Files
AutoGPT/autogpt_platform/frontend
Swifty ab0b537cc7 refactor(backend): optimize find_block response size by removing raw JSON schemas (#12020)
### Changes 🏗️

The `find_block` AutoPilot tool was returning ~90K characters per
response (10 blocks). The bloat came from including full JSON Schema
objects (`input_schema`, `output_schema`) with all nested `$defs`,
`anyOf`, and type definitions for every block.

**What changed:**

- **`BlockInfoSummary` model**: Removed `input_schema` (raw JSON
Schema), `output_schema` (raw JSON Schema), and `categories`. Added
`output_fields` (compact field-level summaries matching the existing
`required_inputs` format).
- **`BlockListResponse` model**: Removed `usage_hint` (info now in
`message`).
- **`FindBlockTool._execute()`**: Now extracts compact `output_fields`
from output schema properties instead of including the entire raw
schema. Credentials handling is unchanged.
- **Test**: Added `test_response_size_average_chars_per_block` with
realistic block schemas (HTTP, Email, Claude Code) to measure and assert
response size stays under 2K chars/block.
- **`CLAUDE.md`**: Clarified `dev` vs `master` branching strategy.

**Result:** Average response size reduced from ~9,000 to ~1,300 chars
per block (~85% reduction). This directly reduces LLM token consumption,
latency, and API costs for AutoPilot interactions.

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Verified models import and serialize correctly
- [x] Verified response size: 3,970 chars for 3 realistic blocks (avg
1,323/block)
- [x] Lint (`ruff check`) and type check (`pyright`) pass on changed
files
- [x] Frontend compatibility preserved: `blocks[].name` and `count`
fields retained for `block_list` handler

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Toran Bruce Richards <toran.richards@gmail.com>
2026-02-13 11:08:51 +01:00
..

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. For Playwright and Storybook testing setup, see TESTING.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.