Show HN: Cogitator – A Python Toolkit for Chain-of-Thought Prompting

Summary

Cogitator A Python Toolkit for Chain-of-Thought Prompting Cogitator is a Python toolkit for experimenting and working with chain-of-thought (CoT) prompting methods in large language models (LLMs). CoT prompting improves LLM performance on complex tasks (like question-answering, reasoning, and problem-solving) by guiding the models to generate intermediate reasoning steps before arriving at the final answer. Additionally, it can be used to improve the interpretability of LLMs by providing insight into the model's reasoning process. The toolkit aims to make it easier to use popular CoT strategies and frameworks for research or integrating them into AI applications. Features Getting Started You can install Cogitator with pip install cogitator Or, if you want to install from the latest version with examples and benchmarks included git clone https://github.com/habedi/cogitator && cd cogitator # Set up Python environment pip install poetry poetry install --with dev # Run the tests to make sure everything is working (optional) poetry run pytest Examples Below is a simple example of using the Self-Consistency CoT with Ollama. import logging from cogitator import SelfConsistency , OllamaLLM # Step 1: Configure logging (optional, but helpful) logging . basicConfig ( level = logging . INFO ) logging . getLogger ( "httpx" ). setLevel ( logging . WARNING ) # Suppress HTTPX logs # Step 2: Initialize the LLM (using Ollama) # Needs Ollama running locally with the model pulled (e.g., `ollama pull gemma3:4b`) try : llm = OllamaLLM ( model = "gemma3:4b" ) except Exception as e : print ( f"Error initializing Ollama LLM: { e } " ) print ( "Please make sure Ollama is running and the model is pulled." ) exit ( 1 ) # Step 3: Choose a CoT strategies (Self-Consistency in this case) # Self-Consistency generates multiple reasoning paths and finds the most common answer sc_strategy = SelfConsistency ( llm , n_samples = 5 , # Number of reasoning paths to generate temperature = 0.7 # Higher tempe...