ReacherX Open-Sourced: Intelligent People Search Tool for X/Twitter and LinkedIn

Summary:ReacherX is an open-source platform designed to help users find specific individuals on X/Twitter and LinkedIn through live searches against current data, ensuring up-to-date results. Unlike traditional tools that rely on static prospect databases, ReacherX uses natural language descriptions to generate ideal profiles and match scores, making it ideal for non-sales professionals. It leverages LLM technology to qualify and enrich search results and supports automated outreach workflows, offering

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ReacherX: An Open-Source Solution for Intelligent People Search

ReacherX, developed by Salman, is an open-source people search tool designed to address the challenge of finding specific individuals on X/Twitter and LinkedIn. Here are the key features and technical highlights of the tool:

Core Features

• Natural Language Description of Targets: Users can describe the people they are looking for in simple English, and ReacherX will generate ideal user profiles and example mock profiles.
• Real-Time Search and Dynamic Updates: Unlike tools that rely on static databases, ReacherX performs live searches on X/Twitter and LinkedIn, ensuring the results are up-to-date.
• Intelligent Matching and Scoring: Leveraging LLM technology, ReacherX qualifies search results and generates match scores (70% and above are surfaced, 90% and above can generate outreach plans).
• Automated Outreach Workflows: Built-in features include DMs, replies, liking/commenting, voice notes, and media attachments. Users can choose to have the Agent handle outreach or do it themselves through the UI.
• Configurable Rate Limits: All operations run through the user's own account, with configurable rate limits to avoid platform flags.

Technical Architecture

ReacherX is built with a full-stack TypeScript stack, based on the Next.js and Convex frameworks. Its core components include:

• Convex Agent: Handles intelligent search and user interactions.
• Workflows and Workpool: Supports continuous running search tasks.
• Memory/RAG: Provides memory functionality, adjusting search strategies based on search performance and user feedback.

Challenges and Future Directions

• Scalability Challenges: The current architecture faces challenges in handling large-scale search tasks, and the developer is exploring optimization strategies.
• Social Data API Costs: The cost of X/Twitter's API is high, and the developer relies on third-party APIs to reduce costs.

Industry Impact and Developer Recommendations

ReacherX's release brings new possibilities to the field of AI-driven social network search, with significant implications in the following areas:

• Non-Salesperson Friendly: Lowers the barrier for non-professionals to use people search tools.
• Open-Source Ecosystem: By being open-source, ReacherX encourages developers to participate in improving and extending its functionality, driving innovation in people search tools.

For developers, ReacherX provides an excellent open-source platform for building more complex AI-driven social network applications. It is recommended to pay attention to its architectural design, LLM integration, and the implementation details of automated outreach workflows to gain inspiration and practical experience.

Conclusion

ReacherX is an innovative open-source people search tool that offers an efficient and precise solution through intelligent matching and automated outreach workflows. Its open-source nature makes it an important tool in the AI and social network fields, pushing the development of people search tools further.

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Source: GitHub AI Trending Releases (2026-09-22)

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Tags: #ReacherX #Open-Source Tool #People Search #AI Agent #LLMs & Foundation Models