ravendb/ravendb
ACID Document Database observed · 2026-08-28
Health v2 · maintenance only
93/100
- Activity 99
- Release rhythm 82
- Longevity 100
Flags: no_license
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: 0.0
- age_days: 6028
- days_rel: 42
- days_push: 7
- n_releases_24m: 65
Adoption not part of the score
3993 stars · 864 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
RavenDB is an open-source (AGPLv3) ACID NoSQL document database written in C#, featuring full-text search, vector search, time series, sharding, and built-in GenAI/LLM integration. It runs on-premises, in the cloud, or at the edge, with official client SDKs for .NET, Java, Python, Node.js, and more.
Use cases
- store and query JSON documents with ACID transactions
- add full-text search to my application without Elasticsearch
- run semantic search over documents using vector embeddings
- run LLM enrichment tasks directly inside my database
- store and aggregate time-series data like IoT sensor readings
- set up a distributed replicated database with automatic failover
- shard data across multiple nodes
- sync data between RavenDB and SQL Server, PostgreSQL, or Kafka
When to choose
- you need a schema-less document database with strong ACID guarantees
- you want built-in search, vector search, and time series without extra infrastructure
- you want LLM/GenAI workflows orchestrated natively in the database
- you need self-optimizing indexes and low-maintenance operations
- you need on-prem, air-gapped, or hybrid cloud deployments with compliance certifications
When to avoid
- you need a fully permissive open-source license for commercial embedding (AGPLv3 applies)
- your workload is purely relational with complex joins across normalized tables
- you are already standardized on another database ecosystem with mature tooling
- you need a lightweight embedded database for a small application
Facets
application · maturity stable
database search-engine vector-database caching etl security databases large-language-models developer-tools iot windows self-hosted cloud dotnet cross-platform nosql document-database acid full-text-search sharding time-series vector-search genai dbms search linux macos docker
10 sources
- readme: https://github.com/ravendb/ravendb · fetched 2026-08-28 · 8c15e62a0693
- homepage: https://ravendb.net · fetched 2026-08-29 · ae63fd844102
- site_page: https://ravendb.net/features · fetched 2026-08-29 · 855fd721cf0c
- site_page: https://docs.ravendb.net · fetched 2026-08-29 · 42ba8d751f46
- site_page: https://ravendb.net/about · fetched 2026-08-29 · a84b074fa5d9
- site_page: https://ravendb.net/features/ai-features/vector-search · fetched 2026-08-29 · 8a69ae8d7a16
- site_page: https://ravendb.net/lp/genai-inside-database · fetched 2026-08-29 · 2eee596ea744
- site_page: https://ravendb.net/integrations · fetched 2026-08-29 · 1aad5d08174e
- site_page: https://cloud.ravendb.net/pricing · fetched 2026-08-29 · edf60991b3c5
- site_page: https://ravendb.net/products/on-prem · fetched 2026-08-29 · 673706d030ed
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ravendb/ravendb | main | 93 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem