Load CAMEL-AI data in Python using dltHub

Build a CAMEL-AI-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

Last updated: Feb 11, 2026

In this guide, we'll set up a complete CAMEL data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def camel_ai_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://camel-ai.org/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            "add", "news", "crawl"
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='camel_ai_pipeline',
        destination='duckdb',
        dataset_name='camel_ai_data', 
    )
    # Load the data
    load_info = pipeline.run(camel_ai_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

Accelerate pipeline development with AI-native context
Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
Build Python notebooks for end users of your data
Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from camel_ai’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

Agent Management: Create and manage various AI agents like ChatAgent, TaskAgent, and specialized agents for different purposes
Memory Operations: Store, retrieve, and manage conversation history and vector-based memory for agents
Task Management: Handle task creation, decomposition, assignment, and lifecycle management with hierarchical structures
Tool Integration: Access various toolkits including search, math, browser automation, and MCP protocol tools
Model Operations: Interface with multiple AI model backends and embedding services
Data Processing: Extract, crawl, and process various file formats and web content
Graph Operations: Interact with knowledge graphs and vector databases for semantic search

You will then debug the CAMEL pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

We suggest using a model like Claude 3.7 Sonnet or better
Index the REST API Source tutorial: https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api/ and add it to context as @dlt rest api
Learn more about our LLM native workflow

Now you're ready to get started!

⚙️ Set up dlt Workspace

Install dlt with duckdb support:
```
pip install "dlt[workspace]"
```
Initialize a dlt pipeline with CAMEL support.
```
dlt init dlthub:camel_ai duckdb
```
The init command will setup the necessary files and folders for the next step.

🤠 Start LLM-assisted coding

Here’s a prompt to get you started:

Prompt
Please generate a REST API Source for CAMEL API, as specified in @camel_ai-docs.yaml 
Start with endpoints add and news and skip incremental loading for now. 
Place the code in camel_ai_pipeline.py and name the pipeline camel_ai_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python camel_ai_pipeline.py and await further instructions.

🔒 Set up credentials

CAMEL uses API key authentication that can be configured via environment variables or .env file. The primary authentication is through OPENAI_API_KEY, though it supports multiple model backends. For Azure deployments, additional API_BASE_URL configuration is required alongside the API key.

To get the appropriate API keys, please visit the original source at https://camel-ai.org/. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

🏃‍♀️ Run the pipeline in the Python terminal in Cursor


python camel_ai_pipeline.py

If your pipeline runs correctly, you’ll see something like the following:


Pipeline camel_ai 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

📈 Debug your pipeline and data with the Pipeline Dashboard

Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:
- Pipeline overview: State, load metrics
- Data’s schema: tables, columns, types, hints
- You can query the data itself
```
dlt pipeline camel_ai_pipeline show
```
🐍 Build a Notebook with data explorations and reports

With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.
```
import dlt

data = dlt.pipeline("camel_ai_pipeline").dataset()
# get d table as Pandas frame
data.d.df().head()
```

Running into errors?

CAMEL has several important considerations: it supports multiple model backends but requires proper API key configuration for each, memory management needs careful consideration for conversation history limits, safe mode is enabled by default which restricts dangerous operations, Docker installation is required for certain interpreters, and the framework maintains session state across operations which can affect performance in distributed environments.

Extra resources:

Learn more with our 1h LLM-assisted coding course!

Next steps

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