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| title: The Agora | |
| emoji: 🌍 | |
| colorFrom: red | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.33.0 | |
| app_file: app.py | |
| pinned: true | |
| license: mit | |
| short_description: Where artificial minds gather to forge wisdom | |
| # The Agora: Where artificial minds gather to forge wisdom | |
| ### TRACK : mcp-server-track | |
| ## 🌟 Project Overview | |
| Agora, also known as "AI Democracy," is an innovative Gradio-based server designed to foster collaborative decision-making among diverse large language models (LLMs). | |
| Imagine an "AI Council" where specialized AI agents deliberate and vote on complex problems, providing reasoned arguments, highlighting disagreements, and ultimately arriving at a synthesized consensus. | |
| This system transcends the limitations of single-model outputs by leveraging the unique strengths of various LLMs, making it perfect for scenarios demanding nuanced. | |
| ## ✨ Features | |
| Multi-Model AI Council: Orchestrates a diverse panel of AI models, each playing a specific role: | |
| - *Anthropic Claude: Specialized in ethical considerations and moral reasoning.* | |
| - *OpenAI GPT (e.g., GPT-4o): Excels in creative problem-solving and brainstorming novel solutions.* | |
| - *Mistral: Focused on robust technical analysis and detailed breakdowns.* | |
| - *Sambanova: Provides rapid, high-throughput inference and quick factual recall.* | |
| - *Hyperbolic Labs (placeholder for specialized models): Integrated for highly specialized tasks or domain-specific knowledge.* | |
| - *Orchestrated AI Debates: | |
| Facilitates structured dialogues and 'debates' between AI models, allowing them to present arguments and counter-arguments.* | |
| - *Transparent Reasoning: Each model's individual reasoning, thought process, and initial stance are transparently displayed.* | |
| - *Disagreement Highlight: Clearly identifies areas of disagreement between models, providing insights into differing perspectives.* | |
| - *Final Consensus & Synthesis: Synthesizes the collective insights and votes into a consolidated, consensus-driven final answer.* | |
| - *Gradio User Interface: Provides an intuitive and interactive web interface for users to submit problems and view the council's deliberations.* | |
| ## 🚀 Workflow: | |
| - *How Agora Reaches Consensus:* | |
| - *Agora operates through a sophisticated, multi-stage process to transform a complex problem into a collective AI consensus.* | |
| - *The system acts as a Multi-Council Orchestration Protocol (MCP) server, managing the flow between the user interface and the various AI models.* | |
| Here's a conceptual workflow: | |
|  | |
| - *User Problem Submission (Gradio UI):* | |
|  | |
|  | |
| IMAGE | |
| A user submits a complex problem or query via the Gradio web interface. The input is typically a natural language prompt, potentially with accompanying data. | |
| Image Description: | |
| A screenshot of a Gradio interface with an input text box for the user's problem and a "Submit" button. | |
|  | |
| - *Problem Parsing & Initial Distribution (MCP Orchestrator):* | |
| - *The MCP Orchestrator (a custom backend server) receives the user's problem.* | |
| - *It parses the input and determines the initial context for the AI Council.* | |
| Based on pre-defined roles, the orchestrator dispatches the problem to specific models or groups of models for initial analysis and proposals. For instance, Claude might get an ethical framing, GPT a creative angle, and Mistral a technical breakdown. | |
| - *A diagram showing the MCP Orchestrator sending the problem to multiple distinct AI models.* | |
|  | |
| - *Individual Model Reasoning & Proposals:* | |
| - *Each designated AI model processes the problem based on its specialty.* | |
| - *Models generate their initial solutions, ethical considerations, technical analyses, or creative approaches.* | |
| - *These individual outputs (including their 'reasoning' and 'confidence scores' if applicable) are sent back to the MCP Orchestrator.* | |
| Debate Orchestration (MCP Orchestrator): Everything happens at backend and Final winner response is displayed in frontend | |
| The orchestrator initiates a multi-turn 'debate' or 'review' phase. | |
| - *Round 1 (Initial Review): Each model's proposal is shared (anonymously or attributed) with other relevant models.* | |
| - *Round 2 (Rebuttal & Refinement): Models respond to critiques, refine their initial proposals, or adjust their positions.* | |
| Image Description: A visual representation of AI models exchanging arguments, possibly with arrows indicating flow of information and feedback loops. | |
| - *Voting & Consensus Formation:* | |
| - *After the debate rounds, the orchestrator prompts each AI model to "vote" on the most optimal solution or to provide a final, refined recommendation.* | |
| - *A consensus algorithm (e.g., majority vote, weighted average based on model confidence/role importance, or a final synthesis by a designated 'moderator' AI) is applied to derive the final collective decision. Disagreements are explicitly logged.* | |
| Result Presentation (Gradio UI): | |
| - *The MCP Orchestrator sends the complete deliberation log, including:-* | |
| - *Each model's initial reasoning.* | |
| - *Key arguments and counter-arguments during the debate.* | |
| - *Areas of significant disagreement.* | |
| - *The final, synthesized consensus or voted-upon solution.* | |
| - *Gradio renders this information to the user in a clear, structured, and interactive format.* | |
| - *A Gradio output screen showing a structured summary of the AI council's deliberation and the final consensus.* | |
|  | |
|  | |
| ## 🛠️ Technologies Used | |
| Frontend: Gradio (for interactive web interface) | |
| Backend: Custom Python MCP Orchestrator (Flask/FastAPI recommended for server implementation) | |
| ### AI Models (via APIs): | |
| - *Anthropic Claude* | |
| - *OpenAI GPT (e.g., GPT-4o)* | |
| - *Mistral AI* | |
| - *Sambanova (or similar, e.g., via Hugging Face Inference API)* | |
| - *Hyperbolic Labs (or other specialized custom models/APIs)* | |
| ## 🎯 Potential Use Cases | |
| - *Medical Diagnoses: AI council reviewing patient data, lab results, and symptoms to propose the most likely diagnosis, considering ethical implications, treatment creativity, and technical accuracy.* | |
| - *Legal Advice: Analyzing case details, precedents, and laws to provide comprehensive legal advice, weighing ethical considerations and strategic options.* | |
| - *Business Strategy: Developing complex business plans, marketing strategies, or investment decisions by leveraging creative, analytical, and ethical AI perspectives.* | |
| - *Scientific Research: Formulating hypotheses, designing experiments, and interpreting results across various scientific disciplines.* | |
| ## ⚙️ Setup and Installation | |
| 1. Clone the repository: | |
| ``` | |
| git clone https://huggingface.co/spaces/Agents-MCP-Hackathon/TheAgora | |
| cd .\TheAgora\ | |
| ``` | |
| 2. Install dependencies: | |
| ``` | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Run the MCP App: | |
| ``` | |
| python app.py | |
| ``` | |
| ## 🤝 Contributing | |
| Aditya Katkar\ | |
| [Github](https://github.com/Addyk-24)\ | |
| [LinkedIn](https://www.linkedin.com/in/aditya-katkar-673930340) |