Open Deep Research

Official Recommended

by Highlight

1 1.2k downloads 5.0 (1) MIT

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About

Open Deep Research is an AI-powered research assistant designed to perform in-depth, iterative research on any topic. It integrates with AI agents via the Model Context Protocol (MCP) for seamless operation. The tool leverages large language models (LLMs) to generate intelligent…

Details

Repository: highlight-ing/deep-research-mcp
License: MIT

Explore

- 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 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

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below
Plugin Name Open Deep Research

Command (node, npx, python, etc.) node
Arguments
- Argument 1 --env-file
- Argument 2 .env.local
- Argument 3 dist/mcp-server.js
Environment
- FIRECRAWL_KEY your_firecrawl_key
- OPENAI_API_KEY your_openai_key
Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "/c",
                "node",
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "/c",
                "node",
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "/c",
                "node",
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "/c",
                "node",
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "/c",
                "node",
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "cmd"
        }
    }
}

JSON

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

From the repository

- Node.js environment (v22.x recommended)
- API keys for:
- Firecrawl API (for web search and content extraction)
- OpenAI API (for o3 mini model)

For standalone usage without MCP, you can use the CLI interface:

npm run start "Your research query here"

To test the MCP server with the inspector:

npx @modelcontextprotocol/inspector node --env-file .env.local dist/mcp-server.js

`deep-research`

Performs deep research on a given query. Parameters: query (string) - the research query, depth (number, 1-5) - how deep to go in the research tree, breadth (number, 1-5) - how broad to make each research level, existingLearnings (string[], optional) - array of existing research findings to build upon.

The deep research functionality is available as an MCP tool that can be used by AI agents. To start the MCP server:

node --env-file .env.local dist/mcp-server.js

The tool provides the following parameters:
- query (string): The research query to investigate
- depth (number, 1-5): How deep to go in the research tree
- breadth (number, 1-5): How broad to make each research level
- existingLearnings (string[], optional): Array of existing research findings to build upon

Example tool usage in an agent:

const result = await mcp.invoke("deep-research", {

  query: "What are the latest developments in quantum computing?",

  depth: 3,

  breadth: 3

});

The tool returns:
- A detailed markdown report of the findings
- List of sources used in the research
- Metadata about learnings and visited URLs

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

{
    "mcpServers": {
        "open deep research": {
            "env": {
                "FIRECRAWL_KEY": "your_firecrawl_key",
                "OPENAI_API_KEY": "your_openai_key"
            },
            "args": [
                "--env-file",
                ".env.local",
                "dist/mcp-server.js"
            ],
            "command": "node"
        }
    }
}

Linux

{
    "env": {
        "FIRECRAWL_KEY": "your_firecrawl_key",
        "OPENAI_API_KEY": "your_openai_key"
    },
    "args": [
        "--env-file",
        ".env.local",
        "dist/mcp-server.js"
    ],
    "command": "node"
}

Macos

{
    "env": {
        "FIRECRAWL_KEY": "your_firecrawl_key",
        "OPENAI_API_KEY": "your_openai_key"
    },
    "args": [
        "--env-file",
        ".env.local",
        "dist/mcp-server.js"
    ],
    "command": "node"
}

Windows

{
    "env": {
        "FIRECRAWL_KEY": "your_firecrawl_key",
        "OPENAI_API_KEY": "your_openai_key"
    },
    "args": [
        "/c",
        "node",
        "--env-file",
        ".env.local",
        "dist/mcp-server.js"
    ],
    "command": "cmd"
}

An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and 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.

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]
%% 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
%% 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

Features

Requirements

- Node.js environment (v22.x recommended)
- API keys for:
- Firecrawl API (for web search and content extraction)
- OpenAI API (for o3 mini model)

Setup

Node.js

1. Clone the repository
2. Install dependencies:

npm install

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

```bash
OPENAI_API_KEY="your_openai_key"
FIRECRAWL_KEY="your_firecrawl_key"

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Details

Explore

Setting up with Highlight

From the repository

deep-research

Claude Desktop / Cursor

Linux

Macos

Windows

How It Works

Features

Requirements

Setup

Node.js

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