create agent

agent

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+import os
+
+import requests
+from dotenv import load_dotenv
+from langchain_core.messages import SystemMessage, HumanMessage
+from langgraph.constants import END
+from langgraph.prebuilt import ToolNode
+
+from prompts import AGENT_SYSTEM_PROMPT, build_question_prompt
+from state import AgentState
+from tools import wikipedia_tool, arxiv_tool, get_current_year, ddg_search_tool
+from langchain_groq import ChatGroq
+
+from langgraph.graph import StateGraph
+
+load_dotenv()
+HF_TOKEN = os.getenv("HF_TOKEN")
+HF_USERNAME = os.getenv("HF_USERNAME")
+RECURSION_LIMIT = 50
+AGENT_CODE_URL = os.getenv("AGENT_CODE_URL", "https://huggingface.co/spaces/manasajanj/Final_Assignment_Template/tree/main")
+llm = ChatGroq(model="openai/gpt-oss-safeguard-20b")
+tools_list = [wikipedia_tool, ddg_search_tool, arxiv_tool, get_current_year]
+llm_with_tools = llm.bind_tools(tools_list)
+
+
+def agent_node(state:AgentState) -> dict:
+    messages = state["messages"]
+    system_message = SystemMessage(content=AGENT_SYSTEM_PROMPT)
+    has_system_message = any(isinstance(m, SystemMessage) for m in messages)
+    if not has_system_message:
+        messages = [system_message]+messages
+    response = llm_with_tools.invoke(messages)
+    return {"messages": [response]}
+def finish_node(state:AgentState) -> dict:
+    last_message = state["messages"][-1]
+    return {"answer": last_message.content.strip()}
+def should_continue(state: AgentState) -> str:
+    last = state["messages"][-1]
+    if hasattr(last, "tool_calls") and last.tool_calls:
+        return "tools"
+    return "end"
+def build_graph() -> StateGraph:
+    graph = StateGraph(AgentState)
+    graph.add_node("agent", agent_node)
+    graph.add_node("tools", ToolNode(tools_list))
+    graph.add_node("finish", finish_node)
+
+    graph.set_entry_point("agent")
+    graph.add_conditional_edges("agent", should_continue, {"tools":"tools", "end":"finish"})
+
+    graph.add_edge("tools", "agent") # go back to agent after tools
+
+    graph.add_edge("finish", END)
+    return graph.compile()
+app = build_graph()
+
+def solve_question(task: dict) -> str:
+    question = task["question"]
+    file_name = task.get("file_name") or ""
+    user_content = build_question_prompt(
+        question=question,
+        file_name=file_name)
+    initial_state: AgentState = AgentState(
+                    messages = [HumanMessage(content=user_content)],
+                    task_id = task["task_id"],
+                    question = question,
+                    file_name=file_name or None,
+                    answer=None,
+    )
+
+    result = app.invoke(initial_state, config={"recursion_limit":RECURSION_LIMIT})
+    return result['answer']
+
+
+
+
+