Use Roo Code as an IDE interface for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior without leaving your editor, with the Orq.ai dashboard available for deeper drill‑down.
Roo Code
Use Roo Code as an IDE interface for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior without leaving your editor, with the Orq.ai dashboard available for deeper drill‑down.
MCP server (remote Streamable HTTP / HTTP command)
Setup time
Quick setup once Roo Code is installed/configured in your editor and an Orq API key is set.
Auth
Orq.ai API key (workspace‑ or project‑level) passed as a bearer token via environment variables or Roo Code’s tool / MCP configuration, depending on how you register the server.
Skills support
Roo Code can call Orq MCP tools when the Orq server is registered and enabled in its tool configuration.
Cloud-based
Roo Code runs inside your editor, while MCP requests from the assistant call Orq’s cloud APIs for workspace data.
Multi‑workspace
Define multiple Orq configurations (for example, different tool entries or env vars) with different API keys to point Roo Code at different Orq workspaces or environments.
Vendor
Roo Code
Pricing
Included with supported Orq.ai workspaces. Roo Code is open‑source; confirm availability of MCP‑style tool integrations in your Roo Code setup and Orq plan.
Why Connect Roo Code to Orq.ai?
Keep your team in one editor
Stop switching between Roo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same VS Code environment your team already uses.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Roo Code’s agents turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Roo Code workflows. From Roo’s Ask/Code/Architect modes, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.
Keep production behavior visible
Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Roo Code brings that visibility into the same environment where you plan, write, and refactor code.
Keep your team in one editor
Stop switching between Roo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same VS Code environment your team already uses.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Roo Code’s agents turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Roo Code workflows. From Roo’s Ask/Code/Architect modes, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.
Keep production behavior visible
Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Roo Code brings that visibility into the same environment where you plan, write, and refactor code.
Keep your team in one editor
Stop switching between Roo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same VS Code environment your team already uses.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Roo Code’s agents turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Roo Code workflows. From Roo’s Ask/Code/Architect modes, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.
Keep production behavior visible
Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Roo Code brings that visibility into the same environment where you plan, write, and refactor code.
Keep your team in one editor
Stop switching between Roo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same VS Code environment your team already uses.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Roo Code’s agents turn that intent into Orq MCP tool calls. No SDKs to learn and no API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Roo Code workflows. From Roo’s Ask/Code/Architect modes, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions.
Keep production behavior visible
Orq.ai gives teams visibility into MCP‑driven activity, including which tools ran, when they ran, and which key or workspace triggered them. Roo Code brings that visibility into the same environment where you plan, write, and refactor code.
Setup
1: Install Roo Code
Install Roo Code in VS Code and sign in / configure your models.
Ensure the following:
Roo Code’s configuration file or settings for tools / MCP servers is accessible
You know where to define new tool / server entries
The environment where Roo Code runs can reach https://my.orq.ai over the network
2: Create an Orq.ai API key
In Orq.ai, create an API key for the workspace or project you want Roo Code to access. Keep this key handy; you’ll reference it in Roo’s MCP configuration or via an environment variable, for example: bash export ORQ_API_KEY=”<your-orq-api-key>“
Keep this key handy; you’ll reference it in Roo Code’s configuration or via an environment variable,
for example:
export ORQ_API_KEY="<your-orq-api-key>"
3: Add the Orq MCP server to Roo Code
Roo Code uses an MCP Servers panel with both global and project‑level JSON configs. Roo gives you two main ways to configure MCP: Option A – Via Roo Code’s MCP settings (UI) 1: In VS Code, open the Roo Code panel. 2: Click the MCP Servers icon in the Roo toolbar. 3: At the bottom of the MCP settings view, choose your configuration level:
A typical configuration pattern looks like:
Name: orq; Type / transport: HTTP or Streamable HTTP; URL / endpoint: https://my.orq.ai/v2/mcp; Auth: Authorization: Bearer <your-orq-api-key> (often provided via ORQ_API_KEY)
Restart Roo Code or reload your editor’s extension so the assistant picks up the new server.
4: A JSON editor will appear with a mcpServers object (similar to other MCP servers like Bright Data Web MCP).
5: Inside mcpServers, add an Orq entry: json { “mcpServers”: { “orq”: { “command”: “npx”, “args”: [ “orq-mcp-client” ], “env”: { “ORQ_API_KEY”: “<your-orq-api-key>“, “ORQ_MCP_URL”: “https://my.orq.ai/v2/mcp” } } } } This pattern mirrors how Roo Code starts other remote MCP servers with npx and env vars. The exact client command may differ depending on how you wrap Orq’s MCP endpoint (direct Streamable HTTP client vs a small node proxy), but the key pieces are the URL and ORQ_API_KEY. 6: Save the file. Back in the MCP settings view, ensure Enable MCP Servers is turned on and that orq appears in the list. 7: Toggle orq on to start it. Roo Code will prompt you the first time an Orq tool runs; approve the request to continue. Option B – Edit .roo/mcp.json directly You can also commit MCP configuration with your repo. At the project root, create .roo/mcp.json: json { “mcpServers”: { “orq”: { “command”: “npx”, “args”: [ “orq-mcp-client” ], “env”: { “ORQ_API_KEY”: “<your-orq-api-key>“, “ORQ_MCP_URL”: “https://my.orq.ai/v2/mcp” } } } } This project‑level config takes precedence over global settings for that workspace. After saving, open the Roo MCP settings and confirm orq is listed and enabled. 4: Start using Orq.ai tools from Cursor With Orq configured: If everything is configured correctly, Roo Code will start the orq MCP server and call Orq’s tools, showing results in your editor.
“What tools do you have access to?” or
“Use Orq to list yesterday’s failed agent runs grouped by error type.”
If configured correctly, Roo Code will call Orq’s MCP tools and show results inline in your IDE.
What Can You Do with Orq.ai + Roo Code
Query observability data in natural language
Use Roo Code to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your VS Code session.
Design and run evaluations
Describe the behavior you want to test, let Roo Code and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.
Compare prompts, models, and configs
From Roo Code, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.
Generate reusable synthetic datasets
Ask Roo Code’s agents to create challenging synthetic test cases for workflows such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.
Debug production regressions as a team
When something breaks, stay in Roo Code. Pull recent traces for a deployment via Orq, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.
Query observability data in natural language
Use Roo Code to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your VS Code session.
Design and run evaluations
Describe the behavior you want to test, let Roo Code and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.
Compare prompts, models, and configs
From Roo Code, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.
Generate reusable synthetic datasets
Ask Roo Code’s agents to create challenging synthetic test cases for workflows such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.
Debug production regressions as a team
When something breaks, stay in Roo Code. Pull recent traces for a deployment via Orq, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.
Query observability data in natural language
Use Roo Code to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your VS Code session.
Design and run evaluations
Describe the behavior you want to test, let Roo Code and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.
Compare prompts, models, and configs
From Roo Code, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.
Generate reusable synthetic datasets
Ask Roo Code’s agents to create challenging synthetic test cases for workflows such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.
Debug production regressions as a team
When something breaks, stay in Roo Code. Pull recent traces for a deployment via Orq, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.
Query observability data in natural language
Use Roo Code to “talk” to your Orq.ai traces. Ask for failed agent runs, slowest requests over the last 24 hours, or errors grouped by model, then apply those insights directly in your VS Code session.
Design and run evaluations
Describe the behavior you want to test, let Roo Code and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without moving into a separate tool.
Compare prompts, models, and configs
From Roo Code, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need more detail.
Generate reusable synthetic datasets
Ask Roo Code’s agents to create challenging synthetic test cases for workflows such as contract analysis or support tickets, and save them as reusable Orq.ai datasets.
Debug production regressions as a team
When something breaks, stay in Roo Code. Pull recent traces for a deployment via Orq, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.
Roo Code Direct vs With Orq.ai MCP
Capability
Roo Code alone
Roo Code + Orq.ai MCP
Query production LLM traces
No built‑in view into Orq.ai’s observability data.
Ask Roo Code to list, filter, and group Orq.ai traces (failures, slow runs, agent tool calls, etc.) from inside the IDE.
Run experiments on prompts
Teams can iterate on prompts manually in chat, but no native experiment tracking in Orq.ai.
Create and run Orq.ai experiments comparing prompts, models, or configs against datasets, directly from your Roo Code sessions.
Generate synthetic eval data
You can prompt Roo Code to generate examples, then copy/paste them elsewhere.
Generate synthetic test cases and save them as reusable Orq.ai datasets for evals and experiments.
Pull cost and usage analytics
No view into Orq router or deployment analytics.
Query Orq.ai’s cost, usage, and performance metrics for models and deployments via MCP tools, then inspect them alongside your code in Roo Code
Run evaluators on datasets
No built‑in concept of Orq evaluators or datasets.
Work with Orq evaluators and datasets from Roo Code, depending on the MCP tools enabled.
FAQs
Do I have to use Roo Code to get value from Orq.ai?
No. Orq.ai works on its own through the UI and API. Roo Code is an optional IDE front‑end for your workspace. You get the same experiments, evals, and observability in Orq; Roo Code simply lets teams drive them from VS Code using natural language.
What can Roo Code see in my Orq.ai workspace, and how is access controlled?
Roo Code only sees what the Orq API key you configure is allowed to access. If you use a project‑level key scoped to a specific workspace or environment, Roo Code can only query traces, experiments, datasets, and deployments inside that scope. Rotate or revoke the key in Orq to instantly cut off access.
Can I point Roo Code at different Orq environments (dev, staging, prod)?
Yes. You can create separate Orq entries in global mcp_settings.json and/or per‑project .roo/mcp.json, or use different API keys per project, then enable the one you need in the MCP Servers panel. That way, you can run evals and inspect traces in dev or staging first, then switch the same Roo Code setup to the production environment.
Does Roo Code connect to Orq directly from my machine?
Yes. Roo Code runs as a VS Code extension and connects to remote MCP servers over the network by starting MCP processes or HTTP clients. Your Orq MCP endpoint must be reachable over the public internet (or via your network/VPN), and Orq handles authentication and scoping via your API key.
