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

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

TL;DR

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.

Use this when you run into the situation in the title.

When not to use this skill: unrelated tasks. It covers only the procedure above.

Compatibility

The steps above apply to the commands named in them. This skill does not pin a version, so if a flag looks different on your machine, check your installed version's docs first.

Details

• 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.

Source: 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.

Verified resolution: 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 URL: https://github.com/ivan-magda/swift-claw/blob/HEAD/docs/research/3b-web-search-backend.report.parallel.md