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Frequently Asked Questions

This page answers some common questions about TRMR.

General​

What is TRMR?​

TRMR.ai is an AI-powered autonomous coding agent that lives in your editor.

How does TRMR work?​

TRMR uses large language models (LLMs) to understand your requests and translate them into actions. It can:

  • Read and write files in your project.
  • Execute commands in your VS Code terminal.
  • Perform web browsing (if enabled).
  • Use external tools via the Model Context Protocol (MCP).

You interact with TRMR through a chat interface, where you provide instructions and review/approve its proposed actions.

What can TRMR.ai Do?​

TRMR can help with a variety of coding tasks, including:

  • Generating code from natural language descriptions.
  • Refactoring existing code.
  • Fixing bugs.
  • Writing documentation.
  • Explaining code.
  • Answering questions about your codebase.
  • Automating repetitive tasks.
  • Creating new files and projects.

Is TRMR free to use?​

The TRMR extension itself is free and open-source. However, TRMR relies on external API providers (like Anthropic, OpenAI, OpenRouter, Requesty, etc.) for its AI capabilities. These providers typically charge for API usage based on the number of tokens processed. You will need to create an account and obtain an API key from your chosen provider. See Setting Up Your First AI Provider for details.

What are the risks of using TRMR?​

TRMR.ai is a powerful tool, and it's important to use it responsibly. Here are some things to keep in mind:

  • TRMR can make mistakes. Always review TRMR's proposed changes carefully before approving them.
  • TRMR can execute commands. Be very cautious about allowing TRMR to run commands, especially if you're using auto-approval.
  • TRMR can access the internet. If you're using a provider that supports web browsing, be aware that TRMR could potentially access sensitive information.

Setup & Installation​

How do I Install TRMR.ai?​

See the Installation Guide for detailed instructions.

Which API providers are supported?​

TRMR supports a wide range of API providers, including:

How do I get an API key?​

Each API provider has its own process for obtaining an API key. See the Setting Up Your First AI Provider for links to the relevant documentation for each provider.

Can I use TRMR with local models?​

Yes, TRMR supports running models locally using Ollama and LM Studio. See Using Local Models for instructions.

Usage​

How do I start a new task?​

Open the TRMR panel () and type your task in the chat box. Be clear and specific about what you want TRMR to do. See Typing Your Requests for best practices.

What are modes in TRMR?​

Modes are different personas that TRMR can adopt, each with a specific focus and set of capabilities. The built-in modes are:

  • Code: For general-purpose coding tasks.
  • Architect: For planning and technical leadership.
  • Ask: For answering questions and providing information.
  • Debug: For systematic problem diagnosis. You can also create Custom Modes.

How do I switch between modes?​

Use the dropdown menu in the chat input area to select a different mode, or use the / command to switch to a specific mode.

What are tools and how do I use them?​

Tools are how TRMR interacts with your system. TRMR automatically selects and uses the appropriate tools to complete your tasks. You don't need to call tools directly. You will be prompted to approve or reject each tool use.

What are context mentions?​

Context mentions are a way to provide TRMR with specific information about your project, such as files, folders, or problems. Use the "@" symbol followed by the item you want to mention (e.g., @/src/file.ts, @problems).

Can TRMR access the internet?​

Yes, if you are using a provider with a model that support web browsing. Be mindful of the security implications of allowing this.

Can TRMR run commands in my terminal?​

Yes, TRMR can execute commands in your VS Code terminal. You will be prompted to approve each command before it's executed, unless you've enabled auto-approval for commands. Be extremely cautious about auto-approving commands. If you're experiencing issues with terminal commands, see the Shell Integration Guide for troubleshooting.

How do I provide feedback to TRMR?​

You can provide feedback by approving or rejecting TRMR's proposed actions. You can provide additional feedback by using the feedback field.

Can I customize TRMR's behavior?​

Yes, you can customize TRMR in several ways:

  • Custom Instructions: Provide general instructions that apply to all modes, or mode-specific instructions.
  • Custom Modes: Create your own modes with tailored prompts and some tool permissions.
  • .roorules Files: Create .roorules files in your project to provide additional guidelines.
  • Settings: Adjust various settings, such as auto-approval, diff editing, and more.

Does TRMR have any auto approval settings?​

Yes, TRMR has a few settings that when enabled will automatically approve actions. Find out more here.

Advanced Features​

Can I use Roo offline?​

Yes, if you use a local model.

What is MCP (Model Context Protocol)?​

MCP is a protocol that allows TRMR to communicate with external servers, extending its capabilities with custom tools and resources.

Can I create my own MCP servers?​

Yes, you can create your own MCP servers to add custom functionality to TRMR. See the MCP documentation for details.

What is Codebase Indexing?​

Codebase Indexing is an experimental feature that creates a semantic search index of your project using AI embeddings. This enables TRMR to better understand and navigate large codebases by finding relevant code based on meaning rather than just keywords.

How much does Codebase Indexing cost?​

Codebase Indexing requires an OpenAI API key for generating embeddings and a Qdrant vector database for storage. Costs depend on your project size and the embedding model used. Initial indexing is the most expensive part; subsequent updates are incremental and much cheaper.

Troubleshooting​

TRMR.ai isn't responding. What should I do?​

  • Make sure your API key is correct and hasn't expired.
  • Check your internet connection.
  • Check the status of your chosen API provider.
  • Try restarting VS Code.
  • If the problem persists, report the issue on GitHub or Discord.

I'm seeing an error message. What does it mean?​

The error message should provide some information about the problem. If you're unsure how to resolve it, seek help in Discord.

TRMR made changes I didn't want. How do I undo them?​

TRMR uses VS Code's built-in file editing capabilities. You can use the standard "Undo" command (Ctrl/Cmd + Z) to revert changes. Also, if experimental checkpoints are enabled, Roo can revert changes made to a file.

TRMR can't write to markdown files. What's wrong?​

If TRMR fails to write to .md files with errors like "Failed to open diff editor" or "write_to_file tool failed", this is typically caused by VS Code extensions or settings that interfere with file editing:

Common causes:

  • Extensions with "format on save" functionality
  • VS Code settings that open markdown files in preview mode by default
  • The Markdown Preview extension or similar markdown processing extensions

Solutions:

  • Disable any extensions that automatically format files on save
  • Remove these settings from your VS Code settings.json: 
    "markdown.preview.openMarkdownLinks": "inPreview",
    "workbench.editorAssociations": {
      "*.md": "vscode.markdown.preview.editor"
    }
  • Temporarily disable markdown-related extensions to test if they're causing the issue
  • Restart VS Code after making these changes

How do I report a bug or suggest a feature?​

Please report bugs or suggest features on the TRMR Issues page and Feature Requests page.

Is this documentation incorrect or incomplete? Report an issue on GitHub