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This example builds a complete search integration from scratch — environment setup, request handling, error recovery, and result parsing.
You need an API key to follow this guide. Get one from the API Console.

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Setup

Store your API key as an environment variable rather than hardcoding it: 
export ANDI_API_KEY="your-api-key"

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Complete example

#!/bin/bash

# Search with error handling
response=$(curl -s -w "\n%{http_code}" \
  "https://search-api.andisearch.com/api/v1/search?q=best+programming+languages+2025&limit=5" \
  -H "x-api-key: $ANDI_API_KEY")

http_code=$(echo "$response" | tail -1)
body=$(echo "$response" | sed '$d')

if [ "$http_code" -eq 200 ]; then
  echo "$body" | jq '.results[] | {title, link, source}'
else
  echo "Error $http_code: $(echo "$body" | jq -r '.error')"
fi

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How it works

  1. API key from environment — loaded from ANDI_API_KEY, never hardcoded
  2. Query parameters — q for the search query, limit to cap results at 5
  3. Status code check — handle success, rate limiting, and other errors separately
  4. Result parsing — each result has title, link, desc, and source

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Variations

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With text extracts

Add extracts=true to get longer text passages from each result page: 
response = requests.get(
    "https://search-api.andisearch.com/api/v1/search",
    params={"q": "best programming languages 2025", "extracts": "true"},
    headers={"x-api-key": api_key},
)

data = response.json()
for result in data["results"]:
    if result.get("extracts"):
        print(f"{result['title']}: {result['extracts'][0][:200]}")

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With domain filtering

Restrict results to specific sites: 
response = requests.get(
    "https://search-api.andisearch.com/api/v1/search",
    params={
        "q": "best programming languages 2025",
        "includeDomains": "stackoverflow.com,github.com",
    },
    headers={"x-api-key": api_key},
)

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Context format for LLMs

Get results as markdown text, ready to pass into an LLM prompt: 
response = requests.get(
    "https://search-api.andisearch.com/api/v1/search",
    params={"q": "best programming languages 2025", "format": "context"},
    headers={"x-api-key": api_key},
)

# Response is markdown text, not JSON
context = response.text

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Next steps

RAG pipeline

Use search results as context for an LLM.

Query parameters

Full parameter reference.

Error handling

Error codes and retry strategies.

Response format

Understand the response structure.