llm-context

Q: How do I install llm-context?

Run npx skills add https://github.com/brave/brave-search-skills --skill llm-context in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-context/ (personal) or .claude/skills/llm-context/ (project).

by brave OfficialAI & Agents

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

Installs: 1.3K
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Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/brave/brave-search-skills --skill llm-context

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads llm-context automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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)
`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

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$site=scholar.google.com`
No paywalls	`$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 · 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", "2025-01-15T13:45:02Z"]
    }
  }
}

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": []
    }
  }
}

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.

FAQ

What does the llm-context skill do?

How do I install llm-context?

Run npx skills add https://github.com/brave/brave-search-skills --skill llm-context in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-context/ (personal) or .claude/skills/llm-context/ (project).

Which AI agents can use llm-context?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is llm-context safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains llm-context?

It is published by brave in brave/brave-search-skills, and it is an official skill from the maker of the technology it covers. It has 1,280 recorded installs.

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llm-context

Install

Install

Describe the task

Review the result

SKILL.md

LLM Context

LLM Context vs AI Grounding

Endpoint

Quick Start

GET Request

POST Request (JSON body)

With Goggles (Inline)

Parameters

Query Parameters

Context Size Parameters

Filtering & Local Parameters

Context Size Guidelines

Threshold Modes

Local Recall

Location Headers

Example: With Coordinates

Example: With Place Name

Goggles (Custom Ranking) — Unique to Brave

Response Format

Standard Response

Local Response (with enable_local)

Response Fields

Use Cases

Best Practices

FAQ

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Local Response (with `enable_local`)