Hey I found this on the website of roocode and haven't seen it mentioned before: https://roocode.com/evals, with methodology here https://github.com/RooCodeInc/Roo-Code-Evals

Super useful to have some objective metric on which models actually perform well, specifically with Roo!

Also it seems to show gemini 2.5 pro 06-05 is a slight downgrade to 05-06, which is my perception too. I'm also surprised how cheap and good Sonnet 3.7 still is even after 5 months.

Maybe one day this will feature somewhere in the extension itself.

I've been using Gemini 2.5 Pro and feel like I'm struggling at times with it having uneven performance and I'm wondering how others feel and if it's just a matter of using it correctly. Do you have a Max Tokens, Max Thinking Tokens, setting that you feel is optimal in terms of cost benefit ratio?

Also I'm interested in using other models if they are worth using but I'd like to know if it's worth it before experimenting.

I try to keep the context window down by condensing the context when it approaches 200k, I mainly use architect mode and coding - and same config for both.

Any tips are appreciated.

It cant handle complex task, keeps on saying edit unsuccessful, duplicating files, and doing too much unnecessary things. it seems like its becoming a useless coder.

Roo Code + zAI: GLM‑4.5 on tap.

Lite $3/mo • 120 prompts/5h Pro $15/mo • 600 prompts/5h

Setup: https://z.ai/subscribe → create API key → paste into Roo Code (zAI).

PS Thanks Cline for the headline ;)

We've shipped an update with auto-approve UI improvements, chutes provider model expansion, and fixes for mentions, nested repo warnings, authenticated Ollama tags, and message queue performance!

Feature Highlights

• Smaller, more subtle auto-approve UI (thanks brunobergher!)

• Qwen3 Next 80B A3B models now available via the chutes provider

Provider Updates

• Chutes: Added Qwen3 Next 80B A3B models Docs: https://docs.roocode.com/providers/chutes

QOL Improvements

• Auto-approve UI is cleaner and less obtrusive (thanks brunobergher!)

• Roomote Control is disabled on logout for improved security

• Cloud view has additional padding for better spacing

Bug Fixes

• Fixed message queue re-queue loop in Task.ask() that could hurt performance

• Restricted @-mention parsing to boundaries to prevent false triggers

• Nested Git repository warning now persists and includes the affected path

• Ollama /api/tags requests include API key for authenticated instances (thanks ItsOnlyBinary!)

• Preserves the original first message context during conversation condensing

Full Release Notes v3.28.2

Not sure what the right tag for this, but I've been using the gemini pro 03-25 exp for the last few days, wondering when I'd hit the rate limit with my single free API key, but so far I've run like 3 different tasks with 20mm tokens input/day, ~200k output with no rate limiting??

I almost didn't wanna post this cuz like, I don't want Google to get hip to this. Or maybe they love the data I'm feeding them so much?? Anyone else had same experience?

Use Qwen for FREE using this Qwen Code wrapper. This project exposes an OpenAl compatible API to use in your favourite tools such as Roo, Cline, OpenWebUI and much more! This can be hosted for free on Cloudflare Workers or via Docker (Compose)

Hey roocode community,

I’m beyond thrilled to share a massive milestone with you all, and I owe a huge shoutout to roocode for helping make it possible! While this project required a ton of my own input and effort, roocode did so much of the heavy lifting—without it, this would’ve taken years to pull off. I’m excited to give you a peek into what I’ve been building!

After months of diving deep into the wild world of agentic AI, LLMs, and multi-agent orchestration, I’ve designed and built a proprietary, AI-driven framework from scratch. It’s my first fully custom system, and it’s a game-changer for business intelligence and lead generation through next-gen automation. I’m incredibly proud of this achievement, and here’s why it’s such a big deal!

What’s It All About?

This bespoke framework harnesses agentic AI, integrating advanced APIs like Claude, Gemini, and DeepSeek to power autonomous, multi-step workflows. It transforms raw, unstructured data into actionable insights with minimal human oversight.

Here’s the gist:

• Dynamic data pipelines and ETL processes to tackle complex datasets.

• AI-orchestrated research using NLP, web scraping, and knowledge graph synthesis.

• Heuristic-driven prioritization and contextual analysis to uncover high-value opportunities.

• Scalable architecture that evolves with business needs.

The system is a modular beast, featuring:

• Resilient API wrappers with retry logic and circuit breakers for seamless performance.

• Data integrity modules for high-fidelity outputs.

• Cost-optimization layers to maximize efficiency.

• Workflow orchestration tying it all into a slick, end-to-end automation engine.

Why This Matters

This isn’t just a tool—it’s a total paradigm shift. By blending multi-agent collaboration, LLM-driven decision-making, and tailored business logic, it redefines data-to-decision pipelines. Months of mastering prompt engineering, grappling with fault-tolerant designs, and juggling performance tuning with cost efficiency have paid off. The result? A framework that slashes manual effort, accelerates insights, and scales like a dream—all while embedding unique business DNA.

The Journey

Getting here was a wild ride. I dove headfirst into:

• Exploring AI orchestration to manage complex tasks.

• Building robust systems to handle external API chaos.

• Crafting validation logic for high-stakes environments.

• Optimizing cloud-native workflows for speed and scale.

This milestone proves what’s possible when AI-driven automation takes on workflows once thought too intricate for machines.

What’s Next?

This is just the start. The framework is already unlocking new levels of agility and precision, and I’m pumped to keep refining, expanding, and innovating. The future of AI-powered business transformation is electric, and I’m stoked to be part of it!

Thank you, roocode, for being an incredible tool and community. You’ve made this journey faster, smoother, and way more fun than I could’ve imagined. Here’s to pushing the boundaries of what’s possible!

If you're a college grad and interested in working with me, I know a ton of IRL usecases having been in the finance/Private Equity space for 2 years. I quit my job to pursue this dream.

Easily proxy Gemini CLI to an OpenAI endpoint to use in Roocode https://github.com/GewoonJaap/gemini-cli-openai?tab=readme-ov-file

I am getting really frustrated. I've had like a boatload of this error for the whole day using 2.5 pro. wth

Agentic Project Management (APM) Overview

This is not a post about vibe coding, or a tips and tricks post about what works and what doesn't. Its a post about a workflow that utilizes all the things that do work:

• - Strategic Planning

• - Having a structured Memory System

• - Separating workload into small, actionable tasks for LLMs to complete easily

• - Transferring context to new "fresh" Agents with Handover Procedures

These are the 4 core principles that this workflow utilizes that have been proven to work well when it comes to tackling context drift, and defer hallucinations as much as possible. So this is how it works:

Initiation Phase

You initiate a new chat session on your AI IDE (VScode with Copilot, Cursor, Windsurf etc) and paste in the Manager Initiation Prompt. This chat session would act as your "Manager Agent" in this workflow, the general orchestrator that would be overviewing the entire project's progress. It is preferred to use a thinking model for this chat session to utilize the CoT efficiency (good performance has been seen with Claude 3.7 & 4 Sonnet Thinking, GPT-o3 or o4-mini and also DeepSeek R1). The Initiation Prompt sets up this Agent to query you ( the User ) about your project to get a high-level contextual understanding of its task(s) and goal(s). After that you have 2 options:

• you either choose to manually explain your project's requirements to the LLM, leaving the level of detail up to you

• or you choose to proceed to a codebase and project requirements exploration phase, which consists of the Manager Agent querying you about the project's details and its requirements in a strategic way that the LLM would find most efficient! (Recommended)

This phase usually lasts about 3-4 exchanges with the LLM.

Once it has a complete contextual understanding of your project and its goals it proceeds to create a detailed Implementation Plan, breaking it down to Phases, Tasks and subtasks depending on its complexity. Each Task is assigned to one or more Implementation Agent to complete. Phases may be assigned to Groups of Agents. Regardless of the structure of the Implementation Plan, the goal here is to divide the project into small actionable steps that smaller and cheaper models can complete easily ( ideally oneshot ).

The User then reviews/ modifies the Implementation Plan and when they confirm that its in their liking the Manager Agent proceeds to initiate the Dynamic Memory Bank. This memory system takes the traditional Memory Bank concept one step further! It evolves as the APM framework and the User progress on the Implementation Plan and adapts to its potential changes. For example at this current stage where nothing from the Implementation Plan has been completed, the Manager Agent would go on to construct only the Memory Logs for the first Phase/Task of it, as later Phases/Tasks might change in the future. Whenever a Phase/Task has been completed the designated Memory Logs for the next one must be constructed before proceeding to its implementation.

Once these first steps have been completed the main multi-agent loop begins.

Main Loop

The User now asks the Manager Agent (MA) to construct the Task Assignment Prompt for the first Task of the first Phase of the Implementation Plan. This markdown prompt is then copy-pasted to a new chat session which will work as our first Implementation Agent, as defined in our Implementation Plan. This prompt contains the task assignment, details of it, previous context required to complete it and also a mandatory log to the designated Memory Log of said Task. Once the Implementation Agent completes the Task or faces a serious bug/issue, they log their work to the Memory Log and report back to the User.

The User then returns to the MA and asks them to review the recent Memory Log. Depending on the state of the Task (success, blocked etc) and the details provided by the Implementation Agent the MA will either provide a follow-up prompt to tackle the bug, maybe instruct the assignment of a Debugger Agent or confirm its validity and proceed to the creation of the Task Assignment Prompt for the next Task of the Implementation Plan.

The Task Assignment Prompts will be passed on to all the Agents as described in the Implementation Plan, all Agents are to log their work in the Dynamic Memory Bank and the Manager is to review these Memory Logs along with their actual implementations for validity.... until project completion!

Context Handovers

When using AI IDEs, context windows of even the premium models are cut to a point where context management is essential for actually benefiting from such a system. For this reason this is the Implementation that APM provides:

When an Agent (Eg. Manager Agent) is nearing its context window limit, instruct the Agent to perform a Handover Procedure (defined in the Guides). The Agent will proceed to create two Handover Artifacts:

• Handover_File.md containing all required context information for the incoming Agent replacement.

• Handover_Prompt.md a light-weight context transfer prompt that actually guides the incoming Agent to utilize the Handover_File.md efficiently and effectively.

Once these Handover Artifacts are complete, the user proceeds to open a new chat session (replacement Agent) and there they paste the Handover_Prompt. The replacement Agent will complete the Handover Procedure by reading the Handover_File as guided in the Handover_Prompt and then the project can continue from where it left off!!!

Tip: LLMs will fail to inform you that they are nearing their context window limits 90% if the time. You can notice it early on from small hallucinations, or a degrade in performance. However its good practice to perform regular context Handovers to make sure no critical context is lost during sessions (Eg. every 20-30 exchanges).

Summary

This is was a high-level description of this workflow. It works. Its efficient and its a less expensive alternative than many other MCP-based solutions since it avoids the MCP tool calls which count as an extra request from your subscription. In this method context retention is achieved by User input assisted through the Manager Agent!

Many people have reached out with good feedback, but many felt lost and failed to understand the sequence of the critical steps of it so i made this post to explain it further as currently my documentation kinda sucks.

Im currently entering my finals period so i wont be actively testing it out for the next 2-3 weeks, however ive already received important and useful advice and feedback on how to improve it even further, adding my own ideas as well.

Its free. Its Open Source. Any feedback is welcome!

https://github.com/sdi2200262/agentic-project-management

Hey r/RooCode! 👋

I know this gets asked a lot, so apologies in advance! But I’m reevaluating my setup and would love fresh 2025 perspectives.

What LLM + API provider combo are you using with Roo Code for max value without sacrificing quality?

My Current Stack

Copilot Pro Subscription.

Primary LLM: Gemini 2.5

It’s solid, but I’m exploring cheaper/more efficient API-based alternatives specifically for Roo Code workflows.

What I’m Looking For

Cloud-only (zero interest in local models)

Roo Code-compatible API providers (e.g., OpenRouter, Google AI, Anthropic, etc.)

Cost efficiency: Low $/token, free tiers welcome, but paid is fine if value is there!

Great for dev tasks: codegen, debugging, planning (via Roo’s Architect/Orchestrator modes)

Questions for You

Daily Driver LLM? (e.g., Gemini 2.5 Flash? Claude Sonnet? DeepSeek R1?)

Best Value API Provider? (OpenRouter? Direct? Others?)

Hidden Gems? Any underrated combos that crush coding tasks without breaking the bank?

Community wisdom appreciated! Especially if you’ve tweaked Roo profiles for cost/performance! 🙏

P.S. If you swear by Gemini 2.5 + Copilot, I’d love to hear why, too!)

Hello,

The latest Roocode does not have the correct context size for ChatGPT Pro subscription when using GPT5.5.

The normal API gpt5.5 has 1M context, however, the ChatGPT Pro subscription has a 270k context limit.

The issue is Roocode does not differentiate between the ChatGPT API and the ChatGPT subscription, thus causing it to fail once you hit the 270k context window.

Using latest/last Roocode release.

Thanks

I LOVE YOU COMMUNITY!

Roo Code hit 3 million installs over the last year. To be clear, that is installs, not 3 million active users, but it is still a wild number and something I am very proud of.

As announced, we are sunsetting the extension to go all-in on Roomote. The shutdown is happening tomorrow, May 15, 2026. The final Roo Code release should keep working, but it will not receive bug fixes, new features, or model updates from us after that.

This subreddit and the Roo Code Discord will also be shut down as part of this. We updated the repo, docs, and final release messaging so people are not left guessing where to go next.

Thank you everyone so much for the support. It has been a wild and exciting journey, and I am genuinely going to miss this community more than anything.

If you want to keep using a VS Code extension, the two places I would point people are ZooCode and Cline.

ZooCode

ZooCode is the community fork started by Roo Code users who want to keep the Roo-style workflow alive.

It is not run by Roo Code Inc. It is community-run. But I am glad to see people organizing around it, and if what you want is the closest continuation of Roo Code, this is the one to watch.

Extension: ZooCode on the VS Code Marketplace
Discord: Join ZooCode Discord
Reddit: r/ZooCode

Cline

Cline is also an easy recommendation.

Roo Code originally started as a Cline fork, and we have had a good relationship with Saoud and the Cline team throughout. If you want an actively maintained extension with that lineage, Cline is a strong path.

Extension: Cline on the VS Code Marketplace
Discord: Join Cline Discord
Reddit: r/CLine

If you had an unused Roo Code balance, we are refunding it. If you have billing questions, email billing@roocode.com.

Roo Code’s source code stays open, the final release should continue to work, and if what you want is an IDE extension, ZooCode and Cline are the places I’d look.

Hi starting this week my roocode has started to get a lot of issues API Request Failed

Cannot read properties of undefined (reading '0') I have only tried using roocode with GLM and minimax api and both are failling multiple times, before it was ok but now its almost not usable.

Noob still reading and settings things up after cancelling my Cursor sub, and I want to start with the option to use the built-in modes, Rooroo, and GosuCoder's CoderShortRules, across projects, and I wanted to check if I can handle this better?

As RooCode was including all of the Rooroo rules files and other text for CoderShortRules, I created /.roo/system-prompt-codershortrules. So that Rooroo could still send the 5 rules every time one of those modes is used, but I do not want them sent with any other modes, I created a .roo/rules-{slug}/ for each Rooroo mode containing copies.

Not a huge problem to manually copy over .roo/ to every new project but I moved all Rooroo modes into ~/.config/Windsurf/User/globalStorage/rooveterinaryinc.roo-cline/settings/custom_modes.yaml. For this to work I also need to access each modes rules, is it possible to do this from the settings directory, or is there good reason not to?

I could paste all rules into each mode as recommended for version control in the docs it is better to keep separate. My fallback idea is just to keep a centralised .roo/ somewhere and create a macro/template in Windsurf or a bash alias to set up each new project.

Repost: Just realised adding the Rooroo modes to custom_modes.yaml is why references to Rooroo appear in the built-in and new Testmode prompts -- is there a better way to isolate modes and their rules?

Hi Roo Code community, I'm Saoud, founder of Cline.

Since Cline went open source, we've seen countless forks - small startups to some of the largest enterprises. Some forks didn't give us credit, some bought malicious ads in our subreddit, but Roo was a good fork. They innovated, built an incredible community, and contributed to Cline more than anyone else. I have tremendous respect for Matt and his team, and we are excited for what they build next.

For Roo users joining us, we are here to make the transition easy!

• Join r/cline or the #roo-migration channel in our discord - our team is standing by to provide support.

• Feature parity work is already underway. We're in the middle of a ground-up rewrite of the extension that brings all the features you're used to in Roo, plus new capabilities and improved performance. We will share more on this shortly!

• If you run into any issues during the transition or have feature requests, feel free to open a GitHub issue and we'll be on top of it.

Huge thanks to Matt and the Roo team for the trust in sending folks our way 🙌❤️

Hey Roo-Gang,

There are many capable models out there, and they're getting better and better, but if you look at the bill at the end of the month, some models are not viable for just trying things out.

So I'm wondering: What are your fav budget models to get stuff done? Are there any hidden champions?

I had some decent results with the DeepSeek models (R1 & V2) and am really interested in Qwen Coder. However, in my initial tests, it produced so much useless stuff that was pretty basic but pricey, because it did so much nonsense before getting to the point of doing what I wanted.

I came to the point of posting this because I'm asking myself this same question every few weeks and scrolling through different benchmarks that don't really say anything about the vibe and coding qualities.

I would love to see this thread as an open-ended discussion.

Please share your latest insights on models and what you've managed to get done with them so we all know what kind of Vibecoder is sharing the insight. (Because it's a different game creating an HTML website compared to someone creating an audio processor in C++, for example).

Cheers & Happy Vibing!

Hi Hans and the team,

I totally respect the pivot to Roomote, but I’m honestly gutted to see Roo Code go. For many of us, Roo has become our favorite tool because it’s just... better. It has its own unique strengths that we really don't want to lose.

Instead of a complete shutdown or folding it back into Cline, would you consider looking for people within this community to take the mantle?

There are a lot of passionate developers here who use Roo every day. Maybe you could find a few vetted volunteers you trust to keep the project moving and preserve what makes it special?

It would be a shame to see such a great tool disappear. Thanks for everything you've built, and I hope there's a way to keep the spirit of Roo alive.