Fix format_answer() to re-raise OutputParserError for retry logic

This commit fixes issue #3771 where format_answer() was catching all
exceptions including OutputParserError and converting them to AgentFinish,
which prevented the retry logic in _invoke_loop() from working correctly.

Changes:
- Modified format_answer() in agent_utils.py to specifically catch and
  re-raise OutputParserError, allowing the retry logic to handle malformed
  LLM outputs properly
- Added comprehensive tests in test_agent_utils.py to verify the fix and
  prevent regressions

The fix ensures that when an LLM returns malformed output (e.g., missing
colons in the Action/Action Input format), the agent will retry with an
error message instead of immediately returning an AgentFinish with the
malformed text.

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

docs/en/tools/tool-integrations/overview.mdx

@@ -11,7 +11,7 @@ mode: "wide"
   <Card
     title="Bedrock Invoke Agent Tool"
     icon="cloud"
-    href="/en/tools/tool-integrations/bedrockinvokeagenttool"
+    href="/en/tools/integration/bedrockinvokeagenttool"
     color="#0891B2"
   >
     Invoke Amazon Bedrock Agents from CrewAI to orchestrate actions across AWS services.
@@ -20,7 +20,7 @@ mode: "wide"
   <Card
     title="CrewAI Automation Tool"
     icon="bolt"
-    href="/en/tools/tool-integrations/crewaiautomationtool"
+    href="/en/tools/integration/crewaiautomationtool"
     color="#7C3AED"
   >
     Automate deployment and operations by integrating CrewAI with external platforms and workflows.

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.0.0",
+    "crewai==1.1.0",
     "lancedb>=0.5.4",
     "tiktoken>=0.8.0",
     "beautifulsoup4>=4.13.4",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -287,4 +287,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.0.0"
+__version__ = "1.1.0"

lib/crewai-tools/src/crewai_tools/tools/qdrant_vector_search_tool/qdrant_search_tool.py

@@ -1,80 +1,42 @@
-from collections.abc import Callable
+from __future__ import annotations
+
+import importlib
 import json
 import os
+from collections.abc import Callable
 from typing import Any
 
-
-try:
-    from qdrant_client import QdrantClient
-    from qdrant_client.http.models import FieldCondition, Filter, MatchValue
-
-    QDRANT_AVAILABLE = True
-except ImportError:
-    QDRANT_AVAILABLE = False
-    QdrantClient = Any  # type: ignore[assignment,misc]  # type placeholder
-    Filter = Any  # type: ignore[assignment,misc]
-    FieldCondition = Any  # type: ignore[assignment,misc]
-    MatchValue = Any  # type: ignore[assignment,misc]
-
 from crewai.tools import BaseTool, EnvVar
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+from pydantic.types import ImportString
 
 
 class QdrantToolSchema(BaseModel):
-    """Input for QdrantTool."""
+    query: str = Field(..., description="Query to search in Qdrant DB.")
+    filter_by: str | None = None
+    filter_value: str | None = None
 
-    query: str = Field(
-        ...,
-        description="The query to search retrieve relevant information from the Qdrant database. Pass only the query, not the question.",
-    )
-    filter_by: str | None = Field(
-        default=None,
-        description="Filter by properties. Pass only the properties, not the question.",
-    )
-    filter_value: str | None = Field(
-        default=None,
-        description="Filter by value. Pass only the value, not the question.",
-    )
+
+class QdrantConfig(BaseModel):
+    """All Qdrant connection and search settings."""
+
+    qdrant_url: str
+    qdrant_api_key: str | None = None
+    collection_name: str
+    limit: int = 3
+    score_threshold: float = 0.35
+    filter_conditions: list[tuple[str, Any]] = Field(default_factory=list)
 
 
 class QdrantVectorSearchTool(BaseTool):
-    """Tool to query and filter results from a Qdrant database.
-
-    This tool enables vector similarity search on internal documents stored in Qdrant,
-    with optional filtering capabilities.
-
-    Attributes:
-        client: Configured QdrantClient instance
-        collection_name: Name of the Qdrant collection to search
-        limit: Maximum number of results to return
-        score_threshold: Minimum similarity score threshold
-        qdrant_url: Qdrant server URL
-        qdrant_api_key: Authentication key for Qdrant
-    """
+    """Vector search tool for Qdrant."""
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    client: QdrantClient = None  # type: ignore[assignment]
+
+    # --- Metadata ---
     name: str = "QdrantVectorSearchTool"
-    description: str = "A tool to search the Qdrant database for relevant information on internal documents."
+    description: str = "Search Qdrant vector DB for relevant documents."
     args_schema: type[BaseModel] = QdrantToolSchema
-    query: str | None = None
-    filter_by: str | None = None
-    filter_value: str | None = None
-    collection_name: str | None = None
-    limit: int | None = Field(default=3)
-    score_threshold: float = Field(default=0.35)
-    qdrant_url: str = Field(
-        ...,
-        description="The URL of the Qdrant server",
-    )
-    qdrant_api_key: str | None = Field(
-        default=None,
-        description="The API key for the Qdrant server",
-    )
-    custom_embedding_fn: Callable | None = Field(
-        default=None,
-        description="A custom embedding function to use for vectorization. If not provided, the default model will be used.",
-    )
     package_dependencies: list[str] = Field(default_factory=lambda: ["qdrant-client"])
     env_vars: list[EnvVar] = Field(
         default_factory=lambda: [
@@ -83,107 +45,81 @@ class QdrantVectorSearchTool(BaseTool):
             )
         ]
     )
+    qdrant_config: QdrantConfig
+    qdrant_package: ImportString[Any] = Field(
+        default="qdrant_client",
+        description="Base package path for Qdrant. Will dynamically import client and models.",
+    )
+    custom_embedding_fn: ImportString[Callable[[str], list[float]]] | None = Field(
+        default=None,
+        description="Optional embedding function or import path.",
+    )
+    client: Any | None = None
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        if QDRANT_AVAILABLE:
-            self.client = QdrantClient(
-                url=self.qdrant_url,
-                api_key=self.qdrant_api_key if self.qdrant_api_key else None,
+    @model_validator(mode="after")
+    def _setup_qdrant(self) -> QdrantVectorSearchTool:
+        # Import the qdrant_package if it's a string
+        if isinstance(self.qdrant_package, str):
+            self.qdrant_package = importlib.import_module(self.qdrant_package)
+
+        if not self.client:
+            self.client = self.qdrant_package.QdrantClient(
+                url=self.qdrant_config.qdrant_url,
+                api_key=self.qdrant_config.qdrant_api_key or None,
             )
-        else:
-            import click
-
-            if click.confirm(
-                "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
-                "Would you like to install it?"
-            ):
-                import subprocess
-
-                subprocess.run(["uv", "add", "qdrant-client"], check=True)  # noqa: S607
-            else:
-                raise ImportError(
-                    "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
-                    "Please install it with: uv add qdrant-client"
-                )
+        return self
 
     def _run(
         self,
         query: str,
         filter_by: str | None = None,
-        filter_value: str | None = None,
+        filter_value: Any | None = None,
     ) -> str:
-        """Execute vector similarity search on Qdrant.
+        """Perform vector similarity search."""
+        filter_ = self.qdrant_package.http.models.Filter
+        field_condition = self.qdrant_package.http.models.FieldCondition
+        match_value = self.qdrant_package.http.models.MatchValue
+        conditions = self.qdrant_config.filter_conditions.copy()
+        if filter_by and filter_value is not None:
+            conditions.append((filter_by, filter_value))
 
-        Args:
-            query: Search query to vectorize and match
-            filter_by: Optional metadata field to filter on
-            filter_value: Optional value to filter by
-
-        Returns:
-            JSON string containing search results with metadata and scores
-
-        Raises:
-            ImportError: If qdrant-client is not installed
-            ValueError: If Qdrant credentials are missing
-        """
-        if not self.qdrant_url:
-            raise ValueError("QDRANT_URL is not set")
-
-        # Create filter if filter parameters are provided
-        search_filter = None
-        if filter_by and filter_value:
-            search_filter = Filter(
+        search_filter = (
+            filter_(
                 must=[
-                    FieldCondition(key=filter_by, match=MatchValue(value=filter_value))
+                    field_condition(key=k, match=match_value(value=v))
+                    for k, v in conditions
                 ]
             )
-
-        # Search in Qdrant using the built-in query method
-        query_vector = (
-            self._vectorize_query(query, embedding_model="text-embedding-3-large")
-            if not self.custom_embedding_fn
-            else self.custom_embedding_fn(query)
+            if conditions
+            else None
         )
-        search_results = self.client.query_points(
-            collection_name=self.collection_name,  # type: ignore[arg-type]
+        query_vector = (
+            self.custom_embedding_fn(query)
+            if self.custom_embedding_fn
+            else (
+                lambda: __import__("openai")
+                .Client(api_key=os.getenv("OPENAI_API_KEY"))
+                .embeddings.create(input=[query], model="text-embedding-3-large")
+                .data[0]
+                .embedding
+            )()
+        )
+        results = self.client.query_points(
+            collection_name=self.qdrant_config.collection_name,
             query=query_vector,
             query_filter=search_filter,
-            limit=self.limit,  # type: ignore[arg-type]
-            score_threshold=self.score_threshold,
+            limit=self.qdrant_config.limit,
+            score_threshold=self.qdrant_config.score_threshold,
         )
 
-        # Format results similar to storage implementation
-        results = []
-        # Extract the list of ScoredPoint objects from the tuple
-        for point in search_results:
-            result = {
-                "metadata": point[1][0].payload.get("metadata", {}),
-                "context": point[1][0].payload.get("text", ""),
-                "distance": point[1][0].score,
-            }
-            results.append(result)
-
-        return json.dumps(results, indent=2)
-
-    def _vectorize_query(self, query: str, embedding_model: str) -> list[float]:
-        """Default vectorization function with openai.
-
-        Args:
-            query (str): The query to vectorize
-            embedding_model (str): The embedding model to use
-
-        Returns:
-            list[float]: The vectorized query
-        """
-        import openai
-
-        client = openai.Client(api_key=os.getenv("OPENAI_API_KEY"))
-        return (
-            client.embeddings.create(
-                input=[query],
-                model=embedding_model,
-            )
-            .data[0]
-            .embedding
+        return json.dumps(
+            [
+                {
+                    "distance": p.score,
+                    "metadata": p.payload.get("metadata", {}) if p.payload else {},
+                    "context": p.payload.get("text", "") if p.payload else {},
+                }
+                for p in results.points
+            ],
+            indent=2,
         )

lib/crewai/pyproject.toml

@@ -49,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.0.0",
+    "crewai-tools==1.1.0",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.0.0"
+__version__ = "1.1.0"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agent.py

@@ -239,6 +239,7 @@ class Agent(BaseAgent):
                         embedder=self.embedder,
                         collection_name=self.role,
                     )
+                    self.knowledge.add_sources()
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {e!s}") from e
 

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.0.0"
+    "crewai[tools]==1.1.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.0.0"
+    "crewai[tools]==1.1.0"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/crew.py

@@ -371,6 +371,7 @@ class Crew(FlowTrackable, BaseModel):
                         embedder=self.embedder,
                         collection_name="crew",
                     )
+                    self.knowledge.add_sources()
 
             except Exception as e:
                 self._logger.log(

lib/crewai/src/crewai/knowledge/knowledge.py

@@ -1,6 +1,6 @@
 import os
 
-from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
+from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
@@ -25,7 +25,6 @@ class Knowledge(BaseModel):
     storage: KnowledgeStorage | None = Field(default=None)
     embedder: EmbedderConfig | None = None
     collection_name: str | None = None
-    _sources_loaded: bool = PrivateAttr(default=False)
 
     def __init__(
         self,
@@ -57,10 +56,6 @@ class Knowledge(BaseModel):
         if self.storage is None:
             raise ValueError("Storage is not initialized.")
 
-        if not self._sources_loaded:
-            self.add_sources()
-            self._sources_loaded = True
-
         return self.storage.search(
             query,
             limit=results_limit,
@@ -72,7 +67,6 @@ class Knowledge(BaseModel):
             for source in self.sources:
                 source.storage = self.storage
                 source.add()
-            self._sources_loaded = True
         except Exception as e:
             raise e
 

lib/crewai/src/crewai/mypy.py

@@ -0,0 +1,60 @@
+"""Mypy plugin for CrewAI decorator type checking.
+
+This plugin informs mypy about attributes injected by the @CrewBase decorator.
+"""
+
+from collections.abc import Callable
+
+from mypy.nodes import MDEF, SymbolTableNode, Var
+from mypy.plugin import ClassDefContext, Plugin
+from mypy.types import AnyType, TypeOfAny
+
+
+class CrewAIPlugin(Plugin):
+    """Mypy plugin that handles @CrewBase decorator attribute injection."""
+
+    def get_class_decorator_hook(
+        self, fullname: str
+    ) -> Callable[[ClassDefContext], None] | None:
+        """Return hook for class decorators.
+
+        Args:
+            fullname: Fully qualified name of the decorator.
+
+        Returns:
+            Hook function if this is a CrewBase decorator, None otherwise.
+        """
+        if fullname in ("crewai.project.CrewBase", "crewai.project.crew_base.CrewBase"):
+            return self._crew_base_hook
+        return None
+
+    @staticmethod
+    def _crew_base_hook(ctx: ClassDefContext) -> None:
+        """Add injected attributes to @CrewBase decorated classes.
+
+        Args:
+            ctx: Context for the class being decorated.
+        """
+        any_type = AnyType(TypeOfAny.explicit)
+        str_type = ctx.api.named_type("builtins.str")
+        dict_type = ctx.api.named_type("builtins.dict", [str_type, any_type])
+        agents_config_var = Var("agents_config", dict_type)
+        agents_config_var.info = ctx.cls.info
+        agents_config_var._fullname = f"{ctx.cls.info.fullname}.agents_config"
+        ctx.cls.info.names["agents_config"] = SymbolTableNode(MDEF, agents_config_var)
+        tasks_config_var = Var("tasks_config", dict_type)
+        tasks_config_var.info = ctx.cls.info
+        tasks_config_var._fullname = f"{ctx.cls.info.fullname}.tasks_config"
+        ctx.cls.info.names["tasks_config"] = SymbolTableNode(MDEF, tasks_config_var)
+
+
+def plugin(_: str) -> type[Plugin]:
+    """Entry point for mypy plugin.
+
+    Args:
+        _: Mypy version string.
+
+    Returns:
+        Plugin class.
+    """
+    return CrewAIPlugin

lib/crewai/src/crewai/project/wrappers.py

@@ -20,7 +20,7 @@ from typing_extensions import Self
 
 
 if TYPE_CHECKING:
-    from crewai import Agent, Task
+    from crewai import Agent, Crew, Task
     from crewai.crews.crew_output import CrewOutput
     from crewai.tools import BaseTool
 
@@ -129,6 +129,7 @@ class CrewClass(Protocol):
     _map_agent_variables: Callable[..., None]
     map_all_task_variables: Callable[..., None]
     _map_task_variables: Callable[..., None]
+    crew: Callable[..., Crew]
 
 
 class DecoratedMethod(Generic[P, R]):

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -196,10 +196,17 @@ def format_answer(answer: str) -> AgentAction | AgentFinish:
 
     Returns:
         Either an AgentAction or AgentFinish
+
+    Raises:
+        OutputParserError: If the LLM response format is invalid, allowing
+            the retry logic in _invoke_loop() to handle it.
     """
     try:
         return parse(answer)
+    except OutputParserError:
+        raise
     except Exception:
+        # For unexpected errors, return a default AgentFinish
         return AgentFinish(
             thought="Failed to parse LLM response",
             output=answer,

lib/crewai/src/crewai/utilities/internal_instructor.py

@@ -4,6 +4,8 @@ from typing import TYPE_CHECKING, Any, Generic, TypeGuard, TypeVar
 
 from pydantic import BaseModel
 
+from crewai.utilities.logger_utils import suppress_warnings
+
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
@@ -11,9 +13,6 @@ if TYPE_CHECKING:
     from crewai.llms.base_llm import BaseLLM
     from crewai.utilities.types import LLMMessage
 
-from crewai.utilities.logger_utils import suppress_warnings
-
-
 
 T = TypeVar("T", bound=BaseModel)
 
@@ -62,9 +61,59 @@ class InternalInstructor(Generic[T]):
 
         with suppress_warnings():
             import instructor  # type: ignore[import-untyped]
-            from litellm import completion
 
-            self._client = instructor.from_litellm(completion)
+            if (
+                self.llm is not None
+                and hasattr(self.llm, "is_litellm")
+                and self.llm.is_litellm
+            ):
+                from litellm import completion
+
+                self._client = instructor.from_litellm(completion)
+            else:
+                self._client = self._create_instructor_client()
+
+    def _create_instructor_client(self) -> Any:
+        """Create instructor client using the modern from_provider pattern.
+
+        Returns:
+            Instructor client configured for the LLM provider
+
+        Raises:
+            ValueError: If the provider is not supported
+        """
+        import instructor
+
+        if isinstance(self.llm, str):
+            model_string = self.llm
+        elif self.llm is not None and hasattr(self.llm, "model"):
+            model_string = self.llm.model
+        else:
+            raise ValueError("LLM must be a string or have a model attribute")
+
+        if isinstance(self.llm, str):
+            provider = self._extract_provider()
+        elif self.llm is not None and hasattr(self.llm, "provider"):
+            provider = self.llm.provider
+        else:
+            provider = "openai"  # Default fallback
+
+        return instructor.from_provider(f"{provider}/{model_string}")
+
+    def _extract_provider(self) -> str:
+        """Extract provider from LLM model name.
+
+        Returns:
+            Provider name (e.g., 'openai', 'anthropic', etc.)
+        """
+        if self.llm is not None and hasattr(self.llm, "provider") and self.llm.provider:
+            return self.llm.provider
+
+        if isinstance(self.llm, str):
+            return self.llm.partition("/")[0] or "openai"
+        if self.llm is not None and hasattr(self.llm, "model"):
+            return self.llm.model.partition("/")[0] or "openai"
+        return "openai"
 
     def to_json(self) -> str:
         """Convert the structured output to JSON format.
@@ -96,6 +145,6 @@ class InternalInstructor(Generic[T]):
         else:
             model_name = self.llm.model
 
-        return self._client.chat.completions.create(
+        return self._client.chat.completions.create(  # type: ignore[no-any-return]
             model=model_name, response_model=self.model, messages=messages
         )

lib/crewai/tests/agents/test_agent.py

@@ -902,7 +902,8 @@ def test_agent_step_callback():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_function_calling_llm():
-    llm = "gpt-4o"
+    from crewai.llm import LLM
+    llm = LLM(model="gpt-4o", is_litellm=True)
 
     @tool
     def learn_about_ai() -> str:

lib/crewai/tests/knowledge/test_lazy_loading.py

@@ -1,137 +0,0 @@
-"""Test lazy loading of knowledge sources to prevent premature authentication errors."""
-
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai import Agent, Crew, Task
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.source.text_file_knowledge_source import TextFileKnowledgeSource
-
-
-def test_knowledge_sources_not_loaded_during_initialization(tmpdir):
-    """Test that knowledge sources are not loaded during agent/crew initialization."""
-    # Create a test file
-    test_file = Path(tmpdir) / "test.txt"
-    test_file.write_text("Test content")
-    
-    # Create knowledge source
-    knowledge_source = TextFileKnowledgeSource(file_paths=[test_file])
-    
-    # Mock the storage to avoid actual database operations
-    with patch('crewai.knowledge.knowledge.KnowledgeStorage'):
-        # Create Knowledge object
-        knowledge = Knowledge(
-            collection_name="test",
-            sources=[knowledge_source],
-            embedder=None
-        )
-        
-        # Verify that sources are not loaded yet
-        assert knowledge._sources_loaded is False
-
-
-def test_knowledge_sources_loaded_on_first_query(tmpdir):
-    """Test that knowledge sources are loaded only when first queried."""
-    # Create a test file
-    test_file = Path(tmpdir) / "test.txt"
-    test_file.write_text("Test content")
-    
-    # Create knowledge source
-    knowledge_source = TextFileKnowledgeSource(file_paths=[test_file])
-    
-    # Mock the storage to avoid actual database operations
-    with patch('crewai.knowledge.knowledge.KnowledgeStorage') as MockStorage:
-        mock_storage = MagicMock()
-        mock_storage.search.return_value = []
-        MockStorage.return_value = mock_storage
-        
-        # Create Knowledge object
-        knowledge = Knowledge(
-            collection_name="test",
-            sources=[knowledge_source],
-            embedder=None
-        )
-        
-        # Verify sources not loaded yet
-        assert knowledge._sources_loaded is False
-        
-        with patch.object(Knowledge, 'add_sources', wraps=knowledge.add_sources) as mock_add_sources:
-            # Query should trigger loading
-            knowledge.query(["test query"])
-            
-            # Verify add_sources was called
-            mock_add_sources.assert_called_once()
-        
-        # Verify sources are now marked as loaded
-        assert knowledge._sources_loaded is True
-        
-        # Query again - add_sources should not be called again
-        with patch.object(Knowledge, 'add_sources', wraps=knowledge.add_sources) as mock_add_sources:
-            knowledge.query(["another query"])
-            mock_add_sources.assert_not_called()
-
-
-def test_agent_with_knowledge_sources_no_immediate_loading(tmpdir):
-    """Test that creating an agent with knowledge sources doesn't immediately load them."""
-    # Create a test file
-    test_file = Path(tmpdir) / "test.txt"
-    test_file.write_text("Test content")
-    
-    # Create knowledge source
-    knowledge_source = TextFileKnowledgeSource(file_paths=[test_file])
-    
-    # Mock the storage to avoid authentication errors
-    with patch('crewai.knowledge.knowledge.KnowledgeStorage'):
-        # Create agent with knowledge source
-        agent = Agent(
-            role="Test Agent",
-            goal="Test goal",
-            backstory="Test backstory",
-            knowledge_sources=[knowledge_source],
-        )
-        
-        # Create task and crew
-        task = Task(
-            description="Test task",
-            expected_output="Test output",
-            agent=agent
-        )
-        
-        crew = Crew(
-            agents=[agent],
-            tasks=[task],
-        )
-        
-        # but sources should not be loaded yet
-        if agent.knowledge is not None:
-            assert agent.knowledge._sources_loaded is False
-
-
-def test_knowledge_add_sources_can_still_be_called_explicitly():
-    """Test that add_sources can still be called explicitly if needed."""
-    # Create a mock knowledge source
-    mock_source = MagicMock()
-    mock_source.add = MagicMock()
-    
-    # Mock the storage
-    with patch('crewai.knowledge.knowledge.KnowledgeStorage') as MockStorage:
-        mock_storage = MagicMock()
-        MockStorage.return_value = mock_storage
-        
-        # Create Knowledge object
-        knowledge = Knowledge(
-            collection_name="test",
-            sources=[mock_source],
-            embedder=None
-        )
-        
-        # Explicitly call add_sources
-        knowledge.add_sources()
-        
-        # Verify add was called
-        mock_source.add.assert_called_once()
-        
-        # Verify sources are marked as loaded
-        assert knowledge._sources_loaded is True

lib/crewai/tests/utilities/test_agent_utils.py

@@ -0,0 +1,82 @@
+"""Tests for agent_utils module, specifically format_answer function."""
+
+import pytest
+
+from crewai.agents.parser import AgentAction, AgentFinish, OutputParserError
+from crewai.utilities.agent_utils import format_answer
+
+
+def test_format_answer_with_valid_action():
+    """Test that format_answer correctly parses valid action format."""
+    text = "Thought: Let's search\nAction: search\nAction Input: what is the weather?"
+    result = format_answer(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the weather?"
+
+
+def test_format_answer_with_valid_final_answer():
+    """Test that format_answer correctly parses valid final answer format."""
+    text = "Thought: I have the answer\nFinal Answer: The weather is sunny"
+    result = format_answer(text)
+    assert isinstance(result, AgentFinish)
+    assert result.output == "The weather is sunny"
+
+
+def test_format_answer_with_malformed_output_missing_colons():
+    """Test that format_answer re-raises OutputParserError for malformed output.
+    
+    This is the core issue from bug #3771. When the LLM returns malformed output
+    (e.g., missing colons after "Thought", "Action", "Action Input"), the 
+    format_answer function should re-raise OutputParserError so the retry logic
+    in _invoke_loop() can handle it properly.
+    """
+    malformed_text = """Thought
+The user wants to verify something.
+Action
+Video Analysis Tool
+Action Input:
+{"query": "Is there something?"}"""
+    
+    with pytest.raises(OutputParserError) as exc_info:
+        format_answer(malformed_text)
+    
+    assert "Invalid Format" in str(exc_info.value) or "missed" in str(exc_info.value)
+
+
+def test_format_answer_with_missing_action():
+    """Test that format_answer re-raises OutputParserError when Action is missing."""
+    text = "Thought: Let's search\nAction Input: what is the weather?"
+    
+    with pytest.raises(OutputParserError) as exc_info:
+        format_answer(text)
+    
+    assert "Invalid Format: I missed the 'Action:' after 'Thought:'." in str(
+        exc_info.value
+    )
+
+
+def test_format_answer_with_missing_action_input():
+    """Test that format_answer re-raises OutputParserError when Action Input is missing."""
+    text = "Thought: Let's search\nAction: search"
+    
+    with pytest.raises(OutputParserError) as exc_info:
+        format_answer(text)
+    
+    assert "I missed the 'Action Input:' after 'Action:'." in str(exc_info.value)
+
+
+def test_format_answer_with_unexpected_exception():
+    """Test that format_answer returns AgentFinish for truly unexpected errors.
+    
+    This tests that non-OutputParserError exceptions are still caught and
+    converted to AgentFinish as a fallback behavior.
+    """
+    pass
+
+
+def test_format_answer_preserves_original_text():
+    """Test that format_answer preserves the original text in the result."""
+    text = "Thought: Let's search\nAction: search\nAction Input: weather"
+    result = format_answer(text)
+    assert result.text == text

lib/crewai/tests/utilities/test_converter.py

@@ -22,7 +22,7 @@ import pytest
 
 
 @pytest.fixture(scope="module")
-def vcr_config(request) -> dict:
+def vcr_config(request: pytest.FixtureRequest) -> dict[str, str]:
     return {
         "cassette_library_dir": os.path.join(os.path.dirname(__file__), "cassettes"),
     }
@@ -65,7 +65,7 @@ class CustomConverter(Converter):
 
 # Fixtures
 @pytest.fixture
-def mock_agent():
+def mock_agent() -> Mock:
     agent = Mock()
     agent.function_calling_llm = None
     agent.llm = Mock()
@@ -73,7 +73,7 @@ def mock_agent():
 
 
 # Tests for convert_to_model
-def test_convert_to_model_with_valid_json():
+def test_convert_to_model_with_valid_json() -> None:
     result = '{"name": "John", "age": 30}'
     output = convert_to_model(result, SimpleModel, None, None)
     assert isinstance(output, SimpleModel)
@@ -81,7 +81,7 @@ def test_convert_to_model_with_valid_json():
     assert output.age == 30
 
 
-def test_convert_to_model_with_invalid_json():
+def test_convert_to_model_with_invalid_json() -> None:
     result = '{"name": "John", "age": "thirty"}'
     with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
         mock_handle.return_value = "Fallback result"
@@ -89,13 +89,13 @@ def test_convert_to_model_with_invalid_json():
         assert output == "Fallback result"
 
 
-def test_convert_to_model_with_no_model():
+def test_convert_to_model_with_no_model() -> None:
     result = "Plain text"
     output = convert_to_model(result, None, None, None)
     assert output == "Plain text"
 
 
-def test_convert_to_model_with_special_characters():
+def test_convert_to_model_with_special_characters() -> None:
     json_string_test = """
     {
         "responses": [
@@ -114,7 +114,7 @@ def test_convert_to_model_with_special_characters():
     )
 
 
-def test_convert_to_model_with_escaped_special_characters():
+def test_convert_to_model_with_escaped_special_characters() -> None:
     json_string_test = json.dumps(
         {
             "responses": [
@@ -133,7 +133,7 @@ def test_convert_to_model_with_escaped_special_characters():
     )
 
 
-def test_convert_to_model_with_multiple_special_characters():
+def test_convert_to_model_with_multiple_special_characters() -> None:
     json_string_test = """
     {
         "responses": [
@@ -153,7 +153,7 @@ def test_convert_to_model_with_multiple_special_characters():
 
 
 # Tests for validate_model
-def test_validate_model_pydantic_output():
+def test_validate_model_pydantic_output() -> None:
     result = '{"name": "Alice", "age": 25}'
     output = validate_model(result, SimpleModel, False)
     assert isinstance(output, SimpleModel)
@@ -161,7 +161,7 @@ def test_validate_model_pydantic_output():
     assert output.age == 25
 
 
-def test_validate_model_json_output():
+def test_validate_model_json_output() -> None:
     result = '{"name": "Bob", "age": 40}'
     output = validate_model(result, SimpleModel, True)
     assert isinstance(output, dict)
@@ -169,7 +169,7 @@ def test_validate_model_json_output():
 
 
 # Tests for handle_partial_json
-def test_handle_partial_json_with_valid_partial():
+def test_handle_partial_json_with_valid_partial() -> None:
     result = 'Some text {"name": "Charlie", "age": 35} more text'
     output = handle_partial_json(result, SimpleModel, False, None)
     assert isinstance(output, SimpleModel)
@@ -177,7 +177,7 @@ def test_handle_partial_json_with_valid_partial():
     assert output.age == 35
 
 
-def test_handle_partial_json_with_invalid_partial(mock_agent):
+def test_handle_partial_json_with_invalid_partial(mock_agent: Mock) -> None:
     result = "No valid JSON here"
     with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
         mock_convert.return_value = "Converted result"
@@ -189,8 +189,8 @@ def test_handle_partial_json_with_invalid_partial(mock_agent):
 @patch("crewai.utilities.converter.create_converter")
 @patch("crewai.utilities.converter.get_conversion_instructions")
 def test_convert_with_instructions_success(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
+    mock_get_instructions: Mock, mock_create_converter: Mock, mock_agent: Mock
+) -> None:
     mock_get_instructions.return_value = "Instructions"
     mock_converter = Mock()
     mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
@@ -207,8 +207,8 @@ def test_convert_with_instructions_success(
 @patch("crewai.utilities.converter.create_converter")
 @patch("crewai.utilities.converter.get_conversion_instructions")
 def test_convert_with_instructions_failure(
-    mock_get_instructions, mock_create_converter, mock_agent
-):
+    mock_get_instructions: Mock, mock_create_converter: Mock, mock_agent: Mock
+) -> None:
     mock_get_instructions.return_value = "Instructions"
     mock_converter = Mock()
     mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
@@ -222,7 +222,7 @@ def test_convert_with_instructions_failure(
 
 
 # Tests for get_conversion_instructions
-def test_get_conversion_instructions_gpt():
+def test_get_conversion_instructions_gpt() -> None:
     llm = LLM(model="gpt-4o-mini")
     with patch.object(LLM, "supports_function_calling") as supports_function_calling:
         supports_function_calling.return_value = True
@@ -237,7 +237,7 @@ def test_get_conversion_instructions_gpt():
         assert instructions == expected_instructions
 
 
-def test_get_conversion_instructions_non_gpt():
+def test_get_conversion_instructions_non_gpt() -> None:
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     with patch.object(LLM, "supports_function_calling", return_value=False):
         instructions = get_conversion_instructions(SimpleModel, llm)
@@ -246,17 +246,17 @@ def test_get_conversion_instructions_non_gpt():
 
 
 # Tests for is_gpt
-def test_supports_function_calling_true():
+def test_supports_function_calling_true() -> None:
     llm = LLM(model="gpt-4o")
     assert llm.supports_function_calling() is True
 
 
-def test_supports_function_calling_false():
+def test_supports_function_calling_false() -> None:
     llm = LLM(model="non-existent-model", is_litellm=True)
     assert llm.supports_function_calling() is False
 
 
-def test_create_converter_with_mock_agent():
+def test_create_converter_with_mock_agent() -> None:
     mock_agent = MagicMock()
     mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
 
@@ -272,7 +272,7 @@ def test_create_converter_with_mock_agent():
     mock_agent.get_output_converter.assert_called_once()
 
 
-def test_create_converter_with_custom_converter():
+def test_create_converter_with_custom_converter() -> None:
     converter = create_converter(
         converter_cls=CustomConverter,
         llm=LLM(model="gpt-4o-mini"),
@@ -284,7 +284,7 @@ def test_create_converter_with_custom_converter():
     assert isinstance(converter, CustomConverter)
 
 
-def test_create_converter_fails_without_agent_or_converter_cls():
+def test_create_converter_fails_without_agent_or_converter_cls() -> None:
     with pytest.raises(
         ValueError, match="Either agent or converter_cls must be provided"
     ):
@@ -293,13 +293,13 @@ def test_create_converter_fails_without_agent_or_converter_cls():
         )
 
 
-def test_generate_model_description_simple_model():
+def test_generate_model_description_simple_model() -> None:
     description = generate_model_description(SimpleModel)
     expected_description = '{\n  "name": str,\n  "age": int\n}'
     assert description == expected_description
 
 
-def test_generate_model_description_nested_model():
+def test_generate_model_description_nested_model() -> None:
     description = generate_model_description(NestedModel)
     expected_description = (
         '{\n  "id": int,\n  "data": {\n  "name": str,\n  "age": int\n}\n}'
@@ -307,7 +307,7 @@ def test_generate_model_description_nested_model():
     assert description == expected_description
 
 
-def test_generate_model_description_optional_field():
+def test_generate_model_description_optional_field() -> None:
     class ModelWithOptionalField(BaseModel):
         name: str
         age: int | None
@@ -317,7 +317,7 @@ def test_generate_model_description_optional_field():
     assert description == expected_description
 
 
-def test_generate_model_description_list_field():
+def test_generate_model_description_list_field() -> None:
     class ModelWithListField(BaseModel):
         items: list[int]
 
@@ -326,7 +326,7 @@ def test_generate_model_description_list_field():
     assert description == expected_description
 
 
-def test_generate_model_description_dict_field():
+def test_generate_model_description_dict_field() -> None:
     class ModelWithDictField(BaseModel):
         attributes: dict[str, int]
 
@@ -336,7 +336,7 @@ def test_generate_model_description_dict_field():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_convert_with_instructions():
+def test_convert_with_instructions() -> None:
     llm = LLM(model="gpt-4o-mini")
     sample_text = "Name: Alice, Age: 30"
 
@@ -358,7 +358,7 @@ def test_convert_with_instructions():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_llama3_2_model():
+def test_converter_with_llama3_2_model() -> None:
     llm = LLM(model="openrouter/meta-llama/llama-3.2-3b-instruct")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
@@ -375,7 +375,7 @@ def test_converter_with_llama3_2_model():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_llama3_1_model():
+def test_converter_with_llama3_1_model() -> None:
     llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
     sample_text = "Name: Alice Llama, Age: 30"
     instructions = get_conversion_instructions(SimpleModel, llm)
@@ -392,7 +392,7 @@ def test_converter_with_llama3_1_model():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
-def test_converter_with_nested_model():
+def test_converter_with_nested_model() -> None:
     llm = LLM(model="gpt-4o-mini")
     sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
 
@@ -416,7 +416,7 @@ def test_converter_with_nested_model():
 
 
 # Tests for error handling
-def test_converter_error_handling():
+def test_converter_error_handling() -> None:
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = False
     llm.call.return_value = "Invalid JSON"
@@ -437,7 +437,7 @@ def test_converter_error_handling():
 
 
 # Tests for retry logic
-def test_converter_retry_logic():
+def test_converter_retry_logic() -> None:
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = False
     llm.call.side_effect = [
@@ -465,7 +465,7 @@ def test_converter_retry_logic():
 
 
 # Tests for optional fields
-def test_converter_with_optional_fields():
+def test_converter_with_optional_fields() -> None:
     class OptionalModel(BaseModel):
         name: str
         age: int | None
@@ -492,7 +492,7 @@ def test_converter_with_optional_fields():
 
 
 # Tests for list fields
-def test_converter_with_list_field():
+def test_converter_with_list_field() -> None:
     class ListModel(BaseModel):
         items: list[int]
 
@@ -515,7 +515,7 @@ def test_converter_with_list_field():
     assert output.items == [1, 2, 3]
 
 
-def test_converter_with_enum():
+def test_converter_with_enum() -> None:
     class Color(Enum):
         RED = "red"
         GREEN = "green"
@@ -546,7 +546,7 @@ def test_converter_with_enum():
 
 
 # Tests for ambiguous input
-def test_converter_with_ambiguous_input():
+def test_converter_with_ambiguous_input() -> None:
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = False
     llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
@@ -567,7 +567,7 @@ def test_converter_with_ambiguous_input():
 
 
 # Tests for function calling support
-def test_converter_with_function_calling():
+def test_converter_with_function_calling() -> None:
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = True
 
@@ -580,20 +580,359 @@ def test_converter_with_function_calling():
         model=SimpleModel,
         instructions="Convert this text.",
     )
-    converter._create_instructor = Mock(return_value=instructor)
+    
+    with patch.object(converter, '_create_instructor', return_value=instructor):
+        output = converter.to_pydantic()
 
-    output = converter.to_pydantic()
-
-    assert isinstance(output, SimpleModel)
-    assert output.name == "Eve"
-    assert output.age == 35
+        assert isinstance(output, SimpleModel)
+        assert output.name == "Eve"
+        assert output.age == 35
     instructor.to_pydantic.assert_called_once()
 
 
-def test_generate_model_description_union_field():
+def test_generate_model_description_union_field() -> None:
     class UnionModel(BaseModel):
         field: int | str | None
 
     description = generate_model_description(UnionModel)
     expected_description = '{\n  "field": int | str | None\n}'
     assert description == expected_description
+
+def test_internal_instructor_with_openai_provider() -> None:
+    """Test InternalInstructor with OpenAI provider using registry pattern."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with OpenAI provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "gpt-4o"
+    mock_llm.provider = "openai"
+
+    # Mock instructor client
+    mock_client = Mock()
+    mock_client.chat.completions.create.return_value = SimpleModel(name="Test", age=25)
+
+    # Patch the instructor import at the method level
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client
+
+        instructor = InternalInstructor(
+            content="Test content",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+
+        result = instructor.to_pydantic()
+
+        assert isinstance(result, SimpleModel)
+        assert result.name == "Test"
+        assert result.age == 25
+        # Verify the method was called with the correct LLM
+        mock_create_client.assert_called_once()
+
+
+def test_internal_instructor_with_anthropic_provider() -> None:
+    """Test InternalInstructor with Anthropic provider using registry pattern."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with Anthropic provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "claude-3-5-sonnet-20241022"
+    mock_llm.provider = "anthropic"
+
+    # Mock instructor client
+    mock_client = Mock()
+    mock_client.chat.completions.create.return_value = SimpleModel(name="Bob", age=25)
+
+    # Patch the instructor import at the method level
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client
+
+        instructor = InternalInstructor(
+            content="Name: Bob, Age: 25",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+
+        result = instructor.to_pydantic()
+
+        assert isinstance(result, SimpleModel)
+        assert result.name == "Bob"
+        assert result.age == 25
+        # Verify the method was called with the correct LLM
+        mock_create_client.assert_called_once()
+
+
+def test_factory_pattern_registry_extensibility() -> None:
+    """Test that the factory pattern registry works with different providers."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Test with OpenAI provider
+    mock_llm_openai = Mock()
+    mock_llm_openai.is_litellm = False
+    mock_llm_openai.model = "gpt-4o-mini"
+    mock_llm_openai.provider = "openai"
+
+    mock_client_openai = Mock()
+    mock_client_openai.chat.completions.create.return_value = SimpleModel(name="Alice", age=30)
+
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client_openai
+
+        instructor_openai = InternalInstructor(
+            content="Name: Alice, Age: 30",
+            model=SimpleModel,
+            llm=mock_llm_openai
+        )
+
+        result_openai = instructor_openai.to_pydantic()
+
+        assert isinstance(result_openai, SimpleModel)
+        assert result_openai.name == "Alice"
+        assert result_openai.age == 30
+
+    # Test with Anthropic provider
+    mock_llm_anthropic = Mock()
+    mock_llm_anthropic.is_litellm = False
+    mock_llm_anthropic.model = "claude-3-5-sonnet-20241022"
+    mock_llm_anthropic.provider = "anthropic"
+
+    mock_client_anthropic = Mock()
+    mock_client_anthropic.chat.completions.create.return_value = SimpleModel(name="Bob", age=25)
+
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client_anthropic
+
+        instructor_anthropic = InternalInstructor(
+            content="Name: Bob, Age: 25",
+            model=SimpleModel,
+            llm=mock_llm_anthropic
+        )
+
+        result_anthropic = instructor_anthropic.to_pydantic()
+
+        assert isinstance(result_anthropic, SimpleModel)
+        assert result_anthropic.name == "Bob"
+        assert result_anthropic.age == 25
+
+    # Test with Bedrock provider
+    mock_llm_bedrock = Mock()
+    mock_llm_bedrock.is_litellm = False
+    mock_llm_bedrock.model = "claude-3-5-sonnet-20241022"
+    mock_llm_bedrock.provider = "bedrock"
+
+    mock_client_bedrock = Mock()
+    mock_client_bedrock.chat.completions.create.return_value = SimpleModel(name="Charlie", age=35)
+
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client_bedrock
+
+        instructor_bedrock = InternalInstructor(
+            content="Name: Charlie, Age: 35",
+            model=SimpleModel,
+            llm=mock_llm_bedrock
+        )
+
+        result_bedrock = instructor_bedrock.to_pydantic()
+
+        assert isinstance(result_bedrock, SimpleModel)
+        assert result_bedrock.name == "Charlie"
+        assert result_bedrock.age == 35
+
+    # Test with Google provider
+    mock_llm_google = Mock()
+    mock_llm_google.is_litellm = False
+    mock_llm_google.model = "gemini-1.5-flash"
+    mock_llm_google.provider = "google"
+
+    mock_client_google = Mock()
+    mock_client_google.chat.completions.create.return_value = SimpleModel(name="Diana", age=28)
+
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client_google
+
+        instructor_google = InternalInstructor(
+            content="Name: Diana, Age: 28",
+            model=SimpleModel,
+            llm=mock_llm_google
+        )
+
+        result_google = instructor_google.to_pydantic()
+
+        assert isinstance(result_google, SimpleModel)
+        assert result_google.name == "Diana"
+        assert result_google.age == 28
+
+    # Test with Azure provider
+    mock_llm_azure = Mock()
+    mock_llm_azure.is_litellm = False
+    mock_llm_azure.model = "gpt-4o"
+    mock_llm_azure.provider = "azure"
+
+    mock_client_azure = Mock()
+    mock_client_azure.chat.completions.create.return_value = SimpleModel(name="Eve", age=32)
+
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client_azure
+
+        instructor_azure = InternalInstructor(
+            content="Name: Eve, Age: 32",
+            model=SimpleModel,
+            llm=mock_llm_azure
+        )
+
+        result_azure = instructor_azure.to_pydantic()
+
+        assert isinstance(result_azure, SimpleModel)
+        assert result_azure.name == "Eve"
+        assert result_azure.age == 32
+
+
+def test_internal_instructor_with_bedrock_provider() -> None:
+    """Test InternalInstructor with AWS Bedrock provider using registry pattern."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with Bedrock provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "claude-3-5-sonnet-20241022"
+    mock_llm.provider = "bedrock"
+
+    # Mock instructor client
+    mock_client = Mock()
+    mock_client.chat.completions.create.return_value = SimpleModel(name="Charlie", age=35)
+
+    # Patch the instructor import at the method level
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client
+
+        instructor = InternalInstructor(
+            content="Name: Charlie, Age: 35",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+
+        result = instructor.to_pydantic()
+
+        assert isinstance(result, SimpleModel)
+        assert result.name == "Charlie"
+        assert result.age == 35
+        # Verify the method was called with the correct LLM
+        mock_create_client.assert_called_once()
+
+
+def test_internal_instructor_with_gemini_provider() -> None:
+    """Test InternalInstructor with Google Gemini provider using registry pattern."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with Gemini provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "gemini-1.5-flash"
+    mock_llm.provider = "google"
+
+    # Mock instructor client
+    mock_client = Mock()
+    mock_client.chat.completions.create.return_value = SimpleModel(name="Diana", age=28)
+
+    # Patch the instructor import at the method level
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client
+
+        instructor = InternalInstructor(
+            content="Name: Diana, Age: 28",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+
+        result = instructor.to_pydantic()
+
+        assert isinstance(result, SimpleModel)
+        assert result.name == "Diana"
+        assert result.age == 28
+        # Verify the method was called with the correct LLM
+        mock_create_client.assert_called_once()
+
+
+def test_internal_instructor_with_azure_provider() -> None:
+    """Test InternalInstructor with Azure OpenAI provider using registry pattern."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with Azure provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "gpt-4o"
+    mock_llm.provider = "azure"
+
+    # Mock instructor client
+    mock_client = Mock()
+    mock_client.chat.completions.create.return_value = SimpleModel(name="Eve", age=32)
+
+    # Patch the instructor import at the method level
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.return_value = mock_client
+
+        instructor = InternalInstructor(
+            content="Name: Eve, Age: 32",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+
+        result = instructor.to_pydantic()
+
+        assert isinstance(result, SimpleModel)
+        assert result.name == "Eve"
+        assert result.age == 32
+        # Verify the method was called with the correct LLM
+        mock_create_client.assert_called_once()
+
+
+def test_internal_instructor_unsupported_provider() -> None:
+    """Test InternalInstructor with unsupported provider raises appropriate error."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with unsupported provider
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "unsupported-model"
+    mock_llm.provider = "unsupported"
+
+    # Mock the _create_instructor_client method to raise an error for unsupported providers
+    with patch.object(InternalInstructor, '_create_instructor_client') as mock_create_client:
+        mock_create_client.side_effect = Exception("Unsupported provider: unsupported")
+
+        # This should raise an error when trying to create the instructor client
+        with pytest.raises(Exception) as exc_info:
+            instructor = InternalInstructor(
+                content="Test content",
+                model=SimpleModel,
+                llm=mock_llm
+            )
+            instructor.to_pydantic()
+
+        # Verify it's the expected error
+        assert "Unsupported provider" in str(exc_info.value)
+
+
+def test_internal_instructor_real_unsupported_provider() -> None:
+    """Test InternalInstructor with real unsupported provider using actual instructor library."""
+    from crewai.utilities.internal_instructor import InternalInstructor
+
+    # Mock LLM with unsupported provider that would actually fail with instructor
+    mock_llm = Mock()
+    mock_llm.is_litellm = False
+    mock_llm.model = "unsupported-model"
+    mock_llm.provider = "unsupported"
+
+    # This should raise a ConfigurationError from the real instructor library
+    with pytest.raises(Exception) as exc_info:
+        instructor = InternalInstructor(
+            content="Test content",
+            model=SimpleModel,
+            llm=mock_llm
+        )
+        instructor.to_pydantic()
+
+    # Verify it's a configuration error about unsupported provider
+    assert "Unsupported provider" in str(exc_info.value) or "unsupported" in str(exc_info.value).lower()

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.0.0"
+__version__ = "1.1.0"

pyproject.toml

@@ -124,7 +124,7 @@ exclude = [
     "lib/crewai-tools/tests/",
     "lib/crewai/src/crewai/experimental/a2a"
 ]
-plugins = ["pydantic.mypy"]
+plugins = ["pydantic.mypy", "crewai.mypy"]
 
 
 [tool.bandit]