VEKTOR vs the field — architecture, ownership, and cost, laid out straight.
Pick the stack that fits your needs.
The most popular cloud memory API vs local-first SQLite. Pricing model, Node.js support, MCP integration, and data ownership.
Read comparison → LiveGraphiti's temporal reasoning vs MAGMA's 4-layer graph. Where Zep's validity windows win, and where local-first 8ms recall wins.
Read comparison → LivePure vector store vs associative memory graph. Why semantic similarity search alone isn't the same as agent memory.
Read comparison → LiveMemGPT's successor vs VEKTOR. Tiered in-context memory model vs persistent graph. Long-horizon task performance compared.
Read comparison → LiveBoth MCP-native, both claim strong LongMemEval numbers. Architecture deep-dive and honest look at the benchmark methodology.
Read comparison →Agentic memory is the infrastructure that lets an AI agent retain, retrieve, and reason over information across sessions, tasks, and model switches. It is not a context window. It is not a vector database. It is not a chat history buffer. It is a purpose-built layer that decides what to store, how to index it, and which parts to surface at the right moment — without blowing up your token budget or losing the information that matters.
A vector database stores and retrieves by semantic similarity. That solves one part of the problem. Production agents also need contradiction resolution (when new information conflicts with what is stored), temporal reasoning (the fact was true in March but not now), entity linking (understanding that "the auth module" and "auth.js" refer to the same thing), and knowledge graph traversal (answering questions that require combining two or more stored facts). VEKTOR's MAGMA graph handles all four. A bare vector store handles one.
| System | LongMemEval | LoCoMo | Metric | Notes |
|---|---|---|---|---|
| VEKTOR Slipstream | 79.0% | 66.9% | QA accuracy | GPT-4o-mini judge, 105q, v1.7.2. Methodology open. |
| Mem0 (Apr 2026) | 94.4% † | 92.5% † | QA accuracy | Self-reported May 2026. Eval framework open-sourced. Pre-May independent reproduction: 73.8%. |
| MemPal (raw, no LLM) | 96.6% ‡ | — | Retrieval R@5 | Different metric to QA accuracy. Not directly comparable. |
| Mastra | 94.87% † | — | QA accuracy | Self-reported, GPT-5-mini judge. Methodology not fully published. |
| GPT-4 (full context) | ~67% | — | QA accuracy | Original LongMemEval paper baseline (ICLR 2025). |
| ReadAgent | ~55% | — | QA accuracy | Original LongMemEval paper baseline (ICLR 2025). |
† Self-reported. Not independently verified at the published score. Independent reproduction found a 19.6-point gap versus Mem0's published figure on the same evaluation.
‡ Retrieval recall (R@5) — not QA accuracy. Measures whether the correct session appears in the top 5 retrieved results. MemPal's own documentation explicitly states this is not comparable to QA accuracy metrics.
VEKTOR's 79.0% is end-to-end QA accuracy on LongMemEval_S (105 questions), judged by GPT-4o-mini, using v1.7.2 routed ingest. Full methodology →
VEKTOR wins on latency (8ms local vs 100–400ms cloud), pricing ($9/mo flat vs usage-based), MCP support (native server for Claude, Cursor, Windsurf), and data ownership (zero egress). Competitors win on Python-first SDKs, managed infrastructure, and in Zep's case, temporal fact reasoning. Pick based on your stack, not the marketing.