If you've explained your tech stack to your coding agent four times this month only to watch it forget by next Tuesday, you're not alone—and it's not a model problem. GPT-4, Claude, and Gemini all have the same fundamental limitation: they're stateless. They ingest 75,000 words in roughly eight seconds but retain nothing beyond the current session without external memory infrastructure. VEKTOR Slipstream v1.6.3, released June 5, 2026, is the local-first SDK that finally addresses what most memory systems skip entirely—not just storing what you tell your agent, but managing what should still be there months later.

The FadeMem Difference

The headline feature in v1.6.3 is the production implementation of FadeMem decay architecture from a February 2026 paper by researchers at Alibaba and Peking University (arXiv:2601.18642). To VEKTOR's knowledge, this marks one of the first production SDK implementations of that research. The system classifies every memory into two tiers: Long-term Memory Layer (LML) with roughly an eleven-day half-life at default settings, or Short-term Memory Layer (SML), which decays four times faster. What drives tier assignment isn't just what you set when storing—it recalculates as a weighted function of semantic relevance to current goals, access frequency, and position in the causal graph.

Conflict Resolution That Doesn't Suck

Most memory systems are append-only stores with sophisticated retrieval but zero opinion about what should still exist. VEKTOR's vektor-conflict.js compares new memories against existing ones above a similarity threshold, classifying relationships across five outcomes: supersession, coexistence, subsumption, generalization absorption, or duplicate dismissal. Trust scores prevent automated sources from overwriting human decisions—a direct user note scores 1.0 trust while an automated bot event scores just 0.28. When you update a decision, the old version gets retired to cold storage rather than deleted entirely, preserved for audit but excluded from active recall.

Local-First Means What It Says

Memory lives in a single SQLite file the user owns. Embeddings run locally on CPU with no API calls and zero per-token cost. MCP connectors spawn as local stdio processes; nothing routes through external services. There's no telemetry, no cloud sync, no account required. When you connect GitHub or your filesystem via the wizard setup, data stays on your machine—answering all four critical questions about agent memory infrastructure (where context lives, who pays per token, where data goes, who can see it) with the same answer: your machine, your data, your rules.

The Numbers Are Real

VEKTOR validated retrieval against the LoCoMo dataset—419 stored dialog turns and 199 annotated question-answer pairs, retrieval only, no LLM assistance at query time. Results: VEKTOR achieved Recall@10 of 71.9% compared to GPT-4 with RAG baseline at 37–42% F1. The human ceiling on LoCoMo sits around 88% F1. The gap between standard RAG and VEKTOR's four-channel recall pipeline (semantic + BM25 + enriched semantic + HyDE, fused via Reciprocal Rank Fusion) is substantial. Running the benchmark also caught a production bug: question marks were reaching SQLite's FTS5 engine as special syntax, silently falling back to semantic-only recall on every conversational query since—yes—every question ends with a question mark.

Standing Queries and the REM Cycle

vektor-standing.js synthesizes current priorities weekly from top-importance recent memories. The output is a small set of embedded goal statements stored in the database; every new memory gets scored for relevance against these goals before tier assignment. A commit directly relevant to an active project receives higher initial importance than one with no connection to your work. The full REM cycle (decay → fusion → prune → standing) completes in 716 milliseconds on a 17,523-node graph—fast enough to run every six hours in the background without user awareness.

MCP Connectors and Provider Agnosticism

Version 1.6.3 ships with vektor-mcp-reader.js and vektor-connector-base.js for syncing external tools into VEKTOR memory. Filesystem and GitHub connectors are now available via setup wizard Step 10, using dedicated fetchGithubItems strategy to sync issues, commits, and pull requests. Staggered ingestion throttles large initial syncs to 200 items per run with 5ms between writes. The system also drops Groq hardcoding across conflict resolution, fusion, standing query, and sleep modules—now supporting all fifteen wizard providers including groq, claude, openai, gemini, mistral, deepseek, together, cohere, xai, minimax, nvidia, perplexity, lmstudio, litellm, and ollama.

Key Takeaways

  • FadeMem dual-tier decay (LML/SML) delivers 45% storage reduction versus append-only systems at equivalent recall quality, per the original Alibaba/Peking research
  • Trust-based conflict resolution prevents CI pipelines from overwriting team decisions—bot events score 0.28 trust while direct user notes score 1.0
  • The causal inference engine deploys four phases (G-Formula, MSM/IPW, IV Bounds, Root Cause Analysis) with zero external dependencies and 31 passing tests
  • SQLite file ownership means full transparency: open the database in any browser to see exactly what your agent knows about you

The Bottom Line

If you've been tolerating an AI agent that contradicts itself across sessions and surfaces stale decisions with equal confidence as current ones, that's not a model limitation—that's a memory management problem VEKTOR v1.6.3 actually solves. Local-first, self-curating, and fast enough to run invisibly in the background. The garden tends itself now.