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.
import osfrom langchain_valyu import ValyuSearchTool# Set your API keyos.environ["VALYU_API_KEY"] = "your-api-key-here"# Initialize the search tooltool = ValyuSearchTool()# Perform a searchsearch_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=20.0)print("Search Results:", search_results)
from langchain_valyu import ValyuSearchToolfrom langchain_anthropic import ChatAnthropicfrom langgraph.prebuilt import create_react_agentfrom langchain_core.messages import HumanMessage, SystemMessage# Create financial research agentfinancial_llm = ChatAnthropic(model="claude-sonnet-4-20250514")valyu_tool = ValyuSearchTool()financial_agent = create_react_agent(financial_llm, [valyu_tool])# Query financial markets with system contextquery = "What are the latest developments in cryptocurrency regulation and their impact on institutional adoption?"system_context = SystemMessage(content="""You are a financial research assistant. Use Valyu to search for:- Real-time market data and news- Academic research on financial models- Economic indicators and analysisAlways cite your sources and provide context about data recency.""")for step in financial_agent.stream( {"messages": [system_context, HumanMessage(content=query)]}, stream_mode="values",): step["messages"][-1].pretty_print()
# Set appropriate price limits based on use casetool = ValyuSearchTool()# For quick lookupsquick_search = tool._run( query="current bitcoin price", max_price=10.0, # Lower cost for simple queries max_num_results=3)# For comprehensive researchdetailed_search = tool._run( query="comprehensive analysis of renewable energy trends", max_price=50.0, # Higher budget for complex queries max_num_results=15, search_type="all")
from langchain_core.messages import SystemMessage, HumanMessage# Optimize agent behavior with good system messagessystem_message = SystemMessage(content="""You are an AI research assistant with access to Valyu search.SEARCH GUIDELINES:- Use search_type="proprietary" for academic/scientific queries- Use search_type="web" for current events and news- Use search_type="all" for comprehensive research- Set higher relevance_threshold (0.6+) for precise results- Use category parameter to guide search context- Always cite sources from search resultsRESPONSE FORMAT:- Provide direct answers based on search results- Include source citations with URLs when available- Mention publication dates for time-sensitive information- Indicate if information might be outdated""")agent = create_react_agent(llm, [ValyuSearchTool()])# Use the agent with system contextfor step in agent.stream( {"messages": [system_message, HumanMessage(content="Your query here")]}, stream_mode="values",): step["messages"][-1].pretty_print()