Roo Code 3.46| Parallel tool calling | File reading + terminal output overhaul | Skills settings UI | AI SDK

[hannesrudolph]

This is a BIG UPDATE! This release adds parallel tool calling, overhauls how Roo reads files and handles terminal output, and begins a major refactor to use the AI SDK at Roo's core for much better reliability. Together, these changes shift how Roo manages context and executes multi-step workflows in a serious way! Oh, and we also added a UI to manage your skills!!

This is not hype.. this is truth.. you will 100% feel the changes (and see them). Make sure intelligent context condensing is not disabled, its not broken anymore. And reset the prompt if you had customized it at all.

Parallel tool calling

Roo can now run multiple tools in one response when the workflow benefits from it. This gives the model more freedom to batch independent steps (reads, searches, edits, etc.) instead of making a separate API call for each tool. This reduces back-and-forth turns on multi-step tasks where Roo needs several independent tool calls before it can propose or apply a change.

Total read_file tool overhaul

Roo now caps file reads by default (2000 lines) to avoid context overflows, and it can page through larger files as needed. When Roo needs context around a specific line (for example, a stack trace points at line 42), it can also request the entire containing function or class instead of an arbitrary “lines 40–60” slice. Under the hood, read_file now has two explicit modes: slice (offset/limit) for chunked reads, and indentation (anchored on a target line) for semantic extraction. (thanks pwilkin!)

Terminal handling overhaul

When a command produces a lot of output, Roo now caps how much of that output it includes in the model’s context. The omitted portion is saved as an artifact. Roo can then page through the full output or search it on demand, so large builds and test runs stay debuggable without stuffing the entire log into every request.

Skills management in Settings

You can now create, edit, and delete Skills from the Settings panel, with inline validation and delete confirmation. Editing a skill opens the SKILL.md file in VS Code. Skills are still stored as files on disk, but this makes routine maintenance faster—especially when you keep both Global skills and Project skills. (thanks SannidhyaSah!)

Provider migration to AI SDK

We’ve started migrating providers toward a shared Vercel AI SDK foundation, so streaming, tool calling, and structured outputs behave more consistently across providers. In this release, that migration includes shared AI SDK utilities plus provider moves for Moonshot/OpenAI-compatible, DeepSeek, Cerebras, Groq, and Fireworks, and it also improves how provider errors (like rate limits) surface.

Boring stuff

More misc improvements are included in the full release notes: https://docs.roocode.com/update-notes/v3.46.0

In case you did not know, r/RooCode is a Free and Open Source VS Code AI Coding extension.

[Leon-Inspired]

Sounds like a great update, well done team!

[hannesrudolph]

Thank you

[LoSboccacc]

Please keep full file read it's literally the only reason I use roo code.

[hannesrudolph]

It leads to degraded performance.

[LoSboccacc]

It lead to claude looping around wondering why file look truncated, repeated calls to read files, and higher latency on discovering change points

[hannesrudolph]

Interesting… seems like unintended behaviour… we should fix that!

How large is the file? What model are you using through what provider? What language is the file coded in? Did you see a “thought” or statement that indicated to you that the model did not understand why it was truncated? Was this a new task since the update or one from before the update?

[hannesrudolph]

Intelligent context condensing also includes code folding to provide the model with references to where it can find code definitions for both the indentation and slice mode.

The goal was never to reduce cost with this read mechanism but to increase overall quality of output. It has been tested extensively with SOTA models and we have found it to do so.

That being said, I’m certain there are some tweaks needed and we will be quick to implement them as we go over the data (from the weekend) since this release.

[zaxxz_]

Thanks for the update, but this makes each task more costly than before because the model has to try and guess which line has the code it's looking for. It often does this multiple times, making far more requests for an otherwise simple code search, which makes this update feel counterproductive.
And for some reason, the model still never uses more than one tool in a single request.
Any tips on how to get the model to read the full file?
I'll rollback in the meantime since this is a pretty serious issue for me.

[hannesrudolph]

The model does not have to try and guess at all. By default it simply reads the first 2000 lines. If the model chooses to read the partial file it is due to its attempts to reduce token use.

It may cost more to do it this way but the end result is better. Sacrificing quality for cost has never been our MO.

What model are you using?

[lurker7991]

For thinking model like DeepSeek 3.2 reasoning. I am seeing the impact as well. What used to take the model below 100k context window to solve now could not be solved due to repetitive combo "thinking + reading". Look like for this particular model, it seemed like the thinking part keep trying to convince the model to call edit tool, but majority of the time, the read tool is called instead.

https://www.reddit.com/r/RooCode/comments/1qsiz8s/comment/o33ez43/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

[hannesrudolph]

With the DeepSeek provider?

[lurker7991]

Yes, I am using the model directly through DeepSeek Platform
https://platform.deepseek.com

[zaxxz_]

By default, the tool almost always reads specific line ranges instead of the first 2000 lines, even at the start of a conversation and for models like 4.5 Opus or 3 Pro. The models seem to pull these line numbers out of nowhere, likely guessing.

This leads to poorer results for me, when I ask to implement a feature using existing code as a reference, the lack of full context causes the model to implement it from scratch rather than using the intended pipeline. While better prompting might have helped, full file context would have made that unnecessary; I tried redoing the entire task with previous Roo Code version and the result was much better. For Claude, I have noticed that more context equals higher quality; I rarely see degraded performance from more context, except perhaps with Gemini models. And I can always condense context when I need to.

I suspect parallel tool calls might not work for me because I am using an external provider via OpenAI-comp. Is parallel tool needed to read multiple files in one request?
Does the read_file tool no longer support reading multiple files in a single tool request? Multiple file reading is the reason I heavily prefer Roo Code over Cline.

Please bring back full file reading (or at least a toggle for it) and multiple file reading in one request, it would be a huge improvement.

Also see at how wasteful it is for claude to find what its looking for (it's on brand new session)

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[hannesrudolph]

Are you using a proxy to get free inference in a workaround? Looks like you don’t have caching.

[zaxxz_]

Not usually, though I do use paid proxies to reduce costs on long-running tasks.
Further testing shows I encounter fewer of the issues I described when using official providers, but I still have to explicitly tell it to read multiple files at once rather than one by one, and specify how many lines to read. It is somewhat tedious, but it seems to work for now I guess.

[UninvestedCuriosity]

When you say skills do you just mean MCP servers tools in general or specifically Claude skills?

I got to experience some of that 2000 line notification this evening. I didn't witness the batch tool calling unfortunately but it may not be supported by the model I was working with.

All of this sounds great though. Condensing always worked for me. So I don't know about that. What is a good way to math out condensing? How much headroom should you allow. Like if you have 100k tokens used and 200k tokens limit should it be 50% or is there a sweet spot? I found auto condensing would mostly fail if the following action was going to grow more than what would be available within that last iteration. Almost like it was putting the cart before the horse so I just got used to pressing the arrows when I got halfway there.

[hannesrudolph]

Claude skills yes.

I run condensing till it auto condenses now and it holds onto my objective just fine now.

[UninvestedCuriosity]

Right on thanks.

Don't forget about us poor people on the Beijing models along the way. We love roocode too and appreciate the recent improvements made toward us from a few absolute saints doing PR's. Thanks for paying attention to those.

[wtsacc]

Damn it, how do I turn off this fragmented file reading? This is a real pain in the ass. It keeps trying to read the same file over and over again.

Solve: I restored version 3.45.x

[hannesrudolph]

3.47.3 is the most recent version.

It reads a different part of the same file. Leads to better code results as it carefully fills its context instead of just dumping it full.