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athena-core

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Basic Information

Athena is a production-ready general-purpose AI agent designed to execute real tasks rather than only provide analysis. The repository provides the core runtime, plugin system and configuration to run Athena locally or in production so users can move from idea to results. It supports terminal interaction and browser-based demos and includes example configurations to connect to language models, web search, scheduling and automation services. The codebase exposes plugins for LLM access, browser automation, file system and Python execution so Athena can perform web scraping, data analysis, file transformations, scheduled monitoring and bot interactions. The README emphasizes practical examples and a quick start path to clone, install dependencies, copy a config file and launch the agent to interact via a CLI.

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App URL
https://github.com/Athena-AI-Lab/athena-core
Github Repository
https://github.com/Athena-AI-Lab/athena-core/blob/main/README.md

App Details

Features
Athena ships with a plugin-based architecture and a list of ready integrations for model access, web search, browser automation and system control. Core features called out include command-line computer control, file and folder access, Python code execution, web browser automation and scheduled tasks. It supports short-term memory for context retention, chat with other language models, and bot functionality for Telegram and Discord. The project provides a configurable minimal config example showing plugins such as cerebrum (LLM), clock, http web search, short-term-memory, file-system, python, shell, browser and a CLI UI. The repo also documents headless/headful browser options and recommends configuration for production use.
Use Cases
Athena is helpful for automating and orchestrating practical workflows that combine web data, code and system operations. The README lists concrete use cases like finding and summarizing top GitHub repositories, searching discussion sites and summarizing threads, checking flight prices daily with trend visualizations, plotting financial indicators, collecting weather history and recommending travel times, translating Word documents while preserving formatting, scraping bestseller lists and generating reading lists, and training a digit recognition model with PyTorch. These examples illustrate how Athena bridges analysis and execution, enabling users to schedule monitors, run experiments, build bots and integrate model-driven automation into real tasks. Community docs and a roadmap signal ongoing improvements in memory, multimodal capabilities and browser reliability.

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