feat(frontend): Disable auto-opening wallet (#11961)

<!-- Clearly explain the need for these changes: -->

### Changes 🏗️

- Disable auto-opening Wallet for first time user and on credit increase
- Remove no longer needed `lastSeenCredits` state and storage

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Wallet doesn't open automatically

.branchlet.json

@@ -29,8 +29,7 @@
   "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/server/server.py` - FastAPI application setup
+- `backend/backend/api/rest_api.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/server/routers/`
+1. Update routes in `/backend/backend/api/features/`
 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/server/middleware/security.py`):
+**Cache Protection Middleware** (`/backend/backend/api/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

@@ -178,5 +178,6 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
+CLAUDE.local.md
 /autogpt_platform/backend/logs
 .next

AGENTS.md

@@ -16,7 +16,6 @@ See `docs/content/platform/getting-started.md` for setup instructions.
 - Format Python code with `poetry run format`.
 - Format frontend code using `pnpm format`.
 
-
 ## Frontend guidelines:
 
 See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
@@ -33,14 +32,17 @@ 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
 
@@ -49,22 +51,8 @@ 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:
+Types: - feat - fix - refactor - ci - dx (developer experience)
-    - feat
+Scopes: - platform - platform/library - platform/marketplace - backend - backend/executor - frontend - frontend/library - frontend/marketplace - blocks
-    - 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://docs.agpt.co/platform/getting-started/)
+👉 [Follow the official self-hosting guide here](https://agpt.co/docs/platform/getting-started/getting-started)
 
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.

autogpt_platform/CLAUDE.md

@@ -6,152 +6,30 @@ 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
+- **Backend** (`backend`): Python FastAPI server with async support
-- **Frontend** (`/frontend`): Next.js React application
+- **Frontend** (`frontend`): Next.js React application
-- **Shared Libraries** (`/autogpt_libs`): Common Python utilities
+- **Shared Libraries** (`autogpt_libs`): Common Python utilities
 
-## Essential Commands
+## Component Documentation
 
-### Backend Development
+- **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
 
-```bash
+## Key Concepts
-# 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/blocks/` that perform specific tasks
+2. **Blocks**: Reusable components in `backend/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)
+- **Backend**: `backend/.env.default` (defaults) → `backend/.env` (user overrides)
-- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
+- **Frontend**: `frontend/.env.default` (defaults) → `frontend/.env` (user overrides)
-- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
+- **Platform**: `.env.default` (Supabase/shared defaults) → `.env` (user overrides)
 
 #### Docker Environment Loading Order
 
@@ -167,83 +45,12 @@ Key models (defined in `/backend/schema.prisma`):
 - 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 aginst the `dev` branch of the repository.
+- Create the PR against the `dev` branch of the repository.
-- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
+- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)
-- Use conventional commit messages (see below)/
+- Use conventional commit messages (see below)
-- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
+- 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/CLAUDE.md

@@ -0,0 +1,170 @@
+# 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/server/conftest.py`:
+Two global auth fixtures are provided by `backend/api/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/features/builder/routes.py

@@ -17,7 +17,7 @@ router = fastapi.APIRouter(
 )
 
 
-# Taken from backend/server/v2/store/db.py
+# Taken from backend/api/features/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

@@ -0,0 +1,368 @@
+"""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

@@ -0,0 +1,344 @@
+"""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

@@ -44,6 +44,48 @@ class ChatConfig(BaseSettings):
         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)",
+    )
+
     # Langfuse Prompt Management Configuration
     # Note: Langfuse credentials are in Settings().secrets (settings.py)
     langfuse_prompt_name: str = Field(
@@ -82,6 +124,14 @@ 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/response_model.py

@@ -52,6 +52,10 @@ 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):

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

@@ -1,19 +1,23 @@
 """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, Query, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, 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()
 
@@ -55,6 +59,15 @@ 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."""
 
@@ -63,6 +76,7 @@ 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):
@@ -81,6 +95,14 @@ 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 ==========
 
 
@@ -166,13 +188,14 @@ 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, or None if not found.
+        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
 
     """
     session = await get_chat_session(session_id, user_id)
@@ -180,11 +203,28 @@ async def get_session(
         raise NotFoundError(f"Session {session_id} not found.")
 
     messages = [message.model_dump() for message in session.messages]
-    logger.info(
+
-        f"Returning session {session_id}: "
+    # Check if there's an active stream for this session
-        f"message_count={len(messages)}, "
+    active_stream_info = None
-        f"roles={[m.get('role') for m in messages]}"
+    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+        session_id, user_id
     )
+    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,
@@ -192,6 +232,7 @@ async def get_session(
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
         messages=messages,
+        active_stream=active_stream_info,
     )
 
 
@@ -211,49 +252,112 @@ 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.
+        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
+        containing the task_id for reconnection.
 
     """
+    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]:
-        chunk_count = 0
+        subscriber_queue = None
-        first_chunk_type: str | None = None
+        try:
-        async for chunk in chat_service.stream_chat_completion(
+            # Subscribe to the task stream (this replays existing messages + live updates)
-            session_id,
+            subscriber_queue = await stream_registry.subscribe_to_task(
-            request.message,
+                task_id=task_id,
-            is_user_message=request.is_user_message,
+                user_id=user_id,
-            user_id=user_id,
+                last_message_id="0-0",  # Get all messages from the beginning
-            session=session,  # Pass pre-fetched session to avoid double-fetch
+            )
-            context=request.context,
+
-        ):
+            if subscriber_queue is None:
-            if chunk_count < 3:
+                yield StreamFinish().to_sse()
-                logger.info(
+                yield "data: [DONE]\n\n"
-                    "Chat stream chunk",
+                return
-                    extra={
+
-                        "session_id": session_id,
+            # Read from the subscriber queue and yield to SSE
-                        "chunk_type": str(chunk.type),
+            while True:
-                    },
+                try:
-                )
+                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-            if not first_chunk_type:
+                    yield chunk.to_sse()
-                first_chunk_type = str(chunk.type)
+
-            chunk_count += 1
+                    # Check for finish signal
-            yield chunk.to_sse()
+                    if isinstance(chunk, StreamFinish):
-        logger.info(
+                        break
-            "Chat stream completed",
+                except asyncio.TimeoutError:
-            extra={
+                    # Send heartbeat to keep connection alive
-                "session_id": session_id,
+                    yield StreamHeartbeat().to_sse()
-                "chunk_count": chunk_count,
+
-                "first_chunk_type": first_chunk_type,
+        except GeneratorExit:
-            },
+            pass  # Client disconnected - background task continues
-        )
+        except Exception as e:
-        # AI SDK protocol termination
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
-        yield "data: [DONE]\n\n"
+        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"
 
     return StreamingResponse(
         event_generator(),
@@ -366,6 +470,251 @@ 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

@@ -0,0 +1,704 @@
+"""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

@@ -0,0 +1,79 @@
+# 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

@@ -10,6 +10,7 @@ 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
@@ -18,6 +19,12 @@ 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
@@ -28,6 +35,7 @@ logger = logging.getLogger(__name__)
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
     "create_agent": CreateAgentTool(),
+    "customize_agent": CustomizeAgentTool(),
     "edit_agent": EditAgentTool(),
     "find_agent": FindAgentTool(),
     "find_block": FindBlockTool(),
@@ -37,6 +45,11 @@ 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

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

@@ -2,27 +2,58 @@
 
 from .core import (
     AgentGeneratorNotConfiguredError,
+    AgentJsonValidationError,
+    AgentSummary,
+    DecompositionResult,
+    DecompositionStep,
+    LibraryAgentSummary,
+    MarketplaceAgentSummary,
+    customize_template,
     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
 
 __all__ = [
-    # Core functions
+    "AgentGeneratorNotConfiguredError",
+    "AgentJsonValidationError",
+    "AgentSummary",
+    "DecompositionResult",
+    "DecompositionStep",
+    "LibraryAgentSummary",
+    "MarketplaceAgentSummary",
+    "check_external_service_health",
+    "customize_template",
     "decompose_goal",
+    "enrich_library_agents_from_steps",
+    "extract_search_terms_from_steps",
+    "extract_uuids_from_text",
     "generate_agent",
     "generate_agent_patch",
-    "save_agent_to_library",
     "get_agent_as_json",
-    "json_to_graph",
+    "get_all_relevant_agents_for_generation",
-    # Exceptions
+    "get_library_agent_by_graph_id",
-    "AgentGeneratorNotConfiguredError",
+    "get_library_agent_by_id",
-    # Service
+    "get_library_agents_for_generation",
+    "get_user_message_for_error",
+    "graph_to_json",
     "is_external_service_configured",
-    "check_external_service_health",
+    "json_to_graph",
+    "save_agent_to_library",
+    "search_marketplace_agents_for_generation",
 ]

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

@@ -1,13 +1,25 @@
 """Core agent generation functions."""
 
 import logging
+import re
 import uuid
-from typing import Any
+from typing import Any, NotRequired, TypedDict
 
 from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node, create_graph
+from backend.api.features.store import db as store_db
+from backend.data.graph import (
+    Graph,
+    Link,
+    Node,
+    create_graph,
+    get_graph,
+    get_graph_all_versions,
+    get_store_listed_graphs,
+)
+from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
+    customize_template_external,
     decompose_goal_external,
     generate_agent_external,
     generate_agent_patch_external,
@@ -16,6 +28,74 @@ from .service import (
 
 logger = logging.getLogger(__name__)
 
+AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
+
+
+class ExecutionSummary(TypedDict):
+    """Summary of a single execution for quality assessment."""
+
+    status: str
+    correctness_score: NotRequired[float]
+    activity_summary: NotRequired[str]
+
+
+class LibraryAgentSummary(TypedDict):
+    """Summary of a library agent for sub-agent composition.
+
+    Includes recent executions to help the LLM decide whether to use this agent.
+    Each execution shows status, correctness_score (0-1), and activity_summary.
+    """
+
+    graph_id: str
+    graph_version: int
+    name: str
+    description: str
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    recent_executions: NotRequired[list[ExecutionSummary]]
+
+
+class MarketplaceAgentSummary(TypedDict):
+    """Summary of a marketplace agent for sub-agent composition."""
+
+    name: str
+    description: str
+    sub_heading: str
+    creator: str
+    is_marketplace_agent: bool
+
+
+class DecompositionStep(TypedDict, total=False):
+    """A single step in decomposed instructions."""
+
+    description: str
+    action: str
+    block_name: str
+    tool: str
+    name: str
+
+
+class DecompositionResult(TypedDict, total=False):
+    """Result from decompose_goal - can be instructions, questions, or error."""
+
+    type: str
+    steps: list[DecompositionStep]
+    questions: list[dict[str, Any]]
+    error: str
+    error_type: str
+
+
+AgentSummary = LibraryAgentSummary | MarketplaceAgentSummary | dict[str, Any]
+
+
+def _to_dict_list(
+    agents: list[AgentSummary] | list[dict[str, Any]] | None,
+) -> list[dict[str, Any]] | None:
+    """Convert typed agent summaries to plain dicts for external service calls."""
+    if agents is None:
+        return None
+    return [dict(a) for a in agents]
+
 
 class AgentGeneratorNotConfiguredError(Exception):
     """Raised when the external Agent Generator service is not configured."""
@@ -36,15 +116,422 @@ def _check_service_configured() -> None:
         )
 
 
-async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
+_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 extract_uuids_from_text(text: str) -> list[str]:
+    """Extract all UUID v4 strings from text.
+
+    Args:
+        text: Text that may contain UUIDs (e.g., user's goal description)
+
+    Returns:
+        List of unique UUIDs found in the text (lowercase)
+    """
+    matches = _UUID_PATTERN.findall(text)
+    return list({m.lower() for m in matches})
+
+
+async def get_library_agent_by_id(
+    user_id: str, agent_id: str
+) -> LibraryAgentSummary | None:
+    """Fetch a specific library agent by its ID (library agent ID or graph_id).
+
+    This function tries multiple lookup strategies:
+    1. First tries to find by graph_id (AgentGraph primary key)
+    2. If not found, tries to find by library agent ID (LibraryAgent primary key)
+
+    This handles both cases:
+    - User provides graph_id (e.g., from AgentExecutorBlock)
+    - User provides library agent ID (e.g., from library URL)
+
+    Args:
+        user_id: The user ID
+        agent_id: The ID to look up (can be graph_id or library agent ID)
+
+    Returns:
+        LibraryAgentSummary 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 LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.debug(f"Could not fetch library agent by graph_id {agent_id}: {e}")
+
+    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 LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+    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
+
+
+get_library_agent_by_graph_id = get_library_agent_by_id
+
+
+async def get_library_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    max_results: int = 15,
+) -> list[LibraryAgentSummary]:
+    """Fetch user's library agents formatted for Agent Generator.
+
+    Uses search-based fetching to return relevant agents instead of all agents.
+    This is more scalable for users with large libraries.
+
+    Includes recent_executions list to help the LLM assess agent quality:
+    - Each execution has status, correctness_score (0-1), and activity_summary
+    - This gives the LLM concrete examples of recent performance
+
+    Args:
+        user_id: The user ID
+        search_query: Optional search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        max_results: Maximum number of agents to return (default 15)
+
+    Returns:
+        List of LibraryAgentSummary with schemas and recent executions for sub-agent composition
+    """
+    try:
+        response = await library_db.list_library_agents(
+            user_id=user_id,
+            search_term=search_query,
+            page=1,
+            page_size=max_results,
+            include_executions=True,
+        )
+
+        results: list[LibraryAgentSummary] = []
+        for agent in response.agents:
+            if exclude_graph_id is not None and agent.graph_id == exclude_graph_id:
+                continue
+
+            summary = LibraryAgentSummary(
+                graph_id=agent.graph_id,
+                graph_version=agent.graph_version,
+                name=agent.name,
+                description=agent.description,
+                input_schema=agent.input_schema,
+                output_schema=agent.output_schema,
+            )
+            if agent.recent_executions:
+                exec_summaries: list[ExecutionSummary] = []
+                for ex in agent.recent_executions:
+                    exec_sum = ExecutionSummary(status=ex.status)
+                    if ex.correctness_score is not None:
+                        exec_sum["correctness_score"] = ex.correctness_score
+                    if ex.activity_summary:
+                        exec_sum["activity_summary"] = ex.activity_summary
+                    exec_summaries.append(exec_sum)
+                summary["recent_executions"] = exec_summaries
+            results.append(summary)
+        return results
+    except DatabaseError:
+        raise
+    except Exception as e:
+        logger.warning(f"Failed to fetch library agents: {e}")
+        return []
+
+
+async def search_marketplace_agents_for_generation(
+    search_query: str,
+    max_results: int = 10,
+) -> list[LibraryAgentSummary]:
+    """Search marketplace agents formatted for Agent Generator.
+
+    Fetches marketplace agents and their full schemas so they can be used
+    as sub-agents in generated workflows.
+
+    Args:
+        search_query: Search term to find relevant public agents
+        max_results: Maximum number of agents to return (default 10)
+
+    Returns:
+        List of LibraryAgentSummary with full input/output schemas
+    """
+    try:
+        response = await store_db.get_store_agents(
+            search_query=search_query,
+            page=1,
+            page_size=max_results,
+        )
+
+        agents_with_graphs = [
+            agent for agent in response.agents if agent.agent_graph_id
+        ]
+
+        if not agents_with_graphs:
+            return []
+
+        graph_ids = [agent.agent_graph_id for agent in agents_with_graphs]
+        graphs = await get_store_listed_graphs(*graph_ids)
+
+        results: list[LibraryAgentSummary] = []
+        for agent in agents_with_graphs:
+            graph_id = agent.agent_graph_id
+            if graph_id and graph_id in graphs:
+                graph = graphs[graph_id]
+                results.append(
+                    LibraryAgentSummary(
+                        graph_id=graph.id,
+                        graph_version=graph.version,
+                        name=agent.agent_name,
+                        description=agent.description,
+                        input_schema=graph.input_schema,
+                        output_schema=graph.output_schema,
+                    )
+                )
+        return results
+    except Exception as e:
+        logger.warning(f"Failed to search marketplace agents: {e}")
+        return []
+
+
+async def get_all_relevant_agents_for_generation(
+    user_id: str,
+    search_query: str | None = None,
+    exclude_graph_id: str | None = None,
+    include_library: bool = True,
+    include_marketplace: bool = True,
+    max_library_results: int = 15,
+    max_marketplace_results: int = 10,
+) -> list[AgentSummary]:
+    """Fetch relevant agents from library and/or marketplace.
+
+    Searches both user's library and marketplace by default.
+    Explicitly mentioned UUIDs in the search query are always looked up.
+
+    Args:
+        user_id: The user ID
+        search_query: Search term to find relevant agents (user's goal/description)
+        exclude_graph_id: Optional graph ID to exclude (prevents circular references)
+        include_library: Whether to search user's library (default True)
+        include_marketplace: Whether to also search marketplace (default True)
+        max_library_results: Max library agents to return (default 15)
+        max_marketplace_results: Max marketplace agents to return (default 10)
+
+    Returns:
+        List of AgentSummary with full schemas (both library and marketplace agents)
+    """
+    agents: list[AgentSummary] = []
+    seen_graph_ids: set[str] = set()
+
+    if search_query:
+        mentioned_uuids = extract_uuids_from_text(search_query)
+        for graph_id in mentioned_uuids:
+            if graph_id == exclude_graph_id:
+                continue
+            agent = await get_library_agent_by_graph_id(user_id, graph_id)
+            agent_graph_id = agent.get("graph_id") if agent else None
+            if agent and agent_graph_id and agent_graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(agent_graph_id)
+                logger.debug(
+                    f"Found explicitly mentioned agent: {agent.get('name') or 'Unknown'}"
+                )
+
+    if include_library:
+        library_agents = await get_library_agents_for_generation(
+            user_id=user_id,
+            search_query=search_query,
+            exclude_graph_id=exclude_graph_id,
+            max_results=max_library_results,
+        )
+        for agent in library_agents:
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
+
+    if include_marketplace and search_query:
+        marketplace_agents = await search_marketplace_agents_for_generation(
+            search_query=search_query,
+            max_results=max_marketplace_results,
+        )
+        for agent in marketplace_agents:
+            graph_id = agent.get("graph_id")
+            if graph_id and graph_id not in seen_graph_ids:
+                agents.append(agent)
+                seen_graph_ids.add(graph_id)
+
+    return agents
+
+
+def extract_search_terms_from_steps(
+    decomposition_result: DecompositionResult | dict[str, Any],
+) -> list[str]:
+    """Extract search terms from decomposed instruction steps.
+
+    Analyzes the decomposition result to extract relevant keywords
+    for additional library agent searches.
+
+    Args:
+        decomposition_result: Result from decompose_goal containing steps
+
+    Returns:
+        List of unique search terms extracted from steps
+    """
+    search_terms: list[str] = []
+
+    if decomposition_result.get("type") != "instructions":
+        return search_terms
+
+    steps = decomposition_result.get("steps", [])
+    if not steps:
+        return search_terms
+
+    step_keys: list[str] = ["description", "action", "block_name", "tool", "name"]
+
+    for step in steps:
+        for key in step_keys:
+            value = step.get(key)  # type: ignore[union-attr]
+            if isinstance(value, str) and len(value) > 3:
+                search_terms.append(value)
+
+    seen: set[str] = set()
+    unique_terms: list[str] = []
+    for term in search_terms:
+        term_lower = term.lower()
+        if term_lower not in seen:
+            seen.add(term_lower)
+            unique_terms.append(term)
+
+    return unique_terms
+
+
+async def enrich_library_agents_from_steps(
+    user_id: str,
+    decomposition_result: DecompositionResult | dict[str, Any],
+    existing_agents: list[AgentSummary] | list[dict[str, Any]],
+    exclude_graph_id: str | None = None,
+    include_marketplace: bool = True,
+    max_additional_results: int = 10,
+) -> list[AgentSummary] | list[dict[str, Any]]:
+    """Enrich library agents list with additional searches based on decomposed steps.
+
+    This implements two-phase search: after decomposition, we search for additional
+    relevant agents based on the specific steps identified.
+
+    Args:
+        user_id: The user ID
+        decomposition_result: Result from decompose_goal containing steps
+        existing_agents: Already fetched library agents from initial search
+        exclude_graph_id: Optional graph ID to exclude
+        include_marketplace: Whether to also search marketplace
+        max_additional_results: Max additional agents per search term (default 10)
+
+    Returns:
+        Combined list of library agents (existing + newly discovered)
+    """
+    search_terms = extract_search_terms_from_steps(decomposition_result)
+
+    if not search_terms:
+        return existing_agents
+
+    existing_ids: set[str] = set()
+    existing_names: set[str] = set()
+
+    for agent in existing_agents:
+        agent_name = agent.get("name")
+        if agent_name and isinstance(agent_name, str):
+            existing_names.add(agent_name.lower())
+        graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+        if graph_id and isinstance(graph_id, str):
+            existing_ids.add(graph_id)
+
+    all_agents: list[AgentSummary] | list[dict[str, Any]] = list(existing_agents)
+
+    for term in search_terms[:3]:
+        try:
+            additional_agents = await get_all_relevant_agents_for_generation(
+                user_id=user_id,
+                search_query=term,
+                exclude_graph_id=exclude_graph_id,
+                include_marketplace=include_marketplace,
+                max_library_results=max_additional_results,
+                max_marketplace_results=5,
+            )
+
+            for agent in additional_agents:
+                agent_name = agent.get("name")
+                if not agent_name or not isinstance(agent_name, str):
+                    continue
+                agent_name_lower = agent_name.lower()
+
+                if agent_name_lower in existing_names:
+                    continue
+
+                graph_id = agent.get("graph_id")  # type: ignore[call-overload]
+                if graph_id and graph_id in existing_ids:
+                    continue
+
+                all_agents.append(agent)
+                existing_names.add(agent_name_lower)
+                if graph_id and isinstance(graph_id, str):
+                    existing_ids.add(graph_id)
+
+        except DatabaseError:
+            logger.error(f"Database error searching for agents with term '{term}'")
+            raise
+        except Exception as e:
+            logger.warning(
+                f"Failed to search for additional agents with term '{term}': {e}"
+            )
+
+    logger.debug(
+        f"Enriched library agents: {len(existing_agents)} initial + "
+        f"{len(all_agents) - len(existing_agents)} additional = {len(all_agents)} total"
+    )
+
+    return all_agents
+
+
+async def decompose_goal(
+    description: str,
+    context: str = "",
+    library_agents: list[AgentSummary] | None = None,
+) -> DecompositionResult | None:
     """Break down a goal into steps or return clarifying questions.
 
     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:
+        DecompositionResult with either:
         - {"type": "clarifying_questions", "questions": [...]}
         - {"type": "instructions", "steps": [...]}
         Or None on error
@@ -54,26 +541,47 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any]
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for decompose_goal")
-    return await decompose_goal_external(description, context)
+    result = await decompose_goal_external(
+        description, context, _to_dict_list(library_agents)
+    )
+    return result  # type: ignore[return-value]
 
 
-async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
+async def generate_agent(
+    instructions: DecompositionResult | dict[str, Any],
+    library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
+) -> dict[str, Any] | None:
     """Generate agent JSON 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
+            completion notification)
+        task_id: Task ID for async processing (enables Redis Streams persistence
+            and SSE delivery)
 
     Returns:
-        Agent JSON dict or None on error
+        Agent JSON dict, {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent")
-    result = await generate_agent_external(instructions)
+    result = await generate_agent_external(
+        dict(instructions), _to_dict_list(library_agents), operation_id, task_id
+    )
+
+    # Don't modify async response
+    if result and result.get("status") == "accepted":
+        return result
+
     if result:
-        # Ensure required fields
+        if isinstance(result, dict) and result.get("type") == "error":
+            return result
         if "id" not in result:
             result["id"] = str(uuid.uuid4())
         if "version" not in result:
@@ -83,6 +591,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
     return result
 
 
+class AgentJsonValidationError(Exception):
+    """Raised when agent JSON is invalid or missing required fields."""
+
+    pass
+
+
 def json_to_graph(agent_json: dict[str, Any]) -> Graph:
     """Convert agent JSON dict to Graph model.
 
@@ -91,25 +605,55 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
 
     Returns:
         Graph ready for saving
+
+    Raises:
+        AgentJsonValidationError: If required fields are missing from nodes or links
     """
     nodes = []
-    for n in agent_json.get("nodes", []):
+    for idx, n in enumerate(agent_json.get("nodes", [])):
+        block_id = n.get("block_id")
+        if not block_id:
+            node_id = n.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Node '{node_id}' is missing required field 'block_id'"
+            )
         node = Node(
             id=n.get("id", str(uuid.uuid4())),
-            block_id=n["block_id"],
+            block_id=block_id,
             input_default=n.get("input_default", {}),
             metadata=n.get("metadata", {}),
         )
         nodes.append(node)
 
     links = []
-    for link_data in agent_json.get("links", []):
+    for idx, link_data in enumerate(agent_json.get("links", [])):
+        source_id = link_data.get("source_id")
+        sink_id = link_data.get("sink_id")
+        source_name = link_data.get("source_name")
+        sink_name = link_data.get("sink_name")
+
+        missing_fields = []
+        if not source_id:
+            missing_fields.append("source_id")
+        if not sink_id:
+            missing_fields.append("sink_id")
+        if not source_name:
+            missing_fields.append("source_name")
+        if not sink_name:
+            missing_fields.append("sink_name")
+
+        if missing_fields:
+            link_id = link_data.get("id", f"index_{idx}")
+            raise AgentJsonValidationError(
+                f"Link '{link_id}' is missing required fields: {', '.join(missing_fields)}"
+            )
+
         link = Link(
             id=link_data.get("id", str(uuid.uuid4())),
-            source_id=link_data["source_id"],
+            source_id=source_id,
-            sink_id=link_data["sink_id"],
+            sink_id=sink_id,
-            source_name=link_data["source_name"],
+            source_name=source_name,
-            sink_name=link_data["sink_name"],
+            sink_name=sink_name,
             is_static=link_data.get("is_static", False),
         )
         links.append(link)
@@ -130,22 +674,40 @@ def _reassign_node_ids(graph: Graph) -> None:
 
     This is needed when creating a new version to avoid unique constraint violations.
     """
-    # Create mapping from old node IDs to new UUIDs
     id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
 
-    # Reassign node IDs
     for node in graph.nodes:
         node.id = id_map[node.id]
 
-    # Update link references to use new node IDs
     for link in graph.links:
-        link.id = str(uuid.uuid4())  # Also give links new IDs
+        link.id = str(uuid.uuid4())
         if link.source_id in id_map:
             link.source_id = id_map[link.source_id]
         if link.sink_id in id_map:
             link.sink_id = id_map[link.sink_id]
 
 
+def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
+    """Populate user_id in AgentExecutorBlock nodes.
+
+    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
+    This function fills in the actual user_id so sub-agents run with correct permissions.
+
+    Args:
+        agent_json: Agent JSON dict (modified in place)
+        user_id: User ID to set
+    """
+    for node in agent_json.get("nodes", []):
+        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
+            input_default = node.get("input_default") or {}
+            if not input_default.get("user_id"):
+                input_default["user_id"] = user_id
+                node["input_default"] = input_default
+                logger.debug(
+                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
+                )
+
+
 async def save_agent_to_library(
     agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -159,33 +721,27 @@ async def save_agent_to_library(
     Returns:
         Tuple of (created Graph, LibraryAgent)
     """
-    from backend.data.graph import get_graph_all_versions
+    # Populate user_id in AgentExecutorBlock nodes before conversion
+    _populate_agent_executor_user_ids(agent_json, user_id)
 
     graph = json_to_graph(agent_json)
 
     if is_update:
-        # For updates, keep the same graph ID but increment version
-        # and reassign node/link IDs to avoid conflicts
         if graph.id:
             existing_versions = await get_graph_all_versions(graph.id, user_id)
             if existing_versions:
                 latest_version = max(v.version for v in existing_versions)
                 graph.version = latest_version + 1
-                # Reassign node IDs (but keep graph ID the same)
                 _reassign_node_ids(graph)
                 logger.info(f"Updating agent {graph.id} to version {graph.version}")
     else:
-        # For new agents, always generate a fresh UUID to avoid collisions
         graph.id = str(uuid.uuid4())
         graph.version = 1
-        # Reassign all node IDs as well
         _reassign_node_ids(graph)
         logger.info(f"Creating new agent with ID {graph.id}")
 
-    # Save to database
     created_graph = await create_graph(graph, user_id)
 
-    # Add to user's library (or update existing library agent)
     library_agents = await library_db.create_library_agent(
         graph=created_graph,
         user_id=user_id,
@@ -196,26 +752,15 @@ async def save_agent_to_library(
     return created_graph, library_agents[0]
 
 
-async def get_agent_as_json(
+def graph_to_json(graph: Graph) -> dict[str, Any]:
-    graph_id: str, user_id: str | None
+    """Convert a Graph object to JSON format for the agent generator.
-) -> dict[str, Any] | None:
-    """Fetch an agent and convert to JSON format for editing.
 
     Args:
-        graph_id: Graph ID or library agent ID
+        graph: Graph object to convert
-        user_id: User ID
 
     Returns:
-        Agent as JSON dict or None if not found
+        Agent as JSON dict
     """
-    from backend.data.graph import get_graph
-
-    # Try to get the graph (version=None gets the active version)
-    graph = await get_graph(graph_id, version=None, user_id=user_id)
-    if not graph:
-        return None
-
-    # Convert to JSON format
     nodes = []
     for node in graph.nodes:
         nodes.append(
@@ -252,8 +797,41 @@ async def get_agent_as_json(
     }
 
 
+async def get_agent_as_json(
+    agent_id: str, user_id: str | None
+) -> dict[str, Any] | None:
+    """Fetch an agent and convert to JSON format for editing.
+
+    Args:
+        agent_id: Graph ID or library agent ID
+        user_id: User ID
+
+    Returns:
+        Agent as JSON dict or None if not found
+    """
+    graph = await get_graph(agent_id, version=None, user_id=user_id)
+
+    if not graph and user_id:
+        try:
+            library_agent = await library_db.get_library_agent(agent_id, user_id)
+            graph = await get_graph(
+                library_agent.graph_id, version=None, user_id=user_id
+            )
+        except NotFoundError:
+            pass
+
+    if not graph:
+        return None
+
+    return graph_to_json(graph)
+
+
 async def generate_agent_patch(
-    update_request: str, current_agent: dict[str, Any]
+    update_request: str,
+    current_agent: dict[str, Any],
+    library_agents: list[AgentSummary] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Update an existing agent using natural language.
 
@@ -265,13 +843,57 @@ async def generate_agent_patch(
     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, or None on error
+        Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
+        {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
     """
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent_patch")
-    return await generate_agent_patch_external(update_request, current_agent)
+    return await generate_agent_patch_external(
+        update_request,
+        current_agent,
+        _to_dict_list(library_agents),
+        operation_id,
+        task_id,
+    )
+
+
+async def customize_template(
+    template_agent: dict[str, Any],
+    modification_request: str,
+    context: str = "",
+) -> dict[str, Any] | None:
+    """Customize a template/marketplace agent using natural language.
+
+    This is used when users want to modify a template or marketplace agent
+    to fit their specific needs before adding it to their library.
+
+    The external Agent Generator service handles:
+    - Understanding the modification request
+    - Applying changes to the template
+    - Fixing and validating the result
+
+    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 {"type": "clarifying_questions", ...},
+        error dict {"type": "error", ...}, or None on unexpected error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
+    """
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for customize_template")
+    return await customize_template_external(
+        template_agent, modification_request, context
+    )

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

@@ -0,0 +1,95 @@
+"""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/service.py

@@ -14,6 +14,70 @@ 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
 
@@ -53,13 +117,16 @@ def _get_client() -> httpx.AsyncClient:
 
 
 async def decompose_goal_external(
-    description: str, context: str = ""
+    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:
@@ -67,15 +134,17 @@ async def decompose_goal_external(
         - {"type": "instructions", "steps": [...]}
         - {"type": "unachievable_goal", ...}
         - {"type": "vague_goal", ...}
-        Or None on error
+        - {"type": "error", "error": "...", "error_type": "..."} on error
+        Or None on unexpected error
     """
     client = _get_client()
 
-    # Build the request payload
-    payload: dict[str, Any] = {"description": description}
     if context:
-        # The external service uses user_instruction for additional context
+        description = f"{description}\n\nAdditional context from user:\n{context}"
-        payload["user_instruction"] = 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)
@@ -83,8 +152,13 @@ async def decompose_goal_external(
         data = response.json()
 
         if not data.get("success"):
-            logger.error(f"External service returned error: {data.get('error')}")
+            error_msg = data.get("error", "Unknown error from Agent Generator")
-            return None
+            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")
@@ -106,88 +180,162 @@ async def decompose_goal_external(
                 "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 None
+            return _create_error_response(
+                f"Unknown response type from Agent Generator: {response_type}",
+                "invalid_response",
+            )
 
     except httpx.HTTPStatusError as e:
-        logger.error(f"HTTP error calling external agent generator: {e}")
+        error_type, error_msg = _classify_http_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
+        error_type, error_msg = _classify_request_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
+        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, "unexpected_error")
 
 
 async def generate_agent_external(
-    instructions: dict[str, Any]
+    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 or None on error
+        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(
+        response = await client.post("/api/generate-agent", json=payload)
-            "/api/generate-agent", json={"instructions": instructions}
+
-        )
+        # 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"):
-            logger.error(f"External service returned error: {data.get('error')}")
+            error_msg = data.get("error", "Unknown error from Agent Generator")
-            return None
+            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:
-        logger.error(f"HTTP error calling external agent generator: {e}")
+        error_type, error_msg = _classify_http_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
+        error_type, error_msg = _classify_request_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
+        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        return None
+        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]
+    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, or None on error
+        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(
+        response = await client.post("/api/update-agent", json=payload)
-            "/api/update-agent",
+
-            json={
+        # Handle 202 Accepted for async processing
-                "update_request": update_request,
+        if response.status_code == 202:
-                "current_agent_json": current_agent,
+            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"):
-            logger.error(f"External service returned error: {data.get('error')}")
+            error_msg = data.get("error", "Unknown error from Agent Generator")
-            return None
+            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":
@@ -196,18 +344,99 @@ async def generate_agent_patch_external(
                 "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:
-        logger.error(f"HTTP error calling external agent generator: {e}")
+        error_type, error_msg = _classify_http_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except httpx.RequestError as e:
-        logger.error(f"Request error calling external agent generator: {e}")
+        error_type, error_msg = _classify_request_error(e)
-        return None
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
     except Exception as e:
-        logger.error(f"Unexpected error calling external agent generator: {e}")
+        error_msg = f"Unexpected error calling Agent Generator: {e}"
-        return None
+        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:

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

@@ -1,13 +1,11 @@
 """Shared agent search functionality for find_agent and find_library_agent tools."""
 
-import asyncio
 import logging
+import re
 from typing import Literal
 
 from backend.api.features.library import db as library_db
 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.util.exceptions import DatabaseError, NotFoundError
 
 from .models import (
@@ -17,12 +15,90 @@ from .models import (
     NoResultsResponse,
     ToolResponseBase,
 )
-from .utils import fetch_graph_from_store_slug
 
 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,
@@ -58,28 +134,7 @@ async def search_agents(
         if source == "marketplace":
             logger.info(f"Searching marketplace for: {query}")
             results = await store_db.get_store_agents(search_query=query, page_size=5)
-
+            for agent in results.agents:
-            # Fetch all graphs in parallel for better performance
-            async def fetch_marketplace_graph(
-                creator: str, slug: str
-            ) -> GraphModel | None:
-                try:
-                    graph, _ = await fetch_graph_from_store_slug(creator, slug)
-                    return graph
-                except Exception as e:
-                    logger.warning(
-                        f"Failed to fetch input schema for {creator}/{slug}: {e}"
-                    )
-                    return None
-
-            graphs = await asyncio.gather(
-                *(
-                    fetch_marketplace_graph(agent.creator, agent.slug)
-                    for agent in results.agents
-                )
-            )
-
-            for agent, graph in zip(results.agents, graphs):
                 agents.append(
                     AgentInfo(
                         id=f"{agent.creator}/{agent.slug}",
@@ -92,58 +147,39 @@ async def search_agents(
                         rating=agent.rating,
                         runs=agent.runs,
                         is_featured=False,
-                        inputs=graph.input_schema if graph else None,
                     )
                 )
-        else:  # library
+        else:
-            logger.info(f"Searching user library for: {query}")
+            if _is_uuid(query):
-            results = await library_db.list_library_agents(
+                logger.info(f"Query looks like UUID, trying direct lookup: {query}")
-                user_id=user_id,  # type: ignore[arg-type]
+                agent = await _get_library_agent_by_id(user_id, query)  # type: ignore[arg-type]
-                search_term=query,
+                if agent:
-                page_size=10,
+                    agents.append(agent)
-            )
+                    logger.info(f"Found agent by direct ID lookup: {agent.name}")
 
-            # Fetch all graphs in parallel for better performance
+            if not agents:
-            # (list_library_agents doesn't include nodes for performance)
+                logger.info(f"Searching user library for: {query}")
-            async def fetch_library_graph(
+                results = await library_db.list_library_agents(
-                graph_id: str, graph_version: int
+                    user_id=user_id,  # type: ignore[arg-type]
-            ) -> GraphModel | None:
+                    search_term=query,
-                try:
+                    page_size=10,
-                    return await graph_db.get_graph(
-                        graph_id=graph_id,
-                        version=graph_version,
-                        user_id=user_id,
-                    )
-                except Exception as e:
-                    logger.warning(
-                        f"Failed to fetch input schema for graph {graph_id}: {e}"
-                    )
-                    return None
-
-            graphs = await asyncio.gather(
-                *(
-                    fetch_library_graph(agent.graph_id, agent.graph_version)
-                    for agent in results.agents
                 )
-            )
+                for agent in results.agents:
-
+                    agents.append(
-            for agent, graph in zip(results.agents, graphs):
+                        AgentInfo(
-                agents.append(
+                            id=agent.id,
-                    AgentInfo(
+                            name=agent.name,
-                        id=agent.id,
+                            description=agent.description or "",
-                        name=agent.name,
+                            source="library",
-                        description=agent.description or "",
+                            in_library=True,
-                        source="library",
+                            creator=agent.creator_name,
-                        in_library=True,
+                            status=agent.status.value,
-                        creator=agent.creator_name,
+                            can_access_graph=agent.can_access_graph,
-                        status=agent.status.value,
+                            has_external_trigger=agent.has_external_trigger,
-                        can_access_graph=agent.can_access_graph,
+                            new_output=agent.new_output,
-                        has_external_trigger=agent.has_external_trigger,
+                            graph_id=agent.graph_id,
-                        new_output=agent.new_output,
+                        )
-                        graph_id=agent.graph_id,
-                        inputs=graph.input_schema if graph else None,
                     )
-                )
         logger.info(f"Found {len(agents)} agents in {source}")
     except NotFoundError:
         pass

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

@@ -8,13 +8,17 @@ from backend.api.features.chat.model import ChatSession
 from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     decompose_goal,
+    enrich_library_agents_from_steps,
     generate_agent,
+    get_all_relevant_agents_for_generation,
+    get_user_message_for_error,
     save_agent_to_library,
 )
 from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
+    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -95,6 +99,10 @@ class CreateAgentTool(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 description:
             return ErrorResponse(
                 message="Please provide a description of what the agent should do.",
@@ -102,9 +110,24 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 1: Decompose goal into steps
+        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}")
+
         try:
-            decomposition_result = await decompose_goal(description, context)
+            decomposition_result = await decompose_goal(
+                description, context, library_agents
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -117,15 +140,31 @@ class CreateAgentTool(BaseTool):
 
         if decomposition_result is None:
             return ErrorResponse(
-                message="Failed to analyze the goal. The agent generation service may be unavailable or timed out. Please try again.",
+                message="Failed to analyze the goal. The agent generation service may be unavailable. Please try again.",
                 error="decomposition_failed",
-                details={
+                details={"description": description[:100]},
-                    "description": description[:100]
+                session_id=session_id,
-                },  # Include context for debugging
+            )
+
+        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,
+                },
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
         if decomposition_result.get("type") == "clarifying_questions":
             questions = decomposition_result.get("questions", [])
             return ClarificationNeededResponse(
@@ -144,7 +183,6 @@ 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", "")
@@ -171,9 +209,27 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Step 2: Generate agent JSON (external service handles fixing and validation)
+        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,
+                )
+                logger.debug(
+                    f"After enrichment: {len(library_agents)} total agents for sub-agent composition"
+                )
+            except Exception as e:
+                logger.warning(f"Failed to enrich library agents from steps: {e}")
+
         try:
-            agent_json = await generate_agent(decomposition_result)
+            agent_json = await generate_agent(
+                decomposition_result,
+                library_agents,
+                operation_id=operation_id,
+                task_id=task_id,
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -186,11 +242,47 @@ class CreateAgentTool(BaseTool):
 
         if agent_json is None:
             return ErrorResponse(
-                message="Failed to generate the agent. The agent generation service may be unavailable or timed out. Please try again.",
+                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]
+                    "description": description[:100],
-                },  # Include context for debugging
+                    "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,
             )
 
@@ -199,7 +291,6 @@ class CreateAgentTool(BaseTool):
         node_count = len(agent_json.get("nodes", []))
         link_count = len(agent_json.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -214,7 +305,6 @@ 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.",
@@ -232,7 +322,7 @@ class CreateAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{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,
             )

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

@@ -0,0 +1,337 @@
+"""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

@@ -9,12 +9,15 @@ from .agent_generator import (
     AgentGeneratorNotConfiguredError,
     generate_agent_patch,
     get_agent_as_json,
+    get_all_relevant_agents_for_generation,
+    get_user_message_for_error,
     save_agent_to_library,
 )
 from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
+    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -102,6 +105,10 @@ 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.",
@@ -116,7 +123,6 @@ 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:
@@ -126,14 +132,34 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Build the update request with context
+        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}")
+
         update_request = changes
         if context:
             update_request = f"{changes}\n\nAdditional context:\n{context}"
 
-        # Step 2: Generate updated agent (external service handles fixing and validation)
         try:
-            result = await generate_agent_patch(update_request, current_agent)
+            result = await generate_agent_patch(
+                update_request,
+                current_agent,
+                library_agents,
+                operation_id=operation_id,
+                task_id=task_id,
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -152,7 +178,42 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Check if LLM returned clarifying questions
+        # 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(
@@ -171,7 +232,6 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
-        # Result is the updated agent JSON
         updated_agent = result
 
         agent_name = updated_agent.get("name", "Updated Agent")
@@ -179,7 +239,6 @@ class EditAgentTool(BaseTool):
         node_count = len(updated_agent.get("nodes", []))
         link_count = len(updated_agent.get("links", []))
 
-        # Step 3: Preview or save
         if not save:
             return AgentPreviewResponse(
                 message=(
@@ -195,7 +254,6 @@ 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.",
@@ -213,7 +271,7 @@ class EditAgentTool(BaseTool):
                 agent_id=created_graph.id,
                 agent_name=created_graph.name,
                 library_agent_id=library_agent.id,
-                library_agent_link=f"/library/{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,
             )

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

@@ -28,6 +28,12 @@ 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"
@@ -366,11 +372,15 @@ class OperationStartedResponse(ToolResponseBase):
 
     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):
@@ -394,3 +404,20 @@ class OperationInProgressResponse(ToolResponseBase):
 
     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_block.py

@@ -1,6 +1,7 @@
 """Tool for executing blocks directly."""
 
 import logging
+import uuid
 from collections import defaultdict
 from typing import Any
 
@@ -10,6 +11,7 @@ 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
 
@@ -250,11 +252,48 @@ class RunBlockTool(BaseTool):
             )
 
         try:
-            # Fetch actual credentials and prepare kwargs for block execution
+            # Get or create user's workspace for CoPilot file operations
-            # Create execution context with defaults (blocks may require it)
+            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
             exec_kwargs: dict[str, Any] = {
                 "user_id": user_id,
-                "execution_context": ExecutionContext(),
+                "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,
             }
 
             for field_name, cred_meta in matched_credentials.items():

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 CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
 
@@ -266,13 +266,14 @@ async def match_user_credentials_to_graph(
         credential_requirements,
         _node_fields,
     ) in aggregated_creds.items():
-        # Find first matching credential by provider and type
+        # Find first matching credential by provider, type, and scopes
         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,
         )
@@ -296,10 +297,17 @@ 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} "
+                f"{credential_field_name} (requires {', '.join(error_parts)})"
-                f"(requires provider in {list(credential_requirements.provider)}, "
-                f"type in {list(credential_requirements.supported_types)})"
             )
 
     logger.info(
@@ -309,6 +317,28 @@ 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

@@ -0,0 +1,620 @@
+"""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/library/db.py

@@ -39,6 +39,7 @@ 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.
@@ -49,6 +50,9 @@ 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.
@@ -76,7 +80,6 @@ async def list_library_agents(
         "isArchived": False,
     }
 
-    # Build search filter if applicable
     if search_term:
         where_clause["OR"] = [
             {
@@ -93,7 +96,6 @@ async def list_library_agents(
             },
         ]
 
-    # Determine sorting
     order_by: prisma.types.LibraryAgentOrderByInput | None = None
 
     if sort_by == library_model.LibraryAgentSort.CREATED_AT:
@@ -105,7 +107,7 @@ 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=False
+                user_id, include_nodes=False, include_executions=include_executions
             ),
             order=order_by,
             skip=(page - 1) * page_size,

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

@@ -9,6 +9,7 @@ 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:
@@ -16,10 +17,10 @@ if TYPE_CHECKING:
 
 
 class LibraryAgentStatus(str, Enum):
-    COMPLETED = "COMPLETED"  # All runs completed
+    COMPLETED = "COMPLETED"
-    HEALTHY = "HEALTHY"  # Agent is running (not all runs have completed)
+    HEALTHY = "HEALTHY"
-    WAITING = "WAITING"  # Agent is queued or waiting to start
+    WAITING = "WAITING"
-    ERROR = "ERROR"  # Agent is in an error state
+    ERROR = "ERROR"
 
 
 class MarketplaceListingCreator(pydantic.BaseModel):
@@ -39,6 +40,30 @@ 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
@@ -48,7 +73,7 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str  # ID of user who owns/created this agent graph
+    owner_user_id: str
 
     image_url: str | None
 
@@ -64,7 +89,7 @@ class LibraryAgent(pydantic.BaseModel):
     description: str
     instructions: str | None = None
 
-    input_schema: dict[str, Any]  # Should be BlockIOObjectSubSchema in frontend
+    input_schema: dict[str, Any]
     output_schema: dict[str, Any]
     credentials_input_schema: dict[str, Any] | None = pydantic.Field(
         description="Input schema for credentials required by the agent",
@@ -81,25 +106,19 @@ 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
-    # Whether the user can access the underlying graph
+    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",
+    )
     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
@@ -123,7 +142,6 @@ 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
@@ -136,7 +154,6 @@ 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
         )
@@ -145,13 +162,55 @@ class LibraryAgent(pydantic.BaseModel):
         status = status_result.status
         new_output = status_result.new_output
 
-        # Check if user can access the graph
+        execution_count = len(executions)
-        can_access_graph = agent.AgentGraph.userId == agent.userId
+        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
 
-        # Hard-coded to True until a method to check is implemented
+            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,
+                )
+            )
+
+        can_access_graph = agent.AgentGraph.userId == agent.userId
         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(
@@ -190,11 +249,15 @@ 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=GraphSettings.model_validate(agent.settings),
+            settings=_parse_settings(agent.settings),
             marketplace_listing=marketplace_listing_data,
         )
 
@@ -220,18 +283,15 @@ 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/store/db.py

@@ -112,6 +112,7 @@ 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:
@@ -170,6 +171,7 @@ 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)

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

@@ -454,6 +454,9 @@ 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):
@@ -465,14 +468,14 @@ async def test_unified_hybrid_search_pagination(
             content_type=ContentType.BLOCK,
             content_id=content_id,
             embedding=mock_embedding,
-            searchable_text=f"pagination test item number {i}",
+            searchable_text=f"{unique_term} item number {i}",
             metadata={"index": i},
             user_id=None,
         )
 
     # Get first page
     page1_results, total1 = await unified_hybrid_search(
-        query="pagination test",
+        query=unique_term,
         content_types=[ContentType.BLOCK],
         page=1,
         page_size=2,
@@ -480,7 +483,7 @@ async def test_unified_hybrid_search_pagination(
 
     # Get second page
     page2_results, total2 = await unified_hybrid_search(
-        query="pagination test",
+        query=unique_term,
         content_types=[ContentType.BLOCK],
         page=2,
         page_size=2,

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

@@ -600,6 +600,7 @@ 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
@@ -659,6 +660,7 @@ async def hybrid_search(
                 featured,
                 is_available,
                 updated_at,
+                "agentGraphId",
                 searchable_text,
                 semantic_score,
                 lexical_score,

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

@@ -38,6 +38,7 @@ 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

@@ -26,11 +26,13 @@ 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():
@@ -46,6 +48,7 @@ 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

@@ -82,6 +82,7 @@ def test_get_agents_featured(
                 description="Featured agent description",
                 runs=100,
                 rating=4.5,
+                agent_graph_id="test-graph-1",
             )
         ],
         pagination=store_model.Pagination(
@@ -127,6 +128,7 @@ 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(
@@ -172,6 +174,7 @@ def test_get_agents_sorted(
                 description="Top agent description",
                 runs=1000,
                 rating=5.0,
+                agent_graph_id="test-graph-3",
             )
         ],
         pagination=store_model.Pagination(
@@ -217,6 +220,7 @@ 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(
@@ -262,6 +266,7 @@ def test_get_agents_category(
                 description="Category agent description",
                 runs=60,
                 rating=4.1,
+                agent_graph_id="test-graph-category",
             )
         ],
         pagination=store_model.Pagination(
@@ -306,6 +311,7 @@ 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

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

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

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

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

@@ -0,0 +1,122 @@
+"""
+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

@@ -32,6 +32,7 @@ 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
@@ -39,6 +40,10 @@ 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
@@ -52,6 +57,7 @@ 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
@@ -116,14 +122,31 @@ 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()
 
 
@@ -315,6 +338,11 @@ 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

@@ -66,18 +66,24 @@ async def event_broadcaster(manager: ConnectionManager):
     execution_bus = AsyncRedisExecutionEventBus()
     notification_bus = AsyncRedisNotificationEventBus()
 
-    async def execution_worker():
+    try:
-        async for event in execution_bus.listen("*"):
-            await manager.send_execution_update(event)
 
-    async def notification_worker():
+        async def execution_worker():
-        async for notification in notification_bus.listen("*"):
+            async for event in execution_bus.listen("*"):
-            await manager.send_notification(
+                await manager.send_execution_update(event)
-                user_id=notification.user_id,
-                payload=notification.payload,
-            )
 
-    await asyncio.gather(execution_worker(), notification_worker())
+        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()
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

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

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -6,6 +6,7 @@ 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,
@@ -13,6 +14,8 @@ 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):
@@ -165,11 +168,13 @@ class AIImageGeneratorBlock(Block):
             test_output=[
                 (
                     "image_url",
-                    "https://replicate.delivery/generated-image.webp",
+                    # Test output is a data URI since we now store images
+                    lambda x: x.startswith("data:image/"),
                 ),
             ],
             test_mock={
-                "_run_client": lambda *args, **kwargs: "https://replicate.delivery/generated-image.webp"
+                # Return a data URI directly so store_media_file doesn't need to download
+                "_run_client": lambda *args, **kwargs: "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
             },
         )
 
@@ -318,11 +323,24 @@ 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, **kwargs):
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ):
         try:
             url = await self.generate_image(input_data, credentials)
             if url:
-                yield "image_url", 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
             else:
                 yield "error", "Image generation returned an empty result."
         except Exception as e:

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -13,6 +13,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -21,7 +22,9 @@ 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",
@@ -271,7 +274,10 @@ class AIShortformVideoCreatorBlock(Block):
                 "voice": Voice.LILY,
                 "video_style": VisualMediaType.STOCK_VIDEOS,
             },
-            test_output=("video_url", "https://example.com/video.mp4"),
+            test_output=(
+                "video_url",
+                lambda x: x.startswith(("workspace://", "data:")),
+            ),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -280,15 +286,21 @@ class AIShortformVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "https://example.com/video.mp4",
+                    "videoUrl": "data:video/mp4;base64,AAAA",
                 },
-                "wait_for_video": lambda *args, **kwargs: "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",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
     ) -> BlockOutput:
         # Create a new Webhook.site URL
         webhook_token, webhook_url = await self.create_webhook()
@@ -340,7 +352,13 @@ class AIShortformVideoCreatorBlock(Block):
             )
             video_url = await self.wait_for_video(credentials.api_key, pid)
             logger.debug(f"Video ready: {video_url}")
-            yield "video_url", 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
 
 
 class AIAdMakerVideoCreatorBlock(Block):
@@ -447,7 +465,10 @@ class AIAdMakerVideoCreatorBlock(Block):
                     "https://cdn.revid.ai/uploads/1747076315114-image.png",
                 ],
             },
-            test_output=("video_url", "https://example.com/ad.mp4"),
+            test_output=(
+                "video_url",
+                lambda x: x.startswith(("workspace://", "data:")),
+            ),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -456,14 +477,21 @@ class AIAdMakerVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "https://example.com/ad.mp4",
+                    "videoUrl": "data:video/mp4;base64,AAAA",
                 },
-                "wait_for_video": lambda *args, **kwargs: "https://example.com/ad.mp4",
+                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -531,7 +559,13 @@ class AIAdMakerVideoCreatorBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        yield "video_url", 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
 
 
 class AIScreenshotToVideoAdBlock(Block):
@@ -626,7 +660,10 @@ class AIScreenshotToVideoAdBlock(Block):
                 "script": "Amazing numbers!",
                 "screenshot_url": "https://cdn.revid.ai/uploads/1747080376028-image.png",
             },
-            test_output=("video_url", "https://example.com/screenshot.mp4"),
+            test_output=(
+                "video_url",
+                lambda x: x.startswith(("workspace://", "data:")),
+            ),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -635,14 +672,21 @@ class AIScreenshotToVideoAdBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "https://example.com/screenshot.mp4",
+                    "videoUrl": "data:video/mp4;base64,AAAA",
                 },
-                "wait_for_video": lambda *args, **kwargs: "https://example.com/screenshot.mp4",
+                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -710,4 +754,10 @@ class AIScreenshotToVideoAdBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        yield "video_url", 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

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

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

autogpt_platform/backend/backend/blocks/basic.py

@@ -9,6 +9,7 @@ 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
@@ -17,10 +18,10 @@ from backend.util.type import MediaFileType, convert
 class FileStoreBlock(Block):
     class Input(BlockSchemaInput):
         file_in: MediaFileType = SchemaField(
-            description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
+            description="The file to download and store. Can be a URL (https://...), data URI, or local path."
         )
         base_64: bool = SchemaField(
-            description="Whether produce an output in base64 format (not recommended, you can pass the string path just fine accross blocks).",
+            description="Whether to produce output in base64 format (not recommended, you can pass the file reference across blocks).",
             default=False,
             advanced=True,
             title="Produce Base64 Output",
@@ -28,13 +29,18 @@ class FileStoreBlock(Block):
 
     class Output(BlockSchemaOutput):
         file_out: MediaFileType = SchemaField(
-            description="The relative path to the stored file in the temporary directory."
+            description="Reference to the stored file. In CoPilot: workspace:// URI (visible in list_workspace_files). In graphs: data URI for passing to other blocks."
         )
 
     def __init__(self):
         super().__init__(
             id="cbb50872-625b-42f0-8203-a2ae78242d8a",
-            description="Stores the input file in the temporary directory.",
+            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."
+            ),
             categories={BlockCategory.BASIC, BlockCategory.MULTIMEDIA},
             input_schema=FileStoreBlock.Input,
             output_schema=FileStoreBlock.Output,
@@ -45,15 +51,18 @@ class FileStoreBlock(Block):
         self,
         input_data: Input,
         *,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        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,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=input_data.base_64,
+            return_format=return_format,
         )
 
 

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

@@ -15,6 +15,7 @@ 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
@@ -666,8 +667,7 @@ class SendDiscordFileBlock(Block):
         file: MediaFileType,
         filename: str,
         message_content: str,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
     ) -> dict:
         intents = discord.Intents.default()
         intents.guilds = True
@@ -731,10 +731,9 @@ 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,
-                        user_id=user_id,
+                        execution_context=execution_context,
-                        return_content=True,  # Get as data URI
+                        return_format="for_external_api",  # Get content to send to Discord
                     )
                     # Now process as data URI
                     header, encoded = stored_file.split(",", 1)
@@ -781,8 +780,7 @@ class SendDiscordFileBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -793,8 +791,7 @@ class SendDiscordFileBlock(Block):
                 file=input_data.file,
                 filename=input_data.filename,
                 message_content=input_data.message_content,
-                graph_exec_id=graph_exec_id,
+                execution_context=execution_context,
-                user_id=user_id,
             )
 
             yield "status", result.get("status", "Unknown error")

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

@@ -17,8 +17,11 @@ 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__)
 
@@ -64,9 +67,13 @@ class AIVideoGeneratorBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
-            test_output=[("video_url", "https://fal.media/files/example/video.mp4")],
+            test_output=[
+                # Output will be a workspace ref or data URI depending on context
+                ("video_url", lambda x: x.startswith(("workspace://", "data:"))),
+            ],
             test_mock={
-                "generate_video": lambda *args, **kwargs: "https://fal.media/files/example/video.mp4"
+                # Use data URI to avoid HTTP requests during tests
+                "generate_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA"
             },
         )
 
@@ -208,11 +215,22 @@ class AIVideoGeneratorBlock(Block):
             raise RuntimeError(f"API request failed: {str(e)}")
 
     async def run(
-        self, input_data: Input, *, credentials: FalCredentials, **kwargs
+        self,
+        input_data: Input,
+        *,
+        credentials: FalCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
     ) -> BlockOutput:
         try:
             video_url = await self.generate_video(input_data, credentials)
-            yield "video_url", 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
         except Exception as e:
             error_message = str(e)
             yield "error", error_message

autogpt_platform/backend/backend/blocks/flux_kontext.py

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

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

@@ -21,6 +21,7 @@ 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
@@ -95,8 +96,7 @@ def _make_mime_text(
 
 async def create_mime_message(
     input_data,
-    graph_exec_id: str,
+    execution_context: ExecutionContext,
-    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,
-                return_content=False,
+                execution_context=execution_context,
+                return_format="for_local_processing",
             )
-            abs_path = get_exec_file_path(graph_exec_id, local_path)
+            assert execution_context.graph_exec_id  # Validated by store_media_file
+            abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
             part = MIMEBase("application", "octet-stream")
             with open(abs_path, "rb") as f:
                 part.set_payload(f.read())
@@ -582,27 +582,25 @@ class GmailSendBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._send_email(
             service,
             input_data,
-            graph_exec_id,
+            execution_context,
-            user_id,
         )
         yield "result", result
 
     async def _send_email(
-        self, service, input_data: Input, graph_exec_id: str, user_id: str
+        self, service, input_data: Input, execution_context: ExecutionContext
     ) -> 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, graph_exec_id, user_id)
+        raw_message = await create_mime_message(input_data, execution_context)
         sent_message = await asyncio.to_thread(
             lambda: service.users()
             .messages()
@@ -692,30 +690,28 @@ class GmailCreateDraftBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._create_draft(
             service,
             input_data,
-            graph_exec_id,
+            execution_context,
-            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, graph_exec_id: str, user_id: str
+        self, service, input_data: Input, execution_context: ExecutionContext
     ) -> 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, graph_exec_id, user_id)
+        raw_message = await create_mime_message(input_data, execution_context)
         draft = await asyncio.to_thread(
             lambda: service.users()
             .drafts()
@@ -1100,7 +1096,7 @@ class GmailGetThreadBlock(GmailBase):
 
 
 async def _build_reply_message(
-    service, input_data, graph_exec_id: str, user_id: str
+    service, input_data, execution_context: ExecutionContext
 ) -> tuple[str, str]:
     """
     Builds a reply MIME message for Gmail threads.
@@ -1190,12 +1186,12 @@ 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,
-            return_content=False,
+            execution_context=execution_context,
+            return_format="for_local_processing",
         )
-        abs_path = get_exec_file_path(graph_exec_id, local_path)
+        assert execution_context.graph_exec_id  # Validated by store_media_file
+        abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
         part = MIMEBase("application", "octet-stream")
         with open(abs_path, "rb") as f:
             part.set_payload(f.read())
@@ -1311,16 +1307,14 @@ class GmailReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         message = await self._reply(
             service,
             input_data,
-            graph_exec_id,
+            execution_context,
-            user_id,
         )
         yield "messageId", message["id"]
         yield "threadId", message.get("threadId", input_data.threadId)
@@ -1343,11 +1337,11 @@ class GmailReplyBlock(GmailBase):
         yield "email", email
 
     async def _reply(
-        self, service, input_data: Input, graph_exec_id: str, user_id: str
+        self, service, input_data: Input, execution_context: ExecutionContext
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, graph_exec_id, user_id
+            service, input_data, execution_context
         )
 
         # Send the message
@@ -1441,16 +1435,14 @@ class GmailDraftReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         draft = await self._create_draft_reply(
             service,
             input_data,
-            graph_exec_id,
+            execution_context,
-            user_id,
         )
         yield "draftId", draft["id"]
         yield "messageId", draft["message"]["id"]
@@ -1458,11 +1450,11 @@ class GmailDraftReplyBlock(GmailBase):
         yield "status", "draft_created"
 
     async def _create_draft_reply(
-        self, service, input_data: Input, graph_exec_id: str, user_id: str
+        self, service, input_data: Input, execution_context: ExecutionContext
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, graph_exec_id, user_id
+            service, input_data, execution_context
         )
 
         # Create draft with proper thread association
@@ -1629,23 +1621,21 @@ class GmailForwardBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._forward_message(
             service,
             input_data,
-            graph_exec_id,
+            execution_context,
-            user_id,
         )
         yield "messageId", result["id"]
         yield "threadId", result.get("threadId", "")
         yield "status", "forwarded"
 
     async def _forward_message(
-        self, service, input_data: Input, graph_exec_id: str, user_id: str
+        self, service, input_data: Input, execution_context: ExecutionContext
     ) -> dict:
         if not input_data.to:
             raise ValueError("At least one recipient is required for forwarding")
@@ -1727,12 +1717,12 @@ 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,
-                return_content=False,
+                execution_context=execution_context,
+                return_format="for_local_processing",
             )
-            abs_path = get_exec_file_path(graph_exec_id, local_path)
+            assert execution_context.graph_exec_id  # Validated by store_media_file
+            abs_path = get_exec_file_path(execution_context.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/http.py

@@ -15,6 +15,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import (
     CredentialsField,
     CredentialsMetaInput,
@@ -116,10 +117,9 @@ class SendWebRequestBlock(Block):
 
     @staticmethod
     async def _prepare_files(
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
         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.
@@ -127,11 +127,16 @@ 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(
-                graph_exec_id, media, user_id, return_content=False
+                file=media,
+                execution_context=execution_context,
+                return_format="for_local_processing",
             )
             abs_path = get_exec_file_path(graph_exec_id, rel_path)
             async with aiofiles.open(abs_path, "rb") as f:
@@ -143,7 +148,7 @@ class SendWebRequestBlock(Block):
         return files_payload
 
     async def run(
-        self, input_data: Input, *, graph_exec_id: str, user_id: str, **kwargs
+        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
     ) -> BlockOutput:
         # ─── Parse/normalise body ────────────────────────────────────
         body = input_data.body
@@ -174,7 +179,7 @@ class SendWebRequestBlock(Block):
         files_payload: list[tuple[str, tuple[str, BytesIO, str]]] = []
         if use_files:
             files_payload = await self._prepare_files(
-                graph_exec_id, input_data.files_name, input_data.files, user_id
+                execution_context, input_data.files_name, input_data.files
             )
 
         # Enforce body format rules
@@ -238,9 +243,8 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
         self,
         input_data: Input,
         *,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
         credentials: HostScopedCredentials,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         # Create SendWebRequestBlock.Input from our input (removing credentials field)
@@ -271,6 +275,6 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
 
         # Use parent class run method
         async for output_name, output_data in super().run(
-            base_input, graph_exec_id=graph_exec_id, user_id=user_id, **kwargs
+            base_input, execution_context=execution_context, **kwargs
         ):
             yield output_name, output_data

autogpt_platform/backend/backend/blocks/io.py

@@ -12,6 +12,7 @@ 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
@@ -462,18 +463,21 @@ class AgentFileInputBlock(AgentInputBlock):
         self,
         input_data: Input,
         *,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        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,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=input_data.base_64,
+            return_format=return_format,
         )
 
 

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_prompt, estimate_token_count
+from backend.util.prompt import compress_context, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
@@ -115,7 +115,6 @@ 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"
@@ -280,9 +279,6 @@ 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
@@ -638,11 +634,18 @@ async def llm_call(
     context_window = llm_model.context_window
 
     if compress_prompt_to_fit:
-        prompt = compress_prompt(
+        result = await compress_context(
             messages=prompt,
             target_tokens=llm_model.context_window // 2,
-            lossy_ok=True,
+            client=None,  # Truncation-only, no LLM summarization
+            reserve=0,  # Caller handles response token budget separately
         )
+        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 Literal, Optional
+from typing import Optional
 
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.fx.Loop import Loop
@@ -13,6 +13,7 @@ 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
 
@@ -46,18 +47,19 @@ class MediaDurationBlock(Block):
         self,
         input_data: Input,
         *,
-        graph_exec_id: str,
+        execution_context: ExecutionContext,
-        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,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=False,
+            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
         )
-        media_abspath = get_exec_file_path(graph_exec_id, local_media_path)
 
         # 2) Load the clip
         if input_data.is_video:
@@ -88,10 +90,6 @@ 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(
@@ -111,17 +109,19 @@ class LoopVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        node_exec_id: str,
+        execution_context: ExecutionContext,
-        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,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=False,
+            return_format="for_local_processing",
         )
         input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
 
@@ -149,12 +149,11 @@ class LoopVideoBlock(Block):
         looped_clip = looped_clip.with_audio(clip.audio)
         looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
 
-        # Return as data URI
+        # Return output - for_block_output returns workspace:// if available, else data URI
         video_out = await store_media_file(
-            graph_exec_id=graph_exec_id,
             file=output_filename,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=input_data.output_return_type == "data_uri",
+            return_format="for_block_output",
         )
 
         yield "video_out", video_out
@@ -177,10 +176,6 @@ 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(
@@ -200,23 +195,24 @@ class AddAudioToVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        node_exec_id: str,
+        execution_context: ExecutionContext,
-        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,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=False,
+            return_format="for_local_processing",
         )
         local_audio_path = await store_media_file(
-            graph_exec_id=graph_exec_id,
             file=input_data.audio_in,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=False,
+            return_format="for_local_processing",
         )
 
         abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
@@ -240,12 +236,11 @@ 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 either path or data URI
+        # 5) Return output - for_block_output returns workspace:// if available, else data URI
         video_out = await store_media_file(
-            graph_exec_id=graph_exec_id,
             file=output_filename,
-            user_id=user_id,
+            execution_context=execution_context,
-            return_content=input_data.output_return_type == "data_uri",
+            return_format="for_block_output",
         )
 
         yield "video_out", video_out

autogpt_platform/backend/backend/blocks/screenshotone.py

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

@@ -7,6 +7,7 @@ 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
@@ -98,7 +99,7 @@ class ReadSpreadsheetBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, graph_exec_id: str, user_id: str, **_kwargs
+        self, input_data: Input, *, execution_context: ExecutionContext, **_kwargs
     ) -> BlockOutput:
         import csv
         from io import StringIO
@@ -106,14 +107,16 @@ 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,
-                return_content=False,
+                execution_context=execution_context,
+                return_format="for_local_processing",
             )
 
             # Get full file path
-            file_path = get_exec_file_path(graph_exec_id, stored_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
+            )
             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_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
 
     @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_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -182,10 +182,7 @@ class StagehandObserveBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}")
+        logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}")
-        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(
@@ -230,7 +227,7 @@ class StagehandActBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -282,10 +279,7 @@ class StagehandActBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.info(f"ACT: Stagehand credentials: {stagehand_credentials}")
+        logger.debug(f"ACT: Using model provider {model_credentials.provider}")
-        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(
@@ -330,7 +324,7 @@ class StagehandExtractBlock(Block):
         model: StagehandRecommendedLlmModel = SchemaField(
             title="LLM Model",
             description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
+            default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET,
             advanced=False,
         )
         model_credentials: AICredentials = AICredentialsField()
@@ -370,10 +364,7 @@ class StagehandExtractBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        logger.info(f"EXTRACT: Stagehand credentials: {stagehand_credentials}")
+        logger.debug(f"EXTRACT: Using model provider {model_credentials.provider}")
-        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

@@ -10,6 +10,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -17,7 +18,9 @@ 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",
@@ -102,7 +105,7 @@ class CreateTalkingAvatarVideoBlock(Block):
             test_output=[
                 (
                     "video_url",
-                    "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
+                    lambda x: x.startswith(("workspace://", "data:")),
                 ),
             ],
             test_mock={
@@ -110,9 +113,10 @@ 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": "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
+                    "result_url": "data:video/mp4;base64,AAAA",
                 },
             },
             test_credentials=TEST_CREDENTIALS,
@@ -138,7 +142,12 @@ class CreateTalkingAvatarVideoBlock(Block):
         return response.json()
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        execution_context: ExecutionContext,
+        **kwargs,
     ) -> BlockOutput:
         # Create the clip
         payload = {
@@ -165,7 +174,14 @@ 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":
-                yield "video_url", status_response["result_url"]
+                # 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
                 return
             elif status_response["status"] == "error":
                 raise RuntimeError(

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

@@ -12,6 +12,7 @@ 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
 
@@ -233,9 +234,12 @@ class TestStoreMediaFileSecurity:
 
         with pytest.raises(ValueError, match="File too large"):
             await store_media_file(
-                graph_exec_id="test",
                 file=MediaFileType(large_data_uri),
-                user_id="test_user",
+                execution_context=ExecutionContext(
+                    user_id="test_user",
+                    graph_exec_id="test",
+                ),
+                return_format="for_local_processing",
             )
 
     @patch("backend.util.file.Path")
@@ -270,9 +274,12 @@ 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
-                user_id="test_user",
+                execution_context=ExecutionContext(
+                    user_id="test_user",
+                    graph_exec_id="test",
+                ),
+                return_format="for_local_processing",
             )

autogpt_platform/backend/backend/blocks/test/test_http.py

@@ -11,10 +11,22 @@ from backend.blocks.http import (
     HttpMethod,
     SendAuthenticatedWebRequestBlock,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import HostScopedCredentials
 from backend.util.request import Response
 
 
+def make_test_context(
+    graph_exec_id: str = "test-exec-id",
+    user_id: str = "test-user-id",
+) -> ExecutionContext:
+    """Helper to create test ExecutionContext."""
+    return ExecutionContext(
+        user_id=user_id,
+        graph_exec_id=graph_exec_id,
+    )
+
+
 class TestHttpBlockWithHostScopedCredentials:
     """Test suite for HTTP block integration with HostScopedCredentials."""
 
@@ -105,8 +117,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=exact_match_credentials,
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -161,8 +172,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=wildcard_credentials,
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -208,8 +218,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=non_matching_credentials,
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -258,8 +267,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=exact_match_credentials,
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -318,8 +326,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=auto_discovered_creds,  # Execution manager found these
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -382,8 +389,7 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=multi_header_creds,
-            graph_exec_id="test-exec-id",
+            execution_context=make_test_context(),
-            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -471,8 +477,7 @@ class TestHttpBlockWithHostScopedCredentials:
             async for output_name, output_data in http_block.run(
                 input_data,
                 credentials=test_creds,
-                graph_exec_id="test-exec-id",
+                execution_context=make_test_context(),
-                user_id="test-user-id",
             ):
                 result.append((output_name, output_data))
 

autogpt_platform/backend/backend/blocks/text.py

@@ -11,6 +11,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util import json, text
 from backend.util.file import get_exec_file_path, store_media_file
@@ -444,18 +445,21 @@ class FileReadBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, graph_exec_id: str, user_id: str, **_kwargs
+        self, input_data: Input, *, execution_context: ExecutionContext, **_kwargs
     ) -> BlockOutput:
         # Store the media file properly (handles URLs, data URIs, etc.)
         stored_file_path = await store_media_file(
-            user_id=user_id,
-            graph_exec_id=graph_exec_id,
             file=input_data.file_input,
-            return_content=False,
+            execution_context=execution_context,
+            return_format="for_local_processing",
         )
 
-        # Get full file path
+        # Get full file path (graph_exec_id validated by store_media_file above)
-        file_path = get_exec_file_path(graph_exec_id, stored_file_path)
+        if not execution_context.graph_exec_id:
+            raise ValueError("execution_context.graph_exec_id is required")
+        file_path = get_exec_file_path(
+            execution_context.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/data/block.py

@@ -873,14 +873,13 @@ def is_block_auth_configured(
 
 
 async def initialize_blocks() -> None:
-    # First, sync all provider costs to blocks
-    # Imported here to avoid circular import
     from backend.sdk.cost_integration import sync_all_provider_costs
+    from backend.util.retry import func_retry
 
     sync_all_provider_costs()
 
-    for cls in get_blocks().values():
+    @func_retry
-        block = cls()
+    async def sync_block_to_db(block: Block) -> None:
         existing_block = await AgentBlock.prisma().find_first(
             where={"OR": [{"id": block.id}, {"name": block.name}]}
         )
@@ -893,7 +892,7 @@ async def initialize_blocks() -> None:
                     outputSchema=json.dumps(block.output_schema.jsonschema()),
                 )
             )
-            continue
+            return
 
         input_schema = json.dumps(block.input_schema.jsonschema())
         output_schema = json.dumps(block.output_schema.jsonschema())
@@ -913,6 +912,25 @@ async def initialize_blocks() -> None:
                 },
             )
 
+    failed_blocks: list[str] = []
+    for cls in get_blocks().values():
+        block = cls()
+        try:
+            await sync_block_to_db(block)
+        except Exception as e:
+            logger.warning(
+                f"Failed to sync block {block.name} to database: {e}. "
+                "Block is still available in memory.",
+                exc_info=True,
+            )
+            failed_blocks.append(block.name)
+
+    if failed_blocks:
+        logger.error(
+            f"Failed to sync {len(failed_blocks)} block(s) to database: "
+            f"{', '.join(failed_blocks)}. These blocks are still available in memory."
+        )
+
 
 # Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
 def get_block(block_id: str) -> AnyBlockSchema | None:

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -81,7 +81,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_5_HAIKU: 4,
     LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
-    LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_HAIKU: 1,
     LlmModel.AIML_API_QWEN2_5_72B: 1,
     LlmModel.AIML_API_LLAMA3_1_70B: 1,

autogpt_platform/backend/backend/data/event_bus.py

@@ -133,10 +133,23 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
+    def __init__(self):
+        self._pubsub: AsyncPubSub | None = None
+
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()
 
+    async def close(self) -> None:
+        """Close the PubSub connection if it exists."""
+        if self._pubsub is not None:
+            try:
+                await self._pubsub.close()
+            except Exception:
+                logger.warning("Failed to close PubSub connection", exc_info=True)
+            finally:
+                self._pubsub = None
+
     async def publish_event(self, event: M, channel_key: str):
         """
         Publish an event to Redis. Gracefully handles connection failures
@@ -157,6 +170,7 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
             await self.connection, channel_key
         )
         assert isinstance(pubsub, AsyncPubSub)
+        self._pubsub = pubsub
 
         if "*" in channel_key:
             await pubsub.psubscribe(full_channel_name)

autogpt_platform/backend/backend/data/execution.py

@@ -83,12 +83,29 @@ class ExecutionContext(BaseModel):
 
     model_config = {"extra": "ignore"}
 
+    # Execution identity
+    user_id: Optional[str] = None
+    graph_id: Optional[str] = None
+    graph_exec_id: Optional[str] = None
+    graph_version: Optional[int] = None
+    node_id: Optional[str] = None
+    node_exec_id: Optional[str] = None
+
+    # Safety settings
     human_in_the_loop_safe_mode: bool = True
     sensitive_action_safe_mode: bool = False
+
+    # User settings
     user_timezone: str = "UTC"
+
+    # Execution hierarchy
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None
 
+    # Workspace
+    workspace_id: Optional[str] = None
+    session_id: Optional[str] = None
+
 
 # -------------------------- Models -------------------------- #
 

autogpt_platform/backend/backend/data/graph.py

@@ -1028,6 +1028,39 @@ async def get_graph(
     return GraphModel.from_db(graph, for_export)
 
 
+async def get_store_listed_graphs(*graph_ids: str) -> dict[str, GraphModel]:
+    """Batch-fetch multiple store-listed graphs by their IDs.
+
+    Only returns graphs that have approved store listings (publicly available).
+    Does not require permission checks since store-listed graphs are public.
+
+    Args:
+        *graph_ids: Variable number of graph IDs to fetch
+
+    Returns:
+        Dict mapping graph_id to GraphModel for graphs with approved store listings
+    """
+    if not graph_ids:
+        return {}
+
+    store_listings = await StoreListingVersion.prisma().find_many(
+        where={
+            "agentGraphId": {"in": list(graph_ids)},
+            "submissionStatus": SubmissionStatus.APPROVED,
+            "isDeleted": False,
+        },
+        include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
+        distinct=["agentGraphId"],
+        order={"agentGraphVersion": "desc"},
+    )
+
+    return {
+        listing.agentGraphId: GraphModel.from_db(listing.AgentGraph)
+        for listing in store_listings
+        if listing.AgentGraph
+    }
+
+
 async def get_graph_as_admin(
     graph_id: str,
     version: int | None = None,

autogpt_platform/backend/backend/data/model.py

@@ -666,10 +666,16 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
         if not (self.discriminator and self.discriminator_mapping):
             return self
 
+        try:
+            provider = self.discriminator_mapping[discriminator_value]
+        except KeyError:
+            raise ValueError(
+                f"Model '{discriminator_value}' is not supported. "
+                "It may have been deprecated. Please update your agent configuration."
+            )
+
         return CredentialsFieldInfo(
-            credentials_provider=frozenset(
+            credentials_provider=frozenset([provider]),
-                [self.discriminator_mapping[discriminator_value]]
-            ),
             credentials_types=self.supported_types,
             credentials_scopes=self.required_scopes,
             discriminator=self.discriminator,

autogpt_platform/backend/backend/data/workspace.py

@@ -0,0 +1,276 @@
+"""
+Database CRUD operations for User Workspace.
+
+This module provides functions for managing user workspaces and workspace files.
+"""
+
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from prisma.models import UserWorkspace, UserWorkspaceFile
+from prisma.types import UserWorkspaceFileWhereInput
+
+from backend.util.json import SafeJson
+
+logger = logging.getLogger(__name__)
+
+
+async def get_or_create_workspace(user_id: str) -> UserWorkspace:
+    """
+    Get user's workspace, creating one if it doesn't exist.
+
+    Uses upsert to handle race conditions when multiple concurrent requests
+    attempt to create a workspace for the same user.
+
+    Args:
+        user_id: The user's ID
+
+    Returns:
+        UserWorkspace instance
+    """
+    workspace = await UserWorkspace.prisma().upsert(
+        where={"userId": user_id},
+        data={
+            "create": {"userId": user_id},
+            "update": {},  # No updates needed if exists
+        },
+    )
+
+    return workspace
+
+
+async def get_workspace(user_id: str) -> Optional[UserWorkspace]:
+    """
+    Get user's workspace if it exists.
+
+    Args:
+        user_id: The user's ID
+
+    Returns:
+        UserWorkspace instance or None
+    """
+    return await UserWorkspace.prisma().find_unique(where={"userId": user_id})
+
+
+async def create_workspace_file(
+    workspace_id: str,
+    file_id: str,
+    name: str,
+    path: str,
+    storage_path: str,
+    mime_type: str,
+    size_bytes: int,
+    checksum: Optional[str] = None,
+    metadata: Optional[dict] = None,
+) -> UserWorkspaceFile:
+    """
+    Create a new workspace file record.
+
+    Args:
+        workspace_id: The workspace ID
+        file_id: The file ID (same as used in storage path for consistency)
+        name: User-visible filename
+        path: Virtual path (e.g., "/documents/report.pdf")
+        storage_path: Actual storage path (GCS or local)
+        mime_type: MIME type of the file
+        size_bytes: File size in bytes
+        checksum: Optional SHA256 checksum
+        metadata: Optional additional metadata
+
+    Returns:
+        Created UserWorkspaceFile instance
+    """
+    # Normalize path to start with /
+    if not path.startswith("/"):
+        path = f"/{path}"
+
+    file = await UserWorkspaceFile.prisma().create(
+        data={
+            "id": file_id,
+            "workspaceId": workspace_id,
+            "name": name,
+            "path": path,
+            "storagePath": storage_path,
+            "mimeType": mime_type,
+            "sizeBytes": size_bytes,
+            "checksum": checksum,
+            "metadata": SafeJson(metadata or {}),
+        }
+    )
+
+    logger.info(
+        f"Created workspace file {file.id} at path {path} "
+        f"in workspace {workspace_id}"
+    )
+    return file
+
+
+async def get_workspace_file(
+    file_id: str,
+    workspace_id: Optional[str] = None,
+) -> Optional[UserWorkspaceFile]:
+    """
+    Get a workspace file by ID.
+
+    Args:
+        file_id: The file ID
+        workspace_id: Optional workspace ID for validation
+
+    Returns:
+        UserWorkspaceFile instance or None
+    """
+    where_clause: dict = {"id": file_id, "isDeleted": False}
+    if workspace_id:
+        where_clause["workspaceId"] = workspace_id
+
+    return await UserWorkspaceFile.prisma().find_first(where=where_clause)
+
+
+async def get_workspace_file_by_path(
+    workspace_id: str,
+    path: str,
+) -> Optional[UserWorkspaceFile]:
+    """
+    Get a workspace file by its virtual path.
+
+    Args:
+        workspace_id: The workspace ID
+        path: Virtual path
+
+    Returns:
+        UserWorkspaceFile instance or None
+    """
+    # Normalize path
+    if not path.startswith("/"):
+        path = f"/{path}"
+
+    return await UserWorkspaceFile.prisma().find_first(
+        where={
+            "workspaceId": workspace_id,
+            "path": path,
+            "isDeleted": False,
+        }
+    )
+
+
+async def list_workspace_files(
+    workspace_id: str,
+    path_prefix: Optional[str] = None,
+    include_deleted: bool = False,
+    limit: Optional[int] = None,
+    offset: int = 0,
+) -> list[UserWorkspaceFile]:
+    """
+    List files in a workspace.
+
+    Args:
+        workspace_id: The workspace ID
+        path_prefix: Optional path prefix to filter (e.g., "/documents/")
+        include_deleted: Whether to include soft-deleted files
+        limit: Maximum number of files to return
+        offset: Number of files to skip
+
+    Returns:
+        List of UserWorkspaceFile instances
+    """
+    where_clause: UserWorkspaceFileWhereInput = {"workspaceId": workspace_id}
+
+    if not include_deleted:
+        where_clause["isDeleted"] = False
+
+    if path_prefix:
+        # Normalize prefix
+        if not path_prefix.startswith("/"):
+            path_prefix = f"/{path_prefix}"
+        where_clause["path"] = {"startswith": path_prefix}
+
+    return await UserWorkspaceFile.prisma().find_many(
+        where=where_clause,
+        order={"createdAt": "desc"},
+        take=limit,
+        skip=offset,
+    )
+
+
+async def count_workspace_files(
+    workspace_id: str,
+    path_prefix: Optional[str] = None,
+    include_deleted: bool = False,
+) -> int:
+    """
+    Count files in a workspace.
+
+    Args:
+        workspace_id: The workspace ID
+        path_prefix: Optional path prefix to filter (e.g., "/sessions/abc123/")
+        include_deleted: Whether to include soft-deleted files
+
+    Returns:
+        Number of files
+    """
+    where_clause: dict = {"workspaceId": workspace_id}
+    if not include_deleted:
+        where_clause["isDeleted"] = False
+
+    if path_prefix:
+        # Normalize prefix
+        if not path_prefix.startswith("/"):
+            path_prefix = f"/{path_prefix}"
+        where_clause["path"] = {"startswith": path_prefix}
+
+    return await UserWorkspaceFile.prisma().count(where=where_clause)
+
+
+async def soft_delete_workspace_file(
+    file_id: str,
+    workspace_id: Optional[str] = None,
+) -> Optional[UserWorkspaceFile]:
+    """
+    Soft-delete a workspace file.
+
+    The path is modified to include a deletion timestamp to free up the original
+    path for new files while preserving the record for potential recovery.
+
+    Args:
+        file_id: The file ID
+        workspace_id: Optional workspace ID for validation
+
+    Returns:
+        Updated UserWorkspaceFile instance or None if not found
+    """
+    # First verify the file exists and belongs to workspace
+    file = await get_workspace_file(file_id, workspace_id)
+    if file is None:
+        return None
+
+    deleted_at = datetime.now(timezone.utc)
+    # Modify path to free up the unique constraint for new files at original path
+    # Format: {original_path}__deleted__{timestamp}
+    deleted_path = f"{file.path}__deleted__{int(deleted_at.timestamp())}"
+
+    updated = await UserWorkspaceFile.prisma().update(
+        where={"id": file_id},
+        data={
+            "isDeleted": True,
+            "deletedAt": deleted_at,
+            "path": deleted_path,
+        },
+    )
+
+    logger.info(f"Soft-deleted workspace file {file_id}")
+    return updated
+
+
+async def get_workspace_total_size(workspace_id: str) -> int:
+    """
+    Get the total size of all files in a workspace.
+
+    Args:
+        workspace_id: The workspace ID
+
+    Returns:
+        Total size in bytes
+    """
+    files = await list_workspace_files(workspace_id)
+    return sum(file.sizeBytes for file in files)

autogpt_platform/backend/backend/executor/database.py

@@ -17,6 +17,7 @@ from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
     get_marketplace_graphs_for_monitoring,
 )
+from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
     create_graph_execution,
@@ -219,6 +220,9 @@ class DatabaseManager(AppService):
     # Onboarding
     increment_onboarding_runs = _(increment_onboarding_runs)
 
+    # OAuth
+    cleanup_expired_oauth_tokens = _(cleanup_expired_oauth_tokens)
+
     # Store
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
@@ -349,6 +353,9 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     # Onboarding
     increment_onboarding_runs = d.increment_onboarding_runs
 
+    # OAuth
+    cleanup_expired_oauth_tokens = d.cleanup_expired_oauth_tokens
+
     # Store
     get_store_agents = d.get_store_agents
     get_store_agent_details = d.get_store_agent_details

autogpt_platform/backend/backend/executor/manager.py

@@ -236,7 +236,14 @@ async def execute_node(
     input_size = len(input_data_str)
     log_metadata.debug("Executed node with input", input=input_data_str)
 
+    # Create node-specific execution context to avoid race conditions
+    # (multiple nodes can execute concurrently and would otherwise mutate shared state)
+    execution_context = execution_context.model_copy(
+        update={"node_id": node_id, "node_exec_id": node_exec_id}
+    )
+
     # Inject extra execution arguments for the blocks via kwargs
+    # Keep individual kwargs for backwards compatibility with existing blocks
     extra_exec_kwargs: dict = {
         "graph_id": graph_id,
         "graph_version": graph_version,

autogpt_platform/backend/backend/executor/scheduler.py

@@ -24,11 +24,9 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy import MetaData, create_engine
 
-from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_onboarding_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -38,7 +36,11 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_database_manager_client, get_scheduler_client
+from backend.util.clients import (
+    get_database_manager_async_client,
+    get_database_manager_client,
+    get_scheduler_client,
+)
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -148,6 +150,7 @@ def execute_graph(**kwargs):
 async def _execute_graph(**kwargs):
     args = GraphExecutionJobArgs(**kwargs)
     start_time = asyncio.get_event_loop().time()
+    db = get_database_manager_async_client()
     try:
         logger.info(f"Executing recurring job for graph #{args.graph_id}")
         graph_exec: GraphExecutionWithNodes = await execution_utils.add_graph_execution(
@@ -157,7 +160,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
-        await increment_onboarding_runs(args.user_id)
+        await db.increment_onboarding_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -246,8 +249,13 @@ def cleanup_expired_files():
 
 def cleanup_oauth_tokens():
     """Clean up expired OAuth tokens from the database."""
+
     # Wait for completion
-    run_async(cleanup_expired_oauth_tokens())
+    async def _cleanup():
+        db = get_database_manager_async_client()
+        return await db.cleanup_expired_oauth_tokens()
+
+    run_async(_cleanup())
 
 
 def execution_accuracy_alerts():

autogpt_platform/backend/backend/executor/utils.py

@@ -892,11 +892,19 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
+            # Execution identity
+            user_id=user_id,
+            graph_id=graph_id,
+            graph_exec_id=graph_exec.id,
+            graph_version=graph_exec.graph_version,
+            # Safety settings
             human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
             sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
+            # User settings
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),
+            # Execution hierarchy
             root_execution_id=graph_exec.id,
         )
 

autogpt_platform/backend/backend/executor/utils_test.py

@@ -348,6 +348,7 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
     mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
+    mock_graph_exec.graph_version = graph_version
     mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Mock the queue and event bus
@@ -434,6 +435,9 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Create a second mock execution for the sanity check
     mock_graph_exec_2 = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec_2.id = "execution-id-456"
+    mock_graph_exec_2.node_executions = []
+    mock_graph_exec_2.status = ExecutionStatus.QUEUED
+    mock_graph_exec_2.graph_version = graph_version
     mock_graph_exec_2.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Reset mocks and set up for second call
@@ -614,6 +618,7 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []
     mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
+    mock_graph_exec.graph_version = graph_version
 
     # Track what's passed to to_graph_execution_entry
     captured_kwargs = {}

autogpt_platform/backend/backend/integrations/webhooks/utils_test.py

@@ -0,0 +1,39 @@
+from urllib.parse import urlparse
+
+import fastapi
+from fastapi.routing import APIRoute
+
+from backend.api.features.integrations.router import router as integrations_router
+from backend.integrations.providers import ProviderName
+from backend.integrations.webhooks import utils as webhooks_utils
+
+
+def test_webhook_ingress_url_matches_route(monkeypatch) -> None:
+    app = fastapi.FastAPI()
+    app.include_router(integrations_router, prefix="/api/integrations")
+
+    provider = ProviderName.GITHUB
+    webhook_id = "webhook_123"
+    base_url = "https://example.com"
+
+    monkeypatch.setattr(webhooks_utils.app_config, "platform_base_url", base_url)
+
+    route = next(
+        route
+        for route in integrations_router.routes
+        if isinstance(route, APIRoute)
+        and route.path == "/{provider}/webhooks/{webhook_id}/ingress"
+        and "POST" in route.methods
+    )
+    expected_path = f"/api/integrations{route.path}".format(
+        provider=provider.value,
+        webhook_id=webhook_id,
+    )
+    actual_url = urlparse(webhooks_utils.webhook_ingress_url(provider, webhook_id))
+    expected_base = urlparse(base_url)
+
+    assert (actual_url.scheme, actual_url.netloc) == (
+        expected_base.scheme,
+        expected_base.netloc,
+    )
+    assert actual_url.path == expected_path

autogpt_platform/backend/backend/util/cloud_storage.py

@@ -13,6 +13,7 @@ import aiohttp
 from gcloud.aio import storage as async_gcs_storage
 from google.cloud import storage as gcs_storage
 
+from backend.util.gcs_utils import download_with_fresh_session, generate_signed_url
 from backend.util.settings import Config
 
 logger = logging.getLogger(__name__)
@@ -251,7 +252,7 @@ class CloudStorageHandler:
             f"in_task: {current_task is not None}"
         )
 
-        # Parse bucket and blob name from path
+        # Parse bucket and blob name from path (path already has gcs:// prefix removed)
         parts = path.split("/", 1)
         if len(parts) != 2:
             raise ValueError(f"Invalid GCS path: {path}")
@@ -261,50 +262,19 @@ class CloudStorageHandler:
         # Authorization check
         self._validate_file_access(blob_name, user_id, graph_exec_id)
 
-        # Use a fresh client for each download to avoid session issues
+        logger.info(
-        # This is less efficient but more reliable with the executor's event loop
+            f"[CloudStorage] About to download from GCS - bucket: {bucket_name}, blob: {blob_name}"
-        logger.info("[CloudStorage] Creating fresh GCS client for download")
-
-        # Create a new session specifically for this download
-        session = aiohttp.ClientSession(
-            connector=aiohttp.TCPConnector(limit=10, force_close=True)
         )
 
-        async_client = None
         try:
-            # Create a new GCS client with the fresh session
+            content = await download_with_fresh_session(bucket_name, blob_name)
-            async_client = async_gcs_storage.Storage(session=session)
-
-            logger.info(
-                f"[CloudStorage] About to download from GCS - bucket: {bucket_name}, blob: {blob_name}"
-            )
-
-            # Download content using the fresh client
-            content = await async_client.download(bucket_name, blob_name)
             logger.info(
                 f"[CloudStorage] GCS download successful - size: {len(content)} bytes"
             )
-
-            # Clean up
-            await async_client.close()
-            await session.close()
-
             return content
-
+        except FileNotFoundError:
+            raise
         except Exception as e:
-            # Always try to clean up
-            if async_client is not None:
-                try:
-                    await async_client.close()
-                except Exception as cleanup_error:
-                    logger.warning(
-                        f"[CloudStorage] Error closing GCS client: {cleanup_error}"
-                    )
-            try:
-                await session.close()
-            except Exception as cleanup_error:
-                logger.warning(f"[CloudStorage] Error closing session: {cleanup_error}")
-
             # Log the specific error for debugging
             logger.error(
                 f"[CloudStorage] GCS download failed - error: {str(e)}, "
@@ -319,10 +289,6 @@ class CloudStorageHandler:
                     f"current_task: {current_task}, "
                     f"bucket: {bucket_name}, blob: redacted for privacy"
                 )
-
-            # Convert gcloud-aio exceptions to standard ones
-            if "404" in str(e) or "Not Found" in str(e):
-                raise FileNotFoundError(f"File not found: gcs://{path}")
             raise
 
     def _validate_file_access(
@@ -445,8 +411,7 @@ class CloudStorageHandler:
         graph_exec_id: str | None = None,
     ) -> str:
         """Generate signed URL for GCS with authorization."""
-
+        # Parse bucket and blob name from path (path already has gcs:// prefix removed)
-        # Parse bucket and blob name from path
         parts = path.split("/", 1)
         if len(parts) != 2:
             raise ValueError(f"Invalid GCS path: {path}")
@@ -456,21 +421,11 @@ class CloudStorageHandler:
         # Authorization check
         self._validate_file_access(blob_name, user_id, graph_exec_id)
 
-        # Use sync client for signed URLs since gcloud-aio doesn't support them
         sync_client = self._get_sync_gcs_client()
-        bucket = sync_client.bucket(bucket_name)
+        return await generate_signed_url(
-        blob = bucket.blob(blob_name)
+            sync_client, bucket_name, blob_name, expiration_hours * 3600
-
-        # Generate signed URL asynchronously using sync client
-        url = await asyncio.to_thread(
-            blob.generate_signed_url,
-            version="v4",
-            expiration=datetime.now(timezone.utc) + timedelta(hours=expiration_hours),
-            method="GET",
         )
 
-        return url
-
     async def delete_expired_files(self, provider: str = "gcs") -> int:
         """
         Delete files that have passed their expiration time.

autogpt_platform/backend/backend/util/file.py

@@ -5,13 +5,26 @@ import shutil
 import tempfile
 import uuid
 from pathlib import Path
+from typing import TYPE_CHECKING, Literal
 from urllib.parse import urlparse
 
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.request import Requests
+from backend.util.settings import Config
 from backend.util.type import MediaFileType
 from backend.util.virus_scanner import scan_content_safe
 
+if TYPE_CHECKING:
+    from backend.data.execution import ExecutionContext
+
+# Return format options for store_media_file
+# - "for_local_processing": Returns local file path - use with ffmpeg, MoviePy, PIL, etc.
+# - "for_external_api": Returns data URI (base64) - use when sending content to external APIs
+# - "for_block_output": Returns best format for output - workspace:// in CoPilot, data URI in graphs
+MediaReturnFormat = Literal[
+    "for_local_processing", "for_external_api", "for_block_output"
+]
+
 TEMP_DIR = Path(tempfile.gettempdir()).resolve()
 
 # Maximum filename length (conservative limit for most filesystems)
@@ -67,42 +80,56 @@ def clean_exec_files(graph_exec_id: str, file: str = "") -> None:
 
 
 async def store_media_file(
-    graph_exec_id: str,
     file: MediaFileType,
-    user_id: str,
+    execution_context: "ExecutionContext",
-    return_content: bool = False,
+    *,
+    return_format: MediaReturnFormat,
 ) -> MediaFileType:
     """
-    Safely handle 'file' (a data URI, a URL, or a local path relative to {temp}/exec_file/{exec_id}),
+    Safely handle 'file' (a data URI, a URL, a workspace:// reference, or a local path
-    placing or verifying it under:
+    relative to {temp}/exec_file/{exec_id}), placing or verifying it under:
         {tempdir}/exec_file/{exec_id}/...
 
-    If 'return_content=True', return a data URI (data:<mime>;base64,<content>).
+    For each MediaFileType input:
-    Otherwise, returns the file media path relative to the exec_id folder.
+    - Data URI: decode and store locally
+    - URL: download and store locally
+    - workspace:// reference: read from workspace, store locally
+    - Local path: verify it exists in exec_file directory
 
-    For each MediaFileType type:
+    Return format options:
-    - Data URI:
+    - "for_local_processing": Returns local file path - use with ffmpeg, MoviePy, PIL, etc.
-      -> decode and store in a new random file in that folder
+    - "for_external_api": Returns data URI (base64) - use when sending to external APIs
-    - URL:
+    - "for_block_output": Returns best format for output - workspace:// in CoPilot, data URI in graphs
-      -> download and store in that folder
-    - Local path:
-      -> interpret as relative to that folder; verify it exists
-         (no copying, as it's presumably already there).
-         We realpath-check so no symlink or '..' can escape the folder.
 
-
+    :param file:               Data URI, URL, workspace://, or local (relative) path.
-    :param graph_exec_id:  The unique ID of the graph execution.
+    :param execution_context:  ExecutionContext with user_id, graph_exec_id, workspace_id.
-    :param file:           Data URI, URL, or local (relative) path.
+    :param return_format:      What to return: "for_local_processing", "for_external_api", or "for_block_output".
-    :param return_content: If True, return a data URI of the file content.
+    :return:                   The requested result based on return_format.
-                           If False, return the *relative* path inside the exec_id folder.
-    :return:               The requested result: data URI or relative path of the media.
     """
+    # Extract values from execution_context
+    graph_exec_id = execution_context.graph_exec_id
+    user_id = execution_context.user_id
+
+    if not graph_exec_id:
+        raise ValueError("execution_context.graph_exec_id is required")
+    if not user_id:
+        raise ValueError("execution_context.user_id is required")
+
+    # Create workspace_manager if we have workspace_id (with session scoping)
+    # Import here to avoid circular import (file.py → workspace.py → data → blocks → file.py)
+    from backend.util.workspace import WorkspaceManager
+
+    workspace_manager: WorkspaceManager | None = None
+    if execution_context.workspace_id:
+        workspace_manager = WorkspaceManager(
+            user_id, execution_context.workspace_id, execution_context.session_id
+        )
     # Build base path
     base_path = Path(get_exec_file_path(graph_exec_id, ""))
     base_path.mkdir(parents=True, exist_ok=True)
 
     # Security fix: Add disk space limits to prevent DoS
-    MAX_FILE_SIZE = 100 * 1024 * 1024  # 100MB per file
+    MAX_FILE_SIZE_BYTES = Config().max_file_size_mb * 1024 * 1024
     MAX_TOTAL_DISK_USAGE = 1024 * 1024 * 1024  # 1GB total per execution directory
 
     # Check total disk usage in base_path
@@ -142,9 +169,57 @@ async def store_media_file(
         """
         return str(absolute_path.relative_to(base))
 
-    # Check if this is a cloud storage path
+    # Get cloud storage handler for checking cloud paths
     cloud_storage = await get_cloud_storage_handler()
-    if cloud_storage.is_cloud_path(file):
+
+    # Track if the input came from workspace (don't re-save it)
+    is_from_workspace = file.startswith("workspace://")
+
+    # Check if this is a workspace file reference
+    if is_from_workspace:
+        if workspace_manager is None:
+            raise ValueError(
+                "Workspace file reference requires workspace context. "
+                "This file type is only available in CoPilot sessions."
+            )
+
+        # Parse workspace reference
+        # workspace://abc123 - by file ID
+        # workspace:///path/to/file.txt - by virtual path
+        file_ref = file[12:]  # Remove "workspace://"
+
+        if file_ref.startswith("/"):
+            # Path reference
+            workspace_content = await workspace_manager.read_file(file_ref)
+            file_info = await workspace_manager.get_file_info_by_path(file_ref)
+            filename = sanitize_filename(
+                file_info.name if file_info else f"{uuid.uuid4()}.bin"
+            )
+        else:
+            # ID reference
+            workspace_content = await workspace_manager.read_file_by_id(file_ref)
+            file_info = await workspace_manager.get_file_info(file_ref)
+            filename = sanitize_filename(
+                file_info.name if file_info else f"{uuid.uuid4()}.bin"
+            )
+
+        try:
+            target_path = _ensure_inside_base(base_path / filename, base_path)
+        except OSError as e:
+            raise ValueError(f"Invalid file path '{filename}': {e}") from e
+
+        # Check file size limit
+        if len(workspace_content) > MAX_FILE_SIZE_BYTES:
+            raise ValueError(
+                f"File too large: {len(workspace_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
+            )
+
+        # Virus scan the workspace content before writing locally
+        await scan_content_safe(workspace_content, filename=filename)
+        target_path.write_bytes(workspace_content)
+
+    # Check if this is a cloud storage path
+    elif cloud_storage.is_cloud_path(file):
         # Download from cloud storage and store locally
         cloud_content = await cloud_storage.retrieve_file(
             file, user_id=user_id, graph_exec_id=graph_exec_id
@@ -159,9 +234,9 @@ async def store_media_file(
             raise ValueError(f"Invalid file path '{filename}': {e}") from e
 
         # Check file size limit
-        if len(cloud_content) > MAX_FILE_SIZE:
+        if len(cloud_content) > MAX_FILE_SIZE_BYTES:
             raise ValueError(
-                f"File too large: {len(cloud_content)} bytes > {MAX_FILE_SIZE} bytes"
+                f"File too large: {len(cloud_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
             )
 
         # Virus scan the cloud content before writing locally
@@ -189,9 +264,9 @@ async def store_media_file(
         content = base64.b64decode(b64_content)
 
         # Check file size limit
-        if len(content) > MAX_FILE_SIZE:
+        if len(content) > MAX_FILE_SIZE_BYTES:
             raise ValueError(
-                f"File too large: {len(content)} bytes > {MAX_FILE_SIZE} bytes"
+                f"File too large: {len(content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
             )
 
         # Virus scan the base64 content before writing
@@ -199,23 +274,31 @@ async def store_media_file(
         target_path.write_bytes(content)
 
     elif file.startswith(("http://", "https://")):
-        # URL
+        # URL - download first to get Content-Type header
+        resp = await Requests().get(file)
+
+        # Check file size limit
+        if len(resp.content) > MAX_FILE_SIZE_BYTES:
+            raise ValueError(
+                f"File too large: {len(resp.content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
+            )
+
+        # Extract filename from URL path
         parsed_url = urlparse(file)
         filename = sanitize_filename(Path(parsed_url.path).name or f"{uuid.uuid4()}")
+
+        # If filename lacks extension, add one from Content-Type header
+        if "." not in filename:
+            content_type = resp.headers.get("Content-Type", "").split(";")[0].strip()
+            if content_type:
+                ext = _extension_from_mime(content_type)
+                filename = f"{filename}{ext}"
+
         try:
             target_path = _ensure_inside_base(base_path / filename, base_path)
         except OSError as e:
             raise ValueError(f"Invalid file path '{filename}': {e}") from e
 
-        # Download and save
-        resp = await Requests().get(file)
-
-        # Check file size limit
-        if len(resp.content) > MAX_FILE_SIZE:
-            raise ValueError(
-                f"File too large: {len(resp.content)} bytes > {MAX_FILE_SIZE} bytes"
-            )
-
         # Virus scan the downloaded content before writing
         await scan_content_safe(resp.content, filename=filename)
         target_path.write_bytes(resp.content)
@@ -230,12 +313,44 @@ async def store_media_file(
         if not target_path.is_file():
             raise ValueError(f"Local file does not exist: {target_path}")
 
-    # Return result
+    # Return based on requested format
-    if return_content:
+    if return_format == "for_local_processing":
-        return MediaFileType(_file_to_data_uri(target_path))
+        # Use when processing files locally with tools like ffmpeg, MoviePy, PIL
-    else:
+        # Returns: relative path in exec_file directory (e.g., "image.png")
         return MediaFileType(_strip_base_prefix(target_path, base_path))
 
+    elif return_format == "for_external_api":
+        # Use when sending content to external APIs that need base64
+        # Returns: data URI (e.g., "data:image/png;base64,iVBORw0...")
+        return MediaFileType(_file_to_data_uri(target_path))
+
+    elif return_format == "for_block_output":
+        # Use when returning output from a block to user/next block
+        # Returns: workspace:// ref (CoPilot) or data URI (graph execution)
+        if workspace_manager is None:
+            # No workspace available (graph execution without CoPilot)
+            # Fallback to data URI so the content can still be used/displayed
+            return MediaFileType(_file_to_data_uri(target_path))
+
+        # Don't re-save if input was already from workspace
+        if is_from_workspace:
+            # Return original workspace reference
+            return MediaFileType(file)
+
+        # Save new content to workspace
+        content = target_path.read_bytes()
+        filename = target_path.name
+
+        file_record = await workspace_manager.write_file(
+            content=content,
+            filename=filename,
+            overwrite=True,
+        )
+        return MediaFileType(f"workspace://{file_record.id}")
+
+    else:
+        raise ValueError(f"Invalid return_format: {return_format}")
+
 
 def get_dir_size(path: Path) -> int:
     """Get total size of directory."""

autogpt_platform/backend/backend/util/file_test.py

@@ -7,10 +7,22 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from backend.data.execution import ExecutionContext
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType
 
 
+def make_test_context(
+    graph_exec_id: str = "test-exec-123",
+    user_id: str = "test-user-123",
+) -> ExecutionContext:
+    """Helper to create test ExecutionContext."""
+    return ExecutionContext(
+        user_id=user_id,
+        graph_exec_id=graph_exec_id,
+    )
+
+
 class TestFileCloudIntegration:
     """Test cases for cloud storage integration in file utilities."""
 
@@ -70,10 +82,9 @@ class TestFileCloudIntegration:
             mock_path_class.side_effect = path_constructor
 
             result = await store_media_file(
-                graph_exec_id,
+                file=MediaFileType(cloud_path),
-                MediaFileType(cloud_path),
+                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                "test-user-123",
+                return_format="for_local_processing",
-                return_content=False,
             )
 
             # Verify cloud storage operations
@@ -144,10 +155,9 @@ class TestFileCloudIntegration:
             mock_path_obj.name = "image.png"
             with patch("backend.util.file.Path", return_value=mock_path_obj):
                 result = await store_media_file(
-                    graph_exec_id,
+                    file=MediaFileType(cloud_path),
-                    MediaFileType(cloud_path),
+                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                    "test-user-123",
+                    return_format="for_external_api",
-                    return_content=True,
                 )
 
             # Verify result is a data URI
@@ -198,10 +208,9 @@ class TestFileCloudIntegration:
             mock_resolved_path.relative_to.return_value = Path("test-uuid-789.txt")
 
             await store_media_file(
-                graph_exec_id,
+                file=MediaFileType(data_uri),
-                MediaFileType(data_uri),
+                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                "test-user-123",
+                return_format="for_local_processing",
-                return_content=False,
             )
 
             # Verify cloud handler was checked but not used for retrieval
@@ -234,5 +243,7 @@ class TestFileCloudIntegration:
                 FileNotFoundError, match="File not found in cloud storage"
             ):
                 await store_media_file(
-                    graph_exec_id, MediaFileType(cloud_path), "test-user-123"
+                    file=MediaFileType(cloud_path),
+                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
+                    return_format="for_local_processing",
                 )

autogpt_platform/backend/backend/util/gcs_utils.py

@@ -0,0 +1,108 @@
+"""
+Shared GCS utilities for workspace and cloud storage backends.
+
+This module provides common functionality for working with Google Cloud Storage,
+including path parsing, client management, and signed URL generation.
+"""
+
+import asyncio
+import logging
+from datetime import datetime, timedelta, timezone
+
+import aiohttp
+from gcloud.aio import storage as async_gcs_storage
+from google.cloud import storage as gcs_storage
+
+logger = logging.getLogger(__name__)
+
+
+def parse_gcs_path(path: str) -> tuple[str, str]:
+    """
+    Parse a GCS path in the format 'gcs://bucket/blob' to (bucket, blob).
+
+    Args:
+        path: GCS path string (e.g., "gcs://my-bucket/path/to/file")
+
+    Returns:
+        Tuple of (bucket_name, blob_name)
+
+    Raises:
+        ValueError: If the path format is invalid
+    """
+    if not path.startswith("gcs://"):
+        raise ValueError(f"Invalid GCS path: {path}")
+
+    path_without_prefix = path[6:]  # Remove "gcs://"
+    parts = path_without_prefix.split("/", 1)
+    if len(parts) != 2:
+        raise ValueError(f"Invalid GCS path format: {path}")
+
+    return parts[0], parts[1]
+
+
+async def download_with_fresh_session(bucket: str, blob: str) -> bytes:
+    """
+    Download file content using a fresh session.
+
+    This approach avoids event loop issues that can occur when reusing
+    sessions across different async contexts (e.g., in executors).
+
+    Args:
+        bucket: GCS bucket name
+        blob: Blob path within the bucket
+
+    Returns:
+        File content as bytes
+
+    Raises:
+        FileNotFoundError: If the file doesn't exist
+    """
+    session = aiohttp.ClientSession(
+        connector=aiohttp.TCPConnector(limit=10, force_close=True)
+    )
+    client: async_gcs_storage.Storage | None = None
+    try:
+        client = async_gcs_storage.Storage(session=session)
+        content = await client.download(bucket, blob)
+        return content
+    except Exception as e:
+        if "404" in str(e) or "Not Found" in str(e):
+            raise FileNotFoundError(f"File not found: gcs://{bucket}/{blob}")
+        raise
+    finally:
+        if client:
+            try:
+                await client.close()
+            except Exception:
+                pass  # Best-effort cleanup
+        await session.close()
+
+
+async def generate_signed_url(
+    sync_client: gcs_storage.Client,
+    bucket_name: str,
+    blob_name: str,
+    expires_in: int,
+) -> str:
+    """
+    Generate a signed URL for temporary access to a GCS file.
+
+    Uses asyncio.to_thread() to run the sync operation without blocking.
+
+    Args:
+        sync_client: Sync GCS client with service account credentials
+        bucket_name: GCS bucket name
+        blob_name: Blob path within the bucket
+        expires_in: URL expiration time in seconds
+
+    Returns:
+        Signed URL string
+    """
+    bucket = sync_client.bucket(bucket_name)
+    blob = bucket.blob(blob_name)
+    return await asyncio.to_thread(
+        blob.generate_signed_url,
+        version="v4",
+        expiration=datetime.now(timezone.utc) + timedelta(seconds=expires_in),
+        method="GET",
+    )

autogpt_platform/backend/backend/util/prompt.py

@@ -1,10 +1,19 @@
+from __future__ import annotations
+
+import logging
 from copy import deepcopy
-from typing import Any
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 from tiktoken import encoding_for_model
 
 from backend.util import json
 
+if TYPE_CHECKING:
+    from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
 # ---------------------------------------------------------------------------#
 #  CONSTANTS                                                                 #
 # ---------------------------------------------------------------------------#
@@ -100,9 +109,17 @@ def _is_objective_message(msg: dict) -> bool:
 def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None:
     """
     Carefully truncate tool message content while preserving tool structure.
-    Only truncates tool_result content, leaves tool_use intact.
+    Handles both Anthropic-style (list content) and OpenAI-style (string content) tool messages.
     """
     content = msg.get("content")
+
+    # OpenAI-style tool message: role="tool" with string content
+    if msg.get("role") == "tool" and isinstance(content, str):
+        if _tok_len(content, enc) > max_tokens:
+            msg["content"] = _truncate_middle_tokens(content, enc, max_tokens)
+        return
+
+    # Anthropic-style: list content with tool_result items
     if not isinstance(content, list):
         return
 
@@ -140,141 +157,6 @@ def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str:
 # ---------------------------------------------------------------------------#
 
 
-def compress_prompt(
-    messages: list[dict],
-    target_tokens: int,
-    *,
-    model: str = "gpt-4o",
-    reserve: int = 2_048,
-    start_cap: int = 8_192,
-    floor_cap: int = 128,
-    lossy_ok: bool = True,
-) -> list[dict]:
-    """
-    Shrink *messages* so that::
-
-        token_count(prompt) + reserve  ≤  target_tokens
-
-    Strategy
-    --------
-    1. **Token-aware truncation** – progressively halve a per-message cap
-       (`start_cap`, `start_cap/2`, … `floor_cap`) and apply it to the
-       *content* of every message except the first and last.  Tool shells
-       are included: we keep the envelope but shorten huge payloads.
-    2. **Middle-out deletion** – if still over the limit, delete whole
-       messages working outward from the centre, **skipping** any message
-       that contains ``tool_calls`` or has ``role == "tool"``.
-    3. **Last-chance trim** – if still too big, truncate the *first* and
-       *last* message bodies down to `floor_cap` tokens.
-    4. If the prompt is *still* too large:
-         • raise ``ValueError``      when ``lossy_ok == False`` (default)
-         • return the partially-trimmed prompt when ``lossy_ok == True``
-
-    Parameters
-    ----------
-    messages        Complete chat history (will be deep-copied).
-    model           Model name; passed to tiktoken to pick the right
-                    tokenizer (gpt-4o → 'o200k_base', others fallback).
-    target_tokens   Hard ceiling for prompt size **excluding** the model's
-                    forthcoming answer.
-    reserve         How many tokens you want to leave available for that
-                    answer (`max_tokens` in your subsequent completion call).
-    start_cap       Initial per-message truncation ceiling (tokens).
-    floor_cap       Lowest cap we'll accept before moving to deletions.
-    lossy_ok        If *True* return best-effort prompt instead of raising
-                    after all trim passes have been exhausted.
-
-    Returns
-    -------
-    list[dict]  – A *new* messages list that abides by the rules above.
-    """
-    enc = encoding_for_model(model)  # best-match tokenizer
-    msgs = deepcopy(messages)  # never mutate caller
-
-    def total_tokens() -> int:
-        """Current size of *msgs* in tokens."""
-        return sum(_msg_tokens(m, enc) for m in msgs)
-
-    original_token_count = total_tokens()
-
-    if original_token_count + reserve <= target_tokens:
-        return msgs
-
-    # ---- STEP 0 : normalise content --------------------------------------
-    # Convert non-string payloads to strings so token counting is coherent.
-    for i, m in enumerate(msgs):
-        if not isinstance(m.get("content"), str) and m.get("content") is not None:
-            if _is_tool_message(m):
-                continue
-
-            # Keep first and last messages intact (unless they're tool messages)
-            if i == 0 or i == len(msgs) - 1:
-                continue
-
-            # Reasonable 20k-char ceiling prevents pathological blobs
-            content_str = json.dumps(m["content"], separators=(",", ":"))
-            if len(content_str) > 20_000:
-                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
-            m["content"] = content_str
-
-    # ---- STEP 1 : token-aware truncation ---------------------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for m in msgs[1:-1]:  # keep first & last intact
-            if _is_tool_message(m):
-                # For tool messages, only truncate tool result content, preserve structure
-                _truncate_tool_message_content(m, enc, cap)
-                continue
-
-            if _is_objective_message(m):
-                # Never truncate objective messages - they contain the core task
-                continue
-
-            content = m.get("content") or ""
-            if _tok_len(content, enc) > cap:
-                m["content"] = _truncate_middle_tokens(content, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 2 : middle-out deletion -----------------------------------
-    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
-        # Identify all deletable messages (not first/last, not tool messages, not objective messages)
-        deletable_indices = []
-        for i in range(1, len(msgs) - 1):  # Skip first and last
-            if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]):
-                deletable_indices.append(i)
-
-        if not deletable_indices:
-            break  # nothing more we can drop
-
-        # Delete from center outward - find the index closest to center
-        centre = len(msgs) // 2
-        to_delete = min(deletable_indices, key=lambda i: abs(i - centre))
-        del msgs[to_delete]
-
-    # ---- STEP 3 : final safety-net trim on first & last ------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for idx in (0, -1):  # first and last
-            if _is_tool_message(msgs[idx]):
-                # For tool messages at first/last position, truncate tool result content only
-                _truncate_tool_message_content(msgs[idx], enc, cap)
-                continue
-
-            text = msgs[idx].get("content") or ""
-            if _tok_len(text, enc) > cap:
-                msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 4 : success or fail-gracefully -----------------------------
-    if total_tokens() + reserve > target_tokens and not lossy_ok:
-        raise ValueError(
-            "compress_prompt: prompt still exceeds budget "
-            f"({total_tokens() + reserve} > {target_tokens})."
-        )
-
-    return msgs
-
-
 def estimate_token_count(
     messages: list[dict],
     *,
@@ -293,7 +175,8 @@ def estimate_token_count(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     return sum(_msg_tokens(m, enc) for m in messages)
 
 
@@ -315,6 +198,543 @@ def estimate_token_count_str(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     text = json.dumps(text) if not isinstance(text, str) else text
     return _tok_len(text, enc)
+
+
+# ---------------------------------------------------------------------------#
+#  UNIFIED CONTEXT COMPRESSION                                               #
+# ---------------------------------------------------------------------------#
+
+# Default thresholds
+DEFAULT_TOKEN_THRESHOLD = 120_000
+DEFAULT_KEEP_RECENT = 15
+
+
+@dataclass
+class CompressResult:
+    """Result of context compression."""
+
+    messages: list[dict]
+    token_count: int
+    was_compacted: bool
+    error: str | None = None
+    original_token_count: int = 0
+    messages_summarized: int = 0
+    messages_dropped: int = 0
+
+
+def _normalize_model_for_tokenizer(model: str) -> str:
+    """Normalize model name for tiktoken tokenizer selection."""
+    if "/" in model:
+        model = model.split("/")[-1]
+    if "claude" in model.lower() or not any(
+        known in model.lower() for known in ["gpt", "o1", "chatgpt", "text-"]
+    ):
+        return "gpt-4o"
+    return model
+
+
+def _extract_tool_call_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs from an assistant message.
+
+    Supports both formats:
+    - OpenAI: {"role": "assistant", "tool_calls": [{"id": "..."}]}
+    - Anthropic: {"role": "assistant", "content": [{"type": "tool_use", "id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs found in the message.
+    """
+    ids: set[str] = set()
+    if msg.get("role") != "assistant":
+        return ids
+
+    # OpenAI format: tool_calls array
+    if msg.get("tool_calls"):
+        for tc in msg["tool_calls"]:
+            tc_id = tc.get("id")
+            if tc_id:
+                ids.add(tc_id)
+
+    # Anthropic format: content list with tool_use blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_use":
+                tc_id = block.get("id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _extract_tool_response_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs that this message is responding to.
+
+    Supports both formats:
+    - OpenAI: {"role": "tool", "tool_call_id": "..."}
+    - Anthropic: {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs this message responds to.
+    """
+    ids: set[str] = set()
+
+    # OpenAI format: role=tool with tool_call_id
+    if msg.get("role") == "tool":
+        tc_id = msg.get("tool_call_id")
+        if tc_id:
+            ids.add(tc_id)
+
+    # Anthropic format: content list with tool_result blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                tc_id = block.get("tool_use_id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _is_tool_response_message(msg: dict) -> bool:
+    """Check if message is a tool response (OpenAI or Anthropic format)."""
+    # OpenAI format
+    if msg.get("role") == "tool":
+        return True
+    # Anthropic format
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                return True
+    return False
+
+
+def _remove_orphan_tool_responses(
+    messages: list[dict], orphan_ids: set[str]
+) -> list[dict]:
+    """
+    Remove tool response messages/blocks that reference orphan tool_call IDs.
+
+    Supports both OpenAI and Anthropic formats.
+    For Anthropic messages with mixed valid/orphan tool_result blocks,
+    filters out only the orphan blocks instead of dropping the entire message.
+    """
+    result = []
+    for msg in messages:
+        # OpenAI format: role=tool - drop entire message if orphan
+        if msg.get("role") == "tool":
+            tc_id = msg.get("tool_call_id")
+            if tc_id and tc_id in orphan_ids:
+                continue
+            result.append(msg)
+            continue
+
+        # Anthropic format: content list may have mixed tool_result blocks
+        content = msg.get("content")
+        if isinstance(content, list):
+            has_tool_results = any(
+                isinstance(b, dict) and b.get("type") == "tool_result" for b in content
+            )
+            if has_tool_results:
+                # Filter out orphan tool_result blocks, keep valid ones
+                filtered_content = [
+                    block
+                    for block in content
+                    if not (
+                        isinstance(block, dict)
+                        and block.get("type") == "tool_result"
+                        and block.get("tool_use_id") in orphan_ids
+                    )
+                ]
+                # Only keep message if it has remaining content
+                if filtered_content:
+                    msg = msg.copy()
+                    msg["content"] = filtered_content
+                    result.append(msg)
+                continue
+
+        result.append(msg)
+    return result
+
+
+def _ensure_tool_pairs_intact(
+    recent_messages: list[dict],
+    all_messages: list[dict],
+    start_index: int,
+) -> list[dict]:
+    """
+    Ensure tool_call/tool_response pairs stay together after slicing.
+
+    When slicing messages for context compaction, a naive slice can separate
+    an assistant message containing tool_calls from its corresponding tool
+    response messages. This causes API validation errors (e.g., Anthropic's
+    "unexpected tool_use_id found in tool_result blocks").
+
+    This function checks for orphan tool responses in the slice and extends
+    backwards to include their corresponding assistant messages.
+
+    Supports both formats:
+    - OpenAI: tool_calls array + role="tool" responses
+    - Anthropic: tool_use blocks + tool_result blocks
+
+    Args:
+        recent_messages: The sliced messages to validate
+        all_messages: The complete message list (for looking up missing assistants)
+        start_index: The index in all_messages where recent_messages begins
+
+    Returns:
+        A potentially extended list of messages with tool pairs intact
+    """
+    if not recent_messages:
+        return recent_messages
+
+    # Collect all tool_call_ids from assistant messages in the slice
+    available_tool_call_ids: set[str] = set()
+    for msg in recent_messages:
+        available_tool_call_ids |= _extract_tool_call_ids_from_message(msg)
+
+    # Find orphan tool responses (responses whose tool_call_id is missing)
+    orphan_tool_call_ids: set[str] = set()
+    for msg in recent_messages:
+        response_ids = _extract_tool_response_ids_from_message(msg)
+        for tc_id in response_ids:
+            if tc_id not in available_tool_call_ids:
+                orphan_tool_call_ids.add(tc_id)
+
+    if not orphan_tool_call_ids:
+        # No orphans, slice is valid
+        return recent_messages
+
+    # Find the assistant messages that contain the orphan tool_call_ids
+    # Search backwards from start_index in all_messages
+    messages_to_prepend: list[dict] = []
+    for i in range(start_index - 1, -1, -1):
+        msg = all_messages[i]
+        msg_tool_ids = _extract_tool_call_ids_from_message(msg)
+        if msg_tool_ids & orphan_tool_call_ids:
+            # This assistant message has tool_calls we need
+            # Also collect its contiguous tool responses that follow it
+            assistant_and_responses: list[dict] = [msg]
+
+            # Scan forward from this assistant to collect tool responses
+            for j in range(i + 1, start_index):
+                following_msg = all_messages[j]
+                following_response_ids = _extract_tool_response_ids_from_message(
+                    following_msg
+                )
+                if following_response_ids and following_response_ids & msg_tool_ids:
+                    assistant_and_responses.append(following_msg)
+                elif not _is_tool_response_message(following_msg):
+                    # Stop at first non-tool-response message
+                    break
+
+            # Prepend the assistant and its tool responses (maintain order)
+            messages_to_prepend = assistant_and_responses + messages_to_prepend
+            # Mark these as found
+            orphan_tool_call_ids -= msg_tool_ids
+            # Also add this assistant's tool_call_ids to available set
+            available_tool_call_ids |= msg_tool_ids
+
+        if not orphan_tool_call_ids:
+            # Found all missing assistants
+            break
+
+    if orphan_tool_call_ids:
+        # Some tool_call_ids couldn't be resolved - remove those tool responses
+        # This shouldn't happen in normal operation but handles edge cases
+        logger.warning(
+            f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. "
+            "Removing orphan tool responses."
+        )
+        recent_messages = _remove_orphan_tool_responses(
+            recent_messages, orphan_tool_call_ids
+        )
+
+    if messages_to_prepend:
+        logger.info(
+            f"Extended recent messages by {len(messages_to_prepend)} to preserve "
+            f"tool_call/tool_response pairs"
+        )
+        return messages_to_prepend + recent_messages
+
+    return recent_messages
+
+
+async def _summarize_messages_llm(
+    messages: list[dict],
+    client: AsyncOpenAI,
+    model: str,
+    timeout: float = 30.0,
+) -> str:
+    """Summarize messages using an LLM."""
+    conversation = []
+    for msg in messages:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if content and role in ("user", "assistant", "tool"):
+            conversation.append(f"{role.upper()}: {content}")
+
+    conversation_text = "\n\n".join(conversation)
+
+    if not conversation_text:
+        return "No conversation history available."
+
+    # Limit to ~100k chars for safety
+    MAX_CHARS = 100_000
+    if len(conversation_text) > MAX_CHARS:
+        conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]"
+
+    response = await client.with_options(timeout=timeout).chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "Create a detailed summary of the conversation so far. "
+                    "This summary will be used as context when continuing the conversation.\n\n"
+                    "Before writing the summary, analyze each message chronologically to identify:\n"
+                    "- User requests and their explicit goals\n"
+                    "- Your approach and key decisions made\n"
+                    "- Technical specifics (file names, tool outputs, function signatures)\n"
+                    "- Errors encountered and resolutions applied\n\n"
+                    "You MUST include ALL of the following sections:\n\n"
+                    "## 1. Primary Request and Intent\n"
+                    "The user's explicit goals and what they are trying to accomplish.\n\n"
+                    "## 2. Key Technical Concepts\n"
+                    "Technologies, frameworks, tools, and patterns being used or discussed.\n\n"
+                    "## 3. Files and Resources Involved\n"
+                    "Specific files examined or modified, with relevant snippets and identifiers.\n\n"
+                    "## 4. Errors and Fixes\n"
+                    "Problems encountered, error messages, and their resolutions. "
+                    "Include any user feedback on fixes.\n\n"
+                    "## 5. Problem Solving\n"
+                    "Issues that have been resolved and how they were addressed.\n\n"
+                    "## 6. All User Messages\n"
+                    "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n"
+                    "## 7. Pending Tasks\n"
+                    "Work items the user explicitly requested that have not yet been completed.\n\n"
+                    "## 8. Current Work\n"
+                    "Precise description of what was being worked on most recently, including relevant context.\n\n"
+                    "## 9. Next Steps\n"
+                    "What should happen next, aligned with the user's most recent requests. "
+                    "Include verbatim quotes of recent instructions if relevant."
+                ),
+            },
+            {"role": "user", "content": f"Summarize:\n\n{conversation_text}"},
+        ],
+        max_tokens=1500,
+        temperature=0.3,
+    )
+
+    return response.choices[0].message.content or "No summary available."
+
+
+async def compress_context(
+    messages: list[dict],
+    target_tokens: int = DEFAULT_TOKEN_THRESHOLD,
+    *,
+    model: str = "gpt-4o",
+    client: AsyncOpenAI | None = None,
+    keep_recent: int = DEFAULT_KEEP_RECENT,
+    reserve: int = 2_048,
+    start_cap: int = 8_192,
+    floor_cap: int = 128,
+) -> CompressResult:
+    """
+    Unified context compression that combines summarization and truncation strategies.
+
+    Strategy (in order):
+    1. **LLM summarization** – If client provided, summarize old messages into a
+       single context message while keeping recent messages intact. This is the
+       primary strategy for chat service.
+    2. **Content truncation** – Progressively halve a per-message cap and truncate
+       bloated message content (tool outputs, large pastes). Preserves all messages
+       but shortens their content. Primary strategy when client=None (LLM blocks).
+    3. **Middle-out deletion** – Delete whole messages one at a time from the center
+       outward, skipping tool messages and objective messages.
+    4. **First/last trim** – Truncate first and last message content as last resort.
+
+    Parameters
+    ----------
+    messages        Complete chat history (will be deep-copied).
+    target_tokens   Hard ceiling for prompt size.
+    model           Model name for tokenization and summarization.
+    client          AsyncOpenAI client. If provided, enables LLM summarization
+                    as the first strategy. If None, skips to truncation strategies.
+    keep_recent     Number of recent messages to preserve during summarization.
+    reserve         Tokens to reserve for model response.
+    start_cap       Initial per-message truncation ceiling (tokens).
+    floor_cap       Lowest cap before moving to deletions.
+
+    Returns
+    -------
+    CompressResult with compressed messages and metadata.
+    """
+    # Guard clause for empty messages
+    if not messages:
+        return CompressResult(
+            messages=[],
+            token_count=0,
+            was_compacted=False,
+            original_token_count=0,
+        )
+
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
+    msgs = deepcopy(messages)
+
+    def total_tokens() -> int:
+        return sum(_msg_tokens(m, enc) for m in msgs)
+
+    original_count = total_tokens()
+
+    # Already under limit
+    if original_count + reserve <= target_tokens:
+        return CompressResult(
+            messages=msgs,
+            token_count=original_count,
+            was_compacted=False,
+            original_token_count=original_count,
+        )
+
+    messages_summarized = 0
+    messages_dropped = 0
+
+    # ---- STEP 1: LLM summarization (if client provided) -------------------
+    # This is the primary compression strategy for chat service.
+    # Summarize old messages while keeping recent ones intact.
+    if client is not None:
+        has_system = len(msgs) > 0 and msgs[0].get("role") == "system"
+        system_msg = msgs[0] if has_system else None
+
+        # Calculate old vs recent messages
+        if has_system:
+            if len(msgs) > keep_recent + 1:
+                old_msgs = msgs[1:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs[1:] if len(msgs) > 1 else []
+        else:
+            if len(msgs) > keep_recent:
+                old_msgs = msgs[:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs
+
+        # Ensure tool pairs stay intact
+        slice_start = max(0, len(msgs) - keep_recent)
+        recent_msgs = _ensure_tool_pairs_intact(recent_msgs, msgs, slice_start)
+
+        if old_msgs:
+            try:
+                summary_text = await _summarize_messages_llm(old_msgs, client, model)
+                summary_msg = {
+                    "role": "assistant",
+                    "content": f"[Previous conversation summary — for context only]: {summary_text}",
+                }
+                messages_summarized = len(old_msgs)
+
+                if has_system:
+                    msgs = [system_msg, summary_msg] + recent_msgs
+                else:
+                    msgs = [summary_msg] + recent_msgs
+
+                logger.info(
+                    f"Context summarized: {original_count} -> {total_tokens()} tokens, "
+                    f"summarized {messages_summarized} messages"
+                )
+            except Exception as e:
+                logger.warning(f"Summarization failed, continuing with truncation: {e}")
+                # Fall through to content truncation
+
+    # ---- STEP 2: Normalize content ----------------------------------------
+    # Convert non-string payloads to strings so token counting is coherent.
+    # Always run this before truncation to ensure consistent token counting.
+    for i, m in enumerate(msgs):
+        if not isinstance(m.get("content"), str) and m.get("content") is not None:
+            if _is_tool_message(m):
+                continue
+            if i == 0 or i == len(msgs) - 1:
+                continue
+            content_str = json.dumps(m["content"], separators=(",", ":"))
+            if len(content_str) > 20_000:
+                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
+            m["content"] = content_str
+
+    # ---- STEP 3: Token-aware content truncation ---------------------------
+    # Progressively halve per-message cap and truncate bloated content.
+    # This preserves all messages but shortens their content.
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for m in msgs[1:-1]:
+            if _is_tool_message(m):
+                _truncate_tool_message_content(m, enc, cap)
+                continue
+            if _is_objective_message(m):
+                continue
+            content = m.get("content") or ""
+            if _tok_len(content, enc) > cap:
+                m["content"] = _truncate_middle_tokens(content, enc, cap)
+        cap //= 2
+
+    # ---- STEP 4: Middle-out deletion --------------------------------------
+    # Delete messages one at a time from the center outward.
+    # This is more granular than dropping all old messages at once.
+    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
+        deletable: list[int] = []
+        for i in range(1, len(msgs) - 1):
+            msg = msgs[i]
+            if (
+                msg is not None
+                and not _is_tool_message(msg)
+                and not _is_objective_message(msg)
+            ):
+                deletable.append(i)
+        if not deletable:
+            break
+        centre = len(msgs) // 2
+        to_delete = min(deletable, key=lambda i: abs(i - centre))
+        del msgs[to_delete]
+        messages_dropped += 1
+
+    # ---- STEP 5: Final trim on first/last ---------------------------------
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for idx in (0, -1):
+            msg = msgs[idx]
+            if msg is None:
+                continue
+            if _is_tool_message(msg):
+                _truncate_tool_message_content(msg, enc, cap)
+                continue
+            text = msg.get("content") or ""
+            if _tok_len(text, enc) > cap:
+                msg["content"] = _truncate_middle_tokens(text, enc, cap)
+        cap //= 2
+
+    # Filter out any None values that may have been introduced
+    final_msgs: list[dict] = [m for m in msgs if m is not None]
+    final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
+    error = None
+    if final_count + reserve > target_tokens:
+        error = f"Could not compress below target ({final_count + reserve} > {target_tokens})"
+        logger.warning(error)
+
+    return CompressResult(
+        messages=final_msgs,
+        token_count=final_count,
+        was_compacted=True,
+        error=error,
+        original_token_count=original_count,
+        messages_summarized=messages_summarized,
+        messages_dropped=messages_dropped,
+    )

autogpt_platform/backend/backend/util/prompt_test.py

@@ -1,10 +1,21 @@
 """Tests for prompt utility functions, especially tool call token counting."""
 
+from unittest.mock import AsyncMock, MagicMock
+
 import pytest
 from tiktoken import encoding_for_model
 
 from backend.util import json
-from backend.util.prompt import _msg_tokens, estimate_token_count
+from backend.util.prompt import (
+    CompressResult,
+    _ensure_tool_pairs_intact,
+    _msg_tokens,
+    _normalize_model_for_tokenizer,
+    _truncate_middle_tokens,
+    _truncate_tool_message_content,
+    compress_context,
+    estimate_token_count,
+)
 
 
 class TestMsgTokens:
@@ -276,3 +287,690 @@ class TestEstimateTokenCount:
 
         assert total_tokens == expected_total
         assert total_tokens > 20  # Should be substantial
+
+
+class TestNormalizeModelForTokenizer:
+    """Test model name normalization for tiktoken."""
+
+    def test_openai_models_unchanged(self):
+        """Test that OpenAI models are returned as-is."""
+        assert _normalize_model_for_tokenizer("gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("gpt-4") == "gpt-4"
+        assert _normalize_model_for_tokenizer("gpt-3.5-turbo") == "gpt-3.5-turbo"
+
+    def test_claude_models_normalized(self):
+        """Test that Claude models are normalized to gpt-4o."""
+        assert _normalize_model_for_tokenizer("claude-3-opus") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("claude-3-sonnet") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-haiku") == "gpt-4o"
+
+    def test_openrouter_paths_extracted(self):
+        """Test that OpenRouter model paths are handled."""
+        assert _normalize_model_for_tokenizer("openai/gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-opus") == "gpt-4o"
+
+    def test_unknown_models_default_to_gpt4o(self):
+        """Test that unknown models default to gpt-4o."""
+        assert _normalize_model_for_tokenizer("some-random-model") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("llama-3-70b") == "gpt-4o"
+
+
+class TestTruncateToolMessageContent:
+    """Test tool message content truncation."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncate_openai_tool_message(self, enc):
+        """Test truncation of OpenAI-style tool message with string content."""
+        long_content = "x" * 10000
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": long_content}
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        assert len(msg["content"]) < len(long_content)
+        assert "…" in msg["content"]  # Has ellipsis marker
+
+    def test_truncate_anthropic_tool_result(self, enc):
+        """Test truncation of Anthropic-style tool_result."""
+        long_content = "y" * 10000
+        msg = {
+            "role": "user",
+            "content": [
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_123",
+                    "content": long_content,
+                }
+            ],
+        }
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        result_content = msg["content"][0]["content"]
+        assert len(result_content) < len(long_content)
+        assert "…" in result_content
+
+    def test_preserve_tool_use_blocks(self, enc):
+        """Test that tool_use blocks are not truncated."""
+        msg = {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "tool_use",
+                    "id": "toolu_123",
+                    "name": "some_function",
+                    "input": {"key": "value" * 1000},  # Large input
+                }
+            ],
+        }
+
+        original = json.dumps(msg["content"][0]["input"])
+        _truncate_tool_message_content(msg, enc, max_tokens=10)
+
+        # tool_use should be unchanged
+        assert json.dumps(msg["content"][0]["input"]) == original
+
+    def test_no_truncation_when_under_limit(self, enc):
+        """Test that short content is not modified."""
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": "Short content"}
+
+        original = msg["content"]
+        _truncate_tool_message_content(msg, enc, max_tokens=1000)
+
+        assert msg["content"] == original
+
+
+class TestTruncateMiddleTokens:
+    """Test middle truncation of text."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncates_long_text(self, enc):
+        """Test that long text is truncated with ellipsis in middle."""
+        long_text = "word " * 1000
+        result = _truncate_middle_tokens(long_text, enc, max_tok=50)
+
+        assert len(enc.encode(result)) <= 52  # Allow some slack for ellipsis
+        assert "…" in result
+        assert result.startswith("word")  # Head preserved
+        assert result.endswith("word ")  # Tail preserved
+
+    def test_preserves_short_text(self, enc):
+        """Test that short text is not modified."""
+        short_text = "Hello world"
+        result = _truncate_middle_tokens(short_text, enc, max_tok=100)
+
+        assert result == short_text
+
+
+class TestEnsureToolPairsIntact:
+    """Test tool call/response pair preservation for both OpenAI and Anthropic formats."""
+
+    # ---- OpenAI Format Tests ----
+
+    def test_openai_adds_missing_tool_call(self):
+        """Test that orphaned OpenAI tool_response gets its tool_call prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool response)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_call message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert "tool_calls" in result[0]
+
+    def test_openai_keeps_complete_pairs(self):
+        """Test that complete OpenAI pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+        ]
+        recent = all_msgs[1:]  # Include both tool_call and response
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_openai_multiple_tool_calls(self):
+        """Test multiple OpenAI tool calls in one assistant message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}},
+                    {"id": "call_2", "type": "function", "function": {"name": "f2"}},
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result1"},
+            {"role": "tool", "tool_call_id": "call_2", "content": "result2"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (first tool response)
+        recent = [all_msgs[2], all_msgs[3], all_msgs[4]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_calls
+        assert len(result) == 4
+        assert result[0]["role"] == "assistant"
+        assert len(result[0]["tool_calls"]) == 2
+
+    # ---- Anthropic Format Tests ----
+
+    def test_anthropic_adds_missing_tool_use(self):
+        """Test that orphaned Anthropic tool_result gets its tool_use prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_123",
+                        "name": "get_weather",
+                        "input": {"location": "SF"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_123",
+                        "content": "22°C and sunny",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_anthropic_keeps_complete_pairs(self):
+        """Test that complete Anthropic pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_456",
+                        "name": "calculator",
+                        "input": {"expr": "2+2"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_456",
+                        "content": "4",
+                    }
+                ],
+            },
+        ]
+        recent = all_msgs[1:]  # Include both tool_use and result
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_anthropic_multiple_tool_uses(self):
+        """Test multiple Anthropic tool_use blocks in one message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "Let me check both..."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_1",
+                        "name": "get_weather",
+                        "input": {"city": "NYC"},
+                    },
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_2",
+                        "name": "get_weather",
+                        "input": {"city": "LA"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_1",
+                        "content": "Cold",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_2",
+                        "content": "Warm",
+                    },
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_uses
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        tool_use_count = sum(
+            1 for b in result[0]["content"] if b.get("type") == "tool_use"
+        )
+        assert tool_use_count == 2
+
+    # ---- Mixed/Edge Case Tests ----
+
+    def test_anthropic_with_type_message_field(self):
+        """Test Anthropic format with 'type': 'message' field (smart_decision_maker style)."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_abc",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "type": "message",  # Extra field from smart_decision_maker
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_abc",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result with 'type': 'message')
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_handles_no_tool_messages(self):
+        """Test messages without tool calls."""
+        all_msgs = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        recent = all_msgs
+        start_index = 0
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert result == all_msgs
+
+    def test_handles_empty_messages(self):
+        """Test empty message list."""
+        result = _ensure_tool_pairs_intact([], [], 0)
+        assert result == []
+
+    def test_mixed_text_and_tool_content(self):
+        """Test Anthropic message with mixed text and tool_use content."""
+        all_msgs = [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you with that."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_mixed",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_mixed",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "assistant", "content": "Here are the results..."},
+        ]
+        # Start from tool_result
+        recent = [all_msgs[1], all_msgs[2]]
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with tool_use
+        assert len(result) == 3
+        assert result[0]["content"][0]["type"] == "text"
+        assert result[0]["content"][1]["type"] == "tool_use"
+
+
+class TestCompressContext:
+    """Test the async compress_context function."""
+
+    @pytest.mark.asyncio
+    async def test_no_compression_needed(self):
+        """Test messages under limit return without compression."""
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hello!"},
+        ]
+
+        result = await compress_context(messages, target_tokens=100000)
+
+        assert isinstance(result, CompressResult)
+        assert result.was_compacted is False
+        assert len(result.messages) == 2
+        assert result.error is None
+
+    @pytest.mark.asyncio
+    async def test_truncation_without_client(self):
+        """Test that truncation works without LLM client."""
+        long_content = "x" * 50000
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": long_content},
+            {"role": "assistant", "content": "Response"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=1000, client=None, reserve=100
+        )
+
+        assert result.was_compacted is True
+        # Should have truncated without summarization
+        assert result.messages_summarized == 0
+
+    @pytest.mark.asyncio
+    async def test_with_mocked_llm_client(self):
+        """Test summarization with mocked LLM client."""
+        # Create many messages to trigger summarization
+        messages = [{"role": "system", "content": "System prompt"}]
+        for i in range(30):
+            messages.append({"role": "user", "content": f"User message {i} " * 100})
+            messages.append(
+                {"role": "assistant", "content": f"Assistant response {i} " * 100}
+            )
+
+        # Mock the AsyncOpenAI client
+        mock_client = AsyncMock()
+        mock_response = MagicMock()
+        mock_response.choices = [MagicMock()]
+        mock_response.choices[0].message.content = "Summary of conversation"
+        mock_client.with_options.return_value.chat.completions.create = AsyncMock(
+            return_value=mock_response
+        )
+
+        result = await compress_context(
+            messages,
+            target_tokens=5000,
+            client=mock_client,
+            keep_recent=5,
+            reserve=500,
+        )
+
+        assert result.was_compacted is True
+        # Should have attempted summarization
+        assert mock_client.with_options.called or result.messages_summarized > 0
+
+    @pytest.mark.asyncio
+    async def test_preserves_tool_pairs(self):
+        """Test that tool call/response pairs stay together."""
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": "Do something"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "func"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "Result " * 1000},
+            {"role": "assistant", "content": "Done!"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=500, client=None, reserve=50
+        )
+
+        # Check that if tool response exists, its call exists too
+        tool_call_ids = set()
+        tool_response_ids = set()
+        for msg in result.messages:
+            if "tool_calls" in msg:
+                for tc in msg["tool_calls"]:
+                    tool_call_ids.add(tc["id"])
+            if msg.get("role") == "tool":
+                tool_response_ids.add(msg.get("tool_call_id"))
+
+        # All tool responses should have their calls
+        assert tool_response_ids <= tool_call_ids
+
+    @pytest.mark.asyncio
+    async def test_returns_error_when_cannot_compress(self):
+        """Test that error is returned when compression fails."""
+        # Single huge message that can't be compressed enough
+        messages = [
+            {"role": "user", "content": "x" * 100000},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=100, client=None, reserve=50
+        )
+
+        # Should have an error since we can't get below 100 tokens
+        assert result.error is not None
+        assert result.was_compacted is True
+
+    @pytest.mark.asyncio
+    async def test_empty_messages(self):
+        """Test that empty messages list returns early without error."""
+        result = await compress_context([], target_tokens=1000)
+
+        assert result.messages == []
+        assert result.token_count == 0
+        assert result.was_compacted is False
+        assert result.error is None
+
+
+class TestRemoveOrphanToolResponses:
+    """Test _remove_orphan_tool_responses helper function."""
+
+    def test_removes_openai_orphan(self):
+        """Test removal of orphan OpenAI tool response."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_orphan", "content": "result"},
+            {"role": "user", "content": "Hello"},
+        ]
+        orphan_ids = {"call_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["role"] == "user"
+
+    def test_keeps_valid_openai_tool(self):
+        """Test that valid OpenAI tool responses are kept."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_valid", "content": "result"},
+        ]
+        orphan_ids = {"call_other"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["tool_call_id"] == "call_valid"
+
+    def test_filters_anthropic_mixed_blocks(self):
+        """Test filtering individual orphan blocks from Anthropic message with mixed valid/orphan."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_valid",
+                        "content": "valid result",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan",
+                        "content": "orphan result",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        # Should only have the valid tool_result, orphan filtered out
+        assert len(result[0]["content"]) == 1
+        assert result[0]["content"][0]["tool_use_id"] == "toolu_valid"
+
+    def test_removes_anthropic_all_orphan(self):
+        """Test removal of Anthropic message when all tool_results are orphans."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan1",
+                        "content": "result1",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan2",
+                        "content": "result2",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan1", "toolu_orphan2"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        # Message should be completely removed since no content left
+        assert len(result) == 0
+
+    def test_preserves_non_tool_messages(self):
+        """Test that non-tool messages are preserved."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        orphan_ids = {"some_id"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert result == messages
+
+
+class TestCompressResultDataclass:
+    """Test CompressResult dataclass."""
+
+    def test_default_values(self):
+        """Test default values are set correctly."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=10,
+            was_compacted=False,
+        )
+
+        assert result.error is None
+        assert result.original_token_count == 0  # Defaults to 0, not None
+        assert result.messages_summarized == 0
+        assert result.messages_dropped == 0
+
+    def test_all_fields(self):
+        """Test all fields can be set."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=100,
+            was_compacted=True,
+            error="Some error",
+            original_token_count=500,
+            messages_summarized=10,
+            messages_dropped=5,
+        )
+
+        assert result.token_count == 100
+        assert result.was_compacted is True
+        assert result.error == "Some error"
+        assert result.original_token_count == 500
+        assert result.messages_summarized == 10
+        assert result.messages_dropped == 5

autogpt_platform/backend/backend/util/settings.py

@@ -263,6 +263,12 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="The name of the Google Cloud Storage bucket for media files",
     )
 
+    workspace_storage_dir: str = Field(
+        default="",
+        description="Local directory for workspace file storage when GCS is not configured. "
+        "If empty, defaults to {app_data}/workspaces. Used for self-hosted deployments.",
+    )
+
     reddit_user_agent: str = Field(
         default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
@@ -389,6 +395,13 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Maximum file size in MB for file uploads (1-1024 MB)",
     )
 
+    max_file_size_mb: int = Field(
+        default=100,
+        ge=1,
+        le=1024,
+        description="Maximum file size in MB for workspace files (1-1024 MB)",
+    )
+
     # AutoMod configuration
     automod_enabled: bool = Field(
         default=False,

autogpt_platform/backend/backend/util/test.py

@@ -140,14 +140,29 @@ async def execute_block_test(block: Block):
             setattr(block, mock_name, mock_obj)
 
     # Populate credentials argument(s)
+    # Generate IDs for execution context
+    graph_id = str(uuid.uuid4())
+    node_id = str(uuid.uuid4())
+    graph_exec_id = str(uuid.uuid4())
+    node_exec_id = str(uuid.uuid4())
+    user_id = str(uuid.uuid4())
+    graph_version = 1  # Default version for tests
+
     extra_exec_kwargs: dict = {
-        "graph_id": str(uuid.uuid4()),
+        "graph_id": graph_id,
-        "node_id": str(uuid.uuid4()),
+        "node_id": node_id,
-        "graph_exec_id": str(uuid.uuid4()),
+        "graph_exec_id": graph_exec_id,
-        "node_exec_id": str(uuid.uuid4()),
+        "node_exec_id": node_exec_id,
-        "user_id": str(uuid.uuid4()),
+        "user_id": user_id,
-        "graph_version": 1,  # Default version for tests
+        "graph_version": graph_version,
-        "execution_context": ExecutionContext(),
+        "execution_context": ExecutionContext(
+            user_id=user_id,
+            graph_id=graph_id,
+            graph_exec_id=graph_exec_id,
+            graph_version=graph_version,
+            node_id=node_id,
+            node_exec_id=node_exec_id,
+        ),
     }
     input_model = cast(type[BlockSchema], block.input_schema)
 

autogpt_platform/backend/backend/util/workspace.py

@@ -0,0 +1,419 @@
+"""
+WorkspaceManager for managing user workspace file operations.
+
+This module provides a high-level interface for workspace file operations,
+combining the storage backend and database layer.
+"""
+
+import logging
+import mimetypes
+import uuid
+from typing import Optional
+
+from prisma.errors import UniqueViolationError
+from prisma.models import UserWorkspaceFile
+
+from backend.data.workspace import (
+    count_workspace_files,
+    create_workspace_file,
+    get_workspace_file,
+    get_workspace_file_by_path,
+    list_workspace_files,
+    soft_delete_workspace_file,
+)
+from backend.util.settings import Config
+from backend.util.workspace_storage import compute_file_checksum, get_workspace_storage
+
+logger = logging.getLogger(__name__)
+
+
+class WorkspaceManager:
+    """
+    Manages workspace file operations.
+
+    Combines storage backend operations with database record management.
+    Supports session-scoped file segmentation where files are stored in
+    session-specific virtual paths: /sessions/{session_id}/{filename}
+    """
+
+    def __init__(
+        self, user_id: str, workspace_id: str, session_id: Optional[str] = None
+    ):
+        """
+        Initialize WorkspaceManager.
+
+        Args:
+            user_id: The user's ID
+            workspace_id: The workspace ID
+            session_id: Optional session ID for session-scoped file access
+        """
+        self.user_id = user_id
+        self.workspace_id = workspace_id
+        self.session_id = session_id
+        # Session path prefix for file isolation
+        self.session_path = f"/sessions/{session_id}" if session_id else ""
+
+    def _resolve_path(self, path: str) -> str:
+        """
+        Resolve a path, defaulting to session folder if session_id is set.
+
+        Cross-session access is allowed by explicitly using /sessions/other-session-id/...
+
+        Args:
+            path: Virtual path (e.g., "/file.txt" or "/sessions/abc123/file.txt")
+
+        Returns:
+            Resolved path with session prefix if applicable
+        """
+        # If path explicitly references a session folder, use it as-is
+        if path.startswith("/sessions/"):
+            return path
+
+        # If we have a session context, prepend session path
+        if self.session_path:
+            # Normalize the path
+            if not path.startswith("/"):
+                path = f"/{path}"
+            return f"{self.session_path}{path}"
+
+        # No session context, use path as-is
+        return path if path.startswith("/") else f"/{path}"
+
+    def _get_effective_path(
+        self, path: Optional[str], include_all_sessions: bool
+    ) -> Optional[str]:
+        """
+        Get effective path for list/count operations based on session context.
+
+        Args:
+            path: Optional path prefix to filter
+            include_all_sessions: If True, don't apply session scoping
+
+        Returns:
+            Effective path prefix for database query
+        """
+        if include_all_sessions:
+            # Normalize path to ensure leading slash (stored paths are normalized)
+            if path is not None and not path.startswith("/"):
+                return f"/{path}"
+            return path
+        elif path is not None:
+            # Resolve the provided path with session scoping
+            return self._resolve_path(path)
+        elif self.session_path:
+            # Default to session folder with trailing slash to prevent prefix collisions
+            # e.g., "/sessions/abc" should not match "/sessions/abc123"
+            return self.session_path.rstrip("/") + "/"
+        else:
+            # No session context, use path as-is
+            return path
+
+    async def read_file(self, path: str) -> bytes:
+        """
+        Read file from workspace by virtual path.
+
+        When session_id is set, paths are resolved relative to the session folder
+        unless they explicitly reference /sessions/...
+
+        Args:
+            path: Virtual path (e.g., "/documents/report.pdf")
+
+        Returns:
+            File content as bytes
+
+        Raises:
+            FileNotFoundError: If file doesn't exist
+        """
+        resolved_path = self._resolve_path(path)
+        file = await get_workspace_file_by_path(self.workspace_id, resolved_path)
+        if file is None:
+            raise FileNotFoundError(f"File not found at path: {resolved_path}")
+
+        storage = await get_workspace_storage()
+        return await storage.retrieve(file.storagePath)
+
+    async def read_file_by_id(self, file_id: str) -> bytes:
+        """
+        Read file from workspace by file ID.
+
+        Args:
+            file_id: The file's ID
+
+        Returns:
+            File content as bytes
+
+        Raises:
+            FileNotFoundError: If file doesn't exist
+        """
+        file = await get_workspace_file(file_id, self.workspace_id)
+        if file is None:
+            raise FileNotFoundError(f"File not found: {file_id}")
+
+        storage = await get_workspace_storage()
+        return await storage.retrieve(file.storagePath)
+
+    async def write_file(
+        self,
+        content: bytes,
+        filename: str,
+        path: Optional[str] = None,
+        mime_type: Optional[str] = None,
+        overwrite: bool = False,
+    ) -> UserWorkspaceFile:
+        """
+        Write file to workspace.
+
+        When session_id is set, files are written to /sessions/{session_id}/...
+        by default. Use explicit /sessions/... paths for cross-session access.
+
+        Args:
+            content: File content as bytes
+            filename: Filename for the file
+            path: Virtual path (defaults to "/{filename}", session-scoped if session_id set)
+            mime_type: MIME type (auto-detected if not provided)
+            overwrite: Whether to overwrite existing file at path
+
+        Returns:
+            Created UserWorkspaceFile instance
+
+        Raises:
+            ValueError: If file exceeds size limit or path already exists
+        """
+        # Enforce file size limit
+        max_file_size = Config().max_file_size_mb * 1024 * 1024
+        if len(content) > max_file_size:
+            raise ValueError(
+                f"File too large: {len(content)} bytes exceeds "
+                f"{Config().max_file_size_mb}MB limit"
+            )
+
+        # Determine path with session scoping
+        if path is None:
+            path = f"/{filename}"
+        elif not path.startswith("/"):
+            path = f"/{path}"
+
+        # Resolve path with session prefix
+        path = self._resolve_path(path)
+
+        # Check if file exists at path (only error for non-overwrite case)
+        # For overwrite=True, we let the write proceed and handle via UniqueViolationError
+        # This ensures the new file is written to storage BEFORE the old one is deleted,
+        # preventing data loss if the new write fails
+        if not overwrite:
+            existing = await get_workspace_file_by_path(self.workspace_id, path)
+            if existing is not None:
+                raise ValueError(f"File already exists at path: {path}")
+
+        # Auto-detect MIME type if not provided
+        if mime_type is None:
+            mime_type, _ = mimetypes.guess_type(filename)
+            mime_type = mime_type or "application/octet-stream"
+
+        # Compute checksum
+        checksum = compute_file_checksum(content)
+
+        # Generate unique file ID for storage
+        file_id = str(uuid.uuid4())
+
+        # Store file in storage backend
+        storage = await get_workspace_storage()
+        storage_path = await storage.store(
+            workspace_id=self.workspace_id,
+            file_id=file_id,
+            filename=filename,
+            content=content,
+        )
+
+        # Create database record - handle race condition where another request
+        # created a file at the same path between our check and create
+        try:
+            file = await create_workspace_file(
+                workspace_id=self.workspace_id,
+                file_id=file_id,
+                name=filename,
+                path=path,
+                storage_path=storage_path,
+                mime_type=mime_type,
+                size_bytes=len(content),
+                checksum=checksum,
+            )
+        except UniqueViolationError:
+            # Race condition: another request created a file at this path
+            if overwrite:
+                # Re-fetch and delete the conflicting file, then retry
+                existing = await get_workspace_file_by_path(self.workspace_id, path)
+                if existing:
+                    await self.delete_file(existing.id)
+                # Retry the create - if this also fails, clean up storage file
+                try:
+                    file = await create_workspace_file(
+                        workspace_id=self.workspace_id,
+                        file_id=file_id,
+                        name=filename,
+                        path=path,
+                        storage_path=storage_path,
+                        mime_type=mime_type,
+                        size_bytes=len(content),
+                        checksum=checksum,
+                    )
+                except Exception:
+                    # Clean up orphaned storage file on retry failure
+                    try:
+                        await storage.delete(storage_path)
+                    except Exception as e:
+                        logger.warning(f"Failed to clean up orphaned storage file: {e}")
+                    raise
+            else:
+                # Clean up the orphaned storage file before raising
+                try:
+                    await storage.delete(storage_path)
+                except Exception as e:
+                    logger.warning(f"Failed to clean up orphaned storage file: {e}")
+                raise ValueError(f"File already exists at path: {path}")
+        except Exception:
+            # Any other database error (connection, validation, etc.) - clean up storage
+            try:
+                await storage.delete(storage_path)
+            except Exception as e:
+                logger.warning(f"Failed to clean up orphaned storage file: {e}")
+            raise
+
+        logger.info(
+            f"Wrote file {file.id} ({filename}) to workspace {self.workspace_id} "
+            f"at path {path}, size={len(content)} bytes"
+        )
+
+        return file
+
+    async def list_files(
+        self,
+        path: Optional[str] = None,
+        limit: Optional[int] = None,
+        offset: int = 0,
+        include_all_sessions: bool = False,
+    ) -> list[UserWorkspaceFile]:
+        """
+        List files in workspace.
+
+        When session_id is set and include_all_sessions is False (default),
+        only files in the current session's folder are listed.
+
+        Args:
+            path: Optional path prefix to filter (e.g., "/documents/")
+            limit: Maximum number of files to return
+            offset: Number of files to skip
+            include_all_sessions: If True, list files from all sessions.
+                                  If False (default), only list current session's files.
+
+        Returns:
+            List of UserWorkspaceFile instances
+        """
+        effective_path = self._get_effective_path(path, include_all_sessions)
+
+        return await list_workspace_files(
+            workspace_id=self.workspace_id,
+            path_prefix=effective_path,
+            limit=limit,
+            offset=offset,
+        )
+
+    async def delete_file(self, file_id: str) -> bool:
+        """
+        Delete a file (soft-delete).
+
+        Args:
+            file_id: The file's ID
+
+        Returns:
+            True if deleted, False if not found
+        """
+        file = await get_workspace_file(file_id, self.workspace_id)
+        if file is None:
+            return False
+
+        # Delete from storage
+        storage = await get_workspace_storage()
+        try:
+            await storage.delete(file.storagePath)
+        except Exception as e:
+            logger.warning(f"Failed to delete file from storage: {e}")
+            # Continue with database soft-delete even if storage delete fails
+
+        # Soft-delete database record
+        result = await soft_delete_workspace_file(file_id, self.workspace_id)
+        return result is not None
+
+    async def get_download_url(self, file_id: str, expires_in: int = 3600) -> str:
+        """
+        Get download URL for a file.
+
+        Args:
+            file_id: The file's ID
+            expires_in: URL expiration in seconds (default 1 hour)
+
+        Returns:
+            Download URL (signed URL for GCS, API endpoint for local)
+
+        Raises:
+            FileNotFoundError: If file doesn't exist
+        """
+        file = await get_workspace_file(file_id, self.workspace_id)
+        if file is None:
+            raise FileNotFoundError(f"File not found: {file_id}")
+
+        storage = await get_workspace_storage()
+        return await storage.get_download_url(file.storagePath, expires_in)
+
+    async def get_file_info(self, file_id: str) -> Optional[UserWorkspaceFile]:
+        """
+        Get file metadata.
+
+        Args:
+            file_id: The file's ID
+
+        Returns:
+            UserWorkspaceFile instance or None
+        """
+        return await get_workspace_file(file_id, self.workspace_id)
+
+    async def get_file_info_by_path(self, path: str) -> Optional[UserWorkspaceFile]:
+        """
+        Get file metadata by path.
+
+        When session_id is set, paths are resolved relative to the session folder
+        unless they explicitly reference /sessions/...
+
+        Args:
+            path: Virtual path
+
+        Returns:
+            UserWorkspaceFile instance or None
+        """
+        resolved_path = self._resolve_path(path)
+        return await get_workspace_file_by_path(self.workspace_id, resolved_path)
+
+    async def get_file_count(
+        self,
+        path: Optional[str] = None,
+        include_all_sessions: bool = False,
+    ) -> int:
+        """
+        Get number of files in workspace.
+
+        When session_id is set and include_all_sessions is False (default),
+        only counts files in the current session's folder.
+
+        Args:
+            path: Optional path prefix to filter (e.g., "/documents/")
+            include_all_sessions: If True, count all files in workspace.
+                                  If False (default), only count current session's files.
+
+        Returns:
+            Number of files
+        """
+        effective_path = self._get_effective_path(path, include_all_sessions)
+
+        return await count_workspace_files(
+            self.workspace_id, path_prefix=effective_path
+        )

autogpt_platform/backend/backend/util/workspace_storage.py

@@ -0,0 +1,398 @@
+"""
+Workspace storage backend abstraction for supporting both cloud and local deployments.
+
+This module provides a unified interface for storing workspace files, with implementations
+for Google Cloud Storage (cloud deployments) and local filesystem (self-hosted deployments).
+"""
+
+import asyncio
+import hashlib
+import logging
+from abc import ABC, abstractmethod
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+import aiofiles
+import aiohttp
+from gcloud.aio import storage as async_gcs_storage
+from google.cloud import storage as gcs_storage
+
+from backend.util.data import get_data_path
+from backend.util.gcs_utils import (
+    download_with_fresh_session,
+    generate_signed_url,
+    parse_gcs_path,
+)
+from backend.util.settings import Config
+
+logger = logging.getLogger(__name__)
+
+
+class WorkspaceStorageBackend(ABC):
+    """Abstract interface for workspace file storage."""
+
+    @abstractmethod
+    async def store(
+        self,
+        workspace_id: str,
+        file_id: str,
+        filename: str,
+        content: bytes,
+    ) -> str:
+        """
+        Store file content, return storage path.
+
+        Args:
+            workspace_id: The workspace ID
+            file_id: Unique file ID for storage
+            filename: Original filename
+            content: File content as bytes
+
+        Returns:
+            Storage path string (cloud path or local path)
+        """
+        pass
+
+    @abstractmethod
+    async def retrieve(self, storage_path: str) -> bytes:
+        """
+        Retrieve file content from storage.
+
+        Args:
+            storage_path: The storage path returned from store()
+
+        Returns:
+            File content as bytes
+        """
+        pass
+
+    @abstractmethod
+    async def delete(self, storage_path: str) -> None:
+        """
+        Delete file from storage.
+
+        Args:
+            storage_path: The storage path to delete
+        """
+        pass
+
+    @abstractmethod
+    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
+        """
+        Get URL for downloading the file.
+
+        Args:
+            storage_path: The storage path
+            expires_in: URL expiration time in seconds (default 1 hour)
+
+        Returns:
+            Download URL (signed URL for GCS, direct API path for local)
+        """
+        pass
+
+
+class GCSWorkspaceStorage(WorkspaceStorageBackend):
+    """Google Cloud Storage implementation for workspace storage."""
+
+    def __init__(self, bucket_name: str):
+        self.bucket_name = bucket_name
+        self._async_client: Optional[async_gcs_storage.Storage] = None
+        self._sync_client: Optional[gcs_storage.Client] = None
+        self._session: Optional[aiohttp.ClientSession] = None
+
+    async def _get_async_client(self) -> async_gcs_storage.Storage:
+        """Get or create async GCS client."""
+        if self._async_client is None:
+            self._session = aiohttp.ClientSession(
+                connector=aiohttp.TCPConnector(limit=100, force_close=False)
+            )
+            self._async_client = async_gcs_storage.Storage(session=self._session)
+        return self._async_client
+
+    def _get_sync_client(self) -> gcs_storage.Client:
+        """Get or create sync GCS client (for signed URLs)."""
+        if self._sync_client is None:
+            self._sync_client = gcs_storage.Client()
+        return self._sync_client
+
+    async def close(self) -> None:
+        """Close all client connections."""
+        if self._async_client is not None:
+            try:
+                await self._async_client.close()
+            except Exception as e:
+                logger.warning(f"Error closing GCS client: {e}")
+            self._async_client = None
+
+        if self._session is not None:
+            try:
+                await self._session.close()
+            except Exception as e:
+                logger.warning(f"Error closing session: {e}")
+            self._session = None
+
+    def _build_blob_name(self, workspace_id: str, file_id: str, filename: str) -> str:
+        """Build the blob path for workspace files."""
+        return f"workspaces/{workspace_id}/{file_id}/{filename}"
+
+    async def store(
+        self,
+        workspace_id: str,
+        file_id: str,
+        filename: str,
+        content: bytes,
+    ) -> str:
+        """Store file in GCS."""
+        client = await self._get_async_client()
+        blob_name = self._build_blob_name(workspace_id, file_id, filename)
+
+        # Upload with metadata
+        upload_time = datetime.now(timezone.utc)
+        await client.upload(
+            self.bucket_name,
+            blob_name,
+            content,
+            metadata={
+                "uploaded_at": upload_time.isoformat(),
+                "workspace_id": workspace_id,
+                "file_id": file_id,
+            },
+        )
+
+        return f"gcs://{self.bucket_name}/{blob_name}"
+
+    async def retrieve(self, storage_path: str) -> bytes:
+        """Retrieve file from GCS."""
+        bucket_name, blob_name = parse_gcs_path(storage_path)
+        return await download_with_fresh_session(bucket_name, blob_name)
+
+    async def delete(self, storage_path: str) -> None:
+        """Delete file from GCS."""
+        bucket_name, blob_name = parse_gcs_path(storage_path)
+        client = await self._get_async_client()
+
+        try:
+            await client.delete(bucket_name, blob_name)
+        except Exception as e:
+            if "404" not in str(e) and "Not Found" not in str(e):
+                raise
+            # File already deleted, that's fine
+
+    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
+        """
+        Generate download URL for GCS file.
+
+        Attempts to generate a signed URL if running with service account credentials.
+        Falls back to an API proxy endpoint if signed URL generation fails
+        (e.g., when running locally with user OAuth credentials).
+        """
+        bucket_name, blob_name = parse_gcs_path(storage_path)
+
+        # Extract file_id from blob_name for fallback: workspaces/{workspace_id}/{file_id}/{filename}
+        blob_parts = blob_name.split("/")
+        file_id = blob_parts[2] if len(blob_parts) >= 3 else None
+
+        # Try to generate signed URL (requires service account credentials)
+        try:
+            sync_client = self._get_sync_client()
+            return await generate_signed_url(
+                sync_client, bucket_name, blob_name, expires_in
+            )
+        except AttributeError as e:
+            # Signed URL generation requires service account with private key.
+            # When running with user OAuth credentials, fall back to API proxy.
+            if "private key" in str(e) and file_id:
+                logger.debug(
+                    "Cannot generate signed URL (no service account credentials), "
+                    "falling back to API proxy endpoint"
+                )
+                return f"/api/workspace/files/{file_id}/download"
+            raise
+
+
+class LocalWorkspaceStorage(WorkspaceStorageBackend):
+    """Local filesystem implementation for workspace storage (self-hosted deployments)."""
+
+    def __init__(self, base_dir: Optional[str] = None):
+        """
+        Initialize local storage backend.
+
+        Args:
+            base_dir: Base directory for workspace storage.
+                     If None, defaults to {app_data}/workspaces
+        """
+        if base_dir:
+            self.base_dir = Path(base_dir)
+        else:
+            self.base_dir = Path(get_data_path()) / "workspaces"
+
+        # Ensure base directory exists
+        self.base_dir.mkdir(parents=True, exist_ok=True)
+
+    def _build_file_path(self, workspace_id: str, file_id: str, filename: str) -> Path:
+        """Build the local file path with path traversal protection."""
+        # Import here to avoid circular import
+        # (file.py imports workspace.py which imports workspace_storage.py)
+        from backend.util.file import sanitize_filename
+
+        # Sanitize filename to prevent path traversal (removes / and \ among others)
+        safe_filename = sanitize_filename(filename)
+        file_path = (self.base_dir / workspace_id / file_id / safe_filename).resolve()
+
+        # Verify the resolved path is still under base_dir
+        if not file_path.is_relative_to(self.base_dir.resolve()):
+            raise ValueError("Invalid filename: path traversal detected")
+
+        return file_path
+
+    def _parse_storage_path(self, storage_path: str) -> Path:
+        """Parse local storage path to filesystem path."""
+        if storage_path.startswith("local://"):
+            relative_path = storage_path[8:]  # Remove "local://"
+        else:
+            relative_path = storage_path
+
+        full_path = (self.base_dir / relative_path).resolve()
+
+        # Security check: ensure path is under base_dir
+        # Use is_relative_to() for robust path containment check
+        # (handles case-insensitive filesystems and edge cases)
+        if not full_path.is_relative_to(self.base_dir.resolve()):
+            raise ValueError("Invalid storage path: path traversal detected")
+
+        return full_path
+
+    async def store(
+        self,
+        workspace_id: str,
+        file_id: str,
+        filename: str,
+        content: bytes,
+    ) -> str:
+        """Store file locally."""
+        file_path = self._build_file_path(workspace_id, file_id, filename)
+
+        # Create parent directories
+        file_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Write file asynchronously
+        async with aiofiles.open(file_path, "wb") as f:
+            await f.write(content)
+
+        # Return relative path as storage path
+        relative_path = file_path.relative_to(self.base_dir)
+        return f"local://{relative_path}"
+
+    async def retrieve(self, storage_path: str) -> bytes:
+        """Retrieve file from local storage."""
+        file_path = self._parse_storage_path(storage_path)
+
+        if not file_path.exists():
+            raise FileNotFoundError(f"File not found: {storage_path}")
+
+        async with aiofiles.open(file_path, "rb") as f:
+            return await f.read()
+
+    async def delete(self, storage_path: str) -> None:
+        """Delete file from local storage."""
+        file_path = self._parse_storage_path(storage_path)
+
+        if file_path.exists():
+            # Remove file
+            file_path.unlink()
+
+            # Clean up empty parent directories
+            parent = file_path.parent
+            while parent != self.base_dir:
+                try:
+                    if parent.exists() and not any(parent.iterdir()):
+                        parent.rmdir()
+                    else:
+                        break
+                except OSError:
+                    break
+                parent = parent.parent
+
+    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
+        """
+        Get download URL for local file.
+
+        For local storage, this returns an API endpoint path.
+        The actual serving is handled by the API layer.
+        """
+        # Parse the storage path to get the components
+        if storage_path.startswith("local://"):
+            relative_path = storage_path[8:]
+        else:
+            relative_path = storage_path
+
+        # Return the API endpoint for downloading
+        # The file_id is extracted from the path: {workspace_id}/{file_id}/{filename}
+        parts = relative_path.split("/")
+        if len(parts) >= 2:
+            file_id = parts[1]  # Second component is file_id
+            return f"/api/workspace/files/{file_id}/download"
+        else:
+            raise ValueError(f"Invalid storage path format: {storage_path}")
+
+
+# Global storage backend instance
+_workspace_storage: Optional[WorkspaceStorageBackend] = None
+_storage_lock = asyncio.Lock()
+
+
+async def get_workspace_storage() -> WorkspaceStorageBackend:
+    """
+    Get the workspace storage backend instance.
+
+    Uses GCS if media_gcs_bucket_name is configured, otherwise uses local storage.
+    """
+    global _workspace_storage
+
+    if _workspace_storage is None:
+        async with _storage_lock:
+            if _workspace_storage is None:
+                config = Config()
+
+                if config.media_gcs_bucket_name:
+                    logger.info(
+                        f"Using GCS workspace storage: {config.media_gcs_bucket_name}"
+                    )
+                    _workspace_storage = GCSWorkspaceStorage(
+                        config.media_gcs_bucket_name
+                    )
+                else:
+                    storage_dir = (
+                        config.workspace_storage_dir
+                        if config.workspace_storage_dir
+                        else None
+                    )
+                    logger.info(
+                        f"Using local workspace storage: {storage_dir or 'default'}"
+                    )
+                    _workspace_storage = LocalWorkspaceStorage(storage_dir)
+
+    return _workspace_storage
+
+
+async def shutdown_workspace_storage() -> None:
+    """
+    Properly shutdown the global workspace storage backend.
+
+    Closes aiohttp sessions and other resources for GCS backend.
+    Should be called during application shutdown.
+    """
+    global _workspace_storage
+
+    if _workspace_storage is not None:
+        async with _storage_lock:
+            if _workspace_storage is not None:
+                if isinstance(_workspace_storage, GCSWorkspaceStorage):
+                    await _workspace_storage.close()
+                _workspace_storage = None
+
+
+def compute_file_checksum(content: bytes) -> str:
+    """Compute SHA256 checksum of file content."""
+    return hashlib.sha256(content).hexdigest()

autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql

@@ -0,0 +1,22 @@
+-- Migrate Claude 3.7 Sonnet to Claude 4.5 Sonnet
+-- This updates all AgentNode blocks that use the deprecated Claude 3.7 Sonnet model
+-- Anthropic is retiring claude-3-7-sonnet-20250219 on February 19, 2026
+
+-- Update AgentNode constant inputs
+UPDATE "AgentNode"
+SET    "constantInput" = JSONB_SET(
+         "constantInput"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "constantInput"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';
+
+-- Update AgentPreset input overrides (stored in AgentNodeExecutionInputOutput)
+UPDATE "AgentNodeExecutionInputOutput"
+SET    "data" = JSONB_SET(
+         "data"::jsonb,
+         '{model}',
+         '"claude-sonnet-4-5-20250929"'::jsonb
+       )
+WHERE  "agentPresetId" IS NOT NULL
+  AND  "data"::jsonb->>'model' = 'claude-3-7-sonnet-20250219';

autogpt_platform/backend/migrations/20260127230419_add_user_workspace/migration.sql

@@ -0,0 +1,52 @@
+-- CreateEnum
+CREATE TYPE "WorkspaceFileSource" AS ENUM ('UPLOAD', 'EXECUTION', 'COPILOT', 'IMPORT');
+
+-- CreateTable
+CREATE TABLE "UserWorkspace" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "userId" TEXT NOT NULL,
+
+    CONSTRAINT "UserWorkspace_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateTable
+CREATE TABLE "UserWorkspaceFile" (
+    "id" TEXT NOT NULL,
+    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    "updatedAt" TIMESTAMP(3) NOT NULL,
+    "workspaceId" TEXT NOT NULL,
+    "name" TEXT NOT NULL,
+    "path" TEXT NOT NULL,
+    "storagePath" TEXT NOT NULL,
+    "mimeType" TEXT NOT NULL,
+    "sizeBytes" BIGINT NOT NULL,
+    "checksum" TEXT,
+    "isDeleted" BOOLEAN NOT NULL DEFAULT false,
+    "deletedAt" TIMESTAMP(3),
+    "source" "WorkspaceFileSource" NOT NULL DEFAULT 'UPLOAD',
+    "sourceExecId" TEXT,
+    "sourceSessionId" TEXT,
+    "metadata" JSONB NOT NULL DEFAULT '{}',
+
+    CONSTRAINT "UserWorkspaceFile_pkey" PRIMARY KEY ("id")
+);
+
+-- CreateIndex
+CREATE UNIQUE INDEX "UserWorkspace_userId_key" ON "UserWorkspace"("userId");
+
+-- CreateIndex
+CREATE INDEX "UserWorkspace_userId_idx" ON "UserWorkspace"("userId");
+
+-- CreateIndex
+CREATE INDEX "UserWorkspaceFile_workspaceId_isDeleted_idx" ON "UserWorkspaceFile"("workspaceId", "isDeleted");
+
+-- CreateIndex
+CREATE UNIQUE INDEX "UserWorkspaceFile_workspaceId_path_key" ON "UserWorkspaceFile"("workspaceId", "path");
+
+-- AddForeignKey
+ALTER TABLE "UserWorkspace" ADD CONSTRAINT "UserWorkspace_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
+
+-- AddForeignKey
+ALTER TABLE "UserWorkspaceFile" ADD CONSTRAINT "UserWorkspaceFile_workspaceId_fkey" FOREIGN KEY ("workspaceId") REFERENCES "UserWorkspace"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/migrations/20260129011611_remove_workspace_file_source/migration.sql

@@ -0,0 +1,16 @@
+/*
+  Warnings:
+
+  - You are about to drop the column `source` on the `UserWorkspaceFile` table. All the data in the column will be lost.
+  - You are about to drop the column `sourceExecId` on the `UserWorkspaceFile` table. All the data in the column will be lost.
+  - You are about to drop the column `sourceSessionId` on the `UserWorkspaceFile` table. All the data in the column will be lost.
+
+*/
+
+-- AlterTable
+ALTER TABLE "UserWorkspaceFile" DROP COLUMN "source",
+DROP COLUMN "sourceExecId",
+DROP COLUMN "sourceSessionId";
+
+-- DropEnum
+DROP TYPE "WorkspaceFileSource";

autogpt_platform/backend/schema.prisma

@@ -63,6 +63,7 @@ model User {
   IntegrationWebhooks   IntegrationWebhook[]
   NotificationBatches   UserNotificationBatch[]
   PendingHumanReviews   PendingHumanReview[]
+  Workspace             UserWorkspace?
 
   // OAuth Provider relations
   OAuthApplications       OAuthApplication[]
@@ -137,6 +138,53 @@ model CoPilotUnderstanding {
   @@index([userId])
 }
 
+////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////
+////////////////   USER WORKSPACE TABLES   /////////////////
+////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////
+
+// User's persistent file storage workspace
+model UserWorkspace {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  userId String @unique
+  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
+
+  Files UserWorkspaceFile[]
+
+  @@index([userId])
+}
+
+// Individual files in a user's workspace
+model UserWorkspaceFile {
+  id        String   @id @default(uuid())
+  createdAt DateTime @default(now())
+  updatedAt DateTime @updatedAt
+
+  workspaceId String
+  Workspace   UserWorkspace @relation(fields: [workspaceId], references: [id], onDelete: Cascade)
+
+  // File metadata
+  name        String // User-visible filename
+  path        String // Virtual path (e.g., "/documents/report.pdf")
+  storagePath String // Actual GCS or local storage path
+  mimeType    String
+  sizeBytes   BigInt
+  checksum    String? // SHA256 for integrity
+
+  // File state
+  isDeleted Boolean   @default(false)
+  deletedAt DateTime?
+
+  metadata Json @default("{}")
+
+  @@unique([workspaceId, path])
+  @@index([workspaceId, isDeleted])
+}
+
 model BuilderSearchHistory {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())

autogpt_platform/backend/snapshots/agts_by_creator

@@ -9,7 +9,8 @@
       "sub_heading": "Creator agent subheading",
       "description": "Creator agent description",
       "runs": 50,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_category

@@ -9,7 +9,8 @@
       "sub_heading": "Category agent subheading",
       "description": "Category agent description",
       "runs": 60,
-      "rating": 4.1
+      "rating": 4.1,
+      "agent_graph_id": "test-graph-category"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_pagination

@@ -9,7 +9,8 @@
       "sub_heading": "Agent 0 subheading",
       "description": "Agent 0 description",
       "runs": 0,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-1",
@@ -20,7 +21,8 @@
       "sub_heading": "Agent 1 subheading",
       "description": "Agent 1 description",
       "runs": 10,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-2",
@@ -31,7 +33,8 @@
       "sub_heading": "Agent 2 subheading",
       "description": "Agent 2 description",
       "runs": 20,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-3",
@@ -42,7 +45,8 @@
       "sub_heading": "Agent 3 subheading",
       "description": "Agent 3 description",
       "runs": 30,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     },
     {
       "slug": "agent-4",
@@ -53,7 +57,8 @@
       "sub_heading": "Agent 4 subheading",
       "description": "Agent 4 description",
       "runs": 40,
-      "rating": 4.0
+      "rating": 4.0,
+      "agent_graph_id": "test-graph-2"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_search

@@ -9,7 +9,8 @@
       "sub_heading": "Search agent subheading",
       "description": "Specific search term description",
       "runs": 75,
-      "rating": 4.2
+      "rating": 4.2,
+      "agent_graph_id": "test-graph-search"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/agts_sorted

@@ -9,7 +9,8 @@
       "sub_heading": "Top agent subheading",
       "description": "Top agent description",
       "runs": 1000,
-      "rating": 5.0
+      "rating": 5.0,
+      "agent_graph_id": "test-graph-3"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/feat_agts

@@ -9,7 +9,8 @@
       "sub_heading": "Featured agent subheading",
       "description": "Featured agent description",
       "runs": 100,
-      "rating": 4.5
+      "rating": 4.5,
+      "agent_graph_id": "test-graph-1"
     }
   ],
   "pagination": {

autogpt_platform/backend/snapshots/lib_agts_search

@@ -31,6 +31,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": true,
       "is_latest_version": true,
       "is_favorite": false,
@@ -72,6 +76,10 @@
       "has_sensitive_action": false,
       "trigger_setup_info": null,
       "new_output": false,
+      "execution_count": 0,
+      "success_rate": null,
+      "avg_correctness_score": null,
+      "recent_executions": [],
       "can_access_graph": false,
       "is_latest_version": true,
       "is_favorite": false,

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -57,7 +57,8 @@ class TestDecomposeGoal:
 
             result = await core.decompose_goal("Build a chatbot")
 
-            mock_external.assert_called_once_with("Build a chatbot", "")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "", None)
             assert result == expected_result
 
     @pytest.mark.asyncio
@@ -74,7 +75,8 @@ class TestDecomposeGoal:
 
             await core.decompose_goal("Build a chatbot", "Use Python")
 
-            mock_external.assert_called_once_with("Build a chatbot", "Use Python")
+            # library_agents defaults to None
+            mock_external.assert_called_once_with("Build a chatbot", "Use Python", None)
 
     @pytest.mark.asyncio
     async def test_returns_none_on_service_failure(self):
@@ -109,8 +111,7 @@ class TestGenerateAgent:
             instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await core.generate_agent(instructions)
 
-            mock_external.assert_called_once_with(instructions)
+            mock_external.assert_called_once_with(instructions, None, None, None)
-            # Result should have id, version, is_active added if not present
             assert result is not None
             assert result["name"] == "Test Agent"
             assert "id" in result
@@ -174,7 +175,9 @@ class TestGenerateAgentPatch:
             current_agent = {"nodes": [], "links": []}
             result = await core.generate_agent_patch("Add a node", current_agent)
 
-            mock_external.assert_called_once_with("Add a node", current_agent)
+            mock_external.assert_called_once_with(
+                "Add a node", current_agent, None, None, None
+            )
             assert result == expected_result
 
     @pytest.mark.asyncio

autogpt_platform/backend/test/agent_generator/test_library_agents.py

@@ -0,0 +1,857 @@
+"""
+Tests for library agent fetching functionality in agent generator.
+
+This test suite verifies the search-based library agent fetching,
+including the combination of library and marketplace agents.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from backend.api.features.chat.tools.agent_generator import core
+
+
+class TestGetLibraryAgentsForGeneration:
+    """Test get_library_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_agents_with_search_term(self):
+        """Test that search_term is passed to the library db."""
+        # Create a mock agent with proper attribute values
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Email Agent"
+        mock_agent.description = "Sends emails"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+        mock_agent.recent_executions = []
+
+        mock_response = MagicMock()
+        mock_response.agents = [mock_agent]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                search_query="send email",
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term="send email",
+                page=1,
+                page_size=15,
+                include_executions=True,
+            )
+
+        # Verify result format
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-123"
+        assert result[0]["name"] == "Email Agent"
+
+    @pytest.mark.asyncio
+    async def test_excludes_specified_graph_id(self):
+        """Test that agents with excluded graph_id are filtered out."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                graph_id="agent-123",
+                graph_version=1,
+                name="Agent 1",
+                description="First agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+            MagicMock(
+                graph_id="agent-456",
+                graph_version=1,
+                name="Agent 2",
+                description="Second agent",
+                input_schema={},
+                output_schema={},
+                recent_executions=[],
+            ),
+        ]
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ):
+            result = await core.get_library_agents_for_generation(
+                user_id="user-123",
+                exclude_graph_id="agent-123",
+            )
+
+        # Verify the excluded agent is not in results
+        assert len(result) == 1
+        assert result[0]["graph_id"] == "agent-456"
+
+    @pytest.mark.asyncio
+    async def test_respects_max_results(self):
+        """Test that max_results parameter limits the page_size."""
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with patch.object(
+            core.library_db,
+            "list_library_agents",
+            new_callable=AsyncMock,
+            return_value=mock_response,
+        ) as mock_list:
+            await core.get_library_agents_for_generation(
+                user_id="user-123",
+                max_results=5,
+            )
+
+            mock_list.assert_called_once_with(
+                user_id="user-123",
+                search_term=None,
+                page=1,
+                page_size=5,
+                include_executions=True,
+            )
+
+
+class TestSearchMarketplaceAgentsForGeneration:
+    """Test search_marketplace_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_searches_marketplace_with_query(self):
+        """Test that marketplace is searched with the query."""
+        mock_response = MagicMock()
+        mock_response.agents = [
+            MagicMock(
+                agent_name="Public Agent",
+                description="A public agent",
+                sub_heading="Does something useful",
+                creator="creator-1",
+                agent_graph_id="graph-123",
+            )
+        ]
+
+        mock_graph = MagicMock()
+        mock_graph.id = "graph-123"
+        mock_graph.version = 1
+        mock_graph.input_schema = {"type": "object"}
+        mock_graph.output_schema = {"type": "object"}
+
+        with (
+            patch(
+                "backend.api.features.store.db.get_store_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ) as mock_search,
+            patch(
+                "backend.api.features.chat.tools.agent_generator.core.get_store_listed_graphs",
+                new_callable=AsyncMock,
+                return_value={"graph-123": mock_graph},
+            ),
+        ):
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="automation",
+                max_results=10,
+            )
+
+            mock_search.assert_called_once_with(
+                search_query="automation",
+                page=1,
+                page_size=10,
+            )
+
+        assert len(result) == 1
+        assert result[0]["name"] == "Public Agent"
+        assert result[0]["graph_id"] == "graph-123"
+
+    @pytest.mark.asyncio
+    async def test_handles_marketplace_error_gracefully(self):
+        """Test that marketplace errors don't crash the function."""
+        with patch(
+            "backend.api.features.store.db.get_store_agents",
+            new_callable=AsyncMock,
+            side_effect=Exception("Marketplace unavailable"),
+        ):
+            result = await core.search_marketplace_agents_for_generation(
+                search_query="test"
+            )
+
+        # Should return empty list, not raise exception
+        assert result == []
+
+
+class TestGetAllRelevantAgentsForGeneration:
+    """Test get_all_relevant_agents_for_generation function."""
+
+    @pytest.mark.asyncio
+    async def test_combines_library_and_marketplace_agents(self):
+        """Test that agents from both sources are combined."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "graph_id": "market-456",
+                "graph_version": 1,
+                "name": "Market Agent",
+                "description": "From marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test query",
+                    include_marketplace=True,
+                )
+
+        # Library agents should come first
+        assert len(result) == 2
+        assert result[0]["name"] == "Library Agent"
+        assert result[1]["name"] == "Market Agent"
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_graph_id(self):
+        """Test that marketplace agents with same graph_id as library are excluded."""
+        library_agents = [
+            {
+                "graph_id": "shared-123",
+                "graph_version": 1,
+                "name": "Shared Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        marketplace_agents = [
+            {
+                "graph_id": "shared-123",  # Same graph_id, should be deduplicated
+                "graph_version": 1,
+                "name": "Shared Agent",
+                "description": "From marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            },
+            {
+                "graph_id": "unique-456",
+                "graph_version": 1,
+                "name": "Unique Agent",
+                "description": "Only in marketplace",
+                "input_schema": {},
+                "output_schema": {},
+            },
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+                return_value=marketplace_agents,
+            ):
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=True,
+                )
+
+        # Shared Agent from marketplace should be excluded by graph_id
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Shared Agent" in names
+        assert "Unique Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_disabled(self):
+        """Test that marketplace is not searched when include_marketplace=False."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query="test",
+                    include_marketplace=False,
+                )
+
+        # Marketplace should not be called
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_skips_marketplace_when_no_search_query(self):
+        """Test that marketplace is not searched without a search query."""
+        library_agents = [
+            {
+                "graph_id": "lib-123",
+                "graph_version": 1,
+                "name": "Library Agent",
+                "description": "From library",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        with patch.object(
+            core,
+            "get_library_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=library_agents,
+        ):
+            with patch.object(
+                core,
+                "search_marketplace_agents_for_generation",
+                new_callable=AsyncMock,
+            ) as mock_marketplace:
+                result = await core.get_all_relevant_agents_for_generation(
+                    user_id="user-123",
+                    search_query=None,  # No search query
+                    include_marketplace=True,
+                )
+
+        # Marketplace should not be called without search query
+        mock_marketplace.assert_not_called()
+        assert len(result) == 1
+
+
+class TestExtractSearchTermsFromSteps:
+    """Test extract_search_terms_from_steps function."""
+
+    def test_extracts_terms_from_instructions_type(self):
+        """Test extraction from valid instructions decomposition result."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {
+                    "description": "Send an email notification",
+                    "block_name": "GmailSendBlock",
+                },
+                {"description": "Fetch weather data", "action": "Get weather API"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "Send an email notification" in result
+        assert "GmailSendBlock" in result
+        assert "Fetch weather data" in result
+        assert "Get weather API" in result
+
+    def test_returns_empty_for_non_instructions_type(self):
+        """Test that non-instructions types return empty list."""
+        decomposition_result = {
+            "type": "clarifying_questions",
+            "questions": [{"question": "What email?"}],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+    def test_deduplicates_terms_case_insensitively(self):
+        """Test that duplicate terms are removed (case-insensitive)."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send Email", "name": "send email"},
+                {"description": "Other task"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        # Should only have one "send email" variant
+        email_terms = [t for t in result if "email" in t.lower()]
+        assert len(email_terms) == 1
+
+    def test_filters_short_terms(self):
+        """Test that terms with 3 or fewer characters are filtered out."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "ab", "action": "xyz"},  # Both too short
+                {"description": "Valid term here"},
+            ],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert "ab" not in result
+        assert "xyz" not in result
+        assert "Valid term here" in result
+
+    def test_handles_empty_steps(self):
+        """Test handling of empty steps list."""
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [],
+        }
+
+        result = core.extract_search_terms_from_steps(decomposition_result)
+
+        assert result == []
+
+
+class TestEnrichLibraryAgentsFromSteps:
+    """Test enrich_library_agents_from_steps function."""
+
+    @pytest.mark.asyncio
+    async def test_enriches_with_additional_agents(self):
+        """Test that additional agents are found based on steps."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        additional_agents = [
+            {
+                "graph_id": "new-456",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "For sending emails",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "Send email notification"},
+            ],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should have both existing and new agents
+        assert len(result) == 2
+        names = [a["name"] for a in result]
+        assert "Existing Agent" in names
+        assert "Email Agent" in names
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_graph_id(self):
+        """Test that agents with same graph_id are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns same agent
+        additional_agents = [
+            {
+                "graph_id": "agent-123",  # Same ID
+                "graph_version": 1,
+                "name": "Existing Agent Copy",
+                "description": "Same agent different name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Some action"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate
+        assert len(result) == 1
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_by_name(self):
+        """Test that agents with same name are not duplicated."""
+        existing_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        # Additional search returns agent with same name but different ID
+        additional_agents = [
+            {
+                "graph_id": "agent-456",  # Different ID
+                "graph_version": 1,
+                "name": "Email Agent",  # Same name
+                "description": "Different agent same name",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [{"description": "Send email"}],
+        }
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            new_callable=AsyncMock,
+            return_value=additional_agents,
+        ):
+            result = await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should not duplicate by name
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "agent-123"  # Original kept
+
+    @pytest.mark.asyncio
+    async def test_returns_existing_when_no_steps(self):
+        """Test that existing agents are returned when no search terms extracted."""
+        existing_agents = [
+            {
+                "graph_id": "existing-123",
+                "graph_version": 1,
+                "name": "Existing Agent",
+                "description": "Already fetched",
+                "input_schema": {},
+                "output_schema": {},
+            }
+        ]
+
+        decomposition_result = {
+            "type": "clarifying_questions",  # Not instructions type
+            "questions": [],
+        }
+
+        result = await core.enrich_library_agents_from_steps(
+            user_id="user-123",
+            decomposition_result=decomposition_result,
+            existing_agents=existing_agents,
+        )
+
+        # Should return existing unchanged
+        assert result == existing_agents
+
+    @pytest.mark.asyncio
+    async def test_limits_search_terms_to_three(self):
+        """Test that only first 3 search terms are used."""
+        existing_agents = []
+
+        decomposition_result = {
+            "type": "instructions",
+            "steps": [
+                {"description": "First action"},
+                {"description": "Second action"},
+                {"description": "Third action"},
+                {"description": "Fourth action"},
+                {"description": "Fifth action"},
+            ],
+        }
+
+        call_count = 0
+
+        async def mock_get_agents(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            return []
+
+        with patch.object(
+            core,
+            "get_all_relevant_agents_for_generation",
+            side_effect=mock_get_agents,
+        ):
+            await core.enrich_library_agents_from_steps(
+                user_id="user-123",
+                decomposition_result=decomposition_result,
+                existing_agents=existing_agents,
+            )
+
+        # Should only make 3 calls (limited to first 3 terms)
+        assert call_count == 3
+
+
+class TestExtractUuidsFromText:
+    """Test extract_uuids_from_text function."""
+
+    def test_extracts_single_uuid(self):
+        """Test extraction of a single UUID from text."""
+        text = "Use my agent 46631191-e8a8-486f-ad90-84f89738321d for this task"
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+        assert "46631191-e8a8-486f-ad90-84f89738321d" in result
+
+    def test_extracts_multiple_uuids(self):
+        """Test extraction of multiple UUIDs from text."""
+        text = (
+            "Combine agents 11111111-1111-4111-8111-111111111111 "
+            "and 22222222-2222-4222-9222-222222222222"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 2
+        assert "11111111-1111-4111-8111-111111111111" in result
+        assert "22222222-2222-4222-9222-222222222222" in result
+
+    def test_deduplicates_uuids(self):
+        """Test that duplicate UUIDs are deduplicated."""
+        text = (
+            "Use 46631191-e8a8-486f-ad90-84f89738321d twice: "
+            "46631191-e8a8-486f-ad90-84f89738321d"
+        )
+        result = core.extract_uuids_from_text(text)
+        assert len(result) == 1
+
+    def test_normalizes_to_lowercase(self):
+        """Test that UUIDs are normalized to lowercase."""
+        text = "Use 46631191-E8A8-486F-AD90-84F89738321D"
+        result = core.extract_uuids_from_text(text)
+        assert result[0] == "46631191-e8a8-486f-ad90-84f89738321d"
+
+    def test_returns_empty_for_no_uuids(self):
+        """Test that empty list is returned when no UUIDs found."""
+        text = "Create an email agent that sends notifications"
+        result = core.extract_uuids_from_text(text)
+        assert result == []
+
+    def test_ignores_invalid_uuids(self):
+        """Test that invalid UUID-like strings are ignored."""
+        text = "Not a valid UUID: 12345678-1234-1234-1234-123456789abc"
+        result = core.extract_uuids_from_text(text)
+        # UUID v4 requires specific patterns (4 in third group, 8/9/a/b in fourth)
+        assert len(result) == 0
+
+
+class TestGetLibraryAgentById:
+    """Test get_library_agent_by_id function (and its alias get_library_agent_by_graph_id)."""
+
+    @pytest.mark.asyncio
+    async def test_returns_agent_when_found_by_graph_id(self):
+        """Test that agent is returned when found by graph_id."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "agent-123"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Test Agent"
+        mock_agent.description = "Test description"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with patch.object(
+            core.library_db,
+            "get_library_agent_by_graph_id",
+            new_callable=AsyncMock,
+            return_value=mock_agent,
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is not None
+        assert result["graph_id"] == "agent-123"
+        assert result["name"] == "Test Agent"
+
+    @pytest.mark.asyncio
+    async def test_falls_back_to_library_agent_id(self):
+        """Test that lookup falls back to library agent ID when graph_id not found."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "graph-456"  # Different from the lookup ID
+        mock_agent.graph_version = 1
+        mock_agent.name = "Library Agent"
+        mock_agent.description = "Found by library ID"
+        mock_agent.input_schema = {"properties": {}}
+        mock_agent.output_schema = {"properties": {}}
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,  # Not found by graph_id
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                return_value=mock_agent,  # Found by library ID
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "library-id-123")
+
+        assert result is not None
+        assert result["graph_id"] == "graph-456"
+        assert result["name"] == "Library Agent"
+
+    @pytest.mark.asyncio
+    async def test_returns_none_when_not_found_by_either_method(self):
+        """Test that None is returned when agent not found by either method."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=None,
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=core.NotFoundError("Not found"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "nonexistent")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_returns_none_on_exception(self):
+        """Test that None is returned when exception occurs in both lookups."""
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+            patch.object(
+                core.library_db,
+                "get_library_agent",
+                new_callable=AsyncMock,
+                side_effect=Exception("Database error"),
+            ),
+        ):
+            result = await core.get_library_agent_by_id("user-123", "agent-123")
+
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_alias_works(self):
+        """Test that get_library_agent_by_graph_id is an alias for get_library_agent_by_id."""
+        assert core.get_library_agent_by_graph_id is core.get_library_agent_by_id
+
+
+class TestGetAllRelevantAgentsWithUuids:
+    """Test UUID extraction in get_all_relevant_agents_for_generation."""
+
+    @pytest.mark.asyncio
+    async def test_fetches_explicitly_mentioned_agents(self):
+        """Test that agents mentioned by UUID are fetched directly."""
+        mock_agent = MagicMock()
+        mock_agent.graph_id = "46631191-e8a8-486f-ad90-84f89738321d"
+        mock_agent.graph_version = 1
+        mock_agent.name = "Mentioned Agent"
+        mock_agent.description = "Explicitly mentioned"
+        mock_agent.input_schema = {}
+        mock_agent.output_schema = {}
+
+        mock_response = MagicMock()
+        mock_response.agents = []
+
+        with (
+            patch.object(
+                core.library_db,
+                "get_library_agent_by_graph_id",
+                new_callable=AsyncMock,
+                return_value=mock_agent,
+            ),
+            patch.object(
+                core.library_db,
+                "list_library_agents",
+                new_callable=AsyncMock,
+                return_value=mock_response,
+            ),
+        ):
+            result = await core.get_all_relevant_agents_for_generation(
+                user_id="user-123",
+                search_query="Use agent 46631191-e8a8-486f-ad90-84f89738321d",
+                include_marketplace=False,
+            )
+
+        assert len(result) == 1
+        assert result[0].get("graph_id") == "46631191-e8a8-486f-ad90-84f89738321d"
+        assert result[0].get("name") == "Mentioned Agent"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -102,7 +102,7 @@ class TestDecomposeGoalExternal:
 
     @pytest.mark.asyncio
     async def test_decompose_goal_with_context(self):
-        """Test decomposition with additional context."""
+        """Test decomposition with additional context enriched into description."""
         mock_response = MagicMock()
         mock_response.json.return_value = {
             "success": True,
@@ -119,9 +119,12 @@ class TestDecomposeGoalExternal:
                 "Build a chatbot", context="Use Python"
             )
 
+        expected_description = (
+            "Build a chatbot\n\nAdditional context from user:\nUse Python"
+        )
         mock_client.post.assert_called_once_with(
             "/api/decompose-description",
-            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+            json={"description": expected_description},
         )
 
     @pytest.mark.asyncio
@@ -151,15 +154,20 @@ class TestDecomposeGoalExternal:
     @pytest.mark.asyncio
     async def test_decompose_goal_handles_http_error(self):
         """Test decomposition handles HTTP errors gracefully."""
+        mock_response = MagicMock()
+        mock_response.status_code = 500
         mock_client = AsyncMock()
         mock_client.post.side_effect = httpx.HTTPStatusError(
-            "Server error", request=MagicMock(), response=MagicMock()
+            "Server error", request=MagicMock(), response=mock_response
         )
 
         with patch.object(service, "_get_client", return_value=mock_client):
             result = await service.decompose_goal_external("Build a chatbot")
 
-        assert result is None
+        assert result is not None
+        assert result.get("type") == "error"
+        assert result.get("error_type") == "http_error"
+        assert "Server error" in result.get("error", "")
 
     @pytest.mark.asyncio
     async def test_decompose_goal_handles_request_error(self):
@@ -170,7 +178,10 @@ class TestDecomposeGoalExternal:
         with patch.object(service, "_get_client", return_value=mock_client):
             result = await service.decompose_goal_external("Build a chatbot")
 
-        assert result is None
+        assert result is not None
+        assert result.get("type") == "error"
+        assert result.get("error_type") == "connection_error"
+        assert "Connection failed" in result.get("error", "")
 
     @pytest.mark.asyncio
     async def test_decompose_goal_handles_service_error(self):
@@ -179,6 +190,7 @@ class TestDecomposeGoalExternal:
         mock_response.json.return_value = {
             "success": False,
             "error": "Internal error",
+            "error_type": "internal_error",
         }
         mock_response.raise_for_status = MagicMock()
 
@@ -188,7 +200,10 @@ class TestDecomposeGoalExternal:
         with patch.object(service, "_get_client", return_value=mock_client):
             result = await service.decompose_goal_external("Build a chatbot")
 
-        assert result is None
+        assert result is not None
+        assert result.get("type") == "error"
+        assert result.get("error") == "Internal error"
+        assert result.get("error_type") == "internal_error"
 
 
 class TestGenerateAgentExternal:
@@ -236,7 +251,10 @@ class TestGenerateAgentExternal:
         with patch.object(service, "_get_client", return_value=mock_client):
             result = await service.generate_agent_external({"steps": []})
 
-        assert result is None
+        assert result is not None
+        assert result.get("type") == "error"
+        assert result.get("error_type") == "connection_error"
+        assert "Connection failed" in result.get("error", "")
 
 
 class TestGenerateAgentPatchExternal:
@@ -418,5 +436,139 @@ class TestGetBlocksExternal:
         assert result is None
 
 
+class TestLibraryAgentsPassthrough:
+    """Test that library_agents are passed correctly in all requests."""
+
+    def setup_method(self):
+        """Reset client singleton before each test."""
+        service._settings = None
+        service._client = None
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_passes_library_agents(self):
+        """Test that library_agents are included in decompose goal payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-123",
+                "graph_version": 1,
+                "name": "Email Sender",
+                "description": "Sends emails",
+                "input_schema": {"properties": {"to": {"type": "string"}}},
+                "output_schema": {"properties": {"sent": {"type": "boolean"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external(
+                "Send an email",
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_passes_library_agents(self):
+        """Test that library_agents are included in generate agent payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-456",
+                "graph_version": 2,
+                "name": "Data Fetcher",
+                "description": "Fetches data from API",
+                "input_schema": {"properties": {"url": {"type": "string"}}},
+                "output_schema": {"properties": {"data": {"type": "object"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Test Agent", "nodes": []},
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.generate_agent_external(
+                {"steps": ["Step 1"]},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_generate_agent_patch_passes_library_agents(self):
+        """Test that library_agents are included in patch generation payload."""
+        library_agents = [
+            {
+                "graph_id": "agent-789",
+                "graph_version": 1,
+                "name": "Slack Notifier",
+                "description": "Sends Slack messages",
+                "input_schema": {"properties": {"message": {"type": "string"}}},
+                "output_schema": {"properties": {"success": {"type": "boolean"}}},
+            },
+        ]
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "agent_json": {"name": "Updated Agent", "nodes": []},
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.generate_agent_patch_external(
+                "Add error handling",
+                {"name": "Original Agent", "nodes": []},
+                library_agents=library_agents,
+            )
+
+        # Verify library_agents was passed in the payload
+        call_args = mock_client.post.call_args
+        assert call_args[1]["json"]["library_agents"] == library_agents
+
+    @pytest.mark.asyncio
+    async def test_decompose_goal_without_library_agents(self):
+        """Test that decompose goal works without library_agents."""
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "success": True,
+            "type": "instructions",
+            "steps": ["Step 1"],
+        }
+        mock_response.raise_for_status = MagicMock()
+
+        mock_client = AsyncMock()
+        mock_client.post.return_value = mock_response
+
+        with patch.object(service, "_get_client", return_value=mock_client):
+            await service.decompose_goal_external("Build a workflow")
+
+        # Verify library_agents was NOT passed when not provided
+        call_args = mock_client.post.call_args
+        assert "library_agents" not in call_args[1]["json"]
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v"])