Fix test mock to use 'context' key instead of 'content' key

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/agent.py

@@ -1,3 +1,4 @@
+import logging
 import re
 import shutil
 import subprocess
@@ -5,6 +6,8 @@ from typing import Any, Dict, List, Literal, Optional, Sequence, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
+logger = logging.getLogger(__name__)
+
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
@@ -207,6 +210,27 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
+        # Check if the task has knowledge first
+        if hasattr(task, 'knowledge') and task.knowledge:
+            """
+            Knowledge is queried in the following priority order:
+            1. Task-specific knowledge
+            2. Agent's knowledge
+            3. Crew's knowledge
+            This ensures the most specific context is considered first.
+            """
+            try:
+                task_knowledge_snippets = task.knowledge.query([task.prompt()])
+                if task_knowledge_snippets:
+                    task_knowledge_context = extract_knowledge_context(
+                        task_knowledge_snippets
+                    )
+                    if task_knowledge_context:
+                        task_prompt += task_knowledge_context
+            except Exception as e:
+                logger.warning(f"Error querying task knowledge: {str(e)}")
+
+        # Then check agent's knowledge
         if self.knowledge:
             agent_knowledge_snippets = self.knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
@@ -216,6 +240,7 @@ class Agent(BaseAgent):
                 if agent_knowledge_context:
                     task_prompt += agent_knowledge_context
 
+        # Finally check crew's knowledge
         if self.crew:
             knowledge_snippets = self.crew.query_knowledge([task.prompt()])
             if knowledge_snippets:

src/crewai/knowledge/__init__.py

@@ -0,0 +1,7 @@
+"""
+Knowledge management module for CrewAI.
+Provides functionality for managing and querying knowledge sources.
+"""
+from crewai.knowledge.knowledge import Knowledge
+
+__all__ = ["Knowledge"]

src/crewai/task.py

@@ -32,6 +32,7 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.knowledge import Knowledge
 from crewai.tasks.guardrail_result import GuardrailResult
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
@@ -144,6 +145,10 @@ class Task(BaseModel):
     end_time: Optional[datetime.datetime] = Field(
         default=None, description="End time of the task execution"
     )
+    knowledge: Optional[Knowledge] = Field(
+        default=None,
+        description="Knowledge sources for the task. This knowledge will be used by the agent when executing the task.",
+    )
 
     @field_validator("guardrail")
     @classmethod
@@ -215,6 +220,24 @@ class Task(BaseModel):
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
 
+    @field_validator("knowledge")
+    @classmethod
+    def validate_knowledge(cls, knowledge):
+        """Validate that the knowledge field is an instance of Knowledge class.
+        
+        Args:
+            knowledge: The knowledge to validate. Can be None or an instance of Knowledge.
+            
+        Returns:
+            The validated knowledge object, or None if no knowledge was provided.
+            
+        Raises:
+            ValueError: If the knowledge is not an instance of Knowledge class.
+        """
+        if knowledge is not None and not isinstance(knowledge, Knowledge):
+            raise ValueError("Knowledge must be an instance of Knowledge class")
+        return knowledge
+        
     @field_validator("output_file")
     @classmethod
     def output_file_validation(cls, value: Optional[str]) -> Optional[str]:

tests/agent_test.py

@@ -10,6 +10,7 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserException
+from crewai.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
@@ -1661,6 +1662,99 @@ def test_agent_with_knowledge_sources_works_with_copy():
             assert isinstance(agent_copy.llm, LLM)
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_uses_task_knowledge():
+    """Test that an agent uses the knowledge provided in the task."""
+    # Create a knowledge source with specific content
+    content = "The capital of France is Paris. The Eiffel Tower is located in Paris."
+    
+    # Create a mock Knowledge object
+    with patch("crewai.knowledge.Knowledge", autospec=True) as MockKnowledge:
+        try:
+            # Configure the mock
+            mock_knowledge = MockKnowledge.return_value
+            mock_knowledge.query.return_value = [{"context": content}]
+            
+            # Create a real LLM but patch its call method
+            agent = Agent(
+                role="Geography Teacher",
+                goal="Provide accurate geographic information",
+                backstory="You are a geography expert who teaches students about world capitals.",
+                llm=LLM(model="gpt-4o-mini"),
+            )
+            
+            # Create a task with knowledge
+            task = Task(
+                description="What is the capital of France?",
+                expected_output="The capital of France.",
+                agent=agent,
+                knowledge=mock_knowledge,
+            )
+            
+            # Mock the agent's execute_task method to avoid actual LLM calls
+            with patch.object(agent.llm, "call") as mock_llm_call:
+                mock_llm_call.return_value = "The capital of France is Paris, where the Eiffel Tower is located."
+                
+                # Execute the task
+                result = agent.execute_task(task)
+                
+                # Assert that the agent provides the correct information
+                assert "paris" in result.lower()
+                assert "eiffel tower" in result.lower()
+                
+                # Verify that the task's knowledge was queried
+                mock_knowledge.query.assert_called_once()
+                
+                # The query should include the task prompt
+                query_arg = mock_knowledge.query.call_args[0][0]
+                assert isinstance(query_arg, list)
+                assert "capital of france" in query_arg[0].lower()
+        finally:
+            MockKnowledge.reset_mock()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_agent_with_empty_task_knowledge():
+    """Test that an agent handles empty task knowledge gracefully."""
+    # Create a mock Knowledge object
+    with patch("crewai.knowledge.Knowledge", autospec=True) as MockKnowledge:
+        try:
+            # Configure the mock to return empty results
+            mock_knowledge = MockKnowledge.return_value
+            mock_knowledge.query.return_value = []
+            
+            # Create a real LLM but patch its call method
+            agent = Agent(
+                role="Geography Teacher",
+                goal="Provide accurate geographic information",
+                backstory="You are a geography expert who teaches students about world capitals.",
+                llm=LLM(model="gpt-4o-mini"),
+            )
+            
+            # Create a task with empty knowledge
+            task = Task(
+                description="What is the capital of France?",
+                expected_output="The capital of France.",
+                agent=agent,
+                knowledge=mock_knowledge,
+            )
+            
+            # Mock the agent's execute_task method to avoid actual LLM calls
+            with patch.object(agent.llm, "call") as mock_llm_call:
+                mock_llm_call.return_value = "The capital of France is Paris."
+                
+                # Execute the task
+                result = agent.execute_task(task)
+                
+                # Assert that the agent still provides a response
+                assert "paris" in result.lower()
+                
+                # Verify that the task's knowledge was queried
+                mock_knowledge.query.assert_called_once()
+        finally:
+            MockKnowledge.reset_mock()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_litellm_auth_error_handling():
     """Test that LiteLLM authentication errors are handled correctly and not retried."""

tests/cassettes/test_agent_uses_task_knowledge.yaml

@@ -0,0 +1,77 @@
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