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agent.py

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+# Install required libraries
+# !pip install streamlit langchain_community duckduckgo-search langchain_google_community langchain_experimental langchain_openai langgraph
+
+import streamlit as st
+import os
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_google_community import GoogleSearchAPIWrapper
+from langchain_community.utilities import GoogleSerperAPIWrapper
+from langchain.tools import DuckDuckGoSearchRun, Tool
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.agents import create_openai_tools_agent, AgentExecutor
+from langgraph.graph import StateGraph, END
+from langchain_core.messages import HumanMessage
+from typing_extensions import TypedDict
+from typing import Annotated, Sequence
+import functools
+import operator
+
+
+# Initialize tools
+llm = ChatOpenAI()
+
+tavily_tool = TavilySearchResults(max_results=5)
+search_google_tool = Tool(
+    name="GoogleSearch",
+    func=GoogleSearchAPIWrapper().run,
+    description="Search information using Google Search API."
+)
+
+duckduck_search_tool = Tool(
+    name="DuckDuckGoSearch",
+    func=DuckDuckGoSearchRun().run,
+    description="Search information using DuckDuckGo."
+)
+
+serper_tool = Tool(
+    name="GoogleSerperSearch",
+    func=GoogleSerperAPIWrapper(max_results=5).run,
+    description="Perform searches using Google Serper API."
+)
+
+tavily_tool_wrapped = Tool(
+    name="TavilySearch",
+    func=tavily_tool.run,
+    description="Retrieve search results from Tavily API."
+)
+
+# Define reusable function for agent creation
+def create_agent(llm: ChatOpenAI, tools: list, system_prompt: str):
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", system_prompt),
+            MessagesPlaceholder(variable_name="messages"),
+            MessagesPlaceholder(variable_name="agent_scratchpad"),
+        ]
+    )
+    agent = create_openai_tools_agent(llm, tools, prompt)
+    executor = AgentExecutor(agent=agent, tools=tools)
+    return executor
+
+
+# Define agents
+def get_agents():
+    cto_agent = create_agent(
+        llm,
+        [duckduck_search_tool],
+        "You are a CTO name finder. Extract the CTO's name from the provided company data."
+    )
+
+    glassdoor_agent = create_agent(
+        llm,
+        [tavily_tool_wrapped, serper_tool],
+        "You are a Glassdoor review scraper. Retrieve reviews about the given company. "
+        "Consider points like Overall Rating, Compensation, Senior Management, Career Opportunities."
+        "Provide stars for each point."
+        "Always scrap the same data"
+    )
+
+    competitor_agent = create_agent(
+        llm,
+        [tavily_tool_wrapped, serper_tool],
+        "You are a competitor finder. Provide details such as a description of competitors and their primary differences."
+        "Output the results in a table format."
+    )
+
+    information_agent = create_agent(
+        llm,
+        [search_google_tool, duckduck_search_tool, serper_tool],
+        "You are an information collector. Retrieve details such as Website, Sector, Industry, Location, Employees, Founding Year, and LinkedIn URL."
+        "Linkedin URL will be always like this https://www.linkedin.com/company/company_name"
+    )
+
+    return cto_agent, glassdoor_agent, competitor_agent, information_agent
+
+
+# Streamlit App
+def main():
+    st.title("Company Insights API")
+    st.write("Enter a company name to fetch details about its CTO, competitors, Glassdoor reviews, and general information.")
+
+    # Input for company name
+    company_name = st.text_input("Enter company name")
+    run_queries = st.button("Run Queries")
+
+    if run_queries:
+        # Prepare agents
+        cto_agent, glassdoor_agent, competitor_agent, information_agent = get_agents()
+
+        # Queries
+        queries = {
+            "CTO": f"Who is the CTO of {company_name}?",
+            "Glassdoor Reviews": f"What are the Glassdoor reviews of {company_name}?",
+            "Competitors": f"What are the competitors of {company_name}?",
+            "Information": f"Give me all information about {company_name}.",
+        }
+
+        results = {}
+        for query_name, query in queries.items():
+            agent = {
+                "CTO": cto_agent,
+                "Glassdoor Reviews": glassdoor_agent,
+                "Competitors": competitor_agent,
+                "Information": information_agent,
+            }[query_name]
+
+            state = {
+                "messages": [HumanMessage(content=query)]
+            }
+
+            try:
+                response = agent.invoke(state)
+                results[query_name] = response.get("output", "No response")
+            except Exception as e:
+                results[query_name] = f"Error: {e}"
+
+        # Display results
+        for query_name, result in results.items():
+            st.subheader(query_name)
+            st.write(result)
+
+
+if __name__ == "__main__":
+    main()

reqirements.txt

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+streamlit
+langchain_community
+duckduckgo-search
+langchain_google_community
+langchain_experimental
+langchain_openai langgraph
+langchain-community