Nexusyn – Long-term memory engine for AI agents (Go, DiskANN, MCP)
Nexusyn Engine Long-Term, Time-Aware Memory Engine for AI Agents Stop building agents that forget. Give your LLMs persistent, grounded memory across sessions. Quickstart • MCP Integration • Architecture • Benchmarks • Cloud vs Self-Hosted • Documentation The Problem: Why Vector Databases Alone Aren't "Memory" Most AI agent frameworks implement memory by taking the last conversation turn, calculating an embedding, and storing it in a vector database.
- ▪Nexusyn Engine Long-Term, Time-Aware Memory Engine for AI Agents Stop building agents that forget.
- ▪Give your LLMs persistent, grounded memory across sessions.
- ▪Quickstart • MCP Integration • Architecture • Benchmarks • Cloud vs Self-Hosted • Documentation The Problem: Why Vector Databases Alone Aren't "Memory" Most AI agent frameworks implement memory by taking the last conversation turn, calculat
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,353 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/nexusyn/engine |
| Publication time | Fri, 02 Oct 2026 17:15:03 +0000 |
| Retrieval time | 2026-10-02T17:15:50.787Z |
| Last seen | 2026-10-02T17:15:50.787Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | N81jTb3Pt-TJ · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Nexusyn Engine Long-Term, Time-Aware Memory Engine for AI Agents Stop building agents that forget. Give your LLMs persistent, grounded memory across sessions. Quickstart • MCP Integration • Architecture • Benchmarks • Cloud vs Self-Hosted • Documentation The Problem: Why Vector Databases Alone Aren't "Memory" Most AI agent frameworks implement memory by taking the last conversation turn, calculating an embedding, and storing it in a vector database. When the agent queries this "memory", it runs a simple nearest-neighbor search. This breaks down in production: Vectors don't understand time: If a user says "I live in Berlin" in January and "I moved to Madrid" in August, both vectors have identical semantic similarity to "Where do I live?".
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.