Added Code

agent.py

@@ -0,0 +1,232 @@
+########## Imports ############
+from langchain_google_genai import ChatGoogleGenerativeAI
+import os
+from typing import TypedDict, List, Dict, Any, Optional
+from langgraph.graph import StateGraph, START, END
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage
+
+from langchain_community.tools import WikipediaQueryRun
+from langchain_community.utilities import WikipediaAPIWrapper
+import string
+
+from langchain_experimental.tools import PythonREPLTool
+import ast, json
+
+from langchain_community.tools import DuckDuckGoSearchRun
+
+
+########## State ############
+class InfoState(TypedDict):
+    question: str
+    answer_type: Optional[str] # WebInfo  - WIKI - MATH
+    answer_code : Optional[str]
+    main_parts: Optional[List[str]]
+    tool_answer : Optional[list[str]]
+    final_answer : Optional[str]
+
+
+
+######### Nodes ############
+def get_wiki_relate(state: InfoState) -> InfoState:
+    """
+    Tool to Get the wikipedia info from keywords extracted from preprocessing at main_parts.
+    
+    Uses: Wikipedia API
+    Returns: tool_answer (summary)
+    """
+    print("Using Wikipedia...")
+    # Create the Wikipedia utility
+    wiki = WikipediaAPIWrapper(
+        lang="en",        # Wikipedia language
+        top_k_results=1,  # how many results to fetch
+        doc_content_chars_max=2000
+    )
+
+    # Make a tool from it
+    wiki_tool = WikipediaQueryRun(api_wrapper=wiki)
+
+    wiki_answer = wiki_tool.run(" ".join(state["main_parts"]))
+    state['tool_answer'] = wiki_answer
+    return state
+
+
+def execute_code(state: InfoState) -> InfoState :
+    """Tool to calculate any math using python code or get current date time."""
+    print("Execut Code...")
+    python_tool = PythonREPLTool()
+    code = state["answer_code"]
+    state["tool_answer"]=python_tool.run(code)
+    return state
+
+def get_code(state:InfoState) -> InfoState:
+    """From prompt get the code to run."""
+    print("Getting Code (Gemini)...")
+    prompt = (
+    f"You are a strict code generator. "
+    f"Given the question: '{state['question']}', "
+    f"return ONLY valid Python code that computes the answer IF the question is about math, date, or time. "
+    f"Otherwise, return exactly: print('not valid')\n\n"
+    f"Rules:\n"
+    f"- Output ONLY the code or print('not valid')\n"
+    f"- No explanations, no markdown, no extra text\n"
+    f"- No quotes around the code\n"
+    f"- Use print() to show the result\n"
+    f"- Import modules only if needed (e.g. datetime, math)"
+)
+
+    # 2️⃣ Call Gemini
+    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
+    response = model.invoke([HumanMessage(content=prompt)]).content.strip()
+
+    state["answer_code"] = response
+    
+
+    return state
+
+
+
+def preprocess_text(state: dict) -> InfoState: 
+    
+    """
+    Preprocess text to get the keywords to help get results directly from wikipedia.
+    
+    Input: raw question
+    Output: main_parts (list of keywords)
+    """
+    print("Preprocess text (Gemini)...")
+    # 1️⃣ Prepare the prompt
+    prompt = (
+    "Extract the most important content words (nouns, proper names, key concepts) from this question that would help find the best-matching Wikipedia article. "
+    "If the question is not in English; translate key terms to English for Wikipedia's English edition. "
+    "Ignore stopwords (like 'who', 'is', 'the', 'of', 'in', 'current', 'what'), filler words, and typos. "
+    "Focus on entities and topics that exist as Wikipedia page titles. "
+    "Correct obvious spelling mistakes and expand common abbreviations if needed for better Wikipedia matching.\n\n"
+    "Question: '" + state["question"] + "'\n\n"
+    "Output ONLY a valid JSON list of 1–4 corrected, title-cased strings (e.g. [\"President of the United States\", \"Joe Biden\"]). "
+    "No explanations, no markdown, no extra text, no quotes around words, no trailing commas."
+    )
+
+    # 2️⃣ Call Gemini
+    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
+    response = model.invoke([HumanMessage(content=prompt)]).content.strip()
+
+    # 3️⃣ Try to safely parse
+    try:
+        # First, try JSON
+        state["main_parts"] = json.loads(response)
+    except json.JSONDecodeError:
+        try:
+            # If not JSON, try Python literal
+            state["main_parts"] = ast.literal_eval(response)
+        except Exception:
+            # If both fail, store fallback info
+            print("⚠️ Model returned invalid content:", response)
+            state["main_parts"] = []
+
+    return state
+
+
+
+
+def get_answer(state: InfoState) -> InfoState :
+    """
+    Final Node that returns the final answer organized.
+    
+    Combines: tool_answer → final_answer
+    """
+    print("Getting Answer (Gemini)...")
+
+    prompt = (
+        "Answer the question based on the context below. "
+        #"If the question cannot be answered using the information provided, answer with 'I don't know'. "
+        "Question: " + state["question"] +
+        "\nContext: " + str(state.get("tool_answer")) +
+        "\nRewrite answer in clearer, simple way."
+    )
+    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
+    state["final_answer"] = (model.invoke([HumanMessage(content=prompt)]).content)
+
+    return state
+
+def get_type(state: InfoState) -> InfoState:
+    """Choose which tool to use based on question type (WIKI, SEARCH, CODE)."""
+    print("Getting Type (Gemini)...")
+
+    prompt = "According to the Question " +state["question"] + " Select the best tool to answer WIKI if it's informatative or science question, WebInfo if it need up to data news,MATH if math or date or time  You're very serious,just give one word from given"
+    model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
+    state["answer_type"] = (model.invoke([HumanMessage(content=prompt)]).content)
+
+    return state
+
+
+
+
+def get_search_results(state: InfoState) -> InfoState:
+    """Tool to search web for results using DuckDuckGo."""
+    print("Searching...")
+
+    search = DuckDuckGoSearchRun()
+
+    # Simple text result
+    state['tool_answer'] = search.run(state["question"])   #" " .join(state["main_parts"]))
+
+    return state
+
+
+def route(state: InfoState):
+    print(state["answer_type"])
+    return state["answer_type"]
+
+
+
+################# Graph ################
+def get_graph():
+        graph = StateGraph(InfoState)
+
+        # Add nodes
+        graph.add_node("get_wiki_relate", get_wiki_relate)
+        graph.add_node("preprocess_text", preprocess_text)
+        graph.add_node("get_answer", get_answer)
+        graph.add_node("get_type", get_type)
+        graph.add_node("get_search_results", get_search_results)
+        graph.add_node("execute_code", execute_code)
+        graph.add_node("get_code", get_code)
+
+        # Add edges
+        graph.add_edge(START, "preprocess_text")
+        graph.add_edge("preprocess_text", "get_type")
+
+
+        # Add conditional edges
+        graph.add_conditional_edges(
+            "get_type",
+            route,
+            {
+                "WebInfo": "get_search_results",
+                "WIKI": "get_wiki_relate",
+                "MATH": "get_code"
+            }
+        )
+
+        # Add final edges
+        graph.add_edge("get_search_results", "get_answer")  
+        graph.add_edge("get_wiki_relate", "get_answer")
+
+        graph.add_edge("get_code", "execute_code")
+        graph.add_edge("execute_code", "get_answer")
+
+        graph.add_edge("get_answer", END)
+
+
+        # Compile the graph
+        compiled_graph = graph.compile()
+        return compiled_graph
+
+def ask(compiled_graph,question):
+        legitimate_result = compiled_graph.invoke({
+            "question": question,
+
+        })
+
+        return legitimate_result['final_answer']

app.py

@@ -1,196 +1,197 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")  
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
-
-    # 3. Run your Agent
-    results_log = []
-    answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
-        except Exception as e:
-             print(f"Error running agent on task {task_id}: {e}")
-             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission 
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **Instructions:**
-
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
-        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
-        ---
-        **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
+import os
+import gradio as gr
+import requests
+import inspect
+import pandas as pd
+from agent import get_graph,ask
+# (Keep Constants as is)
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Basic Agent Definition ---
+# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+class BasicAgent:
+    def __init__(self):
+        print("BasicAgent initialized.")  
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        compiled_graph = get_graph()
+        fixed_answer = ask(compiled_graph,question)
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
+
+def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
+
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+             print("Fetched questions list is empty.")
+             return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         print(f"Response text: {response.text[:500]}")
+         return f"Error decoding server response for questions: {e}", None
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
+
+    # 3. Run your Agent
+    results_log = []
+    answers_payload = []
+    print(f"Running agent on {len(questions_data)} questions...")
+    for item in questions_data:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
+            continue
+        try:
+            submitted_answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+        except Exception as e:
+             print(f"Error running agent on task {task_id}: {e}")
+             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
+    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
+    print(status_update)
+
+    # 5. Submit
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"Submission Successful!\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
+            f"Message: {result_data.get('message', 'No message received.')}"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.HTTPError as e:
+        error_detail = f"Server responded with status {e.response.status_code}."
+        try:
+            error_json = e.response.json()
+            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
+        except requests.exceptions.JSONDecodeError:
+            error_detail += f" Response: {e.response.text[:500]}"
+        status_message = f"Submission Failed: {error_detail}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.Timeout:
+        status_message = "Submission Failed: The request timed out."
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except requests.exceptions.RequestException as e:
+        status_message = f"Submission Failed: Network error - {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+    except Exception as e:
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface using Blocks ---
+with gr.Blocks() as demo:
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("Run Evaluation & Submit All Answers")
+
+    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
+    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
+    space_host_startup = os.getenv("SPACE_HOST")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
+
+    if space_host_startup:
+        print(f"✅ SPACE_HOST found: {space_host_startup}")
+        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
+
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
+        print(f"✅ SPACE_ID found: {space_id_startup}")
+        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
+        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
+    else:
+        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
+
+    print("-"*(60 + len(" App Starting ")) + "\n")
+
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)

requirements.txt

@@ -1,2 +1,13 @@
-gradio
-requests
+gradio
+requests
+langchain==0.2.16 
+langchain-core==0.2.38 
+langchain-openai==0.1.22 
+google-generativeai
+langchain-google-genai==1.0.10 
+langchain-experimental==0.0.65 
+langchain-community==0.2.16 
+langgraph==0.1.19 
+ddgs
+duckduckgo-search==5.3.1
+wikipedia