Bolt.new AI and Screaming Frog integration: Step-by-Step Guide 2025

You cannot “integrate Bolt.new with Screaming Frog” directly because Screaming Frog is a desktop application that does not expose a native API for external control. The real, practical integration pattern is: export data from Screaming Frog → feed that data into a Bolt.new app → process, enrich, or transform it with your own logic or LLM-powered workflows. If you want automation, Screaming Frog provides a Command Line Interface (CLI)API-like scheduled automation via config files, which you can orchestrate from a Bolt.new backend using standard subprocess calls or scripts running outside Bolt. The integration is file-based and task-based, not live API-based.

 

What This Actually Means

 

Screaming Frog SEO Spider is a desktop crawler. It does not publish a REST API, GraphQL API, SDK, or webhooks. That’s why Bolt.new (or any external system) cannot “connect” to it over the network. But Screaming Frog has two very real integration surfaces:

• Exports (CSV, XLSX, JSON, custom extraction, bulk export folders)
• CLI mode (run crawls headless, output files automatically)

Bolt.new apps can read + parse these files, run AI/LLM logic on them, and build dashboards or workflows around the data. That’s the valid, correct method.

 

Integration Pattern That Actually Works

 

Here is the real pattern most teams use:

• You run a crawl in Screaming Frog (manually or via CLI).
• Screaming Frog outputs data to a folder (CSV/XLSX/JSON).
• You upload those exports into your Bolt.new app (via a file upload input).
• Your backend code parses the file and uses AI logic to analyze, cluster, or generate recommendations.

This pattern is stable, secure, and doesn’t rely on any fictional API.

 

How to Use Screaming Frog CLI for Automated Flows

 

If you want repeatable automation, you can call Screaming Frog from a local machine or server using its CLI:

ScreamingFrogSEOSpiderCli \
 --crawl https://example.com \
 --output-folder "/Users/me/results" \
 --headless \
 --save-crawl \
 --export-tabs "Internal:All" \
 --export-format csv

This produces consistent CSV files that your Bolt.new backend can ingest.

 

How to Process Screaming Frog Files Inside Bolt.new

 

Inside a Bolt.new project, you build a standard file-upload endpoint or UI. Example backend using Node/Express (Bolt’s default stack works fine):

// routes/upload.js
import express from "express";
import multer from "multer";
import csv from "csv-parser"; // npm install csv-parser

const router = express.Router();
const upload = multer({ dest: "uploads/" });

router.post("/upload", upload.single("file"), async (req, res) => {
  const results = [];

  // Parse the Screaming Frog CSV
  fs.createReadStream(req.file.path)
    .pipe(csv())
    .on("data", (row) => {
      results.push(row);
    })
    .on("end", () => {
      // Now you can send this to your LLM / processing logic
      res.json({ rows: results });
    });
});

export default router;

Then your front‑end simply provides a file input.

 

How to Add AI Processing (Bolt.new’s Actual Value)

 

Once you have the data parsed, you can send rows into an LLM for analysis. Example (OpenAI SDK):

import OpenAI from "openai";

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

export async function getSeoInsights(rows) {
  const prompt = `
Here is Screaming Frog crawl data.
Generate technical SEO insights and group issues by type.

Rows:
${JSON.stringify(rows.slice(0, 50))} // send only a sample to avoid overloading the model
`;

  const response = await client.chat.completions.create({
    model: "gpt-4.1",
    messages: [{ role: "user", content: prompt }]
  });

  return response.choices[0].message.content;
}

This is where Bolt.new becomes useful: orchestrating real workflows using real data.

 

Optional: Automate Importing Using a Watch Folder (Outside Bolt)

 

If you want full automation:

• Screaming Frog CLI dumps CSV into a folder.
• A local script detects new files and sends them to your Bolt.new backend via REST.

Example watcher script:

import fs from "fs";
import axios from "axios";

fs.watch("./results", async (event, filename) => {
  if (filename.endsWith(".csv")) {
    const file = fs.createReadStream(`./results/${filename}`);

    await axios.post("https://your-bolt-app.com/upload", file, {
      headers: { "Content-Type": "multipart/form-data" }
    });
  }
});

This part runs on your machine or server, not inside Bolt.

 

Summary

 

You integrate Bolt.new with Screaming Frog by using file-based exports or CLI-driven automated exports, then parsing those files inside a Bolt.new application. There is no native API, no direct connection, and nothing “automatic” — but the file-based workflow is reliable and the industry standard. Once the data is inside Bolt, you can perform any AI, reporting, or transformation workflow you want.