Update openapi.json

- Fix TruncatedLogger calls to use f-strings instead of %s format args (4 calls)
- Fix get_parallel_tool_calls_param return type: NotGiven → Omit
- Add comment clarifying LlmModelMigration is system-level data
- Add pagination input validation to prevent division by zero

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

@@ -41,18 +41,13 @@ jobs:
         ports:
           - 6379:6379
       rabbitmq:
-        image: rabbitmq:4.1.4
+        image: rabbitmq:3.12-management
         ports:
           - 5672:5672
+          - 15672:15672
         env:
           RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
           RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
-        options: >-
-          --health-cmd "rabbitmq-diagnostics -q ping"
-          --health-interval 30s
-          --health-timeout 10s
-          --health-retries 5
-          --health-start-period 10s
       clamav:
         image: clamav/clamav-debian:latest
         ports:

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

@@ -6,16 +6,10 @@ on:
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
-      - "autogpt_platform/backend/Dockerfile"
-      - "autogpt_platform/docker-compose.yml"
-      - "autogpt_platform/docker-compose.platform.yml"
   pull_request:
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
-      - "autogpt_platform/backend/Dockerfile"
-      - "autogpt_platform/docker-compose.yml"
-      - "autogpt_platform/docker-compose.platform.yml"
   merge_group:
   workflow_dispatch:
 

autogpt_platform/backend/Dockerfile

@@ -53,6 +53,63 @@ COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/parti
 COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
 RUN poetry run prisma generate && poetry run gen-prisma-stub
 
+# ============================== BACKEND SERVER ============================== #
+
+FROM debian:13-slim AS server
+
+WORKDIR /app
+
+ENV POETRY_HOME=/opt/poetry \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_CREATE=true \
+    POETRY_VIRTUALENVS_IN_PROJECT=true \
+    DEBIAN_FRONTEND=noninteractive
+ENV PATH=/opt/poetry/bin:$PATH
+
+# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use.
+# bubblewrap provides OS-level sandbox (whitelist-only FS + no network)
+# for the bash_exec MCP tool.
+# Using --no-install-recommends saves ~650MB by skipping unnecessary deps like llvm, mesa, etc.
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    python3.13 \
+    python3-pip \
+    ffmpeg \
+    imagemagick \
+    jq \
+    ripgrep \
+    tree \
+    bubblewrap \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
+COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
+# Copy Node.js installation for Prisma
+COPY --from=builder /usr/bin/node /usr/bin/node
+COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
+COPY --from=builder /usr/bin/npm /usr/bin/npm
+COPY --from=builder /usr/bin/npx /usr/bin/npx
+COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
+
+WORKDIR /app/autogpt_platform/backend
+
+# Copy only the .venv from builder (not the entire /app directory)
+# The .venv includes the generated Prisma client
+COPY --from=builder /app/autogpt_platform/backend/.venv ./.venv
+ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
+
+# Copy dependency files + autogpt_libs (path dependency)
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml ./
+
+# Copy backend code + docs (for Copilot docs search)
+COPY autogpt_platform/backend ./
+COPY docs /app/docs
+RUN poetry install --no-ansi --only-root
+
+ENV PORT=8000
+
+CMD ["poetry", "run", "rest"]
+
 # =============================== DB MIGRATOR =============================== #
 
 # Lightweight migrate stage - only needs Prisma CLI, not full Python environment
@@ -84,59 +141,3 @@ COPY autogpt_platform/backend/schema.prisma ./
 COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
 COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
 COPY autogpt_platform/backend/migrations ./migrations
-
-# ============================== BACKEND SERVER ============================== #
-
-FROM debian:13-slim AS server
-
-WORKDIR /app
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use.
-# bubblewrap provides OS-level sandbox (whitelist-only FS + no network)
-# for the bash_exec MCP tool.
-# Using --no-install-recommends saves ~650MB by skipping unnecessary deps like llvm, mesa, etc.
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.13 \
-    python3-pip \
-    ffmpeg \
-    imagemagick \
-    jq \
-    ripgrep \
-    tree \
-    bubblewrap \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy poetry (build-time only, for `poetry install --only-root` to create entry points)
-COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
-COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
-# Copy Node.js installation for Prisma
-COPY --from=builder /usr/bin/node /usr/bin/node
-COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
-COPY --from=builder /usr/bin/npm /usr/bin/npm
-COPY --from=builder /usr/bin/npx /usr/bin/npx
-COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
-
-WORKDIR /app/autogpt_platform/backend
-
-# Copy only the .venv from builder (not the entire /app directory)
-# The .venv includes the generated Prisma client
-COPY --from=builder /app/autogpt_platform/backend/.venv ./.venv
-ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
-
-# Copy dependency files + autogpt_libs (path dependency)
-COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
-COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml ./
-
-# Copy backend code + docs (for Copilot docs search)
-COPY autogpt_platform/backend ./
-COPY docs /app/docs
-# Install the project package to create entry point scripts in .venv/bin/
-# (e.g., rest, executor, ws, db, scheduler, notification - see [tool.poetry.scripts])
-RUN POETRY_VIRTUALENVS_CREATE=true POETRY_VIRTUALENVS_IN_PROJECT=true \
-    poetry install --no-ansi --only-root
-
-ENV PORT=8000
-
-CMD ["rest"]

autogpt_platform/backend/backend/api/conftest.py

@@ -1,9 +1,4 @@
-"""Common test fixtures for server tests.
+"""Common test fixtures for server tests."""
-
-Note: Common fixtures like test_user_id, admin_user_id, target_user_id,
-setup_test_user, and setup_admin_user are defined in the parent conftest.py
-(backend/conftest.py) and are available here automatically.
-"""
 
 import pytest
 from pytest_snapshot.plugin import Snapshot
@@ -16,6 +11,54 @@ def configured_snapshot(snapshot: Snapshot) -> Snapshot:
     return snapshot
 
 
+@pytest.fixture
+def test_user_id() -> str:
+    """Test user ID fixture."""
+    return "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+
+
+@pytest.fixture
+def admin_user_id() -> str:
+    """Admin user ID fixture."""
+    return "4e53486c-cf57-477e-ba2a-cb02dc828e1b"
+
+
+@pytest.fixture
+def target_user_id() -> str:
+    """Target user ID fixture."""
+    return "5e53486c-cf57-477e-ba2a-cb02dc828e1c"
+
+
+@pytest.fixture
+async def setup_test_user(test_user_id):
+    """Create test user in database before tests."""
+    from backend.data.user import get_or_create_user
+
+    # Create the test user in the database using JWT token format
+    user_data = {
+        "sub": test_user_id,
+        "email": "test@example.com",
+        "user_metadata": {"name": "Test User"},
+    }
+    await get_or_create_user(user_data)
+    return test_user_id
+
+
+@pytest.fixture
+async def setup_admin_user(admin_user_id):
+    """Create admin user in database before tests."""
+    from backend.data.user import get_or_create_user
+
+    # Create the admin user in the database using JWT token format
+    user_data = {
+        "sub": admin_user_id,
+        "email": "test-admin@example.com",
+        "user_metadata": {"name": "Test Admin"},
+    }
+    await get_or_create_user(user_data)
+    return admin_user_id
+
+
 @pytest.fixture
 def mock_jwt_user(test_user_id):
     """Provide mock JWT payload for regular user testing."""

autogpt_platform/backend/backend/api/conn_manager.py

@@ -122,6 +122,24 @@ 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/external/v1/tools.py

@@ -15,9 +15,9 @@ from prisma.enums import APIKeyPermission
 from pydantic import BaseModel, Field
 
 from backend.api.external.middleware import require_permission
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.tools import find_agent_tool, run_agent_tool
+from backend.api.features.chat.tools import find_agent_tool, run_agent_tool
-from backend.copilot.tools.models import ToolResponseBase
+from backend.api.features.chat.tools.models import ToolResponseBase
 from backend.data.auth.base import APIAuthorizationInfo
 
 logger = logging.getLogger(__name__)

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

@@ -176,30 +176,64 @@ async def get_execution_analytics_config(
         # Return with provider prefix for clarity
         return f"{provider_name}: {model_name}"
 
-    # Include all LlmModel values (no more filtering by hardcoded list)
+    # Get all models from the registry (dynamic, not hardcoded enum)
-    recommended_model = LlmModel.GPT4O_MINI.value
+    from backend.data import llm_registry
-    for model in LlmModel:
+    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 = LlmModel(registry_model.slug)
         label = generate_model_label(model)
         # Add "(Recommended)" suffix to the recommended model
-        if model.value == recommended_model:
+        if registry_model.slug == recommended_model_slug:
             label += " (Recommended)"
 
         available_models.append(
             ModelInfo(
-                value=model.value,
+                value=registry_model.slug,
                 label=label,
-                provider=model.provider,
+                provider=registry_model.metadata.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=recommended_model,
+        recommended_model=final_recommended,
     )
 
 

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

@@ -0,0 +1,593 @@
+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()
+        await refresh_llm_costs()
+
+        # Clear block schema caches so they're regenerated with updated model options
+        from backend.blocks._base 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
+        try:
+            from backend.api.features.builder import db as builder_db
+
+            builder_db._get_all_providers.cache_clear()
+            logger.info("Cleared v2 builder providers cache")
+            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 /api/llm/admin/models/{id}?replacement_model_slug=gpt-4o
+    Example (no usage): DELETE /api/llm/admin/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

@@ -0,0 +1,491 @@
+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

@@ -20,6 +20,7 @@ from backend.blocks._base import (
 )
 from backend.blocks.llm import LlmModel
 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
@@ -36,7 +37,14 @@ from .model import (
 )
 
 logger = logging.getLogger(__name__)
-llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
+
+
+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()
+    ]
+
 
 MAX_LIBRARY_AGENT_RESULTS = 100
 MAX_MARKETPLACE_AGENT_RESULTS = 100
@@ -501,8 +509,10 @@ 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
+            # Normalize query same as model slugs (lowercase, hyphens to spaces)
-            if any(query in name for name in llm_models):
+            normalized_model_query = query.lower().replace("-", " ")
+            # Check if query matches any value in llm_models from registry
+            if any(normalized_model_query in name for name in _get_llm_models()):
                 return True
     return False
 

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

@@ -37,10 +37,12 @@ stale pending messages from dead consumers.
 
 import asyncio
 import logging
+import os
 import uuid
 from typing import Any
 
 import orjson
+from prisma import Prisma
 from pydantic import BaseModel
 from redis.exceptions import ResponseError
 
@@ -67,8 +69,8 @@ class OperationCompleteMessage(BaseModel):
 class ChatCompletionConsumer:
     """Consumer for chat operation completion messages from Redis Streams.
 
-    Database operations are handled through the chat_db() accessor, which
+    This consumer initializes its own Prisma client in start() to ensure
-    routes through DatabaseManager RPC when Prisma is not directly connected.
+    database operations work correctly within this async context.
 
     Uses Redis consumer groups to allow multiple platform pods to consume
     messages reliably with automatic redelivery on failure.
@@ -77,6 +79,7 @@ class ChatCompletionConsumer:
     def __init__(self):
         self._consumer_task: asyncio.Task | None = None
         self._running = False
+        self._prisma: Prisma | None = None
         self._consumer_name = f"consumer-{uuid.uuid4().hex[:8]}"
 
     async def start(self) -> None:
@@ -112,6 +115,15 @@ class ChatCompletionConsumer:
             f"Chat completion consumer started (consumer: {self._consumer_name})"
         )
 
+    async def _ensure_prisma(self) -> Prisma:
+        """Lazily initialize Prisma client on first use."""
+        if self._prisma is None:
+            database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
+            self._prisma = Prisma(datasource={"url": database_url})
+            await self._prisma.connect()
+            logger.info("[COMPLETION] Consumer Prisma client connected (lazy init)")
+        return self._prisma
+
     async def stop(self) -> None:
         """Stop the completion consumer."""
         self._running = False
@@ -124,6 +136,11 @@ class ChatCompletionConsumer:
                 pass
             self._consumer_task = None
 
+        if self._prisma:
+            await self._prisma.disconnect()
+            self._prisma = None
+            logger.info("[COMPLETION] Consumer Prisma client disconnected")
+
         logger.info("Chat completion consumer stopped")
 
     async def _consume_messages(self) -> None:
@@ -235,7 +252,7 @@ class ChatCompletionConsumer:
             # XAUTOCLAIM after min_idle_time expires
 
     async def _handle_message(self, body: bytes) -> None:
-        """Handle a completion message."""
+        """Handle a completion message using our own Prisma client."""
         try:
             data = orjson.loads(body)
             message = OperationCompleteMessage(**data)
@@ -285,7 +302,8 @@ class ChatCompletionConsumer:
         message: OperationCompleteMessage,
     ) -> None:
         """Handle successful operation completion."""
-        await process_operation_success(task, message.result)
+        prisma = await self._ensure_prisma()
+        await process_operation_success(task, message.result, prisma)
 
     async def _handle_failure(
         self,
@@ -293,7 +311,8 @@ class ChatCompletionConsumer:
         message: OperationCompleteMessage,
     ) -> None:
         """Handle failed operation completion."""
-        await process_operation_failure(task, message.error)
+        prisma = await self._ensure_prisma()
+        await process_operation_failure(task, message.error, prisma)
 
 
 # Module-level consumer instance

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

@@ -9,8 +9,7 @@ import logging
 from typing import Any
 
 import orjson
-
+from prisma import Prisma
-from backend.data.db_accessors import chat_db
 
 from . import service as chat_service
 from . import stream_registry
@@ -73,40 +72,48 @@ async def _update_tool_message(
     session_id: str,
     tool_call_id: str,
     content: str,
+    prisma_client: Prisma | None,
 ) -> None:
-    """Update tool message in database using the chat_db accessor.
+    """Update tool message in database.
-
-    Routes through DatabaseManager RPC when Prisma is not directly
-    connected (e.g. in the CoPilot Executor microservice).
 
     Args:
         session_id: The session ID
         tool_call_id: The tool call ID to update
         content: The new content for the message
+        prisma_client: Optional Prisma client. If None, uses chat_service.
 
     Raises:
-        ToolMessageUpdateError: If the database update fails.
+        ToolMessageUpdateError: If the database update fails. The caller should
+            handle this to avoid marking the task as completed with inconsistent state.
     """
     try:
-        updated = await chat_db().update_tool_message_content(
+        if prisma_client:
-            session_id=session_id,
+            # Use provided Prisma client (for consumer with its own connection)
-            tool_call_id=tool_call_id,
+            updated_count = await prisma_client.chatmessage.update_many(
-            new_content=content,
+                where={
-        )
+                    "sessionId": session_id,
-        if not updated:
+                    "toolCallId": tool_call_id,
-            raise ToolMessageUpdateError(
+                },
-                f"No message found with tool_call_id="
+                data={"content": content},
-                f"{tool_call_id} in session {session_id}"
+            )
+            # Check if any rows were updated - 0 means message not found
+            if updated_count == 0:
+                raise ToolMessageUpdateError(
+                    f"No message found with tool_call_id={tool_call_id} in session {session_id}"
+                )
+        else:
+            # Use service function (for webhook endpoint)
+            await chat_service._update_pending_operation(
+                session_id=session_id,
+                tool_call_id=tool_call_id,
+                result=content,
             )
     except ToolMessageUpdateError:
         raise
     except Exception as e:
-        logger.error(
+        logger.error(f"[COMPLETION] Failed to update tool message: {e}", exc_info=True)
-            f"[COMPLETION] Failed to update tool message: {e}",
-            exc_info=True,
-        )
         raise ToolMessageUpdateError(
-            f"Failed to update tool message for tool call #{tool_call_id}: {e}"
+            f"Failed to update tool message for tool_call_id={tool_call_id}: {e}"
         ) from e
 
 
@@ -195,6 +202,7 @@ async def _save_agent_from_result(
 async def process_operation_success(
     task: stream_registry.ActiveTask,
     result: dict | str | None,
+    prisma_client: Prisma | None = None,
 ) -> None:
     """Handle successful operation completion.
 
@@ -204,10 +212,12 @@ async def process_operation_success(
     Args:
         task: The active task that completed
         result: The result data from the operation
+        prisma_client: Optional Prisma client for database operations.
+            If None, uses chat_service._update_pending_operation instead.
 
     Raises:
-        ToolMessageUpdateError: If the database update fails. The task
+        ToolMessageUpdateError: If the database update fails. The task will be
-            will be marked as failed instead of completed.
+            marked as failed instead of completed to avoid inconsistent state.
     """
     # For agent generation tools, save the agent to library
     if task.tool_name in AGENT_GENERATION_TOOLS and isinstance(result, dict):
@@ -240,6 +250,7 @@ async def process_operation_success(
             session_id=task.session_id,
             tool_call_id=task.tool_call_id,
             content=result_str,
+            prisma_client=prisma_client,
         )
     except ToolMessageUpdateError:
         # DB update failed - mark task as failed to avoid inconsistent state
@@ -282,15 +293,18 @@ async def process_operation_success(
 async def process_operation_failure(
     task: stream_registry.ActiveTask,
     error: str | None,
+    prisma_client: Prisma | None = None,
 ) -> None:
     """Handle failed operation completion.
 
-    Publishes the error to the stream registry, updates the database
+    Publishes the error to the stream registry, updates the database with
-    with the error response, and marks the task as failed.
+    the error response, and marks the task as failed.
 
     Args:
         task: The active task that failed
         error: The error message from the operation
+        prisma_client: Optional Prisma client for database operations.
+            If None, uses chat_service._update_pending_operation instead.
     """
     error_msg = error or "Operation failed"
 
@@ -311,6 +325,7 @@ async def process_operation_failure(
             session_id=task.session_id,
             tool_call_id=task.tool_call_id,
             content=error_response.model_dump_json(),
+            prisma_client=prisma_client,
         )
     except ToolMessageUpdateError:
         # DB update failed - log but continue with cleanup

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

@@ -14,27 +14,29 @@ from prisma.types import (
     ChatSessionWhereInput,
 )
 
-from backend.data import db
+from backend.data.db import transaction
 from backend.util.json import SafeJson
 
-from .model import ChatMessage, ChatSession, ChatSessionInfo
-
 logger = logging.getLogger(__name__)
 
 
-async def get_chat_session(session_id: str) -> ChatSession | None:
+async def get_chat_session(session_id: str) -> PrismaChatSession | None:
     """Get a chat session by ID from the database."""
     session = await PrismaChatSession.prisma().find_unique(
         where={"id": session_id},
-        include={"Messages": {"order_by": {"sequence": "asc"}}},
+        include={"Messages": True},
     )
-    return ChatSession.from_db(session) if session else None
+    if session and session.Messages:
+        # Sort messages by sequence in Python - Prisma Python client doesn't support
+        # order_by in include clauses (unlike Prisma JS), so we sort after fetching
+        session.Messages.sort(key=lambda m: m.sequence)
+    return session
 
 
 async def create_chat_session(
     session_id: str,
     user_id: str,
-) -> ChatSessionInfo:
+) -> PrismaChatSession:
     """Create a new chat session in the database."""
     data = ChatSessionCreateInput(
         id=session_id,
@@ -43,8 +45,7 @@ async def create_chat_session(
         successfulAgentRuns=SafeJson({}),
         successfulAgentSchedules=SafeJson({}),
     )
-    prisma_session = await PrismaChatSession.prisma().create(data=data)
+    return await PrismaChatSession.prisma().create(data=data)
-    return ChatSessionInfo.from_db(prisma_session)
 
 
 async def update_chat_session(
@@ -55,7 +56,7 @@ async def update_chat_session(
     total_prompt_tokens: int | None = None,
     total_completion_tokens: int | None = None,
     title: str | None = None,
-) -> ChatSession | None:
+) -> PrismaChatSession | None:
     """Update a chat session's metadata."""
     data: ChatSessionUpdateInput = {"updatedAt": datetime.now(UTC)}
 
@@ -75,9 +76,12 @@ async def update_chat_session(
     session = await PrismaChatSession.prisma().update(
         where={"id": session_id},
         data=data,
-        include={"Messages": {"order_by": {"sequence": "asc"}}},
+        include={"Messages": True},
     )
-    return ChatSession.from_db(session) if session else None
+    if session and session.Messages:
+        # Sort in Python - Prisma Python doesn't support order_by in include clauses
+        session.Messages.sort(key=lambda m: m.sequence)
+    return session
 
 
 async def add_chat_message(
@@ -90,7 +94,7 @@ async def add_chat_message(
     refusal: str | None = None,
     tool_calls: list[dict[str, Any]] | None = None,
     function_call: dict[str, Any] | None = None,
-) -> ChatMessage:
+) -> PrismaChatMessage:
     """Add a message to a chat session."""
     # Build input dict dynamically rather than using ChatMessageCreateInput directly
     # because Prisma's TypedDict validation rejects optional fields set to None.
@@ -125,14 +129,14 @@ async def add_chat_message(
         ),
         PrismaChatMessage.prisma().create(data=cast(ChatMessageCreateInput, data)),
     )
-    return ChatMessage.from_db(message)
+    return message
 
 
 async def add_chat_messages_batch(
     session_id: str,
     messages: list[dict[str, Any]],
     start_sequence: int,
-) -> list[ChatMessage]:
+) -> list[PrismaChatMessage]:
     """Add multiple messages to a chat session in a batch.
 
     Uses a transaction for atomicity - if any message creation fails,
@@ -143,7 +147,7 @@ async def add_chat_messages_batch(
 
     created_messages = []
 
-    async with db.transaction() as tx:
+    async with transaction() as tx:
         for i, msg in enumerate(messages):
             # Build input dict dynamically rather than using ChatMessageCreateInput
             # directly because Prisma's TypedDict validation rejects optional fields
@@ -183,22 +187,21 @@ async def add_chat_messages_batch(
             data={"updatedAt": datetime.now(UTC)},
         )
 
-    return [ChatMessage.from_db(m) for m in created_messages]
+    return created_messages
 
 
 async def get_user_chat_sessions(
     user_id: str,
     limit: int = 50,
     offset: int = 0,
-) -> list[ChatSessionInfo]:
+) -> list[PrismaChatSession]:
     """Get chat sessions for a user, ordered by most recent."""
-    prisma_sessions = await PrismaChatSession.prisma().find_many(
+    return await PrismaChatSession.prisma().find_many(
         where={"userId": user_id},
         order={"updatedAt": "desc"},
         take=limit,
         skip=offset,
     )
-    return [ChatSessionInfo.from_db(s) for s in prisma_sessions]
 
 
 async def get_user_session_count(user_id: str) -> int:

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

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any, Self, cast
+from typing import Any, cast
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -23,17 +23,26 @@ from prisma.models import ChatMessage as PrismaChatMessage
 from prisma.models import ChatSession as PrismaChatSession
 from pydantic import BaseModel
 
-from backend.data.db_accessors import chat_db
 from backend.data.redis_client import get_redis_async
 from backend.util import json
 from backend.util.exceptions import DatabaseError, RedisError
 
+from . import db as chat_db
 from .config import ChatConfig
 
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 
 
+def _parse_json_field(value: str | dict | list | None, default: Any = None) -> Any:
+    """Parse a JSON field that may be stored as string or already parsed."""
+    if value is None:
+        return default
+    if isinstance(value, str):
+        return json.loads(value)
+    return value
+
+
 # Redis cache key prefix for chat sessions
 CHAT_SESSION_CACHE_PREFIX = "chat:session:"
 
@@ -43,7 +52,28 @@ def _get_session_cache_key(session_id: str) -> str:
     return f"{CHAT_SESSION_CACHE_PREFIX}{session_id}"
 
 
-# ===================== Chat data models ===================== #
+# Session-level locks to prevent race conditions during concurrent upserts.
+# Uses WeakValueDictionary to automatically garbage collect locks when no longer referenced,
+# preventing unbounded memory growth while maintaining lock semantics for active sessions.
+# Invalidation: Locks are auto-removed by GC when no coroutine holds a reference (after
+# async with lock: completes). Explicit cleanup also occurs in delete_chat_session().
+_session_locks: WeakValueDictionary[str, asyncio.Lock] = WeakValueDictionary()
+_session_locks_mutex = asyncio.Lock()
+
+
+async def _get_session_lock(session_id: str) -> asyncio.Lock:
+    """Get or create a lock for a specific session to prevent concurrent upserts.
+
+    Uses WeakValueDictionary for automatic cleanup: locks are garbage collected
+    when no coroutine holds a reference to them, preventing memory leaks from
+    unbounded growth of session locks.
+    """
+    async with _session_locks_mutex:
+        lock = _session_locks.get(session_id)
+        if lock is None:
+            lock = asyncio.Lock()
+            _session_locks[session_id] = lock
+        return lock
 
 
 class ChatMessage(BaseModel):
@@ -55,19 +85,6 @@ class ChatMessage(BaseModel):
     tool_calls: list[dict] | None = None
     function_call: dict | None = None
 
-    @staticmethod
-    def from_db(prisma_message: PrismaChatMessage) -> "ChatMessage":
-        """Convert a Prisma ChatMessage to a Pydantic ChatMessage."""
-        return ChatMessage(
-            role=prisma_message.role,
-            content=prisma_message.content,
-            name=prisma_message.name,
-            tool_call_id=prisma_message.toolCallId,
-            refusal=prisma_message.refusal,
-            tool_calls=_parse_json_field(prisma_message.toolCalls),
-            function_call=_parse_json_field(prisma_message.functionCall),
-        )
-
 
 class Usage(BaseModel):
     prompt_tokens: int
@@ -75,10 +92,11 @@ class Usage(BaseModel):
     total_tokens: int
 
 
-class ChatSessionInfo(BaseModel):
+class ChatSession(BaseModel):
     session_id: str
     user_id: str
     title: str | None = None
+    messages: list[ChatMessage]
     usage: list[Usage]
     credentials: dict[str, dict] = {}  # Map of provider -> credential metadata
     started_at: datetime
@@ -86,9 +104,60 @@ class ChatSessionInfo(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
-    @classmethod
+    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
-    def from_db(cls, prisma_session: PrismaChatSession) -> Self:
+        """Attach a tool_call to the current turn's assistant message.
-        """Convert Prisma ChatSession to Pydantic ChatSession."""
+
+        Searches backwards for the most recent assistant message (stopping at
+        any user message boundary). If found, appends the tool_call to it.
+        Otherwise creates a new assistant message with the tool_call.
+        """
+        for msg in reversed(self.messages):
+            if msg.role == "user":
+                break
+            if msg.role == "assistant":
+                if not msg.tool_calls:
+                    msg.tool_calls = []
+                msg.tool_calls.append(tool_call)
+                return
+
+        self.messages.append(
+            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
+        )
+
+    @staticmethod
+    def new(user_id: str) -> "ChatSession":
+        return ChatSession(
+            session_id=str(uuid.uuid4()),
+            user_id=user_id,
+            title=None,
+            messages=[],
+            usage=[],
+            credentials={},
+            started_at=datetime.now(UTC),
+            updated_at=datetime.now(UTC),
+        )
+
+    @staticmethod
+    def from_db(
+        prisma_session: PrismaChatSession,
+        prisma_messages: list[PrismaChatMessage] | None = None,
+    ) -> "ChatSession":
+        """Convert Prisma models to Pydantic ChatSession."""
+        messages = []
+        if prisma_messages:
+            for msg in prisma_messages:
+                messages.append(
+                    ChatMessage(
+                        role=msg.role,
+                        content=msg.content,
+                        name=msg.name,
+                        tool_call_id=msg.toolCallId,
+                        refusal=msg.refusal,
+                        tool_calls=_parse_json_field(msg.toolCalls),
+                        function_call=_parse_json_field(msg.functionCall),
+                    )
+                )
+
         # Parse JSON fields from Prisma
         credentials = _parse_json_field(prisma_session.credentials, default={})
         successful_agent_runs = _parse_json_field(
@@ -110,10 +179,11 @@ class ChatSessionInfo(BaseModel):
                 )
             )
 
-        return cls(
+        return ChatSession(
             session_id=prisma_session.id,
             user_id=prisma_session.userId,
             title=prisma_session.title,
+            messages=messages,
             usage=usage,
             credentials=credentials,
             started_at=prisma_session.createdAt,
@@ -122,55 +192,46 @@ class ChatSessionInfo(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
+    @staticmethod
+    def _merge_consecutive_assistant_messages(
+        messages: list[ChatCompletionMessageParam],
+    ) -> list[ChatCompletionMessageParam]:
+        """Merge consecutive assistant messages into single messages.
 
-class ChatSession(ChatSessionInfo):
+        Long-running tool flows can create split assistant messages: one with
-    messages: list[ChatMessage]
+        text content and another with tool_calls. Anthropic's API requires
-
+        tool_result blocks to reference a tool_use in the immediately preceding
-    @classmethod
+        assistant message, so these splits cause 400 errors via OpenRouter.
-    def new(cls, user_id: str) -> Self:
-        return cls(
-            session_id=str(uuid.uuid4()),
-            user_id=user_id,
-            title=None,
-            messages=[],
-            usage=[],
-            credentials={},
-            started_at=datetime.now(UTC),
-            updated_at=datetime.now(UTC),
-        )
-
-    @classmethod
-    def from_db(cls, prisma_session: PrismaChatSession) -> Self:
-        """Convert Prisma ChatSession to Pydantic ChatSession."""
-        if prisma_session.Messages is None:
-            raise ValueError(
-                f"Prisma session {prisma_session.id} is missing Messages relation"
-            )
-
-        return cls(
-            **ChatSessionInfo.from_db(prisma_session).model_dump(),
-            messages=[ChatMessage.from_db(m) for m in prisma_session.Messages],
-        )
-
-    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
-        """Attach a tool_call to the current turn's assistant message.
-
-        Searches backwards for the most recent assistant message (stopping at
-        any user message boundary). If found, appends the tool_call to it.
-        Otherwise creates a new assistant message with the tool_call.
         """
-        for msg in reversed(self.messages):
+        if len(messages) < 2:
-            if msg.role == "user":
+            return messages
-                break
-            if msg.role == "assistant":
-                if not msg.tool_calls:
-                    msg.tool_calls = []
-                msg.tool_calls.append(tool_call)
-                return
 
-        self.messages.append(
+        result: list[ChatCompletionMessageParam] = [messages[0]]
-            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
+        for msg in messages[1:]:
-        )
+            prev = result[-1]
+            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
+                result.append(msg)
+                continue
+
+            prev = cast(ChatCompletionAssistantMessageParam, prev)
+            curr = cast(ChatCompletionAssistantMessageParam, msg)
+
+            curr_content = curr.get("content") or ""
+            if curr_content:
+                prev_content = prev.get("content") or ""
+                prev["content"] = (
+                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
+                )
+
+            curr_tool_calls = curr.get("tool_calls")
+            if curr_tool_calls:
+                prev_tool_calls = prev.get("tool_calls")
+                prev["tool_calls"] = (
+                    list(prev_tool_calls) + list(curr_tool_calls)
+                    if prev_tool_calls
+                    else list(curr_tool_calls)
+                )
+        return result
 
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
@@ -260,70 +321,40 @@ class ChatSession(ChatSessionInfo):
                 )
         return self._merge_consecutive_assistant_messages(messages)
 
-    @staticmethod
-    def _merge_consecutive_assistant_messages(
-        messages: list[ChatCompletionMessageParam],
-    ) -> list[ChatCompletionMessageParam]:
-        """Merge consecutive assistant messages into single messages.
 
-        Long-running tool flows can create split assistant messages: one with
+async def _get_session_from_cache(session_id: str) -> ChatSession | None:
-        text content and another with tool_calls. Anthropic's API requires
+    """Get a chat session from Redis cache."""
-        tool_result blocks to reference a tool_use in the immediately preceding
+    redis_key = _get_session_cache_key(session_id)
-        assistant message, so these splits cause 400 errors via OpenRouter.
+    async_redis = await get_redis_async()
-        """
+    raw_session: bytes | None = await async_redis.get(redis_key)
-        if len(messages) < 2:
-            return messages
 
-        result: list[ChatCompletionMessageParam] = [messages[0]]
+    if raw_session is None:
-        for msg in messages[1:]:
+        return None
-            prev = result[-1]
-            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
-                result.append(msg)
-                continue
 
-            prev = cast(ChatCompletionAssistantMessageParam, prev)
+    try:
-            curr = cast(ChatCompletionAssistantMessageParam, msg)
+        session = ChatSession.model_validate_json(raw_session)
-
+        logger.info(
-            curr_content = curr.get("content") or ""
+            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
-            if curr_content:
+            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
-                prev_content = prev.get("content") or ""
+        )
-                prev["content"] = (
+        return session
-                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
+    except Exception as e:
-                )
+        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
-
+        raise RedisError(f"Corrupted session data for {session_id}") from e
-            curr_tool_calls = curr.get("tool_calls")
-            if curr_tool_calls:
-                prev_tool_calls = prev.get("tool_calls")
-                prev["tool_calls"] = (
-                    list(prev_tool_calls) + list(curr_tool_calls)
-                    if prev_tool_calls
-                    else list(curr_tool_calls)
-                )
-        return result
 
 
-def _parse_json_field(value: str | dict | list | None, default: Any = None) -> Any:
+async def _cache_session(session: ChatSession) -> None:
-    """Parse a JSON field that may be stored as string or already parsed."""
+    """Cache a chat session in Redis."""
-    if value is None:
-        return default
-    if isinstance(value, str):
-        return json.loads(value)
-    return value
-
-
-# ================ Chat cache + DB operations ================ #
-
-# NOTE: Database calls are automatically routed through DatabaseManager if Prisma is not
-#       connected directly.
-
-
-async def cache_chat_session(session: ChatSession) -> None:
-    """Cache a chat session in Redis (without persisting to the database)."""
     redis_key = _get_session_cache_key(session.session_id)
     async_redis = await get_redis_async()
     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 invalidate_session_cache(session_id: str) -> None:
     """Invalidate a chat session from Redis cache.
 
@@ -339,6 +370,77 @@ async def invalidate_session_cache(session_id: str) -> None:
         logger.warning(f"Failed to invalidate session cache for {session_id}: {e}")
 
 
+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)
+    if not prisma_session:
+        return None
+
+    messages = prisma_session.Messages
+    logger.debug(
+        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
+        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
+    )
+
+    return ChatSession.from_db(prisma_session, messages)
+
+
+async def _save_session_to_db(
+    session: ChatSession, existing_message_count: int
+) -> None:
+    """Save or update a chat session in the database."""
+    # Check if session exists in DB
+    existing = await chat_db.get_chat_session(session.session_id)
+
+    if not existing:
+        # Create new session
+        await chat_db.create_chat_session(
+            session_id=session.session_id,
+            user_id=session.user_id,
+        )
+        existing_message_count = 0
+
+    # Calculate total tokens from usage
+    total_prompt = sum(u.prompt_tokens for u in session.usage)
+    total_completion = sum(u.completion_tokens for u in session.usage)
+
+    # Update session metadata
+    await chat_db.update_chat_session(
+        session_id=session.session_id,
+        credentials=session.credentials,
+        successful_agent_runs=session.successful_agent_runs,
+        successful_agent_schedules=session.successful_agent_schedules,
+        total_prompt_tokens=total_prompt,
+        total_completion_tokens=total_completion,
+    )
+
+    # Add new messages (only those after existing count)
+    new_messages = session.messages[existing_message_count:]
+    if new_messages:
+        messages_data = []
+        for msg in new_messages:
+            messages_data.append(
+                {
+                    "role": msg.role,
+                    "content": msg.content,
+                    "name": msg.name,
+                    "tool_call_id": msg.tool_call_id,
+                    "refusal": msg.refusal,
+                    "tool_calls": msg.tool_calls,
+                    "function_call": msg.function_call,
+                }
+            )
+        logger.debug(
+            f"[DB] Saving {len(new_messages)} messages to session {session.session_id}, "
+            f"roles={[m['role'] for m in messages_data]}"
+        )
+        await chat_db.add_chat_messages_batch(
+            session_id=session.session_id,
+            messages=messages_data,
+            start_sequence=existing_message_count,
+        )
+
+
 async def get_chat_session(
     session_id: str,
     user_id: str | None = None,
@@ -386,53 +488,16 @@ async def get_chat_session(
 
     # Cache the session from DB
     try:
-        await cache_chat_session(session)
+        await _cache_session(session)
-        logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
 
     return session
 
 
-async def _get_session_from_cache(session_id: str) -> ChatSession | None:
+async def upsert_chat_session(
-    """Get a chat session from Redis cache."""
+    session: ChatSession,
-    redis_key = _get_session_cache_key(session_id)
+) -> ChatSession:
-    async_redis = await get_redis_async()
-    raw_session: bytes | None = await async_redis.get(redis_key)
-
-    if raw_session is None:
-        return None
-
-    try:
-        session = ChatSession.model_validate_json(raw_session)
-        logger.info(
-            f"Loading session {session_id} from cache: "
-            f"message_count={len(session.messages)}, "
-            f"roles={[m.role for m in session.messages]}"
-        )
-        return session
-    except Exception as e:
-        logger.error(f"Failed to deserialize session {session_id}: {e}", exc_info=True)
-        raise RedisError(f"Corrupted session data for {session_id}") from e
-
-
-async def _get_session_from_db(session_id: str) -> ChatSession | None:
-    """Get a chat session from the database."""
-    session = await chat_db().get_chat_session(session_id)
-    if not session:
-        return None
-
-    logger.info(
-        f"Loaded session {session_id} from DB: "
-        f"has_messages={bool(session.messages)}, "
-        f"message_count={len(session.messages)}, "
-        f"roles={[m.role for m in session.messages]}"
-    )
-
-    return session
-
-
-async def upsert_chat_session(session: ChatSession) -> ChatSession:
     """Update a chat session in both cache and database.
 
     Uses session-level locking to prevent race conditions when concurrent
@@ -450,7 +515,7 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
 
     async with lock:
         # Get existing message count from DB for incremental saves
-        existing_message_count = await chat_db().get_chat_session_message_count(
+        existing_message_count = await chat_db.get_chat_session_message_count(
             session.session_id
         )
 
@@ -467,7 +532,7 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
 
         # Save to cache (best-effort, even if DB failed)
         try:
-            await cache_chat_session(session)
+            await _cache_session(session)
         except Exception as e:
             # If DB succeeded but cache failed, raise cache error
             if db_error is None:
@@ -488,65 +553,6 @@ async def upsert_chat_session(session: ChatSession) -> ChatSession:
         return session
 
 
-async def _save_session_to_db(
-    session: ChatSession, existing_message_count: int
-) -> None:
-    """Save or update a chat session in the database."""
-    db = chat_db()
-
-    # Check if session exists in DB
-    existing = await db.get_chat_session(session.session_id)
-
-    if not existing:
-        # Create new session
-        await db.create_chat_session(
-            session_id=session.session_id,
-            user_id=session.user_id,
-        )
-        existing_message_count = 0
-
-    # Calculate total tokens from usage
-    total_prompt = sum(u.prompt_tokens for u in session.usage)
-    total_completion = sum(u.completion_tokens for u in session.usage)
-
-    # Update session metadata
-    await db.update_chat_session(
-        session_id=session.session_id,
-        credentials=session.credentials,
-        successful_agent_runs=session.successful_agent_runs,
-        successful_agent_schedules=session.successful_agent_schedules,
-        total_prompt_tokens=total_prompt,
-        total_completion_tokens=total_completion,
-    )
-
-    # Add new messages (only those after existing count)
-    new_messages = session.messages[existing_message_count:]
-    if new_messages:
-        messages_data = []
-        for msg in new_messages:
-            messages_data.append(
-                {
-                    "role": msg.role,
-                    "content": msg.content,
-                    "name": msg.name,
-                    "tool_call_id": msg.tool_call_id,
-                    "refusal": msg.refusal,
-                    "tool_calls": msg.tool_calls,
-                    "function_call": msg.function_call,
-                }
-            )
-        logger.info(
-            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}, "
-            f"start_sequence={existing_message_count}"
-        )
-        await db.add_chat_messages_batch(
-            session_id=session.session_id,
-            messages=messages_data,
-            start_sequence=existing_message_count,
-        )
-
-
 async def append_and_save_message(session_id: str, message: ChatMessage) -> ChatSession:
     """Atomically append a message to a session and persist it.
 
@@ -562,7 +568,7 @@ async def append_and_save_message(session_id: str, message: ChatMessage) -> Chat
             raise ValueError(f"Session {session_id} not found")
 
         session.messages.append(message)
-        existing_message_count = await chat_db().get_chat_session_message_count(
+        existing_message_count = await chat_db.get_chat_session_message_count(
             session_id
         )
 
@@ -574,7 +580,7 @@ async def append_and_save_message(session_id: str, message: ChatMessage) -> Chat
             ) from e
 
         try:
-            await cache_chat_session(session)
+            await _cache_session(session)
         except Exception as e:
             logger.warning(f"Cache write failed for session {session_id}: {e}")
 
@@ -593,7 +599,7 @@ async def create_chat_session(user_id: str) -> ChatSession:
 
     # Create in database first - fail fast if this fails
     try:
-        await chat_db().create_chat_session(
+        await chat_db.create_chat_session(
             session_id=session.session_id,
             user_id=user_id,
         )
@@ -605,7 +611,7 @@ async def create_chat_session(user_id: str) -> ChatSession:
 
     # Cache the session (best-effort optimization, DB is source of truth)
     try:
-        await cache_chat_session(session)
+        await _cache_session(session)
     except Exception as e:
         logger.warning(f"Failed to cache new session {session.session_id}: {e}")
 
@@ -616,16 +622,20 @@ async def get_user_sessions(
     user_id: str,
     limit: int = 50,
     offset: int = 0,
-) -> tuple[list[ChatSessionInfo], int]:
+) -> tuple[list[ChatSession], int]:
     """Get chat sessions for a user from the database with total count.
 
     Returns:
         A tuple of (sessions, total_count) where total_count is the overall
         number of sessions for the user (not just the current page).
     """
-    db = chat_db()
+    prisma_sessions = await chat_db.get_user_chat_sessions(user_id, limit, offset)
-    sessions = await db.get_user_chat_sessions(user_id, limit, offset)
+    total_count = await chat_db.get_user_session_count(user_id)
-    total_count = await db.get_user_session_count(user_id)
+
+    sessions = []
+    for prisma_session in prisma_sessions:
+        # Convert without messages for listing (lighter weight)
+        sessions.append(ChatSession.from_db(prisma_session, None))
 
     return sessions, total_count
 
@@ -643,7 +653,7 @@ async def delete_chat_session(session_id: str, user_id: str | None = None) -> bo
     """
     # Delete from database first (with optional user_id validation)
     # This confirms ownership before invalidating cache
-    deleted = await chat_db().delete_chat_session(session_id, user_id)
+    deleted = await chat_db.delete_chat_session(session_id, user_id)
 
     if not deleted:
         return False
@@ -678,7 +688,7 @@ async def update_session_title(session_id: str, title: str) -> bool:
         True if updated successfully, False otherwise.
     """
     try:
-        result = await chat_db().update_chat_session(session_id=session_id, title=title)
+        result = await chat_db.update_chat_session(session_id=session_id, title=title)
         if result is None:
             logger.warning(f"Session {session_id} not found for title update")
             return False
@@ -690,7 +700,7 @@ async def update_session_title(session_id: str, title: str) -> bool:
             cached = await _get_session_from_cache(session_id)
             if cached:
                 cached.title = title
-                await cache_chat_session(cached)
+                await _cache_session(cached)
         except Exception as e:
             # Not critical - title will be correct on next full cache refresh
             logger.warning(
@@ -701,29 +711,3 @@ async def update_session_title(session_id: str, title: str) -> bool:
     except Exception as e:
         logger.error(f"Failed to update title for session {session_id}: {e}")
         return False
-
-
-# ==================== Chat session locks ==================== #
-
-_session_locks: WeakValueDictionary[str, asyncio.Lock] = WeakValueDictionary()
-_session_locks_mutex = asyncio.Lock()
-
-
-async def _get_session_lock(session_id: str) -> asyncio.Lock:
-    """Get or create a lock for a specific session to prevent concurrent upserts.
-
-    This was originally added to solve the specific problem of race conditions between
-    the session title thread and the conversation thread, which always occurs on the
-    same instance as we prevent rapid request sends on the frontend.
-
-    Uses WeakValueDictionary for automatic cleanup: locks are garbage collected
-    when no coroutine holds a reference to them, preventing memory leaks from
-    unbounded growth of session locks. Explicit cleanup also occurs
-    in `delete_chat_session()`.
-    """
-    async with _session_locks_mutex:
-        lock = _session_locks.get(session_id)
-        if lock is None:
-            lock = asyncio.Lock()
-            _session_locks[session_id] = lock
-        return lock

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

@@ -11,25 +11,24 @@ from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response,
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
-from backend.copilot import service as chat_service
+from backend.util.exceptions import NotFoundError
-from backend.copilot import stream_registry
+from backend.util.feature_flag import Flag, is_feature_enabled
-from backend.copilot.completion_handler import (
+
-    process_operation_failure,
+from . import service as chat_service
-    process_operation_success,
+from . import stream_registry
-)
+from .completion_handler import process_operation_failure, process_operation_success
-from backend.copilot.config import ChatConfig
+from .config import ChatConfig
-from backend.copilot.executor.utils import enqueue_copilot_task
+from .model import (
-from backend.copilot.model import (
     ChatMessage,
     ChatSession,
     append_and_save_message,
     create_chat_session,
-    delete_chat_session,
     get_chat_session,
     get_user_sessions,
 )
-from backend.copilot.response_model import StreamError, StreamFinish, StreamHeartbeat
+from .response_model import StreamError, StreamFinish, StreamHeartbeat, StreamStart
-from backend.copilot.tools.models import (
+from .sdk import service as sdk_service
+from .tools.models import (
     AgentDetailsResponse,
     AgentOutputResponse,
     AgentPreviewResponse,
@@ -52,8 +51,7 @@ from backend.copilot.tools.models import (
     SetupRequirementsResponse,
     UnderstandingUpdatedResponse,
 )
-from backend.copilot.tracking import track_user_message
+from .tracking import track_user_message
-from backend.util.exceptions import NotFoundError
 
 config = ChatConfig()
 
@@ -213,43 +211,6 @@ async def create_session(
     )
 
 
-@router.delete(
-    "/sessions/{session_id}",
-    dependencies=[Security(auth.requires_user)],
-    status_code=204,
-    responses={404: {"description": "Session not found or access denied"}},
-)
-async def delete_session(
-    session_id: str,
-    user_id: Annotated[str, Security(auth.get_user_id)],
-) -> Response:
-    """
-    Delete a chat session.
-
-    Permanently removes a chat session and all its messages.
-    Only the owner can delete their sessions.
-
-    Args:
-        session_id: The session ID to delete.
-        user_id: The authenticated user's ID.
-
-    Returns:
-        204 No Content on success.
-
-    Raises:
-        HTTPException: 404 if session not found or not owned by user.
-    """
-    deleted = await delete_chat_session(session_id, user_id)
-
-    if not deleted:
-        raise HTTPException(
-            status_code=404,
-            detail=f"Session {session_id} not found or access denied",
-        )
-
-    return Response(status_code=204)
-
-
 @router.get(
     "/sessions/{session_id}",
 )
@@ -355,7 +316,7 @@ async def stream_chat_post(
         f"user={user_id}, message_len={len(request.message)}",
         extra={"json_fields": log_meta},
     )
-    await _validate_and_get_session(session_id, user_id)
+    session = await _validate_and_get_session(session_id, user_id)
     logger.info(
         f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
         extra={
@@ -382,7 +343,7 @@ async def stream_chat_post(
                 message_length=len(request.message),
             )
         logger.info(f"[STREAM] Saving user message to session {session_id}")
-        await append_and_save_message(session_id, message)
+        session = await append_and_save_message(session_id, message)
         logger.info(f"[STREAM] User message saved for session {session_id}")
 
     # Create a task in the stream registry for reconnection support
@@ -409,19 +370,125 @@ async def stream_chat_post(
         },
     )
 
-    await enqueue_copilot_task(
+    # Background task that runs the AI generation independently of SSE connection
-        task_id=task_id,
+    async def run_ai_generation():
-        session_id=session_id,
+        import time as time_module
-        user_id=user_id,
-        operation_id=operation_id,
-        message=request.message,
-        is_user_message=request.is_user_message,
-        context=request.context,
-    )
 
+        gen_start_time = time_module.perf_counter()
+        logger.info(
+            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
+            extra={"json_fields": log_meta},
+        )
+        first_chunk_time, ttfc = None, None
+        chunk_count = 0
+        try:
+            # Emit a start event with task_id for reconnection
+            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
+            await stream_registry.publish_chunk(task_id, start_chunk)
+            logger.info(
+                f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time) * 1000:.1f}ms",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "elapsed_ms": (time_module.perf_counter() - gen_start_time)
+                        * 1000,
+                    }
+                },
+            )
+
+            # Choose service based on LaunchDarkly flag (falls back to config default)
+            use_sdk = await is_feature_enabled(
+                Flag.COPILOT_SDK,
+                user_id or "anonymous",
+                default=config.use_claude_agent_sdk,
+            )
+            stream_fn = (
+                sdk_service.stream_chat_completion_sdk
+                if use_sdk
+                else chat_service.stream_chat_completion
+            )
+            logger.info(
+                f"[TIMING] Calling {'sdk' if use_sdk else 'standard'} stream_chat_completion",
+                extra={"json_fields": log_meta},
+            )
+            # Pass message=None since we already added it to the session above
+            async for chunk in stream_fn(
+                session_id,
+                None,  # Message already in session
+                is_user_message=request.is_user_message,
+                user_id=user_id,
+                session=session,  # Pass session with message already added
+                context=request.context,
+            ):
+                # Skip duplicate StreamStart — we already published one above
+                if isinstance(chunk, StreamStart):
+                    continue
+                chunk_count += 1
+                if first_chunk_time is None:
+                    first_chunk_time = time_module.perf_counter()
+                    ttfc = first_chunk_time - gen_start_time
+                    logger.info(
+                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
+                        extra={
+                            "json_fields": {
+                                **log_meta,
+                                "chunk_type": type(chunk).__name__,
+                                "time_to_first_chunk_ms": ttfc * 1000,
+                            }
+                        },
+                    )
+                # Write to Redis (subscribers will receive via XREAD)
+                await stream_registry.publish_chunk(task_id, chunk)
+
+            gen_end_time = time_module.perf_counter()
+            total_time = (gen_end_time - gen_start_time) * 1000
+            logger.info(
+                f"[TIMING] run_ai_generation FINISHED in {total_time / 1000:.1f}s; "
+                f"task={task_id}, session={session_id}, "
+                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "total_time_ms": total_time,
+                        "time_to_first_chunk_ms": (
+                            ttfc * 1000 if ttfc is not None else None
+                        ),
+                        "n_chunks": chunk_count,
+                    }
+                },
+            )
+            await stream_registry.mark_task_completed(task_id, "completed")
+        except Exception as e:
+            elapsed = time_module.perf_counter() - gen_start_time
+            logger.error(
+                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "elapsed_ms": elapsed * 1000,
+                        "error": str(e),
+                    }
+                },
+            )
+            # Publish a StreamError so the frontend can display an error message
+            try:
+                await stream_registry.publish_chunk(
+                    task_id,
+                    StreamError(
+                        errorText="An error occurred. Please try again.",
+                        code="stream_error",
+                    ),
+                )
+            except Exception:
+                pass  # Best-effort; mark_task_completed will publish StreamFinish
+            await stream_registry.mark_task_completed(task_id, "failed")
+
+    # Start the AI generation in a background task
+    bg_task = asyncio.create_task(run_ai_generation())
+    await stream_registry.set_task_asyncio_task(task_id, bg_task)
     setup_time = (time.perf_counter() - stream_start_time) * 1000
     logger.info(
-        f"[TIMING] Task enqueued to RabbitMQ, setup={setup_time:.1f}ms",
+        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
         extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
     )
 

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py

@@ -20,7 +20,7 @@ from claude_agent_sdk import (
     UserMessage,
 )
 
-from backend.copilot.response_model import (
+from backend.api.features.chat.response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
@@ -34,8 +34,10 @@ from backend.copilot.response_model import (
     StreamToolInputStart,
     StreamToolOutputAvailable,
 )
-
+from backend.api.features.chat.sdk.tool_adapter import (
-from .tool_adapter import MCP_TOOL_PREFIX, pop_pending_tool_output
+    MCP_TOOL_PREFIX,
+    pop_pending_tool_output,
+)
 
 logger = logging.getLogger(__name__)
 

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py

@@ -10,7 +10,7 @@ from claude_agent_sdk import (
     UserMessage,
 )
 
-from backend.copilot.response_model import (
+from backend.api.features.chat.response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py

@@ -11,7 +11,7 @@ import re
 from collections.abc import Callable
 from typing import Any, cast
 
-from .tool_adapter import (
+from backend.api.features.chat.sdk.tool_adapter import (
     BLOCKED_TOOLS,
     DANGEROUS_PATTERNS,
     MCP_TOOL_PREFIX,

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks_test.py

@@ -1,9 +1,4 @@
-"""Tests for SDK security hooks — workspace paths, tool access, and deny messages.
+"""Unit tests for SDK security hooks."""
-
-These are pure unit tests with no external dependencies (no SDK, no DB, no server).
-They validate that the security hooks correctly block unauthorized paths,
-tool access, and dangerous input patterns.
-"""
 
 import os
 
@@ -17,10 +12,6 @@ def _is_denied(result: dict) -> bool:
     return hook.get("permissionDecision") == "deny"
 
 
-def _reason(result: dict) -> str:
-    return result.get("hookSpecificOutput", {}).get("permissionDecisionReason", "")
-
-
 # -- Blocked tools -----------------------------------------------------------
 
 
@@ -172,19 +163,3 @@ def test_non_workspace_tool_passes_isolation():
         "find_agent", {"query": "email"}, user_id="user-1"
     )
     assert result == {}
-
-
-# -- Deny message quality ----------------------------------------------------
-
-
-def test_blocked_tool_message_clarity():
-    """Deny messages must include [SECURITY] and 'cannot be bypassed'."""
-    reason = _reason(_validate_tool_access("bash", {}))
-    assert "[SECURITY]" in reason
-    assert "cannot be bypassed" in reason
-
-
-def test_bash_builtin_blocked_message_clarity():
-    reason = _reason(_validate_tool_access("Bash", {"command": "echo hello"}))
-    assert "[SECURITY]" in reason
-    assert "cannot be bypassed" in reason

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

@@ -255,7 +255,7 @@ def _build_sdk_env() -> dict[str, str]:
 def _make_sdk_cwd(session_id: str) -> str:
     """Create a safe, session-specific working directory path.
 
-    Delegates to :func:`~backend.copilot.tools.sandbox.make_session_path`
+    Delegates to :func:`~backend.api.features.chat.tools.sandbox.make_session_path`
     (single source of truth for path sanitization) and adds a defence-in-depth
     assertion.
     """
@@ -440,16 +440,12 @@ async def stream_chat_completion_sdk(
             f"Session {session_id} not found. Please create a new session first."
         )
 
-    # Append the new message to the session if it's not already there
+    if message:
-    new_message_role = "user" if is_user_message else "assistant"
+        session.messages.append(
-    if message and (
+            ChatMessage(
-        len(session.messages) == 0
+                role="user" if is_user_message else "assistant", content=message
-        or not (
+            )
-            session.messages[-1].role == new_message_role
-            and session.messages[-1].content == message
         )
-    ):
-        session.messages.append(ChatMessage(role=new_message_role, content=message))
         if is_user_message:
             track_user_message(
                 user_id=user_id, session_id=session_id, message_length=len(message)
@@ -693,15 +689,11 @@ async def stream_chat_completion_sdk(
                     await asyncio.sleep(0.5)
                     raw_transcript = read_transcript_file(captured_transcript.path)
                     if raw_transcript:
-                        try:
+                        task = asyncio.create_task(
-                            async with asyncio.timeout(30):
+                            _upload_transcript_bg(user_id, session_id, raw_transcript)
-                                await _upload_transcript_bg(
+                        )
-                                    user_id, session_id, raw_transcript
+                        _background_tasks.add(task)
-                                )
+                        task.add_done_callback(_background_tasks.discard)
-                        except asyncio.TimeoutError:
-                            logger.warning(
-                                f"[SDK] Transcript upload timed out for {session_id}"
-                            )
                     else:
                         logger.debug("[SDK] Stop hook fired but transcript not usable")
 

autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py

@@ -18,9 +18,9 @@ from collections.abc import Awaitable, Callable
 from contextvars import ContextVar
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.tools import TOOL_REGISTRY
+from backend.api.features.chat.tools import TOOL_REGISTRY
-from backend.copilot.tools.base import BaseTool
+from backend.api.features.chat.tools.base import BaseTool
 
 logger = logging.getLogger(__name__)
 

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

@@ -27,18 +27,20 @@ from openai.types.chat import (
     ChatCompletionToolParam,
 )
 
-from backend.data.db_accessors import chat_db, understanding_db
 from backend.data.redis_client import get_redis_async
-from backend.data.understanding import format_understanding_for_prompt
+from backend.data.understanding import (
+    format_understanding_for_prompt,
+    get_business_understanding,
+)
 from backend.util.exceptions import NotFoundError
 from backend.util.settings import AppEnvironment, Settings
 
+from . import db as chat_db
 from . import stream_registry
 from .config import ChatConfig
 from .model import (
     ChatMessage,
     ChatSession,
-    ChatSessionInfo,
     Usage,
     cache_chat_session,
     get_chat_session,
@@ -261,7 +263,7 @@ async def _build_system_prompt(
     understanding = None
     if user_id:
         try:
-            understanding = await understanding_db().get_business_understanding(user_id)
+            understanding = await get_business_understanding(user_id)
         except Exception as e:
             logger.warning(f"Failed to fetch business understanding: {e}")
             understanding = None
@@ -337,7 +339,7 @@ async def _generate_session_title(
 async def assign_user_to_session(
     session_id: str,
     user_id: str,
-) -> ChatSessionInfo:
+) -> ChatSession:
     """
     Assign a user to a chat session.
     """
@@ -426,16 +428,12 @@ async def stream_chat_completion(
             f"Session {session_id} not found. Please create a new session first."
         )
 
-    # Append the new message to the session if it's not already there
+    if message:
-    new_message_role = "user" if is_user_message else "assistant"
+        session.messages.append(
-    if message and (
+            ChatMessage(
-        len(session.messages) == 0
+                role="user" if is_user_message else "assistant", content=message
-        or not (
+            )
-            session.messages[-1].role == new_message_role
-            and session.messages[-1].content == message
         )
-    ):
-        session.messages.append(ChatMessage(role=new_message_role, content=message))
         logger.info(
             f"Appended message (role={'user' if is_user_message else 'assistant'}), "
             f"new message_count={len(session.messages)}"
@@ -1772,7 +1770,7 @@ async def _update_pending_operation(
     This is called by background tasks when long-running operations complete.
     """
     # Update the message in database
-    updated = await chat_db().update_tool_message_content(
+    updated = await chat_db.update_tool_message_content(
         session_id=session_id,
         tool_call_id=tool_call_id,
         new_content=result,

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

@@ -3,8 +3,8 @@ from typing import TYPE_CHECKING, Any
 
 from openai.types.chat import ChatCompletionToolParam
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.tracking import track_tool_called
+from backend.api.features.chat.tracking import track_tool_called
 
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
@@ -31,7 +31,7 @@ from .workspace_files import (
 )
 
 if TYPE_CHECKING:
-    from backend.copilot.response_model import StreamToolOutputAvailable
+    from backend.api.features.chat.response_model import StreamToolOutputAvailable
 
 logger = logging.getLogger(__name__)
 

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

@@ -6,11 +6,11 @@ import pytest
 from prisma.types import ProfileCreateInput
 from pydantic import SecretStr
 
+from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.blocks.firecrawl.scrape import FirecrawlScrapeBlock
 from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.llm import AITextGeneratorBlock
-from backend.copilot.model import ChatSession
 from backend.data.db import prisma
 from backend.data.graph import Graph, Link, Node, create_graph
 from backend.data.model import APIKeyCredentials

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

@@ -3,9 +3,11 @@
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.data.db_accessors import understanding_db
+from backend.data.understanding import (
-from backend.data.understanding import BusinessUnderstandingInput
+    BusinessUnderstandingInput,
+    upsert_business_understanding,
+)
 
 from .base import BaseTool
 from .models import ErrorResponse, ToolResponseBase, UnderstandingUpdatedResponse
@@ -97,9 +99,7 @@ and automations for the user's specific needs."""
         ]
 
         # Upsert with merge
-        understanding = await understanding_db().upsert_business_understanding(
+        understanding = await upsert_business_understanding(user_id, input_data)
-            user_id, input_data
-        )
 
         # Build current understanding summary (filter out empty values)
         current_understanding = {

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

@@ -5,8 +5,9 @@ import re
 import uuid
 from typing import Any, NotRequired, TypedDict
 
-from backend.data.db_accessors import graph_db, library_db, store_db
+from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node
+from backend.api.features.store import db as store_db
+from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
@@ -144,9 +145,8 @@ async def get_library_agent_by_id(
     Returns:
         LibraryAgentSummary if found, None otherwise
     """
-    db = library_db()
     try:
-        agent = await db.get_library_agent_by_graph_id(user_id, agent_id)
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
         if agent:
             logger.debug(f"Found library agent by graph_id: {agent.name}")
             return LibraryAgentSummary(
@@ -163,7 +163,7 @@ async def get_library_agent_by_id(
         logger.debug(f"Could not fetch library agent by graph_id {agent_id}: {e}")
 
     try:
-        agent = await db.get_library_agent(agent_id, user_id)
+        agent = await library_db.get_library_agent(agent_id, user_id)
         if agent:
             logger.debug(f"Found library agent by library_id: {agent.name}")
             return LibraryAgentSummary(
@@ -215,7 +215,7 @@ async def get_library_agents_for_generation(
         List of LibraryAgentSummary with schemas and recent executions for sub-agent composition
     """
     try:
-        response = await library_db().list_library_agents(
+        response = await library_db.list_library_agents(
             user_id=user_id,
             search_term=search_query,
             page=1,
@@ -272,7 +272,7 @@ async def search_marketplace_agents_for_generation(
         List of LibraryAgentSummary with full input/output schemas
     """
     try:
-        response = await store_db().get_store_agents(
+        response = await store_db.get_store_agents(
             search_query=search_query,
             page=1,
             page_size=max_results,
@@ -286,7 +286,7 @@ async def search_marketplace_agents_for_generation(
             return []
 
         graph_ids = [agent.agent_graph_id for agent in agents_with_graphs]
-        graphs = await graph_db().get_store_listed_graphs(graph_ids)
+        graphs = await get_store_listed_graphs(*graph_ids)
 
         results: list[LibraryAgentSummary] = []
         for agent in agents_with_graphs:
@@ -673,10 +673,9 @@ async def save_agent_to_library(
         Tuple of (created Graph, LibraryAgent)
     """
     graph = json_to_graph(agent_json)
-    db = library_db()
     if is_update:
-        return await db.update_graph_in_library(graph, user_id)
+        return await library_db.update_graph_in_library(graph, user_id)
-    return await db.create_graph_in_library(graph, user_id)
+    return await library_db.create_graph_in_library(graph, user_id)
 
 
 def graph_to_json(graph: Graph) -> dict[str, Any]:
@@ -736,14 +735,12 @@ async def get_agent_as_json(
     Returns:
         Agent as JSON dict or None if not found
     """
-    db = graph_db()
+    graph = await get_graph(agent_id, version=None, user_id=user_id)
-
-    graph = await db.get_graph(agent_id, version=None, user_id=user_id)
 
     if not graph and user_id:
         try:
-            library_agent = await library_db().get_library_agent(agent_id, user_id)
+            library_agent = await library_db.get_library_agent(agent_id, user_id)
-            graph = await db.get_graph(
+            graph = await get_graph(
                 library_agent.graph_id, version=None, user_id=user_id
             )
         except NotFoundError:

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

@@ -7,9 +7,10 @@ from typing import Any
 
 from pydantic import BaseModel, field_validator
 
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.library import db as library_db
 from backend.api.features.library.model import LibraryAgent
-from backend.copilot.model import ChatSession
+from backend.data import execution as execution_db
-from backend.data.db_accessors import execution_db, library_db
 from backend.data.execution import ExecutionStatus, GraphExecution, GraphExecutionMeta
 
 from .base import BaseTool
@@ -164,12 +165,10 @@ class AgentOutputTool(BaseTool):
         Resolve agent from provided identifiers.
         Returns (library_agent, error_message).
         """
-        lib_db = library_db()
-
         # Priority 1: Exact library agent ID
         if library_agent_id:
             try:
-                agent = await lib_db.get_library_agent(library_agent_id, user_id)
+                agent = await library_db.get_library_agent(library_agent_id, user_id)
                 return agent, None
             except Exception as e:
                 logger.warning(f"Failed to get library agent by ID: {e}")
@@ -183,7 +182,7 @@ class AgentOutputTool(BaseTool):
                 return None, f"Agent '{store_slug}' not found in marketplace"
 
             # Find in user's library by graph_id
-            agent = await lib_db.get_library_agent_by_graph_id(user_id, graph.id)
+            agent = await library_db.get_library_agent_by_graph_id(user_id, graph.id)
             if not agent:
                 return (
                     None,
@@ -195,7 +194,7 @@ class AgentOutputTool(BaseTool):
         # Priority 3: Fuzzy name search in library
         if agent_name:
             try:
-                response = await lib_db.list_library_agents(
+                response = await library_db.list_library_agents(
                     user_id=user_id,
                     search_term=agent_name,
                     page_size=5,
@@ -229,11 +228,9 @@ class AgentOutputTool(BaseTool):
         Fetch execution(s) based on filters.
         Returns (single_execution, available_executions_meta, error_message).
         """
-        exec_db = execution_db()
-
         # If specific execution_id provided, fetch it directly
         if execution_id:
-            execution = await exec_db.get_graph_execution(
+            execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=execution_id,
                 include_node_executions=False,
@@ -243,7 +240,7 @@ class AgentOutputTool(BaseTool):
             return execution, [], None
 
         # Get completed executions with time filters
-        executions = await exec_db.get_graph_executions(
+        executions = await execution_db.get_graph_executions(
             graph_id=graph_id,
             user_id=user_id,
             statuses=[ExecutionStatus.COMPLETED],
@@ -257,7 +254,7 @@ class AgentOutputTool(BaseTool):
 
         # If only one execution, fetch full details
         if len(executions) == 1:
-            full_execution = await exec_db.get_graph_execution(
+            full_execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=executions[0].id,
                 include_node_executions=False,
@@ -265,7 +262,7 @@ class AgentOutputTool(BaseTool):
             return full_execution, [], None
 
         # Multiple executions - return latest with full details, plus list of available
-        full_execution = await exec_db.get_graph_execution(
+        full_execution = await execution_db.get_graph_execution(
             user_id=user_id,
             execution_id=executions[0].id,
             include_node_executions=False,
@@ -383,7 +380,7 @@ class AgentOutputTool(BaseTool):
             and not input_data.store_slug
         ):
             # Fetch execution directly to get graph_id
-            execution = await execution_db().get_graph_execution(
+            execution = await execution_db.get_graph_execution(
                 user_id=user_id,
                 execution_id=input_data.execution_id,
                 include_node_executions=False,
@@ -395,7 +392,7 @@ class AgentOutputTool(BaseTool):
                 )
 
             # Find library agent by graph_id
-            agent = await library_db().get_library_agent_by_graph_id(
+            agent = await library_db.get_library_agent_by_graph_id(
                 user_id, execution.graph_id
             )
             if not agent:

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

@@ -4,7 +4,8 @@ import logging
 import re
 from typing import Literal
 
-from backend.data.db_accessors import library_db, store_db
+from backend.api.features.library import db as library_db
+from backend.api.features.store import db as store_db
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .models import (
@@ -44,10 +45,8 @@ async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | N
     Returns:
         AgentInfo if found, None otherwise
     """
-    lib_db = library_db()
-
     try:
-        agent = await lib_db.get_library_agent_by_graph_id(user_id, agent_id)
+        agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id)
         if agent:
             logger.debug(f"Found library agent by graph_id: {agent.name}")
             return AgentInfo(
@@ -72,7 +71,7 @@ async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | N
         )
 
     try:
-        agent = await lib_db.get_library_agent(agent_id, user_id)
+        agent = await library_db.get_library_agent(agent_id, user_id)
         if agent:
             logger.debug(f"Found library agent by library_id: {agent.name}")
             return AgentInfo(
@@ -134,7 +133,7 @@ async def search_agents(
     try:
         if source == "marketplace":
             logger.info(f"Searching marketplace for: {query}")
-            results = await store_db().get_store_agents(search_query=query, page_size=5)
+            results = await store_db.get_store_agents(search_query=query, page_size=5)
             for agent in results.agents:
                 agents.append(
                     AgentInfo(
@@ -160,7 +159,7 @@ async def search_agents(
 
             if not agents:
                 logger.info(f"Searching user library for: {query}")
-                results = await library_db().list_library_agents(
+                results = await library_db.list_library_agents(
                     user_id=user_id,  # type: ignore[arg-type]
                     search_term=query,
                     page_size=10,

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

@@ -5,8 +5,8 @@ from typing import Any
 
 from openai.types.chat import ChatCompletionToolParam
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.response_model import StreamToolOutputAvailable
+from backend.api.features.chat.response_model import StreamToolOutputAvailable
 
 from .models import ErrorResponse, NeedLoginResponse, ToolResponseBase
 

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

@@ -11,11 +11,18 @@ available (e.g. macOS development).
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-
+from backend.api.features.chat.tools.base import BaseTool
-from .base import BaseTool
+from backend.api.features.chat.tools.models import (
-from .models import BashExecResponse, ErrorResponse, ToolResponseBase
+    BashExecResponse,
-from .sandbox import get_workspace_dir, has_full_sandbox, run_sandboxed
+    ErrorResponse,
+    ToolResponseBase,
+)
+from backend.api.features.chat.tools.sandbox import (
+    get_workspace_dir,
+    has_full_sandbox,
+    run_sandboxed,
+)
 
 logger = logging.getLogger(__name__)
 

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

@@ -3,10 +3,13 @@
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-
+from backend.api.features.chat.tools.base import BaseTool
-from .base import BaseTool
+from backend.api.features.chat.tools.models import (
-from .models import ErrorResponse, ResponseType, ToolResponseBase
+    ErrorResponse,
+    ResponseType,
+    ToolResponseBase,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -75,7 +78,7 @@ class CheckOperationStatusTool(BaseTool):
         session: ChatSession,
         **kwargs,
     ) -> ToolResponseBase:
-        from backend.copilot import stream_registry
+        from backend.api.features.chat import stream_registry
 
         operation_id = (kwargs.get("operation_id") or "").strip()
         task_id = (kwargs.get("task_id") or "").strip()

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

@@ -3,7 +3,7 @@
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,

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

@@ -3,9 +3,9 @@
 import logging
 from typing import Any
 
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.store import db as store_db
 from backend.api.features.store.exceptions import AgentNotFoundError
-from backend.copilot.model import ChatSession
-from backend.data.db_accessors import store_db as get_store_db
 
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,
@@ -137,8 +137,6 @@ class CustomizeAgentTool(BaseTool):
 
         creator_username, agent_slug = parts
 
-        store_db = get_store_db()
-
         # Fetch the marketplace agent details
         try:
             agent_details = await store_db.get_store_agent_details(

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

@@ -3,7 +3,7 @@
 import logging
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,

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

@@ -5,14 +5,9 @@ from typing import Any
 
 from pydantic import SecretStr
 
-from backend.blocks.linear._api import LinearClient
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool
-from backend.data.db_accessors import user_db
+from backend.api.features.chat.tools.models import (
-from backend.data.model import APIKeyCredentials
-from backend.util.settings import Settings
-
-from .base import BaseTool
-from .models import (
     ErrorResponse,
     FeatureRequestCreatedResponse,
     FeatureRequestInfo,
@@ -20,6 +15,10 @@ from .models import (
     NoResultsResponse,
     ToolResponseBase,
 )
+from backend.blocks.linear._api import LinearClient
+from backend.data.model import APIKeyCredentials
+from backend.data.user import get_user_email_by_id
+from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
 
@@ -105,8 +104,8 @@ def _get_linear_config() -> tuple[LinearClient, str, str]:
     Raises RuntimeError if any required setting is missing.
     """
     secrets = _get_settings().secrets
-    if not secrets.copilot_linear_api_key:
+    if not secrets.linear_api_key:
-        raise RuntimeError("COPILOT_LINEAR_API_KEY is not configured")
+        raise RuntimeError("LINEAR_API_KEY is not configured")
     if not secrets.linear_feature_request_project_id:
         raise RuntimeError("LINEAR_FEATURE_REQUEST_PROJECT_ID is not configured")
     if not secrets.linear_feature_request_team_id:
@@ -115,7 +114,7 @@ def _get_linear_config() -> tuple[LinearClient, str, str]:
     credentials = APIKeyCredentials(
         id="system-linear",
         provider="linear",
-        api_key=SecretStr(secrets.copilot_linear_api_key),
+        api_key=SecretStr(secrets.linear_api_key),
         title="System Linear API Key",
     )
     client = LinearClient(credentials=credentials)
@@ -333,9 +332,7 @@ class CreateFeatureRequestTool(BaseTool):
         # Resolve a human-readable name (email) for the Linear customer record.
         # Fall back to user_id if the lookup fails or returns None.
         try:
-            customer_display_name = (
+            customer_display_name = await get_user_email_by_id(user_id) or user_id
-                await user_db().get_user_email_by_id(user_id) or user_id
-            )
         except Exception:
             customer_display_name = user_id
 

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

@@ -1,18 +1,22 @@
 """Tests for SearchFeatureRequestsTool and CreateFeatureRequestTool."""
 
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import AsyncMock, patch
 
 import pytest
 
-from ._test_data import make_session
+from backend.api.features.chat.tools.feature_requests import (
-from .feature_requests import CreateFeatureRequestTool, SearchFeatureRequestsTool
+    CreateFeatureRequestTool,
-from .models import (
+    SearchFeatureRequestsTool,
+)
+from backend.api.features.chat.tools.models import (
     ErrorResponse,
     FeatureRequestCreatedResponse,
     FeatureRequestSearchResponse,
     NoResultsResponse,
 )
 
+from ._test_data import make_session
+
 _TEST_USER_ID = "test-user-feature-requests"
 _TEST_USER_EMAIL = "testuser@example.com"
 
@@ -35,7 +39,7 @@ def _mock_linear_config(*, query_return=None, mutate_return=None):
         client.mutate.return_value = mutate_return
     return (
         patch(
-            "backend.copilot.tools.feature_requests._get_linear_config",
+            "backend.api.features.chat.tools.feature_requests._get_linear_config",
             return_value=(client, _FAKE_PROJECT_ID, _FAKE_TEAM_ID),
         ),
         client,
@@ -204,7 +208,7 @@ class TestSearchFeatureRequestsTool:
     async def test_linear_client_init_failure(self):
         session = make_session(user_id=_TEST_USER_ID)
         with patch(
-            "backend.copilot.tools.feature_requests._get_linear_config",
+            "backend.api.features.chat.tools.feature_requests._get_linear_config",
             side_effect=RuntimeError("No API key"),
         ):
             tool = SearchFeatureRequestsTool()
@@ -227,11 +231,10 @@ class TestCreateFeatureRequestTool:
 
     @pytest.fixture(autouse=True)
     def _patch_email_lookup(self):
-        mock_user_db = MagicMock()
-        mock_user_db.get_user_email_by_id = AsyncMock(return_value=_TEST_USER_EMAIL)
         with patch(
-            "backend.copilot.tools.feature_requests.user_db",
+            "backend.api.features.chat.tools.feature_requests.get_user_email_by_id",
-            return_value=mock_user_db,
+            new_callable=AsyncMock,
+            return_value=_TEST_USER_EMAIL,
         ):
             yield
 
@@ -344,7 +347,7 @@ class TestCreateFeatureRequestTool:
     async def test_linear_client_init_failure(self):
         session = make_session(user_id=_TEST_USER_ID)
         with patch(
-            "backend.copilot.tools.feature_requests._get_linear_config",
+            "backend.api.features.chat.tools.feature_requests._get_linear_config",
             side_effect=RuntimeError("No API key"),
         ):
             tool = CreateFeatureRequestTool()

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

@@ -2,7 +2,7 @@
 
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
 from .base import BaseTool

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

@@ -3,18 +3,17 @@ from typing import Any
 
 from prisma.enums import ContentType
 
-from backend.blocks import get_block
+from backend.api.features.chat.model import ChatSession
-from backend.blocks._base import BlockType
+from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.tools.models import (
-from backend.data.db_accessors import search
-
-from .base import BaseTool, ToolResponseBase
-from .models import (
     BlockInfoSummary,
     BlockListResponse,
     ErrorResponse,
     NoResultsResponse,
 )
+from backend.api.features.store.hybrid_search import unified_hybrid_search
+from backend.blocks import get_block
+from backend.blocks._base import BlockType
 
 logger = logging.getLogger(__name__)
 
@@ -108,7 +107,7 @@ class FindBlockTool(BaseTool):
 
         try:
             # Search for blocks using hybrid search
-            results, total = await search().unified_hybrid_search(
+            results, total = await unified_hybrid_search(
                 query=query,
                 content_types=[ContentType.BLOCK],
                 page=1,

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

@@ -4,15 +4,15 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.blocks._base import BlockType
+from backend.api.features.chat.tools.find_block import (
-
-from ._test_data import make_session
-from .find_block import (
     COPILOT_EXCLUDED_BLOCK_IDS,
     COPILOT_EXCLUDED_BLOCK_TYPES,
     FindBlockTool,
 )
-from .models import BlockListResponse
+from backend.api.features.chat.tools.models import BlockListResponse
+from backend.blocks._base import BlockType
+
+from ._test_data import make_session
 
 _TEST_USER_ID = "test-user-find-block"
 
@@ -84,17 +84,13 @@ class TestFindBlockFiltering:
                 "standard-block-id": standard_block,
             }.get(block_id)
 
-        mock_search_db = MagicMock()
-        mock_search_db.unified_hybrid_search = AsyncMock(
-            return_value=(search_results, 2)
-        )
-
         with patch(
-            "backend.copilot.tools.find_block.search",
+            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            return_value=mock_search_db,
+            new_callable=AsyncMock,
+            return_value=(search_results, 2),
         ):
             with patch(
-                "backend.copilot.tools.find_block.get_block",
+                "backend.api.features.chat.tools.find_block.get_block",
                 side_effect=mock_get_block,
             ):
                 tool = FindBlockTool()
@@ -132,17 +128,13 @@ class TestFindBlockFiltering:
                 "normal-block-id": normal_block,
             }.get(block_id)
 
-        mock_search_db = MagicMock()
-        mock_search_db.unified_hybrid_search = AsyncMock(
-            return_value=(search_results, 2)
-        )
-
         with patch(
-            "backend.copilot.tools.find_block.search",
+            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            return_value=mock_search_db,
+            new_callable=AsyncMock,
+            return_value=(search_results, 2),
         ):
             with patch(
-                "backend.copilot.tools.find_block.get_block",
+                "backend.api.features.chat.tools.find_block.get_block",
                 side_effect=mock_get_block,
             ):
                 tool = FindBlockTool()
@@ -361,16 +353,12 @@ class TestFindBlockFiltering:
             for d in block_defs
         }
 
-        mock_search_db = MagicMock()
-        mock_search_db.unified_hybrid_search = AsyncMock(
-            return_value=(search_results, len(search_results))
-        )
-
         with patch(
-            "backend.copilot.tools.find_block.search",
+            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            return_value=mock_search_db,
+            new_callable=AsyncMock,
+            return_value=(search_results, len(search_results)),
         ), patch(
-            "backend.copilot.tools.find_block.get_block",
+            "backend.api.features.chat.tools.find_block.get_block",
             side_effect=lambda bid: mock_blocks.get(bid),
         ):
             tool = FindBlockTool()

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

@@ -2,7 +2,7 @@
 
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
 from .base import BaseTool

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

@@ -4,10 +4,13 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-
+from backend.api.features.chat.tools.base import BaseTool
-from .base import BaseTool
+from backend.api.features.chat.tools.models import (
-from .models import DocPageResponse, ErrorResponse, ToolResponseBase
+    DocPageResponse,
+    ErrorResponse,
+    ToolResponseBase,
+)
 
 logger = logging.getLogger(__name__)
 

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

@@ -5,12 +5,16 @@ from typing import Any
 
 from pydantic import BaseModel, Field, field_validator
 
-from backend.copilot.config import ChatConfig
+from backend.api.features.chat.config import ChatConfig
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.copilot.tracking import track_agent_run_success, track_agent_scheduled
+from backend.api.features.chat.tracking import (
-from backend.data.db_accessors import graph_db, library_db, user_db
+    track_agent_run_success,
+    track_agent_scheduled,
+)
+from backend.api.features.library import db as library_db
 from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
+from backend.data.user import get_user_by_id
 from backend.executor import utils as execution_utils
 from backend.util.clients import get_scheduler_client
 from backend.util.exceptions import DatabaseError, NotFoundError
@@ -196,7 +200,7 @@ class RunAgentTool(BaseTool):
 
             # Priority: library_agent_id if provided
             if has_library_id:
-                library_agent = await library_db().get_library_agent(
+                library_agent = await library_db.get_library_agent(
                     params.library_agent_id, user_id
                 )
                 if not library_agent:
@@ -205,7 +209,9 @@ class RunAgentTool(BaseTool):
                         session_id=session_id,
                     )
                 # Get the graph from the library agent
-                graph = await graph_db().get_graph(
+                from backend.data.graph import get_graph
+
+                graph = await get_graph(
                     library_agent.graph_id,
                     library_agent.graph_version,
                     user_id=user_id,
@@ -516,7 +522,7 @@ class RunAgentTool(BaseTool):
         library_agent = await get_or_create_library_agent(graph, user_id)
 
         # Get user timezone
-        user = await user_db().get_user_by_id(user_id)
+        user = await get_user_by_id(user_id)
         user_timezone = get_user_timezone_or_utc(user.timezone if user else timezone)
 
         # Create schedule

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

@@ -7,17 +7,20 @@ from typing import Any
 
 from pydantic_core import PydanticUndefined
 
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.find_block import (
+    COPILOT_EXCLUDED_BLOCK_IDS,
+    COPILOT_EXCLUDED_BLOCK_TYPES,
+)
 from backend.blocks import get_block
 from backend.blocks._base import AnyBlockSchema
-from backend.copilot.model import ChatSession
-from backend.data.db_accessors import workspace_db
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 
 from .base import BaseTool
-from .find_block import COPILOT_EXCLUDED_BLOCK_IDS, COPILOT_EXCLUDED_BLOCK_TYPES
 from .helpers import get_inputs_from_schema
 from .models import (
     BlockDetails,
@@ -273,7 +276,7 @@ class RunBlockTool(BaseTool):
 
         try:
             # Get or create user's workspace for CoPilot file operations
-            workspace = await workspace_db().get_or_create_workspace(user_id)
+            workspace = await get_or_create_workspace(user_id)
 
             # Generate synthetic IDs for CoPilot context
             # Each chat session is treated as its own agent with one continuous run

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

@@ -4,16 +4,16 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.blocks._base import BlockType
+from backend.api.features.chat.tools.models import (
-
-from ._test_data import make_session
-from .models import (
     BlockDetailsResponse,
     BlockOutputResponse,
     ErrorResponse,
     InputValidationErrorResponse,
 )
-from .run_block import RunBlockTool
+from backend.api.features.chat.tools.run_block import RunBlockTool
+from backend.blocks._base import BlockType
+
+from ._test_data import make_session
 
 _TEST_USER_ID = "test-user-run-block"
 
@@ -77,7 +77,7 @@ class TestRunBlockFiltering:
         input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=input_block,
         ):
             tool = RunBlockTool()
@@ -103,7 +103,7 @@ class TestRunBlockFiltering:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=smart_block,
         ):
             tool = RunBlockTool()
@@ -127,7 +127,7 @@ class TestRunBlockFiltering:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=standard_block,
         ):
             tool = RunBlockTool()
@@ -183,7 +183,7 @@ class TestRunBlockInputValidation:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=mock_block,
         ):
             tool = RunBlockTool()
@@ -222,7 +222,7 @@ class TestRunBlockInputValidation:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=mock_block,
         ):
             tool = RunBlockTool()
@@ -263,7 +263,7 @@ class TestRunBlockInputValidation:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=mock_block,
         ):
             tool = RunBlockTool()
@@ -302,19 +302,15 @@ class TestRunBlockInputValidation:
 
         mock_block.execute = mock_execute
 
-        mock_workspace_db = MagicMock()
-        mock_workspace_db.get_or_create_workspace = AsyncMock(
-            return_value=MagicMock(id="test-workspace-id")
-        )
-
         with (
             patch(
-                "backend.copilot.tools.run_block.get_block",
+                "backend.api.features.chat.tools.run_block.get_block",
                 return_value=mock_block,
             ),
             patch(
-                "backend.copilot.tools.run_block.workspace_db",
+                "backend.api.features.chat.tools.run_block.get_or_create_workspace",
-                return_value=mock_workspace_db,
+                new_callable=AsyncMock,
+                return_value=MagicMock(id="test-workspace-id"),
             ),
         ):
             tool = RunBlockTool()
@@ -348,7 +344,7 @@ class TestRunBlockInputValidation:
         )
 
         with patch(
-            "backend.copilot.tools.run_block.get_block",
+            "backend.api.features.chat.tools.run_block.get_block",
             return_value=mock_block,
         ):
             tool = RunBlockTool()

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

@@ -5,17 +5,16 @@ from typing import Any
 
 from prisma.enums import ContentType
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.data.db_accessors import search
+from backend.api.features.chat.tools.base import BaseTool
-
+from backend.api.features.chat.tools.models import (
-from .base import BaseTool
-from .models import (
     DocSearchResult,
     DocSearchResultsResponse,
     ErrorResponse,
     NoResultsResponse,
     ToolResponseBase,
 )
+from backend.api.features.store.hybrid_search import unified_hybrid_search
 
 logger = logging.getLogger(__name__)
 
@@ -118,7 +117,7 @@ class SearchDocsTool(BaseTool):
 
         try:
             # Search using hybrid search for DOCUMENTATION content type only
-            results, total = await search().unified_hybrid_search(
+            results, total = await unified_hybrid_search(
                 query=query,
                 content_types=[ContentType.DOCUMENTATION],
                 page=1,

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

@@ -4,13 +4,13 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from backend.api.features.chat.tools.models import BlockDetailsResponse
+from backend.api.features.chat.tools.run_block import RunBlockTool
 from backend.blocks._base import BlockType
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.providers import ProviderName
 
 from ._test_data import make_session
-from .models import BlockDetailsResponse
-from .run_block import RunBlockTool
 
 _TEST_USER_ID = "test-user-run-block-details"
 
@@ -61,7 +61,7 @@ async def test_run_block_returns_details_when_no_input_provided():
     )
 
     with patch(
-        "backend.copilot.tools.run_block.get_block",
+        "backend.api.features.chat.tools.run_block.get_block",
         return_value=http_block,
     ):
         # Mock credentials check to return no missing credentials
@@ -120,7 +120,7 @@ async def test_run_block_returns_details_when_only_credentials_provided():
     }
 
     with patch(
-        "backend.copilot.tools.run_block.get_block",
+        "backend.api.features.chat.tools.run_block.get_block",
         return_value=mock,
     ):
         with patch.object(

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

@@ -3,8 +3,9 @@
 import logging
 from typing import Any
 
+from backend.api.features.library import db as library_db
 from backend.api.features.library import model as library_model
-from backend.data.db_accessors import library_db, store_db
+from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
     Credentials,
@@ -38,14 +39,13 @@ async def fetch_graph_from_store_slug(
     Raises:
         DatabaseError: If there's a database error during lookup.
     """
-    sdb = store_db()
     try:
-        store_agent = await sdb.get_store_agent_details(username, agent_name)
+        store_agent = await store_db.get_store_agent_details(username, agent_name)
     except NotFoundError:
         return None, None
 
     # Get the graph from store listing version
-    graph = await sdb.get_available_graph(
+    graph = await store_db.get_available_graph(
         store_agent.store_listing_version_id, hide_nodes=False
     )
     return graph, store_agent
@@ -210,13 +210,13 @@ async def get_or_create_library_agent(
     Returns:
         LibraryAgent instance
     """
-    existing = await library_db().get_library_agent_by_graph_id(
+    existing = await library_db.get_library_agent_by_graph_id(
         graph_id=graph.id, user_id=user_id
     )
     if existing:
         return existing
 
-    library_agents = await library_db().create_library_agent(
+    library_agents = await library_db.create_library_agent(
         graph=graph,
         user_id=user_id,
         create_library_agents_for_sub_graphs=False,

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

@@ -6,12 +6,15 @@ from typing import Any
 import aiohttp
 import html2text
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool
+from backend.api.features.chat.tools.models import (
+    ErrorResponse,
+    ToolResponseBase,
+    WebFetchResponse,
+)
 from backend.util.request import Requests
 
-from .base import BaseTool
-from .models import ErrorResponse, ToolResponseBase, WebFetchResponse
-
 logger = logging.getLogger(__name__)
 
 # Limits

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

@@ -6,8 +6,8 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from backend.copilot.model import ChatSession
+from backend.api.features.chat.model import ChatSession
-from backend.data.db_accessors import workspace_db
+from backend.data.workspace import get_or_create_workspace
 from backend.util.settings import Config
 from backend.util.virus_scanner import scan_content_safe
 from backend.util.workspace import WorkspaceManager
@@ -148,7 +148,7 @@ class ListWorkspaceFilesTool(BaseTool):
         include_all_sessions: bool = kwargs.get("include_all_sessions", False)
 
         try:
-            workspace = await workspace_db().get_or_create_workspace(user_id)
+            workspace = await get_or_create_workspace(user_id)
             # Pass session_id for session-scoped file access
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 
@@ -167,8 +167,8 @@ class ListWorkspaceFilesTool(BaseTool):
                     file_id=f.id,
                     name=f.name,
                     path=f.path,
-                    mime_type=f.mime_type,
+                    mime_type=f.mimeType,
-                    size_bytes=f.size_bytes,
+                    size_bytes=f.sizeBytes,
                 )
                 for f in files
             ]
@@ -284,7 +284,7 @@ class ReadWorkspaceFileTool(BaseTool):
             )
 
         try:
-            workspace = await workspace_db().get_or_create_workspace(user_id)
+            workspace = await get_or_create_workspace(user_id)
             # Pass session_id for session-scoped file access
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 
@@ -309,8 +309,8 @@ class ReadWorkspaceFileTool(BaseTool):
                 target_file_id = file_info.id
 
             # Decide whether to return inline content or metadata+URL
-            is_small_file = file_info.size_bytes <= self.MAX_INLINE_SIZE_BYTES
+            is_small_file = file_info.sizeBytes <= self.MAX_INLINE_SIZE_BYTES
-            is_text_file = self._is_text_mime_type(file_info.mime_type)
+            is_text_file = self._is_text_mime_type(file_info.mimeType)
 
             # Return inline content for small text files (unless force_download_url)
             if is_small_file and is_text_file and not force_download_url:
@@ -321,7 +321,7 @@ class ReadWorkspaceFileTool(BaseTool):
                     file_id=file_info.id,
                     name=file_info.name,
                     path=file_info.path,
-                    mime_type=file_info.mime_type,
+                    mime_type=file_info.mimeType,
                     content_base64=content_b64,
                     message=f"Successfully read file: {file_info.name}",
                     session_id=session_id,
@@ -350,11 +350,11 @@ class ReadWorkspaceFileTool(BaseTool):
                 file_id=file_info.id,
                 name=file_info.name,
                 path=file_info.path,
-                mime_type=file_info.mime_type,
+                mime_type=file_info.mimeType,
-                size_bytes=file_info.size_bytes,
+                size_bytes=file_info.sizeBytes,
                 download_url=download_url,
                 preview=preview,
-                message=f"File: {file_info.name} ({file_info.size_bytes} bytes). Use download_url to retrieve content.",
+                message=f"File: {file_info.name} ({file_info.sizeBytes} bytes). Use download_url to retrieve content.",
                 session_id=session_id,
             )
 
@@ -484,7 +484,7 @@ class WriteWorkspaceFileTool(BaseTool):
             # Virus scan
             await scan_content_safe(content, filename=filename)
 
-            workspace = await workspace_db().get_or_create_workspace(user_id)
+            workspace = await get_or_create_workspace(user_id)
             # Pass session_id for session-scoped file access
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 
@@ -500,7 +500,7 @@ class WriteWorkspaceFileTool(BaseTool):
                 file_id=file_record.id,
                 name=file_record.name,
                 path=file_record.path,
-                size_bytes=file_record.size_bytes,
+                size_bytes=file_record.sizeBytes,
                 message=f"Successfully wrote file: {file_record.name}",
                 session_id=session_id,
             )
@@ -583,7 +583,7 @@ class DeleteWorkspaceFileTool(BaseTool):
             )
 
         try:
-            workspace = await workspace_db().get_or_create_workspace(user_id)
+            workspace = await get_or_create_workspace(user_id)
             # Pass session_id for session-scoped file access
             manager = WorkspaceManager(user_id, workspace.id, session_id)
 

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

@@ -393,6 +393,7 @@ 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/features/workspace/routes.py

@@ -11,7 +11,7 @@ import fastapi
 from autogpt_libs.auth.dependencies import get_user_id, requires_user
 from fastapi.responses import Response
 
-from backend.data.workspace import WorkspaceFile, get_workspace, get_workspace_file
+from backend.data.workspace import get_workspace, get_workspace_file
 from backend.util.workspace_storage import get_workspace_storage
 
 
@@ -44,11 +44,11 @@ router = fastapi.APIRouter(
 )
 
 
-def _create_streaming_response(content: bytes, file: WorkspaceFile) -> Response:
+def _create_streaming_response(content: bytes, file) -> Response:
     """Create a streaming response for file content."""
     return Response(
         content=content,
-        media_type=file.mime_type,
+        media_type=file.mimeType,
         headers={
             "Content-Disposition": _sanitize_filename_for_header(file.name),
             "Content-Length": str(len(content)),
@@ -56,7 +56,7 @@ def _create_streaming_response(content: bytes, file: WorkspaceFile) -> Response:
     )
 
 
-async def _create_file_download_response(file: WorkspaceFile) -> Response:
+async def _create_file_download_response(file) -> Response:
     """
     Create a download response for a workspace file.
 
@@ -66,33 +66,33 @@ async def _create_file_download_response(file: WorkspaceFile) -> Response:
     storage = await get_workspace_storage()
 
     # For local storage, stream the file directly
-    if file.storage_path.startswith("local://"):
+    if file.storagePath.startswith("local://"):
-        content = await storage.retrieve(file.storage_path)
+        content = await storage.retrieve(file.storagePath)
         return _create_streaming_response(content, file)
 
     # For GCS, try to redirect to signed URL, fall back to streaming
     try:
-        url = await storage.get_download_url(file.storage_path, expires_in=300)
+        url = await storage.get_download_url(file.storagePath, expires_in=300)
         # If we got back an API path (fallback), stream directly instead
         if url.startswith("/api/"):
-            content = await storage.retrieve(file.storage_path)
+            content = await storage.retrieve(file.storagePath)
             return _create_streaming_response(content, file)
         return fastapi.responses.RedirectResponse(url=url, status_code=302)
     except Exception as e:
         # Log the signed URL failure with context
         logger.error(
             f"Failed to get signed URL for file {file.id} "
-            f"(storagePath={file.storage_path}): {e}",
+            f"(storagePath={file.storagePath}): {e}",
             exc_info=True,
         )
         # Fall back to streaming directly from GCS
         try:
-            content = await storage.retrieve(file.storage_path)
+            content = await storage.retrieve(file.storagePath)
             return _create_streaming_response(content, file)
         except Exception as fallback_error:
             logger.error(
                 f"Fallback streaming also failed for file {file.id} "
-                f"(storagePath={file.storage_path}): {fallback_error}",
+                f"(storagePath={file.storagePath}): {fallback_error}",
                 exc_info=True,
             )
             raise

autogpt_platform/backend/backend/api/rest_api.py

@@ -18,6 +18,7 @@ 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
@@ -39,13 +40,15 @@ 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.blocks.llm import DEFAULT_LLM_MODEL
+from backend.api.features.chat.completion_consumer import (
-from backend.copilot.completion_consumer import (
     start_completion_consumer,
     stop_completion_consumer,
 )
+from backend.data import llm_registry
+from backend.data.block_cost_config import refresh_llm_costs
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.monitoring.instrumentation import instrument_fastapi
@@ -116,11 +119,27 @@ 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()
+    await refresh_llm_costs()
+
+    # Clear block schema caches so they're regenerated with updated discriminator_mapping
+    from backend.blocks._base 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()
-    await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
+    # 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.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
     # Start chat completion consumer for Redis Streams notifications
@@ -322,6 +341,16 @@ 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

@@ -79,11 +79,49 @@ async def event_broadcaster(manager: ConnectionManager):
                     payload=notification.payload,
                 )
 
-        await asyncio.gather(execution_worker(), notification_worker())
+        # Track registry pubsub for cleanup
+        registry_pubsub = None
+
+        async def registry_refresh_worker():
+            """Listen for LLM registry refresh notifications and broadcast to all clients."""
+            nonlocal registry_pubsub
+            from backend.data.llm_registry import REGISTRY_REFRESH_CHANNEL
+            from backend.data.redis_client import connect_async
+
+            redis = await connect_async()
+            registry_pubsub = redis.pubsub()
+            await registry_pubsub.subscribe(REGISTRY_REFRESH_CHANNEL)
+            logger.info(
+                "Subscribed to LLM registry refresh notifications for WebSocket broadcast"
+            )
+
+            async for message in registry_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(),
+        )
     finally:
         # Ensure PubSub connections are closed on any exit to prevent leaks
         await execution_bus.close()
         await notification_bus.close()
+        if registry_pubsub:
+            await registry_pubsub.close()
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/app.py

@@ -38,9 +38,7 @@ def main(**kwargs):
 
     from backend.api.rest_api import AgentServer
     from backend.api.ws_api import WebsocketServer
-    from backend.copilot.executor.manager import CoPilotExecutor
+    from backend.executor import DatabaseManager, ExecutionManager, Scheduler
-    from backend.data.db_manager import DatabaseManager
-    from backend.executor import ExecutionManager, Scheduler
     from backend.notifications import NotificationManager
 
     run_processes(
@@ -50,7 +48,6 @@ def main(**kwargs):
         WebsocketServer(),
         AgentServer(),
         ExecutionManager(),
-        CoPilotExecutor(),
         **kwargs,
     )
 

autogpt_platform/backend/backend/blocks/_base.py

@@ -134,7 +134,26 @@ class BlockInfo(BaseModel):
 
 
 class BlockSchema(BaseModel):
-    cached_jsonschema: ClassVar[dict[str, Any]]
+    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)
 
     @classmethod
     def jsonschema(cls) -> dict[str, Any]:
@@ -225,7 +244,8 @@ class BlockSchema(BaseModel):
         super().__pydantic_init_subclass__(**kwargs)
 
         # Reset cached JSON schema to prevent inheriting it from parent class
-        cls.cached_jsonschema = {}
+        # Use None instead of {} because {} is truthy and would prevent regeneration
+        cls.cached_jsonschema = None
 
         credentials_fields = cls.get_credentials_fields()
 

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -7,7 +7,6 @@ from backend.blocks._base import (
     BlockSchemaOutput,
 )
 from backend.blocks.llm import (
-    DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
     AIBlockBase,
@@ -16,6 +15,7 @@ from backend.blocks.llm import (
     LlmModel,
     LLMResponse,
     llm_call,
+    llm_model_schema_extra,
 )
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
 
@@ -50,9 +50,10 @@ class AIConditionBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             description="The language model to use for evaluating the condition.",
             advanced=False,
+            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
 
@@ -82,7 +83,7 @@ class AIConditionBlock(AIBlockBase):
                 "condition": "the input is an email address",
                 "yes_value": "Valid email",
                 "no_value": "Not an email",
-                "model": DEFAULT_LLM_MODEL,
+                "model": LlmModel.default(),
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,

autogpt_platform/backend/backend/blocks/llm.py

@@ -4,16 +4,18 @@ import logging
 import re
 import secrets
 from abc import ABC
-from enum import Enum, EnumMeta
+from dataclasses import dataclass
+from enum import Enum
 from json import JSONDecodeError
-from typing import Any, Iterable, List, Literal, NamedTuple, Optional
+from typing import Any, Iterable, List, Literal, Optional
 
 import anthropic
 import ollama
 import openai
 from anthropic.types import ToolParam
 from groq import AsyncGroq
-from pydantic import BaseModel, SecretStr
+from pydantic import BaseModel, GetCoreSchemaHandler, SecretStr
+from pydantic_core import CoreSchema, core_schema
 
 from backend.blocks._base import (
     Block,
@@ -22,6 +24,8 @@ from backend.blocks._base import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.data import llm_registry
+from backend.data.llm_registry import ModelMetadata
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -66,114 +70,123 @@ 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={
+        discriminator_mapping=mapping,  # May be empty initially, refreshed later
-            model.value: model.metadata.provider for model in LlmModel
-        },
     )
 
 
-class ModelMetadata(NamedTuple):
+def llm_model_schema_extra() -> dict[str, Any]:
-    provider: str
+    return {"options": llm_registry.get_llm_model_schema_options()}
-    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(EnumMeta):
+class LlmModelMeta(type):
-    pass
+    """
+    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
+        slug = name.lower().replace("_", "-")
+
+        # Check for exact match in registry first (e.g., "o1" stays "o1")
+        registry_slugs = llm_registry.get_dynamic_model_slugs()
+        if slug in registry_slugs:
+            return cls(slug)
+
+        # If no exact match, try inserting hyphen between letter and digit
+        # e.g., gpt4o -> gpt-4o
+        transformed_slug = re.sub(r"([a-z])(\d)", r"\1-\2", slug)
+        return cls(transformed_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 LlmModel(str, Enum, metaclass=LlmModelMeta):
+class LlmModel(str, metaclass=LlmModelMeta):
-    # OpenAI models
+    """
-    O3_MINI = "o3-mini"
+    Dynamic LLM model type that accepts any model slug from the registry.
-    O3 = "o3-2025-04-16"
+
-    O1 = "o1"
+    This is a string subclass (not an Enum) that allows any model slug value.
-    O1_MINI = "o1-mini"
+    All models are managed via the LLM Registry in the database.
-    # GPT-5 models
+
-    GPT5_2 = "gpt-5.2-2025-12-11"
+    Usage:
-    GPT5_1 = "gpt-5.1-2025-11-13"
+        model = LlmModel("gpt-4o")  # Direct construction
-    GPT5 = "gpt-5-2025-08-07"
+        model = LlmModel.GPT4O      # Attribute access (converted to "gpt-4o")
-    GPT5_MINI = "gpt-5-mini-2025-08-07"
+        model.value                  # Returns the slug string
-    GPT5_NANO = "gpt-5-nano-2025-08-07"
+        model.provider               # Returns the provider from registry
-    GPT5_CHAT = "gpt-5-chat-latest"
+    """
-    GPT41 = "gpt-4.1-2025-04-14"
+
-    GPT41_MINI = "gpt-4.1-mini-2025-04-14"
+    def __new__(cls, value: str):
-    GPT4O_MINI = "gpt-4o-mini"
+        if isinstance(value, LlmModel):
-    GPT4O = "gpt-4o"
+            return value
-    GPT4_TURBO = "gpt-4-turbo"
+        return str.__new__(cls, value)
-    GPT3_5_TURBO = "gpt-3.5-turbo"
+
-    # Anthropic models
+    @classmethod
-    CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
+    def __get_pydantic_core_schema__(
-    CLAUDE_4_OPUS = "claude-opus-4-20250514"
+        cls, source_type: Any, handler: GetCoreSchemaHandler
-    CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
+    ) -> CoreSchema:
-    CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
+        """
-    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
+        Tell Pydantic how to validate LlmModel.
-    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
+
-    CLAUDE_4_6_OPUS = "claude-opus-4-6"
+        Accepts strings and converts them to LlmModel instances.
-    CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
+        """
-    # AI/ML API models
+        return core_schema.no_info_after_validator_function(
-    AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
+            cls,  # The validator function (LlmModel constructor)
-    AIML_API_LLAMA3_1_70B = "nvidia/llama-3.1-nemotron-70b-instruct"
+            core_schema.str_schema(),  # Accept string input
-    AIML_API_LLAMA3_3_70B = "meta-llama/Llama-3.3-70B-Instruct-Turbo"
+            serialization=core_schema.to_string_ser_schema(),  # Serialize as string
-    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
+    @property
-    LLAMA3_3_70B = "llama-3.3-70b-versatile"
+    def value(self) -> str:
-    LLAMA3_1_8B = "llama-3.1-8b-instant"
+        """Return the model slug (for compatibility with enum-style access)."""
-    # Ollama models
+        return str(self)
-    OLLAMA_LLAMA3_3 = "llama3.3"
+
-    OLLAMA_LLAMA3_2 = "llama3.2"
+    @classmethod
-    OLLAMA_LLAMA3_8B = "llama3"
+    def default(cls) -> "LlmModel":
-    OLLAMA_LLAMA3_405B = "llama3.1:405b"
+        """
-    OLLAMA_DOLPHIN = "dolphin-mistral:latest"
+        Get the default model from the registry.
-    # OpenRouter models
+
-    OPENAI_GPT_OSS_120B = "openai/gpt-oss-120b"
+        Returns the recommended model if set, otherwise gpt-4o if available
-    OPENAI_GPT_OSS_20B = "openai/gpt-oss-20b"
+        and enabled, otherwise the first enabled model from the registry.
-    GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
+        Falls back to "gpt-4o" if registry is empty (e.g., at module import time).
-    GEMINI_3_PRO_PREVIEW = "google/gemini-3-pro-preview"
+        """
-    GEMINI_2_5_FLASH = "google/gemini-2.5-flash"
+        from backend.data.llm_registry import get_default_model_slug
-    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"
+        slug = get_default_model_slug()
-    GEMINI_2_0_FLASH_LITE = "google/gemini-2.0-flash-lite-001"
+        if slug is None:
-    MISTRAL_NEMO = "mistralai/mistral-nemo"
+            # Registry is empty (e.g., at module import time before DB connection).
-    COHERE_COMMAND_R_08_2024 = "cohere/command-r-08-2024"
+            # Fall back to gpt-4o for backward compatibility.
-    COHERE_COMMAND_R_PLUS_08_2024 = "cohere/command-r-plus-08-2024"
+            slug = "gpt-4o"
-    DEEPSEEK_CHAT = "deepseek/deepseek-chat"  # Actually: DeepSeek V3
+        return cls(slug)
-    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):
@@ -181,7 +194,15 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
         llm_model_metadata = {}
         for model in cls:
             model_name = model.value
-            metadata = model.metadata
+            # Skip disabled models - only show enabled models in the picker
+            if not llm_registry.is_model_enabled(model_name):
+                continue
+            # 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
             llm_model_metadata[model_name] = {
                 "creator": metadata.creator_name,
                 "creator_name": metadata.creator_name,
@@ -197,7 +218,12 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
 
     @property
     def metadata(self) -> ModelMetadata:
-        return MODEL_METADATA[self]
+        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."
+        )
 
     @property
     def provider(self) -> str:
@@ -212,300 +238,125 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
         return self.metadata.max_output_tokens
 
 
-MODEL_METADATA = {
+# Default model constant for backward compatibility
-    # https://platform.openai.com/docs/models
+# Uses the dynamic registry to get the default model
-    LlmModel.O3: ModelMetadata("openai", 200000, 100000, "O3", "OpenAI", "OpenAI", 2),
+DEFAULT_LLM_MODEL = LlmModel.default()
-    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_6_OPUS: ModelMetadata(
-        "anthropic", 200000, 128000, "Claude Opus 4.6", "Anthropic", "Anthropic", 3
-    ),  # claude-opus-4-6
-    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_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_LLM_MODEL = LlmModel.GPT5_2
 
-for model in LlmModel:
+class ModelUnavailableError(ValueError):
-    if model not in MODEL_METADATA:
+    """Raised when a requested LLM model cannot be resolved for use."""
-        raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
+
+    pass
+
+
+@dataclass
+class ResolvedModel:
+    """Result of resolving a model for an LLM call."""
+
+    slug: str  # The actual model slug to use (may differ from requested if fallback)
+    provider: str
+    context_window: int
+    max_output_tokens: int
+    used_fallback: bool = False
+    original_slug: str | None = None  # Set if fallback was used
+
+
+async def resolve_model_for_call(llm_model: LlmModel) -> ResolvedModel:
+    """
+    Resolve a model for use in an LLM call.
+
+    Handles:
+    - Checking if the model exists in the registry
+    - Falling back to an enabled model from the same provider if disabled
+    - Refreshing the registry cache if model not found (with DB access)
+
+    Args:
+        llm_model: The requested LlmModel
+
+    Returns:
+        ResolvedModel with all necessary metadata for the call
+
+    Raises:
+        ModelUnavailableError: If model cannot be resolved (not found, disabled with no fallback)
+    """
+    from backend.data.llm_registry import (
+        get_fallback_model_for_disabled,
+        get_model_info,
+    )
+
+    model_info = get_model_info(llm_model.value)
+
+    # Case 1: Model found and disabled - try fallback
+    if model_info and not model_info.is_enabled:
+        fallback = get_fallback_model_for_disabled(llm_model.value)
+        if fallback:
+            logger.warning(
+                f"Model '{llm_model.value}' is disabled. Using fallback "
+                f"'{fallback.slug}' from same provider ({fallback.metadata.provider})."
+            )
+            return ResolvedModel(
+                slug=fallback.slug,
+                provider=fallback.metadata.provider,
+                context_window=fallback.metadata.context_window,
+                max_output_tokens=fallback.metadata.max_output_tokens or 2**15,
+                used_fallback=True,
+                original_slug=llm_model.value,
+            )
+        raise ModelUnavailableError(
+            f"Model '{llm_model.value}' is disabled and no fallback from the same "
+            f"provider is available. Enable the model or select a different one."
+        )
+
+    # Case 2: Model found and enabled - use it directly
+    if model_info:
+        return ResolvedModel(
+            slug=llm_model.value,
+            provider=model_info.metadata.provider,
+            context_window=model_info.metadata.context_window,
+            max_output_tokens=model_info.metadata.max_output_tokens or 2**15,
+        )
+
+    # Case 3: Model not in registry - try refresh if DB available
+    logger.warning(f"Model '{llm_model.value}' not found in registry cache")
+
+    from backend.data.db import is_connected
+
+    if not is_connected():
+        raise ModelUnavailableError(
+            f"Model '{llm_model.value}' not found in registry. "
+            f"The registry may need to be refreshed via the admin UI."
+        )
+
+    # Try refreshing the registry
+    try:
+        logger.info(f"Refreshing LLM registry for model '{llm_model.value}'")
+        await llm_registry.refresh_llm_registry()
+    except Exception as e:
+        raise ModelUnavailableError(
+            f"Model '{llm_model.value}' not found and registry refresh failed: {e}"
+        ) from e
+
+    # Check again after refresh
+    model_info = get_model_info(llm_model.value)
+    if not model_info:
+        raise ModelUnavailableError(
+            f"Model '{llm_model.value}' not found in registry. "
+            f"Add it via the admin UI at /admin/llms."
+        )
+
+    if not model_info.is_enabled:
+        raise ModelUnavailableError(
+            f"Model '{llm_model.value}' exists but is disabled. "
+            f"Enable it via the admin UI at /admin/llms."
+        )
+
+    logger.info(f"Model '{llm_model.value}' loaded after registry refresh")
+    return ResolvedModel(
+        slug=llm_model.value,
+        provider=model_info.metadata.provider,
+        context_window=model_info.metadata.context_window,
+        max_output_tokens=model_info.metadata.max_output_tokens or 2**15,
+    )
 
 
 class ToolCall(BaseModel):
@@ -531,12 +382,12 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.Omit:
+) -> Iterable[ToolParam] | anthropic.NotGiven:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
     if not openai_tools or len(openai_tools) == 0:
-        return anthropic.omit
+        return anthropic.NOT_GIVEN
 
     anthropic_tools = []
     for tool in openai_tools:
@@ -598,7 +449,12 @@ def get_parallel_tool_calls_param(
     llm_model: LlmModel, parallel_tool_calls: bool | None
 ) -> bool | openai.Omit:
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
-    if llm_model.startswith("o") or parallel_tool_calls is None:
+    # Check for o-series models (o1, o1-mini, o3-mini, etc.) which don't support
+    # parallel tool calls. Handle both bare slugs ("o1-mini") and provider-prefixed
+    # slugs ("openai/o1-mini"). The pattern matches "o" followed by a digit at the
+    # start of the string or after a "/" separator.
+    is_o_series = re.search(r"(^|/)o\d", llm_model) is not None
+    if is_o_series or parallel_tool_calls is None:
         return openai.omit
     return parallel_tool_calls
 
@@ -634,15 +490,22 @@ async def llm_call(
             - prompt_tokens: The number of tokens used in the prompt.
             - completion_tokens: The number of tokens used in the completion.
     """
-    provider = llm_model.metadata.provider
+    # Resolve the model - handles disabled models, fallbacks, and cache misses
-    context_window = llm_model.context_window
+    resolved = await resolve_model_for_call(llm_model)
+
+    model_to_use = resolved.slug
+    provider = resolved.provider
+    context_window = resolved.context_window
+    model_max_output = resolved.max_output_tokens
+
+    # Create effective model for model-specific parameter resolution (e.g., o-series check)
+    effective_model = LlmModel(model_to_use)
 
     if compress_prompt_to_fit:
         result = await compress_context(
             messages=prompt,
-            target_tokens=llm_model.context_window // 2,
+            target_tokens=context_window // 2,
             client=None,  # Truncation-only, no LLM summarization
-            reserve=0,  # Caller handles response token budget separately
         )
         if result.error:
             logger.warning(
@@ -653,7 +516,7 @@ async def llm_call(
 
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)
-    model_max_output = llm_model.max_output_tokens or int(2**15)
+    # model_max_output already set above
     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)
@@ -664,14 +527,14 @@ async def llm_call(
         response_format = None
 
         parallel_tool_calls = get_parallel_tool_calls_param(
-            llm_model, parallel_tool_calls
+            effective_model, parallel_tool_calls
         )
 
         if force_json_output:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
-            model=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_completion_tokens=max_tokens,
@@ -718,7 +581,7 @@ async def llm_call(
         )
         try:
             resp = await client.messages.create(
-                model=llm_model.value,
+                model=model_to_use,
                 system=sysprompt,
                 messages=messages,
                 max_tokens=max_tokens,
@@ -782,7 +645,7 @@ 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=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_tokens=max_tokens,
@@ -804,7 +667,7 @@ 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=llm_model.value,
+            model=model_to_use,
             prompt=f"{sys_messages}\n\n{usr_messages}",
             stream=False,
             options={"num_ctx": max_tokens},
@@ -826,7 +689,7 @@ async def llm_call(
         )
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            llm_model, parallel_tool_calls
+            effective_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
@@ -834,7 +697,7 @@ async def llm_call(
                 "HTTP-Referer": "https://agpt.co",
                 "X-Title": "AutoGPT",
             },
-            model=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
@@ -868,7 +731,7 @@ async def llm_call(
         )
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            llm_model, parallel_tool_calls
+            effective_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
@@ -876,7 +739,7 @@ async def llm_call(
                 "HTTP-Referer": "https://agpt.co",
                 "X-Title": "AutoGPT",
             },
-            model=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
             tools=tools_param,  # type: ignore
@@ -903,7 +766,7 @@ async def llm_call(
             reasoning=reasoning,
         )
     elif provider == "aiml_api":
-        client = openai.OpenAI(
+        client = openai.AsyncOpenAI(
             base_url="https://api.aimlapi.com/v2",
             api_key=credentials.api_key.get_secret_value(),
             default_headers={
@@ -913,8 +776,8 @@ async def llm_call(
             },
         )
 
-        completion = client.chat.completions.create(
+        completion = await client.chat.completions.create(
-            model=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             max_tokens=max_tokens,
         )
@@ -942,11 +805,11 @@ async def llm_call(
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
-            llm_model, parallel_tool_calls
+            effective_model, parallel_tool_calls
         )
 
         response = await client.chat.completions.create(
-            model=llm_model.value,
+            model=model_to_use,
             messages=prompt,  # type: ignore
             response_format=response_format,  # type: ignore
             max_tokens=max_tokens,
@@ -997,9 +860,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             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",
@@ -1062,7 +926,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             input_schema=AIStructuredResponseGeneratorBlock.Input,
             output_schema=AIStructuredResponseGeneratorBlock.Output,
             test_input={
-                "model": DEFAULT_LLM_MODEL,
+                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "expected_format": {
                     "key1": "value1",
@@ -1428,9 +1292,10 @@ class AITextGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             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(
@@ -1524,8 +1389,9 @@ class AITextSummarizerBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             description="The language model to use for summarizing the text.",
+            json_schema_extra=llm_model_schema_extra(),
         )
         focus: str = SchemaField(
             title="Focus",
@@ -1741,8 +1607,9 @@ class AIConversationBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             description="The language model to use for the conversation.",
+            json_schema_extra=llm_model_schema_extra(),
         )
         credentials: AICredentials = AICredentialsField()
         max_tokens: int | None = SchemaField(
@@ -1779,7 +1646,7 @@ class AIConversationBlock(AIBlockBase):
                     },
                     {"role": "user", "content": "Where was it played?"},
                 ],
-                "model": DEFAULT_LLM_MODEL,
+                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
@@ -1842,9 +1709,10 @@ class AIListGeneratorBlock(AIBlockBase):
         )
         model: LlmModel = SchemaField(
             title="LLM Model",
-            default=DEFAULT_LLM_MODEL,
+            default_factory=LlmModel.default,
             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(
@@ -1899,7 +1767,7 @@ class AIListGeneratorBlock(AIBlockBase):
                     "drawing explorers to uncover its mysteries. Each planet showcases the limitless possibilities of "
                     "fictional worlds."
                 ),
-                "model": DEFAULT_LLM_MODEL,
+                "model": "gpt-4o",  # Using string value - enum accepts any model slug dynamically
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
                 "force_json_output": False,

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -226,9 +226,10 @@ class SmartDecisionMakerBlock(Block):
         )
         model: llm.LlmModel = SchemaField(
             title="LLM Model",
-            default=llm.DEFAULT_LLM_MODEL,
+            default_factory=llm.LlmModel.default,
             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 = MODEL_METADATA[LlmModel(self.value)]
+        model_metadata = self.metadata
         model_name = self.value
 
         if len(model_name.split("/")) == 1 and not self.value.startswith(
@@ -102,24 +102,28 @@ class StagehandRecommendedLlmModel(str, Enum):
             ), "Logic failed and open_router provider attempted to be prepended to model name! in stagehand/_config.py"
             model_name = f"{model_metadata.provider}/{model_name}"
 
-        logger.error(f"Model name: {model_name}")
+        logger.debug(f"Model name: {model_name}")
         return model_name
 
     @property
     def provider(self) -> str:
-        return MODEL_METADATA[LlmModel(self.value)].provider
+        return self.metadata.provider
 
     @property
     def metadata(self) -> ModelMetadata:
-        return MODEL_METADATA[LlmModel(self.value)]
+        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
 
     @property
     def context_window(self) -> int:
-        return MODEL_METADATA[LlmModel(self.value)].context_window
+        return self.metadata.context_window
 
     @property
     def max_output_tokens(self) -> int | None:
-        return MODEL_METADATA[LlmModel(self.value)].max_output_tokens
+        return self.metadata.max_output_tokens
 
 
 class StagehandObserveBlock(Block):

autogpt_platform/backend/backend/conftest.py

@@ -1,7 +1,6 @@
 import logging
 import os
 
-import pytest
 import pytest_asyncio
 from dotenv import load_dotenv
 
@@ -28,54 +27,6 @@ async def server():
         yield server
 
 
-@pytest.fixture
-def test_user_id() -> str:
-    """Test user ID fixture."""
-    return "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
-
-
-@pytest.fixture
-def admin_user_id() -> str:
-    """Admin user ID fixture."""
-    return "4e53486c-cf57-477e-ba2a-cb02dc828e1b"
-
-
-@pytest.fixture
-def target_user_id() -> str:
-    """Target user ID fixture."""
-    return "5e53486c-cf57-477e-ba2a-cb02dc828e1c"
-
-
-@pytest.fixture
-async def setup_test_user(test_user_id):
-    """Create test user in database before tests."""
-    from backend.data.user import get_or_create_user
-
-    # Create the test user in the database using JWT token format
-    user_data = {
-        "sub": test_user_id,
-        "email": "test@example.com",
-        "user_metadata": {"name": "Test User"},
-    }
-    await get_or_create_user(user_data)
-    return test_user_id
-
-
-@pytest.fixture
-async def setup_admin_user(admin_user_id):
-    """Create admin user in database before tests."""
-    from backend.data.user import get_or_create_user
-
-    # Create the admin user in the database using JWT token format
-    user_data = {
-        "sub": admin_user_id,
-        "email": "test-admin@example.com",
-        "user_metadata": {"name": "Test Admin"},
-    }
-    await get_or_create_user(user_data)
-    return admin_user_id
-
-
 @pytest_asyncio.fixture(scope="session", loop_scope="session", autouse=True)
 async def graph_cleanup(server):
     created_graph_ids = []

autogpt_platform/backend/backend/copilot/__init__.py

@@ -1 +0,0 @@
-

autogpt_platform/backend/backend/copilot/executor/__main__.py

@@ -1,18 +0,0 @@
-"""Entry point for running the CoPilot Executor service.
-
-Usage:
-    python -m backend.copilot.executor
-"""
-
-from backend.app import run_processes
-
-from .manager import CoPilotExecutor
-
-
-def main():
-    """Run the CoPilot Executor service."""
-    run_processes(CoPilotExecutor())
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/backend/copilot/executor/manager.py

@@ -1,521 +0,0 @@
-"""CoPilot Executor Manager - main service for CoPilot task execution.
-
-This module contains the CoPilotExecutor class that consumes chat tasks from
-RabbitMQ and processes them using a thread pool, following the graph executor pattern.
-"""
-
-import asyncio
-import logging
-import os
-import threading
-import time
-import uuid
-from concurrent.futures import Future, ThreadPoolExecutor
-
-from pika.adapters.blocking_connection import BlockingChannel
-from pika.exceptions import AMQPChannelError, AMQPConnectionError
-from pika.spec import Basic, BasicProperties
-from prometheus_client import Gauge, start_http_server
-
-from backend.data import redis_client as redis
-from backend.data.rabbitmq import SyncRabbitMQ
-from backend.executor.cluster_lock import ClusterLock
-from backend.util.decorator import error_logged
-from backend.util.logging import TruncatedLogger
-from backend.util.process import AppProcess
-from backend.util.retry import continuous_retry
-from backend.util.settings import Settings
-
-from .processor import execute_copilot_task, init_worker
-from .utils import (
-    COPILOT_CANCEL_QUEUE_NAME,
-    COPILOT_EXECUTION_QUEUE_NAME,
-    GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS,
-    CancelCoPilotEvent,
-    CoPilotExecutionEntry,
-    create_copilot_queue_config,
-)
-
-logger = TruncatedLogger(logging.getLogger(__name__), prefix="[CoPilotExecutor]")
-settings = Settings()
-
-# Prometheus metrics
-active_tasks_gauge = Gauge(
-    "copilot_executor_active_tasks",
-    "Number of active CoPilot tasks",
-)
-pool_size_gauge = Gauge(
-    "copilot_executor_pool_size",
-    "Maximum number of CoPilot executor workers",
-)
-utilization_gauge = Gauge(
-    "copilot_executor_utilization_ratio",
-    "Ratio of active tasks to pool size",
-)
-
-
-class CoPilotExecutor(AppProcess):
-    """CoPilot Executor service for processing chat generation tasks.
-
-    This service consumes tasks from RabbitMQ, processes them using a thread pool,
-    and publishes results to Redis Streams. It follows the graph executor pattern
-    for reliable message handling and graceful shutdown.
-
-    Key features:
-    - RabbitMQ-based task distribution with manual acknowledgment
-    - Thread pool executor for concurrent task processing
-    - Cluster lock for duplicate prevention across pods
-    - Graceful shutdown with timeout for in-flight tasks
-    - FANOUT exchange for cancellation broadcast
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.pool_size = settings.config.num_copilot_workers
-        self.active_tasks: dict[str, tuple[Future, threading.Event]] = {}
-        self.executor_id = str(uuid.uuid4())
-
-        self._executor = None
-        self._stop_consuming = None
-
-        self._cancel_thread = None
-        self._cancel_client = None
-        self._run_thread = None
-        self._run_client = None
-
-        self._task_locks: dict[str, ClusterLock] = {}
-        self._active_tasks_lock = threading.Lock()
-
-    # ============ Main Entry Points (AppProcess interface) ============ #
-
-    def run(self):
-        """Main service loop - consume from RabbitMQ."""
-        logger.info(f"Pod assigned executor_id: {self.executor_id}")
-        logger.info(f"Spawn max-{self.pool_size} workers...")
-
-        pool_size_gauge.set(self.pool_size)
-        self._update_metrics()
-        start_http_server(settings.config.copilot_executor_port)
-
-        self.cancel_thread.start()
-        self.run_thread.start()
-
-        while True:
-            time.sleep(1e5)
-
-    def cleanup(self):
-        """Graceful shutdown with active execution waiting."""
-        pid = os.getpid()
-        logger.info(f"[cleanup {pid}] Starting graceful shutdown...")
-
-        # Signal the consumer thread to stop
-        try:
-            self.stop_consuming.set()
-            run_channel = self.run_client.get_channel()
-            run_channel.connection.add_callback_threadsafe(
-                lambda: run_channel.stop_consuming()
-            )
-            logger.info(f"[cleanup {pid}] Consumer has been signaled to stop")
-        except Exception as e:
-            logger.error(f"[cleanup {pid}] Error stopping consumer: {e}")
-
-        # Wait for active executions to complete
-        if self.active_tasks:
-            logger.info(
-                f"[cleanup {pid}] Waiting for {len(self.active_tasks)} active tasks to complete (timeout: {GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS}s)..."
-            )
-
-            start_time = time.monotonic()
-            last_refresh = start_time
-            lock_refresh_interval = settings.config.cluster_lock_timeout / 10
-
-            while (
-                self.active_tasks
-                and (time.monotonic() - start_time) < GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS
-            ):
-                self._cleanup_completed_tasks()
-                if not self.active_tasks:
-                    break
-
-                # Refresh cluster locks periodically
-                current_time = time.monotonic()
-                if current_time - last_refresh >= lock_refresh_interval:
-                    for lock in list(self._task_locks.values()):
-                        try:
-                            lock.refresh()
-                        except Exception as e:
-                            logger.warning(
-                                f"[cleanup {pid}] Failed to refresh lock: {e}"
-                            )
-                    last_refresh = current_time
-
-                logger.info(
-                    f"[cleanup {pid}] {len(self.active_tasks)} tasks still active, waiting..."
-                )
-                time.sleep(10.0)
-
-        # Stop message consumers
-        if self._run_thread:
-            self._stop_message_consumers(
-                self._run_thread, self.run_client, "[cleanup][run]"
-            )
-        if self._cancel_thread:
-            self._stop_message_consumers(
-                self._cancel_thread, self.cancel_client, "[cleanup][cancel]"
-            )
-
-        # Clean up worker threads (closes per-loop workspace storage sessions)
-        if self._executor:
-            from .processor import cleanup_worker
-
-            logger.info(f"[cleanup {pid}] Cleaning up workers...")
-            futures = []
-            for _ in range(self._executor._max_workers):
-                futures.append(self._executor.submit(cleanup_worker))
-            for f in futures:
-                try:
-                    f.result(timeout=10)
-                except Exception as e:
-                    logger.warning(f"[cleanup {pid}] Worker cleanup error: {e}")
-
-            logger.info(f"[cleanup {pid}] Shutting down executor...")
-            self._executor.shutdown(wait=False)
-
-        # Release any remaining locks
-        for task_id, lock in list(self._task_locks.items()):
-            try:
-                lock.release()
-                logger.info(f"[cleanup {pid}] Released lock for {task_id}")
-            except Exception as e:
-                logger.error(
-                    f"[cleanup {pid}] Failed to release lock for {task_id}: {e}"
-                )
-
-        logger.info(f"[cleanup {pid}] Graceful shutdown completed")
-
-    # ============ RabbitMQ Consumer Methods ============ #
-
-    @continuous_retry()
-    def _consume_cancel(self):
-        """Consume cancellation messages from FANOUT exchange."""
-        if self.stop_consuming.is_set() and not self.active_tasks:
-            logger.info("Stop reconnecting cancel consumer - service cleaned up")
-            return
-
-        if not self.cancel_client.is_ready:
-            self.cancel_client.disconnect()
-        self.cancel_client.connect()
-
-        # Check again after connect - shutdown may have been requested
-        if self.stop_consuming.is_set() and not self.active_tasks:
-            logger.info("Stop consuming requested during reconnect - disconnecting")
-            self.cancel_client.disconnect()
-            return
-
-        cancel_channel = self.cancel_client.get_channel()
-        cancel_channel.basic_consume(
-            queue=COPILOT_CANCEL_QUEUE_NAME,
-            on_message_callback=self._handle_cancel_message,
-            auto_ack=True,
-        )
-        logger.info("Starting to consume cancel messages...")
-        cancel_channel.start_consuming()
-        if not self.stop_consuming.is_set() or self.active_tasks:
-            raise RuntimeError("Cancel message consumer stopped unexpectedly")
-        logger.info("Cancel message consumer stopped gracefully")
-
-    @continuous_retry()
-    def _consume_run(self):
-        """Consume run messages from DIRECT exchange."""
-        if self.stop_consuming.is_set():
-            logger.info("Stop reconnecting run consumer - service cleaned up")
-            return
-
-        if not self.run_client.is_ready:
-            self.run_client.disconnect()
-        self.run_client.connect()
-
-        # Check again after connect - shutdown may have been requested
-        if self.stop_consuming.is_set():
-            logger.info("Stop consuming requested during reconnect - disconnecting")
-            self.run_client.disconnect()
-            return
-
-        run_channel = self.run_client.get_channel()
-        run_channel.basic_qos(prefetch_count=self.pool_size)
-
-        run_channel.basic_consume(
-            queue=COPILOT_EXECUTION_QUEUE_NAME,
-            on_message_callback=self._handle_run_message,
-            auto_ack=False,
-            consumer_tag="copilot_execution_consumer",
-        )
-        logger.info("Starting to consume run messages...")
-        run_channel.start_consuming()
-        if not self.stop_consuming.is_set():
-            raise RuntimeError("Run message consumer stopped unexpectedly")
-        logger.info("Run message consumer stopped gracefully")
-
-    # ============ Message Handlers ============ #
-
-    @error_logged(swallow=True)
-    def _handle_cancel_message(
-        self,
-        _channel: BlockingChannel,
-        _method: Basic.Deliver,
-        _properties: BasicProperties,
-        body: bytes,
-    ):
-        """Handle cancel message from FANOUT exchange."""
-        request = CancelCoPilotEvent.model_validate_json(body)
-        task_id = request.task_id
-        if not task_id:
-            logger.warning("Cancel message missing 'task_id'")
-            return
-        if task_id not in self.active_tasks:
-            logger.debug(f"Cancel received for {task_id} but not active")
-            return
-
-        _, cancel_event = self.active_tasks[task_id]
-        logger.info(f"Received cancel for {task_id}")
-        if not cancel_event.is_set():
-            cancel_event.set()
-        else:
-            logger.debug(f"Cancel already set for {task_id}")
-
-    def _handle_run_message(
-        self,
-        _channel: BlockingChannel,
-        method: Basic.Deliver,
-        _properties: BasicProperties,
-        body: bytes,
-    ):
-        """Handle run message from DIRECT exchange."""
-        delivery_tag = method.delivery_tag
-        # Capture the channel used at message delivery time to ensure we ack
-        # on the correct channel. Delivery tags are channel-scoped and become
-        # invalid if the channel is recreated after reconnection.
-        delivery_channel = _channel
-
-        def ack_message(reject: bool, requeue: bool):
-            """Acknowledge or reject the message.
-
-            Uses the channel from the original message delivery. If the channel
-            is no longer open (e.g., after reconnection), logs a warning and
-            skips the ack - RabbitMQ will redeliver the message automatically.
-            """
-            try:
-                if not delivery_channel.is_open:
-                    logger.warning(
-                        f"Channel closed, cannot ack delivery_tag={delivery_tag}. "
-                        "Message will be redelivered by RabbitMQ."
-                    )
-                    return
-
-                if reject:
-                    delivery_channel.connection.add_callback_threadsafe(
-                        lambda: delivery_channel.basic_nack(
-                            delivery_tag, requeue=requeue
-                        )
-                    )
-                else:
-                    delivery_channel.connection.add_callback_threadsafe(
-                        lambda: delivery_channel.basic_ack(delivery_tag)
-                    )
-            except (AMQPChannelError, AMQPConnectionError) as e:
-                # Channel/connection errors indicate stale delivery tag - don't retry
-                logger.warning(
-                    f"Cannot ack delivery_tag={delivery_tag} due to channel/connection "
-                    f"error: {e}. Message will be redelivered by RabbitMQ."
-                )
-            except Exception as e:
-                # Other errors might be transient, but log and skip to avoid blocking
-                logger.error(
-                    f"Unexpected error acking delivery_tag={delivery_tag}: {e}"
-                )
-
-        # Check if we're shutting down
-        if self.stop_consuming.is_set():
-            logger.info("Rejecting new task during shutdown")
-            ack_message(reject=True, requeue=True)
-            return
-
-        # Check if we can accept more tasks
-        self._cleanup_completed_tasks()
-        if len(self.active_tasks) >= self.pool_size:
-            ack_message(reject=True, requeue=True)
-            return
-
-        try:
-            entry = CoPilotExecutionEntry.model_validate_json(body)
-        except Exception as e:
-            logger.error(f"Could not parse run message: {e}, body={body}")
-            ack_message(reject=True, requeue=False)
-            return
-
-        task_id = entry.task_id
-
-        # Check for local duplicate - task is already running on this executor
-        if task_id in self.active_tasks:
-            logger.warning(
-                f"Task {task_id} already running locally, rejecting duplicate"
-            )
-            ack_message(reject=True, requeue=False)
-            return
-
-        # Try to acquire cluster-wide lock
-        cluster_lock = ClusterLock(
-            redis=redis.get_redis(),
-            key=f"copilot:task:{task_id}:lock",
-            owner_id=self.executor_id,
-            timeout=settings.config.cluster_lock_timeout,
-        )
-        current_owner = cluster_lock.try_acquire()
-        if current_owner != self.executor_id:
-            if current_owner is not None:
-                logger.warning(f"Task {task_id} already running on pod {current_owner}")
-                ack_message(reject=True, requeue=False)
-            else:
-                logger.warning(
-                    f"Could not acquire lock for {task_id} - Redis unavailable"
-                )
-                ack_message(reject=True, requeue=True)
-            return
-
-        # Execute the task
-        try:
-            self._task_locks[task_id] = cluster_lock
-
-            logger.info(
-                f"Acquired cluster lock for {task_id}, executor_id={self.executor_id}"
-            )
-
-            cancel_event = threading.Event()
-            future = self.executor.submit(
-                execute_copilot_task, entry, cancel_event, cluster_lock
-            )
-            self.active_tasks[task_id] = (future, cancel_event)
-        except Exception as e:
-            logger.warning(f"Failed to setup execution for {task_id}: {e}")
-            cluster_lock.release()
-            if task_id in self._task_locks:
-                del self._task_locks[task_id]
-            ack_message(reject=True, requeue=True)
-            return
-
-        self._update_metrics()
-
-        def on_run_done(f: Future):
-            logger.info(f"Run completed for {task_id}")
-            try:
-                if exec_error := f.exception():
-                    logger.error(f"Execution for {task_id} failed: {exec_error}")
-                    # Don't requeue failed tasks - they've been marked as failed
-                    # in the stream registry. Requeuing would cause infinite retries
-                    # for deterministic failures.
-                    ack_message(reject=True, requeue=False)
-                else:
-                    ack_message(reject=False, requeue=False)
-            except BaseException as e:
-                logger.exception(f"Error in run completion callback: {e}")
-            finally:
-                # Release the cluster lock
-                if task_id in self._task_locks:
-                    logger.info(f"Releasing cluster lock for {task_id}")
-                    self._task_locks[task_id].release()
-                    del self._task_locks[task_id]
-                self._cleanup_completed_tasks()
-
-        future.add_done_callback(on_run_done)
-
-    # ============ Helper Methods ============ #
-
-    def _cleanup_completed_tasks(self) -> list[str]:
-        """Remove completed futures from active_tasks and update metrics."""
-        completed_tasks = []
-        with self._active_tasks_lock:
-            for task_id, (future, _) in list(self.active_tasks.items()):
-                if future.done():
-                    completed_tasks.append(task_id)
-                    self.active_tasks.pop(task_id, None)
-                    logger.info(f"Cleaned up completed task {task_id}")
-
-        self._update_metrics()
-        return completed_tasks
-
-    def _update_metrics(self):
-        """Update Prometheus metrics."""
-        active_count = len(self.active_tasks)
-        active_tasks_gauge.set(active_count)
-        if self.stop_consuming.is_set():
-            utilization_gauge.set(1.0)
-        else:
-            utilization_gauge.set(
-                active_count / self.pool_size if self.pool_size > 0 else 0
-            )
-
-    def _stop_message_consumers(
-        self, thread: threading.Thread, client: SyncRabbitMQ, prefix: str
-    ):
-        """Stop a message consumer thread."""
-        try:
-            channel = client.get_channel()
-            channel.connection.add_callback_threadsafe(lambda: channel.stop_consuming())
-
-            thread.join(timeout=300)
-            if thread.is_alive():
-                logger.error(
-                    f"{prefix} Thread did not finish in time, forcing disconnect"
-                )
-
-            client.disconnect()
-            logger.info(f"{prefix} Client disconnected")
-        except Exception as e:
-            logger.error(f"{prefix} Error disconnecting client: {e}")
-
-    # ============ Lazy-initialized Properties ============ #
-
-    @property
-    def cancel_thread(self) -> threading.Thread:
-        if self._cancel_thread is None:
-            self._cancel_thread = threading.Thread(
-                target=lambda: self._consume_cancel(),
-                daemon=True,
-            )
-        return self._cancel_thread
-
-    @property
-    def run_thread(self) -> threading.Thread:
-        if self._run_thread is None:
-            self._run_thread = threading.Thread(
-                target=lambda: self._consume_run(),
-                daemon=True,
-            )
-        return self._run_thread
-
-    @property
-    def stop_consuming(self) -> threading.Event:
-        if self._stop_consuming is None:
-            self._stop_consuming = threading.Event()
-        return self._stop_consuming
-
-    @property
-    def executor(self) -> ThreadPoolExecutor:
-        if self._executor is None:
-            self._executor = ThreadPoolExecutor(
-                max_workers=self.pool_size,
-                initializer=init_worker,
-            )
-        return self._executor
-
-    @property
-    def cancel_client(self) -> SyncRabbitMQ:
-        if self._cancel_client is None:
-            self._cancel_client = SyncRabbitMQ(create_copilot_queue_config())
-        return self._cancel_client
-
-    @property
-    def run_client(self) -> SyncRabbitMQ:
-        if self._run_client is None:
-            self._run_client = SyncRabbitMQ(create_copilot_queue_config())
-        return self._run_client

autogpt_platform/backend/backend/copilot/executor/processor.py

@@ -1,287 +0,0 @@
-"""CoPilot execution processor - per-worker execution logic.
-
-This module contains the processor class that handles CoPilot task execution
-in a thread-local context, following the graph executor pattern.
-"""
-
-import asyncio
-import logging
-import threading
-import time
-
-from backend.copilot import service as copilot_service
-from backend.copilot import stream_registry
-from backend.copilot.config import ChatConfig
-from backend.copilot.response_model import StreamError, StreamFinish, StreamFinishStep
-from backend.copilot.sdk import service as sdk_service
-from backend.executor.cluster_lock import ClusterLock
-from backend.util.decorator import error_logged
-from backend.util.feature_flag import Flag, is_feature_enabled
-from backend.util.logging import TruncatedLogger, configure_logging
-from backend.util.process import set_service_name
-from backend.util.retry import func_retry
-
-from .utils import CoPilotExecutionEntry, CoPilotLogMetadata
-
-logger = TruncatedLogger(logging.getLogger(__name__), prefix="[CoPilotExecutor]")
-
-
-# ============ Module Entry Points ============ #
-
-# Thread-local storage for processor instances
-_tls = threading.local()
-
-
-def execute_copilot_task(
-    entry: CoPilotExecutionEntry,
-    cancel: threading.Event,
-    cluster_lock: ClusterLock,
-):
-    """Execute a CoPilot task using the thread-local processor.
-
-    This function is the entry point called by the thread pool executor.
-
-    Args:
-        entry: The task payload
-        cancel: Threading event to signal cancellation
-        cluster_lock: Distributed lock for this execution
-    """
-    processor: CoPilotProcessor = _tls.processor
-    return processor.execute(entry, cancel, cluster_lock)
-
-
-def init_worker():
-    """Initialize the processor for the current worker thread.
-
-    This function is called by the thread pool executor when a new worker
-    thread is created. It ensures each worker has its own processor instance.
-    """
-    _tls.processor = CoPilotProcessor()
-    _tls.processor.on_executor_start()
-
-
-def cleanup_worker():
-    """Clean up the processor for the current worker thread.
-
-    Should be called before the worker thread's event loop is destroyed so
-    that event-loop-bound resources (e.g. ``aiohttp.ClientSession``) are
-    closed on the correct loop.
-    """
-    processor: CoPilotProcessor | None = getattr(_tls, "processor", None)
-    if processor is not None:
-        processor.cleanup()
-
-
-# ============ Processor Class ============ #
-
-
-class CoPilotProcessor:
-    """Per-worker execution logic for CoPilot tasks.
-
-    This class is instantiated once per worker thread and handles the execution
-    of CoPilot chat generation tasks. It maintains an async event loop for
-    running the async service code.
-
-    The execution flow:
-        1. CoPilot task is picked from RabbitMQ queue
-        2. Manager submits task to thread pool
-        3. Processor executes the task in its event loop
-        4. Results are published to Redis Streams
-    """
-
-    @func_retry
-    def on_executor_start(self):
-        """Initialize the processor when the worker thread starts.
-
-        This method is called once per worker thread to set up the async event
-        loop and initialize any required resources.
-
-        Database is accessed only through DatabaseManager, so we don't need to connect
-        to Prisma directly.
-        """
-        configure_logging()
-        set_service_name("CoPilotExecutor")
-        self.tid = threading.get_ident()
-        self.execution_loop = asyncio.new_event_loop()
-        self.execution_thread = threading.Thread(
-            target=self.execution_loop.run_forever, daemon=True
-        )
-        self.execution_thread.start()
-
-        logger.info(f"[CoPilotExecutor] Worker {self.tid} started")
-
-    def cleanup(self):
-        """Clean up event-loop-bound resources before the loop is destroyed.
-
-        Shuts down the workspace storage instance that belongs to this
-        worker's event loop, ensuring ``aiohttp.ClientSession.close()``
-        runs on the same loop that created the session.
-        """
-        from backend.util.workspace_storage import shutdown_workspace_storage
-
-        try:
-            future = asyncio.run_coroutine_threadsafe(
-                shutdown_workspace_storage(), self.execution_loop
-            )
-            future.result(timeout=5)
-        except Exception as e:
-            logger.warning(f"[CoPilotExecutor] Worker {self.tid} cleanup error: {e}")
-
-        # Stop the event loop
-        self.execution_loop.call_soon_threadsafe(self.execution_loop.stop)
-        self.execution_thread.join(timeout=5)
-        logger.info(f"[CoPilotExecutor] Worker {self.tid} cleaned up")
-
-    @error_logged(swallow=False)
-    def execute(
-        self,
-        entry: CoPilotExecutionEntry,
-        cancel: threading.Event,
-        cluster_lock: ClusterLock,
-    ):
-        """Execute a CoPilot task.
-
-        This is the main entry point for task execution. It runs the async
-        execution logic in the worker's event loop and handles errors.
-
-        Args:
-            entry: The task payload containing session and message info
-            cancel: Threading event to signal cancellation
-            cluster_lock: Distributed lock to prevent duplicate execution
-        """
-        log = CoPilotLogMetadata(
-            logging.getLogger(__name__),
-            task_id=entry.task_id,
-            session_id=entry.session_id,
-            user_id=entry.user_id,
-        )
-        log.info("Starting execution")
-
-        start_time = time.monotonic()
-
-        try:
-            # Run the async execution in our event loop
-            future = asyncio.run_coroutine_threadsafe(
-                self._execute_async(entry, cancel, cluster_lock, log),
-                self.execution_loop,
-            )
-
-            # Wait for completion, checking cancel periodically
-            while not future.done():
-                try:
-                    future.result(timeout=1.0)
-                except asyncio.TimeoutError:
-                    if cancel.is_set():
-                        log.info("Cancellation requested")
-                        future.cancel()
-                        break
-                    # Refresh cluster lock to maintain ownership
-                    cluster_lock.refresh()
-
-            if not future.cancelled():
-                # Get result to propagate any exceptions
-                future.result()
-
-            elapsed = time.monotonic() - start_time
-            log.info(f"Execution completed in {elapsed:.2f}s")
-
-        except Exception as e:
-            elapsed = time.monotonic() - start_time
-            log.error(f"Execution failed after {elapsed:.2f}s: {e}")
-            # Note: _execute_async already marks the task as failed before re-raising,
-            # so we don't call _mark_task_failed here to avoid duplicate error events.
-            raise
-
-    async def _execute_async(
-        self,
-        entry: CoPilotExecutionEntry,
-        cancel: threading.Event,
-        cluster_lock: ClusterLock,
-        log: CoPilotLogMetadata,
-    ):
-        """Async execution logic for CoPilot task.
-
-        This method calls the existing stream_chat_completion service function
-        and publishes results to the stream registry.
-
-        Args:
-            entry: The task payload
-            cancel: Threading event to signal cancellation
-            cluster_lock: Distributed lock for refresh
-            log: Structured logger for this task
-        """
-        last_refresh = time.monotonic()
-        refresh_interval = 30.0  # Refresh lock every 30 seconds
-
-        try:
-            # Choose service based on LaunchDarkly flag
-            config = ChatConfig()
-            use_sdk = await is_feature_enabled(
-                Flag.COPILOT_SDK,
-                entry.user_id or "anonymous",
-                default=config.use_claude_agent_sdk,
-            )
-            stream_fn = (
-                sdk_service.stream_chat_completion_sdk
-                if use_sdk
-                else copilot_service.stream_chat_completion
-            )
-            log.info(f"Using {'SDK' if use_sdk else 'standard'} service")
-
-            # Stream chat completion and publish chunks to Redis
-            async for chunk in stream_fn(
-                session_id=entry.session_id,
-                message=entry.message if entry.message else None,
-                is_user_message=entry.is_user_message,
-                user_id=entry.user_id,
-                context=entry.context,
-            ):
-                # Check for cancellation
-                if cancel.is_set():
-                    log.info("Cancelled during streaming")
-                    await stream_registry.publish_chunk(
-                        entry.task_id, StreamError(errorText="Operation cancelled")
-                    )
-                    await stream_registry.publish_chunk(
-                        entry.task_id, StreamFinishStep()
-                    )
-                    await stream_registry.publish_chunk(entry.task_id, StreamFinish())
-                    await stream_registry.mark_task_completed(
-                        entry.task_id, status="failed"
-                    )
-                    return
-
-                # Refresh cluster lock periodically
-                current_time = time.monotonic()
-                if current_time - last_refresh >= refresh_interval:
-                    cluster_lock.refresh()
-                    last_refresh = current_time
-
-                # Publish chunk to stream registry
-                await stream_registry.publish_chunk(entry.task_id, chunk)
-
-            # Mark task as completed
-            await stream_registry.mark_task_completed(entry.task_id, status="completed")
-            log.info("Task completed successfully")
-
-        except asyncio.CancelledError:
-            log.info("Task cancelled")
-            await stream_registry.mark_task_completed(entry.task_id, status="failed")
-            raise
-
-        except Exception as e:
-            log.error(f"Task failed: {e}")
-            await self._mark_task_failed(entry.task_id, str(e))
-            raise
-
-    async def _mark_task_failed(self, task_id: str, error_message: str):
-        """Mark a task as failed and publish error to stream registry."""
-        try:
-            await stream_registry.publish_chunk(
-                task_id, StreamError(errorText=error_message)
-            )
-            await stream_registry.publish_chunk(task_id, StreamFinishStep())
-            await stream_registry.publish_chunk(task_id, StreamFinish())
-            await stream_registry.mark_task_completed(task_id, status="failed")
-        except Exception as e:
-            logger.error(f"Failed to mark task {task_id} as failed: {e}")

autogpt_platform/backend/backend/copilot/executor/utils.py

@@ -1,207 +0,0 @@
-"""RabbitMQ queue configuration for CoPilot executor.
-
-Defines two exchanges and queues following the graph executor pattern:
-- 'copilot_execution' (DIRECT) for chat generation tasks
-- 'copilot_cancel' (FANOUT) for cancellation requests
-"""
-
-import logging
-
-from pydantic import BaseModel
-
-from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.util.logging import TruncatedLogger, is_structured_logging_enabled
-
-logger = logging.getLogger(__name__)
-
-
-# ============ Logging Helper ============ #
-
-
-class CoPilotLogMetadata(TruncatedLogger):
-    """Structured logging helper for CoPilot executor.
-
-    In cloud environments (structured logging enabled), uses a simple prefix
-    and passes metadata via json_fields. In local environments, uses a detailed
-    prefix with all metadata key-value pairs for easier debugging.
-
-    Args:
-        logger: The underlying logger instance
-        max_length: Maximum log message length before truncation
-        **kwargs: Metadata key-value pairs (e.g., task_id="abc", session_id="xyz")
-            These are added to json_fields in cloud mode, or to the prefix in local mode.
-    """
-
-    def __init__(
-        self,
-        logger: logging.Logger,
-        max_length: int = 1000,
-        **kwargs: str | None,
-    ):
-        # Filter out None values
-        metadata = {k: v for k, v in kwargs.items() if v is not None}
-        metadata["component"] = "CoPilotExecutor"
-
-        if is_structured_logging_enabled():
-            prefix = "[CoPilotExecutor]"
-        else:
-            # Build prefix from metadata key-value pairs
-            meta_parts = "|".join(
-                f"{k}:{v}" for k, v in metadata.items() if k != "component"
-            )
-            prefix = (
-                f"[CoPilotExecutor|{meta_parts}]" if meta_parts else "[CoPilotExecutor]"
-            )
-
-        super().__init__(
-            logger,
-            max_length=max_length,
-            prefix=prefix,
-            metadata=metadata,
-        )
-
-
-# ============ Exchange and Queue Configuration ============ #
-
-COPILOT_EXECUTION_EXCHANGE = Exchange(
-    name="copilot_execution",
-    type=ExchangeType.DIRECT,
-    durable=True,
-    auto_delete=False,
-)
-COPILOT_EXECUTION_QUEUE_NAME = "copilot_execution_queue"
-COPILOT_EXECUTION_ROUTING_KEY = "copilot.run"
-
-COPILOT_CANCEL_EXCHANGE = Exchange(
-    name="copilot_cancel",
-    type=ExchangeType.FANOUT,
-    durable=True,
-    auto_delete=False,
-)
-COPILOT_CANCEL_QUEUE_NAME = "copilot_cancel_queue"
-
-# CoPilot operations can include extended thinking and agent generation
-# which may take 30+ minutes to complete
-COPILOT_CONSUMER_TIMEOUT_SECONDS = 60 * 60  # 1 hour
-
-# Graceful shutdown timeout - allow in-flight operations to complete
-GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS = 30 * 60  # 30 minutes
-
-
-def create_copilot_queue_config() -> RabbitMQConfig:
-    """Create RabbitMQ configuration for CoPilot executor.
-
-    Defines two exchanges and queues:
-    - 'copilot_execution' (DIRECT) for chat generation tasks
-    - 'copilot_cancel' (FANOUT) for cancellation requests
-
-    Returns:
-        RabbitMQConfig with exchanges and queues defined
-    """
-    run_queue = Queue(
-        name=COPILOT_EXECUTION_QUEUE_NAME,
-        exchange=COPILOT_EXECUTION_EXCHANGE,
-        routing_key=COPILOT_EXECUTION_ROUTING_KEY,
-        durable=True,
-        auto_delete=False,
-        arguments={
-            # Extended consumer timeout for long-running LLM operations
-            # Default 30-minute timeout is insufficient for extended thinking
-            # and agent generation which can take 30+ minutes
-            "x-consumer-timeout": COPILOT_CONSUMER_TIMEOUT_SECONDS
-            * 1000,
-        },
-    )
-    cancel_queue = Queue(
-        name=COPILOT_CANCEL_QUEUE_NAME,
-        exchange=COPILOT_CANCEL_EXCHANGE,
-        routing_key="",  # not used for FANOUT
-        durable=True,
-        auto_delete=False,
-    )
-    return RabbitMQConfig(
-        vhost="/",
-        exchanges=[COPILOT_EXECUTION_EXCHANGE, COPILOT_CANCEL_EXCHANGE],
-        queues=[run_queue, cancel_queue],
-    )
-
-
-# ============ Message Models ============ #
-
-
-class CoPilotExecutionEntry(BaseModel):
-    """Task payload for CoPilot AI generation.
-
-    This model represents a chat generation task to be processed by the executor.
-    """
-
-    task_id: str
-    """Unique identifier for this task (used for stream registry)"""
-
-    session_id: str
-    """Chat session ID"""
-
-    user_id: str | None
-    """User ID (may be None for anonymous users)"""
-
-    operation_id: str
-    """Operation ID for webhook callbacks and completion tracking"""
-
-    message: str
-    """User's message to process"""
-
-    is_user_message: bool = True
-    """Whether the message is from the user (vs system/assistant)"""
-
-    context: dict[str, str] | None = None
-    """Optional context for the message (e.g., {url: str, content: str})"""
-
-
-class CancelCoPilotEvent(BaseModel):
-    """Event to cancel a CoPilot operation."""
-
-    task_id: str
-    """Task ID to cancel"""
-
-
-# ============ Queue Publishing Helpers ============ #
-
-
-async def enqueue_copilot_task(
-    task_id: str,
-    session_id: str,
-    user_id: str | None,
-    operation_id: str,
-    message: str,
-    is_user_message: bool = True,
-    context: dict[str, str] | None = None,
-) -> None:
-    """Enqueue a CoPilot task for processing by the executor service.
-
-    Args:
-        task_id: Unique identifier for this task (used for stream registry)
-        session_id: Chat session ID
-        user_id: User ID (may be None for anonymous users)
-        operation_id: Operation ID for webhook callbacks and completion tracking
-        message: User's message to process
-        is_user_message: Whether the message is from the user (vs system/assistant)
-        context: Optional context for the message (e.g., {url: str, content: str})
-    """
-    from backend.util.clients import get_async_copilot_queue
-
-    entry = CoPilotExecutionEntry(
-        task_id=task_id,
-        session_id=session_id,
-        user_id=user_id,
-        operation_id=operation_id,
-        message=message,
-        is_user_message=is_user_message,
-        context=context,
-    )
-
-    queue_client = await get_async_copilot_queue()
-    await queue_client.publish_message(
-        routing_key=COPILOT_EXECUTION_ROUTING_KEY,
-        message=entry.model_dump_json(),
-        exchange=COPILOT_EXECUTION_EXCHANGE,
-    )

autogpt_platform/backend/backend/data/block.py

@@ -19,6 +19,30 @@ CompletedBlockOutput = dict[str, list[Any]]  # Completed stream, collected as a
 
 
 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()
+            await 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.blocks import get_blocks
     from backend.sdk.cost_integration import sync_all_provider_costs
     from backend.util.retry import func_retry

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -1,5 +1,8 @@
+import logging
 from typing import Type
 
+import prisma.models
+
 from backend.blocks._base import Block, BlockCost, BlockCostType
 from backend.blocks.ai_image_customizer import AIImageCustomizerBlock, GeminiImageModel
 from backend.blocks.ai_image_generator_block import AIImageGeneratorBlock, ImageGenModel
@@ -24,13 +27,11 @@ 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
@@ -38,6 +39,7 @@ 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.blocks.video.narration import VideoNarrationBlock
+from backend.data import llm_registry
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
     anthropic_credentials,
@@ -57,210 +59,116 @@ from backend.integrations.credentials_store import (
     v0_credentials,
 )
 
-# =============== Configure the cost for each LLM Model call =============== #
+logger = logging.getLogger(__name__)
 
-MODEL_COST: dict[LlmModel, int] = {
+PROVIDER_CREDENTIALS = {
-    LlmModel.O3: 4,
+    "openai": openai_credentials,
-    LlmModel.O3_MINI: 2,
+    "anthropic": anthropic_credentials,
-    LlmModel.O1: 16,
+    "groq": groq_credentials,
-    LlmModel.O1_MINI: 4,
+    "open_router": open_router_credentials,
-    # GPT-5 models
+    "llama_api": llama_api_credentials,
-    LlmModel.GPT5_2: 6,
+    "aiml_api": aiml_api_credentials,
-    LlmModel.GPT5_1: 5,
+    "v0": v0_credentials,
-    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_6_OPUS: 14,
-    LlmModel.CLAUDE_4_5_HAIKU: 4,
-    LlmModel.CLAUDE_4_5_OPUS: 14,
-    LlmModel.CLAUDE_4_5_SONNET: 9,
-    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,
 }
 
-for model in LlmModel:
+# =============== Configure the cost for each LLM Model call =============== #
-    if model not in MODEL_COST:
+# All LLM costs now come from the database via llm_registry
-        raise ValueError(f"Missing MODEL_COST for model: {model}")
+
+LLM_COST: list[BlockCost] = []
 
 
-LLM_COST = (
+async def _build_llm_costs_from_registry() -> list[BlockCost]:
-    # Anthropic Models
+    """
-    [
+    Build BlockCost list from all models in the LLM registry.
-        BlockCost(
+
-            cost_type=BlockCostType.RUN,
+    This function checks for active model migrations with customCreditCost overrides.
-            cost_filter={
+    When a model has been migrated with a custom price, that price is used instead
-                "model": model,
+    of the target model's default cost.
+    """
+    # Query active migrations with custom pricing overrides.
+    # Note: LlmModelMigration is system-level data (no userId field) and this function
+    # is only called during app startup and admin operations, so no user ID filter needed.
+    migration_overrides: dict[str, int] = {}
+    try:
+        active_migrations = await prisma.models.LlmModelMigration.prisma().find_many(
+            where={
+                "isReverted": False,
+                "customCreditCost": {"not": None},
+            }
+        )
+        # Key by targetModelSlug since that's the model nodes are now using
+        # after migration. The custom cost applies to the target model.
+        migration_overrides = {
+            migration.targetModelSlug: migration.customCreditCost
+            for migration in active_migrations
+            if migration.customCreditCost is not None
+        }
+        if migration_overrides:
+            logger.info(
+                "Found %d active model migrations with custom pricing overrides",
+                len(migration_overrides),
+            )
+    except Exception as exc:
+        logger.warning(
+            "Failed to query model migration overrides: %s. Proceeding with default costs.",
+            exc,
+            exc_info=True,
+        )
+
+    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
+
+            # Check if this model has a custom cost override from migration
+            cost_amount = migration_overrides.get(model.slug, cost.credit_cost)
+
+            if model.slug in migration_overrides:
+                logger.debug(
+                    "Applying custom cost override for model %s: %d credits (default: %d)",
+                    model.slug,
+                    cost_amount,
+                    cost.credit_cost,
+                )
+
+            cost_filter = {
+                "model": model.slug,
                 "credentials": {
-                    "id": anthropic_credentials.id,
+                    "id": credentials.id,
-                    "provider": anthropic_credentials.provider,
+                    "provider": credentials.provider,
-                    "type": anthropic_credentials.type,
+                    "type": credentials.type,
                 },
-            },
+            }
-            cost_amount=cost,
+            costs.append(
-        )
+                BlockCost(
-        for model, cost in MODEL_COST.items()
+                    cost_type=BlockCostType.RUN,
-        if MODEL_METADATA[model].provider == "anthropic"
+                    cost_filter=cost_filter,
-    ]
+                    cost_amount=cost_amount,
-    # OpenAI Models
+                )
-    + [
+            )
-        BlockCost(
+    return costs
-            cost_type=BlockCostType.RUN,
+
-            cost_filter={
+
-                "model": model,
+async def refresh_llm_costs() -> None:
-                "credentials": {
+    """
-                    "id": openai_credentials.id,
+    Refresh LLM costs from the registry. All costs now come from the database.
-                    "provider": openai_credentials.provider,
+
-                    "type": openai_credentials.type,
+    This function also checks for active model migrations with custom pricing overrides
-                },
+    and applies them to ensure accurate billing.
-            },
+    """
-            cost_amount=cost,
+    LLM_COST.clear()
-        )
+    LLM_COST.extend(await _build_llm_costs_from_registry())
-        for model, cost in MODEL_COST.items()
+
-        if MODEL_METADATA[model].provider == "openai"
+
-    ]
+# Initial load will happen after registry is refreshed at startup
-    # Groq Models
+# Don't call refresh_llm_costs() here - it will be called after registry refresh
-    + [
-        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/db_accessors.py

@@ -1,118 +0,0 @@
-from backend.data import db
-
-
-def chat_db():
-    if db.is_connected():
-        from backend.copilot import db as _chat_db
-
-        chat_db = _chat_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        chat_db = get_database_manager_async_client()
-
-    return chat_db
-
-
-def graph_db():
-    if db.is_connected():
-        from backend.data import graph as _graph_db
-
-        graph_db = _graph_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        graph_db = get_database_manager_async_client()
-
-    return graph_db
-
-
-def library_db():
-    if db.is_connected():
-        from backend.api.features.library import db as _library_db
-
-        library_db = _library_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        library_db = get_database_manager_async_client()
-
-    return library_db
-
-
-def store_db():
-    if db.is_connected():
-        from backend.api.features.store import db as _store_db
-
-        store_db = _store_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        store_db = get_database_manager_async_client()
-
-    return store_db
-
-
-def search():
-    if db.is_connected():
-        from backend.api.features.store import hybrid_search as _search
-
-        search = _search
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        search = get_database_manager_async_client()
-
-    return search
-
-
-def execution_db():
-    if db.is_connected():
-        from backend.data import execution as _execution_db
-
-        execution_db = _execution_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        execution_db = get_database_manager_async_client()
-
-    return execution_db
-
-
-def user_db():
-    if db.is_connected():
-        from backend.data import user as _user_db
-
-        user_db = _user_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        user_db = get_database_manager_async_client()
-
-    return user_db
-
-
-def understanding_db():
-    if db.is_connected():
-        from backend.data import understanding as _understanding_db
-
-        understanding_db = _understanding_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        understanding_db = get_database_manager_async_client()
-
-    return understanding_db
-
-
-def workspace_db():
-    if db.is_connected():
-        from backend.data import workspace as _workspace_db
-
-        workspace_db = _workspace_db
-    else:
-        from backend.util.clients import get_database_manager_async_client
-
-        workspace_db = get_database_manager_async_client()
-
-    return workspace_db

autogpt_platform/backend/backend/data/graph.py

@@ -1147,14 +1147,14 @@ async def get_graph(
     return GraphModel.from_db(graph, for_export)
 
 
-async def get_store_listed_graphs(graph_ids: list[str]) -> dict[str, GraphModel]:
+async def get_store_listed_graphs(*graph_ids: str) -> dict[str, GraphModel]:
     """Batch-fetch multiple store-listed graphs by their IDs.
 
     Only returns graphs that have approved store listings (publicly available).
     Does not require permission checks since store-listed graphs are public.
 
     Args:
-        graph_ids: List of graph IDs to fetch
+        *graph_ids: Variable number of graph IDs to fetch
 
     Returns:
         Dict mapping graph_id to GraphModel for graphs with approved store listings
@@ -1663,8 +1663,10 @@ async def migrate_llm_models(migrate_to: LlmModel):
             if field.annotation == LlmModel:
                 llm_model_fields[block.id] = field_name
 
-    # Convert enum values to a list of strings for the SQL query
+    # Get all model slugs from the registry (dynamic, not hardcoded enum)
-    enum_values = [v.value for v in LlmModel]
+    from backend.data import llm_registry
+
+    enum_values = list(llm_registry.get_all_model_slugs_for_validation())
     escaped_enum_values = repr(tuple(enum_values))  # hack but works
 
     # Update each block

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

@@ -0,0 +1,72 @@
+"""
+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

@@ -0,0 +1,25 @@
+"""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

@@ -0,0 +1,89 @@
+"""
+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

@@ -0,0 +1,388 @@
+"""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

@@ -0,0 +1,130 @@
+"""
+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

@@ -40,6 +40,7 @@ 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.request import parse_url
@@ -570,7 +571,9 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
             else:
                 schema["credentials_provider"] = allowed_providers
             schema["credentials_types"] = model_class.allowed_cred_types()
-        # Do not return anything, just mutate schema in place
+
+        # Ensure LLM discriminators are populated (delegates to shared helper)
+        update_schema_with_llm_registry(schema, model_class)
 
     model_config = ConfigDict(
         json_schema_extra=_add_json_schema_extra,  # type: ignore
@@ -732,16 +735,20 @@ def CredentialsField(
     This is enforced by the `BlockSchema` base class.
     """
 
-    field_schema_extra = {
+    # Build field_schema_extra - always include discriminator and mapping if discriminator is set
-        k: v
+    field_schema_extra: dict[str, Any] = {}
-        for k, v in {
+
-            "credentials_scopes": list(required_scopes) or None,
+    # Always include discriminator if provided
-            "discriminator": discriminator,
+    if discriminator is not None:
-            "discriminator_mapping": discriminator_mapping,
+        field_schema_extra["discriminator"] = discriminator
-            "discriminator_values": discriminator_values,
+        # Always include discriminator_mapping when discriminator is set (even if empty initially)
-        }.items()
+        field_schema_extra["discriminator_mapping"] = discriminator_mapping or {}
-        if v is not None
+
-    }
+    # 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
 
     # Merge any json_schema_extra passed in kwargs
     if "json_schema_extra" in kwargs:

autogpt_platform/backend/backend/data/workspace.py

@@ -8,7 +8,6 @@ import logging
 from datetime import datetime, timezone
 from typing import Optional
 
-import pydantic
 from prisma.models import UserWorkspace, UserWorkspaceFile
 from prisma.types import UserWorkspaceFileWhereInput
 
@@ -17,61 +16,7 @@ from backend.util.json import SafeJson
 logger = logging.getLogger(__name__)
 
 
-class Workspace(pydantic.BaseModel):
+async def get_or_create_workspace(user_id: str) -> UserWorkspace:
-    """Pydantic model for UserWorkspace, safe for RPC transport."""
-
-    id: str
-    user_id: str
-    created_at: datetime
-    updated_at: datetime
-
-    @staticmethod
-    def from_db(workspace: "UserWorkspace") -> "Workspace":
-        return Workspace(
-            id=workspace.id,
-            user_id=workspace.userId,
-            created_at=workspace.createdAt,
-            updated_at=workspace.updatedAt,
-        )
-
-
-class WorkspaceFile(pydantic.BaseModel):
-    """Pydantic model for UserWorkspaceFile, safe for RPC transport."""
-
-    id: str
-    workspace_id: str
-    created_at: datetime
-    updated_at: datetime
-    name: str
-    path: str
-    storage_path: str
-    mime_type: str
-    size_bytes: int
-    checksum: Optional[str] = None
-    is_deleted: bool = False
-    deleted_at: Optional[datetime] = None
-    metadata: dict = pydantic.Field(default_factory=dict)
-
-    @staticmethod
-    def from_db(file: "UserWorkspaceFile") -> "WorkspaceFile":
-        return WorkspaceFile(
-            id=file.id,
-            workspace_id=file.workspaceId,
-            created_at=file.createdAt,
-            updated_at=file.updatedAt,
-            name=file.name,
-            path=file.path,
-            storage_path=file.storagePath,
-            mime_type=file.mimeType,
-            size_bytes=file.sizeBytes,
-            checksum=file.checksum,
-            is_deleted=file.isDeleted,
-            deleted_at=file.deletedAt,
-            metadata=file.metadata if isinstance(file.metadata, dict) else {},
-        )
-
-
-async def get_or_create_workspace(user_id: str) -> Workspace:
     """
     Get user's workspace, creating one if it doesn't exist.
 
@@ -82,7 +27,7 @@ async def get_or_create_workspace(user_id: str) -> Workspace:
         user_id: The user's ID
 
     Returns:
-        Workspace instance
+        UserWorkspace instance
     """
     workspace = await UserWorkspace.prisma().upsert(
         where={"userId": user_id},
@@ -92,10 +37,10 @@ async def get_or_create_workspace(user_id: str) -> Workspace:
         },
     )
 
-    return Workspace.from_db(workspace)
+    return workspace
 
 
-async def get_workspace(user_id: str) -> Optional[Workspace]:
+async def get_workspace(user_id: str) -> Optional[UserWorkspace]:
     """
     Get user's workspace if it exists.
 
@@ -103,10 +48,9 @@ async def get_workspace(user_id: str) -> Optional[Workspace]:
         user_id: The user's ID
 
     Returns:
-        Workspace instance or None
+        UserWorkspace instance or None
     """
-    workspace = await UserWorkspace.prisma().find_unique(where={"userId": user_id})
+    return await UserWorkspace.prisma().find_unique(where={"userId": user_id})
-    return Workspace.from_db(workspace) if workspace else None
 
 
 async def create_workspace_file(
@@ -119,7 +63,7 @@ async def create_workspace_file(
     size_bytes: int,
     checksum: Optional[str] = None,
     metadata: Optional[dict] = None,
-) -> WorkspaceFile:
+) -> UserWorkspaceFile:
     """
     Create a new workspace file record.
 
@@ -135,7 +79,7 @@ async def create_workspace_file(
         metadata: Optional additional metadata
 
     Returns:
-        Created WorkspaceFile instance
+        Created UserWorkspaceFile instance
     """
     # Normalize path to start with /
     if not path.startswith("/"):
@@ -159,37 +103,34 @@ async def create_workspace_file(
         f"Created workspace file {file.id} at path {path} "
         f"in workspace {workspace_id}"
     )
-    return WorkspaceFile.from_db(file)
+    return file
 
 
 async def get_workspace_file(
     file_id: str,
-    workspace_id: str,
+    workspace_id: Optional[str] = None,
-) -> Optional[WorkspaceFile]:
+) -> Optional[UserWorkspaceFile]:
     """
     Get a workspace file by ID.
 
     Args:
         file_id: The file ID
-        workspace_id: Workspace ID for scoping (required)
+        workspace_id: Optional workspace ID for validation
 
     Returns:
-        WorkspaceFile instance or None
+        UserWorkspaceFile instance or None
     """
-    where_clause: UserWorkspaceFileWhereInput = {
+    where_clause: dict = {"id": file_id, "isDeleted": False}
-        "id": file_id,
+    if workspace_id:
-        "isDeleted": False,
+        where_clause["workspaceId"] = workspace_id
-        "workspaceId": workspace_id,
-    }
 
-    file = await UserWorkspaceFile.prisma().find_first(where=where_clause)
+    return await UserWorkspaceFile.prisma().find_first(where=where_clause)
-    return WorkspaceFile.from_db(file) if file else None
 
 
 async def get_workspace_file_by_path(
     workspace_id: str,
     path: str,
-) -> Optional[WorkspaceFile]:
+) -> Optional[UserWorkspaceFile]:
     """
     Get a workspace file by its virtual path.
 
@@ -198,20 +139,19 @@ async def get_workspace_file_by_path(
         path: Virtual path
 
     Returns:
-        WorkspaceFile instance or None
+        UserWorkspaceFile instance or None
     """
     # Normalize path
     if not path.startswith("/"):
         path = f"/{path}"
 
-    file = await UserWorkspaceFile.prisma().find_first(
+    return await UserWorkspaceFile.prisma().find_first(
         where={
             "workspaceId": workspace_id,
             "path": path,
             "isDeleted": False,
         }
     )
-    return WorkspaceFile.from_db(file) if file else None
 
 
 async def list_workspace_files(
@@ -220,7 +160,7 @@ async def list_workspace_files(
     include_deleted: bool = False,
     limit: Optional[int] = None,
     offset: int = 0,
-) -> list[WorkspaceFile]:
+) -> list[UserWorkspaceFile]:
     """
     List files in a workspace.
 
@@ -232,7 +172,7 @@ async def list_workspace_files(
         offset: Number of files to skip
 
     Returns:
-        List of WorkspaceFile instances
+        List of UserWorkspaceFile instances
     """
     where_clause: UserWorkspaceFileWhereInput = {"workspaceId": workspace_id}
 
@@ -245,13 +185,12 @@ async def list_workspace_files(
             path_prefix = f"/{path_prefix}"
         where_clause["path"] = {"startswith": path_prefix}
 
-    files = await UserWorkspaceFile.prisma().find_many(
+    return await UserWorkspaceFile.prisma().find_many(
         where=where_clause,
         order={"createdAt": "desc"},
         take=limit,
         skip=offset,
     )
-    return [WorkspaceFile.from_db(f) for f in files]
 
 
 async def count_workspace_files(
@@ -270,7 +209,7 @@ async def count_workspace_files(
     Returns:
         Number of files
     """
-    where_clause: UserWorkspaceFileWhereInput = {"workspaceId": workspace_id}
+    where_clause: dict = {"workspaceId": workspace_id}
     if not include_deleted:
         where_clause["isDeleted"] = False
 
@@ -285,8 +224,8 @@ async def count_workspace_files(
 
 async def soft_delete_workspace_file(
     file_id: str,
-    workspace_id: str,
+    workspace_id: Optional[str] = None,
-) -> Optional[WorkspaceFile]:
+) -> Optional[UserWorkspaceFile]:
     """
     Soft-delete a workspace file.
 
@@ -295,10 +234,10 @@ async def soft_delete_workspace_file(
 
     Args:
         file_id: The file ID
-        workspace_id: Workspace ID for scoping (required)
+        workspace_id: Optional workspace ID for validation
 
     Returns:
-        Updated WorkspaceFile instance or None if not found
+        Updated UserWorkspaceFile instance or None if not found
     """
     # First verify the file exists and belongs to workspace
     file = await get_workspace_file(file_id, workspace_id)
@@ -320,7 +259,7 @@ async def soft_delete_workspace_file(
     )
 
     logger.info(f"Soft-deleted workspace file {file_id}")
-    return WorkspaceFile.from_db(updated) if updated else None
+    return updated
 
 
 async def get_workspace_total_size(workspace_id: str) -> int:
@@ -334,4 +273,4 @@ async def get_workspace_total_size(workspace_id: str) -> int:
         Total size in bytes
     """
     files = await list_workspace_files(workspace_id)
-    return sum(file.size_bytes for file in files)
+    return sum(file.sizeBytes for file in files)

autogpt_platform/backend/backend/db.py

@@ -1,5 +1,5 @@
 from backend.app import run_processes
-from backend.data.db_manager import DatabaseManager
+from backend.executor import DatabaseManager
 
 
 def main():

autogpt_platform/backend/backend/executor/__init__.py

@@ -1,7 +1,11 @@
+from .database import DatabaseManager, DatabaseManagerAsyncClient, DatabaseManagerClient
 from .manager import ExecutionManager
 from .scheduler import Scheduler
 
 __all__ = [
+    "DatabaseManager",
+    "DatabaseManagerClient",
+    "DatabaseManagerAsyncClient",
     "ExecutionManager",
     "Scheduler",
 ]

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -22,7 +22,7 @@ from backend.util.settings import Settings
 from backend.util.truncate import truncate
 
 if TYPE_CHECKING:
-    from backend.data.db_manager import DatabaseManagerAsyncClient
+    from backend.executor import DatabaseManagerAsyncClient
 
 logger = logging.getLogger(__name__)
 

autogpt_platform/backend/backend/executor/automod/manager.py

@@ -4,7 +4,7 @@ import logging
 from typing import TYPE_CHECKING, Any, Literal
 
 if TYPE_CHECKING:
-    from backend.data.db_manager import DatabaseManagerAsyncClient
+    from backend.executor import DatabaseManagerAsyncClient
 
 from pydantic import ValidationError
 

autogpt_platform/backend/backend/executor/cluster_lock.py

@@ -1,7 +1,6 @@
 """Redis-based distributed locking for cluster coordination."""
 
 import logging
-import threading
 import time
 from typing import TYPE_CHECKING
 
@@ -20,7 +19,6 @@ class ClusterLock:
         self.owner_id = owner_id
         self.timeout = timeout
         self._last_refresh = 0.0
-        self._refresh_lock = threading.Lock()
 
     def try_acquire(self) -> str | None:
         """Try to acquire the lock.
@@ -33,8 +31,7 @@ class ClusterLock:
         try:
             success = self.redis.set(self.key, self.owner_id, nx=True, ex=self.timeout)
             if success:
-                with self._refresh_lock:
+                self._last_refresh = time.time()
-                    self._last_refresh = time.time()
                 return self.owner_id  # Successfully acquired
 
             # Failed to acquire, get current owner
@@ -60,27 +57,23 @@ class ClusterLock:
         Rate limited to at most once every timeout/10 seconds (minimum 1 second).
         During rate limiting, still verifies lock existence but skips TTL extension.
         Setting _last_refresh to 0 bypasses rate limiting for testing.
-
-        Thread-safe: uses _refresh_lock to protect _last_refresh access.
         """
         # Calculate refresh interval: max(timeout // 10, 1)
         refresh_interval = max(self.timeout // 10, 1)
         current_time = time.time()
 
-        # Check if we're within the rate limit period (thread-safe read)
+        # Check if we're within the rate limit period
         # _last_refresh == 0 forces a refresh (bypasses rate limiting for testing)
-        with self._refresh_lock:
-            last_refresh = self._last_refresh
         is_rate_limited = (
-            last_refresh > 0 and (current_time - last_refresh) < refresh_interval
+            self._last_refresh > 0
+            and (current_time - self._last_refresh) < refresh_interval
         )
 
         try:
             # Always verify lock existence, even during rate limiting
             current_value = self.redis.get(self.key)
             if not current_value:
-                with self._refresh_lock:
+                self._last_refresh = 0
-                    self._last_refresh = 0
                 return False
 
             stored_owner = (
@@ -89,8 +82,7 @@ class ClusterLock:
                 else str(current_value)
             )
             if stored_owner != self.owner_id:
-                with self._refresh_lock:
+                self._last_refresh = 0
-                    self._last_refresh = 0
                 return False
 
             # If rate limited, return True but don't update TTL or timestamp
@@ -99,30 +91,25 @@ class ClusterLock:
 
             # Perform actual refresh
             if self.redis.expire(self.key, self.timeout):
-                with self._refresh_lock:
+                self._last_refresh = current_time
-                    self._last_refresh = current_time
                 return True
 
-            with self._refresh_lock:
+            self._last_refresh = 0
-                self._last_refresh = 0
             return False
 
         except Exception as e:
             logger.error(f"ClusterLock.refresh failed for key {self.key}: {e}")
-            with self._refresh_lock:
+            self._last_refresh = 0
-                self._last_refresh = 0
             return False
 
     def release(self):
         """Release the lock."""
-        with self._refresh_lock:
+        if self._last_refresh == 0:
-            if self._last_refresh == 0:
+            return
-                return
 
         try:
             self.redis.delete(self.key)
         except Exception:
             pass
 
-        with self._refresh_lock:
+        self._last_refresh = 0.0
-            self._last_refresh = 0.0

autogpt_platform/backend/backend/executor/database.py

@@ -4,26 +4,14 @@ from typing import TYPE_CHECKING, Callable, Concatenate, ParamSpec, TypeVar, cas
 
 from backend.api.features.library.db import (
     add_store_agent_to_library,
-    create_graph_in_library,
-    create_library_agent,
-    get_library_agent,
-    get_library_agent_by_graph_id,
     list_library_agents,
-    update_graph_in_library,
-)
-from backend.api.features.store.db import (
-    get_agent,
-    get_available_graph,
-    get_store_agent_details,
-    get_store_agents,
 )
+from backend.api.features.store.db import get_store_agent_details, get_store_agents
 from backend.api.features.store.embeddings import (
     backfill_missing_embeddings,
     cleanup_orphaned_embeddings,
     get_embedding_stats,
 )
-from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.copilot import db as chat_db
 from backend.data import db
 from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
@@ -60,7 +48,6 @@ from backend.data.graph import (
     get_graph_metadata,
     get_graph_settings,
     get_node,
-    get_store_listed_graphs,
     validate_graph_execution_permissions,
 )
 from backend.data.human_review import (
@@ -80,10 +67,6 @@ from backend.data.notifications import (
     remove_notifications_from_batch,
 )
 from backend.data.onboarding import increment_onboarding_runs
-from backend.data.understanding import (
-    get_business_understanding,
-    upsert_business_understanding,
-)
 from backend.data.user import (
     get_active_user_ids_in_timerange,
     get_user_by_id,
@@ -93,15 +76,6 @@ from backend.data.user import (
     get_user_notification_preference,
     update_user_integrations,
 )
-from backend.data.workspace import (
-    count_workspace_files,
-    create_workspace_file,
-    get_or_create_workspace,
-    get_workspace_file,
-    get_workspace_file_by_path,
-    list_workspace_files,
-    soft_delete_workspace_file,
-)
 from backend.util.service import (
     AppService,
     AppServiceClient,
@@ -133,13 +107,6 @@ async def _get_credits(user_id: str) -> int:
 
 
 class DatabaseManager(AppService):
-    """Database connection pooling service.
-
-    This service connects to the Prisma engine and exposes database
-    operations via RPC endpoints. It acts as a centralized connection pool
-    for all services that need database access.
-    """
-
     @asynccontextmanager
     async def lifespan(self, app: "FastAPI"):
         async with super().lifespan(app):
@@ -175,15 +142,11 @@ class DatabaseManager(AppService):
     def _(
         f: Callable[P, R], name: str | None = None
     ) -> Callable[Concatenate[object, P], R]:
-        """
-        Exposes a function as an RPC endpoint, and adds a virtual `self` param
-        to the function's type so it can be bound as a method.
-        """
         if name is not None:
             f.__name__ = name
         return cast(Callable[Concatenate[object, P], R], expose(f))
 
-    # ============ Graph Executions ============ #
+    # Executions
     get_child_graph_executions = _(get_child_graph_executions)
     get_graph_executions = _(get_graph_executions)
     get_graph_executions_count = _(get_graph_executions_count)
@@ -207,37 +170,36 @@ class DatabaseManager(AppService):
     get_frequently_executed_graphs = _(get_frequently_executed_graphs)
     get_marketplace_graphs_for_monitoring = _(get_marketplace_graphs_for_monitoring)
 
-    # ============ Graphs ============ #
+    # Graphs
     get_node = _(get_node)
     get_graph = _(get_graph)
     get_connected_output_nodes = _(get_connected_output_nodes)
     get_graph_metadata = _(get_graph_metadata)
     get_graph_settings = _(get_graph_settings)
-    get_store_listed_graphs = _(get_store_listed_graphs)
 
-    # ============ Credits ============ #
+    # Credits
     spend_credits = _(_spend_credits, name="spend_credits")
     get_credits = _(_get_credits, name="get_credits")
 
-    # ============ User + Integrations ============ #
+    # User + User Metadata + User Integrations
-    get_user_by_id = _(get_user_by_id)
     get_user_integrations = _(get_user_integrations)
     update_user_integrations = _(update_user_integrations)
 
-    # ============ User Comms ============ #
+    # User Comms - async
     get_active_user_ids_in_timerange = _(get_active_user_ids_in_timerange)
+    get_user_by_id = _(get_user_by_id)
     get_user_email_by_id = _(get_user_email_by_id)
     get_user_email_verification = _(get_user_email_verification)
     get_user_notification_preference = _(get_user_notification_preference)
 
-    # ============ Human In The Loop ============ #
+    # Human In The Loop
     cancel_pending_reviews_for_execution = _(cancel_pending_reviews_for_execution)
     check_approval = _(check_approval)
     get_or_create_human_review = _(get_or_create_human_review)
     has_pending_reviews_for_graph_exec = _(has_pending_reviews_for_graph_exec)
     update_review_processed_status = _(update_review_processed_status)
 
-    # ============ Notifications ============ #
+    # Notifications - async
     clear_all_user_notification_batches = _(clear_all_user_notification_batches)
     create_or_add_to_user_notification_batch = _(
         create_or_add_to_user_notification_batch
@@ -250,62 +212,29 @@ class DatabaseManager(AppService):
         get_user_notification_oldest_message_in_batch
     )
 
-    # ============ Library ============ #
+    # Library
     list_library_agents = _(list_library_agents)
     add_store_agent_to_library = _(add_store_agent_to_library)
-    create_graph_in_library = _(create_graph_in_library)
-    create_library_agent = _(create_library_agent)
-    get_library_agent = _(get_library_agent)
-    get_library_agent_by_graph_id = _(get_library_agent_by_graph_id)
-    update_graph_in_library = _(update_graph_in_library)
     validate_graph_execution_permissions = _(validate_graph_execution_permissions)
 
-    # ============ Onboarding ============ #
+    # Onboarding
     increment_onboarding_runs = _(increment_onboarding_runs)
 
-    # ============ OAuth ============ #
+    # OAuth
     cleanup_expired_oauth_tokens = _(cleanup_expired_oauth_tokens)
 
-    # ============ Store ============ #
+    # Store
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
-    get_agent = _(get_agent)
-    get_available_graph = _(get_available_graph)
 
-    # ============ Search ============ #
+    # Store Embeddings
     get_embedding_stats = _(get_embedding_stats)
     backfill_missing_embeddings = _(backfill_missing_embeddings)
     cleanup_orphaned_embeddings = _(cleanup_orphaned_embeddings)
-    unified_hybrid_search = _(unified_hybrid_search)
 
-    # ============ Summary Data ============ #
+    # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
 
-    # ============ Workspace ============ #
-    count_workspace_files = _(count_workspace_files)
-    create_workspace_file = _(create_workspace_file)
-    get_or_create_workspace = _(get_or_create_workspace)
-    get_workspace_file = _(get_workspace_file)
-    get_workspace_file_by_path = _(get_workspace_file_by_path)
-    list_workspace_files = _(list_workspace_files)
-    soft_delete_workspace_file = _(soft_delete_workspace_file)
-
-    # ============ Understanding ============ #
-    get_business_understanding = _(get_business_understanding)
-    upsert_business_understanding = _(upsert_business_understanding)
-
-    # ============ CoPilot Chat Sessions ============ #
-    get_chat_session = _(chat_db.get_chat_session)
-    create_chat_session = _(chat_db.create_chat_session)
-    update_chat_session = _(chat_db.update_chat_session)
-    add_chat_message = _(chat_db.add_chat_message)
-    add_chat_messages_batch = _(chat_db.add_chat_messages_batch)
-    get_user_chat_sessions = _(chat_db.get_user_chat_sessions)
-    get_user_session_count = _(chat_db.get_user_session_count)
-    delete_chat_session = _(chat_db.delete_chat_session)
-    get_chat_session_message_count = _(chat_db.get_chat_session_message_count)
-    update_tool_message_content = _(chat_db.update_tool_message_content)
-
 
 class DatabaseManagerClient(AppServiceClient):
     d = DatabaseManager
@@ -367,50 +296,43 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     def get_service_type(cls):
         return DatabaseManager
 
-    # ============ Graph Executions ============ #
     create_graph_execution = d.create_graph_execution
     get_child_graph_executions = d.get_child_graph_executions
     get_connected_output_nodes = d.get_connected_output_nodes
     get_latest_node_execution = d.get_latest_node_execution
-    get_graph_execution = d.get_graph_execution
-    get_graph_execution_meta = d.get_graph_execution_meta
-    get_graph_executions = d.get_graph_executions
-    get_node_execution = d.get_node_execution
-    get_node_executions = d.get_node_executions
-    update_graph_execution_stats = d.update_graph_execution_stats
-    update_node_execution_status = d.update_node_execution_status
-    update_node_execution_status_batch = d.update_node_execution_status_batch
-    upsert_execution_input = d.upsert_execution_input
-    upsert_execution_output = d.upsert_execution_output
-    get_execution_outputs_by_node_exec_id = d.get_execution_outputs_by_node_exec_id
-    get_execution_kv_data = d.get_execution_kv_data
-    set_execution_kv_data = d.set_execution_kv_data
-
-    # ============ Graphs ============ #
     get_graph = d.get_graph
     get_graph_metadata = d.get_graph_metadata
     get_graph_settings = d.get_graph_settings
+    get_graph_execution = d.get_graph_execution
+    get_graph_execution_meta = d.get_graph_execution_meta
     get_node = d.get_node
-    get_store_listed_graphs = d.get_store_listed_graphs
+    get_node_execution = d.get_node_execution
-
+    get_node_executions = d.get_node_executions
-    # ============ User + Integrations ============ #
     get_user_by_id = d.get_user_by_id
     get_user_integrations = d.get_user_integrations
+    upsert_execution_input = d.upsert_execution_input
+    upsert_execution_output = d.upsert_execution_output
+    get_execution_outputs_by_node_exec_id = d.get_execution_outputs_by_node_exec_id
+    update_graph_execution_stats = d.update_graph_execution_stats
+    update_node_execution_status = d.update_node_execution_status
+    update_node_execution_status_batch = d.update_node_execution_status_batch
     update_user_integrations = d.update_user_integrations
+    get_execution_kv_data = d.get_execution_kv_data
+    set_execution_kv_data = d.set_execution_kv_data
 
-    # ============ Human In The Loop ============ #
+    # Human In The Loop
     cancel_pending_reviews_for_execution = d.cancel_pending_reviews_for_execution
     check_approval = d.check_approval
     get_or_create_human_review = d.get_or_create_human_review
     update_review_processed_status = d.update_review_processed_status
 
-    # ============ User Comms ============ #
+    # User Comms
     get_active_user_ids_in_timerange = d.get_active_user_ids_in_timerange
     get_user_email_by_id = d.get_user_email_by_id
     get_user_email_verification = d.get_user_email_verification
     get_user_notification_preference = d.get_user_notification_preference
 
-    # ============ Notifications ============ #
+    # Notifications
     clear_all_user_notification_batches = d.clear_all_user_notification_batches
     create_or_add_to_user_notification_batch = (
         d.create_or_add_to_user_notification_batch
@@ -423,55 +345,20 @@ class DatabaseManagerAsyncClient(AppServiceClient):
         d.get_user_notification_oldest_message_in_batch
     )
 
-    # ============ Library ============ #
+    # Library
     list_library_agents = d.list_library_agents
     add_store_agent_to_library = d.add_store_agent_to_library
-    create_graph_in_library = d.create_graph_in_library
-    create_library_agent = d.create_library_agent
-    get_library_agent = d.get_library_agent
-    get_library_agent_by_graph_id = d.get_library_agent_by_graph_id
-    update_graph_in_library = d.update_graph_in_library
     validate_graph_execution_permissions = d.validate_graph_execution_permissions
 
-    # ============ Onboarding ============ #
+    # Onboarding
     increment_onboarding_runs = d.increment_onboarding_runs
 
-    # ============ OAuth ============ #
+    # OAuth
     cleanup_expired_oauth_tokens = d.cleanup_expired_oauth_tokens
 
-    # ============ Store ============ #
+    # Store
     get_store_agents = d.get_store_agents
     get_store_agent_details = d.get_store_agent_details
-    get_agent = d.get_agent
-    get_available_graph = d.get_available_graph
 
-    # ============ Search ============ #
+    # Summary data
-    unified_hybrid_search = d.unified_hybrid_search
-
-    # ============ Summary Data ============ #
     get_user_execution_summary_data = d.get_user_execution_summary_data
-
-    # ============ Workspace ============ #
-    count_workspace_files = d.count_workspace_files
-    create_workspace_file = d.create_workspace_file
-    get_or_create_workspace = d.get_or_create_workspace
-    get_workspace_file = d.get_workspace_file
-    get_workspace_file_by_path = d.get_workspace_file_by_path
-    list_workspace_files = d.list_workspace_files
-    soft_delete_workspace_file = d.soft_delete_workspace_file
-
-    # ============ Understanding ============ #
-    get_business_understanding = d.get_business_understanding
-    upsert_business_understanding = d.upsert_business_understanding
-
-    # ============ CoPilot Chat Sessions ============ #
-    get_chat_session = d.get_chat_session
-    create_chat_session = d.create_chat_session
-    update_chat_session = d.update_chat_session
-    add_chat_message = d.add_chat_message
-    add_chat_messages_batch = d.add_chat_messages_batch
-    get_user_chat_sessions = d.get_user_chat_sessions
-    get_user_session_count = d.get_user_session_count
-    delete_chat_session = d.delete_chat_session
-    get_chat_session_message_count = d.get_chat_session_message_count
-    update_tool_message_content = d.update_tool_message_content

autogpt_platform/backend/backend/executor/llm_registry_init.py

@@ -0,0 +1,67 @@
+"""
+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.blocks._base import BlockSchema
+from backend.data import db, llm_registry
+from backend.data.block import 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()
+        await 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)