NamiDB is a graph database designed to operate directly within object storage platforms such as AWS S3, Cloudflare R2, Google Cloud Storage, Azure Blob, MinIO, and Tigris. The tool consolidates graph, vector, and full-text search capabilities into a single engine, allowing users to perform graph traversal, vector ranking, and keyword search within the same query and dataset. By leveraging object storage, NamiDB eliminates the need for dedicated database servers, enabling users to scale to zero when idle and pay only for the storage they consume.
The platform supports Cypher queries, both synchronously and asynchronously from Python, with GQL support planned. It includes built-in algorithms such as PageRank, connected components, triangle counting, and shortest path. Vector search is facilitated through cosine similarity over embeddings stored as node properties, and hybrid search combines keyword relevance with vector similarity using reciprocal rank fusion. NamiDB is also capable of transforming Obsidian or Markdown folders into a live, queryable graph, where notes become nodes, wikilinks become edges, and tags form a hierarchical structure. This integration enables AI agents, such as those using Claude Code or Cursor, to access and query the resulting graph via a hosted MCP endpoint, supporting semantic search, backlinks, neighbors, orphans, shared tags, and raw Cypher queries.
NamiDB offers multiple deployment options: as a server (a single Rust binary interfacing with object storage and serving REST and Bolt endpoints), as an embedded library (usable in notebooks, scripts, or CI), and as a managed, multi-tenant cloud service with namespace isolation per tenant or project. The open-source version can be installed via pip or run as a Docker container. Pricing is based on object storage usage rather than memory allocation, with idle namespaces incurring no compute costs. Backups and disaster recovery are handled through standard object storage operations, as the database consists of files within the user's bucket, avoiding proprietary formats or vendor lock-in.
NamiDB is open source, with all architectural decisions documented and benchmarks published. The tool is suitable for teams seeking to unify graph, vector, and search workloads on existing object storage infrastructure without managing traditional database servers.
NamiDB is a Databases (SQL, NoSQL, vector, graph) product. Enabling teams to run a scalable, multi-modal database directly in S3, combining graph, vector, and search capabilities. It is built as an open-source project for developers and data engineers needing embedded or cloud-native graph/vector databases. NamiDB is open source under the Open Source license. The product ships for the command line and API, and it can be self-hosted.
It is developed by NamiDB, and the product first shipped in 2026. Development happens publicly on GitHub with 109 stars and 339 commits in the last 90 days. PulseGate's similarity index finds few close equivalents — NamiDB occupies a relatively distinct niche. Key capabilities include graph database, vector search, and full-text search. It exposes integrations via an MCP server.
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