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liteagent supports apps and mcps (#3794)
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* liteagent supports apps and mcps * generated cassettes for these
This commit is contained in:
@@ -1186,6 +1186,15 @@ class Agent(BaseAgent):
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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if self.apps:
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platform_tools = self.get_platform_tools(self.apps)
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if platform_tools:
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self.tools.extend(platform_tools)
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if self.mcps:
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mcps = self.get_mcp_tools(self.mcps)
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if mcps:
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self.tools.extend(mcps)
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lite_agent = LiteAgent(
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id=self.id,
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role=self.role,
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@@ -591,3 +591,81 @@ def test_lite_agent_with_invalid_llm():
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llm="invalid-model",
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)
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assert "Expected LLM instance of type BaseLLM" in str(exc_info.value)
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@patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
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@patch("crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get")
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_kickoff_with_platform_tools(mock_get):
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"""Test that Agent.kickoff() properly integrates platform tools with LiteAgent"""
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mock_response = Mock()
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mock_response.raise_for_status.return_value = None
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mock_response.json.return_value = {
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"actions": {
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"github": [
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{
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"name": "create_issue",
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"description": "Create a GitHub issue",
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"parameters": {
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"type": "object",
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"properties": {
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"title": {"type": "string", "description": "Issue title"},
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"body": {"type": "string", "description": "Issue body"},
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},
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"required": ["title"],
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},
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}
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]
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}
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}
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mock_get.return_value = mock_response
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agent = Agent(
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role="Test Agent",
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goal="Test goal",
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backstory="Test backstory",
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llm=LLM(model="gpt-3.5-turbo"),
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apps=["github"],
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verbose=True
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)
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result = agent.kickoff("Create a GitHub issue")
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assert isinstance(result, LiteAgentOutput)
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assert result.raw is not None
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@patch.dict("os.environ", {"EXA_API_KEY": "test_exa_key"})
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@patch("crewai.agent.Agent._get_external_mcp_tools")
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_agent_kickoff_with_mcp_tools(mock_get_mcp_tools):
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"""Test that Agent.kickoff() properly integrates MCP tools with LiteAgent"""
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# Setup mock MCP tools - create a proper BaseTool instance
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class MockMCPTool(BaseTool):
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name: str = "exa_search"
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description: str = "Search the web using Exa"
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def _run(self, query: str) -> str:
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return f"Mock search results for: {query}"
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mock_get_mcp_tools.return_value = [MockMCPTool()]
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# Create agent with MCP servers
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agent = Agent(
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role="Test Agent",
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goal="Test goal",
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backstory="Test backstory",
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llm=LLM(model="gpt-3.5-turbo"),
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mcps=["https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research"],
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verbose=True
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)
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# Execute kickoff
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result = agent.kickoff("Search for information about AI")
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# Verify the result is a LiteAgentOutput
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assert isinstance(result, LiteAgentOutput)
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assert result.raw is not None
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# Verify MCP tools were retrieved
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mock_get_mcp_tools.assert_called_once_with("https://mcp.exa.ai/mcp?api_key=test_exa_key&profile=research")
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and should NEVER make up tools that are not listed here:\n\nTool Name: exa_search\nTool
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Arguments: {''query'': {''description'': None, ''type'': ''str''}}\nTool Description:
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Search the web using Exa\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
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you should always think about what to do\nAction: the action to take, only one
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name of [exa_search], just the name, exactly as it''s written.\nAction Input:
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the input to the action, just a simple JSON object, enclosed in curly braces,
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using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce
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all necessary information is gathered, return the following format:\n\n```\nThought:
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I now know the final answer\nFinal Answer: the final answer to the original
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|
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|
||||
Search the web using Exa\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
|
||||
you should always think about what to do\nAction: the action to take, only one
|
||||
name of [exa_search], just the name, exactly as it''s written.\nAction Input:
|
||||
the input to the action, just a simple JSON object, enclosed in curly braces,
|
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|
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