Benchmark enterprise RAG agents with EnterpriseRAG-Bench
Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
Install
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/benchmark-enterprise-rag-agents-with-enterpriserag-bench
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install agentskillexchange-skills@llmmart
git clone https://github.com/agentskillexchange/skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole agentskillexchange/skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Benchmark enterprise RAG agents with EnterpriseRAG-Bench
Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
Prerequisites
Python 3.10+, EnterpriseRAG-Bench dataset and questions, a RAG or knowledge-agent system under test, OpenAI or Anthropic compatible LLM credentials for evaluation
Installation
Install or set up from the source-backed instructions:
Clone https://github.com/onyx-dot-app/EnterpriseRAG-Bench, install Python dependencies with pip install -r requirements.txt, set LLM_PROVIDER and LLM_API_KEY, download the benchmark dataset from the latest GitHub release or Hugging Face, write system outputs to answer_evaluation/answers.jsonl, then run python -m src.scripts.answer_evaluation.metrics_based_eval --answers-file answer_evaluation/answers.jsonl.
Documentation
Source
Files (skills)
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SKILL.md 1.8 KB
--- name: "Benchmark enterprise RAG agents with EnterpriseRAG-Bench" slug: "benchmark-enterprise-rag-agents-with-enterpriserag-bench" description: "Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring." github_stars: 562 verification: "security_reviewed" source: "https://github.com/onyx-dot-app/EnterpriseRAG-Bench" author: "Onyx" publisher_type: "organization" category: "Security & Verification" framework: "Multi-Framework" tool_ecosystem: github_repo: "onyx-dot-app/EnterpriseRAG-Bench" github_stars: 562 --- # Benchmark enterprise RAG agents with EnterpriseRAG-Bench Use EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring. ## Prerequisites Python 3.10+, EnterpriseRAG-Bench dataset and questions, a RAG or knowledge-agent system under test, OpenAI or Anthropic compatible LLM credentials for evaluation ## Installation Install or set up from the source-backed instructions: Clone https://github.com/onyx-dot-app/EnterpriseRAG-Bench, install Python dependencies with pip install -r requirements.txt, set LLM_PROVIDER and LLM_API_KEY, download the benchmark dataset from the latest GitHub release or Hugging Face, write system outputs to answer_evaluation/answers.jsonl, then run python -m src.scripts.answer_evaluation.metrics_based_eval --answers-file answer_evaluation/answers.jsonl. - Source: https://github.com/onyx-dot-app/EnterpriseRAG-Bench ## Documentation - https://github.com/onyx-dot-app/EnterpriseRAG-Bench/blob/main/quickstart.md ## Source - [Agent Skill Exchange](https://agentskillexchange.com/skills/benchmark-enterprise-rag-agents-with-enterpriserag-bench/)
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