Revert to pytest-recording to fix CI --block-network flag

The previous commit changed pytest-recording to pytest-vcr to resolve a local conflict, but this broke CI because: - pytest-recording provides the --block-network flag used in CI - pytest-vcr does not provide this flag - The two plugins are fundamentally incompatible This commit reverts to pytest-recording (the original dependency) to restore the --block-network flag and fix the CI test failures. Co-Authored-By: João <joao@crewai.com>
Fix agent output sanitization to prevent internal ReAct fields from leaking
2026-01-07 15:18:29 +00:00 · 2025-11-10 11:42:40 +00:00 · 2025-11-10 11:32:48 +00:00 · 2025-11-07 11:19:07 -08:00 · 2025-11-07 13:54:11 -05:00 · 2025-11-07 13:34:15 -05:00
22 changed files with 5642 additions and 4046 deletions
--- a/lib/crewai-tools/pyproject.toml
+++ b/lib/crewai-tools/pyproject.toml
@@ -12,7 +12,7 @@ dependencies = [
    "pytube>=15.0.0",
    "requests>=2.32.5",
    "docker>=7.1.0",
-    "crewai==1.4.0",
+    "crewai==1.4.1",
    "lancedb>=0.5.4",
    "tiktoken>=0.8.0",
    "beautifulsoup4>=4.13.4",
--- a/lib/crewai-tools/src/crewai_tools/init.py
+++ b/lib/crewai-tools/src/crewai_tools/init.py
@@ -287,4 +287,4 @@ __all__ = [
    "ZapierActionTools",
 ]

-__version__ = "1.4.0"
+__version__ = "1.4.1"
--- a/lib/crewai/pyproject.toml
+++ b/lib/crewai/pyproject.toml
@@ -48,7 +48,7 @@ Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.4.0",
+    "crewai-tools==1.4.1",
 ]
 embeddings = [
    "tiktoken~=0.8.0"
--- a/lib/crewai/src/crewai/init.py
+++ b/lib/crewai/src/crewai/init.py
@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:

 _suppress_pydantic_deprecation_warnings()

-__version__ = "1.4.0"
+__version__ = "1.4.1"
 _telemetry_submitted = False


--- a/lib/crewai/src/crewai/agents/crew_agent_executor.py
+++ b/lib/crewai/src/crewai/agents/crew_agent_executor.py
@@ -214,6 +214,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                        llm=self.llm,
                        callbacks=self.callbacks,
                    )
+                    break

                enforce_rpm_limit(self.request_within_rpm_limit)

@@ -226,7 +227,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                    from_agent=self.agent,
                    response_model=self.response_model,
                )
-                formatted_answer = process_llm_response(answer, self.use_stop_words)
+                formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]

                if isinstance(formatted_answer, AgentAction):
                    # Extract agent fingerprint if available
@@ -258,11 +259,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                        formatted_answer, tool_result
                    )

-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text)
+                self._invoke_step_callback(formatted_answer)  # type: ignore[arg-type]
+                self._append_message(formatted_answer.text)  # type: ignore[union-attr,attr-defined]

-            except OutputParserError as e:  # noqa: PERF203
-                formatted_answer = handle_output_parser_exception(
+            except OutputParserError as e:
+                formatted_answer = handle_output_parser_exception(  # type: ignore[assignment]
                    e=e,
                    messages=self.messages,
                    iterations=self.iterations,
--- a/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml
+++ b/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]==1.4.0"
+    "crewai[tools]==1.4.1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml
+++ b/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]==1.4.0"
+    "crewai[tools]==1.4.1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/llm.py
+++ b/lib/crewai/src/crewai/llm.py
@@ -38,6 +38,13 @@ from crewai.events.types.tool_usage_events import (
    ToolUsageStartedEvent,
 )
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.constants import (
+    ANTHROPIC_MODELS,
+    AZURE_MODELS,
+    BEDROCK_MODELS,
+    GEMINI_MODELS,
+    OPENAI_MODELS,
+)
 from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
    LLMContextLengthExceededError,
@@ -323,18 +330,64 @@ class LLM(BaseLLM):
    completion_cost: float | None = None

    def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
-        """Factory method that routes to native SDK or falls back to LiteLLM."""
+        """Factory method that routes to native SDK or falls back to LiteLLM.
+
+        Routing priority:
+            1. If 'provider' kwarg is present, use that provider with constants
+            2. If only 'model' kwarg, use constants to infer provider
+            3. If "/" in model name:
+               - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
+               - If yes, validate model against constants
+               - If valid, route to native SDK; otherwise route to LiteLLM
+        """
        if not model or not isinstance(model, str):
            raise ValueError("Model must be a non-empty string")

-        provider = model.partition("/")[0] if "/" in model else "openai"
+        explicit_provider = kwargs.get("provider")

-        native_class = cls._get_native_provider(provider)
+        if explicit_provider:
+            provider = explicit_provider
+            use_native = True
+            model_string = model
+        elif "/" in model:
+            prefix, _, model_part = model.partition("/")
+
+            provider_mapping = {
+                "openai": "openai",
+                "anthropic": "anthropic",
+                "claude": "anthropic",
+                "azure": "azure",
+                "azure_openai": "azure",
+                "google": "gemini",
+                "gemini": "gemini",
+                "bedrock": "bedrock",
+                "aws": "bedrock",
+            }
+
+            canonical_provider = provider_mapping.get(prefix.lower())
+
+            if canonical_provider and cls._validate_model_in_constants(
+                model_part, canonical_provider
+            ):
+                provider = canonical_provider
+                use_native = True
+                model_string = model_part
+            else:
+                provider = prefix
+                use_native = False
+                model_string = model_part
+        else:
+            provider = cls._infer_provider_from_model(model)
+            use_native = True
+            model_string = model
+
+        native_class = cls._get_native_provider(provider) if use_native else None
        if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
            try:
-                model_string = model.partition("/")[2] if "/" in model else model
+                # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
+                kwargs_copy = {k: v for k, v in kwargs.items() if k != 'provider'}
                return cast(
-                    Self, native_class(model=model_string, provider=provider, **kwargs)
+                    Self, native_class(model=model_string, provider=provider, **kwargs_copy)
                )
            except NotImplementedError:
                raise
@@ -351,6 +404,63 @@ class LLM(BaseLLM):
        instance.is_litellm = True
        return instance

+    @classmethod
+    def _validate_model_in_constants(cls, model: str, provider: str) -> bool:
+        """Validate if a model name exists in the provider's constants.
+
+        Args:
+            model: The model name to validate
+            provider: The provider to check against (canonical name)
+
+        Returns:
+            True if the model exists in the provider's constants, False otherwise
+        """
+        if provider == "openai":
+            return model in OPENAI_MODELS
+
+        if provider == "anthropic" or provider == "claude":
+            return model in ANTHROPIC_MODELS
+
+        if provider == "gemini":
+            return model in GEMINI_MODELS
+
+        if provider == "bedrock":
+            return model in BEDROCK_MODELS
+
+        if provider == "azure":
+            # azure does not provide a list of available models, determine a better way to handle this
+            return True
+
+        return False
+
+    @classmethod
+    def _infer_provider_from_model(cls, model: str) -> str:
+        """Infer the provider from the model name.
+
+        Args:
+            model: The model name without provider prefix
+
+        Returns:
+            The inferred provider name, defaults to "openai"
+        """
+
+        if model in OPENAI_MODELS:
+            return "openai"
+
+        if model in ANTHROPIC_MODELS:
+            return "anthropic"
+
+        if model in GEMINI_MODELS:
+            return "gemini"
+
+        if model in BEDROCK_MODELS:
+            return "bedrock"
+
+        if model in AZURE_MODELS:
+            return "azure"
+
+        return "openai"
+
    @classmethod
    def _get_native_provider(cls, provider: str) -> type | None:
        """Get native provider class if available."""
--- a/lib/crewai/src/crewai/llms/constants.py
+++ b/lib/crewai/src/crewai/llms/constants.py
@@ -0,0 +1,558 @@
+from typing import Literal, TypeAlias
+
+
+OpenAIModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+OPENAI_MODELS: list[OpenAIModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+
+
+AnthropicModels: TypeAlias = Literal[
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+ANTHROPIC_MODELS: list[AnthropicModels] = [
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+
+GeminiModels: TypeAlias = Literal[
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+GEMINI_MODELS: list[GeminiModels] = [
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+
+
+AzureModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+AZURE_MODELS: list[AzureModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+
+
+BedrockModels: TypeAlias = Literal[
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]
+BEDROCK_MODELS: list[BedrockModels] = [
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]
--- a/lib/crewai/src/crewai/utilities/agent_utils.py
+++ b/lib/crewai/src/crewai/utilities/agent_utils.py
@@ -51,6 +51,40 @@ class SummaryContent(TypedDict):
 console = Console()

 _MULTIPLE_NEWLINES: Final[re.Pattern[str]] = re.compile(r"\n+")
+_REACT_FIELD_PATTERN: Final[re.Pattern[str]] = re.compile(
+    r"^(Thought|Action|Action Input|Observation):\s*",
+    re.MULTILINE
+)
+
+
+def sanitize_react_output(text: str) -> str:
+    """Sanitize agent output by removing internal ReAct fields.
+
+    This function removes lines that start with internal ReAct formatting
+    markers like "Thought:", "Action:", "Action Input:", and "Observation:".
+    These fields are used internally by the agent execution loop but should
+    not be exposed in final user-facing outputs.
+
+    Args:
+        text: The raw agent output text that may contain ReAct fields.
+
+    Returns:
+        Sanitized text with internal ReAct fields removed.
+    """
+    if not text:
+        return text
+
+    lines = text.split("\n")
+    sanitized_lines = [
+        line for line in lines if not _REACT_FIELD_PATTERN.match(line)
+    ]
+
+    result = "\n".join(sanitized_lines).strip()
+
+    if not result:
+        return "Unable to complete the task."
+
+    return result


 def parse_tools(tools: list[BaseTool]) -> list[CrewStructuredTool]:
@@ -127,7 +161,7 @@ def handle_max_iterations_exceeded(
    messages: list[LLMMessage],
    llm: LLM | BaseLLM,
    callbacks: list[TokenCalcHandler],
-) -> AgentAction | AgentFinish:
+) -> AgentFinish:
    """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.

    Args:
@@ -139,7 +173,7 @@ def handle_max_iterations_exceeded(
        callbacks: List of callbacks for the LLM call.

    Returns:
-        The final formatted answer after exceeding max iterations.
+        AgentFinish with the final answer after exceeding max iterations.
    """
    printer.print(
        content="Maximum iterations reached. Requesting final answer.",
@@ -157,7 +191,7 @@ def handle_max_iterations_exceeded(

    # Perform one more LLM call to get the final answer
    answer = llm.call(
-        messages,  # type: ignore[arg-type]
+        messages,
        callbacks=callbacks,
    )

@@ -168,8 +202,19 @@ def handle_max_iterations_exceeded(
        )
        raise ValueError("Invalid response from LLM call - None or empty.")

-    # Return the formatted answer, regardless of its type
-    return format_answer(answer=answer)
+    formatted = format_answer(answer=answer)
+
+    # If format_answer returned an AgentAction, convert it to AgentFinish
+    if isinstance(formatted, AgentFinish):
+        return formatted
+
+    sanitized_output = sanitize_react_output(formatted.text)
+
+    return AgentFinish(
+        thought=formatted.thought,
+        output=sanitized_output,
+        text=sanitized_output,
+    )


 def format_message_for_llm(
@@ -201,10 +246,11 @@ def format_answer(answer: str) -> AgentAction | AgentFinish:
    try:
        return parse(answer)
    except Exception:
+        sanitized_output = sanitize_react_output(answer)
        return AgentFinish(
            thought="Failed to parse LLM response",
-            output=answer,
-            text=answer,
+            output=sanitized_output,
+            text=sanitized_output,
        )


@@ -249,10 +295,10 @@ def get_llm_response(
    """
    try:
        answer = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
            callbacks=callbacks,
            from_task=from_task,
-            from_agent=from_agent,
+            from_agent=from_agent,  # type: ignore[arg-type]
            response_model=response_model,
        )
    except Exception as e:
@@ -294,8 +340,8 @@ def handle_agent_action_core(
    formatted_answer: AgentAction,
    tool_result: ToolResult,
    messages: list[LLMMessage] | None = None,
-    step_callback: Callable | None = None,
-    show_logs: Callable | None = None,
+    step_callback: Callable | None = None,  # type: ignore[type-arg]
+    show_logs: Callable | None = None,  # type: ignore[type-arg]
 ) -> AgentAction | AgentFinish:
    """Core logic for handling agent actions and tool results.

@@ -481,7 +527,7 @@ def summarize_messages(
            ),
        ]
        summary = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
            callbacks=callbacks,
        )
        summarized_contents.append({"content": str(summary)})
--- a/lib/crewai/tests/agents/test_agent.py
+++ b/lib/crewai/tests/agents/test_agent.py
@@ -508,7 +508,47 @@ def test_agent_custom_max_iterations():
    assert isinstance(result, str)
    assert len(result) > 0
    assert call_count > 0
-    assert call_count == 3
+    # With max_iter=1, expect 2 calls:
+    # - Call 1: iteration 0
+    # - Call 2: iteration 1 (max reached, handle_max_iterations_exceeded called, then loop breaks)
+    assert call_count == 2
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.timeout(30)
+def test_agent_max_iterations_stops_loop():
+    """Test that agent execution terminates when max_iter is reached."""
+
+    @tool
+    def get_data(step: str) -> str:
+        """Get data for a step. Always returns data requiring more steps."""
+        return f"Data for {step}: incomplete, need to query more steps."
+
+    agent = Agent(
+        role="data collector",
+        goal="collect data using the get_data tool",
+        backstory="You must use the get_data tool extensively",
+        max_iter=2,
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Use get_data tool for step1, step2, step3, step4, step5, step6, step7, step8, step9, and step10. Do NOT stop until you've called it for ALL steps.",
+        expected_output="A summary of all data collected",
+    )
+
+    result = agent.execute_task(
+        task=task,
+        tools=[get_data],
+    )
+
+    assert result is not None
+    assert isinstance(result, str)
+
+    assert agent.agent_executor.iterations <= agent.max_iter + 2, (
+        f"Agent ran {agent.agent_executor.iterations} iterations "
+        f"but should stop around {agent.max_iter + 1}. "
+    )


@pytest.mark.vcr(filter_headers=["authorization"])
--- a/lib/crewai/tests/cassettes/test_agent_max_iterations_stops_loop.yaml
+++ b/lib/crewai/tests/cassettes/test_agent_max_iterations_stops_loop.yaml
@@ -0,0 +1,495 @@
+interactions:
+- request:
+    body: '{"trace_id": "REDACTED_TRACE_ID", "execution_type":
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+      "crew_name": "Unknown Crew", "flow_name": null, "crewai_version": "1.4.0", "privacy_level":
+      "standard"}, "execution_metadata": {"expected_duration_estimate": 300, "agent_count":
+      0, "task_count": 0, "flow_method_count": 0, "execution_started_at": "2025-11-07T18:27:07.650947+00:00"}}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '434'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - CrewAI-CLI/1.4.0
+      X-Crewai-Version:
+      - 1.4.0
+    method: POST
+    uri: https://app.crewai.com/crewai_plus/api/v1/tracing/batches
+  response:
+    body:
+      string: '{"error":"bad_credentials","message":"Bad credentials"}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '55'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Fri, 07 Nov 2025 18:27:07 GMT
+      cache-control:
+      - no-store
+      content-security-policy:
+      - 'default-src ''self'' *.app.crewai.com app.crewai.com; script-src ''self''
+        ''unsafe-inline'' *.app.crewai.com app.crewai.com https://cdn.jsdelivr.net/npm/apexcharts
+        https://www.gstatic.com https://run.pstmn.io https://apis.google.com https://apis.google.com/js/api.js
+        https://accounts.google.com https://accounts.google.com/gsi/client https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.1/normalize.min.css.map
+        https://*.google.com https://docs.google.com https://slides.google.com https://js.hs-scripts.com
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+        https://js-na1.hs-scripts.com https://js.hubspot.com http://js-na1.hs-scripts.com
+        https://bat.bing.com https://cdn.amplitude.com https://cdn.segment.com https://d1d3n03t5zntha.cloudfront.net/
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--- a/lib/crewai/tests/llms/anthropic/test_anthropic.py
+++ b/lib/crewai/tests/llms/anthropic/test_anthropic.py
@@ -36,7 +36,7 @@ def test_anthropic_completion_is_used_when_claude_provider():

    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
    assert isinstance(llm, AnthropicCompletion)
-    assert llm.provider == "claude"
+    assert llm.provider == "anthropic"
    assert llm.model == "claude-3-5-sonnet-20241022"


--- a/lib/crewai/tests/llms/azure/test_azure.py
+++ b/lib/crewai/tests/llms/azure/test_azure.py
@@ -39,7 +39,7 @@ def test_azure_completion_is_used_when_azure_openai_provider():

    from crewai.llms.providers.azure.completion import AzureCompletion
    assert isinstance(llm, AzureCompletion)
-    assert llm.provider == "azure_openai"
+    assert llm.provider == "azure"
    assert llm.model == "gpt-4"


--- a/lib/crewai/tests/llms/google/test_google.py
+++ b/lib/crewai/tests/llms/google/test_google.py
@@ -24,7 +24,7 @@ def test_gemini_completion_is_used_when_google_provider():
    llm = LLM(model="google/gemini-2.0-flash-001")

    assert llm.__class__.__name__ == "GeminiCompletion"
-    assert llm.provider == "google"
+    assert llm.provider == "gemini"
    assert llm.model == "gemini-2.0-flash-001"


--- a/lib/crewai/tests/llms/hooks/test_unsupported_providers.py
+++ b/lib/crewai/tests/llms/hooks/test_unsupported_providers.py
@@ -154,7 +154,7 @@ class TestGeminiProviderInterceptor:
        # Gemini provider should raise NotImplementedError
        with pytest.raises(NotImplementedError) as exc_info:
            LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                interceptor=interceptor,
                api_key="test-gemini-key",
            )
@@ -169,7 +169,7 @@ class TestGeminiProviderInterceptor:

        with pytest.raises(NotImplementedError) as exc_info:
            LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                interceptor=interceptor,
                api_key="test-gemini-key",
            )
@@ -181,7 +181,7 @@ class TestGeminiProviderInterceptor:
    def test_gemini_without_interceptor_works(self) -> None:
        """Test that Gemini LLM works without interceptor."""
        llm = LLM(
-            model="gemini/gemini-pro",
+            model="gemini/gemini-2.5-pro",
            api_key="test-gemini-key",
        )

@@ -231,7 +231,7 @@ class TestUnsupportedProviderMessages:

        with pytest.raises(NotImplementedError) as exc_info:
            LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                interceptor=interceptor,
                api_key="test-gemini-key",
            )
@@ -282,7 +282,7 @@ class TestProviderSupportMatrix:
        # Gemini - NOT SUPPORTED
        with pytest.raises(NotImplementedError):
            LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                interceptor=interceptor,
                api_key="test",
            )
@@ -315,5 +315,5 @@ class TestProviderSupportMatrix:
        assert not hasattr(bedrock_llm, 'interceptor') or bedrock_llm.interceptor is None

        # Gemini - doesn't have interceptor attribute
-        gemini_llm = LLM(model="gemini/gemini-pro", api_key="test")
-        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None
+        gemini_llm = LLM(model="gemini/gemini-2.5-pro", api_key="test")
+        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None
--- a/lib/crewai/tests/llms/openai/test_openai.py
+++ b/lib/crewai/tests/llms/openai/test_openai.py
@@ -16,7 +16,7 @@ def test_openai_completion_is_used_when_openai_provider():
    """
    Test that OpenAICompletion from completion.py is used when LLM uses provider 'openai'
    """
-    llm = LLM(model="openai/gpt-4o")
+    llm = LLM(model="gpt-4o")

    assert llm.__class__.__name__ == "OpenAICompletion"
    assert llm.provider == "openai"
@@ -70,7 +70,7 @@ def test_openai_completion_module_is_imported():
        del sys.modules[module_name]

    # Create LLM instance - this should trigger the import
-    LLM(model="openai/gpt-4o")
+    LLM(model="gpt-4o")

    # Verify the module was imported
    assert module_name in sys.modules
@@ -97,7 +97,7 @@ def test_native_openai_raises_error_when_initialization_fails():

        # This should raise ImportError, not fall back to LiteLLM
        with pytest.raises(ImportError) as excinfo:
-            LLM(model="openai/gpt-4o")
+            LLM(model="gpt-4o")

        assert "Error importing native provider" in str(excinfo.value)
        assert "Native SDK failed" in str(excinfo.value)
@@ -108,7 +108,7 @@ def test_openai_completion_initialization_parameters():
    Test that OpenAICompletion is initialized with correct parameters
    """
    llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
        temperature=0.7,
        max_tokens=1000,
        api_key="test-key"
@@ -311,7 +311,7 @@ def test_openai_completion_call_returns_usage_metrics():
        role="Research Assistant",
        goal="Find information about the population of Tokyo",
        backstory="You are a helpful research assistant.",
-        llm=LLM(model="openai/gpt-4o"),
+        llm=LLM(model="gpt-4o"),
        verbose=True,
    )

@@ -331,6 +331,7 @@ def test_openai_completion_call_returns_usage_metrics():
    assert result.token_usage.cached_prompt_tokens == 0


+@pytest.mark.skip(reason="Allow for litellm")
 def test_openai_raises_error_when_model_not_supported():
    """Test that OpenAICompletion raises ValueError when model not supported"""

@@ -354,7 +355,7 @@ def test_openai_client_setup_with_extra_arguments():
    Test that OpenAICompletion is initialized with correct parameters
    """
    llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
        temperature=0.7,
        max_tokens=1000,
        top_p=0.5,
@@ -391,7 +392,7 @@ def test_extra_arguments_are_passed_to_openai_completion():
    """
    Test that extra arguments are passed to OpenAICompletion
    """
-    llm = LLM(model="openai/gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
+    llm = LLM(model="gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)

    with patch.object(llm.client.chat.completions, 'create') as mock_create:
        mock_create.return_value = MagicMock(
--- a/lib/crewai/tests/test_agent_output_sanitization.py
+++ b/lib/crewai/tests/test_agent_output_sanitization.py
@@ -0,0 +1,167 @@
+"""Tests for agent output sanitization to prevent internal fields from leaking."""
+
+import pytest
+from unittest.mock import Mock, patch
+
+from crewai import Agent, Crew, Task
+from crewai.agents.parser import AgentAction, AgentFinish
+from crewai.process import Process
+from crewai.utilities.agent_utils import (
+    format_answer,
+    handle_max_iterations_exceeded,
+)
+
+
+@pytest.fixture
+def mock_llm():
+    """Create a mock LLM that returns ReAct-style output."""
+    llm = Mock()
+    llm.call = Mock()
+    llm.supports_stop_words = Mock(return_value=True)
+    llm.get_context_window_size = Mock(return_value=4096)
+    return llm
+
+
+@pytest.fixture
+def mock_printer():
+    """Create a mock printer."""
+    printer = Mock()
+    printer.print = Mock()
+    return printer
+
+
+@pytest.fixture
+def mock_i18n():
+    """Create a mock i18n."""
+    i18n = Mock()
+    i18n.errors = Mock(return_value="Please provide a final answer.")
+    return i18n
+
+
+def test_handle_max_iterations_with_agent_action_should_not_leak_internal_fields(
+    mock_llm, mock_printer, mock_i18n
+):
+    """Test that when max iterations is exceeded and we have an AgentAction,
+    the final output doesn't contain internal ReAct fields like 'Thought:' and 'Action:'.
+    
+    This reproduces issue #3873 where hierarchical crews would return internal
+    fields in the final answer when delegated tasks failed.
+    """
+    formatted_answer = AgentAction(
+        thought="I need to fetch the database tables",
+        tool="PostgresTool",
+        tool_input="list_tables",
+        text="Thought: I need to fetch the database tables\nAction: PostgresTool\nAction Input: list_tables",
+    )
+    
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Fetch list of tables from postgres db"},
+    ]
+    
+    mock_llm.call.return_value = (
+        "Thought: I should try to connect to the database\n"
+        "Action: PostgresTool\n"
+        "Action Input: connect"
+    )
+    
+    callbacks = []
+    
+    result = handle_max_iterations_exceeded(
+        formatted_answer=formatted_answer,
+        printer=mock_printer,
+        i18n=mock_i18n,
+        messages=messages,
+        llm=mock_llm,
+        callbacks=callbacks,
+    )
+    
+    assert isinstance(result, AgentFinish)
+    
+    assert "Thought:" not in result.output, (
+        f"Output should not contain 'Thought:' but got: {result.output}"
+    )
+    assert "Action:" not in result.output, (
+        f"Output should not contain 'Action:' but got: {result.output}"
+    )
+    assert "Action Input:" not in result.output, (
+        f"Output should not contain 'Action Input:' but got: {result.output}"
+    )
+
+
+def test_format_answer_with_unparseable_output_should_not_leak_internal_fields():
+    """Test that when format_answer receives unparseable output with ReAct fields,
+    it sanitizes them from the final output.
+    """
+    raw_answer = (
+        "Thought: I tried to connect to the database but failed\n"
+        "Action: PostgresTool\n"
+        "Action Input: connect\n"
+        "Observation: Error: Database configuration not found"
+    )
+    
+    with patch("crewai.utilities.agent_utils.parse") as mock_parse:
+        mock_parse.side_effect = Exception("Failed to parse")
+        
+        result = format_answer(raw_answer)
+    
+    assert isinstance(result, AgentFinish)
+    
+    assert "Thought:" not in result.output, (
+        f"Output should not contain 'Thought:' but got: {result.output}"
+    )
+    assert "Action:" not in result.output, (
+        f"Output should not contain 'Action:' but got: {result.output}"
+    )
+    assert "Action Input:" not in result.output, (
+        f"Output should not contain 'Action Input:' but got: {result.output}"
+    )
+    assert "Observation:" not in result.output, (
+        f"Output should not contain 'Observation:' but got: {result.output}"
+    )
+
+
+def test_hierarchical_crew_with_failing_task_should_not_leak_internal_fields():
+    """Integration test: hierarchical crew with a failing delegated task
+    should not leak internal ReAct fields in the final output.
+    
+    This is a full integration test that reproduces issue #3873.
+    
+    Note: This test is skipped for now as it requires VCR cassettes.
+    The unit tests above cover the core functionality.
+    """
+    pytest.skip("Integration test requires VCR cassettes - covered by unit tests")
+    expert = Agent(
+        role="Database Expert",
+        goal="Fetch database information",
+        backstory="You are an expert in database operations.",
+        max_iter=2,  # Set low max_iter to trigger the bug
+        verbose=True,
+    )
+    
+    task = Task(
+        description="Fetch list of tables from postgres database",
+        expected_output="A list of database tables",
+        agent=expert,
+    )
+    
+    crew = Crew(
+        agents=[expert],
+        tasks=[task],
+        process=Process.hierarchical,
+        manager_llm="gpt-4o",
+        verbose=True,
+    )
+    
+    # Execute the crew
+    result = crew.kickoff()
+    
+    assert "Thought:" not in result.raw, (
+        f"Final output should not contain 'Thought:' but got: {result.raw}"
+    )
+    assert "Action:" not in result.raw, (
+        f"Final output should not contain 'Action:' but got: {result.raw}"
+    )
+    assert "Action Input:" not in result.raw, (
+        f"Final output should not contain 'Action Input:' but got: {result.raw}"
+    )
--- a/lib/crewai/tests/test_llm.py
+++ b/lib/crewai/tests/test_llm.py
@@ -710,7 +710,7 @@ def test_native_provider_raises_error_when_supported_but_fails():
            mock_get_native.return_value = mock_provider

            with pytest.raises(ImportError) as excinfo:
-                LLM(model="openai/gpt-4", is_litellm=False)
+                LLM(model="gpt-4", is_litellm=False)

            assert "Error importing native provider" in str(excinfo.value)
            assert "Native provider initialization failed" in str(excinfo.value)
@@ -725,3 +725,113 @@ def test_native_provider_falls_back_to_litellm_when_not_in_supported_list():
        # Should fall back to LiteLLM
        assert llm.is_litellm is True
        assert llm.model == "groq/llama-3.1-70b-versatile"
+
+
+def test_prefixed_models_with_valid_constants_use_native_sdk():
+    """Test that models with native provider prefixes use native SDK when model is in constants."""
+    # Test openai/ prefix with actual OpenAI model in constants → Native SDK
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="openai/gpt-4o", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # Test anthropic/ prefix with Claude model in constants → Native SDK
+    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+        llm2 = LLM(model="anthropic/claude-opus-4-0", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "anthropic"
+
+    # Test gemini/ prefix with Gemini model in constants → Native SDK
+    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+        llm3 = LLM(model="gemini/gemini-2.5-pro", is_litellm=False)
+        assert llm3.is_litellm is False
+        assert llm3.provider == "gemini"
+
+
+def test_prefixed_models_with_invalid_constants_use_litellm():
+    """Test that models with native provider prefixes use LiteLLM when model is NOT in constants."""
+    # Test openai/ prefix with non-OpenAI model (not in OPENAI_MODELS) → LiteLLM
+    llm = LLM(model="openai/gemini-2.5-flash", is_litellm=False)
+    assert llm.is_litellm is True
+    assert llm.model == "openai/gemini-2.5-flash"
+
+    # Test openai/ prefix with unknown future model → LiteLLM
+    llm2 = LLM(model="openai/gpt-future-6", is_litellm=False)
+    assert llm2.is_litellm is True
+    assert llm2.model == "openai/gpt-future-6"
+
+    # Test anthropic/ prefix with non-Anthropic model → LiteLLM
+    llm3 = LLM(model="anthropic/gpt-4o", is_litellm=False)
+    assert llm3.is_litellm is True
+    assert llm3.model == "anthropic/gpt-4o"
+
+
+def test_prefixed_models_with_non_native_providers_use_litellm():
+    """Test that models with non-native provider prefixes always use LiteLLM."""
+    # Test groq/ prefix (not a native provider) → LiteLLM
+    llm = LLM(model="groq/llama-3.3-70b", is_litellm=False)
+    assert llm.is_litellm is True
+    assert llm.model == "groq/llama-3.3-70b"
+
+    # Test together/ prefix (not a native provider) → LiteLLM
+    llm2 = LLM(model="together/qwen-2.5-72b", is_litellm=False)
+    assert llm2.is_litellm is True
+    assert llm2.model == "together/qwen-2.5-72b"
+
+
+def test_unprefixed_models_use_native_sdk():
+    """Test that unprefixed models use native SDK when model is in constants."""
+    # gpt-4o is in OPENAI_MODELS → Native OpenAI SDK
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="gpt-4o", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # claude-opus-4-0 is in ANTHROPIC_MODELS → Native Anthropic SDK
+    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+        llm2 = LLM(model="claude-opus-4-0", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "anthropic"
+
+    # gemini-2.5-pro is in GEMINI_MODELS → Native Gemini SDK
+    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+        llm3 = LLM(model="gemini-2.5-pro", is_litellm=False)
+        assert llm3.is_litellm is False
+        assert llm3.provider == "gemini"
+
+
+def test_explicit_provider_kwarg_takes_priority():
+    """Test that explicit provider kwarg takes priority over model name inference."""
+    # Explicit provider=openai should use OpenAI even if model name suggests otherwise
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="gpt-4o", provider="openai", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # Explicit provider for a model with "/" should still use that provider
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm2 = LLM(model="gpt-4o", provider="openai", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "openai"
+
+
+def test_validate_model_in_constants():
+    """Test the _validate_model_in_constants method."""
+    # OpenAI models
+    assert LLM._validate_model_in_constants("gpt-4o", "openai") is True
+    assert LLM._validate_model_in_constants("gpt-future-6", "openai") is False
+
+    # Anthropic models
+    assert LLM._validate_model_in_constants("claude-opus-4-0", "claude") is True
+    assert LLM._validate_model_in_constants("claude-future-5", "claude") is False
+
+    # Gemini models
+    assert LLM._validate_model_in_constants("gemini-2.5-pro", "gemini") is True
+    assert LLM._validate_model_in_constants("gemini-future", "gemini") is False
+
+    # Azure models
+    assert LLM._validate_model_in_constants("gpt-4o", "azure") is True
+    assert LLM._validate_model_in_constants("gpt-35-turbo", "azure") is True
+
+    # Bedrock models
+    assert LLM._validate_model_in_constants("anthropic.claude-opus-4-1-20250805-v1:0", "bedrock") is True
--- a/lib/devtools/src/crewai_devtools/init.py
+++ b/lib/devtools/src/crewai_devtools/init.py
@@ -1,3 +1,3 @@
 """CrewAI development tools."""

-__version__ = "1.4.0"
+__version__ = "1.4.1"
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ dev = [
    "pytest>=8.4.2",
    "pytest-asyncio>=1.2.0",
    "pytest-subprocess>=1.5.3",
-    "vcrpy==7.0.0", # pinned, less versions break pytest-recording
+    "vcrpy==7.0.0",
    "pytest-recording>=0.13.4",
    "pytest-randomly>=4.0.1",
    "pytest-timeout>=2.4.0",
--- a/uv.lock
+++ b/uv.lock
Author	SHA1	Message	Date
Devin AI	eee3556b52	Revert to pytest-recording to fix CI --block-network flag The previous commit changed pytest-recording to pytest-vcr to resolve a local conflict, but this broke CI because: - pytest-recording provides the --block-network flag used in CI - pytest-vcr does not provide this flag - The two plugins are fundamentally incompatible This commit reverts to pytest-recording (the original dependency) to restore the --block-network flag and fix the CI test failures. Co-Authored-By: João <joao@crewai.com>	2025-11-10 11:42:40 +00:00
Devin AI	efb005afaf	Fix agent output sanitization to prevent internal ReAct fields from leaking Fixes #3873 This commit addresses a bug where internal ReAct-style fields like 'Thought:', 'Action:', 'Action Input:', and 'Observation:' were leaking into the final agent output, particularly in hierarchical crews when delegated tasks failed or when agents hit max iterations. Changes: - Added sanitize_react_output() utility function to strip internal ReAct fields - Applied sanitization in handle_max_iterations_exceeded() when converting AgentAction to AgentFinish - Applied sanitization in format_answer() exception handling to prevent leaks when LLM responses cannot be parsed - Added comprehensive unit tests to verify the fix and prevent regressions - Updated pyproject.toml to use pytest-vcr instead of pytest-recording to resolve plugin conflicts The sanitization function removes lines starting with ReAct field markers while preserving legitimate content, ensuring clean user-facing outputs. Co-Authored-By: João <joao@crewai.com>	2025-11-10 11:32:48 +00:00
Lorenze Jay	0f1c173d02	feat: bump versions to 1.4.1 (#3862 ) Some checks failed CodeQL Advanced / Analyze (actions) (push) Has been cancelled Details CodeQL Advanced / Analyze (python) (push) Has been cancelled Details Notify Downstream / notify-downstream (push) Has been cancelled Details Mark stale issues and pull requests / stale (push) Has been cancelled Details Build uv cache / build-cache (3.10) (push) Has been cancelled Details Build uv cache / build-cache (3.11) (push) Has been cancelled Details Build uv cache / build-cache (3.12) (push) Has been cancelled Details Build uv cache / build-cache (3.13) (push) Has been cancelled Details * feat: bump versions to 1.4.1 * chore: update crewAI tools dependency to version 1.4.1 in project templates	2025-11-07 11:19:07 -08:00
Greyson LaLonde	19c5b9a35e	fix: properly handle agent max iterations fixes #3847	2025-11-07 13:54:11 -05:00
Greyson LaLonde	1ed307b58c	fix: route llm model syntax to litellm * fix: route llm model syntax to litellm * wip: add list of supported models	2025-11-07 13:34:15 -05:00