Hi everyone — I’ve been working on an open-source Obsidian vault framework called Loreloom.
GitHub:
https://github.com/yi-john-huang/loreloom
The project started from a concern I had with many AI-assisted knowledge systems:
An AI-generated summary can be useful, but it should not quietly become the source of truth.
Loreloom therefore separates the vault into three main layers:
- Sources preserve evidence and provenance.
- Knowledge contains atomic claims reviewed by a human.
- Wiki pages are AI-generated synthesis that can be rebuilt when models or prompts improve.
The basic principle is:
A useful criticism changed the framework
After I shared the project, someone raised an important issue:
What happens when the distortion occurs before AI is involved?
For example, I might read a paper quickly, type what I think it said, and then save that note as a Source. Even with perfect downstream synthesis, the original evidence was never faithfully captured.
That feedback led to a new capture-fidelity model.
Loreloom now records two separate properties:
capture_method: manual-entry
capture_mode: unknown
capture_method describes how content entered the vault.
capture_mode describes how the captured representation relates to the original evidence.
Examples include:
capture_method: asset
capture_mode: preserved-original
capture_method: file-extraction
capture_mode: extracted
capture_method: manual-entry
capture_mode: paraphrased
For hand-typed content without supporting evidence, Loreloom defaults to:
capture_method: manual-entry
capture_mode: unknown
I chose unknown rather than automatically labeling it paraphrased, because uncertainty should remain explicit instead of being replaced by another confident classification.
However, unknown is handled conservatively. It receives no more trust than a paraphrase until the original evidence, exact locator, and human review support a stronger classification.
The risk is asymmetric:
- Calling a verbatim quote a paraphrase mostly reduces confidence.
- Calling a paraphrase verbatim can turn distortion into apparent evidence.
These limitations also propagate into downstream Knowledge notes, so an AI agent should not silently rewrite a paraphrase as though it were a direct claim from the original source.
The implementation is included in this merged PR:
https://github.com/yi-john-huang/loreloom/pull/3
What Loreloom currently includes
- Markdown-first Obsidian vault structure
- Git-based history and review
- Source provenance and local Asset hashing
- Human review gates
- Atomic Knowledge notes
- Rebuildable AI-generated Wiki pages
- Maps of Content
- Schemas and validation scripts
- Codex-compatible agent policies and workflows
- Explicit capture-fidelity classification
It does not require a vector database or a specific model provider.
Feedback I’m looking for
I would especially appreciate criticism from people who maintain research notes, technical documentation, or long-lived personal knowledge bases:
- Is separating Sources, reviewed Knowledge, and generated Wiki pages useful in practice?
- Is
unknown the correct default for unverified manual capture?
- Is the framework too rigorous for personal use?
- What would prevent you from trying it with a private vault?
This is still an early-stage project, so workflow and onboarding feedback are more valuable to me than GitHub stars.