Frags framework for precision in AI agents
Welcome to Frags Note: The project is still in development, but you can already try it out. Frags is an advanced AI/LLM Agent dedicated to executing complex workflows of data retrieval, transformation, extraction and aggregation. It is designed to be highly customizable and extensible, allowing you to integrate it with your own tools and processes.
- ▪Welcome to Frags Note: The project is still in development, but you can already try it out.
- ▪Frags is an advanced AI/LLM Agent dedicated to executing complex workflows of data retrieval, transformation, extraction and aggregation.
- ▪It is designed to be highly customizable and extensible, allowing you to integrate it with your own tools and processes.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,428 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/theirish81/frags |
| Publication time | Tue, 11 Aug 2026 12:11:51 +0000 |
| Retrieval time | 2026-08-11T12:25:42.918Z |
| Last seen | 2026-08-11T12:25:42.918Z |
| 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 | 60c1_oH2KLZY · 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
Welcome to Frags Note: The project is still in development, but you can already try it out. What is Frags? Frags is an advanced AI/LLM Agent dedicated to executing complex workflows of data retrieval, transformation, extraction and aggregation. It is designed to be highly customizable and extensible, allowing you to integrate it with your own tools and processes. Its main goal is precision and focus, and it's a system dedicated to engineers and specialists rather than a code-free quick fix. Frags comes as a CLI tool and as a library to be integrated into Golang projects. Main features Multi LLM: Frags supports multiple LLMs, allowing you to choose the one that best suits your needs.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.