GibRAM: An In-Memory GraphRAG Runtime for Ephemeral Tasks

Working with regulation-heavy documents often reveals a fundamental flaw in standard retrieval systems: they struggle to group related articles connected by references, definitions, or clauses. This limitation prompted the creation of a new experimental tool designed to rethink how we access and analyze complex information.

GibRAM, or Graph in-buffer Retrieval and Associative Memory, is an in-memory GraphRAG runtime that challenges the conventional separation of graph storage and vector indexing. By keeping all data components in a single process, it offers a streamlined approach for specific, short-lived analytical tasks.

Traditional flat RAG pipelines frequently fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses. This fragmentation makes it difficult to build a coherent understanding of interconnected legal or technical documents. After testing several setups, the creator of GibRAM subjectively felt that GraphRAG provided a better mental model for this type of data.

The Microsoft GraphRAG paper and reference implementation served as helpful starting points. However, a recurring friction point emerged in practice: graph storage and vector indexing are typically handled by separate systems. This separation felt unnecessarily heavy for short-lived analysis tasks, where the overhead of managing multiple systems can outweigh the benefits.

• Difficulty retrieving interconnected articles
• Fragmented understanding of complex documents
• Overhead from separate storage systems

To explore this tradeoff, GibRAM was built as an experimental, in-memory GraphRAG runtime. In this system, entities, relationships, text units, and embeddings live side by side in a single process. This architecture is intentionally ephemeral, designed specifically 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 (Time-To-Live). There are no durability guarantees, and recomputation is considered cheaper than persistence for the intended use cases. The creator explicitly notes that 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.

The core design philosophy of GibRAM revolves around the concept of memory as the primary constraint. By eliminating the need for persistent storage and separate indexing systems, the runtime reduces complexity for specific use cases. This approach is ideal for scenarios where data is temporary and the focus is on rapid analysis rather than long-term retention.

Key architectural decisions include:

• Single-process operation for all data components
• Session-scoped data with automatic TTL cleanup
• No durability guarantees, prioritizing speed over persistence
• Explicit tradeoffs acknowledging technical debt

The project is presented as an exploration of what GraphRAG can become when freed from the constraints of traditional database architecture. It is not intended for production environments but serves as a valuable proof-of-concept for specific analytical workflows.

GibRAM is available as an open-source project, inviting feedback from the community. The creator is particularly interested in insights from professionals working on RAG, search infrastructure, or graph-based retrieval systems. This collaborative approach aims to refine the concept and identify potential flaws or improvements.

The project repository is hosted on GitHub under the gibram-io organization. It represents an ongoing experiment rather than a finished product, with the creator openly acknowledging that technical debt exists and many tradeoffs are explicit. The goal is to foster discussion around alternative approaches to document retrieval.

• Available on GitHub for public review
• Seeking feedback from RAG and search experts
• Open to discussion on potential flaws

GibRAM represents a focused experiment in rethinking GraphRAG architecture for ephemeral tasks. By concentrating on memory efficiency and process simplicity, it offers a potential alternative for analyzing regulation-heavy documents where traditional systems feel cumbersome. The project highlights the importance of matching tool design to specific use cases rather than adopting one-size-fits-all solutions.

As an open-source initiative, its future development will likely depend on community engagement and practical feedback. For those working with complex, interconnected documents, GibRAM provides a compelling case study in balancing retrieval quality with system overhead.