feat: bump versions to 1.10.0a1

docs/en/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ CrewAI Enterprise provides a comprehensive Human-in-the-Loop (HITL) management s
 Configure human review checkpoints within your Flows using the `@human_feedback` decorator. When execution reaches a review point, the system pauses, notifies the assignee via email, and waits for a response.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # AI generates content
         return "Generated marketing copy for Q1 campaign..."
 
+    @listen(generate_content)
     @human_feedback(
         message="Please review this content for brand compliance:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Marketing copy for review..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"Content rejected. Reason: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"Revision requested: {result.feedback}")
 ```
 
 For complete implementation details, see the [Human Feedback in Flows](/en/learn/human-feedback-in-flows) guide.

docs/en/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 When you specify `emit`, the decorator becomes a router. The human's free-form feedback is interpreted by an LLM and collapsed into one of the specified outcomes:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="Do you approve this content for publication?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "Draft blog post content here..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "Draft blog post content here..."
+@listen("approved")
+def publish(self, result):
+    print(f"Publishing! User said: {result.feedback}")
 
-    @human_feedback(
-        message="Do you approve this content for publication?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Draft blog post content here..."
+@listen("rejected")
+def discard(self, result):
+    print(f"Discarding. Reason: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"Publishing! User said: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"Discarding. Reason: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"Revising based on: {result.feedback}")
 ```
 
-When the human says something like "needs more detail", the LLM collapses that to `"needs_revision"`, which triggers `review_content` again via `or_()` — creating a revision loop. The loop continues until the outcome is `"approved"` or `"rejected"`.
-
 <Tip>
 The LLM uses structured outputs (function calling) when available to guarantee the response is one of your specified outcomes. This makes routing reliable and predictable.
 </Tip>
 
-<Warning>
-A `@start()` method only runs once at the beginning of the flow. If you need a revision loop, separate the start method from the review method and use `@listen(or_("trigger", "revision_outcome"))` on the review method to enable the self-loop.
-</Warning>
-
 ## HumanFeedbackResult
 
 The `HumanFeedbackResult` dataclass contains all information about a human feedback interaction:
@@ -198,183 +188,127 @@ Each `HumanFeedbackResult` is appended to `human_feedback_history`, so multiple
 
 ## Complete Example: Content Approval Workflow
 
-Here's a full example implementing a content review and approval workflow with a revision loop:
+Here's a full example implementing a content review and approval workflow:
 
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """A flow that generates content and loops until the human approves."""
+    """A flow that generates content and gets human approval."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# AI Safety\n\nThis is a draft about AI Safety..."
+    def get_topic(self):
+        self.state.topic = input("What topic should I write about? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # In real use, this would call an LLM
+        self.state.draft = f"# {topic}\n\nThis is a draft about {topic}..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="Please review this draft. Approve, reject, or describe what needs changing:",
+        message="Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"Content approved and published! Reviewer said: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ Content approved and published!")
+        print(f"Reviewer comment: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"Content rejected. Reason: {result.feedback}")
+        print("\n❌ Content rejected")
+        print(f"Reason: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 Revision #{self.state.revision_count} requested")
+        print(f"Feedback: {result.feedback}")
 
+        # In a real flow, you might loop back to generate_draft
+        # For this example, we just acknowledge
+        return "revision_requested"
+
+
+# Run the flow
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow completed. Status: {flow.state.status}, Reviews: {flow.state.revision_count}")
+print(f"\nFlow completed. Revisions requested: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# AI Safety
-
-This is a draft about AI Safety... (v1)
-==================================================
-
-Please review this draft. Approve, reject, or describe what needs changing:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: Needs more detail on alignment research
+What topic should I write about? AI Safety
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
 # AI Safety
 
-This is a draft about AI Safety... (v2)
+This is a draft about AI Safety...
 ==================================================
 
-Please review this draft. Approve, reject, or describe what needs changing:
+Please review this draft. Reply 'approved', 'rejected', or provide revision feedback:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: Looks good, approved!
 
-Content approved and published! Reviewer said: Looks good, approved!
+✅ Content approved and published!
+Reviewer comment: Looks good, approved!
 
-Flow completed. Status: published, Reviews: 2
+Flow completed. Revisions requested: 0
 ```
 
 </CodeGroup>
 
-The key pattern is `@listen(or_("generate_draft", "needs_revision"))` — the review method listens to both the initial trigger and its own revision outcome, creating a self-loop that repeats until the human approves or rejects.
-
 ## Combining with Other Decorators
 
-The `@human_feedback` decorator works with `@start()`, `@listen()`, and `or_()`. Both decorator orderings work — the framework propagates attributes in both directions — but the recommended patterns are:
+The `@human_feedback` decorator works with other flow decorators. Place it as the innermost decorator (closest to the function):
 
 ```python Code
-# One-shot review at the start of a flow (no self-loop)
+# Correct: @human_feedback is innermost (closest to the function)
 @start()
-@human_feedback(message="Review this:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="Review this:")
 def my_start_method(self):
     return "content"
 
-# Linear review on a listener (no self-loop)
 @listen(other_method)
-@human_feedback(message="Review this too:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="Review this too:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: review that can loop back for revisions
-@human_feedback(message="Approve or revise?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Self-loop pattern
-
-To create a revision loop, the review method must listen to **both** an upstream trigger and its own revision outcome using `or_()`:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="Approve or request changes?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-When the outcome is `"revise"`, the flow routes back to `review` (because it listens to `"revise"` via `or_()`). When the outcome is `"approved"`, the flow continues to `publish`. This works because the flow engine exempts routers from the "fire once" rule, allowing them to re-execute on each loop iteration.
-
-### Chained routers
-
-A listener triggered by one router's outcome can itself be a router:
-
-```python Code
-@start()
-def generate(self):
-    return "draft content"
-
-@human_feedback(message="First review:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-@listen("generate")
-def first_review(self):
-    return "draft content"
-
-@human_feedback(message="Final review:", emit=["publish", "hold"], llm="gpt-4o-mini")
-@listen("approved")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-
-@listen("hold")
-def on_hold(self, prev):
-    return "held for later"
-```
-
-### Limitations
-
-- **`@start()` methods run once**: A `@start()` method cannot self-loop. If you need a revision cycle, use a separate `@start()` method as the entry point and put the `@human_feedback` on a `@listen()` method.
-- **No `@start()` + `@listen()` on the same method**: This is a Flow framework constraint. A method is either a start point or a listener, not both.
+<Tip>
+Place `@human_feedback` as the innermost decorator (last/closest to the function) so it wraps the method directly and can capture the return value before passing to the flow system.
+</Tip>
 
 ## Best Practices
 
 ### 1. Write Clear Request Messages
 
-The `message` parameter is what the human sees. Make it actionable:
+The `request` parameter is what the human sees. Make it actionable:
 
 ```python Code
 # ✅ Good - clear and actionable
@@ -582,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="Approve this content for publication?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -600,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"Archived. Reason: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"Queued for revision: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Starting the flow (will pause and wait for Slack response)
 def start_content_pipeline():
@@ -655,22 +594,22 @@ Over time, the human sees progressively better pre-reviewed output because each
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
         message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
         learn=True,  # enable HITL learning
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **First run**: The human sees the raw output and says "Always include citations for factual claims." The lesson is distilled and stored in memory.

docs/ko/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ CrewAI Enterprise는 AI 워크플로우를 협업적인 인간-AI 프로세스
 `@human_feedback` 데코레이터를 사용하여 Flow 내에 인간 검토 체크포인트를 구성합니다. 실행이 검토 포인트에 도달하면 시스템이 일시 중지되고, 담당자에게 이메일로 알리며, 응답을 기다립니다.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # AI가 콘텐츠 생성
         return "Q1 캠페인용 마케팅 카피 생성..."
 
+    @listen(generate_content)
     @human_feedback(
         message="브랜드 준수를 위해 이 콘텐츠를 검토해 주세요:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "검토용 마케팅 카피..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"콘텐츠 거부됨. 사유: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"수정 요청: {result.feedback}")
 ```
 
 완전한 구현 세부 사항은 [Flow에서 인간 피드백](/ko/learn/human-feedback-in-flows) 가이드를 참조하세요.

docs/ko/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 `emit`을 지정하면, 데코레이터는 라우터가 됩니다. 인간의 자유 형식 피드백이 LLM에 의해 해석되어 지정된 outcome 중 하나로 매핑됩니다:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="이 콘텐츠의 출판을 승인하시겠습니까?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "블로그 게시물 초안 내용..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "블로그 게시물 초안 내용..."
+@listen("approved")
+def publish(self, result):
+    print(f"출판 중! 사용자 의견: {result.feedback}")
 
-    @human_feedback(
-        message="이 콘텐츠의 출판을 승인하시겠습니까?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "블로그 게시물 초안 내용..."
+@listen("rejected")
+def discard(self, result):
+    print(f"폐기됨. 이유: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"출판 중! 사용자 의견: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"폐기됨. 이유: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"다음을 기반으로 수정 중: {result.feedback}")
 ```
 
-사용자가 "더 자세한 내용이 필요합니다"와 같이 말하면, LLM이 이를 `"needs_revision"`으로 매핑하고, `or_()`를 통해 `review_content`가 다시 트리거됩니다 — 수정 루프가 생성됩니다. outcome이 `"approved"` 또는 `"rejected"`가 될 때까지 루프가 계속됩니다.
-
 <Tip>
 LLM은 가능한 경우 구조화된 출력(function calling)을 사용하여 응답이 지정된 outcome 중 하나임을 보장합니다. 이로 인해 라우팅이 신뢰할 수 있고 예측 가능해집니다.
 </Tip>
 
-<Warning>
-`@start()` 메서드는 flow 시작 시 한 번만 실행됩니다. 수정 루프가 필요한 경우, start 메서드를 review 메서드와 분리하고 review 메서드에 `@listen(or_("trigger", "revision_outcome"))`를 사용하여 self-loop을 활성화하세요.
-</Warning>
-
 ## HumanFeedbackResult
 
 `HumanFeedbackResult` 데이터클래스는 인간 피드백 상호작용에 대한 모든 정보를 포함합니다:
@@ -203,162 +193,116 @@ def summarize(self):
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """콘텐츠를 생성하고 승인될 때까지 반복하는 Flow."""
+    """콘텐츠를 생성하고 인간의 승인을 받는 Flow입니다."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# AI 안전\n\nAI 안전에 대한 초안..."
+    def get_topic(self):
+        self.state.topic = input("어떤 주제에 대해 글을 쓸까요? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # 실제 사용에서는 LLM을 호출합니다
+        self.state.draft = f"# {topic}\n\n{topic}에 대한 초안입니다..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:",
+        message="이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"콘텐츠 승인 및 게시! 리뷰어 의견: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ 콘텐츠가 승인되어 출판되었습니다!")
+        print(f"검토자 코멘트: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"콘텐츠 거부됨. 이유: {result.feedback}")
+        print("\n❌ 콘텐츠가 거부되었습니다")
+        print(f"이유: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 수정 #{self.state.revision_count} 요청됨")
+        print(f"피드백: {result.feedback}")
 
+        # 실제 Flow에서는 generate_draft로 돌아갈 수 있습니다
+        # 이 예제에서는 단순히 확인합니다
+        return "revision_requested"
+
+
+# Flow 실행
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow 완료. 상태: {flow.state.status}, 검토 횟수: {flow.state.revision_count}")
+print(f"\nFlow 완료. 요청된 수정: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# AI 안전
-
-AI 안전에 대한 초안... (v1)
-==================================================
-
-이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: 더 자세한 내용이 필요합니다
+어떤 주제에 대해 글을 쓸까요? AI 안전
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
 # AI 안전
 
-AI 안전에 대한 초안... (v2)
+AI 안전에 대한 초안입니다...
 ==================================================
 
-이 초안을 검토해 주세요. 승인, 거부 또는 변경이 필요한 사항을 설명해 주세요:
+이 초안을 검토해 주세요. 'approved', 'rejected'로 답하거나 수정 피드백을 제공해 주세요:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: 좋아 보입니다, 승인!
 
-콘텐츠 승인 및 게시! 리뷰어 의견: 좋아 보입니다, 승인!
+✅ 콘텐츠가 승인되어 출판되었습니다!
+검토자 코멘트: 좋아 보입니다, 승인!
 
-Flow 완료. 상태: published, 검토 횟수: 2
+Flow 완료. 요청된 수정: 0
 ```
 
 </CodeGroup>
 
 ## 다른 데코레이터와 결합하기
 
-`@human_feedback` 데코레이터는 `@start()`, `@listen()`, `or_()`와 함께 작동합니다. 데코레이터 순서는 두 가지 모두 동작합니다—프레임워크가 양방향으로 속성을 전파합니다—하지만 권장 패턴은 다음과 같습니다:
+`@human_feedback` 데코레이터는 다른 Flow 데코레이터와 함께 작동합니다. 가장 안쪽 데코레이터(함수에 가장 가까운)로 배치하세요:
 
 ```python Code
-# Flow 시작 시 일회성 검토 (self-loop 없음)
+# 올바름: @human_feedback이 가장 안쪽(함수에 가장 가까움)
 @start()
-@human_feedback(message="이것을 검토해 주세요:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="이것을 검토해 주세요:")
 def my_start_method(self):
     return "content"
 
-# 리스너에서 선형 검토 (self-loop 없음)
 @listen(other_method)
-@human_feedback(message="이것도 검토해 주세요:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="이것도 검토해 주세요:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: 수정을 위해 반복할 수 있는 검토
-@human_feedback(message="승인 또는 수정 요청?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Self-loop 패턴
-
-수정 루프를 만들려면 `or_()`를 사용하여 검토 메서드가 **상위 트리거**와 **자체 수정 outcome**을 모두 리스닝해야 합니다:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="승인하시겠습니까, 아니면 변경을 요청하시겠습니까?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-outcome이 `"revise"`이면 flow가 `review`로 다시 라우팅됩니다 (`or_()`를 통해 `"revise"`를 리스닝하기 때문). outcome이 `"approved"`이면 flow가 `publish`로 계속됩니다. flow 엔진이 라우터를 "한 번만 실행" 규칙에서 제외하여 각 루프 반복마다 재실행할 수 있기 때문에 이 패턴이 동작합니다.
-
-### 체인된 라우터
-
-한 라우터의 outcome으로 트리거된 리스너가 그 자체로 라우터가 될 수 있습니다:
-
-```python Code
-@start()
-@human_feedback(message="첫 번째 검토:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-def draft(self):
-    return "draft content"
-
-@listen("approved")
-@human_feedback(message="최종 검토:", emit=["publish", "revise"], llm="gpt-4o-mini")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-```
-
-### 제한 사항
-
-- **`@start()` 메서드는 한 번만 실행**: `@start()` 메서드는 self-loop할 수 없습니다. 수정 주기가 필요하면 별도의 `@start()` 메서드를 진입점으로 사용하고 `@listen()` 메서드에 `@human_feedback`를 배치하세요.
-- **동일 메서드에 `@start()` + `@listen()` 불가**: 이는 Flow 프레임워크 제약입니다. 메서드는 시작점이거나 리스너여야 하며, 둘 다일 수 없습니다.
+<Tip>
+`@human_feedback`를 가장 안쪽 데코레이터(마지막/함수에 가장 가까움)로 배치하여 메서드를 직접 래핑하고 Flow 시스템에 전달하기 전에 반환 값을 캡처할 수 있도록 하세요.
+</Tip>
 
 ## 모범 사례
 
@@ -572,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="이 콘텐츠의 출판을 승인하시겠습니까?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -590,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"보관됨. 이유: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"수정 대기열에 추가됨: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Flow 시작 (Slack 응답을 기다리며 일시 중지)
 def start_content_pipeline():
@@ -645,22 +594,22 @@ async def on_slack_feedback_async(flow_id: str, slack_message: str):
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
-        message="이 글 초안을 검토해 주세요:",
+        message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
-        learn=True,
+        learn=True,  # HITL 학습 활성화
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **첫 번째 실행**: 인간이 원시 출력을 보고 "사실에 대한 주장에는 항상 인용을 포함하세요."라고 말합니다. 교훈이 추출되어 메모리에 저장됩니다.

docs/pt-BR/enterprise/features/flow-hitl-management.mdx

@@ -38,21 +38,22 @@ O CrewAI Enterprise oferece um sistema abrangente de gerenciamento Human-in-the-
 Configure checkpoints de revisão humana em seus Flows usando o decorador `@human_feedback`. Quando a execução atinge um ponto de revisão, o sistema pausa, notifica o responsável via email e aguarda uma resposta.
 
 ```python
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 
 class ContentApprovalFlow(Flow):
     @start()
     def generate_content(self):
+        # IA gera conteúdo
         return "Texto de marketing gerado para campanha Q1..."
 
+    @listen(generate_content)
     @human_feedback(
         message="Por favor, revise este conteúdo para conformidade com a marca:",
         emit=["approved", "rejected", "needs_revision"],
     )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Texto de marketing para revisão..."
+    def review_content(self, content):
+        return content
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
@@ -61,6 +62,10 @@ class ContentApprovalFlow(Flow):
     @listen("rejected")
     def archive_content(self, result: HumanFeedbackResult):
         print(f"Conteúdo rejeitado. Motivo: {result.feedback}")
+
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        print(f"Revisão solicitada: {result.feedback}")
 ```
 
 Para detalhes completos de implementação, consulte o guia [Feedback Humano em Flows](/pt-BR/learn/human-feedback-in-flows).

docs/pt-BR/learn/human-feedback-in-flows.mdx

@@ -98,43 +98,33 @@ def handle_feedback(self, result):
 Quando você especifica `emit`, o decorador se torna um roteador. O feedback livre do humano é interpretado por um LLM e mapeado para um dos outcomes especificados:
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
-from crewai.flow.human_feedback import human_feedback
+@start()
+@human_feedback(
+    message="Você aprova este conteúdo para publicação?",
+    emit=["approved", "rejected", "needs_revision"],
+    llm="gpt-4o-mini",
+    default_outcome="needs_revision",
+)
+def review_content(self):
+    return "Rascunho do post do blog aqui..."
 
-class ReviewFlow(Flow):
-    @start()
-    def generate_content(self):
-        return "Rascunho do post do blog aqui..."
+@listen("approved")
+def publish(self, result):
+    print(f"Publicando! Usuário disse: {result.feedback}")
 
-    @human_feedback(
-        message="Você aprova este conteúdo para publicação?",
-        emit=["approved", "rejected", "needs_revision"],
-        llm="gpt-4o-mini",
-        default_outcome="needs_revision",
-    )
-    @listen(or_("generate_content", "needs_revision"))
-    def review_content(self):
-        return "Rascunho do post do blog aqui..."
+@listen("rejected")
+def discard(self, result):
+    print(f"Descartando. Motivo: {result.feedback}")
 
-    @listen("approved")
-    def publish(self, result):
-        print(f"Publicando! Usuário disse: {result.feedback}")
-
-    @listen("rejected")
-    def discard(self, result):
-        print(f"Descartando. Motivo: {result.feedback}")
+@listen("needs_revision")
+def revise(self, result):
+    print(f"Revisando baseado em: {result.feedback}")
 ```
 
-Quando o humano diz algo como "precisa de mais detalhes", o LLM mapeia para `"needs_revision"`, que dispara `review_content` novamente via `or_()` — criando um loop de revisão. O loop continua até que o outcome seja `"approved"` ou `"rejected"`.
-
 <Tip>
 O LLM usa saídas estruturadas (function calling) quando disponível para garantir que a resposta seja um dos seus outcomes especificados. Isso torna o roteamento confiável e previsível.
 </Tip>
 
-<Warning>
-Um método `@start()` só executa uma vez no início do flow. Se você precisa de um loop de revisão, separe o método start do método de revisão e use `@listen(or_("trigger", "revision_outcome"))` no método de revisão para habilitar o self-loop.
-</Warning>
-
 ## HumanFeedbackResult
 
 O dataclass `HumanFeedbackResult` contém todas as informações sobre uma interação de feedback humano:
@@ -203,162 +193,116 @@ Aqui está um exemplo completo implementando um fluxo de revisão e aprovação
 <CodeGroup>
 
 ```python Code
-from crewai.flow.flow import Flow, start, listen, or_
+from crewai.flow.flow import Flow, start, listen
 from crewai.flow.human_feedback import human_feedback, HumanFeedbackResult
 from pydantic import BaseModel
 
 
 class ContentState(BaseModel):
+    topic: str = ""
     draft: str = ""
+    final_content: str = ""
     revision_count: int = 0
-    status: str = "pending"
 
 
 class ContentApprovalFlow(Flow[ContentState]):
-    """Um flow que gera conteúdo e faz loop até o humano aprovar."""
+    """Um flow que gera conteúdo e obtém aprovação humana."""
 
     @start()
-    def generate_draft(self):
-        self.state.draft = "# IA Segura\n\nEste é um rascunho sobre IA Segura..."
+    def get_topic(self):
+        self.state.topic = input("Sobre qual tópico devo escrever? ")
+        return self.state.topic
+
+    @listen(get_topic)
+    def generate_draft(self, topic):
+        # Em uso real, isso chamaria um LLM
+        self.state.draft = f"# {topic}\n\nEste é um rascunho sobre {topic}..."
         return self.state.draft
 
+    @listen(generate_draft)
     @human_feedback(
-        message="Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:",
+        message="Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:",
         emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
         default_outcome="needs_revision",
     )
-    @listen(or_("generate_draft", "needs_revision"))
-    def review_draft(self):
-        self.state.revision_count += 1
-        return f"{self.state.draft} (v{self.state.revision_count})"
+    def review_draft(self, draft):
+        return draft
 
     @listen("approved")
     def publish_content(self, result: HumanFeedbackResult):
-        self.state.status = "published"
-        print(f"Conteúdo aprovado e publicado! Revisor disse: {result.feedback}")
+        self.state.final_content = result.output
+        print("\n✅ Conteúdo aprovado e publicado!")
+        print(f"Comentário do revisor: {result.feedback}")
         return "published"
 
     @listen("rejected")
     def handle_rejection(self, result: HumanFeedbackResult):
-        self.state.status = "rejected"
-        print(f"Conteúdo rejeitado. Motivo: {result.feedback}")
+        print("\n❌ Conteúdo rejeitado")
+        print(f"Motivo: {result.feedback}")
         return "rejected"
 
+    @listen("needs_revision")
+    def revise_content(self, result: HumanFeedbackResult):
+        self.state.revision_count += 1
+        print(f"\n📝 Revisão #{self.state.revision_count} solicitada")
+        print(f"Feedback: {result.feedback}")
 
+        # Em um flow real, você pode voltar para generate_draft
+        # Para este exemplo, apenas reconhecemos
+        return "revision_requested"
+
+
+# Executar o flow
 flow = ContentApprovalFlow()
 result = flow.kickoff()
-print(f"\nFlow finalizado. Status: {flow.state.status}, Revisões: {flow.state.revision_count}")
+print(f"\nFlow concluído. Revisões solicitadas: {flow.state.revision_count}")
 ```
 
 ```text Output
-==================================================
-OUTPUT FOR REVIEW:
-==================================================
-# IA Segura
-
-Este é um rascunho sobre IA Segura... (v1)
-==================================================
-
-Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:
-(Press Enter to skip, or type your feedback)
-
-Your feedback: Preciso de mais detalhes sobre segurança em IA.
+Sobre qual tópico devo escrever? Segurança em IA
 
 ==================================================
 OUTPUT FOR REVIEW:
 ==================================================
-# IA Segura
+# Segurança em IA
 
-Este é um rascunho sobre IA Segura... (v2)
+Este é um rascunho sobre Segurança em IA...
 ==================================================
 
-Por favor, revise este rascunho. Aprove, rejeite ou descreva o que precisa mudar:
+Por favor, revise este rascunho. Responda 'approved', 'rejected', ou forneça feedback de revisão:
 (Press Enter to skip, or type your feedback)
 
 Your feedback: Parece bom, aprovado!
 
-Conteúdo aprovado e publicado! Revisor disse: Parece bom, aprovado!
+✅ Conteúdo aprovado e publicado!
+Comentário do revisor: Parece bom, aprovado!
 
-Flow finalizado. Status: published, Revisões: 2
+Flow concluído. Revisões solicitadas: 0
 ```
 
 </CodeGroup>
 
 ## Combinando com Outros Decoradores
 
-O decorador `@human_feedback` funciona com `@start()`, `@listen()` e `or_()`. Ambas as ordens de decoradores funcionam — o framework propaga atributos em ambas as direções — mas os padrões recomendados são:
+O decorador `@human_feedback` funciona com outros decoradores de flow. Coloque-o como o decorador mais interno (mais próximo da função):
 
 ```python Code
-# Revisão única no início do flow (sem self-loop)
+# Correto: @human_feedback é o mais interno (mais próximo da função)
 @start()
-@human_feedback(message="Revise isto:", emit=["approved", "rejected"], llm="gpt-4o-mini")
+@human_feedback(message="Revise isto:")
 def my_start_method(self):
     return "content"
 
-# Revisão linear em um listener (sem self-loop)
 @listen(other_method)
-@human_feedback(message="Revise isto também:", emit=["good", "bad"], llm="gpt-4o-mini")
+@human_feedback(message="Revise isto também:")
 def my_listener(self, data):
     return f"processed: {data}"
-
-# Self-loop: revisão que pode voltar para revisões
-@human_feedback(message="Aprovar ou revisar?", emit=["approved", "revise"], llm="gpt-4o-mini")
-@listen(or_("upstream_method", "revise"))
-def review_with_loop(self):
-    return "content for review"
 ```
 
-### Padrão de self-loop
-
-Para criar um loop de revisão, o método de revisão deve escutar **ambos** um gatilho upstream e seu próprio outcome de revisão usando `or_()`:
-
-```python Code
-@start()
-def generate(self):
-    return "initial draft"
-
-@human_feedback(
-    message="Aprovar ou solicitar alterações?",
-    emit=["revise", "approved"],
-    llm="gpt-4o-mini",
-    default_outcome="approved",
-)
-@listen(or_("generate", "revise"))
-def review(self):
-    return "content"
-
-@listen("approved")
-def publish(self):
-    return "published"
-```
-
-Quando o outcome é `"revise"`, o flow roteia de volta para `review` (porque ele escuta `"revise"` via `or_()`). Quando o outcome é `"approved"`, o flow continua para `publish`. Isso funciona porque o engine de flow isenta roteadores da regra "fire once", permitindo que eles re-executem em cada iteração do loop.
-
-### Roteadores encadeados
-
-Um listener disparado pelo outcome de um roteador pode ser ele mesmo um roteador:
-
-```python Code
-@start()
-@human_feedback(message="Primeira revisão:", emit=["approved", "rejected"], llm="gpt-4o-mini")
-def draft(self):
-    return "draft content"
-
-@listen("approved")
-@human_feedback(message="Revisão final:", emit=["publish", "revise"], llm="gpt-4o-mini")
-def final_review(self, prev):
-    return "final content"
-
-@listen("publish")
-def on_publish(self, prev):
-    return "published"
-```
-
-### Limitações
-
-- **Métodos `@start()` executam uma vez**: Um método `@start()` não pode fazer self-loop. Se você precisa de um ciclo de revisão, use um método `@start()` separado como ponto de entrada e coloque o `@human_feedback` em um método `@listen()`.
-- **Sem `@start()` + `@listen()` no mesmo método**: Esta é uma restrição do framework de Flow. Um método é ou um ponto de início ou um listener, não ambos.
+<Tip>
+Coloque `@human_feedback` como o decorador mais interno (último/mais próximo da função) para que ele envolva o método diretamente e possa capturar o valor de retorno antes de passar para o sistema de flow.
+</Tip>
 
 ## Melhores Práticas
 
@@ -572,9 +516,9 @@ class ContentPipeline(Flow):
     @start()
     @human_feedback(
         message="Aprova este conteúdo para publicação?",
-        emit=["approved", "rejected"],
+        emit=["approved", "rejected", "needs_revision"],
         llm="gpt-4o-mini",
-        default_outcome="rejected",
+        default_outcome="needs_revision",
         provider=SlackNotificationProvider("#content-reviews"),
     )
     def generate_content(self):
@@ -590,6 +534,11 @@ class ContentPipeline(Flow):
         print(f"Arquivado. Motivo: {result.feedback}")
         return {"status": "archived"}
 
+    @listen("needs_revision")
+    def queue_revision(self, result):
+        print(f"Na fila para revisão: {result.feedback}")
+        return {"status": "revision_needed"}
+
 
 # Iniciando o flow (vai pausar e aguardar resposta do Slack)
 def start_content_pipeline():
@@ -645,22 +594,22 @@ Com o tempo, o humano vê saídas pré-revisadas progressivamente melhores porqu
 ```python Code
 class ArticleReviewFlow(Flow):
     @start()
-    def generate_article(self):
-        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
-
     @human_feedback(
-        message="Revise este rascunho do artigo:",
+        message="Review this article draft:",
         emit=["approved", "needs_revision"],
         llm="gpt-4o-mini",
         learn=True,  # enable HITL learning
     )
-    @listen(or_("generate_article", "needs_revision"))
-    def review_article(self):
-        return self.last_human_feedback.output if self.last_human_feedback else "article draft"
+    def generate_article(self):
+        return self.crew.kickoff(inputs={"topic": "AI Safety"}).raw
 
     @listen("approved")
     def publish(self):
         print(f"Publishing: {self.last_human_feedback.output}")
+
+    @listen("needs_revision")
+    def revise(self):
+        print("Revising based on feedback...")
 ```
 
 **Primeira execução**: O humano vê a saída bruta e diz "Sempre inclua citações para afirmações factuais." A lição é destilada e armazenada na memória.

lib/crewai-files/src/crewai_files/__init__.py

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.9.3"
+__version__ = "1.10.0a1"

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.9.3",
+    "crewai==1.10.0a1",
     "lancedb~=0.5.4",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",

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

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.9.3"
+__version__ = "1.10.0a1"

lib/crewai-tools/tool.specs.json

@@ -20117,18 +20117,6 @@
       "humanized_name": "Web Automation Tool",
       "init_params_schema": {
         "$defs": {
-          "AvailableModel": {
-            "enum": [
-              "gpt-4o",
-              "gpt-4o-mini",
-              "claude-3-5-sonnet-latest",
-              "claude-3-7-sonnet-latest",
-              "computer-use-preview",
-              "gemini-2.0-flash"
-            ],
-            "title": "AvailableModel",
-            "type": "string"
-          },
           "EnvVar": {
             "properties": {
               "default": {
@@ -20206,17 +20194,6 @@
             "default": null,
             "title": "Model Api Key"
           },
-          "model_name": {
-            "anyOf": [
-              {
-                "$ref": "#/$defs/AvailableModel"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": "claude-3-7-sonnet-latest"
-          },
           "project_id": {
             "anyOf": [
               {

lib/crewai/pyproject.toml

@@ -52,7 +52,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.9.3",
+    "crewai-tools==1.10.0a1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -41,7 +41,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.9.3"
+__version__ = "1.10.0a1"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -7,7 +7,6 @@ and memory management.
 from __future__ import annotations
 
 from collections.abc import Callable
-from concurrent.futures import ThreadPoolExecutor, as_completed
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast
 
@@ -686,138 +685,30 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             AgentFinish if tool has result_as_answer=True, None otherwise.
         """
+        from datetime import datetime
+        import json
+
+        from crewai.events import crewai_event_bus
+        from crewai.events.types.tool_usage_events import (
+            ToolUsageErrorEvent,
+            ToolUsageFinishedEvent,
+            ToolUsageStartedEvent,
+        )
+
         if not tool_calls:
             return None
 
-        parsed_calls = [
-            parsed
-            for tool_call in tool_calls
-            if (parsed := self._parse_native_tool_call(tool_call)) is not None
-        ]
-        if not parsed_calls:
-            return None
+        # Only process the FIRST tool call for sequential execution with reflection
+        tool_call = tool_calls[0]
 
-        original_tools_by_name: dict[str, Any] = {}
-        for tool in self.original_tools or []:
-            original_tools_by_name[sanitize_tool_name(tool.name)] = tool
-
-        if len(parsed_calls) > 1:
-            has_result_as_answer_in_batch = any(
-                bool(
-                    original_tools_by_name.get(func_name)
-                    and getattr(
-                        original_tools_by_name.get(func_name), "result_as_answer", False
-                    )
-                )
-                for _, func_name, _ in parsed_calls
-            )
-            has_max_usage_count_in_batch = any(
-                bool(
-                    original_tools_by_name.get(func_name)
-                    and getattr(
-                        original_tools_by_name.get(func_name),
-                        "max_usage_count",
-                        None,
-                    )
-                    is not None
-                )
-                for _, func_name, _ in parsed_calls
-            )
-
-            # Preserve historical sequential behavior for result_as_answer batches.
-            # Also avoid threading around usage counters for max_usage_count tools.
-            if has_result_as_answer_in_batch or has_max_usage_count_in_batch:
-                logger.debug(
-                    "Skipping parallel native execution because batch includes result_as_answer or max_usage_count tool"
-                )
-            else:
-                execution_plan: list[
-                    tuple[str, str, str | dict[str, Any], Any | None]
-                ] = []
-                for call_id, func_name, func_args in parsed_calls:
-                    original_tool = original_tools_by_name.get(func_name)
-                    execution_plan.append((call_id, func_name, func_args, original_tool))
-
-                self._append_assistant_tool_calls_message(
-                    [
-                        (call_id, func_name, func_args)
-                        for call_id, func_name, func_args, _ in execution_plan
-                    ]
-                )
-
-                max_workers = min(8, len(execution_plan))
-                ordered_results: list[dict[str, Any] | None] = [None] * len(execution_plan)
-                with ThreadPoolExecutor(max_workers=max_workers) as pool:
-                    futures = {
-                        pool.submit(
-                            self._execute_single_native_tool_call,
-                            call_id=call_id,
-                            func_name=func_name,
-                            func_args=func_args,
-                            available_functions=available_functions,
-                            original_tool=original_tool,
-                            should_execute=True,
-                        ): idx
-                        for idx, (
-                            call_id,
-                            func_name,
-                            func_args,
-                            original_tool,
-                        ) in enumerate(execution_plan)
-                    }
-                    for future in as_completed(futures):
-                        idx = futures[future]
-                        ordered_results[idx] = future.result()
-
-                for execution_result in ordered_results:
-                    if not execution_result:
-                        continue
-                    tool_finish = self._append_tool_result_and_check_finality(
-                        execution_result
-                    )
-                    if tool_finish:
-                        return tool_finish
-
-                reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-                reasoning_message: LLMMessage = {
-                    "role": "user",
-                    "content": reasoning_prompt,
-                }
-                self.messages.append(reasoning_message)
-                return None
-
-        # Sequential behavior: process only first tool call, then force reflection.
-        call_id, func_name, func_args = parsed_calls[0]
-        self._append_assistant_tool_calls_message([(call_id, func_name, func_args)])
-
-        execution_result = self._execute_single_native_tool_call(
-            call_id=call_id,
-            func_name=func_name,
-            func_args=func_args,
-            available_functions=available_functions,
-            original_tool=original_tools_by_name.get(func_name),
-            should_execute=True,
-        )
-        tool_finish = self._append_tool_result_and_check_finality(execution_result)
-        if tool_finish:
-            return tool_finish
-
-        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
-        reasoning_message: LLMMessage = {
-            "role": "user",
-            "content": reasoning_prompt,
-        }
-        self.messages.append(reasoning_message)
-        return None
-
-    def _parse_native_tool_call(
-        self, tool_call: Any
-    ) -> tuple[str, str, str | dict[str, Any]] | None:
+        # Extract tool call info - handle OpenAI-style, Anthropic-style, and Gemini-style
         if hasattr(tool_call, "function"):
+            # OpenAI-style: has .function.name and .function.arguments
             call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
             func_name = sanitize_tool_name(tool_call.function.name)
-            return call_id, func_name, tool_call.function.arguments
-        if hasattr(tool_call, "function_call") and tool_call.function_call:
+            func_args = tool_call.function.arguments
+        elif hasattr(tool_call, "function_call") and tool_call.function_call:
+            # Gemini-style: has .function_call.name and .function_call.args
             call_id = f"call_{id(tool_call)}"
             func_name = sanitize_tool_name(tool_call.function_call.name)
             func_args = (
@@ -825,12 +716,13 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 if tool_call.function_call.args
                 else {}
             )
-            return call_id, func_name, func_args
-        if hasattr(tool_call, "name") and hasattr(tool_call, "input"):
+        elif hasattr(tool_call, "name") and hasattr(tool_call, "input"):
+            # Anthropic format: has .name and .input (ToolUseBlock)
             call_id = getattr(tool_call, "id", f"call_{id(tool_call)}")
             func_name = sanitize_tool_name(tool_call.name)
-            return call_id, func_name, tool_call.input
-        if isinstance(tool_call, dict):
+            func_args = tool_call.input  # Already a dict in Anthropic
+        elif isinstance(tool_call, dict):
+            # Support OpenAI "id", Bedrock "toolUseId", or generate one
             call_id = (
                 tool_call.get("id")
                 or tool_call.get("toolUseId")
@@ -841,15 +733,10 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 func_info.get("name", "") or tool_call.get("name", "")
             )
             func_args = func_info.get("arguments", "{}") or tool_call.get("input", {})
-            return call_id, func_name, func_args
-        return None
-
-    def _append_assistant_tool_calls_message(
-        self,
-        parsed_calls: list[tuple[str, str, str | dict[str, Any]]],
-    ) -> None:
-        import json
+        else:
+            return None
 
+        # Append assistant message with single tool call
         assistant_message: LLMMessage = {
             "role": "assistant",
             "content": None,
@@ -864,30 +751,12 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         else json.dumps(func_args),
                     },
                 }
-                for call_id, func_name, func_args in parsed_calls
             ],
         }
+
         self.messages.append(assistant_message)
 
-    def _execute_single_native_tool_call(
-        self,
-        *,
-        call_id: str,
-        func_name: str,
-        func_args: str | dict[str, Any],
-        available_functions: dict[str, Callable[..., Any]],
-        original_tool: Any | None = None,
-        should_execute: bool = True,
-    ) -> dict[str, Any]:
-        from datetime import datetime
-        import json
-
-        from crewai.events.types.tool_usage_events import (
-            ToolUsageErrorEvent,
-            ToolUsageFinishedEvent,
-            ToolUsageStartedEvent,
-        )
-
+        # Parse arguments for the single tool call
         if isinstance(func_args, str):
             try:
                 args_dict = json.loads(func_args)
@@ -896,26 +765,28 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         else:
             args_dict = func_args
 
-        if original_tool is None:
-            for tool in self.original_tools or []:
-                if sanitize_tool_name(tool.name) == func_name:
-                    original_tool = tool
-                    break
+        agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
 
+        # Find original tool by matching sanitized name (needed for cache_function and result_as_answer)
+
+        original_tool = None
+        for tool in self.original_tools or []:
+            if sanitize_tool_name(tool.name) == func_name:
+                original_tool = tool
+                break
+
+        # Check if tool has reached max usage count
         max_usage_reached = False
-        if not should_execute and original_tool:
-            max_usage_reached = True
-        elif (
-            should_execute
-            and original_tool
-            and getattr(original_tool, "max_usage_count", None) is not None
-            and getattr(original_tool, "current_usage_count", 0)
-            >= original_tool.max_usage_count
-        ):
-            max_usage_reached = True
+        if original_tool:
+            if (
+                hasattr(original_tool, "max_usage_count")
+                and original_tool.max_usage_count is not None
+                and original_tool.current_usage_count >= original_tool.max_usage_count
+            ):
+                max_usage_reached = True
 
+        # Check cache before executing
         from_cache = False
-        result: str = "Tool not found"
         input_str = json.dumps(args_dict) if args_dict else ""
         if self.tools_handler and self.tools_handler.cache:
             cached_result = self.tools_handler.cache.read(
@@ -929,7 +800,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 )
                 from_cache = True
 
-        agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
+        # Emit tool usage started event
         started_at = datetime.now()
         crewai_event_bus.emit(
             self,
@@ -945,12 +816,14 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
         track_delegation_if_needed(func_name, args_dict, self.task)
 
+        # Find the structured tool for hook context
         structured_tool: CrewStructuredTool | None = None
         for structured in self.tools or []:
             if sanitize_tool_name(structured.name) == func_name:
                 structured_tool = structured
                 break
 
+        # Execute before_tool_call hooks
         hook_blocked = False
         before_hook_context = ToolCallHookContext(
             tool_name=func_name,
@@ -974,44 +847,58 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     color="red",
                 )
 
+        # If hook blocked execution, set result and skip tool execution
         if hook_blocked:
             result = f"Tool execution blocked by hook. Tool: {func_name}"
+        # Execute the tool (only if not cached, not at max usage, and not blocked by hook)
+        elif not from_cache and not max_usage_reached:
+            result = "Tool not found"
+            if func_name in available_functions:
+                try:
+                    tool_func = available_functions[func_name]
+                    raw_result = tool_func(**args_dict)
+
+                    # Add to cache after successful execution (before string conversion)
+                    if self.tools_handler and self.tools_handler.cache:
+                        should_cache = True
+                        if (
+                            original_tool
+                            and hasattr(original_tool, "cache_function")
+                            and callable(original_tool.cache_function)
+                        ):
+                            should_cache = original_tool.cache_function(
+                                args_dict, raw_result
+                            )
+                        if should_cache:
+                            self.tools_handler.cache.add(
+                                tool=func_name, input=input_str, output=raw_result
+                            )
+
+                    # Convert to string for message
+                    result = (
+                        str(raw_result)
+                        if not isinstance(raw_result, str)
+                        else raw_result
+                    )
+                except Exception as e:
+                    result = f"Error executing tool: {e}"
+                    if self.task:
+                        self.task.increment_tools_errors()
+                    crewai_event_bus.emit(
+                        self,
+                        event=ToolUsageErrorEvent(
+                            tool_name=func_name,
+                            tool_args=args_dict,
+                            from_agent=self.agent,
+                            from_task=self.task,
+                            agent_key=agent_key,
+                            error=e,
+                        ),
+                    )
+                    error_event_emitted = True
         elif max_usage_reached and original_tool:
+            # Return error message when max usage limit is reached
             result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
-        elif not from_cache and func_name in available_functions:
-            try:
-                raw_result = available_functions[func_name](**args_dict)
-
-                if self.tools_handler and self.tools_handler.cache:
-                    should_cache = True
-                    if (
-                        original_tool
-                        and hasattr(original_tool, "cache_function")
-                        and callable(original_tool.cache_function)
-                    ):
-                        should_cache = original_tool.cache_function(args_dict, raw_result)
-                    if should_cache:
-                        self.tools_handler.cache.add(
-                            tool=func_name, input=input_str, output=raw_result
-                        )
-
-                result = str(raw_result) if not isinstance(raw_result, str) else raw_result
-            except Exception as e:
-                result = f"Error executing tool: {e}"
-                if self.task:
-                    self.task.increment_tools_errors()
-                crewai_event_bus.emit(
-                    self,
-                    event=ToolUsageErrorEvent(
-                        tool_name=func_name,
-                        tool_args=args_dict,
-                        from_agent=self.agent,
-                        from_task=self.task,
-                        agent_key=agent_key,
-                        error=e,
-                    ),
-                )
-                error_event_emitted = True
 
         after_hook_context = ToolCallHookContext(
             tool_name=func_name,
@@ -1051,23 +938,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 ),
             )
 
-        return {
-            "call_id": call_id,
-            "func_name": func_name,
-            "result": result,
-            "from_cache": from_cache,
-            "original_tool": original_tool,
-        }
-
-    def _append_tool_result_and_check_finality(
-        self, execution_result: dict[str, Any]
-    ) -> AgentFinish | None:
-        call_id = cast(str, execution_result["call_id"])
-        func_name = cast(str, execution_result["func_name"])
-        result = cast(str, execution_result["result"])
-        from_cache = cast(bool, execution_result["from_cache"])
-        original_tool = execution_result["original_tool"]
-
+        # Append tool result message
         tool_message: LLMMessage = {
             "role": "tool",
             "tool_call_id": call_id,
@@ -1076,6 +947,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         }
         self.messages.append(tool_message)
 
+        # Log the tool execution
         if self.agent and self.agent.verbose:
             cache_info = " (from cache)" if from_cache else ""
             self._printer.print(
@@ -1088,11 +960,20 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             and hasattr(original_tool, "result_as_answer")
             and original_tool.result_as_answer
         ):
+            # Return immediately with tool result as final answer
             return AgentFinish(
                 thought="Tool result is the final answer",
                 output=result,
                 text=result,
             )
+
+        # Inject post-tool reasoning prompt to enforce analysis
+        reasoning_prompt = self._i18n.slice("post_tool_reasoning")
+        reasoning_message: LLMMessage = {
+            "role": "user",
+            "content": reasoning_prompt,
+        }
+        self.messages.append(reasoning_message)
         return None
 
     async def ainvoke(self, inputs: dict[str, Any]) -> dict[str, Any]:

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]==1.9.3"
+    "crewai[tools]==1.10.0a1"
 ]
 
 [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]==1.9.3"
+    "crewai[tools]==1.10.0a1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]>=0.203.1"
+    "crewai[tools]==1.10.0a1"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/experimental/agent_executor.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 from collections.abc import Callable, Coroutine
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime
 import json
 import threading
@@ -669,12 +668,9 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
         if not self.state.pending_tool_calls:
             return "native_tool_completed"
 
-        pending_tool_calls = list(self.state.pending_tool_calls)
-        self.state.pending_tool_calls.clear()
-
         # Group all tool calls into a single assistant message
         tool_calls_to_report = []
-        for tool_call in pending_tool_calls:
+        for tool_call in self.state.pending_tool_calls:
             info = extract_tool_call_info(tool_call)
             if not info:
                 continue
@@ -699,85 +695,201 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
                 "content": None,
                 "tool_calls": tool_calls_to_report,
             }
-            if all(type(tc).__qualname__ == "Part" for tc in pending_tool_calls):
-                assistant_message["raw_tool_call_parts"] = list(pending_tool_calls)
+            if all(
+                type(tc).__qualname__ == "Part" for tc in self.state.pending_tool_calls
+            ):
+                assistant_message["raw_tool_call_parts"] = list(
+                    self.state.pending_tool_calls
+                )
             self.state.messages.append(assistant_message)
 
-        runnable_tool_calls = [
-            tool_call
-            for tool_call in pending_tool_calls
-            if extract_tool_call_info(tool_call) is not None
-        ]
-        should_parallelize = self._should_parallelize_native_tool_calls(
-            runnable_tool_calls
-        )
+        # Now execute each tool
+        while self.state.pending_tool_calls:
+            tool_call = self.state.pending_tool_calls.pop(0)
+            info = extract_tool_call_info(tool_call)
+            if not info:
+                continue
 
-        execution_results: list[dict[str, Any]] = []
-        if should_parallelize:
-            max_workers = min(8, len(runnable_tool_calls))
-            with ThreadPoolExecutor(max_workers=max_workers) as pool:
-                future_to_idx = {
-                    pool.submit(self._execute_single_native_tool_call, tool_call): idx
-                    for idx, tool_call in enumerate(runnable_tool_calls)
-                }
-                ordered_results: list[dict[str, Any] | None] = [None] * len(
-                    runnable_tool_calls
+            call_id, func_name, func_args = info
+
+            # Parse arguments
+            if isinstance(func_args, str):
+                try:
+                    args_dict = json.loads(func_args)
+                except json.JSONDecodeError:
+                    args_dict = {}
+            else:
+                args_dict = func_args
+
+            # Get agent_key for event tracking
+            agent_key = (
+                getattr(self.agent, "key", "unknown") if self.agent else "unknown"
+            )
+
+            # Find original tool by matching sanitized name (needed for cache_function and result_as_answer)
+            original_tool = None
+            for tool in self.original_tools or []:
+                if sanitize_tool_name(tool.name) == func_name:
+                    original_tool = tool
+                    break
+
+            # Check if tool has reached max usage count
+            max_usage_reached = False
+            if (
+                original_tool
+                and original_tool.max_usage_count is not None
+                and original_tool.current_usage_count >= original_tool.max_usage_count
+            ):
+                max_usage_reached = True
+
+            # Check cache before executing
+            from_cache = False
+            input_str = json.dumps(args_dict) if args_dict else ""
+            if self.tools_handler and self.tools_handler.cache:
+                cached_result = self.tools_handler.cache.read(
+                    tool=func_name, input=input_str
                 )
-                for future in as_completed(future_to_idx):
-                    idx = future_to_idx[future]
-                    ordered_results[idx] = future.result()
-                execution_results = [
-                    result for result in ordered_results if result is not None
-                ]
-        else:
-            # Execute sequentially so result_as_answer tools can short-circuit
-            # immediately without running remaining calls.
-            for tool_call in runnable_tool_calls:
-                execution_result = self._execute_single_native_tool_call(tool_call)
-                call_id = cast(str, execution_result["call_id"])
-                func_name = cast(str, execution_result["func_name"])
-                result = cast(str, execution_result["result"])
-                from_cache = cast(bool, execution_result["from_cache"])
-                original_tool = execution_result["original_tool"]
+                if cached_result is not None:
+                    result = (
+                        str(cached_result)
+                        if not isinstance(cached_result, str)
+                        else cached_result
+                    )
+                    from_cache = True
 
-                tool_message: LLMMessage = {
-                    "role": "tool",
-                    "tool_call_id": call_id,
-                    "name": func_name,
-                    "content": result,
-                }
-                self.state.messages.append(tool_message)
+            # Emit tool usage started event
+            started_at = datetime.now()
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageStartedEvent(
+                    tool_name=func_name,
+                    tool_args=args_dict,
+                    from_agent=self.agent,
+                    from_task=self.task,
+                    agent_key=agent_key,
+                ),
+            )
+            error_event_emitted = False
 
-                # Log the tool execution
-                if self.agent and self.agent.verbose:
-                    cache_info = " (from cache)" if from_cache else ""
+            track_delegation_if_needed(func_name, args_dict, self.task)
+
+            structured_tool: CrewStructuredTool | None = None
+            for structured in self.tools or []:
+                if sanitize_tool_name(structured.name) == func_name:
+                    structured_tool = structured
+                    break
+
+            hook_blocked = False
+            before_hook_context = ToolCallHookContext(
+                tool_name=func_name,
+                tool_input=args_dict,
+                tool=structured_tool,  # type: ignore[arg-type]
+                agent=self.agent,
+                task=self.task,
+                crew=self.crew,
+            )
+            before_hooks = get_before_tool_call_hooks()
+            try:
+                for hook in before_hooks:
+                    hook_result = hook(before_hook_context)
+                    if hook_result is False:
+                        hook_blocked = True
+                        break
+            except Exception as hook_error:
+                if self.agent.verbose:
                     self._printer.print(
-                        content=f"Tool {func_name} executed with result{cache_info}: {result[:200]}...",
-                        color="green",
+                        content=f"Error in before_tool_call hook: {hook_error}",
+                        color="red",
                     )
 
-                if (
-                    original_tool
-                    and hasattr(original_tool, "result_as_answer")
-                    and original_tool.result_as_answer
-                ):
-                    self.state.current_answer = AgentFinish(
-                        thought="Tool result is the final answer",
+            if hook_blocked:
+                result = f"Tool execution blocked by hook. Tool: {func_name}"
+            elif not from_cache and not max_usage_reached:
+                result = "Tool not found"
+                if func_name in self._available_functions:
+                    try:
+                        tool_func = self._available_functions[func_name]
+                        raw_result = tool_func(**args_dict)
+
+                        # Add to cache after successful execution (before string conversion)
+                        if self.tools_handler and self.tools_handler.cache:
+                            should_cache = True
+                            if original_tool:
+                                should_cache = original_tool.cache_function(
+                                    args_dict, raw_result
+                                )
+                            if should_cache:
+                                self.tools_handler.cache.add(
+                                    tool=func_name, input=input_str, output=raw_result
+                                )
+
+                        # Convert to string for message
+                        result = (
+                            str(raw_result)
+                            if not isinstance(raw_result, str)
+                            else raw_result
+                        )
+                    except Exception as e:
+                        result = f"Error executing tool: {e}"
+                        if self.task:
+                            self.task.increment_tools_errors()
+                        # Emit tool usage error event
+                        crewai_event_bus.emit(
+                            self,
+                            event=ToolUsageErrorEvent(
+                                tool_name=func_name,
+                                tool_args=args_dict,
+                                from_agent=self.agent,
+                                from_task=self.task,
+                                agent_key=agent_key,
+                                error=e,
+                            ),
+                        )
+                        error_event_emitted = True
+            elif max_usage_reached and original_tool:
+                # Return error message when max usage limit is reached
+                result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
+
+            # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring)
+            after_hook_context = ToolCallHookContext(
+                tool_name=func_name,
+                tool_input=args_dict,
+                tool=structured_tool,  # type: ignore[arg-type]
+                agent=self.agent,
+                task=self.task,
+                crew=self.crew,
+                tool_result=result,
+            )
+            after_hooks = get_after_tool_call_hooks()
+            try:
+                for after_hook in after_hooks:
+                    after_hook_result = after_hook(after_hook_context)
+                    if after_hook_result is not None:
+                        result = after_hook_result
+                        after_hook_context.tool_result = result
+            except Exception as hook_error:
+                if self.agent.verbose:
+                    self._printer.print(
+                        content=f"Error in after_tool_call hook: {hook_error}",
+                        color="red",
+                    )
+
+            if not error_event_emitted:
+                crewai_event_bus.emit(
+                    self,
+                    event=ToolUsageFinishedEvent(
                         output=result,
-                        text=result,
-                    )
-                    self.state.is_finished = True
-                    return "tool_result_is_final"
-
-            return "native_tool_completed"
-
-        for execution_result in execution_results:
-            call_id = cast(str, execution_result["call_id"])
-            func_name = cast(str, execution_result["func_name"])
-            result = cast(str, execution_result["result"])
-            from_cache = cast(bool, execution_result["from_cache"])
-            original_tool = execution_result["original_tool"]
+                        tool_name=func_name,
+                        tool_args=args_dict,
+                        from_agent=self.agent,
+                        from_task=self.task,
+                        agent_key=agent_key,
+                        started_at=started_at,
+                        finished_at=datetime.now(),
+                    ),
+                )
 
+            # Append tool result message
             tool_message: LLMMessage = {
                 "role": "tool",
                 "tool_call_id": call_id,
@@ -810,224 +922,6 @@ class AgentExecutor(Flow[AgentReActState], CrewAgentExecutorMixin):
 
         return "native_tool_completed"
 
-    def _should_parallelize_native_tool_calls(self, tool_calls: list[Any]) -> bool:
-        """Determine if native tool calls are safe to run in parallel."""
-        if len(tool_calls) <= 1:
-            return False
-
-        for tool_call in tool_calls:
-            info = extract_tool_call_info(tool_call)
-            if not info:
-                continue
-            _, func_name, _ = info
-
-            original_tool = None
-            for tool in self.original_tools or []:
-                if sanitize_tool_name(tool.name) == func_name:
-                    original_tool = tool
-                    break
-
-            if not original_tool:
-                continue
-
-            if getattr(original_tool, "result_as_answer", False):
-                return False
-            if getattr(original_tool, "max_usage_count", None) is not None:
-                return False
-
-        return True
-
-    def _execute_single_native_tool_call(self, tool_call: Any) -> dict[str, Any]:
-        """Execute a single native tool call and return metadata/result."""
-        info = extract_tool_call_info(tool_call)
-        if not info:
-            raise ValueError("Invalid native tool call format")
-
-        call_id, func_name, func_args = info
-
-        # Parse arguments
-        if isinstance(func_args, str):
-            try:
-                args_dict = json.loads(func_args)
-            except json.JSONDecodeError:
-                args_dict = {}
-        else:
-            args_dict = func_args
-
-        # Get agent_key for event tracking
-        agent_key = getattr(self.agent, "key", "unknown") if self.agent else "unknown"
-
-        # Find original tool by matching sanitized name (needed for cache_function and result_as_answer)
-        original_tool = None
-        for tool in self.original_tools or []:
-            if sanitize_tool_name(tool.name) == func_name:
-                original_tool = tool
-                break
-
-        # Check if tool has reached max usage count
-        max_usage_reached = False
-        if (
-            original_tool
-            and original_tool.max_usage_count is not None
-            and original_tool.current_usage_count >= original_tool.max_usage_count
-        ):
-            max_usage_reached = True
-
-        # Check cache before executing
-        from_cache = False
-        input_str = json.dumps(args_dict) if args_dict else ""
-        if self.tools_handler and self.tools_handler.cache:
-            cached_result = self.tools_handler.cache.read(
-                tool=func_name, input=input_str
-            )
-            if cached_result is not None:
-                result = (
-                    str(cached_result)
-                    if not isinstance(cached_result, str)
-                    else cached_result
-                )
-                from_cache = True
-
-        # Emit tool usage started event
-        started_at = datetime.now()
-        crewai_event_bus.emit(
-            self,
-            event=ToolUsageStartedEvent(
-                tool_name=func_name,
-                tool_args=args_dict,
-                from_agent=self.agent,
-                from_task=self.task,
-                agent_key=agent_key,
-            ),
-        )
-        error_event_emitted = False
-
-        track_delegation_if_needed(func_name, args_dict, self.task)
-
-        structured_tool: CrewStructuredTool | None = None
-        for structured in self.tools or []:
-            if sanitize_tool_name(structured.name) == func_name:
-                structured_tool = structured
-                break
-
-        hook_blocked = False
-        before_hook_context = ToolCallHookContext(
-            tool_name=func_name,
-            tool_input=args_dict,
-            tool=structured_tool,  # type: ignore[arg-type]
-            agent=self.agent,
-            task=self.task,
-            crew=self.crew,
-        )
-        before_hooks = get_before_tool_call_hooks()
-        try:
-            for hook in before_hooks:
-                hook_result = hook(before_hook_context)
-                if hook_result is False:
-                    hook_blocked = True
-                    break
-        except Exception as hook_error:
-            if self.agent.verbose:
-                self._printer.print(
-                    content=f"Error in before_tool_call hook: {hook_error}",
-                    color="red",
-                )
-
-        if hook_blocked:
-            result = f"Tool execution blocked by hook. Tool: {func_name}"
-        elif not from_cache and not max_usage_reached:
-            result = "Tool not found"
-            if func_name in self._available_functions:
-                try:
-                    tool_func = self._available_functions[func_name]
-                    raw_result = tool_func(**args_dict)
-
-                    # Add to cache after successful execution (before string conversion)
-                    if self.tools_handler and self.tools_handler.cache:
-                        should_cache = True
-                        if original_tool:
-                            should_cache = original_tool.cache_function(
-                                args_dict, raw_result
-                            )
-                        if should_cache:
-                            self.tools_handler.cache.add(
-                                tool=func_name, input=input_str, output=raw_result
-                            )
-
-                    # Convert to string for message
-                    result = (
-                        str(raw_result)
-                        if not isinstance(raw_result, str)
-                        else raw_result
-                    )
-                except Exception as e:
-                    result = f"Error executing tool: {e}"
-                    if self.task:
-                        self.task.increment_tools_errors()
-                    # Emit tool usage error event
-                    crewai_event_bus.emit(
-                        self,
-                        event=ToolUsageErrorEvent(
-                            tool_name=func_name,
-                            tool_args=args_dict,
-                            from_agent=self.agent,
-                            from_task=self.task,
-                            agent_key=agent_key,
-                            error=e,
-                        ),
-                    )
-                    error_event_emitted = True
-        elif max_usage_reached and original_tool:
-            # Return error message when max usage limit is reached
-            result = f"Tool '{func_name}' has reached its usage limit of {original_tool.max_usage_count} times and cannot be used anymore."
-
-        # Execute after_tool_call hooks (even if blocked, to allow logging/monitoring)
-        after_hook_context = ToolCallHookContext(
-            tool_name=func_name,
-            tool_input=args_dict,
-            tool=structured_tool,  # type: ignore[arg-type]
-            agent=self.agent,
-            task=self.task,
-            crew=self.crew,
-            tool_result=result,
-        )
-        after_hooks = get_after_tool_call_hooks()
-        try:
-            for after_hook in after_hooks:
-                after_hook_result = after_hook(after_hook_context)
-                if after_hook_result is not None:
-                    result = after_hook_result
-                    after_hook_context.tool_result = result
-        except Exception as hook_error:
-            if self.agent.verbose:
-                self._printer.print(
-                    content=f"Error in after_tool_call hook: {hook_error}",
-                    color="red",
-                )
-
-        if not error_event_emitted:
-            crewai_event_bus.emit(
-                self,
-                event=ToolUsageFinishedEvent(
-                    output=result,
-                    tool_name=func_name,
-                    tool_args=args_dict,
-                    from_agent=self.agent,
-                    from_task=self.task,
-                    agent_key=agent_key,
-                    started_at=started_at,
-                    finished_at=datetime.now(),
-                ),
-            )
-
-        return {
-            "call_id": call_id,
-            "func_name": func_name,
-            "result": result,
-            "from_cache": from_cache,
-            "original_tool": original_tool,
-        }
-
     def _extract_tool_name(self, tool_call: Any) -> str:
         """Extract tool name from various tool call formats."""
         if hasattr(tool_call, "function"):

lib/crewai/src/crewai/flow/flow.py

@@ -10,7 +10,6 @@ import asyncio
 from collections.abc import (
     Callable,
     ItemsView,
-    Iterable,
     Iterator,
     KeysView,
     Sequence,
@@ -18,7 +17,6 @@ from collections.abc import (
 )
 from concurrent.futures import Future
 import copy
-import enum
 import inspect
 import logging
 import threading
@@ -29,10 +27,8 @@ from typing import (
     Generic,
     Literal,
     ParamSpec,
-    SupportsIndex,
     TypeVar,
     cast,
-    overload,
 )
 from uuid import uuid4
 
@@ -81,12 +77,7 @@ from crewai.flow.flow_wrappers import (
     StartMethod,
 )
 from crewai.flow.persistence.base import FlowPersistence
-from crewai.flow.types import (
-    FlowExecutionData,
-    FlowMethodName,
-    InputHistoryEntry,
-    PendingListenerKey,
-)
+from crewai.flow.types import FlowExecutionData, FlowMethodName, InputHistoryEntry, PendingListenerKey
 from crewai.flow.utils import (
     _extract_all_methods,
     _extract_all_methods_recursive,
@@ -435,7 +426,8 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
     """
 
     def __init__(self, lst: list[T], lock: threading.Lock) -> None:
-        super().__init__()  # empty builtin list; all access goes through self._list
+        # Do NOT call super().__init__() -- we don't want to copy data into
+        # the builtin list storage. All access goes through self._list.
         self._list = lst
         self._lock = lock
 
@@ -443,11 +435,11 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             self._list.append(item)
 
-    def extend(self, items: Iterable[T]) -> None:
+    def extend(self, items: list[T]) -> None:
         with self._lock:
             self._list.extend(items)
 
-    def insert(self, index: SupportsIndex, item: T) -> None:
+    def insert(self, index: int, item: T) -> None:
         with self._lock:
             self._list.insert(index, item)
 
@@ -455,7 +447,7 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             self._list.remove(item)
 
-    def pop(self, index: SupportsIndex = -1) -> T:
+    def pop(self, index: int = -1) -> T:
         with self._lock:
             return self._list.pop(index)
 
@@ -463,23 +455,15 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             self._list.clear()
 
-    @overload
-    def __setitem__(self, index: SupportsIndex, value: T) -> None: ...
-    @overload
-    def __setitem__(self, index: slice, value: Iterable[T]) -> None: ...
-    def __setitem__(self, index: Any, value: Any) -> None:
+    def __setitem__(self, index: int, value: T) -> None:
         with self._lock:
             self._list[index] = value
 
-    def __delitem__(self, index: SupportsIndex | slice) -> None:
+    def __delitem__(self, index: int) -> None:
         with self._lock:
             del self._list[index]
 
-    @overload
-    def __getitem__(self, index: SupportsIndex) -> T: ...
-    @overload
-    def __getitem__(self, index: slice) -> list[T]: ...
-    def __getitem__(self, index: Any) -> Any:
+    def __getitem__(self, index: int) -> T:
         return self._list[index]
 
     def __len__(self) -> int:
@@ -497,7 +481,7 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
     def __bool__(self) -> bool:
         return bool(self._list)
 
-    def __eq__(self, other: object) -> bool:
+    def __eq__(self, other: object) -> bool:  # type: ignore[override]
         """Compare based on the underlying list contents."""
         if isinstance(other, LockedListProxy):
             # Avoid deadlocks by acquiring locks in a consistent order.
@@ -508,7 +492,7 @@ class LockedListProxy(list, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             return self._list == other
 
-    def __ne__(self, other: object) -> bool:
+    def __ne__(self, other: object) -> bool:  # type: ignore[override]
         return not self.__eq__(other)
 
 
@@ -521,7 +505,8 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
     """
 
     def __init__(self, d: dict[str, T], lock: threading.Lock) -> None:
-        super().__init__()  # empty builtin dict; all access goes through self._dict
+        # Do NOT call super().__init__() -- we don't want to copy data into
+        # the builtin dict storage. All access goes through self._dict.
         self._dict = d
         self._lock = lock
 
@@ -533,11 +518,11 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             del self._dict[key]
 
-    def pop(self, key: str, *default: T) -> T:  # type: ignore[override]
+    def pop(self, key: str, *default: T) -> T:
         with self._lock:
             return self._dict.pop(key, *default)
 
-    def update(self, other: dict[str, T]) -> None:  # type: ignore[override]
+    def update(self, other: dict[str, T]) -> None:
         with self._lock:
             self._dict.update(other)
 
@@ -545,7 +530,7 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             self._dict.clear()
 
-    def setdefault(self, key: str, default: T) -> T:  # type: ignore[override]
+    def setdefault(self, key: str, default: T) -> T:
         with self._lock:
             return self._dict.setdefault(key, default)
 
@@ -561,16 +546,16 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
     def __contains__(self, key: object) -> bool:
         return key in self._dict
 
-    def keys(self) -> KeysView[str]:  # type: ignore[override]
+    def keys(self) -> KeysView[str]:
         return self._dict.keys()
 
-    def values(self) -> ValuesView[T]:  # type: ignore[override]
+    def values(self) -> ValuesView[T]:
         return self._dict.values()
 
-    def items(self) -> ItemsView[str, T]:  # type: ignore[override]
+    def items(self) -> ItemsView[str, T]:
         return self._dict.items()
 
-    def get(self, key: str, default: T | None = None) -> T | None:  # type: ignore[override]
+    def get(self, key: str, default: T | None = None) -> T | None:
         return self._dict.get(key, default)
 
     def __repr__(self) -> str:
@@ -579,7 +564,7 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
     def __bool__(self) -> bool:
         return bool(self._dict)
 
-    def __eq__(self, other: object) -> bool:
+    def __eq__(self, other: object) -> bool:  # type: ignore[override]
         """Compare based on the underlying dict contents."""
         if isinstance(other, LockedDictProxy):
             # Avoid deadlocks by acquiring locks in a consistent order.
@@ -590,7 +575,7 @@ class LockedDictProxy(dict, Generic[T]):  # type: ignore[type-arg]
         with self._lock:
             return self._dict == other
 
-    def __ne__(self, other: object) -> bool:
+    def __ne__(self, other: object) -> bool:  # type: ignore[override]
         return not self.__eq__(other)
 
 
@@ -752,9 +737,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     name: str | None = None
     tracing: bool | None = None
     stream: bool = False
-    memory: Any = (
-        None  # Memory | MemoryScope | MemorySlice | None; auto-created if not set
-    )
+    memory: Any = None  # Memory | MemoryScope | MemorySlice | None; auto-created if not set
     input_provider: Any = None  # InputProvider | None; per-flow override for self.ask()
 
     def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:
@@ -898,8 +881,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """
         if self.memory is None:
             raise ValueError("No memory configured for this flow")
-        result: list[str] = self.memory.extract_memories(content)
-        return result
+        return self.memory.extract_memories(content)
 
     def _mark_or_listener_fired(self, listener_name: FlowMethodName) -> bool:
         """Mark an OR listener as fired atomically.
@@ -1370,10 +1352,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ValueError: If structured state model lacks 'id' field
             TypeError: If state is neither BaseModel nor dictionary
         """
-        init_state = self.initial_state
-
         # Handle case where initial_state is None but we have a type parameter
-        if init_state is None and hasattr(self, "_initial_state_t"):
+        if self.initial_state is None and hasattr(self, "_initial_state_t"):
             state_type = self._initial_state_t
             if isinstance(state_type, type):
                 if issubclass(state_type, FlowState):
@@ -1397,12 +1377,12 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     return cast(T, {"id": str(uuid4())})
 
         # Handle case where no initial state is provided
-        if init_state is None:
+        if self.initial_state is None:
             return cast(T, {"id": str(uuid4())})
 
         # Handle case where initial_state is a type (class)
-        if isinstance(init_state, type):
-            state_class = init_state
+        if isinstance(self.initial_state, type):
+            state_class: type[T] = self.initial_state
             if issubclass(state_class, FlowState):
                 return state_class()
             if issubclass(state_class, BaseModel):
@@ -1413,19 +1393,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 if not getattr(model_instance, "id", None):
                     object.__setattr__(model_instance, "id", str(uuid4()))
                 return model_instance
-            if init_state is dict:
+            if self.initial_state is dict:
                 return cast(T, {"id": str(uuid4())})
 
         # Handle dictionary instance case
-        if isinstance(init_state, dict):
-            new_state = dict(init_state)  # Copy to avoid mutations
+        if isinstance(self.initial_state, dict):
+            new_state = dict(self.initial_state)  # Copy to avoid mutations
             if "id" not in new_state:
                 new_state["id"] = str(uuid4())
             return cast(T, new_state)
 
         # Handle BaseModel instance case
-        if isinstance(init_state, BaseModel):
-            model = cast(BaseModel, init_state)
+        if isinstance(self.initial_state, BaseModel):
+            model = cast(BaseModel, self.initial_state)
             if not hasattr(model, "id"):
                 raise ValueError("Flow state model must have an 'id' field")
 
@@ -2198,8 +2178,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             from crewai.flow.async_feedback.types import HumanFeedbackPending
 
             if isinstance(e, HumanFeedbackPending):
-                e.context.method_name = method_name
-
                 # Auto-save pending feedback (create default persistence if needed)
                 if self._persistence is None:
                     from crewai.flow.persistence import SQLiteFlowPersistence
@@ -2299,23 +2277,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     router_name, router_input, current_triggering_event_id
                 )
                 if router_result:  # Only add non-None results
-                    router_result_str = (
-                        router_result.value
-                        if isinstance(router_result, enum.Enum)
-                        else str(router_result)
-                    )
-                    router_results.append(FlowMethodName(router_result_str))
+                    router_results.append(FlowMethodName(str(router_result)))
                     # If this was a human_feedback router, map the outcome to the feedback
                     if self.last_human_feedback is not None:
-                        router_result_to_feedback[router_result_str] = (
+                        router_result_to_feedback[str(router_result)] = (
                             self.last_human_feedback
                         )
                 current_trigger = (
-                    FlowMethodName(
-                        router_result.value
-                        if isinstance(router_result, enum.Enum)
-                        else str(router_result)
-                    )
+                    FlowMethodName(str(router_result))
                     if router_result is not None
                     else FlowMethodName("")  # Update for next iteration of router chain
                 )
@@ -2732,10 +2701,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
                     return topic
             ```
         """
-        from concurrent.futures import (
-            ThreadPoolExecutor,
-            TimeoutError as FuturesTimeoutError,
-        )
+        from concurrent.futures import ThreadPoolExecutor, TimeoutError as FuturesTimeoutError
         from datetime import datetime
 
         from crewai.events.types.flow_events import (
@@ -2804,16 +2770,14 @@ class Flow(Generic[T], metaclass=FlowMeta):
             response = None
 
         # Record in history
-        self._input_history.append(
-            {
-                "message": message,
-                "response": response,
-                "method_name": method_name,
-                "timestamp": datetime.now(),
-                "metadata": metadata,
-                "response_metadata": response_metadata,
-            }
-        )
+        self._input_history.append({
+            "message": message,
+            "response": response,
+            "method_name": method_name,
+            "timestamp": datetime.now(),
+            "metadata": metadata,
+            "response_metadata": response_metadata,
+        })
 
         # Emit input received event
         crewai_event_bus.emit(

lib/crewai/tests/agents/test_agent_executor.py

@@ -4,7 +4,6 @@ Tests the Flow-based agent executor implementation including state management,
 flow methods, routing logic, and error handling.
 """
 
-import time
 from unittest.mock import Mock, patch
 
 import pytest
@@ -463,176 +462,3 @@ class TestFlowInvoke:
 
         assert result == {"output": "Done"}
         assert len(executor.state.messages) >= 2
-
-
-class TestNativeToolExecution:
-    """Test native tool execution behavior."""
-
-    @pytest.fixture
-    def mock_dependencies(self):
-        llm = Mock()
-        llm.supports_stop_words.return_value = True
-
-        task = Mock()
-        task.name = "Test Task"
-        task.description = "Test"
-        task.human_input = False
-        task.response_model = None
-
-        crew = Mock()
-        crew._memory = None
-        crew.verbose = False
-        crew._train = False
-
-        agent = Mock()
-        agent.id = "test-agent-id"
-        agent.role = "Test Agent"
-        agent.verbose = False
-        agent.key = "test-key"
-
-        prompt = {"prompt": "Test {input} {tool_names} {tools}"}
-
-        tools_handler = Mock()
-        tools_handler.cache = None
-
-        return {
-            "llm": llm,
-            "task": task,
-            "crew": crew,
-            "agent": agent,
-            "prompt": prompt,
-            "max_iter": 10,
-            "tools": [],
-            "tools_names": "",
-            "stop_words": [],
-            "tools_description": "",
-            "tools_handler": tools_handler,
-        }
-
-    def test_execute_native_tool_runs_parallel_for_multiple_calls(
-        self, mock_dependencies
-    ):
-        executor = AgentExecutor(**mock_dependencies)
-
-        def slow_one() -> str:
-            time.sleep(0.2)
-            return "one"
-
-        def slow_two() -> str:
-            time.sleep(0.2)
-            return "two"
-
-        executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two}
-        executor.state.pending_tool_calls = [
-            {
-                "id": "call_1",
-                "function": {"name": "slow_one", "arguments": "{}"},
-            },
-            {
-                "id": "call_2",
-                "function": {"name": "slow_two", "arguments": "{}"},
-            },
-        ]
-
-        started = time.perf_counter()
-        result = executor.execute_native_tool()
-        elapsed = time.perf_counter() - started
-
-        assert result == "native_tool_completed"
-        assert elapsed < 0.5
-        tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"]
-        assert len(tool_messages) == 2
-        assert tool_messages[0]["tool_call_id"] == "call_1"
-        assert tool_messages[1]["tool_call_id"] == "call_2"
-
-    def test_execute_native_tool_falls_back_to_sequential_for_result_as_answer(
-        self, mock_dependencies
-    ):
-        executor = AgentExecutor(**mock_dependencies)
-
-        def slow_one() -> str:
-            time.sleep(0.2)
-            return "one"
-
-        def slow_two() -> str:
-            time.sleep(0.2)
-            return "two"
-
-        result_tool = Mock()
-        result_tool.name = "slow_one"
-        result_tool.result_as_answer = True
-        result_tool.max_usage_count = None
-        result_tool.current_usage_count = 0
-
-        executor.original_tools = [result_tool]
-        executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two}
-        executor.state.pending_tool_calls = [
-            {
-                "id": "call_1",
-                "function": {"name": "slow_one", "arguments": "{}"},
-            },
-            {
-                "id": "call_2",
-                "function": {"name": "slow_two", "arguments": "{}"},
-            },
-        ]
-
-        started = time.perf_counter()
-        result = executor.execute_native_tool()
-        elapsed = time.perf_counter() - started
-
-        assert result == "tool_result_is_final"
-        assert elapsed >= 0.2
-        assert elapsed < 0.8
-        assert isinstance(executor.state.current_answer, AgentFinish)
-        assert executor.state.current_answer.output == "one"
-
-    def test_execute_native_tool_result_as_answer_short_circuits_remaining_calls(
-        self, mock_dependencies
-    ):
-        executor = AgentExecutor(**mock_dependencies)
-        call_counts = {"slow_one": 0, "slow_two": 0}
-
-        def slow_one() -> str:
-            call_counts["slow_one"] += 1
-            time.sleep(0.2)
-            return "one"
-
-        def slow_two() -> str:
-            call_counts["slow_two"] += 1
-            time.sleep(0.2)
-            return "two"
-
-        result_tool = Mock()
-        result_tool.name = "slow_one"
-        result_tool.result_as_answer = True
-        result_tool.max_usage_count = None
-        result_tool.current_usage_count = 0
-
-        executor.original_tools = [result_tool]
-        executor._available_functions = {"slow_one": slow_one, "slow_two": slow_two}
-        executor.state.pending_tool_calls = [
-            {
-                "id": "call_1",
-                "function": {"name": "slow_one", "arguments": "{}"},
-            },
-            {
-                "id": "call_2",
-                "function": {"name": "slow_two", "arguments": "{}"},
-            },
-        ]
-
-        started = time.perf_counter()
-        result = executor.execute_native_tool()
-        elapsed = time.perf_counter() - started
-
-        assert result == "tool_result_is_final"
-        assert isinstance(executor.state.current_answer, AgentFinish)
-        assert executor.state.current_answer.output == "one"
-        assert call_counts["slow_one"] == 1
-        assert call_counts["slow_two"] == 0
-        assert elapsed < 0.5
-
-        tool_messages = [m for m in executor.state.messages if m.get("role") == "tool"]
-        assert len(tool_messages) == 1
-        assert tool_messages[0]["tool_call_id"] == "call_1"

lib/crewai/tests/agents/test_native_tool_calling.py

@@ -6,20 +6,13 @@ when the LLM supports it, across multiple providers.
 
 from __future__ import annotations
 
-from collections.abc import Generator
 import os
-import threading
-import time
-from collections import Counter
 from unittest.mock import patch
 
 import pytest
 from pydantic import BaseModel, Field
 
 from crewai import Agent, Crew, Task
-from crewai.events import crewai_event_bus
-from crewai.hooks import register_after_tool_call_hook, register_before_tool_call_hook
-from crewai.hooks.tool_hooks import ToolCallHookContext
 from crewai.llm import LLM
 from crewai.tools.base_tool import BaseTool
 
@@ -71,73 +64,6 @@ class FailingTool(BaseTool):
     def _run(self) -> str:
         raise Exception("This tool always fails")
 
-
-class LocalSearchInput(BaseModel):
-    query: str = Field(description="Search query")
-
-
-class ParallelProbe:
-    """Thread-safe in-memory recorder for tool execution windows."""
-
-    _lock = threading.Lock()
-    _windows: list[tuple[str, float, float]] = []
-
-    @classmethod
-    def reset(cls) -> None:
-        with cls._lock:
-            cls._windows = []
-
-    @classmethod
-    def record(cls, tool_name: str, start: float, end: float) -> None:
-        with cls._lock:
-            cls._windows.append((tool_name, start, end))
-
-    @classmethod
-    def windows(cls) -> list[tuple[str, float, float]]:
-        with cls._lock:
-            return list(cls._windows)
-
-
-def _parallel_prompt() -> str:
-    return (
-        "This is a tool-calling compliance test. "
-        "In your next assistant turn, emit exactly 3 tool calls in the same response (parallel tool calls), in this order: "
-        "1) parallel_local_search_one(query='latest OpenAI model release notes'), "
-        "2) parallel_local_search_two(query='latest Anthropic model release notes'), "
-        "3) parallel_local_search_three(query='latest Gemini model release notes'). "
-        "Do not call any other tools and do not answer before those 3 tool calls are emitted. "
-        "After the tool results return, provide a one paragraph summary."
-    )
-
-
-def _max_concurrency(windows: list[tuple[str, float, float]]) -> int:
-    points: list[tuple[float, int]] = []
-    for _, start, end in windows:
-        points.append((start, 1))
-        points.append((end, -1))
-    points.sort(key=lambda p: (p[0], p[1]))
-
-    current = 0
-    maximum = 0
-    for _, delta in points:
-        current += delta
-        if current > maximum:
-            maximum = current
-    return maximum
-
-
-def _assert_tools_overlapped() -> None:
-    windows = ParallelProbe.windows()
-    local_windows = [
-        w
-        for w in windows
-        if w[0].startswith("parallel_local_search_")
-    ]
-
-    assert len(local_windows) >= 3, f"Expected at least 3 local tool calls, got {len(local_windows)}"
-    assert _max_concurrency(local_windows) >= 2, "Expected overlapping local tool executions"
-
-
 @pytest.fixture
 def calculator_tool() -> CalculatorTool:
     """Create a calculator tool for testing."""
@@ -156,65 +82,6 @@ def failing_tool() -> BaseTool:
 
     )
 
-
-@pytest.fixture
-def parallel_tools() -> list[BaseTool]:
-    """Create local tools used to verify native parallel execution deterministically."""
-
-    class ParallelLocalSearchOne(BaseTool):
-        name: str = "parallel_local_search_one"
-        description: str = "Local search tool #1 for concurrency testing."
-        args_schema: type[BaseModel] = LocalSearchInput
-
-        def _run(self, query: str) -> str:
-            start = time.perf_counter()
-            time.sleep(1.0)
-            end = time.perf_counter()
-            ParallelProbe.record(self.name, start, end)
-            return f"[one] {query}"
-
-    class ParallelLocalSearchTwo(BaseTool):
-        name: str = "parallel_local_search_two"
-        description: str = "Local search tool #2 for concurrency testing."
-        args_schema: type[BaseModel] = LocalSearchInput
-
-        def _run(self, query: str) -> str:
-            start = time.perf_counter()
-            time.sleep(1.0)
-            end = time.perf_counter()
-            ParallelProbe.record(self.name, start, end)
-            return f"[two] {query}"
-
-    class ParallelLocalSearchThree(BaseTool):
-        name: str = "parallel_local_search_three"
-        description: str = "Local search tool #3 for concurrency testing."
-        args_schema: type[BaseModel] = LocalSearchInput
-
-        def _run(self, query: str) -> str:
-            start = time.perf_counter()
-            time.sleep(1.0)
-            end = time.perf_counter()
-            ParallelProbe.record(self.name, start, end)
-            return f"[three] {query}"
-
-    return [
-        ParallelLocalSearchOne(),
-        ParallelLocalSearchTwo(),
-        ParallelLocalSearchThree(),
-    ]
-
-
-def _attach_parallel_probe_handler() -> None:
-    @crewai_event_bus.on(ToolUsageFinishedEvent)
-    def _capture_tool_window(_source, event: ToolUsageFinishedEvent):
-        if not event.tool_name.startswith("parallel_local_search_"):
-            return
-        ParallelProbe.record(
-            event.tool_name,
-            event.started_at.timestamp(),
-            event.finished_at.timestamp(),
-        )
-
 # =============================================================================
 # OpenAI Provider Tests
 # =============================================================================
@@ -255,7 +122,7 @@ class TestOpenAINativeToolCalling:
         self, calculator_tool: CalculatorTool
     ) -> None:
         """Test OpenAI agent kickoff with mocked LLM call."""
-        llm = LLM(model="gpt-5-nano")
+        llm = LLM(model="gpt-4o-mini")
 
         with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
             agent = Agent(
@@ -279,174 +146,6 @@ class TestOpenAINativeToolCalling:
             assert mock_call.called
             assert result is not None
 
-    @pytest.mark.vcr()
-    @pytest.mark.timeout(180)
-    def test_openai_parallel_native_tool_calling_test_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="gpt-5-nano", temperature=1),
-            verbose=False,
-            max_iter=3,
-        )
-        task = Task(
-            description=_parallel_prompt(),
-            expected_output="A one sentence summary of both tool outputs",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    @pytest.mark.timeout(180)
-    def test_openai_parallel_native_tool_calling_test_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="gpt-4o-mini"),
-            verbose=False,
-            max_iter=3,
-        )
-        result = agent.kickoff(_parallel_prompt())
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    @pytest.mark.timeout(180)
-    def test_openai_parallel_native_tool_calling_tool_hook_parity_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []}
-
-        def before_hook(context: ToolCallHookContext) -> bool | None:
-            if context.tool_name.startswith("parallel_local_search_"):
-                hook_calls["before"].append(
-                    {
-                        "tool_name": context.tool_name,
-                        "query": str(context.tool_input.get("query", "")),
-                    }
-                )
-            return None
-
-        def after_hook(context: ToolCallHookContext) -> str | None:
-            if context.tool_name.startswith("parallel_local_search_"):
-                hook_calls["after"].append(
-                    {
-                        "tool_name": context.tool_name,
-                        "query": str(context.tool_input.get("query", "")),
-                    }
-                )
-            return None
-
-        register_before_tool_call_hook(before_hook)
-        register_after_tool_call_hook(after_hook)
-
-        try:
-            agent = Agent(
-                role="Parallel Tool Agent",
-                goal="Use both tools exactly as instructed",
-                backstory="You follow tool instructions precisely.",
-                tools=parallel_tools,
-                llm=LLM(model="gpt-5-nano", temperature=1),
-                verbose=False,
-                max_iter=3,
-            )
-            task = Task(
-                description=_parallel_prompt(),
-                expected_output="A one sentence summary of both tool outputs",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task])
-            result = crew.kickoff()
-
-            assert result is not None
-            _assert_tools_overlapped()
-
-            before_names = [call["tool_name"] for call in hook_calls["before"]]
-            after_names = [call["tool_name"] for call in hook_calls["after"]]
-            assert len(before_names) >= 3, "Expected before hooks for all parallel calls"
-            assert Counter(before_names) == Counter(after_names)
-            assert all(call["query"] for call in hook_calls["before"])
-            assert all(call["query"] for call in hook_calls["after"])
-        finally:
-            from crewai.hooks import (
-                unregister_after_tool_call_hook,
-                unregister_before_tool_call_hook,
-            )
-
-            unregister_before_tool_call_hook(before_hook)
-            unregister_after_tool_call_hook(after_hook)
-
-    @pytest.mark.vcr()
-    @pytest.mark.timeout(180)
-    def test_openai_parallel_native_tool_calling_tool_hook_parity_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        hook_calls: dict[str, list[dict[str, str]]] = {"before": [], "after": []}
-
-        def before_hook(context: ToolCallHookContext) -> bool | None:
-            if context.tool_name.startswith("parallel_local_search_"):
-                hook_calls["before"].append(
-                    {
-                        "tool_name": context.tool_name,
-                        "query": str(context.tool_input.get("query", "")),
-                    }
-                )
-            return None
-
-        def after_hook(context: ToolCallHookContext) -> str | None:
-            if context.tool_name.startswith("parallel_local_search_"):
-                hook_calls["after"].append(
-                    {
-                        "tool_name": context.tool_name,
-                        "query": str(context.tool_input.get("query", "")),
-                    }
-                )
-            return None
-
-        register_before_tool_call_hook(before_hook)
-        register_after_tool_call_hook(after_hook)
-
-        try:
-            agent = Agent(
-                role="Parallel Tool Agent",
-                goal="Use both tools exactly as instructed",
-                backstory="You follow tool instructions precisely.",
-                tools=parallel_tools,
-                llm=LLM(model="gpt-5-nano", temperature=1),
-                verbose=False,
-                max_iter=3,
-            )
-            result = agent.kickoff(_parallel_prompt())
-
-            assert result is not None
-            _assert_tools_overlapped()
-
-            before_names = [call["tool_name"] for call in hook_calls["before"]]
-            after_names = [call["tool_name"] for call in hook_calls["after"]]
-            assert len(before_names) >= 3, "Expected before hooks for all parallel calls"
-            assert Counter(before_names) == Counter(after_names)
-            assert all(call["query"] for call in hook_calls["before"])
-            assert all(call["query"] for call in hook_calls["after"])
-        finally:
-            from crewai.hooks import (
-                unregister_after_tool_call_hook,
-                unregister_before_tool_call_hook,
-            )
-
-            unregister_before_tool_call_hook(before_hook)
-            unregister_after_tool_call_hook(after_hook)
-
 
 # =============================================================================
 # Anthropic Provider Tests
@@ -518,46 +217,6 @@ class TestAnthropicNativeToolCalling:
             assert mock_call.called
             assert result is not None
 
-    @pytest.mark.vcr()
-    def test_anthropic_parallel_native_tool_calling_test_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="anthropic/claude-sonnet-4-6"),
-            verbose=False,
-            max_iter=3,
-        )
-        task = Task(
-            description=_parallel_prompt(),
-            expected_output="A one sentence summary of both tool outputs",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    def test_anthropic_parallel_native_tool_calling_test_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="anthropic/claude-sonnet-4-6"),
-            verbose=False,
-            max_iter=3,
-        )
-        result = agent.kickoff(_parallel_prompt())
-        assert result is not None
-        _assert_tools_overlapped()
-
 
 # =============================================================================
 # Google/Gemini Provider Tests
@@ -588,7 +247,7 @@ class TestGeminiNativeToolCalling:
             goal="Help users with mathematical calculations",
             backstory="You are a helpful math assistant.",
             tools=[calculator_tool],
-            llm=LLM(model="gemini/gemini-2.5-flash"),
+            llm=LLM(model="gemini/gemini-2.0-flash-exp"),
         )
 
         task = Task(
@@ -607,7 +266,7 @@ class TestGeminiNativeToolCalling:
         self, calculator_tool: CalculatorTool
     ) -> None:
         """Test Gemini agent kickoff with mocked LLM call."""
-        llm = LLM(model="gemini/gemini-2.5-flash")
+        llm = LLM(model="gemini/gemini-2.0-flash-001")
 
         with patch.object(llm, "call", return_value="The answer is 120.") as mock_call:
             agent = Agent(
@@ -631,46 +290,6 @@ class TestGeminiNativeToolCalling:
             assert mock_call.called
             assert result is not None
 
-    @pytest.mark.vcr()
-    def test_gemini_parallel_native_tool_calling_test_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="gemini/gemini-2.5-flash"),
-            verbose=False,
-            max_iter=3,
-        )
-        task = Task(
-            description=_parallel_prompt(),
-            expected_output="A one sentence summary of both tool outputs",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    def test_gemini_parallel_native_tool_calling_test_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="gemini/gemini-2.5-flash"),
-            verbose=False,
-            max_iter=3,
-        )
-        result = agent.kickoff(_parallel_prompt())
-        assert result is not None
-        _assert_tools_overlapped()
-
 
 # =============================================================================
 # Azure Provider Tests
@@ -705,7 +324,7 @@ class TestAzureNativeToolCalling:
             goal="Help users with mathematical calculations",
             backstory="You are a helpful math assistant.",
             tools=[calculator_tool],
-            llm=LLM(model="azure/gpt-5-nano"),
+            llm=LLM(model="azure/gpt-4o-mini"),
             verbose=False,
             max_iter=3,
         )
@@ -728,7 +347,7 @@ class TestAzureNativeToolCalling:
     ) -> None:
         """Test Azure agent kickoff with mocked LLM call."""
         llm = LLM(
-            model="azure/gpt-5-nano",
+            model="azure/gpt-4o-mini",
             api_key="test-key",
             base_url="https://test.openai.azure.com",
         )
@@ -755,46 +374,6 @@ class TestAzureNativeToolCalling:
             assert mock_call.called
             assert result is not None
 
-    @pytest.mark.vcr()
-    def test_azure_parallel_native_tool_calling_test_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="azure/gpt-5-nano"),
-            verbose=False,
-            max_iter=3,
-        )
-        task = Task(
-            description=_parallel_prompt(),
-            expected_output="A one sentence summary of both tool outputs",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    def test_azure_parallel_native_tool_calling_test_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="azure/gpt-5-nano"),
-            verbose=False,
-            max_iter=3,
-        )
-        result = agent.kickoff(_parallel_prompt())
-        assert result is not None
-        _assert_tools_overlapped()
-
 
 # =============================================================================
 # Bedrock Provider Tests
@@ -805,30 +384,18 @@ class TestBedrockNativeToolCalling:
     """Tests for native tool calling with AWS Bedrock models."""
 
     @pytest.fixture(autouse=True)
-    def validate_bedrock_credentials_for_live_recording(self):
-        """Run Bedrock tests only when explicitly enabled."""
-        run_live_bedrock = os.getenv("RUN_BEDROCK_LIVE_TESTS", "false").lower() == "true"
-
-        if not run_live_bedrock:
-            pytest.skip(
-                "Skipping Bedrock tests by default. "
-                "Set RUN_BEDROCK_LIVE_TESTS=true with valid AWS credentials to enable."
-            )
-
-        access_key = os.getenv("AWS_ACCESS_KEY_ID", "")
-        secret_key = os.getenv("AWS_SECRET_ACCESS_KEY", "")
-        if (
-            not access_key
-            or not secret_key
-            or access_key.startswith(("fake-", "test-"))
-            or secret_key.startswith(("fake-", "test-"))
-        ):
-            pytest.skip(
-                "Skipping Bedrock tests: valid AWS credentials are required when "
-                "RUN_BEDROCK_LIVE_TESTS=true."
-            )
-
-        yield
+    def mock_aws_env(self):
+        """Mock AWS environment variables for tests."""
+        env_vars = {
+        "AWS_ACCESS_KEY_ID": "test-key",
+        "AWS_SECRET_ACCESS_KEY": "test-secret",
+        "AWS_REGION": "us-east-1",
+        }
+        if "AWS_ACCESS_KEY_ID" not in os.environ:
+            with patch.dict(os.environ, env_vars):
+                yield
+        else:
+            yield
 
     @pytest.mark.vcr()
     def test_bedrock_agent_kickoff_with_tools_mocked(
@@ -860,46 +427,6 @@ class TestBedrockNativeToolCalling:
         assert result.raw is not None
         assert "120" in str(result.raw)
 
-    @pytest.mark.vcr()
-    def test_bedrock_parallel_native_tool_calling_test_crew(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"),
-            verbose=False,
-            max_iter=3,
-        )
-        task = Task(
-            description=_parallel_prompt(),
-            expected_output="A one sentence summary of both tool outputs",
-            agent=agent,
-        )
-        crew = Crew(agents=[agent], tasks=[task])
-        result = crew.kickoff()
-        assert result is not None
-        _assert_tools_overlapped()
-
-    @pytest.mark.vcr()
-    def test_bedrock_parallel_native_tool_calling_test_agent_kickoff(
-        self, parallel_tools: list[BaseTool]
-    ) -> None:
-        agent = Agent(
-            role="Parallel Tool Agent",
-            goal="Use both tools exactly as instructed",
-            backstory="You follow tool instructions precisely.",
-            tools=parallel_tools,
-            llm=LLM(model="bedrock/anthropic.claude-3-haiku-20240307-v1:0"),
-            verbose=False,
-            max_iter=3,
-        )
-        result = agent.kickoff(_parallel_prompt())
-        assert result is not None
-        _assert_tools_overlapped()
-
 
 # =============================================================================
 # Cross-Provider Native Tool Calling Behavior Tests
@@ -912,7 +439,7 @@ class TestNativeToolCallingBehavior:
     def test_supports_function_calling_check(self) -> None:
         """Test that supports_function_calling() is properly checked."""
         # OpenAI should support function calling
-        openai_llm = LLM(model="gpt-5-nano")
+        openai_llm = LLM(model="gpt-4o-mini")
         assert hasattr(openai_llm, "supports_function_calling")
         assert openai_llm.supports_function_calling() is True
 
@@ -948,7 +475,7 @@ class TestNativeToolCallingTokenUsage:
             goal="Perform calculations efficiently",
             backstory="You calculate things.",
             tools=[calculator_tool],
-            llm=LLM(model="gpt-5-nano"),
+            llm=LLM(model="gpt-4o-mini"),
             verbose=False,
             max_iter=3,
         )
@@ -992,7 +519,7 @@ def test_native_tool_calling_error_handling(failing_tool: FailingTool):
         goal="Perform calculations efficiently",
         backstory="You calculate things.",
         tools=[failing_tool],
-        llm=LLM(model="gpt-5-nano"),
+        llm=LLM(model="gpt-4o-mini"),
         verbose=False,
         max_iter=3,
     )
@@ -1051,7 +578,7 @@ class TestMaxUsageCountWithNativeToolCalling:
             goal="Call the counting tool multiple times",
             backstory="You are an agent that counts things.",
             tools=[tool],
-            llm=LLM(model="gpt-5-nano"),
+            llm=LLM(model="gpt-4o-mini"),
             verbose=False,
             max_iter=5,
         )
@@ -1079,7 +606,7 @@ class TestMaxUsageCountWithNativeToolCalling:
             goal="Use the counting tool as many times as requested",
             backstory="You are an agent that counts things. You must try to use the tool for each value requested.",
             tools=[tool],
-            llm=LLM(model="gpt-5-nano"),
+            llm=LLM(model="gpt-4o-mini"),
             verbose=False,
             max_iter=5,
         )
@@ -1111,7 +638,7 @@ class TestMaxUsageCountWithNativeToolCalling:
             goal="Use the counting tool exactly as requested",
             backstory="You are an agent that counts things precisely.",
             tools=[tool],
-            llm=LLM(model="gpt-5-nano"),
+            llm=LLM(model="gpt-4o-mini"),
             verbose=False,
             max_iter=5,
         )
@@ -1126,6 +653,5 @@ class TestMaxUsageCountWithNativeToolCalling:
         result = crew.kickoff()
 
         assert result is not None
-        # Verify the requested calls occurred while keeping usage bounded.
-        assert tool.current_usage_count >= 2
-        assert tool.current_usage_count <= tool.max_usage_count
+        # Verify usage count was incremented for each successful call
+        assert tool.current_usage_count == 2

lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_agent_kickoff.yaml

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lib/crewai/tests/cassettes/agents/TestAnthropicNativeToolCalling.test_anthropic_parallel_native_tool_calling_test_crew.yaml

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-      anthropic-ratelimit-requests-remaining:
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-      anthropic-ratelimit-requests-reset:
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-      anthropic-ratelimit-tokens-limit:
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-      anthropic-ratelimit-tokens-remaining:
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-      strict-transport-security:
-      - STS-XXX
-      x-envoy-upstream-service-time:
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-    status:
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lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_agent_with_native_tool_calling.yaml

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-      "type": "string"}}, "required": ["expression"], "type": "object", "additionalProperties":
-      false}}, "type": "function"}]}'
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+      "tools": [{"function": {"name": "calculator", "description": "Perform mathematical
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+      {"expression": {"description": "Mathematical expression to evaluate", "title":
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     headers:
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       Connection:
       - keep-alive
       Content-Length:
-      - '828'
+      - '883'
       Content-Type:
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       User-Agent:
@@ -31,20 +32,20 @@ interactions:
       x-ms-client-request-id:
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     method: POST
-    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview
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     headers:
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+      - '1058'
       Content-Type:
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+      - Thu, 22 Jan 2026 22:48:34 GMT
       Strict-Transport-Security:
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       x-ms-deployment-name:
-      - gpt-5-nano
+      - gpt-4o-mini
       x-ms-rai-invoked:
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       x-ms-region:
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-      "Expression", "type": "string"}}, "required": ["expression"], "type": "object",
-      "additionalProperties": false}}, "type": "function"}]}'
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+      "content": "The result of 15 * 8 is 120"}, {"role": "user", "content": "Analyze
+      the tool result. If requirements are met, provide the Final Answer. Otherwise,
+      call the next tool. Deliver only the answer without meta-commentary."}], "stream":
+      false, "stop": ["\nObservation:"], "tool_choice": "auto", "tools": [{"function":
+      {"name": "calculator", "description": "Perform mathematical calculations. Use
+      this for any math operations.", "parameters": {"properties": {"expression":
+      {"description": "Mathematical expression to evaluate", "title": "Expression",
+      "type": "string"}}, "required": ["expression"], "type": "object"}}, "type":
+      "function"}]}'
     headers:
       Accept:
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       Connection:
       - keep-alive
       Content-Length:
-      - '1320'
+      - '1375'
       Content-Type:
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       User-Agent:
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     method: POST
-    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-5-nano/chat/completions?api-version=2024-12-01-preview
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-12-01-preview
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+      - '1250'
       Content-Type:
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+      - Thu, 22 Jan 2026 22:48:34 GMT
       Strict-Transport-Security:
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+      - gpt-4o-mini
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lib/crewai/tests/cassettes/agents/TestAzureNativeToolCalling.test_azure_parallel_native_tool_calling_test_agent_kickoff.yaml

@@ -1,198 +0,0 @@
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