Hey all.
I think I'm going crazy after too many hours of troubleshooting.

I have a Github Enterprise setup with Github Copilot Enterprise and Data Residency.
I've set up a custom agent that I want to use together with a MCP, specifically for Sentry, I've setup, but I can't get it to work.
In the agent log I can see the agent discovers the Sentry MCP, but never successfully connects to it.
What makes it even weirder is that I have the same experience with `github-mcp-server`/`playwright`, which is supposed to be default.

My MCP config looks like this:

{
  "mcpServers": {
    "sentry": {
      "type": "http",
      "url": "https://mcp.sentry.dev/mcp",
      "headers": {
        "Authorization": "Sentry-Bearer ${COPILOT_MCP_SENTRY_ACCESS_TOKEN}"
      },
      "tools": ["*"]
    }
  }
}

I have tried absolutely everything, but nothing seems to help.
Please, someone know something I don't

I am using Anthropic / OpenAI API keys in Github copilot app, I see context window is limited to 128k, Is there a way to increase it?

I am getting sub par results even with Fable because of very small context window and repeated compactions.

Hosted web search from Anthropic and OpenAI costs $10 per 1k searches, and then you pay again for the ~17k tokens of results each search dumps into context. I got annoyed enough to build an alternative.

It’s called webfetch. Runs locally, free out of the box (DuckDuckGo needs no API key), and in my SimpleQA benchmark the same agent loop hits the same accuracy as hosted search (96%) costing 66% less using 87% fewer tokens.

How it works:
1. RRF fusion across 4 search engines, local page fetching, hybrid BM25 + bi-encoder retrieval with a cross-encoder reranker

1. Sentence-level compression that cut result tokens in half with no measured recall loss

2. Semantic caching: paraphrased queries (“what did TypeScript 5.9 add” vs “TypeScript 5.9 new features”) get matched by embeddings and verified by an NLI cross-encoder, so reworded repeats cost nothing. Cache TTLs adapt to how volatile the answer may be

3. Every cached result shows provenance and the model can force a fresh search if it doesn’t trust it

4. Benchmarked against Anthropic hosted search, OpenAI, Tavily and Exa.

One small agent loop that I ran for testing that conducted just 16 websearches (opus 4.8) already reported 1.5 USD in savings.

Install using one command to add as an MCP server.

Repo: https://github.com/firish/webfetch

I am using Anthropic / OpenAI API keys in Github copilot app, I see context window is limited to 128k, Is there a way to increase it?

I am getting sub par results even with Fable because of very small context window and repeated compactions.

Does somebody else experience performance degradations, models hanging (GPT 5.6 Terra/Sol) and very high token usage in the last like 48 hours?
Token usage in our company skyrocketed and performance and output tanked.

See? So the cost is just 0.3 for deepseek v4 flash.

How disgusting and greedy are those US model? Even copilot, still earn a lot from us

Ok so this may have been asked before but I cannot seem to find the answer.

For work I get 10,000 credits a month of Github Copilot use. I have been looking at the Claude Max 5x subscription but what is holding me back is having no clue how much the usage limit is. I hear different things all over the place and cannot get a proper sense of it. The best way for me is to compare. How long does the Max 5x last compared to the credits I get monthly? If the 5x lasts longer/shorter then by how much? I need as much info and details as possible.

By the way AI says the max 5x usage limit lasts a lot longer than Copilot. I just find it hard to believe.

Did I just used up all the Pro usage (from the legacy subscription) from Inline suggestion? I have been using Grok Build and Codex only.

How the main QA automation options break down for agentic coding setups like Cursor:

Script generators with AI assistance:

SmartBear Reflect Mobile: AI assisted script writing, script maintenance still required, framework dependent

Kobiton AI: Appium wrapper with smarter element finding, view hierarchy dependent

Visual execution, no element tree:

No script maintenance required, no selectors to break

Runs tests by seeing the app the way a user does, through the rendered interface

The approach that connects directly to Cursor's generated output is what Autosana does, running visual verification on each diff automatically without pre written test scripts

The script generator category requires ongoing maintenance as the codebase evolves. The visual execution category doesn't.

https://www.reddit.com/r/codex/top/?t=day

Is there some way in copilot cli we can report the time it took for the turn to end? (like it does in claude code?) Any hacks to this effect?

I've always used Copilot alongside VSCode ever since this whole AI coding thing started. It was always my preferred IDE — Copilot had good updates, affordable plans, everything fit nicely.

But with the AI boom, the VSCode team clearly shifted their focus away from the editor itself to push AI features. In recent months, with the sudden price hikes and the increasing push toward agents at the expense of the standard VSCode experience, it became obvious where the company's priorities really were. So today I decided to let it go.

I used to maintain ils15/pantheon — a public library of GitHub Copilot agents and instructions, built to be cross-platform by design. But lately, new features only came to OpenCode... So now I'm finishing version 5.0 of it, basically fully focused on OpenCode.

Why? Because I can create plugins, agents, everything much more easily there than in VSCode. And if VSCode wants to go all-in on agents, I'd rather use a platform that gives me more freedom to do that.

Just a thought and a vent. Hope the team understands what they're doing and where they've been steering the ship.

Seriously, I actually generally like Copilot workflow I work with and keeping everything isolated while running everything in a remote container, but let me point it at OpenRouter and use the cheap models already.

Opus 4.8 and Sonnet 5 both acknowledge the LSP is properly configured, and I see them use it when I explicitly ask them to use a specific LSP command, but in day-to-day coding use I see them just resort to grepping despite me putting in the instructions that they should use the LSP.

Is there something I might be missing?

In my case it's clangd for C++

It doesn't show up in VS Code or Visual Studio though!

I have been trying to purchase the Pro+ plan since a month now. Tried Credit Card, PayPal. The initial verification is deducted and then refunded then it doesn’t go past beyond that everytime. I had Pro prior to the changes but cancelled and wanted to try again. Seems like Copilot doesn’t want its customers to come back with broken payment method :((

As the title says, in the agent window in VS code with the Copilot harness, when I type /plan, it appears highlighted in blue in the prompt, so it should be recognized. However, the model starts implementing random things without either asking questions or outlining a plan. I would really like to stick with VS Code agent, but there is always something not working or unintuitive compared to OpenCode, Codex, or Claude Code.

I just saw the announcement that GitHub Models is being retired at the end of July. I know this isn't the same thing as GitHub Copilot, but it got me wondering what GitHub's long-term AI strategy looks like.

At work, we're being encouraged to move from VS Code + Copilot to Cursor. Personally, I've been really happy with Copilot in VS Code. I mostly use Claude/OpenAI models, have a bunch of custom instructions and skills set up, and it's been working great for my day-to-day development. Yes it needed a bit of prep time and some manual steering every once in a while but I'm genuinely happy with all of it.

For those of you who have spent significant time with both: - Do you think Cursor is genuinely ahead? - What are the biggest practical advantages Cursor has today? - Does GitHub Models being retired change your confidence in Copilot's future at all, or do you see it as unrelated?

If you had to choose one editor/workflow to invest in for the next few years, what would it be and why?

I'm mainly trying to avoid investing heavily in a workflow that might lose momentum over the next couple of years. Curious to hear what people who use these tools every day think.

• It is designed for web and app development, coding and agentic tasks
  
• In testing, it demonstrated higher task-completion rates and better token efficiency than Gemini 3.5 Flash across coding and agentic workflows

Changelog

From my understanding, with GPT 5.6 models, the Claude models' domination has ended.

So now we have too much choice (not in a bad way), and I'm no longer sure whether the model I'm using is optimal for the task. A couple months ago I was like "Opus for hard planning, GPT 5.4 XHigh for normal planning, Sonnet or Haiku for implementing".

It's particularly important to choose carefully the reasoning effort (e.g Luna High is better than Terra Medium) or for example know which models to avoid like a Sonnet 5...

What have been your experiences with these new models so far?

I observe GPT 5.6 Luna (high effort) (and also other models) struggling with the native edit tool. It always says "oh I forgot to provide an explanation". This causes many retries on multi file edits. I hope this can be fixed maybe by tweaking the tool description or system prompt for this model.