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Basic Information

This repository implements an autonomous AI agent designed to manage social media accounts starting with Twitter. The agent reads mentions, replies, and direct messages, generates and publishes tweets using integrated large language models, maintains rolling memory and conversational context, monitors topics and hashtags, and engages with users through likes and replies. It also supports cryptocurrency tipping by generating and securely storing a wallet and private keys, sending small transactions to users who meet criteria, and tracking transaction history. The project is written in TypeScript for type safety and scalability. The stated long-term goal is to expand beyond Twitter to multiple social platforms and to allow integration with different LLM providers and blockchain networks.

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App Details

Features
The codebase is a TypeScript backend with async/await patterns and a modular structure. It includes Twitter API integration using a twitter-api-v2 client and LLM integration via the Claude API for context-aware generation and custom prompt engineering. Wallet and transaction management is implemented with ethers.js, plus environment-based configuration and secure key handling. The architecture is event-driven with periodic mention fetching, rate limiting, exponential backoff, retry logic, persistent state and conversation tracking, and audit logging. Safety features include prompt and code-level guardrails, content filtering, moderation, and error handling. The project is intended to be extensible to other platforms and LLMs.
Use Cases
This project automates social media engagement tasks to reduce manual workload for account owners and communities. It can autonomously publish content, respond to user interactions with contextual replies, monitor topics of interest, and perform community-building actions like liking and replying. Built-in wallet management enables micro-tipping via cryptocurrency, allowing reward flows tied to interaction criteria while tracking transactions for accountability. Persistence of conversation state and memory improves continuity in interactions. Safety controls, rate limiting, and audit logs help manage abuse and compliance risk. The TypeScript foundation and modular services make it straightforward for contributors to add new conversation strategies, platforms, or security improvements.

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