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settings and tell us where it helps and where it doesn t see you in the pull request queue the post how pull request limits are cutting down the noise appeared first on the github blog 96841 getting more from each token how copilot improves context handling and model routing https github blog ai and ml github copilot getting more from each token how copilot improves context handling and model routing wed 17 jun 2026 19 41 46 0000 https github blog p 96832 how github copilot is making more of each session go toward useful work so your credits go further the post getting more from each token how copilot improves context handling and model routing appeared first on the github blog as copilot takes on more agentic work from planning and editing to debugging reviewing and calling tools across longer sessions efficiency means more than using fewer tokens it means being smarter about how you use them increasing efficiency starts with reducing what copilot has to repeat from turn to turn including context tool definitions and cached state it continues with choosing the right model for the job a quick explanation a focused edit and a complex multi file change should not all be treated the same way we are working on both improving the copilot harness so more of each session goes toward the task itself and expanding auto so copilot can pick the model that fits the work without asking developers to make that choice every time this post focuses on harness improvements in github copilot for vs code and on ongoing work to expand auto across copilot surfaces increased prompt caching and deferred tools in longer github copilot sessions in vs code the harness prepares a lot of recurring information for the model instructions repository context conversation history available tools and the current state of the task some of that context is needed some of it can be cached deferred or loaded only when it becomes relevant two improvements in github copilot for vs code are doing most of the work here prompt caching helps copilot reuse model state for repeated prompt prefixes instead of recomputing the same prefix on every request tool search lets the model load tool definitions on demand instead of sending every full tool schema into context on every turn that matters more as agents use more tools a session may need access to mcp tools terminal commands file operations workspace search and product specific actions loading every full tool definition up front adds fixed cost to each turn even when only a small number of tools are relevant to the task with tool search copilot can keep the available toolset broad while sending less unnecessary tool schema into the model for a deeper technical look at the implementation including prompt caching cache control breakpoints provider specific tool search and how these changes work across long running agentic sessions read the vs code technical deep dive where github copilot auto model selection fits in auto answers a practical question which model is the best fit for this task right now after your first prompt copilot uses task intent and current model health to choose a model that best fits the task different kinds of work like quick explanations focused edits or multi file changes do not all benefit from the same level of reasoning so auto makes that call without requiring you to tune model settings in our evaluations no single model consistently performed best across tasks in many cases a more efficient model reached the same outcome while stronger models mattered most when the task required deeper reasoning auto learns where stronger reasoning improves the result it routes up when the task demands it and stays more efficient when it does not the goal is not to trade quality for cost but to use the model that best fits the work how auto selects the right model auto combines two signals what model is healthy and available right now and what kind of work copilot is being asked to do real time model health a dynamic engine tracks model availability utilization speed error rates and cost a model may be capable of handling a task but that does not mean it is the best choice at that moment auto takes current system conditions into account so copilot can route to a model that is both capable and ready to respond task aware routing with hydra a routing model that considers factors like reasoning depth code complexity debugging difficulty and tool orchestration needs hydra identifies models that can meet the quality bar for the task then chooses the best fit among them figure 1 three hydra operating points illustrate tunability peak exceeds sonnet at 12 9 savings agg balances quality for 72 5 savings figure 2 hydra cons ties openrouter auto on resolution rate 70 8 at 3 3x the savings hydra agg outperforms both azure foundry operating modes taken together these signals let auto avoid a one size fits all approach the point is not to send every task to the biggest model or every task to the cheapest one it is to choose the model that fits the work making auto work in practice getting routing right in evaluations is only part of the problem to make auto useful in real workflows we also had to account for how developers actually use copilot conversations get longer context builds up tasks shift and developers work in many languages cache aware routing switching models on every turn may sound flexible but it can work against efficiency when a conversation stays on the same model the prompt prefix can be cached and reused across turns switching models mid conversation breaks that cache which can cost more than the routing change saves auto avoids that by routing at natural cache boundaries on the first turn when there is no cache to lose and after compaction when copilot summarizes older turns and the prompt prefix resets between those points the selected model stays in place so the cache can keep building routing across languages copilot serves developers around the world so routing has to work in languages other than english we trained the routing model on conversations across 16 language families including cjk european and others in evaluations routing accuracy stayed within four points of the english baseline across language groups with no statistically significant quality gap figure 3 intelligent routing stays within 4 points of english baseline model evaluations across english european cjk and other script families based on a held out evaluation set sampled from production vs code chat telemetry across 19 languages learning when escalation matters instead of labeling tasks as simply easy or hard we trained the router to learn where models actually diverge for each training query responses from a less capable model and a more capable model are scored across quality dimensions the router learns when the stronger model adds value and when a more efficient model can produce an equally good result for context dependent messages in longer agentic sessions the router is trained on complete multi turn conversations including the original user intent recent assistant responses and conversation metadata auto with task intent is expanding auto with task intent is already live in visual studio code github com and mobile it gives copilot more signal about the kind of work you are doing whether that is coding debugging planning or using tools so it can make a better model choice for the task we are continuing to expand that experience across copilot next we are bringing auto with task intent to more surfaces and adding more ways for teams to make auto the default auto with task intent is coming to copilot cli github app and additional ides copilot free and student plans will be simplified to leverage auto as the only model selection option admin controls will let organizations set auto as the default or enforce auto as the only option getting more value from your ai credits copilot is getting more efficient by default but a few habits can help your credits go further start with auto auto is the strong default for many tasks because it chooses a model based on what you are trying to do without making you pick one manually every time keep context focused start a new session when you switch tasks compact long running sessions when needed and mention the files you want copilot to use when you already know where the relevant code lives less unnecessary context means more of the session goes toward the actual work avoid changing models or settings mid session switching models reasoning levels context size or tool configuration can break cache reuse and make copilot rebuild context set up the session the way you want it then keep related work together plan before parallelizing for larger tasks ask copilot to plan first parallel agents can be useful when work can truly be split up but they also consume credits in parallel so use them deliberately use only the tools you need tools and mcp servers are powerful but broad toolsets can add extra context enable what is relevant to the task and turn off what you do not need check out agent finder in github copilot to help streamline your tool usage check your usage your ai usage page shows where credits are going across features and models in copilot cli session level usage can also help you spot expensive patterns while you work for the full guide see how to get more out of your ai credits get started auto model selection is available today across supported copilot experiences to learn more see the auto model selection docs you can also share feedback in copilot discussions we are continuing to make copilot more efficient across the system so more of your credits go toward useful work without requiring you to tune every model choice yourself this post contains contributions from nhu do and aashna garg the post getting more from each token how copilot improves context handling and model routing appeared first on the github blog 96832 what are git worktrees and why should i use them https github blog ai and ml github copilot what are git worktrees and why should i use them tue 16 jun 2026 20 58 54 0000 https github blog p 96793 git worktrees have been around since 2015 but it wasn t until recently they became popular learn what they are how to use them and why you might the post what are git worktrees and why should i use them appeared first on the github blog it seems like the latest hotness in git these days is the concept of worktrees which is kind of funny because they ve been around since 2015 but nevertheless they are cool and you might be wondering why you d use them how they differ from branches and why they are suddenly so popular let s talk about it context switching with branches and stashing let s say you lived in a worktree less world and were working on a ticket and suddenly an urgent bug came to you and you had to switch contexts first you might stash your work git stash wip feature login then you d switch to your main branch and update git checkout main git pull origin main then make a bugfix branch git checkout b hotfix bug then you d fix everything commit and push the branch git add git commit m fix broken submit button git push origin hotfix bug then after merging a pull request you might return back to your computer and pull main and remove the bug branch git checkout main git pull origin main git branch d hotfix bug and then you could go back to the feature you were working on git checkout feature login git stash pop phew where were we the mental overhead of switching around reloading files reinstalling node_modules based on whatever changed and so on is a lot the context switching burden is heavy now this is a basic example but sometimes developers would work around this kind of chaos with doing some more complicated git stash commands or even multiple clones of the same repo i m guilty of that one until worktrees context switching with worktrees with worktrees you never leave your branch and you never stash and your editor context for your original feature stays untouched git worktree add hotfix workspace b hotfix bug main this instantly creates a sibling folder called hotfix workspace and bases it on main and checks out a new branch called hotfix bug now you can open that folder in a new editor window or cd into it and fix the bug your original editor window stays exactly as you left it cd hotfix workspace fix fix fix git add git commit m fix broken submit button git push origin hotfix bug you merge the pull request online just like before and once it s merged you can simply delete the temporary folder cd main project git worktree remove hotfix workspace this is so much smoother worktrees can go beyond the git command line too for example vs code has full worktree support built in you have options and no matter where you work worktrees give you zero risk of stash conflicts there s no editor disruption and you can truly work in parallel so why now for a really long time worktrees were relatively unknown most developers had never heard of them because either git guis didn t support them or treated them as second class citizens or because they just usually followed the known pattern of feature branch then work then pr then merge then repeat now our work as developers has changed ai has made us work in parallel more than we ever have before in the history of software development developers run so many sessions in parallel and code review culture is growing beyond code writing culture agents and humans can do more in parallel with worktrees it s the default mode for the github copilot app and for many other modern tools what s the catch worktrees do solve a whole lot of issues but there s definitely some things to watch out for dependency bloat each worktree folder requires its own copy of your project dependencies if you re running npm install or pip install across multiple of them your computer might get very full very quickly folder management you have to delete the worktree folders to avoid cluttering your parent directory over time apps like the github copilot app do often handle this for you but it s still something you might have to do yourself if you re operating in the terminal yourself global gitignore requirements if you create worktree folders inside your main repo directory you have to manually add them to gitignore to not accidentally track them you can make these worktrees outside of your main repo and many apps do that by default but it s worth noting one branch limits git prevents you from checking out the exact same branch in two different worktrees at the same time to prevent data corruption how do i use worktrees in the github copilot app great question what s awesome is they just work out of the box when you open the app there s a dropdown that asks you where you want to run your new session on the home screen the default is a new worktree then once you kick off a new session you can click the session name at the top of the app and you ll se...