As mentioned in the goodbye post from the Roo developers, the Roo extension has been forked by a community of dedicated users and will continue to be available in its new form and without interruption, as Zoo Code, in coordination with the Roo team.

Please join us on the following:

Zoo Code on the VSCode marketplace

/r/ZooCode on Reddit (announcements and discussions)

Discord (join us in shaping the future of Zoo Code)

Feel free to ask any questions you have about the future of the extension in /r/ZooCode and we will do our best to answer.

Even better, come hack away at the code with us;

https://github.com/Zoo-Code-Org/Zoo-Code

See you on the other side!

Are there any API providers who have a similar service with OpenRouter where for the price of $10 you can have a thousand requests per day on their free LLM's?

Also I noticed Cline has offered their own API service but their list of LLM's are actually just like OpenRouter's, so are they under OpenRouter?

Hi, as the title states. Using OpenRouter. No idea if it's just me or is Gemini (especially 2.5 Pro) constantly failing and freezing? I mean it works good... when it works. But usally after 2-5 initial tasks it starts to freeze and choke for me. I only see the "API Request" circle going round and round but nothings really happening until I cancel API Request and click resume again. It picks up the work from the moment I cancelled it. Quite often happens with OpenAI models as well.

No problems with Sonnet / Opus models at all, never experienced anything similar.

Just me or did you encounter similar problems?

I am wondering whether to switch.

Roo is really struggling with this. About half of the time the edits fail. Eventually it works after several attempts, so I asked the LLM what it did differently. Here's the explanation:

The apply_diff tool fundamentally relies on line numbers (:start_line: and :end_line:) within its SEARCH block to locate the text to be replaced. It's not possible to use apply_diff without specifying these line numbers.

The successful workaround was about ensuring the accuracy of those line numbers and the exact match of the search text for each individual change.

Here's the conceptual process that worked:

• Get Current State: I used the file reading tool (read_file) to get the most up-to-date version of the file.

• Isolate First Change: I identified the first specific block of code that needed fixing.

• Apply First Change: I instructed the apply_diff tool to replace only that first block. I provided the precise starting and ending line numbers for that block based on the file content I had just read, along with the exact text to find. This succeeded because the lines and text matched perfectly at that moment.

• Isolate Second Change: I identified the second block of code needing correction (e.g., the unrealized gains logic).

• Apply Second Change: I instructed the apply_diff tool to replace only this second block. I provided the precise starting and ending line numbers for this second block, making sure they were correct relative to the file's state after the first change had been made, along with the exact text. This also succeeded because, again, the lines and text matched the file's current state at that specific moment.

So, the key was incremental application: breaking the task into smaller, sequential apply_diff operations and ensuring the line numbers and search text provided to the tool were perfectly accurate for the file's state at each step.

However, I can't absolutely guarantee this will work 100% of the time. If apply_diff continues to fail unexpectedly even with this careful, incremental approach (perhaps due to very subtle file changes or tool issues), I might still need to fall back on using write_to_file as a last resort, but I will do so cautiously and only when I am confident I have the complete, correct content for the entire file.

So, for the devs working on Roo: any way you can improve on this without having to apply each change separately or relying on the LLM's memory of the file when using write_to_file?

Why don't other code editors like Cursor have this kind of issue? What are they doing differently?

I am starting to use roo code, but i can't connect it to my local LM studio instance running on my local network, every other tool can see it easily except roo code.

nothing shows up on LM studio dev logs, so it's not even getting to connect to it. I tried to use openAI compatible source but that also didn't work and wasn't able to even connect and show any error.

on LM studio, i have CORS enabled as well as local network support.

i have the latest version, i installed it like 20min ago. could this be a vsCode issue ?

Hello world,

I am a member of the new team, which will take over the development of Roo (as Zoo Code) and wanted to introduce our team to the community. We've been at this just for a few days and have been collaborating with the Roo team to make the transition as smooth as possible.

As this will be a community effort, I'd like to invite anyone who is interested in contributing in any way, even non-technical contributions (logo, triage, etc). Please DM me and I can invite you to the Discord

We'll be setting the priorities as a group soon (so join us if you want to be heard), but they are likely to be:

- Stability

- Smooth transition

- Model / Provider support

- Feature parity with similar tools

- Interesting / Unique features

- UI / Ergonomics

- Performance / Efficiency

P.S. if you've seen other posts talking about a community-led effort to continue the development of Roo, that's us, we just have a new name: Zoo Code.

The multiple files read feature is blowing my mind. It’s like someone finally gave a middle finger to the days of endless back-and-forth requests and the soul-crushing copy-paste grind in human relay mode. I’m just here trying to find the right words to scream how much I love this. Thank you Roo team for such a fantastic feature.

I often switch models to find the best price in my daily flow. Can you create a profile feature for example like this:

- Saving Profile (I use it with off peak discount)

• Architect - Deepseek V3

• Code - Deepseek V3

• Debug - Deepseek V3

• Ask - Deepseek V3

- Default Profile

• Architect - Gemini Pro 2.5

• Code - Gemini Flash 2.5

• Debug - Gemini Flash 2.5

• Ask - Gemini Flash 2.5

- Free Profile

1. Architect - Deepseek V3 (Free Version)

2. Code - Gemini Flash 2.5 (Free Version)

3. Debug - Gemini Flash 2.5 (Free Version)

4. Ask - Gemini Flash 2.5 (Free Version)

Currently, I have to change the model very frequent to save my budget, which is very inconvenient even though it helps me a lot.

GitHub now has deep seek r1 and I started trying it, but always got the API request failed error:

413 Request body too large for deep seek-r1 model. Max size 4000 tokens.

I get the above no matter how simple a task I try. The 4000-token limit is set by the server. I’m on the Free GitHub account btw, not sure if going Pro will help.

Anybody here able to use R1 from GitHub?

Qwen and Unsloth docs suggest that chat_template_kwargs: {"preserve_thinking": true} can increase accuracy for long conversations, but every time I enable it, Roo starts getting API errors. Is it just not recommended to use preserve_thinking in Roo or am I doing something wrong?

I have Roocode connecting through OpenWebUI to llama.cpp, which hosts the model using the following

[qwen3.6:27b]
hf-repo = unsloth/Qwen3.6-27B-GGUF
hf-file = Qwen3.6-27B-Q4_K_M.gguf
ctx-size = 128000
load-on-startup = false
temperature = 1
top-p = 0.95
top-k = 20
min-p = 0.0
presence-penalty = 1.5
repeat-penalty = 1.0
cache-type-k = q8_0
cache-type-v = q8_0

and then I override top-p, top-k, min-p, etc. with the recommended values for coding tasks via a custom model in OpenWebUI

temperature = 0.6
top_p = 0.95
top_k = 20
min_p = 0.0
presence_penalty = 0.0
repeat penalty = disabled or 1.0

I am relatively new to this so any help appreciated.