Merge branch 'fix/integrations-credential-type' into swiftyos/dev

autogpt_platform/Makefile

@@ -44,7 +44,7 @@ test-data:
 	cd backend && poetry run python test/test_data_creator.py
 
 load-store-agents:
-	cd backend && poetry run load-store-agents
+	cd backend && poetry run python test/load_store_agents.py
 
 help:
 	@echo "Usage: make <target>"

autogpt_platform/backend/backend/blocks/airtable/_api.py

@@ -1371,7 +1371,7 @@ async def create_base(
     if tables:
         params["tables"] = tables
 
-    logger.debug(f"Creating Airtable base with params: {params}")
+    print(params)
 
     response = await Requests().post(
         "https://api.airtable.com/v0/meta/bases",

autogpt_platform/backend/backend/blocks/github/example_payloads/discussion.created.json

@@ -1,108 +0,0 @@
-{
-  "action": "created",
-  "discussion": {
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "category": {
-      "id": 12345678,
-      "node_id": "DIC_kwDOJKSTjM4CXXXX",
-      "repository_id": 614765452,
-      "emoji": ":pray:",
-      "name": "Q&A",
-      "description": "Ask the community for help",
-      "created_at": "2023-03-16T09:21:07Z",
-      "updated_at": "2023-03-16T09:21:07Z",
-      "slug": "q-a",
-      "is_answerable": true
-    },
-    "answer_html_url": null,
-    "answer_chosen_at": null,
-    "answer_chosen_by": null,
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/discussions/9999",
-    "id": 5000000001,
-    "node_id": "D_kwDOJKSTjM4AYYYY",
-    "number": 9999,
-    "title": "How do I configure custom blocks?",
-    "user": {
-      "login": "curious-user",
-      "id": 22222222,
-      "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-      "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-      "url": "https://api.github.com/users/curious-user",
-      "html_url": "https://github.com/curious-user",
-      "type": "User",
-      "site_admin": false
-    },
-    "state": "open",
-    "state_reason": null,
-    "locked": false,
-    "comments": 0,
-    "created_at": "2024-12-01T17:00:00Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Question\n\nI'm trying to create a custom block for my specific use case. I've read the documentation but I'm not sure how to:\n\n1. Define the input/output schema\n2. Handle authentication\n3. Test my block locally\n\nCan someone point me to examples or provide guidance?\n\n## Environment\n\n- AutoGPT Platform version: latest\n- Python: 3.11",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/timeline"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "has_discussions": true,
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "curious-user",
-    "id": 22222222,
-    "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/curious-user",
-    "html_url": "https://github.com/curious-user",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/issues.opened.json

@@ -1,112 +0,0 @@
-{
-  "action": "opened",
-  "issue": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345",
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "labels_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/labels{/name}",
-    "comments_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/comments",
-    "events_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/events",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/issues/12345",
-    "id": 2000000001,
-    "node_id": "I_kwDOJKSTjM5wXXXX",
-    "number": 12345,
-    "title": "Bug: Application crashes when processing large files",
-    "user": {
-      "login": "bug-reporter",
-      "id": 11111111,
-      "node_id": "MDQ6VXNlcjExMTExMTEx",
-      "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-      "url": "https://api.github.com/users/bug-reporter",
-      "html_url": "https://github.com/bug-reporter",
-      "type": "User",
-      "site_admin": false
-    },
-    "labels": [
-      {
-        "id": 5272676214,
-        "node_id": "LA_kwDOJKSTjM8AAAABOkandg",
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/labels/bug",
-        "name": "bug",
-        "color": "d73a4a",
-        "default": true,
-        "description": "Something isn't working"
-      }
-    ],
-    "state": "open",
-    "locked": false,
-    "assignee": null,
-    "assignees": [],
-    "milestone": null,
-    "comments": 0,
-    "created_at": "2024-12-01T16:00:00Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "closed_at": null,
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Description\n\nWhen I try to process a file larger than 100MB, the application crashes with an out of memory error.\n\n## Steps to Reproduce\n\n1. Open the application\n2. Select a file larger than 100MB\n3. Click 'Process'\n4. Application crashes\n\n## Expected Behavior\n\nThe application should handle large files gracefully.\n\n## Environment\n\n- OS: Ubuntu 22.04\n- Python: 3.11\n- AutoGPT Version: 1.0.0",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/timeline",
-    "state_reason": null
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "open_issues_count": 190,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "bug-reporter",
-    "id": 11111111,
-    "node_id": "MDQ6VXNlcjExMTExMTEx",
-    "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/bug-reporter",
-    "html_url": "https://github.com/bug-reporter",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/release.published.json

@@ -1,97 +0,0 @@
-{
-  "action": "published",
-  "release": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789",
-    "assets_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets",
-    "upload_url": "https://uploads.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets{?name,label}",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/tag/v1.0.0",
-    "id": 123456789,
-    "author": {
-      "login": "ntindle",
-      "id": 12345678,
-      "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-      "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-      "gravatar_id": "",
-      "url": "https://api.github.com/users/ntindle",
-      "html_url": "https://github.com/ntindle",
-      "type": "User",
-      "site_admin": false
-    },
-    "node_id": "RE_kwDOJKSTjM4HWwAA",
-    "tag_name": "v1.0.0",
-    "target_commitish": "master",
-    "name": "AutoGPT Platform v1.0.0",
-    "draft": false,
-    "prerelease": false,
-    "created_at": "2024-12-01T10:00:00Z",
-    "published_at": "2024-12-01T12:00:00Z",
-    "assets": [
-      {
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/assets/987654321",
-        "id": 987654321,
-        "node_id": "RA_kwDOJKSTjM4HWwBB",
-        "name": "autogpt-v1.0.0.zip",
-        "label": "Release Package",
-        "content_type": "application/zip",
-        "state": "uploaded",
-        "size": 52428800,
-        "download_count": 0,
-        "created_at": "2024-12-01T11:30:00Z",
-        "updated_at": "2024-12-01T11:35:00Z",
-        "browser_download_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/download/v1.0.0/autogpt-v1.0.0.zip"
-      }
-    ],
-    "tarball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/tarball/v1.0.0",
-    "zipball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/zipball/v1.0.0",
-    "body": "## What's New\n\n- Feature 1: Amazing new capability\n- Feature 2: Performance improvements\n- Bug fixes and stability improvements\n\n## Breaking Changes\n\nNone\n\n## Contributors\n\nThanks to all our contributors!"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T12:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "ntindle",
-    "id": 12345678,
-    "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-    "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/ntindle",
-    "html_url": "https://github.com/ntindle",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/star.created.json

@@ -1,53 +0,0 @@
-{
-  "action": "created",
-  "starred_at": "2024-12-01T15:30:00Z",
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T15:30:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170001,
-    "watchers_count": 170001,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "awesome-contributor",
-    "id": 98765432,
-    "node_id": "MDQ6VXNlcjk4NzY1NDMy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/98765432?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/awesome-contributor",
-    "html_url": "https://github.com/awesome-contributor",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -159,391 +159,3 @@ class GithubPullRequestTriggerBlock(GitHubTriggerBase, Block):
 
 
 # --8<-- [end:GithubTriggerExample]
-
-
-class GithubStarTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub star events - useful for milestone celebrations."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "star.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#star
-            """
-
-            created: bool = False
-            deleted: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The star events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The star event that triggered the webhook ('created' or 'deleted')"
-        )
-        starred_at: str = SchemaField(
-            description="ISO timestamp when the repo was starred (empty if deleted)"
-        )
-        stargazers_count: int = SchemaField(
-            description="Current number of stars on the repository"
-        )
-        repository_name: str = SchemaField(
-            description="Full name of the repository (owner/repo)"
-        )
-        repository_url: str = SchemaField(description="URL to the repository")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="551e0a35-100b-49b7-89b8-3031322239b6",
-            description="This block triggers on GitHub star events. "
-            "Useful for celebrating milestones (e.g., 1k, 10k stars) or tracking engagement.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubStarTriggerBlock.Input,
-            output_schema=GithubStarTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="star.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("starred_at", example_payload.get("starred_at", "")),
-                ("stargazers_count", example_payload["repository"]["stargazers_count"]),
-                ("repository_name", example_payload["repository"]["full_name"]),
-                ("repository_url", example_payload["repository"]["html_url"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        yield "event", input_data.payload["action"]
-        yield "starred_at", input_data.payload.get("starred_at", "")
-        yield "stargazers_count", input_data.payload["repository"]["stargazers_count"]
-        yield "repository_name", input_data.payload["repository"]["full_name"]
-        yield "repository_url", input_data.payload["repository"]["html_url"]
-
-
-class GithubReleaseTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub release events - ideal for announcing new versions."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "release.published.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#release
-            """
-
-            published: bool = False
-            unpublished: bool = False
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            prereleased: bool = False
-            released: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The release events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The release event that triggered the webhook (e.g., 'published')"
-        )
-        release: dict = SchemaField(description="The full release object")
-        release_url: str = SchemaField(description="URL to the release page")
-        tag_name: str = SchemaField(description="The release tag name (e.g., 'v1.0.0')")
-        release_name: str = SchemaField(description="Human-readable release name")
-        body: str = SchemaField(description="Release notes/description")
-        prerelease: bool = SchemaField(description="Whether this is a prerelease")
-        draft: bool = SchemaField(description="Whether this is a draft release")
-        assets: list = SchemaField(description="List of release assets/files")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="2052dd1b-74e1-46ac-9c87-c7a0e057b60b",
-            description="This block triggers on GitHub release events. "
-            "Perfect for automating announcements to Discord, Twitter, or other platforms.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubReleaseTriggerBlock.Input,
-            output_schema=GithubReleaseTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="release.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"published": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("release", example_payload["release"]),
-                ("release_url", example_payload["release"]["html_url"]),
-                ("tag_name", example_payload["release"]["tag_name"]),
-                ("release_name", example_payload["release"]["name"]),
-                ("body", example_payload["release"]["body"]),
-                ("prerelease", example_payload["release"]["prerelease"]),
-                ("draft", example_payload["release"]["draft"]),
-                ("assets", example_payload["release"]["assets"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        release = input_data.payload["release"]
-        yield "event", input_data.payload["action"]
-        yield "release", release
-        yield "release_url", release["html_url"]
-        yield "tag_name", release["tag_name"]
-        yield "release_name", release.get("name", "")
-        yield "body", release.get("body", "")
-        yield "prerelease", release["prerelease"]
-        yield "draft", release["draft"]
-        yield "assets", release["assets"]
-
-
-class GithubIssuesTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub issues events - great for triage and notifications."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "issues.opened.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#issues
-            """
-
-            opened: bool = False
-            edited: bool = False
-            deleted: bool = False
-            closed: bool = False
-            reopened: bool = False
-            assigned: bool = False
-            unassigned: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            transferred: bool = False
-            milestoned: bool = False
-            demilestoned: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The issue events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The issue event that triggered the webhook (e.g., 'opened')"
-        )
-        number: int = SchemaField(description="The issue number")
-        issue: dict = SchemaField(description="The full issue object")
-        issue_url: str = SchemaField(description="URL to the issue")
-        issue_title: str = SchemaField(description="The issue title")
-        issue_body: str = SchemaField(description="The issue body/description")
-        labels: list = SchemaField(description="List of labels on the issue")
-        assignees: list = SchemaField(description="List of assignees")
-        state: str = SchemaField(description="Issue state ('open' or 'closed')")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="b2605464-e486-4bf4-aad3-d8a213c8a48a",
-            description="This block triggers on GitHub issues events. "
-            "Useful for automated triage, notifications, and welcoming first-time contributors.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubIssuesTriggerBlock.Input,
-            output_schema=GithubIssuesTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="issues.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"opened": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["issue"]["number"]),
-                ("issue", example_payload["issue"]),
-                ("issue_url", example_payload["issue"]["html_url"]),
-                ("issue_title", example_payload["issue"]["title"]),
-                ("issue_body", example_payload["issue"]["body"]),
-                ("labels", example_payload["issue"]["labels"]),
-                ("assignees", example_payload["issue"]["assignees"]),
-                ("state", example_payload["issue"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        issue = input_data.payload["issue"]
-        yield "event", input_data.payload["action"]
-        yield "number", issue["number"]
-        yield "issue", issue
-        yield "issue_url", issue["html_url"]
-        yield "issue_title", issue["title"]
-        yield "issue_body", issue.get("body") or ""
-        yield "labels", issue["labels"]
-        yield "assignees", issue["assignees"]
-        yield "state", issue["state"]
-
-
-class GithubDiscussionTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub discussion events - perfect for community Q&A sync."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "discussion.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#discussion
-            """
-
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            answered: bool = False
-            unanswered: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            category_changed: bool = False
-            transferred: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The discussion events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The discussion event that triggered the webhook"
-        )
-        number: int = SchemaField(description="The discussion number")
-        discussion: dict = SchemaField(description="The full discussion object")
-        discussion_url: str = SchemaField(description="URL to the discussion")
-        title: str = SchemaField(description="The discussion title")
-        body: str = SchemaField(description="The discussion body")
-        category: dict = SchemaField(description="The discussion category object")
-        category_name: str = SchemaField(description="Name of the category")
-        state: str = SchemaField(description="Discussion state")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="87f847b3-d81a-424e-8e89-acadb5c9d52b",
-            description="This block triggers on GitHub Discussions events. "
-            "Great for syncing Q&A to Discord or auto-responding to common questions. "
-            "Note: Discussions must be enabled on the repository.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubDiscussionTriggerBlock.Input,
-            output_schema=GithubDiscussionTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="discussion.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["discussion"]["number"]),
-                ("discussion", example_payload["discussion"]),
-                ("discussion_url", example_payload["discussion"]["html_url"]),
-                ("title", example_payload["discussion"]["title"]),
-                ("body", example_payload["discussion"]["body"]),
-                ("category", example_payload["discussion"]["category"]),
-                ("category_name", example_payload["discussion"]["category"]["name"]),
-                ("state", example_payload["discussion"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        discussion = input_data.payload["discussion"]
-        yield "event", input_data.payload["action"]
-        yield "number", discussion["number"]
-        yield "discussion", discussion
-        yield "discussion_url", discussion["html_url"]
-        yield "title", discussion["title"]
-        yield "body", discussion.get("body") or ""
-        yield "category", discussion["category"]
-        yield "category_name", discussion["category"]["name"]
-        yield "state", discussion["state"]

autogpt_platform/backend/backend/blocks/reddit.py

@@ -1,4 +1,3 @@
-import logging
 from datetime import datetime, timezone
 from typing import Iterator, Literal
 
@@ -65,7 +64,6 @@ class RedditComment(BaseModel):
 
 
 settings = Settings()
-logger = logging.getLogger(__name__)
 
 
 def get_praw(creds: RedditCredentials) -> praw.Reddit:
@@ -79,7 +77,7 @@ def get_praw(creds: RedditCredentials) -> praw.Reddit:
     me = client.user.me()
     if not me:
         raise ValueError("Invalid Reddit credentials.")
-    logger.info(f"Logged in as Reddit user: {me.name}")
+    print(f"Logged in as Reddit user: {me.name}")
     return client
 
 

autogpt_platform/backend/backend/data/analytics.py

@@ -1,45 +1,12 @@
 import logging
-from datetime import datetime, timedelta, timezone
-from typing import Optional
 
 import prisma.types
-from pydantic import BaseModel
 
-from backend.data.db import query_raw_with_schema
 from backend.util.json import SafeJson
 
 logger = logging.getLogger(__name__)
 
 
-class AccuracyAlertData(BaseModel):
-    """Alert data when accuracy drops significantly."""
-
-    graph_id: str
-    user_id: Optional[str]
-    drop_percent: float
-    three_day_avg: float
-    seven_day_avg: float
-    detected_at: datetime
-
-
-class AccuracyLatestData(BaseModel):
-    """Latest execution accuracy data point."""
-
-    date: datetime
-    daily_score: Optional[float]
-    three_day_avg: Optional[float]
-    seven_day_avg: Optional[float]
-    fourteen_day_avg: Optional[float]
-
-
-class AccuracyTrendsResponse(BaseModel):
-    """Response model for accuracy trends and alerts."""
-
-    latest_data: AccuracyLatestData
-    alert: Optional[AccuracyAlertData]
-    historical_data: Optional[list[AccuracyLatestData]] = None
-
-
 async def log_raw_analytics(
     user_id: str,
     type: str,
@@ -76,217 +43,3 @@ async def log_raw_metric(
     )
 
     return result
-
-
-async def get_accuracy_trends_and_alerts(
-    graph_id: str,
-    days_back: int = 30,
-    user_id: Optional[str] = None,
-    drop_threshold: float = 10.0,
-    include_historical: bool = False,
-) -> AccuracyTrendsResponse:
-    """Get accuracy trends and detect alerts for a specific graph."""
-    query_template = """
-    WITH daily_scores AS (
-        SELECT 
-            DATE(e."createdAt") as execution_date,
-            AVG(CASE 
-                WHEN e.stats IS NOT NULL 
-                AND e.stats::json->>'correctness_score' IS NOT NULL
-                AND e.stats::json->>'correctness_score' != 'null'
-                THEN (e.stats::json->>'correctness_score')::float * 100
-                ELSE NULL 
-            END) as daily_score
-        FROM {schema_prefix}"AgentGraphExecution" e
-        WHERE e."agentGraphId" = $1::text
-            AND e."isDeleted" = false
-            AND e."createdAt" >= $2::timestamp
-            AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-            {user_filter}
-        GROUP BY DATE(e."createdAt")
-        HAVING COUNT(*) >= 3  -- Need at least 3 executions per day
-    ),
-    trends AS (
-        SELECT 
-            execution_date,
-            daily_score,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
-            ) as three_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
-            ) as seven_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 13 PRECEDING AND CURRENT ROW
-            ) as fourteen_day_avg
-        FROM daily_scores
-    )
-    SELECT *,
-        CASE 
-            WHEN three_day_avg IS NOT NULL AND seven_day_avg IS NOT NULL AND seven_day_avg > 0
-            THEN ((seven_day_avg - three_day_avg) / seven_day_avg * 100)
-            ELSE NULL
-        END as drop_percent
-    FROM trends
-    ORDER BY execution_date DESC
-    {limit_clause}
-    """
-
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    params = [graph_id, start_date]
-    user_filter = ""
-    if user_id:
-        user_filter = 'AND e."userId" = $3::text'
-        params.append(user_id)
-
-    # Determine limit clause
-    limit_clause = "" if include_historical else "LIMIT 1"
-
-    final_query = query_template.format(
-        schema_prefix="{schema_prefix}",
-        user_filter=user_filter,
-        limit_clause=limit_clause,
-    )
-
-    result = await query_raw_with_schema(final_query, *params)
-
-    if not result:
-        return AccuracyTrendsResponse(
-            latest_data=AccuracyLatestData(
-                date=datetime.now(timezone.utc),
-                daily_score=None,
-                three_day_avg=None,
-                seven_day_avg=None,
-                fourteen_day_avg=None,
-            ),
-            alert=None,
-        )
-
-    latest = result[0]
-
-    alert = None
-    if (
-        latest["drop_percent"] is not None
-        and latest["drop_percent"] >= drop_threshold
-        and latest["three_day_avg"] is not None
-        and latest["seven_day_avg"] is not None
-    ):
-        alert = AccuracyAlertData(
-            graph_id=graph_id,
-            user_id=user_id,
-            drop_percent=float(latest["drop_percent"]),
-            three_day_avg=float(latest["three_day_avg"]),
-            seven_day_avg=float(latest["seven_day_avg"]),
-            detected_at=datetime.now(timezone.utc),
-        )
-
-    # Prepare historical data if requested
-    historical_data = None
-    if include_historical:
-        historical_data = []
-        for row in result:
-            historical_data.append(
-                AccuracyLatestData(
-                    date=row["execution_date"],
-                    daily_score=(
-                        float(row["daily_score"])
-                        if row["daily_score"] is not None
-                        else None
-                    ),
-                    three_day_avg=(
-                        float(row["three_day_avg"])
-                        if row["three_day_avg"] is not None
-                        else None
-                    ),
-                    seven_day_avg=(
-                        float(row["seven_day_avg"])
-                        if row["seven_day_avg"] is not None
-                        else None
-                    ),
-                    fourteen_day_avg=(
-                        float(row["fourteen_day_avg"])
-                        if row["fourteen_day_avg"] is not None
-                        else None
-                    ),
-                )
-            )
-
-    return AccuracyTrendsResponse(
-        latest_data=AccuracyLatestData(
-            date=latest["execution_date"],
-            daily_score=(
-                float(latest["daily_score"])
-                if latest["daily_score"] is not None
-                else None
-            ),
-            three_day_avg=(
-                float(latest["three_day_avg"])
-                if latest["three_day_avg"] is not None
-                else None
-            ),
-            seven_day_avg=(
-                float(latest["seven_day_avg"])
-                if latest["seven_day_avg"] is not None
-                else None
-            ),
-            fourteen_day_avg=(
-                float(latest["fourteen_day_avg"])
-                if latest["fourteen_day_avg"] is not None
-                else None
-            ),
-        ),
-        alert=alert,
-        historical_data=historical_data,
-    )
-
-
-class MarketplaceGraphData(BaseModel):
-    """Data structure for marketplace graph monitoring."""
-
-    graph_id: str
-    user_id: Optional[str]
-    execution_count: int
-
-
-async def get_marketplace_graphs_for_monitoring(
-    days_back: int = 30,
-    min_executions: int = 10,
-) -> list[MarketplaceGraphData]:
-    """Get published marketplace graphs with recent executions for monitoring."""
-    query_template = """
-    WITH marketplace_graphs AS (
-        SELECT DISTINCT 
-            slv."agentGraphId" as graph_id,
-            slv."agentGraphVersion" as graph_version
-        FROM {schema_prefix}"StoreListing" sl
-        JOIN {schema_prefix}"StoreListingVersion" slv ON sl."activeVersionId" = slv."id"
-        WHERE sl."hasApprovedVersion" = true
-            AND sl."isDeleted" = false
-    )
-    SELECT DISTINCT 
-        mg.graph_id,
-        NULL as user_id,  -- Marketplace graphs don't have a specific user_id for monitoring
-        COUNT(*) as execution_count
-    FROM marketplace_graphs mg
-    JOIN {schema_prefix}"AgentGraphExecution" e ON e."agentGraphId" = mg.graph_id
-    WHERE e."createdAt" >= $1::timestamp
-        AND e."isDeleted" = false
-        AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-    GROUP BY mg.graph_id
-    HAVING COUNT(*) >= $2
-    ORDER BY execution_count DESC
-    """
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    result = await query_raw_with_schema(query_template, start_date, min_executions)
-
-    return [
-        MarketplaceGraphData(
-            graph_id=row["graph_id"],
-            user_id=row["user_id"],
-            execution_count=int(row["execution_count"]),
-        )
-        for row in result
-    ]

autogpt_platform/backend/backend/data/block.py

@@ -601,18 +601,14 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             async for output_name, output_data in self._execute(input_data, **kwargs):
                 yield output_name, output_data
         except Exception as ex:
-            if isinstance(ex, BlockError):
-                raise ex
-            else:
-                raise (
-                    BlockExecutionError
-                    if isinstance(ex, ValueError)
-                    else BlockUnknownError
-                )(
+            if not isinstance(ex, BlockError):
+                raise BlockUnknownError(
                     message=str(ex),
                     block_name=self.name,
                     block_id=self.id,
                 ) from ex
+            else:
+                raise ex
 
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
         if error := self.input_schema.validate_data(input_data):

autogpt_platform/backend/backend/data/execution.py

@@ -1465,35 +1465,3 @@ async def get_graph_execution_by_share_token(
         created_at=execution.createdAt,
         outputs=outputs,
     )
-
-
-async def get_frequently_executed_graphs(
-    days_back: int = 30,
-    min_executions: int = 10,
-) -> list[dict]:
-    """Get graphs that have been frequently executed for monitoring."""
-    query_template = """
-    SELECT DISTINCT 
-        e."agentGraphId" as graph_id,
-        e."userId" as user_id,
-        COUNT(*) as execution_count
-    FROM {schema_prefix}"AgentGraphExecution" e
-    WHERE e."createdAt" >= $1::timestamp
-        AND e."isDeleted" = false
-        AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-    GROUP BY e."agentGraphId", e."userId"
-    HAVING COUNT(*) >= $2
-    ORDER BY execution_count DESC
-    """
-
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    result = await query_raw_with_schema(query_template, start_date, min_executions)
-
-    return [
-        {
-            "graph_id": row["graph_id"],
-            "user_id": row["user_id"],
-            "execution_count": int(row["execution_count"]),
-        }
-        for row in result
-    ]

autogpt_platform/backend/backend/data/model.py

@@ -22,7 +22,7 @@ from typing import (
 from urllib.parse import urlparse
 from uuid import uuid4
 
-from prisma.enums import CreditTransactionType, OnboardingStep
+from prisma.enums import CreditTransactionType
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -868,20 +868,3 @@ class UserExecutionSummaryStats(BaseModel):
     total_execution_time: float = Field(default=0)
     average_execution_time: float = Field(default=0)
     cost_breakdown: dict[str, float] = Field(default_factory=dict)
-
-
-class UserOnboarding(BaseModel):
-    userId: str
-    completedSteps: list[OnboardingStep]
-    walletShown: bool
-    notified: list[OnboardingStep]
-    rewardedFor: list[OnboardingStep]
-    usageReason: Optional[str]
-    integrations: list[str]
-    otherIntegrations: Optional[str]
-    selectedStoreListingVersionId: Optional[str]
-    agentInput: Optional[dict[str, Any]]
-    onboardingAgentExecutionId: Optional[str]
-    agentRuns: int
-    lastRunAt: Optional[datetime]
-    consecutiveRunDays: int

autogpt_platform/backend/backend/data/notification_bus.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from typing import AsyncGenerator
 
-from pydantic import BaseModel, field_serializer
+from pydantic import BaseModel
 
 from backend.data.event_bus import AsyncRedisEventBus
 from backend.server.model import NotificationPayload
@@ -15,11 +15,6 @@ class NotificationEvent(BaseModel):
     user_id: str
     payload: NotificationPayload
 
-    @field_serializer("payload")
-    def serialize_payload(self, payload: NotificationPayload):
-        """Ensure extra fields survive Redis serialization."""
-        return payload.model_dump()
-
 
 class AsyncRedisNotificationEventBus(AsyncRedisEventBus[NotificationEvent]):
     Model = NotificationEvent  # type: ignore

autogpt_platform/backend/backend/data/onboarding.py

@@ -1,7 +1,6 @@
 import re
-from datetime import datetime, timedelta, timezone
-from typing import Any, Literal, Optional
-from zoneinfo import ZoneInfo
+from datetime import datetime
+from typing import Any, Optional
 
 import prisma
 import pydantic
@@ -9,18 +8,17 @@ from prisma.enums import OnboardingStep
 from prisma.models import UserOnboarding
 from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
 
-from backend.data import execution as execution_db
+from backend.data.block import get_blocks
 from backend.data.credit import get_user_credit_model
+from backend.data.model import CredentialsMetaInput
 from backend.data.notification_bus import (
     AsyncRedisNotificationEventBus,
     NotificationEvent,
 )
-from backend.data.user import get_user_by_id
 from backend.server.model import OnboardingNotificationPayload
 from backend.server.v2.store.model import StoreAgentDetails
 from backend.util.cache import cached
 from backend.util.json import SafeJson
-from backend.util.timezone_utils import get_user_timezone_or_utc
 
 # Mapping from user reason id to categories to search for when choosing agent to show
 REASON_MAPPING: dict[str, list[str]] = {
@@ -33,20 +31,9 @@ REASON_MAPPING: dict[str, list[str]] = {
 POINTS_AGENT_COUNT = 50  # Number of agents to calculate points for
 MIN_AGENT_COUNT = 2  # Minimum number of marketplace agents to enable onboarding
 
-FrontendOnboardingStep = Literal[
-    OnboardingStep.WELCOME,
-    OnboardingStep.USAGE_REASON,
-    OnboardingStep.INTEGRATIONS,
-    OnboardingStep.AGENT_CHOICE,
-    OnboardingStep.AGENT_NEW_RUN,
-    OnboardingStep.AGENT_INPUT,
-    OnboardingStep.CONGRATS,
-    OnboardingStep.MARKETPLACE_VISIT,
-    OnboardingStep.BUILDER_OPEN,
-]
-
 
 class UserOnboardingUpdate(pydantic.BaseModel):
+    completedSteps: Optional[list[OnboardingStep]] = None
     walletShown: Optional[bool] = None
     notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
@@ -55,6 +42,9 @@ class UserOnboardingUpdate(pydantic.BaseModel):
     selectedStoreListingVersionId: Optional[str] = None
     agentInput: Optional[dict[str, Any]] = None
     onboardingAgentExecutionId: Optional[str] = None
+    agentRuns: Optional[int] = None
+    lastRunAt: Optional[datetime] = None
+    consecutiveRunDays: Optional[int] = None
 
 
 async def get_user_onboarding(user_id: str):
@@ -93,6 +83,14 @@ async def reset_user_onboarding(user_id: str):
 async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
     update: UserOnboardingUpdateInput = {}
     onboarding = await get_user_onboarding(user_id)
+    if data.completedSteps is not None:
+        update["completedSteps"] = list(
+            set(data.completedSteps + onboarding.completedSteps)
+        )
+        for step in data.completedSteps:
+            if step not in onboarding.completedSteps:
+                await _reward_user(user_id, onboarding, step)
+                await _send_onboarding_notification(user_id, step)
     if data.walletShown:
         update["walletShown"] = data.walletShown
     if data.notified is not None:
@@ -109,6 +107,12 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["agentInput"] = SafeJson(data.agentInput)
     if data.onboardingAgentExecutionId is not None:
         update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
+    if data.agentRuns is not None and data.agentRuns > onboarding.agentRuns:
+        update["agentRuns"] = data.agentRuns
+    if data.lastRunAt is not None:
+        update["lastRunAt"] = data.lastRunAt
+    if data.consecutiveRunDays is not None:
+        update["consecutiveRunDays"] = data.consecutiveRunDays
 
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
@@ -157,12 +161,14 @@ async def _reward_user(user_id: str, onboarding: UserOnboarding, step: Onboardin
     if step in onboarding.rewardedFor:
         return
 
+    onboarding.rewardedFor.append(step)
     user_credit_model = await get_user_credit_model(user_id)
     await user_credit_model.onboarding_reward(user_id, reward, step)
     await UserOnboarding.prisma().update(
         where={"userId": user_id},
         data={
-            "rewardedFor": list(set(onboarding.rewardedFor + [step])),
+            "completedSteps": list(set(onboarding.completedSteps + [step])),
+            "rewardedFor": onboarding.rewardedFor,
         },
     )
 
@@ -171,52 +177,31 @@ async def complete_onboarding_step(user_id: str, step: OnboardingStep):
     """
     Completes the specified onboarding step for the user if not already completed.
     """
+
     onboarding = await get_user_onboarding(user_id)
     if step not in onboarding.completedSteps:
-        await UserOnboarding.prisma().update(
-            where={"userId": user_id},
-            data={
-                "completedSteps": list(set(onboarding.completedSteps + [step])),
-            },
+        await update_user_onboarding(
+            user_id,
+            UserOnboardingUpdate(completedSteps=onboarding.completedSteps + [step]),
         )
-        await _reward_user(user_id, onboarding, step)
         await _send_onboarding_notification(user_id, step)
 
 
-async def _send_onboarding_notification(
-    user_id: str, step: OnboardingStep | None, event: str = "step_completed"
-):
+async def _send_onboarding_notification(user_id: str, step: OnboardingStep):
     """
-    Sends an onboarding notification to the user.
+    Sends an onboarding notification to the user for the specified step.
     """
     payload = OnboardingNotificationPayload(
         type="onboarding",
-        event=event,
-        step=step,
+        event="step_completed",
+        step=step.value,
     )
     await AsyncRedisNotificationEventBus().publish(
         NotificationEvent(user_id=user_id, payload=payload)
     )
 
 
-async def complete_re_run_agent(user_id: str, graph_id: str) -> None:
-    """
-    Complete RE_RUN_AGENT step when a user runs a graph they've run before.
-    Keeps overhead low by only counting executions if the step is still pending.
-    """
-    onboarding = await get_user_onboarding(user_id)
-    if OnboardingStep.RE_RUN_AGENT in onboarding.completedSteps:
-        return
-
-    # Includes current execution, so count > 1 means there was at least one prior run.
-    previous_exec_count = await execution_db.get_graph_executions_count(
-        user_id=user_id, graph_id=graph_id
-    )
-    if previous_exec_count > 1:
-        await complete_onboarding_step(user_id, OnboardingStep.RE_RUN_AGENT)
-
-
-def _clean_and_split(text: str) -> list[str]:
+def clean_and_split(text: str) -> list[str]:
     """
     Removes all special characters from a string, truncates it to 100 characters,
     and splits it by whitespace and commas.
@@ -239,7 +224,7 @@ def _clean_and_split(text: str) -> list[str]:
     return words
 
 
-def _calculate_points(
+def calculate_points(
     agent, categories: list[str], custom: list[str], integrations: list[str]
 ) -> int:
     """
@@ -283,85 +268,18 @@ def _calculate_points(
     return int(points)
 
 
-def _normalize_datetime(value: datetime | None) -> datetime | None:
-    if value is None:
-        return None
-    if value.tzinfo is None:
-        return value.replace(tzinfo=timezone.utc)
-    return value.astimezone(timezone.utc)
+def get_credentials_blocks() -> dict[str, str]:
+    # Returns a dictionary of block id to credentials field name
+    creds: dict[str, str] = {}
+    blocks = get_blocks()
+    for id, block in blocks.items():
+        for field_name, field_info in block().input_schema.model_fields.items():
+            if field_info.annotation == CredentialsMetaInput:
+                creds[id] = field_name
+    return creds
 
 
-def _calculate_consecutive_run_days(
-    last_run_at: datetime | None, current_consecutive_days: int, user_timezone: str
-) -> tuple[datetime, int]:
-    tz = ZoneInfo(user_timezone)
-    local_now = datetime.now(tz)
-    normalized_last_run = _normalize_datetime(last_run_at)
-
-    if normalized_last_run is None:
-        return local_now.astimezone(timezone.utc), 1
-
-    last_run_local = normalized_last_run.astimezone(tz)
-    last_run_date = last_run_local.date()
-    today = local_now.date()
-
-    if last_run_date == today:
-        return local_now.astimezone(timezone.utc), current_consecutive_days
-
-    if last_run_date == today - timedelta(days=1):
-        return local_now.astimezone(timezone.utc), current_consecutive_days + 1
-
-    return local_now.astimezone(timezone.utc), 1
-
-
-def _get_run_milestone_steps(
-    new_run_count: int, consecutive_days: int
-) -> list[OnboardingStep]:
-    milestones: list[OnboardingStep] = []
-    if new_run_count >= 10:
-        milestones.append(OnboardingStep.RUN_AGENTS)
-    if new_run_count >= 100:
-        milestones.append(OnboardingStep.RUN_AGENTS_100)
-    if consecutive_days >= 3:
-        milestones.append(OnboardingStep.RUN_3_DAYS)
-    if consecutive_days >= 14:
-        milestones.append(OnboardingStep.RUN_14_DAYS)
-    return milestones
-
-
-async def _get_user_timezone(user_id: str) -> str:
-    user = await get_user_by_id(user_id)
-    return get_user_timezone_or_utc(user.timezone if user else None)
-
-
-async def increment_runs(user_id: str):
-    """
-    Increment a user's run counters and trigger any onboarding milestones.
-    """
-    user_timezone = await _get_user_timezone(user_id)
-    onboarding = await get_user_onboarding(user_id)
-    new_run_count = onboarding.agentRuns + 1
-    last_run_at, consecutive_run_days = _calculate_consecutive_run_days(
-        onboarding.lastRunAt, onboarding.consecutiveRunDays, user_timezone
-    )
-
-    await UserOnboarding.prisma().update(
-        where={"userId": user_id},
-        data={
-            "agentRuns": {"increment": 1},
-            "lastRunAt": last_run_at,
-            "consecutiveRunDays": consecutive_run_days,
-        },
-    )
-
-    milestones = _get_run_milestone_steps(new_run_count, consecutive_run_days)
-    new_steps = [step for step in milestones if step not in onboarding.completedSteps]
-
-    for step in new_steps:
-        await complete_onboarding_step(user_id, step)
-    # Send progress notification if no steps were completed, so client refetches onboarding state
-    if not new_steps:
-        await _send_onboarding_notification(user_id, None, event="increment_runs")
+CREDENTIALS_FIELDS: dict[str, str] = get_credentials_blocks()
 
 
 async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
@@ -370,7 +288,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
 
     where_clause: dict[str, Any] = {}
 
-    custom = _clean_and_split((user_onboarding.usageReason or "").lower())
+    custom = clean_and_split((user_onboarding.usageReason or "").lower())
 
     if categories:
         where_clause["OR"] = [
@@ -418,7 +336,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
     # Calculate points for the first X agents and choose the top 2
     agent_points = []
     for agent in storeAgents[:POINTS_AGENT_COUNT]:
-        points = _calculate_points(
+        points = calculate_points(
             agent, categories, custom, user_onboarding.integrations
         )
         agent_points.append((agent, points))
@@ -432,7 +350,6 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
             slug=agent.slug,
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
-            agent_output_demo=agent.agent_output_demo or "",
             agent_image=agent.agent_image,
             creator=agent.creator_username,
             creator_avatar=agent.creator_avatar,

autogpt_platform/backend/backend/executor/database.py

@@ -3,17 +3,12 @@ from contextlib import asynccontextmanager
 from typing import TYPE_CHECKING, Callable, Concatenate, ParamSpec, TypeVar, cast
 
 from backend.data import db
-from backend.data.analytics import (
-    get_accuracy_trends_and_alerts,
-    get_marketplace_graphs_for_monitoring,
-)
 from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
     create_graph_execution,
     get_block_error_stats,
     get_child_graph_executions,
     get_execution_kv_data,
-    get_frequently_executed_graphs,
     get_graph_execution_meta,
     get_graph_executions,
     get_graph_executions_count,
@@ -150,9 +145,6 @@ class DatabaseManager(AppService):
     get_execution_kv_data = _(get_execution_kv_data)
     set_execution_kv_data = _(set_execution_kv_data)
     get_block_error_stats = _(get_block_error_stats)
-    get_accuracy_trends_and_alerts = _(get_accuracy_trends_and_alerts)
-    get_frequently_executed_graphs = _(get_frequently_executed_graphs)
-    get_marketplace_graphs_for_monitoring = _(get_marketplace_graphs_for_monitoring)
 
     # Graphs
     get_node = _(get_node)
@@ -234,10 +226,6 @@ class DatabaseManagerClient(AppServiceClient):
 
     # Block error monitoring
     get_block_error_stats = _(d.get_block_error_stats)
-    # Execution accuracy monitoring
-    get_accuracy_trends_and_alerts = _(d.get_accuracy_trends_and_alerts)
-    get_frequently_executed_graphs = _(d.get_frequently_executed_graphs)
-    get_marketplace_graphs_for_monitoring = _(d.get_marketplace_graphs_for_monitoring)
 
     # Human In The Loop
     has_pending_reviews_for_graph_exec = _(d.has_pending_reviews_for_graph_exec)

autogpt_platform/backend/backend/executor/scheduler.py

@@ -26,14 +26,12 @@ from sqlalchemy import MetaData, create_engine
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
     process_existing_batches,
     process_weekly_summary,
     report_block_error_rates,
-    report_execution_accuracy_alerts,
     report_late_executions,
 )
 from backend.util.clients import get_scheduler_client
@@ -155,7 +153,6 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
-        await increment_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -242,11 +239,6 @@ def cleanup_expired_files():
     run_async(cleanup_expired_files_async())
 
 
-def execution_accuracy_alerts():
-    """Check execution accuracy and send alerts if drops are detected."""
-    return report_execution_accuracy_alerts()
-
-
 # Monitoring functions are now imported from monitoring module
 
 
@@ -446,17 +438,6 @@ class Scheduler(AppService):
                 jobstore=Jobstores.EXECUTION.value,
             )
 
-            # Execution Accuracy Monitoring - configurable interval
-            self.scheduler.add_job(
-                execution_accuracy_alerts,
-                id="report_execution_accuracy_alerts",
-                trigger="interval",
-                replace_existing=True,
-                seconds=config.execution_accuracy_check_interval_hours
-                * 3600,  # Convert hours to seconds
-                jobstore=Jobstores.EXECUTION.value,
-            )
-
         self.scheduler.add_listener(job_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
         self.scheduler.add_listener(job_missed_listener, EVENT_JOB_MISSED)
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
@@ -604,11 +585,6 @@ class Scheduler(AppService):
         """Manually trigger cleanup of expired cloud storage files."""
         return cleanup_expired_files()
 
-    @expose
-    def execute_report_execution_accuracy_alerts(self):
-        """Manually trigger execution accuracy alert checking."""
-        return execution_accuracy_alerts()
-
 
 class SchedulerClient(AppServiceClient):
     @classmethod

autogpt_platform/backend/backend/integrations/embeddings.py

@@ -0,0 +1,156 @@
+"""
+Embedding service for generating text embeddings using OpenAI.
+
+Used for vector-based semantic search in the store.
+"""
+
+import logging
+from typing import Optional
+
+import openai
+
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+# Model configuration
+# Using text-embedding-3-small (1536 dimensions) for compatibility with pgvector indexes
+# pgvector IVFFlat/HNSW indexes have dimension limits (2000 for IVFFlat, varies for HNSW)
+EMBEDDING_MODEL = "text-embedding-3-small"
+EMBEDDING_DIMENSIONS = 1536
+
+# Input validation limits
+# OpenAI text-embedding-3-large supports up to 8191 tokens (~32k chars)
+# We set a conservative limit to prevent abuse
+MAX_TEXT_LENGTH = 10000  # characters
+MAX_BATCH_SIZE = 100  # maximum texts per batch request
+
+
+class EmbeddingService:
+    """Service for generating text embeddings using OpenAI."""
+
+    def __init__(self, api_key: Optional[str] = None):
+        settings = Settings()
+        self.api_key = (
+            api_key
+            or settings.secrets.openai_internal_api_key
+            or settings.secrets.openai_api_key
+        )
+        if not self.api_key:
+            raise ValueError(
+                "OpenAI API key not configured. "
+                "Set OPENAI_API_KEY or OPENAI_INTERNAL_API_KEY environment variable."
+            )
+        self.client = openai.AsyncOpenAI(api_key=self.api_key)
+
+    async def generate_embedding(self, text: str) -> list[float]:
+        """
+        Generate embedding for a single text string.
+
+        Args:
+            text: The text to generate an embedding for.
+
+        Returns:
+            A list of floats representing the embedding vector.
+
+        Raises:
+            ValueError: If the text is empty or exceeds maximum length.
+            openai.APIError: If the OpenAI API call fails.
+        """
+        # Input validation
+        if not text or not text.strip():
+            raise ValueError("Text cannot be empty")
+        if len(text) > MAX_TEXT_LENGTH:
+            raise ValueError(
+                f"Text exceeds maximum length of {MAX_TEXT_LENGTH} characters"
+            )
+
+        try:
+            response = await self.client.embeddings.create(
+                model=EMBEDDING_MODEL,
+                input=text,
+                dimensions=EMBEDDING_DIMENSIONS,
+            )
+            return response.data[0].embedding
+        except openai.APIError as e:
+            logger.error(f"OpenAI API error generating embedding: {e}")
+            raise
+
+    async def generate_embeddings(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts (batch).
+
+        Args:
+            texts: List of texts to generate embeddings for.
+
+        Returns:
+            List of embedding vectors, one per input text.
+
+        Raises:
+            ValueError: If any text is invalid or batch size exceeds limit.
+            openai.APIError: If the OpenAI API call fails.
+        """
+        # Input validation
+        if not texts:
+            raise ValueError("Texts list cannot be empty")
+        if len(texts) > MAX_BATCH_SIZE:
+            raise ValueError(f"Batch size exceeds maximum of {MAX_BATCH_SIZE} texts")
+        for i, text in enumerate(texts):
+            if not text or not text.strip():
+                raise ValueError(f"Text at index {i} cannot be empty")
+            if len(text) > MAX_TEXT_LENGTH:
+                raise ValueError(
+                    f"Text at index {i} exceeds maximum length of {MAX_TEXT_LENGTH} characters"
+                )
+
+        try:
+            response = await self.client.embeddings.create(
+                model=EMBEDDING_MODEL,
+                input=texts,
+                dimensions=EMBEDDING_DIMENSIONS,
+            )
+            # Sort by index to ensure correct ordering
+            sorted_data = sorted(response.data, key=lambda x: x.index)
+            return [item.embedding for item in sorted_data]
+        except openai.APIError as e:
+            logger.error(f"OpenAI API error generating embeddings: {e}")
+            raise
+
+
+def create_search_text(name: str, sub_heading: str, description: str) -> str:
+    """
+    Combine fields into searchable text for embedding.
+
+    This creates a single text string from the agent's name, sub-heading,
+    and description, which is then converted to an embedding vector.
+
+    Args:
+        name: The agent name.
+        sub_heading: The agent sub-heading/tagline.
+        description: The agent description.
+
+    Returns:
+        A single string combining all non-empty fields.
+    """
+    parts = [name or "", sub_heading or "", description or ""]
+    return " ".join(filter(None, parts)).strip()
+
+
+# Singleton instance
+_embedding_service: Optional[EmbeddingService] = None
+
+
+async def get_embedding_service() -> EmbeddingService:
+    """
+    Get or create the embedding service singleton.
+
+    Returns:
+        The shared EmbeddingService instance.
+
+    Raises:
+        ValueError: If OpenAI API key is not configured.
+    """
+    global _embedding_service
+    if _embedding_service is None:
+        _embedding_service = EmbeddingService()
+    return _embedding_service

autogpt_platform/backend/backend/integrations/embeddings_test.py

@@ -0,0 +1,231 @@
+"""Tests for the embedding service.
+
+This module tests:
+- create_search_text utility function
+- EmbeddingService input validation
+- EmbeddingService API interaction (mocked)
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.integrations.embeddings import (
+    EMBEDDING_DIMENSIONS,
+    MAX_BATCH_SIZE,
+    MAX_TEXT_LENGTH,
+    EmbeddingService,
+    create_search_text,
+)
+
+
+class TestCreateSearchText:
+    """Tests for the create_search_text utility function."""
+
+    def test_combines_all_fields(self):
+        result = create_search_text("Agent Name", "A cool agent", "Does amazing things")
+        assert result == "Agent Name A cool agent Does amazing things"
+
+    def test_handles_empty_name(self):
+        result = create_search_text("", "Sub heading", "Description")
+        assert result == "Sub heading Description"
+
+    def test_handles_empty_sub_heading(self):
+        result = create_search_text("Name", "", "Description")
+        assert result == "Name Description"
+
+    def test_handles_empty_description(self):
+        result = create_search_text("Name", "Sub heading", "")
+        assert result == "Name Sub heading"
+
+    def test_handles_all_empty(self):
+        result = create_search_text("", "", "")
+        assert result == ""
+
+    def test_handles_none_values(self):
+        # The function expects strings but should handle None gracefully
+        result = create_search_text(None, None, None)  # type: ignore
+        assert result == ""
+
+    def test_preserves_content_strips_outer_whitespace(self):
+        # The function joins parts and strips the outer result
+        # Internal whitespace in each part is preserved
+        result = create_search_text("  Name  ", "  Sub  ", "  Desc  ")
+        # Each part is joined with space, then outer strip applied
+        assert result == "Name     Sub     Desc"
+
+    def test_handles_only_whitespace(self):
+        # Parts that are only whitespace become empty after filter
+        result = create_search_text("   ", "   ", "   ")
+        assert result == ""
+
+
+class TestEmbeddingServiceValidation:
+    """Tests for EmbeddingService input validation."""
+
+    @pytest.fixture
+    def mock_settings(self):
+        """Mock settings with a test API key."""
+        with patch("backend.integrations.embeddings.Settings") as mock:
+            mock_instance = MagicMock()
+            mock_instance.secrets.openai_internal_api_key = "test-api-key"
+            mock_instance.secrets.openai_api_key = ""
+            mock.return_value = mock_instance
+            yield mock
+
+    @pytest.fixture
+    def service(self, mock_settings):
+        """Create an EmbeddingService instance with mocked settings."""
+        with patch("backend.integrations.embeddings.openai.AsyncOpenAI"):
+            return EmbeddingService()
+
+    def test_init_requires_api_key(self):
+        """Test that initialization fails without an API key."""
+        with patch("backend.integrations.embeddings.Settings") as mock:
+            mock_instance = MagicMock()
+            mock_instance.secrets.openai_internal_api_key = ""
+            mock_instance.secrets.openai_api_key = ""
+            mock.return_value = mock_instance
+
+            with pytest.raises(ValueError, match="OpenAI API key not configured"):
+                EmbeddingService()
+
+    def test_init_accepts_explicit_api_key(self):
+        """Test that explicit API key overrides settings."""
+        with patch("backend.integrations.embeddings.Settings") as mock:
+            mock_instance = MagicMock()
+            mock_instance.secrets.openai_internal_api_key = ""
+            mock_instance.secrets.openai_api_key = ""
+            mock.return_value = mock_instance
+
+            with patch("backend.integrations.embeddings.openai.AsyncOpenAI"):
+                service = EmbeddingService(api_key="explicit-key")
+                assert service.api_key == "explicit-key"
+
+    @pytest.mark.asyncio
+    async def test_generate_embedding_empty_text(self, service):
+        """Test that empty text raises ValueError."""
+        with pytest.raises(ValueError, match="Text cannot be empty"):
+            await service.generate_embedding("")
+
+    @pytest.mark.asyncio
+    async def test_generate_embedding_whitespace_only(self, service):
+        """Test that whitespace-only text raises ValueError."""
+        with pytest.raises(ValueError, match="Text cannot be empty"):
+            await service.generate_embedding("   ")
+
+    @pytest.mark.asyncio
+    async def test_generate_embedding_exceeds_max_length(self, service):
+        """Test that text exceeding max length raises ValueError."""
+        long_text = "a" * (MAX_TEXT_LENGTH + 1)
+        with pytest.raises(ValueError, match="exceeds maximum length"):
+            await service.generate_embedding(long_text)
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_empty_list(self, service):
+        """Test that empty list raises ValueError."""
+        with pytest.raises(ValueError, match="Texts list cannot be empty"):
+            await service.generate_embeddings([])
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_exceeds_batch_size(self, service):
+        """Test that batch exceeding max size raises ValueError."""
+        texts = ["text"] * (MAX_BATCH_SIZE + 1)
+        with pytest.raises(ValueError, match="Batch size exceeds maximum"):
+            await service.generate_embeddings(texts)
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_empty_text_in_batch(self, service):
+        """Test that empty text in batch raises ValueError with index."""
+        with pytest.raises(ValueError, match="Text at index 1 cannot be empty"):
+            await service.generate_embeddings(["valid", "", "also valid"])
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_long_text_in_batch(self, service):
+        """Test that long text in batch raises ValueError with index."""
+        long_text = "a" * (MAX_TEXT_LENGTH + 1)
+        with pytest.raises(ValueError, match="Text at index 2 exceeds maximum length"):
+            await service.generate_embeddings(["short", "also short", long_text])
+
+
+class TestEmbeddingServiceAPI:
+    """Tests for EmbeddingService API interaction."""
+
+    @pytest.fixture
+    def mock_openai_client(self):
+        """Create a mock OpenAI client."""
+        mock_client = MagicMock()
+        mock_client.embeddings = MagicMock()
+        return mock_client
+
+    @pytest.fixture
+    def service_with_mock_client(self, mock_openai_client):
+        """Create an EmbeddingService with a mocked OpenAI client."""
+        with patch("backend.integrations.embeddings.Settings") as mock_settings:
+            mock_instance = MagicMock()
+            mock_instance.secrets.openai_internal_api_key = "test-key"
+            mock_instance.secrets.openai_api_key = ""
+            mock_settings.return_value = mock_instance
+
+            with patch(
+                "backend.integrations.embeddings.openai.AsyncOpenAI"
+            ) as mock_openai:
+                mock_openai.return_value = mock_openai_client
+                service = EmbeddingService()
+                return service, mock_openai_client
+
+    @pytest.mark.asyncio
+    async def test_generate_embedding_success(self, service_with_mock_client):
+        """Test successful embedding generation."""
+        service, mock_client = service_with_mock_client
+
+        # Create mock response
+        mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
+        mock_response = MagicMock()
+        mock_response.data = [MagicMock(embedding=mock_embedding)]
+        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+        result = await service.generate_embedding("test text")
+
+        assert result == mock_embedding
+        mock_client.embeddings.create.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_success(self, service_with_mock_client):
+        """Test successful batch embedding generation."""
+        service, mock_client = service_with_mock_client
+
+        # Create mock response with multiple embeddings
+        mock_embeddings = [[0.1] * EMBEDDING_DIMENSIONS, [0.2] * EMBEDDING_DIMENSIONS]
+        mock_response = MagicMock()
+        mock_response.data = [
+            MagicMock(embedding=mock_embeddings[0], index=0),
+            MagicMock(embedding=mock_embeddings[1], index=1),
+        ]
+        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+        result = await service.generate_embeddings(["text1", "text2"])
+
+        assert result == mock_embeddings
+        mock_client.embeddings.create.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_generate_embeddings_preserves_order(self, service_with_mock_client):
+        """Test that batch embeddings are returned in correct order even if API returns out of order."""
+        service, mock_client = service_with_mock_client
+
+        # Create mock response with embeddings out of order
+        mock_embeddings = [[0.1] * EMBEDDING_DIMENSIONS, [0.2] * EMBEDDING_DIMENSIONS]
+        mock_response = MagicMock()
+        # Return in reverse order
+        mock_response.data = [
+            MagicMock(embedding=mock_embeddings[1], index=1),
+            MagicMock(embedding=mock_embeddings[0], index=0),
+        ]
+        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+        result = await service.generate_embeddings(["text1", "text2"])
+
+        # Should be sorted by index
+        assert result[0] == mock_embeddings[0]
+        assert result[1] == mock_embeddings[1]

autogpt_platform/backend/backend/integrations/webhooks/_manual_base.py

@@ -18,9 +18,7 @@ class ManualWebhookManagerBase(BaseWebhooksManager[WT]):
         ingress_url: str,
         secret: str,
     ) -> tuple[str, dict]:
-        # TODO: pass ingress_url to user in frontend
-        # See: https://github.com/Significant-Gravitas/AutoGPT/issues/8537
-        logger.debug(f"Manual webhook registered with ingress URL: {ingress_url}")
+        print(ingress_url)  # FIXME: pass URL to user in front end
 
         return "", {}
 

autogpt_platform/backend/backend/monitoring/__init__.py

@@ -1,6 +1,5 @@
 """Monitoring module for platform health and alerting."""
 
-from .accuracy_monitor import AccuracyMonitor, report_execution_accuracy_alerts
 from .block_error_monitor import BlockErrorMonitor, report_block_error_rates
 from .late_execution_monitor import (
     LateExecutionException,
@@ -14,12 +13,10 @@ from .notification_monitor import (
 )
 
 __all__ = [
-    "AccuracyMonitor",
     "BlockErrorMonitor",
     "LateExecutionMonitor",
     "LateExecutionException",
     "NotificationJobArgs",
-    "report_execution_accuracy_alerts",
     "report_block_error_rates",
     "report_late_executions",
     "process_existing_batches",

autogpt_platform/backend/backend/monitoring/accuracy_monitor.py

@@ -1,107 +0,0 @@
-"""Execution accuracy monitoring module."""
-
-import logging
-
-from backend.util.clients import (
-    get_database_manager_client,
-    get_notification_manager_client,
-)
-from backend.util.metrics import DiscordChannel, sentry_capture_error
-from backend.util.settings import Config
-
-logger = logging.getLogger(__name__)
-config = Config()
-
-
-class AccuracyMonitor:
-    """Monitor execution accuracy trends and send alerts for drops."""
-
-    def __init__(self, drop_threshold: float = 10.0):
-        self.config = config
-        self.notification_client = get_notification_manager_client()
-        self.database_client = get_database_manager_client()
-        self.drop_threshold = drop_threshold
-
-    def check_execution_accuracy_alerts(self) -> str:
-        """Check marketplace agents for accuracy drops and send alerts."""
-        try:
-            logger.info("Checking execution accuracy for marketplace agents")
-
-            # Get marketplace graphs using database client
-            graphs = self.database_client.get_marketplace_graphs_for_monitoring(
-                days_back=30, min_executions=10
-            )
-
-            alerts_found = 0
-
-            for graph_data in graphs:
-                result = self.database_client.get_accuracy_trends_and_alerts(
-                    graph_id=graph_data.graph_id,
-                    user_id=graph_data.user_id,
-                    days_back=21,  # 3 weeks
-                    drop_threshold=self.drop_threshold,
-                )
-
-                if result.alert:
-                    alert = result.alert
-
-                    # Get graph details for better alert info
-                    try:
-                        graph_info = self.database_client.get_graph_metadata(
-                            graph_id=alert.graph_id
-                        )
-                        graph_name = graph_info.name if graph_info else "Unknown Agent"
-                    except Exception:
-                        graph_name = "Unknown Agent"
-
-                    # Create detailed alert message
-                    alert_msg = (
-                        f"🚨 **AGENT ACCURACY DROP DETECTED**\n\n"
-                        f"**Agent:** {graph_name}\n"
-                        f"**Graph ID:** `{alert.graph_id}`\n"
-                        f"**Accuracy Drop:** {alert.drop_percent:.1f}%\n"
-                        f"**Recent Performance:**\n"
-                        f"  • 3-day average: {alert.three_day_avg:.1f}%\n"
-                        f"  • 7-day average: {alert.seven_day_avg:.1f}%\n"
-                    )
-
-                    if alert.user_id:
-                        alert_msg += f"**Owner:** {alert.user_id}\n"
-
-                    # Send individual alert for each agent (not batched)
-                    self.notification_client.discord_system_alert(
-                        alert_msg, DiscordChannel.PRODUCT
-                    )
-                    alerts_found += 1
-                    logger.warning(
-                        f"Sent accuracy alert for agent: {graph_name} ({alert.graph_id})"
-                    )
-
-            if alerts_found > 0:
-                return f"Alert sent for {alerts_found} agents with accuracy drops"
-
-            logger.info("No execution accuracy alerts detected")
-            return "No accuracy alerts detected"
-
-        except Exception as e:
-            logger.exception(f"Error checking execution accuracy alerts: {e}")
-
-            error = Exception(f"Error checking execution accuracy alerts: {e}")
-            msg = str(error)
-            sentry_capture_error(error)
-            self.notification_client.discord_system_alert(msg, DiscordChannel.PRODUCT)
-            return msg
-
-
-def report_execution_accuracy_alerts(drop_threshold: float = 10.0) -> str:
-    """
-    Check execution accuracy and send alerts if drops are detected.
-
-    Args:
-        drop_threshold: Percentage drop threshold to trigger alerts (default 10.0%)
-
-    Returns:
-        Status message indicating results of the check
-    """
-    monitor = AccuracyMonitor(drop_threshold=drop_threshold)
-    return monitor.check_execution_accuracy_alerts()

autogpt_platform/backend/backend/server/integrations/router.py

@@ -33,11 +33,7 @@ from backend.data.model import (
     OAuth2Credentials,
     UserIntegrations,
 )
-from backend.data.onboarding import (
-    OnboardingStep,
-    complete_onboarding_step,
-    increment_runs,
-)
+from backend.data.onboarding import OnboardingStep, complete_onboarding_step
 from backend.data.user import get_user_integrations
 from backend.executor.utils import add_graph_execution
 from backend.integrations.ayrshare import AyrshareClient, SocialPlatform
@@ -381,7 +377,6 @@ async def webhook_ingress_generic(
         return
 
     await complete_onboarding_step(user_id, OnboardingStep.TRIGGER_WEBHOOK)
-    await increment_runs(user_id)
 
     # Execute all triggers concurrently for better performance
     tasks = []

autogpt_platform/backend/backend/server/model.py

@@ -1,8 +1,7 @@
 import enum
-from typing import Any, Literal, Optional
+from typing import Any, Optional
 
 import pydantic
-from prisma.enums import OnboardingStep
 
 from backend.data.api_key import APIKeyInfo, APIKeyPermission
 from backend.data.graph import Graph
@@ -36,13 +35,8 @@ class WSSubscribeGraphExecutionsRequest(pydantic.BaseModel):
     graph_id: str
 
 
-GraphCreationSource = Literal["builder", "upload"]
-GraphExecutionSource = Literal["builder", "library", "onboarding"]
-
-
 class CreateGraph(pydantic.BaseModel):
     graph: Graph
-    source: GraphCreationSource | None = None
 
 
 class CreateAPIKeyRequest(pydantic.BaseModel):
@@ -89,8 +83,6 @@ class NotificationPayload(pydantic.BaseModel):
     type: str
     event: str
 
-    model_config = pydantic.ConfigDict(extra="allow")
-
 
 class OnboardingNotificationPayload(NotificationPayload):
-    step: OnboardingStep | None
+    step: str

autogpt_platform/backend/backend/server/routers/v1.py

@@ -5,7 +5,7 @@ import time
 import uuid
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import Annotated, Any, Sequence, get_args
+from typing import Annotated, Any, Sequence
 
 import pydantic
 import stripe
@@ -45,17 +45,12 @@ from backend.data.credit import (
     set_auto_top_up,
 )
 from backend.data.graph import GraphSettings
-from backend.data.model import CredentialsMetaInput, UserOnboarding
+from backend.data.model import CredentialsMetaInput
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
 from backend.data.onboarding import (
-    FrontendOnboardingStep,
-    OnboardingStep,
     UserOnboardingUpdate,
-    complete_onboarding_step,
-    complete_re_run_agent,
     get_recommended_agents,
     get_user_onboarding,
-    increment_runs,
     onboarding_enabled,
     reset_user_onboarding,
     update_user_onboarding,
@@ -83,7 +78,6 @@ from backend.server.model import (
     CreateAPIKeyRequest,
     CreateAPIKeyResponse,
     CreateGraph,
-    GraphExecutionSource,
     RequestTopUp,
     SetGraphActiveVersion,
     TimezoneResponse,
@@ -91,7 +85,6 @@ from backend.server.model import (
     UpdateTimezoneRequest,
     UploadFileResponse,
 )
-from backend.server.v2.store.model import StoreAgentDetails
 from backend.util.cache import cached
 from backend.util.clients import get_scheduler_client
 from backend.util.cloud_storage import get_cloud_storage_handler
@@ -281,10 +274,9 @@ async def update_preferences(
 
 @v1_router.get(
     "/onboarding",
-    summary="Onboarding state",
+    summary="Get onboarding status",
     tags=["onboarding"],
     dependencies=[Security(requires_user)],
-    response_model=UserOnboarding,
 )
 async def get_onboarding(user_id: Annotated[str, Security(get_user_id)]):
     return await get_user_onboarding(user_id)
@@ -292,10 +284,9 @@ async def get_onboarding(user_id: Annotated[str, Security(get_user_id)]):
 
 @v1_router.patch(
     "/onboarding",
-    summary="Update onboarding state",
+    summary="Update onboarding progress",
     tags=["onboarding"],
     dependencies=[Security(requires_user)],
-    response_model=UserOnboarding,
 )
 async def update_onboarding(
     user_id: Annotated[str, Security(get_user_id)], data: UserOnboardingUpdate
@@ -303,39 +294,25 @@ async def update_onboarding(
     return await update_user_onboarding(user_id, data)
 
 
-@v1_router.post(
-    "/onboarding/step",
-    summary="Complete onboarding step",
-    tags=["onboarding"],
-    dependencies=[Security(requires_user)],
-)
-async def onboarding_complete_step(
-    user_id: Annotated[str, Security(get_user_id)], step: FrontendOnboardingStep
-):
-    if step not in get_args(FrontendOnboardingStep):
-        raise HTTPException(status_code=400, detail="Invalid onboarding step")
-    return await complete_onboarding_step(user_id, step)
-
-
 @v1_router.get(
     "/onboarding/agents",
-    summary="Recommended onboarding agents",
+    summary="Get recommended agents",
     tags=["onboarding"],
     dependencies=[Security(requires_user)],
 )
 async def get_onboarding_agents(
     user_id: Annotated[str, Security(get_user_id)],
-) -> list[StoreAgentDetails]:
+):
     return await get_recommended_agents(user_id)
 
 
 @v1_router.get(
     "/onboarding/enabled",
-    summary="Is onboarding enabled",
+    summary="Check onboarding enabled",
     tags=["onboarding", "public"],
     dependencies=[Security(requires_user)],
 )
-async def is_onboarding_enabled() -> bool:
+async def is_onboarding_enabled():
     return await onboarding_enabled()
 
 
@@ -344,7 +321,6 @@ async def is_onboarding_enabled() -> bool:
     summary="Reset onboarding progress",
     tags=["onboarding"],
     dependencies=[Security(requires_user)],
-    response_model=UserOnboarding,
 )
 async def reset_onboarding(user_id: Annotated[str, Security(get_user_id)]):
     return await reset_user_onboarding(user_id)
@@ -833,12 +809,7 @@ async def create_new_graph(
     # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
     await library_db.create_library_agent(graph, user_id=user_id)
-    activated_graph = await on_graph_activate(graph, user_id=user_id)
-
-    if create_graph.source == "builder":
-        await complete_onboarding_step(user_id, OnboardingStep.BUILDER_SAVE_AGENT)
-
-    return activated_graph
+    return await on_graph_activate(graph, user_id=user_id)
 
 
 @v1_router.delete(
@@ -996,7 +967,6 @@ async def execute_graph(
     credentials_inputs: Annotated[
         dict[str, CredentialsMetaInput], Body(..., embed=True, default_factory=dict)
     ],
-    source: Annotated[GraphExecutionSource | None, Body(embed=True)] = None,
     graph_version: Optional[int] = None,
     preset_id: Optional[str] = None,
 ) -> execution_db.GraphExecutionMeta:
@@ -1020,14 +990,6 @@ async def execute_graph(
         # Record successful graph execution
         record_graph_execution(graph_id=graph_id, status="success", user_id=user_id)
         record_graph_operation(operation="execute", status="success")
-        await increment_runs(user_id)
-        await complete_re_run_agent(user_id, graph_id)
-        if source == "library":
-            await complete_onboarding_step(
-                user_id, OnboardingStep.MARKETPLACE_RUN_AGENT
-            )
-        elif source == "builder":
-            await complete_onboarding_step(user_id, OnboardingStep.BUILDER_RUN_AGENT)
         return result
     except GraphValidationError as e:
         # Record failed graph execution
@@ -1141,15 +1103,6 @@ async def list_graph_executions(
     filtered_executions = await hide_activity_summaries_if_disabled(
         paginated_result.executions, user_id
     )
-    onboarding = await get_user_onboarding(user_id)
-    if (
-        onboarding.onboardingAgentExecutionId
-        and onboarding.onboardingAgentExecutionId
-        in [exec.id for exec in filtered_executions]
-        and OnboardingStep.GET_RESULTS not in onboarding.completedSteps
-    ):
-        await complete_onboarding_step(user_id, OnboardingStep.GET_RESULTS)
-
     return execution_db.GraphExecutionsPaginated(
         executions=filtered_executions, pagination=paginated_result.pagination
     )
@@ -1187,12 +1140,6 @@ async def get_graph_execution(
 
     # Apply feature flags to filter out disabled features
     result = await hide_activity_summary_if_disabled(result, user_id)
-    onboarding = await get_user_onboarding(user_id)
-    if (
-        onboarding.onboardingAgentExecutionId == graph_exec_id
-        and OnboardingStep.GET_RESULTS not in onboarding.completedSteps
-    ):
-        await complete_onboarding_step(user_id, OnboardingStep.GET_RESULTS)
 
     return result
 
@@ -1369,8 +1316,6 @@ async def create_graph_execution_schedule(
             result.next_run_time, user_timezone
         )
 
-    await complete_onboarding_step(user_id, OnboardingStep.SCHEDULE_AGENT)
-
     return result
 
 

autogpt_platform/backend/backend/server/v2/admin/execution_analytics_routes.py

@@ -8,10 +8,6 @@ from fastapi import APIRouter, HTTPException, Security
 from pydantic import BaseModel, Field
 
 from backend.blocks.llm import LlmModel
-from backend.data.analytics import (
-    AccuracyTrendsResponse,
-    get_accuracy_trends_and_alerts,
-)
 from backend.data.execution import (
     ExecutionStatus,
     GraphExecutionMeta,
@@ -87,18 +83,6 @@ class ExecutionAnalyticsConfig(BaseModel):
     recommended_model: str
 
 
-class AccuracyTrendsRequest(BaseModel):
-    graph_id: str = Field(..., description="Graph ID to analyze", min_length=1)
-    user_id: Optional[str] = Field(None, description="Optional user ID filter")
-    days_back: int = Field(30, description="Number of days to look back", ge=7, le=90)
-    drop_threshold: float = Field(
-        10.0, description="Alert threshold percentage", ge=1.0, le=50.0
-    )
-    include_historical: bool = Field(
-        False, description="Include historical data for charts"
-    )
-
-
 router = APIRouter(
     prefix="/admin",
     tags=["admin", "execution_analytics"],
@@ -435,40 +419,3 @@ async def _process_batch(
     return await asyncio.gather(
         *[process_single_execution(execution) for execution in executions]
     )
-
-
-@router.get(
-    "/execution_accuracy_trends",
-    response_model=AccuracyTrendsResponse,
-    summary="Get Execution Accuracy Trends and Alerts",
-)
-async def get_execution_accuracy_trends(
-    graph_id: str,
-    user_id: Optional[str] = None,
-    days_back: int = 30,
-    drop_threshold: float = 10.0,
-    include_historical: bool = False,
-    admin_user_id: str = Security(get_user_id),
-) -> AccuracyTrendsResponse:
-    """
-    Get execution accuracy trends with moving averages and alert detection.
-    Simple single-query approach.
-    """
-    logger.info(
-        f"Admin user {admin_user_id} requesting accuracy trends for graph {graph_id}"
-    )
-
-    try:
-        result = await get_accuracy_trends_and_alerts(
-            graph_id=graph_id,
-            days_back=days_back,
-            user_id=user_id,
-            drop_threshold=drop_threshold,
-            include_historical=include_historical,
-        )
-
-        return result
-
-    except Exception as e:
-        logger.exception(f"Error getting accuracy trends for graph {graph_id}: {e}")
-        raise HTTPException(status_code=500, detail=str(e))

autogpt_platform/backend/backend/server/v2/builder/db.py

@@ -1,16 +1,9 @@
 import logging
-from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
-from difflib import SequenceMatcher
-from typing import Sequence
 
 import prisma
 
 import backend.data.block
-import backend.server.v2.library.db as library_db
-import backend.server.v2.library.model as library_model
-import backend.server.v2.store.db as store_db
-import backend.server.v2.store.model as store_model
 from backend.blocks import load_all_blocks
 from backend.blocks.llm import LlmModel
 from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
@@ -21,36 +14,17 @@ from backend.server.v2.builder.model import (
     BlockResponse,
     BlockType,
     CountResponse,
-    FilterType,
     Provider,
     ProviderResponse,
-    SearchEntry,
+    SearchBlocksResponse,
 )
 from backend.util.cache import cached
 from backend.util.models import Pagination
 
 logger = logging.getLogger(__name__)
 llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
-
-MAX_LIBRARY_AGENT_RESULTS = 100
-MAX_MARKETPLACE_AGENT_RESULTS = 100
-MIN_SCORE_FOR_FILTERED_RESULTS = 10.0
-
-SearchResultItem = BlockInfo | library_model.LibraryAgent | store_model.StoreAgent
-
-
-@dataclass
-class _ScoredItem:
-    item: SearchResultItem
-    filter_type: FilterType
-    score: float
-    sort_key: str
-
-
-@dataclass
-class _SearchCacheEntry:
-    items: list[SearchResultItem]
-    total_items: dict[FilterType, int]
+_static_counts_cache: dict | None = None
+_suggested_blocks: list[BlockInfo] | None = None
 
 
 def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse]:
@@ -156,244 +130,71 @@ def get_block_by_id(block_id: str) -> BlockInfo | None:
     return None
 
 
-async def update_search(user_id: str, search: SearchEntry) -> str:
+def search_blocks(
+    include_blocks: bool = True,
+    include_integrations: bool = True,
+    query: str = "",
+    page: int = 1,
+    page_size: int = 50,
+) -> SearchBlocksResponse:
     """
-    Upsert a search request for the user and return the search ID.
+    Get blocks based on the filter and query.
+    `providers` only applies for `integrations` filter.
     """
-    if search.search_id:
-        # Update existing search
-        await prisma.models.BuilderSearchHistory.prisma().update(
-            where={
-                "id": search.search_id,
-            },
-            data={
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            },
-        )
-        return search.search_id
-    else:
-        # Create new search
-        new_search = await prisma.models.BuilderSearchHistory.prisma().create(
-            data={
-                "userId": user_id,
-                "searchQuery": search.search_query or "",
-                "filter": search.filter or [],  # type: ignore
-                "byCreator": search.by_creator or [],
-            }
-        )
-        return new_search.id
+    blocks: list[AnyBlockSchema] = []
+    query = query.lower()
 
-
-async def get_recent_searches(user_id: str, limit: int = 5) -> list[SearchEntry]:
-    """
-    Get the user's most recent search requests.
-    """
-    searches = await prisma.models.BuilderSearchHistory.prisma().find_many(
-        where={
-            "userId": user_id,
-        },
-        order={
-            "updatedAt": "desc",
-        },
-        take=limit,
-    )
-    return [
-        SearchEntry(
-            search_query=s.searchQuery,
-            filter=s.filter,  # type: ignore
-            by_creator=s.byCreator,
-            search_id=s.id,
-        )
-        for s in searches
-    ]
-
-
-async def get_sorted_search_results(
-    *,
-    user_id: str,
-    search_query: str | None,
-    filters: Sequence[FilterType],
-    by_creator: Sequence[str] | None = None,
-) -> _SearchCacheEntry:
-    normalized_filters: tuple[FilterType, ...] = tuple(sorted(set(filters or [])))
-    normalized_creators: tuple[str, ...] = tuple(sorted(set(by_creator or [])))
-    return await _build_cached_search_results(
-        user_id=user_id,
-        search_query=search_query or "",
-        filters=normalized_filters,
-        by_creator=normalized_creators,
-    )
-
-
-@cached(ttl_seconds=300, shared_cache=True)
-async def _build_cached_search_results(
-    user_id: str,
-    search_query: str,
-    filters: tuple[FilterType, ...],
-    by_creator: tuple[str, ...],
-) -> _SearchCacheEntry:
-    normalized_query = (search_query or "").strip().lower()
-
-    include_blocks = "blocks" in filters
-    include_integrations = "integrations" in filters
-    include_library_agents = "my_agents" in filters
-    include_marketplace_agents = "marketplace_agents" in filters
-
-    scored_items: list[_ScoredItem] = []
-    total_items: dict[FilterType, int] = {
-        "blocks": 0,
-        "integrations": 0,
-        "marketplace_agents": 0,
-        "my_agents": 0,
-    }
-
-    block_results, block_total, integration_total = _collect_block_results(
-        normalized_query=normalized_query,
-        include_blocks=include_blocks,
-        include_integrations=include_integrations,
-    )
-    scored_items.extend(block_results)
-    total_items["blocks"] = block_total
-    total_items["integrations"] = integration_total
-
-    if include_library_agents:
-        library_response = await library_db.list_library_agents(
-            user_id=user_id,
-            search_term=search_query or None,
-            page=1,
-            page_size=MAX_LIBRARY_AGENT_RESULTS,
-        )
-        total_items["my_agents"] = library_response.pagination.total_items
-        scored_items.extend(
-            _build_library_items(
-                agents=library_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    if include_marketplace_agents:
-        marketplace_response = await store_db.get_store_agents(
-            creators=list(by_creator) or None,
-            search_query=search_query or None,
-            page=1,
-            page_size=MAX_MARKETPLACE_AGENT_RESULTS,
-        )
-        total_items["marketplace_agents"] = marketplace_response.pagination.total_items
-        scored_items.extend(
-            _build_marketplace_items(
-                agents=marketplace_response.agents,
-                normalized_query=normalized_query,
-            )
-        )
-
-    sorted_items = sorted(
-        scored_items,
-        key=lambda entry: (-entry.score, entry.sort_key, entry.filter_type),
-    )
-
-    return _SearchCacheEntry(
-        items=[entry.item for entry in sorted_items],
-        total_items=total_items,
-    )
-
-
-def _collect_block_results(
-    *,
-    normalized_query: str,
-    include_blocks: bool,
-    include_integrations: bool,
-) -> tuple[list[_ScoredItem], int, int]:
-    results: list[_ScoredItem] = []
+    total = 0
+    skip = (page - 1) * page_size
+    take = page_size
     block_count = 0
     integration_count = 0
 
-    if not include_blocks and not include_integrations:
-        return results, block_count, integration_count
-
     for block_type in load_all_blocks().values():
         block: AnyBlockSchema = block_type()
+        # Skip disabled blocks
         if block.disabled:
             continue
-
-        block_info = block.get_info()
+        # Skip blocks that don't match the query
+        if (
+            query not in block.name.lower()
+            and query not in block.description.lower()
+            and not _matches_llm_model(block.input_schema, query)
+        ):
+            continue
+        keep = False
         credentials = list(block.input_schema.get_credentials_fields().values())
-        is_integration = len(credentials) > 0
-
-        if is_integration and not include_integrations:
-            continue
-        if not is_integration and not include_blocks:
-            continue
-
-        score = _score_block(block, block_info, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
-        filter_type: FilterType = "integrations" if is_integration else "blocks"
-        if is_integration:
+        if include_integrations and len(credentials) > 0:
+            keep = True
             integration_count += 1
-        else:
+        if include_blocks and len(credentials) == 0:
+            keep = True
             block_count += 1
 
-        results.append(
-            _ScoredItem(
-                item=block_info,
-                filter_type=filter_type,
-                score=score,
-                sort_key=_get_item_name(block_info),
-            )
-        )
-
-    return results, block_count, integration_count
-
-
-def _build_library_items(
-    *,
-    agents: list[library_model.LibraryAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_library_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
+        if not keep:
             continue
 
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="my_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
-
-
-def _build_marketplace_items(
-    *,
-    agents: list[store_model.StoreAgent],
-    normalized_query: str,
-) -> list[_ScoredItem]:
-    results: list[_ScoredItem] = []
-
-    for agent in agents:
-        score = _score_store_agent(agent, normalized_query)
-        if not _should_include_item(score, normalized_query):
+        total += 1
+        if skip > 0:
+            skip -= 1
             continue
+        if take > 0:
+            take -= 1
+            blocks.append(block)
 
-        results.append(
-            _ScoredItem(
-                item=agent,
-                filter_type="marketplace_agents",
-                score=score,
-                sort_key=_get_item_name(agent),
-            )
-        )
-
-    return results
+    return SearchBlocksResponse(
+        blocks=BlockResponse(
+            blocks=[b.get_info() for b in blocks],
+            pagination=Pagination(
+                total_items=total,
+                total_pages=(total + page_size - 1) // page_size,
+                current_page=page,
+                page_size=page_size,
+            ),
+        ),
+        total_block_count=block_count,
+        total_integration_count=integration_count,
+    )
 
 
 def get_providers(
@@ -450,12 +251,16 @@ async def get_counts(user_id: str) -> CountResponse:
     )
 
 
-@cached(ttl_seconds=3600)
 async def _get_static_counts():
     """
     Get counts of blocks, integrations, and marketplace agents.
     This is cached to avoid unnecessary database queries and calculations.
+    Can't use functools.cache here because the function is async.
     """
+    global _static_counts_cache
+    if _static_counts_cache is not None:
+        return _static_counts_cache
+
     all_blocks = 0
     input_blocks = 0
     action_blocks = 0
@@ -482,7 +287,7 @@ async def _get_static_counts():
 
     marketplace_agents = await prisma.models.StoreAgent.prisma().count()
 
-    return {
+    _static_counts_cache = {
         "all_blocks": all_blocks,
         "input_blocks": input_blocks,
         "action_blocks": action_blocks,
@@ -491,6 +296,8 @@ async def _get_static_counts():
         "marketplace_agents": marketplace_agents,
     }
 
+    return _static_counts_cache
+
 
 def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
     for field in schema_cls.model_fields.values():
@@ -501,123 +308,6 @@ def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
     return False
 
 
-def _score_block(
-    block: AnyBlockSchema,
-    block_info: BlockInfo,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = block_info.name.lower()
-    description = block_info.description.lower()
-    score = _score_primary_fields(name, description, normalized_query)
-
-    category_text = " ".join(
-        category.get("category", "").lower() for category in block_info.categories
-    )
-    score += _score_additional_field(category_text, normalized_query, 12, 6)
-
-    credentials_info = block.input_schema.get_credentials_fields_info().values()
-    provider_names = [
-        provider.value.lower()
-        for info in credentials_info
-        for provider in info.provider
-    ]
-    provider_text = " ".join(provider_names)
-    score += _score_additional_field(provider_text, normalized_query, 15, 6)
-
-    if _matches_llm_model(block.input_schema, normalized_query):
-        score += 20
-
-    return score
-
-
-def _score_library_agent(
-    agent: library_model.LibraryAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.name.lower()
-    description = (agent.description or "").lower()
-    instructions = (agent.instructions or "").lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(instructions, normalized_query, 15, 6)
-    score += _score_additional_field(
-        agent.creator_name.lower(), normalized_query, 10, 5
-    )
-
-    return score
-
-
-def _score_store_agent(
-    agent: store_model.StoreAgent,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = agent.agent_name.lower()
-    description = agent.description.lower()
-    sub_heading = agent.sub_heading.lower()
-
-    score = _score_primary_fields(name, description, normalized_query)
-    score += _score_additional_field(sub_heading, normalized_query, 12, 6)
-    score += _score_additional_field(agent.creator.lower(), normalized_query, 10, 5)
-
-    return score
-
-
-def _score_primary_fields(name: str, description: str, query: str) -> float:
-    score = 0.0
-    if name == query:
-        score += 120
-    elif name.startswith(query):
-        score += 90
-    elif query in name:
-        score += 60
-
-    score += SequenceMatcher(None, name, query).ratio() * 50
-    if description:
-        if query in description:
-            score += 30
-        score += SequenceMatcher(None, description, query).ratio() * 25
-    return score
-
-
-def _score_additional_field(
-    value: str,
-    query: str,
-    contains_weight: float,
-    similarity_weight: float,
-) -> float:
-    if not value or not query:
-        return 0.0
-
-    score = 0.0
-    if query in value:
-        score += contains_weight
-    score += SequenceMatcher(None, value, query).ratio() * similarity_weight
-    return score
-
-
-def _should_include_item(score: float, normalized_query: str) -> bool:
-    if not normalized_query:
-        return True
-    return score >= MIN_SCORE_FOR_FILTERED_RESULTS
-
-
-def _get_item_name(item: SearchResultItem) -> str:
-    if isinstance(item, BlockInfo):
-        return item.name.lower()
-    if isinstance(item, library_model.LibraryAgent):
-        return item.name.lower()
-    return item.agent_name.lower()
-
-
 @cached(ttl_seconds=3600)
 def _get_all_providers() -> dict[ProviderName, Provider]:
     providers: dict[ProviderName, Provider] = {}
@@ -639,9 +329,13 @@ def _get_all_providers() -> dict[ProviderName, Provider]:
     return providers
 
 
-@cached(ttl_seconds=3600)
 async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
-    suggested_blocks = []
+    global _suggested_blocks
+
+    if _suggested_blocks is not None and len(_suggested_blocks) >= count:
+        return _suggested_blocks[:count]
+
+    _suggested_blocks = []
     # Sum the number of executions for each block type
     # Prisma cannot group by nested relations, so we do a raw query
     # Calculate the cutoff timestamp
@@ -682,7 +376,7 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
     # Sort blocks by execution count
     blocks.sort(key=lambda x: x[1], reverse=True)
 
-    suggested_blocks = [block[0] for block in blocks]
+    _suggested_blocks = [block[0] for block in blocks]
 
     # Return the top blocks
-    return suggested_blocks[:count]
+    return _suggested_blocks[:count]

autogpt_platform/backend/backend/server/v2/builder/model.py

@@ -18,17 +18,10 @@ FilterType = Literal[
 BlockType = Literal["all", "input", "action", "output"]
 
 
-class SearchEntry(BaseModel):
-    search_query: str | None = None
-    filter: list[FilterType] | None = None
-    by_creator: list[str] | None = None
-    search_id: str | None = None
-
-
 # Suggestions
 class SuggestionsResponse(BaseModel):
     otto_suggestions: list[str]
-    recent_searches: list[SearchEntry]
+    recent_searches: list[str]
     providers: list[ProviderName]
     top_blocks: list[BlockInfo]
 
@@ -39,7 +32,7 @@ class BlockCategoryResponse(BaseModel):
     total_blocks: int
     blocks: list[BlockInfo]
 
-    model_config = {"use_enum_values": False}  # Use enum names like "AI"
+    model_config = {"use_enum_values": False}  # <== use enum names like "AI"
 
 
 # Input/Action/Output and see all for block categories
@@ -60,11 +53,17 @@ class ProviderResponse(BaseModel):
     pagination: Pagination
 
 
+class SearchBlocksResponse(BaseModel):
+    blocks: BlockResponse
+    total_block_count: int
+    total_integration_count: int
+
+
 class SearchResponse(BaseModel):
     items: list[BlockInfo | library_model.LibraryAgent | store_model.StoreAgent]
-    search_id: str
     total_items: dict[FilterType, int]
-    pagination: Pagination
+    page: int
+    more_pages: bool
 
 
 class CountResponse(BaseModel):

autogpt_platform/backend/backend/server/v2/builder/routes.py

@@ -6,6 +6,10 @@ from autogpt_libs.auth.dependencies import get_user_id, requires_user
 
 import backend.server.v2.builder.db as builder_db
 import backend.server.v2.builder.model as builder_model
+import backend.server.v2.library.db as library_db
+import backend.server.v2.library.model as library_model
+import backend.server.v2.store.db as store_db
+import backend.server.v2.store.model as store_model
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 
@@ -41,9 +45,7 @@ def sanitize_query(query: str | None) -> str | None:
     summary="Get Builder suggestions",
     response_model=builder_model.SuggestionsResponse,
 )
-async def get_suggestions(
-    user_id: Annotated[str, fastapi.Security(get_user_id)],
-) -> builder_model.SuggestionsResponse:
+async def get_suggestions() -> builder_model.SuggestionsResponse:
     """
     Get all suggestions for the Blocks Menu.
     """
@@ -53,7 +55,11 @@ async def get_suggestions(
             "Help me create a list",
             "Help me feed my data to Google Maps",
         ],
-        recent_searches=await builder_db.get_recent_searches(user_id),
+        recent_searches=[
+            "image generation",
+            "deepfake",
+            "competitor analysis",
+        ],
         providers=[
             ProviderName.TWITTER,
             ProviderName.GITHUB,
@@ -141,6 +147,7 @@ async def get_providers(
     )
 
 
+# Not using post method because on frontend, orval doesn't support Infinite Query with POST method.
 @router.get(
     "/search",
     summary="Builder search",
@@ -150,7 +157,7 @@ async def get_providers(
 async def search(
     user_id: Annotated[str, fastapi.Security(get_user_id)],
     search_query: Annotated[str | None, fastapi.Query()] = None,
-    filter: Annotated[list[builder_model.FilterType] | None, fastapi.Query()] = None,
+    filter: Annotated[list[str] | None, fastapi.Query()] = None,
     search_id: Annotated[str | None, fastapi.Query()] = None,
     by_creator: Annotated[list[str] | None, fastapi.Query()] = None,
     page: Annotated[int, fastapi.Query()] = 1,
@@ -169,43 +176,69 @@ async def search(
         ]
     search_query = sanitize_query(search_query)
 
-    # Get all possible results
-    cached_results = await builder_db.get_sorted_search_results(
-        user_id=user_id,
-        search_query=search_query,
-        filters=filter,
-        by_creator=by_creator,
-    )
-
-    # Paginate results
-    total_combined_items = len(cached_results.items)
-    pagination = Pagination(
-        total_items=total_combined_items,
-        total_pages=(total_combined_items + page_size - 1) // page_size,
-        current_page=page,
-        page_size=page_size,
-    )
-
-    start_idx = (page - 1) * page_size
-    end_idx = start_idx + page_size
-    paginated_items = cached_results.items[start_idx:end_idx]
-
-    # Update the search entry by id
-    search_id = await builder_db.update_search(
-        user_id,
-        builder_model.SearchEntry(
-            search_query=search_query,
-            filter=filter,
-            by_creator=by_creator,
-            search_id=search_id,
+    # Blocks&Integrations
+    blocks = builder_model.SearchBlocksResponse(
+        blocks=builder_model.BlockResponse(
+            blocks=[],
+            pagination=Pagination.empty(),
         ),
+        total_block_count=0,
+        total_integration_count=0,
     )
+    if "blocks" in filter or "integrations" in filter:
+        blocks = builder_db.search_blocks(
+            include_blocks="blocks" in filter,
+            include_integrations="integrations" in filter,
+            query=search_query or "",
+            page=page,
+            page_size=page_size,
+        )
+
+    # Library Agents
+    my_agents = library_model.LibraryAgentResponse(
+        agents=[],
+        pagination=Pagination.empty(),
+    )
+    if "my_agents" in filter:
+        my_agents = await library_db.list_library_agents(
+            user_id=user_id,
+            search_term=search_query,
+            page=page,
+            page_size=page_size,
+        )
+
+    # Marketplace Agents
+    marketplace_agents = store_model.StoreAgentsResponse(
+        agents=[],
+        pagination=Pagination.empty(),
+    )
+    if "marketplace_agents" in filter:
+        marketplace_agents = await store_db.get_store_agents(
+            creators=by_creator,
+            search_query=search_query,
+            page=page,
+            page_size=page_size,
+        )
+
+    more_pages = False
+    if (
+        blocks.blocks.pagination.current_page < blocks.blocks.pagination.total_pages
+        or my_agents.pagination.current_page < my_agents.pagination.total_pages
+        or marketplace_agents.pagination.current_page
+        < marketplace_agents.pagination.total_pages
+    ):
+        more_pages = True
 
     return builder_model.SearchResponse(
-        items=paginated_items,
-        search_id=search_id,
-        total_items=cached_results.total_items,
-        pagination=pagination,
+        items=blocks.blocks.blocks + my_agents.agents + marketplace_agents.agents,
+        total_items={
+            "blocks": blocks.total_block_count,
+            "integrations": blocks.total_integration_count,
+            "marketplace_agents": marketplace_agents.pagination.total_items,
+            "my_agents": my_agents.pagination.total_items,
+        },
+        page=page,
+        more_pages=more_pages,
     )
 
 

autogpt_platform/backend/backend/server/v2/executions/review/routes.py

@@ -23,7 +23,7 @@ logger = logging.getLogger(__name__)
 
 
 router = APIRouter(
-    tags=["v2", "executions", "review"],
+    tags=["executions", "review", "private"],
     dependencies=[Security(autogpt_auth_lib.requires_user)],
 )
 

autogpt_platform/backend/backend/server/v2/library/routes/agents.py

@@ -1,15 +1,13 @@
 import logging
-from typing import Literal, Optional
+from typing import Optional
 
 import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, Body, HTTPException, Query, Security, status
 from fastapi.responses import Response
-from prisma.enums import OnboardingStep
 
 import backend.server.v2.library.db as library_db
 import backend.server.v2.library.model as library_model
 import backend.server.v2.store.exceptions as store_exceptions
-from backend.data.onboarding import complete_onboarding_step
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 logger = logging.getLogger(__name__)
@@ -202,9 +200,6 @@ async def get_library_agent_by_store_listing_version_id(
 )
 async def add_marketplace_agent_to_library(
     store_listing_version_id: str = Body(embed=True),
-    source: Literal["onboarding", "marketplace"] = Body(
-        default="marketplace", embed=True
-    ),
     user_id: str = Security(autogpt_auth_lib.get_user_id),
 ) -> library_model.LibraryAgent:
     """
@@ -222,15 +217,10 @@ async def add_marketplace_agent_to_library(
         HTTPException(500): If a server/database error occurs.
     """
     try:
-        agent = await library_db.add_store_agent_to_library(
+        return await library_db.add_store_agent_to_library(
             store_listing_version_id=store_listing_version_id,
             user_id=user_id,
         )
-        if source != "onboarding":
-            await complete_onboarding_step(
-                user_id, OnboardingStep.MARKETPLACE_ADD_AGENT
-            )
-        return agent
 
     except store_exceptions.AgentNotFoundError as e:
         logger.warning(

autogpt_platform/backend/backend/server/v2/library/routes/presets.py

@@ -10,7 +10,6 @@ from backend.data.execution import GraphExecutionMeta
 from backend.data.graph import get_graph
 from backend.data.integrations import get_webhook
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_runs
 from backend.executor.utils import add_graph_execution, make_node_credentials_input_map
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks import get_webhook_manager
@@ -402,8 +401,6 @@ async def execute_preset(
     merged_node_input = preset.inputs | inputs
     merged_credential_inputs = preset.credentials | credential_inputs
 
-    await increment_runs(user_id)
-
     return await add_graph_execution(
         user_id=user_id,
         graph_id=preset.graph_id,

autogpt_platform/backend/backend/server/v2/library/routes_test.py

@@ -1,6 +1,5 @@
 import datetime
 import json
-from unittest.mock import AsyncMock
 
 import fastapi.testclient
 import pytest
@@ -226,10 +225,6 @@ def test_add_agent_to_library_success(
         "backend.server.v2.library.db.add_store_agent_to_library"
     )
     mock_db_call.return_value = mock_library_agent
-    mock_complete_onboarding = mocker.patch(
-        "backend.server.v2.library.routes.agents.complete_onboarding_step",
-        new_callable=AsyncMock,
-    )
 
     response = client.post(
         "/agents", json={"store_listing_version_id": "test-version-id"}
@@ -244,7 +239,6 @@ def test_add_agent_to_library_success(
     mock_db_call.assert_called_once_with(
         store_listing_version_id="test-version-id", user_id=test_user_id
     )
-    mock_complete_onboarding.assert_awaited_once()
 
 
 def test_add_agent_to_library_error(mocker: pytest_mock.MockFixture, test_user_id: str):

autogpt_platform/backend/backend/server/v2/store/backfill_embeddings.py

@@ -0,0 +1,168 @@
+"""
+Script to backfill embeddings for existing store listing versions.
+
+This script should be run after the migration to add the embedding column
+to populate embeddings for all existing store listing versions.
+
+Usage:
+    poetry run python -m backend.server.v2.store.backfill_embeddings
+    poetry run python -m backend.server.v2.store.backfill_embeddings --dry-run
+    poetry run python -m backend.server.v2.store.backfill_embeddings --batch-size 25
+"""
+
+import argparse
+import asyncio
+import logging
+import sys
+
+from backend.data.db import connect, disconnect, query_raw_with_schema
+from backend.integrations.embeddings import EmbeddingService, create_search_text
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(message)s",
+)
+logger = logging.getLogger(__name__)
+
+# Default batch size for processing
+DEFAULT_BATCH_SIZE = 50
+
+# Delay between batches to avoid rate limits (seconds)
+BATCH_DELAY_SECONDS = 1.0
+
+
+async def backfill_embeddings(
+    dry_run: bool = False,
+    batch_size: int = DEFAULT_BATCH_SIZE,
+) -> tuple[int, int]:
+    """
+    Backfill embeddings for all store listing versions without embeddings.
+
+    Args:
+        dry_run: If True, don't make any changes, just report what would be done.
+        batch_size: Number of versions to process in each batch.
+
+    Returns:
+        Tuple of (processed_count, error_count)
+    """
+    await connect()
+
+    try:
+        embedding_service = EmbeddingService()
+
+        # Get all versions without embeddings
+        versions = await query_raw_with_schema(
+            """
+            SELECT id, name, "subHeading", description
+            FROM {schema_prefix}"StoreListingVersion"
+            WHERE embedding IS NULL
+            ORDER BY "createdAt" DESC
+            """
+        )
+
+        total = len(versions)
+        logger.info(f"Found {total} versions without embeddings")
+
+        if dry_run:
+            logger.info("Dry run mode - no changes will be made")
+            return (0, 0)
+
+        if total == 0:
+            logger.info("No versions need embeddings")
+            return (0, 0)
+
+        processed = 0
+        errors = 0
+
+        for i in range(0, total, batch_size):
+            batch = versions[i : i + batch_size]
+            batch_num = (i // batch_size) + 1
+            total_batches = (total + batch_size - 1) // batch_size
+
+            logger.info(f"Processing batch {batch_num}/{total_batches}")
+
+            for version in batch:
+                version_id = version["id"]
+                try:
+                    search_text = create_search_text(
+                        version["name"] or "",
+                        version["subHeading"] or "",
+                        version["description"] or "",
+                    )
+
+                    if not search_text:
+                        logger.warning(f"Skipping {version_id} - no searchable text")
+                        continue
+
+                    embedding = await embedding_service.generate_embedding(search_text)
+                    embedding_str = "[" + ",".join(map(str, embedding)) + "]"
+
+                    await query_raw_with_schema(
+                        """
+                        UPDATE {schema_prefix}"StoreListingVersion"
+                        SET embedding = $1::vector
+                        WHERE id = $2
+                        """,
+                        embedding_str,
+                        version_id,
+                    )
+
+                    processed += 1
+
+                except Exception as e:
+                    logger.error(f"Error processing {version_id}: {e}")
+                    errors += 1
+
+            logger.info(f"Progress: {processed}/{total} processed, {errors} errors")
+
+            # Rate limit: wait between batches to avoid hitting API limits
+            if i + batch_size < total:
+                await asyncio.sleep(BATCH_DELAY_SECONDS)
+
+        logger.info(f"Backfill complete: {processed} processed, {errors} errors")
+        return (processed, errors)
+
+    finally:
+        await disconnect()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Backfill embeddings for store listing versions"
+    )
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Don't make any changes, just report what would be done",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=DEFAULT_BATCH_SIZE,
+        help=f"Number of versions to process in each batch (default: {DEFAULT_BATCH_SIZE})",
+    )
+
+    args = parser.parse_args()
+
+    try:
+        processed, errors = asyncio.run(
+            backfill_embeddings(dry_run=args.dry_run, batch_size=args.batch_size)
+        )
+
+        if errors > 0:
+            logger.warning(f"Completed with {errors} errors")
+            sys.exit(1)
+        else:
+            logger.info("Completed successfully")
+            sys.exit(0)
+
+    except KeyboardInterrupt:
+        logger.info("Interrupted by user")
+        sys.exit(130)
+    except Exception as e:
+        logger.error(f"Fatal error: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

autogpt_platform/backend/backend/server/v2/store/db.py

@@ -26,6 +26,7 @@ from backend.data.notifications import (
     AgentRejectionData,
     NotificationEventModel,
 )
+from backend.integrations.embeddings import create_search_text, get_embedding_service
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
 from backend.util.settings import Settings
@@ -56,31 +57,40 @@ async def get_store_agents(
     )
 
     try:
-        # If search_query is provided, use full-text search
+        # If search_query is provided, use vector similarity search
         if search_query:
             offset = (page - 1) * page_size
 
+            # Generate embedding for search query
+            embedding_service = await get_embedding_service()
+            query_embedding = await embedding_service.generate_embedding(search_query)
+            # Convert embedding to PostgreSQL array format
+            embedding_str = "[" + ",".join(map(str, query_embedding)) + "]"
+
             # Whitelist allowed order_by columns
+            # For vector search, we use similarity instead of rank
             ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
+                "rating": "rating DESC, similarity DESC",
+                "runs": "runs DESC, similarity DESC",
+                "name": "agent_name ASC, similarity DESC",
+                "updated_at": "updated_at DESC, similarity DESC",
             }
 
             # Validate and get order clause
             if sorted_by and sorted_by in ALLOWED_ORDER_BY:
                 order_by_clause = ALLOWED_ORDER_BY[sorted_by]
             else:
-                order_by_clause = "updated_at DESC, rank DESC"
+                # Default: order by vector similarity (most similar first)
+                order_by_clause = "similarity DESC, updated_at DESC"
 
             # Build WHERE conditions and parameters list
             where_parts: list[str] = []
-            params: list[typing.Any] = [search_query]  # $1 - search term
+            params: list[typing.Any] = [embedding_str]  # $1 - query embedding
             param_index = 2  # Start at $2 for next parameter
 
-            # Always filter for available agents
+            # Always filter for available agents and agents with embeddings
             where_parts.append("is_available = true")
+            where_parts.append("embedding IS NOT NULL")
 
             if featured:
                 where_parts.append("featured = true")
@@ -103,7 +113,9 @@ async def get_store_agents(
             limit_param = f"${param_index}"
             offset_param = f"${param_index + 1}"
 
-            # Execute full-text search query with parameterized values
+            # Vector similarity search query using cosine distance
+            # The <=> operator returns cosine distance (0 = identical, 2 = opposite)
+            # We convert to similarity: 1 - distance/2 gives range [0, 1]
             sql_query = f"""
                 SELECT
                     slug,
@@ -119,22 +131,18 @@ async def get_store_agents(
                     featured,
                     is_available,
                     updated_at,
-                    ts_rank_cd(search, query) AS rank
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
+                    1 - (embedding <=> $1::vector) AS similarity
+                FROM {{schema_prefix}}"StoreAgent"
                 WHERE {sql_where_clause}
-                    AND search @@ query
                 ORDER BY {order_by_clause}
                 LIMIT {limit_param} OFFSET {offset_param}
             """
 
-            # Count query for pagination - only uses search term parameter
+            # Count query for pagination
             count_query = f"""
                 SELECT COUNT(*) as count
-                FROM {{schema_prefix}}"StoreAgent",
-                    plainto_tsquery('english', $1) AS query
+                FROM {{schema_prefix}}"StoreAgent"
                 WHERE {sql_where_clause}
-                    AND search @@ query
             """
 
             # Execute both queries with parameters
@@ -255,6 +263,56 @@ async def log_search_term(search_query: str):
         logger.error(f"Error logging search term: {e}")
 
 
+async def _generate_and_store_embedding(
+    store_listing_version_id: str,
+    name: str,
+    sub_heading: str,
+    description: str,
+) -> None:
+    """
+    Generate and store embedding for a store listing version.
+
+    This creates a vector embedding from the agent's name, sub_heading, and
+    description, which is used for semantic search.
+
+    Args:
+        store_listing_version_id: The ID of the store listing version.
+        name: The agent name.
+        sub_heading: The agent sub-heading/tagline.
+        description: The agent description.
+    """
+    try:
+        embedding_service = await get_embedding_service()
+        search_text = create_search_text(name, sub_heading, description)
+
+        if not search_text:
+            logger.warning(
+                f"No searchable text for version {store_listing_version_id}, "
+                "skipping embedding generation"
+            )
+            return
+
+        embedding = await embedding_service.generate_embedding(search_text)
+        embedding_str = "[" + ",".join(map(str, embedding)) + "]"
+
+        await query_raw_with_schema(
+            """
+            UPDATE {schema_prefix}"StoreListingVersion"
+            SET embedding = $1::vector
+            WHERE id = $2
+            """,
+            embedding_str,
+            store_listing_version_id,
+        )
+        logger.debug(f"Generated embedding for version {store_listing_version_id}")
+    except Exception as e:
+        # Log error but don't fail the whole operation
+        # Embeddings can be generated later via backfill
+        logger.error(
+            f"Failed to generate embedding for {store_listing_version_id}: {e}"
+        )
+
+
 async def get_store_agent_details(
     username: str, agent_name: str
 ) -> backend.server.v2.store.model.StoreAgentDetails:
@@ -327,7 +385,6 @@ async def get_store_agent_details(
             slug=agent.slug,
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
-            agent_output_demo=agent.agent_output_demo or "",
             agent_image=agent.agent_image,
             creator=agent.creator_username or "",
             creator_avatar=agent.creator_avatar or "",
@@ -398,7 +455,6 @@ async def get_store_agent_by_version_id(
             slug=agent.slug,
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
-            agent_output_demo=agent.agent_output_demo or "",
             agent_image=agent.agent_image,
             creator=agent.creator_username or "",
             creator_avatar=agent.creator_avatar or "",
@@ -685,7 +741,6 @@ async def create_store_submission(
     slug: str,
     name: str,
     video_url: str | None = None,
-    agent_output_demo_url: str | None = None,
     image_urls: list[str] = [],
     description: str = "",
     instructions: str | None = None,
@@ -780,7 +835,6 @@ async def create_store_submission(
                         agentGraphVersion=agent_version,
                         name=name,
                         videoUrl=video_url,
-                        agentOutputDemoUrl=agent_output_demo_url,
                         imageUrls=image_urls,
                         description=description,
                         instructions=instructions,
@@ -805,6 +859,12 @@ async def create_store_submission(
             else None
         )
 
+        # Generate embedding for semantic search
+        if store_listing_version_id:
+            await _generate_and_store_embedding(
+                store_listing_version_id, name, sub_heading, description
+            )
+
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return backend.server.v2.store.model.StoreSubmission(
@@ -853,7 +913,6 @@ async def edit_store_submission(
     store_listing_version_id: str,
     name: str,
     video_url: str | None = None,
-    agent_output_demo_url: str | None = None,
     image_urls: list[str] = [],
     description: str = "",
     sub_heading: str = "",
@@ -935,7 +994,6 @@ async def edit_store_submission(
                 store_listing_id=current_version.storeListingId,
                 name=name,
                 video_url=video_url,
-                agent_output_demo_url=agent_output_demo_url,
                 image_urls=image_urls,
                 description=description,
                 sub_heading=sub_heading,
@@ -953,7 +1011,6 @@ async def edit_store_submission(
                 data=prisma.types.StoreListingVersionUpdateInput(
                     name=name,
                     videoUrl=video_url,
-                    agentOutputDemoUrl=agent_output_demo_url,
                     imageUrls=image_urls,
                     description=description,
                     categories=categories,
@@ -970,6 +1027,12 @@ async def edit_store_submission(
 
             if not updated_version:
                 raise DatabaseError("Failed to update store listing version")
+
+            # Regenerate embedding with updated content
+            await _generate_and_store_embedding(
+                store_listing_version_id, name, sub_heading, description
+            )
+
             return backend.server.v2.store.model.StoreSubmission(
                 agent_id=current_version.agentGraphId,
                 agent_version=current_version.agentGraphVersion,
@@ -1015,7 +1078,6 @@ async def create_store_version(
     store_listing_id: str,
     name: str,
     video_url: str | None = None,
-    agent_output_demo_url: str | None = None,
     image_urls: list[str] = [],
     description: str = "",
     instructions: str | None = None,
@@ -1085,7 +1147,6 @@ async def create_store_version(
                 agentGraphVersion=agent_version,
                 name=name,
                 videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
                 imageUrls=image_urls,
                 description=description,
                 instructions=instructions,
@@ -1102,6 +1163,12 @@ async def create_store_version(
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
+
+        # Generate embedding for semantic search
+        await _generate_and_store_embedding(
+            new_version.id, name, sub_heading, description
+        )
+
         # Return submission details
         return backend.server.v2.store.model.StoreSubmission(
             agent_id=agent_id,

autogpt_platform/backend/backend/server/v2/store/db_test.py

@@ -405,3 +405,237 @@ async def test_get_store_agents_search_category_array_injection():
     # Verify the query executed without error
     # Category should be parameterized, preventing SQL injection
     assert isinstance(result.agents, list)
+
+
+# Vector search tests
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_store_agents_vector_search_mocked(mocker):
+    """Test vector search uses embedding service and executes query safely."""
+    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
+
+    # Mock embedding service
+    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock(
+        return_value=mock_embedding
+    )
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Mock query_raw_with_schema to return empty results
+    mocker.patch(
+        "backend.server.v2.store.db.query_raw_with_schema",
+        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
+    )
+
+    # Call function with search query
+    result = await db.get_store_agents(search_query="test query")
+
+    # Verify embedding service was called
+    mock_embedding_service.generate_embedding.assert_called_once_with("test query")
+
+    # Verify results
+    assert isinstance(result.agents, list)
+    assert len(result.agents) == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_store_agents_vector_search_with_results(mocker):
+    """Test vector search returns properly formatted results."""
+    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
+
+    # Mock embedding service
+    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock(
+        return_value=mock_embedding
+    )
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Mock query results
+    mock_agents = [
+        {
+            "slug": "test-agent",
+            "agent_name": "Test Agent",
+            "agent_image": ["image.jpg"],
+            "creator_username": "creator",
+            "creator_avatar": "avatar.jpg",
+            "sub_heading": "Test heading",
+            "description": "Test description",
+            "runs": 10,
+            "rating": 4.5,
+            "categories": ["test"],
+            "featured": False,
+            "is_available": True,
+            "updated_at": datetime.now(),
+            "similarity": 0.95,
+        }
+    ]
+    mock_count = [{"count": 1}]
+
+    mocker.patch(
+        "backend.server.v2.store.db.query_raw_with_schema",
+        mocker.AsyncMock(side_effect=[mock_agents, mock_count]),
+    )
+
+    # Call function with search query
+    result = await db.get_store_agents(search_query="test query")
+
+    # Verify results
+    assert len(result.agents) == 1
+    assert result.agents[0].slug == "test-agent"
+    assert result.agents[0].agent_name == "Test Agent"
+    assert result.pagination.total_items == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_store_agents_vector_search_with_filters(mocker):
+    """Test vector search works correctly with additional filters."""
+    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
+
+    # Mock embedding service
+    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock(
+        return_value=mock_embedding
+    )
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Mock query_raw_with_schema
+    mock_query = mocker.patch(
+        "backend.server.v2.store.db.query_raw_with_schema",
+        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
+    )
+
+    # Call function with search query and filters
+    await db.get_store_agents(
+        search_query="test query",
+        featured=True,
+        creators=["creator1", "creator2"],
+        category="AI",
+        sorted_by="rating",
+    )
+
+    # Verify query was called with parameterized values
+    # First call is the main query, second is count
+    assert mock_query.call_count == 2
+
+    # Check that the SQL query includes proper parameterization
+    first_call_args = mock_query.call_args_list[0]
+    sql_query = first_call_args[0][0]
+
+    # Verify key elements of the query
+    assert "embedding <=> $1::vector" in sql_query
+    assert "featured = true" in sql_query
+    assert "creator_username = ANY($" in sql_query
+    assert "= ANY(categories)" in sql_query
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_and_store_embedding_success(mocker):
+    """Test that embedding generation and storage works correctly."""
+    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
+
+    # Mock embedding service
+    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock(
+        return_value=mock_embedding
+    )
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Mock query_raw_with_schema
+    mock_query = mocker.patch(
+        "backend.server.v2.store.db.query_raw_with_schema",
+        mocker.AsyncMock(return_value=[]),
+    )
+
+    # Call the internal function
+    await db._generate_and_store_embedding(
+        store_listing_version_id="version-123",
+        name="Test Agent",
+        sub_heading="A test agent",
+        description="Does testing",
+    )
+
+    # Verify embedding service was called with combined text
+    mock_embedding_service.generate_embedding.assert_called_once_with(
+        "Test Agent A test agent Does testing"
+    )
+
+    # Verify database update was called
+    mock_query.assert_called_once()
+    call_args = mock_query.call_args
+    assert "UPDATE" in call_args[0][0]
+    assert "embedding = $1::vector" in call_args[0][0]
+    assert call_args[0][2] == "version-123"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_and_store_embedding_empty_text(mocker):
+    """Test that embedding is not generated for empty text."""
+    # Mock embedding service
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock()
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Mock query_raw_with_schema
+    mock_query = mocker.patch(
+        "backend.server.v2.store.db.query_raw_with_schema",
+        mocker.AsyncMock(return_value=[]),
+    )
+
+    # Call with empty fields
+    await db._generate_and_store_embedding(
+        store_listing_version_id="version-123",
+        name="",
+        sub_heading="",
+        description="",
+    )
+
+    # Verify embedding service was NOT called
+    mock_embedding_service.generate_embedding.assert_not_called()
+
+    # Verify database was NOT updated
+    mock_query.assert_not_called()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_generate_and_store_embedding_handles_error(mocker):
+    """Test that embedding generation errors don't crash the operation."""
+    # Mock embedding service to raise an error
+    mock_embedding_service = mocker.MagicMock()
+    mock_embedding_service.generate_embedding = mocker.AsyncMock(
+        side_effect=Exception("API error")
+    )
+    mocker.patch(
+        "backend.server.v2.store.db.get_embedding_service",
+        mocker.AsyncMock(return_value=mock_embedding_service),
+    )
+
+    # Call should not raise - errors are logged but not propagated
+    await db._generate_and_store_embedding(
+        store_listing_version_id="version-123",
+        name="Test Agent",
+        sub_heading="A test agent",
+        description="Does testing",
+    )
+
+    # Verify embedding service was called (and failed)
+    mock_embedding_service.generate_embedding.assert_called_once()

autogpt_platform/backend/backend/server/v2/store/model.py

@@ -44,7 +44,6 @@ class StoreAgentDetails(pydantic.BaseModel):
     slug: str
     agent_name: str
     agent_video: str
-    agent_output_demo: str
     agent_image: list[str]
     creator: str
     creator_avatar: str
@@ -122,7 +121,6 @@ class StoreSubmission(pydantic.BaseModel):
 
     # Additional fields for editing
     video_url: str | None = None
-    agent_output_demo_url: str | None = None
     categories: list[str] = []
 
 
@@ -159,7 +157,6 @@ class StoreSubmissionRequest(pydantic.BaseModel):
     name: str
     sub_heading: str
     video_url: str | None = None
-    agent_output_demo_url: str | None = None
     image_urls: list[str] = []
     description: str = ""
     instructions: str | None = None
@@ -172,7 +169,6 @@ class StoreSubmissionEditRequest(pydantic.BaseModel):
     name: str
     sub_heading: str
     video_url: str | None = None
-    agent_output_demo_url: str | None = None
     image_urls: list[str] = []
     description: str = ""
     instructions: str | None = None

autogpt_platform/backend/backend/server/v2/store/model_test.py

@@ -62,7 +62,6 @@ def test_store_agent_details():
         slug="test-agent",
         agent_name="Test Agent",
         agent_video="video.mp4",
-        agent_output_demo="demo.mp4",
         agent_image=["image1.jpg", "image2.jpg"],
         creator="creator1",
         creator_avatar="avatar.jpg",

autogpt_platform/backend/backend/server/v2/store/routes.py

@@ -438,7 +438,6 @@ async def create_submission(
         slug=submission_request.slug,
         name=submission_request.name,
         video_url=submission_request.video_url,
-        agent_output_demo_url=submission_request.agent_output_demo_url,
         image_urls=submission_request.image_urls,
         description=submission_request.description,
         instructions=submission_request.instructions,
@@ -482,7 +481,6 @@ async def edit_submission(
         store_listing_version_id=store_listing_version_id,
         name=submission_request.name,
         video_url=submission_request.video_url,
-        agent_output_demo_url=submission_request.agent_output_demo_url,
         image_urls=submission_request.image_urls,
         description=submission_request.description,
         instructions=submission_request.instructions,

autogpt_platform/backend/backend/server/v2/store/routes_test.py

@@ -378,7 +378,6 @@ def test_get_agent_details(
         slug="test-agent",
         agent_name="Test Agent",
         agent_video="video.mp4",
-        agent_output_demo="demo.mp4",
         agent_image=["image1.jpg", "image2.jpg"],
         creator="creator1",
         creator_avatar="avatar1.jpg",

autogpt_platform/backend/backend/util/settings.py

@@ -185,12 +185,6 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Number of top blocks with most errors to show when no blocks exceed threshold (0 to disable).",
     )
 
-    # Execution Accuracy Monitoring
-    execution_accuracy_check_interval_hours: int = Field(
-        default=24,
-        description="Interval in hours between execution accuracy alert checks.",
-    )
-
     model_config = SettingsConfigDict(
         env_file=".env",
         extra="allow",

autogpt_platform/backend/migrations/20251204012214_add_marketplace_agent_output_video_column/migration.sql

@@ -1,64 +0,0 @@
--- AlterTable
-ALTER TABLE "StoreListingVersion" ADD COLUMN     "agentOutputDemoUrl" TEXT;
-
--- Drop and recreate the StoreAgent view with agentOutputDemoUrl field
-DROP VIEW IF EXISTS "StoreAgent";
-
-CREATE OR REPLACE VIEW "StoreAgent" AS
-WITH latest_versions AS (
-    SELECT 
-        "storeListingId",
-        MAX(version) AS max_version
-    FROM "StoreListingVersion"
-    WHERE "submissionStatus" = 'APPROVED'
-    GROUP BY "storeListingId"
-),
-agent_versions AS (
-    SELECT 
-        "storeListingId",
-        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
-    FROM "StoreListingVersion"
-    WHERE "submissionStatus" = 'APPROVED'
-    GROUP BY "storeListingId"
-)
-SELECT
-    sl.id AS listing_id,
-    slv.id AS "storeListingVersionId",
-    slv."createdAt" AS updated_at,
-    sl.slug,
-    COALESCE(slv.name, '') AS agent_name,
-    slv."videoUrl" AS agent_video,
-    slv."agentOutputDemoUrl" AS agent_output_demo,
-    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
-    slv."isFeatured" AS featured,
-    p.username AS creator_username,  -- Allow NULL for malformed sub-agents
-    p."avatarUrl" AS creator_avatar,  -- Allow NULL for malformed sub-agents
-    slv."subHeading" AS sub_heading,
-    slv.description,
-    slv.categories,
-    slv.search,
-    COALESCE(ar.run_count, 0::bigint) AS runs,
-    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
-    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
-    slv."isAvailable" AS is_available,
-    COALESCE(sl."useForOnboarding", false) AS "useForOnboarding"
-FROM "StoreListing" sl
-JOIN latest_versions lv 
-    ON sl.id = lv."storeListingId"
-JOIN "StoreListingVersion" slv 
-    ON slv."storeListingId" = lv."storeListingId"
-   AND slv.version = lv.max_version
-   AND slv."submissionStatus" = 'APPROVED'
-JOIN "AgentGraph" a 
-    ON slv."agentGraphId" = a.id 
-    AND slv."agentGraphVersion" = a.version
-LEFT JOIN "Profile" p 
-    ON sl."owningUserId" = p."userId"
-LEFT JOIN "mv_review_stats" rs 
-    ON sl.id = rs."storeListingId"
-LEFT JOIN "mv_agent_run_counts" ar 
-    ON a.id = ar."agentGraphId"
-LEFT JOIN agent_versions av 
-    ON sl.id = av."storeListingId"
-WHERE sl."isDeleted" = false
-  AND sl."hasApprovedVersion" = true;

autogpt_platform/backend/migrations/20251204121212_add_vector_search/migration.sql

@@ -0,0 +1,92 @@
+-- Migration: Replace full-text search with pgvector-based vector search
+-- This migration:
+-- 1. Enables the pgvector extension
+-- 2. Drops the StoreAgent view (depends on search column)
+-- 3. Removes the full-text search infrastructure (trigger, function, tsvector column)
+-- 4. Adds a vector embedding column for semantic search
+-- 5. Creates an index for fast vector similarity search
+-- 6. Recreates the StoreAgent view with the embedding column
+
+-- Enable pgvector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- First drop the view that depends on the search column
+DROP VIEW IF EXISTS "StoreAgent";
+
+-- Remove full-text search infrastructure
+DROP TRIGGER IF EXISTS "update_tsvector" ON "StoreListingVersion";
+DROP FUNCTION IF EXISTS update_tsvector_column();
+
+-- Drop the tsvector search column
+ALTER TABLE "StoreListingVersion" DROP COLUMN IF EXISTS "search";
+
+-- Add embedding column for vector search (1536 dimensions for text-embedding-3-small)
+ALTER TABLE "StoreListingVersion"
+ADD COLUMN IF NOT EXISTS "embedding" vector(1536);
+
+-- Create IVFFlat index for fast similarity search
+-- Using cosine distance (vector_cosine_ops) which is standard for text embeddings
+-- lists = 100 is appropriate for datasets under 1M rows
+CREATE INDEX IF NOT EXISTS idx_store_listing_version_embedding
+ON "StoreListingVersion"
+USING ivfflat (embedding vector_cosine_ops)
+WITH (lists = 100);
+
+-- Recreate StoreAgent view WITHOUT search column, WITH embedding column
+CREATE OR REPLACE VIEW "StoreAgent" AS
+WITH latest_versions AS (
+    SELECT
+        "storeListingId",
+        MAX(version) AS max_version
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+),
+agent_versions AS (
+    SELECT
+        "storeListingId",
+        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+)
+SELECT
+    sl.id AS listing_id,
+    slv.id AS "storeListingVersionId",
+    slv."createdAt" AS updated_at,
+    sl.slug,
+    COALESCE(slv.name, '') AS agent_name,
+    slv."videoUrl" AS agent_video,
+    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
+    slv."isFeatured" AS featured,
+    p.username AS creator_username,
+    p."avatarUrl" AS creator_avatar,
+    slv."subHeading" AS sub_heading,
+    slv.description,
+    slv.categories,
+    slv.embedding,
+    COALESCE(ar.run_count, 0::bigint) AS runs,
+    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
+    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
+    COALESCE(sl."useForOnboarding", false) AS "useForOnboarding",
+    slv."isAvailable" AS is_available
+FROM "StoreListing" sl
+JOIN latest_versions lv
+    ON sl.id = lv."storeListingId"
+JOIN "StoreListingVersion" slv
+    ON slv."storeListingId" = lv."storeListingId"
+   AND slv.version = lv.max_version
+   AND slv."submissionStatus" = 'APPROVED'
+JOIN "AgentGraph" a
+    ON slv."agentGraphId" = a.id
+    AND slv."agentGraphVersion" = a.version
+LEFT JOIN "Profile" p
+    ON sl."owningUserId" = p."userId"
+LEFT JOIN "mv_review_stats" rs
+    ON sl.id = rs."storeListingId"
+LEFT JOIN "mv_agent_run_counts" ar
+    ON a.id = ar."agentGraphId"
+LEFT JOIN agent_versions av
+    ON sl.id = av."storeListingId"
+WHERE sl."isDeleted" = false
+  AND sl."hasApprovedVersion" = true;

autogpt_platform/backend/migrations/20251209182537_add_builder_search/migration.sql

@@ -1,15 +0,0 @@
--- Create BuilderSearchHistory table
-CREATE TABLE "BuilderSearchHistory" (
-    "id" TEXT NOT NULL,
-    "userId" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "searchQuery" TEXT NOT NULL,
-    "filter" TEXT[] DEFAULT ARRAY[]::TEXT[],
-    "byCreator" TEXT[] DEFAULT ARRAY[]::TEXT[],
-
-    CONSTRAINT "BuilderSearchHistory_pkey" PRIMARY KEY ("id")
-);
-
--- Define User foreign relation
-ALTER TABLE "BuilderSearchHistory" ADD CONSTRAINT "BuilderSearchHistory_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/pyproject.toml

@@ -114,7 +114,6 @@ cli = "backend.cli:main"
 format = "linter:format"
 lint = "linter:lint"
 test = "run_tests:test"
-load-store-agents = "test.load_store_agents:run"
 
 [tool.isort]
 profile = "black"

autogpt_platform/backend/schema.prisma

@@ -53,7 +53,6 @@ model User {
 
   Profile               Profile[]
   UserOnboarding        UserOnboarding?
-  BuilderSearchHistory  BuilderSearchHistory[]
   StoreListings         StoreListing[]
   StoreListingReviews   StoreListingReview[]
   StoreVersionsReviewed StoreListingVersion[]
@@ -115,19 +114,6 @@ model UserOnboarding {
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 }
 
-model BuilderSearchHistory {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @default(now()) @updatedAt
-
-  searchQuery String
-  filter      String[]  @default([])
-  byCreator   String[]  @default([])
-
-  userId String
-  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
-}
-
 // This model describes the Agent Graph/Flow (Multi Agent System).
 model AgentGraph {
   id        String    @default(uuid())
@@ -715,11 +701,10 @@ view StoreAgent {
   storeListingVersionId String
   updated_at            DateTime
 
-  slug                String
-  agent_name          String
-  agent_video         String?
-  agent_output_demo   String?
-  agent_image         String[]
+  slug        String
+  agent_name  String
+  agent_video String?
+  agent_image String[]
 
   featured         Boolean                  @default(false)
   creator_username String?
@@ -727,7 +712,7 @@ view StoreAgent {
   sub_heading      String
   description      String
   categories       String[]
-  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  embedding        Unsupported("vector(1536)")?
   runs             Int
   rating           Float
   versions         String[]
@@ -848,14 +833,13 @@ model StoreListingVersion {
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
 
   // Content fields
-  name                String
-  subHeading          String
-  videoUrl            String?
-  agentOutputDemoUrl  String?
-  imageUrls           String[]
-  description         String
-  instructions        String?
-  categories          String[]
+  name         String
+  subHeading   String
+  videoUrl     String?
+  imageUrls    String[]
+  description  String
+  instructions String?
+  categories   String[]
 
   isFeatured Boolean @default(false)
 
@@ -863,7 +847,8 @@ model StoreListingVersion {
   // Old versions can be made unavailable by the author if desired
   isAvailable Boolean @default(true)
 
-  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  // Vector embedding for semantic search (replaces tsvector full-text search)
+  embedding Unsupported("vector(1536)")?
 
   // Version workflow state
   submissionStatus SubmissionStatus @default(DRAFT)

autogpt_platform/backend/snapshots/agt_details

@@ -3,7 +3,6 @@
   "slug": "test-agent",
   "agent_name": "Test Agent",
   "agent_video": "video.mp4",
-  "agent_output_demo": "demo.mp4",
   "agent_image": [
     "image1.jpg",
     "image2.jpg"

autogpt_platform/backend/snapshots/sub_success

@@ -23,7 +23,6 @@
       "reviewed_at": null,
       "changes_summary": null,
       "video_url": "test.mp4",
-      "agent_output_demo_url": null,
       "categories": [
         "test-category"
       ]

autogpt_platform/backend/test/load_store_agents.py

@@ -10,7 +10,7 @@ It creates:
 
 Usage:
     cd backend
-    poetry run load-store-agents
+    poetry run python test/load_store_agents.py
 """
 
 import asyncio
@@ -37,8 +37,7 @@ from prisma.types import (
 AGENTS_DIR = Path(__file__).parent.parent / "agents"
 CSV_FILE = AGENTS_DIR / "StoreAgent_rows.csv"
 
-# User constants for the autogpt creator (test data, not production)
-# Fixed uuid4 for idempotency - same user is reused across script runs
+# Fixed user ID for the autogpt creator (test data, not production)
 AUTOGPT_USER_ID = "79d96c73-e6f5-4656-a83a-185b41ee0d06"
 AUTOGPT_EMAIL = "autogpt-test@agpt.co"
 AUTOGPT_USERNAME = "autogpt"
@@ -254,12 +253,7 @@ async def create_agent_graph(
     for node in nodes:
         block_id = node["block_id"]
         # Ensure the block exists (create placeholder if needed)
-        block_exists = await ensure_block_exists(db, block_id, known_blocks)
-        if not block_exists:
-            print(
-                f"    Skipping node {node['id']} - block {block_id} could not be created"
-            )
-            continue
+        await ensure_block_exists(db, block_id, known_blocks)
 
         await db.agentnode.create(
             data=AgentNodeCreateInput(
@@ -359,7 +353,7 @@ async def create_store_listing(
     if is_approved:
         await db.storelisting.update(
             where={"id": listing_id},
-            data={"ActiveVersion": {"connect": {"id": version_id}}},
+            data={"activeVersionId": version_id},
         )
 
 
@@ -393,7 +387,6 @@ async def main():
 
         # Build mapping from version_id to json file
         loaded_graphs = {}  # graph_id -> (graph_id, version)
-        failed_agents = []
 
         for json_file in json_files:
             # Extract the version ID from filename (agent_<version_id>.json)
@@ -406,25 +399,17 @@ async def main():
                 continue
 
             metadata = csv_metadata[version_id]
-            agent_name = metadata["agent_name"]
-            print(f"\nProcessing: {agent_name}")
+            print(f"\nProcessing: {metadata['agent_name']}")
 
-            # Use a transaction per agent to prevent dangling resources
-            try:
-                async with db.tx() as tx:
-                    # Load and create the agent graph
-                    agent_data = await load_agent_json(json_file)
-                    graph_id, graph_version = await create_agent_graph(
-                        tx, agent_data, known_blocks
-                    )
-                    loaded_graphs[graph_id] = (graph_id, graph_version)
+            # Load and create the agent graph
+            agent_data = await load_agent_json(json_file)
+            graph_id, graph_version = await create_agent_graph(
+                db, agent_data, known_blocks
+            )
+            loaded_graphs[graph_id] = (graph_id, graph_version)
 
-                    # Create store listing
-                    await create_store_listing(tx, graph_id, graph_version, metadata)
-            except Exception as e:
-                print(f"  Error loading agent '{agent_name}': {e}")
-                failed_agents.append(agent_name)
-                continue
+            # Create store listing
+            await create_store_listing(db, graph_id, graph_version, metadata)
 
         # Step 4: Refresh materialized views
         print("\n[Step 4] Refreshing materialized views...")
@@ -436,20 +421,11 @@ async def main():
 
         print("\n" + "=" * 60)
         print(f"Successfully loaded {len(loaded_graphs)} agents")
-        if failed_agents:
-            print(
-                f"Failed to load {len(failed_agents)} agents: {', '.join(failed_agents)}"
-            )
         print("=" * 60)
 
     finally:
         await db.disconnect()
 
 
-def run():
-    """Entry point for poetry script."""
-    asyncio.run(main())
-
-
 if __name__ == "__main__":
-    run()
+    asyncio.run(main())

autogpt_platform/frontend/package.json

@@ -82,7 +82,7 @@
     "lodash": "4.17.21",
     "lucide-react": "0.552.0",
     "moment": "2.30.1",
-    "next": "15.4.10",
+    "next": "15.4.7",
     "next-themes": "0.4.6",
     "nuqs": "2.7.2",
     "party-js": "2.2.0",

autogpt_platform/frontend/pnpm-lock.yaml

@@ -16,7 +16,7 @@ importers:
         version: 5.2.2(react-hook-form@7.66.0(react@18.3.1))
       '@next/third-parties':
         specifier: 15.4.6
-        version: 15.4.6(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
+        version: 15.4.6(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
       '@phosphor-icons/react':
         specifier: 2.1.10
         version: 2.1.10(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -88,7 +88,7 @@ importers:
         version: 5.24.13(@rjsf/utils@5.24.13(react@18.3.1))
       '@sentry/nextjs':
         specifier: 10.27.0
-        version: 10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))
+        version: 10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))
       '@supabase/ssr':
         specifier: 0.7.0
         version: 0.7.0(@supabase/supabase-js@2.78.0)
@@ -106,10 +106,10 @@ importers:
         version: 0.2.4
       '@vercel/analytics':
         specifier: 1.5.0
-        version: 1.5.0(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
+        version: 1.5.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
       '@vercel/speed-insights':
         specifier: 1.2.0
-        version: 1.2.0(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
+        version: 1.2.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
       '@xyflow/react':
         specifier: 12.9.2
         version: 12.9.2(@types/react@18.3.17)(immer@10.1.3)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
@@ -148,7 +148,7 @@ importers:
         version: 12.23.24(@emotion/is-prop-valid@1.2.2)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       geist:
         specifier: 1.5.1
-        version: 1.5.1(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))
+        version: 1.5.1(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))
       highlight.js:
         specifier: 11.11.1
         version: 11.11.1
@@ -171,14 +171,14 @@ importers:
         specifier: 2.30.1
         version: 2.30.1
       next:
-        specifier: 15.4.10
-        version: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+        specifier: 15.4.7
+        version: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       next-themes:
         specifier: 0.4.6
         version: 0.4.6(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       nuqs:
         specifier: 2.7.2
-        version: 2.7.2(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
+        version: 2.7.2(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)
       party-js:
         specifier: 2.2.0
         version: 2.2.0
@@ -284,7 +284,7 @@ importers:
         version: 9.1.5(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))
       '@storybook/nextjs':
         specifier: 9.1.5
-        version: 9.1.5(esbuild@0.25.9)(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))
+        version: 9.1.5(esbuild@0.25.9)(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))
       '@tanstack/eslint-plugin-query':
         specifier: 5.91.2
         version: 5.91.2(eslint@8.57.1)(typescript@5.9.3)
@@ -1602,56 +1602,56 @@ packages:
   '@neoconfetti/react@1.0.0':
     resolution: {integrity: sha512-klcSooChXXOzIm+SE5IISIAn3bYzYfPjbX7D7HoqZL84oAfgREeSg5vSIaSFH+DaGzzvImTyWe1OyrJ67vik4A==}
 
-  '@next/env@15.4.10':
-    resolution: {integrity: sha512-knhmoJ0Vv7VRf6pZEPSnciUG1S4bIhWx+qTYBW/AjxEtlzsiNORPk8sFDCEvqLfmKuey56UB9FL1UdHEV3uBrg==}
+  '@next/env@15.4.7':
+    resolution: {integrity: sha512-PrBIpO8oljZGTOe9HH0miix1w5MUiGJ/q83Jge03mHEE0E3pyqzAy2+l5G6aJDbXoobmxPJTVhbCuwlLtjSHwg==}
 
   '@next/eslint-plugin-next@15.5.2':
     resolution: {integrity: sha512-lkLrRVxcftuOsJNhWatf1P2hNVfh98k/omQHrCEPPriUypR6RcS13IvLdIrEvkm9AH2Nu2YpR5vLqBuy6twH3Q==}
 
-  '@next/swc-darwin-arm64@15.4.8':
-    resolution: {integrity: sha512-Pf6zXp7yyQEn7sqMxur6+kYcywx5up1J849psyET7/8pG2gQTVMjU3NzgIt8SeEP5to3If/SaWmaA6H6ysBr1A==}
+  '@next/swc-darwin-arm64@15.4.7':
+    resolution: {integrity: sha512-2Dkb+VUTp9kHHkSqtws4fDl2Oxms29HcZBwFIda1X7Ztudzy7M6XF9HDS2dq85TmdN47VpuhjE+i6wgnIboVzQ==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [darwin]
 
-  '@next/swc-darwin-x64@15.4.8':
-    resolution: {integrity: sha512-xla6AOfz68a6kq3gRQccWEvFC/VRGJmA/QuSLENSO7CZX5WIEkSz7r1FdXUjtGCQ1c2M+ndUAH7opdfLK1PQbw==}
+  '@next/swc-darwin-x64@15.4.7':
+    resolution: {integrity: sha512-qaMnEozKdWezlmh1OGDVFueFv2z9lWTcLvt7e39QA3YOvZHNpN2rLs/IQLwZaUiw2jSvxW07LxMCWtOqsWFNQg==}
     engines: {node: '>= 10'}
     cpu: [x64]
     os: [darwin]
 
-  '@next/swc-linux-arm64-gnu@15.4.8':
-    resolution: {integrity: sha512-y3fmp+1Px/SJD+5ntve5QLZnGLycsxsVPkTzAc3zUiXYSOlTPqT8ynfmt6tt4fSo1tAhDPmryXpYKEAcoAPDJw==}
+  '@next/swc-linux-arm64-gnu@15.4.7':
+    resolution: {integrity: sha512-ny7lODPE7a15Qms8LZiN9wjNWIeI+iAZOFDOnv2pcHStncUr7cr9lD5XF81mdhrBXLUP9yT9RzlmSWKIazWoDw==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [linux]
 
-  '@next/swc-linux-arm64-musl@15.4.8':
-    resolution: {integrity: sha512-DX/L8VHzrr1CfwaVjBQr3GWCqNNFgyWJbeQ10Lx/phzbQo3JNAxUok1DZ8JHRGcL6PgMRgj6HylnLNndxn4Z6A==}
+  '@next/swc-linux-arm64-musl@15.4.7':
+    resolution: {integrity: sha512-4SaCjlFR/2hGJqZLLWycccy1t+wBrE/vyJWnYaZJhUVHccpGLG5q0C+Xkw4iRzUIkE+/dr90MJRUym3s1+vO8A==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [linux]
 
-  '@next/swc-linux-x64-gnu@15.4.8':
-    resolution: {integrity: sha512-9fLAAXKAL3xEIFdKdzG5rUSvSiZTLLTCc6JKq1z04DR4zY7DbAPcRvNm3K1inVhTiQCs19ZRAgUerHiVKMZZIA==}
+  '@next/swc-linux-x64-gnu@15.4.7':
+    resolution: {integrity: sha512-2uNXjxvONyRidg00VwvlTYDwC9EgCGNzPAPYbttIATZRxmOZ3hllk/YYESzHZb65eyZfBR5g9xgCZjRAl9YYGg==}
     engines: {node: '>= 10'}
     cpu: [x64]
     os: [linux]
 
-  '@next/swc-linux-x64-musl@15.4.8':
-    resolution: {integrity: sha512-s45V7nfb5g7dbS7JK6XZDcapicVrMMvX2uYgOHP16QuKH/JA285oy6HcxlKqwUNaFY/UC6EvQ8QZUOo19cBKSA==}
+  '@next/swc-linux-x64-musl@15.4.7':
+    resolution: {integrity: sha512-ceNbPjsFgLscYNGKSu4I6LYaadq2B8tcK116nVuInpHHdAWLWSwVK6CHNvCi0wVS9+TTArIFKJGsEyVD1H+4Kg==}
     engines: {node: '>= 10'}
     cpu: [x64]
     os: [linux]
 
-  '@next/swc-win32-arm64-msvc@15.4.8':
-    resolution: {integrity: sha512-KjgeQyOAq7t/HzAJcWPGA8X+4WY03uSCZ2Ekk98S9OgCFsb6lfBE3dbUzUuEQAN2THbwYgFfxX2yFTCMm8Kehw==}
+  '@next/swc-win32-arm64-msvc@15.4.7':
+    resolution: {integrity: sha512-pZyxmY1iHlZJ04LUL7Css8bNvsYAMYOY9JRwFA3HZgpaNKsJSowD09Vg2R9734GxAcLJc2KDQHSCR91uD6/AAw==}
     engines: {node: '>= 10'}
     cpu: [arm64]
     os: [win32]
 
-  '@next/swc-win32-x64-msvc@15.4.8':
-    resolution: {integrity: sha512-Exsmf/+42fWVnLMaZHzshukTBxZrSwuuLKFvqhGHJ+mC1AokqieLY/XzAl3jc/CqhXLqLY3RRjkKJ9YnLPcRWg==}
+  '@next/swc-win32-x64-msvc@15.4.7':
+    resolution: {integrity: sha512-HjuwPJ7BeRzgl3KrjKqD2iDng0eQIpIReyhpF5r4yeAHFwWRuAhfW92rWv/r3qeQHEwHsLRzFDvMqRjyM5DI6A==}
     engines: {node: '>= 10'}
     cpu: [x64]
     os: [win32]
@@ -5920,8 +5920,8 @@ packages:
       react: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
       react-dom: ^16.8 || ^17 || ^18 || ^19 || ^19.0.0-rc
 
-  next@15.4.10:
-    resolution: {integrity: sha512-itVlc79QjpKMFMRhP+kbGKaSG/gZM6RCvwhEbwmCNF06CdDiNaoHcbeg0PqkEa2GOcn8KJ0nnc7+yL7EjoYLHQ==}
+  next@15.4.7:
+    resolution: {integrity: sha512-OcqRugwF7n7mC8OSYjvsZhhG1AYSvulor1EIUsIkbbEbf1qoE5EbH36Swj8WhF4cHqmDgkiam3z1c1W0J1Wifg==}
     engines: {node: ^18.18.0 || ^19.8.0 || >= 20.0.0}
     hasBin: true
     peerDependencies:
@@ -9003,39 +9003,39 @@ snapshots:
 
   '@neoconfetti/react@1.0.0': {}
 
-  '@next/env@15.4.10': {}
+  '@next/env@15.4.7': {}
 
   '@next/eslint-plugin-next@15.5.2':
     dependencies:
       fast-glob: 3.3.1
 
-  '@next/swc-darwin-arm64@15.4.8':
+  '@next/swc-darwin-arm64@15.4.7':
     optional: true
 
-  '@next/swc-darwin-x64@15.4.8':
+  '@next/swc-darwin-x64@15.4.7':
     optional: true
 
-  '@next/swc-linux-arm64-gnu@15.4.8':
+  '@next/swc-linux-arm64-gnu@15.4.7':
     optional: true
 
-  '@next/swc-linux-arm64-musl@15.4.8':
+  '@next/swc-linux-arm64-musl@15.4.7':
     optional: true
 
-  '@next/swc-linux-x64-gnu@15.4.8':
+  '@next/swc-linux-x64-gnu@15.4.7':
     optional: true
 
-  '@next/swc-linux-x64-musl@15.4.8':
+  '@next/swc-linux-x64-musl@15.4.7':
     optional: true
 
-  '@next/swc-win32-arm64-msvc@15.4.8':
+  '@next/swc-win32-arm64-msvc@15.4.7':
     optional: true
 
-  '@next/swc-win32-x64-msvc@15.4.8':
+  '@next/swc-win32-x64-msvc@15.4.7':
     optional: true
 
-  '@next/third-parties@15.4.6(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
+  '@next/third-parties@15.4.6(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
     dependencies:
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       react: 18.3.1
       third-party-capital: 1.0.20
 
@@ -10267,7 +10267,7 @@ snapshots:
 
   '@sentry/core@10.27.0': {}
 
-  '@sentry/nextjs@10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))':
+  '@sentry/nextjs@10.27.0(@opentelemetry/context-async-hooks@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/core@2.2.0(@opentelemetry/api@1.9.0))(@opentelemetry/sdk-trace-base@2.2.0(@opentelemetry/api@1.9.0))(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)(webpack@5.101.3(esbuild@0.25.9))':
     dependencies:
       '@opentelemetry/api': 1.9.0
       '@opentelemetry/semantic-conventions': 1.37.0
@@ -10280,7 +10280,7 @@ snapshots:
       '@sentry/react': 10.27.0(react@18.3.1)
       '@sentry/vercel-edge': 10.27.0
       '@sentry/webpack-plugin': 4.3.0(webpack@5.101.3(esbuild@0.25.9))
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       resolve: 1.22.8
       rollup: 4.52.2
       stacktrace-parser: 0.1.11
@@ -10642,7 +10642,7 @@ snapshots:
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
 
-  '@storybook/nextjs@9.1.5(esbuild@0.25.9)(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))':
+  '@storybook/nextjs@9.1.5(esbuild@0.25.9)(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(type-fest@4.41.0)(typescript@5.9.3)(webpack-hot-middleware@2.26.1)(webpack@5.101.3(esbuild@0.25.9))':
     dependencies:
       '@babel/core': 7.28.4
       '@babel/plugin-syntax-bigint': 7.8.3(@babel/core@7.28.4)
@@ -10666,7 +10666,7 @@ snapshots:
       css-loader: 6.11.0(webpack@5.101.3(esbuild@0.25.9))
       image-size: 2.0.2
       loader-utils: 3.3.1
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       node-polyfill-webpack-plugin: 2.0.1(webpack@5.101.3(esbuild@0.25.9))
       postcss: 8.5.6
       postcss-loader: 8.2.0(postcss@8.5.6)(typescript@5.9.3)(webpack@5.101.3(esbuild@0.25.9))
@@ -11271,14 +11271,14 @@ snapshots:
   '@unrs/resolver-binding-win32-x64-msvc@1.11.1':
     optional: true
 
-  '@vercel/analytics@1.5.0(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
+  '@vercel/analytics@1.5.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
     optionalDependencies:
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       react: 18.3.1
 
-  '@vercel/speed-insights@1.2.0(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
+  '@vercel/speed-insights@1.2.0(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1)':
     optionalDependencies:
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
       react: 18.3.1
 
   '@vitest/expect@3.2.4':
@@ -12954,9 +12954,9 @@ snapshots:
 
   functions-have-names@1.2.3: {}
 
-  geist@1.5.1(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)):
+  geist@1.5.1(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)):
     dependencies:
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
 
   gensync@1.0.0-beta.2: {}
 
@@ -14226,9 +14226,9 @@ snapshots:
       react: 18.3.1
       react-dom: 18.3.1(react@18.3.1)
 
-  next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
+  next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1):
     dependencies:
-      '@next/env': 15.4.10
+      '@next/env': 15.4.7
       '@swc/helpers': 0.5.15
       caniuse-lite: 1.0.30001741
       postcss: 8.4.31
@@ -14236,14 +14236,14 @@ snapshots:
       react-dom: 18.3.1(react@18.3.1)
       styled-jsx: 5.1.6(@babel/core@7.28.4)(react@18.3.1)
     optionalDependencies:
-      '@next/swc-darwin-arm64': 15.4.8
-      '@next/swc-darwin-x64': 15.4.8
-      '@next/swc-linux-arm64-gnu': 15.4.8
-      '@next/swc-linux-arm64-musl': 15.4.8
-      '@next/swc-linux-x64-gnu': 15.4.8
-      '@next/swc-linux-x64-musl': 15.4.8
-      '@next/swc-win32-arm64-msvc': 15.4.8
-      '@next/swc-win32-x64-msvc': 15.4.8
+      '@next/swc-darwin-arm64': 15.4.7
+      '@next/swc-darwin-x64': 15.4.7
+      '@next/swc-linux-arm64-gnu': 15.4.7
+      '@next/swc-linux-arm64-musl': 15.4.7
+      '@next/swc-linux-x64-gnu': 15.4.7
+      '@next/swc-linux-x64-musl': 15.4.7
+      '@next/swc-win32-arm64-msvc': 15.4.7
+      '@next/swc-win32-x64-msvc': 15.4.7
       '@opentelemetry/api': 1.9.0
       '@playwright/test': 1.56.1
       sharp: 0.34.3
@@ -14321,12 +14321,12 @@ snapshots:
     dependencies:
       boolbase: 1.0.0
 
-  nuqs@2.7.2(next@15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1):
+  nuqs@2.7.2(next@15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1))(react@18.3.1):
     dependencies:
       '@standard-schema/spec': 1.0.0
       react: 18.3.1
     optionalDependencies:
-      next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      next: 15.4.7(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
 
   oas-kit-common@1.0.8:
     dependencies:
@@ -15340,7 +15340,7 @@ snapshots:
     dependencies:
       color: 4.2.3
       detect-libc: 2.0.4
-      semver: 7.7.3
+      semver: 7.7.2
     optionalDependencies:
       '@img/sharp-darwin-arm64': 0.34.3
       '@img/sharp-darwin-x64': 0.34.3

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/4-agent/page.tsx

@@ -1,4 +1,6 @@
 "use client";
+import { StoreAgentDetails } from "@/lib/autogpt-server-api";
+import { useBackendAPI } from "@/lib/autogpt-server-api/context";
 import { isEmptyOrWhitespace } from "@/lib/utils";
 import { useRouter } from "next/navigation";
 import { useEffect, useState } from "react";
@@ -11,17 +13,15 @@ import {
   OnboardingStep,
 } from "../components/OnboardingStep";
 import { OnboardingText } from "../components/OnboardingText";
-import { getV1RecommendedOnboardingAgents } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
-import { resolveResponse } from "@/app/api/helpers";
-import { StoreAgentDetails } from "@/app/api/__generated__/models/storeAgentDetails";
 
 export default function Page() {
   const { state, updateState, completeStep } = useOnboarding(4, "INTEGRATIONS");
   const [agents, setAgents] = useState<StoreAgentDetails[]>([]);
+  const api = useBackendAPI();
   const router = useRouter();
 
   useEffect(() => {
-    resolveResponse(getV1RecommendedOnboardingAgents()).then((agents) => {
+    api.getOnboardingAgents().then((agents) => {
       if (agents.length < 2) {
         completeStep("CONGRATS");
         router.replace("/");

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/useOnboardingRunStep.tsx

@@ -12,9 +12,6 @@ import {
   useGetV2GetAgentByVersion,
   useGetV2GetAgentGraph,
 } from "@/app/api/__generated__/endpoints/store/store";
-import { resolveResponse } from "@/app/api/helpers";
-import { postV2AddMarketplaceAgent } from "@/app/api/__generated__/endpoints/library/library";
-import { GraphID } from "@/lib/autogpt-server-api";
 
 export function useOnboardingRunStep() {
   const onboarding = useOnboarding(undefined, "AGENT_CHOICE");
@@ -80,7 +77,12 @@ export function useOnboardingRunStep() {
 
     setShowInput(true);
     onboarding.setStep(6);
-    onboarding.completeStep("AGENT_NEW_RUN");
+    onboarding.updateState({
+      completedSteps: [
+        ...(onboarding.state.completedSteps || []),
+        "AGENT_NEW_RUN",
+      ],
+    });
   }
 
   function handleSetAgentInput(key: string, value: string) {
@@ -109,22 +111,21 @@ export function useOnboardingRunStep() {
     setRunningAgent(true);
 
     try {
-      const libraryAgent = await resolveResponse(
-        postV2AddMarketplaceAgent({
-          store_listing_version_id: storeAgent?.store_listing_version_id || "",
-          source: "onboarding",
-        }),
+      const libraryAgent = await api.addMarketplaceAgentToLibrary(
+        storeAgent?.store_listing_version_id || "",
       );
 
       const { id: runID } = await api.executeGraph(
-        libraryAgent.graph_id as GraphID,
+        libraryAgent.graph_id,
         libraryAgent.graph_version,
         onboarding.state.agentInput || {},
         inputCredentials,
-        "onboarding",
       );
 
-      onboarding.updateState({ onboardingAgentExecutionId: runID });
+      onboarding.updateState({
+        onboardingAgentExecutionId: runID,
+        agentRuns: (onboarding.state.agentRuns || 0) + 1,
+      });
 
       router.push("/onboarding/6-congrats");
     } catch (error) {

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/6-congrats/page.tsx

@@ -5,9 +5,6 @@ import { useRouter } from "next/navigation";
 import * as party from "party-js";
 import { useEffect, useRef, useState } from "react";
 import { useOnboarding } from "../../../../providers/onboarding/onboarding-provider";
-import { resolveResponse } from "@/app/api/helpers";
-import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
-import { postV2AddMarketplaceAgent } from "@/app/api/__generated__/endpoints/library/library";
 
 export default function Page() {
   const { completeStep } = useOnboarding(7, "AGENT_INPUT");
@@ -40,15 +37,11 @@ export default function Page() {
       completeStep("CONGRATS");
 
       try {
-        const onboarding = await resolveResponse(getV1OnboardingState());
+        const onboarding = await api.getUserOnboarding();
         if (onboarding?.selectedStoreListingVersionId) {
           try {
-            const libraryAgent = await resolveResponse(
-              postV2AddMarketplaceAgent({
-                store_listing_version_id:
-                  onboarding.selectedStoreListingVersionId,
-                source: "onboarding",
-              }),
+            const libraryAgent = await api.addMarketplaceAgentToLibrary(
+              onboarding.selectedStoreListingVersionId,
             );
             router.replace(`/library/agents/${libraryAgent.id}`);
           } catch (error) {

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/components/OnboardingAgentCard.tsx

@@ -1,7 +1,7 @@
 import { cn } from "@/lib/utils";
 import StarRating from "./StarRating";
+import { StoreAgentDetails } from "@/lib/autogpt-server-api";
 import SmartImage from "@/components/__legacy__/SmartImage";
-import { StoreAgentDetails } from "@/app/api/__generated__/models/storeAgentDetails";
 
 type OnboardingAgentCardProps = {
   agent?: StoreAgentDetails;

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx

@@ -1,24 +1,24 @@
 "use client";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { useBackendAPI } from "@/lib/autogpt-server-api/context";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
-import { resolveResponse, shouldShowOnboarding } from "@/app/api/helpers";
-import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
 
 export default function OnboardingPage() {
   const router = useRouter();
+  const api = useBackendAPI();
 
   useEffect(() => {
     async function redirectToStep() {
       try {
         // Check if onboarding is enabled
-        const isEnabled = await shouldShowOnboarding();
+        const isEnabled = await api.isOnboardingEnabled();
         if (!isEnabled) {
           router.replace("/");
           return;
         }
 
-        const onboarding = await resolveResponse(getV1OnboardingState());
+        const onboarding = await api.getUserOnboarding();
 
         // Handle completed onboarding
         if (onboarding.completedSteps.includes("GET_RESULTS")) {
@@ -66,7 +66,7 @@ export default function OnboardingPage() {
     }
 
     redirectToStep();
-  }, [router]);
+  }, [api, router]);
 
   return <LoadingSpinner size="large" cover />;
 }

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/components/ExecutionAnalyticsForm.tsx

@@ -1,16 +1,6 @@
 "use client";
 
 import { useState, useEffect } from "react";
-import {
-  LineChart,
-  Line,
-  XAxis,
-  YAxis,
-  CartesianGrid,
-  Tooltip,
-  Legend,
-  ResponsiveContainer,
-} from "recharts";
 import { Button } from "@/components/atoms/Button/Button";
 import { Input } from "@/components/__legacy__/ui/input";
 import { Label } from "@/components/__legacy__/ui/label";
@@ -28,12 +18,9 @@ import { useToast } from "@/components/molecules/Toast/use-toast";
 import {
   usePostV2GenerateExecutionAnalytics,
   useGetV2GetExecutionAnalyticsConfiguration,
-  useGetV2GetExecutionAccuracyTrendsAndAlerts,
 } from "@/app/api/__generated__/endpoints/admin/admin";
 import type { ExecutionAnalyticsRequest } from "@/app/api/__generated__/models/executionAnalyticsRequest";
 import type { ExecutionAnalyticsResponse } from "@/app/api/__generated__/models/executionAnalyticsResponse";
-import type { AccuracyTrendsResponse } from "@/app/api/__generated__/models/accuracyTrendsResponse";
-import type { AccuracyLatestData } from "@/app/api/__generated__/models/accuracyLatestData";
 
 // Use the generated type with minimal adjustment for form handling
 interface FormData extends Omit<ExecutionAnalyticsRequest, "created_after"> {
@@ -46,133 +33,8 @@ export function ExecutionAnalyticsForm() {
   const [results, setResults] = useState<ExecutionAnalyticsResponse | null>(
     null,
   );
-  const [trendsData, setTrendsData] = useState<AccuracyTrendsResponse | null>(
-    null,
-  );
   const { toast } = useToast();
 
-  // State for accuracy trends query parameters
-  const [accuracyParams, setAccuracyParams] = useState<{
-    graph_id: string;
-    user_id?: string;
-    days_back: number;
-    drop_threshold: number;
-    include_historical?: boolean;
-  } | null>(null);
-
-  // Use the generated API client for accuracy trends (GET)
-  const { data: accuracyApiResponse, error: accuracyError } =
-    useGetV2GetExecutionAccuracyTrendsAndAlerts(
-      accuracyParams || {
-        graph_id: "",
-        days_back: 30,
-        drop_threshold: 10.0,
-        include_historical: false,
-      },
-      {
-        query: {
-          enabled: !!accuracyParams?.graph_id,
-        },
-      },
-    );
-
-  // Update local state when data changes and handle success/error
-  useEffect(() => {
-    if (accuracyError) {
-      console.error("Failed to fetch trends:", accuracyError);
-      toast({
-        title: "Trends Error",
-        description:
-          (accuracyError as any)?.message || "Failed to fetch accuracy trends",
-        variant: "destructive",
-      });
-      return;
-    }
-
-    const data = accuracyApiResponse?.data;
-    if (data && "latest_data" in data) {
-      setTrendsData(data);
-
-      // Check for alerts
-      if (data.alert) {
-        toast({
-          title: "🚨 Accuracy Alert Detected",
-          description: `${data.alert.drop_percent.toFixed(1)}% accuracy drop detected for this agent`,
-          variant: "destructive",
-        });
-      }
-    }
-  }, [accuracyApiResponse, accuracyError, toast]);
-
-  // Chart component for accuracy trends
-  function AccuracyChart({ data }: { data: AccuracyLatestData[] }) {
-    const chartData = data.map((item) => ({
-      date: new Date(item.date).toLocaleDateString(),
-      "Daily Score": item.daily_score,
-      "3-Day Avg": item.three_day_avg,
-      "7-Day Avg": item.seven_day_avg,
-      "14-Day Avg": item.fourteen_day_avg,
-    }));
-
-    return (
-      <ResponsiveContainer width="100%" height={400}>
-        <LineChart
-          data={chartData}
-          margin={{ top: 5, right: 30, left: 20, bottom: 5 }}
-        >
-          <CartesianGrid strokeDasharray="3 3" />
-          <XAxis dataKey="date" />
-          <YAxis domain={[0, 100]} />
-          <Tooltip
-            formatter={(value) => [`${Number(value).toFixed(2)}%`, ""]}
-          />
-          <Legend />
-          <Line
-            type="monotone"
-            dataKey="Daily Score"
-            stroke="#3b82f6"
-            strokeWidth={2}
-            dot={{ r: 3 }}
-          />
-          <Line
-            type="monotone"
-            dataKey="3-Day Avg"
-            stroke="#10b981"
-            strokeWidth={2}
-            dot={{ r: 3 }}
-          />
-          <Line
-            type="monotone"
-            dataKey="7-Day Avg"
-            stroke="#f59e0b"
-            strokeWidth={2}
-            dot={{ r: 3 }}
-          />
-          <Line
-            type="monotone"
-            dataKey="14-Day Avg"
-            stroke="#8b5cf6"
-            strokeWidth={2}
-            dot={{ r: 3 }}
-          />
-        </LineChart>
-      </ResponsiveContainer>
-    );
-  }
-
-  // Function to fetch accuracy trends using generated API client
-  const fetchAccuracyTrends = (graphId: string, userId?: string) => {
-    if (!graphId.trim()) return;
-
-    setAccuracyParams({
-      graph_id: graphId.trim(),
-      user_id: userId?.trim() || undefined,
-      days_back: 30,
-      drop_threshold: 10.0,
-      include_historical: showAccuracyChart, // Include historical data when chart is enabled
-    });
-  };
-
   // Fetch configuration from API
   const {
     data: config,
@@ -188,7 +50,6 @@ export function ExecutionAnalyticsForm() {
         }
         const result = res.data;
         setResults(result);
-
         toast({
           title: "Analytics Generated",
           description: `Processed ${result.processed_executions} executions. ${result.successful_analytics} successful, ${result.failed_analytics} failed, ${result.skipped_executions} skipped.`,
@@ -197,21 +58,11 @@ export function ExecutionAnalyticsForm() {
       },
       onError: (error: any) => {
         console.error("Analytics generation error:", error);
-
-        const errorMessage =
-          error?.message || error?.detail || "An unexpected error occurred";
-        const isOpenAIError = errorMessage.includes(
-          "OpenAI API key not configured",
-        );
-
         toast({
-          title: isOpenAIError
-            ? "Analytics Generation Skipped"
-            : "Analytics Generation Failed",
-          description: isOpenAIError
-            ? "Analytics generation requires OpenAI configuration, but accuracy trends are still available above."
-            : errorMessage,
-          variant: isOpenAIError ? "default" : "destructive",
+          title: "Analytics Generation Failed",
+          description:
+            error?.message || error?.detail || "An unexpected error occurred",
+          variant: "destructive",
         });
       },
     },
@@ -226,9 +77,6 @@ export function ExecutionAnalyticsForm() {
     user_prompt: "", // Will use config default when empty
   });
 
-  // State for accuracy trends chart toggle
-  const [showAccuracyChart, setShowAccuracyChart] = useState(true);
-
   // Update form defaults when config loads
   useEffect(() => {
     if (config?.data && config.status === 200 && !formData.model_name) {
@@ -253,11 +101,6 @@ export function ExecutionAnalyticsForm() {
 
     setResults(null);
 
-    // Fetch accuracy trends if chart is enabled
-    if (showAccuracyChart) {
-      fetchAccuracyTrends(formData.graph_id, formData.user_id || undefined);
-    }
-
     // Prepare the request payload
     const payload: ExecutionAnalyticsRequest = {
       graph_id: formData.graph_id.trim(),
@@ -419,18 +262,6 @@ export function ExecutionAnalyticsForm() {
                 </Label>
               </div>
 
-              {/* Show Accuracy Chart Checkbox */}
-              <div className="flex items-center space-x-2">
-                <Checkbox
-                  id="show_accuracy_chart"
-                  checked={showAccuracyChart}
-                  onCheckedChange={(checked) => setShowAccuracyChart(!!checked)}
-                />
-                <Label htmlFor="show_accuracy_chart" className="text-sm">
-                  Show accuracy trends chart and historical data visualization
-                </Label>
-              </div>
-
               {/* Custom System Prompt */}
               <div className="space-y-2">
                 <Label htmlFor="system_prompt">
@@ -539,98 +370,6 @@ export function ExecutionAnalyticsForm() {
         </div>
       </form>
 
-      {/* Accuracy Trends Display */}
-      {trendsData && (
-        <div className="space-y-4">
-          <h3 className="text-lg font-semibold">Execution Accuracy Trends</h3>
-
-          {/* Alert Section */}
-          {trendsData.alert && (
-            <div className="rounded-lg border-l-4 border-red-500 bg-red-50 p-4">
-              <div className="flex items-start">
-                <span className="text-2xl">🚨</span>
-                <div className="ml-3 space-y-2">
-                  <h4 className="text-lg font-semibold text-red-800">
-                    Accuracy Alert Detected
-                  </h4>
-                  <p className="text-red-700">
-                    <strong>
-                      {trendsData.alert.drop_percent.toFixed(1)}% accuracy drop
-                    </strong>{" "}
-                    detected for agent{" "}
-                    <code className="rounded bg-red-100 px-1 text-sm">
-                      {formData.graph_id}
-                    </code>
-                  </p>
-                  <div className="space-y-1 text-sm text-red-600">
-                    <p>
-                      • 3-day average:{" "}
-                      <strong>
-                        {trendsData.alert.three_day_avg.toFixed(2)}%
-                      </strong>
-                    </p>
-                    <p>
-                      • 7-day average:{" "}
-                      <strong>
-                        {trendsData.alert.seven_day_avg.toFixed(2)}%
-                      </strong>
-                    </p>
-                    <p>
-                      • Detected at:{" "}
-                      <strong>
-                        {new Date(
-                          trendsData.alert.detected_at,
-                        ).toLocaleString()}
-                      </strong>
-                    </p>
-                  </div>
-                </div>
-              </div>
-            </div>
-          )}
-
-          {/* Latest Data Summary */}
-          <div className="grid grid-cols-2 gap-4 md:grid-cols-4">
-            <div className="rounded-lg border bg-white p-4 text-center">
-              <div className="text-2xl font-bold text-blue-600">
-                {trendsData.latest_data.daily_score?.toFixed(2) || "N/A"}
-              </div>
-              <div className="text-sm text-gray-600">Daily Score</div>
-            </div>
-            <div className="rounded-lg border bg-white p-4 text-center">
-              <div className="text-2xl font-bold text-green-600">
-                {trendsData.latest_data.three_day_avg?.toFixed(2) || "N/A"}
-              </div>
-              <div className="text-sm text-gray-600">3-Day Avg</div>
-            </div>
-            <div className="rounded-lg border bg-white p-4 text-center">
-              <div className="text-2xl font-bold text-orange-600">
-                {trendsData.latest_data.seven_day_avg?.toFixed(2) || "N/A"}
-              </div>
-              <div className="text-sm text-gray-600">7-Day Avg</div>
-            </div>
-            <div className="rounded-lg border bg-white p-4 text-center">
-              <div className="text-2xl font-bold text-purple-600">
-                {trendsData.latest_data.fourteen_day_avg?.toFixed(2) || "N/A"}
-              </div>
-              <div className="text-sm text-gray-600">14-Day Avg</div>
-            </div>
-          </div>
-
-          {/* Chart Section - only show when toggle is enabled and historical data exists */}
-          {showAccuracyChart && trendsData?.historical_data && (
-            <div className="mt-6">
-              <h4 className="mb-4 text-lg font-semibold">
-                Execution Accuracy Trends Chart
-              </h4>
-              <div className="rounded-lg border bg-white p-6">
-                <AccuracyChart data={trendsData.historical_data} />
-              </div>
-            </div>
-          )}
-        </div>
-      )}
-
       {results && <AnalyticsResultsTable results={results} />}
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/admin/execution-analytics/page.tsx

@@ -17,13 +17,12 @@ function ExecutionAnalyticsDashboard() {
         </div>
 
         <div className="rounded-lg border bg-white p-6 shadow-sm">
-          <h2 className="mb-4 text-xl font-semibold">
-            Execution Analytics & Accuracy Monitoring
-          </h2>
+          <h2 className="mb-4 text-xl font-semibold">Analytics Generation</h2>
           <p className="mb-6 text-gray-600">
-            Generate missing activity summaries and success scores for agent
-            executions. After generation, accuracy trends and alerts will
-            automatically be displayed to help monitor agent health over time.
+            This tool will identify completed executions missing activity
+            summaries or success scores and generate them using AI. Only
+            executions that meet the criteria and are missing these fields will
+            be processed.
           </p>
 
           <Suspense

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

@@ -1,4 +1,4 @@
-import { OAuthPopupResultMessage } from "@/components/renderers/input-renderer/fields/CredentialField/models/OAuthCredentialModal/useOAuthCredentialModal";
+import { OAuthPopupResultMessage } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import { NextResponse } from "next/server";
 
 // This route is intended to be used as the callback for integration OAuth flows,

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunGraph/useRunGraph.ts

@@ -9,8 +9,6 @@ import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { useShallow } from "zustand/react/shallow";
 import { useState } from "react";
 import { useSaveGraph } from "@/app/(platform)/build/hooks/useSaveGraph";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import { ApiError } from "@/lib/autogpt-server-api/helpers"; // Check if this exists
 
 export const useRunGraph = () => {
   const { saveGraph, isSaving } = useSaveGraph({
@@ -26,13 +24,6 @@ export const useRunGraph = () => {
   );
   const [openRunInputDialog, setOpenRunInputDialog] = useState(false);
 
-  const setNodeErrorsForBackendId = useNodeStore(
-    useShallow((state) => state.setNodeErrorsForBackendId),
-  );
-  const clearAllNodeErrors = useNodeStore(
-    useShallow((state) => state.clearAllNodeErrors),
-  );
-
   const [{ flowID, flowVersion, flowExecutionID }, setQueryStates] =
     useQueryStates({
       flowID: parseAsString,
@@ -44,49 +35,19 @@ export const useRunGraph = () => {
     usePostV1ExecuteGraphAgent({
       mutation: {
         onSuccess: (response: any) => {
-          clearAllNodeErrors();
           const { id } = response.data as GraphExecutionMeta;
           setQueryStates({
             flowExecutionID: id,
           });
         },
         onError: (error: any) => {
+          // Reset running state on error
           setIsGraphRunning(false);
-          if (error instanceof ApiError && error.isGraphValidationError?.()) {
-            const errorData = error.response?.detail;
-
-            if (errorData?.node_errors) {
-              Object.entries(errorData.node_errors).forEach(
-                ([backendId, nodeErrors]) => {
-                  setNodeErrorsForBackendId(
-                    backendId,
-                    nodeErrors as { [key: string]: string },
-                  );
-                },
-              );
-
-              useNodeStore.getState().nodes.forEach((node) => {
-                const backendId = node.data.metadata?.backend_id || node.id;
-                if (!errorData.node_errors[backendId as string]) {
-                  useNodeStore.getState().updateNodeErrors(node.id, {});
-                }
-              });
-            }
-
-            toast({
-              title: errorData?.message || "Graph validation failed",
-              description:
-                "Please fix the validation errors on the highlighted nodes and try again.",
-              variant: "destructive",
-            });
-          } else {
-            toast({
-              title:
-                (error.detail as string) ?? "An unexpected error occurred.",
-              description: "An unexpected error occurred.",
-              variant: "destructive",
-            });
-          }
+          toast({
+            title: (error.detail as string) ?? "An unexpected error occurred.",
+            description: "An unexpected error occurred.",
+            variant: "destructive",
+          });
         },
       },
     });
@@ -116,7 +77,7 @@ export const useRunGraph = () => {
       await executeGraph({
         graphId: flowID ?? "",
         graphVersion: flowVersion || null,
-        data: { inputs: {}, credentials_inputs: {}, source: "builder" },
+        data: { inputs: {}, credentials_inputs: {} },
       });
     }
   };

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -79,11 +79,7 @@ export const useRunInputDialog = ({
     await executeGraph({
       graphId: flowID ?? "",
       graphVersion: flowVersion || null,
-      data: {
-        inputs: inputValues,
-        credentials_inputs: credentialValues,
-        source: "builder",
-      },
+      data: { inputs: inputValues, credentials_inputs: credentialValues },
     });
     // Optimistically set running state immediately for responsive UI
     setIsGraphRunning(true);

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/useCopyPaste.ts

@@ -1,25 +1,24 @@
 import { useCallback } from "react";
 import { useReactFlow } from "@xyflow/react";
+import { Key, storage } from "@/services/storage/local-storage";
 import { v4 as uuidv4 } from "uuid";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useEdgeStore } from "../../../stores/edgeStore";
 import { CustomNode } from "../nodes/CustomNode/CustomNode";
 import { CustomEdge } from "../edges/CustomEdge";
-import { useToast } from "@/components/molecules/Toast/use-toast";
 
 interface CopyableData {
   nodes: CustomNode[];
   edges: CustomEdge[];
 }
 
-const CLIPBOARD_PREFIX = "autogpt-flow-data:";
-
 export function useCopyPaste() {
+  // Only use useReactFlow for viewport (not managed by stores)
   const { getViewport } = useReactFlow();
-  const { toast } = useToast();
 
   const handleCopyPaste = useCallback(
     (event: KeyboardEvent) => {
+      // Prevent copy/paste if any modal is open or if the focus is on an input element
       const activeElement = document.activeElement;
       const isInputField =
         activeElement?.tagName === "INPUT" ||
@@ -29,6 +28,7 @@ export function useCopyPaste() {
       if (isInputField) return;
 
       if (event.ctrlKey || event.metaKey) {
+        // COPY: Ctrl+C or Cmd+C
         if (event.key === "c" || event.key === "C") {
           const { nodes } = useNodeStore.getState();
           const { edges } = useEdgeStore.getState();
@@ -53,102 +53,81 @@ export function useCopyPaste() {
             edges: selectedEdges,
           };
 
-          const clipboardText = `${CLIPBOARD_PREFIX}${JSON.stringify(copiedData)}`;
-          navigator.clipboard
-            .writeText(clipboardText)
-            .then(() => {
-              toast({
-                title: "Copied successfully",
-                description: `${selectedNodes.length} node(s) copied to clipboard`,
-              });
-            })
-            .catch((error) => {
-              console.error("Failed to copy to clipboard:", error);
-            });
+          storage.set(Key.COPIED_FLOW_DATA, JSON.stringify(copiedData));
         }
 
+        // PASTE: Ctrl+V or Cmd+V
         if (event.key === "v" || event.key === "V") {
-          navigator.clipboard
-            .readText()
-            .then((clipboardText) => {
-              if (!clipboardText.startsWith(CLIPBOARD_PREFIX)) {
-                return; // Not our data, ignore
-              }
+          const copiedDataString = storage.get(Key.COPIED_FLOW_DATA);
+          if (copiedDataString) {
+            const copiedData = JSON.parse(copiedDataString) as CopyableData;
+            const oldToNewIdMap: Record<string, string> = {};
 
-              const jsonString = clipboardText.slice(CLIPBOARD_PREFIX.length);
-              const copiedData = JSON.parse(jsonString) as CopyableData;
-              const oldToNewIdMap: Record<string, string> = {};
+            // Get fresh viewport values at paste time to ensure correct positioning
+            const { x, y, zoom } = getViewport();
+            const viewportCenter = {
+              x: (window.innerWidth / 2 - x) / zoom,
+              y: (window.innerHeight / 2 - y) / zoom,
+            };
 
-              const { x, y, zoom } = getViewport();
-              const viewportCenter = {
-                x: (window.innerWidth / 2 - x) / zoom,
-                y: (window.innerHeight / 2 - y) / zoom,
+            let minX = Infinity,
+              minY = Infinity,
+              maxX = -Infinity,
+              maxY = -Infinity;
+            copiedData.nodes.forEach((node) => {
+              minX = Math.min(minX, node.position.x);
+              minY = Math.min(minY, node.position.y);
+              maxX = Math.max(maxX, node.position.x);
+              maxY = Math.max(maxY, node.position.y);
+            });
+
+            const offsetX = viewportCenter.x - (minX + maxX) / 2;
+            const offsetY = viewportCenter.y - (minY + maxY) / 2;
+
+            // Deselect existing nodes first
+            useNodeStore.setState((state) => ({
+              nodes: state.nodes.map((node) => ({ ...node, selected: false })),
+            }));
+
+            // Create and add new nodes with UNIQUE IDs using UUID
+            copiedData.nodes.forEach((node) => {
+              const newNodeId = uuidv4();
+              oldToNewIdMap[node.id] = newNodeId;
+
+              const newNode: CustomNode = {
+                ...node,
+                id: newNodeId,
+                selected: true,
+                position: {
+                  x: node.position.x + offsetX,
+                  y: node.position.y + offsetY,
+                },
               };
 
-              let minX = Infinity,
-                minY = Infinity,
-                maxX = -Infinity,
-                maxY = -Infinity;
-              copiedData.nodes.forEach((node) => {
-                minX = Math.min(minX, node.position.x);
-                minY = Math.min(minY, node.position.y);
-                maxX = Math.max(maxX, node.position.x);
-                maxY = Math.max(maxY, node.position.y);
-              });
-
-              const offsetX = viewportCenter.x - (minX + maxX) / 2;
-              const offsetY = viewportCenter.y - (minY + maxY) / 2;
-
-              // Deselect existing nodes first
-              useNodeStore.setState((state) => ({
-                nodes: state.nodes.map((node) => ({
-                  ...node,
-                  selected: false,
-                })),
-              }));
-
-              // Create and add new nodes with UNIQUE IDs using UUID
-              copiedData.nodes.forEach((node) => {
-                const newNodeId = uuidv4();
-                oldToNewIdMap[node.id] = newNodeId;
-
-                const newNode: CustomNode = {
-                  ...node,
-                  id: newNodeId,
-                  selected: true,
-                  position: {
-                    x: node.position.x + offsetX,
-                    y: node.position.y + offsetY,
-                  },
-                };
-
-                useNodeStore.getState().addNode(newNode);
-              });
-
-              // Add edges with updated source/target IDs
-              const { addEdge } = useEdgeStore.getState();
-              copiedData.edges.forEach((edge) => {
-                const newSourceId = oldToNewIdMap[edge.source] ?? edge.source;
-                const newTargetId = oldToNewIdMap[edge.target] ?? edge.target;
-
-                addEdge({
-                  source: newSourceId,
-                  target: newTargetId,
-                  sourceHandle: edge.sourceHandle ?? "",
-                  targetHandle: edge.targetHandle ?? "",
-                  data: {
-                    ...edge.data,
-                  },
-                });
-              });
-            })
-            .catch((error) => {
-              console.error("Failed to read from clipboard:", error);
+              useNodeStore.getState().addNode(newNode);
             });
+
+            // Add edges with updated source/target IDs
+            const { addEdge } = useEdgeStore.getState();
+            copiedData.edges.forEach((edge) => {
+              const newSourceId = oldToNewIdMap[edge.source] ?? edge.source;
+              const newTargetId = oldToNewIdMap[edge.target] ?? edge.target;
+
+              addEdge({
+                source: newSourceId,
+                target: newTargetId,
+                sourceHandle: edge.sourceHandle ?? "",
+                targetHandle: edge.targetHandle ?? "",
+                data: {
+                  ...edge.data,
+                },
+              });
+            });
+          }
         }
       }
     },
-    [getViewport, toast],
+    [getViewport],
   );
 
   return handleCopyPaste;

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/useFlow.ts

@@ -42,12 +42,11 @@ export const useFlow = () => {
   const setBlockMenuOpen = useControlPanelStore(
     useShallow((state) => state.setBlockMenuOpen),
   );
-  const [{ flowID, flowVersion, flowExecutionID }, setQueryStates] =
-    useQueryStates({
-      flowID: parseAsString,
-      flowVersion: parseAsInteger,
-      flowExecutionID: parseAsString,
-    });
+  const [{ flowID, flowVersion, flowExecutionID }] = useQueryStates({
+    flowID: parseAsString,
+    flowVersion: parseAsInteger,
+    flowExecutionID: parseAsString,
+  });
 
   const { data: executionDetails } = useGetV1GetExecutionDetails(
     flowID || "",
@@ -82,7 +81,7 @@ export const useFlow = () => {
       {
         query: {
           select: (res) => res.data as BlockInfo[],
-          enabled: !!flowID && !!blockIds && blockIds.length > 0,
+          enabled: !!flowID && !!blockIds,
         },
       },
     );
@@ -103,9 +102,6 @@ export const useFlow = () => {
   // load graph schemas
   useEffect(() => {
     if (graph) {
-      setQueryStates({
-        flowVersion: graph.version ?? 1,
-      });
       setGraphSchemas(
         graph.input_schema as Record<string, any> | null,
         graph.credentials_input_schema as Record<string, any> | null,

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/CustomNode.tsx

@@ -37,7 +37,6 @@ export type CustomNodeData = {
   costs: BlockCost[];
   categories: BlockInfoCategoriesItem[];
   metadata?: NodeModelMetadata;
-  errors?: { [key: string]: string };
 };
 
 export type CustomNode = XYNode<CustomNodeData, "custom">;
@@ -72,24 +71,10 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
         ? (data.hardcodedValues.output_schema ?? {})
         : data.outputSchema;
 
-    const hasConfigErrors =
-      data.errors &&
-      Object.values(data.errors).some(
-        (value) => value !== null && value !== undefined && value !== "",
-      );
-
-    const outputData = data.nodeExecutionResult?.output_data;
-    const hasOutputError =
-      typeof outputData === "object" &&
-      outputData !== null &&
-      "error" in outputData;
-
-    const hasErrors = hasConfigErrors || hasOutputError;
-
     // Currently all blockTypes design are similar - that's why i am using the same component for all of them
     // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
     return (
-      <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
+      <NodeContainer selected={selected} nodeId={nodeId}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
           {isWebhook && <WebhookDisclaimer nodeId={nodeId} />}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeContainer.tsx

@@ -3,18 +3,15 @@ import { nodeStyleBasedOnStatus } from "../helpers";
 
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { useShallow } from "zustand/react/shallow";
-import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
 
 export const NodeContainer = ({
   children,
   nodeId,
   selected,
-  hasErrors, // these are configuration errors that occur before executing the graph -- more like validation errors
 }: {
   children: React.ReactNode;
   nodeId: string;
   selected: boolean;
-  hasErrors?: boolean;
 }) => {
   const status = useNodeStore(
     useShallow((state) => state.getNodeStatus(nodeId)),
@@ -25,7 +22,6 @@ export const NodeContainer = ({
         "z-12 max-w-[370px] rounded-xlarge ring-1 ring-slate-200/60",
         selected && "shadow-lg ring-2 ring-slate-200",
         status && nodeStyleBasedOnStatus[status],
-        hasErrors ? nodeStyleBasedOnStatus[AgentExecutionStatus.FAILED] : "",
       )}
     >
       {children}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeContextMenu.tsx

@@ -28,7 +28,6 @@ export const NodeContextMenu = ({
       })),
     }));
 
-    useCopyPasteStore.getState().copySelectedNodes();
     useCopyPasteStore.getState().pasteNodes();
   };
 

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/useBlockMenuSearch.ts

@@ -1,7 +1,7 @@
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { useGetV2BuilderSearchInfinite } from "@/app/api/__generated__/endpoints/store/store";
 import { SearchResponse } from "@/app/api/__generated__/models/searchResponse";
-import { useCallback, useEffect, useState } from "react";
+import { useState } from "react";
 import { useAddAgentToBuilder } from "../hooks/useAddAgentToBuilder";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getV2GetSpecificAgent } from "@/app/api/__generated__/endpoints/store/store";
@@ -9,27 +9,16 @@ import {
   getGetV2ListLibraryAgentsQueryKey,
   usePostV2AddMarketplaceAgent,
 } from "@/app/api/__generated__/endpoints/library/library";
-import {
-  getGetV2GetBuilderItemCountsQueryKey,
-  getGetV2GetBuilderSuggestionsQueryKey,
-} from "@/app/api/__generated__/endpoints/default/default";
+import { getGetV2GetBuilderItemCountsQueryKey } from "@/app/api/__generated__/endpoints/default/default";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import * as Sentry from "@sentry/nextjs";
 
 export const useBlockMenuSearch = () => {
-  const { searchQuery, searchId, setSearchId } = useBlockMenuStore();
+  const { searchQuery } = useBlockMenuStore();
   const { toast } = useToast();
   const { addAgentToBuilder, addLibraryAgentToBuilder } =
     useAddAgentToBuilder();
-  const queryClient = getQueryClient();
-
-  const resetSearchSession = useCallback(() => {
-    setSearchId(undefined);
-    queryClient.invalidateQueries({
-      queryKey: getGetV2GetBuilderSuggestionsQueryKey(),
-    });
-  }, [queryClient, setSearchId]);
 
   const [addingLibraryAgentId, setAddingLibraryAgentId] = useState<
     string | null
@@ -49,19 +38,13 @@ export const useBlockMenuSearch = () => {
       page: 1,
       page_size: 8,
       search_query: searchQuery,
-      search_id: searchId,
     },
     {
       query: {
-        getNextPageParam: (lastPage) => {
-          const response = lastPage.data as SearchResponse;
-          const { pagination } = response;
-          if (!pagination) {
-            return undefined;
-          }
-
-          const { current_page, total_pages } = pagination;
-          return current_page < total_pages ? current_page + 1 : undefined;
+        getNextPageParam: (lastPage, allPages) => {
+          const pagination = lastPage.data as SearchResponse;
+          const isMore = pagination.more_pages;
+          return isMore ? allPages.length + 1 : undefined;
         },
       },
     },
@@ -70,6 +53,7 @@ export const useBlockMenuSearch = () => {
   const { mutateAsync: addMarketplaceAgent } = usePostV2AddMarketplaceAgent({
     mutation: {
       onSuccess: () => {
+        const queryClient = getQueryClient();
         queryClient.invalidateQueries({
           queryKey: getGetV2ListLibraryAgentsQueryKey(),
         });
@@ -91,24 +75,6 @@ export const useBlockMenuSearch = () => {
     },
   });
 
-  useEffect(() => {
-    if (!searchData?.pages?.length) {
-      return;
-    }
-
-    const latestPage = searchData.pages[searchData.pages.length - 1];
-    const response = latestPage?.data as SearchResponse;
-    if (response?.search_id && response.search_id !== searchId) {
-      setSearchId(response.search_id);
-    }
-  }, [searchData, searchId, setSearchId]);
-
-  useEffect(() => {
-    if (searchId && !searchQuery) {
-      resetSearchSession();
-    }
-  }, [resetSearchSession, searchId, searchQuery]);
-
   const allSearchData =
     searchData?.pages?.flatMap((page) => {
       const response = page.data as SearchResponse;

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

@@ -1,32 +1,30 @@
 import { debounce } from "lodash";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
-import { getQueryClient } from "@/lib/react-query/queryClient";
-import { getGetV2GetBuilderSuggestionsQueryKey } from "@/app/api/__generated__/endpoints/default/default";
 
 const SEARCH_DEBOUNCE_MS = 300;
 
 export const useBlockMenuSearchBar = () => {
   const inputRef = useRef<HTMLInputElement>(null);
   const [localQuery, setLocalQuery] = useState("");
-  const { setSearchQuery, setSearchId, searchQuery } = useBlockMenuStore();
-  const queryClient = getQueryClient();
+  const { setSearchQuery, setSearchId, searchId, searchQuery } =
+    useBlockMenuStore();
 
-  const clearSearchSession = useCallback(() => {
-    setSearchId(undefined);
-    queryClient.invalidateQueries({
-      queryKey: getGetV2GetBuilderSuggestionsQueryKey(),
-    });
-  }, [queryClient, setSearchId]);
+  const searchIdRef = useRef(searchId);
+  useEffect(() => {
+    searchIdRef.current = searchId;
+  }, [searchId]);
 
   const debouncedSetSearchQuery = useCallback(
     debounce((value: string) => {
       setSearchQuery(value);
       if (value.length === 0) {
-        clearSearchSession();
+        setSearchId(undefined);
+      } else if (!searchIdRef.current) {
+        setSearchId(crypto.randomUUID());
       }
     }, SEARCH_DEBOUNCE_MS),
-    [clearSearchSession, setSearchQuery],
+    [setSearchQuery, setSearchId],
   );
 
   useEffect(() => {
@@ -38,13 +36,13 @@ export const useBlockMenuSearchBar = () => {
   const handleClear = () => {
     setLocalQuery("");
     setSearchQuery("");
-    clearSearchSession();
+    setSearchId(undefined);
     debouncedSetSearchQuery.cancel();
   };
 
   useEffect(() => {
     setLocalQuery(searchQuery);
-  }, [searchQuery]);
+  }, []);
 
   return {
     handleClear,

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx

@@ -1,109 +0,0 @@
-import React, { useEffect, useRef, useState } from "react";
-import { ArrowLeftIcon, ArrowRightIcon } from "@phosphor-icons/react";
-import { cn } from "@/lib/utils";
-
-interface HorizontalScrollAreaProps {
-  children: React.ReactNode;
-  wrapperClassName?: string;
-  scrollContainerClassName?: string;
-  scrollAmount?: number;
-  dependencyList?: React.DependencyList;
-}
-
-const defaultDependencies: React.DependencyList = [];
-const baseScrollClasses =
-  "flex gap-2 overflow-x-auto px-8 [scrollbar-width:none] [-ms-overflow-style:'none'] [&::-webkit-scrollbar]:hidden";
-
-export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
-  children,
-  wrapperClassName,
-  scrollContainerClassName,
-  scrollAmount = 300,
-  dependencyList = defaultDependencies,
-}) => {
-  const scrollRef = useRef<HTMLDivElement | null>(null);
-  const [canScrollLeft, setCanScrollLeft] = useState(false);
-  const [canScrollRight, setCanScrollRight] = useState(false);
-
-  const scrollByDelta = (delta: number) => {
-    if (!scrollRef.current) {
-      return;
-    }
-    scrollRef.current.scrollBy({ left: delta, behavior: "smooth" });
-  };
-
-  const updateScrollState = () => {
-    const element = scrollRef.current;
-    if (!element) {
-      setCanScrollLeft(false);
-      setCanScrollRight(false);
-      return;
-    }
-    setCanScrollLeft(element.scrollLeft > 0);
-    setCanScrollRight(
-      Math.ceil(element.scrollLeft + element.clientWidth) < element.scrollWidth,
-    );
-  };
-
-  useEffect(() => {
-    updateScrollState();
-    const element = scrollRef.current;
-    if (!element) {
-      return;
-    }
-    const handleScroll = () => updateScrollState();
-    element.addEventListener("scroll", handleScroll);
-    window.addEventListener("resize", handleScroll);
-    return () => {
-      element.removeEventListener("scroll", handleScroll);
-      window.removeEventListener("resize", handleScroll);
-    };
-  }, dependencyList);
-
-  return (
-    <div className={wrapperClassName}>
-      <div className="group relative">
-        <div
-          ref={scrollRef}
-          className={cn(baseScrollClasses, scrollContainerClassName)}
-        >
-          {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" />
-        )}
-        {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" />
-        )}
-        {canScrollLeft && (
-          <button
-            type="button"
-            aria-label="Scroll left"
-            className="pointer-events-none absolute left-2 top-5 -translate-y-1/2 opacity-0 transition-opacity duration-200 group-hover:pointer-events-auto group-hover:opacity-100"
-            onClick={() => scrollByDelta(-scrollAmount)}
-          >
-            <ArrowLeftIcon
-              size={28}
-              className="rounded-full bg-zinc-700 p-1 text-white drop-shadow"
-              weight="light"
-            />
-          </button>
-        )}
-        {canScrollRight && (
-          <button
-            type="button"
-            aria-label="Scroll right"
-            className="pointer-events-none absolute right-2 top-5 -translate-y-1/2 opacity-0 transition-opacity duration-200 group-hover:pointer-events-auto group-hover:opacity-100"
-            onClick={() => scrollByDelta(scrollAmount)}
-          >
-            <ArrowRightIcon
-              size={28}
-              className="rounded-full bg-zinc-700 p-1 text-white drop-shadow"
-              weight="light"
-            />
-          </button>
-        )}
-      </div>
-    </div>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/SuggestionContent/SuggestionContent.tsx

@@ -6,15 +6,10 @@ import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { blockMenuContainerStyle } from "../style";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { DefaultStateType } from "../types";
-import { SearchHistoryChip } from "../SearchHistoryChip";
-import { HorizontalScroll } from "../HorizontalScroll";
 
 export const SuggestionContent = () => {
-  const { setIntegration, setDefaultState, setSearchQuery, setSearchId } =
-    useBlockMenuStore();
+  const { setIntegration, setDefaultState } = useBlockMenuStore();
   const { data, isLoading, isError, error, refetch } = useSuggestionContent();
-  const suggestions = data?.suggestions;
-  const hasRecentSearches = (suggestions?.recent_searches?.length ?? 0) > 0;
 
   if (isError) {
     return (
@@ -34,45 +29,11 @@ export const SuggestionContent = () => {
     );
   }
 
+  const suggestions = data?.suggestions;
+
   return (
     <div className={blockMenuContainerStyle}>
       <div className="w-full space-y-6 pb-4">
-        {/* Recent searches */}
-        {hasRecentSearches && (
-          <div className="space-y-2.5 px-4">
-            <p className="font-sans text-sm font-medium leading-[1.375rem] text-zinc-800">
-              Recent searches
-            </p>
-            <HorizontalScroll
-              wrapperClassName="-mx-8"
-              scrollContainerClassName="flex gap-2 overflow-x-auto px-8 [scrollbar-width:none] [-ms-overflow-style:'none'] [&::-webkit-scrollbar]:hidden"
-              dependencyList={[
-                suggestions?.recent_searches?.length ?? 0,
-                isLoading,
-              ]}
-            >
-              {!isLoading && suggestions
-                ? suggestions.recent_searches.map((entry, index) => (
-                    <SearchHistoryChip
-                      key={entry.search_id || `${entry.search_query}-${index}`}
-                      content={entry.search_query || "Untitled search"}
-                      onClick={() => {
-                        setSearchQuery(entry.search_query || "");
-                        setSearchId(entry.search_id || undefined);
-                      }}
-                    />
-                  ))
-                : Array(3)
-                    .fill(0)
-                    .map((_, index) => (
-                      <SearchHistoryChip.Skeleton
-                        key={`recent-search-skeleton-${index}`}
-                      />
-                    ))}
-            </HorizontalScroll>
-          </div>
-        )}
-
         {/* Integrations */}
         <div className="space-y-2.5 px-4">
           <p className="font-sans text-sm font-medium leading-[1.375rem] text-zinc-800">

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeInputs.tsx

@@ -981,17 +981,15 @@ const NodeArrayInput: FC<{
           );
         return (
           <div key={entryKey}>
-            {schema.items && (
-              <NodeHandle
-                title={`#${index + 1}`}
-                className="text-sm text-gray-500"
-                keyName={entryKey}
-                schema={schema.items}
-                isConnected={isConnected}
-                isRequired={false}
-                side="left"
-              />
-            )}
+            <NodeHandle
+              title={`#${index + 1}`}
+              className="text-sm text-gray-500"
+              keyName={entryKey}
+              schema={schema.items!}
+              isConnected={isConnected}
+              isRequired={false}
+              side="left"
+            />
             <div className="mb-2 flex space-x-2">
               {!isConnected &&
                 (schema.items ? (

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/RunnerInputUI.tsx

@@ -83,6 +83,7 @@ export function RunnerInputDialog({
           onRun={doRun ? undefined : doClose}
           doCreateSchedule={doCreateSchedule ? handleSchedule : undefined}
           onCreateSchedule={doCreateSchedule ? undefined : doClose}
+          runCount={0}
         />
       </DialogContent>
     </Dialog>

autogpt_platform/frontend/src/app/(platform)/build/hooks/useSaveGraph.ts

@@ -152,9 +152,7 @@ export const useSaveGraph = ({
           links: graphLinks,
         };
 
-        const response = await createNewGraph({
-          data: { graph: data, source: "builder" },
-        });
+        const response = await createNewGraph({ data: { graph: data } });
         const graphData = response.data as GraphModel;
         setGraphSchemas(
           graphData.input_schema,

autogpt_platform/frontend/src/app/(platform)/build/stores/nodeStore.ts

@@ -53,15 +53,6 @@ type NodeStore = {
   getNodeExecutionResult: (nodeId: string) => NodeExecutionResult | undefined;
   getNodeBlockUIType: (nodeId: string) => BlockUIType;
   hasWebhookNodes: () => boolean;
-
-  updateNodeErrors: (nodeId: string, errors: { [key: string]: string }) => void;
-  clearNodeErrors: (nodeId: string) => void;
-  getNodeErrors: (nodeId: string) => { [key: string]: string } | undefined;
-  setNodeErrorsForBackendId: (
-    backendId: string,
-    errors: { [key: string]: string },
-  ) => void;
-  clearAllNodeErrors: () => void; // Add this
 };
 
 export const useNodeStore = create<NodeStore>((set, get) => ({
@@ -262,47 +253,4 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       [BlockUIType.WEBHOOK, BlockUIType.WEBHOOK_MANUAL].includes(n.data.uiType),
     );
   },
-
-  updateNodeErrors: (nodeId: string, errors: { [key: string]: string }) => {
-    set((state) => ({
-      nodes: state.nodes.map((n) =>
-        n.id === nodeId ? { ...n, data: { ...n.data, errors } } : n,
-      ),
-    }));
-  },
-
-  clearNodeErrors: (nodeId: string) => {
-    set((state) => ({
-      nodes: state.nodes.map((n) =>
-        n.id === nodeId ? { ...n, data: { ...n.data, errors: undefined } } : n,
-      ),
-    }));
-  },
-
-  getNodeErrors: (nodeId: string) => {
-    return get().nodes.find((n) => n.id === nodeId)?.data?.errors;
-  },
-
-  setNodeErrorsForBackendId: (
-    backendId: string,
-    errors: { [key: string]: string },
-  ) => {
-    set((state) => ({
-      nodes: state.nodes.map((n) => {
-        // Match by backend_id if nodes have it, or by id
-        const matches =
-          n.data.metadata?.backend_id === backendId || n.id === backendId;
-        return matches ? { ...n, data: { ...n.data, errors } } : n;
-      }),
-    }));
-  },
-
-  clearAllNodeErrors: () => {
-    set((state) => ({
-      nodes: state.nodes.map((n) => ({
-        ...n,
-        data: { ...n.data, errors: undefined },
-      })),
-    }));
-  },
 }));

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatCredentialsSetup/ChatCredentialsSetup.tsx

@@ -141,6 +141,7 @@ export function ChatCredentialsSetup({
                     onSelectCredentials={(credMeta) =>
                       handleCredentialSelect(cred.provider, credMeta)
                     }
+                    hideIfSingleCredentialAvailable={false}
                   />
                 </div>
               );

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/NewAgentLibraryView.tsx

@@ -1,5 +1,6 @@
 "use client";
 
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
 import { Button } from "@/components/atoms/Button/Button";
 import { Breadcrumbs } from "@/components/molecules/Breadcrumbs/Breadcrumbs";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
@@ -10,27 +11,20 @@ import { AgentRunsLoading } from "./components/other/AgentRunsLoading";
 import { EmptySchedules } from "./components/other/EmptySchedules";
 import { EmptyTasks } from "./components/other/EmptyTasks";
 import { EmptyTemplates } from "./components/other/EmptyTemplates";
-import { EmptyTriggers } from "./components/other/EmptyTriggers";
 import { SectionWrap } from "./components/other/SectionWrap";
-import { LoadingSelectedContent } from "./components/selected-views/LoadingSelectedContent";
 import { SelectedRunView } from "./components/selected-views/SelectedRunView/SelectedRunView";
 import { SelectedScheduleView } from "./components/selected-views/SelectedScheduleView/SelectedScheduleView";
-import { SelectedTemplateView } from "./components/selected-views/SelectedTemplateView/SelectedTemplateView";
-import { SelectedTriggerView } from "./components/selected-views/SelectedTriggerView/SelectedTriggerView";
-import { SelectedViewLayout } from "./components/selected-views/SelectedViewLayout";
 import { SidebarRunsList } from "./components/sidebar/SidebarRunsList/SidebarRunsList";
 import { AGENT_LIBRARY_SECTION_PADDING_X } from "./helpers";
 import { useNewAgentLibraryView } from "./useNewAgentLibraryView";
 
 export function NewAgentLibraryView() {
   const {
-    agentId,
     agent,
-    ready,
-    activeTemplate,
-    isTemplateLoading,
-    error,
     hasAnyItems,
+    ready,
+    error,
+    agentId,
     activeItem,
     sidebarLoading,
     activeTab,
@@ -38,9 +32,6 @@ export function NewAgentLibraryView() {
     handleSelectRun,
     handleCountsChange,
     handleClearSelectedRun,
-    onRunInitiated,
-    onTriggerSetup,
-    onScheduleCreated,
   } = useNewAgentLibraryView();
 
   if (error) {
@@ -70,19 +61,14 @@ export function NewAgentLibraryView() {
           />
         </div>
         <div className="flex min-h-0 flex-1">
-          <EmptyTasks
-            agent={agent}
-            onRun={onRunInitiated}
-            onTriggerSetup={onTriggerSetup}
-            onScheduleCreated={onScheduleCreated}
-          />
+          <EmptyTasks agent={agent} />
         </div>
       </div>
     );
   }
 
   return (
-    <div className="mx-4 grid h-full grid-cols-1 gap-0 pt-3 md:ml-4 md:mr-0 md:gap-4 lg:grid-cols-[25%_70%]">
+    <div className="ml-4 grid h-full grid-cols-1 gap-0 pt-3 md:gap-4 lg:grid-cols-[25%_70%]">
       <SectionWrap className="mb-3 block">
         <div
           className={cn(
@@ -92,21 +78,16 @@ export function NewAgentLibraryView() {
         >
           <RunAgentModal
             triggerSlot={
-              <Button
-                variant="primary"
-                size="large"
-                className="w-full"
-                disabled={isTemplateLoading && activeTab === "templates"}
-              >
+              <Button variant="primary" size="large" className="w-full">
                 <PlusIcon size={20} /> New task
               </Button>
             }
             agent={agent}
-            onRunCreated={onRunInitiated}
-            onScheduleCreated={onScheduleCreated}
-            onTriggerSetup={onTriggerSetup}
-            initialInputValues={activeTemplate?.inputs}
-            initialInputCredentials={activeTemplate?.credentials}
+            agentId={agent.id.toString()}
+            onRunCreated={(execution) => handleSelectRun(execution.id, "runs")}
+            onScheduleCreated={(schedule) =>
+              handleSelectRun(schedule.id, "scheduled")
+            }
           />
         </div>
 
@@ -120,60 +101,49 @@ export function NewAgentLibraryView() {
         />
       </SectionWrap>
 
-      {activeItem ? (
-        activeTab === "scheduled" ? (
-          <SelectedScheduleView
-            agent={agent}
-            scheduleId={activeItem}
-            onClearSelectedRun={handleClearSelectedRun}
+      <SectionWrap className="mb-3">
+        <div
+          className={`${AGENT_LIBRARY_SECTION_PADDING_X} border-b border-zinc-100 pb-4`}
+        >
+          <Breadcrumbs
+            items={[
+              { name: "My Library", link: "/library" },
+              { name: agent.name, link: `/library/agents/${agentId}` },
+            ]}
           />
-        ) : activeTab === "templates" ? (
-          <SelectedTemplateView
-            agent={agent}
-            templateId={activeItem}
-            onClearSelectedRun={handleClearSelectedRun}
-            onRunCreated={(execution) => handleSelectRun(execution.id, "runs")}
-            onSwitchToRunsTab={() => setActiveTab("runs")}
-          />
-        ) : activeTab === "triggers" ? (
-          <SelectedTriggerView
-            agent={agent}
-            triggerId={activeItem}
-            onClearSelectedRun={handleClearSelectedRun}
-            onSwitchToRunsTab={() => setActiveTab("runs")}
-          />
-        ) : (
-          <SelectedRunView
-            agent={agent}
-            runId={activeItem}
-            onSelectRun={handleSelectRun}
-            onClearSelectedRun={handleClearSelectedRun}
-          />
-        )
-      ) : sidebarLoading ? (
-        <LoadingSelectedContent agentName={agent.name} agentId={agent.id} />
-      ) : activeTab === "scheduled" ? (
-        <SelectedViewLayout agentName={agent.name} agentId={agent.id}>
-          <EmptySchedules />
-        </SelectedViewLayout>
-      ) : activeTab === "templates" ? (
-        <SelectedViewLayout agentName={agent.name} agentId={agent.id}>
-          <EmptyTemplates />
-        </SelectedViewLayout>
-      ) : activeTab === "triggers" ? (
-        <SelectedViewLayout agentName={agent.name} agentId={agent.id}>
-          <EmptyTriggers />
-        </SelectedViewLayout>
-      ) : (
-        <SelectedViewLayout agentName={agent.name} agentId={agent.id}>
-          <EmptyTasks
-            agent={agent}
-            onRun={onRunInitiated}
-            onTriggerSetup={onTriggerSetup}
-            onScheduleCreated={onScheduleCreated}
-          />
-        </SelectedViewLayout>
-      )}
+        </div>
+        <div className="flex min-h-0 flex-1 flex-col">
+          {activeItem ? (
+            activeTab === "scheduled" ? (
+              <SelectedScheduleView
+                agent={agent}
+                scheduleId={activeItem}
+                onClearSelectedRun={handleClearSelectedRun}
+              />
+            ) : (
+              <SelectedRunView
+                agent={agent}
+                runId={activeItem}
+                onSelectRun={handleSelectRun}
+                onClearSelectedRun={handleClearSelectedRun}
+              />
+            )
+          ) : sidebarLoading ? (
+            <div className="flex flex-col gap-4">
+              <Skeleton className="h-8 w-full bg-slate-100" />
+              <Skeleton className="h-12 w-full bg-slate-100" />
+              <Skeleton className="h-64 w-full bg-slate-100" />
+              <Skeleton className="h-32 w-full bg-slate-100" />
+            </div>
+          ) : activeTab === "scheduled" ? (
+            <EmptySchedules />
+          ) : activeTab === "templates" ? (
+            <EmptyTemplates />
+          ) : (
+            <EmptyTasks agent={agent} />
+          )}
+        </div>
+      </SectionWrap>
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/AgentInputsReadOnly/AgentInputsReadOnly.tsx

@@ -1,14 +1,13 @@
 "use client";
 
+import React from "react";
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import type {
-  BlockIOSubSchema,
-  CredentialsMetaInput,
-} from "@/lib/autogpt-server-api/types";
-import { CredentialsInput } from "../CredentialsInputs/CredentialsInputs";
+import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
+import { toDisplayName } from "@/providers/agent-credentials/helper";
 import {
   getAgentCredentialsFields,
   getAgentInputFields,
+  getCredentialTypeDisplayName,
   renderValue,
 } from "./helpers";
 
@@ -23,21 +22,13 @@ export function AgentInputsReadOnly({
   inputs,
   credentialInputs,
 }: Props) {
-  const inputFields = getAgentInputFields(agent);
-  const credentialFieldEntries = Object.entries(
-    getAgentCredentialsFields(agent),
-  );
+  const fields = getAgentInputFields(agent);
+  const credentialFields = getAgentCredentialsFields(agent);
+  const inputEntries = Object.entries(fields);
+  const credentialEntries = Object.entries(credentialFields);
 
-  // Take actual input entries as leading; augment with schema from input fields.
-  // TODO: ensure consistent ordering.
-  const inputEntries =
-    inputs &&
-    Object.entries(inputs).map<[string, [BlockIOSubSchema | undefined, any]]>(
-      ([k, v]) => [k, [inputFields[k], v]],
-    );
-
-  const hasInputs = inputEntries && inputEntries.length > 0;
-  const hasCredentials = credentialInputs && credentialFieldEntries.length > 0;
+  const hasInputs = inputs && inputEntries.length > 0;
+  const hasCredentials = credentialInputs && credentialEntries.length > 0;
 
   if (!hasInputs && !hasCredentials) {
     return <div className="text-neutral-600">No input for this run.</div>;
@@ -48,13 +39,11 @@ export function AgentInputsReadOnly({
       {/* Regular inputs */}
       {hasInputs && (
         <div className="flex flex-col gap-4">
-          {inputEntries.map(([key, [schema, value]]) => (
+          {inputEntries.map(([key, sub]) => (
             <div key={key} className="flex flex-col gap-1.5">
-              <label className="text-sm font-medium">
-                {schema?.title || key}
-              </label>
+              <label className="text-sm font-medium">{sub?.title || key}</label>
               <p className="whitespace-pre-wrap break-words text-sm text-neutral-700">
-                {renderValue(value)}
+                {renderValue((inputs as Record<string, any>)[key])}
               </p>
             </div>
           ))}
@@ -65,18 +54,32 @@ export function AgentInputsReadOnly({
       {hasCredentials && (
         <div className="flex flex-col gap-6">
           {hasInputs && <div className="border-t border-neutral-200 pt-4" />}
-          {credentialFieldEntries.map(([key, inputSubSchema]) => {
+          {credentialEntries.map(([key, _sub]) => {
             const credential = credentialInputs![key];
             if (!credential) return null;
 
             return (
-              <CredentialsInput
-                key={key}
-                schema={{ ...inputSubSchema, discriminator: undefined } as any}
-                selectedCredentials={credential}
-                onSelectCredentials={() => {}}
-                readOnly={true}
-              />
+              <div key={key} className="flex flex-col gap-4">
+                <h3 className="text-lg font-medium text-neutral-900">
+                  {toDisplayName(credential.provider)} credentials
+                </h3>
+                <div className="flex flex-col gap-3">
+                  <div className="flex items-center justify-between text-sm">
+                    <span className="text-neutral-600">Name</span>
+                    <span className="text-neutral-600">
+                      {getCredentialTypeDisplayName(credential.type)}
+                    </span>
+                  </div>
+                  <div className="flex items-center justify-between text-sm">
+                    <span className="text-neutral-900">
+                      {credential.title || "Untitled"}
+                    </span>
+                    <span className="font-mono text-neutral-400">
+                      {"*".repeat(25)}
+                    </span>
+                  </div>
+                </div>
+              </div>
             );
           })}
         </div>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/AgentInputsReadOnly/helpers.ts

@@ -13,8 +13,7 @@ export function getCredentialTypeDisplayName(type: string): string {
 }
 
 export function getAgentInputFields(agent: LibraryAgent): Record<string, any> {
-  const schema = (agent.trigger_setup_info?.config_schema ??
-    agent.input_schema) as unknown as {
+  const schema = agent.input_schema as unknown as {
     properties?: Record<string, any>;
   } | null;
   if (!schema || !schema.properties) return {};

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs.tsx

@@ -1,59 +1,189 @@
+import {
+  IconKey,
+  IconKeyPlus,
+  IconUserPlus,
+} from "@/components/__legacy__/ui/icons";
+import {
+  Select,
+  SelectContent,
+  SelectItem,
+  SelectSeparator,
+  SelectTrigger,
+  SelectValue,
+} from "@/components/__legacy__/ui/select";
 import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
+import useCredentials from "@/hooks/useCredentials";
+import { useBackendAPI } from "@/lib/autogpt-server-api/context";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 import { cn } from "@/lib/utils";
-import { toDisplayName } from "@/providers/agent-credentials/helper";
-import { APIKeyCredentialsModal } from "./components/APIKeyCredentialsModal/APIKeyCredentialsModal";
-import { CredentialRow } from "./components/CredentialRow/CredentialRow";
-import { CredentialsSelect } from "./components/CredentialsSelect/CredentialsSelect";
-import { DeleteConfirmationModal } from "./components/DeleteConfirmationModal/DeleteConfirmationModal";
-import { HostScopedCredentialsModal } from "./components/HotScopedCredentialsModal/HotScopedCredentialsModal";
-import { OAuthFlowWaitingModal } from "./components/OAuthWaitingModal/OAuthWaitingModal";
-import { PasswordCredentialsModal } from "./components/PasswordCredentialsModal/PasswordCredentialsModal";
-import { getCredentialDisplayName } from "./helpers";
-import { useCredentialsInputs } from "./useCredentialsInputs";
+import { getHostFromUrl } from "@/lib/utils/url";
+import { NotionLogoIcon } from "@radix-ui/react-icons";
+import { FC, useEffect, useMemo, useState } from "react";
+import {
+  FaDiscord,
+  FaGithub,
+  FaGoogle,
+  FaHubspot,
+  FaKey,
+  FaMedium,
+  FaTwitter,
+} from "react-icons/fa";
+import { APIKeyCredentialsModal } from "./APIKeyCredentialsModal/APIKeyCredentialsModal";
+import { HostScopedCredentialsModal } from "./HotScopedCredentialsModal/HotScopedCredentialsModal";
+import { OAuthFlowWaitingModal } from "./OAuthWaitingModal/OAuthWaitingModal";
+import { PasswordCredentialsModal } from "./PasswordCredentialsModal/PasswordCredentialsModal";
 
-type UseCredentialsInputsReturn = ReturnType<typeof useCredentialsInputs>;
+const fallbackIcon = FaKey;
 
-function isLoaded(
-  data: UseCredentialsInputsReturn,
-): data is Extract<UseCredentialsInputsReturn, { isLoading: false }> {
-  return data.isLoading === false;
-}
+// --8<-- [start:ProviderIconsEmbed]
+// Provider icons mapping - uses fallback for unknown providers
+export const providerIcons: Partial<
+  Record<string, React.FC<{ className?: string }>>
+> = {
+  aiml_api: fallbackIcon,
+  anthropic: fallbackIcon,
+  apollo: fallbackIcon,
+  e2b: fallbackIcon,
+  github: FaGithub,
+  google: FaGoogle,
+  groq: fallbackIcon,
+  http: fallbackIcon,
+  notion: NotionLogoIcon,
+  nvidia: fallbackIcon,
+  discord: FaDiscord,
+  d_id: fallbackIcon,
+  google_maps: FaGoogle,
+  jina: fallbackIcon,
+  ideogram: fallbackIcon,
+  linear: fallbackIcon,
+  medium: FaMedium,
+  mem0: fallbackIcon,
+  ollama: fallbackIcon,
+  openai: fallbackIcon,
+  openweathermap: fallbackIcon,
+  open_router: fallbackIcon,
+  llama_api: fallbackIcon,
+  pinecone: fallbackIcon,
+  enrichlayer: fallbackIcon,
+  slant3d: fallbackIcon,
+  screenshotone: fallbackIcon,
+  smtp: fallbackIcon,
+  replicate: fallbackIcon,
+  reddit: fallbackIcon,
+  fal: fallbackIcon,
+  revid: fallbackIcon,
+  twitter: FaTwitter,
+  unreal_speech: fallbackIcon,
+  exa: fallbackIcon,
+  hubspot: FaHubspot,
+  smartlead: fallbackIcon,
+  todoist: fallbackIcon,
+  zerobounce: fallbackIcon,
+};
+// --8<-- [end:ProviderIconsEmbed]
 
-type Props = {
+export type OAuthPopupResultMessage = { message_type: "oauth_popup_result" } & (
+  | {
+      success: true;
+      code: string;
+      state: string;
+    }
+  | {
+      success: false;
+      message: string;
+    }
+);
+
+export const CredentialsInput: FC<{
   schema: BlockIOCredentialsSubSchema;
   className?: string;
   selectedCredentials?: CredentialsMetaInput;
-  siblingInputs?: Record<string, any>;
   onSelectCredentials: (newValue?: CredentialsMetaInput) => void;
+  siblingInputs?: Record<string, any>;
+  hideIfSingleCredentialAvailable?: boolean;
   onLoaded?: (loaded: boolean) => void;
-  readOnly?: boolean;
-};
-
-export function CredentialsInput({
+}> = ({
   schema,
   className,
   selectedCredentials,
   onSelectCredentials,
   siblingInputs,
+  hideIfSingleCredentialAvailable = true,
   onLoaded,
-  readOnly = false,
-}: Props) {
-  const hookData = useCredentialsInputs({
-    schema,
-    selectedCredentials,
-    onSelectCredentials,
-    siblingInputs,
-    onLoaded,
-    readOnly,
-  });
+}) => {
+  const [isAPICredentialsModalOpen, setAPICredentialsModalOpen] =
+    useState(false);
+  const [
+    isUserPasswordCredentialsModalOpen,
+    setUserPasswordCredentialsModalOpen,
+  ] = useState(false);
+  const [isHostScopedCredentialsModalOpen, setHostScopedCredentialsModalOpen] =
+    useState(false);
+  const [isOAuth2FlowInProgress, setOAuth2FlowInProgress] = useState(false);
+  const [oAuthPopupController, setOAuthPopupController] =
+    useState<AbortController | null>(null);
+  const [oAuthError, setOAuthError] = useState<string | null>(null);
 
-  if (!isLoaded(hookData)) {
+  const api = useBackendAPI();
+  const credentials = useCredentials(schema, siblingInputs);
+
+  // Report loaded state to parent
+  useEffect(() => {
+    if (onLoaded) {
+      onLoaded(Boolean(credentials && credentials.isLoading === false));
+    }
+  }, [credentials, onLoaded]);
+
+  // Deselect credentials if they do not exist (e.g. provider was changed)
+  useEffect(() => {
+    if (!credentials || !("savedCredentials" in credentials)) return;
+    if (
+      selectedCredentials &&
+      !credentials.savedCredentials.some((c) => c.id === selectedCredentials.id)
+    ) {
+      onSelectCredentials(undefined);
+    }
+  }, [credentials, selectedCredentials, onSelectCredentials]);
+
+  const { hasRelevantCredentials, singleCredential } = useMemo(() => {
+    if (!credentials || !("savedCredentials" in credentials)) {
+      return {
+        hasRelevantCredentials: false,
+        singleCredential: null,
+      };
+    }
+
+    // Simple logic: if we have any saved credentials, we have relevant credentials
+    const hasRelevant = credentials.savedCredentials.length > 0;
+
+    // Auto-select single credential if only one exists
+    const single =
+      credentials.savedCredentials.length === 1
+        ? credentials.savedCredentials[0]
+        : null;
+
+    return {
+      hasRelevantCredentials: hasRelevant,
+      singleCredential: single,
+    };
+  }, [credentials]);
+
+  // If only 1 credential is available, auto-select it and hide this input
+  useEffect(() => {
+    if (singleCredential && !selectedCredentials) {
+      onSelectCredentials(singleCredential);
+    }
+  }, [singleCredential, selectedCredentials, onSelectCredentials]);
+
+  if (
+    !credentials ||
+    credentials.isLoading ||
+    (singleCredential && hideIfSingleCredentialAvailable)
+  ) {
     return null;
   }
 
@@ -64,158 +194,309 @@ export function CredentialsInput({
     supportsOAuth2,
     supportsUserPassword,
     supportsHostScoped,
-    credentialsToShow,
-    oAuthError,
-    isAPICredentialsModalOpen,
-    isUserPasswordCredentialsModalOpen,
-    isHostScopedCredentialsModalOpen,
-    isOAuth2FlowInProgress,
-    oAuthPopupController,
-    credentialToDelete,
-    deleteCredentialsMutation,
-    actionButtonText,
-    setAPICredentialsModalOpen,
-    setUserPasswordCredentialsModalOpen,
-    setHostScopedCredentialsModalOpen,
-    setCredentialToDelete,
-    handleActionButtonClick,
-    handleCredentialSelect,
-    handleDeleteCredential,
-    handleDeleteConfirm,
-  } = hookData;
+    savedCredentials,
+    oAuthCallback,
+  } = credentials;
 
-  const displayName = toDisplayName(provider);
-  const hasCredentialsToShow = credentialsToShow.length > 0;
+  async function handleOAuthLogin() {
+    setOAuthError(null);
+    const { login_url, state_token } = await api.oAuthLogin(
+      provider,
+      schema.credentials_scopes,
+    );
+    setOAuth2FlowInProgress(true);
+    const popup = window.open(login_url, "_blank", "popup=true");
 
-  return (
-    <div className={cn("mb-6", className)}>
-      <div className="mb-2 flex items-center gap-2">
-        <Text variant="large-medium">{displayName} credentials</Text>
-        {schema.description && (
-          <InformationTooltip description={schema.description} />
-        )}
-      </div>
+    if (!popup) {
+      throw new Error(
+        "Failed to open popup window. Please allow popups for this site.",
+      );
+    }
 
-      {hasCredentialsToShow ? (
-        <>
-          {credentialsToShow.length > 1 && !readOnly ? (
-            <CredentialsSelect
-              credentials={credentialsToShow}
-              provider={provider}
-              displayName={displayName}
-              selectedCredentials={selectedCredentials}
-              onSelectCredential={handleCredentialSelect}
-              readOnly={readOnly}
-            />
-          ) : (
-            <div className="mb-4 space-y-2">
-              {credentialsToShow.map((credential) => {
-                return (
-                  <CredentialRow
-                    key={credential.id}
-                    credential={credential}
-                    provider={provider}
-                    displayName={displayName}
-                    onSelect={() => handleCredentialSelect(credential.id)}
-                    onDelete={() =>
-                      handleDeleteCredential({
-                        id: credential.id,
-                        title: getCredentialDisplayName(
-                          credential,
-                          displayName,
-                        ),
-                      })
-                    }
-                    readOnly={readOnly}
-                  />
-                );
-              })}
-            </div>
-          )}
-          {!readOnly && (
-            <Button
-              variant="secondary"
-              size="small"
-              onClick={handleActionButtonClick}
-              className="w-fit"
-            >
-              {actionButtonText}
+    const controller = new AbortController();
+    setOAuthPopupController(controller);
+    controller.signal.onabort = () => {
+      console.debug("OAuth flow aborted");
+      setOAuth2FlowInProgress(false);
+      popup.close();
+    };
+
+    const handleMessage = async (e: MessageEvent<OAuthPopupResultMessage>) => {
+      console.debug("Message received:", e.data);
+      if (
+        typeof e.data != "object" ||
+        !("message_type" in e.data) ||
+        e.data.message_type !== "oauth_popup_result"
+      ) {
+        console.debug("Ignoring irrelevant message");
+        return;
+      }
+
+      if (!e.data.success) {
+        console.error("OAuth flow failed:", e.data.message);
+        setOAuthError(`OAuth flow failed: ${e.data.message}`);
+        setOAuth2FlowInProgress(false);
+        return;
+      }
+
+      if (e.data.state !== state_token) {
+        console.error("Invalid state token received");
+        setOAuthError("Invalid state token received");
+        setOAuth2FlowInProgress(false);
+        return;
+      }
+
+      try {
+        console.debug("Processing OAuth callback");
+        const credentials = await oAuthCallback(e.data.code, e.data.state);
+        console.debug("OAuth callback processed successfully");
+        onSelectCredentials({
+          id: credentials.id,
+          type: "oauth2",
+          title: credentials.title,
+          provider,
+        });
+      } catch (error) {
+        console.error("Error in OAuth callback:", error);
+        setOAuthError(
+          // type of error is unkown so we need to use String(error)
+          `Error in OAuth callback: ${
+            error instanceof Error ? error.message : String(error)
+          }`,
+        );
+      } finally {
+        console.debug("Finalizing OAuth flow");
+        setOAuth2FlowInProgress(false);
+        controller.abort("success");
+      }
+    };
+
+    console.debug("Adding message event listener");
+    window.addEventListener("message", handleMessage, {
+      signal: controller.signal,
+    });
+
+    setTimeout(
+      () => {
+        console.debug("OAuth flow timed out");
+        controller.abort("timeout");
+        setOAuth2FlowInProgress(false);
+        setOAuthError("OAuth flow timed out");
+      },
+      5 * 60 * 1000,
+    );
+  }
+
+  const ProviderIcon = providerIcons[provider] || fallbackIcon;
+  const modals = (
+    <>
+      {supportsApiKey && (
+        <APIKeyCredentialsModal
+          schema={schema}
+          open={isAPICredentialsModalOpen}
+          onClose={() => setAPICredentialsModalOpen(false)}
+          onCredentialsCreate={(credsMeta) => {
+            onSelectCredentials(credsMeta);
+            setAPICredentialsModalOpen(false);
+          }}
+          siblingInputs={siblingInputs}
+        />
+      )}
+      {supportsOAuth2 && (
+        <OAuthFlowWaitingModal
+          open={isOAuth2FlowInProgress}
+          onClose={() => oAuthPopupController?.abort("canceled")}
+          providerName={providerName}
+        />
+      )}
+      {supportsUserPassword && (
+        <PasswordCredentialsModal
+          schema={schema}
+          open={isUserPasswordCredentialsModalOpen}
+          onClose={() => setUserPasswordCredentialsModalOpen(false)}
+          onCredentialsCreate={(creds) => {
+            onSelectCredentials(creds);
+            setUserPasswordCredentialsModalOpen(false);
+          }}
+          siblingInputs={siblingInputs}
+        />
+      )}
+      {supportsHostScoped && (
+        <HostScopedCredentialsModal
+          schema={schema}
+          open={isHostScopedCredentialsModalOpen}
+          onClose={() => setHostScopedCredentialsModalOpen(false)}
+          onCredentialsCreate={(creds) => {
+            onSelectCredentials(creds);
+            setHostScopedCredentialsModalOpen(false);
+          }}
+          siblingInputs={siblingInputs}
+        />
+      )}
+    </>
+  );
+
+  const fieldHeader = (
+    <div className="mb-2 flex gap-1">
+      <span className="text-m green text-gray-900">
+        {providerName} Credentials
+      </span>
+      <InformationTooltip description={schema.description} />
+    </div>
+  );
+
+  // Show credentials creation UI when no relevant credentials exist
+  if (!hasRelevantCredentials) {
+    return (
+      <div className="mb-4">
+        {fieldHeader}
+
+        <div className={cn("flex flex-row space-x-2", className)}>
+          {supportsOAuth2 && (
+            <Button onClick={handleOAuthLogin} size="small">
+              <ProviderIcon className="mr-2 h-4 w-4" />
+              {"Sign in with " + providerName}
             </Button>
           )}
-        </>
-      ) : (
-        !readOnly && (
-          <Button
-            variant="secondary"
-            size="small"
-            onClick={handleActionButtonClick}
-            className="w-fit"
-          >
-            {actionButtonText}
-          </Button>
-        )
-      )}
+          {supportsApiKey && (
+            <Button
+              onClick={() => setAPICredentialsModalOpen(true)}
+              size="small"
+            >
+              <ProviderIcon className="mr-2 h-4 w-4" />
+              Enter API key
+            </Button>
+          )}
+          {supportsUserPassword && (
+            <Button
+              onClick={() => setUserPasswordCredentialsModalOpen(true)}
+              size="small"
+            >
+              <ProviderIcon className="mr-2 h-4 w-4" />
+              Enter username and password
+            </Button>
+          )}
+          {supportsHostScoped && credentials.discriminatorValue && (
+            <Button
+              onClick={() => setHostScopedCredentialsModalOpen(true)}
+              size="small"
+            >
+              <ProviderIcon className="mr-2 h-4 w-4" />
+              {`Enter sensitive headers for ${getHostFromUrl(credentials.discriminatorValue)}`}
+            </Button>
+          )}
+        </div>
+        {modals}
+        {oAuthError && (
+          <div className="mt-2 text-red-500">Error: {oAuthError}</div>
+        )}
+      </div>
+    );
+  }
 
-      {!readOnly && (
-        <>
-          {supportsApiKey ? (
-            <APIKeyCredentialsModal
-              schema={schema}
-              open={isAPICredentialsModalOpen}
-              onClose={() => setAPICredentialsModalOpen(false)}
-              onCredentialsCreate={(credsMeta) => {
-                onSelectCredentials(credsMeta);
-                setAPICredentialsModalOpen(false);
-              }}
-              siblingInputs={siblingInputs}
-            />
-          ) : null}
-          {supportsOAuth2 ? (
-            <OAuthFlowWaitingModal
-              open={isOAuth2FlowInProgress}
-              onClose={() => oAuthPopupController?.abort("canceled")}
-              providerName={providerName}
-            />
-          ) : null}
-          {supportsUserPassword ? (
-            <PasswordCredentialsModal
-              schema={schema}
-              open={isUserPasswordCredentialsModalOpen}
-              onClose={() => setUserPasswordCredentialsModalOpen(false)}
-              onCredentialsCreate={(creds) => {
-                onSelectCredentials(creds);
-                setUserPasswordCredentialsModalOpen(false);
-              }}
-              siblingInputs={siblingInputs}
-            />
-          ) : null}
-          {supportsHostScoped ? (
-            <HostScopedCredentialsModal
-              schema={schema}
-              open={isHostScopedCredentialsModalOpen}
-              onClose={() => setHostScopedCredentialsModalOpen(false)}
-              onCredentialsCreate={(creds) => {
-                onSelectCredentials(creds);
-                setHostScopedCredentialsModalOpen(false);
-              }}
-              siblingInputs={siblingInputs}
-            />
-          ) : null}
+  function handleValueChange(newValue: string) {
+    if (newValue === "sign-in") {
+      // Trigger OAuth2 sign in flow
+      handleOAuthLogin();
+    } else if (newValue === "add-api-key") {
+      // Open API key dialog
+      setAPICredentialsModalOpen(true);
+    } else if (newValue === "add-user-password") {
+      // Open user password dialog
+      setUserPasswordCredentialsModalOpen(true);
+    } else if (newValue === "add-host-scoped") {
+      // Open host-scoped credentials dialog
+      setHostScopedCredentialsModalOpen(true);
+    } else {
+      const selectedCreds = savedCredentials.find((c) => c.id == newValue)!;
 
-          {oAuthError ? (
-            <Text variant="body" className="mt-2 text-red-500">
-              Error: {oAuthError}
-            </Text>
-          ) : null}
+      onSelectCredentials({
+        id: selectedCreds.id,
+        type: selectedCreds.type,
+        provider: provider,
+        // title: customTitle, // TODO: add input for title
+      });
+    }
+  }
 
-          <DeleteConfirmationModal
-            credentialToDelete={credentialToDelete}
-            isDeleting={deleteCredentialsMutation.isPending}
-            onClose={() => setCredentialToDelete(null)}
-            onConfirm={handleDeleteConfirm}
-          />
-        </>
+  // Saved credentials exist
+  return (
+    <div>
+      {fieldHeader}
+
+      <Select value={selectedCredentials?.id} onValueChange={handleValueChange}>
+        <SelectTrigger>
+          <SelectValue placeholder={schema.placeholder} />
+        </SelectTrigger>
+        <SelectContent className="nodrag">
+          {savedCredentials
+            .filter((c) => c.type == "oauth2")
+            .map((credentials, index) => (
+              <SelectItem key={index} value={credentials.id}>
+                <ProviderIcon className="mr-2 inline h-4 w-4" />
+                {credentials.title ||
+                  credentials.username ||
+                  `Your ${providerName} account`}
+              </SelectItem>
+            ))}
+          {savedCredentials
+            .filter((c) => c.type == "api_key")
+            .map((credentials, index) => (
+              <SelectItem key={index} value={credentials.id}>
+                <ProviderIcon className="mr-2 inline h-4 w-4" />
+                <IconKey className="mr-1.5 inline" />
+                {credentials.title}
+              </SelectItem>
+            ))}
+          {savedCredentials
+            .filter((c) => c.type == "user_password")
+            .map((credentials, index) => (
+              <SelectItem key={index} value={credentials.id}>
+                <ProviderIcon className="mr-2 inline h-4 w-4" />
+                <IconUserPlus className="mr-1.5 inline" />
+                {credentials.title}
+              </SelectItem>
+            ))}
+          {savedCredentials
+            .filter((c) => c.type == "host_scoped")
+            .map((credentials, index) => (
+              <SelectItem key={index} value={credentials.id}>
+                <ProviderIcon className="mr-2 inline h-4 w-4" />
+                <IconKey className="mr-1.5 inline" />
+                {credentials.title}
+              </SelectItem>
+            ))}
+          <SelectSeparator />
+          {supportsOAuth2 && (
+            <SelectItem value="sign-in">
+              <IconUserPlus className="mr-1.5 inline" />
+              Sign in with {providerName}
+            </SelectItem>
+          )}
+          {supportsApiKey && (
+            <SelectItem value="add-api-key">
+              <IconKeyPlus className="mr-1.5 inline" />
+              Add new API key
+            </SelectItem>
+          )}
+          {supportsUserPassword && (
+            <SelectItem value="add-user-password">
+              <IconUserPlus className="mr-1.5 inline" />
+              Add new user password
+            </SelectItem>
+          )}
+          {supportsHostScoped && (
+            <SelectItem value="add-host-scoped">
+              <IconKey className="mr-1.5 inline" />
+              Add host-scoped headers
+            </SelectItem>
+          )}
+        </SelectContent>
+      </Select>
+      {modals}
+      {oAuthError && (
+        <div className="mt-2 text-red-500">Error: {oAuthError}</div>
       )}
     </div>
   );
-}
+};

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/PasswordCredentialsModal/PasswordCredentialsModal.tsx

@@ -1,15 +1,15 @@
-import { Form, FormField } from "@/components/__legacy__/ui/form";
-import { Button } from "@/components/atoms/Button/Button";
+import { z } from "zod";
+import { useForm } from "react-hook-form";
+import { zodResolver } from "@hookform/resolvers/zod";
 import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "@/components/atoms/Button/Button";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { Form, FormField } from "@/components/__legacy__/ui/form";
 import useCredentials from "@/hooks/useCredentials";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
-import { zodResolver } from "@hookform/resolvers/zod";
-import { useForm } from "react-hook-form";
-import { z } from "zod";
 
 type Props = {
   schema: BlockIOCredentialsSubSchema;
@@ -85,7 +85,7 @@ export function PasswordCredentialsModal({
         <Form {...form}>
           <form
             onSubmit={form.handleSubmit(onSubmit)}
-            className="w-[98%] space-y-2 pt-4"
+            className="space-y-2 pt-4"
           >
             <FormField
               control={form.control}
@@ -96,6 +96,7 @@ export function PasswordCredentialsModal({
                   label="Username"
                   type="text"
                   placeholder="Enter username..."
+                  size="small"
                   {...field}
                 />
               )}
@@ -109,6 +110,7 @@ export function PasswordCredentialsModal({
                   label="Password"
                   type="password"
                   placeholder="Enter password..."
+                  size="small"
                   {...field}
                 />
               )}
@@ -122,12 +124,12 @@ export function PasswordCredentialsModal({
                   label="Name"
                   type="text"
                   placeholder="Enter a name for this user login..."
-                  className="mb-8"
+                  size="small"
                   {...field}
                 />
               )}
             />
-            <Button type="submit" className="w-full">
+            <Button type="submit" size="small" className="min-w-68">
               Save & use this user login
             </Button>
           </form>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/CredentialRow/CredentialRow.tsx

@@ -1,102 +0,0 @@
-import { IconKey } from "@/components/__legacy__/ui/icons";
-import { Text } from "@/components/atoms/Text/Text";
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuTrigger,
-} from "@/components/molecules/DropdownMenu/DropdownMenu";
-import { cn } from "@/lib/utils";
-import { CaretDown, DotsThreeVertical } from "@phosphor-icons/react";
-import {
-  fallbackIcon,
-  getCredentialDisplayName,
-  MASKED_KEY_LENGTH,
-  providerIcons,
-} from "../../helpers";
-
-type CredentialRowProps = {
-  credential: {
-    id: string;
-    title?: string;
-    username?: string;
-    type: string;
-    provider: string;
-  };
-  provider: string;
-  displayName: string;
-  onSelect: () => void;
-  onDelete: () => void;
-  readOnly?: boolean;
-  showCaret?: boolean;
-  asSelectTrigger?: boolean;
-};
-
-export function CredentialRow({
-  credential,
-  provider,
-  displayName,
-  onSelect,
-  onDelete,
-  readOnly = false,
-  showCaret = false,
-  asSelectTrigger = false,
-}: CredentialRowProps) {
-  const ProviderIcon = providerIcons[provider] || fallbackIcon;
-
-  return (
-    <div
-      className={cn(
-        "flex items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
-        asSelectTrigger ? "border-0 bg-transparent" : readOnly ? "w-fit" : "",
-      )}
-      onClick={readOnly || showCaret || asSelectTrigger ? undefined : onSelect}
-      style={
-        readOnly || showCaret || asSelectTrigger
-          ? { cursor: showCaret || asSelectTrigger ? "pointer" : "default" }
-          : undefined
-      }
-    >
-      <div className="flex h-6 w-6 shrink-0 items-center justify-center rounded-full bg-gray-900">
-        <ProviderIcon className="h-3 w-3 text-white" />
-      </div>
-      <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
-      <div className="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
-        <Text variant="body" className="tracking-tight">
-          {getCredentialDisplayName(credential, displayName)}
-        </Text>
-        <Text
-          variant="large"
-          className="relative top-1 font-mono tracking-tight"
-        >
-          {"*".repeat(MASKED_KEY_LENGTH)}
-        </Text>
-      </div>
-      {showCaret && !asSelectTrigger && (
-        <CaretDown className="h-4 w-4 shrink-0 text-gray-400" />
-      )}
-      {!readOnly && !showCaret && !asSelectTrigger && (
-        <DropdownMenu>
-          <DropdownMenuTrigger asChild>
-            <button
-              className="ml-auto shrink-0 rounded p-1 hover:bg-gray-100"
-              onClick={(e) => e.stopPropagation()}
-            >
-              <DotsThreeVertical className="h-5 w-5 text-gray-400" />
-            </button>
-          </DropdownMenuTrigger>
-          <DropdownMenuContent align="end">
-            <DropdownMenuItem
-              onClick={(e) => {
-                e.stopPropagation();
-                onDelete();
-              }}
-            >
-              Delete
-            </DropdownMenuItem>
-          </DropdownMenuContent>
-        </DropdownMenu>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/CredentialsSelect/CredentialsSelect.tsx

@@ -1,86 +0,0 @@
-import {
-  Select,
-  SelectContent,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} from "@/components/__legacy__/ui/select";
-import { Text } from "@/components/atoms/Text/Text";
-import { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
-import { useEffect } from "react";
-import { getCredentialDisplayName } from "../../helpers";
-import { CredentialRow } from "../CredentialRow/CredentialRow";
-
-interface Props {
-  credentials: Array<{
-    id: string;
-    title?: string;
-    username?: string;
-    type: string;
-    provider: string;
-  }>;
-  provider: string;
-  displayName: string;
-  selectedCredentials?: CredentialsMetaInput;
-  onSelectCredential: (credentialId: string) => void;
-  readOnly?: boolean;
-}
-
-export function CredentialsSelect({
-  credentials,
-  provider,
-  displayName,
-  selectedCredentials,
-  onSelectCredential,
-  readOnly = false,
-}: Props) {
-  // Auto-select first credential if none is selected
-  useEffect(() => {
-    if (!selectedCredentials && credentials.length > 0) {
-      onSelectCredential(credentials[0].id);
-    }
-  }, [selectedCredentials, credentials, onSelectCredential]);
-
-  return (
-    <div className="mb-4 w-full">
-      <Select
-        value={selectedCredentials?.id || ""}
-        onValueChange={(value) => onSelectCredential(value)}
-      >
-        <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
-          {selectedCredentials ? (
-            <SelectValue key={selectedCredentials.id} asChild>
-              <CredentialRow
-                credential={{
-                  id: selectedCredentials.id,
-                  title: selectedCredentials.title || undefined,
-                  type: selectedCredentials.type,
-                  provider: selectedCredentials.provider,
-                }}
-                provider={provider}
-                displayName={displayName}
-                onSelect={() => {}}
-                onDelete={() => {}}
-                readOnly={readOnly}
-                asSelectTrigger={true}
-              />
-            </SelectValue>
-          ) : (
-            <SelectValue key="placeholder" placeholder="Select credential" />
-          )}
-        </SelectTrigger>
-        <SelectContent>
-          {credentials.map((credential) => (
-            <SelectItem key={credential.id} value={credential.id}>
-              <div className="flex items-center gap-2">
-                <Text variant="body" className="tracking-tight">
-                  {getCredentialDisplayName(credential, displayName)}
-                </Text>
-              </div>
-            </SelectItem>
-          ))}
-        </SelectContent>
-      </Select>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/DeleteConfirmationModal/DeleteConfirmationModal.tsx

@@ -1,49 +0,0 @@
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-
-interface Props {
-  credentialToDelete: { id: string; title: string } | null;
-  isDeleting: boolean;
-  onClose: () => void;
-  onConfirm: () => void;
-}
-
-export function DeleteConfirmationModal({
-  credentialToDelete,
-  isDeleting,
-  onClose,
-  onConfirm,
-}: Props) {
-  return (
-    <Dialog
-      controlled={{
-        isOpen: credentialToDelete !== null,
-        set: (open) => {
-          if (!open) onClose();
-        },
-      }}
-      title="Delete credential"
-      styling={{ maxWidth: "32rem" }}
-    >
-      <Dialog.Content>
-        <Text variant="large">
-          Are you sure you want to delete &quot;{credentialToDelete?.title}
-          &quot;? This action cannot be undone.
-        </Text>
-        <Dialog.Footer>
-          <Button variant="secondary" onClick={onClose} disabled={isDeleting}>
-            Cancel
-          </Button>
-          <Button
-            variant="destructive"
-            onClick={onConfirm}
-            loading={isDeleting}
-          >
-            Delete
-          </Button>
-        </Dialog.Footer>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/helpers.ts

@@ -1,102 +0,0 @@
-import { KeyIcon } from "@phosphor-icons/react";
-import { NotionLogoIcon } from "@radix-ui/react-icons";
-import {
-  FaDiscord,
-  FaGithub,
-  FaGoogle,
-  FaHubspot,
-  FaMedium,
-  FaTwitter,
-} from "react-icons/fa";
-
-export const fallbackIcon = KeyIcon;
-
-export const providerIcons: Partial<
-  Record<string, React.FC<{ className?: string }>>
-> = {
-  aiml_api: fallbackIcon,
-  anthropic: fallbackIcon,
-  apollo: fallbackIcon,
-  e2b: fallbackIcon,
-  github: FaGithub,
-  google: FaGoogle,
-  groq: fallbackIcon,
-  http: fallbackIcon,
-  notion: NotionLogoIcon,
-  nvidia: fallbackIcon,
-  discord: FaDiscord,
-  d_id: fallbackIcon,
-  google_maps: FaGoogle,
-  jina: fallbackIcon,
-  ideogram: fallbackIcon,
-  linear: fallbackIcon,
-  medium: FaMedium,
-  mem0: fallbackIcon,
-  ollama: fallbackIcon,
-  openai: fallbackIcon,
-  openweathermap: fallbackIcon,
-  open_router: fallbackIcon,
-  llama_api: fallbackIcon,
-  pinecone: fallbackIcon,
-  enrichlayer: fallbackIcon,
-  slant3d: fallbackIcon,
-  screenshotone: fallbackIcon,
-  smtp: fallbackIcon,
-  replicate: fallbackIcon,
-  reddit: fallbackIcon,
-  fal: fallbackIcon,
-  revid: fallbackIcon,
-  twitter: FaTwitter,
-  unreal_speech: fallbackIcon,
-  exa: fallbackIcon,
-  hubspot: FaHubspot,
-  smartlead: fallbackIcon,
-  todoist: fallbackIcon,
-  zerobounce: fallbackIcon,
-};
-
-export type OAuthPopupResultMessage = { message_type: "oauth_popup_result" } & (
-  | {
-      success: true;
-      code: string;
-      state: string;
-    }
-  | {
-      success: false;
-      message: string;
-    }
-);
-
-export function getActionButtonText(
-  supportsOAuth2: boolean,
-  supportsApiKey: boolean,
-  supportsUserPassword: boolean,
-  supportsHostScoped: boolean,
-  hasExistingCredentials: boolean,
-): string {
-  if (hasExistingCredentials) {
-    if (supportsOAuth2) return "Connect another account";
-    if (supportsApiKey) return "Use a new API key";
-    if (supportsUserPassword) return "Add a new username and password";
-    if (supportsHostScoped) return "Add new headers";
-    return "Add new credentials";
-  } else {
-    if (supportsOAuth2) return "Add account";
-    if (supportsApiKey) return "Add API key";
-    if (supportsUserPassword) return "Add username and password";
-    if (supportsHostScoped) return "Add headers";
-    return "Add credentials";
-  }
-}
-
-export function getCredentialDisplayName(
-  credential: { title?: string; username?: string },
-  displayName: string,
-): string {
-  return (
-    credential.title || credential.username || `Your ${displayName} account`
-  );
-}
-
-export const OAUTH_TIMEOUT_MS = 5 * 60 * 1000;
-export const MASKED_KEY_LENGTH = 30;

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/useCredentialsInputs.ts

@@ -1,318 +0,0 @@
-import { useDeleteV1DeleteCredentials } from "@/app/api/__generated__/endpoints/integrations/integrations";
-import useCredentials from "@/hooks/useCredentials";
-import { useBackendAPI } from "@/lib/autogpt-server-api/context";
-import {
-  BlockIOCredentialsSubSchema,
-  CredentialsMetaInput,
-} from "@/lib/autogpt-server-api/types";
-import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
-import { useQueryClient } from "@tanstack/react-query";
-import { useContext, useEffect, useMemo, useState } from "react";
-import {
-  getActionButtonText,
-  OAUTH_TIMEOUT_MS,
-  OAuthPopupResultMessage,
-} from "./helpers";
-
-type Args = {
-  schema: BlockIOCredentialsSubSchema;
-  selectedCredentials?: CredentialsMetaInput;
-  onSelectCredentials: (newValue?: CredentialsMetaInput) => void;
-  siblingInputs?: Record<string, any>;
-  onLoaded?: (loaded: boolean) => void;
-  readOnly?: boolean;
-};
-
-export function useCredentialsInputs({
-  schema,
-  selectedCredentials,
-  onSelectCredentials,
-  siblingInputs,
-  onLoaded,
-  readOnly = false,
-}: Args) {
-  const [isAPICredentialsModalOpen, setAPICredentialsModalOpen] =
-    useState(false);
-  const [
-    isUserPasswordCredentialsModalOpen,
-    setUserPasswordCredentialsModalOpen,
-  ] = useState(false);
-  const [isHostScopedCredentialsModalOpen, setHostScopedCredentialsModalOpen] =
-    useState(false);
-  const [isOAuth2FlowInProgress, setOAuth2FlowInProgress] = useState(false);
-  const [oAuthPopupController, setOAuthPopupController] =
-    useState<AbortController | null>(null);
-  const [oAuthError, setOAuthError] = useState<string | null>(null);
-  const [credentialToDelete, setCredentialToDelete] = useState<{
-    id: string;
-    title: string;
-  } | null>(null);
-
-  const api = useBackendAPI();
-  const queryClient = useQueryClient();
-  const credentials = useCredentials(schema, siblingInputs);
-  const allProviders = useContext(CredentialsProvidersContext);
-
-  const deleteCredentialsMutation = useDeleteV1DeleteCredentials({
-    mutation: {
-      onSuccess: () => {
-        queryClient.invalidateQueries({
-          queryKey: ["/api/integrations/credentials"],
-        });
-        queryClient.invalidateQueries({
-          queryKey: [`/api/integrations/${credentials?.provider}/credentials`],
-        });
-        setCredentialToDelete(null);
-        if (selectedCredentials?.id === credentialToDelete?.id) {
-          onSelectCredentials(undefined);
-        }
-      },
-    },
-  });
-
-  const rawProvider = credentials
-    ? allProviders?.[credentials.provider as keyof typeof allProviders]
-    : null;
-
-  useEffect(() => {
-    if (onLoaded) {
-      onLoaded(Boolean(credentials && credentials.isLoading === false));
-    }
-  }, [credentials, onLoaded]);
-
-  useEffect(() => {
-    if (readOnly) return;
-    if (!credentials || !("savedCredentials" in credentials)) return;
-    if (
-      selectedCredentials &&
-      !credentials.savedCredentials.some((c) => c.id === selectedCredentials.id)
-    ) {
-      onSelectCredentials(undefined);
-    }
-  }, [credentials, selectedCredentials, onSelectCredentials, readOnly]);
-
-  const { singleCredential } = useMemo(() => {
-    if (!credentials || !("savedCredentials" in credentials)) {
-      return {
-        singleCredential: null,
-      };
-    }
-
-    const single =
-      credentials.savedCredentials.length === 1
-        ? credentials.savedCredentials[0]
-        : null;
-
-    return {
-      singleCredential: single,
-    };
-  }, [credentials]);
-
-  useEffect(() => {
-    if (readOnly) return;
-    if (singleCredential && !selectedCredentials) {
-      onSelectCredentials(singleCredential);
-    }
-  }, [singleCredential, selectedCredentials, onSelectCredentials, readOnly]);
-
-  if (
-    !credentials ||
-    credentials.isLoading ||
-    !("savedCredentials" in credentials)
-  ) {
-    return {
-      isLoading: true,
-    };
-  }
-
-  const {
-    provider,
-    providerName,
-    supportsApiKey,
-    supportsOAuth2,
-    supportsUserPassword,
-    supportsHostScoped,
-    savedCredentials,
-    oAuthCallback,
-  } = credentials;
-
-  const allSavedCredentials = rawProvider?.savedCredentials || savedCredentials;
-
-  const credentialsToShow = (() => {
-    const creds = [...allSavedCredentials];
-    if (
-      !readOnly &&
-      selectedCredentials &&
-      !creds.some((c) => c.id === selectedCredentials.id)
-    ) {
-      creds.push({
-        id: selectedCredentials.id,
-        type: selectedCredentials.type,
-        title: selectedCredentials.title || "Selected credential",
-        provider: provider,
-      } as any);
-    }
-    return creds;
-  })();
-
-  async function handleOAuthLogin() {
-    setOAuthError(null);
-    const { login_url, state_token } = await api.oAuthLogin(
-      provider,
-      schema.credentials_scopes,
-    );
-    setOAuth2FlowInProgress(true);
-    const popup = window.open(login_url, "_blank", "popup=true");
-
-    if (!popup) {
-      throw new Error(
-        "Failed to open popup window. Please allow popups for this site.",
-      );
-    }
-
-    const controller = new AbortController();
-    setOAuthPopupController(controller);
-    controller.signal.onabort = () => {
-      console.debug("OAuth flow aborted");
-      setOAuth2FlowInProgress(false);
-      popup.close();
-    };
-
-    const handleMessage = async (e: MessageEvent<OAuthPopupResultMessage>) => {
-      console.debug("Message received:", e.data);
-      if (
-        typeof e.data != "object" ||
-        !("message_type" in e.data) ||
-        e.data.message_type !== "oauth_popup_result"
-      ) {
-        console.debug("Ignoring irrelevant message");
-        return;
-      }
-
-      if (!e.data.success) {
-        console.error("OAuth flow failed:", e.data.message);
-        setOAuthError(`OAuth flow failed: ${e.data.message}`);
-        setOAuth2FlowInProgress(false);
-        return;
-      }
-
-      if (e.data.state !== state_token) {
-        console.error("Invalid state token received");
-        setOAuthError("Invalid state token received");
-        setOAuth2FlowInProgress(false);
-        return;
-      }
-
-      try {
-        console.debug("Processing OAuth callback");
-        const credentials = await oAuthCallback(e.data.code, e.data.state);
-        console.debug("OAuth callback processed successfully");
-        onSelectCredentials({
-          id: credentials.id,
-          type: "oauth2",
-          title: credentials.title,
-          provider,
-        });
-      } catch (error) {
-        console.error("Error in OAuth callback:", error);
-        setOAuthError(
-          `Error in OAuth callback: ${
-            error instanceof Error ? error.message : String(error)
-          }`,
-        );
-      } finally {
-        console.debug("Finalizing OAuth flow");
-        setOAuth2FlowInProgress(false);
-        controller.abort("success");
-      }
-    };
-
-    console.debug("Adding message event listener");
-    window.addEventListener("message", handleMessage, {
-      signal: controller.signal,
-    });
-
-    setTimeout(() => {
-      console.debug("OAuth flow timed out");
-      controller.abort("timeout");
-      setOAuth2FlowInProgress(false);
-      setOAuthError("OAuth flow timed out");
-    }, OAUTH_TIMEOUT_MS);
-  }
-
-  function handleActionButtonClick() {
-    if (supportsOAuth2) {
-      handleOAuthLogin();
-    } else if (supportsApiKey) {
-      setAPICredentialsModalOpen(true);
-    } else if (supportsUserPassword) {
-      setUserPasswordCredentialsModalOpen(true);
-    } else if (supportsHostScoped) {
-      setHostScopedCredentialsModalOpen(true);
-    }
-  }
-
-  function handleCredentialSelect(credentialId: string) {
-    const selectedCreds = credentialsToShow.find((c) => c.id === credentialId);
-    if (selectedCreds) {
-      onSelectCredentials({
-        id: selectedCreds.id,
-        type: selectedCreds.type,
-        provider: provider,
-        title: (selectedCreds as any).title,
-      });
-    }
-  }
-
-  function handleDeleteCredential(credential: { id: string; title: string }) {
-    setCredentialToDelete(credential);
-  }
-
-  function handleDeleteConfirm() {
-    if (credentialToDelete && credentials) {
-      deleteCredentialsMutation.mutate({
-        provider: credentials.provider,
-        credId: credentialToDelete.id,
-      });
-    }
-  }
-
-  return {
-    isLoading: false as const,
-    provider,
-    providerName,
-    supportsApiKey,
-    supportsOAuth2,
-    supportsUserPassword,
-    supportsHostScoped,
-    credentialsToShow,
-    selectedCredentials,
-    oAuthError,
-    isAPICredentialsModalOpen,
-    isUserPasswordCredentialsModalOpen,
-    isHostScopedCredentialsModalOpen,
-    isOAuth2FlowInProgress,
-    oAuthPopupController,
-    credentialToDelete,
-    deleteCredentialsMutation,
-    actionButtonText: getActionButtonText(
-      supportsOAuth2,
-      supportsApiKey,
-      supportsUserPassword,
-      supportsHostScoped,
-      credentialsToShow.length > 0,
-    ),
-    setAPICredentialsModalOpen,
-    setUserPasswordCredentialsModalOpen,
-    setHostScopedCredentialsModalOpen,
-    setCredentialToDelete,
-    handleActionButtonClick,
-    handleCredentialSelect,
-    handleDeleteCredential,
-    handleDeleteConfirm,
-    handleOAuthLogin,
-    onSelectCredentials,
-    schema,
-    siblingInputs,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal.tsx

@@ -3,17 +3,14 @@
 import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
 import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
 import { Button } from "@/components/atoms/Button/Button";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipProvider,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { AlarmIcon } from "@phosphor-icons/react";
 import { useState } from "react";
 import { ScheduleAgentModal } from "../ScheduleAgentModal/ScheduleAgentModal";
+import { AgentCostSection } from "./components/AgentCostSection/AgentCostSection";
+import { AgentDetails } from "./components/AgentDetails/AgentDetails";
+import { AgentSectionHeader } from "./components/AgentSectionHeader/AgentSectionHeader";
 import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { ModalRunSection } from "./components/ModalRunSection/ModalRunSection";
 import { RunActions } from "./components/RunActions/RunActions";
@@ -23,20 +20,16 @@ import { useAgentRunModal } from "./useAgentRunModal";
 interface Props {
   triggerSlot: React.ReactNode;
   agent: LibraryAgent;
-  initialInputValues?: Record<string, any>;
-  initialInputCredentials?: Record<string, any>;
+  agentId: string;
+  agentVersion?: number;
   onRunCreated?: (execution: GraphExecutionMeta) => void;
-  onTriggerSetup?: (preset: LibraryAgentPreset) => void;
   onScheduleCreated?: (schedule: GraphExecutionJobInfo) => void;
 }
 
 export function RunAgentModal({
   triggerSlot,
   agent,
-  initialInputValues,
-  initialInputCredentials,
   onRunCreated,
-  onTriggerSetup,
   onScheduleCreated,
 }: Props) {
   const {
@@ -76,9 +69,6 @@ export function RunAgentModal({
     handleRun,
   } = useAgentRunModal(agent, {
     onRun: onRunCreated,
-    onSetupTrigger: onTriggerSetup,
-    initialInputValues,
-    initialInputCredentials,
   });
 
   const [isScheduleModalOpen, setIsScheduleModalOpen] = useState(false);
@@ -87,8 +77,6 @@ export function RunAgentModal({
     Object.keys(agentInputFields || {}).length > 0 ||
     Object.keys(agentCredentialsInputFields || {}).length > 0;
 
-  const isTriggerRunType = defaultRunType.includes("trigger");
-
   function handleInputChange(key: string, value: string) {
     setInputValues((prev) => ({
       ...prev,
@@ -134,74 +122,70 @@ export function RunAgentModal({
       >
         <Dialog.Trigger>{triggerSlot}</Dialog.Trigger>
         <Dialog.Content>
-          {/* Header */}
-          <ModalHeader agent={agent} />
-
-          {/* Content */}
-          {hasAnySetupFields ? (
-            <div className="mt-10">
-              <RunAgentModalContextProvider
-                value={{
-                  agent,
-                  defaultRunType,
-                  presetName,
-                  setPresetName,
-                  presetDescription,
-                  setPresetDescription,
-                  inputValues,
-                  setInputValue: handleInputChange,
-                  agentInputFields,
-                  inputCredentials,
-                  setInputCredentialsValue: handleCredentialsChange,
-                  agentCredentialsInputFields,
-                }}
-              >
-                <ModalRunSection />
-              </RunAgentModalContextProvider>
+          <div className="flex h-full flex-col pb-4">
+            {/* Header */}
+            <div className="flex-shrink-0">
+              <ModalHeader agent={agent} />
+              <AgentCostSection flowId={agent.graph_id} />
             </div>
-          ) : null}
 
-          <Dialog.Footer className="mt-6 bg-white pt-4">
+            {/* Scrollable content */}
+            <div className="flex-1 pr-1" style={{ scrollbarGutter: "stable" }}>
+              {/* Setup Section */}
+              <div className="mt-10">
+                {hasAnySetupFields ? (
+                  <RunAgentModalContextProvider
+                    value={{
+                      agent,
+                      defaultRunType,
+                      presetName,
+                      setPresetName,
+                      presetDescription,
+                      setPresetDescription,
+                      inputValues,
+                      setInputValue: handleInputChange,
+                      agentInputFields,
+                      inputCredentials,
+                      setInputCredentialsValue: handleCredentialsChange,
+                      agentCredentialsInputFields,
+                    }}
+                  >
+                    <>
+                      <AgentSectionHeader
+                        title={
+                          defaultRunType === "automatic-trigger"
+                            ? "Trigger Setup"
+                            : "Agent Setup"
+                        }
+                      />
+                      <ModalRunSection />
+                    </>
+                  </RunAgentModalContextProvider>
+                ) : null}
+              </div>
+
+              {/* Agent Details Section */}
+              <div className="mt-8">
+                <AgentSectionHeader title="Agent Details" />
+                <AgentDetails agent={agent} />
+              </div>
+            </div>
+          </div>
+          <Dialog.Footer
+            className="fixed bottom-1 left-0 z-10 w-full bg-white p-4"
+            style={{ boxShadow: "0px -8px 10px white" }}
+          >
             <div className="flex items-center justify-end gap-3">
-              {isTriggerRunType ? null : !allRequiredInputsAreSet ? (
-                <TooltipProvider>
-                  <Tooltip>
-                    <TooltipTrigger asChild>
-                      <span>
-                        <Button
-                          variant="secondary"
-                          onClick={handleOpenScheduleModal}
-                          disabled={
-                            isExecuting ||
-                            isSettingUpTrigger ||
-                            !allRequiredInputsAreSet
-                          }
-                        >
-                          Schedule Task
-                        </Button>
-                      </span>
-                    </TooltipTrigger>
-                    <TooltipContent>
-                      <p>
-                        Please set up all required inputs and credentials before
-                        scheduling
-                      </p>
-                    </TooltipContent>
-                  </Tooltip>
-                </TooltipProvider>
-              ) : (
-                <Button
-                  variant="secondary"
-                  onClick={handleOpenScheduleModal}
-                  disabled={
-                    isExecuting ||
-                    isSettingUpTrigger ||
-                    !allRequiredInputsAreSet
-                  }
-                >
-                  Schedule Task
-                </Button>
-              )}
+              <Button
+                variant="secondary"
+                onClick={handleOpenScheduleModal}
+                disabled={
+                  isExecuting || isSettingUpTrigger || !allRequiredInputsAreSet
+                }
+              >
+                <AlarmIcon size={16} />
+                Schedule Agent
+              </Button>
               <RunActions
                 defaultRunType={defaultRunType}
                 onRun={handleRun}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/AgentCostSection/AgentCostSection.tsx

@@ -0,0 +1,31 @@
+import { Button } from "@/components/atoms/Button/Button";
+
+interface Props {
+  flowId: string;
+}
+
+export function AgentCostSection({ flowId }: Props) {
+  return (
+    <div className="mt-6 flex items-center justify-between">
+      {/* TODO: enable once we have an API to show estimated cost for an agent run */}
+      {/* <div className="flex items-center gap-2">
+        <Text variant="body-medium">Cost</Text>
+        <Text variant="body">{cost}</Text>
+      </div> */}
+      <div className="flex items-center gap-2">
+        <Button
+          variant="outline"
+          size="small"
+          as="NextLink"
+          href={`/build?flowID=${flowId}`}
+        >
+          Open in builder
+        </Button>
+        {/* TODO: enable once we can easily link to the agent listing page from the library agent response */}
+        {/* <Button variant="outline" size="small">
+          View listing <ArrowSquareOutIcon size={16} />
+        </Button> */}
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/AgentDetails/AgentDetails.tsx

@@ -0,0 +1,48 @@
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Text } from "@/components/atoms/Text/Text";
+import { Badge } from "@/components/atoms/Badge/Badge";
+import { formatDate } from "@/lib/utils/time";
+
+interface Props {
+  agent: LibraryAgent;
+}
+
+export function AgentDetails({ agent }: Props) {
+  return (
+    <div className="mt-4 flex flex-col gap-5">
+      <div>
+        <Text variant="body-medium" className="mb-1 !text-black">
+          Version
+        </Text>
+        <div className="flex items-center gap-2">
+          <Text variant="body" className="!text-zinc-700">
+            v{agent.graph_version}
+          </Text>
+          {agent.is_latest_version && (
+            <Badge variant="success" size="small">
+              Latest
+            </Badge>
+          )}
+        </div>
+      </div>
+      <div>
+        <Text variant="body-medium" className="mb-1 !text-black">
+          Last Updated
+        </Text>
+        <Text variant="body" className="!text-zinc-700">
+          {formatDate(agent.updated_at)}
+        </Text>
+      </div>
+      {agent.has_external_trigger && (
+        <div>
+          <Text variant="body-medium" className="mb-1">
+            Trigger Type
+          </Text>
+          <Text variant="body" className="!text-neutral-700">
+            External Webhook
+          </Text>
+        </div>
+      )}
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/AgentSectionHeader/AgentSectionHeader.tsx

@@ -0,0 +1,15 @@
+import { Text } from "@/components/atoms/Text/Text";
+
+interface Props {
+  title: string;
+}
+
+export function AgentSectionHeader({ title }: Props) {
+  return (
+    <div className="border-t border-zinc-400 px-0 pb-2 pt-1">
+      <Text variant="label" className="!text-zinc-700">
+        {title}
+      </Text>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/ModalHeader/ModalHeader.tsx

@@ -1,8 +1,8 @@
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Badge } from "@/components/atoms/Badge/Badge";
-import { Link } from "@/components/atoms/Link/Link";
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Text } from "@/components/atoms/Text/Text";
 import { ShowMoreText } from "@/components/molecules/ShowMoreText/ShowMoreText";
+import { ClockIcon, InfoIcon } from "@phosphor-icons/react";
 import { humanizeCronExpression } from "@/lib/cron-expression-utils";
 
 interface ModalHeaderProps {
@@ -10,56 +10,49 @@ interface ModalHeaderProps {
 }
 
 export function ModalHeader({ agent }: ModalHeaderProps) {
-  const creator = agent.marketplace_listing?.creator;
+  const isUnknownCreator = agent.creator_name === "Unknown";
 
   return (
-    <div className="flex flex-col gap-4">
-      <Badge variant="info" className="w-fit">
-        New Task
-      </Badge>
+    <div className="space-y-4">
+      <div className="flex items-center gap-3">
+        <Badge variant="info">New Run</Badge>
+      </div>
       <div>
-        <Text variant="h2">{agent.name}</Text>
-        {creator ? (
-          <Link href={`/marketplace/creator/${creator.slug}`} isExternal>
-            by {creator.name}
-          </Link>
+        <Text variant="h3">{agent.name}</Text>
+        {!isUnknownCreator ? (
+          <Text variant="body-medium">by {agent.creator_name}</Text>
         ) : null}
+        <ShowMoreText
+          previewLimit={80}
+          variant="small"
+          className="mt-4 !text-zinc-700"
+        >
+          {agent.description}
+        </ShowMoreText>
 
-        {agent.description ? (
-          <ShowMoreText
-            previewLimit={400}
-            variant="small"
-            className="mt-4 !text-zinc-700"
-          >
-            {agent.description}
-          </ShowMoreText>
-        ) : null}
-
-        {agent.recommended_schedule_cron && !agent.has_external_trigger ? (
-          <div className="flex flex-col gap-4 rounded-medium border border-blue-100 bg-blue-50 p-4">
-            <Text variant="lead-semibold" className="text-blue-600">
-              Tip
-            </Text>
-            <Text variant="body">
-              For best results, run this agent{" "}
+        {/* Schedule recommendation tip */}
+        {agent.recommended_schedule_cron && !agent.has_external_trigger && (
+          <div className="mt-4 flex items-center gap-2">
+            <ClockIcon className="h-4 w-4 text-gray-500" />
+            <p className="text-sm text-gray-600">
+              <strong>Tip:</strong> For best results, run this agent{" "}
               {humanizeCronExpression(
                 agent.recommended_schedule_cron,
               ).toLowerCase()}
-            </Text>
+            </p>
           </div>
-        ) : null}
+        )}
 
-        {agent.instructions ? (
-          <div className="flex flex-col gap-4 rounded-medium border border-purple-100 bg-[#F1EBFE/5] p-4">
-            <Text variant="lead-semibold" className="text-purple-600">
-              Instructions
-            </Text>
-
-            <div className="h-px w-full bg-purple-100" />
-
-            <Text variant="body">{agent.instructions}</Text>
+        {/* Setup Instructions */}
+        {agent.instructions && (
+          <div className="mt-4 flex items-start gap-2">
+            <InfoIcon className="mt-0.5 h-4 w-4 flex-shrink-0 text-gray-500" />
+            <div className="text-sm text-gray-600">
+              <strong>Setup Instructions:</strong>{" "}
+              <span className="whitespace-pre-wrap">{agent.instructions}</span>
+            </div>
           </div>
-        ) : null}
+        )}
       </div>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/ModalRunSection/ModalRunSection.tsx

@@ -1,13 +1,17 @@
 import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInputs";
 import { Input } from "@/components/atoms/Input/Input";
+import { Text } from "@/components/atoms/Text/Text";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
+import { toDisplayName } from "@/providers/agent-credentials/helper";
+import { InfoIcon } from "@phosphor-icons/react";
 import { RunAgentInputs } from "../../../RunAgentInputs/RunAgentInputs";
 import { useRunAgentModalContext } from "../../context";
-import { ModalSection } from "../ModalSection/ModalSection";
 import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBanner";
+import { getCredentialTypeDisplayName } from "./helpers";
 
 export function ModalRunSection() {
   const {
+    agent,
     defaultRunType,
     presetName,
     setPresetName,
@@ -21,105 +25,147 @@ export function ModalRunSection() {
     agentCredentialsInputFields,
   } = useRunAgentModalContext();
 
-  const inputFields = Object.entries(agentInputFields || {});
-  const credentialFields = Object.entries(agentCredentialsInputFields || {});
-
   return (
-    <div className="flex flex-col gap-4">
-      {defaultRunType === "automatic-trigger" ||
-      defaultRunType === "manual-trigger" ? (
-        <ModalSection
-          title="Task Trigger"
-          subtitle="Set up a trigger for the agent to run this task automatically"
-        >
-          <WebhookTriggerBanner />
-          <div className="flex flex-col gap-4">
-            <div className="flex flex-col space-y-2">
-              <label className="flex items-center gap-1 text-sm font-medium">
-                Trigger Name
-                <InformationTooltip description="Name of the trigger you are setting up" />
-              </label>
-              <Input
-                id="trigger_name"
-                label="Trigger Name"
-                size="small"
-                hideLabel
-                value={presetName}
-                placeholder="Enter trigger name"
-                onChange={(e) => setPresetName(e.target.value)}
-              />
-            </div>
-            <div className="flex flex-col space-y-2">
-              <label className="flex items-center gap-1 text-sm font-medium">
-                Trigger Description
-                <InformationTooltip description="Description of the trigger you are setting up" />
-              </label>
-              <Input
-                id="trigger_description"
-                label="Trigger Description"
-                size="small"
-                hideLabel
-                value={presetDescription}
-                placeholder="Enter trigger description"
-                onChange={(e) => setPresetDescription(e.target.value)}
-              />
-            </div>
+    <div className="mb-10 mt-4">
+      {defaultRunType === "automatic-trigger" && <WebhookTriggerBanner />}
+
+      {/* Preset/Trigger fields */}
+      {defaultRunType === "automatic-trigger" && (
+        <div className="flex flex-col gap-4">
+          <div className="flex flex-col space-y-2">
+            <label className="flex items-center gap-1 text-sm font-medium">
+              Trigger Name
+              <InformationTooltip description="Name of the trigger you are setting up" />
+            </label>
+            <Input
+              id="trigger_name"
+              label="Trigger Name"
+              size="small"
+              hideLabel
+              value={presetName}
+              placeholder="Enter trigger name"
+              onChange={(e) => setPresetName(e.target.value)}
+            />
           </div>
-        </ModalSection>
-      ) : null}
-
-      {inputFields.length > 0 ? (
-        <ModalSection
-          title="Task Inputs"
-          subtitle="Enter the information you want to provide to the agent for this task"
-        >
-          {/* Regular inputs */}
-          {inputFields.map(([key, inputSubSchema]) => (
-            <div key={key} className="flex w-full flex-col gap-0 space-y-2">
-              <label className="flex items-center gap-1 text-sm font-medium">
-                {inputSubSchema.title || key}
-                <InformationTooltip description={inputSubSchema.description} />
-              </label>
-
-              <RunAgentInputs
-                schema={inputSubSchema}
-                value={inputValues[key] ?? inputSubSchema.default}
-                placeholder={inputSubSchema.description}
-                onChange={(value) => setInputValue(key, value)}
-                data-testid={`agent-input-${key}`}
-              />
-            </div>
-          ))}
-        </ModalSection>
-      ) : null}
-
-      {credentialFields.length > 0 ? (
-        <ModalSection
-          title="Task Credentials"
-          subtitle="These are the credentials the agent will use to perform this task"
-        >
-          <div className="space-y-6">
-            {Object.entries(agentCredentialsInputFields || {}).map(
-              ([key, inputSubSchema]) => (
-                <CredentialsInput
-                  key={key}
-                  schema={
-                    { ...inputSubSchema, discriminator: undefined } as any
-                  }
-                  selectedCredentials={
-                    (inputCredentials && inputCredentials[key]) ??
-                    inputSubSchema.default
-                  }
-                  onSelectCredentials={(value) =>
-                    setInputCredentialsValue(key, value)
-                  }
-                  siblingInputs={inputValues}
-                />
-              ),
-            )}
+          <div className="flex flex-col space-y-2">
+            <label className="flex items-center gap-1 text-sm font-medium">
+              Trigger Description
+              <InformationTooltip description="Description of the trigger you are setting up" />
+            </label>
+            <Input
+              id="trigger_description"
+              label="Trigger Description"
+              size="small"
+              hideLabel
+              value={presetDescription}
+              placeholder="Enter trigger description"
+              onChange={(e) => setPresetDescription(e.target.value)}
+            />
           </div>
-        </ModalSection>
-      ) : null}
+        </div>
+      )}
+
+      {/* Instructions */}
+      {agent.instructions && (
+        <div className="mb-4 flex items-start gap-2 rounded-md border border-blue-200 bg-blue-50 p-3">
+          <InfoIcon className="mt-0.5 h-4 w-4 text-blue-600" />
+          <div>
+            <h4 className="text-sm font-medium text-blue-900">
+              How to use this agent
+            </h4>
+            <p className="mt-1 whitespace-pre-wrap text-sm text-blue-800">
+              {agent.instructions}
+            </p>
+          </div>
+        </div>
+      )}
+
+      {/* Credentials inputs */}
+      {Object.entries(agentCredentialsInputFields || {}).map(
+        ([key, inputSubSchema]) => (
+          <CredentialsInput
+            key={key}
+            schema={{ ...inputSubSchema, discriminator: undefined } as any}
+            selectedCredentials={
+              (inputCredentials && inputCredentials[key]) ??
+              inputSubSchema.default
+            }
+            onSelectCredentials={(value) =>
+              setInputCredentialsValue(key, value)
+            }
+            siblingInputs={inputValues}
+            hideIfSingleCredentialAvailable={!agent.has_external_trigger}
+          />
+        ),
+      )}
+
+      {/* Regular inputs */}
+      {Object.entries(agentInputFields || {}).map(([key, inputSubSchema]) => (
+        <div key={key} className="flex w-full flex-col gap-0 space-y-2">
+          <label className="flex items-center gap-1 text-sm font-medium">
+            {inputSubSchema.title || key}
+            <InformationTooltip description={inputSubSchema.description} />
+          </label>
+
+          <RunAgentInputs
+            schema={inputSubSchema}
+            value={inputValues[key] ?? inputSubSchema.default}
+            placeholder={inputSubSchema.description}
+            onChange={(value) => setInputValue(key, value)}
+            data-testid={`agent-input-${key}`}
+          />
+        </div>
+      ))}
+
+      {/* Selected Credentials Preview */}
+      {Object.keys(inputCredentials).length > 0 && (
+        <div className="mt-6 flex flex-col gap-6">
+          {Object.entries(agentCredentialsInputFields || {}).map(
+            ([key, _sub]) => {
+              const credential = inputCredentials[key];
+              if (!credential) return null;
+
+              return (
+                <div key={key} className="flex flex-col gap-4">
+                  <Text variant="body-medium" as="h3">
+                    {toDisplayName(credential.provider)} credentials
+                  </Text>
+                  <div className="flex flex-col gap-3">
+                    <div className="flex items-center justify-between text-sm">
+                      <Text
+                        variant="body"
+                        as="span"
+                        className="!text-neutral-600"
+                      >
+                        Name
+                      </Text>
+                      <Text
+                        variant="body"
+                        as="span"
+                        className="!text-neutral-600"
+                      >
+                        {getCredentialTypeDisplayName(credential.type)}
+                      </Text>
+                    </div>
+                    <div className="flex items-center justify-between text-sm">
+                      <Text
+                        variant="body"
+                        as="span"
+                        className="!text-neutral-900"
+                      >
+                        {credential.title || "Untitled"}
+                      </Text>
+                      <span className="font-mono text-neutral-400">
+                        {"*".repeat(25)}
+                      </span>
+                    </div>
+                  </div>
+                </div>
+              );
+            },
+          )}
+        </div>
+      )}
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/ModalSection/ModalSection.tsx

@@ -1,21 +0,0 @@
-import { Text } from "@/components/atoms/Text/Text";
-
-interface Props {
-  title: string;
-  subtitle: string;
-  children: React.ReactNode;
-}
-
-export function ModalSection({ title, subtitle, children }: Props) {
-  return (
-    <div className="rounded-medium border border-zinc-200 p-6">
-      <div className="mb-4 flex flex-col gap-1 border-b border-zinc-100 pb-4">
-        <Text variant="lead-semibold">{title}</Text>
-        <Text variant="body" className="text-zinc-500">
-          {subtitle}
-        </Text>
-      </div>
-      {children}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/RunActions/RunActions.tsx

@@ -24,10 +24,9 @@ export function RunActions({
         disabled={!isRunReady || isExecuting || isSettingUpTrigger}
         loading={isExecuting || isSettingUpTrigger}
       >
-        {defaultRunType === "automatic-trigger" ||
-        defaultRunType === "manual-trigger"
+        {defaultRunType === "automatic-trigger"
           ? "Set up Trigger"
-          : "Start Task"}
+          : "Run Agent"}
       </Button>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/WebhookTriggerBanner/WebhookTriggerBanner.tsx

@@ -1,6 +1,6 @@
 export function WebhookTriggerBanner() {
   return (
-    <div className="mb-4 rounded-lg border border-blue-200 bg-blue-50 p-4">
+    <div className="rounded-lg border border-blue-200 bg-blue-50 p-4">
       <div className="flex items-start">
         <div className="flex-shrink-0">
           <svg

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/errorHelpers.tsx

@@ -1,100 +0,0 @@
-import { ApiError } from "@/lib/autogpt-server-api/helpers";
-import Link from "next/link";
-import React from "react";
-
-type ValidationErrorDetail = {
-  type: string;
-  message?: string;
-  node_errors?: Record<string, Record<string, string>>;
-};
-
-type AgentInfo = {
-  graph_id: string;
-  graph_version: number;
-};
-
-export function formatValidationError(
-  error: any,
-  agentInfo?: AgentInfo,
-): string | React.ReactNode {
-  if (
-    !(error instanceof ApiError) ||
-    !error.isGraphValidationError() ||
-    !error.response?.detail
-  ) {
-    return error.message || "An unexpected error occurred.";
-  }
-
-  const detail: ValidationErrorDetail = error.response.detail;
-
-  // Format validation errors nicely
-  if (detail.type === "validation_error" && detail.node_errors) {
-    const nodeErrors = detail.node_errors;
-    const errorItems: React.ReactNode[] = [];
-
-    // Collect all field errors
-    Object.entries(nodeErrors).forEach(([nodeId, fields]) => {
-      if (fields && typeof fields === "object") {
-        Object.entries(fields).forEach(([fieldName, fieldError]) => {
-          errorItems.push(
-            <div key={`${nodeId}-${fieldName}`} className="mt-1">
-              <span className="font-medium">{fieldName}:</span>{" "}
-              {String(fieldError)}
-            </div>,
-          );
-        });
-      }
-    });
-
-    if (errorItems.length > 0) {
-      return (
-        <div className="space-y-1">
-          <div className="font-medium text-white">
-            {detail.message || "Validation failed"}
-          </div>
-          <div className="mt-2 space-y-1 text-xs">{errorItems}</div>
-          {agentInfo && (
-            <div className="mt-3 text-xs">
-              Check the agent graph and try to run from there for further
-              details.{" "}
-              <Link
-                href={`/build?flowID=${agentInfo.graph_id}&flowVersion=${agentInfo.graph_version}`}
-                target="_blank"
-                rel="noopener noreferrer"
-                className="cursor-pointer underline hover:no-underline"
-              >
-                Open in builder
-              </Link>
-            </div>
-          )}
-        </div>
-      );
-    } else {
-      return detail.message || "Validation failed";
-    }
-  }
-
-  return detail.message || error.message || "An unexpected error occurred.";
-}
-
-export function showExecutionErrorToast(
-  toast: (options: {
-    title: string;
-    description: string | React.ReactNode;
-    variant: "destructive";
-    duration: number;
-    dismissable: boolean;
-  }) => void,
-  error: any,
-  agentInfo?: AgentInfo,
-) {
-  const errorMessage = formatValidationError(error, agentInfo);
-
-  toast({
-    title: "Failed to execute agent",
-    description: errorMessage,
-    variant: "destructive",
-    duration: 10000, // 10 seconds - long enough to read and close
-    dismissable: true,
-  });
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/useAgentRunModal.ts

@@ -1,20 +1,23 @@
-import {
-  getGetV1ListGraphExecutionsQueryKey,
-  usePostV1ExecuteGraphAgent,
-} from "@/app/api/__generated__/endpoints/graphs/graphs";
-import {
-  getGetV2ListPresetsQueryKey,
-  usePostV2SetupTrigger,
-} from "@/app/api/__generated__/endpoints/presets/presets";
-import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
+import { useState, useCallback, useMemo } from "react";
+import { useQueryClient } from "@tanstack/react-query";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { isEmpty } from "@/lib/utils";
+import {
+  usePostV1ExecuteGraphAgent,
+  getGetV1ListGraphExecutionsInfiniteQueryOptions,
+} from "@/app/api/__generated__/endpoints/graphs/graphs";
+import {
+  usePostV1CreateExecutionSchedule as useCreateSchedule,
+  getGetV1ListExecutionSchedulesForAGraphQueryKey,
+} from "@/app/api/__generated__/endpoints/schedules/schedules";
+import { usePostV2SetupTrigger } from "@/app/api/__generated__/endpoints/presets/presets";
+import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
+import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
+import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
+import { useGetV1GetUserTimezone } from "@/app/api/__generated__/endpoints/auth/auth";
+import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
 import { analytics } from "@/services/analytics";
-import { useQueryClient } from "@tanstack/react-query";
-import { useCallback, useEffect, useMemo, useState } from "react";
-import { showExecutionErrorToast } from "./errorHelpers";
 
 export type RunVariant =
   | "manual"
@@ -24,9 +27,8 @@ export type RunVariant =
 
 interface UseAgentRunModalCallbacks {
   onRun?: (execution: GraphExecutionMeta) => void;
+  onCreateSchedule?: (schedule: GraphExecutionJobInfo) => void;
   onSetupTrigger?: (preset: LibraryAgentPreset) => void;
-  initialInputValues?: Record<string, any>;
-  initialInputCredentials?: Record<string, any>;
 }
 
 export function useAgentRunModal(
@@ -36,28 +38,32 @@ export function useAgentRunModal(
   const { toast } = useToast();
   const queryClient = useQueryClient();
   const [isOpen, setIsOpen] = useState(false);
-  const [inputValues, setInputValues] = useState<Record<string, any>>(
-    callbacks?.initialInputValues || {},
-  );
+  const [showScheduleView, setShowScheduleView] = useState(false);
+  const [inputValues, setInputValues] = useState<Record<string, any>>({});
   const [inputCredentials, setInputCredentials] = useState<Record<string, any>>(
-    callbacks?.initialInputCredentials || {},
+    {},
   );
   const [presetName, setPresetName] = useState<string>("");
   const [presetDescription, setPresetDescription] = useState<string>("");
+  const defaultScheduleName = useMemo(() => `Run ${agent.name}`, [agent.name]);
+  const [scheduleName, setScheduleName] = useState(defaultScheduleName);
+  const [cronExpression, setCronExpression] = useState(
+    agent.recommended_schedule_cron || "0 9 * * 1",
+  );
+  const { completeStep: completeOnboardingStep } = useOnboarding();
+
+  // Get user timezone for scheduling
+  const { data: userTimezone } = useGetV1GetUserTimezone({
+    query: {
+      select: (res) => (res.status === 200 ? res.data.timezone : undefined),
+    },
+  });
 
   // Determine the default run type based on agent capabilities
-  const defaultRunType: RunVariant = agent.trigger_setup_info
-    ? agent.trigger_setup_info.credentials_input_name
-      ? "automatic-trigger"
-      : "manual-trigger"
+  const defaultRunType: RunVariant = agent.has_external_trigger
+    ? "automatic-trigger"
     : "manual";
 
-  // Update input values/credentials if template is selected/unselected
-  useEffect(() => {
-    setInputValues(callbacks?.initialInputValues || {});
-    setInputCredentials(callbacks?.initialInputCredentials || {});
-  }, [callbacks?.initialInputValues, callbacks?.initialInputCredentials]);
-
   // API mutations
   const executeGraphMutation = usePostV1ExecuteGraphAgent({
     mutation: {
@@ -66,11 +72,13 @@ export function useAgentRunModal(
           toast({
             title: "Agent execution started",
           });
+          callbacks?.onRun?.(response.data as unknown as GraphExecutionMeta);
           // Invalidate runs list for this graph
           queryClient.invalidateQueries({
-            queryKey: getGetV1ListGraphExecutionsQueryKey(agent.graph_id),
+            queryKey: getGetV1ListGraphExecutionsInfiniteQueryOptions(
+              agent.graph_id,
+            ).queryKey,
           });
-          callbacks?.onRun?.(response.data);
           analytics.sendDatafastEvent("run_agent", {
             name: agent.name,
             id: agent.graph_id,
@@ -79,9 +87,46 @@ export function useAgentRunModal(
         }
       },
       onError: (error: any) => {
-        showExecutionErrorToast(toast, error, {
-          graph_id: agent.graph_id,
-          graph_version: agent.graph_version,
+        const errorMessage = error.isGraphValidationError()
+          ? error.response.detail.message
+          : error.message;
+
+        toast({
+          title: "❌ Failed to execute agent",
+          description: errorMessage || "An unexpected error occurred.",
+          variant: "destructive",
+        });
+      },
+    },
+  });
+
+  const createScheduleMutation = useCreateSchedule({
+    mutation: {
+      onSuccess: (response) => {
+        if (response.status === 200) {
+          toast({
+            title: "Schedule created",
+          });
+          callbacks?.onCreateSchedule?.(response.data);
+          // Invalidate schedules list for this graph
+          queryClient.invalidateQueries({
+            queryKey: getGetV1ListExecutionSchedulesForAGraphQueryKey(
+              agent.graph_id,
+            ),
+          });
+          analytics.sendDatafastEvent("schedule_agent", {
+            name: agent.name,
+            id: agent.graph_id,
+            cronExpression: cronExpression,
+          });
+          setIsOpen(false);
+        }
+      },
+      onError: (error: any) => {
+        toast({
+          title: "❌ Failed to create schedule",
+          description: error.message || "An unexpected error occurred.",
+          variant: "destructive",
         });
       },
     },
@@ -89,14 +134,11 @@ export function useAgentRunModal(
 
   const setupTriggerMutation = usePostV2SetupTrigger({
     mutation: {
-      onSuccess: (response) => {
+      onSuccess: (response: any) => {
         if (response.status === 200) {
           toast({
             title: "Trigger setup complete",
           });
-          queryClient.invalidateQueries({
-            queryKey: getGetV2ListPresetsQueryKey({ graph_id: agent.graph_id }),
-          });
           callbacks?.onSetupTrigger?.(response.data);
           setIsOpen(false);
         }
@@ -111,13 +153,11 @@ export function useAgentRunModal(
     },
   });
 
-  // Input schema validation (use trigger schema for triggered agents)
-  const agentInputSchema = useMemo(() => {
-    if (agent.trigger_setup_info?.config_schema) {
-      return agent.trigger_setup_info.config_schema;
-    }
-    return agent.input_schema || { properties: {}, required: [] };
-  }, [agent.input_schema, agent.trigger_setup_info]);
+  // Input schema validation
+  const agentInputSchema = useMemo(
+    () => agent.input_schema || { properties: {}, required: [] },
+    [agent.input_schema],
+  );
 
   const agentInputFields = useMemo(() => {
     if (
@@ -186,25 +226,33 @@ export function useAgentRunModal(
     [allRequiredInputsAreSetRaw, credentialsRequired, allCredentialsAreSet],
   );
 
-  const notifyMissingRequirements = useCallback(() => {
-    const allMissingFields = missingInputs.concat(
-      credentialsRequired && !allCredentialsAreSet
-        ? missingCredentials.map((k) => `credentials:${k}`)
-        : [],
-    );
+  const notifyMissingRequirements = useCallback(
+    (needScheduleName: boolean = false) => {
+      const allMissingFields = (
+        needScheduleName && !scheduleName ? ["schedule_name"] : []
+      )
+        .concat(missingInputs)
+        .concat(
+          credentialsRequired && !allCredentialsAreSet
+            ? missingCredentials.map((k) => `credentials:${k}`)
+            : [],
+        );
 
-    toast({
-      title: "⚠️ Missing required inputs",
-      description: `Please provide: ${allMissingFields.map((k) => `"${k}"`).join(", ")}`,
-      variant: "destructive",
-    });
-  }, [
-    missingInputs,
-    toast,
-    credentialsRequired,
-    allCredentialsAreSet,
-    missingCredentials,
-  ]);
+      toast({
+        title: "⚠️ Missing required inputs",
+        description: `Please provide: ${allMissingFields.map((k) => `"${k}"`).join(", ")}`,
+        variant: "destructive",
+      });
+    },
+    [
+      missingInputs,
+      scheduleName,
+      toast,
+      credentialsRequired,
+      allCredentialsAreSet,
+      missingCredentials,
+    ],
+  );
 
   // Action handlers
   const handleRun = useCallback(() => {
@@ -213,12 +261,9 @@ export function useAgentRunModal(
       return;
     }
 
-    if (
-      defaultRunType === "automatic-trigger" ||
-      defaultRunType === "manual-trigger"
-    ) {
+    if (defaultRunType === "automatic-trigger") {
       // Setup trigger
-      if (!presetName.trim()) {
+      if (!scheduleName.trim()) {
         toast({
           title: "⚠️ Trigger name required",
           description: "Please provide a name for your trigger.",
@@ -229,7 +274,7 @@ export function useAgentRunModal(
 
       setupTriggerMutation.mutate({
         data: {
-          name: presetName,
+          name: presetName || scheduleName,
           description: presetDescription || `Trigger for ${agent.name}`,
           graph_id: agent.graph_id,
           graph_version: agent.graph_version,
@@ -245,13 +290,13 @@ export function useAgentRunModal(
         data: {
           inputs: inputValues,
           credentials_inputs: inputCredentials,
-          source: "library",
         },
       });
     }
   }, [
     allRequiredInputsAreSet,
     defaultRunType,
+    scheduleName,
     inputValues,
     inputCredentials,
     agent,
@@ -263,6 +308,72 @@ export function useAgentRunModal(
     toast,
   ]);
 
+  const handleSchedule = useCallback(() => {
+    if (!allRequiredInputsAreSet) {
+      notifyMissingRequirements(true);
+      return;
+    }
+
+    if (!scheduleName.trim()) {
+      toast({
+        title: "⚠️ Schedule name required",
+        description: "Please provide a name for your schedule.",
+        variant: "destructive",
+      });
+      return;
+    }
+
+    createScheduleMutation.mutate({
+      graphId: agent.graph_id,
+      data: {
+        name: presetName || scheduleName,
+        cron: cronExpression,
+        inputs: inputValues,
+        graph_version: agent.graph_version,
+        credentials: inputCredentials,
+        timezone:
+          userTimezone && userTimezone !== "not-set" ? userTimezone : undefined,
+      },
+    });
+
+    completeOnboardingStep("SCHEDULE_AGENT");
+  }, [
+    allRequiredInputsAreSet,
+    scheduleName,
+    cronExpression,
+    inputValues,
+    inputCredentials,
+    agent,
+    notifyMissingRequirements,
+    createScheduleMutation,
+    toast,
+    userTimezone,
+  ]);
+
+  function handleShowSchedule() {
+    // Initialize with sensible defaults when entering schedule view
+    setScheduleName((prev) => prev || defaultScheduleName);
+    setCronExpression(
+      (prev) => prev || agent.recommended_schedule_cron || "0 9 * * 1",
+    );
+    setShowScheduleView(true);
+  }
+
+  function handleGoBack() {
+    setShowScheduleView(false);
+    // Reset schedule fields on exit
+    setScheduleName(defaultScheduleName);
+    setCronExpression(agent.recommended_schedule_cron || "0 9 * * 1");
+  }
+
+  function handleSetScheduleName(name: string) {
+    setScheduleName(name);
+  }
+
+  function handleSetCronExpression(expression: string) {
+    setCronExpression(expression);
+  }
+
   const hasInputFields = useMemo(() => {
     return Object.keys(agentInputFields).length > 0;
   }, [agentInputFields]);
@@ -271,9 +382,10 @@ export function useAgentRunModal(
     // UI state
     isOpen,
     setIsOpen,
+    showScheduleView,
 
     // Run mode
-    defaultRunType: defaultRunType as RunVariant,
+    defaultRunType,
 
     // Form: regular inputs
     inputValues,
@@ -289,6 +401,10 @@ export function useAgentRunModal(
     setPresetName,
     setPresetDescription,
 
+    // Scheduling
+    scheduleName,
+    cronExpression,
+
     // Validation/readiness
     allRequiredInputsAreSet,
     missingInputs,
@@ -300,9 +416,15 @@ export function useAgentRunModal(
 
     // Async states
     isExecuting: executeGraphMutation.isPending,
+    isCreatingSchedule: createScheduleMutation.isPending,
     isSettingUpTrigger: setupTriggerMutation.isPending,
 
     // Actions
     handleRun,
+    handleSchedule,
+    handleShowSchedule,
+    handleGoBack,
+    handleSetScheduleName,
+    handleSetCronExpression,
   };
 }