LangChain

pip install -U langchain-valyu

npm install @valyu/langchain valyu-js @langchain/core

export VALYU_API_KEY="your-valyu-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"

export VALYU_API_KEY="your-valyu-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"

from langchain_valyu import ValyuSearchTool

tool = ValyuSearchTool()

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,
)

print(results.results)

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

const client = new ValyuAdapter(process.env.VALYU_API_KEY!);
const tool = new ValyuSearchTool({ client });

const results = 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,
});

console.log(JSON.parse(results));

from langchain_valyu import ValyuContentsTool

contents_tool = ValyuContentsTool()

extracted = contents_tool._run(urls=[
    "https://arxiv.org/abs/2301.00001",
    "https://example.com/article",
])

for result in extracted.results:
    print(result["url"], result["title"])
    print(result["content"][:200], "...")

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

const client = new ValyuAdapter(process.env.VALYU_API_KEY!);
const contentsTool = new ValyuContentsTool({ client });

const extracted = JSON.parse(
  await contentsTool.invoke({
    urls: ["https://arxiv.org/abs/2301.00001", "https://example.com/article"],
  }),
);

for (const result of extracted.results ?? []) {
  console.log(result.url, result.title);
  console.log(result.content?.slice(0, 200), "...");
}

pip install langchain-anthropic langgraph

npm install @langchain/anthropic @langchain/langgraph

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

llm = ChatAnthropic(model="claude-sonnet-5")
agent = create_react_agent(llm, [ValyuSearchTool()])

question = (
    "What are the key factors driving recent stock market volatility, "
    "and how do macroeconomic indicators influence equity prices across sectors?"
)

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

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

const client = new ValyuAdapter(process.env.VALYU_API_KEY!);
const llm = new ChatAnthropic({ model: "claude-sonnet-5" });
const agent = createReactAgent({ llm, tools: [new ValyuSearchTool({ client })] });

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

const stream = await agent.stream({ messages: [new HumanMessage(question)] });
for await (const step of stream) {
  console.log(step.messages.at(-1)?.content);
}

results = tool._run(
    query="quantum computing breakthroughs 2024",
    max_num_results=10,          # 1-20; above 20 needs a key permission
    relevance_threshold=0.6,     # 0.0-1.0
    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 an int
    country_code="US",
)

const results = await tool.invoke({
  query: "quantum computing breakthroughs 2024",
  max_num_results: 10,          // 1-20; above 20 needs a key permission
  relevance_threshold: 0.6,     // 0.0-1.0
  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 a number
  country_code: "US",
});