Exa AI Python API Docs | dltHub

Exa AI is an internet-scale search and content extraction API that performs embeddings-based and traditional web search, returns search results, page contents, summaries, and highlights. The REST API base URL is https://api.exa.ai and API key required in request headers (x-api-key or Authorization: Bearer).

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 Exa AI data in under 10 minutes.

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What data can I load from Exa AI?

Here are some of the endpoints you can load from Exa AI:

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How do I authenticate with the Exa AI API?

Exa requires an API key. Provide the key in the x-api-key HTTP header or in the Authorization header using the Bearer scheme. Content-Type: application/json is required for JSON bodies.

1. Get your credentials

1. Sign in to the Exa Dashboard at https://dashboard.exa.ai. 2) Open the API Keys section (https://dashboard.exa.ai/api-keys). 3) Create or copy an existing API key. 4) Store it securely and pass it in requests as x-api-key or Authorization: Bearer .

2. Add them to .dlt/secrets.toml

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[sources.exa_ai_source]
api_key = "your_exa_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 Exa AI 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 exa_ai_pipeline.py

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

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Pipeline exa_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset exa_ai_data
The duckdb destination used duckdb:/exa_ai.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 exa_ai_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 search and contents from the Exa AI 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 exa_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.exa.ai",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "search", "data_selector": "results"}},
            {"name": "contents", "endpoint": {"path": "contents", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="exa_ai_pipeline",
        destination="duckdb",
        dataset_name="exa_ai_data",
    )
    load_info = pipeline.run(exa_ai_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("exa_ai_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM exa_ai_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("exa_ai_pipeline").dataset()
data.search.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 Exa AI 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 the API key is missing or invalid, requests return 401 Unauthorized. Ensure the x-api-key header contains a valid key or use Authorization: Bearer . Confirm the key from https://dashboard.exa.ai/api-keys.

Rate limits and costs

The OpenAPI spec documents cost fields (costDollars) per result and mentions usage-based pricing; although specific rate limits are not published in the reference, respect exponential backoff on 429 responses and check dashboard for account-specific limits.

Contents fetch errors and per-URL statuses

The /contents response contains a "statuses" array with entries: id, status (success|error), and error object when present. Error tags include: CRAWL_NOT_FOUND, CRAWL_TIMEOUT, CRAWL_LIVECRAWL_TIMEOUT, SOURCE_NOT_AVAILABLE, UNSUPPORTED_URL, CRAWL_UNKNOWN_ERROR. Use id to map failures to requested URLs.

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