metadata

title: Multi-Agent AI Collaboration System
colorFrom: gray
colorTo: gray
sdk: gradio
sdk_version: 5.41.1
app_file: app.py
pinned: false
license: mit
short_description: Specialized AI agents collaborate to solve complex problems

Multi-Agent AI Collaboration System

This project is an enterprise-grade simulation of a multi-agent AI system designed to solve complex problems through collaboration. It features a team of specialized AI agents, each with a distinct role, working together under the direction of a central coordinator. The system demonstrates intelligent task decomposition, parallel workflow execution, and the synthesis of diverse information into a single, actionable output.

This is a simulation environment created for demonstration and educational purposes. It showcases an advanced software architecture for AI collaboration.

Core Features

This system models a complete, end-to-end collaborative workflow with the following key components:

Specialized Agent Team: The system is composed of a team of five distinct agents, each powered by a Large Language Model (e.g., GPT-4) to fulfill a specific role:
- Researcher: Gathers comprehensive, objective information on a given topic.
- Analyst: Analyzes the collected information to identify patterns, trends, and key insights.
- Critic: Provides critical evaluation and quality assurance, identifying weaknesses, gaps, and potential biases in the analysis.
- Synthesizer: Integrates the diverse findings from all agents into a single, coherent, and actionable narrative or plan.
- Coordinator: The master agent that manages the entire workflow, from problem decomposition to final output.
Intelligent Task Decomposition: When presented with a complex problem, the CoordinatorAgent uses its LLM to break it down into a series of smaller, actionable sub-tasks, each assigned to the most appropriate specialist agent.
Parallel Workflow Execution: The system supports both sequential and parallel task execution. In parallel mode, it uses a thread pool to run independent tasks concurrently, significantly improving performance and mimicking the efficiency of a real-world team.
Advanced Performance Analytics: A built-in PerformanceTracker monitors the system's efficiency, tracking metrics like task completion time, agent utilization, and collaboration frequency. It compares the system's performance against a pre-defined single-agent baseline to quantify the benefits of the multi-agent approach.
Interactive Visualization Dashboard: The entire process is visualized through a comprehensive Gradio dashboard, featuring:
- An interactive collaboration network graph showing agent interactions.
- A task execution timeline (Gantt chart).
- A performance heatmap for comparing agent contributions.
Comprehensive PDF Reporting: A ReportGenerator module can produce detailed, professional PDF reports of the analysis, including an executive summary, key findings, agent contributions, and final recommendations.
Full Demo Mode: The system can be run entirely in a "Demo Mode" without requiring an OpenAI API key. In this mode, agents produce high-quality, pre-defined simulated results, allowing for full exploration of the system's architecture and features.

How It Works

The platform follows a logical, coordinated workflow that mirrors a high-functioning team of human experts:

Initialization: The user initializes the system, either by providing an OpenAI API key for "Live Mode" or by selecting "Demo Mode." The CoordinatorAgent then creates and registers its team of specialist agents.
Problem Decomposition: The user inputs a complex problem. The CoordinatorAgent analyzes the problem and breaks it into a dependency graph of sub-tasks, assigning each to the agent best suited for the job (e.g., research tasks to the Researcher, analysis tasks to the Analyst).
Coordinated Execution: The Coordinator executes the workflow plan. In "Parallel" mode, it dispatches all tasks with resolved dependencies to the available agents for concurrent processing. In "Live Mode", this involves agents making parallel calls to the OpenAI API.
Information Sharing & Collaboration: As agents complete tasks, their results are shared with other relevant agents via an internal messaging system. For example, the Critic automatically receives the Analyst's output for evaluation. This entire process is tracked in the collaboration network.
Synthesis and Final Output: The SynthesizerAgent receives the findings and critiques from all other agents and is tasked with creating the final, integrated result, which includes an executive summary and actionable recommendations.
Visualization and Reporting: The Gradio interface updates in real-time to display the collaboration graph, task timeline, and performance metrics. Once the analysis is complete, the user can generate a full PDF report summarizing the entire process and its outcomes.

Technical Stack

The project leverages a modern stack for AI, data processing, and visualization:

AI & Language Models: LangChain, langchain-openai, OpenAI
Web Interface & Dashboard: Gradio
Data & Computation: Pandas, NumPy, SciPy
Visualization: Plotly, NetworkX, Matplotlib
PDF Reporting: ReportLab
Asynchronous Processing: asyncio, aiohttp
Large File Management: The repository is configured with Git LFS to handle large model or data files efficiently.

How to Use the Demo

Configure the System: In the main interface, either enter your OpenAI API key to run in "Live Mode" or check the "Demo Mode" box to use the system without a key. Click "Initialize Agents".
Analyze a Problem: Navigate to the "Problem Analysis" tab.
- Enter a complex problem statement in the text box.
- Alternatively, go to the "Example Problems" tab and click "Load This Example" to use a pre-defined problem.
Run the Analysis: Click the "Analyze Problem" button. Watch as the status updates and the visualization panes populate with data.
Review the Results:
- Examine the Agent Collaboration Network to see how the agents interacted.
- Analyze the Task Execution Timeline to understand the workflow's duration and parallelism.
- Review the Performance Metrics and Performance Comparison charts to see how the system performed.
Drill Down: Go to the "Agent Details" tab to see status, performance, and recent tasks for each individual agent.
Generate a Report: Go to the "Report Generation" tab, select the sections you wish to include, and click "Generate PDF Report" to download a comprehensive summary of the analysis.

Disclaimer

This project is a simulation designed to demonstrate a sophisticated multi-agent system architecture. It is for educational and illustrative purposes only and does not represent a commercial product. The analysis and recommendations generated by the system should not be considered professional advice.