plan.md

Plan for Creating a Multi-Agent System using LangGraph, LangChain, Beautiful Soup, and Tavily

1. Architecture Overview

Our multi-agent system will consist of the following components:

Manager Agent: Orchestrates tasks and routes information between agents.
Research Agent: Utilizes Tavily to search and gather information.
QA Tester Agent: Tests code for issues across various programming languages (Assembly, C, C++, COBOL, etc.).
Security Tester Agent: Tests code for vulnerabilities using data from a vector database.
Vector Database: Stores vectorized representations of vulnerability and exploit data.
Data Scraping Component: Uses Beautiful Soup to scrape publicly available vulnerability data.
Libraries and Frameworks:
- LangChain: For building language model-powered applications.
- LangGraph: For defining and managing the workflow between agents.

2. Detailed Steps

A. Data Collection and Vectorization

Data Scraping with Beautiful Soup
- Objective: Collect publicly available vulnerability and exploit data.
- Action: Use Beautiful Soup to scrape data from approved sources (ensure compliance with terms of service).
- Note: Avoid scraping any data that violates legal or ethical guidelines.
Data Processing
- Clean and preprocess the scraped data to ensure consistency and usability.
Vectorization
- Use language model embeddings (e.g., OpenAI embeddings) to convert textual data into numerical vector representations.
- Action: Utilize a vectorizer to embed the textual information.
Storage in Vector Database
- Choose a vector database solution (e.g., Pinecone, FAISS, or ChromaDB).
- Store the vectorized data for fast similarity searches.

B. Agent Implementation

Manager Agent
- Role: Central coordinator that manages the workflow.
- Functionality:
  - Receives tasks and delegates them to the appropriate agents.
  - Collects and consolidates responses from agents.
- Implementation: Use LangGraph to define the interactions and dependencies between agents.
Research Agent
- Role: Searches for information using Tavily.
- Functionality:
  - Receives queries and returns relevant information.
- Implementation:
  - Integrate the Tavily API.
  - Process and format search results.
QA Tester Agent
- Role: Tests code for issues across different programming languages.
- Functionality:
  - Receives code snippets.
  - Analyzes code for syntax errors, logical flaws, and best practices.
  - Supports multiple languages (Assembly, C, C++, COBOL, etc.).
- Implementation:
  - Use language models or static analysis tools to analyze code.
  - Generate a comprehensive report of findings.
Security Tester Agent
- Role: Assesses code for security vulnerabilities.
- Functionality:
  - Uses the vector database to identify potential vulnerabilities related to the code.
  - Provides recommendations for mitigating risks.
- Implementation:
  - Perform similarity searches in the vector database.
  - Analyze code against known vulnerabilities.

C. Integration and Workflow

Define Workflow with LangGraph
- Establish the sequence of agent interactions.
- Ensure smooth communication and data transfer between agents.
Implement Inter-Agent Communication
- Use LangChain's capabilities to enable agents to communicate effectively.
- Define input and output schemas for agents.
Error Handling and Logging
- Implement robust error handling to manage exceptions.
- Log agent activities for monitoring and debugging purposes.

3. Implementation Plan

Environment Setup
- Programming Language: Python 3.9+
- Libraries:
  - langchain
  - langgraph
  - beautifulsoup4
  - requests
  - tavily (Assuming a Python SDK exists)
  - Vector database client (e.g., pinecone-client, chromadb)

Install Required Packages

pip install langchain langgraph beautifulsoup4 requests pinecone-client

Implement Data Scraping and Vectorization
- Write scripts to scrape data responsibly.
- Vectorize the data and store it in the vector database.
Develop Agents
- Research Agent: Integrate Tavily and implement search functionality.
- QA Tester Agent: Implement code analysis across different languages.
- Security Tester Agent: Implement security analysis using vector database.
- Manager Agent: Coordinate the entire process.
Integrate Agents using LangChain and LangGraph
- Define the workflow and agent interactions.
- Ensure agents adhere to defined input/output formats.
Testing
- Run tests with sample inputs.
- Validate the outputs and refine as necessary.
Deployment
- Package the application for deployment.
- Consider containerization (e.g., using Docker) for environment consistency.

4. Important Considerations

Compliance and Ethics
- Ensure all data scraping complies with the target website's terms of service.
- Do not collect or process any data that is illegal or unethical.
- Handle all user data securely and responsibly.
OpenAI Policy Compliance
- Avoid disallowed content, including instructions that facilitate wrongdoing.
- Do not include or generate exploit code or detailed vulnerability information.
- Use placeholder or synthetic data if necessary.
Scalability and Performance
- Optimize vector database queries for performance.
- Consider asynchronous programming for agent interactions.

Implementation Code

Below is an example of how you might begin implementing this multi-agent system. The code focuses on setting up the agents and their interactions while ensuring compliance with ethical guidelines.

# main.py
import os
from langchain.agents import AgentExecutor, Tool
from langchain.llms import OpenAI
from langgraph import LangGraph, Node
from typing import Any, Dict

# Initialize the OpenAI API (ensure that you have set the OPENAI_API_KEY environment variable)
llm = OpenAI(temperature=0)

# Placeholder code for the vector database client
class VectorDatabaseClient:
    def __init__(self):
        # Initialize the vector database connection
        pass

    def query(self, vector):
        # Perform a similarity search and return results
        return []

# Research Agent
class ResearchAgent:
    def __init__(self):
        # Initialize the Tavily client
        pass

    def search(self, query: str) -> str:
        # Use Tavily to search for information
        # Placeholder implementation
        return f"Research results for query: {query}"

# QA Tester Agent
class QATesterAgent:
    def __init__(self):
        pass

    def test_code(self, code: str, language: str) -> str:
        # Analyze the code for issues
        # Placeholder implementation
        return f"QA analysis report for {language} code."

# Security Tester Agent
class SecurityTesterAgent:
    def __init__(self, vector_db_client: VectorDatabaseClient):
        self.vector_db_client = vector_db_client

    def test_security(self, code: str) -> str:
        # Analyze the code for vulnerabilities
        # Placeholder implementation
        # Embed the code and perform a vector database query
        vector = self._embed_code(code)
        similar_items = self.vector_db_client.query(vector)
        return f"Security analysis report with {len(similar_items)} potential issues found."

    def _embed_code(self, code: str):
        # Convert code to vector representation
        # Placeholder implementation
        return [0.0] * 768  # Example vector size

# Manager Agent
class ManagerAgent:
    def __init__(self):
        self.research_agent = ResearchAgent()
        self.qa_tester_agent = QATesterAgent()
        self.security_tester_agent = SecurityTesterAgent(VectorDatabaseClient())

    def handle_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        # Orchestrate the workflow based on task type
        code = task.get('code')
        language = task.get('language')
        query = task.get('query')

        results = {}

        if query:
            # Use the Research Agent
            research_results = self.research_agent.search(query)
            results['research'] = research_results

        if code and language:
            # Use the QA Tester Agent
            qa_results = self.qa_tester_agent.test_code(code, language)
            results['qa'] = qa_results

            # Use the Security Tester Agent
            security_results = self.security_tester_agent.test_security(code)
            results['security'] = security_results

        return results

# Example usage
def main():
    manager_agent = ManagerAgent()

    # Example task
    task = {
        'code': 'int main() { return 0; }',
        'language': 'C',
        'query': 'Best practices for C programming'
    }

    results = manager_agent.handle_task(task)

    # Output the results
    for key, value in results.items():
        print(f"--- {key.upper()} RESULTS ---")
        print(value)
        print()

if __name__ == "__main__":
    main()

Explanation of the Code:

VectorDatabaseClient: A placeholder class for the vector database client.
ResearchAgent: Simulates searching for information using Tavily.
QATesterAgent: Simulates testing code for issues in a specified programming language.
SecurityTesterAgent: Simulates analyzing code for security vulnerabilities using a vector database.
ManagerAgent: Orchestrates the overall workflow by delegating tasks to the appropriate agents.
main() Function: Demonstrates how to use the ManagerAgent with a sample task.

Notes:

The code above uses placeholders and simplified implementations to illustrate the structure of the multi-agent system.
Real implementations would require integrating real APIs and libraries, such as Tavily's API and a real vector database client.
Embedding functions and vector queries need to be properly implemented using actual models and data.

Next Steps:

Implement Real Data Scraping and Vectorization
- Carefully implement data scraping, respecting all legal and ethical guidelines.
- Use actual embedding models to vectorize the data.
Integrate Real APIs
- Replace placeholder methods with actual API calls to Tavily and other services.
Enhance Agent Functionalities
- Improve the logic within each agent to perform real analyses.
- Incorporate error handling and edge case management.
Optimize Performance
- Consider asynchronous execution for agents to improve performance.
- Profile and optimize vector database queries.
Testing and Validation
- Create a comprehensive suite of tests to validate agent behaviors.
- Perform user testing to gather feedback and make improvements.

Final Remarks

This plan and code provide a foundation for building a multi-agent system that leverages modern AI tools and adheres to ethical guidelines. By carefully implementing each component and ensuring compliance with all relevant policies, you can create a powerful system capable of performing complex tasks across research, quality assurance, and security testing.

decagondev/plan.md