> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Run Locally

> Run AgentOS and Postgres locally with Docker.

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

* AgentOS on FastAPI
* Postgres + pgvector

## 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

**1. Clone the template**

```bash theme={null}
git clone https://github.com/agno-agi/agentos-railway.git agent-platform
cd agent-platform
```

> [!TIP]
>
> To make this codebase yours, run `rm -rf .git` and push to your own git repo.

**2. Configure your environment**

```bash theme={null}
cp example.env .env
```

Open `.env` and set `OPENAI_API_KEY`. Everything else has sensible defaults.

**3. Start the platform**

```bash theme={null}
docker compose up -d --build
```

This runs two containers: a FastAPI app on port 8000 and a Postgres database on port 5432. The first build takes a few minutes.

**4. 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.

![AgentOS API](https://mintcdn.com/agno-v2/4Z2CVTSHBoFZK7mc/videos/first-agent-api.gif?s=f709fc7060609e0055ecf1f6864157d3)

AgentOS API

You now have an agent platform made of AgentOS on FastAPI and Postgres. The AgentOS server exposes 80+ endpoints for runs, sessions, memory, knowledge, and evals.

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 sign in.
2. Click **Connect OS**, enter `http://localhost:8000` as the URL, and name it **Local AgentOS**.
3. Click **Connect**.

You should see three agents:

| Agent | Pattern | What it does |
| - | - | - |
| **Agent Builder** | Studio builder | Builds agents, teams, and workflows from a chat prompt. |
| **Platform Manager** | Context provider | Monitors the platform and answers questions about the codebase. |
| **WebSearch** | Direct tools | Searches the web and synthesizes answers grounded in citations. |

Try a prompt against each:

> *"Build an agent that tracks AI news and writes a daily brief"* → **Agent Builder** walks you through the agent development process.

> *"How healthy is the platform?"* → **Platform Manager** answers from eval history, deployment checks, and schedules.

> *"What did Anthropic publish about agents recently?"* → **WebSearch** returns a summary with citations.

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 80+ endpoints.
* A Postgres database for storing sessions, memory, knowledge, and traces.
* Eight coding-agent skills in `.agents/skills/`: setup, create, extend, improve, create evals, repair eval failures, review, and deploy.

> [!TIP]
>
> Hot-reload is on. Edits to Python files in the source (`agents/`, `app/`, `db/`, `evals/`, `workflows/`) are live in \~2s.

## Next

[Create an agent →](https://agno-v2.mintlify.site/agent-platform/create-agent.md)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.