Use Grok Build as a terminal‑first coding agent for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior from Grok Build’s CLI (or its editor integrations), with the Orq.ai dashboard available for deeper drill‑down.
Grok Build
Use Grok Build as a terminal‑first coding agent for your Orq.ai workspace. Query traces, run evals, inspect deployments, and debug production LLM behavior from Grok Build’s CLI (or its editor integrations), with the Orq.ai dashboard available for deeper drill‑down.
MCP server (remote Streaming HTTP / SSE via Grok’s Remote MCP Tools)
Setup time
Quick setup once Grok Build 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 Grok Build’s tool / MCP configuration, depending on how you register the server.
Skills support
Grok Build can call Orq MCP tools when the Orq server is registered and enabled in its tool configuration.
Cloud-based
Grok Build 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 Grok Build at different Orq workspaces or environments.
Vendor
xAI
Pricing
Included with supported Orq.ai workspaces. Grok Build is open‑source; confirm availability of MCP‑style tool integrations in your Grok Build setup and Orq plan.
Why Connect Grok Build to Orq.ai?
Keep your team in one editor
Stop switching between Grok Build, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same terminal agent that already plans, edits, and runs your code.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Grok Build turn that intent into Orq MCP tool calls. No SDKs to learn and no Orq API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Grok Build workflows. From the CLI or Grok Build’s VS Code integration, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions or headless scripts.
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. Grok Build brings that visibility into the same environment that’s already orchestrating builds, tests, and deployments.
Keep your team in one editor
Stop switching between Grok Build, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same terminal agent that already plans, edits, and runs your code.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Grok Build turn that intent into Orq MCP tool calls. No SDKs to learn and no Orq API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Grok Build workflows. From the CLI or Grok Build’s VS Code integration, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions or headless scripts.
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. Grok Build brings that visibility into the same environment that’s already orchestrating builds, tests, and deployments.
Keep your team in one editor
Stop switching between Grok Build, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same terminal agent that already plans, edits, and runs your code.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Grok Build turn that intent into Orq MCP tool calls. No SDKs to learn and no Orq API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Grok Build workflows. From the CLI or Grok Build’s VS Code integration, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions or headless scripts.
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. Grok Build brings that visibility into the same environment that’s already orchestrating builds, tests, and deployments.
Keep your team in one editor
Stop switching between Grok Build, the Orq.ai dashboard, and separate eval scripts. Query traces, run experiments, and inspect deployments from the same terminal agent that already plans, edits, and runs your code.
Ask operational questions in natural language
Ask questions like “Show me yesterday’s failed agent runs grouped by error type” and let Grok Build turn that intent into Orq MCP tool calls. No SDKs to learn and no Orq API URLs to memorize.
Connect evals to your development workflow
Use Orq.ai inside your existing Grok Build workflows. From the CLI or Grok Build’s VS Code integration, pull trace data, design and run evals, and kick off experiments as part of your normal coding sessions or headless scripts.
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. Grok Build brings that visibility into the same environment that’s already orchestrating builds, tests, and deployments.
Setup
1: Install Grok Build
Install the Grok CLI from xAI. macOS / Linux: bash curl -fsSL https://x.ai/cli/install.sh | bash Windows (PowerShell): powershell irm https://x.ai/cli/install.ps1 | iex Then sign in: bash grok /login or export an API key if you’re in a non‑browser environment: bash export XAI_API_KEY=”xai-…” grok
Ensure the following:
Grok Build’s configuration file or settings for tools / MCP servers is accessible
You know where to define new tool / server entries
The environment where Grok Build 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 Grok Build to access. Export it in your shell so Grok can reference it: bash export ORQ_API_KEY=”<your-orq-api-key>“ You can use different env vars (like ORQ_API_KEY_DEV, ORQ_API_KEY_PROD) if you want separate keys per environment.
Keep this key handy; you’ll reference it in Grok Build’s configuration or via an environment variable,
for example:
export ORQ_API_KEY="<your-orq-api-key>"
3: Add the Orq MCP server to Grok Build
Grok Build uses “Remote MCP Tools” to connect to third‑party MCP servers. In your Grok tool or model configuration, you define a remote MCP server entry with: A conceptual JSON example (exact structure depends on which Grok SDK or config file you’re using): json { “tools”: [ { “type”: “remote_mcp”, “server_url”: “https://my.orq.ai/v2/mcp”, “server_label”: “orq”, “allowed_tool_names”: [], “env”: { “ORQ_API_KEY”: “<your-orq-api-key>“ } } ] } Once configured, Grok Build will know that an MCP server labeled orq is available at https://my.orq.ai/v2/mcp and can route tool calls there when your prompts require Orq functionality.
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 Grok Build or reload your editor’s extension so the assistant picks up the new server.
4: Start using Orq.ai tools from Grok Build
From a Grok Build session in your project directory: bash grok -p “List the top failure modes in my Orq traces over the last 24 hours.” Ask Grok to: “List available Orq tools and query recent failed agent runs.” “Run Orq evals on this agent configuration and summarize the results.” If your remote MCP tool is configured correctly, Grok Build will call Orq’s MCP tools as part of these requests.
“What tools do you have access to?” or
“Use Orq to list yesterday’s failed agent runs grouped by error type.”
If configured correctly, Grok Build will call Orq’s MCP tools and show results inline in your IDE.
What Can You Do with Orq.ai + Grok Build
Query observability data in natural language
Use Grok Build 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 keep that context in the same terminal session where you’re editing and running code.
Design and run evaluations
Describe the behavior you want to test, let Grok Build and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without leaving the Grok CLI.
Compare prompts, models, and configs
From Grok Build, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai for more information.
Generate reusable synthetic datasets
Ask Grok Build 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 Grok Build. 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 Grok Build 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 keep that context in the same terminal session where you’re editing and running code.
Design and run evaluations
Describe the behavior you want to test, let Grok Build and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without leaving the Grok CLI.
Compare prompts, models, and configs
From Grok Build, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai for more information.
Generate reusable synthetic datasets
Ask Grok Build 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 Grok Build. 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 Grok Build 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 keep that context in the same terminal session where you’re editing and running code.
Design and run evaluations
Describe the behavior you want to test, let Grok Build and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without leaving the Grok CLI.
Compare prompts, models, and configs
From Grok Build, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai for more information.
Generate reusable synthetic datasets
Ask Grok Build 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 Grok Build. 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 Grok Build 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 keep that context in the same terminal session where you’re editing and running code.
Design and run evaluations
Describe the behavior you want to test, let Grok Build and Orq.ai scaffold evaluators and datasets, then run evals against your deployments without leaving the Grok CLI.
Compare prompts, models, and configs
From Grok Build, create experiments that compare prompts, models, or configurations, run them on real or synthetic datasets, and inspect results in Orq.ai for more information.
Generate reusable synthetic datasets
Ask Grok Build 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 Grok Build. 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.
Grok Build Direct vs With Orq.ai MCP
Capability
Grok Build alone
Grok Build + Orq.ai MCP
Query production LLM traces
No built‑in view into Orq.ai’s observability data.
Ask Grok Build to list, filter, and group Orq.ai traces (failures, slow runs, agent tool calls, etc.) from your terminal sessions.
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 Grok Build sessions.
Generate synthetic eval data
You can prompt Grok Build 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 bring those numbers into Grok Build’s reports or logs.
Run evaluators on datasets
No built‑in concept of Orq evaluators or datasets.
Work with Orq evaluators and datasets from Grok Build, depending on the Orq MCP tools enabled.
FAQs
Do I have to use Grok Build to get value from Orq.ai?
No. Orq.ai works on its own through the UI and API. Grok Build is an optional terminal‑first agent front‑end for your workspace. You get the same experiments, evals, and observability in Orq; Grok Build simply lets teams drive them from xAI’s coding agent using natural language.
What can Grok Build see in my Orq.ai workspace, and how is access controlled?
Grok Build 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, Grok Build 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 Grok Build at different Orq environments (dev, staging, prod)?
Yes. You can create separate remote MCP tool entries (or use different env vars) per Orq project or environment, then choose which one Grok Build uses for a given session or script. That way, you can run evals and inspect traces in dev or staging first, then switch the same Grok Build setup to the production environment.
Does Grok Build connect to Orq directly from my machine?
Yes. Grok Build runs in your local terminal and connects to remote MCP servers over the network using Remote MCP Tools. 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.
