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Multi Ai Agent

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository provides a Python implementation of a multi-agent AI system built from scratch to demonstrate how agentic systems can be assembled without external orchestration frameworks. It uses OpenAI's GPT-4o as the underlying language model and a Streamlit web interface to let users submit tasks, view outputs, and receive validation feedback. The system centers on an Agent Manager that routes requests to specialized main agents and corresponding validator agents. Primary domain examples in the repo focus on medical workflows, including summarizing medical texts, drafting research articles, and sanitizing Protected Health Information (PHI). The project includes logging, example Streamlit app code, and setup instructions so learners can run the app locally with an OpenAI API key and inspect how agent coordination, validation, and logging are implemented.

Links

Stars
15
App URL
https://github.com/milojeBtc/Multi-Ai-Agent
Github Repository
https://github.com/milojeBtc/Multi-Ai-Agent/blob/main/README.md

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App Details

Features
The codebase implements key capabilities for a compact multi-agent demo: a Streamlit user interface for task submission and result display; an Agent Manager that delegates work to specialized agents; three main agents for summarization, article drafting, and PHI sanitization; validator agents paired with each main agent to check quality, refine drafts, and confirm PHI removal; robust logging configured with loguru and stored in a logs/ directory including multi_agent_system.log; dependency and environment guidance requiring Python 3.8+, an OpenAI API key, and installation via requirements.txt; example architecture diagrams and clear usage steps that show how to run streamlit run app.py and inspect agent interactions.
Use Cases
For developers and learners interested in agent design, this repository is a hands-on resource demonstrating how to build multi-agent workflows without relying on orchestration libraries. It is helpful for prototyping domain-specific pipelines by showing agent decomposition, validator patterns, and centralized coordination via an Agent Manager. The included medical examples illustrate practical uses: generating concise summaries of long texts, creating and refining research article drafts, and removing PHI from datasets. Streamlit makes the system easy to run and test locally while loguru logging provides traceability for debugging and quality monitoring. The project also documents installation and environment setup so users with an OpenAI API key can reproduce and adapt the architecture for other tasks.

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