How to Parse Resumes Using Manus AI and Fastio Metadata Views

Only 17% of organizations have deployed AI agents to date, despite aggressive plans by over 60% to adopt them within the next two years [Gartner Press Release 2025-10-21]. The massive gap between pilot projects and production systems stems from a single challenge: the handoff. Many AI agents run in isolated containers. When the agent completes its run, the container terminates, and any files generated during execution are deleted.

This is especially true in recruitment. If an autonomous agent parses a resume and ranks a candidate, the resulting files must go somewhere permanent. Candidate evaluations are traditionally siloed on local computers or inside separate SaaS dashboards. Ephemeral sandboxes purge all local files upon termination, leading to data leaks or loss of candidate history. Connecting ephemeral agent sandboxes to persistent storage prevents file loss when task containers terminate [Fast.io Resources].

Recruiters need a pipeline that moves files from sandboxes to shared workspaces automatically. While developers could build custom integrations with Amazon S3 or local disks, these options lack user friendly interfaces. S3 requires complex access controls, bucket policies, and IAM roles, while local storage limits collaboration and siloes data. Google Drive struggles with webhook latency and does not support agent-friendly MCP tools. Fast.io solves this by providing a version-controlled cloud workspace where humans and agents collaborate.

The pipeline starts by sending resumes to Manus AI. The Manus AI v2 API allows file attachments and outputs structured JSON data via its API [Manus AI Official Documentation]. To process a resume, the system must upload the PDF file to Manus and launch a parsing task.

The upload process requires two steps. First, the application requests a presigned upload URL from Manus by calling the /v2/file.upload endpoint. Second, the application uploads the binary PDF file to the returned URL.

Here is a Node.js example showing how to request an upload URL, upload the file, and create a task:

import fetch from 'node-fetch';
import fs from 'fs';
async function uploadAndParse(filePath) {
  const uploadResponse = await fetch('https://api.manus.ai/v2/file.upload', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.MANUS_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({ filename: 'resume.pdf' })
  });
  const { upload_url, file_id } = await uploadResponse.json();
  const fileStream = fs.createReadStream(filePath);
  await fetch(upload_url, {
    method: 'PUT',
    headers: { 'Content-Type': 'application/pdf' },
    body: fileStream
  });
  const taskResponse = await fetch('https://api.manus.ai/v2/task.create', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.MANUS_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      prompt: 'Extract candidate details, including name, email, phone, years of experience, and primary skills. Output the results as structured JSON.',
      attachments: [file_id]
    })
  });
  const task = await taskResponse.json();
  return task.id;
}

Using the file identifier ensures the agent receives the correct resume context. The agent parses the document, isolates the requested attributes, and outputs the structured candidate profile. Teams can configure storage for agents on Fast.io to centralize these incoming files.

Manus AI utilizes an advanced vision-language processing engine to read handwritten text, scanned images, and multi-page PDFs. To ensure high-quality parsing, write prompts that instruct the agent to return JSON conforming to standard recruiter schemas. For candidates who omit contact details, instruct the agent to output null values rather than guessing.

When Manus AI finishes parsing the resume, it triggers a webhook to notify the system. Webhooks are essential for building reactive workflows without polling. The system must listen for this notification, extract the temporary download links, and move the files to permanent storage.

Fast.io simplifies this step with its Cloud Import features. Instead of downloading files locally and uploading them to Fast.io, the integration service calls the Fast.io API endpoint /imports/url. Fast.io then pulls the files directly from Manus, saving server bandwidth and memory.

Here is a Node.js example of a webhook handler that receives the task completion notification, retrieves the file URLs, and sends them to Fast.io:

import express from 'express';
import fetch from 'node-fetch';
const app = express();
app.use(express.json());
app.post('/webhook/manus', async (req, res) => {
  const { event, task_id } = req.body;
  if (event !== 'task.completed') {
    return res.status(200).send('Event ignored');
  }
  const taskDetailsResponse = await fetch(`https://api.manus.ai/v2/tasks/${task_id}`, {
    headers: { 'Authorization': `Bearer ${process.env.MANUS_API_KEY}` }
  });
  const { files } = await taskDetailsResponse.json();
  for (const file of files) {
    await fetch('https://mcp.fast.io/mcp/key', {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${process.env.FASTIO_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        jsonrpc: '2.0',
        id: 1,
        method: 'tools/call',
        params: {
          name: 'upload',
          arguments: {
            action: 'web-import',
            url: file.download_url,
            path: `/Recruitment/Resumes/${file.name}`
          }
        }
      })
    });
  }
  res.status(200).send('Handoff complete');
});

This automated transition secures the files. The documents are now safely stored in Fast.io. The team can collaborate on candidate files using comments and attachments directly in the workspace. Fast.io version history ensures that if a candidate submits an updated resume, the platform appends it as a new version, preserving the audit trail without cluttering the folder.

When handling high-volume recruitment pipelines, processing services should implement exponential backoff retry logic. If a temporary network drop occurs during URL import, the handler retries the transfer, ensuring candidate profiles are not lost.

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Moving resumes to the cloud is only the first step. To make this data useful, recruitment teams need to query it. Fast.io Metadata Views turn documents into a live, queryable database and support real-time sorting and filtering by attributes [Fast.io Document Data Extraction Product Page].

Metadata Views differ from Intelligence Mode. While Intelligence Mode auto indexes files for semantic search and summaries, Metadata Views are the structured extraction layer. The feature extracts typed schema columns from files without requiring custom OCR rules or templates. Use Fast.io Metadata Views to configure this system.

To set up a candidate database, the team defines the extraction schema in natural language. Users describe the fields they want, and Fast.io's AI builds a typed schema that supports the following formats:

• Text: For candidate names, university names, and previous company titles.

• Integer: For years of professional experience and coding language counts.

• Decimal: For cumulative GPA scores and performance ratings.

• Boolean: For relocation willingness and visa sponsorship requirements.

• URL: For candidate portfolio sites and LinkedIn profiles.

• JSON: For skill tags, certification lists, and historical timeline details.

• Date & Time: For graduation dates, availability dates, or interview schedules.

Once defined, Fast.io processes uploaded resumes and populates the spreadsheet view. Recruiters can sort candidates by years of experience or filter them by specific skills. Adding new columns is simple: if you add a relocation column later, Fast.io extracts it from existing documents dynamically without reprocessing files, saving API token costs.

The pipeline allows human recruiters and developers to work alongside AI agents. Fast.io workspaces are org owned, meaning all candidate data belongs to the company, not individual accounts. Human team members review files, leave comments anchored to PDF page numbers, and edit Collaborative Notes in real time. The append-only audit log records every action, keeping hiring processes auditable.

Fast.io does not offer a permanent free plan. Every organization runs on a paid subscription. The plans are Starter for $29/mo, Business for $99/mo, and Growth for $299/mo [Fast.io Pricing]. Every organization gets a 14-day free trial that requires a credit card [Fast.io Pricing].

Developers can build and test this system using a free user account. The agent flow works as follows: the developer signs up, configures the workspace, sets up the Metadata Views, and tests the Manus pipeline. Once complete, the developer uses the ownership transfer feature to hand over the organization to the human client. The client enters their credit card to start the paid subscription and 14-day free trial [Fast.io Pricing]. The developer can retain administrator access to manage the pipeline. Refer to the Fast.io pricing page for a detailed comparison of features.