mirror of
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532 lines
21 KiB
Python
532 lines
21 KiB
Python
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"""
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Integration tests for Agent Manager (ReAct Pattern) Service
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These tests verify the end-to-end functionality of the Agent Manager service,
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testing the ReAct pattern (Think-Act-Observe), tool coordination, multi-step reasoning,
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and conversation state management.
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Following the TEST_STRATEGY.md approach for integration testing.
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"""
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import pytest
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import json
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from unittest.mock import AsyncMock, MagicMock, patch
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from trustgraph.agent.react.agent_manager import AgentManager
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from trustgraph.agent.react.tools import KnowledgeQueryImpl, TextCompletionImpl, McpToolImpl
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from trustgraph.agent.react.types import Action, Final, Tool, Argument
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from trustgraph.schema import AgentRequest, AgentResponse, AgentStep, Error
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@pytest.mark.integration
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class TestAgentManagerIntegration:
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"""Integration tests for Agent Manager ReAct pattern coordination"""
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@pytest.fixture
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def mock_flow_context(self):
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"""Mock flow context for service coordination"""
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context = MagicMock()
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# Mock prompt client
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prompt_client = AsyncMock()
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prompt_client.agent_react.return_value = {
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"thought": "I need to search for information about machine learning",
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"action": "knowledge_query",
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"arguments": {"question": "What is machine learning?"}
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}
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# Mock graph RAG client
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graph_rag_client = AsyncMock()
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graph_rag_client.rag.return_value = "Machine learning is a subset of AI that enables computers to learn from data."
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# Mock text completion client
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text_completion_client = AsyncMock()
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text_completion_client.question.return_value = "Machine learning involves algorithms that improve through experience."
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# Mock MCP tool client
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mcp_tool_client = AsyncMock()
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mcp_tool_client.invoke.return_value = "Tool execution successful"
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# Configure context to return appropriate clients
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def context_router(service_name):
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if service_name == "prompt-request":
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return prompt_client
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elif service_name == "graph-rag-request":
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return graph_rag_client
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elif service_name == "prompt-request":
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return text_completion_client
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elif service_name == "mcp-tool-request":
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return mcp_tool_client
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else:
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return AsyncMock()
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context.side_effect = context_router
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return context
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@pytest.fixture
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def sample_tools(self):
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"""Sample tool configuration for testing"""
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return {
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"knowledge_query": Tool(
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name="knowledge_query",
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description="Query the knowledge graph for information",
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arguments={
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"question": Argument(
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name="question",
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type="string",
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description="The question to ask the knowledge graph"
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)
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},
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implementation=KnowledgeQueryImpl,
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config={}
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),
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"text_completion": Tool(
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name="text_completion",
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description="Generate text completion using LLM",
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arguments={
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"question": Argument(
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name="question",
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type="string",
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description="The question to ask the LLM"
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)
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},
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implementation=TextCompletionImpl,
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config={}
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),
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"web_search": Tool(
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name="web_search",
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description="Search the web for information",
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arguments={
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"query": Argument(
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name="query",
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type="string",
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description="The search query"
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)
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},
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implementation=lambda context: AsyncMock(invoke=AsyncMock(return_value="Web search results")),
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config={}
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)
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}
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@pytest.fixture
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def agent_manager(self, sample_tools):
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"""Create agent manager with sample tools"""
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return AgentManager(
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tools=sample_tools,
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additional_context="You are a helpful AI assistant with access to knowledge and tools."
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)
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@pytest.mark.asyncio
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async def test_agent_manager_reasoning_cycle(self, agent_manager, mock_flow_context):
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"""Test basic reasoning cycle with tool selection"""
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# Arrange
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question = "What is machine learning?"
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history = []
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# Act
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action = await agent_manager.reason(question, history, mock_flow_context)
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# Assert
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assert isinstance(action, Action)
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assert action.thought == "I need to search for information about machine learning"
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assert action.name == "knowledge_query"
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assert action.arguments == {"question": "What is machine learning?"}
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assert action.observation == ""
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# Verify prompt client was called correctly
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prompt_client = mock_flow_context("prompt-request")
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prompt_client.agent_react.assert_called_once()
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# Verify the prompt variables passed to agent_react
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call_args = prompt_client.agent_react.call_args
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variables = call_args[0][0]
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assert variables["question"] == question
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assert len(variables["tools"]) == 3 # knowledge_query, text_completion, web_search
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assert variables["context"] == "You are a helpful AI assistant with access to knowledge and tools."
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@pytest.mark.asyncio
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async def test_agent_manager_final_answer(self, agent_manager, mock_flow_context):
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"""Test agent manager returning final answer"""
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# Arrange
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mock_flow_context("prompt-request").agent_react.return_value = {
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"thought": "I have enough information to answer the question",
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"final-answer": "Machine learning is a field of AI that enables computers to learn from data."
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}
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question = "What is machine learning?"
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history = []
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# Act
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action = await agent_manager.reason(question, history, mock_flow_context)
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# Assert
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assert isinstance(action, Final)
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assert action.thought == "I have enough information to answer the question"
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assert action.final == "Machine learning is a field of AI that enables computers to learn from data."
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@pytest.mark.asyncio
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async def test_agent_manager_react_with_tool_execution(self, agent_manager, mock_flow_context):
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"""Test full ReAct cycle with tool execution"""
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# Arrange
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question = "What is machine learning?"
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history = []
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think_callback = AsyncMock()
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observe_callback = AsyncMock()
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# Act
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action = await agent_manager.react(question, history, think_callback, observe_callback, mock_flow_context)
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# Assert
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assert isinstance(action, Action)
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assert action.thought == "I need to search for information about machine learning"
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assert action.name == "knowledge_query"
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assert action.arguments == {"question": "What is machine learning?"}
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assert action.observation == "Machine learning is a subset of AI that enables computers to learn from data."
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# Verify callbacks were called
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think_callback.assert_called_once_with("I need to search for information about machine learning")
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observe_callback.assert_called_once_with("Machine learning is a subset of AI that enables computers to learn from data.")
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# Verify tool was executed
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graph_rag_client = mock_flow_context("graph-rag-request")
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graph_rag_client.rag.assert_called_once_with("What is machine learning?")
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@pytest.mark.asyncio
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async def test_agent_manager_react_with_final_answer(self, agent_manager, mock_flow_context):
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"""Test ReAct cycle ending with final answer"""
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# Arrange
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mock_flow_context("prompt-request").agent_react.return_value = {
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"thought": "I can provide a direct answer",
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"final-answer": "Machine learning is a branch of artificial intelligence."
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}
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question = "What is machine learning?"
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history = []
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think_callback = AsyncMock()
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observe_callback = AsyncMock()
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# Act
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action = await agent_manager.react(question, history, think_callback, observe_callback, mock_flow_context)
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# Assert
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assert isinstance(action, Final)
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assert action.thought == "I can provide a direct answer"
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assert action.final == "Machine learning is a branch of artificial intelligence."
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# Verify only think callback was called (no observation for final answer)
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think_callback.assert_called_once_with("I can provide a direct answer")
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observe_callback.assert_not_called()
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@pytest.mark.asyncio
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async def test_agent_manager_with_conversation_history(self, agent_manager, mock_flow_context):
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"""Test agent manager with conversation history"""
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# Arrange
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question = "Can you tell me more about neural networks?"
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history = [
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Action(
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thought="I need to search for information about machine learning",
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name="knowledge_query",
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arguments={"question": "What is machine learning?"},
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observation="Machine learning is a subset of AI that enables computers to learn from data."
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)
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]
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# Act
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action = await agent_manager.reason(question, history, mock_flow_context)
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# Assert
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assert isinstance(action, Action)
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# Verify history was included in prompt variables
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prompt_client = mock_flow_context("prompt-request")
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call_args = prompt_client.agent_react.call_args
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variables = call_args[0][0]
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assert len(variables["history"]) == 1
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assert variables["history"][0]["thought"] == "I need to search for information about machine learning"
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assert variables["history"][0]["action"] == "knowledge_query"
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assert variables["history"][0]["observation"] == "Machine learning is a subset of AI that enables computers to learn from data."
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@pytest.mark.asyncio
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async def test_agent_manager_tool_selection(self, agent_manager, mock_flow_context):
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"""Test agent manager selecting different tools"""
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# Test different tool selections
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tool_scenarios = [
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("knowledge_query", "graph-rag-request"),
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("text_completion", "prompt-request"),
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]
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for tool_name, expected_service in tool_scenarios:
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# Arrange
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mock_flow_context("prompt-request").agent_react.return_value = {
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"thought": f"I need to use {tool_name}",
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"action": tool_name,
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"arguments": {"question": "test question"}
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}
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think_callback = AsyncMock()
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observe_callback = AsyncMock()
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# Act
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action = await agent_manager.react("test question", [], think_callback, observe_callback, mock_flow_context)
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# Assert
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assert isinstance(action, Action)
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assert action.name == tool_name
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# Verify correct service was called
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if tool_name == "knowledge_query":
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mock_flow_context("graph-rag-request").rag.assert_called()
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elif tool_name == "text_completion":
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mock_flow_context("prompt-request").question.assert_called()
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# Reset mocks for next iteration
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for service in ["prompt-request", "graph-rag-request", "prompt-request"]:
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mock_flow_context(service).reset_mock()
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@pytest.mark.asyncio
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async def test_agent_manager_unknown_tool_error(self, agent_manager, mock_flow_context):
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"""Test agent manager error handling for unknown tool"""
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# Arrange
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mock_flow_context("prompt-request").agent_react.return_value = {
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"thought": "I need to use an unknown tool",
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"action": "unknown_tool",
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"arguments": {"param": "value"}
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}
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think_callback = AsyncMock()
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observe_callback = AsyncMock()
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# Act & Assert
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with pytest.raises(RuntimeError) as exc_info:
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await agent_manager.react("test question", [], think_callback, observe_callback, mock_flow_context)
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assert "No action for unknown_tool!" in str(exc_info.value)
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@pytest.mark.asyncio
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async def test_agent_manager_tool_execution_error(self, agent_manager, mock_flow_context):
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"""Test agent manager handling tool execution errors"""
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# Arrange
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mock_flow_context("graph-rag-request").rag.side_effect = Exception("Tool execution failed")
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think_callback = AsyncMock()
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observe_callback = AsyncMock()
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# Act & Assert
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with pytest.raises(Exception) as exc_info:
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await agent_manager.react("test question", [], think_callback, observe_callback, mock_flow_context)
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assert "Tool execution failed" in str(exc_info.value)
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@pytest.mark.asyncio
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async def test_agent_manager_multiple_tools_coordination(self, agent_manager, mock_flow_context):
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"""Test agent manager coordination with multiple available tools"""
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# Arrange
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question = "Find information about AI and summarize it"
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# Mock multi-step reasoning
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mock_flow_context("prompt-request").agent_react.return_value = {
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"thought": "I need to search for AI information first",
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"action": "knowledge_query",
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"arguments": {"question": "What is artificial intelligence?"}
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}
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# Act
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action = await agent_manager.reason(question, [], mock_flow_context)
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# Assert
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assert isinstance(action, Action)
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assert action.name == "knowledge_query"
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# Verify tool information was passed to prompt
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prompt_client = mock_flow_context("prompt-request")
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call_args = prompt_client.agent_react.call_args
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variables = call_args[0][0]
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# Should have all 3 tools available
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tool_names = [tool["name"] for tool in variables["tools"]]
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assert "knowledge_query" in tool_names
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assert "text_completion" in tool_names
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assert "web_search" in tool_names
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@pytest.mark.asyncio
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async def test_agent_manager_tool_argument_validation(self, agent_manager, mock_flow_context):
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"""Test agent manager with various tool argument patterns"""
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# Arrange
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test_cases = [
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{
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"action": "knowledge_query",
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"arguments": {"question": "What is deep learning?"},
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"expected_service": "graph-rag-request"
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},
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{
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"action": "text_completion",
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"arguments": {"question": "Explain neural networks"},
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"expected_service": "prompt-request"
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},
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{
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"action": "web_search",
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"arguments": {"query": "latest AI research"},
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"expected_service": None # Custom mock
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}
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]
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for test_case in test_cases:
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||
|
|
# Arrange
|
||
|
|
mock_flow_context("prompt-request").agent_react.return_value = {
|
||
|
|
"thought": f"Using {test_case['action']}",
|
||
|
|
"action": test_case['action'],
|
||
|
|
"arguments": test_case['arguments']
|
||
|
|
}
|
||
|
|
|
||
|
|
think_callback = AsyncMock()
|
||
|
|
observe_callback = AsyncMock()
|
||
|
|
|
||
|
|
# Act
|
||
|
|
action = await agent_manager.react("test", [], think_callback, observe_callback, mock_flow_context)
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
assert isinstance(action, Action)
|
||
|
|
assert action.name == test_case['action']
|
||
|
|
assert action.arguments == test_case['arguments']
|
||
|
|
|
||
|
|
# Reset mocks
|
||
|
|
for service in ["prompt-request", "graph-rag-request", "prompt-request"]:
|
||
|
|
mock_flow_context(service).reset_mock()
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
async def test_agent_manager_context_integration(self, agent_manager, mock_flow_context):
|
||
|
|
"""Test agent manager integration with additional context"""
|
||
|
|
# Arrange
|
||
|
|
agent_with_context = AgentManager(
|
||
|
|
tools={"knowledge_query": agent_manager.tools["knowledge_query"]},
|
||
|
|
additional_context="You are an expert in machine learning research."
|
||
|
|
)
|
||
|
|
|
||
|
|
question = "What are the latest developments in AI?"
|
||
|
|
|
||
|
|
# Act
|
||
|
|
action = await agent_with_context.reason(question, [], mock_flow_context)
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
prompt_client = mock_flow_context("prompt-request")
|
||
|
|
call_args = prompt_client.agent_react.call_args
|
||
|
|
variables = call_args[0][0]
|
||
|
|
|
||
|
|
assert variables["context"] == "You are an expert in machine learning research."
|
||
|
|
assert variables["question"] == question
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
async def test_agent_manager_empty_tools(self, mock_flow_context):
|
||
|
|
"""Test agent manager with no tools available"""
|
||
|
|
# Arrange
|
||
|
|
agent_no_tools = AgentManager(tools={}, additional_context="")
|
||
|
|
|
||
|
|
question = "What is machine learning?"
|
||
|
|
|
||
|
|
# Act
|
||
|
|
action = await agent_no_tools.reason(question, [], mock_flow_context)
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
prompt_client = mock_flow_context("prompt-request")
|
||
|
|
call_args = prompt_client.agent_react.call_args
|
||
|
|
variables = call_args[0][0]
|
||
|
|
|
||
|
|
assert len(variables["tools"]) == 0
|
||
|
|
assert variables["tool_names"] == ""
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
async def test_agent_manager_tool_response_processing(self, agent_manager, mock_flow_context):
|
||
|
|
"""Test agent manager processing different tool response types"""
|
||
|
|
# Arrange
|
||
|
|
response_scenarios = [
|
||
|
|
"Simple text response",
|
||
|
|
"Multi-line response\nwith several lines\nof information",
|
||
|
|
"Response with special characters: @#$%^&*()_+-=[]{}|;':\",./<>?",
|
||
|
|
" Response with whitespace ",
|
||
|
|
"" # Empty response
|
||
|
|
]
|
||
|
|
|
||
|
|
for expected_response in response_scenarios:
|
||
|
|
# Set up mock response
|
||
|
|
mock_flow_context("graph-rag-request").rag.return_value = expected_response
|
||
|
|
|
||
|
|
think_callback = AsyncMock()
|
||
|
|
observe_callback = AsyncMock()
|
||
|
|
|
||
|
|
# Act
|
||
|
|
action = await agent_manager.react("test question", [], think_callback, observe_callback, mock_flow_context)
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
assert isinstance(action, Action)
|
||
|
|
assert action.observation == expected_response.strip()
|
||
|
|
observe_callback.assert_called_with(expected_response.strip())
|
||
|
|
|
||
|
|
# Reset mocks
|
||
|
|
mock_flow_context("graph-rag-request").reset_mock()
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
@pytest.mark.slow
|
||
|
|
async def test_agent_manager_performance_with_large_history(self, agent_manager, mock_flow_context):
|
||
|
|
"""Test agent manager performance with large conversation history"""
|
||
|
|
# Arrange
|
||
|
|
large_history = [
|
||
|
|
Action(
|
||
|
|
thought=f"Step {i} thinking",
|
||
|
|
name="knowledge_query",
|
||
|
|
arguments={"question": f"Question {i}"},
|
||
|
|
observation=f"Observation {i}"
|
||
|
|
)
|
||
|
|
for i in range(50) # Large history
|
||
|
|
]
|
||
|
|
|
||
|
|
question = "Final question"
|
||
|
|
|
||
|
|
# Act
|
||
|
|
import time
|
||
|
|
start_time = time.time()
|
||
|
|
|
||
|
|
action = await agent_manager.reason(question, large_history, mock_flow_context)
|
||
|
|
|
||
|
|
end_time = time.time()
|
||
|
|
execution_time = end_time - start_time
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
assert isinstance(action, Action)
|
||
|
|
assert execution_time < 5.0 # Should complete within reasonable time
|
||
|
|
|
||
|
|
# Verify history was processed correctly
|
||
|
|
prompt_client = mock_flow_context("prompt-request")
|
||
|
|
call_args = prompt_client.agent_react.call_args
|
||
|
|
variables = call_args[0][0]
|
||
|
|
assert len(variables["history"]) == 50
|
||
|
|
|
||
|
|
@pytest.mark.asyncio
|
||
|
|
async def test_agent_manager_json_serialization(self, agent_manager, mock_flow_context):
|
||
|
|
"""Test agent manager handling of JSON serialization in prompts"""
|
||
|
|
# Arrange
|
||
|
|
complex_history = [
|
||
|
|
Action(
|
||
|
|
thought="Complex thinking with special characters: \"quotes\", 'apostrophes', and symbols",
|
||
|
|
name="knowledge_query",
|
||
|
|
arguments={"question": "What about JSON serialization?", "complex": {"nested": "value"}},
|
||
|
|
observation="Response with JSON: {\"key\": \"value\"}"
|
||
|
|
)
|
||
|
|
]
|
||
|
|
|
||
|
|
question = "Handle JSON properly"
|
||
|
|
|
||
|
|
# Act
|
||
|
|
action = await agent_manager.reason(question, complex_history, mock_flow_context)
|
||
|
|
|
||
|
|
# Assert
|
||
|
|
assert isinstance(action, Action)
|
||
|
|
|
||
|
|
# Verify JSON was properly serialized in prompt
|
||
|
|
prompt_client = mock_flow_context("prompt-request")
|
||
|
|
call_args = prompt_client.agent_react.call_args
|
||
|
|
variables = call_args[0][0]
|
||
|
|
|
||
|
|
# Should not raise JSON serialization errors
|
||
|
|
json_str = json.dumps(variables, indent=4)
|
||
|
|
assert len(json_str) > 0
|