> ## Documentation Index
> Fetch the complete documentation index at: https://flokoa.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Flokoa Documentation — Kubernetes AI Agent Platform

> Flokoa is a Kubernetes-native open-source platform for deploying and managing AI agents declaratively using Custom Resource Definitions.

Welcome to the Flokoa documentation. Flokoa lets you deploy, configure, and scale AI agents on Kubernetes using familiar declarative YAML — the same way you manage any other workload in your cluster. Whether you're running a single prototype or a fleet of production agents, Flokoa gives you the operational primitives to do it reliably.

<CardGroup cols={2}>
  <Card title="Introduction" icon="book-open" href="/introduction">
    Learn what Flokoa is, how it works, and the six Custom Resource Definitions that power your AI agents.
  </Card>

  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Install the operator, connect an LLM provider, and deploy your first agent in under five minutes.
  </Card>

  <Card title="Agent Resource" icon="robot" href="/resources/agent">
    Explore the full Agent CRD spec — runtime modes, model references, tool bindings, and lifecycle options.
  </Card>

  <Card title="SDK Overview" icon="code" href="/sdk/overview">
    Use the Flokoa Python SDK and CLI to build, run, and interact with agents programmatically.
  </Card>
</CardGroup>

## Get up and running

Follow these four steps to go from a fresh Kubernetes cluster to a running AI agent.

<Steps>
  <Step title="Install the operator">
    Apply the single-file install manifest to your cluster. The Flokoa operator is deployed into the `flokoa-system` namespace and immediately begins watching for Flokoa Custom Resources.
  </Step>

  <Step title="Configure an LLM provider">
    Create a Kubernetes Secret with your API key, then declare a `ModelProvider` resource pointing to OpenAI, Anthropic, Google Gemini, or AWS Bedrock. A `Model` resource wires a specific model name and parameters to that provider.
  </Step>

  <Step title="Deploy your first agent">
    Apply an `Agent` resource that references your `Model` and specifies a container image. The operator creates a Kubernetes Deployment and Service automatically — no Helm chart or manual pod spec required.
  </Step>

  <Step title="Call it via A2A">
    Once the agent's status shows `Running`, reach it through its cluster-internal Service endpoint using the Agent-to-Agent (A2A) protocol or any HTTP client.
  </Step>
</Steps>

## Explore the resource types

Flokoa exposes six Custom Resource Definitions under the `agent.flokoa.ai/v1alpha1` API group. Each resource plays a distinct role in the agent deployment pipeline.

<CardGroup cols={3}>
  <Card title="Agent" icon="robot" href="/resources/agent">
    The primary resource. Declares the agent runtime, model reference, tool bindings, and scaling configuration.
  </Card>

  <Card title="ModelProvider" icon="plug" href="/resources/model-provider">
    Stores LLM provider connection config and API credentials, shared across any number of Model resources.
  </Card>

  <Card title="Model" icon="microchip" href="/resources/model">
    Selects a specific model name and tuning parameters, referencing a ModelProvider for the connection.
  </Card>

  <Card title="AgentTool" icon="wrench" href="/resources/agent-tool">
    Gives agents access to external APIs and internal Kubernetes services via OpenAPI specifications.
  </Card>

  <Card title="AgentWorkflow" icon="diagram-project" href="/resources/agent-workflow">
    Composes multiple agents into multi-step workflows, compiled to Argo WorkflowTemplates automatically.
  </Card>
</CardGroup>