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

# Introduction

> Self-hosted platform for discovering and running AI agents

## Self-hosted platform for discovering and running AI agents

Deploy specialized agents on your infrastructure—without building from scratch or using SaaS.

<Info>
  **What is AgentSystems?** A self-hosted platform that combines a federated agent ecosystem, provider portability, and container isolation. Browse community agents, deploy on your infrastructure, and switch between AI providers through configuration.
</Info>

## Why AgentSystems?

Teams want to use specialized AI agents but face a dilemma:

* 🔒 **SaaS agents** require sending data to third parties
* 🛠️ **Building from scratch** takes weeks of development per agent (most teams lack ML expertise)
* 🐳 **Manual Docker orchestration** means configuring networks, volumes, proxies, and API keys for each agent

**AgentSystems provides** a standardized runtime and ecosystem:

* **Federated Agent Ecosystem**: Git-based agent index where developers publish via GitHub forks
* **Provider Portability**: Switch from OpenAI to Anthropic to Ollama through configuration
* **Container Isolation**: Each agent runs in its own Docker container with configurable egress filtering
* **Audit Trail**: Hash-chained logs for operation tracking

## Key Capabilities

<Columns cols={2}>
  <Card title="Community Indexes" icon="globe">
    Browse and add agents from community indexes
  </Card>

  <Card title="Local Execution" icon="server">
    Deploy AI agents on your hardware with Docker
  </Card>

  <Card title="Multiple Providers" icon="shuffle">
    Use OpenAI, Anthropic, AWS Bedrock, or local models
  </Card>

  <Card title="Audit Logging" icon="file-signature">
    Operation logging to PostgreSQL database
  </Card>
</Columns>

## Use Cases

The platform targets scenarios requiring local AI execution:

* Processing documents on your infrastructure
* Running AI workloads on-premise
* Keeping data within your own environment
* Testing multiple AI providers
* Building containerized AI agents