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news-agents

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Basic Information

This repository is an experimental terminal-based news aggregation system that demonstrates how to orchestrate multiple agents to fetch and summarize articles using Amazon Q, the Model Context Protocol (MCP), and tmux. The project runs entirely in the terminal and is designed to show a main agent that reads feed URLs from feeds.txt, splits work into chunks, spawns sub agents in separate tmux panes, and collects per-feed summaries into a central digest. It includes feed-specific processors in the src folder for sources such as Hacker News, TechCrunch, Wired, and the Wall Street Journal. The README documents how to install and run Amazon Q, synchronize dependencies, start the agent chat session, and add context files to steer the multi-agent workflow.

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App URL
https://github.com/eugeneyan/news-agents
Github Repository
https://github.com/eugeneyan/news-agents/blob/main/README.md

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App Details

Features
The system uses Amazon Q CLI as an agent harness and MCP as the tool/interface for parsing RSS feeds and invoking feed processors. Tmux is used to create visible parallel tmux panes so a main agent can monitor sub agents in real time. The main agent reads feeds.txt, splits feeds into three chunks, spawns three sub agents, and aggregates results. Sub agents download articles, parse items using feed-specific handlers in src, write summaries to summaries/[feed-name].md, and report back to the main agent which produces main-summary.md. The repo contains context files to seed agent behavior and includes debugging commands such as running MCP in dev mode. The README also describes dependency sync and basic validation commands.
Use Cases
This project is helpful as a practical demonstration and starting point for developers learning to build and orchestrate multi-agent pipelines in the terminal. It shows how to parallelize feed processing, monitor agent activity with tmux, and combine distributed outputs into a single digest. The per-source parsers and context instructions make it straightforward to extend the system to new feeds or to customize summarization behavior. It provides concrete commands to set up Amazon Q and MCP, examples for debugging, and a simple output structure with individual summaries and a consolidated main-summary.md, making it useful for prototyping agent-driven news aggregation workflows.

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