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dragiter: Deterministic RAG Iterator

License: AGPL v3 PyPI Python

A modular CLI for structured, reproducible LLM workflows.

dragiter is a modular command-line interface (CLI) designed to integrate Large Language Models (LLMs) directly into your automated terminal workflows. It acts as a bridge between your local file system and AI APIs, eliminating "copy-paste fatigue" by allowing you to chain AI agents exactly like standard Unix pipes.

Source & Project - GitLab Repository (Code) - Releases - Issues

Why dragiter?

If you want an AI to review an entire project, manually gathering files, stripping out noise, and pasting them into a web chat is tedious. dragiter solves this through "Prompt as Code."

  • Automated Context Assembly: Use wildcards (like src/**/*.py) and regex patterns to surgically extract exactly what the AI needs to see.
  • Version-Controllable Prompts: Define your AI instructions and data context in standard .toml files so your workflows are repeatable and shareable.
  • Advanced Batch Processing: Feed dragiter a .jsonl loop file to automatically iterate through translation tasks, report summaries, or data extraction without writing custom Python scripts.
  • Vendor Independence: Switch from cloud providers like OpenAI, Grok, or Google to a completely local, private model like Ollama just by changing a single CLI flag.

Installation

You can install dragiter easily via pip:

pip install dragiter

Quick Start

The core philosophy of dragiter is to keep your resources (material, context) and your prompts (instructions) separate. The easiest way to explore dragiter is by using the included examples.

1. Extract the Examples

First, extract them into your current directory by running:

dragiter-gen-examples .

You will find the examples in the ../examples subdirectory. To follow along with the first example, navigate into it:

cd examples/01_md_sample

2. Test Safely with Simulation Mode

It is highly recommended to always run a simulation first. This allows you to safely verify your workflow and file routing without making actual API calls or spending your API credits. You can do this by adding the -s flag to your command.

Run the simulation by typing:

dragiter -s -p 01_prompt_md.toml -r 01_resource_md.toml -l 01_loop_md.txt

3. Run with Ollama

The file config-ollama.toml is ready to use out of the box, provided that Ollama is installed and running locally with its default settings. When using Ollama, it is recommended to run the command with the -v (verbose) flag:

dragiter -v -c config-ollama.toml -p 01_prompt_md.toml -r 01_resource_md.toml -l 01_loop_md.txt

Tool Chaining (The Unix Way)

dragiter is built to play nicely with other CLI tools. You can fetch live data and pipe it straight to your AI workflow:

cat server_logs.txt | grep "ERROR" | dragiter -p analyze_errors.toml

Note: When piping data into dragiter with a prompt template (-p), the template must contain the [STDIN] placeholder. The piped content is inserted at that position. Very large inputs are currently not automatically chunked via STDIN; for big log files it is usually better to write the filtered data to a file and load it through a resource definition (-r) with proper chunking.

Documentation

You are currently looking at the official documentation. To learn more about practical examples (code review, batch report analysis, marketing copy generation, tool chaining, etc.) and advanced features such as context window management and JSONL processing, please read the Manual.

Acknowledgements

The development of dragiter has been a journey of continuous learning. Bringing this project to life would not have been possible without the support of some extraordinary tools and communities.

A massive thank you to the AI models Grok and Gemini. As tireless pair-programming partners, your guidance, code reviews, and structural suggestions were invaluable in adapting the Python code for this project.

Equally important is the global Python community. The rich ecosystem, extensive documentation, and open-source spirit provide the foundation for tools like dragiter. Thank you to all the developers who make Python such a powerful language to work with. ```