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

# Run Locally

> Run your agent platform locally.

Today we're going to run an agent platform made of:

* AgentOS on FastAPI
* Postgres + PgVector

Two containers. Stateless FastAPI in front of Postgres, the same stack you run in production.

## Prerequisites

* [Docker Desktop](https://docs.docker.com/desktop/) installed and running
* An [OpenAI API key](https://platform.openai.com/api-keys) for models and embeddings

## Run your agent platform

<Steps>
  <Step title="Clone the template">
    ```bash theme={null}
    git clone https://github.com/agno-agi/agent-platform-railway.git agent-platform
    cd agent-platform
    ```

    <Tip>
      To make this codebase yours, run `rm -rf .git` and push to your own git repo.
    </Tip>
  </Step>

  <Step title="Configure your environment">
    ```bash theme={null}
    cp example.env .env
    ```

    Open `.env` and set `OPENAI_API_KEY`. Everything else has sensible defaults.
  </Step>

  <Step title="Start the platform">
    ```bash theme={null}
    docker compose up -d --build
    ```

    This runs two containers: a FastAPI app on port 8000 and a Postgres on port 5432.
  </Step>

  <Step title="Verify it's running">
    Open [http://localhost:8000/docs](http://localhost:8000/docs).

    You'll see the OpenAPI spec: every agent action exposed as a REST endpoint.

    <Frame caption="AgentOS API">
      <img src="https://mintcdn.com/agno-v2/4Z2CVTSHBoFZK7mc/videos/first-agent-api.gif?s=f709fc7060609e0055ecf1f6864157d3" alt="AgentOS API" width="800" height="524" data-path="videos/first-agent-api.gif" />
    </Frame>
  </Step>
</Steps>

You now have an agent platform made of AgentOS on FastAPI and Postgres. The AgentOS server exposes 50+ actions through a REST API.

AgentOS also comes with a UI at [os.agno.com](https://os.agno.com).

## Connect the AgentOS UI

1. Open [os.agno.com](https://os.agno.com) and log in.
2. Click **Connect OS**, choose **Local**, enter `http://localhost:8000` as the URL, and name it **Local AgentOS**.
3. Click **Connect**.

You should see two agents:

| Agent            | Pattern          | What it does                                                           |
| ---------------- | ---------------- | ---------------------------------------------------------------------- |
| **code\_search** | Context provider | Reads files in the workspace and answers questions about the codebase. |
| **web\_search**  | Direct tools     | Searches the web and synthesizes answers grounded in citations.        |

Try a prompt against each:

> *"What's in this repo?"* → `code_search` lists the directory and explains the structure.

> *"What did Anthropic publish about agents recently?"* → `web_search` returns a summary.

Open **Sessions** and **Traces** in the sidebar. Every run is captured with full message history, tool calls, and timing. This is what powers the iteration loop on the next page.

## Summary

We now have a locally running agent platform with:

* Our agent runtime (AgentOS) running on port 8000 with request isolation, session management, scheduling, and 50+ endpoints.
* A Postgres database for storing sessions, memory, knowledge, and traces.
* A `docs/` folder of Claude Code prompts for managing the entire agent development lifecycle: create, improve, extend, eval, and review.

<Tip>
  Hot-reload is on. Edits to `agents/*.py` and `app/*.py` should be live in \~2s.
</Tip>

## Next

[Create an agent →](/agent-platform/create-agent)