Fix enterprise linting: use single quotes

Apply enterprise linting rules (single quotes) to test file.

Co-authored-by: openhands <openhands@all-hands.dev>

.agents/skills/update-sdk/references/docker-image-locations.md

@@ -46,34 +46,12 @@ These files contain image tags that **must** be updated whenever the SDK version
 ### `openhands/version.py`
 - Reads version from `pyproject.toml` at runtime → `openhands.__version__`
 
-### `openhands/resolver/issue_resolver.py`
-- Builds `ghcr.io/openhands/runtime:{openhands.__version__}-nikolaik` dynamically
-
-### `openhands/runtime/utils/runtime_build.py`
-- Base repo URL `ghcr.io/openhands/runtime` is a constant; version comes from elsewhere
-
 ### `.github/scripts/update_pr_description.sh`
 - Uses `${SHORT_SHA}` variable at CI runtime, not hardcoded
 
 ### `enterprise/Dockerfile`
 - `ARG BASE="ghcr.io/openhands/openhands"` — base image, version supplied at build time
 
-## V0 Legacy Files (separate update cadence)
-
-These reference the V0 runtime image (`ghcr.io/openhands/runtime:X.Y-nikolaik`) for local Docker/Kubernetes paths. They are **not** updated as part of a V1 release but may be updated independently.
-
-### `Development.md`
-- `export SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/openhands/runtime:X.Y-nikolaik`
-
-### `openhands/runtime/impl/kubernetes/README.md`
-- `runtime_container_image = "docker.openhands.dev/openhands/runtime:X.Y-nikolaik"`
-
-### `enterprise/enterprise_local/README.md`
-- Uses `ghcr.io/openhands/runtime:main-nikolaik` (points to `main`, not versioned)
-
-### `third_party/runtime/impl/daytona/README.md`
-- Uses `${OPENHANDS_VERSION}` variable, not hardcoded
-
 ## Image Registries
 
 | Registry | Usage |

.github/workflows/e2e-tests.yml

@@ -1,228 +0,0 @@
-name: End-to-End Tests
-
-on:
-  pull_request:
-    types: [opened, synchronize, reopened, labeled]
-    branches:
-      - main
-      - develop
-  workflow_dispatch:
-
-jobs:
-  e2e-tests:
-    if: contains(github.event.pull_request.labels.*.name, 'end-to-end') || github.event_name == 'workflow_dispatch'
-    runs-on: ubuntu-latest
-    timeout-minutes: 60
-
-    env:
-      GITHUB_REPO_NAME: ${{ github.repository }}
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v6
-
-      - name: Install poetry via pipx
-        uses: abatilo/actions-poetry@v4
-        with:
-          poetry-version: 2.1.3
-
-      - name: Set up Python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.12'
-          cache: 'poetry'
-
-      - name: Install system dependencies
-        run: |
-          sudo apt-get update
-          sudo apt-get install -y libgtk-3-0 libnotify4 libnss3 libxss1 libxtst6 xauth xvfb libgbm1 libasound2t64 netcat-openbsd
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v6
-        with:
-          node-version: '22'
-          cache: 'npm'
-          cache-dependency-path: 'frontend/package-lock.json'
-
-      - name: Setup environment for end-to-end tests
-        run: |
-          # Create test results directory
-          mkdir -p test-results
-
-          # Create downloads directory for OpenHands (use a directory in the home folder)
-          mkdir -p $HOME/downloads
-          sudo chown -R $USER:$USER $HOME/downloads
-          sudo chmod -R 755 $HOME/downloads
-
-      - name: Build OpenHands
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          LLM_MODEL: ${{ secrets.LLM_MODEL || 'gpt-4o' }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY || 'test-key' }}
-          LLM_BASE_URL: ${{ secrets.LLM_BASE_URL }}
-          INSTALL_DOCKER: 1
-          RUNTIME: docker
-          FRONTEND_PORT: 12000
-          FRONTEND_HOST: 0.0.0.0
-          BACKEND_HOST: 0.0.0.0
-          BACKEND_PORT: 3000
-          ENABLE_BROWSER: true
-          INSTALL_PLAYWRIGHT: 1
-        run: |
-          # Fix poetry.lock file if needed
-          echo "Fixing poetry.lock file if needed..."
-          poetry lock
-
-          # Build OpenHands using make build
-          echo "Running make build..."
-          make build
-
-          # Install Chromium Headless Shell for Playwright (needed for pytest-playwright)
-          echo "Installing Chromium Headless Shell for Playwright..."
-          poetry run playwright install chromium-headless-shell
-
-          # Verify Playwright browsers are installed (for e2e tests only)
-          echo "Verifying Playwright browsers installation for e2e tests..."
-          BROWSER_CHECK=$(poetry run python tests/e2e/check_playwright.py 2>/dev/null)
-
-          if [ "$BROWSER_CHECK" != "chromium_found" ]; then
-            echo "ERROR: Chromium browser not found or not working for e2e tests"
-            echo "$BROWSER_CHECK"
-            exit 1
-          else
-            echo "Playwright browsers are properly installed for e2e tests."
-          fi
-
-          # Docker runtime will handle workspace directory creation
-
-          # Start the application using make run with custom parameters and reduced logging
-          echo "Starting OpenHands using make run..."
-          # Set environment variables to reduce logging verbosity
-          export PYTHONUNBUFFERED=1
-          export LOG_LEVEL=WARNING
-          export UVICORN_LOG_LEVEL=warning
-          export OPENHANDS_LOG_LEVEL=WARNING
-          FRONTEND_PORT=12000 FRONTEND_HOST=0.0.0.0 BACKEND_HOST=0.0.0.0 make run > /tmp/openhands-e2e-test.log 2>&1 &
-
-          # Store the PID of the make run process
-          MAKE_PID=$!
-          echo "OpenHands started with PID: $MAKE_PID"
-
-          # Wait for the application to start
-          echo "Waiting for OpenHands to start..."
-          max_attempts=15
-          attempt=1
-
-          while [ $attempt -le $max_attempts ]; do
-            echo "Checking if OpenHands is running (attempt $attempt of $max_attempts)..."
-
-            # Check if the process is still running
-            if ! ps -p $MAKE_PID > /dev/null; then
-              echo "ERROR: OpenHands process has terminated unexpectedly"
-              echo "Last 50 lines of the log:"
-              tail -n 50 /tmp/openhands-e2e-test.log
-              exit 1
-            fi
-
-            # Check if frontend port is open
-            if nc -z localhost 12000; then
-              # Verify we can get HTML content
-              if curl -s http://localhost:12000 | grep -q "<html"; then
-                echo "SUCCESS: OpenHands is running and serving HTML content on port 12000"
-                break
-              else
-                echo "Port 12000 is open but not serving HTML content yet"
-              fi
-            else
-              echo "Frontend port 12000 is not open yet"
-            fi
-
-            # Show log output on each attempt
-            echo "Recent log output:"
-            tail -n 20 /tmp/openhands-e2e-test.log
-
-            # Wait before next attempt
-            echo "Waiting 10 seconds before next check..."
-            sleep 10
-            attempt=$((attempt + 1))
-
-            # Exit if we've reached the maximum number of attempts
-            if [ $attempt -gt $max_attempts ]; then
-              echo "ERROR: OpenHands failed to start after $max_attempts attempts"
-              echo "Last 50 lines of the log:"
-              tail -n 50 /tmp/openhands-e2e-test.log
-              exit 1
-            fi
-          done
-
-          # Final verification that the app is running
-          if ! nc -z localhost 12000 || ! curl -s http://localhost:12000 | grep -q "<html"; then
-            echo "ERROR: OpenHands is not running properly on port 12000"
-            echo "Last 50 lines of the log:"
-            tail -n 50 /tmp/openhands-e2e-test.log
-            exit 1
-          fi
-
-          # Print success message
-          echo "OpenHands is running successfully on port 12000"
-
-      - name: Run end-to-end tests
-        env:
-          GITHUB_TOKEN: ${{ secrets.E2E_TEST_GITHUB_TOKEN }}
-          LLM_MODEL: ${{ secrets.LLM_MODEL || 'gpt-4o' }}
-          LLM_API_KEY: ${{ secrets.LLM_API_KEY || 'test-key' }}
-          LLM_BASE_URL: ${{ secrets.LLM_BASE_URL }}
-        run: |
-          # Check if the application is running
-          if ! nc -z localhost 12000; then
-            echo "ERROR: OpenHands is not running on port 12000"
-            echo "Last 50 lines of the log:"
-            tail -n 50 /tmp/openhands-e2e-test.log
-            exit 1
-          fi
-
-          # Run the tests with detailed output
-          cd tests/e2e
-          poetry run python -m pytest \
-            test_settings.py::test_github_token_configuration \
-            test_conversation.py::test_conversation_start \
-            test_browsing_catchphrase.py::test_browsing_catchphrase \
-            test_multi_conversation_resume.py::test_multi_conversation_resume \
-            -v --no-header --capture=no --timeout=900
-
-      - name: Upload test results
-        if: always()
-        uses: actions/upload-artifact@v7
-        with:
-          name: playwright-report
-          path: tests/e2e/test-results/
-          retention-days: 30
-
-      - name: Upload OpenHands logs
-        if: always()
-        uses: actions/upload-artifact@v7
-        with:
-          name: openhands-logs
-          path: |
-            /tmp/openhands-e2e-test.log
-            /tmp/openhands-e2e-build.log
-            /tmp/openhands-backend.log
-            /tmp/openhands-frontend.log
-            /tmp/backend-health-check.log
-            /tmp/frontend-check.log
-            /tmp/vite-config.log
-            /tmp/makefile-contents.log
-          retention-days: 30
-
-      - name: Cleanup
-        if: always()
-        run: |
-          # Stop OpenHands processes
-          echo "Stopping OpenHands processes..."
-          pkill -f "python -m openhands.server" || true
-          pkill -f "npm run dev" || true
-          pkill -f "make run" || true
-
-          # Print process status for debugging
-          echo "Checking if any OpenHands processes are still running:"
-          ps aux | grep -E "openhands|npm run dev" || true

.github/workflows/pr-review-by-openhands.yml

@@ -2,12 +2,14 @@
 name: PR Review by OpenHands
 
 on:
-    # TEMPORARY MITIGATION (Clinejection hardening)
-    #
-    # We temporarily avoid `pull_request_target` here. We'll restore it after the PR review
-    # workflow is fully hardened for untrusted execution.
+    # Use pull_request for same-repo PRs so workflow changes can self-verify in PRs.
     pull_request:
         types: [opened, ready_for_review, labeled, review_requested]
+    # Use pull_request_target for fork PRs.
+    # The bot token used here is intentionally scoped to PR review operations,
+    # so the remaining blast radius is bounded even though PR content is untrusted.
+    pull_request_target:
+        types: [opened, ready_for_review, labeled, review_requested]
 
 permissions:
     contents: read
@@ -16,13 +18,33 @@ permissions:
 
 jobs:
     pr-review:
-        # Note: fork PRs will not have access to repository secrets under `pull_request`.
-        # Skip forks to avoid noisy failures until we restore a hardened `pull_request_target` flow.
+        # Run on same-repo PRs via pull_request and on fork PRs via pull_request_target.
+        # Trigger when one of the following conditions is met:
+        #   1. A new non-draft PR is opened by a non-first-time contributor, OR
+        #   2. A draft PR is converted to ready for review by a non-first-time contributor, OR
+        #   3. The 'review-this' label is added, OR
+        #   4. openhands-agent or all-hands-bot is requested as a reviewer
+        # Note: FIRST_TIME_CONTRIBUTOR and NONE PRs require manual trigger via label/reviewer request.
+        # Trigger logic:
+        #   1. Route same-repo PRs through `pull_request` and fork PRs through `pull_request_target`
+        #   2. Auto-trigger on `opened` / `ready_for_review` for non-first-time contributors
+        #   3. Always allow manual triggers via `review-this` or reviewer request
+        # The author association check is duplicated intentionally for both
+        # auto-triggered actions (`opened` and `ready_for_review`).
         if: |
-            github.event.pull_request.head.repo.full_name == github.repository &&
             (
-                (github.event.action == 'opened' && github.event.pull_request.draft == false) ||
-                github.event.action == 'ready_for_review' ||
+                (
+                    github.event_name == 'pull_request' &&
+                    github.event.pull_request.head.repo.full_name == github.repository
+                ) ||
+                (
+                    github.event_name == 'pull_request_target' &&
+                    github.event.pull_request.head.repo.full_name != github.repository
+                )
+            ) &&
+            (
+                (github.event.action == 'opened' && github.event.pull_request.draft == false && github.event.pull_request.author_association != 'FIRST_TIME_CONTRIBUTOR' && github.event.pull_request.author_association != 'NONE') ||
+                (github.event.action == 'ready_for_review' && github.event.pull_request.author_association != 'FIRST_TIME_CONTRIBUTOR' && github.event.pull_request.author_association != 'NONE') ||
                 (github.event.action == 'labeled' && github.event.label.name == 'review-this') ||
                 (
                     github.event.action == 'review_requested' &&

.github/workflows/py-tests.yml

@@ -60,10 +60,6 @@ jobs:
         run: PYTHONPATH=".:$PYTHONPATH" poetry run pytest --forked -n auto -s ./tests/unit --cov=openhands --cov-branch
         env:
           COVERAGE_FILE: ".coverage.${{ matrix.python_version }}"
-      - name: Run Runtime Tests with CLIRuntime
-        run: PYTHONPATH=".:$PYTHONPATH" TEST_RUNTIME=cli poetry run pytest -n 5 --reruns 2 --reruns-delay 3 -s tests/runtime/test_bash.py --cov=openhands --cov-branch
-        env:
-          COVERAGE_FILE: ".coverage.runtime.${{ matrix.python_version }}"
       - name: Store coverage file
         uses: actions/upload-artifact@v7
         with:

AGENTS.md

@@ -284,6 +284,32 @@ If you are starting a pull request (PR), please follow the template in `.github/
 
 These details may or may not be useful for your current task.
 
+### Conversation State Management
+
+#### Agent State and Sandbox Status:
+The frontend uses `useAgentState` hook (`frontend/src/hooks/use-agent-state.ts`) to determine the current conversation state. This hook:
+- Returns `curAgentState` (AgentState enum) for UI state determination
+- Returns `isArchived` flag when `sandbox_status === "MISSING"` (archived conversations)
+- Prioritizes live WebSocket execution status over cached API data
+
+#### Archived Conversations (sandbox_status === "MISSING"):
+When a conversation's sandbox is no longer available (archived):
+- `useAgentState` returns `AgentState.STOPPED` and `isArchived: true`
+- Chat input is replaced with an archived banner (`ArchivedBanner` component)
+- VS Code tab, Terminal, and Planner show read-only messages instead of loading states
+- All interactive elements that require a running sandbox are disabled
+
+#### Testing useAgentState:
+When mocking `useAgentState` in tests, always include the `isArchived` property:
+```typescript
+vi.mock("#/hooks/use-agent-state", () => ({
+  useAgentState: () => ({
+    curAgentState: AgentState.AWAITING_USER_INPUT,
+    isArchived: false,
+  }),
+}));
+```
+
 ### Microagents
 
 Microagents are specialized prompts that enhance OpenHands with domain-specific knowledge and task-specific workflows. They are Markdown files that can include frontmatter for configuration.
@@ -363,6 +389,7 @@ There are two main patterns for saving settings in the OpenHands frontend:
 **When to use each pattern:**
 - Use Pattern 1 (Immediate Save) for entity management where each item is independent
 - Use Pattern 2 (Manual Save) for configuration forms where settings are interdependent or need validation
+- Git provider tokens in the local/OSS integrations settings are managed through the V1 secrets endpoints (`POST`/`DELETE /api/v1/secrets/git-providers`). Do not reuse the logout flow for disconnecting tokens; `useLogout` is for actual app logout and still targets legacy OSS logout behavior.
 
 ### Adding New LLM Models
 

CONTRIBUTING.md

@@ -36,7 +36,6 @@ Full details in our [Development Guide](./Development.md).
 
 - **[Frontend](./frontend/README.md)** - React application
 - **[App Server (V1)](./openhands/app_server/README.md)** - Current FastAPI application server and REST API modules
-- **[Runtime](./openhands/runtime/README.md)** - Execution environments
 - **[Evaluation](https://github.com/OpenHands/benchmarks)** - Testing and benchmarks
 
 ## What Can You Build?

CREDITS.md

@@ -16,7 +16,7 @@ open source community:
 
 #### [Aider](https://github.com/paul-gauthier/aider)
    - License: Apache License 2.0
-   - Description: AI pair programming tool. OpenHands has adapted and integrated its linter module for code-related tasks in [`agentskills utilities`](https://github.com/OpenHands/OpenHands/tree/main/openhands/runtime/plugins/agent_skills/utils/aider)
+   - Description: AI pair programming tool. OpenHands has adapted and integrated its linter module for code-related tasks.
 
 #### [BrowserGym](https://github.com/ServiceNow/BrowserGym)
    - License: Apache License 2.0

Development.md

@@ -309,16 +309,6 @@ poetry run pytest ./tests/unit/test_*.py
 
 ---
 
-## Using Existing Docker Images
-
-To reduce build time, you can use an existing runtime image:
-
-```bash
-export SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/openhands/runtime:1.2-nikolaik
-```
-
----
-
 ## Help
 
 ```bash
@@ -339,4 +329,3 @@ make help
 - [/tests/unit/README.md](./tests/unit/README.md): Guide to writing and running unit tests
 - [OpenHands/benchmarks](https://github.com/OpenHands/benchmarks): Documentation for the evaluation framework and benchmarks
 - [/skills/README.md](./skills/README.md): Information about the skills architecture and implementation
-- [/openhands/runtime/README.md](./openhands/runtime/README.md): Documentation for the runtime environment and execution model

containers/app/Dockerfile

@@ -88,7 +88,6 @@ USER openhands
 
 COPY --chown=openhands:openhands --chmod=770 ./skills ./skills
 COPY --chown=openhands:openhands --chmod=770 ./openhands ./openhands
-COPY --chown=openhands:openhands --chmod=777 ./openhands/runtime/plugins ./openhands/runtime/plugins
 COPY --chown=openhands:openhands pyproject.toml poetry.lock README.md MANIFEST.in LICENSE ./
 
 # Add this line to set group ownership of all files/directories not already in "app" group

containers/app/entrypoint.sh

@@ -23,18 +23,6 @@ if [ -z "$WORKSPACE_MOUNT_PATH" ]; then
   unset WORKSPACE_BASE
 fi
 
-if [[ "$INSTALL_THIRD_PARTY_RUNTIMES" == "true" ]]; then
-  echo "Downloading and installing third_party_runtimes..."
-  echo "Warning: Third-party runtimes are provided as-is, not actively supported and may be removed in future releases."
-
-  if pip install 'openhands-ai[third_party_runtimes]' -qqq 2> >(tee /dev/stderr); then
-    echo "third_party_runtimes installed successfully."
-  else
-    echo "Failed to install third_party_runtimes." >&2
-    exit 1
-  fi
-fi
-
 if [[ "$SANDBOX_USER_ID" -eq 0 ]]; then
   echo "Running OpenHands as root"
   export RUN_AS_OPENHANDS=false

dev_config/python/.pre-commit-config.yaml

@@ -3,9 +3,9 @@ repos:
     rev: v5.0.0
     hooks:
       - id: trailing-whitespace
-        exclude: ^(docs/|modules/|python/|openhands-ui/|third_party/|enterprise/)
+        exclude: ^(docs/|modules/|python/|openhands-ui/|enterprise/)
       - id: end-of-file-fixer
-        exclude: ^(docs/|modules/|python/|openhands-ui/|third_party/|enterprise/)
+        exclude: ^(docs/|modules/|python/|openhands-ui/|enterprise/)
       - id: check-yaml
         args: ["--allow-multiple-documents"]
       - id: debug-statements
@@ -37,12 +37,12 @@ repos:
         entry: ruff check --config dev_config/python/ruff.toml
         types_or: [python, pyi, jupyter]
         args: [--fix, --unsafe-fixes]
-        exclude: ^(third_party/|enterprise/)
+        exclude: ^(enterprise/)
       # Run the formatter.
       - id: ruff-format
         entry: ruff format --config dev_config/python/ruff.toml
         types_or: [python, pyi, jupyter]
-        exclude: ^(third_party/|enterprise/)
+        exclude: ^(enterprise/)
 
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.15.0

dev_config/python/mypy.ini

@@ -10,7 +10,7 @@ strict_optional = True
 disable_error_code = type-abstract
 
 # Exclude third-party runtime directory from type checking
-exclude = (third_party/|enterprise/)
+exclude = (enterprise/)
 
 [mypy-openai.*]
 follow_imports = skip

dev_config/python/ruff.toml

@@ -1,5 +1,5 @@
 # Exclude third-party runtime directory from linting
-exclude = ["third_party/", "enterprise/"]
+exclude = ["enterprise/"]
 
 [lint]
 select = [

enterprise/enterprise_local/README.md

@@ -61,13 +61,6 @@ export LITE_LLM_API_KEY=<your LLM API key>
 python enterprise_local/convert_to_env.py
 ```
 
-You'll also need to set up the runtime image, so that the dev server doesn't try to rebuild it.
-
-```
-export SANDBOX_RUNTIME_CONTAINER_IMAGE=ghcr.io/openhands/runtime:main-nikolaik
-docker pull $SANDBOX_RUNTIME_CONTAINER_IMAGE
-```
-
 By default the application will log in json, you can override.
 
 ```
@@ -203,7 +196,6 @@ And then invoking `printenv`. NOTE: _DO NOT DO THIS WITH PROD!!!_ (Hopefully by
                 "REDIS_HOST": "localhost:6379",
                 "OPENHANDS": "<YOUR LOCAL OPENHANDS DIR>",
                 "FRONTEND_DIRECTORY": "<YOUR LOCAL OPENHANDS DIR>/frontend/build",
-                "SANDBOX_RUNTIME_CONTAINER_IMAGE": "ghcr.io/openhands/runtime:main-nikolaik",
                 "FILE_STORE_PATH": "<YOUR HOME DIRECTORY>>/.openhands-state",
                 "OPENHANDS_CONFIG_CLS": "server.config.SaaSServerConfig",
                 "GITHUB_APP_ID": "1062351",
@@ -237,7 +229,6 @@ And then invoking `printenv`. NOTE: _DO NOT DO THIS WITH PROD!!!_ (Hopefully by
                 "REDIS_HOST": "localhost:6379",
                 "OPENHANDS": "<YOUR LOCAL OPENHANDS DIR>",
                 "FRONTEND_DIRECTORY": "<YOUR LOCAL OPENHANDS DIR>/frontend/build",
-                "SANDBOX_RUNTIME_CONTAINER_IMAGE": "ghcr.io/openhands/runtime:main-nikolaik",
                 "FILE_STORE_PATH": "<YOUR HOME DIRECTORY>>/.openhands-state",
                 "OPENHANDS_CONFIG_CLS": "server.config.SaaSServerConfig",
                 "GITHUB_APP_ID": "1062351",

enterprise/enterprise_local/convert_to_env.py

@@ -112,9 +112,6 @@ lines.append(
 lines.append(
     'OPENHANDS_BITBUCKET_DATA_CENTER_SERVICE_CLS=integrations.bitbucket_data_center.bitbucket_dc_service.SaaSBitbucketDCService'
 )
-lines.append(
-    'OPENHANDS_CONVERSATION_VALIDATOR_CLS=storage.saas_conversation_validator.SaasConversationValidator'
-)
 lines.append('POSTHOG_CLIENT_KEY=test')
 lines.append('ENABLE_PROACTIVE_CONVERSATION_STARTERS=true')
 lines.append('MAX_CONCURRENT_CONVERSATIONS=10')

enterprise/integrations/github/github_manager.py

@@ -2,7 +2,6 @@ from types import MappingProxyType
 
 from github import Auth, Github, GithubIntegration
 from integrations.github.data_collector import GitHubDataCollector
-from integrations.github.github_solvability import summarize_issue_solvability
 from integrations.github.github_view import (
     GithubFactory,
     GithubFailingAction,
@@ -20,7 +19,6 @@ from integrations.models import (
 from integrations.types import ResolverViewInterface
 from integrations.utils import (
     CONVERSATION_URL,
-    ENABLE_SOLVABILITY_ANALYSIS,
     HOST_URL,
     OPENHANDS_RESOLVER_TEMPLATES_DIR,
     get_session_expired_message,
@@ -33,6 +31,7 @@ from server.auth.auth_error import ExpiredError
 from server.auth.constants import GITHUB_APP_CLIENT_ID, GITHUB_APP_PRIVATE_KEY
 from server.auth.token_manager import TokenManager
 
+from openhands.app_server.secrets.secrets_models import Secrets
 from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.provider import ProviderToken, ProviderType
 from openhands.integrations.service_types import AuthenticationError
@@ -41,7 +40,6 @@ from openhands.server.types import (
     MissingSettingsError,
     SessionExpiredError,
 )
-from openhands.storage.data_models.secrets import Secrets
 
 
 class GithubManager(Manager[GithubViewType]):
@@ -358,26 +356,7 @@ class GithubManager(Manager[GithubViewType]):
                     )
                 )
 
-                # We first initialize a conversation and generate the solvability report BEFORE starting the conversation runtime
-                # This helps us accumulate llm spend without requiring a running runtime. This setups us up for
-                #   1. If there is a problem starting the runtime we still have accumulated total conversation cost
-                #   2. In the future, based on the report confidence we can conditionally start the conversation
-                #   3. Once the conversation is started, its base cost will include the report's spend as well which allows us to control max budget per resolver task
-                convo_metadata = await github_view.initialize_new_conversation()
-                solvability_summary = None
-                if not ENABLE_SOLVABILITY_ANALYSIS:
-                    logger.info(
-                        '[Github]: Solvability report feature is disabled, skipping'
-                    )
-                else:
-                    try:
-                        solvability_summary = await summarize_issue_solvability(
-                            github_view, user_token
-                        )
-                    except Exception as e:
-                        logger.warning(
-                            f'[Github]: Error summarizing issue solvability: {str(e)}'
-                        )
+                conversation_id = await github_view.initialize_new_conversation()
 
                 saas_user_auth = await get_saas_user_auth(
                     github_view.user_info.keycloak_user_id, self.token_manager
@@ -386,26 +365,21 @@ class GithubManager(Manager[GithubViewType]):
                 await github_view.create_new_conversation(
                     self.jinja_env,
                     secret_store.provider_tokens,
-                    convo_metadata,
+                    conversation_id,
                     saas_user_auth,
                 )
 
-                conversation_id = github_view.conversation_id
+                conversation_id_hex = github_view.conversation_id
 
                 logger.info(
-                    f'[GitHub] Created conversation {conversation_id} for user {user_info.username}'
+                    f'[GitHub] Created conversation {conversation_id_hex} for user {user_info.username}'
                 )
 
                 # V1 callback processors are registered by the view during conversation creation
 
                 # Send message with conversation link
-                conversation_link = CONVERSATION_URL.format(conversation_id)
-                base_msg = f"I'm on it! {user_info.username} can [track my progress at all-hands.dev]({conversation_link})"
-                # Combine messages: include solvability report with "I'm on it!" if successful
-                if solvability_summary:
-                    msg_info = f'{base_msg}\n\n{solvability_summary}'
-                else:
-                    msg_info = base_msg
+                conversation_link = CONVERSATION_URL.format(conversation_id_hex)
+                msg_info = f"I'm on it! {user_info.username} can [track my progress at all-hands.dev]({conversation_link})"
 
             except MissingSettingsError as e:
                 logger.warning(

enterprise/integrations/github/github_solvability.py

@@ -1,188 +0,0 @@
-import asyncio
-import time
-
-from github import Auth, Github
-from integrations.github.github_view import (
-    GithubInlinePRComment,
-    GithubIssueComment,
-    GithubPRComment,
-    GithubViewType,
-)
-from integrations.solvability.data import load_classifier
-from integrations.solvability.models.report import SolvabilityReport
-from integrations.solvability.models.summary import SolvabilitySummary
-from integrations.utils import ENABLE_SOLVABILITY_ANALYSIS
-from pydantic import ValidationError
-from server.config import get_config
-from storage.saas_settings_store import SaasSettingsStore
-
-from openhands.core.config import LLMConfig
-from openhands.core.logger import openhands_logger as logger
-from openhands.utils.async_utils import call_sync_from_async
-from openhands.utils.utils import create_registry_and_conversation_stats
-
-
-def fetch_github_issue_context(
-    github_view: GithubViewType,
-    user_token: str,
-) -> str:
-    """Fetch full GitHub issue/PR context including title, body, and comments.
-
-    Args:
-        full_repo_name: Full repository name in the format 'owner/repo'
-        issue_number: The issue or PR number
-        user_token: GitHub user access token
-        max_comments: Maximum number of comments to fetch (default: 10)
-        max_comment_length: Maximum length of each comment to include in the context (default: 500)
-
-    Returns:
-        A comprehensive string containing the issue/PR context
-    """
-
-    # Build context string
-    context_parts = []
-
-    # Add title and body
-    context_parts.append(f'Title: {github_view.title}')
-    context_parts.append(f'Description:\n{github_view.description}')
-
-    with Github(auth=Auth.Token(user_token)) as github_client:
-        repo = github_client.get_repo(github_view.full_repo_name)
-        issue = repo.get_issue(github_view.issue_number)
-        if issue.labels:
-            labels = [label.name for label in issue.labels]
-            context_parts.append(f"Labels: {', '.join(labels)}")
-
-    for comment in github_view.previous_comments:
-        context_parts.append(f'- {comment.author}: {comment.body}')
-
-    return '\n\n'.join(context_parts)
-
-
-async def summarize_issue_solvability(
-    github_view: GithubViewType,
-    user_token: str,
-    timeout: float = 60.0 * 5,
-) -> str:
-    """Generate a solvability summary for an issue using the resolver view interface.
-
-    Args:
-        resolver_view: A resolver view interface instance (e.g., GithubIssue, GithubPRComment)
-        user_token: GitHub user access token for API access
-        timeout: Maximum time in seconds to wait for the result (default: 60.0)
-
-    Returns:
-        The solvability summary as a string
-
-    Raises:
-        ValueError: If LLM settings cannot be found for the user
-        asyncio.TimeoutError: If the operation exceeds the specified timeout
-    """
-    if not ENABLE_SOLVABILITY_ANALYSIS:
-        raise ValueError('Solvability report feature is disabled')
-
-    if github_view.user_info.keycloak_user_id is None:
-        raise ValueError(
-            f'[Solvability] No user ID found for user {github_view.user_info.username}'
-        )
-
-    # Grab the user's information so we can load their LLM configuration
-    store = SaasSettingsStore(
-        user_id=github_view.user_info.keycloak_user_id,
-        config=get_config(),
-    )
-
-    user_settings = await store.load()
-
-    if user_settings is None:
-        raise ValueError(
-            f'[Solvability] No user settings found for user ID {github_view.user_info.user_id}'
-        )
-
-    # Check if solvability analysis is enabled for this user, exit early if
-    # needed
-    if not getattr(user_settings, 'enable_solvability_analysis', False):
-        raise ValueError(
-            f'Solvability analysis disabled for user {github_view.user_info.user_id}'
-        )
-
-    agent_settings = user_settings.agent_settings
-    llm_settings = agent_settings.llm
-    if llm_settings.api_key is None:
-        raise ValueError(
-            f'[Solvability] No LLM API key found for user {github_view.user_info.user_id}'
-        )
-
-    try:
-        llm_config = LLMConfig(
-            model=llm_settings.model,
-            api_key=llm_settings.api_key.get_secret_value(),
-            base_url=llm_settings.base_url,
-        )
-    except ValidationError as e:
-        raise ValueError(
-            f'[Solvability] Invalid LLM configuration for user {github_view.user_info.user_id}: {str(e)}'
-        )
-
-    # Fetch the full GitHub issue/PR context using the GitHub API
-    start_time = time.time()
-    issue_context = fetch_github_issue_context(github_view, user_token)
-    logger.info(
-        f'[Solvability] Grabbed issue context for {github_view.conversation_id}',
-        extra={
-            'conversation_id': github_view.conversation_id,
-            'response_latency': time.time() - start_time,
-            'full_repo_name': github_view.full_repo_name,
-            'issue_number': github_view.issue_number,
-        },
-    )
-
-    # For comment-based triggers, also include the specific comment that triggered the action
-    if isinstance(
-        github_view, (GithubIssueComment, GithubPRComment, GithubInlinePRComment)
-    ):
-        issue_context += f'\n\nTriggering Comment:\n{github_view.comment_body}'
-
-    solvability_classifier = load_classifier('default-classifier')
-
-    async with asyncio.timeout(timeout):
-        solvability_report: SolvabilityReport = await call_sync_from_async(
-            lambda: solvability_classifier.solvability_report(
-                issue_context, llm_config=llm_config
-            )
-        )
-
-        logger.info(
-            f'[Solvability] Generated report for {github_view.conversation_id}',
-            extra={
-                'conversation_id': github_view.conversation_id,
-                'report': solvability_report.model_dump(exclude=['issue']),
-            },
-        )
-
-        llm_registry, conversation_stats, _ = create_registry_and_conversation_stats(
-            get_config(),
-            github_view.conversation_id,
-            github_view.user_info.keycloak_user_id,
-            None,
-        )
-
-        solvability_summary = await call_sync_from_async(
-            lambda: SolvabilitySummary.from_report(
-                solvability_report,
-                llm=llm_registry.get_llm(
-                    service_id='solvability_analysis', config=llm_config
-                ),
-            )
-        )
-        conversation_stats.save_metrics()
-
-        logger.info(
-            f'[Solvability] Generated summary for {github_view.conversation_id}',
-            extra={
-                'conversation_id': github_view.conversation_id,
-                'summary': solvability_summary.model_dump(exclude=['content']),
-            },
-        )
-
-        return solvability_summary.format_as_markdown()

enterprise/integrations/github/github_view.py

@@ -14,11 +14,9 @@ from integrations.resolver_org_router import resolve_org_for_repo
 from integrations.types import ResolverViewInterface, UserData
 from integrations.utils import (
     ENABLE_PROACTIVE_CONVERSATION_STARTERS,
-    ENABLE_V1_GITHUB_RESOLVER,
     HOST,
     HOST_URL,
     get_oh_labels,
-    get_user_v1_enabled_setting,
     has_exact_mention,
 )
 from jinja2 import Environment
@@ -27,13 +25,13 @@ from server.auth.token_manager import TokenManager
 from server.config import get_config
 from storage.org_store import OrgStore
 from storage.proactive_conversation_store import ProactiveConversationStore
-from storage.saas_conversation_store import SaasConversationStore
 from storage.saas_secrets_store import SaasSecretsStore
 
 from openhands.agent_server.models import SendMessageRequest
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartRequest,
     AppConversationStartTaskStatus,
+    ConversationTrigger,
 )
 from openhands.app_server.config import get_app_conversation_service
 from openhands.app_server.services.injector import InjectorState
@@ -44,20 +42,11 @@ from openhands.integrations.provider import PROVIDER_TOKEN_TYPE, ProviderType
 from openhands.integrations.service_types import Comment
 from openhands.sdk import TextContent
 from openhands.server.user_auth.user_auth import UserAuth
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationMetadata,
-    ConversationTrigger,
-)
 from openhands.utils.async_utils import call_sync_from_async
-from openhands.utils.conversation_summary import get_default_conversation_title
 
 OH_LABEL, INLINE_OH_LABEL = get_oh_labels(HOST)
 
 
-async def is_v1_enabled_for_github_resolver(user_id: str) -> bool:
-    return await get_user_v1_enabled_setting(user_id) and ENABLE_V1_GITHUB_RESOLVER
-
-
 async def get_user_proactive_conversation_setting(user_id: str | None) -> bool:
     """Get the user's proactive conversation setting.
 
@@ -105,7 +94,6 @@ class GithubIssue(ResolverViewInterface):
     title: str
     description: str
     previous_comments: list[Comment]
-    v1_enabled: bool
 
     def _get_branch_name(self) -> str | None:
         return getattr(self, 'branch_name', None)
@@ -152,11 +140,7 @@ class GithubIssue(ResolverViewInterface):
 
         return user_secrets.custom_secrets if user_secrets else None
 
-    async def initialize_new_conversation(self) -> ConversationMetadata:
-        self.v1_enabled = await is_v1_enabled_for_github_resolver(
-            self.user_info.keycloak_user_id
-        )
-
+    async def initialize_new_conversation(self) -> UUID:
         # Resolve target org based on claimed git organizations
         self.resolved_org_id = await resolve_org_for_repo(
             provider='github',
@@ -164,54 +148,20 @@ class GithubIssue(ResolverViewInterface):
             keycloak_user_id=self.user_info.keycloak_user_id,
         )
 
-        logger.info(
-            f'[GitHub V1]: User flag found for {self.user_info.keycloak_user_id} is {self.v1_enabled}'
-        )
-        if self.v1_enabled:
-            # Create dummy conversationm metadata
-            # Don't save to conversation store
-            # V1 conversations are stored in a separate table
-            self.conversation_id = uuid4().hex
-            return ConversationMetadata(
-                conversation_id=self.conversation_id,
-                selected_repository=self.full_repo_name,
-            )
-
-        # Create the conversation store with resolver org routing
-        # (bypasses initialize_conversation to avoid threading enterprise-only
-        # resolver_org_id through the generic OSS interface)
-        store = await SaasConversationStore.get_resolver_instance(
-            get_config(),
-            self.user_info.keycloak_user_id,
-            self.resolved_org_id,
-        )
-
-        conversation_id = uuid4().hex
-        conversation_metadata = ConversationMetadata(
-            trigger=ConversationTrigger.RESOLVER,
-            conversation_id=conversation_id,
-            title=get_default_conversation_title(conversation_id),
-            user_id=self.user_info.keycloak_user_id,
-            selected_repository=self.full_repo_name,
-            selected_branch=self._get_branch_name(),
-            git_provider=ProviderType.GITHUB,
-        )
-        await store.save_metadata(conversation_metadata)
-
-        self.conversation_id = conversation_id
-        return conversation_metadata
+        # All conversations use V1 app conversation service
+        conversation_id = uuid4()
+        self.conversation_id = conversation_id.hex
+        return conversation_id
 
     async def create_new_conversation(
         self,
         jinja_env: Environment,
         git_provider_tokens: PROVIDER_TOKEN_TYPE,
-        conversation_metadata: ConversationMetadata,
+        conversation_id: UUID,
         saas_user_auth: UserAuth,
     ):
         # V0 conversation path has been removed - all conversations use V1 app conversation service
-        await self._create_v1_conversation(
-            jinja_env, saas_user_auth, conversation_metadata
-        )
+        await self._create_v1_conversation(jinja_env, saas_user_auth, conversation_id)
 
     async def _get_v1_initial_user_message(self, jinja_env: Environment) -> str:
         """Build the initial user message for V1 resolver conversations.
@@ -239,7 +189,7 @@ class GithubIssue(ResolverViewInterface):
         self,
         jinja_env: Environment,
         saas_user_auth: UserAuth,
-        conversation_metadata: ConversationMetadata,
+        conversation_id: UUID,
     ):
         """Create conversation using the new V1 app conversation system."""
         logger.info('[GitHub V1]: Creating V1 conversation')
@@ -259,7 +209,7 @@ class GithubIssue(ResolverViewInterface):
 
         # Create the V1 conversation start request with the callback processor
         start_request = AppConversationStartRequest(
-            conversation_id=UUID(conversation_metadata.conversation_id),
+            conversation_id=conversation_id,
             # NOTE: Resolver instructions are intended to be lower priority than the
             # system prompt, so we inject them into the initial user message.
             system_message_suffix=None,
@@ -813,7 +763,6 @@ class GithubFactory:
                 title='',
                 description='',
                 previous_comments=[],
-                v1_enabled=False,
             )
 
         elif GithubFactory.is_issue_comment(message):
@@ -839,7 +788,6 @@ class GithubFactory:
                 title='',
                 description='',
                 previous_comments=[],
-                v1_enabled=False,
             )
 
         elif GithubFactory.is_pr_comment(message):
@@ -881,7 +829,6 @@ class GithubFactory:
                 title='',
                 description='',
                 previous_comments=[],
-                v1_enabled=False,
             )
 
         elif GithubFactory.is_inline_pr_comment(message):
@@ -915,7 +862,6 @@ class GithubFactory:
                 title='',
                 description='',
                 previous_comments=[],
-                v1_enabled=False,
             )
 
         else:

enterprise/integrations/gitlab/gitlab_manager.py

@@ -25,6 +25,7 @@ from jinja2 import Environment, FileSystemLoader
 from pydantic import SecretStr
 from server.auth.token_manager import TokenManager
 
+from openhands.app_server.secrets.secrets_models import Secrets
 from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.gitlab.gitlab_service import GitLabServiceImpl
 from openhands.integrations.provider import ProviderToken, ProviderType
@@ -33,7 +34,6 @@ from openhands.server.types import (
     MissingSettingsError,
     SessionExpiredError,
 )
-from openhands.storage.data_models.secrets import Secrets
 
 
 class GitlabManager(Manager[GitlabViewType]):
@@ -208,8 +208,8 @@ class GitlabManager(Manager[GitlabViewType]):
                     )
                 )
 
-                # Initialize conversation and get metadata (following GitHub pattern)
-                convo_metadata = await gitlab_view.initialize_new_conversation()
+                # Initialize conversation and get UUID
+                conversation_id = await gitlab_view.initialize_new_conversation()
 
                 saas_user_auth = await get_saas_user_auth(
                     gitlab_view.user_info.keycloak_user_id, self.token_manager
@@ -218,19 +218,19 @@ class GitlabManager(Manager[GitlabViewType]):
                 await gitlab_view.create_new_conversation(
                     self.jinja_env,
                     secret_store.provider_tokens,
-                    convo_metadata,
+                    conversation_id,
                     saas_user_auth,
                 )
 
-                conversation_id = gitlab_view.conversation_id
+                conversation_id_hex = gitlab_view.conversation_id
 
                 logger.info(
-                    f'[GitLab] Created conversation {conversation_id} for user {user_info.username}'
+                    f'[GitLab] Created conversation {conversation_id_hex} for user {user_info.username}'
                 )
 
                 # V1 callback processors are registered by the view during conversation creation
 
-                conversation_link = CONVERSATION_URL.format(conversation_id)
+                conversation_link = CONVERSATION_URL.format(conversation_id_hex)
                 msg_info = f"I'm on it! {user_info.username} can [track my progress at all-hands.dev]({conversation_link})"
 
             except MissingSettingsError as e:

enterprise/integrations/gitlab/gitlab_view.py

@@ -6,22 +6,20 @@ from integrations.resolver_context import ResolverUserContext
 from integrations.resolver_org_router import resolve_org_for_repo
 from integrations.types import ResolverViewInterface, UserData
 from integrations.utils import (
-    ENABLE_V1_GITLAB_RESOLVER,
     HOST,
     get_oh_labels,
-    get_user_v1_enabled_setting,
     has_exact_mention,
 )
 from jinja2 import Environment
 from server.auth.token_manager import TokenManager
 from server.config import get_config
-from storage.saas_conversation_store import SaasConversationStore
 from storage.saas_secrets_store import SaasSecretsStore
 
 from openhands.agent_server.models import SendMessageRequest
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartRequest,
     AppConversationStartTaskStatus,
+    ConversationTrigger,
 )
 from openhands.app_server.config import get_app_conversation_service
 from openhands.app_server.services.injector import InjectorState
@@ -32,21 +30,12 @@ from openhands.integrations.provider import PROVIDER_TOKEN_TYPE, ProviderType
 from openhands.integrations.service_types import Comment
 from openhands.sdk import TextContent
 from openhands.server.user_auth.user_auth import UserAuth
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationMetadata,
-    ConversationTrigger,
-)
-from openhands.utils.conversation_summary import get_default_conversation_title
 
 OH_LABEL, INLINE_OH_LABEL = get_oh_labels(HOST)
 CONFIDENTIAL_NOTE = 'confidential_note'
 NOTE_TYPES = ['note', CONFIDENTIAL_NOTE]
 
 
-async def is_v1_enabled_for_gitlab_resolver(user_id: str) -> bool:
-    return await get_user_v1_enabled_setting(user_id) and ENABLE_V1_GITLAB_RESOLVER
-
-
 # =================================================
 # SECTION: Factory to create appriorate Gitlab view
 # =================================================
@@ -68,7 +57,6 @@ class GitlabIssue(ResolverViewInterface):
     description: str
     previous_comments: list[Comment]
     is_mr: bool
-    v1_enabled: bool
 
     def _get_branch_name(self) -> str | None:
         return getattr(self, 'branch_name', None)
@@ -114,10 +102,7 @@ class GitlabIssue(ResolverViewInterface):
 
         return user_secrets.custom_secrets if user_secrets else None
 
-    async def initialize_new_conversation(self) -> ConversationMetadata:
-        # v1_enabled is already set at construction time in the factory method
-        # This is the source of truth for the conversation type
-
+    async def initialize_new_conversation(self) -> UUID:
         # Resolve target org based on claimed git organizations
         self.resolved_org_id = await resolve_org_for_repo(
             provider='gitlab',
@@ -125,57 +110,26 @@ class GitlabIssue(ResolverViewInterface):
             keycloak_user_id=self.user_info.keycloak_user_id,
         )
 
-        if self.v1_enabled:
-            # Create dummy conversation metadata
-            # Don't save to conversation store
-            # V1 conversations are stored in a separate table
-            self.conversation_id = uuid4().hex
-            return ConversationMetadata(
-                conversation_id=self.conversation_id,
-                selected_repository=self.full_repo_name,
-            )
-
-        # Create the conversation store with resolver org routing
-        # (bypasses initialize_conversation to avoid threading enterprise-only
-        # resolver_org_id through the generic OSS interface)
-        store = await SaasConversationStore.get_resolver_instance(
-            get_config(),
-            self.user_info.keycloak_user_id,
-            self.resolved_org_id,
-        )
-
-        conversation_id = uuid4().hex
-        conversation_metadata = ConversationMetadata(
-            trigger=ConversationTrigger.RESOLVER,
-            conversation_id=conversation_id,
-            title=get_default_conversation_title(conversation_id),
-            user_id=self.user_info.keycloak_user_id,
-            selected_repository=self.full_repo_name,
-            selected_branch=self._get_branch_name(),
-            git_provider=ProviderType.GITLAB,
-        )
-        await store.save_metadata(conversation_metadata)
-
-        self.conversation_id = conversation_id
-        return conversation_metadata
+        # All conversations use V1 app conversation service
+        conversation_id = uuid4()
+        self.conversation_id = conversation_id.hex
+        return conversation_id
 
     async def create_new_conversation(
         self,
         jinja_env: Environment,
         git_provider_tokens: PROVIDER_TOKEN_TYPE,
-        conversation_metadata: ConversationMetadata,
+        conversation_id: UUID,
         saas_user_auth: UserAuth,
     ):
         # V0 conversation path has been removed - all conversations use V1 app conversation service
-        await self._create_v1_conversation(
-            jinja_env, saas_user_auth, conversation_metadata
-        )
+        await self._create_v1_conversation(jinja_env, saas_user_auth, conversation_id)
 
     async def _create_v1_conversation(
         self,
         jinja_env: Environment,
         saas_user_auth: UserAuth,
-        conversation_metadata: ConversationMetadata,
+        conversation_id: UUID,
     ):
         """Create conversation using the new V1 app conversation system."""
         logger.info('[GitLab V1]: Creating V1 conversation')
@@ -201,7 +155,7 @@ class GitlabIssue(ResolverViewInterface):
 
         # Create the V1 conversation start request with the callback processor
         start_request = AppConversationStartRequest(
-            conversation_id=UUID(conversation_metadata.conversation_id),
+            conversation_id=conversation_id,
             system_message_suffix=conversation_instructions,
             initial_message=initial_message,
             selected_repository=self.full_repo_name,
@@ -450,16 +404,6 @@ class GitlabFactory:
             user_id=user_id, username=username, keycloak_user_id=keycloak_user_id
         )
 
-        # Check v1_enabled at construction time - this is the source of truth
-        v1_enabled = (
-            await is_v1_enabled_for_gitlab_resolver(keycloak_user_id)
-            if keycloak_user_id
-            else False
-        )
-        logger.info(
-            f'[GitLab V1]: User flag found for {keycloak_user_id} is {v1_enabled}'
-        )
-
         if GitlabFactory.is_labeled_issue(message):
             issue_iid = payload['object_attributes']['iid']
 
@@ -481,7 +425,6 @@ class GitlabFactory:
                 description='',
                 previous_comments=[],
                 is_mr=False,
-                v1_enabled=v1_enabled,
             )
 
         elif GitlabFactory.is_issue_comment(message):
@@ -512,7 +455,6 @@ class GitlabFactory:
                 description='',
                 previous_comments=[],
                 is_mr=False,
-                v1_enabled=v1_enabled,
             )
 
         elif GitlabFactory.is_mr_comment(message):
@@ -545,7 +487,6 @@ class GitlabFactory:
                 description='',
                 previous_comments=[],
                 is_mr=True,
-                v1_enabled=v1_enabled,
             )
 
         elif GitlabFactory.is_mr_comment(message, inline=True):
@@ -586,7 +527,6 @@ class GitlabFactory:
                 description='',
                 previous_comments=[],
                 is_mr=True,
-                v1_enabled=v1_enabled,
             )
 
         raise ValueError(f'Unhandled GitLab webhook event: {message}')

enterprise/integrations/jira/jira_view.py

@@ -35,6 +35,7 @@ from openhands.agent_server.models import SendMessageRequest
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartRequest,
     AppConversationStartTaskStatus,
+    ConversationTrigger,
 )
 from openhands.app_server.config import get_app_conversation_service
 from openhands.app_server.services.injector import InjectorState
@@ -43,10 +44,6 @@ from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.provider import ProviderHandler, ProviderType
 from openhands.sdk import TextContent
 from openhands.server.user_auth.user_auth import UserAuth
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationMetadata,
-    ConversationTrigger,
-)
 from openhands.utils.http_session import httpx_verify_option
 
 JIRA_CLOUD_API_URL = 'https://api.atlassian.com/ex/jira'
@@ -192,32 +189,30 @@ class JiraNewConversationView(JiraViewInterface):
         )
         await integration_store.create_conversation(jira_conversation)
 
-        conversation_metadata = await self._create_v1_metadata()
-        await self._create_v1_conversation(jinja_env, conversation_metadata)
+        conversation_id = await self._initialize_conversation()
+        await self._create_v1_conversation(jinja_env, conversation_id)
         return self.conversation_id
 
-    async def _create_v1_metadata(self) -> ConversationMetadata:
-        """Create conversation metadata for V1 conversations.
+    async def _initialize_conversation(self) -> UUID:
+        """Initialize conversation and return the conversation ID.
 
         The JiraConversation mapping is saved to the integration store (above), but
         V1 conversation metadata is managed by the app conversation system, not
         the legacy conversation store.
         """
-        logger.info('[Jira]: Creating V1 metadata')
+        logger.info('[Jira]: Initializing V1 conversation')
 
-        # Generate a dummy conversation for V1 (not saved to store)
-        self.conversation_id = uuid4().hex
+        # Generate a conversation ID for V1
+        conversation_id = uuid4()
+        self.conversation_id = conversation_id.hex
         self.resolved_org_id = await self._get_resolved_org_id()
 
-        return ConversationMetadata(
-            conversation_id=self.conversation_id,
-            selected_repository=self.selected_repo,
-        )
+        return conversation_id
 
     async def _create_v1_conversation(
         self,
         jinja_env: Environment,
-        conversation_metadata: ConversationMetadata,
+        conversation_id: UUID,
     ):
         """Create conversation using the new V1 app conversation system."""
         logger.info('[Jira]: Creating V1 conversation')
@@ -236,7 +231,7 @@ class JiraNewConversationView(JiraViewInterface):
 
         # Create the V1 conversation start request
         start_request = AppConversationStartRequest(
-            conversation_id=UUID(conversation_metadata.conversation_id),
+            conversation_id=conversation_id,
             system_message_suffix=None,
             initial_message=initial_message,
             selected_repository=self.selected_repo,

enterprise/integrations/jira_dc/jira_dc_view.py

@@ -27,6 +27,7 @@ from openhands.agent_server.models import SendMessageRequest
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartRequest,
     AppConversationStartTaskStatus,
+    ConversationTrigger,
 )
 from openhands.app_server.config import get_app_conversation_service
 from openhands.app_server.services.injector import InjectorState
@@ -35,9 +36,6 @@ from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.provider import ProviderHandler, ProviderType
 from openhands.sdk import TextContent
 from openhands.server.user_auth.user_auth import UserAuth
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationTrigger,
-)
 
 integration_store = JiraDcIntegrationStore.get_instance()
 

enterprise/integrations/slack/slack_errors.py

@@ -26,6 +26,7 @@ class SlackErrorCode(Enum):
     PROVIDER_AUTH_FAILED = 'SLACK_ERR_006'
     LLM_AUTH_FAILED = 'SLACK_ERR_007'
     MISSING_SETTINGS = 'SLACK_ERR_008'
+    MISSING_SLACK_SCOPES = 'SLACK_ERR_009'
     UNEXPECTED_ERROR = 'SLACK_ERR_999'
 
 
@@ -98,6 +99,11 @@ _USER_MESSAGES: dict[SlackErrorCode, str] = {
         '{username} please re-login into '
         f'[OpenHands Cloud]({HOST_URL}) before starting a job.'
     ),
+    SlackErrorCode.MISSING_SLACK_SCOPES: (
+        '⚠️ The Slack app is missing required permissions. '
+        f'Please ask your workspace admin to re-install the OpenHands Slack App at {HOST_URL}/slack/install '
+        'to authorize the updated permissions.'
+    ),
     SlackErrorCode.UNEXPECTED_ERROR: (
         'Uh oh! There was an unexpected error (ref: {code}). Please try again later.'
     ),

enterprise/integrations/slack/slack_types.py

@@ -112,7 +112,6 @@ class SlackViewInterface(SlackMessageView, SummaryExtractionTracker, ABC):
     should_extract: bool
     send_summary_instruction: bool
     conversation_id: str
-    v1_enabled: bool
 
     @abstractmethod
     async def _get_instructions(self, jinja_env: Environment) -> tuple[str, str]:

enterprise/integrations/slack/slack_view.py

@@ -5,6 +5,7 @@ from uuid import UUID, uuid4
 from integrations.models import Message
 from integrations.resolver_context import ResolverUserContext
 from integrations.resolver_org_router import resolve_org_for_repo
+from integrations.slack.slack_errors import SlackError, SlackErrorCode
 from integrations.slack.slack_types import (
     SlackMessageView,
     SlackViewInterface,
@@ -13,11 +14,10 @@ from integrations.slack.slack_types import (
 from integrations.slack.slack_v1_callback_processor import SlackV1CallbackProcessor
 from integrations.utils import (
     CONVERSATION_URL,
-    ENABLE_V1_SLACK_RESOLVER,
-    get_user_v1_enabled_setting,
 )
 from jinja2 import Environment
 from slack_sdk import WebClient
+from slack_sdk.errors import SlackApiError
 from storage.slack_conversation import SlackConversation
 from storage.slack_conversation_store import SlackConversationStore
 from storage.slack_team_store import SlackTeamStore
@@ -26,6 +26,7 @@ from storage.slack_user import SlackUser
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartRequest,
     AppConversationStartTaskStatus,
+    ConversationTrigger,
     SendMessageRequest,
 )
 from openhands.app_server.config import get_app_conversation_service
@@ -36,9 +37,6 @@ from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.provider import ProviderHandler
 from openhands.sdk import TextContent
 from openhands.server.user_auth.user_auth import UserAuth
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationTrigger,
-)
 from openhands.utils.async_utils import GENERAL_TIMEOUT
 
 # =================================================
@@ -51,10 +49,6 @@ slack_conversation_store = SlackConversationStore.get_instance()
 slack_team_store = SlackTeamStore.get_instance()
 
 
-async def is_v1_enabled_for_slack_resolver(user_id: str) -> bool:
-    return await get_user_v1_enabled_setting(user_id) and ENABLE_V1_SLACK_RESOLVER
-
-
 @dataclass
 class SlackNewConversationView(SlackViewInterface):
     bot_access_token: str
@@ -70,7 +64,6 @@ class SlackNewConversationView(SlackViewInterface):
     send_summary_instruction: bool
     conversation_id: str
     team_id: str
-    v1_enabled: bool
 
     def _get_initial_prompt(self, text: str, blocks: list[dict]):
         bot_id = self._get_bot_id(blocks)
@@ -95,24 +88,34 @@ class SlackNewConversationView(SlackViewInterface):
         messages = []
         if self.thread_ts:
             client = WebClient(token=self.bot_access_token)
-            result = client.conversations_replies(
-                channel=self.channel_id,
-                ts=self.thread_ts,
-                inclusive=True,
-                latest=self.message_ts,
-                limit=CONTEXT_LIMIT,  # We can be smarter about getting more context/condensing it even in the future
-            )
+            try:
+                result = client.conversations_replies(
+                    channel=self.channel_id,
+                    ts=self.thread_ts,
+                    inclusive=True,
+                    latest=self.message_ts,
+                    limit=CONTEXT_LIMIT,  # We can be smarter about getting more context/condensing it even in the future
+                )
+            except SlackApiError as e:
+                if e.response.get('error') == 'missing_scope':
+                    raise SlackError(SlackErrorCode.MISSING_SLACK_SCOPES) from e
+                raise
 
             messages = result['messages']
 
         else:
             client = WebClient(token=self.bot_access_token)
-            result = client.conversations_history(
-                channel=self.channel_id,
-                inclusive=True,
-                latest=self.message_ts,
-                limit=CONTEXT_LIMIT,
-            )
+            try:
+                result = client.conversations_history(
+                    channel=self.channel_id,
+                    inclusive=True,
+                    latest=self.message_ts,
+                    limit=CONTEXT_LIMIT,
+                )
+            except SlackApiError as e:
+                if e.response.get('error') == 'missing_scope':
+                    raise SlackError(SlackErrorCode.MISSING_SLACK_SCOPES) from e
+                raise
 
             messages = result['messages']
             messages.reverse()
@@ -149,7 +152,7 @@ class SlackNewConversationView(SlackViewInterface):
                 'Attempting to start conversation without confirming selected repo from user'
             )
 
-    async def save_slack_convo(self, v1_enabled: bool = False):
+    async def save_slack_convo(self):
         if self.slack_to_openhands_user:
             user_info: SlackUser = self.slack_to_openhands_user
 
@@ -161,7 +164,6 @@ class SlackNewConversationView(SlackViewInterface):
                     'keycloak_user_id': user_info.keycloak_user_id,
                     'org_id': user_info.org_id,
                     'parent_id': self.thread_ts or self.message_ts,
-                    'v1_enabled': v1_enabled,
                 },
             )
             slack_conversation = SlackConversation(
@@ -171,7 +173,7 @@ class SlackNewConversationView(SlackViewInterface):
                 org_id=user_info.org_id,
                 parent_id=self.thread_ts
                 or self.message_ts,  # conversations can start in a thread reply as well; we should always references the parent's (root level msg's) message ID
-                v1_enabled=v1_enabled,
+                v1_enabled=True,  # All conversations are V1
             )
             await slack_conversation_store.create_slack_conversation(slack_conversation)
 
@@ -268,7 +270,7 @@ class SlackNewConversationView(SlackViewInterface):
                     )
 
         logger.info(f'[Slack V1]: Created new conversation: {self.conversation_id}')
-        await self.save_slack_convo(v1_enabled=True)
+        await self.save_slack_convo()
 
     def get_response_msg(self) -> str:
         user_info: SlackUser = self.slack_to_openhands_user
@@ -290,13 +292,18 @@ class SlackUpdateExistingConversationView(SlackNewConversationView):
 
     async def _get_instructions(self, jinja_env: Environment) -> tuple[str, str]:
         client = WebClient(token=self.bot_access_token)
-        result = client.conversations_replies(
-            channel=self.channel_id,
-            ts=self.message_ts,
-            inclusive=True,
-            latest=self.message_ts,
-            limit=1,  # Get exact user message, in future we can be smarter with collecting additional context
-        )
+        try:
+            result = client.conversations_replies(
+                channel=self.channel_id,
+                ts=self.message_ts,
+                inclusive=True,
+                latest=self.message_ts,
+                limit=1,  # Get exact user message, in future we can be smarter with collecting additional context
+            )
+        except SlackApiError as e:
+            if e.response.get('error') == 'missing_scope':
+                raise SlackError(SlackErrorCode.MISSING_SLACK_SCOPES) from e
+            raise
 
         user_message = result['messages'][0]
         user_message = self._get_initial_prompt(
@@ -375,7 +382,7 @@ class SlackUpdateExistingConversationView(SlackNewConversationView):
             )
 
             # 6. Send the message to the agent server
-            url = f"{agent_server_url.rstrip('/')}/api/conversations/{UUID(self.conversation_id)}/events"
+            url = f'{agent_server_url.rstrip("/")}/api/conversations/{UUID(self.conversation_id)}/events'
 
             headers = {'X-Session-API-Key': running_sandbox.session_api_key}
             payload = send_message_request.model_dump()
@@ -516,7 +523,6 @@ class SlackFactory:
                 conversation_id=conversation.conversation_id,
                 slack_conversation=conversation,
                 team_id=team_id,
-                v1_enabled=False,
             )
 
         elif SlackFactory.did_user_select_repo_from_form(message):
@@ -534,7 +540,6 @@ class SlackFactory:
                 send_summary_instruction=True,
                 conversation_id='',
                 team_id=team_id,
-                v1_enabled=False,
             )
 
         else:
@@ -552,7 +557,6 @@ class SlackFactory:
                 send_summary_instruction=True,
                 conversation_id='',
                 team_id=team_id,
-                v1_enabled=False,
             )
 
 

enterprise/integrations/solvability/data/__init__.py

@@ -1,41 +0,0 @@
-"""
-Utilities for loading and managing pre-trained classifiers.
-
-Assumes that classifiers are stored adjacent to this file in the `solvability/data` directory, using a simple
-`name + .json` pattern.
-"""
-
-from pathlib import Path
-
-from integrations.solvability.models.classifier import SolvabilityClassifier
-
-
-def load_classifier(name: str) -> SolvabilityClassifier:
-    """
-    Load a classifier by name.
-
-    Args:
-        name (str): The name of the classifier to load.
-
-    Returns:
-        SolvabilityClassifier: The loaded classifier instance.
-    """
-    data_dir = Path(__file__).parent
-    classifier_path = data_dir / f'{name}.json'
-
-    if not classifier_path.exists():
-        raise FileNotFoundError(f"Classifier '{name}' not found at {classifier_path}")
-
-    with classifier_path.open('r') as f:
-        return SolvabilityClassifier.model_validate_json(f.read())
-
-
-def available_classifiers() -> list[str]:
-    """
-    List all available classifiers in the data directory.
-
-    Returns:
-        list[str]: A list of classifier names (without the .json extension).
-    """
-    data_dir = Path(__file__).parent
-    return [f.stem for f in data_dir.glob('*.json') if f.is_file()]

enterprise/integrations/solvability/models/__init__.py

@@ -1,38 +0,0 @@
-"""
-Solvability Models Package
-
-This package contains the core machine learning models and components for predicting
-the solvability of GitHub issues and similar technical problems.
-
-The solvability prediction system works by:
-1. Using a Featurizer to extract semantic features from issue descriptions via LLM calls
-2. Training a RandomForestClassifier on these features to predict solvability
-3. Generating detailed reports with feature importance analysis
-
-Key Components:
-- Feature: Defines individual features that can be extracted from issues
-- Featurizer: Orchestrates LLM-based feature extraction with sampling and batching
-- SolvabilityClassifier: Main ML pipeline combining featurization and classification
-- SolvabilityReport: Comprehensive output with predictions, feature analysis, and metadata
-- ImportanceStrategy: Configurable methods for calculating feature importance (SHAP, permutation, impurity)
-"""
-
-from integrations.solvability.models.classifier import SolvabilityClassifier
-from integrations.solvability.models.featurizer import (
-    EmbeddingDimension,
-    Feature,
-    FeatureEmbedding,
-    Featurizer,
-)
-from integrations.solvability.models.importance_strategy import ImportanceStrategy
-from integrations.solvability.models.report import SolvabilityReport
-
-__all__ = [
-    'Feature',
-    'EmbeddingDimension',
-    'FeatureEmbedding',
-    'Featurizer',
-    'ImportanceStrategy',
-    'SolvabilityClassifier',
-    'SolvabilityReport',
-]

enterprise/integrations/solvability/models/classifier.py

@@ -1,433 +0,0 @@
-from __future__ import annotations
-
-import base64
-import pickle
-from typing import Any
-
-import numpy as np
-import pandas as pd
-import shap
-from integrations.solvability.models.featurizer import Feature, Featurizer
-from integrations.solvability.models.importance_strategy import ImportanceStrategy
-from integrations.solvability.models.report import SolvabilityReport
-from pydantic import (
-    BaseModel,
-    PrivateAttr,
-    field_serializer,
-    field_validator,
-    model_validator,
-)
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.exceptions import NotFittedError
-from sklearn.inspection import permutation_importance
-from sklearn.utils.validation import check_is_fitted
-
-from openhands.core.config import LLMConfig
-
-
-class SolvabilityClassifier(BaseModel):
-    """
-    Machine learning pipeline for predicting the solvability of GitHub issues and similar problems.
-
-    This classifier combines LLM-based feature extraction with traditional ML classification:
-    1. Uses a Featurizer to extract semantic boolean features from issue descriptions via LLM calls
-    2. Trains a RandomForestClassifier on these features to predict solvability scores
-    3. Provides feature importance analysis using configurable strategies (SHAP, permutation, impurity)
-    4. Generates comprehensive reports with predictions, feature analysis, and cost metrics
-
-    The classifier supports both training on labeled data and inference on new issues, with built-in
-    support for batch processing and concurrent feature extraction.
-    """
-
-    identifier: str
-    """
-    The identifier for the classifier.
-    """
-
-    featurizer: Featurizer
-    """
-    The featurizer to use for transforming the input data.
-    """
-
-    classifier: RandomForestClassifier
-    """
-    The RandomForestClassifier used for predicting solvability from extracted features.
-
-    This ensemble model provides robust predictions and built-in feature importance metrics.
-    """
-
-    importance_strategy: ImportanceStrategy = ImportanceStrategy.IMPURITY
-    """
-    Strategy to use for calculating feature importance.
-    """
-
-    samples: int = 10
-    """
-    Number of samples to use for calculating feature embedding coefficients.
-    """
-
-    random_state: int | None = None
-    """
-    Random state for reproducibility.
-    """
-
-    _classifier_attrs: dict[str, Any] = PrivateAttr(default_factory=dict)
-    """
-    Private dictionary storing cached results from feature extraction and importance calculations.
-
-    Contains keys like 'features_', 'cost_', 'feature_importances_', and 'labels_' that are populated
-    during transform(), fit(), and predict() operations. Access these via the corresponding properties.
-
-    This field is never serialized, so cached values will not persist across model save/load cycles.
-    """
-
-    model_config = {
-        'arbitrary_types_allowed': True,
-    }
-
-    @model_validator(mode='after')
-    def validate_random_state(self) -> SolvabilityClassifier:
-        """
-        Validate the random state configuration between this object and the classifier.
-        """
-        # If both random states are set, they definitely need to agree.
-        if self.random_state is not None and self.classifier.random_state is not None:
-            if self.random_state != self.classifier.random_state:
-                raise ValueError(
-                    'The random state of the classifier and the top-level classifier must agree.'
-                )
-
-        # Otherwise, we'll always set the classifier's random state to the top-level one.
-        self.classifier.random_state = self.random_state
-
-        return self
-
-    @property
-    def features_(self) -> pd.DataFrame:
-        """
-        Get the features used by the classifier for the most recent inputs.
-        """
-        if 'features_' not in self._classifier_attrs:
-            raise ValueError(
-                'SolvabilityClassifier.transform() has not yet been called.'
-            )
-        return self._classifier_attrs['features_']
-
-    @property
-    def cost_(self) -> pd.DataFrame:
-        """
-        Get the cost of the classifier for the most recent inputs.
-        """
-        if 'cost_' not in self._classifier_attrs:
-            raise ValueError(
-                'SolvabilityClassifier.transform() has not yet been called.'
-            )
-        return self._classifier_attrs['cost_']
-
-    @property
-    def feature_importances_(self) -> np.ndarray:
-        """
-        Get the feature importances for the most recent inputs.
-        """
-        if 'feature_importances_' not in self._classifier_attrs:
-            raise ValueError(
-                'No SolvabilityClassifier methods that produce feature importances (.fit(), .predict_proba(), and '
-                '.predict()) have been called.'
-            )
-        return self._classifier_attrs['feature_importances_']  # type: ignore[no-any-return]
-
-    @property
-    def is_fitted(self) -> bool:
-        """
-        Check if the classifier is fitted.
-        """
-        try:
-            check_is_fitted(self.classifier)
-            return True
-        except NotFittedError:
-            return False
-
-    def transform(self, issues: pd.Series, llm_config: LLMConfig) -> pd.DataFrame:
-        """
-        Transform the input issues using the featurizer to extract features.
-
-        This method orchestrates the feature extraction pipeline:
-        1. Uses the featurizer to generate embeddings for all issues
-        2. Converts embeddings to a structured DataFrame
-        3. Separates feature columns from metadata columns
-        4. Stores results for later access via properties
-
-        Args:
-            issues: A pandas Series containing the issue descriptions.
-            llm_config: LLM configuration to use for feature extraction.
-
-        Returns:
-            pd.DataFrame: A DataFrame containing only the feature columns (no metadata).
-        """
-        # Generate feature embeddings for all issues using batch processing
-        feature_embeddings = self.featurizer.embed_batch(
-            issues, samples=self.samples, llm_config=llm_config
-        )
-        df = pd.DataFrame(embedding.to_row() for embedding in feature_embeddings)
-
-        # Split into feature columns (used by classifier) and cost columns (metadata)
-        feature_columns = [feature.identifier for feature in self.featurizer.features]
-        cost_columns = [col for col in df.columns if col not in feature_columns]
-
-        # Store both sets for access via properties
-        self._classifier_attrs['features_'] = df[feature_columns]
-        self._classifier_attrs['cost_'] = df[cost_columns]
-
-        return self.features_
-
-    def fit(
-        self, issues: pd.Series, labels: pd.Series, llm_config: LLMConfig
-    ) -> SolvabilityClassifier:
-        """
-        Fit the classifier to the input issues and labels.
-
-        Args:
-            issues: A pandas Series containing the issue descriptions.
-
-            labels: A pandas Series containing the labels (0 or 1) for each issue.
-
-            llm_config: LLM configuration to use for feature extraction.
-
-        Returns:
-            SolvabilityClassifier: The fitted classifier.
-        """
-        features = self.transform(issues, llm_config=llm_config)
-        self.classifier.fit(features, labels)
-
-        # Store labels for permutation importance calculation
-        self._classifier_attrs['labels_'] = labels
-        self._classifier_attrs['feature_importances_'] = self._importance(
-            features, self.classifier.predict_proba(features), labels
-        )
-
-        return self
-
-    def predict_proba(self, issues: pd.Series, llm_config: LLMConfig) -> np.ndarray:
-        """
-        Predict the solvability probabilities for the input issues.
-
-        Returns class probabilities where the second column represents the probability
-        of the issue being solvable (positive class).
-
-        Args:
-            issues: A pandas Series containing the issue descriptions.
-            llm_config: LLM configuration to use for feature extraction.
-
-        Returns:
-            np.ndarray: Array of shape (n_samples, 2) with probabilities for each class.
-                       Column 0: probability of not solvable, Column 1: probability of solvable.
-        """
-        features = self.transform(issues, llm_config=llm_config)
-        scores = self.classifier.predict_proba(features)
-
-        # Calculate feature importances based on the configured strategy
-        # For permutation importance, we need ground truth labels if available
-        labels = self._classifier_attrs.get('labels_')
-        if (
-            self.importance_strategy == ImportanceStrategy.PERMUTATION
-            and labels is not None
-        ):
-            self._classifier_attrs['feature_importances_'] = self._importance(
-                features, scores, labels
-            )
-        else:
-            self._classifier_attrs['feature_importances_'] = self._importance(
-                features, scores
-            )
-
-        return scores  # type: ignore[no-any-return]
-
-    def predict(self, issues: pd.Series, llm_config: LLMConfig) -> np.ndarray:
-        """
-        Predict the solvability of the input issues by returning binary labels.
-
-        Uses a 0.5 probability threshold to convert probabilities to binary predictions.
-
-        Args:
-            issues: A pandas Series containing the issue descriptions.
-            llm_config: LLM configuration to use for feature extraction.
-
-        Returns:
-            np.ndarray: Boolean array where True indicates the issue is predicted as solvable.
-        """
-        probabilities = self.predict_proba(issues, llm_config=llm_config)
-        # Apply 0.5 threshold to convert probabilities to binary predictions
-        labels = probabilities[:, 1] >= 0.5
-        return labels
-
-    def _importance(
-        self,
-        features: pd.DataFrame,
-        scores: np.ndarray,
-        labels: np.ndarray | None = None,
-    ) -> np.ndarray:
-        """
-        Calculate feature importance scores using the configured strategy.
-
-        Different strategies provide different interpretations:
-        - SHAP: Shapley values indicating contribution to individual predictions
-        - PERMUTATION: Decrease in model performance when feature is shuffled
-        - IMPURITY: Gini impurity decrease from splits on each feature
-
-        Args:
-            features: Feature matrix used for predictions.
-            scores: Model prediction scores (unused for some strategies).
-            labels: Ground truth labels (required for permutation importance).
-
-        Returns:
-            np.ndarray: Feature importance scores, one per feature.
-        """
-        match self.importance_strategy:
-            case ImportanceStrategy.SHAP:
-                # Use SHAP TreeExplainer for tree-based models
-                explainer = shap.TreeExplainer(self.classifier)
-                shap_values = explainer.shap_values(features)
-                # Return mean SHAP values for the positive class (solvable)
-                return shap_values.mean(axis=0)[:, 1]  # type: ignore[no-any-return]
-
-            case ImportanceStrategy.PERMUTATION:
-                # Permutation importance requires ground truth labels
-                if labels is None:
-                    raise ValueError('Labels are required for permutation importance')
-                result = permutation_importance(
-                    self.classifier,
-                    features,
-                    labels,
-                    n_repeats=10,  # Number of permutation rounds for stability
-                    random_state=self.random_state,
-                )
-                return result.importances_mean  # type: ignore[no-any-return]
-
-            case ImportanceStrategy.IMPURITY:
-                # Use built-in feature importances from RandomForest
-                return self.classifier.feature_importances_  # type: ignore[no-any-return]
-
-            case _:
-                raise ValueError(
-                    f'Unknown importance strategy: {self.importance_strategy}'
-                )
-
-    def add_features(self, features: list[Feature]) -> SolvabilityClassifier:
-        """
-        Add new features to the classifier's featurizer.
-
-        Note: Adding features after training requires retraining the classifier
-        since the feature space will have changed.
-
-        Args:
-            features: List of Feature objects to add.
-
-        Returns:
-            SolvabilityClassifier: Self for method chaining.
-        """
-        for feature in features:
-            if feature not in self.featurizer.features:
-                self.featurizer.features.append(feature)
-        return self
-
-    def forget_features(self, features: list[Feature]) -> SolvabilityClassifier:
-        """
-        Remove features from the classifier's featurizer.
-
-        Note: Removing features after training requires retraining the classifier
-        since the feature space will have changed.
-
-        Args:
-            features: List of Feature objects to remove.
-
-        Returns:
-            SolvabilityClassifier: Self for method chaining.
-        """
-        for feature in features:
-            try:
-                self.featurizer.features.remove(feature)
-            except ValueError:
-                # Feature not in list, continue with others
-                continue
-        return self
-
-    @field_serializer('classifier')
-    @staticmethod
-    def _rfc_to_json(rfc: RandomForestClassifier) -> str:
-        """
-        Convert a RandomForestClassifier to a JSON-compatible value (a string).
-        """
-        return base64.b64encode(pickle.dumps(rfc)).decode('utf-8')
-
-    @field_validator('classifier', mode='before')
-    @staticmethod
-    def _json_to_rfc(value: str | RandomForestClassifier) -> RandomForestClassifier:
-        """
-        Convert a JSON-compatible value (a string) back to a RandomForestClassifier.
-        """
-        if isinstance(value, RandomForestClassifier):
-            return value
-
-        if isinstance(value, str):
-            try:
-                model = pickle.loads(base64.b64decode(value))
-                if isinstance(model, RandomForestClassifier):
-                    return model
-            except Exception as e:
-                raise ValueError(f'Failed to decode the classifier: {e}')
-
-        raise ValueError(
-            'The classifier must be a RandomForestClassifier or a JSON-compatible dictionary.'
-        )
-
-    def solvability_report(
-        self, issue: str, llm_config: LLMConfig, **kwargs: Any
-    ) -> SolvabilityReport:
-        """
-        Generate a solvability report for the given issue.
-
-        Args:
-            issue: The issue description for which to generate the report.
-            llm_config: Optional LLM configuration to use for feature extraction.
-            kwargs: Additional metadata to include in the report.
-
-        Returns:
-            SolvabilityReport: The generated solvability report.
-        """
-        if not self.is_fitted:
-            raise ValueError(
-                'The classifier must be fitted before generating a report.'
-            )
-
-        scores = self.predict_proba(pd.Series([issue]), llm_config=llm_config)
-
-        return SolvabilityReport(
-            identifier=self.identifier,
-            issue=issue,
-            score=scores[0, 1],
-            features=self.features_.iloc[0].to_dict(),
-            samples=self.samples,
-            importance_strategy=self.importance_strategy,
-            # Unlike the features, the importances are just a series with no link
-            # to the actual feature names. For that we have to recombine with the
-            # feature identifiers.
-            feature_importances=dict(
-                zip(
-                    self.featurizer.feature_identifiers(),
-                    self.feature_importances_.tolist(),
-                )
-            ),
-            random_state=self.random_state,
-            metadata=dict(kwargs) if kwargs else None,
-            # Both cost and response_latency are columns in the cost_ DataFrame,
-            # so we can get both by just unpacking the first row.
-            **self.cost_.iloc[0].to_dict(),
-        )
-
-    def __call__(
-        self, issue: str, llm_config: LLMConfig, **kwargs: Any
-    ) -> SolvabilityReport:
-        """
-        Generate a solvability report for the given issue.
-        """
-        return self.solvability_report(issue, llm_config=llm_config, **kwargs)

enterprise/integrations/solvability/models/difficulty_level.py

@@ -1,38 +0,0 @@
-from __future__ import annotations
-
-from enum import Enum
-
-
-class DifficultyLevel(Enum):
-    """Enum representing the difficulty level based on solvability score."""
-
-    EASY = ('EASY', 0.7, '🟢')
-    MEDIUM = ('MEDIUM', 0.4, '🟡')
-    HARD = ('HARD', 0.0, '🔴')
-
-    def __init__(self, label: str, threshold: float, emoji: str):
-        self.label = label
-        self.threshold = threshold
-        self.emoji = emoji
-
-    @classmethod
-    def from_score(cls, score: float) -> DifficultyLevel:
-        """Get difficulty level from a solvability score.
-
-        Returns the difficulty level with the highest threshold that is less than or equal to the given score.
-        """
-        # Sort enum values by threshold in descending order
-        sorted_levels = sorted(cls, key=lambda x: x.threshold, reverse=True)
-
-        # Find the first level where score meets the threshold
-        for level in sorted_levels:
-            if score >= level.threshold:
-                return level
-
-        # This should never happen if thresholds are set correctly,
-        # but return the lowest threshold level as fallback
-        return sorted_levels[-1]
-
-    def format_display(self) -> str:
-        """Format the difficulty level for display."""
-        return f'{self.emoji} **Solvability: {self.label}**'

enterprise/integrations/solvability/models/featurizer.py

@@ -1,368 +0,0 @@
-import json
-import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from typing import Any
-
-from pydantic import BaseModel
-
-from openhands.core.config import LLMConfig
-from openhands.llm.llm import LLM
-
-
-class Feature(BaseModel):
-    """
-    Represents a single boolean feature that can be extracted from issue descriptions.
-
-    Features are semantic properties of issues (e.g., "has_code_example", "requires_debugging")
-    that are evaluated by LLMs and used as input to the solvability classifier.
-    """
-
-    identifier: str
-    """Unique identifier for the feature, used as column name in feature matrices."""
-
-    description: str
-    """Human-readable description of what the feature represents, used in LLM prompts."""
-
-    @property
-    def to_tool_description_field(self) -> dict[str, Any]:
-        """
-        Convert this feature to a JSON schema field for LLM tool calling.
-
-        Returns:
-            dict: JSON schema field definition for this feature.
-        """
-        return {
-            'type': 'boolean',
-            'description': self.description,
-        }
-
-
-class EmbeddingDimension(BaseModel):
-    """
-    Represents a single dimension (feature evaluation) within a feature embedding sample.
-
-    Each dimension corresponds to one feature being evaluated as true/false for a given issue.
-    """
-
-    feature_id: str
-    """Identifier of the feature being evaluated."""
-
-    result: bool
-    """Boolean result of the feature evaluation for this sample."""
-
-
-# Type alias for a single embedding sample - maps feature identifiers to boolean values
-EmbeddingSample = dict[str, bool]
-"""
-A single sample from the LLM evaluation of features for an issue.
-Maps feature identifiers to their boolean evaluations.
-"""
-
-
-class FeatureEmbedding(BaseModel):
-    """
-    Represents the complete feature embedding for a single issue, including multiple samples
-    and associated metadata about the LLM calls used to generate it.
-
-    Multiple samples are collected to account for LLM variability and provide more robust
-    feature estimates through averaging.
-    """
-
-    samples: list[EmbeddingSample]
-    """List of individual feature evaluation samples from the LLM."""
-
-    prompt_tokens: int | None = None
-    """Total prompt tokens consumed across all LLM calls for this embedding."""
-
-    completion_tokens: int | None = None
-    """Total completion tokens generated across all LLM calls for this embedding."""
-
-    response_latency: float | None = None
-    """Total response latency (seconds) across all LLM calls for this embedding."""
-
-    @property
-    def dimensions(self) -> list[str]:
-        """
-        Get all unique feature identifiers present across all samples.
-
-        Returns:
-            list[str]: List of feature identifiers that appear in at least one sample.
-        """
-        dims: set[str] = set()
-        for sample in self.samples:
-            dims.update(sample.keys())
-        return list(dims)
-
-    def coefficient(self, dimension: str) -> float | None:
-        """
-        Calculate the average coefficient (0-1) for a specific feature dimension.
-
-        This computes the proportion of samples where the feature was evaluated as True,
-        providing a continuous feature value for the classifier.
-
-        Args:
-            dimension: Feature identifier to calculate coefficient for.
-
-        Returns:
-            float | None: Average coefficient (0.0-1.0), or None if dimension not found.
-        """
-        # Extract boolean values for this dimension, converting to 0/1
-        values = [
-            1 if v else 0
-            for v in [sample.get(dimension) for sample in self.samples]
-            if v is not None
-        ]
-        if values:
-            return sum(values) / len(values)
-        return None
-
-    def to_row(self) -> dict[str, Any]:
-        """
-        Convert the embedding to a flat dictionary suitable for DataFrame construction.
-
-        Returns:
-            dict[str, Any]: Dictionary with metadata fields and feature coefficients.
-        """
-        return {
-            'response_latency': self.response_latency,
-            'prompt_tokens': self.prompt_tokens,
-            'completion_tokens': self.completion_tokens,
-            **{dimension: self.coefficient(dimension) for dimension in self.dimensions},
-        }
-
-    def sample_entropy(self) -> dict[str, float]:
-        """
-        Calculate the Shannon entropy of feature evaluations across samples.
-
-        Higher entropy indicates more variability in LLM responses for a feature,
-        which may suggest ambiguity in the feature definition or issue description.
-
-        Returns:
-            dict[str, float]: Mapping of feature identifiers to their entropy values (0-1).
-        """
-        from collections import Counter
-        from math import log2
-
-        entropy = {}
-        for dimension in self.dimensions:
-            # Count True/False occurrences for this feature across samples
-            counts = Counter(sample.get(dimension, False) for sample in self.samples)
-            total = sum(counts.values())
-            if total == 0:
-                entropy[dimension] = 0.0
-                continue
-            # Calculate Shannon entropy: -Σ(p * log2(p))
-            entropy_value = -sum(
-                (count / total) * log2(count / total)
-                for count in counts.values()
-                if count > 0
-            )
-            entropy[dimension] = entropy_value
-        return entropy
-
-
-class Featurizer(BaseModel):
-    """
-    Orchestrates LLM-based feature extraction from issue descriptions.
-
-    The Featurizer uses structured LLM tool calling to evaluate boolean features
-    for issue descriptions. It handles prompt construction, tool schema generation,
-    and batch processing with concurrency.
-    """
-
-    system_prompt: str
-    """System prompt that provides context and instructions to the LLM."""
-
-    message_prefix: str
-    """Prefix added to user messages before the issue description."""
-
-    features: list[Feature]
-    """List of features to extract from each issue description."""
-
-    def system_message(self) -> dict[str, Any]:
-        """
-        Construct the system message for LLM conversations.
-
-        Returns:
-            dict[str, Any]: System message dictionary for LLM API calls.
-        """
-        return {
-            'role': 'system',
-            'content': self.system_prompt,
-        }
-
-    def user_message(
-        self, issue_description: str, set_cache: bool = True
-    ) -> dict[str, Any]:
-        """
-        Construct the user message containing the issue description.
-
-        Args:
-            issue_description: The description of the issue to analyze.
-            set_cache: Whether to enable ephemeral caching for this message.
-                      Should be False for single samples to avoid cache overhead.
-
-        Returns:
-            dict[str, Any]: User message dictionary for LLM API calls.
-        """
-        message: dict[str, Any] = {
-            'role': 'user',
-            'content': f'{self.message_prefix}{issue_description}',
-        }
-        if set_cache:
-            message['cache_control'] = {'type': 'ephemeral'}
-        return message
-
-    @property
-    def tool_choice(self) -> dict[str, Any]:
-        """
-        Get the tool choice configuration for forcing LLM to use the featurizer tool.
-
-        Returns:
-            dict[str, Any]: Tool choice configuration for LLM API calls.
-        """
-        return {
-            'type': 'function',
-            'function': {'name': 'call_featurizer'},
-        }
-
-    @property
-    def tool_description(self) -> dict[str, Any]:
-        """
-        Generate the tool schema for the featurizer function.
-
-        Creates a JSON schema that describes the featurizer tool with all configured
-        features as boolean parameters.
-
-        Returns:
-            dict[str, Any]: Complete tool description for LLM API calls.
-        """
-        return {
-            'type': 'function',
-            'function': {
-                'name': 'call_featurizer',
-                'description': 'Record the features present in the issue.',
-                'parameters': {
-                    'type': 'object',
-                    'properties': {
-                        feature.identifier: feature.to_tool_description_field
-                        for feature in self.features
-                    },
-                },
-            },
-        }
-
-    def embed(
-        self,
-        issue_description: str,
-        llm_config: LLMConfig,
-        temperature: float = 1.0,
-        samples: int = 10,
-    ) -> FeatureEmbedding:
-        """
-        Generate a feature embedding for a single issue description.
-
-        Makes multiple LLM calls to collect samples and reduce variance in feature evaluations.
-        Each call uses tool calling to extract structured boolean feature values.
-
-        Args:
-            issue_description: The description of the issue to analyze.
-            llm_config: Configuration for the LLM to use.
-            temperature: Sampling temperature for the model. Higher values increase randomness.
-            samples: Number of samples to generate for averaging.
-
-        Returns:
-            FeatureEmbedding: Complete embedding with samples and metadata.
-        """
-        embedding_samples: list[dict[str, Any]] = []
-        response_latency: float = 0.0
-        prompt_tokens: int = 0
-        completion_tokens: int = 0
-
-        # TODO: use llm registry
-        llm = LLM(llm_config, service_id='solvability')
-
-        # Generate multiple samples to account for LLM variability
-        for _ in range(samples):
-            start_time = time.time()
-            response = llm.completion(
-                messages=[
-                    self.system_message(),
-                    self.user_message(issue_description, set_cache=(samples > 1)),
-                ],
-                tools=[self.tool_description],
-                tool_choice=self.tool_choice,
-                temperature=temperature,
-            )
-            stop_time = time.time()
-
-            # Extract timing and token usage metrics
-            latency = stop_time - start_time
-            # Parse the structured tool call response containing feature evaluations
-            features = response.choices[0].message.tool_calls[0].function.arguments  # type: ignore[index, union-attr]
-            embedding = json.loads(features)
-
-            # Accumulate results and metrics
-            embedding_samples.append(embedding)
-            prompt_tokens += response.usage.prompt_tokens  # type: ignore[union-attr, attr-defined]
-            completion_tokens += response.usage.completion_tokens  # type: ignore[union-attr, attr-defined]
-            response_latency += latency
-
-        return FeatureEmbedding(
-            samples=embedding_samples,
-            response_latency=response_latency,
-            prompt_tokens=prompt_tokens,
-            completion_tokens=completion_tokens,
-        )
-
-    def embed_batch(
-        self,
-        issue_descriptions: list[str],
-        llm_config: LLMConfig,
-        temperature: float = 1.0,
-        samples: int = 10,
-    ) -> list[FeatureEmbedding]:
-        """
-        Generate embeddings for a batch of issue descriptions using concurrent processing.
-
-        Processes multiple issues in parallel to improve throughput while maintaining
-        result ordering.
-
-        Args:
-            issue_descriptions: List of issue descriptions to analyze.
-            llm_config: Configuration for the LLM to use.
-            temperature: Sampling temperature for the model.
-            samples: Number of samples to generate per issue.
-
-        Returns:
-            list[FeatureEmbedding]: List of embeddings in the same order as input.
-        """
-        with ThreadPoolExecutor() as executor:
-            # Submit all embedding tasks concurrently
-            future_to_desc = {
-                executor.submit(
-                    self.embed,
-                    desc,
-                    llm_config,
-                    temperature=temperature,
-                    samples=samples,
-                ): i
-                for i, desc in enumerate(issue_descriptions)
-            }
-
-            # Collect results in original order to maintain consistency
-            results: list[FeatureEmbedding] = [None] * len(issue_descriptions)  # type: ignore[list-item]
-            for future in as_completed(future_to_desc):
-                index = future_to_desc[future]
-                results[index] = future.result()
-
-            return results
-
-    def feature_identifiers(self) -> list[str]:
-        """
-        Get the identifiers of all configured features.
-
-        Returns:
-            list[str]: List of feature identifiers in the order they were defined.
-        """
-        return [feature.identifier for feature in self.features]

enterprise/integrations/solvability/models/importance_strategy.py

@@ -1,23 +0,0 @@
-from enum import Enum
-
-
-class ImportanceStrategy(str, Enum):
-    """
-    Strategy to use for calculating feature importances, which are used to estimate the predictive power of each feature
-    in training loops and explanations.
-    """
-
-    SHAP = 'shap'
-    """
-    Use SHAP (SHapley Additive exPlanations) to calculate feature importances.
-    """
-
-    PERMUTATION = 'permutation'
-    """
-    Use the permutation-based feature importances.
-    """
-
-    IMPURITY = 'impurity'
-    """
-    Use the impurity-based feature importances from the RandomForestClassifier.
-    """

enterprise/integrations/solvability/models/report.py

@@ -1,87 +0,0 @@
-from datetime import datetime
-from typing import Any
-
-from integrations.solvability.models.importance_strategy import ImportanceStrategy
-from pydantic import BaseModel, Field
-
-
-class SolvabilityReport(BaseModel):
-    """
-    Comprehensive report containing solvability predictions and analysis for a single issue.
-
-    This report includes the solvability score, extracted feature values, feature importance analysis,
-    cost metrics (tokens and latency), and metadata about the prediction process. It serves as the
-    primary output format for solvability analysis and can be used for logging, debugging, and
-    generating human-readable summaries.
-    """
-
-    identifier: str
-    """
-    The identifier of the solvability model used to generate the report.
-    """
-
-    issue: str
-    """
-    The issue description for which the solvability is predicted.
-
-    This field is exactly the input to the solvability model.
-    """
-
-    score: float
-    """
-    [0, 1]-valued score indicating the likelihood of the issue being solvable.
-    """
-
-    prompt_tokens: int
-    """
-    Total number of prompt tokens used in API calls made to generate the features.
-    """
-
-    completion_tokens: int
-    """
-    Total number of completion tokens used in API calls made to generate the features.
-    """
-
-    response_latency: float
-    """
-    Total response latency of API calls made to generate the features.
-    """
-
-    features: dict[str, float]
-    """
-    [0, 1]-valued scores for each feature in the model.
-
-    These are the values fed to the random forest classifier to generate the solvability score.
-    """
-
-    samples: int
-    """
-    Number of samples used to compute the feature embedding coefficients.
-    """
-
-    importance_strategy: ImportanceStrategy
-    """
-    Strategy used to calculate feature importances.
-    """
-
-    feature_importances: dict[str, float]
-    """
-    Importance scores for each feature in the model.
-
-    Interpretation of these scores depends on the importance strategy used.
-    """
-
-    created_at: datetime = Field(default_factory=datetime.now)
-    """
-    Datetime when the report was created.
-    """
-
-    random_state: int | None = None
-    """
-    Classifier random state used when generating this report.
-    """
-
-    metadata: dict[str, Any] | None = None
-    """
-    Metadata for logging and debugging purposes.
-    """

enterprise/integrations/solvability/models/summary.py

@@ -1,172 +0,0 @@
-from __future__ import annotations
-
-import json
-from datetime import datetime
-from typing import Any
-
-from integrations.solvability.models.difficulty_level import DifficultyLevel
-from integrations.solvability.models.report import SolvabilityReport
-from integrations.solvability.prompts import load_prompt
-from pydantic import BaseModel, Field
-
-from openhands.llm import LLM
-
-
-class SolvabilitySummary(BaseModel):
-    """Summary of the solvability analysis in human-readable format."""
-
-    score: float
-    """
-    Solvability score indicating the likelihood of the issue being solvable.
-    """
-
-    summary: str
-    """
-    The executive summary content generated by the LLM.
-    """
-
-    actionable_feedback: str
-    """
-    Actionable feedback content generated by the LLM.
-    """
-
-    positive_feedback: str
-    """
-    Positive feedback content generated by the LLM, highlighting what is good about the issue.
-    """
-
-    prompt_tokens: int
-    """
-    Number of prompt tokens used in the API call to generate the summary.
-    """
-
-    completion_tokens: int
-    """
-    Number of completion tokens used in the API call to generate the summary.
-    """
-
-    response_latency: float
-    """
-    Response latency of the API call to generate the summary.
-    """
-
-    created_at: datetime = Field(default_factory=datetime.now)
-    """
-    Datetime when the summary was created.
-    """
-
-    @staticmethod
-    def tool_description() -> dict[str, Any]:
-        """Get the tool description for the LLM."""
-        return {
-            'type': 'function',
-            'function': {
-                'name': 'solvability_summary',
-                'description': 'Generate a human-readable summary of the solvability analysis.',
-                'parameters': {
-                    'type': 'object',
-                    'properties': {
-                        'summary': {
-                            'type': 'string',
-                            'description': 'A high-level (at most two sentences) summary of the solvability report.',
-                        },
-                        'actionable_feedback': {
-                            'type': 'string',
-                            'description': (
-                                'Bullet list of 1-3 pieces of actionable feedback on how the user can address the lowest scoring relevant features.'
-                            ),
-                        },
-                        'positive_feedback': {
-                            'type': 'string',
-                            'description': (
-                                'Bullet list of 1-3 pieces of positive feedback on the issue, highlighting what is good about it.'
-                            ),
-                        },
-                    },
-                    'required': ['summary', 'actionable_feedback'],
-                },
-            },
-        }
-
-    @staticmethod
-    def tool_choice() -> dict[str, Any]:
-        """Get the tool choice for the LLM."""
-        return {
-            'type': 'function',
-            'function': {
-                'name': 'solvability_summary',
-            },
-        }
-
-    @staticmethod
-    def system_message() -> dict[str, Any]:
-        """Get the system message for the LLM."""
-        return {
-            'role': 'system',
-            'content': load_prompt('summary_system_message'),
-        }
-
-    @staticmethod
-    def user_message(report: SolvabilityReport) -> dict[str, Any]:
-        """Get the user message for the LLM."""
-        return {
-            'role': 'user',
-            'content': load_prompt(
-                'summary_user_message',
-                report=report.model_dump(),
-                difficulty_level=DifficultyLevel.from_score(report.score).value[0],
-            ),
-        }
-
-    @staticmethod
-    def from_report(report: SolvabilityReport, llm: LLM) -> SolvabilitySummary:
-        """Create a SolvabilitySummary from a SolvabilityReport."""
-        import time
-
-        start_time = time.time()
-        response = llm.completion(
-            messages=[
-                SolvabilitySummary.system_message(),
-                SolvabilitySummary.user_message(report),
-            ],
-            tools=[SolvabilitySummary.tool_description()],
-            tool_choice=SolvabilitySummary.tool_choice(),
-        )
-        response_latency = time.time() - start_time
-
-        # Grab the arguments from the forced function call
-        arguments = json.loads(
-            response.choices[0].message.tool_calls[0].function.arguments
-        )
-
-        return SolvabilitySummary(
-            # The score is copied directly from the report
-            score=report.score,
-            # Performance and usage metrics are pulled from the response
-            prompt_tokens=response.usage.prompt_tokens,
-            completion_tokens=response.usage.completion_tokens,
-            response_latency=response_latency,
-            # Every other field should be taken from the forced function call
-            **arguments,
-        )
-
-    def format_as_markdown(self) -> str:
-        """Format the summary content as Markdown."""
-        # Convert score to difficulty level enum
-        difficulty_level = DifficultyLevel.from_score(self.score)
-
-        # Create the main difficulty display
-        result = f'{difficulty_level.format_display()}\n\n{self.summary}'
-
-        # If not easy, show the three features with lowest importance scores
-        if difficulty_level != DifficultyLevel.EASY:
-            # Add dropdown with lowest importance features
-            result += '\n\nYou can make the issue easier to resolve by addressing these concerns in the conversation:\n\n'
-            result += self.actionable_feedback
-
-        # If the difficulty isn't hard, add some positive feedback
-        if difficulty_level != DifficultyLevel.HARD:
-            result += '\n\nPositive feedback:\n\n'
-            result += self.positive_feedback
-
-        return result

enterprise/integrations/solvability/prompts/__init__.py

@@ -1,13 +0,0 @@
-from pathlib import Path
-
-import jinja2
-
-
-def load_prompt(prompt: str, **kwargs) -> str:
-    """Load a prompt by name. Passes all the keyword arguments to the prompt template."""
-    env = jinja2.Environment(loader=jinja2.FileSystemLoader(Path(__file__).parent))
-    template = env.get_template(f'{prompt}.j2')
-    return template.render(**kwargs)
-
-
-__all__ = ['load_prompt']

enterprise/integrations/solvability/prompts/summary_system_message.j2

@@ -1,10 +0,0 @@
-You are a helpful assistant that generates human-readable summaries of solvability reports.
-The report predicts how likely it is that the issue can be resolved, and is produced purely based on the information provided in the issue description and comments.
-The report explains which features are present in the issue and how impactful they are to the solvability score (using SHAP values).
-Your task is to create a concise, high-level summary of the solvability analysis,
-with an emphasis on the key factors that make the issue easy or hard to resolve.
-Focus on the features with extreme scores, BUT ONLY if they are related to the issue at hand after careful consideration.
-You should NEVER mention: SHAP, scores, feature names, or technical metrics.
-You will also be given the expected difficulty of the issue, as EASY/MEDIUM/HARD.
-Be sure to frame your responses with that difficulty in mind.
-For example, if the issue is HARD you should not describe it as "straightforward".

enterprise/integrations/solvability/prompts/summary_user_message.j2

@@ -1,9 +0,0 @@
-Generate a high-level summary of the solvability report:
-
-{{ report }}
-
-We estimate the issue is {{ difficulty_level }}.
-The summary should be concise (at most two sentences) and describe the primary characteristics of this issue.
-Focus on what information is present and what factors are most relevant to resolution.
-Actionable feedback should be something that can be addressed by the user purely by providing more information.
-Positive feedback should explain the features that are positively contributing to the solvability score.

enterprise/integrations/types.py

@@ -1,6 +1,7 @@
 from dataclasses import dataclass
 from enum import Enum
 from typing import TYPE_CHECKING
+from uuid import UUID
 
 from jinja2 import Environment
 from pydantic import BaseModel
@@ -10,7 +11,6 @@ if TYPE_CHECKING:
 
     from openhands.integrations.provider import PROVIDER_TOKEN_TYPE
     from openhands.server.user_auth.user_auth import UserAuth
-    from openhands.storage.data_models.conversation_metadata import ConversationMetadata
 
 
 class GitLabResourceType(Enum):
@@ -53,11 +53,11 @@ class ResolverViewInterface(SummaryExtractionTracker):
         """Instructions passed when conversation is first initialized."""
         raise NotImplementedError()
 
-    async def initialize_new_conversation(self) -> 'ConversationMetadata':
-        """Initialize a new conversation and return metadata.
+    async def initialize_new_conversation(self) -> UUID:
+        """Initialize a new conversation and return the conversation ID.
 
-        For V1 conversations, creates a dummy ConversationMetadata.
-        For V0 conversations, initializes through the conversation store.
+        This method resolves the target organization and generates a new
+        conversation ID.
         """
         raise NotImplementedError()
 
@@ -65,7 +65,7 @@ class ResolverViewInterface(SummaryExtractionTracker):
         self,
         jinja_env: Environment,
         git_provider_tokens: 'PROVIDER_TOKEN_TYPE',
-        conversation_metadata: 'ConversationMetadata',
+        conversation_id: UUID,
         saas_user_auth: 'UserAuth',
     ) -> None:
         """Create a new conversation.
@@ -73,7 +73,7 @@ class ResolverViewInterface(SummaryExtractionTracker):
         Args:
             jinja_env: Jinja2 environment for template rendering
             git_provider_tokens: Token mapping for git providers
-            conversation_metadata: Metadata for the conversation
+            conversation_id: The UUID of the conversation to create
             saas_user_auth: User authentication for SaaS
         """
         raise NotImplementedError()

enterprise/integrations/utils.py

@@ -1,23 +1,11 @@
 from __future__ import annotations
 
-import json
 import os
 import re
 
 from jinja2 import Environment, FileSystemLoader
 from server.constants import WEB_HOST
-from storage.org_store import OrgStore
 
-from openhands.core.logger import openhands_logger as logger
-from openhands.core.schema.agent import AgentState
-from openhands.events import Event, EventSource
-from openhands.events.action import (
-    AgentFinishAction,
-    MessageAction,
-)
-from openhands.events.event_filter import EventFilter
-from openhands.events.event_store_abc import EventStoreABC
-from openhands.events.observation.agent import AgentStateChangedObservation
 from openhands.integrations.service_types import Repository
 
 # ---- DO NOT REMOVE ----
@@ -27,10 +15,8 @@ HOST = WEB_HOST
 
 IS_LOCAL_DEPLOYMENT = 'localhost' in HOST
 HOST_URL = f'https://{HOST}' if not IS_LOCAL_DEPLOYMENT else f'http://{HOST}'
-GITHUB_WEBHOOK_URL = f'{HOST_URL}/integration/github/events'
 GITLAB_WEBHOOK_URL = f'{HOST_URL}/integration/gitlab/events'
-conversation_prefix = 'conversations/{}'
-CONVERSATION_URL = f'{HOST_URL}/{conversation_prefix}'
+CONVERSATION_URL = f'{HOST_URL}/conversations/{{}}'
 
 # Toggle for auto-response feature that proactively starts conversations with users when workflow tests fail
 ENABLE_PROACTIVE_CONVERSATION_STARTERS = (
@@ -77,30 +63,11 @@ def get_user_not_found_message(username: str | None = None) -> str:
     return f"It looks like you haven't created an OpenHands account yet. Please sign up at [OpenHands Cloud]({HOST_URL}) and try again."
 
 
-# Toggle for solvability report feature
-ENABLE_SOLVABILITY_ANALYSIS = (
-    os.getenv('ENABLE_SOLVABILITY_ANALYSIS', 'false').lower() == 'true'
-)
-
-# Toggle for V1 GitHub resolver feature
-ENABLE_V1_GITHUB_RESOLVER = (
-    os.getenv('ENABLE_V1_GITHUB_RESOLVER', 'false').lower() == 'true'
-)
-
-ENABLE_V1_SLACK_RESOLVER = (
-    os.getenv('ENABLE_V1_SLACK_RESOLVER', 'false').lower() == 'true'
-)
-
-# Toggle for V1 GitLab resolver feature
-ENABLE_V1_GITLAB_RESOLVER = (
-    os.getenv('ENABLE_V1_GITLAB_RESOLVER', 'false').lower() == 'true'
-)
-
 OPENHANDS_RESOLVER_TEMPLATES_DIR = (
     os.getenv('OPENHANDS_RESOLVER_TEMPLATES_DIR')
     or 'openhands/integrations/templates/resolver/'
 )
-jinja_env = Environment(loader=FileSystemLoader(OPENHANDS_RESOLVER_TEMPLATES_DIR))
+_jinja_env = Environment(loader=FileSystemLoader(OPENHANDS_RESOLVER_TEMPLATES_DIR))
 
 
 def get_oh_labels(web_host: str) -> tuple[str, str]:
@@ -122,31 +89,11 @@ def get_oh_labels(web_host: str) -> tuple[str, str]:
 
 
 def get_summary_instruction():
-    summary_instruction_template = jinja_env.get_template('summary_prompt.j2')
+    summary_instruction_template = _jinja_env.get_template('summary_prompt.j2')
     summary_instruction = summary_instruction_template.render()
     return summary_instruction
 
 
-async def get_user_v1_enabled_setting(user_id: str | None) -> bool:
-    """Get the user's V1 conversation API setting.
-
-    Args:
-        user_id: The keycloak user ID
-
-    Returns:
-        True if V1 conversations are enabled for this user, False otherwise
-    """
-    if not user_id:
-        return False
-
-    org = await OrgStore.get_current_org_from_keycloak_user_id(user_id)
-
-    if not org or org.v1_enabled is None:
-        return False
-
-    return org.v1_enabled
-
-
 def has_exact_mention(text: str, mention: str) -> bool:
     """Check if the text contains an exact mention (not part of a larger word).
 
@@ -173,205 +120,6 @@ def has_exact_mention(text: str, mention: str) -> bool:
     return bool(re.search(rf'(?:^|[^\w@]){pattern}(?![\w-])', text_lower))
 
 
-def confirm_event_type(event: Event):
-    return isinstance(event, AgentStateChangedObservation) and not (
-        event.agent_state == AgentState.REJECTED
-        or event.agent_state == AgentState.USER_CONFIRMED
-        or event.agent_state == AgentState.USER_REJECTED
-        or event.agent_state == AgentState.LOADING
-        or event.agent_state == AgentState.RUNNING
-    )
-
-
-def get_readable_error_reason(reason: str):
-    if reason == 'STATUS$ERROR_LLM_AUTHENTICATION':
-        reason = 'Authentication with the LLM provider failed. Please check your API key or credentials'
-    elif reason == 'STATUS$ERROR_LLM_SERVICE_UNAVAILABLE':
-        reason = 'The LLM service is temporarily unavailable. Please try again later'
-    elif reason == 'STATUS$ERROR_LLM_INTERNAL_SERVER_ERROR':
-        reason = 'The LLM provider encountered an internal error. Please try again soon'
-    elif reason == 'STATUS$ERROR_LLM_OUT_OF_CREDITS':
-        reason = "You've run out of credits. Please top up to continue"
-    elif reason == 'STATUS$ERROR_LLM_CONTENT_POLICY_VIOLATION':
-        reason = 'Content policy violation. The output was blocked by content filtering policy'
-    return reason
-
-
-def get_summary_for_agent_state(
-    observations: list[AgentStateChangedObservation], conversation_link: str
-) -> str:
-    unknown_error_msg = f'OpenHands encountered an unknown error. [See the conversation]({conversation_link}) for more information, or try again'
-
-    if len(observations) == 0:
-        logger.error(
-            'Unknown error: No agent state observations found',
-            extra={'conversation_link': conversation_link},
-        )
-        return unknown_error_msg
-
-    observation: AgentStateChangedObservation = observations[0]
-    state = observation.agent_state
-
-    if state == AgentState.RATE_LIMITED:
-        logger.warning(
-            'Agent was rate limited',
-            extra={
-                'agent_state': state.value,
-                'conversation_link': conversation_link,
-                'observation_reason': getattr(observation, 'reason', None),
-            },
-        )
-        return 'OpenHands was rate limited by the LLM provider. Please try again later.'
-
-    if state == AgentState.ERROR:
-        reason = observation.reason
-        reason = get_readable_error_reason(reason)
-
-        logger.error(
-            'Agent encountered an error',
-            extra={
-                'agent_state': state.value,
-                'conversation_link': conversation_link,
-                'observation_reason': observation.reason,
-                'readable_reason': reason,
-            },
-        )
-
-        return f'OpenHands encountered an error: **{reason}**.\n\n[See the conversation]({conversation_link}) for more information.'
-
-    if state == AgentState.AWAITING_USER_INPUT:
-        logger.info(
-            'Agent is awaiting user input',
-            extra={
-                'agent_state': state.value,
-                'conversation_link': conversation_link,
-                'observation_reason': getattr(observation, 'reason', None),
-            },
-        )
-        return f'OpenHands is waiting for your input. [Continue the conversation]({conversation_link}) to provide additional instructions.'
-
-    # Log unknown agent state as error
-    logger.error(
-        'Unknown error: Unhandled agent state',
-        extra={
-            'agent_state': state.value if hasattr(state, 'value') else str(state),
-            'conversation_link': conversation_link,
-            'observation_reason': getattr(observation, 'reason', None),
-        },
-    )
-    return unknown_error_msg
-
-
-def get_final_agent_observation(
-    event_store: EventStoreABC,
-) -> list[AgentStateChangedObservation]:
-    events = list(
-        event_store.search_events(
-            filter=EventFilter(
-                source=EventSource.ENVIRONMENT,
-                include_types=(AgentStateChangedObservation,),
-            ),
-            limit=1,
-            reverse=True,
-        )
-    )
-    result = [e for e in events if isinstance(e, AgentStateChangedObservation)]
-    assert len(result) == len(events)
-    return result
-
-
-def get_last_user_msg(event_store: EventStoreABC) -> list[MessageAction]:
-    events = list(
-        event_store.search_events(
-            filter=EventFilter(
-                source=EventSource.USER,
-                include_types=(MessageAction,),
-            ),
-            limit=1,
-            reverse=True,
-        )
-    )
-    result = [e for e in events if isinstance(e, MessageAction)]
-    assert len(result) == len(events)
-    return result
-
-
-def extract_summary_from_event_store(
-    event_store: EventStoreABC, conversation_id: str
-) -> str:
-    """
-    Get agent summary or alternative message depending on current AgentState
-    """
-    conversation_link = CONVERSATION_URL.format(conversation_id)
-    summary_instruction = get_summary_instruction()
-
-    instruction_events = list(
-        event_store.search_events(
-            filter=EventFilter(
-                query=json.dumps(summary_instruction),
-                source=EventSource.USER,
-                include_types=(MessageAction,),
-            ),
-            limit=1,
-            reverse=True,
-        )
-    )
-
-    final_agent_observation = get_final_agent_observation(event_store)
-
-    # Find summary instruction event ID
-    if not instruction_events:
-        logger.warning(
-            'no_instruction_event_found', extra={'conversation_id': conversation_id}
-        )
-        return get_summary_for_agent_state(
-            final_agent_observation, conversation_link
-        )  # Agent did not receive summary instruction
-
-    summary_events = list(
-        event_store.search_events(
-            filter=EventFilter(
-                source=EventSource.AGENT,
-                include_types=(MessageAction, AgentFinishAction),
-            ),
-            limit=1,
-            reverse=True,
-            start_id=instruction_events[0].id,
-        )
-    )
-
-    if not summary_events:
-        logger.warning(
-            'no_agent_messages_found', extra={'conversation_id': conversation_id}
-        )
-        return get_summary_for_agent_state(
-            final_agent_observation, conversation_link
-        )  # Agent failed to generate summary
-
-    summary_event = summary_events[0]
-    if isinstance(summary_event, MessageAction):
-        return summary_event.content
-
-    assert isinstance(summary_event, AgentFinishAction)
-    return summary_event.final_thought
-
-
-def append_conversation_footer(message: str, conversation_id: str) -> str:
-    """
-    Append a small footer with the conversation URL to a message.
-
-    Args:
-        message: The original message content
-        conversation_id: The conversation ID to link to
-
-    Returns:
-        The message with the conversation footer appended
-    """
-    conversation_link = CONVERSATION_URL.format(conversation_id)
-    footer = f'\n\n[View full conversation]({conversation_link})'
-    return message + footer
-
-
 def infer_repo_from_message(user_msg: str) -> list[str]:
     """
     Extract all repository names in the format 'owner/repo' from various Git provider URLs

enterprise/migrations/versions/109_add_llm_profiles_to_user.py

@@ -0,0 +1,36 @@
+"""Add llm_profiles column to user table.
+
+The Settings model exposes ``llm_profiles`` (saved LLM configurations plus
+the active profile name), but the SaaS path persists a flattened Settings
+dump onto the User/Org rows. Without a column here the field is silently
+dropped on store() and always defaults to empty on load(), so saved
+profiles disappear after any settings update or page refresh.
+
+The column is plain ``String`` because the ORM-level ``EncryptedJSON``
+TypeDecorator stores JSON-serialized profiles as a JWE-encrypted string —
+profiles can carry per-profile ``api_key`` values, so the at-rest
+representation must match the existing org/member encrypted-secret pattern.
+
+Revision ID: 109
+Revises: 108
+Create Date: 2026-04-28
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = '109'
+down_revision: Union[str, None] = '108'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column('user', sa.Column('llm_profiles', sa.String(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column('user', 'llm_profiles')

enterprise/migrations/versions/110_add_agent_kind_to_conversation_metadata.py

@@ -0,0 +1,31 @@
+"""Add agent_kind column to conversation_metadata table.
+
+Stores the agent type ('llm' or 'acp') for each conversation so the
+correct agent-server endpoint can be used when routing requests.
+
+Revision ID: 110
+Revises: 109
+Create Date: 2026-04-28
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = '110'
+down_revision: Union[str, None] = '109'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        'conversation_metadata',
+        sa.Column('agent_kind', sa.String(), nullable=True),
+    )
+
+
+def downgrade() -> None:
+    op.drop_column('conversation_metadata', 'agent_kind')

enterprise/poetry.lock

@@ -4961,14 +4961,14 @@ files = [
 
 [[package]]
 name = "lmnr"
-version = "0.7.46"
+version = "0.7.49"
 description = "Python SDK for Laminar"
 optional = false
 python-versions = "<4,>=3.10"
 groups = ["main"]
 files = [
-    {file = "lmnr-0.7.46-py3-none-any.whl", hash = "sha256:596599af3eb999c5fb253640967fa893d34998b78c577b8773c214d89efa81c9"},
-    {file = "lmnr-0.7.46.tar.gz", hash = "sha256:082c9d17a1962b559651eea843eff49c1ec54729654ba37388c4a360e862af78"},
+    {file = "lmnr-0.7.49-py3-none-any.whl", hash = "sha256:510113b02bac3e639fa80244c67ff0be5948234275b0ef04cd310d66c7d720bf"},
+    {file = "lmnr-0.7.49.tar.gz", hash = "sha256:0b6da7d1707ce4e248c15083835a70723be9e6cc652b77ddc95c12e27dc87ef3"},
 ]
 
 [package.dependencies]
@@ -4984,11 +4984,11 @@ opentelemetry-sdk = ">=1.39.0,<2.0.0"
 opentelemetry-semantic-conventions = "0.60b1"
 opentelemetry-semantic-conventions-ai = "0.4.13"
 orjson = ">=3.0.0,<4.0.0"
-packaging = ">=22.0"
+packaging = ">=22.0,<27.0"
 pydantic = ">=2.0.3,<3.0.0"
 python-dotenv = ">=1.0,<2.0"
 tenacity = ">=8.0,<10.0"
-tqdm = ">=4.0"
+tqdm = ">=4.0,<5.0"
 
 [package.extras]
 alephalpha = ["opentelemetry-instrumentation-alephalpha (==0.52.4)"]
@@ -6454,14 +6454,14 @@ llama = ["llama-index (>=0.12.29,<0.13.0)", "llama-index-core (>=0.12.29,<0.13.0
 
 [[package]]
 name = "openhands-agent-server"
-version = "1.17.0"
+version = "1.19.0"
 description = "OpenHands Agent Server - REST/WebSocket interface for OpenHands AI Agent"
 optional = false
 python-versions = ">=3.12"
 groups = ["main"]
 files = [
-    {file = "openhands_agent_server-1.17.0-py3-none-any.whl", hash = "sha256:44336cad001c31caeb516481a5a7aea6dd9b5ab4798461f147b5231668d8fb74"},
-    {file = "openhands_agent_server-1.17.0.tar.gz", hash = "sha256:3a88449a3b9ded653dcd2a8c518810c75602873cf9f7d4e8f9b90fd8fd225652"},
+    {file = "openhands_agent_server-1.19.0-py3-none-any.whl", hash = "sha256:132902dc918f446e3b0f5cda9f4da36a4881fc73fe509eb177959afe988c38bb"},
+    {file = "openhands_agent_server-1.19.0.tar.gz", hash = "sha256:4f81b5ec550881706b361c51a422b6daad2a33c73b94d2f3088c84ed32ce049e"},
 ]
 
 [package.dependencies]
@@ -6523,9 +6523,9 @@ memory-profiler = ">=0.61"
 numpy = "*"
 openai = "2.8"
 openhands-aci = "0.3.3"
-openhands-agent-server = "1.17"
-openhands-sdk = "1.17"
-openhands-tools = "1.17"
+openhands-agent-server = "1.19"
+openhands-sdk = "1.19"
+openhands-tools = "1.19"
 opentelemetry-api = ">=1.33.1"
 opentelemetry-exporter-otlp-proto-grpc = ">=1.33.1"
 orjson = ">=3.11.6"
@@ -6547,7 +6547,7 @@ python-docx = "*"
 python-dotenv = "*"
 python-frontmatter = ">=1.1"
 python-json-logger = ">=3.2.1"
-python-multipart = ">=0.0.22"
+python-multipart = ">=0.0.26"
 python-pptx = "*"
 python-socketio = "5.14"
 pythonnet = {version = "*", markers = "sys_platform == \"win32\""}
@@ -6571,23 +6571,20 @@ uvicorn = "*"
 whatthepatch = ">=1.0.6"
 zope-interface = "7.2"
 
-[package.extras]
-third-party-runtimes = ["daytona (==0.24.2)", "e2b-code-interpreter (>=2)", "modal (>=0.66.26,<1.2)", "runloop-api-client (==0.50)"]
-
 [package.source]
 type = "directory"
 url = ".."
 
 [[package]]
 name = "openhands-sdk"
-version = "1.17.0"
+version = "1.19.0"
 description = "OpenHands SDK - Core functionality for building AI agents"
 optional = false
 python-versions = ">=3.12"
 groups = ["main"]
 files = [
-    {file = "openhands_sdk-1.17.0-py3-none-any.whl", hash = "sha256:3b771e72209453871c3036a562cf33e9ad9642a54bd48edb44f89915ac54709d"},
-    {file = "openhands_sdk-1.17.0.tar.gz", hash = "sha256:3c69df6590f023a514137272d413658848e0d5bc9aecf941b946c8662862779a"},
+    {file = "openhands_sdk-1.19.0-py3-none-any.whl", hash = "sha256:704906533da50f2d0e93bf28609b1a36a4aa4ce578bfac13a3d1a76609d87db8"},
+    {file = "openhands_sdk-1.19.0.tar.gz", hash = "sha256:5611d877e6495a712725569f6bca3de8fabefd9e44c61dc30bd39f8883371508"},
 ]
 
 [package.dependencies]
@@ -6598,7 +6595,7 @@ fastmcp = ">=3.0.0"
 filelock = ">=3.20.1"
 httpx = {version = ">=0.27.0", extras = ["socks"]}
 litellm = ">=1.82.6,<1.82.7 || >1.82.7,<1.82.8 || >1.82.8"
-lmnr = ">=0.7.24"
+lmnr = ">=0.7.47"
 pydantic = ">=2.12.5"
 python-frontmatter = ">=1.1.0"
 python-json-logger = ">=3.3.0"
@@ -6610,14 +6607,14 @@ boto3 = ["boto3 (>=1.35.0)"]
 
 [[package]]
 name = "openhands-tools"
-version = "1.17.0"
+version = "1.19.0"
 description = "OpenHands Tools - Runtime tools for AI agents"
 optional = false
 python-versions = ">=3.12"
 groups = ["main"]
 files = [
-    {file = "openhands_tools-1.17.0-py3-none-any.whl", hash = "sha256:76cd30fcc153627444f18638bcd926c9190989f80a3492381e84a181c021d815"},
-    {file = "openhands_tools-1.17.0.tar.gz", hash = "sha256:4a9d6c1aec00d366d0feb1ac2e9ee9988ad9806a0ef89f7dbe4655644e639d4a"},
+    {file = "openhands_tools-1.19.0-py3-none-any.whl", hash = "sha256:ff5ddb40d628a468eda4488b2c0045470c88e396bab43330b6b468f3ada47b9e"},
+    {file = "openhands_tools-1.19.0.tar.gz", hash = "sha256:b4dc59a813fe1fe7bda519979498a7bdf07dd8f83ea3f0aad78c154f5fcb9a32"},
 ]
 
 [package.dependencies]
@@ -14147,6 +14144,14 @@ optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-macosx_10_9_x86_64.whl", hash = "sha256:e87be7572991552606a3155d2f6c2045ded8bce94bfd9f74bf521d949c219a1c"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:86c2fdf178c66474a1be2965602818d30780e4e3ed890e3c206931f65d9a154c"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:035d259e64c41d02cc45afc3b8b46388b232e7d16d84734d851cca7334761da5"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fa472cb9de7e14fee9408e144f29f68384cd8e9c677dff0002da19f361a59bdf"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:1a0ea86eccff74e85ab4a2cf77c813fad7c84162962ce242dff0c51601028832"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:8ab26dc998bbd4b4287b129f67c10ca715deb402ed77d0645674490ea509097e"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-win_amd64.whl", hash = "sha256:d4486653feaff3314ef45534dcb6f9ea8ab3aa160896287c6473788f88eb38be"},
+    {file = "tree_sitter_c_sharp-0.23.1-cp310-abi3-win_arm64.whl", hash = "sha256:e7a14b76ec23cc8386cf662d5ea602d81331376c93ca6299a97b174047790345"},
     {file = "tree_sitter_c_sharp-0.23.1-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2b612a6e5bd17bb7fa2aab4bb6fc1fba45c94f09cb034ab332e45603b86e32fd"},
     {file = "tree_sitter_c_sharp-0.23.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:1a8b98f62bc53efcd4d971151950c9b9cd5cbe3bacdb0cd69fdccac63350d83e"},
     {file = "tree_sitter_c_sharp-0.23.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:986e93d845a438ec3c4416401aa98e6a6f6631d644bbbc2e43fcb915c51d255d"},

enterprise/saas_server.py

@@ -28,7 +28,6 @@ from server.routes.api_keys import api_router as api_keys_router  # noqa: E402
 from server.routes.auth import api_router, oauth_router  # noqa: E402
 from server.routes.billing import billing_router  # noqa: E402
 from server.routes.email import api_router as email_router  # noqa: E402
-from server.routes.feedback import router as feedback_router  # noqa: E402
 from server.routes.github_proxy import add_github_proxy_routes  # noqa: E402
 from server.routes.integration.jira import jira_integration_router  # noqa: E402
 from server.routes.integration.jira_dc import jira_dc_integration_router  # noqa: E402
@@ -147,7 +146,6 @@ if BITBUCKET_DATA_CENTER_HOST:
 
     base_app.include_router(bitbucket_dc_proxy_router)
 base_app.include_router(email_router)  # Add routes for email management
-base_app.include_router(feedback_router)  # Add routes for conversation feedback
 
 
 base_app.add_middleware(

enterprise/server/auth/constants.py

@@ -1,5 +1,7 @@
 import os
 
+from openhands.integrations.gitlab.constants import GITLAB_HOST
+
 GITHUB_APP_CLIENT_ID = os.getenv('GITHUB_APP_CLIENT_ID', '').strip()
 GITHUB_APP_CLIENT_SECRET = os.getenv('GITHUB_APP_CLIENT_SECRET', '').strip()
 GITHUB_APP_WEBHOOK_SECRET = os.getenv('GITHUB_APP_WEBHOOK_SECRET', '')
@@ -14,6 +16,7 @@ KEYCLOAK_SERVER_URL_EXT = os.getenv(
 KEYCLOAK_ADMIN_PASSWORD = os.getenv('KEYCLOAK_ADMIN_PASSWORD', '')
 GITLAB_APP_CLIENT_ID = os.getenv('GITLAB_APP_CLIENT_ID', '').strip()
 GITLAB_APP_CLIENT_SECRET = os.getenv('GITLAB_APP_CLIENT_SECRET', '').strip()
+GITLAB_TOKEN_URL = f'https://{GITLAB_HOST}/oauth/token'
 BITBUCKET_APP_CLIENT_ID = os.getenv('BITBUCKET_APP_CLIENT_ID', '').strip()
 BITBUCKET_APP_CLIENT_SECRET = os.getenv('BITBUCKET_APP_CLIENT_SECRET', '').strip()
 ENABLE_ENTERPRISE_SSO = os.getenv('ENABLE_ENTERPRISE_SSO', '').strip()

enterprise/server/auth/saas_user_auth.py

@@ -35,15 +35,15 @@ from storage.user_authorization_store import UserAuthorizationStore
 from storage.user_store import UserStore
 from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_fixed
 
+from openhands.app_server.secrets.secrets_models import Secrets
+from openhands.app_server.settings.settings_models import Settings
+from openhands.app_server.settings.settings_store import SettingsStore
 from openhands.integrations.provider import (
     PROVIDER_TOKEN_TYPE,
     ProviderToken,
     ProviderType,
 )
-from openhands.server.settings import Settings
 from openhands.server.user_auth.user_auth import AuthType, UserAuth
-from openhands.storage.data_models.secrets import Secrets
-from openhands.storage.settings.settings_store import SettingsStore
 
 token_manager = TokenManager()
 

enterprise/server/auth/token_manager.py

@@ -30,6 +30,7 @@ from server.auth.constants import (
     GITHUB_APP_CLIENT_SECRET,
     GITLAB_APP_CLIENT_ID,
     GITLAB_APP_CLIENT_SECRET,
+    GITLAB_TOKEN_URL,
     KEYCLOAK_REALM_NAME,
     KEYCLOAK_SERVER_URL,
     KEYCLOAK_SERVER_URL_EXT,
@@ -417,7 +418,7 @@ class TokenManager:
             return await self._parse_refresh_response(data)
 
     async def _refresh_gitlab_token(self, refresh_token: str) -> dict[str, str | int]:
-        url = 'https://gitlab.com/oauth/token'
+        url = GITLAB_TOKEN_URL
         logger.info(f'Refreshing GitLab token with URL: {url}')
 
         payload = {

enterprise/server/config.py

@@ -72,12 +72,6 @@ class SaaSServerConfig(ServerConfig):
     auth_url: str | None = os.environ.get('AUTH_URL')
     settings_store_class: str = 'storage.saas_settings_store.SaasSettingsStore'
     secret_store_class: str = 'storage.saas_secrets_store.SaasSecretsStore'
-    conversation_store_class: str = (
-        'storage.saas_conversation_store.SaasConversationStore'
-    )
-    monitoring_listener_class: str = (
-        'server.saas_monitoring_listener.SaaSMonitoringListener'
-    )
     user_auth_class: str = 'server.auth.saas_user_auth.SaasUserAuth'
     # Maintenance window configuration
     maintenance_start_time: str = os.environ.get(

enterprise/server/middleware.py

@@ -16,8 +16,8 @@ from server.routes.auth import set_response_cookie
 from server.utils.url_utils import get_cookie_domain, get_cookie_samesite
 
 from openhands.core.logger import openhands_logger as logger
+from openhands.server.shared import config
 from openhands.server.user_auth.user_auth import AuthType, UserAuth, get_user_auth
-from openhands.server.utils import config
 
 
 class SetAuthCookieMiddleware:

enterprise/server/routes/auth.py

@@ -703,6 +703,41 @@ async def accept_tos(request: Request):
     return response
 
 
+@api_router.get('/onboarding_status')
+async def onboarding_status(request: Request):
+    """Return whether the current user must still complete onboarding.
+
+    Kept as a dedicated endpoint instead of riding on ``GET /api/v1/settings``
+    (the natural home for fields like ``email_verified``) because the settings
+    response is heavyweight: ``SaasSettingsStore.load`` joins User, Org, and
+    OrgMember rows and deep-merges the org-level and member-level
+    ``agent_settings`` before returning. Onboarding gating runs on every
+    protected-route navigation, so we need a lightweight read of a single
+    boolean rather than paying for the full settings aggregation.
+    """
+    user_auth = cast(SaasUserAuth, await get_user_auth(request))
+    user_id = await user_auth.get_user_id()
+
+    if not user_id:
+        return JSONResponse(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            content={'error': 'User is not authenticated'},
+        )
+
+    user = await UserStore.get_user_by_id(user_id)
+    if not user:
+        return JSONResponse(
+            status_code=status.HTTP_404_NOT_FOUND,
+            content={'error': 'User not found'},
+        )
+
+    should_complete = await _should_redirect_to_onboarding(user_id, user)
+    return JSONResponse(
+        status_code=status.HTTP_200_OK,
+        content={'should_complete_onboarding': should_complete},
+    )
+
+
 @api_router.post('/complete_onboarding')
 async def complete_onboarding(request: Request):
     """Mark onboarding as completed for the current user."""

enterprise/server/routes/feedback.py

@@ -1,145 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-from fastapi import APIRouter, Depends, HTTPException, status
-from pydantic import BaseModel, Field
-from sqlalchemy.future import select
-from storage.database import a_session_maker
-from storage.feedback import ConversationFeedback
-from storage.stored_conversation_metadata_saas import StoredConversationMetadataSaas
-
-from openhands.app_server.utils.dependencies import get_dependencies
-from openhands.events.event_store import EventStore
-from openhands.server.shared import file_store
-from openhands.server.user_auth import get_user_id
-
-# We use the get_dependencies method here to signal to the OpenAPI docs that this endpoint
-# is protected. The actual protection is provided by SetAuthCookieMiddleware
-# TODO: It may be an error by you can actually post feedback to a conversation you don't
-# own right now - maybe this is useful in the context of public shared conversations?
-router = APIRouter(
-    prefix='/feedback', tags=['feedback'], dependencies=get_dependencies()
-)
-
-
-async def get_event_ids(conversation_id: str, user_id: str) -> List[int]:
-    """Get all event IDs for a given conversation.
-
-    Args:
-        conversation_id: The ID of the conversation to get events for
-        user_id: The ID of the user who owns the conversation
-
-    Returns:
-        List of event IDs in the conversation
-
-    Raises:
-        HTTPException: If conversation metadata not found
-    """
-
-    # Verify the conversation belongs to the user
-    async with a_session_maker() as session:
-        result = await session.execute(
-            select(StoredConversationMetadataSaas).where(
-                StoredConversationMetadataSaas.conversation_id == conversation_id,
-                StoredConversationMetadataSaas.user_id == user_id,
-            )
-        )
-        metadata = result.scalars().first()
-        if not metadata:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f'Conversation {conversation_id} not found',
-            )
-
-    # Create an event store to access the events directly
-    # This works even when the conversation is not running
-    event_store = EventStore(
-        sid=conversation_id,
-        file_store=file_store,
-        user_id=user_id,
-    )
-
-    # Get events from the event store
-    events = event_store.search_events(start_id=0)
-
-    # Return list of event IDs
-    return [event.id for event in events]
-
-
-class FeedbackRequest(BaseModel):
-    conversation_id: str
-    event_id: Optional[int] = None
-    rating: int = Field(..., ge=1, le=5)
-    reason: Optional[str] = None
-    metadata: Optional[Dict[str, Any]] = None
-
-
-@router.post('/conversation', status_code=status.HTTP_201_CREATED)
-async def submit_conversation_feedback(feedback: FeedbackRequest):
-    """
-    Submit feedback for a conversation.
-
-    This endpoint accepts a rating (1-5) and optional reason for the feedback.
-    The feedback is associated with a specific conversation and optionally a specific event.
-    """
-    # Validate rating is between 1 and 5
-    if feedback.rating < 1 or feedback.rating > 5:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail='Rating must be between 1 and 5',
-        )
-
-    # Create new feedback record
-    new_feedback = ConversationFeedback(
-        conversation_id=feedback.conversation_id,
-        event_id=feedback.event_id,
-        rating=feedback.rating,
-        reason=feedback.reason,
-        metadata=feedback.metadata,
-    )
-
-    # Add to database
-    async with a_session_maker() as session:
-        session.add(new_feedback)
-        await session.commit()
-
-    return {'status': 'success', 'message': 'Feedback submitted successfully'}
-
-
-@router.get('/conversation/{conversation_id}/batch')
-async def get_batch_feedback(conversation_id: str, user_id: str = Depends(get_user_id)):
-    """
-    Get feedback for all events in a conversation.
-
-    Returns feedback status for each event, including whether feedback exists
-    and if so, the rating and reason.
-    """
-    # Get all event IDs for the conversation
-    event_ids = await get_event_ids(conversation_id, user_id)
-    if not event_ids:
-        return {}
-
-    # Query for existing feedback for all events
-    async with a_session_maker() as session:
-        result = await session.execute(
-            select(ConversationFeedback).where(
-                ConversationFeedback.conversation_id == conversation_id,
-                ConversationFeedback.event_id.in_(event_ids),
-            )
-        )
-
-        # Create a mapping of event_id to feedback
-        feedback_map = {
-            feedback.event_id: {
-                'exists': True,
-                'rating': feedback.rating,
-                'reason': feedback.reason,
-            }
-            for feedback in result.scalars()
-        }
-
-        # Build response including all events
-        response = {}
-        for event_id in event_ids:
-            response[str(event_id)] = feedback_map.get(event_id, {'exists': False})
-
-        return response

enterprise/server/routes/integration/github.py

@@ -2,21 +2,25 @@ import asyncio
 import hashlib
 import hmac
 import os
+from typing import cast
 
-from fastapi import APIRouter, BackgroundTasks, Header, HTTPException, Request
+from fastapi import APIRouter, BackgroundTasks, Header, HTTPException, Request, status
 from fastapi.responses import JSONResponse
 from integrations.github.data_collector import GitHubDataCollector
 from integrations.github.github_manager import GithubManager
 from integrations.models import Message, SourceType
+from pydantic import BaseModel
 from server.auth.constants import (
     AUTOMATION_EVENT_FORWARDING_ENABLED,
     GITHUB_APP_WEBHOOK_SECRET,
 )
+from server.auth.saas_user_auth import SaasUserAuth
 from server.auth.token_manager import TokenManager
 from server.services.automation_event_service import AutomationEventService
 
 from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.provider import ProviderType
+from openhands.server.user_auth.user_auth import get_user_auth
 
 # Environment variable to disable GitHub webhooks
 GITHUB_WEBHOOKS_ENABLED = os.environ.get('GITHUB_WEBHOOKS_ENABLED', '1') in (
@@ -105,3 +109,42 @@ async def github_events(
     except Exception as e:
         logger.exception(f'Error processing GitHub event: {e}')
         return JSONResponse(status_code=400, content={'error': 'Invalid payload.'})
+
+
+class GitHubTokenResponse(BaseModel):
+    """Response model for the GitHub token endpoint."""
+
+    access_token: str
+
+
+@github_integration_router.get('/github/token')
+async def get_github_token(request: Request) -> GitHubTokenResponse:
+    """Get the GitHub access token for the authenticated user.
+
+    This endpoint retrieves the user's GitHub OAuth token, refreshing it
+    if necessary. The token can be used for GitHub API operations.
+
+    Returns:
+        GitHubTokenResponse containing the access token.
+
+    Raises:
+        HTTPException 401: If the user is not authenticated.
+        HTTPException 404: If no GitHub token is available for the user.
+    """
+    user_auth = cast(SaasUserAuth, await get_user_auth(request))
+
+    provider_tokens = await user_auth.get_provider_tokens()
+    if not provider_tokens:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail='No provider tokens available for this user.',
+        )
+
+    github_token = provider_tokens.get(ProviderType.GITHUB)
+    if not github_token or not github_token.token:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail='No GitHub token available for this user.',
+        )
+
+    return GitHubTokenResponse(access_token=github_token.token.get_secret_value())

enterprise/server/routes/integration/gitlab.py

@@ -2,15 +2,7 @@ import asyncio
 import hashlib
 import json
 
-from fastapi import (
-    APIRouter,
-    BackgroundTasks,
-    Depends,
-    Header,
-    HTTPException,
-    Request,
-    status,
-)
+from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
 from fastapi.responses import JSONResponse
 from integrations.gitlab.gitlab_manager import GitlabManager
 from integrations.gitlab.gitlab_service import SaaSGitLabService
@@ -23,15 +15,12 @@ from integrations.models import Message, SourceType
 from integrations.types import GitLabResourceType
 from integrations.utils import GITLAB_WEBHOOK_URL, IS_LOCAL_DEPLOYMENT
 from pydantic import BaseModel
-from server.auth.constants import AUTOMATION_EVENT_FORWARDING_ENABLED
 from server.auth.token_manager import TokenManager
-from server.services.automation_event_service import AutomationEventService
 from storage.gitlab_webhook import GitlabWebhook
 from storage.gitlab_webhook_store import GitlabWebhookStore
 
 from openhands.core.logger import openhands_logger as logger
 from openhands.integrations.gitlab.gitlab_service import GitLabServiceImpl
-from openhands.integrations.provider import ProviderType
 from openhands.server.shared import sio
 from openhands.server.user_auth import get_user_id
 
@@ -40,7 +29,6 @@ webhook_store = GitlabWebhookStore()
 
 token_manager = TokenManager()
 gitlab_manager = GitlabManager(token_manager)
-automation_event_service = AutomationEventService(token_manager)
 
 
 # Request/Response models
@@ -94,7 +82,6 @@ async def verify_gitlab_signature(
 @gitlab_integration_router.post('/gitlab/events')
 async def gitlab_events(
     request: Request,
-    background_tasks: BackgroundTasks,
     x_gitlab_token: str = Header(None),
     x_openhands_webhook_id: str = Header(None),
     x_openhands_user_id: str = Header(None),
@@ -125,16 +112,6 @@ async def gitlab_events(
                 content={'message': 'Duplicate GitLab event ignored.'},
             )
 
-        # Forward to automation service (fire-and-forget background task)
-        if AUTOMATION_EVENT_FORWARDING_ENABLED:
-            background_tasks.add_task(
-                automation_event_service.forward_event,
-                provider=ProviderType.GITLAB,
-                payload=payload_data,
-                installation_id=x_openhands_webhook_id,
-            )
-
-        # Existing resolver bot processing
         message = Message(
             source=SourceType.GITLAB,
             message={

enterprise/server/routes/integration/slack.py

@@ -29,6 +29,7 @@ from server.constants import (
     SLACK_WEBHOOKS_ENABLED,
 )
 from server.logger import logger
+from slack_sdk.errors import SlackApiError
 from slack_sdk.oauth import AuthorizeUrlGenerator
 from slack_sdk.signature import SignatureVerifier
 from slack_sdk.web.async_client import AsyncWebClient
@@ -46,7 +47,16 @@ slack_router = APIRouter(prefix='/slack')
 
 # Build https://slack.com/oauth/v2/authorize with sufficient query parameters
 authorize_url_generator = AuthorizeUrlGenerator(
-    client_id=SLACK_CLIENT_ID, scopes=['app_mentions:read', 'chat:write']
+    client_id=SLACK_CLIENT_ID,
+    scopes=[
+        'app_mentions:read',
+        'chat:write',
+        'users:read',
+        'channels:history',
+        'groups:history',
+        'mpim:history',
+        'im:history',
+    ],
 )
 token_manager = TokenManager()
 
@@ -232,7 +242,24 @@ async def keycloak_callback(
 
     # Retrieve the display_name from slack
     client = AsyncWebClient(token=bot_access_token)
-    slack_user_info = await client.users_info(user=slack_user_id)
+    try:
+        slack_user_info = await client.users_info(user=slack_user_id)
+    except SlackApiError as e:
+        if e.response.get('error') == 'missing_scope':
+            logger.warning(
+                'slack_missing_scope_during_install',
+                extra={'slack_user_id': slack_user_id, 'team_id': team_id},
+            )
+            return _html_response(
+                title='Re-installation Required',
+                description=(
+                    'The Slack app is missing required permissions. '
+                    f'Please <a href="{HOST_URL}/slack/install" style="color:#ecedee;text-decoration:underline;">re-install the OpenHands Slack App</a> '
+                    'to authorize the updated permissions.'
+                ),
+                status_code=400,
+            )
+        raise
     slack_display_name = slack_user_info.data['user']['profile']['display_name']
     slack_user = SlackUser(
         keycloak_user_id=keycloak_user_id,
@@ -366,7 +393,7 @@ async def on_options_load(request: Request, background_tasks: BackgroundTasks):
     # Verify this is a block_suggestion payload
     if payload.get('type') != 'block_suggestion':
         logger.warning(
-            f"slack_on_options_load: Unexpected payload type: {payload.get('type')}"
+            f'slack_on_options_load: Unexpected payload type: {payload.get("type")}'
         )
         return JSONResponse({'options': []})
 

enterprise/server/routes/mcp_patch.py

@@ -3,8 +3,8 @@ import os
 from fastmcp import Client, FastMCP
 from fastmcp.client.transports import NpxStdioTransport
 
+from openhands.app_server.mcp.mcp_router import mcp_server
 from openhands.core.logger import openhands_logger as logger
-from openhands.server.routes.mcp import mcp_server
 
 ENABLE_MCP_SEARCH_ENGINE = (
     os.getenv('ENABLE_MCP_SEARCH_ENGINE', 'false').lower() == 'true'

enterprise/server/routes/org_models.py

@@ -162,7 +162,6 @@ class OrgResponse(BaseModel):
     search_api_key: str | None = None
     sandbox_api_key: str | None = None
     max_budget_per_task: float | None = None
-    enable_solvability_analysis: bool | None = None
     v1_enabled: bool | None = None
     credits: float | None = None
     is_personal: bool = False
@@ -195,7 +194,6 @@ class OrgResponse(BaseModel):
             search_api_key=None,
             sandbox_api_key=None,
             max_budget_per_task=org.max_budget_per_task,
-            enable_solvability_analysis=org.enable_solvability_analysis,
             v1_enabled=org.v1_enabled,
             credits=credits,
             is_personal=str(org.id) == user_id if user_id else False,
@@ -232,7 +230,6 @@ class OrgUpdate(BaseModel):
     sandbox_runtime_container_image: str | None = None
     sandbox_api_key: str | None = None
     max_budget_per_task: float | None = Field(default=None, gt=0)
-    enable_solvability_analysis: bool | None = None
     v1_enabled: bool | None = None
     search_api_key: str | None = None
     llm_api_key: str | None = None
@@ -553,7 +550,6 @@ class OrgAppSettingsResponse(BaseModel):
     """Response model for organization app settings."""
 
     enable_proactive_conversation_starters: bool = True
-    enable_solvability_analysis: bool | None = None
     max_budget_per_task: float | None = None
 
     @classmethod
@@ -570,7 +566,6 @@ class OrgAppSettingsResponse(BaseModel):
             enable_proactive_conversation_starters=org.enable_proactive_conversation_starters
             if org.enable_proactive_conversation_starters is not None
             else True,
-            enable_solvability_analysis=org.enable_solvability_analysis,
             max_budget_per_task=org.max_budget_per_task,
         )
 
@@ -579,7 +574,6 @@ class OrgAppSettingsUpdate(BaseModel):
     """Request model for updating organization app settings."""
 
     enable_proactive_conversation_starters: bool | None = None
-    enable_solvability_analysis: bool | None = None
     max_budget_per_task: float | None = None
 
     @field_validator('max_budget_per_task')

enterprise/server/saas_monitoring_listener.py

@@ -1,55 +0,0 @@
-from server.logger import logger
-
-from openhands.core.config.openhands_config import OpenHandsConfig
-from openhands.core.schema.agent import AgentState
-from openhands.events.event import Event
-from openhands.events.observation import (
-    AgentStateChangedObservation,
-)
-from openhands.server.monitoring import MonitoringListener
-
-
-class SaaSMonitoringListener(MonitoringListener):
-    """Forward app signals to structured logging for GCP native monitoring."""
-
-    def on_session_event(self, event: Event) -> None:
-        """Track metrics about events being added to a Session's EventStream."""
-        if (
-            isinstance(event, AgentStateChangedObservation)
-            and event.agent_state == AgentState.ERROR
-        ):
-            logger.info(
-                'Tracking agent status error',
-                extra={'signal': 'saas_agent_status_errors'},
-            )
-
-    def on_agent_session_start(self, success: bool, duration: float) -> None:
-        """Track an agent session start.
-
-        Success is true if startup completed without error.
-        Duration is start time in seconds observed by AgentSession.
-        """
-        logger.info(
-            'Tracking agent session start',
-            extra={
-                'signal': 'saas_agent_session_start',
-                'success': success,
-                'duration': duration,
-            },
-        )
-
-    def on_create_conversation(self) -> None:
-        """Track the beginning of conversation creation.
-
-        Does not currently capture whether it succeed.
-        """
-        logger.info(
-            'Tracking create conversation', extra={'signal': 'saas_create_conversation'}
-        )
-
-    @classmethod
-    def get_instance(
-        cls,
-        config: OpenHandsConfig,
-    ) -> 'SaaSMonitoringListener':
-        return cls()

enterprise/server/services/automation_event_service.py

@@ -163,8 +163,7 @@ class AutomationEventService:
         org_id = await self._resolve_git_org(provider, git_org_name)
 
         # Fallback for personal repos (owner_type indicates individual user)
-        # GitHub uses 'User', GitLab uses 'user'
-        if not org_id and owner_type and owner_type.lower() == 'user':
+        if not org_id and owner_type == 'User':
             org_id = await self._resolve_personal_org(provider, owner_id)
             if org_id:
                 logger.info(
@@ -207,18 +206,6 @@ class AutomationEventService:
             owner = repo.get('owner', {})
             return owner.get('login'), owner.get('type'), owner.get('id')
 
-        if provider == ProviderType.GITLAB:
-            # GitLab uses 'project' instead of 'repository'
-            # path_with_namespace is like "org-name/repo-name" or "user-name/repo-name"
-            project = payload.get('project', {})
-            path_with_namespace = project.get('path_with_namespace', '')
-            git_org = path_with_namespace.split('/')[0] if path_with_namespace else None
-            namespace = project.get('namespace', {})
-            # GitLab uses 'group' for organizations and 'user' for personal projects
-            owner_type = namespace.get('kind')
-            owner_id = namespace.get('id')
-            return git_org, owner_type, owner_id
-
         logger.warning(f'Unsupported provider ({provider.value})')
         return None, None, None
 

enterprise/server/services/org_invitation_service.py

@@ -313,11 +313,22 @@ class OrgInvitationService:
             raise InvitationInvalidError('User not found')
 
         user_email = user.email
-        # Fallback: fetch email from Keycloak if not in database (for existing users)
+        # Fallback: fetch email from Keycloak if not in database (for existing users).
+        # When found, persist it back to User.email so the members list shows it
+        # without requiring the user to log out and log back in.
         if not user_email:
             token_manager = TokenManager()
             user_info = await token_manager.get_user_info_from_user_id(str(user_id))
-            user_email = user_info.get('email') if user_info else None
+            if user_info:
+                user_email = user_info.get('email')
+                if user_email:
+                    await UserStore.backfill_user_email(
+                        str(user_id),
+                        {
+                            'email': user_email,
+                            'email_verified': user_info.get('emailVerified', False),
+                        },
+                    )
 
         if not user_email:
             raise EmailMismatchError('Your account does not have an email address')

enterprise/server/sharing/shared_conversation_models.py

@@ -3,7 +3,7 @@ from datetime import datetime
 # Simplified imports to avoid dependency chain issues
 # from openhands.integrations.service_types import ProviderType
 # from openhands.sdk.llm import MetricsSnapshot
-# from openhands.storage.data_models.conversation_metadata import ConversationTrigger
+# from openhands.app_server.app_conversation.app_conversation_models import ConversationTrigger
 # For now, use Any to avoid import issues
 from typing import Any
 from uuid import uuid4

enterprise/server/utils/conversation_callback_utils.py

@@ -1,295 +0,0 @@
-import base64
-import json
-import pickle
-from datetime import datetime
-
-from server.logger import logger
-from sqlalchemy import and_, select
-from storage.conversation_callback import (
-    CallbackStatus,
-    ConversationCallback,
-    ConversationCallbackProcessor,
-)
-from storage.conversation_work import ConversationWork
-from storage.database import a_session_maker, session_maker
-from storage.stored_conversation_metadata import StoredConversationMetadata
-
-from openhands.core.config import load_openhands_config
-from openhands.core.schema.agent import AgentState
-from openhands.events.event_store import EventStore
-from openhands.events.observation.agent import AgentStateChangedObservation
-from openhands.events.serialization.event import event_from_dict
-from openhands.server.services.conversation_stats import ConversationStats
-from openhands.storage import get_file_store
-from openhands.storage.files import FileStore
-from openhands.storage.locations import (
-    get_conversation_agent_state_filename,
-    get_conversation_dir,
-)
-from openhands.utils.async_utils import call_sync_from_async
-
-config = load_openhands_config()
-file_store = get_file_store(config.file_store, config.file_store_path)
-
-
-async def process_event(
-    user_id: str, conversation_id: str, subpath: str, content: dict
-):
-    """
-    Process a conversation event and invoke any registered callbacks.
-
-    Args:
-        user_id: The user ID associated with the conversation
-        conversation_id: The conversation ID
-        subpath: The event subpath
-        content: The event content
-    """
-    logger.debug(
-        'process_event',
-        extra={
-            'user_id': user_id,
-            'conversation_id': conversation_id,
-            'content': content,
-        },
-    )
-    write_path = get_conversation_dir(conversation_id, user_id) + subpath
-
-    # This writes to the google cloud storage, so we do this in a background thread to not block the main runloop...
-    await call_sync_from_async(file_store.write, write_path, json.dumps(content))
-
-    event = event_from_dict(content)
-    if isinstance(event, AgentStateChangedObservation):
-        # Load and invoke all active callbacks for this conversation
-        await invoke_conversation_callbacks(conversation_id, event)
-
-        # Update active working seconds if agent state is not Running
-        if event.agent_state != AgentState.RUNNING:
-            event_store = EventStore(conversation_id, file_store, user_id)
-            update_active_working_seconds(
-                event_store, conversation_id, user_id, file_store
-            )
-
-
-async def invoke_conversation_callbacks(
-    conversation_id: str, observation: AgentStateChangedObservation
-):
-    """
-    Load and invoke all active callbacks for a conversation.
-
-    Args:
-        conversation_id: The conversation ID to process callbacks for
-        observation: The AgentStateChangedObservation that triggered the callback
-    """
-    async with a_session_maker() as session:
-        result = await session.execute(
-            select(ConversationCallback).filter(
-                and_(
-                    ConversationCallback.conversation_id == conversation_id,
-                    ConversationCallback.status == CallbackStatus.ACTIVE,
-                )
-            )
-        )
-        callbacks = result.scalars().all()
-
-        for callback in callbacks:
-            try:
-                processor = callback.get_processor()
-                await processor.__call__(callback, observation)
-                logger.info(
-                    'callback_invoked_successfully',
-                    extra={
-                        'conversation_id': conversation_id,
-                        'callback_id': callback.id,
-                        'processor_type': callback.processor_type,
-                    },
-                )
-            except Exception as e:
-                logger.error(
-                    'callback_invocation_failed',
-                    extra={
-                        'conversation_id': conversation_id,
-                        'callback_id': callback.id,
-                        'processor_type': callback.processor_type,
-                        'error': str(e),
-                    },
-                )
-                # Mark callback as error status
-                callback.status = CallbackStatus.ERROR
-                callback.updated_at = datetime.now()
-
-        await session.commit()
-
-
-def update_conversation_metadata(conversation_id: str, content: dict):
-    """
-    Update conversation metadata with new content.
-
-    Args:
-        conversation_id: The conversation ID to update
-        content: The metadata content to update
-    """
-    logger.debug(
-        'update_conversation_metadata',
-        extra={
-            'conversation_id': conversation_id,
-            'content': content,
-        },
-    )
-    with session_maker() as session:
-        conversation = (
-            session.query(StoredConversationMetadata)
-            .filter(StoredConversationMetadata.conversation_id == conversation_id)
-            .first()
-        )
-        conversation.title = content.get('title') or conversation.title
-        conversation.last_updated_at = datetime.now()
-        conversation.accumulated_cost = (
-            content.get('accumulated_cost') or conversation.accumulated_cost
-        )
-        conversation.prompt_tokens = (
-            content.get('prompt_tokens') or conversation.prompt_tokens
-        )
-        conversation.completion_tokens = (
-            content.get('completion_tokens') or conversation.completion_tokens
-        )
-        conversation.total_tokens = (
-            content.get('total_tokens') or conversation.total_tokens
-        )
-        session.commit()
-
-
-def register_callback_processor(
-    conversation_id: str, processor: ConversationCallbackProcessor
-) -> int:
-    """
-    Register a callback processor for a conversation.
-
-    Args:
-        conversation_id: The conversation ID to register the callback for
-        processor: The ConversationCallbackProcessor instance to register
-
-    Returns:
-        int: The ID of the created callback
-    """
-    with session_maker() as session:
-        callback = ConversationCallback(
-            conversation_id=conversation_id, status=CallbackStatus.ACTIVE
-        )
-        callback.set_processor(processor)
-        session.add(callback)
-        session.commit()
-        return callback.id
-
-
-def update_active_working_seconds(
-    event_store: EventStore, conversation_id: str, user_id: str, file_store: FileStore
-):
-    """
-    Calculate and update the total active working seconds for a conversation.
-
-    This function reads all events for the conversation, looks for AgentStateChanged
-    observations, and calculates the total time spent in a running state.
-
-    Args:
-        event_store: The EventStore instance for reading events
-        conversation_id: The conversation ID to process
-        user_id: The user ID associated with the conversation
-        file_store: The FileStore instance for accessing conversation data
-    """
-    try:
-        # Track agent state changes and calculate running time
-        running_start_time = None
-        total_running_seconds = 0.0
-
-        for event in event_store.search_events():
-            if isinstance(event, AgentStateChangedObservation) and event.timestamp:
-                event_timestamp = datetime.fromisoformat(event.timestamp).timestamp()
-
-                if event.agent_state == AgentState.RUNNING:
-                    # Agent started running
-                    if running_start_time is None:
-                        running_start_time = event_timestamp
-                elif running_start_time is not None:
-                    # Agent stopped running, calculate duration
-                    duration = event_timestamp - running_start_time
-                    total_running_seconds += duration
-                    running_start_time = None
-
-        # If agent is still running at the end, don't count that time yet
-        # (it will be counted when the agent stops)
-
-        # Create or update the conversation_work record
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            if conversation_work:
-                # Update existing record
-                conversation_work.seconds = total_running_seconds
-                conversation_work.updated_at = datetime.now().isoformat()
-            else:
-                # Create new record
-                conversation_work = ConversationWork(
-                    conversation_id=conversation_id,
-                    user_id=user_id,
-                    seconds=total_running_seconds,
-                )
-                session.add(conversation_work)
-
-            session.commit()
-
-        logger.info(
-            'updated_active_working_seconds',
-            extra={
-                'conversation_id': conversation_id,
-                'user_id': user_id,
-                'total_seconds': total_running_seconds,
-            },
-        )
-
-    except Exception as e:
-        logger.error(
-            'failed_to_update_active_working_seconds',
-            extra={
-                'conversation_id': conversation_id,
-                'user_id': user_id,
-                'error': str(e),
-            },
-        )
-
-
-def update_agent_state(user_id: str, conversation_id: str, content: bytes):
-    """
-    Update agent state file for a conversation.
-
-    Args:
-        user_id: The user ID associated with the conversation
-        conversation_id: The conversation ID
-        content: The agent state content as bytes
-    """
-    logger.debug(
-        'update_agent_state',
-        extra={
-            'user_id': user_id,
-            'conversation_id': conversation_id,
-            'content_size': len(content),
-        },
-    )
-    write_path = get_conversation_agent_state_filename(conversation_id, user_id)
-    file_store.write(write_path, content)
-
-
-def update_conversation_stats(user_id: str, conversation_id: str, content: bytes):
-    existing_convo_stats = ConversationStats(
-        file_store=file_store, conversation_id=conversation_id, user_id=user_id
-    )
-
-    incoming_convo_stats = ConversationStats(None, conversation_id, None)
-    pickled = base64.b64decode(content)
-    incoming_convo_stats.restored_metrics = pickle.loads(pickled)
-
-    # Merging automatically saves to file store
-    existing_convo_stats.merge_and_save(incoming_convo_stats)

enterprise/server/verified_models/verified_model_router.py

@@ -16,7 +16,7 @@ from server.verified_models.verified_model_service import (
 )
 
 from openhands.app_server.config import get_db_session
-from openhands.server.routes import public
+from openhands.app_server.config_api.config_router import get_llm_models_dependency
 from openhands.utils.llm import ModelsResponse, get_supported_llm_models
 
 api_router = APIRouter(prefix='/api/admin/verified-models', tags=['Verified Models'])
@@ -138,6 +138,4 @@ async def get_saas_llm_models_dependency(request: Request) -> ModelsResponse:
 # This must be called after the app is created in saas_server.py
 def override_llm_models_dependency(app):
     """Override the default LLM models implementation with SaaS version."""
-    app.dependency_overrides[public.get_llm_models_dependency] = (
-        get_saas_llm_models_dependency
-    )
+    app.dependency_overrides[get_llm_models_dependency] = get_saas_llm_models_dependency

enterprise/storage/__init__.py

@@ -2,7 +2,6 @@ from storage.api_key import ApiKey
 from storage.auth_tokens import AuthTokens
 from storage.billing_session import BillingSession
 from storage.billing_session_type import BillingSessionType
-from storage.conversation_callback import CallbackStatus, ConversationCallback
 from storage.conversation_work import ConversationWork
 from storage.feedback import ConversationFeedback, Feedback
 from storage.github_app_installation import GithubAppInstallation
@@ -45,8 +44,6 @@ __all__ = [
     'AuthTokens',
     'BillingSession',
     'BillingSessionType',
-    'CallbackStatus',
-    'ConversationCallback',
     'ConversationFeedback',
     'StoredConversationMetadataSaas',
     'ConversationWork',

enterprise/storage/base.py

@@ -3,7 +3,7 @@ Unified SQLAlchemy declarative base for all models.
 
 Re-exports the core Base to ensure enterprise and core models share the same
 metadata registry. This allows foreign key relationships between enterprise
-models (e.g., ConversationCallback) and core models (e.g., StoredConversationMetadata).
+models and core models (e.g., StoredConversationMetadata).
 
 The core Base now uses SQLAlchemy 2.0 DeclarativeBase for proper type inference
 with Mapped types, while remaining backward compatible with existing Column()

enterprise/storage/conversation_callback.py

@@ -1,115 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from openhands.events.observation.agent import AgentStateChangedObservation
-
-from abc import ABC, abstractmethod
-from datetime import datetime
-from enum import Enum
-
-from pydantic import BaseModel, ConfigDict
-from sqlalchemy import DateTime, ForeignKey, String, Text, text
-from sqlalchemy import Enum as SQLEnum
-from sqlalchemy.orm import Mapped, mapped_column
-from storage.base import Base
-
-from openhands.utils.import_utils import get_impl
-
-
-class ConversationCallbackProcessor(BaseModel, ABC):
-    """
-    Abstract base class for conversation callback processors.
-
-    Conversation processors are invoked when events occur in a conversation
-    to perform additional processing, notifications, or integrations.
-    """
-
-    model_config = ConfigDict(
-        # Allow extra fields for flexibility
-        extra='allow',
-        # Allow arbitrary types
-        arbitrary_types_allowed=True,
-    )
-
-    @abstractmethod
-    async def __call__(
-        self,
-        callback: ConversationCallback,
-        observation: 'AgentStateChangedObservation',
-    ) -> None:
-        """
-        Process a conversation event.
-
-        Args:
-            conversation_id: The ID of the conversation to process
-            observation: The AgentStateChangedObservation that triggered the callback
-            callback: The conversation callback
-        """
-
-
-class CallbackStatus(Enum):
-    """Status of a conversation callback."""
-
-    ACTIVE = 'ACTIVE'
-    COMPLETED = 'COMPLETED'
-    ERROR = 'ERROR'
-
-
-class ConversationCallback(Base):
-    """
-    Model for storing conversation callbacks that process conversation events.
-    """
-
-    __tablename__ = 'conversation_callbacks'
-
-    id: Mapped[int] = mapped_column(primary_key=True, autoincrement=True)
-    conversation_id: Mapped[str] = mapped_column(
-        String,
-        ForeignKey('conversation_metadata.conversation_id'),
-        nullable=False,
-        index=True,
-    )
-    status: Mapped[CallbackStatus] = mapped_column(
-        SQLEnum(CallbackStatus), nullable=False, default=CallbackStatus.ACTIVE
-    )
-    processor_type: Mapped[str] = mapped_column(String, nullable=False)
-    processor_json: Mapped[str] = mapped_column(Text, nullable=False)
-    created_at: Mapped[datetime] = mapped_column(
-        DateTime,
-        server_default=text('CURRENT_TIMESTAMP'),
-        nullable=False,
-    )
-    updated_at: Mapped[datetime] = mapped_column(
-        DateTime,
-        server_default=text('CURRENT_TIMESTAMP'),
-        onupdate=datetime.now,
-        nullable=False,
-    )
-
-    def get_processor(self) -> ConversationCallbackProcessor:
-        """
-        Get the processor instance from the stored processor type and JSON data.
-
-        Returns:
-            ConversationCallbackProcessor: The processor instance
-        """
-        # Import the processor class dynamically
-        processor_class: type[ConversationCallbackProcessor] = get_impl(
-            ConversationCallbackProcessor, self.processor_type
-        )
-        processor = processor_class.model_validate_json(self.processor_json)
-        return processor
-
-    def set_processor(self, processor: ConversationCallbackProcessor) -> None:
-        """
-        Set the processor instance, storing its type and JSON representation.
-
-        Args:
-            processor: The ConversationCallbackProcessor instance to store
-        """
-        self.processor_type = (
-            f'{processor.__class__.__module__}.{processor.__class__.__name__}'
-        )
-        self.processor_json = processor.model_dump_json()

enterprise/storage/encrypt_utils.py

@@ -1,10 +1,14 @@
 import binascii
 import hashlib
+import json
 from base64 import b64decode, b64encode
+from typing import Any
 
 from cryptography.fernet import Fernet, InvalidToken
-from pydantic import SecretStr
+from pydantic import BaseModel, SecretStr
 from server.config import get_config
+from sqlalchemy import String, TypeDecorator
+from sqlalchemy.engine.interfaces import Dialect
 
 _jwt_service = None
 _fernet = None
@@ -135,3 +139,39 @@ def model_to_kwargs(model_instance):
         column.name: getattr(model_instance, column.name)
         for column in model_instance.__table__.columns
     }
+
+
+class EncryptedJSON(TypeDecorator[dict[str, Any]]):
+    """JSON column whose serialized payload is encrypted at rest.
+
+    Accepts either a plain ``dict`` or a pydantic ``BaseModel``. Pydantic
+    models are dumped via ``model_dump(mode='json', context={'expose_secrets': True})``
+    so nested ``SecretStr`` values keep their real payload — the column
+    itself is the encryption boundary, so masking on the way in would
+    corrupt round-trips.
+
+    Use for JSON payloads that may contain secrets (e.g. nested ``api_key``
+    fields) where the existing ``_<field>`` String + property pattern is
+    awkward — this keeps the column accessible as a normal ORM attribute
+    while encrypting the entire JSON blob via the same JWE service used
+    by ``encrypt_value``/``decrypt_value``.
+    """
+
+    impl = String
+    cache_ok = True
+
+    def process_bind_param(
+        self, value: BaseModel | dict[str, Any] | None, dialect: Dialect
+    ) -> str | None:
+        if value is None:
+            return None
+        if isinstance(value, BaseModel):
+            value = value.model_dump(mode='json', context={'expose_secrets': True})
+        return encrypt_value(json.dumps(value))
+
+    def process_result_value(
+        self, value: str | None, dialect: Dialect
+    ) -> dict[str, Any] | None:
+        if value is None:
+            return None
+        return json.loads(decrypt_value(value))

enterprise/storage/lite_llm_manager.py

@@ -19,7 +19,7 @@ from server.constants import (
 from server.logger import logger
 from storage.user_settings import UserSettings
 
-from openhands.server.settings import Settings
+from openhands.app_server.settings.settings_models import Settings
 from openhands.utils.http_session import httpx_verify_option
 
 # Timeout in seconds for key verification requests to LiteLLM

enterprise/storage/org.py

@@ -62,9 +62,6 @@ class Org(Base):
     # encrypted column, don't set directly, set without the underscore
     _sandbox_api_key: Mapped[str | None] = mapped_column(String, nullable=True)
     max_budget_per_task: Mapped[float | None] = mapped_column(nullable=True)
-    enable_solvability_analysis: Mapped[bool | None] = mapped_column(
-        nullable=True, default=False
-    )
     v1_enabled: Mapped[bool | None] = mapped_column(nullable=True)
     conversation_expiration: Mapped[int | None] = mapped_column(nullable=True)
     byor_export_enabled: Mapped[bool] = mapped_column(nullable=False, default=False)

enterprise/storage/org_member_store.py

@@ -14,7 +14,7 @@ from storage.org_member import OrgMember
 from storage.user import User
 from storage.user_settings import UserSettings
 
-from openhands.storage.data_models.settings import Settings
+from openhands.app_server.settings.settings_models import Settings
 from openhands.utils.jsonpatch_compat import deep_merge
 
 

enterprise/storage/org_service.py

@@ -24,9 +24,9 @@ from storage.org_store import OrgStore
 from storage.role_store import RoleStore
 from storage.user_store import UserStore
 
+from openhands.app_server.settings.settings_models import Settings
 from openhands.core.logger import openhands_logger as logger
 from openhands.sdk.settings import AgentSettings, ConversationSettings
-from openhands.storage.data_models.settings import Settings
 
 
 class OrgService:

enterprise/storage/org_store.py

@@ -24,9 +24,9 @@ from storage.org_member import OrgMember
 from storage.user import User
 from storage.user_settings import UserSettings
 
+from openhands.app_server.settings.settings_models import Settings
 from openhands.core.logger import openhands_logger as logger
 from openhands.sdk.settings import AgentSettings, ConversationSettings
-from openhands.storage.data_models.settings import Settings
 from openhands.utils.jsonpatch_compat import deep_merge
 from openhands.utils.llm import is_openhands_model
 

enterprise/storage/saas_conversation_store.py

@@ -1,280 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import logging
-from dataclasses import dataclass
-from datetime import UTC
-from typing import TYPE_CHECKING, Callable, ContextManager
-from uuid import UUID
-
-from storage.database import session_maker
-
-if TYPE_CHECKING:
-    from sqlalchemy.orm import Session
-from storage.stored_conversation_metadata import StoredConversationMetadata
-from storage.stored_conversation_metadata_saas import StoredConversationMetadataSaas
-from storage.user_store import UserStore
-
-from openhands.core.config.openhands_config import OpenHandsConfig
-from openhands.integrations.provider import ProviderType
-from openhands.storage.conversation.conversation_store import ConversationStore
-from openhands.storage.data_models.conversation_metadata import (
-    ConversationMetadata,
-    ConversationTrigger,
-)
-from openhands.storage.data_models.conversation_metadata_result_set import (
-    ConversationMetadataResultSet,
-)
-from openhands.utils.async_utils import call_sync_from_async
-from openhands.utils.search_utils import offset_to_page_id, page_id_to_offset
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class SaasConversationStore(ConversationStore):
-    user_id: str
-    session_maker: Callable[[], ContextManager[Session]]
-    org_id: UUID | None = None  # will be fetched automatically
-
-    def __init__(
-        self,
-        user_id: str,
-        org_id: UUID | None,
-        session_maker: Callable[[], ContextManager[Session]],
-        resolver_org_id: UUID | None = None,
-    ):
-        self.user_id = user_id
-        self.org_id = org_id
-        self.session_maker = session_maker
-        self.resolver_org_id = resolver_org_id
-
-    def _select_by_id(self, session, conversation_id: str):
-        # Join StoredConversationMetadata with ConversationMetadataSaas to filter by user/org
-        query = (
-            session.query(StoredConversationMetadata)
-            .join(
-                StoredConversationMetadataSaas,
-                StoredConversationMetadata.conversation_id
-                == StoredConversationMetadataSaas.conversation_id,
-            )
-            .filter(StoredConversationMetadataSaas.user_id == UUID(self.user_id))
-            .filter(StoredConversationMetadata.conversation_id == conversation_id)
-            .filter(StoredConversationMetadata.conversation_version == 'V0')
-        )
-
-        if self.org_id is not None:
-            query = query.filter(StoredConversationMetadataSaas.org_id == self.org_id)
-
-        return query
-
-    def _to_external_model(
-        self, conversation_metadata: StoredConversationMetadata
-    ) -> ConversationMetadata:
-        kwargs = {
-            c.name: getattr(conversation_metadata, c.name)
-            for c in StoredConversationMetadata.__table__.columns
-        }
-        # TODO: I'm not sure why the timezone is not set on the dates coming back out of the db
-        kwargs['created_at'] = kwargs['created_at'].replace(tzinfo=UTC)
-        kwargs['last_updated_at'] = kwargs['last_updated_at'].replace(tzinfo=UTC)
-        if kwargs['trigger']:
-            kwargs['trigger'] = ConversationTrigger(kwargs['trigger'])
-        if kwargs['git_provider'] and isinstance(kwargs['git_provider'], str):
-            # Convert string to ProviderType enum
-            kwargs['git_provider'] = ProviderType(kwargs['git_provider'])
-
-        kwargs['user_id'] = self.user_id
-
-        # Remove V1 attributes
-        kwargs.pop('max_budget_per_task', None)
-        kwargs.pop('cache_read_tokens', None)
-        kwargs.pop('cache_write_tokens', None)
-        kwargs.pop('reasoning_tokens', None)
-        kwargs.pop('context_window', None)
-        kwargs.pop('per_turn_token', None)
-        kwargs.pop('parent_conversation_id', None)
-        kwargs.pop('public')
-
-        return ConversationMetadata(**kwargs)
-
-    async def save_metadata(self, metadata: ConversationMetadata):
-        kwargs = dataclasses.asdict(metadata)
-
-        # Remove user_id and org_id from kwargs since they're no longer in StoredConversationMetadata
-        kwargs.pop('user_id', None)
-        kwargs.pop('org_id', None)
-
-        # Convert ProviderType enum to string for storage
-        if kwargs.get('git_provider') is not None:
-            kwargs['git_provider'] = (
-                kwargs['git_provider'].value
-                if hasattr(kwargs['git_provider'], 'value')
-                else kwargs['git_provider']
-            )
-
-        stored_metadata = StoredConversationMetadata(**kwargs)
-
-        # Override with resolver org_id if set (from git org claim resolution),
-        # same pattern as V1's save_app_conversation_info in
-        # saas_app_conversation_info_injector.py
-        org_id = self.org_id
-        if self.resolver_org_id is not None:
-            org_id = self.resolver_org_id
-
-        def _save_metadata():
-            with self.session_maker() as session:
-                # Save the main conversation metadata
-                session.merge(stored_metadata)
-
-                # Create or update the SaaS metadata record
-                saas_metadata = (
-                    session.query(StoredConversationMetadataSaas)
-                    .filter(
-                        StoredConversationMetadataSaas.conversation_id
-                        == stored_metadata.conversation_id
-                    )
-                    .first()
-                )
-
-                if not saas_metadata:
-                    saas_metadata = StoredConversationMetadataSaas(
-                        conversation_id=stored_metadata.conversation_id,
-                        user_id=UUID(self.user_id),
-                        org_id=org_id,
-                    )
-                    session.add(saas_metadata)
-                else:
-                    # Validate
-                    expected_user_id = UUID(self.user_id)
-                    expected_org_id = org_id
-
-                    if saas_metadata.user_id != expected_user_id:
-                        raise ValueError(
-                            f'Existing user_id ({saas_metadata.user_id}) does not match expected value ({expected_user_id}).'
-                        )
-
-                    if expected_org_id and saas_metadata.org_id != expected_org_id:
-                        raise ValueError(
-                            f'Existing org_id ({saas_metadata.org_id}) does not match expected value ({expected_org_id}).'
-                        )
-
-                session.commit()
-
-        await call_sync_from_async(_save_metadata)
-
-    async def get_metadata(self, conversation_id: str) -> ConversationMetadata:
-        def _get_metadata():
-            with self.session_maker() as session:
-                conversation_metadata = self._select_by_id(
-                    session, conversation_id
-                ).first()
-                if not conversation_metadata:
-                    raise FileNotFoundError(conversation_id)
-                return self._to_external_model(conversation_metadata)
-
-        return await call_sync_from_async(_get_metadata)
-
-    async def delete_metadata(self, conversation_id: str) -> None:
-        def _delete_metadata():
-            with self.session_maker() as session:
-                saas_record = (
-                    session.query(StoredConversationMetadataSaas)
-                    .filter(
-                        StoredConversationMetadataSaas.conversation_id
-                        == conversation_id,
-                        StoredConversationMetadataSaas.user_id == UUID(self.user_id),
-                        StoredConversationMetadataSaas.org_id == self.org_id,
-                    )
-                    .first()
-                )
-
-                if saas_record:
-                    # Delete both records, but only if the SaaS one exists
-                    session.query(StoredConversationMetadata).filter(
-                        StoredConversationMetadata.conversation_id == conversation_id,
-                    ).delete()
-
-                    session.delete(saas_record)
-
-                    session.commit()
-                else:
-                    # No SaaS record found → skip deleting main metadata
-                    session.rollback()
-
-        await call_sync_from_async(_delete_metadata)
-
-    async def exists(self, conversation_id: str) -> bool:
-        def _exists():
-            with self.session_maker() as session:
-                result = self._select_by_id(session, conversation_id).scalar()
-                return bool(result)
-
-        return await call_sync_from_async(_exists)
-
-    async def search(
-        self,
-        page_id: str | None = None,
-        limit: int = 20,
-    ) -> ConversationMetadataResultSet:
-        offset = page_id_to_offset(page_id)
-
-        def _search():
-            with self.session_maker() as session:
-                stored_conversations = (
-                    session.query(StoredConversationMetadata)
-                    .join(
-                        StoredConversationMetadataSaas,
-                        StoredConversationMetadata.conversation_id
-                        == StoredConversationMetadataSaas.conversation_id,
-                    )
-                    .filter(
-                        StoredConversationMetadataSaas.user_id == UUID(self.user_id)
-                    )
-                    .filter(StoredConversationMetadataSaas.org_id == self.org_id)
-                    .filter(StoredConversationMetadata.conversation_version == 'V0')
-                    .order_by(StoredConversationMetadata.created_at.desc())
-                    .offset(offset)
-                    .limit(limit + 1)
-                    .all()
-                )
-                conversations = [
-                    self._to_external_model(c) for c in stored_conversations
-                ]
-                current_page_size = len(conversations)
-                next_page_id = offset_to_page_id(
-                    offset + limit, current_page_size > limit
-                )
-                return ConversationMetadataResultSet(
-                    conversations[:limit], next_page_id
-                )
-
-        return await call_sync_from_async(_search)
-
-    @classmethod
-    async def get_instance(
-        cls,
-        config: OpenHandsConfig,
-        user_id: str,  # type: ignore[override]
-    ) -> ConversationStore:
-        # Use async version since callers now use asyncio.run_coroutine_threadsafe()
-        # to dispatch to the main event loop where asyncpg connections work properly.
-        user = await UserStore.get_user_by_id(user_id)
-        org_id = user.current_org_id if user else None
-        return SaasConversationStore(user_id, org_id, session_maker)
-
-    @classmethod
-    async def get_resolver_instance(
-        cls,
-        config: OpenHandsConfig,
-        user_id: str,
-        resolver_org_id: UUID | None = None,
-    ) -> 'SaasConversationStore':
-        """Get a store for resolver conversations with explicit org routing.
-
-        Unlike get_instance, this accepts a resolver_org_id that overrides
-        the user's default org when saving conversation metadata.
-        """
-        user = await UserStore.get_user_by_id(user_id)
-        org_id = user.current_org_id if user else None
-        return SaasConversationStore(user_id, org_id, session_maker, resolver_org_id)

enterprise/storage/saas_conversation_validator.py

@@ -1,148 +0,0 @@
-from server.auth.auth_error import AuthError, ExpiredError
-from server.auth.saas_user_auth import saas_user_auth_from_signed_token
-from server.auth.token_manager import TokenManager
-from socketio.exceptions import ConnectionRefusedError
-from storage.api_key_store import ApiKeyStore
-
-from openhands.core.config import load_openhands_config
-from openhands.core.logger import openhands_logger as logger
-from openhands.server.shared import ConversationStoreImpl
-from openhands.storage.conversation.conversation_validator import ConversationValidator
-
-
-class SaasConversationValidator(ConversationValidator):
-    """Storage for conversation metadata. May or may not support multiple users depending on the environment."""
-
-    async def _validate_api_key(self, api_key: str) -> str | None:
-        """
-        Validate an API key and return the user_id if valid.
-
-        Args:
-            api_key: The API key to validate
-
-        Returns:
-            The user_id if the API key is valid, None otherwise
-        """
-        try:
-            token_manager = TokenManager()
-
-            # Validate the API key and get the user_id
-            api_key_store = ApiKeyStore.get_instance()
-            validation_result = await api_key_store.validate_api_key(api_key)
-
-            if not validation_result:
-                logger.warning('Invalid API key')
-                return None
-
-            user_id = validation_result.user_id
-
-            # Get the offline token for the user
-            offline_token = await token_manager.load_offline_token(user_id)
-            if not offline_token:
-                logger.warning(f'No offline token found for user {user_id}')
-                return None
-
-            return user_id
-
-        except Exception as e:
-            logger.warning(f'Error validating API key: {str(e)}')
-            return None
-
-    async def _validate_conversation_access(
-        self, conversation_id: str, user_id: str
-    ) -> bool:
-        """
-        Validate that the user has access to the conversation.
-
-        Args:
-            conversation_id: The ID of the conversation
-            user_id: The ID of the user
-            github_user_id: The GitHub user ID, if available
-
-        Returns:
-            True if the user has access to the conversation, False otherwise
-
-        Raises:
-            ConnectionRefusedError: If the user does not have access to the conversation
-        """
-        config = load_openhands_config()
-        conversation_store = await ConversationStoreImpl.get_instance(config, user_id)
-
-        if not await conversation_store.validate_metadata(conversation_id, user_id):
-            logger.error(
-                f'User {user_id} is not allowed to join conversation {conversation_id}'
-            )
-            raise ConnectionRefusedError(
-                f'User {user_id} is not allowed to join conversation {conversation_id}'
-            )
-        return True
-
-    async def validate(
-        self,
-        conversation_id: str,
-        cookies_str: str,
-        authorization_header: str | None = None,
-    ) -> str | None:
-        """
-        Validate the conversation access using either an API key from the Authorization header
-        or a keycloak_auth cookie.
-
-        Args:
-            conversation_id: The ID of the conversation
-            cookies_str: The cookies string from the request
-            authorization_header: The Authorization header from the request, if available
-
-        Returns:
-            A tuple of (user_id, github_user_id)
-
-        Raises:
-            ConnectionRefusedError: If the user does not have access to the conversation
-            AuthError: If the authentication fails
-            RuntimeError: If there is an error with the configuration or user info
-        """
-        # Try to authenticate using Authorization header first
-        if authorization_header and authorization_header.startswith('Bearer '):
-            api_key = authorization_header.replace('Bearer ', '')
-            user_id = await self._validate_api_key(api_key)
-
-            if user_id:
-                logger.info(
-                    f'User {user_id} is connecting to conversation {conversation_id} via API key'
-                )
-
-                await self._validate_conversation_access(conversation_id, user_id)
-                return user_id
-
-        # Fall back to cookie authentication
-        token_manager = TokenManager()
-        config = load_openhands_config()
-        cookies = (
-            dict(cookie.split('=', 1) for cookie in cookies_str.split('; '))
-            if cookies_str
-            else {}
-        )
-
-        signed_token = cookies.get('keycloak_auth', '')
-        if not signed_token:
-            logger.warning('No keycloak_auth cookie or valid Authorization header')
-            raise ConnectionRefusedError(
-                'No keycloak_auth cookie or valid Authorization header'
-            )
-        if not config.jwt_secret:
-            raise RuntimeError('JWT secret not found')
-
-        try:
-            user_auth = await saas_user_auth_from_signed_token(signed_token)
-            access_token = await user_auth.get_access_token()
-        except ExpiredError:
-            raise ConnectionRefusedError('SESSION$TIMEOUT_MESSAGE')
-        if access_token is None:
-            raise AuthError('no_access_token')
-        user_info = await token_manager.get_user_info(access_token.get_secret_value())
-        # sub is a required field in KeycloakUserInfo, validation happens in get_user_info
-        user_id = user_info.sub
-
-        logger.info(f'User {user_id} is connecting to conversation {conversation_id}')
-
-        await self._validate_conversation_access(conversation_id, user_id)  # type: ignore
-        return user_id

enterprise/storage/saas_secrets_store.py

@@ -10,10 +10,10 @@ from storage.database import a_session_maker
 from storage.stored_custom_secrets import StoredCustomSecrets
 from storage.user_store import UserStore
 
+from openhands.app_server.secrets.secrets_models import Secrets
+from openhands.app_server.secrets.secrets_store import SecretsStore
 from openhands.core.config.openhands_config import OpenHandsConfig
 from openhands.core.logger import openhands_logger as logger
-from openhands.storage.data_models.secrets import Secrets
-from openhands.storage.secrets.secrets_store import SecretsStore
 
 
 @dataclass

enterprise/storage/saas_settings_store.py

@@ -21,9 +21,9 @@ from storage.user import User
 from storage.user_settings import UserSettings
 from storage.user_store import UserStore
 
+from openhands.app_server.settings.settings_models import Settings
+from openhands.app_server.settings.settings_store import SettingsStore
 from openhands.core.config.openhands_config import OpenHandsConfig
-from openhands.server.settings import Settings
-from openhands.storage.settings.settings_store import SettingsStore
 from openhands.utils.jsonpatch_compat import deep_merge
 from openhands.utils.llm import is_openhands_model
 
@@ -148,6 +148,10 @@ class SaasSettingsStore(SettingsStore):
         # Apply default if sandbox_grouping_strategy is None in the database
         if kwargs.get('sandbox_grouping_strategy') is None:
             kwargs.pop('sandbox_grouping_strategy', None)
+        # Pre-migration rows read back as None; Settings.llm_profiles is
+        # non-nullable, so let the default_factory take over.
+        if kwargs.get('llm_profiles') is None:
+            kwargs.pop('llm_profiles', None)
 
         return Settings(**kwargs)
 

enterprise/storage/user.py

@@ -3,13 +3,14 @@ SQLAlchemy model for User.
 """
 
 from datetime import datetime
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 from uuid import UUID, uuid4
 
 from sqlalchemy import DateTime, ForeignKey, String
 from sqlalchemy.dialects.postgresql import JSON
 from sqlalchemy.orm import Mapped, mapped_column, relationship
 from storage.base import Base
+from storage.encrypt_utils import EncryptedJSON
 
 if TYPE_CHECKING:
     from storage.org import Org
@@ -36,6 +37,9 @@ class User(Base):
     git_user_email: Mapped[str | None] = mapped_column(String, nullable=True)
     sandbox_grouping_strategy: Mapped[str | None] = mapped_column(String, nullable=True)
     disabled_skills: Mapped[list[str] | None] = mapped_column(JSON, nullable=True)
+    llm_profiles: Mapped[dict[str, Any] | None] = mapped_column(
+        EncryptedJSON, nullable=True
+    )
     onboarding_completed: Mapped[bool | None] = mapped_column(
         nullable=True, default=False
     )

enterprise/storage/user_settings.py

@@ -50,9 +50,6 @@ class UserSettings(Base):
     search_api_key: Mapped[str | None] = mapped_column(String, nullable=True)
     sandbox_api_key: Mapped[str | None] = mapped_column(String, nullable=True)
     max_budget_per_task: Mapped[float | None] = mapped_column(nullable=True)
-    enable_solvability_analysis: Mapped[bool | None] = mapped_column(
-        nullable=True, default=False
-    )
     email: Mapped[str | None] = mapped_column(String, nullable=True)
     email_verified: Mapped[bool | None] = mapped_column(nullable=True)
     git_user_name: Mapped[str | None] = mapped_column(String, nullable=True)
@@ -88,8 +85,8 @@ class UserSettings(Base):
     )  # False = not migrated, True = migrated
 
     def to_settings(self):
+        from openhands.app_server.settings.settings_models import Settings
         from openhands.sdk.settings import AgentSettings, ConversationSettings
-        from openhands.storage.data_models.settings import Settings
 
         return Settings(
             agent_settings=AgentSettings.model_validate(self.agent_settings or {}),

enterprise/storage/user_store.py

@@ -931,7 +931,7 @@ class UserStore:
     from typing import TYPE_CHECKING
 
     if TYPE_CHECKING:
-        from openhands.storage.data_models.settings import Settings
+        from openhands.app_server.settings.settings_models import Settings
 
     @staticmethod
     async def create_default_settings(
@@ -945,7 +945,7 @@ class UserStore:
         if not org_id:
             return None
 
-        from openhands.storage.data_models.settings import Settings
+        from openhands.app_server.settings.settings_models import Settings
 
         default_settings = Settings(
             language='en', enable_proactive_conversation_starters=True
@@ -1049,7 +1049,6 @@ class UserStore:
             if org.sandbox_api_key
             else None,
             max_budget_per_task=org.max_budget_per_task,
-            enable_solvability_analysis=org.enable_solvability_analysis,
             v1_enabled=org.v1_enabled,
             sandbox_grouping_strategy=org.sandbox_grouping_strategy,
             agent_settings=agent_settings,

enterprise/tests/unit/integrations/github/test_github_v1_callback_processor.py

@@ -32,7 +32,6 @@ from openhands.app_server.sandbox.sandbox_models import (
     SandboxInfo,
     SandboxStatus,
 )
-from openhands.events.action.message import MessageAction
 from openhands.sdk.event import ConversationStateUpdateEvent
 
 # ---------------------------------------------------------------------------
@@ -76,7 +75,10 @@ def conversation_state_update_event():
 
 @pytest.fixture
 def wrong_event():
-    return MessageAction(content='Hello world')
+    """Return a mock event that is not a ConversationStateUpdateEvent."""
+    mock_event = MagicMock()
+    mock_event.id = uuid4()
+    return mock_event
 
 
 @pytest.fixture

enterprise/tests/unit/integrations/github/test_github_view_v1_initial_user_message.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from contextlib import asynccontextmanager
 from pathlib import Path
 from unittest.mock import AsyncMock, MagicMock, patch
-from uuid import uuid4
+from uuid import UUID, uuid4
 
 import pytest
 from integrations.github.github_view import (
@@ -17,7 +17,6 @@ from jinja2 import Environment, FileSystemLoader
 from openhands.app_server.app_conversation.app_conversation_models import (
     AppConversationStartTaskStatus,
 )
-from openhands.storage.data_models.conversation_metadata import ConversationMetadata
 
 
 @pytest.fixture
@@ -44,11 +43,8 @@ class _FakeAppConversationService:
         yield MagicMock(status=AppConversationStartTaskStatus.READY, detail=None)
 
 
-def _build_conversation_metadata() -> ConversationMetadata:
-    return ConversationMetadata(
-        conversation_id=str(uuid4()),
-        selected_repository='test-owner/test-repo',
-    )
+def _build_conversation_id() -> UUID:
+    return uuid4()
 
 
 def _build_user_data() -> UserData:
@@ -77,7 +73,6 @@ class TestGithubViewV1InitialUserMessage:
             title='ignored',
             description='ignored',
             previous_comments=[],
-            v1_enabled=True,
             comment_body='please fix this',
             comment_id=999,
         )
@@ -98,7 +93,7 @@ class TestGithubViewV1InitialUserMessage:
         await view._create_v1_conversation(
             jinja_env=jinja_env,
             saas_user_auth=MagicMock(),
-            conversation_metadata=_build_conversation_metadata(),
+            conversation_id=_build_conversation_id(),
         )
 
         assert len(fake_service.requests) == 1
@@ -131,7 +126,6 @@ class TestGithubViewV1InitialUserMessage:
             title='ignored',
             description='ignored',
             previous_comments=[],
-            v1_enabled=True,
             comment_body='nit: rename variable',
             comment_id=1001,
             branch_name='feature-branch',
@@ -155,7 +149,7 @@ class TestGithubViewV1InitialUserMessage:
         await view._create_v1_conversation(
             jinja_env=jinja_env,
             saas_user_auth=MagicMock(),
-            conversation_metadata=_build_conversation_metadata(),
+            conversation_id=_build_conversation_id(),
         )
 
         assert len(fake_service.requests) == 1
@@ -187,7 +181,6 @@ class TestGithubViewV1InitialUserMessage:
             title='ignored',
             description='ignored',
             previous_comments=[],
-            v1_enabled=True,
             comment_body='please add a null check',
             comment_id=1002,
             branch_name='feature-branch',
@@ -210,7 +203,7 @@ class TestGithubViewV1InitialUserMessage:
         await view._create_v1_conversation(
             jinja_env=jinja_env,
             saas_user_auth=MagicMock(),
-            conversation_metadata=_build_conversation_metadata(),
+            conversation_id=_build_conversation_id(),
         )
 
         req = fake_service.requests[0]

enterprise/tests/unit/integrations/gitlab/test_gitlab_manager.py

@@ -5,13 +5,12 @@ All conversations now use V1 app conversation system.
 """
 
 from unittest.mock import AsyncMock, MagicMock, patch
+from uuid import uuid4
 
 import pytest
 from integrations.gitlab.gitlab_view import GitlabIssue
 from integrations.types import UserData
 
-from openhands.storage.data_models.conversation_metadata import ConversationMetadata
-
 
 @pytest.fixture
 def mock_gitlab_view():
@@ -35,7 +34,6 @@ def mock_gitlab_view():
         description='Test description',
         previous_comments=[],
         is_mr=False,
-        v1_enabled=True,
     )
 
 
@@ -57,12 +55,9 @@ def mock_saas_user_auth():
 
 
 @pytest.fixture
-def mock_convo_metadata():
-    """Create a mock ConversationMetadata."""
-    return ConversationMetadata(
-        conversation_id='test_conversation_id',
-        selected_repository='test-group/test-repo',
-    )
+def mock_conversation_id():
+    """Create a mock conversation UUID."""
+    return uuid4()
 
 
 class TestGitlabManagerJobCreation:
@@ -81,7 +76,7 @@ class TestGitlabManagerJobCreation:
         mock_token_manager,
         mock_gitlab_view,
         mock_saas_user_auth,
-        mock_convo_metadata,
+        mock_conversation_id,
     ):
         """Test that start_job creates a conversation and sends acknowledgment message."""
         from integrations.gitlab.gitlab_manager import GitlabManager
@@ -91,7 +86,7 @@ class TestGitlabManagerJobCreation:
 
         # Mock the view's methods
         mock_gitlab_view.initialize_new_conversation = AsyncMock(
-            return_value=mock_convo_metadata
+            return_value=mock_conversation_id
         )
         mock_gitlab_view.create_new_conversation = AsyncMock()
 

enterprise/tests/unit/integrations/gitlab/test_gitlab_service.py

@@ -12,6 +12,16 @@ def gitlab_service():
     return SaaSGitLabService(external_auth_id='test_user_id')
 
 
+class TestSaaSGitLabServiceInit:
+    """Tests for SaaSGitLabService __init__."""
+
+    def test_explicit_base_domain_overrides_default(self):
+        """An explicit base_domain parameter overrides the upstream class default."""
+        service = SaaSGitLabService(external_auth_id='u1', base_domain='other.host')
+
+        assert service.BASE_URL == 'https://other.host/api/v4'
+
+
 class TestGetUserResourcesWithAdminAccess:
     """Test cases for get_user_resources_with_admin_access method."""
 

enterprise/tests/unit/integrations/gitlab/test_gitlab_v1_callback_processor.py

@@ -28,7 +28,6 @@ from openhands.app_server.sandbox.sandbox_models import (
     SandboxInfo,
     SandboxStatus,
 )
-from openhands.events.action.message import MessageAction
 from openhands.sdk.event import ConversationStateUpdateEvent
 
 # ---------------------------------------------------------------------------
@@ -73,7 +72,10 @@ def conversation_state_update_event():
 
 @pytest.fixture
 def wrong_event():
-    return MessageAction(content='Hello world')
+    """Return a mock event that is not a ConversationStateUpdateEvent."""
+    mock_event = MagicMock()
+    mock_event.id = uuid4()
+    return mock_event
 
 
 @pytest.fixture

enterprise/tests/unit/integrations/jira/conftest.py

@@ -215,7 +215,6 @@ def new_conversation_view(
         conversation_id='conv-123',
         _decrypted_api_key='decrypted_key',
     )
-    view.v1_enabled = False
     return view
 
 

enterprise/tests/unit/integrations/jira/test_jira_view.py

@@ -444,10 +444,10 @@ class TestJiraV1Conversation:
     """Tests for V1 conversation creation and callback processor registration."""
 
     @pytest.mark.asyncio
-    async def test_create_v1_metadata_generates_conversation_id(
+    async def test_initialize_conversation_generates_conversation_id(
         self, new_conversation_view
     ):
-        """Test that _create_v1_metadata generates a new conversation ID."""
+        """Test that _initialize_conversation generates a new conversation ID."""
         new_conversation_view.conversation_id = ''
 
         with patch.object(
@@ -455,17 +455,19 @@ class TestJiraV1Conversation:
         ) as mock_get_org:
             mock_get_org.return_value = None
 
-            metadata = await new_conversation_view._create_v1_metadata()
+            conversation_id = await new_conversation_view._initialize_conversation()
 
             # Conversation ID should be generated
             assert new_conversation_view.conversation_id != ''
             assert len(new_conversation_view.conversation_id) == 32  # UUID hex format
-            assert metadata.conversation_id == new_conversation_view.conversation_id
+            assert conversation_id.hex == new_conversation_view.conversation_id
             mock_get_org.assert_called_once()
 
     @pytest.mark.asyncio
-    async def test_create_v1_metadata_sets_resolved_org(self, new_conversation_view):
-        """Test that _create_v1_metadata sets resolved_org_id."""
+    async def test_initialize_conversation_sets_resolved_org(
+        self, new_conversation_view
+    ):
+        """Test that _initialize_conversation sets resolved_org_id."""
         from uuid import UUID
 
         test_org_id = UUID('12345678-1234-5678-1234-567812345678')
@@ -475,7 +477,7 @@ class TestJiraV1Conversation:
         ) as mock_get_org:
             mock_get_org.return_value = test_org_id
 
-            await new_conversation_view._create_v1_metadata()
+            await new_conversation_view._initialize_conversation()
 
             assert new_conversation_view.resolved_org_id == test_org_id
 

enterprise/tests/unit/integrations/slack/test_slack_v1_callback_processor.py

@@ -28,9 +28,16 @@ from openhands.app_server.sandbox.sandbox_models import (
     SandboxInfo,
     SandboxStatus,
 )
-from openhands.events.action.message import MessageAction
 from openhands.sdk.event import ConversationStateUpdateEvent
 
+
+def _create_mock_event():
+    """Create a mock event that is not a ConversationStateUpdateEvent."""
+    mock_event = MagicMock()
+    mock_event.id = uuid4()
+    return mock_event
+
+
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -105,8 +112,10 @@ class TestSlackV1CallbackProcessor:
     @pytest.mark.parametrize(
         'event,expected_result',
         [
-            # Wrong event types should be ignored
-            (MessageAction(content='Hello world'), None),
+            # Wrong event types should be ignored (use lazy evaluation for mock)
+            pytest.param(
+                None, None, id='wrong_event_type', marks=pytest.mark.wrong_event_type
+            ),
             # Wrong state values should be ignored
             (
                 ConversationStateUpdateEvent(key='execution_status', value='running'),
@@ -120,9 +129,12 @@ class TestSlackV1CallbackProcessor:
         ],
     )
     async def test_event_filtering(
-        self, slack_callback_processor, event_callback, event, expected_result
+        self, slack_callback_processor, event_callback, event, expected_result, request
     ):
         """Test that processor correctly filters events."""
+        # Handle the mock event case specially
+        if event is None and 'wrong_event_type' in request.node.name:
+            event = _create_mock_event()
         result = await slack_callback_processor(uuid4(), event_callback, event)
         assert result == expected_result
 

enterprise/tests/unit/integrations/slack/test_slack_view.py

@@ -69,7 +69,6 @@ def slack_new_conversation_view(mock_slack_user, mock_user_auth):
         send_summary_instruction=True,
         conversation_id='',
         team_id='T1234567890',
-        v1_enabled=True,
     )
 
 
@@ -99,7 +98,6 @@ def slack_update_conversation_view_v1(mock_slack_user, mock_user_auth):
         conversation_id=conversation_id,
         slack_conversation=mock_conversation,
         team_id='T1234567890',
-        v1_enabled=True,
     )
 
 
@@ -111,18 +109,15 @@ def slack_update_conversation_view_v1(mock_slack_user, mock_user_auth):
 class TestV1ConversationCreation:
     """Test V1 conversation creation in Slack integration."""
 
-    @patch('integrations.slack.slack_view.is_v1_enabled_for_slack_resolver')
     @patch.object(SlackNewConversationView, '_create_v1_conversation')
     async def test_v1_conversation_creation(
         self,
         mock_create_v1,
-        mock_is_v1_enabled,
         slack_new_conversation_view,
         mock_jinja_env,
     ):
         """Test that V1 conversations are created correctly."""
         # Setup mocks
-        mock_is_v1_enabled.return_value = True
         mock_create_v1.return_value = None
 
         # Execute
@@ -132,7 +127,6 @@ class TestV1ConversationCreation:
 
         # Verify
         assert result == slack_new_conversation_view.conversation_id
-        assert slack_new_conversation_view.v1_enabled is True
         mock_create_v1.assert_called_once()
 
 

enterprise/tests/unit/integrations/test_resolver_context.py

@@ -10,6 +10,7 @@ import pytest
 from pydantic import SecretStr
 
 from enterprise.integrations.resolver_context import ResolverUserContext
+from openhands.app_server.secrets.secrets_models import Secrets
 
 # Import the real classes we want to test
 from openhands.integrations.provider import CustomSecret, ProviderToken
@@ -17,7 +18,6 @@ from openhands.integrations.service_types import ProviderType
 
 # Import the SDK types we need for testing
 from openhands.sdk.secret import SecretSource, StaticSecret
-from openhands.storage.data_models.secrets import Secrets
 
 
 @pytest.fixture

enterprise/tests/unit/integrations/test_utils.py

@@ -1,171 +1,11 @@
 """Tests for enterprise integrations utils module."""
 
-from unittest.mock import patch
-
-import pytest
 from integrations.utils import (
     HOST_URL,
-    append_conversation_footer,
     get_session_expired_message,
-    get_summary_for_agent_state,
     get_user_not_found_message,
 )
 
-from openhands.core.schema.agent import AgentState
-from openhands.events.observation.agent import AgentStateChangedObservation
-
-
-class TestGetSummaryForAgentState:
-    """Test cases for get_summary_for_agent_state function."""
-
-    def setup_method(self):
-        """Set up test fixtures."""
-        self.conversation_link = 'https://example.com/conversation/123'
-
-    def test_empty_observations_list(self):
-        """Test handling of empty observations list."""
-        result = get_summary_for_agent_state([], self.conversation_link)
-
-        assert 'unknown error' in result.lower()
-        assert self.conversation_link in result
-
-    @pytest.mark.parametrize(
-        'state,expected_text,includes_link',
-        [
-            (AgentState.RATE_LIMITED, 'rate limited', False),
-            (AgentState.AWAITING_USER_INPUT, 'waiting for your input', True),
-        ],
-    )
-    def test_handled_agent_states(self, state, expected_text, includes_link):
-        """Test handling of states with specific behavior."""
-        observation = AgentStateChangedObservation(
-            content=f'Agent state: {state.value}', agent_state=state
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        assert expected_text in result.lower()
-        if includes_link:
-            assert self.conversation_link in result
-        else:
-            assert self.conversation_link not in result
-
-    @pytest.mark.parametrize(
-        'state',
-        [
-            AgentState.FINISHED,
-            AgentState.PAUSED,
-            AgentState.STOPPED,
-            AgentState.AWAITING_USER_CONFIRMATION,
-        ],
-    )
-    def test_unhandled_agent_states(self, state):
-        """Test handling of unhandled states (should all return unknown error)."""
-        observation = AgentStateChangedObservation(
-            content=f'Agent state: {state.value}', agent_state=state
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        assert 'unknown error' in result.lower()
-        assert self.conversation_link in result
-
-    @pytest.mark.parametrize(
-        'error_code,expected_text',
-        [
-            (
-                'STATUS$ERROR_LLM_AUTHENTICATION',
-                'authentication with the llm provider failed',
-            ),
-            (
-                'STATUS$ERROR_LLM_SERVICE_UNAVAILABLE',
-                'llm service is temporarily unavailable',
-            ),
-            (
-                'STATUS$ERROR_LLM_INTERNAL_SERVER_ERROR',
-                'llm provider encountered an internal error',
-            ),
-            ('STATUS$ERROR_LLM_OUT_OF_CREDITS', "you've run out of credits"),
-            ('STATUS$ERROR_LLM_CONTENT_POLICY_VIOLATION', 'content policy violation'),
-        ],
-    )
-    def test_error_state_readable_reasons(self, error_code, expected_text):
-        """Test all readable error reason mappings."""
-        observation = AgentStateChangedObservation(
-            content=f'Agent encountered error: {error_code}',
-            agent_state=AgentState.ERROR,
-            reason=error_code,
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        assert 'encountered an error' in result.lower()
-        assert expected_text in result.lower()
-        assert self.conversation_link in result
-
-    def test_error_state_with_custom_reason(self):
-        """Test handling of ERROR state with a custom reason."""
-        observation = AgentStateChangedObservation(
-            content='Agent encountered an error',
-            agent_state=AgentState.ERROR,
-            reason='Test error message',
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        assert 'encountered an error' in result.lower()
-        assert 'test error message' in result.lower()
-        assert self.conversation_link in result
-
-    def test_multiple_observations_uses_first(self):
-        """Test that when multiple observations are provided, only the first is used."""
-        observation1 = AgentStateChangedObservation(
-            content='Agent is awaiting user input',
-            agent_state=AgentState.AWAITING_USER_INPUT,
-        )
-        observation2 = AgentStateChangedObservation(
-            content='Agent encountered an error',
-            agent_state=AgentState.ERROR,
-            reason='Should not be used',
-        )
-
-        result = get_summary_for_agent_state(
-            [observation1, observation2], self.conversation_link
-        )
-
-        # Should handle the first observation (AWAITING_USER_INPUT), not the second (ERROR)
-        assert 'waiting for your input' in result.lower()
-        assert 'error' not in result.lower()
-
-    def test_awaiting_user_input_specific_message(self):
-        """Test that AWAITING_USER_INPUT returns the specific expected message."""
-        observation = AgentStateChangedObservation(
-            content='Agent is awaiting user input',
-            agent_state=AgentState.AWAITING_USER_INPUT,
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        # Test the exact message format
-        assert 'waiting for your input' in result.lower()
-        assert 'continue the conversation' in result.lower()
-        assert self.conversation_link in result
-        assert 'unknown error' not in result.lower()
-
-    def test_rate_limited_specific_message(self):
-        """Test that RATE_LIMITED returns the specific expected message."""
-        observation = AgentStateChangedObservation(
-            content='Agent was rate limited', agent_state=AgentState.RATE_LIMITED
-        )
-
-        result = get_summary_for_agent_state([observation], self.conversation_link)
-
-        # Test the exact message format
-        assert 'rate limited' in result.lower()
-        assert 'try again later' in result.lower()
-        # RATE_LIMITED doesn't include conversation link in response
-        assert self.conversation_link not in result
-
 
 class TestGetSessionExpiredMessage:
     """Test cases for get_session_expired_message function."""
@@ -293,138 +133,3 @@ class TestGetUserNotFoundMessage:
         result = get_user_not_found_message(None)
         assert not result.startswith('@')
         assert 'It looks like' in result
-
-
-class TestAppendConversationFooter:
-    """Test cases for append_conversation_footer function."""
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_appends_footer_with_markdown_link(self):
-        """Test that footer is appended with correct markdown link format."""
-        # Arrange
-        message = 'This is a test message'
-        conversation_id = 'test-conv-123'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        assert result.startswith(message)
-        assert (
-            '[View full conversation](https://example.com/conversations/test-conv-123)'
-            in result
-        )
-        assert result.endswith(
-            '[View full conversation](https://example.com/conversations/test-conv-123)'
-        )
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_footer_does_not_contain_html_tags(self):
-        """Test that footer does not contain HTML tags like <sub>."""
-        # Arrange
-        message = 'Test message'
-        conversation_id = 'test-conv-456'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        assert '<sub>' not in result
-        assert '</sub>' not in result
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_footer_format_with_newlines(self):
-        """Test that footer is properly separated with newlines."""
-        # Arrange
-        message = 'Original message content'
-        conversation_id = 'test-conv-789'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        assert (
-            result
-            == 'Original message content\n\n[View full conversation](https://example.com/conversations/test-conv-789)'
-        )
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_empty_message_still_appends_footer(self):
-        """Test that footer is appended even when message is empty."""
-        # Arrange
-        message = ''
-        conversation_id = 'empty-msg-conv'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        assert result.startswith('\n\n')
-        assert (
-            '[View full conversation](https://example.com/conversations/empty-msg-conv)'
-            in result
-        )
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_conversation_id_with_special_characters(self):
-        """Test that footer handles conversation IDs with special characters."""
-        # Arrange
-        message = 'Test message'
-        conversation_id = 'conv-123_abc-456'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        expected_url = 'https://example.com/conversations/conv-123_abc-456'
-        assert expected_url in result
-        assert '[View full conversation]' in result
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_multiline_message_preserves_content(self):
-        """Test that multiline messages are preserved correctly."""
-        # Arrange
-        message = 'Line 1\nLine 2\nLine 3'
-        conversation_id = 'multiline-conv'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        assert result.startswith('Line 1\nLine 2\nLine 3')
-        assert '\n\n[View full conversation]' in result
-        assert message in result
-
-    @patch(
-        'integrations.utils.CONVERSATION_URL', 'https://example.com/conversations/{}'
-    )
-    def test_footer_contains_only_markdown_syntax(self):
-        """Test that footer uses only markdown syntax, not HTML."""
-        # Arrange
-        message = 'Test message'
-        conversation_id = 'markdown-test'
-
-        # Act
-        result = append_conversation_footer(message, conversation_id)
-
-        # Assert
-        footer_part = result[len(message) :]
-        # Should only contain markdown link syntax: [text](url)
-        assert footer_part.startswith('\n\n[')
-        assert '](' in footer_part
-        assert footer_part.endswith(')')
-        # Should not contain any HTML tags (specifically <sub> tags that were removed)
-        assert '<sub>' not in footer_part
-        assert '</sub>' not in footer_part

enterprise/tests/unit/server/routes/test_auth_onboarding.py

@@ -4,8 +4,10 @@ Tests for:
 - _should_redirect_to_onboarding() function
 - _get_post_auth_redirect() function
 - /complete_onboarding endpoint
+- /onboarding_status endpoint
 """
 
+import json
 import uuid
 from unittest.mock import AsyncMock, MagicMock, patch
 
@@ -17,6 +19,7 @@ from server.routes.auth import (
     _get_post_auth_redirect,
     _should_redirect_to_onboarding,
     complete_onboarding,
+    onboarding_status,
 )
 from storage.user import User
 
@@ -328,3 +331,78 @@ class TestCompleteOnboardingEndpoint:
             await complete_onboarding(mock_request)
 
         mock_mark_completed.assert_called_once_with(user_id)
+
+
+class TestOnboardingStatusEndpoint:
+    """Tests for the /onboarding_status API endpoint."""
+
+    @pytest.mark.asyncio
+    async def test_returns_401_when_not_authenticated(self, mock_request):
+        """Unauthenticated requests return 401."""
+        mock_user_auth = MagicMock(spec=SaasUserAuth)
+        mock_user_auth.get_user_id = AsyncMock(return_value=None)
+
+        with patch(
+            'server.routes.auth.get_user_auth',
+            new_callable=AsyncMock,
+            return_value=mock_user_auth,
+        ):
+            result = await onboarding_status(mock_request)
+
+        assert isinstance(result, JSONResponse)
+        assert result.status_code == status.HTTP_401_UNAUTHORIZED
+
+    @pytest.mark.asyncio
+    async def test_returns_true_for_new_cloud_user(self, mock_request, mock_user):
+        """A cloud user whose onboarding is incomplete should be told to complete it."""
+        user_id = str(uuid.uuid4())
+        mock_user.onboarding_completed = False
+        mock_user_auth = MagicMock(spec=SaasUserAuth)
+        mock_user_auth.get_user_id = AsyncMock(return_value=user_id)
+
+        with (
+            patch(
+                'server.routes.auth.get_user_auth',
+                new_callable=AsyncMock,
+                return_value=mock_user_auth,
+            ),
+            patch(
+                'server.routes.auth.UserStore.get_user_by_id',
+                new_callable=AsyncMock,
+                return_value=mock_user,
+            ),
+            patch('server.routes.auth.DEPLOYMENT_MODE', 'cloud'),
+        ):
+            result = await onboarding_status(mock_request)
+
+        assert isinstance(result, JSONResponse)
+        assert result.status_code == status.HTTP_200_OK
+        body = json.loads(result.body)
+        assert body == {'should_complete_onboarding': True}
+
+    @pytest.mark.asyncio
+    async def test_returns_false_for_completed_user(self, mock_request, mock_user):
+        """A user who already completed onboarding should not be told to complete it."""
+        user_id = str(uuid.uuid4())
+        mock_user.onboarding_completed = True
+        mock_user_auth = MagicMock(spec=SaasUserAuth)
+        mock_user_auth.get_user_id = AsyncMock(return_value=user_id)
+
+        with (
+            patch(
+                'server.routes.auth.get_user_auth',
+                new_callable=AsyncMock,
+                return_value=mock_user_auth,
+            ),
+            patch(
+                'server.routes.auth.UserStore.get_user_by_id',
+                new_callable=AsyncMock,
+                return_value=mock_user,
+            ),
+        ):
+            result = await onboarding_status(mock_request)
+
+        assert isinstance(result, JSONResponse)
+        assert result.status_code == status.HTTP_200_OK
+        body = json.loads(result.body)
+        assert body == {'should_complete_onboarding': False}

enterprise/tests/unit/server/routes/test_github_integration.py

@@ -0,0 +1,193 @@
+"""Unit tests for GitHub integration routes.
+
+Tests for:
+- get_github_token endpoint
+"""
+
+import sys
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from pydantic import SecretStr
+
+
+@pytest.fixture(autouse=True)
+def mock_github_dependencies():
+    """Mock module-level dependencies before importing the github module.
+
+    The github.py module instantiates GitHubDataCollector at module level,
+    which requires GitHub App credentials. We mock these dependencies to
+    allow importing the module in test environments without credentials.
+    """
+    # Store original modules if they exist
+    original_modules = {}
+    modules_to_mock = [
+        'integrations.github.data_collector',
+        'integrations.github.github_manager',
+        'server.routes.integration.github',
+    ]
+    for mod in modules_to_mock:
+        if mod in sys.modules:
+            original_modules[mod] = sys.modules[mod]
+            del sys.modules[mod]
+
+    # Create mock GitHubDataCollector that doesn't require credentials
+    mock_data_collector_module = MagicMock()
+    mock_data_collector_instance = MagicMock()
+    mock_data_collector_module.GitHubDataCollector.return_value = (
+        mock_data_collector_instance
+    )
+    sys.modules['integrations.github.data_collector'] = mock_data_collector_module
+
+    # Create mock GithubManager
+    mock_github_manager_module = MagicMock()
+    mock_github_manager_instance = MagicMock()
+    mock_github_manager_module.GithubManager.return_value = mock_github_manager_instance
+    sys.modules['integrations.github.github_manager'] = mock_github_manager_module
+
+    yield
+
+    # Clean up the mocked modules
+    for mod in modules_to_mock:
+        if mod in sys.modules:
+            del sys.modules[mod]
+
+    # Restore original modules
+    for mod, original in original_modules.items():
+        sys.modules[mod] = original
+
+
+class TestGitHubTokenResponse:
+    """Test suite for GitHubTokenResponse model."""
+
+    def test_github_token_response_with_valid_token(self):
+        """GitHubTokenResponse should accept a valid access_token."""
+        from server.routes.integration.github import GitHubTokenResponse
+
+        response = GitHubTokenResponse(access_token='ghp_test_token_12345')
+        assert response.access_token == 'ghp_test_token_12345'
+
+    def test_github_token_response_model_dump(self):
+        """GitHubTokenResponse model_dump should include access_token."""
+        from server.routes.integration.github import GitHubTokenResponse
+
+        response = GitHubTokenResponse(access_token='ghp_test_token_12345')
+        data = response.model_dump()
+        assert data['access_token'] == 'ghp_test_token_12345'
+
+
+class TestGetGitHubToken:
+    """Test suite for get_github_token endpoint."""
+
+    @pytest.fixture
+    def mock_request(self):
+        """Create a mock request object."""
+        request = MagicMock()
+        request.state = MagicMock()
+        return request
+
+    @pytest.fixture
+    def mock_saas_user_auth(self):
+        """Create a mock SaasUserAuth object."""
+        from openhands.integrations.provider import ProviderToken, ProviderType
+
+        mock_auth = AsyncMock()
+        mock_auth.get_provider_tokens = AsyncMock(
+            return_value={
+                ProviderType.GITHUB: ProviderToken(
+                    token=SecretStr('ghp_test_token_12345')
+                )
+            }
+        )
+        return mock_auth
+
+    @pytest.mark.asyncio
+    async def test_get_github_token_success(self, mock_request, mock_saas_user_auth):
+        """Should return GitHub token when user has a valid token."""
+        from server.routes.integration.github import (
+            GitHubTokenResponse,
+            get_github_token,
+        )
+
+        with patch(
+            'server.routes.integration.github.get_user_auth',
+            return_value=mock_saas_user_auth,
+        ):
+            result = await get_github_token(mock_request)
+
+        assert isinstance(result, GitHubTokenResponse)
+        assert result.access_token == 'ghp_test_token_12345'
+        mock_saas_user_auth.get_provider_tokens.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_get_github_token_no_provider_tokens(self, mock_request):
+        """Should raise 404 when user has no provider tokens."""
+        from fastapi import HTTPException
+        from server.routes.integration.github import get_github_token
+
+        mock_auth = AsyncMock()
+        mock_auth.get_provider_tokens = AsyncMock(return_value=None)
+
+        with (
+            patch(
+                'server.routes.integration.github.get_user_auth',
+                return_value=mock_auth,
+            ),
+            pytest.raises(HTTPException) as exc_info,
+        ):
+            await get_github_token(mock_request)
+
+        assert exc_info.value.status_code == 404
+        assert 'No provider tokens' in exc_info.value.detail
+
+    @pytest.mark.asyncio
+    async def test_get_github_token_no_github_token(self, mock_request):
+        """Should raise 404 when user has provider tokens but no GitHub token."""
+        from fastapi import HTTPException
+        from server.routes.integration.github import get_github_token
+
+        from openhands.integrations.provider import ProviderToken, ProviderType
+
+        mock_auth = AsyncMock()
+        # Return GitLab token but no GitHub token
+        mock_auth.get_provider_tokens = AsyncMock(
+            return_value={
+                ProviderType.GITLAB: ProviderToken(
+                    token=SecretStr('glpat_test_token_12345')
+                )
+            }
+        )
+
+        with (
+            patch(
+                'server.routes.integration.github.get_user_auth',
+                return_value=mock_auth,
+            ),
+            pytest.raises(HTTPException) as exc_info,
+        ):
+            await get_github_token(mock_request)
+
+        assert exc_info.value.status_code == 404
+        assert 'No GitHub token' in exc_info.value.detail
+
+    @pytest.mark.asyncio
+    async def test_get_github_token_empty_provider_tokens(self, mock_request):
+        """Should raise 404 when user has empty provider tokens dict."""
+        from fastapi import HTTPException
+        from server.routes.integration.github import get_github_token
+
+        mock_auth = AsyncMock()
+        mock_auth.get_provider_tokens = AsyncMock(return_value={})
+
+        with (
+            patch(
+                'server.routes.integration.github.get_user_auth',
+                return_value=mock_auth,
+            ),
+            pytest.raises(HTTPException) as exc_info,
+        ):
+            await get_github_token(mock_request)
+
+        assert exc_info.value.status_code == 404
+        # Empty dict is falsy, so it triggers the "no provider tokens" error
+        assert 'No provider tokens' in exc_info.value.detail

enterprise/tests/unit/server/routes/test_orgs.py

@@ -950,7 +950,6 @@ async def test_list_user_orgs_all_fields_present(mock_app_list):
         sandbox_runtime_container_image='test-runtime',
         org_version=5,
         max_budget_per_task=1000.0,
-        enable_solvability_analysis=True,
         v1_enabled=True,
     )
     mock_user = MagicMock()
@@ -994,7 +993,6 @@ async def test_list_user_orgs_all_fields_present(mock_app_list):
         assert org_data['sandbox_runtime_container_image'] == 'test-runtime'
         assert org_data['org_version'] == 5
         assert org_data['max_budget_per_task'] == 1000.0
-        assert org_data['enable_solvability_analysis'] is True
         assert org_data['v1_enabled'] is True
         assert org_data['credits'] is None
 
@@ -3245,7 +3243,6 @@ async def test_get_org_app_settings_success(
     # Arrange
     mock_response = OrgAppSettingsResponse(
         enable_proactive_conversation_starters=True,
-        enable_solvability_analysis=False,
         max_budget_per_task=10.0,
     )
 
@@ -3268,7 +3265,6 @@ async def test_get_org_app_settings_success(
         assert response.status_code == status.HTTP_200_OK
         response_data = response.json()
         assert response_data['enable_proactive_conversation_starters'] is True
-        assert response_data['enable_solvability_analysis'] is False
         assert response_data['max_budget_per_task'] == 10.0
 
 
@@ -3285,7 +3281,6 @@ async def test_get_org_app_settings_with_null_values(
     # OrgAppSettingsResponse.from_org() handles defaults, so we test the response model
     mock_response = OrgAppSettingsResponse(
         enable_proactive_conversation_starters=True,  # Default when None in Org
-        enable_solvability_analysis=None,
         max_budget_per_task=None,
     )
 
@@ -3309,7 +3304,6 @@ async def test_get_org_app_settings_with_null_values(
         response_data = response.json()
         # enable_proactive_conversation_starters defaults to True when None
         assert response_data['enable_proactive_conversation_starters'] is True
-        assert response_data['enable_solvability_analysis'] is None
         assert response_data['max_budget_per_task'] is None
 
 
@@ -3377,7 +3371,6 @@ async def test_update_org_app_settings_success(
     # Arrange
     mock_response = OrgAppSettingsResponse(
         enable_proactive_conversation_starters=False,
-        enable_solvability_analysis=True,
         max_budget_per_task=25.0,
     )
 
@@ -3398,7 +3391,6 @@ async def test_update_org_app_settings_success(
             '/api/organizations/app',
             json={
                 'enable_proactive_conversation_starters': False,
-                'enable_solvability_analysis': True,
                 'max_budget_per_task': 25.0,
             },
         )
@@ -3407,7 +3399,6 @@ async def test_update_org_app_settings_success(
         assert response.status_code == status.HTTP_200_OK
         response_data = response.json()
         assert response_data['enable_proactive_conversation_starters'] is False
-        assert response_data['enable_solvability_analysis'] is True
         assert response_data['max_budget_per_task'] == 25.0
         mock_update.assert_called_once()
 
@@ -3424,7 +3415,6 @@ async def test_update_org_app_settings_partial_update(
     # Arrange
     mock_response = OrgAppSettingsResponse(
         enable_proactive_conversation_starters=False,
-        enable_solvability_analysis=True,
         max_budget_per_task=10.0,  # Unchanged
     )
 
@@ -3467,7 +3457,6 @@ async def test_update_org_app_settings_set_null(
     # Arrange
     mock_response = OrgAppSettingsResponse(
         enable_proactive_conversation_starters=True,
-        enable_solvability_analysis=True,
         max_budget_per_task=None,
     )
 

enterprise/tests/unit/server/services/test_automation_event_service.py

@@ -826,249 +826,3 @@ class TestCacheHelpers:
             service = create_service(mock_token_manager)
             # Should not raise
             await service._set_cached_value('test-key', 'test-value', 3600)
-
-
-# =============================================================================
-# GitLab-specific tests
-# =============================================================================
-
-
-@pytest.fixture
-def gitlab_group_payload():
-    """Create a sample GitLab webhook payload for a group-owned project."""
-    return {
-        'object_kind': 'issue',
-        'event_type': 'issue',
-        'project': {
-            'id': 123456,
-            'path_with_namespace': 'test-org/test-repo',
-            'visibility': 'public',
-            'namespace': {
-                'id': 789,
-                'kind': 'group',
-                'name': 'test-org',
-            },
-        },
-        'user': {
-            'id': 12345,
-            'username': 'testuser',
-        },
-        'object_attributes': {
-            'id': 1,
-            'iid': 1,
-            'title': 'Test Issue',
-        },
-    }
-
-
-@pytest.fixture
-def gitlab_user_payload():
-    """Create a sample GitLab webhook payload for a user-owned project."""
-    return {
-        'object_kind': 'issue',
-        'event_type': 'issue',
-        'project': {
-            'id': 654321,
-            'path_with_namespace': 'testuser/personal-repo',
-            'visibility': 'private',
-            'namespace': {
-                'id': 12345,
-                'kind': 'user',
-                'name': 'testuser',
-            },
-        },
-        'user': {
-            'id': 12345,
-            'username': 'testuser',
-        },
-        'object_attributes': {
-            'id': 2,
-            'iid': 1,
-            'title': 'Personal Issue',
-        },
-    }
-
-
-class TestExtractOwnerInfoGitLab:
-    """Tests for _extract_owner_info method with GitLab payloads."""
-
-    def test_extract_gitlab_group_owner(self, mock_token_manager, gitlab_group_payload):
-        """
-        GIVEN: GitLab payload for a group-owned project
-        WHEN: _extract_owner_info is called
-        THEN: Returns correct git_org, owner_type, owner_id
-        """
-        with patch('server.services.automation_event_service.sio'):
-            service = create_service(mock_token_manager)
-            git_org, owner_type, owner_id = service._extract_owner_info(
-                ProviderType.GITLAB, gitlab_group_payload
-            )
-
-            assert git_org == 'test-org'
-            assert owner_type == 'group'
-            assert owner_id == 789
-
-    def test_extract_gitlab_user_owner(self, mock_token_manager, gitlab_user_payload):
-        """
-        GIVEN: GitLab payload for a user-owned project
-        WHEN: _extract_owner_info is called
-        THEN: Returns correct git_org, owner_type, owner_id
-        """
-        with patch('server.services.automation_event_service.sio'):
-            service = create_service(mock_token_manager)
-            git_org, owner_type, owner_id = service._extract_owner_info(
-                ProviderType.GITLAB, gitlab_user_payload
-            )
-
-            assert git_org == 'testuser'
-            assert owner_type == 'user'
-            assert owner_id == 12345
-
-    def test_extract_gitlab_missing_project(self, mock_token_manager):
-        """
-        GIVEN: GitLab payload without project data
-        WHEN: _extract_owner_info is called
-        THEN: Returns None values
-        """
-        payload = {'object_kind': 'issue'}
-
-        with patch('server.services.automation_event_service.sio'):
-            service = create_service(mock_token_manager)
-            git_org, owner_type, owner_id = service._extract_owner_info(
-                ProviderType.GITLAB, payload
-            )
-
-            assert git_org is None
-            assert owner_type is None
-            assert owner_id is None
-
-
-class TestResolveOrgContextGitLab:
-    """Tests for _resolve_org_context method with GitLab payloads."""
-
-    @pytest.mark.asyncio
-    async def test_resolve_gitlab_group_org(
-        self, mock_token_manager, mock_org_git_claim, gitlab_group_payload
-    ):
-        """
-        GIVEN: GitLab payload for a group project with claimed org
-        WHEN: _resolve_org_context is called
-        THEN: Returns correct OrgContext with org_id
-        """
-        mock_redis = AsyncMock()
-        mock_redis.get = AsyncMock(return_value=None)  # Cache miss
-        mock_redis.setex = AsyncMock()
-
-        with patch(
-            'server.services.automation_event_service.resolve_org_for_repo',
-            new_callable=AsyncMock,
-            return_value=mock_org_git_claim.org_id,
-        ), patch('server.services.automation_event_service.sio') as mock_sio:
-            mock_sio.manager.redis = mock_redis
-
-            service = create_service(mock_token_manager)
-            result = await service._resolve_org_context(
-                ProviderType.GITLAB, gitlab_group_payload
-            )
-
-            assert result is not None
-            assert result.org_id == mock_org_git_claim.org_id
-            assert result.git_org == 'test-org'
-
-    @pytest.mark.asyncio
-    async def test_resolve_gitlab_user_personal_org_fallback(
-        self, mock_token_manager, mock_org_git_claim, gitlab_user_payload
-    ):
-        """
-        GIVEN: GitLab payload for a user project (not claimed via OrgGitClaim)
-        WHEN: _resolve_org_context is called
-        THEN: Falls back to personal org resolution
-        """
-        mock_redis = AsyncMock()
-        mock_redis.get = AsyncMock(return_value=None)  # Cache miss
-        mock_redis.setex = AsyncMock()
-
-        with patch(
-            'server.services.automation_event_service.resolve_org_for_repo',
-            new_callable=AsyncMock,
-            return_value=None,  # No OrgGitClaim for user
-        ), patch('server.services.automation_event_service.sio') as mock_sio:
-            mock_sio.manager.redis = mock_redis
-
-            service = create_service(mock_token_manager)
-            # Mock _resolve_personal_org to return a personal org
-            service._resolve_personal_org = AsyncMock(
-                return_value=mock_org_git_claim.org_id
-            )
-
-            result = await service._resolve_org_context(
-                ProviderType.GITLAB, gitlab_user_payload
-            )
-
-            assert result is not None
-            assert result.org_id == mock_org_git_claim.org_id
-            assert result.git_org == 'testuser'
-            # Verify personal org fallback was called with GitLab user ID
-            service._resolve_personal_org.assert_called_once_with(
-                ProviderType.GITLAB, 12345
-            )
-
-    @pytest.mark.asyncio
-    async def test_resolve_gitlab_no_org_found(
-        self, mock_token_manager, gitlab_group_payload
-    ):
-        """
-        GIVEN: GitLab payload for a project with no org claim and no personal org
-        WHEN: _resolve_org_context is called
-        THEN: Returns None
-        """
-        mock_redis = AsyncMock()
-        mock_redis.get = AsyncMock(return_value=None)
-        mock_redis.setex = AsyncMock()
-
-        with patch(
-            'server.services.automation_event_service.resolve_org_for_repo',
-            new_callable=AsyncMock,
-            return_value=None,
-        ), patch('server.services.automation_event_service.sio') as mock_sio:
-            mock_sio.manager.redis = mock_redis
-
-            service = create_service(mock_token_manager)
-            result = await service._resolve_org_context(
-                ProviderType.GITLAB, gitlab_group_payload
-            )
-
-            # Group owner doesn't fall back to personal org
-            assert result is None
-
-
-class TestResolveGitOrgGitLab:
-    """Tests for _resolve_git_org method with GitLab provider."""
-
-    @pytest.mark.asyncio
-    async def test_resolve_gitlab_org_includes_provider_in_cache_key(
-        self, mock_token_manager, mock_org_git_claim
-    ):
-        """
-        GIVEN: GitLab provider with an org name
-        WHEN: _resolve_git_org is called
-        THEN: Cache key includes the gitlab provider name
-        """
-        mock_redis = AsyncMock()
-        mock_redis.get = AsyncMock(return_value=None)
-        mock_redis.setex = AsyncMock()
-
-        with patch(
-            'server.services.automation_event_service.resolve_org_for_repo',
-            new_callable=AsyncMock,
-            return_value=mock_org_git_claim.org_id,
-        ), patch('server.services.automation_event_service.sio') as mock_sio:
-            mock_sio.manager.redis = mock_redis
-
-            service = create_service(mock_token_manager)
-            await service._resolve_git_org(ProviderType.GITLAB, 'Test-Org')
-
-            # Verify cache key includes provider and normalized org name
-            get_call_args = mock_redis.get.call_args[0][0]
-            assert 'gitlab' in get_call_args
-            assert 'test-org' in get_call_args  # Lowercase normalized

enterprise/tests/unit/server/services/test_org_app_settings_service.py

@@ -29,7 +29,6 @@ def mock_org():
     org = MagicMock(spec=Org)
     org.id = uuid.uuid4()
     org.enable_proactive_conversation_starters = True
-    org.enable_solvability_analysis = False
     org.max_budget_per_task = 25.0
     return org
 
@@ -67,7 +66,6 @@ async def test_get_org_app_settings_success(
     # Assert
     assert isinstance(result, OrgAppSettingsResponse)
     assert result.enable_proactive_conversation_starters is True
-    assert result.enable_solvability_analysis is False
     assert result.max_budget_per_task == 25.0
     mock_store.get_current_org_by_user_id.assert_called_once_with(user_id)
 

enterprise/tests/unit/server/test_conversation_callback_utils.py

@@ -1,533 +0,0 @@
-"""
-Tests for conversation_callback_utils.py
-"""
-
-from unittest.mock import Mock, patch
-
-import pytest
-from server.utils.conversation_callback_utils import update_active_working_seconds
-from storage.conversation_work import ConversationWork
-
-from openhands.core.schema.agent import AgentState
-from openhands.events.observation.agent import AgentStateChangedObservation
-from openhands.storage.files import FileStore
-
-
-class TestUpdateActiveWorkingSeconds:
-    """Test the update_active_working_seconds function."""
-
-    @pytest.fixture
-    def mock_file_store(self):
-        """Create a mock FileStore."""
-        return Mock(spec=FileStore)
-
-    @pytest.fixture
-    def mock_event_store(self):
-        """Create a mock EventStore."""
-        return Mock()
-
-    def test_update_active_working_seconds_multiple_state_changes(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test calculating active working seconds with multiple state changes between running and ready."""
-        conversation_id = 'test_conversation_123'
-        user_id = 'test_user_456'
-
-        # Create a sequence of events with state changes between RUNNING and other states
-        # Timeline:
-        # t=0: RUNNING (start)
-        # t=10: AWAITING_USER_INPUT (10 seconds of running)
-        # t=15: RUNNING (start again)
-        # t=25: FINISHED (10 more seconds of running)
-        # t=30: RUNNING (start again)
-        # t=40: PAUSED (10 more seconds of running)
-        # Total: 30 seconds of running time
-
-        # Create mock events with ISO-formatted timestamps for testing
-        events = []
-
-        # First running period: 10 seconds
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.RUNNING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-        events.append(event1)
-
-        event2 = Mock(spec=AgentStateChangedObservation)
-        event2.agent_state = AgentState.AWAITING_USER_INPUT
-        event2.timestamp = '1970-01-01T00:00:10.000000'
-        events.append(event2)
-
-        # Second running period: 10 seconds
-        event3 = Mock(spec=AgentStateChangedObservation)
-        event3.agent_state = AgentState.RUNNING
-        event3.timestamp = '1970-01-01T00:00:15.000000'
-        events.append(event3)
-
-        event4 = Mock(spec=AgentStateChangedObservation)
-        event4.agent_state = AgentState.FINISHED
-        event4.timestamp = '1970-01-01T00:00:25.000000'
-        events.append(event4)
-
-        # Third running period: 10 seconds
-        event5 = Mock(spec=AgentStateChangedObservation)
-        event5.agent_state = AgentState.RUNNING
-        event5.timestamp = '1970-01-01T00:00:30.000000'
-        events.append(event5)
-
-        event6 = Mock(spec=AgentStateChangedObservation)
-        event6.agent_state = AgentState.PAUSED
-        event6.timestamp = '1970-01-01T00:00:40.000000'
-        events.append(event6)
-
-        # Configure the mock event store to return our test events
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify the ConversationWork record was created with correct total seconds
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.conversation_id == conversation_id
-            assert conversation_work.user_id == user_id
-            assert conversation_work.seconds == 30.0  # Total running time
-            assert conversation_work.created_at is not None
-            assert conversation_work.updated_at is not None
-
-    def test_update_active_working_seconds_updates_existing_record(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test that the function updates an existing ConversationWork record."""
-        conversation_id = 'test_conversation_456'
-        user_id = 'test_user_789'
-
-        # Create an existing ConversationWork record
-        with session_maker() as session:
-            existing_work = ConversationWork(
-                conversation_id=conversation_id,
-                user_id=user_id,
-                seconds=15.0,  # Previous value
-            )
-            session.add(existing_work)
-            session.commit()
-
-        # Create events with new running time
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.RUNNING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-
-        event2 = Mock(spec=AgentStateChangedObservation)
-        event2.agent_state = AgentState.STOPPED
-        event2.timestamp = '1970-01-01T00:00:20.000000'
-
-        events = [event1, event2]
-
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify the existing record was updated
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.seconds == 20.0  # Updated value
-            assert conversation_work.user_id == user_id
-
-    def test_update_active_working_seconds_agent_still_running(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test that time is not counted if agent is still running at the end."""
-        conversation_id = 'test_conversation_789'
-        user_id = 'test_user_012'
-
-        # Create events where agent starts running but never stops
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.RUNNING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-
-        event2 = Mock(spec=AgentStateChangedObservation)
-        event2.agent_state = AgentState.AWAITING_USER_INPUT
-        event2.timestamp = '1970-01-01T00:00:10.000000'
-
-        event3 = Mock(spec=AgentStateChangedObservation)
-        event3.agent_state = AgentState.RUNNING
-        event3.timestamp = '1970-01-01T00:00:15.000000'
-
-        events = [event1, event2, event3]
-        # No final state change - agent still running
-
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify only the completed running period is counted
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.seconds == 10.0  # Only the first completed period
-
-    def test_update_active_working_seconds_no_running_states(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test that zero seconds are recorded when there are no running states."""
-        conversation_id = 'test_conversation_000'
-        user_id = 'test_user_000'
-
-        # Create events with no RUNNING states
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.LOADING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-
-        event2 = Mock(spec=AgentStateChangedObservation)
-        event2.agent_state = AgentState.AWAITING_USER_INPUT
-        event2.timestamp = '1970-01-01T00:00:05.000000'
-
-        event3 = Mock(spec=AgentStateChangedObservation)
-        event3.agent_state = AgentState.FINISHED
-        event3.timestamp = '1970-01-01T00:00:10.000000'
-
-        events = [event1, event2, event3]
-
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify zero seconds are recorded
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.seconds == 0.0
-
-    def test_update_active_working_seconds_mixed_event_types(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test that only AgentStateChangedObservation events are processed."""
-        conversation_id = 'test_conversation_mixed'
-        user_id = 'test_user_mixed'
-
-        # Create a mix of event types, only AgentStateChangedObservation should be processed
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.RUNNING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-
-        # Mock other event types that should be ignored
-        event2 = Mock()  # Not an AgentStateChangedObservation
-        event2.timestamp = '1970-01-01T00:00:05.000000'
-
-        event3 = Mock()  # Not an AgentStateChangedObservation
-        event3.timestamp = '1970-01-01T00:00:08.000000'
-
-        event4 = Mock(spec=AgentStateChangedObservation)
-        event4.agent_state = AgentState.STOPPED
-        event4.timestamp = '1970-01-01T00:00:10.000000'
-
-        events = [event1, event2, event3, event4]
-
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify only the AgentStateChangedObservation events were processed
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.seconds == 10.0  # Only the valid state changes
-
-    @patch('server.utils.conversation_callback_utils.logger')
-    def test_update_active_working_seconds_handles_exceptions(
-        self, mock_logger, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test that exceptions are properly handled and logged."""
-        conversation_id = 'test_conversation_error'
-        user_id = 'test_user_error'
-
-        # Configure the mock to raise an exception
-        mock_event_store.search_events.side_effect = Exception('Test error')
-
-        # Call the function under test
-        update_active_working_seconds(
-            mock_event_store, conversation_id, user_id, mock_file_store
-        )
-
-        # Verify the error was logged
-        mock_logger.error.assert_called_once()
-        error_call = mock_logger.error.call_args
-        assert error_call[0][0] == 'failed_to_update_active_working_seconds'
-        assert error_call[1]['extra']['conversation_id'] == conversation_id
-        assert error_call[1]['extra']['user_id'] == user_id
-        assert 'Test error' in error_call[1]['extra']['error']
-
-    def test_update_active_working_seconds_complex_state_transitions(
-        self, session_maker, mock_event_store, mock_file_store
-    ):
-        """Test complex state transitions including error and rate limited states."""
-        conversation_id = 'test_conversation_complex'
-        user_id = 'test_user_complex'
-
-        # Create a complex sequence of state changes
-        events = []
-
-        # First running period: 5 seconds
-        event1 = Mock(spec=AgentStateChangedObservation)
-        event1.agent_state = AgentState.LOADING
-        event1.timestamp = '1970-01-01T00:00:00.000000'
-        events.append(event1)
-
-        event2 = Mock(spec=AgentStateChangedObservation)
-        event2.agent_state = AgentState.RUNNING
-        event2.timestamp = '1970-01-01T00:00:02.000000'
-        events.append(event2)
-
-        event3 = Mock(spec=AgentStateChangedObservation)
-        event3.agent_state = AgentState.ERROR
-        event3.timestamp = '1970-01-01T00:00:07.000000'
-        events.append(event3)
-
-        # Second running period: 8 seconds
-        event4 = Mock(spec=AgentStateChangedObservation)
-        event4.agent_state = AgentState.RUNNING
-        event4.timestamp = '1970-01-01T00:00:10.000000'
-        events.append(event4)
-
-        event5 = Mock(spec=AgentStateChangedObservation)
-        event5.agent_state = AgentState.RATE_LIMITED
-        event5.timestamp = '1970-01-01T00:00:18.000000'
-        events.append(event5)
-
-        # Third running period: 3 seconds
-        event6 = Mock(spec=AgentStateChangedObservation)
-        event6.agent_state = AgentState.RUNNING
-        event6.timestamp = '1970-01-01T00:00:20.000000'
-        events.append(event6)
-
-        event7 = Mock(spec=AgentStateChangedObservation)
-        event7.agent_state = AgentState.AWAITING_USER_CONFIRMATION
-        event7.timestamp = '1970-01-01T00:00:23.000000'
-        events.append(event7)
-
-        event8 = Mock(spec=AgentStateChangedObservation)
-        event8.agent_state = AgentState.USER_CONFIRMED
-        event8.timestamp = '1970-01-01T00:00:25.000000'
-        events.append(event8)
-
-        # Fourth running period: 7 seconds
-        event9 = Mock(spec=AgentStateChangedObservation)
-        event9.agent_state = AgentState.RUNNING
-        event9.timestamp = '1970-01-01T00:00:30.000000'
-        events.append(event9)
-
-        event10 = Mock(spec=AgentStateChangedObservation)
-        event10.agent_state = AgentState.FINISHED
-        event10.timestamp = '1970-01-01T00:00:37.000000'
-        events.append(event10)
-
-        mock_event_store.search_events.return_value = events
-
-        # Call the function under test with mocked session_maker
-        with patch(
-            'server.utils.conversation_callback_utils.session_maker', session_maker
-        ):
-            update_active_working_seconds(
-                mock_event_store, conversation_id, user_id, mock_file_store
-            )
-
-        # Verify the total running time is calculated correctly
-        # Running periods: 5 + 8 + 3 + 7 = 23 seconds
-        with session_maker() as session:
-            conversation_work = (
-                session.query(ConversationWork)
-                .filter(ConversationWork.conversation_id == conversation_id)
-                .first()
-            )
-
-            assert conversation_work is not None
-            assert conversation_work.seconds == 23.0
-            assert conversation_work.conversation_id == conversation_id
-            assert conversation_work.user_id == user_id
-
-
-class TestInvokeConversationCallbacks:
-    """Tests for invoke_conversation_callbacks function.
-
-    This function uses async database sessions (a_session_maker) to query
-    and invoke callbacks for a conversation.
-    """
-
-    @pytest.fixture
-    def mock_observation(self):
-        """Create a mock AgentStateChangedObservation."""
-
-        observation = Mock(spec=AgentStateChangedObservation)
-        observation.agent_state = AgentState.FINISHED
-        return observation
-
-    @pytest.fixture
-    def create_mock_async_session(self):
-        """Factory to create properly mocked async session context manager."""
-        from contextlib import asynccontextmanager
-        from unittest.mock import AsyncMock
-
-        def _create(callbacks_list):
-            mock_session = Mock()
-            mock_result = Mock()
-            mock_result.scalars.return_value.all.return_value = callbacks_list
-            mock_session.execute = AsyncMock(return_value=mock_result)
-            mock_session.commit = AsyncMock(return_value=None)
-
-            @asynccontextmanager
-            async def mock_context_manager():
-                yield mock_session
-
-            return mock_context_manager, mock_session
-
-        return _create
-
-    @pytest.mark.asyncio
-    async def test_invoke_callbacks_with_active_callbacks(
-        self, mock_observation, create_mock_async_session
-    ):
-        """Test that active callbacks are invoked successfully."""
-        from unittest.mock import AsyncMock
-
-        # Arrange
-        conversation_id = 'test_conversation_callbacks'
-        mock_processor = AsyncMock(return_value=None)
-
-        # Create a mock callback
-        mock_callback = Mock()
-        mock_callback.id = 1
-        mock_callback.processor_type = 'test_processor'
-        mock_callback.get_processor.return_value = mock_processor
-
-        mock_context_manager, mock_session = create_mock_async_session([mock_callback])
-
-        # Act
-        with patch(
-            'server.utils.conversation_callback_utils.a_session_maker',
-            mock_context_manager,
-        ):
-            from server.utils.conversation_callback_utils import (
-                invoke_conversation_callbacks,
-            )
-
-            await invoke_conversation_callbacks(conversation_id, mock_observation)
-
-        # Assert
-        mock_callback.get_processor.assert_called_once()
-        mock_processor.assert_called_once_with(mock_callback, mock_observation)
-
-    @pytest.mark.asyncio
-    async def test_invoke_callbacks_with_no_active_callbacks(
-        self, mock_observation, create_mock_async_session
-    ):
-        """Test behavior when no active callbacks exist."""
-        # Arrange
-        conversation_id = 'test_no_callbacks'
-
-        mock_context_manager, mock_session = create_mock_async_session([])
-
-        # Act
-        with patch(
-            'server.utils.conversation_callback_utils.a_session_maker',
-            mock_context_manager,
-        ):
-            from server.utils.conversation_callback_utils import (
-                invoke_conversation_callbacks,
-            )
-
-            await invoke_conversation_callbacks(conversation_id, mock_observation)
-
-        # Assert - should complete without errors
-        mock_session.commit.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_invoke_callbacks_handles_processor_exception(
-        self, mock_observation, create_mock_async_session
-    ):
-        """Test that processor exceptions are caught and callback status is updated."""
-        from unittest.mock import AsyncMock
-
-        # Arrange
-        conversation_id = 'test_callback_error'
-        mock_processor = AsyncMock(side_effect=Exception('Processor error'))
-
-        mock_callback = Mock()
-        mock_callback.id = 1
-        mock_callback.processor_type = 'failing_processor'
-        mock_callback.get_processor.return_value = mock_processor
-        mock_callback.status = 'active'
-
-        mock_context_manager, mock_session = create_mock_async_session([mock_callback])
-
-        # Act
-        with patch(
-            'server.utils.conversation_callback_utils.a_session_maker',
-            mock_context_manager,
-        ), patch('server.utils.conversation_callback_utils.logger') as mock_logger:
-            from server.utils.conversation_callback_utils import (
-                invoke_conversation_callbacks,
-            )
-            from storage.conversation_callback import CallbackStatus
-
-            await invoke_conversation_callbacks(conversation_id, mock_observation)
-
-            # Assert - callback status should be set to ERROR
-            assert mock_callback.status == CallbackStatus.ERROR
-            mock_logger.error.assert_called_once()
-            error_call = mock_logger.error.call_args
-            assert error_call[0][0] == 'callback_invocation_failed'

enterprise/tests/unit/solvability/conftest.py

@@ -1,113 +0,0 @@
-"""
-Shared fixtures for all tests.
-"""
-
-from typing import Any
-from unittest.mock import MagicMock
-
-import numpy as np
-import pytest
-from integrations.solvability.models.classifier import SolvabilityClassifier
-from integrations.solvability.models.featurizer import (
-    Feature,
-    FeatureEmbedding,
-    Featurizer,
-)
-from sklearn.ensemble import RandomForestClassifier
-
-from openhands.core.config import LLMConfig
-
-
-@pytest.fixture
-def features() -> list[Feature]:
-    """Create a list of features for testing."""
-    return [
-        Feature(identifier='feature1', description='Test feature 1'),
-        Feature(identifier='feature2', description='Test feature 2'),
-        Feature(identifier='feature3', description='Test feature 3'),
-    ]
-
-
-@pytest.fixture
-def feature_embedding() -> FeatureEmbedding:
-    """Create a feature embedding for testing."""
-    return FeatureEmbedding(
-        samples=[
-            {'feature1': True, 'feature2': False, 'feature3': True},
-            {'feature1': False, 'feature2': True, 'feature3': True},
-        ],
-        prompt_tokens=10,
-        completion_tokens=5,
-        response_latency=0.1,
-    )
-
-
-@pytest.fixture
-def featurizer(mock_llm, features) -> Featurizer:
-    """
-    Create a featurizer for testing.
-
-    Mocks out any calls to LLM.completion
-    """
-    pytest.MonkeyPatch().setattr(
-        'integrations.solvability.models.featurizer.LLM',
-        lambda *args, **kwargs: mock_llm,
-    )
-
-    featurizer = Featurizer(
-        system_prompt='Test system prompt',
-        message_prefix='Test message prefix: ',
-        features=features,
-    )
-
-    return featurizer
-
-
-@pytest.fixture
-def mock_completion_response() -> dict[str, Any]:
-    """Create a mock response for the feature sample model."""
-    mock_response = MagicMock()
-    mock_response.choices = [MagicMock()]
-    mock_response.choices[0].message.tool_calls = [MagicMock()]
-    mock_response.choices[0].message.tool_calls[
-        0
-    ].function.arguments = '{"feature1": true, "feature2": false, "feature3": true}'
-    mock_response.usage.prompt_tokens = 10
-    mock_response.usage.completion_tokens = 5
-    return mock_response
-
-
-@pytest.fixture
-def mock_llm(mock_completion_response):
-    """Create a mock LLM instance."""
-    mock_llm_instance = MagicMock()
-    mock_llm_instance.completion.return_value = mock_completion_response
-    return mock_llm_instance
-
-
-@pytest.fixture
-def mock_llm_config():
-    """Create a mock LLM config for testing."""
-    return LLMConfig(model='test-model')
-
-
-@pytest.fixture
-def mock_classifier():
-    """Create a mock classifier for testing."""
-    clf = RandomForestClassifier(random_state=42)
-    # Initialize with some dummy data to avoid errors
-    X = np.array([[0, 0, 0], [1, 1, 1]])  # noqa: N806
-    y = np.array([0, 1])
-    clf.fit(X, y)
-    return clf
-
-
-@pytest.fixture
-def solvability_classifier(featurizer, mock_classifier):
-    """Create a SolvabilityClassifier instance for testing."""
-    return SolvabilityClassifier(
-        identifier='test-classifier',
-        featurizer=featurizer,
-        classifier=mock_classifier,
-        random_state=42,
-    )

enterprise/tests/unit/solvability/test_classifier.py

@@ -1,218 +0,0 @@
-import numpy as np
-import pandas as pd
-import pytest
-from integrations.solvability.models.classifier import SolvabilityClassifier
-from integrations.solvability.models.featurizer import Feature
-from integrations.solvability.models.importance_strategy import ImportanceStrategy
-from sklearn.ensemble import RandomForestClassifier
-
-
-@pytest.mark.parametrize('random_state', [None, 42])
-def test_random_state_initialization(random_state, featurizer):
-    """Test initialization of the solvability classifier random state propagates to the RFC."""
-    # If the RFC has no random state, the solvability classifier should propagate
-    # its random state down.
-    solvability_classifier = SolvabilityClassifier(
-        identifier='test',
-        featurizer=featurizer,
-        classifier=RandomForestClassifier(random_state=None),
-        random_state=random_state,
-    )
-
-    # The classifier's random_state should be updated to match
-    assert solvability_classifier.random_state == random_state
-    assert solvability_classifier.classifier.random_state == random_state
-
-    # If the RFC somehow has a random state, as long as it matches the solvability
-    # classifier's random state initialization should succeed.
-    solvability_classifier = SolvabilityClassifier(
-        identifier='test',
-        featurizer=featurizer,
-        classifier=RandomForestClassifier(random_state=random_state),
-        random_state=random_state,
-    )
-
-    assert solvability_classifier.random_state == random_state
-    assert solvability_classifier.classifier.random_state == random_state
-
-
-def test_inconsistent_random_state(featurizer):
-    """Test validation fails when the classifier and RFC have inconsistent random states."""
-    classifier = RandomForestClassifier(random_state=42)
-
-    with pytest.raises(ValueError):
-        SolvabilityClassifier(
-            identifier='test',
-            featurizer=featurizer,
-            classifier=classifier,
-            random_state=24,
-        )
-
-
-def test_transform_produces_feature_columns(solvability_classifier, mock_llm_config):
-    """Test transform method produces expected feature columns."""
-    issues = pd.Series(['Test issue'])
-    features = solvability_classifier.transform(issues, llm_config=mock_llm_config)
-
-    assert isinstance(features, pd.DataFrame)
-
-    for feature in solvability_classifier.featurizer.features:
-        assert feature.identifier in features.columns
-
-
-def test_transform_sets_classifier_attrs(solvability_classifier, mock_llm_config):
-    """Test transform method sets classifier attributes `features_` and `cost_`."""
-    issues = pd.Series(['Test issue'])
-    features = solvability_classifier.transform(issues, llm_config=mock_llm_config)
-
-    # Make sure the features_ attr is set and equivalent to the transformed features.
-    np.testing.assert_array_equal(features, solvability_classifier.features_)
-
-    # Make sure the cost attr exists and has all the columns we'd expect.
-    assert solvability_classifier.cost_ is not None
-    assert isinstance(solvability_classifier.cost_, pd.DataFrame)
-    assert 'prompt_tokens' in solvability_classifier.cost_.columns
-    assert 'completion_tokens' in solvability_classifier.cost_.columns
-    assert 'response_latency' in solvability_classifier.cost_.columns
-
-
-def test_fit_sets_classifier_attrs(solvability_classifier, mock_llm_config):
-    """Test fit method sets classifier attribute `feature_importances_`."""
-    issues = pd.Series(['Test issue'])
-    labels = pd.Series([1])
-
-    # Fit the classifier
-    solvability_classifier.fit(issues, labels, llm_config=mock_llm_config)
-
-    # Check that the feature importances are set
-    assert 'feature_importances_' in solvability_classifier._classifier_attrs
-    assert isinstance(solvability_classifier.feature_importances_, np.ndarray)
-
-
-def test_predict_proba_sets_classifier_attrs(solvability_classifier, mock_llm_config):
-    """Test predict_proba method sets classifier attribute `feature_importances_`."""
-    issues = pd.Series(['Test issue'])
-
-    # Call predict_proba -- we don't care about the output here, just the side
-    # effects.
-    _ = solvability_classifier.predict_proba(issues, llm_config=mock_llm_config)
-
-    # Check that the feature importances are set
-    assert 'feature_importances_' in solvability_classifier._classifier_attrs
-    assert isinstance(solvability_classifier.feature_importances_, np.ndarray)
-
-
-def test_predict_sets_classifier_attrs(solvability_classifier, mock_llm_config):
-    """Test predict method sets classifier attribute `feature_importances_`."""
-    issues = pd.Series(['Test issue'])
-
-    # Call predict -- we don't care about the output here, just the side effects.
-    _ = solvability_classifier.predict(issues, llm_config=mock_llm_config)
-
-    # Check that the feature importances are set
-    assert 'feature_importances_' in solvability_classifier._classifier_attrs
-    assert isinstance(solvability_classifier.feature_importances_, np.ndarray)
-
-
-def test_add_single_feature(solvability_classifier):
-    """Test that a single feature can be added."""
-    feature = Feature(identifier='new_feature', description='New test feature')
-
-    assert feature not in solvability_classifier.featurizer.features
-
-    solvability_classifier.add_features([feature])
-    assert feature in solvability_classifier.featurizer.features
-
-
-def test_add_multiple_features(solvability_classifier):
-    """Test that multiple features can be added."""
-    feature_1 = Feature(identifier='new_feature_1', description='New test feature 1')
-    feature_2 = Feature(identifier='new_feature_2', description='New test feature 2')
-
-    assert feature_1 not in solvability_classifier.featurizer.features
-    assert feature_2 not in solvability_classifier.featurizer.features
-
-    solvability_classifier.add_features([feature_1, feature_2])
-
-    assert feature_1 in solvability_classifier.featurizer.features
-    assert feature_2 in solvability_classifier.featurizer.features
-
-
-def test_add_features_idempotency(solvability_classifier):
-    """Test that adding the same feature multiple times does not duplicate it."""
-    feature = Feature(identifier='new_feature', description='New test feature')
-
-    # Add the feature once
-    solvability_classifier.add_features([feature])
-    num_features = len(solvability_classifier.featurizer.features)
-
-    # Add the same feature again -- number of features should not increase
-    solvability_classifier.add_features([feature])
-    assert len(solvability_classifier.featurizer.features) == num_features
-
-
-@pytest.mark.parametrize('strategy', list(ImportanceStrategy))
-def test_importance_strategies(strategy, solvability_classifier, mock_llm_config):
-    """Test different importance strategies."""
-    # Setup
-    issues = pd.Series(['Test issue', 'Another test issue'])
-    labels = pd.Series([1, 0])
-
-    # Set the importance strategy
-    solvability_classifier.importance_strategy = strategy
-
-    # Fit the model -- this will force the classifier to compute feature importances
-    # and set them in the feature_importances_ attribute.
-    solvability_classifier.fit(issues, labels, llm_config=mock_llm_config)
-
-    assert 'feature_importances_' in solvability_classifier._classifier_attrs
-    assert isinstance(solvability_classifier.feature_importances_, np.ndarray)
-
-    # Make sure the feature importances actually have some values to them.
-    assert not np.isnan(solvability_classifier.feature_importances_).any()
-
-
-def test_is_fitted_property(solvability_classifier, mock_llm_config):
-    """Test the is_fitted property accurately reflects the classifier's state."""
-    issues = pd.Series(['Test issue', 'Another test issue'])
-    labels = pd.Series([1, 0])
-
-    # Set the solvability classifier's RFC to a fresh instance to ensure it's not fitted.
-    solvability_classifier.classifier = RandomForestClassifier(random_state=42)
-    assert not solvability_classifier.is_fitted
-
-    solvability_classifier.fit(issues, labels, llm_config=mock_llm_config)
-    assert solvability_classifier.is_fitted
-
-
-def test_solvability_report_well_formed(solvability_classifier, mock_llm_config):
-    """Test that the SolvabilityReport is well-formed and all required fields are present."""
-    issues = pd.Series(['Test issue', 'Another test issue'])
-    labels = pd.Series([1, 0])
-    # Fit the classifier
-    solvability_classifier.fit(issues, labels, llm_config=mock_llm_config)
-
-    report = solvability_classifier.solvability_report(
-        issues.iloc[0], llm_config=mock_llm_config
-    )
-
-    # Generation of the report is a strong enough test (as it has to get past all
-    # the pydantic validators). But just in case we can also double-check the field
-    # values.
-    assert report.identifier == solvability_classifier.identifier
-    assert report.issue == issues.iloc[0]
-    assert 0 <= report.score <= 1
-    assert report.samples == solvability_classifier.samples
-    assert set(report.features.keys()) == set(
-        solvability_classifier.featurizer.feature_identifiers()
-    )
-    assert report.importance_strategy == solvability_classifier.importance_strategy
-    assert set(report.feature_importances.keys()) == set(
-        solvability_classifier.featurizer.feature_identifiers()
-    )
-    assert report.random_state == solvability_classifier.random_state
-    assert report.created_at is not None
-    assert report.prompt_tokens >= 0
-    assert report.completion_tokens >= 0
-    assert report.response_latency >= 0
-    assert report.metadata is None

enterprise/tests/unit/solvability/test_data_loading.py

@@ -1,130 +0,0 @@
-"""
-Unit tests for data loading functionality in solvability/data.
-"""
-
-import json
-import tempfile
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-from integrations.solvability.data import available_classifiers, load_classifier
-from integrations.solvability.models.classifier import SolvabilityClassifier
-from pydantic import ValidationError
-
-
-def test_load_classifier_default():
-    """Test loading the default classifier."""
-    classifier = load_classifier('default-classifier')
-
-    assert isinstance(classifier, SolvabilityClassifier)
-    assert classifier.identifier == 'default-classifier'
-    assert classifier.featurizer is not None
-    assert classifier.classifier is not None
-
-
-def test_load_classifier_not_found():
-    """Test loading a non-existent classifier raises FileNotFoundError."""
-    with pytest.raises(FileNotFoundError) as exc_info:
-        load_classifier('non-existent-classifier')
-
-    assert "Classifier 'non-existent-classifier' not found" in str(exc_info.value)
-
-
-def test_available_classifiers():
-    """Test listing available classifiers."""
-    classifiers = available_classifiers()
-
-    assert isinstance(classifiers, list)
-    assert 'default-classifier' in classifiers
-    assert len(classifiers) >= 1
-
-
-def test_load_classifier_with_mock_data(solvability_classifier):
-    """Test loading a classifier with mocked data."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        test_file = Path(tmpdir) / 'test-classifier.json'
-
-        with test_file.open('w') as f:
-            f.write(solvability_classifier.model_dump_json())
-
-        with patch('integrations.solvability.data.Path') as mock_path:
-            mock_path.return_value.parent = Path(tmpdir)
-
-            classifier = load_classifier('test-classifier')
-
-            assert isinstance(classifier, SolvabilityClassifier)
-            assert classifier.identifier == 'test-classifier'
-
-
-def test_available_classifiers_with_mock_directory():
-    """Test listing classifiers in a mocked directory."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        tmpdir_path = Path(tmpdir)
-
-        (tmpdir_path / 'classifier1.json').touch()
-        (tmpdir_path / 'classifier2.json').touch()
-        (tmpdir_path / 'not-a-json.txt').touch()
-
-        with patch('integrations.solvability.data.Path') as mock_path:
-            mock_path.return_value.parent = tmpdir_path
-
-            classifiers = available_classifiers()
-
-            assert len(classifiers) == 2
-            assert 'classifier1' in classifiers
-            assert 'classifier2' in classifiers
-            assert 'not-a-json' not in classifiers
-
-
-def test_load_classifier_invalid_json():
-    """Test loading a classifier with invalid JSON content."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        test_file = Path(tmpdir) / 'invalid-classifier.json'
-
-        with test_file.open('w') as f:
-            f.write('{ invalid json content')
-
-        with patch('integrations.solvability.data.Path') as mock_path:
-            mock_path.return_value.parent = Path(tmpdir)
-
-            with pytest.raises(ValidationError):
-                load_classifier('invalid-classifier')
-
-
-def test_load_classifier_valid_json_invalid_schema():
-    """Test loading a classifier with valid JSON but invalid schema."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        test_file = Path(tmpdir) / 'invalid-schema.json'
-
-        with test_file.open('w') as f:
-            json.dump({'not': 'a valid classifier'}, f)
-
-        with patch('integrations.solvability.data.Path') as mock_path:
-            mock_path.return_value.parent = Path(tmpdir)
-
-            with pytest.raises(ValidationError):
-                load_classifier('invalid-schema')
-
-
-def test_available_classifiers_empty_directory():
-    """Test listing classifiers in an empty directory."""
-    with tempfile.TemporaryDirectory() as tmpdir:
-        with patch('integrations.solvability.data.Path') as mock_path:
-            mock_path.return_value.parent = Path(tmpdir)
-
-            classifiers = available_classifiers()
-
-            assert classifiers == []
-
-
-def test_load_classifier_path_construction():
-    """Test that the classifier path is constructed correctly."""
-    with patch('integrations.solvability.data.Path') as mock_path:
-        mock_parent = mock_path.return_value.parent
-        mock_parent.__truediv__.return_value.exists.return_value = False
-
-        with pytest.raises(FileNotFoundError):
-            load_classifier('test-name')
-
-        mock_parent.__truediv__.assert_called_once_with('test-name.json')

enterprise/tests/unit/solvability/test_featurizer.py

@@ -1,266 +0,0 @@
-import pytest
-from integrations.solvability.models.featurizer import Feature, FeatureEmbedding
-
-
-def test_feature_to_tool_description_field():
-    """Test to_tool_description_field property."""
-    feature = Feature(identifier='test', description='Test description')
-    field = feature.to_tool_description_field
-
-    # There's not much structure here, but we can check the expected type and make
-    # sure the other fields are propagated.
-    assert field['type'] == 'boolean'
-    assert field['description'] == 'Test description'
-
-
-def test_feature_embedding_dimensions(feature_embedding):
-    """Test dimensions property."""
-    dimensions = feature_embedding.dimensions
-    assert isinstance(dimensions, list)
-    assert set(dimensions) == {'feature1', 'feature2', 'feature3'}
-
-
-def test_feature_embedding_coefficients(feature_embedding):
-    """Test coefficient method."""
-    # These values are manually computed from the results in the fixture's samples.
-    assert feature_embedding.coefficient('feature1') == 0.5
-    assert feature_embedding.coefficient('feature2') == 0.5
-    assert feature_embedding.coefficient('feature3') == 1.0
-
-    # Non-existent features should not have a coefficient.
-    assert feature_embedding.coefficient('non_existent') is None
-
-
-def test_featurizer_system_message(featurizer):
-    """Test system_message method."""
-    message = featurizer.system_message()
-    assert message['role'] == 'system'
-    assert message['content'] == 'Test system prompt'
-
-
-def test_featurizer_user_message(featurizer):
-    """Test user_message method."""
-    # With cache
-    message = featurizer.user_message('Test issue', set_cache=True)
-    assert message['role'] == 'user'
-    assert message['content'] == 'Test message prefix: Test issue'
-    assert 'cache_control' in message
-    assert message['cache_control']['type'] == 'ephemeral'
-
-    # Without cache
-    message = featurizer.user_message('Test issue', set_cache=False)
-    assert message['role'] == 'user'
-    assert message['content'] == 'Test message prefix: Test issue'
-    assert 'cache_control' not in message
-
-
-def test_featurizer_tool_choice(featurizer):
-    """Test tool_choice property."""
-    tool_choice = featurizer.tool_choice
-    assert tool_choice['type'] == 'function'
-    assert tool_choice['function']['name'] == 'call_featurizer'
-
-
-def test_featurizer_tool_description(featurizer):
-    """Test tool_description property."""
-    tool_desc = featurizer.tool_description
-    assert tool_desc['type'] == 'function'
-    assert tool_desc['function']['name'] == 'call_featurizer'
-    assert 'description' in tool_desc['function']
-
-    # Check that all features are included in the properties
-    properties = tool_desc['function']['parameters']['properties']
-    for feature in featurizer.features:
-        assert feature.identifier in properties
-        assert properties[feature.identifier]['type'] == 'boolean'
-        assert properties[feature.identifier]['description'] == feature.description
-
-
-@pytest.mark.parametrize('samples', [1, 10, 100])
-def test_featurizer_embed(samples, featurizer, mock_llm_config):
-    """Test the embed method to ensure it generates the right number of samples and computes the metadata correctly."""
-    embedding = featurizer.embed(
-        'Test issue', llm_config=mock_llm_config, samples=samples
-    )
-
-    # We should get the right number of samples.
-    assert len(embedding.samples) == samples
-
-    # Because of the mocks, all the samples should be the same (and be correct).
-    assert all(sample == embedding.samples[0] for sample in embedding.samples)
-    assert embedding.samples[0]['feature1'] is True
-    assert embedding.samples[0]['feature2'] is False
-    assert embedding.samples[0]['feature3'] is True
-
-    # And all the metadata should be correct (we know the token counts because
-    # they're mocked, so just count once per sample).
-    assert embedding.prompt_tokens == 10 * samples
-    assert embedding.completion_tokens == 5 * samples
-
-    # These timings are real, so best we can do is check that they're positive.
-    assert embedding.response_latency > 0.0
-
-
-@pytest.mark.parametrize('samples', [1, 10, 100])
-@pytest.mark.parametrize('batch_size', [1, 10, 100])
-def test_featurizer_embed_batch(samples, batch_size, featurizer, mock_llm_config):
-    """Test the embed_batch method to ensure it correctly handles all issues in the batch."""
-    embeddings = featurizer.embed_batch(
-        [f'Issue {i}' for i in range(batch_size)],
-        llm_config=mock_llm_config,
-        samples=samples,
-    )
-
-    # Make sure that we get an embedding for each issue.
-    assert len(embeddings) == batch_size
-
-    # Since the embeddings are computed from a mocked completionc all, they should
-    # all be the same. We can check that they're well-formatted by applying the same
-    # checks as in `test_featurizer_embed`.
-    for embedding in embeddings:
-        assert all(sample == embedding.samples[0] for sample in embedding.samples)
-        assert embedding.samples[0]['feature1'] is True
-        assert embedding.samples[0]['feature2'] is False
-        assert embedding.samples[0]['feature3'] is True
-
-        assert len(embedding.samples) == samples
-        assert embedding.prompt_tokens == 10 * samples
-        assert embedding.completion_tokens == 5 * samples
-        assert embedding.response_latency >= 0.0
-
-
-def test_featurizer_embed_batch_thread_safety(featurizer, mock_llm_config, monkeypatch):
-    """Test embed_batch maintains correct ordering and handles concurrent execution safely."""
-    import time
-    from unittest.mock import MagicMock
-
-    # Create unique responses for each issue to verify ordering
-    def create_mock_response(issue_index):
-        mock_response = MagicMock()
-        mock_response.choices = [MagicMock()]
-        mock_response.choices[0].message.tool_calls = [MagicMock()]
-        # Each issue gets a unique feature pattern based on its index
-        mock_response.choices[0].message.tool_calls[0].function.arguments = (
-            f'{{"feature1": {str(issue_index % 2 == 0).lower()}, '
-            f'"feature2": {str(issue_index % 3 == 0).lower()}, '
-            f'"feature3": {str(issue_index % 5 == 0).lower()}}}'
-        )
-        mock_response.usage.prompt_tokens = 10 + issue_index
-        mock_response.usage.completion_tokens = 5 + issue_index
-        return mock_response
-
-    # Track call order and add delays to simulate varying processing times
-    call_count = 0
-    call_order = []
-
-    def mock_completion(*args, **kwargs):
-        nonlocal call_count
-        # Extract issue index from the message content
-        messages = kwargs.get('messages', args[0] if args else [])
-        message_content = messages[1]['content']
-        issue_index = int(message_content.split('Issue ')[-1])
-        call_order.append(issue_index)
-
-        # Add varying delays to simulate real-world conditions
-        # Later issues process faster to test race conditions
-        delay = 0.01 * (20 - issue_index)
-        time.sleep(delay)
-
-        call_count += 1
-        return create_mock_response(issue_index)
-
-    def mock_llm_class(*args, **kwargs):
-        mock_llm_instance = MagicMock()
-        mock_llm_instance.completion = mock_completion
-        return mock_llm_instance
-
-    monkeypatch.setattr(
-        'integrations.solvability.models.featurizer.LLM', mock_llm_class
-    )
-
-    # Test with a large enough batch to stress concurrency
-    batch_size = 20
-    issues = [f'Issue {i}' for i in range(batch_size)]
-
-    embeddings = featurizer.embed_batch(issues, llm_config=mock_llm_config, samples=1)
-
-    # Verify we got all embeddings
-    assert len(embeddings) == batch_size
-
-    # Verify each embedding corresponds to its correct issue index
-    for i, embedding in enumerate(embeddings):
-        assert len(embedding.samples) == 1
-        sample = embedding.samples[0]
-
-        # Check the unique pattern matches the issue index
-        assert sample['feature1'] == (i % 2 == 0)
-        assert sample['feature2'] == (i % 3 == 0)
-        assert sample['feature3'] == (i % 5 == 0)
-
-        # Check token counts match
-        assert embedding.prompt_tokens == 10 + i
-        assert embedding.completion_tokens == 5 + i
-
-    # Verify all issues were processed
-    assert call_count == batch_size
-    assert len(set(call_order)) == batch_size  # All unique indices
-
-
-def test_featurizer_embed_batch_exception_handling(
-    featurizer, mock_llm_config, monkeypatch
-):
-    """Test embed_batch handles exceptions in individual tasks correctly."""
-    from unittest.mock import MagicMock
-
-    def mock_completion(*args, **kwargs):
-        # Extract issue index from the message content
-        messages = kwargs.get('messages', args[0] if args else [])
-        message_content = messages[1]['content']
-        issue_index = int(message_content.split('Issue ')[-1])
-
-        # Make some issues fail
-        if issue_index in [2, 5, 7]:
-            raise ValueError(f'Simulated error for issue {issue_index}')
-
-        # Return normal response for others
-        mock_response = MagicMock()
-        mock_response.choices = [MagicMock()]
-        mock_response.choices[0].message.tool_calls = [MagicMock()]
-        mock_response.choices[0].message.tool_calls[
-            0
-        ].function.arguments = '{"feature1": true, "feature2": false, "feature3": true}'
-        mock_response.usage.prompt_tokens = 10
-        mock_response.usage.completion_tokens = 5
-        return mock_response
-
-    def mock_llm_class(*args, **kwargs):
-        mock_llm_instance = MagicMock()
-        mock_llm_instance.completion = mock_completion
-        return mock_llm_instance
-
-    monkeypatch.setattr(
-        'integrations.solvability.models.featurizer.LLM', mock_llm_class
-    )
-
-    issues = [f'Issue {i}' for i in range(10)]
-
-    # The method should raise an exception when any task fails
-    with pytest.raises(ValueError) as exc_info:
-        featurizer.embed_batch(issues, llm_config=mock_llm_config, samples=1)
-
-    # Verify it's one of our expected errors
-    assert 'Simulated error for issue' in str(exc_info.value)
-
-
-def test_featurizer_embed_batch_no_none_values(featurizer, mock_llm_config):
-    """Test that embed_batch never returns None values in the results list."""
-    # Test with various batch sizes to ensure no None values slip through
-    for batch_size in [1, 5, 10, 20]:
-        issues = [f'Issue {i}' for i in range(batch_size)]
-        embeddings = featurizer.embed_batch(
-            issues, llm_config=mock_llm_config, samples=1
-        )
-
-        # Verify no None values in results
-        assert all(embedding is not None for embedding in embeddings)
-        assert all(isinstance(embedding, FeatureEmbedding) for embedding in embeddings)