Agenten stärken
Der eigene Befehl des Herausgebers. Kein PlugMCP-Endpunkt.
Host a copy with our keys and logs.
mcpb https://github.com/cyberlife-coder/VelesDB/releases/download/velesdb-memory-v0.17.1/velesdb-memory-0.17.1-aarch64-apple-darwin.mcpbClaude Code hat keinen Installationslink für einen lokalen Befehl. Kopieren Sie den Befehl unten und führen Sie ihn in einem Terminal aus.
claude mcp add io.github.cyberlife-coder-velesdb-memory -- mcpb https://github.com/cyberlife-coder/VelesDB/releases/download/velesdb-memory-v0.17.1/velesdb-memory-0.17.1-aarch64-apple-darwin.mcpbIn einem Terminal ausführen.
{ "mcpServers": { "io.github.cyberlife-coder-velesdb-memory": { "command": "mcpb", "args": [ "https://github.com/cyberlife-coder/VelesDB/releases/download/velesdb-memory-v0.17.1/velesdb-memory-0.17.1-aarch64-apple-darwin.mcpb" ] } }}claude_desktop_config.json
Remote-URL oder ein Befehl auf Ihrem Rechner? Worin sich die beiden Installationen unterscheiden
Auf PlugMCP ausführen
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
StartenVom Herausgeber
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 ; its first run downloads .
Offline agentic memory: remember/recall/relate/forget/why over a fused vector+graph+columnar engine
all-MiniLM-L6-v2Expected 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).
Memory used continuously, not just available: integrations/agent-hooks/ wires five Claude Code hooks — SessionStart/Stop/PreCompact resume and save the working context, PreToolUse requires successful recall before an opted-in repository edit, and PostToolUse both records that recall and compiles an oversized tool result before it enters the transcript. One global install covers every project without enabling the edit guard outside explicitly configured repositories.
One memory shared by several clients (Claude Code, Codex CLI, Claude Desktop, Windsurf, Devin CLI): scripts/install-memory-daemon.sh runs velesdb-memory as a single local daemon — HTTPS by default, with a natively generated local CA.
Cargo (Rust + REST server): cargo install velesdb-server velesdb-cli — Docker (multi-arch linux/amd64 + linux/arm64): docker run -d -p 8080:8080 -v velesdb_data:/data --name velesdb ghcr.io/cyberlife-coder/velesdb:latest, then curl http://localhost:8080/health.
Browser / edge: the WASM build is ~710 KB gzipped and runs entirely client-side (TypeScript SDK). REST: 54 REST endpoints (OpenAPI spec). Full matrix: installation guide.
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 toremember_extractedand 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.
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
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
MemoryServicedefaults to the offlinehashembedder: deterministic, but lexical rather than semantic, so unrelated wording can score0.000. Opening it now says so once on stderr. For meaning-based recall, passembedder="ollama"; follow Real semantic recall in 5 minutes.
Memories are permanent by default; forget(id) deletes one, ttl_seconds gives a fact a durable expiry. Every remember auto-stamps its storage day, so recency-weighted recall works with zero setup. Same wedge in Python, Node, the MCP server, and in-memory in the TypeScript SDK.
Proof it is not a weak-embedder trick — four runnable demos in which recall stays blind to the reason even under a real semantic embedder (ollama / all-minilm), because the reason is connected by a decision rather than by surface similarity: why_across_sessions.py (survives a process restart) · why_magic_constant.py (a business reason sharing no words with the code) · memory_builds_its_own_graph.py (raw prose in, auto-wired graph out) · why_magic_constant.mjs (Node). Benchmark position, including LoCoMo and why cross-lab scores are not fairly comparable: BENCHMARK.md · Agent Memory guide.
Agents burn most of their budget re-reading redundant context. compile_context / compile_transcript (MCP, or ContextCompiler in Rust) shrink it with no LLM and no network:
explain_compilation gives every kept or dropped fragment a stable rule id, a reason and a risk level.ctx://source/ handle; retrieve_context_source brings the original bytes back on demand.remember.Code, URLs, numbers and negative constraints survive verbatim. The velesdb-context-optimizer skill teaches the workflow — including when not to compress.
No figure here is an estimate from a slide; each links to the log or script in this repo that produced it.
Same campaign, less flattering: 10.9 % on cropped screenshots, 14.7 % on a 36-turn day-scale arc, 15.1 % input tokens on the direct Messages API, and 2.5 % for the no-screenshots variant — that spread is the measured value of the media mechanisms, so we publish it as prominently as the headline. Honest reading, limitations and full protocol. Every number on this page is CI-guarded by a promise contract that pins the README to its committed sources.
Billed A/B sessions (2026-07-19, claude-sonnet-5; raw logs committed verbatim):
* Two turns' grading key was later found defective (both arms scored full marks there; the parity conclusion stands) — disclosure. Over a 36-turn session compiled context grows 1.7× slower, so one session lasts far longer before hitting the window.
Memory retrieval quality, public test sets, no AI grader in the loop: +7.2 pts multi-hop (HotpotQA), +9.7 pts time-scoped recall (TimeQA), +29 pts on a controlled task needing both engines at once — BENCHMARK.md.
End-to-end search (canonical): search p50 450 us (10K, 384D, WAL ON; measured 2026-03-27 on 1.7.2, Balanced then at ef 128 — harness, report) · quantization PQ (2 × dim / m smaller: dim/4 at the default m = 8), RaBitQ (32x smaller), SQ8 (4x smaller), Binary (32x smaller) — scope & caveats.
Index-only micro-benchmarks (no WAL, no payload, hot cache — not comparable to the end-to-end figure above), each reproducible with cargo bench -p velesdb-core --bench <name>: HNSW Search index-only (10K/768D, k=10) 55 us (hnsw_benchmark -- hnsw_search_latency) · SIMD Dot Product (768D, AVX2) 21.7 ns (simd_benchmark) · Recall@10 balanced 99.8% and accurate mode 100% (10K/128D, recall_benchmark) · BM25 Sparse Search index-only (10K docs, top-10) 57.6 us (sparse_benchmark -- top10_10k_corpus).
ef_search is each preset's base value, used as-is for k ≤ 32 on up to 10K vectors; ef_search_for_scale raises it beyond. Recall@10: recall_benchmark, 10K/128D, 2026-09-10 on Apple M5 Pro.
Distance metrics — 5 with SIMD acceleration (AVX-512, AVX2, NEON). At 768D on the i9-14900KF reference (AVX2, hot cache; the April 3, 2026 run in docs/BENCHMARKS.md §1): Cosine 32 ns · Euclidean 26 ns · Dot Product 22 ns · Hamming 35 ns · Jaccard 27 ns.
ColumnStore — typed columnar filtering, 130x faster than JSON scanning at 100K rows on the i9-14900KF reference (JSON scan 3.84 ms → ColumnStore 29.5 us). The ratio is hardware-dependent: on Apple Silicon (M5 Pro, 2026-07-20) the JSON scan itself runs ~2.8× faster, so the same bench measures ~50–105x while the ColumnStore's absolute time holds (~27 µs).
Provenance: Intel Core i9-14900KF (x86_64, AVX2). Per-machine figures vary; Apple-Silicon cross-checks, the SIFT1M standardized ANN run and the full methodology live in docs/BENCHMARKS.md. Reproduce the end-to-end figure with
python benchmarks/velesdb_benchmark.py --recall.
Tool parity per surface is published honestly — including where a surface is still behind: memory crate README. Worked examples: examples/.
Full API reference: docs/reference/api-reference.md | OpenAPI spec: docs/openapi.yaml | Server security: docs/guides/SERVER_SECURITY.md
Sweet spot: vector + graph + structured filtering in one engine, local-first, auditable. Not the best fit (yet): a managed cloud service with a multi-node distributed cluster. Competitor figures are typical public ranges, not a head-to-head run we performed — run your own. Detailed comparison against agent-memory products (Mem0, Zep, Letta), as of mid-2026: docs/WHY_VELESDB.md.
The core engine is source-available and stays that way. Premium adds the company-grade layer on top of the same binary, for organizations running agent fleets on sensitive data: RBAC on every endpoint including the memory and context-compiler surfaces · audit trail (who, what, when — metadata only, GDPR-conscious) with forensic replay · multi-tenancy with hard per-tenant isolation and two-level deletion rights · clustering and air-gapped deployment · a WebAdmin UI for operators.
Pricing on quote — [email protected] · velesdb.com. Built by Wiscale (France; GDPR and data-sovereignty native).
The items below are deliberate trade-offs or Premium-tracked features, not correctness gaps — the Community Edition is production-ready for single-node, local-first deployments. We publish them next to the strengths, including the ones we have not fixed yet.
Internal technical limitations (query-planner approximations, plan-cache semantics): docs/reference/KNOWN_LIMITATIONS.md.
Quality bar: cargo test --workspace — 9k+ tests across Rust, TypeScript and Python run in CI on every merge; exact commands in QUALITY_BAR.md.
Contributions welcome — start with CONTRIBUTING.md and the good first issues. Security reports: SECURITY.md. Roadmap: ROADMAP.md · Changelog · DeepWiki.
License: VelesDB Core License 1.0 (source-available). Premium: commercial license. Contact: [email protected] · velesdb.com
The name nods to Veles, a deity of old Slavic myth — a keeper of hidden knowledge and boundaries.
Offline agentic memory: remember/recall/relate/forget/why over a fused vector+graph+columnar engine
{ "mcpServers": { "io.github.cyberlife-coder-velesdb-memory": { "command": "mcpb", "args": [ "https://github.com/cyberlife-coder/VelesDB/releases/download/velesdb-memory-v0.17.1/velesdb-memory-0.17.1-aarch64-apple-darwin.mcpb" ] } }}Tools
Noch keine Tool-Liste. Remote-Server erhalten eine nach dem ersten Health-Check.
FAQ
Kopieren Sie auf dieser Seite die stdio-Konfiguration für Cursor, Claude Desktop, Claude Code oder VS Code. Sie nutzt die Installation des Herausgebers, keinen PlugMCP-Proxy.
In diesem Eintrag ist keine erforderliche Umgebungsvariable aufgeführt.
VelesDB Memory läuft lokal über stdio.
Zuletzt geprüft: 2026-11-13. Offline agentic memory: remember/recall/relate/forget/why over a fused vector+graph+columnar engine