Files
AutoGPT/autogpt_platform/frontend
Zamil Majdy d6ee402483 feat(platform): Add execution analytics admin endpoint with feature flag bypass (#11327)
This PR adds a comprehensive execution analytics admin endpoint that
generates AI-powered activity summaries and correctness scores for graph
executions, with proper feature flag bypass for admin use.

### Changes 🏗️

**Backend Changes:**
- Added admin endpoint: `/api/executions/admin/execution_analytics`
- Implemented feature flag bypass with `skip_feature_flag=True`
parameter for admin operations
- Fixed async database client usage (`get_db_async_client`) to resolve
async/await errors
- Added batch processing with configurable size limits to handle large
datasets
- Comprehensive error handling and logging for troubleshooting
- Renamed entire feature from "Activity Backfill" to "Execution
Analytics" for clarity

**Frontend Changes:**
- Created clean admin UI for execution analytics generation at
`/admin/execution-analytics`
- Built form with graph ID input, model selection dropdown, and optional
filters
- Implemented results table with status badges and detailed execution
information
- Added CSV export functionality for analytics results
- Integrated with generated TypeScript API client for proper
authentication
- Added proper error handling with toast notifications and loading
states

**Database & API:**
- Fixed critical async/await issue by switching from sync to async
database client
- Updated router configuration and endpoint naming for consistency
- Generated proper TypeScript types and API client integration
- Applied feature flag filtering at API level while bypassing for admin
operations

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:

**Test Plan:**
- [x] Admin can access execution analytics page at
`/admin/execution-analytics`
- [x] Form validation works correctly (requires graph ID, validates
inputs)
- [x] API endpoint `/api/executions/admin/execution_analytics` responds
correctly
- [x] Authentication works properly through generated API client
- [x] Analytics generation works with different LLM models (gpt-4o-mini,
gpt-4o, etc.)
- [x] Results display correctly with appropriate status badges
(success/failed/skipped)
- [x] CSV export functionality downloads correct data
- [x] Error handling displays appropriate toast messages
- [x] Feature flag bypass works for admin users (generates analytics
regardless of user flags)
- [x] Batch processing handles multiple executions correctly
- [x] Loading states show proper feedback during processing

#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] No configuration changes required for this feature

**Related to:** PR #11325 (base correctness score functionality)

🤖 Generated with [Claude Code](https://claude.ai/code)

---------

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>
Co-authored-by: Zamil Majdy <majdyz@users.noreply.github.com>
2025-11-10 10:27:44 +00:00
..

This is the frontend for AutoGPT's next generation

🧢 Getting Started

This project uses pnpm as the package manager via corepack. Corepack is a Node.js tool that automatically manages package managers without requiring global installations.

For architecture, conventions, data fetching, feature flags, design system usage, state management, and PR process, see CONTRIBUTING.md.

Prerequisites

Make sure you have Node.js 16.10+ installed. Corepack is included with Node.js by default.

Setup

1. Enable corepack (run this once on your system):

corepack enable

This enables corepack to automatically manage pnpm based on the packageManager field in package.json.

2. Install dependencies:

pnpm i

3. Start the development server:

Running the Front-end & Back-end separately

We recommend this approach if you are doing active development on the project. First spin up the Back-end:

# on `autogpt_platform`
docker compose --profile local up deps_backend -d
# on `autogpt_platform/backend`
poetry run app

Then start the Front-end:

# on `autogpt_platform/frontend`
pnpm dev

Open http://localhost:3000 with your browser to see the result. If the server starts on http://localhost:3001 it means the Front-end is already running via Docker. You have to kill the container then or do docker compose down.

You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.

Running both the Front-end and Back-end via Docker

If you run:

# on `autogpt_platform`
docker compose up -d

It will spin up the Back-end and Front-end via Docker. The Front-end will start on port 3000. This might not be what you want when actively contributing to the Front-end as you won't have direct/easy access to the Next.js dev server.

Subsequent Runs

For subsequent development sessions, you only need to run:

pnpm dev

Every time a new Front-end dependency is added by you or others, you will need to run pnpm i to install the new dependencies.

Available Scripts

  • pnpm dev - Start development server
  • pnpm build - Build for production
  • pnpm start - Start production server
  • pnpm lint - Run ESLint and Prettier checks
  • pnpm format - Format code with Prettier
  • pnpm types - Run TypeScript type checking
  • pnpm test - Run Playwright tests
  • pnpm test-ui - Run Playwright tests with UI
  • pnpm fetch:openapi - Fetch OpenAPI spec from backend
  • pnpm generate:api-client - Generate API client from OpenAPI spec
  • pnpm generate:api - Fetch OpenAPI spec and generate API client

This project uses next/font to automatically optimize and load Inter, a custom Google Font.

🔄 Data Fetching

See CONTRIBUTING.md for guidance on generated API hooks, SSR + hydration patterns, and usage examples. You generally do not need to run OpenAPI commands unless adding/modifying backend endpoints.

🚩 Feature Flags

See CONTRIBUTING.md for feature flag usage patterns, local development with mocks, and how to add new flags.

🚚 Deploy

TODO

📙 Storybook

Storybook is a powerful development environment for UI components. It allows you to build UI components in isolation, making it easier to develop, test, and document your components independently from your main application.

Purpose in the Development Process

  1. Component Development: Develop and test UI components in isolation.
  2. Visual Testing: Easily spot visual regressions.
  3. Documentation: Automatically document components and their props.
  4. Collaboration: Share components with your team or stakeholders for feedback.

How to Use Storybook

  1. Start Storybook: Run the following command to start the Storybook development server:

    pnpm storybook
    

    This will start Storybook on port 6006. Open http://localhost:6006 in your browser to view your component library.

  2. Build Storybook: To build a static version of Storybook for deployment, use:

    pnpm build-storybook
    
  3. Running Storybook Tests: Storybook tests can be run using:

    pnpm test-storybook
    
  4. Writing Stories: Create .stories.tsx files alongside your components to define different states and variations of your components.

By integrating Storybook into our development workflow, we can streamline UI development, improve component reusability, and maintain a consistent design system across the project.

🔭 Tech Stack

Core Framework & Language

  • Next.js - React framework with App Router
  • React - UI library for building user interfaces
  • TypeScript - Typed JavaScript for better developer experience

Styling & UI Components

Development & Testing

Backend & Services

  • Supabase - Backend-as-a-Service (database, auth, storage)
  • Sentry - Error monitoring and performance tracking

Package Management

  • pnpm - Fast, disk space efficient package manager
  • Corepack - Node.js package manager management

Additional Libraries

Development Tools

  • NEXT_PUBLIC_REACT_QUERY_DEVTOOL - Enable React Query DevTools. Set to true to enable.