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

# Quickstart

> Get started with Graphora in minutes

# Quickstart

Graphora offers three ways to get started, from zero-install to full local development. Pick the one that fits your situation.

## Option 1: Try the Demo (30 seconds)

No signup, no install. Visit [demo.graphora.io](https://demo.graphora.io), upload a document, and see Graphora extract a knowledge graph in real time.

This is the fastest way to see what Graphora does.

## Option 2: Google Colab Notebook (2 minutes)

Run the interactive quickstart notebook in your browser. It walks you through defining an ontology, extracting entities from a sample document, and visualizing the resulting graph -- all without installing anything locally.

<Card title="Open in Google Colab" icon="flask" href="https://colab.research.google.com/github/graphora/graphora-api/blob/main/examples/quickstart.ipynb">
  Launch the quickstart notebook
</Card>

The notebook uses `pip install graphora[cli]` and walks you through configuring your Gemini API key. You'll need a Google account and a [free Gemini API key](https://aistudio.google.com/apikey).

## Option 3: CLI (5 minutes)

Install the Graphora CLI and extract knowledge graphs from the command line. The CLI includes an embedded mode that runs the API locally, so you do not need a separate server.

<Steps>
  <Step title="Install the CLI">
    ```bash theme={null}
    pip install graphora[cli]
    ```

    This installs both the Python client library and the `graphora` CLI command.
  </Step>

  <Step title="Configure your Gemini API key">
    Graphora uses Google Gemini for AI-powered extraction. Get a free API key from [Google AI Studio](https://aistudio.google.com/apikey), then configure it:

    ```bash theme={null}
    graphora config init --api-key "YOUR_GEMINI_API_KEY"
    ```

    You can also set it as an environment variable: `export GEMINI_API_KEY="YOUR_KEY"`
  </Step>

  <Step title="Extract a knowledge graph">
    ```bash theme={null}
    graphora extract document.pdf --output graph.json
    ```

    The CLI will process the document and write the extracted graph to `graph.json`. Schema is inferred automatically from your document content.
  </Step>

  <Step title="Explore the output">
    The output is a JSON file containing nodes (entities) and edges (relationships) extracted from your document. You can load it into any graph visualization tool or use it in your application.

    ```bash theme={null}
    # Quick peek at what was extracted
    python -c "
    import json
    graph = json.load(open('graph.json'))
    print(f'Nodes: {len(graph[\"nodes\"])}')
    print(f'Edges: {len(graph[\"edges\"])}')
    for node in graph['nodes'][:5]:
        print(f'  {node[\"labels\"]}: {node[\"properties\"].get(\"name\", node[\"id\"])}')
    "
    ```
  </Step>
</Steps>

For more CLI options, see the [CLI Reference](/platform/cli).

## Next Steps

Once you have seen Graphora in action, you can dive deeper:

<CardGroup cols={2}>
  <Card title="Platform User Guide" icon="map" href="/platform/user-guide">
    Complete walkthrough of the web UI workflow
  </Card>

  <Card title="Schema Copilot" icon="wand-magic-sparkles" href="/platform/schema-copilot">
    Build ontology schemas through natural language chat
  </Card>

  <Card title="Local Development" icon="laptop-code" href="/platform/local-dev">
    Run Graphora locally with zero-config in-memory mode
  </Card>

  <Card title="Python Client Library" icon="python" href="/client/introduction">
    Integrate Graphora into your data pipelines programmatically
  </Card>
</CardGroup>