CAMEL-AI Python API Docs | dltHub

CAMEL-AI is a platform and framework for building multi‑agent AI systems and providing an API for connecting data sources, knowledge bases, reference queries and embedding an interactive iframe for data‑driven AI chat. The REST API base URL is https://api.camelai.com and All requests require a Bearer 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 CAMEL-AI data in under 10 minutes.

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What data can I load from CAMEL-AI?

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

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How do I authenticate with the CAMEL-AI API?

Authenticate by creating an API key in the CamelAI web console and include it on every request as the HTTP Authorization header: "Authorization: Bearer YOUR_API_KEY".

1. Get your credentials

1. Sign in to the CamelAI web console. 2) Open "API keys" in the account/settings area. 3) Click "Create New API Key". 4) Copy and securely store the generated key (it is shown once). 5) Use that key in the Authorization header for API requests.

2. Add them to .dlt/secrets.toml

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[sources.camel_ai_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 CAMEL-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 camel_ai_pipeline.py

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

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Pipeline camel_ai_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset camel_ai_data
The duckdb destination used duckdb:/camel_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 camel_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 sources and iframe_create from the CAMEL-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 camel_ai_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.camelai.com",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "sources", "endpoint": {"path": "api/v1/sources/", "data_selector": "results"}},
            {"name": "iframe_create", "endpoint": {"path": "api/v1/iframe/create"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="camel_ai_pipeline",
        destination="duckdb",
        dataset_name="camel_ai_data",
    )
    load_info = pipeline.run(camel_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("camel_ai_pipeline").dataset()
sessions_df = data.sources.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM camel_ai_data.sources LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("camel_ai_pipeline").dataset()
data.sources.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 CAMEL-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 you receive 401/403 responses, check that the Authorization header is present and uses the Bearer scheme: "Authorization: Bearer YOUR_API_KEY". Ensure the API key hasn't expired or been revoked and that you copied it exactly (keys are shown only once in the console).

Pagination

List endpoints (e.g. GET /api/v1/sources/) return a paginated object with keys: count, next, previous, and results. Use the page and page_size query parameters to navigate pages and read records from the "results" array.

Rate limits and server errors

The docs indicate standard HTTP status codes are used. On 429 (rate limit) back off and retry after a delay. For 5xx errors, retry with exponential backoff and contact Camel support if persistent.

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