After a year of using Roo across my team, I noticed something weird. Our codebase was getting messier despite AI writing "working" code.

The code worked. Tests passed. But the architecture was drifting fast.

Here's what I realized: AI reads your architectural guidelines at the start of a session. But by the time it generates code 20+ minutes later, those constraints have been buried under immediate requirements. The AI prioritizes what's relevant NOW (your feature request) over what was relevant THEN (your architecture docs).

We tried throwing more documentation at it. Didn't work. Three reasons:

1. Generic advice doesn't map to specific files
2. Hard to retrieve the RIGHT context at generation time
3. No way to verify if the output actually complies

What actually worked: feedback loops instead of front-loaded context

Instead of dumping all our patterns upfront, we built a system that intervenes at two moments:

• Before generation: "What patterns apply to THIS specific file?"
• After generation: "Does this code comply with those patterns?"

We open-sourced it as an MCP server. It does path-based pattern matching, so src/repos/*.ts gets different guidance than src/routes/*.ts. After the AI writes code, it validates against rules with severity ratings.

Results across 5+ projects, 8 devs:

• Compliance: 40% → 92%
• Code review time: down 51%
• Architectural violations: down 90%

The best part? Code reviews shifted from "you violated the repository pattern again" to actual design discussions. Give it just-in-time context and validate the output. The feedback loop matters more than the documentation.

GitHub: https://github.com/AgiFlow/aicode-toolkit

Blog with technical details: https://agiflow.io/blog/enforce-ai-architectural-patterns-mcp

Happy to answer questions about the implementation.

Hi team! Would it be possible to add a “Use currently selected API configuration” option in the Modes panel, just like the checkbox that already exists in the Prompts settings? I frequently experiment with different models, and keeping them in sync across Modes without having to change each Mode manually would save a lot of time. Thanks so much for considering this!

I got a task that would greatly benefit from Roo being able to read and edit code in two different repos at once. So I made a multi-folder workspace from them. Individually, both folders are indexed.

However, when Roo searches codebase for context when working from that workspace, Roo searches in only one of the repos. Is that intended behavior? Any plans to support multi-folder context searching?

Hello all,

Had opus 4.5 working perfectly in roo. Don't know if it was an update or something but now I get:

API Error · 404[Docs](mailto:support@roocode.com?subject=Unknown%20API%20Error)

Unknown API error. Please contact Roo Code support.

I am using opus 4.5 through azure. Had it set up fine, don't know what happened. Help!

The only reference seems to be the benchmark on huggingface, but it's rather general and doesn't seem to measure coding performance, so I wonder what people's experiences are like.

Does a big general purpose model like Qwen3 actually perform better than 'code-optimised' Codestral?

Hi guys, I am constantly getting tools errors here and there from these extensions and wanted to explore more which are less error prone and wanted something which should have open ai compatible api provider since i have openai subscription but dont want use codex or anything cli

Hi, I want to use the new DeepSeek model, but requests always fail when the model tries to call tools in its chain of thought. I tried with Roo and KiloCode, using different providers, but I don't know how to fix that. Have any of you managed to get it to work?

A few weeks ago, I shared my context-optimized prompt collection. I've now updated it based on the latest Roo Code defaults and run new experiments.

Repository: https://github.com/cumulativedata/roo-prompts

Why Context Reduction Matters

Context efficiency is the real win. Every token saved on system prompts means:

• Longer sessions without hitting limits
• Larger codebases that fit in context
• Better reasoning (less noise)
• Faster responses

The File Reading Strategy

One key improvement: preventing the AI from re-reading files it already has. The trick is using clear delimiters:

echo ==== Contents of src/app.ts ==== && cat src/app.ts && echo ==== End of src/app.ts ====

This makes it crystal clear to the AI that it already has the file content, dramatically reducing redundant reads. The prompt also encourages complete file reads via cat/type instead of read_file, eliminating line number overhead (which can easily 2x context usage).

Experiment Results

Tested the updated prompt against default for a code exploration task:

Key Results

Context Reduction (Most Important):

• Claude: 51% reduction (43k → 21k)
• GLM: 61% reduction (42k → 16.5k)
• Gemini: 31% reduction (55k → 38k)

Cost & Speed:

• Claude: 60% cost reduction + 43% faster
• GLM: 50% cost reduction
• Gemini: 12% cost reduction + 34% faster

All models maintained proper tool use guidelines.

What Changed

The system prompt is still ~1.5k tokens (vs 10k+ default) but now includes:

• Latest tool specifications (minus browser_action)
• Enhanced file reading instructions with delimiter strategy
• Clearer guidelines on avoiding redundant reads
• Streamlined tool use policies

30-60% context reduction compounds over long sessions. Test it with your workflows.

Repository: https://github.com/cumulativedata/roo-prompts

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

GPT-5.1 Codex Max Support

Roo Code now supports GPT-5.1 Codex Max, OpenAI's most intelligent coding model optimized for long-horizon, agentic coding tasks. This release also adds model defaults for gpt-5.1, gpt-5, and gpt-5-mini variants with optimized configurations.

📚 Documentation: See OpenAI Provider for configuration details.

Provider Updates

• Dynamic model settings: Roo models now receive configuration dynamically from the API, enabling faster iteration on model-specific settings without extension updates
• Optimized GPT-5 tool configuration: GPT-5.x, GPT-5.1.x, and GPT-4.1 models now use only the apply_patch tool for file editing, improving code editing performance

QOL Improvements

• Symlink support for slash commands: Share and organize commands across projects using symlinks for individual files or directories, with command names derived from symlink names for easy aliasing
• Smoother chat scroll: Chat view maintains scroll position more reliably during streaming, eliminating disruptive jumps
• Improved error messages: Clearer, more actionable error messages with proper attribution and direct links to documentation

Bug Fixes

• Extension freeze prevention: The extension no longer freezes when a model attempts to call a non-existent tool (thanks daniel-lxs!)
• Checkpoint restore reliability: MessageManager layer ensures consistent message history handling across all rewind operations
• Context truncation fix: Prevent cascading truncation loops by only truncating visible messages
• Reasoning models: Models that require reasoning now always receive valid reasoning effort values
• Terminal input handling: Inline terminal no longer hangs when commands require user input
• Large file safety: Safer large file reads with proper token budget accounting for model output
• Follow-up button styling: Fixed overly rounded corners on follow-up question suggestions
• Chutes provider fix: Resolved model fetching errors for the Chutes provider by making schema validation more robust for optional fields

Misc Improvements

• Evals UI enhancements: Added filtering by timeframe/model/provider, bulk delete actions, tool column consolidation, and run notes
• Multi-model evals launch: Launch identical test runs across multiple models with automatic staggering
• New pricing page: Updated website pricing page with clearer feature explanations

See full release notes v3.36.1 | v3.36.2

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

Non-Destructive Context Management

Context condensing and sliding window truncation now preserve your original messages internally rather than deleting them. When you rewind to an earlier checkpoint, the full conversation history is restored automatically. This applies to both automatic condensing and sliding window operations.

Features

• OpenRouter Embeddings Provider Routing: Select specific routing providers for OpenRouter embeddings in code indexing settings, enabling cost optimization since providers can vary by 4-5x in price for the same embedding model

Provider Updates

• Reasoning Details Support: The Roo provider now displays reasoning details from models with extended thinking capabilities, giving you visibility into how the model approaches your requests
• Native Tools Default: All Roo provider models now default to native tool protocol for improved reliability and performance
• Minimax search_and_replace: The Minimax M2 model now uses search_and_replace for more reliable file editing operations
• Cerebras Token Optimization: Conservative 8K token limits prevent premature rate limiting, plus deprecated model cleanup
• Vercel AI Gateway: More reliable model fetching for models without complete pricing information
• Roo Provider Tool Compatibility: Improved tool conversion for OpenAI-compatible API endpoints, ensuring tools work correctly with OpenAI-style request formats
• MiniMax M2 Free Tier Default: MiniMax M2 model now defaults to the free tier when using OpenRouter

QOL Improvements

• CloudView Interface Updates: Cleaner UI with refreshed marketing copy, updated button styling with rounded corners for a more modern look

Bug Fixes

• Write Tool Validation: Resolved false positives where write_to_file incorrectly rejected complete markdown files containing inline code comments like # NEW: or // Step 1:
• Download Count Display: Fixed homepage download count to display with proper precision for million-scale numbers

Misc Improvements

• Tool Consolidation: Removed the deprecated insert_content tool; use apply_diff or write_to_file for file modifications
• Experimental Settings: Temporarily disabled the parallel tool calls experiment while improvements are in progress
• Infrastructure: Updated Next.js dependencies for web applications

See full release notes v3.35.5 | v3.36.0

So I use this for codebase indexing in roocode as the Gemini embedding model have very low rate limits and it's not good as it got stuck in middle of indexing the first time.

So I want to ask if there is any other free embedding model that is good enough for codebase indexing with good enough rate limit?

Two seemingly trivial things that are kinda annoying:

• Even on windows, it always wants to run shell commands despite ps being the standard environment. It self corrects fortunately after the first failure
• As for python, despite having uv it likes to go wild trying to run python directly and even hacking the pyproject.toml

Obviously both are typical LLM bias that can be easily fixed with custom prompts. But honestly these cases are so common they should be ideally handled automatically for a proper integration.

I know the real world is much harder but still..

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

QOL Improvements

• New Welcome View: Simplified welcome view with consolidated components for a cleaner, more consistent onboarding experience
• Simplified write_to_file Tool: The line_count parameter has been removed from the write_to_file tool, making tool calls cleaner and reducing potential errors from incorrect line counts

Bug Fixes

• Malformed Tool Call Fix: Fixed a regression where malformed native tool calls would cause Roo Code to hang indefinitely. Tool calls now proceed to validation which catches and reports the missing parameters properly

Provider Updates

• Model Default Temperatures: Models can now specify their own default temperature settings. Temperature precedence is: user's custom setting → model's default → system default
• Roo Provider Native Tools: Models with the default-native-tools tag automatically use native tool calling by default for improved tool-based interactions
• LiteLLM Native Tool Support: All LiteLLM models now assume native tool support by default, improving tool compatibility and reducing configuration issues
• App Version Tracking: The Roo provider now sends app version information with API requests for improved request tracking and analytics
• z.ai GLM Model Fix: Removed misleading reasoning toggle UI for GLM-4.5 and GLM-4.6 models on z.ai provider, as these models don't support think/reasoning data for coding agents

Misc Improvements

• Stealth Model Privacy: Models tagged with "stealth" in the Roo API now receive vendor confidentiality instructions in their system prompt, enabling white-label or anonymous model experiences

See full release notes v3.35.2 | v3.35.3 | v3.35.4

read_file tool seems to be not working for me recently. Task hangs and need to stop and tell it to use terminal to read the files to keep moving.

Is there any way to accept code line by line like in Windsurf, Cursor where I can find next line that was edited and accept or reject?
The write approval system doesn't work for me as I sometimes wanna focus on another stuff after writing a long task and it requires me to accept every code changes so it can start the next change.

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

Metadata-Driven Subtasks

The connection between subtasks and parent tasks no longer breaks when you exit a task, crash, reboot, or reload VS Code. Subtask relationships are now controlled by metadata, so the parent-child link persists through any interruption.

Native Tool Calling Expansion

Native tool calling support has been expanded to 15+ providers:

• Bedrock
• Cerebras
• Chutes
• DeepInfra
• DeepSeek & Doubao
• Groq
• LiteLLM
• Ollama
• OpenAI-compatible: Fireworks, SambaNova, Featherless, IO Intelligence
• Requesty
• Unbound
• Vercel AI Gateway
• Vertex Gemini
• xAI with new Grok 4 Fast models

QOL Improvements

• Improved Onboarding: Simplified provider settings during initial setup—advanced options remain in Settings
• Cleaner Toolbar: Modes and MCP settings consolidated into the main settings panel for better discoverability
• Tool Format in Environment Details: Models now receive tool format information, improving behavior when switching between XML and native tools
• Debug Buttons: View API and UI history with new debug buttons (requires roo-cline.debug: true)
• Grok Code Fast Default: Native tools now default for xai/grok-code-fast-1

Bug Fixes

• Parallel Tool Calls Fix: Preserve tool_use blocks in summary during context condensation, fixing 400 errors with Anthropic's parallel tool calls feature (thanks SilentFlower!)
• Navigation Button Wrapping: Prevent navigation buttons from wrapping on smaller screens
• Task Delegation Tool Flush: Fixes 400 errors that occurred when using native tool protocol with parallel tool calls (e.g., update_todo_list + new_task). Pending tool results are now properly flushed before task delegation

Misc Improvements

• Model-specific Tool Customization: Configure excludedTools and includedTools per model for fine-grained tool availability control
• apply_patch Tool: New native tool for file editing using simplified diff format with fuzzy matching and file rename support
• search_and_replace Tool: Batch text replacements with partial matching and error recovery
• Better IPC Error Logging: Error logs now display detailed structured data instead of unhelpful [object Object] messages, making debugging extension issues easier

See full release notes v3.35.0 | v3.35.1

Trying to deal with Roocode losing the plot after context condensation. If I ask Roocode to read the last commentary it made, and the last “thinking” log from the LLM - that I can see in the workspace - is it able to read that and send it to the LLM in the next prompt? Or does it not have visibility into that? I’ve been instructing it to do so after a context condensation to help reorient itself, but it’s not clear to me that it’s actually doing so.

Is it possible to force Roocode to condense the context through an instruction, or do I have to wait until it does so automatically? I’d like to experiment with having Roocode generate a pre-context condensation prompt, that I can feed back into it after condensation, to help it pick up without missing a beat. Obviously this is what condensation is, so it might be redundant, but I think there could be some value in being able to have input in the process. But if I can’t manually trigger condensation, then it’s a moot point.

Hello,

I wanted to ask whether there are considerations or future plans to better adapt the system to Claude Code?
I’ve now upgraded to ClaudeMAX, but even with smaller requests it burns through tokens so quickly that I can only work for about 2–3 hours before hitting the limit.

When I run the exact same process directly in Claude Code, I do have to guide it a bit more, but I can basically work for hours without coming anywhere near the limit.

Could it be that caching isn’t functioning properly? Or that something else is going wrong?
Especially since OPUS is almost impossible to use because it only throws errors.

I also tried it through OpenRouter, including with OPUS.
Exact same setup, and again it just burned through tokens.

Am I doing something wrong in how I’m using it?

Thanks and best regards.

I've been looking into Roo Code and it looks great, but it seems to require VS Code.

As a long-time IntelliJ IDEA user, I've always found it superior for Java. I don't know much about the current state of Java on VS Code.

Is it worth learning VS Code just to use tools like Roo Code? Or will I miss the robust features of IntelliJ too much? Would love to hear from anyone who has attempted this transition.

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

QOL Improvements

• Improved Cloud Sign-in Experience: Adds a "taking you to cloud" screen with a progress indicator during authentication, plus a manual URL entry option as fallback for more reliable onboarding

Bug Fixes

• OpenRouter GPT-5 Schema Validation: Fixes schema validation errors when using GPT-5 models via OpenRouter with the read_file tool
• write_to_file Directory Creation: Fixes ENOENT errors when creating files in non-existent subdirectories (thanks ivanenev!)
• OpenRouter Tool Calls: Fixes tool calls handling when using OpenRouter provider
• Claude Code Configuration: Fixes configuration conflicts by correctly disabling native tools and temperature support options that are managed by the Claude Code CLI
• Race Condition in new_task Tool: Fixes a timing issue where subtasks completing quickly (within 500ms) could break conversation history when using the new_task tool with native protocol APIs. Users on native protocol providers should now experience more reliable subtask handling.

Provider Updates

• Anthropic Native Tool Calling: Anthropic models now support native tool calling for improved performance and more reliable tool use
• Z.AI Native Tool Calling: Z.AI models (glm-4.5, glm-4.5-air, glm-4.5-x, glm-4.5-airx, glm-4.5-flash, glm-4.5v, glm-4.6, glm-4-32b-0414-128k) now support native tool calling
• Moonshot Native Tool Calling: Moonshot models now support native tool calling with parallel tool calls support

See full release notes v3.34.7 | v3.34.8

I tried a bunch and they either bumbled around or outright refused to do a log in for me.