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

# Action Llama

> Deploy agentic workflows to your favourite VPS

Open source and MIT Licensed.

Use Claude, OpenAI, or other models to:

* **Automate your dev workflow**: agents handle issues, PRs, monitoring
* **Automate communications**: have an agent create a summary of product changes for the last week and tweet it out

You're **progamming with prompts**: any process can be automated using an agent.

<CardGroup cols={2}>
  <Card title="First Steps" icon="rocket" href="/first-steps/getting-started">
    Zero to running agent in minutes
  </Card>

  <Card title="Guides" icon="book" href="/guides/dynamic-context">
    Deploy, scale, optimize, and orchestrate
  </Card>

  <Card title="Concepts" icon="lightbulb" href="/concepts/agents">
    Understand agents, scheduling, and locks
  </Card>

  <Card title="Reference" icon="terminal" href="/reference/cli-commands">
    Every CLI flag, config field, and command
  </Card>
</CardGroup>

## Key features

* **Docker isolation** — each run gets its own container with only the credentials it needs
* **Git-native** — define agents in a git repo, add custom ones, share them across teams
* **BYOM** — bring your own model (Anthropic, OpenAI, Groq, Google Gemini, xAI, Mistral, OpenRouter, or any custom provider)
* **Deploy anywhere** — run locally for development or deploy to Cloud Run for production
* **Webhook + cron** — react to GitHub issues, Sentry alerts, Linear tickets, or poll on a schedule
* **Multi-agent** — agents can call other agents with `call_agent` and collect results with `check_call`
* **Resource locks** — coordinate parallel instances with automatic deadlock detection
* **Web dashboard** — live agent status and streaming logs in your browser

## Philosophy

* **Agents are infrastructure**: they should be versioned, repeatable, and deployable. Agents work together, so they should be bundled together.
* **Stateless by default**: state is easy to add to a stateless system. Dynamic agent context strategies can be added if desired.
* **Bring your own models**: there are many models out there, each appropriate to a different task. You should be able to use any of them.
* **Minimize the harness and lean on the model**: models are improving constantly, that's why this framework lets you program with prompts. The harness should focus on orchestration only.