workflow Automation for small businesses in Africa
# Multi-Agent Workflow Optimizer
An experimental multi-agent system built in python that simulates how specialized agents can plan, optimize, and execute tasks collaboratively.
This project started as a leraning exercise and has since grown into a more strucutured application with a backened (FastAPI), database logging, and a frontend interface for user interaction
## Purpose
The project's core purpose is to explore multi-agent systems in a practical way while learning modern development practices.
### Key goals:
- Understand object-oriented programming (oop) and agent-based design in python
- Simulate collaborative workflows between different agents (planner, Optimizer, Executor, Coordinator).
- Gain hands-on experience with FastAPI, databases, and frontend-backend integration.
- Practice software engineering principles: logging, modularity, and system design.
- Lay the foundation for future integration with AI/LLMs for smarter decision-making.
## What it does:
- Accepts a user query via the frontend (form input).
- Planner Agent: Interprets the query and creates a task plan.
- Optimizer Agent: Refines the plan for efficiency.
- Executor Agent: Simulates carrying out the task.
- Coordinator Agent: Logs the full workflow and maintains history.
- All actions are logged into a SQLite database for traceability.
- Users can view task history and workflow logs via API endpoints.
## Tech stack:
- Backend: FastAPI (Python)
- Frontend: JS + Bootstrap (form-based interface)
- Database: SQLite (lightweight logging & workflow storage)
- Server: Uvicorn (ASGI server for FastAPI)
- Architecture: Modular agent-based design with orchestrator
## Usage
1. Run the FastAPI app:
uvicorn main:app --reload
2. Open the frontend (with form connected to /ai-simulate)
3. Enter a query
4. Agents collaborate to: Plan-optimize-execute-log
5. View results in the UI or query logs via endpoints
## Current Features
- Multi-agent collaboration (Planner, Optimizer, Executor, Coordin …