Show HN: Anansi – open-source memory API for LLM apps
Anansi A self-hostable memory engine for AI agents that can distinguish what was true from what the agent knew at the time. Anansi gives an AI system a durable understanding of how an organization actually works — and how that changed over time. You feed it the exhaust your company already produces: conversations, docs, tickets, meeting transcripts.
- ▪Anansi A self-hostable memory engine for AI agents that can distinguish what was true from what the agent knew at the time.
- ▪Anansi gives an AI system a durable understanding of how an organization actually works — and how that changed over time.
- ▪You feed it the exhaust your company already produces: conversations, docs, tickets, meeting transcripts.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,407 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/g-33-L/anansi |
| Publication time | Tue, 11 Aug 2026 11:02:19 +0000 |
| Retrieval time | 2026-08-11T11:05:44.223Z |
| Last seen | 2026-08-11T11:05:44.223Z |
| 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 | 383UGTkT93ev · 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
Anansi A self-hostable memory engine for AI agents that can distinguish what was true from what the agent knew at the time. Anansi gives an AI system a durable understanding of how an organization actually works — and how that changed over time. You feed it the exhaust your company already produces: conversations, docs, tickets, meeting transcripts. Anansi turns that into structured memory your agent can read before it answers, and keeps every version of it. So you can ask not just "what is our escalation process?" but "what did we think it was in March, and when did it change?" — and get an answer with a citation. TL;DR: MIT-licensed self-hostable core, with a commercial enterprise/hosted layer. See License.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.