# Getting Started

This page walks you through installing OpenHuman, going through the in-app onboarding, and running your first request.

OpenHuman is open source under the GNU GPL3 license. The codebase is at [github.com/tinyhumansai/openhuman](https://github.com/tinyhumansai/openhuman).

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## System requirements

OpenHuman runs on **macOS, Windows and Linux** desktops. 4 GB+ RAM is recommended; 16 GB+ if you intend to ingest very large mailboxes or repos, or run a [local model](/openhuman/features/model-routing/local-ai.md) on the same machine.

### Permissions

The first time you launch OpenHuman, the OS will prompt for the permissions the app needs (Accessibility on macOS, Input Monitoring for the voice hotkey, Camera/Microphone if you plan to use the [Meeting Agent](/openhuman/features/mascot/meeting-agents.md)). You can review and adjust these any time under **Settings → Automation & Channels**.

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## 1. Download and install

Get the OpenHuman desktop app from <http://tinyhumans.ai/openhuman> or via your platform's package manager. Open the app once it's installed.

## 2. Sign in

The first screen is **"Sign in! Let's Cook"**. Multiple sign-in options are available, including social login. There's also an **Advanced** panel for pointing the app at a custom core RPC URL if you're running your own backend; most users can ignore it.

{% hint style="info" %}
**No permanent lock-in.** Signing in does not grant OpenHuman ongoing access to anything. All third-party access requires explicit OAuth approval per integration in the steps below.
{% endhint %}

## 3. Run your first request

Once Gmail has been ingested (the first auto-fetch tick happens within twenty minutes), try prompts like:

**Briefings**

* "What do I need to know from the last 12 hours?"
* "What's waiting on me?"

**Cross-source queries**

* "Summarize what I missed today."
* "What are the key decisions from this week?"
* "Extract action items from my recent conversations."
* "What did Sarah say about the project across email and chat?"

OpenHuman picks the right model for each task automatically. See [Automatic Model Routing](/openhuman/features/model-routing.md).

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## 4. Open the Obsidian vault

The Memory tab has a **View vault in Obsidian** button. Click it to open `<workspace>/wiki/` in [Obsidian](https://obsidian.md). You can browse the agent's summaries, drop in your own notes, and even build manual links - the agent will pick up your edits on the next ingest. See [Obsidian-Style Memory](/openhuman/features/obsidian-wiki.md).

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## 5. Let the mascot do more

Now that the agent has memory and a model, the rest of the product is about giving it more surfaces:

* [**Meeting Agents**](/openhuman/features/mascot/meeting-agents.md) - drop a Google Meet link in and the mascot joins as a real participant: it listens, takes notes into the Memory Tree, speaks back into the call, and uses tools live.
* [**Auto-fetch from Integrations**](/openhuman/features/obsidian-wiki/auto-fetch.md) - connect more sources from **Settings**; every twenty minutes the scheduler pulls fresh data into your tree.
* [**Native Voice**](/openhuman/features/native-tools/voice.md) - push-to-talk dictation and TTS replies so you can talk to OpenHuman instead of typing.
* [**Subconscious Loop**](/openhuman/features/subconscious.md) - let the mascot keep working on standing tasks while you're away.

## Join the community

OpenHuman is in early beta. Feedback and contributions make a real difference at this stage.

* **GitHub:** [github.com/tinyhumansai/openhuman](https://github.com/tinyhumansai/openhuman)
* **Discord:** [discord.tinyhumans.ai](https://discord.tinyhumans.ai)


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://tinyhumans.gitbook.io/openhuman/overview/getting-started.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.