Deep Research Agent 🔍

This project implements a sophisticated, AI-powered multi-agent system designed to perform deep research on any given topic. The agent leverages a team of specialized AI agents to gather requirements, plan a research strategy, retrieve real-time information from the web and reasoning that increases the brain storming of Agent .

� Features

• Multi-Agent System: Utilizes a team of specialized agents for a structured research process:
  • Requirement Gathering Agent: Clarifies and understands the user's research needs.
  • Planning Agent: Develops a strategic plan to tackle the research query.
  • Lead Agent: Coordinates the workflow between other agents and synthesizes the final report.
• Advanced Web Search: Integrates with the Tavily API for accurate, up-to-date information retrieval.
• Interactive Chat Interface: A user-friendly interface built with Chainlit for a seamless conversational experience.
• Real-time Streaming: Streams responses and thought processes of the agents in real-time.
• Session and Agentic Memory It gives the Session as well as Agentic Memory that increase the consistency and performance of the chatbot.
• Asynchronous Architecture: Built with asyncio for efficient, non-blocking I/O operations, especially for web requests.

🛠️ How It Works

The system operates as a pipeline of agents. When a user provides a research query, it's first passed to the requirement_gathering_agent. Once the requirements are clear, the planning_agent creates a step-by-step research plan. The lead_agent then executes this plan, using the available tools to gather information.

Web Search Tool (web_search.py)

A core component of this system is the web_search tool, which provides real-time access to web information.

• Technology: Here are the technologies used in the Deep Research Chatbot as follows :

1. It uses the Tavily Python client to perform efficient and comprehensive web searches.
2. It use the Mem0 for Agentic Memory . It stores the important data about user .

• Functionality: The web_search function is an asynchronous tool that takes a search query string.
• Output: It fetches search results from the Tavily API, formats them into a clean, readable Markdown format, including the title, a snippet of the content, and a direct link to the source URL. This ensures all information is verifiable.

# Example of a formatted result from web_search
"""
### Title of the Web Page
Snippet of the content from the page...
##### [Source](https://example.com/source-url)
---
"""

� Getting Started

Prerequisites

• Python 3.13+
• An active internet connection

⚙️ Configuration

1. Environment Variables: Create a .env file in the root of the project directory and add your API keys:

   GEMINI_API_KEY="your_gemini_key_here"
   TAVILY_API_KEY="your_tavily_key_here"

2. Model Settings: The primary model can be configured in deep_research_system.py. The default is set to gemini-2.5-flash.

   model = OpenAIChatCompletionsModel(
       openai_client=provider,
       model="gemini-2.5-flash"
   )

📦 Installation & Setup

1. Clone the repository:

   git clone <your-repo-url>
   cd _Projects/Deep_Research_Agent

2. Set up a virtual environment:

   #install uv if you haven't already
   pip install uv
   
   # Create and activate the virtual environment
   uv venv
   source .venv/bin/activate  # On Unix/macOS
   .venv\Scripts\activate    # On Windows

3. Install dependencies:

   uv pip install -r requirements.txt 

   Note: If a pyproject.toml is used for dependencies, you might use uv install.

4. Run the application:

   uv run chainlit run deep_research_system.py

📁 Project Structure

---
*This project is for educational and research purposes.*
Deep_Research_Agent/
├── .env                    # Environment variables (API keys)
├── deep_research_system.py # Main application logic and agent definitions
├── main.py                 # CLI Agent (Alternative entry point)
├── pyproject.toml          # Project metadata and dependencies
├── README.md               # This file is for Documentaion
├── research_agents.py      # Definitions for specialized research agents
├── tools.py                # Additional tools for agents
├── uv.lock                 # uv lock file
├── web_search.py           # Web search tool implementation
└── Workflow_of_Agent.png   # Diagram of the agent workflow

🎯 Usage Examples

You can interact with the agent through the Chainlit interface with queries like:

• "Analyze the latest developments in quantum computing."
• "Provide a summary of recent AI regulations in the European Union."
• "Compare and contrast different approaches to sustainable energy generation."

How it works

• Tools are defined with @function_tool.
• The agent registers: tools=[web_search, get_info, save_user_memory,search_user_memory].
• Local context is passed as a dataclass instance: Runner.run_streamed(..., context=Info(...)).
• Inside tools, access context via Wrapper.context (type-safe with RunContextWrapper[Info]).
• Responses are streamed using Runner.run_streamed(...).stream_events().

Usage examples

• “Search the latest news about AI regulation and summarize key points.”
• “What is my profile info?” (invokes get_info and returns values from Info)

Configuration tips

• Model: adjust model="gemini-2.5-flash" in main.py as needed.
• Tool choice: ModelSettings(tool_choice="auto") lets the model decide when to use tools.
• Temperature: increase for creativity, decrease for factual responses.

🔒 Security Best Practices

1. API Key Management

   • All API keys stored in .env
   • Environment variable validation
   • No hardcoded credentials

2. Data Privacy

   • Local context management for sensitive data
   • Minimal PII exposure in prompts
   • Secure data handling in tools

❗ Troubleshooting

Common Issues and Solutions

1. API Connection Issues

   • Verify API keys in .env
   • Check network connectivity
   • Validate API endpoint status

2. Agent Behavior Issues

   • Confirm correct tool registration
   • Check agent configuration
   • Verify model settings

3. Performance Optimization

   • Adjust model temperature
   • Fine-tune tool usage settings
   • Optimize search parameters

📄 License

MIT License - Feel free to use and modify as needed.

👨‍💻 Author

Abdullah Malik (@AbdullahMalik17)

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For bug reports, feature requests, or contributions, please create an issue in the repository.