ae6ef8c0c2
### Changes ️
The previous implementation of the `LaunchDarklyProvider` had a race
condition where it would only initialize after the user's authentication
state was fully resolved. This caused two primary issues:
1. A delay in evaluating any feature flags, leading to a "flash of
un-styled/un-flagged content" until the user session was loaded.
2. An unreliable transition from an un-flagged state to a flagged state,
which could cause UI flicker or incorrect flag evaluations upon login.
This pull request refactors the provider to follow a more robust,
industry-standard pattern. It now initializes immediately with an
`anonymous` context, ensuring flags are available from the very start of
the application lifecycle. When the user logs in and their session
becomes available, the provider seamlessly transitions to an
authenticated context, guaranteeing that the correct flags are evaluated
consistently.
### Checklist
#### For code changes:
- I have clearly listed my changes in the PR description
- I have made a test plan
- I have tested my changes according to the test plan:
**Test Plan:**
- [x] **Anonymous User:** Load the application in an incognito window
without logging in. Verify that feature flags are evaluated correctly
for an anonymous user. Check the browser console for the
`[LaunchDarklyProvider] Using anonymous context` message.
- [x] **Login Flow:** While on the site, log in. Verify that the UI
updates with the correct feature flags for the authenticated user. Check
the console for the `[LaunchDarklyProvider] Using authenticated context`
message and confirm the LaunchDarkly client re-initializes.
- [x] **Authenticated User (Page Refresh):** As a logged-in user,
refresh the page. Verify that the application loads directly with the
authenticated user's flags, leveraging the cached session and
bootstrapped flags from `localStorage`.
- [x] **Logout Flow:** While logged in, log out. Verify that the UI
reverts to the anonymous user's state and flags. The provider `key`
should change back to "anonymous", triggering another re-mount.
<details><summary>Summary of Code Changes</summary>
- Refactored `LaunchDarklyProvider` to handle user authentication state
changes gracefully.
- The provider now initializes immediately with an `anonymous` user
context while the Supabase user session is loading.
- Once the user is authenticated, the provider's context is updated to
reflect the logged-in user's details.
- Added a `key` prop to the `<LDProvider>` component, using the user's
ID (or "anonymous"). This forces React to re-mount the provider when the
user's identity changes, ensuring a clean re-initialization of the
LaunchDarkly SDK.
- Enabled `localStorage` bootstrapping (`options={{ bootstrap:
"localStorage" }}`) to cache flags and improve performance on subsequent
page loads.
- Added `console.debug` statements for improved observability into the
provider's state (anonymous vs. authenticated).
</details>
#### For configuration changes:
- `.env.example` is updated or already compatible with my changes
- `docker-compose.yml` is updated or already compatible with my changes
- I have included a list of my configuration changes in the PR
description (under **Changes**)
<details>
<summary>Configuration Changes</summary>
- No configuration changes were made. This PR relies on existing
environment variables (`NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID` and
`NEXT_PUBLIC_LAUNCHDARKLY_ENABLED`).
</details>
---------
Co-authored-by: Lluis Agusti <hi@llu.lu>
AutoGPT: Build, Deploy, and Run AI Agents
AutoGPT is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows.
Hosting Options
- Download to self-host
- Join the Waitlist for the cloud-hosted beta
How to Setup for Self-Hosting
Note
Setting up and hosting the AutoGPT Platform yourself is a technical process. If you'd rather something that just works, we recommend joining the waitlist for the cloud-hosted beta.
System Requirements
Before proceeding with the installation, ensure your system meets the following requirements:
Hardware Requirements
- CPU: 4+ cores recommended
- RAM: Minimum 8GB, 16GB recommended
- Storage: At least 10GB of free space
Software Requirements
- Operating Systems:
- Linux (Ubuntu 20.04 or newer recommended)
- macOS (10.15 or newer)
- Windows 10/11 with WSL2
- Required Software (with minimum versions):
- Docker Engine (20.10.0 or newer)
- Docker Compose (2.0.0 or newer)
- Git (2.30 or newer)
- Node.js (16.x or newer)
- npm (8.x or newer)
- VSCode (1.60 or newer) or any modern code editor
Network Requirements
- Stable internet connection
- Access to required ports (will be configured in Docker)
- Ability to make outbound HTTPS connections
Updated Setup Instructions:
We've moved to a fully maintained and regularly updated documentation site.
👉 Follow the official self-hosting guide here
This tutorial assumes you have Docker, VSCode, git and npm installed.
⚡ Quick Setup with One-Line Script (Recommended for Local Hosting)
Skip the manual steps and get started in minutes using our automatic setup script.
For macOS/Linux:
curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.sh
For Windows (PowerShell):
powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"
This will install dependencies, configure Docker, and launch your local instance — all in one go.
🧱 AutoGPT Frontend
The AutoGPT frontend is where users interact with our powerful AI automation platform. It offers multiple ways to engage with and leverage our AI agents. This is the interface where you'll bring your AI automation ideas to life:
Agent Builder: For those who want to customize, our intuitive, low-code interface allows you to design and configure your own AI agents.
Workflow Management: Build, modify, and optimize your automation workflows with ease. You build your agent by connecting blocks, where each block performs a single action.
Deployment Controls: Manage the lifecycle of your agents, from testing to production.
Ready-to-Use Agents: Don't want to build? Simply select from our library of pre-configured agents and put them to work immediately.
Agent Interaction: Whether you've built your own or are using pre-configured agents, easily run and interact with them through our user-friendly interface.
Monitoring and Analytics: Keep track of your agents' performance and gain insights to continually improve your automation processes.
Read this guide to learn how to build your own custom blocks.
💽 AutoGPT Server
The AutoGPT Server is the powerhouse of our platform This is where your agents run. Once deployed, agents can be triggered by external sources and can operate continuously. It contains all the essential components that make AutoGPT run smoothly.
Source Code: The core logic that drives our agents and automation processes.
Infrastructure: Robust systems that ensure reliable and scalable performance.
Marketplace: A comprehensive marketplace where you can find and deploy a wide range of pre-built agents.
🐙 Example Agents
Here are two examples of what you can do with AutoGPT:
-
Generate Viral Videos from Trending Topics
- This agent reads topics on Reddit.
- It identifies trending topics.
- It then automatically creates a short-form video based on the content.
-
Identify Top Quotes from Videos for Social Media
- This agent subscribes to your YouTube channel.
- When you post a new video, it transcribes it.
- It uses AI to identify the most impactful quotes to generate a summary.
- Then, it writes a post to automatically publish to your social media.
These examples show just a glimpse of what you can achieve with AutoGPT! You can create customized workflows to build agents for any use case.
Mission and Licencing
Our mission is to provide the tools, so that you can focus on what matters:
- 🏗️ Building - Lay the foundation for something amazing.
- 🧪 Testing - Fine-tune your agent to perfection.
- 🤝 Delegating - Let AI work for you, and have your ideas come to life.
Be part of the revolution! AutoGPT is here to stay, at the forefront of AI innovation.
📖 Documentation | 🚀 Contributing
Licensing:
MIT License: The majority of the AutoGPT repository is under the MIT License.
Polyform Shield License: This license applies to the autogpt_platform folder.
For more information, see https://agpt.co/blog/introducing-the-autogpt-platform
🤖 AutoGPT Classic
Below is information about the classic version of AutoGPT.
🛠️ Build your own Agent - Quickstart
🏗️ Forge
Forge your own agent! – Forge is a ready-to-go toolkit to build your own agent application. It handles most of the boilerplate code, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from forge can also be used individually to speed up development and reduce boilerplate in your agent project.
🚀 Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
📘 Learn More about Forge
🎯 Benchmark
Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
📦 agbenchmark on Pypi
|
📘 Learn More about the Benchmark
💻 UI
Makes agents easy to use! The frontend gives you a user-friendly interface to control and monitor your agents. It connects to agents through the agent protocol, ensuring compatibility with many agents from both inside and outside of our ecosystem.
The frontend works out-of-the-box with all agents in the repo. Just use the CLI to run your agent of choice!
📘 Learn More about the Frontend
⌨️ CLI
To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
Just clone the repo, install dependencies with ./run setup, and you should be good to go!
🤔 Questions? Problems? Suggestions?
Get help - Discord 💬
To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasn't created an issue for the same topic.
🤝 Sister projects
🔄 Agent Protocol
To maintain a uniform standard and ensure seamless compatibility with many current and future applications, AutoGPT employs the agent protocol standard by the AI Engineer Foundation. This standardizes the communication pathways from your agent to the frontend and benchmark.
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