Bumble Advanced Search

This project provides an automated workflow that enhances and accelerates Bumble Advanced Search tasks on Android devices. It solves the bottleneck of manually filtering profiles and applying structured criteria at scale. The result is a consistent, fast, and repeatable profile discovery pipeline that improves accuracy and saves significant labor hours.

Introduction

This automation tool performs optimized search filtering on the Bumble Android app, applying criteria such as distance, interests, age ranges, and profile attributes without requiring manual interaction. It removes the repetitive workflow of navigating menus, applying filters, and scanning profiles. The primary benefit is higher throughput and consistent data collection for research, analytics, or lead-generation cases that rely on Bumble Advanced Search signals.

Automated Mobile Filtering & Discovery

• Executes filter adjustments with deterministic UI interactions via Appilot/UI Automator.
• Applies multi-criteria search rules with precise timing and retry logic.
• Minimizes user input delays, accelerating end-to-end search cycles.
• Captures structured output in JSON or CSV form for downstream analysis.
• Supports optional proxy rotation and multi-device scaling.

Core Features

Feature	Description
UI Automation Engine	Uses Appilot/UI Automator actions to navigate and apply Bumble filters reliably.
ADB-less Interaction	Operates without requiring raw ADB commands by relying on high-level automation APIs.
Filter Rule Builder	Dynamically composes search criteria including age, distance, and interests.
Profile Data Parser	Extracts essential on-screen profile info and stores it in structured formats.
Multi-Device Scaling	Supports parallel execution through sharded task queues.
Scheduler Integration	Runs recurring jobs for continuous search cycles with automatic resets.
Proxy & Network Cycling	Optional network layer rotation to maintain stable, isolated sessions.
Error & Retry Mechanism	Applies exponential backoff, screenshot logging, and fallback steps.
Analytics Exporter	Outputs results as JSON/CSV for BI, ML, or CRM workflows.
Activity Logging	Centralized logs for events, warnings, error traces, and device-specific activity.

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How It Works

1. Input or Trigger — User or scheduler provides search criteria and execution interval.
2. Core Logic — Automation engine navigates Bumble, applies filters, scrolls profiles, and collects data.
3. Output or Action — Extracted profile metadata is saved into JSON/CSV for analysis.
4. Other Functionalities — Optional proxy cycling, multi-device routing, and async task handling.
5. Safety Controls — Includes action throttling, randomized waits, UI-state verification, and retry limits.

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Tech Stack

Language: Python Frameworks: Appilot, UI Automator, lightweight async workers Tools: Scheduler, proxy manager, structured logger Infrastructure: Local or distributed Android device farm, optional containerized workers

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Directory Structure

automation-bot/
├── src/
│   ├── main.py
│   ├── automation/
│   │   ├── tasks.py
│   │   ├── scheduler.py
│   │   └── utils/
│   │       ├── logger.py
│   │       ├── proxy_manager.py
│   │       └── config_loader.py
├── config/
│   ├── settings.yaml
│   ├── credentials.env
├── logs/
│   └── activity.log
├── output/
│   ├── results.json
│   └── report.csv
├── requirements.txt
└── README.md

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Use Cases

• Data analysts use it to gather structured profile samples, so they can generate behavioral insights.
• Growth teams use it to automate interest-based profile discovery, so they can optimize targeting experiments.
• Researchers use it to run controlled, rule-based searches, so they can examine demographic or location-based patterns.
• Operations teams use it to process large batches of search cycles, so they can reduce manual workload.

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FAQs

Does this require root access? No, it uses high-level automation frameworks without root permissions.

Can it run continuously? Yes, the scheduler supports recurring cycles with auto-recovery.

Is real-time monitoring included? Activity logs provide near real-time visibility into device actions.

Can it be integrated with a CRM? Exports in JSON/CSV make integration straightforward via ETL pipelines.

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Performance & Reliability Benchmarks

Execution Speed: Typically 20–35 actions/min on standard Android device farm setups. Success Rate: ~93–94% across long-running automation tasks with intelligent retries. Scalability: Designed for 300–1,000 Android devices via horizontally scaled workers and sharded queues. Resource Efficiency: Average 1 vCPU and 350–500 MB RAM per worker/device instance. Error Handling: Automatic retries, exponential backoff, structured logging, screenshot capture, and recovery flows ensure stability during UI drift or network inconsistencies.