.15.0"
 
 [build-system]

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

@@ -93,6 +93,12 @@ 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):

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

@@ -45,10 +45,7 @@ async def create_chat_session(
         successfulAgentRuns=SafeJson({}),
         successfulAgentSchedules=SafeJson({}),
     )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
+    return await PrismaChatSession.prisma().create(data=data)
 
 
 async def update_chat_session(

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

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any
+from typing import Any, cast
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -104,6 +104,26 @@ class ChatSession(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
+    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
+        """Attach a tool_call to the current turn's assistant message.
+
+        Searches backwards for the most recent assistant message (stopping at
+        any user message boundary). If found, appends the tool_call to it.
+        Otherwise creates a new assistant message with the tool_call.
+        """
+        for msg in reversed(self.messages):
+            if msg.role == "user":
+                break
+            if msg.role == "assistant":
+                if not msg.tool_calls:
+                    msg.tool_calls = []
+                msg.tool_calls.append(tool_call)
+                return
+
+        self.messages.append(
+            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
+        )
+
     @staticmethod
     def new(user_id: str) -> "ChatSession":
         return ChatSession(
@@ -172,6 +192,47 @@ class ChatSession(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
+    @staticmethod
+    def _merge_consecutive_assistant_messages(
+        messages: list[ChatCompletionMessageParam],
+    ) -> list[ChatCompletionMessageParam]:
+        """Merge consecutive assistant messages into single messages.
+
+        Long-running tool flows can create split assistant messages: one with
+        text content and another with tool_calls. Anthropic's API requires
+        tool_result blocks to reference a tool_use in the immediately preceding
+        assistant message, so these splits cause 400 errors via OpenRouter.
+        """
+        if len(messages) < 2:
+            return messages
+
+        result: list[ChatCompletionMessageParam] = [messages[0]]
+        for msg in messages[1:]:
+            prev = result[-1]
+            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
+                result.append(msg)
+                continue
+
+            prev = cast(ChatCompletionAssistantMessageParam, prev)
+            curr = cast(ChatCompletionAssistantMessageParam, msg)
+
+            curr_content = curr.get("content") or ""
+            if curr_content:
+                prev_content = prev.get("content") or ""
+                prev["content"] = (
+                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
+                )
+
+            curr_tool_calls = curr.get("tool_calls")
+            if curr_tool_calls:
+                prev_tool_calls = prev.get("tool_calls")
+                prev["tool_calls"] = (
+                    list(prev_tool_calls) + list(curr_tool_calls)
+                    if prev_tool_calls
+                    else list(curr_tool_calls)
+                )
+        return result
+
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -258,7 +319,7 @@ class ChatSession(BaseModel):
                         name=message.name or "",
                     )
                 )
-        return messages
+        return self._merge_consecutive_assistant_messages(messages)
 
 
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:

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

@@ -1,4 +1,16 @@
+from typing import cast
+
 import pytest
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionMessageParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionUserMessageParam,
+)
+from openai.types.chat.chat_completion_message_tool_call_param import (
+    ChatCompletionMessageToolCallParam,
+    Function,
+)
 
 from .model import (
     ChatMessage,
@@ -117,3 +129,205 @@ async def test_chatsession_db_storage(setup_test_user, test_user_id):
                 loaded.tool_calls is not None
             ), f"Tool calls missing for {orig.role} message"
             assert len(orig.tool_calls) == len(loaded.tool_calls)
+
+
+# --------------------------------------------------------------------------- #
+#  _merge_consecutive_assistant_messages                                       #
+# --------------------------------------------------------------------------- #
+
+_tc = ChatCompletionMessageToolCallParam(
+    id="tc1", type="function", function=Function(name="do_stuff", arguments="{}")
+)
+_tc2 = ChatCompletionMessageToolCallParam(
+    id="tc2", type="function", function=Function(name="other", arguments="{}")
+)
+
+
+def test_merge_noop_when_no_consecutive_assistants():
+    """Messages without consecutive assistants are returned unchanged."""
+    msgs = [
+        ChatCompletionUserMessageParam(role="user", content="hi"),
+        ChatCompletionAssistantMessageParam(role="assistant", content="hello"),
+        ChatCompletionUserMessageParam(role="user", content="bye"),
+    ]
+    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
+    assert len(merged) == 3
+    assert [m["role"] for m in merged] == ["user", "assistant", "user"]
+
+
+def test_merge_splits_text_and_tool_calls():
+    """The exact bug scenario: text-only assistant followed by tool_calls-only assistant."""
+    msgs = [
+        ChatCompletionUserMessageParam(role="user", content="build agent"),
+        ChatCompletionAssistantMessageParam(
+            role="assistant", content="Let me build that"
+        ),
+        ChatCompletionAssistantMessageParam(
+            role="assistant", content="", tool_calls=[_tc]
+        ),
+        ChatCompletionToolMessageParam(role="tool", content="ok", tool_call_id="tc1"),
+    ]
+    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
+
+    assert len(merged) == 3
+    assert merged[0]["role"] == "user"
+    assert merged[2]["role"] == "tool"
+    a = cast(ChatCompletionAssistantMessageParam, merged[1])
+    assert a["role"] == "assistant"
+    assert a.get("content") == "Let me build that"
+    assert a.get("tool_calls") == [_tc]
+
+
+def test_merge_combines_tool_calls_from_both():
+    """Both consecutive assistants have tool_calls — they get merged."""
+    msgs: list[ChatCompletionAssistantMessageParam] = [
+        ChatCompletionAssistantMessageParam(
+            role="assistant", content="text", tool_calls=[_tc]
+        ),
+        ChatCompletionAssistantMessageParam(
+            role="assistant", content="", tool_calls=[_tc2]
+        ),
+    ]
+    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
+
+    assert len(merged) == 1
+    a = cast(ChatCompletionAssistantMessageParam, merged[0])
+    assert a.get("tool_calls") == [_tc, _tc2]
+    assert a.get("content") == "text"
+
+
+def test_merge_three_consecutive_assistants():
+    """Three consecutive assistants collapse into one."""
+    msgs: list[ChatCompletionAssistantMessageParam] = [
+        ChatCompletionAssistantMessageParam(role="assistant", content="a"),
+        ChatCompletionAssistantMessageParam(role="assistant", content="b"),
+        ChatCompletionAssistantMessageParam(
+            role="assistant", content="", tool_calls=[_tc]
+        ),
+    ]
+    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
+
+    assert len(merged) == 1
+    a = cast(ChatCompletionAssistantMessageParam, merged[0])
+    assert a.get("content") == "a\nb"
+    assert a.get("tool_calls") == [_tc]
+
+
+def test_merge_empty_and_single_message():
+    """Edge cases: empty list and single message."""
+    assert ChatSession._merge_consecutive_assistant_messages([]) == []
+
+    single: list[ChatCompletionMessageParam] = [
+        ChatCompletionUserMessageParam(role="user", content="hi")
+    ]
+    assert ChatSession._merge_consecutive_assistant_messages(single) == single
+
+
+# --------------------------------------------------------------------------- #
+#  add_tool_call_to_current_turn                                               #
+# --------------------------------------------------------------------------- #
+
+_raw_tc = {
+    "id": "tc1",
+    "type": "function",
+    "function": {"name": "f", "arguments": "{}"},
+}
+_raw_tc2 = {
+    "id": "tc2",
+    "type": "function",
+    "function": {"name": "g", "arguments": "{}"},
+}
+
+
+def test_add_tool_call_appends_to_existing_assistant():
+    """When the last assistant is from the current turn, tool_call is added to it."""
+    session = ChatSession.new(user_id="u")
+    session.messages = [
+        ChatMessage(role="user", content="hi"),
+        ChatMessage(role="assistant", content="working on it"),
+    ]
+    session.add_tool_call_to_current_turn(_raw_tc)
+
+    assert len(session.messages) == 2  # no new message created
+    assert session.messages[1].tool_calls == [_raw_tc]
+
+
+def test_add_tool_call_creates_assistant_when_none_exists():
+    """When there's no current-turn assistant, a new one is created."""
+    session = ChatSession.new(user_id="u")
+    session.messages = [
+        ChatMessage(role="user", content="hi"),
+    ]
+    session.add_tool_call_to_current_turn(_raw_tc)
+
+    assert len(session.messages) == 2
+    assert session.messages[1].role == "assistant"
+    assert session.messages[1].tool_calls == [_raw_tc]
+
+
+def test_add_tool_call_does_not_cross_user_boundary():
+    """A user message acts as a boundary — previous assistant is not modified."""
+    session = ChatSession.new(user_id="u")
+    session.messages = [
+        ChatMessage(role="assistant", content="old turn"),
+        ChatMessage(role="user", content="new message"),
+    ]
+    session.add_tool_call_to_current_turn(_raw_tc)
+
+    assert len(session.messages) == 3  # new assistant was created
+    assert session.messages[0].tool_calls is None  # old assistant untouched
+    assert session.messages[2].role == "assistant"
+    assert session.messages[2].tool_calls == [_raw_tc]
+
+
+def test_add_tool_call_multiple_times():
+    """Multiple long-running tool calls accumulate on the same assistant."""
+    session = ChatSession.new(user_id="u")
+    session.messages = [
+        ChatMessage(role="user", content="hi"),
+        ChatMessage(role="assistant", content="doing stuff"),
+    ]
+    session.add_tool_call_to_current_turn(_raw_tc)
+    # Simulate a pending tool result in between (like _yield_tool_call does)
+    session.messages.append(
+        ChatMessage(role="tool", content="pending", tool_call_id="tc1")
+    )
+    session.add_tool_call_to_current_turn(_raw_tc2)
+
+    assert len(session.messages) == 3  # user, assistant, tool — no extra assistant
+    assert session.messages[1].tool_calls == [_raw_tc, _raw_tc2]
+
+
+def test_to_openai_messages_merges_split_assistants():
+    """End-to-end: session with split assistants produces valid OpenAI messages."""
+    session = ChatSession.new(user_id="u")
+    session.messages = [
+        ChatMessage(role="user", content="build agent"),
+        ChatMessage(role="assistant", content="Let me build that"),
+        ChatMessage(
+            role="assistant",
+            content="",
+            tool_calls=[
+                {
+                    "id": "tc1",
+                    "type": "function",
+                    "function": {"name": "create_agent", "arguments": "{}"},
+                }
+            ],
+        ),
+        ChatMessage(role="tool", content="done", tool_call_id="tc1"),
+        ChatMessage(role="assistant", content="Saved!"),
+        ChatMessage(role="user", content="show me an example run"),
+    ]
+    openai_msgs = session.to_openai_messages()
+
+    # The two consecutive assistants at index 1,2 should be merged
+    roles = [m["role"] for m in openai_msgs]
+    assert roles == ["user", "assistant", "tool", "assistant", "user"]
+
+    # The merged assistant should have both content and tool_calls
+    merged = cast(ChatCompletionAssistantMessageParam, openai_msgs[1])
+    assert merged.get("content") == "Let me build that"
+    tc_list = merged.get("tool_calls")
+    assert tc_list is not None and len(list(tc_list)) == 1
+    assert list(tc_list)[0]["id"] == "tc1"

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

@@ -18,6 +18,10 @@ 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"
@@ -57,6 +61,16 @@ 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."""
@@ -64,6 +78,26 @@ 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 ==========
 
 
@@ -117,7 +151,7 @@ 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")
-    # Additional fields for internal use (not part of AI SDK spec but useful)
+    # Keep these for internal backend use
     toolName: str | None = Field(
         default=None, description="Name of the tool that was executed"
     )
@@ -125,6 +159,17 @@ 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 ==========
 

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, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
@@ -17,7 +17,29 @@ 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, StreamStart
+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,
+)
 
 config = ChatConfig()
 
@@ -266,12 +288,36 @@ 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,
@@ -280,14 +326,28 @@ 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():
-        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)
+        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:
             async for chunk in chat_service.stream_chat_completion(
                 session_id,
                 request.message,
@@ -295,25 +355,79 @@ 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)
 
-            # Mark task as completed
+            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,
+                    }
+                },
+            )
             await stream_registry.mark_task_completed(task_id, "completed")
         except Exception as e:
+            elapsed = time_module.perf_counter() - gen_start_time
             logger.error(
-                f"Error in background AI generation for session {session_id}: {e}"
+                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "elapsed_ms": elapsed * 1000,
+                        "error": str(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(
@@ -328,22 +442,70 @@ 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:
-            logger.error(f"Error in SSE stream for task {task_id}: {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)}
+                },
+            )
         finally:
             # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
@@ -357,6 +519,18 @@ 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(
@@ -374,63 +548,90 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def stream_chat_get(
+async def resume_session_stream(
     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),
 ):
     """
-    Stream chat responses for a session (GET - legacy endpoint).
+    Resume an active stream for a session.
 
-    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
+    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.
 
     Args:
-        session_id: The chat session identifier to associate with the streamed messages.
-        message: The user's new message to process.
+        session_id: The chat session identifier.
         user_id: Optional authenticated user ID.
-        is_user_message: Whether the message is a user message.
-    Returns:
-        StreamingResponse: SSE-formatted response chunks.
 
+    Returns:
+        StreamingResponse (SSE) when an active stream exists,
+        or 204 No Content when there is nothing to resume.
     """
-    session = await _validate_and_get_session(session_id, user_id)
+    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)
 
     async def event_generator() -> AsyncGenerator[str, None]:
         chunk_count = 0
         first_chunk_type: str | None = None
-        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),
-                    },
+        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
                 )
-            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"
+            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"
 
     return StreamingResponse(
         event_generator(),
@@ -438,8 +639,8 @@ async def stream_chat_get(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
+            "X-Accel-Buffering": "no",
+            "x-vercel-ai-ui-message-stream": "v1",
         },
     )
 
@@ -751,3 +952,42 @@ 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")

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

@@ -52,8 +52,10 @@ from .response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
+    StreamFinishStep,
     StreamHeartbeat,
     StreamStart,
+    StreamStartStep,
     StreamTextDelta,
     StreamTextEnd,
     StreamTextStart,
@@ -351,6 +353,10 @@ 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.
 
@@ -371,21 +377,45 @@ 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"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
+        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,
+            }
+        },
     )
 
     # 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"Fetched session from Redis: {session.session_id if session else 'None'}, "
-            f"message_count={len(session.messages) if session else 0}"
+            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,
+                }
+            },
         )
     else:
         logger.info(
-            f"Using provided session object: {session.session_id}, "
-            f"message_count={len(session.messages)}"
+            f"[TIMING] Using provided session, messages={len(session.messages)}",
+            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
         )
 
     if not session:
@@ -406,17 +436,25 @@ 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}},
+            )
 
-    logger.info(
-        f"Upserting session: {session.session_id} with user id {session.user_id}, "
-        f"message_count={len(session.messages)}"
-    )
+    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}},
+    )
     assert session, "Session not found"
 
     # Generate title for new sessions on first user message (non-blocking)
@@ -454,7 +492,13 @@ 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(
@@ -479,13 +523,27 @@ async def stream_chat_completion(
     # Generate unique IDs for AI SDK protocol
     import uuid as uuid_module
 
-    message_id = str(uuid_module.uuid4())
+    is_continuation = _continuation_message_id is not None
+    message_id = _continuation_message_id or str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
-    # Yield message start
-    yield StreamStart(messageId=message_id)
+    # 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()
 
     try:
+        logger.info(
+            "[TIMING] Calling _stream_chat_chunks",
+            extra={"json_fields": log_meta},
+        )
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
@@ -585,6 +643,10 @@ 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:
@@ -616,6 +678,8 @@ 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
@@ -665,6 +729,10 @@ 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
@@ -700,6 +768,7 @@ async def stream_chat_completion(
                 error_response = StreamError(errorText=error_message)
                 yield error_response
             if not has_yielded_end:
+                yield StreamFinishStep()
                 yield StreamFinish()
             return
 
@@ -714,6 +783,8 @@ 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
@@ -729,9 +800,13 @@ async def stream_chat_completion(
         # Build the messages list in the correct order
         messages_to_save: list[ChatMessage] = []
 
-        # Add assistant message with tool_calls if any
+        # Add assistant message with tool_calls if any.
+        # Use extend (not assign) to preserve tool_calls already added by
+        # _yield_tool_call for long-running tools.
         if accumulated_tool_calls:
-            assistant_response.tool_calls = accumulated_tool_calls
+            if not assistant_response.tool_calls:
+                assistant_response.tool_calls = []
+            assistant_response.tool_calls.extend(accumulated_tool_calls)
             logger.info(
                 f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
             )
@@ -783,6 +858,8 @@ 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
 
@@ -893,9 +970,21 @@ 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
 
-    logger.info("Starting pure chat stream")
+    # 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)}},
+    )
 
     messages = session.to_openai_messages()
     if system_prompt:
@@ -906,12 +995,18 @@ 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:
@@ -946,9 +1041,19 @@ 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"Creating OpenAI chat completion stream..."
-                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
+                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
+                    extra={
+                        "json_fields": {
+                            **log_meta,
+                            "elapsed_ms": elapsed,
+                            "retry_count": retry_count,
+                        }
+                    },
                 )
 
                 # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -965,6 +1070,11 @@ 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),
@@ -974,6 +1084,11 @@ 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]] = []
@@ -984,10 +1099,13 @@ 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,
@@ -1010,6 +1128,23 @@ 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 "",
@@ -1066,7 +1201,21 @@ async def _stream_chat_chunks(
                                         toolName=tool_calls[idx]["function"]["name"],
                                     )
                                     emitted_start_for_idx.add(idx)
-                logger.info(f"Stream complete. Finish reason: {finish_reason}")
+                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),
+                        }
+                    },
+                )
 
                 # Yield all accumulated tool calls after the stream is complete
                 # This ensures all tool call arguments have been fully received
@@ -1086,6 +1235,12 @@ 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:
@@ -1253,13 +1408,9 @@ async def _yield_tool_call(
                 operation_id=operation_id,
             )
 
-            # Save assistant message with tool_call FIRST (required by LLM)
-            assistant_message = ChatMessage(
-                role="assistant",
-                content="",
-                tool_calls=[tool_calls[yield_idx]],
-            )
-            session.messages.append(assistant_message)
+            # Attach the tool_call to the current turn's assistant message
+            # (or create one if this is a tool-only response with no text).
+            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
 
             # Then save pending tool result
             pending_message = ChatMessage(
@@ -1565,6 +1716,7 @@ 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(
@@ -1681,6 +1833,10 @@ 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
@@ -1811,6 +1967,10 @@ 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
 
@@ -1822,6 +1982,7 @@ async def _generate_llm_continuation_with_streaming(
 
         # 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
@@ -1845,6 +2006,7 @@ 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
@@ -1886,4 +2048,5 @@ 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())

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

@@ -104,6 +104,24 @@ 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,
@@ -114,10 +132,18 @@ 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={
@@ -131,12 +157,22 @@ 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)
 
-    logger.debug(f"Created task {task_id} for session {session_id}")
+    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}},
+    )
 
     return task
 
@@ -156,26 +192,60 @@ 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"Failed to publish chunk for task {task_id}: {e}",
+            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
+            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
             exc_info=True,
         )
 
@@ -200,24 +270,61 @@ 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:
-        logger.debug(f"Task {task_id} not found in Redis")
+        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",
+                }
+            },
+        )
         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"User {user_id} denied access to task {task_id} "
-                f"owned by {task_user_id}"
+                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",
+                    }
+                },
             )
             return None
 
@@ -225,7 +332,19 @@ 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
@@ -244,19 +363,48 @@ async def subscribe_to_task(
                     except Exception as e:
                         logger.warning(f"Failed to replay message: {e}")
 
-    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
+    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,
+            }
+        },
+    )
 
     # 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)
+            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
         )
         # 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
 
 
@@ -264,6 +412,7 @@ 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.
 
@@ -274,10 +423,27 @@ 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()
@@ -287,9 +453,39 @@ 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
@@ -326,10 +522,30 @@ 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"Subscriber queue full for task {task_id}, "
-                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
+                                    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",
+                                        }
+                                    },
                                 )
                                 # Send overflow error with recovery info
                                 try:
@@ -351,15 +567,44 @@ 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}")
+                        logger.warning(
+                            f"Error processing stream message: {e}",
+                            extra={"json_fields": {**log_meta, "error": str(e)}},
+                        )
 
     except asyncio.CancelledError:
-        logger.debug(f"Stream listener cancelled for task {task_id}")
+        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",
+                }
+            },
+        )
         raise  # Re-raise to propagate cancellation
     except Exception as e:
-        logger.error(f"Stream listener error for task {task_id}: {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)}},
+        )
         # On error, send finish to unblock subscriber
         try:
             await asyncio.wait_for(
@@ -368,10 +613,24 @@ async def _stream_listener(
             )
         except (asyncio.TimeoutError, asyncio.QueueFull):
             logger.warning(
-                f"Could not deliver finish event for task {task_id} after error"
+                "Could not deliver finish event after error",
+                extra={"json_fields": log_meta},
             )
     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)
 
 
@@ -598,8 +857,10 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
         ResponseType,
         StreamError,
         StreamFinish,
+        StreamFinishStep,
         StreamHeartbeat,
         StreamStart,
+        StreamStartStep,
         StreamTextDelta,
         StreamTextEnd,
         StreamTextStart,
@@ -613,6 +874,8 @@ 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,

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

@@ -13,10 +13,32 @@ from backend.api.features.chat.tools.models import (
     NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import get_block
+from backend.data.block import BlockType, get_block
 
 logger = logging.getLogger(__name__)
 
+_TARGET_RESULTS = 10
+# Over-fetch to compensate for post-hoc filtering of graph-only blocks.
+# 40 is 2x current removed; speed of query 10 vs 40 is minimial
+_OVERFETCH_PAGE_SIZE = 40
+
+# Block types that only work within graphs and cannot run standalone in CoPilot.
+COPILOT_EXCLUDED_BLOCK_TYPES = {
+    BlockType.INPUT,  # Graph interface definition - data enters via chat, not graph inputs
+    BlockType.OUTPUT,  # Graph interface definition - data exits via chat, not graph outputs
+    BlockType.WEBHOOK,  # Wait for external events - would hang forever in CoPilot
+    BlockType.WEBHOOK_MANUAL,  # Same as WEBHOOK
+    BlockType.NOTE,  # Visual annotation only - no runtime behavior
+    BlockType.HUMAN_IN_THE_LOOP,  # Pauses for human approval - CoPilot IS human-in-the-loop
+    BlockType.AGENT,  # AgentExecutorBlock requires execution_context - use run_agent tool
+}
+
+# Specific block IDs excluded from CoPilot (STANDARD type but still require graph context)
+COPILOT_EXCLUDED_BLOCK_IDS = {
+    # SmartDecisionMakerBlock - dynamically discovers downstream blocks via graph topology
+    "3b191d9f-356f-482d-8238-ba04b6d18381",
+}
+
 
 class FindBlockTool(BaseTool):
     """Tool for searching available blocks."""
@@ -88,7 +110,7 @@ class FindBlockTool(BaseTool):
                 query=query,
                 content_types=[ContentType.BLOCK],
                 page=1,
-                page_size=10,
+                page_size=_OVERFETCH_PAGE_SIZE,
             )
 
             if not results:
@@ -108,60 +130,90 @@ class FindBlockTool(BaseTool):
                 block = get_block(block_id)
 
                 # Skip disabled blocks
-                if block and not block.disabled:
-                    # Get input/output schemas
-                    input_schema = {}
-                    output_schema = {}
-                    try:
-                        input_schema = block.input_schema.jsonschema()
-                    except Exception:
-                        pass
-                    try:
-                        output_schema = block.output_schema.jsonschema()
-                    except Exception:
-                        pass
+                if not block or block.disabled:
+                    continue
 
-                    # Get categories from block instance
-                    categories = []
-                    if hasattr(block, "categories") and block.categories:
-                        categories = [cat.value for cat in block.categories]
+                # Skip blocks excluded from CoPilot (graph-only blocks)
+                if (
+                    block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
+                    or block.id in COPILOT_EXCLUDED_BLOCK_IDS
+                ):
+                    continue
 
-                    # Extract required inputs for easier use
-                    required_inputs: list[BlockInputFieldInfo] = []
-                    if input_schema:
-                        properties = input_schema.get("properties", {})
-                        required_fields = set(input_schema.get("required", []))
-                        # Get credential field names to exclude from required inputs
-                        credentials_fields = set(
-                            block.input_schema.get_credentials_fields().keys()
-                        )
-
-                        for field_name, field_schema in properties.items():
-                            # Skip credential fields - they're handled separately
-                            if field_name in credentials_fields:
-                                continue
-
-                            required_inputs.append(
-                                BlockInputFieldInfo(
-                                    name=field_name,
-                                    type=field_schema.get("type", "string"),
-                                    description=field_schema.get("description", ""),
-                                    required=field_name in required_fields,
-                                    default=field_schema.get("default"),
-                                )
-                            )
-
-                    blocks.append(
-                        BlockInfoSummary(
-                            id=block_id,
-                            name=block.name,
-                            description=block.description or "",
-                            categories=categories,
-                            input_schema=input_schema,
-                            output_schema=output_schema,
-                            required_inputs=required_inputs,
-                        )
+                # Get input/output schemas
+                input_schema = {}
+                output_schema = {}
+                try:
+                    input_schema = block.input_schema.jsonschema()
+                except Exception as e:
+                    logger.debug(
+                        "Failed to generate input schema for block %s: %s",
+                        block_id,
+                        e,
                     )
+                try:
+                    output_schema = block.output_schema.jsonschema()
+                except Exception as e:
+                    logger.debug(
+                        "Failed to generate output schema for block %s: %s",
+                        block_id,
+                        e,
+                    )
+
+                # Get categories from block instance
+                categories = []
+                if hasattr(block, "categories") and block.categories:
+                    categories = [cat.value for cat in block.categories]
+
+                # Extract required inputs for easier use
+                required_inputs: list[BlockInputFieldInfo] = []
+                if input_schema:
+                    properties = input_schema.get("properties", {})
+                    required_fields = set(input_schema.get("required", []))
+                    # Get credential field names to exclude from required inputs
+                    credentials_fields = set(
+                        block.input_schema.get_credentials_fields().keys()
+                    )
+
+                    for field_name, field_schema in properties.items():
+                        # Skip credential fields - they're handled separately
+                        if field_name in credentials_fields:
+                            continue
+
+                        required_inputs.append(
+                            BlockInputFieldInfo(
+                                name=field_name,
+                                type=field_schema.get("type", "string"),
+                                description=field_schema.get("description", ""),
+                                required=field_name in required_fields,
+                                default=field_schema.get("default"),
+                            )
+                        )
+
+                blocks.append(
+                    BlockInfoSummary(
+                        id=block_id,
+                        name=block.name,
+                        description=block.description or "",
+                        categories=categories,
+                        input_schema=input_schema,
+                        output_schema=output_schema,
+                        required_inputs=required_inputs,
+                    )
+                )
+
+                if len(blocks) >= _TARGET_RESULTS:
+                    break
+
+            if blocks and len(blocks) < _TARGET_RESULTS:
+                logger.debug(
+                    "find_block returned %d/%d results for query '%s' "
+                    "(filtered %d excluded/disabled blocks)",
+                    len(blocks),
+                    _TARGET_RESULTS,
+                    query,
+                    len(results) - len(blocks),
+                )
 
             if not blocks:
                 return NoResultsResponse(

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

@@ -0,0 +1,139 @@
+"""Tests for block filtering in FindBlockTool."""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.find_block import (
+    COPILOT_EXCLUDED_BLOCK_IDS,
+    COPILOT_EXCLUDED_BLOCK_TYPES,
+    FindBlockTool,
+)
+from backend.api.features.chat.tools.models import BlockListResponse
+from backend.data.block import BlockType
+
+from ._test_data import make_session
+
+_TEST_USER_ID = "test-user-find-block"
+
+
+def make_mock_block(
+    block_id: str, name: str, block_type: BlockType, disabled: bool = False
+):
+    """Create a mock block for testing."""
+    mock = MagicMock()
+    mock.id = block_id
+    mock.name = name
+    mock.description = f"{name} description"
+    mock.block_type = block_type
+    mock.disabled = disabled
+    mock.input_schema = MagicMock()
+    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
+    mock.input_schema.get_credentials_fields.return_value = {}
+    mock.output_schema = MagicMock()
+    mock.output_schema.jsonschema.return_value = {}
+    mock.categories = []
+    return mock
+
+
+class TestFindBlockFiltering:
+    """Tests for block filtering in FindBlockTool."""
+
+    def test_excluded_block_types_contains_expected_types(self):
+        """Verify COPILOT_EXCLUDED_BLOCK_TYPES contains all graph-only types."""
+        assert BlockType.INPUT in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.OUTPUT in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.WEBHOOK in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.WEBHOOK_MANUAL in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.NOTE in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.HUMAN_IN_THE_LOOP in COPILOT_EXCLUDED_BLOCK_TYPES
+        assert BlockType.AGENT in COPILOT_EXCLUDED_BLOCK_TYPES
+
+    def test_excluded_block_ids_contains_smart_decision_maker(self):
+        """Verify SmartDecisionMakerBlock is in COPILOT_EXCLUDED_BLOCK_IDS."""
+        assert "3b191d9f-356f-482d-8238-ba04b6d18381" in COPILOT_EXCLUDED_BLOCK_IDS
+
+    @pytest.mark.asyncio(loop_scope="session")
+    async def test_excluded_block_type_filtered_from_results(self):
+        """Verify blocks with excluded BlockTypes are filtered from search results."""
+        session = make_session(user_id=_TEST_USER_ID)
+
+        # Mock search returns an INPUT block (excluded) and a STANDARD block (included)
+        search_results = [
+            {"content_id": "input-block-id", "score": 0.9},
+            {"content_id": "standard-block-id", "score": 0.8},
+        ]
+
+        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
+        standard_block = make_mock_block(
+            "standard-block-id", "HTTP Request", BlockType.STANDARD
+        )
+
+        def mock_get_block(block_id):
+            return {
+                "input-block-id": input_block,
+                "standard-block-id": standard_block,
+            }.get(block_id)
+
+        with patch(
+            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
+            new_callable=AsyncMock,
+            return_value=(search_results, 2),
+        ):
+            with patch(
+                "backend.api.features.chat.tools.find_block.get_block",
+                side_effect=mock_get_block,
+            ):
+                tool = FindBlockTool()
+                response = await tool._execute(
+                    user_id=_TEST_USER_ID, session=session, query="test"
+                )
+
+        # Should only return the standard block, not the INPUT block
+        assert isinstance(response, BlockListResponse)
+        assert len(response.blocks) == 1
+        assert response.blocks[0].id == "standard-block-id"
+
+    @pytest.mark.asyncio(loop_scope="session")
+    async def test_excluded_block_id_filtered_from_results(self):
+        """Verify SmartDecisionMakerBlock is filtered from search results."""
+        session = make_session(user_id=_TEST_USER_ID)
+
+        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
+        search_results = [
+            {"content_id": smart_decision_id, "score": 0.9},
+            {"content_id": "normal-block-id", "score": 0.8},
+        ]
+
+        # SmartDecisionMakerBlock has STANDARD type but is excluded by ID
+        smart_block = make_mock_block(
+            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
+        )
+        normal_block = make_mock_block(
+            "normal-block-id", "Normal Block", BlockType.STANDARD
+        )
+
+        def mock_get_block(block_id):
+            return {
+                smart_decision_id: smart_block,
+                "normal-block-id": normal_block,
+            }.get(block_id)
+
+        with patch(
+            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
+            new_callable=AsyncMock,
+            return_value=(search_results, 2),
+        ):
+            with patch(
+                "backend.api.features.chat.tools.find_block.get_block",
+                side_effect=mock_get_block,
+            ):
+                tool = FindBlockTool()
+                response = await tool._execute(
+                    user_id=_TEST_USER_ID, session=session, query="decision"
+                )
+
+        # Should only return normal block, not SmartDecisionMakerBlock
+        assert isinstance(response, BlockListResponse)
+        assert len(response.blocks) == 1
+        assert response.blocks[0].id == "normal-block-id"

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

@@ -0,0 +1,29 @@
+"""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
+    ]

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

@@ -24,6 +24,7 @@ from backend.util.timezone_utils import (
 )
 
 from .base import BaseTool
+from .helpers import get_inputs_from_schema
 from .models import (
     AgentDetails,
     AgentDetailsResponse,
@@ -261,7 +262,7 @@ class RunAgentTool(BaseTool):
                         ),
                         requirements={
                             "credentials": requirements_creds_list,
-                            "inputs": self._get_inputs_list(graph.input_schema),
+                            "inputs": get_inputs_from_schema(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },
                     ),
@@ -369,22 +370,6 @@ 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
@@ -398,7 +383,7 @@ class RunAgentTool(BaseTool):
         suffix: str,
     ) -> str:
         """Build a message describing available inputs for an agent."""
-        inputs_list = self._get_inputs_list(graph.input_schema)
+        inputs_list = get_inputs_from_schema(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"]]
 

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

@@ -8,14 +8,19 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 
 from backend.api.features.chat.model import ChatSession
-from backend.data.block import get_block
+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.execution import ExecutionContext
-from backend.data.model import CredentialsMetaInput
+from backend.data.model import CredentialsFieldInfo, 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,
@@ -24,7 +29,10 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import build_missing_credentials_from_field_info
+from .utils import (
+    build_missing_credentials_from_field_info,
+    match_credentials_to_requirements,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -73,91 +81,6 @@ 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,
@@ -212,11 +135,24 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Check if block is excluded from CoPilot (graph-only blocks)
+        if (
+            block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
+            or block.id in COPILOT_EXCLUDED_BLOCK_IDS
+        ):
+            return ErrorResponse(
+                message=(
+                    f"Block '{block.name}' cannot be run directly in CoPilot. "
+                    "This block is designed for use within graphs only."
+                ),
+                session_id=session_id,
+            )
+
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block, input_data
+        matched_credentials, missing_credentials = (
+            await self._resolve_block_credentials(user_id, block, input_data)
         )
 
         if missing_credentials:
@@ -345,29 +281,75 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
+    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]]:
         """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)
 
-        for field_name, field_schema in properties.items():
-            # Skip credential fields
-            if field_name in credentials_fields:
-                continue
+    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 {}
 
-            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,
-                }
-            )
+        resolved: dict[str, CredentialsFieldInfo] = {}
 
-        return inputs_list
+        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

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

@@ -0,0 +1,106 @@
+"""Tests for block execution guards in RunBlockTool."""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.models import ErrorResponse
+from backend.api.features.chat.tools.run_block import RunBlockTool
+from backend.data.block import BlockType
+
+from ._test_data import make_session
+
+_TEST_USER_ID = "test-user-run-block"
+
+
+def make_mock_block(
+    block_id: str, name: str, block_type: BlockType, disabled: bool = False
+):
+    """Create a mock block for testing."""
+    mock = MagicMock()
+    mock.id = block_id
+    mock.name = name
+    mock.block_type = block_type
+    mock.disabled = disabled
+    mock.input_schema = MagicMock()
+    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
+    mock.input_schema.get_credentials_fields_info.return_value = []
+    return mock
+
+
+class TestRunBlockFiltering:
+    """Tests for block execution guards in RunBlockTool."""
+
+    @pytest.mark.asyncio(loop_scope="session")
+    async def test_excluded_block_type_returns_error(self):
+        """Attempting to execute a block with excluded BlockType returns error."""
+        session = make_session(user_id=_TEST_USER_ID)
+
+        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
+
+        with patch(
+            "backend.api.features.chat.tools.run_block.get_block",
+            return_value=input_block,
+        ):
+            tool = RunBlockTool()
+            response = await tool._execute(
+                user_id=_TEST_USER_ID,
+                session=session,
+                block_id="input-block-id",
+                input_data={},
+            )
+
+        assert isinstance(response, ErrorResponse)
+        assert "cannot be run directly in CoPilot" in response.message
+        assert "designed for use within graphs only" in response.message
+
+    @pytest.mark.asyncio(loop_scope="session")
+    async def test_excluded_block_id_returns_error(self):
+        """Attempting to execute SmartDecisionMakerBlock returns error."""
+        session = make_session(user_id=_TEST_USER_ID)
+
+        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
+        smart_block = make_mock_block(
+            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
+        )
+
+        with patch(
+            "backend.api.features.chat.tools.run_block.get_block",
+            return_value=smart_block,
+        ):
+            tool = RunBlockTool()
+            response = await tool._execute(
+                user_id=_TEST_USER_ID,
+                session=session,
+                block_id=smart_decision_id,
+                input_data={},
+            )
+
+        assert isinstance(response, ErrorResponse)
+        assert "cannot be run directly in CoPilot" in response.message
+
+    @pytest.mark.asyncio(loop_scope="session")
+    async def test_non_excluded_block_passes_guard(self):
+        """Non-excluded blocks pass the filtering guard (may fail later for other reasons)."""
+        session = make_session(user_id=_TEST_USER_ID)
+
+        standard_block = make_mock_block(
+            "standard-id", "HTTP Request", BlockType.STANDARD
+        )
+
+        with patch(
+            "backend.api.features.chat.tools.run_block.get_block",
+            return_value=standard_block,
+        ):
+            tool = RunBlockTool()
+            response = await tool._execute(
+                user_id=_TEST_USER_ID,
+                session=session,
+                block_id="standard-id",
+                input_data={},
+            )
+
+        # Should NOT be an ErrorResponse about CoPilot exclusion
+        # (may be other errors like missing credentials, but not the exclusion guard)
+        if isinstance(response, ErrorResponse):
+            assert "cannot be run directly in CoPilot" not in response.message

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

@@ -8,6 +8,7 @@ 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,
@@ -223,6 +224,99 @@ 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,
@@ -331,8 +425,6 @@ 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)
 
 

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
+import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -686,7 +691,11 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
 
@@ -718,6 +727,87 @@ 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

autogpt_platform/backend/backend/data/execution_queue_test.py

@@ -3,8 +3,6 @@
 import queue
 import threading
 
-import pytest
-
 from backend.data.execution import ExecutionQueue
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -743,6 +743,11 @@ 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 {}
             )

autogpt_platform/backend/backend/data/rabbitmq.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -225,6 +226,10 @@ 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)
@@ -235,7 +240,17 @@ class AsyncRabbitMQ(RabbitMQBase):
 
     @conn_retry("AsyncRabbitMQ", "Acquiring async connection")
     async def connect(self):
-        if self.is_connected:
+        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()
             return
 
         self._connection = await aio_pika.connect_robust(
@@ -291,24 +306,46 @@ class AsyncRabbitMQ(RabbitMQBase):
                     exchange, routing_key=queue.routing_key or queue.name
                 )
 
-    @func_retry
-    async def publish_message(
+    @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(
         self,
         routing_key: str,
         message: str,
         exchange: Optional[Exchange] = None,
         persistent: bool = True,
     ) -> None:
-        if not self.is_ready:
-            await self.connect()
-
-        if self._channel is None:
-            raise RuntimeError("Channel should be established after connect")
+        channel = await self._ensure_channel()
 
         if exchange:
-            exchange_obj = await self._channel.get_exchange(exchange.name)
+            exchange_obj = await channel.get_exchange(exchange.name)
         else:
-            exchange_obj = self._channel.default_exchange
+            exchange_obj = channel.default_exchange
 
         await exchange_obj.publish(
             aio_pika.Message(
@@ -322,9 +359,23 @@ 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:
-        if not self.is_ready:
-            await self.connect()
-        if self._channel is None:
-            raise RuntimeError("Channel should be established after connect")
-        return self._channel
+        return await self._ensure_channel()

autogpt_platform/backend/backend/executor/manager.py

@@ -213,6 +213,9 @@ 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:

autogpt_platform/backend/poetry.lock

@@ -46,14 +46,14 @@ pycares = ">=4.9.0,<5"
 
 [[package]]
 name = "aiofiles"
-version = "24.1.0"
+version = "25.1.0"
 description = "File support for asyncio."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
-    {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
+    {file = "aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695"},
+    {file = "aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2"},
 ]
 
 [[package]]
@@ -1382,14 +1382,14 @@ tzdata = "*"
 
 [[package]]
 name = "fastapi"
-version = "0.128.5"
+version = "0.128.6"
 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-0.128.5-py3-none-any.whl", hash = "sha256:bceec0de8aa6564599c5bcc0593b0d287703562c848271fca8546fd2c87bf4dd"},
-    {file = "fastapi-0.128.5.tar.gz", hash = "sha256:a7173579fc162d6471e3c6fbd9a4b7610c7a3b367bcacf6c4f90d5d022cab711"},
+    {file = "fastapi-0.128.6-py3-none-any.whl", hash = "sha256:bb1c1ef87d6086a7132d0ab60869d6f1ee67283b20fbf84ec0003bd335099509"},
+    {file = "fastapi-0.128.6.tar.gz", hash = "sha256:0cb3946557e792d731b26a42b04912f16367e3c3135ea8290f620e234f2b604f"},
 ]
 
 [package.dependencies]
@@ -3078,14 +3078,14 @@ type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]
 
 [[package]]
 name = "langfuse"
-version = "3.13.0"
+version = "3.14.1"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "langfuse-3.13.0-py3-none-any.whl", hash = "sha256:71912ddac1cc831a65df895eae538a556f564c094ae51473e747426e9ded1a9d"},
-    {file = "langfuse-3.13.0.tar.gz", hash = "sha256:dacea8111ca4442e97dbfec4f8d676cf9709b35357a26e468f8887b95de0012f"},
+    {file = "langfuse-3.14.1-py3-none-any.whl", hash = "sha256:17bed605dbfc9947cbd1738a715f6d27c1b80b6da9f2946586171958fa5820d0"},
+    {file = "langfuse-3.14.1.tar.gz", hash = "sha256:404a6104cd29353d7829aa417ec46565b04917e5599afdda96c5b0865f4bc991"},
 ]
 
 [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 = "14686ee0e2dc446a75d0db145b08dc410dc31c357e25085bb0f9b0174711c4b1"
+content-hash = "c06e96ad49388ba7a46786e9ea55ea2c1a57408e15613237b4bee40a592a12af"

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 = "^0.128.5"
+fastapi = "^0.128.6"
 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 = "^3.11.0"
+langfuse = "^3.14.1"
 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 = "^24.1.0"
+aiofiles = "^25.1.0"
 tiktoken = "^0.12.0"
 aioclamd = "^1.0.0"
 setuptools = "^80.9.0"

autogpt_platform/frontend/Dockerfile

@@ -25,8 +25,12 @@ 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 [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi
+RUN if [ "$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
 
 # 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

autogpt_platform/frontend/next.config.mjs

@@ -1,8 +1,12 @@
 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: true,
+  productionBrowserSourceMaps: enableSourceMaps,
   // Externalize OpenTelemetry packages to fix Turbopack HMR issues
   serverExternalPackages: [
     "@opentelemetry/instrumentation",
@@ -14,9 +18,37 @@ 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: [
@@ -54,9 +86,16 @@ const nextConfig = {
   transpilePackages: ["geist"],
 };
 
-const isDevelopmentBuild = process.env.NODE_ENV !== "production";
+// 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;
 
-export default isDevelopmentBuild
+export default skipSentryPlugin
   ? nextConfig
   : withSentryConfig(nextConfig, {
       // For all available options, see:
@@ -96,7 +135,7 @@ export default isDevelopmentBuild
 
       // This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
       sourcemaps: {
-        disable: false,
+        disable: !enableSourceMaps,
         assets: [".next/**/*.js", ".next/**/*.js.map"],
         ignore: ["**/node_modules/**"],
         deleteSourcemapsAfterUpload: false, // Source is public anyway :)

autogpt_platform/frontend/package.json

@@ -7,7 +7,7 @@
   },
   "scripts": {
     "dev": "pnpm run generate:api:force && next dev --turbo",
-    "build": "next build",
+    "build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
     "start": "next start",
     "start:standalone": "cd .next/standalone && node server.js",
     "lint": "next lint && prettier --check .",
@@ -30,6 +30,7 @@
     "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",
@@ -60,6 +61,10 @@
     "@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",
@@ -68,6 +73,7 @@
     "@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",
@@ -87,7 +93,6 @@
     "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",
@@ -112,9 +117,11 @@
     "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",
@@ -172,7 +179,8 @@
   },
   "pnpm": {
     "overrides": {
-      "@opentelemetry/instrumentation": "0.209.0"
+      "@opentelemetry/instrumentation": "0.209.0",
+      "lodash-es": "4.17.23"
     }
   },
   "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts

@@ -1,4 +1,4 @@
-import { debounce } from "lodash";
+import debounce from "lodash/debounce";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { getQueryClient } from "@/lib/react-query/queryClient";

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-white via-white/80 to-white/0" />
+          <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" />
         )}
         {canScrollRight && (
-          <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" />
+          <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" />
         )}
         {canScrollLeft && (
           <button

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

@@ -0,0 +1,80 @@
+"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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx

@@ -0,0 +1,74 @@
+"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>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/ChatInput.tsx

@@ -6,17 +6,19 @@ import {
   MicrophoneIcon,
   StopIcon,
 } from "@phosphor-icons/react";
+import { ChangeEvent, useCallback } from "react";
 import { RecordingIndicator } from "./components/RecordingIndicator";
 import { useChatInput } from "./useChatInput";
 import { useVoiceRecording } from "./useVoiceRecording";
 
 export interface Props {
-  onSend: (message: string) => void;
+  onSend: (message: string) => void | Promise<void>;
   disabled?: boolean;
   isStreaming?: boolean;
   onStop?: () => void;
   placeholder?: string;
   className?: string;
+  inputId?: string;
 }
 
 export function ChatInput({
@@ -26,14 +28,14 @@ export function ChatInput({
   onStop,
   placeholder = "Type your message...",
   className,
+  inputId = "chat-input",
 }: Props) {
-  const inputId = "chat-input";
   const {
     value,
     setValue,
     handleKeyDown: baseHandleKeyDown,
     handleSubmit,
-    handleChange,
+    handleChange: baseHandleChange,
     hasMultipleLines,
   } = useChatInput({
     onSend,
@@ -60,6 +62,15 @@ export function ChatInput({
     inputId,
   });
 
+  // Block text changes when recording
+  const handleChange = useCallback(
+    (e: ChangeEvent<HTMLTextAreaElement>) => {
+      if (isRecording) return;
+      baseHandleChange(e);
+    },
+    [isRecording, baseHandleChange],
+  );
+
   return (
     <form onSubmit={handleSubmit} className={cn("relative flex-1", className)}>
       <div className="relative">

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/useChatInput.ts

@@ -21,6 +21,7 @@ export function useChatInput({
 }: Args) {
   const [value, setValue] = useState("");
   const [hasMultipleLines, setHasMultipleLines] = useState(false);
+  const [isSending, setIsSending] = useState(false);
 
   useEffect(
     function focusOnMount() {
@@ -100,34 +101,40 @@ export function useChatInput({
     }
   }, [value, maxRows, inputId]);
 
-  const handleSend = () => {
-    if (disabled || !value.trim()) return;
-    onSend(value.trim());
-    setValue("");
-    setHasMultipleLines(false);
-    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
-    const wrapper = document.getElementById(
-      `${inputId}-wrapper`,
-    ) as HTMLDivElement;
-    if (textarea) {
-      textarea.style.height = "auto";
+  async function handleSend() {
+    if (disabled || isSending || !value.trim()) return;
+
+    setIsSending(true);
+    try {
+      await onSend(value.trim());
+      setValue("");
+      setHasMultipleLines(false);
+      const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+      const wrapper = document.getElementById(
+        `${inputId}-wrapper`,
+      ) as HTMLDivElement;
+      if (textarea) {
+        textarea.style.height = "auto";
+      }
+      if (wrapper) {
+        wrapper.style.height = "";
+        wrapper.style.maxHeight = "";
+      }
+    } finally {
+      setIsSending(false);
     }
-    if (wrapper) {
-      wrapper.style.height = "";
-      wrapper.style.maxHeight = "";
-    }
-  };
+  }
 
   function handleKeyDown(event: KeyboardEvent<HTMLTextAreaElement>) {
     if (event.key === "Enter" && !event.shiftKey) {
       event.preventDefault();
-      handleSend();
+      void handleSend();
     }
   }
 
   function handleSubmit(e: FormEvent<HTMLFormElement>) {
     e.preventDefault();
-    handleSend();
+    void handleSend();
   }
 
   function handleChange(e: ChangeEvent<HTMLTextAreaElement>) {
@@ -142,5 +149,6 @@ export function useChatInput({
     handleSubmit,
     handleChange,
     hasMultipleLines,
+    isSending,
   };
 }

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatInput/useVoiceRecording.ts

@@ -38,9 +38,13 @@ export function useVoiceRecording({
   const streamRef = useRef<MediaStream | null>(null);
   const isRecordingRef = useRef(false);
 
-  const isSupported =
-    typeof window !== "undefined" &&
-    !!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia);
+  const [isSupported, setIsSupported] = useState(false);
+
+  useEffect(() => {
+    setIsSupported(
+      !!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia),
+    );
+  }, []);
 
   const clearTimer = useCallback(() => {
     if (timerRef.current) {
@@ -214,17 +218,33 @@ export function useVoiceRecording({
 
   const handleKeyDown = useCallback(
     (event: KeyboardEvent<HTMLTextAreaElement>) => {
-      if (event.key === " " && !value.trim() && !isTranscribing) {
+      // Allow space to toggle recording (start when empty, stop when recording)
+      if (event.key === " " && !isTranscribing) {
+        if (isRecordingRef.current) {
+          // Stop recording on space
+          event.preventDefault();
+          stopRecording();
+          return;
+        } else if (!value.trim()) {
+          // Start recording on space when input is empty
+          event.preventDefault();
+          void startRecording();
+          return;
+        }
+      }
+      // Block all key events when recording (except space handled above)
+      if (isRecordingRef.current) {
         event.preventDefault();
-        toggleRecording();
         return;
       }
       baseHandleKeyDown(event);
     },
-    [value, isTranscribing, toggleRecording, baseHandleKeyDown],
+    [value, isTranscribing, stopRecording, startRecording, baseHandleKeyDown],
   );
 
   const showMicButton = isSupported;
+  // Don't include isRecording in disabled state - we need key events to work
+  // Text input is blocked via handleKeyDown instead
   const isInputDisabled = disabled || isStreaming || isTranscribing;
 
   // Cleanup on unmount

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx

@@ -0,0 +1,274 @@
+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 { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
+import { useEffect, 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);
+
+  useEffect(() => {
+    if (status === "submitted") {
+      setThinkingPhrase(getRandomPhrase());
+    }
+  }, [status]);
+
+  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-3 text-red-600">
+            Error: {error.message}
+          </div>
+        )}
+      </ConversationContent>
+      <ConversationScrollButton />
+    </Conversation>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx

@@ -0,0 +1,188 @@
+"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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx

@@ -0,0 +1,16 @@
+"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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts

@@ -0,0 +1,23 @@
+"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 };

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx

@@ -1,99 +0,0 @@
-"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>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx

@@ -1,70 +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 { 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>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx

@@ -1,91 +0,0 @@
-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>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts

@@ -1,24 +0,0 @@
-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,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx

@@ -1,80 +0,0 @@
-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>
-        );
-      }}
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts

@@ -1,91 +0,0 @@
-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,
-  };
-}

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

@@ -1,106 +0,0 @@
-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");
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts

@@ -1,124 +0,0 @@
-"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,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts

@@ -1,113 +0,0 @@
-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,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx

@@ -0,0 +1,111 @@
+"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>
+  );
+}

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

@@ -1,6 +1,26 @@
-import type { User } from "@supabase/supabase-js";
+import { User } from "@supabase/supabase-js";
 
-export function getGreetingName(user?: User | null): string {
+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) {
   if (!user) return "there";
   const metadata = user.user_metadata as Record<string, unknown> | undefined;
   const fullName = metadata?.full_name;
@@ -16,30 +36,3 @@ export function getGreetingName(user?: User | null): string {
   }
   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...'";
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx

@@ -0,0 +1,140 @@
+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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx

@@ -0,0 +1,54 @@
+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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css

@@ -0,0 +1,69 @@
+.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;
+  }
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx

@@ -0,0 +1,28 @@
+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
+      }
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx

@@ -0,0 +1,26 @@
+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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css

@@ -0,0 +1,34 @@
+.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;
+  }
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx

@@ -0,0 +1,16 @@
+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 }}
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.module.css

@@ -0,0 +1,35 @@
+.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;
+  }
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.tsx

@@ -0,0 +1,16 @@
+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 }}
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css

@@ -0,0 +1,57 @@
+.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;
+  }
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx

@@ -0,0 +1,16 @@
+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 }}
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx

@@ -0,0 +1,235 @@
+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(
+        "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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx

@@ -0,0 +1,102 @@
+"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>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts

@@ -0,0 +1,32 @@
+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 };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts

@@ -1,56 +0,0 @@
-"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 });
-  },
-}));

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

@@ -0,0 +1,128 @@
+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;
+}

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/Untitled

@@ -7,4 +7,4 @@ export function useCopilotSessionId() {
   );
 
   return { urlSessionId, setUrlSessionId };
-}
+}

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/useAsymptoticProgress.ts

@@ -5,17 +5,16 @@ import { useEffect, useRef, useState } from "react";
  * asymptotically approaching but never reaching the max value.
  *
  * Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
- * This creates the "game loading bar" effect where:
+ * This creates a "loading bar" effect where:
  * - 50% is reached at halfLifeSeconds
  * - 75% is reached at 2 * halfLifeSeconds
  * - 87.5% is reached at 3 * halfLifeSeconds
- * - and so on...
  *
  * @param isActive - Whether the progress should be animating
  * @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
  * @param maxProgress - Maximum progress value to approach (default: 100)
  * @param intervalMs - Update interval in milliseconds (default: 100)
- * @returns Current progress value (0-maxProgress)
+ * @returns Current progress value (0–maxProgress)
  */
 export function useAsymptoticProgress(
   isActive: boolean,
@@ -35,8 +34,6 @@ export function useAsymptoticProgress(
 
     const interval = setInterval(() => {
       elapsedTimeRef.current += intervalMs / 1000;
-      // Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
-      // At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
       const newProgress =
         maxProgress *
         (1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));

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

@@ -1,13 +0,0 @@
-"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>
-  );
-}

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

@@ -1,149 +1,13 @@
 "use client";
 
-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>
-    );
-  }
+import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
+import { Flag } from "@/services/feature-flags/use-get-flag";
+import { CopilotPage } from "./CopilotPage";
 
+export default function Page() {
   return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
-      <div className="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>
+    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
+      <CopilotPage />
+    </FeatureFlagPage>
   );
 }

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

@@ -0,0 +1,235 @@
+"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 "./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: <OrbitLoader size={32} />,
+      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>
+  );
+}

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

@@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react";
-import { useState, useEffect, useRef } from "react";
+import { useEffect, useRef, useState } from "react";
 
 export interface ClarifyingQuestion {
   question: string;
@@ -24,12 +24,7 @@ interface Props {
   className?: string;
 }
 
-function getStorageKey(sessionId?: string): string | null {
-  if (!sessionId) return null;
-  return `clarification_answers_${sessionId}`;
-}
-
-export function ClarificationQuestionsWidget({
+export function ClarificationQuestionsCard({
   questions,
   message,
   sessionId,
@@ -241,3 +236,8 @@ export function ClarificationQuestionsWidget({
     </div>
   );
 }
+
+function getStorageKey(sessionId?: string): string | null {
+  if (!sessionId) return null;
+  return `clarification_answers_${sessionId}`;
+}

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

@@ -0,0 +1,186 @@
+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()}…`;
+}

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

@@ -0,0 +1,231 @@
+"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>
+  );
+}

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

@@ -0,0 +1,188 @@
+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()}…`;
+}

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

@@ -0,0 +1,127 @@
+"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>
+  );
+}

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

@@ -0,0 +1,187 @@
+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" />;
+}

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

@@ -0,0 +1,92 @@
+"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>
+  );
+}

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

@@ -0,0 +1,75 @@
+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" />;
+}

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

@@ -0,0 +1,86 @@
+"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>
+  );
+}