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

# Agno Provider

> Deploy and manage AI agents, teams, and their dependencies on Kubernetes

The Agno provider integrates the [Agno](https://agno.com) AI agent framework with Pragmatiks, enabling you to define, configure, and deploy AI agents and multi-agent teams as declarative resources.

## What is Agno?

Agno is a Python framework for building AI agents with support for models, tools, knowledge bases, and memory. The Agno provider lets you manage the full agent lifecycle declaratively — from model configuration to Kubernetes deployment.

## Setup

The Agno provider is a first-party provider maintained by Pragmatiks. Install it with:

```bash theme={"theme":{"light":"min-light","dark":"min-dark"}}
pragma providers push --deploy
```

## Resources

| Resource                                 | Type                                                                                                 | Description                                          |
| ---------------------------------------- | ---------------------------------------------------------------------------------------------------- | ---------------------------------------------------- |
| [Agent](/providers/agno/agent)           | `agno/agent`                                                                                         | AI agent definition with model, tools, and knowledge |
| [Team](/providers/agno/team)             | `agno/team`                                                                                          | Multi-agent team with coordinated members            |
| [Models](/providers/agno/models)         | `agno/models/anthropic`, `agno/models/openai`                                                        | LLM configuration (Claude, GPT)                      |
| [Tools](/providers/agno/tools)           | `agno/tools/mcp`, `agno/tools/websearch`                                                             | Agent tool integrations                              |
| [Knowledge](/providers/agno/knowledge)   | `agno/knowledge`, `agno/vectordb/qdrant`, `agno/knowledge/embedder/openai`, `agno/knowledge/content` | Semantic search and RAG                              |
| [Memory](/providers/agno/memory)         | `agno/memory/manager`                                                                                | Persistent agent memory                              |
| [Storage](/providers/agno/storage)       | `agno/db/postgres`                                                                                   | PostgreSQL storage for sessions and memory           |
| [Deployment](/providers/agno/deployment) | `agno/runner`                                                                                        | Deploy agents/teams to Kubernetes                    |

## Architecture

Resources form a dependency graph. A typical deployment looks like:

```
Secret (API key)
  └─▶ Model (Claude/GPT)
        └─▶ Agent
              ├─▶ Tools (MCP, WebSearch)
              ├─▶ Knowledge (VectorDB + Embedder + Content)
              ├─▶ Memory (Manager + DB)
              └─▶ Runner (Kubernetes Deployment)
```

Changes propagate automatically. Updating a model triggers the agent and runner to rebuild.

## Dependencies

The Agno provider depends on:

* **GCP provider** — for `gcp/gke` clusters (required by `agno/runner`)
* **GCP provider** — for `gcp/secret` (commonly used for API keys)