--- a
+++ b/aiagents4pharma/talk2biomodels/tests/test_integration.py
@@ -0,0 +1,128 @@
+'''
+Test cases for Talk2Biomodels.
+'''
+
+import pandas as pd
+from langchain_core.messages import HumanMessage, ToolMessage
+from langchain_openai import ChatOpenAI
+from ..agents.t2b_agent import get_app
+
+LLM_MODEL = ChatOpenAI(model='gpt-4o-mini', temperature=0)
+
+def test_integration():
+    '''
+    Test the integration of the tools.
+    '''
+    unique_id = 1234567
+    app = get_app(unique_id, llm_model=LLM_MODEL)
+    config = {"configurable": {"thread_id": unique_id}}
+    # ##########################################
+    # ## Test simulate_model tool
+    # ##########################################
+    prompt = '''Simulate the model BIOMD0000000537 for 100 hours and time intervals
+    100 with an initial concentration of `DoseQ2W` set to 300 and `Dose`
+    set to 0. Reset the concentration of `Ab{serum}` to 100 every 25 hours.'''
+    # Test the tool get_modelinfo
+    response = app.invoke(
+                        {"messages": [HumanMessage(content=prompt)]},
+                        config=config
+                    )
+    assistant_msg = response["messages"][-1].content
+    print (assistant_msg)
+    # Check if the assistant message is a string
+    assert isinstance(assistant_msg, str)
+    ##########################################
+    # Test ask_question tool when simulation
+    # results are available
+    ##########################################
+    # Update state
+    app.update_state(config, {"llm_model": LLM_MODEL})
+    prompt = """What is the concentration of CRP in serum after 100 hours?
+    Round off the value to 2 decimal places."""
+    # Test the tool get_modelinfo
+    response = app.invoke(
+                        {"messages": [HumanMessage(content=prompt)]},
+                        config=config
+                    )
+    assistant_msg = response["messages"][-1].content
+    # print (assistant_msg)
+    # Check if the assistant message is a string
+    assert '211' in assistant_msg
+
+    ##########################################
+    # Test the custom_plotter tool when the
+    # simulation results are available but
+    # the species is not available
+    ##########################################
+    prompt = """Call the custom_plotter tool to make a plot
+        showing only species 'Infected cases'. Let me
+        know if these species were not found. Do not
+        invoke any other tool."""
+    # Update state
+    app.update_state(config, {"llm_model": LLM_MODEL}
+                    )
+    # Test the tool get_modelinfo
+    response = app.invoke(
+                        {"messages": [HumanMessage(content=prompt)]},
+                        config=config
+                    )
+    assistant_msg = response["messages"][-1].content
+    current_state = app.get_state(config)
+    # Get the messages from the current state
+    # and reverse the order
+    reversed_messages = current_state.values["messages"][::-1]
+    # Loop through the reversed messages until a
+    # ToolMessage is found.
+    predicted_artifact = []
+    for msg in reversed_messages:
+        if isinstance(msg, ToolMessage):
+            # Work on the message if it is a ToolMessage
+            # These may contain additional visuals that
+            # need to be displayed to the user.
+            if msg.name == "custom_plotter":
+                predicted_artifact = msg.artifact
+                break
+    # Check if the the predicted artifact is `None`
+    assert predicted_artifact is None
+
+    ##########################################
+    # Test custom_plotter tool when the
+    # simulation results are available
+    ##########################################
+    prompt = "Plot only CRP related species."
+
+    # Update state
+    app.update_state(config, {"llm_model": LLM_MODEL}
+                    )
+    # Test the tool get_modelinfo
+    response = app.invoke(
+                        {"messages": [HumanMessage(content=prompt)]},
+                        config=config
+                    )
+    assistant_msg = response["messages"][-1].content
+    current_state = app.get_state(config)
+    # Get the messages from the current state
+    # and reverse the order
+    reversed_messages = current_state.values["messages"][::-1]
+    # Loop through the reversed messages
+    # until a ToolMessage is found.
+    expected_header = ['Time', 'CRP{serum}', 'CRPExtracellular']
+    expected_header += ['CRP Suppression (%)', 'CRP (% of baseline)']
+    expected_header += ['CRP{liver}']
+    predicted_artifact = []
+    for msg in reversed_messages:
+        if isinstance(msg, ToolMessage):
+            # Work on the message if it is a ToolMessage
+            # These may contain additional visuals that
+            # need to be displayed to the user.
+            if msg.name == "custom_plotter":
+                predicted_artifact = msg.artifact['dic_data']
+                break
+    # Convert the artifact into a pandas dataframe
+    # for easy comparison
+    df = pd.DataFrame(predicted_artifact)
+    # Extract the headers from the dataframe
+    predicted_header = df.columns.tolist()
+    # Check if the header is in the expected_header
+    # assert expected_header in predicted_artifact
+    assert set(expected_header).issubset(set(predicted_header))