Story

Hi HN,

I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.

After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, which felt unnecessarily heavy for short-lived analysis tasks.

To explore this tradeoff, I built GibRAM (Graph in-buffer Retrieval and Associative Memory). It is an experimental, in-memory GraphRAG runtime where entities, relationships, text units, and embeddings live side by side in a single process.

GibRAM is intentionally ephemeral. It is designed for exploratory tasks like summarization or conversational querying over a bounded document set. Data lives in memory, scoped by session, and is automatically cleaned up via TTL. There are no durability guarantees, and recomputation is considered cheaper than persistence for the intended use cases.

This is not a database and not a production-ready system. It is a casual project, largely vibe-coded, meant to explore what GraphRAG looks like when memory is the primary constraint instead of storage. Technical debt exists, and many tradeoffs are explicit.

The project is open source, and I would really appreciate feedback, especially from people working on RAG, search infrastructure, or graph-based retrieval.

GitHub: https://github.com/gibram-io/gibram

Happy to answer questions or hear why this approach might be flawed.