Open-Source Framework

Proposal Summary

This project proposes the development of an open-source framework for creating auditable LLM workflows, where each decision step taken by a large language model is traceable and explainable. The aim is to provide developers and researchers with tools to log intermediate reasoning, prompt stages, and final outputs—making AI behavior transparent and verifiable.

Objectives

Build a modular framework that captures the reasoning chain of LLMs during complex tasks
Enable traceability of prompts, context injections, and outputs
Support integrations with leading LLM APIs and open models
Pilot test the framework in real-world scenarios like AI tutoring and legal document analysis

Motivation

As large language models are adopted in high-stakes fields (education, legal, medical), it becomes essential to audit their reasoning. This project addresses a critical gap: while models are powerful, their decision paths remain opaque. By making these steps transparent, we enable accountability, debugging, and trust in AI systems.

Deliverables

Python-based framework with documentation and tutorials
Example notebooks: AI tutor, legal assistant, and document summarizer
Open-source repository with MIT License
Case studies published on ResearchHub and GitHub

Budget Justification

Developer time to build and document the framework
Model/API usage (OpenAI, Anthropic, etc.)
Hosting for public demos and examples
Community outreach (e.g. ResearchHub post, Twitter demo threads)

Timeline

	PhaseDuration
Design + Architecture	Week 1–2
Core Development	Week 3–5
Testing with Use Cases	Week 6–7
Public Release & Documentation	Week 8
Community Feedback + Improvements	Week 9–10

Impact

This tool will allow researchers, educators, and developers to audit LLM outputs and build trustworthy AI systems. It can also serve as a foundation for future research in explainable AI and compliance auditing, aligning with growing regulatory interest in AI transparency.

Open-Source Framework

Open-Source Framework

Funding Progress

Related Funding

AI for Transparent and Accountable Research

Nonprofit Recipient

Topics

DOI