Egoist-Machines / LodeDB
World's fastest and most compact embedded vector database: exact by default, multimodal, local-first, and GPU-accelerated
Why this job: says: vector database; topics: rag, vector-database, embeddings, semantic-search, hybrid-search
What it does
LodeDB is great for local RAG; it's _extremely fast_, exact by default, in-process, and on-disk. We're the best drop-in durable memory backend for LangChain, LlamaIndex, and mem0: the most compact on disk, the fastest per single query, GPU-accelerated for batched search, and durable in about a millisecond per write. Point any of them at LodeDB instead of its default store. Over 17.5k documents, per framework default:
Get started
pip install "lodedb[embeddings]"
# vector store + built-in text embedding (ONNX, no PyTorch)- Maintenance
- Active
- Last commit 22 days ago
- Stars
- 104
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- 2 systems
- Mac, Linux
- Latest version
- v2.3.1
- Released 22 days ago
- Built with
- Python
- 50% of the code
Is it looked after?
- Contributors
- 2
- Last 90 days
- Issues
- 74% closed
- 8 still open
- Pull requests
- 90 merged
- 6 waiting
- Releases
- 28
- 28 in the last year
- Contributing guide (opens in new tab)
- Security policy (opens in new tab)
- No code of conduct
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More: download files, languages, topics and project record
Download files, v2.3.1
- Mac4 files, 6 downloads
- Linux2 files, 4 downloads
Languages
- Python 50%
- Rust 45%
- Swift 4%
- C <1%
Topics
- ann
- clip
- db
- embeddings
- gpu-acceleration
- hybrid-search
- image-search
- ivf
- local-first
- multimodal
- onnx
- privacy
Project record
- JobRAG and search
- StatusListed
- Commits211
- Forks11
- Watching2
- Stars in 28 days+3
- CreatedJun 14, 2026
- First seen by usNov 7, 2026
- CheckedNov 7, 2026