Use Cline as an IDE assistant 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.
Cline
Use Cline as an IDE assistant 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 Cline 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 Cline’s tool / MCP configuration, depending on how you register the server.
Skills support
Cline can call Orq MCP tools when the Orq server is registered and enabled in its tool configuration.
Cloud-based
Cline 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 Cline at different Orq workspaces or environments.
Vendor
Cline
Pricing
Included with supported Orq.ai workspaces. Cline is open‑source; confirm availability of MCP‑style tool integrations in your Cline setup and Orq plan.
Why Connect Cline to Orq.ai?
Keep your team in one editor
Stop switching between Cline, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE‑native assistant 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 Cline translate 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 Cline workflows. From the assistant 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. Cline brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Cline, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE‑native assistant 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 Cline translate 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 Cline workflows. From the assistant 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. Cline brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Cline, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE‑native assistant 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 Cline translate 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 Cline workflows. From the assistant 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. Cline brings that visibility into the same environment where you write and review code.
Keep your team in one editor
Stop switching between Cline, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same IDE‑native assistant 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 Cline translate 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 Cline workflows. From the assistant 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. Cline brings that visibility into the same environment where you write and review code.
Setup
1: Install Cline
Install Cline in your editor (for example, as a VS Code extension or local agent) and make sure it’s able to call external tools or MCP‑style servers in your setup.
Ensure the following:
Cline’s configuration file or settings for tools / MCP servers is accessible
You know where to define new tool / server entries
The environment where Cline 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 Cline to access.
Keep this key handy; you’ll reference it in Cline’s configuration or via an environment variable,
for example:
export ORQ_API_KEY="<your-orq-api-key>"
3: Add the Orq MCP server to Cline
Cline is configured by defining tools or MCP‑style servers in its configuration (for example, a JSON/YAML file, or a settings block in your editor). You’ll add Orq as a remote MCP server using the Orq MCP endpoint and your API key.
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 Cline or reload your editor’s extension so the assistant picks up the new server.
4: Start using Orq.ai tools from Cline
Open your editor’s Cline panel or command palette and ask:
“What tools do you have access to?” or
“Use Orq to list yesterday’s failed agent runs grouped by error type.”
If configured correctly, Cline will call Orq’s MCP tools and show results inline in your IDE.
What Can You Do with Orq.ai + Cline
Query observability data in natural language
Use Cline 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 Cline 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 Cline, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need deeper drill‑down.
Generate reusable synthetic datasets
Ask Cline to create challenging synthetic test cases for a workflow, 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 Cline. Pull recent traces for a deployment, 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 Cline 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 Cline 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 Cline, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need deeper drill‑down.
Generate reusable synthetic datasets
Ask Cline to create challenging synthetic test cases for a workflow, 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 Cline. Pull recent traces for a deployment, 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 Cline 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 Cline 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 Cline, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need deeper drill‑down.
Generate reusable synthetic datasets
Ask Cline to create challenging synthetic test cases for a workflow, 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 Cline. Pull recent traces for a deployment, 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 Cline 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 Cline 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 Cline, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai when you need deeper drill‑down.
Generate reusable synthetic datasets
Ask Cline to create challenging synthetic test cases for a workflow, 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 Cline. Pull recent traces for a deployment, filter by failure pattern, and use those examples to guide prompt or model changes backed by experiments and evals.
Cline Direct vs With Orq.ai MCP
Capability
Cline alone
Cline + Orq.ai MCP
Query production LLM traces
No built‑in view into Orq.ai’s observability data.
Ask Cline 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 Cline sessions.
Generate synthetic eval data
You can prompt Cline 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 Cline.
Run evaluators on datasets
No built‑in concept of Orq evaluators or datasets.
Work with Orq evaluators and datasets from Cline, depending on the MCP tools enabled.
FAQs
Do I have to use Cline to get value from Orq.ai?
No. Orq.ai works on its own through the UI and API. Cline is an optional IDE front‑end for your workspace. You get the same experiments, evals, and observability in Orq; Cline simply lets teams drive them from their editor using natural language.
What can Cline see in my Orq.ai workspace, and how is access controlled?
Cline 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, Cline 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 Cline at different Orq environments (dev, staging, prod)?
Yes. You can create separate Orq configurations (different server names or env vars) per environment, then select or enable the one you need per project. That way, you can run evals and inspect traces in dev or staging first, then switch the same Cline setup to the production environment.
Does Cline connect to Orq directly from my machine?
