--- a
+++ b/aiagents4pharma/talk2cells/agents/scp_agent.py
@@ -0,0 +1,85 @@
+#/usr/bin/env python3
+
+'''
+This is the agent file for the Talk2Cells graph.
+'''
+
+import logging
+import os
+from langchain_openai import ChatOpenAI
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import create_react_agent, ToolNode
+from ..tools.scp_agent.search_studies import search_studies
+from ..tools.scp_agent.display_studies import display_studies
+from ..states.state_talk2cells import Talk2Cells
+
+# Initialize logger
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def get_app(uniq_id):
+    '''
+    This function returns the langraph app.
+    '''
+    def agent_scp_node(state: Talk2Cells):
+        '''
+        This function calls the model.
+        '''
+        logger.log(logging.INFO, "Creating SCP_Agent node with thread_id %s", uniq_id)
+        # Get the messages from the state
+        # messages = state['messages']
+        # Call the model
+        # inputs = {'messages': messages}
+        response = model.invoke(state, {"configurable": {"thread_id": uniq_id}})
+        # The response is a list of messages and may contain `tool calls`
+        # We return a list, because this will get added to the existing list
+        # return {"messages": [response]}
+        return response
+
+    # Define the tools
+    # tools = [search_studies, display_studies]
+    tools = ToolNode([search_studies, display_studies])
+
+    # Create the LLM
+    # And bind the tools to it
+    # model = ChatOpenAI(model="gpt-4o-mini", temperature=0).bind_tools(tools)
+
+    # Create an environment variable to store the LLM model
+    # Check if the environment variable AIAGENTS4PHARMA_LLM_MODEL is set
+    # If not, set it to 'gpt-4o-mini'
+    llm_model = os.getenv('AIAGENTS4PHARMA_LLM_MODEL', 'gpt-4o-mini')
+    # print (f'LLM model: {llm_model}')
+    # llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+    llm = ChatOpenAI(model=llm_model, temperature=0)
+    model = create_react_agent(
+                            llm,
+                            tools=tools,
+                            state_schema=Talk2Cells,
+                            state_modifier=(
+                                            "You are Talk2Cells agent."
+                                            ),
+                            checkpointer=MemorySaver()
+                        )
+
+    # Define a new graph
+    workflow = StateGraph(Talk2Cells)
+
+    # Define the two nodes we will cycle between
+    workflow.add_node("agent_scp", agent_scp_node)
+
+    # Set the entrypoint as `agent`
+    # This means that this node is the first one called
+    workflow.add_edge(START, "agent_scp")
+
+    # Initialize memory to persist state between graph runs
+    checkpointer = MemorySaver()
+
+    # Finally, we compile it!
+    # This compiles it into a LangChain Runnable,
+    # meaning you can use it as you would any other runnable.
+    # Note that we're (optionally) passing the memory when compiling the graph
+    app = workflow.compile(checkpointer=checkpointer)
+    logger.log(logging.INFO, "Compiled the graph")
+
+    return app