Quickstart

Agent quick-start

Your agent installs and configures the Andi Search API itself — paste one line:

Paste into Claude Code, Cursor, or Codex

Set up Andi search for this project: andiai.com

Your agent fetches andiai.com, follows it to the install guide at api.andiai.com, then gets an API key from the API Console, connects the MCP server, and verifies the setup.

Are you an AI agent? Fetch https://api.andiai.com/install.md and follow it to configure Andi Search — MCP server, API key, and verification.

CLI quick-start

Search from your terminal without writing any code — get a key from the API Console, then install the CLI and run it:

export ANDI_API_KEY=YOUR_API_KEY
npm install -g @andiai/cli

andi search "what is RAG"
andi fetch https://en.wikipedia.org/wiki/Retrieval-augmented_generation

Or run it without installing:

npx -y @andiai/cli search "what is RAG"

See the CLI guide for output formats and the local MCP server.

Manual setup

You need an API key to follow these steps. Get one from the API Console.

Get your API key

Sign in to the API Console and create an API key. Copy it — you’ll need it for the next step. See authentication for key management and security practices.

Make your first request

Send a search query using the x-api-key header:

curl "https://api.andiai.com/api/v1/search?q=what+is+RAG" \
  -H "x-api-key: YOUR_API_KEY"

import requests

response = requests.get(
    "https://api.andiai.com/api/v1/search",
    params={"q": "what is RAG"},
    headers={"x-api-key": "YOUR_API_KEY"}
)

data = response.json()
for result in data["results"]:
    print(result["title"], result["link"])

const response = await fetch(
  "https://api.andiai.com/api/v1/search?q=what+is+RAG",
  { headers: { "x-api-key": "YOUR_API_KEY" } }
);

const data = await response.json();
data.results.forEach(r => console.log(r.title, r.link));

Read the response

The API returns a JSON object with top-level fields and a results array. Each result includes a title, link, desc, and source:

{
  "results_type": "Search",
  "answer": "",
  "type": "Search",
  "title": "what is RAG",
  "results": [
    {
      "title": "Retrieval-Augmented Generation (RAG) Explained",
      "link": "https://example.com/rag-explained",
      "desc": "RAG combines a retrieval system with a language model to generate responses grounded in retrieved documents.",
      "source": "example.com"
    }
  ],
  "metrics": {
    "query": "what is RAG",
    "intent": "FallbackSearchIntent",
    "timestamp": "2026-07-14T21:30:00.000Z",
    "duration": 1240,
    "queries_executed": 1,
    "api_requests_count": 1,
    "results_returned": 10,
    "total_results_found": 42,
    "cost_dollars": 0.0031
  }
}

The metrics.cost_dollars field shows the amount charged for this request in USD.

Fetch a page

The second core endpoint, /api/v1/fetch, retrieves a single page as clean extracted content — use it to read a result in full after searching:

curl "https://api.andiai.com/api/v1/fetch?url=https://en.wikipedia.org/wiki/Retrieval-augmented_generation" \
  -H "x-api-key: YOUR_API_KEY"

import requests

response = requests.get(
    "https://api.andiai.com/api/v1/fetch",
    params={"url": "https://en.wikipedia.org/wiki/Retrieval-augmented_generation"},
    headers={"x-api-key": "YOUR_API_KEY"}
)

page = response.json()
print(page["title"], page["word_count"])

const response = await fetch(
  "https://api.andiai.com/api/v1/fetch?url=" +
    encodeURIComponent("https://en.wikipedia.org/wiki/Retrieval-augmented_generation"),
  { headers: { "x-api-key": "YOUR_API_KEY" } }
);

const page = await response.json();
console.log(page.title, page.word_count);

The response includes the page title, extracted content and markdown, word_count, and metrics.cost_dollars. Add format=context for LLM-ready markdown, or query=... for extracts focused on what you want from the page. See content retrieval for the full reference.

Next steps

Search modes

Automatic effort by default, manual control when you want it.

Build with AI agents

Connect via MCP for Claude Code, Cursor, and other tools.

Content retrieval

Full fetch endpoint reference.

Query parameters

Full parameter reference for the search endpoint.

Getting started

Search

Features

Resources

Agent quick-start

CLI quick-start

Manual setup

Next steps

Search modes

Build with AI agents

Content retrieval

Query parameters

​Agent quick-start

​CLI quick-start

​Manual setup

​Next steps

Search modes

Build with AI agents

Content retrieval

Query parameters

Agent quick-start

CLI quick-start

Manual setup

Next steps