README

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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.


  


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      Getting Started
    
    
      Technical Details
    
    
      Get Involved
    
    
      Reference
    
  



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 Getting Started

- Your first agent
- Installation guide

 Technical Details

 Core Concepts

- Agents: Agents are the main building block of kagent. An Agent is a Kubernetes custom resource that brings together an agent's behavior and runtime configuration.
- Agent Templates: A system prompt, a set of tools and subagents, and an LLM configuration define an agent's behavior. You can define this directly in an Agent or share it across agents using an AgentTemplate resource.
- Harnesses: A Harness defines how an agent runs. Kagent supports its own Go and Python ADKs, Codex, Claude, and custom runtimes, so you can choose the runtime that fits your agent.
- LLM Providers: Kagent supports multiple LLM providers, including OpenAI, Azure OpenAI, Anthropic, Google Vertex AI, Ollama and custom providers and models accessible via AI gateways. Models are configured through the ModelConfig resource, with provider support depending on the chosen Harness.
- MCP Tools: Agents can connect to MCP servers that provide tools. Kagent comes with an MCP server with tools for Kubernetes, Istio, Helm, Argo, Prometheus, Grafana, Cilium, and others. Agents connect through RemoteMCPServer resources, which can be shared across agents.
- Sessions: Sessions keep conversations and task history across interactions. You can suspend and resume an agent, or create a checkpoint and fork a new conversation from it.
- Observability: Kagent supports OpenTelemetry tracing, which allows you to monitor what's happening with your agents and tools.

 Core Principles

- Kubernetes Native: Manage agent and tool configuration through Kubernetes APIs and familiar kubectl workflows.
- Extensible: Add custom tools, skills, and runtimes to connect agents to your own systems.
- Flexible: Choose the models and runtimes that fit your workload, and reuse agent templates across compatible harnesses.
- Observable: Trace agent, model, and tool execution with OpenTelemetry and your existing observability stack.
- Declarative: Define agents in YAML, keep configuration in version control, and deploy through GitOps workflows.
- Testable: Test agents through their public APIs and use task history and traces to diagnose failures.

 Architecture

mermaid
flowchart TD
    UI[UI] -- CONTROLLER[kagent Controller]
    CLI[CLI] -- CONTROLLER
    CONFIG[Kubernetes resources] -- CONTROLLER
    CONTROLLER -- DB[(PostgreSQL)]
    CONTROLLER -- SUBSTRATE[Substrate]
    SUBSTRATE -- RUNTIMES[Agent runtimes]
    CONT

Top issues

Discussions

Latest release

What's Changed
 Features
 feat(streaming): configurable nginx, controller, and UI inactivity ti… by @yanivmn in https://github.com/kagent-dev/kagent/pull/1973
 feat(helm): add controller service annotations by @anthonyhaussman in https://github.com/kagent-dev/kagent/pull/2088
 feat: enable sharing of chat sessions by @onematchfox in https://github.com/kagent-dev/kagent/pull/1935
 feat: substrate support for BYO and python runtimes for SandboxAgent CR by @jmhbh in https://github.com/kagent-dev/kagent/pull/2043
 feat(substrate): bump agent substrate to 0.0.7 by @supreme-gg-gg in https://github.c