fix: preserve non-dict error bodies as truncated strings

Addresses CodeRabbit feedback: non-dict error bodies (e.g., HTML error
pages from proxies) were silently discarded, losing diagnostic info.
Now returns truncated string representation instead of None.

.github/dependabot.yml

@@ -1,5 +1,29 @@
 version: 2
 updates:
+  # autogpt_libs (Poetry project)
+  - package-ecosystem: "pip"
+    directory: "autogpt_platform/autogpt_libs"
+    schedule:
+      interval: "weekly"
+    open-pull-requests-limit: 10
+    target-branch: "dev"
+    commit-message:
+      prefix: "chore(libs/deps)"
+      prefix-development: "chore(libs/deps-dev)"
+    ignore:
+      - dependency-name: "poetry"
+    groups:
+      production-dependencies:
+        dependency-type: "production"
+        update-types:
+          - "minor"
+          - "patch"
+      development-dependencies:
+        dependency-type: "development"
+        update-types:
+          - "minor"
+          - "patch"
+
   # backend (Poetry project)
   - package-ecosystem: "pip"
     directory: "autogpt_platform/backend"

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

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

.github/workflows/claude-dependabot.yml

@@ -30,7 +30,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
@@ -78,7 +78,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 

.github/workflows/claude.yml

@@ -40,7 +40,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 
@@ -94,7 +94,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 

.github/workflows/codeql.yml

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

.github/workflows/copilot-setup-steps.yml

@@ -27,7 +27,7 @@ jobs:
     # If you do not check out your code, Copilot will do this for you.
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true
@@ -76,7 +76,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22"
 

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

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

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

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

.github/workflows/docs-enhance.yml

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

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

@@ -25,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
@@ -52,7 +52,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.ref_name || 'master' }}
 
@@ -45,7 +45,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -6,11 +6,13 @@ on:
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
+      - "autogpt_platform/autogpt_libs/**"
   pull_request:
     branches: [master, dev, release-*]
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
+      - "autogpt_platform/autogpt_libs/**"
   merge_group:
 
 concurrency:
@@ -66,7 +68,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true

.github/workflows/platform-dev-deploy-event-dispatcher.yml

@@ -82,7 +82,7 @@ jobs:
           
       - name: Dispatch Deploy Event
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -110,7 +110,7 @@ jobs:
 
       - name: Dispatch Undeploy Event (from comment)
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -168,7 +168,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -31,7 +31,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Check for component changes
         uses: dorny/paths-filter@v3
@@ -42,7 +42,7 @@ jobs:
               - 'autogpt_platform/frontend/src/components/**'
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -71,10 +71,10 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -107,12 +107,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -148,12 +148,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -277,12 +277,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 

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

@@ -29,10 +29,10 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -56,19 +56,19 @@ jobs:
         run: pnpm install --frozen-lockfile
 
   types:
-    runs-on: big-boi
+    runs-on: ubuntu-latest
     needs: setup
     strategy:
       fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
         with:
           node-version: "22.18.0"
 
@@ -85,7 +85,7 @@ jobs:
 
       - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
 
       - name: Restore dependencies cache
         uses: actions/cache@v5

.github/workflows/repo-workflow-checker.yml

@@ -11,7 +11,7 @@ jobs:
     steps:
       # - name: Wait some time for all actions to start
       #   run: sleep 30
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
         # with:
           # fetch-depth: 0
       - name: Set up Python

autogpt_platform/CLAUDE.md

@@ -8,7 +8,7 @@ AutoGPT Platform is a monorepo containing:
 
 - **Backend** (`backend`): Python FastAPI server with async support
 - **Frontend** (`frontend`): Next.js React application
-- **Shared Libraries** (`backend/api/auth`, `backend/logging`): Auth, logging, and common utilities integrated into backend
+- **Shared Libraries** (`autogpt_libs`): Common Python utilities
 
 ## Component Documentation
 

autogpt_platform/autogpt_libs/README.md

@@ -0,0 +1,3 @@
+# AutoGPT Libs
+
+This is a new project to store shared functionality across different services in the AutoGPT Platform (e.g. authentication)

autogpt_platform/autogpt_libs/autogpt_libs/api_key/test_keysmith.py

@@ -1,6 +1,6 @@
 import hashlib
 
-from backend.api.auth.api_key.keysmith import APIKeySmith
+from autogpt_libs.api_key.keysmith import APIKeySmith
 
 
 def test_generate_api_key():

autogpt_platform/autogpt_libs/autogpt_libs/auth/config_test.py

@@ -9,7 +9,7 @@ import os
 import pytest
 from pytest_mock import MockerFixture
 
-from backend.api.auth.config import AuthConfigError, Settings
+from autogpt_libs.auth.config import AuthConfigError, Settings
 
 
 def test_environment_variable_precedence(mocker: MockerFixture):
@@ -228,7 +228,7 @@ def test_no_crypto_warning(mocker: MockerFixture, caplog: pytest.LogCaptureFixtu
     mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
 
     # Mock has_crypto to return False
-    mocker.patch("backend.api.auth.config.has_crypto", False)
+    mocker.patch("autogpt_libs.auth.config.has_crypto", False)
 
     with caplog.at_level(logging.WARNING):
         Settings()

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -43,7 +43,7 @@ def get_optional_user_id(
 
     try:
         # Parse JWT token to get user ID
-        from backend.api.auth.jwt_utils import parse_jwt_token
+        from autogpt_libs.auth.jwt_utils import parse_jwt_token
 
         payload = parse_jwt_token(credentials.credentials)
         return payload.get("sub")

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -11,12 +11,12 @@ from fastapi import FastAPI, HTTPException, Request, Security
 from fastapi.testclient import TestClient
 from pytest_mock import MockerFixture
 
-from backend.api.auth.dependencies import (
+from autogpt_libs.auth.dependencies import (
     get_user_id,
     requires_admin_user,
     requires_user,
 )
-from backend.api.auth.models import User
+from autogpt_libs.auth.models import User
 
 
 class TestAuthDependencies:
@@ -53,7 +53,7 @@ class TestAuthDependencies:
 
         # Mock get_jwt_payload to return our test payload
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
         user = await requires_user(jwt_payload)
         assert isinstance(user, User)
@@ -70,7 +70,7 @@ class TestAuthDependencies:
         }
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
         user = await requires_user(jwt_payload)
         assert user.user_id == "admin-456"
@@ -105,7 +105,7 @@ class TestAuthDependencies:
         }
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
         user = await requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
@@ -137,7 +137,7 @@ class TestAuthDependencies:
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
         user_id = await get_user_id(request, jwt_payload)
         assert user_id == "user-id-xyz"
@@ -344,7 +344,7 @@ class TestAuthDependenciesEdgeCases:
     ):
         """Test that errors propagate correctly through dependencies."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
-        from backend.api.auth.jwt_utils import verify_user
+        from autogpt_libs.auth.jwt_utils import verify_user
 
         with pytest.raises(HTTPException) as exc_info:
             verify_user(payload, admin_only=admin_only)
@@ -354,7 +354,7 @@ class TestAuthDependenciesEdgeCases:
     async def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
-        from backend.api.auth.jwt_utils import verify_user
+        from autogpt_libs.auth.jwt_utils import verify_user
 
         # Valid case
         user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
@@ -376,16 +376,16 @@ class TestAdminImpersonation:
         }
 
         # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("backend.api.auth.dependencies.verify_user")
+        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
         mock_verify_user.return_value = Mock(
             user_id="admin-456", email="admin@example.com", role="admin"
         )
 
         # Mock logger to verify audit logging
-        mock_logger = mocker.patch("backend.api.auth.dependencies.logger")
+        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)
@@ -412,13 +412,13 @@ class TestAdminImpersonation:
         }
 
         # Mock verify_user to return regular user data
-        mock_verify_user = mocker.patch("backend.api.auth.dependencies.verify_user")
+        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
         mock_verify_user.return_value = Mock(
             user_id="regular-user", email="user@example.com", role="user"
         )
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         with pytest.raises(HTTPException) as exc_info:
@@ -439,7 +439,7 @@ class TestAdminImpersonation:
         }
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)
@@ -459,7 +459,7 @@ class TestAdminImpersonation:
         }
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)
@@ -479,16 +479,16 @@ class TestAdminImpersonation:
         }
 
         # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("backend.api.auth.dependencies.verify_user")
+        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
         mock_verify_user.return_value = Mock(
             user_id="admin-999", email="superadmin@company.com", role="admin"
         )
 
         # Mock logger to capture audit trail
-        mock_logger = mocker.patch("backend.api.auth.dependencies.logger")
+        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)
@@ -515,7 +515,7 @@ class TestAdminImpersonation:
         }
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)
@@ -535,16 +535,16 @@ class TestAdminImpersonation:
         }
 
         # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("backend.api.auth.dependencies.verify_user")
+        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
         mock_verify_user.return_value = Mock(
             user_id="admin-456", email="admin@example.com", role="admin"
         )
 
         # Mock logger
-        mock_logger = mocker.patch("backend.api.auth.dependencies.logger")
+        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
 
         mocker.patch(
-            "backend.api.auth.dependencies.get_jwt_payload", return_value=jwt_payload
+            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
 
         user_id = await get_user_id(request, jwt_payload)

autogpt_platform/autogpt_libs/autogpt_libs/auth/helpers_test.py

@@ -3,11 +3,13 @@ Comprehensive tests for auth helpers module to achieve 100% coverage.
 Tests OpenAPI schema generation and authentication response handling.
 """
 
+from unittest import mock
+
 from fastapi import FastAPI
 from fastapi.openapi.utils import get_openapi
 
-from backend.api.auth.helpers import add_auth_responses_to_openapi
-from backend.api.auth.jwt_utils import bearer_jwt_auth
+from autogpt_libs.auth.helpers import add_auth_responses_to_openapi
+from autogpt_libs.auth.jwt_utils import bearer_jwt_auth
 
 
 def test_add_auth_responses_to_openapi_basic():
@@ -17,7 +19,7 @@ def test_add_auth_responses_to_openapi_basic():
     # Add some test endpoints with authentication
     from fastapi import Depends
 
-    from backend.api.auth.dependencies import requires_user
+    from autogpt_libs.auth.dependencies import requires_user
 
     @app.get("/protected", dependencies=[Depends(requires_user)])
     def protected_endpoint():
@@ -62,7 +64,7 @@ def test_add_auth_responses_to_openapi_with_security():
     # Mock endpoint with security
     from fastapi import Security
 
-    from backend.api.auth.dependencies import get_user_id
+    from autogpt_libs.auth.dependencies import get_user_id
 
     @app.get("/secured")
     def secured_endpoint(user_id: str = Security(get_user_id)):
@@ -128,7 +130,7 @@ def test_add_auth_responses_to_openapi_existing_responses():
 
     from fastapi import Security
 
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     @app.get(
         "/with-responses",
@@ -195,8 +197,8 @@ def test_add_auth_responses_to_openapi_multiple_security_schemes():
 
     from fastapi import Security
 
-    from backend.api.auth.dependencies import requires_admin_user, requires_user
-    from backend.api.auth.models import User
+    from autogpt_libs.auth.dependencies import requires_admin_user, requires_user
+    from autogpt_libs.auth.models import User
 
     @app.get("/multi-auth")
     def multi_auth(
@@ -225,29 +227,26 @@ def test_add_auth_responses_to_openapi_empty_components():
     """Test when OpenAPI schema has no components section initially."""
     app = FastAPI()
 
-    def mock_openapi():
-        schema = get_openapi(
-            title=app.title,
-            version=app.version,
-            routes=app.routes,
-        )
-        # Remove components if it exists to test component creation
+    # Mock get_openapi to return schema without components
+    original_get_openapi = get_openapi
+
+    def mock_get_openapi(*args, **kwargs):
+        schema = original_get_openapi(*args, **kwargs)
+        # Remove components if it exists
         if "components" in schema:
             del schema["components"]
         return schema
 
-    # Replace app's openapi method
-    app.openapi = mock_openapi
+    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
+        # Apply customization
+        add_auth_responses_to_openapi(app)
 
-    # Apply customization (this wraps our mock)
-    add_auth_responses_to_openapi(app)
+        schema = app.openapi()
 
-    schema = app.openapi()
-
-    # Components should be created
-    assert "components" in schema
-    assert "responses" in schema["components"]
-    assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
+        # Components should be created
+        assert "components" in schema
+        assert "responses" in schema["components"]
+        assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
 
 
 def test_add_auth_responses_to_openapi_all_http_methods():
@@ -256,7 +255,7 @@ def test_add_auth_responses_to_openapi_all_http_methods():
 
     from fastapi import Security
 
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     @app.get("/resource")
     def get_resource(jwt: dict = Security(get_jwt_payload)):
@@ -334,59 +333,53 @@ def test_endpoint_without_responses_section():
     app = FastAPI()
 
     from fastapi import Security
+    from fastapi.openapi.utils import get_openapi as original_get_openapi
 
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     # Create endpoint
     @app.get("/no-responses")
     def endpoint_without_responses(jwt: dict = Security(get_jwt_payload)):
         return {"data": "test"}
 
-    # Create a mock openapi method that removes responses from the endpoint
-    def mock_openapi():
-        schema = get_openapi(
-            title=app.title,
-            version=app.version,
-            routes=app.routes,
-        )
-        # Remove responses from our endpoint to test response creation
+    # Mock get_openapi to remove responses from the endpoint
+    def mock_get_openapi(*args, **kwargs):
+        schema = original_get_openapi(*args, **kwargs)
+        # Remove responses from our endpoint to trigger line 40
         if "/no-responses" in schema.get("paths", {}):
             if "get" in schema["paths"]["/no-responses"]:
+                # Delete responses to force the code to create it
                 if "responses" in schema["paths"]["/no-responses"]["get"]:
                     del schema["paths"]["/no-responses"]["get"]["responses"]
         return schema
 
-    # Replace app's openapi method
-    app.openapi = mock_openapi
+    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
+        # Apply customization
+        add_auth_responses_to_openapi(app)
 
-    # Apply customization (this wraps our mock)
-    add_auth_responses_to_openapi(app)
+        # Get schema and verify 401 was added
+        schema = app.openapi()
 
-    # Get schema and verify 401 was added
-    schema = app.openapi()
-
-    # The endpoint should now have 401 response
-    if "/no-responses" in schema["paths"]:
-        if "get" in schema["paths"]["/no-responses"]:
-            responses = schema["paths"]["/no-responses"]["get"].get("responses", {})
-            assert "401" in responses
-            assert (
-                responses["401"]["$ref"]
-                == "#/components/responses/HTTP401NotAuthenticatedError"
-            )
+        # The endpoint should now have 401 response
+        if "/no-responses" in schema["paths"]:
+            if "get" in schema["paths"]["/no-responses"]:
+                responses = schema["paths"]["/no-responses"]["get"].get("responses", {})
+                assert "401" in responses
+                assert (
+                    responses["401"]["$ref"]
+                    == "#/components/responses/HTTP401NotAuthenticatedError"
+                )
 
 
 def test_components_with_existing_responses():
     """Test when components already has a responses section."""
     app = FastAPI()
 
-    # Create a mock openapi method that adds existing components/responses
-    def mock_openapi():
-        schema = get_openapi(
-            title=app.title,
-            version=app.version,
-            routes=app.routes,
-        )
+    # Mock get_openapi to return schema with existing components/responses
+    from fastapi.openapi.utils import get_openapi as original_get_openapi
+
+    def mock_get_openapi(*args, **kwargs):
+        schema = original_get_openapi(*args, **kwargs)
         # Add existing components/responses
         if "components" not in schema:
             schema["components"] = {}
@@ -395,21 +388,21 @@ def test_components_with_existing_responses():
         }
         return schema
 
-    # Replace app's openapi method
-    app.openapi = mock_openapi
+    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
+        # Apply customization
+        add_auth_responses_to_openapi(app)
 
-    # Apply customization (this wraps our mock)
-    add_auth_responses_to_openapi(app)
+        schema = app.openapi()
 
-    schema = app.openapi()
+        # Both responses should exist
+        assert "ExistingResponse" in schema["components"]["responses"]
+        assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
 
-    # Both responses should exist
-    assert "ExistingResponse" in schema["components"]["responses"]
-    assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-    # Verify our 401 response structure
-    error_response = schema["components"]["responses"]["HTTP401NotAuthenticatedError"]
-    assert error_response["description"] == "Authentication required"
+        # Verify our 401 response structure
+        error_response = schema["components"]["responses"][
+            "HTTP401NotAuthenticatedError"
+        ]
+        assert error_response["description"] == "Authentication required"
 
 
 def test_openapi_schema_persistence():
@@ -418,7 +411,7 @@ def test_openapi_schema_persistence():
 
     from fastapi import Security
 
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     @app.get("/test")
     def test_endpoint(jwt: dict = Security(get_jwt_payload)):

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -12,9 +12,9 @@ from fastapi import HTTPException
 from fastapi.security import HTTPAuthorizationCredentials
 from pytest_mock import MockerFixture
 
-from backend.api.auth import config, jwt_utils
-from backend.api.auth.config import Settings
-from backend.api.auth.models import User
+from autogpt_libs.auth import config, jwt_utils
+from autogpt_libs.auth.config import Settings
+from autogpt_libs.auth.models import User
 
 MOCK_JWT_SECRET = "test-secret-key-with-at-least-32-characters"
 TEST_USER_PAYLOAD = {

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/config.py

@@ -0,0 +1,33 @@
+from typing import Optional
+
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class RateLimitSettings(BaseSettings):
+    redis_host: str = Field(
+        default="redis://localhost:6379",
+        description="Redis host",
+        validation_alias="REDIS_HOST",
+    )
+
+    redis_port: str = Field(
+        default="6379", description="Redis port", validation_alias="REDIS_PORT"
+    )
+
+    redis_password: Optional[str] = Field(
+        default=None,
+        description="Redis password",
+        validation_alias="REDIS_PASSWORD",
+    )
+
+    requests_per_minute: int = Field(
+        default=60,
+        description="Maximum number of requests allowed per minute per API key",
+        validation_alias="RATE_LIMIT_REQUESTS_PER_MINUTE",
+    )
+
+    model_config = SettingsConfigDict(case_sensitive=True, extra="ignore")
+
+
+RATE_LIMIT_SETTINGS = RateLimitSettings()

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/limiter.py

@@ -0,0 +1,51 @@
+import time
+from typing import Tuple
+
+from redis import Redis
+
+from .config import RATE_LIMIT_SETTINGS
+
+
+class RateLimiter:
+    def __init__(
+        self,
+        redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
+        redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
+        redis_password: str | None = RATE_LIMIT_SETTINGS.redis_password,
+        requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
+    ):
+        self.redis = Redis(
+            host=redis_host,
+            port=int(redis_port),
+            password=redis_password,
+            decode_responses=True,
+        )
+        self.window = 60
+        self.max_requests = requests_per_minute
+
+    async def check_rate_limit(self, api_key_id: str) -> Tuple[bool, int, int]:
+        """
+        Check if request is within rate limits.
+
+        Args:
+            api_key_id: The API key identifier to check
+
+        Returns:
+            Tuple of (is_allowed, remaining_requests, reset_time)
+        """
+        now = time.time()
+        window_start = now - self.window
+        key = f"ratelimit:{api_key_id}:1min"
+
+        pipe = self.redis.pipeline()
+        pipe.zremrangebyscore(key, 0, window_start)
+        pipe.zadd(key, {str(now): now})
+        pipe.zcount(key, window_start, now)
+        pipe.expire(key, self.window)
+
+        _, _, request_count, _ = pipe.execute()
+
+        remaining = max(0, self.max_requests - request_count)
+        reset_time = int(now + self.window)
+
+        return request_count <= self.max_requests, remaining, reset_time

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/middleware.py

@@ -0,0 +1,32 @@
+from fastapi import HTTPException, Request
+from starlette.middleware.base import RequestResponseEndpoint
+
+from .limiter import RateLimiter
+
+
+async def rate_limit_middleware(request: Request, call_next: RequestResponseEndpoint):
+    """FastAPI middleware for rate limiting API requests."""
+    limiter = RateLimiter()
+
+    if not request.url.path.startswith("/api"):
+        return await call_next(request)
+
+    api_key = request.headers.get("Authorization")
+    if not api_key:
+        return await call_next(request)
+
+    api_key = api_key.replace("Bearer ", "")
+
+    is_allowed, remaining, reset_time = await limiter.check_rate_limit(api_key)
+
+    if not is_allowed:
+        raise HTTPException(
+            status_code=429, detail="Rate limit exceeded. Please try again later."
+        )
+
+    response = await call_next(request)
+    response.headers["X-RateLimit-Limit"] = str(limiter.max_requests)
+    response.headers["X-RateLimit-Remaining"] = str(remaining)
+    response.headers["X-RateLimit-Reset"] = str(reset_time)
+
+    return response

autogpt_platform/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/types.py

@@ -0,0 +1,76 @@
+from typing import Annotated, Any, Literal, Optional, TypedDict
+from uuid import uuid4
+
+from pydantic import BaseModel, Field, SecretStr, field_serializer
+
+
+class _BaseCredentials(BaseModel):
+    id: str = Field(default_factory=lambda: str(uuid4()))
+    provider: str
+    title: Optional[str]
+
+    @field_serializer("*")
+    def dump_secret_strings(value: Any, _info):
+        if isinstance(value, SecretStr):
+            return value.get_secret_value()
+        return value
+
+
+class OAuth2Credentials(_BaseCredentials):
+    type: Literal["oauth2"] = "oauth2"
+    username: Optional[str]
+    """Username of the third-party service user that these credentials belong to"""
+    access_token: SecretStr
+    access_token_expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the access token expires (if at all)"""
+    refresh_token: Optional[SecretStr]
+    refresh_token_expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the refresh token expires (if at all)"""
+    scopes: list[str]
+    metadata: dict[str, Any] = Field(default_factory=dict)
+
+    def bearer(self) -> str:
+        return f"Bearer {self.access_token.get_secret_value()}"
+
+
+class APIKeyCredentials(_BaseCredentials):
+    type: Literal["api_key"] = "api_key"
+    api_key: SecretStr
+    expires_at: Optional[int]
+    """Unix timestamp (seconds) indicating when the API key expires (if at all)"""
+
+    def bearer(self) -> str:
+        return f"Bearer {self.api_key.get_secret_value()}"
+
+
+Credentials = Annotated[
+    OAuth2Credentials | APIKeyCredentials,
+    Field(discriminator="type"),
+]
+
+
+CredentialsType = Literal["api_key", "oauth2"]
+
+
+class OAuthState(BaseModel):
+    token: str
+    provider: str
+    expires_at: int
+    code_verifier: Optional[str] = None
+    scopes: list[str]
+    """Unix timestamp (seconds) indicating when this OAuth state expires"""
+
+
+class UserMetadata(BaseModel):
+    integration_credentials: list[Credentials] = Field(default_factory=list)
+    integration_oauth_states: list[OAuthState] = Field(default_factory=list)
+
+
+class UserMetadataRaw(TypedDict, total=False):
+    integration_credentials: list[dict]
+    integration_oauth_states: list[dict]
+
+
+class UserIntegrations(BaseModel):
+    credentials: list[Credentials] = Field(default_factory=list)
+    oauth_states: list[OAuthState] = Field(default_factory=list)

autogpt_platform/autogpt_libs/pyproject.toml

@@ -0,0 +1,40 @@
+[tool.poetry]
+name = "autogpt-libs"
+version = "0.2.0"
+description = "Shared libraries across AutoGPT Platform"
+authors = ["AutoGPT team <info@agpt.co>"]
+readme = "README.md"
+packages = [{ include = "autogpt_libs" }]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<4.0"
+colorama = "^0.4.6"
+cryptography = "^46.0"
+expiringdict = "^1.2.2"
+fastapi = "^0.128.0"
+google-cloud-logging = "^3.13.0"
+launchdarkly-server-sdk = "^9.14.1"
+pydantic = "^2.12.5"
+pydantic-settings = "^2.12.0"
+pyjwt = { version = "^2.11.0", extras = ["crypto"] }
+redis = "^6.2.0"
+supabase = "^2.27.2"
+uvicorn = "^0.40.0"
+
+[tool.poetry.group.dev.dependencies]
+pyright = "^1.1.408"
+pytest = "^8.4.1"
+pytest-asyncio = "^1.3.0"
+pytest-mock = "^3.15.1"
+pytest-cov = "^6.2.1"
+ruff = "^0.15.0"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+line-length = 88
+
+[tool.ruff.lint]
+extend-select = ["I"]  # sort dependencies

autogpt_platform/backend/Dockerfile

@@ -39,7 +39,8 @@ ENV PATH=/opt/poetry/bin:$PATH
 
 RUN pip3 install poetry --break-system-packages
 
-# Copy and install dependencies (autogpt_libs merged into backend - OPEN-2998)
+# Copy and install dependencies
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
 COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
 WORKDIR /app/autogpt_platform/backend
 RUN poetry install --no-ansi --no-root
@@ -82,9 +83,11 @@ COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-pyth
 
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
 
-# autogpt_libs merged into backend (OPEN-2998)
+RUN mkdir -p /app/autogpt_platform/autogpt_libs
 RUN mkdir -p /app/autogpt_platform/backend
 
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+
 COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
 
 WORKDIR /app/autogpt_platform/backend

autogpt_platform/backend/TESTING.md

@@ -132,7 +132,7 @@ def test_endpoint_success(snapshot: Snapshot):
 
 ### Testing with Authentication
 
-For the main API routes that use JWT authentication, auth is provided by the `backend.api.auth` module. If the test actually uses the `user_id`, the recommended approach for testing is to mock the `get_jwt_payload` function, which underpins all higher-level auth functions used in the API (`requires_user`, `requires_admin_user`, `get_user_id`).
+For the main API routes that use JWT authentication, auth is provided by the `autogpt_libs.auth` module. If the test actually uses the `user_id`, the recommended approach for testing is to mock the `get_jwt_payload` function, which underpins all higher-level auth functions used in the API (`requires_user`, `requires_admin_user`, `get_user_id`).
 
 If the test doesn't need the `user_id` specifically, mocking is not necessary as during tests auth is disabled anyway (see `conftest.py`).
 
@@ -158,7 +158,7 @@ client = fastapi.testclient.TestClient(app)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user):
     """Setup auth overrides for all tests in this module"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user['get_jwt_payload']
     yield
@@ -171,7 +171,7 @@ For admin-only endpoints, use `mock_jwt_admin` instead:
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_admin):
     """Setup auth overrides for admin tests"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_admin['get_jwt_payload']
     yield

autogpt_platform/backend/backend/api/features/admin/credit_admin_routes.py

@@ -1,10 +1,10 @@
 import logging
 import typing
 
+from autogpt_libs.auth import get_user_id, requires_admin_user
 from fastapi import APIRouter, Body, Security
 from prisma.enums import CreditTransactionType
 
-from backend.api.auth import get_user_id, requires_admin_user
 from backend.data.credit import admin_get_user_history, get_user_credit_model
 from backend.util.json import SafeJson
 

autogpt_platform/backend/backend/api/features/admin/credit_admin_routes_test.py

@@ -6,9 +6,9 @@ import fastapi.testclient
 import prisma.enums
 import pytest
 import pytest_mock
+from autogpt_libs.auth.jwt_utils import get_jwt_payload
 from pytest_snapshot.plugin import Snapshot
 
-from backend.api.auth.jwt_utils import get_jwt_payload
 from backend.data.model import UserTransaction
 from backend.util.json import SafeJson
 from backend.util.models import Pagination

autogpt_platform/backend/backend/api/features/admin/execution_analytics_routes.py

@@ -3,10 +3,10 @@ import logging
 from datetime import datetime
 from typing import Optional
 
+from autogpt_libs.auth import get_user_id, requires_admin_user
 from fastapi import APIRouter, HTTPException, Security
 from pydantic import BaseModel, Field
 
-from backend.api.auth import get_user_id, requires_admin_user
 from backend.blocks.llm import LlmModel
 from backend.data.analytics import (
     AccuracyTrendsResponse,

autogpt_platform/backend/backend/api/features/admin/store_admin_routes.py

@@ -2,11 +2,11 @@ import logging
 import tempfile
 import typing
 
+import autogpt_libs.auth
 import fastapi
 import fastapi.responses
 import prisma.enums
 
-import backend.api.auth
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
@@ -17,7 +17,7 @@ logger = logging.getLogger(__name__)
 router = fastapi.APIRouter(
     prefix="/admin",
     tags=["store", "admin"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_admin_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
 )
 
 
@@ -73,7 +73,7 @@ async def get_admin_listings_with_versions(
 async def review_submission(
     store_listing_version_id: str,
     request: store_model.ReviewSubmissionRequest,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Review a store listing submission.
@@ -117,7 +117,7 @@ async def review_submission(
     tags=["store", "admin"],
 )
 async def admin_download_agent_file(
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
     store_listing_version_id: str = fastapi.Path(
         ..., description="The ID of the agent to download"
     ),

autogpt_platform/backend/backend/api/features/analytics.py

@@ -5,10 +5,10 @@ from typing import Annotated
 
 import fastapi
 import pydantic
+from autogpt_libs.auth import get_user_id
+from autogpt_libs.auth.dependencies import requires_user
 
 import backend.data.analytics
-from backend.api.auth import get_user_id
-from backend.api.auth.dependencies import requires_user
 
 router = fastapi.APIRouter(dependencies=[fastapi.Security(requires_user)])
 logger = logging.getLogger(__name__)

autogpt_platform/backend/backend/api/features/analytics_test.py

@@ -20,7 +20,7 @@ client = fastapi.testclient.TestClient(app)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user):
     """Setup auth overrides for all tests in this module."""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
     yield

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

@@ -2,8 +2,8 @@ import logging
 from typing import Annotated, Sequence
 
 import fastapi
+from autogpt_libs.auth.dependencies import get_user_id, requires_user
 
-from backend.api.auth.dependencies import get_user_id, requires_user
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 

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

@@ -93,12 +93,6 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
-    # Extended thinking configuration for Claude models
-    thinking_enabled: bool = Field(
-        default=True,
-        description="Enable adaptive thinking for Claude models via OpenRouter",
-    )
-
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):

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

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

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

@@ -10,8 +10,6 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from backend.util.json import dumps as json_dumps
-
 
 class ResponseType(str, Enum):
     """Types of streaming responses following AI SDK protocol."""
@@ -20,10 +18,6 @@ class ResponseType(str, Enum):
     START = "start"
     FINISH = "finish"
 
-    # Step lifecycle (one LLM API call within a message)
-    START_STEP = "start-step"
-    FINISH_STEP = "finish-step"
-
     # Text streaming
     TEXT_START = "text-start"
     TEXT_DELTA = "text-delta"
@@ -63,16 +57,6 @@ class StreamStart(StreamBaseResponse):
         description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
-        data: dict[str, Any] = {
-            "type": self.type.value,
-            "messageId": self.messageId,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 class StreamFinish(StreamBaseResponse):
     """End of message/stream."""
@@ -80,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
     type: ResponseType = ResponseType.FINISH
 
 
-class StreamStartStep(StreamBaseResponse):
-    """Start of a step (one LLM API call within a message).
-
-    The AI SDK uses this to add a step-start boundary to message.parts,
-    enabling visual separation between multiple LLM calls in a single message.
-    """
-
-    type: ResponseType = ResponseType.START_STEP
-
-
-class StreamFinishStep(StreamBaseResponse):
-    """End of a step (one LLM API call within a message).
-
-    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
-    so the next LLM call in a tool-call continuation starts with clean state.
-    """
-
-    type: ResponseType = ResponseType.FINISH_STEP
-
-
 # ========== Text Streaming ==========
 
 
@@ -153,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
     type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
     toolCallId: str = Field(..., description="Tool call ID this responds to")
     output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
     toolName: str | None = Field(
         default=None, description="Name of the tool that was executed"
     )
@@ -161,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
         default=True, description="Whether the tool execution succeeded"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-spec fields."""
-        import json
-
-        data = {
-            "type": self.type.value,
-            "toolCallId": self.toolCallId,
-            "output": self.output,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 # ========== Other ==========
 
@@ -195,18 +148,6 @@ class StreamError(StreamBaseResponse):
         default=None, description="Additional error details"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, only emitting fields required by AI SDK protocol.
-
-        The AI SDK uses z.strictObject({type, errorText}) which rejects
-        any extra fields like `code` or `details`.
-        """
-        data = {
-            "type": self.type.value,
-            "errorText": self.errorText,
-        }
-        return f"data: {json_dumps(data)}\n\n"
-
 
 class StreamHeartbeat(StreamBaseResponse):
     """Heartbeat to keep SSE connection alive during long-running operations.

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

@@ -5,11 +5,11 @@ import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from autogpt_libs import auth
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
-from backend.api import auth
 from backend.util.exceptions import NotFoundError
 
 from . import service as chat_service
@@ -17,29 +17,7 @@ from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-from .response_model import StreamFinish, StreamHeartbeat
-from .tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 
 config = ChatConfig()
 
@@ -288,36 +266,12 @@ async def stream_chat_post(
 
     """
     import asyncio
-    import time
-
-    stream_start_time = time.perf_counter()
-    log_meta = {"component": "ChatStream", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
-        f"user={user_id}, message_len={len(request.message)}",
-        extra={"json_fields": log_meta},
-    )
 
     session = await _validate_and_get_session(session_id, user_id)
-    logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
-            }
-        },
-    )
 
     # Create a task in the stream registry for reconnection support
     task_id = str(uuid_module.uuid4())
     operation_id = str(uuid_module.uuid4())
-    log_meta["task_id"] = task_id
-
-    task_create_start = time.perf_counter()
     await stream_registry.create_task(
         task_id=task_id,
         session_id=session_id,
@@ -326,28 +280,14 @@ async def stream_chat_post(
         tool_name="chat",
         operation_id=operation_id,
     )
-    logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
-            }
-        },
-    )
 
     # Background task that runs the AI generation independently of SSE connection
     async def run_ai_generation():
-        import time as time_module
-
-        gen_start_time = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
-            extra={"json_fields": log_meta},
-        )
-        first_chunk_time, ttfc = None, None
-        chunk_count = 0
         try:
+            # Emit a start event with task_id for reconnection
+            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
+            await stream_registry.publish_chunk(task_id, start_chunk)
+
             async for chunk in chat_service.stream_chat_completion(
                 session_id,
                 request.message,
@@ -355,79 +295,25 @@ async def stream_chat_post(
                 user_id=user_id,
                 session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
-                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
             ):
-                chunk_count += 1
-                if first_chunk_time is None:
-                    first_chunk_time = time_module.perf_counter()
-                    ttfc = first_chunk_time - gen_start_time
-                    logger.info(
-                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
-                        extra={
-                            "json_fields": {
-                                **log_meta,
-                                "chunk_type": type(chunk).__name__,
-                                "time_to_first_chunk_ms": ttfc * 1000,
-                            }
-                        },
-                    )
                 # Write to Redis (subscribers will receive via XREAD)
                 await stream_registry.publish_chunk(task_id, chunk)
 
-            gen_end_time = time_module.perf_counter()
-            total_time = (gen_end_time - gen_start_time) * 1000
-            logger.info(
-                f"[TIMING] run_ai_generation FINISHED in {total_time / 1000:.1f}s; "
-                f"task={task_id}, session={session_id}, "
-                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "time_to_first_chunk_ms": (
-                            ttfc * 1000 if ttfc is not None else None
-                        ),
-                        "n_chunks": chunk_count,
-                    }
-                },
-            )
+            # Mark task as completed
             await stream_registry.mark_task_completed(task_id, "completed")
         except Exception as e:
-            elapsed = time_module.perf_counter() - gen_start_time
             logger.error(
-                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": elapsed * 1000,
-                        "error": str(e),
-                    }
-                },
+                f"Error in background AI generation for session {session_id}: {e}"
             )
             await stream_registry.mark_task_completed(task_id, "failed")
 
     # Start the AI generation in a background task
     bg_task = asyncio.create_task(run_ai_generation())
     await stream_registry.set_task_asyncio_task(task_id, bg_task)
-    setup_time = (time.perf_counter() - stream_start_time) * 1000
-    logger.info(
-        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
 
     # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        import time as time_module
-
-        event_gen_start = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
-            f"user={user_id}",
-            extra={"json_fields": log_meta},
-        )
         subscriber_queue = None
-        first_chunk_yielded = False
-        chunks_yielded = 0
         try:
             # Subscribe to the task stream (this replays existing messages + live updates)
             subscriber_queue = await stream_registry.subscribe_to_task(
@@ -442,70 +328,22 @@ async def stream_chat_post(
                 return
 
             # Read from the subscriber queue and yield to SSE
-            logger.info(
-                "[TIMING] Starting to read from subscriber_queue",
-                extra={"json_fields": log_meta},
-            )
             while True:
                 try:
                     chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    chunks_yielded += 1
-
-                    if not first_chunk_yielded:
-                        first_chunk_yielded = True
-                        elapsed = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
-                            f"type={type(chunk).__name__}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunk_type": type(chunk).__name__,
-                                    "elapsed_ms": elapsed * 1000,
-                                }
-                            },
-                        )
-
                     yield chunk.to_sse()
 
                     # Check for finish signal
                     if isinstance(chunk, StreamFinish):
-                        total_time = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
-                            f"n_chunks={chunks_yielded}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunks_yielded": chunks_yielded,
-                                    "total_time_ms": total_time * 1000,
-                                }
-                            },
-                        )
                         break
                 except asyncio.TimeoutError:
+                    # Send heartbeat to keep connection alive
                     yield StreamHeartbeat().to_sse()
 
         except GeneratorExit:
-            logger.info(
-                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "chunks_yielded": chunks_yielded,
-                        "reason": "client_disconnect",
-                    }
-                },
-            )
             pass  # Client disconnected - background task continues
         except Exception as e:
-            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
-            logger.error(
-                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
-                extra={
-                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
-                },
-            )
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
         finally:
             # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
@@ -519,18 +357,6 @@ async def stream_chat_post(
                         exc_info=True,
                     )
             # AI SDK protocol termination - always yield even if unsubscribe fails
-            total_time = time_module.perf_counter() - event_gen_start
-            logger.info(
-                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
-                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time * 1000,
-                        "chunks_yielded": chunks_yielded,
-                    }
-                },
-            )
             yield "data: [DONE]\n\n"
 
     return StreamingResponse(
@@ -548,90 +374,63 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
     session_id: str,
+    message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
+    is_user_message: bool = Query(default=True),
 ):
     """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).
 
-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
-    Checks for an active (in-progress) task on the session and either replays
-    the full SSE stream or returns 204 No Content if nothing is running.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+      - Text fragments as they are generated
+      - Tool call UI elements (if invoked)
+      - Tool execution results
 
     Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
+        message: The user's new message to process.
         user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
     Returns:
-        StreamingResponse (SSE) when an active stream exists,
-        or 204 No Content when there is nothing to resume.
+        StreamingResponse: SSE-formatted response chunks.
+
     """
-    import asyncio
-
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-
-    if not active_task:
-        return Response(status_code=204)
-
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
-        user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
-    )
-
-    if subscriber_queue is None:
-        return Response(status_code=204)
+    session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
         chunk_count = 0
         first_chunk_type: str | None = None
-        try:
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    if chunk_count < 3:
-                        logger.info(
-                            "Resume stream chunk",
-                            extra={
-                                "session_id": session_id,
-                                "chunk_type": str(chunk.type),
-                            },
-                        )
-                    if not first_chunk_type:
-                        first_chunk_type = str(chunk.type)
-                    chunk_count += 1
-                    yield chunk.to_sse()
-
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-        except GeneratorExit:
-            pass
-        except Exception as e:
-            logger.error(f"Error in resume stream for session {session_id}: {e}")
-        finally:
-            try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            message,
+            is_user_message=is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+        ):
+            if chunk_count < 3:
+                logger.info(
+                    "Chat stream chunk",
+                    extra={
+                        "session_id": session_id,
+                        "chunk_type": str(chunk.type),
+                    },
                 )
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            logger.info(
-                "Resume stream completed",
-                extra={
-                    "session_id": session_id,
-                    "n_chunks": chunk_count,
-                    "first_chunk_type": first_chunk_type,
-                },
-            )
-            yield "data: [DONE]\n\n"
+            if not first_chunk_type:
+                first_chunk_type = str(chunk.type)
+            chunk_count += 1
+            yield chunk.to_sse()
+        logger.info(
+            "Chat stream completed",
+            extra={
+                "session_id": session_id,
+                "chunk_count": chunk_count,
+                "first_chunk_type": first_chunk_type,
+            },
+        )
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -639,8 +438,8 @@ async def resume_session_stream(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -952,42 +751,3 @@ async def health_check() -> dict:
         "service": "chat",
         "version": "0.1.0",
     }
-
-
-# ========== Schema Export (for OpenAPI / Orval codegen) ==========
-
-ToolResponseUnion = (
-    AgentsFoundResponse
-    | NoResultsResponse
-    | AgentDetailsResponse
-    | SetupRequirementsResponse
-    | ExecutionStartedResponse
-    | NeedLoginResponse
-    | ErrorResponse
-    | InputValidationErrorResponse
-    | AgentOutputResponse
-    | UnderstandingUpdatedResponse
-    | AgentPreviewResponse
-    | AgentSavedResponse
-    | ClarificationNeededResponse
-    | BlockListResponse
-    | BlockOutputResponse
-    | DocSearchResultsResponse
-    | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
-)
-
-
-@router.get(
-    "/schema/tool-responses",
-    response_model=ToolResponseUnion,
-    include_in_schema=True,
-    summary="[Dummy] Tool response type export for codegen",
-    description="This endpoint is not meant to be called. It exists solely to "
-    "expose tool response models in the OpenAPI schema for frontend codegen.",
-)
-async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
-    """Never called at runtime. Exists only so Orval generates TS types."""
-    raise HTTPException(status_code=501, detail="Schema-only endpoint")

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

@@ -52,10 +52,8 @@ from .response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
-    StreamFinishStep,
     StreamHeartbeat,
     StreamStart,
-    StreamStartStep,
     StreamTextDelta,
     StreamTextEnd,
     StreamTextStart,
@@ -353,10 +351,6 @@ async def stream_chat_completion(
     retry_count: int = 0,
     session: ChatSession | None = None,
     context: dict[str, str] | None = None,  # {url: str, content: str}
-    _continuation_message_id: (
-        str | None
-    ) = None,  # Internal: reuse message ID for tool call continuations
-    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """Main entry point for streaming chat completions with database handling.
 
@@ -377,45 +371,21 @@ async def stream_chat_completion(
         ValueError: If max_context_messages is exceeded
 
     """
-    completion_start = time.monotonic()
-
-    # Build log metadata for structured logging
-    log_meta = {"component": "ChatService", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
     logger.info(
-        f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
-        f"message_len={len(message) if message else 0}, is_user={is_user_message}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "message_len": len(message) if message else 0,
-                "is_user_message": is_user_message,
-            }
-        },
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
     )
 
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
-        fetch_start = time.monotonic()
         session = await get_chat_session(session_id, user_id)
-        fetch_time = (time.monotonic() - fetch_start) * 1000
         logger.info(
-            f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
-            f"n_messages={len(session.messages) if session else 0}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "duration_ms": fetch_time,
-                    "n_messages": len(session.messages) if session else 0,
-                }
-            },
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
+            f"message_count={len(session.messages) if session else 0}"
         )
     else:
         logger.info(
-            f"[TIMING] Using provided session, messages={len(session.messages)}",
-            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
+            f"Using provided session object: {session.session_id}, "
+            f"message_count={len(session.messages)}"
         )
 
     if not session:
@@ -436,25 +406,17 @@ async def stream_chat_completion(
 
         # Track user message in PostHog
         if is_user_message:
-            posthog_start = time.monotonic()
             track_user_message(
                 user_id=user_id,
                 session_id=session_id,
                 message_length=len(message),
             )
-            posthog_time = (time.monotonic() - posthog_start) * 1000
-            logger.info(
-                f"[TIMING] track_user_message took {posthog_time:.1f}ms",
-                extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
-            )
 
-    upsert_start = time.monotonic()
-    session = await upsert_chat_session(session)
-    upsert_time = (time.monotonic() - upsert_start) * 1000
     logger.info(
-        f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
+        f"message_count={len(session.messages)}"
     )
+    session = await upsert_chat_session(session)
     assert session, "Session not found"
 
     # Generate title for new sessions on first user message (non-blocking)
@@ -492,13 +454,7 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    prompt_start = time.monotonic()
     system_prompt, understanding = await _build_system_prompt(user_id)
-    prompt_time = (time.monotonic() - prompt_start) * 1000
-    logger.info(
-        f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
-    )
 
     # Initialize variables for streaming
     assistant_response = ChatMessage(
@@ -523,27 +479,13 @@ async def stream_chat_completion(
     # Generate unique IDs for AI SDK protocol
     import uuid as uuid_module
 
-    is_continuation = _continuation_message_id is not None
-    message_id = _continuation_message_id or str(uuid_module.uuid4())
+    message_id = str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
-    # Only yield message start for the initial call, not for continuations.
-    setup_time = (time.monotonic() - completion_start) * 1000
-    logger.info(
-        f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
-    if not is_continuation:
-        yield StreamStart(messageId=message_id, taskId=_task_id)
-
-    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
-    yield StreamStartStep()
+    # Yield message start
+    yield StreamStart(messageId=message_id)
 
     try:
-        logger.info(
-            "[TIMING] Calling _stream_chat_chunks",
-            extra={"json_fields": log_meta},
-        )
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
@@ -643,10 +585,6 @@ async def stream_chat_completion(
                     )
                 yield chunk
             elif isinstance(chunk, StreamFinish):
-                if has_done_tool_call:
-                    # Tool calls happened — close the step but don't send message-level finish.
-                    # The continuation will open a new step, and finish will come at the end.
-                    yield StreamFinishStep()
                 if not has_done_tool_call:
                     # Emit text-end before finish if we received text but haven't closed it
                     if has_received_text and not text_streaming_ended:
@@ -678,8 +616,6 @@ async def stream_chat_completion(
                             has_saved_assistant_message = True
 
                     has_yielded_end = True
-                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
-                    yield StreamFinishStep()
                     yield chunk
             elif isinstance(chunk, StreamError):
                 has_yielded_error = True
@@ -729,10 +665,6 @@ async def stream_chat_completion(
             logger.info(
                 f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
             )
-            # Close the current step before retrying so the recursive call's
-            # StreamStartStep doesn't produce unbalanced step events.
-            if not has_yielded_end:
-                yield StreamFinishStep()
             should_retry = True
         else:
             # Non-retryable error or max retries exceeded
@@ -768,7 +700,6 @@ async def stream_chat_completion(
                 error_response = StreamError(errorText=error_message)
                 yield error_response
             if not has_yielded_end:
-                yield StreamFinishStep()
                 yield StreamFinish()
             return
 
@@ -783,8 +714,6 @@ async def stream_chat_completion(
             retry_count=retry_count + 1,
             session=session,
             context=context,
-            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
-            _task_id=_task_id,
         ):
             yield chunk
         return  # Exit after retry to avoid double-saving in finally block
@@ -854,8 +783,6 @@ async def stream_chat_completion(
             session=session,  # Pass session object to avoid Redis refetch
             context=context,
             tool_call_response=str(tool_response_messages),
-            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
-            _task_id=_task_id,
         ):
             yield chunk
 
@@ -966,21 +893,9 @@ async def _stream_chat_chunks(
         SSE formatted JSON response objects
 
     """
-    import time as time_module
-
-    stream_chunks_start = time_module.perf_counter()
     model = config.model
 
-    # Build log metadata for structured logging
-    log_meta = {"component": "ChatService", "session_id": session.session_id}
-    if session.user_id:
-        log_meta["user_id"] = session.user_id
-
-    logger.info(
-        f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
-        f"user={session.user_id}, n_messages={len(session.messages)}",
-        extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
-    )
+    logger.info("Starting pure chat stream")
 
     messages = session.to_openai_messages()
     if system_prompt:
@@ -991,18 +906,12 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    context_start = time_module.perf_counter()
     context_result = await _manage_context_window(
         messages=messages,
         model=model,
         api_key=config.api_key,
         base_url=config.base_url,
     )
-    context_time = (time_module.perf_counter() - context_start) * 1000
-    logger.info(
-        f"[TIMING] _manage_context_window took {context_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": context_time}},
-    )
 
     if context_result.error:
         if "System prompt dropped" in context_result.error:
@@ -1037,19 +946,9 @@ async def _stream_chat_chunks(
 
         while retry_count <= MAX_RETRIES:
             try:
-                elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
-                retry_info = (
-                    f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
-                )
                 logger.info(
-                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "elapsed_ms": elapsed,
-                            "retry_count": retry_count,
-                        }
-                    },
+                    f"Creating OpenAI chat completion stream..."
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
                 )
 
                 # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1066,11 +965,6 @@ async def _stream_chat_chunks(
                         :128
                     ]  # OpenRouter limit
 
-                # Enable adaptive thinking for Anthropic models via OpenRouter
-                if config.thinking_enabled and "anthropic" in model.lower():
-                    extra_body["reasoning"] = {"enabled": True}
-
-                api_call_start = time_module.perf_counter()
                 stream = await client.chat.completions.create(
                     model=model,
                     messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1080,11 +974,6 @@ async def _stream_chat_chunks(
                     stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
                     extra_body=extra_body,
                 )
-                api_init_time = (time_module.perf_counter() - api_call_start) * 1000
-                logger.info(
-                    f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
-                    extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
-                )
 
                 # Variables to accumulate tool calls
                 tool_calls: list[dict[str, Any]] = []
@@ -1095,13 +984,10 @@ async def _stream_chat_chunks(
 
                 # Track if we've started the text block
                 text_started = False
-                first_content_chunk = True
-                chunk_count = 0
 
                 # Process the stream
                 chunk: ChatCompletionChunk
                 async for chunk in stream:
-                    chunk_count += 1
                     if chunk.usage:
                         yield StreamUsage(
                             promptTokens=chunk.usage.prompt_tokens,
@@ -1124,23 +1010,6 @@ async def _stream_chat_chunks(
                             if not text_started and text_block_id:
                                 yield StreamTextStart(id=text_block_id)
                                 text_started = True
-                            # Log timing for first content chunk
-                            if first_content_chunk:
-                                first_content_chunk = False
-                                ttfc = (
-                                    time_module.perf_counter() - api_call_start
-                                ) * 1000
-                                logger.info(
-                                    f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
-                                    f"(since API call), n_chunks={chunk_count}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "time_to_first_chunk_ms": ttfc,
-                                            "n_chunks": chunk_count,
-                                        }
-                                    },
-                                )
                             # Stream the text delta
                             text_response = StreamTextDelta(
                                 id=text_block_id or "",
@@ -1197,21 +1066,7 @@ async def _stream_chat_chunks(
                                         toolName=tool_calls[idx]["function"]["name"],
                                     )
                                     emitted_start_for_idx.add(idx)
-                stream_duration = time_module.perf_counter() - api_call_start
-                logger.info(
-                    f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
-                    f"duration={stream_duration:.2f}s, "
-                    f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "stream_duration_ms": stream_duration * 1000,
-                            "finish_reason": finish_reason,
-                            "n_chunks": chunk_count,
-                            "n_tool_calls": len(tool_calls),
-                        }
-                    },
-                )
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")
 
                 # Yield all accumulated tool calls after the stream is complete
                 # This ensures all tool call arguments have been fully received
@@ -1231,16 +1086,11 @@ async def _stream_chat_chunks(
                         # Re-raise to trigger retry logic in the parent function
                         raise
 
-                total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
-                logger.info(
-                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time / 1000:.1f}s; "
-                    f"session={session.session_id}, user={session.user_id}",
-                    extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-                )
                 yield StreamFinish()
                 return
             except Exception as e:
                 last_error = e
+
                 if _is_retryable_error(e) and retry_count < MAX_RETRIES:
                     retry_count += 1
                     # Calculate delay with exponential backoff
@@ -1256,12 +1106,26 @@ async def _stream_chat_chunks(
                     continue  # Retry the stream
                 else:
                     # Non-retryable error or max retries exceeded
-                    logger.error(
-                        f"Error in stream (not retrying): {e!s}",
-                        exc_info=True,
+                    _log_api_error(
+                        error=e,
+                        session_id=session.session_id if session else None,
+                        message_count=len(messages) if messages else None,
+                        model=model,
+                        retry_count=retry_count,
                     )
                     error_code = None
                     error_text = str(e)
+
+                    error_details = _extract_api_error_details(e)
+                    if error_details.get("response_body"):
+                        body = error_details["response_body"]
+                        if isinstance(body, dict):
+                            err = body.get("error")
+                            if isinstance(err, dict) and err.get("message"):
+                                error_text = err["message"]
+                            elif body.get("message"):
+                                error_text = body["message"]
+
                     if _is_region_blocked_error(e):
                         error_code = "MODEL_NOT_AVAILABLE_REGION"
                         error_text = (
@@ -1278,9 +1142,12 @@ async def _stream_chat_chunks(
 
         # If we exit the retry loop without returning, it means we exhausted retries
         if last_error:
-            logger.error(
-                f"Max retries ({MAX_RETRIES}) exceeded. Last error: {last_error!s}",
-                exc_info=True,
+            _log_api_error(
+                error=last_error,
+                session_id=session.session_id if session else None,
+                message_count=len(messages) if messages else None,
+                model=model,
+                retry_count=MAX_RETRIES,
             )
             yield StreamError(errorText=f"Max retries exceeded: {last_error!s}")
             yield StreamFinish()
@@ -1716,7 +1583,6 @@ async def _execute_long_running_tool_with_streaming(
             task_id,
             StreamError(errorText=str(e)),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())
 
         await _update_pending_operation(
@@ -1833,10 +1699,6 @@ async def _generate_llm_continuation(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
         retry_count = 0
         last_error: Exception | None = None
         response = None
@@ -1857,6 +1719,7 @@ async def _generate_llm_continuation(
                 break  # Success, exit retry loop
             except Exception as e:
                 last_error = e
+
                 if _is_retryable_error(e) and retry_count < MAX_RETRIES:
                     retry_count += 1
                     delay = min(
@@ -1870,17 +1733,23 @@ async def _generate_llm_continuation(
                     await asyncio.sleep(delay)
                     continue
                 else:
-                    # Non-retryable error - log and exit gracefully
-                    logger.error(
-                        f"Non-retryable error in LLM continuation: {e!s}",
-                        exc_info=True,
+                    # Non-retryable error - log details and exit gracefully
+                    _log_api_error(
+                        error=e,
+                        session_id=session_id,
+                        message_count=len(messages) if messages else None,
+                        model=config.model,
+                        retry_count=retry_count,
                     )
                     return
 
         if last_error:
-            logger.error(
-                f"Max retries ({MAX_RETRIES}) exceeded for LLM continuation. "
-                f"Last error: {last_error!s}"
+            _log_api_error(
+                error=last_error,
+                session_id=session_id,
+                message_count=len(messages) if messages else None,
+                model=config.model,
+                retry_count=MAX_RETRIES,
             )
             return
 
@@ -1920,6 +1789,89 @@ async def _generate_llm_continuation(
         logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True)
 
 
+def _log_api_error(
+    error: Exception,
+    session_id: str | None = None,
+    message_count: int | None = None,
+    model: str | None = None,
+    retry_count: int = 0,
+) -> None:
+    """Log detailed API error information for debugging."""
+    details = _extract_api_error_details(error)
+    details["session_id"] = session_id
+    details["message_count"] = message_count
+    details["model"] = model
+    details["retry_count"] = retry_count
+
+    if isinstance(error, RateLimitError):
+        logger.warning(f"Rate limit error: {details}")
+    elif isinstance(error, APIConnectionError):
+        logger.warning(f"API connection error: {details}")
+    elif isinstance(error, APIStatusError) and error.status_code >= 500:
+        logger.error(f"API server error (5xx): {details}")
+    else:
+        logger.error(f"API error: {details}")
+
+
+def _extract_api_error_details(error: Exception) -> dict[str, Any]:
+    """Extract detailed information from OpenAI/OpenRouter API errors."""
+    error_msg = str(error)
+    details: dict[str, Any] = {
+        "error_type": type(error).__name__,
+        "error_message": error_msg[:500] + "..." if len(error_msg) > 500 else error_msg,
+    }
+
+    if hasattr(error, "code"):
+        details["code"] = getattr(error, "code", None)
+    if hasattr(error, "param"):
+        details["param"] = getattr(error, "param", None)
+
+    if isinstance(error, APIStatusError):
+        details["status_code"] = error.status_code
+        details["request_id"] = getattr(error, "request_id", None)
+
+        if hasattr(error, "body") and error.body:
+            details["response_body"] = _sanitize_error_body(error.body)
+
+        if hasattr(error, "response") and error.response:
+            headers = error.response.headers
+            details["openrouter_provider"] = headers.get("x-openrouter-provider")
+            details["openrouter_model"] = headers.get("x-openrouter-model")
+            details["retry_after"] = headers.get("retry-after")
+            details["rate_limit_remaining"] = headers.get("x-ratelimit-remaining")
+
+    return details
+
+
+def _sanitize_error_body(
+    body: Any, max_length: int = 2000
+) -> dict[str, Any] | str | None:
+    """Extract only safe fields from error response body to avoid logging sensitive data."""
+    if not isinstance(body, dict):
+        # Non-dict bodies (e.g., HTML error pages) - return truncated string
+        if body is not None:
+            body_str = str(body)
+            if len(body_str) > max_length:
+                return body_str[:max_length] + "...[truncated]"
+            return body_str
+        return None
+
+    safe_fields = ("message", "type", "code", "param", "error")
+    sanitized: dict[str, Any] = {}
+
+    for field in safe_fields:
+        if field in body:
+            value = body[field]
+            if field == "error" and isinstance(value, dict):
+                sanitized[field] = _sanitize_error_body(value, max_length)
+            elif isinstance(value, str) and len(value) > max_length:
+                sanitized[field] = value[:max_length] + "...[truncated]"
+            else:
+                sanitized[field] = value
+
+    return sanitized if sanitized else None
+
+
 async def _generate_llm_continuation_with_streaming(
     session_id: str,
     user_id: str | None,
@@ -1967,10 +1919,6 @@ async def _generate_llm_continuation_with_streaming(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
         # Make streaming LLM call (no tools - just text response)
         from typing import cast
 
@@ -1982,7 +1930,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish start event
         await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
-        await stream_registry.publish_chunk(task_id, StreamStartStep())
         await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
 
         # Stream the response
@@ -2006,7 +1953,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish end events
         await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
 
         if assistant_content:
             # Reload session from DB to avoid race condition with user messages
@@ -2048,5 +1994,4 @@ async def _generate_llm_continuation_with_streaming(
             task_id,
             StreamError(errorText=f"Failed to generate response: {e}"),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())

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

@@ -104,24 +104,6 @@ async def create_task(
     Returns:
         The created ActiveTask instance (metadata only)
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata for structured logging
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "session_id": session_id,
-    }
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
-        extra={"json_fields": log_meta},
-    )
-
     task = ActiveTask(
         task_id=task_id,
         session_id=session_id,
@@ -132,18 +114,10 @@ async def create_task(
     )
 
     # Store metadata in Redis
-    redis_start = time.perf_counter()
     redis = await get_redis_async()
-    redis_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] get_redis_async took {redis_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
-    )
-
     meta_key = _get_task_meta_key(task_id)
     op_key = _get_operation_mapping_key(operation_id)
 
-    hset_start = time.perf_counter()
     await redis.hset(  # type: ignore[misc]
         meta_key,
         mapping={
@@ -157,22 +131,12 @@ async def create_task(
             "created_at": task.created_at.isoformat(),
         },
     )
-    hset_time = (time.perf_counter() - hset_start) * 1000
-    logger.info(
-        f"[TIMING] redis.hset took {hset_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
-    )
-
     await redis.expire(meta_key, config.stream_ttl)
 
     # Create operation_id -> task_id mapping for webhook lookups
     await redis.set(op_key, task_id, ex=config.stream_ttl)
 
-    total_time = (time.perf_counter() - start_time) * 1000
-    logger.info(
-        f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
-        extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-    )
+    logger.debug(f"Created task {task_id} for session {session_id}")
 
     return task
 
@@ -192,60 +156,26 @@ async def publish_chunk(
     Returns:
         The Redis Stream message ID
     """
-    import time
-
-    start_time = time.perf_counter()
-    chunk_type = type(chunk).__name__
     chunk_json = chunk.model_dump_json()
     message_id = "0-0"
 
-    # Build log metadata
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "chunk_type": chunk_type,
-    }
-
     try:
         redis = await get_redis_async()
         stream_key = _get_task_stream_key(task_id)
 
         # Write to Redis Stream for persistence and real-time delivery
-        xadd_start = time.perf_counter()
         raw_id = await redis.xadd(
             stream_key,
             {"data": chunk_json},
             maxlen=config.stream_max_length,
         )
-        xadd_time = (time.perf_counter() - xadd_start) * 1000
         message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
 
         # Set TTL on stream to match task metadata TTL
         await redis.expire(stream_key, config.stream_ttl)
-
-        total_time = (time.perf_counter() - start_time) * 1000
-        # Only log timing for significant chunks or slow operations
-        if (
-            chunk_type
-            in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
-            or total_time > 50
-        ):
-            logger.info(
-                f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "xadd_time_ms": xadd_time,
-                        "message_id": message_id,
-                    }
-                },
-            )
     except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
         logger.error(
-            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
+            f"Failed to publish chunk for task {task_id}: {e}",
             exc_info=True,
         )
 
@@ -270,61 +200,24 @@ async def subscribe_to_task(
         An asyncio Queue that will receive stream chunks, or None if task not found
         or user doesn't have access
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata
-    log_meta = {"component": "StreamRegistry", "task_id": task_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
-        extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
-    )
-
-    redis_start = time.perf_counter()
     redis = await get_redis_async()
     meta_key = _get_task_meta_key(task_id)
     meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-    hgetall_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
-    )
 
     if not meta:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "reason": "task_not_found",
-                }
-            },
-        )
+        logger.debug(f"Task {task_id} not found in Redis")
         return None
 
     # Note: Redis client uses decode_responses=True, so keys are strings
     task_status = meta.get("status", "")
     task_user_id = meta.get("user_id", "") or None
-    log_meta["session_id"] = meta.get("session_id", "")
 
     # Validate ownership - if task has an owner, requester must match
     if task_user_id:
         if user_id != task_user_id:
             logger.warning(
-                f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "task_owner": task_user_id,
-                        "reason": "access_denied",
-                    }
-                },
+                f"User {user_id} denied access to task {task_id} "
+                f"owned by {task_user_id}"
             )
             return None
 
@@ -332,19 +225,7 @@ async def subscribe_to_task(
     stream_key = _get_task_stream_key(task_id)
 
     # Step 1: Replay messages from Redis Stream
-    xread_start = time.perf_counter()
     messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
-    xread_time = (time.perf_counter() - xread_start) * 1000
-    logger.info(
-        f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": xread_time,
-                "task_status": task_status,
-            }
-        },
-    )
 
     replayed_count = 0
     replay_last_id = last_message_id
@@ -363,48 +244,19 @@ async def subscribe_to_task(
                     except Exception as e:
                         logger.warning(f"Failed to replay message: {e}")
 
-    logger.info(
-        f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "n_messages_replayed": replayed_count,
-                "replay_last_id": replay_last_id,
-            }
-        },
-    )
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
 
     # Step 2: If task is still running, start stream listener for live updates
     if task_status == "running":
-        logger.info(
-            "[TIMING] Task still running, starting _stream_listener",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
         listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
+            _stream_listener(task_id, subscriber_queue, replay_last_id)
         )
         # Track listener task for cleanup on unsubscribe
         _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
     else:
         # Task is completed/failed - add finish marker
-        logger.info(
-            f"[TIMING] Task already {task_status}, adding StreamFinish",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
         await subscriber_queue.put(StreamFinish())
 
-    total_time = (time.perf_counter() - start_time) * 1000
-    logger.info(
-        f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
-        f"n_messages_replayed={replayed_count}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "total_time_ms": total_time,
-                "n_messages_replayed": replayed_count,
-            }
-        },
-    )
     return subscriber_queue
 
 
@@ -412,7 +264,6 @@ async def _stream_listener(
     task_id: str,
     subscriber_queue: asyncio.Queue[StreamBaseResponse],
     last_replayed_id: str,
-    log_meta: dict | None = None,
 ) -> None:
     """Listen to Redis Stream for new messages using blocking XREAD.
 
@@ -423,27 +274,10 @@ async def _stream_listener(
         task_id: Task ID to listen for
         subscriber_queue: Queue to deliver messages to
         last_replayed_id: Last message ID from replay (continue from here)
-        log_meta: Structured logging metadata
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Use provided log_meta or build minimal one
-    if log_meta is None:
-        log_meta = {"component": "StreamRegistry", "task_id": task_id}
-
-    logger.info(
-        f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
-        extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
-    )
-
     queue_id = id(subscriber_queue)
     # Track the last successfully delivered message ID for recovery hints
     last_delivered_id = last_replayed_id
-    messages_delivered = 0
-    first_message_time = None
-    xread_count = 0
 
     try:
         redis = await get_redis_async()
@@ -453,39 +287,9 @@ async def _stream_listener(
         while True:
             # Block for up to 30 seconds waiting for new messages
             # This allows periodic checking if task is still running
-            xread_start = time.perf_counter()
-            xread_count += 1
             messages = await redis.xread(
                 {stream_key: current_id}, block=30000, count=100
             )
-            xread_time = (time.perf_counter() - xread_start) * 1000
-
-            if messages:
-                msg_count = sum(len(msgs) for _, msgs in messages)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "n_messages": msg_count,
-                            "duration_ms": xread_time,
-                        }
-                    },
-                )
-            elif xread_time > 1000:
-                # Only log timeouts (30s blocking)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "duration_ms": xread_time,
-                            "reason": "timeout",
-                        }
-                    },
-                )
 
             if not messages:
                 # Timeout - check if task is still running
@@ -522,30 +326,10 @@ async def _stream_listener(
                                 )
                                 # Update last delivered ID on successful delivery
                                 last_delivered_id = current_id
-                                messages_delivered += 1
-                                if first_message_time is None:
-                                    first_message_time = time.perf_counter()
-                                    elapsed = (first_message_time - start_time) * 1000
-                                    logger.info(
-                                        f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
-                                        extra={
-                                            "json_fields": {
-                                                **log_meta,
-                                                "elapsed_ms": elapsed,
-                                                "chunk_type": type(chunk).__name__,
-                                            }
-                                        },
-                                    )
                             except asyncio.TimeoutError:
                                 logger.warning(
-                                    f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "timeout_s": QUEUE_PUT_TIMEOUT,
-                                            "reason": "queue_full",
-                                        }
-                                    },
+                                    f"Subscriber queue full for task {task_id}, "
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
                                 )
                                 # Send overflow error with recovery info
                                 try:
@@ -567,44 +351,15 @@ async def _stream_listener(
 
                             # Stop listening on finish
                             if isinstance(chunk, StreamFinish):
-                                total_time = (time.perf_counter() - start_time) * 1000
-                                logger.info(
-                                    f"[TIMING] StreamFinish received in {total_time / 1000:.1f}s; delivered={messages_delivered}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "total_time_ms": total_time,
-                                            "messages_delivered": messages_delivered,
-                                        }
-                                    },
-                                )
                                 return
                     except Exception as e:
-                        logger.warning(
-                            f"Error processing stream message: {e}",
-                            extra={"json_fields": {**log_meta, "error": str(e)}},
-                        )
+                        logger.warning(f"Error processing stream message: {e}")
 
     except asyncio.CancelledError:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "messages_delivered": messages_delivered,
-                    "reason": "cancelled",
-                }
-            },
-        )
+        logger.debug(f"Stream listener cancelled for task {task_id}")
         raise  # Re-raise to propagate cancellation
     except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.error(
-            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
-        )
+        logger.error(f"Stream listener error for task {task_id}: {e}")
         # On error, send finish to unblock subscriber
         try:
             await asyncio.wait_for(
@@ -613,24 +368,10 @@ async def _stream_listener(
             )
         except (asyncio.TimeoutError, asyncio.QueueFull):
             logger.warning(
-                "Could not deliver finish event after error",
-                extra={"json_fields": log_meta},
+                f"Could not deliver finish event for task {task_id} after error"
             )
     finally:
         # Clean up listener task mapping on exit
-        total_time = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] _stream_listener FINISHED in {total_time / 1000:.1f}s; task={task_id}, "
-            f"delivered={messages_delivered}, xread_count={xread_count}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "total_time_ms": total_time,
-                    "messages_delivered": messages_delivered,
-                    "xread_count": xread_count,
-                }
-            },
-        )
         _listener_tasks.pop(queue_id, None)
 
 
@@ -857,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
         ResponseType,
         StreamError,
         StreamFinish,
-        StreamFinishStep,
         StreamHeartbeat,
         StreamStart,
-        StreamStartStep,
         StreamTextDelta,
         StreamTextEnd,
         StreamTextStart,
@@ -874,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
     type_to_class: dict[str, type[StreamBaseResponse]] = {
         ResponseType.START.value: StreamStart,
         ResponseType.FINISH.value: StreamFinish,
-        ResponseType.START_STEP.value: StreamStartStep,
-        ResponseType.FINISH_STEP.value: StreamFinishStep,
         ResponseType.TEXT_START.value: StreamTextStart,
         ResponseType.TEXT_DELTA.value: StreamTextDelta,
         ResponseType.TEXT_END.value: StreamTextEnd,

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

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

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

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

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

@@ -1,29 +0,0 @@
-"""Shared helpers for chat tools."""
-
-from typing import Any
-
-
-def get_inputs_from_schema(
-    input_schema: dict[str, Any],
-    exclude_fields: set[str] | None = None,
-) -> list[dict[str, Any]]:
-    """Extract input field info from JSON schema."""
-    if not isinstance(input_schema, dict):
-        return []
-
-    exclude = exclude_fields or set()
-    properties = input_schema.get("properties", {})
-    required = set(input_schema.get("required", []))
-
-    return [
-        {
-            "name": name,
-            "title": schema.get("title", name),
-            "type": schema.get("type", "string"),
-            "description": schema.get("description", ""),
-            "required": name in required,
-            "default": schema.get("default"),
-        }
-        for name, schema in properties.items()
-        if name not in exclude
-    ]

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

@@ -24,7 +24,6 @@ from backend.util.timezone_utils import (
 )
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
     AgentDetails,
     AgentDetailsResponse,
@@ -262,7 +261,7 @@ class RunAgentTool(BaseTool):
                         ),
                         requirements={
                             "credentials": requirements_creds_list,
-                            "inputs": get_inputs_from_schema(graph.input_schema),
+                            "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },
                     ),
@@ -370,6 +369,22 @@ class RunAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
+        """Extract inputs list from schema."""
+        inputs_list = []
+        if isinstance(input_schema, dict) and "properties" in input_schema:
+            for field_name, field_schema in input_schema["properties"].items():
+                inputs_list.append(
+                    {
+                        "name": field_name,
+                        "title": field_schema.get("title", field_name),
+                        "type": field_schema.get("type", "string"),
+                        "description": field_schema.get("description", ""),
+                        "required": field_name in input_schema.get("required", []),
+                    }
+                )
+        return inputs_list
+
     def _get_execution_modes(self, graph: GraphModel) -> list[str]:
         """Get available execution modes for the graph."""
         trigger_info = graph.trigger_setup_info
@@ -383,7 +398,7 @@ class RunAgentTool(BaseTool):
         suffix: str,
     ) -> str:
         """Build a message describing available inputs for an agent."""
-        inputs_list = get_inputs_from_schema(graph.input_schema)
+        inputs_list = self._get_inputs_list(graph.input_schema)
         required_names = [i["name"] for i in inputs_list if i["required"]]
         optional_names = [i["name"] for i in inputs_list if not i["required"]]
 

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

@@ -8,19 +8,14 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.find_block import (
-    COPILOT_EXCLUDED_BLOCK_IDS,
-    COPILOT_EXCLUDED_BLOCK_TYPES,
-)
-from backend.data.block import AnyBlockSchema, get_block
+from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
     BlockOutputResponse,
     ErrorResponse,
@@ -29,10 +24,7 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import (
-    build_missing_credentials_from_field_info,
-    match_credentials_to_requirements,
-)
+from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -81,6 +73,91 @@ class RunBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    async def _check_block_credentials(
+        self,
+        user_id: str,
+        block: Any,
+        input_data: dict[str, Any] | None = None,
+    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
+        """
+        Check if user has required credentials for a block.
+
+        Args:
+            user_id: User ID
+            block: Block to check credentials for
+            input_data: Input data for the block (used to determine provider via discriminator)
+
+        Returns:
+            tuple[matched_credentials, missing_credentials]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+        input_data = input_data or {}
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            effective_field_info = field_info
+            if field_info.discriminator and field_info.discriminator_mapping:
+                # Get discriminator from input, falling back to schema default
+                discriminator_value = input_data.get(field_info.discriminator)
+                if discriminator_value is None:
+                    field = block.input_schema.model_fields.get(
+                        field_info.discriminator
+                    )
+                    if field and field.default is not PydanticUndefined:
+                        discriminator_value = field.default
+
+                if (
+                    discriminator_value
+                    and discriminator_value in field_info.discriminator_mapping
+                ):
+                    effective_field_info = field_info.discriminate(discriminator_value)
+                    logger.debug(
+                        f"Discriminated provider for {field_name}: "
+                        f"{discriminator_value} -> {effective_field_info.provider}"
+                    )
+
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in effective_field_info.provider
+                    and cred.type in effective_field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(effective_field_info.provider), "unknown")
+                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
     async def _execute(
         self,
         user_id: str | None,
@@ -135,26 +212,12 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if block is excluded from CoPilot (graph-only blocks)
-        if (
-            block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
-            or block.id in COPILOT_EXCLUDED_BLOCK_IDS
-        ):
-            return ErrorResponse(
-                message=(
-                    f"Block '{block.name}' cannot be run directly in CoPilot. "
-                    "This block is designed for use within graphs only."
-                ),
-                session_id=session_id,
-            )
-
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()
-        (
-            matched_credentials,
-            missing_credentials,
-        ) = await self._resolve_block_credentials(user_id, block, input_data)
+        matched_credentials, missing_credentials = await self._check_block_credentials(
+            user_id, block, input_data
+        )
 
         if missing_credentials:
             # Return setup requirements response with missing credentials
@@ -282,75 +345,29 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-    async def _resolve_block_credentials(
-        self,
-        user_id: str,
-        block: AnyBlockSchema,
-        input_data: dict[str, Any] | None = None,
-    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-        """
-        Resolve credentials for a block by matching user's available credentials.
-
-        Args:
-            user_id: User ID
-            block: Block to resolve credentials for
-            input_data: Input data for the block (used to determine provider via discriminator)
-
-        Returns:
-            tuple of (matched_credentials, missing_credentials) - matched credentials
-            are used for block execution, missing ones indicate setup requirements.
-        """
-        input_data = input_data or {}
-        requirements = self._resolve_discriminated_credentials(block, input_data)
-
-        if not requirements:
-            return {}, []
-
-        return await match_credentials_to_requirements(user_id, requirements)
-
-    def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
         """Extract non-credential inputs from block schema."""
+        inputs_list = []
         schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
         credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-        return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
 
-    def _resolve_discriminated_credentials(
-        self,
-        block: AnyBlockSchema,
-        input_data: dict[str, Any],
-    ) -> dict[str, CredentialsFieldInfo]:
-        """Resolve credential requirements, applying discriminator logic where needed."""
-        credentials_fields_info = block.input_schema.get_credentials_fields_info()
-        if not credentials_fields_info:
-            return {}
+        for field_name, field_schema in properties.items():
+            # Skip credential fields
+            if field_name in credentials_fields:
+                continue
 
-        resolved: dict[str, CredentialsFieldInfo] = {}
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )
 
-        for field_name, field_info in credentials_fields_info.items():
-            effective_field_info = field_info
-
-            if field_info.discriminator and field_info.discriminator_mapping:
-                discriminator_value = input_data.get(field_info.discriminator)
-                if discriminator_value is None:
-                    field = block.input_schema.model_fields.get(
-                        field_info.discriminator
-                    )
-                    if field and field.default is not PydanticUndefined:
-                        discriminator_value = field.default
-
-                if (
-                    discriminator_value
-                    and discriminator_value in field_info.discriminator_mapping
-                ):
-                    effective_field_info = field_info.discriminate(discriminator_value)
-                    # For host-scoped credentials, add the discriminator value
-                    # (e.g., URL) so _credential_is_for_host can match it
-                    effective_field_info.discriminator_values.add(discriminator_value)
-                    logger.debug(
-                        f"Discriminated provider for {field_name}: "
-                        f"{discriminator_value} -> {effective_field_info.provider}"
-                    )
-
-            resolved[field_name] = effective_field_info
-
-        return resolved
+        return inputs_list

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

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

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

@@ -8,7 +8,6 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
-    Credentials,
     CredentialsFieldInfo,
     CredentialsMetaInput,
     HostScopedCredentials,
@@ -224,99 +223,6 @@ async def get_or_create_library_agent(
     return library_agents[0]
 
 
-async def match_credentials_to_requirements(
-    user_id: str,
-    requirements: dict[str, CredentialsFieldInfo],
-) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-    """
-    Match user's credentials against a dictionary of credential requirements.
-
-    This is the core matching logic shared by both graph and block credential matching.
-    """
-    matched: dict[str, CredentialsMetaInput] = {}
-    missing: list[CredentialsMetaInput] = []
-
-    if not requirements:
-        return matched, missing
-
-    available_creds = await get_user_credentials(user_id)
-
-    for field_name, field_info in requirements.items():
-        matching_cred = find_matching_credential(available_creds, field_info)
-
-        if matching_cred:
-            try:
-                matched[field_name] = create_credential_meta_from_match(matching_cred)
-            except Exception as e:
-                logger.error(
-                    f"Failed to create CredentialsMetaInput for field '{field_name}': "
-                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
-                    f"credential_id={matching_cred.id}",
-                    exc_info=True,
-                )
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=f"{field_name} (validation failed: {e})",
-                    )
-                )
-        else:
-            provider = next(iter(field_info.provider), "unknown")
-            cred_type = next(iter(field_info.supported_types), "api_key")
-            missing.append(
-                CredentialsMetaInput(
-                    id=field_name,
-                    provider=provider,  # type: ignore
-                    type=cred_type,  # type: ignore
-                    title=field_name.replace("_", " ").title(),
-                )
-            )
-
-    return matched, missing
-
-
-async def get_user_credentials(user_id: str) -> list[Credentials]:
-    """Get all available credentials for a user."""
-    creds_manager = IntegrationCredentialsManager()
-    return await creds_manager.store.get_all_creds(user_id)
-
-
-def find_matching_credential(
-    available_creds: list[Credentials],
-    field_info: CredentialsFieldInfo,
-) -> Credentials | None:
-    """Find a credential that matches the required provider, type, scopes, and host."""
-    for cred in available_creds:
-        if cred.provider not in field_info.provider:
-            continue
-        if cred.type not in field_info.supported_types:
-            continue
-        if cred.type == "oauth2" and not _credential_has_required_scopes(
-            cred, field_info
-        ):
-            continue
-        if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
-            continue
-        return cred
-    return None
-
-
-def create_credential_meta_from_match(
-    matching_cred: Credentials,
-) -> CredentialsMetaInput:
-    """Create a CredentialsMetaInput from a matched credential."""
-    return CredentialsMetaInput(
-        id=matching_cred.id,
-        provider=matching_cred.provider,  # type: ignore
-        type=matching_cred.type,
-        title=matching_cred.title,
-    )
-
-
 async def match_user_credentials_to_graph(
     user_id: str,
     graph: GraphModel,
@@ -425,6 +331,8 @@ def _credential_has_required_scopes(
     # If no scopes are required, any credential matches
     if not requirements.required_scopes:
         return True
+
+    # Check that credential scopes are a superset of required scopes
     return set(credential.scopes).issuperset(requirements.required_scopes)
 
 

autogpt_platform/backend/backend/api/features/executions/review/review_routes_test.py

@@ -25,7 +25,7 @@ FIXED_NOW = datetime.datetime(2023, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc)
 @pytest_asyncio.fixture(loop_scope="session")
 async def client(server, mock_jwt_user) -> AsyncGenerator[httpx.AsyncClient, None]:
     """Create async HTTP client with auth overrides"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     # Override get_jwt_payload dependency to return our test user
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]

autogpt_platform/backend/backend/api/features/executions/review/routes.py

@@ -2,10 +2,10 @@ import asyncio
 import logging
 from typing import Any, List
 
+import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, HTTPException, Query, Security, status
 from prisma.enums import ReviewStatus
 
-import backend.api.auth as autogpt_auth_lib
 from backend.data.execution import (
     ExecutionContext,
     ExecutionStatus,

autogpt_platform/backend/backend/api/features/integrations/router.py

@@ -3,6 +3,7 @@ import logging
 from datetime import datetime, timedelta, timezone
 from typing import TYPE_CHECKING, Annotated, List, Literal
 
+from autogpt_libs.auth import get_user_id
 from fastapi import (
     APIRouter,
     Body,
@@ -16,7 +17,6 @@ from fastapi import (
 from pydantic import BaseModel, Field, SecretStr
 from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR, HTTP_502_BAD_GATEWAY
 
-from backend.api.auth import get_user_id
 from backend.api.features.library.db import set_preset_webhook, update_preset
 from backend.api.features.library.model import LibraryAgentPreset
 from backend.data.graph import NodeModel, get_graph, set_node_webhook

autogpt_platform/backend/backend/api/features/library/routes/agents.py

@@ -1,10 +1,10 @@
 from typing import Literal, 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.api.auth as autogpt_auth_lib
 from backend.data.onboarding import complete_onboarding_step
 
 from .. import db as library_db

autogpt_platform/backend/backend/api/features/library/routes/presets.py

@@ -1,9 +1,9 @@
 import logging
 from typing import Any, Optional
 
+import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, Body, HTTPException, Query, Security, status
 
-import backend.api.auth as autogpt_auth_lib
 from backend.data.execution import GraphExecutionMeta
 from backend.data.graph import get_graph
 from backend.data.integrations import get_webhook

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -23,7 +23,7 @@ FIXED_NOW = datetime.datetime(2023, 1, 1, 0, 0, 0)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user):
     """Setup auth overrides for all tests in this module"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
     yield

autogpt_platform/backend/backend/api/features/oauth.py

@@ -21,13 +21,13 @@ from datetime import datetime
 from typing import Literal, Optional
 from urllib.parse import urlencode
 
+from autogpt_libs.auth import get_user_id
 from fastapi import APIRouter, Body, HTTPException, Security, UploadFile, status
 from gcloud.aio import storage as async_storage
 from PIL import Image
 from prisma.enums import APIKeyPermission
 from pydantic import BaseModel, Field
 
-from backend.api.auth import get_user_id
 from backend.data.auth.oauth import (
     InvalidClientError,
     InvalidGrantError,

autogpt_platform/backend/backend/api/features/oauth_test.py

@@ -21,6 +21,7 @@ from typing import AsyncGenerator
 import httpx
 import pytest
 import pytest_asyncio
+from autogpt_libs.api_key.keysmith import APIKeySmith
 from prisma.enums import APIKeyPermission
 from prisma.models import OAuthAccessToken as PrismaOAuthAccessToken
 from prisma.models import OAuthApplication as PrismaOAuthApplication
@@ -28,7 +29,6 @@ from prisma.models import OAuthAuthorizationCode as PrismaOAuthAuthorizationCode
 from prisma.models import OAuthRefreshToken as PrismaOAuthRefreshToken
 from prisma.models import User as PrismaUser
 
-from backend.api.auth.api_key.keysmith import APIKeySmith
 from backend.api.rest_api import app
 
 keysmith = APIKeySmith()
@@ -134,7 +134,7 @@ async def client(server, test_user: str) -> AsyncGenerator[httpx.AsyncClient, No
     Depends on `server` to ensure the DB is connected and `test_user` to ensure
     the user exists in the database before running tests.
     """
-    from backend.api.auth import get_user_id
+    from autogpt_libs.auth import get_user_id
 
     # Override get_user_id dependency to return our test user
     def override_get_user_id():

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

@@ -1,9 +1,8 @@
 import logging
 
+from autogpt_libs.auth import get_user_id, requires_user
 from fastapi import APIRouter, HTTPException, Security
 
-from backend.api.auth import get_user_id, requires_user
-
 from .models import ApiResponse, ChatRequest
 from .service import OttoService
 

autogpt_platform/backend/backend/api/features/otto/routes_test.py

@@ -19,7 +19,7 @@ client = fastapi.testclient.TestClient(app)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user):
     """Setup auth overrides for all tests in this module"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
     yield

autogpt_platform/backend/backend/api/features/postmark/postmark.py

@@ -57,7 +57,7 @@ async def postmark_webhook_handler(
     webhook: Annotated[
         PostmarkWebhook,
         Body(discriminator="RecordType"),
-    ],
+    ]
 ):
     logger.info(f"Received webhook from Postmark: {webhook}")
     match webhook:

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

@@ -164,7 +164,7 @@ class BlockHandler(ContentHandler):
         block_ids = list(all_blocks.keys())
 
         # Query for existing embeddings
-        placeholders = ",".join([f"${i + 1}" for i in range(len(block_ids))])
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
         existing_result = await query_raw_with_schema(
             f"""
             SELECT "contentId"
@@ -265,7 +265,7 @@ class BlockHandler(ContentHandler):
             return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
 
         block_ids = enabled_block_ids
-        placeholders = ",".join([f"${i + 1}" for i in range(len(block_ids))])
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
 
         embedded_result = await query_raw_with_schema(
             f"""
@@ -508,7 +508,7 @@ class DocumentationHandler(ContentHandler):
         ]
 
         # Check which ones have embeddings
-        placeholders = ",".join([f"${i + 1}" for i in range(len(section_content_ids))])
+        placeholders = ",".join([f"${i+1}" for i in range(len(section_content_ids))])
         existing_result = await query_raw_with_schema(
             f"""
             SELECT "contentId"

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

@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
-import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -363,11 +362,7 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     total = results[0]["total_count"] if results else 0
 
@@ -727,87 +718,6 @@ async def hybrid_search_simple(
     return await hybrid_search(query=query, page=page, page_size=page_size)
 
 
-# ============================================================================
-# Diagnostics
-# ============================================================================
-
-# Rate limit: only log vector error diagnostics once per this interval
-_VECTOR_DIAG_INTERVAL_SECONDS = 60
-_last_vector_diag_time: float = 0
-
-
-async def _log_vector_error_diagnostics(error: Exception) -> None:
-    """Log diagnostic info when 'type vector does not exist' error occurs.
-
-    Note: Diagnostic queries use query_raw_with_schema which may run on a different
-    pooled connection than the one that failed. Session-level search_path can differ,
-    so these diagnostics show cluster-wide state, not necessarily the failed session.
-
-    Includes rate limiting to avoid log spam - only logs once per minute.
-    Caller should re-raise the error after calling this function.
-    """
-    global _last_vector_diag_time
-
-    # Check if this is the vector type error
-    error_str = str(error).lower()
-    if not (
-        "type" in error_str and "vector" in error_str and "does not exist" in error_str
-    ):
-        return
-
-    # Rate limit: only log once per interval
-    now = time.time()
-    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
-        return
-    _last_vector_diag_time = now
-
-    try:
-        diagnostics: dict[str, object] = {}
-
-        try:
-            search_path_result = await query_raw_with_schema("SHOW search_path")
-            diagnostics["search_path"] = search_path_result
-        except Exception as e:
-            diagnostics["search_path"] = f"Error: {e}"
-
-        try:
-            schema_result = await query_raw_with_schema("SELECT current_schema()")
-            diagnostics["current_schema"] = schema_result
-        except Exception as e:
-            diagnostics["current_schema"] = f"Error: {e}"
-
-        try:
-            user_result = await query_raw_with_schema(
-                "SELECT current_user, session_user, current_database()"
-            )
-            diagnostics["user_info"] = user_result
-        except Exception as e:
-            diagnostics["user_info"] = f"Error: {e}"
-
-        try:
-            # Check pgvector extension installation (cluster-wide, stable info)
-            ext_result = await query_raw_with_schema(
-                "SELECT extname, extversion, nspname as schema "
-                "FROM pg_extension e "
-                "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                "WHERE extname = 'vector'"
-            )
-            diagnostics["pgvector_extension"] = ext_result
-        except Exception as e:
-            diagnostics["pgvector_extension"] = f"Error: {e}"
-
-        logger.error(
-            f"Vector type error diagnostics:\n"
-            f"  Error: {error}\n"
-            f"  search_path: {diagnostics.get('search_path')}\n"
-            f"  current_schema: {diagnostics.get('current_schema')}\n"
-            f"  user_info: {diagnostics.get('user_info')}\n"
-            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
-        )
-    except Exception as diag_error:
-        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
-
-
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights

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

@@ -47,7 +47,7 @@ def mock_storage_client(mocker):
 
 async def test_upload_media_success(mock_settings, mock_storage_client):
     # Create test JPEG data with valid signature
-    test_data = b"\xff\xd8\xff" + b"test data"
+    test_data = b"\xFF\xD8\xFF" + b"test data"
 
     test_file = fastapi.UploadFile(
         filename="laptop.jpeg",
@@ -85,7 +85,7 @@ async def test_upload_media_missing_credentials(monkeypatch):
 
     test_file = fastapi.UploadFile(
         filename="laptop.jpeg",
-        file=io.BytesIO(b"\xff\xd8\xff" + b"test data"),  # Valid JPEG signature
+        file=io.BytesIO(b"\xFF\xD8\xFF" + b"test data"),  # Valid JPEG signature
         headers=starlette.datastructures.Headers({"content-type": "image/jpeg"}),
     )
 
@@ -110,7 +110,7 @@ async def test_upload_media_video_type(mock_settings, mock_storage_client):
 
 
 async def test_upload_media_file_too_large(mock_settings, mock_storage_client):
-    large_data = b"\xff\xd8\xff" + b"x" * (
+    large_data = b"\xFF\xD8\xFF" + b"x" * (
         50 * 1024 * 1024 + 1
     )  # 50MB + 1 byte with valid JPEG signature
     test_file = fastapi.UploadFile(

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

@@ -4,11 +4,11 @@ import typing
 import urllib.parse
 from typing import Literal
 
+import autogpt_libs.auth
 import fastapi
 import fastapi.responses
 import prisma.enums
 
-import backend.api.auth
 import backend.data.graph
 import backend.util.json
 from backend.util.models import Pagination
@@ -34,11 +34,11 @@ router = fastapi.APIRouter()
     "/profile",
     summary="Get user profile",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.ProfileDetails,
 )
 async def get_profile(
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Get the profile details for the authenticated user.
@@ -57,12 +57,12 @@ async def get_profile(
     "/profile",
     summary="Update user profile",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.CreatorDetails,
 )
 async def update_or_create_profile(
     profile: store_model.Profile,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Update the store profile for the authenticated user.
@@ -169,7 +169,7 @@ async def unified_search(
     page: int = 1,
     page_size: int = 20,
     user_id: str | None = fastapi.Security(
-        backend.api.auth.get_optional_user_id, use_cache=False
+        autogpt_libs.auth.get_optional_user_id, use_cache=False
     ),
 ):
     """
@@ -274,7 +274,7 @@ async def get_agent(
     "/graph/{store_listing_version_id}",
     summary="Get agent graph",
     tags=["store"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
 )
 async def get_graph_meta_by_store_listing_version_id(
     store_listing_version_id: str,
@@ -290,7 +290,7 @@ async def get_graph_meta_by_store_listing_version_id(
     "/agents/{store_listing_version_id}",
     summary="Get agent by version",
     tags=["store"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.StoreAgentDetails,
 )
 async def get_store_agent(store_listing_version_id: str):
@@ -306,14 +306,14 @@ async def get_store_agent(store_listing_version_id: str):
     "/agents/{username}/{agent_name}/review",
     summary="Create agent review",
     tags=["store"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.StoreReview,
 )
 async def create_review(
     username: str,
     agent_name: str,
     review: store_model.StoreReviewCreate,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Create a review for a store agent.
@@ -417,11 +417,11 @@ async def get_creator(
     "/myagents",
     summary="Get my agents",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.MyAgentsResponse,
 )
 async def get_my_agents(
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
     page: typing.Annotated[int, fastapi.Query(ge=1)] = 1,
     page_size: typing.Annotated[int, fastapi.Query(ge=1)] = 20,
 ):
@@ -436,12 +436,12 @@ async def get_my_agents(
     "/submissions/{submission_id}",
     summary="Delete store submission",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=bool,
 )
 async def delete_submission(
     submission_id: str,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Delete a store listing submission.
@@ -465,11 +465,11 @@ async def delete_submission(
     "/submissions",
     summary="List my submissions",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.StoreSubmissionsResponse,
 )
 async def get_submissions(
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
     page: int = 1,
     page_size: int = 20,
 ):
@@ -508,12 +508,12 @@ async def get_submissions(
     "/submissions",
     summary="Create store submission",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.StoreSubmission,
 )
 async def create_submission(
     submission_request: store_model.StoreSubmissionRequest,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Create a new store listing submission.
@@ -552,13 +552,13 @@ async def create_submission(
     "/submissions/{store_listing_version_id}",
     summary="Edit store submission",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
     response_model=store_model.StoreSubmission,
 )
 async def edit_submission(
     store_listing_version_id: str,
     submission_request: store_model.StoreSubmissionEditRequest,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Edit an existing store listing submission.
@@ -596,11 +596,11 @@ async def edit_submission(
     "/submissions/media",
     summary="Upload submission media",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
 )
 async def upload_submission_media(
     file: fastapi.UploadFile,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ):
     """
     Upload media (images/videos) for a store listing submission.
@@ -623,11 +623,11 @@ async def upload_submission_media(
     "/submissions/generate_image",
     summary="Generate submission image",
     tags=["store", "private"],
-    dependencies=[fastapi.Security(backend.api.auth.requires_user)],
+    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
 )
 async def generate_image(
     agent_id: str,
-    user_id: str = fastapi.Security(backend.api.auth.get_user_id),
+    user_id: str = fastapi.Security(autogpt_libs.auth.get_user_id),
 ) -> fastapi.responses.Response:
     """
     Generate an image for a store listing submission.

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

@@ -24,7 +24,7 @@ client = fastapi.testclient.TestClient(app)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user):
     """Setup auth overrides for all tests in this module"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     app.dependency_overrides[get_jwt_payload] = mock_jwt_user["get_jwt_payload"]
     yield

autogpt_platform/backend/backend/api/features/v1.py

@@ -9,6 +9,8 @@ from typing import Annotated, Any, Sequence, get_args
 
 import pydantic
 import stripe
+from autogpt_libs.auth import get_user_id, requires_user
+from autogpt_libs.auth.jwt_utils import get_jwt_payload
 from fastapi import (
     APIRouter,
     Body,
@@ -26,8 +28,6 @@ from pydantic import BaseModel
 from starlette.status import HTTP_204_NO_CONTENT, HTTP_404_NOT_FOUND
 from typing_extensions import Optional, TypedDict
 
-from backend.api.auth import get_user_id, requires_user
-from backend.api.auth.jwt_utils import get_jwt_payload
 from backend.api.model import (
     CreateAPIKeyRequest,
     CreateAPIKeyResponse,

autogpt_platform/backend/backend/api/features/v1_test.py

@@ -25,7 +25,7 @@ client = fastapi.testclient.TestClient(app)
 @pytest.fixture(autouse=True)
 def setup_app_auth(mock_jwt_user, setup_test_user):
     """Setup auth overrides for all tests in this module"""
-    from backend.api.auth.jwt_utils import get_jwt_payload
+    from autogpt_libs.auth.jwt_utils import get_jwt_payload
 
     # setup_test_user fixture already executed and user is created in database
     # It returns the user_id which we don't need to await
@@ -499,12 +499,10 @@ async def test_upload_file_success(test_user_id: str):
     )
 
     # Mock dependencies
-    with (
-        patch("backend.api.features.v1.scan_content_safe") as mock_scan,
-        patch(
-            "backend.api.features.v1.get_cloud_storage_handler"
-        ) as mock_handler_getter,
-    ):
+    with patch("backend.api.features.v1.scan_content_safe") as mock_scan, patch(
+        "backend.api.features.v1.get_cloud_storage_handler"
+    ) as mock_handler_getter:
+
         mock_scan.return_value = None
         mock_handler = AsyncMock()
         mock_handler.store_file.return_value = "gcs://test-bucket/uploads/123/test.txt"
@@ -553,12 +551,10 @@ async def test_upload_file_no_filename(test_user_id: str):
         ),
     )
 
-    with (
-        patch("backend.api.features.v1.scan_content_safe") as mock_scan,
-        patch(
-            "backend.api.features.v1.get_cloud_storage_handler"
-        ) as mock_handler_getter,
-    ):
+    with patch("backend.api.features.v1.scan_content_safe") as mock_scan, patch(
+        "backend.api.features.v1.get_cloud_storage_handler"
+    ) as mock_handler_getter:
+
         mock_scan.return_value = None
         mock_handler = AsyncMock()
         mock_handler.store_file.return_value = (
@@ -636,12 +632,10 @@ async def test_upload_file_cloud_storage_failure(test_user_id: str):
         headers=starlette.datastructures.Headers({"content-type": "text/plain"}),
     )
 
-    with (
-        patch("backend.api.features.v1.scan_content_safe") as mock_scan,
-        patch(
-            "backend.api.features.v1.get_cloud_storage_handler"
-        ) as mock_handler_getter,
-    ):
+    with patch("backend.api.features.v1.scan_content_safe") as mock_scan, patch(
+        "backend.api.features.v1.get_cloud_storage_handler"
+    ) as mock_handler_getter:
+
         mock_scan.return_value = None
         mock_handler = AsyncMock()
         mock_handler.store_file.side_effect = RuntimeError("Storage error!")
@@ -685,12 +679,10 @@ async def test_upload_file_gcs_not_configured_fallback(test_user_id: str):
         headers=starlette.datastructures.Headers({"content-type": "text/plain"}),
     )
 
-    with (
-        patch("backend.api.features.v1.scan_content_safe") as mock_scan,
-        patch(
-            "backend.api.features.v1.get_cloud_storage_handler"
-        ) as mock_handler_getter,
-    ):
+    with patch("backend.api.features.v1.scan_content_safe") as mock_scan, patch(
+        "backend.api.features.v1.get_cloud_storage_handler"
+    ) as mock_handler_getter:
+
         mock_scan.return_value = None
         mock_handler = AsyncMock()
         mock_handler.config.gcs_bucket_name = ""  # Simulate no GCS bucket configured

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

@@ -8,9 +8,9 @@ from typing import Annotated
 from urllib.parse import quote
 
 import fastapi
+from autogpt_libs.auth.dependencies import get_user_id, requires_user
 from fastapi.responses import Response
 
-from backend.api.auth.dependencies import get_user_id, requires_user
 from backend.data.workspace import get_workspace, get_workspace_file
 from backend.util.workspace_storage import get_workspace_storage
 

autogpt_platform/backend/backend/api/rest_api.py

@@ -9,6 +9,8 @@ import fastapi.responses
 import pydantic
 import starlette.middleware.cors
 import uvicorn
+from autogpt_libs.auth import add_auth_responses_to_openapi
+from autogpt_libs.auth import verify_settings as verify_auth_settings
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.gzip import GZipMiddleware
 from fastapi.routing import APIRoute
@@ -38,8 +40,6 @@ import backend.data.user
 import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
-from backend.api.auth import add_auth_responses_to_openapi
-from backend.api.auth import verify_settings as verify_auth_settings
 from backend.api.features.chat.completion_consumer import (
     start_completion_consumer,
     stop_completion_consumer,
@@ -69,7 +69,7 @@ from .utils.openapi import sort_openapi
 settings = backend.util.settings.Settings()
 logger = logging.getLogger(__name__)
 
-logging.getLogger("backend.api.auth").setLevel(logging.INFO)
+logging.getLogger("autogpt_libs").setLevel(logging.INFO)
 
 
 @contextlib.contextmanager

autogpt_platform/backend/backend/api/utils/api_key_auth_test.py

@@ -457,8 +457,7 @@ async def test_api_key_with_unicode_characters_normalization_attack(mock_request
     """Test that Unicode normalization doesn't bypass validation."""
     # Create auth with composed Unicode character
     auth = APIKeyAuthenticator(
-        header_name="X-API-Key",
-        expected_token="café",  # é is composed
+        header_name="X-API-Key", expected_token="café"  # é is composed
     )
 
     # Try with decomposed version (c + a + f + e + ´)
@@ -523,8 +522,8 @@ async def test_api_keys_with_newline_variations(mock_request):
         "valid\r\ntoken",  # Windows newline
         "valid\rtoken",  # Mac newline
         "valid\x85token",  # NEL (Next Line)
-        "valid\x0btoken",  # Vertical Tab
-        "valid\x0ctoken",  # Form Feed
+        "valid\x0Btoken",  # Vertical Tab
+        "valid\x0Ctoken",  # Form Feed
     ]
 
     for api_key in newline_variations:

autogpt_platform/backend/backend/api/ws_api.py

@@ -5,10 +5,10 @@ from typing import Protocol
 
 import pydantic
 import uvicorn
+from autogpt_libs.auth.jwt_utils import parse_jwt_token
 from fastapi import Depends, FastAPI, WebSocket, WebSocketDisconnect
 from starlette.middleware.cors import CORSMiddleware
 
-from backend.api.auth.jwt_utils import parse_jwt_token
 from backend.api.conn_manager import ConnectionManager
 from backend.api.model import (
     WSMessage,

autogpt_platform/backend/backend/api/ws_api_test.py

@@ -44,12 +44,9 @@ def test_websocket_server_uses_cors_helper(mocker) -> None:
         "backend.api.ws_api.build_cors_params", return_value=cors_params
     )
 
-    with (
-        override_config(
-            settings, "backend_cors_allow_origins", cors_params["allow_origins"]
-        ),
-        override_config(settings, "app_env", AppEnvironment.LOCAL),
-    ):
+    with override_config(
+        settings, "backend_cors_allow_origins", cors_params["allow_origins"]
+    ), override_config(settings, "app_env", AppEnvironment.LOCAL):
         WebsocketServer().run()
 
     build_cors.assert_called_once_with(
@@ -68,12 +65,9 @@ def test_websocket_server_uses_cors_helper(mocker) -> None:
 def test_websocket_server_blocks_localhost_in_production(mocker) -> None:
     mocker.patch("backend.api.ws_api.uvicorn.run")
 
-    with (
-        override_config(
-            settings, "backend_cors_allow_origins", ["http://localhost:3000"]
-        ),
-        override_config(settings, "app_env", AppEnvironment.PRODUCTION),
-    ):
+    with override_config(
+        settings, "backend_cors_allow_origins", ["http://localhost:3000"]
+    ), override_config(settings, "app_env", AppEnvironment.PRODUCTION):
         with pytest.raises(ValueError):
             WebsocketServer().run()
 

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -174,9 +174,7 @@ class AIImageGeneratorBlock(Block):
             ],
             test_mock={
                 # Return a data URI directly so store_media_file doesn't need to download
-                "_run_client": lambda *args, **kwargs: (
-                    "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
-                )
+                "_run_client": lambda *args, **kwargs: "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
             },
         )
 

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -142,9 +142,7 @@ class AIMusicGeneratorBlock(Block):
                 ),
             ],
             test_mock={
-                "run_model": lambda api_key, music_gen_model_version, prompt, duration, temperature, top_k, top_p, classifier_free_guidance, output_format, normalization_strategy: (
-                    "https://replicate.com/output/generated-audio-url.wav"
-                ),
+                "run_model": lambda api_key, music_gen_model_version, prompt, duration, temperature, top_k, top_p, classifier_free_guidance, output_format, normalization_strategy: "https://replicate.com/output/generated-audio-url.wav",
             },
             test_credentials=TEST_CREDENTIALS,
         )

autogpt_platform/backend/backend/blocks/ayrshare/post_to_bluesky.py

@@ -69,18 +69,12 @@ class PostToBlueskyBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Validate character limit for Bluesky
         if len(input_data.post) > 300:
-            yield (
-                "error",
-                f"Post text exceeds Bluesky's 300 character limit ({len(input_data.post)} characters)",
-            )
+            yield "error", f"Post text exceeds Bluesky's 300 character limit ({len(input_data.post)} characters)"
             return
 
         # Validate media constraints for Bluesky

autogpt_platform/backend/backend/blocks/ayrshare/post_to_facebook.py

@@ -131,10 +131,7 @@ class PostToFacebookBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Convert datetime to ISO format if provided

autogpt_platform/backend/backend/blocks/ayrshare/post_to_gmb.py

@@ -120,18 +120,12 @@ class PostToGMBBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Validate GMB constraints
         if len(input_data.media_urls) > 1:
-            yield (
-                "error",
-                "Google My Business supports only one image or video per post",
-            )
+            yield "error", "Google My Business supports only one image or video per post"
             return
 
         # Validate offer coupon code length

autogpt_platform/backend/backend/blocks/ayrshare/post_to_instagram.py

@@ -123,25 +123,16 @@ class PostToInstagramBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Validate Instagram constraints
         if len(input_data.post) > 2200:
-            yield (
-                "error",
-                f"Instagram post text exceeds 2,200 character limit ({len(input_data.post)} characters)",
-            )
+            yield "error", f"Instagram post text exceeds 2,200 character limit ({len(input_data.post)} characters)"
             return
 
         if len(input_data.media_urls) > 10:
-            yield (
-                "error",
-                "Instagram supports a maximum of 10 images/videos in a carousel",
-            )
+            yield "error", "Instagram supports a maximum of 10 images/videos in a carousel"
             return
 
         if len(input_data.collaborators) > 3:
@@ -156,10 +147,7 @@ class PostToInstagramBlock(Block):
         ]
 
         if any(reel_options) and not all(reel_options):
-            yield (
-                "error",
-                "When posting a reel, all reel options must be set: share_reels_feed, audio_name, and either thumbnail or thumbnail_offset",
-            )
+            yield "error", "When posting a reel, all reel options must be set: share_reels_feed, audio_name, and either thumbnail or thumbnail_offset"
             return
 
         # Count hashtags and mentions
@@ -167,17 +155,11 @@ class PostToInstagramBlock(Block):
         mention_count = input_data.post.count("@")
 
         if hashtag_count > 30:
-            yield (
-                "error",
-                f"Instagram allows maximum 30 hashtags ({hashtag_count} found)",
-            )
+            yield "error", f"Instagram allows maximum 30 hashtags ({hashtag_count} found)"
             return
 
         if mention_count > 3:
-            yield (
-                "error",
-                f"Instagram allows maximum 3 @mentions ({mention_count} found)",
-            )
+            yield "error", f"Instagram allows maximum 3 @mentions ({mention_count} found)"
             return
 
         # Convert datetime to ISO format if provided
@@ -209,10 +191,7 @@ class PostToInstagramBlock(Block):
             # Validate alt text length
             for i, alt in enumerate(input_data.alt_text):
                 if len(alt) > 1000:
-                    yield (
-                        "error",
-                        f"Alt text {i + 1} exceeds 1,000 character limit ({len(alt)} characters)",
-                    )
+                    yield "error", f"Alt text {i+1} exceeds 1,000 character limit ({len(alt)} characters)"
                     return
             instagram_options["altText"] = input_data.alt_text
 
@@ -227,19 +206,13 @@ class PostToInstagramBlock(Block):
                 try:
                     tag_obj = InstagramUserTag(**tag)
                 except Exception as e:
-                    yield (
-                        "error",
-                        f"Invalid user tag: {e}, tages need to be a dictionary with a 3 items: username (str), x (float) and y (float)",
-                    )
+                    yield "error", f"Invalid user tag: {e}, tages need to be a dictionary with a 3 items: username (str), x (float) and y (float)"
                     return
                 tag_dict: dict[str, float | str] = {"username": tag_obj.username}
                 if tag_obj.x is not None and tag_obj.y is not None:
                     # Validate coordinates
                     if not (0.0 <= tag_obj.x <= 1.0) or not (0.0 <= tag_obj.y <= 1.0):
-                        yield (
-                            "error",
-                            f"User tag coordinates must be between 0.0 and 1.0 (user: {tag_obj.username})",
-                        )
+                        yield "error", f"User tag coordinates must be between 0.0 and 1.0 (user: {tag_obj.username})"
                         return
                     tag_dict["x"] = tag_obj.x
                     tag_dict["y"] = tag_obj.y

autogpt_platform/backend/backend/blocks/ayrshare/post_to_linkedin.py

@@ -123,18 +123,12 @@ class PostToLinkedInBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Validate LinkedIn constraints
         if len(input_data.post) > 3000:
-            yield (
-                "error",
-                f"LinkedIn post text exceeds 3,000 character limit ({len(input_data.post)} characters)",
-            )
+            yield "error", f"LinkedIn post text exceeds 3,000 character limit ({len(input_data.post)} characters)"
             return
 
         if len(input_data.media_urls) > 9:
@@ -142,19 +136,13 @@ class PostToLinkedInBlock(Block):
             return
 
         if input_data.document_title and len(input_data.document_title) > 400:
-            yield (
-                "error",
-                f"LinkedIn document title exceeds 400 character limit ({len(input_data.document_title)} characters)",
-            )
+            yield "error", f"LinkedIn document title exceeds 400 character limit ({len(input_data.document_title)} characters)"
             return
 
         # Validate visibility option
         valid_visibility = ["public", "connections", "loggedin"]
         if input_data.visibility not in valid_visibility:
-            yield (
-                "error",
-                f"LinkedIn visibility must be one of: {', '.join(valid_visibility)}",
-            )
+            yield "error", f"LinkedIn visibility must be one of: {', '.join(valid_visibility)}"
             return
 
         # Check for document extensions

autogpt_platform/backend/backend/blocks/ayrshare/post_to_pinterest.py

@@ -103,32 +103,20 @@ class PostToPinterestBlock(Block):
 
         client = create_ayrshare_client()
         if not client:
-            yield (
-                "error",
-                "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY.",
-            )
+            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
             return
 
         # Validate Pinterest constraints
         if len(input_data.post) > 500:
-            yield (
-                "error",
-                f"Pinterest pin description exceeds 500 character limit ({len(input_data.post)} characters)",
-            )
+            yield "error", f"Pinterest pin description exceeds 500 character limit ({len(input_data.post)} characters)"
             return
 
         if len(input_data.pin_title) > 100:
-            yield (
-                "error",
-                f"Pinterest pin title exceeds 100 character limit ({len(input_data.pin_title)} characters)",
-            )
+            yield "error", f"Pinterest pin title exceeds 100 character limit ({len(input_data.pin_title)} characters)"
             return
 
         if len(input_data.link) > 2048:
-            yield (
-                "error",
-                f"Pinterest link URL exceeds 2048 character limit ({len(input_data.link)} characters)",
-            )
+            yield "error", f"Pinterest link URL exceeds 2048 character limit ({len(input_data.link)} characters)"
             return
 
         if len(input_data.media_urls) == 0:
@@ -153,10 +141,7 @@ class PostToPinterestBlock(Block):
         # Validate alt text length
         for i, alt in enumerate(input_data.alt_text):
             if len(alt) > 500:
-                yield (
-                    "error",
-                    f"Pinterest alt text {i + 1} exceeds 500 character limit ({len(alt)} characters)",
-                )
+                yield "error", f"Pinterest alt text {i+1} exceeds 500 character limit ({len(alt)} characters)"
                 return
 
         # Convert datetime to ISO format if provided