PyCharm, a JetBrains IDE

The only Python IDE you need. Built by @JetBrains, part of @JetBrainsData

[PyCharm, a JetBrains IDE @pycharm]

PyCharm debugging just got a serious upgrade in 2026.1 🚀

Introducing debugpy – Microsoft’s open-source debugger. It’s now available as a debugger backend in PyCharm and offers:

✅ DAP-based communication
✅ Better async debugging
✅ Improved “Attach to Process” support
✅ Stronger alignment with the Python ecosystem

Enable it via:
Settings | Python | Debugger | debugpy

Explore what’s new in PyCharm 2026.1 → https://t.co/1az5wxkpWs

[PyCharm, a JetBrains IDE @pycharm]

At #PyConUS 2026, the PyCharm team brought live demos, AI agent talks, and plenty of coffee to the JetBrains booth! ☕

It was great meeting so many Python developers, sharing new features, and hearing how you use PyCharm every day.

Thanks to everyone who stopped by!

Watch the full tour here → https://t.co/0SAjw0W1mW

[PyCharm, a JetBrains IDE @pycharm]

Before your agent writes code, give it a constitution:

✅ Mission
✅ Tech stack
✅ Roadmap
✅ Rules for what comes next

That’s spec-driven development – turning “build this” into a workflow the agent can follow and you can review.

Watch the full video on #FastAPI + Claude Code + PyCharm, with spec-driven guardrails: ▶️ https://t.co/jltHQwvYYB

[PyCharm, a JetBrains IDE @pycharm]

Building an AI agent? Don’t start by asking “Which framework is best?”
Start by asking “What kind of system am I building?”

Agentic AI frameworks are becoming a core layer for building autonomous apps, but choosing one depends on what you need most:

• Control → LangGraph, OpenAI Agents SDK
• Fast prototyping → LangChain, CrewAI
• Knowledge-heavy agents → LlamaIndex, Haystack
• Enterprise governance → Semantic Kernel
• Lightweight experiments → smolagents
• Tool-heavy workflows → Phidata

Compare the top agentic AI frameworks for 2026 and find the right fit for your project: https://t.co/5N5sl67aUU

[PyCharm, a JetBrains IDE @pycharm]

Looking for a practical AI project that is more than just another classifier?

Build a real-time object detection app with #TensorFlow and PyCharm!

You’ll start with your laptop webcam, use a pretrained SSD MobileNet V2 model, draw live bounding boxes with OpenCV, and validate the pipeline in a PyCharm notebook.

Then, as an optional next step, deploy it to #ReachyMini and turn detections into object tracking, antenna reactions, and a live dashboard.

Read the tutorial: 🔗 https://t.co/N9PJ4V7OTl 🔗

[PyCharm, a JetBrains IDE @pycharm]

AI agent costs don’t always explode overnight.

Sometimes they creep up quietly:

• Extra reasoning steps
• Repeated retrievals
• Unnecessary tool calls
• Verbose prompts
• Slow model responses
• Workflows that take longer than expected

In a demo, this may be invisible.

In production, it becomes a budget and user experience problem.

That’s why cost and latency should be part of agent evaluation from the start – not something teams discover through surprise bills or user complaints.

👉 Read the full guide to learn which production metrics you should monitor: 🔗 https://t.co/XdGJSi18aA 🔗

[PyCharm, a JetBrains IDE @pycharm]

“What should we actually measure?” is where AI agent evaluation often gets messy.

There isn’t one “quality” score.

We break down 8 metrics you can use to make AI agents easier to test, monitor, and improve:

1. Hallucination rate

Does the agent generate claims that are unsupported or factually wrong?
Use it to evaluate factual accuracy and user trust.

2. Toxicity scores

Could the system produce harmful, offensive, biased, or inappropriate content?
Use toxicity checks as a safety guardrail for public-facing agents.

3. RAGAS

For RAG-based systems, check:
• Did it retrieve relevant documents?
• Did it generate an answer grounded in those documents?

4. DeepEval

Use evaluation frameworks to test more than basic accuracy.
DeepEval can help evaluate safety, RAG pipelines, chatbots, agent behavior, and security risks.

5. Task completion rate

Did the agent actually complete the task?
A workflow can fail even if one step succeeds.

6. Tool usage correctness

• Did the agent choose the right tool?
• Did it pass the right parameters?
• Did it use the result correctly?

7. Reasoning quality

Were the steps logical, necessary, and correctly ordered?
A correct answer can still come from a weak process.

8. Cost, latency, and regressions

Track what happens in production:
• Token usage
• Response time
• Cost per interaction
• Changes after model or prompt updates

Different metrics answer different questions. That’s why agent evaluation needs more than one score.

Read the full blog post for more details: 🔗 https://t.co/1SjVURo0L3 🔗

[JetBrains @jetbrains]

Our full conversation with Andrew Kelley, creator of Zig, is out now.

No AI policy, leaving GitHub, a $670K nonprofit, and why there's no 1.0 (yet).

[PyCharm, a JetBrains IDE @pycharm]

Attending PyCon Italy this week? Come say hi 👋

We're sponsoring the event in Bologna on May 28–30:

💬 Find our team onsite to talk about the IDE, Python, or just to chat.
🧠 Take our quiz to test your Python knowledge – look for screens around the venue.
🎨 Grab PyCharm stickers before they're gone.
☕ Enjoy a coffee break (our treat!).

See you there!

#PyConIT #PyConIT26

[PyCharm, a JetBrains IDE @pycharm]

Turn your webcam into a real-time object detection app with TensorFlow and PyCharm.

In this tutorial by @iuliaferoli, you’ll use a laptop webcam and PyCharm notebook to:
1️⃣ Capture webcam frames.
2️⃣ Convert them into TensorFlow tensors.
3️⃣ Run SSD MobileNet V2 inference from TensorFlow Hub.
4️⃣ Filter detections by confidence score.
5️⃣ Draw bounding boxes with OpenCV.
6️⃣ Validate everything locally.

Then you can deploy the same pipeline to #ReachyMini by @huggingface and @pollenrobotics – allowing the robot to follow detected objects, react with its antennas, and show the results on a live dashboard.

Code snippets and the full GitHub repo are provided, so you can follow along or adapt your own project.

➡️ Read the tutorial: https://t.co/nkCTGJkoLj

[PyCharm, a JetBrains IDE @pycharm]

Before you ship an AI agent, don’t just ask: “Does the answer look good?”

Ask: “Can we trust how it got there?”

Read these 7 practical checks before putting an AI agent in front of users:

1. Test the full task

An agent can complete one step and still fail the workflow.
Check whether it reaches the actual end goal.

2. Check tool choice

Did the agent pick the right tool for the job?
Wrong tool = unreliable result.

3. Inspect the inputs

The right tool is not enough.
Check IDs, filters, formats, parameters, and context passed to the tool.

4. Verify groundedness

For RAG-based agents, ask:
Is the answer supported by the retrieved sources?
Plausible ≠ grounded.

5. Trace the reasoning path

A correct answer can hide bad decision-making.
Log the steps, tool calls, retrieved data, and intermediate outputs.

6. Monitor cost and latency

Agents can get expensive quickly.
Track token usage, response time, and cost per interaction.

7. Watch for regressions

- New model?
- New prompt?
- Updated workflow?

Re-test completion rate, groundedness, latency, and cost.

Want the full guide? 👉 Read the blog post for more details: https://t.co/1SjVURo0L3

[PyCharm, a JetBrains IDE @pycharm]

What do you enjoy most about coding?

For Johannes Rüschel, it’s seeing a problem being solved.

He also enjoys that software is never really finished. There’s always an opportunity to improve the design, handle new edge cases, or rethink a solution.

Watch the full interview about building better developer tools: 🔗 https://t.co/Gm4YAbf2xp 🔗

[PyCharm, a JetBrains IDE @pycharm]

LLMs don’t just need text. They need context.

Parsing PDFs isn’t just extracting words. It’s capturing structure, sidebars, and relationships between sections.

That’s where many AI systems still fall short.

▶️ Watch our Python conference recap: https://t.co/PUTuRFRXl8

[PyCharm, a JetBrains IDE @pycharm]

Python monorepos can quickly turn into absolute dependency chaos.

Here are five ways PyCharm makes working with uv workspaces easier:

1️⃣ Detects when you open a workspace
2️⃣ Sets up or detects the workspace virtual environment
3️⃣ Syncs dependencies directly from pyproject.toml
4️⃣ Supports navigation and refactoring across workspace members
5️⃣ Runs and debugs projects using the workspace interpreter

It works with #uv, #Hatch, and #Poetry workspaces, too!

👉 Watch the full video to see the full workflow in action: https://t.co/qZWp4BH64g

[PyCharm, a JetBrains IDE @pycharm]

“Coding is only a means to an end.”

Johannes Rüschel – software engineer, technical coach, open-source contributor, and longtime PyCharm power user – shared his perspectives on software engineering with us.

▶️ Watch the full conversation: 🔗 https://t.co/hl6XE6oPtr 🔗

[PyCharm, a JetBrains IDE @pycharm]

You can now enable Pyrefly as an external type provider in PyCharm to increase the speed of code insight features. Engineered in Rust by Meta, this next generation type checker is built for high performance analysis of large scale Python codebases.

Delegating analysis to this engine delivers immediate feedback for type inference, diagnostics, and inlay hints. To switch, select Pyrefly from the ‘Type’ widget at the bottom of your window – PyCharm will handle the installation automatically.

Read the blog: https://t.co/xozLeypn52

#Python #PyCharm #Rust #Meta #LSP

[PyCharm, a JetBrains IDE @pycharm]

AI agents are harder to evaluate than regular LLMs because they don’t just generate answers – they make decisions.

Before putting an agent into production, ask these questions:

• Can it complete the full task, not just one step?
• Does it choose the right tools and APIs?
• Does it pass the right parameters?
• Is the final answer grounded in the source data?
• Can you trace how it reached the result?
• Are cost and latency acceptable at scale?
• Do you have monitoring in place for regressions after updates?

LLM evaluation helps you test whether the agent works.

AI observability helps you see whether it is working once real users, edge cases, and production costs appear.

Read the full blog post by Naa Ashiorkor (@ashiorkornortey) for more details: 🔗 https://t.co/1SjVURo0L3 🔗

[PyCharm, a JetBrains IDE @pycharm]

The logic for Poetry and Hatch workspaces follows this exact same workflow. PyCharm detects projects via their pyproject.toml files and manages the environments with the same automated precision.

The only minor difference is in tool selection – the suggested environment tool is determined by what you have specified in your pyproject.toml. If no tool is specified, PyCharm will prioritize uv (if installed) or a standard virtual environment to get you up and running quickly.

This Beta version of the functionality is just the beginning of our focus on supporting complex workspace structures. We are already working on expanding the UI to allow creating new projects, linking dependencies, and activating the terminal for specific projects.

Watch the deep dive by Paul Everitt to see exactly how it works: https://t.co/qZWp4BH64g 👨‍🔧

[PyCharm, a JetBrains IDE @pycharm]

Python monorepos can break your flow fast.

Managing multiple virtual environments and drifting dependencies across interdependent projects is a constant headache. PyCharm now solves this with built-in support for uv, Poetry, and Hatch workspaces (Beta). 🧵👇

[PyCharm, a JetBrains IDE @pycharm]

While PyCharm automates the backend execution of commands – such as uv sync --all-packages – it still remains fully transparent.

You can track all executed commands and their live output in the Python Process Output tool window. If synchronization fails for an environment, you can analyze the specific error logs to quickly identify the root cause.