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## Summary Implement comprehensive k6 load testing infrastructure for the AutoGPT Platform with clean file organization, unified test runner, and cloud integration. ## Key Features ### 🗂️ Clean File Organization - **tests/basic/**: Simple validation tests (connectivity, single endpoints) - **tests/api/**: Core functionality tests (API endpoints, graph execution) - **tests/marketplace/**: User-facing feature tests (public/library access) - **tests/comprehensive/**: End-to-end scenario tests (complete user journeys) - **orchestrator/**: Advanced test orchestration for full suites ### 🚀 Unified Test Runner - **Single entry point**: `run-tests.js` for both local and cloud execution - **7 available tests**: From basic connectivity to comprehensive platform journeys - **Flexible execution**: Run individual tests, comma-separated lists, or all tests - **Auto-configuration**: Different VU/duration settings for local vs cloud execution ### 🔐 Advanced Authentication - **Pre-authenticated tokens**: 24-hour JWT tokens eliminate Supabase rate limiting - **Configurable generation**: Default 10 tokens, scalable to 150+ for high concurrency - **Graceful handling**: Proper auth failure detection and recovery - **ES module compatible**: Modern JavaScript with full import/export support ### ☁️ k6 Cloud Integration - **Cloud execution**: Tests run on k6 cloud infrastructure for consistent results - **Real-time monitoring**: Live dashboards with performance metrics - **URL tracking**: Automatic test result URL capture and storage - **Sequential orchestration**: Proper delays between tests for resource management ## Test Coverage ### Performance Validated - **Core API**: 100 VUs successfully testing `/api/credits`, `/api/graphs`, `/api/blocks`, `/api/executions` - **Graph Execution**: 80 VUs for complete workflow pipeline testing - **Marketplace**: 150 VUs for public browsing, 100 VUs for authenticated library operations - **Authentication**: 150+ concurrent users with pre-authenticated token scaling ### User Journey Simulation - **Dashboard workflows**: Credits checking, graph management, execution monitoring - **Marketplace browsing**: Public search, agent discovery, category filtering - **Library operations**: Agent adding, favoriting, forking, detailed views - **Complete workflows**: End-to-end platform usage with realistic user behavior ## Technical Implementation ### ES Module Compatibility - Full ES module support with modern JavaScript imports/exports - Proper module execution patterns for Node.js compatibility - Clean separation between CommonJS legacy and modern ES modules ### Error Handling & Monitoring - **Separate metrics**: HTTP status, authentication, JSON validation, overall success - **Graceful degradation**: Auth failures don't crash VUs, proper error tracking - **Performance thresholds**: Configurable P95/P99 latency and error rate limits - **Custom counters**: Track operation types, success rates, user journey completion ### Infrastructure Benefits - **Rate limit protection**: Pre-auth tokens prevent Supabase auth bottlenecks - **Scalable testing**: Support for 150+ concurrent users with proper token management - **Cloud consistency**: Tests run on dedicated k6 cloud servers for reliable results - **Development workflow**: Local execution for debugging, cloud for performance validation ## Usage ### Quick Start ```bash # Setup and verification export SUPABASE_SERVICE_KEY="your-service-key" node generate-tokens.js node run-tests.js verify # Local testing (development) node run-tests.js run core-api-test DEV # Cloud testing (performance) node run-tests.js cloud all DEV ``` ### NPM Scripts ```bash npm run verify # Quick setup check npm test # All tests locally npm run cloud # All tests in k6 cloud ``` ## Validation Results ✅ **Authentication**: 100% success rate with fresh 24-hour tokens ✅ **File Structure**: All imports and references verified correct ✅ **Test Execution**: All 7 tests execute successfully with proper metrics ✅ **Cloud Integration**: k6 cloud execution working with proper credentials ✅ **Documentation**: Complete README with usage examples and troubleshooting ## Files Changed ### Core Infrastructure - `run-tests.js`: Unified test runner supporting local/cloud execution - `generate-tokens.js`: ES module compatible token generation with 24-hour expiry - `README.md`: Comprehensive documentation with updated file references ### Organized Test Structure - `tests/basic/connectivity-test.js`: Basic connectivity validation - `tests/basic/single-endpoint-test.js`: Individual API endpoint testing - `tests/api/core-api-test.js`: Core authenticated API endpoints - `tests/api/graph-execution-test.js`: Graph workflow pipeline testing - `tests/marketplace/public-access-test.js`: Public marketplace browsing - `tests/marketplace/library-access-test.js`: Authenticated marketplace/library operations - `tests/comprehensive/platform-journey-test.js`: Complete user journey simulation ### Configuration - `configs/environment.js`: Environment URLs and performance settings - `package.json`: NPM scripts and dependencies for unified workflow This infrastructure provides a solid foundation for continuous performance monitoring and load testing of the AutoGPT Platform. 🤖 Generated with [Claude Code](https://claude.ai/code) --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Nicholas Tindle <nicholas.tindle@agpt.co> Co-authored-by: Reinier van der Leer <pwuts@agpt.co>
AutoGPT: Build, Deploy, and Run AI Agents
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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 (Free!)
- Join the Waitlist for the cloud-hosted beta (Closed Beta - Public release Coming Soon!)
How to Self-Host the AutoGPT Platform
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.
License Overview:
🛡️ Polyform Shield License:
All code and content within the autogpt_platform folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.
Read more about this effort
🦉 MIT License:
All other portions of the AutoGPT repository (i.e., everything outside the autogpt_platform folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI.
We also publish additional work under the MIT Licence in other repositories, such as GravitasML which is developed for and used in the AutoGPT Platform. See also our MIT Licenced Code Ability project.
Mission
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
🤖 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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