What Intelligent Terminal With a Local LLM Gives You
Microsoft’s Intelligent Terminal with a local LLM is an experimental fork of Windows Terminal that adds an AI pane powered by any compatible agent, including open‑source models running on your own hardware instead of remote Copilot services, so you can keep shell context on‑device while still getting code help, error analysis, and command suggestions in real time.
Think of this as the same terminal you know, plus an extra panel that understands your commands and errors and can respond like an assistant who lives in your shell. Intelligent Terminal installs alongside the regular terminal and does not replace it, so you can opt in only if you want AI help. It ships with GitHub Copilot as the default agent, but the important detail is that it is not locked to Copilot at all. You can point it at other agents and models, including local ones that run through tools such as llama.cpp or Ollama. This is ideal if you want a Copilot alternative terminal that keeps source code, logs, and scripts away from cloud services while still adding AI features.
Why Local Models and ACP Make This Different
Under the hood, Intelligent Terminal uses the Agent Client Protocol (ACP), an open standard that connects the terminal to any compatible AI agent over standard input and output. In practice, the terminal does not care which large language model you use; it cares only that something on the other side of the ACP pipe can read shell context and send back responses. If you want Copilot, you can use Copilot; if you want Claude Code or Gemini, those are available too. And if you want a local model running on your own GPU with an open‑source agent harness, that is equally possible.
This local LLM approach matters for privacy. One author who spends a lot of time wiring AI agents into their home lab explains that they “refuse to give those agents long‑standing access to my file system,” preferring setups where data stays on the machine. Running an Intelligent Terminal local LLM fits that mindset: your prompts, command history, and project files do not have to leave your network. Instead of enabling a system‑wide agent that asks for broad read and write access to folders such as Documents, Downloads, Desktop, Music, Pictures, and Videos, you confine the AI to your terminal workflow and the files you explicitly expose to it.
Step‑by‑Step: Microsoft Terminal AI Setup With a Local LLM
You do not need deep AI knowledge to run local language models in Intelligent Terminal. In one test, getting a Qwen 3.6 27B model wired in took about five minutes from a clean install. Below is the same flow, broken into clear steps so you can follow it like you would from a friend sitting next to you.
- Install Intelligent Terminal alongside your existing Windows Terminal so you can experiment without touching your main setup.
- Launch Intelligent Terminal; when it offers GitHub Copilot via WinGet, skip it if you plan to use a local LLM as your Copilot alternative terminal.
- Install Pi, the open‑source coding agent that can talk to local models through tools like llama.cpp or Ollama, and add the pi-acp extension so it can speak ACP.
- Open Intelligent Terminal’s settings and switch the active agent in the dropdown from the default Copilot entry to pi-acp so the terminal talks to Pi instead.
- Edit Pi’s models.json and set the default Pi model to your local or self‑hosted model, for example Qwen 3.6 27B served from your home server through llama.cpp.
- Restart or reload Intelligent Terminal, then run a few real shell tasks so the agent pane can see errors and context and confirm that responses are coming from your local language model.
That setup was described as straightforward: install the pi-acp extension, point Intelligent Terminal’s agent setting at it, and select Qwen 3.6 27B in models.json, with the model running on a home server through llama.cpp. Once those pieces were in place, the result was a Microsoft terminal talking to a community‑built ACP bridge, driving an open‑source coding agent, backed by a model from Alibaba, side by side with regular shell work.
What It’s Like to Run Commands With a Local LLM
Once you have the Intelligent Terminal local LLM setup working, it behaves like a helpful teammate sitting in the bottom of your window. You get a persistent agent bar, a dockable pane you can toggle with a shortcut, and an error detector that lights up when a command fails. Hitting a key combo loads the failing command and its context directly into the agent, so you do not need to copy error messages or explain what you were trying to do.
In one example, Pi running Qwen 3.6 27B spotted that nano is a Linux text editor not installed on Windows and laid out the options: install it via WinGet, Chocolatey, or Scoop, or use Notepad or Visual Studio Code instead. That is the kind of small but constant guidance that keeps you moving without breaking focus. The agent management panel tracks active and past sessions across tabs so you can resume older work with the same context instead of starting over every time. For day‑to‑day use, this feels like an integrated Copilot alternative terminal that speaks shell fluently but leaves your data on your machines.
Caveats, Privacy Benefits, and Whether It’s Worth It
There is one honest caveat: Intelligent Terminal is a version 0.1 release and you can feel that it is basic. Features are still thin, and you will not find a huge settings UI or polished onboarding flow yet. That said, the design choice to ship it as a separate app, instead of bolting AI into the main terminal used by millions, means experiments cannot break your stable environment. You can remove it or ignore it without consequences if you decide it is not ready for daily work.
On the privacy side, keeping AI agents scoped to terminal work and local models means you avoid giving broad access to personal folders like Documents, Downloads, Desktop, Music, Pictures, and Videos, which can contain sensitive material from tax records to configuration files. One user sums up the safer pattern: keeping agents away from folders they do not need and letting private data stay on‑device. If that resonates with you, spending a few minutes on this Microsoft Terminal AI setup is worth it. You gain a Copilot alternative terminal that can run local language models without tying your workflows to any one cloud provider.
