Load MemGPT data in Python using dltHub

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

In this guide, we'll set up a complete MemGPT 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 memgpt_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://memgpt.localhost/openai/v1/threads/thread_id/runs/run_id",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "cancel", "submit_tool_outputs"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='memgpt_pipeline',
        destination='duckdb',
        dataset_name='memgpt_data', 
    )
    # Load the data
    load_info = pipeline.run(memgpt_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 memgpt’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Chat Completions: Endpoint for generating chat-based responses and completions.
  • Threads: Manages conversation threads, allowing for organization and retrieval of chat interactions.
  • Assistants: Handles different assistant configurations and settings.
  • Authentication: Manages user authentication and authorization.
  • Admin Organizations: Admin-level access to manage organizational settings and configurations.
  • Admin Users: Admin-level access to manage user accounts and permissions.
  • Jobs: Endpoint for managing background jobs and tasks.
  • Blocks: Handles various content blocks used in chat or responses.
  • Models: Endpoint to access and manage different AI models available.
  • Agents: Manages autonomous agents that can act on behalf of users.
  • Sources: Access and manage external data sources integrated into the system.
  • Tools: Manages various tools available for use within threads or tasks.

You will then debug the MemGPT 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:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install "dlt[workspace]"

    Initialize a dlt pipeline with MemGPT support.

    dlt init dlthub:memgpt duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for MemGPT API, as specified in @memgpt-docs.yaml 
Start with endpoints cancel and submit_tool_outputs and skip incremental loading for now. 
Place the code in memgpt_pipeline.py and name the pipeline memgpt_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 memgpt_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    The bearer token is required to identify the user, and it should be applied in the authorization header.

    To get the appropriate API keys, please visit the original source at https://memgpt.readme.io/reference/api. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python memgpt_pipeline.py

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

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

  5. 📈 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 memgpt_pipeline show

  6. 🐍 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("memgpt_pipeline").dataset()
# get "cancel" table as Pandas frame
data."cancel".df().head()

Extra resources:

Next steps