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Overview

Best for: building Valyu into your product / agents. Use Valyu as a search and content tool inside LangChain (Python) agents.
Viewing the Python integration. For the TypeScript version of this guide, see the TypeScript 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 (Python) agent.

Open in Cursor

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Installation

Install the official LangChain Valyu package: 
pip install -U langchain-valyu
Configure credentials by setting the following environment variable: 
export VALYU_API_KEY="your-valyu-api-key-here"
Or set it programmatically: 
import os
os.environ["VALYU_API_KEY"] = "your-valyu-api-key-here"
For agent examples, you’ll also need: 
export ANTHROPIC_API_KEY="your-anthropic-api-key"  # For Claude examples
export OPENAI_API_KEY="your-openai-api-key"        # For OpenAI examples

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 os
from langchain_valyu import ValyuSearchTool

# Set your API key
os.environ["VALYU_API_KEY"] = "your-api-key-here"

# Initialize the search tool
tool = ValyuSearchTool()

# Perform a search
search_results = tool._run(
    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
)

print("Search Results:", search_results.results)

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Using ValyuContentsTool for Content Extraction

Extract clean, structured content from specific URLs: 
import os
from langchain_valyu import ValyuContentsTool

# Set your API key
os.environ["VALYU_API_KEY"] = "your-api-key-here"

# Initialize the contents tool
contents_tool = ValyuContentsTool()

# Extract content from URLs
urls = [
    "https://arxiv.org/abs/2301.00001",
    "https://example.com/article",
]

extracted_content = contents_tool._run(urls=urls)
print("Extracted Content:", extracted_content.results)

# Print individual results
for result in extracted_content.results:
    print(f"URL: {result['url']}")
    print(f"Title: {result['title']}")
    print(f"Content: {result['content'][:200]}...")
    print(f"Status: {result['status']}")
    print("---")

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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: 
pip install langchain-anthropic langgraph

import os
from langchain_valyu import ValyuSearchTool
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage

# Set API keys
os.environ["VALYU_API_KEY"] = "your-valyu-api-key"
os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"

# Initialize components
llm = ChatAnthropic(model="claude-sonnet-4-20250514")
valyu_search_tool = ValyuSearchTool()

# Create agent with Valyu search capability
agent = create_react_agent(llm, [valyu_search_tool])

# Use the agent
user_input = "What are the key factors driving recent stock market volatility, and how do macroeconomic indicators influence equity prices across different sectors?"

for step in agent.stream(
    {"messages": [HumanMessage(content=user_input)]},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

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Advanced configuration

results = tool._run(
    query="quantum computing breakthroughs 2024",
    max_num_results=10,  # 1-20 (up to 100 with a special API key)
    relevance_threshold=0.6,  # 0.0-1.0
    max_price=30.0,  # max cost per 1k retrievals (CPM)
    start_date="2024-01-01",  # YYYY-MM-DD
    end_date="2024-12-31",
    included_sources=["valyu/valyu-arxiv", "valyu/valyu-pubmed"],
    excluded_sources=["example.com"],
    response_length="medium",  # "short", "medium", "large", "max", or int
    country_code="US",  # 2-letter ISO code
    fast_mode=False,  # faster but shorter results
)
ValyuContentsTool takes a single required urls argument (max 10 per request).
Bind a SystemMessage to guide tool selection. Use search_type="all" for broad coverage (web + proprietary), or "web" for current events only; raise relevance_threshold (0.6+) for precise results; cite sources; and use natural-language queries, not operators.See the Prompting Guide for full guidelines.

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Additional Resources

LangChain Valyu Tool

Official LangChain 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)