What is Agentic Deep Researcher

Agentic Deep Researcher is a multi-agent system powered by MCP that conducts real-time web research. It utilizes a team of specialized agents to perform web searches, analyze the results, and synthesize the findings into structured insights, providing users with comprehensive and well-cited information.

Use cases

This tool is ideal for academic research, market analysis, content creation, and information gathering across various topics. It supports users looking for in-depth insights by automating the research process and synthesizing data from multiple sources into a cohesive output.

How to use

To use the Agentic Deep Researcher, clone the repository, set up a Python virtual environment, install the required dependencies, and configure your API keys in a .env file. Start the Streamlit application to interact with the multi-agent researcher through a chat interface or run the FastMCP RPC server for orchestration via API.

Key features

Key features include an interactive chat UI, an automated research pipeline that consists of web searching, analysis, and Markdown drafting, configurable settings via environment variables, and an extensible architecture that allows the addition of new agents and tools.

Where to use

Agentic Deep Researcher can be used locally on user machines through the provided Streamlit UI or deployed as an RPC service in various applications needing automated research capabilities, making it suitable for developers, researchers, and professionals requiring efficient information synthesis.

Agentic Deep Researcher

This is a MCP-powered multi-agent deep researcher that performs real-time web research using agents through CrewAI, LinkUp API, and DeepSeek R1 as a local LLM. It uses a team of agents to web search, analyze the research, and finally synthesize the information into structured, cited insights.
It orchestrates three roles:

• Web Searcher
  Formulates and executes web searches via LinkUp API
• Research Analyst
  Synthesizes raw search results into structured insights with citations of sources.
• Technical Writer
  Produces clear, well-formatted Markdown responses with source links.

Under the hood, the pipeline is defined in backend/agents.py and runs sequentially with built-in error handling and in-memory context storage.

Features

• Interactive chat UI (Streamlit) with history tracking and clear history functionality
• Automated multi-step research pipeline:
  1. Web searching process via LinkUp API
  2. Structured analysis of collected results
  3. Final drafting of Markdown responses
• Configurable via environment variables
• Extensible architecture: add new agents, tools, or output formats

Installation

1. Clone the repository:

   git clone https://github.com/ShivamKGate/agentic-deep-researcher.git
   cd agentic-deep-researcher
   

2. Create and activate a Python virtual environment:

   python -m venv .venv
   .\.venv\Scripts\Activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Obtain your LinkUp API key from LinkUp and configure API keys and model settings in a .env file:

   LINKUP_API_KEY=<your linkup api key>
   

5. Download and install Ollama locally, then pull the deepseek-r1:7b model by running:

   ollama pull deepseek-r1:7b
   

   Note: Feel free to choose any DeepSeek model with higher or lower parameters (1.5b, 7b, 8b, 14b, 32b, 70b, or 671b) based on your available GPU resources and the desired level of reasoning quality during the model selection in the terminal.

Usage

Streamlit Chat UI

streamlit run backend/app.py

Open your browser at http://localhost:8501 to chat with the multi-agent deep researcher!

FastMCP RPC Server

python backend/server.py

Exposes an async tool crew_research for orchestration via FastMCP protocols.

Configuration

All other configuration values are defined in code and do not require manual setup.

Project Structure

root/
├── backend/
│   ├── agents.py    # Agent definitions, tool wrappers, and pipeline orchestration
│   ├── app.py       # Streamlit UI application
│   └── server.py    # FastMCP RPC server endpoint
├── LICENSE
├── README.md
└── requirements.txt # Python dependencies

Contributing

Contributions, issues, and feature requests are welcome! Please open an issue or pull request on the GitHub repository.

Future Implementations

• Full Async & Reactive Pipeline
  Convert all research steps (web search, analysis, writing) to non-blocking async operations with event-driven scheduling and responsive I/O for improved throughput.

• Enhanced Error Handling & Retry Mechanisms
  Implement granular retry strategies, circuit-breaker patterns, backoff policies, and comprehensive structured logging to handle transient and persistent failures at each step.

• Memory Agent
  Introduce a dedicated agent to capture and persist conversational context and research state into the LLM’s memory store for long-term continuity through the agents’ sequential run.

• Feedback Agent
  Add an agent to validate and refine outputs from each agent in parallel by comparing raw results against quality criteria and user feedback loops before final drafting.

• Cleaner & Customizable Outputs
  Introduce configurable Markdown templates, support for HTML/PDF exports, and user-defined formatting options for polished, presentation-ready reports.

License

This project is licensed under the MIT License. See LICENSE for details.