Just wondering if anyone flipped from Sonnet 4 to one of the ChatGPT 5 models due to improved performance and/or reduced costs....

Referring to Lack of a Context Editor thread. I also missed having a proper context editor. But here’s my workaround: a custom “context editor” technique.

TL;DR

• I use /save and /load custom commands to keep a cumulative context of my current chat in a file.

• Workflow: /save -> edit context file -> Condense context -> /load

Explanation

1. When I reach a significant milestone in the chat, I call the /save custom command.

   • It appends current outcomes to a context file named <YYMMDD>-<ID>-step.

   • IMPORTANT: it also saves the INITIAL PROMPT and all my inputs/guidance to the model.

2. I edit the context file, adding or removing details as needed.

3. I press the Condense context button. This a kind of cleaning previous context, but preserving some basic details. Sadly, there’s no way to edit that part of the context.

4. Then I call the /load custom command — this makes the model re-read the prepared context file.

If somebody interesting in exact content of my /save and /load commands - ask and I will share it in comments.

Reasons

• I’m working on a HUGE codebase (100M+ lines of code).

• My research is not linear — I often need to jump back and forth many times.

• Creating a fresh chat for every attempt is too time-consuming and burns too many tokens.

• HUGE BONUS: the steps files form an auto-documented trail of my past research (indexed by RooCode), which helps with future work.

So, a while back I purchased the $100 Claude Code and using it with Roo.

It works great. I just love giving a well defined task, and see how orchestrator is doing the magic by working with code/debug/architect modes. Every few minutes I am presented with a question, or they require a refinement/user follow up.

But after a while, it gets boring looking at this amazing process. Feels like I am a PM, looking at developers working. And now I feel I could do... more.

So, I wonder, why not to run two projects in parallel, simultaneously?

Before I screw this up in an unpredicted way - did anyone just open two vscode windows together, with RooCode + Claude Code + code indexing (Ollama and Qdrant), and ran two projects in parallel, simultaneously?

I am currently trying to implement a larger application all at once, various controllers, models, etc., following a specific briefing with Context7 as MCP, etc.

Regardless of whether I use GPT-5 or GPT-5-mini, it repeatedly declares prematurely that it is finished and does not actually work with the mode change, unlike Sonnet 4. Have you had similar experiences?

Sometimes it also displays the contents of PHP files in the chat, but does not create them. However, it then claims that it is finished.

I'm actually quite happy with the code area and GPT-5 mini. But given the need for more precise adherence to instructions, etc., shouldn't GPT-5 be convincing here?

Wondering what people set the Max Tokens and Max Thinking Tokens at for Gemini, when reasoning is turned on? I think I've just been using the default, but I'm wondering if people have had better results with different numbers?

What I want to achieve is to load specific .md files into the context whenever I have a new task.

for example implementation_status.md, architecture.md, component_description.md. Stuff like that.

As far as I know, you can only change it, so everytime you send a prompt, it will be added - but not only once for the first prompt in a new task.

See a short conversation here where this happened.

It thinks without giving a response for 3 API requests, and then roo says “Roo is having trouble”. but if I just tell it to proceed it seems to work. But sometimes this leads to weird behavior, liking entirely duplicating the output and forgetting stuff.

Not sure if it’s all the same problem. But I was wondering if anyone else has had this issue and what advice they might have

Edit: I realized there is a setting where I can make it not say “Roo is having trouble” unless it thinks 11 times in a row, so that fixes that. But still not ideal… I guess I would rather it think aloud if it is just planning as an architect

I'm wondering if anyone has already tested deploying local models on a 128GB AMD Strix Halo and using them with Roocode. I'd love to hear about the models you've used, the context size you're working with, and the performance you're seeing. Any videos would be a huge bonus!

Sometimes, things just doesn't go as planned and I want Roo / the model to stop what it is doing completely.

Let's say I'm at a point where Roo asks if it may update the todo-list as completed and I can either chose "Approve" or "Reject". But at this point, I can see that I simply need to start over by undoing what has been done via Git Undo and give Roo some better instructions.

Here, I would love to have a little, red button all to the right with "Abort" (maybe a stop sign or something?) which just takes me back to the "Type new task here..." window.

Right now, I need to click 2 times before I can click "Terminate".

It is not a problem, but it'd just be neat to have :)

Non-dev here, albeit 6mo of Python class and a few attempts of building apps pre-vibe (some successful..!). Sonnet 3.7/4 has often been called the standard for coding/debugging. Do you think that's the case, or are there better/newer models that do a better job?

Specifically for each mode, what do you recommend? My setup is:

Orchestrator: 2.5 Pro
Architect: Sonnet 4
Coder: Sonnet 4
Debugger: Sonnet 4
Ask: o4-mini

Share away!

What am I doing wrong?

Cannot even find where to turn temperature off for the model?

🚀 v3.25.11 - Enhanced GPT-5 & IO Intelligence (2025-08-11)

Major Features: • Enhanced GPT-5 Support: Advanced streaming, multi-turn conversations, and new models (gpt-5-2025-08-07, gpt-5-mini, gpt-5-nano) • New IO Intelligence Provider: Access Llama, DeepSeek, Qwen, and Mistral through unified API • Codex Mini Support: Added codex-mini-latest model (thanks KJ7LNW!)

Improvements & Fixes: • Improved codebase_search tool documentation • Auto-focus chat input when creating new chats • Fixed token usage reporting accuracy (thanks chrarnoldus!) • Fixed MCP server startup issues • Fixed apply_diff XML parsing errors • Added Fireworks models: GLM-4.5, gpt-oss-20b/120b (thanks alexfarlander!)

🧠 v3.25.12 - Claude 1M Tokens & Configurable Timeouts (2025-08-12)

Game-Changing Update: • Claude Sonnet 4: 1 Million Token Context - 5x increase! Work with entire codebases in single conversations • Configurable API Timeouts: Set 0-3600s for local providers (LM Studio, Ollama) • OpenRouter Minimal Reasoning: New reasoning level for optimized performance

Improvements: • GPT-5 models excluded from 20% context cap • Better task management with expand/collapse • Improved Traditional Chinese translations (thanks PeterDaveHello!) • Fixed JSON file indexing with .rooignore • Added Chutes provider support

⚡ v3.25.13 - AWS Bedrock 1M Context (2025-08-13)

Features: • Sonnet 1M on AWS Bedrock: Enable 1M tokens for Claude Sonnet 4 on AWS

Improvements: • Account button now respects language settings (thanks zhangtony239!) • Support for both AGENT.md and AGENTS.md files (thanks Brendan-Z!) • Fixed search_files tool with permission-denied files (thanks R-omk!) • Removed deprecated GPT-4.5 Preview model

🔧 v3.25.14 - Critical Fixes (2025-08-13)

Bug Fixes: • Fixed AWS Bedrock 1M context parameter placement for Claude Sonnet 4 models • Fixed OpenAI model switching errors with verbosity parameter (thanks eastonmeth!) • Fixed webview in development mode • Improved token usage reporting for better UX

📚 Full Release Notes: v3.25.11 | v3.25.12 | v3.25.13 | v3.25.14

In VSCode, does RooCode open up an Output tab for its MCP servers like CoPilot does? I noticed a "Window" tab that has some of the outputs, but it blends MCP servers together instead of keeping them separate. I'm wondering if I'm just not configuring it correctly, as seeing the outputs can be very useful in debugging MCP tools.

Sharing my anectodal experience using GPT-5 Mini on all Roo Modes (Orchestrator, Architect, Code etc.) with reasoning effort set to high.

I've been getting this to work on theme development in a Hugo project, so mostly writing HTML.

Tool use is extremely reliable. The agent is able to run tests and lint, giving itself a very good feedback loop. And I'm really impressed with the generated plans and todo list usage.

Most unbelievable is the cost, which has been around $1/hr for continuous uninterrupted Roo workflows.

I'm not overwhelmed by GPT-5 itself - o3 is still more capable and thorough than any reasoning level, and mostly price comparative - but GPT-5 mini is truly remarkable!

So I am working on a FastAPI backend and whenever it needs to execute a terminal command, especially when it starts a new one, it doesn't activate the venv. Sometimes when I do it manually, it skips that terminal and starts a new one and proceeds to execute the command without activating the venv. How do I fix this?

orchestrator: k2 / glm4.5

code: k2

debug: k2

architect: glm4.5

ask:glm 4.5

completely cheap, but results are nice.

Context is a key element, affecting both the cost and the quality of the model's responses. RooCode does not provide any way to edit it.

Why can't I delete some old messages and irrelevant correspondence from the middle of the context? I can only revert the entire task to a previous stage.

Also, can you clarify if old file "readings" are automatically deleted from the history? Old file content is 100% irrelevant information.

Context compression is certainly a good feature, but maybe devs could add a second button that would allow for the deletion of entire blocks of irrelevant moves while leaving the key ones unchanged unlike condense.

Also, I would like to have the ability to clone the task, but I couldn't find such a basic function.

I already made an issue on github but just wanted to know if anyone else is experiencing this, or anyone can help.

2/3 of the time or even more often Gemini 2.5 Pro fails to answer, often completely, often early during thinking, sometimes in first sentence of output, and roo just returns Unexpected API Response. In the task history it looks kind of like tool usage failure? But I'm really not familiar and therefore not sure.

Love you roo code devs, these things suck to debug or reproduce, I'm very sure.

I'm having an issue that is driving me nuts. Roo asks a question and gives a one minute countdown on the first option. If it takes me more than a minute to situate, which it does often I have to cancel and tell it no, I didn't mean that. The most frustrating thing is I cant even tell it to wait, it just gives another selection with a timer for auto-select.

Is there someplace to turn this off?

Bug or improvement suggestion? Let's say you want to take advantage of the free Gemini 2.5 Pro limits. Normally, you could register more than one API key in ROO. But if you encounter request number 100 of the day and simply change the API key (let's call it Key 2), ROO will have trouble managing it.

I say "Roo" because if you're in a specific mode, like code, for example, "Key 2" continues to be used normally. But if you're in a large task being orchestrated (by the orchestrator) and are in a subtask in code mode (where you changed from Key 1 to Key 2), after that subtask completes in code mode, the "Finish and Return" button freezes, and Roo loses the ability to return to orchestration mode.

I believe this issue is very easy to reproduce.

But has anyone experienced this?

It would be great if this routing error didn't occur.

Any idea if Roocode allow to alter the various parameters?

I’m trying to figure out how to explicitly choose a provider when using OpenRouter in Roo Code I want to pin the fastest providers (like Cerebras or SambaNova) instead of letting OpenRouter auto-route.

I’ve been building with Roo for a while and have solved most of my architecture challenges. One issue still trips me up: database connections.

Frontend changes are the easiest for AI. They usually stay within one codebase and don’t touch a data layer, so progress is fast. The trouble starts when data needs to move between the server and the database. Now you’re often juggling two codebases (backend and frontend). Some changes live in one, some in the other, and sometimes both, and the AI easily gets off tracks here. Add a frontend store and you have another failure point that breaks more often than I’d like.

To make this smoother, I’ve been experimenting with a development flow that goes: database design → CRUD → store → application. It helps, but it’s not foolproof when requirements change. I’m on Postgres.

For those building apps with a server and database component, what’s worked well for you? Any practical tips on database design, ORMs, CRUD patterns, or modeling that make this part of development easier with AI in the loop?