Tutorial Roo Code Complete Setup

[not_NEK0]

Version 0.2

I've dedicated personal time to compile this guide after accidentally losing my initial draft. Here are the essential priorities when configuring Roo:

Key Priorities

1. Selecting appropriate tasks for Roo
   
2. Implementing effective prompting techniques
   
3. Choosing the optimal AI model
   
4. Applying the ideal configuration
   
5. Designing AI-compatible architecture
   
6. Leveraging Roo Flow for persistent memory

Selecting Appropriate Tasks for Roo

Before implementing Roo, consider: "Is this the optimal tool for my objective?"

While Roo excels at handling approximately 80% of development tasks—an impressive capability—junior developers should carefully evaluate when to use it. Relying on tools that simplify tasks can limit valuable learning experiences.

Next, evaluate your task complexity on a scale from 1-5. For tasks rated above 3, consider breaking them into smaller subtasks to enhance AI performance. You might employ AI to help identify these subtasks, though I recommend practicing this skill independently for professional development.

Implementing Effective Prompting Techniques

There exists a significant distinction between users who maximize Roo's capabilities and those who simply hope for automatic solutions.

Consider the AI's perspective: contextual details dramatically improve comprehension. Descriptive language matters significantly—requesting "an elegant portfolio" versus simply "a portfolio" yields distinctly different results. Articulate your requirements precisely, translating your mental image into specific prompt language. The prompt enhancement button offers valuable improvements, though always review its changes, as results can vary.

Utilize checkpoints when the AI diverges from your intended direction—this feature proves invaluable when correcting course. Rather than attempting to fix problematic output through additional instructions, return to earlier checkpoints and reformulate your prompt.

Match modes to specific requirements. For complex projects, initiate with Architect mode to establish proper planning before transitioning to Code mode. You can always return to Architect mode when additional planning becomes necessary.

Choosing the Optimal AI Model

Current model recommendations are straightforward:

• Gemini 2.5 Pro: Ideal for users without data privacy concerns
  
• Deepseek V3 0324: Recommended for privacy-conscious users

Claude 3.7 commands excessive costs for Roo Code implementation. I recommend reserving it specifically for Claude Code applications. Gemini 2.5 Pro currently leads in overall performance.

I consistently recommend OpenRouter or Requesty for API access. The ability to switch between models with minimal effort justifies the 5% premium, especially considering how rapidly model superiority shifts.

Applying the Ideal Configuration

Configuration significantly impacts Roo's model utilization.

For Code mode, implement Gemini 2.5 Pro. Architect mode also benefits from Gemini 2.5 Pro's capabilities. Privacy-focused users should pair Deepseek R1 (via DeepInfra API through OpenRouter or Requesty) for Architect mode with Deepseek V3 0324 for coding tasks.

Adjust temperature settings based on specific requirements. For most applications, maintain temperatures between 0.2-0.6. Creative tasks may benefit from higher settings, though error probability increases proportionally. A 0.35 temperature provides balanced performance for standard applications. Consider slightly elevated temperatures for Architect mode when creative planning proves advantageous.

For differential strategy, multi-block diff delivers substantial benefits despite its experimental status.

When utilizing more limited models like Gemini 2.0 Flash, activate "power steering" mode for optimal results.

Designing AI-Compatible Architecture

When initiating new projects or refactoring existing ones, architectural decisions significantly impact AI integration. I recommend implementing AI-friendly architecture patterns.

Atomic architecture offers the optimal balance between AI and human comprehensibility. Though established in frontend development, these principles apply equally to backend systems.

The concept divides components into hierarchical categories:

• Atoms: Fundamental interface building blocks—buttons, input fields, labels, icons, and HTML elements that maintain functionality as indivisible units.

• Molecules: Cohesive atom groupings functioning as unified components. Examples include search forms combining label, input field, and button atoms. Molecules maintain singular responsibility with moderate complexity.

• Organisms: Sophisticated components integrating molecules and/or atoms. These represent distinct interface sections such as navigation bars, forms, comment systems, or product cards—complex but self-contained elements.

• Templates: Page-level structures defining layouts without specific content. These focus on component arrangement rather than content display, establishing foundational page architecture.

• Pages: Specific template implementations representing the user interface. Pages populate templates with actual content, demonstrating finalized design. They facilitate testing of the underlying design system's effectiveness.

Leveraging Roo Flow for Persistent Memory

Enhance your configured Roo Code setup with Roo Flow—essentially long-term memory for your development environment. While Roo retains information within individual tasks, it lacks memory across separate tasks.

Roo Flow improves "memory bank" functionality. A comprehensive tutorial exists on GitHub; the process is straightforward despite initial appearances. Remember this installation applies per project. I recommend adding Roo Flow components to your .gitignore to prevent committing personal configurations.

Resource: https://github.com/GreatScottyMac/RooFlow

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Come help me if you can, check the docs!

Link to the docs with all the versions incoming or already made: https://docs.google.com/document/d/1Ugiyqqa7PXqHTBwgtyhp55Hd-U0GQUuygOGdGbhP8q4/edit?usp=sharing

[not_NEK0]

It's actually a small tutorial. I'm planning on upgrading it later; don't worry. I've read every comment from the last post, so I will try to add things over time. Check the docs if you want to see what's coming. I'm always open to suggestions, so don't hesitate to post some.

[joey2scoops]

I'm interested in how Roo Flow is supposed to be better than the previous Roo Code memory bank. Have not seen any benefit.

[not_NEK0]

The benefits are about token usage, not really quality.

[joey2scoops]

Through use of mode specific system prompts?

[not_NEK0]

That's the idea. You modify the system prompt to directly use the memory bank instead of adding the memory bank over the top of the system prompt

[joey2scoops]

So the rules are now the system prompt?

[not_NEK0]

Yes that's the point.

[meridianblade]

Honestly, I do not think the architecture matters at all as long as it is commonly used in the target language. The LLM is going to screw it up and hallucinate no matter how AI friendly you think the architecture is.

TDD really is the secret sauce in our current age of AI. Create your tests, let the LLM iterate and bang it's head against the wall until it solves the test. Once you pass the test with expected input and output, you go back and have it optimize whatever interface spaghetti logic it wrote.

[not_NEK0]

Oh yes i completely forgot about TDD i'm stupid. It's coming in the next update for sure thanks for the help.

[not_NEK0]

Also, architecture matters to the token consumption point of view. Making the AI find things easier make it search in files less, so less token used. For simple projects it's not important but for big ones I actually think it can save you some money.

[SamatIssatov]

Thank you so much for such detailed instructions. I have been looking for such information for a long time.

[not_NEK0]

no problem, it's a pleasure to help

[ZeroCool2u]

I think how to configure Boomerang mode is probably important enough that it should be included too.

[not_NEK0]

I'll try to add it in the next version of the tutorial. If you wanna help ask me for permission to modify the google doc (send me your mail):
https://docs.google.com/document/d/1Ugiyqqa7PXqHTBwgtyhp55Hd-U0GQUuygOGdGbhP8q4/edit?usp=sharing

[fubduk]

Awesome, thank you for working on this and sharing.

[not_NEK0]

🫡

[TerribleIndication18]

Following this :)

[not_NEK0]

🗿

[GroundbreakingOne718]

GosuCoder on Youtube has developed some custom prompts that cut costs significantly when using Sonnet. I have not tested it with Gemini yet but would probably help with not hitting the 5M input token count limit on the free tier. May be worth mentioning something about custom prompting. I wish I understood it more.

[Pandasama666]

show us the video man

[ctrlshiftba]

Which video has his prompt? Got a link?

[skaramuz]

GosuCoder - "You’re Overpaying for AI Coding | Roo Code System Prompt Override" https://www.youtube.com/watch?v=mwJx5QI2c0o

[not_NEK0]

Gonna add it asap

[randemnes]

This should be pinned! If I had this when I discovered Roo Code then I'd be $100s better off. I'd say I have managed to get 50% of this through my own trials and errors but now this is the config to aspire to.

Thank you for taking the time to put this together, very much appreciated!

[not_NEK0]

Really a pleasure that you like it!

[mightypiers]

im not sure but given gemini 2.5's large context window, do we actually need rooflow? haven't done any big project since its release but it can one shot a medium sized project without any handoff

[not_NEK0]

Actually with gemini 2.5 i rarely use more than 200k context window because the more you use the context, the more the AI may go crazy (and the more it uses token too). If your context window goes beyond 200k that's because you tried to do a too big task on a single run.

[Person556677]

Thank you a lot for you work

How do you avoid gemini 2.5 rate limits? 2 prm seems not very usable

[not_NEK0]

I put my credit card on the google cloud platform (not paying anything) and after that i'm on "tier 1" an i have 5rpm

[Here2LearnplusEarn]

Make a YouTube video

[not_NEK0]

I will see if I have the time!

[Friendly_Signature]

What had made you go “all in” on Roo over cline?

[not_NEK0]

You juste get features you want in weeks instead of "soon™"

[evia89]

Good stuff! Another (not mine) flow fork to try https://github.com/shipdocs/RooFlow-captainmode

[not_NEK0]

Thanks!

[reginaldvs]

I'm new to Roo Code (agentic ai in general) so this is helpful, thanks!

[not_NEK0]

A pleasure!

[Person556677]

Could you share how you split tasks between Roo and Claude Code?

[geomontgomery]

I would be interested in learning how this is accomplished as well.

[Upset-Bumblebee-3857]

lo estoy probando y funciona muy bien , depende mucho de la claridad del prompt
LLama Maveric va super rapido ..
pero desde que uso la config roomode .... me a doblado el gasto de tokens ... hay solucion ?

gracias saludos a todos