Test check

.branchlet.json

@@ -29,7 +29,8 @@
   "postCreateCmd": [
     "cd autogpt_platform/autogpt_libs && poetry install",
     "cd autogpt_platform/backend && poetry install && poetry run prisma generate",
-    "cd autogpt_platform/frontend && pnpm install"
+    "cd autogpt_platform/frontend && pnpm install",
+    "cd docs && pip install -r requirements.txt"
   ],
   "terminalCommand": "code .",
   "deleteBranchWithWorktree": false

.github/copilot-instructions.md

@@ -160,7 +160,7 @@ pnpm storybook                      # Start component development server
 
 **Backend Entry Points:**
 
-- `backend/backend/api/rest_api.py` - FastAPI application setup
+- `backend/backend/server/server.py` - FastAPI application setup
 - `backend/backend/data/` - Database models and user management
 - `backend/blocks/` - Agent execution blocks and logic
 
@@ -219,7 +219,7 @@ Agents are built using a visual block-based system where each block performs a s
 
 ### API Development
 
-1. Update routes in `/backend/backend/api/features/`
+1. Update routes in `/backend/backend/server/routers/`
 2. Add/update Pydantic models in same directory
 3. Write tests alongside route files
 4. For `data/*.py` changes, validate user ID checks
@@ -285,7 +285,7 @@ Agents are built using a visual block-based system where each block performs a s
 
 ### Security Guidelines
 
-**Cache Protection Middleware** (`/backend/backend/api/middleware/security.py`):
+**Cache Protection Middleware** (`/backend/backend/server/middleware/security.py`):
 
 - Default: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
 - Uses allow list approach for cacheable paths (static assets, health checks, public pages)

.gitignore

@@ -178,6 +178,4 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-CLAUDE.local.md
 /autogpt_platform/backend/logs
-.next

AGENTS.md

@@ -16,6 +16,7 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
+
 ## Frontend guidelines:
 
 See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
@@ -32,17 +33,14 @@ See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
 5. **Testing**: Add Storybook stories for new components, Playwright for E2E
 6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
-
 - Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
 - Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
 - Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
 - Avoid large hooks, abstract logic into `helpers.ts` files when sensible
 - Use function declarations for components, arrow functions only for callbacks
 - No barrel files or `index.ts` re-exports
+- Do not use `useCallback` or `useMemo` unless strictly needed
 - Avoid comments at all times unless the code is very complex
-- Do not use `useCallback` or `useMemo` unless asked to optimise a given function
-- Do not type hook returns, let Typescript infer as much as possible
-- Never type with `any`, if not types available use `unknown`
 
 ## Testing
 
@@ -51,8 +49,22 @@ See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
 
 Always run the relevant linters and tests before committing.
 Use conventional commit messages for all commits (e.g. `feat(backend): add API`).
-Types: - feat - fix - refactor - ci - dx (developer experience)
-Scopes: - platform - platform/library - platform/marketplace - backend - backend/executor - frontend - frontend/library - frontend/marketplace - blocks
+  Types:
+    - feat
+    - fix
+    - refactor
+    - ci
+    - dx (developer experience)
+  Scopes:
+    - platform
+      - platform/library
+      - platform/marketplace
+      - backend
+        - backend/executor
+      - frontend
+        - frontend/library
+        - frontend/marketplace
+      - blocks
 
 ## Pull requests
 

README.md

@@ -54,7 +54,7 @@ Before proceeding with the installation, ensure your system meets the following
 ### Updated Setup Instructions:
 We've moved to a fully maintained and regularly updated documentation site.
 
-👉 [Follow the official self-hosting guide here](https://agpt.co/docs/platform/getting-started/getting-started)
+👉 [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/)
 
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.

autogpt_platform/CLAUDE.md

@@ -6,30 +6,152 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 AutoGPT Platform is a monorepo containing:
 
-- **Backend** (`backend`): Python FastAPI server with async support
-- **Frontend** (`frontend`): Next.js React application
-- **Shared Libraries** (`autogpt_libs`): Common Python utilities
+- **Backend** (`/backend`): Python FastAPI server with async support
+- **Frontend** (`/frontend`): Next.js React application
+- **Shared Libraries** (`/autogpt_libs`): Common Python utilities
 
-## Component Documentation
+## Essential Commands
 
-- **Backend**: See @backend/CLAUDE.md for backend-specific commands, architecture, and development tasks
-- **Frontend**: See @frontend/CLAUDE.md for frontend-specific commands, architecture, and development patterns
+### Backend Development
 
-## Key Concepts
+```bash
+# Install dependencies
+cd backend && poetry install
+
+# Run database migrations
+poetry run prisma migrate dev
+
+# Start all services (database, redis, rabbitmq, clamav)
+docker compose up -d
+
+# Run the backend server
+poetry run serve
+
+# Run tests
+poetry run test
+
+# Run specific test
+poetry run pytest path/to/test_file.py::test_function_name
+
+# Run block tests (tests that validate all blocks work correctly)
+poetry run pytest backend/blocks/test/test_block.py -xvs
+
+# Run tests for a specific block (e.g., GetCurrentTimeBlock)
+poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
+
+# Lint and format
+# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
+poetry run format  # Black + isort
+poetry run lint    # ruff
+```
+
+More details can be found in TESTING.md
+
+#### Creating/Updating Snapshots
+
+When you first write a test or when the expected output changes:
+
+```bash
+poetry run pytest path/to/test.py --snapshot-update
+```
+
+⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
+
+### Frontend Development
+
+```bash
+# Install dependencies
+cd frontend && pnpm i
+
+# Generate API client from OpenAPI spec
+pnpm generate:api
+
+# Start development server
+pnpm dev
+
+# Run E2E tests
+pnpm test
+
+# Run Storybook for component development
+pnpm storybook
+
+# Build production
+pnpm build
+
+# Format and lint
+pnpm format
+
+# Type checking
+pnpm types
+```
+
+**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
+
+**Key Frontend Conventions:**
+
+- Separate render logic from data/behavior in components
+- Use generated API hooks from `@/app/api/__generated__/endpoints/`
+- Use function declarations (not arrow functions) for components/handlers
+- Use design system components from `src/components/` (atoms, molecules, organisms)
+- Only use Phosphor Icons
+- Never use `src/components/__legacy__/*` or deprecated `BackendAPI`
+
+## Architecture Overview
+
+### Backend Architecture
+
+- **API Layer**: FastAPI with REST and WebSocket endpoints
+- **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
+- **Queue System**: RabbitMQ for async task processing
+- **Execution Engine**: Separate executor service processes agent workflows
+- **Authentication**: JWT-based with Supabase integration
+- **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
+
+### Frontend Architecture
+
+- **Framework**: Next.js 15 App Router (client-first approach)
+- **Data Fetching**: Type-safe generated API hooks via Orval + React Query
+- **State Management**: React Query for server state, co-located UI state in components/hooks
+- **Component Structure**: Separate render logic (`.tsx`) from business logic (`use*.ts` hooks)
+- **Workflow Builder**: Visual graph editor using @xyflow/react
+- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
+- **Icons**: Phosphor Icons only
+- **Feature Flags**: LaunchDarkly integration
+- **Error Handling**: ErrorCard for render errors, toast for mutations, Sentry for exceptions
+- **Testing**: Playwright for E2E, Storybook for component development
+
+### Key Concepts
 
 1. **Agent Graphs**: Workflow definitions stored as JSON, executed by the backend
-2. **Blocks**: Reusable components in `backend/backend/blocks/` that perform specific tasks
+2. **Blocks**: Reusable components in `/backend/blocks/` that perform specific tasks
 3. **Integrations**: OAuth and API connections stored per user
 4. **Store**: Marketplace for sharing agent templates
 5. **Virus Scanning**: ClamAV integration for file upload security
 
+### Testing Approach
+
+- Backend uses pytest with snapshot testing for API responses
+- Test files are colocated with source files (`*_test.py`)
+- Frontend uses Playwright for E2E tests
+- Component testing via Storybook
+
+### Database Schema
+
+Key models (defined in `/backend/schema.prisma`):
+
+- `User`: Authentication and profile data
+- `AgentGraph`: Workflow definitions with version control
+- `AgentGraphExecution`: Execution history and results
+- `AgentNode`: Individual nodes in a workflow
+- `StoreListing`: Marketplace listings for sharing agents
+
 ### Environment Configuration
 
 #### Configuration Files
 
-- **Backend**: `backend/.env.default` (defaults) → `backend/.env` (user overrides)
-- **Frontend**: `frontend/.env.default` (defaults) → `frontend/.env` (user overrides)
-- **Platform**: `.env.default` (Supabase/shared defaults) → `.env` (user overrides)
+- **Backend**: `/backend/.env.default` (defaults) → `/backend/.env` (user overrides)
+- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
+- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
 
 #### Docker Environment Loading Order
 
@@ -45,12 +167,83 @@ AutoGPT Platform is a monorepo containing:
 - Backend/Frontend services use YAML anchors for consistent configuration
 - Supabase services (`db/docker/docker-compose.yml`) follow the same pattern
 
+### Common Development Tasks
+
+**Adding a new block:**
+
+Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-sdk-guide.md) which covers:
+
+- Provider configuration with `ProviderBuilder`
+- Block schema definition
+- Authentication (API keys, OAuth, webhooks)
+- Testing and validation
+- File organization
+
+Quick steps:
+
+1. Create new file in `/backend/backend/blocks/`
+2. Configure provider using `ProviderBuilder` in `_config.py`
+3. Inherit from `Block` base class
+4. Define input/output schemas using `BlockSchema`
+5. Implement async `run` method
+6. Generate unique block ID using `uuid.uuid4()`
+7. Test with `poetry run pytest backend/blocks/test/test_block.py`
+
+Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
+ex: do the inputs and outputs tie well together?
+
+If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
+
+**Modifying the API:**
+
+1. Update route in `/backend/backend/server/routers/`
+2. Add/update Pydantic models in same directory
+3. Write tests alongside the route file
+4. Run `poetry run test` to verify
+
+### Frontend guidelines:
+
+See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
+
+1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
+   - Add `usePageName.ts` hook for logic
+   - Put sub-components in local `components/` folder
+2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
+   - Use design system components from `src/components/` (atoms, molecules, organisms)
+   - Never use `src/components/__legacy__/*`
+3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
+   - Regenerate with `pnpm generate:api`
+   - Pattern: `use{Method}{Version}{OperationName}`
+4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
+5. **Testing**: Add Storybook stories for new components, Playwright for E2E
+6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
+- Component props should be `interface Props { ... }` (not exported) unless the interface needs to be used outside the component
+- Separate render logic from business logic (component.tsx + useComponent.ts + helpers.ts)
+- Colocate state when possible and avoid creating large components, use sub-components ( local `/components` folder next to the parent component ) when sensible
+- Avoid large hooks, abstract logic into `helpers.ts` files when sensible
+- Use function declarations for components, arrow functions only for callbacks
+- No barrel files or `index.ts` re-exports
+- Do not use `useCallback` or `useMemo` unless strictly needed
+- Avoid comments at all times unless the code is very complex
+
+### Security Implementation
+
+**Cache Protection Middleware:**
+
+- Located in `/backend/backend/server/middleware/security.py`
+- Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
+- Uses an allow list approach - only explicitly permitted paths can be cached
+- Cacheable paths include: static assets (`/static/*`, `/_next/static/*`), health checks, public store pages, documentation
+- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
+- To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
+- Applied to both main API server and external API applications
+
 ### Creating Pull Requests
 
-- Create the PR against the `dev` branch of the repository.
-- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)
-- Use conventional commit messages (see below)
-- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description
+- Create the PR aginst the `dev` branch of the repository.
+- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
+- Use conventional commit messages (see below)/
+- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
 - Run the github pre-commit hooks to ensure code quality.
 
 ### Reviewing/Revising Pull Requests

autogpt_platform/backend/.env.default

@@ -178,10 +178,5 @@ AYRSHARE_JWT_KEY=
 SMARTLEAD_API_KEY=
 ZEROBOUNCE_API_KEY=
 
-# PostHog Analytics
-# Get API key from https://posthog.com - Project Settings > Project API Key
-POSTHOG_API_KEY=
-POSTHOG_HOST=https://eu.i.posthog.com
-
 # Other Services
 AUTOMOD_API_KEY=

autogpt_platform/backend/CLAUDE.md

@@ -1,170 +0,0 @@
-# CLAUDE.md - Backend
-
-This file provides guidance to Claude Code when working with the backend.
-
-## Essential Commands
-
-To run something with Python package dependencies you MUST use `poetry run ...`.
-
-```bash
-# Install dependencies
-poetry install
-
-# Run database migrations
-poetry run prisma migrate dev
-
-# Start all services (database, redis, rabbitmq, clamav)
-docker compose up -d
-
-# Run the backend as a whole
-poetry run app
-
-# Run tests
-poetry run test
-
-# Run specific test
-poetry run pytest path/to/test_file.py::test_function_name
-
-# Run block tests (tests that validate all blocks work correctly)
-poetry run pytest backend/blocks/test/test_block.py -xvs
-
-# Run tests for a specific block (e.g., GetCurrentTimeBlock)
-poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
-
-# Lint and format
-# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
-poetry run format  # Black + isort
-poetry run lint    # ruff
-```
-
-More details can be found in @TESTING.md
-
-### Creating/Updating Snapshots
-
-When you first write a test or when the expected output changes:
-
-```bash
-poetry run pytest path/to/test.py --snapshot-update
-```
-
-⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
-
-## Architecture
-
-- **API Layer**: FastAPI with REST and WebSocket endpoints
-- **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
-- **Queue System**: RabbitMQ for async task processing
-- **Execution Engine**: Separate executor service processes agent workflows
-- **Authentication**: JWT-based with Supabase integration
-- **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
-
-## Testing Approach
-
-- Uses pytest with snapshot testing for API responses
-- Test files are colocated with source files (`*_test.py`)
-
-## Database Schema
-
-Key models (defined in `schema.prisma`):
-
-- `User`: Authentication and profile data
-- `AgentGraph`: Workflow definitions with version control
-- `AgentGraphExecution`: Execution history and results
-- `AgentNode`: Individual nodes in a workflow
-- `StoreListing`: Marketplace listings for sharing agents
-
-## Environment Configuration
-
-- **Backend**: `.env.default` (defaults) → `.env` (user overrides)
-
-## Common Development Tasks
-
-### Adding a new block
-
-Follow the comprehensive [Block SDK Guide](@../../docs/content/platform/block-sdk-guide.md) which covers:
-
-- Provider configuration with `ProviderBuilder`
-- Block schema definition
-- Authentication (API keys, OAuth, webhooks)
-- Testing and validation
-- File organization
-
-Quick steps:
-
-1. Create new file in `backend/blocks/`
-2. Configure provider using `ProviderBuilder` in `_config.py`
-3. Inherit from `Block` base class
-4. Define input/output schemas using `BlockSchema`
-5. Implement async `run` method
-6. Generate unique block ID using `uuid.uuid4()`
-7. Test with `poetry run pytest backend/blocks/test/test_block.py`
-
-Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph-based editor or would they struggle to connect productively?
-ex: do the inputs and outputs tie well together?
-
-If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
-
-#### Handling files in blocks with `store_media_file()`
-
-When blocks need to work with files (images, videos, documents), use `store_media_file()` from `backend.util.file`. The `return_format` parameter determines what you get back:
-
-| Format | Use When | Returns |
-|--------|----------|---------|
-| `"for_local_processing"` | Processing with local tools (ffmpeg, MoviePy, PIL) | Local file path (e.g., `"image.png"`) |
-| `"for_external_api"` | Sending content to external APIs (Replicate, OpenAI) | Data URI (e.g., `"data:image/png;base64,..."`) |
-| `"for_block_output"` | Returning output from your block | Smart: `workspace://` in CoPilot, data URI in graphs |
-
-**Examples:**
-
-```python
-# INPUT: Need to process file locally with ffmpeg
-local_path = await store_media_file(
-    file=input_data.video,
-    execution_context=execution_context,
-    return_format="for_local_processing",
-)
-# local_path = "video.mp4" - use with Path/ffmpeg/etc
-
-# INPUT: Need to send to external API like Replicate
-image_b64 = await store_media_file(
-    file=input_data.image,
-    execution_context=execution_context,
-    return_format="for_external_api",
-)
-# image_b64 = "data:image/png;base64,iVBORw0..." - send to API
-
-# OUTPUT: Returning result from block
-result_url = await store_media_file(
-    file=generated_image_url,
-    execution_context=execution_context,
-    return_format="for_block_output",
-)
-yield "image_url", result_url
-# In CoPilot: result_url = "workspace://abc123"
-# In graphs:  result_url = "data:image/png;base64,..."
-```
-
-**Key points:**
-
-- `for_block_output` is the ONLY format that auto-adapts to execution context
-- Always use `for_block_output` for block outputs unless you have a specific reason not to
-- Never hardcode workspace checks - let `for_block_output` handle it
-
-### Modifying the API
-
-1. Update route in `backend/api/features/`
-2. Add/update Pydantic models in same directory
-3. Write tests alongside the route file
-4. Run `poetry run test` to verify
-
-## Security Implementation
-
-### Cache Protection Middleware
-
-- Located in `backend/api/middleware/security.py`
-- Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
-- Uses an allow list approach - only explicitly permitted paths can be cached
-- Cacheable paths include: static assets (`static/*`, `_next/static/*`), health checks, public store pages, documentation
-- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
-- To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
-- Applied to both main API server and external API applications

autogpt_platform/backend/TESTING.md

@@ -138,7 +138,7 @@ If the test doesn't need the `user_id` specifically, mocking is not necessary as
 
 #### Using Global Auth Fixtures
 
-Two global auth fixtures are provided by `backend/api/conftest.py`:
+Two global auth fixtures are provided by `backend/server/conftest.py`:
 
 - `mock_jwt_user` - Regular user with `test_user_id` ("test-user-id")
 - `mock_jwt_admin` - Admin user with `admin_user_id` ("admin-user-id")

autogpt_platform/backend/backend/api/external/v1/routes.py

@@ -86,8 +86,6 @@ async def execute_graph_block(
     obj = backend.data.block.get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
-    if obj.disabled:
-        raise HTTPException(status_code=403, detail=f"Block #{block_id} is disabled.")
 
     output = defaultdict(list)
     async for name, data in obj.execute(data):

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

@@ -17,7 +17,7 @@ router = fastapi.APIRouter(
 )
 
 
-# Taken from backend/api/features/store/db.py
+# Taken from backend/server/v2/store/db.py
 def sanitize_query(query: str | None) -> str | None:
     if query is None:
         return query

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

@@ -1,368 +0,0 @@
-"""Redis Streams consumer for operation completion messages.
-
-This module provides a consumer (ChatCompletionConsumer) that listens for
-completion notifications (OperationCompleteMessage) from external services
-(like Agent Generator) and triggers the appropriate stream registry and
-chat service updates via process_operation_success/process_operation_failure.
-
-Why Redis Streams instead of RabbitMQ?
---------------------------------------
-While the project typically uses RabbitMQ for async task queues (e.g., execution
-queue), Redis Streams was chosen for chat completion notifications because:
-
-1. **Unified Infrastructure**: The SSE reconnection feature already uses Redis
-   Streams (via stream_registry) for message persistence and replay. Using Redis
-   Streams for completion notifications keeps all chat streaming infrastructure
-   in one system, simplifying operations and reducing cross-system coordination.
-
-2. **Message Replay**: Redis Streams support XREAD with arbitrary message IDs,
-   allowing consumers to replay missed messages after reconnection. This aligns
-   with the SSE reconnection pattern where clients can resume from last_message_id.
-
-3. **Consumer Groups with XAUTOCLAIM**: Redis consumer groups provide automatic
-   load balancing across pods with explicit message claiming (XAUTOCLAIM) for
-   recovering from dead consumers - ideal for the completion callback pattern.
-
-4. **Lower Latency**: For real-time SSE updates, Redis (already in-memory for
-   stream_registry) provides lower latency than an additional RabbitMQ hop.
-
-5. **Atomicity with Task State**: Completion processing often needs to update
-   task metadata stored in Redis. Keeping both in Redis enables simpler
-   transactional semantics without distributed coordination.
-
-The consumer uses Redis Streams with consumer groups for reliable message
-processing across multiple platform pods, with XAUTOCLAIM for reclaiming
-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
-
-from backend.data.redis_client import get_redis_async
-
-from . import stream_registry
-from .completion_handler import process_operation_failure, process_operation_success
-from .config import ChatConfig
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-
-class OperationCompleteMessage(BaseModel):
-    """Message format for operation completion notifications."""
-
-    operation_id: str
-    task_id: str
-    success: bool
-    result: dict | str | None = None
-    error: str | None = None
-
-
-class ChatCompletionConsumer:
-    """Consumer for chat operation completion messages from Redis Streams.
-
-    This consumer initializes its own Prisma client in start() to ensure
-    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.
-    """
-
-    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:
-        """Start the completion consumer."""
-        if self._running:
-            logger.warning("Completion consumer already running")
-            return
-
-        # Create consumer group if it doesn't exist
-        try:
-            redis = await get_redis_async()
-            await redis.xgroup_create(
-                config.stream_completion_name,
-                config.stream_consumer_group,
-                id="0",
-                mkstream=True,
-            )
-            logger.info(
-                f"Created consumer group '{config.stream_consumer_group}' "
-                f"on stream '{config.stream_completion_name}'"
-            )
-        except ResponseError as e:
-            if "BUSYGROUP" in str(e):
-                logger.debug(
-                    f"Consumer group '{config.stream_consumer_group}' already exists"
-                )
-            else:
-                raise
-
-        self._running = True
-        self._consumer_task = asyncio.create_task(self._consume_messages())
-        logger.info(
-            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
-
-        if self._consumer_task:
-            self._consumer_task.cancel()
-            try:
-                await self._consumer_task
-            except asyncio.CancelledError:
-                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:
-        """Main message consumption loop with retry logic."""
-        max_retries = 10
-        retry_delay = 5  # seconds
-        retry_count = 0
-        block_timeout = 5000  # milliseconds
-
-        while self._running and retry_count < max_retries:
-            try:
-                redis = await get_redis_async()
-
-                # Reset retry count on successful connection
-                retry_count = 0
-
-                while self._running:
-                    # First, claim any stale pending messages from dead consumers
-                    # Redis does NOT auto-redeliver pending messages; we must explicitly
-                    # claim them using XAUTOCLAIM
-                    try:
-                        claimed_result = await redis.xautoclaim(
-                            name=config.stream_completion_name,
-                            groupname=config.stream_consumer_group,
-                            consumername=self._consumer_name,
-                            min_idle_time=config.stream_claim_min_idle_ms,
-                            start_id="0-0",
-                            count=10,
-                        )
-                        # xautoclaim returns: (next_start_id, [(id, data), ...], [deleted_ids])
-                        if claimed_result and len(claimed_result) >= 2:
-                            claimed_entries = claimed_result[1]
-                            if claimed_entries:
-                                logger.info(
-                                    f"Claimed {len(claimed_entries)} stale pending messages"
-                                )
-                                for entry_id, data in claimed_entries:
-                                    if not self._running:
-                                        return
-                                    await self._process_entry(redis, entry_id, data)
-                    except Exception as e:
-                        logger.warning(f"XAUTOCLAIM failed (non-fatal): {e}")
-
-                    # Read new messages from the stream
-                    messages = await redis.xreadgroup(
-                        groupname=config.stream_consumer_group,
-                        consumername=self._consumer_name,
-                        streams={config.stream_completion_name: ">"},
-                        block=block_timeout,
-                        count=10,
-                    )
-
-                    if not messages:
-                        continue
-
-                    for stream_name, entries in messages:
-                        for entry_id, data in entries:
-                            if not self._running:
-                                return
-                            await self._process_entry(redis, entry_id, data)
-
-            except asyncio.CancelledError:
-                logger.info("Consumer cancelled")
-                return
-            except Exception as e:
-                retry_count += 1
-                logger.error(
-                    f"Consumer error (retry {retry_count}/{max_retries}): {e}",
-                    exc_info=True,
-                )
-                if self._running and retry_count < max_retries:
-                    await asyncio.sleep(retry_delay)
-                else:
-                    logger.error("Max retries reached, stopping consumer")
-                    return
-
-    async def _process_entry(
-        self, redis: Any, entry_id: str, data: dict[str, Any]
-    ) -> None:
-        """Process a single stream entry and acknowledge it on success.
-
-        Args:
-            redis: Redis client connection
-            entry_id: The stream entry ID
-            data: The entry data dict
-        """
-        try:
-            # Handle the message
-            message_data = data.get("data")
-            if message_data:
-                await self._handle_message(
-                    message_data.encode()
-                    if isinstance(message_data, str)
-                    else message_data
-                )
-
-            # Acknowledge the message after successful processing
-            await redis.xack(
-                config.stream_completion_name,
-                config.stream_consumer_group,
-                entry_id,
-            )
-        except Exception as e:
-            logger.error(
-                f"Error processing completion message {entry_id}: {e}",
-                exc_info=True,
-            )
-            # Message remains in pending state and will be claimed by
-            # XAUTOCLAIM after min_idle_time expires
-
-    async def _handle_message(self, body: bytes) -> None:
-        """Handle a completion message using our own Prisma client."""
-        try:
-            data = orjson.loads(body)
-            message = OperationCompleteMessage(**data)
-        except Exception as e:
-            logger.error(f"Failed to parse completion message: {e}")
-            return
-
-        logger.info(
-            f"[COMPLETION] Received completion for operation {message.operation_id} "
-            f"(task_id={message.task_id}, success={message.success})"
-        )
-
-        # Find task in registry
-        task = await stream_registry.find_task_by_operation_id(message.operation_id)
-        if task is None:
-            task = await stream_registry.get_task(message.task_id)
-
-        if task is None:
-            logger.warning(
-                f"[COMPLETION] Task not found for operation {message.operation_id} "
-                f"(task_id={message.task_id})"
-            )
-            return
-
-        logger.info(
-            f"[COMPLETION] Found task: task_id={task.task_id}, "
-            f"session_id={task.session_id}, tool_call_id={task.tool_call_id}"
-        )
-
-        # Guard against empty task fields
-        if not task.task_id or not task.session_id or not task.tool_call_id:
-            logger.error(
-                f"[COMPLETION] Task has empty critical fields! "
-                f"task_id={task.task_id!r}, session_id={task.session_id!r}, "
-                f"tool_call_id={task.tool_call_id!r}"
-            )
-            return
-
-        if message.success:
-            await self._handle_success(task, message)
-        else:
-            await self._handle_failure(task, message)
-
-    async def _handle_success(
-        self,
-        task: stream_registry.ActiveTask,
-        message: OperationCompleteMessage,
-    ) -> None:
-        """Handle successful operation completion."""
-        prisma = await self._ensure_prisma()
-        await process_operation_success(task, message.result, prisma)
-
-    async def _handle_failure(
-        self,
-        task: stream_registry.ActiveTask,
-        message: OperationCompleteMessage,
-    ) -> None:
-        """Handle failed operation completion."""
-        prisma = await self._ensure_prisma()
-        await process_operation_failure(task, message.error, prisma)
-
-
-# Module-level consumer instance
-_consumer: ChatCompletionConsumer | None = None
-
-
-async def start_completion_consumer() -> None:
-    """Start the global completion consumer."""
-    global _consumer
-    if _consumer is None:
-        _consumer = ChatCompletionConsumer()
-    await _consumer.start()
-
-
-async def stop_completion_consumer() -> None:
-    """Stop the global completion consumer."""
-    global _consumer
-    if _consumer:
-        await _consumer.stop()
-        _consumer = None
-
-
-async def publish_operation_complete(
-    operation_id: str,
-    task_id: str,
-    success: bool,
-    result: dict | str | None = None,
-    error: str | None = None,
-) -> None:
-    """Publish an operation completion message to Redis Streams.
-
-    Args:
-        operation_id: The operation ID that completed.
-        task_id: The task ID associated with the operation.
-        success: Whether the operation succeeded.
-        result: The result data (for success).
-        error: The error message (for failure).
-    """
-    message = OperationCompleteMessage(
-        operation_id=operation_id,
-        task_id=task_id,
-        success=success,
-        result=result,
-        error=error,
-    )
-
-    redis = await get_redis_async()
-    await redis.xadd(
-        config.stream_completion_name,
-        {"data": message.model_dump_json()},
-        maxlen=config.stream_max_length,
-    )
-    logger.info(f"Published completion for operation {operation_id}")

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

@@ -1,344 +0,0 @@
-"""Shared completion handling for operation success and failure.
-
-This module provides common logic for handling operation completion from both:
-- The Redis Streams consumer (completion_consumer.py)
-- The HTTP webhook endpoint (routes.py)
-"""
-
-import logging
-from typing import Any
-
-import orjson
-from prisma import Prisma
-
-from . import service as chat_service
-from . import stream_registry
-from .response_model import StreamError, StreamToolOutputAvailable
-from .tools.models import ErrorResponse
-
-logger = logging.getLogger(__name__)
-
-# Tools that produce agent_json that needs to be saved to library
-AGENT_GENERATION_TOOLS = {"create_agent", "edit_agent"}
-
-# Keys that should be stripped from agent_json when returning in error responses
-SENSITIVE_KEYS = frozenset(
-    {
-        "api_key",
-        "apikey",
-        "api_secret",
-        "password",
-        "secret",
-        "credentials",
-        "credential",
-        "token",
-        "access_token",
-        "refresh_token",
-        "private_key",
-        "privatekey",
-        "auth",
-        "authorization",
-    }
-)
-
-
-def _sanitize_agent_json(obj: Any) -> Any:
-    """Recursively sanitize agent_json by removing sensitive keys.
-
-    Args:
-        obj: The object to sanitize (dict, list, or primitive)
-
-    Returns:
-        Sanitized copy with sensitive keys removed/redacted
-    """
-    if isinstance(obj, dict):
-        return {
-            k: "[REDACTED]" if k.lower() in SENSITIVE_KEYS else _sanitize_agent_json(v)
-            for k, v in obj.items()
-        }
-    elif isinstance(obj, list):
-        return [_sanitize_agent_json(item) for item in obj]
-    else:
-        return obj
-
-
-class ToolMessageUpdateError(Exception):
-    """Raised when updating a tool message in the database fails."""
-
-    pass
-
-
-async def _update_tool_message(
-    session_id: str,
-    tool_call_id: str,
-    content: str,
-    prisma_client: Prisma | None,
-) -> None:
-    """Update tool message in database.
-
-    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. The caller should
-            handle this to avoid marking the task as completed with inconsistent state.
-    """
-    try:
-        if prisma_client:
-            # Use provided Prisma client (for consumer with its own connection)
-            updated_count = await prisma_client.chatmessage.update_many(
-                where={
-                    "sessionId": session_id,
-                    "toolCallId": tool_call_id,
-                },
-                data={"content": content},
-            )
-            # 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(f"[COMPLETION] Failed to update tool message: {e}", exc_info=True)
-        raise ToolMessageUpdateError(
-            f"Failed to update tool message for tool_call_id={tool_call_id}: {e}"
-        ) from e
-
-
-def serialize_result(result: dict | list | str | int | float | bool | None) -> str:
-    """Serialize result to JSON string with sensible defaults.
-
-    Args:
-        result: The result to serialize. Can be a dict, list, string,
-            number, boolean, or None.
-
-    Returns:
-        JSON string representation of the result. Returns '{"status": "completed"}'
-        only when result is explicitly None.
-    """
-    if isinstance(result, str):
-        return result
-    if result is None:
-        return '{"status": "completed"}'
-    return orjson.dumps(result).decode("utf-8")
-
-
-async def _save_agent_from_result(
-    result: dict[str, Any],
-    user_id: str | None,
-    tool_name: str,
-) -> dict[str, Any]:
-    """Save agent to library if result contains agent_json.
-
-    Args:
-        result: The result dict that may contain agent_json
-        user_id: The user ID to save the agent for
-        tool_name: The tool name (create_agent or edit_agent)
-
-    Returns:
-        Updated result dict with saved agent details, or original result if no agent_json
-    """
-    if not user_id:
-        logger.warning("[COMPLETION] Cannot save agent: no user_id in task")
-        return result
-
-    agent_json = result.get("agent_json")
-    if not agent_json:
-        logger.warning(
-            f"[COMPLETION] {tool_name} completed but no agent_json in result"
-        )
-        return result
-
-    try:
-        from .tools.agent_generator import save_agent_to_library
-
-        is_update = tool_name == "edit_agent"
-        created_graph, library_agent = await save_agent_to_library(
-            agent_json, user_id, is_update=is_update
-        )
-
-        logger.info(
-            f"[COMPLETION] Saved agent '{created_graph.name}' to library "
-            f"(graph_id={created_graph.id}, library_agent_id={library_agent.id})"
-        )
-
-        # Return a response similar to AgentSavedResponse
-        return {
-            "type": "agent_saved",
-            "message": f"Agent '{created_graph.name}' has been saved to your library!",
-            "agent_id": created_graph.id,
-            "agent_name": created_graph.name,
-            "library_agent_id": library_agent.id,
-            "library_agent_link": f"/library/agents/{library_agent.id}",
-            "agent_page_link": f"/build?flowID={created_graph.id}",
-        }
-    except Exception as e:
-        logger.error(
-            f"[COMPLETION] Failed to save agent to library: {e}",
-            exc_info=True,
-        )
-        # Return error but don't fail the whole operation
-        # Sanitize agent_json to remove sensitive keys before returning
-        return {
-            "type": "error",
-            "message": f"Agent was generated but failed to save: {str(e)}",
-            "error": str(e),
-            "agent_json": _sanitize_agent_json(agent_json),
-        }
-
-
-async def process_operation_success(
-    task: stream_registry.ActiveTask,
-    result: dict | str | None,
-    prisma_client: Prisma | None = None,
-) -> None:
-    """Handle successful operation completion.
-
-    Publishes the result to the stream registry, updates the database,
-    generates LLM continuation, and marks the task as completed.
-
-    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 will be
-            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):
-        result = await _save_agent_from_result(result, task.user_id, task.tool_name)
-
-    # Serialize result for output (only substitute default when result is exactly None)
-    result_output = result if result is not None else {"status": "completed"}
-    output_str = (
-        result_output
-        if isinstance(result_output, str)
-        else orjson.dumps(result_output).decode("utf-8")
-    )
-
-    # Publish result to stream registry
-    await stream_registry.publish_chunk(
-        task.task_id,
-        StreamToolOutputAvailable(
-            toolCallId=task.tool_call_id,
-            toolName=task.tool_name,
-            output=output_str,
-            success=True,
-        ),
-    )
-
-    # Update pending operation in database
-    # If this fails, we must not continue to mark the task as completed
-    result_str = serialize_result(result)
-    try:
-        await _update_tool_message(
-            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
-        logger.error(
-            f"[COMPLETION] DB update failed for task {task.task_id}, "
-            "marking as failed instead of completed"
-        )
-        await stream_registry.publish_chunk(
-            task.task_id,
-            StreamError(errorText="Failed to save operation result to database"),
-        )
-        await stream_registry.mark_task_completed(task.task_id, status="failed")
-        raise
-
-    # Generate LLM continuation with streaming
-    try:
-        await chat_service._generate_llm_continuation_with_streaming(
-            session_id=task.session_id,
-            user_id=task.user_id,
-            task_id=task.task_id,
-        )
-    except Exception as e:
-        logger.error(
-            f"[COMPLETION] Failed to generate LLM continuation: {e}",
-            exc_info=True,
-        )
-
-    # Mark task as completed and release Redis lock
-    await stream_registry.mark_task_completed(task.task_id, status="completed")
-    try:
-        await chat_service._mark_operation_completed(task.tool_call_id)
-    except Exception as e:
-        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
-
-    logger.info(
-        f"[COMPLETION] Successfully processed completion for task {task.task_id}"
-    )
-
-
-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 with
-    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"
-
-    # Publish error to stream registry
-    await stream_registry.publish_chunk(
-        task.task_id,
-        StreamError(errorText=error_msg),
-    )
-
-    # Update pending operation with error
-    # If this fails, we still continue to mark the task as failed
-    error_response = ErrorResponse(
-        message=error_msg,
-        error=error,
-    )
-    try:
-        await _update_tool_message(
-            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
-        logger.error(
-            f"[COMPLETION] DB update failed while processing failure for task {task.task_id}, "
-            "continuing with cleanup"
-        )
-
-    # Mark task as failed and release Redis lock
-    await stream_registry.mark_task_completed(task.task_id, status="failed")
-    try:
-        await chat_service._mark_operation_completed(task.tool_call_id)
-    except Exception as e:
-        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
-
-    logger.info(f"[COMPLETION] Processed failure for task {task.task_id}: {error_msg}")

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

@@ -33,57 +33,9 @@ class ChatConfig(BaseSettings):
 
     stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
     max_retries: int = Field(default=3, description="Maximum number of retries")
-    max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
+    max_agent_runs: int = Field(default=3, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
-        default=30, description="Maximum number of agent schedules"
-    )
-
-    # Long-running operation configuration
-    long_running_operation_ttl: int = Field(
-        default=600,
-        description="TTL in seconds for long-running operation tracking in Redis (safety net if pod dies)",
-    )
-
-    # Stream registry configuration for SSE reconnection
-    stream_ttl: int = Field(
-        default=3600,
-        description="TTL in seconds for stream data in Redis (1 hour)",
-    )
-    stream_max_length: int = Field(
-        default=10000,
-        description="Maximum number of messages to store per stream",
-    )
-
-    # Redis Streams configuration for completion consumer
-    stream_completion_name: str = Field(
-        default="chat:completions",
-        description="Redis Stream name for operation completions",
-    )
-    stream_consumer_group: str = Field(
-        default="chat_consumers",
-        description="Consumer group name for completion stream",
-    )
-    stream_claim_min_idle_ms: int = Field(
-        default=60000,
-        description="Minimum idle time in milliseconds before claiming pending messages from dead consumers",
-    )
-
-    # Redis key prefixes for stream registry
-    task_meta_prefix: str = Field(
-        default="chat:task:meta:",
-        description="Prefix for task metadata hash keys",
-    )
-    task_stream_prefix: str = Field(
-        default="chat:stream:",
-        description="Prefix for task message stream keys",
-    )
-    task_op_prefix: str = Field(
-        default="chat:task:op:",
-        description="Prefix for operation ID to task ID mapping keys",
-    )
-    internal_api_key: str | None = Field(
-        default=None,
-        description="API key for internal webhook callbacks (env: CHAT_INTERNAL_API_KEY)",
+        default=3, description="Maximum number of agent schedules"
     )
 
     # Langfuse Prompt Management Configuration
@@ -124,14 +76,6 @@ class ChatConfig(BaseSettings):
                 v = "https://openrouter.ai/api/v1"
         return v
 
-    @field_validator("internal_api_key", mode="before")
-    @classmethod
-    def get_internal_api_key(cls, v):
-        """Get internal API key from environment if not provided."""
-        if v is None:
-            v = os.getenv("CHAT_INTERNAL_API_KEY")
-        return v
-
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

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

@@ -247,45 +247,3 @@ async def get_chat_session_message_count(session_id: str) -> int:
     """Get the number of messages in a chat session."""
     count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
     return count
-
-
-async def update_tool_message_content(
-    session_id: str,
-    tool_call_id: str,
-    new_content: str,
-) -> bool:
-    """Update the content of a tool message in chat history.
-
-    Used by background tasks to update pending operation messages with final results.
-
-    Args:
-        session_id: The chat session ID.
-        tool_call_id: The tool call ID to find the message.
-        new_content: The new content to set.
-
-    Returns:
-        True if a message was updated, False otherwise.
-    """
-    try:
-        result = await PrismaChatMessage.prisma().update_many(
-            where={
-                "sessionId": session_id,
-                "toolCallId": tool_call_id,
-            },
-            data={
-                "content": new_content,
-            },
-        )
-        if result == 0:
-            logger.warning(
-                f"No message found to update for session {session_id}, "
-                f"tool_call_id {tool_call_id}"
-            )
-            return False
-        return True
-    except Exception as e:
-        logger.error(
-            f"Failed to update tool message for session {session_id}, "
-            f"tool_call_id {tool_call_id}: {e}"
-        )
-        return False

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

@@ -295,21 +295,6 @@ async def cache_chat_session(session: ChatSession) -> None:
     await _cache_session(session)
 
 
-async def invalidate_session_cache(session_id: str) -> None:
-    """Invalidate a chat session from Redis cache.
-
-    Used by background tasks to ensure fresh data is loaded on next access.
-    This is best-effort - Redis failures are logged but don't fail the operation.
-    """
-    try:
-        redis_key = _get_session_cache_key(session_id)
-        async_redis = await get_redis_async()
-        await async_redis.delete(redis_key)
-    except Exception as e:
-        # Best-effort: log but don't fail - cache will expire naturally
-        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)

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

@@ -31,7 +31,6 @@ class ResponseType(str, Enum):
     # Other
     ERROR = "error"
     USAGE = "usage"
-    HEARTBEAT = "heartbeat"
 
 
 class StreamBaseResponse(BaseModel):
@@ -52,10 +51,6 @@ class StreamStart(StreamBaseResponse):
 
     type: ResponseType = ResponseType.START
     messageId: str = Field(..., description="Unique message ID")
-    taskId: str | None = Field(
-        default=None,
-        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
-    )
 
 
 class StreamFinish(StreamBaseResponse):
@@ -147,20 +142,3 @@ class StreamError(StreamBaseResponse):
     details: dict[str, Any] | None = Field(
         default=None, description="Additional error details"
     )
-
-
-class StreamHeartbeat(StreamBaseResponse):
-    """Heartbeat to keep SSE connection alive during long-running operations.
-
-    Uses SSE comment format (: comment) which is ignored by clients but keeps
-    the connection alive through proxies and load balancers.
-    """
-
-    type: ResponseType = ResponseType.HEARTBEAT
-    toolCallId: str | None = Field(
-        default=None, description="Tool call ID if heartbeat is for a specific tool"
-    )
-
-    def to_sse(self) -> str:
-        """Convert to SSE comment format to keep connection alive."""
-        return ": heartbeat\n\n"

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

@@ -1,23 +1,19 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
 import logging
-import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
+from fastapi import APIRouter, Depends, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
 from backend.util.exceptions import NotFoundError
 
 from . import service as chat_service
-from . import stream_registry
-from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 
 config = ChatConfig()
 
@@ -59,15 +55,6 @@ class CreateSessionResponse(BaseModel):
     user_id: str | None
 
 
-class ActiveStreamInfo(BaseModel):
-    """Information about an active stream for reconnection."""
-
-    task_id: str
-    last_message_id: str  # Redis Stream message ID for resumption
-    operation_id: str  # Operation ID for completion tracking
-    tool_name: str  # Name of the tool being executed
-
-
 class SessionDetailResponse(BaseModel):
     """Response model providing complete details for a chat session, including messages."""
 
@@ -76,7 +63,6 @@ class SessionDetailResponse(BaseModel):
     updated_at: str
     user_id: str | None
     messages: list[dict]
-    active_stream: ActiveStreamInfo | None = None  # Present if stream is still active
 
 
 class SessionSummaryResponse(BaseModel):
@@ -95,14 +81,6 @@ class ListSessionsResponse(BaseModel):
     total: int
 
 
-class OperationCompleteRequest(BaseModel):
-    """Request model for external completion webhook."""
-
-    success: bool
-    result: dict | str | None = None
-    error: str | None = None
-
-
 # ========== Routes ==========
 
 
@@ -188,14 +166,13 @@ async def get_session(
     Retrieve the details of a specific chat session.
 
     Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
-    If there's an active stream for this session, returns the task_id for reconnection.
 
     Args:
         session_id: The unique identifier for the desired chat session.
         user_id: The optional authenticated user ID, or None for anonymous access.
 
     Returns:
-        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
+        SessionDetailResponse: Details for the requested session, or None if not found.
 
     """
     session = await get_chat_session(session_id, user_id)
@@ -203,28 +180,11 @@ async def get_session(
         raise NotFoundError(f"Session {session_id} not found.")
 
     messages = [message.model_dump() for message in session.messages]
-
-    # Check if there's an active stream for this session
-    active_stream_info = None
-    active_task, last_message_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
+    logger.info(
+        f"Returning session {session_id}: "
+        f"message_count={len(messages)}, "
+        f"roles={[m.get('role') for m in messages]}"
     )
-    if active_task:
-        # Filter out the in-progress assistant message from the session response.
-        # The client will receive the complete assistant response through the SSE
-        # stream replay instead, preventing duplicate content.
-        if messages and messages[-1].get("role") == "assistant":
-            messages = messages[:-1]
-
-        # Use "0-0" as last_message_id to replay the stream from the beginning.
-        # Since we filtered out the cached assistant message, the client needs
-        # the full stream to reconstruct the response.
-        active_stream_info = ActiveStreamInfo(
-            task_id=active_task.task_id,
-            last_message_id="0-0",
-            operation_id=active_task.operation_id,
-            tool_name=active_task.tool_name,
-        )
 
     return SessionDetailResponse(
         id=session.session_id,
@@ -232,7 +192,6 @@ async def get_session(
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
         messages=messages,
-        active_stream=active_stream_info,
     )
 
 
@@ -252,112 +211,49 @@ async def stream_chat_post(
       - Tool call UI elements (if invoked)
       - Tool execution results
 
-    The AI generation runs in a background task that continues even if the client disconnects.
-    All chunks are written to Redis for reconnection support. If the client disconnects,
-    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
-
     Args:
         session_id: The chat session identifier to associate with the streamed messages.
         request: Request body containing message, is_user_message, and optional context.
         user_id: Optional authenticated user ID.
     Returns:
-        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
-        containing the task_id for reconnection.
+        StreamingResponse: SSE-formatted response chunks.
 
     """
-    import asyncio
-
     session = await _validate_and_get_session(session_id, user_id)
 
-    # Create a task in the stream registry for reconnection support
-    task_id = str(uuid_module.uuid4())
-    operation_id = str(uuid_module.uuid4())
-    await stream_registry.create_task(
-        task_id=task_id,
-        session_id=session_id,
-        user_id=user_id,
-        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
-        tool_name="chat",
-        operation_id=operation_id,
-    )
-
-    # Background task that runs the AI generation independently of SSE connection
-    async def run_ai_generation():
-        try:
-            # Emit a start event with task_id for reconnection
-            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
-            await stream_registry.publish_chunk(task_id, start_chunk)
-
-            async for chunk in chat_service.stream_chat_completion(
-                session_id,
-                request.message,
-                is_user_message=request.is_user_message,
-                user_id=user_id,
-                session=session,  # Pass pre-fetched session to avoid double-fetch
-                context=request.context,
-            ):
-                # Write to Redis (subscribers will receive via XREAD)
-                await stream_registry.publish_chunk(task_id, chunk)
-
-            # Mark task as completed
-            await stream_registry.mark_task_completed(task_id, "completed")
-        except Exception as e:
-            logger.error(
-                f"Error in background AI generation for session {session_id}: {e}"
-            )
-            await stream_registry.mark_task_completed(task_id, "failed")
-
-    # Start the AI generation in a background task
-    bg_task = asyncio.create_task(run_ai_generation())
-    await stream_registry.set_task_asyncio_task(task_id, bg_task)
-
-    # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        subscriber_queue = None
-        try:
-            # Subscribe to the task stream (this replays existing messages + live updates)
-            subscriber_queue = await stream_registry.subscribe_to_task(
-                task_id=task_id,
-                user_id=user_id,
-                last_message_id="0-0",  # Get all messages from the beginning
-            )
-
-            if subscriber_queue is None:
-                yield StreamFinish().to_sse()
-                yield "data: [DONE]\n\n"
-                return
-
-            # Read from the subscriber queue and yield to SSE
-            while True:
-                try:
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    yield chunk.to_sse()
-
-                    # Check for finish signal
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    # Send heartbeat to keep connection alive
-                    yield StreamHeartbeat().to_sse()
-
-        except GeneratorExit:
-            pass  # Client disconnected - background task continues
-        except Exception as e:
-            logger.error(f"Error in SSE stream for task {task_id}: {e}")
-        finally:
-            # Unsubscribe when client disconnects or stream ends to prevent resource leak
-            if subscriber_queue is not None:
-                try:
-                    await stream_registry.unsubscribe_from_task(
-                        task_id, subscriber_queue
-                    )
-                except Exception as unsub_err:
-                    logger.error(
-                        f"Error unsubscribing from task {task_id}: {unsub_err}",
-                        exc_info=True,
-                    )
-            # AI SDK protocol termination - always yield even if unsubscribe fails
-            yield "data: [DONE]\n\n"
+        chunk_count = 0
+        first_chunk_type: str | None = None
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            request.message,
+            is_user_message=request.is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+            context=request.context,
+        ):
+            if chunk_count < 3:
+                logger.info(
+                    "Chat stream chunk",
+                    extra={
+                        "session_id": session_id,
+                        "chunk_type": str(chunk.type),
+                    },
+                )
+            if not first_chunk_type:
+                first_chunk_type = str(chunk.type)
+            chunk_count += 1
+            yield chunk.to_sse()
+        logger.info(
+            "Chat stream completed",
+            extra={
+                "session_id": session_id,
+                "chunk_count": chunk_count,
+                "first_chunk_type": first_chunk_type,
+            },
+        )
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -470,251 +366,6 @@ async def session_assign_user(
     return {"status": "ok"}
 
 
-# ========== Task Streaming (SSE Reconnection) ==========
-
-
-@router.get(
-    "/tasks/{task_id}/stream",
-)
-async def stream_task(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-    last_message_id: str = Query(
-        default="0-0",
-        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
-    ),
-):
-    """
-    Reconnect to a long-running task's SSE stream.
-
-    When a long-running operation (like agent generation) starts, the client
-    receives a task_id. If the connection drops, the client can reconnect
-    using this endpoint to resume receiving updates.
-
-    Args:
-        task_id: The task ID from the operation_started response.
-        user_id: Authenticated user ID for ownership validation.
-        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
-
-    Returns:
-        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
-
-    Raises:
-        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
-    """
-    # Check task existence and expiry before subscribing
-    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
-
-    if error_code == "TASK_EXPIRED":
-        raise HTTPException(
-            status_code=410,
-            detail={
-                "code": "TASK_EXPIRED",
-                "message": "This operation has expired. Please try again.",
-            },
-        )
-
-    if error_code == "TASK_NOT_FOUND":
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found.",
-            },
-        )
-
-    # Validate ownership if task has an owner
-    if task and task.user_id and user_id != task.user_id:
-        raise HTTPException(
-            status_code=403,
-            detail={
-                "code": "ACCESS_DENIED",
-                "message": "You do not have access to this task.",
-            },
-        )
-
-    # Get subscriber queue from stream registry
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=task_id,
-        user_id=user_id,
-        last_message_id=last_message_id,
-    )
-
-    if subscriber_queue is None:
-        raise HTTPException(
-            status_code=404,
-            detail={
-                "code": "TASK_NOT_FOUND",
-                "message": f"Task {task_id} not found or access denied.",
-            },
-        )
-
-    async def event_generator() -> AsyncGenerator[str, None]:
-        import asyncio
-
-        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
-        try:
-            while True:
-                try:
-                    # Wait for next chunk with timeout for heartbeats
-                    chunk = await asyncio.wait_for(
-                        subscriber_queue.get(), timeout=heartbeat_interval
-                    )
-                    yield chunk.to_sse()
-
-                    # Check for finish signal
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    # Send heartbeat to keep connection alive
-                    yield StreamHeartbeat().to_sse()
-        except Exception as e:
-            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
-        finally:
-            # Unsubscribe when client disconnects or stream ends
-            try:
-                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
-            except Exception as unsub_err:
-                logger.error(
-                    f"Error unsubscribing from task {task_id}: {unsub_err}",
-                    exc_info=True,
-                )
-            # AI SDK protocol termination - always yield even if unsubscribe fails
-            yield "data: [DONE]\n\n"
-
-    return StreamingResponse(
-        event_generator(),
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
-        },
-    )
-
-
-@router.get(
-    "/tasks/{task_id}",
-)
-async def get_task_status(
-    task_id: str,
-    user_id: str | None = Depends(auth.get_user_id),
-) -> dict:
-    """
-    Get the status of a long-running task.
-
-    Args:
-        task_id: The task ID to check.
-        user_id: Authenticated user ID for ownership validation.
-
-    Returns:
-        dict: Task status including task_id, status, tool_name, and operation_id.
-
-    Raises:
-        NotFoundError: If task_id is not found or user doesn't have access.
-    """
-    task = await stream_registry.get_task(task_id)
-
-    if task is None:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    # Validate ownership - if task has an owner, requester must match
-    if task.user_id and user_id != task.user_id:
-        raise NotFoundError(f"Task {task_id} not found.")
-
-    return {
-        "task_id": task.task_id,
-        "session_id": task.session_id,
-        "status": task.status,
-        "tool_name": task.tool_name,
-        "operation_id": task.operation_id,
-        "created_at": task.created_at.isoformat(),
-    }
-
-
-# ========== External Completion Webhook ==========
-
-
-@router.post(
-    "/operations/{operation_id}/complete",
-    status_code=200,
-)
-async def complete_operation(
-    operation_id: str,
-    request: OperationCompleteRequest,
-    x_api_key: str | None = Header(default=None),
-) -> dict:
-    """
-    External completion webhook for long-running operations.
-
-    Called by Agent Generator (or other services) when an operation completes.
-    This triggers the stream registry to publish completion and continue LLM generation.
-
-    Args:
-        operation_id: The operation ID to complete.
-        request: Completion payload with success status and result/error.
-        x_api_key: Internal API key for authentication.
-
-    Returns:
-        dict: Status of the completion.
-
-    Raises:
-        HTTPException: If API key is invalid or operation not found.
-    """
-    # Validate internal API key - reject if not configured or invalid
-    if not config.internal_api_key:
-        logger.error(
-            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
-        )
-        raise HTTPException(
-            status_code=503,
-            detail="Webhook not available: internal API key not configured",
-        )
-    if x_api_key != config.internal_api_key:
-        raise HTTPException(status_code=401, detail="Invalid API key")
-
-    # Find task by operation_id
-    task = await stream_registry.find_task_by_operation_id(operation_id)
-    if task is None:
-        raise HTTPException(
-            status_code=404,
-            detail=f"Operation {operation_id} not found",
-        )
-
-    logger.info(
-        f"Received completion webhook for operation {operation_id} "
-        f"(task_id={task.task_id}, success={request.success})"
-    )
-
-    if request.success:
-        await process_operation_success(task, request.result)
-    else:
-        await process_operation_failure(task, request.error)
-
-    return {"status": "ok", "task_id": task.task_id}
-
-
-# ========== Configuration ==========
-
-
-@router.get("/config/ttl", status_code=200)
-async def get_ttl_config() -> dict:
-    """
-    Get the stream TTL configuration.
-
-    Returns the Time-To-Live settings for chat streams, which determines
-    how long clients can reconnect to an active stream.
-
-    Returns:
-        dict: TTL configuration with seconds and milliseconds values.
-    """
-    return {
-        "stream_ttl_seconds": config.stream_ttl,
-        "stream_ttl_ms": config.stream_ttl * 1000,
-    }
-
-
 # ========== Health Check ==========
 
 

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

@@ -1,704 +0,0 @@
-"""Stream registry for managing reconnectable SSE streams.
-
-This module provides a registry for tracking active streaming tasks and their
-messages. It uses Redis for all state management (no in-memory state), making
-pods stateless and horizontally scalable.
-
-Architecture:
-- Redis Stream: Persists all messages for replay and real-time delivery
-- Redis Hash: Task metadata (status, session_id, etc.)
-
-Subscribers:
-1. Replay missed messages from Redis Stream (XREAD)
-2. Listen for live updates via blocking XREAD
-3. No in-memory state required on the subscribing pod
-"""
-
-import asyncio
-import logging
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from typing import Any, Literal
-
-import orjson
-
-from backend.data.redis_client import get_redis_async
-
-from .config import ChatConfig
-from .response_model import StreamBaseResponse, StreamError, StreamFinish
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Track background tasks for this pod (just the asyncio.Task reference, not subscribers)
-_local_tasks: dict[str, asyncio.Task] = {}
-
-# Track listener tasks per subscriber queue for cleanup
-# Maps queue id() to (task_id, asyncio.Task) for proper cleanup on unsubscribe
-_listener_tasks: dict[int, tuple[str, asyncio.Task]] = {}
-
-# Timeout for putting chunks into subscriber queues (seconds)
-# If the queue is full and doesn't drain within this time, send an overflow error
-QUEUE_PUT_TIMEOUT = 5.0
-
-# Lua script for atomic compare-and-swap status update (idempotent completion)
-# Returns 1 if status was updated, 0 if already completed/failed
-COMPLETE_TASK_SCRIPT = """
-local current = redis.call("HGET", KEYS[1], "status")
-if current == "running" then
-    redis.call("HSET", KEYS[1], "status", ARGV[1])
-    return 1
-end
-return 0
-"""
-
-
-@dataclass
-class ActiveTask:
-    """Represents an active streaming task (metadata only, no in-memory queues)."""
-
-    task_id: str
-    session_id: str
-    user_id: str | None
-    tool_call_id: str
-    tool_name: str
-    operation_id: str
-    status: Literal["running", "completed", "failed"] = "running"
-    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
-    asyncio_task: asyncio.Task | None = None
-
-
-def _get_task_meta_key(task_id: str) -> str:
-    """Get Redis key for task metadata."""
-    return f"{config.task_meta_prefix}{task_id}"
-
-
-def _get_task_stream_key(task_id: str) -> str:
-    """Get Redis key for task message stream."""
-    return f"{config.task_stream_prefix}{task_id}"
-
-
-def _get_operation_mapping_key(operation_id: str) -> str:
-    """Get Redis key for operation_id to task_id mapping."""
-    return f"{config.task_op_prefix}{operation_id}"
-
-
-async def create_task(
-    task_id: str,
-    session_id: str,
-    user_id: str | None,
-    tool_call_id: str,
-    tool_name: str,
-    operation_id: str,
-) -> ActiveTask:
-    """Create a new streaming task in Redis.
-
-    Args:
-        task_id: Unique identifier for the task
-        session_id: Chat session ID
-        user_id: User ID (may be None for anonymous)
-        tool_call_id: Tool call ID from the LLM
-        tool_name: Name of the tool being executed
-        operation_id: Operation ID for webhook callbacks
-
-    Returns:
-        The created ActiveTask instance (metadata only)
-    """
-    task = ActiveTask(
-        task_id=task_id,
-        session_id=session_id,
-        user_id=user_id,
-        tool_call_id=tool_call_id,
-        tool_name=tool_name,
-        operation_id=operation_id,
-    )
-
-    # Store metadata in Redis
-    redis = await get_redis_async()
-    meta_key = _get_task_meta_key(task_id)
-    op_key = _get_operation_mapping_key(operation_id)
-
-    await redis.hset(  # type: ignore[misc]
-        meta_key,
-        mapping={
-            "task_id": task_id,
-            "session_id": session_id,
-            "user_id": user_id or "",
-            "tool_call_id": tool_call_id,
-            "tool_name": tool_name,
-            "operation_id": operation_id,
-            "status": task.status,
-            "created_at": task.created_at.isoformat(),
-        },
-    )
-    await redis.expire(meta_key, config.stream_ttl)
-
-    # Create operation_id -> task_id mapping for webhook lookups
-    await redis.set(op_key, task_id, ex=config.stream_ttl)
-
-    logger.debug(f"Created task {task_id} for session {session_id}")
-
-    return task
-
-
-async def publish_chunk(
-    task_id: str,
-    chunk: StreamBaseResponse,
-) -> str:
-    """Publish a chunk to Redis Stream.
-
-    All delivery is via Redis Streams - no in-memory state.
-
-    Args:
-        task_id: Task ID to publish to
-        chunk: The stream response chunk to publish
-
-    Returns:
-        The Redis Stream message ID
-    """
-    chunk_json = chunk.model_dump_json()
-    message_id = "0-0"
-
-    try:
-        redis = await get_redis_async()
-        stream_key = _get_task_stream_key(task_id)
-
-        # Write to Redis Stream for persistence and real-time delivery
-        raw_id = await redis.xadd(
-            stream_key,
-            {"data": chunk_json},
-            maxlen=config.stream_max_length,
-        )
-        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
-
-        # Set TTL on stream to match task metadata TTL
-        await redis.expire(stream_key, config.stream_ttl)
-    except Exception as e:
-        logger.error(
-            f"Failed to publish chunk for task {task_id}: {e}",
-            exc_info=True,
-        )
-
-    return message_id
-
-
-async def subscribe_to_task(
-    task_id: str,
-    user_id: str | None,
-    last_message_id: str = "0-0",
-) -> asyncio.Queue[StreamBaseResponse] | None:
-    """Subscribe to a task's stream with replay of missed messages.
-
-    This is fully stateless - uses Redis Stream for replay and pub/sub for live updates.
-
-    Args:
-        task_id: Task ID to subscribe to
-        user_id: User ID for ownership validation
-        last_message_id: Last Redis Stream message ID received ("0-0" for full replay)
-
-    Returns:
-        An asyncio Queue that will receive stream chunks, or None if task not found
-        or user doesn't have access
-    """
-    redis = await get_redis_async()
-    meta_key = _get_task_meta_key(task_id)
-    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-
-    if not meta:
-        logger.debug(f"Task {task_id} not found in Redis")
-        return None
-
-    # Note: Redis client uses decode_responses=True, so keys are strings
-    task_status = meta.get("status", "")
-    task_user_id = meta.get("user_id", "") or None
-
-    # Validate ownership - if task has an owner, requester must match
-    if task_user_id:
-        if user_id != task_user_id:
-            logger.warning(
-                f"User {user_id} denied access to task {task_id} "
-                f"owned by {task_user_id}"
-            )
-            return None
-
-    subscriber_queue: asyncio.Queue[StreamBaseResponse] = asyncio.Queue()
-    stream_key = _get_task_stream_key(task_id)
-
-    # Step 1: Replay messages from Redis Stream
-    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
-
-    replayed_count = 0
-    replay_last_id = last_message_id
-    if messages:
-        for _stream_name, stream_messages in messages:
-            for msg_id, msg_data in stream_messages:
-                replay_last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
-                # Note: Redis client uses decode_responses=True, so keys are strings
-                if "data" in msg_data:
-                    try:
-                        chunk_data = orjson.loads(msg_data["data"])
-                        chunk = _reconstruct_chunk(chunk_data)
-                        if chunk:
-                            await subscriber_queue.put(chunk)
-                            replayed_count += 1
-                    except Exception as e:
-                        logger.warning(f"Failed to replay message: {e}")
-
-    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
-
-    # Step 2: If task is still running, start stream listener for live updates
-    if task_status == "running":
-        listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id)
-        )
-        # Track listener task for cleanup on unsubscribe
-        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
-    else:
-        # Task is completed/failed - add finish marker
-        await subscriber_queue.put(StreamFinish())
-
-    return subscriber_queue
-
-
-async def _stream_listener(
-    task_id: str,
-    subscriber_queue: asyncio.Queue[StreamBaseResponse],
-    last_replayed_id: str,
-) -> None:
-    """Listen to Redis Stream for new messages using blocking XREAD.
-
-    This approach avoids the duplicate message issue that can occur with pub/sub
-    when messages are published during the gap between replay and subscription.
-
-    Args:
-        task_id: Task ID to listen for
-        subscriber_queue: Queue to deliver messages to
-        last_replayed_id: Last message ID from replay (continue from here)
-    """
-    queue_id = id(subscriber_queue)
-    # Track the last successfully delivered message ID for recovery hints
-    last_delivered_id = last_replayed_id
-
-    try:
-        redis = await get_redis_async()
-        stream_key = _get_task_stream_key(task_id)
-        current_id = last_replayed_id
-
-        while True:
-            # Block for up to 30 seconds waiting for new messages
-            # This allows periodic checking if task is still running
-            messages = await redis.xread(
-                {stream_key: current_id}, block=30000, count=100
-            )
-
-            if not messages:
-                # Timeout - check if task is still running
-                meta_key = _get_task_meta_key(task_id)
-                status = await redis.hget(meta_key, "status")  # type: ignore[misc]
-                if status and status != "running":
-                    try:
-                        await asyncio.wait_for(
-                            subscriber_queue.put(StreamFinish()),
-                            timeout=QUEUE_PUT_TIMEOUT,
-                        )
-                    except asyncio.TimeoutError:
-                        logger.warning(
-                            f"Timeout delivering finish event for task {task_id}"
-                        )
-                    break
-                continue
-
-            for _stream_name, stream_messages in messages:
-                for msg_id, msg_data in stream_messages:
-                    current_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
-
-                    if "data" not in msg_data:
-                        continue
-
-                    try:
-                        chunk_data = orjson.loads(msg_data["data"])
-                        chunk = _reconstruct_chunk(chunk_data)
-                        if chunk:
-                            try:
-                                await asyncio.wait_for(
-                                    subscriber_queue.put(chunk),
-                                    timeout=QUEUE_PUT_TIMEOUT,
-                                )
-                                # Update last delivered ID on successful delivery
-                                last_delivered_id = current_id
-                            except asyncio.TimeoutError:
-                                logger.warning(
-                                    f"Subscriber queue full for task {task_id}, "
-                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
-                                )
-                                # Send overflow error with recovery info
-                                try:
-                                    overflow_error = StreamError(
-                                        errorText="Message delivery timeout - some messages may have been missed",
-                                        code="QUEUE_OVERFLOW",
-                                        details={
-                                            "last_delivered_id": last_delivered_id,
-                                            "recovery_hint": f"Reconnect with last_message_id={last_delivered_id}",
-                                        },
-                                    )
-                                    subscriber_queue.put_nowait(overflow_error)
-                                except asyncio.QueueFull:
-                                    # Queue is completely stuck, nothing more we can do
-                                    logger.error(
-                                        f"Cannot deliver overflow error for task {task_id}, "
-                                        "queue completely blocked"
-                                    )
-
-                            # Stop listening on finish
-                            if isinstance(chunk, StreamFinish):
-                                return
-                    except Exception as e:
-                        logger.warning(f"Error processing stream message: {e}")
-
-    except asyncio.CancelledError:
-        logger.debug(f"Stream listener cancelled for task {task_id}")
-        raise  # Re-raise to propagate cancellation
-    except Exception as e:
-        logger.error(f"Stream listener error for task {task_id}: {e}")
-        # On error, send finish to unblock subscriber
-        try:
-            await asyncio.wait_for(
-                subscriber_queue.put(StreamFinish()),
-                timeout=QUEUE_PUT_TIMEOUT,
-            )
-        except (asyncio.TimeoutError, asyncio.QueueFull):
-            logger.warning(
-                f"Could not deliver finish event for task {task_id} after error"
-            )
-    finally:
-        # Clean up listener task mapping on exit
-        _listener_tasks.pop(queue_id, None)
-
-
-async def mark_task_completed(
-    task_id: str,
-    status: Literal["completed", "failed"] = "completed",
-) -> bool:
-    """Mark a task as completed and publish finish event.
-
-    This is idempotent - calling multiple times with the same task_id is safe.
-    Uses atomic compare-and-swap via Lua script to prevent race conditions.
-    Status is updated first (source of truth), then finish event is published (best-effort).
-
-    Args:
-        task_id: Task ID to mark as completed
-        status: Final status ("completed" or "failed")
-
-    Returns:
-        True if task was newly marked completed, False if already completed/failed
-    """
-    redis = await get_redis_async()
-    meta_key = _get_task_meta_key(task_id)
-
-    # Atomic compare-and-swap: only update if status is "running"
-    # This prevents race conditions when multiple callers try to complete simultaneously
-    result = await redis.eval(COMPLETE_TASK_SCRIPT, 1, meta_key, status)  # type: ignore[misc]
-
-    if result == 0:
-        logger.debug(f"Task {task_id} already completed/failed, skipping")
-        return False
-
-    # THEN publish finish event (best-effort - listeners can detect via status polling)
-    try:
-        await publish_chunk(task_id, StreamFinish())
-    except Exception as e:
-        logger.error(
-            f"Failed to publish finish event for task {task_id}: {e}. "
-            "Listeners will detect completion via status polling."
-        )
-
-    # Clean up local task reference if exists
-    _local_tasks.pop(task_id, None)
-    return True
-
-
-async def find_task_by_operation_id(operation_id: str) -> ActiveTask | None:
-    """Find a task by its operation ID.
-
-    Used by webhook callbacks to locate the task to update.
-
-    Args:
-        operation_id: Operation ID to search for
-
-    Returns:
-        ActiveTask if found, None otherwise
-    """
-    redis = await get_redis_async()
-    op_key = _get_operation_mapping_key(operation_id)
-    task_id = await redis.get(op_key)
-
-    if not task_id:
-        return None
-
-    task_id_str = task_id.decode() if isinstance(task_id, bytes) else task_id
-    return await get_task(task_id_str)
-
-
-async def get_task(task_id: str) -> ActiveTask | None:
-    """Get a task by its ID from Redis.
-
-    Args:
-        task_id: Task ID to look up
-
-    Returns:
-        ActiveTask if found, None otherwise
-    """
-    redis = await get_redis_async()
-    meta_key = _get_task_meta_key(task_id)
-    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-
-    if not meta:
-        return None
-
-    # Note: Redis client uses decode_responses=True, so keys/values are strings
-    return ActiveTask(
-        task_id=meta.get("task_id", ""),
-        session_id=meta.get("session_id", ""),
-        user_id=meta.get("user_id", "") or None,
-        tool_call_id=meta.get("tool_call_id", ""),
-        tool_name=meta.get("tool_name", ""),
-        operation_id=meta.get("operation_id", ""),
-        status=meta.get("status", "running"),  # type: ignore[arg-type]
-    )
-
-
-async def get_task_with_expiry_info(
-    task_id: str,
-) -> tuple[ActiveTask | None, str | None]:
-    """Get a task by its ID with expiration detection.
-
-    Returns (task, error_code) where error_code is:
-    - None if task found
-    - "TASK_EXPIRED" if stream exists but metadata is gone (TTL expired)
-    - "TASK_NOT_FOUND" if neither exists
-
-    Args:
-        task_id: Task ID to look up
-
-    Returns:
-        Tuple of (ActiveTask or None, error_code or None)
-    """
-    redis = await get_redis_async()
-    meta_key = _get_task_meta_key(task_id)
-    stream_key = _get_task_stream_key(task_id)
-
-    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-
-    if not meta:
-        # Check if stream still has data (metadata expired but stream hasn't)
-        stream_len = await redis.xlen(stream_key)
-        if stream_len > 0:
-            return None, "TASK_EXPIRED"
-        return None, "TASK_NOT_FOUND"
-
-    # Note: Redis client uses decode_responses=True, so keys/values are strings
-    return (
-        ActiveTask(
-            task_id=meta.get("task_id", ""),
-            session_id=meta.get("session_id", ""),
-            user_id=meta.get("user_id", "") or None,
-            tool_call_id=meta.get("tool_call_id", ""),
-            tool_name=meta.get("tool_name", ""),
-            operation_id=meta.get("operation_id", ""),
-            status=meta.get("status", "running"),  # type: ignore[arg-type]
-        ),
-        None,
-    )
-
-
-async def get_active_task_for_session(
-    session_id: str,
-    user_id: str | None = None,
-) -> tuple[ActiveTask | None, str]:
-    """Get the active (running) task for a session, if any.
-
-    Scans Redis for tasks matching the session_id with status="running".
-
-    Args:
-        session_id: Session ID to look up
-        user_id: User ID for ownership validation (optional)
-
-    Returns:
-        Tuple of (ActiveTask if found and running, last_message_id from Redis Stream)
-    """
-
-    redis = await get_redis_async()
-
-    # Scan Redis for task metadata keys
-    cursor = 0
-    tasks_checked = 0
-
-    while True:
-        cursor, keys = await redis.scan(
-            cursor, match=f"{config.task_meta_prefix}*", count=100
-        )
-
-        for key in keys:
-            tasks_checked += 1
-            meta: dict[Any, Any] = await redis.hgetall(key)  # type: ignore[misc]
-            if not meta:
-                continue
-
-            # Note: Redis client uses decode_responses=True, so keys/values are strings
-            task_session_id = meta.get("session_id", "")
-            task_status = meta.get("status", "")
-            task_user_id = meta.get("user_id", "") or None
-            task_id = meta.get("task_id", "")
-
-            if task_session_id == session_id and task_status == "running":
-                # Validate ownership - if task has an owner, requester must match
-                if task_user_id and user_id != task_user_id:
-                    continue
-
-                # Get the last message ID from Redis Stream
-                stream_key = _get_task_stream_key(task_id)
-                last_id = "0-0"
-                try:
-                    messages = await redis.xrevrange(stream_key, count=1)
-                    if messages:
-                        msg_id = messages[0][0]
-                        last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
-                except Exception as e:
-                    logger.warning(f"Failed to get last message ID: {e}")
-
-                return (
-                    ActiveTask(
-                        task_id=task_id,
-                        session_id=task_session_id,
-                        user_id=task_user_id,
-                        tool_call_id=meta.get("tool_call_id", ""),
-                        tool_name=meta.get("tool_name", ""),
-                        operation_id=meta.get("operation_id", ""),
-                        status="running",
-                    ),
-                    last_id,
-                )
-
-        if cursor == 0:
-            break
-
-    return None, "0-0"
-
-
-def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
-    """Reconstruct a StreamBaseResponse from JSON data.
-
-    Args:
-        chunk_data: Parsed JSON data from Redis
-
-    Returns:
-        Reconstructed response object, or None if unknown type
-    """
-    from .response_model import (
-        ResponseType,
-        StreamError,
-        StreamFinish,
-        StreamHeartbeat,
-        StreamStart,
-        StreamTextDelta,
-        StreamTextEnd,
-        StreamTextStart,
-        StreamToolInputAvailable,
-        StreamToolInputStart,
-        StreamToolOutputAvailable,
-        StreamUsage,
-    )
-
-    # Map response types to their corresponding classes
-    type_to_class: dict[str, type[StreamBaseResponse]] = {
-        ResponseType.START.value: StreamStart,
-        ResponseType.FINISH.value: StreamFinish,
-        ResponseType.TEXT_START.value: StreamTextStart,
-        ResponseType.TEXT_DELTA.value: StreamTextDelta,
-        ResponseType.TEXT_END.value: StreamTextEnd,
-        ResponseType.TOOL_INPUT_START.value: StreamToolInputStart,
-        ResponseType.TOOL_INPUT_AVAILABLE.value: StreamToolInputAvailable,
-        ResponseType.TOOL_OUTPUT_AVAILABLE.value: StreamToolOutputAvailable,
-        ResponseType.ERROR.value: StreamError,
-        ResponseType.USAGE.value: StreamUsage,
-        ResponseType.HEARTBEAT.value: StreamHeartbeat,
-    }
-
-    chunk_type = chunk_data.get("type")
-    chunk_class = type_to_class.get(chunk_type)  # type: ignore[arg-type]
-
-    if chunk_class is None:
-        logger.warning(f"Unknown chunk type: {chunk_type}")
-        return None
-
-    try:
-        return chunk_class(**chunk_data)
-    except Exception as e:
-        logger.warning(f"Failed to reconstruct chunk of type {chunk_type}: {e}")
-        return None
-
-
-async def set_task_asyncio_task(task_id: str, asyncio_task: asyncio.Task) -> None:
-    """Track the asyncio.Task for a task (local reference only).
-
-    This is just for cleanup purposes - the task state is in Redis.
-
-    Args:
-        task_id: Task ID
-        asyncio_task: The asyncio Task to track
-    """
-    _local_tasks[task_id] = asyncio_task
-
-
-async def unsubscribe_from_task(
-    task_id: str,
-    subscriber_queue: asyncio.Queue[StreamBaseResponse],
-) -> None:
-    """Clean up when a subscriber disconnects.
-
-    Cancels the XREAD-based listener task associated with this subscriber queue
-    to prevent resource leaks.
-
-    Args:
-        task_id: Task ID
-        subscriber_queue: The subscriber's queue used to look up the listener task
-    """
-    queue_id = id(subscriber_queue)
-    listener_entry = _listener_tasks.pop(queue_id, None)
-
-    if listener_entry is None:
-        logger.debug(
-            f"No listener task found for task {task_id} queue {queue_id} "
-            "(may have already completed)"
-        )
-        return
-
-    stored_task_id, listener_task = listener_entry
-
-    if stored_task_id != task_id:
-        logger.warning(
-            f"Task ID mismatch in unsubscribe: expected {task_id}, "
-            f"found {stored_task_id}"
-        )
-
-    if listener_task.done():
-        logger.debug(f"Listener task for task {task_id} already completed")
-        return
-
-    # Cancel the listener task
-    listener_task.cancel()
-
-    try:
-        # Wait for the task to be cancelled with a timeout
-        await asyncio.wait_for(listener_task, timeout=5.0)
-    except asyncio.CancelledError:
-        # Expected - the task was successfully cancelled
-        pass
-    except asyncio.TimeoutError:
-        logger.warning(
-            f"Timeout waiting for listener task cancellation for task {task_id}"
-        )
-    except Exception as e:
-        logger.error(f"Error during listener task cancellation for task {task_id}: {e}")
-
-    logger.debug(f"Successfully unsubscribed from task {task_id}")

autogpt_platform/backend/backend/api/features/chat/tools/IDEAS.md

@@ -1,79 +0,0 @@
-# CoPilot Tools - Future Ideas
-
-## Multimodal Image Support for CoPilot
-
-**Problem:** CoPilot uses a vision-capable model but can't "see" workspace images. When a block generates an image and returns `workspace://abc123`, CoPilot can't evaluate it (e.g., checking blog thumbnail quality).
-
-**Backend Solution:**
-When preparing messages for the LLM, detect `workspace://` image references and convert them to proper image content blocks:
-
-```python
-# Before sending to LLM, scan for workspace image references
-# and inject them as image content parts
-
-# Example message transformation:
-# FROM: {"role": "assistant", "content": "Generated image: workspace://abc123"}
-# TO:   {"role": "assistant", "content": [
-#         {"type": "text", "text": "Generated image: workspace://abc123"},
-#         {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
-#       ]}
-```
-
-**Where to implement:**
-- In the chat stream handler before calling the LLM
-- Or in a message preprocessing step
-- Need to fetch image from workspace, convert to base64, add as image content
-
-**Considerations:**
-- Only do this for image MIME types (image/png, image/jpeg, etc.)
-- May want a size limit (don't pass 10MB images)
-- Track which images were "shown" to the AI for frontend indicator
-- Cost implications - vision API calls are more expensive
-
-**Frontend Solution:**
-Show visual indicator on workspace files in chat:
-- If AI saw the image: normal display
-- If AI didn't see it: overlay icon saying "AI can't see this image"
-
-Requires response metadata indicating which `workspace://` refs were passed to the model.
-
----
-
-## Output Post-Processing Layer for run_block
-
-**Problem:** Many blocks produce large outputs that:
-- Consume massive context (100KB base64 image = ~133KB tokens)
-- Can't fit in conversation
-- Break things and cause high LLM costs
-
-**Proposed Solution:** Instead of modifying individual blocks or `store_media_file()`, implement a centralized output processor in `run_block.py` that handles outputs before they're returned to CoPilot.
-
-**Benefits:**
-1. **Centralized** - one place to handle all output processing
-2. **Future-proof** - new blocks automatically get output processing
-3. **Keeps blocks pure** - they don't need to know about context constraints
-4. **Handles all large outputs** - not just images
-
-**Processing Rules:**
-- Detect base64 data URIs → save to workspace, return `workspace://` reference
-- Truncate very long strings (>N chars) with truncation note
-- Summarize large arrays/lists (e.g., "Array with 1000 items, first 5: [...]")
-- Handle nested large outputs in dicts recursively
-- Cap total output size
-
-**Implementation Location:** `run_block.py` after block execution, before returning `BlockOutputResponse`
-
-**Example:**
-```python
-def _process_outputs_for_context(
-    outputs: dict[str, list[Any]],
-    workspace_manager: WorkspaceManager,
-    max_string_length: int = 10000,
-    max_array_preview: int = 5,
-) -> dict[str, list[Any]]:
-    """Process block outputs to prevent context bloat."""
-    processed = {}
-    for name, values in outputs.items():
-        processed[name] = [_process_value(v, workspace_manager) for v in values]
-    return processed
-```

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

@@ -1,16 +1,13 @@
-import logging
 from typing import TYPE_CHECKING, Any
 
 from openai.types.chat import ChatCompletionToolParam
 
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tracking import track_tool_called
 
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .create_agent import CreateAgentTool
-from .customize_agent import CustomizeAgentTool
 from .edit_agent import EditAgentTool
 from .find_agent import FindAgentTool
 from .find_block import FindBlockTool
@@ -19,23 +16,14 @@ from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
 from .run_block import RunBlockTool
 from .search_docs import SearchDocsTool
-from .workspace_files import (
-    DeleteWorkspaceFileTool,
-    ListWorkspaceFilesTool,
-    ReadWorkspaceFileTool,
-    WriteWorkspaceFileTool,
-)
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolOutputAvailable
 
-logger = logging.getLogger(__name__)
-
 # Single source of truth for all tools
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
     "create_agent": CreateAgentTool(),
-    "customize_agent": CustomizeAgentTool(),
     "edit_agent": EditAgentTool(),
     "find_agent": FindAgentTool(),
     "find_block": FindBlockTool(),
@@ -45,11 +33,6 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "view_agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
-    # Workspace tools for CoPilot file operations
-    "list_workspace_files": ListWorkspaceFilesTool(),
-    "read_workspace_file": ReadWorkspaceFileTool(),
-    "write_workspace_file": WriteWorkspaceFileTool(),
-    "delete_workspace_file": DeleteWorkspaceFileTool(),
 }
 
 # Export individual tool instances for backwards compatibility
@@ -62,11 +45,6 @@ tools: list[ChatCompletionToolParam] = [
 ]
 
 
-def get_tool(tool_name: str) -> BaseTool | None:
-    """Get a tool instance by name."""
-    return TOOL_REGISTRY.get(tool_name)
-
-
 async def execute_tool(
     tool_name: str,
     parameters: dict[str, Any],
@@ -75,20 +53,7 @@ async def execute_tool(
     tool_call_id: str,
 ) -> "StreamToolOutputAvailable":
     """Execute a tool by name."""
-    tool = get_tool(tool_name)
+    tool = TOOL_REGISTRY.get(tool_name)
     if not tool:
         raise ValueError(f"Tool {tool_name} not found")
-
-    # Track tool call in PostHog
-    logger.info(
-        f"Tracking tool call: tool={tool_name}, user={user_id}, "
-        f"session={session.session_id}, call_id={tool_call_id}"
-    )
-    track_tool_called(
-        user_id=user_id,
-        session_id=session.session_id,
-        tool_name=tool_name,
-        tool_call_id=tool_call_id,
-    )
-
     return await tool.execute(user_id, session, tool_call_id, **parameters)

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

@@ -3,6 +3,8 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -59,6 +61,7 @@ and automations for the user's specific needs."""
         """Requires authentication to store user-specific data."""
         return True
 
+    @observe(as_type="tool", name="add_understanding")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -1,59 +1,29 @@
 """Agent generator package - Creates agents from natural language."""
 
 from .core import (
-    AgentGeneratorNotConfiguredError,
-    AgentJsonValidationError,
-    AgentSummary,
-    DecompositionResult,
-    DecompositionStep,
-    LibraryAgentSummary,
-    MarketplaceAgentSummary,
-    customize_template,
+    apply_agent_patch,
     decompose_goal,
-    enrich_library_agents_from_steps,
-    extract_search_terms_from_steps,
-    extract_uuids_from_text,
     generate_agent,
     generate_agent_patch,
     get_agent_as_json,
-    get_all_relevant_agents_for_generation,
-    get_library_agent_by_graph_id,
-    get_library_agent_by_id,
-    get_library_agents_for_generation,
-    graph_to_json,
-    json_to_graph,
     save_agent_to_library,
-    search_marketplace_agents_for_generation,
 )
-from .errors import get_user_message_for_error
-from .service import health_check as check_external_service_health
-from .service import is_external_service_configured
+from .fixer import apply_all_fixes
+from .utils import get_blocks_info
+from .validator import validate_agent
 
 __all__ = [
-    "AgentGeneratorNotConfiguredError",
-    "AgentJsonValidationError",
-    "AgentSummary",
-    "DecompositionResult",
-    "DecompositionStep",
-    "LibraryAgentSummary",
-    "MarketplaceAgentSummary",
-    "check_external_service_health",
-    "customize_template",
+    # Core functions
     "decompose_goal",
-    "enrich_library_agents_from_steps",
-    "extract_search_terms_from_steps",
-    "extract_uuids_from_text",
     "generate_agent",
     "generate_agent_patch",
-    "get_agent_as_json",
-    "get_all_relevant_agents_for_generation",
-    "get_library_agent_by_graph_id",
-    "get_library_agent_by_id",
-    "get_library_agents_for_generation",
-    "get_user_message_for_error",
-    "graph_to_json",
-    "is_external_service_configured",
-    "json_to_graph",
+    "apply_agent_patch",
     "save_agent_to_library",
-    "search_marketplace_agents_for_generation",
+    "get_agent_as_json",
+    # Fixer
+    "apply_all_fixes",
+    # Validator
+    "validate_agent",
+    # Utils
+    "get_blocks_info",
 ]

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

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

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

@@ -1,95 +0,0 @@
-"""Error handling utilities for agent generator."""
-
-import re
-
-
-def _sanitize_error_details(details: str) -> str:
-    """Sanitize error details to remove sensitive information.
-
-    Strips common patterns that could expose internal system info:
-    - File paths (Unix and Windows)
-    - Database connection strings
-    - URLs with credentials
-    - Stack trace internals
-
-    Args:
-        details: Raw error details string
-
-    Returns:
-        Sanitized error details safe for user display
-    """
-    sanitized = re.sub(
-        r"/[a-zA-Z0-9_./\-]+\.(py|js|ts|json|yaml|yml)", "[path]", details
-    )
-    sanitized = re.sub(r"[A-Z]:\\[a-zA-Z0-9_\\.\\-]+", "[path]", sanitized)
-    sanitized = re.sub(
-        r"(postgres|mysql|mongodb|redis)://[^\s]+", "[database_url]", sanitized
-    )
-    sanitized = re.sub(r"https?://[^:]+:[^@]+@[^\s]+", "[url]", sanitized)
-    sanitized = re.sub(r", line \d+", "", sanitized)
-    sanitized = re.sub(r'File "[^"]+",?', "", sanitized)
-
-    return sanitized.strip()
-
-
-def get_user_message_for_error(
-    error_type: str,
-    operation: str = "process the request",
-    llm_parse_message: str | None = None,
-    validation_message: str | None = None,
-    error_details: str | None = None,
-) -> str:
-    """Get a user-friendly error message based on error type.
-
-    This function maps internal error types to user-friendly messages,
-    providing a consistent experience across different agent operations.
-
-    Args:
-        error_type: The error type from the external service
-            (e.g., "llm_parse_error", "timeout", "rate_limit")
-        operation: Description of what operation failed, used in the default
-            message (e.g., "analyze the goal", "generate the agent")
-        llm_parse_message: Custom message for llm_parse_error type
-        validation_message: Custom message for validation_error type
-        error_details: Optional additional details about the error
-
-    Returns:
-        User-friendly error message suitable for display to the user
-    """
-    base_message = ""
-
-    if error_type == "llm_parse_error":
-        base_message = (
-            llm_parse_message
-            or "The AI had trouble processing this request. Please try again."
-        )
-    elif error_type == "validation_error":
-        base_message = (
-            validation_message
-            or "The generated agent failed validation. "
-            "This usually happens when the agent structure doesn't match "
-            "what the platform expects. Please try simplifying your goal "
-            "or breaking it into smaller parts."
-        )
-    elif error_type == "patch_error":
-        base_message = (
-            "Failed to apply the changes. The modification couldn't be "
-            "validated. Please try a different approach or simplify the change."
-        )
-    elif error_type in ("timeout", "llm_timeout"):
-        base_message = (
-            "The request took too long to process. This can happen with "
-            "complex agents. Please try again or simplify your goal."
-        )
-    elif error_type in ("rate_limit", "llm_rate_limit"):
-        base_message = "The service is currently busy. Please try again in a moment."
-    else:
-        base_message = f"Failed to {operation}. Please try again."
-
-    if error_details:
-        details = _sanitize_error_details(error_details)
-        if len(details) > 200:
-            details = details[:200] + "..."
-        base_message += f"\n\nTechnical details: {details}"
-
-    return base_message

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

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

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

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

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

@@ -1,498 +0,0 @@
-"""External Agent Generator service client.
-
-This module provides a client for communicating with the external Agent Generator
-microservice. When AGENTGENERATOR_HOST is configured, the agent generation functions
-will delegate to the external service instead of using the built-in LLM-based implementation.
-"""
-
-import logging
-from typing import Any
-
-import httpx
-
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-
-
-def _create_error_response(
-    error_message: str,
-    error_type: str = "unknown",
-    details: dict[str, Any] | None = None,
-) -> dict[str, Any]:
-    """Create a standardized error response dict.
-
-    Args:
-        error_message: Human-readable error message
-        error_type: Machine-readable error type
-        details: Optional additional error details
-
-    Returns:
-        Error dict with type="error" and error details
-    """
-    response: dict[str, Any] = {
-        "type": "error",
-        "error": error_message,
-        "error_type": error_type,
-    }
-    if details:
-        response["details"] = details
-    return response
-
-
-def _classify_http_error(e: httpx.HTTPStatusError) -> tuple[str, str]:
-    """Classify an HTTP error into error_type and message.
-
-    Args:
-        e: The HTTP status error
-
-    Returns:
-        Tuple of (error_type, error_message)
-    """
-    status = e.response.status_code
-    if status == 429:
-        return "rate_limit", f"Agent Generator rate limited: {e}"
-    elif status == 503:
-        return "service_unavailable", f"Agent Generator unavailable: {e}"
-    elif status == 504 or status == 408:
-        return "timeout", f"Agent Generator timed out: {e}"
-    else:
-        return "http_error", f"HTTP error calling Agent Generator: {e}"
-
-
-def _classify_request_error(e: httpx.RequestError) -> tuple[str, str]:
-    """Classify a request error into error_type and message.
-
-    Args:
-        e: The request error
-
-    Returns:
-        Tuple of (error_type, error_message)
-    """
-    error_str = str(e).lower()
-    if "timeout" in error_str or "timed out" in error_str:
-        return "timeout", f"Agent Generator request timed out: {e}"
-    elif "connect" in error_str:
-        return "connection_error", f"Could not connect to Agent Generator: {e}"
-    else:
-        return "request_error", f"Request error calling Agent Generator: {e}"
-
-
-_client: httpx.AsyncClient | None = None
-_settings: Settings | None = None
-
-
-def _get_settings() -> Settings:
-    """Get or create settings singleton."""
-    global _settings
-    if _settings is None:
-        _settings = Settings()
-    return _settings
-
-
-def is_external_service_configured() -> bool:
-    """Check if external Agent Generator service is configured."""
-    settings = _get_settings()
-    return bool(settings.config.agentgenerator_host)
-
-
-def _get_base_url() -> str:
-    """Get the base URL for the external service."""
-    settings = _get_settings()
-    host = settings.config.agentgenerator_host
-    port = settings.config.agentgenerator_port
-    return f"http://{host}:{port}"
-
-
-def _get_client() -> httpx.AsyncClient:
-    """Get or create the HTTP client for the external service."""
-    global _client
-    if _client is None:
-        settings = _get_settings()
-        _client = httpx.AsyncClient(
-            base_url=_get_base_url(),
-            timeout=httpx.Timeout(settings.config.agentgenerator_timeout),
-        )
-    return _client
-
-
-async def decompose_goal_external(
-    description: str,
-    context: str = "",
-    library_agents: list[dict[str, Any]] | None = None,
-) -> dict[str, Any] | None:
-    """Call the external service to decompose a goal.
-
-    Args:
-        description: Natural language goal description
-        context: Additional context (e.g., answers to previous questions)
-        library_agents: User's library agents available for sub-agent composition
-
-    Returns:
-        Dict with either:
-        - {"type": "clarifying_questions", "questions": [...]}
-        - {"type": "instructions", "steps": [...]}
-        - {"type": "unachievable_goal", ...}
-        - {"type": "vague_goal", ...}
-        - {"type": "error", "error": "...", "error_type": "..."} on error
-        Or None on unexpected error
-    """
-    client = _get_client()
-
-    if context:
-        description = f"{description}\n\nAdditional context from user:\n{context}"
-
-    payload: dict[str, Any] = {"description": description}
-    if library_agents:
-        payload["library_agents"] = library_agents
-
-    try:
-        response = await client.post("/api/decompose-description", json=payload)
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator decomposition failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Map the response to the expected format
-        response_type = data.get("type")
-        if response_type == "instructions":
-            return {"type": "instructions", "steps": data.get("steps", [])}
-        elif response_type == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-        elif response_type == "unachievable_goal":
-            return {
-                "type": "unachievable_goal",
-                "reason": data.get("reason"),
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        elif response_type == "vague_goal":
-            return {
-                "type": "vague_goal",
-                "suggested_goal": data.get("suggested_goal"),
-            }
-        elif response_type == "error":
-            # Pass through error from the service
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-        else:
-            logger.error(
-                f"Unknown response type from external service: {response_type}"
-            )
-            return _create_error_response(
-                f"Unknown response type from Agent Generator: {response_type}",
-                "invalid_response",
-            )
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except Exception as e:
-        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        logger.error(error_msg)
-        return _create_error_response(error_msg, "unexpected_error")
-
-
-async def generate_agent_external(
-    instructions: dict[str, Any],
-    library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
-) -> dict[str, Any] | None:
-    """Call the external service to generate an agent from instructions.
-
-    Args:
-        instructions: Structured instructions from decompose_goal
-        library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams callback)
-        task_id: Task ID for async processing (enables Redis Streams callback)
-
-    Returns:
-        Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
-    """
-    client = _get_client()
-
-    # Build request payload
-    payload: dict[str, Any] = {"instructions": instructions}
-    if library_agents:
-        payload["library_agents"] = library_agents
-    if operation_id and task_id:
-        payload["operation_id"] = operation_id
-        payload["task_id"] = task_id
-
-    try:
-        response = await client.post("/api/generate-agent", json=payload)
-
-        # Handle 202 Accepted for async processing
-        if response.status_code == 202:
-            logger.info(
-                f"Agent Generator accepted async request "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return {
-                "status": "accepted",
-                "operation_id": operation_id,
-                "task_id": task_id,
-            }
-
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator generation failed: {error_msg} (type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except Exception as e:
-        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        logger.error(error_msg)
-        return _create_error_response(error_msg, "unexpected_error")
-
-
-async def generate_agent_patch_external(
-    update_request: str,
-    current_agent: dict[str, Any],
-    library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
-) -> dict[str, Any] | None:
-    """Call the external service to generate a patch for an existing agent.
-
-    Args:
-        update_request: Natural language description of changes
-        current_agent: Current agent JSON
-        library_agents: User's library agents available for sub-agent composition
-        operation_id: Operation ID for async processing (enables Redis Streams callback)
-        task_id: Task ID for async processing (enables Redis Streams callback)
-
-    Returns:
-        Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
-    """
-    client = _get_client()
-
-    # Build request payload
-    payload: dict[str, Any] = {
-        "update_request": update_request,
-        "current_agent_json": current_agent,
-    }
-    if library_agents:
-        payload["library_agents"] = library_agents
-    if operation_id and task_id:
-        payload["operation_id"] = operation_id
-        payload["task_id"] = task_id
-
-    try:
-        response = await client.post("/api/update-agent", json=payload)
-
-        # Handle 202 Accepted for async processing
-        if response.status_code == 202:
-            logger.info(
-                f"Agent Generator accepted async update request "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return {
-                "status": "accepted",
-                "operation_id": operation_id,
-                "task_id": task_id,
-            }
-
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator patch generation failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Check if it's clarifying questions
-        if data.get("type") == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-
-        # Check if it's an error passed through
-        if data.get("type") == "error":
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-
-        # Otherwise return the updated agent JSON
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except Exception as e:
-        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        logger.error(error_msg)
-        return _create_error_response(error_msg, "unexpected_error")
-
-
-async def customize_template_external(
-    template_agent: dict[str, Any],
-    modification_request: str,
-    context: str = "",
-) -> dict[str, Any] | None:
-    """Call the external service to customize a template/marketplace agent.
-
-    Args:
-        template_agent: The template agent JSON to customize
-        modification_request: Natural language description of customizations
-        context: Additional context (e.g., answers to previous questions)
-
-    Returns:
-        Customized agent JSON, clarifying questions dict, or error dict on error
-    """
-    client = _get_client()
-
-    request = modification_request
-    if context:
-        request = f"{modification_request}\n\nAdditional context from user:\n{context}"
-
-    payload: dict[str, Any] = {
-        "template_agent_json": template_agent,
-        "modification_request": request,
-    }
-
-    try:
-        response = await client.post("/api/template-modification", json=payload)
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            error_msg = data.get("error", "Unknown error from Agent Generator")
-            error_type = data.get("error_type", "unknown")
-            logger.error(
-                f"Agent Generator template customization failed: {error_msg} "
-                f"(type: {error_type})"
-            )
-            return _create_error_response(error_msg, error_type)
-
-        # Check if it's clarifying questions
-        if data.get("type") == "clarifying_questions":
-            return {
-                "type": "clarifying_questions",
-                "questions": data.get("questions", []),
-            }
-
-        # Check if it's an error passed through
-        if data.get("type") == "error":
-            return _create_error_response(
-                data.get("error", "Unknown error"),
-                data.get("error_type", "unknown"),
-            )
-
-        # Otherwise return the customized agent JSON
-        return data.get("agent_json")
-
-    except httpx.HTTPStatusError as e:
-        error_type, error_msg = _classify_http_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except httpx.RequestError as e:
-        error_type, error_msg = _classify_request_error(e)
-        logger.error(error_msg)
-        return _create_error_response(error_msg, error_type)
-    except Exception as e:
-        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        logger.error(error_msg)
-        return _create_error_response(error_msg, "unexpected_error")
-
-
-async def get_blocks_external() -> list[dict[str, Any]] | None:
-    """Get available blocks from the external service.
-
-    Returns:
-        List of block info dicts or None on error
-    """
-    client = _get_client()
-
-    try:
-        response = await client.get("/api/blocks")
-        response.raise_for_status()
-        data = response.json()
-
-        if not data.get("success"):
-            logger.error("External service returned error getting blocks")
-            return None
-
-        return data.get("blocks", [])
-
-    except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error getting blocks from external service: {e}")
-        return None
-    except httpx.RequestError as e:
-        logger.error(f"Request error getting blocks from external service: {e}")
-        return None
-    except Exception as e:
-        logger.error(f"Unexpected error getting blocks from external service: {e}")
-        return None
-
-
-async def health_check() -> bool:
-    """Check if the external service is healthy.
-
-    Returns:
-        True if healthy, False otherwise
-    """
-    if not is_external_service_configured():
-        return False
-
-    client = _get_client()
-
-    try:
-        response = await client.get("/health")
-        response.raise_for_status()
-        data = response.json()
-        return data.get("status") == "healthy" and data.get("blocks_loaded", False)
-    except Exception as e:
-        logger.warning(f"External agent generator health check failed: {e}")
-        return False
-
-
-async def close_client() -> None:
-    """Close the HTTP client."""
-    global _client
-    if _client is not None:
-        await _client.aclose()
-        _client = None

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

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

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

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

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

@@ -5,6 +5,7 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
+from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -328,6 +329,7 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
+    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -1,7 +1,6 @@
 """Shared agent search functionality for find_agent and find_library_agent tools."""
 
 import logging
-import re
 from typing import Literal
 
 from backend.api.features.library import db as library_db
@@ -20,85 +19,6 @@ logger = logging.getLogger(__name__)
 
 SearchSource = Literal["marketplace", "library"]
 
-_UUID_PATTERN = re.compile(
-    r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$",
-    re.IGNORECASE,
-)
-
-
-def _is_uuid(text: str) -> bool:
-    """Check if text is a valid UUID v4."""
-    return bool(_UUID_PATTERN.match(text.strip()))
-
-
-async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | None:
-    """Fetch a library agent by ID (library agent ID or graph_id).
-
-    Tries multiple lookup strategies:
-    1. First by graph_id (AgentGraph primary key)
-    2. Then by library agent ID (LibraryAgent primary key)
-
-    Args:
-        user_id: The user ID
-        agent_id: The ID to look up (can be graph_id or library agent ID)
-
-    Returns:
-        AgentInfo if found, None otherwise
-    """
-    try:
-        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(
-                id=agent.id,
-                name=agent.name,
-                description=agent.description or "",
-                source="library",
-                in_library=True,
-                creator=agent.creator_name,
-                status=agent.status.value,
-                can_access_graph=agent.can_access_graph,
-                has_external_trigger=agent.has_external_trigger,
-                new_output=agent.new_output,
-                graph_id=agent.graph_id,
-            )
-    except DatabaseError:
-        raise
-    except Exception as e:
-        logger.warning(
-            f"Could not fetch library agent by graph_id {agent_id}: {e}",
-            exc_info=True,
-        )
-
-    try:
-        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(
-                id=agent.id,
-                name=agent.name,
-                description=agent.description or "",
-                source="library",
-                in_library=True,
-                creator=agent.creator_name,
-                status=agent.status.value,
-                can_access_graph=agent.can_access_graph,
-                has_external_trigger=agent.has_external_trigger,
-                new_output=agent.new_output,
-                graph_id=agent.graph_id,
-            )
-    except NotFoundError:
-        logger.debug(f"Library agent not found by library_id: {agent_id}")
-    except DatabaseError:
-        raise
-    except Exception as e:
-        logger.warning(
-            f"Could not fetch library agent by library_id {agent_id}: {e}",
-            exc_info=True,
-        )
-
-    return None
-
 
 async def search_agents(
     query: str,
@@ -149,37 +69,29 @@ async def search_agents(
                         is_featured=False,
                     )
                 )
-        else:
-            if _is_uuid(query):
-                logger.info(f"Query looks like UUID, trying direct lookup: {query}")
-                agent = await _get_library_agent_by_id(user_id, query)  # type: ignore[arg-type]
-                if agent:
-                    agents.append(agent)
-                    logger.info(f"Found agent by direct ID lookup: {agent.name}")
-
-            if not agents:
-                logger.info(f"Searching user library for: {query}")
-                results = await library_db.list_library_agents(
-                    user_id=user_id,  # type: ignore[arg-type]
-                    search_term=query,
-                    page_size=10,
-                )
-                for agent in results.agents:
-                    agents.append(
-                        AgentInfo(
-                            id=agent.id,
-                            name=agent.name,
-                            description=agent.description or "",
-                            source="library",
-                            in_library=True,
-                            creator=agent.creator_name,
-                            status=agent.status.value,
-                            can_access_graph=agent.can_access_graph,
-                            has_external_trigger=agent.has_external_trigger,
-                            new_output=agent.new_output,
-                            graph_id=agent.graph_id,
-                        )
+        else:  # library
+            logger.info(f"Searching user library for: {query}")
+            results = await library_db.list_library_agents(
+                user_id=user_id,  # type: ignore[arg-type]
+                search_term=query,
+                page_size=10,
+            )
+            for agent in results.agents:
+                agents.append(
+                    AgentInfo(
+                        id=agent.id,
+                        name=agent.name,
+                        description=agent.description or "",
+                        source="library",
+                        in_library=True,
+                        creator=agent.creator_name,
+                        status=agent.status.value,
+                        can_access_graph=agent.can_access_graph,
+                        has_external_trigger=agent.has_external_trigger,
+                        new_output=agent.new_output,
+                        graph_id=agent.graph_id,
                     )
+                )
         logger.info(f"Found {len(agents)} agents in {source}")
     except NotFoundError:
         pass

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

@@ -36,16 +36,6 @@ class BaseTool:
         """Whether this tool requires authentication."""
         return False
 
-    @property
-    def is_long_running(self) -> bool:
-        """Whether this tool is long-running and should execute in background.
-
-        Long-running tools (like agent generation) are executed via background
-        tasks to survive SSE disconnections. The result is persisted to chat
-        history and visible when the user refreshes.
-        """
-        return False
-
     def as_openai_tool(self) -> ChatCompletionToolParam:
         """Convert to OpenAI tool format."""
         return ChatCompletionToolParam(

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

@@ -3,22 +3,22 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    AgentGeneratorNotConfiguredError,
+    apply_all_fixes,
     decompose_goal,
-    enrich_library_agents_from_steps,
     generate_agent,
-    get_all_relevant_agents_for_generation,
-    get_user_message_for_error,
+    get_blocks_info,
     save_agent_to_library,
+    validate_agent,
 )
 from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
-    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -27,6 +27,9 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
+# Maximum retries for agent generation with validation feedback
+MAX_GENERATION_RETRIES = 2
+
 
 class CreateAgentTool(BaseTool):
     """Tool for creating agents from natural language descriptions."""
@@ -46,10 +49,6 @@ class CreateAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {
@@ -81,6 +80,7 @@ class CreateAgentTool(BaseTool):
             "required": ["description"],
         }
 
+    @observe(as_type="tool", name="create_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -91,18 +91,15 @@ class CreateAgentTool(BaseTool):
 
         Flow:
         1. Decompose the description into steps (may return clarifying questions)
-        2. Generate agent JSON (external service handles fixing and validation)
-        3. Preview or save based on the save parameter
+        2. Generate agent JSON from the steps
+        3. Apply fixes to correct common LLM errors
+        4. Preview or save based on the save parameter
         """
         description = kwargs.get("description", "").strip()
         context = kwargs.get("context", "")
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        # Extract async processing params (passed by long-running tool handler)
-        operation_id = kwargs.get("_operation_id")
-        task_id = kwargs.get("_task_id")
-
         if not description:
             return ErrorResponse(
                 message="Please provide a description of what the agent should do.",
@@ -110,61 +107,25 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        library_agents = None
-        if user_id:
-            try:
-                library_agents = await get_all_relevant_agents_for_generation(
-                    user_id=user_id,
-                    search_query=description,
-                    include_marketplace=True,
-                )
-                logger.debug(
-                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
-                )
-            except Exception as e:
-                logger.warning(f"Failed to fetch library agents: {e}")
-
+        # Step 1: Decompose goal into steps
         try:
-            decomposition_result = await decompose_goal(
-                description, context, library_agents
-            )
-        except AgentGeneratorNotConfiguredError:
+            decomposition_result = await decompose_goal(description, context)
+        except ValueError as e:
+            # Handle missing API key or configuration errors
             return ErrorResponse(
-                message=(
-                    "Agent generation is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
+                message=f"Agent generation is not configured: {str(e)}",
+                error="configuration_error",
                 session_id=session_id,
             )
 
         if decomposition_result is None:
             return ErrorResponse(
-                message="Failed to analyze the goal. The agent generation service may be unavailable. Please try again.",
-                error="decomposition_failed",
-                details={"description": description[:100]},
-                session_id=session_id,
-            )
-
-        if decomposition_result.get("type") == "error":
-            error_msg = decomposition_result.get("error", "Unknown error")
-            error_type = decomposition_result.get("error_type", "unknown")
-            user_message = get_user_message_for_error(
-                error_type,
-                operation="analyze the goal",
-                llm_parse_message="The AI had trouble understanding this request. Please try rephrasing your goal.",
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"decomposition_failed:{error_type}",
-                details={
-                    "description": description[:100],
-                    "service_error": error_msg,
-                    "error_type": error_type,
-                },
+                message="Failed to analyze the goal. Please try rephrasing.",
+                error="Decomposition failed",
                 session_id=session_id,
             )
 
+        # Check if LLM returned clarifying questions
         if decomposition_result.get("type") == "clarifying_questions":
             questions = decomposition_result.get("questions", [])
             return ClarificationNeededResponse(
@@ -183,6 +144,7 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Check for unachievable/vague goals
         if decomposition_result.get("type") == "unachievable_goal":
             suggested = decomposition_result.get("suggested_goal", "")
             reason = decomposition_result.get("reason", "")
@@ -209,88 +171,72 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        if user_id and library_agents is not None:
-            try:
-                library_agents = await enrich_library_agents_from_steps(
-                    user_id=user_id,
-                    decomposition_result=decomposition_result,
-                    existing_agents=library_agents,
-                    include_marketplace=True,
+        # Step 2: Generate agent JSON with retry on validation failure
+        blocks_info = get_blocks_info()
+        agent_json = None
+        validation_errors = None
+
+        for attempt in range(MAX_GENERATION_RETRIES + 1):
+            # Generate agent (include validation errors from previous attempt)
+            if attempt == 0:
+                agent_json = await generate_agent(decomposition_result)
+            else:
+                # Retry with validation error feedback
+                logger.info(
+                    f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
                 )
-                logger.debug(
-                    f"After enrichment: {len(library_agents)} total agents for sub-agent composition"
+                retry_instructions = {
+                    **decomposition_result,
+                    "previous_errors": validation_errors,
+                    "retry_instructions": (
+                        "The previous generation had validation errors. "
+                        "Please fix these issues in the new generation:\n"
+                        f"{validation_errors}"
+                    ),
+                }
+                agent_json = await generate_agent(retry_instructions)
+
+            if agent_json is None:
+                if attempt == MAX_GENERATION_RETRIES:
+                    return ErrorResponse(
+                        message="Failed to generate the agent. Please try again.",
+                        error="Generation failed",
+                        session_id=session_id,
+                    )
+                continue
+
+            # Step 3: Apply fixes to correct common errors
+            agent_json = apply_all_fixes(agent_json, blocks_info)
+
+            # Step 4: Validate the agent
+            is_valid, validation_errors = validate_agent(agent_json, blocks_info)
+
+            if is_valid:
+                logger.info(f"Agent generated successfully on attempt {attempt + 1}")
+                break
+
+            logger.warning(
+                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
+            )
+
+            if attempt == MAX_GENERATION_RETRIES:
+                # Return error with validation details
+                return ErrorResponse(
+                    message=(
+                        f"Generated agent has validation errors after {MAX_GENERATION_RETRIES + 1} attempts. "
+                        f"Please try rephrasing your request or simplify the workflow."
+                    ),
+                    error="validation_failed",
+                    details={"validation_errors": validation_errors},
+                    session_id=session_id,
                 )
-            except Exception as e:
-                logger.warning(f"Failed to enrich library agents from steps: {e}")
-
-        try:
-            agent_json = await generate_agent(
-                decomposition_result,
-                library_agents,
-                operation_id=operation_id,
-                task_id=task_id,
-            )
-        except AgentGeneratorNotConfiguredError:
-            return ErrorResponse(
-                message=(
-                    "Agent generation is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
-                session_id=session_id,
-            )
-
-        if agent_json is None:
-            return ErrorResponse(
-                message="Failed to generate the agent. The agent generation service may be unavailable. Please try again.",
-                error="generation_failed",
-                details={"description": description[:100]},
-                session_id=session_id,
-            )
-
-        if isinstance(agent_json, dict) and agent_json.get("type") == "error":
-            error_msg = agent_json.get("error", "Unknown error")
-            error_type = agent_json.get("error_type", "unknown")
-            user_message = get_user_message_for_error(
-                error_type,
-                operation="generate the agent",
-                llm_parse_message="The AI had trouble generating the agent. Please try again or simplify your goal.",
-                validation_message=(
-                    "I wasn't able to create a valid agent for this request. "
-                    "The generated workflow had some structural issues. "
-                    "Please try simplifying your goal or breaking it into smaller steps."
-                ),
-                error_details=error_msg,
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"generation_failed:{error_type}",
-                details={
-                    "description": description[:100],
-                    "service_error": error_msg,
-                    "error_type": error_type,
-                },
-                session_id=session_id,
-            )
-
-        # Check if Agent Generator accepted for async processing
-        if agent_json.get("status") == "accepted":
-            logger.info(
-                f"Agent generation delegated to async processing "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return AsyncProcessingResponse(
-                message="Agent generation started. You'll be notified when it's complete.",
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-            )
 
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
+        # Step 4: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -305,6 +251,7 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Save to library
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -322,7 +269,7 @@ class CreateAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/agents/{library_agent.id}",
+                library_agent_link=f"/library/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

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

@@ -1,337 +0,0 @@
-"""CustomizeAgentTool - Customizes marketplace/template agents using natural language."""
-
-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 .agent_generator import (
-    AgentGeneratorNotConfiguredError,
-    customize_template,
-    get_user_message_for_error,
-    graph_to_json,
-    save_agent_to_library,
-)
-from .base import BaseTool
-from .models import (
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    ClarificationNeededResponse,
-    ClarifyingQuestion,
-    ErrorResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class CustomizeAgentTool(BaseTool):
-    """Tool for customizing marketplace/template agents using natural language."""
-
-    @property
-    def name(self) -> str:
-        return "customize_agent"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Customize a marketplace or template agent using natural language. "
-            "Takes an existing agent from the marketplace and modifies it based on "
-            "the user's requirements before adding to their library."
-        )
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "agent_id": {
-                    "type": "string",
-                    "description": (
-                        "The marketplace agent ID in format 'creator/slug' "
-                        "(e.g., 'autogpt/newsletter-writer'). "
-                        "Get this from find_agent results."
-                    ),
-                },
-                "modifications": {
-                    "type": "string",
-                    "description": (
-                        "Natural language description of how to customize the agent. "
-                        "Be specific about what changes you want to make."
-                    ),
-                },
-                "context": {
-                    "type": "string",
-                    "description": (
-                        "Additional context or answers to previous clarifying questions."
-                    ),
-                },
-                "save": {
-                    "type": "boolean",
-                    "description": (
-                        "Whether to save the customized agent to the user's library. "
-                        "Default is true. Set to false for preview only."
-                    ),
-                    "default": True,
-                },
-            },
-            "required": ["agent_id", "modifications"],
-        }
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Execute the customize_agent tool.
-
-        Flow:
-        1. Parse the agent ID to get creator/slug
-        2. Fetch the template agent from the marketplace
-        3. Call customize_template with the modification request
-        4. Preview or save based on the save parameter
-        """
-        agent_id = kwargs.get("agent_id", "").strip()
-        modifications = kwargs.get("modifications", "").strip()
-        context = kwargs.get("context", "")
-        save = kwargs.get("save", True)
-        session_id = session.session_id if session else None
-
-        if not agent_id:
-            return ErrorResponse(
-                message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
-                error="missing_agent_id",
-                session_id=session_id,
-            )
-
-        if not modifications:
-            return ErrorResponse(
-                message="Please describe how you want to customize this agent.",
-                error="missing_modifications",
-                session_id=session_id,
-            )
-
-        # Parse agent_id in format "creator/slug"
-        parts = [p.strip() for p in agent_id.split("/")]
-        if len(parts) != 2 or not parts[0] or not parts[1]:
-            return ErrorResponse(
-                message=(
-                    f"Invalid agent ID format: '{agent_id}'. "
-                    "Expected format is 'creator/agent-name' "
-                    "(e.g., 'autogpt/newsletter-writer')."
-                ),
-                error="invalid_agent_id_format",
-                session_id=session_id,
-            )
-
-        creator_username, agent_slug = parts
-
-        # Fetch the marketplace agent details
-        try:
-            agent_details = await store_db.get_store_agent_details(
-                username=creator_username, agent_name=agent_slug
-            )
-        except AgentNotFoundError:
-            return ErrorResponse(
-                message=(
-                    f"Could not find marketplace agent '{agent_id}'. "
-                    "Please check the agent ID and try again."
-                ),
-                error="agent_not_found",
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
-            return ErrorResponse(
-                message="Failed to fetch the marketplace agent. Please try again.",
-                error="fetch_error",
-                session_id=session_id,
-            )
-
-        if not agent_details.store_listing_version_id:
-            return ErrorResponse(
-                message=(
-                    f"The agent '{agent_id}' does not have an available version. "
-                    "Please try a different agent."
-                ),
-                error="no_version_available",
-                session_id=session_id,
-            )
-
-        # Get the full agent graph
-        try:
-            graph = await store_db.get_agent(agent_details.store_listing_version_id)
-            template_agent = graph_to_json(graph)
-        except Exception as e:
-            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
-            return ErrorResponse(
-                message="Failed to fetch the agent configuration. Please try again.",
-                error="graph_fetch_error",
-                session_id=session_id,
-            )
-
-        # Call customize_template
-        try:
-            result = await customize_template(
-                template_agent=template_agent,
-                modification_request=modifications,
-                context=context,
-            )
-        except AgentGeneratorNotConfiguredError:
-            return ErrorResponse(
-                message=(
-                    "Agent customization is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error calling customize_template for {agent_id}: {e}")
-            return ErrorResponse(
-                message=(
-                    "Failed to customize the agent due to a service error. "
-                    "Please try again."
-                ),
-                error="customization_service_error",
-                session_id=session_id,
-            )
-
-        if result is None:
-            return ErrorResponse(
-                message=(
-                    "Failed to customize the agent. "
-                    "The agent generation service may be unavailable or timed out. "
-                    "Please try again."
-                ),
-                error="customization_failed",
-                session_id=session_id,
-            )
-
-        # Handle error response
-        if isinstance(result, dict) and result.get("type") == "error":
-            error_msg = result.get("error", "Unknown error")
-            error_type = result.get("error_type", "unknown")
-            user_message = get_user_message_for_error(
-                error_type,
-                operation="customize the agent",
-                llm_parse_message=(
-                    "The AI had trouble customizing the agent. "
-                    "Please try again or simplify your request."
-                ),
-                validation_message=(
-                    "The customized agent failed validation. "
-                    "Please try rephrasing your request."
-                ),
-                error_details=error_msg,
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"customization_failed:{error_type}",
-                session_id=session_id,
-            )
-
-        # Handle clarifying questions
-        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
-            questions = result.get("questions") or []
-            if not isinstance(questions, list):
-                logger.error(
-                    f"Unexpected clarifying questions format: {type(questions)}"
-                )
-                questions = []
-            return ClarificationNeededResponse(
-                message=(
-                    "I need some more information to customize this agent. "
-                    "Please answer the following questions:"
-                ),
-                questions=[
-                    ClarifyingQuestion(
-                        question=q.get("question", ""),
-                        keyword=q.get("keyword", ""),
-                        example=q.get("example"),
-                    )
-                    for q in questions
-                    if isinstance(q, dict)
-                ],
-                session_id=session_id,
-            )
-
-        # Result should be the customized agent JSON
-        if not isinstance(result, dict):
-            logger.error(f"Unexpected customize_template response type: {type(result)}")
-            return ErrorResponse(
-                message="Failed to customize the agent due to an unexpected response.",
-                error="unexpected_response_type",
-                session_id=session_id,
-            )
-
-        customized_agent = result
-
-        agent_name = customized_agent.get(
-            "name", f"Customized {agent_details.agent_name}"
-        )
-        agent_description = customized_agent.get("description", "")
-        nodes = customized_agent.get("nodes")
-        links = customized_agent.get("links")
-        node_count = len(nodes) if isinstance(nodes, list) else 0
-        link_count = len(links) if isinstance(links, list) else 0
-
-        if not save:
-            return AgentPreviewResponse(
-                message=(
-                    f"I've customized the agent '{agent_details.agent_name}'. "
-                    f"The customized agent has {node_count} blocks. "
-                    f"Review it and call customize_agent with save=true to save it."
-                ),
-                agent_json=customized_agent,
-                agent_name=agent_name,
-                description=agent_description,
-                node_count=node_count,
-                link_count=link_count,
-                session_id=session_id,
-            )
-
-        if not user_id:
-            return ErrorResponse(
-                message="You must be logged in to save agents.",
-                error="auth_required",
-                session_id=session_id,
-            )
-
-        # Save to user's library
-        try:
-            created_graph, library_agent = await save_agent_to_library(
-                customized_agent, user_id, is_update=False
-            )
-
-            return AgentSavedResponse(
-                message=(
-                    f"Customized agent '{created_graph.name}' "
-                    f"(based on '{agent_details.agent_name}') "
-                    f"has been saved to your library!"
-                ),
-                agent_id=created_graph.id,
-                agent_name=created_graph.name,
-                library_agent_id=library_agent.id,
-                library_agent_link=f"/library/agents/{library_agent.id}",
-                agent_page_link=f"/build?flowID={created_graph.id}",
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error saving customized agent: {e}")
-            return ErrorResponse(
-                message="Failed to save the customized agent. Please try again.",
-                error="save_failed",
-                session_id=session_id,
-            )

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

@@ -3,21 +3,23 @@
 import logging
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
-    AgentGeneratorNotConfiguredError,
+    apply_agent_patch,
+    apply_all_fixes,
     generate_agent_patch,
     get_agent_as_json,
-    get_all_relevant_agents_for_generation,
-    get_user_message_for_error,
+    get_blocks_info,
     save_agent_to_library,
+    validate_agent,
 )
 from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
-    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -26,6 +28,9 @@ from .models import (
 
 logger = logging.getLogger(__name__)
 
+# Maximum retries for patch generation with validation feedback
+MAX_GENERATION_RETRIES = 2
+
 
 class EditAgentTool(BaseTool):
     """Tool for editing existing agents using natural language."""
@@ -38,17 +43,13 @@ class EditAgentTool(BaseTool):
     def description(self) -> str:
         return (
             "Edit an existing agent from the user's library using natural language. "
-            "Generates updates to the agent while preserving unchanged parts."
+            "Generates a patch to update the agent while preserving unchanged parts."
         )
 
     @property
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {
@@ -86,6 +87,7 @@ class EditAgentTool(BaseTool):
             "required": ["agent_id", "changes"],
         }
 
+    @observe(as_type="tool", name="edit_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -96,8 +98,9 @@ class EditAgentTool(BaseTool):
 
         Flow:
         1. Fetch the current agent
-        2. Generate updated agent (external service handles fixing and validation)
-        3. Preview or save based on the save parameter
+        2. Generate a patch based on the requested changes
+        3. Apply the patch to create an updated agent
+        4. Preview or save based on the save parameter
         """
         agent_id = kwargs.get("agent_id", "").strip()
         changes = kwargs.get("changes", "").strip()
@@ -105,10 +108,6 @@ class EditAgentTool(BaseTool):
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
-        # Extract async processing params (passed by long-running tool handler)
-        operation_id = kwargs.get("_operation_id")
-        task_id = kwargs.get("_task_id")
-
         if not agent_id:
             return ErrorResponse(
                 message="Please provide the agent ID to edit.",
@@ -123,6 +122,7 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Step 1: Fetch current agent
         current_agent = await get_agent_as_json(agent_id, user_id)
 
         if current_agent is None:
@@ -132,117 +132,126 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        library_agents = None
-        if user_id:
-            try:
-                graph_id = current_agent.get("id")
-                library_agents = await get_all_relevant_agents_for_generation(
-                    user_id=user_id,
-                    search_query=changes,
-                    exclude_graph_id=graph_id,
-                    include_marketplace=True,
-                )
-                logger.debug(
-                    f"Found {len(library_agents)} relevant agents for sub-agent composition"
-                )
-            except Exception as e:
-                logger.warning(f"Failed to fetch library agents: {e}")
-
+        # Build the update request with context
         update_request = changes
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        try:
-            result = await generate_agent_patch(
-                update_request,
-                current_agent,
-                library_agents,
-                operation_id=operation_id,
-                task_id=task_id,
-            )
-        except AgentGeneratorNotConfiguredError:
-            return ErrorResponse(
-                message=(
-                    "Agent editing is not available. "
-                    "The Agent Generator service is not configured."
-                ),
-                error="service_not_configured",
-                session_id=session_id,
-            )
+        # Step 2: Generate patch with retry on validation failure
+        blocks_info = get_blocks_info()
+        updated_agent = None
+        validation_errors = None
+        intent = "Applied requested changes"
 
-        if result is None:
-            return ErrorResponse(
-                message="Failed to generate changes. The agent generation service may be unavailable or timed out. Please try again.",
-                error="update_generation_failed",
-                details={"agent_id": agent_id, "changes": changes[:100]},
-                session_id=session_id,
-            )
-
-        # Check if Agent Generator accepted for async processing
-        if result.get("status") == "accepted":
-            logger.info(
-                f"Agent edit delegated to async processing "
-                f"(operation_id={operation_id}, task_id={task_id})"
-            )
-            return AsyncProcessingResponse(
-                message="Agent edit started. You'll be notified when it's complete.",
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-            )
-
-        # Check if the result is an error from the external service
-        if isinstance(result, dict) and result.get("type") == "error":
-            error_msg = result.get("error", "Unknown error")
-            error_type = result.get("error_type", "unknown")
-            user_message = get_user_message_for_error(
-                error_type,
-                operation="generate the changes",
-                llm_parse_message="The AI had trouble generating the changes. Please try again or simplify your request.",
-                validation_message="The generated changes failed validation. Please try rephrasing your request.",
-                error_details=error_msg,
-            )
-            return ErrorResponse(
-                message=user_message,
-                error=f"update_generation_failed:{error_type}",
-                details={
-                    "agent_id": agent_id,
-                    "changes": changes[:100],
-                    "service_error": error_msg,
-                    "error_type": error_type,
-                },
-                session_id=session_id,
-            )
-
-        if result.get("type") == "clarifying_questions":
-            questions = result.get("questions", [])
-            return ClarificationNeededResponse(
-                message=(
-                    "I need some more information about the changes. "
-                    "Please answer the following questions:"
-                ),
-                questions=[
-                    ClarifyingQuestion(
-                        question=q.get("question", ""),
-                        keyword=q.get("keyword", ""),
-                        example=q.get("example"),
+        for attempt in range(MAX_GENERATION_RETRIES + 1):
+            # Generate patch (include validation errors from previous attempt)
+            try:
+                if attempt == 0:
+                    patch_result = await generate_agent_patch(
+                        update_request, current_agent
                     )
-                    for q in questions
-                ],
-                session_id=session_id,
+                else:
+                    # Retry with validation error feedback
+                    logger.info(
+                        f"Retry {attempt}/{MAX_GENERATION_RETRIES} with validation feedback"
+                    )
+                    retry_request = (
+                        f"{update_request}\n\n"
+                        f"IMPORTANT: The previous edit had validation errors. "
+                        f"Please fix these issues:\n{validation_errors}"
+                    )
+                    patch_result = await generate_agent_patch(
+                        retry_request, current_agent
+                    )
+            except ValueError as e:
+                # Handle missing API key or configuration errors
+                return ErrorResponse(
+                    message=f"Agent generation is not configured: {str(e)}",
+                    error="configuration_error",
+                    session_id=session_id,
+                )
+
+            if patch_result is None:
+                if attempt == MAX_GENERATION_RETRIES:
+                    return ErrorResponse(
+                        message="Failed to generate changes. Please try rephrasing.",
+                        error="Patch generation failed",
+                        session_id=session_id,
+                    )
+                continue
+
+            # Check if LLM returned clarifying questions
+            if patch_result.get("type") == "clarifying_questions":
+                questions = patch_result.get("questions", [])
+                return ClarificationNeededResponse(
+                    message=(
+                        "I need some more information about the changes. "
+                        "Please answer the following questions:"
+                    ),
+                    questions=[
+                        ClarifyingQuestion(
+                            question=q.get("question", ""),
+                            keyword=q.get("keyword", ""),
+                            example=q.get("example"),
+                        )
+                        for q in questions
+                    ],
+                    session_id=session_id,
+                )
+
+            # Step 3: Apply patch and fixes
+            try:
+                updated_agent = apply_agent_patch(current_agent, patch_result)
+                updated_agent = apply_all_fixes(updated_agent, blocks_info)
+            except Exception as e:
+                if attempt == MAX_GENERATION_RETRIES:
+                    return ErrorResponse(
+                        message=f"Failed to apply changes: {str(e)}",
+                        error="patch_apply_failed",
+                        details={"exception": str(e)},
+                        session_id=session_id,
+                    )
+                validation_errors = str(e)
+                continue
+
+            # Step 4: Validate the updated agent
+            is_valid, validation_errors = validate_agent(updated_agent, blocks_info)
+
+            if is_valid:
+                logger.info(f"Agent edited successfully on attempt {attempt + 1}")
+                intent = patch_result.get("intent", "Applied requested changes")
+                break
+
+            logger.warning(
+                f"Validation failed on attempt {attempt + 1}: {validation_errors}"
             )
 
-        updated_agent = result
+            if attempt == MAX_GENERATION_RETRIES:
+                # Return error with validation details
+                return ErrorResponse(
+                    message=(
+                        f"Updated agent has validation errors after "
+                        f"{MAX_GENERATION_RETRIES + 1} attempts. "
+                        f"Please try rephrasing your request or simplify the changes."
+                    ),
+                    error="validation_failed",
+                    details={"validation_errors": validation_errors},
+                    session_id=session_id,
+                )
+
+        # At this point, updated_agent is guaranteed to be set (we return on all failure paths)
+        assert updated_agent is not None
 
         agent_name = updated_agent.get("name", "Updated Agent")
         agent_description = updated_agent.get("description", "")
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
+        # Step 5: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
-                    f"I've updated the agent. "
+                    f"I've updated the agent. Changes: {intent}. "
                     f"The agent now has {node_count} blocks. "
                     f"Review it and call edit_agent with save=true to save the changes."
                 ),
@@ -254,6 +263,7 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Save to library (creates a new version)
         if not user_id:
             return ErrorResponse(
                 message="You must be logged in to save agents.",
@@ -267,11 +277,14 @@ class EditAgentTool(BaseTool):
             )
 
             return AgentSavedResponse(
-                message=f"Updated agent '{created_graph.name}' has been saved to your library!",
+                message=(
+                    f"Updated agent '{created_graph.name}' has been saved to your library! "
+                    f"Changes: {intent}"
+                ),
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/agents/{library_agent.id}",
+                library_agent_link=f"/library/{library_agent.id}",
                 agent_page_link=f"/build?flowID={created_graph.id}",
                 session_id=session_id,
             )

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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -35,6 +37,7 @@ class FindAgentTool(BaseTool):
             "required": ["query"],
         }
 
+    @observe(as_type="tool", name="find_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -1,6 +1,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -55,6 +56,7 @@ class FindBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    @observe(as_type="tool", name="find_block")
     async def _execute(
         self,
         user_id: str | None,
@@ -107,8 +109,7 @@ class FindBlockTool(BaseTool):
                 block_id = result["content_id"]
                 block = get_block(block_id)
 
-                # Skip disabled blocks
-                if block and not block.disabled:
+                if block:
                     # Get input/output schemas
                     input_schema = {}
                     output_schema = {}

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

@@ -2,6 +2,8 @@
 
 from typing import Any
 
+from langfuse import observe
+
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -41,6 +43,7 @@ class FindLibraryAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    @observe(as_type="tool", name="find_library_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

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

@@ -4,6 +4,8 @@ import logging
 from pathlib import Path
 from typing import Any
 
+from langfuse import observe
+
 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 (
@@ -71,6 +73,7 @@ class GetDocPageTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
+    @observe(as_type="tool", name="get_doc_page")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -28,18 +28,6 @@ class ResponseType(str, Enum):
     BLOCK_OUTPUT = "block_output"
     DOC_SEARCH_RESULTS = "doc_search_results"
     DOC_PAGE = "doc_page"
-    # Workspace response types
-    WORKSPACE_FILE_LIST = "workspace_file_list"
-    WORKSPACE_FILE_CONTENT = "workspace_file_content"
-    WORKSPACE_FILE_METADATA = "workspace_file_metadata"
-    WORKSPACE_FILE_WRITTEN = "workspace_file_written"
-    WORKSPACE_FILE_DELETED = "workspace_file_deleted"
-    # Long-running operation types
-    OPERATION_STARTED = "operation_started"
-    OPERATION_PENDING = "operation_pending"
-    OPERATION_IN_PROGRESS = "operation_in_progress"
-    # Input validation
-    INPUT_VALIDATION_ERROR = "input_validation_error"
 
 
 # Base response model
@@ -70,10 +58,6 @@ class AgentInfo(BaseModel):
     has_external_trigger: bool | None = None
     new_output: bool | None = None
     graph_id: str | None = None
-    inputs: dict[str, Any] | None = Field(
-        default=None,
-        description="Input schema for the agent, including field names, types, and defaults",
-    )
 
 
 class AgentsFoundResponse(ToolResponseBase):
@@ -200,20 +184,6 @@ class ErrorResponse(ToolResponseBase):
     details: dict[str, Any] | None = None
 
 
-class InputValidationErrorResponse(ToolResponseBase):
-    """Response when run_agent receives unknown input fields."""
-
-    type: ResponseType = ResponseType.INPUT_VALIDATION_ERROR
-    unrecognized_fields: list[str] = Field(
-        description="List of input field names that were not recognized"
-    )
-    inputs: dict[str, Any] = Field(
-        description="The agent's valid input schema for reference"
-    )
-    graph_id: str | None = None
-    graph_version: int | None = None
-
-
 # Agent output models
 class ExecutionOutputInfo(BaseModel):
     """Summary of a single execution's outputs."""
@@ -364,60 +334,3 @@ class BlockOutputResponse(ToolResponseBase):
     block_name: str
     outputs: dict[str, list[Any]]
     success: bool = True
-
-
-# Long-running operation models
-class OperationStartedResponse(ToolResponseBase):
-    """Response when a long-running operation has been started in the background.
-
-    This is returned immediately to the client while the operation continues
-    to execute. The user can close the tab and check back later.
-
-    The task_id can be used to reconnect to the SSE stream via
-    GET /chat/tasks/{task_id}/stream?last_idx=0
-    """
-
-    type: ResponseType = ResponseType.OPERATION_STARTED
-    operation_id: str
-    tool_name: str
-    task_id: str | None = None  # For SSE reconnection
-
-
-class OperationPendingResponse(ToolResponseBase):
-    """Response stored in chat history while a long-running operation is executing.
-
-    This is persisted to the database so users see a pending state when they
-    refresh before the operation completes.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_PENDING
-    operation_id: str
-    tool_name: str
-
-
-class OperationInProgressResponse(ToolResponseBase):
-    """Response when an operation is already in progress.
-
-    Returned for idempotency when the same tool_call_id is requested again
-    while the background task is still running.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_IN_PROGRESS
-    tool_call_id: str
-
-
-class AsyncProcessingResponse(ToolResponseBase):
-    """Response when an operation has been delegated to async processing.
-
-    This is returned by tools when the external service accepts the request
-    for async processing (HTTP 202 Accepted). The Redis Streams completion
-    consumer will handle the result when the external service completes.
-
-    The status field is specifically "accepted" to allow the long-running tool
-    handler to detect this response and skip LLM continuation.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_STARTED
-    status: str = "accepted"  # Must be "accepted" for detection
-    operation_id: str | None = None
-    task_id: str | None = None

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

@@ -3,14 +3,11 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tracking import (
-    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
@@ -30,7 +27,6 @@ from .models import (
     ErrorResponse,
     ExecutionOptions,
     ExecutionStartedResponse,
-    InputValidationErrorResponse,
     SetupInfo,
     SetupRequirementsResponse,
     ToolResponseBase,
@@ -159,6 +155,7 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
+    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,
@@ -274,22 +271,6 @@ class RunAgentTool(BaseTool):
             input_properties = graph.input_schema.get("properties", {})
             required_fields = set(graph.input_schema.get("required", []))
             provided_inputs = set(params.inputs.keys())
-            valid_fields = set(input_properties.keys())
-
-            # Check for unknown input fields
-            unrecognized_fields = provided_inputs - valid_fields
-            if unrecognized_fields:
-                return InputValidationErrorResponse(
-                    message=(
-                        f"Unknown input field(s) provided: {', '.join(sorted(unrecognized_fields))}. "
-                        f"Agent was not executed. Please use the correct field names from the schema."
-                    ),
-                    session_id=session_id,
-                    unrecognized_fields=sorted(unrecognized_fields),
-                    inputs=graph.input_schema,
-                    graph_id=graph.id,
-                    graph_version=graph.version,
-                )
 
             # If agent has inputs but none were provided AND use_defaults is not set,
             # always show what's available first so user can decide
@@ -472,16 +453,6 @@ class RunAgentTool(BaseTool):
             session.successful_agent_runs.get(library_agent.graph_id, 0) + 1
         )
 
-        # Track in PostHog
-        track_agent_run_success(
-            user_id=user_id,
-            session_id=session_id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            execution_id=execution.id,
-            library_agent_id=library_agent.id,
-        )
-
         library_agent_link = f"/library/agents/{library_agent.id}"
         return ExecutionStartedResponse(
             message=(
@@ -563,18 +534,6 @@ class RunAgentTool(BaseTool):
             session.successful_agent_schedules.get(library_agent.graph_id, 0) + 1
         )
 
-        # Track in PostHog
-        track_agent_scheduled(
-            user_id=user_id,
-            session_id=session_id,
-            graph_id=library_agent.graph_id,
-            graph_name=library_agent.name,
-            schedule_id=result.id,
-            schedule_name=schedule_name,
-            cron=cron,
-            library_agent_id=library_agent.id,
-        )
-
         library_agent_link = f"/library/agents/{library_agent.id}"
         return ExecutionStartedResponse(
             message=(

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

@@ -29,7 +29,7 @@ def mock_embedding_functions():
         yield
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent(setup_test_data):
     """Test that the run_agent tool successfully executes an approved agent"""
     # Use test data from fixture
@@ -70,7 +70,7 @@ async def test_run_agent(setup_test_data):
     assert result_data["graph_name"] == "Test Agent"
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_missing_inputs(setup_test_data):
     """Test that the run_agent tool returns error when inputs are missing"""
     # Use test data from fixture
@@ -106,7 +106,7 @@ async def test_run_agent_missing_inputs(setup_test_data):
     assert "message" in result_data
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_invalid_agent_id(setup_test_data):
     """Test that the run_agent tool returns error for invalid agent ID"""
     # Use test data from fixture
@@ -141,7 +141,7 @@ async def test_run_agent_invalid_agent_id(setup_test_data):
     )
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_with_llm_credentials(setup_llm_test_data):
     """Test that run_agent works with an agent requiring LLM credentials"""
     # Use test data from fixture
@@ -185,7 +185,7 @@ async def test_run_agent_with_llm_credentials(setup_llm_test_data):
     assert result_data["graph_name"] == "LLM Test Agent"
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_shows_available_inputs_when_none_provided(setup_test_data):
     """Test that run_agent returns available inputs when called without inputs or use_defaults."""
     user = setup_test_data["user"]
@@ -219,7 +219,7 @@ async def test_run_agent_shows_available_inputs_when_none_provided(setup_test_da
     assert "inputs" in result_data["message"].lower()
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_with_use_defaults(setup_test_data):
     """Test that run_agent executes successfully with use_defaults=True."""
     user = setup_test_data["user"]
@@ -251,7 +251,7 @@ async def test_run_agent_with_use_defaults(setup_test_data):
     assert result_data["graph_id"] == graph.id
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_missing_credentials(setup_firecrawl_test_data):
     """Test that run_agent returns setup_requirements when credentials are missing."""
     user = setup_firecrawl_test_data["user"]
@@ -285,7 +285,7 @@ async def test_run_agent_missing_credentials(setup_firecrawl_test_data):
     assert len(setup_info["user_readiness"]["missing_credentials"]) > 0
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_invalid_slug_format(setup_test_data):
     """Test that run_agent returns error for invalid slug format (no slash)."""
     user = setup_test_data["user"]
@@ -313,7 +313,7 @@ async def test_run_agent_invalid_slug_format(setup_test_data):
     assert "username/agent-name" in result_data["message"]
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_unauthenticated():
     """Test that run_agent returns need_login for unauthenticated users."""
     tool = RunAgentTool()
@@ -340,7 +340,7 @@ async def test_run_agent_unauthenticated():
     assert "sign in" in result_data["message"].lower()
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_schedule_without_cron(setup_test_data):
     """Test that run_agent returns error when scheduling without cron expression."""
     user = setup_test_data["user"]
@@ -372,7 +372,7 @@ async def test_run_agent_schedule_without_cron(setup_test_data):
     assert "cron" in result_data["message"].lower()
 
 
-@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.asyncio(scope="session")
 async def test_run_agent_schedule_without_name(setup_test_data):
     """Test that run_agent returns error when scheduling without schedule_name."""
     user = setup_test_data["user"]
@@ -402,42 +402,3 @@ async def test_run_agent_schedule_without_name(setup_test_data):
     # Should return error about missing schedule_name
     assert result_data.get("type") == "error"
     assert "schedule_name" in result_data["message"].lower()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_run_agent_rejects_unknown_input_fields(setup_test_data):
-    """Test that run_agent returns input_validation_error for unknown input fields."""
-    user = setup_test_data["user"]
-    store_submission = setup_test_data["store_submission"]
-
-    tool = RunAgentTool()
-    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
-    session = make_session(user_id=user.id)
-
-    # Execute with unknown input field names
-    response = await tool.execute(
-        user_id=user.id,
-        session_id=str(uuid.uuid4()),
-        tool_call_id=str(uuid.uuid4()),
-        username_agent_slug=agent_marketplace_id,
-        inputs={
-            "unknown_field": "some value",
-            "another_unknown": "another value",
-        },
-        session=session,
-    )
-
-    assert response is not None
-    assert hasattr(response, "output")
-    assert isinstance(response.output, str)
-    result_data = orjson.loads(response.output)
-
-    # Should return input_validation_error type with unrecognized fields
-    assert result_data.get("type") == "input_validation_error"
-    assert "unrecognized_fields" in result_data
-    assert set(result_data["unrecognized_fields"]) == {
-        "another_unknown",
-        "unknown_field",
-    }
-    assert "inputs" in result_data  # Contains the valid schema
-    assert "Agent was not executed" in result_data["message"]

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

@@ -1,17 +1,15 @@
 """Tool for executing blocks directly."""
 
 import logging
-import uuid
 from collections import defaultdict
 from typing import Any
 
-from pydantic_core import PydanticUndefined
+from langfuse import observe
 
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsMetaInput
-from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 
@@ -77,22 +75,15 @@ class RunBlockTool(BaseTool):
         self,
         user_id: str,
         block: Any,
-        input_data: dict[str, Any] | None = None,
     ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
         """
         Check if user has required credentials for a block.
 
-        Args:
-            user_id: User ID
-            block: Block to check credentials for
-            input_data: Input data for the block (used to determine provider via discriminator)
-
         Returns:
             tuple[matched_credentials, missing_credentials]
         """
         matched_credentials: dict[str, CredentialsMetaInput] = {}
         missing_credentials: list[CredentialsMetaInput] = []
-        input_data = input_data or {}
 
         # Get credential field info from block's input schema
         credentials_fields_info = block.input_schema.get_credentials_fields_info()
@@ -105,33 +96,14 @@ class RunBlockTool(BaseTool):
         available_creds = await creds_manager.store.get_all_creds(user_id)
 
         for field_name, field_info in credentials_fields_info.items():
-            effective_field_info = field_info
-            if field_info.discriminator and field_info.discriminator_mapping:
-                # Get discriminator from input, falling back to schema default
-                discriminator_value = input_data.get(field_info.discriminator)
-                if discriminator_value is None:
-                    field = block.input_schema.model_fields.get(
-                        field_info.discriminator
-                    )
-                    if field and field.default is not PydanticUndefined:
-                        discriminator_value = field.default
-
-                if (
-                    discriminator_value
-                    and discriminator_value in field_info.discriminator_mapping
-                ):
-                    effective_field_info = field_info.discriminate(discriminator_value)
-                    logger.debug(
-                        f"Discriminated provider for {field_name}: "
-                        f"{discriminator_value} -> {effective_field_info.provider}"
-                    )
-
+            # field_info.provider is a frozenset of acceptable providers
+            # field_info.supported_types is a frozenset of acceptable types
             matching_cred = next(
                 (
                     cred
                     for cred in available_creds
-                    if cred.provider in effective_field_info.provider
-                    and cred.type in effective_field_info.supported_types
+                    if cred.provider in field_info.provider
+                    and cred.type in field_info.supported_types
                 ),
                 None,
             )
@@ -145,8 +117,8 @@ class RunBlockTool(BaseTool):
                 )
             else:
                 # Create a placeholder for the missing credential
-                provider = next(iter(effective_field_info.provider), "unknown")
-                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                provider = next(iter(field_info.provider), "unknown")
+                cred_type = next(iter(field_info.supported_types), "api_key")
                 missing_credentials.append(
                     CredentialsMetaInput(
                         id=field_name,
@@ -158,6 +130,7 @@ class RunBlockTool(BaseTool):
 
         return matched_credentials, missing_credentials
 
+    @observe(as_type="tool", name="run_block")
     async def _execute(
         self,
         user_id: str | None,
@@ -206,17 +179,13 @@ class RunBlockTool(BaseTool):
                 message=f"Block '{block_id}' not found",
                 session_id=session_id,
             )
-        if block.disabled:
-            return ErrorResponse(
-                message=f"Block '{block_id}' is disabled",
-                session_id=session_id,
-            )
 
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
+        # Check credentials
         creds_manager = IntegrationCredentialsManager()
         matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block, input_data
+            user_id, block
         )
 
         if missing_credentials:
@@ -252,48 +221,11 @@ class RunBlockTool(BaseTool):
             )
 
         try:
-            # Get or create user's workspace for CoPilot file operations
-            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
-            # This means:
-            # - graph_id (agent) = session (memories scoped to session when limit_to_agent=True)
-            # - graph_exec_id (run) = session (memories scoped to session when limit_to_run=True)
-            # - node_exec_id = unique per block execution
-            synthetic_graph_id = f"copilot-session-{session.session_id}"
-            synthetic_graph_exec_id = f"copilot-session-{session.session_id}"
-            synthetic_node_id = f"copilot-node-{block_id}"
-            synthetic_node_exec_id = (
-                f"copilot-{session.session_id}-{uuid.uuid4().hex[:8]}"
-            )
-
-            # Create unified execution context with all required fields
-            execution_context = ExecutionContext(
-                # Execution identity
-                user_id=user_id,
-                graph_id=synthetic_graph_id,
-                graph_exec_id=synthetic_graph_exec_id,
-                graph_version=1,  # Versions are 1-indexed
-                node_id=synthetic_node_id,
-                node_exec_id=synthetic_node_exec_id,
-                # Workspace with session scoping
-                workspace_id=workspace.id,
-                session_id=session.session_id,
-            )
-
-            # Prepare kwargs for block execution
-            # Keep individual kwargs for backwards compatibility with existing blocks
+            # Fetch actual credentials and prepare kwargs for block execution
+            # Create execution context with defaults (blocks may require it)
             exec_kwargs: dict[str, Any] = {
                 "user_id": user_id,
-                "execution_context": execution_context,
-                # Legacy: individual kwargs for blocks not yet using execution_context
-                "workspace_id": workspace.id,
-                "graph_exec_id": synthetic_graph_exec_id,
-                "node_exec_id": synthetic_node_exec_id,
-                "node_id": synthetic_node_id,
-                "graph_version": 1,  # Versions are 1-indexed
-                "graph_id": synthetic_graph_id,
+                "execution_context": ExecutionContext(),
             }
 
             for field_name, cred_meta in matched_credentials.items():

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

@@ -3,6 +3,7 @@
 import logging
 from typing import Any
 
+from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -87,6 +88,7 @@ class SearchDocsTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
+    @observe(as_type="tool", name="search_docs")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -266,14 +266,13 @@ async def match_user_credentials_to_graph(
         credential_requirements,
         _node_fields,
     ) in aggregated_creds.items():
-        # Find first matching credential by provider, type, and scopes
+        # Find first matching credential by provider and type
         matching_cred = next(
             (
                 cred
                 for cred in available_creds
                 if cred.provider in credential_requirements.provider
                 and cred.type in credential_requirements.supported_types
-                and _credential_has_required_scopes(cred, credential_requirements)
             ),
             None,
         )
@@ -297,17 +296,10 @@ async def match_user_credentials_to_graph(
                     f"{credential_field_name} (validation failed: {e})"
                 )
         else:
-            # Build a helpful error message including scope requirements
-            error_parts = [
-                f"provider in {list(credential_requirements.provider)}",
-                f"type in {list(credential_requirements.supported_types)}",
-            ]
-            if credential_requirements.required_scopes:
-                error_parts.append(
-                    f"scopes including {list(credential_requirements.required_scopes)}"
-                )
             missing_creds.append(
-                f"{credential_field_name} (requires {', '.join(error_parts)})"
+                f"{credential_field_name} "
+                f"(requires provider in {list(credential_requirements.provider)}, "
+                f"type in {list(credential_requirements.supported_types)})"
             )
 
     logger.info(
@@ -317,28 +309,6 @@ async def match_user_credentials_to_graph(
     return graph_credentials_inputs, missing_creds
 
 
-def _credential_has_required_scopes(
-    credential: Credentials,
-    requirements: CredentialsFieldInfo,
-) -> bool:
-    """
-    Check if a credential has all the scopes required by the block.
-
-    For OAuth2 credentials, verifies that the credential's scopes are a superset
-    of the required scopes. For other credential types, returns True (no scope check).
-    """
-    # Only OAuth2 credentials have scopes to check
-    if credential.type != "oauth2":
-        return True
-
-    # If no scopes are required, any credential matches
-    if not requirements.required_scopes:
-        return True
-
-    # Check that credential scopes are a superset of required scopes
-    return set(credential.scopes).issuperset(requirements.required_scopes)
-
-
 async def check_user_has_required_credentials(
     user_id: str,
     required_credentials: list[CredentialsMetaInput],

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

@@ -1,620 +0,0 @@
-"""CoPilot tools for workspace file operations."""
-
-import base64
-import logging
-from typing import Any, Optional
-
-from pydantic import BaseModel
-
-from backend.api.features.chat.model import ChatSession
-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
-
-from .base import BaseTool
-from .models import ErrorResponse, ResponseType, ToolResponseBase
-
-logger = logging.getLogger(__name__)
-
-
-class WorkspaceFileInfoData(BaseModel):
-    """Data model for workspace file information (not a response itself)."""
-
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    size_bytes: int
-
-
-class WorkspaceFileListResponse(ToolResponseBase):
-    """Response containing list of workspace files."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_LIST
-    files: list[WorkspaceFileInfoData]
-    total_count: int
-
-
-class WorkspaceFileContentResponse(ToolResponseBase):
-    """Response containing workspace file content (legacy, for small text files)."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_CONTENT
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    content_base64: str
-
-
-class WorkspaceFileMetadataResponse(ToolResponseBase):
-    """Response containing workspace file metadata and download URL (prevents context bloat)."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_METADATA
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    size_bytes: int
-    download_url: str
-    preview: str | None = None  # First 500 chars for text files
-
-
-class WorkspaceWriteResponse(ToolResponseBase):
-    """Response after writing a file to workspace."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_WRITTEN
-    file_id: str
-    name: str
-    path: str
-    size_bytes: int
-
-
-class WorkspaceDeleteResponse(ToolResponseBase):
-    """Response after deleting a file from workspace."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_DELETED
-    file_id: str
-    success: bool
-
-
-class ListWorkspaceFilesTool(BaseTool):
-    """Tool for listing files in user's workspace."""
-
-    @property
-    def name(self) -> str:
-        return "list_workspace_files"
-
-    @property
-    def description(self) -> str:
-        return (
-            "List files in the user's workspace. "
-            "Returns file names, paths, sizes, and metadata. "
-            "Optionally filter by path prefix."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "path_prefix": {
-                    "type": "string",
-                    "description": (
-                        "Optional path prefix to filter files "
-                        "(e.g., '/documents/' to list only files in documents folder). "
-                        "By default, only files from the current session are listed."
-                    ),
-                },
-                "limit": {
-                    "type": "integer",
-                    "description": "Maximum number of files to return (default 50, max 100)",
-                    "minimum": 1,
-                    "maximum": 100,
-                },
-                "include_all_sessions": {
-                    "type": "boolean",
-                    "description": (
-                        "If true, list files from all sessions. "
-                        "Default is false (only current session's files)."
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        path_prefix: Optional[str] = kwargs.get("path_prefix")
-        limit = min(kwargs.get("limit", 50), 100)
-        include_all_sessions: bool = kwargs.get("include_all_sessions", False)
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            files = await manager.list_files(
-                path=path_prefix,
-                limit=limit,
-                include_all_sessions=include_all_sessions,
-            )
-            total = await manager.get_file_count(
-                path=path_prefix,
-                include_all_sessions=include_all_sessions,
-            )
-
-            file_infos = [
-                WorkspaceFileInfoData(
-                    file_id=f.id,
-                    name=f.name,
-                    path=f.path,
-                    mime_type=f.mimeType,
-                    size_bytes=f.sizeBytes,
-                )
-                for f in files
-            ]
-
-            scope_msg = "all sessions" if include_all_sessions else "current session"
-            return WorkspaceFileListResponse(
-                files=file_infos,
-                total_count=total,
-                message=f"Found {len(files)} files in workspace ({scope_msg})",
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error listing workspace files: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to list workspace files: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class ReadWorkspaceFileTool(BaseTool):
-    """Tool for reading file content from workspace."""
-
-    # Size threshold for returning full content vs metadata+URL
-    # Files larger than this return metadata with download URL to prevent context bloat
-    MAX_INLINE_SIZE_BYTES = 32 * 1024  # 32KB
-    # Preview size for text files
-    PREVIEW_SIZE = 500
-
-    @property
-    def name(self) -> str:
-        return "read_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Read a file from the user's workspace. "
-            "Specify either file_id or path to identify the file. "
-            "For small text files, returns content directly. "
-            "For large or binary files, returns metadata and a download URL. "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "file_id": {
-                    "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
-                },
-                "force_download_url": {
-                    "type": "boolean",
-                    "description": (
-                        "If true, always return metadata+URL instead of inline content. "
-                        "Default is false (auto-selects based on file size/type)."
-                    ),
-                },
-            },
-            "required": [],  # At least one must be provided
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    def _is_text_mime_type(self, mime_type: str) -> bool:
-        """Check if the MIME type is a text-based type."""
-        text_types = [
-            "text/",
-            "application/json",
-            "application/xml",
-            "application/javascript",
-            "application/x-python",
-            "application/x-sh",
-        ]
-        return any(mime_type.startswith(t) for t in text_types)
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        file_id: Optional[str] = kwargs.get("file_id")
-        path: Optional[str] = kwargs.get("path")
-        force_download_url: bool = kwargs.get("force_download_url", False)
-
-        if not file_id and not path:
-            return ErrorResponse(
-                message="Please provide either file_id or path",
-                session_id=session_id,
-            )
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            # Get file info
-            if file_id:
-                file_info = await manager.get_file_info(file_id)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found: {file_id}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_id
-            else:
-                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_info.id
-
-            # Decide whether to return inline content or metadata+URL
-            is_small_file = file_info.sizeBytes <= self.MAX_INLINE_SIZE_BYTES
-            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:
-                content = await manager.read_file_by_id(target_file_id)
-                content_b64 = base64.b64encode(content).decode("utf-8")
-
-                return WorkspaceFileContentResponse(
-                    file_id=file_info.id,
-                    name=file_info.name,
-                    path=file_info.path,
-                    mime_type=file_info.mimeType,
-                    content_base64=content_b64,
-                    message=f"Successfully read file: {file_info.name}",
-                    session_id=session_id,
-                )
-
-            # Return metadata + workspace:// reference for large or binary files
-            # This prevents context bloat (100KB file = ~133KB as base64)
-            # Use workspace:// format so frontend urlTransform can add proxy prefix
-            download_url = f"workspace://{target_file_id}"
-
-            # Generate preview for text files
-            preview: str | None = None
-            if is_text_file:
-                try:
-                    content = await manager.read_file_by_id(target_file_id)
-                    preview_text = content[: self.PREVIEW_SIZE].decode(
-                        "utf-8", errors="replace"
-                    )
-                    if len(content) > self.PREVIEW_SIZE:
-                        preview_text += "..."
-                    preview = preview_text
-                except Exception:
-                    pass  # Preview is optional
-
-            return WorkspaceFileMetadataResponse(
-                file_id=file_info.id,
-                name=file_info.name,
-                path=file_info.path,
-                mime_type=file_info.mimeType,
-                size_bytes=file_info.sizeBytes,
-                download_url=download_url,
-                preview=preview,
-                message=f"File: {file_info.name} ({file_info.sizeBytes} bytes). Use download_url to retrieve content.",
-                session_id=session_id,
-            )
-
-        except FileNotFoundError as e:
-            return ErrorResponse(
-                message=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error reading workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to read workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class WriteWorkspaceFileTool(BaseTool):
-    """Tool for writing files to workspace."""
-
-    @property
-    def name(self) -> str:
-        return "write_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Write or create a file in the user's workspace. "
-            "Provide the content as a base64-encoded string. "
-            f"Maximum file size is {Config().max_file_size_mb}MB. "
-            "Files are saved to the current session's folder by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "filename": {
-                    "type": "string",
-                    "description": "Name for the file (e.g., 'report.pdf')",
-                },
-                "content_base64": {
-                    "type": "string",
-                    "description": "Base64-encoded file content",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "Optional virtual path where to save the file "
-                        "(e.g., '/documents/report.pdf'). "
-                        "Defaults to '/{filename}'. Scoped to current session."
-                    ),
-                },
-                "mime_type": {
-                    "type": "string",
-                    "description": (
-                        "Optional MIME type of the file. "
-                        "Auto-detected from filename if not provided."
-                    ),
-                },
-                "overwrite": {
-                    "type": "boolean",
-                    "description": "Whether to overwrite if file exists at path (default: false)",
-                },
-            },
-            "required": ["filename", "content_base64"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        filename: str = kwargs.get("filename", "")
-        content_b64: str = kwargs.get("content_base64", "")
-        path: Optional[str] = kwargs.get("path")
-        mime_type: Optional[str] = kwargs.get("mime_type")
-        overwrite: bool = kwargs.get("overwrite", False)
-
-        if not filename:
-            return ErrorResponse(
-                message="Please provide a filename",
-                session_id=session_id,
-            )
-
-        if not content_b64:
-            return ErrorResponse(
-                message="Please provide content_base64",
-                session_id=session_id,
-            )
-
-        # Decode content
-        try:
-            content = base64.b64decode(content_b64)
-        except Exception:
-            return ErrorResponse(
-                message="Invalid base64-encoded content",
-                session_id=session_id,
-            )
-
-        # Check size
-        max_file_size = Config().max_file_size_mb * 1024 * 1024
-        if len(content) > max_file_size:
-            return ErrorResponse(
-                message=f"File too large. Maximum size is {Config().max_file_size_mb}MB",
-                session_id=session_id,
-            )
-
-        try:
-            # Virus scan
-            await scan_content_safe(content, filename=filename)
-
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            file_record = await manager.write_file(
-                content=content,
-                filename=filename,
-                path=path,
-                mime_type=mime_type,
-                overwrite=overwrite,
-            )
-
-            return WorkspaceWriteResponse(
-                file_id=file_record.id,
-                name=file_record.name,
-                path=file_record.path,
-                size_bytes=file_record.sizeBytes,
-                message=f"Successfully wrote file: {file_record.name}",
-                session_id=session_id,
-            )
-
-        except ValueError as e:
-            return ErrorResponse(
-                message=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error writing workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to write workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class DeleteWorkspaceFileTool(BaseTool):
-    """Tool for deleting files from workspace."""
-
-    @property
-    def name(self) -> str:
-        return "delete_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Delete a file from the user's workspace. "
-            "Specify either file_id or path to identify the file. "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "file_id": {
-                    "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
-                },
-            },
-            "required": [],  # At least one must be provided
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        file_id: Optional[str] = kwargs.get("file_id")
-        path: Optional[str] = kwargs.get("path")
-
-        if not file_id and not path:
-            return ErrorResponse(
-                message="Please provide either file_id or path",
-                session_id=session_id,
-            )
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            # Determine the file_id to delete
-            target_file_id: str
-            if file_id:
-                target_file_id = file_id
-            else:
-                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_info.id
-
-            success = await manager.delete_file(target_file_id)
-
-            if not success:
-                return ErrorResponse(
-                    message=f"File not found: {target_file_id}",
-                    session_id=session_id,
-                )
-
-            return WorkspaceDeleteResponse(
-                file_id=target_file_id,
-                success=True,
-                message="File deleted successfully",
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error deleting workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to delete workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )

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

@@ -1,250 +0,0 @@
-"""PostHog analytics tracking for the chat system."""
-
-import atexit
-import logging
-from typing import Any
-
-from posthog import Posthog
-
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-# PostHog client instance (lazily initialized)
-_posthog_client: Posthog | None = None
-
-
-def _shutdown_posthog() -> None:
-    """Flush and shutdown PostHog client on process exit."""
-    if _posthog_client is not None:
-        _posthog_client.flush()
-        _posthog_client.shutdown()
-
-
-atexit.register(_shutdown_posthog)
-
-
-def _get_posthog_client() -> Posthog | None:
-    """Get or create the PostHog client instance."""
-    global _posthog_client
-    if _posthog_client is not None:
-        return _posthog_client
-
-    if not settings.secrets.posthog_api_key:
-        logger.debug("PostHog API key not configured, analytics disabled")
-        return None
-
-    _posthog_client = Posthog(
-        settings.secrets.posthog_api_key,
-        host=settings.secrets.posthog_host,
-    )
-    logger.info(
-        f"PostHog client initialized with host: {settings.secrets.posthog_host}"
-    )
-    return _posthog_client
-
-
-def _get_base_properties() -> dict[str, Any]:
-    """Get base properties included in all events."""
-    return {
-        "environment": settings.config.app_env.value,
-        "source": "chat_copilot",
-    }
-
-
-def track_user_message(
-    user_id: str | None,
-    session_id: str,
-    message_length: int,
-) -> None:
-    """Track when a user sends a message in chat.
-
-    Args:
-        user_id: The user's ID (or None for anonymous)
-        session_id: The chat session ID
-        message_length: Length of the user's message
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "message_length": message_length,
-        }
-        client.capture(
-            distinct_id=user_id or f"anonymous_{session_id}",
-            event="copilot_message_sent",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track user message: {e}")
-
-
-def track_tool_called(
-    user_id: str | None,
-    session_id: str,
-    tool_name: str,
-    tool_call_id: str,
-) -> None:
-    """Track when a tool is called in chat.
-
-    Args:
-        user_id: The user's ID (or None for anonymous)
-        session_id: The chat session ID
-        tool_name: Name of the tool being called
-        tool_call_id: Unique ID of the tool call
-    """
-    client = _get_posthog_client()
-    if not client:
-        logger.info("PostHog client not available for tool tracking")
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "tool_name": tool_name,
-            "tool_call_id": tool_call_id,
-        }
-        distinct_id = user_id or f"anonymous_{session_id}"
-        logger.info(
-            f"Sending copilot_tool_called event to PostHog: distinct_id={distinct_id}, "
-            f"tool_name={tool_name}"
-        )
-        client.capture(
-            distinct_id=distinct_id,
-            event="copilot_tool_called",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track tool call: {e}")
-
-
-def track_agent_run_success(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    execution_id: str,
-    library_agent_id: str,
-) -> None:
-    """Track when an agent is successfully run.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        execution_id: ID of the execution
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "execution_id": execution_id,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_agent_run_success",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track agent run: {e}")
-
-
-def track_agent_scheduled(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    schedule_id: str,
-    schedule_name: str,
-    cron: str,
-    library_agent_id: str,
-) -> None:
-    """Track when an agent is successfully scheduled.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        schedule_id: ID of the schedule
-        schedule_name: Name of the schedule
-        cron: Cron expression for the schedule
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "schedule_id": schedule_id,
-            "schedule_name": schedule_name,
-            "cron": cron,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_agent_scheduled",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track agent schedule: {e}")
-
-
-def track_trigger_setup(
-    user_id: str,
-    session_id: str,
-    graph_id: str,
-    graph_name: str,
-    trigger_type: str,
-    library_agent_id: str,
-) -> None:
-    """Track when a trigger is set up for an agent.
-
-    Args:
-        user_id: The user's ID
-        session_id: The chat session ID
-        graph_id: ID of the agent graph
-        graph_name: Name of the agent
-        trigger_type: Type of trigger (e.g., 'webhook')
-        library_agent_id: ID of the library agent
-    """
-    client = _get_posthog_client()
-    if not client:
-        return
-
-    try:
-        properties = {
-            **_get_base_properties(),
-            "session_id": session_id,
-            "graph_id": graph_id,
-            "graph_name": graph_name,
-            "trigger_type": trigger_type,
-            "library_agent_id": library_agent_id,
-        }
-        client.capture(
-            distinct_id=user_id,
-            event="copilot_trigger_setup",
-            properties=properties,
-        )
-    except Exception as e:
-        logger.warning(f"Failed to track trigger setup: {e}")

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

@@ -23,7 +23,6 @@ class PendingHumanReviewModel(BaseModel):
         id: Unique identifier for the review record
         user_id: ID of the user who must perform the review
         node_exec_id: ID of the node execution that created this review
-        node_id: ID of the node definition (for grouping reviews from same node)
         graph_exec_id: ID of the graph execution containing the node
         graph_id: ID of the graph template being executed
         graph_version: Version number of the graph template
@@ -38,10 +37,6 @@ class PendingHumanReviewModel(BaseModel):
     """
 
     node_exec_id: str = Field(description="Node execution ID (primary key)")
-    node_id: str = Field(
-        description="Node definition ID (for grouping)",
-        default="",  # Temporary default for test compatibility
-    )
     user_id: str = Field(description="User ID associated with the review")
     graph_exec_id: str = Field(description="Graph execution ID")
     graph_id: str = Field(description="Graph ID")
@@ -71,9 +66,7 @@ class PendingHumanReviewModel(BaseModel):
     )
 
     @classmethod
-    def from_db(
-        cls, review: "PendingHumanReview", node_id: str
-    ) -> "PendingHumanReviewModel":
+    def from_db(cls, review: "PendingHumanReview") -> "PendingHumanReviewModel":
         """
         Convert a database model to a response model.
 
@@ -81,14 +74,9 @@ class PendingHumanReviewModel(BaseModel):
         payload, instructions, and editable flag.
 
         Handles invalid data gracefully by using safe defaults.
-
-        Args:
-            review: Database review object
-            node_id: Node definition ID (fetched from NodeExecution)
         """
         return cls(
             node_exec_id=review.nodeExecId,
-            node_id=node_id,
             user_id=review.userId,
             graph_exec_id=review.graphExecId,
             graph_id=review.graphId,
@@ -119,13 +107,6 @@ class ReviewItem(BaseModel):
     reviewed_data: SafeJsonData | None = Field(
         None, description="Optional edited data (ignored if approved=False)"
     )
-    auto_approve_future: bool = Field(
-        default=False,
-        description=(
-            "If true and this review is approved, future executions of this same "
-            "block (node) will be automatically approved. This only affects approved reviews."
-        ),
-    )
 
     @field_validator("reviewed_data")
     @classmethod
@@ -193,9 +174,6 @@ class ReviewRequest(BaseModel):
     This request must include ALL pending reviews for a graph execution.
     Each review will be either approved (with optional data modifications)
     or rejected (data ignored). The execution will resume only after ALL reviews are processed.
-
-    Each review item can individually specify whether to auto-approve future executions
-    of the same block via the `auto_approve_future` field on ReviewItem.
     """
 
     reviews: List[ReviewItem] = Field(

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

@@ -1,27 +1,17 @@
-import asyncio
 import logging
-from typing import Any, List
+from typing import List
 
 import autogpt_libs.auth as autogpt_auth_lib
 from fastapi import APIRouter, HTTPException, Query, Security, status
 from prisma.enums import ReviewStatus
 
-from backend.data.execution import (
-    ExecutionContext,
-    ExecutionStatus,
-    get_graph_execution_meta,
-)
-from backend.data.graph import get_graph_settings
+from backend.data.execution import get_graph_execution_meta
 from backend.data.human_review import (
-    create_auto_approval_record,
     get_pending_reviews_for_execution,
     get_pending_reviews_for_user,
-    get_reviews_by_node_exec_ids,
     has_pending_reviews_for_graph_exec,
     process_all_reviews_for_execution,
 )
-from backend.data.model import USER_TIMEZONE_NOT_SET
-from backend.data.user import get_user_by_id
 from backend.executor.utils import add_graph_execution
 
 from .model import PendingHumanReviewModel, ReviewRequest, ReviewResponse
@@ -137,70 +127,17 @@ async def process_review_action(
             detail="At least one review must be provided",
         )
 
-    # Batch fetch all requested reviews (regardless of status for idempotent handling)
-    reviews_map = await get_reviews_by_node_exec_ids(
-        list(all_request_node_ids), user_id
-    )
-
-    # Validate all reviews were found (must exist, any status is OK for now)
-    missing_ids = all_request_node_ids - set(reviews_map.keys())
-    if missing_ids:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Review(s) not found: {', '.join(missing_ids)}",
-        )
-
-    # Validate all reviews belong to the same execution
-    graph_exec_ids = {review.graph_exec_id for review in reviews_map.values()}
-    if len(graph_exec_ids) > 1:
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail="All reviews in a single request must belong to the same execution.",
-        )
-
-    graph_exec_id = next(iter(graph_exec_ids))
-
-    # Validate execution status before processing reviews
-    graph_exec_meta = await get_graph_execution_meta(
-        user_id=user_id, execution_id=graph_exec_id
-    )
-
-    if not graph_exec_meta:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail=f"Graph execution #{graph_exec_id} not found",
-        )
-
-    # Only allow processing reviews if execution is paused for review
-    # or incomplete (partial execution with some reviews already processed)
-    if graph_exec_meta.status not in (
-        ExecutionStatus.REVIEW,
-        ExecutionStatus.INCOMPLETE,
-    ):
-        raise HTTPException(
-            status_code=status.HTTP_409_CONFLICT,
-            detail=f"Cannot process reviews while execution status is {graph_exec_meta.status}. "
-            f"Reviews can only be processed when execution is paused (REVIEW status). "
-            f"Current status: {graph_exec_meta.status}",
-        )
-
-    # Build review decisions map and track which reviews requested auto-approval
-    # Auto-approved reviews use original data (no modifications allowed)
+    # Build review decisions map
     review_decisions = {}
-    auto_approve_requests = {}  # Map node_exec_id -> auto_approve_future flag
-
     for review in request.reviews:
         review_status = (
             ReviewStatus.APPROVED if review.approved else ReviewStatus.REJECTED
         )
-        # If this review requested auto-approval, don't allow data modifications
-        reviewed_data = None if review.auto_approve_future else review.reviewed_data
         review_decisions[review.node_exec_id] = (
             review_status,
-            reviewed_data,
+            review.reviewed_data,
             review.message,
         )
-        auto_approve_requests[review.node_exec_id] = review.auto_approve_future
 
     # Process all reviews
     updated_reviews = await process_all_reviews_for_execution(
@@ -208,87 +145,6 @@ async def process_review_action(
         review_decisions=review_decisions,
     )
 
-    # Create auto-approval records for approved reviews that requested it
-    # Deduplicate by node_id to avoid race conditions when multiple reviews
-    # for the same node are processed in parallel
-    async def create_auto_approval_for_node(
-        node_id: str, review_result
-    ) -> tuple[str, bool]:
-        """
-        Create auto-approval record for a node.
-        Returns (node_id, success) tuple for tracking failures.
-        """
-        try:
-            await create_auto_approval_record(
-                user_id=user_id,
-                graph_exec_id=review_result.graph_exec_id,
-                graph_id=review_result.graph_id,
-                graph_version=review_result.graph_version,
-                node_id=node_id,
-                payload=review_result.payload,
-            )
-            return (node_id, True)
-        except Exception as e:
-            logger.error(
-                f"Failed to create auto-approval record for node {node_id}",
-                exc_info=e,
-            )
-            return (node_id, False)
-
-    # Collect node_exec_ids that need auto-approval
-    node_exec_ids_needing_auto_approval = [
-        node_exec_id
-        for node_exec_id, review_result in updated_reviews.items()
-        if review_result.status == ReviewStatus.APPROVED
-        and auto_approve_requests.get(node_exec_id, False)
-    ]
-
-    # Batch-fetch node executions to get node_ids
-    nodes_needing_auto_approval: dict[str, Any] = {}
-    if node_exec_ids_needing_auto_approval:
-        from backend.data.execution import get_node_executions
-
-        node_execs = await get_node_executions(
-            graph_exec_id=graph_exec_id, include_exec_data=False
-        )
-        node_exec_map = {node_exec.node_exec_id: node_exec for node_exec in node_execs}
-
-        for node_exec_id in node_exec_ids_needing_auto_approval:
-            node_exec = node_exec_map.get(node_exec_id)
-            if node_exec:
-                review_result = updated_reviews[node_exec_id]
-                # Use the first approved review for this node (deduplicate by node_id)
-                if node_exec.node_id not in nodes_needing_auto_approval:
-                    nodes_needing_auto_approval[node_exec.node_id] = review_result
-            else:
-                logger.error(
-                    f"Failed to create auto-approval record for {node_exec_id}: "
-                    f"Node execution not found. This may indicate a race condition "
-                    f"or data inconsistency."
-                )
-
-    # Execute all auto-approval creations in parallel (deduplicated by node_id)
-    auto_approval_results = await asyncio.gather(
-        *[
-            create_auto_approval_for_node(node_id, review_result)
-            for node_id, review_result in nodes_needing_auto_approval.items()
-        ],
-        return_exceptions=True,
-    )
-
-    # Count auto-approval failures
-    auto_approval_failed_count = 0
-    for result in auto_approval_results:
-        if isinstance(result, Exception):
-            # Unexpected exception during auto-approval creation
-            auto_approval_failed_count += 1
-            logger.error(
-                f"Unexpected exception during auto-approval creation: {result}"
-            )
-        elif isinstance(result, tuple) and len(result) == 2 and not result[1]:
-            # Auto-approval creation failed (returned False)
-            auto_approval_failed_count += 1
-
     # Count results
     approved_count = sum(
         1
@@ -301,53 +157,30 @@ async def process_review_action(
         if review.status == ReviewStatus.REJECTED
     )
 
-    # Resume execution only if ALL pending reviews for this execution have been processed
+    # Resume execution if we processed some reviews
     if updated_reviews:
+        # Get graph execution ID from any processed review
+        first_review = next(iter(updated_reviews.values()))
+        graph_exec_id = first_review.graph_exec_id
+
+        # Check if any pending reviews remain for this execution
         still_has_pending = await has_pending_reviews_for_graph_exec(graph_exec_id)
 
         if not still_has_pending:
-            # Get the graph_id from any processed review
-            first_review = next(iter(updated_reviews.values()))
-
+            # Resume execution
             try:
-                # Fetch user and settings to build complete execution context
-                user = await get_user_by_id(user_id)
-                settings = await get_graph_settings(
-                    user_id=user_id, graph_id=first_review.graph_id
-                )
-
-                # Preserve user's timezone preference when resuming execution
-                user_timezone = (
-                    user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
-                )
-
-                execution_context = ExecutionContext(
-                    human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
-                    sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
-                    user_timezone=user_timezone,
-                )
-
                 await add_graph_execution(
                     graph_id=first_review.graph_id,
                     user_id=user_id,
                     graph_exec_id=graph_exec_id,
-                    execution_context=execution_context,
                 )
                 logger.info(f"Resumed execution {graph_exec_id}")
             except Exception as e:
                 logger.error(f"Failed to resume execution {graph_exec_id}: {str(e)}")
 
-    # Build error message if auto-approvals failed
-    error_message = None
-    if auto_approval_failed_count > 0:
-        error_message = (
-            f"{auto_approval_failed_count} auto-approval setting(s) could not be saved. "
-            f"You may need to manually approve these reviews in future executions."
-        )
-
     return ReviewResponse(
         approved_count=approved_count,
         rejected_count=rejected_count,
-        failed_count=auto_approval_failed_count,
-        error=error_message,
+        failed_count=0,
+        error=None,
     )

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

@@ -21,7 +21,7 @@ from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
 from backend.util.clients import get_scheduler_client
-from backend.util.exceptions import DatabaseError, InvalidInputError, NotFoundError
+from backend.util.exceptions import DatabaseError, NotFoundError
 from backend.util.json import SafeJson
 from backend.util.models import Pagination
 from backend.util.settings import Config
@@ -39,7 +39,6 @@ async def list_library_agents(
     sort_by: library_model.LibraryAgentSort = library_model.LibraryAgentSort.UPDATED_AT,
     page: int = 1,
     page_size: int = 50,
-    include_executions: bool = False,
 ) -> library_model.LibraryAgentResponse:
     """
     Retrieves a paginated list of LibraryAgent records for a given user.
@@ -50,9 +49,6 @@ async def list_library_agents(
         sort_by: Sorting field (createdAt, updatedAt, isFavorite, isCreatedByUser).
         page: Current page (1-indexed).
         page_size: Number of items per page.
-        include_executions: Whether to include execution data for status calculation.
-            Defaults to False for performance (UI fetches status separately).
-            Set to True when accurate status/metrics are needed (e.g., agent generator).
 
     Returns:
         A LibraryAgentResponse containing the list of agents and pagination details.
@@ -68,11 +64,11 @@ async def list_library_agents(
 
     if page < 1 or page_size < 1:
         logger.warning(f"Invalid pagination: page={page}, page_size={page_size}")
-        raise InvalidInputError("Invalid pagination input")
+        raise DatabaseError("Invalid pagination input")
 
     if search_term and len(search_term.strip()) > 100:
         logger.warning(f"Search term too long: {repr(search_term)}")
-        raise InvalidInputError("Search term is too long")
+        raise DatabaseError("Search term is too long")
 
     where_clause: prisma.types.LibraryAgentWhereInput = {
         "userId": user_id,
@@ -80,6 +76,7 @@ async def list_library_agents(
         "isArchived": False,
     }
 
+    # Build search filter if applicable
     if search_term:
         where_clause["OR"] = [
             {
@@ -96,6 +93,7 @@ async def list_library_agents(
             },
         ]
 
+    # Determine sorting
     order_by: prisma.types.LibraryAgentOrderByInput | None = None
 
     if sort_by == library_model.LibraryAgentSort.CREATED_AT:
@@ -107,7 +105,7 @@ async def list_library_agents(
         library_agents = await prisma.models.LibraryAgent.prisma().find_many(
             where=where_clause,
             include=library_agent_include(
-                user_id, include_nodes=False, include_executions=include_executions
+                user_id, include_nodes=False, include_executions=False
             ),
             order=order_by,
             skip=(page - 1) * page_size,
@@ -177,7 +175,7 @@ async def list_favorite_library_agents(
 
     if page < 1 or page_size < 1:
         logger.warning(f"Invalid pagination: page={page}, page_size={page_size}")
-        raise InvalidInputError("Invalid pagination input")
+        raise DatabaseError("Invalid pagination input")
 
     where_clause: prisma.types.LibraryAgentWhereInput = {
         "userId": user_id,
@@ -585,13 +583,7 @@ async def update_library_agent(
             )
         update_fields["isDeleted"] = is_deleted
     if settings is not None:
-        existing_agent = await get_library_agent(id=library_agent_id, user_id=user_id)
-        current_settings_dict = (
-            existing_agent.settings.model_dump() if existing_agent.settings else {}
-        )
-        new_settings = settings.model_dump(exclude_unset=True)
-        merged_settings = {**current_settings_dict, **new_settings}
-        update_fields["settings"] = SafeJson(merged_settings)
+        update_fields["settings"] = SafeJson(settings.model_dump())
 
     try:
         # If graph_version is provided, update to that specific version

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

@@ -9,7 +9,6 @@ import pydantic
 from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
 from backend.data.model import CredentialsMetaInput, is_credentials_field_name
-from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
 
 if TYPE_CHECKING:
@@ -17,10 +16,10 @@ if TYPE_CHECKING:
 
 
 class LibraryAgentStatus(str, Enum):
-    COMPLETED = "COMPLETED"
-    HEALTHY = "HEALTHY"
-    WAITING = "WAITING"
-    ERROR = "ERROR"
+    COMPLETED = "COMPLETED"  # All runs completed
+    HEALTHY = "HEALTHY"  # Agent is running (not all runs have completed)
+    WAITING = "WAITING"  # Agent is queued or waiting to start
+    ERROR = "ERROR"  # Agent is in an error state
 
 
 class MarketplaceListingCreator(pydantic.BaseModel):
@@ -40,30 +39,6 @@ class MarketplaceListing(pydantic.BaseModel):
     creator: MarketplaceListingCreator
 
 
-class RecentExecution(pydantic.BaseModel):
-    """Summary of a recent execution for quality assessment.
-
-    Used by the LLM to understand the agent's recent performance with specific examples
-    rather than just aggregate statistics.
-    """
-
-    status: str
-    correctness_score: float | None = None
-    activity_summary: str | None = None
-
-
-def _parse_settings(settings: dict | str | None) -> GraphSettings:
-    """Parse settings from database, handling both dict and string formats."""
-    if settings is None:
-        return GraphSettings()
-    try:
-        if isinstance(settings, str):
-            settings = json_loads(settings)
-        return GraphSettings.model_validate(settings)
-    except Exception:
-        return GraphSettings()
-
-
 class LibraryAgent(pydantic.BaseModel):
     """
     Represents an agent in the library, including metadata for display and
@@ -73,7 +48,7 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str
+    owner_user_id: str  # ID of user who owns/created this agent graph
 
     image_url: str | None
 
@@ -89,7 +64,7 @@ class LibraryAgent(pydantic.BaseModel):
     description: str
     instructions: str | None = None
 
-    input_schema: dict[str, Any]
+    input_schema: dict[str, Any]  # Should be BlockIOObjectSubSchema in frontend
     output_schema: dict[str, Any]
     credentials_input_schema: dict[str, Any] | None = pydantic.Field(
         description="Input schema for credentials required by the agent",
@@ -106,19 +81,25 @@ class LibraryAgent(pydantic.BaseModel):
     )
     trigger_setup_info: Optional[GraphTriggerInfo] = None
 
+    # Indicates whether there's a new output (based on recent runs)
     new_output: bool
-    execution_count: int = 0
-    success_rate: float | None = None
-    avg_correctness_score: float | None = None
-    recent_executions: list[RecentExecution] = pydantic.Field(
-        default_factory=list,
-        description="List of recent executions with status, score, and summary",
-    )
+
+    # Whether the user can access the underlying graph
     can_access_graph: bool
+
+    # Indicates if this agent is the latest version
     is_latest_version: bool
+
+    # Whether the agent is marked as favorite by the user
     is_favorite: bool
+
+    # Recommended schedule cron (from marketplace agents)
     recommended_schedule_cron: str | None = None
+
+    # User-specific settings for this library agent
     settings: GraphSettings = pydantic.Field(default_factory=GraphSettings)
+
+    # Marketplace listing information if the agent has been published
     marketplace_listing: Optional["MarketplaceListing"] = None
 
     @staticmethod
@@ -142,6 +123,7 @@ class LibraryAgent(pydantic.BaseModel):
         agent_updated_at = agent.AgentGraph.updatedAt
         lib_agent_updated_at = agent.updatedAt
 
+        # Compute updated_at as the latest between library agent and graph
         updated_at = (
             max(agent_updated_at, lib_agent_updated_at)
             if agent_updated_at
@@ -154,6 +136,7 @@ class LibraryAgent(pydantic.BaseModel):
             creator_name = agent.Creator.name or "Unknown"
             creator_image_url = agent.Creator.avatarUrl or ""
 
+        # Logic to calculate status and new_output
         week_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta(
             days=7
         )
@@ -162,55 +145,13 @@ class LibraryAgent(pydantic.BaseModel):
         status = status_result.status
         new_output = status_result.new_output
 
-        execution_count = len(executions)
-        success_rate: float | None = None
-        avg_correctness_score: float | None = None
-        if execution_count > 0:
-            success_count = sum(
-                1
-                for e in executions
-                if e.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED
-            )
-            success_rate = (success_count / execution_count) * 100
-
-            correctness_scores = []
-            for e in executions:
-                if e.stats and isinstance(e.stats, dict):
-                    score = e.stats.get("correctness_score")
-                    if score is not None and isinstance(score, (int, float)):
-                        correctness_scores.append(float(score))
-            if correctness_scores:
-                avg_correctness_score = sum(correctness_scores) / len(
-                    correctness_scores
-                )
-
-        recent_executions: list[RecentExecution] = []
-        for e in executions:
-            exec_score: float | None = None
-            exec_summary: str | None = None
-            if e.stats and isinstance(e.stats, dict):
-                score = e.stats.get("correctness_score")
-                if score is not None and isinstance(score, (int, float)):
-                    exec_score = float(score)
-                summary = e.stats.get("activity_status")
-                if summary is not None and isinstance(summary, str):
-                    exec_summary = summary
-            exec_status = (
-                e.executionStatus.value
-                if hasattr(e.executionStatus, "value")
-                else str(e.executionStatus)
-            )
-            recent_executions.append(
-                RecentExecution(
-                    status=exec_status,
-                    correctness_score=exec_score,
-                    activity_summary=exec_summary,
-                )
-            )
-
+        # Check if user can access the graph
         can_access_graph = agent.AgentGraph.userId == agent.userId
+
+        # Hard-coded to True until a method to check is implemented
         is_latest_version = True
 
+        # Build marketplace_listing if available
         marketplace_listing_data = None
         if store_listing and store_listing.ActiveVersion and profile:
             creator_data = MarketplaceListingCreator(
@@ -249,15 +190,11 @@ class LibraryAgent(pydantic.BaseModel):
             has_sensitive_action=graph.has_sensitive_action,
             trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
-            execution_count=execution_count,
-            success_rate=success_rate,
-            avg_correctness_score=avg_correctness_score,
-            recent_executions=recent_executions,
             can_access_graph=can_access_graph,
             is_latest_version=is_latest_version,
             is_favorite=agent.isFavorite,
             recommended_schedule_cron=agent.AgentGraph.recommendedScheduleCron,
-            settings=_parse_settings(agent.settings),
+            settings=GraphSettings.model_validate(agent.settings),
             marketplace_listing=marketplace_listing_data,
         )
 
@@ -283,15 +220,18 @@ def _calculate_agent_status(
     if not executions:
         return AgentStatusResult(status=LibraryAgentStatus.COMPLETED, new_output=False)
 
+    # Track how many times each execution status appears
     status_counts = {status: 0 for status in prisma.enums.AgentExecutionStatus}
     new_output = False
 
     for execution in executions:
+        # Check if there's a completed run more recent than `recent_threshold`
         if execution.createdAt >= recent_threshold:
             if execution.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED:
                 new_output = True
         status_counts[execution.executionStatus] += 1
 
+    # Determine the final status based on counts
     if status_counts[prisma.enums.AgentExecutionStatus.FAILED] > 0:
         return AgentStatusResult(status=LibraryAgentStatus.ERROR, new_output=new_output)
     elif status_counts[prisma.enums.AgentExecutionStatus.QUEUED] > 0:

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

@@ -1,3 +1,4 @@
+import logging
 from typing import Literal, Optional
 
 import autogpt_libs.auth as autogpt_auth_lib
@@ -5,11 +6,15 @@ from fastapi import APIRouter, Body, HTTPException, Query, Security, status
 from fastapi.responses import Response
 from prisma.enums import OnboardingStep
 
+import backend.api.features.store.exceptions as store_exceptions
 from backend.data.onboarding import complete_onboarding_step
+from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .. import db as library_db
 from .. import model as library_model
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter(
     prefix="/agents",
     tags=["library", "private"],
@@ -21,6 +26,10 @@ router = APIRouter(
     "",
     summary="List Library Agents",
     response_model=library_model.LibraryAgentResponse,
+    responses={
+        200: {"description": "List of library agents"},
+        500: {"description": "Server error", "content": {"application/json": {}}},
+    },
 )
 async def list_library_agents(
     user_id: str = Security(autogpt_auth_lib.get_user_id),
@@ -44,19 +53,43 @@ async def list_library_agents(
 ) -> library_model.LibraryAgentResponse:
     """
     Get all agents in the user's library (both created and saved).
+
+    Args:
+        user_id: ID of the authenticated user.
+        search_term: Optional search term to filter agents by name/description.
+        filter_by: List of filters to apply (favorites, created by user).
+        sort_by: List of sorting criteria (created date, updated date).
+        page: Page number to retrieve.
+        page_size: Number of agents per page.
+
+    Returns:
+        A LibraryAgentResponse containing agents and pagination metadata.
+
+    Raises:
+        HTTPException: If a server/database error occurs.
     """
-    return await library_db.list_library_agents(
-        user_id=user_id,
-        search_term=search_term,
-        sort_by=sort_by,
-        page=page,
-        page_size=page_size,
-    )
+    try:
+        return await library_db.list_library_agents(
+            user_id=user_id,
+            search_term=search_term,
+            sort_by=sort_by,
+            page=page,
+            page_size=page_size,
+        )
+    except Exception as e:
+        logger.error(f"Could not list library agents for user #{user_id}: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=str(e),
+        ) from e
 
 
 @router.get(
     "/favorites",
     summary="List Favorite Library Agents",
+    responses={
+        500: {"description": "Server error", "content": {"application/json": {}}},
+    },
 )
 async def list_favorite_library_agents(
     user_id: str = Security(autogpt_auth_lib.get_user_id),
@@ -73,12 +106,30 @@ async def list_favorite_library_agents(
 ) -> library_model.LibraryAgentResponse:
     """
     Get all favorite agents in the user's library.
+
+    Args:
+        user_id: ID of the authenticated user.
+        page: Page number to retrieve.
+        page_size: Number of agents per page.
+
+    Returns:
+        A LibraryAgentResponse containing favorite agents and pagination metadata.
+
+    Raises:
+        HTTPException: If a server/database error occurs.
     """
-    return await library_db.list_favorite_library_agents(
-        user_id=user_id,
-        page=page,
-        page_size=page_size,
-    )
+    try:
+        return await library_db.list_favorite_library_agents(
+            user_id=user_id,
+            page=page,
+            page_size=page_size,
+        )
+    except Exception as e:
+        logger.error(f"Could not list favorite library agents for user #{user_id}: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=str(e),
+        ) from e
 
 
 @router.get("/{library_agent_id}", summary="Get Library Agent")
@@ -111,6 +162,10 @@ async def get_library_agent_by_graph_id(
     summary="Get Agent By Store ID",
     tags=["store", "library"],
     response_model=library_model.LibraryAgent | None,
+    responses={
+        200: {"description": "Library agent found"},
+        404: {"description": "Agent not found"},
+    },
 )
 async def get_library_agent_by_store_listing_version_id(
     store_listing_version_id: str,
@@ -119,15 +174,32 @@ async def get_library_agent_by_store_listing_version_id(
     """
     Get Library Agent from Store Listing Version ID.
     """
-    return await library_db.get_library_agent_by_store_version_id(
-        store_listing_version_id, user_id
-    )
+    try:
+        return await library_db.get_library_agent_by_store_version_id(
+            store_listing_version_id, user_id
+        )
+    except NotFoundError as e:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=str(e),
+        )
+    except Exception as e:
+        logger.error(f"Could not fetch library agent from store version ID: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=str(e),
+        ) from e
 
 
 @router.post(
     "",
     summary="Add Marketplace Agent",
     status_code=status.HTTP_201_CREATED,
+    responses={
+        201: {"description": "Agent added successfully"},
+        404: {"description": "Store listing version not found"},
+        500: {"description": "Server error"},
+    },
 )
 async def add_marketplace_agent_to_library(
     store_listing_version_id: str = Body(embed=True),
@@ -138,19 +210,59 @@ async def add_marketplace_agent_to_library(
 ) -> library_model.LibraryAgent:
     """
     Add an agent from the marketplace to the user's library.
+
+    Args:
+        store_listing_version_id: ID of the store listing version to add.
+        user_id: ID of the authenticated user.
+
+    Returns:
+        library_model.LibraryAgent: Agent added to the library
+
+    Raises:
+        HTTPException(404): If the listing version is not found.
+        HTTPException(500): If a server/database error occurs.
     """
-    agent = await library_db.add_store_agent_to_library(
-        store_listing_version_id=store_listing_version_id,
-        user_id=user_id,
-    )
-    if source != "onboarding":
-        await complete_onboarding_step(user_id, OnboardingStep.MARKETPLACE_ADD_AGENT)
-    return agent
+    try:
+        agent = await library_db.add_store_agent_to_library(
+            store_listing_version_id=store_listing_version_id,
+            user_id=user_id,
+        )
+        if source != "onboarding":
+            await complete_onboarding_step(
+                user_id, OnboardingStep.MARKETPLACE_ADD_AGENT
+            )
+        return agent
+
+    except store_exceptions.AgentNotFoundError as e:
+        logger.warning(
+            f"Could not find store listing version {store_listing_version_id} "
+            "to add to library"
+        )
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(e))
+    except DatabaseError as e:
+        logger.error(f"Database error while adding agent to library: {e}", e)
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail={"message": str(e), "hint": "Inspect DB logs for details."},
+        ) from e
+    except Exception as e:
+        logger.error(f"Unexpected error while adding agent to library: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail={
+                "message": str(e),
+                "hint": "Check server logs for more information.",
+            },
+        ) from e
 
 
 @router.patch(
     "/{library_agent_id}",
     summary="Update Library Agent",
+    responses={
+        200: {"description": "Agent updated successfully"},
+        500: {"description": "Server error"},
+    },
 )
 async def update_library_agent(
     library_agent_id: str,
@@ -159,21 +271,52 @@ async def update_library_agent(
 ) -> library_model.LibraryAgent:
     """
     Update the library agent with the given fields.
+
+    Args:
+        library_agent_id: ID of the library agent to update.
+        payload: Fields to update (auto_update_version, is_favorite, etc.).
+        user_id: ID of the authenticated user.
+
+    Raises:
+        HTTPException(500): If a server/database error occurs.
     """
-    return await library_db.update_library_agent(
-        library_agent_id=library_agent_id,
-        user_id=user_id,
-        auto_update_version=payload.auto_update_version,
-        graph_version=payload.graph_version,
-        is_favorite=payload.is_favorite,
-        is_archived=payload.is_archived,
-        settings=payload.settings,
-    )
+    try:
+        return await library_db.update_library_agent(
+            library_agent_id=library_agent_id,
+            user_id=user_id,
+            auto_update_version=payload.auto_update_version,
+            graph_version=payload.graph_version,
+            is_favorite=payload.is_favorite,
+            is_archived=payload.is_archived,
+            settings=payload.settings,
+        )
+    except NotFoundError as e:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=str(e),
+        ) from e
+    except DatabaseError as e:
+        logger.error(f"Database error while updating library agent: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail={"message": str(e), "hint": "Verify DB connection."},
+        ) from e
+    except Exception as e:
+        logger.error(f"Unexpected error while updating library agent: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail={"message": str(e), "hint": "Check server logs."},
+        ) from e
 
 
 @router.delete(
     "/{library_agent_id}",
     summary="Delete Library Agent",
+    responses={
+        204: {"description": "Agent deleted successfully"},
+        404: {"description": "Agent not found"},
+        500: {"description": "Server error"},
+    },
 )
 async def delete_library_agent(
     library_agent_id: str,
@@ -181,11 +324,28 @@ async def delete_library_agent(
 ) -> Response:
     """
     Soft-delete the specified library agent.
+
+    Args:
+        library_agent_id: ID of the library agent to delete.
+        user_id: ID of the authenticated user.
+
+    Returns:
+        204 No Content if successful.
+
+    Raises:
+        HTTPException(404): If the agent does not exist.
+        HTTPException(500): If a server/database error occurs.
     """
-    await library_db.delete_library_agent(
-        library_agent_id=library_agent_id, user_id=user_id
-    )
-    return Response(status_code=status.HTTP_204_NO_CONTENT)
+    try:
+        await library_db.delete_library_agent(
+            library_agent_id=library_agent_id, user_id=user_id
+        )
+        return Response(status_code=status.HTTP_204_NO_CONTENT)
+    except NotFoundError as e:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=str(e),
+        ) from e
 
 
 @router.post("/{library_agent_id}/fork", summary="Fork Library Agent")

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

@@ -118,6 +118,21 @@ async def test_get_library_agents_success(
     )
 
 
+def test_get_library_agents_error(mocker: pytest_mock.MockFixture, test_user_id: str):
+    mock_db_call = mocker.patch("backend.api.features.library.db.list_library_agents")
+    mock_db_call.side_effect = Exception("Test error")
+
+    response = client.get("/agents?search_term=test")
+    assert response.status_code == 500
+    mock_db_call.assert_called_once_with(
+        user_id=test_user_id,
+        search_term="test",
+        sort_by=library_model.LibraryAgentSort.UPDATED_AT,
+        page=1,
+        page_size=15,
+    )
+
+
 @pytest.mark.asyncio
 async def test_get_favorite_library_agents_success(
     mocker: pytest_mock.MockFixture,
@@ -175,6 +190,23 @@ async def test_get_favorite_library_agents_success(
     )
 
 
+def test_get_favorite_library_agents_error(
+    mocker: pytest_mock.MockFixture, test_user_id: str
+):
+    mock_db_call = mocker.patch(
+        "backend.api.features.library.db.list_favorite_library_agents"
+    )
+    mock_db_call.side_effect = Exception("Test error")
+
+    response = client.get("/agents/favorites")
+    assert response.status_code == 500
+    mock_db_call.assert_called_once_with(
+        user_id=test_user_id,
+        page=1,
+        page_size=15,
+    )
+
+
 def test_add_agent_to_library_success(
     mocker: pytest_mock.MockFixture, test_user_id: str
 ):
@@ -226,3 +258,19 @@ def test_add_agent_to_library_success(
         store_listing_version_id="test-version-id", user_id=test_user_id
     )
     mock_complete_onboarding.assert_awaited_once()
+
+
+def test_add_agent_to_library_error(mocker: pytest_mock.MockFixture, test_user_id: str):
+    mock_db_call = mocker.patch(
+        "backend.api.features.library.db.add_store_agent_to_library"
+    )
+    mock_db_call.side_effect = Exception("Test error")
+
+    response = client.post(
+        "/agents", json={"store_listing_version_id": "test-version-id"}
+    )
+    assert response.status_code == 500
+    assert "detail" in response.json()  # Verify error response structure
+    mock_db_call.assert_called_once_with(
+        store_listing_version_id="test-version-id", user_id=test_user_id
+    )

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

@@ -20,7 +20,6 @@ from typing import AsyncGenerator
 
 import httpx
 import pytest
-import pytest_asyncio
 from autogpt_libs.api_key.keysmith import APIKeySmith
 from prisma.enums import APIKeyPermission
 from prisma.models import OAuthAccessToken as PrismaOAuthAccessToken
@@ -39,13 +38,13 @@ keysmith = APIKeySmith()
 # ============================================================================
 
 
-@pytest.fixture(scope="session")
+@pytest.fixture
 def test_user_id() -> str:
     """Test user ID for OAuth tests."""
     return str(uuid.uuid4())
 
 
-@pytest_asyncio.fixture(scope="session", loop_scope="session")
+@pytest.fixture
 async def test_user(server, test_user_id: str):
     """Create a test user in the database."""
     await PrismaUser.prisma().create(
@@ -68,7 +67,7 @@ async def test_user(server, test_user_id: str):
     await PrismaUser.prisma().delete(where={"id": test_user_id})
 
 
-@pytest_asyncio.fixture
+@pytest.fixture
 async def test_oauth_app(test_user: str):
     """Create a test OAuth application in the database."""
     app_id = str(uuid.uuid4())
@@ -123,7 +122,7 @@ def pkce_credentials() -> tuple[str, str]:
     return generate_pkce()
 
 
-@pytest_asyncio.fixture
+@pytest.fixture
 async def client(server, test_user: str) -> AsyncGenerator[httpx.AsyncClient, None]:
     """
     Create an async HTTP client that talks directly to the FastAPI app.
@@ -166,7 +165,7 @@ async def client(server, test_user: str) -> AsyncGenerator[httpx.AsyncClient, No
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_authorize_creates_code_in_database(
+async def test_authorize_creates_code_in_database_test(
     client: httpx.AsyncClient,
     test_user: str,
     test_oauth_app: dict,
@@ -288,7 +287,7 @@ async def test_authorize_invalid_client_returns_error(
     assert query_params["error"][0] == "invalid_client"
 
 
-@pytest_asyncio.fixture
+@pytest.fixture
 async def inactive_oauth_app(test_user: str):
     """Create an inactive test OAuth application in the database."""
     app_id = str(uuid.uuid4())
@@ -1005,7 +1004,7 @@ async def test_token_refresh_revoked(
     assert "revoked" in response.json()["detail"].lower()
 
 
-@pytest_asyncio.fixture
+@pytest.fixture
 async def other_oauth_app(test_user: str):
     """Create a second OAuth application for cross-app tests."""
     app_id = str(uuid.uuid4())

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

@@ -188,10 +188,6 @@ class BlockHandler(ContentHandler):
             try:
                 block_instance = block_cls()
 
-                # Skip disabled blocks - they shouldn't be indexed
-                if block_instance.disabled:
-                    continue
-
                 # Build searchable text from block metadata
                 parts = []
                 if hasattr(block_instance, "name") and block_instance.name:
@@ -252,19 +248,12 @@ class BlockHandler(ContentHandler):
         from backend.data.block import get_blocks
 
         all_blocks = get_blocks()
-
-        # Filter out disabled blocks - they're not indexed
-        enabled_block_ids = [
-            block_id
-            for block_id, block_cls in all_blocks.items()
-            if not block_cls().disabled
-        ]
-        total_blocks = len(enabled_block_ids)
+        total_blocks = len(all_blocks)
 
         if total_blocks == 0:
             return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
 
-        block_ids = enabled_block_ids
+        block_ids = list(all_blocks.keys())
         placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
 
         embedded_result = await query_raw_with_schema(

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

@@ -81,7 +81,6 @@ async def test_block_handler_get_missing_items(mocker):
     mock_block_instance.name = "Calculator Block"
     mock_block_instance.description = "Performs calculations"
     mock_block_instance.categories = [MagicMock(value="MATH")]
-    mock_block_instance.disabled = False
     mock_block_instance.input_schema.model_json_schema.return_value = {
         "properties": {"expression": {"description": "Math expression to evaluate"}}
     }
@@ -117,18 +116,11 @@ async def test_block_handler_get_stats(mocker):
     """Test BlockHandler returns correct stats."""
     handler = BlockHandler()
 
-    # Mock get_blocks - each block class returns an instance with disabled=False
-    def make_mock_block_class():
-        mock_class = MagicMock()
-        mock_instance = MagicMock()
-        mock_instance.disabled = False
-        mock_class.return_value = mock_instance
-        return mock_class
-
+    # Mock get_blocks
     mock_blocks = {
-        "block-1": make_mock_block_class(),
-        "block-2": make_mock_block_class(),
-        "block-3": make_mock_block_class(),
+        "block-1": MagicMock(),
+        "block-2": MagicMock(),
+        "block-3": MagicMock(),
     }
 
     # Mock embedded count query (2 blocks have embeddings)
@@ -317,7 +309,6 @@ async def test_block_handler_handles_missing_attributes():
     mock_block_class = MagicMock()
     mock_block_instance = MagicMock()
     mock_block_instance.name = "Minimal Block"
-    mock_block_instance.disabled = False
     # No description, categories, or schema
     del mock_block_instance.description
     del mock_block_instance.categories
@@ -351,7 +342,6 @@ async def test_block_handler_skips_failed_blocks():
     good_instance.name = "Good Block"
     good_instance.description = "Works fine"
     good_instance.categories = []
-    good_instance.disabled = False
     good_block.return_value = good_instance
 
     bad_block = MagicMock()

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

@@ -112,7 +112,6 @@ async def get_store_agents(
                             description=agent["description"],
                             runs=agent["runs"],
                             rating=agent["rating"],
-                            agent_graph_id=agent.get("agentGraphId", ""),
                         )
                         store_agents.append(store_agent)
                     except Exception as e:
@@ -171,7 +170,6 @@ async def get_store_agents(
                         description=agent.description,
                         runs=agent.runs,
                         rating=agent.rating,
-                        agent_graph_id=agent.agentGraphId,
                     )
                     # Add to the list only if creation was successful
                     store_agents.append(store_agent)
@@ -1554,7 +1552,7 @@ async def review_store_submission(
 
                 # Generate embedding for approved listing (blocking - admin operation)
                 # Inside transaction: if embedding fails, entire transaction rolls back
-                await ensure_embedding(
+                embedding_success = await ensure_embedding(
                     version_id=store_listing_version_id,
                     name=store_listing_version.name,
                     description=store_listing_version.description,
@@ -1562,6 +1560,12 @@ async def review_store_submission(
                     categories=store_listing_version.categories or [],
                     tx=tx,
                 )
+                if not embedding_success:
+                    raise ValueError(
+                        f"Failed to generate embedding for listing {store_listing_version_id}. "
+                        "This is likely due to OpenAI API being unavailable. "
+                        "Please try again later or contact support if the issue persists."
+                    )
 
                 await prisma.models.StoreListing.prisma(tx).update(
                     where={"id": store_listing_version.StoreListing.id},

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

@@ -21,6 +21,7 @@ from backend.util.json import dumps
 
 logger = logging.getLogger(__name__)
 
+
 # OpenAI embedding model configuration
 EMBEDDING_MODEL = "text-embedding-3-small"
 # Embedding dimension for the model above
@@ -62,42 +63,49 @@ def build_searchable_text(
     return " ".join(parts)
 
 
-async def generate_embedding(text: str) -> list[float]:
+async def generate_embedding(text: str) -> list[float] | None:
     """
     Generate embedding for text using OpenAI API.
 
-    Raises exceptions on failure - caller should handle.
+    Returns None if embedding generation fails.
+    Fail-fast: no retries to maintain consistency with approval flow.
     """
-    client = get_openai_client()
-    if not client:
-        raise RuntimeError("openai_internal_api_key not set, cannot generate embedding")
+    try:
+        client = get_openai_client()
+        if not client:
+            logger.error("openai_internal_api_key not set, cannot generate embedding")
+            return None
 
-    # Truncate text to token limit using tiktoken
-    # Character-based truncation is insufficient because token ratios vary by content type
-    enc = encoding_for_model(EMBEDDING_MODEL)
-    tokens = enc.encode(text)
-    if len(tokens) > EMBEDDING_MAX_TOKENS:
-        tokens = tokens[:EMBEDDING_MAX_TOKENS]
-        truncated_text = enc.decode(tokens)
-        logger.info(
-            f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
+        # Truncate text to token limit using tiktoken
+        # Character-based truncation is insufficient because token ratios vary by content type
+        enc = encoding_for_model(EMBEDDING_MODEL)
+        tokens = enc.encode(text)
+        if len(tokens) > EMBEDDING_MAX_TOKENS:
+            tokens = tokens[:EMBEDDING_MAX_TOKENS]
+            truncated_text = enc.decode(tokens)
+            logger.info(
+                f"Truncated text from {len(enc.encode(text))} to {len(tokens)} tokens"
+            )
+        else:
+            truncated_text = text
+
+        start_time = time.time()
+        response = await client.embeddings.create(
+            model=EMBEDDING_MODEL,
+            input=truncated_text,
         )
-    else:
-        truncated_text = text
+        latency_ms = (time.time() - start_time) * 1000
 
-    start_time = time.time()
-    response = await client.embeddings.create(
-        model=EMBEDDING_MODEL,
-        input=truncated_text,
-    )
-    latency_ms = (time.time() - start_time) * 1000
+        embedding = response.data[0].embedding
+        logger.info(
+            f"Generated embedding: {len(embedding)} dims, "
+            f"{len(tokens)} tokens, {latency_ms:.0f}ms"
+        )
+        return embedding
 
-    embedding = response.data[0].embedding
-    logger.info(
-        f"Generated embedding: {len(embedding)} dims, "
-        f"{len(tokens)} tokens, {latency_ms:.0f}ms"
-    )
-    return embedding
+    except Exception as e:
+        logger.error(f"Failed to generate embedding: {e}")
+        return None
 
 
 async def store_embedding(
@@ -136,45 +144,48 @@ async def store_content_embedding(
 
     New function for unified content embedding storage.
     Uses raw SQL since Prisma doesn't natively support pgvector.
-
-    Raises exceptions on failure - caller should handle.
     """
-    client = tx if tx else prisma.get_client()
+    try:
+        client = tx if tx else prisma.get_client()
 
-    # Convert embedding to PostgreSQL vector format
-    embedding_str = embedding_to_vector_string(embedding)
-    metadata_json = dumps(metadata or {})
+        # Convert embedding to PostgreSQL vector format
+        embedding_str = embedding_to_vector_string(embedding)
+        metadata_json = dumps(metadata or {})
 
-    # Upsert the embedding
-    # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
-    # Use unqualified ::vector - pgvector is in search_path on all environments
-    await execute_raw_with_schema(
-        """
-        INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
-            "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
+        # Upsert the embedding
+        # WHERE clause in DO UPDATE prevents PostgreSQL 15 bug with NULLS NOT DISTINCT
+        # Use unqualified ::vector - pgvector is in search_path on all environments
+        await execute_raw_with_schema(
+            """
+            INSERT INTO {schema_prefix}"UnifiedContentEmbedding" (
+                "id", "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
+            )
+            VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
+            ON CONFLICT ("contentType", "contentId", "userId")
+            DO UPDATE SET
+                "embedding" = $4::vector,
+                "searchableText" = $5,
+                "metadata" = $6::jsonb,
+                "updatedAt" = NOW()
+            WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
+                AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
+                AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+            embedding_str,
+            searchable_text,
+            metadata_json,
+            client=client,
         )
-        VALUES (gen_random_uuid()::text, $1::{schema_prefix}"ContentType", $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
-        ON CONFLICT ("contentType", "contentId", "userId")
-        DO UPDATE SET
-            "embedding" = $4::vector,
-            "searchableText" = $5,
-            "metadata" = $6::jsonb,
-            "updatedAt" = NOW()
-        WHERE {schema_prefix}"UnifiedContentEmbedding"."contentType" = $1::{schema_prefix}"ContentType"
-            AND {schema_prefix}"UnifiedContentEmbedding"."contentId" = $2
-            AND ({schema_prefix}"UnifiedContentEmbedding"."userId" = $3 OR ($3 IS NULL AND {schema_prefix}"UnifiedContentEmbedding"."userId" IS NULL))
-        """,
-        content_type,
-        content_id,
-        user_id,
-        embedding_str,
-        searchable_text,
-        metadata_json,
-        client=client,
-    )
 
-    logger.info(f"Stored embedding for {content_type}:{content_id}")
-    return True
+        logger.info(f"Stored embedding for {content_type}:{content_id}")
+        return True
+
+    except Exception as e:
+        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
+        return False
 
 
 async def get_embedding(version_id: str) -> dict[str, Any] | None:
@@ -206,31 +217,34 @@ async def get_content_embedding(
 
     New function for unified content embedding retrieval.
     Returns dict with contentType, contentId, embedding, timestamps or None if not found.
-
-    Raises exceptions on failure - caller should handle.
     """
-    result = await query_raw_with_schema(
-        """
-        SELECT
-            "contentType",
-            "contentId",
-            "userId",
-            "embedding"::text as "embedding",
-            "searchableText",
-            "metadata",
-            "createdAt",
-            "updatedAt"
-        FROM {schema_prefix}"UnifiedContentEmbedding"
-        WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
-        """,
-        content_type,
-        content_id,
-        user_id,
-    )
+    try:
+        result = await query_raw_with_schema(
+            """
+            SELECT
+                "contentType",
+                "contentId",
+                "userId",
+                "embedding"::text as "embedding",
+                "searchableText",
+                "metadata",
+                "createdAt",
+                "updatedAt"
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = $1::{schema_prefix}"ContentType" AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
+            """,
+            content_type,
+            content_id,
+            user_id,
+        )
 
-    if result and len(result) > 0:
-        return result[0]
-    return None
+        if result and len(result) > 0:
+            return result[0]
+        return None
+
+    except Exception as e:
+        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
+        return None
 
 
 async def ensure_embedding(
@@ -258,38 +272,46 @@ async def ensure_embedding(
         tx: Optional transaction client
 
     Returns:
-        True if embedding exists/was created
-
-    Raises exceptions on failure - caller should handle.
+        True if embedding exists/was created, False on failure
     """
-    # Check if embedding already exists
-    if not force:
-        existing = await get_embedding(version_id)
-        if existing and existing.get("embedding"):
-            logger.debug(f"Embedding for version {version_id} already exists")
-            return True
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_embedding(version_id)
+            if existing and existing.get("embedding"):
+                logger.debug(f"Embedding for version {version_id} already exists")
+                return True
 
-    # Build searchable text for embedding
-    searchable_text = build_searchable_text(name, description, sub_heading, categories)
+        # Build searchable text for embedding
+        searchable_text = build_searchable_text(
+            name, description, sub_heading, categories
+        )
 
-    # Generate new embedding
-    embedding = await generate_embedding(searchable_text)
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(f"Could not generate embedding for version {version_id}")
+            return False
 
-    # Store the embedding with metadata using new function
-    metadata = {
-        "name": name,
-        "subHeading": sub_heading,
-        "categories": categories,
-    }
-    return await store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=version_id,
-        embedding=embedding,
-        searchable_text=searchable_text,
-        metadata=metadata,
-        user_id=None,  # Store agents are public
-        tx=tx,
-    )
+        # Store the embedding with metadata using new function
+        metadata = {
+            "name": name,
+            "subHeading": sub_heading,
+            "categories": categories,
+        }
+        return await store_content_embedding(
+            content_type=ContentType.STORE_AGENT,
+            content_id=version_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata,
+            user_id=None,  # Store agents are public
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
+        return False
 
 
 async def delete_embedding(version_id: str) -> bool:
@@ -454,7 +476,6 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
     total_processed = 0
     total_success = 0
     total_failed = 0
-    all_errors: dict[str, int] = {}  # Aggregate errors across all content types
 
     # Process content types in explicit order
     processing_order = [
@@ -500,13 +521,6 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
             success = sum(1 for result in results if result is True)
             failed = len(results) - success
 
-            # Aggregate errors across all content types
-            if failed > 0:
-                for result in results:
-                    if isinstance(result, Exception):
-                        error_key = f"{type(result).__name__}: {str(result)}"
-                        all_errors[error_key] = all_errors.get(error_key, 0) + 1
-
             results_by_type[content_type.value] = {
                 "processed": len(missing_items),
                 "success": success,
@@ -532,13 +546,6 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
                 "error": str(e),
             }
 
-    # Log aggregated errors once at the end
-    if all_errors:
-        error_details = ", ".join(
-            f"{error} ({count}x)" for error, count in all_errors.items()
-        )
-        logger.error(f"Embedding backfill errors: {error_details}")
-
     return {
         "by_type": results_by_type,
         "totals": {
@@ -550,12 +557,11 @@ async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
     }
 
 
-async def embed_query(query: str) -> list[float]:
+async def embed_query(query: str) -> list[float] | None:
     """
     Generate embedding for a search query.
 
     Same as generate_embedding but with clearer intent.
-    Raises exceptions on failure - caller should handle.
     """
     return await generate_embedding(query)
 
@@ -588,30 +594,40 @@ async def ensure_content_embedding(
         tx: Optional transaction client
 
     Returns:
-        True if embedding exists/was created
-
-    Raises exceptions on failure - caller should handle.
+        True if embedding exists/was created, False on failure
     """
-    # Check if embedding already exists
-    if not force:
-        existing = await get_content_embedding(content_type, content_id, user_id)
-        if existing and existing.get("embedding"):
-            logger.debug(f"Embedding for {content_type}:{content_id} already exists")
-            return True
+    try:
+        # Check if embedding already exists
+        if not force:
+            existing = await get_content_embedding(content_type, content_id, user_id)
+            if existing and existing.get("embedding"):
+                logger.debug(
+                    f"Embedding for {content_type}:{content_id} already exists"
+                )
+                return True
 
-    # Generate new embedding
-    embedding = await generate_embedding(searchable_text)
+        # Generate new embedding
+        embedding = await generate_embedding(searchable_text)
+        if embedding is None:
+            logger.warning(
+                f"Could not generate embedding for {content_type}:{content_id}"
+            )
+            return False
 
-    # Store the embedding
-    return await store_content_embedding(
-        content_type=content_type,
-        content_id=content_id,
-        embedding=embedding,
-        searchable_text=searchable_text,
-        metadata=metadata or {},
-        user_id=user_id,
-        tx=tx,
-    )
+        # Store the embedding
+        return await store_content_embedding(
+            content_type=content_type,
+            content_id=content_id,
+            embedding=embedding,
+            searchable_text=searchable_text,
+            metadata=metadata or {},
+            user_id=user_id,
+            tx=tx,
+        )
+
+    except Exception as e:
+        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
+        return False
 
 
 async def cleanup_orphaned_embeddings() -> dict[str, Any]:
@@ -838,8 +854,9 @@ async def semantic_search(
         limit = 100
 
     # Generate query embedding
-    try:
-        query_embedding = await embed_query(query)
+    query_embedding = await embed_query(query)
+
+    if query_embedding is not None:
         # Semantic search with embeddings
         embedding_str = embedding_to_vector_string(query_embedding)
 
@@ -890,21 +907,24 @@ async def semantic_search(
         """
         )
 
-        results = await query_raw_with_schema(sql, *params)
-        return [
-            {
-                "content_id": row["content_id"],
-                "content_type": row["content_type"],
-                "searchable_text": row["searchable_text"],
-                "metadata": row["metadata"],
-                "similarity": float(row["similarity"]),
-            }
-            for row in results
-        ]
-    except Exception as e:
-        logger.warning(f"Semantic search failed, falling back to lexical search: {e}")
+        try:
+            results = await query_raw_with_schema(sql, *params)
+            return [
+                {
+                    "content_id": row["content_id"],
+                    "content_type": row["content_type"],
+                    "searchable_text": row["searchable_text"],
+                    "metadata": row["metadata"],
+                    "similarity": float(row["similarity"]),
+                }
+                for row in results
+            ]
+        except Exception as e:
+            logger.error(f"Semantic search failed: {e}")
+            # Fall through to lexical search below
 
     # Fallback to lexical search if embeddings unavailable
+    logger.warning("Falling back to lexical search (embeddings unavailable)")
 
     params_lexical: list[Any] = [limit]
     user_filter = ""

autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py

@@ -454,9 +454,6 @@ async def test_unified_hybrid_search_pagination(
     cleanup_embeddings: list,
 ):
     """Test unified search pagination works correctly."""
-    # Use a unique search term to avoid matching other test data
-    unique_term = f"xyzpagtest{uuid.uuid4().hex[:8]}"
-
     # Create multiple items
     content_ids = []
     for i in range(5):
@@ -468,14 +465,14 @@ async def test_unified_hybrid_search_pagination(
             content_type=ContentType.BLOCK,
             content_id=content_id,
             embedding=mock_embedding,
-            searchable_text=f"{unique_term} item number {i}",
+            searchable_text=f"pagination test item number {i}",
             metadata={"index": i},
             user_id=None,
         )
 
     # Get first page
     page1_results, total1 = await unified_hybrid_search(
-        query=unique_term,
+        query="pagination test",
         content_types=[ContentType.BLOCK],
         page=1,
         page_size=2,
@@ -483,7 +480,7 @@ async def test_unified_hybrid_search_pagination(
 
     # Get second page
     page2_results, total2 = await unified_hybrid_search(
-        query=unique_term,
+        query="pagination test",
         content_types=[ContentType.BLOCK],
         page=2,
         page_size=2,

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

@@ -298,16 +298,17 @@ async def test_schema_handling_error_cases():
             mock_client.execute_raw.side_effect = Exception("Database error")
             mock_get_client.return_value = mock_client
 
-            # Should raise exception on error
-            with pytest.raises(Exception, match="Database error"):
-                await embeddings.store_content_embedding(
-                    content_type=ContentType.STORE_AGENT,
-                    content_id="test-id",
-                    embedding=[0.1] * EMBEDDING_DIM,
-                    searchable_text="test",
-                    metadata=None,
-                    user_id=None,
-                )
+            result = await embeddings.store_content_embedding(
+                content_type=ContentType.STORE_AGENT,
+                content_id="test-id",
+                embedding=[0.1] * EMBEDDING_DIM,
+                searchable_text="test",
+                metadata=None,
+                user_id=None,
+            )
+
+            # Should return False on error, not raise
+            assert result is False
 
 
 if __name__ == "__main__":

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

@@ -80,8 +80,9 @@ async def test_generate_embedding_no_api_key():
     ) as mock_get_client:
         mock_get_client.return_value = None
 
-        with pytest.raises(RuntimeError, match="openai_internal_api_key not set"):
-            await embeddings.generate_embedding("test text")
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -96,8 +97,9 @@ async def test_generate_embedding_api_error():
     ) as mock_get_client:
         mock_get_client.return_value = mock_client
 
-        with pytest.raises(Exception, match="API Error"):
-            await embeddings.generate_embedding("test text")
+        result = await embeddings.generate_embedding("test text")
+
+        assert result is None
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -171,10 +173,11 @@ async def test_store_embedding_database_error(mocker):
 
     embedding = [0.1, 0.2, 0.3]
 
-    with pytest.raises(Exception, match="Database error"):
-        await embeddings.store_embedding(
-            version_id="test-version-id", embedding=embedding, tx=mock_client
-        )
+    result = await embeddings.store_embedding(
+        version_id="test-version-id", embedding=embedding, tx=mock_client
+    )
+
+    assert result is False
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -274,16 +277,17 @@ async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
 async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
     """Test ensure_embedding when generation fails."""
     mock_get.return_value = None
-    mock_generate.side_effect = Exception("Generation failed")
+    mock_generate.return_value = None
 
-    with pytest.raises(Exception, match="Generation failed"):
-        await embeddings.ensure_embedding(
-            version_id="test-id",
-            name="Test",
-            description="Test description",
-            sub_heading="Test heading",
-            categories=["test"],
-        )
+    result = await embeddings.ensure_embedding(
+        version_id="test-id",
+        name="Test",
+        description="Test description",
+        sub_heading="Test heading",
+        categories=["test"],
+    )
+
+    assert result is False
 
 
 @pytest.mark.asyncio(loop_scope="session")

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

@@ -186,12 +186,13 @@ async def unified_hybrid_search(
 
     offset = (page - 1) * page_size
 
-    # Generate query embedding with graceful degradation
-    try:
-        query_embedding = await embed_query(query)
-    except Exception as e:
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Graceful degradation if embedding unavailable
+    if query_embedding is None or not query_embedding:
         logger.warning(
-            f"Failed to generate query embedding - falling back to lexical-only search: {e}. "
+            "Failed to generate query embedding - falling back to lexical-only search. "
             "Check that openai_internal_api_key is configured and OpenAI API is accessible."
         )
         query_embedding = [0.0] * EMBEDDING_DIM
@@ -463,12 +464,13 @@ async def hybrid_search(
 
     offset = (page - 1) * page_size
 
-    # Generate query embedding with graceful degradation
-    try:
-        query_embedding = await embed_query(query)
-    except Exception as e:
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Graceful degradation
+    if query_embedding is None or not query_embedding:
         logger.warning(
-            f"Failed to generate query embedding - falling back to lexical-only search: {e}"
+            "Failed to generate query embedding - falling back to lexical-only search."
         )
         query_embedding = [0.0] * EMBEDDING_DIM
         total_non_semantic = (
@@ -600,7 +602,6 @@ async def hybrid_search(
                 sa.featured,
                 sa.is_available,
                 sa.updated_at,
-                sa."agentGraphId",
                 -- Searchable text for BM25 reranking
                 COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
                 -- Semantic score
@@ -660,7 +661,6 @@ async def hybrid_search(
                 featured,
                 is_available,
                 updated_at,
-                "agentGraphId",
                 searchable_text,
                 semantic_score,
                 lexical_score,

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

@@ -172,8 +172,8 @@ async def test_hybrid_search_without_embeddings():
         with patch(
             "backend.api.features.store.hybrid_search.query_raw_with_schema"
         ) as mock_query:
-            # Simulate embedding failure by raising exception
-            mock_embed.side_effect = Exception("Embedding generation failed")
+            # Simulate embedding failure
+            mock_embed.return_value = None
             mock_query.return_value = mock_results
 
             # Should NOT raise - graceful degradation
@@ -613,9 +613,7 @@ async def test_unified_hybrid_search_graceful_degradation():
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
             mock_query.return_value = mock_results
-            mock_embed.side_effect = Exception(
-                "Embedding generation failed"
-            )  # Embedding failure
+            mock_embed.return_value = None  # Embedding failure
 
             # Should NOT raise - graceful degradation
             results, total = await unified_hybrid_search(

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

@@ -38,7 +38,6 @@ class StoreAgent(pydantic.BaseModel):
     description: str
     runs: int
     rating: float
-    agent_graph_id: str
 
 
 class StoreAgentsResponse(pydantic.BaseModel):

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

@@ -26,13 +26,11 @@ def test_store_agent():
         description="Test description",
         runs=50,
         rating=4.5,
-        agent_graph_id="test-graph-id",
     )
     assert agent.slug == "test-agent"
     assert agent.agent_name == "Test Agent"
     assert agent.runs == 50
     assert agent.rating == 4.5
-    assert agent.agent_graph_id == "test-graph-id"
 
 
 def test_store_agents_response():
@@ -48,7 +46,6 @@ def test_store_agents_response():
                 description="Test description",
                 runs=50,
                 rating=4.5,
-                agent_graph_id="test-graph-id",
             )
         ],
         pagination=store_model.Pagination(

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

@@ -82,7 +82,6 @@ def test_get_agents_featured(
                 description="Featured agent description",
                 runs=100,
                 rating=4.5,
-                agent_graph_id="test-graph-1",
             )
         ],
         pagination=store_model.Pagination(
@@ -128,7 +127,6 @@ def test_get_agents_by_creator(
                 description="Creator agent description",
                 runs=50,
                 rating=4.0,
-                agent_graph_id="test-graph-2",
             )
         ],
         pagination=store_model.Pagination(
@@ -174,7 +172,6 @@ def test_get_agents_sorted(
                 description="Top agent description",
                 runs=1000,
                 rating=5.0,
-                agent_graph_id="test-graph-3",
             )
         ],
         pagination=store_model.Pagination(
@@ -220,7 +217,6 @@ def test_get_agents_search(
                 description="Specific search term description",
                 runs=75,
                 rating=4.2,
-                agent_graph_id="test-graph-search",
             )
         ],
         pagination=store_model.Pagination(
@@ -266,7 +262,6 @@ def test_get_agents_category(
                 description="Category agent description",
                 runs=60,
                 rating=4.1,
-                agent_graph_id="test-graph-category",
             )
         ],
         pagination=store_model.Pagination(
@@ -311,7 +306,6 @@ def test_get_agents_pagination(
                 description=f"Agent {i} description",
                 runs=i * 10,
                 rating=4.0,
-                agent_graph_id="test-graph-2",
             )
             for i in range(5)
         ],

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

@@ -33,7 +33,6 @@ class TestCacheDeletion:
                     description="Test description",
                     runs=100,
                     rating=4.5,
-                    agent_graph_id="test-graph-id",
                 )
             ],
             pagination=Pagination(

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

@@ -261,36 +261,14 @@ async def get_onboarding_agents(
     return await get_recommended_agents(user_id)
 
 
-class OnboardingStatusResponse(pydantic.BaseModel):
-    """Response for onboarding status check."""
-
-    is_onboarding_enabled: bool
-    is_chat_enabled: bool
-
-
 @v1_router.get(
     "/onboarding/enabled",
     summary="Is onboarding enabled",
     tags=["onboarding", "public"],
-    response_model=OnboardingStatusResponse,
+    dependencies=[Security(requires_user)],
 )
-async def is_onboarding_enabled(
-    user_id: Annotated[str, Security(get_user_id)],
-) -> OnboardingStatusResponse:
-    # Check if chat is enabled for user
-    is_chat_enabled = await is_feature_enabled(Flag.CHAT, user_id, False)
-
-    # If chat is enabled, skip legacy onboarding
-    if is_chat_enabled:
-        return OnboardingStatusResponse(
-            is_onboarding_enabled=False,
-            is_chat_enabled=True,
-        )
-
-    return OnboardingStatusResponse(
-        is_onboarding_enabled=await onboarding_enabled(),
-        is_chat_enabled=False,
-    )
+async def is_onboarding_enabled() -> bool:
+    return await onboarding_enabled()
 
 
 @v1_router.post(
@@ -386,8 +364,6 @@ async def execute_graph_block(
     obj = get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
-    if obj.disabled:
-        raise HTTPException(status_code=403, detail=f"Block #{block_id} is disabled.")
 
     user = await get_user_by_id(user_id)
     if not user:

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

@@ -138,7 +138,6 @@ def test_execute_graph_block(
     """Test execute block endpoint"""
     # Mock block
     mock_block = Mock()
-    mock_block.disabled = False
 
     async def mock_execute(*args, **kwargs):
         yield "output1", {"data": "result1"}

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

@@ -1 +0,0 @@
-# Workspace API feature module

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

@@ -1,122 +0,0 @@
-"""
-Workspace API routes for managing user file storage.
-"""
-
-import logging
-import re
-from typing import Annotated
-from urllib.parse import quote
-
-import fastapi
-from autogpt_libs.auth.dependencies import get_user_id, requires_user
-from fastapi.responses import Response
-
-from backend.data.workspace import get_workspace, get_workspace_file
-from backend.util.workspace_storage import get_workspace_storage
-
-
-def _sanitize_filename_for_header(filename: str) -> str:
-    """
-    Sanitize filename for Content-Disposition header to prevent header injection.
-
-    Removes/replaces characters that could break the header or inject new headers.
-    Uses RFC5987 encoding for non-ASCII characters.
-    """
-    # Remove CR, LF, and null bytes (header injection prevention)
-    sanitized = re.sub(r"[\r\n\x00]", "", filename)
-    # Escape quotes
-    sanitized = sanitized.replace('"', '\\"')
-    # For non-ASCII, use RFC5987 filename* parameter
-    # Check if filename has non-ASCII characters
-    try:
-        sanitized.encode("ascii")
-        return f'attachment; filename="{sanitized}"'
-    except UnicodeEncodeError:
-        # Use RFC5987 encoding for UTF-8 filenames
-        encoded = quote(sanitized, safe="")
-        return f"attachment; filename*=UTF-8''{encoded}"
-
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter(
-    dependencies=[fastapi.Security(requires_user)],
-)
-
-
-def _create_streaming_response(content: bytes, file) -> Response:
-    """Create a streaming response for file content."""
-    return Response(
-        content=content,
-        media_type=file.mimeType,
-        headers={
-            "Content-Disposition": _sanitize_filename_for_header(file.name),
-            "Content-Length": str(len(content)),
-        },
-    )
-
-
-async def _create_file_download_response(file) -> Response:
-    """
-    Create a download response for a workspace file.
-
-    Handles both local storage (direct streaming) and GCS (signed URL redirect
-    with fallback to streaming).
-    """
-    storage = await get_workspace_storage()
-
-    # For local storage, stream the file directly
-    if file.storagePath.startswith("local://"):
-        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.storagePath, expires_in=300)
-        # If we got back an API path (fallback), stream directly instead
-        if url.startswith("/api/"):
-            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.storagePath}): {e}",
-            exc_info=True,
-        )
-        # Fall back to streaming directly from GCS
-        try:
-            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.storagePath}): {fallback_error}",
-                exc_info=True,
-            )
-            raise
-
-
-@router.get(
-    "/files/{file_id}/download",
-    summary="Download file by ID",
-)
-async def download_file(
-    user_id: Annotated[str, fastapi.Security(get_user_id)],
-    file_id: str,
-) -> Response:
-    """
-    Download a file by its ID.
-
-    Returns the file content directly or redirects to a signed URL for GCS.
-    """
-    workspace = await get_workspace(user_id)
-    if workspace is None:
-        raise fastapi.HTTPException(status_code=404, detail="Workspace not found")
-
-    file = await get_workspace_file(file_id, workspace.id)
-    if file is None:
-        raise fastapi.HTTPException(status_code=404, detail="File not found")
-
-    return await _create_file_download_response(file)

autogpt_platform/backend/backend/api/rest_api.py

@@ -32,7 +32,6 @@ import backend.api.features.postmark.postmark
 import backend.api.features.store.model
 import backend.api.features.store.routes
 import backend.api.features.v1
-import backend.api.features.workspace.routes as workspace_routes
 import backend.data.block
 import backend.data.db
 import backend.data.graph
@@ -40,10 +39,6 @@ import backend.data.user
 import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
-from backend.api.features.chat.completion_consumer import (
-    start_completion_consumer,
-    stop_completion_consumer,
-)
 from backend.blocks.llm import DEFAULT_LLM_MODEL
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
@@ -57,7 +52,6 @@ from backend.util.exceptions import (
 )
 from backend.util.feature_flag import initialize_launchdarkly, shutdown_launchdarkly
 from backend.util.service import UnhealthyServiceError
-from backend.util.workspace_storage import shutdown_workspace_storage
 
 from .external.fastapi_app import external_api
 from .features.analytics import router as analytics_router
@@ -122,31 +116,14 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
-    # Start chat completion consumer for Redis Streams notifications
-    try:
-        await start_completion_consumer()
-    except Exception as e:
-        logger.warning(f"Could not start chat completion consumer: {e}")
-
     with launch_darkly_context():
         yield
 
-    # Stop chat completion consumer
-    try:
-        await stop_completion_consumer()
-    except Exception as e:
-        logger.warning(f"Error stopping chat completion consumer: {e}")
-
     try:
         await shutdown_cloud_storage_handler()
     except Exception as e:
         logger.warning(f"Error shutting down cloud storage handler: {e}")
 
-    try:
-        await shutdown_workspace_storage()
-    except Exception as e:
-        logger.warning(f"Error shutting down workspace storage: {e}")
-
     await backend.data.db.disconnect()
 
 
@@ -338,11 +315,6 @@ app.include_router(
     tags=["v2", "chat"],
     prefix="/api/chat",
 )
-app.include_router(
-    workspace_routes.router,
-    tags=["workspace"],
-    prefix="/api/workspace",
-)
 app.include_router(
     backend.api.features.oauth.router,
     tags=["oauth"],

autogpt_platform/backend/backend/api/ws_api.py

@@ -66,24 +66,18 @@ async def event_broadcaster(manager: ConnectionManager):
     execution_bus = AsyncRedisExecutionEventBus()
     notification_bus = AsyncRedisNotificationEventBus()
 
-    try:
+    async def execution_worker():
+        async for event in execution_bus.listen("*"):
+            await manager.send_execution_update(event)
 
-        async def execution_worker():
-            async for event in execution_bus.listen("*"):
-                await manager.send_execution_update(event)
+    async def notification_worker():
+        async for notification in notification_bus.listen("*"):
+            await manager.send_notification(
+                user_id=notification.user_id,
+                payload=notification.payload,
+            )
 
-        async def notification_worker():
-            async for notification in notification_bus.listen("*"):
-                await manager.send_notification(
-                    user_id=notification.user_id,
-                    payload=notification.payload,
-                )
-
-        await asyncio.gather(execution_worker(), notification_worker())
-    finally:
-        # Ensure PubSub connections are closed on any exit to prevent leaks
-        await execution_bus.close()
-        await notification_bus.close()
+    await asyncio.gather(execution_worker(), notification_worker())
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -13,7 +13,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -118,13 +117,11 @@ class AIImageCustomizerBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("image_url", lambda x: x.startswith(("workspace://", "data:"))),
+                ("image_url", "https://replicate.delivery/generated-image.jpg"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
                 "run_model": lambda *args, **kwargs: MediaFileType(
-                    "data:image/jpeg;base64,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"
+                    "https://replicate.delivery/generated-image.jpg"
                 ),
             },
             test_credentials=TEST_CREDENTIALS,
@@ -135,7 +132,8 @@ class AIImageCustomizerBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -143,9 +141,10 @@ class AIImageCustomizerBlock(Block):
             processed_images = await asyncio.gather(
                 *(
                     store_media_file(
+                        graph_exec_id=graph_exec_id,
                         file=img,
-                        execution_context=execution_context,
-                        return_format="for_external_api",  # Get content for Replicate API
+                        user_id=user_id,
+                        return_content=True,
                     )
                     for img in input_data.images
                 )
@@ -159,14 +158,7 @@ class AIImageCustomizerBlock(Block):
                 aspect_ratio=input_data.aspect_ratio.value,
                 output_format=input_data.output_format.value,
             )
-
-            # Store the generated image to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=result,
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "image_url", stored_url
+            yield "image_url", result
         except Exception as e:
             yield "error", str(e)
 

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -6,7 +6,6 @@ from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
 from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -14,8 +13,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
 
 
 class ImageSize(str, Enum):
@@ -168,13 +165,11 @@ class AIImageGeneratorBlock(Block):
             test_output=[
                 (
                     "image_url",
-                    # Test output is a data URI since we now store images
-                    lambda x: x.startswith("data:image/"),
+                    "https://replicate.delivery/generated-image.webp",
                 ),
             ],
             test_mock={
-                # Return a data URI directly so store_media_file doesn't need to download
-                "_run_client": lambda *args, **kwargs: "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
+                "_run_client": lambda *args, **kwargs: "https://replicate.delivery/generated-image.webp"
             },
         )
 
@@ -323,24 +318,11 @@ class AIImageGeneratorBlock(Block):
         style_text = style_map.get(style, "")
         return f"{style_text} of" if style_text else ""
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         try:
             url = await self.generate_image(input_data, credentials)
             if url:
-                # Store the generated image to the user's workspace/execution folder
-                stored_url = await store_media_file(
-                    file=MediaFileType(url),
-                    execution_context=execution_context,
-                    return_format="for_block_output",
-                )
-                yield "image_url", stored_url
+                yield "image_url", url
             else:
                 yield "error", "Image generation returned an empty result."
         except Exception as e:

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -13,7 +13,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -22,9 +21,7 @@ from backend.data.model import (
 )
 from backend.integrations.providers import ProviderName
 from backend.util.exceptions import BlockExecutionError
-from backend.util.file import store_media_file
 from backend.util.request import Requests
-from backend.util.type import MediaFileType
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -274,10 +271,7 @@ class AIShortformVideoCreatorBlock(Block):
                 "voice": Voice.LILY,
                 "video_style": VisualMediaType.STOCK_VIDEOS,
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/video.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -286,21 +280,15 @@ class AIShortformVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/video.mp4",
                 },
-                # Use data URI to avoid HTTP requests during tests
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/video.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Create a new Webhook.site URL
         webhook_token, webhook_url = await self.create_webhook()
@@ -352,13 +340,7 @@ class AIShortformVideoCreatorBlock(Block):
             )
             video_url = await self.wait_for_video(credentials.api_key, pid)
             logger.debug(f"Video ready: {video_url}")
-            # Store the generated video to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=MediaFileType(video_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "video_url", stored_url
+            yield "video_url", video_url
 
 
 class AIAdMakerVideoCreatorBlock(Block):
@@ -465,10 +447,7 @@ class AIAdMakerVideoCreatorBlock(Block):
                     "https://cdn.revid.ai/uploads/1747076315114-image.png",
                 ],
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/ad.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -477,21 +456,14 @@ class AIAdMakerVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/ad.mp4",
                 },
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/ad.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -559,13 +531,7 @@ class AIAdMakerVideoCreatorBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        # Store the generated video to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=MediaFileType(video_url),
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "video_url", stored_url
+        yield "video_url", video_url
 
 
 class AIScreenshotToVideoAdBlock(Block):
@@ -660,10 +626,7 @@ class AIScreenshotToVideoAdBlock(Block):
                 "script": "Amazing numbers!",
                 "screenshot_url": "https://cdn.revid.ai/uploads/1747080376028-image.png",
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/screenshot.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -672,21 +635,14 @@ class AIScreenshotToVideoAdBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/screenshot.mp4",
                 },
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/screenshot.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -754,10 +710,4 @@ class AIScreenshotToVideoAdBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        # Store the generated video to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=MediaFileType(video_url),
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "video_url", stored_url
+        yield "video_url", video_url

autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py

@@ -6,7 +6,6 @@ if TYPE_CHECKING:
 
 from pydantic import SecretStr
 
-from backend.data.execution import ExecutionContext
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -18,8 +17,6 @@ from backend.sdk import (
     Requests,
     SchemaField,
 )
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
 
 from ._config import bannerbear
 
@@ -138,17 +135,15 @@ class BannerbearTextOverlayBlock(Block):
             },
             test_output=[
                 ("success", True),
-                # Output will be a workspace ref or data URI depending on context
-                ("image_url", lambda x: x.startswith(("workspace://", "data:"))),
+                ("image_url", "https://cdn.bannerbear.com/test-image.jpg"),
                 ("uid", "test-uid-123"),
                 ("status", "completed"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
                 "_make_api_request": lambda *args, **kwargs: {
                     "uid": "test-uid-123",
                     "status": "completed",
-                    "image_url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAABAAEBAREA/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APn+v//Z",
+                    "image_url": "https://cdn.bannerbear.com/test-image.jpg",
                 }
             },
             test_credentials=TEST_CREDENTIALS,
@@ -182,12 +177,7 @@ class BannerbearTextOverlayBlock(Block):
             raise Exception(error_msg)
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Build the modifications array
         modifications = []
@@ -244,18 +234,6 @@ class BannerbearTextOverlayBlock(Block):
 
         # Synchronous request - image should be ready
         yield "success", True
-
-        # Store the generated image to workspace for persistence
-        image_url = data.get("image_url", "")
-        if image_url:
-            stored_url = await store_media_file(
-                file=MediaFileType(image_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "image_url", stored_url
-        else:
-            yield "image_url", ""
-
+        yield "image_url", data.get("image_url", "")
         yield "uid", data.get("uid", "")
         yield "status", data.get("status", "completed")

autogpt_platform/backend/backend/blocks/basic.py

@@ -9,7 +9,6 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType, convert
@@ -18,10 +17,10 @@ from backend.util.type import MediaFileType, convert
 class FileStoreBlock(Block):
     class Input(BlockSchemaInput):
         file_in: MediaFileType = SchemaField(
-            description="The file to download and store. Can be a URL (https://...), data URI, or local path."
+            description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
         )
         base_64: bool = SchemaField(
-            description="Whether to produce output in base64 format (not recommended, you can pass the file reference across blocks).",
+            description="Whether produce an output in base64 format (not recommended, you can pass the string path just fine accross blocks).",
             default=False,
             advanced=True,
             title="Produce Base64 Output",
@@ -29,18 +28,13 @@ class FileStoreBlock(Block):
 
     class Output(BlockSchemaOutput):
         file_out: MediaFileType = SchemaField(
-            description="Reference to the stored file. In CoPilot: workspace:// URI (visible in list_workspace_files). In graphs: data URI for passing to other blocks."
+            description="The relative path to the stored file in the temporary directory."
         )
 
     def __init__(self):
         super().__init__(
             id="cbb50872-625b-42f0-8203-a2ae78242d8a",
-            description=(
-                "Downloads and stores a file from a URL, data URI, or local path. "
-                "Use this to fetch images, documents, or other files for processing. "
-                "In CoPilot: saves to workspace (use list_workspace_files to see it). "
-                "In graphs: outputs a data URI to pass to other blocks."
-            ),
+            description="Stores the input file in the temporary directory.",
             categories={BlockCategory.BASIC, BlockCategory.MULTIMEDIA},
             input_schema=FileStoreBlock.Input,
             output_schema=FileStoreBlock.Output,
@@ -51,18 +45,15 @@ class FileStoreBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        # Determine return format based on user preference
-        # for_external_api: always returns data URI (base64) - honors "Produce Base64 Output"
-        # for_block_output: smart format - workspace:// in CoPilot, data URI in graphs
-        return_format = "for_external_api" if input_data.base_64 else "for_block_output"
-
         yield "file_out", await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.file_in,
-            execution_context=execution_context,
-            return_format=return_format,
+            user_id=user_id,
+            return_content=input_data.base_64,
         )
 
 
@@ -125,7 +116,6 @@ class PrintToConsoleBlock(Block):
             input_schema=PrintToConsoleBlock.Input,
             output_schema=PrintToConsoleBlock.Output,
             test_input={"text": "Hello, World!"},
-            is_sensitive_action=True,
             test_output=[
                 ("output", "Hello, World!"),
                 ("status", "printed"),

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -15,7 +15,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import APIKeyCredentials, SchemaField
 from backend.util.file import store_media_file
 from backend.util.request import Requests
@@ -667,7 +666,8 @@ class SendDiscordFileBlock(Block):
         file: MediaFileType,
         filename: str,
         message_content: str,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
     ) -> dict:
         intents = discord.Intents.default()
         intents.guilds = True
@@ -731,9 +731,10 @@ class SendDiscordFileBlock(Block):
                     # Local file path - read from stored media file
                     # This would be a path from a previous block's output
                     stored_file = await store_media_file(
+                        graph_exec_id=graph_exec_id,
                         file=file,
-                        execution_context=execution_context,
-                        return_format="for_external_api",  # Get content to send to Discord
+                        user_id=user_id,
+                        return_content=True,  # Get as data URI
                     )
                     # Now process as data URI
                     header, encoded = stored_file.split(",", 1)
@@ -780,7 +781,8 @@ class SendDiscordFileBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -791,7 +793,8 @@ class SendDiscordFileBlock(Block):
                 file=input_data.file,
                 filename=input_data.filename,
                 message_content=input_data.message_content,
-                execution_context=execution_context,
+                graph_exec_id=graph_exec_id,
+                user_id=user_id,
             )
 
             yield "status", result.get("status", "Unknown error")

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -17,11 +17,8 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
-from backend.util.file import store_media_file
 from backend.util.request import ClientResponseError, Requests
-from backend.util.type import MediaFileType
 
 logger = logging.getLogger(__name__)
 
@@ -67,13 +64,9 @@ class AIVideoGeneratorBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("video_url", lambda x: x.startswith(("workspace://", "data:"))),
-            ],
+            test_output=[("video_url", "https://fal.media/files/example/video.mp4")],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
-                "generate_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA"
+                "generate_video": lambda *args, **kwargs: "https://fal.media/files/example/video.mp4"
             },
         )
 
@@ -215,22 +208,11 @@ class AIVideoGeneratorBlock(Block):
             raise RuntimeError(f"API request failed: {str(e)}")
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: FalCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: FalCredentials, **kwargs
     ) -> BlockOutput:
         try:
             video_url = await self.generate_video(input_data, credentials)
-            # Store the generated video to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=MediaFileType(video_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "video_url", stored_url
+            yield "video_url", video_url
         except Exception as e:
             error_message = str(e)
             yield "error", error_message

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -12,7 +12,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -122,12 +121,10 @@ class AIImageEditorBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("output_image", lambda x: x.startswith(("workspace://", "data:"))),
+                ("output_image", "https://replicate.com/output/edited-image.png"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
-                "run_model": lambda *args, **kwargs: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==",
+                "run_model": lambda *args, **kwargs: "https://replicate.com/output/edited-image.png",
             },
             test_credentials=TEST_CREDENTIALS,
         )
@@ -137,7 +134,8 @@ class AIImageEditorBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         result = await self.run_model(
@@ -146,25 +144,20 @@ class AIImageEditorBlock(Block):
             prompt=input_data.prompt,
             input_image_b64=(
                 await store_media_file(
+                    graph_exec_id=graph_exec_id,
                     file=input_data.input_image,
-                    execution_context=execution_context,
-                    return_format="for_external_api",  # Get content for Replicate API
+                    user_id=user_id,
+                    return_content=True,
                 )
                 if input_data.input_image
                 else None
             ),
             aspect_ratio=input_data.aspect_ratio.value,
             seed=input_data.seed,
-            user_id=execution_context.user_id or "",
-            graph_exec_id=execution_context.graph_exec_id or "",
+            user_id=user_id,
+            graph_exec_id=graph_exec_id,
         )
-        # Store the generated image to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=result,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "output_image", stored_url
+        yield "output_image", result
 
     async def run_model(
         self,

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -21,7 +21,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 from backend.util.settings import Settings
@@ -96,7 +95,8 @@ def _make_mime_text(
 
 async def create_mime_message(
     input_data,
-    execution_context: ExecutionContext,
+    graph_exec_id: str,
+    user_id: str,
 ) -> str:
     """Create a MIME message with attachments and return base64-encoded raw message."""
 
@@ -117,12 +117,12 @@ async def create_mime_message(
     if input_data.attachments:
         for attach in input_data.attachments:
             local_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=attach,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+            abs_path = get_exec_file_path(graph_exec_id, local_path)
             part = MIMEBase("application", "octet-stream")
             with open(abs_path, "rb") as f:
                 part.set_payload(f.read())
@@ -582,25 +582,27 @@ class GmailSendBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._send_email(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "result", result
 
     async def _send_email(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to or not input_data.subject or not input_data.body:
             raise ValueError(
                 "At least one recipient, subject, and body are required for sending an email"
             )
-        raw_message = await create_mime_message(input_data, execution_context)
+        raw_message = await create_mime_message(input_data, graph_exec_id, user_id)
         sent_message = await asyncio.to_thread(
             lambda: service.users()
             .messages()
@@ -690,28 +692,30 @@ class GmailCreateDraftBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._create_draft(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "result", GmailDraftResult(
             id=result["id"], message_id=result["message"]["id"], status="draft_created"
         )
 
     async def _create_draft(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to or not input_data.subject:
             raise ValueError(
                 "At least one recipient and subject are required for creating a draft"
             )
 
-        raw_message = await create_mime_message(input_data, execution_context)
+        raw_message = await create_mime_message(input_data, graph_exec_id, user_id)
         draft = await asyncio.to_thread(
             lambda: service.users()
             .drafts()
@@ -1096,7 +1100,7 @@ class GmailGetThreadBlock(GmailBase):
 
 
 async def _build_reply_message(
-    service, input_data, execution_context: ExecutionContext
+    service, input_data, graph_exec_id: str, user_id: str
 ) -> tuple[str, str]:
     """
     Builds a reply MIME message for Gmail threads.
@@ -1186,12 +1190,12 @@ async def _build_reply_message(
     # Handle attachments
     for attach in input_data.attachments:
         local_path = await store_media_file(
+            user_id=user_id,
+            graph_exec_id=graph_exec_id,
             file=attach,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            return_content=False,
         )
-        assert execution_context.graph_exec_id  # Validated by store_media_file
-        abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+        abs_path = get_exec_file_path(graph_exec_id, local_path)
         part = MIMEBase("application", "octet-stream")
         with open(abs_path, "rb") as f:
             part.set_payload(f.read())
@@ -1307,14 +1311,16 @@ class GmailReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         message = await self._reply(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "messageId", message["id"]
         yield "threadId", message.get("threadId", input_data.threadId)
@@ -1337,11 +1343,11 @@ class GmailReplyBlock(GmailBase):
         yield "email", email
 
     async def _reply(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, execution_context
+            service, input_data, graph_exec_id, user_id
         )
 
         # Send the message
@@ -1435,14 +1441,16 @@ class GmailDraftReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         draft = await self._create_draft_reply(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "draftId", draft["id"]
         yield "messageId", draft["message"]["id"]
@@ -1450,11 +1458,11 @@ class GmailDraftReplyBlock(GmailBase):
         yield "status", "draft_created"
 
     async def _create_draft_reply(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, execution_context
+            service, input_data, graph_exec_id, user_id
         )
 
         # Create draft with proper thread association
@@ -1621,21 +1629,23 @@ class GmailForwardBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._forward_message(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "messageId", result["id"]
         yield "threadId", result.get("threadId", "")
         yield "status", "forwarded"
 
     async def _forward_message(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to:
             raise ValueError("At least one recipient is required for forwarding")
@@ -1717,12 +1727,12 @@ To: {original_to}
         # Add any additional attachments
         for attach in input_data.additionalAttachments:
             local_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=attach,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+            abs_path = get_exec_file_path(graph_exec_id, local_path)
             part = MIMEBase("application", "octet-stream")
             with open(abs_path, "rb") as f:
                 part.set_payload(f.read())

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -9,7 +9,7 @@ from typing import Any, Optional
 from prisma.enums import ReviewStatus
 from pydantic import BaseModel
 
-from backend.data.execution import ExecutionStatus
+from backend.data.execution import ExecutionContext, ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.executor.manager import async_update_node_execution_status
 from backend.util.clients import get_database_manager_async_client
@@ -28,11 +28,6 @@ class ReviewDecision(BaseModel):
 class HITLReviewHelper:
     """Helper class for Human-In-The-Loop review operations."""
 
-    @staticmethod
-    async def check_approval(**kwargs) -> Optional[ReviewResult]:
-        """Check if there's an existing approval for this node execution."""
-        return await get_database_manager_async_client().check_approval(**kwargs)
-
     @staticmethod
     async def get_or_create_human_review(**kwargs) -> Optional[ReviewResult]:
         """Create or retrieve a human review from the database."""
@@ -60,11 +55,11 @@ class HITLReviewHelper:
     async def _handle_review_request(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
         graph_version: int,
+        execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
     ) -> Optional[ReviewResult]:
@@ -74,11 +69,11 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
             graph_version: Version of the graph
+            execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
 
@@ -88,41 +83,15 @@ class HITLReviewHelper:
         Raises:
             Exception: If review creation or status update fails
         """
-        # Note: Safe mode checks (human_in_the_loop_safe_mode, sensitive_action_safe_mode)
-        # are handled by the caller:
-        # - HITL blocks check human_in_the_loop_safe_mode in their run() method
-        # - Sensitive action blocks check sensitive_action_safe_mode in is_block_exec_need_review()
-        # This function only handles checking for existing approvals.
-
-        # Check if this node has already been approved (normal or auto-approval)
-        if approval_result := await HITLReviewHelper.check_approval(
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            node_id=node_id,
-            user_id=user_id,
-            input_data=input_data,
-        ):
+        # Skip review if safe mode is disabled - return auto-approved result
+        if not execution_context.human_in_the_loop_safe_mode:
             logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - "
-                f"found existing approval"
-            )
-            # Return a new ReviewResult with the current node_exec_id but approved status
-            # For auto-approvals, always use current input_data
-            # For normal approvals, use approval_result.data unless it's None
-            is_auto_approval = approval_result.node_exec_id != node_exec_id
-            approved_data = (
-                input_data
-                if is_auto_approval
-                else (
-                    approval_result.data
-                    if approval_result.data is not None
-                    else input_data
-                )
+                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
             )
             return ReviewResult(
-                data=approved_data,
+                data=input_data,
                 status=ReviewStatus.APPROVED,
-                message=approval_result.message,
+                message="Auto-approved (safe mode disabled)",
                 processed=True,
                 node_exec_id=node_exec_id,
             )
@@ -134,7 +103,7 @@ class HITLReviewHelper:
             graph_id=graph_id,
             graph_version=graph_version,
             input_data=input_data,
-            message=block_name,  # Use block_name directly as the message
+            message=f"Review required for {block_name} execution",
             editable=editable,
         )
 
@@ -160,11 +129,11 @@ class HITLReviewHelper:
     async def handle_review_decision(
         input_data: Any,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
         graph_version: int,
+        execution_context: ExecutionContext,
         block_name: str = "Block",
         editable: bool = False,
     ) -> Optional[ReviewDecision]:
@@ -174,11 +143,11 @@ class HITLReviewHelper:
         Args:
             input_data: The input data to be reviewed
             user_id: ID of the user requesting the review
-            node_id: ID of the node in the graph definition
             node_exec_id: ID of the node execution
             graph_exec_id: ID of the graph execution
             graph_id: ID of the graph
             graph_version: Version of the graph
+            execution_context: Current execution context
             block_name: Name of the block requesting review
             editable: Whether the reviewer can edit the data
 
@@ -189,11 +158,11 @@ class HITLReviewHelper:
         review_result = await HITLReviewHelper._handle_review_request(
             input_data=input_data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
+            execution_context=execution_context,
             block_name=block_name,
             editable=editable,
         )

autogpt_platform/backend/backend/blocks/http.py

@@ -15,7 +15,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     CredentialsField,
     CredentialsMetaInput,
@@ -117,9 +116,10 @@ class SendWebRequestBlock(Block):
 
     @staticmethod
     async def _prepare_files(
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
         files_name: str,
         files: list[MediaFileType],
+        user_id: str,
     ) -> list[tuple[str, tuple[str, BytesIO, str]]]:
         """
         Prepare files for the request by storing them and reading their content.
@@ -127,16 +127,11 @@ class SendWebRequestBlock(Block):
         (files_name, (filename, BytesIO, mime_type))
         """
         files_payload: list[tuple[str, tuple[str, BytesIO, str]]] = []
-        graph_exec_id = execution_context.graph_exec_id
-        if graph_exec_id is None:
-            raise ValueError("graph_exec_id is required for file operations")
 
         for media in files:
             # Normalise to a list so we can repeat the same key
             rel_path = await store_media_file(
-                file=media,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                graph_exec_id, media, user_id, return_content=False
             )
             abs_path = get_exec_file_path(graph_exec_id, rel_path)
             async with aiofiles.open(abs_path, "rb") as f:
@@ -148,7 +143,7 @@ class SendWebRequestBlock(Block):
         return files_payload
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
+        self, input_data: Input, *, graph_exec_id: str, user_id: str, **kwargs
     ) -> BlockOutput:
         # ─── Parse/normalise body ────────────────────────────────────
         body = input_data.body
@@ -179,7 +174,7 @@ class SendWebRequestBlock(Block):
         files_payload: list[tuple[str, tuple[str, BytesIO, str]]] = []
         if use_files:
             files_payload = await self._prepare_files(
-                execution_context, input_data.files_name, input_data.files
+                graph_exec_id, input_data.files_name, input_data.files, user_id
             )
 
         # Enforce body format rules
@@ -243,8 +238,9 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
         credentials: HostScopedCredentials,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         # Create SendWebRequestBlock.Input from our input (removing credentials field)
@@ -275,6 +271,6 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
 
         # Use parent class run method
         async for output_name, output_data in super().run(
-            base_input, execution_context=execution_context, **kwargs
+            base_input, graph_exec_id=graph_exec_id, user_id=user_id, **kwargs
         ):
             yield output_name, output_data

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -97,7 +97,6 @@ class HumanInTheLoopBlock(Block):
         input_data: Input,
         *,
         user_id: str,
-        node_id: str,
         node_exec_id: str,
         graph_exec_id: str,
         graph_id: str,
@@ -116,12 +115,12 @@ class HumanInTheLoopBlock(Block):
         decision = await self.handle_review_decision(
             input_data=input_data.data,
             user_id=user_id,
-            node_id=node_id,
             node_exec_id=node_exec_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             graph_version=graph_version,
-            block_name=input_data.name,  # Use user-provided name instead of block type
+            execution_context=execution_context,
+            block_name=self.name,
             editable=input_data.editable,
         )
 

autogpt_platform/backend/backend/blocks/io.py

@@ -12,7 +12,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
 from backend.util.mock import MockObject
@@ -463,21 +462,18 @@ class AgentFileInputBlock(AgentInputBlock):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         if not input_data.value:
             return
 
-        # Determine return format based on user preference
-        # for_external_api: always returns data URI (base64) - honors "Produce Base64 Output"
-        # for_block_output: smart format - workspace:// in CoPilot, data URI in graphs
-        return_format = "for_external_api" if input_data.base_64 else "for_block_output"
-
         yield "result", await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.value,
-            execution_context=execution_context,
-            return_format=return_format,
+            user_id=user_id,
+            return_content=input_data.base_64,
         )
 
 

autogpt_platform/backend/backend/blocks/linear/_api.py

@@ -162,16 +162,8 @@ class LinearClient:
                 "searchTerm": team_name,
             }
 
-            result = await self.query(query, variables)
-            nodes = result["teams"]["nodes"]
-
-            if not nodes:
-                raise LinearAPIException(
-                    f"Team '{team_name}' not found. Check the team name or key and try again.",
-                    status_code=404,
-                )
-
-            return nodes[0]["id"]
+            team_id = await self.query(query, variables)
+            return team_id["teams"]["nodes"][0]["id"]
         except LinearAPIException as e:
             raise e
 
@@ -248,44 +240,17 @@ class LinearClient:
         except LinearAPIException as e:
             raise e
 
-    async def try_search_issues(
-        self,
-        term: str,
-        max_results: int = 10,
-        team_id: str | None = None,
-    ) -> list[Issue]:
+    async def try_search_issues(self, term: str) -> list[Issue]:
         try:
             query = """
-                query SearchIssues(
-                    $term: String!,
-                    $first: Int,
-                    $teamId: String
-                ) {
-                    searchIssues(
-                        term: $term,
-                        first: $first,
-                        teamId: $teamId
-                    ) {
+                query SearchIssues($term: String!, $includeComments: Boolean!) {
+                    searchIssues(term: $term, includeComments: $includeComments) {
                         nodes {
                             id
                             identifier
                             title
                             description
                             priority
-                            createdAt
-                            state {
-                                id
-                                name
-                                type
-                            }
-                            project {
-                                id
-                                name
-                            }
-                            assignee {
-                                id
-                                name
-                            }
                         }
                     }
                 }
@@ -293,8 +258,7 @@ class LinearClient:
 
             variables: dict[str, Any] = {
                 "term": term,
-                "first": max_results,
-                "teamId": team_id,
+                "includeComments": True,
             }
 
             issues = await self.query(query, variables)

autogpt_platform/backend/backend/blocks/linear/issues.py

@@ -17,7 +17,7 @@ from ._config import (
     LinearScope,
     linear,
 )
-from .models import CreateIssueResponse, Issue, State
+from .models import CreateIssueResponse, Issue
 
 
 class LinearCreateIssueBlock(Block):
@@ -135,20 +135,9 @@ class LinearSearchIssuesBlock(Block):
             description="Linear credentials with read permissions",
             required_scopes={LinearScope.READ},
         )
-        max_results: int = SchemaField(
-            description="Maximum number of results to return",
-            default=10,
-            ge=1,
-            le=100,
-        )
-        team_name: str | None = SchemaField(
-            description="Optional team name to filter results (e.g., 'Internal', 'Open Source')",
-            default=None,
-        )
 
     class Output(BlockSchemaOutput):
         issues: list[Issue] = SchemaField(description="List of issues")
-        error: str = SchemaField(description="Error message if the search failed")
 
     def __init__(self):
         super().__init__(
@@ -156,11 +145,8 @@ class LinearSearchIssuesBlock(Block):
             description="Searches for issues on Linear",
             input_schema=self.Input,
             output_schema=self.Output,
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.ISSUE_TRACKING},
             test_input={
                 "term": "Test issue",
-                "max_results": 10,
-                "team_name": None,
                 "credentials": TEST_CREDENTIALS_INPUT_OAUTH,
             },
             test_credentials=TEST_CREDENTIALS_OAUTH,
@@ -170,14 +156,10 @@ class LinearSearchIssuesBlock(Block):
                     [
                         Issue(
                             id="abc123",
-                            identifier="TST-123",
+                            identifier="abc123",
                             title="Test issue",
                             description="Test description",
                             priority=1,
-                            state=State(
-                                id="state1", name="In Progress", type="started"
-                            ),
-                            createdAt="2026-01-15T10:00:00.000Z",
                         )
                     ],
                 )
@@ -186,12 +168,10 @@ class LinearSearchIssuesBlock(Block):
                 "search_issues": lambda *args, **kwargs: [
                     Issue(
                         id="abc123",
-                        identifier="TST-123",
+                        identifier="abc123",
                         title="Test issue",
                         description="Test description",
                         priority=1,
-                        state=State(id="state1", name="In Progress", type="started"),
-                        createdAt="2026-01-15T10:00:00.000Z",
                     )
                 ]
             },
@@ -201,22 +181,10 @@ class LinearSearchIssuesBlock(Block):
     async def search_issues(
         credentials: OAuth2Credentials | APIKeyCredentials,
         term: str,
-        max_results: int = 10,
-        team_name: str | None = None,
     ) -> list[Issue]:
         client = LinearClient(credentials=credentials)
-
-        # Resolve team name to ID if provided
-        # Raises LinearAPIException with descriptive message if team not found
-        team_id: str | None = None
-        if team_name:
-            team_id = await client.try_get_team_by_name(team_name=team_name)
-
-        return await client.try_search_issues(
-            term=term,
-            max_results=max_results,
-            team_id=team_id,
-        )
+        response: list[Issue] = await client.try_search_issues(term=term)
+        return response
 
     async def run(
         self,
@@ -228,10 +196,7 @@ class LinearSearchIssuesBlock(Block):
         """Execute the issue search"""
         try:
             issues = await self.search_issues(
-                credentials=credentials,
-                term=input_data.term,
-                max_results=input_data.max_results,
-                team_name=input_data.team_name,
+                credentials=credentials, term=input_data.term
             )
             yield "issues", issues
         except LinearAPIException as e:

autogpt_platform/backend/backend/blocks/linear/models.py

@@ -36,21 +36,12 @@ class Project(BaseModel):
     content: str | None = None
 
 
-class State(BaseModel):
-    id: str
-    name: str
-    type: str | None = (
-        None  # Workflow state type (e.g., "triage", "backlog", "started", "completed", "canceled")
-    )
-
-
 class Issue(BaseModel):
     id: str
     identifier: str
     title: str
     description: str | None
     priority: int
-    state: State | None = None
     project: Project | None = None
     createdAt: str | None = None
     comments: list[Comment] | None = None

autogpt_platform/backend/backend/blocks/llm.py

@@ -32,7 +32,7 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.logging import TruncatedLogger
-from backend.util.prompt import compress_context, estimate_token_count
+from backend.util.prompt import compress_prompt, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
@@ -115,6 +115,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
+    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -279,6 +280,9 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2
     ),  # claude-haiku-4-5-20251001
+    LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
+        "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2
+    ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_HAIKU: ModelMetadata(
         "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1
     ),  # claude-3-haiku-20240307
@@ -634,18 +638,11 @@ async def llm_call(
     context_window = llm_model.context_window
 
     if compress_prompt_to_fit:
-        result = await compress_context(
+        prompt = compress_prompt(
             messages=prompt,
             target_tokens=llm_model.context_window // 2,
-            client=None,  # Truncation-only, no LLM summarization
-            reserve=0,  # Caller handles response token budget separately
+            lossy_ok=True,
         )
-        if result.error:
-            logger.warning(
-                f"Prompt compression did not meet target: {result.error}. "
-                f"Proceeding with {result.token_count} tokens."
-            )
-        prompt = result.messages
 
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)

autogpt_platform/backend/backend/blocks/media.py

@@ -1,6 +1,6 @@
 import os
 import tempfile
-from typing import Optional
+from typing import Literal, Optional
 
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.fx.Loop import Loop
@@ -13,7 +13,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 
@@ -47,19 +46,18 @@ class MediaDurationBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         # 1) Store the input media locally
         local_media_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.media_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
-        )
-        assert execution_context.graph_exec_id is not None
-        media_abspath = get_exec_file_path(
-            execution_context.graph_exec_id, local_media_path
+            user_id=user_id,
+            return_content=False,
         )
+        media_abspath = get_exec_file_path(graph_exec_id, local_media_path)
 
         # 2) Load the clip
         if input_data.is_video:
@@ -90,6 +88,10 @@ class LoopVideoBlock(Block):
             default=None,
             ge=1,
         )
+        output_return_type: Literal["file_path", "data_uri"] = SchemaField(
+            description="How to return the output video. Either a relative path or base64 data URI.",
+            default="file_path",
+        )
 
     class Output(BlockSchemaOutput):
         video_out: str = SchemaField(
@@ -109,19 +111,17 @@ class LoopVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        node_exec_id: str,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
         # 1) Store the input video locally
         local_video_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
         input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
 
@@ -149,11 +149,12 @@ class LoopVideoBlock(Block):
         looped_clip = looped_clip.with_audio(clip.audio)
         looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
 
-        # Return output - for_block_output returns workspace:// if available, else data URI
+        # Return as data URI
         video_out = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
+            user_id=user_id,
+            return_content=input_data.output_return_type == "data_uri",
         )
 
         yield "video_out", video_out
@@ -176,6 +177,10 @@ class AddAudioToVideoBlock(Block):
             description="Volume scale for the newly attached audio track (1.0 = original).",
             default=1.0,
         )
+        output_return_type: Literal["file_path", "data_uri"] = SchemaField(
+            description="Return the final output as a relative path or base64 data URI.",
+            default="file_path",
+        )
 
     class Output(BlockSchemaOutput):
         video_out: MediaFileType = SchemaField(
@@ -195,24 +200,23 @@ class AddAudioToVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        node_exec_id: str,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        assert execution_context.graph_exec_id is not None
-        assert execution_context.node_exec_id is not None
-        graph_exec_id = execution_context.graph_exec_id
-        node_exec_id = execution_context.node_exec_id
-
         # 1) Store the inputs locally
         local_video_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
         local_audio_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.audio_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
 
         abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
@@ -236,11 +240,12 @@ class AddAudioToVideoBlock(Block):
         output_abspath = os.path.join(abs_temp_dir, output_filename)
         final_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
 
-        # 5) Return output - for_block_output returns workspace:// if available, else data URI
+        # 5) Return either path or data URI
         video_out = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
+            user_id=user_id,
+            return_content=input_data.output_return_type == "data_uri",
         )
 
         yield "video_out", video_out

autogpt_platform/backend/backend/blocks/screenshotone.py

@@ -11,7 +11,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -113,7 +112,8 @@ class ScreenshotWebPageBlock(Block):
     @staticmethod
     async def take_screenshot(
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         url: str,
         viewport_width: int,
         viewport_height: int,
@@ -155,11 +155,12 @@ class ScreenshotWebPageBlock(Block):
 
         return {
             "image": await store_media_file(
+                graph_exec_id=graph_exec_id,
                 file=MediaFileType(
                     f"data:image/{format.value};base64,{b64encode(content).decode('utf-8')}"
                 ),
-                execution_context=execution_context,
-                return_format="for_block_output",
+                user_id=user_id,
+                return_content=True,
             )
         }
 
@@ -168,13 +169,15 @@ class ScreenshotWebPageBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
             screenshot_data = await self.take_screenshot(
                 credentials=credentials,
-                execution_context=execution_context,
+                graph_exec_id=graph_exec_id,
+                user_id=user_id,
                 url=input_data.url,
                 viewport_width=input_data.viewport_width,
                 viewport_height=input_data.viewport_height,

autogpt_platform/backend/backend/blocks/spreadsheet.py

@@ -7,7 +7,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import ContributorDetails, SchemaField
 from backend.util.file import get_exec_file_path, store_media_file
 from backend.util.type import MediaFileType
@@ -99,7 +98,7 @@ class ReadSpreadsheetBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **_kwargs
+        self, input_data: Input, *, graph_exec_id: str, user_id: str, **_kwargs
     ) -> BlockOutput:
         import csv
         from io import StringIO
@@ -107,16 +106,14 @@ class ReadSpreadsheetBlock(Block):
         # Determine data source - prefer file_input if provided, otherwise use contents
         if input_data.file_input:
             stored_file_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=input_data.file_input,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
 
             # Get full file path
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            file_path = get_exec_file_path(
-                execution_context.graph_exec_id, stored_file_path
-            )
+            file_path = get_exec_file_path(graph_exec_id, stored_file_path)
             if not Path(file_path).exists():
                 raise ValueError(f"File does not exist: {file_path}")
 

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

@@ -83,7 +83,7 @@ class StagehandRecommendedLlmModel(str, Enum):
     GPT41_MINI = "gpt-4.1-mini-2025-04-14"
 
     # Anthropic
-    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
+    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
 
     @property
     def provider_name(self) -> str:
@@ -137,7 +137,7 @@ class StagehandObserveBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -182,7 +182,10 @@ class StagehandObserveBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}")
+        logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}")
+        logger.info(
+            f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
+        )
 
         with disable_signal_handling():
             stagehand = Stagehand(
@@ -227,7 +230,7 @@ class StagehandActBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -279,7 +282,10 @@ class StagehandActBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.debug(f"ACT: Using model provider {model_credentials.provider}")
+        logger.info(f"ACT: Stagehand credentials: {stagehand_credentials}")
+        logger.info(
+            f"ACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
+        )
 
         with disable_signal_handling():
             stagehand = Stagehand(
@@ -324,7 +330,7 @@ class StagehandExtractBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -364,7 +370,10 @@ class StagehandExtractBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.debug(f"EXTRACT: Using model provider {model_credentials.provider}")
+        logger.info(f"EXTRACT: Stagehand credentials: {stagehand_credentials}")
+        logger.info(
+            f"EXTRACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
+        )
 
         with disable_signal_handling():
             stagehand = Stagehand(

autogpt_platform/backend/backend/blocks/talking_head.py

@@ -10,7 +10,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -18,9 +17,7 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import store_media_file
 from backend.util.request import Requests
-from backend.util.type import MediaFileType
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -105,7 +102,7 @@ class CreateTalkingAvatarVideoBlock(Block):
             test_output=[
                 (
                     "video_url",
-                    lambda x: x.startswith(("workspace://", "data:")),
+                    "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
                 ),
             ],
             test_mock={
@@ -113,10 +110,9 @@ class CreateTalkingAvatarVideoBlock(Block):
                     "id": "abcd1234-5678-efgh-ijkl-mnopqrstuvwx",
                     "status": "created",
                 },
-                # Use data URI to avoid HTTP requests during tests
                 "get_clip_status": lambda *args, **kwargs: {
                     "status": "done",
-                    "result_url": "data:video/mp4;base64,AAAA",
+                    "result_url": "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
                 },
             },
             test_credentials=TEST_CREDENTIALS,
@@ -142,12 +138,7 @@ class CreateTalkingAvatarVideoBlock(Block):
         return response.json()
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Create the clip
         payload = {
@@ -174,14 +165,7 @@ class CreateTalkingAvatarVideoBlock(Block):
         for _ in range(input_data.max_polling_attempts):
             status_response = await self.get_clip_status(credentials.api_key, clip_id)
             if status_response["status"] == "done":
-                # Store the generated video to the user's workspace for persistence
-                video_url = status_response["result_url"]
-                stored_url = await store_media_file(
-                    file=MediaFileType(video_url),
-                    execution_context=execution_context,
-                    return_format="for_block_output",
-                )
-                yield "video_url", stored_url
+                yield "video_url", status_response["result_url"]
                 return
             elif status_response["status"] == "error":
                 raise RuntimeError(

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -12,7 +12,6 @@ from backend.blocks.iteration import StepThroughItemsBlock
 from backend.blocks.llm import AITextSummarizerBlock
 from backend.blocks.text import ExtractTextInformationBlock
 from backend.blocks.xml_parser import XMLParserBlock
-from backend.data.execution import ExecutionContext
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType
 
@@ -234,12 +233,9 @@ class TestStoreMediaFileSecurity:
 
         with pytest.raises(ValueError, match="File too large"):
             await store_media_file(
+                graph_exec_id="test",
                 file=MediaFileType(large_data_uri),
-                execution_context=ExecutionContext(
-                    user_id="test_user",
-                    graph_exec_id="test",
-                ),
-                return_format="for_local_processing",
+                user_id="test_user",
             )
 
     @patch("backend.util.file.Path")
@@ -274,12 +270,9 @@ class TestStoreMediaFileSecurity:
         # Should raise an error when directory size exceeds limit
         with pytest.raises(ValueError, match="Disk usage limit exceeded"):
             await store_media_file(
+                graph_exec_id="test",
                 file=MediaFileType(
                     "data:text/plain;base64,dGVzdA=="
                 ),  # Small test file
-                execution_context=ExecutionContext(
-                    user_id="test_user",
-                    graph_exec_id="test",
-                ),
-                return_format="for_local_processing",
+                user_id="test_user",
             )