Gaia Python API Docs | dltHub

Gaia is an OpenAI-compatible node API platform that provides chat, embeddings, retrieval, and model info endpoints for running LLM-powered services. The REST API base URL is https://{node_id}.gaia.domains/v1 and all requests require a Bearer token (API key) in the Authorization header.

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Gaia data in under 10 minutes.

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What data can I load from Gaia?

Here are some of the endpoints you can load from Gaia:

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How do I authenticate with the Gaia API?

Authentication uses API keys sent in the Authorization header as a Bearer token. Include header: Authorization: Bearer YOUR_API_KEY_GOES_HERE. API keys are created in the Gaia dashboard after connecting a wallet.

1. Get your credentials

1. Go to https://gaianet.ai and click Launch App.
2. Connect your MetaMask wallet via CONNECT.
3. Open the profile dropdown -> Settings.
4. Under Settings -> Gaia API Keys click Create API Key, provide a name, and save the displayed key securely.

2. Add them to .dlt/secrets.toml

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[sources.gaia_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Gaia API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python gaia_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline gaia_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset gaia_data
The duckdb destination used duckdb:/gaia.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline gaia_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads chat/completions and embeddings from the Gaia API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def gaia_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{node_id}.gaia.domains/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "chat_completions", "endpoint": {"path": "chat/completions", "data_selector": "choices"}},
            {"name": "embeddings", "endpoint": {"path": "embeddings", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="gaia_pipeline",
        destination="duckdb",
        dataset_name="gaia_data",
    )
    load_info = pipeline.run(gaia_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("gaia_pipeline").dataset()
sessions_df = data.chat_completions.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM gaia_data.chat_completions LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("gaia_pipeline").dataset()
data.chat_completions.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load Gaia data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized or missing API key errors, ensure the Authorization header is set to "Authorization: Bearer YOUR_API_KEY" and that the key has been created in the Gaia dashboard. Do not expose API keys in client-side code.

Rate limits and errors

The docs list common HTTP status codes including 400 (Bad Request), 404 (Not Found) and 500 (Internal Server Error). For 500 errors verify the model name and node availability.

Pagination and data selectors

Responses for list endpoints use top-level keys named data, choices, or points per endpoint (see table). For streaming chat responses the API returns incremental SSE chunks with JSON objects (object: "chat.completion.chunk").

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack