Show HN: FAB – A benchmark for AI agents doing financial due diligence
FAB — Finance Agents Benchmark FAB is an open-source project for benchmarking LLM agents' ability to perform financial due diligence in a synthetic company data room. FAB consists of two parts: a dataset of tasks containing agent instructions, documents and rubrics, and an execution harness for running and evaluating agents against those tasks. The current release contains 50 tasks, 160 documents and 231 grading criteria for one company, Meridian Industrial Supply LLC.
- ▪FAB — Finance Agents Benchmark FAB is an open-source project for benchmarking LLM agents' ability to perform financial due diligence in a synthetic company data room.
- ▪FAB consists of two parts: a dataset of tasks containing agent instructions, documents and rubrics, and an execution harness for running and evaluating agents against those tasks.
- ▪The current release contains 50 tasks, 160 documents and 231 grading criteria for one company, Meridian Industrial Supply LLC.
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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/SecondState-ai/finance-agents-benchmark |
| Publication time | Mon, 28 Sep 2026 06:34:40 +0000 |
| Retrieval time | 2026-09-28T06:51:07.135Z |
| Last seen | 2026-09-28T06:51:07.135Z |
| 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 | 3_GWxGSYcTug · 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
FAB — Finance Agents Benchmark FAB is an open-source project for benchmarking LLM agents' ability to perform financial due diligence in a synthetic company data room. FAB consists of two parts: a dataset of tasks containing agent instructions, documents and rubrics, and an execution harness for running and evaluating agents against those tasks. The current release contains 50 tasks, 160 documents and 231 grading criteria for one company, Meridian Industrial Supply LLC. Getting Started Start with the walkthrough for setup, task inspection, running an agent and reviewing its scores. Requires Python 3.11+, uv, Docker or Podman, and model API credentials.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.