local-deep-research Agent工作流

Build Your Knowledge Base

Every research session finds valuable sources. Download them directly into your encrypted library—academic papers from ArXiv, PubMed articles, web pages. LDR extracts text, indexes everything, and makes it searchable. Next time you research, ask questions across your own documents and the live web together. Your knowledge compounds over time.

Installation

```bash

Install with MCP extras

pip install "local-deep-research[mcp]" ```

Getting Started

• Installation Guide
• Frequently Asked Questions
• API Quickstart
• Configuration Guide
• Full Configuration Reference

⚡ Quick Start

Option 1: Docker Run (Linux) ```bash

💻 Usage Examples

Example Usage

"Use quick_research to find information about quantum computing applications"
"Search arxiv for recent papers on diffusion models"
"Generate a detailed research report on renewable energy trends"

Examples & Tutorials

• API Examples
• Benchmark Examples
• Optimization Examples

Option 1: Simplest - one line research

summary = quick_query("username", "password", "What is quantum computing?") print(summary)

Option 2: Client for multiple operations

client = LDRClient() client.login("username", "password") result = client.quick_research("What are the latest advances in quantum computing?") print(result["summary"]) ```

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "local-deep-research": {
      "command": "ldr-mcp",
      "env": {
        "LDR_LLM_PROVIDER": "openai",
        "LDR_LLM_OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Claude Code Configuration

Add to your .mcp.json (project-level) or ~/.claude/mcp.json (global):

{
  "mcpServers": {
    "local-deep-research": {
      "command": "ldr-mcp",
      "env": {
        "LDR_LLM_PROVIDER": "ollama",
        "LDR_LLM_OLLAMA_URL": "http://localhost:11434"
      }
    }
  }
}

Python API

```python from local_deep_research.api import LDRClient, quick_query

HTTP API

The code example below shows the basic API structure - for working examples, see the link below

```python import requests from bs4 import BeautifulSoup

Login and get API CSRF token

session.post("http://localhost:5000/auth/login", data={"username": "user", "password": "pass", "csrf_token": login_csrf}) csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]

Make API request

response = session.post("http://localhost:5000/api/start_research", json={"query": "Your research question"}, headers={"X-CSRF-Token": csrf}) ```

🚀 Ready-to-use HTTP API Examples → examples/api_usage/http/ - ✅ Automatic user creation - works out of the box - ✅ Complete authentication with CSRF handling - ✅ Result retry logic - waits until research completes - ✅ Progress monitoring and error handling

🔗 Enterprise Integration

Connect LDR to your existing knowledge base:

```python from local_deep_research.api import quick_summary

🔌 MCP Server (Claude Integration)

LDR provides an MCP (Model Context Protocol) server that allows AI assistants like Claude Desktop and Claude Code to perform deep research. Full setup details in the MCP Server guide.

⚠️ Security Note: This MCP server is designed for local use only via STDIO transport (e.g., Claude Desktop). It has no built-in authentication or rate limiting. Do not expose over a network without implementing proper security controls. See the MCP Security Guide for network deployment requirements.