langchain-talordata 0.1.5

LangChain integration for Talor SERP API — 33 search engines in one tool

LangChain integration for TalorData SERP API

Installation • Quick Start • Tools • Resources

PyPI version Python versions License: MIT

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

Overview

langchain-talordata provides LangChain 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

This package provides:

• TalorDataSerpAPIWrapper for direct sync and async API access
• TalorDataSerpTool for creating LangChain tools
• 20+ search types across four major search engines
• support for search, history, and statistics endpoints

Installation

pip install langchain-talordata

Quick start

1. Get your API key

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

2. Set up authentication

import os
os.environ["TALOR_API_KEY"] = "your-token"

3. Modern agent usage

from langchain_talordata import TalorDataSerpTool
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tool = TalorDataSerpTool.from_env()

# Tool calling without langchain_classic agents
model_with_tools = llm.bind_tools([tool])
response = model_with_tools.invoke("Search for the latest LangChain news")
print(response)

4. Search tool

from langchain_talordata import TalorDataSerpTool

search_tool = TalorDataSerpTool.from_env()

result = search_tool.invoke({
    "query": "LangChain tutorial",
    "engine": "google",
    "params": {
        "gl": "us",
        "hl": "en",
        "device": "desktop",
    },
})

print(result)

Search parameters:

• query: optional search query text
• engine: optional engine key such as google, google_news, google_images, bing, duckduckgo
• params: optional engine-specific parameter object
• common params fields include gl, hl, device, location, and no_cache
• use talor_serp_list_engines to inspect detailed parameters for a specific engine params also accepts a JSON string when returned by a model tool call, for example:

result = search_tool.invoke({
    "query": "LangChain tutorial",
    "engine": "google",
    "params": "{\"hl\": \"zh-CN\", \"gl\": \"cn\"}",
})

5. History tool

from langchain_talordata import TalorDataSerpTool

history_tool = TalorDataSerpTool.history_from_env()

result = history_tool.invoke({
    "page": 1,
    "page_size": 20,
    "search_query": "langchain",
    "search_engine": "google",
    "status": "success",
    "timezone": "Asia/Shanghai",
})

print(result)

History parameters:

• page: page number, default 1
• page_size: page size, default 20
• search_query: optional keyword filter
• search_engine: optional engine filter such as google or bing
• status: all, success, or error
• start_time: optional unix timestamp in seconds
• end_time: optional unix timestamp in seconds
• timezone: optional timezone header such as Asia/Shanghai or +08:00 6. Statistics tool

from langchain_talordata import TalorDataSerpTool

statistics_tool = TalorDataSerpTool.statistics_from_env()

result = statistics_tool.invoke({
    "start_date": "2026-06-01",
    "end_date": "2026-06-05",
    "engines": "google,bing",
    "timezone": "+08:00",
})

print(result)

Statistics parameters:

• start_date: required, format YYYY-MM-DD
• end_date: required, format YYYY-MM-DD
• engines: optional comma-separated engine keys such as google,bing
• timezone: optional timezone offset such as +08:00 7. Bind multiple tools

from langchain_talordata import TalorDataSerpTool
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = TalorDataSerpTool.tools_from_env()

model_with_tools = llm.bind_tools(tools)
response = model_with_tools.invoke("Show my SERP usage statistics for 2026-06-01 to 2026-06-05")
print(response)

bind_tools() only lets the model generate tool_calls. To actually execute the selected tool, either:

• call tool.invoke(...) directly, or

• use an agent workflow that executes tools automatically Tools

• talor_serp_search - search the web with engine-specific parameters

• talor_serp_list_engines - inspect supported engines and detailed parameter schemas

• talor_serp_history - query historical SERP requests

• talor_serp_statistics - query usage statistics for a date range Compatibility note

If you are using modern package versions such as:

• langchain-core>=1.0
• langchain-classic>=1.0
• langchain-openai>=1.0 avoid langchain_classic.agents.create_openai_functions_agent() and other legacy initialize_agent / AgentType.OPENAI_FUNCTIONS flows with chat models. Those classic agent paths call llm.invoke(..., callbacks=...), while modern BaseChatModel.invoke() forwards callbacks through config, which can lead to:

TypeError: BaseLLM.generate_prompt() got multiple values for keyword argument 'callbacks'

Use one of these approaches instead:

• llm.bind_tools([tool]) for direct tool calling

• langchain.agents.create_agent(...) for new agent workflows

• agent.invoke(...) instead of deprecated agent.run(...) Features

• compatible with LangChain tool workflows

• supports multiple Talor SERP engines such as Google, Bing, Yandex, and DuckDuckGo

• exposes engine schema metadata for parameter-aware integrations

• includes helper APIs for usage history and statistics Resources

• PyPI: langchain-talordata

• TalorData: talordata.com

Support

For issues with the LangChain 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 LangChain?

Explore the TalorData LangChain Integration Guide

Read the Integration Documentation

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