fix: wrap generate_agent call in try/except for consistency

Add exception handler for AgentGeneratorNotConfiguredError in generate_agent
call for defensive consistency, even though decompose_goal would typically
catch it first.

Addresses CodeRabbit review suggestion.

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

@@ -128,7 +128,7 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           exitOnceUploaded: true
 
-  e2e_test:
+  test:
     runs-on: big-boi
     needs: setup
     strategy:
@@ -258,39 +258,3 @@ jobs:
       - name: Print Final Docker Compose logs
         if: always()
         run: docker compose -f ../docker-compose.yml logs
-
-  integration_test:
-    runs-on: ubuntu-latest
-    needs: setup
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          submodules: recursive
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22.18.0"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Restore dependencies cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ needs.setup.outputs.cache-key }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Generate API client
-        run: pnpm generate:api
-
-      - name: Run Integration Tests
-        run: pnpm test:unit

AGENTS.md

@@ -16,32 +16,6 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
-
-## Frontend guidelines:
-
-See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
-
-1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
-   - Add `usePageName.ts` hook for logic
-   - Put sub-components in local `components/` folder
-2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
-   - Use design system components from `src/components/` (atoms, molecules, organisms)
-   - Never use `src/components/__legacy__/*`
-3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
-   - Regenerate with `pnpm generate:api`
-   - Pattern: `use{Method}{Version}{OperationName}`
-4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
-5. **Testing**: Add Storybook stories for new components, Playwright for E2E
-6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
-- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
-- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
-- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
-- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
-- Use function declarations for components, arrow functions only for callbacks
-- No barrel files or `index.ts` re-exports
-- Do not use `useCallback` or `useMemo` unless strictly needed
-- Avoid comments at all times unless the code is very complex
-
 ## Testing
 
 - Backend: `poetry run test` (runs pytest with a docker based postgres + prisma).

autogpt_platform/CLAUDE.md

@@ -201,7 +201,7 @@ If you get any pushback or hit complex block conditions check the new_blocks gui
 3. Write tests alongside the route file
 4. Run `poetry run test` to verify
 
-### Frontend guidelines:
+**Frontend feature development:**
 
 See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 
@@ -217,14 +217,6 @@ See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
 5. **Testing**: Add Storybook stories for new components, Playwright for E2E
 6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
-- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
-- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
-- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
-- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
-- Use function declarations for components, arrow functions only for callbacks
-- No barrel files or `index.ts` re-exports
-- Do not use `useCallback` or `useMemo` unless strictly needed
-- Avoid comments at all times unless the code is very complex
 
 ### Security Implementation
 

autogpt_platform/backend/backend/api/conn_manager.py

@@ -122,24 +122,6 @@ class ConnectionManager:
 
         return len(connections)
 
-    async def broadcast_to_all(self, *, method: WSMethod, data: dict) -> int:
-        """Broadcast a message to all active websocket connections."""
-        message = WSMessage(
-            method=method,
-            data=data,
-        ).model_dump_json()
-
-        connections = tuple(self.active_connections)
-        if not connections:
-            return 0
-
-        await asyncio.gather(
-            *(connection.send_text(message) for connection in connections),
-            return_exceptions=True,
-        )
-
-        return len(connections)
-
     async def _subscribe(self, channel_key: str, websocket: WebSocket) -> str:
         if channel_key not in self.subscriptions:
             self.subscriptions[channel_key] = set()

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

@@ -176,64 +176,30 @@ async def get_execution_analytics_config(
         # Return with provider prefix for clarity
         return f"{provider_name}: {model_name}"
 
-    # Get all models from the registry (dynamic, not hardcoded enum)
-    from backend.data import llm_registry
-    from backend.server.v2.llm import db as llm_db
-
-    # Get the recommended model from the database (configurable via admin UI)
-    recommended_model_slug = await llm_db.get_recommended_model_slug()
-
-    # Build the available models list
-    first_enabled_slug = None
-    for registry_model in llm_registry.iter_dynamic_models():
-        # Only include enabled models in the list
-        if not registry_model.is_enabled:
-            continue
-
-        # Track first enabled model as fallback
-        if first_enabled_slug is None:
-            first_enabled_slug = registry_model.slug
-
-        model_enum = LlmModel(registry_model.slug)  # Create enum instance from slug
-        label = generate_model_label(model_enum)
+    # Include all LlmModel values (no more filtering by hardcoded list)
+    recommended_model = LlmModel.GPT4O_MINI.value
+    for model in LlmModel:
+        label = generate_model_label(model)
         # Add "(Recommended)" suffix to the recommended model
-        if registry_model.slug == recommended_model_slug:
+        if model.value == recommended_model:
             label += " (Recommended)"
 
         available_models.append(
             ModelInfo(
-                value=registry_model.slug,
+                value=model.value,
                 label=label,
-                provider=registry_model.metadata.provider,
+                provider=model.provider,
             )
         )
 
     # Sort models by provider and name for better UX
     available_models.sort(key=lambda x: (x.provider, x.label))
 
-    # Handle case where no models are available
-    if not available_models:
-        logger.warning(
-            "No enabled LLM models found in registry. "
-            "Ensure models are configured and enabled in the LLM Registry."
-        )
-        # Provide a placeholder entry so admins see meaningful feedback
-        available_models.append(
-            ModelInfo(
-                value="",
-                label="No models available - configure in LLM Registry",
-                provider="none",
-            )
-        )
-
-    # Use the DB recommended model, or fallback to first enabled model
-    final_recommended = recommended_model_slug or first_enabled_slug or ""
-
     return ExecutionAnalyticsConfig(
         available_models=available_models,
         default_system_prompt=DEFAULT_SYSTEM_PROMPT,
         default_user_prompt=DEFAULT_USER_PROMPT,
-        recommended_model=final_recommended,
+        recommended_model=recommended_model,
     )
 
 

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

@@ -1,595 +0,0 @@
-import logging
-
-import autogpt_libs.auth
-import fastapi
-
-from backend.data import llm_registry
-from backend.data.block_cost_config import refresh_llm_costs
-from backend.server.v2.llm import db as llm_db
-from backend.server.v2.llm import model as llm_model
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter(
-    tags=["llm", "admin"],
-    dependencies=[fastapi.Security(autogpt_libs.auth.requires_admin_user)],
-)
-
-
-async def _refresh_runtime_state() -> None:
-    """Refresh the LLM registry and clear all related caches to ensure real-time updates."""
-    logger.info("Refreshing LLM registry runtime state...")
-    try:
-        # Refresh registry from database
-        await llm_registry.refresh_llm_registry()
-        refresh_llm_costs()
-
-        # Clear block schema caches so they're regenerated with updated model options
-        from backend.data.block import BlockSchema
-
-        BlockSchema.clear_all_schema_caches()
-        logger.info("Cleared all block schema caches")
-
-        # Clear the /blocks endpoint cache so frontend gets updated schemas
-        try:
-            from backend.api.features.v1 import _get_cached_blocks
-
-            _get_cached_blocks.cache_clear()
-            logger.info("Cleared /blocks endpoint cache")
-        except Exception as e:
-            logger.warning("Failed to clear /blocks cache: %s", e)
-
-        # Clear the v2 builder caches (if they exist)
-        try:
-            from backend.api.features.builder import db as builder_db
-
-            if hasattr(builder_db, "_get_all_providers"):
-                builder_db._get_all_providers.cache_clear()
-                logger.info("Cleared v2 builder providers cache")
-            if hasattr(builder_db, "_build_cached_search_results"):
-                builder_db._build_cached_search_results.cache_clear()
-                logger.info("Cleared v2 builder search results cache")
-        except Exception as e:
-            logger.debug("Could not clear v2 builder cache: %s", e)
-
-        # Notify all executor services to refresh their registry cache
-        from backend.data.llm_registry import publish_registry_refresh_notification
-
-        await publish_registry_refresh_notification()
-        logger.info("Published registry refresh notification")
-    except Exception as exc:
-        logger.exception(
-            "LLM runtime state refresh failed; caches may be stale: %s", exc
-        )
-
-
-@router.get(
-    "/providers",
-    summary="List LLM providers",
-    response_model=llm_model.LlmProvidersResponse,
-)
-async def list_llm_providers(include_models: bool = True):
-    providers = await llm_db.list_providers(include_models=include_models)
-    return llm_model.LlmProvidersResponse(providers=providers)
-
-
-@router.post(
-    "/providers",
-    summary="Create LLM provider",
-    response_model=llm_model.LlmProvider,
-)
-async def create_llm_provider(request: llm_model.UpsertLlmProviderRequest):
-    provider = await llm_db.upsert_provider(request=request)
-    await _refresh_runtime_state()
-    return provider
-
-
-@router.patch(
-    "/providers/{provider_id}",
-    summary="Update LLM provider",
-    response_model=llm_model.LlmProvider,
-)
-async def update_llm_provider(
-    provider_id: str,
-    request: llm_model.UpsertLlmProviderRequest,
-):
-    provider = await llm_db.upsert_provider(request=request, provider_id=provider_id)
-    await _refresh_runtime_state()
-    return provider
-
-
-@router.delete(
-    "/providers/{provider_id}",
-    summary="Delete LLM provider",
-    response_model=dict,
-)
-async def delete_llm_provider(provider_id: str):
-    """
-    Delete an LLM provider.
-
-    A provider can only be deleted if it has no associated models.
-    Delete all models from the provider first before deleting the provider.
-    """
-    try:
-        await llm_db.delete_provider(provider_id)
-        await _refresh_runtime_state()
-        logger.info("Deleted LLM provider '%s'", provider_id)
-        return {"success": True, "message": "Provider deleted successfully"}
-    except ValueError as e:
-        logger.warning("Failed to delete provider '%s': %s", provider_id, e)
-        raise fastapi.HTTPException(status_code=400, detail=str(e))
-    except Exception as e:
-        logger.exception("Failed to delete provider '%s': %s", provider_id, e)
-        raise fastapi.HTTPException(status_code=500, detail=str(e))
-
-
-@router.get(
-    "/models",
-    summary="List LLM models",
-    response_model=llm_model.LlmModelsResponse,
-)
-async def list_llm_models(
-    provider_id: str | None = fastapi.Query(default=None),
-    page: int = fastapi.Query(default=1, ge=1, description="Page number (1-indexed)"),
-    page_size: int = fastapi.Query(
-        default=50, ge=1, le=100, description="Number of models per page"
-    ),
-):
-    return await llm_db.list_models(
-        provider_id=provider_id, page=page, page_size=page_size
-    )
-
-
-@router.post(
-    "/models",
-    summary="Create LLM model",
-    response_model=llm_model.LlmModel,
-)
-async def create_llm_model(request: llm_model.CreateLlmModelRequest):
-    model = await llm_db.create_model(request=request)
-    await _refresh_runtime_state()
-    return model
-
-
-@router.patch(
-    "/models/{model_id}",
-    summary="Update LLM model",
-    response_model=llm_model.LlmModel,
-)
-async def update_llm_model(
-    model_id: str,
-    request: llm_model.UpdateLlmModelRequest,
-):
-    model = await llm_db.update_model(model_id=model_id, request=request)
-    await _refresh_runtime_state()
-    return model
-
-
-@router.patch(
-    "/models/{model_id}/toggle",
-    summary="Toggle LLM model availability",
-    response_model=llm_model.ToggleLlmModelResponse,
-)
-async def toggle_llm_model(
-    model_id: str,
-    request: llm_model.ToggleLlmModelRequest,
-):
-    """
-    Toggle a model's enabled status, optionally migrating workflows when disabling.
-
-    If disabling a model and `migrate_to_slug` is provided, all workflows using
-    this model will be migrated to the specified replacement model before disabling.
-    A migration record is created which can be reverted later using the revert endpoint.
-
-    Optional fields:
-    - `migration_reason`: Reason for the migration (e.g., "Provider outage")
-    - `custom_credit_cost`: Custom pricing override for billing during migration
-    """
-    try:
-        result = await llm_db.toggle_model(
-            model_id=model_id,
-            is_enabled=request.is_enabled,
-            migrate_to_slug=request.migrate_to_slug,
-            migration_reason=request.migration_reason,
-            custom_credit_cost=request.custom_credit_cost,
-        )
-        await _refresh_runtime_state()
-        if result.nodes_migrated > 0:
-            logger.info(
-                "Toggled model '%s' to %s and migrated %d nodes to '%s' (migration_id=%s)",
-                result.model.slug,
-                "enabled" if request.is_enabled else "disabled",
-                result.nodes_migrated,
-                result.migrated_to_slug,
-                result.migration_id,
-            )
-        return result
-    except ValueError as exc:
-        logger.warning("Model toggle validation failed: %s", exc)
-        raise fastapi.HTTPException(status_code=400, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to toggle LLM model %s: %s", model_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to toggle model availability",
-        ) from exc
-
-
-@router.get(
-    "/models/{model_id}/usage",
-    summary="Get model usage count",
-    response_model=llm_model.LlmModelUsageResponse,
-)
-async def get_llm_model_usage(model_id: str):
-    """Get the number of workflow nodes using this model."""
-    try:
-        return await llm_db.get_model_usage(model_id=model_id)
-    except ValueError as exc:
-        raise fastapi.HTTPException(status_code=404, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to get model usage %s: %s", model_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to get model usage",
-        ) from exc
-
-
-@router.delete(
-    "/models/{model_id}",
-    summary="Delete LLM model and migrate workflows",
-    response_model=llm_model.DeleteLlmModelResponse,
-)
-async def delete_llm_model(
-    model_id: str,
-    replacement_model_slug: str | None = fastapi.Query(
-        default=None,
-        description="Slug of the model to migrate existing workflows to (required only if workflows use this model)",
-    ),
-):
-    """
-    Delete a model and optionally migrate workflows using it to a replacement model.
-
-    If no workflows are using this model, it can be deleted without providing a
-    replacement. If workflows exist, replacement_model_slug is required.
-
-    This endpoint:
-    1. Counts how many workflow nodes use the model being deleted
-    2. If nodes exist, validates the replacement model and migrates them
-    3. Deletes the model record
-    4. Refreshes all caches and notifies executors
-
-    Example: DELETE /admin/llm/models/{id}?replacement_model_slug=gpt-4o
-    Example (no usage): DELETE /admin/llm/models/{id}
-    """
-    try:
-        result = await llm_db.delete_model(
-            model_id=model_id, replacement_model_slug=replacement_model_slug
-        )
-        await _refresh_runtime_state()
-        logger.info(
-            "Deleted model '%s' and migrated %d nodes to '%s'",
-            result.deleted_model_slug,
-            result.nodes_migrated,
-            result.replacement_model_slug,
-        )
-        return result
-    except ValueError as exc:
-        # Validation errors (model not found, replacement invalid, etc.)
-        logger.warning("Model deletion validation failed: %s", exc)
-        raise fastapi.HTTPException(status_code=400, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to delete LLM model %s: %s", model_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to delete model and migrate workflows",
-        ) from exc
-
-
-# ============================================================================
-# Migration Management Endpoints
-# ============================================================================
-
-
-@router.get(
-    "/migrations",
-    summary="List model migrations",
-    response_model=llm_model.LlmMigrationsResponse,
-)
-async def list_llm_migrations(
-    include_reverted: bool = fastapi.Query(
-        default=False, description="Include reverted migrations in the list"
-    ),
-):
-    """
-    List all model migrations.
-
-    Migrations are created when disabling a model with the migrate_to_slug option.
-    They can be reverted to restore the original model configuration.
-    """
-    try:
-        migrations = await llm_db.list_migrations(include_reverted=include_reverted)
-        return llm_model.LlmMigrationsResponse(migrations=migrations)
-    except Exception as exc:
-        logger.exception("Failed to list migrations: %s", exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to list migrations",
-        ) from exc
-
-
-@router.get(
-    "/migrations/{migration_id}",
-    summary="Get migration details",
-    response_model=llm_model.LlmModelMigration,
-)
-async def get_llm_migration(migration_id: str):
-    """Get details of a specific migration."""
-    try:
-        migration = await llm_db.get_migration(migration_id)
-        if not migration:
-            raise fastapi.HTTPException(
-                status_code=404, detail=f"Migration '{migration_id}' not found"
-            )
-        return migration
-    except fastapi.HTTPException:
-        raise
-    except Exception as exc:
-        logger.exception("Failed to get migration %s: %s", migration_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to get migration",
-        ) from exc
-
-
-@router.post(
-    "/migrations/{migration_id}/revert",
-    summary="Revert a model migration",
-    response_model=llm_model.RevertMigrationResponse,
-)
-async def revert_llm_migration(
-    migration_id: str,
-    request: llm_model.RevertMigrationRequest | None = None,
-):
-    """
-    Revert a model migration, restoring affected workflows to their original model.
-
-    This only reverts the specific nodes that were part of the migration.
-    The source model must exist for the revert to succeed.
-
-    Options:
-    - `re_enable_source_model`: Whether to re-enable the source model if disabled (default: True)
-
-    Response includes:
-    - `nodes_reverted`: Number of nodes successfully reverted
-    - `nodes_already_changed`: Number of nodes that were modified since migration (not reverted)
-    - `source_model_re_enabled`: Whether the source model was re-enabled
-
-    Requirements:
-    - Migration must not already be reverted
-    - Source model must exist
-    """
-    try:
-        re_enable = request.re_enable_source_model if request else True
-        result = await llm_db.revert_migration(
-            migration_id,
-            re_enable_source_model=re_enable,
-        )
-        await _refresh_runtime_state()
-        logger.info(
-            "Reverted migration '%s': %d nodes restored from '%s' to '%s' "
-            "(%d already changed, source re-enabled=%s)",
-            migration_id,
-            result.nodes_reverted,
-            result.target_model_slug,
-            result.source_model_slug,
-            result.nodes_already_changed,
-            result.source_model_re_enabled,
-        )
-        return result
-    except ValueError as exc:
-        logger.warning("Migration revert validation failed: %s", exc)
-        raise fastapi.HTTPException(status_code=400, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to revert migration %s: %s", migration_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to revert migration",
-        ) from exc
-
-
-# ============================================================================
-# Creator Management Endpoints
-# ============================================================================
-
-
-@router.get(
-    "/creators",
-    summary="List model creators",
-    response_model=llm_model.LlmCreatorsResponse,
-)
-async def list_llm_creators():
-    """
-    List all model creators.
-
-    Creators are organizations that create/train models (e.g., OpenAI, Meta, Anthropic).
-    This is distinct from providers who host/serve the models (e.g., OpenRouter).
-    """
-    try:
-        creators = await llm_db.list_creators()
-        return llm_model.LlmCreatorsResponse(creators=creators)
-    except Exception as exc:
-        logger.exception("Failed to list creators: %s", exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to list creators",
-        ) from exc
-
-
-@router.get(
-    "/creators/{creator_id}",
-    summary="Get creator details",
-    response_model=llm_model.LlmModelCreator,
-)
-async def get_llm_creator(creator_id: str):
-    """Get details of a specific model creator."""
-    try:
-        creator = await llm_db.get_creator(creator_id)
-        if not creator:
-            raise fastapi.HTTPException(
-                status_code=404, detail=f"Creator '{creator_id}' not found"
-            )
-        return creator
-    except fastapi.HTTPException:
-        raise
-    except Exception as exc:
-        logger.exception("Failed to get creator %s: %s", creator_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to get creator",
-        ) from exc
-
-
-@router.post(
-    "/creators",
-    summary="Create model creator",
-    response_model=llm_model.LlmModelCreator,
-)
-async def create_llm_creator(request: llm_model.UpsertLlmCreatorRequest):
-    """
-    Create a new model creator.
-
-    A creator represents an organization that creates/trains AI models,
-    such as OpenAI, Anthropic, Meta, or Google.
-    """
-    try:
-        creator = await llm_db.upsert_creator(request=request)
-        await _refresh_runtime_state()
-        logger.info("Created model creator '%s' (%s)", creator.display_name, creator.id)
-        return creator
-    except Exception as exc:
-        logger.exception("Failed to create creator: %s", exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to create creator",
-        ) from exc
-
-
-@router.patch(
-    "/creators/{creator_id}",
-    summary="Update model creator",
-    response_model=llm_model.LlmModelCreator,
-)
-async def update_llm_creator(
-    creator_id: str,
-    request: llm_model.UpsertLlmCreatorRequest,
-):
-    """Update an existing model creator."""
-    try:
-        creator = await llm_db.upsert_creator(request=request, creator_id=creator_id)
-        await _refresh_runtime_state()
-        logger.info("Updated model creator '%s' (%s)", creator.display_name, creator_id)
-        return creator
-    except Exception as exc:
-        logger.exception("Failed to update creator %s: %s", creator_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to update creator",
-        ) from exc
-
-
-@router.delete(
-    "/creators/{creator_id}",
-    summary="Delete model creator",
-    response_model=dict,
-)
-async def delete_llm_creator(creator_id: str):
-    """
-    Delete a model creator.
-
-    This will remove the creator association from all models that reference it
-    (sets creatorId to NULL), but will not delete the models themselves.
-    """
-    try:
-        await llm_db.delete_creator(creator_id)
-        await _refresh_runtime_state()
-        logger.info("Deleted model creator '%s'", creator_id)
-        return {"success": True, "message": f"Creator '{creator_id}' deleted"}
-    except ValueError as exc:
-        logger.warning("Creator deletion validation failed: %s", exc)
-        raise fastapi.HTTPException(status_code=404, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to delete creator %s: %s", creator_id, exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to delete creator",
-        ) from exc
-
-
-# ============================================================================
-# Recommended Model Endpoints
-# ============================================================================
-
-
-@router.get(
-    "/recommended-model",
-    summary="Get recommended model",
-    response_model=llm_model.RecommendedModelResponse,
-)
-async def get_recommended_model():
-    """
-    Get the currently recommended LLM model.
-
-    The recommended model is shown to users as the default/suggested option
-    in model selection dropdowns.
-    """
-    try:
-        model = await llm_db.get_recommended_model()
-        return llm_model.RecommendedModelResponse(
-            model=model,
-            slug=model.slug if model else None,
-        )
-    except Exception as exc:
-        logger.exception("Failed to get recommended model: %s", exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to get recommended model",
-        ) from exc
-
-
-@router.post(
-    "/recommended-model",
-    summary="Set recommended model",
-    response_model=llm_model.SetRecommendedModelResponse,
-)
-async def set_recommended_model(request: llm_model.SetRecommendedModelRequest):
-    """
-    Set a model as the recommended model.
-
-    This clears the recommended flag from any other model and sets it on
-    the specified model. The model must be enabled to be set as recommended.
-
-    The recommended model is displayed to users as the default/suggested
-    option in model selection dropdowns throughout the platform.
-    """
-    try:
-        model, previous_slug = await llm_db.set_recommended_model(request.model_id)
-        await _refresh_runtime_state()
-        logger.info(
-            "Set recommended model to '%s' (previous: %s)",
-            model.slug,
-            previous_slug or "none",
-        )
-        return llm_model.SetRecommendedModelResponse(
-            model=model,
-            previous_recommended_slug=previous_slug,
-            message=f"Model '{model.display_name}' is now the recommended model",
-        )
-    except ValueError as exc:
-        logger.warning("Set recommended model validation failed: %s", exc)
-        raise fastapi.HTTPException(status_code=400, detail=str(exc)) from exc
-    except Exception as exc:
-        logger.exception("Failed to set recommended model: %s", exc)
-        raise fastapi.HTTPException(
-            status_code=500,
-            detail="Failed to set recommended model",
-        ) from exc

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

@@ -1,491 +0,0 @@
-import json
-from unittest.mock import AsyncMock
-
-import fastapi
-import fastapi.testclient
-import pytest
-import pytest_mock
-from autogpt_libs.auth.jwt_utils import get_jwt_payload
-from pytest_snapshot.plugin import Snapshot
-
-import backend.api.features.admin.llm_routes as llm_routes
-from backend.server.v2.llm import model as llm_model
-from backend.util.models import Pagination
-
-app = fastapi.FastAPI()
-app.include_router(llm_routes.router, prefix="/admin/llm")
-
-client = fastapi.testclient.TestClient(app)
-
-
-@pytest.fixture(autouse=True)
-def setup_app_admin_auth(mock_jwt_admin):
-    """Setup admin auth overrides for all tests in this module"""
-    app.dependency_overrides[get_jwt_payload] = mock_jwt_admin["get_jwt_payload"]
-    yield
-    app.dependency_overrides.clear()
-
-
-def test_list_llm_providers_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful listing of LLM providers"""
-    # Mock the database function
-    mock_providers = [
-        {
-            "id": "provider-1",
-            "name": "openai",
-            "display_name": "OpenAI",
-            "description": "OpenAI LLM provider",
-            "supports_tools": True,
-            "supports_json_output": True,
-            "supports_reasoning": False,
-            "supports_parallel_tool": True,
-            "metadata": {},
-            "models": [],
-        },
-        {
-            "id": "provider-2",
-            "name": "anthropic",
-            "display_name": "Anthropic",
-            "description": "Anthropic LLM provider",
-            "supports_tools": True,
-            "supports_json_output": True,
-            "supports_reasoning": False,
-            "supports_parallel_tool": True,
-            "metadata": {},
-            "models": [],
-        },
-    ]
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.list_providers",
-        new=AsyncMock(return_value=mock_providers),
-    )
-
-    response = client.get("/admin/llm/providers")
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert len(response_data["providers"]) == 2
-    assert response_data["providers"][0]["name"] == "openai"
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "list_llm_providers_success.json",
-    )
-
-
-def test_list_llm_models_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful listing of LLM models with pagination"""
-    # Mock the database function - now returns LlmModelsResponse
-    mock_model = llm_model.LlmModel(
-        id="model-1",
-        slug="gpt-4o",
-        display_name="GPT-4o",
-        description="GPT-4 Optimized",
-        provider_id="provider-1",
-        context_window=128000,
-        max_output_tokens=16384,
-        is_enabled=True,
-        capabilities={},
-        metadata={},
-        costs=[
-            llm_model.LlmModelCost(
-                id="cost-1",
-                credit_cost=10,
-                credential_provider="openai",
-                metadata={},
-            )
-        ],
-    )
-
-    mock_response = llm_model.LlmModelsResponse(
-        models=[mock_model],
-        pagination=Pagination(
-            total_items=1,
-            total_pages=1,
-            current_page=1,
-            page_size=50,
-        ),
-    )
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.list_models",
-        new=AsyncMock(return_value=mock_response),
-    )
-
-    response = client.get("/admin/llm/models")
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert len(response_data["models"]) == 1
-    assert response_data["models"][0]["slug"] == "gpt-4o"
-    assert response_data["pagination"]["total_items"] == 1
-    assert response_data["pagination"]["page_size"] == 50
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "list_llm_models_success.json",
-    )
-
-
-def test_create_llm_provider_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful creation of LLM provider"""
-    mock_provider = {
-        "id": "new-provider-id",
-        "name": "groq",
-        "display_name": "Groq",
-        "description": "Groq LLM provider",
-        "supports_tools": True,
-        "supports_json_output": True,
-        "supports_reasoning": False,
-        "supports_parallel_tool": False,
-        "metadata": {},
-    }
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.upsert_provider",
-        new=AsyncMock(return_value=mock_provider),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    request_data = {
-        "name": "groq",
-        "display_name": "Groq",
-        "description": "Groq LLM provider",
-        "supports_tools": True,
-        "supports_json_output": True,
-        "supports_reasoning": False,
-        "supports_parallel_tool": False,
-        "metadata": {},
-    }
-
-    response = client.post("/admin/llm/providers", json=request_data)
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["name"] == "groq"
-    assert response_data["display_name"] == "Groq"
-
-    # Verify refresh was called
-    mock_refresh.assert_called_once()
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "create_llm_provider_success.json",
-    )
-
-
-def test_create_llm_model_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful creation of LLM model"""
-    mock_model = {
-        "id": "new-model-id",
-        "slug": "gpt-4.1-mini",
-        "display_name": "GPT-4.1 Mini",
-        "description": "Latest GPT-4.1 Mini model",
-        "provider_id": "provider-1",
-        "context_window": 128000,
-        "max_output_tokens": 16384,
-        "is_enabled": True,
-        "capabilities": {},
-        "metadata": {},
-        "costs": [
-            {
-                "id": "cost-id",
-                "credit_cost": 5,
-                "credential_provider": "openai",
-                "metadata": {},
-            }
-        ],
-    }
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.create_model",
-        new=AsyncMock(return_value=mock_model),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    request_data = {
-        "slug": "gpt-4.1-mini",
-        "display_name": "GPT-4.1 Mini",
-        "description": "Latest GPT-4.1 Mini model",
-        "provider_id": "provider-1",
-        "context_window": 128000,
-        "max_output_tokens": 16384,
-        "is_enabled": True,
-        "capabilities": {},
-        "metadata": {},
-        "costs": [
-            {
-                "credit_cost": 5,
-                "credential_provider": "openai",
-                "metadata": {},
-            }
-        ],
-    }
-
-    response = client.post("/admin/llm/models", json=request_data)
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["slug"] == "gpt-4.1-mini"
-    assert response_data["is_enabled"] is True
-
-    # Verify refresh was called
-    mock_refresh.assert_called_once()
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "create_llm_model_success.json",
-    )
-
-
-def test_update_llm_model_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful update of LLM model"""
-    mock_model = {
-        "id": "model-1",
-        "slug": "gpt-4o",
-        "display_name": "GPT-4o Updated",
-        "description": "Updated description",
-        "provider_id": "provider-1",
-        "context_window": 256000,
-        "max_output_tokens": 32768,
-        "is_enabled": True,
-        "capabilities": {},
-        "metadata": {},
-        "costs": [
-            {
-                "id": "cost-1",
-                "credit_cost": 15,
-                "credential_provider": "openai",
-                "metadata": {},
-            }
-        ],
-    }
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.update_model",
-        new=AsyncMock(return_value=mock_model),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    request_data = {
-        "display_name": "GPT-4o Updated",
-        "description": "Updated description",
-        "context_window": 256000,
-        "max_output_tokens": 32768,
-    }
-
-    response = client.patch("/admin/llm/models/model-1", json=request_data)
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["display_name"] == "GPT-4o Updated"
-    assert response_data["context_window"] == 256000
-
-    # Verify refresh was called
-    mock_refresh.assert_called_once()
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "update_llm_model_success.json",
-    )
-
-
-def test_toggle_llm_model_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful toggling of LLM model enabled status"""
-    # Create a proper mock model object
-    mock_model = llm_model.LlmModel(
-        id="model-1",
-        slug="gpt-4o",
-        display_name="GPT-4o",
-        description="GPT-4 Optimized",
-        provider_id="provider-1",
-        context_window=128000,
-        max_output_tokens=16384,
-        is_enabled=False,
-        capabilities={},
-        metadata={},
-        costs=[],
-    )
-
-    # Create a proper ToggleLlmModelResponse
-    mock_response = llm_model.ToggleLlmModelResponse(
-        model=mock_model,
-        nodes_migrated=0,
-        migrated_to_slug=None,
-        migration_id=None,
-    )
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.toggle_model",
-        new=AsyncMock(return_value=mock_response),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    request_data = {"is_enabled": False}
-
-    response = client.patch("/admin/llm/models/model-1/toggle", json=request_data)
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["model"]["is_enabled"] is False
-
-    # Verify refresh was called
-    mock_refresh.assert_called_once()
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "toggle_llm_model_success.json",
-    )
-
-
-def test_delete_llm_model_success(
-    mocker: pytest_mock.MockFixture,
-    configured_snapshot: Snapshot,
-) -> None:
-    """Test successful deletion of LLM model with migration"""
-    # Create a proper DeleteLlmModelResponse
-    mock_response = llm_model.DeleteLlmModelResponse(
-        deleted_model_slug="gpt-3.5-turbo",
-        deleted_model_display_name="GPT-3.5 Turbo",
-        replacement_model_slug="gpt-4o-mini",
-        nodes_migrated=42,
-        message="Successfully deleted model 'GPT-3.5 Turbo' (gpt-3.5-turbo) "
-        "and migrated 42 workflow node(s) to 'gpt-4o-mini'.",
-    )
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.delete_model",
-        new=AsyncMock(return_value=mock_response),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    response = client.delete(
-        "/admin/llm/models/model-1?replacement_model_slug=gpt-4o-mini"
-    )
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["deleted_model_slug"] == "gpt-3.5-turbo"
-    assert response_data["nodes_migrated"] == 42
-    assert response_data["replacement_model_slug"] == "gpt-4o-mini"
-
-    # Verify refresh was called
-    mock_refresh.assert_called_once()
-
-    # Snapshot test the response (must be string)
-    configured_snapshot.assert_match(
-        json.dumps(response_data, indent=2, sort_keys=True),
-        "delete_llm_model_success.json",
-    )
-
-
-def test_delete_llm_model_validation_error(
-    mocker: pytest_mock.MockFixture,
-) -> None:
-    """Test deletion fails with proper error when validation fails"""
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.delete_model",
-        new=AsyncMock(side_effect=ValueError("Replacement model 'invalid' not found")),
-    )
-
-    response = client.delete("/admin/llm/models/model-1?replacement_model_slug=invalid")
-
-    assert response.status_code == 400
-    assert "Replacement model 'invalid' not found" in response.json()["detail"]
-
-
-def test_delete_llm_model_no_replacement_with_usage(
-    mocker: pytest_mock.MockFixture,
-) -> None:
-    """Test deletion fails when nodes exist but no replacement is provided"""
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.delete_model",
-        new=AsyncMock(
-            side_effect=ValueError(
-                "Cannot delete model 'test-model': 5 workflow node(s) are using it. "
-                "Please provide a replacement_model_slug to migrate them."
-            )
-        ),
-    )
-
-    response = client.delete("/admin/llm/models/model-1")
-
-    assert response.status_code == 400
-    assert "workflow node(s) are using it" in response.json()["detail"]
-
-
-def test_delete_llm_model_no_replacement_no_usage(
-    mocker: pytest_mock.MockFixture,
-) -> None:
-    """Test deletion succeeds when no nodes use the model and no replacement is provided"""
-    mock_response = llm_model.DeleteLlmModelResponse(
-        deleted_model_slug="unused-model",
-        deleted_model_display_name="Unused Model",
-        replacement_model_slug=None,
-        nodes_migrated=0,
-        message="Successfully deleted model 'Unused Model' (unused-model). No workflows were using this model.",
-    )
-
-    mocker.patch(
-        "backend.api.features.admin.llm_routes.llm_db.delete_model",
-        new=AsyncMock(return_value=mock_response),
-    )
-
-    mock_refresh = mocker.patch(
-        "backend.api.features.admin.llm_routes._refresh_runtime_state",
-        new=AsyncMock(),
-    )
-
-    response = client.delete("/admin/llm/models/model-1")
-
-    assert response.status_code == 200
-    response_data = response.json()
-    assert response_data["deleted_model_slug"] == "unused-model"
-    assert response_data["nodes_migrated"] == 0
-    assert response_data["replacement_model_slug"] is None
-    mock_refresh.assert_called_once()

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

@@ -15,7 +15,6 @@ from backend.blocks import load_all_blocks
 from backend.blocks.llm import LlmModel
 from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
 from backend.data.db import query_raw_with_schema
-from backend.data.llm_registry import get_all_model_slugs_for_validation
 from backend.integrations.providers import ProviderName
 from backend.util.cache import cached
 from backend.util.models import Pagination
@@ -32,14 +31,7 @@ from .model import (
 )
 
 logger = logging.getLogger(__name__)
-
-
-def _get_llm_models() -> list[str]:
-    """Get LLM model names for search matching from the registry."""
-    return [
-        slug.lower().replace("-", " ") for slug in get_all_model_slugs_for_validation()
-    ]
-
+llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
 
 MAX_LIBRARY_AGENT_RESULTS = 100
 MAX_MARKETPLACE_AGENT_RESULTS = 100
@@ -504,8 +496,8 @@ async def _get_static_counts():
 def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
     for field in schema_cls.model_fields.values():
         if field.annotation == LlmModel:
-            # Check if query matches any value in llm_models from registry
-            if any(query in name for name in _get_llm_models()):
+            # Check if query matches any value in llm_models
+            if any(query in name for name in llm_models):
                 return True
     return False
 

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

@@ -290,11 +290,6 @@ async def _cache_session(session: ChatSession) -> None:
     await async_redis.setex(redis_key, config.session_ttl, session.model_dump_json())
 
 
-async def cache_chat_session(session: ChatSession) -> None:
-    """Cache a chat session without persisting to the database."""
-    await _cache_session(session)
-
-
 async def _get_session_from_db(session_id: str) -> ChatSession | None:
     """Get a chat session from the database."""
     prisma_session = await chat_db.get_chat_session(session_id)

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

@@ -172,12 +172,12 @@ async def get_session(
         user_id: The optional authenticated user ID, or None for anonymous access.
 
     Returns:
-        SessionDetailResponse: Details for the requested session, or None if not found.
+        SessionDetailResponse: Details for the requested session; raises NotFoundError if not found.
 
     """
     session = await get_chat_session(session_id, user_id)
     if not session:
-        raise NotFoundError(f"Session {session_id} not found.")
+        raise NotFoundError(f"Session {session_id} not found")
 
     messages = [message.model_dump() for message in session.messages]
     logger.info(
@@ -222,8 +222,6 @@ async def stream_chat_post(
     session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
         async for chunk in chat_service.stream_chat_completion(
             session_id,
             request.message,
@@ -232,26 +230,7 @@ async def stream_chat_post(
             session=session,  # Pass pre-fetched session to avoid double-fetch
             context=request.context,
         ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
             yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
         # AI SDK protocol termination
         yield "data: [DONE]\n\n"
 
@@ -296,8 +275,6 @@ async def stream_chat_get(
     session = await _validate_and_get_session(session_id, user_id)
 
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
         async for chunk in chat_service.stream_chat_completion(
             session_id,
             message,
@@ -305,26 +282,7 @@ async def stream_chat_get(
             user_id=user_id,
             session=session,  # Pass pre-fetched session to avoid double-fetch
         ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
             yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
         # AI SDK protocol termination
         yield "data: [DONE]\n\n"
 

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

@@ -1,20 +1,12 @@
 import asyncio
 import logging
-import time
-from asyncio import CancelledError
 from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
 from langfuse import get_client, propagate_attributes
 from langfuse.openai import openai  # type: ignore
-from openai import (
-    APIConnectionError,
-    APIError,
-    APIStatusError,
-    PermissionDeniedError,
-    RateLimitError,
-)
+from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -29,7 +21,6 @@ from .model import (
     ChatMessage,
     ChatSession,
     Usage,
-    cache_chat_session,
     get_chat_session,
     update_session_title,
     upsert_chat_session,
@@ -305,10 +296,6 @@ async def stream_chat_completion(
                 content="",
             )
             accumulated_tool_calls: list[dict[str, Any]] = []
-            has_saved_assistant_message = False
-            has_appended_streaming_message = False
-            last_cache_time = 0.0
-            last_cache_content_len = 0
 
             # Wrap main logic in try/finally to ensure Langfuse observations are always ended
             has_yielded_end = False
@@ -345,23 +332,6 @@ async def stream_chat_completion(
                         assert assistant_response.content is not None
                         assistant_response.content += delta
                         has_received_text = True
-                        if not has_appended_streaming_message:
-                            session.messages.append(assistant_response)
-                            has_appended_streaming_message = True
-                        current_time = time.monotonic()
-                        content_len = len(assistant_response.content)
-                        if (
-                            current_time - last_cache_time >= 1.0
-                            and content_len > last_cache_content_len
-                        ):
-                            try:
-                                await cache_chat_session(session)
-                            except Exception as e:
-                                logger.warning(
-                                    f"Failed to cache partial session {session.session_id}: {e}"
-                                )
-                            last_cache_time = current_time
-                            last_cache_content_len = content_len
                         yield chunk
                     elif isinstance(chunk, StreamTextEnd):
                         # Emit text-end after text completes
@@ -420,42 +390,10 @@ async def stream_chat_completion(
                             if has_received_text and not text_streaming_ended:
                                 yield StreamTextEnd(id=text_block_id)
                                 text_streaming_ended = True
-
-                            # Save assistant message before yielding finish to ensure it's persisted
-                            # even if client disconnects immediately after receiving StreamFinish
-                            if not has_saved_assistant_message:
-                                messages_to_save_early: list[ChatMessage] = []
-                                if accumulated_tool_calls:
-                                    assistant_response.tool_calls = (
-                                        accumulated_tool_calls
-                                    )
-                                if not has_appended_streaming_message and (
-                                    assistant_response.content
-                                    or assistant_response.tool_calls
-                                ):
-                                    messages_to_save_early.append(assistant_response)
-                                messages_to_save_early.extend(tool_response_messages)
-
-                                if messages_to_save_early:
-                                    session.messages.extend(messages_to_save_early)
-                                    logger.info(
-                                        f"Saving assistant message before StreamFinish: "
-                                        f"content_len={len(assistant_response.content or '')}, "
-                                        f"tool_calls={len(assistant_response.tool_calls or [])}, "
-                                        f"tool_responses={len(tool_response_messages)}"
-                                    )
-                                if (
-                                    messages_to_save_early
-                                    or has_appended_streaming_message
-                                ):
-                                    await upsert_chat_session(session)
-                                    has_saved_assistant_message = True
-
                             has_yielded_end = True
                             yield chunk
                     elif isinstance(chunk, StreamError):
                         has_yielded_error = True
-                        yield chunk
                     elif isinstance(chunk, StreamUsage):
                         session.usage.append(
                             Usage(
@@ -475,27 +413,6 @@ async def stream_chat_completion(
                     langfuse.update_current_trace(output=str(tool_response_messages))
                     langfuse.update_current_span(output=str(tool_response_messages))
 
-            except CancelledError:
-                if not has_saved_assistant_message:
-                    if accumulated_tool_calls:
-                        assistant_response.tool_calls = accumulated_tool_calls
-                    if assistant_response.content:
-                        assistant_response.content = (
-                            f"{assistant_response.content}\n\n[interrupted]"
-                        )
-                    else:
-                        assistant_response.content = "[interrupted]"
-                    if not has_appended_streaming_message:
-                        session.messages.append(assistant_response)
-                    if tool_response_messages:
-                        session.messages.extend(tool_response_messages)
-                    try:
-                        await upsert_chat_session(session)
-                    except Exception as e:
-                        logger.warning(
-                            f"Failed to save interrupted session {session.session_id}: {e}"
-                        )
-                raise
             except Exception as e:
                 logger.error(f"Error during stream: {e!s}", exc_info=True)
 
@@ -517,19 +434,14 @@ async def stream_chat_completion(
                     # Add assistant message if it has content or tool calls
                     if accumulated_tool_calls:
                         assistant_response.tool_calls = accumulated_tool_calls
-                    if not has_appended_streaming_message and (
-                        assistant_response.content or assistant_response.tool_calls
-                    ):
+                    if assistant_response.content or assistant_response.tool_calls:
                         messages_to_save.append(assistant_response)
 
                     # Add tool response messages after assistant message
                     messages_to_save.extend(tool_response_messages)
 
-                    if not has_saved_assistant_message:
-                        if messages_to_save:
-                            session.messages.extend(messages_to_save)
-                        if messages_to_save or has_appended_streaming_message:
-                            await upsert_chat_session(session)
+                    session.messages.extend(messages_to_save)
+                    await upsert_chat_session(session)
 
                     if not has_yielded_error:
                         error_message = str(e)
@@ -560,49 +472,38 @@ async def stream_chat_completion(
                 return  # Exit after retry to avoid double-saving in finally block
 
             # Normal completion path - save session and handle tool call continuation
-            # Only save if we haven't already saved when StreamFinish was received
-            if not has_saved_assistant_message:
+            logger.info(
+                f"Normal completion path: session={session.session_id}, "
+                f"current message_count={len(session.messages)}"
+            )
+
+            # Build the messages list in the correct order
+            messages_to_save: list[ChatMessage] = []
+
+            # Add assistant message with tool_calls if any
+            if accumulated_tool_calls:
+                assistant_response.tool_calls = accumulated_tool_calls
                 logger.info(
-                    f"Normal completion path: session={session.session_id}, "
-                    f"current message_count={len(session.messages)}"
+                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+                )
+            if assistant_response.content or assistant_response.tool_calls:
+                messages_to_save.append(assistant_response)
+                logger.info(
+                    f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
                 )
 
-                # Build the messages list in the correct order
-                messages_to_save: list[ChatMessage] = []
+            # Add tool response messages after assistant message
+            messages_to_save.extend(tool_response_messages)
+            logger.info(
+                f"Saving {len(tool_response_messages)} tool response messages, "
+                f"total_to_save={len(messages_to_save)}"
+            )
 
-                # Add assistant message with tool_calls if any
-                if accumulated_tool_calls:
-                    assistant_response.tool_calls = accumulated_tool_calls
-                    logger.info(
-                        f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-                    )
-                if not has_appended_streaming_message and (
-                    assistant_response.content or assistant_response.tool_calls
-                ):
-                    messages_to_save.append(assistant_response)
-                    logger.info(
-                        f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
-                    )
-
-                # Add tool response messages after assistant message
-                messages_to_save.extend(tool_response_messages)
-                logger.info(
-                    f"Saving {len(tool_response_messages)} tool response messages, "
-                    f"total_to_save={len(messages_to_save)}"
-                )
-
-                if messages_to_save:
-                    session.messages.extend(messages_to_save)
-                    logger.info(
-                        f"Extended session messages, new message_count={len(session.messages)}"
-                    )
-                if messages_to_save or has_appended_streaming_message:
-                    await upsert_chat_session(session)
-            else:
-                logger.info(
-                    "Assistant message already saved when StreamFinish was received, "
-                    "skipping duplicate save"
-                )
+            session.messages.extend(messages_to_save)
+            logger.info(
+                f"Extended session messages, new message_count={len(session.messages)}"
+            )
+            await upsert_chat_session(session)
 
             # If we did a tool call, stream the chat completion again to get the next response
             if has_done_tool_call:
@@ -644,12 +545,6 @@ def _is_retryable_error(error: Exception) -> bool:
     return False
 
 
-def _is_region_blocked_error(error: Exception) -> bool:
-    if isinstance(error, PermissionDeniedError):
-        return "not available in your region" in str(error).lower()
-    return "not available in your region" in str(error).lower()
-
-
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
@@ -842,18 +737,7 @@ async def _stream_chat_chunks(
                         f"Error in stream (not retrying): {e!s}",
                         exc_info=True,
                     )
-                    error_code = None
-                    error_text = str(e)
-                    if _is_region_blocked_error(e):
-                        error_code = "MODEL_NOT_AVAILABLE_REGION"
-                        error_text = (
-                            "This model is not available in your region. "
-                            "Please connect via VPN and try again."
-                        )
-                    error_response = StreamError(
-                        errorText=error_text,
-                        code=error_code,
-                    )
+                    error_response = StreamError(errorText=str(e))
                     yield error_response
                     yield StreamFinish()
                     return

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py

@@ -1,29 +1,28 @@
 """Agent generator package - Creates agents from natural language."""
 
 from .core import (
-    apply_agent_patch,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
+    json_to_graph,
     save_agent_to_library,
 )
-from .fixer import apply_all_fixes
-from .utils import get_blocks_info
-from .validator import validate_agent
+from .service import health_check as check_external_service_health
+from .service import is_external_service_configured
 
 __all__ = [
     # Core functions
     "decompose_goal",
     "generate_agent",
     "generate_agent_patch",
-    "apply_agent_patch",
     "save_agent_to_library",
     "get_agent_as_json",
-    # Fixer
-    "apply_all_fixes",
-    # Validator
-    "validate_agent",
-    # Utils
-    "get_blocks_info",
+    "json_to_graph",
+    # Exceptions
+    "AgentGeneratorNotConfiguredError",
+    # Service
+    "is_external_service_configured",
+    "check_external_service_health",
 ]

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/client.py

@@ -1,25 +0,0 @@
-"""OpenRouter client configuration for agent generation."""
-
-import os
-
-from openai import AsyncOpenAI
-
-# Configuration - use OPEN_ROUTER_API_KEY for consistency with chat/config.py
-OPENROUTER_API_KEY = os.getenv("OPEN_ROUTER_API_KEY")
-AGENT_GENERATOR_MODEL = os.getenv("AGENT_GENERATOR_MODEL", "anthropic/claude-opus-4.5")
-
-# OpenRouter client (OpenAI-compatible API)
-_client: AsyncOpenAI | None = None
-
-
-def get_client() -> AsyncOpenAI:
-    """Get or create the OpenRouter client."""
-    global _client
-    if _client is None:
-        if not OPENROUTER_API_KEY:
-            raise ValueError("OPENROUTER_API_KEY environment variable is required")
-        _client = AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=OPENROUTER_API_KEY,
-        )
-    return _client

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -1,7 +1,5 @@
 """Core agent generation functions."""
 
-import copy
-import json
 import logging
 import uuid
 from typing import Any
@@ -9,13 +7,35 @@ from typing import Any
 from backend.api.features.library import db as library_db
 from backend.data.graph import Graph, Link, Node, create_graph
 
-from .client import AGENT_GENERATOR_MODEL, get_client
-from .prompts import DECOMPOSITION_PROMPT, GENERATION_PROMPT, PATCH_PROMPT
-from .utils import get_block_summaries, parse_json_from_llm
+from .service import (
+    decompose_goal_external,
+    generate_agent_external,
+    generate_agent_patch_external,
+    is_external_service_configured,
+)
 
 logger = logging.getLogger(__name__)
 
 
+class AgentGeneratorNotConfiguredError(Exception):
+    """Raised when the external Agent Generator service is not configured."""
+
+    pass
+
+
+def _check_service_configured() -> None:
+    """Check if the external Agent Generator service is configured.
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the service is not configured.
+    """
+    if not is_external_service_configured():
+        raise AgentGeneratorNotConfiguredError(
+            "Agent Generator service is not configured. "
+            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
+        )
+
+
 async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
     """Break down a goal into steps or return clarifying questions.
 
@@ -28,40 +48,13 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = DECOMPOSITION_PROMPT.format(block_summaries=get_block_summaries())
-
-    full_description = description
-    if context:
-        full_description = f"{description}\n\nAdditional context:\n{context}"
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": full_description},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for decomposition")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse decomposition response: {content[:200]}")
-            return None
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error decomposing goal: {e}")
-        return None
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for decompose_goal")
+    return await decompose_goal_external(description, context)
 
 
 async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
@@ -72,31 +65,14 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
 
     Returns:
         Agent JSON dict or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = GENERATION_PROMPT.format(block_summaries=get_block_summaries())
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": json.dumps(instructions, indent=2)},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for agent generation")
-            return None
-
-        result = parse_json_from_llm(content)
-
-        if result is None:
-            logger.error(f"Failed to parse agent JSON: {content[:200]}")
-            return None
-
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent")
+    result = await generate_agent_external(instructions)
+    if result:
         # Ensure required fields
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
@@ -104,12 +80,7 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
             result["version"] = 1
         if "is_active" not in result:
             result["is_active"] = True
-
-        return result
-
-    except Exception as e:
-        logger.error(f"Error generating agent: {e}")
-        return None
+    return result
 
 
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
@@ -284,108 +255,23 @@ async def get_agent_as_json(
 async def generate_agent_patch(
     update_request: str, current_agent: dict[str, Any]
 ) -> dict[str, Any] | None:
-    """Generate a patch to update an existing agent.
+    """Update an existing agent using natural language.
+
+    The external Agent Generator service handles:
+    - Generating the patch
+    - Applying the patch
+    - Fixing and validating the result
 
     Args:
         update_request: Natural language description of changes
         current_agent: Current agent JSON
 
     Returns:
-        Patch dict or clarifying questions, or None on error
+        Updated agent JSON, clarifying questions dict, or None on error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
-    client = get_client()
-    prompt = PATCH_PROMPT.format(
-        current_agent=json.dumps(current_agent, indent=2),
-        block_summaries=get_block_summaries(),
-    )
-
-    try:
-        response = await client.chat.completions.create(
-            model=AGENT_GENERATOR_MODEL,
-            messages=[
-                {"role": "system", "content": prompt},
-                {"role": "user", "content": update_request},
-            ],
-            temperature=0,
-        )
-
-        content = response.choices[0].message.content
-        if content is None:
-            logger.error("LLM returned empty content for patch generation")
-            return None
-
-        return parse_json_from_llm(content)
-
-    except Exception as e:
-        logger.error(f"Error generating patch: {e}")
-        return None
-
-
-def apply_agent_patch(
-    current_agent: dict[str, Any], patch: dict[str, Any]
-) -> dict[str, Any]:
-    """Apply a patch to an existing agent.
-
-    Args:
-        current_agent: Current agent JSON
-        patch: Patch dict with operations
-
-    Returns:
-        Updated agent JSON
-    """
-    agent = copy.deepcopy(current_agent)
-    patches = patch.get("patches", [])
-
-    for p in patches:
-        patch_type = p.get("type")
-
-        if patch_type == "modify":
-            node_id = p.get("node_id")
-            changes = p.get("changes", {})
-
-            for node in agent.get("nodes", []):
-                if node["id"] == node_id:
-                    _deep_update(node, changes)
-                    logger.debug(f"Modified node {node_id}")
-                    break
-
-        elif patch_type == "add":
-            new_nodes = p.get("new_nodes", [])
-            new_links = p.get("new_links", [])
-
-            agent["nodes"] = agent.get("nodes", []) + new_nodes
-            agent["links"] = agent.get("links", []) + new_links
-            logger.debug(f"Added {len(new_nodes)} nodes, {len(new_links)} links")
-
-        elif patch_type == "remove":
-            node_ids_to_remove = set(p.get("node_ids", []))
-            link_ids_to_remove = set(p.get("link_ids", []))
-
-            # Remove nodes
-            agent["nodes"] = [
-                n for n in agent.get("nodes", []) if n["id"] not in node_ids_to_remove
-            ]
-
-            # Remove links (both explicit and those referencing removed nodes)
-            agent["links"] = [
-                link
-                for link in agent.get("links", [])
-                if link["id"] not in link_ids_to_remove
-                and link["source_id"] not in node_ids_to_remove
-                and link["sink_id"] not in node_ids_to_remove
-            ]
-
-            logger.debug(
-                f"Removed {len(node_ids_to_remove)} nodes, {len(link_ids_to_remove)} links"
-            )
-
-    return agent
-
-
-def _deep_update(target: dict, source: dict) -> None:
-    """Recursively update a dict with another dict."""
-    for key, value in source.items():
-        if key in target and isinstance(target[key], dict) and isinstance(value, dict):
-            _deep_update(target[key], value)
-        else:
-            target[key] = value
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for generate_agent_patch")
+    return await generate_agent_patch_external(update_request, current_agent)

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/fixer.py

@@ -1,606 +0,0 @@
-"""Agent fixer - Fixes common LLM generation errors."""
-
-import logging
-import re
-import uuid
-from typing import Any
-
-from .utils import (
-    ADDTODICTIONARY_BLOCK_ID,
-    ADDTOLIST_BLOCK_ID,
-    CODE_EXECUTION_BLOCK_ID,
-    CONDITION_BLOCK_ID,
-    CREATEDICT_BLOCK_ID,
-    CREATELIST_BLOCK_ID,
-    DATA_SAMPLING_BLOCK_ID,
-    DOUBLE_CURLY_BRACES_BLOCK_IDS,
-    GET_CURRENT_DATE_BLOCK_ID,
-    STORE_VALUE_BLOCK_ID,
-    UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-    get_blocks_info,
-    is_valid_uuid,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def fix_agent_ids(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix invalid UUIDs in agent and link IDs."""
-    # Fix agent ID
-    if not is_valid_uuid(agent.get("id", "")):
-        agent["id"] = str(uuid.uuid4())
-        logger.debug(f"Fixed agent ID: {agent['id']}")
-
-    # Fix node IDs
-    id_mapping = {}  # Old ID -> New ID
-    for node in agent.get("nodes", []):
-        if not is_valid_uuid(node.get("id", "")):
-            old_id = node.get("id", "")
-            new_id = str(uuid.uuid4())
-            id_mapping[old_id] = new_id
-            node["id"] = new_id
-            logger.debug(f"Fixed node ID: {old_id} -> {new_id}")
-
-    # Fix link IDs and update references
-    for link in agent.get("links", []):
-        if not is_valid_uuid(link.get("id", "")):
-            link["id"] = str(uuid.uuid4())
-            logger.debug(f"Fixed link ID: {link['id']}")
-
-        # Update source/sink IDs if they were remapped
-        if link.get("source_id") in id_mapping:
-            link["source_id"] = id_mapping[link["source_id"]]
-        if link.get("sink_id") in id_mapping:
-            link["sink_id"] = id_mapping[link["sink_id"]]
-
-    return agent
-
-
-def fix_double_curly_braces(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix single curly braces to double in template blocks."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") not in DOUBLE_CURLY_BRACES_BLOCK_IDS:
-            continue
-
-        input_data = node.get("input_default", {})
-        for key in ("prompt", "format"):
-            if key in input_data and isinstance(input_data[key], str):
-                original = input_data[key]
-                # Fix simple variable references: {var} -> {{var}}
-                fixed = re.sub(
-                    r"(?<!\{)\{([a-zA-Z_][a-zA-Z0-9_]*)\}(?!\})",
-                    r"{{\1}}",
-                    original,
-                )
-                if fixed != original:
-                    input_data[key] = fixed
-                    logger.debug(f"Fixed curly braces in {key}")
-
-    return agent
-
-
-def fix_storevalue_before_condition(agent: dict[str, Any]) -> dict[str, Any]:
-    """Add StoreValueBlock before ConditionBlock if needed for value2."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    # Find all ConditionBlock nodes
-    condition_node_ids = {
-        node["id"] for node in nodes if node.get("block_id") == CONDITION_BLOCK_ID
-    }
-
-    if not condition_node_ids:
-        return agent
-
-    new_nodes = []
-    new_links = []
-    processed_conditions = set()
-
-    for link in links:
-        sink_id = link.get("sink_id")
-        sink_name = link.get("sink_name")
-
-        # Check if this link goes to a ConditionBlock's value2
-        if sink_id in condition_node_ids and sink_name == "value2":
-            source_node = next(
-                (n for n in nodes if n["id"] == link.get("source_id")), None
-            )
-
-            # Skip if source is already a StoreValueBlock
-            if source_node and source_node.get("block_id") == STORE_VALUE_BLOCK_ID:
-                continue
-
-            # Skip if we already processed this condition
-            if sink_id in processed_conditions:
-                continue
-
-            processed_conditions.add(sink_id)
-
-            # Create StoreValueBlock
-            store_node_id = str(uuid.uuid4())
-            store_node = {
-                "id": store_node_id,
-                "block_id": STORE_VALUE_BLOCK_ID,
-                "input_default": {"data": None},
-                "metadata": {"position": {"x": 0, "y": -100}},
-            }
-            new_nodes.append(store_node)
-
-            # Create link: original source -> StoreValueBlock
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": store_node_id,
-                    "sink_name": "input",
-                    "is_static": False,
-                }
-            )
-
-            # Update original link: StoreValueBlock -> ConditionBlock
-            link["source_id"] = store_node_id
-            link["source_name"] = "output"
-
-            logger.debug(f"Added StoreValueBlock before ConditionBlock {sink_id}")
-
-    if new_nodes:
-        agent["nodes"] = nodes + new_nodes
-
-    return agent
-
-
-def fix_addtolist_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToList blocks by adding prerequisite empty AddToList block.
-
-    When an AddToList block is found:
-    1. Checks if there's a CreateListBlock before it
-    2. Removes CreateListBlock if linked directly to AddToList
-    3. Adds an empty AddToList block before the original
-    4. Ensures the original has a self-referencing link
-    """
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    new_nodes = []
-    original_addtolist_ids = set()
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    # First pass: identify CreateListBlock nodes to remove
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATELIST_BLOCK_ID
-            and sink_node.get("block_id") == ADDTOLIST_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateListBlock {source_node.get('id')}")
-
-    # Second pass: process AddToList blocks
-    filtered_nodes = []
-    for node in nodes:
-        if node.get("id") in nodes_to_remove:
-            continue
-
-        if node.get("block_id") == ADDTOLIST_BLOCK_ID:
-            original_addtolist_ids.add(node.get("id"))
-            node_id = node.get("id")
-            pos = node.get("metadata", {}).get("position", {"x": 0, "y": 0})
-
-            # Check if already has prerequisite
-            has_prereq = any(
-                link.get("sink_id") == node_id
-                and link.get("sink_name") == "list"
-                and link.get("source_name") == "updated_list"
-                for link in links
-            )
-
-            if not has_prereq:
-                # Remove links to "list" input (except self-reference)
-                for link in links:
-                    if (
-                        link.get("sink_id") == node_id
-                        and link.get("sink_name") == "list"
-                        and link.get("source_id") != node_id
-                        and link not in links_to_remove
-                    ):
-                        links_to_remove.append(link)
-
-                # Create prerequisite AddToList block
-                prereq_id = str(uuid.uuid4())
-                prereq_node = {
-                    "id": prereq_id,
-                    "block_id": ADDTOLIST_BLOCK_ID,
-                    "input_default": {"list": [], "entry": None, "entries": []},
-                    "metadata": {
-                        "position": {"x": pos.get("x", 0) - 800, "y": pos.get("y", 0)}
-                    },
-                }
-                new_nodes.append(prereq_node)
-
-                # Link prerequisite to original
-                links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": prereq_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added prerequisite AddToList block for {node_id}")
-
-        filtered_nodes.append(node)
-
-    # Remove marked links
-    filtered_links = [link for link in links if link not in links_to_remove]
-
-    # Add self-referencing links for original AddToList blocks
-    for node in filtered_nodes + new_nodes:
-        if (
-            node.get("block_id") == ADDTOLIST_BLOCK_ID
-            and node.get("id") in original_addtolist_ids
-        ):
-            node_id = node.get("id")
-            has_self_ref = any(
-                link["source_id"] == node_id
-                and link["sink_id"] == node_id
-                and link["source_name"] == "updated_list"
-                and link["sink_name"] == "list"
-                for link in filtered_links
-            )
-            if not has_self_ref:
-                filtered_links.append(
-                    {
-                        "id": str(uuid.uuid4()),
-                        "source_id": node_id,
-                        "source_name": "updated_list",
-                        "sink_id": node_id,
-                        "sink_name": "list",
-                        "is_static": False,
-                    }
-                )
-                logger.debug(f"Added self-reference for AddToList {node_id}")
-
-    agent["nodes"] = filtered_nodes + new_nodes
-    agent["links"] = filtered_links
-    return agent
-
-
-def fix_addtodictionary_blocks(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix AddToDictionary blocks by removing empty CreateDictionary nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    nodes_to_remove = set()
-    links_to_remove = []
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        sink_node = next((n for n in nodes if n.get("id") == link.get("sink_id")), None)
-
-        if (
-            source_node
-            and sink_node
-            and source_node.get("block_id") == CREATEDICT_BLOCK_ID
-            and sink_node.get("block_id") == ADDTODICTIONARY_BLOCK_ID
-        ):
-            nodes_to_remove.add(source_node.get("id"))
-            links_to_remove.append(link)
-            logger.debug(f"Removing CreateDictionary {source_node.get('id')}")
-
-    agent["nodes"] = [n for n in nodes if n.get("id") not in nodes_to_remove]
-    agent["links"] = [link for link in links if link not in links_to_remove]
-    return agent
-
-
-def fix_code_execution_output(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix CodeExecutionBlock output: change 'response' to 'stdout_logs'."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-
-    for link in links:
-        source_node = next(
-            (n for n in nodes if n.get("id") == link.get("source_id")), None
-        )
-        if (
-            source_node
-            and source_node.get("block_id") == CODE_EXECUTION_BLOCK_ID
-            and link.get("source_name") == "response"
-        ):
-            link["source_name"] = "stdout_logs"
-            logger.debug("Fixed CodeExecutionBlock output: response -> stdout_logs")
-
-    return agent
-
-
-def fix_data_sampling_sample_size(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix DataSamplingBlock by setting sample_size to 1 as default."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    links_to_remove = []
-
-    for node in nodes:
-        if node.get("block_id") == DATA_SAMPLING_BLOCK_ID:
-            node_id = node.get("id")
-            input_default = node.get("input_default", {})
-
-            # Remove links to sample_size
-            for link in links:
-                if (
-                    link.get("sink_id") == node_id
-                    and link.get("sink_name") == "sample_size"
-                ):
-                    links_to_remove.append(link)
-
-            # Set default
-            input_default["sample_size"] = 1
-            node["input_default"] = input_default
-            logger.debug(f"Fixed DataSamplingBlock {node_id} sample_size to 1")
-
-    if links_to_remove:
-        agent["links"] = [link for link in links if link not in links_to_remove]
-
-    return agent
-
-
-def fix_node_x_coordinates(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix node x-coordinates to ensure 800+ unit spacing between linked nodes."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    for link in links:
-        source_id = link.get("source_id")
-        sink_id = link.get("sink_id")
-
-        source_node = node_lookup.get(source_id)
-        sink_node = node_lookup.get(sink_id)
-
-        if not source_node or not sink_node:
-            continue
-
-        source_pos = source_node.get("metadata", {}).get("position", {})
-        sink_pos = sink_node.get("metadata", {}).get("position", {})
-
-        source_x = source_pos.get("x", 0)
-        sink_x = sink_pos.get("x", 0)
-
-        if abs(sink_x - source_x) < 800:
-            new_x = source_x + 800
-            if "metadata" not in sink_node:
-                sink_node["metadata"] = {}
-            if "position" not in sink_node["metadata"]:
-                sink_node["metadata"]["position"] = {}
-            sink_node["metadata"]["position"]["x"] = new_x
-            logger.debug(f"Fixed node {sink_id} x: {sink_x} -> {new_x}")
-
-    return agent
-
-
-def fix_getcurrentdate_offset(agent: dict[str, Any]) -> dict[str, Any]:
-    """Fix GetCurrentDateBlock offset to ensure it's positive."""
-    for node in agent.get("nodes", []):
-        if node.get("block_id") == GET_CURRENT_DATE_BLOCK_ID:
-            input_default = node.get("input_default", {})
-            if "offset" in input_default:
-                offset = input_default["offset"]
-                if isinstance(offset, (int, float)) and offset < 0:
-                    input_default["offset"] = abs(offset)
-                    logger.debug(f"Fixed offset: {offset} -> {abs(offset)}")
-
-    return agent
-
-
-def fix_ai_model_parameter(
-    agent: dict[str, Any],
-    blocks_info: list[dict[str, Any]],
-    default_model: str = "gpt-4o",
-) -> dict[str, Any]:
-    """Add default model parameter to AI blocks if missing."""
-    block_map = {b.get("id"): b for b in blocks_info}
-
-    for node in agent.get("nodes", []):
-        block_id = node.get("block_id")
-        block = block_map.get(block_id)
-
-        if not block:
-            continue
-
-        # Check if block has AI category
-        categories = block.get("categories", [])
-        is_ai_block = any(
-            cat.get("category") == "AI" for cat in categories if isinstance(cat, dict)
-        )
-
-        if is_ai_block:
-            input_default = node.get("input_default", {})
-            if "model" not in input_default:
-                input_default["model"] = default_model
-                node["input_default"] = input_default
-                logger.debug(
-                    f"Added model '{default_model}' to AI block {node.get('id')}"
-                )
-
-    return agent
-
-
-def fix_link_static_properties(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix is_static property based on source block's staticOutput."""
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-    for link in agent.get("links", []):
-        source_node = node_lookup.get(link.get("source_id"))
-        if not source_node:
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        if not source_block:
-            continue
-
-        static_output = source_block.get("staticOutput", False)
-        if link.get("is_static") != static_output:
-            link["is_static"] = static_output
-            logger.debug(f"Fixed link {link.get('id')} is_static to {static_output}")
-
-    return agent
-
-
-def fix_data_type_mismatch(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-) -> dict[str, Any]:
-    """Fix data type mismatches by inserting UniversalTypeConverterBlock."""
-    nodes = agent.get("nodes", [])
-    links = agent.get("links", [])
-    block_map = {b.get("id"): b for b in blocks_info}
-    node_lookup = {n.get("id"): n for n in nodes}
-
-    def get_property_type(schema: dict, name: str) -> str | None:
-        if "_#_" in name:
-            parent, child = name.split("_#_", 1)
-            parent_schema = schema.get(parent, {})
-            if "properties" in parent_schema:
-                return parent_schema["properties"].get(child, {}).get("type")
-            return None
-        return schema.get(name, {}).get("type")
-
-    def are_types_compatible(src: str, sink: str) -> bool:
-        if {src, sink} <= {"integer", "number"}:
-            return True
-        return src == sink
-
-    type_mapping = {
-        "string": "string",
-        "text": "string",
-        "integer": "number",
-        "number": "number",
-        "float": "number",
-        "boolean": "boolean",
-        "bool": "boolean",
-        "array": "list",
-        "list": "list",
-        "object": "dictionary",
-        "dict": "dictionary",
-        "dictionary": "dictionary",
-    }
-
-    new_links = []
-    nodes_to_add = []
-
-    for link in links:
-        source_node = node_lookup.get(link.get("source_id"))
-        sink_node = node_lookup.get(link.get("sink_id"))
-
-        if not source_node or not sink_node:
-            new_links.append(link)
-            continue
-
-        source_block = block_map.get(source_node.get("block_id"))
-        sink_block = block_map.get(sink_node.get("block_id"))
-
-        if not source_block or not sink_block:
-            new_links.append(link)
-            continue
-
-        source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-        sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-        source_type = get_property_type(source_outputs, link.get("source_name", ""))
-        sink_type = get_property_type(sink_inputs, link.get("sink_name", ""))
-
-        if (
-            source_type
-            and sink_type
-            and not are_types_compatible(source_type, sink_type)
-        ):
-            # Insert type converter
-            converter_id = str(uuid.uuid4())
-            target_type = type_mapping.get(sink_type, sink_type)
-
-            converter_node = {
-                "id": converter_id,
-                "block_id": UNIVERSAL_TYPE_CONVERTER_BLOCK_ID,
-                "input_default": {"type": target_type},
-                "metadata": {"position": {"x": 0, "y": 100}},
-            }
-            nodes_to_add.append(converter_node)
-
-            # source -> converter
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": link["source_id"],
-                    "source_name": link["source_name"],
-                    "sink_id": converter_id,
-                    "sink_name": "value",
-                    "is_static": False,
-                }
-            )
-
-            # converter -> sink
-            new_links.append(
-                {
-                    "id": str(uuid.uuid4()),
-                    "source_id": converter_id,
-                    "source_name": "value",
-                    "sink_id": link["sink_id"],
-                    "sink_name": link["sink_name"],
-                    "is_static": False,
-                }
-            )
-
-            logger.debug(f"Inserted type converter: {source_type} -> {target_type}")
-        else:
-            new_links.append(link)
-
-    if nodes_to_add:
-        agent["nodes"] = nodes + nodes_to_add
-        agent["links"] = new_links
-
-    return agent
-
-
-def apply_all_fixes(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> dict[str, Any]:
-    """Apply all fixes to an agent JSON.
-
-    Args:
-        agent: Agent JSON dict
-        blocks_info: Optional list of block info dicts for advanced fixes
-
-    Returns:
-        Fixed agent JSON
-    """
-    # Basic fixes (no block info needed)
-    agent = fix_agent_ids(agent)
-    agent = fix_double_curly_braces(agent)
-    agent = fix_storevalue_before_condition(agent)
-    agent = fix_addtolist_blocks(agent)
-    agent = fix_addtodictionary_blocks(agent)
-    agent = fix_code_execution_output(agent)
-    agent = fix_data_sampling_sample_size(agent)
-    agent = fix_node_x_coordinates(agent)
-    agent = fix_getcurrentdate_offset(agent)
-
-    # Advanced fixes (require block info)
-    if blocks_info is None:
-        blocks_info = get_blocks_info()
-
-    agent = fix_ai_model_parameter(agent, blocks_info)
-    agent = fix_link_static_properties(agent, blocks_info)
-    agent = fix_data_type_mismatch(agent, blocks_info)
-
-    return agent

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/prompts.py

@@ -1,225 +0,0 @@
-"""Prompt templates for agent generation."""
-
-DECOMPOSITION_PROMPT = """
-You are an expert AutoGPT Workflow Decomposer. Your task is to analyze a user's high-level goal and break it down into a clear, step-by-step plan using the available blocks.
-
-Each step should represent a distinct, automatable action suitable for execution by an AI automation system.
-
----
-
-FIRST: Analyze the user's goal and determine:
-1) Design-time configuration (fixed settings that won't change per run)
-2) Runtime inputs (values the agent's end-user will provide each time it runs)
-
-For anything that can vary per run (email addresses, names, dates, search terms, etc.):
-- DO NOT ask for the actual value
-- Instead, define it as an Agent Input with a clear name, type, and description
-
-Only ask clarifying questions about design-time config that affects how you build the workflow:
-- Which external service to use (e.g., "Gmail vs Outlook", "Notion vs Google Docs")
-- Required formats or structures (e.g., "CSV, JSON, or PDF output?")
-- Business rules that must be hard-coded
-
-IMPORTANT CLARIFICATIONS POLICY:
-- Ask no more than five essential questions
-- Do not ask for concrete values that can be provided at runtime as Agent Inputs
-- Do not ask for API keys or credentials; the platform handles those directly
-- If there is enough information to infer reasonable defaults, prefer to propose defaults
-
----
-
-GUIDELINES:
-1. List each step as a numbered item
-2. Describe the action clearly and specify inputs/outputs
-3. Ensure steps are in logical, sequential order
-4. Mention block names naturally (e.g., "Use GetWeatherByLocationBlock to...")
-5. Help the user reach their goal efficiently
-
----
-
-RULES:
-1. OUTPUT FORMAT: Only output either clarifying questions OR step-by-step instructions, not both
-2. USE ONLY THE BLOCKS PROVIDED
-3. ALL required_input fields must be provided
-4. Data types of linked properties must match
-5. Write expert-level prompts for AI-related blocks
-
----
-
-CRITICAL BLOCK RESTRICTIONS:
-1. AddToListBlock: Outputs updated list EVERY addition, not after all additions
-2. SendEmailBlock: Draft the email for user review; set SMTP config based on email type
-3. ConditionBlock: value2 is reference, value1 is contrast
-4. CodeExecutionBlock: DO NOT USE - use AI blocks instead
-5. ReadCsvBlock: Only use the 'rows' output, not 'row'
-
----
-
-OUTPUT FORMAT:
-
-If more information is needed:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "Which email provider should be used? (Gmail, Outlook, custom SMTP)",
-      "keyword": "email_provider",
-      "example": "Gmail"
-    }}
-  ]
-}}
-```
-
-If ready to proceed:
-```json
-{{
-  "type": "instructions",
-  "steps": [
-    {{
-      "step_number": 1,
-      "block_name": "AgentShortTextInputBlock",
-      "description": "Get the URL of the content to analyze.",
-      "inputs": [{{"name": "name", "value": "URL"}}],
-      "outputs": [{{"name": "result", "description": "The URL entered by user"}}]
-    }}
-  ]
-}}
-```
-
----
-
-AVAILABLE BLOCKS:
-{block_summaries}
-"""
-
-GENERATION_PROMPT = """
-You are an expert AI workflow builder. Generate a valid agent JSON from the given instructions.
-
----
-
-NODES:
-Each node must include:
-- `id`: Unique UUID v4 (e.g. `a8f5b1e2-c3d4-4e5f-8a9b-0c1d2e3f4a5b`)
-- `block_id`: The block identifier (must match an Allowed Block)
-- `input_default`: Dict of inputs (can be empty if no static inputs needed)
-- `metadata`: Must contain:
-  - `position`: {{"x": number, "y": number}} - adjacent nodes should differ by 800+ in X
-  - `customized_name`: Clear name describing this block's purpose in the workflow
-
----
-
-LINKS:
-Each link connects a source node's output to a sink node's input:
-- `id`: MUST be UUID v4 (NOT "link-1", "link-2", etc.)
-- `source_id`: ID of the source node
-- `source_name`: Output field name from the source block
-- `sink_id`: ID of the sink node
-- `sink_name`: Input field name on the sink block
-- `is_static`: true only if source block has static_output: true
-
-CRITICAL: All IDs must be valid UUID v4 format!
-
----
-
-AGENT (GRAPH):
-Wrap nodes and links in:
-- `id`: UUID of the agent
-- `name`: Short, generic name (avoid specific company names, URLs)
-- `description`: Short, generic description
-- `nodes`: List of all nodes
-- `links`: List of all links
-- `version`: 1
-- `is_active`: true
-
----
-
-TIPS:
-- All required_input fields must be provided via input_default or a valid link
-- Ensure consistent source_id and sink_id references
-- Avoid dangling links
-- Input/output pins must match block schemas
-- Do not invent unknown block_ids
-
----
-
-ALLOWED BLOCKS:
-{block_summaries}
-
----
-
-Generate the complete agent JSON. Output ONLY valid JSON, no explanation.
-"""
-
-PATCH_PROMPT = """
-You are an expert at modifying AutoGPT agent workflows. Given the current agent and a modification request, generate a JSON patch to update the agent.
-
-CURRENT AGENT:
-{current_agent}
-
-AVAILABLE BLOCKS:
-{block_summaries}
-
----
-
-PATCH FORMAT:
-Return a JSON object with the following structure:
-
-```json
-{{
-  "type": "patch",
-  "intent": "Brief description of what the patch does",
-  "patches": [
-    {{
-      "type": "modify",
-      "node_id": "uuid-of-node-to-modify",
-      "changes": {{
-        "input_default": {{"field": "new_value"}},
-        "metadata": {{"customized_name": "New Name"}}
-      }}
-    }},
-    {{
-      "type": "add",
-      "new_nodes": [
-        {{
-          "id": "new-uuid",
-          "block_id": "block-uuid",
-          "input_default": {{}},
-          "metadata": {{"position": {{"x": 0, "y": 0}}, "customized_name": "Name"}}
-        }}
-      ],
-      "new_links": [
-        {{
-          "id": "link-uuid",
-          "source_id": "source-node-id",
-          "source_name": "output_field",
-          "sink_id": "sink-node-id",
-          "sink_name": "input_field"
-        }}
-      ]
-    }},
-    {{
-      "type": "remove",
-      "node_ids": ["uuid-of-node-to-remove"],
-      "link_ids": ["uuid-of-link-to-remove"]
-    }}
-  ]
-}}
-```
-
-If you need more information, return:
-```json
-{{
-  "type": "clarifying_questions",
-  "questions": [
-    {{
-      "question": "What specific change do you want?",
-      "keyword": "change_type",
-      "example": "Add error handling"
-    }}
-  ]
-}}
-```
-
-Generate the minimal patch needed. Output ONLY valid JSON.
-"""

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

@@ -0,0 +1,269 @@
+"""External Agent Generator service client.
+
+This module provides a client for communicating with the external Agent Generator
+microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
+will delegate to the external service instead of using the built-in LLM-based implementation.
+"""
+
+import logging
+from typing import Any
+
+import httpx
+
+from backend.util.settings import Settings
+
+logger = logging.getLogger(__name__)
+
+_client: httpx.AsyncClient | None = None
+_settings: Settings | None = None
+
+
+def _get_settings() -> Settings:
+    """Get or create settings singleton."""
+    global _settings
+    if _settings is None:
+        _settings = Settings()
+    return _settings
+
+
+def is_external_service_configured() -> bool:
+    """Check if external Agent Generator service is configured."""
+    settings = _get_settings()
+    return bool(settings.config.agentgenerator_host)
+
+
+def _get_base_url() -> str:
+    """Get the base URL for the external service."""
+    settings = _get_settings()
+    host = settings.config.agentgenerator_host
+    port = settings.config.agentgenerator_port
+    return f"http://{host}:{port}"
+
+
+def _get_client() -> httpx.AsyncClient:
+    """Get or create the HTTP client for the external service."""
+    global _client
+    if _client is None:
+        settings = _get_settings()
+        _client = httpx.AsyncClient(
+            base_url=_get_base_url(),
+            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
+        )
+    return _client
+
+
+async def decompose_goal_external(
+    description: str, context: str = ""
+) -> dict[str, Any] | None:
+    """Call the external service to decompose a goal.
+
+    Args:
+        description: Natural language goal description
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Dict with either:
+        - {"type": "clarifying_questions", "questions": [...]}
+        - {"type": "instructions", "steps": [...]}
+        - {"type": "unachievable_goal", ...}
+        - {"type": "vague_goal", ...}
+        Or None on error
+    """
+    client = _get_client()
+
+    # Build the request payload
+    payload: dict[str, Any] = {"description": description}
+    if context:
+        # The external service uses user_instruction for additional context
+        payload["user_instruction"] = context
+
+    try:
+        response = await client.post("/api/decompose-description", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Map the response to the expected format
+        response_type = data.get("type")
+        if response_type == "instructions":
+            return {"type": "instructions", "steps": data.get("steps", [])}
+        elif response_type == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+        elif response_type == "unachievable_goal":
+            return {
+                "type": "unachievable_goal",
+                "reason": data.get("reason"),
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        elif response_type == "vague_goal":
+            return {
+                "type": "vague_goal",
+                "suggested_goal": data.get("suggested_goal"),
+            }
+        else:
+            logger.error(
+                f"Unknown response type from external service: {response_type}"
+            )
+            return None
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_external(
+    instructions: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate an agent from instructions.
+
+    Args:
+        instructions: Structured instructions from decompose_goal
+
+    Returns:
+        Agent JSON dict or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def generate_agent_patch_external(
+    update_request: str, current_agent: dict[str, Any]
+) -> dict[str, Any] | None:
+    """Call the external service to generate a patch for an existing agent.
+
+    Args:
+        update_request: Natural language description of changes
+        current_agent: Current agent JSON
+
+    Returns:
+        Updated agent JSON, clarifying questions dict, or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.post(
+            "/api/update-agent",
+            json={
+                "update_request": update_request,
+                "current_agent_json": current_agent,
+            },
+        )
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error(f"External service returned error: {data.get('error')}")
+            return None
+
+        # Check if it's clarifying questions
+        if data.get("type") == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+
+        # Otherwise return the updated agent JSON
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error calling external agent generator: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error calling external agent generator: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error calling external agent generator: {e}")
+        return None
+
+
+async def get_blocks_external() -> list[dict[str, Any]] | None:
+    """Get available blocks from the external service.
+
+    Returns:
+        List of block info dicts or None on error
+    """
+    client = _get_client()
+
+    try:
+        response = await client.get("/api/blocks")
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            logger.error("External service returned error getting blocks")
+            return None
+
+        return data.get("blocks", [])
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"HTTP error getting blocks from external service: {e}")
+        return None
+    except httpx.RequestError as e:
+        logger.error(f"Request error getting blocks from external service: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Unexpected error getting blocks from external service: {e}")
+        return None
+
+
+async def health_check() -> bool:
+    """Check if the external service is healthy.
+
+    Returns:
+        True if healthy, False otherwise
+    """
+    if not is_external_service_configured():
+        return False
+
+    client = _get_client()
+
+    try:
+        response = await client.get("/health")
+        response.raise_for_status()
+        data = response.json()
+        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
+    except Exception as e:
+        logger.warning(f"External agent generator health check failed: {e}")
+        return False
+
+
+async def close_client() -> None:
+    """Close the HTTP client."""
+    global _client
+    if _client is not None:
+        await _client.aclose()
+        _client = None

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

@@ -1,213 +0,0 @@
-"""Utilities for agent generation."""
-
-import json
-import re
-from typing import Any
-
-from backend.data.block import get_blocks
-
-# UUID validation regex
-UUID_REGEX = re.compile(
-    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$"
-)
-
-# Block IDs for various fixes
-STORE_VALUE_BLOCK_ID = "1ff065e9-88e8-4358-9d82-8dc91f622ba9"
-CONDITION_BLOCK_ID = "715696a0-e1da-45c8-b209-c2fa9c3b0be6"
-ADDTOLIST_BLOCK_ID = "aeb08fc1-2fc1-4141-bc8e-f758f183a822"
-ADDTODICTIONARY_BLOCK_ID = "31d1064e-7446-4693-a7d4-65e5ca1180d1"
-CREATELIST_BLOCK_ID = "a912d5c7-6e00-4542-b2a9-8034136930e4"
-CREATEDICT_BLOCK_ID = "b924ddf4-de4f-4b56-9a85-358930dcbc91"
-CODE_EXECUTION_BLOCK_ID = "0b02b072-abe7-11ef-8372-fb5d162dd712"
-DATA_SAMPLING_BLOCK_ID = "4a448883-71fa-49cf-91cf-70d793bd7d87"
-UNIVERSAL_TYPE_CONVERTER_BLOCK_ID = "95d1b990-ce13-4d88-9737-ba5c2070c97b"
-GET_CURRENT_DATE_BLOCK_ID = "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1"
-
-DOUBLE_CURLY_BRACES_BLOCK_IDS = [
-    "44f6c8ad-d75c-4ae1-8209-aad1c0326928",  # FillTextTemplateBlock
-    "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-    "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-    "363ae599-353e-4804-937e-b2ee3cef3da4",  # AgentOutputBlock
-    "3b191d9f-356f-482d-8238-ba04b6d18381",
-    "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-    "3a7c4b8d-6e2f-4a5d-b9c1-f8d23c5a9b0e",
-    "ed1ae7a0-b770-4089-b520-1f0005fad19a",
-    "a892b8d9-3e4e-4e9c-9c1e-75f8efcf1bfa",
-    "b29c1b50-5d0e-4d9f-8f9d-1b0e6fcbf0b1",
-    "716a67b3-6760-42e7-86dc-18645c6e00fc",
-    "530cf046-2ce0-4854-ae2c-659db17c7a46",
-    "ed55ac19-356e-4243-a6cb-bc599e9b716f",
-    "1f292d4a-41a4-4977-9684-7c8d560b9f91",  # LLM blocks
-    "32a87eab-381e-4dd4-bdb8-4c47151be35a",
-]
-
-
-def is_valid_uuid(value: str) -> bool:
-    """Check if a string is a valid UUID v4."""
-    return isinstance(value, str) and UUID_REGEX.match(value) is not None
-
-
-def _compact_schema(schema: dict) -> dict[str, str]:
-    """Extract compact type info from a JSON schema properties dict.
-
-    Returns a dict of {field_name: type_string} for essential info only.
-    """
-    props = schema.get("properties", {})
-    result = {}
-
-    for name, prop in props.items():
-        # Skip internal/complex fields
-        if name.startswith("_"):
-            continue
-
-        # Get type string
-        type_str = prop.get("type", "any")
-
-        # Handle anyOf/oneOf (optional types)
-        if "anyOf" in prop:
-            types = [t.get("type", "?") for t in prop["anyOf"] if t.get("type")]
-            type_str = "|".join(types) if types else "any"
-        elif "allOf" in prop:
-            type_str = "object"
-
-        # Add array item type if present
-        if type_str == "array" and "items" in prop:
-            items = prop["items"]
-            if isinstance(items, dict):
-                item_type = items.get("type", "any")
-                type_str = f"array[{item_type}]"
-
-        result[name] = type_str
-
-    return result
-
-
-def get_block_summaries(include_schemas: bool = True) -> str:
-    """Generate compact block summaries for prompts.
-
-    Args:
-        include_schemas: Whether to include input/output type info
-
-    Returns:
-        Formatted string of block summaries (compact format)
-    """
-    blocks = get_blocks()
-    summaries = []
-
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        name = block.name
-        desc = getattr(block, "description", "") or ""
-
-        # Truncate description
-        if len(desc) > 150:
-            desc = desc[:147] + "..."
-
-        if not include_schemas:
-            summaries.append(f"- {name} (id: {block_id}): {desc}")
-        else:
-            # Compact format with type info only
-            inputs = {}
-            outputs = {}
-            required = []
-
-            if hasattr(block, "input_schema"):
-                try:
-                    schema = block.input_schema.jsonschema()
-                    inputs = _compact_schema(schema)
-                    required = schema.get("required", [])
-                except Exception:
-                    pass
-
-            if hasattr(block, "output_schema"):
-                try:
-                    schema = block.output_schema.jsonschema()
-                    outputs = _compact_schema(schema)
-                except Exception:
-                    pass
-
-            # Build compact line format
-            # Format: NAME (id): desc | in: {field:type, ...} [required] | out: {field:type}
-            in_str = ", ".join(f"{k}:{v}" for k, v in inputs.items())
-            out_str = ", ".join(f"{k}:{v}" for k, v in outputs.items())
-            req_str = f" req=[{','.join(required)}]" if required else ""
-
-            static = " [static]" if getattr(block, "static_output", False) else ""
-
-            line = f"- {name} (id: {block_id}): {desc}"
-            if in_str:
-                line += f"\n  in: {{{in_str}}}{req_str}"
-            if out_str:
-                line += f"\n  out: {{{out_str}}}{static}"
-
-            summaries.append(line)
-
-    return "\n".join(summaries)
-
-
-def get_blocks_info() -> list[dict[str, Any]]:
-    """Get block information with schemas for validation and fixing."""
-    blocks = get_blocks()
-    blocks_info = []
-    for block_id, block_cls in blocks.items():
-        block = block_cls()
-        blocks_info.append(
-            {
-                "id": block_id,
-                "name": block.name,
-                "description": getattr(block, "description", ""),
-                "categories": getattr(block, "categories", []),
-                "staticOutput": getattr(block, "static_output", False),
-                "inputSchema": (
-                    block.input_schema.jsonschema()
-                    if hasattr(block, "input_schema")
-                    else {}
-                ),
-                "outputSchema": (
-                    block.output_schema.jsonschema()
-                    if hasattr(block, "output_schema")
-                    else {}
-                ),
-            }
-        )
-    return blocks_info
-
-
-def parse_json_from_llm(text: str) -> dict[str, Any] | None:
-    """Extract JSON from LLM response (handles markdown code blocks)."""
-    if not text:
-        return None
-
-    # Try fenced code block
-    match = re.search(r"```(?:json)?\s*([\s\S]*?)```", text, re.IGNORECASE)
-    if match:
-        try:
-            return json.loads(match.group(1).strip())
-        except json.JSONDecodeError:
-            pass
-
-    # Try raw text
-    try:
-        return json.loads(text.strip())
-    except json.JSONDecodeError:
-        pass
-
-    # Try finding {...} span
-    start = text.find("{")
-    end = text.rfind("}")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    # Try finding [...] span
-    start = text.find("[")
-    end = text.rfind("]")
-    if start != -1 and end > start:
-        try:
-            return json.loads(text[start : end + 1])
-        except json.JSONDecodeError:
-            pass
-
-    return None

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/validator.py

@@ -1,279 +0,0 @@
-"""Agent validator - Validates agent structure and connections."""
-
-import logging
-import re
-from typing import Any
-
-from .utils import get_blocks_info
-
-logger = logging.getLogger(__name__)
-
-
-class AgentValidator:
-    """Validator for AutoGPT agents with detailed error reporting."""
-
-    def __init__(self):
-        self.errors: list[str] = []
-
-    def add_error(self, error: str) -> None:
-        """Add an error message."""
-        self.errors.append(error)
-
-    def validate_block_existence(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate all block IDs exist in the blocks library."""
-        valid = True
-        valid_block_ids = {b.get("id") for b in blocks_info if b.get("id")}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            node_id = node.get("id")
-
-            if not block_id:
-                self.add_error(f"Node '{node_id}' is missing 'block_id' field.")
-                valid = False
-                continue
-
-            if block_id not in valid_block_ids:
-                self.add_error(
-                    f"Node '{node_id}' references block_id '{block_id}' which does not exist."
-                )
-                valid = False
-
-        return valid
-
-    def validate_link_node_references(self, agent: dict[str, Any]) -> bool:
-        """Validate all node IDs referenced in links exist."""
-        valid = True
-        valid_node_ids = {n.get("id") for n in agent.get("nodes", []) if n.get("id")}
-
-        for link in agent.get("links", []):
-            link_id = link.get("id", "Unknown")
-            source_id = link.get("source_id")
-            sink_id = link.get("sink_id")
-
-            if not source_id:
-                self.add_error(f"Link '{link_id}' is missing 'source_id'.")
-                valid = False
-            elif source_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent source_id '{source_id}'."
-                )
-                valid = False
-
-            if not sink_id:
-                self.add_error(f"Link '{link_id}' is missing 'sink_id'.")
-                valid = False
-            elif sink_id not in valid_node_ids:
-                self.add_error(
-                    f"Link '{link_id}' references non-existent sink_id '{sink_id}'."
-                )
-                valid = False
-
-        return valid
-
-    def validate_required_inputs(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate required inputs are provided."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-
-        for node in agent.get("nodes", []):
-            block_id = node.get("block_id")
-            block = block_map.get(block_id)
-
-            if not block:
-                continue
-
-            required_inputs = block.get("inputSchema", {}).get("required", [])
-            input_defaults = node.get("input_default", {})
-            node_id = node.get("id")
-
-            # Get linked inputs
-            linked_inputs = {
-                link["sink_name"]
-                for link in agent.get("links", [])
-                if link.get("sink_id") == node_id
-            }
-
-            for req_input in required_inputs:
-                if (
-                    req_input not in input_defaults
-                    and req_input not in linked_inputs
-                    and req_input != "credentials"
-                ):
-                    block_name = block.get("name", "Unknown Block")
-                    self.add_error(
-                        f"Node '{node_id}' ({block_name}) is missing required input '{req_input}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate_data_type_compatibility(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate linked data types are compatible."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        def get_type(schema: dict, name: str) -> str | None:
-            if "_#_" in name:
-                parent, child = name.split("_#_", 1)
-                parent_schema = schema.get(parent, {})
-                if "properties" in parent_schema:
-                    return parent_schema["properties"].get(child, {}).get("type")
-                return None
-            return schema.get(name, {}).get("type")
-
-        def are_compatible(src: str, sink: str) -> bool:
-            if {src, sink} <= {"integer", "number"}:
-                return True
-            return src == sink
-
-        for link in agent.get("links", []):
-            source_node = node_lookup.get(link.get("source_id"))
-            sink_node = node_lookup.get(link.get("sink_id"))
-
-            if not source_node or not sink_node:
-                continue
-
-            source_block = block_map.get(source_node.get("block_id"))
-            sink_block = block_map.get(sink_node.get("block_id"))
-
-            if not source_block or not sink_block:
-                continue
-
-            source_outputs = source_block.get("outputSchema", {}).get("properties", {})
-            sink_inputs = sink_block.get("inputSchema", {}).get("properties", {})
-
-            source_type = get_type(source_outputs, link.get("source_name", ""))
-            sink_type = get_type(sink_inputs, link.get("sink_name", ""))
-
-            if source_type and sink_type and not are_compatible(source_type, sink_type):
-                self.add_error(
-                    f"Type mismatch: {source_block.get('name')} output '{link['source_name']}' "
-                    f"({source_type}) -> {sink_block.get('name')} input '{link['sink_name']}' ({sink_type})."
-                )
-                valid = False
-
-        return valid
-
-    def validate_nested_sink_links(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]]
-    ) -> bool:
-        """Validate nested sink links (with _#_ notation)."""
-        valid = True
-        block_map = {b.get("id"): b for b in blocks_info}
-        node_lookup = {n.get("id"): n for n in agent.get("nodes", [])}
-
-        for link in agent.get("links", []):
-            sink_name = link.get("sink_name", "")
-
-            if "_#_" in sink_name:
-                parent, child = sink_name.split("_#_", 1)
-
-                sink_node = node_lookup.get(link.get("sink_id"))
-                if not sink_node:
-                    continue
-
-                block = block_map.get(sink_node.get("block_id"))
-                if not block:
-                    continue
-
-                input_props = block.get("inputSchema", {}).get("properties", {})
-                parent_schema = input_props.get(parent)
-
-                if not parent_schema:
-                    self.add_error(
-                        f"Invalid nested link '{sink_name}': parent '{parent}' not found."
-                    )
-                    valid = False
-                    continue
-
-                if not parent_schema.get("additionalProperties"):
-                    if not (
-                        isinstance(parent_schema, dict)
-                        and "properties" in parent_schema
-                        and child in parent_schema.get("properties", {})
-                    ):
-                        self.add_error(
-                            f"Invalid nested link '{sink_name}': child '{child}' not found in '{parent}'."
-                        )
-                        valid = False
-
-        return valid
-
-    def validate_prompt_spaces(self, agent: dict[str, Any]) -> bool:
-        """Validate prompts don't have spaces in template variables."""
-        valid = True
-
-        for node in agent.get("nodes", []):
-            input_default = node.get("input_default", {})
-            prompt = input_default.get("prompt", "")
-
-            if not isinstance(prompt, str):
-                continue
-
-            # Find {{...}} with spaces
-            matches = re.finditer(r"\{\{([^}]+)\}\}", prompt)
-            for match in matches:
-                content = match.group(1)
-                if " " in content:
-                    self.add_error(
-                        f"Node '{node.get('id')}' has spaces in template variable: "
-                        f"'{{{{{content}}}}}' should be '{{{{{content.replace(' ', '_')}}}}}'."
-                    )
-                    valid = False
-
-        return valid
-
-    def validate(
-        self, agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-    ) -> tuple[bool, str | None]:
-        """Run all validations.
-
-        Returns:
-            Tuple of (is_valid, error_message)
-        """
-        self.errors = []
-
-        if blocks_info is None:
-            blocks_info = get_blocks_info()
-
-        checks = [
-            self.validate_block_existence(agent, blocks_info),
-            self.validate_link_node_references(agent),
-            self.validate_required_inputs(agent, blocks_info),
-            self.validate_data_type_compatibility(agent, blocks_info),
-            self.validate_nested_sink_links(agent, blocks_info),
-            self.validate_prompt_spaces(agent),
-        ]
-
-        all_passed = all(checks)
-
-        if all_passed:
-            logger.info("Agent validation successful")
-            return True, None
-
-        error_message = "Agent validation failed:\n"
-        for i, error in enumerate(self.errors, 1):
-            error_message += f"{i}. {error}\n"
-
-        logger.warning(f"Agent validation failed with {len(self.errors)} errors")
-        return False, error_message
-
-
-def validate_agent(
-    agent: dict[str, Any], blocks_info: list[dict[str, Any]] | None = None
-) -> tuple[bool, str | None]:
-    """Convenience function to validate an agent.
-
-    Returns:
-        Tuple of (is_valid, error_message)
-    """
-    validator = AgentValidator()
-    return validator.validate(agent, blocks_info)

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

@@ -8,12 +8,10 @@ from langfuse import observe
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     decompose_goal,
     generate_agent,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -27,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for agent generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class CreateAgentTool(BaseTool):
     """Tool for creating agents from natural language descriptions."""
@@ -91,9 +86,8 @@ class CreateAgentTool(BaseTool):
 
         Flow:
         1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON from the steps
-        3. Apply fixes to correct common LLM errors
-        4. Preview or save based on the save parameter
+        2. Generate agent JSON (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         description = kwargs.get("description", "").strip()
         context = kwargs.get("context", "")
@@ -110,11 +104,13 @@ class CreateAgentTool(BaseTool):
         # Step 1: Decompose goal into steps
         try:
             decomposition_result = await decompose_goal(description, context)
-        except ValueError as e:
-            # Handle missing API key or configuration errors
+        except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
-                message=f"Agent generation is not configured: {str(e)}",
-                error="configuration_error",
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
                 session_id=session_id,
             )
 
@@ -171,72 +167,32 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON with retry on validation failure
-        blocks_info = get_blocks_info()
-        agent_json = None
-        validation_errors = None
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate agent (include validation errors from previous attempt)
-            if attempt == 0:
-                agent_json = await generate_agent(decomposition_result)
-            else:
-                # Retry with validation error feedback
-                logger.info(
-                    f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                )
-                retry_instructions = {
-                    **decomposition_result,
-                    "previous_errors": validation_errors,
-                    "retry_instructions": (
-                        "The previous generation had validation errors. "
-                        "Please fix these issues in the new generation:\n"
-                        f"{validation_errors}"
-                    ),
-                }
-                agent_json = await generate_agent(retry_instructions)
-
-            if agent_json is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate the agent. Please try again.",
-                        error="Generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Step 3: Apply fixes to correct common errors
-            agent_json = apply_all_fixes(agent_json, blocks_info)
-
-            # Step 4: Validate the agent
-            is_valid, validation_errors = validate_agent(agent_json, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent generated successfully on attempt {attempt + 1}")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        try:
+            agent_json = await generate_agent(decomposition_result)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent generation is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Generated agent has validation errors after {MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the workflow."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if agent_json is None:
+            return ErrorResponse(
+                message="Failed to generate the agent. Please try again.",
+                error="Generation failed",
+                session_id=session_id,
+            )
 
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 4: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(

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

@@ -8,13 +8,10 @@ from langfuse import observe
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    apply_agent_patch,
-    apply_all_fixes,
+    AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
-    get_blocks_info,
     save_agent_to_library,
-    validate_agent,
 )
 from .base import BaseTool
 from .models import (
@@ -28,9 +25,6 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
-# Maximum retries for patch generation with validation feedback
-MAX_GENERATION_RETRIES = 2
-
 
 class EditAgentTool(BaseTool):
     """Tool for editing existing agents using natural language."""
@@ -43,7 +37,7 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates a patch to update the agent while preserving unchanged parts."
+            "Generates updates to the agent while preserving unchanged parts."
         )
 
     @property
@@ -98,9 +92,8 @@ class EditAgentTool(BaseTool):
 
         Flow:
         1. Fetch the current agent
-        2. Generate a patch based on the requested changes
-        3. Apply the patch to create an updated agent
-        4. Preview or save based on the save parameter
+        2. Generate updated agent (external service handles fixing and validation)
+        3. Preview or save based on the save parameter
         """
         agent_id = kwargs.get("agent_id", "").strip()
         changes = kwargs.get("changes", "").strip()
@@ -137,121 +130,58 @@ class EditAgentTool(BaseTool):
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate patch with retry on validation failure
-        blocks_info = get_blocks_info()
-        updated_agent = None
-        validation_errors = None
-        intent = "Applied requested changes"
-
-        for attempt in range(MAX_GENERATION_RETRIES + 1):
-            # Generate patch (include validation errors from previous attempt)
-            try:
-                if attempt == 0:
-                    patch_result = await generate_agent_patch(
-                        update_request, current_agent
-                    )
-                else:
-                    # Retry with validation error feedback
-                    logger.info(
-                        f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
-                    )
-                    retry_request = (
-                        f"{update_request}\n\n"
-                        f"IMPORTANT: The previous edit had validation errors. "
-                        f"Please fix these issues:\n{validation_errors}"
-                    )
-                    patch_result = await generate_agent_patch(
-                        retry_request, current_agent
-                    )
-            except ValueError as e:
-                # Handle missing API key or configuration errors
-                return ErrorResponse(
-                    message=f"Agent generation is not configured: {str(e)}",
-                    error="configuration_error",
-                    session_id=session_id,
-                )
-
-            if patch_result is None:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message="Failed to generate changes. Please try rephrasing.",
-                        error="Patch generation failed",
-                        session_id=session_id,
-                    )
-                continue
-
-            # Check if LLM returned clarifying questions
-            if patch_result.get("type") == "clarifying_questions":
-                questions = patch_result.get("questions", [])
-                return ClarificationNeededResponse(
-                    message=(
-                        "I need some more information about the changes. "
-                        "Please answer the following questions:"
-                    ),
-                    questions=[
-                        ClarifyingQuestion(
-                            question=q.get("question", ""),
-                            keyword=q.get("keyword", ""),
-                            example=q.get("example"),
-                        )
-                        for q in questions
-                    ],
-                    session_id=session_id,
-                )
-
-            # Step 3: Apply patch and fixes
-            try:
-                updated_agent = apply_agent_patch(current_agent, patch_result)
-                updated_agent = apply_all_fixes(updated_agent, blocks_info)
-            except Exception as e:
-                if attempt == MAX_GENERATION_RETRIES:
-                    return ErrorResponse(
-                        message=f"Failed to apply changes: {str(e)}",
-                        error="patch_apply_failed",
-                        details={"exception": str(e)},
-                        session_id=session_id,
-                    )
-                validation_errors = str(e)
-                continue
-
-            # Step 4: Validate the updated agent
-            is_valid, validation_errors = validate_agent(updated_agent, blocks_info)
-
-            if is_valid:
-                logger.info(f"Agent edited successfully on attempt {attempt + 1}")
-                intent = patch_result.get("intent", "Applied requested changes")
-                break
-
-            logger.warning(
-                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+        # Step 2: Generate updated agent (external service handles fixing and validation)
+        try:
+            result = await generate_agent_patch(update_request, current_agent)
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent editing is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
             )
 
-            if attempt == MAX_GENERATION_RETRIES:
-                # Return error with validation details
-                return ErrorResponse(
-                    message=(
-                        f"Updated agent has validation errors after "
-                        f"{MAX_GENERATION_RETRIES + 1} attempts. "
-                        f"Please try rephrasing your request or simplify the changes."
-                    ),
-                    error="validation_failed",
-                    details={"validation_errors": validation_errors},
-                    session_id=session_id,
-                )
+        if result is None:
+            return ErrorResponse(
+                message="Failed to generate changes. Please try rephrasing.",
+                error="Update generation failed",
+                session_id=session_id,
+            )
 
-        # At this point, updated_agent is guaranteed to be set (we return on all failure paths)
-        assert updated_agent is not None
+        # Check if LLM returned clarifying questions
+        if result.get("type") == "clarifying_questions":
+            questions = result.get("questions", [])
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information about the changes. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                ],
+                session_id=session_id,
+            )
+
+        # Result is the updated agent JSON
+        updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
         agent_description = updated_agent.get("description", "")
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 5: Preview or save
+        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
-                    f"I've updated the agent. Changes: {intent}. "
+                    f"I've updated the agent. "
                     f"The agent now has {node_count} blocks. "
                     f"Review it and call edit_agent with save=true to save the changes."
                 ),
@@ -277,10 +207,7 @@ class EditAgentTool(BaseTool):
             )
 
             return AgentSavedResponse(
-                message=(
-                    f"Updated agent '{created_graph.name}' has been saved to your library! "
-                    f"Changes: {intent}"
-                ),
+                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,

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

@@ -393,7 +393,6 @@ async def get_creators(
 @router.get(
     "/creator/{username}",
     summary="Get creator details",
-    operation_id="getV2GetCreatorDetails",
     tags=["store", "public"],
     response_model=store_model.CreatorDetails,
 )

autogpt_platform/backend/backend/api/rest_api.py

@@ -18,7 +18,6 @@ from prisma.errors import PrismaError
 
 import backend.api.features.admin.credit_admin_routes
 import backend.api.features.admin.execution_analytics_routes
-import backend.api.features.admin.llm_routes
 import backend.api.features.admin.store_admin_routes
 import backend.api.features.builder
 import backend.api.features.builder.routes
@@ -38,11 +37,9 @@ import backend.data.db
 import backend.data.graph
 import backend.data.user
 import backend.integrations.webhooks.utils
-import backend.server.v2.llm.routes as public_llm_routes
 import backend.util.service
 import backend.util.settings
-from backend.data import llm_registry
-from backend.data.block_cost_config import refresh_llm_costs
+from backend.blocks.llm import DEFAULT_LLM_MODEL
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.monitoring.instrumentation import instrument_fastapi
@@ -112,27 +109,11 @@ async def lifespan_context(app: fastapi.FastAPI):
 
     AutoRegistry.patch_integrations()
 
-    # Refresh LLM registry before initializing blocks so blocks can use registry data
-    await llm_registry.refresh_llm_registry()
-    refresh_llm_costs()
-
-    # Clear block schema caches so they're regenerated with updated discriminator_mapping
-    from backend.data.block import BlockSchema
-
-    BlockSchema.clear_all_schema_caches()
-
     await backend.data.block.initialize_blocks()
 
     await backend.data.user.migrate_and_encrypt_user_integrations()
     await backend.data.graph.fix_llm_provider_credentials()
-    # migrate_llm_models uses registry default model
-    from backend.blocks.llm import LlmModel
-
-    default_model_slug = llm_registry.get_default_model_slug()
-    if default_model_slug:
-        await backend.data.graph.migrate_llm_models(LlmModel(default_model_slug))
-    else:
-        logger.warning("Skipping LLM model migration: no default model available")
+    await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
     with launch_darkly_context():
@@ -317,16 +298,6 @@ app.include_router(
     tags=["v2", "executions", "review"],
     prefix="/api/review",
 )
-app.include_router(
-    backend.api.features.admin.llm_routes.router,
-    tags=["v2", "admin", "llm"],
-    prefix="/api/llm/admin",
-)
-app.include_router(
-    public_llm_routes.router,
-    tags=["v2", "llm"],
-    prefix="/api",
-)
 app.include_router(
     backend.api.features.library.routes.router, tags=["v2"], prefix="/api/library"
 )

autogpt_platform/backend/backend/api/ws_api.py

@@ -77,39 +77,7 @@ async def event_broadcaster(manager: ConnectionManager):
                 payload=notification.payload,
             )
 
-    async def registry_refresh_worker():
-        """Listen for LLM registry refresh notifications and broadcast to all clients."""
-        from backend.data.llm_registry import REGISTRY_REFRESH_CHANNEL
-        from backend.data.redis_client import connect_async
-
-        redis = await connect_async()
-        pubsub = redis.pubsub()
-        await pubsub.subscribe(REGISTRY_REFRESH_CHANNEL)
-        logger.info(
-            "Subscribed to LLM registry refresh notifications for WebSocket broadcast"
-        )
-
-        async for message in pubsub.listen():
-            if (
-                message["type"] == "message"
-                and message["channel"] == REGISTRY_REFRESH_CHANNEL
-            ):
-                logger.info(
-                    "Broadcasting LLM registry refresh to all WebSocket clients"
-                )
-                await manager.broadcast_to_all(
-                    method=WSMethod.NOTIFICATION,
-                    data={
-                        "type": "LLM_REGISTRY_REFRESH",
-                        "event": "registry_updated",
-                    },
-                )
-
-    await asyncio.gather(
-        execution_worker(),
-        notification_worker(),
-        registry_refresh_worker(),
-    )
+    await asyncio.gather(execution_worker(), notification_worker())
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,6 +1,7 @@
 from typing import Any
 
 from backend.blocks.llm import (
+    DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
     AIBlockBase,
@@ -9,7 +10,6 @@ from backend.blocks.llm import (
     LlmModel,
     LLMResponse,
     llm_call,
-    llm_model_schema_extra,
 )
 from backend.data.block import (
     BlockCategory,
@@ -50,10 +50,9 @@ class AIConditionBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for evaluating the condition.",
             advanced=False,
-            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
 
@@ -83,7 +82,7 @@ class AIConditionBlock(AIBlockBase):
                 "condition": "the input is an email address",
                 "yes_value": "Valid email",
                 "no_value": "Not an email",
-                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
+                "model": DEFAULT_LLM_MODEL,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,

autogpt_platform/backend/backend/blocks/llm.py

@@ -4,19 +4,17 @@ import logging
 import re
 import secrets
 from abc import ABC
-from enum import Enum
+from enum import Enum, EnumMeta
 from json import JSONDecodeError
-from typing import Any, Iterable, List, Literal, Optional
+from typing import Any, Iterable, List, Literal, NamedTuple, Optional
 
 import anthropic
 import ollama
 import openai
 from anthropic.types import ToolParam
 from groq import AsyncGroq
-from pydantic import BaseModel, GetCoreSchemaHandler, SecretStr
-from pydantic_core import CoreSchema, core_schema
+from pydantic import BaseModel, SecretStr
 
-from backend.data import llm_registry
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -24,7 +22,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.llm_registry import ModelMetadata
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -69,117 +66,114 @@ TEST_CREDENTIALS_INPUT = {
 
 
 def AICredentialsField() -> AICredentials:
-    """
-    Returns a CredentialsField for LLM providers.
-    The discriminator_mapping will be refreshed when the schema is generated
-    if it's empty, ensuring the LLM registry is loaded.
-    """
-    # Get the mapping now - it may be empty initially, but will be refreshed
-    # when the schema is generated via CredentialsMetaInput._add_json_schema_extra
-    mapping = llm_registry.get_llm_discriminator_mapping()
-
     return CredentialsField(
         description="API key for the LLM provider.",
         discriminator="model",
-        discriminator_mapping=mapping,  # May be empty initially, refreshed later
+        discriminator_mapping={
+            model.value: model.metadata.provider for model in LlmModel
+        },
     )
 
 
-def llm_model_schema_extra() -> dict[str, Any]:
-    return {"options": llm_registry.get_llm_model_schema_options()}
+class ModelMetadata(NamedTuple):
+    provider: str
+    context_window: int
+    max_output_tokens: int | None
+    display_name: str
+    provider_name: str
+    creator_name: str
+    price_tier: Literal[1, 2, 3]
 
 
-class LlmModelMeta(type):
-    """
-    Metaclass for LlmModel that enables attribute-style access to dynamic models.
-
-    This allows code like `LlmModel.GPT4O` to work by converting the attribute
-    name to a slug format:
-    - GPT4O -> gpt-4o
-    - GPT4O_MINI -> gpt-4o-mini
-    - CLAUDE_3_5_SONNET -> claude-3-5-sonnet
-    """
-
-    def __getattr__(cls, name: str):
-        # Don't intercept private/dunder attributes
-        if name.startswith("_"):
-            raise AttributeError(f"type object 'LlmModel' has no attribute '{name}'")
-
-        # Convert attribute name to slug format:
-        # 1. Lowercase: GPT4O -> gpt4o
-        # 2. Underscores to hyphens: GPT4O_MINI -> gpt4o-mini
-        # 3. Insert hyphen between letter and digit: gpt4o -> gpt-4o
-        slug = name.lower().replace("_", "-")
-        slug = re.sub(r"([a-z])(\d)", r"\1-\2", slug)
-
-        return cls(slug)
-
-    def __iter__(cls):
-        """Iterate over all models from the registry.
-
-        Yields LlmModel instances for each model in the dynamic registry.
-        Used by __get_pydantic_json_schema__ to build model metadata.
-        """
-        for model in llm_registry.iter_dynamic_models():
-            yield cls(model.slug)
+class LlmModelMeta(EnumMeta):
+    pass
 
 
-class LlmModel(str, metaclass=LlmModelMeta):
-    """
-    Dynamic LLM model type that accepts any model slug from the registry.
-
-    This is a string subclass (not an Enum) that allows any model slug value.
-    All models are managed via the LLM Registry in the database.
-
-    Usage:
-        model = LlmModel("gpt-4o")  # Direct construction
-        model = LlmModel.GPT4O      # Attribute access (converted to "gpt-4o")
-        model.value                  # Returns the slug string
-        model.provider               # Returns the provider from registry
-    """
-
-    def __new__(cls, value: str):
-        if isinstance(value, LlmModel):
-            return value
-        return str.__new__(cls, value)
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, source_type: Any, handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """
-        Tell Pydantic how to validate LlmModel.
-
-        Accepts strings and converts them to LlmModel instances.
-        """
-        return core_schema.no_info_after_validator_function(
-            cls,  # The validator function (LlmModel constructor)
-            core_schema.str_schema(),  # Accept string input
-            serialization=core_schema.to_string_ser_schema(),  # Serialize as string
-        )
-
-    @property
-    def value(self) -> str:
-        """Return the model slug (for compatibility with enum-style access)."""
-        return str(self)
-
-    @classmethod
-    def default(cls) -> "LlmModel":
-        """
-        Get the default model from the registry.
-
-        Returns the recommended model if set, otherwise gpt-4o if available
-        and enabled, otherwise the first enabled model from the registry.
-        Falls back to "gpt-4o" if registry is empty (e.g., at module import time).
-        """
-        from backend.data.llm_registry import get_default_model_slug
-
-        slug = get_default_model_slug()
-        if slug is None:
-            # Registry is empty (e.g., at module import time before DB connection).
-            # Fall back to gpt-4o for backward compatibility.
-            slug = "gpt-4o"
-        return cls(slug)
+class LlmModel(str, Enum, metaclass=LlmModelMeta):
+    # OpenAI models
+    O3_MINI = "o3-mini"
+    O3 = "o3-2025-04-16"
+    O1 = "o1"
+    O1_MINI = "o1-mini"
+    # GPT-5 models
+    GPT5_2 = "gpt-5.2-2025-12-11"
+    GPT5_1 = "gpt-5.1-2025-11-13"
+    GPT5 = "gpt-5-2025-08-07"
+    GPT5_MINI = "gpt-5-mini-2025-08-07"
+    GPT5_NANO = "gpt-5-nano-2025-08-07"
+    GPT5_CHAT = "gpt-5-chat-latest"
+    GPT41 = "gpt-4.1-2025-04-14"
+    GPT41_MINI = "gpt-4.1-mini-2025-04-14"
+    GPT4O_MINI = "gpt-4o-mini"
+    GPT4O = "gpt-4o"
+    GPT4_TURBO = "gpt-4-turbo"
+    GPT3_5_TURBO = "gpt-3.5-turbo"
+    # Anthropic models
+    CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
+    CLAUDE_4_OPUS = "claude-opus-4-20250514"
+    CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
+    CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
+    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
+    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
+    # AI/ML API models
+    AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
+    AIML_API_LLAMA3_1_70B = "nvidia/llama-3.1-nemotron-70b-instruct"
+    AIML_API_LLAMA3_3_70B = "meta-llama/Llama-3.3-70B-Instruct-Turbo"
+    AIML_API_META_LLAMA_3_1_70B = "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
+    AIML_API_LLAMA_3_2_3B = "meta-llama/Llama-3.2-3B-Instruct-Turbo"
+    # Groq models
+    LLAMA3_3_70B = "llama-3.3-70b-versatile"
+    LLAMA3_1_8B = "llama-3.1-8b-instant"
+    # Ollama models
+    OLLAMA_LLAMA3_3 = "llama3.3"
+    OLLAMA_LLAMA3_2 = "llama3.2"
+    OLLAMA_LLAMA3_8B = "llama3"
+    OLLAMA_LLAMA3_405B = "llama3.1:405b"
+    OLLAMA_DOLPHIN = "dolphin-mistral:latest"
+    # OpenRouter models
+    OPENAI_GPT_OSS_120B = "openai/gpt-oss-120b"
+    OPENAI_GPT_OSS_20B = "openai/gpt-oss-20b"
+    GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
+    GEMINI_3_PRO_PREVIEW = "google/gemini-3-pro-preview"
+    GEMINI_2_5_FLASH = "google/gemini-2.5-flash"
+    GEMINI_2_0_FLASH = "google/gemini-2.0-flash-001"
+    GEMINI_2_5_FLASH_LITE_PREVIEW = "google/gemini-2.5-flash-lite-preview-06-17"
+    GEMINI_2_0_FLASH_LITE = "google/gemini-2.0-flash-lite-001"
+    MISTRAL_NEMO = "mistralai/mistral-nemo"
+    COHERE_COMMAND_R_08_2024 = "cohere/command-r-08-2024"
+    COHERE_COMMAND_R_PLUS_08_2024 = "cohere/command-r-plus-08-2024"
+    DEEPSEEK_CHAT = "deepseek/deepseek-chat"  # Actually: DeepSeek V3
+    DEEPSEEK_R1_0528 = "deepseek/deepseek-r1-0528"
+    PERPLEXITY_SONAR = "perplexity/sonar"
+    PERPLEXITY_SONAR_PRO = "perplexity/sonar-pro"
+    PERPLEXITY_SONAR_DEEP_RESEARCH = "perplexity/sonar-deep-research"
+    NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B = "nousresearch/hermes-3-llama-3.1-405b"
+    NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B = "nousresearch/hermes-3-llama-3.1-70b"
+    AMAZON_NOVA_LITE_V1 = "amazon/nova-lite-v1"
+    AMAZON_NOVA_MICRO_V1 = "amazon/nova-micro-v1"
+    AMAZON_NOVA_PRO_V1 = "amazon/nova-pro-v1"
+    MICROSOFT_WIZARDLM_2_8X22B = "microsoft/wizardlm-2-8x22b"
+    GRYPHE_MYTHOMAX_L2_13B = "gryphe/mythomax-l2-13b"
+    META_LLAMA_4_SCOUT = "meta-llama/llama-4-scout"
+    META_LLAMA_4_MAVERICK = "meta-llama/llama-4-maverick"
+    GROK_4 = "x-ai/grok-4"
+    GROK_4_FAST = "x-ai/grok-4-fast"
+    GROK_4_1_FAST = "x-ai/grok-4.1-fast"
+    GROK_CODE_FAST_1 = "x-ai/grok-code-fast-1"
+    KIMI_K2 = "moonshotai/kimi-k2"
+    QWEN3_235B_A22B_THINKING = "qwen/qwen3-235b-a22b-thinking-2507"
+    QWEN3_CODER = "qwen/qwen3-coder"
+    # Llama API models
+    LLAMA_API_LLAMA_4_SCOUT = "Llama-4-Scout-17B-16E-Instruct-FP8"
+    LLAMA_API_LLAMA4_MAVERICK = "Llama-4-Maverick-17B-128E-Instruct-FP8"
+    LLAMA_API_LLAMA3_3_8B = "Llama-3.3-8B-Instruct"
+    LLAMA_API_LLAMA3_3_70B = "Llama-3.3-70B-Instruct"
+    # v0 by Vercel models
+    V0_1_5_MD = "v0-1.5-md"
+    V0_1_5_LG = "v0-1.5-lg"
+    V0_1_0_MD = "v0-1.0-md"
 
     @classmethod
     def __get_pydantic_json_schema__(cls, schema, handler):
@@ -187,12 +181,7 @@ class LlmModel(str, metaclass=LlmModelMeta):
         llm_model_metadata = {}
         for model in cls:
             model_name = model.value
-            # Use registry directly with None check to gracefully handle
-            # missing metadata during startup/import before registry is populated
-            metadata = llm_registry.get_llm_model_metadata(model_name)
-            if metadata is None:
-                # Skip models without metadata (registry not yet populated)
-                continue
+            metadata = model.metadata
             llm_model_metadata[model_name] = {
                 "creator": metadata.creator_name,
                 "creator_name": metadata.creator_name,
@@ -208,12 +197,7 @@ class LlmModel(str, metaclass=LlmModelMeta):
 
     @property
     def metadata(self) -> ModelMetadata:
-        metadata = llm_registry.get_llm_model_metadata(self.value)
-        if metadata:
-            return metadata
-        raise ValueError(
-            f"Missing metadata for model: {self.value}. Model not found in LLM registry."
-        )
+        return MODEL_METADATA[self]
 
     @property
     def provider(self) -> str:
@@ -228,11 +212,300 @@ class LlmModel(str, metaclass=LlmModelMeta):
         return self.metadata.max_output_tokens
 
 
-# MODEL_METADATA removed - all models now come from the database via llm_registry
+MODEL_METADATA = {
+    # https://platform.openai.com/docs/models
+    LlmModel.O3: ModelMetadata("openai", 200000, 100000, "O3", "OpenAI", "OpenAI", 2),
+    LlmModel.O3_MINI: ModelMetadata(
+        "openai", 200000, 100000, "O3 Mini", "OpenAI", "OpenAI", 1
+    ),  # o3-mini-2025-01-31
+    LlmModel.O1: ModelMetadata(
+        "openai", 200000, 100000, "O1", "OpenAI", "OpenAI", 3
+    ),  # o1-2024-12-17
+    LlmModel.O1_MINI: ModelMetadata(
+        "openai", 128000, 65536, "O1 Mini", "OpenAI", "OpenAI", 2
+    ),  # o1-mini-2024-09-12
+    # GPT-5 models
+    LlmModel.GPT5_2: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5.2", "OpenAI", "OpenAI", 3
+    ),
+    LlmModel.GPT5_1: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5.1", "OpenAI", "OpenAI", 2
+    ),
+    LlmModel.GPT5: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_MINI: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5 Mini", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_NANO: ModelMetadata(
+        "openai", 400000, 128000, "GPT-5 Nano", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT5_CHAT: ModelMetadata(
+        "openai", 400000, 16384, "GPT-5 Chat Latest", "OpenAI", "OpenAI", 2
+    ),
+    LlmModel.GPT41: ModelMetadata(
+        "openai", 1047576, 32768, "GPT-4.1", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT41_MINI: ModelMetadata(
+        "openai", 1047576, 32768, "GPT-4.1 Mini", "OpenAI", "OpenAI", 1
+    ),
+    LlmModel.GPT4O_MINI: ModelMetadata(
+        "openai", 128000, 16384, "GPT-4o Mini", "OpenAI", "OpenAI", 1
+    ),  # gpt-4o-mini-2024-07-18
+    LlmModel.GPT4O: ModelMetadata(
+        "openai", 128000, 16384, "GPT-4o", "OpenAI", "OpenAI", 2
+    ),  # gpt-4o-2024-08-06
+    LlmModel.GPT4_TURBO: ModelMetadata(
+        "openai", 128000, 4096, "GPT-4 Turbo", "OpenAI", "OpenAI", 3
+    ),  # gpt-4-turbo-2024-04-09
+    LlmModel.GPT3_5_TURBO: ModelMetadata(
+        "openai", 16385, 4096, "GPT-3.5 Turbo", "OpenAI", "OpenAI", 1
+    ),  # gpt-3.5-turbo-0125
+    # https://docs.anthropic.com/en/docs/about-claude/models
+    LlmModel.CLAUDE_4_1_OPUS: ModelMetadata(
+        "anthropic", 200000, 32000, "Claude Opus 4.1", "Anthropic", "Anthropic", 3
+    ),  # claude-opus-4-1-20250805
+    LlmModel.CLAUDE_4_OPUS: ModelMetadata(
+        "anthropic", 200000, 32000, "Claude Opus 4", "Anthropic", "Anthropic", 3
+    ),  # claude-4-opus-20250514
+    LlmModel.CLAUDE_4_SONNET: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
+    ),  # claude-4-sonnet-20250514
+    LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
+    ),  # claude-opus-4-5-20251101
+    LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude Sonnet 4.5", "Anthropic", "Anthropic", 3
+    ),  # claude-sonnet-4-5-20250929
+    LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
+    ),  # claude-haiku-4-5-20251001
+    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
+    ),  # claude-3-7-sonnet-20250219
+    LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
+        "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
+    ),  # claude-3-haiku-20240307
+    # https://docs.aimlapi.com/api-overview/model-database/text-models
+    LlmModel.AIML_API_QWEN2_5_72B: ModelMetadata(
+        "aiml_api", 32000, 8000, "Qwen 2.5 72B Instruct Turbo", "AI/ML", "Qwen", 1
+    ),
+    LlmModel.AIML_API_LLAMA3_1_70B: ModelMetadata(
+        "aiml_api",
+        128000,
+        40000,
+        "Llama 3.1 Nemotron 70B Instruct",
+        "AI/ML",
+        "Nvidia",
+        1,
+    ),
+    LlmModel.AIML_API_LLAMA3_3_70B: ModelMetadata(
+        "aiml_api", 128000, None, "Llama 3.3 70B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata(
+        "aiml_api", 131000, 2000, "Llama 3.1 70B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata(
+        "aiml_api", 128000, None, "Llama 3.2 3B Instruct Turbo", "AI/ML", "Meta", 1
+    ),
+    # https://console.groq.com/docs/models
+    LlmModel.LLAMA3_3_70B: ModelMetadata(
+        "groq", 128000, 32768, "Llama 3.3 70B Versatile", "Groq", "Meta", 1
+    ),
+    LlmModel.LLAMA3_1_8B: ModelMetadata(
+        "groq", 128000, 8192, "Llama 3.1 8B Instant", "Groq", "Meta", 1
+    ),
+    # https://ollama.com/library
+    LlmModel.OLLAMA_LLAMA3_3: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.3", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_2: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.2", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata(
+        "ollama", 8192, None, "Llama 3", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata(
+        "ollama", 8192, None, "Llama 3.1 405B", "Ollama", "Meta", 1
+    ),
+    LlmModel.OLLAMA_DOLPHIN: ModelMetadata(
+        "ollama", 32768, None, "Dolphin Mistral Latest", "Ollama", "Mistral AI", 1
+    ),
+    # https://openrouter.ai/models
+    LlmModel.GEMINI_2_5_PRO: ModelMetadata(
+        "open_router",
+        1050000,
+        8192,
+        "Gemini 2.5 Pro Preview 03.25",
+        "OpenRouter",
+        "Google",
+        2,
+    ),
+    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata(
+        "open_router", 1048576, 65535, "Gemini 3 Pro Preview", "OpenRouter", "Google", 2
+    ),
+    LlmModel.GEMINI_2_5_FLASH: ModelMetadata(
+        "open_router", 1048576, 65535, "Gemini 2.5 Flash", "OpenRouter", "Google", 1
+    ),
+    LlmModel.GEMINI_2_0_FLASH: ModelMetadata(
+        "open_router", 1048576, 8192, "Gemini 2.0 Flash 001", "OpenRouter", "Google", 1
+    ),
+    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
+        "open_router",
+        1048576,
+        65535,
+        "Gemini 2.5 Flash Lite Preview 06.17",
+        "OpenRouter",
+        "Google",
+        1,
+    ),
+    LlmModel.GEMINI_2_0_FLASH_LITE: ModelMetadata(
+        "open_router",
+        1048576,
+        8192,
+        "Gemini 2.0 Flash Lite 001",
+        "OpenRouter",
+        "Google",
+        1,
+    ),
+    LlmModel.MISTRAL_NEMO: ModelMetadata(
+        "open_router", 128000, 4096, "Mistral Nemo", "OpenRouter", "Mistral AI", 1
+    ),
+    LlmModel.COHERE_COMMAND_R_08_2024: ModelMetadata(
+        "open_router", 128000, 4096, "Command R 08.2024", "OpenRouter", "Cohere", 1
+    ),
+    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata(
+        "open_router", 128000, 4096, "Command R Plus 08.2024", "OpenRouter", "Cohere", 2
+    ),
+    LlmModel.DEEPSEEK_CHAT: ModelMetadata(
+        "open_router", 64000, 2048, "DeepSeek Chat", "OpenRouter", "DeepSeek", 1
+    ),
+    LlmModel.DEEPSEEK_R1_0528: ModelMetadata(
+        "open_router", 163840, 163840, "DeepSeek R1 0528", "OpenRouter", "DeepSeek", 1
+    ),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata(
+        "open_router", 127000, 8000, "Sonar", "OpenRouter", "Perplexity", 1
+    ),
+    LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata(
+        "open_router", 200000, 8000, "Sonar Pro", "OpenRouter", "Perplexity", 2
+    ),
+    LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
+        "open_router",
+        128000,
+        16000,
+        "Sonar Deep Research",
+        "OpenRouter",
+        "Perplexity",
+        3,
+    ),
+    LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
+        "open_router",
+        131000,
+        4096,
+        "Hermes 3 Llama 3.1 405B",
+        "OpenRouter",
+        "Nous Research",
+        1,
+    ),
+    LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: ModelMetadata(
+        "open_router",
+        12288,
+        12288,
+        "Hermes 3 Llama 3.1 70B",
+        "OpenRouter",
+        "Nous Research",
+        1,
+    ),
+    LlmModel.OPENAI_GPT_OSS_120B: ModelMetadata(
+        "open_router", 131072, 131072, "GPT-OSS 120B", "OpenRouter", "OpenAI", 1
+    ),
+    LlmModel.OPENAI_GPT_OSS_20B: ModelMetadata(
+        "open_router", 131072, 32768, "GPT-OSS 20B", "OpenRouter", "OpenAI", 1
+    ),
+    LlmModel.AMAZON_NOVA_LITE_V1: ModelMetadata(
+        "open_router", 300000, 5120, "Nova Lite V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.AMAZON_NOVA_MICRO_V1: ModelMetadata(
+        "open_router", 128000, 5120, "Nova Micro V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.AMAZON_NOVA_PRO_V1: ModelMetadata(
+        "open_router", 300000, 5120, "Nova Pro V1", "OpenRouter", "Amazon", 1
+    ),
+    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: ModelMetadata(
+        "open_router", 65536, 4096, "WizardLM 2 8x22B", "OpenRouter", "Microsoft", 1
+    ),
+    LlmModel.GRYPHE_MYTHOMAX_L2_13B: ModelMetadata(
+        "open_router", 4096, 4096, "MythoMax L2 13B", "OpenRouter", "Gryphe", 1
+    ),
+    LlmModel.META_LLAMA_4_SCOUT: ModelMetadata(
+        "open_router", 131072, 131072, "Llama 4 Scout", "OpenRouter", "Meta", 1
+    ),
+    LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata(
+        "open_router", 1048576, 1000000, "Llama 4 Maverick", "OpenRouter", "Meta", 1
+    ),
+    LlmModel.GROK_4: ModelMetadata(
+        "open_router", 256000, 256000, "Grok 4", "OpenRouter", "xAI", 3
+    ),
+    LlmModel.GROK_4_FAST: ModelMetadata(
+        "open_router", 2000000, 30000, "Grok 4 Fast", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.GROK_4_1_FAST: ModelMetadata(
+        "open_router", 2000000, 30000, "Grok 4.1 Fast", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.GROK_CODE_FAST_1: ModelMetadata(
+        "open_router", 256000, 10000, "Grok Code Fast 1", "OpenRouter", "xAI", 1
+    ),
+    LlmModel.KIMI_K2: ModelMetadata(
+        "open_router", 131000, 131000, "Kimi K2", "OpenRouter", "Moonshot AI", 1
+    ),
+    LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata(
+        "open_router",
+        262144,
+        262144,
+        "Qwen 3 235B A22B Thinking 2507",
+        "OpenRouter",
+        "Qwen",
+        1,
+    ),
+    LlmModel.QWEN3_CODER: ModelMetadata(
+        "open_router", 262144, 262144, "Qwen 3 Coder", "OpenRouter", "Qwen", 3
+    ),
+    # Llama API models
+    LlmModel.LLAMA_API_LLAMA_4_SCOUT: ModelMetadata(
+        "llama_api",
+        128000,
+        4028,
+        "Llama 4 Scout 17B 16E Instruct FP8",
+        "Llama API",
+        "Meta",
+        1,
+    ),
+    LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata(
+        "llama_api",
+        128000,
+        4028,
+        "Llama 4 Maverick 17B 128E Instruct FP8",
+        "Llama API",
+        "Meta",
+        1,
+    ),
+    LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata(
+        "llama_api", 128000, 4028, "Llama 3.3 8B Instruct", "Llama API", "Meta", 1
+    ),
+    LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata(
+        "llama_api", 128000, 4028, "Llama 3.3 70B Instruct", "Llama API", "Meta", 1
+    ),
+    # v0 by Vercel models
+    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000, "v0 1.5 MD", "V0", "V0", 1),
+    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000, "v0 1.5 LG", "V0", "V0", 1),
+    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000, "v0 1.0 MD", "V0", "V0", 1),
+}
 
-# Default model constant for backward compatibility
-# Uses the dynamic registry to get the default model
-DEFAULT_LLM_MODEL = LlmModel.default()
+DEFAULT_LLM_MODEL = LlmModel.GPT5_2
+
+for model in LlmModel:
+    if model not in MODEL_METADATA:
+        raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
 
 
 class ToolCall(BaseModel):
@@ -361,98 +634,19 @@ async def llm_call(
             - prompt_tokens: The number of tokens used in the prompt.
             - completion_tokens: The number of tokens used in the completion.
     """
-    # Get model metadata and check if enabled - with fallback support
-    # The model we'll actually use (may differ if original is disabled)
-    model_to_use = llm_model.value
-
-    # Check if model is in registry and if it's enabled
-    from backend.data.llm_registry import (
-        get_fallback_model_for_disabled,
-        get_model_info,
-    )
-
-    model_info = get_model_info(llm_model.value)
-
-    if model_info and not model_info.is_enabled:
-        # Model is disabled - try to find a fallback from the same provider
-        fallback = get_fallback_model_for_disabled(llm_model.value)
-        if fallback:
-            logger.warning(
-                f"Model '{llm_model.value}' is disabled. Using fallback model '{fallback.slug}' from the same provider ({fallback.metadata.provider})."
-            )
-            model_to_use = fallback.slug
-            # Use fallback model's metadata
-            provider = fallback.metadata.provider
-            context_window = fallback.metadata.context_window
-            model_max_output = fallback.metadata.max_output_tokens or int(2**15)
-        else:
-            # No fallback available - raise error
-            raise ValueError(
-                f"LLM model '{llm_model.value}' is disabled and no fallback model "
-                f"from the same provider is available. Please enable the model or "
-                f"select a different model in the block configuration."
-            )
-    else:
-        # Model is enabled or not in registry (legacy/static model)
-        try:
-            provider = llm_model.metadata.provider
-            context_window = llm_model.context_window
-            model_max_output = llm_model.max_output_tokens or int(2**15)
-        except ValueError:
-            # Model not in cache - try refreshing the registry once if we have DB access
-            logger.warning(f"Model {llm_model.value} not found in registry cache")
-
-            # Try refreshing the registry if we have database access
-            from backend.data.db import is_connected
-
-            if is_connected():
-                try:
-                    logger.info(
-                        f"Refreshing LLM registry and retrying lookup for {llm_model.value}"
-                    )
-                    await llm_registry.refresh_llm_registry()
-                    # Try again after refresh
-                    try:
-                        provider = llm_model.metadata.provider
-                        context_window = llm_model.context_window
-                        model_max_output = llm_model.max_output_tokens or int(2**15)
-                        logger.info(
-                            f"Successfully loaded model {llm_model.value} metadata after registry refresh"
-                        )
-                    except ValueError:
-                        # Still not found after refresh
-                        raise ValueError(
-                            f"LLM model '{llm_model.value}' not found in registry after refresh. "
-                            "Please ensure the model is added and enabled in the LLM registry via the admin UI."
-                        )
-                except Exception as refresh_exc:
-                    logger.error(f"Failed to refresh LLM registry: {refresh_exc}")
-                    raise ValueError(
-                        f"LLM model '{llm_model.value}' not found in registry and failed to refresh. "
-                        "Please ensure the model is added to the LLM registry via the admin UI."
-                    ) from refresh_exc
-            else:
-                # No DB access (e.g., in executor without direct DB connection)
-                # The registry should have been loaded on startup
-                raise ValueError(
-                    f"LLM model '{llm_model.value}' not found in registry cache. "
-                    "The registry may need to be refreshed. Please contact support or try again later."
-                )
-
-    # Create effective model for model-specific parameter resolution (e.g., o-series check)
-    # This uses the resolved model_to_use which may differ from llm_model if fallback occurred
-    effective_model = LlmModel(model_to_use)
+    provider = llm_model.metadata.provider
+    context_window = llm_model.context_window
 
     if compress_prompt_to_fit:
         prompt = compress_prompt(
             messages=prompt,
-            target_tokens=context_window // 2,
+            target_tokens=llm_model.context_window // 2,
             lossy_ok=True,
         )
 
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)
-    # model_max_output already set above
+    model_max_output = llm_model.max_output_tokens or int(2**15)
     user_max = max_tokens or model_max_output
     available_tokens = max(context_window - estimated_input_tokens, 0)
     max_tokens = max(min(available_tokens, model_max_output, user_max), 1)
@@ -463,14 +657,14 @@ async def llm_call(
         response_format = None
 
         parallel_tool_calls = get_parallel_tool_calls_param(
-            effective_model, parallel_tool_calls
+            llm_model, parallel_tool_calls
         )
 
         if force_json_output:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
-            model=model_to_use,
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_completion_tokens=max_tokens,
@@ -517,7 +711,7 @@ async def llm_call(
         )
         try:
             resp = await client.messages.create(
-                model=model_to_use,
+                model=llm_model.value,
                 system=sysprompt,
                 messages=messages,
                 max_tokens=max_tokens,
@@ -581,7 +775,7 @@ async def llm_call(
         client = AsyncGroq(api_key=credentials.api_key.get_secret_value())
         response_format = {"type": "json_object"} if force_json_output else None
         response = await client.chat.completions.create(
-            model=model_to_use,
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_tokens=max_tokens,
@@ -603,7 +797,7 @@ async def llm_call(
         sys_messages = [p["content"] for p in prompt if p["role"] == "system"]
         usr_messages = [p["content"] for p in prompt if p["role"] != "system"]
         response = await client.generate(
-            model=model_to_use,
+            model=llm_model.value,
             prompt=f"{sys_messages}\n\n{usr_messages}",
             stream=False,
             options={"num_ctx": max_tokens},
@@ -625,7 +819,7 @@ async def llm_call(
         )
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            effective_model, parallel_tool_calls
+            llm_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
@@ -633,7 +827,7 @@ async def llm_call(
                 "HTTP-Referer": "https://agpt.co",
                 "X-Title": "AutoGPT",
             },
-            model=model_to_use,
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
@@ -667,7 +861,7 @@ async def llm_call(
         )
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            effective_model, parallel_tool_calls
+            llm_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
@@ -675,7 +869,7 @@ async def llm_call(
                 "HTTP-Referer": "https://agpt.co",
                 "X-Title": "AutoGPT",
             },
-            model=model_to_use,
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
@@ -702,7 +896,7 @@ async def llm_call(
             reasoning=reasoning,
         )
     elif provider == "aiml_api":
-        client = openai.AsyncOpenAI(
+        client = openai.OpenAI(
             base_url="https://api.aimlapi.com/v2",
             api_key=credentials.api_key.get_secret_value(),
             default_headers={
@@ -712,8 +906,8 @@ async def llm_call(
             },
         )
 
-        completion = await client.chat.completions.create(
-            model=model_to_use,
+        completion = client.chat.completions.create(
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
         )
@@ -741,11 +935,11 @@ async def llm_call(
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            effective_model, parallel_tool_calls
+            llm_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
-            model=model_to_use,
+            model=llm_model.value,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_tokens=max_tokens,
@@ -796,10 +990,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for answering the prompt.",
             advanced=False,
-            json_schema_extra=llm_model_schema_extra(),
         )
         force_json_output: bool = SchemaField(
             title="Restrict LLM to pure JSON output",
@@ -862,7 +1055,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
             test_input={
-                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
+                "model": DEFAULT_LLM_MODEL,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "expected_format": {
                     "key1": "value1",
@@ -1228,10 +1421,9 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for answering the prompt.",
             advanced=False,
-            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
         sys_prompt: str = SchemaField(
@@ -1325,9 +1517,8 @@ class AITextSummarizerBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for summarizing the text.",
-            json_schema_extra=llm_model_schema_extra(),
         )
         focus: str = SchemaField(
             title="Focus",
@@ -1543,9 +1734,8 @@ class AIConversationBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for the conversation.",
-            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
         max_tokens: int | None = SchemaField(
@@ -1582,7 +1772,7 @@ class AIConversationBlock(AIBlockBase):
                     },
                     {"role": "user", "content": "Where was it played?"},
                 ],
-                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
+                "model": DEFAULT_LLM_MODEL,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
@@ -1645,10 +1835,9 @@ class AIListGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=LlmModel.default,
+            default=DEFAULT_LLM_MODEL,
             description="The language model to use for generating the list.",
             advanced=True,
-            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
         max_retries: int = SchemaField(
@@ -1703,7 +1892,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
                     "fictional worlds."
                 ),
-                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
+                "model": DEFAULT_LLM_MODEL,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
                 "force_json_output": False,

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -226,10 +226,9 @@ class SmartDecisionMakerBlock(Block):
         )
         model: llm.LlmModel = SchemaField(
             title="LLM Model",
-            default_factory=llm.LlmModel.default,
+            default=llm.DEFAULT_LLM_MODEL,
             description="The language model to use for answering the prompt.",
             advanced=False,
-            json_schema_extra=llm.llm_model_schema_extra(),
         )
         credentials: llm.AICredentials = llm.AICredentialsField()
         multiple_tool_calls: bool = SchemaField(

autogpt_platform/backend/backend/blocks/stagehand/blocks.py

@@ -10,13 +10,13 @@ import stagehand.main
 from stagehand import Stagehand
 
 from backend.blocks.llm import (
+    MODEL_METADATA,
     AICredentials,
     AICredentialsField,
     LlmModel,
     ModelMetadata,
 )
 from backend.blocks.stagehand._config import stagehand as stagehand_provider
-from backend.data import llm_registry
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -91,7 +91,7 @@ class StagehandRecommendedLlmModel(str, Enum):
         Returns the provider name for the model in the required format for Stagehand:
         provider/model_name
         """
-        model_metadata = self.metadata
+        model_metadata = MODEL_METADATA[LlmModel(self.value)]
         model_name = self.value
 
         if len(model_name.split("/")) == 1 and not self.value.startswith(
@@ -107,23 +107,19 @@ class StagehandRecommendedLlmModel(str, Enum):
 
     @property
     def provider(self) -> str:
-        return self.metadata.provider
+        return MODEL_METADATA[LlmModel(self.value)].provider
 
     @property
     def metadata(self) -> ModelMetadata:
-        metadata = llm_registry.get_llm_model_metadata(self.value)
-        if metadata:
-            return metadata
-        # Fallback to LlmModel enum if registry lookup fails
-        return LlmModel(self.value).metadata
+        return MODEL_METADATA[LlmModel(self.value)]
 
     @property
     def context_window(self) -> int:
-        return self.metadata.context_window
+        return MODEL_METADATA[LlmModel(self.value)].context_window
 
     @property
     def max_output_tokens(self) -> int | None:
-        return self.metadata.max_output_tokens
+        return MODEL_METADATA[LlmModel(self.value)].max_output_tokens
 
 
 class StagehandObserveBlock(Block):

autogpt_platform/backend/backend/data/block.py

@@ -25,7 +25,6 @@ from prisma.models import AgentBlock
 from prisma.types import AgentBlockCreateInput
 from pydantic import BaseModel
 
-from backend.data.llm_registry import update_schema_with_llm_registry
 from backend.data.model import NodeExecutionStats
 from backend.integrations.providers import ProviderName
 from backend.util import json
@@ -144,59 +143,35 @@ class BlockInfo(BaseModel):
 
 
 class BlockSchema(BaseModel):
-    cached_jsonschema: ClassVar[dict[str, Any] | None] = None
-
-    @classmethod
-    def clear_schema_cache(cls) -> None:
-        """Clear the cached JSON schema for this class."""
-        # Use None instead of {} because {} is truthy and would prevent regeneration
-        cls.cached_jsonschema = None  # type: ignore
-
-    @staticmethod
-    def clear_all_schema_caches() -> None:
-        """Clear cached JSON schemas for all BlockSchema subclasses."""
-
-        def clear_recursive(cls: type) -> None:
-            """Recursively clear cache for class and all subclasses."""
-            if hasattr(cls, "clear_schema_cache"):
-                cls.clear_schema_cache()
-            for subclass in cls.__subclasses__():
-                clear_recursive(subclass)
-
-        clear_recursive(BlockSchema)
+    cached_jsonschema: ClassVar[dict[str, Any]]
 
     @classmethod
     def jsonschema(cls) -> dict[str, Any]:
-        # Generate schema if not cached
-        if not cls.cached_jsonschema:
-            model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
+        if cls.cached_jsonschema:
+            return cls.cached_jsonschema
 
-            def ref_to_dict(obj):
-                if isinstance(obj, dict):
-                    # OpenAPI <3.1 does not support sibling fields that has a $ref key
-                    # So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
-                    keys = {"allOf", "anyOf", "oneOf"}
-                    one_key = next(
-                        (k for k in keys if k in obj and len(obj[k]) == 1), None
-                    )
-                    if one_key:
-                        obj.update(obj[one_key][0])
+        model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
 
-                    return {
-                        key: ref_to_dict(value)
-                        for key, value in obj.items()
-                        if not key.startswith("$") and key != one_key
-                    }
-                elif isinstance(obj, list):
-                    return [ref_to_dict(item) for item in obj]
+        def ref_to_dict(obj):
+            if isinstance(obj, dict):
+                # OpenAPI <3.1 does not support sibling fields that has a $ref key
+                # So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
+                keys = {"allOf", "anyOf", "oneOf"}
+                one_key = next((k for k in keys if k in obj and len(obj[k]) == 1), None)
+                if one_key:
+                    obj.update(obj[one_key][0])
 
-                return obj
+                return {
+                    key: ref_to_dict(value)
+                    for key, value in obj.items()
+                    if not key.startswith("$") and key != one_key
+                }
+            elif isinstance(obj, list):
+                return [ref_to_dict(item) for item in obj]
 
-            cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
+            return obj
 
-        # Always post-process to ensure LLM registry data is up-to-date
-        # This refreshes model options and discriminator mappings even if schema was cached
-        update_schema_with_llm_registry(cls.cached_jsonschema, cls)
+        cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
 
         return cls.cached_jsonschema
 
@@ -259,7 +234,7 @@ class BlockSchema(BaseModel):
         super().__pydantic_init_subclass__(**kwargs)
 
         # Reset cached JSON schema to prevent inheriting it from parent class
-        cls.cached_jsonschema = None
+        cls.cached_jsonschema = {}
 
         credentials_fields = cls.get_credentials_fields()
 
@@ -896,28 +871,6 @@ def is_block_auth_configured(
 
 
 async def initialize_blocks() -> None:
-    # Refresh LLM registry before initializing blocks so blocks can use registry data
-    # This ensures the registry cache is populated even in executor context
-    try:
-        from backend.data import llm_registry
-        from backend.data.block_cost_config import refresh_llm_costs
-
-        # Only refresh if we have DB access (check if Prisma is connected)
-        from backend.data.db import is_connected
-
-        if is_connected():
-            await llm_registry.refresh_llm_registry()
-            refresh_llm_costs()
-            logger.info("LLM registry refreshed during block initialization")
-        else:
-            logger.warning(
-                "Prisma not connected, skipping LLM registry refresh during block initialization"
-            )
-    except Exception as exc:
-        logger.warning(
-            "Failed to refresh LLM registry during block initialization: %s", exc
-        )
-
     # First, sync all provider costs to blocks
     # Imported here to avoid circular import
     from backend.sdk.cost_integration import sync_all_provider_costs

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -1,4 +1,3 @@
-import logging
 from typing import Type
 
 from backend.blocks.ai_image_customizer import AIImageCustomizerBlock, GeminiImageModel
@@ -24,18 +23,19 @@ from backend.blocks.ideogram import IdeogramModelBlock
 from backend.blocks.jina.embeddings import JinaEmbeddingBlock
 from backend.blocks.jina.search import ExtractWebsiteContentBlock, SearchTheWebBlock
 from backend.blocks.llm import (
+    MODEL_METADATA,
     AIConversationBlock,
     AIListGeneratorBlock,
     AIStructuredResponseGeneratorBlock,
     AITextGeneratorBlock,
     AITextSummarizerBlock,
+    LlmModel,
 )
 from backend.blocks.replicate.flux_advanced import ReplicateFluxAdvancedModelBlock
 from backend.blocks.replicate.replicate_block import ReplicateModelBlock
 from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
-from backend.data import llm_registry
 from backend.data.block import Block, BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
@@ -55,63 +55,210 @@ from backend.integrations.credentials_store import (
     v0_credentials,
 )
 
-logger = logging.getLogger(__name__)
+# =============== Configure the cost for each LLM Model call =============== #
 
-PROVIDER_CREDENTIALS = {
-    "openai": openai_credentials,
-    "anthropic": anthropic_credentials,
-    "groq": groq_credentials,
-    "open_router": open_router_credentials,
-    "llama_api": llama_api_credentials,
-    "aiml_api": aiml_api_credentials,
-    "v0": v0_credentials,
+MODEL_COST: dict[LlmModel, int] = {
+    LlmModel.O3: 4,
+    LlmModel.O3_MINI: 2,
+    LlmModel.O1: 16,
+    LlmModel.O1_MINI: 4,
+    # GPT-5 models
+    LlmModel.GPT5_2: 6,
+    LlmModel.GPT5_1: 5,
+    LlmModel.GPT5: 2,
+    LlmModel.GPT5_MINI: 1,
+    LlmModel.GPT5_NANO: 1,
+    LlmModel.GPT5_CHAT: 5,
+    LlmModel.GPT41: 2,
+    LlmModel.GPT41_MINI: 1,
+    LlmModel.GPT4O_MINI: 1,
+    LlmModel.GPT4O: 3,
+    LlmModel.GPT4_TURBO: 10,
+    LlmModel.GPT3_5_TURBO: 1,
+    LlmModel.CLAUDE_4_1_OPUS: 21,
+    LlmModel.CLAUDE_4_OPUS: 21,
+    LlmModel.CLAUDE_4_SONNET: 5,
+    LlmModel.CLAUDE_4_5_HAIKU: 4,
+    LlmModel.CLAUDE_4_5_OPUS: 14,
+    LlmModel.CLAUDE_4_5_SONNET: 9,
+    LlmModel.CLAUDE_3_7_SONNET: 5,
+    LlmModel.CLAUDE_3_HAIKU: 1,
+    LlmModel.AIML_API_QWEN2_5_72B: 1,
+    LlmModel.AIML_API_LLAMA3_1_70B: 1,
+    LlmModel.AIML_API_LLAMA3_3_70B: 1,
+    LlmModel.AIML_API_META_LLAMA_3_1_70B: 1,
+    LlmModel.AIML_API_LLAMA_3_2_3B: 1,
+    LlmModel.LLAMA3_3_70B: 1,
+    LlmModel.LLAMA3_1_8B: 1,
+    LlmModel.OLLAMA_LLAMA3_3: 1,
+    LlmModel.OLLAMA_LLAMA3_2: 1,
+    LlmModel.OLLAMA_LLAMA3_8B: 1,
+    LlmModel.OLLAMA_LLAMA3_405B: 1,
+    LlmModel.OLLAMA_DOLPHIN: 1,
+    LlmModel.OPENAI_GPT_OSS_120B: 1,
+    LlmModel.OPENAI_GPT_OSS_20B: 1,
+    LlmModel.GEMINI_2_5_PRO: 4,
+    LlmModel.GEMINI_3_PRO_PREVIEW: 5,
+    LlmModel.GEMINI_2_5_FLASH: 1,
+    LlmModel.GEMINI_2_0_FLASH: 1,
+    LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
+    LlmModel.GEMINI_2_0_FLASH_LITE: 1,
+    LlmModel.MISTRAL_NEMO: 1,
+    LlmModel.COHERE_COMMAND_R_08_2024: 1,
+    LlmModel.COHERE_COMMAND_R_PLUS_08_2024: 3,
+    LlmModel.DEEPSEEK_CHAT: 2,
+    LlmModel.DEEPSEEK_R1_0528: 1,
+    LlmModel.PERPLEXITY_SONAR: 1,
+    LlmModel.PERPLEXITY_SONAR_PRO: 5,
+    LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: 10,
+    LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: 1,
+    LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_70B: 1,
+    LlmModel.AMAZON_NOVA_LITE_V1: 1,
+    LlmModel.AMAZON_NOVA_MICRO_V1: 1,
+    LlmModel.AMAZON_NOVA_PRO_V1: 1,
+    LlmModel.MICROSOFT_WIZARDLM_2_8X22B: 1,
+    LlmModel.GRYPHE_MYTHOMAX_L2_13B: 1,
+    LlmModel.META_LLAMA_4_SCOUT: 1,
+    LlmModel.META_LLAMA_4_MAVERICK: 1,
+    LlmModel.LLAMA_API_LLAMA_4_SCOUT: 1,
+    LlmModel.LLAMA_API_LLAMA4_MAVERICK: 1,
+    LlmModel.LLAMA_API_LLAMA3_3_8B: 1,
+    LlmModel.LLAMA_API_LLAMA3_3_70B: 1,
+    LlmModel.GROK_4: 9,
+    LlmModel.GROK_4_FAST: 1,
+    LlmModel.GROK_4_1_FAST: 1,
+    LlmModel.GROK_CODE_FAST_1: 1,
+    LlmModel.KIMI_K2: 1,
+    LlmModel.QWEN3_235B_A22B_THINKING: 1,
+    LlmModel.QWEN3_CODER: 9,
+    # v0 by Vercel models
+    LlmModel.V0_1_5_MD: 1,
+    LlmModel.V0_1_5_LG: 2,
+    LlmModel.V0_1_0_MD: 1,
 }
 
-# =============== Configure the cost for each LLM Model call =============== #
-# All LLM costs now come from the database via llm_registry
-
-LLM_COST: list[BlockCost] = []
+for model in LlmModel:
+    if model not in MODEL_COST:
+        raise ValueError(f"Missing MODEL_COST for model: {model}")
 
 
-def _build_llm_costs_from_registry() -> list[BlockCost]:
-    """Build BlockCost list from all models in the LLM registry."""
-    costs: list[BlockCost] = []
-    for model in llm_registry.iter_dynamic_models():
-        for cost in model.costs:
-            credentials = PROVIDER_CREDENTIALS.get(cost.credential_provider)
-            if not credentials:
-                logger.warning(
-                    "Skipping cost entry for %s due to unknown credentials provider %s",
-                    model.slug,
-                    cost.credential_provider,
-                )
-                continue
-            cost_filter = {
-                "model": model.slug,
+LLM_COST = (
+    # Anthropic Models
+    [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
                 "credentials": {
-                    "id": credentials.id,
-                    "provider": credentials.provider,
-                    "type": credentials.type,
+                    "id": anthropic_credentials.id,
+                    "provider": anthropic_credentials.provider,
+                    "type": anthropic_credentials.type,
                 },
-            }
-            costs.append(
-                BlockCost(
-                    cost_type=BlockCostType.RUN,
-                    cost_filter=cost_filter,
-                    cost_amount=cost.credit_cost,
-                )
-            )
-    return costs
-
-
-def refresh_llm_costs() -> None:
-    """Refresh LLM costs from the registry. All costs now come from the database."""
-    LLM_COST.clear()
-    LLM_COST.extend(_build_llm_costs_from_registry())
-
-
-# Initial load will happen after registry is refreshed at startup
-# Don't call refresh_llm_costs() here - it will be called after registry refresh
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "anthropic"
+    ]
+    # OpenAI Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": openai_credentials.id,
+                    "provider": openai_credentials.provider,
+                    "type": openai_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "openai"
+    ]
+    # Groq Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {"id": groq_credentials.id},
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "groq"
+    ]
+    # Open Router Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": open_router_credentials.id,
+                    "provider": open_router_credentials.provider,
+                    "type": open_router_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "open_router"
+    ]
+    # Llama API Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": llama_api_credentials.id,
+                    "provider": llama_api_credentials.provider,
+                    "type": llama_api_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "llama_api"
+    ]
+    # v0 by Vercel Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": v0_credentials.id,
+                    "provider": v0_credentials.provider,
+                    "type": v0_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "v0"
+    ]
+    # AI/ML Api Models
+    + [
+        BlockCost(
+            cost_type=BlockCostType.RUN,
+            cost_filter={
+                "model": model,
+                "credentials": {
+                    "id": aiml_api_credentials.id,
+                    "provider": aiml_api_credentials.provider,
+                    "type": aiml_api_credentials.type,
+                },
+            },
+            cost_amount=cost,
+        )
+        for model, cost in MODEL_COST.items()
+        if MODEL_METADATA[model].provider == "aiml_api"
+    ]
+)
 
 # =============== This is the exhaustive list of cost for each Block =============== #
 

autogpt_platform/backend/backend/data/graph.py

@@ -1511,10 +1511,8 @@ async def migrate_llm_models(migrate_to: LlmModel):
             if field.annotation == LlmModel:
                 llm_model_fields[block.id] = field_name
 
-    # Get all model slugs from the registry (dynamic, not hardcoded enum)
-    from backend.data import llm_registry
-
-    enum_values = list(llm_registry.get_all_model_slugs_for_validation())
+    # Convert enum values to a list of strings for the SQL query
+    enum_values = [v.value for v in LlmModel]
     escaped_enum_values = repr(tuple(enum_values))  # hack but works
 
     # Update each block

autogpt_platform/backend/backend/data/llm_registry/__init__.py

@@ -1,72 +0,0 @@
-"""
-LLM Registry module for managing LLM models, providers, and costs dynamically.
-
-This module provides a database-driven registry system for LLM models,
-replacing hardcoded model configurations with a flexible admin-managed system.
-"""
-
-from backend.data.llm_registry.model import ModelMetadata
-
-# Re-export for backwards compatibility
-from backend.data.llm_registry.notifications import (
-    REGISTRY_REFRESH_CHANNEL,
-    publish_registry_refresh_notification,
-    subscribe_to_registry_refresh,
-)
-from backend.data.llm_registry.registry import (
-    RegistryModel,
-    RegistryModelCost,
-    RegistryModelCreator,
-    get_all_model_slugs_for_validation,
-    get_default_model_slug,
-    get_dynamic_model_slugs,
-    get_fallback_model_for_disabled,
-    get_llm_discriminator_mapping,
-    get_llm_model_cost,
-    get_llm_model_metadata,
-    get_llm_model_schema_options,
-    get_model_info,
-    is_model_enabled,
-    iter_dynamic_models,
-    refresh_llm_registry,
-    register_static_costs,
-    register_static_metadata,
-)
-from backend.data.llm_registry.schema_utils import (
-    is_llm_model_field,
-    refresh_llm_discriminator_mapping,
-    refresh_llm_model_options,
-    update_schema_with_llm_registry,
-)
-
-__all__ = [
-    # Types
-    "ModelMetadata",
-    "RegistryModel",
-    "RegistryModelCost",
-    "RegistryModelCreator",
-    # Registry functions
-    "get_all_model_slugs_for_validation",
-    "get_default_model_slug",
-    "get_dynamic_model_slugs",
-    "get_fallback_model_for_disabled",
-    "get_llm_discriminator_mapping",
-    "get_llm_model_cost",
-    "get_llm_model_metadata",
-    "get_llm_model_schema_options",
-    "get_model_info",
-    "is_model_enabled",
-    "iter_dynamic_models",
-    "refresh_llm_registry",
-    "register_static_costs",
-    "register_static_metadata",
-    # Notifications
-    "REGISTRY_REFRESH_CHANNEL",
-    "publish_registry_refresh_notification",
-    "subscribe_to_registry_refresh",
-    # Schema utilities
-    "is_llm_model_field",
-    "refresh_llm_discriminator_mapping",
-    "refresh_llm_model_options",
-    "update_schema_with_llm_registry",
-]

autogpt_platform/backend/backend/data/llm_registry/model.py

@@ -1,25 +0,0 @@
-"""Type definitions for LLM model metadata."""
-
-from typing import Literal, NamedTuple
-
-
-class ModelMetadata(NamedTuple):
-    """Metadata for an LLM model.
-
-    Attributes:
-        provider: The provider identifier (e.g., "openai", "anthropic")
-        context_window: Maximum context window size in tokens
-        max_output_tokens: Maximum output tokens (None if unlimited)
-        display_name: Human-readable name for the model
-        provider_name: Human-readable provider name (e.g., "OpenAI", "Anthropic")
-        creator_name: Name of the organization that created the model
-        price_tier: Relative cost tier (1=cheapest, 2=medium, 3=expensive)
-    """
-
-    provider: str
-    context_window: int
-    max_output_tokens: int | None
-    display_name: str
-    provider_name: str
-    creator_name: str
-    price_tier: Literal[1, 2, 3]

autogpt_platform/backend/backend/data/llm_registry/notifications.py

@@ -1,89 +0,0 @@
-"""
-Redis pub/sub notifications for LLM registry updates.
-
-When models are added/updated/removed via the admin UI, this module
-publishes notifications to Redis that all executor services subscribe to,
-ensuring they refresh their registry cache in real-time.
-"""
-
-import asyncio
-import logging
-from typing import Any
-
-from backend.data.redis_client import connect_async
-
-logger = logging.getLogger(__name__)
-
-# Redis channel name for LLM registry refresh notifications
-REGISTRY_REFRESH_CHANNEL = "llm_registry:refresh"
-
-
-async def publish_registry_refresh_notification() -> None:
-    """
-    Publish a notification to Redis that the LLM registry has been updated.
-    All executor services subscribed to this channel will refresh their registry.
-    """
-    try:
-        redis = await connect_async()
-        await redis.publish(REGISTRY_REFRESH_CHANNEL, "refresh")
-        logger.info("Published LLM registry refresh notification to Redis")
-    except Exception as exc:
-        logger.warning(
-            "Failed to publish LLM registry refresh notification: %s",
-            exc,
-            exc_info=True,
-        )
-
-
-async def subscribe_to_registry_refresh(
-    on_refresh: Any,  # Async callable that takes no args
-) -> None:
-    """
-    Subscribe to Redis notifications for LLM registry updates.
-    This runs in a loop and processes messages as they arrive.
-
-    Args:
-        on_refresh: Async callable to execute when a refresh notification is received
-    """
-    try:
-        redis = await connect_async()
-        pubsub = redis.pubsub()
-        await pubsub.subscribe(REGISTRY_REFRESH_CHANNEL)
-        logger.info(
-            "Subscribed to LLM registry refresh notifications on channel: %s",
-            REGISTRY_REFRESH_CHANNEL,
-        )
-
-        # Process messages in a loop
-        while True:
-            try:
-                message = await pubsub.get_message(
-                    ignore_subscribe_messages=True, timeout=1.0
-                )
-                if (
-                    message
-                    and message["type"] == "message"
-                    and message["channel"] == REGISTRY_REFRESH_CHANNEL
-                ):
-                    logger.info("Received LLM registry refresh notification")
-                    try:
-                        await on_refresh()
-                    except Exception as exc:
-                        logger.error(
-                            "Error refreshing LLM registry from notification: %s",
-                            exc,
-                            exc_info=True,
-                        )
-            except Exception as exc:
-                logger.warning(
-                    "Error processing registry refresh message: %s", exc, exc_info=True
-                )
-                # Continue listening even if one message fails
-                await asyncio.sleep(1)
-    except Exception as exc:
-        logger.error(
-            "Failed to subscribe to LLM registry refresh notifications: %s",
-            exc,
-            exc_info=True,
-        )
-        raise

autogpt_platform/backend/backend/data/llm_registry/registry.py

@@ -1,388 +0,0 @@
-"""Core LLM registry implementation for managing models dynamically."""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-from dataclasses import dataclass, field
-from typing import Any, Iterable
-
-import prisma.models
-
-from backend.data.llm_registry.model import ModelMetadata
-
-logger = logging.getLogger(__name__)
-
-
-def _json_to_dict(value: Any) -> dict[str, Any]:
-    """Convert Prisma Json type to dict, with fallback to empty dict."""
-    if value is None:
-        return {}
-    if isinstance(value, dict):
-        return value
-    # Prisma Json type should always be a dict at runtime
-    return dict(value) if value else {}
-
-
-@dataclass(frozen=True)
-class RegistryModelCost:
-    """Cost configuration for an LLM model."""
-
-    credit_cost: int
-    credential_provider: str
-    credential_id: str | None
-    credential_type: str | None
-    currency: str | None
-    metadata: dict[str, Any]
-
-
-@dataclass(frozen=True)
-class RegistryModelCreator:
-    """Creator information for an LLM model."""
-
-    id: str
-    name: str
-    display_name: str
-    description: str | None
-    website_url: str | None
-    logo_url: str | None
-
-
-@dataclass(frozen=True)
-class RegistryModel:
-    """Represents a model in the LLM registry."""
-
-    slug: str
-    display_name: str
-    description: str | None
-    metadata: ModelMetadata
-    capabilities: dict[str, Any]
-    extra_metadata: dict[str, Any]
-    provider_display_name: str
-    is_enabled: bool
-    is_recommended: bool = False
-    costs: tuple[RegistryModelCost, ...] = field(default_factory=tuple)
-    creator: RegistryModelCreator | None = None
-
-
-_static_metadata: dict[str, ModelMetadata] = {}
-_static_costs: dict[str, int] = {}
-_dynamic_models: dict[str, RegistryModel] = {}
-_schema_options: list[dict[str, str]] = []
-_discriminator_mapping: dict[str, str] = {}
-_lock = asyncio.Lock()
-
-
-def register_static_metadata(metadata: dict[Any, ModelMetadata]) -> None:
-    """Register static metadata for legacy models (deprecated)."""
-    _static_metadata.update({str(key): value for key, value in metadata.items()})
-    _refresh_cached_schema()
-
-
-def register_static_costs(costs: dict[Any, int]) -> None:
-    """Register static costs for legacy models (deprecated)."""
-    _static_costs.update({str(key): value for key, value in costs.items()})
-
-
-def _build_schema_options() -> list[dict[str, str]]:
-    """Build schema options for model selection dropdown. Only includes enabled models."""
-    options: list[dict[str, str]] = []
-    # Only include enabled models in the dropdown options
-    for model in sorted(_dynamic_models.values(), key=lambda m: m.display_name.lower()):
-        if model.is_enabled:
-            options.append(
-                {
-                    "label": model.display_name,
-                    "value": model.slug,
-                    "group": model.metadata.provider,
-                    "description": model.description or "",
-                }
-            )
-
-    for slug, metadata in _static_metadata.items():
-        if slug in _dynamic_models:
-            continue
-        options.append(
-            {
-                "label": slug,
-                "value": slug,
-                "group": metadata.provider,
-                "description": "",
-            }
-        )
-    return options
-
-
-async def refresh_llm_registry() -> None:
-    """Refresh the LLM registry from the database. Loads all models (enabled and disabled)."""
-    async with _lock:
-        try:
-            records = await prisma.models.LlmModel.prisma().find_many(
-                include={
-                    "Provider": True,
-                    "Costs": True,
-                    "Creator": True,
-                }
-            )
-            logger.debug("Found %d LLM model records in database", len(records))
-        except Exception as exc:
-            logger.error(
-                "Failed to refresh LLM registry from DB: %s", exc, exc_info=True
-            )
-            return
-
-        dynamic: dict[str, RegistryModel] = {}
-        for record in records:
-            provider_name = (
-                record.Provider.name if record.Provider else record.providerId
-            )
-            provider_display_name = (
-                record.Provider.displayName if record.Provider else record.providerId
-            )
-            # Creator name: prefer Creator.name, fallback to provider display name
-            creator_name = (
-                record.Creator.name if record.Creator else provider_display_name
-            )
-            # Price tier: default to 1 (cheapest) if not set
-            price_tier = getattr(record, "priceTier", 1) or 1
-            # Clamp to valid range 1-3
-            price_tier = max(1, min(3, price_tier))
-
-            metadata = ModelMetadata(
-                provider=provider_name,
-                context_window=record.contextWindow,
-                max_output_tokens=record.maxOutputTokens,
-                display_name=record.displayName,
-                provider_name=provider_display_name,
-                creator_name=creator_name,
-                price_tier=price_tier,  # type: ignore[arg-type]
-            )
-            costs = tuple(
-                RegistryModelCost(
-                    credit_cost=cost.creditCost,
-                    credential_provider=cost.credentialProvider,
-                    credential_id=cost.credentialId,
-                    credential_type=cost.credentialType,
-                    currency=cost.currency,
-                    metadata=_json_to_dict(cost.metadata),
-                )
-                for cost in (record.Costs or [])
-            )
-
-            # Map creator if present
-            creator = None
-            if record.Creator:
-                creator = RegistryModelCreator(
-                    id=record.Creator.id,
-                    name=record.Creator.name,
-                    display_name=record.Creator.displayName,
-                    description=record.Creator.description,
-                    website_url=record.Creator.websiteUrl,
-                    logo_url=record.Creator.logoUrl,
-                )
-
-            dynamic[record.slug] = RegistryModel(
-                slug=record.slug,
-                display_name=record.displayName,
-                description=record.description,
-                metadata=metadata,
-                capabilities=_json_to_dict(record.capabilities),
-                extra_metadata=_json_to_dict(record.metadata),
-                provider_display_name=(
-                    record.Provider.displayName
-                    if record.Provider
-                    else record.providerId
-                ),
-                is_enabled=record.isEnabled,
-                is_recommended=record.isRecommended,
-                costs=costs,
-                creator=creator,
-            )
-
-        # Atomic swap - build new structures then replace references
-        # This ensures readers never see partially updated state
-        global _dynamic_models
-        _dynamic_models = dynamic
-        _refresh_cached_schema()
-        logger.info(
-            "LLM registry refreshed with %s dynamic models (enabled: %s, disabled: %s)",
-            len(dynamic),
-            sum(1 for m in dynamic.values() if m.is_enabled),
-            sum(1 for m in dynamic.values() if not m.is_enabled),
-        )
-
-
-def _refresh_cached_schema() -> None:
-    """Refresh cached schema options and discriminator mapping."""
-    global _schema_options, _discriminator_mapping
-
-    # Build new structures
-    new_options = _build_schema_options()
-    new_mapping = {
-        slug: entry.metadata.provider for slug, entry in _dynamic_models.items()
-    }
-    for slug, metadata in _static_metadata.items():
-        new_mapping.setdefault(slug, metadata.provider)
-
-    # Atomic swap - replace references to ensure readers see consistent state
-    _schema_options = new_options
-    _discriminator_mapping = new_mapping
-
-
-def get_llm_model_metadata(slug: str) -> ModelMetadata | None:
-    """Get model metadata by slug. Checks dynamic models first, then static metadata."""
-    if slug in _dynamic_models:
-        return _dynamic_models[slug].metadata
-    return _static_metadata.get(slug)
-
-
-def get_llm_model_cost(slug: str) -> tuple[RegistryModelCost, ...]:
-    """Get model cost configuration by slug."""
-    if slug in _dynamic_models:
-        return _dynamic_models[slug].costs
-    cost_value = _static_costs.get(slug)
-    if cost_value is None:
-        return tuple()
-    return (
-        RegistryModelCost(
-            credit_cost=cost_value,
-            credential_provider="static",
-            credential_id=None,
-            credential_type=None,
-            currency=None,
-            metadata={},
-        ),
-    )
-
-
-def get_llm_model_schema_options() -> list[dict[str, str]]:
-    """
-    Get schema options for LLM model selection dropdown.
-
-    Returns a copy of cached schema options that are refreshed when the registry is
-    updated via refresh_llm_registry() (called on startup and via Redis pub/sub).
-    """
-    # Return a copy to prevent external mutation
-    return list(_schema_options)
-
-
-def get_llm_discriminator_mapping() -> dict[str, str]:
-    """
-    Get discriminator mapping for LLM models.
-
-    Returns a copy of cached discriminator mapping that is refreshed when the registry
-    is updated via refresh_llm_registry() (called on startup and via Redis pub/sub).
-    """
-    # Return a copy to prevent external mutation
-    return dict(_discriminator_mapping)
-
-
-def get_dynamic_model_slugs() -> set[str]:
-    """Get all dynamic model slugs from the registry."""
-    return set(_dynamic_models.keys())
-
-
-def get_all_model_slugs_for_validation() -> set[str]:
-    """
-    Get ALL model slugs (both enabled and disabled) for validation purposes.
-
-    This is used for JSON schema enum validation - we need to accept any known
-    model value (even disabled ones) so that existing graphs don't fail validation.
-    The actual fallback/enforcement happens at runtime in llm_call().
-    """
-    all_slugs = set(_dynamic_models.keys())
-    all_slugs.update(_static_metadata.keys())
-    return all_slugs
-
-
-def iter_dynamic_models() -> Iterable[RegistryModel]:
-    """Iterate over all dynamic models in the registry."""
-    return tuple(_dynamic_models.values())
-
-
-def get_fallback_model_for_disabled(disabled_model_slug: str) -> RegistryModel | None:
-    """
-    Find a fallback model when the requested model is disabled.
-
-    Looks for an enabled model from the same provider. Prefers models with
-    similar names or capabilities if possible.
-
-    Args:
-        disabled_model_slug: The slug of the disabled model
-
-    Returns:
-        An enabled RegistryModel from the same provider, or None if no fallback found
-    """
-    disabled_model = _dynamic_models.get(disabled_model_slug)
-    if not disabled_model:
-        return None
-
-    provider = disabled_model.metadata.provider
-
-    # Find all enabled models from the same provider
-    candidates = [
-        model
-        for model in _dynamic_models.values()
-        if model.is_enabled and model.metadata.provider == provider
-    ]
-
-    if not candidates:
-        return None
-
-    # Sort by: prefer models with similar context window, then by name
-    candidates.sort(
-        key=lambda m: (
-            abs(m.metadata.context_window - disabled_model.metadata.context_window),
-            m.display_name.lower(),
-        )
-    )
-
-    return candidates[0]
-
-
-def is_model_enabled(model_slug: str) -> bool:
-    """Check if a model is enabled in the registry."""
-    model = _dynamic_models.get(model_slug)
-    if not model:
-        # Model not in registry - assume it's a static/legacy model and allow it
-        return True
-    return model.is_enabled
-
-
-def get_model_info(model_slug: str) -> RegistryModel | None:
-    """Get model info from the registry."""
-    return _dynamic_models.get(model_slug)
-
-
-def get_default_model_slug() -> str | None:
-    """
-    Get the default model slug to use for block defaults.
-
-    Returns the recommended model if set (configured via admin UI),
-    otherwise returns the first enabled model alphabetically.
-    Returns None if no models are available or enabled.
-    """
-    # Return the recommended model if one is set and enabled
-    for model in _dynamic_models.values():
-        if model.is_recommended and model.is_enabled:
-            return model.slug
-
-    # No recommended model set - find first enabled model alphabetically
-    for model in sorted(_dynamic_models.values(), key=lambda m: m.display_name.lower()):
-        if model.is_enabled:
-            logger.warning(
-                "No recommended model set, using '%s' as default",
-                model.slug,
-            )
-            return model.slug
-
-    # No enabled models available
-    if _dynamic_models:
-        logger.error(
-            "No enabled models found in registry (%d models registered but all disabled)",
-            len(_dynamic_models),
-        )
-    else:
-        logger.error("No models registered in LLM registry")
-
-    return None

autogpt_platform/backend/backend/data/llm_registry/schema_utils.py

@@ -1,130 +0,0 @@
-"""
-Helper utilities for LLM registry integration with block schemas.
-
-This module handles the dynamic injection of discriminator mappings
-and model options from the LLM registry into block schemas.
-"""
-
-import logging
-from typing import Any
-
-from backend.data.llm_registry.registry import (
-    get_all_model_slugs_for_validation,
-    get_default_model_slug,
-    get_llm_discriminator_mapping,
-    get_llm_model_schema_options,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def is_llm_model_field(field_name: str, field_info: Any) -> bool:
-    """
-    Check if a field is an LLM model selection field.
-
-    Returns True if the field has 'options' in json_schema_extra
-    (set by llm_model_schema_extra() in blocks/llm.py).
-    """
-    if not hasattr(field_info, "json_schema_extra"):
-        return False
-
-    extra = field_info.json_schema_extra
-    if isinstance(extra, dict):
-        return "options" in extra
-
-    return False
-
-
-def refresh_llm_model_options(field_schema: dict[str, Any]) -> None:
-    """
-    Refresh LLM model options from the registry.
-
-    Updates 'options' (for frontend dropdown) to show only enabled models,
-    but keeps the 'enum' (for validation) inclusive of ALL known models.
-
-    This is important because:
-    - Options: What users see in the dropdown (enabled models only)
-    - Enum: What values pass validation (all known models, including disabled)
-
-    Existing graphs may have disabled models selected - they should pass validation
-    and the fallback logic in llm_call() will handle using an alternative model.
-    """
-    fresh_options = get_llm_model_schema_options()
-    if not fresh_options:
-        return
-
-    # Update options array (UI dropdown) - only enabled models
-    if "options" in field_schema:
-        field_schema["options"] = fresh_options
-
-    all_known_slugs = get_all_model_slugs_for_validation()
-    if all_known_slugs and "enum" in field_schema:
-        existing_enum = set(field_schema.get("enum", []))
-        combined_enum = existing_enum | all_known_slugs
-        field_schema["enum"] = sorted(combined_enum)
-
-    # Set the default value from the registry (gpt-4o if available, else first enabled)
-    # This ensures new blocks have a sensible default pre-selected
-    default_slug = get_default_model_slug()
-    if default_slug:
-        field_schema["default"] = default_slug
-
-
-def refresh_llm_discriminator_mapping(field_schema: dict[str, Any]) -> None:
-    """
-    Refresh discriminator_mapping for fields that use model-based discrimination.
-
-    The discriminator is already set when AICredentialsField() creates the field.
-    We only need to refresh the mapping when models are added/removed.
-    """
-    if field_schema.get("discriminator") != "model":
-        return
-
-    # Always refresh the mapping to get latest models
-    fresh_mapping = get_llm_discriminator_mapping()
-    if fresh_mapping is not None:
-        field_schema["discriminator_mapping"] = fresh_mapping
-
-
-def update_schema_with_llm_registry(
-    schema: dict[str, Any], model_class: type | None = None
-) -> None:
-    """
-    Update a JSON schema with current LLM registry data.
-
-    Refreshes:
-    1. Model options for LLM model selection fields (dropdown choices)
-    2. Discriminator mappings for credentials fields (model → provider)
-
-    Args:
-        schema: The JSON schema to update (mutated in-place)
-        model_class: The Pydantic model class (optional, for field introspection)
-    """
-    properties = schema.get("properties", {})
-
-    for field_name, field_schema in properties.items():
-        if not isinstance(field_schema, dict):
-            continue
-
-        # Refresh model options for LLM model fields
-        if model_class and hasattr(model_class, "model_fields"):
-            field_info = model_class.model_fields.get(field_name)
-            if field_info and is_llm_model_field(field_name, field_info):
-                try:
-                    refresh_llm_model_options(field_schema)
-                except Exception as exc:
-                    logger.warning(
-                        "Failed to refresh LLM options for field %s: %s",
-                        field_name,
-                        exc,
-                    )
-
-        # Refresh discriminator mapping for fields that use model discrimination
-        try:
-            refresh_llm_discriminator_mapping(field_schema)
-        except Exception as exc:
-            logger.warning(
-                "Failed to refresh discriminator mapping for field %s: %s",
-                field_name,
-                exc,
-            )

autogpt_platform/backend/backend/data/model.py

@@ -40,7 +40,6 @@ from pydantic_core import (
 )
 from typing_extensions import TypedDict
 
-from backend.data.llm_registry import update_schema_with_llm_registry
 from backend.integrations.providers import ProviderName
 from backend.util.json import loads as json_loads
 from backend.util.settings import Secrets
@@ -545,9 +544,7 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
             else:
                 schema["credentials_provider"] = allowed_providers
             schema["credentials_types"] = model_class.allowed_cred_types()
-
-        # Ensure LLM discriminators are populated (delegates to shared helper)
-        update_schema_with_llm_registry(schema, model_class)
+        # Do not return anything, just mutate schema in place
 
     model_config = ConfigDict(
         json_schema_extra=_add_json_schema_extra,  # type: ignore
@@ -696,20 +693,16 @@ def CredentialsField(
     This is enforced by the `BlockSchema` base class.
     """
 
-    # Build field_schema_extra - always include discriminator and mapping if discriminator is set
-    field_schema_extra: dict[str, Any] = {}
-
-    # Always include discriminator if provided
-    if discriminator is not None:
-        field_schema_extra["discriminator"] = discriminator
-        # Always include discriminator_mapping when discriminator is set (even if empty initially)
-        field_schema_extra["discriminator_mapping"] = discriminator_mapping or {}
-
-    # Include other optional fields (only if not None)
-    if required_scopes:
-        field_schema_extra["credentials_scopes"] = list(required_scopes)
-    if discriminator_values:
-        field_schema_extra["discriminator_values"] = discriminator_values
+    field_schema_extra = {
+        k: v
+        for k, v in {
+            "credentials_scopes": list(required_scopes) or None,
+            "discriminator": discriminator,
+            "discriminator_mapping": discriminator_mapping,
+            "discriminator_values": discriminator_values,
+        }.items()
+        if v is not None
+    }
 
     # Merge any json_schema_extra passed in kwargs
     if "json_schema_extra" in kwargs:

autogpt_platform/backend/backend/executor/llm_registry_init.py

@@ -1,66 +0,0 @@
-"""
-Helper functions for LLM registry initialization in executor context.
-
-These functions handle refreshing the LLM registry when the executor starts
-and subscribing to real-time updates via Redis pub/sub.
-"""
-
-import logging
-
-from backend.data import db, llm_registry
-from backend.data.block import BlockSchema, initialize_blocks
-from backend.data.block_cost_config import refresh_llm_costs
-from backend.data.llm_registry import subscribe_to_registry_refresh
-
-logger = logging.getLogger(__name__)
-
-
-async def initialize_registry_for_executor() -> None:
-    """
-    Initialize blocks and refresh LLM registry in the executor context.
-
-    This must run in the executor's event loop to have access to the database.
-    """
-    try:
-        # Connect to database if not already connected
-        if not db.is_connected():
-            await db.connect()
-            logger.info("[GraphExecutor] Connected to database for registry refresh")
-
-        # Initialize blocks (internally refreshes LLM registry and costs)
-        await initialize_blocks()
-        logger.info("[GraphExecutor] Blocks initialized")
-    except Exception as exc:
-        logger.warning(
-            "[GraphExecutor] Failed to refresh LLM registry on startup: %s",
-            exc,
-            exc_info=True,
-        )
-
-
-async def refresh_registry_on_notification() -> None:
-    """Refresh LLM registry when notified via Redis pub/sub."""
-    try:
-        # Ensure DB is connected
-        if not db.is_connected():
-            await db.connect()
-
-        # Refresh registry and costs
-        await llm_registry.refresh_llm_registry()
-        refresh_llm_costs()
-
-        # Clear block schema caches so they regenerate with new model options
-        BlockSchema.clear_all_schema_caches()
-
-        logger.info("[GraphExecutor] LLM registry refreshed from notification")
-    except Exception as exc:
-        logger.error(
-            "[GraphExecutor] Failed to refresh LLM registry from notification: %s",
-            exc,
-            exc_info=True,
-        )
-
-
-async def subscribe_to_registry_updates() -> None:
-    """Subscribe to Redis pub/sub for LLM registry refresh notifications."""
-    await subscribe_to_registry_refresh(refresh_registry_on_notification)

autogpt_platform/backend/backend/executor/manager.py

@@ -702,20 +702,6 @@ class ExecutionProcessor:
         )
         self.node_execution_thread.start()
         self.node_evaluation_thread.start()
-
-        # Initialize LLM registry and subscribe to updates
-        from backend.executor.llm_registry_init import (
-            initialize_registry_for_executor,
-            subscribe_to_registry_updates,
-        )
-
-        asyncio.run_coroutine_threadsafe(
-            initialize_registry_for_executor(), self.node_execution_loop
-        )
-        asyncio.run_coroutine_threadsafe(
-            subscribe_to_registry_updates(), self.node_execution_loop
-        )
-
         logger.info(f"[GraphExecutor] {self.tid} started")
 
     @error_logged(swallow=False)

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

@@ -1,935 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Iterable, Sequence, cast
-
-import prisma
-import prisma.models
-
-from backend.data.db import transaction
-from backend.server.v2.llm import model as llm_model
-from backend.util.models import Pagination
-
-
-def _json_dict(value: Any | None) -> dict[str, Any]:
-    if not value:
-        return {}
-    if isinstance(value, dict):
-        return value
-    return {}
-
-
-def _map_cost(record: prisma.models.LlmModelCost) -> llm_model.LlmModelCost:
-    return llm_model.LlmModelCost(
-        id=record.id,
-        unit=record.unit,
-        credit_cost=record.creditCost,
-        credential_provider=record.credentialProvider,
-        credential_id=record.credentialId,
-        credential_type=record.credentialType,
-        currency=record.currency,
-        metadata=_json_dict(record.metadata),
-    )
-
-
-def _map_creator(
-    record: prisma.models.LlmModelCreator,
-) -> llm_model.LlmModelCreator:
-    return llm_model.LlmModelCreator(
-        id=record.id,
-        name=record.name,
-        display_name=record.displayName,
-        description=record.description,
-        website_url=record.websiteUrl,
-        logo_url=record.logoUrl,
-        metadata=_json_dict(record.metadata),
-    )
-
-
-def _map_model(record: prisma.models.LlmModel) -> llm_model.LlmModel:
-    costs = []
-    if record.Costs:
-        costs = [_map_cost(cost) for cost in record.Costs]
-
-    creator = None
-    if hasattr(record, "Creator") and record.Creator:
-        creator = _map_creator(record.Creator)
-
-    return llm_model.LlmModel(
-        id=record.id,
-        slug=record.slug,
-        display_name=record.displayName,
-        description=record.description,
-        provider_id=record.providerId,
-        creator_id=record.creatorId,
-        creator=creator,
-        context_window=record.contextWindow,
-        max_output_tokens=record.maxOutputTokens,
-        is_enabled=record.isEnabled,
-        is_recommended=record.isRecommended,
-        capabilities=_json_dict(record.capabilities),
-        metadata=_json_dict(record.metadata),
-        costs=costs,
-    )
-
-
-def _map_provider(record: prisma.models.LlmProvider) -> llm_model.LlmProvider:
-    models: list[llm_model.LlmModel] = []
-    if record.Models:
-        models = [_map_model(model) for model in record.Models]
-
-    return llm_model.LlmProvider(
-        id=record.id,
-        name=record.name,
-        display_name=record.displayName,
-        description=record.description,
-        default_credential_provider=record.defaultCredentialProvider,
-        default_credential_id=record.defaultCredentialId,
-        default_credential_type=record.defaultCredentialType,
-        supports_tools=record.supportsTools,
-        supports_json_output=record.supportsJsonOutput,
-        supports_reasoning=record.supportsReasoning,
-        supports_parallel_tool=record.supportsParallelTool,
-        metadata=_json_dict(record.metadata),
-        models=models,
-    )
-
-
-async def list_providers(
-    include_models: bool = True, enabled_only: bool = False
-) -> list[llm_model.LlmProvider]:
-    """
-    List all LLM providers.
-
-    Args:
-        include_models: Whether to include models for each provider
-        enabled_only: If True, only include enabled models (for public routes)
-    """
-    include: Any = None
-    if include_models:
-        model_where = {"isEnabled": True} if enabled_only else None
-        include = {
-            "Models": {
-                "include": {"Costs": True, "Creator": True},
-                "where": model_where,
-            }
-        }
-    records = await prisma.models.LlmProvider.prisma().find_many(include=include)
-    return [_map_provider(record) for record in records]
-
-
-async def upsert_provider(
-    request: llm_model.UpsertLlmProviderRequest,
-    provider_id: str | None = None,
-) -> llm_model.LlmProvider:
-    data: Any = {
-        "name": request.name,
-        "displayName": request.display_name,
-        "description": request.description,
-        "defaultCredentialProvider": request.default_credential_provider,
-        "defaultCredentialId": request.default_credential_id,
-        "defaultCredentialType": request.default_credential_type,
-        "supportsTools": request.supports_tools,
-        "supportsJsonOutput": request.supports_json_output,
-        "supportsReasoning": request.supports_reasoning,
-        "supportsParallelTool": request.supports_parallel_tool,
-        "metadata": prisma.Json(request.metadata or {}),
-    }
-    include: Any = {"Models": {"include": {"Costs": True, "Creator": True}}}
-    if provider_id:
-        record = await prisma.models.LlmProvider.prisma().update(
-            where={"id": provider_id},
-            data=data,
-            include=include,
-        )
-    else:
-        record = await prisma.models.LlmProvider.prisma().create(
-            data=data,
-            include=include,
-        )
-    if record is None:
-        raise ValueError("Failed to create/update provider")
-    return _map_provider(record)
-
-
-async def delete_provider(provider_id: str) -> bool:
-    """
-    Delete an LLM provider.
-
-    A provider can only be deleted if it has no associated models.
-    Due to onDelete: Restrict on LlmModel.Provider, the database will
-    block deletion if models exist.
-
-    Args:
-        provider_id: UUID of the provider to delete
-
-    Returns:
-        True if deleted successfully
-
-    Raises:
-        ValueError: If provider not found or has associated models
-    """
-    # Check if provider exists
-    provider = await prisma.models.LlmProvider.prisma().find_unique(
-        where={"id": provider_id},
-        include={"Models": True},
-    )
-    if not provider:
-        raise ValueError(f"Provider with id '{provider_id}' not found")
-
-    # Check if provider has any models
-    model_count = len(provider.Models) if provider.Models else 0
-    if model_count > 0:
-        raise ValueError(
-            f"Cannot delete provider '{provider.displayName}' because it has "
-            f"{model_count} model(s). Delete all models first."
-        )
-
-    # Safe to delete
-    await prisma.models.LlmProvider.prisma().delete(where={"id": provider_id})
-    return True
-
-
-async def list_models(
-    provider_id: str | None = None,
-    enabled_only: bool = False,
-    page: int = 1,
-    page_size: int = 50,
-) -> llm_model.LlmModelsResponse:
-    """
-    List LLM models with pagination.
-
-    Args:
-        provider_id: Optional filter by provider ID
-        enabled_only: If True, only return enabled models (for public routes)
-        page: Page number (1-indexed)
-        page_size: Number of models per page
-    """
-    where: Any = {}
-    if provider_id:
-        where["providerId"] = provider_id
-    if enabled_only:
-        where["isEnabled"] = True
-
-    # Get total count for pagination
-    total_items = await prisma.models.LlmModel.prisma().count(
-        where=where if where else None
-    )
-
-    # Calculate pagination
-    skip = (page - 1) * page_size
-    total_pages = (total_items + page_size - 1) // page_size if total_items > 0 else 0
-
-    records = await prisma.models.LlmModel.prisma().find_many(
-        where=where if where else None,
-        include={"Costs": True, "Creator": True},
-        skip=skip,
-        take=page_size,
-    )
-    models = [_map_model(record) for record in records]
-
-    return llm_model.LlmModelsResponse(
-        models=models,
-        pagination=Pagination(
-            total_items=total_items,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-def _cost_create_payload(
-    costs: Sequence[llm_model.LlmModelCostInput],
-) -> dict[str, Iterable[dict[str, Any]]]:
-
-    create_items = []
-    for cost in costs:
-        item: dict[str, Any] = {
-            "unit": cost.unit,
-            "creditCost": cost.credit_cost,
-            "credentialProvider": cost.credential_provider,
-        }
-        # Only include optional fields if they have values
-        if cost.credential_id:
-            item["credentialId"] = cost.credential_id
-        if cost.credential_type:
-            item["credentialType"] = cost.credential_type
-        if cost.currency:
-            item["currency"] = cost.currency
-        # Handle metadata - use Prisma Json type
-        if cost.metadata is not None and cost.metadata != {}:
-            item["metadata"] = prisma.Json(cost.metadata)
-        create_items.append(item)
-    return {"create": create_items}
-
-
-async def create_model(
-    request: llm_model.CreateLlmModelRequest,
-) -> llm_model.LlmModel:
-    data: Any = {
-        "slug": request.slug,
-        "displayName": request.display_name,
-        "description": request.description,
-        "Provider": {"connect": {"id": request.provider_id}},
-        "contextWindow": request.context_window,
-        "maxOutputTokens": request.max_output_tokens,
-        "isEnabled": request.is_enabled,
-        "capabilities": prisma.Json(request.capabilities or {}),
-        "metadata": prisma.Json(request.metadata or {}),
-        "Costs": _cost_create_payload(request.costs),
-    }
-    if request.creator_id:
-        data["Creator"] = {"connect": {"id": request.creator_id}}
-
-    record = await prisma.models.LlmModel.prisma().create(
-        data=data,
-        include={"Costs": True, "Creator": True, "Provider": True},
-    )
-    return _map_model(record)
-
-
-async def update_model(
-    model_id: str,
-    request: llm_model.UpdateLlmModelRequest,
-) -> llm_model.LlmModel:
-    # Build scalar field updates (non-relation fields)
-    scalar_data: Any = {}
-    if request.display_name is not None:
-        scalar_data["displayName"] = request.display_name
-    if request.description is not None:
-        scalar_data["description"] = request.description
-    if request.context_window is not None:
-        scalar_data["contextWindow"] = request.context_window
-    if request.max_output_tokens is not None:
-        scalar_data["maxOutputTokens"] = request.max_output_tokens
-    if request.is_enabled is not None:
-        scalar_data["isEnabled"] = request.is_enabled
-    if request.capabilities is not None:
-        scalar_data["capabilities"] = request.capabilities
-    if request.metadata is not None:
-        scalar_data["metadata"] = request.metadata
-    # Foreign keys can be updated directly as scalar fields
-    if request.provider_id is not None:
-        scalar_data["providerId"] = request.provider_id
-    if request.creator_id is not None:
-        # Empty string means remove the creator
-        scalar_data["creatorId"] = request.creator_id if request.creator_id else None
-
-    # If we have costs to update, we need to handle them separately
-    # because nested writes have different constraints
-    if request.costs is not None:
-        # Wrap cost replacement in a transaction for atomicity
-        async with transaction() as tx:
-            # First update scalar fields
-            if scalar_data:
-                await tx.llmmodel.update(
-                    where={"id": model_id},
-                    data=scalar_data,
-                )
-            # Then handle costs: delete existing and create new
-            await tx.llmmodelcost.delete_many(where={"llmModelId": model_id})
-            if request.costs:
-                cost_payload = _cost_create_payload(request.costs)
-                for cost_item in cost_payload["create"]:
-                    cost_item["llmModelId"] = model_id
-                    await tx.llmmodelcost.create(data=cast(Any, cost_item))
-        # Fetch the updated record (outside transaction)
-        record = await prisma.models.LlmModel.prisma().find_unique(
-            where={"id": model_id},
-            include={"Costs": True, "Creator": True},
-        )
-    else:
-        # No costs update - simple update
-        record = await prisma.models.LlmModel.prisma().update(
-            where={"id": model_id},
-            data=scalar_data,
-            include={"Costs": True, "Creator": True},
-        )
-
-    if not record:
-        raise ValueError(f"Model with id '{model_id}' not found")
-    return _map_model(record)
-
-
-async def toggle_model(
-    model_id: str,
-    is_enabled: bool,
-    migrate_to_slug: str | None = None,
-    migration_reason: str | None = None,
-    custom_credit_cost: int | None = None,
-) -> llm_model.ToggleLlmModelResponse:
-    """
-    Toggle a model's enabled status, optionally migrating workflows when disabling.
-
-    Args:
-        model_id: UUID of the model to toggle
-        is_enabled: New enabled status
-        migrate_to_slug: If disabling and this is provided, migrate all workflows
-                         using this model to the specified replacement model
-        migration_reason: Optional reason for the migration (e.g., "Provider outage")
-        custom_credit_cost: Optional custom pricing override for migrated workflows.
-                           When set, the billing system should use this cost instead
-                           of the target model's cost for affected nodes.
-
-    Returns:
-        ToggleLlmModelResponse with the updated model and optional migration stats
-    """
-    import json
-
-    # Get the model being toggled
-    model = await prisma.models.LlmModel.prisma().find_unique(
-        where={"id": model_id}, include={"Costs": True}
-    )
-    if not model:
-        raise ValueError(f"Model with id '{model_id}' not found")
-
-    nodes_migrated = 0
-    migration_id: str | None = None
-
-    # If disabling with migration, perform migration first
-    if not is_enabled and migrate_to_slug:
-        # Validate replacement model exists and is enabled
-        replacement = await prisma.models.LlmModel.prisma().find_unique(
-            where={"slug": migrate_to_slug}
-        )
-        if not replacement:
-            raise ValueError(f"Replacement model '{migrate_to_slug}' not found")
-        if not replacement.isEnabled:
-            raise ValueError(
-                f"Replacement model '{migrate_to_slug}' is disabled. "
-                f"Please enable it before using it as a replacement."
-            )
-
-        # Perform all operations atomically within a single transaction
-        # This ensures no nodes are missed between query and update
-        async with transaction() as tx:
-            # Get the IDs of nodes that will be migrated (inside transaction for consistency)
-            node_ids_result = await tx.query_raw(
-                """
-                SELECT id
-                FROM "AgentNode"
-                WHERE "constantInput"::jsonb->>'model' = $1
-                FOR UPDATE
-                """,
-                model.slug,
-            )
-            migrated_node_ids = (
-                [row["id"] for row in node_ids_result] if node_ids_result else []
-            )
-            nodes_migrated = len(migrated_node_ids)
-
-            if nodes_migrated > 0:
-                # Update by IDs to ensure we only update the exact nodes we queried
-                # Use JSON array and jsonb_array_elements_text for safe parameterization
-                node_ids_json = json.dumps(migrated_node_ids)
-                await tx.execute_raw(
-                    """
-                    UPDATE "AgentNode"
-                    SET "constantInput" = JSONB_SET(
-                        "constantInput"::jsonb,
-                        '{model}',
-                        to_jsonb($1::text)
-                    )
-                    WHERE id::text IN (
-                        SELECT jsonb_array_elements_text($2::jsonb)
-                    )
-                    """,
-                    migrate_to_slug,
-                    node_ids_json,
-                )
-
-            record = await tx.llmmodel.update(
-                where={"id": model_id},
-                data={"isEnabled": is_enabled},
-                include={"Costs": True},
-            )
-
-            # Create migration record for revert capability
-            if nodes_migrated > 0:
-                migration_data: Any = {
-                    "sourceModelSlug": model.slug,
-                    "targetModelSlug": migrate_to_slug,
-                    "reason": migration_reason,
-                    "migratedNodeIds": json.dumps(migrated_node_ids),
-                    "nodeCount": nodes_migrated,
-                    "customCreditCost": custom_credit_cost,
-                }
-                migration_record = await tx.llmmodelmigration.create(
-                    data=migration_data
-                )
-                migration_id = migration_record.id
-    else:
-        # Simple toggle without migration
-        record = await prisma.models.LlmModel.prisma().update(
-            where={"id": model_id},
-            data={"isEnabled": is_enabled},
-            include={"Costs": True},
-        )
-
-    if record is None:
-        raise ValueError(f"Model with id '{model_id}' not found")
-    return llm_model.ToggleLlmModelResponse(
-        model=_map_model(record),
-        nodes_migrated=nodes_migrated,
-        migrated_to_slug=migrate_to_slug if nodes_migrated > 0 else None,
-        migration_id=migration_id,
-    )
-
-
-async def get_model_usage(model_id: str) -> llm_model.LlmModelUsageResponse:
-    """Get usage count for a model."""
-    import prisma as prisma_module
-
-    model = await prisma.models.LlmModel.prisma().find_unique(where={"id": model_id})
-    if not model:
-        raise ValueError(f"Model with id '{model_id}' not found")
-
-    count_result = await prisma_module.get_client().query_raw(
-        """
-        SELECT COUNT(*) as count
-        FROM "AgentNode"
-        WHERE "constantInput"::jsonb->>'model' = $1
-        """,
-        model.slug,
-    )
-    node_count = int(count_result[0]["count"]) if count_result else 0
-
-    return llm_model.LlmModelUsageResponse(model_slug=model.slug, node_count=node_count)
-
-
-async def delete_model(
-    model_id: str, replacement_model_slug: str | None = None
-) -> llm_model.DeleteLlmModelResponse:
-    """
-    Delete a model and optionally migrate all AgentNodes using it to a replacement model.
-
-    This performs an atomic operation within a database transaction:
-    1. Validates the model exists
-    2. Counts affected nodes
-    3. If nodes exist, validates replacement model and migrates them
-    4. Deletes the LlmModel record (CASCADE deletes costs)
-
-    Args:
-        model_id: UUID of the model to delete
-        replacement_model_slug: Slug of the model to migrate to (required only if nodes use this model)
-
-    Returns:
-        DeleteLlmModelResponse with migration stats
-
-    Raises:
-        ValueError: If model not found, nodes exist but no replacement provided,
-                    replacement not found, or replacement is disabled
-    """
-    # 1. Get the model being deleted (validation - outside transaction)
-    model = await prisma.models.LlmModel.prisma().find_unique(
-        where={"id": model_id}, include={"Costs": True}
-    )
-    if not model:
-        raise ValueError(f"Model with id '{model_id}' not found")
-
-    deleted_slug = model.slug
-    deleted_display_name = model.displayName
-
-    # 2. Count affected nodes first to determine if replacement is needed
-    import prisma as prisma_module
-
-    count_result = await prisma_module.get_client().query_raw(
-        """
-        SELECT COUNT(*) as count
-        FROM "AgentNode"
-        WHERE "constantInput"::jsonb->>'model' = $1
-        """,
-        deleted_slug,
-    )
-    nodes_to_migrate = int(count_result[0]["count"]) if count_result else 0
-
-    # 3. Validate replacement model only if there are nodes to migrate
-    if nodes_to_migrate > 0:
-        if not replacement_model_slug:
-            raise ValueError(
-                f"Cannot delete model '{deleted_slug}': {nodes_to_migrate} workflow node(s) "
-                f"are using it. Please provide a replacement_model_slug to migrate them."
-            )
-        replacement = await prisma.models.LlmModel.prisma().find_unique(
-            where={"slug": replacement_model_slug}
-        )
-        if not replacement:
-            raise ValueError(f"Replacement model '{replacement_model_slug}' not found")
-        if not replacement.isEnabled:
-            raise ValueError(
-                f"Replacement model '{replacement_model_slug}' is disabled. "
-                f"Please enable it before using it as a replacement."
-            )
-
-    # 4. Perform migration (if needed) and deletion atomically within a transaction
-    async with transaction() as tx:
-        # Migrate all AgentNode.constantInput->model to replacement
-        if nodes_to_migrate > 0 and replacement_model_slug:
-            await tx.execute_raw(
-                """
-                UPDATE "AgentNode"
-                SET "constantInput" = JSONB_SET(
-                    "constantInput"::jsonb,
-                    '{model}',
-                    to_jsonb($1::text)
-                )
-                WHERE "constantInput"::jsonb->>'model' = $2
-                """,
-                replacement_model_slug,
-                deleted_slug,
-            )
-
-        # Delete the model (CASCADE will delete costs automatically)
-        await tx.llmmodel.delete(where={"id": model_id})
-
-    # Build appropriate message based on whether migration happened
-    if nodes_to_migrate > 0:
-        message = (
-            f"Successfully deleted model '{deleted_display_name}' ({deleted_slug}) "
-            f"and migrated {nodes_to_migrate} workflow node(s) to '{replacement_model_slug}'."
-        )
-    else:
-        message = (
-            f"Successfully deleted model '{deleted_display_name}' ({deleted_slug}). "
-            f"No workflows were using this model."
-        )
-
-    return llm_model.DeleteLlmModelResponse(
-        deleted_model_slug=deleted_slug,
-        deleted_model_display_name=deleted_display_name,
-        replacement_model_slug=replacement_model_slug,
-        nodes_migrated=nodes_to_migrate,
-        message=message,
-    )
-
-
-def _map_migration(
-    record: prisma.models.LlmModelMigration,
-) -> llm_model.LlmModelMigration:
-    return llm_model.LlmModelMigration(
-        id=record.id,
-        source_model_slug=record.sourceModelSlug,
-        target_model_slug=record.targetModelSlug,
-        reason=record.reason,
-        node_count=record.nodeCount,
-        custom_credit_cost=record.customCreditCost,
-        is_reverted=record.isReverted,
-        created_at=record.createdAt.isoformat(),
-        reverted_at=record.revertedAt.isoformat() if record.revertedAt else None,
-    )
-
-
-async def list_migrations(
-    include_reverted: bool = False,
-) -> list[llm_model.LlmModelMigration]:
-    """
-    List model migrations, optionally including reverted ones.
-
-    Args:
-        include_reverted: If True, include reverted migrations. Default is False.
-
-    Returns:
-        List of LlmModelMigration records
-    """
-    where: Any = None if include_reverted else {"isReverted": False}
-    records = await prisma.models.LlmModelMigration.prisma().find_many(
-        where=where,
-        order={"createdAt": "desc"},
-    )
-    return [_map_migration(record) for record in records]
-
-
-async def get_migration(migration_id: str) -> llm_model.LlmModelMigration | None:
-    """Get a specific migration by ID."""
-    record = await prisma.models.LlmModelMigration.prisma().find_unique(
-        where={"id": migration_id}
-    )
-    return _map_migration(record) if record else None
-
-
-async def revert_migration(
-    migration_id: str,
-    re_enable_source_model: bool = True,
-) -> llm_model.RevertMigrationResponse:
-    """
-    Revert a model migration, restoring affected nodes to their original model.
-
-    This only reverts the specific nodes that were migrated, not all nodes
-    currently using the target model.
-
-    Args:
-        migration_id: UUID of the migration to revert
-        re_enable_source_model: Whether to re-enable the source model if it's disabled
-
-    Returns:
-        RevertMigrationResponse with revert stats
-
-    Raises:
-        ValueError: If migration not found, already reverted, or source model not available
-    """
-    import json
-    from datetime import datetime, timezone
-
-    # Get the migration record
-    migration = await prisma.models.LlmModelMigration.prisma().find_unique(
-        where={"id": migration_id}
-    )
-    if not migration:
-        raise ValueError(f"Migration with id '{migration_id}' not found")
-
-    if migration.isReverted:
-        raise ValueError(
-            f"Migration '{migration_id}' has already been reverted "
-            f"on {migration.revertedAt.isoformat() if migration.revertedAt else 'unknown date'}"
-        )
-
-    # Check if source model exists
-    source_model = await prisma.models.LlmModel.prisma().find_unique(
-        where={"slug": migration.sourceModelSlug}
-    )
-    if not source_model:
-        raise ValueError(
-            f"Source model '{migration.sourceModelSlug}' no longer exists. "
-            f"Cannot revert migration."
-        )
-
-    # Get the migrated node IDs (Prisma auto-parses JSONB to list)
-    migrated_node_ids: list[str] = (
-        migration.migratedNodeIds
-        if isinstance(migration.migratedNodeIds, list)
-        else json.loads(migration.migratedNodeIds)  # type: ignore
-    )
-    if not migrated_node_ids:
-        raise ValueError("No nodes to revert in this migration")
-
-    # Track if we need to re-enable the source model
-    source_model_was_disabled = not source_model.isEnabled
-    should_re_enable = source_model_was_disabled and re_enable_source_model
-    source_model_re_enabled = False
-
-    # Perform revert atomically
-    async with transaction() as tx:
-        # Re-enable the source model if requested and it was disabled
-        if should_re_enable:
-            await tx.llmmodel.update(
-                where={"id": source_model.id},
-                data={"isEnabled": True},
-            )
-            source_model_re_enabled = True
-
-        # Update only the specific nodes that were migrated
-        # We need to check that they still have the target model (haven't been changed since)
-        # Use a single batch update for efficiency
-        # Use JSON array and jsonb_array_elements_text for safe parameterization
-        node_ids_json = json.dumps(migrated_node_ids)
-        result = await tx.execute_raw(
-            """
-            UPDATE "AgentNode"
-            SET "constantInput" = JSONB_SET(
-                "constantInput"::jsonb,
-                '{model}',
-                to_jsonb($1::text)
-            )
-            WHERE id::text IN (
-                SELECT jsonb_array_elements_text($2::jsonb)
-            )
-            AND "constantInput"::jsonb->>'model' = $3
-            """,
-            migration.sourceModelSlug,
-            node_ids_json,
-            migration.targetModelSlug,
-        )
-        nodes_reverted = result if result else 0
-
-        # Mark migration as reverted
-        await tx.llmmodelmigration.update(
-            where={"id": migration_id},
-            data={
-                "isReverted": True,
-                "revertedAt": datetime.now(timezone.utc),
-            },
-        )
-
-    # Calculate nodes that were already changed since migration
-    nodes_already_changed = len(migrated_node_ids) - nodes_reverted
-
-    # Build appropriate message
-    message_parts = [
-        f"Successfully reverted migration: {nodes_reverted} node(s) restored "
-        f"from '{migration.targetModelSlug}' to '{migration.sourceModelSlug}'."
-    ]
-    if nodes_already_changed > 0:
-        message_parts.append(
-            f" {nodes_already_changed} node(s) were already changed and not reverted."
-        )
-    if source_model_re_enabled:
-        message_parts.append(
-            f" Model '{migration.sourceModelSlug}' has been re-enabled."
-        )
-
-    return llm_model.RevertMigrationResponse(
-        migration_id=migration_id,
-        source_model_slug=migration.sourceModelSlug,
-        target_model_slug=migration.targetModelSlug,
-        nodes_reverted=nodes_reverted,
-        nodes_already_changed=nodes_already_changed,
-        source_model_re_enabled=source_model_re_enabled,
-        message="".join(message_parts),
-    )
-
-
-# ============================================================================
-# Creator CRUD operations
-# ============================================================================
-
-
-async def list_creators() -> list[llm_model.LlmModelCreator]:
-    """List all LLM model creators."""
-    records = await prisma.models.LlmModelCreator.prisma().find_many(
-        order={"displayName": "asc"}
-    )
-    return [_map_creator(record) for record in records]
-
-
-async def get_creator(creator_id: str) -> llm_model.LlmModelCreator | None:
-    """Get a specific creator by ID."""
-    record = await prisma.models.LlmModelCreator.prisma().find_unique(
-        where={"id": creator_id}
-    )
-    return _map_creator(record) if record else None
-
-
-async def upsert_creator(
-    request: llm_model.UpsertLlmCreatorRequest,
-    creator_id: str | None = None,
-) -> llm_model.LlmModelCreator:
-    """Create or update a model creator."""
-    data: Any = {
-        "name": request.name,
-        "displayName": request.display_name,
-        "description": request.description,
-        "websiteUrl": request.website_url,
-        "logoUrl": request.logo_url,
-        "metadata": prisma.Json(request.metadata or {}),
-    }
-    if creator_id:
-        record = await prisma.models.LlmModelCreator.prisma().update(
-            where={"id": creator_id},
-            data=data,
-        )
-    else:
-        record = await prisma.models.LlmModelCreator.prisma().create(data=data)
-    if record is None:
-        raise ValueError("Failed to create/update creator")
-    return _map_creator(record)
-
-
-async def delete_creator(creator_id: str) -> bool:
-    """
-    Delete a model creator.
-
-    This will set creatorId to NULL on all associated models (due to onDelete: SetNull).
-
-    Args:
-        creator_id: UUID of the creator to delete
-
-    Returns:
-        True if deleted successfully
-
-    Raises:
-        ValueError: If creator not found
-    """
-    creator = await prisma.models.LlmModelCreator.prisma().find_unique(
-        where={"id": creator_id}
-    )
-    if not creator:
-        raise ValueError(f"Creator with id '{creator_id}' not found")
-
-    await prisma.models.LlmModelCreator.prisma().delete(where={"id": creator_id})
-    return True
-
-
-async def get_recommended_model() -> llm_model.LlmModel | None:
-    """
-    Get the currently recommended LLM model.
-
-    Returns:
-        The recommended model, or None if no model is marked as recommended.
-    """
-    record = await prisma.models.LlmModel.prisma().find_first(
-        where={"isRecommended": True, "isEnabled": True},
-        include={"Costs": True, "Creator": True},
-    )
-    return _map_model(record) if record else None
-
-
-async def set_recommended_model(
-    model_id: str,
-) -> tuple[llm_model.LlmModel, str | None]:
-    """
-    Set a model as the recommended model.
-
-    This will clear the isRecommended flag from any other model and set it
-    on the specified model. The model must be enabled.
-
-    Args:
-        model_id: UUID of the model to set as recommended
-
-    Returns:
-        Tuple of (the updated model, previous recommended model slug or None)
-
-    Raises:
-        ValueError: If model not found or not enabled
-    """
-    # First, verify the model exists and is enabled
-    target_model = await prisma.models.LlmModel.prisma().find_unique(
-        where={"id": model_id}
-    )
-    if not target_model:
-        raise ValueError(f"Model with id '{model_id}' not found")
-    if not target_model.isEnabled:
-        raise ValueError(
-            f"Cannot set disabled model '{target_model.slug}' as recommended"
-        )
-
-    # Get the current recommended model (if any)
-    current_recommended = await prisma.models.LlmModel.prisma().find_first(
-        where={"isRecommended": True}
-    )
-    previous_slug = current_recommended.slug if current_recommended else None
-
-    # Use a transaction to ensure atomicity
-    async with transaction() as tx:
-        # Clear isRecommended from all models
-        await tx.llmmodel.update_many(
-            where={"isRecommended": True},
-            data={"isRecommended": False},
-        )
-        # Set the new recommended model
-        await tx.llmmodel.update(
-            where={"id": model_id},
-            data={"isRecommended": True},
-        )
-
-    # Fetch and return the updated model
-    updated_record = await prisma.models.LlmModel.prisma().find_unique(
-        where={"id": model_id},
-        include={"Costs": True, "Creator": True},
-    )
-    if not updated_record:
-        raise ValueError("Failed to fetch updated model")
-
-    return _map_model(updated_record), previous_slug
-
-
-async def get_recommended_model_slug() -> str | None:
-    """
-    Get the slug of the currently recommended LLM model.
-
-    Returns:
-        The slug of the recommended model, or None if no model is marked as recommended.
-    """
-    record = await prisma.models.LlmModel.prisma().find_first(
-        where={"isRecommended": True, "isEnabled": True},
-    )
-    return record.slug if record else None

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

@@ -1,235 +0,0 @@
-from __future__ import annotations
-
-import re
-from datetime import datetime
-from typing import Any, Optional
-
-import prisma.enums
-import pydantic
-
-from backend.util.models import Pagination
-
-# Pattern for valid model slugs: alphanumeric start, then alphanumeric, dots, underscores, slashes, hyphens
-SLUG_PATTERN = re.compile(r"^[a-zA-Z0-9][a-zA-Z0-9._/-]*$")
-
-
-class LlmModelCost(pydantic.BaseModel):
-    id: str
-    unit: prisma.enums.LlmCostUnit = prisma.enums.LlmCostUnit.RUN
-    credit_cost: int
-    credential_provider: str
-    credential_id: Optional[str] = None
-    credential_type: Optional[str] = None
-    currency: Optional[str] = None
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-
-
-class LlmModelCreator(pydantic.BaseModel):
-    """Represents the organization that created/trained the model (e.g., OpenAI, Meta)."""
-
-    id: str
-    name: str
-    display_name: str
-    description: Optional[str] = None
-    website_url: Optional[str] = None
-    logo_url: Optional[str] = None
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-
-
-class LlmModel(pydantic.BaseModel):
-    id: str
-    slug: str
-    display_name: str
-    description: Optional[str] = None
-    provider_id: str
-    creator_id: Optional[str] = None
-    creator: Optional[LlmModelCreator] = None
-    context_window: int
-    max_output_tokens: Optional[int] = None
-    is_enabled: bool = True
-    is_recommended: bool = False
-    capabilities: dict[str, Any] = pydantic.Field(default_factory=dict)
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-    costs: list[LlmModelCost] = pydantic.Field(default_factory=list)
-
-
-class LlmProvider(pydantic.BaseModel):
-    id: str
-    name: str
-    display_name: str
-    description: Optional[str] = None
-    default_credential_provider: Optional[str] = None
-    default_credential_id: Optional[str] = None
-    default_credential_type: Optional[str] = None
-    supports_tools: bool = True
-    supports_json_output: bool = True
-    supports_reasoning: bool = False
-    supports_parallel_tool: bool = False
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-    models: list[LlmModel] = pydantic.Field(default_factory=list)
-
-
-class LlmProvidersResponse(pydantic.BaseModel):
-    providers: list[LlmProvider]
-
-
-class LlmModelsResponse(pydantic.BaseModel):
-    models: list[LlmModel]
-    pagination: Optional[Pagination] = None
-
-
-class LlmCreatorsResponse(pydantic.BaseModel):
-    creators: list[LlmModelCreator]
-
-
-class UpsertLlmProviderRequest(pydantic.BaseModel):
-    name: str
-    display_name: str
-    description: Optional[str] = None
-    default_credential_provider: Optional[str] = None
-    default_credential_id: Optional[str] = None
-    default_credential_type: Optional[str] = "api_key"
-    supports_tools: bool = True
-    supports_json_output: bool = True
-    supports_reasoning: bool = False
-    supports_parallel_tool: bool = False
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-
-
-class UpsertLlmCreatorRequest(pydantic.BaseModel):
-    name: str
-    display_name: str
-    description: Optional[str] = None
-    website_url: Optional[str] = None
-    logo_url: Optional[str] = None
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-
-
-class LlmModelCostInput(pydantic.BaseModel):
-    unit: prisma.enums.LlmCostUnit = prisma.enums.LlmCostUnit.RUN
-    credit_cost: int
-    credential_provider: str
-    credential_id: Optional[str] = None
-    credential_type: Optional[str] = "api_key"
-    currency: Optional[str] = None
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-
-
-class CreateLlmModelRequest(pydantic.BaseModel):
-    slug: str
-    display_name: str
-    description: Optional[str] = None
-    provider_id: str
-    creator_id: Optional[str] = None
-    context_window: int
-    max_output_tokens: Optional[int] = None
-    is_enabled: bool = True
-    capabilities: dict[str, Any] = pydantic.Field(default_factory=dict)
-    metadata: dict[str, Any] = pydantic.Field(default_factory=dict)
-    costs: list[LlmModelCostInput]
-
-    @pydantic.field_validator("slug")
-    @classmethod
-    def validate_slug(cls, v: str) -> str:
-        if not v or len(v) > 100:
-            raise ValueError("Slug must be 1-100 characters")
-        if not SLUG_PATTERN.match(v):
-            raise ValueError(
-                "Slug must start with alphanumeric and contain only "
-                "alphanumeric characters, dots, underscores, slashes, or hyphens"
-            )
-        return v
-
-
-class UpdateLlmModelRequest(pydantic.BaseModel):
-    display_name: Optional[str] = None
-    description: Optional[str] = None
-    context_window: Optional[int] = None
-    max_output_tokens: Optional[int] = None
-    is_enabled: Optional[bool] = None
-    capabilities: Optional[dict[str, Any]] = None
-    metadata: Optional[dict[str, Any]] = None
-    provider_id: Optional[str] = None
-    creator_id: Optional[str] = None
-    costs: Optional[list[LlmModelCostInput]] = None
-
-
-class ToggleLlmModelRequest(pydantic.BaseModel):
-    is_enabled: bool
-    migrate_to_slug: Optional[str] = None
-    migration_reason: Optional[str] = None  # e.g., "Provider outage"
-    # Custom pricing override for migrated workflows. When set, billing should use
-    # this cost instead of the target model's cost for affected nodes.
-    # See LlmModelMigration in schema.prisma for full documentation.
-    custom_credit_cost: Optional[int] = None
-
-
-class ToggleLlmModelResponse(pydantic.BaseModel):
-    model: LlmModel
-    nodes_migrated: int = 0
-    migrated_to_slug: Optional[str] = None
-    migration_id: Optional[str] = None  # ID of the migration record for revert
-
-
-class DeleteLlmModelResponse(pydantic.BaseModel):
-    deleted_model_slug: str
-    deleted_model_display_name: str
-    replacement_model_slug: Optional[str] = None
-    nodes_migrated: int
-    message: str
-
-
-class LlmModelUsageResponse(pydantic.BaseModel):
-    model_slug: str
-    node_count: int
-
-
-# Migration tracking models
-class LlmModelMigration(pydantic.BaseModel):
-    id: str
-    source_model_slug: str
-    target_model_slug: str
-    reason: Optional[str] = None
-    node_count: int
-    # Custom pricing override - billing should use this instead of target model's cost
-    custom_credit_cost: Optional[int] = None
-    is_reverted: bool = False
-    created_at: datetime
-    reverted_at: Optional[datetime] = None
-
-
-class LlmMigrationsResponse(pydantic.BaseModel):
-    migrations: list[LlmModelMigration]
-
-
-class RevertMigrationRequest(pydantic.BaseModel):
-    re_enable_source_model: bool = (
-        True  # Whether to re-enable the source model if disabled
-    )
-
-
-class RevertMigrationResponse(pydantic.BaseModel):
-    migration_id: str
-    source_model_slug: str
-    target_model_slug: str
-    nodes_reverted: int
-    nodes_already_changed: int = (
-        0  # Nodes that were modified since migration (not reverted)
-    )
-    source_model_re_enabled: bool = False  # Whether the source model was re-enabled
-    message: str
-
-
-class SetRecommendedModelRequest(pydantic.BaseModel):
-    model_id: str
-
-
-class SetRecommendedModelResponse(pydantic.BaseModel):
-    model: LlmModel
-    previous_recommended_slug: Optional[str] = None
-    message: str
-
-
-class RecommendedModelResponse(pydantic.BaseModel):
-    model: Optional[LlmModel] = None
-    slug: Optional[str] = None

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

@@ -1,29 +0,0 @@
-import autogpt_libs.auth
-import fastapi
-
-from backend.server.v2.llm import db as llm_db
-from backend.server.v2.llm import model as llm_model
-
-router = fastapi.APIRouter(
-    prefix="/llm",
-    tags=["llm"],
-    dependencies=[fastapi.Security(autogpt_libs.auth.requires_user)],
-)
-
-
-@router.get("/models", response_model=llm_model.LlmModelsResponse)
-async def list_models(
-    page: int = fastapi.Query(default=1, ge=1, description="Page number (1-indexed)"),
-    page_size: int = fastapi.Query(
-        default=50, ge=1, le=100, description="Number of models per page"
-    ),
-):
-    """List all enabled LLM models available to users."""
-    return await llm_db.list_models(enabled_only=True, page=page, page_size=page_size)
-
-
-@router.get("/providers", response_model=llm_model.LlmProvidersResponse)
-async def list_providers():
-    """List all LLM providers with their enabled models."""
-    providers = await llm_db.list_providers(include_models=True, enabled_only=True)
-    return llm_model.LlmProvidersResponse(providers=providers)

autogpt_platform/backend/backend/util/settings.py

@@ -350,6 +350,19 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Whether to mark failed scans as clean or not",
     )
 
+    agentgenerator_host: str = Field(
+        default="",
+        description="The host for the Agent Generator service (empty to use built-in)",
+    )
+    agentgenerator_port: int = Field(
+        default=8000,
+        description="The port for the Agent Generator service",
+    )
+    agentgenerator_timeout: int = Field(
+        default=120,
+        description="The timeout in seconds for Agent Generator service requests",
+    )
+
     enable_example_blocks: bool = Field(
         default=False,
         description="Whether to enable example blocks in production",

autogpt_platform/backend/migrations/20251126113000_add_llm_registry/migration.sql

@@ -1,81 +0,0 @@
--- CreateEnum
-CREATE TYPE "LlmCostUnit" AS ENUM ('RUN', 'TOKENS');
-
--- CreateTable
-CREATE TABLE "LlmProvider" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "name" TEXT NOT NULL,
-    "displayName" TEXT NOT NULL,
-    "description" TEXT,
-    "defaultCredentialProvider" TEXT,
-    "defaultCredentialId" TEXT,
-    "defaultCredentialType" TEXT,
-    "supportsTools" BOOLEAN NOT NULL DEFAULT TRUE,
-    "supportsJsonOutput" BOOLEAN NOT NULL DEFAULT TRUE,
-    "supportsReasoning" BOOLEAN NOT NULL DEFAULT FALSE,
-    "supportsParallelTool" BOOLEAN NOT NULL DEFAULT FALSE,
-    "metadata" JSONB NOT NULL DEFAULT '{}'::jsonb,
-
-    CONSTRAINT "LlmProvider_pkey" PRIMARY KEY ("id"),
-    CONSTRAINT "LlmProvider_name_key" UNIQUE ("name")
-);
-
--- CreateTable
-CREATE TABLE "LlmModel" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "slug" TEXT NOT NULL,
-    "displayName" TEXT NOT NULL,
-    "description" TEXT,
-    "providerId" TEXT NOT NULL,
-    "contextWindow" INTEGER NOT NULL,
-    "maxOutputTokens" INTEGER,
-    "isEnabled" BOOLEAN NOT NULL DEFAULT TRUE,
-    "capabilities" JSONB NOT NULL DEFAULT '{}'::jsonb,
-    "metadata" JSONB NOT NULL DEFAULT '{}'::jsonb,
-
-    CONSTRAINT "LlmModel_pkey" PRIMARY KEY ("id"),
-    CONSTRAINT "LlmModel_slug_key" UNIQUE ("slug")
-);
-
--- CreateTable
-CREATE TABLE "LlmModelCost" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "unit" "LlmCostUnit" NOT NULL DEFAULT 'RUN',
-    "creditCost" INTEGER NOT NULL,
-    "credentialProvider" TEXT NOT NULL,
-    "credentialId" TEXT,
-    "credentialType" TEXT,
-    "currency" TEXT,
-    "metadata" JSONB NOT NULL DEFAULT '{}'::jsonb,
-    "llmModelId" TEXT NOT NULL,
-
-    CONSTRAINT "LlmModelCost_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE INDEX "LlmModel_providerId_isEnabled_idx" ON "LlmModel"("providerId", "isEnabled");
-
--- CreateIndex
-CREATE INDEX "LlmModel_slug_idx" ON "LlmModel"("slug");
-
--- CreateIndex
-CREATE INDEX "LlmModelCost_llmModelId_idx" ON "LlmModelCost"("llmModelId");
-
--- CreateIndex
-CREATE INDEX "LlmModelCost_credentialProvider_idx" ON "LlmModelCost"("credentialProvider");
-
--- CreateIndex
-CREATE UNIQUE INDEX "LlmModelCost_llmModelId_credentialProvider_unit_key" ON "LlmModelCost"("llmModelId", "credentialProvider", "unit");
-
--- AddForeignKey
-ALTER TABLE "LlmModel" ADD CONSTRAINT "LlmModel_providerId_fkey" FOREIGN KEY ("providerId") REFERENCES "LlmProvider"("id") ON DELETE RESTRICT ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "LlmModelCost" ADD CONSTRAINT "LlmModelCost_llmModelId_fkey" FOREIGN KEY ("llmModelId") REFERENCES "LlmModel"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-

autogpt_platform/backend/migrations/20251126120000_seed_llm_registry/migration.sql

@@ -1,226 +0,0 @@
--- Seed LLM Registry from existing hard-coded data
--- This migration populates the LlmProvider, LlmModel, and LlmModelCost tables
--- with data from the existing MODEL_METADATA and MODEL_COST dictionaries
-
--- Insert Providers
-INSERT INTO "LlmProvider" ("id", "name", "displayName", "description", "defaultCredentialProvider", "defaultCredentialType", "supportsTools", "supportsJsonOutput", "supportsReasoning", "supportsParallelTool", "metadata")
-VALUES
-    (gen_random_uuid(), 'openai', 'OpenAI', 'OpenAI language models', 'openai', 'api_key', true, true, true, true, '{}'::jsonb),
-    (gen_random_uuid(), 'anthropic', 'Anthropic', 'Anthropic Claude models', 'anthropic', 'api_key', true, true, true, false, '{}'::jsonb),
-    (gen_random_uuid(), 'groq', 'Groq', 'Groq inference API', 'groq', 'api_key', false, true, false, false, '{}'::jsonb),
-    (gen_random_uuid(), 'open_router', 'OpenRouter', 'OpenRouter unified API', 'open_router', 'api_key', true, true, false, false, '{}'::jsonb),
-    (gen_random_uuid(), 'aiml_api', 'AI/ML API', 'AI/ML API models', 'aiml_api', 'api_key', false, true, false, false, '{}'::jsonb),
-    (gen_random_uuid(), 'ollama', 'Ollama', 'Ollama local models', 'ollama', 'api_key', false, true, false, false, '{}'::jsonb),
-    (gen_random_uuid(), 'llama_api', 'Llama API', 'Llama API models', 'llama_api', 'api_key', false, true, false, false, '{}'::jsonb),
-    (gen_random_uuid(), 'v0', 'v0', 'v0 by Vercel models', 'v0', 'api_key', true, true, false, false, '{}'::jsonb)
-ON CONFLICT ("name") DO NOTHING;
-
--- Insert Models (using CTEs to reference provider IDs)
-WITH provider_ids AS (
-    SELECT "id", "name" FROM "LlmProvider"
-)
-INSERT INTO "LlmModel" ("id", "slug", "displayName", "description", "providerId", "contextWindow", "maxOutputTokens", "isEnabled", "capabilities", "metadata")
-SELECT
-    gen_random_uuid(),
-    model_slug,
-    model_display_name,
-    NULL,
-    p."id",
-    context_window,
-    max_output_tokens,
-    true,
-    '{}'::jsonb,
-    '{}'::jsonb
-FROM (VALUES
-    -- OpenAI models
-    ('o3', 'O3', 'openai', 200000, 100000),
-    ('o3-mini', 'O3 Mini', 'openai', 200000, 100000),
-    ('o1', 'O1', 'openai', 200000, 100000),
-    ('o1-mini', 'O1 Mini', 'openai', 128000, 65536),
-    ('gpt-5-2025-08-07', 'GPT 5', 'openai', 400000, 128000),
-    ('gpt-5.1-2025-11-13', 'GPT 5.1', 'openai', 400000, 128000),
-    ('gpt-5-mini-2025-08-07', 'GPT 5 Mini', 'openai', 400000, 128000),
-    ('gpt-5-nano-2025-08-07', 'GPT 5 Nano', 'openai', 400000, 128000),
-    ('gpt-5-chat-latest', 'GPT 5 Chat', 'openai', 400000, 16384),
-    ('gpt-4.1-2025-04-14', 'GPT 4.1', 'openai', 1000000, 32768),
-    ('gpt-4.1-mini-2025-04-14', 'GPT 4.1 Mini', 'openai', 1047576, 32768),
-    ('gpt-4o-mini', 'GPT 4o Mini', 'openai', 128000, 16384),
-    ('gpt-4o', 'GPT 4o', 'openai', 128000, 16384),
-    ('gpt-4-turbo', 'GPT 4 Turbo', 'openai', 128000, 4096),
-    ('gpt-3.5-turbo', 'GPT 3.5 Turbo', 'openai', 16385, 4096),
-    -- Anthropic models
-    ('claude-opus-4-1-20250805', 'Claude 4.1 Opus', 'anthropic', 200000, 32000),
-    ('claude-opus-4-20250514', 'Claude 4 Opus', 'anthropic', 200000, 32000),
-    ('claude-sonnet-4-20250514', 'Claude 4 Sonnet', 'anthropic', 200000, 64000),
-    ('claude-opus-4-5-20251101', 'Claude 4.5 Opus', 'anthropic', 200000, 64000),
-    ('claude-sonnet-4-5-20250929', 'Claude 4.5 Sonnet', 'anthropic', 200000, 64000),
-    ('claude-haiku-4-5-20251001', 'Claude 4.5 Haiku', 'anthropic', 200000, 64000),
-    ('claude-3-7-sonnet-20250219', 'Claude 3.7 Sonnet', 'anthropic', 200000, 64000),
-    ('claude-3-haiku-20240307', 'Claude 3 Haiku', 'anthropic', 200000, 4096),
-    -- AI/ML API models
-    ('Qwen/Qwen2.5-72B-Instruct-Turbo', 'Qwen 2.5 72B', 'aiml_api', 32000, 8000),
-    ('nvidia/llama-3.1-nemotron-70b-instruct', 'Llama 3.1 Nemotron 70B', 'aiml_api', 128000, 40000),
-    ('meta-llama/Llama-3.3-70B-Instruct-Turbo', 'Llama 3.3 70B', 'aiml_api', 128000, NULL),
-    ('meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', 'Meta Llama 3.1 70B', 'aiml_api', 131000, 2000),
-    ('meta-llama/Llama-3.2-3B-Instruct-Turbo', 'Llama 3.2 3B', 'aiml_api', 128000, NULL),
-    -- Groq models
-    ('llama-3.3-70b-versatile', 'Llama 3.3 70B', 'groq', 128000, 32768),
-    ('llama-3.1-8b-instant', 'Llama 3.1 8B', 'groq', 128000, 8192),
-    -- Ollama models
-    ('llama3.3', 'Llama 3.3', 'ollama', 8192, NULL),
-    ('llama3.2', 'Llama 3.2', 'ollama', 8192, NULL),
-    ('llama3', 'Llama 3', 'ollama', 8192, NULL),
-    ('llama3.1:405b', 'Llama 3.1 405B', 'ollama', 8192, NULL),
-    ('dolphin-mistral:latest', 'Dolphin Mistral', 'ollama', 32768, NULL),
-    -- OpenRouter models
-    ('google/gemini-2.5-pro-preview-03-25', 'Gemini 2.5 Pro', 'open_router', 1050000, 8192),
-    ('google/gemini-3-pro-preview', 'Gemini 3 Pro Preview', 'open_router', 1048576, 65535),
-    ('google/gemini-2.5-flash', 'Gemini 2.5 Flash', 'open_router', 1048576, 65535),
-    ('google/gemini-2.0-flash-001', 'Gemini 2.0 Flash', 'open_router', 1048576, 8192),
-    ('google/gemini-2.5-flash-lite-preview-06-17', 'Gemini 2.5 Flash Lite Preview', 'open_router', 1048576, 65535),
-    ('google/gemini-2.0-flash-lite-001', 'Gemini 2.0 Flash Lite', 'open_router', 1048576, 8192),
-    ('mistralai/mistral-nemo', 'Mistral Nemo', 'open_router', 128000, 4096),
-    ('cohere/command-r-08-2024', 'Command R', 'open_router', 128000, 4096),
-    ('cohere/command-r-plus-08-2024', 'Command R Plus', 'open_router', 128000, 4096),
-    ('deepseek/deepseek-chat', 'DeepSeek Chat', 'open_router', 64000, 2048),
-    ('deepseek/deepseek-r1-0528', 'DeepSeek R1', 'open_router', 163840, 163840),
-    ('perplexity/sonar', 'Perplexity Sonar', 'open_router', 127000, 8000),
-    ('perplexity/sonar-pro', 'Perplexity Sonar Pro', 'open_router', 200000, 8000),
-    ('perplexity/sonar-deep-research', 'Perplexity Sonar Deep Research', 'open_router', 128000, 16000),
-    ('nousresearch/hermes-3-llama-3.1-405b', 'Hermes 3 Llama 3.1 405B', 'open_router', 131000, 4096),
-    ('nousresearch/hermes-3-llama-3.1-70b', 'Hermes 3 Llama 3.1 70B', 'open_router', 12288, 12288),
-    ('openai/gpt-oss-120b', 'GPT OSS 120B', 'open_router', 131072, 131072),
-    ('openai/gpt-oss-20b', 'GPT OSS 20B', 'open_router', 131072, 32768),
-    ('amazon/nova-lite-v1', 'Amazon Nova Lite', 'open_router', 300000, 5120),
-    ('amazon/nova-micro-v1', 'Amazon Nova Micro', 'open_router', 128000, 5120),
-    ('amazon/nova-pro-v1', 'Amazon Nova Pro', 'open_router', 300000, 5120),
-    ('microsoft/wizardlm-2-8x22b', 'WizardLM 2 8x22B', 'open_router', 65536, 4096),
-    ('gryphe/mythomax-l2-13b', 'MythoMax L2 13B', 'open_router', 4096, 4096),
-    ('meta-llama/llama-4-scout', 'Llama 4 Scout', 'open_router', 131072, 131072),
-    ('meta-llama/llama-4-maverick', 'Llama 4 Maverick', 'open_router', 1048576, 1000000),
-    ('x-ai/grok-4', 'Grok 4', 'open_router', 256000, 256000),
-    ('x-ai/grok-4-fast', 'Grok 4 Fast', 'open_router', 2000000, 30000),
-    ('x-ai/grok-4.1-fast', 'Grok 4.1 Fast', 'open_router', 2000000, 30000),
-    ('x-ai/grok-code-fast-1', 'Grok Code Fast 1', 'open_router', 256000, 10000),
-    ('moonshotai/kimi-k2', 'Kimi K2', 'open_router', 131000, 131000),
-    ('qwen/qwen3-235b-a22b-thinking-2507', 'Qwen 3 235B Thinking', 'open_router', 262144, 262144),
-    ('qwen/qwen3-coder', 'Qwen 3 Coder', 'open_router', 262144, 262144),
-    -- Llama API models
-    ('Llama-4-Scout-17B-16E-Instruct-FP8', 'Llama 4 Scout', 'llama_api', 128000, 4028),
-    ('Llama-4-Maverick-17B-128E-Instruct-FP8', 'Llama 4 Maverick', 'llama_api', 128000, 4028),
-    ('Llama-3.3-8B-Instruct', 'Llama 3.3 8B', 'llama_api', 128000, 4028),
-    ('Llama-3.3-70B-Instruct', 'Llama 3.3 70B', 'llama_api', 128000, 4028),
-    -- v0 models
-    ('v0-1.5-md', 'v0 1.5 MD', 'v0', 128000, 64000),
-    ('v0-1.5-lg', 'v0 1.5 LG', 'v0', 512000, 64000),
-    ('v0-1.0-md', 'v0 1.0 MD', 'v0', 128000, 64000)
-) AS models(model_slug, model_display_name, provider_name, context_window, max_output_tokens)
-JOIN provider_ids p ON p."name" = models.provider_name
-ON CONFLICT ("slug") DO NOTHING;
-
--- Insert Costs (using CTEs to reference model IDs)
-WITH model_ids AS (
-    SELECT "id", "slug", "providerId" FROM "LlmModel"
-),
-provider_ids AS (
-    SELECT "id", "name" FROM "LlmProvider"
-)
-INSERT INTO "LlmModelCost" ("id", "unit", "creditCost", "credentialProvider", "credentialId", "credentialType", "currency", "metadata", "llmModelId")
-SELECT
-    gen_random_uuid(),
-    'RUN'::"LlmCostUnit",
-    cost,
-    p."name",
-    NULL,
-    'api_key',
-    NULL,
-    '{}'::jsonb,
-    m."id"
-FROM (VALUES
-    -- OpenAI costs
-    ('o3', 4),
-    ('o3-mini', 2),
-    ('o1', 16),
-    ('o1-mini', 4),
-    ('gpt-5-2025-08-07', 2),
-    ('gpt-5.1-2025-11-13', 5),
-    ('gpt-5-mini-2025-08-07', 1),
-    ('gpt-5-nano-2025-08-07', 1),
-    ('gpt-5-chat-latest', 5),
-    ('gpt-4.1-2025-04-14', 2),
-    ('gpt-4.1-mini-2025-04-14', 1),
-    ('gpt-4o-mini', 1),
-    ('gpt-4o', 3),
-    ('gpt-4-turbo', 10),
-    ('gpt-3.5-turbo', 1),
-    -- Anthropic costs
-    ('claude-opus-4-1-20250805', 21),
-    ('claude-opus-4-20250514', 21),
-    ('claude-sonnet-4-20250514', 5),
-    ('claude-haiku-4-5-20251001', 4),
-    ('claude-opus-4-5-20251101', 14),
-    ('claude-sonnet-4-5-20250929', 9),
-    ('claude-3-7-sonnet-20250219', 5),
-    ('claude-3-haiku-20240307', 1),
-    -- AI/ML API costs
-    ('Qwen/Qwen2.5-72B-Instruct-Turbo', 1),
-    ('nvidia/llama-3.1-nemotron-70b-instruct', 1),
-    ('meta-llama/Llama-3.3-70B-Instruct-Turbo', 1),
-    ('meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', 1),
-    ('meta-llama/Llama-3.2-3B-Instruct-Turbo', 1),
-    -- Groq costs
-    ('llama-3.3-70b-versatile', 1),
-    ('llama-3.1-8b-instant', 1),
-    -- Ollama costs
-    ('llama3.3', 1),
-    ('llama3.2', 1),
-    ('llama3', 1),
-    ('llama3.1:405b', 1),
-    ('dolphin-mistral:latest', 1),
-    -- OpenRouter costs
-    ('google/gemini-2.5-pro-preview-03-25', 4),
-    ('google/gemini-3-pro-preview', 5),
-    ('mistralai/mistral-nemo', 1),
-    ('cohere/command-r-08-2024', 1),
-    ('cohere/command-r-plus-08-2024', 3),
-    ('deepseek/deepseek-chat', 2),
-    ('perplexity/sonar', 1),
-    ('perplexity/sonar-pro', 5),
-    ('perplexity/sonar-deep-research', 10),
-    ('nousresearch/hermes-3-llama-3.1-405b', 1),
-    ('nousresearch/hermes-3-llama-3.1-70b', 1),
-    ('amazon/nova-lite-v1', 1),
-    ('amazon/nova-micro-v1', 1),
-    ('amazon/nova-pro-v1', 1),
-    ('microsoft/wizardlm-2-8x22b', 1),
-    ('gryphe/mythomax-l2-13b', 1),
-    ('meta-llama/llama-4-scout', 1),
-    ('meta-llama/llama-4-maverick', 1),
-    ('x-ai/grok-4', 9),
-    ('x-ai/grok-4-fast', 1),
-    ('x-ai/grok-4.1-fast', 1),
-    ('x-ai/grok-code-fast-1', 1),
-    ('moonshotai/kimi-k2', 1),
-    ('qwen/qwen3-235b-a22b-thinking-2507', 1),
-    ('qwen/qwen3-coder', 9),
-    ('google/gemini-2.5-flash', 1),
-    ('google/gemini-2.0-flash-001', 1),
-    ('google/gemini-2.5-flash-lite-preview-06-17', 1),
-    ('google/gemini-2.0-flash-lite-001', 1),
-    ('deepseek/deepseek-r1-0528', 1),
-    ('openai/gpt-oss-120b', 1),
-    ('openai/gpt-oss-20b', 1),
-    -- Llama API costs
-    ('Llama-4-Scout-17B-16E-Instruct-FP8', 1),
-    ('Llama-4-Maverick-17B-128E-Instruct-FP8', 1),
-    ('Llama-3.3-8B-Instruct', 1),
-    ('Llama-3.3-70B-Instruct', 1),
-    -- v0 costs
-    ('v0-1.5-md', 1),
-    ('v0-1.5-lg', 2),
-    ('v0-1.0-md', 1)
-) AS costs(model_slug, cost)
-JOIN model_ids m ON m."slug" = costs.model_slug
-JOIN provider_ids p ON p."id" = m."providerId"
-ON CONFLICT ("llmModelId", "credentialProvider", "unit") DO NOTHING;
-

autogpt_platform/backend/migrations/20251218100000_add_llm_model_migration/migration.sql

@@ -1,25 +0,0 @@
--- CreateTable
-CREATE TABLE "LlmModelMigration" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "sourceModelSlug" TEXT NOT NULL,
-    "targetModelSlug" TEXT NOT NULL,
-    "reason" TEXT,
-    "migratedNodeIds" JSONB NOT NULL DEFAULT '[]',
-    "nodeCount" INTEGER NOT NULL,
-    "customCreditCost" INTEGER,
-    "isReverted" BOOLEAN NOT NULL DEFAULT false,
-    "revertedAt" TIMESTAMP(3),
-
-    CONSTRAINT "LlmModelMigration_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE INDEX "LlmModelMigration_sourceModelSlug_idx" ON "LlmModelMigration"("sourceModelSlug");
-
--- CreateIndex
-CREATE INDEX "LlmModelMigration_targetModelSlug_idx" ON "LlmModelMigration"("targetModelSlug");
-
--- CreateIndex
-CREATE INDEX "LlmModelMigration_isReverted_idx" ON "LlmModelMigration"("isReverted");

autogpt_platform/backend/migrations/20251224100000_add_llm_model_creator/migration.sql

@@ -1,127 +0,0 @@
--- Add LlmModelCreator table
--- Creator represents who made/trained the model (e.g., OpenAI, Meta)
--- This is distinct from Provider who hosts/serves the model (e.g., OpenRouter)
-
--- Create the LlmModelCreator table
-CREATE TABLE "LlmModelCreator" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "name" TEXT NOT NULL,
-    "displayName" TEXT NOT NULL,
-    "description" TEXT,
-    "websiteUrl" TEXT,
-    "logoUrl" TEXT,
-    "metadata" JSONB NOT NULL DEFAULT '{}',
-
-    CONSTRAINT "LlmModelCreator_pkey" PRIMARY KEY ("id")
-);
-
--- Create unique index on name
-CREATE UNIQUE INDEX "LlmModelCreator_name_key" ON "LlmModelCreator"("name");
-
--- Add creatorId column to LlmModel
-ALTER TABLE "LlmModel" ADD COLUMN "creatorId" TEXT;
-
--- Add foreign key constraint
-ALTER TABLE "LlmModel" ADD CONSTRAINT "LlmModel_creatorId_fkey"
-    FOREIGN KEY ("creatorId") REFERENCES "LlmModelCreator"("id") ON DELETE SET NULL ON UPDATE CASCADE;
-
--- Create index on creatorId
-CREATE INDEX "LlmModel_creatorId_idx" ON "LlmModel"("creatorId");
-
--- Seed creators based on known model creators
-INSERT INTO "LlmModelCreator" ("id", "updatedAt", "name", "displayName", "description", "websiteUrl", "metadata")
-VALUES
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'openai', 'OpenAI', 'Creator of GPT models', 'https://openai.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'anthropic', 'Anthropic', 'Creator of Claude models', 'https://anthropic.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'meta', 'Meta', 'Creator of Llama models', 'https://ai.meta.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'google', 'Google', 'Creator of Gemini models', 'https://deepmind.google', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'mistral', 'Mistral AI', 'Creator of Mistral models', 'https://mistral.ai', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'cohere', 'Cohere', 'Creator of Command models', 'https://cohere.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'deepseek', 'DeepSeek', 'Creator of DeepSeek models', 'https://deepseek.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'perplexity', 'Perplexity AI', 'Creator of Sonar models', 'https://perplexity.ai', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'qwen', 'Qwen (Alibaba)', 'Creator of Qwen models', 'https://qwenlm.github.io', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'xai', 'xAI', 'Creator of Grok models', 'https://x.ai', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'amazon', 'Amazon', 'Creator of Nova models', 'https://aws.amazon.com/bedrock', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'microsoft', 'Microsoft', 'Creator of WizardLM models', 'https://microsoft.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'moonshot', 'Moonshot AI', 'Creator of Kimi models', 'https://moonshot.cn', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'nvidia', 'NVIDIA', 'Creator of Nemotron models', 'https://nvidia.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'nous_research', 'Nous Research', 'Creator of Hermes models', 'https://nousresearch.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'vercel', 'Vercel', 'Creator of v0 models', 'https://vercel.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'cognitive_computations', 'Cognitive Computations', 'Creator of Dolphin models', 'https://erichartford.com', '{}'),
-    (gen_random_uuid(), CURRENT_TIMESTAMP, 'gryphe', 'Gryphe', 'Creator of MythoMax models', 'https://huggingface.co/Gryphe', '{}')
-ON CONFLICT ("name") DO NOTHING;
-
--- Update existing models with their creators
--- OpenAI models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'openai')
-WHERE "slug" LIKE 'gpt-%' OR "slug" LIKE 'o1%' OR "slug" LIKE 'o3%' OR "slug" LIKE 'openai/%';
-
--- Anthropic models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'anthropic')
-WHERE "slug" LIKE 'claude-%';
-
--- Meta/Llama models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'meta')
-WHERE "slug" LIKE 'llama%' OR "slug" LIKE 'Llama%' OR "slug" LIKE 'meta-llama/%' OR "slug" LIKE '%/llama-%';
-
--- Google models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'google')
-WHERE "slug" LIKE 'google/%' OR "slug" LIKE 'gemini%';
-
--- Mistral models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'mistral')
-WHERE "slug" LIKE 'mistral%' OR "slug" LIKE 'mistralai/%';
-
--- Cohere models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'cohere')
-WHERE "slug" LIKE 'cohere/%' OR "slug" LIKE 'command-%';
-
--- DeepSeek models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'deepseek')
-WHERE "slug" LIKE 'deepseek/%' OR "slug" LIKE 'deepseek-%';
-
--- Perplexity models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'perplexity')
-WHERE "slug" LIKE 'perplexity/%' OR "slug" LIKE 'sonar%';
-
--- Qwen models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'qwen')
-WHERE "slug" LIKE 'Qwen/%' OR "slug" LIKE 'qwen/%';
-
--- xAI/Grok models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'xai')
-WHERE "slug" LIKE 'x-ai/%' OR "slug" LIKE 'grok%';
-
--- Amazon models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'amazon')
-WHERE "slug" LIKE 'amazon/%' OR "slug" LIKE 'nova-%';
-
--- Microsoft models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'microsoft')
-WHERE "slug" LIKE 'microsoft/%' OR "slug" LIKE 'wizardlm%';
-
--- Moonshot models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'moonshot')
-WHERE "slug" LIKE 'moonshotai/%' OR "slug" LIKE 'kimi%';
-
--- NVIDIA models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'nvidia')
-WHERE "slug" LIKE 'nvidia/%' OR "slug" LIKE '%nemotron%';
-
--- Nous Research models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'nous_research')
-WHERE "slug" LIKE 'nousresearch/%' OR "slug" LIKE 'hermes%';
-
--- Vercel/v0 models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'vercel')
-WHERE "slug" LIKE 'v0-%';
-
--- Dolphin models (Cognitive Computations / Eric Hartford)
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'cognitive_computations')
-WHERE "slug" LIKE 'dolphin-%';
-
--- Gryphe models
-UPDATE "LlmModel" SET "creatorId" = (SELECT "id" FROM "LlmModelCreator" WHERE "name" = 'gryphe')
-WHERE "slug" LIKE 'gryphe/%' OR "slug" LIKE 'mythomax%';

autogpt_platform/backend/migrations/20260105120000_add_agent_node_model_index/migration.sql

@@ -1,4 +0,0 @@
--- CreateIndex
--- Index for efficient LLM model lookups on AgentNode.constantInput->>'model'
--- This improves performance of model migration queries in the LLM registry
-CREATE INDEX "AgentNode_constantInput_model_idx" ON "AgentNode" ((("constantInput"->>'model')));

autogpt_platform/backend/migrations/20260106150000_add_gpt52_model/migration.sql

@@ -1,52 +0,0 @@
--- Add GPT-5.2 model and update O3 slug
--- This migration adds the new GPT-5.2 model added in dev branch
-
--- Update O3 slug to match dev branch format
-UPDATE "LlmModel"
-SET "slug" = 'o3-2025-04-16'
-WHERE "slug" = 'o3';
-
--- Update cost reference for O3 if needed
--- (costs are linked by model ID, so no update needed)
-
--- Add GPT-5.2 model
-WITH provider_id AS (
-    SELECT "id" FROM "LlmProvider" WHERE "name" = 'openai'
-)
-INSERT INTO "LlmModel" ("id", "slug", "displayName", "description", "providerId", "contextWindow", "maxOutputTokens", "isEnabled", "capabilities", "metadata")
-SELECT
-    gen_random_uuid(),
-    'gpt-5.2-2025-12-11',
-    'GPT 5.2',
-    'OpenAI GPT-5.2 model',
-    p."id",
-    400000,
-    128000,
-    true,
-    '{}'::jsonb,
-    '{}'::jsonb
-FROM provider_id p
-ON CONFLICT ("slug") DO NOTHING;
-
--- Add cost for GPT-5.2
-WITH model_id AS (
-    SELECT m."id", p."name" as provider_name
-    FROM "LlmModel" m
-    JOIN "LlmProvider" p ON p."id" = m."providerId"
-    WHERE m."slug" = 'gpt-5.2-2025-12-11'
-)
-INSERT INTO "LlmModelCost" ("id", "unit", "creditCost", "credentialProvider", "credentialId", "credentialType", "currency", "metadata", "llmModelId")
-SELECT
-    gen_random_uuid(),
-    'RUN'::"LlmCostUnit",
-    3,  -- Same cost tier as GPT-5.1
-    m.provider_name,
-    NULL,
-    'api_key',
-    NULL,
-    '{}'::jsonb,
-    m."id"
-FROM model_id m
-WHERE NOT EXISTS (
-    SELECT 1 FROM "LlmModelCost" c WHERE c."llmModelId" = m."id"
-);

autogpt_platform/backend/migrations/20260107100000_add_llm_recommended_model/migration.sql

@@ -1,11 +0,0 @@
--- Add isRecommended field to LlmModel table
--- This allows admins to mark a model as the recommended default
-
-ALTER TABLE "LlmModel" ADD COLUMN "isRecommended" BOOLEAN NOT NULL DEFAULT false;
-
--- Set gpt-4o-mini as the default recommended model (if it exists)
-UPDATE "LlmModel" SET "isRecommended" = true WHERE "slug" = 'gpt-4o-mini' AND "isEnabled" = true;
-
--- Create unique partial index to enforce only one recommended model at the database level
--- This prevents multiple rows from having isRecommended = true
-CREATE UNIQUE INDEX "LlmModel_single_recommended_idx" ON "LlmModel" ("isRecommended") WHERE "isRecommended" = true;

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -1,37 +1,12 @@
 -- CreateExtension
 -- Supabase: pgvector must be enabled via Dashboard → Database → Extensions first
--- Ensures vector extension is in the current schema (from DATABASE_URL ?schema= param)
--- If it exists in a different schema (e.g., public), we drop and recreate it in the current schema
+-- Creates extension in current schema (determined by search_path from DATABASE_URL ?schema= param)
 -- This ensures vector type is in the same schema as tables, making ::vector work without explicit qualification
 DO $$
-DECLARE
-    current_schema_name text;
-    vector_schema text;
 BEGIN
-    -- Get the current schema from search_path
-    SELECT current_schema() INTO current_schema_name;
-
-    -- Check if vector extension exists and which schema it's in
-    SELECT n.nspname INTO vector_schema
-    FROM pg_extension e
-    JOIN pg_namespace n ON e.extnamespace = n.oid
-    WHERE e.extname = 'vector';
-
-    -- Handle removal if in wrong schema
-    IF vector_schema IS NOT NULL AND vector_schema != current_schema_name THEN
-        BEGIN
-            -- Vector exists in a different schema, drop it first
-            RAISE WARNING 'pgvector found in schema "%" but need it in "%". Dropping and reinstalling...',
-                vector_schema, current_schema_name;
-            EXECUTE 'DROP EXTENSION IF EXISTS vector CASCADE';
-        EXCEPTION WHEN OTHERS THEN
-            RAISE EXCEPTION 'Failed to drop pgvector from schema "%": %. You may need to drop it manually.',
-                vector_schema, SQLERRM;
-        END;
-    END IF;
-
-    -- Create extension in current schema (let it fail naturally if not available)
-    EXECUTE format('CREATE EXTENSION IF NOT EXISTS vector SCHEMA %I', current_schema_name);
+    CREATE EXTENSION IF NOT EXISTS "vector";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'vector extension not available or already exists, skipping';
 END $$;
 
 -- CreateEnum

autogpt_platform/backend/migrations/20260112173500_add_supabase_extensions_to_platform_schema/migration.sql

@@ -0,0 +1,71 @@
+-- Acknowledge Supabase-managed extensions to prevent drift warnings
+-- These extensions are pre-installed by Supabase in specific schemas
+-- This migration ensures they exist where available (Supabase) or skips gracefully (CI)
+
+-- Create schemas (safe in both CI and Supabase)
+CREATE SCHEMA IF NOT EXISTS "extensions";
+
+-- Extensions that exist in both CI and Supabase
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgcrypto" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgcrypto extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "uuid-ossp" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'uuid-ossp extension not available, skipping';
+END $$;
+
+-- Supabase-specific extensions (skip gracefully in CI)
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_stat_statements" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_stat_statements extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pg_net" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_net extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE EXTENSION IF NOT EXISTS "pgjwt" WITH SCHEMA "extensions";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgjwt extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "graphql";
+    CREATE EXTENSION IF NOT EXISTS "pg_graphql" WITH SCHEMA "graphql";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pg_graphql extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "pgsodium";
+    CREATE EXTENSION IF NOT EXISTS "pgsodium" WITH SCHEMA "pgsodium";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'pgsodium extension not available, skipping';
+END $$;
+
+DO $$
+BEGIN
+    CREATE SCHEMA IF NOT EXISTS "vault";
+    CREATE EXTENSION IF NOT EXISTS "supabase_vault" WITH SCHEMA "vault";
+EXCEPTION WHEN OTHERS THEN
+    RAISE NOTICE 'supabase_vault extension not available, skipping';
+END $$;
+
+
+-- Return to platform
+CREATE SCHEMA IF NOT EXISTS "platform";

autogpt_platform/backend/migrations/20260122000000_add_llm_price_tier/migration.sql

@@ -1,61 +0,0 @@
--- Add new columns to LlmModel table for extended model metadata
--- These columns support the LLM Picker UI enhancements
-
--- Add priceTier column: 1=cheapest, 2=medium, 3=expensive
-ALTER TABLE "LlmModel" ADD COLUMN IF NOT EXISTS "priceTier" INTEGER NOT NULL DEFAULT 1;
-
--- Add creatorId column for model creator relationship (if not exists)
-ALTER TABLE "LlmModel" ADD COLUMN IF NOT EXISTS "creatorId" TEXT;
-
--- Add isRecommended column (if not exists)
-ALTER TABLE "LlmModel" ADD COLUMN IF NOT EXISTS "isRecommended" BOOLEAN NOT NULL DEFAULT FALSE;
-
--- Add index on creatorId if not exists
-CREATE INDEX IF NOT EXISTS "LlmModel_creatorId_idx" ON "LlmModel"("creatorId");
-
--- Add foreign key for creatorId if not exists
-DO $$
-BEGIN
-    IF NOT EXISTS (SELECT 1 FROM pg_constraint WHERE conname = 'LlmModel_creatorId_fkey') THEN
-        -- Only add FK if LlmModelCreator table exists
-        IF EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = 'LlmModelCreator') THEN
-            ALTER TABLE "LlmModel" ADD CONSTRAINT "LlmModel_creatorId_fkey"
-            FOREIGN KEY ("creatorId") REFERENCES "LlmModelCreator"("id") ON DELETE SET NULL ON UPDATE CASCADE;
-        END IF;
-    END IF;
-END $$;
-
--- Update priceTier values for existing models based on original MODEL_METADATA
--- Tier 1 = cheapest, Tier 2 = medium, Tier 3 = expensive
-
--- OpenAI models
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" = 'o3';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'o3-mini';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" = 'o1';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" = 'o1-mini';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" = 'gpt-5.2';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" = 'gpt-5.1';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'gpt-5';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'gpt-5-mini';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'gpt-5-nano';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" = 'gpt-5-chat-latest';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" LIKE 'gpt-4.1%';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'gpt-4o-mini';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" = 'gpt-4o';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" = 'gpt-4-turbo';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'gpt-3.5-turbo';
-
--- Anthropic models
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" LIKE 'claude-opus%';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" LIKE 'claude-sonnet%';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" LIKE 'claude%-4-5-sonnet%';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" LIKE 'claude%-haiku%';
-UPDATE "LlmModel" SET "priceTier" = 1 WHERE "slug" = 'claude-3-haiku-20240307';
-
--- OpenRouter models - Pro/expensive tiers
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" LIKE 'google/gemini%-pro%';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" LIKE '%command-r-plus%';
-UPDATE "LlmModel" SET "priceTier" = 2 WHERE "slug" LIKE '%sonar-pro%';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" LIKE '%sonar-deep-research%';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" = 'x-ai/grok-4';
-UPDATE "LlmModel" SET "priceTier" = 3 WHERE "slug" LIKE '%qwen3-coder%';

autogpt_platform/backend/schema.prisma

@@ -1096,153 +1096,6 @@ enum APIKeyStatus {
 
 ////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////
-/////////////   LLM REGISTRY AND BILLING DATA   /////////////
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-
-// LlmCostUnit: Defines how LLM MODEL costs are calculated (per run or per token).
-// This is distinct from BlockCostType (in backend/data/block.py) which defines
-// how BLOCK EXECUTION costs are calculated (per run, per byte, or per second).
-// LlmCostUnit is for pricing individual LLM model API calls in the registry,
-// while BlockCostType is for billing platform block executions.
-enum LlmCostUnit {
-  RUN
-  TOKENS
-}
-
-model LlmModelCreator {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  name        String  @unique // e.g., "openai", "anthropic", "meta"
-  displayName String // e.g., "OpenAI", "Anthropic", "Meta"
-  description String?
-  websiteUrl  String? // Link to creator's website
-  logoUrl     String? // URL to creator's logo
-
-  metadata Json @default("{}")
-
-  Models LlmModel[]
-}
-
-model LlmProvider {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  name        String @unique
-  displayName String
-  description String?
-
-  defaultCredentialProvider String?
-  defaultCredentialId       String?
-  defaultCredentialType     String?
-
-  supportsTools        Boolean @default(true)
-  supportsJsonOutput   Boolean @default(true)
-  supportsReasoning    Boolean @default(false)
-  supportsParallelTool Boolean @default(false)
-
-  metadata Json @default("{}")
-
-  Models LlmModel[]
-}
-
-model LlmModel {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  slug        String @unique
-  displayName String
-  description String?
-
-  providerId String
-  Provider   LlmProvider @relation(fields: [providerId], references: [id], onDelete: Restrict)
-
-  // Creator is the organization that created/trained the model (e.g., OpenAI, Meta)
-  // This is distinct from the provider who hosts/serves the model (e.g., OpenRouter)
-  creatorId String?
-  Creator   LlmModelCreator? @relation(fields: [creatorId], references: [id], onDelete: SetNull)
-
-  contextWindow   Int
-  maxOutputTokens Int?
-  priceTier       Int     @default(1) // 1=cheapest, 2=medium, 3=expensive
-  isEnabled       Boolean @default(true)
-  isRecommended   Boolean @default(false)
-
-  capabilities Json @default("{}")
-  metadata     Json @default("{}")
-
-  Costs LlmModelCost[]
-
-  @@index([providerId, isEnabled])
-  @@index([creatorId])
-  @@index([slug])
-}
-
-model LlmModelCost {
-  id        String     @id @default(uuid())
-  createdAt DateTime   @default(now())
-  updatedAt DateTime   @updatedAt
-  unit      LlmCostUnit @default(RUN)
-
-  creditCost Int
-
-  credentialProvider String
-  credentialId       String?
-  credentialType     String?
-  currency           String?
-
-  metadata Json @default("{}")
-
-  llmModelId String
-  Model      LlmModel @relation(fields: [llmModelId], references: [id], onDelete: Cascade)
-
-  @@unique([llmModelId, credentialProvider, unit])
-  @@index([llmModelId])
-  @@index([credentialProvider])
-}
-
-// Tracks model migrations for revert capability
-// When a model is disabled with migration, we record which nodes were affected
-// so they can be reverted when the original model is back online
-model LlmModelMigration {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  sourceModelSlug String // The original model that was disabled
-  targetModelSlug String // The model workflows were migrated to
-  reason          String? // Why the migration happened (e.g., "Provider outage")
-
-  // Track affected nodes as JSON array of node IDs
-  // Format: ["node-uuid-1", "node-uuid-2", ...]
-  migratedNodeIds Json @default("[]")
-  nodeCount       Int // Number of nodes migrated
-
-  // Custom pricing override for migrated workflows during the migration period.
-  // Use case: When migrating users from an expensive model (e.g., GPT-4) to a cheaper
-  // one (e.g., GPT-3.5), you may want to temporarily maintain the original pricing
-  // to avoid billing surprises, or offer a discount during the transition.
-  //
-  // IMPORTANT: This field is intended for integration with the billing system.
-  // When billing calculates costs for nodes affected by this migration, it should
-  // check if customCreditCost is set and use it instead of the target model's cost.
-  // If null, the target model's normal cost applies.
-  //
-  // TODO: Integrate with billing system to apply this override during cost calculation.
-  customCreditCost Int?
-
-  // Revert tracking
-  isReverted Boolean   @default(false)
-  revertedAt DateTime?
-
-  @@index([sourceModelSlug])
-  @@index([targetModelSlug])
-  @@index([isReverted])
-}
 //////////////   OAUTH PROVIDER TABLES    //////////////////
 ////////////////////////////////////////////////////////////
 ////////////////////////////////////////////////////////////

autogpt_platform/backend/test/agent_generator/__init__.py

@@ -0,0 +1 @@
+"""Tests for agent generator module."""

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -0,0 +1,273 @@
+"""
+Tests for the Agent Generator core module.
+
+This test suite verifies that the core functions correctly delegate to
+the external Agent Generator service.
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+from backend.api.features.chat.tools.agent_generator.core import (
+    AgentGeneratorNotConfiguredError,
+)
+
+
+class TestServiceNotConfigured:
+    """Test that functions raise AgentGeneratorNotConfiguredError when service is not configured."""
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_raises_when_not_configured(self):
+        """Test that decompose_goal raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.decompose_goal("Build a chatbot")
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_raises_when_not_configured(self):
+        """Test that generate_agent raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent({"steps": []})
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_raises_when_not_configured(self):
+        """Test that generate_agent_patch raises error when service not configured."""
+        with patch.object(core, "is_external_service_configured", return_value=False):
+            with pytest.raises(AgentGeneratorNotConfiguredError):
+                await core.generate_agent_patch("Add a node", {"nodes": []})
+
+
+class TestDecomposeGoal:
+    """Test decompose_goal function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that decompose_goal calls the external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            mock_external.assert_called_once_with("Build a chatbot", "")
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_passes_context_to_external_service(self):
+        """Test that decompose_goal passes context to external service."""
+        expected_result = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            await core.decompose_goal("Build a chatbot", "Use Python")
+
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_service_failure(self):
+        """Test that decompose_goal returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "decompose_goal_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.decompose_goal("Build a chatbot")
+
+            assert result is None
+
+
+class TestGenerateAgent:
+    """Test generate_agent function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent calls the external service."""
+        expected_result = {"name": "Test Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            instructions = {"type": "instructions", "steps": ["Step 1"]}
+            result = await core.generate_agent(instructions)
+
+            mock_external.assert_called_once_with(instructions)
+            # Result should have id, version, is_active added if not present
+            assert result is not None
+            assert result["name"] == "Test Agent"
+            assert "id" in result
+            assert result["version"] == 1
+            assert result["is_active"] is True
+
+    @pytest.mark.asyncio
+    async def test_preserves_existing_id_and_version(self):
+        """Test that external service result preserves existing id and version."""
+        expected_result = {
+            "id": "existing-id",
+            "version": 3,
+            "is_active": False,
+            "name": "Test Agent",
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result.copy()
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is not None
+            assert result["id"] == "existing-id"
+            assert result["version"] == 3
+            assert result["is_active"] is False
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent returns None when external service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent({"steps": []})
+
+            assert result is None
+
+
+class TestGenerateAgentPatch:
+    """Test generate_agent_patch function service delegation."""
+
+    @pytest.mark.asyncio
+    async def test_calls_external_service(self):
+        """Test that generate_agent_patch calls the external service."""
+        expected_result = {"name": "Updated Agent", "nodes": [], "links": []}
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            current_agent = {"nodes": [], "links": []}
+            result = await core.generate_agent_patch("Add a node", current_agent)
+
+            mock_external.assert_called_once_with("Add a node", current_agent)
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_clarifying_questions(self):
+        """Test that generate_agent_patch returns clarifying questions."""
+        expected_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What type of node?"}],
+        }
+
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = expected_result
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result == expected_result
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_external_service_fails(self):
+        """Test that generate_agent_patch returns None when service fails."""
+        with patch.object(
+            core, "is_external_service_configured", return_value=True
+        ), patch.object(
+            core, "generate_agent_patch_external", new_callable=AsyncMock
+        ) as mock_external:
+            mock_external.return_value = None
+
+            result = await core.generate_agent_patch("Add a node", {"nodes": []})
+
+            assert result is None
+
+
+class TestJsonToGraph:
+    """Test json_to_graph function."""
+
+    def test_converts_agent_json_to_graph(self):
+        """Test conversion of agent JSON to Graph model."""
+        agent_json = {
+            "id": "test-id",
+            "version": 2,
+            "is_active": True,
+            "name": "Test Agent",
+            "description": "A test agent",
+            "nodes": [
+                {
+                    "id": "node1",
+                    "block_id": "block1",
+                    "input_default": {"key": "value"},
+                    "metadata": {"x": 100},
+                }
+            ],
+            "links": [
+                {
+                    "id": "link1",
+                    "source_id": "node1",
+                    "sink_id": "output",
+                    "source_name": "result",
+                    "sink_name": "input",
+                    "is_static": False,
+                }
+            ],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id == "test-id"
+        assert graph.version == 2
+        assert graph.is_active is True
+        assert graph.name == "Test Agent"
+        assert graph.description == "A test agent"
+        assert len(graph.nodes) == 1
+        assert graph.nodes[0].id == "node1"
+        assert graph.nodes[0].block_id == "block1"
+        assert len(graph.links) == 1
+        assert graph.links[0].source_id == "node1"
+
+    def test_generates_ids_if_missing(self):
+        """Test that missing IDs are generated."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [{"block_id": "block1"}],
+            "links": [],
+        }
+
+        graph = core.json_to_graph(agent_json)
+
+        assert graph.id is not None
+        assert graph.nodes[0].id is not None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -0,0 +1,422 @@
+"""
+Tests for the Agent Generator external service client.
+
+This test suite verifies the external Agent Generator service integration,
+including service detection, API calls, and error handling.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import service
+
+
+class TestServiceConfiguration:
+    """Test service configuration detection."""
+
+    def setup_method(self):
+        """Reset settings singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    def test_external_service_not_configured_when_host_empty(self):
+        """Test that external service is not configured when host is empty."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = ""
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is False
+
+    def test_external_service_configured_when_host_set(self):
+        """Test that external service is configured when host is set."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            assert service.is_external_service_configured() is True
+
+    def test_get_base_url(self):
+        """Test base URL construction."""
+        mock_settings = MagicMock()
+        mock_settings.config.agentgenerator_host = "agent-generator.local"
+        mock_settings.config.agentgenerator_port = 8000
+
+        with patch.object(service, "_get_settings", return_value=mock_settings):
+            url = service._get_base_url()
+            assert url == "http://agent-generator.local:8000"
+
+
+class TestDecomposeGoalExternal:
+    """Test decompose_goal_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_instructions(self):
+        """Test successful decomposition returning instructions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1", "Step 2"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result == {"type": "instructions", "steps": ["Step 1", "Step 2"]}
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description", json={"description": "Build a chatbot"}
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_clarifying_questions(self):
+        """Test decomposition returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build something")
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What platform?", "What language?"],
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_with_context(self):
+        """Test decomposition with additional context."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external(
+                "Build a chatbot", context="Use Python"
+            )
+
+        mock_client.post.assert_called_once_with(
+            "/api/decompose-description",
+            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+        )
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_returns_unachievable_goal(self):
+        """Test decomposition returning unachievable goal response."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Do something impossible")
+
+        assert result == {
+            "type": "unachievable_goal",
+            "reason": "Cannot do X",
+            "suggested_goal": "Try Y instead",
+        }
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_http_error(self):
+        """Test decomposition handles HTTP errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.HTTPStatusError(
+            "Server error", request=MagicMock(), response=MagicMock()
+        )
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_request_error(self):
+        """Test decomposition handles request errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_handles_service_error(self):
+        """Test decomposition handles service returning error."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": False,
+            "error": "Internal error",
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.decompose_goal_external("Build a chatbot")
+
+        assert result is None
+
+
+class TestGenerateAgentExternal:
+    """Test generate_agent_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_success(self):
+        """Test successful agent generation."""
+        agent_json = {
+            "name": "Test Agent",
+            "nodes": [],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": agent_json,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        instructions = {"type": "instructions", "steps": ["Step 1"]}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external(instructions)
+
+        assert result == agent_json
+        mock_client.post.assert_called_once_with(
+            "/api/generate-agent", json={"instructions": instructions}
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_handles_error(self):
+        """Test agent generation handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.post.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_external({"steps": []})
+
+        assert result is None
+
+
+class TestGenerateAgentPatchExternal:
+    """Test generate_agent_patch_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_updated_agent(self):
+        """Test successful patch generation returning updated agent."""
+        updated_agent = {
+            "name": "Updated Agent",
+            "nodes": [{"id": "1", "block_id": "test"}],
+            "links": [],
+        }
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": updated_agent,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        current_agent = {"name": "Old Agent", "nodes": [], "links": []}
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add a new node", current_agent
+            )
+
+        assert result == updated_agent
+        mock_client.post.assert_called_once_with(
+            "/api/update-agent",
+            json={
+                "update_request": "Add a new node",
+                "current_agent_json": current_agent,
+            },
+        )
+
+    @pytest.mark.asyncio
+    async def test_generate_patch_returns_clarifying_questions(self):
+        """Test patch generation returning clarifying questions."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.generate_agent_patch_external(
+                "Add something", {"nodes": []}
+            )
+
+        assert result == {
+            "type": "clarifying_questions",
+            "questions": ["What type of node?"],
+        }
+
+
+class TestHealthCheck:
+    """Test health_check function."""
+
+    def setup_method(self):
+        """Reset singletons before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_configured(self):
+        """Test health check returns False when service not configured."""
+        with patch.object(
+            service, "is_external_service_configured", return_value=False
+        ):
+            result = await service.health_check()
+            assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_true_when_healthy(self):
+        """Test health check returns True when service is healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "healthy",
+            "blocks_loaded": True,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is True
+        mock_client.get.assert_called_once_with("/health")
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_when_not_healthy(self):
+        """Test health check returns False when service is not healthy."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "status": "unhealthy",
+            "blocks_loaded": False,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+    @pytest.mark.asyncio
+    async def test_health_check_returns_false_on_error(self):
+        """Test health check returns False on connection error."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "is_external_service_configured", return_value=True):
+            with patch.object(service, "_get_client", return_value=mock_client):
+                result = await service.health_check()
+
+        assert result is False
+
+
+class TestGetBlocksExternal:
+    """Test get_blocks_external function."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_success(self):
+        """Test successful blocks retrieval."""
+        blocks = [
+            {"id": "block1", "name": "Block 1"},
+            {"id": "block2", "name": "Block 2"},
+        ]
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "blocks": blocks,
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.get.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result == blocks
+        mock_client.get.assert_called_once_with("/api/blocks")
+
+    @pytest.mark.asyncio
+    async def test_get_blocks_handles_error(self):
+        """Test blocks retrieval handles errors gracefully."""
+        mock_client = AsyncMock()
+        mock_client.get.side_effect = httpx.RequestError("Connection failed")
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            result = await service.get_blocks_external()
+
+        assert result is None
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/frontend/.env.default

@@ -29,4 +29,4 @@ NEXT_PUBLIC_CLOUDFLARE_TURNSTILE_SITE_KEY=
 NEXT_PUBLIC_TURNSTILE=disabled
 
 # PR previews
-NEXT_PUBLIC_PREVIEW_STEALING_DEV=
+NEXT_PUBLIC_PREVIEW_STEALING_DEV=

autogpt_platform/frontend/CONTRIBUTING.md

@@ -175,8 +175,6 @@ While server components and actions are cool and cutting-edge, they introduce a
 
 - Prefer [React Query](https://tanstack.com/query/latest/docs/framework/react/overview) for server state, colocated near consumers (see [state colocation](https://kentcdodds.com/blog/state-colocation-will-make-your-react-app-faster))
 - Co-locate UI state inside components/hooks; keep global state minimal
-- Avoid `useMemo` and `useCallback` unless you have a measured performance issue
-- Do not abuse `useEffect`; prefer state colocation and derive values directly when possible
 
 ### Styling and components
 
@@ -551,48 +549,9 @@ Files:
 Types:
 
 - Prefer `interface` for object shapes
-- Component props should be `interface Props { ... }` (not exported)
-- Only use specific exported names (e.g., `export interface MyComponentProps`) when the interface needs to be used outside the component
-- Keep type definitions inline with the component - do not create separate `types.ts` files unless types are shared across multiple files
+- Component props should be `interface Props { ... }`
 - Use precise types; avoid `any` and unsafe casts
 
-**Props naming examples:**
-
-```tsx
-// ✅ Good - internal props, not exported
-interface Props {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: Props) {
-  // ...
-}
-
-// ✅ Good - exported when needed externally
-export interface ModalProps {
-  title: string;
-  onClose: () => void;
-}
-
-export function Modal({ title, onClose }: ModalProps) {
-  // ...
-}
-
-// ❌ Bad - unnecessarily specific name for internal use
-interface ModalComponentProps {
-  title: string;
-  onClose: () => void;
-}
-
-// ❌ Bad - separate types.ts file for single component
-// types.ts
-export interface ModalProps { ... }
-
-// Modal.tsx
-import type { ModalProps } from './types';
-```
-
 Parameters:
 
 - If more than one parameter is needed, pass a single `Args` object for clarity

autogpt_platform/frontend/orval.config.ts

@@ -16,12 +16,6 @@ export default defineConfig({
       client: "react-query",
       httpClient: "fetch",
       indexFiles: false,
-      mock: {
-        type: "msw",
-        baseUrl: "http://localhost:3000/api/proxy",
-        generateEachHttpStatus: true,
-        delay: 0,
-      },
       override: {
         mutator: {
           path: "./mutators/custom-mutator.ts",

autogpt_platform/frontend/package.json

@@ -15,8 +15,6 @@
     "types": "tsc --noEmit",
     "test": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test",
     "test-ui": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test --ui",
-    "test:unit": "vitest run",
-    "test:unit:watch": "vitest",
     "test:no-build": "playwright test",
     "gentests": "playwright codegen http://localhost:3000",
     "storybook": "storybook dev -p 6006",
@@ -120,7 +118,6 @@
   },
   "devDependencies": {
     "@chromatic-com/storybook": "4.1.2",
-    "happy-dom": "20.3.4",
     "@opentelemetry/instrumentation": "0.209.0",
     "@playwright/test": "1.56.1",
     "@storybook/addon-a11y": "9.1.5",
@@ -130,8 +127,6 @@
     "@storybook/nextjs": "9.1.5",
     "@tanstack/eslint-plugin-query": "5.91.2",
     "@tanstack/react-query-devtools": "5.90.2",
-    "@testing-library/dom": "10.4.1",
-    "@testing-library/react": "16.3.2",
     "@types/canvas-confetti": "1.9.0",
     "@types/lodash": "4.17.20",
     "@types/negotiator": "0.6.4",
@@ -140,7 +135,6 @@
     "@types/react-dom": "18.3.5",
     "@types/react-modal": "3.16.3",
     "@types/react-window": "1.8.8",
-    "@vitejs/plugin-react": "5.1.2",
     "axe-playwright": "2.2.2",
     "chromatic": "13.3.3",
     "concurrently": "9.2.1",
@@ -159,9 +153,7 @@
     "require-in-the-middle": "8.0.1",
     "storybook": "9.1.5",
     "tailwindcss": "3.4.17",
-    "typescript": "5.9.3",
-    "vite-tsconfig-paths": "6.0.4",
-    "vitest": "4.0.17"
+    "typescript": "5.9.3"
   },
   "msw": {
     "workerDirectory": [

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

@@ -1,58 +0,0 @@
-"use client";
-
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useRouter } from "next/navigation";
-import { useEffect, useRef } from "react";
-
-const LOGOUT_REDIRECT_DELAY_MS = 400;
-
-function wait(ms: number): Promise<void> {
-  return new Promise(function resolveAfterDelay(resolve) {
-    setTimeout(resolve, ms);
-  });
-}
-
-export default function LogoutPage() {
-  const { logOut } = useSupabase();
-  const { toast } = useToast();
-  const router = useRouter();
-  const hasStartedRef = useRef(false);
-
-  useEffect(
-    function handleLogoutEffect() {
-      if (hasStartedRef.current) return;
-      hasStartedRef.current = true;
-
-      async function runLogout() {
-        try {
-          await logOut();
-        } catch {
-          toast({
-            title: "Failed to log out. Redirecting to login.",
-            variant: "destructive",
-          });
-        } finally {
-          await wait(LOGOUT_REDIRECT_DELAY_MS);
-          router.replace("/login");
-        }
-      }
-
-      void runLogout();
-    },
-    [logOut, router, toast],
-  );
-
-  return (
-    <div className="flex min-h-screen items-center justify-center px-4">
-      <div className="flex flex-col items-center justify-center gap-4 py-8">
-        <LoadingSpinner size="large" />
-        <Text variant="body" className="text-center">
-          Logging you out...
-        </Text>
-      </div>
-    </div>
-  );
-}

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

@@ -1,8 +1,5 @@
-"use client";
-
 import { Sidebar } from "@/components/__legacy__/Sidebar";
 import { Users, DollarSign, UserSearch, FileText } from "lucide-react";
-import { Cpu } from "@phosphor-icons/react";
 
 import { IconSliders } from "@/components/__legacy__/ui/icons";
 
@@ -29,11 +26,6 @@ const sidebarLinkGroups = [
         href: "/admin/execution-analytics",
         icon: <FileText className="h-6 w-6" />,
       },
-      {
-        text: "LLM Registry",
-        href: "/admin/llms",
-        icon: <Cpu size={24} />,
-      },
       {
         text: "Admin User Management",
         href: "/admin/settings",

autogpt_platform/frontend/src/app/(platform)/admin/llms/actions.ts

@@ -1,493 +0,0 @@
-"use server";
-
-import { revalidatePath } from "next/cache";
-
-// Generated API functions
-import {
-  getV2ListLlmProviders,
-  postV2CreateLlmProvider,
-  patchV2UpdateLlmProvider,
-  deleteV2DeleteLlmProvider,
-  getV2ListLlmModels,
-  postV2CreateLlmModel,
-  patchV2UpdateLlmModel,
-  patchV2ToggleLlmModelAvailability,
-  deleteV2DeleteLlmModelAndMigrateWorkflows,
-  getV2GetModelUsageCount,
-  getV2ListModelMigrations,
-  postV2RevertAModelMigration,
-  getV2ListModelCreators,
-  postV2CreateModelCreator,
-  patchV2UpdateModelCreator,
-  deleteV2DeleteModelCreator,
-  postV2SetRecommendedModel,
-} from "@/app/api/__generated__/endpoints/admin/admin";
-
-// Generated types
-import type { LlmProvidersResponse } from "@/app/api/__generated__/models/llmProvidersResponse";
-import type { LlmModelsResponse } from "@/app/api/__generated__/models/llmModelsResponse";
-import type { UpsertLlmProviderRequest } from "@/app/api/__generated__/models/upsertLlmProviderRequest";
-import type { CreateLlmModelRequest } from "@/app/api/__generated__/models/createLlmModelRequest";
-import type { UpdateLlmModelRequest } from "@/app/api/__generated__/models/updateLlmModelRequest";
-import type { ToggleLlmModelRequest } from "@/app/api/__generated__/models/toggleLlmModelRequest";
-import type { LlmMigrationsResponse } from "@/app/api/__generated__/models/llmMigrationsResponse";
-import type { LlmCreatorsResponse } from "@/app/api/__generated__/models/llmCreatorsResponse";
-import type { UpsertLlmCreatorRequest } from "@/app/api/__generated__/models/upsertLlmCreatorRequest";
-import type { LlmModelUsageResponse } from "@/app/api/__generated__/models/llmModelUsageResponse";
-import { LlmCostUnit } from "@/app/api/__generated__/models/llmCostUnit";
-
-const ADMIN_LLM_PATH = "/admin/llms";
-
-// =============================================================================
-// Utilities
-// =============================================================================
-
-/**
- * Extracts and validates a required string field from FormData.
- * Throws an error if the field is missing or empty.
- */
-function getRequiredFormField(
-  formData: FormData,
-  fieldName: string,
-  displayName?: string,
-): string {
-  const raw = formData.get(fieldName);
-  const value = raw ? String(raw).trim() : "";
-  if (!value) {
-    throw new Error(`${displayName || fieldName} is required`);
-  }
-  return value;
-}
-
-/**
- * Extracts and validates a required positive number field from FormData.
- * Throws an error if the field is missing, empty, or not a positive number.
- */
-function getRequiredPositiveNumber(
-  formData: FormData,
-  fieldName: string,
-  displayName?: string,
-): number {
-  const raw = formData.get(fieldName);
-  const value = Number(raw);
-  if (raw === null || raw === "" || !Number.isFinite(value) || value <= 0) {
-    throw new Error(`${displayName || fieldName} must be a positive number`);
-  }
-  return value;
-}
-
-/**
- * Extracts and validates a required number field from FormData.
- * Throws an error if the field is missing, empty, or not a finite number.
- */
-function getRequiredNumber(
-  formData: FormData,
-  fieldName: string,
-  displayName?: string,
-): number {
-  const raw = formData.get(fieldName);
-  const value = Number(raw);
-  if (raw === null || raw === "" || !Number.isFinite(value)) {
-    throw new Error(`${displayName || fieldName} is required`);
-  }
-  return value;
-}
-
-// =============================================================================
-// Provider Actions
-// =============================================================================
-
-export async function fetchLlmProviders(): Promise<LlmProvidersResponse> {
-  const response = await getV2ListLlmProviders({ include_models: true });
-  if (response.status !== 200) {
-    throw new Error("Failed to fetch LLM providers");
-  }
-  return response.data;
-}
-
-export async function createLlmProviderAction(formData: FormData) {
-  const payload: UpsertLlmProviderRequest = {
-    name: String(formData.get("name") || "").trim(),
-    display_name: String(formData.get("display_name") || "").trim(),
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    default_credential_provider: formData.get("default_credential_provider")
-      ? String(formData.get("default_credential_provider")).trim()
-      : undefined,
-    default_credential_id: formData.get("default_credential_id")
-      ? String(formData.get("default_credential_id")).trim()
-      : undefined,
-    default_credential_type: formData.get("default_credential_type")
-      ? String(formData.get("default_credential_type")).trim()
-      : "api_key",
-    supports_tools: formData.getAll("supports_tools").includes("on"),
-    supports_json_output: formData
-      .getAll("supports_json_output")
-      .includes("on"),
-    supports_reasoning: formData.getAll("supports_reasoning").includes("on"),
-    supports_parallel_tool: formData
-      .getAll("supports_parallel_tool")
-      .includes("on"),
-    metadata: {},
-  };
-
-  const response = await postV2CreateLlmProvider(payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to create LLM provider");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function deleteLlmProviderAction(
-  formData: FormData,
-): Promise<void> {
-  const providerId = getRequiredFormField(
-    formData,
-    "provider_id",
-    "Provider id",
-  );
-
-  const response = await deleteV2DeleteLlmProvider(providerId);
-  if (response.status !== 200) {
-    const errorData = response.data as { detail?: string };
-    throw new Error(errorData?.detail || "Failed to delete provider");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function updateLlmProviderAction(formData: FormData) {
-  const providerId = getRequiredFormField(
-    formData,
-    "provider_id",
-    "Provider id",
-  );
-
-  const payload: UpsertLlmProviderRequest = {
-    name: String(formData.get("name") || "").trim(),
-    display_name: String(formData.get("display_name") || "").trim(),
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    default_credential_provider: formData.get("default_credential_provider")
-      ? String(formData.get("default_credential_provider")).trim()
-      : undefined,
-    default_credential_id: formData.get("default_credential_id")
-      ? String(formData.get("default_credential_id")).trim()
-      : undefined,
-    default_credential_type: formData.get("default_credential_type")
-      ? String(formData.get("default_credential_type")).trim()
-      : "api_key",
-    supports_tools: formData.getAll("supports_tools").includes("on"),
-    supports_json_output: formData
-      .getAll("supports_json_output")
-      .includes("on"),
-    supports_reasoning: formData.getAll("supports_reasoning").includes("on"),
-    supports_parallel_tool: formData
-      .getAll("supports_parallel_tool")
-      .includes("on"),
-    metadata: {},
-  };
-
-  const response = await patchV2UpdateLlmProvider(providerId, payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to update LLM provider");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-// =============================================================================
-// Model Actions
-// =============================================================================
-
-export async function fetchLlmModels(): Promise<LlmModelsResponse> {
-  const response = await getV2ListLlmModels();
-  if (response.status !== 200) {
-    throw new Error("Failed to fetch LLM models");
-  }
-  return response.data;
-}
-
-export async function createLlmModelAction(formData: FormData) {
-  const providerId = getRequiredFormField(formData, "provider_id", "Provider");
-  const creatorId = formData.get("creator_id");
-  const contextWindow = getRequiredPositiveNumber(
-    formData,
-    "context_window",
-    "Context window",
-  );
-  const creditCost = getRequiredNumber(formData, "credit_cost", "Credit cost");
-
-  // Fetch provider to get default credentials
-  const providersResponse = await getV2ListLlmProviders({
-    include_models: false,
-  });
-  if (providersResponse.status !== 200) {
-    throw new Error("Failed to fetch providers");
-  }
-  const provider = providersResponse.data.providers.find(
-    (p) => p.id === providerId,
-  );
-
-  if (!provider) {
-    throw new Error("Provider not found");
-  }
-
-  const payload: CreateLlmModelRequest = {
-    slug: String(formData.get("slug") || "").trim(),
-    display_name: String(formData.get("display_name") || "").trim(),
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    provider_id: providerId,
-    creator_id: creatorId ? String(creatorId) : undefined,
-    context_window: contextWindow,
-    max_output_tokens: formData.get("max_output_tokens")
-      ? Number(formData.get("max_output_tokens"))
-      : undefined,
-    is_enabled: formData.getAll("is_enabled").includes("on"),
-    capabilities: {},
-    metadata: {},
-    costs: [
-      {
-        unit: (formData.get("unit") as LlmCostUnit) || LlmCostUnit.RUN,
-        credit_cost: creditCost,
-        credential_provider:
-          provider.default_credential_provider || provider.name,
-        credential_id: provider.default_credential_id || undefined,
-        credential_type: provider.default_credential_type || "api_key",
-        metadata: {},
-      },
-    ],
-  };
-
-  const response = await postV2CreateLlmModel(payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to create LLM model");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function updateLlmModelAction(formData: FormData) {
-  const modelId = getRequiredFormField(formData, "model_id", "Model id");
-  const creatorId = formData.get("creator_id");
-
-  const payload: UpdateLlmModelRequest = {
-    display_name: formData.get("display_name")
-      ? String(formData.get("display_name"))
-      : undefined,
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    provider_id: formData.get("provider_id")
-      ? String(formData.get("provider_id"))
-      : undefined,
-    creator_id: creatorId ? String(creatorId) : undefined,
-    context_window: formData.get("context_window")
-      ? Number(formData.get("context_window"))
-      : undefined,
-    max_output_tokens: formData.get("max_output_tokens")
-      ? Number(formData.get("max_output_tokens"))
-      : undefined,
-    is_enabled: formData.has("is_enabled")
-      ? formData.getAll("is_enabled").includes("on")
-      : undefined,
-    costs: formData.get("credit_cost")
-      ? [
-          {
-            unit: (formData.get("unit") as LlmCostUnit) || LlmCostUnit.RUN,
-            credit_cost: Number(formData.get("credit_cost")),
-            credential_provider: String(
-              formData.get("credential_provider") || "",
-            ).trim(),
-            credential_id: formData.get("credential_id")
-              ? String(formData.get("credential_id"))
-              : undefined,
-            credential_type: formData.get("credential_type")
-              ? String(formData.get("credential_type"))
-              : undefined,
-            metadata: {},
-          },
-        ]
-      : undefined,
-  };
-
-  const response = await patchV2UpdateLlmModel(modelId, payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to update LLM model");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function toggleLlmModelAction(formData: FormData): Promise<void> {
-  const modelId = getRequiredFormField(formData, "model_id", "Model id");
-  const shouldEnable = formData.get("is_enabled") === "true";
-  const migrateToSlug = formData.get("migrate_to_slug");
-  const migrationReason = formData.get("migration_reason");
-  const customCreditCost = formData.get("custom_credit_cost");
-
-  const payload: ToggleLlmModelRequest = {
-    is_enabled: shouldEnable,
-    migrate_to_slug: migrateToSlug ? String(migrateToSlug) : undefined,
-    migration_reason: migrationReason ? String(migrationReason) : undefined,
-    custom_credit_cost: customCreditCost ? Number(customCreditCost) : undefined,
-  };
-
-  const response = await patchV2ToggleLlmModelAvailability(modelId, payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to toggle LLM model");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function deleteLlmModelAction(formData: FormData): Promise<void> {
-  const modelId = getRequiredFormField(formData, "model_id", "Model id");
-  const rawReplacement = formData.get("replacement_model_slug");
-  const replacementModelSlug =
-    rawReplacement && String(rawReplacement).trim()
-      ? String(rawReplacement).trim()
-      : undefined;
-
-  const response = await deleteV2DeleteLlmModelAndMigrateWorkflows(modelId, {
-    replacement_model_slug: replacementModelSlug,
-  });
-  if (response.status !== 200) {
-    throw new Error("Failed to delete model");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function fetchLlmModelUsage(
-  modelId: string,
-): Promise<LlmModelUsageResponse> {
-  const response = await getV2GetModelUsageCount(modelId);
-  if (response.status !== 200) {
-    throw new Error("Failed to fetch model usage");
-  }
-  return response.data;
-}
-
-// =============================================================================
-// Migration Actions
-// =============================================================================
-
-export async function fetchLlmMigrations(
-  includeReverted: boolean = false,
-): Promise<LlmMigrationsResponse> {
-  const response = await getV2ListModelMigrations({
-    include_reverted: includeReverted,
-  });
-  if (response.status !== 200) {
-    throw new Error("Failed to fetch migrations");
-  }
-  return response.data;
-}
-
-export async function revertLlmMigrationAction(
-  formData: FormData,
-): Promise<void> {
-  const migrationId = getRequiredFormField(
-    formData,
-    "migration_id",
-    "Migration id",
-  );
-
-  const response = await postV2RevertAModelMigration(migrationId, null);
-  if (response.status !== 200) {
-    throw new Error("Failed to revert migration");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-// =============================================================================
-// Creator Actions
-// =============================================================================
-
-export async function fetchLlmCreators(): Promise<LlmCreatorsResponse> {
-  const response = await getV2ListModelCreators();
-  if (response.status !== 200) {
-    throw new Error("Failed to fetch creators");
-  }
-  return response.data;
-}
-
-export async function createLlmCreatorAction(
-  formData: FormData,
-): Promise<void> {
-  const payload: UpsertLlmCreatorRequest = {
-    name: String(formData.get("name") || "").trim(),
-    display_name: String(formData.get("display_name") || "").trim(),
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    website_url: formData.get("website_url")
-      ? String(formData.get("website_url")).trim()
-      : undefined,
-    logo_url: formData.get("logo_url")
-      ? String(formData.get("logo_url")).trim()
-      : undefined,
-    metadata: {},
-  };
-
-  const response = await postV2CreateModelCreator(payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to create creator");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function updateLlmCreatorAction(
-  formData: FormData,
-): Promise<void> {
-  const creatorId = getRequiredFormField(formData, "creator_id", "Creator id");
-
-  const payload: UpsertLlmCreatorRequest = {
-    name: String(formData.get("name") || "").trim(),
-    display_name: String(formData.get("display_name") || "").trim(),
-    description: formData.get("description")
-      ? String(formData.get("description"))
-      : undefined,
-    website_url: formData.get("website_url")
-      ? String(formData.get("website_url")).trim()
-      : undefined,
-    logo_url: formData.get("logo_url")
-      ? String(formData.get("logo_url")).trim()
-      : undefined,
-    metadata: {},
-  };
-
-  const response = await patchV2UpdateModelCreator(creatorId, payload);
-  if (response.status !== 200) {
-    throw new Error("Failed to update creator");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-export async function deleteLlmCreatorAction(
-  formData: FormData,
-): Promise<void> {
-  const creatorId = getRequiredFormField(formData, "creator_id", "Creator id");
-
-  const response = await deleteV2DeleteModelCreator(creatorId);
-  if (response.status !== 200) {
-    throw new Error("Failed to delete creator");
-  }
-  revalidatePath(ADMIN_LLM_PATH);
-}
-
-// =============================================================================
-// Recommended Model Actions
-// =============================================================================
-
-export async function setRecommendedModelAction(
-  formData: FormData,
-): Promise<void> {
-  const modelId = getRequiredFormField(formData, "model_id", "Model id");
-
-  const response = await postV2SetRecommendedModel({ model_id: modelId });
-  if (response.status !== 200) {
-    throw new Error("Failed to set recommended model");
-  }
-
-  revalidatePath(ADMIN_LLM_PATH);
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/AddCreatorModal.tsx

@@ -1,147 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import { createLlmCreatorAction } from "../actions";
-import { useRouter } from "next/navigation";
-
-export function AddCreatorModal() {
-  const [open, setOpen] = useState(false);
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await createLlmCreatorAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to create creator");
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Add Creator"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "512px" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="primary" size="small">
-          Add Creator
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Add a new model creator (the organization that made/trained the
-          model).
-        </div>
-
-        <form action={handleSubmit} className="space-y-4">
-          <div className="grid gap-4 sm:grid-cols-2">
-            <div className="space-y-2">
-              <label
-                htmlFor="name"
-                className="text-sm font-medium text-foreground"
-              >
-                Name (slug) <span className="text-destructive">*</span>
-              </label>
-              <input
-                id="name"
-                required
-                name="name"
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="openai"
-              />
-              <p className="text-xs text-muted-foreground">
-                Lowercase identifier (e.g., openai, meta, anthropic)
-              </p>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="display_name"
-                className="text-sm font-medium text-foreground"
-              >
-                Display Name <span className="text-destructive">*</span>
-              </label>
-              <input
-                id="display_name"
-                required
-                name="display_name"
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="OpenAI"
-              />
-            </div>
-          </div>
-
-          <div className="space-y-2">
-            <label
-              htmlFor="description"
-              className="text-sm font-medium text-foreground"
-            >
-              Description
-            </label>
-            <textarea
-              id="description"
-              name="description"
-              rows={2}
-              className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-              placeholder="Creator of GPT models..."
-            />
-          </div>
-
-          <div className="space-y-2">
-            <label
-              htmlFor="website_url"
-              className="text-sm font-medium text-foreground"
-            >
-              Website URL
-            </label>
-            <input
-              id="website_url"
-              name="website_url"
-              type="url"
-              className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-              placeholder="https://openai.com"
-            />
-          </div>
-
-          {error && (
-            <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-              {error}
-            </div>
-          )}
-
-          <Dialog.Footer>
-            <Button
-              variant="ghost"
-              size="small"
-              type="button"
-              onClick={() => {
-                setOpen(false);
-                setError(null);
-              }}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Creating..." : "Add Creator"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/AddModelModal.tsx

@@ -1,314 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import { createLlmModelAction } from "../actions";
-import { useRouter } from "next/navigation";
-
-interface Props {
-  providers: LlmProvider[];
-  creators: LlmModelCreator[];
-}
-
-export function AddModelModal({ providers, creators }: Props) {
-  const [open, setOpen] = useState(false);
-  const [selectedCreatorId, setSelectedCreatorId] = useState("");
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await createLlmModelAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to create model");
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  // When provider changes, auto-select matching creator if one exists
-  function handleProviderChange(providerId: string) {
-    const provider = providers.find((p) => p.id === providerId);
-    if (provider) {
-      // Find creator with same name as provider (e.g., "openai" -> "openai")
-      const matchingCreator = creators.find((c) => c.name === provider.name);
-      if (matchingCreator) {
-        setSelectedCreatorId(matchingCreator.id);
-      } else {
-        // No matching creator (e.g., OpenRouter hosts other creators' models)
-        setSelectedCreatorId("");
-      }
-    }
-  }
-
-  return (
-    <Dialog
-      title="Add Model"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "768px", maxHeight: "90vh", overflowY: "auto" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="primary" size="small">
-          Add Model
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Register a new model slug, metadata, and pricing.
-        </div>
-
-        <form action={handleSubmit} className="space-y-6">
-          {/* Basic Information */}
-          <div className="space-y-4">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Basic Information
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Core model details
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="slug"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Model Slug <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="slug"
-                  required
-                  name="slug"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="gpt-4.1-mini-2025-04-14"
-                />
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="display_name"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Display Name <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="display_name"
-                  required
-                  name="display_name"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="GPT 4.1 Mini"
-                />
-              </div>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="description"
-                className="text-sm font-medium text-foreground"
-              >
-                Description
-              </label>
-              <textarea
-                id="description"
-                name="description"
-                rows={3}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="Optional description..."
-              />
-            </div>
-          </div>
-
-          {/* Model Configuration */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Model Configuration
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Model capabilities and limits
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="provider_id"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Provider <span className="text-destructive">*</span>
-                </label>
-                <select
-                  id="provider_id"
-                  required
-                  name="provider_id"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  defaultValue=""
-                  onChange={(e) => handleProviderChange(e.target.value)}
-                >
-                  <option value="" disabled>
-                    Select provider
-                  </option>
-                  {providers.map((provider) => (
-                    <option key={provider.id} value={provider.id}>
-                      {provider.display_name} ({provider.name})
-                    </option>
-                  ))}
-                </select>
-                <p className="text-xs text-muted-foreground">
-                  Who hosts/serves the model
-                </p>
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="creator_id"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Creator
-                </label>
-                <select
-                  id="creator_id"
-                  name="creator_id"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  value={selectedCreatorId}
-                  onChange={(e) => setSelectedCreatorId(e.target.value)}
-                >
-                  <option value="">No creator selected</option>
-                  {creators.map((creator) => (
-                    <option key={creator.id} value={creator.id}>
-                      {creator.display_name} ({creator.name})
-                    </option>
-                  ))}
-                </select>
-                <p className="text-xs text-muted-foreground">
-                  Who made/trained the model (e.g., OpenAI, Meta)
-                </p>
-              </div>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="context_window"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Context Window <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="context_window"
-                  required
-                  type="number"
-                  name="context_window"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="128000"
-                  min={1}
-                />
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="max_output_tokens"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Max Output Tokens
-                </label>
-                <input
-                  id="max_output_tokens"
-                  type="number"
-                  name="max_output_tokens"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="16384"
-                  min={1}
-                />
-              </div>
-            </div>
-          </div>
-
-          {/* Pricing */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">Pricing</h3>
-              <p className="text-xs text-muted-foreground">
-                Credit cost per run (credentials are managed via the provider)
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-1">
-              <div className="space-y-2">
-                <label
-                  htmlFor="credit_cost"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Credit Cost <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="credit_cost"
-                  required
-                  type="number"
-                  name="credit_cost"
-                  step="1"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="5"
-                  min={0}
-                />
-              </div>
-            </div>
-            <p className="text-xs text-muted-foreground">
-              Credit cost is always in platform credits. Credentials are
-              inherited from the selected provider.
-            </p>
-          </div>
-
-          {/* Enabled Toggle */}
-          <div className="flex items-center gap-3 border-t border-border pt-6">
-            <input type="hidden" name="is_enabled" value="off" />
-            <input
-              id="is_enabled"
-              type="checkbox"
-              name="is_enabled"
-              defaultChecked
-              className="h-4 w-4 rounded border-input"
-            />
-            <label
-              htmlFor="is_enabled"
-              className="text-sm font-medium text-foreground"
-            >
-              Enabled by default
-            </label>
-          </div>
-
-          {error && (
-            <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-              {error}
-            </div>
-          )}
-
-          <Dialog.Footer>
-            <Button
-              variant="ghost"
-              size="small"
-              type="button"
-              onClick={() => {
-                setOpen(false);
-                setError(null);
-              }}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Creating..." : "Save Model"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/AddProviderModal.tsx

@@ -1,268 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import { createLlmProviderAction } from "../actions";
-import { useRouter } from "next/navigation";
-
-export function AddProviderModal() {
-  const [open, setOpen] = useState(false);
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await createLlmProviderAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(
-        err instanceof Error ? err.message : "Failed to create provider",
-      );
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Add Provider"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "768px", maxHeight: "90vh", overflowY: "auto" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="primary" size="small">
-          Add Provider
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Define a new upstream provider and default credential information.
-        </div>
-
-        {/* Setup Instructions */}
-        <div className="mb-6 rounded-lg border border-primary/30 bg-primary/5 p-4">
-          <div className="space-y-2">
-            <h4 className="text-sm font-semibold text-foreground">
-              Before Adding a Provider
-            </h4>
-            <p className="text-xs text-muted-foreground">
-              To use a new provider, you must first configure its credentials in
-              the backend:
-            </p>
-            <ol className="list-inside list-decimal space-y-1 text-xs text-muted-foreground">
-              <li>
-                Add the credential to{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono">
-                  backend/integrations/credentials_store.py
-                </code>{" "}
-                with a UUID, provider name, and settings secret reference
-              </li>
-              <li>
-                Add it to the{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono">
-                  PROVIDER_CREDENTIALS
-                </code>{" "}
-                dictionary in{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono">
-                  backend/data/block_cost_config.py
-                </code>
-              </li>
-              <li>
-                Use the <strong>same provider name</strong> in the
-                &quot;Credential Provider&quot; field below that matches the key
-                in{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono">
-                  PROVIDER_CREDENTIALS
-                </code>
-              </li>
-            </ol>
-          </div>
-        </div>
-
-        <form action={handleSubmit} className="space-y-6">
-          {/* Basic Information */}
-          <div className="space-y-4">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Basic Information
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Core provider details
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="name"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Provider Slug <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="name"
-                  required
-                  name="name"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="e.g. openai"
-                />
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="display_name"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Display Name <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="display_name"
-                  required
-                  name="display_name"
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="OpenAI"
-                />
-              </div>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="description"
-                className="text-sm font-medium text-foreground"
-              >
-                Description
-              </label>
-              <textarea
-                id="description"
-                name="description"
-                rows={3}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="Optional description..."
-              />
-            </div>
-          </div>
-
-          {/* Default Credentials */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Default Credentials
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Credential provider name that matches the key in{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono text-xs">
-                  PROVIDER_CREDENTIALS
-                </code>
-              </p>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="default_credential_provider"
-                className="text-sm font-medium text-foreground"
-              >
-                Credential Provider <span className="text-destructive">*</span>
-              </label>
-              <input
-                id="default_credential_provider"
-                name="default_credential_provider"
-                required
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="openai"
-              />
-              <p className="text-xs text-muted-foreground">
-                <strong>Important:</strong> This must exactly match the key in
-                the{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono text-xs">
-                  PROVIDER_CREDENTIALS
-                </code>{" "}
-                dictionary in{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono text-xs">
-                  block_cost_config.py
-                </code>
-                . Common values: &quot;openai&quot;, &quot;anthropic&quot;,
-                &quot;groq&quot;, &quot;open_router&quot;, etc.
-              </p>
-            </div>
-          </div>
-
-          {/* Capabilities */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Capabilities
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Provider feature flags
-              </p>
-            </div>
-            <div className="grid gap-3 sm:grid-cols-2">
-              {[
-                { name: "supports_tools", label: "Supports tools" },
-                { name: "supports_json_output", label: "Supports JSON output" },
-                { name: "supports_reasoning", label: "Supports reasoning" },
-                {
-                  name: "supports_parallel_tool",
-                  label: "Supports parallel tool calls",
-                },
-              ].map(({ name, label }) => (
-                <div
-                  key={name}
-                  className="flex items-center gap-3 rounded-md border border-border bg-muted/30 px-4 py-3 transition-colors hover:bg-muted/50"
-                >
-                  <input type="hidden" name={name} value="off" />
-                  <input
-                    id={name}
-                    type="checkbox"
-                    name={name}
-                    defaultChecked={
-                      name !== "supports_reasoning" &&
-                      name !== "supports_parallel_tool"
-                    }
-                    className="h-4 w-4 rounded border-input"
-                  />
-                  <label
-                    htmlFor={name}
-                    className="text-sm font-medium text-foreground"
-                  >
-                    {label}
-                  </label>
-                </div>
-              ))}
-            </div>
-          </div>
-
-          {error && (
-            <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-              {error}
-            </div>
-          )}
-
-          <Dialog.Footer>
-            <Button
-              variant="ghost"
-              size="small"
-              type="button"
-              onClick={() => {
-                setOpen(false);
-                setError(null);
-              }}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Creating..." : "Save Provider"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/CreatorsTable.tsx

@@ -1,195 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/atoms/Table/Table";
-import { Button } from "@/components/atoms/Button/Button";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { updateLlmCreatorAction } from "../actions";
-import { useRouter } from "next/navigation";
-import { DeleteCreatorModal } from "./DeleteCreatorModal";
-
-export function CreatorsTable({ creators }: { creators: LlmModelCreator[] }) {
-  if (!creators.length) {
-    return (
-      <div className="rounded-lg border border-dashed border-border p-6 text-center text-sm text-muted-foreground">
-        No creators registered yet.
-      </div>
-    );
-  }
-
-  return (
-    <div className="rounded-lg border">
-      <Table>
-        <TableHeader>
-          <TableRow>
-            <TableHead>Creator</TableHead>
-            <TableHead>Description</TableHead>
-            <TableHead>Website</TableHead>
-            <TableHead>Actions</TableHead>
-          </TableRow>
-        </TableHeader>
-        <TableBody>
-          {creators.map((creator) => (
-            <TableRow key={creator.id}>
-              <TableCell>
-                <div className="font-medium">{creator.display_name}</div>
-                <div className="text-xs text-muted-foreground">
-                  {creator.name}
-                </div>
-              </TableCell>
-              <TableCell>
-                <span className="text-sm text-muted-foreground">
-                  {creator.description || "—"}
-                </span>
-              </TableCell>
-              <TableCell>
-                {creator.website_url ? (
-                  <a
-                    href={creator.website_url}
-                    target="_blank"
-                    rel="noopener noreferrer"
-                    className="text-sm text-primary hover:underline"
-                  >
-                    {(() => {
-                      try {
-                        return new URL(creator.website_url).hostname;
-                      } catch {
-                        return creator.website_url;
-                      }
-                    })()}
-                  </a>
-                ) : (
-                  <span className="text-muted-foreground">—</span>
-                )}
-              </TableCell>
-              <TableCell>
-                <div className="flex items-center justify-end gap-2">
-                  <EditCreatorModal creator={creator} />
-                  <DeleteCreatorModal creator={creator} />
-                </div>
-              </TableCell>
-            </TableRow>
-          ))}
-        </TableBody>
-      </Table>
-    </div>
-  );
-}
-
-function EditCreatorModal({ creator }: { creator: LlmModelCreator }) {
-  const [open, setOpen] = useState(false);
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await updateLlmCreatorAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to update creator");
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Edit Creator"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "512px" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="outline" size="small" className="min-w-0">
-          Edit
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <form action={handleSubmit} className="space-y-4">
-          <input type="hidden" name="creator_id" value={creator.id} />
-
-          <div className="grid gap-4 sm:grid-cols-2">
-            <div className="space-y-2">
-              <label className="text-sm font-medium">Name (slug)</label>
-              <input
-                required
-                name="name"
-                defaultValue={creator.name}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm"
-              />
-            </div>
-            <div className="space-y-2">
-              <label className="text-sm font-medium">Display Name</label>
-              <input
-                required
-                name="display_name"
-                defaultValue={creator.display_name}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm"
-              />
-            </div>
-          </div>
-
-          <div className="space-y-2">
-            <label className="text-sm font-medium">Description</label>
-            <textarea
-              name="description"
-              rows={2}
-              defaultValue={creator.description ?? ""}
-              className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm"
-            />
-          </div>
-
-          <div className="space-y-2">
-            <label className="text-sm font-medium">Website URL</label>
-            <input
-              name="website_url"
-              type="url"
-              defaultValue={creator.website_url ?? ""}
-              className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm"
-            />
-          </div>
-
-          {error && (
-            <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-              {error}
-            </div>
-          )}
-
-          <Dialog.Footer>
-            <Button
-              variant="ghost"
-              size="small"
-              type="button"
-              onClick={() => {
-                setOpen(false);
-                setError(null);
-              }}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Updating..." : "Update"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/DeleteCreatorModal.tsx

@@ -1,107 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useRouter } from "next/navigation";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import { deleteLlmCreatorAction } from "../actions";
-
-export function DeleteCreatorModal({ creator }: { creator: LlmModelCreator }) {
-  const [open, setOpen] = useState(false);
-  const [isDeleting, setIsDeleting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleDelete(formData: FormData) {
-    setIsDeleting(true);
-    setError(null);
-    try {
-      await deleteLlmCreatorAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to delete creator");
-    } finally {
-      setIsDeleting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Delete Creator"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "480px" }}
-    >
-      <Dialog.Trigger>
-        <Button
-          type="button"
-          variant="outline"
-          size="small"
-          className="min-w-0 text-destructive hover:bg-destructive/10"
-        >
-          Delete
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="space-y-4">
-          <div className="rounded-lg border border-amber-500/30 bg-amber-500/10 p-4 dark:border-amber-400/30 dark:bg-amber-400/10">
-            <div className="flex items-start gap-3">
-              <div className="flex-shrink-0 text-amber-600 dark:text-amber-400">
-                ⚠️
-              </div>
-              <div className="text-sm text-foreground">
-                <p className="font-semibold">You are about to delete:</p>
-                <p className="mt-1">
-                  <span className="font-medium">{creator.display_name}</span>{" "}
-                  <span className="text-muted-foreground">
-                    ({creator.name})
-                  </span>
-                </p>
-                <p className="mt-2 text-muted-foreground">
-                  Models using this creator will have their creator field
-                  cleared. This is safe and won&apos;t affect model
-                  functionality.
-                </p>
-              </div>
-            </div>
-          </div>
-
-          <form action={handleDelete} className="space-y-4">
-            <input type="hidden" name="creator_id" value={creator.id} />
-
-            {error && (
-              <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-                {error}
-              </div>
-            )}
-
-            <Dialog.Footer>
-              <Button
-                variant="ghost"
-                size="small"
-                onClick={() => {
-                  setOpen(false);
-                  setError(null);
-                }}
-                disabled={isDeleting}
-                type="button"
-              >
-                Cancel
-              </Button>
-              <Button
-                type="submit"
-                variant="primary"
-                size="small"
-                disabled={isDeleting}
-                className="bg-destructive text-destructive-foreground hover:bg-destructive/90"
-              >
-                {isDeleting ? "Deleting..." : "Delete Creator"}
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/DeleteModelModal.tsx

@@ -1,224 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useRouter } from "next/navigation";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import { deleteLlmModelAction, fetchLlmModelUsage } from "../actions";
-
-export function DeleteModelModal({
-  model,
-  availableModels,
-}: {
-  model: LlmModel;
-  availableModels: LlmModel[];
-}) {
-  const router = useRouter();
-  const [open, setOpen] = useState(false);
-  const [selectedReplacement, setSelectedReplacement] = useState<string>("");
-  const [isDeleting, setIsDeleting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const [usageCount, setUsageCount] = useState<number | null>(null);
-  const [usageLoading, setUsageLoading] = useState(false);
-  const [usageError, setUsageError] = useState<string | null>(null);
-
-  // Filter out the current model and disabled models from replacement options
-  const replacementOptions = availableModels.filter(
-    (m) => m.id !== model.id && m.is_enabled,
-  );
-
-  // Check if migration is required (has blocks using this model)
-  const requiresMigration = usageCount !== null && usageCount > 0;
-
-  async function fetchUsage() {
-    setUsageLoading(true);
-    setUsageError(null);
-    try {
-      const usage = await fetchLlmModelUsage(model.id);
-      setUsageCount(usage.node_count);
-    } catch (err) {
-      console.error("Failed to fetch model usage:", err);
-      setUsageError("Failed to load usage count");
-      setUsageCount(null);
-    } finally {
-      setUsageLoading(false);
-    }
-  }
-
-  async function handleDelete(formData: FormData) {
-    setIsDeleting(true);
-    setError(null);
-    try {
-      await deleteLlmModelAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to delete model");
-    } finally {
-      setIsDeleting(false);
-    }
-  }
-
-  // Determine if delete button should be enabled
-  const canDelete =
-    !isDeleting &&
-    !usageLoading &&
-    usageCount !== null &&
-    (requiresMigration
-      ? selectedReplacement && replacementOptions.length > 0
-      : true);
-
-  return (
-    <Dialog
-      title="Delete Model"
-      controlled={{
-        isOpen: open,
-        set: async (isOpen) => {
-          setOpen(isOpen);
-          if (isOpen) {
-            setUsageCount(null);
-            setUsageError(null);
-            setError(null);
-            setSelectedReplacement("");
-            await fetchUsage();
-          }
-        },
-      }}
-      styling={{ maxWidth: "600px" }}
-    >
-      <Dialog.Trigger>
-        <Button
-          type="button"
-          variant="outline"
-          size="small"
-          className="min-w-0 text-destructive hover:bg-destructive/10"
-        >
-          Delete
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          {requiresMigration
-            ? "This action cannot be undone. All workflows using this model will be migrated to the replacement model you select."
-            : "This action cannot be undone."}
-        </div>
-
-        <div className="space-y-4">
-          <div className="rounded-lg border border-amber-500/30 bg-amber-500/10 p-4 dark:border-amber-400/30 dark:bg-amber-400/10">
-            <div className="flex items-start gap-3">
-              <div className="flex-shrink-0 text-amber-600 dark:text-amber-400">
-                ⚠️
-              </div>
-              <div className="text-sm text-foreground">
-                <p className="font-semibold">You are about to delete:</p>
-                <p className="mt-1">
-                  <span className="font-medium">{model.display_name}</span>{" "}
-                  <span className="text-muted-foreground">({model.slug})</span>
-                </p>
-                {usageLoading && (
-                  <p className="mt-2 text-muted-foreground">
-                    Loading usage count...
-                  </p>
-                )}
-                {usageError && (
-                  <p className="mt-2 text-destructive">{usageError}</p>
-                )}
-                {!usageLoading && !usageError && usageCount !== null && (
-                  <p className="mt-2 font-semibold">
-                    Impact: {usageCount} block{usageCount !== 1 ? "s" : ""}{" "}
-                    currently use this model
-                  </p>
-                )}
-                {requiresMigration && (
-                  <p className="mt-2 text-muted-foreground">
-                    All workflows currently using this model will be
-                    automatically updated to use the replacement model you
-                    choose below.
-                  </p>
-                )}
-                {!usageLoading && usageCount === 0 && (
-                  <p className="mt-2 text-muted-foreground">
-                    No workflows are using this model. It can be safely deleted.
-                  </p>
-                )}
-              </div>
-            </div>
-          </div>
-
-          <form action={handleDelete} className="space-y-4">
-            <input type="hidden" name="model_id" value={model.id} />
-            <input
-              type="hidden"
-              name="replacement_model_slug"
-              value={selectedReplacement}
-            />
-
-            {requiresMigration && (
-              <label className="text-sm font-medium">
-                <span className="mb-2 block">
-                  Select Replacement Model{" "}
-                  <span className="text-destructive">*</span>
-                </span>
-                <select
-                  required
-                  value={selectedReplacement}
-                  onChange={(e) => setSelectedReplacement(e.target.value)}
-                  className="w-full rounded border border-input bg-background p-2 text-sm"
-                >
-                  <option value="">-- Choose a replacement model --</option>
-                  {replacementOptions.map((m) => (
-                    <option key={m.id} value={m.slug}>
-                      {m.display_name} ({m.slug})
-                    </option>
-                  ))}
-                </select>
-                {replacementOptions.length === 0 && (
-                  <p className="mt-2 text-xs text-destructive">
-                    No replacement models available. You must have at least one
-                    other enabled model before deleting this one.
-                  </p>
-                )}
-              </label>
-            )}
-
-            {error && (
-              <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-                {error}
-              </div>
-            )}
-
-            <Dialog.Footer>
-              <Button
-                variant="ghost"
-                size="small"
-                type="button"
-                onClick={() => {
-                  setOpen(false);
-                  setSelectedReplacement("");
-                  setError(null);
-                }}
-                disabled={isDeleting}
-              >
-                Cancel
-              </Button>
-              <Button
-                type="submit"
-                variant="primary"
-                size="small"
-                disabled={!canDelete}
-                className="bg-destructive text-destructive-foreground hover:bg-destructive/90"
-              >
-                {isDeleting
-                  ? "Deleting..."
-                  : requiresMigration
-                    ? "Delete and Migrate"
-                    : "Delete"}
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/DeleteProviderModal.tsx

@@ -1,129 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useRouter } from "next/navigation";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import { deleteLlmProviderAction } from "../actions";
-
-export function DeleteProviderModal({ provider }: { provider: LlmProvider }) {
-  const [open, setOpen] = useState(false);
-  const [isDeleting, setIsDeleting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  const modelCount = provider.models?.length ?? 0;
-  const hasModels = modelCount > 0;
-
-  async function handleDelete(formData: FormData) {
-    setIsDeleting(true);
-    setError(null);
-    try {
-      await deleteLlmProviderAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(
-        err instanceof Error ? err.message : "Failed to delete provider",
-      );
-    } finally {
-      setIsDeleting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Delete Provider"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "480px" }}
-    >
-      <Dialog.Trigger>
-        <Button
-          type="button"
-          variant="outline"
-          size="small"
-          className="min-w-0 text-destructive hover:bg-destructive/10"
-        >
-          Delete
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="space-y-4">
-          <div
-            className={`rounded-lg border p-4 ${
-              hasModels
-                ? "border-destructive/30 bg-destructive/10"
-                : "border-amber-500/30 bg-amber-500/10 dark:border-amber-400/30 dark:bg-amber-400/10"
-            }`}
-          >
-            <div className="flex items-start gap-3">
-              <div
-                className={`flex-shrink-0 ${
-                  hasModels
-                    ? "text-destructive"
-                    : "text-amber-600 dark:text-amber-400"
-                }`}
-              >
-                {hasModels ? "🚫" : "⚠️"}
-              </div>
-              <div className="text-sm text-foreground">
-                <p className="font-semibold">You are about to delete:</p>
-                <p className="mt-1">
-                  <span className="font-medium">{provider.display_name}</span>{" "}
-                  <span className="text-muted-foreground">
-                    ({provider.name})
-                  </span>
-                </p>
-                {hasModels ? (
-                  <p className="mt-2 text-destructive">
-                    This provider has {modelCount} model(s). You must delete all
-                    models before you can delete this provider.
-                  </p>
-                ) : (
-                  <p className="mt-2 text-muted-foreground">
-                    This provider has no models and can be safely deleted.
-                  </p>
-                )}
-              </div>
-            </div>
-          </div>
-
-          <form action={handleDelete} className="space-y-4">
-            <input type="hidden" name="provider_id" value={provider.id} />
-
-            {error && (
-              <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-                {error}
-              </div>
-            )}
-
-            <Dialog.Footer>
-              <Button
-                variant="ghost"
-                size="small"
-                onClick={() => {
-                  setOpen(false);
-                  setError(null);
-                }}
-                disabled={isDeleting}
-                type="button"
-              >
-                Cancel
-              </Button>
-              <Button
-                type="submit"
-                variant="primary"
-                size="small"
-                disabled={isDeleting || hasModels}
-                className="bg-destructive text-destructive-foreground hover:bg-destructive/90 disabled:opacity-50"
-              >
-                {isDeleting ? "Deleting..." : "Delete Provider"}
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/DisableModelModal.tsx

@@ -1,288 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import { toggleLlmModelAction, fetchLlmModelUsage } from "../actions";
-
-export function DisableModelModal({
-  model,
-  availableModels,
-}: {
-  model: LlmModel;
-  availableModels: LlmModel[];
-}) {
-  const [open, setOpen] = useState(false);
-  const [isDisabling, setIsDisabling] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const [usageCount, setUsageCount] = useState<number | null>(null);
-  const [selectedMigration, setSelectedMigration] = useState<string>("");
-  const [wantsMigration, setWantsMigration] = useState(false);
-  const [migrationReason, setMigrationReason] = useState("");
-  const [customCreditCost, setCustomCreditCost] = useState<string>("");
-
-  // Filter out the current model and disabled models from replacement options
-  const migrationOptions = availableModels.filter(
-    (m) => m.id !== model.id && m.is_enabled,
-  );
-
-  async function fetchUsage() {
-    try {
-      const usage = await fetchLlmModelUsage(model.id);
-      setUsageCount(usage.node_count);
-    } catch {
-      setUsageCount(null);
-    }
-  }
-
-  async function handleDisable(formData: FormData) {
-    setIsDisabling(true);
-    setError(null);
-    try {
-      await toggleLlmModelAction(formData);
-      setOpen(false);
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to disable model");
-    } finally {
-      setIsDisabling(false);
-    }
-  }
-
-  function resetState() {
-    setError(null);
-    setSelectedMigration("");
-    setWantsMigration(false);
-    setMigrationReason("");
-    setCustomCreditCost("");
-  }
-
-  const hasUsage = usageCount !== null && usageCount > 0;
-
-  return (
-    <Dialog
-      title="Disable Model"
-      controlled={{
-        isOpen: open,
-        set: async (isOpen) => {
-          setOpen(isOpen);
-          if (isOpen) {
-            setUsageCount(null);
-            resetState();
-            await fetchUsage();
-          }
-        },
-      }}
-      styling={{ maxWidth: "600px" }}
-    >
-      <Dialog.Trigger>
-        <Button
-          type="button"
-          variant="outline"
-          size="small"
-          className="min-w-0"
-        >
-          Disable
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Disabling a model will hide it from users when creating new workflows.
-        </div>
-
-        <div className="space-y-4">
-          <div className="rounded-lg border border-amber-500/30 bg-amber-500/10 p-4 dark:border-amber-400/30 dark:bg-amber-400/10">
-            <div className="flex items-start gap-3">
-              <div className="flex-shrink-0 text-amber-600 dark:text-amber-400">
-                ⚠️
-              </div>
-              <div className="text-sm text-foreground">
-                <p className="font-semibold">You are about to disable:</p>
-                <p className="mt-1">
-                  <span className="font-medium">{model.display_name}</span>{" "}
-                  <span className="text-muted-foreground">({model.slug})</span>
-                </p>
-                {usageCount === null ? (
-                  <p className="mt-2 text-muted-foreground">
-                    Loading usage data...
-                  </p>
-                ) : usageCount > 0 ? (
-                  <p className="mt-2 font-semibold">
-                    Impact: {usageCount} block{usageCount !== 1 ? "s" : ""}{" "}
-                    currently use this model
-                  </p>
-                ) : (
-                  <p className="mt-2 text-muted-foreground">
-                    No workflows are currently using this model.
-                  </p>
-                )}
-              </div>
-            </div>
-          </div>
-
-          {hasUsage && (
-            <div className="space-y-4 rounded-lg border border-border bg-muted/50 p-4">
-              <label className="flex items-start gap-3">
-                <input
-                  type="checkbox"
-                  checked={wantsMigration}
-                  onChange={(e) => {
-                    setWantsMigration(e.target.checked);
-                    if (!e.target.checked) {
-                      setSelectedMigration("");
-                    }
-                  }}
-                  className="mt-1"
-                />
-                <div className="text-sm">
-                  <span className="font-medium">
-                    Migrate existing workflows to another model
-                  </span>
-                  <p className="mt-1 text-muted-foreground">
-                    Creates a revertible migration record. If unchecked,
-                    existing workflows will use automatic fallback to an enabled
-                    model from the same provider.
-                  </p>
-                </div>
-              </label>
-
-              {wantsMigration && (
-                <div className="space-y-4 border-t border-border pt-4">
-                  <label className="block text-sm font-medium">
-                    <span className="mb-2 block">
-                      Replacement Model{" "}
-                      <span className="text-destructive">*</span>
-                    </span>
-                    <select
-                      required
-                      value={selectedMigration}
-                      onChange={(e) => setSelectedMigration(e.target.value)}
-                      className="w-full rounded border border-input bg-background p-2 text-sm"
-                    >
-                      <option value="">-- Choose a replacement model --</option>
-                      {migrationOptions.map((m) => (
-                        <option key={m.id} value={m.slug}>
-                          {m.display_name} ({m.slug})
-                        </option>
-                      ))}
-                    </select>
-                    {migrationOptions.length === 0 && (
-                      <p className="mt-2 text-xs text-destructive">
-                        No other enabled models available for migration.
-                      </p>
-                    )}
-                  </label>
-
-                  <label className="block text-sm font-medium">
-                    <span className="mb-2 block">
-                      Migration Reason{" "}
-                      <span className="font-normal text-muted-foreground">
-                        (optional)
-                      </span>
-                    </span>
-                    <input
-                      type="text"
-                      value={migrationReason}
-                      onChange={(e) => setMigrationReason(e.target.value)}
-                      placeholder="e.g., Provider outage, Cost reduction"
-                      className="w-full rounded border border-input bg-background p-2 text-sm"
-                    />
-                    <p className="mt-1 text-xs text-muted-foreground">
-                      Helps track why the migration was made
-                    </p>
-                  </label>
-
-                  <label className="block text-sm font-medium">
-                    <span className="mb-2 block">
-                      Custom Credit Cost{" "}
-                      <span className="font-normal text-muted-foreground">
-                        (optional)
-                      </span>
-                    </span>
-                    <input
-                      type="number"
-                      min="0"
-                      value={customCreditCost}
-                      onChange={(e) => setCustomCreditCost(e.target.value)}
-                      placeholder="Leave blank to use target model's cost"
-                      className="w-full rounded border border-input bg-background p-2 text-sm"
-                    />
-                    <p className="mt-1 text-xs text-muted-foreground">
-                      Override pricing for migrated workflows. When set, billing
-                      will use this cost instead of the target model&apos;s
-                      cost.
-                    </p>
-                  </label>
-                </div>
-              )}
-            </div>
-          )}
-
-          <form action={handleDisable} className="space-y-4">
-            <input type="hidden" name="model_id" value={model.id} />
-            <input type="hidden" name="is_enabled" value="false" />
-            {wantsMigration && selectedMigration && (
-              <>
-                <input
-                  type="hidden"
-                  name="migrate_to_slug"
-                  value={selectedMigration}
-                />
-                {migrationReason && (
-                  <input
-                    type="hidden"
-                    name="migration_reason"
-                    value={migrationReason}
-                  />
-                )}
-                {customCreditCost && (
-                  <input
-                    type="hidden"
-                    name="custom_credit_cost"
-                    value={customCreditCost}
-                  />
-                )}
-              </>
-            )}
-
-            {error && (
-              <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-                {error}
-              </div>
-            )}
-
-            <Dialog.Footer>
-              <Button
-                variant="ghost"
-                size="small"
-                onClick={() => {
-                  setOpen(false);
-                  resetState();
-                }}
-                disabled={isDisabling}
-              >
-                Cancel
-              </Button>
-              <Button
-                type="submit"
-                variant="primary"
-                size="small"
-                disabled={
-                  isDisabling ||
-                  (wantsMigration && !selectedMigration) ||
-                  usageCount === null
-                }
-              >
-                {isDisabling
-                  ? "Disabling..."
-                  : wantsMigration && selectedMigration
-                    ? "Disable & Migrate"
-                    : "Disable Model"}
-              </Button>
-            </Dialog.Footer>
-          </form>
-        </div>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/EditModelModal.tsx

@@ -1,223 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useRouter } from "next/navigation";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import { updateLlmModelAction } from "../actions";
-
-export function EditModelModal({
-  model,
-  providers,
-  creators,
-}: {
-  model: LlmModel;
-  providers: LlmProvider[];
-  creators: LlmModelCreator[];
-}) {
-  const router = useRouter();
-  const [open, setOpen] = useState(false);
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const cost = model.costs?.[0];
-  const provider = providers.find((p) => p.id === model.provider_id);
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await updateLlmModelAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to update model");
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Edit Model"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "768px", maxHeight: "90vh", overflowY: "auto" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="outline" size="small" className="min-w-0">
-          Edit
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Update model metadata and pricing information.
-        </div>
-        {error && (
-          <div className="mb-4 rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-            {error}
-          </div>
-        )}
-        <form action={handleSubmit} className="space-y-4">
-          <input type="hidden" name="model_id" value={model.id} />
-
-          <div className="grid gap-4 md:grid-cols-2">
-            <label className="text-sm font-medium">
-              Display Name
-              <input
-                required
-                name="display_name"
-                defaultValue={model.display_name}
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-              />
-            </label>
-            <label className="text-sm font-medium">
-              Provider
-              <select
-                required
-                name="provider_id"
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-                defaultValue={model.provider_id}
-              >
-                {providers.map((p) => (
-                  <option key={p.id} value={p.id}>
-                    {p.display_name} ({p.name})
-                  </option>
-                ))}
-              </select>
-              <span className="text-xs text-muted-foreground">
-                Who hosts/serves the model
-              </span>
-            </label>
-          </div>
-
-          <div className="grid gap-4 md:grid-cols-2">
-            <label className="text-sm font-medium">
-              Creator
-              <select
-                name="creator_id"
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-                defaultValue={model.creator_id ?? ""}
-              >
-                <option value="">No creator selected</option>
-                {creators.map((c) => (
-                  <option key={c.id} value={c.id}>
-                    {c.display_name} ({c.name})
-                  </option>
-                ))}
-              </select>
-              <span className="text-xs text-muted-foreground">
-                Who made/trained the model (e.g., OpenAI, Meta)
-              </span>
-            </label>
-          </div>
-
-          <label className="text-sm font-medium">
-            Description
-            <textarea
-              name="description"
-              rows={2}
-              defaultValue={model.description ?? ""}
-              className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-              placeholder="Optional description..."
-            />
-          </label>
-
-          <div className="grid gap-4 md:grid-cols-2">
-            <label className="text-sm font-medium">
-              Context Window
-              <input
-                required
-                type="number"
-                name="context_window"
-                defaultValue={model.context_window}
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-                min={1}
-              />
-            </label>
-            <label className="text-sm font-medium">
-              Max Output Tokens
-              <input
-                type="number"
-                name="max_output_tokens"
-                defaultValue={model.max_output_tokens ?? undefined}
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-                min={1}
-              />
-            </label>
-          </div>
-
-          <div className="grid gap-4 md:grid-cols-2">
-            <label className="text-sm font-medium">
-              Credit Cost
-              <input
-                required
-                type="number"
-                name="credit_cost"
-                defaultValue={cost?.credit_cost ?? 0}
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-                min={0}
-              />
-              <span className="text-xs text-muted-foreground">
-                Credits charged per run
-              </span>
-            </label>
-            <label className="text-sm font-medium">
-              Credential Provider
-              <select
-                required
-                name="credential_provider"
-                defaultValue={cost?.credential_provider ?? provider?.name ?? ""}
-                className="mt-1 w-full rounded border border-input bg-background p-2 text-sm"
-              >
-                <option value="" disabled>
-                  Select provider
-                </option>
-                {providers.map((p) => (
-                  <option key={p.id} value={p.name}>
-                    {p.display_name} ({p.name})
-                  </option>
-                ))}
-              </select>
-              <span className="text-xs text-muted-foreground">
-                Must match a key in PROVIDER_CREDENTIALS
-              </span>
-            </label>
-          </div>
-          {/* Hidden defaults for credential_type and unit */}
-          <input
-            type="hidden"
-            name="credential_type"
-            value={
-              cost?.credential_type ??
-              provider?.default_credential_type ??
-              "api_key"
-            }
-          />
-          <input type="hidden" name="unit" value={cost?.unit ?? "RUN"} />
-
-          <Dialog.Footer>
-            <Button
-              type="button"
-              variant="ghost"
-              size="small"
-              onClick={() => setOpen(false)}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Updating..." : "Update Model"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/EditProviderModal.tsx

@@ -1,263 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { Button } from "@/components/atoms/Button/Button";
-import { updateLlmProviderAction } from "../actions";
-import { useRouter } from "next/navigation";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-
-export function EditProviderModal({ provider }: { provider: LlmProvider }) {
-  const [open, setOpen] = useState(false);
-  const [isSubmitting, setIsSubmitting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const router = useRouter();
-
-  async function handleSubmit(formData: FormData) {
-    setIsSubmitting(true);
-    setError(null);
-    try {
-      await updateLlmProviderAction(formData);
-      setOpen(false);
-      router.refresh();
-    } catch (err) {
-      setError(
-        err instanceof Error ? err.message : "Failed to update provider",
-      );
-    } finally {
-      setIsSubmitting(false);
-    }
-  }
-
-  return (
-    <Dialog
-      title="Edit Provider"
-      controlled={{ isOpen: open, set: setOpen }}
-      styling={{ maxWidth: "768px", maxHeight: "90vh", overflowY: "auto" }}
-    >
-      <Dialog.Trigger>
-        <Button variant="outline" size="small">
-          Edit
-        </Button>
-      </Dialog.Trigger>
-      <Dialog.Content>
-        <div className="mb-4 text-sm text-muted-foreground">
-          Update provider configuration and capabilities.
-        </div>
-
-        <form action={handleSubmit} className="space-y-6">
-          <input type="hidden" name="provider_id" value={provider.id} />
-
-          {/* Basic Information */}
-          <div className="space-y-4">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Basic Information
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Core provider details
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="name"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Provider Slug <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="name"
-                  required
-                  name="name"
-                  defaultValue={provider.name}
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="e.g. openai"
-                />
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="display_name"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Display Name <span className="text-destructive">*</span>
-                </label>
-                <input
-                  id="display_name"
-                  required
-                  name="display_name"
-                  defaultValue={provider.display_name}
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="OpenAI"
-                />
-              </div>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="description"
-                className="text-sm font-medium text-foreground"
-              >
-                Description
-              </label>
-              <textarea
-                id="description"
-                name="description"
-                rows={3}
-                defaultValue={provider.description ?? ""}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="Optional description..."
-              />
-            </div>
-          </div>
-
-          {/* Default Credentials */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Default Credentials
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Credential provider name that matches the key in{" "}
-                <code className="rounded bg-muted px-1 py-0.5 font-mono text-xs">
-                  PROVIDER_CREDENTIALS
-                </code>
-              </p>
-            </div>
-            <div className="grid gap-4 sm:grid-cols-2">
-              <div className="space-y-2">
-                <label
-                  htmlFor="default_credential_provider"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Credential Provider
-                </label>
-                <input
-                  id="default_credential_provider"
-                  name="default_credential_provider"
-                  defaultValue={provider.default_credential_provider ?? ""}
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="openai"
-                />
-              </div>
-              <div className="space-y-2">
-                <label
-                  htmlFor="default_credential_id"
-                  className="text-sm font-medium text-foreground"
-                >
-                  Credential ID
-                </label>
-                <input
-                  id="default_credential_id"
-                  name="default_credential_id"
-                  defaultValue={provider.default_credential_id ?? ""}
-                  className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                  placeholder="Optional credential ID"
-                />
-              </div>
-            </div>
-            <div className="space-y-2">
-              <label
-                htmlFor="default_credential_type"
-                className="text-sm font-medium text-foreground"
-              >
-                Credential Type
-              </label>
-              <input
-                id="default_credential_type"
-                name="default_credential_type"
-                defaultValue={provider.default_credential_type ?? "api_key"}
-                className="w-full rounded-md border border-input bg-background px-3 py-2 text-sm transition-colors placeholder:text-muted-foreground focus:border-primary focus:outline-none focus:ring-2 focus:ring-primary/20"
-                placeholder="api_key"
-              />
-            </div>
-          </div>
-
-          {/* Capabilities */}
-          <div className="space-y-4 border-t border-border pt-6">
-            <div className="space-y-1">
-              <h3 className="text-sm font-semibold text-foreground">
-                Capabilities
-              </h3>
-              <p className="text-xs text-muted-foreground">
-                Provider feature flags
-              </p>
-            </div>
-            <div className="grid gap-3 sm:grid-cols-2">
-              {[
-                {
-                  name: "supports_tools",
-                  label: "Supports tools",
-                  checked: provider.supports_tools,
-                },
-                {
-                  name: "supports_json_output",
-                  label: "Supports JSON output",
-                  checked: provider.supports_json_output,
-                },
-                {
-                  name: "supports_reasoning",
-                  label: "Supports reasoning",
-                  checked: provider.supports_reasoning,
-                },
-                {
-                  name: "supports_parallel_tool",
-                  label: "Supports parallel tool calls",
-                  checked: provider.supports_parallel_tool,
-                },
-              ].map(({ name, label, checked }) => (
-                <div
-                  key={name}
-                  className="flex items-center gap-3 rounded-md border border-border bg-muted/30 px-4 py-3 transition-colors hover:bg-muted/50"
-                >
-                  <input type="hidden" name={name} value="off" />
-                  <input
-                    id={name}
-                    type="checkbox"
-                    name={name}
-                    defaultChecked={checked}
-                    className="h-4 w-4 rounded border-input"
-                  />
-                  <label
-                    htmlFor={name}
-                    className="text-sm font-medium text-foreground"
-                  >
-                    {label}
-                  </label>
-                </div>
-              ))}
-            </div>
-          </div>
-
-          {error && (
-            <div className="rounded-lg border border-destructive/30 bg-destructive/10 p-3 text-sm text-destructive">
-              {error}
-            </div>
-          )}
-
-          <Dialog.Footer>
-            <Button
-              variant="ghost"
-              size="small"
-              type="button"
-              onClick={() => {
-                setOpen(false);
-                setError(null);
-              }}
-              disabled={isSubmitting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="primary"
-              size="small"
-              type="submit"
-              disabled={isSubmitting}
-            >
-              {isSubmitting ? "Saving..." : "Save Changes"}
-            </Button>
-          </Dialog.Footer>
-        </form>
-      </Dialog.Content>
-    </Dialog>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/LlmRegistryDashboard.tsx

@@ -1,131 +0,0 @@
-"use client";
-
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import type { LlmModelMigration } from "@/app/api/__generated__/models/llmModelMigration";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import { ErrorBoundary } from "@/components/molecules/ErrorBoundary/ErrorBoundary";
-import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { AddProviderModal } from "./AddProviderModal";
-import { AddModelModal } from "./AddModelModal";
-import { AddCreatorModal } from "./AddCreatorModal";
-import { ProviderList } from "./ProviderList";
-import { ModelsTable } from "./ModelsTable";
-import { MigrationsTable } from "./MigrationsTable";
-import { CreatorsTable } from "./CreatorsTable";
-import { RecommendedModelSelector } from "./RecommendedModelSelector";
-
-interface Props {
-  providers: LlmProvider[];
-  models: LlmModel[];
-  migrations: LlmModelMigration[];
-  creators: LlmModelCreator[];
-}
-
-function AdminErrorFallback() {
-  return (
-    <div className="mx-auto max-w-xl p-6">
-      <ErrorCard
-        responseError={{
-          message:
-            "An error occurred while loading the LLM Registry. Please refresh the page.",
-        }}
-        context="llm-registry"
-        onRetry={() => window.location.reload()}
-      />
-    </div>
-  );
-}
-
-export function LlmRegistryDashboard({
-  providers,
-  models,
-  migrations,
-  creators,
-}: Props) {
-  return (
-    <ErrorBoundary fallback={<AdminErrorFallback />} context="llm-registry">
-      <div className="mx-auto p-6">
-        <div className="flex flex-col gap-6">
-          {/* Header */}
-          <div>
-            <h1 className="text-3xl font-bold">LLM Registry</h1>
-            <p className="text-muted-foreground">
-              Manage providers, creators, models, and credit pricing
-            </p>
-          </div>
-
-          {/* Active Migrations Section - Only show if there are migrations */}
-          {migrations.length > 0 && (
-            <div className="rounded-lg border border-primary/30 bg-primary/5 p-6 shadow-sm">
-              <div className="mb-4">
-                <h2 className="text-xl font-semibold">Active Migrations</h2>
-                <p className="mt-1 text-sm text-muted-foreground">
-                  These migrations can be reverted to restore workflows to their
-                  original model
-                </p>
-              </div>
-              <MigrationsTable migrations={migrations} />
-            </div>
-          )}
-
-          {/* Providers & Creators Section - Side by Side */}
-          <div className="grid gap-6 lg:grid-cols-2">
-            {/* Providers */}
-            <div className="rounded-lg border bg-card p-6 shadow-sm">
-              <div className="mb-4 flex items-center justify-between">
-                <div>
-                  <h2 className="text-xl font-semibold">Providers</h2>
-                  <p className="mt-1 text-sm text-muted-foreground">
-                    Who hosts/serves the models
-                  </p>
-                </div>
-                <AddProviderModal />
-              </div>
-              <ProviderList providers={providers} />
-            </div>
-
-            {/* Creators */}
-            <div className="rounded-lg border bg-card p-6 shadow-sm">
-              <div className="mb-4 flex items-center justify-between">
-                <div>
-                  <h2 className="text-xl font-semibold">Creators</h2>
-                  <p className="mt-1 text-sm text-muted-foreground">
-                    Who made/trained the models
-                  </p>
-                </div>
-                <AddCreatorModal />
-              </div>
-              <CreatorsTable creators={creators} />
-            </div>
-          </div>
-
-          {/* Models Section */}
-          <div className="rounded-lg border bg-card p-6 shadow-sm">
-            <div className="mb-4 flex items-center justify-between">
-              <div>
-                <h2 className="text-xl font-semibold">Models</h2>
-                <p className="mt-1 text-sm text-muted-foreground">
-                  Toggle availability, adjust context windows, and update credit
-                  pricing
-                </p>
-              </div>
-              <AddModelModal providers={providers} creators={creators} />
-            </div>
-
-            {/* Recommended Model Selector */}
-            <div className="mb-6">
-              <RecommendedModelSelector models={models} />
-            </div>
-
-            <ModelsTable
-              models={models}
-              providers={providers}
-              creators={creators}
-            />
-          </div>
-        </div>
-      </div>
-    </ErrorBoundary>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/MigrationsTable.tsx

@@ -1,133 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import type { LlmModelMigration } from "@/app/api/__generated__/models/llmModelMigration";
-import { Button } from "@/components/atoms/Button/Button";
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/atoms/Table/Table";
-import { revertLlmMigrationAction } from "../actions";
-
-export function MigrationsTable({
-  migrations,
-}: {
-  migrations: LlmModelMigration[];
-}) {
-  if (!migrations.length) {
-    return (
-      <div className="rounded-lg border border-dashed border-border p-6 text-center text-sm text-muted-foreground">
-        No active migrations. Migrations are created when you disable a model
-        with the &quot;Migrate existing workflows&quot; option.
-      </div>
-    );
-  }
-
-  return (
-    <div className="rounded-lg border">
-      <Table>
-        <TableHeader>
-          <TableRow>
-            <TableHead>Migration</TableHead>
-            <TableHead>Reason</TableHead>
-            <TableHead>Nodes Affected</TableHead>
-            <TableHead>Custom Cost</TableHead>
-            <TableHead>Created</TableHead>
-            <TableHead className="text-right">Actions</TableHead>
-          </TableRow>
-        </TableHeader>
-        <TableBody>
-          {migrations.map((migration) => (
-            <MigrationRow key={migration.id} migration={migration} />
-          ))}
-        </TableBody>
-      </Table>
-    </div>
-  );
-}
-
-function MigrationRow({ migration }: { migration: LlmModelMigration }) {
-  const [isReverting, setIsReverting] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-
-  async function handleRevert(formData: FormData) {
-    setIsReverting(true);
-    setError(null);
-    try {
-      await revertLlmMigrationAction(formData);
-    } catch (err) {
-      setError(
-        err instanceof Error ? err.message : "Failed to revert migration",
-      );
-    } finally {
-      setIsReverting(false);
-    }
-  }
-
-  const createdDate = new Date(migration.created_at);
-
-  return (
-    <>
-      <TableRow>
-        <TableCell>
-          <div className="text-sm">
-            <span className="font-medium">{migration.source_model_slug}</span>
-            <span className="mx-2 text-muted-foreground">→</span>
-            <span className="font-medium">{migration.target_model_slug}</span>
-          </div>
-        </TableCell>
-        <TableCell>
-          <div className="text-sm text-muted-foreground">
-            {migration.reason || "—"}
-          </div>
-        </TableCell>
-        <TableCell>
-          <div className="text-sm">{migration.node_count}</div>
-        </TableCell>
-        <TableCell>
-          <div className="text-sm">
-            {migration.custom_credit_cost !== null &&
-            migration.custom_credit_cost !== undefined
-              ? `${migration.custom_credit_cost} credits`
-              : "—"}
-          </div>
-        </TableCell>
-        <TableCell>
-          <div className="text-sm text-muted-foreground">
-            {createdDate.toLocaleDateString()}{" "}
-            {createdDate.toLocaleTimeString([], {
-              hour: "2-digit",
-              minute: "2-digit",
-            })}
-          </div>
-        </TableCell>
-        <TableCell className="text-right">
-          <form action={handleRevert} className="inline">
-            <input type="hidden" name="migration_id" value={migration.id} />
-            <Button
-              type="submit"
-              variant="outline"
-              size="small"
-              disabled={isReverting}
-            >
-              {isReverting ? "Reverting..." : "Revert"}
-            </Button>
-          </form>
-        </TableCell>
-      </TableRow>
-      {error && (
-        <TableRow>
-          <TableCell colSpan={6}>
-            <div className="rounded border border-destructive/30 bg-destructive/10 p-2 text-sm text-destructive">
-              {error}
-            </div>
-          </TableCell>
-        </TableRow>
-      )}
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/ModelsTable.tsx

@@ -1,172 +0,0 @@
-"use client";
-
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import type { LlmModelCreator } from "@/app/api/__generated__/models/llmModelCreator";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/atoms/Table/Table";
-import { Button } from "@/components/atoms/Button/Button";
-import { toggleLlmModelAction } from "../actions";
-import { DeleteModelModal } from "./DeleteModelModal";
-import { DisableModelModal } from "./DisableModelModal";
-import { EditModelModal } from "./EditModelModal";
-import { Star } from "@phosphor-icons/react";
-
-export function ModelsTable({
-  models,
-  providers,
-  creators,
-}: {
-  models: LlmModel[];
-  providers: LlmProvider[];
-  creators: LlmModelCreator[];
-}) {
-  if (!models.length) {
-    return (
-      <div className="rounded-lg border border-dashed border-border p-6 text-center text-sm text-muted-foreground">
-        No models registered yet.
-      </div>
-    );
-  }
-
-  const providerLookup = new Map(
-    providers.map((provider) => [provider.id, provider]),
-  );
-
-  return (
-    <div className="rounded-lg border">
-      <Table>
-        <TableHeader>
-          <TableRow>
-            <TableHead>Model</TableHead>
-            <TableHead>Provider</TableHead>
-            <TableHead>Creator</TableHead>
-            <TableHead>Context Window</TableHead>
-            <TableHead>Max Output</TableHead>
-            <TableHead>Cost</TableHead>
-            <TableHead>Status</TableHead>
-            <TableHead>Actions</TableHead>
-          </TableRow>
-        </TableHeader>
-        <TableBody>
-          {models.map((model) => {
-            const cost = model.costs?.[0];
-            const provider = providerLookup.get(model.provider_id);
-            return (
-              <TableRow
-                key={model.id}
-                className={model.is_enabled ? "" : "opacity-60"}
-              >
-                <TableCell>
-                  <div className="font-medium">{model.display_name}</div>
-                  <div className="text-xs text-muted-foreground">
-                    {model.slug}
-                  </div>
-                </TableCell>
-                <TableCell>
-                  {provider ? (
-                    <>
-                      <div>{provider.display_name}</div>
-                      <div className="text-xs text-muted-foreground">
-                        {provider.name}
-                      </div>
-                    </>
-                  ) : (
-                    model.provider_id
-                  )}
-                </TableCell>
-                <TableCell>
-                  {model.creator ? (
-                    <>
-                      <div>{model.creator.display_name}</div>
-                      <div className="text-xs text-muted-foreground">
-                        {model.creator.name}
-                      </div>
-                    </>
-                  ) : (
-                    <span className="text-muted-foreground">—</span>
-                  )}
-                </TableCell>
-                <TableCell>{model.context_window.toLocaleString()}</TableCell>
-                <TableCell>
-                  {model.max_output_tokens
-                    ? model.max_output_tokens.toLocaleString()
-                    : "—"}
-                </TableCell>
-                <TableCell>
-                  {cost ? (
-                    <>
-                      <div className="font-medium">
-                        {cost.credit_cost} credits
-                      </div>
-                      <div className="text-xs text-muted-foreground">
-                        {cost.credential_provider}
-                      </div>
-                    </>
-                  ) : (
-                    "—"
-                  )}
-                </TableCell>
-                <TableCell>
-                  <div className="flex flex-col gap-1">
-                    <span
-                      className={`inline-flex rounded-full px-2.5 py-1 text-xs font-semibold ${
-                        model.is_enabled
-                          ? "bg-primary/10 text-primary"
-                          : "bg-muted text-muted-foreground"
-                      }`}
-                    >
-                      {model.is_enabled ? "Enabled" : "Disabled"}
-                    </span>
-                    {model.is_recommended && (
-                      <span className="inline-flex items-center gap-1 rounded-full bg-amber-500/10 px-2.5 py-1 text-xs font-semibold text-amber-600 dark:text-amber-400">
-                        <Star size={12} weight="fill" />
-                        Recommended
-                      </span>
-                    )}
-                  </div>
-                </TableCell>
-                <TableCell>
-                  <div className="flex items-center justify-end gap-2">
-                    {model.is_enabled ? (
-                      <DisableModelModal
-                        model={model}
-                        availableModels={models}
-                      />
-                    ) : (
-                      <EnableModelButton modelId={model.id} />
-                    )}
-                    <EditModelModal
-                      model={model}
-                      providers={providers}
-                      creators={creators}
-                    />
-                    <DeleteModelModal model={model} availableModels={models} />
-                  </div>
-                </TableCell>
-              </TableRow>
-            );
-          })}
-        </TableBody>
-      </Table>
-    </div>
-  );
-}
-
-function EnableModelButton({ modelId }: { modelId: string }) {
-  return (
-    <form action={toggleLlmModelAction} className="inline">
-      <input type="hidden" name="model_id" value={modelId} />
-      <input type="hidden" name="is_enabled" value="true" />
-      <Button type="submit" variant="outline" size="small" className="min-w-0">
-        Enable
-      </Button>
-    </form>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/ProviderList.tsx

@@ -1,94 +0,0 @@
-"use client";
-
-import {
-  Table,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/atoms/Table/Table";
-import type { LlmProvider } from "@/app/api/__generated__/models/llmProvider";
-import { DeleteProviderModal } from "./DeleteProviderModal";
-import { EditProviderModal } from "./EditProviderModal";
-
-export function ProviderList({ providers }: { providers: LlmProvider[] }) {
-  if (!providers.length) {
-    return (
-      <div className="rounded-lg border border-dashed border-border p-6 text-center text-sm text-muted-foreground">
-        No providers configured yet.
-      </div>
-    );
-  }
-
-  return (
-    <div className="rounded-lg border">
-      <Table>
-        <TableHeader>
-          <TableRow>
-            <TableHead>Name</TableHead>
-            <TableHead>Display Name</TableHead>
-            <TableHead>Default Credential</TableHead>
-            <TableHead>Capabilities</TableHead>
-            <TableHead>Models</TableHead>
-            <TableHead className="w-[100px]">Actions</TableHead>
-          </TableRow>
-        </TableHeader>
-        <TableBody>
-          {providers.map((provider) => (
-            <TableRow key={provider.id}>
-              <TableCell className="font-medium">{provider.name}</TableCell>
-              <TableCell>{provider.display_name}</TableCell>
-              <TableCell>
-                {provider.default_credential_provider
-                  ? `${provider.default_credential_provider} (${provider.default_credential_id ?? "id?"})`
-                  : "—"}
-              </TableCell>
-              <TableCell className="text-sm text-muted-foreground">
-                <div className="flex flex-wrap gap-2">
-                  {provider.supports_tools && (
-                    <span className="rounded bg-muted px-2 py-0.5 text-xs">
-                      Tools
-                    </span>
-                  )}
-                  {provider.supports_json_output && (
-                    <span className="rounded bg-muted px-2 py-0.5 text-xs">
-                      JSON
-                    </span>
-                  )}
-                  {provider.supports_reasoning && (
-                    <span className="rounded bg-muted px-2 py-0.5 text-xs">
-                      Reasoning
-                    </span>
-                  )}
-                  {provider.supports_parallel_tool && (
-                    <span className="rounded bg-muted px-2 py-0.5 text-xs">
-                      Parallel Tools
-                    </span>
-                  )}
-                </div>
-              </TableCell>
-              <TableCell className="text-sm">
-                <span
-                  className={
-                    (provider.models?.length ?? 0) > 0
-                      ? "text-foreground"
-                      : "text-muted-foreground"
-                  }
-                >
-                  {provider.models?.length ?? 0}
-                </span>
-              </TableCell>
-              <TableCell>
-                <div className="flex gap-2">
-                  <EditProviderModal provider={provider} />
-                  <DeleteProviderModal provider={provider} />
-                </div>
-              </TableCell>
-            </TableRow>
-          ))}
-        </TableBody>
-      </Table>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/components/RecommendedModelSelector.tsx

@@ -1,87 +0,0 @@
-"use client";
-
-import { useState } from "react";
-import { useRouter } from "next/navigation";
-import type { LlmModel } from "@/app/api/__generated__/models/llmModel";
-import { Button } from "@/components/atoms/Button/Button";
-import { setRecommendedModelAction } from "../actions";
-import { Star } from "@phosphor-icons/react";
-
-export function RecommendedModelSelector({ models }: { models: LlmModel[] }) {
-  const router = useRouter();
-  const enabledModels = models.filter((m) => m.is_enabled);
-  const currentRecommended = models.find((m) => m.is_recommended);
-
-  const [selectedModelId, setSelectedModelId] = useState<string>(
-    currentRecommended?.id || "",
-  );
-  const [isSaving, setIsSaving] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-
-  const hasChanges = selectedModelId !== (currentRecommended?.id || "");
-
-  async function handleSave() {
-    if (!selectedModelId) return;
-
-    setIsSaving(true);
-    setError(null);
-    try {
-      const formData = new FormData();
-      formData.set("model_id", selectedModelId);
-      await setRecommendedModelAction(formData);
-      router.refresh();
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "Failed to save");
-    } finally {
-      setIsSaving(false);
-    }
-  }
-
-  return (
-    <div className="rounded-lg border border-border bg-card p-4">
-      <div className="mb-3 flex items-center gap-2">
-        <Star size={20} weight="fill" className="text-amber-500" />
-        <h3 className="text-sm font-semibold">Recommended Model</h3>
-      </div>
-      <p className="mb-3 text-xs text-muted-foreground">
-        The recommended model is shown as the default suggestion in model
-        selection dropdowns throughout the platform.
-      </p>
-
-      <div className="flex items-center gap-3">
-        <select
-          value={selectedModelId}
-          onChange={(e) => setSelectedModelId(e.target.value)}
-          className="flex-1 rounded-md border border-input bg-background px-3 py-2 text-sm"
-          disabled={isSaving}
-        >
-          <option value="">-- Select a model --</option>
-          {enabledModels.map((model) => (
-            <option key={model.id} value={model.id}>
-              {model.display_name} ({model.slug})
-            </option>
-          ))}
-        </select>
-
-        <Button
-          type="button"
-          variant="primary"
-          size="small"
-          onClick={handleSave}
-          disabled={!hasChanges || !selectedModelId || isSaving}
-        >
-          {isSaving ? "Saving..." : "Save"}
-        </Button>
-      </div>
-
-      {error && <p className="mt-2 text-xs text-destructive">{error}</p>}
-
-      {currentRecommended && !hasChanges && (
-        <p className="mt-2 text-xs text-muted-foreground">
-          Currently set to:{" "}
-          <span className="font-medium">{currentRecommended.display_name}</span>
-        </p>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/getLlmRegistryPage.ts

@@ -1,46 +0,0 @@
-/**
- * Server-side data fetching for LLM Registry page.
- */
-
-import {
-  fetchLlmCreators,
-  fetchLlmMigrations,
-  fetchLlmModels,
-  fetchLlmProviders,
-} from "./actions";
-
-export async function getLlmRegistryPageData() {
-  // Fetch providers and models (required)
-  const [providersResponse, modelsResponse] = await Promise.all([
-    fetchLlmProviders(),
-    fetchLlmModels(),
-  ]);
-
-  // Fetch migrations separately with fallback (table might not exist yet)
-  let migrations: Awaited<ReturnType<typeof fetchLlmMigrations>>["migrations"] =
-    [];
-  try {
-    const migrationsResponse = await fetchLlmMigrations(false);
-    migrations = migrationsResponse.migrations;
-  } catch {
-    // Migrations table might not exist yet - that's ok, just show empty list
-    console.warn("Could not fetch migrations - table may not exist yet");
-  }
-
-  // Fetch creators separately with fallback (table might not exist yet)
-  let creators: Awaited<ReturnType<typeof fetchLlmCreators>>["creators"] = [];
-  try {
-    const creatorsResponse = await fetchLlmCreators();
-    creators = creatorsResponse.creators;
-  } catch {
-    // Creators table might not exist yet - that's ok, just show empty list
-    console.warn("Could not fetch creators - table may not exist yet");
-  }
-
-  return {
-    providers: providersResponse.providers,
-    models: modelsResponse.models,
-    migrations,
-    creators,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/admin/llms/page.tsx

@@ -1,14 +0,0 @@
-import { withRoleAccess } from "@/lib/withRoleAccess";
-import { getLlmRegistryPageData } from "./getLlmRegistryPage";
-import { LlmRegistryDashboard } from "./components/LlmRegistryDashboard";
-
-async function LlmRegistryPage() {
-  const data = await getLlmRegistryPageData();
-  return <LlmRegistryDashboard {...data} />;
-}
-
-export default async function AdminLlmRegistryPage() {
-  const withAdminAccess = await withRoleAccess(["admin"]);
-  const ProtectedLlmRegistryPage = await withAdminAccess(LlmRegistryPage);
-  return <ProtectedLlmRegistryPage />;
-}

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

@@ -9,7 +9,7 @@ export async function GET(request: Request) {
   const { searchParams, origin } = new URL(request.url);
   const code = searchParams.get("code");
 
-  let next = "/";
+  let next = "/marketplace";
 
   if (code) {
     const supabase = await getServerSupabase();

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

@@ -7,9 +7,8 @@ import { BlockCategoryResponse } from "@/app/api/__generated__/models/blockCateg
 import { BlockResponse } from "@/app/api/__generated__/models/blockResponse";
 import * as Sentry from "@sentry/nextjs";
 import { getQueryClient } from "@/lib/react-query/queryClient";
-import { useState, useEffect } from "react";
+import { useState } from "react";
 import { useToast } from "@/components/molecules/Toast/use-toast";
-import BackendApi from "@/lib/autogpt-server-api";
 
 export const useAllBlockContent = () => {
   const { toast } = useToast();
@@ -94,32 +93,6 @@ export const useAllBlockContent = () => {
   const isErrorOnLoadingMore = (categoryName: string) =>
     errorLoadingCategories.has(categoryName);
 
-  // Listen for LLM registry refresh notifications
-  useEffect(() => {
-    const api = new BackendApi();
-    const queryClient = getQueryClient();
-
-    const handleNotification = (notification: any) => {
-      if (
-        notification?.type === "LLM_REGISTRY_REFRESH" ||
-        notification?.event === "registry_updated"
-      ) {
-        // Invalidate all block-related queries to force refresh
-        const categoriesQueryKey = getGetV2GetBuilderBlockCategoriesQueryKey();
-        queryClient.invalidateQueries({ queryKey: categoriesQueryKey });
-      }
-    };
-
-    const unsubscribe = api.onWebSocketMessage(
-      "notification",
-      handleNotification,
-    );
-
-    return () => {
-      unsubscribe();
-    };
-  }, []);
-
   return {
     data,
     isLoading,

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

@@ -610,11 +610,8 @@ const NodeOneOfDiscriminatorField: FC<{
 
     return oneOfVariants
       .map((variant) => {
-        const discProperty = variant.properties?.[discriminatorProperty];
-        const variantDiscValue =
-          discProperty && "const" in discProperty
-            ? (discProperty.const as string)
-            : undefined; // NOTE: can discriminators only be strings?
+        const variantDiscValue = variant.properties?.[discriminatorProperty]
+          ?.const as string; // NOTE: can discriminators only be strings?
 
         return {
           value: variantDiscValue,
@@ -1127,47 +1124,9 @@ const NodeStringInput: FC<{
   displayName,
 }) => {
   value ||= schema.default || "";
-
-  // Check if we have options with labels (e.g., LLM model picker)
-  const hasOptions = schema.options && schema.options.length > 0;
-  const hasEnum = schema.enum && schema.enum.length > 0;
-
-  // Helper to get display label for a value
-  const getDisplayLabel = (val: string) => {
-    if (hasOptions) {
-      const option = schema.options!.find((opt) => opt.value === val);
-      return option?.label || beautifyString(val);
-    }
-    return beautifyString(val);
-  };
-
   return (
     <div className={className}>
-      {hasOptions ? (
-        // Render options with proper labels (used by LLM model picker)
-        <Select
-          defaultValue={value}
-          onValueChange={(newValue) => handleInputChange(selfKey, newValue)}
-        >
-          <SelectTrigger>
-            <SelectValue placeholder={schema.placeholder || displayName}>
-              {value ? getDisplayLabel(value) : undefined}
-            </SelectValue>
-          </SelectTrigger>
-          <SelectContent className="nodrag">
-            {schema.options!.map((option, index) => (
-              <SelectItem
-                key={index}
-                value={option.value}
-                title={option.description}
-              >
-                {option.label || beautifyString(option.value)}
-              </SelectItem>
-            ))}
-          </SelectContent>
-        </Select>
-      ) : hasEnum ? (
-        // Fallback to enum with beautified strings
+      {schema.enum && schema.enum.length > 0 ? (
         <Select
           defaultValue={value}
           onValueChange={(newValue) => handleInputChange(selfKey, newValue)}
@@ -1176,8 +1135,8 @@ const NodeStringInput: FC<{
             <SelectValue placeholder={schema.placeholder || displayName} />
           </SelectTrigger>
           <SelectContent className="nodrag">
-            {schema
-              .enum!.filter((option) => option)
+            {schema.enum
+              .filter((option) => option)
               .map((option, index) => (
                 <SelectItem key={index} value={option}>
                   {beautifyString(option)}

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

@@ -0,0 +1,134 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { List } from "@phosphor-icons/react";
+import React, { useState } from "react";
+import { ChatContainer } from "./components/ChatContainer/ChatContainer";
+import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoadingState } from "./components/ChatLoadingState/ChatLoadingState";
+import { SessionsDrawer } from "./components/SessionsDrawer/SessionsDrawer";
+import { useChat } from "./useChat";
+
+export interface ChatProps {
+  className?: string;
+  headerTitle?: React.ReactNode;
+  showHeader?: boolean;
+  showSessionInfo?: boolean;
+  showNewChatButton?: boolean;
+  onNewChat?: () => void;
+  headerActions?: React.ReactNode;
+}
+
+export function Chat({
+  className,
+  headerTitle = "AutoGPT Copilot",
+  showHeader = true,
+  showSessionInfo = true,
+  showNewChatButton = true,
+  onNewChat,
+  headerActions,
+}: ChatProps) {
+  const {
+    messages,
+    isLoading,
+    isCreating,
+    error,
+    sessionId,
+    createSession,
+    clearSession,
+    loadSession,
+  } = useChat();
+
+  const [isSessionsDrawerOpen, setIsSessionsDrawerOpen] = useState(false);
+
+  const handleNewChat = () => {
+    clearSession();
+    onNewChat?.();
+  };
+
+  const handleSelectSession = async (sessionId: string) => {
+    try {
+      await loadSession(sessionId);
+    } catch (err) {
+      console.error("Failed to load session:", err);
+    }
+  };
+
+  return (
+    <div className={cn("flex h-full flex-col", className)}>
+      {/* Header */}
+      {showHeader && (
+        <header className="shrink-0 border-t border-zinc-200 bg-white p-3">
+          <div className="flex items-center justify-between">
+            <div className="flex items-center gap-3">
+              <button
+                aria-label="View sessions"
+                onClick={() => setIsSessionsDrawerOpen(true)}
+                className="flex size-8 items-center justify-center rounded hover:bg-zinc-100"
+              >
+                <List width="1.25rem" height="1.25rem" />
+              </button>
+              {typeof headerTitle === "string" ? (
+                <Text variant="h2" className="text-lg font-semibold">
+                  {headerTitle}
+                </Text>
+              ) : (
+                headerTitle
+              )}
+            </div>
+            <div className="flex items-center gap-3">
+              {showSessionInfo && sessionId && (
+                <>
+                  {showNewChatButton && (
+                    <Button
+                      variant="outline"
+                      size="small"
+                      onClick={handleNewChat}
+                    >
+                      New Chat
+                    </Button>
+                  )}
+                </>
+              )}
+              {headerActions}
+            </div>
+          </div>
+        </header>
+      )}
+
+      {/* Main Content */}
+      <main className="flex min-h-0 flex-1 flex-col overflow-hidden">
+        {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
+        {(isLoading || isCreating || (!sessionId && !error)) && (
+          <ChatLoadingState
+            message={isCreating ? "Creating session..." : "Loading..."}
+          />
+        )}
+
+        {/* Error State */}
+        {error && !isLoading && (
+          <ChatErrorState error={error} onRetry={createSession} />
+        )}
+
+        {/* Session Content */}
+        {sessionId && !isLoading && !error && (
+          <ChatContainer
+            sessionId={sessionId}
+            initialMessages={messages}
+            className="flex-1"
+          />
+        )}
+      </main>
+
+      {/* Sessions Drawer */}
+      <SessionsDrawer
+        isOpen={isSessionsDrawerOpen}
+        onClose={() => setIsSessionsDrawerOpen(false)}
+        onSelectSession={handleSelectSession}
+        currentSessionId={sessionId}
+      />
+    </div>
+  );
+}

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

@@ -21,7 +21,7 @@ export function AuthPromptWidget({
   message,
   sessionId,
   agentInfo,
-  returnUrl = "/copilot/chat",
+  returnUrl = "/chat",
   className,
 }: AuthPromptWidgetProps) {
   const router = useRouter();

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

@@ -0,0 +1,88 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { cn } from "@/lib/utils";
+import { useCallback } from "react";
+import { usePageContext } from "../../usePageContext";
+import { ChatInput } from "../ChatInput/ChatInput";
+import { MessageList } from "../MessageList/MessageList";
+import { QuickActionsWelcome } from "../QuickActionsWelcome/QuickActionsWelcome";
+import { useChatContainer } from "./useChatContainer";
+
+export interface ChatContainerProps {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  className?: string;
+}
+
+export function ChatContainer({
+  sessionId,
+  initialMessages,
+  className,
+}: ChatContainerProps) {
+  const { messages, streamingChunks, isStreaming, sendMessage } =
+    useChatContainer({
+      sessionId,
+      initialMessages,
+    });
+  const { capturePageContext } = usePageContext();
+
+  // Wrap sendMessage to automatically capture page context
+  const sendMessageWithContext = useCallback(
+    async (content: string, isUserMessage: boolean = true) => {
+      const context = capturePageContext();
+      await sendMessage(content, isUserMessage, context);
+    },
+    [sendMessage, capturePageContext],
+  );
+
+  const quickActions = [
+    "Find agents for social media management",
+    "Show me agents for content creation",
+    "Help me automate my business",
+    "What can you help me with?",
+  ];
+
+  return (
+    <div
+      className={cn("flex h-full min-h-0 flex-col", className)}
+      style={{
+        backgroundColor: "#ffffff",
+        backgroundImage:
+          "radial-gradient(#e5e5e5 0.5px, transparent 0.5px), radial-gradient(#e5e5e5 0.5px, #ffffff 0.5px)",
+        backgroundSize: "20px 20px",
+        backgroundPosition: "0 0, 10px 10px",
+      }}
+    >
+      {/* Messages or Welcome Screen */}
+      <div className="flex min-h-0 flex-1 flex-col overflow-hidden pb-24">
+        {messages.length === 0 ? (
+          <QuickActionsWelcome
+            title="Welcome to AutoGPT Copilot"
+            description="Start a conversation to discover and run AI agents."
+            actions={quickActions}
+            onActionClick={sendMessageWithContext}
+            disabled={isStreaming || !sessionId}
+          />
+        ) : (
+          <MessageList
+            messages={messages}
+            streamingChunks={streamingChunks}
+            isStreaming={isStreaming}
+            onSendMessage={sendMessageWithContext}
+            className="flex-1"
+          />
+        )}
+      </div>
+
+      {/* Input - Always visible */}
+      <div className="fixed bottom-0 left-0 right-0 z-50 border-t border-zinc-200 bg-white p-4">
+        <ChatInput
+          onSend={sendMessageWithContext}
+          disabled={isStreaming || !sessionId}
+          placeholder={
+            sessionId ? "Type your message..." : "Creating session..."
+          }
+        />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/createStreamEventDispatcher.ts

@@ -1,6 +1,6 @@
 import { toast } from "sonner";
 import { StreamChunk } from "../../useChatStream";
-import type { HandlerDependencies } from "./handlers";
+import type { HandlerDependencies } from "./useChatContainer.handlers";
 import {
   handleError,
   handleLoginNeeded,
@@ -9,30 +9,12 @@ import {
   handleTextEnded,
   handleToolCallStart,
   handleToolResponse,
-  isRegionBlockedError,
-} from "./handlers";
+} from "./useChatContainer.handlers";
 
 export function createStreamEventDispatcher(
   deps: HandlerDependencies,
 ): (chunk: StreamChunk) => void {
   return function dispatchStreamEvent(chunk: StreamChunk): void {
-    if (
-      chunk.type === "text_chunk" ||
-      chunk.type === "tool_call_start" ||
-      chunk.type === "tool_response" ||
-      chunk.type === "login_needed" ||
-      chunk.type === "need_login" ||
-      chunk.type === "error"
-    ) {
-      if (!deps.hasResponseRef.current) {
-        console.info("[ChatStream] First response chunk:", {
-          type: chunk.type,
-          sessionId: deps.sessionId,
-        });
-      }
-      deps.hasResponseRef.current = true;
-    }
-
     switch (chunk.type) {
       case "text_chunk":
         handleTextChunk(chunk, deps);
@@ -56,23 +38,15 @@ export function createStreamEventDispatcher(
         break;
 
       case "stream_end":
-        console.info("[ChatStream] Stream ended:", {
-          sessionId: deps.sessionId,
-          hasResponse: deps.hasResponseRef.current,
-          chunkCount: deps.streamingChunksRef.current.length,
-        });
         handleStreamEnd(chunk, deps);
         break;
 
       case "error":
-        const isRegionBlocked = isRegionBlockedError(chunk);
         handleError(chunk, deps);
         // Show toast at dispatcher level to avoid circular dependencies
-        if (!isRegionBlocked) {
-          toast.error("Chat Error", {
-            description: chunk.message || chunk.content || "An error occurred",
-          });
-        }
+        toast.error("Chat Error", {
+          description: chunk.message || chunk.content || "An error occurred",
+        });
         break;
 
       case "usage":

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/helpers.ts

@@ -1,33 +1,6 @@
-import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
 import type { ToolResult } from "@/types/chat";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 
-export function hasSentInitialPrompt(sessionId: string): boolean {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    return sent[sessionId] === true;
-  } catch {
-    return false;
-  }
-}
-
-export function markInitialPromptSent(sessionId: string): void {
-  try {
-    const sent = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_SENT_INITIAL_PROMPTS) || "{}",
-    );
-    sent[sessionId] = true;
-    sessionStorage.set(
-      SessionKey.CHAT_SENT_INITIAL_PROMPTS,
-      JSON.stringify(sent),
-    );
-  } catch {
-    // Ignore storage errors
-  }
-}
-
 export function removePageContext(content: string): string {
   // Remove "Page URL: ..." pattern at start of line (case insensitive, handles various formats)
   let cleaned = content.replace(/^\s*Page URL:\s*[^\n\r]*/gim, "");
@@ -234,22 +207,12 @@ export function parseToolResponse(
     if (responseType === "setup_requirements") {
       return null;
     }
-    if (responseType === "understanding_updated") {
-      return {
-        type: "tool_response",
-        toolId,
-        toolName,
-        result: (parsedResult || result) as ToolResult,
-        success: true,
-        timestamp: timestamp || new Date(),
-      };
-    }
   }
   return {
     type: "tool_response",
     toolId,
     toolName,
-    result: parsedResult ? (parsedResult as ToolResult) : result,
+    result,
     success: true,
     timestamp: timestamp || new Date(),
   };

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.handlers.ts

@@ -7,30 +7,15 @@ import {
   parseToolResponse,
 } from "./helpers";
 
-function isToolCallMessage(
-  message: ChatMessageData,
-): message is Extract<ChatMessageData, { type: "tool_call" }> {
-  return message.type === "tool_call";
-}
-
 export interface HandlerDependencies {
   setHasTextChunks: Dispatch<SetStateAction<boolean>>;
   setStreamingChunks: Dispatch<SetStateAction<string[]>>;
   streamingChunksRef: MutableRefObject<string[]>;
-  hasResponseRef: MutableRefObject<boolean>;
   setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
   setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
-  setIsRegionBlockedModalOpen: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
 }
 
-export function isRegionBlockedError(chunk: StreamChunk): boolean {
-  if (chunk.code === "MODEL_NOT_AVAILABLE_REGION") return true;
-  const message = chunk.message || chunk.content;
-  if (typeof message !== "string") return false;
-  return message.toLowerCase().includes("not available in your region");
-}
-
 export function handleTextChunk(chunk: StreamChunk, deps: HandlerDependencies) {
   if (!chunk.content) return;
   deps.setHasTextChunks(true);
@@ -45,17 +30,16 @@ export function handleTextEnded(
   _chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  console.log("[Text Ended] Saving streamed text as assistant message");
   const completedText = deps.streamingChunksRef.current.join("");
   if (completedText.trim()) {
-    deps.setMessages((prev) => {
-      const assistantMessage: ChatMessageData = {
-        type: "message",
-        role: "assistant",
-        content: completedText,
-        timestamp: new Date(),
-      };
-      return [...prev, assistantMessage];
-    });
+    const assistantMessage: ChatMessageData = {
+      type: "message",
+      role: "assistant",
+      content: completedText,
+      timestamp: new Date(),
+    };
+    deps.setMessages((prev) => [...prev, assistantMessage]);
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
@@ -66,45 +50,30 @@ export function handleToolCallStart(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
-  const toolCallMessage: Extract<ChatMessageData, { type: "tool_call" }> = {
+  const toolCallMessage: ChatMessageData = {
     type: "tool_call",
     toolId: chunk.tool_id || `tool-${Date.now()}-${chunk.idx || 0}`,
-    toolName: chunk.tool_name || "Executing",
+    toolName: chunk.tool_name || "Executing...",
     arguments: chunk.arguments || {},
     timestamp: new Date(),
   };
-
-  function updateToolCallMessages(prev: ChatMessageData[]) {
-    const existingIndex = prev.findIndex(function findToolCallIndex(msg) {
-      return isToolCallMessage(msg) && msg.toolId === toolCallMessage.toolId;
-    });
-    if (existingIndex === -1) {
-      return [...prev, toolCallMessage];
-    }
-    const nextMessages = [...prev];
-    const existing = nextMessages[existingIndex];
-    if (!isToolCallMessage(existing)) return prev;
-    const nextArguments =
-      toolCallMessage.arguments &&
-      Object.keys(toolCallMessage.arguments).length > 0
-        ? toolCallMessage.arguments
-        : existing.arguments;
-    nextMessages[existingIndex] = {
-      ...existing,
-      toolName: toolCallMessage.toolName || existing.toolName,
-      arguments: nextArguments,
-      timestamp: toolCallMessage.timestamp,
-    };
-    return nextMessages;
-  }
-
-  deps.setMessages(updateToolCallMessages);
+  deps.setMessages((prev) => [...prev, toolCallMessage]);
+  console.log("[Tool Call Start]", {
+    toolId: toolCallMessage.toolId,
+    toolName: toolCallMessage.toolName,
+    timestamp: new Date().toISOString(),
+  });
 }
 
 export function handleToolResponse(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  console.log("[Tool Response] Received:", {
+    toolId: chunk.tool_id,
+    toolName: chunk.tool_name,
+    timestamp: new Date().toISOString(),
+  });
   let toolName = chunk.tool_name || "unknown";
   if (!chunk.tool_name || chunk.tool_name === "unknown") {
     deps.setMessages((prev) => {
@@ -158,15 +127,22 @@ export function handleToolResponse(
     const toolCallIndex = prev.findIndex(
       (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id,
     );
-    const hasResponse = prev.some(
-      (msg) => msg.type === "tool_response" && msg.toolId === chunk.tool_id,
-    );
-    if (hasResponse) return prev;
     if (toolCallIndex !== -1) {
       const newMessages = [...prev];
-      newMessages.splice(toolCallIndex + 1, 0, responseMessage);
+      newMessages[toolCallIndex] = responseMessage;
+      console.log(
+        "[Tool Response] Replaced tool_call with matching tool_id:",
+        chunk.tool_id,
+        "at index:",
+        toolCallIndex,
+      );
       return newMessages;
     }
+    console.warn(
+      "[Tool Response] No tool_call found with tool_id:",
+      chunk.tool_id,
+      "appending instead",
+    );
     return [...prev, responseMessage];
   });
 }
@@ -191,38 +167,55 @@ export function handleStreamEnd(
   deps: HandlerDependencies,
 ) {
   const completedContent = deps.streamingChunksRef.current.join("");
-  if (!completedContent.trim() && !deps.hasResponseRef.current) {
-    deps.setMessages((prev) => [
-      ...prev,
-      {
-        type: "message",
-        role: "assistant",
-        content: "No response received. Please try again.",
-        timestamp: new Date(),
-      },
-    ]);
-  }
+  // Only save message if there are uncommitted chunks
+  // (text_ended already saved if there were tool calls)
   if (completedContent.trim()) {
+    console.log(
+      "[Stream End] Saving remaining streamed text as assistant message",
+    );
     const assistantMessage: ChatMessageData = {
       type: "message",
       role: "assistant",
       content: completedContent,
       timestamp: new Date(),
     };
-    deps.setMessages((prev) => [...prev, assistantMessage]);
+    deps.setMessages((prev) => {
+      const updated = [...prev, assistantMessage];
+      console.log("[Stream End] Final state:", {
+        localMessages: updated.map((m) => ({
+          type: m.type,
+          ...(m.type === "message" && {
+            role: m.role,
+            contentLength: m.content.length,
+          }),
+          ...(m.type === "tool_call" && {
+            toolId: m.toolId,
+            toolName: m.toolName,
+          }),
+          ...(m.type === "tool_response" && {
+            toolId: m.toolId,
+            toolName: m.toolName,
+            success: m.success,
+          }),
+        })),
+        streamingChunks: deps.streamingChunksRef.current,
+        timestamp: new Date().toISOString(),
+      });
+      return updated;
+    });
+  } else {
+    console.log("[Stream End] No uncommitted chunks, message already saved");
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
   deps.setHasTextChunks(false);
   deps.setIsStreamingInitiated(false);
+  console.log("[Stream End] Stream complete, messages in local state");
 }
 
 export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
   const errorMessage = chunk.message || chunk.content || "An error occurred";
   console.error("Stream error:", errorMessage);
-  if (isRegionBlockedError(chunk)) {
-    deps.setIsRegionBlockedModalOpen(true);
-  }
   deps.setIsStreamingInitiated(false);
   deps.setHasTextChunks(false);
   deps.setStreamingChunks([]);

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.ts

@@ -1,17 +1,14 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { useCallback, useMemo, useRef, useState } from "react";
 import { toast } from "sonner";
 import { useChatStream } from "../../useChatStream";
-import { usePageContext } from "../../usePageContext";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
 import {
   createUserMessage,
   filterAuthMessages,
-  hasSentInitialPrompt,
   isToolCallArray,
   isValidMessage,
-  markInitialPromptSent,
   parseToolResponse,
   removePageContext,
 } from "./helpers";
@@ -19,45 +16,20 @@ import {
 interface Args {
   sessionId: string | null;
   initialMessages: SessionDetailResponse["messages"];
-  initialPrompt?: string;
 }
 
-export function useChatContainer({
-  sessionId,
-  initialMessages,
-  initialPrompt,
-}: Args) {
+export function useChatContainer({ sessionId, initialMessages }: Args) {
   const [messages, setMessages] = useState<ChatMessageData[]>([]);
   const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
   const [hasTextChunks, setHasTextChunks] = useState(false);
   const [isStreamingInitiated, setIsStreamingInitiated] = useState(false);
-  const [isRegionBlockedModalOpen, setIsRegionBlockedModalOpen] =
-    useState(false);
-  const hasResponseRef = useRef(false);
   const streamingChunksRef = useRef<string[]>([]);
-  const previousSessionIdRef = useRef<string | null>(null);
-  const {
-    error,
-    sendMessage: sendStreamMessage,
-    stopStreaming,
-  } = useChatStream();
+  const { error, sendMessage: sendStreamMessage } = useChatStream();
   const isStreaming = isStreamingInitiated || hasTextChunks;
 
-  useEffect(() => {
-    if (sessionId !== previousSessionIdRef.current) {
-      stopStreaming(previousSessionIdRef.current ?? undefined, true);
-      previousSessionIdRef.current = sessionId;
-      setMessages([]);
-      setStreamingChunks([]);
-      streamingChunksRef.current = [];
-      setHasTextChunks(false);
-      setIsStreamingInitiated(false);
-      hasResponseRef.current = false;
-    }
-  }, [sessionId, stopStreaming]);
-
   const allMessages = useMemo(() => {
     const processedInitialMessages: ChatMessageData[] = [];
+    // Map to track tool calls by their ID so we can look up tool names for tool responses
     const toolCallMap = new Map<string, string>();
 
     for (const msg of initialMessages) {
@@ -73,9 +45,13 @@ export function useChatContainer({
         ? new Date(msg.timestamp as string)
         : undefined;
 
+      // Remove page context from user messages when loading existing sessions
       if (role === "user") {
         content = removePageContext(content);
-        if (!content.trim()) continue;
+        // Skip user messages that become empty after removing page context
+        if (!content.trim()) {
+          continue;
+        }
         processedInitialMessages.push({
           type: "message",
           role: "user",
@@ -85,15 +61,19 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle assistant messages first (before tool messages) to build tool call map
       if (role === "assistant") {
+        // Strip <thinking> tags from content
         content = content
           .replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
           .trim();
 
+        // If assistant has tool calls, create tool_call messages for each
         if (toolCalls && isToolCallArray(toolCalls) && toolCalls.length > 0) {
           for (const toolCall of toolCalls) {
             const toolName = toolCall.function.name;
             const toolId = toolCall.id;
+            // Store tool name for later lookup
             toolCallMap.set(toolId, toolName);
 
             try {
@@ -116,6 +96,7 @@ export function useChatContainer({
               });
             }
           }
+          // Only add assistant message if there's content after stripping thinking tags
           if (content.trim()) {
             processedInitialMessages.push({
               type: "message",
@@ -125,6 +106,7 @@ export function useChatContainer({
             });
           }
         } else if (content.trim()) {
+          // Assistant message without tool calls, but with content
           processedInitialMessages.push({
             type: "message",
             role: "assistant",
@@ -135,6 +117,7 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle tool messages - look up tool name from tool call map
       if (role === "tool") {
         const toolCallId = (msg.tool_call_id as string) || "";
         const toolName = toolCallMap.get(toolCallId) || "unknown";
@@ -150,6 +133,7 @@ export function useChatContainer({
         continue;
       }
 
+      // Handle other message types (system, etc.)
       if (content.trim()) {
         processedInitialMessages.push({
           type: "message",
@@ -170,10 +154,9 @@ export function useChatContainer({
       context?: { url: string; content: string },
     ) {
       if (!sessionId) {
-        console.error("[useChatContainer] Cannot send message: no session ID");
+        console.error("Cannot send message: no session ID");
         return;
       }
-      setIsRegionBlockedModalOpen(false);
       if (isUserMessage) {
         const userMessage = createUserMessage(content);
         setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
@@ -184,19 +167,14 @@ export function useChatContainer({
       streamingChunksRef.current = [];
       setHasTextChunks(false);
       setIsStreamingInitiated(true);
-      hasResponseRef.current = false;
-
       const dispatcher = createStreamEventDispatcher({
         setHasTextChunks,
         setStreamingChunks,
         streamingChunksRef,
-        hasResponseRef,
         setMessages,
-        setIsRegionBlockedModalOpen,
         sessionId,
         setIsStreamingInitiated,
       });
-
       try {
         await sendStreamMessage(
           sessionId,
@@ -206,12 +184,8 @@ export function useChatContainer({
           context,
         );
       } catch (err) {
-        console.error("[useChatContainer] Failed to send message:", err);
+        console.error("Failed to send message:", err);
         setIsStreamingInitiated(false);
-
-        // Don't show error toast for AbortError (expected during cleanup)
-        if (err instanceof Error && err.name === "AbortError") return;
-
         const errorMessage =
           err instanceof Error ? err.message : "Failed to send message";
         toast.error("Failed to send message", {
@@ -222,63 +196,11 @@ export function useChatContainer({
     [sessionId, sendStreamMessage],
   );
 
-  const handleStopStreaming = useCallback(() => {
-    stopStreaming();
-    setStreamingChunks([]);
-    streamingChunksRef.current = [];
-    setHasTextChunks(false);
-    setIsStreamingInitiated(false);
-  }, [stopStreaming]);
-
-  const { capturePageContext } = usePageContext();
-
-  // Send initial prompt if provided (for new sessions from homepage)
-  useEffect(
-    function handleInitialPrompt() {
-      if (!initialPrompt || !sessionId) return;
-      if (initialMessages.length > 0) return;
-      if (hasSentInitialPrompt(sessionId)) return;
-
-      markInitialPromptSent(sessionId);
-      const context = capturePageContext();
-      sendMessage(initialPrompt, true, context);
-    },
-    [
-      initialPrompt,
-      sessionId,
-      initialMessages.length,
-      sendMessage,
-      capturePageContext,
-    ],
-  );
-
-  async function sendMessageWithContext(
-    content: string,
-    isUserMessage: boolean = true,
-  ) {
-    const context = capturePageContext();
-    await sendMessage(content, isUserMessage, context);
-  }
-
-  function handleRegionModalOpenChange(open: boolean) {
-    setIsRegionBlockedModalOpen(open);
-  }
-
-  function handleRegionModalClose() {
-    setIsRegionBlockedModalOpen(false);
-  }
-
   return {
     messages: allMessages,
     streamingChunks,
     isStreaming,
     error,
-    isRegionBlockedModalOpen,
-    setIsRegionBlockedModalOpen,
-    sendMessageWithContext,
-    handleRegionModalOpenChange,
-    handleRegionModalClose,
     sendMessage,
-    stopStreaming: handleStopStreaming,
   };
 }

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

@@ -0,0 +1,64 @@
+import { Input } from "@/components/atoms/Input/Input";
+import { cn } from "@/lib/utils";
+import { ArrowUpIcon } from "@phosphor-icons/react";
+import { useChatInput } from "./useChatInput";
+
+export interface ChatInputProps {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  placeholder?: string;
+  className?: string;
+}
+
+export function ChatInput({
+  onSend,
+  disabled = false,
+  placeholder = "Type your message...",
+  className,
+}: ChatInputProps) {
+  const inputId = "chat-input";
+  const { value, setValue, handleKeyDown, handleSend } = useChatInput({
+    onSend,
+    disabled,
+    maxRows: 5,
+    inputId,
+  });
+
+  return (
+    <div className={cn("relative flex-1", className)}>
+      <Input
+        id={inputId}
+        label="Chat message input"
+        hideLabel
+        type="textarea"
+        value={value}
+        onChange={(e) => setValue(e.target.value)}
+        onKeyDown={handleKeyDown}
+        placeholder={placeholder}
+        disabled={disabled}
+        rows={1}
+        wrapperClassName="mb-0 relative"
+        className="pr-12"
+      />
+      <span id="chat-input-hint" className="sr-only">
+        Press Enter to send, Shift+Enter for new line
+      </span>
+
+      <button
+        onClick={handleSend}
+        disabled={disabled || !value.trim()}
+        className={cn(
+          "absolute right-3 top-1/2 flex h-8 w-8 -translate-y-1/2 items-center justify-center rounded-full",
+          "border border-zinc-800 bg-zinc-800 text-white",
+          "hover:border-zinc-900 hover:bg-zinc-900",
+          "disabled:border-zinc-200 disabled:bg-zinc-200 disabled:text-white disabled:opacity-50",
+          "transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-neutral-950",
+          "disabled:pointer-events-none",
+        )}
+        aria-label="Send message"
+      >
+        <ArrowUpIcon className="h-3 w-3" weight="bold" />
+      </button>
+    </div>
+  );
+}

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

@@ -0,0 +1,60 @@
+import { KeyboardEvent, useCallback, useEffect, useState } from "react";
+
+interface UseChatInputArgs {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  maxRows?: number;
+  inputId?: string;
+}
+
+export function useChatInput({
+  onSend,
+  disabled = false,
+  maxRows = 5,
+  inputId = "chat-input",
+}: UseChatInputArgs) {
+  const [value, setValue] = useState("");
+
+  useEffect(() => {
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    if (!textarea) return;
+    textarea.style.height = "auto";
+    const lineHeight = parseInt(
+      window.getComputedStyle(textarea).lineHeight,
+      10,
+    );
+    const maxHeight = lineHeight * maxRows;
+    const newHeight = Math.min(textarea.scrollHeight, maxHeight);
+    textarea.style.height = `${newHeight}px`;
+    textarea.style.overflowY =
+      textarea.scrollHeight > maxHeight ? "auto" : "hidden";
+  }, [value, maxRows, inputId]);
+
+  const handleSend = useCallback(() => {
+    if (disabled || !value.trim()) return;
+    onSend(value.trim());
+    setValue("");
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    if (textarea) {
+      textarea.style.height = "auto";
+    }
+  }, [value, onSend, disabled, inputId]);
+
+  const handleKeyDown = useCallback(
+    (event: KeyboardEvent<HTMLInputElement | HTMLTextAreaElement>) => {
+      if (event.key === "Enter" && !event.shiftKey) {
+        event.preventDefault();
+        handleSend();
+      }
+      // Shift+Enter allows default behavior (new line) - no need to handle explicitly
+    },
+    [handleSend],
+  );
+
+  return {
+    value,
+    setValue,
+    handleKeyDown,
+    handleSend,
+  };
+}