One 6 MB binary. Three engines. One query language. Zero cloud dependency.
Vector + Graph + ColumnStore — unified under VelesQL

Download v1.12.0 • Quick Start • Documentation • DeepWiki

Every AI agent today stitches together 3 databases for memory — vectors for "what feels similar", a graph for "what is connected", and SQL for "what I know for sure". That's 3 deployments, 3 configs, 3 query languages, and a pile of glue code.

VelesDB replaces all of that with a single Rust binary that fits on a floppy disk.

VelesDB was born in France out of a simple observation: EU data sovereignty is an architectural problem, not a legal one.

The US Cloud Act, FISA 702, and PATRIOT Act give US authorities multiple legal paths to reach data held by any US company — regardless of where the servers are. Hosting on AWS eu-west-1 is a latency decision, not a sovereignty decision. The EU's Data Privacy Framework has been invalidated twice (Schrems I, Schrems II), and a third challenge is pending.

For European developers building AI agents that handle health data, legal documents, or financial records, the typical 2026 stack sends embeddings to Pinecone (US), graphs to Neo4j Aura (US), and metadata to PostgreSQL on AWS (US provider). Every one of these is reachable by a FISA warrant.

VelesDB removes the US provider from the chain entirely. One Rust binary, local-first by design. No API key, no cloud account, no data processor. Your data stays in a directory you control — on your laptop, your server, your jurisdiction.

Read the full story: "I built a database in France because the Cloud Act makes EU data sovereignty impossible"

VelesDB is a local-first database for AI agents that fuses three engines into a single 6 MB binary:

All three are queried through VelesQL — a single SQL-like language with vector, graph, and columnar extensions:

MATCH (doc:Document)-[:AUTHORED_BY]->(author:Person)
WHERE similarity(doc.embedding, $question) > 0.8
  AND author.department = 'Engineering'
RETURN author.name, doc.title
ORDER BY similarity() DESC LIMIT 5

Built-in Agent Memory SDK provides semantic, episodic, and procedural memory for AI agents — no external services needed.

One binary. No cloud. No glue code. Runs on server, browser, mobile, and desktop.

Built-in memory for AI agents — semantic, episodic, and procedural. No external services needed.

from velesdb import Database, AgentMemory

db = Database("./agent_data")
memory = AgentMemory(db, dimension=384)

memory.semantic.store(1, "Paris is the capital of France", embedding)
memory.episodic.record(1, "User asked about geography", timestamp, embedding)
memory.procedural.learn(1, "answer_geography", steps, embedding, confidence=0.8)

Full guide: docs/guides/AGENT_MEMORY.md | Source code

Competitor latencies are typical ranges from public benchmarks and vendor documentation. Direct comparison is approximate — architectures differ (embedded vs client-server, durable vs in-memory, recall levels). Run your own benchmarks for accurate comparison.

VelesDB's sweet spot: When you need vector + graph + structured filtering in a single engine, local-first deployment, or a lightweight binary that runs anywhere.

Not the best fit (yet): If you need a managed cloud service with a multi-node distributed cluster.

Cargo (Rust):

cargo install velesdb-server velesdb-cli

Python:

pip install velesdb

Docker:

# Build the image locally
git clone https://github.com/cyberlife-coder/VelesDB.git && cd VelesDB
docker build -t velesdb .

# Run with persistent data (named volume)
docker run -d -p 8080:8080 -v velesdb_data:/data --name velesdb velesdb

# Verify it's running
curl http://localhost:8080/health

Data is stored in the /data directory inside the container. The named volume velesdb_data persists data across container restarts. The built-in health check polls GET /health every 30 seconds.

git clone https://github.com/cyberlife-coder/VelesDB.git && cd VelesDB
docker-compose up -d

npm install @wiscale/velesdb-wasm

curl -fsSL https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/scripts/install.sh | bash

irm https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/scripts/install.ps1 | iex

velesdb-server --data-dir ./my_data &

# Create collection + insert + search
curl -X POST http://localhost:8080/collections \
  -d '{"name": "docs", "dimension": 4, "metric": "cosine"}' -H "Content-Type: application/json"

curl -X POST http://localhost:8080/collections/docs/points \
  -d '{"points": [
    {"id": 1, "vector": [1.0, 0.0, 0.0, 0.0], "payload": {"title": "AI Intro", "category": "tech"}},
    {"id": 2, "vector": [0.0, 1.0, 0.0, 0.0], "payload": {"title": "ML Basics", "category": "tech"}},
    {"id": 3, "vector": [0.0, 0.0, 1.0, 0.0], "payload": {"title": "History of Computing", "category": "history"}}
  ]}' -H "Content-Type: application/json"

curl -X POST http://localhost:8080/collections/docs/search \
  -d '{"vector": [0.9, 0.1, 0.0, 0.0], "top_k": 2}' -H "Content-Type: application/json"
# [{"id":1,"score":0.995,"payload":{"title":"AI Intro","category":"tech"}}, ...]

Full installation guide: docs/guides/INSTALLATION.md

Native HNSW index with SIMD-accelerated distance kernels. Sub-millisecond search on commodity hardware.

5 search quality modes (Fast → Perfect), adaptive two-phase ef, AutoTune.

5 metrics with SIMD acceleration (AVX-512, AVX2, NEON, WASM SIMD128):

-- Choose metric at collection creation
CREATE COLLECTION docs (dimension = 768, metric = 'cosine');
CREATE COLLECTION images (dimension = 512, metric = 'euclidean');
CREATE COLLECTION fingerprints (dimension = 256, metric = 'hamming');

SELECT * FROM docs WHERE vector NEAR $v AND category = 'tech' LIMIT 5

Full benchmarks and methodology: docs/BENCHMARKS.md | velesdb-benchmarks repo | Quantization guide: docs/guides/QUANTIZATION.md

Property graph with BFS/DFS traversal, edge labels, and Cypher-inspired MATCH queries — integrated with vector search.

-- Vector + Graph fusion in ONE statement
MATCH (doc:Document)-[:AUTHORED_BY]->(author:Person)
WHERE similarity(doc.embedding, $question) > 0.8
RETURN author.name, doc.title
ORDER BY similarity() DESC LIMIT 5

Cross-collection MATCH with @collection annotation:

MATCH (p:Product@products)-[:STORED_IN]->(inv:Inventory@inventory)
RETURN p.name, inv.price, inv.stock
LIMIT 20

Graph patterns guide: docs/guides/GRAPH_PATTERNS.md

Typed columnar storage — the same approach DuckDB and ClickHouse use. 130x faster than JSON scanning at 100K rows.

JSON scan: 3.84 ms @ 100K    →    ColumnStore: 29.5 us @ 100K (130x faster)

SELECT * FROM products
WHERE vector NEAR $query AND in_stock = true AND price < 50.0
LIMIT 10

Pre-filter or post-filter automatically optimized by the query planner.

Your agent needs to remember conversations, learn from mistakes, and recall relevant knowledge. VelesDB provides all three memory types in a single embedded database — no Redis, no Pinecone, no Neo4j.

memory = AgentMemory(db, dimension=384)
memory.semantic.store(1, "User prefers dark mode", embedding)
memory.episodic.record(2, "User asked about billing", timestamp, embedding)
memory.procedural.learn(3, "handle_refund", steps, embedding, confidence=0.9)

Vector search alone returns noise. VelesDB's ColumnStore filters eliminate irrelevant results 130x faster than JSON scanning.

SELECT * FROM docs
WHERE vector NEAR $query AND department = 'engineering' AND updated_at > NOW() - INTERVAL '30 days'
LIMIT 10

Find products similar to a query, filter by price/stock, and traverse co-purchase relationships — all in a single VelesQL statement.

MATCH (product)-[:BOUGHT_TOGETHER]->(related)
WHERE similarity(product.embedding, $query) > 0.7
  AND related.price < 200 AND related.in_stock = true
RETURN related.name, related.price
ORDER BY similarity() DESC LIMIT 20

Ship AI features without a server. VelesDB embeds directly into Tauri, iOS, and Android apps.

VelesDB Core is open-source. Enterprise features (distributed replication, managed cloud, RBAC) are available separately via VelesDB Premium.

We ship weekly. Full changelog | Contributing guide

INSERT                      INDEX                       SEARCH
┌──────────┐  upsert   ┌──────────────┐  build   ┌──────────────┐
│ Your App │──────────> │ WAL (append) │────────> │  HNSW Graph  │
│          │           │ + mmap store │         │  (in-memory) │
└──────────┘           └──────┬───────┘         └──────┬───────┘
                              │                        │
                       ┌──────▼───────┐                │ search
                       │  ColumnStore  │  filter   ┌────▼─────────┐
                       │ (typed cols)  │────────> │ SIMD Distance│
                       └──────────────┘          │(AVX-512/NEON)│
                        RESULT                    └──────┬───────┘
┌──────────┐  top-k    ┌──────────────┐  rank           │
│ Your App │<──────────│   Payload    │<────────────────┘
│          │           │  Hydration   │
└──────────┘           └──────────────┘

Key design choices:

• Local-first: In-process or single binary — no network hops, no cloud dependency
• Memory-mapped storage: OS manages paging between RAM and disk
• WAL durability: Every write is journaled. Crash-safe by default (fsync mode). Deferred sync during bulk insert for throughput
• ColumnStore: Typed columns with string interning, RoaringBitmap tombstones, PostgreSQL-inspired auto-vacuum

# Build and run locally
docker build -t velesdb .
docker run -d -p 8080:8080 -v velesdb_data:/data --name velesdb velesdb
curl http://localhost:8080/health

# Or with docker-compose (builds + auto-restart)
docker-compose up -d

cd examples/ecommerce_recommendation && cargo run --release

git clone https://github.com/cyberlife-coder/VelesDB.git && cd VelesDB
cargo test --workspace --features persistence,gpu,update-check --exclude velesdb-python -- --test-threads=1

Looking for a place to start? Check out issues labeled good first issue.

Tell us about your project and get featured on the "Powered by VelesDB" showcase on velesdb.com. We highlight companies and projects that build with VelesDB — from RAG pipelines to sovereign AI agents.

How to get listed:

• Open a GitHub Discussion describing your use case
• Or email [email protected] with your project name, logo, and a one-liner

Your feedback shapes the roadmap.

VelesDB Core License 1.0 (based on ELv2). Free for production use, including commercial applications. Two restrictions: no offering VelesDB as a hosted/managed database service, and no building a competing database product. Read the full license.

VelesDB — The Local Knowledge Engine for AI Agents
velesdb.com • GitHub