Alternatives
Products that do what TuringDB – The fastest analytical in-memory graph database in C++ does
Hi HN, I am one of the cofounders of http://turingdb.ai. We built TuringDB while working on large biological knowledge graphs and graph-based digital twins with pharma & hospitals, where existing graph databases were unusable for deep graph traversals with hundreds or thousands of hops on (crappy) machines you can find in a hospital. https://github.com/turing-db/turingdb TuringDB is a new in-memory, column-oriented graph database optimised for read-heavy analytical workloads: - Milliseconds (1) for multi-hop queries on graphs with 10M+ nodes/edges -…
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2020 · terminusdb.com
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We have been using graph DBs more and more at work. I found them painful to work with locally and decided to try and build something better.
12d ago · github.com
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2020 · github.com
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2020 · github.com
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2020 · github.com
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2018 · github.com
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2017 · github.com
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2016 · graphene-python.org
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2016 · scaphold.io
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2019 · 8base.com
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2016 · github.com
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2020 · github.com
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2015 · github.com
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2017 · github.com
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Hey HN, I’m Roi, one of the co-creators of FalkorDB. We’re a growing team working on a graph database designed for production workloads and GraphRAG systems. The new release (v4.10.0) is out, and I wanted to share some of the updates and ask for feedback from folks who care about performance, memory efficiency in graph-heavy systems. FalkorDB is an open-source property graph database that supports OpenCypher (with our own extensions) and is used under the hood for retrieval-augmented generation setups where accuracy matters. The big problem we’re working on is scaling graph databases without…
2025
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2015 · github.com
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Input a SMILES string (or pick one molecule from the examples) and it returns up to 100k molecules closest in 3-D shape or electrostatic similarity – from 10+ billion scale databases — typically in under 5-10 s. *Why it might interest HN* * Entire index lives on disk — no GPU at query-time, less than ~10 GB RAM total. * Built from scratch (no FAISS index / Milvus / Pinecone). * Index-build cost: one Nvidia T4 (~ 300USD) for one 5.5B database. * Open to anyone, predict ADMET, export results as CSV/SDF. Full write-up & benchmarks (DUD-E, LIT-PCBA, SVS) in the pre-print:…
2025 · cheese-new.deepmedchem.com
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Hey HN, I've been working on a multi-model database called NodeDB. Originally, i've found out the idea of SurrealDB quite good. However, it doesn't have some graph and vector features that I need. And since it is just a KV wrapper, instead of purpose-built engine, the performance will never be close to the specialized databases (like Neo4j, Pinecone, Clickhouse, etc). And i've asked myself, what if, there is a database that have the same idea, but built differently? Instead of just treating it as KV database, we build specialized engines for the data. Besides that, I want it to be able to…
May 2026 · github.com
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