Context Graph Agent
The Context Graph Agent (CGA) has been updated to version 1.30.47, enhancing its capabilities for AI coding agents. It significantly reduces prompt tokens and lowers hallucination pressure, allowing for faster code retrieval and analysis. The new features include work briefing aggregation and schedule automation for improved project management.
- ▪CGA reduces prompt tokens by 90.44% on average and lowers hallucination pressure by 13.34%.
- ▪It provides focused evidence packs instead of broad keyword searches, improving efficiency in code retrieval.
- ▪The update includes built-in work activity aggregation and an admin-only schedule surface for automation jobs.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 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/nascousa/cga |
| Publication time | Wed, 03 Jun 2026 05:26:48 +0000 |
| Retrieval time | 2026-06-03T05:41:56.797Z |
| Last seen | 2026-06-03T05:41:56.797Z |
| 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 | cemgvvqpgGuq |
| 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
CGA (Context Graph Agent) Version: 1.30.47 Status: Published Author: Nate Scott Date: 2026-06-02 (branding and website copy refresh) CGA, aka Context Graph Agent, helps AI coding agents work with much smaller, more relevant code context. In the current live multi-project benchmark, CGA reduced prompt tokens by 90.44% on average while lowering hallucination pressure by 13.34%, which helps agents answer, edit, and search through repositories faster. Instead of sending whole files or broad keyword-search results to the model, CGA returns focused evidence packs: target symbol excerpts, nearby relationship context, dependency paths, and recent project facts. Under the hood, CGA is a local-first graph context service for AI-assisted development.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.