905373a712
## Summary
Addresses SENTRY-1051: Shiki warning about multiple highlighter
instances.
## Problem
The `@streamdown/code` package creates a **new Shiki highlighter for
each language** encountered. When users view AI chat responses with code
blocks in multiple languages (JavaScript, Python, JSON, YAML, etc.),
this creates 10+ highlighter instances, triggering Shiki's warning:
> "10 instances have been created. Shiki is supposed to be used as a
singleton, consider refactoring your code to cache your highlighter
instance"
This causes memory bloat and performance degradation.
## Solution
Introduced a custom code highlighting plugin that properly implements
the singleton pattern:
### New files:
- `src/lib/shiki-highlighter.ts` - Singleton highlighter management
- `src/lib/streamdown-code-plugin.ts` - Drop-in replacement for
`@streamdown/code`
### Key features:
- **Single shared highlighter** - One instance serves all code blocks
- **Preloaded common languages** - JS, TS, Python, JSON, Bash, YAML,
etc.
- **Lazy loading** - Additional languages loaded on demand
- **Result caching** - Avoids re-highlighting identical code blocks
### Changes:
- Added `shiki` as direct dependency
- Updated `message.tsx` to use the new plugin
## Testing
- [ ] Verify code blocks render correctly in AI chat
- [ ] Confirm no Shiki singleton warnings in console
- [ ] Test with multiple languages in same conversation
## Related
- Linear: SENTRY-1051
- Sentry: Multiple Shiki instances warning
<!-- greptile_comment -->
<details><summary><h3>Greptile Summary</h3></summary>
Replaced `@streamdown/code` with a custom singleton-based Shiki
highlighter implementation to resolve memory bloat from creating
multiple highlighter instances per language. The new implementation
creates a single shared highlighter with preloaded common languages (JS,
TS, Python, JSON, etc.) and lazy-loads additional languages on demand.
Results are cached to avoid re-highlighting identical code blocks.
**Key changes:**
- Added `shiki` v3.21.0 as a direct dependency
- Created `shiki-highlighter.ts` with singleton pattern and language
management utilities
- Created `streamdown-code-plugin.ts` as a drop-in replacement for
`@streamdown/code`
- Updated `message.tsx` to import from the new plugin instead of
`@streamdown/code`
The implementation follows React best practices with async highlighting
and callback-based notifications. The cache key uses code length +
prefix/suffix for efficient lookups on large code blocks.
</details>
<details><summary><h3>Confidence Score: 4/5</h3></summary>
- Safe to merge with minor considerations for edge cases
- The implementation is solid with proper singleton pattern, caching,
and async handling. The code is well-structured and addresses the stated
problem. However, there's a subtle potential race condition in the
callback handling where multiple concurrent requests for the same cache
key could trigger duplicate highlight operations before the first
completes. The cache key generation using prefix/suffix could
theoretically cause false cache hits for large files with identical
prefixes and suffixes. Despite these edge cases, the implementation
should work correctly for the vast majority of use cases.
- No files require special attention
</details>
<details><summary><h3>Sequence Diagram</h3></summary>
```mermaid
sequenceDiagram
participant UI as Streamdown Component
participant Plugin as Custom Code Plugin
participant Cache as Token Cache
participant Singleton as Shiki Highlighter (Singleton)
participant Callbacks as Pending Callbacks
UI->>Plugin: highlight(code, lang)
Plugin->>Cache: Check cache key
alt Cache hit
Cache-->>Plugin: Return cached result
Plugin-->>UI: Return highlighted tokens
else Cache miss
Plugin->>Callbacks: Register callback
Plugin->>Singleton: Get highlighter instance
alt First call
Singleton->>Singleton: Create highlighter with preloaded languages
end
Singleton-->>Plugin: Return highlighter
alt Language not loaded
Plugin->>Singleton: Load language dynamically
end
Plugin->>Singleton: codeToTokens(code, lang, themes)
Singleton-->>Plugin: Return tokens
Plugin->>Cache: Store result
Plugin->>Callbacks: Notify all waiting callbacks
Callbacks-->>UI: Async callback with result
end
```
</details>
<sub>Last reviewed commit: 96c793b</sub>
<!-- greptile_other_comments_section -->
<!-- /greptile_comment -->
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 serverpnpm build- Build for productionpnpm start- Start production serverpnpm lint- Run ESLint and Prettier checkspnpm format- Format code with Prettierpnpm types- Run TypeScript type checkingpnpm test- Run Playwright testspnpm test-ui- Run Playwright tests with UIpnpm fetch:openapi- Fetch OpenAPI spec from backendpnpm generate:api-client- Generate API client from OpenAPI specpnpm 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
- Component Development: Develop and test UI components in isolation.
- Visual Testing: Easily spot visual regressions.
- Documentation: Automatically document components and their props.
- Collaboration: Share components with your team or stakeholders for feedback.
How to Use Storybook
-
Start Storybook: Run the following command to start the Storybook development server:
pnpm storybookThis will start Storybook on port 6006. Open http://localhost:6006 in your browser to view your component library.
-
Build Storybook: To build a static version of Storybook for deployment, use:
pnpm build-storybook -
Running Storybook Tests: Storybook tests can be run using:
pnpm test-storybook -
Writing Stories: Create
.stories.tsxfiles 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
- Tailwind CSS - Utility-first CSS framework
- shadcn/ui - Re-usable components built with Radix UI and Tailwind CSS
- Radix UI - Headless UI components for accessibility
- Phosphor Icons - Icon set used across the app
- Framer Motion - Animation library for React
Development & Testing
- Storybook - Component development environment
- Playwright - End-to-end testing framework
- ESLint - JavaScript/TypeScript linting
- Prettier - Code formatting
Backend & Services
- Supabase - Backend-as-a-Service (database, auth, storage)
- Sentry - Error monitoring and performance tracking
Package Management
Additional Libraries
- React Hook Form - Forms with easy validation
- Zod - TypeScript-first schema validation
- React Table - Headless table library
- React Flow - Interactive node-based diagrams
- React Query - Data fetching and caching
- React Query DevTools - Debugging tool for React Query
Development Tools
NEXT_PUBLIC_REACT_QUERY_DEVTOOL- Enable React Query DevTools. Set totrueto enable.