Ross ROSS = Recommend OSS · open-source software intelligence for agents

datalevin/datalevin

A simple, fast and versatile Datalog database observed · 2026-08-28

github.com/datalevin/datalevin · homepage · Clojure · EPL-2.0 (copyleft) observed · 2026-08-28

Health v2 · maintenance only

98/100

  • Activity 99
  • Release rhythm 97
  • Longevity 100
How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 3.0
  • age_days: 2277
  • days_rel: 22
  • days_push: 7
  • n_releases_24m: 31

Full methodology

Adoption not part of the score

1470 stars · 85 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Datalevin is a durable Datalog database built on LMDB that unifies relational, graph, document, key-value, full-text, and vector search in a single ACID store. It runs embedded or client-server, with bindings for Clojure, Java, Python, and JavaScript, plus AI-native features like built-in embeddings and MCP tools.

Use cases

  • query data with datalog instead of sql
  • embed a fast durable database in my application
  • combine full-text and vector search in one database
  • replace datomic with a simpler open-source alternative
  • store and query graph relationships with recursive rules
  • build a rag application with hybrid retrieval
  • run a client-server database with acid transactions

When to choose

  • you prefer Datomic-style Datalog over SQL and want an open-source, simpler option
  • you need multiple data models (relational, graph, document, KV) plus full-text and vector search without running separate stores
  • you want an embedded database that can later scale to client-server or cluster deployment without data migration
  • you build AI applications needing unified retrieval and MCP integration

When to avoid

  • your team and ecosystem are deeply SQL-oriented and you need broad SQL tooling compatibility
  • you require the temporal/bitemporal history features of Datomic
  • you need a managed cloud database service or very wide language ecosystem support
  • your stack cannot run a JVM (Java 21+) or native LMDB dependencies

Facets

library · maturity active

database vector-database search-engine rag mcp databases artificial-intelligence developer-tools jvm python cross-platform self-hosted datalog datomic-alternative lmdb embedded-database fulltext-search graph-database document-database key-value-store clojure search nodejs

10 sources

Member repositories

RepositoryRoleHealth v2
datalevin/datalevinmain98

For agents

markdown · JSON · MCP: product_card(name="datalevin/datalevin")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem