In-Depth Insights. Clear Outcomes.

LLMFlow Search is an agent that finds accurate answers to complex questions using a smart search strategy. It automatically refines queries: if the initial results are incomplete or inaccurate, the agent generates additional queries to fill in the gaps.

The agent explores information from various sources — Wikipedia, DuckDuckGo, and websites directly. It:

• Analyzes search intent to optimize queries
• Identifies which parts need verification
• Expands or narrows the search as needed
• Detects and resolves contradictions
• Chooses more precise wording

The result is a coherent, verified answer based on real data. It works in multiple languages and can bypass site restrictions.

Requirements

• Python 3.8+
• LLM API key (supported providers in config.json)
• Chrome/Chromium (for Selenium-based web searches)

An example of a report compiled by LLMFlow Search agent

Installation

1. Clone the repository:

   git clone https://github.com/KazKozDev/LLMFlow-Search.git
   cd LLMFlow-Search

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   echo "# API Keys for LLM Providers
   OPENAI_API_KEY=" > .env

   Edit the .env file, adding your LLM provider API key.

Execution

Start the application:

python main.py

Configuration

The system uses a config.json file for configuration. Example configuration:

{
    "llm": {
        "provider": "default_provider",
        "model": "default_model",
        "temperature": 0.2,
        "max_tokens": 2048
    },
    "search": {
        "max_results": 5,
        "safe_search": true,
        "parse_top_results": 3,
        "use_selenium": true,
        "use_cache": true
    },
    "memory": {
        "path": "./memory",
        "max_items": 100
    },
    "report": {
        "output_file": "report.md",
        "include_sources": true,
        "max_source_length": 1500
    },
    "intent_analyzer": {
        "enabled": true,
        "cache_results": true
    }
}

Architecture

LLMFlow Search Agent consists of the following main modules:

1. Planning Module: Analyzes queries and creates a search plan
2. Tools Module: Provides tools for searching DuckDuckGo, Wikipedia, and web pages
3. Memory Module: Stores and retrieves information for context-aware processing
4. Report Generator: Synthesizes information into comprehensive reports
5. LLM Service: Provides interaction with language models

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For questions, feedback, or support, reach out to:

Artem KK | MIT LICENSE