Use Kilo 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.
Kilo Code
Use Kilo 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.
Quick setup once Kilo 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 Kilo Code’s tool / MCP configuration, depending on how you register the server.
Skills support
Kilo Code can call Orq MCP tools when the Orq server is registered and enabled in its tool configuration.
Cloud-based
Kilo 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 Kilo Code at different Orq workspaces or environments.
Vendor
Kilo Code
Pricing
Included with supported Orq.ai workspaces. Kilo Code is open‑source; confirm availability of MCP‑style tool integrations in your Kilo Code setup and Orq plan.
Why Connect Kilo Code to Orq.ai?
Keep your team in one editor
Stop switching between Kilo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE 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 Kilo Code’s agent 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 Kilo Code workflows. From the AI panel, 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. Kilo Code brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Kilo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE 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 Kilo Code’s agent 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 Kilo Code workflows. From the AI panel, 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. Kilo Code brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Kilo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE 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 Kilo Code’s agent 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 Kilo Code workflows. From the AI panel, 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. Kilo Code brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Kilo Code, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE 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 Kilo Code’s agent 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 Kilo Code workflows. From the AI panel, 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. Kilo Code brings that visibility into the same environment where you write and review code.
Setup
1: Install Kilo Code
Install Kilo Code as an extension in your IDE (for example, VS Code or a compatible VS Code‑series IDE), then sign in or configure your model provider as usual. Enable MCP support:
Ensure the following:
Kilo Code’s configuration file or settings for tools / MCP servers is accessible
You know where to define new tool / server entries
The environment where Kilo 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 Kilo Code to access. Keep this key handy; you’ll reference it in Kilo’s MCP config 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 Kilo 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 Kilo Code
Kilo Code gives you two main ways to add MCP servers: via the MCP Marketplace UI or by editing the MCP JSON config directly. Option A – Via the MCP Marketplace and settings UI Open Settings → MCP Server in Kilo Code. Open the MCP Marketplace. If Orq is listed as a server, select it and click Install. If Orq is not listed, go to the Install tab and choose Edit Global MCP or Project MCP; this opens the MCP JSON config file for the chosen scope. Paste the Orq MCP server JSON configuration, pointing to Orq’s MCP endpoint (for example https://my.orq.ai/v2/mcp) and referencing your API key. Make sure the server type for remote HTTP is compatible with Kilo (for example streamable-http for remote servers, as used by other MCP integrations like GitHub MCP): Option B – Edit the MCP JSON config directly Kilo Code stores MCP configuration in JSON files that you can open from the Edit Global MCP or Project MCP options in the MCP settings. Add an Orq entry using a remote transport, following the pattern Kilo uses for other remote MCP servers (like GitHub MCP). A conceptual example: json { “mcpServers”: { “orq”: { “type”: “streamable-http”, “url”: “https://my.orq.ai/v2/mcp”, “env”: { “ORQ_API_KEY”: “<your-orq-api-key>“ } } } } Save the file, then reload MCP servers from the Kilo UI or restart your IDE so the agent picks up the new server.
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 Kilo Code or reload your editor’s extension so the assistant picks up the new server.
4: Start using Orq.ai tools from Kilo Code
Open the Kilo Code AI panel or command palette and ask: Kilo Code will call Orq’s MCP tools if everything is configured correctly.
“What tools do you have access to?” or
“Use Orq to list yesterday’s failed agent runs grouped by error type.”
If configured correctly, Kilo Code will call Orq’s MCP tools and show results inline in your IDE.
What Can You Do with Orq.ai + Kilo Code
Query observability data in natural language
Use Kilo 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 editor.
Design and run evaluations
Describe the behavior you want to test, let Kilo 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 Kilo 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 Kilo Code 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 Kilo 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 Kilo 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 editor.
Design and run evaluations
Describe the behavior you want to test, let Kilo 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 Kilo 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 Kilo Code 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 Kilo 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 Kilo 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 editor.
Design and run evaluations
Describe the behavior you want to test, let Kilo 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 Kilo 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 Kilo Code 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 Kilo 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 Kilo 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 editor.
Design and run evaluations
Describe the behavior you want to test, let Kilo 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 Kilo 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 Kilo Code 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 Kilo 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.
Kilo Code Direct vs With Orq.ai MCP
Capability
Kilo Code alone
Kilo Code + Orq.ai MCP
Query production LLM traces
No built‑in view into Orq.ai’s observability data.
Ask Kilo 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 Kilo Code sessions.
Generate synthetic eval data
You can prompt Kilo 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 Kilo Code
Run evaluators on datasets
No built‑in concept of Orq evaluators or datasets.
Work with Orq evaluators and datasets from Kilo Code, depending on the MCP tools enabled.
FAQs
Do I have to use Kilo Code to get value from Orq.ai?
No. Orq.ai works on its own through the UI and API. Kilo Code is an optional IDE front‑end for your workspace. You get the same experiments, evals, and observability in Orq; Kilo Code simply lets teams drive them from their editor using natural language.
What can Kilo Code see in my Orq.ai workspace, and how is access controlled?
Kilo 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, Kilo 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 Kilo Code at different Orq environments (dev, staging, prod)?
Yes. You can create separate Orq entries in the MCP JSON config (or use different API keys per project), then enable the one you need per workspace. That way, you can run evals and inspect traces in dev or staging first, then switch the same Kilo Code setup to the production environment.
Does Kilo Code connect to Orq directly from my machine?
Yes. Kilo Code runs as a local IDE extension and connects to remote MCP servers over the network. 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.
