RinDig/Interpretable-Context-Methodology
A methodology that uses folder structure and markdown files as an AI agent architecture, replacing framework-level orchestration.
Collecting fresh signals — velocity needs a few days of history.
collecting data…
star history
Interpretable Context Methodology (ICM) organizes agent workflows as numbered folders where markdown files carry the prompts and context for each stage. A single AI agent reads the appropriate files at each step, performing sequential work that would otherwise require a multi-agent framework. It is positioned as an alternative to frameworks like CrewAI, LangChain, and AutoGen and is accompanied by a research paper.
Frequently asked
- What is RinDig/Interpretable-Context-Methodology?
- A methodology that uses folder structure and markdown files as an AI agent architecture, replacing framework-level orchestration.
- Is Interpretable-Context-Methodology open source?
- Yes — RinDig/Interpretable-Context-Methodology is open source, released under the MIT license.
- What language is Interpretable-Context-Methodology written in?
- RinDig/Interpretable-Context-Methodology is primarily written in Python.
- How popular is Interpretable-Context-Methodology?
- RinDig/Interpretable-Context-Methodology has 1.3k stars on GitHub.
- Where can I find Interpretable-Context-Methodology?
- RinDig/Interpretable-Context-Methodology is on GitHub at https://github.com/RinDig/Interpretable-Context-Methodology.