An AI-native embedded database
Zero config. Single file. Built for ML workloads.
pip install synadb
Built for AI workflows
Everything you need for ML development in a single embedded database.
Vector Search
Native HNSW index for similarity search. Optional FAISS backend for billion-scale deployments.
Experiment Tracking
Log parameters, metrics, and artifacts locally. No external server required.
Tensor Storage
Store and retrieve tensors directly to NumPy and PyTorch with zero serialization overhead.
Model Registry
Version models with SHA-256 checksums. Promote through development, staging, and production stages.
Single File
Your entire database in one portable file. No server process, no configuration files.
High Performance
Written in Rust. 100K+ writes per second. Sub-millisecond reads with in-memory indexing.
Get started
pip install synadbcargo add synadbfrom synadb import SynaDB, VectorStore
# Key-value storage
db = SynaDB("project.db")
db.put_float("accuracy", 0.95)
db.put_text("model_name", "bert-base")
# Vector similarity search
vectors = VectorStore("embeddings.db", dimensions=768)
vectors.insert("doc_1", embedding)
results = vectors.search(query, k=10)Three paradigms, one database
SynaDB synthesizes the best ideas from three database traditions.
SQLite
Single file, zero config, embedded library. No server to manage.
DuckDB
Columnar history, efficient tensor extraction, analytical queries.
MongoDB
Schema-free storage, flexible data types, no migrations needed.