Deep Research MCP Server 🚀

📚 Table of Contents

- ✨ How It Works
- 🌟 Features
- ⚙️ Requirements
- 🛠️ Setup
- 🚀 Usage
- 📜 License

🤖 Deep Research Gemini

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and Gemini large language models. Available as a Model Context Protocol (MCP) tool for seamless integration with AI agents.

The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction over time and deep dive into a topic. Goal is to keep the repo size at <500 LoC so it is easy to understand and build on top of.

🔄 Research Agent + PostgreSQL Integration

The research agent works seamlessly with PostgreSQL to create an efficient research system:

1. Knowledge Persistence: Each research finding and URL is stored in PostgreSQL, creating a growing knowledge base
2. Smart Caching: Previously processed URLs are tracked to avoid duplicate processing
3. Learning Context: The agent can reference past findings to guide new research directions
4. Query Optimization: Similar research queries can leverage existing database knowledge
5. Efficient Retrieval: Fast access to historical research data through indexed PostgreSQL queries

This integration enables the agent to build upon previous research while maintaining a lightweight codebase.

✨ How It Works

flowchart TB
    subgraph Input
        Q[User Query]
        B[Breadth Parameter]
        D[Depth Parameter]
    end
DR[Deep Research] -->

    SQ[SERP Queries] -->

    PR[Process Results]
subgraph Results[Results]

        direction TB

        NL((Learnings))

        ND((Directions))

    end
PR --> NL

    PR --> ND
DP{depth > 0?}
RD["Next Direction:

    - Prior Goals

    - New Questions

    - Learnings"]
MR[Markdown Report]

    DB[PostgreSQL Database]
%% Main Flow

    Q & B & D --> DR
%% Results to Decision

    NL & ND --> DP
%% Circular Flow

    DP -->|Yes| RD

    RD -->|New Context| DR
%% Final Output

    DP -->|No| MR

    DR --> DB

    DB --> NL
%% Styling

    classDef input fill:#7bed9f,stroke:#2ed573,color:black

    classDef process fill:#70a1ff,stroke:#1e90ff,color:black

    classDef recursive fill:#ffa502,stroke:#ff7f50,color:black

    classDef output fill:#ff4757,stroke:#ff6b81,color:black

    classDef results fill:#a8e6cf,stroke:#3b7a57,color:black
class Q,B,D input

    class DR,SQ,PR process

    class DP,RD recursive

    class MR output

    class NL,ND results

    class DB output

🌟 Features

- MCP Integration: Available as a Model Context Protocol tool for seamless integration with AI agents
- Iterative Research: Performs deep research by iteratively generating search queries, processing results, and diving deeper based on findings
- Intelligent Query Generation: Uses Gemini LLMs to generate targeted search queries based on research goals and previous findings
- Depth & Breadth Control: Configurable parameters to control how wide (breadth) and deep (depth) the research goes
- Smart Follow-up: Generates follow-up questions to better understand research needs
- Comprehensive Reports: Produces detailed markdown reports with findings and sources
- Concurrent Processing: Handles multiple searches and result processing in parallel for efficiency
- Persistent Knowledge with PostgreSQL: 🐘 Leverages a PostgreSQL database for storing research data, ensuring data persistence across sessions and enabling efficient retrieval of past findings. This allows the agent to build upon previous knowledge and avoid redundant research.

⚙️ Requirements

- Node.js environment (v22.x recommended)
- API keys for:
- Firecrawl API 🕸️ (for web search and content extraction)
- Gemini API 🧠 (for Gemini 2.0 models)
- PostgreSQL database 🐘 (running locally or remotely)

🛠️ Setup

Node.js

1. Clone the repository
2. Install dependencies:

npm install

- Set up environment variables in a .env.local file:

```bash
GEMINI_API_KEY="your_gemini_key"
FIRECRAWL_KEY="your_firecrawl_key"