khojkar 0.3.2

A tool for conducting deep research on a given topic, using a combination of LLMs, search engines, and other tools.

khojkar

khojkar (pa: ਖੋਜਕਾਰ, ipa: /kʰoːd͡ʒ.kɑːɾ/) is a deep research agent.

Installation

Prerequisites

• Python 3.12
• uv - Fast Python package installer and resolver

Option 1: Direct Usage with uv

You can run khojkar directly without cloning the repository using uvx:

uvx khojkar --from git+https://github.com/amandeepsp/khojkar.git research --topic "Your research topic" --output report.md

To set up credentials when using this method, create a .env file in your current directory with your API keys. uvx will automatically load environment variables from a .env file in the current working directory.

Option 2: Clone and Install

# Clone the repository
git clone https://github.com/yourusername/khojkar.git
cd khojkar

# Install using uv
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -e .

Setup Credentials

khojkar uses LLMs via the LiteLLM library, which requires API keys for the models you want to use.

1. Create a .env file in the root directory (if running via Option 2: Clone and Install) or in your current working directory (if running via Option 1: Direct Usage with uv).

   touch .env
   

2. Add your API keys to the .env file. You only need the key for the model you intend to use. The default is gemini/gemini-2.0-flash, which requires the GEMINI_API_KEY. For other providers like OpenAI or Anthropic, you would use OPENAI_API_KEY or ANTHROPIC_API_KEY respectively. You also need to add credentials for the Google Programmable Search Engine: SEARCH_ENGINE_ID and SEARCH_ENGINE_API_KEY.

   # Required for the default model (gemini/gemini-2.0-flash)
   GEMINI_API_KEY=your_gemini_api_key
   
   # Required for Google Programmable Search Engine
   SEARCH_ENGINE_ID=your_search_engine_id
   SEARCH_ENGINE_API_KEY=your_search_engine_api_key
   
   # Optional: add other LLM API keys as needed
   # OPENAI_API_KEY=your_openai_api_key
   # ANTHROPIC_API_KEY=your_anthropic_api_key
   

   Note: Refer to the LiteLLM documentation for the specific environment variable names required for different LLM providers.

Usage

Basic Usage:

khojkar research --topic "Your research topic" --output report.md

Advanced Options:

# Use a different model
khojkar research --topic "Your research topic" --model "openai/gpt-4o" --output report.md

# Limit research steps
khojkar research --topic "Your research topic" --max-steps 5 --output report.md

# Use multi-agent research mode
khojkar research --topic "Your research topic" --multi-agent --output report.md

License

See LICENSE file for details.