LlamaIndex integration for TalorData SERP API

TalorData helps developers and AI applications connect to real-time, structured, and reliable search data through a single SERP API. With support for Google, Bing, News, Images, Shopping, Maps, Scholar, Trends, and more, TalorData makes it easier to build AI agents, search copilots, SEO workflows, and data-driven automations powered by live search results.

The llama-index-tools-talordata-serp package brings TalorData’s real-time search capabilities into LlamaIndex, so you can add live search, engine inspection, request history, and usage analytics directly to your LLM workflows and AI agent systems.

Overview

llama-index-tools-talordata-serp provides LlamaIndex tools for TalorData SERP API, enabling your AI agents to:

• Search - Query search engines with geo-targeting and language customization

• Inspect engines - Discover supported engines and engine-specific parameters

• Query history - Fetch SERP request history with filters

• View statistics - Retrieve usage statistics by date range and engine

Installation

Install from PyPI:

python -m pip install llama-index-tools-talordata-serp

Authentication

1. Get your API key

Sign up at TalorData and get your API key from the dashboard.

2.Use a Talordata SERP API key:

sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Do not use a Talordata dashboard login JWT. Dashboard JWT tokens are for dashboard APIs, while this package calls the SERP request API with Authorization: Bearer <SERP_API_KEY>.

3.You can pass the API key directly:

from llama_index.tools.talordata_serp import TalordataSerpToolSpec

tool_spec = TalordataSerpToolSpec(api_key="sk_xxx")

Or read it from an environment variable in your application code.

Basic Usage

from llama_index.tools.talordata_serp import TalordataSerpToolSpec

tool_spec = TalordataSerpToolSpec(api_key="sk_xxx")
tools = tool_spec.to_tool_list()

Direct ToolSpec Calls

import json

from llama_index.tools.talordata_serp import TalordataSerpToolSpec

tool_spec = TalordataSerpToolSpec(api_key="sk_xxx")

result = tool_spec.search_engine(
    query="latest AI search trends",
    engine="google",
    country="us",
    language="en",
    num=5,
)

print(json.loads(result))

Agent Usage

This example requires the openai extra:

python -m pip install "llama-index-llms-openai>=0.3.0"

import asyncio

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.talordata_serp import TalordataSerpToolSpec


async def main() -> None:
    tool_spec = TalordataSerpToolSpec(api_key="sk_xxx")

    agent = FunctionAgent(
        tools=tool_spec.to_tool_list(),
        llm=OpenAI(model="gpt-4.1"),
    )

    response = await agent.run("Find three recent search results for coffee market trends.")
    print(response)


asyncio.run(main())

Tools

search_engine

Search Google, Bing, Yandex, or DuckDuckGo and return normalized web results.

result = tool_spec.search_engine(
    query="coffee",
    engine="google",
    country="us",
    language="en",
    num=3,
)

image_search

Search Bing Images or Google Images and return normalized image results.

result = tool_spec.image_search(
    query="coffee",
    engine="bing_images",
    country="us",
    language="en",
    count=3,
)

news_search

Search Google News or Bing News and return normalized news-style results.

result = tool_spec.news_search(
    query="markets",
    engine="google_news",
    country="us",
    language="en",
)

raw_serp_request

Pass an engine and extra JSON parameters directly to the Talordata SERP API. Use this when you need engine-specific parameters that are not exposed by the normalized helpers.

result = tool_spec.raw_serp_request(
    engine="google",
    query="coffee",
    params_json='{"num": 3, "country": "us", "language": "en"}',
)

Response Shape

Normal search tools return a JSON string with this shape:

{
  "query": "coffee",
  "engine": "google",
  "results": [
    {
      "title": "Coffee",
      "link": "https://example.com",
      "snippet": "Coffee article",
      "source": "Example"
    }
  ]
}

Agent-friendly errors are returned as JSON strings:

{
  "error": {
    "type": "SerpApiError",
    "status_code": 401,
    "message": "API key authentication failed"
  }
}

Set include_raw=True on normal tools when the caller needs the full upstream payload.

Resources

• PyPI: LlamaIndex-talordata

• TalorData: talordata.com

Support

For issues with the LlamaIndex integration package, report an issue in the GitHub repository.

For TalorData SERP API account, quota, or API key issues, contact TalorData support through the support channel listed in your TalorData account or dashboard.

For detailed integration tutorials and API documentation, visit the TalorData Documentation.

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Learn More

Ready to build AI agents with real-time search in LlamaIndex?

Explore the TalorData LlamaIndex Integration Guide

Read the Integration Documentation

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TalorData brings real‑time search to LlamaIndex, enabling developers to build AI agents and workflows with fresh, structured, and reliable search data.