USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.

LLM Context Requires API Key : Get one at https://api.search.brave.com Plan : Included in the Search plan. See https://api dashboard.search.brave.com/app/subscriptions/subscribe Brave LLM Context API delivers pre extracted, relevance ranked web content optimized for grounding LLM responses in real time search results. Unlike traditional web search APIs that return links and snippets, LLM Context extracts the actual page content—text chunks, tables, code blocks, and structured data—so your LLM or AI agent can reason over it directly. LLM Context vs AI Grounding Feature LLM Context (this) AI Grounding ( answers ) Output Raw extracted content for YOUR LLM End to end AI answers with citations Interface REST API (GET/POST) OpenAI compatible /chat/completions Searches Single search per request Multi search (iterative research) Speed Fast (<1s) Slower Plan Search Answers Endpoint /res/v1/llm/context /res/v1/chat/completions Best for AI agents, RAG pipelines, tool calls Chat interfaces, research mode Endpoint Authentication : X Subscription Token: <API KEY header Optional Headers : Accept Encoding: gzip — Enable gzip compression Quick Start GET Request POST Request (JSON body) With Goggles (Inline) Parameters Query Parameters Parameter Type Required Default Description q string Yes Search query (1 400 chars, max 50 words) country string No US Search country (2 letter country code or ALL ) search lang string No en Language preference (2+ char language code) count int No 20 Max search results to consider (1 50) spellcheck bool No true Whether to spellcheck the query before searching freshness string No "" 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. Supported values: pd (24h or less), pw (7 days or less), pm (31 days or less), py (365 days or less), or a custom date range YYYY MM DDtoYYYY MM DD (e.g. 2022 04 01to2022 07 30 ). Context Size Parameters Parameter Type Required Default Description maximum number of urls int No 20 Max URLs in response (1 50) maximum number of tokens int No 8192 Approximate max tokens in context (1024 32768) maximum number of snippets int No 50 Max snippets across all URLs (1 256) maximum number of tokens per url int No 4096 Max tokens per individual URL (512 8192) maximum number of snippets per url int No 50 Max snippets per individual URL (1 100) Filtering & Local Parameters Parameter Type Required Default Description context threshold mode string No null Relevance threshold for including content ( strict / balanced / lenient / disabled ) safesearch string No null Adult content filter ( off / moderate / strict ); not set means no filtering, except local recall which stays strict enable local bool No null Local recall control ( true / false / null , see below) goggles string/list No null Goggle URL or inline definition for custom re ranking enable source metadata bool No false Adds site name , favicon , thumbnail and description to each sources[url] entry Context Size Guidelines Task Type count max tokens Example Simple factual 5 2048 "What year was Python created?" Standard queries 20 8192 "Best practices for React hooks" Complex research 50 16384 "Compare AI frameworks for production" Larger context windows provide more information but increase latency and cost (of your inference). Start with defaults and adjust. Threshold Modes Mode Behavior null (not set) Default — resolves to lenient on the current API version strict Higher threshold — fewer but more relevant results balanced Good balance between coverage and relevance lenient Lower threshold — more results, may include less relevant content disabled No threshold filtering — return all extracted content Local Recall The enable local parameter controls location aware recall: Value Behavior null (not set) Auto detect — local recall enabled when any location header is provided true Force local — always use local recall, even without location headers false Force standard — always use standard web ranking, even with location headers For most use cases, omit enable local and let the API auto detect from location headers. Location Headers Header Type Description X Loc Lat float Latitude ( 90.0 to 90.0) X Loc Long float Longitude ( 180.0 to 180.0) X Loc City string City name X Loc State string State/region code (ISO 3166 2) X Loc State Name string State/region name X Loc Country string 2 letter country code X Loc Postal Code string Postal code Priority : X Loc Lat + X Loc Long take precedence. When provided, text based headers (City, State, Country, Postal Code) are not used for location resolution. Provide text based headers only when you don't have coordinates. Example: With Coordinates Example: With Place Name Goggles (Custom Ranking) — Unique to Brave Goggles let you control which sources ground your LLM — essential for RAG quality. Use Case Goggle Rules Official docs only $discard\n$site=docs.python.org Exclude user content $discard,site=reddit.com\n$discard,site=stackoverflow.com Academic sources $discard\n$site=arxiv.org\n$site=scholar.google.com No paywalls $discard,site=medium.com Method Example Hosted data urlencode "goggles=https://raw.githubusercontent.com/brave/goggles quickstart/main/goggles/1k short.goggle" Inline data urlencode 'goggles=$discard\n$site=example.com' Hosted goggles must be on GitHub/GitLab, include ! name: , ! description: , ! author: headers, and be registered at https://search.brave.com/goggles/create. Inline rules need no registration. Syntax : Rules start with $ + comma separated options. Actions (pick one): discard , boost[=N] , downrank[=N] — N is an integer 1–10. Site filter : site=DOMAIN . Example: $site=example.com,boost=3 . Separate rules with \n ( %0A ). Allow list : $discard\n$site=docs.python.org\n$site=developer.mozilla.org — Block list : $discard,site=pinterest.com\n$discard,site=quora.com Resources : [Discover](https://search.brave.com/goggles/discover) · [Syntax](https://search.brave.com/help/goggles) · [Quickstart](https://github.com/brave/goggles quickstart) Response Format Standard Response Local Response (with enable local ) Response Fields Field Type Description grounding object Container for all grounding content by type grounding.generic array Array of URL objects with extracted content (main grounding data) grounding.generic[].url string Source URL grounding.generic[].title string Page title grounding.generic[].snippets array Extracted smart chunks relevant to the query grounding.poi object/null Point of interest data (only with local recall) grounding.poi.name string/null Point of interest name grounding.poi.url string/null POI source URL grounding.poi.title string/null POI page title grounding.poi.snippets array/null POI text snippets grounding.map array Map/place results (only with local recall) grounding.map[].name string/null Place name grounding.map[].url string/null Place source URL grounding.map[].title string/null Place page title grounding.map[].snippets array/null Place text snippets sources object Metadata for all referenced URLs, keyed by URL sources[url].title string Page title sources[url].hostname string Source hostname sources[url].age array The page's date in four fixed positions: full date, YYYY MM DD , relative age, ISO 8601 timestamp. Empty when the page has no known date sources[url].description string The page's own description, independent of the query. Requires enable source metadata sources[url].site name string Site name. Requires enable source metadata sources[url].favicon string Favicon URL. Requires enable source metadata sources[url].thumbnail object/null Page thumbnail ( src , original ). Requires enable source metadata sources[url].snippet string? Best snippet for the page Note : Snippets may contain plain text OR JSON serialized structured data (tables, schemas, code blocks). LLMs handle this mixed format well. Use Cases AI Agents : Give your agent a web search tool that returns ready to use content in a single call RAG Pipelines : Ground LLM responses in fresh, relevant web content AI Assistants & Chatbots : Provide factual answers backed by real sources Question Answering : Retrieve focused context for specific queries Fact Checking : Verify claims against current web content Content Research : Gather source material on any topic with one API call Best Practices Token budget : Start with defaults ( maximum number of tokens=8192 , count=20 ). Reduce for simple lookups, increase for complex research. Source quality : Use Goggles to restrict to trusted sources. Set context threshold mode=strict when precision recall. Performance : Use smallest count and maximum number of tokens that meet your needs. For local queries, provide location headers.

llm-context