llm-context

name	llm-context
description	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

GET  https://api.search.brave.com/res/v1/llm/context
POST https://api.search.brave.com/res/v1/llm/context

Authentication: X-Subscription-Token: <API_KEY> header

Optional Headers:

• Accept-Encoding: gzip — Enable gzip compression

Quick Start

GET Request

curl -s "https://api.search.brave.com/res/v1/llm/context?q=tallest+mountains+in+the+world" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}"

POST Request (JSON body)

curl -s --compressed -X POST "https://api.search.brave.com/res/v1/llm/context" \
  -H "Accept: application/json" \
  -H "Accept-Encoding: gzip" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"q": "tallest mountains in the world"}'

With Goggles (Inline)

curl -s "https://api.search.brave.com/res/v1/llm/context" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}" \
  -G \
  --data-urlencode "q=rust programming" \
  --data-urlencode 'goggles=$discard
$site=docs.rs
$site=rust-lang.org'

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)

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 (1-100)
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	balanced	Relevance threshold for including content (strict/balanced/lenient)
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

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
strict	Higher threshold — fewer but more relevant results
balanced	Default — good balance between coverage and relevance
lenient	Lower threshold — more results, may include less relevant 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

curl -s "https://api.search.brave.com/res/v1/llm/context" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}" \
  -H "X-Loc-Lat: 37.7749" \
  -H "X-Loc-Long: -122.4194" \
  -G \
  --data-urlencode "q=best coffee shops near me"

Example: With Place Name

curl -s "https://api.search.brave.com/res/v1/llm/context" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}" \
  -H "X-Loc-City: San Francisco" \
  -H "X-Loc-State: CA" \
  -H "X-Loc-Country: US" \
  -G \
  --data-urlencode "q=best coffee shops near me"

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=.edu
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: $boost=N / $downrank=N (1–10), $discard, $site=example.com. Combine with commas: $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 · Syntax · Quickstart

Response Format

Standard Response

{
  "grounding": {
    "generic": [
      {
        "url": "https://example.com/page",
        "title": "Page Title",
        "snippets": [
          "Relevant text chunk extracted from the page...",
          "Another relevant passage from the same page..."
        ]
      }
    ],
    "map": []
  },
  "sources": {
    "https://example.com/page": {
      "title": "Page Title",
      "hostname": "example.com",
      "age": ["Wednesday, January 15, 2025", "2025-01-15", "392 days ago"]
    }
  }
}

Local Response (with enable_local)

{
  "grounding": {
    "generic": [...],
    "poi": {
      "name": "Business Name",
      "url": "https://business.com",
      "title": "Title of business.com website",
      "snippets": ["Business details and information..."]
    },
    "map": [
      {
        "name": "Place Name",
        "url": "https://place.com",
        "title": "Title of place.com website",
        "snippets": ["Place information and details..."]
      }
    ]
  },
  "sources": {
    "https://business.com": {
      "title": "Business Name",
      "hostname": "business.com",
      "age": null
    }
  }
}

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/null	Page modification dates (when available)

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.