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AIsa Brave LLM Context
web-searchfinancial-dataprediction-marketsweathertraveldata
AIsa Brave LLM Context is a paid API for AI agents from AIsa MPP Data APIs at api.aisa.one, paid per call via MPP, $0.005/call, status unknown (last checked 2026-10-18).
Runs a web search query via Brave and returns LLM-ready summarized context with sources, snippets, and metadata
Price / call$0.005
Success—
StatusUnknown
How this service works
<!-- aisa-pricing:start --> Published reference price: $0.005 USD per successful request. Account-specific pricing and final settlement may differ. <!-- aisa-pricing:end -->
Output
A JSON object containing a 'sources' map keyed by URL, where each source includes the page title, hostname, site name, a query-relevant snippet, favicon URL, thumbnail image info, and page age details (formatted date, ISO timestamp, relative age). Designed to be fed directly into an LLM as grounded context with citations.
Example use cases
Real-time news grounding for AI
“Search the web for the latest news on the Fed interest rate decision this week and give me summarized context with sources — I need up-to-date grounded info for my report, US results only, freshness within the last 7 days.”
Product research with cited sources
“Find me web context on 'top electric vehicles under $40,000 in 2024' — I want snippets and source URLs I can cite, up to 30 results, US market.”
Country-specific market lookup
“Do a Brave web search for 'current mortgage rates in Germany' and return LLM-ready context with sources, filtering to German results and pages from the past month.”
Technical details
Endpoint
POSThttps://api.aisa.one/apis/v1/brave/llm/context?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need web search results pre-formatted for LLM ingestion with rich source metadata (snippets, titles, favicons, ages) rather than raw HTML or full page content. Prefer it over generic search APIs when building RAG pipelines, agentic reasoning loops, or citation-grounded AI responses. It is particularly strong when you need freshness filtering, country-specific results, or Brave's Goggles re-ranking for custom result sets.
Side effects
- Empty query (q is blank) returns validation error
- Query exceeding 600 characters or 75 words is rejected
- Unsupported country code returns error
- Count value outside 1-50 range is rejected
- Invalid freshness date range format causes error
- No results found for obscure or malformed queries returns empty sources map
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| qrequired | string | The user’s search query term. Query can not be empty. Maximum of 600 characters and 75 words in the query. |
| count | integer | The maximum number of search results considered to select the LLM context data. The default is 20 and the maximum is 50. |
| country | — | The search query country, where the results come from. The country string is limited to 2 character country codes of supported countries. |
| goggles | — | Goggles act as a custom re-ranking on top of Brave’s search index. The parameter supports both a url where the Goggle is hosted or the definition of the Goggle. For more details, see the <a href="/documentation/resources/goggles">Goggles documentation</a>. The parameter can be a single Goggle or a list of up to 3 Goggles. |
| freshness | string | <p>Filters search results by page age. The age of a page is determined by the most relevant date reported by the content, such as its published or last modified date. The following values are supported:</p> <ul> <li><strong>pd</strong> - Pages aged 24 hours or less.</li> <li><strong>pw</strong> - Pages aged 7 days or less.</li> <li><strong>pm</strong> - Pages aged 31 days or less.</li> <li><strong>py</strong> - Pages aged 365 days or less.</li> <li><strong>YYYY-MM-DDtoYYYY-MM-DD</strong> - A custom date range is also supported by specifying start and end dates e.g. <code>2022-04-01to2022-07-30</code>.</li> </ul> |
| safesearch | — | <p>Filters results for adult content. The following values are supported:</p> <ul> <li><strong>off</strong> - No filtering is done.</li> <li><strong>moderate</strong> - Drops adult content from the results used for context.</li> <li><strong>strict</strong> - Drops all adult content from the results used for context.</li> </ul> <p>When not set, no filtering is applied — except for local recall, which stays <code>strict</code>.</p> |
| spellcheck | boolean | Whether to enable spellcheck on the query. |
| search_lang | — | The search language preference. The 2 or more character language code for which the search results are provided. |
| enable_local | — | Whether to enable local recall. Not setting this value means auto-detect: local recall is used if a location is provided (<code>X-Loc-Lat</code> with <code>X-Loc-Long</code>, or <code>X-Loc-City</code>). |
| context_threshold_mode | — | The mode to use to determine the threshold for including content in context. |
| enable_source_metadata | boolean | Enable source metadata enrichment (site_name, favicon, thumbnail, description, fetched_content_timestamp) in the sources attribute of the response. |
| maximum_number_of_urls | integer | Maximum number of different URLs to include in LLM context. |
| maximum_number_of_tokens | integer | Approximate maximum number of tokens to include in context. The default is <code>8192</code> and maximum is <code>32768</code>. |
| maximum_number_of_snippets | integer | Maximum number of different snippets (or chunks of text) to include in LLM context. The default is <code>50</code> and maximum is <code>256</code>. |
| maximum_number_of_tokens_per_url | integer | Maximum number of tokens to include per URL. The default is <code>4096</code> and maximum is <code>8192</code>. |
| maximum_number_of_snippets_per_url | integer | Maximum number of snippets to include per URL. The default is <code>50</code> and maximum is <code>100</code>. |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| sources | object | Metadata for all referenced URLs, keyed by URL. |
| grounding | object | Container for all LLM context content by type. |
Tags
web-searchfinancial-dataprediction-marketsweathertraveldatasearch
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