Initial commit - Search Engine Tool Agent

Standalone Streamlit app with LangChain agent using DuckDuckGo, Wikipedia, and Arxiv tools.

.env.example

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+OPENAI_API_KEY=your_openai_api_key
+GROQ_API_KEY=your_groq_api_key
+LANGCHAIN_API_KEY=your_langchain_api_key
+LANGCHAIN_PROJECT=SearchEngineToolAgent

.gitignore

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+.env
+__pycache__/
+*.pyc
+.venv/

app.py

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+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# LangSmith tracking
+os.environ['LANGCHAIN_TRACING_V2'] = 'true'
+os.environ['LANGCHAIN_PROJECT'] = os.getenv('LANGCHAIN_PROJECT', 'SearchEngineToolAgent')
+
+import streamlit as st
+from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun
+from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper
+from langchain_groq import ChatGroq
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+
+# Arxiv and Wikipedia tools
+api_wrapper_wiki = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+wiki_tool = WikipediaQueryRun(api_wrapper=api_wrapper_wiki)
+
+api_wrapper_arxiv = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=250)
+arxiv_tool = ArxivQueryRun(api_wrapper=api_wrapper_arxiv)
+
+search_tool = DuckDuckGoSearchRun(name="ddgsSearch")
+
+tools = [wiki_tool, arxiv_tool, search_tool]
+
+# LLM
+llm = ChatOpenAI(model="gpt-5-nano-2025-08-07", streaming=True)
+
+system_prompt = """
+You are a helpful assistant. You have access to tools to search the web, wikipedia and arxiv. Use them only when needed.
+When facing an issue with one tool, try another tool. Your answer should be anchored to the tools.
+Always respond in the same language as the user. Don't stop when facing an issue with one tool.
+If none of the tools work, summarize the situation and say you can't find the answer.
+"""
+search_agent = create_agent(model=llm, tools=tools, system_prompt=system_prompt)
+
+# Streamlit UI
+st.title("🔍 Langchain - Chat with Search")
+st.markdown("""
+In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
+Try more LangChain 🤝 Streamlit Agent examples at [https://blog.langchain.com/langchain-streamlit/]
+""")
+
+# Sidebar for settings
+st.sidebar.title("Settings")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "assistant", "content": "👋 Hi, I am a chatbot 🤖 that can search the web 🌐. How can I help you?"}
+    ]
+
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).markdown(msg["content"])
+
+if prompt := st.chat_input(placeholder="Ask me anything"):
+
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        # Convert session messages to proper format
+        input_messages = [{"role": msg["role"], "content": msg["content"]} for msg in st.session_state.messages]
+
+        config = {"configurable": {"thread_id": "chat_session"}}
+
+        final_response = ""
+
+        with st.spinner("Thinking..."):
+            step = 0
+            for chunk in search_agent.stream(
+                {"messages": input_messages},
+                config=config,
+                stream_mode="values"
+            ):
+                latest_message = chunk["messages"][-1]
+                step += 1
+
+                if isinstance(latest_message, AIMessage):
+                    if latest_message.content and latest_message.tool_calls:
+                        with st.expander(f"💭 Step {step} : Reasoning", expanded=False):
+                            st.markdown(latest_message.content)
+                    if latest_message.tool_calls:
+                        for tool_call in latest_message.tool_calls:
+                            st.info(f"🔧 Step {step} - Calling **{tool_call['name']}** with: `{tool_call['args']}`")
+                    if latest_message.content and not latest_message.tool_calls:
+                        final_response = latest_message.content
+
+                elif isinstance(latest_message, ToolMessage):
+                    with st.expander(f"📄 Step {step} : Result from **{latest_message.name}**", expanded=False):
+                        st.markdown(latest_message.content[:100])
+
+        if final_response:
+            st.session_state.messages.append({"role": "assistant", "content": final_response})
+            st.markdown(final_response)
+        else:
+            st.session_state.messages.append({"role": "assistant", "content": "I'm sorry, I couldn't find any information about that."})
+            st.markdown("I'm sorry, I couldn't find any information about that.")

requirements.txt

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+streamlit
+python-dotenv
+langchain
+langchain-core
+langchain-community
+langchain-openai
+langchain-groq
+langgraph
+ddgs
+wikipedia
+arxiv