Implement LangChain Agents and Tools

Implement the core LangChain agents and their respective tools (including Browserbase) that will form nodes in your LangGraph. Show how to initialize each agent with OpenAI o4-mini and equip them with the necessary tools for web research and data processing. Provide Python code for at least two agent definitions and one custom tool integration. ```python
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent, Tool
from langchain_core.prompts import PromptTemplate # Initialize OpenAI o4-mini model
llm = ChatOpenAI(model="GPT-5-mini", temperature=0.7, openai_api_key="YOUR_OPENAI_API_KEY") # Example Browserbase tool (you'll integrate the actual Browserbase SDK)
def browserbase_scrape(url: str): # Your Browserbase scraping logic here print(f"Scraping URL: {url}") return "Content from website" bb_tool = Tool( name="BrowserbaseScraper", func=browserbase_scrape, description="Useful for scraping content from specified URLs."
) # Example Agent Prompt
market_scanner_prompt = PromptTemplate.from_template( "You are a market scanner for defense AI startups. Your goal is to find relevant companies using the BrowserbaseScraper tool. Query: {query}"
) # Create a basic ReAct agent (this will be part of your LangGraph node)
market_scanner_agent = create_react_agent(llm, [bb_tool], market_scanner_prompt)
market_scanner_executor = AgentExecutor(agent=market_scanner_agent, tools=[bb_tool], verbose=True) # Further implementation for LangGraph nodes and edges...
```