What is the Article Assistant?
The Article Assistant is a multi-agent system designed to assist users in drafting articles and conducting web research using a custom Agent-to-Agent (A2A) communication protocol and a Model Context Protocol (MCP)-inspired architecture.
How to use the Article Assistant?
Users can interact with the system through a Streamlit web interface, where they can submit requests for article drafts or web research on specific topics. The system processes these requests through a Supervisor LLM for validation and then delegates tasks to specialized agents.
Key features of the Article Assistant?
- Article Drafting: Automatically generates article drafts based on user-defined topics and styles, saving them to Google Cloud Storage.
- Web Research: Conducts simulated web research and provides summarized findings.
- Input Validation: Ensures user inputs are safe and correctly formatted before processing.
- Asynchronous Processing: Handles tasks asynchronously, allowing for efficient management of multiple requests.
- Microservice Architecture: Built using Docker for easy deployment and scalability.
Use cases of the Article Assistant?
- Drafting articles for blogs or academic purposes.
- Conducting research on various topics to gather information quickly.
- Automating content generation for marketing or educational materials.
FAQ from the Article Assistant?
- What technologies are used in the Article Assistant?
The system is built using Python, FastAPI, Langchain, and Docker, leveraging Anthropic LLMs for various functionalities.
- Is the Article Assistant free to use?
Yes, the Article Assistant is open-source and available for anyone to use.
- How does the A2A communication work?
The A2A protocol allows different agents within the system to communicate and coordinate tasks effectively, ensuring efficient processing of user requests.