Memory-friendly vector engine for on-device AI memory.
Why · Quick Start · Performance · How it works · Docs
AsterVec is a vector search engine that keeps its index on disk and puts as little as possible in memory. RAM holds only a small navigation structure and caches, so an application can search millions of embeddings within a few hundred MB of memory. It is built for local AI agents and desktop RAG.
Why AsterVec
- Minimize your memory usage. Other vector engines are designed around RAM and take as much of it as your data demands. AsterVec is designed around disk: the index lives there, and memory holds only the minimal part needed to navigate it. Set a memory budget and it holds, at 100K vectors or at 10 million.
- Insert-friendly. AsterVec assumes your data keeps growing. The index absorbs new vectors as they arrive, updates and deletes included, and never stops for a rebuild.
- Embeddable. A thread-safe Python and C++ library that runs inside your app, with no separate service to deploy. An HTTP server ships in the box if you want one.
Quick start
pip install aster-vec
import astervec
opts = astervec.AsterVecDBOptions()
opts.dim = 128
opts.vector_file_path = "./db/vectors.bin"
db = astervec.AsterVecDB.open("./db", opts)
db.insert(1, [0.1] * 128, metadata={"source": "notes"})
hits = db.search([0.1] * 128, k=10, filter={"source": "notes"})
for h in hits:
print(h["id"], h["distance"])
db.close()
The index takes inserts, updates and deletes in place. There is no rebuild step:
db.update(7, new_vector) # replace a vector in place
db.delete(3) # frees the space for reuse
db.bulk_build(embeddings) # fast initial load, NumPy accepted
Performance
Measured on SIFT at ef_search 32, same machine, same HNSW parameters, default settings for every engine. At 100K the working set is mostly cached, so AsterVec is effectively in memory too, and it compares well across the board. At 1M it is genuinely serving from disk. The memory gap widens there, and query speed stays roughly comparable. That is what the disk-oriented design is for: comparable performance while most of the index stays out of RAM. Full results and methodology: docs/BENCHMARKS.md.
How it works
At the heart of AsterVec is one architectural decision: on disk, the graph and the vectors are stored separately, because the two parts are updated and accessed in essentially different patterns.
- Graph — edges churn in small, scattered writes, so they live in Aster, a graph-oriented LSM-tree that absorbs them out of place.
- Vectors — they rarely change once written, so they live in packed pages built for bulk reads.
Your app's process
└─ AsterVec
├─ Memory — upper navigation layers + caches (small, bounded)
└─ Disk — two separate stores, updated independently:
├─ Graph store — base-layer edges in Aster, a graph-oriented LSM-tree, built for small, frequent link updates
└─ Vector store — related vectors stored in nearby pages, compressed
Keeping the two apart is what makes updates cheap. When graph and vectors share one disk record, recording a single new edge can move about 8 KB of vector data that did not change. In AsterVec, that edge lands in the graph store alone. On the vector side, related vectors are packed onto the same pages, so one read serves many candidate evaluations.
Documentation
| Guide | |
|---|---|
| Python & C++ API reference | docs/API_REFERENCE.md |
| Python SDK guide | docs/python_sdk_guide.md |
| Configuration options | docs/API_REFERENCE.md §7 |
| HTTP server (optional) | docs/HTTP_API.md |
| Troubleshooting | docs/TROUBLESHOOTING.md |
| Build from source | CONTRIBUTING.md |
The optional astervec_http server exposes the same engine over HTTP.
See docs/HTTP_API.md.
Contributing
Contributions are welcome — CONTRIBUTING.md has build steps, tests, and PR expectations.
License
Apache-2.0 — see LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for aster-vec 0.2.2
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