VEKTOR Memory

https://dev.to/vektor_memory_43f51a32376/vektor-openai-agents-sdk-production-memory-in-three-lines-59p6

Node.js/TypeScript-native memory layer. SQLite + local Transformers.js embeddings (~80MB model via WebAssembly). AUDN loop (Add/Update/Delete/None) resolves contradictions before write rather than at retrieval — read-after-write consistency.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 34 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T2
Created
searched not found fix
Latest release
v1.7.8 Preview (2026-07-16) repo fix
Funding
searched not found fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
commercial repo fix
Deployment locus
local vendor fix
Platform vs product
both vendor fix
Protocols
mcp-server vendor fix

Memory taxonomy

storage
relational
retrieval
similarity
persistence
cross-session
update
extraction
unit
fact
governance
inspectable
conflict
llm-arbitrate

When to use

Optimised for: TypeScript-native developer experience

Anti-fit: not for Python-only stacks

Pros & cons

Pros

Vector-native memory framework with built-in embedding pipeline; minimal setup for new agents.

Cons

Vector-only — no graph or KV layer; smaller ecosystem.

Claims & capabilities

Only production-grade memory library targeting Node.js agents specifically with no cloud dependency. Python port on roadmap.

Technical surface

API surface
searched not found
Backend storage
searched not found
Deployment
Local-only (Node.js; Transformers.js via WebAssembly; SQLite; no cloud infrastructure)
Embedding model
searched not found
Multi-tenancy
searched not found
MCP
yes — bundled MCP server (npx vektor mcp) with 50+ tools; Claude Code/Desktop integration
A2A
not documented publicly
OpenTelemetry
not documented publicly

Similar systems

Other dedicated memory layers in the catalog, ranked by inbound references.

  • Mem0 T1

    Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.

  • Zep & Graphiti T1

    Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.

  • Cognee T1

    "Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.

  • Hindsight (Vectorize) T1

    Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.

  • Letta / MemGPT T1

    Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.

  • Memori T2

    Structured, persistent memory layer for AI agents. Open source.

Sources (33 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-22
    • License “not applicable — not OSS” → “proprietary (commercial licence)” source
    • Latest release “not applicable — not OSS” → “v1.7.8 Preview (2026-07-16)” source
    • Pricing “$9/month subscription OR one-time $159 commercial license (activates on up to 3 machines)” → “Solo $9/mo (3 licences); Team $35/mo (5); Studio $59/mo (10); Enterprise $99/mo (25)” source
    • MCP support first recorded as “yes — bundled MCP server (npx vektor mcp) with 50+ tools; Claude Code/Desktop integration” source
    #219