V0.0.4

Release v0.0.4

We're pleased to announce the release of v0.0.4 for our Python package. This release includes several updates and improvements that enhance the usability and compatibility of our library with OpenAI functions. Here's what's new:

What's Changed

• Type Hinting and Stubs: We've added a py.typed file and updated setup.py to fix library stub errors, making our package fully compliant with PEP 561. This means type checkers like mypy can now use the type annotations provided with the package to perform static type checking.

• License Badge Update: The README file now includes an updated license badge that reflects the current licensing of our project.

• OpenAI Function Wrappers: To facilitate the use of our package with OpenAI functions, we've introduced OpenAI function wrappers. These are designed to provide a seamless integration experience. Accompanying the new feature, we've updated the documentation, added a comprehensive suite of tests, and included a linter bash script to ensure code quality.

• Version Bump: The package version has been upgraded from 0.0.3 to 0.0.4, marking a new stage in our ongoing development process.

Upgrading

To upgrade to the latest version, run:

pip install --upgrade kin-kernel

We recommend all users to upgrade to this latest version to take advantage of the improvements.

Acknowledgments

A big thank you to our contributors and users for your feedback, contributions, and support. Your input is invaluable and helps us make our package better with every release.

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Thank you for using our package, and we hope you enjoy the new features and enhancements in v0.0.4!

V0.0.3

KinKernel v0.0.3 Release

🚀 It's a pleasure to announce that KinKernel v0.0.3 is now available! This update brings key enhancements and improvements that will elevate your experience in building and deploying autonomous agents, or Cells, as part of the Internet of Agents (IoA) ecosystem.

What's New

The latest version of KinKernel introduces several significant updates:

• Improved Documentation: We've fixed the README for better clarity and guidance. Now, getting started with KinKernel is easier than ever.
• Sphinx Documentation: Dive into our comprehensive documentation generated with Sphinx. It's now seamlessly integrated and accessible for developers.
• GitHub Pages Deployment: Our documentation is automatically deployed to GitHub Pages through GitHub Actions, ensuring you always have access to the latest information.
• Asynchronous Execution: We've added support for asynchronous operation in the run and _execute functions of the BaseCell class, allowing for multi-threading and more efficient handling of concurrent processes.

Enhancements

• Readme Fix: Clarifications and updates have been made to the README file to streamline the onboarding process.
• Sphinx Documentation: An extensive set of documentation has been added, detailing every aspect of KinKernel.
• GitHub Actions for Docs: The documentation is now automatically built and deployed to GitHub Pages on each pr on the main, ensuring that you always have access to the latest instructions and reference materials.
• Async Support: With the integration of asynchronous methods, your Cells can now handle multiple tasks more effectively, improving overall performance and scalability.
• opentelemetry Support: With the integration of opentelemetry methods, you can now track your Cells performances.

Getting Started

To update to the latest version of KinKernel, simply install it from pypi:

pip install kin-kernel
# or
pip install kin-kernel==0.0.3

For developers, remember to also update the development dependencies:

pip install -r requirements/dev.txt

Usage

Creating and interacting with Cells remains straightforward but is now more powerful with asynchronous support:

1. Subclass the Cell class.
2. Define your input and output models using Pydantic.
3. Implement the execute method with your custom logic, now with async capabilities.

Explore the updated simple_cell_example.py in our repository for an example of how to leverage these new features.

Linters, Testing, and Documentation

Maintain high-quality code and functionality with the same suite of linters and testing commands, and now enjoy the improved documentation workflow:

flake8 kinkernel
black kinkernel --check --diff
mypy kinkernel
pylint kinkernel

Run unit tests as usual:

pytest

Generate and view documentation locally:

sphinx-build -b html docs/ docs/_build/html

Contribute and Support

Your contributions and feedback are what make KinKernel a robust toolkit for the IoA. If you have any issues, questions, or suggestions, please reach out to us at contact@digitalkin.ai.

Acknowledgments

We're grateful for your continued support and are excited to see the innovative agents you'll craft using these new capabilities. Together, we're shaping the future of autonomous agents in the IoA.

🔗 Upgrade to KinKernel v0.0.3 now and take your Cells to the next level of performance and efficiency.

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👾 Happy agent-crafting! 👾

© 2023 DigitalKin.ai. All Rights Reserved.

KinKernel v0.0.1 Release

KinKernel v0.0.1 Release

🎉 We are thrilled to announce the first release of KinKernel, v0.0.1! As the foundational building block of the Internet of Agents (IoA) ecosystem, KinKernel is designed to empower developers to create and integrate autonomous agents, known as Cells, into a dynamic and evolving digital landscape.

Features

In this initial release, we've focused on providing a solid core with the following features:

• Abstract Base Classes: Establish a standardized approach for Cell creation, ensuring all agents can operate seamlessly within the IoA.
• Response Models: Facilitate consistent communication between Cells with predefined response structures.
• Schema Access Helpers: Simplify the retrieval of schema information, making it easier to manage and interact with Cell data.
• Example Implementation: Get started quickly with an example Cell implementation provided in the repository.

Getting Started

To get your hands on KinKernel, follow these steps:

git clone https://github.com/DigitalKin/kin-kernel.git
cd kin-kernel-kit
pip install -r requirements/prod.txt

For developers, we also offer a set of development dependencies to facilitate a robust development workflow:

pip install -r requirements/dev.txt

Usage

Creating a new Cell is straightforward:

1. Subclass the Cell class.
2. Define input and output models using Pydantic.
3. Implement the execute method with your custom logic.

Check out our repository for a simple_cell_example.py to jumpstart your Cell development.

Linters and Testing

Ensure code quality and functionality with our suite of linters and test commands:

flake8 kinkernel
black kinkernel --check --diff
mypy kinkernel
pylint kinkernel

Run unit tests easily:

pytest

Contribute and Support

Join our journey in enhancing KinKernel by contributing to the project or sharing your feedback. If you encounter any issues or have questions, don't hesitate to reach out to contact@digitalkin.ai.

Acknowledgments

Thank you for your interest in KinKernel. We are excited to see the innovative solutions and Cells you will create with this toolkit.

🔗 Download KinKernel v0.0.1 now and start building the future of autonomous agents within the Internet of Agents.

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✨ Happy coding! ✨

© 2023 DigitalKin.ai. All Rights Reserved.