--- a
+++ b/aiagents4pharma/talk2scholars/agents/pdf_agent.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""
+Agent for interacting with PDF documents via question and answer.
+
+This module initializes and compiles a LangGraph application that enables users to query PDF
+documents using a question_and_answer tool. It integrates a language model and follows
+the ReAct pattern to process and answer queries related to PDF content.
+
+Usage:
+    >>> app = get_app("unique_thread_id")
+    >>> response = app.invoke(initial_state)
+"""
+
+import logging
+import hydra
+from langchain_core.language_models.chat_models import BaseChatModel
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import create_react_agent, ToolNode
+from langgraph.checkpoint.memory import MemorySaver
+from ..state.state_talk2scholars import Talk2Scholars
+from ..tools.pdf.question_and_answer import question_and_answer_tool
+from ..tools.s2.query_results import query_results
+
+# Initialize logger
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def get_app(uniq_id, llm_model: BaseChatModel):
+    """
+    Initializes and returns the LangGraph application for the PDF agent.
+
+    This function sets up the PDF agent by loading configuration settings via Hydra,
+    initializing a model, and creating a workflow graph that incorporates
+    PDF-specific tools. The agent is built using the ReAct pattern to facilitate interactive
+    querying and processing of PDF documents.
+
+    Args:
+        uniq_id (str): A unique identifier for the current conversation session or thread.
+        llm_model (BaseChatModel, optional): The language model instance to be used.
+            Defaults to ChatOpenAI(model="gpt-4o-mini", temperature=0).
+
+    Returns:
+        StateGraph: A compiled LangGraph application capable of handling PDF interactions.
+
+    Example:
+        >>> app = get_app("thread_123")
+        >>> result = app.invoke(initial_state)
+    """
+    # Load configuration using Hydra.
+    with hydra.initialize(version_base=None, config_path="../configs"):
+        cfg = hydra.compose(
+            config_name="config",
+            overrides=["agents/talk2scholars/pdf_agent=default"],
+        )
+        cfg = cfg.agents.talk2scholars.pdf_agent
+        logger.info("Loaded pdf_agent configuration.")
+
+    def agent_pdf_node(state: Talk2Scholars):
+        """
+        Processes the current state by invoking the language model for PDF question and answer.
+
+        Args:
+            state (Talk2Scholars): The current conversation state containing query details
+            and context.
+
+        Returns:
+            Any: The response generated by the language model after processing the state.
+        """
+        logger.info("Creating Agent_PDF node with thread_id %s", uniq_id)
+        response = model.invoke(state, {"configurable": {"thread_id": uniq_id}})
+        return response
+
+    # Define the tool node that includes the PDF QnA tool.
+    tools = ToolNode([question_and_answer_tool, query_results])
+
+    logger.info("Using OpenAI model %s", llm_model)
+
+    # Create the agent using the provided BaseChatModel instance.
+    model = create_react_agent(
+        llm_model,
+        tools=tools,
+        state_schema=Talk2Scholars,
+        prompt=cfg.pdf_agent,
+        checkpointer=MemorySaver(),
+    )
+
+    # Define a new workflow graph with the state schema.
+    workflow = StateGraph(Talk2Scholars)
+    workflow.add_node("agent_pdf", agent_pdf_node)
+    workflow.add_edge(START, "agent_pdf")
+
+    # Initialize memory to persist state between runs.
+    checkpointer = MemorySaver()
+
+    # Compile the graph into a runnable app.
+    app = workflow.compile(checkpointer=checkpointer, name="agent_pdf")
+    logger.info("Compiled the PDF agent graph.")
+
+    return app