Kagent

What is Kagent

kagent is a Kubernetes native framework designed for building, deploying, and managing AI agents in a cloud-native environment. It simplifies the process of working with AI agents by leveraging Kubernetes orchestration capabilities.

Use cases

Use cases for kagent include developing intelligent chatbots, automating data processing tasks, creating recommendation systems, and integrating AI capabilities into existing applications running on Kubernetes.

How to use

To use kagent, follow the quick start guide available on the official documentation site. Installation instructions are also provided to help set up the framework in your Kubernetes environment.

Key features

Key features of kagent include its Kubernetes native design, ease of use, flexibility in building AI agents, and the ability to define agents and tools as Kubernetes custom resources.

Where to use

kagent can be used in various fields that require AI agent deployment and management, such as cloud computing, data analysis, automated customer support, and more within Kubernetes environments.

kagent is a Kubernetes native framework for building AI agents. Kubernetes is the most popular orchestration platform for running workloads, and kagent makes it easy to build, deploy and manage AI agents in kubernetes. The kagent framework is designed to be easy to understand and use, and to provide a flexible and powerful way to build and manage AI agents.

Get started

• Quick Start
• Installation guide

Documentation

The kagent documentation is available at kagent.dev/docs.

Core Concepts

• Agents: Agents are the main building block of kagent. They are a system prompt, a set of tools, and a model configuration.
• Tools: Tools are any external tool that can be used by an agent. They are defined as Kubernetes custom resources and can be used by multiple agents.

All of the above are defined as Kubernetes custom resources, which makes them easy to manage and modify.

Core Principles

• Kubernetes Native: Kagent is designed to be easy to understand and use, and to provide a flexible and powerful way to build and manage AI agents.
• Extensible: Kagent is designed to be extensible, so you can add your own agents and tools.
• Flexible: Kagent is designed to be flexible, to suit any AI agent use case.
• Observable: Kagent is designed to be observable, so you can monitor the agents and tools using all common monitoring frameworks.
• Declarative: Kagent is designed to be declarative, so you can define the agents and tools in a yaml file.
• Testable: Kagent is designed to be tested and debugged easily. This is especially important for AI agent applications.

Architecture

The kagent framework is designed to be easy to understand and use, and to provide a flexible and powerful way to build and manage AI agents.

Kagent has 4 core components:

• Controller: The controller is a Kubernetes controller that watches the kagent custom resources and creates the necessary resources to run the agents.
• UI: The UI is a web UI that allows you to manage the agents and tools.
• Engine: The engine is a Python application that runs the agents and tools. The engine is built using Autogen.
• CLI: The CLI is a command line tool that allows you to manage the agents and tools.

Roadmap

kagent is currently in active development. The following is a list of features that are planned for the next few releases.

• [ ] Observability improvements:
  • [ ] More powerful Tracing capabilities
  • [ ] Tighter oTEL integration
  • [ ] Metrics
• [ ] Feedback/Testing:
  • [ ] Eval framework/integrations
  • [ ] Debugging/Time travel
  • [ ] Guided Learning
• [ ] Runtime/Engine improvements:
  • [ ] Multi-Agent support
  • [ ] True Graph Execution
  • [ ] Workflows
  • [x] Multiple LLM Provider support
• [ ] Tools:
  • [x] Tool Discovery
  • [x] Expose built-in tools as MCP server

Local development

For instructions on how to run everything locally, see the DEVELOPMENT.md file.

Contributing

For instructions on how to contribute to the kagent project, see the CONTRIBUTION.md file.