Decorative header graphic for the PyCharm and Jupyter setup guide

Jupyter in PyCharm + Kumo Coding Agent

Overview

This guide walks you through setting up the KumoSDK and Kumo Coding Agent for use with the KumoRFM Pre-Trained model in Jupyter notebooks inside JetBrains PyCharm and IntelliJ development environments. This guide assumes minimal prior experience with Jupyter, PyCharm or IntelliJ editors, and coding agents. Experienced users may skip directly to the relevant sections.

Prerequisites

Make sure you have:

PyCharm installed (Download) (or any python compatible IntelliJ editor)
Python installed at the system level
Access to a KumoRFM environment, including an API key and any required API URL
Required PyCharm plugins enabled
- Jupyter plugin
- Markdown plugin
These plugins are usually installed and enabled by default, so you may not need to do anything. If needed, you can review, enable, or install plugins from PyCharm -> Settings -> Plugins.

You may also need:

An OpenAI or Anthropic subscription (recommended - required for use with the Kumo Coding Agent)
- OpenAI Codex via JetBrains AI subscription, or OpenAI subscription
- Anthropic Claude Code via JetBrains AI subscription, or Claude subscription
Homebrew (macOS package manager) (Install)
GitHub CLI installed and authenticated (required for cloning coding agent context from GitHub)

🖥 Terminal

brew install gh     # install via Homebrew
gh auth status      # check if authenticated
gh auth login       # authenticate

Part 1: Setup for Jupyter in PyCharm + KumoSDK

Step 1: Create or Open a PyCharm Project

To create a new notebook:

Create a new project
- Select or create a local python environment for the project
  - Project type: Pure Python or Jupyter
  - Environment type: Venv (recommended) or Conda (if the Anaconda distribution is installed)
  - Select a Python version between 3.10 and 3.13

Create a notebook .ipynb file inside your project
Run a Cell (e.g., print("Hello Kumo!") to Start the Jupyter Server and notebook kernel
Install or upgrade ipywidgets

Pycharm will automatically install the required IPython but does not by default install related ipywidgets package, which may lead to downstream warnings 📓 Notebook cell

%pip install --upgrade jupyter ipywidgets

A project-specific interpreter keeps KumoSDK and notebook dependencies separate from your system Python and from unrelated projects.

Step 2: Install KumoSDK

Install the KumoSDK in the same notebook environment: 📓 Notebook cell

%pip install kumoai

Using %pip instead of !pip helps ensure the package installs into the active notebook interpreter.

Verify installation: 📓 Notebook cell

import kumoai.rfm as rfm

print("Kumo SDK loaded successfully")

Step 3: Authenticate KumoSDK

You will need an API key to make calls to KumoRFM. There are two common approaches:

Use the interactive code block below to set KUMO_API_KEY from the notebook
Set KUMO_API_KEY manually with the API key provided for your KumoRFM environment

Interactive authentication: 📓 Notebook cell

import os
import kumoai.rfm as rfm

if not os.environ.get("KUMO_API_KEY"):
    rfm.authenticate()

rfm.init(api_key=os.environ.get("KUMO_API_KEY"))

Manual authentication: 📓 Notebook cell

import os
import kumoai.rfm as rfm

os.environ["KUMO_API_KEY"] = "YOUR_API_KEY_HERE"
rfm.init(api_key=os.environ.get("KUMO_API_KEY"))

Step 4: Optional Dependencies

Install Graphviz:

The KumoSDK allows you to define and visualize a Kumo Graph, which represents data tables and the relationships between them. To enable Kumo Graph visualization with KumoSDK, the Graphviz library must be installed correctly. Installation requires:

dot — an executable that is part of the Graphviz library, installed at the system level
The graphviz Python package installed in the notebook kernel

Check if dot is installed: 📓 Notebook cell

import shutil
print(shutil.which("dot"))

If this returns None, install Graphviz: 🖥 Terminal

brew install graphviz

Install the Python package: 📓 Notebook cell

%pip install graphviz

Test that a simple graph renders: 📓 Notebook cell

from graphviz import Digraph

g = Digraph()
g.edge("Hello", "Kumo")
g

Install Jinja2:

The KumoSDK requires Jinja2, a Python library used to style displayed outputs. 📓 Notebook cell

%pip show Jinja2   # check if Jinja2 is installed
%pip install jinja2  # install

Part 2: Add a Coding Agent (Optional)

Choose one of the following to add an AI coding agent to your workflow:

Flow A — OpenAI Codex
Flow B — Anthropic Claude Code

Step 1: Open JetBrains AI and Select CodexJetBrains documents that Codex is available directly in JetBrains AI chat starting from IDE version 2025.3. Make sure you are using a recent PyCharm version and the latest AI Assistant plugin (enabled by default).To get started:

Open the JetBrains AI widget in the top-right corner of PyCharm
Follow the setup and authentication flow if JetBrains AI subscription or OpenAI subscription has not been enabled yet
Open the AI chat and choose Codex from the agent picker

Step 2: Install Kumo Coding Agent ContextThe Kumo Coding Agent has two parts:

Context (knowledge base): Documentation, PQL rules, workflow guides, and data connector references that teach the agent how to use the Kumo platform. Installed by cloning the repository.
Skills (slash commands): Actions like /kumo-issue and /kumo-pr for reporting bugs and contributing fixes. Installed via the skill installer.

Install the context:🖥 Terminal

cd your-project
git clone https://github.com/kumo-ai/kumo-coding-agent.git kumo-coding-agent

This action adds a directory named kumo-coding-agent to your project. It contains the Kumo Coding Agent’s knowledge base. Confirm that this directory appears in your project.Codex reads AGENTS.md automatically. No extra configuration needed.Install the skills (optional).Inside a Codex session or chat interface, run the following command and approve the requested actions:🤖 Codex

$skill-installer install https://github.com/kumo-ai/kumo-coding-agent

To confirm the Kumo slash-command skills were installed correctly, list the available skills and look for:

/kumo-issue to report an issue to Kumo
/kumo-pr to contribute fixes

🤖 Codex

/skills

Step 3: Use the Kumo Coding AgentTry a real prediction:🤖 Codex

wrap-code

Use the active VS Code notebook with the kumo-coding-agent skill to load the RelBench F1 dataset and use KumoRFM to predict whether each driver will finish in the top 3 in their next race.

The agent will inspect the data, build a graph, and write PQL. You can then run the notebook end-to-end. If needed, authenticate the KumoSDK within the notebook before running the prediction.

Before asking Codex to modify the notebook, save your changes first. If the notebook is not saved, PyCharm may keep your unsaved version as the active notebook view and treat Codex’s edits as the previous file version. If you do not see Codex’s changes, try one of the following:

Use the “Revert File” option in PyCharm to refresh the notebook (Cmd + Shift + P -> Revert File)
Close the notebook tab and reopen it

You are now fully set up with KumoSDK and the Kumo Coding Agent. You can proceed to:

the Kumo Coding Agent Quick Start for coding agent examples
the Kumo SDK Quick Start to get familiar with core SDK functionality

Step 4 (Optional): UpgradeUpgrade the KumoSDK:📓 Notebook cell

%pip install --upgrade kumoai

Upgrade the Kumo Coding Agent:🖥 Terminal

cd kumo-coding-agent && git pull

To pin to a specific version:

cd kumo-coding-agent && git checkout v1.0.0

Step 1: Open Claude Code Alongside PyCharmClaude Code is best used here as a project-aware coding assistant that runs alongside PyCharm rather than inside the notebook UI itself.The simplest pattern looks like this:

Keep the notebook open in PyCharm
Open a terminal in the same project folder
Run Claude Code there

Step 2: Install Kumo Coding AgentThe Kumo Coding Agent has two parts:

Context (knowledge base): Documentation, PQL rules, workflow guides, and data connector references that teach the agent how to use the Kumo platform. Install this by cloning the repository into your project.
Skills (slash commands): Actions like /kumo-issue and /kumo-pr for reporting bugs and contributing fixes. These can be installed separately if you want them.

Install the context:🖥 Terminal

cd your-project
git clone https://github.com/kumo-ai/kumo-coding-agent.git kumo-coding-agent
echo 'Also read kumo-coding-agent/CLAUDE.md for Kumo agent capabilities.' >> CLAUDE.md

This action adds a directory named kumo-coding-agent to your project. It contains the Kumo Coding Agent’s knowledge base. Confirm that this directory appears in your project.The second line tells Claude Code to read the agent’s knowledge base on startup.Install the skills (optional):To run the following command, you will need Node.js installed on your system.🖥 Terminal

npx skills add kumo-ai/kumo-coding-agent --agent claude-code

Step 3: Use Claude Code with the Notebook and ProjectStart with a setup-oriented prompt:✨ Claude

Help me set up this PyCharm Jupyter notebook for KumoSDK and generate a first validation cell.

Then try a fuller prediction workflow:✨ Claude

wrap-code

Use the active PyCharm notebook and the kumo-coding-agent context in this project to load the RelBench F1 dataset and use KumoRFM to predict whether each driver will finish in the top 3 in their next race.

The agent can inspect the notebook and nearby project files, help build a graph, and draft PQL. You can then run the notebook end-to-end in PyCharm. If needed, authenticate the KumoSDK within the notebook before running the prediction.

Before asking Claude to modify the notebook, save your changes first. If the notebook is not saved, PyCharm may keep your unsaved version as the active view and you may not immediately see the agent’s edits. If needed, save the notebook again or close and reopen it before continuing.

You are now fully set up with KumoSDK and the Kumo Coding Agent in PyCharm. You can proceed to:

the Kumo Coding Agent Quick Start for coding agent examples
the Kumo SDK Quick Start to get familiar with core SDK functionality

Step 4 (Optional): UpgradeUpgrade the KumoSDK:📓 Notebook cell

%pip install --upgrade kumoai

Upgrade the Kumo Coding Agent:🖥 Terminal

cd kumo-coding-agent && git pull

To pin to a specific version:

cd kumo-coding-agent && git checkout v1.0.0

Troubleshooting

If the notebook opens but cells do not run, re-check the selected interpreter and whether the Jupyter server started successfully.
If %pip install kumoai succeeds but import kumoai.rfm fails, verify that the notebook is using the same interpreter where the package was installed.
If authentication fails, confirm KUMO_API_KEY is set or repeat rfm.authenticate().
If Codex is not available in PyCharm, verify your PyCharm version, the AI Assistant plugin version, and that Codex appears in the JetBrains AI agent picker.
If Claude Code helps with project files but not notebook behavior, keep notebook execution inside PyCharm and use Claude Code mainly for project-side code and explanations.

Get Started

KumoRFM SDK

KumoRFM Fine-Tuning SDK

KumoRFM Fine-Tuning Platform

Jupyter in PyCharm

Jupyter in PyCharm + Kumo Coding Agent

Overview

Prerequisites

Part 1: Setup for Jupyter in PyCharm + KumoSDK

Step 1: Create or Open a PyCharm Project

Step 2: Install KumoSDK

Step 3: Authenticate KumoSDK

Step 4: Optional Dependencies

Install Graphviz:

Install Jinja2:

Part 2: Add a Coding Agent (Optional)

Troubleshooting

Next Steps

​Jupyter in PyCharm + Kumo Coding Agent

​Overview

​Prerequisites

​Part 1: Setup for Jupyter in PyCharm + KumoSDK

​Step 1: Create or Open a PyCharm Project

​Step 2: Install KumoSDK

​Step 3: Authenticate KumoSDK

​Step 4: Optional Dependencies

​Install Graphviz:

​Install Jinja2:

​Part 2: Add a Coding Agent (Optional)

​Troubleshooting

​Next Steps

Jupyter in PyCharm + Kumo Coding Agent

Overview

Prerequisites

Part 1: Setup for Jupyter in PyCharm + KumoSDK

Step 1: Create or Open a PyCharm Project

Step 2: Install KumoSDK

Step 3: Authenticate KumoSDK

Step 4: Optional Dependencies

Install Graphviz:

Install Jinja2:

Part 2: Add a Coding Agent (Optional)

Troubleshooting

Next Steps