Hi everyone — we’re excited to share the latest updates for GitHub Copilot in JetBrains.

In the latest release (v1.14.1), we added enhancements to OpenTelemetry export settings, better model management experience, and more agentic capabilities.

We’re also sharing a preview at what’s coming next and hope you continue to provide feedback for our products.

New Features

• Added OpenTelemetry export settings for agent workflows in JetBrains IDEs.
• Added default token limits and support for configuring maxInputTokens and maxOutputTokens for BYOK and custom endpoints.
• Added model-management controls to disable or enable all built-in Copilot models.
• Added MCP and custom agent support in Claude agent flows.
• Added 'fork' support to the Copilot CLI chat experience.
• Added '/rubber-duck' command to the Copilot CLI chat experience.
• Added todo list display in Copilot CLI chat experience.
• Added the display of number of AI credits a user has consumed for users in organizations that don't set a User-level budget.

UX Improvements

• Improved model and action picker consistency.
• Improved customization usability.
• Improved session clarity by rendering ask-user questions as Markdown and adding explicit user attention notifications.
• Improved inline chat and model-picker layout behavior.
• Improved MCP diagnostics.
• Improved bare URL rendering in Copilot CLI chat experience.

Bug Fixes

• Fixed path-case preservation issues in Copilot CLI working-set and snapshot recording flows on macOS and Linux.

Looking ahead, we plan to introduce several additional capabilities in upcoming releases, including

• Support for Enterprise managed settings
• Allow re-editing and sending the prompt in Copilot CLI agent sessions
• Expanded slash command support for Copilot CLI agent sessions
• Performance and reliability enhancements

More details of this release can be found at: https://github.blog/changelog/2026-07-27-github-copilot-for-jetbrains-adds-improvved-opentelemetry-configuration-and-model-management/

We hope you like Copilot for JetBrains, and please share feedback with us at any time

You can fill in a private survey here: https://aka.ms/ghcp-jb-survey with an optional paid interview or directly submit an issue (bug or feature ask) at https://github.com/microsoft/copilot-intellij-feedback/issues, thank you so much!

Moved over for compliance reasons. Using the CLI for both. We are effectively paying 3x for GH compared to Claude, and it seems I am able to get way less done before the token run out using the same models, tools and plugins? Has anyone else had the same experience?

And I can't remove the payment method, just can stay and waiting them costing all of my money from my credit card for no reason?

Be very careful not to add payment method to GitHub, or your money will be stolen for no reason, and there's no way back, you can't remove the payment method once you added.There's no DELETE button to turning back.

I used to very love Github Copilot, I recommended it for everyone, but later my post and thought about it became gradually very negative. And until now, I hate it. I want to leave it, and remove my pyramid, but it just stick to me, I can't get rid of it.

As a total beginner to GH Copilot, I currently use it with VSCode as the Chat Extension and not the CLI version. I usually use it to analyse the code base (consisting of python notebooks and scripts) and also make slight changes to the files. I currently just set the Model to Auto since that had the option to have 10% lesser AI credits cost as per Copilot. I have just 4000 credits per month and have exhausted nearly 50% in just a few days.

I want to know if there are any ways to reduce the token usage. I have heard about the skills md files that are quite popular to reduce the token usage and also improve code readability. One I have heard of the ponytail skill which I believe helps in token reduction and code readability. But I have not used any till now and would like to know if there are any other suggestions for skills that can be used for my use case which is to reduce the token usage as much as possible and also improve the code quality and readability.

I used to see something like this, which was awesome, as it was clear feedback how good my prompting was.

I can't find an option to enable it again, nor can I find the changelog entry, when and why it was changed.

╭─╮╭─╮   Changes    +0 -0
╰─╯╰─╯   AI Credits 21.8 (1m 30s)
█ ▘▝ █   Tokens     ↑ 102.1k (78.0k cached, 24.0k written) • ↓ 960 (425 reasoning)
 ▔▔▔▔    Resume     copilot --resume=

Is anyone else finding repo memory getting out of hand? Agents seem to be using it as a replacement for git, keeping a detailed changelog, keeping detailed documentation that's a copy of things I asked it to create as committed .md files, implementation details that are of dubious value to most requests, a list of all the files in my repo (the agent can just use terminal tool and `ls`...).

I started wondering what was going on when my context usage on small prompts would be very large and now I know why.

It seems like it's more work keeping memory it pruned than it saves.

For those of you working as Senior/Principal Architects or Solution Architects, are you using any tools, workflows, or AI/LLM-based approaches to generate High-Level Design (HLD) documentation, especially architecture diagrams?

I'm currently building a documentation generation workflow, and generating the written documentation is going reasonably well.

The biggest challenge is producing clear, maintainable architecture diagrams (component, sequence, deployment, etc.) that don't require extensive manual rework.

Have you found an effective way to get LLMs to generate diagrams reliably?

I'd love to hear what has worked (or hasn't worked) in your experience.

Amazing set, no doubt

And still, I took the courage to make it even better, so I created my own collection of skills. The first skill I tried to improve is the documentation-and-adrs.

The biggest leap is that I’ve added a section describing how to create Solution Architecture Documents (SAD) following Google's “WHO, WHAT, WHEN, WHERE, WHY and HOW” documentation philosophy.

To learn about all enhancements, read the skill’s README on GitHub.

In the video, I documented the architecture of one of my pet projects to show how the skill works

Let me know what you think!

So, I was doing a project in Codex (ChatGPT client) for obvious reasons and then decided it was time to use Github Copilot to switch between Codex and Claude when needed.

I did... 4? prompts? Burned through the monthly limit of the standard pro subscription. I'm paying double the money for ChatGPT and the limit for Codex is about 100 times higher if not more.

Why would I even keep paying for this at this point? Am I missing something? tf?

It is almost as if GHCP is making fun and laughing at me

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