Prompting and Patterns Reference

Durable usage patterns for Exa queries, output control, and content retrieval.

Canonical Docs Links

Base docs URL: https://exa.ai/docs
Search best practices: /reference/search-best-practices
Contents best practices: /reference/contents-best-practices
Answer reference: /reference/answer
Context reference: /reference/context

Query formulation
systemPrompt vs outputSchema
Highlights vs text
Freshness patterns
Endpoint-selection patterns
Tool calling pattern
Low-latency recipe
Structured output patterns

Query Formulation

Exa works best with explicit natural-language intent. Good queries usually encode:

subject
constraint
time window if freshness matters
source preference if domain-specific sources matter

Examples:

"recent battery recycling policy changes in the EU"
"AI startups that raised Series A funding in 2026"
"senior ML engineers at fintech companies"

`systemPrompt` vs `outputSchema`

Use them together, but give them different jobs:

systemPrompt: source preferences, output style, dedup behavior, emphasis
outputSchema: exact response shape

Pattern:

{
  "query": "Compare recent frontier model launches",
  "type": "deep",
  "systemPrompt": "Prefer official vendor announcements and avoid duplicate reporting.",
  "outputSchema": {
    "type": "object",
    "properties": {
      "summary": {"type": "string"},
      "models": {"type": "array", "items": {"type": "string"}}
    },
    "required": ["summary", "models"]
  }
}

Highlights vs Text

Start with one by default:
use highlights when the caller mainly needs the most relevant excerpts
use text when broad page context is necessary
use summary only when you explicitly want Exa to run per-result LLM compression

Stacking text, highlights, and summary is usually the wrong default. summary adds a per-result LLM call, which means N results create N extra synthesis steps and higher latency. Combining text and highlights also increases billing for two views of the same page. For many agent workflows, highlights: true is the safest default. Bare highlights: true auto-selects an appropriate excerpt length per page. Only set maxCharacters when you have a fixed budget. Below about 400 characters usually truncates too aggressively for downstream LLM use.

Freshness Patterns

Use maxAgeHours to express how fresh the content must be:

omit it for the default balanced behavior
set a small value when near-real-time freshness matters
set 0 only when the app truly requires live crawling every time
set -1 when the latency-critical path should stay cache-only

Freshness is most important on:

news
company announcements
live policy or market updates

It matters less on:

historical content
stable docs
evergreen educational material

Endpoint-Selection Patterns

Question-first UI: consider /answer
Search-results-first UI: use /search
Known URLs: use /contents
Code retrieval: use /context
Repeated recurring tracking: use /monitors
Verified list-building and enrichment: use /websets/v0

Tool Calling Pattern

For existing agent loops, the common Exa integration is to expose exa.search as a tool the LLM picks. This is different from the OpenAI-compatible endpoints in openai-compat.md: tool calling keeps your existing LLM provider, the compat endpoints replace it. OpenAI tool definition:

TOOLS = [{
    "type": "function",
    "function": {
        "name": "exa_search",
        "description": "Search the live web and return the most relevant URLs and content. Use for fresh information beyond the model's training data.",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"],
        },
    },
}]

Anthropic tool definition:

TOOLS = [{
    "name": "exa_search",
    "description": "Search the live web and return the most relevant URLs and content. Use for fresh information beyond the model's training data.",
    "input_schema": {
        "type": "object",
        "properties": {"query": {"type": "string"}},
        "required": ["query"],
    },
}]

The tool body is the same Exa call either way:

def exa_search(query: str):
    return exa.search(query, type="auto", contents={"highlights": True})

Design tips:

Say “search the live web” in the description so the LLM picks Exa for fresh-info queries
Keep query the only required field; let the LLM compose natural-language queries
Return highlights or summary rather than full text to keep tool-result tokens small
Prefer highlights over summary for tool results unless you specifically need Exa-side per-result synthesis
Add separate exa_answer or exa_get_contents tools instead of overloading one search tool when the agent also needs grounded answers or known-URL extraction
Echo tool_call_id (OpenAI) or tool_use_id (Anthropic) back exactly; mismatches fail silently

Low-Latency Recipe

For a latency-critical UX, start with:

{
  "type": "instant",
  "numResults": 3,
  "contents": {
    "highlights": { "maxCharacters": 1000 },
    "maxAgeHours": -1
  }
}

This keeps the path fast: instant minimizes retrieval latency, highlights keeps payloads compact, and maxAgeHours: -1 skips live-crawl overhead by using cache only.

Structured Output Patterns

Keep schemas:

small
bounded
explicit

Prefer:

1 to 5 root fields
simple arrays or flat objects

Avoid:

deeply nested schemas
loose catch-all maps
asking the model to invent citation fields that Exa already provides elsewhere

API

Data

MCP

SDKs

Integrations

Admin

Additional Resources

Prompting and patterns

Prompting and Patterns Reference

Canonical Docs Links

Contents

Query Formulation

`systemPrompt` vs `outputSchema`

Highlights vs Text

Freshness Patterns

Endpoint-Selection Patterns

Tool Calling Pattern

Low-Latency Recipe

Structured Output Patterns

​Prompting and Patterns Reference

​Canonical Docs Links

​Contents

​Query Formulation

​systemPrompt vs outputSchema

​Highlights vs Text

​Freshness Patterns

​Endpoint-Selection Patterns

​Tool Calling Pattern

​Low-Latency Recipe

​Structured Output Patterns

Prompting and Patterns Reference

Canonical Docs Links

Contents

Query Formulation

`systemPrompt` vs `outputSchema`

Highlights vs Text

Freshness Patterns

Endpoint-Selection Patterns

Tool Calling Pattern

Low-Latency Recipe

Structured Output Patterns