Article Assistant
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Orchestrates multiple specialized agents to draft articles and conduct web research using a custom communication protocol and an MCP-inspired architecture.
关于
Article Assistant is a multi-agent system designed to automate article drafting and web research tasks. It utilizes a custom Agent-to-Agent (A2A) communication protocol for seamless inter-agent coordination and a Model Context Protocol (MCP)-inspired architecture to interact with external tool servers, enabling tasks such as article generation, web searching, and cloud storage. The system is built using Python, Langchain, Anthropic LLMs, FastAPI, and Docker, providing a robust and scalable solution for automated content creation and research.
主要功能
- Utilizes an MCP-inspired architecture for interacting with external tool servers.
- Employs a custom Agent-to-Agent (A2A) protocol for inter-agent communication.
- Performs simulated web research and summarizes findings.
- 0 GitHub stars
- Generates article drafts on specific topics and styles, saving them to Google Cloud Storage.
- Validates user inputs with a Supervisor LLM to ensure safety and accuracy.
- Asynchronous task processing with UI polling for updates
使用案例
- Automated drafting of articles on various topics.
- Building custom LLM-powered agent systems with specialized tasks.
- Conducting automated web research for information gathering.
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