A curated, non-BS library of the best resources for evaluating agents
The Awesome Agent Evals library is a curated collection of resources for building and evaluating AI agents, including papers, blog posts, talks, courses, tools, and benchmarks. The library is annotated and verified, with every entry including a description of what it is and why it belongs, as well as checked URLs and quotes. The library was assembled through a combination of academic citation crawls, practitioner-web discovery, and gap audits with adversarial verification.
- ▪The library contains over 443 curated links and 146 deep reading notes, with markers indicating newly released or updated content and caveats where applicable.
- ▪The library includes a playbook with real, runnable code and worked examples for evaluating AI agents, including patterns for LLM-as-judge, pass@k/pass^k, error analysis, and more.
- ▪The library is maintained by BenchFlow and is open to contributions, with a contributing guide available for those interested in adding to the collection.
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/benchflow-ai/awesome-evals |
| Publication time | Fri, 26 Jun 2026 07:06:16 +0000 |
| Retrieval time | 2026-06-26T07:37:30.198Z |
| Last seen | 2026-06-26T07:37:30.198Z |
| 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 | Wc8hiSlXo81c |
| 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
Awesome Agent Evals A curated, opinionated, non-BS library of the best resources for building and evaluating AI agents — papers, blog posts, talks, courses, tools, and benchmarks. Maintained by BenchFlow · Most "awesome" lists are link dumps. This one is annotated and verified: every entry says what it is and why it belongs, URLs are checked, quotes are verbatim, and dead/abandoned tools are pruned (not silently listed). It was assembled by: a depth-4 recursive citation crawl (11.6k papers, ranked by in-degree) to surface the academic canon, targeted practitioner-web discovery for the industry sources citation graphs miss (Eugene Yan, Han-Chung Lee, Hamel Husain, Shreya Shankar, Nathan Lambert, …), 47 talks & podcasts transcribed and deep-noted (verbatim + timestamps), and per-section gap…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.