AI Agents for Linux: Terminal Tools, Context, and Control
Several AI coding agents run natively on Linux and integrate with the terminal or VS Code. Here is how to compare them on installation, shell access, and review workflow, and how to keep project context and restart notes across sessions and tools.
Updated 7 min read
Linux developers have several AI coding agents available that run in the terminal or integrate with VS Code. Choosing between them involves installation requirements, how much shell access each agent has, and how you want to review its work before it changes anything. Tools like fenbs keep the project requirements and handoff notes that travel across agent sessions, so you are not re-explaining the project when you switch tools or start a new terminal session.
Linux support varies between tools. Some install natively via npm or a package; some are VS Code forks; some have Docker wrappers. Check the current documentation for each tool against your distribution and version before committing to a workflow, as support levels and installation paths change between releases.
Comparing AI agents for Linux development
| Tool | How it runs on Linux | What to check |
|---|---|---|
| Claude Code | Installs as an npm package. Runs in any Linux terminal with Node installed. Full shell and file access in the project directory. | Review each proposed file change and shell command before accepting. Check the Node version requirement against your system. |
| GitHub Copilot cloud agent | Runs through VS Code on Linux, or as agentic features in the GitHub CLI (gh). Requires a GitHub Copilot subscription. | Agentic behavior differs from the inline completion extension. Check the current Linux support level for the agentic features you need. |
| Cursor | Provides a Linux AppImage and a Debian package. A VS Code fork with an AI panel that can run multi-step tasks. | Check the current Linux release version against your distribution. Extension compatibility with the VS Code fork may vary. |
| Devin Desktop | Previously Windsurf. Check the vendor site for the current Linux installation instructions and supported distributions. | Linux support status and installation method change between versions. Verify on the vendor documentation before installing. |
| fenbs | Web and phone app. Keeps project requirements, tasks, Decisions and rules, and Where we left off across all the tools above. | Connects over MCP to the agents above. Use as the shared project record, not as an execution environment. |
Shell access and permissions
Agents that run in the Linux terminal can execute shell commands directly in your project environment. That is powerful and requires careful review. Before confirming any proposed command, check what it will write, whether it goes outside the project directory, and whether it needs root access. A command that looks helpful in context can modify system files or package managers in ways that take time to undo.
Run agent sessions as a regular user, not as root, so that commands requiring elevated privileges fail rather than silently proceeding. If a task genuinely requires root, review the specific command, run it separately, and do not give the agent persistent elevated access for a session.
Be specific about what the agent may do at the start of each session: "You may read and edit files in this directory, run make and pytest, and inspect environment variables. Do not run anything that modifies /etc or installs system packages." That instruction narrows the agent's action space to the work at hand.
Project context across tools and sessions
The main friction with using AI agents on Linux is that each tool stores its context differently and each new session starts from scratch. You end up pasting the same project description, the same architecture notes, and the same "do not touch this module" warnings into every chat. That repetition is both time-consuming and unreliable — it is easy to leave something out.
A project record outside any one tool solves this. Write the goal, the architecture decisions, the open tasks, and the stopping point in a place that every agent reads at the start of a session. When you move from Claude Code to Cursor, or come back after a week, the first step is to read that record, not to reconstruct it.
Keep project context where every agent can read it
fenbs stores the project requirements, tasks, and Decisions and rules that each session needs. When you switch Linux tools or start a new terminal session, a connected agent reads the fenbs context first and starts with the same requirements instead of asking you to explain the project again. Connect your tools at Integrations.
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Reviewing changes before accepting them
Read every diff before accepting it. In the terminal, that means looking at each file change the agent proposes rather than accepting a batch with a single confirmation. In VS Code-based tools, it means reviewing the changes panel before accepting a block of edits.
Pay particular attention to changes that: add a new dependency to a build file, modify a configuration file outside the project root, delete a file rather than clearing its contents, or change a function signature used in multiple places. These have downstream effects that are not always visible in the immediate diff.
Run the test suite after each substantive change. On Linux, this is straightforward from the terminal: run your test command and check the output before the agent's next step. If a test fails after a change, that is the time to investigate, not after three more changes have stacked on top.
Handling session restarts
Linux terminal sessions get interrupted: a dropped SSH connection, a system update, or simply closing the terminal. When a session stops, the agent's context is gone. What remains is only what was written to disk and what you recorded in a persistent note.
Before closing a session, ask the agent to summarize what it completed, what it changed, and what the next step is. Save that summary where the next session can read it. The investment is small: thirty seconds of summarizing avoids ten minutes of reconstructing where things were.
Save the stopping point before you close the terminal
fenbs Where we left off records where each session stopped. Ask your agent to save progress in fenbs before closing the terminal. The next session — on the same tool or a different one — reads that note and picks up from the current state rather than starting over. Set up the connection once at Integrations and the agent saves its own stopping point.
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Container and virtual machine workflows
Many Linux development workflows involve containers or virtual machines. An AI agent that has terminal access inside a container can read and modify project files, run build commands, and report the output, just as on a bare Linux system. The important difference is the blast radius of a mistake: a command that damages a container is usually easier to recover from than one that damages a working development machine. Containers are a good environment for tasks where you want the agent to run commands more freely while keeping the risk contained.
Give the agent a clear picture of which environment it is working in. If the project runs in Docker Compose with several services, tell the agent which service is relevant and what the others do. If the environment uses a virtual machine with a specific base image, describe the base so the agent's package management commands use the right package manager and the right repository.
Use separate environments for risky tasks. If an agent needs to test a system configuration change or install a package the project does not normally use, a throwaway container or a snapshot-capable virtual machine is the right place. Test the result there, confirm it works, then apply the change to the project environment with a clear record of what was changed and why.
Container images also give each session a reproducible starting point. If an agent's setup works in a fresh container built from the project's Docker configuration, you can be more confident it will work for another team member. Record the working configuration in the project record so the next session can reference it rather than starting the environment setup from scratch.
How to do this with fenbs
Start with the one piece of context you always have to explain at the start of a new Linux development session. Write it in fenbs, connect your agent, and check that the next session starts with that context already loaded.
- Create a free account. Add the project with a description of the goal, the Linux environment, and the main tools in the stack.
- Add current open tasks: what is in progress, what is blocked, and what is next. Include what has already been tried on any stuck task.
- Write development rules in Decisions and rules: what the agent may and may not do, which directories are out of scope, coding conventions, and system constraints. Mark each as a standing rule.
- Connect your preferred agent at Integrations. Ask it to read the project context at the start of each session and describe what it understands before running any commands.
- Before closing each session, ask the agent to update the task and project record with what completed and what is next. The next session starts from that record regardless of which tool you use.