Microsoft has released Visual Studio Code 1.130, an update to the code editor that brings several improvements to the Agents window, along with enhancements to the agent host and the terminal.
With the new release, the Agents window gets updates that make it easier to review changes and manage chats. File-level diff statistics help users assess the size of each file’s changes when scanning a multi-file diff. The window also gets a more compact multi-file diff that makes it easier to review changes, Microsoft said. The Agents window is a dedicated window in VS Code that lets users run and track multiple agent sessions in parallel across their projects, without opening each workspace in a separate window.
Also with VS Code 1.130, assisted permissions for agent tool calls are available in the agent host. With assisted permissions, the LLM evaluates the risk of each tool call and decides whether the tool can run or should require the user’s approval. The setting to enable assisted permissions is chat.assistedPermissions.enabled. In another agent host improvement, quick chats running on the agent host now use compact, single-line rows in the sessions list. Regular sessions retain a second line with change statistics, status, and timestamps.
VS Code users now can open file links from Git diff output in the terminal when Git’s diff.mnemonicPrefix option is enabled. VS Code recognizes prefixes such as i/ for the index and w/ for the working tree, and removes the prefix from the link target so the correct file opens. When mnemonic prefixes are enabled, VS Code also recognizes the numeric prefixes produced by git diff --no-index.
Timestamps for chat requests and responses now are displayed when users hover over the message toolbar. You can disable this through the chat.verbose setting.
Microsoft has released Visual Studio Code 1.129, an update to its free, popular code editor that features a dedicated agent host and an editor panel in the Agents window. The updated editor also allows users to run terminal commands from chat prompts with a !prefix and offers a preview of a modern UI for the workbench.
Microsoft is rearchitecting how agent sessions work in VS Code around the agent host — a dedicated process that runs agent harnesses such as Claude, Copilot, and Codex, based on the Agent Host Protocol (AHP). Since a session lives in its own process, the same session can be connected to and rendered from multiple VS Code windows at once. The agent host’s Copilot agent is powered by the Copilot SDK, which means that its behavior and functionality is aligned with the Copilot CLI, the standalone GitHub Copilot app, and other Copilot products. The agent host is being rolled out to users in the editor window and the Agents window. It’s enabled through the chat.agentHost.enabled setting.
The Agents window shows conversation with an agent next to a detail area for the files and changes it produces. This release introduces a redesigned editor panel that brings the editor and the detail area together into one docked pane with a shared tab bar, so reviewing an agent’s work feels like working in the main editor instead of switching between separate panels.
Other new features and improvements in VS Code 1.129:
Users now can prefix chat messages with a ! to run their contents as terminal commands. This works in agent host sessions, both in the editor and in the Agents window.
Developers can now use Bring Your Own Key (BYOK) models in the Agents window when selecting the Copilot harness running on the agent host.
Developers can preview a modernized VS Code UI that updates the look and feel of the editor workbench. This is currently an experimental feature that can be enabled with the workbench.experimental.modernUIsetting.
VS Code 1.129 follows Visual Studio Code 1.128, released July 13. An update, VS Code 1.129.1, fixes three bugs including remote agent hosts failing to start.
Visual Studio Code 1.128, the most recent edition of Microsoft’s code editor, adds support for multi-chat agent sessions using Anthropic Claude agents.
Introduced July 8, VS Code 1.28 can be downloaded for Windows, Linux, or Mac from code.visualstudio.com.
With VS Code 1.28, multi-chat agent sessions now support using Anthropic Claude agents. With multiple chats, a session can contain related chats, allowing users to compare approaches, branch from an earlier turn, and run work in parallel. Users can add chats, fork a chat from an existing turn, switch between peer chats, and send turns concurrently. Each chat keeps its own history, title, and model selection, and restores with the parent session after restart, Microsoft said.
VS Code 1.28 also makes Copilot Vision generally available. With Copilot Vision, VS Code users can attach images and PDFs by pasting them into chat, dragging and dropping them, or using the context menu. Images also may be read by the agent via a tool call, Microsoft said.
Also in VS Code 1.28:
Quick chats allow users to ask a question in the Agents window without opening a workspace first.
VS Code can contribute OS-level keyboard shortcuts that take effect even if VS Code is not in focus. Users can add systemWide to a keybinding definition in keybindings.json to make it OS-level.
Developers can configure a location for browser tabs to open via a new setting, workbench.browser.newTabPlacement. The setting provides three options: to open browser tabs in the active editor group, in a dedicated group to the side, or in a dedicated auxiliary window.
VS Code can contribute OS-level keyboard shortcuts. These shortcuts take effect even if VS Code is not in focus.
Administrators can configure where GitHub Copilot sends OpenTelemetry (OTel) data, allowing organizations to enforce telemetry flows to an approved collector without each developer setting OTEL_* environment variables. The managed configuration applies to both the Copilot Chat extension and the agent host process.
Microsoft has been pushing hard to make Visual Studio Code a major way to consume its AI services, mostly in the form of GitHub Copilot. GitHub Copilot’s deep integration with VS Code brings many conveniences — inline autocomplete, for instance — but it’s frustrating for those, like me, who would rather use another model provider, or even a locally hosted LLM, for those functions.
Visual Studio Code 1.122 introduced a new feature, “Use BYOK [Bring Your Own Key] without a GitHub sign-in,” that allows you to “use chat, tools, and MCP servers in air-gapped or restricted environments where GitHub sign-in isn’t possible.” More importantly, it “enables fully offline workflows with local models like Ollama.”
In other words, you can now use locally hosted LLMs for chat, tools, and Model Context Protocol servers inside Visual Studio Code. The one thing you still can’t do is use a local LLM for inline and next-edit suggestions — at least, not without additional tooling.
Choosing a model for BYOK mode
If you want to use a local LLM with VS Code’s bring-your-own-model system, the first thing you need is a way to host the model. VS Code lacks a model-hosting mechanism of its own, although it’s conceivable that a VS Code extension may offer something like that in the future. That said, hosting models is complicated enough that a dedicated app is really needed for the job.
One easy way to host models is via a product like LM Studio, a convenient GUI for standing up, serving, and managing LLMs on one’s own hardware. The model host does not have to be the same system you run VS Code on, either. It can be on a server box you control, or on a cloud instance.
The choice of model is also important. Many models are powerful but won’t run well on commodity hardware because they’re simply too big. A good rule of thumb is to choose a model that fits into existing VRAM, along with the memory needed for a sizable token context (the more, the better). Also, the model should be suited to coding and development work. Some models in this vein that fit comfortably into 8GB VRAM include:
Once you have a model up and running, you can integrate it with Visual Studio Code. If you’ve disabled VS Code’s AI features, you will need to turn them on. Make sure the setting chat.disableAIFeatures is turned off. You can find it in Settings | Chat | Miscellaneous.
Third-party language models are managed through Visual Studio Code’s language model list. Press Ctrl-Shift-P and type Manage Language Models to open the list of existing language models.
Managing VS Code’s AI language model list. The models available by default are only models available as external APIs, not models that run locally.
Foundry
First you will see a list of the built-in models, which are all externally hosted. To add a new model, select Add Models at the top right and select Custom Endpoint.
You’ll then get a series of prompts:
Group Name: This is “Custom Endpoint” by default, but you can choose any name you want. The name is strictly for organizing the model list and doesn’t affect things like model recognition or connectivity.
API Key: If you’ve configured LM Studio to use an API key for serving models, provide it here. If you’re hosting the model locally and you haven’t explicitly set up API keys, you can leave this blank.
API Type: The options here are Chat Completions, Responses, and Messages. Most of the time you’ll want to use Responses, as it’s the most general-purpose option of the three.
Once you finish providing those answers, you’ll be dropped into a modal editor for a JSON file that holds the details about the endpoint you’re configuring.
A newly created custom endpoint for a locally hosted model. The ID, name, and URL still need to be defined for this model to be useful.
Foundry
You’ll need to provide a few more details by typing them into the labeled fields:
id: A text field that uniquely identifies this particular entry. The choice of ID is pretty much arbitrary; if you’re using only a single model, the ID could be the model name.
name: The name of the model that is used to identify it on the model server. In LM Studio, you can get this name by clicking on My Models in the main interface, then selecting the three-dot icon for the model in question and clicking Copy Default Identifier. For Qwen 2.5, for instance, name might be something like qwen2.5-coder-7b-instruct.
url: The URL to the server’s endpoint. On LM Studio, this defaults to something like http://127.0.0.1:1234/v1. The /v1 at the end is important because that endpoint is used for autodiscovery of models and their capabilities.
The other fields generally don’t need editing. Most models have tool calling functionality. If you know for a fact that the model you’re using doesn’t have vision support, then set vision to false.
Once you have these fields filled in, you can close the modal editor to save the changes. If you reload the Manage Language Models page, you’ll now see your new endpoint:
A newly created local endpoint. The choice of name and group is arbitrary. “Custom Endpoint” is the default name for a newly created group of endpoints.
Foundry
You should now be able to launch the chat window and use the defined model for conversation and utilities:
Conversing with the local model using VS Code’s chat window. Note the selected code block in the left pane that is being used as the context for the conversation.
Foundry
One current, and major, limitation of Visual Studio Code’s BYOK functionality is that it only works for chat and utility tasks. It doesn’t allow you to use a local model for inline suggestions or code completions. The only way to take advantage of local models for expanded functionality with VS Code is to use a third-party tool like Continue.
It isn’t clear if Microsoft will eventually lift this restriction. GitHub Copilot integration in VS Code is a large part of how Copilot as a service reaches its target audience. For the time being, you can certainly use third-party and local models for a significant part of your AI-assisted development work in VS Code, and you can close the functionality gap with additional tooling.
Login
