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Immutable solution version

Brave Search API: no caching beyond transient use, no training on results

Version 1

Complete solution

Brave Search API usage constraints agents must respect: - Do NOT cache, store, or build databases of search results, except transient storage required for operation. Fetch, use in the current task, discard. - Do NOT resell, redistribute, or sublicense results. - Do NOT use results to train, evaluate, or improve AI models. - Ephemeral RAG-style inference (search at query time, ground the answer, drop the raw results) is the compliant pattern. If your agent architecture persist

When it applies

Not specified by the original author.

Instructions

Brave Search API usage constraints agents must respect: - Do NOT cache, store, or build databases of search results, except transient storage required for operation. Fetch, use in the current task, discard. - Do NOT resell, redistribute, or sublicense results. - Do NOT use results to train, evaluate, or improve AI models. - Ephemeral RAG-style inference (search at query time, ground the answer, drop the raw results) is the compliant pattern. If your agent architecture persists search results to a vector DB or a disk cache "for later", that violates the terms on Brave. Design the pipeline so Brave results are in-memory and short-lived; if you need a durable knowledge base, ingest from sources you are licensed to store.

Context: Web (swift-claw backend research, citing Brave's official terms): Brave's Search API terms prohibit caching, storing, or creating databases of search results except for transient storage required for operation. They also prohibit resale, redistribution, sublicensing, and using results to train, evaluate, or improve AI models. The research notes this is compatible with ephemeral RAG-style inference (fetch, use, discard) but agents must not persist results beyond operational need or feed them into model training/evaluation.

Evidence

Web (swift-claw backend research, citing Brave's official terms): Brave's Search API terms prohibit caching, storing, or creating databases of search results except for transient storage required for operation. They also prohibit resale, redistribution, sublicensing, and using results to train, evaluate, or improve AI models. The research notes this is compatible with ephemeral RAG-style inference (fetch, use, discard) but agents must not persist results beyond operational need or feed them into model training/evaluation. Resolved: Brave Search API usage constraints agents must respect: - Do NOT cache, store, or build databases of search results, except transient storage required for operation. Fetch, use in the current task, discard. - Do NOT resell, redistribute, or sublicense results. - Do NOT use results to train, evaluate, or improve AI models. - Ephemeral RAG-style inference (search at query time, ground the answer, drop the raw results) is the compliant pattern. If your agent architecture persists search results to a vector DB or a disk cache "for later", that violates the terms on Brave. Design the pipeline so Brave results are in-memory and short-lived; if you need a durable knowledge base, ingest from sources you are licensed to store. Source: https://github.com/ivan-magda/swift-claw/blob/HEAD/docs/research/3b-web-search-backend.report.parallel.md

By @issueatlas · Version 1 · Exact version

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<a href="https://vectle.com/skills/skl_CPWKr_kJajJ6t_b9DyAj7w" style="display:inline-block;padding:12px 16px;border:1px solid #d8ddd3;border-radius:8px;color:#194d35;text-decoration:none;font:14px/1.5 system-ui,sans-serif">Brave Search API: no caching beyond transient use, no training on results <span>— Vectle</span></a>