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Overview

Best for: building Valyu into your product / agents. Use Valyu as a search and content tool inside LangChain (TypeScript) agents.
Viewing the TypeScript integration. For the Python version of this guide, see the Python LangChain integration.
Valyu integrates seamlessly with LangChain as a search tool, allowing you to enhance your AI agents and RAG applications with real-time web search and proprietary data sources. The integration provides LLM-ready context from multiple sources including web pages, academic journals, financial data, and more. The package includes two main tools:
  • ValyuSearchTool: Search operations with comprehensive parameter control
  • ValyuContentsTool: Extract clean content from specific URLs

Paste into your AI assistant to wire Valyu into a LangChain (TypeScript) agent.

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Integration details

ComponentSourcePackage
ValyuSearchToolProprietary + public web content (search)@valyu/langchain
ValyuContentsToolWeb pages (content extraction)@valyu/langchain

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Installation

Install the official LangChain Valyu package and the Valyu SDK: 
npm install @valyu/langchain valyu-js @langchain/core

yarn add @valyu/langchain valyu-js @langchain/core

pnpm add @valyu/langchain valyu-js @langchain/core
Configure credentials by setting the following environment variable: 
process.env.VALYU_API_KEY = "your-valyu-api-key-here";
Or set it programmatically: 
import * as dotenv from "dotenv";
dotenv.config();
For agent examples, you’ll also need: 
process.env.ANTHROPIC_API_KEY = "your-anthropic-api-key";
process.env.OPENAI_API_KEY = "your-openai-api-key";

Free Credits

Get your API key with $10 free credits ($20 with a work email) from the Valyu Platform.

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Basic Usage

import { ValyuSearchTool, ValyuAdapter } from "@valyu/langchain";

const valyuClient = new ValyuAdapter(process.env.VALYU_API_KEY!);

const tool = new ValyuSearchTool({
  client: valyuClient,
});

const searchResults = await tool.invoke({
  query: "What are agentic search-enhanced large reasoning models?",
  search_type: "all", // "all", "web", or "proprietary"
  max_num_results: 5,
  relevance_threshold: 0.5,
  max_price: 30.0,
});

console.log("Search Results:", JSON.parse(searchResults));

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Using ValyuContentsTool for Content Extraction

Extract clean, structured content from specific URLs: 
import { ValyuContentsTool, ValyuAdapter } from "@valyu/langchain";

const valyuClient = new ValyuAdapter(process.env.VALYU_API_KEY!);

const contentsTool = new ValyuContentsTool({
  client: valyuClient,
});

const urls = [
  "https://arxiv.org/abs/2301.00001",
  "https://example.com/article",
];

const extractedContent = await contentsTool.invoke({ urls });
const results = JSON.parse(extractedContent);

console.log("Extracted Content:", results.results);

for (const result of results.results || []) {
  console.log(`URL: ${result.url}`);
  console.log(`Title: ${result.title}`);
  console.log(`Content: ${result.content?.substring(0, 200)}...`);
  console.log(`Status: ${result.status}`);
  console.log("---");
}

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Using with LangChain Agents

The most powerful way to use Valyu is within LangChain agents, where the AI can dynamically decide when and how to search:
npm
npm install @langchain/anthropic @langchain/langgraph

import { ValyuSearchTool, ValyuAdapter } from "@valyu/langchain";
import { ChatAnthropic } from "@langchain/anthropic";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { HumanMessage } from "@langchain/core/messages";

const valyuClient = new ValyuAdapter(process.env.VALYU_API_KEY!);

const llm = new ChatAnthropic({
  model: "claude-sonnet-4-20250514",
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const valyuSearchTool = new ValyuSearchTool({
  client: valyuClient,
});

const agent = createReactAgent({ llm, tools: [valyuSearchTool] });

const userInput =
  "What are the key factors driving recent stock market volatility, and how do macroeconomic indicators influence equity prices across different sectors?";

const stream = await agent.stream({
  messages: [new HumanMessage(userInput)],
});

for await (const step of stream) {
  console.log(step.messages[step.messages.length - 1].content);
}

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Advanced configuration

tool.invoke() accepts:
  • query (required) - natural-language query string
  • search_type - "all", "web", or "proprietary" (use "all" when passing included_sources URLs)
  • max_num_results - 1-20 (up to 100 with a special API key)
  • relevance_threshold - 0.0-1.0
  • max_price - max cost per 1k retrievals (CPM)
  • start_date / end_date - YYYY-MM-DD
  • included_sources / excluded_sources - arrays of URLs, domains, or dataset ids
  • response_length - "short", "medium", "large", "max", or a character count
  • country_code - 2-letter ISO code (e.g. "US", "GB")
  • fast_mode - faster but shorter results
The tool returns a JSON string - parse it with JSON.parse().
Invoke with a required urls array (max 10 per request). Constructor options: summary (boolean/string/object), extract_effort ("normal", "high", "auto"), response_length ("short", "medium", "large", "max", or a number).
Bind a SystemMessage: use search_type="all" for broad coverage (web + proprietary), or "web" for current events only; raise relevance_threshold (0.6+) for precision; cite sources; use natural-language queries, not operators. See the Prompting Guide.

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Additional Resources

LangChain Valyu Tool

Official LangChain Python integration documentation

API Reference

Complete Valyu API documentation

LangGraph Agents

Build advanced agent workflows

Get API Key

Sign up for $10 free credits ($20 with a work email)