Show HN: IngotDB – SQL-based memory for LLM agents
Turn tool results or documents into strucutred memories. Docs · Why · Benchmarks · SDK · Example agent Your agent's memory lives in your Postgres and your bucket as Parquet files. Every row can be read with SQL, searched via text, browsed in the dashboard, or opened with any tool that reads Parquet.
- ▪Turn tool results or documents into strucutred memories.
- ▪Docs · Why · Benchmarks · SDK · Example agent Your agent's memory lives in your Postgres and your bucket as Parquet files.
- ▪Every row can be read with SQL, searched via text, browsed in the dashboard, or opened with any tool that reads Parquet.
2 outlets in our directory ran this story. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ A-Mem: Agentic Memory for LLM Agents — arXiv.org
Hacker News (AI / LLM) files mainly under ai. We currently carry 6,773 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/tjbroodryk/ingot |
| Publication time | Mon, 28 Sep 2026 14:15:01 +0000 |
| Retrieval time | 2026-09-28T15:43:41.789Z |
| Last seen | 2026-09-28T15:43:41.789Z |
| 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 | UpMkA7F5wLoR · 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
Ingot Open-source memory for agents. Turn tool results or documents into strucutred memories. Docs · Why · Benchmarks · SDK · Example agent Your agent's memory lives in your Postgres and your bucket as Parquet files. Every row can be read with SQL, searched via text, browsed in the dashboard, or opened with any tool that reads Parquet. No model calls by default. Storing and querying runs without an API key. Embeddings, summaries and OCR are opt-in. A drop-in solution for tool result storage and document ingestion. Each document type is chunked on its own boundaries: PDFs by page, slides by slide, Markdown and HTML by heading, CSVs into typed rows. With Ingot you can query tool results and documents in one pass.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.