Question 1

What kind of custom rules can I enforce with Knoten?

Accepted Answer

You can define custom graph rules and vocabulary in a `graph.yaml` file. Knoten enforces these rules (e.g., required result fields, node types, statuses, minimum data points) via a Git commit hook, ensuring data integrity and consistency specific to your research domain.

Question 2

How does Knoten integrate with AI agents and LLMs?

Accepted Answer

Knoten provides an MCP (Multi-Agent Communication Protocol) server, allowing AI agents (like Claude) to query the accumulated research knowledge, commit new findings (including failures), and attach relevant scripts or plots. This enables agents to learn and build knowledge across sessions.

Question 3

How does Knoten store and manage research information?

Accepted Answer

Knoten stores research nodes as Markdown files within a Git repository. Each file includes structured frontmatter for machines (like status, links, and results) and prose for humans, alongside embedded code for reproducibility. Git manages version control for the entire graph.

Question 4

What is Knoten and what problem does it solve?

Accepted Answer

Knoten is a Git-based research graph tool that tracks experimental ideas and their outcomes (alive, dead, retracted). It prevents re-running failed experiments by documenting reasons for failure and reproducibility, building cumulative knowledge for data science and ML teams.

Question 5

Can Knoten help prevent me from repeating past experiments?

Accepted Answer

Yes, this is Knoten's primary purpose. It explicitly tracks 'dead' or 'retracted' experiments with their causes of failure and reproducibility steps. You can easily query past attempts to avoid redoing forgotten work and learn from previous results.

Knoten

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