What is A2a Mcp Financial Risk And Compliance Manager

A2A-MCP-Financial-Risk-and-Compliance-Manager is a modular, multi-agent system designed to automate compliance analysis for financial institutions, utilizing the Agent-to-Agent (A2A) protocol and Model Context Protocol (MCP) server architecture.

Use cases

Use cases include automating the generation of compliance reports, conducting risk assessments, and ensuring adherence to regulatory standards through efficient document analysis.

How to use

Users can interact with the system through a user-friendly Streamlit frontend to submit compliance queries and view results, leveraging the capabilities of specialized agents for analysis.

Key features

Key features include inter-agent communication via the A2A protocol, access to Generative AI tools through the MCP server, collaborative workflows among specialized agents, and the ability to index and retrieve relevant regulatory documents for compliance checks.

Where to use

This system is primarily used in the financial sector, particularly by financial institutions that require automated compliance analysis and risk management.

Financial Risk and Compliance Manager with A2A Protocol and MCP Server Architecture

🎥 Video Demo and Technical Report

Please see the Video Demo for a walkthrough of the system and Technical Report

🚀 Overview

The Financial Risk and Compliance Manager with A2A Protocol and MCP Server Architecture is a modular, multi-agent system designed to automate compliance analysis for financial institutions. It leverages the Agent-to-Agent (A2A) protocol for inter-agent communication and the Model Context Protocol (MCP) server for standardized access to Generative AI tools, such as Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). The system indexes regulation documents, retrieves relevant context, and generates detailed compliance reports through a collaborative workflow involving multiple agents. This approach offers improved accuracy, efficiency, and scalability compared to traditional compliance systems.

🧩 Architecture

The system architecture consists of the following components:

• Streamlit Frontend: A user-friendly interface for submitting compliance queries and viewing results.
• MCP Server: A centralized server that provides access to Generative AI tools, such as Retrieval-Augmented Generation (RAG) using LangChain and FAISS, and Large Language Models (LLMs), using the Model Context Protocol (MCP).
• Agents: A set of specialized agents, including the Profile Checker, Risk Reporter, and Compliance Querier, that collaborate using the Agent-to-Agent (A2A) protocol to perform compliance analysis.
• Regulation PDFs: A collection of regulatory documents that serve as the knowledge base for compliance checks, indexed by the MCP Server’s RAG functionality.

The following sequence diagram illustrates the chronological flow of interactions between components, highlighting the A2A communication between agents and the MCP Server’s role in providing RAG services:

sequenceDiagram
    participant UI as Streamlit Frontend
    participant MCP as MCP Server (Flask)
    participant Agent2 as Risk Reporter
    participant Agent1 as Profile Checker
    participant Agent3 as Compliance Querier

    UI->>Agent2: 1. Sends query (via MCP)
    Agent2->>Agent1: 2. Requests client profile (A2A)
    Agent1-->>Agent2: 3. Returns profile data (A2A)
    Agent2->>MCP: 4. Queries regulation context
    MCP-->>Agent2: 5. Returns context
    Agent2->>Agent3: 6. Sends risk report (A2A)
    Agent3->>MCP: 7. Queries compliance details
    MCP-->>Agent3: 8. Returns compliance response
    Agent3-->>Agent2: 9. Sends final report (A2A)
    Agent2-->>UI: 10. Returns result (via MCP)

Notes on the Diagram:

• A2A Communication: Direct Agent-to-Agent interactions (e.g., Agent 2 to Agent 1, Agent 2 to Agent 3) are shown using the A2A protocol.
• MCP Server Role: The MCP Server acts as a centralized hub, providing access to RAG services (using LangChain and FAISS) and LLMs. Agents interact with the MCP Server to retrieve regulation context and compliance details.
• Integrated RAG Services: The RAG functionality is embedded within the MCP Server, which indexes regulation documents and handles retrieval requests from agents.

📚 Regulation Data

✨ Features

• Multi-agent Architecture: The system employs a modular design with specialized agents (Profile Checker, Risk Reporter, Compliance Querier) that collaborate to perform compliance analysis.
• Retrieval-Augmented Generation (RAG): Integrates LangChain and FAISS for efficient ingestion and semantic search of regulation documents.
• Interactive User Interface: A modern, card-based Streamlit UI for easy interaction and visualization of compliance results.
• Contextual Compliance Insights: Provides detailed compliance reports with citations from relevant regulation documents.
• Optional Google Cloud AI Integration: Enhances the system with advanced AI capabilities for more sophisticated analysis.
• Extensible Codebase: A modular Python codebase that allows for easy customization and extension.

⚙️ Setup & Installation

1. Clone the repository:

   git clone https://github.com/Pyligent/A2A-MCP-Compilance-Check.git
   cd A2A-MCP-Compilance-Check
   

2. Install dependencies:

   pip install -r requirements.txt
   

   Or, if using Poetry or pip-tools, use pyproject.toml.

3. Prepare regulation files:

   • Place your regulation PDFs in the data/ directory.

4. Prepare client profiles:

   • Place your client JSON files in the client/ directory.

5. Set environment variables (if needed):

   • For example, for OpenAI:

     export OPENAI_API_KEY=your-openai-api-key
     

   • You can also use a .env file.

6. Start the MCP server:

   python mcp_server.py
   

7. Start the agents (in separate terminals or as background processes):

   python agents/agent1_server.py
   python agents/agent2_server.py
   python agents/agent3_server.py
   

8. Run the Streamlit UI:

   streamlit run app.py
   

9. Access the application:

   • Open your browser and go to: http://localhost:8501

Summary Table for Manual Startup:

🛠️ Configuration

• Client profiles: Place JSON files in the client/ directory.
• Regulation Data: Place in data/.
• RAG settings: Configure chunk size, overlap, and vector DB in the UI sidebar.
• Environment variables: Use a .env file for API keys (e.g., OpenAI, Google Cloud).

🤝 Contributing

Pull requests and issues are welcome! Please open an issue to discuss major changes.

📄 License

MIT License. See LICENSE for details.