OpenViking

OpenViking is a context database built specifically for AI agents. Instead of the usual pile of vector embeddings, it organizes an agents memory, retrieved documents, and skills the way a filesystem organizes files: directories you can navigate, tag, and search recursively. It is open source under AGPL-3.0.

Setup is heavier than a typical pip install. You will need Python 3.10+, a Rust toolchain for building the RAGFS component, and a C/C++ compiler for the core extensions, on top of wiring up a vision-language model and an embedding model (Volcengine Doubao, OpenAI, or others) before it does anything useful. Once running, it compresses conversation history automatically and shows you the retrieval path it took to answer a query, so debugging why an agent recalled the wrong thing is actually possible.

There is a hosted demo, OpenViking Studio, if you want to try the retrieval and memory features before committing to the build. Solo devs experimenting with agent memory: worth the setup time if you are hitting the limits of flat vector search. Teams building production agents: the tiered loading, it only pulls in the context tier it actually needs, is the real selling point for cutting token costs at scale.

It is AGPL-3.0, not MIT or Apache. If you build a hosted product on top of OpenViking and offer it as a network service, the license requires you to open source your modifications too. That is a real constraint for anyone planning to build a commercial product on this, not just an internal tool.