Fix import order in litellm_update_test.py

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

docs/concepts/knowledge.mdx

@@ -42,16 +42,6 @@ CrewAI supports various types of knowledge sources out of the box:
 | `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
 | `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
 
-
-<Tip>
-Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
-Simply add the relevant knowledge sources your agent or crew needs to function.
-
-Knowledge sources can be added at the agent or crew level.
-Crew level knowledge sources will be used by **all agents** in the crew.
-Agent level knowledge sources will be used by the **specific agent** that is preloaded with the knowledge.
-</Tip>
-
 ## Quickstart Example
 
 <Tip>
@@ -156,26 +146,6 @@ result = crew.kickoff(
 )
 ```
 
-## Knowledge Configuration
-
-You can configure the knowledge configuration for the crew or agent.
-
-```python Code
-from crewai.knowledge.knowledge_config import KnowledgeConfig
-
-knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
-
-agent = Agent(
-    ...
-    knowledge_config=knowledge_config
-)
-```
-
-<Tip>
-  `results_limit`: is the number of relevant documents to return. Default is 3.
-  `score_threshold`: is the minimum score for a document to be considered relevant. Default is 0.35.
-</Tip>
-
 ## More Examples
 
 Here are examples of how to use different types of knowledge sources:

docs/examples/jinja_templating.md

@@ -1,133 +0,0 @@
-# Enhanced Templating with Jinja2
-
-CrewAI now supports enhanced templating using Jinja2, while maintaining compatibility with the existing templating system.
-
-## Basic Usage
-
-The basic templating syntax remains the same:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Define inputs
-inputs = {
-    "topic": "Artificial Intelligence",
-    "year": 2024,
-    "count": 5
-}
-
-# Create an agent with template variables
-researcher = Agent(
-    role="{topic} Researcher",
-    goal="Research the latest developments in {topic} for {year}",
-    backstory="You're an expert in {topic} with years of experience"
-)
-
-# Create a task with template variables
-research_task = Task(
-    description="Research {topic} and provide {count} key insights",
-    expected_output="A list of {count} key insights about {topic} in {year}",
-    agent=researcher
-)
-
-# Create a crew and pass inputs
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    inputs=inputs
-)
-
-# Run the crew
-result = crew.kickoff()
-```
-
-## Advanced Features
-
-The new templating system adds support for container types, object attributes, conditional statements, loops, and filters:
-
-### Container Types
-
-```python
-inputs = {
-    "topics": ["AI", "Machine Learning", "Data Science"],
-    "details": {"main_theme": "Technology Trends", "subtopics": ["Ethics", "Applications"]}
-}
-
-# Access list items
-task = Task(
-    description="Research {{topics[0]}} and {{topics[1]}}",
-    expected_output="Analysis of the topics"
-)
-
-# Access dictionary items
-task = Task(
-    description="Research {{details.main_theme}} with focus on {{details.subtopics[0]}}",
-    expected_output="Detailed analysis"
-)
-```
-
-### Conditional Statements
-
-```python
-inputs = {
-    "topic": "AI",
-    "priority": "high",
-    "deadline": "2024-12-31"
-}
-
-task = Task(
-    description="{% if priority == 'high' %}URGENT: {% endif %}Research {topic}{% if deadline %} by {{deadline}}{% endif %}",
-    expected_output="A report on {topic}"
-)
-```
-
-### Loop Statements
-
-```python
-inputs = {
-    "topics": ["AI", "Machine Learning", "Data Science"]
-}
-
-task = Task(
-    description="Research the following topics: {% for topic in topics %}{{topic}}{% if not loop.last %}, {% endif %}{% endfor %}",
-    expected_output="A report covering multiple topics"
-)
-```
-
-### Filters
-
-```python
-from datetime import datetime
-
-inputs = {
-    "topic": "AI",
-    "date": datetime.now()
-}
-
-task = Task(
-    description="Research {topic} as of {{date|date('%Y-%m-%d')}}",
-    expected_output="A report on {topic}"
-)
-```
-
-### Custom Objects
-
-```python
-from pydantic import BaseModel
-
-class Person(BaseModel):
-    name: str
-    age: int
-    
-    def __str__(self):
-        return f"{self.name} ({self.age})"
-
-inputs = {
-    "author": Person(name="John Doe", age=35)
-}
-
-task = Task(
-    description="Write a report authored by {author}",
-    expected_output="A report by {{author.name}}"
-)
-```

pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.60.2",
+    "litellm==1.66.3",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",

src/crewai/agent.py

@@ -114,14 +114,6 @@ class Agent(BaseAgent):
         default=None,
         description="Embedder configuration for the agent.",
     )
-    agent_knowledge_context: Optional[str] = Field(
-        default=None,
-        description="Knowledge context for the agent.",
-    )
-    crew_knowledge_context: Optional[str] = Field(
-        default=None,
-        description="Knowledge context for the crew.",
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -242,30 +234,22 @@ class Agent(BaseAgent):
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
-        knowledge_config = (
-            self.knowledge_config.model_dump() if self.knowledge_config else {}
-        )
+
         if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query(
-                [task.prompt()], **knowledge_config
-            )
+            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
-                self.agent_knowledge_context = extract_knowledge_context(
+                agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
                 )
-                if self.agent_knowledge_context:
-                    task_prompt += self.agent_knowledge_context
+                if agent_knowledge_context:
+                    task_prompt += agent_knowledge_context
 
         if self.crew:
-            knowledge_snippets = self.crew.query_knowledge(
-                [task.prompt()], **knowledge_config
-            )
+            knowledge_snippets = self.crew.query_knowledge([task.prompt()])
             if knowledge_snippets:
-                self.crew_knowledge_context = extract_knowledge_context(
-                    knowledge_snippets
-                )
-                if self.crew_knowledge_context:
-                    task_prompt += self.crew_knowledge_context
+                crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
+                if crew_knowledge_context:
+                    task_prompt += crew_knowledge_context
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)

src/crewai/agents/agent_builder/base_agent.py

@@ -19,7 +19,6 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
@@ -156,10 +155,6 @@ class BaseAgent(ABC, BaseModel):
     adapted_agent: bool = Field(
         default=False, description="Whether the agent is adapted"
     )
-    knowledge_config: Optional[KnowledgeConfig] = Field(
-        default=None,
-        description="Knowledge configuration for the agent such as limits and threshold",
-    )
 
     @model_validator(mode="before")
     @classmethod

src/crewai/crew.py

@@ -304,7 +304,9 @@ class Crew(BaseModel):
         """Initialize private memory attributes."""
         self._external_memory = (
             # External memory doesn’t support a default value since it was designed to be managed entirely externally
-            self.external_memory.set_crew(self) if self.external_memory else None
+            self.external_memory.set_crew(self)
+            if self.external_memory
+            else None
         )
 
         self._long_term_memory = self.long_term_memory
@@ -1134,13 +1136,9 @@ class Crew(BaseModel):
         result = self._execute_tasks(self.tasks, start_index, True)
         return result
 
-    def query_knowledge(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> Union[List[Dict[str, Any]], None]:
+    def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
         if self.knowledge:
-            return self.knowledge.query(
-                query, results_limit=results_limit, score_threshold=score_threshold
-            )
+            return self.knowledge.query(query)
         return None
 
     def fetch_inputs(self) -> Set[str]:
@@ -1222,13 +1220,9 @@ class Crew(BaseModel):
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
         if self.short_term_memory:
-            copied_data["short_term_memory"] = self.short_term_memory.model_copy(
-                deep=True
-            )
+            copied_data["short_term_memory"] = self.short_term_memory.model_copy(deep=True)
         if self.long_term_memory:
-            copied_data["long_term_memory"] = self.long_term_memory.model_copy(
-                deep=True
-            )
+            copied_data["long_term_memory"] = self.long_term_memory.model_copy(deep=True)
         if self.entity_memory:
             copied_data["entity_memory"] = self.entity_memory.model_copy(deep=True)
         if self.external_memory:
@@ -1236,6 +1230,7 @@ class Crew(BaseModel):
         if self.user_memory:
             copied_data["user_memory"] = self.user_memory.model_copy(deep=True)
 
+
         copied_data.pop("agents", None)
         copied_data.pop("tasks", None)
 
@@ -1408,10 +1403,7 @@ class Crew(BaseModel):
             "short": (getattr(self, "_short_term_memory", None), "short term"),
             "entity": (getattr(self, "_entity_memory", None), "entity"),
             "knowledge": (getattr(self, "knowledge", None), "knowledge"),
-            "kickoff_outputs": (
-                getattr(self, "_task_output_handler", None),
-                "task output",
-            ),
+            "kickoff_outputs": (getattr(self, "_task_output_handler", None), "task output"),
             "external": (getattr(self, "_external_memory", None), "external"),
         }
 

src/crewai/knowledge/knowledge.py

@@ -43,9 +43,7 @@ class Knowledge(BaseModel):
         self.storage.initialize_knowledge_storage()
         self._add_sources()
 
-    def query(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> List[Dict[str, Any]]:
+    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -58,8 +56,7 @@ class Knowledge(BaseModel):
 
         results = self.storage.search(
             query,
-            limit=results_limit,
-            score_threshold=score_threshold,
+            limit,
         )
         return results
 

src/crewai/knowledge/knowledge_config.py

@@ -1,16 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class KnowledgeConfig(BaseModel):
-    """Configuration for knowledge retrieval.
-
-    Args:
-        results_limit (int): The number of relevant documents to return.
-        score_threshold (float): The minimum score for a document to be considered relevant.
-    """
-
-    results_limit: int = Field(default=3, description="The number of results to return")
-    score_threshold: float = Field(
-        default=0.35,
-        description="The minimum score for a result to be considered relevant",
-    )

src/crewai/knowledge/storage/knowledge_storage.py

@@ -4,7 +4,7 @@ import io
 import logging
 import os
 import shutil
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union, cast
 
 import chromadb
 import chromadb.errors

src/crewai/task.py

@@ -485,19 +485,6 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-    def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]) -> None:
-        """Interpolate inputs into the task description, expected output, and output file path.
-
-        Args:
-            inputs: Dictionary mapping template variables to their values.
-               Supported value types are strings, integers, floats, dicts, lists,
-               and other objects with string representation.
-
-        Raises:
-            ValueError: If a required template variable is missing from inputs.
-        """
-        self.interpolate_inputs_and_add_conversation_history(inputs)
-        
     def interpolate_inputs_and_add_conversation_history(
         self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]
     ) -> None:
@@ -506,8 +493,7 @@ class Task(BaseModel):
 
         Args:
             inputs: Dictionary mapping template variables to their values.
-                   Supported value types are strings, integers, floats, dicts, lists,
-                   and other objects with string representation.
+                   Supported value types are strings, integers, and floats.
 
         Raises:
             ValueError: If a required template variable is missing from inputs.
@@ -522,65 +508,23 @@ class Task(BaseModel):
         if not inputs:
             return
 
-        # Check for complex indexing patterns like {topics[0]} in the description
-        has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", self._original_description)
-        
-        if has_complex_indexing:
-            complex_patterns = re.findall(r"\{([A-Za-z_][A-Za-z0-9_]*)\[([0-9]+)\]\}", self._original_description)
-            result = self._original_description
-            
-            for var_name, index in complex_patterns:
-                if var_name in inputs and isinstance(inputs[var_name], list):
-                    try:
-                        idx = int(index)
-                        list_value = inputs[var_name]
-                        if isinstance(list_value, list) and 0 <= idx < len(list_value):
-                            placeholder = f"{{{var_name}[{index}]}}"
-                            value = str(list_value[idx])
-                            result = result.replace(placeholder, value)
-                    except (ValueError, IndexError):
-                        pass
-            
-            self.description = result
-        else:
-            try:
-                self.description = interpolate_only(
-                    input_string=self._original_description, inputs=inputs
-                )
-            except KeyError as e:
-                raise ValueError(
-                    f"Missing required template variable '{e.args[0]}' in description"
-                ) from e
-            except ValueError as e:
-                raise ValueError(f"Error interpolating description: {str(e)}") from e
+        try:
+            self.description = interpolate_only(
+                input_string=self._original_description, inputs=inputs
+            )
+        except KeyError as e:
+            raise ValueError(
+                f"Missing required template variable '{e.args[0]}' in description"
+            ) from e
+        except ValueError as e:
+            raise ValueError(f"Error interpolating description: {str(e)}") from e
 
-        # Check for complex indexing patterns in the expected output
-        has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", self._original_expected_output)
-        
-        if has_complex_indexing:
-            complex_patterns = re.findall(r"\{([A-Za-z_][A-Za-z0-9_]*)\[([0-9]+)\]\}", self._original_expected_output)
-            result = self._original_expected_output
-            
-            for var_name, index in complex_patterns:
-                if var_name in inputs and isinstance(inputs[var_name], list):
-                    try:
-                        idx = int(index)
-                        list_value = inputs[var_name]
-                        if isinstance(list_value, list) and 0 <= idx < len(list_value):
-                            placeholder = f"{{{var_name}[{index}]}}"
-                            value = str(list_value[idx])
-                            result = result.replace(placeholder, value)
-                    except (ValueError, IndexError):
-                        pass
-            
-            self.expected_output = result
-        else:
-            try:
-                self.expected_output = interpolate_only(
-                    input_string=self._original_expected_output, inputs=inputs
-                )
-            except (KeyError, ValueError) as e:
-                raise ValueError(f"Error interpolating expected_output: {str(e)}") from e
+        try:
+            self.expected_output = interpolate_only(
+                input_string=self._original_expected_output, inputs=inputs
+            )
+        except (KeyError, ValueError) as e:
+            raise ValueError(f"Error interpolating expected_output: {str(e)}") from e
 
         if self.output_file is not None:
             try:

src/crewai/utilities/jinja_templating.py

@@ -1,98 +0,0 @@
-import re
-from datetime import datetime
-from typing import Any, Dict, List, Optional, Union
-
-import jinja2
-
-
-def to_jinja_template(input_string: str) -> str:
-    """
-    Convert CrewAI-style {var} templates to Jinja2-style {{var}} templates.
-    
-    This function preserves existing Jinja2 syntax if present and only converts
-    CrewAI-style variables.
-    
-    Args:
-        input_string: String containing CrewAI-style templates.
-        
-    Returns:
-        String with CrewAI-style templates converted to Jinja2 syntax.
-    """
-    if not input_string or ("{" not in input_string and "}" not in input_string):
-        return input_string
-        
-    pattern = r'(?<!\{)\{([A-Za-z_][A-Za-z0-9_]*)\}(?!\})'
-    
-    return re.sub(pattern, r'{{\1}}', input_string)
-
-def render_template(
-    input_string: Optional[str],
-    inputs: Dict[str, Any],
-) -> str:
-    """
-    Render a template string using Jinja2 with the provided inputs.
-    
-    This function supports:
-    - Container types (List, Dict, Set)
-    - Standard objects (datetime, time)
-    - Custom objects
-    - Conditional and loop statements
-    - Filtering options
-    
-    Args:
-        input_string: The string containing template variables to interpolate.
-                     Can be None or empty, in which case an empty string is returned.
-        inputs: Dictionary mapping template variables to their values.
-               Supports all types of values.
-               
-    Returns:
-        The rendered template string.
-        
-    Raises:
-        ValueError: If inputs dictionary is empty when interpolating variables.
-        jinja2.exceptions.TemplateError: If there's an error in the template syntax.
-        KeyError: If a required template variable is missing from inputs.
-    """
-    if input_string is None or not input_string:
-        return ""
-        
-    if "{" not in input_string and "}" not in input_string:
-        return input_string
-        
-    if not inputs:
-        raise ValueError("Inputs dictionary cannot be empty when interpolating variables")
-        
-    jinja_template = to_jinja_template(input_string)
-    
-    # Create a custom undefined class that allows loop variables
-    class LoopUndefined(jinja2.StrictUndefined):
-        """Custom undefined class that allows loop variables."""
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            
-        def __str__(self):
-            if self._undefined_name in ('loop', 'item', 'topic'):
-                return ''
-            return super().__str__()
-            
-        def __getattr__(self, name):
-            if self._undefined_name in ('loop', 'item', 'topic'):
-                return self
-            return super().__getattr__(name)
-    
-    env = jinja2.Environment(
-        undefined=LoopUndefined,  # Use custom undefined class for loop variables
-        autoescape=True  # Enable autoescaping for security
-    )
-    
-    env.filters['date'] = lambda d, format='%Y-%m-%d': d.strftime(format) if isinstance(d, datetime) else str(d)
-    
-    template = env.from_string(jinja_template)
-    
-    try:
-        return template.render(**inputs)
-    except jinja2.exceptions.UndefinedError as e:
-        var_name = str(e).split("'")[1] if "'" in str(e) else None
-        if var_name:
-            raise KeyError(f"Template variable '{var_name}' not found in inputs dictionary")
-        raise KeyError(f"Missing required template variable: {str(e)}")

src/crewai/utilities/string_utils.py

@@ -1,39 +1,31 @@
 import re
-from datetime import datetime
 from typing import Any, Dict, List, Optional, Union
 
-from crewai.utilities.jinja_templating import render_template
-
 
 def interpolate_only(
     input_string: Optional[str],
-    inputs: Dict[str, Any],
+    inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
 ) -> str:
     """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
     Only interpolates placeholders that follow the pattern {variable_name} where
     variable_name starts with a letter/underscore and contains only letters, numbers, and underscores.
-    
-    This function now supports advanced Jinja2 templating features:
-    - Container types (List, Dict, Set)
-    - Standard objects (datetime, time)
-    - Custom objects
-    - Conditional and loop statements
-    - Filtering options
 
     Args:
         input_string: The string containing template variables to interpolate.
                      Can be None or empty, in which case an empty string is returned.
         inputs: Dictionary mapping template variables to their values.
-               Supports all types of values including complex objects.
+               Supported value types are strings, integers, floats, and dicts/lists
+               containing only these types and other nested dicts/lists.
 
     Returns:
         The interpolated string with all template variables replaced with their values.
         Empty string if input_string is None or empty.
 
     Raises:
-        ValueError: If inputs dictionary is empty when interpolating variables.
-        KeyError: If a required template variable is missing from inputs.
+        ValueError: If a value contains unsupported types or a template variable is missing
     """
+
+    # Validation function for recursive type checking
     def validate_type(value: Any) -> None:
         if value is None:
             return
@@ -43,21 +35,12 @@ def interpolate_only(
             for item in value.values() if isinstance(value, dict) else value:
                 validate_type(item)
             return
-        if isinstance(value, datetime):
-            return
-        # Check if it's a Pydantic model or other known custom type
-        try:
-            from pydantic import BaseModel
-            if isinstance(value, BaseModel):
-                return
-        except ImportError:
-            pass
-            
         raise ValueError(
             f"Unsupported type {type(value).__name__} in inputs. "
-            "Only str, int, float, bool, dict, list, datetime, and custom objects are allowed."
+            "Only str, int, float, bool, dict, and list are allowed."
         )
 
+    # Validate all input values
     for key, value in inputs.items():
         try:
             validate_type(value)
@@ -73,13 +56,6 @@ def interpolate_only(
             "Inputs dictionary cannot be empty when interpolating variables"
         )
 
-    # Check if the template contains Jinja2 syntax ({% ... %} or {{ ... }})
-    has_jinja_syntax = "{{" in input_string or "{%" in input_string
-    has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", input_string)
-    
-    if has_jinja_syntax or has_complex_indexing:
-        return render_template(input_string, inputs)
-    
     # The regex pattern to find valid variable placeholders
     # Matches {variable_name} where variable_name starts with a letter/underscore
     # and contains only letters, numbers, and underscores
@@ -87,7 +63,8 @@ def interpolate_only(
 
     # Find all matching variables in the input string
     variables = re.findall(pattern, input_string)
-    
+    result = input_string
+
     # Check if all variables exist in inputs
     missing_vars = [var for var in variables if var not in inputs]
     if missing_vars:
@@ -95,10 +72,11 @@ def interpolate_only(
             f"Template variable '{missing_vars[0]}' not found in inputs dictionary"
         )
 
-    result = input_string
+    # Replace each variable with its value
     for var in variables:
         if var in inputs:
             placeholder = "{" + var + "}"
             value = str(inputs[var])
             result = result.replace(placeholder, value)
+
     return result

tests/agent_test.py

@@ -10,8 +10,6 @@ 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 CrewAgentParser, OutputParserException
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
@@ -261,9 +259,7 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (
-        patch.object(CacheHandler, "read") as read,
-    ):
+    with (patch.object(CacheHandler, "read") as read,):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1615,78 +1611,6 @@ def test_agent_with_knowledge_sources():
         assert "red" in result.raw.lower()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-    knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-        with patch.object(Knowledge, "query") as mock_knowledge_query:
-            agent = Agent(
-                role="Information Agent",
-                goal="Provide information based on knowledge sources",
-                backstory="You have access to specific knowledge sources.",
-                llm=LLM(model="gpt-4o-mini"),
-                knowledge_sources=[string_source],
-                knowledge_config=knowledge_config,
-            )
-            task = Task(
-                description="What is Brandon's favorite color?",
-                expected_output="Brandon's favorite color.",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task])
-            crew.kickoff()
-
-            assert agent.knowledge is not None
-            mock_knowledge_query.assert_called_once_with(
-                [task.prompt()],
-                **knowledge_config.model_dump(),
-            )
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-    knowledge_config = KnowledgeConfig()
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-        with patch.object(Knowledge, "query") as mock_knowledge_query:
-            string_source = StringKnowledgeSource(content=content)
-            knowledge_config = KnowledgeConfig()
-            agent = Agent(
-                role="Information Agent",
-                goal="Provide information based on knowledge sources",
-                backstory="You have access to specific knowledge sources.",
-                llm=LLM(model="gpt-4o-mini"),
-                knowledge_sources=[string_source],
-                knowledge_config=knowledge_config,
-            )
-            task = Task(
-                description="What is Brandon's favorite color?",
-                expected_output="Brandon's favorite color.",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task])
-            crew.kickoff()
-
-            assert agent.knowledge is not None
-            mock_knowledge_query.assert_called_once_with(
-                [task.prompt()],
-                **knowledge_config.model_dump(),
-            )
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources_extensive_role():
     content = "Brandon's favorite color is red and he likes Mexican food."

tests/cassettes/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml

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-    headers:
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-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
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-      content-length:
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-      content-type:
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-      host:
-      - api.openai.com
-      user-agent:
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-      x-stainless-async:
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-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.68.2
-      x-stainless-read-timeout:
-      - '600'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
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tests/litellm_update_test.py

@@ -0,0 +1,48 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai.llm import LLM
+
+
+def test_llm_call_with_litellm_1_66_3():
+    """Test that the LLM class works with litellm v1.66.3+"""
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        temperature=0.7,
+        max_tokens=50,
+        stop=["STOP"],
+        presence_penalty=0.1,
+        frequency_penalty=0.1,
+    )
+    messages = [{"role": "user", "content": "Say 'Hello, World!' and then say STOP"}]
+
+    with patch("litellm.completion") as mocked_completion:
+        mock_message = MagicMock()
+        mock_message.content = "Hello, World! I won't say the stop word."
+        mock_choice = MagicMock()
+        mock_choice.message = mock_message
+        mock_response = MagicMock()
+        mock_response.choices = [mock_choice]
+        mock_response.usage = {
+            "prompt_tokens": 10,
+            "completion_tokens": 10,
+            "total_tokens": 20,
+        }
+
+        mocked_completion.return_value = mock_response
+
+        response = llm.call(messages)
+
+        mocked_completion.assert_called_once()
+
+        assert "Hello, World!" in response
+        assert "STOP" not in response
+
+        _, kwargs = mocked_completion.call_args
+        assert kwargs["model"] == "gpt-3.5-turbo"
+        assert kwargs["temperature"] == 0.7
+        assert kwargs["max_tokens"] == 50
+        assert kwargs["stop"] == ["STOP"]
+        assert kwargs["presence_penalty"] == 0.1
+        assert kwargs["frequency_penalty"] == 0.1

tests/test_templating.py

@@ -1,91 +0,0 @@
-import datetime
-from typing import Dict, List
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.task import Task
-
-
-class TestTemplating:
-    def test_task_interpolation(self):
-        task = Task(
-            description="Research about {topic} and provide {count} key points",
-            expected_output="A list of {count} key points about {topic}"
-        )
-        
-        inputs = {"topic": "AI", "count": 5}
-        task.interpolate_inputs(inputs)
-        
-        assert task.description == "Research about AI and provide 5 key points"
-        assert task.expected_output == "A list of 5 key points about AI"
-        
-        task = Task(
-            description="Research about {topics[0]} and {topics[1]}",
-            expected_output="Analysis of {{data.main_theme}}"
-        )
-        
-        inputs = {
-            "topics": ["AI", "Machine Learning"],
-            "data": {"main_theme": "Technology Trends"}
-        }
-        
-        task.interpolate_inputs(inputs)
-        
-        assert task.description == "Research about AI and Machine Learning"
-        assert task.expected_output == "Analysis of Technology Trends"
-    
-    def test_agent_interpolation(self):
-        agent = Agent(
-            role="{industry} Researcher",
-            goal="Research {count} key developments in {industry}",
-            backstory="You are a senior researcher in the {industry} field with {experience} years of experience"
-        )
-        
-        inputs = {"industry": "Healthcare", "count": 5, "experience": 10}
-        agent.interpolate_inputs(inputs)
-        
-        assert agent.role == "Healthcare Researcher"
-        assert agent.goal == "Research 5 key developments in Healthcare"
-        assert agent.backstory == "You are a senior researcher in the Healthcare field with 10 years of experience"
-        
-        agent = Agent(
-            role="{{specialties[0]}} and {{specialties[1]}} Specialist",
-            goal="Analyze trends in {{fields.primary}} sector",
-            backstory="Expert in {{fields.primary}} and {{fields.secondary}}"
-        )
-        
-        inputs = {
-            "specialties": ["AI", "Data Science"],
-            "fields": {"primary": "Healthcare", "secondary": "Finance"}
-        }
-        
-        agent.interpolate_inputs(inputs)
-        
-        assert agent.role == "AI and Data Science Specialist"
-        assert agent.goal == "Analyze trends in Healthcare sector"
-        assert agent.backstory == "Expert in Healthcare and Finance"
-    
-    def test_conditional_templating(self):
-        task = Task(
-            description="{% if priority == 'high' %}URGENT: {% endif %}Research {topic}",
-            expected_output="A report on {topic}"
-        )
-        
-        inputs = {"topic": "AI", "priority": "high"}
-        task.interpolate_inputs(inputs)
-        assert task.description == "URGENT: Research AI"
-        
-        inputs = {"topic": "AI", "priority": "low"}
-        task.interpolate_inputs(inputs)
-        assert task.description == "Research AI"
-    
-    def test_loop_templating(self):
-        task = Task(
-            description="Research the following topics: {% for topic in topics %}{{topic}}{% if not loop.last %}, {% endif %}{% endfor %}",
-            expected_output="A report on multiple topics"
-        )
-        
-        inputs = {"topics": ["AI", "Machine Learning", "Data Science"]}
-        task.interpolate_inputs(inputs)
-        assert task.description == "Research the following topics: AI, Machine Learning, Data Science"

tests/utilities/test_jinja_templating.py

@@ -1,84 +0,0 @@
-import datetime
-from typing import Any, Dict, List
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.utilities.jinja_templating import render_template, to_jinja_template
-
-
-class Person(BaseModel):
-    name: str
-    age: int
-    
-    def __str__(self):
-        return f"{self.name} ({self.age})"
-
-class TestJinjaTemplating:
-    def test_to_jinja_template(self):
-        assert to_jinja_template("Hello {name}!") == "Hello {{name}}!"
-        
-        assert to_jinja_template("Hello {{name}}!") == "Hello {{name}}!"
-        
-        assert to_jinja_template("Hello {name} and {{title}}!") == "Hello {{name}} and {{title}}!"
-        
-        assert to_jinja_template("") == ""
-        
-        assert to_jinja_template("Hello world!") == "Hello world!"
-    
-    def test_render_template_simple_types(self):
-        inputs = {"name": "John", "age": 30, "active": True, "height": 1.85}
-        
-        assert render_template("Hello {name}!", inputs) == "Hello John!"
-        assert render_template("Age: {age}", inputs) == "Age: 30"
-        assert render_template("Active: {active}", inputs) == "Active: True"
-        assert render_template("Height: {height}", inputs) == "Height: 1.85"
-        
-        assert render_template("{name} is {age} years old", inputs) == "John is 30 years old"
-    
-    def test_render_template_container_types(self):
-        inputs = {
-            "items": ["apple", "banana", "orange"],
-            "person": {"name": "John", "age": 30}
-        }
-        
-        assert render_template("First item: {{items[0]}}", inputs) == "First item: apple"
-        
-        assert render_template("Person name: {{person.name}}", inputs) == "Person name: John"
-        
-        assert render_template(
-            "Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}",
-            inputs
-        ) == "Items: apple, banana, orange"
-        
-        assert render_template(
-            "{% if items|length > 2 %}Many items{% else %}Few items{% endif %}",
-            inputs
-        ) == "Many items"
-    
-    def test_render_template_datetime(self):
-        today = datetime.datetime.now()
-        inputs = {"today": today}
-        
-        assert render_template("Today: {{today|date}}", inputs) == f"Today: {today.strftime('%Y-%m-%d')}"
-        
-        assert render_template("Today: {{today|date('%d/%m/%Y')}}", inputs) == f"Today: {today.strftime('%d/%m/%Y')}"
-    
-    def test_render_template_custom_objects(self):
-        person = Person(name="John", age=30)
-        inputs = {"person": person}
-        
-        assert render_template("Person: {person}", inputs) == "Person: John (30)"
-        
-        assert render_template("Person name: {{person.name}}", inputs) == "Person name: John"
-    
-    def test_render_template_error_handling(self):
-        inputs = {"name": "John"}
-        
-        with pytest.raises(KeyError) as excinfo:
-            render_template("Hello {age}!", inputs)
-        assert "Template variable 'age' not found" in str(excinfo.value)
-        
-        with pytest.raises(ValueError) as excinfo:
-            render_template("Hello {name}!", {})
-        assert "Inputs dictionary cannot be empty" in str(excinfo.value)

tests/utilities/test_string_utils.py

@@ -1,8 +1,6 @@
-import datetime
 from typing import Any, Dict, List, Union
 
 import pytest
-from pydantic import BaseModel
 
 from crewai.utilities.string_utils import interpolate_only
 
@@ -187,96 +185,3 @@ class TestInterpolateOnly:
             interpolate_only(template, inputs)
 
         assert "inputs dictionary cannot be empty" in str(excinfo.value).lower()
-        
-    
-    def test_container_types_list_access(self):
-        """Test accessing list items with Jinja2 syntax."""
-        template = "First item: {{items[0]}}, Second item: {{items[1]}}"
-        inputs = {
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "First item: apple, Second item: banana"
-    
-    def test_container_types_dict_access(self):
-        """Test accessing dictionary items with Jinja2 syntax."""
-        template = "Name: {{person.name}}, Age: {{person.age}}"
-        inputs = {
-            "person": {"name": "John", "age": 30}
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Name: John, Age: 30"
-    
-    def test_conditional_statements(self):
-        """Test conditional statements with Jinja2 syntax."""
-        template = "{% if priority == 'high' %}URGENT: {% endif %}Task: {task}"
-        
-        inputs_high = {
-            "task": "Fix bug",
-            "priority": "high"
-        }
-        result_high = interpolate_only(template, inputs_high)
-        assert result_high == "URGENT: Task: Fix bug"
-        
-        inputs_low = {
-            "task": "Fix bug",
-            "priority": "low"
-        }
-        result_low = interpolate_only(template, inputs_low)
-        assert result_low == "Task: Fix bug"
-    
-    def test_loop_statements(self):
-        """Test loop statements with Jinja2 syntax."""
-        template = "Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}"
-        inputs = {
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Items: apple, banana, orange"
-    
-    def test_datetime_formatting(self):
-        """Test datetime formatting with Jinja2 filters."""
-        today = datetime.datetime(2024, 4, 20)
-        inputs = {"today": today}
-        
-        template = "Date: {{today|date}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Date: 2024-04-20"
-        
-        template = "Date: {{today|date('%d/%m/%Y')}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Date: 20/04/2024"
-    
-    def test_custom_objects(self):
-        """Test custom objects with Jinja2 syntax."""
-        class Person(BaseModel):
-            name: str
-            age: int
-            
-            def __str__(self):
-                return f"{self.name} ({self.age})"
-        
-        person = Person(name="John", age=30)
-        inputs = {"person": person}
-        
-        template = "Person: {person}"
-        result = interpolate_only(template, inputs)
-        assert result == "Person: John (30)"
-        
-        template = "Name: {{person.name}}, Age: {{person.age}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Name: John, Age: 30"
-    
-    def test_mixed_syntax(self):
-        """Test mixed CrewAI and Jinja2 syntax."""
-        template = "Hello {name}! Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}"
-        inputs = {
-            "name": "John",
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Hello John! Items: apple, banana, orange"

uv.lock

@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
     "python_full_version < '3.11' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
@@ -715,7 +714,7 @@ requires-dist = [
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.60.2" },
+    { name = "litellm", specifier = "==1.66.3" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -735,7 +734,6 @@ requires-dist = [
     { name = "tomli-w", specifier = ">=1.1.0" },
     { name = "uv", specifier = ">=0.4.25" },
 ]
-provides-extras = ["tools", "embeddings", "agentops", "fastembed", "pdfplumber", "pandas", "openpyxl", "mem0", "docling", "aisuite"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -2266,7 +2264,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.60.2"
+version = "1.66.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2281,9 +2279,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/94/8f/704cdb0fdbdd49dc5062a39ae5f1a8f308ae0ffd746df6e0137fc1776b8a/litellm-1.60.2.tar.gz", hash = "sha256:a8170584fcfd6f5175201d869e61ccd8a40ffe3264fc5e53c5b805ddf8a6e05a", size = 6447447 }
+sdist = { url = "https://files.pythonhosted.org/packages/0a/10/e5f4824ce69d83c2208397a6522df50e0132ca626779101580121b9d342b/litellm-1.66.3.tar.gz", hash = "sha256:909564f5dc33d7dac236de6cc8066512834467bcebe3494a664d72ae6506a5ca", size = 7223923 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8a/ba/0eaec9aee9f99fdf46ef1c0bddcfe7f5720b182f84f6ed27f13145d5ded2/litellm-1.60.2-py3-none-any.whl", hash = "sha256:1cb08cda04bf8c5ef3e690171a779979e4b16a5e3a24cd8dc1f198e7f198d5c4", size = 6746809 },
+    { url = "https://files.pythonhosted.org/packages/20/a1/5e44417a06f3fecdfb164d0774992301293ad73a67763e49c6b97ed61db2/litellm-1.66.3-py3-none-any.whl", hash = "sha256:f1c662afec14225cee3bae7c93961857edf13fcece42fe46d921d9df50f70dd2", size = 7582219 },
 ]
 
 [[package]]
@@ -2988,6 +2986,7 @@ name = "nvidia-nccl-cu12"
 version = "2.20.5"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/c1/bb/d09dda47c881f9ff504afd6f9ca4f502ded6d8fc2f572cacc5e39da91c28/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01", size = 176238458 },
     { url = "https://files.pythonhosted.org/packages/4b/2a/0a131f572aa09f741c30ccd45a8e56316e8be8dfc7bc19bf0ab7cfef7b19/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56", size = 176249402 },
 ]
 
@@ -2997,6 +2996,7 @@ version = "12.6.85"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9d/d7/c5383e47c7e9bf1c99d5bd2a8c935af2b6d705ad831a7ec5c97db4d82f4f/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a", size = 19744971 },
+    { url = "https://files.pythonhosted.org/packages/31/db/dc71113d441f208cdfe7ae10d4983884e13f464a6252450693365e166dcf/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41", size = 19270338 },
 ]
 
 [[package]]