Added multi agent tests (#13)

* Added multi agent tests

* Added changelog

* Added change to make tests backwards compatible with new httpx_mock version

* Update test_simple_agent.py

* Update test_simple_chat_agent.py

* Update test_tools.py

* Added changelog

* Reformatted with proper ruff version

* code review

* new ruff version

* Code review

* Code review

* Code review

CHANGELOG.md

@@ -26,6 +26,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 - Update readme
+- Extra multi agent unit tests
 - Extra unit tests for dependencies.py
 
 ### Removed

pyproject.toml

@@ -104,6 +104,7 @@ filterwarnings = [
     "ignore:Degrees of freedom:RuntimeWarning",
     "ignore:Exception ignored in:pytest.PytestUnraisableExceptionWarning",
     "ignore:This API is in beta:langchain_core._api.beta_decorator.LangChainBetaWarning",
+    "ignore:The configuration option 'asyncio_default_fixture_loop_scope' is unset."
 ]
 
 addopts = "--cov=src/ --cov=tests/ -v --cov-report=term-missing --durations=20 --no-cov-on-fail"

tests/conftest.py

@@ -109,6 +109,9 @@ class FakeFuntionChatModel(GenericFakeChatModel):
         def bind_tools(self, functions: list):
             return self
 
+        def bind_functions(self, **kwargs):
+            return self
+
     # If you need another fake response to use different tools,
     # you can do in your test
     # ```python

tests/multi_agents/test_supervisor_multi_agent.py

@@ -0,0 +1,82 @@
+from unittest.mock import AsyncMock, MagicMock, Mock
+
+import pytest
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from neuroagent.multi_agents.supervisor_multi_agent import AgentState
+from src.neuroagent.multi_agents import SupervisorMultiAgent
+
+
+def test_create_main_agent_initialization():
+    mock_llm = Mock()
+    bind_function_result = MagicMock()
+    bind_function_result.__ror__.return_value = {}
+    mock_llm.bind_functions.return_value = bind_function_result
+    data = {"llm": mock_llm, "agents": [("agent1",)]}
+
+    result = SupervisorMultiAgent.create_main_agent(data)
+    assert "main_agent" in result
+    assert "summarizer" in result
+
+
+@pytest.mark.asyncio
+async def test_agent_node():
+    mock_message = HumanMessage(
+        content="hello",
+        name="test_agent",
+    )
+
+    async def mock_ainvoke(_):
+        return {"messages": [mock_message]}
+
+    agent_state = Mock()
+    agent = Mock()
+    agent.ainvoke = mock_ainvoke
+
+    agent_node_test = await SupervisorMultiAgent.agent_node(
+        agent_state, agent, "test_agent"
+    )
+
+    assert isinstance(agent_node_test, dict)
+    assert "messages" in agent_node_test
+    assert len(agent_node_test["messages"]) == 1
+    assert agent_node_test["messages"][0].content == "hello"
+    assert agent_node_test["messages"][0].name == "test_agent"
+
+
+@pytest.mark.asyncio
+async def test_summarizer_node(fake_llm_with_tools):
+    fake_state = AgentState(
+        messages=[
+            HumanMessage(
+                content="What is the airspeed velocity of an unladen swallow?"
+            ),
+            SystemMessage(content="11 m/s"),
+        ]
+    )
+
+    mock_llm, _, _ = await anext(fake_llm_with_tools)
+    agent = SupervisorMultiAgent(agents=[("agent1", [])], llm=mock_llm)
+
+    mock_message = SystemMessage(
+        content="hello",
+        name="test_agent",
+    )
+
+    mock_summarizer = Mock()
+    mock_summarizer.ainvoke = AsyncMock()
+    mock_summarizer.ainvoke.return_value = mock_message
+    agent.summarizer = mock_summarizer
+    result = await agent.summarizer_node(fake_state)
+    assert result["messages"][0].content == "hello"
+
+
+@pytest.mark.asyncio
+async def test_create_graph(fake_llm_with_tools):
+    mock_llm, _, _ = await anext(fake_llm_with_tools)
+    agent = SupervisorMultiAgent(agents=[("agent1", [])], llm=mock_llm)
+    result = agent.create_graph()
+    nodes = result.nodes
+    assert "agent1" in nodes
+    assert "Supervisor" in nodes
+    assert "Summarizer" in nodes