Search Best Practices

Exa’s Search API returns a list of webpages and their contents based on a natural language search query. Results are optimized for LLM consumption, enabling higher-quality completions with clean, token efficient data. Recommended: Try our Coding Agent Quickstart — get a working search call in under a minute, then come back here for the full reference.

Key Benefits

Token efficient: Use highlights to get key excerpts relevant to your query, reducing token usage by 10x compared to full text, without adding latency.
Specialized index coverage: State of the art search performance on people, company, and code using Exa’s in-house search indexes.
Incredible speed: From auto (default) to fast for sub-second latency to instant for sub-200ms latency, Exa provides the fastest search available without compromising on quality, enabling real-time workflows like autocomplete and live suggestions.

Request Fields

The query parameter is required for all search requests. The remaining fields are optional. See the API Reference for complete parameter details.

Field	Type	Notes	Example
query	string	The search query. Supports long, semantically rich descriptions for finding niche content.	”blog post about embeddings and vector search”
type	string	Search method: `auto` (default balance of speed and quality), `fast` (low latency), `instant` (lowest latency), `deep-lite`, `deep`, or `deep-reasoning`.	”auto”
systemPrompt	string	Instructions that guide synthesized output and, for deep-search variants, search planning.	”Prefer official sources and avoid duplicate results”
outputSchema	object	JSON schema that controls `output.content`. When set, `/search` returns `output` for any search type.	`{ "type": "text", "description": "one sentence" }`
stream	boolean	If true, `/search` streams OpenAI-compatible chat completion chunks over SSE instead of returning one JSON payload.	`true`
numResults	int	Number of results to return (1-100). Defaults to 10.	10
highlights	bool/obj	Return token-efficient excerpts most relevant to your query. You can also request full text if needed—see the API Reference.	`true`
maxAgeHours	int	Maximum age of indexed content in hours. If older, fetches with livecrawl. `0` = always livecrawl, `-1` = never livecrawl (cache only).	24
category	string	Target specific content types: `company`, `people`, `news`	”company”

Search Types

The type parameter selects the search method:

auto (default): Exa’s default search. Start here unless you have a specific latency target or need a deeper synthesized mode.
fast: Low latency search using optimized versions of the search models. A good middle ground when you need speed without sacrificing too much quality.
instant: Lowest latency search optimized for real-time applications like autocomplete or live suggestions.
deep-lite: Lightweight synthesized search output with lower latency than the deeper research modes.
deep: Deep web research with structured outputs. Best for complicated queries that require multi steps of search, reasoning, and structured json outputs.
deep-reasoning: More deliberate deep-search mode when you want more reasoning than deep.

Some older docs and payloads still use legacy search-type names. For new integrations, prefer the search types above.

Token Efficiency

Choosing the right content mode can significantly reduce token usage while maintaining answer quality.

Mode	Best For
text	Deep analysis, when you need full context, comprehensive research
highlights	Factual questions, specific lookups, multi-step agent workflows

Use highlights for agentic workflows: When building multi-step agents that make repeated search calls, highlights provide the most relevant excerpts without flooding context windows.

{
  "query": "What is the current Fed interest rate?",
  "contents": {
    "highlights": true
  }
}

Use full text for deep research: When the task requires comprehensive understanding or when you’re unsure which parts of the page matter, request full text. Use maxCharacters to cap token usage.

{
  "query": "detailed analysis of transformer architecture innovations",
  "contents": {
    "text": { "maxCharacters": 15000 }
  },
  "numResults": 5
}

Combine modes strategically: You can request both highlights and text together—use highlights for quick answers and fall back to full text only when needed.

Content Freshness

Control whether results come from Exa’s index or are freshly crawled using maxAgeHours:

maxAgeHours: 24: Use cache if less than 24 hours old, otherwise livecrawl. Good for daily-fresh content.
maxAgeHours: 0: Always livecrawl (ignore cache). Use when cached data is unacceptable.
maxAgeHours: -1: Never livecrawl (cache only). Maximum speed, historical/static content.
Omit (recommended): Default behavior — livecrawl as fallback if no cache exists.

{
  "query": "latest announcements from OpenAI",
  "includeDomains": ["openai.com"],
  "maxAgeHours": 72,
  "contents": {
    "highlights": true
  }
}

Output Schema

For any search type, you can pass outputSchema (or output_schema in Python SDK) to control output.content format.

type: "text": return plain text output (optionally guided with a description)
type: "object": return structured JSON output

Do not include citation or confidence fields in outputSchema/output_schema. /search already returns grounding and citations automatically in output.grounding.

Including citations/confidence inside your schema is usually worse:

Redundant: duplicates data that is already returned, increasing tokens and latency.
Less reliable: model-generated citation fields inside output.content are generally less reliable than built-in grounding.

Simpler schemas perform better. Defining clear primitive/object/array fields works best, while string properties that try to embed JSON blobs usually perform poorly.

{
  "query": "what's the fastest web search api",
  "type": "deep",
  "outputSchema": {
    "type": "text",
    "description": "Short one to two sentence answer"
  }
}

{
  "query": "top aerospace companies",
  "type": "deep",
  "outputSchema": {
    "type": "object",
    "required": ["companies"],
    "properties": {
      "companies": {
        "type": "array",
        "description": "A list of aerospace companies",
        "items": {
          "type": "object",
          "required": ["company_name", "ceo_name", "stock_price"],
          "properties": {
            "company_name": {
              "type": "string",
              "description": "The name of the aerospace company"
            },
            "ceo_name": {
              "type": "string",
              "description": "The name of the company's CEO"
            },
            "stock_price": {
              "type": "number",
              "description": "Current stock price of the company"
            }
          }
        }
      }
    }
  }
}

Object schema limits:

Maximum nesting depth: 2
Maximum total properties: 10

System Prompt

For any search type, you can also pass systemPrompt (or system_prompt in Python SDK) to guide how the endpoint synthesizes the final returned result. On deep-search variants, it also guides search planning. Use this for instructions like:

prefer official or primary sources
emphasize novelty or avoid duplicate findings
keep the answer concise or highly structured

Use outputSchema/output_schema for shape, and systemPrompt/system_prompt for behavior.

{
  "query": "compare the latest frontier AI model releases",
  "type": "deep-reasoning",
  "systemPrompt": "Prefer official sources and avoid duplicate results",
  "outputSchema": {
    "type": "object",
    "required": ["models"],
    "properties": {
      "models": {
        "type": "array",
        "items": {
          "type": "object",
          "required": ["name", "notable_claims"],
          "properties": {
            "name": { "type": "string" },
            "notable_claims": { "type": "array", "items": { "type": "string" } }
          }
        }
      }
    }
  }
}

Streaming

Set stream: true to receive text/event-stream responses from /search. Each SSE frame contains an OpenAI-compatible chat completion chunk, so you should read partial text from choices[0].delta.content instead of expecting a single JSON body. Use category to target specific content types where Exa has specialized coverage:

Category	Best For	Restrictions
`company`	Company pages, LinkedIn company profiles	Does not support `startPublishedDate`, `endPublishedDate`, `startCrawlDate`, `endCrawlDate`, or `excludeDomains`.
`people`	Multi-source data on people, LinkedIn profiles	Does not support `startPublishedDate`, `endPublishedDate`, `startCrawlDate`, `endCrawlDate`, or `excludeDomains`.
`research paper`	Academic papers, arXiv, peer-reviewed research	—
`news`	Current events, journalism	—
`personal site`	Blogs, personal pages (Exa’s unique strength)	—
`financial report`	SEC filings, earnings reports	—

{
  "query": "agtech companies in the US that have raised series A",
  "type": "auto",
  "category": "company",
  "numResults": 10,
  "contents": {
    "highlights": true
  }
}

​Key Benefits

​Request Fields

​Search Types

​Token Efficiency

​Content Freshness

​Output Schema

​System Prompt

​Streaming

​Category Filters

Key Benefits

Request Fields

Search Types

Token Efficiency

Content Freshness

Output Schema

System Prompt

Streaming

Category Filters