PyCharm for AI Engineers

AI Toolkit. Everything AI Engineers Need – Natively in PyCharm.

Build, experiment, debug, fine-tune, evaluate, and deploy AI systems – all inside PyCharm.

Why Use the AI Toolkit in PyCharm?

Integrated AI dev tooling

No more jumping between notebooks, dashboards, cloud UIs, and scripts. The AI Toolkit integrates AI development directly into PyCharm, bringing your AI workflows into one familiar environment and helping you iterate faster and stay focused.

AI best practices without overhead

The AI Toolkit brings machine learning best practices into the software development workflow.

No need for pre-built datasets or complex infrastructure – evaluate and improve your AI system as you build, using the data and context you already have.

Full AI workflow coverage

The AI Toolkit supports every stage of your workflow from writing your first AI-powered feature to moving into production. Share prototypes with your team, gather feedback, debug, and evaluate quality easily.

AI Toolkit Features

AI Playground

bundled in Pro

Experiment with system prompts and model behavior. Adjust parameters like creativity and randomness, and compare multiple configurations side by side – all before integrating them into your project.

Choose between:

Models included in your JetBrains AI subscription (experimental)
Custom providers via API keys: OpenAI, Gemini, Anthropic, and Mistral
Locally running models: Ollama

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AI Agents Debugger

Pro

Debug, inspect, and optimize agent-based AI systems effortlessly, all from within your IDE. Get visibility into agent decisions, actions, and workflows to refine them efficiently.

The AI Agents Debugger breaks open the black box of agentic systems, revealing each agent node's thought processes, metadata, inputs, and outputs.

Download the plugin

AI Toolkit Roadmap

Share your feedback to shape the future of AI development tooling in PyCharm.

Evaluation

Turn manual tests into continuous insights. Capture evaluation data as you develop, transforming one-off tests into structured, repeatable benchmarks.

Fine tuning

Create custom versions of open-source foundation models using your own data. Improve model accuracy, relevance, and alignment for your specific tasks or domains.

Prototyping and deployment

Turn prototypes into shareable apps without switching tools or rewriting code. Deploy agents, pipelines, and AI-powered apps directly from PyCharm.

Data collection

Collect logs, user feedback, and real-world interactions from your deployed AI systems. Use it to improve your AI workflows or prepare datasets for fine tuning.

Share your feedback

Take the survey

Why PyCharm for AI Engineers?

PyCharm provides powerful tools to speed up and streamline your AI workflows.

Fine-tuning LLMs
Cadence is a PyCharm plugin that combines the comfort of local development with the power of cloud computing, making it easy to scale and optimize ML projects. It simplifies the training and execution of ML models by enabling code to run as-is, automating data syncs, and providing on-demand access to a variety of GPUs – all directly from inside PyCharm. Watch how to fine-tune your LLM.
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AI-powered Jupyter notebooks
Easily work in PyCharm with both local and remote notebooks using Python or a no-code approach. Speed up development with AI-assisted code completion, smart refactorings, and seamless navigation. Generate code with AI cells, convert notebooks to Python scripts in one click, and enjoy full IDE support.
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Hugging Face integration
PyCharm suggests relevant pre-trained Hugging Face models that best suit your needs in Jupyter notebooks and Python scripts. Use and manage models, access documentation, and explore datasets as interactive dataframes – all without leaving the IDE.
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Databricks integration
Enhance your data intelligence platform with a powerful Python IDE. Connect to your cluster, write and run code in the cloud without extra setup, and monitor execution directly in the IDE. Run Python scripts or Jupyter notebooks as Databricks workflows and sync project files seamlessly with your Databricks workspace.
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