add entrypoint to pyproject.toml

## Summary

Enables Anthropic's extended thinking feature for Claude models in
CoPilot via OpenRouter. This keeps the model's chain-of-thought
reasoning internal rather than outputting it to users.

## Problem

The CoPilot prompt was designed for a thinking agent (with
`<internal_reasoning>` tags), but extended thinking wasn't enabled on
the API side. This caused the model to output its reasoning as regular
text, leaking internal analysis to users.

## Solution

Added thinking configuration to the OpenRouter `extra_body` for
Anthropic models:
```python
extra_body["provider"] = {
    "anthropic": {
        "thinking": {
            "type": "enabled",
            "budget_tokens": config.thinking_budget_tokens,
        }
    }
}
```

## Configuration

New settings in `ChatConfig`:
| Setting | Default | Description |
|---------|---------|-------------|
| `thinking_enabled` | `True` | Enable extended thinking for Claude
models |
| `thinking_budget_tokens` | `10000` | Token budget for thinking
(1000-100000) |

## Changes

- `config.py`: Added `thinking_enabled` and `thinking_budget_tokens`
settings
- `service.py`: Added thinking config to all 3 places where `extra_body`
is built for LLM calls

## Testing

- Verify CoPilot responses no longer include internal reasoning text
- Check that Claude's extended thinking is working (should see thinking
tokens in usage)
- Confirm non-Anthropic models are unaffected

## Related

Discussion:
https://discord.com/channels/1126875755960336515/1126875756925046928/1470779843552612607

---------

Co-authored-by: Swifty <craigswift13@gmail.com>

.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 docs && pip install -r requirements.txt"
+    "cd autogpt_platform/frontend && pnpm install"
   ],
   "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)

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

@@ -49,7 +49,7 @@ jobs:
 
       - name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
         if: github.event_name == 'push'
-        uses: peter-evans/create-pull-request@v7
+        uses: peter-evans/create-pull-request@v8
         with:
           add-paths: classic/frontend/build/web
           base: ${{ github.ref_name }}

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -42,7 +42,7 @@ jobs:
 
       - name: Get CI failure details
         id: failure_details
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             const run = await github.rest.actions.getWorkflowRun({

.github/workflows/claude-dependabot.yml

@@ -41,7 +41,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -78,7 +78,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22"
 
@@ -91,7 +91,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -124,7 +124,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes
@@ -309,6 +309,7 @@ jobs:
         uses: anthropics/claude-code-action@v1
         with:
           claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          allowed_bots: "dependabot[bot]"
           claude_args: |
             --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
           prompt: |

.github/workflows/claude.yml

@@ -57,7 +57,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -94,7 +94,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22"
 
@@ -107,7 +107,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -140,7 +140,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes

.github/workflows/copilot-setup-steps.yml

@@ -39,7 +39,7 @@ jobs:
           python-version: "3.11"  # Use standard version matching CI
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -76,7 +76,7 @@ jobs:
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22"
 
@@ -89,7 +89,7 @@ jobs:
           echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
 
       - name: Cache frontend dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -132,7 +132,7 @@ jobs:
       # Phase 1: Cache and load Docker images for faster setup
       - name: Set up Docker image cache
         id: docker-cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/docker-cache
           # Use a versioned key for cache invalidation when image list changes

.github/workflows/docs-block-sync.yml

@@ -33,7 +33,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/docs-claude-review.yml

@@ -33,7 +33,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/docs-enhance.yml

@@ -38,7 +38,7 @@ jobs:
           python-version: "3.11"
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

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

@@ -88,7 +88,7 @@ jobs:
         run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.cache/pypoetry
           key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}

.github/workflows/platform-dev-deploy-event-dispatcher.yml

@@ -17,7 +17,7 @@ jobs:
       - name: Check comment permissions and deployment status
         id: check_status
         if: github.event_name == 'issue_comment' && github.event.issue.pull_request
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             const commentBody = context.payload.comment.body.trim();
@@ -55,7 +55,7 @@ jobs:
 
       - name: Post permission denied comment
         if: steps.check_status.outputs.permission_denied == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             await github.rest.issues.createComment({
@@ -68,7 +68,7 @@ jobs:
       - name: Get PR details for deployment
         id: pr_details
         if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             const pr = await github.rest.pulls.get({
@@ -98,7 +98,7 @@ jobs:
 
       - name: Post deploy success comment
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             await github.rest.issues.createComment({
@@ -126,7 +126,7 @@ jobs:
 
       - name: Post undeploy success comment
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             await github.rest.issues.createComment({
@@ -139,7 +139,7 @@ jobs:
       - name: Check deployment status on PR close
         id: check_pr_close
         if: github.event_name == 'pull_request' && github.event.action == 'closed'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             const comments = await github.rest.issues.listComments({
@@ -187,7 +187,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v7
+        uses: actions/github-script@v8
         with:
           script: |
             await github.rest.issues.createComment({

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

@@ -27,13 +27,22 @@ jobs:
     runs-on: ubuntu-latest
     outputs:
       cache-key: ${{ steps.cache-key.outputs.key }}
+      components-changed: ${{ steps.filter.outputs.components }}
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
 
+      - name: Check for component changes
+        uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            components:
+              - 'autogpt_platform/frontend/src/components/**'
+
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -45,7 +54,7 @@ jobs:
         run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
       - name: Cache dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ steps.cache-key.outputs.key }}
@@ -65,7 +74,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -73,7 +82,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -90,8 +99,11 @@ jobs:
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
-    # Only run on dev branch pushes or PRs targeting dev
-    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
+    # Disabled: to re-enable, remove 'false &&' from the condition below
+    if: >-
+      false
+      && (github.ref == 'refs/heads/dev' || github.base_ref == 'dev')
+      && needs.setup.outputs.components-changed == 'true'
 
     steps:
       - name: Checkout repository
@@ -100,7 +112,7 @@ jobs:
           fetch-depth: 0
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -108,7 +120,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -141,7 +153,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -164,7 +176,7 @@ jobs:
         uses: docker/setup-buildx-action@v3
 
       - name: Cache Docker layers
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: /tmp/.buildx-cache
           key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
@@ -219,7 +231,7 @@ jobs:
           fi
 
       - name: Restore dependencies cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}
@@ -270,7 +282,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -278,7 +290,7 @@ jobs:
         run: corepack enable
 
       - name: Restore dependencies cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}

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

@@ -32,7 +32,7 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -44,7 +44,7 @@ jobs:
         run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
 
       - name: Cache dependencies
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ steps.cache-key.outputs.key }}
@@ -56,7 +56,7 @@ jobs:
         run: pnpm install --frozen-lockfile
 
   types:
-    runs-on: ubuntu-latest
+    runs-on: big-boi
     needs: setup
     strategy:
       fail-fast: false
@@ -68,7 +68,7 @@ jobs:
           submodules: recursive
 
       - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
 
@@ -85,10 +85,10 @@ jobs:
 
       - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
+          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
 
       - name: Restore dependencies cache
-        uses: actions/cache@v4
+        uses: actions/cache@v5
         with:
           path: ~/.pnpm-store
           key: ${{ needs.setup.outputs.cache-key }}

.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:
-    - feat
-    - fix
-    - refactor
-    - ci
-    - dx (developer experience)
-  Scopes:
-    - platform
-      - platform/library
-      - platform/marketplace
-      - backend
-        - backend/executor
-      - frontend
-        - frontend/library
-        - frontend/marketplace
-      - blocks
+Types: - feat - fix - refactor - ci - dx (developer experience)
+Scopes: - platform - platform/library - platform/marketplace - backend - backend/executor - frontend - frontend/library - frontend/marketplace - blocks
 
 ## Pull requests
 

README.md

@@ -54,7 +54,7 @@ Before proceeding with the installation, ensure your system meets the following
 ### Updated Setup Instructions:
 We've moved to a fully maintained and regularly updated documentation site.
 
-👉 [Follow the official self-hosting guide here](https://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
-- **Frontend** (`/frontend`): Next.js React application
-- **Shared Libraries** (`/autogpt_libs`): Common Python utilities
+- **Backend** (`backend`): Python FastAPI server with async support
+- **Frontend** (`frontend`): Next.js React application
+- **Shared Libraries** (`autogpt_libs`): Common Python utilities
 
-## 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
-# 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
+## 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)
-- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
-- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
+- **Backend**: `backend/.env.default` (defaults) → `backend/.env` (user overrides)
+- **Frontend**: `frontend/.env.default` (defaults) → `frontend/.env` (user overrides)
+- **Platform**: `.env.default` (Supabase/shared defaults) → `.env` (user overrides)
 
 #### Docker Environment Loading Order
 
@@ -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.
-- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
-- Use conventional commit messages (see below)/
-- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
+- Create the PR against the `dev` branch of the repository.
+- Ensure the branch name is descriptive (e.g., `feature/add-new-block`)
+- Use conventional commit messages (see below)
+- Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description
 - Run the github pre-commit hooks to ensure code quality.
 
 ### Reviewing/Revising Pull Requests

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,25 +9,25 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^45.0"
+cryptography = "^46.0"
 expiringdict = "^1.2.2"
-fastapi = "^0.116.1"
-google-cloud-logging = "^3.12.1"
-launchdarkly-server-sdk = "^9.12.0"
-pydantic = "^2.11.7"
-pydantic-settings = "^2.10.1"
-pyjwt = { version = "^2.10.1", extras = ["crypto"] }
+fastapi = "^0.128.0"
+google-cloud-logging = "^3.13.0"
+launchdarkly-server-sdk = "^9.14.1"
+pydantic = "^2.12.5"
+pydantic-settings = "^2.12.0"
+pyjwt = { version = "^2.11.0", extras = ["crypto"] }
 redis = "^6.2.0"
-supabase = "^2.16.0"
-uvicorn = "^0.35.0"
+supabase = "^2.27.2"
+uvicorn = "^0.40.0"
 
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.404"
+pyright = "^1.1.408"
 pytest = "^8.4.1"
-pytest-asyncio = "^1.1.0"
-pytest-mock = "^3.14.1"
-pytest-cov = "^6.2.1"
-ruff = "^0.12.11"
+pytest-asyncio = "^1.3.0"
+pytest-mock = "^3.15.1"
+pytest-cov = "^7.0.0"
+ruff = "^0.15.0"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.default

@@ -152,6 +152,7 @@ REPLICATE_API_KEY=
 REVID_API_KEY=
 SCREENSHOTONE_API_KEY=
 UNREAL_SPEECH_API_KEY=
+ELEVENLABS_API_KEY=
 
 # Data & Search Services
 E2B_API_KEY=

autogpt_platform/backend/.gitignore

@@ -19,3 +19,6 @@ load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
 migrations/*/rollback*.sql
+
+# Workspace files
+workspaces/

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/Dockerfile

@@ -62,10 +62,12 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python without upgrading system-managed packages
+# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
 RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
+    ffmpeg \
+    imagemagick \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder

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/external/v1/tools.py

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

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/routes.py

@@ -1,20 +1,54 @@
 """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, Response, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
+from backend.copilot import service as chat_service
+from backend.copilot import stream_registry
+from backend.copilot.completion_handler import (
+    process_operation_failure,
+    process_operation_success,
+)
+from backend.copilot.config import ChatConfig
+from backend.copilot.executor.utils import enqueue_copilot_task
+from backend.copilot.model import (
+    ChatSession,
+    create_chat_session,
+    get_chat_session,
+    get_user_sessions,
+)
+from backend.copilot.response_model import StreamFinish, StreamHeartbeat
+from backend.copilot.tools.models import (
+    AgentDetailsResponse,
+    AgentOutputResponse,
+    AgentPreviewResponse,
+    AgentSavedResponse,
+    AgentsFoundResponse,
+    BlockListResponse,
+    BlockOutputResponse,
+    ClarificationNeededResponse,
+    DocPageResponse,
+    DocSearchResultsResponse,
+    ErrorResponse,
+    ExecutionStartedResponse,
+    InputValidationErrorResponse,
+    NeedLoginResponse,
+    NoResultsResponse,
+    OperationInProgressResponse,
+    OperationPendingResponse,
+    OperationStartedResponse,
+    SetupRequirementsResponse,
+    UnderstandingUpdatedResponse,
+)
 from backend.util.exceptions import NotFoundError
 
-from . import service as chat_service
-from .config import ChatConfig
-from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-
 config = ChatConfig()
 
 
@@ -55,6 +89,15 @@ 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 +106,7 @@ 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 +125,14 @@ 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 +218,14 @@ 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 +233,28 @@ 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}: "
-        f"message_count={len(messages)}, "
-        f"roles={[m.get('role') for m in messages]}"
+
+    # Check if there's an active stream for this session
+    active_stream_info = None
+    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+        session_id, user_id
     )
+    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 +262,7 @@ 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 +282,202 @@ 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.
 
     """
-    session = await _validate_and_get_session(session_id, user_id)
+    import asyncio
+    import time
 
+    stream_start_time = time.perf_counter()
+    log_meta = {"component": "ChatStream", "session_id": session_id}
+    if user_id:
+        log_meta["user_id"] = user_id
+
+    logger.info(
+        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
+        f"user={user_id}, message_len={len(request.message)}",
+        extra={"json_fields": log_meta},
+    )
+
+    _session = await _validate_and_get_session(session_id, user_id)  # noqa: F841
+    logger.info(
+        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
+        extra={
+            "json_fields": {
+                **log_meta,
+                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
+            }
+        },
+    )
+
+    # Create a task in the stream registry for reconnection support
+    task_id = str(uuid_module.uuid4())
+    operation_id = str(uuid_module.uuid4())
+    log_meta["task_id"] = task_id
+
+    task_create_start = time.perf_counter()
+    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,
+    )
+    logger.info(
+        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
+        extra={
+            "json_fields": {
+                **log_meta,
+                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
+            }
+        },
+    )
+
+    # Enqueue the task to RabbitMQ for processing by the CoPilot executor
+    await enqueue_copilot_task(
+        task_id=task_id,
+        session_id=session_id,
+        user_id=user_id,
+        operation_id=operation_id,
+        message=request.message,
+        is_user_message=request.is_user_message,
+        context=request.context,
+    )
+
+    setup_time = (time.perf_counter() - stream_start_time) * 1000
+    logger.info(
+        f"[TIMING] Task enqueued to RabbitMQ, setup={setup_time:.1f}ms",
+        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
+    )
+
+    # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            request.message,
-            is_user_message=request.is_user_message,
-            user_id=user_id,
-            session=session,  # Pass pre-fetched session to avoid double-fetch
-            context=request.context,
-        ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
-            yield chunk.to_sse()
+        import time as time_module
+
+        event_gen_start = time_module.perf_counter()
         logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
+            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
+            f"user={user_id}",
+            extra={"json_fields": log_meta},
         )
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
+        subscriber_queue = None
+        first_chunk_yielded = False
+        chunks_yielded = 0
+        try:
+            # Subscribe to the task stream (this replays existing messages + live updates)
+            subscriber_queue = await stream_registry.subscribe_to_task(
+                task_id=task_id,
+                user_id=user_id,
+                last_message_id="0-0",  # Get all messages from the beginning
+            )
+
+            if subscriber_queue is None:
+                yield StreamFinish().to_sse()
+                yield "data: [DONE]\n\n"
+                return
+
+            # Read from the subscriber queue and yield to SSE
+            logger.info(
+                "[TIMING] Starting to read from subscriber_queue",
+                extra={"json_fields": log_meta},
+            )
+            while True:
+                try:
+                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
+                    chunks_yielded += 1
+
+                    if not first_chunk_yielded:
+                        first_chunk_yielded = True
+                        elapsed = time_module.perf_counter() - event_gen_start
+                        logger.info(
+                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
+                            f"type={type(chunk).__name__}",
+                            extra={
+                                "json_fields": {
+                                    **log_meta,
+                                    "chunk_type": type(chunk).__name__,
+                                    "elapsed_ms": elapsed * 1000,
+                                }
+                            },
+                        )
+
+                    yield chunk.to_sse()
+
+                    # Check for finish signal
+                    if isinstance(chunk, StreamFinish):
+                        total_time = time_module.perf_counter() - event_gen_start
+                        logger.info(
+                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
+                            f"n_chunks={chunks_yielded}",
+                            extra={
+                                "json_fields": {
+                                    **log_meta,
+                                    "chunks_yielded": chunks_yielded,
+                                    "total_time_ms": total_time * 1000,
+                                }
+                            },
+                        )
+                        break
+                except asyncio.TimeoutError:
+                    yield StreamHeartbeat().to_sse()
+
+        except GeneratorExit:
+            logger.info(
+                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "chunks_yielded": chunks_yielded,
+                        "reason": "client_disconnect",
+                    }
+                },
+            )
+            pass  # Client disconnected - background task continues
+        except Exception as e:
+            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
+            logger.error(
+                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
+                extra={
+                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
+                },
+            )
+        finally:
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
+            if subscriber_queue is not None:
+                try:
+                    await stream_registry.unsubscribe_from_task(
+                        task_id, subscriber_queue
+                    )
+                except Exception as unsub_err:
+                    logger.error(
+                        f"Error unsubscribing from task {task_id}: {unsub_err}",
+                        exc_info=True,
+                    )
+            # AI SDK protocol termination - always yield even if unsubscribe fails
+            total_time = time_module.perf_counter() - event_gen_start
+            logger.info(
+                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
+                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
+                extra={
+                    "json_fields": {
+                        **log_meta,
+                        "total_time_ms": total_time * 1000,
+                        "chunks_yielded": chunks_yielded,
+                    }
+                },
+            )
+            yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -270,63 +494,90 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def stream_chat_get(
+async def resume_session_stream(
     session_id: str,
-    message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
-    is_user_message: bool = Query(default=True),
 ):
     """
-    Stream chat responses for a session (GET - legacy endpoint).
+    Resume an active stream for a session.
 
-    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
-      - Text fragments as they are generated
-      - Tool call UI elements (if invoked)
-      - Tool execution results
+    Called by the AI SDK's ``useChat(resume: true)`` on page load.
+    Checks for an active (in-progress) task on the session and either replays
+    the full SSE stream or returns 204 No Content if nothing is running.
 
     Args:
-        session_id: The chat session identifier to associate with the streamed messages.
-        message: The user's new message to process.
+        session_id: The chat session identifier.
         user_id: Optional authenticated user ID.
-        is_user_message: Whether the message is a user message.
-    Returns:
-        StreamingResponse: SSE-formatted response chunks.
 
+    Returns:
+        StreamingResponse (SSE) when an active stream exists,
+        or 204 No Content when there is nothing to resume.
     """
-    session = await _validate_and_get_session(session_id, user_id)
+    import asyncio
+
+    active_task, _last_id = await stream_registry.get_active_task_for_session(
+        session_id, user_id
+    )
+
+    if not active_task:
+        return Response(status_code=204)
+
+    subscriber_queue = await stream_registry.subscribe_to_task(
+        task_id=active_task.task_id,
+        user_id=user_id,
+        last_message_id="0-0",  # Full replay so useChat rebuilds the message
+    )
+
+    if subscriber_queue is None:
+        return Response(status_code=204)
 
     async def event_generator() -> AsyncGenerator[str, None]:
         chunk_count = 0
         first_chunk_type: str | None = None
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            message,
-            is_user_message=is_user_message,
-            user_id=user_id,
-            session=session,  # Pass pre-fetched session to avoid double-fetch
-        ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
+        try:
+            while True:
+                try:
+                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
+                    if chunk_count < 3:
+                        logger.info(
+                            "Resume stream chunk",
+                            extra={
+                                "session_id": session_id,
+                                "chunk_type": str(chunk.type),
+                            },
+                        )
+                    if not first_chunk_type:
+                        first_chunk_type = str(chunk.type)
+                    chunk_count += 1
+                    yield chunk.to_sse()
+
+                    if isinstance(chunk, StreamFinish):
+                        break
+                except asyncio.TimeoutError:
+                    yield StreamHeartbeat().to_sse()
+        except GeneratorExit:
+            pass
+        except Exception as e:
+            logger.error(f"Error in resume stream for session {session_id}: {e}")
+        finally:
+            try:
+                await stream_registry.unsubscribe_from_task(
+                    active_task.task_id, subscriber_queue
                 )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
-            yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
+            except Exception as unsub_err:
+                logger.error(
+                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
+                    exc_info=True,
+                )
+            logger.info(
+                "Resume stream completed",
+                extra={
+                    "session_id": session_id,
+                    "n_chunks": chunk_count,
+                    "first_chunk_type": first_chunk_type,
+                },
+            )
+            yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -334,8 +585,8 @@ async def stream_chat_get(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
+            "X-Accel-Buffering": "no",
+            "x-vercel-ai-ui-message-stream": "v1",
         },
     )
 
@@ -366,6 +617,251 @@ 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 ==========
 
 
@@ -402,3 +898,42 @@ async def health_check() -> dict:
         "service": "chat",
         "version": "0.1.0",
     }
+
+
+# ========== Schema Export (for OpenAPI / Orval codegen) ==========
+
+ToolResponseUnion = (
+    AgentsFoundResponse
+    | NoResultsResponse
+    | AgentDetailsResponse
+    | SetupRequirementsResponse
+    | ExecutionStartedResponse
+    | NeedLoginResponse
+    | ErrorResponse
+    | InputValidationErrorResponse
+    | AgentOutputResponse
+    | UnderstandingUpdatedResponse
+    | AgentPreviewResponse
+    | AgentSavedResponse
+    | ClarificationNeededResponse
+    | BlockListResponse
+    | BlockOutputResponse
+    | DocSearchResultsResponse
+    | DocPageResponse
+    | OperationStartedResponse
+    | OperationPendingResponse
+    | OperationInProgressResponse
+)
+
+
+@router.get(
+    "/schema/tool-responses",
+    response_model=ToolResponseUnion,
+    include_in_schema=True,
+    summary="[Dummy] Tool response type export for codegen",
+    description="This endpoint is not meant to be called. It exists solely to "
+    "expose tool response models in the OpenAPI schema for frontend codegen.",
+)
+async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
+    """Never called at runtime. Exists only so Orval generates TS types."""
+    raise HTTPException(status_code=501, detail="Schema-only endpoint")

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

@@ -1,28 +0,0 @@
-"""Agent generator package - Creates agents from natural language."""
-
-from .core import (
-    AgentGeneratorNotConfiguredError,
-    decompose_goal,
-    generate_agent,
-    generate_agent_patch,
-    get_agent_as_json,
-    json_to_graph,
-    save_agent_to_library,
-)
-from .service import health_check as check_external_service_health
-from .service import is_external_service_configured
-
-__all__ = [
-    # Core functions
-    "decompose_goal",
-    "generate_agent",
-    "generate_agent_patch",
-    "save_agent_to_library",
-    "get_agent_as_json",
-    "json_to_graph",
-    # Exceptions
-    "AgentGeneratorNotConfiguredError",
-    # Service
-    "is_external_service_configured",
-    "check_external_service_health",
-]

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

@@ -1,277 +0,0 @@
-"""Core agent generation functions."""
-
-import logging
-import uuid
-from typing import Any
-
-from backend.api.features.library import db as library_db
-from backend.data.graph import Graph, Link, Node, create_graph
-
-from .service import (
-    decompose_goal_external,
-    generate_agent_external,
-    generate_agent_patch_external,
-    is_external_service_configured,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class AgentGeneratorNotConfiguredError(Exception):
-    """Raised when the external Agent Generator service is not configured."""
-
-    pass
-
-
-def _check_service_configured() -> None:
-    """Check if the external Agent Generator service is configured.
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the service is not configured.
-    """
-    if not is_external_service_configured():
-        raise AgentGeneratorNotConfiguredError(
-            "Agent Generator service is not configured. "
-            "Set AGENTGENERATOR_HOST environment variable to enable agent generation."
-        )
-
-
-async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None:
-    """Break down a goal into steps or return clarifying questions.
-
-    Args:
-        description: Natural language goal description
-        context: Additional context (e.g., answers to previous questions)
-
-    Returns:
-        Dict with either:
-        - {"type": "clarifying_questions", "questions": [...]}
-        - {"type": "instructions", "steps": [...]}
-        Or None on error
-
-    Raises:
-        AgentGeneratorNotConfiguredError: If the external service is not configured.
-    """
-    _check_service_configured()
-    logger.info("Calling external Agent Generator service for decompose_goal")
-    return await decompose_goal_external(description, context)
-
-
-async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None:
-    """Generate agent JSON from instructions.
-
-    Args:
-        instructions: Structured instructions from decompose_goal
-
-    Returns:
-        Agent JSON dict 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)
-    if result:
-        # Ensure required fields
-        if "id" not in result:
-            result["id"] = str(uuid.uuid4())
-        if "version" not in result:
-            result["version"] = 1
-        if "is_active" not in result:
-            result["is_active"] = True
-    return result
-
-
-def json_to_graph(agent_json: dict[str, Any]) -> Graph:
-    """Convert agent JSON dict to Graph model.
-
-    Args:
-        agent_json: Agent JSON with nodes and links
-
-    Returns:
-        Graph ready for saving
-    """
-    nodes = []
-    for n in agent_json.get("nodes", []):
-        node = Node(
-            id=n.get("id", str(uuid.uuid4())),
-            block_id=n["block_id"],
-            input_default=n.get("input_default", {}),
-            metadata=n.get("metadata", {}),
-        )
-        nodes.append(node)
-
-    links = []
-    for link_data in agent_json.get("links", []):
-        link = Link(
-            id=link_data.get("id", str(uuid.uuid4())),
-            source_id=link_data["source_id"],
-            sink_id=link_data["sink_id"],
-            source_name=link_data["source_name"],
-            sink_name=link_data["sink_name"],
-            is_static=link_data.get("is_static", False),
-        )
-        links.append(link)
-
-    return Graph(
-        id=agent_json.get("id", str(uuid.uuid4())),
-        version=agent_json.get("version", 1),
-        is_active=agent_json.get("is_active", True),
-        name=agent_json.get("name", "Generated Agent"),
-        description=agent_json.get("description", ""),
-        nodes=nodes,
-        links=links,
-    )
-
-
-def _reassign_node_ids(graph: Graph) -> None:
-    """Reassign all node and link IDs to new UUIDs.
-
-    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
-        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]
-
-
-async def save_agent_to_library(
-    agent_json: dict[str, Any], user_id: str, is_update: bool = False
-) -> tuple[Graph, Any]:
-    """Save agent to database and user's library.
-
-    Args:
-        agent_json: Agent JSON dict
-        user_id: User ID
-        is_update: Whether this is an update to an existing agent
-
-    Returns:
-        Tuple of (created Graph, LibraryAgent)
-    """
-    from backend.data.graph import get_graph_all_versions
-
-    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,
-        sensitive_action_safe_mode=True,
-        create_library_agents_for_sub_graphs=False,
-    )
-
-    return created_graph, library_agents[0]
-
-
-async def get_agent_as_json(
-    graph_id: str, user_id: str | None
-) -> dict[str, Any] | None:
-    """Fetch an agent and convert to JSON format for editing.
-
-    Args:
-        graph_id: Graph ID or library agent ID
-        user_id: User ID
-
-    Returns:
-        Agent as JSON dict or None if not found
-    """
-    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(
-            {
-                "id": node.id,
-                "block_id": node.block_id,
-                "input_default": node.input_default,
-                "metadata": node.metadata,
-            }
-        )
-
-    links = []
-    for node in graph.nodes:
-        for link in node.output_links:
-            links.append(
-                {
-                    "id": link.id,
-                    "source_id": link.source_id,
-                    "sink_id": link.sink_id,
-                    "source_name": link.source_name,
-                    "sink_name": link.sink_name,
-                    "is_static": link.is_static,
-                }
-            )
-
-    return {
-        "id": graph.id,
-        "name": graph.name,
-        "description": graph.description,
-        "version": graph.version,
-        "is_active": graph.is_active,
-        "nodes": nodes,
-        "links": links,
-    }
-
-
-async def generate_agent_patch(
-    update_request: str, current_agent: dict[str, Any]
-) -> dict[str, Any] | None:
-    """Update an existing agent using natural language.
-
-    The external Agent Generator service handles:
-    - Generating the patch
-    - Applying the patch
-    - Fixing and validating the result
-
-    Args:
-        update_request: Natural language description of changes
-        current_agent: Current agent JSON
-
-    Returns:
-        Updated agent JSON, clarifying questions dict, 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)

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

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

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

@@ -1,151 +0,0 @@
-"""Shared agent search functionality for find_agent and find_library_agent tools."""
-
-import logging
-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.util.exceptions import DatabaseError, NotFoundError
-
-from .models import (
-    AgentInfo,
-    AgentsFoundResponse,
-    ErrorResponse,
-    NoResultsResponse,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-SearchSource = Literal["marketplace", "library"]
-
-
-async def search_agents(
-    query: str,
-    source: SearchSource,
-    session_id: str | None,
-    user_id: str | None = None,
-) -> ToolResponseBase:
-    """
-    Search for agents in marketplace or user library.
-
-    Args:
-        query: Search query string
-        source: "marketplace" or "library"
-        session_id: Chat session ID
-        user_id: User ID (required for library search)
-
-    Returns:
-        AgentsFoundResponse, NoResultsResponse, or ErrorResponse
-    """
-    if not query:
-        return ErrorResponse(
-            message="Please provide a search query", session_id=session_id
-        )
-
-    if source == "library" and not user_id:
-        return ErrorResponse(
-            message="User authentication required to search library",
-            session_id=session_id,
-        )
-
-    agents: list[AgentInfo] = []
-    try:
-        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:
-                agents.append(
-                    AgentInfo(
-                        id=f"{agent.creator}/{agent.slug}",
-                        name=agent.agent_name,
-                        description=agent.description or "",
-                        source="marketplace",
-                        in_library=False,
-                        creator=agent.creator,
-                        category="general",
-                        rating=agent.rating,
-                        runs=agent.runs,
-                        is_featured=False,
-                    )
-                )
-        else:  # library
-            logger.info(f"Searching user library for: {query}")
-            results = await library_db.list_library_agents(
-                user_id=user_id,  # type: ignore[arg-type]
-                search_term=query,
-                page_size=10,
-            )
-            for agent in results.agents:
-                agents.append(
-                    AgentInfo(
-                        id=agent.id,
-                        name=agent.name,
-                        description=agent.description or "",
-                        source="library",
-                        in_library=True,
-                        creator=agent.creator_name,
-                        status=agent.status.value,
-                        can_access_graph=agent.can_access_graph,
-                        has_external_trigger=agent.has_external_trigger,
-                        new_output=agent.new_output,
-                        graph_id=agent.graph_id,
-                    )
-                )
-        logger.info(f"Found {len(agents)} agents in {source}")
-    except NotFoundError:
-        pass
-    except DatabaseError as e:
-        logger.error(f"Error searching {source}: {e}", exc_info=True)
-        return ErrorResponse(
-            message=f"Failed to search {source}. Please try again.",
-            error=str(e),
-            session_id=session_id,
-        )
-
-    if not agents:
-        suggestions = (
-            [
-                "Try more general terms",
-                "Browse categories in the marketplace",
-                "Check spelling",
-            ]
-            if source == "marketplace"
-            else [
-                "Try different keywords",
-                "Use find_agent to search the marketplace",
-                "Check your library at /library",
-            ]
-        )
-        no_results_msg = (
-            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
-            if source == "marketplace"
-            else f"No agents matching '{query}' found in your library."
-        )
-        return NoResultsResponse(
-            message=no_results_msg, session_id=session_id, suggestions=suggestions
-        )
-
-    title = f"Found {len(agents)} agent{'s' if len(agents) != 1 else ''} "
-    title += (
-        f"for '{query}'"
-        if source == "marketplace"
-        else f"in your library for '{query}'"
-    )
-
-    message = (
-        "Now you have found some options for the user to choose from. "
-        "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents."
-        if source == "marketplace"
-        else "Found agents in the user's library. You can provide a link to view an agent at: "
-        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
-    )
-
-    return AgentsFoundResponse(
-        message=message,
-        title=title,
-        agents=agents,
-        count=len(agents),
-        session_id=session_id,
-    )

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

@@ -1,193 +0,0 @@
-import logging
-from typing import Any
-
-from prisma.enums import ContentType
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
-from backend.api.features.chat.tools.models import (
-    BlockInfoSummary,
-    BlockInputFieldInfo,
-    BlockListResponse,
-    ErrorResponse,
-    NoResultsResponse,
-)
-from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import get_block
-
-logger = logging.getLogger(__name__)
-
-
-class FindBlockTool(BaseTool):
-    """Tool for searching available blocks."""
-
-    @property
-    def name(self) -> str:
-        return "find_block"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Search for available blocks by name or description. "
-            "Blocks are reusable components that perform specific tasks like "
-            "sending emails, making API calls, processing text, etc. "
-            "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
-            "The response includes each block's id, required_inputs, and input_schema."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "query": {
-                    "type": "string",
-                    "description": (
-                        "Search query to find blocks by name or description. "
-                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
-                    ),
-                },
-            },
-            "required": ["query"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        """Search for blocks matching the query.
-
-        Args:
-            user_id: User ID (required)
-            session: Chat session
-            query: Search query
-
-        Returns:
-            BlockListResponse: List of matching blocks
-            NoResultsResponse: No blocks found
-            ErrorResponse: Error message
-        """
-        query = kwargs.get("query", "").strip()
-        session_id = session.session_id
-
-        if not query:
-            return ErrorResponse(
-                message="Please provide a search query",
-                session_id=session_id,
-            )
-
-        try:
-            # Search for blocks using hybrid search
-            results, total = await unified_hybrid_search(
-                query=query,
-                content_types=[ContentType.BLOCK],
-                page=1,
-                page_size=10,
-            )
-
-            if not results:
-                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
-                    suggestions=[
-                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
-                        "Check spelling of technical terms",
-                    ],
-                    session_id=session_id,
-                )
-
-            # Enrich results with full block information
-            blocks: list[BlockInfoSummary] = []
-            for result in results:
-                block_id = result["content_id"]
-                block = get_block(block_id)
-
-                # Skip disabled blocks
-                if block and not block.disabled:
-                    # Get input/output schemas
-                    input_schema = {}
-                    output_schema = {}
-                    try:
-                        input_schema = block.input_schema.jsonschema()
-                    except Exception:
-                        pass
-                    try:
-                        output_schema = block.output_schema.jsonschema()
-                    except Exception:
-                        pass
-
-                    # Get categories from block instance
-                    categories = []
-                    if hasattr(block, "categories") and block.categories:
-                        categories = [cat.value for cat in block.categories]
-
-                    # Extract required inputs for easier use
-                    required_inputs: list[BlockInputFieldInfo] = []
-                    if input_schema:
-                        properties = input_schema.get("properties", {})
-                        required_fields = set(input_schema.get("required", []))
-                        # Get credential field names to exclude from required inputs
-                        credentials_fields = set(
-                            block.input_schema.get_credentials_fields().keys()
-                        )
-
-                        for field_name, field_schema in properties.items():
-                            # Skip credential fields - they're handled separately
-                            if field_name in credentials_fields:
-                                continue
-
-                            required_inputs.append(
-                                BlockInputFieldInfo(
-                                    name=field_name,
-                                    type=field_schema.get("type", "string"),
-                                    description=field_schema.get("description", ""),
-                                    required=field_name in required_fields,
-                                    default=field_schema.get("default"),
-                                )
-                            )
-
-                    blocks.append(
-                        BlockInfoSummary(
-                            id=block_id,
-                            name=block.name,
-                            description=block.description or "",
-                            categories=categories,
-                            input_schema=input_schema,
-                            output_schema=output_schema,
-                            required_inputs=required_inputs,
-                        )
-                    )
-
-            if not blocks:
-                return NoResultsResponse(
-                    message=f"No blocks found for '{query}'",
-                    suggestions=[
-                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
-                    ],
-                    session_id=session_id,
-                )
-
-            return BlockListResponse(
-                message=(
-                    f"Found {len(blocks)} block(s) matching '{query}'. "
-                    "To execute a block, use run_block with the block's 'id' field "
-                    "and provide 'input_data' matching the block's input_schema."
-                ),
-                blocks=blocks,
-                count=len(blocks),
-                query=query,
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error searching blocks: {e}", exc_info=True)
-            return ErrorResponse(
-                message="Failed to search blocks",
-                error=str(e),
-                session_id=session_id,
-            )

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

@@ -19,7 +19,10 @@ from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
+from backend.integrations.webhooks.graph_lifecycle_hooks import (
+    on_graph_activate,
+    on_graph_deactivate,
+)
 from backend.util.clients import get_scheduler_client
 from backend.util.exceptions import DatabaseError, InvalidInputError, NotFoundError
 from backend.util.json import SafeJson
@@ -39,6 +42,7 @@ 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 +53,9 @@ 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 +83,6 @@ async def list_library_agents(
         "isArchived": False,
     }
 
-    # Build search filter if applicable
     if search_term:
         where_clause["OR"] = [
             {
@@ -93,7 +99,6 @@ async def list_library_agents(
             },
         ]
 
-    # Determine sorting
     order_by: prisma.types.LibraryAgentOrderByInput | None = None
 
     if sort_by == library_model.LibraryAgentSort.CREATED_AT:
@@ -105,7 +110,7 @@ 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,
@@ -369,7 +374,7 @@ async def get_library_agent_by_graph_id(
 
 
 async def add_generated_agent_image(
-    graph: graph_db.BaseGraph,
+    graph: graph_db.GraphBaseMeta,
     user_id: str,
     library_agent_id: str,
 ) -> Optional[prisma.models.LibraryAgent]:
@@ -535,6 +540,92 @@ async def update_agent_version_in_library(
     return library_model.LibraryAgent.from_db(lib)
 
 
+async def create_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new graph and add it to the user's library."""
+    graph.version = 1
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=True)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agents = await create_library_agent(
+        graph=created_graph,
+        user_id=user_id,
+        sensitive_action_safe_mode=True,
+        create_library_agents_for_sub_graphs=False,
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+
+    return created_graph, library_agents[0]
+
+
+async def update_graph_in_library(
+    graph: graph_db.Graph,
+    user_id: str,
+) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
+    """Create a new version of an existing graph and update the library entry."""
+    existing_versions = await graph_db.get_graph_all_versions(graph.id, user_id)
+    current_active_version = (
+        next((v for v in existing_versions if v.is_active), None)
+        if existing_versions
+        else None
+    )
+    graph.version = (
+        max(v.version for v in existing_versions) + 1 if existing_versions else 1
+    )
+
+    graph_model = graph_db.make_graph_model(graph, user_id)
+    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=False)
+
+    created_graph = await graph_db.create_graph(graph_model, user_id)
+
+    library_agent = await get_library_agent_by_graph_id(user_id, created_graph.id)
+    if not library_agent:
+        raise NotFoundError(f"Library agent not found for graph {created_graph.id}")
+
+    library_agent = await update_library_agent_version_and_settings(
+        user_id, created_graph
+    )
+
+    if created_graph.is_active:
+        created_graph = await on_graph_activate(created_graph, user_id=user_id)
+        await graph_db.set_graph_active_version(
+            graph_id=created_graph.id,
+            version=created_graph.version,
+            user_id=user_id,
+        )
+        if current_active_version:
+            await on_graph_deactivate(current_active_version, user_id=user_id)
+
+    return created_graph, library_agent
+
+
+async def update_library_agent_version_and_settings(
+    user_id: str, agent_graph: graph_db.GraphModel
+) -> library_model.LibraryAgent:
+    """Update library agent to point to new graph version and sync settings."""
+    library = await update_agent_version_in_library(
+        user_id, agent_graph.id, agent_graph.version
+    )
+    updated_settings = GraphSettings.from_graph(
+        graph=agent_graph,
+        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
+        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
+    )
+    if updated_settings != library.settings:
+        library = await update_library_agent(
+            library_agent_id=library.id,
+            user_id=user_id,
+            settings=updated_settings,
+        )
+    return library
+
+
 async def update_library_agent(
     library_agent_id: str,
     user_id: str,

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
-    HEALTHY = "HEALTHY"  # Agent is running (not all runs have completed)
-    WAITING = "WAITING"  # Agent is queued or waiting to start
-    ERROR = "ERROR"  # Agent is in an error state
+    COMPLETED = "COMPLETED"
+    HEALTHY = "HEALTHY"
+    WAITING = "WAITING"
+    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
-
-    # Whether the user can access the underlying graph
+    execution_count: int = 0
+    success_rate: float | None = None
+    avg_correctness_score: float | None = None
+    recent_executions: list[RecentExecution] = pydantic.Field(
+        default_factory=list,
+        description="List of recent executions with status, score, and summary",
+    )
     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
-        can_access_graph = agent.AgentGraph.userId == agent.userId
+        execution_count = len(executions)
+        success_rate: float | None = None
+        avg_correctness_score: float | None = None
+        if execution_count > 0:
+            success_count = sum(
+                1
+                for e in executions
+                if e.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED
+            )
+            success_rate = (success_count / execution_count) * 100
 
-        # 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

@@ -1,7 +1,7 @@
 import asyncio
 import logging
 from datetime import datetime, timezone
-from typing import Any, Literal
+from typing import Any, Literal, overload
 
 import fastapi
 import prisma.enums
@@ -11,8 +11,8 @@ import prisma.types
 
 from backend.data.db import transaction
 from backend.data.graph import (
-    GraphMeta,
     GraphModel,
+    GraphModelWithoutNodes,
     get_graph,
     get_graph_as_admin,
     get_sub_graphs,
@@ -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)
@@ -332,7 +334,22 @@ async def get_store_agent_details(
         raise DatabaseError("Failed to fetch agent details") from e
 
 
-async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
+@overload
+async def get_available_graph(
+    store_listing_version_id: str, hide_nodes: Literal[False]
+) -> GraphModel: ...
+
+
+@overload
+async def get_available_graph(
+    store_listing_version_id: str, hide_nodes: Literal[True] = True
+) -> GraphModelWithoutNodes: ...
+
+
+async def get_available_graph(
+    store_listing_version_id: str,
+    hide_nodes: bool = True,
+) -> GraphModelWithoutNodes | GraphModel:
     try:
         # Get avaialble, non-deleted store listing version
         store_listing_version = (
@@ -342,7 +359,7 @@ async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
                     "isAvailable": True,
                     "isDeleted": False,
                 },
-                include={"AgentGraph": {"include": {"Nodes": True}}},
+                include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
             )
         )
 
@@ -352,7 +369,9 @@ async def get_available_graph(store_listing_version_id: str) -> GraphMeta:
                 detail=f"Store listing version {store_listing_version_id} not found",
             )
 
-        return GraphModel.from_db(store_listing_version.AgentGraph).meta()
+        return (GraphModelWithoutNodes if hide_nodes else GraphModel).from_db(
+            store_listing_version.AgentGraph
+        )
 
     except Exception as e:
         logger.error(f"Error getting agent: {e}")

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

@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
+import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -600,6 +605,7 @@ 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 +665,7 @@ async def hybrid_search(
                 featured,
                 is_available,
                 updated_at,
+                "agentGraphId",
                 searchable_text,
                 semantic_score,
                 lexical_score,
@@ -684,7 +691,11 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    results = await query_raw_with_schema(sql_query, *params)
+    try:
+        results = await query_raw_with_schema(sql_query, *params)
+    except Exception as e:
+        await _log_vector_error_diagnostics(e)
+        raise
 
     total = results[0]["total_count"] if results else 0
 
@@ -716,6 +727,87 @@ async def hybrid_search_simple(
     return await hybrid_search(query=query, page=page, page_size=page_size)
 
 
+# ============================================================================
+# Diagnostics
+# ============================================================================
+
+# Rate limit: only log vector error diagnostics once per this interval
+_VECTOR_DIAG_INTERVAL_SECONDS = 60
+_last_vector_diag_time: float = 0
+
+
+async def _log_vector_error_diagnostics(error: Exception) -> None:
+    """Log diagnostic info when 'type vector does not exist' error occurs.
+
+    Note: Diagnostic queries use query_raw_with_schema which may run on a different
+    pooled connection than the one that failed. Session-level search_path can differ,
+    so these diagnostics show cluster-wide state, not necessarily the failed session.
+
+    Includes rate limiting to avoid log spam - only logs once per minute.
+    Caller should re-raise the error after calling this function.
+    """
+    global _last_vector_diag_time
+
+    # Check if this is the vector type error
+    error_str = str(error).lower()
+    if not (
+        "type" in error_str and "vector" in error_str and "does not exist" in error_str
+    ):
+        return
+
+    # Rate limit: only log once per interval
+    now = time.time()
+    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
+        return
+    _last_vector_diag_time = now
+
+    try:
+        diagnostics: dict[str, object] = {}
+
+        try:
+            search_path_result = await query_raw_with_schema("SHOW search_path")
+            diagnostics["search_path"] = search_path_result
+        except Exception as e:
+            diagnostics["search_path"] = f"Error: {e}"
+
+        try:
+            schema_result = await query_raw_with_schema("SELECT current_schema()")
+            diagnostics["current_schema"] = schema_result
+        except Exception as e:
+            diagnostics["current_schema"] = f"Error: {e}"
+
+        try:
+            user_result = await query_raw_with_schema(
+                "SELECT current_user, session_user, current_database()"
+            )
+            diagnostics["user_info"] = user_result
+        except Exception as e:
+            diagnostics["user_info"] = f"Error: {e}"
+
+        try:
+            # Check pgvector extension installation (cluster-wide, stable info)
+            ext_result = await query_raw_with_schema(
+                "SELECT extname, extversion, nspname as schema "
+                "FROM pg_extension e "
+                "JOIN pg_namespace n ON e.extnamespace = n.oid "
+                "WHERE extname = 'vector'"
+            )
+            diagnostics["pgvector_extension"] = ext_result
+        except Exception as e:
+            diagnostics["pgvector_extension"] = f"Error: {e}"
+
+        logger.error(
+            f"Vector type error diagnostics:\n"
+            f"  Error: {error}\n"
+            f"  search_path: {diagnostics.get('search_path')}\n"
+            f"  current_schema: {diagnostics.get('current_schema')}\n"
+            f"  user_info: {diagnostics.get('user_info')}\n"
+            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
+        )
+    except Exception as diag_error:
+        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
+
+
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights

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

@@ -16,7 +16,7 @@ from backend.blocks.ideogram import (
     StyleType,
     UpscaleOption,
 )
-from backend.data.graph import BaseGraph
+from backend.data.graph import GraphBaseMeta
 from backend.data.model import CredentialsMetaInput, ProviderName
 from backend.integrations.credentials_store import ideogram_credentials
 from backend.util.request import Requests
@@ -34,14 +34,14 @@ class ImageStyle(str, Enum):
     DIGITAL_ART = "digital art"
 
 
-async def generate_agent_image(agent: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image(agent: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     if settings.config.use_agent_image_generation_v2:
         return await generate_agent_image_v2(graph=agent)
     else:
         return await generate_agent_image_v1(agent=agent)
 
 
-async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     """
     Generate an image for an agent using Ideogram model.
     Returns:
@@ -54,14 +54,17 @@ async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
     description = f"{name} ({graph.description})" if graph.description else name
 
     prompt = (
-        f"Create a visually striking retro-futuristic vector pop art illustration prominently featuring "
-        f'"{name}" in bold typography. The image clearly and literally depicts a {description}, '
-        f"along with recognizable objects directly associated with the primary function of a {name}. "
-        f"Ensure the imagery is concrete, intuitive, and immediately understandable, clearly conveying the "
-        f"purpose of a {name}. Maintain vibrant, limited-palette colors, sharp vector lines, geometric "
-        f"shapes, flat illustration techniques, and solid colors without gradients or shading. Preserve a "
-        f"retro-futuristic aesthetic influenced by mid-century futurism and 1960s psychedelia, "
-        f"prioritizing clear visual storytelling and thematic clarity above all else."
+        "Create a visually striking retro-futuristic vector pop art illustration "
+        f'prominently featuring "{name}" in bold typography. The image clearly and '
+        f"literally depicts a {description}, along with recognizable objects directly "
+        f"associated with the primary function of a {name}. "
+        f"Ensure the imagery is concrete, intuitive, and immediately understandable, "
+        f"clearly conveying the purpose of a {name}. "
+        "Maintain vibrant, limited-palette colors, sharp vector lines, "
+        "geometric shapes, flat illustration techniques, and solid colors "
+        "without gradients or shading. Preserve a retro-futuristic aesthetic "
+        "influenced by mid-century futurism and 1960s psychedelia, "
+        "prioritizing clear visual storytelling and thematic clarity above all else."
     )
 
     custom_colors = [
@@ -99,12 +102,12 @@ async def generate_agent_image_v2(graph: BaseGraph | AgentGraph) -> io.BytesIO:
     return io.BytesIO(response.content)
 
 
-async def generate_agent_image_v1(agent: BaseGraph | AgentGraph) -> io.BytesIO:
+async def generate_agent_image_v1(agent: GraphBaseMeta | AgentGraph) -> io.BytesIO:
     """
     Generate an image for an agent using Flux model via Replicate API.
 
     Args:
-        agent (Graph): The agent to generate an image for
+        agent (GraphBaseMeta | AgentGraph): The agent to generate an image for
 
     Returns:
         io.BytesIO: The generated image as bytes
@@ -114,7 +117,13 @@ async def generate_agent_image_v1(agent: BaseGraph | AgentGraph) -> io.BytesIO:
             raise ValueError("Missing Replicate API key in settings")
 
         # Construct prompt from agent details
-        prompt = f"Create a visually engaging app store thumbnail for the AI agent that highlights what it does in a clear and captivating way:\n- **Name**: {agent.name}\n- **Description**: {agent.description}\nFocus on showcasing its core functionality with an appealing design."
+        prompt = (
+            "Create a visually engaging app store thumbnail for the AI agent "
+            "that highlights what it does in a clear and captivating way:\n"
+            f"- **Name**: {agent.name}\n"
+            f"- **Description**: {agent.description}\n"
+            f"Focus on showcasing its core functionality with an appealing design."
+        )
 
         # Set up Replicate client
         client = ReplicateClient(api_token=settings.secrets.replicate_api_key)

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.py

@@ -278,7 +278,7 @@ async def get_agent(
 )
 async def get_graph_meta_by_store_listing_version_id(
     store_listing_version_id: str,
-) -> backend.data.graph.GraphMeta:
+) -> backend.data.graph.GraphModelWithoutNodes:
     """
     Get Agent Graph from Store Listing Version ID.
     """

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/v1.py

@@ -101,7 +101,6 @@ from backend.util.timezone_utils import (
 from backend.util.virus_scanner import scan_content_safe
 
 from .library import db as library_db
-from .library import model as library_model
 from .store.model import StoreAgentDetails
 
 
@@ -823,18 +822,16 @@ async def update_graph(
     graph: graph_db.Graph,
     user_id: Annotated[str, Security(get_user_id)],
 ) -> graph_db.GraphModel:
-    # Sanity check
     if graph.id and graph.id != graph_id:
         raise HTTPException(400, detail="Graph ID does not match ID in URI")
 
-    # Determine new version
     existing_versions = await graph_db.get_graph_all_versions(graph_id, user_id=user_id)
     if not existing_versions:
         raise HTTPException(404, detail=f"Graph #{graph_id} not found")
-    latest_version_number = max(g.version for g in existing_versions)
-    graph.version = latest_version_number + 1
 
+    graph.version = max(g.version for g in existing_versions) + 1
     current_active_version = next((v for v in existing_versions if v.is_active), None)
+
     graph = graph_db.make_graph_model(graph, user_id)
     graph.reassign_ids(user_id=user_id, reassign_graph_id=False)
     graph.validate_graph(for_run=False)
@@ -842,27 +839,23 @@ async def update_graph(
     new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
 
     if new_graph_version.is_active:
-        # Keep the library agent up to date with the new active version
-        await _update_library_agent_version_and_settings(user_id, new_graph_version)
-
-        # Handle activation of the new graph first to ensure continuity
+        await library_db.update_library_agent_version_and_settings(
+            user_id, new_graph_version
+        )
         new_graph_version = await on_graph_activate(new_graph_version, user_id=user_id)
-        # Ensure new version is the only active version
         await graph_db.set_graph_active_version(
             graph_id=graph_id, version=new_graph_version.version, user_id=user_id
         )
         if current_active_version:
-            # Handle deactivation of the previously active version
             await on_graph_deactivate(current_active_version, user_id=user_id)
 
-    # Fetch new graph version *with sub-graphs* (needed for credentials input schema)
     new_graph_version_with_subgraphs = await graph_db.get_graph(
         graph_id,
         new_graph_version.version,
         user_id=user_id,
         include_subgraphs=True,
     )
-    assert new_graph_version_with_subgraphs  # make type checker happy
+    assert new_graph_version_with_subgraphs
     return new_graph_version_with_subgraphs
 
 
@@ -900,33 +893,15 @@ async def set_graph_active_version(
     )
 
     # Keep the library agent up to date with the new active version
-    await _update_library_agent_version_and_settings(user_id, new_active_graph)
+    await library_db.update_library_agent_version_and_settings(
+        user_id, new_active_graph
+    )
 
     if current_active_graph and current_active_graph.version != new_active_version:
         # Handle deactivation of the previously active version
         await on_graph_deactivate(current_active_graph, user_id=user_id)
 
 
-async def _update_library_agent_version_and_settings(
-    user_id: str, agent_graph: graph_db.GraphModel
-) -> library_model.LibraryAgent:
-    library = await library_db.update_agent_version_in_library(
-        user_id, agent_graph.id, agent_graph.version
-    )
-    updated_settings = GraphSettings.from_graph(
-        graph=agent_graph,
-        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
-        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
-            user_id=user_id,
-            settings=updated_settings,
-        )
-    return library
-
-
 @v1_router.patch(
     path="/graphs/{graph_id}/settings",
     summary="Update graph settings",

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
@@ -40,6 +41,10 @@ import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
 from backend.blocks.llm import DEFAULT_LLM_MODEL
+from backend.copilot.completion_consumer import (
+    start_completion_consumer,
+    stop_completion_consumer,
+)
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
 from backend.monitoring.instrumentation import instrument_fastapi
@@ -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():
-        async for event in execution_bus.listen("*"):
-            await manager.send_execution_update(event)
+    try:
 
-    async def notification_worker():
-        async for notification in notification_bus.listen("*"):
-            await manager.send_notification(
-                user_id=notification.user_id,
-                payload=notification.payload,
-            )
+        async def execution_worker():
+            async for event in execution_bus.listen("*"):
+                await manager.send_execution_update(event)
 
-    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/app.py

@@ -38,6 +38,7 @@ def main(**kwargs):
 
     from backend.api.rest_api import AgentServer
     from backend.api.ws_api import WebsocketServer
+    from backend.copilot.executor.manager import CoPilotExecutor
     from backend.executor import DatabaseManager, ExecutionManager, Scheduler
     from backend.notifications import NotificationManager
 
@@ -48,6 +49,7 @@ def main(**kwargs):
         WebsocketServer(),
         AgentServer(),
         ExecutionManager(),
+        CoPilotExecutor(),
         **kwargs,
     )
 

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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **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,
-                        return_content=True,
+                        execution_context=execution_context,
+                        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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **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,
-            return_content=input_data.base_64,
+            execution_context=execution_context,
+            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,
-        user_id: str,
+        execution_context: ExecutionContext,
     ) -> 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,
-                        return_content=True,  # Get as data URI
+                        execution_context=execution_context,
+                        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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **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,
-                user_id=user_id,
+                execution_context=execution_context,
             )
 
             yield "status", result.get("status", "Unknown error")

autogpt_platform/backend/backend/blocks/elevenlabs/_auth.py

@@ -0,0 +1,28 @@
+"""ElevenLabs integration blocks - test credentials and shared utilities."""
+
+from typing import Literal
+
+from pydantic import SecretStr
+
+from backend.data.model import APIKeyCredentials, CredentialsMetaInput
+from backend.integrations.providers import ProviderName
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="elevenlabs",
+    api_key=SecretStr("mock-elevenlabs-api-key"),
+    title="Mock ElevenLabs API key",
+    expires_at=None,
+)
+
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}
+
+ElevenLabsCredentials = APIKeyCredentials
+ElevenLabsCredentialsInput = CredentialsMetaInput[
+    Literal[ProviderName.ELEVENLABS], Literal["api_key"]
+]

autogpt_platform/backend/backend/blocks/encoder_block.py

@@ -0,0 +1,77 @@
+"""Text encoding block for converting special characters to escape sequences."""
+
+import codecs
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.model import SchemaField
+
+
+class TextEncoderBlock(Block):
+    """
+    Encodes a string by converting special characters into escape sequences.
+
+    This block is the inverse of TextDecoderBlock. It takes text containing
+    special characters (like newlines, tabs, etc.) and converts them into
+    their escape sequence representations (e.g., newline becomes \\n).
+    """
+
+    class Input(BlockSchemaInput):
+        """Input schema for TextEncoderBlock."""
+
+        text: str = SchemaField(
+            description="A string containing special characters to be encoded",
+            placeholder="Your text with newlines and quotes to encode",
+        )
+
+    class Output(BlockSchemaOutput):
+        """Output schema for TextEncoderBlock."""
+
+        encoded_text: str = SchemaField(
+            description="The encoded text with special characters converted to escape sequences"
+        )
+        error: str = SchemaField(description="Error message if encoding fails")
+
+    def __init__(self):
+        super().__init__(
+            id="5185f32e-4b65-4ecf-8fbb-873f003f09d6",
+            description="Encodes a string by converting special characters into escape sequences",
+            categories={BlockCategory.TEXT},
+            input_schema=TextEncoderBlock.Input,
+            output_schema=TextEncoderBlock.Output,
+            test_input={
+                "text": """Hello
+World!
+This is a "quoted" string."""
+            },
+            test_output=[
+                (
+                    "encoded_text",
+                    """Hello\\nWorld!\\nThis is a "quoted" string.""",
+                )
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        """
+        Encode the input text by converting special characters to escape sequences.
+
+        Args:
+            input_data: The input containing the text to encode.
+            **kwargs: Additional keyword arguments (unused).
+
+        Yields:
+            The encoded text with escape sequences, or an error message if encoding fails.
+        """
+        try:
+            encoded_text = codecs.encode(input_data.text, "unicode_escape").decode(
+                "utf-8"
+            )
+            yield "encoded_text", encoded_text
+        except Exception as e:
+            yield "error", f"Encoding error: {str(e)}"

autogpt_platform/backend/backend/blocks/exa/websets.py

@@ -478,7 +478,7 @@ class ExaCreateOrFindWebsetBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         try:
-            webset = aexa.websets.get(id=input_data.external_id)
+            webset = await aexa.websets.get(id=input_data.external_id)
             webset_result = Webset.model_validate(webset.model_dump(by_alias=True))
 
             yield "webset", webset_result
@@ -494,7 +494,7 @@ class ExaCreateOrFindWebsetBlock(Block):
                         count=input_data.search_count,
                     )
 
-                webset = aexa.websets.create(
+                webset = await aexa.websets.create(
                     params=CreateWebsetParameters(
                         search=search_params,
                         external_id=input_data.external_id,
@@ -554,7 +554,7 @@ class ExaUpdateWebsetBlock(Block):
         if input_data.metadata is not None:
             payload["metadata"] = input_data.metadata
 
-        sdk_webset = aexa.websets.update(id=input_data.webset_id, params=payload)
+        sdk_webset = await aexa.websets.update(id=input_data.webset_id, params=payload)
 
         status_str = (
             sdk_webset.status.value
@@ -617,7 +617,7 @@ class ExaListWebsetsBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = aexa.websets.list(
+        response = await aexa.websets.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
         )
@@ -678,7 +678,7 @@ class ExaGetWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_webset = aexa.websets.get(id=input_data.webset_id)
+        sdk_webset = await aexa.websets.get(id=input_data.webset_id)
 
         status_str = (
             sdk_webset.status.value
@@ -748,7 +748,7 @@ class ExaDeleteWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_webset = aexa.websets.delete(id=input_data.webset_id)
+        deleted_webset = await aexa.websets.delete(id=input_data.webset_id)
 
         status_str = (
             deleted_webset.status.value
@@ -798,7 +798,7 @@ class ExaCancelWebsetBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_webset = aexa.websets.cancel(id=input_data.webset_id)
+        canceled_webset = await aexa.websets.cancel(id=input_data.webset_id)
 
         status_str = (
             canceled_webset.status.value
@@ -968,7 +968,7 @@ class ExaPreviewWebsetBlock(Block):
                 entity["description"] = input_data.entity_description
             payload["entity"] = entity
 
-        sdk_preview = aexa.websets.preview(params=payload)
+        sdk_preview = await aexa.websets.preview(params=payload)
 
         preview = PreviewWebsetModel.from_sdk(sdk_preview)
 
@@ -1051,7 +1051,7 @@ class ExaWebsetStatusBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = aexa.websets.get(id=input_data.webset_id)
+        webset = await aexa.websets.get(id=input_data.webset_id)
 
         status = (
             webset.status.value
@@ -1185,7 +1185,7 @@ class ExaWebsetSummaryBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = aexa.websets.get(id=input_data.webset_id)
+        webset = await aexa.websets.get(id=input_data.webset_id)
 
         # Extract basic info
         webset_id = webset.id
@@ -1211,7 +1211,7 @@ class ExaWebsetSummaryBlock(Block):
         total_items = 0
 
         if input_data.include_sample_items and input_data.sample_size > 0:
-            items_response = aexa.websets.items.list(
+            items_response = await aexa.websets.items.list(
                 webset_id=input_data.webset_id, limit=input_data.sample_size
             )
             sample_items_data = [
@@ -1362,7 +1362,7 @@ class ExaWebsetReadyCheckBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get webset details
-        webset = aexa.websets.get(id=input_data.webset_id)
+        webset = await aexa.websets.get(id=input_data.webset_id)
 
         status = (
             webset.status.value

autogpt_platform/backend/backend/blocks/exa/websets_enrichment.py

@@ -202,7 +202,7 @@ class ExaCreateEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_enrichment = aexa.websets.enrichments.create(
+        sdk_enrichment = await aexa.websets.enrichments.create(
             webset_id=input_data.webset_id, params=payload
         )
 
@@ -223,7 +223,7 @@ class ExaCreateEnrichmentBlock(Block):
             items_enriched = 0
 
             while time.time() - poll_start < input_data.polling_timeout:
-                current_enrich = aexa.websets.enrichments.get(
+                current_enrich = await aexa.websets.enrichments.get(
                     webset_id=input_data.webset_id, id=enrichment_id
                 )
                 current_status = (
@@ -234,7 +234,7 @@ class ExaCreateEnrichmentBlock(Block):
 
                 if current_status in ["completed", "failed", "cancelled"]:
                     # Estimate items from webset searches
-                    webset = aexa.websets.get(id=input_data.webset_id)
+                    webset = await aexa.websets.get(id=input_data.webset_id)
                     if webset.searches:
                         for search in webset.searches:
                             if search.progress:
@@ -329,7 +329,7 @@ class ExaGetEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_enrichment = aexa.websets.enrichments.get(
+        sdk_enrichment = await aexa.websets.enrichments.get(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
@@ -474,7 +474,7 @@ class ExaDeleteEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_enrichment = aexa.websets.enrichments.delete(
+        deleted_enrichment = await aexa.websets.enrichments.delete(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
@@ -525,13 +525,13 @@ class ExaCancelEnrichmentBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_enrichment = aexa.websets.enrichments.cancel(
+        canceled_enrichment = await aexa.websets.enrichments.cancel(
             webset_id=input_data.webset_id, id=input_data.enrichment_id
         )
 
         # Try to estimate how many items were enriched before cancellation
         items_enriched = 0
-        items_response = aexa.websets.items.list(
+        items_response = await aexa.websets.items.list(
             webset_id=input_data.webset_id, limit=100
         )
 

autogpt_platform/backend/backend/blocks/exa/websets_import_export.py

@@ -222,7 +222,7 @@ class ExaCreateImportBlock(Block):
     def _create_test_mock():
         """Create test mocks for the AsyncExa SDK."""
         from datetime import datetime
-        from unittest.mock import MagicMock
+        from unittest.mock import AsyncMock, MagicMock
 
         # Create mock SDK import object
         mock_import = MagicMock()
@@ -247,7 +247,7 @@ class ExaCreateImportBlock(Block):
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
                 websets=MagicMock(
-                    imports=MagicMock(create=lambda *args, **kwargs: mock_import)
+                    imports=MagicMock(create=AsyncMock(return_value=mock_import))
                 )
             )
         }
@@ -294,7 +294,7 @@ class ExaCreateImportBlock(Block):
         if input_data.metadata:
             payload["metadata"] = input_data.metadata
 
-        sdk_import = aexa.websets.imports.create(
+        sdk_import = await aexa.websets.imports.create(
             params=payload, csv_data=input_data.csv_data
         )
 
@@ -360,7 +360,7 @@ class ExaGetImportBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_import = aexa.websets.imports.get(import_id=input_data.import_id)
+        sdk_import = await aexa.websets.imports.get(import_id=input_data.import_id)
 
         import_obj = ImportModel.from_sdk(sdk_import)
 
@@ -426,7 +426,7 @@ class ExaListImportsBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = aexa.websets.imports.list(
+        response = await aexa.websets.imports.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
         )
@@ -474,7 +474,9 @@ class ExaDeleteImportBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_import = aexa.websets.imports.delete(import_id=input_data.import_id)
+        deleted_import = await aexa.websets.imports.delete(
+            import_id=input_data.import_id
+        )
 
         yield "import_id", deleted_import.id
         yield "success", "true"
@@ -573,14 +575,14 @@ class ExaExportWebsetBlock(Block):
             }
         )
 
-        # Create mock iterator
-        mock_items = [mock_item1, mock_item2]
+        # Create async iterator for list_all
+        async def async_item_iterator(*args, **kwargs):
+            for item in [mock_item1, mock_item2]:
+                yield item
 
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
-                websets=MagicMock(
-                    items=MagicMock(list_all=lambda *args, **kwargs: iter(mock_items))
-                )
+                websets=MagicMock(items=MagicMock(list_all=async_item_iterator))
             )
         }
 
@@ -602,7 +604,7 @@ class ExaExportWebsetBlock(Block):
                 webset_id=input_data.webset_id, limit=input_data.max_items
             )
 
-            for sdk_item in item_iterator:
+            async for sdk_item in item_iterator:
                 if len(all_items) >= input_data.max_items:
                     break
 

autogpt_platform/backend/backend/blocks/exa/websets_items.py

@@ -178,7 +178,7 @@ class ExaGetWebsetItemBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_item = aexa.websets.items.get(
+        sdk_item = await aexa.websets.items.get(
             webset_id=input_data.webset_id, id=input_data.item_id
         )
 
@@ -269,7 +269,7 @@ class ExaListWebsetItemsBlock(Block):
             response = None
 
             while time.time() - start_time < input_data.wait_timeout:
-                response = aexa.websets.items.list(
+                response = await aexa.websets.items.list(
                     webset_id=input_data.webset_id,
                     cursor=input_data.cursor,
                     limit=input_data.limit,
@@ -282,13 +282,13 @@ class ExaListWebsetItemsBlock(Block):
                 interval = min(interval * 1.2, 10)
 
             if not response:
-                response = aexa.websets.items.list(
+                response = await aexa.websets.items.list(
                     webset_id=input_data.webset_id,
                     cursor=input_data.cursor,
                     limit=input_data.limit,
                 )
         else:
-            response = aexa.websets.items.list(
+            response = await aexa.websets.items.list(
                 webset_id=input_data.webset_id,
                 cursor=input_data.cursor,
                 limit=input_data.limit,
@@ -340,7 +340,7 @@ class ExaDeleteWebsetItemBlock(Block):
     ) -> BlockOutput:
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_item = aexa.websets.items.delete(
+        deleted_item = await aexa.websets.items.delete(
             webset_id=input_data.webset_id, id=input_data.item_id
         )
 
@@ -408,7 +408,7 @@ class ExaBulkWebsetItemsBlock(Block):
             webset_id=input_data.webset_id, limit=input_data.max_items
         )
 
-        for sdk_item in item_iterator:
+        async for sdk_item in item_iterator:
             if len(all_items) >= input_data.max_items:
                 break
 
@@ -475,7 +475,7 @@ class ExaWebsetItemsSummaryBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        webset = aexa.websets.get(id=input_data.webset_id)
+        webset = await aexa.websets.get(id=input_data.webset_id)
 
         entity_type = "unknown"
         if webset.searches:
@@ -495,7 +495,7 @@ class ExaWebsetItemsSummaryBlock(Block):
         # Get sample items if requested
         sample_items: List[WebsetItemModel] = []
         if input_data.sample_size > 0:
-            items_response = aexa.websets.items.list(
+            items_response = await aexa.websets.items.list(
                 webset_id=input_data.webset_id, limit=input_data.sample_size
             )
             # Convert to our stable models
@@ -569,7 +569,7 @@ class ExaGetNewItemsBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get items starting from cursor
-        response = aexa.websets.items.list(
+        response = await aexa.websets.items.list(
             webset_id=input_data.webset_id,
             cursor=input_data.since_cursor,
             limit=input_data.max_items,

autogpt_platform/backend/backend/blocks/exa/websets_monitor.py

@@ -233,7 +233,7 @@ class ExaCreateMonitorBlock(Block):
     def _create_test_mock():
         """Create test mocks for the AsyncExa SDK."""
         from datetime import datetime
-        from unittest.mock import MagicMock
+        from unittest.mock import AsyncMock, MagicMock
 
         # Create mock SDK monitor object
         mock_monitor = MagicMock()
@@ -263,7 +263,7 @@ class ExaCreateMonitorBlock(Block):
         return {
             "_get_client": lambda *args, **kwargs: MagicMock(
                 websets=MagicMock(
-                    monitors=MagicMock(create=lambda *args, **kwargs: mock_monitor)
+                    monitors=MagicMock(create=AsyncMock(return_value=mock_monitor))
                 )
             )
         }
@@ -320,7 +320,7 @@ class ExaCreateMonitorBlock(Block):
         if input_data.metadata:
             payload["metadata"] = input_data.metadata
 
-        sdk_monitor = aexa.websets.monitors.create(params=payload)
+        sdk_monitor = await aexa.websets.monitors.create(params=payload)
 
         monitor = MonitorModel.from_sdk(sdk_monitor)
 
@@ -384,7 +384,7 @@ class ExaGetMonitorBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_monitor = aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
+        sdk_monitor = await aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
 
         monitor = MonitorModel.from_sdk(sdk_monitor)
 
@@ -476,7 +476,7 @@ class ExaUpdateMonitorBlock(Block):
         if input_data.metadata is not None:
             payload["metadata"] = input_data.metadata
 
-        sdk_monitor = aexa.websets.monitors.update(
+        sdk_monitor = await aexa.websets.monitors.update(
             monitor_id=input_data.monitor_id, params=payload
         )
 
@@ -522,7 +522,9 @@ class ExaDeleteMonitorBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        deleted_monitor = aexa.websets.monitors.delete(monitor_id=input_data.monitor_id)
+        deleted_monitor = await aexa.websets.monitors.delete(
+            monitor_id=input_data.monitor_id
+        )
 
         yield "monitor_id", deleted_monitor.id
         yield "success", "true"
@@ -579,7 +581,7 @@ class ExaListMonitorsBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        response = aexa.websets.monitors.list(
+        response = await aexa.websets.monitors.list(
             cursor=input_data.cursor,
             limit=input_data.limit,
             webset_id=input_data.webset_id,

autogpt_platform/backend/backend/blocks/exa/websets_polling.py

@@ -121,7 +121,7 @@ class ExaWaitForWebsetBlock(Block):
                 WebsetTargetStatus.IDLE,
                 WebsetTargetStatus.ANY_COMPLETE,
             ]:
-                final_webset = aexa.websets.wait_until_idle(
+                final_webset = await aexa.websets.wait_until_idle(
                     id=input_data.webset_id,
                     timeout=input_data.timeout,
                     poll_interval=input_data.check_interval,
@@ -164,7 +164,7 @@ class ExaWaitForWebsetBlock(Block):
                 interval = input_data.check_interval
                 while time.time() - start_time < input_data.timeout:
                     # Get current webset status
-                    webset = aexa.websets.get(id=input_data.webset_id)
+                    webset = await aexa.websets.get(id=input_data.webset_id)
                     current_status = (
                         webset.status.value
                         if hasattr(webset.status, "value")
@@ -209,7 +209,7 @@ class ExaWaitForWebsetBlock(Block):
 
                 # Timeout reached
                 elapsed = time.time() - start_time
-                webset = aexa.websets.get(id=input_data.webset_id)
+                webset = await aexa.websets.get(id=input_data.webset_id)
                 final_status = (
                     webset.status.value
                     if hasattr(webset.status, "value")
@@ -345,7 +345,7 @@ class ExaWaitForSearchBlock(Block):
         try:
             while time.time() - start_time < input_data.timeout:
                 # Get current search status using SDK
-                search = aexa.websets.searches.get(
+                search = await aexa.websets.searches.get(
                     webset_id=input_data.webset_id, id=input_data.search_id
                 )
 
@@ -401,7 +401,7 @@ class ExaWaitForSearchBlock(Block):
             elapsed = time.time() - start_time
 
             # Get last known status
-            search = aexa.websets.searches.get(
+            search = await aexa.websets.searches.get(
                 webset_id=input_data.webset_id, id=input_data.search_id
             )
             final_status = (
@@ -503,7 +503,7 @@ class ExaWaitForEnrichmentBlock(Block):
         try:
             while time.time() - start_time < input_data.timeout:
                 # Get current enrichment status using SDK
-                enrichment = aexa.websets.enrichments.get(
+                enrichment = await aexa.websets.enrichments.get(
                     webset_id=input_data.webset_id, id=input_data.enrichment_id
                 )
 
@@ -548,7 +548,7 @@ class ExaWaitForEnrichmentBlock(Block):
             elapsed = time.time() - start_time
 
             # Get last known status
-            enrichment = aexa.websets.enrichments.get(
+            enrichment = await aexa.websets.enrichments.get(
                 webset_id=input_data.webset_id, id=input_data.enrichment_id
             )
             final_status = (
@@ -575,7 +575,7 @@ class ExaWaitForEnrichmentBlock(Block):
     ) -> tuple[list[SampleEnrichmentModel], int]:
         """Get sample enriched data and count."""
         # Get a few items to see enrichment results using SDK
-        response = aexa.websets.items.list(webset_id=webset_id, limit=5)
+        response = await aexa.websets.items.list(webset_id=webset_id, limit=5)
 
         sample_data: list[SampleEnrichmentModel] = []
         enriched_count = 0

autogpt_platform/backend/backend/blocks/exa/websets_search.py

@@ -317,7 +317,7 @@ class ExaCreateWebsetSearchBlock(Block):
 
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_search = aexa.websets.searches.create(
+        sdk_search = await aexa.websets.searches.create(
             webset_id=input_data.webset_id, params=payload
         )
 
@@ -350,7 +350,7 @@ class ExaCreateWebsetSearchBlock(Block):
             poll_start = time.time()
 
             while time.time() - poll_start < input_data.polling_timeout:
-                current_search = aexa.websets.searches.get(
+                current_search = await aexa.websets.searches.get(
                     webset_id=input_data.webset_id, id=search_id
                 )
                 current_status = (
@@ -442,7 +442,7 @@ class ExaGetWebsetSearchBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        sdk_search = aexa.websets.searches.get(
+        sdk_search = await aexa.websets.searches.get(
             webset_id=input_data.webset_id, id=input_data.search_id
         )
 
@@ -523,7 +523,7 @@ class ExaCancelWebsetSearchBlock(Block):
         # Use AsyncExa SDK
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        canceled_search = aexa.websets.searches.cancel(
+        canceled_search = await aexa.websets.searches.cancel(
             webset_id=input_data.webset_id, id=input_data.search_id
         )
 
@@ -604,7 +604,7 @@ class ExaFindOrCreateSearchBlock(Block):
         aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
         # Get webset to check existing searches
-        webset = aexa.websets.get(id=input_data.webset_id)
+        webset = await aexa.websets.get(id=input_data.webset_id)
 
         # Look for existing search with same query
         existing_search = None
@@ -636,7 +636,7 @@ class ExaFindOrCreateSearchBlock(Block):
             if input_data.entity_type != SearchEntityType.AUTO:
                 payload["entity"] = {"type": input_data.entity_type.value}
 
-            sdk_search = aexa.websets.searches.create(
+            sdk_search = await aexa.websets.searches.create(
                 webset_id=input_data.webset_id, params=payload
             )
 

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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **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,
-                    return_content=True,
+                    execution_context=execution_context,
+                    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,
-            graph_exec_id=graph_exec_id,
+            user_id=execution_context.user_id or "",
+            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,
-    user_id: str,
+    execution_context: ExecutionContext,
 ) -> 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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._send_email(
             service,
             input_data,
-            graph_exec_id,
-            user_id,
+            execution_context,
         )
         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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._create_draft(
             service,
             input_data,
-            graph_exec_id,
-            user_id,
+            execution_context,
         )
         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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         message = await self._reply(
             service,
             input_data,
-            graph_exec_id,
-            user_id,
+            execution_context,
         )
         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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         draft = await self._create_draft_reply(
             service,
             input_data,
-            graph_exec_id,
-            user_id,
+            execution_context,
         )
         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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._forward_message(
             service,
             input_data,
-            graph_exec_id,
-            user_id,
+            execution_context,
         )
         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,
-        user_id: str,
+        execution_context: ExecutionContext,
         **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,
-            return_content=input_data.base_64,
+            execution_context=execution_context,
+            return_format=return_format,
         )
 
 

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/llm.py

@@ -32,7 +32,7 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.logging import TruncatedLogger
-from backend.util.prompt import compress_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 +115,7 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_4_6_OPUS = "claude-opus-4-6"
     CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
     # AI/ML API models
     AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -271,6 +271,9 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
     ),  # claude-4-sonnet-20250514
+    LlmModel.CLAUDE_4_6_OPUS: ModelMetadata(
+        "anthropic", 200000, 128000, "Claude Opus 4.6", "Anthropic", "Anthropic", 3
+    ),  # claude-opus-4-6
     LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
         "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
     ),  # claude-opus-4-5-20251101
@@ -280,9 +283,6 @@ 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
@@ -531,12 +531,12 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.NotGiven:
+) -> Iterable[ToolParam] | anthropic.Omit:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
     if not openai_tools or len(openai_tools) == 0:
-        return anthropic.NOT_GIVEN
+        return anthropic.omit
 
     anthropic_tools = []
     for tool in openai_tools:
@@ -596,10 +596,10 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
 
 def get_parallel_tool_calls_param(
     llm_model: LlmModel, parallel_tool_calls: bool | None
-):
+) -> bool | openai.Omit:
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
-        return openai.NOT_GIVEN
+        return openai.omit
     return parallel_tool_calls
 
 
@@ -638,11 +638,18 @@ 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

@@ -1,251 +0,0 @@
-import os
-import tempfile
-from typing import Literal, Optional
-
-from moviepy.audio.io.AudioFileClip import AudioFileClip
-from moviepy.video.fx.Loop import Loop
-from moviepy.video.io.VideoFileClip import VideoFileClip
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.model import SchemaField
-from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
-
-
-class MediaDurationBlock(Block):
-
-    class Input(BlockSchemaInput):
-        media_in: MediaFileType = SchemaField(
-            description="Media input (URL, data URI, or local path)."
-        )
-        is_video: bool = SchemaField(
-            description="Whether the media is a video (True) or audio (False).",
-            default=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        duration: float = SchemaField(
-            description="Duration of the media file (in seconds)."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
-            description="Block to get the duration of a media file.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=MediaDurationBlock.Input,
-            output_schema=MediaDurationBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        graph_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # 1) Store the input media locally
-        local_media_path = await store_media_file(
-            graph_exec_id=graph_exec_id,
-            file=input_data.media_in,
-            user_id=user_id,
-            return_content=False,
-        )
-        media_abspath = get_exec_file_path(graph_exec_id, local_media_path)
-
-        # 2) Load the clip
-        if input_data.is_video:
-            clip = VideoFileClip(media_abspath)
-        else:
-            clip = AudioFileClip(media_abspath)
-
-        yield "duration", clip.duration
-
-
-class LoopVideoBlock(Block):
-    """
-    Block for looping (repeating) a video clip until a given duration or number of loops.
-    """
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="The input video (can be a URL, data URI, or local path)."
-        )
-        # Provide EITHER a `duration` or `n_loops` or both. We'll demonstrate `duration`.
-        duration: Optional[float] = SchemaField(
-            description="Target duration (in seconds) to loop the video to. If omitted, defaults to no looping.",
-            default=None,
-            ge=0.0,
-        )
-        n_loops: Optional[int] = SchemaField(
-            description="Number of times to repeat the video. If omitted, defaults to 1 (no repeat).",
-            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(
-            description="Looped video returned either as a relative path or a data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8bf9eef6-5451-4213-b265-25306446e94b",
-            description="Block to loop a video to a given duration or number of repeats.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=LoopVideoBlock.Input,
-            output_schema=LoopVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        node_exec_id: str,
-        graph_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # 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,
-            return_content=False,
-        )
-        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
-
-        # 2) Load the clip
-        clip = VideoFileClip(input_abspath)
-
-        # 3) Apply the loop effect
-        looped_clip = clip
-        if input_data.duration:
-            # Loop until we reach the specified duration
-            looped_clip = looped_clip.with_effects([Loop(duration=input_data.duration)])
-        elif input_data.n_loops:
-            looped_clip = looped_clip.with_effects([Loop(n=input_data.n_loops)])
-        else:
-            raise ValueError("Either 'duration' or 'n_loops' must be provided.")
-
-        assert isinstance(looped_clip, VideoFileClip)
-
-        # 4) Save the looped output
-        output_filename = MediaFileType(
-            f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
-        )
-        output_abspath = get_exec_file_path(graph_exec_id, output_filename)
-
-        looped_clip = looped_clip.with_audio(clip.audio)
-        looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
-
-        # Return as data URI
-        video_out = await store_media_file(
-            graph_exec_id=graph_exec_id,
-            file=output_filename,
-            user_id=user_id,
-            return_content=input_data.output_return_type == "data_uri",
-        )
-
-        yield "video_out", video_out
-
-
-class AddAudioToVideoBlock(Block):
-    """
-    Block that adds (attaches) an audio track to an existing video.
-    Optionally scale the volume of the new track.
-    """
-
-    class Input(BlockSchemaInput):
-        video_in: MediaFileType = SchemaField(
-            description="Video input (URL, data URI, or local path)."
-        )
-        audio_in: MediaFileType = SchemaField(
-            description="Audio input (URL, data URI, or local path)."
-        )
-        volume: float = SchemaField(
-            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(
-            description="Final video (with attached audio), as a path or data URI."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3503748d-62b6-4425-91d6-725b064af509",
-            description="Block to attach an audio file to a video file using moviepy.",
-            categories={BlockCategory.MULTIMEDIA},
-            input_schema=AddAudioToVideoBlock.Input,
-            output_schema=AddAudioToVideoBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        node_exec_id: str,
-        graph_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        # 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,
-            return_content=False,
-        )
-        local_audio_path = await store_media_file(
-            graph_exec_id=graph_exec_id,
-            file=input_data.audio_in,
-            user_id=user_id,
-            return_content=False,
-        )
-
-        abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
-        video_abspath = os.path.join(abs_temp_dir, local_video_path)
-        audio_abspath = os.path.join(abs_temp_dir, local_audio_path)
-
-        # 2) Load video + audio with moviepy
-        video_clip = VideoFileClip(video_abspath)
-        audio_clip = AudioFileClip(audio_abspath)
-        # Optionally scale volume
-        if input_data.volume != 1.0:
-            audio_clip = audio_clip.with_volume_scaled(input_data.volume)
-
-        # 3) Attach the new audio track
-        final_clip = video_clip.with_audio(audio_clip)
-
-        # 4) Write to output file
-        output_filename = MediaFileType(
-            f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
-        )
-        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
-        video_out = await store_media_file(
-            graph_exec_id=graph_exec_id,
-            file=output_filename,
-            user_id=user_id,
-            return_content=input_data.output_return_type == "data_uri",
-        )
-
-        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/screenshotone.py

@@ -11,6 +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,
-        user_id: str,
+        execution_context: ExecutionContext,
         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,
-                return_content=True,
+                execution_context=execution_context,
+                return_format="for_block_output",
             )
         }
 
@@ -169,15 +168,13 @@ class ScreenshotWebPageBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        graph_exec_id: str,
-        user_id: str,
+        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
         try:
             screenshot_data = await self.take_screenshot(
                 credentials=credentials,
-                graph_exec_id=graph_exec_id,
-                user_id=user_id,
+                execution_context=execution_context,
                 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.info(
-            f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
+        logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}")
 
         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.info(
-            f"ACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
+        logger.debug(f"ACT: Using model provider {model_credentials.provider}")
 
         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.info(
-            f"EXTRACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
+        logger.debug(f"EXTRACT: Using model provider {model_credentials.provider}")
 
         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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-            user_id="test-user-id",
+            execution_context=make_test_context(),
         ):
             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",
-                user_id="test-user-id",
+                execution_context=make_test_context(),
             ):
                 result.append((output_name, output_data))
 

autogpt_platform/backend/backend/blocks/test/test_text_encoder.py

@@ -0,0 +1,77 @@
+import pytest
+
+from backend.blocks.encoder_block import TextEncoderBlock
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_basic():
+    """Test basic encoding of newlines and special characters."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello\nWorld")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == "Hello\\nWorld"
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_multiple_escapes():
+    """Test encoding of multiple escape sequences."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(
+        TextEncoderBlock.Input(text="Line1\nLine2\tTabbed\rCarriage")
+    ):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert "\\n" in result[0][1]
+    assert "\\t" in result[0][1]
+    assert "\\r" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_unicode():
+    """Test that unicode characters are handled correctly."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="Hello 世界\n")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    # Unicode characters should be escaped as \uXXXX sequences
+    assert "\\n" in result[0][1]
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_empty_string():
+    """Test encoding of an empty string."""
+    block = TextEncoderBlock()
+    result = []
+    async for output in block.run(TextEncoderBlock.Input(text="")):
+        result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "encoded_text"
+    assert result[0][1] == ""
+
+
+@pytest.mark.asyncio
+async def test_text_encoder_error_handling():
+    """Test that encoding errors are handled gracefully."""
+    from unittest.mock import patch
+
+    block = TextEncoderBlock()
+    result = []
+
+    with patch("codecs.encode", side_effect=Exception("Mocked encoding error")):
+        async for output in block.run(TextEncoderBlock.Input(text="test")):
+            result.append(output)
+
+    assert len(result) == 1
+    assert result[0][0] == "error"
+    assert "Mocked encoding error" in result[0][1]

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
-        file_path = get_exec_file_path(graph_exec_id, stored_file_path)
+        # Get full file path (graph_exec_id validated by store_media_file above)
+        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/blocks/video/__init__.py

@@ -0,0 +1,37 @@
+"""Video editing blocks for AutoGPT Platform.
+
+This module provides blocks for:
+- Downloading videos from URLs (YouTube, Vimeo, news sites, direct links)
+- Clipping/trimming video segments
+- Concatenating multiple videos
+- Adding text overlays
+- Adding AI-generated narration
+- Getting media duration
+- Looping videos
+- Adding audio to videos
+
+Dependencies:
+- yt-dlp: For video downloading
+- moviepy: For video editing operations
+- elevenlabs: For AI narration (optional)
+"""
+
+from backend.blocks.video.add_audio import AddAudioToVideoBlock
+from backend.blocks.video.clip import VideoClipBlock
+from backend.blocks.video.concat import VideoConcatBlock
+from backend.blocks.video.download import VideoDownloadBlock
+from backend.blocks.video.duration import MediaDurationBlock
+from backend.blocks.video.loop import LoopVideoBlock
+from backend.blocks.video.narration import VideoNarrationBlock
+from backend.blocks.video.text_overlay import VideoTextOverlayBlock
+
+__all__ = [
+    "AddAudioToVideoBlock",
+    "LoopVideoBlock",
+    "MediaDurationBlock",
+    "VideoClipBlock",
+    "VideoConcatBlock",
+    "VideoDownloadBlock",
+    "VideoNarrationBlock",
+    "VideoTextOverlayBlock",
+]

autogpt_platform/backend/backend/blocks/video/_utils.py

@@ -0,0 +1,131 @@
+"""Shared utilities for video blocks."""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+import subprocess
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+# Known operation tags added by video blocks
+_VIDEO_OPS = (
+    r"(?:clip|overlay|narrated|looped|concat|audio_attached|with_audio|narration)"
+)
+
+# Matches: {node_exec_id}_{operation}_ where node_exec_id contains a UUID
+_BLOCK_PREFIX_RE = re.compile(
+    r"^[a-zA-Z0-9_-]*"
+    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+    r"[a-zA-Z0-9_-]*"
+    r"_" + _VIDEO_OPS + r"_"
+)
+
+# Matches: a lone {node_exec_id}_ prefix (no operation keyword, e.g. download output)
+_UUID_PREFIX_RE = re.compile(
+    r"^[a-zA-Z0-9_-]*"
+    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
+    r"[a-zA-Z0-9_-]*_"
+)
+
+
+def extract_source_name(input_path: str, max_length: int = 50) -> str:
+    """Extract the original source filename by stripping block-generated prefixes.
+
+    Iteratively removes {node_exec_id}_{operation}_ prefixes that accumulate
+    when chaining video blocks, recovering the original human-readable name.
+
+    Safe for plain filenames (no UUID -> no stripping).
+    Falls back to "video" if everything is stripped.
+    """
+    stem = Path(input_path).stem
+
+    # Pass 1: strip {node_exec_id}_{operation}_ prefixes iteratively
+    while _BLOCK_PREFIX_RE.match(stem):
+        stem = _BLOCK_PREFIX_RE.sub("", stem, count=1)
+
+    # Pass 2: strip a lone {node_exec_id}_ prefix (e.g. from download block)
+    if _UUID_PREFIX_RE.match(stem):
+        stem = _UUID_PREFIX_RE.sub("", stem, count=1)
+
+    if not stem:
+        return "video"
+
+    return stem[:max_length]
+
+
+def get_video_codecs(output_path: str) -> tuple[str, str]:
+    """Get appropriate video and audio codecs based on output file extension.
+
+    Args:
+        output_path: Path to the output file (used to determine extension)
+
+    Returns:
+        Tuple of (video_codec, audio_codec)
+
+    Codec mappings:
+        - .mp4: H.264 + AAC (universal compatibility)
+        - .webm: VP8 + Vorbis (web streaming)
+        - .mkv: H.264 + AAC (container supports many codecs)
+        - .mov: H.264 + AAC (Apple QuickTime, widely compatible)
+        - .m4v: H.264 + AAC (Apple iTunes/devices)
+        - .avi: MPEG-4 + MP3 (legacy Windows)
+    """
+    ext = os.path.splitext(output_path)[1].lower()
+
+    codec_map: dict[str, tuple[str, str]] = {
+        ".mp4": ("libx264", "aac"),
+        ".webm": ("libvpx", "libvorbis"),
+        ".mkv": ("libx264", "aac"),
+        ".mov": ("libx264", "aac"),
+        ".m4v": ("libx264", "aac"),
+        ".avi": ("mpeg4", "libmp3lame"),
+    }
+
+    return codec_map.get(ext, ("libx264", "aac"))
+
+
+def strip_chapters_inplace(video_path: str) -> None:
+    """Strip chapter metadata from a media file in-place using ffmpeg.
+
+    MoviePy 2.x crashes with IndexError when parsing files with embedded
+    chapter metadata (https://github.com/Zulko/moviepy/issues/2419).
+    This strips chapters without re-encoding.
+
+    Args:
+        video_path: Absolute path to the media file to strip chapters from.
+    """
+    base, ext = os.path.splitext(video_path)
+    tmp_path = base + ".tmp" + ext
+    try:
+        result = subprocess.run(
+            [
+                "ffmpeg",
+                "-y",
+                "-i",
+                video_path,
+                "-map_chapters",
+                "-1",
+                "-codec",
+                "copy",
+                tmp_path,
+            ],
+            capture_output=True,
+            text=True,
+            timeout=300,
+        )
+        if result.returncode != 0:
+            logger.warning(
+                "ffmpeg chapter strip failed (rc=%d): %s",
+                result.returncode,
+                result.stderr,
+            )
+            return
+        os.replace(tmp_path, video_path)
+    except FileNotFoundError:
+        logger.warning("ffmpeg not found; skipping chapter strip")
+    finally:
+        if os.path.exists(tmp_path):
+            os.unlink(tmp_path)

autogpt_platform/backend/backend/blocks/video/add_audio.py

@@ -0,0 +1,113 @@
+"""AddAudioToVideoBlock - Attach an audio track to a video file."""
+
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    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
+
+
+class AddAudioToVideoBlock(Block):
+    """Add (attach) an audio track to an existing video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Video input (URL, data URI, or local path)."
+        )
+        audio_in: MediaFileType = SchemaField(
+            description="Audio input (URL, data URI, or local path)."
+        )
+        volume: float = SchemaField(
+            description="Volume scale for the newly attached audio track (1.0 = original).",
+            default=1.0,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Final video (with attached audio), as a path or data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3503748d-62b6-4425-91d6-725b064af509",
+            description="Block to attach an audio file to a video file using moviepy.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=AddAudioToVideoBlock.Input,
+            output_schema=AddAudioToVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **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(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        local_audio_path = await store_media_file(
+            file=input_data.audio_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+        video_abspath = get_exec_file_path(graph_exec_id, local_video_path)
+        audio_abspath = get_exec_file_path(graph_exec_id, local_audio_path)
+
+        # 2) Load video + audio with moviepy
+        strip_chapters_inplace(video_abspath)
+        strip_chapters_inplace(audio_abspath)
+        video_clip = None
+        audio_clip = None
+        final_clip = None
+        try:
+            video_clip = VideoFileClip(video_abspath)
+            audio_clip = AudioFileClip(audio_abspath)
+            # Optionally scale volume
+            if input_data.volume != 1.0:
+                audio_clip = audio_clip.with_volume_scaled(input_data.volume)
+
+            # 3) Attach the new audio track
+            final_clip = video_clip.with_audio(audio_clip)
+
+            # 4) Write to output file
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_with_audio_{source}.mp4")
+            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
+            final_clip.write_videofile(
+                output_abspath, codec="libx264", audio_codec="aac"
+            )
+        finally:
+            if final_clip:
+                final_clip.close()
+            if audio_clip:
+                audio_clip.close()
+            if video_clip:
+                video_clip.close()
+
+        # 5) Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/clip.py

@@ -0,0 +1,167 @@
+"""VideoClipBlock - Extract a segment from a video file."""
+
+from typing import Literal
+
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoClipBlock(Block):
+    """Extract a time segment from a video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        start_time: float = SchemaField(description="Start time in seconds", ge=0.0)
+        end_time: float = SchemaField(description="End time in seconds", ge=0.0)
+        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
+            description="Output format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Clipped video file (path or data URI)"
+        )
+        duration: float = SchemaField(description="Clip duration in seconds")
+
+    def __init__(self):
+        super().__init__(
+            id="8f539119-e580-4d86-ad41-86fbcb22abb1",
+            description="Extract a time segment from a video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "video_in": "/tmp/test.mp4",
+                "start_time": 0.0,
+                "end_time": 10.0,
+            },
+            test_output=[("video_out", str), ("duration", float)],
+            test_mock={
+                "_clip_video": lambda *args: 10.0,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "clip_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _clip_video(
+        self,
+        video_abspath: str,
+        output_abspath: str,
+        start_time: float,
+        end_time: float,
+    ) -> float:
+        """Extract a clip from a video. Extracted for testability."""
+        clip = None
+        subclip = None
+        try:
+            strip_chapters_inplace(video_abspath)
+            clip = VideoFileClip(video_abspath)
+            subclip = clip.subclipped(start_time, end_time)
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            subclip.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+            return subclip.duration
+        finally:
+            if subclip:
+                subclip.close()
+            if clip:
+                clip.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate time range
+        if input_data.end_time <= input_data.start_time:
+            raise BlockExecutionError(
+                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Build output path
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(
+                f"{node_exec_id}_clip_{source}.{input_data.output_format}"
+            )
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            duration = self._clip_video(
+                video_abspath,
+                output_abspath,
+                input_data.start_time,
+                input_data.end_time,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+            yield "duration", duration
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to clip video: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/concat.py

@@ -0,0 +1,227 @@
+"""VideoConcatBlock - Concatenate multiple video clips into one."""
+
+from typing import Literal
+
+from moviepy import concatenate_videoclips
+from moviepy.video.fx import CrossFadeIn, CrossFadeOut, FadeIn, FadeOut
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoConcatBlock(Block):
+    """Merge multiple video clips into one continuous video."""
+
+    class Input(BlockSchemaInput):
+        videos: list[MediaFileType] = SchemaField(
+            description="List of video files to concatenate (in order)"
+        )
+        transition: Literal["none", "crossfade", "fade_black"] = SchemaField(
+            description="Transition between clips", default="none"
+        )
+        transition_duration: int = SchemaField(
+            description="Transition duration in seconds",
+            default=1,
+            ge=0,
+            advanced=True,
+        )
+        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
+            description="Output format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Concatenated video file (path or data URI)"
+        )
+        total_duration: float = SchemaField(description="Total duration in seconds")
+
+    def __init__(self):
+        super().__init__(
+            id="9b0f531a-1118-487f-aeec-3fa63ea8900a",
+            description="Merge multiple video clips into one continuous video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "videos": ["/tmp/a.mp4", "/tmp/b.mp4"],
+            },
+            test_output=[
+                ("video_out", str),
+                ("total_duration", float),
+            ],
+            test_mock={
+                "_concat_videos": lambda *args: 20.0,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "concat_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _concat_videos(
+        self,
+        video_abspaths: list[str],
+        output_abspath: str,
+        transition: str,
+        transition_duration: int,
+    ) -> float:
+        """Concatenate videos. Extracted for testability.
+
+        Returns:
+            Total duration of the concatenated video.
+        """
+        clips = []
+        faded_clips = []
+        final = None
+        try:
+            # Load clips
+            for v in video_abspaths:
+                strip_chapters_inplace(v)
+                clips.append(VideoFileClip(v))
+
+            # Validate transition_duration against shortest clip
+            if transition in {"crossfade", "fade_black"} and transition_duration > 0:
+                min_duration = min(c.duration for c in clips)
+                if transition_duration >= min_duration:
+                    raise BlockExecutionError(
+                        message=(
+                            f"transition_duration ({transition_duration}s) must be "
+                            f"shorter than the shortest clip ({min_duration:.2f}s)"
+                        ),
+                        block_name=self.name,
+                        block_id=str(self.id),
+                    )
+
+            if transition == "crossfade":
+                for i, clip in enumerate(clips):
+                    effects = []
+                    if i > 0:
+                        effects.append(CrossFadeIn(transition_duration))
+                    if i < len(clips) - 1:
+                        effects.append(CrossFadeOut(transition_duration))
+                    if effects:
+                        clip = clip.with_effects(effects)
+                    faded_clips.append(clip)
+                final = concatenate_videoclips(
+                    faded_clips,
+                    method="compose",
+                    padding=-transition_duration,
+                )
+            elif transition == "fade_black":
+                for clip in clips:
+                    faded = clip.with_effects(
+                        [FadeIn(transition_duration), FadeOut(transition_duration)]
+                    )
+                    faded_clips.append(faded)
+                final = concatenate_videoclips(faded_clips)
+            else:
+                final = concatenate_videoclips(clips)
+
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+            return final.duration
+        finally:
+            if final:
+                final.close()
+            for clip in faded_clips:
+                clip.close()
+            for clip in clips:
+                clip.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate minimum clips
+        if len(input_data.videos) < 2:
+            raise BlockExecutionError(
+                message="At least 2 videos are required for concatenation",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store all input videos locally
+            video_abspaths = []
+            for video in input_data.videos:
+                local_path = await self._store_input_video(execution_context, video)
+                video_abspaths.append(
+                    get_exec_file_path(execution_context.graph_exec_id, local_path)
+                )
+
+            # Build output path
+            source = (
+                extract_source_name(video_abspaths[0]) if video_abspaths else "video"
+            )
+            output_filename = MediaFileType(
+                f"{node_exec_id}_concat_{source}.{input_data.output_format}"
+            )
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            total_duration = self._concat_videos(
+                video_abspaths,
+                output_abspath,
+                input_data.transition,
+                input_data.transition_duration,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+            yield "total_duration", total_duration
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to concatenate videos: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/download.py

@@ -0,0 +1,172 @@
+"""VideoDownloadBlock - Download video from URL (YouTube, Vimeo, news sites, direct links)."""
+
+import os
+import typing
+from typing import Literal
+
+import yt_dlp
+
+if typing.TYPE_CHECKING:
+    from yt_dlp import _Params
+
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoDownloadBlock(Block):
+    """Download video from URL using yt-dlp."""
+
+    class Input(BlockSchemaInput):
+        url: str = SchemaField(
+            description="URL of the video to download (YouTube, Vimeo, direct link, etc.)",
+            placeholder="https://www.youtube.com/watch?v=...",
+        )
+        quality: Literal["best", "1080p", "720p", "480p", "audio_only"] = SchemaField(
+            description="Video quality preference", default="720p"
+        )
+        output_format: Literal["mp4", "webm", "mkv"] = SchemaField(
+            description="Output video format", default="mp4", advanced=True
+        )
+
+    class Output(BlockSchemaOutput):
+        video_file: MediaFileType = SchemaField(
+            description="Downloaded video (path or data URI)"
+        )
+        duration: float = SchemaField(description="Video duration in seconds")
+        title: str = SchemaField(description="Video title from source")
+        source_url: str = SchemaField(description="Original source URL")
+
+    def __init__(self):
+        super().__init__(
+            id="c35daabb-cd60-493b-b9ad-51f1fe4b50c4",
+            description="Download video from URL (YouTube, Vimeo, news sites, direct links)",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            disabled=True,  # Disable until we can sandbox yt-dlp and handle security implications
+            test_input={
+                "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
+                "quality": "480p",
+            },
+            test_output=[
+                ("video_file", str),
+                ("duration", float),
+                ("title", str),
+                ("source_url", str),
+            ],
+            test_mock={
+                "_download_video": lambda *args: (
+                    "video.mp4",
+                    212.0,
+                    "Test Video",
+                ),
+                "_store_output_video": lambda *args, **kwargs: "video.mp4",
+            },
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _get_format_string(self, quality: str) -> str:
+        formats = {
+            "best": "bestvideo+bestaudio/best",
+            "1080p": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
+            "720p": "bestvideo[height<=720]+bestaudio/best[height<=720]",
+            "480p": "bestvideo[height<=480]+bestaudio/best[height<=480]",
+            "audio_only": "bestaudio/best",
+        }
+        return formats.get(quality, formats["720p"])
+
+    def _download_video(
+        self,
+        url: str,
+        quality: str,
+        output_format: str,
+        output_dir: str,
+        node_exec_id: str,
+    ) -> tuple[str, float, str]:
+        """Download video. Extracted for testability."""
+        output_template = os.path.join(
+            output_dir, f"{node_exec_id}_%(title).50s.%(ext)s"
+        )
+
+        ydl_opts: "_Params" = {
+            "format": f"{self._get_format_string(quality)}/best",
+            "outtmpl": output_template,
+            "merge_output_format": output_format,
+            "quiet": True,
+            "no_warnings": True,
+        }
+
+        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+            info = ydl.extract_info(url, download=True)
+            video_path = ydl.prepare_filename(info)
+
+            # Handle format conversion in filename
+            if not video_path.endswith(f".{output_format}"):
+                video_path = video_path.rsplit(".", 1)[0] + f".{output_format}"
+
+            # Return just the filename, not the full path
+            filename = os.path.basename(video_path)
+
+            return (
+                filename,
+                info.get("duration") or 0.0,
+                info.get("title") or "Unknown",
+            )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Get the exec file directory
+            output_dir = get_exec_file_path(execution_context.graph_exec_id, "")
+            os.makedirs(output_dir, exist_ok=True)
+
+            filename, duration, title = self._download_video(
+                input_data.url,
+                input_data.quality,
+                input_data.output_format,
+                output_dir,
+                node_exec_id,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, MediaFileType(filename)
+            )
+
+            yield "video_file", video_out
+            yield "duration", duration
+            yield "title", title
+            yield "source_url", input_data.url
+
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to download video: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/duration.py

@@ -0,0 +1,77 @@
+"""MediaDurationBlock - Get the duration of a media file."""
+
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    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
+
+
+class MediaDurationBlock(Block):
+    """Get the duration of a media file (video or audio)."""
+
+    class Input(BlockSchemaInput):
+        media_in: MediaFileType = SchemaField(
+            description="Media input (URL, data URI, or local path)."
+        )
+        is_video: bool = SchemaField(
+            description="Whether the media is a video (True) or audio (False).",
+            default=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        duration: float = SchemaField(
+            description="Duration of the media file (in seconds)."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
+            description="Block to get the duration of a media file.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=MediaDurationBlock.Input,
+            output_schema=MediaDurationBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
+        # 1) Store the input media locally
+        local_media_path = await store_media_file(
+            file=input_data.media_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        assert execution_context.graph_exec_id is not None
+        media_abspath = get_exec_file_path(
+            execution_context.graph_exec_id, local_media_path
+        )
+
+        # 2) Strip chapters to avoid MoviePy crash, then load the clip
+        strip_chapters_inplace(media_abspath)
+        clip = None
+        try:
+            if input_data.is_video:
+                clip = VideoFileClip(media_abspath)
+            else:
+                clip = AudioFileClip(media_abspath)
+
+            duration = clip.duration
+        finally:
+            if clip:
+                clip.close()
+
+        yield "duration", duration

autogpt_platform/backend/backend/blocks/video/loop.py

@@ -0,0 +1,115 @@
+"""LoopVideoBlock - Loop a video to a given duration or number of repeats."""
+
+from typing import Optional
+
+from moviepy.video.fx.Loop import Loop
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    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
+
+
+class LoopVideoBlock(Block):
+    """Loop (repeat) a video clip until a given duration or number of loops."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="The input video (can be a URL, data URI, or local path)."
+        )
+        duration: Optional[float] = SchemaField(
+            description="Target duration (in seconds) to loop the video to. Either duration or n_loops must be provided.",
+            default=None,
+            ge=0.0,
+            le=3600.0,  # Max 1 hour to prevent disk exhaustion
+        )
+        n_loops: Optional[int] = SchemaField(
+            description="Number of times to repeat the video. Either n_loops or duration must be provided.",
+            default=None,
+            ge=1,
+            le=10,  # Max 10 loops to prevent disk exhaustion
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Looped video returned either as a relative path or a data URI."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="8bf9eef6-5451-4213-b265-25306446e94b",
+            description="Block to loop a video to a given duration or number of repeats.",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=LoopVideoBlock.Input,
+            output_schema=LoopVideoBlock.Output,
+        )
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        **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(
+            file=input_data.video_in,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
+
+        # 2) Load the clip
+        strip_chapters_inplace(input_abspath)
+        clip = None
+        looped_clip = None
+        try:
+            clip = VideoFileClip(input_abspath)
+
+            # 3) Apply the loop effect
+            if input_data.duration:
+                # Loop until we reach the specified duration
+                looped_clip = clip.with_effects([Loop(duration=input_data.duration)])
+            elif input_data.n_loops:
+                looped_clip = clip.with_effects([Loop(n=input_data.n_loops)])
+            else:
+                raise ValueError("Either 'duration' or 'n_loops' must be provided.")
+
+            assert isinstance(looped_clip, VideoFileClip)
+
+            # 4) Save the looped output
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_looped_{source}.mp4")
+            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
+
+            looped_clip = looped_clip.with_audio(clip.audio)
+            looped_clip.write_videofile(
+                output_abspath, codec="libx264", audio_codec="aac"
+            )
+        finally:
+            if looped_clip:
+                looped_clip.close()
+            if clip:
+                clip.close()
+
+        # Return output - for_block_output returns workspace:// if available, else data URI
+        video_out = await store_media_file(
+            file=output_filename,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+        yield "video_out", video_out

autogpt_platform/backend/backend/blocks/video/narration.py

@@ -0,0 +1,267 @@
+"""VideoNarrationBlock - Generate AI voice narration and add to video."""
+
+import os
+from typing import Literal
+
+from elevenlabs import ElevenLabs
+from moviepy import CompositeAudioClip
+from moviepy.audio.io.AudioFileClip import AudioFileClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.elevenlabs._auth import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    ElevenLabsCredentials,
+    ElevenLabsCredentialsInput,
+)
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import CredentialsField, SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoNarrationBlock(Block):
+    """Generate AI narration and add to video."""
+
+    class Input(BlockSchemaInput):
+        credentials: ElevenLabsCredentialsInput = CredentialsField(
+            description="ElevenLabs API key for voice synthesis"
+        )
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        script: str = SchemaField(description="Narration script text")
+        voice_id: str = SchemaField(
+            description="ElevenLabs voice ID", default="21m00Tcm4TlvDq8ikWAM"  # Rachel
+        )
+        model_id: Literal[
+            "eleven_multilingual_v2",
+            "eleven_flash_v2_5",
+            "eleven_turbo_v2_5",
+            "eleven_turbo_v2",
+        ] = SchemaField(
+            description="ElevenLabs TTS model",
+            default="eleven_multilingual_v2",
+        )
+        mix_mode: Literal["replace", "mix", "ducking"] = SchemaField(
+            description="How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'.",
+            default="ducking",
+        )
+        narration_volume: float = SchemaField(
+            description="Narration volume (0.0 to 2.0)",
+            default=1.0,
+            ge=0.0,
+            le=2.0,
+            advanced=True,
+        )
+        original_volume: float = SchemaField(
+            description="Original audio volume when mixing (0.0 to 1.0)",
+            default=0.3,
+            ge=0.0,
+            le=1.0,
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Video with narration (path or data URI)"
+        )
+        audio_file: MediaFileType = SchemaField(
+            description="Generated audio file (path or data URI)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3d036b53-859c-4b17-9826-ca340f736e0e",
+            description="Generate AI narration and add to video",
+            categories={BlockCategory.MULTIMEDIA, BlockCategory.AI},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            test_input={
+                "video_in": "/tmp/test.mp4",
+                "script": "Hello world",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[("video_out", str), ("audio_file", str)],
+            test_mock={
+                "_generate_narration_audio": lambda *args: b"mock audio content",
+                "_add_narration_to_video": lambda *args: None,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "narrated_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _generate_narration_audio(
+        self, api_key: str, script: str, voice_id: str, model_id: str
+    ) -> bytes:
+        """Generate narration audio via ElevenLabs API."""
+        client = ElevenLabs(api_key=api_key)
+        audio_generator = client.text_to_speech.convert(
+            voice_id=voice_id,
+            text=script,
+            model_id=model_id,
+        )
+        # The SDK returns a generator, collect all chunks
+        return b"".join(audio_generator)
+
+    def _add_narration_to_video(
+        self,
+        video_abspath: str,
+        audio_abspath: str,
+        output_abspath: str,
+        mix_mode: str,
+        narration_volume: float,
+        original_volume: float,
+    ) -> None:
+        """Add narration audio to video. Extracted for testability."""
+        video = None
+        final = None
+        narration_original = None
+        narration_scaled = None
+        original = None
+
+        try:
+            strip_chapters_inplace(video_abspath)
+            video = VideoFileClip(video_abspath)
+            narration_original = AudioFileClip(audio_abspath)
+            narration_scaled = narration_original.with_volume_scaled(narration_volume)
+            narration = narration_scaled
+
+            if mix_mode == "replace":
+                final_audio = narration
+            elif mix_mode == "mix":
+                if video.audio:
+                    original = video.audio.with_volume_scaled(original_volume)
+                    final_audio = CompositeAudioClip([original, narration])
+                else:
+                    final_audio = narration
+            else:  # ducking - apply stronger attenuation
+                if video.audio:
+                    # Ducking uses a much lower volume for original audio
+                    ducking_volume = original_volume * 0.3
+                    original = video.audio.with_volume_scaled(ducking_volume)
+                    final_audio = CompositeAudioClip([original, narration])
+                else:
+                    final_audio = narration
+
+            final = video.with_audio(final_audio)
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+        finally:
+            if original:
+                original.close()
+            if narration_scaled:
+                narration_scaled.close()
+            if narration_original:
+                narration_original.close()
+            if final:
+                final.close()
+            if video:
+                video.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: ElevenLabsCredentials,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Generate narration audio via ElevenLabs
+            audio_content = self._generate_narration_audio(
+                credentials.api_key.get_secret_value(),
+                input_data.script,
+                input_data.voice_id,
+                input_data.model_id,
+            )
+
+            # Save audio to exec file path
+            audio_filename = MediaFileType(f"{node_exec_id}_narration.mp3")
+            audio_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, audio_filename
+            )
+            os.makedirs(os.path.dirname(audio_abspath), exist_ok=True)
+            with open(audio_abspath, "wb") as f:
+                f.write(audio_content)
+
+            # Add narration to video
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_narrated_{source}.mp4")
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            self._add_narration_to_video(
+                video_abspath,
+                audio_abspath,
+                output_abspath,
+                input_data.mix_mode,
+                input_data.narration_volume,
+                input_data.original_volume,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+            audio_out = await self._store_output_video(
+                execution_context, audio_filename
+            )
+
+            yield "video_out", video_out
+            yield "audio_file", audio_out
+
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to add narration: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/video/text_overlay.py

@@ -0,0 +1,231 @@
+"""VideoTextOverlayBlock - Add text overlay to video."""
+
+from typing import Literal
+
+from moviepy import CompositeVideoClip, TextClip
+from moviepy.video.io.VideoFileClip import VideoFileClip
+
+from backend.blocks.video._utils import (
+    extract_source_name,
+    get_video_codecs,
+    strip_chapters_inplace,
+)
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.execution import ExecutionContext
+from backend.data.model import SchemaField
+from backend.util.exceptions import BlockExecutionError
+from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
+
+
+class VideoTextOverlayBlock(Block):
+    """Add text overlay/caption to video."""
+
+    class Input(BlockSchemaInput):
+        video_in: MediaFileType = SchemaField(
+            description="Input video (URL, data URI, or local path)"
+        )
+        text: str = SchemaField(description="Text to overlay on video")
+        position: Literal[
+            "top",
+            "center",
+            "bottom",
+            "top-left",
+            "top-right",
+            "bottom-left",
+            "bottom-right",
+        ] = SchemaField(description="Position of text on screen", default="bottom")
+        start_time: float | None = SchemaField(
+            description="When to show text (seconds). None = entire video",
+            default=None,
+            advanced=True,
+        )
+        end_time: float | None = SchemaField(
+            description="When to hide text (seconds). None = until end",
+            default=None,
+            advanced=True,
+        )
+        font_size: int = SchemaField(
+            description="Font size", default=48, ge=12, le=200, advanced=True
+        )
+        font_color: str = SchemaField(
+            description="Font color (hex or name)", default="white", advanced=True
+        )
+        bg_color: str | None = SchemaField(
+            description="Background color behind text (None for transparent)",
+            default=None,
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        video_out: MediaFileType = SchemaField(
+            description="Video with text overlay (path or data URI)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="8ef14de6-cc90-430a-8cfa-3a003be92454",
+            description="Add text overlay/caption to video",
+            categories={BlockCategory.MULTIMEDIA},
+            input_schema=self.Input,
+            output_schema=self.Output,
+            disabled=True,  # Disable until we can lockdown imagemagick security policy
+            test_input={"video_in": "/tmp/test.mp4", "text": "Hello World"},
+            test_output=[("video_out", str)],
+            test_mock={
+                "_add_text_overlay": lambda *args: None,
+                "_store_input_video": lambda *args, **kwargs: "test.mp4",
+                "_store_output_video": lambda *args, **kwargs: "overlay_test.mp4",
+            },
+        )
+
+    async def _store_input_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store input video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_local_processing",
+        )
+
+    async def _store_output_video(
+        self, execution_context: ExecutionContext, file: MediaFileType
+    ) -> MediaFileType:
+        """Store output video. Extracted for testability."""
+        return await store_media_file(
+            file=file,
+            execution_context=execution_context,
+            return_format="for_block_output",
+        )
+
+    def _add_text_overlay(
+        self,
+        video_abspath: str,
+        output_abspath: str,
+        text: str,
+        position: str,
+        start_time: float | None,
+        end_time: float | None,
+        font_size: int,
+        font_color: str,
+        bg_color: str | None,
+    ) -> None:
+        """Add text overlay to video. Extracted for testability."""
+        video = None
+        final = None
+        txt_clip = None
+        try:
+            strip_chapters_inplace(video_abspath)
+            video = VideoFileClip(video_abspath)
+
+            txt_clip = TextClip(
+                text=text,
+                font_size=font_size,
+                color=font_color,
+                bg_color=bg_color,
+            )
+
+            # Position mapping
+            pos_map = {
+                "top": ("center", "top"),
+                "center": ("center", "center"),
+                "bottom": ("center", "bottom"),
+                "top-left": ("left", "top"),
+                "top-right": ("right", "top"),
+                "bottom-left": ("left", "bottom"),
+                "bottom-right": ("right", "bottom"),
+            }
+
+            txt_clip = txt_clip.with_position(pos_map[position])
+
+            # Set timing
+            start = start_time or 0
+            end = end_time or video.duration
+            duration = max(0, end - start)
+            txt_clip = txt_clip.with_start(start).with_end(end).with_duration(duration)
+
+            final = CompositeVideoClip([video, txt_clip])
+            video_codec, audio_codec = get_video_codecs(output_abspath)
+            final.write_videofile(
+                output_abspath, codec=video_codec, audio_codec=audio_codec
+            )
+
+        finally:
+            if txt_clip:
+                txt_clip.close()
+            if final:
+                final.close()
+            if video:
+                video.close()
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        execution_context: ExecutionContext,
+        node_exec_id: str,
+        **kwargs,
+    ) -> BlockOutput:
+        # Validate time range if both are provided
+        if (
+            input_data.start_time is not None
+            and input_data.end_time is not None
+            and input_data.end_time <= input_data.start_time
+        ):
+            raise BlockExecutionError(
+                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
+                block_name=self.name,
+                block_id=str(self.id),
+            )
+
+        try:
+            assert execution_context.graph_exec_id is not None
+
+            # Store the input video locally
+            local_video_path = await self._store_input_video(
+                execution_context, input_data.video_in
+            )
+            video_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, local_video_path
+            )
+
+            # Build output path
+            source = extract_source_name(local_video_path)
+            output_filename = MediaFileType(f"{node_exec_id}_overlay_{source}.mp4")
+            output_abspath = get_exec_file_path(
+                execution_context.graph_exec_id, output_filename
+            )
+
+            self._add_text_overlay(
+                video_abspath,
+                output_abspath,
+                input_data.text,
+                input_data.position,
+                input_data.start_time,
+                input_data.end_time,
+                input_data.font_size,
+                input_data.font_color,
+                input_data.bg_color,
+            )
+
+            # Return as workspace path or data URI based on context
+            video_out = await self._store_output_video(
+                execution_context, output_filename
+            )
+
+            yield "video_out", video_out
+
+        except BlockExecutionError:
+            raise
+        except Exception as e:
+            raise BlockExecutionError(
+                message=f"Failed to add text overlay: {e}",
+                block_name=self.name,
+                block_id=str(self.id),
+            ) from e

autogpt_platform/backend/backend/blocks/youtube.py

@@ -165,10 +165,13 @@ class TranscribeYoutubeVideoBlock(Block):
         credentials: WebshareProxyCredentials,
         **kwargs,
     ) -> BlockOutput:
-        video_id = self.extract_video_id(input_data.youtube_url)
-        yield "video_id", video_id
+        try:
+            video_id = self.extract_video_id(input_data.youtube_url)
+            transcript = self.get_transcript(video_id, credentials)
+            transcript_text = self.format_transcript(transcript=transcript)
 
-        transcript = self.get_transcript(video_id, credentials)
-        transcript_text = self.format_transcript(transcript=transcript)
-
-        yield "transcript", transcript_text
+            # Only yield after all operations succeed
+            yield "video_id", video_id
+            yield "transcript", transcript_text
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/copilot/__init__.py

@@ -0,0 +1,8 @@
+"""CoPilot module - AI assistant for AutoGPT platform.
+
+This module contains the core CoPilot functionality including:
+- AI generation service (LLM calls)
+- Tool execution
+- Session management
+- Stream registry for SSE reconnection
+"""

autogpt_platform/backend/backend/copilot/completion_consumer.py

@@ -0,0 +1,369 @@
+"""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")
+            prisma = Prisma(datasource={"url": database_url})
+            await prisma.connect()
+            self._prisma = prisma
+            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/copilot/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/copilot/config.py

@@ -11,7 +11,7 @@ class ChatConfig(BaseSettings):
 
     # OpenAI API Configuration
     model: str = Field(
-        default="anthropic/claude-opus-4.5", description="Default model to use"
+        default="anthropic/claude-opus-4.6", description="Default model to use"
     )
     title_model: str = Field(
         default="openai/gpt-4o-mini",
@@ -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(
@@ -51,6 +93,12 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
+    # Extended thinking configuration for Claude models
+    thinking_enabled: bool = Field(
+        default=True,
+        description="Enable adaptive thinking for Claude models via OpenRouter",
+    )
+
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):
@@ -82,6 +130,14 @@ 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/copilot/db.py

@@ -45,10 +45,7 @@ async def create_chat_session(
         successfulAgentRuns=SafeJson({}),
         successfulAgentSchedules=SafeJson({}),
     )
-    return await PrismaChatSession.prisma().create(
-        data=data,
-        include={"Messages": True},
-    )
+    return await PrismaChatSession.prisma().create(data=data)
 
 
 async def update_chat_session(

autogpt_platform/backend/backend/copilot/executor/__init__.py

@@ -0,0 +1,5 @@
+"""CoPilot Executor - Dedicated service for AI generation and tool execution.
+
+This module contains the executor service that processes CoPilot tasks
+from RabbitMQ, following the graph executor pattern.
+"""