VelesDB

One ~14 MB binary fuses vector + graph + columnar under a single query language — with an agent memory that shows its evidence and a deterministic context compiler that cuts your real, billed token spend.
Local-first: nothing leaves the machine, no LLM and no API key in the memory path. Every number below links to a committed harness you can rerun.

Quick start • Proof • Limitations • Architecture • Roadmap • velesdb.com

Start here — three commands that work

pip install velesdb
curl -O https://raw.githubusercontent.com/cyberlife-coder/VelesDB/main/examples/python/hello_velesdb.py
python hello_velesdb.py

To search your own text instead of hand-written vectors, install the opt-in local adapter (pip install "velesdb[embed-sentence-transformers]") and run hello_velesdb_text.py; its first run downloads all-MiniLM-L6-v2.

Expected output, byte-for-byte (read the script — no server, no embedding model):

Query: "tech"
  score=1.000  Rust 1.89 release notes
  score=0.600  AI-generated jazz: the new wave
  score=0.000  Best ramen in Tokyo

Query: "tech + music"
  score=0.990  AI-generated jazz: the new wave
  score=0.707  Rust 1.89 release notes
  score=0.707  Miles Davis discography

An embedding model determines the vector dimension, while your similarity semantics determine the metric. Both are fixed when a collection is created; to change either, create a new collection and re-index your documents.

Give your agent a persistent memory — three more commands:

cargo install velesdb-memory                                    # the local MCP memory server
claude mcp add velesdb-memory -- ~/.cargo/bin/velesdb-memory    # any MCP client works
curl -L https://github.com/cyberlife-coder/VelesDB/releases/latest/download/velesdb-skills.tar.gz | tar -xz -C ~/.claude/skills/

No Rust toolchain? npm i @wiscale/velesdb-memory-node, or grab a prebuilt .mcpb bundle from the official MCP Registry (io.github.cyberlife-coder/velesdb-memory).

Why VelesDB

• One database instead of three. Vectors for "what feels similar", a graph for "what is connected", typed columns for "what I know for sure" — normally three deployments, three query languages, and glue code. Here it is one binary and one language.
• A memory that can be audited, not just queried. Every recall can show the evidence behind it; every compression decision carries a rule id, a reason, and a risk level. Deterministic by construction — no model in the write path, so no drift and nothing to re-litigate.
• Local-first is a sovereignty decision, not a latency one. No cloud, no API key, no data processor: air-gapped if you want it, in your jurisdiction by default. Why that matters · positioning in depth.

How it works, in plain terms

Four things happen, and none of them calls an AI provider.

1 · It stores facts, not conversations. You give it one statement — "the API port is 6333 because 3000 collided with the web UI" — and it lands in a local file store. No model call, nothing sent anywhere.

2 · It finds them by meaning. Asking "which port did we settle on" reaches that fact even though none of the words match. A local embedding model turns text into coordinates; close meaning means close coordinates.

3 · It connects them, and that is the part a search engine cannot do. Each fact is linked to the topics it mentions. why() starts from the best match and then walks those links, so it returns the answer plus the facts that explain it — including ones sharing no vocabulary with your question.

The links have to exist. Store facts one by one and the graph stays flat, so why() behaves like a search. Hand a paragraph to remember_extracted and it splits it into facts and wires the links for you.

4 · It compresses what is too big, before you pay for it. Give the compiler your accumulated context and a token budget; it returns a smaller version with one recorded decision per fragment — kept, abstracted, or dropped — and a handle to fetch any original back verbatim. Same input, same bytes out, every time. That is what the 82.5 % below measures.

What no one else combines

1 · Three engines, one query

One statement crosses all three — similarity, relations and typed filters, no glue code:

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

2 · A memory that shows its evidence — why()

Most "agent memory" is vector recall: it finds text that looks like your query. VelesDB connects memories with typed links, so it can answer why something happened by walking the graph to context that shares no words with your question — across process restarts, offline, no API key:

from velesdb import MemoryService            # pip install velesdb

mem = MemoryService("./agent_memory")        # a real on-disk store; survives restarts
reason = mem.remember("Robert is recovering from knee surgery")
mem.remember("Booked the aisle seat on Robert's flight", links=[(reason, "because")])

# A *new* process, weeks later, reopens the same store and asks why:
mem.why("why the aisle seat on Robert's flight?")   # walks booking → reason — recall() can't

MemoryService defaults to the offline hash embedder: deterministic, but lexical rather than semantic, so unrelated wording can score 0.000. Opening it now says so once on stderr. For meaning-based recall, pass embedder="ollama"; follow Real semantic recall in 5 minutes.