9f9097c62f
Bumps [cryptography](https://github.com/pyca/cryptography) from 43.0.3 to 44.0.0. <details> <summary>Changelog</summary> <p><em>Sourced from <a href="https://github.com/pyca/cryptography/blob/main/CHANGELOG.rst">cryptography's changelog</a>.</em></p> <blockquote> <p>44.0.0 - 2024-11-27</p> <pre><code> * **BACKWARDS INCOMPATIBLE:** Dropped support for LibreSSL < 3.9. * Deprecated Python 3.7 support. Python 3.7 is no longer supported by the Python core team. Support for Python 3.7 will be removed in a future ``cryptography`` release. * Updated Windows, macOS, and Linux wheels to be compiled with OpenSSL 3.4.0. * macOS wheels are now built against the macOS 10.13 SDK. Users on older versions of macOS should upgrade, or they will need to build ``cryptography`` themselves. * Enforce the :rfc:`5280` requirement that extended key usage extensions must not be empty. * Added support for timestamp extraction to the :class:`~cryptography.fernet.MultiFernet` class. * Relax the Authority Key Identifier requirements on root CA certificates during X.509 verification to allow fields permitted by :rfc:`5280` but forbidden by the CA/Browser BRs. * Added support for :class:`~cryptography.hazmat.primitives.kdf.argon2.Argon2id` when using OpenSSL 3.2.0+. * Added support for the :class:`~cryptography.x509.Admissions` certificate extension. * Added basic support for PKCS7 decryption (including S/MIME 3.2) via :func:`~cryptography.hazmat.primitives.serialization.pkcs7.pkcs7_decrypt_der`, :func:`~cryptography.hazmat.primitives.serialization.pkcs7.pkcs7_decrypt_pem`, and :func:`~cryptography.hazmat.primitives.serialization.pkcs7.pkcs7_decrypt_smime`. <p>.. _v43-0-3:<br /> </code></pre></p> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><a href=" |
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| .github | ||
| .vscode | ||
| assets | ||
| autogpt_platform | ||
| classic | ||
| docs | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .pr_agent.toml | ||
| .pre-commit-config.yaml | ||
| CITATION.cff | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
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.
https://github.com/user-attachments/assets/d04273a5-b36a-4a37-818e-f631ce72d603
This tutorial assumes you have Docker, VSCode, git and npm installed.
🧱 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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