Show HN: I built AgeDB, a database for AI agents to store context and data
AgeDB A database your AI agent can talk to, that tells you how it read the question and says so when it can't answer. AI agents collect things as they work: leads, prices, support tickets, research notes. AgeDB gives them somewhere to keep those records and lets them ask questions in plain English, such as "which companies look most likely to convert?".
- ▪AgeDB A database your AI agent can talk to, that tells you how it read the question and says so when it can't answer.
- ▪AI agents collect things as they work: leads, prices, support tickets, research notes.
- ▪AgeDB gives them somewhere to keep those records and lets them ask questions in plain English, such as "which companies look most likely to convert?".
2 outlets in our directory ran this story, first to last over 12 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,261 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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/sivsivsree/agedb |
| Publication time | Thu, 24 Sep 2026 10:34:05 +0000 |
| Retrieval time | 2026-09-24T10:40:26.282Z |
| Last seen | 2026-09-24T10:40:26.282Z |
| 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 | hRSQKCBFJ2wo · 2 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
AgeDB A database your AI agent can talk to, that tells you how it read the question and says so when it can't answer. AI agents collect things as they work: leads, prices, support tickets, research notes. AgeDB gives them somewhere to keep those records and lets them ask questions in plain English, such as "which companies look most likely to convert?". It runs on your machine as one small program. There is no SQL to write, and no extra AI call is made to understand the question. Agent: "Create a table for the leads I'm collecting." Agent: "Store these 4,000 leads." Agent: "Which companies look most likely to convert?" AgeDB: acme 0.99, company 210 0.79, company 139 0.74, ...
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.