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Overview

Endpoint: POST https://api.exa.ai/search. Code search is integrated into the main search endpoint. No category parameter needed. What it searches: Billions of GitHub repositories, documentation pages, Stack Overflow posts, and developer blogs. Semantic search matches natural language queries to real, working code examples ranked by relevance. Reduces hallucinated imports and outdated syntax.

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Minimal Working Example

curl -X POST "https://api.exa.ai/search" \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{"query": "how to use Vercel AI SDK streaming", "type": "fast", "contents": {"highlights": {"maxCharacters": 4000}}}'

from exa_py import Exa
exa = Exa(api_key="YOUR_API_KEY")
result = exa.search("how to use Vercel AI SDK streaming", type="fast", contents={"highlights": {"max_characters": 4000}})

import Exa from "exa-js";
const exa = new Exa("YOUR_API_KEY");
const result = await exa.search("how to use Vercel AI SDK streaming", { type: "fast", contents: { highlights: { maxCharacters: 4000 } } });

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Supported Parameters

Code search is integrated into the main search endpoint. All standard search parameters are supported:
ParameterTypeNotes
querystringNatural language describing the code you need. Be specific about language, framework, and version.
typestring"fast" recommended for code search. All search types supported.
numResultsinteger1–100. Default 10.
includeDomainsstring[]Restrict to specific sources (e.g. ["github.com", "stackoverflow.com"]).
excludeDomainsstring[]Exclude specific sources.
startPublishedDatestringISO 8601. Filter for recent code examples.
endPublishedDatestringISO 8601.
contentsobjecttext, highlights, summary, all nested under contents.

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Query Patterns

Library usage: 
"how to use Exa search in python with livecrawl"
"pandas dataframe filtering and groupby operations"
API syntax: 
"correct syntax for vercel ai sdk to call gpt-5"
"Next.js 14 app router with TypeScript configuration"
Development setup: 
"how to set up a reproducible Nix Rust development environment"
"Docker Compose for PostgreSQL and Redis"
Framework-specific: 
"React Server Components data fetching patterns"
"FastAPI dependency injection with SQLAlchemy"

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Common Mistakes

WrongCorrect
Vague queries like "python code"Be specific: "python asyncio gather with error handling". The more specific the query, the better the code results.
Using text: true for code extractionUse highlights for targeted code snippets, or text with maxCharacters to cap output size. Raw text: true returns entire pages.

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Patterns and Gotchas

  • Code search is integrated into the main search endpoint. No category parameter needed. Just use descriptive code-related queries with type: "fast".
  • Be specific about language and framework. “how to stream responses with Vercel AI SDK in Next.js” returns much better results than “streaming API”.
  • Use highlights to extract code snippets. Highlights pull the most relevant code blocks from pages, avoiding boilerplate and navigation text.
  • includeDomains is useful for source quality. Restrict to ["github.com"] for raw code, ["stackoverflow.com"] for Q&A, or official docs domains.
  • Date filters work for code. Use startPublishedDate to get recent examples, useful for fast-moving frameworks.
  • Python SDK uses snake_case. numResults → num_results, maxCharacters → max_characters.
  • Use text with maxCharacters for full context. When you need the complete code file or tutorial, request text with a character cap rather than just highlights.