Lightning-Fast Local Agents: Groq Desktop + Vectorize

Groq Desktop gives you fast access to Groq’s models — but what if those models could actually answer questions about your team’s internal documents?

In this post, we’ll show how to connect Groq Desktop to a Vectorize agent, so you can ask real questions about changelogs, design specs, and runbooks — and get structured answers with citations, right on your desktop. No UI to build. No fine-tuning. Just a fast, desktop-native agent experience.

Ask Questions Like These

Once connected, Groq Desktop becomes a chat window into your organization’s knowledge. Some real queries you might try:

“What were the most recent changes to our auth service?”
→ Cites two changelogs and a migration note, scoped by metadata.

“Where are the runbooks for the payment stack?”
→ Filters to document_type: runbook and service: payment.

“What is our current API rate limit policy?”
→ Pulls a paragraph from the latest internal API spec.

Vectorize makes this possible with structured context and metadata filters like team, service, document_type, and last_updated.

How It Works

Vectorize agents can expose a structured search tool over your docs. Groq Desktop connects via MCP and gives you a conversational interface. Here’s the setup:

Step 1: Create a Vectorize Agent

Create an MCP agent in the Vectorize UI or via the API. This agent hosts your tools and manages retrieval over your data. This agent will host your tools and manage reasoning over your data.

Step 2: Create a New Tool for Your Agent

Add a tool to the agent and point it at the pipeline it should query. You can expose standard parameters (e.g., question, k) and link custom parameters to pipeline metadata (e.g., team, service) for precise filtering.

Step 3: Run an MCP Server

Open Groq Desktop’s settings.json and add an entry for your Vectorize agent:

"mcpServers": {
  "vectorize-mcp": {
    "command": "npx",
    "args": [ "-y", "mcp-remote@latest", "https://agents.vectorize.io/api/agents/YOUR_AGENT_ID/mcp", ... ],
    "env": { "VECTORIZE_API_KEY": "YOUR_API_KEY" },
    "transport": "stdio"
  }
}

This tells Groq Desktop to auto-start your Vectorize MCP agent whenever the app launches.

Step 4: Connect Groq Desktop

Restart Groq Desktop. You’ll see vectorize-mcp listed under Tools. From there, you can start chatting with your docs.

Why Speed Matters

Groq’s LPU™ is designed for low-latency inference. Paired with Vectorize retrieval, responses come back quickly — depending on model, context size, and network.

This makes agents practical for live development sessions and incident response. Instead of waiting, you get answers quickly enough to feel interactive.

Advanced: Metadata + Structure

Vectorize pipelines capture rich metadata. Combine this with Groq’s speed for tightly scoped queries:

{
  "metadata": {
    "team": "platform",
    "service": "auth",
    "document_type": "runbook",
    "last_updated": "2025-01-15"
  }
}

Then ask: ‘P0 runbooks for platform team updated this month’ → The tool scopes results to matching docs and responds quickly.

Use Cases

1. Incident Response
   “Show me the last 3 incidents similar to high memory usage in the payment service.”
2. Code Review Context
   “What are the downstream dependencies of the order-processing service?”
3. Onboarding Acceleration
   “How do I set up the local development environment for the API gateway?”

Try It Yourself!

Ready to test it out?

• Sign up for Vectorize.
• Install Groq Desktop.
• Follow our Groq Desktop integration guide to connect your first agent.
• Spin up an agent, point it at your docs, and get fast answers right in your desktop chat window.