fix(ci): add type annotations to _SSRFSafeAdapter.send and fix test mocks

- Add proper type annotations to _SSRFSafeAdapter.send() to satisfy mypy
- Add 'Any' import from typing
- Update webpage_loader tests to mock safe_get instead of requests.get
  (the loader now uses safe_get for SSRF protection)

.github/security.md

@@ -5,7 +5,10 @@ CrewAI ecosystem.
 
 ### How to Report
 
-Please submit reports to **crewai-vdp-ess@submit.bugcrowd.com**
+Please submit reports through one of the following channels:
+
+- **crewai-vdp-ess@submit.bugcrowd.com**
+- https://security.crewai.com
 
 - **Please do not** disclose vulnerabilities via public GitHub issues, pull requests,
   or social media

docs/ar/api-reference/introduction.mdx

@@ -26,7 +26,7 @@ mode: "wide"
 </Step>
 
   <Step title="مراقبة التقدم">
-    استخدم `GET /{kickoff_id}/status` للتحقق من حالة التنفيذ واسترجاع النتائج.
+    استخدم `GET /status/{kickoff_id}` للتحقق من حالة التنفيذ واسترجاع النتائج.
   </Step>
 </Steps>
 
@@ -65,7 +65,7 @@ https://your-crew-name.crewai.com
 
 1. **الاكتشاف**: استدعِ `GET /inputs` لفهم ما يحتاجه طاقمك
 2. **التنفيذ**: أرسل المدخلات عبر `POST /kickoff` لبدء المعالجة
-3. **المراقبة**: استعلم عن `GET /{kickoff_id}/status` حتى الاكتمال
+3. **المراقبة**: استعلم عن `GET /status/{kickoff_id}` حتى الاكتمال
 4. **النتائج**: استخرج المخرجات النهائية من الاستجابة المكتملة
 
 ## معالجة الأخطاء

docs/ar/api-reference/status.mdx

@@ -1,6 +1,6 @@
 ---
-title: "GET /{kickoff_id}/status"
+title: "GET /status/{kickoff_id}"
 description: "الحصول على حالة التنفيذ"
-openapi: "/enterprise-api.en.yaml GET /{kickoff_id}/status"
+openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
 mode: "wide"
 ---

docs/ar/changelog.mdx

@@ -4,6 +4,99 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="4 مايو 2026">
+  ## v1.14.5a2
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a2)
+
+  ## ما الذي تغير
+
+  ### إصلاحات الأخطاء
+  - إصلاح استعادة مخرجات المهام في كتلة finally
+  - تضمين `thoughts_token_count` في رموز الإكمال
+  - الحفاظ على مخرجات المهام عبر تفريغ دفعات غير متزامنة
+  - تمرير kwargs إلى استدعاءات المحمل في `CrewAIRagAdapter`
+  - منع `result_as_answer` من إرجاع رسالة كتلة الخطاف كإجابة نهائية
+  - منع `result_as_answer` من إرجاع خطأ كإجابة نهائية
+  - استخدام `acall` لتحويل المخرجات في المسارات غير المتزامنة
+  - منع تغيير كلمات التوقف المشتركة في LLM عبر الوكلاء
+  - التعامل مع مدخلات `BaseModel` في `convert_to_model`
+
+  ### الوثائق
+  - توثيق متغيرات البيئة الإضافية
+  - تحديث سجل التغييرات والإصدار لـ v1.14.5a1
+
+  ## المساهمون
+
+  @NIK-TIGER-BILL, @greysonlalonde, @lorenzejay, @minasami-pr, @theCyberTech, @wishhyt
+
+</Update>
+
+<Update label="1 مايو 2026">
+  ## v1.14.5a1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة معلمة بدء `restore_from_state_id`
+  - إضافة تسليط الضوء على ExaSearchTool وإعادة تسميته من EXASearchTool
+
+  ### إصلاحات الأخطاء
+  - إصلاح المواقع المفقودة لـ crewai في تدفق الإصدار
+  - ضمان تحميل أحداث المهارات للآثار
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.4
+
+  ## المساهمون
+
+  @akaKuruma, @github-actions[bot], @greysonlalonde, @lorenzejay, @theishangoswami
+
+</Update>
+
+<Update label="1 مايو 2026">
+  ## v1.14.4
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.4)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة دعم لمفتاح الاستمرارية المخصص في @persist
+  - إضافة دعم واجهة برمجة التطبيقات للردود لمزود Azure OpenAI
+  - تمرير credential_scopes إلى عميل Azure AI Inference
+  - إضافة دليل إعداد هوية عبء العمل لـ Vertex AI
+  - إضافة Tavily Research والحصول على Research
+  - إضافة أدوات MCP من You.com للبحث، البحث، واستخراج المحتوى
+
+  ### إصلاحات الأخطاء
+  - إصلاح مشكلة السقوط عند عدم تطابق تعبير JSON regex مع JSON صالح
+  - إصلاح للحفاظ على tool_calls عندما تحتوي الاستجابة أيضًا على نص
+  - إصلاح لتمرير base_url و api_key إلى instructor.from_provider
+  - إصلاح لتحذير وإرجاع فارغ عندما لا يُرجع خادم MCP الأصلي أي أدوات
+  - إصلاح لاستخدام متغير الرسائل الموثقة في معالجات غير البث
+  - إصلاح لحماية مساعدي وصف دردشة الطاقم ضد فشل LLM
+  - إصلاح لإعادة تعيين الرسائل والتكرارات بين الاستدعاءات
+  - إصلاح لتمرير ملف trained-agents من خلال replay و test
+  - إصلاح لاحترام ملف trained-agents المخصص في الاستدلال
+  - إصلاح لربط الوكلاء المخصصين بالمهام فقط بالطاقم لملفات الإدخال متعددة الأنماط
+  - إصلاح لتسلسل callable الحواجز كـ null لتسجيل JSON
+  - إصلاح إعادة تسمية force_final_answer لتجنب توجيه ذاتي
+  - إصلاح زيادة litellm لإصلاح SSTI؛ تجاهل CVE غير القابل للإصلاح في pip
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.4a1
+  - إضافة صفحة أدوات E2B Sandbox
+  - إضافة وثائق أدوات صندوق Daytona
+
+  ## المساهمون
+
+  @EdwardIrby, @dependabot[bot], @factory-droid-oss, @factory-droid[bot], @greysonlalonde, @kunalk16, @lorenzejay, @lucasgomide, @manisrinivasan2k1, @mattatcha, @vinibrsl
+
+</Update>
+
 <Update label="29 أبريل 2026">
   ## v1.14.4a1
 

docs/ar/concepts/flows.mdx

@@ -380,32 +380,41 @@ class AnotherFlow(Flow[dict]):
         print("Method-level persisted runs:", self.state["runs"])
 ```
 
-### مفتاح استمرارية مخصص
+### تفرع الحالة المستمرة
 
-افتراضيًا، يستخدم `@persist` الحقل `state.id` المُولّد تلقائيًا كمفتاح للاستمرارية. إذا كان لتدفقك معرّف خاص به — مثل `conversation_id` مشترك بين عدة جلسات — يمكنك تمرير الوسيط `key` ليستخدم `@persist` تلك السمة كـ UUID للتدفق:
+يدعم `@persist` نمطين متميزين للترطيب في `kickoff` / `kickoff_async`:
+
+- `kickoff(inputs={"id": <uuid>})` — **استئناف**: يحمّل أحدث لقطة لـ UUID المقدم ويستمر في الكتابة تحت نفس `flow_uuid`. يمتد التاريخ.
+- `kickoff(restore_from_state_id=<uuid>)` — **تفرع**: يحمّل أحدث لقطة لـ UUID المقدم، يرطّب حالة التشغيل الجديد منها، ثم يعيّن `state.id` جديدًا (مولّدًا تلقائيًا، أو `inputs["id"]` إذا تم تثبيته). تذهب كتابات `@persist` للتشغيل الجديد تحت `state.id` الجديد؛ يتم الحفاظ على تاريخ تدفق المصدر.
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    turn: int = 0
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")  # استخدام حقل مخصص كمفتاح للاستمرارية
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def begin(self):
-        self.state.turn += 1
-        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")
+    def step(self):
+        self.state.counter += 1
+        print(f"[id={self.state.id}] counter={self.state.counter}")
 
-# إعادة تشغيل المحادثة بنفس conversation_id يُعيد تحميل الحالة السابقة
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# التشغيل 1: حالة جديدة، العداد 0 -> 1، محفوظ تحت flow_1.state.id
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# التفرع: ترطيب من أحدث لقطة لـ flow_1، لكن باستخدام state.id جديد
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# يبدأ flow_2.state.counter بـ 1 (مرطّب)، ثم تزيده step() إلى 2.
+# flow_2.state.id != flow_1.state.id؛ تاريخ flow_1 لم يتغيّر.
 ```
 
-يقرأ المزخرف القيمة من `state[key]` للحالات من نوع dict، ومن `getattr(state, key)` للحالات من نوع Pydantic / كائن. إذا كانت السمة المحددة غير موجودة أو قيمتها falsy عند الحفظ، يُطلق `@persist` خطأ `ValueError` مثل `Flow state is missing required persistence key 'conversation_id'`. عند حذف `key`، يظل السلوك الأصلي قائمًا ويُستخدم `state.id`.
+إذا لم يطابق `restore_from_state_id` المقدم أي حالة مستمرة، يعود kickoff بصمت إلى السلوك الافتراضي — نفس سلوك `inputs["id"]` عند عدم العثور عليه. الجمع بين `restore_from_state_id` و `from_checkpoint` يطلق `ValueError`؛ اختر مصدر ترطيب واحدًا. تثبيت `inputs["id"]` أثناء التفرع يشارك مفتاح الاستمرارية مع تدفق آخر — عادةً ما تريد استخدام `restore_from_state_id` فقط.
 
 ### كيف تعمل
 

docs/ar/concepts/production-architecture.mdx

@@ -146,15 +146,14 @@ class ProductionFlow(Flow[AppState]):
     # ...
 ```
 
-افتراضيًا، يستخدم `@persist` الحقل `state.id` المُولّد تلقائيًا كمفتاح للحالة المحفوظة. إذا كان تطبيقك يمتلك معرّفًا طبيعيًا بالفعل — مثل `conversation_id` يربط عدة تشغيلات بنفس جلسة المستخدم — مرّره كـ `key` ليستخدمه المزخرف كـ UUID للتدفق. يُطلق `ValueError` إذا كانت السمة المحددة غير موجودة أو قيمتها falsy عند الحفظ.
+افتراضيًا، يستأنف `@persist` تدفقًا عند توفير `kickoff(inputs={"id": <uuid>})`، مما يمدّ نفس تاريخ `flow_uuid`. لـ **تفرع** تدفق مستمر إلى نسبٍ جديد — ترطيب الحالة من تشغيل سابق ولكن الكتابة تحت `state.id` جديد — مرّر `restore_from_state_id`:
 
 ```python
-@persist(key="conversation_id")
-class ProductionFlow(Flow[AppState]):
-    # يجب أن يحتوي AppState على conversation_id؛ استئناف الجلسة يُعيد تحميل الحالة السابقة
-    ...
+flow.kickoff(restore_from_state_id="<previous-run-state-id>")
 ```
 
+يحصل التشغيل الجديد على `state.id` جديد (مولّد تلقائيًا، أو `inputs["id"]` إذا تم تثبيته) لذا لا تمتد كتابات `@persist` الخاصة به إلى تاريخ المصدر. الجمع مع `from_checkpoint` يطلق `ValueError`؛ اختر مصدر ترطيب واحدًا.
+
 ## الخلاصة
 
 - **ابدأ بتدفق.**

docs/ar/concepts/tools.mdx

@@ -133,7 +133,7 @@ crew.kickoff()
 | **DirectorySearchTool**          | أداة RAG للبحث في المجلدات، مفيدة للتنقل في أنظمة الملفات.       |
 | **DOCXSearchTool**               | أداة RAG للبحث في مستندات DOCX، مثالية لمعالجة ملفات Word.          |
 | **DirectoryReadTool**            | تسهّل قراءة ومعالجة هياكل المجلدات ومحتوياتها.                 |
-| **EXASearchTool**                | أداة مصممة لإجراء عمليات بحث شاملة عبر مصادر بيانات متنوعة.                |
+| **ExaSearchTool**                | أداة مصممة لإجراء عمليات بحث شاملة عبر مصادر بيانات متنوعة.                |
 | **FileReadTool**                 | تُمكّن قراءة واستخراج البيانات من الملفات، مع دعم تنسيقات ملفات متنوعة.               |
 | **FirecrawlSearchTool**          | أداة للبحث في صفحات الويب باستخدام Firecrawl وإرجاع النتائج.                              |
 | **FirecrawlCrawlWebsiteTool**    | أداة لزحف صفحات الويب باستخدام Firecrawl.                                                  |

docs/ar/guides/flows/mastering-flow-state.mdx

@@ -116,32 +116,47 @@ class PersistentCounterFlow(Flow[CounterState]):
         return self.state.value
 ```
 
-### استخدام مفتاح استمرارية مخصص
+#### تفرع الحالة المستمرة
 
-افتراضيًا، يستخدم `@persist()` الحقل `state.id` المُولّد تلقائيًا كمفتاح للحالة المحفوظة. عندما يكون لمجالك معرّف طبيعي بالفعل — مثل `conversation_id` يربط عدة تشغيلات للتدفق بنفس جلسة المستخدم — مرّره كوسيط `key` ليستخدمه `@persist` كـ UUID للتدفق بدلًا من `id`:
+يدعم `@persist` نمطين متميزين للترطيب في `kickoff` / `kickoff_async`. استخدم **استئناف** (`inputs["id"]`) لمواصلة نفس النسب؛ استخدم **تفرع** (`restore_from_state_id`) لبدء نسبٍ جديد من لقطة:
+
+| | `state.id` بعد kickoff | كتابات `@persist` تذهب إلى |
+|---|---|---|
+| `inputs["id"]` (استئناف) | المعرّف المقدم | المعرّف المقدم (يمد التاريخ) |
+| `restore_from_state_id` (تفرع) | معرّف جديد، أو `inputs["id"]` إذا ثُبّت | المعرّف الجديد (المصدر محفوظ) |
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    history: list[str] = []
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def greet(self):
-        self.state.history.append("hello")
-        return self.state.history
+    def step(self):
+        self.state.counter += 1
 
-# تشغيل ثانٍ بنفس conversation_id يُعيد تحميل الحالة السابقة
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# التشغيل 1: حالة جديدة، العداد 0 -> 1
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# التفرع: الترطيب من أحدث لقطة لـ flow_1، لكن الكتابة تحت state.id جديد
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# يبدأ flow_2 بـ counter=1 (مرطّب)، ثم تزيده step() إلى 2.
+# تاريخ flow_uuid لـ flow_1 لم يتغيّر.
 ```
 
-بالنسبة للحالات من نوع dict يقرأ `@persist` القيمة من `state[key]`، ولحالات Pydantic / الكائنات يقرأها من `getattr(state, key)`. إذا كانت السمة المحددة غير موجودة أو قيمتها falsy عند حفظ الحالة، يُطلق `@persist` خطأ `ValueError` مثل `Flow state is missing required persistence key 'conversation_id'`، فيظهر الفشل فورًا بدلًا من فقد بيانات الاستمرارية بصمت. استدعاء `@persist()` بدون `key` يحافظ على السلوك الأصلي ويستخدم `state.id`.
+ملاحظات السلوك:
+
+- `restore_from_state_id` غير موجود في الاستمرارية → يعود kickoff بصمت إلى السلوك الافتراضي (يعكس سلوك `inputs["id"]` عند عدم العثور عليه). لا يُطلق أي استثناء.
+- الجمع بين `restore_from_state_id` و `from_checkpoint` يطلق `ValueError` — يستهدفان نظامي حالة مختلفين (`@persist` مقابل Checkpointing) ولا يمكن الجمع بينهما.
+- `restore_from_state_id=None` (افتراضي) متطابق بايت ببايت مع kickoff بدون المعامل.
+- تثبيت `inputs["id"]` أثناء التفرع يعني أن التشغيل الجديد يشارك مفتاح الاستمرارية مع تدفق آخر — عادةً ما تريد فقط `restore_from_state_id`.
 
 ## أنماط حالة متقدمة
 

docs/ar/tools/search-research/exasearchtool.mdx

@@ -1,11 +1,11 @@
 ---
 title: "أداة بحث Exa"
-description: "ابحث في الويب باستخدام Exa Search API للعثور على النتائج الأكثر صلة لأي استعلام، مع خيارات لمحتوى الصفحة الكامل والمقتطفات والملخصات."
+description: "ابحث في الويب باستخدام Exa Search API للعثور على النتائج الأكثر صلة لأي استعلام، مع خيارات لمحتوى الصفحة الكامل والمقتطفات."
 icon: "magnifying-glass"
 mode: "wide"
 ---
 
-تتيح أداة `EXASearchTool` لوكلاء CrewAI البحث في الويب باستخدام [Exa](https://exa.ai/) search API. تُرجع النتائج الأكثر صلة لأي استعلام، مع خيارات لمحتوى الصفحة الكامل والملخصات المولّدة بالذكاء الاصطناعي.
+تتيح أداة `ExaSearchTool` لوكلاء CrewAI البحث في الويب باستخدام [Exa](https://exa.ai/) search API. تُرجع النتائج الأكثر صلة لأي استعلام، مع خيارات لمحتوى الصفحة الكامل والمقتطفات الموفرة للرموز.
 
 ## التثبيت
 
@@ -27,15 +27,15 @@ export EXA_API_KEY='your_exa_api_key'
 
 ## مثال على الاستخدام
 
-إليك كيفية استخدام `EXASearchTool` مع وكيل CrewAI:
+إليك كيفية استخدام `ExaSearchTool` مع وكيل CrewAI:
 
 ```python
 import os
 from crewai import Agent, Task, Crew
-from crewai_tools import EXASearchTool
+from crewai_tools import ExaSearchTool
 
 # Initialize the tool
-exa_tool = EXASearchTool()
+exa_tool = ExaSearchTool()
 
 # Create an agent that uses the tool
 researcher = Agent(
@@ -66,11 +66,11 @@ print(result)
 
 ## خيارات التكوين
 
-تقبل أداة `EXASearchTool` المعاملات التالية أثناء التهيئة:
+تقبل أداة `ExaSearchTool` المعاملات التالية أثناء التهيئة:
 
 - `type` (str، اختياري): نوع البحث المستخدم. الافتراضي هو `"auto"`. الخيارات: `"auto"`، `"instant"`، `"fast"`، `"deep"`.
+- `highlights` (bool أو dict، اختياري): إرجاع مقتطفات موفرة للرموز أكثر صلة بالاستعلام بدلاً من الصفحة الكاملة. الافتراضي هو `True`. مرر قاموسًا مثل `{"max_characters": 4000}` للتكوين، أو `False` للتعطيل.
 - `content` (bool، اختياري): ما إذا كان يجب تضمين محتوى الصفحة الكامل في النتائج. الافتراضي هو `False`.
-- `summary` (bool، اختياري): ما إذا كان يجب تضمين ملخصات مولّدة بالذكاء الاصطناعي لكل نتيجة. يتطلب `content=True`. الافتراضي هو `False`.
 - `api_key` (str، اختياري): مفتاح Exa API الخاص بك. يعود إلى متغير البيئة `EXA_API_KEY` إذا لم يتم تقديمه.
 - `base_url` (str، اختياري): عنوان URL مخصص لخادم API. يعود إلى متغير البيئة `EXA_BASE_URL` إذا لم يتم تقديمه.
 
@@ -86,25 +86,52 @@ print(result)
 يمكنك تكوين الأداة بمعاملات مخصصة للحصول على نتائج أغنى:
 
 ```python
-# Get full page content with AI summaries
-exa_tool = EXASearchTool(
-    content=True,
-    summary=True,
+# Use 'deep' for thorough, multi-step searches
+exa_tool = ExaSearchTool(
+    highlights=True,
     type="deep"
 )
 
 # Use it in an agent
 agent = Agent(
     role="Deep Researcher",
-    goal="Conduct thorough research with full content and summaries",
+    goal="Conduct thorough research",
     tools=[exa_tool]
 )
 ```
 
+## استخدام Exa عبر MCP
+
+يمكنك أيضًا ربط وكيلك بخادم MCP المستضاف من Exa. مرّر مفتاح API الخاص بك عبر ترويسة `x-api-key`:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerHTTP
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information on the web",
+    backstory="Expert researcher with access to Exa's tools",
+    mcps=[
+        MCPServerHTTP(
+            url="https://mcp.exa.ai/mcp",
+            headers={"x-api-key": "YOUR_EXA_API_KEY"},
+        ),
+    ],
+)
+```
+
+احصل على مفتاح API من [لوحة تحكم Exa](https://dashboard.exa.ai/api-keys). لمزيد من المعلومات حول MCP في CrewAI، راجع [نظرة عامة على MCP](/ar/mcp/overview).
+
 ## الميزات
 
+- **مقتطفات موفرة للرموز**: الحصول على المقتطفات الأكثر صلة من كل نتيجة، باستخدام رموز أقل بكثير من النص الكامل
 - **البحث الدلالي**: العثور على نتائج بناءً على المعنى، وليس الكلمات المفتاحية فقط
 - **استرجاع المحتوى الكامل**: الحصول على النص الكامل لصفحات الويب مع نتائج البحث
-- **ملخصات الذكاء الاصطناعي**: الحصول على ملخصات موجزة مولّدة بالذكاء الاصطناعي لكل نتيجة
 - **تصفية التاريخ**: تقييد النتائج لفترات زمنية محددة باستخدام فلاتر تاريخ النشر
-- **تصفية النطاقات**: تقييد عمليات البحث على نطاقات محددة
+- **تصفية النطاقات**: تقييد عمليات البحث على نطاقات محددة
+
+## موارد
+
+- [توثيق Exa](https://exa.ai/docs)
+- [لوحة تحكم Exa — إدارة مفاتيح API والاستخدام](https://dashboard.exa.ai)

docs/en/api-reference/introduction.mdx

@@ -26,7 +26,7 @@ Welcome to the CrewAI AMP API reference. This API allows you to programmatically
 </Step>
 
   <Step title="Monitor Progress">
-    Use `GET /{kickoff_id}/status` to check execution status and retrieve results.
+    Use `GET /status/{kickoff_id}` to check execution status and retrieve results.
   </Step>
 </Steps>
 
@@ -65,7 +65,7 @@ Replace `your-crew-name` with your actual crew's URL from the dashboard.
 
 1. **Discovery**: Call `GET /inputs` to understand what your crew needs
 2. **Execution**: Submit inputs via `POST /kickoff` to start processing
-3. **Monitoring**: Poll `GET /{kickoff_id}/status` until completion
+3. **Monitoring**: Poll `GET /status/{kickoff_id}` until completion
 4. **Results**: Extract the final output from the completed response
 
 ## Error Handling

docs/en/api-reference/status.mdx

@@ -1,6 +1,6 @@
 ---
-title: "GET /{kickoff_id}/status"
+title: "GET /status/{kickoff_id}"
 description: "Get execution status"
-openapi: "/enterprise-api.en.yaml GET /{kickoff_id}/status"
+openapi: "/enterprise-api.en.yaml GET /status/{kickoff_id}"
 mode: "wide"
 ---

docs/en/changelog.mdx

@@ -4,6 +4,99 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="May 04, 2026">
+  ## v1.14.5a2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a2)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Fix task output restoration in finally block
+  - Include `thoughts_token_count` in completion tokens
+  - Preserve task outputs across async batch flush
+  - Forward kwargs to loader calls in `CrewAIRagAdapter`
+  - Prevent `result_as_answer` from returning hook-block message as final answer
+  - Prevent `result_as_answer` from returning error as final answer
+  - Use `acall` for output conversion in async paths
+  - Prevent shared LLM stop words mutation across agents
+  - Handle `BaseModel` input in `convert_to_model`
+
+  ### Documentation
+  - Document additional environment variables
+  - Update changelog and version for v1.14.5a1
+
+  ## Contributors
+
+  @NIK-TIGER-BILL, @greysonlalonde, @lorenzejay, @minasami-pr, @theCyberTech, @wishhyt
+
+</Update>
+
+<Update label="May 01, 2026">
+  ## v1.14.5a1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a1)
+
+  ## What's Changed
+
+  ### Features
+  - Add `restore_from_state_id` kickoff parameter
+  - Add highlights to ExaSearchTool and rename from EXASearchTool
+
+  ### Bug Fixes
+  - Fix missing crewai pin sites in release flow
+  - Ensure skills loading events for traces
+
+  ### Documentation
+  - Update changelog and version for v1.14.4
+
+  ## Contributors
+
+  @akaKuruma, @github-actions[bot], @greysonlalonde, @lorenzejay, @theishangoswami
+
+</Update>
+
+<Update label="May 01, 2026">
+  ## v1.14.4
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.4)
+
+  ## What's Changed
+
+  ### Features
+  - Add support for custom persistence key in @persist
+  - Add Responses API support for Azure OpenAI provider
+  - Forward credential_scopes to Azure AI Inference client
+  - Add Vertex AI workload identity setup guide
+  - Add Tavily Research and get Research
+  - Add You.com MCP tools for search, research, and content extraction
+
+  ### Bug Fixes
+  - Fix fall through when JSON regex match isn't valid JSON
+  - Fix to preserve tool_calls when response also contains text
+  - Fix to forward base_url and api_key to instructor.from_provider
+  - Fix to warn and return empty when native MCP server returns no tools
+  - Fix to use validated messages variable in non-streaming handlers
+  - Fix to guard crew chat description helpers against LLM failures
+  - Fix to reset messages and iterations between invocations
+  - Fix to forward trained-agents file through replay and test
+  - Fix to honor custom trained-agents file at inference
+  - Fix to bind task-only agents to crew for multimodal input_files
+  - Fix to serialize guardrail callables as null for JSON checkpointing
+  - Fix renaming of force_final_answer to avoid self-referential router
+  - Fix bump of litellm for SSTI fix; ignore unfixable pip CVE
+
+  ### Documentation
+  - Update changelog and version for v1.14.4a1
+  - Add E2B Sandbox Tools page
+  - Add Daytona sandbox tools documentation
+
+  ## Contributors
+
+  @EdwardIrby, @dependabot[bot], @factory-droid-oss, @factory-droid[bot], @greysonlalonde, @kunalk16, @lorenzejay, @lucasgomide, @manisrinivasan2k1, @mattatcha, @vinibrsl
+
+</Update>
+
 <Update label="Apr 29, 2026">
   ## v1.14.4a1
 

docs/en/concepts/flows.mdx

@@ -380,32 +380,41 @@ class AnotherFlow(Flow[dict]):
         print("Method-level persisted runs:", self.state["runs"])
 ```
 
-### Custom Persistence Key
+### Forking Persisted State
 
-By default, `@persist` uses the auto-generated `state.id` field as the persistence key. If your flow models its own identifier — for example a `conversation_id` shared across sessions — you can pass a `key` argument and `@persist` will use that attribute as the flow UUID instead:
+`@persist` supports two distinct hydration modes on `kickoff` / `kickoff_async`:
+
+- `kickoff(inputs={"id": <uuid>})` — **resume**: load the latest snapshot for the supplied UUID and continue writing under the same `flow_uuid`. The history extends.
+- `kickoff(restore_from_state_id=<uuid>)` — **fork**: load the latest snapshot for the supplied UUID, hydrate the new run's state from it, and assign a fresh `state.id` (auto-generated, or `inputs["id"]` if pinned). The new run's `@persist` writes land under the new `state.id`; the source flow's history is preserved.
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    turn: int = 0
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")  # Use a custom field as the persistence key
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def begin(self):
-        self.state.turn += 1
-        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")
+    def step(self):
+        self.state.counter += 1
+        print(f"[id={self.state.id}] counter={self.state.counter}")
 
-# Resuming the same conversation reloads its prior state by conversation_id
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# Run 1: fresh state, counter 0 -> 1, persisted under flow_1.state.id
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# Fork: hydrate from flow_1's latest snapshot, but use a NEW state.id
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2.state.counter starts at 1 (hydrated), then step() bumps it to 2.
+# flow_2.state.id != flow_1.state.id; flow_1's history is unchanged.
 ```
 
-The decorator reads the value at `state[key]` for dict states, or `getattr(state, key)` for Pydantic / object states. If the named attribute is missing or falsy at save time, `@persist` raises a `ValueError` such as `Flow state is missing required persistence key 'conversation_id'`. When `key` is omitted, the existing behavior is preserved and `state.id` is used.
+If the supplied `restore_from_state_id` does not match any persisted state, the kickoff falls back silently — same as the existing `inputs["id"]` resume not-found behavior. Combining `restore_from_state_id` with `from_checkpoint` raises a `ValueError`; pick one hydration source. Pinning `inputs["id"]` while forking shares a persistence key with another flow — usually you want only `restore_from_state_id`.
 
 ### How It Works
 

docs/en/concepts/production-architecture.mdx

@@ -146,15 +146,14 @@ class ProductionFlow(Flow[AppState]):
     # ...
 ```
 
-By default `@persist` keys saved state by the auto-generated `state.id`. If your application already has a natural identifier — for example a `conversation_id` that ties multiple runs to the same user session — pass it as `key` and the decorator will use that attribute as the flow UUID. A `ValueError` is raised if the named attribute is missing or falsy at save time.
+By default, `@persist` resumes a flow when `kickoff(inputs={"id": <uuid>})` is supplied, extending the same `flow_uuid` history. To **fork** a persisted flow into a new lineage — hydrate state from a previous run but write under a fresh `state.id` — pass `restore_from_state_id`:
 
 ```python
-@persist(key="conversation_id")
-class ProductionFlow(Flow[AppState]):
-    # AppState must expose conversation_id; resuming a session reloads its prior state
-    ...
+flow.kickoff(restore_from_state_id="<previous-run-state-id>")
 ```
 
+The new run gets a fresh `state.id` (auto-generated, or `inputs["id"]` if pinned) so its `@persist` writes don't extend the source's history. Combining with `from_checkpoint` raises a `ValueError`; pick one hydration source.
+
 ## Summary
 
 - **Start with a Flow.**

docs/en/concepts/tools.mdx

@@ -133,7 +133,7 @@ Here is a list of the available tools and their descriptions:
 | **DirectorySearchTool**          | A RAG tool for searching within directories, useful for navigating through file systems.       |
 | **DOCXSearchTool**               | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
 | **DirectoryReadTool**            | Facilitates reading and processing of directory structures and their contents.                 |
-| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
+| **ExaSearchTool**                | Search the web with Exa, the fastest and most accurate web search API. Supports token-efficient highlights and full page content. |
 | **FileReadTool**                 | Enables reading and extracting data from files, supporting various file formats.               |
 | **FirecrawlSearchTool**          | A tool to search webpages using Firecrawl and return the results.                              |
 | **FirecrawlCrawlWebsiteTool**    | A tool for crawling webpages using Firecrawl.                                                  |

docs/en/guides/flows/mastering-flow-state.mdx

@@ -346,32 +346,47 @@ class SelectivePersistFlow(Flow):
         return f"Complete with count {self.state['count']}"
 ```
 
-#### Using a Custom Persistence Key
+#### Forking Persisted State
 
-By default, `@persist()` keys persisted state by the flow's auto-generated `state.id`. When your domain already has a natural identifier — for example a `conversation_id` that ties multiple flow runs to the same user session — pass it as the `key` argument and `@persist` will use that attribute as the flow UUID instead of `id`:
+`@persist` supports two distinct hydration modes on `kickoff` / `kickoff_async`. Use **resume** (`inputs["id"]`) to continue the same lineage; use **fork** (`restore_from_state_id`) to start a new lineage seeded from a snapshot:
+
+| | `state.id` after kickoff | `@persist` writes land under |
+|---|---|---|
+| `inputs["id"]` (resume) | supplied id | supplied id (extends history) |
+| `restore_from_state_id` (fork) | fresh id, or `inputs["id"]` if pinned | new id (source preserved) |
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    history: list[str] = []
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def greet(self):
-        self.state.history.append("hello")
-        return self.state.history
+    def step(self):
+        self.state.counter += 1
 
-# A second run with the same conversation_id reloads the prior state
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# Run 1: fresh state, counter 0 -> 1
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# Fork: hydrate from flow_1's latest snapshot, but write under a NEW state.id
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2 starts with counter=1 (hydrated), then step() bumps it to 2.
+# flow_1's flow_uuid history is unchanged.
 ```
 
-For dict-based states `@persist` reads `state[key]`, and for Pydantic / object states it reads `getattr(state, key)`. If the named attribute is missing or falsy when state is being saved, `@persist` raises a `ValueError` like `Flow state is missing required persistence key 'conversation_id'`, so the failure surfaces immediately rather than silently dropping persisted data. Calling `@persist()` without `key` keeps the original behavior of using `state.id`.
+Behavior notes:
+
+- `restore_from_state_id` not found in persistence → the kickoff falls back silently to default behavior (mirrors the existing `inputs["id"]` resume not-found behavior). No exception is raised.
+- Combining `restore_from_state_id` with `from_checkpoint` raises a `ValueError` — they target different state systems (`@persist` vs. Checkpointing) and cannot be combined.
+- `restore_from_state_id=None` (default) is byte-identical to a kickoff without the parameter.
+- Pinning `inputs["id"]` while forking means the new run shares a persistence key with another flow — usually you want only `restore_from_state_id`.
 
 
 ## Advanced State Patterns

docs/en/tools/search-research/exasearchtool.mdx

@@ -1,11 +1,11 @@
 ---
 title: "Exa Search Tool"
-description: "Search the web using the Exa Search API to find the most relevant results for any query, with options for full page content, highlights, and summaries."
+description: "Search the web with Exa, the fastest and most accurate web search API. Get token-efficient highlights and full page content."
 icon: "magnifying-glass"
 mode: "wide"
 ---
 
-The `EXASearchTool` lets CrewAI agents search the web using the [Exa](https://exa.ai/) search API. It returns the most relevant results for any query, with options for full page content and AI-generated summaries.
+The `ExaSearchTool` lets CrewAI agents search the web using [Exa](https://exa.ai/), the fastest and most accurate web search API. It returns the most relevant results for any query, with options for token-efficient highlights and full page content.
 
 ## Installation
 
@@ -27,15 +27,15 @@ Get an API key from the [Exa dashboard](https://dashboard.exa.ai/api-keys).
 
 ## Example Usage
 
-Here's how to use the `EXASearchTool` within a CrewAI agent:
+Here's how to use the `ExaSearchTool` within a CrewAI agent:
 
 ```python
 import os
 from crewai import Agent, Task, Crew
-from crewai_tools import EXASearchTool
+from crewai_tools import ExaSearchTool
 
 # Initialize the tool
-exa_tool = EXASearchTool()
+exa_tool = ExaSearchTool()
 
 # Create an agent that uses the tool
 researcher = Agent(
@@ -66,11 +66,11 @@ print(result)
 
 ## Configuration Options
 
-The `EXASearchTool` accepts the following parameters during initialization:
+The `ExaSearchTool` accepts the following parameters during initialization:
 
 - `type` (str, optional): The search type to use. Defaults to `"auto"`. Options: `"auto"`, `"instant"`, `"fast"`, `"deep"`.
+- `highlights` (bool or dict, optional): Return token-efficient excerpts most relevant to the query instead of the full page. Defaults to `True`. Pass a dict like `{"max_characters": 4000}` to configure, or `False` to disable.
 - `content` (bool, optional): Whether to include full page content in results. Defaults to `False`.
-- `summary` (bool, optional): Whether to include AI-generated summaries of each result. Requires `content=True`. Defaults to `False`.
 - `api_key` (str, optional): Your Exa API key. Falls back to the `EXA_API_KEY` environment variable if not provided.
 - `base_url` (str, optional): Custom API server URL. Falls back to the `EXA_BASE_URL` environment variable if not provided.
 
@@ -83,28 +83,70 @@ When calling the tool (or when an agent invokes it), the following search parame
 
 ## Advanced Usage
 
-You can configure the tool with custom parameters for richer results:
+For most agent workflows we recommend `highlights` — it returns the most relevant excerpts from each result and uses far fewer tokens than full page content:
 
 ```python
-# Get full page content with AI summaries
-exa_tool = EXASearchTool(
-    content=True,
-    summary=True,
-    type="deep"
+# Get token-efficient excerpts most relevant to the query
+exa_tool = ExaSearchTool(
+    highlights=True,
+    type="auto",
 )
 
 # Use it in an agent
 agent = Agent(
-    role="Deep Researcher",
-    goal="Conduct thorough research with full content and summaries",
+    role="Researcher",
+    goal="Answer questions with current web data",
     tools=[exa_tool]
 )
 ```
 
+For thorough, multi-step searches, use `type="deep"`:
+
+```python
+exa_tool = ExaSearchTool(
+    highlights=True,
+    type="deep",
+)
+```
+
+For more on choosing between highlights and full content, see the [Exa search best practices](https://exa.ai/docs/reference/search-best-practices).
+
+## Using Exa via MCP
+
+You can also connect your agent to Exa's hosted MCP server. Pass your API key with the `x-api-key` header:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerHTTP
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information on the web",
+    backstory="Expert researcher with access to Exa's tools",
+    mcps=[
+        MCPServerHTTP(
+            url="https://mcp.exa.ai/mcp",
+            headers={"x-api-key": "YOUR_EXA_API_KEY"},
+        ),
+    ],
+)
+```
+
+Get your API key from the [Exa dashboard](https://dashboard.exa.ai/api-keys). For more on MCP in CrewAI, see the [MCP overview](/en/mcp/overview).
+
 ## Features
 
+- **Token-Efficient Highlights**: Get the most relevant excerpts from each result, ~10x fewer tokens than full text
 - **Semantic Search**: Find results based on meaning, not just keywords
 - **Full Content Retrieval**: Get the full text of web pages alongside search results
-- **AI Summaries**: Get concise, AI-generated summaries of each result
 - **Date Filtering**: Limit results to specific time periods with published date filters
 - **Domain Filtering**: Restrict searches to specific domains
+
+<Note>
+`EXASearchTool` is a deprecated alias for `ExaSearchTool`. Existing imports continue to work but will emit a deprecation warning; please migrate to `ExaSearchTool`.
+</Note>
+
+## Resources
+
+- [Exa documentation](https://exa.ai/docs)
+- [Exa dashboard — manage API keys and usage](https://dashboard.exa.ai)

docs/enterprise-api.base.yaml

@@ -35,7 +35,7 @@ info:
 
     1. **Discover inputs** using `GET /inputs`
     2. **Start execution** using `POST /kickoff`
-    3. **Monitor progress** using `GET /{kickoff_id}/status`
+    3. **Monitor progress** using `GET /status/{kickoff_id}`
   version: 1.0.0
   contact:
     name: CrewAI Support
@@ -207,7 +207,7 @@ paths:
         "500":
           $ref: "#/components/responses/ServerError"
 
-  /{kickoff_id}/status:
+  /status/{kickoff_id}:
     get:
       summary: Get Execution Status
       description: |

docs/enterprise-api.en.yaml

@@ -35,7 +35,7 @@ info:
 
     1. **Discover inputs** using `GET /inputs`
     2. **Start execution** using `POST /kickoff`
-    3. **Monitor progress** using `GET /{kickoff_id}/status`
+    3. **Monitor progress** using `GET /status/{kickoff_id}`
   version: 1.0.0
   contact:
     name: CrewAI Support
@@ -207,7 +207,7 @@ paths:
         "500":
           $ref: "#/components/responses/ServerError"
 
-  /{kickoff_id}/status:
+  /status/{kickoff_id}:
     get:
       summary: Get Execution Status
       description: |

docs/enterprise-api.ko.yaml

@@ -84,7 +84,7 @@ paths:
         '500':
           $ref: '#/components/responses/ServerError'
 
-  /{kickoff_id}/status:
+  /status/{kickoff_id}:
     get:
       summary: 실행 상태 조회
       description: |

docs/enterprise-api.pt-BR.yaml

@@ -35,7 +35,7 @@ info:
 
     1. **Descubra os inputs** usando `GET /inputs`
     2. **Inicie a execução** usando `POST /kickoff`
-    3. **Monitore o progresso** usando `GET /{kickoff_id}/status`
+    3. **Monitore o progresso** usando `GET /status/{kickoff_id}`
   version: 1.0.0
   contact:
     name: CrewAI Suporte
@@ -120,7 +120,7 @@ paths:
         "500":
           $ref: "#/components/responses/ServerError"
 
-  /{kickoff_id}/status:
+  /status/{kickoff_id}:
     get:
       summary: Obter Status da Execução
       description: |

docs/ko/api-reference/introduction.mdx

@@ -26,7 +26,7 @@ CrewAI 엔터프라이즈 API 참고 자료에 오신 것을 환영합니다.
 </Step>
 
   <Step title="진행 상황 모니터링">
-    `GET /{kickoff_id}/status`를 사용하여 실행 상태를 확인하고 결과를 조회하세요.
+    `GET /status/{kickoff_id}`를 사용하여 실행 상태를 확인하고 결과를 조회하세요.
   </Step>
 </Steps>
 
@@ -65,7 +65,7 @@ https://your-crew-name.crewai.com
 
 1. **탐색**: `GET /inputs`를 호출하여 crew가 필요한 것을 파악합니다.
 2. **실행**: `POST /kickoff`를 통해 입력값을 제출하여 처리를 시작합니다.
-3. **모니터링**: 완료될 때까지 `GET /{kickoff_id}/status`를 주기적으로 조회합니다.
+3. **모니터링**: 완료될 때까지 `GET /status/{kickoff_id}`를 주기적으로 조회합니다.
 4. **결과**: 완료된 응답에서 최종 출력을 추출합니다.
 
 ## 오류 처리

docs/ko/api-reference/status.mdx

@@ -1,6 +1,6 @@
 ---
-title: "GET /{kickoff_id}/status"
+title: "GET /status/{kickoff_id}"
 description: "실행 상태 조회"
-openapi: "/enterprise-api.ko.yaml GET /{kickoff_id}/status"
+openapi: "/enterprise-api.ko.yaml GET /status/{kickoff_id}"
 mode: "wide"
 ---

docs/ko/changelog.mdx

@@ -4,6 +4,99 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 5월 4일">
+  ## v1.14.5a2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a2)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - finally 블록에서 작업 출력 복원 수정
+  - 완료 토큰에 `thoughts_token_count` 포함
+  - 비동기 배치 플러시 간 작업 출력 보존
+  - `CrewAIRagAdapter`의 로더 호출에 kwargs 전달
+  - `result_as_answer`가 후크 차단 메시지를 최종 답변으로 반환하지 않도록 방지
+  - `result_as_answer`가 오류를 최종 답변으로 반환하지 않도록 방지
+  - 비동기 경로에서 출력 변환을 위해 `acall` 사용
+  - 에이전트 간 공유 LLM 중지 단어 변형 방지
+  - `convert_to_model`에서 `BaseModel` 입력 처리
+
+  ### 문서화
+  - 추가 환경 변수 문서화
+  - v1.14.5a1에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @NIK-TIGER-BILL, @greysonlalonde, @lorenzejay, @minasami-pr, @theCyberTech, @wishhyt
+
+</Update>
+
+<Update label="2026년 5월 1일">
+  ## v1.14.5a1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a1)
+
+  ## 변경 사항
+
+  ### 기능
+  - `restore_from_state_id` 시작 매개변수 추가
+  - ExaSearchTool에 하이라이트 추가 및 EXASearchTool에서 이름 변경
+
+  ### 버그 수정
+  - 릴리스 흐름에서 crewai 핀 사이트 누락 수정
+  - 트레이스를 위한 기술 로딩 이벤트 보장
+
+  ### 문서
+  - v1.14.4에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @akaKuruma, @github-actions[bot], @greysonlalonde, @lorenzejay, @theishangoswami
+
+</Update>
+
+<Update label="2026년 5월 1일">
+  ## v1.14.4
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.4)
+
+  ## 변경 사항
+
+  ### 기능
+  - @persist에서 사용자 정의 지속성 키 지원 추가
+  - Azure OpenAI 공급자를 위한 응답 API 지원 추가
+  - Azure AI 추론 클라이언트에 credential_scopes 전달
+  - Vertex AI 작업 부하 신원 설정 가이드 추가
+  - Tavily Research 및 Research 가져오기 추가
+  - 검색, 연구 및 콘텐츠 추출을 위한 You.com MCP 도구 추가
+
+  ### 버그 수정
+  - JSON 정규 표현식이 유효한 JSON이 아닐 때의 fall through 수정
+  - 응답에 텍스트가 포함될 때 tool_calls를 보존하도록 수정
+  - instructor.from_provider에 base_url 및 api_key를 전달하도록 수정
+  - 기본 MCP 서버가 도구를 반환하지 않을 때 경고하고 빈 값을 반환하도록 수정
+  - 비스트리밍 핸들러에서 검증된 메시지 변수를 사용하도록 수정
+  - LLM 실패에 대한 크루 채팅 설명 도우미를 보호하도록 수정
+  - 호출 간 메시지 및 반복을 재설정하도록 수정
+  - replay 및 test를 통해 훈련된 에이전트 파일을 전달하도록 수정
+  - 추론 시 사용자 정의 훈련된 에이전트 파일을 존중하도록 수정
+  - 다중 모드 input_files에 대해 작업 전용 에이전트를 크루에 바인딩하도록 수정
+  - JSON 체크포인팅을 위해 가드레일 호출 가능 항목을 null로 직렬화하도록 수정
+  - 자기 참조 라우터를 피하기 위해 force_final_answer의 이름 변경 수정
+  - SSTI 수정을 위한 litellm 버전 증가; 수정할 수 없는 pip CVE 무시
+
+  ### 문서
+  - v1.14.4a1에 대한 변경 로그 및 버전 업데이트
+  - E2B 샌드박스 도구 페이지 추가
+  - Daytona 샌드박스 도구 문서 추가
+
+  ## 기여자
+
+  @EdwardIrby, @dependabot[bot], @factory-droid-oss, @factory-droid[bot], @greysonlalonde, @kunalk16, @lorenzejay, @lucasgomide, @manisrinivasan2k1, @mattatcha, @vinibrsl
+
+</Update>
+
 <Update label="2026년 4월 29일">
   ## v1.14.4a1
 

docs/ko/concepts/flows.mdx

@@ -373,32 +373,41 @@ class AnotherFlow(Flow[dict]):
         print("Method-level persisted runs:", self.state["runs"])
 ```
 
-### 사용자 지정 영속성 키
+### 영속 상태 포크하기
 
-기본적으로 `@persist`는 자동 생성된 `state.id` 필드를 영속성 키로 사용합니다. 여러 세션에 걸쳐 공유되는 `conversation_id`처럼 플로우에 자체 식별자가 있는 경우, `key` 인자를 전달하면 `@persist`가 해당 속성을 플로우 UUID로 사용합니다:
+`@persist`는 `kickoff` / `kickoff_async`에서 두 가지 별개의 하이드레이션 모드를 지원합니다:
+
+- `kickoff(inputs={"id": <uuid>})` — **재개(resume)**: 제공된 UUID에 대한 최신 스냅샷을 로드하고 동일한 `flow_uuid` 아래에서 계속 기록합니다. 기록이 확장됩니다.
+- `kickoff(restore_from_state_id=<uuid>)` — **포크(fork)**: 제공된 UUID에 대한 최신 스냅샷을 로드하고 새 실행의 상태를 하이드레이트한 후, 새로운 `state.id`(자동 생성, 또는 `inputs["id"]`가 고정된 경우 그 값)를 할당합니다. 새 실행의 `@persist` 기록은 새로운 `state.id` 아래에 저장되며, 원본 플로우의 기록은 보존됩니다.
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    turn: int = 0
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")  # 사용자 지정 필드를 영속성 키로 사용
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def begin(self):
-        self.state.turn += 1
-        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")
+    def step(self):
+        self.state.counter += 1
+        print(f"[id={self.state.id}] counter={self.state.counter}")
 
-# 동일한 conversation_id로 다시 실행하면 이전 상태가 다시 로드됩니다
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# 실행 1: 새 상태, counter 0 -> 1, flow_1.state.id 아래에 저장됨
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# 포크: flow_1의 최신 스냅샷에서 하이드레이트하지만, 새 state.id를 사용
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2.state.counter는 1(하이드레이트)로 시작하고, step()이 2로 증가시킵니다.
+# flow_2.state.id != flow_1.state.id; flow_1의 기록은 변경되지 않습니다.
 ```
 
-이 데코레이터는 dict 상태의 경우 `state[key]`에서, Pydantic / 객체 상태의 경우 `getattr(state, key)`에서 값을 읽습니다. 저장 시점에 지정된 속성이 없거나 falsy 값이면, `@persist`는 `Flow state is missing required persistence key 'conversation_id'`와 같은 `ValueError`를 발생시킵니다. `key`를 생략하면 기존 동작이 유지되어 `state.id`가 사용됩니다.
+제공된 `restore_from_state_id`가 어떤 영속 상태와도 일치하지 않으면, kickoff는 조용히 기본 동작으로 폴백됩니다 — 기존 `inputs["id"]`의 미발견 동작과 동일합니다. `restore_from_state_id`를 `from_checkpoint`와 결합하면 `ValueError`가 발생합니다; 하나의 하이드레이션 소스를 선택하세요. 포크 중 `inputs["id"]`를 고정하면 다른 플로우와 영속 키를 공유하게 됩니다 — 일반적으로 `restore_from_state_id`만 사용하는 것이 좋습니다.
 
 ### 작동 방식
 

docs/ko/concepts/production-architecture.mdx

@@ -146,15 +146,14 @@ class ProductionFlow(Flow[AppState]):
     # ...
 ```
 
-기본적으로 `@persist`는 자동 생성된 `state.id`를 저장된 상태의 키로 사용합니다. 애플리케이션에 이미 자연스러운 식별자가 있는 경우 — 예를 들어 같은 사용자 세션에 속한 여러 실행을 묶는 `conversation_id` — `key`로 전달하면 데코레이터가 해당 속성을 플로우 UUID로 사용합니다. 저장 시점에 지정된 속성이 없거나 falsy 값이면 `ValueError`가 발생합니다.
+기본적으로, `@persist`는 `kickoff(inputs={"id": <uuid>})`가 제공될 때 플로우를 재개하여 동일한 `flow_uuid` 기록을 확장합니다. 영속된 플로우를 새 계보로 **포크**하려면 — 이전 실행에서 상태를 하이드레이트하지만 새로운 `state.id` 아래에 기록 — `restore_from_state_id`를 전달하세요:
 
 ```python
-@persist(key="conversation_id")
-class ProductionFlow(Flow[AppState]):
-    # AppState는 conversation_id를 노출해야 합니다; 세션을 재개하면 이전 상태가 다시 로드됩니다
-    ...
+flow.kickoff(restore_from_state_id="<previous-run-state-id>")
 ```
 
+새 실행은 새로운 `state.id`(자동 생성, 또는 `inputs["id"]`가 고정된 경우 그 값)를 받아 `@persist` 기록이 원본의 기록을 확장하지 않도록 합니다. `from_checkpoint`와 결합하면 `ValueError`가 발생합니다; 하나의 하이드레이션 소스를 선택하세요.
+
 ## 요약
 
 - **Flow로 시작하세요.**

docs/ko/concepts/tools.mdx

@@ -132,7 +132,7 @@ crew.kickoff()
 | **DirectorySearchTool**           | 디렉터리 내에서 검색하는 RAG 도구로, 파일 시스템을 탐색할 때 유용합니다.                              |
 | **DOCXSearchTool**                | DOCX 문서 내에서 검색하는 데 특화된 RAG 도구로, Word 파일을 처리할 때 이상적입니다.                   |
 | **DirectoryReadTool**             | 디렉터리 구조와 그 내용을 읽고 처리하도록 지원하는 도구입니다.                                        |
-| **EXASearchTool**                 | 다양한 데이터 소스를 폭넓게 검색하기 위해 설계된 도구입니다.                                          |
+| **ExaSearchTool**                 | 다양한 데이터 소스를 폭넓게 검색하기 위해 설계된 도구입니다.                                          |
 | **FileReadTool**                  | 다양한 파일 형식을 지원하며 파일에서 데이터를 읽고 추출할 수 있는 도구입니다.                         |
 | **FirecrawlSearchTool**           | Firecrawl을 이용해 웹페이지를 검색하고 결과를 반환하는 도구입니다.                                    |
 | **FirecrawlCrawlWebsiteTool**     | Firecrawl을 사용해 웹페이지를 크롤링하는 도구입니다.                                                  |

docs/ko/guides/flows/mastering-flow-state.mdx

@@ -346,32 +346,47 @@ class SelectivePersistFlow(Flow):
         return f"Complete with count {self.state['count']}"
 ```
 
-#### 사용자 지정 영속성 키 사용하기
+#### 영속 상태 포크하기
 
-기본적으로 `@persist()`는 자동 생성된 `state.id`를 영속 상태의 키로 사용합니다. 도메인에 이미 자연스러운 식별자가 있는 경우 — 예를 들어 같은 사용자 세션에 속한 여러 플로우 실행을 묶는 `conversation_id` — `key` 인자로 전달하면 `@persist`는 `id` 대신 해당 속성을 플로우 UUID로 사용합니다:
+`@persist`는 `kickoff` / `kickoff_async`에서 두 가지 별개의 하이드레이션 모드를 지원합니다. 동일한 계보를 계속하려면 **재개**(`inputs["id"]`)를 사용하고, 스냅샷에서 시작하는 새 계보를 시작하려면 **포크**(`restore_from_state_id`)를 사용하세요:
+
+| | kickoff 후 `state.id` | `@persist` 기록 위치 |
+|---|---|---|
+| `inputs["id"]` (재개) | 제공된 id | 제공된 id (기록 확장) |
+| `restore_from_state_id` (포크) | 새 id, 또는 고정 시 `inputs["id"]` | 새 id (원본 보존) |
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    history: list[str] = []
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def greet(self):
-        self.state.history.append("hello")
-        return self.state.history
+    def step(self):
+        self.state.counter += 1
 
-# 동일한 conversation_id로 두 번째 실행 시 이전 상태가 다시 로드됩니다
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# 실행 1: 새 상태, counter 0 -> 1
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# 포크: flow_1의 최신 스냅샷에서 하이드레이트, 단 새 state.id에 기록
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2는 counter=1(하이드레이트)로 시작하고, step()이 2로 증가시킵니다.
+# flow_1의 flow_uuid 기록은 변경되지 않습니다.
 ```
 
-dict 기반 상태의 경우 `@persist`는 `state[key]`를 읽고, Pydantic / 객체 상태의 경우 `getattr(state, key)`를 읽습니다. 상태가 저장될 때 지정된 속성이 없거나 falsy 값이면 `@persist`는 `Flow state is missing required persistence key 'conversation_id'`와 같은 `ValueError`를 발생시켜, 영속 데이터가 조용히 손실되는 대신 즉시 실패가 드러나도록 합니다. `key` 없이 `@persist()`를 호출하면 기존 동작대로 `state.id`가 사용됩니다.
+동작 노트:
+
+- `restore_from_state_id`가 영속에서 발견되지 않음 → kickoff는 조용히 기본 동작으로 폴백됩니다 (기존 `inputs["id"]`의 미발견 동작 미러링). 예외는 발생하지 않습니다.
+- `restore_from_state_id`를 `from_checkpoint`와 결합하면 `ValueError`가 발생합니다 — 서로 다른 상태 시스템(`@persist` 대 Checkpointing)을 대상으로 하므로 결합할 수 없습니다.
+- `restore_from_state_id=None`(기본값)은 매개변수 없는 kickoff와 바이트 단위로 동일합니다.
+- 포크 중 `inputs["id"]`를 고정하면 새 실행이 다른 플로우와 영속 키를 공유함을 의미합니다 — 일반적으로 `restore_from_state_id`만 사용하는 것이 좋습니다.
 
 ## 고급 상태 패턴
 

docs/ko/tools/search-research/exasearchtool.mdx

@@ -1,15 +1,15 @@
 ---
 title: EXA 검색 웹 로더
-description: EXASearchTool은 인터넷 전반에 걸쳐 텍스트의 내용에서 지정된 쿼리에 대한 시맨틱 검색을 수행하도록 설계되었습니다.
+description: ExaSearchTool은 인터넷 전반에 걸쳐 텍스트의 내용에서 지정된 쿼리에 대한 시맨틱 검색을 수행하도록 설계되었습니다.
 icon: globe-pointer
 mode: "wide"
 ---
 
-# `EXASearchTool`
+# `ExaSearchTool`
 
 ## 설명
 
-EXASearchTool은 텍스트의 내용을 기반으로 지정된 쿼리를 인터넷 전반에 걸쳐 의미론적으로 검색하도록 설계되었습니다.
+ExaSearchTool은 텍스트의 내용을 기반으로 지정된 쿼리를 인터넷 전반에 걸쳐 의미론적으로 검색하도록 설계되었습니다.
 사용자가 제공한 쿼리를 기반으로 가장 관련성 높은 검색 결과를 가져오고 표시하기 위해 [exa.ai](https://exa.ai/) API를 활용합니다.
 
 ## 설치
@@ -25,15 +25,15 @@ pip install 'crewai[tools]'
 다음 예제는 도구를 초기화하고 주어진 쿼리로 검색을 실행하는 방법을 보여줍니다:
 
 ```python Code
-from crewai_tools import EXASearchTool
+from crewai_tools import ExaSearchTool
 
 # Initialize the tool for internet searching capabilities
-tool = EXASearchTool()
+tool = ExaSearchTool()
 ```
 
 ## 시작 단계
 
-EXASearchTool을 효과적으로 사용하려면 다음 단계를 따르세요:
+ExaSearchTool을 효과적으로 사용하려면 다음 단계를 따르세요:
 
 <Steps>
     <Step title="패키지 설치">
@@ -47,7 +47,35 @@ EXASearchTool을 효과적으로 사용하려면 다음 단계를 따르세요:
     </Step>
 </Steps>
 
+## MCP를 통한 Exa 사용
+
+Exa가 호스팅하는 MCP 서버에 에이전트를 연결할 수도 있습니다. API 키는 `x-api-key` 헤더로 전달하세요:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerHTTP
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information on the web",
+    backstory="Expert researcher with access to Exa's tools",
+    mcps=[
+        MCPServerHTTP(
+            url="https://mcp.exa.ai/mcp",
+            headers={"x-api-key": "YOUR_EXA_API_KEY"},
+        ),
+    ],
+)
+```
+
+API 키는 [Exa 대시보드](https://dashboard.exa.ai/api-keys)에서 발급받을 수 있습니다. CrewAI에서의 MCP 사용에 대한 자세한 내용은 [MCP 개요](/ko/mcp/overview)를 참고하세요.
+
 ## 결론
 
-`EXASearchTool`을 Python 프로젝트에 통합함으로써, 사용자는 애플리케이션 내에서 실시간으로 인터넷을 직접 검색할 수 있는 능력을 얻게 됩니다.
+`ExaSearchTool`을 Python 프로젝트에 통합함으로써, 사용자는 애플리케이션 내에서 실시간으로 인터넷을 직접 검색할 수 있는 능력을 얻게 됩니다.
 제공된 설정 및 사용 지침을 따르면, 이 도구를 프로젝트에 포함하는 과정이 간편하고 직관적입니다.
+
+## 참고 자료
+
+- [Exa 공식 문서](https://exa.ai/docs)
+- [Exa 대시보드 — API 키 및 사용량 관리](https://dashboard.exa.ai)

docs/pt-BR/api-reference/introduction.mdx

@@ -26,7 +26,7 @@ Bem-vindo à referência da API do CrewAI AMP. Esta API permite que você intera
 </Step>
 
   <Step title="Monitore o Progresso">
-    Use `GET /{kickoff_id}/status` para checar o status da execução e recuperar os resultados.
+    Use `GET /status/{kickoff_id}` para checar o status da execução e recuperar os resultados.
   </Step>
 </Steps>
 
@@ -65,7 +65,7 @@ Substitua `your-crew-name` pela URL real do seu crew no painel.
 
 1. **Descoberta**: Chame `GET /inputs` para entender o que seu crew precisa
 2. **Execução**: Envie os inputs via `POST /kickoff` para iniciar o processamento
-3. **Monitoramento**: Faça polling em `GET /{kickoff_id}/status` até a conclusão
+3. **Monitoramento**: Faça polling em `GET /status/{kickoff_id}` até a conclusão
 4. **Resultados**: Extraia o output final da resposta concluída
 
 ## Tratamento de Erros

docs/pt-BR/api-reference/status.mdx

@@ -1,6 +1,6 @@
 ---
-title: "GET /{kickoff_id}/status"
+title: "GET /status/{kickoff_id}"
 description: "Obter o status da execução"
-openapi: "/enterprise-api.pt-BR.yaml GET /{kickoff_id}/status"
+openapi: "/enterprise-api.pt-BR.yaml GET /status/{kickoff_id}"
 mode: "wide"
 ---

docs/pt-BR/changelog.mdx

@@ -4,6 +4,99 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="04 mai 2026">
+  ## v1.14.5a2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a2)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Corrigir a restauração da saída da tarefa no bloco finally
+  - Incluir `thoughts_token_count` nos tokens de conclusão
+  - Preservar as saídas das tarefas durante o descarregamento assíncrono em lote
+  - Encaminhar kwargs para chamadas de carregador em `CrewAIRagAdapter`
+  - Impedir que `result_as_answer` retorne mensagem de bloqueio de hook como resposta final
+  - Impedir que `result_as_answer` retorne erro como resposta final
+  - Usar `acall` para conversão de saída em caminhos assíncronos
+  - Prevenir a mutação de palavras de parada compartilhadas do LLM entre agentes
+  - Lidar com entrada `BaseModel` em `convert_to_model`
+
+  ### Documentação
+  - Documentar variáveis de ambiente adicionais
+  - Atualizar changelog e versão para v1.14.5a1
+
+  ## Contribuidores
+
+  @NIK-TIGER-BILL, @greysonlalonde, @lorenzejay, @minasami-pr, @theCyberTech, @wishhyt
+
+</Update>
+
+<Update label="01 mai 2026">
+  ## v1.14.5a1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.5a1)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar parâmetro de início `restore_from_state_id`
+  - Adicionar destaques ao ExaSearchTool e renomear de EXASearchTool
+
+  ### Correções de Bugs
+  - Corrigir sites de pinos do crewai ausentes no fluxo de lançamento
+  - Garantir eventos de carregamento de habilidades para rastros
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.4
+
+  ## Contribuidores
+
+  @akaKuruma, @github-actions[bot], @greysonlalonde, @lorenzejay, @theishangoswami
+
+</Update>
+
+<Update label="01 mai 2026">
+  ## v1.14.4
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.4)
+
+  ## O que mudou
+
+  ### Recursos
+  - Adicionar suporte para chave de persistência personalizada em @persist
+  - Adicionar suporte à API de Respostas para o provedor Azure OpenAI
+  - Encaminhar credential_scopes para o cliente de Inferência da Azure AI
+  - Adicionar guia de configuração de identidade de carga de trabalho do Vertex AI
+  - Adicionar Tavily Research e obter Pesquisa
+  - Adicionar ferramentas MCP do You.com para pesquisa, pesquisa e extração de conteúdo
+
+  ### Correções de Bugs
+  - Corrigir falha quando a correspondência de regex JSON não é um JSON válido
+  - Corrigir para preservar tool_calls quando a resposta também contém texto
+  - Corrigir para encaminhar base_url e api_key para instructor.from_provider
+  - Corrigir para avisar e retornar vazio quando o servidor MCP nativo não retorna ferramentas
+  - Corrigir para usar a variável de mensagens validadas em manipuladores não-streaming
+  - Corrigir para proteger os ajudantes de descrição do chat da equipe contra falhas do LLM
+  - Corrigir para redefinir mensagens e iterações entre invocações
+  - Corrigir para encaminhar o arquivo de agentes treinados através de replay e teste
+  - Corrigir para honrar o arquivo de agentes treinados personalizados na inferência
+  - Corrigir para vincular agentes apenas de tarefa à equipe para arquivos de entrada multimodal
+  - Corrigir para serializar chamadas de guardrail como nulas para checkpointing JSON
+  - Corrigir renomeação de force_final_answer para evitar roteador autorreferencial
+  - Corrigir aumento de litellm para correção de SSTI; ignorar CVE pip não corrigível
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.4a1
+  - Adicionar página de Ferramentas do Sandbox E2B
+  - Adicionar documentação de ferramentas do sandbox Daytona
+
+  ## Contributors
+
+  @EdwardIrby, @dependabot[bot], @factory-droid-oss, @factory-droid[bot], @greysonlalonde, @kunalk16, @lorenzejay, @lucasgomide, @manisrinivasan2k1, @mattatcha, @vinibrsl
+
+</Update>
+
 <Update label="29 abr 2026">
   ## v1.14.4a1
 

docs/pt-BR/concepts/flows.mdx

@@ -193,32 +193,41 @@ Para um controle mais granular, você pode aplicar @persist em métodos específ
 # (O código não é traduzido)
 ```
 
-### Chave de Persistência Personalizada
+### Forking de Estado Persistido
 
-Por padrão, `@persist` usa o campo `state.id` gerado automaticamente como chave de persistência. Se o seu flow já possui um identificador natural — por exemplo um `conversation_id` compartilhado entre sessões — você pode passar o argumento `key` e `@persist` usará esse atributo como UUID do flow:
+`@persist` suporta dois modos distintos de hidratação em `kickoff` / `kickoff_async`:
+
+- `kickoff(inputs={"id": <uuid>})` — **resume**: carrega o snapshot mais recente do UUID informado e continua escrevendo sob o mesmo `flow_uuid`. O histórico se estende.
+- `kickoff(restore_from_state_id=<uuid>)` — **fork**: carrega o snapshot mais recente do UUID informado, hidrata o estado da nova execução a partir dele, e atribui um novo `state.id` (auto-gerado, ou `inputs["id"]` se fixado). As escritas do `@persist` da nova execução vão para o novo `state.id`; o histórico do flow de origem é preservado.
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    turn: int = 0
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")  # Usa um campo personalizado como chave de persistência
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def begin(self):
-        self.state.turn += 1
-        print(f"Conversa {self.state.conversation_id} turno {self.state.turn}")
+    def step(self):
+        self.state.counter += 1
+        print(f"[id={self.state.id}] counter={self.state.counter}")
 
-# Retomar a mesma conversa recarrega o estado anterior pelo conversation_id
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# Execução 1: estado novo, counter 0 -> 1, persistido sob flow_1.state.id
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# Fork: hidrata do snapshot mais recente de flow_1, mas usa um state.id NOVO
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2.state.counter começa em 1 (hidratado), e step() incrementa para 2.
+# flow_2.state.id != flow_1.state.id; o histórico de flow_1 não é alterado.
 ```
 
-O decorador lê o valor em `state[key]` para estados do tipo dicionário ou `getattr(state, key)` para estados Pydantic / objetos. Se o atributo informado estiver ausente ou for *falsy* no momento de salvar, `@persist` lança um `ValueError` como `Flow state is missing required persistence key 'conversation_id'`. Quando `key` é omitido, o comportamento original é preservado e `state.id` continua sendo usado.
+Se o `restore_from_state_id` informado não corresponder a nenhum estado persistido, o kickoff retorna silenciosamente ao comportamento padrão — o mesmo comportamento do `inputs["id"]` quando não encontrado. Combinar `restore_from_state_id` com `from_checkpoint` lança um `ValueError`; escolha uma única fonte de hidratação. Fixar `inputs["id"]` durante o fork compartilha uma chave de persistência com outro flow — geralmente você quer apenas `restore_from_state_id`.
 
 ### Como Funciona
 

docs/pt-BR/concepts/production-architecture.mdx

@@ -146,15 +146,14 @@ class ProductionFlow(Flow[AppState]):
     # ...
 ```
 
-Por padrão, `@persist` usa o `state.id` gerado automaticamente como chave do estado salvo. Se a sua aplicação já tem um identificador natural — por exemplo um `conversation_id` que liga várias execuções à mesma sessão de usuário — passe-o como `key` e o decorador usará esse atributo como UUID do flow. Um `ValueError` é lançado se o atributo informado estiver ausente ou for *falsy* no momento de salvar.
+Por padrão, `@persist` retoma um flow quando `kickoff(inputs={"id": <uuid>})` é informado, estendendo o mesmo histórico do `flow_uuid`. Para **forkar** um flow persistido em uma nova linhagem — hidratar o estado a partir de uma execução anterior mas escrever sob um novo `state.id` — passe `restore_from_state_id`:
 
 ```python
-@persist(key="conversation_id")
-class ProductionFlow(Flow[AppState]):
-    # AppState precisa expor conversation_id; retomar a sessão recarrega o estado anterior
-    ...
+flow.kickoff(restore_from_state_id="<previous-run-state-id>")
 ```
 
+A nova execução recebe um novo `state.id` (auto-gerado, ou `inputs["id"]` se fixado), então suas escritas do `@persist` não estendem o histórico da origem. Combinar com `from_checkpoint` lança um `ValueError`; escolha uma única fonte de hidratação.
+
 ## Resumo
 
 - **Comece com um Flow.**

docs/pt-BR/concepts/tools.mdx

@@ -133,7 +133,7 @@ Aqui está uma lista das ferramentas disponíveis e suas descrições:
 | **DirectorySearchTool**          | Ferramenta RAG para busca em diretórios, útil para navegação em sistemas de arquivos.                |
 | **DOCXSearchTool**               | Ferramenta RAG voltada para busca em documentos DOCX, ideal para processar arquivos Word.            |
 | **DirectoryReadTool**            | Facilita a leitura e processamento de estruturas de diretórios e seus conteúdos.                     |
-| **EXASearchTool**                | Ferramenta projetada para buscas exaustivas em diversas fontes de dados.                             |
+| **ExaSearchTool**                | Ferramenta projetada para buscas exaustivas em diversas fontes de dados.                             |
 | **FileReadTool**                 | Permite a leitura e extração de dados de arquivos, suportando diversos formatos.                     |
 | **FirecrawlSearchTool**          | Ferramenta para buscar páginas web usando Firecrawl e retornar os resultados.                        |
 | **FirecrawlCrawlWebsiteTool**    | Ferramenta para rastrear páginas web utilizando o Firecrawl.                                         |

docs/pt-BR/guides/flows/mastering-flow-state.mdx

@@ -167,32 +167,47 @@ Para mais controle, você pode aplicar `@persist()` em métodos específicos:
 # código não traduzido
 ```
 
-#### Usando uma Chave de Persistência Personalizada
+#### Forking de Estado Persistido
 
-Por padrão, `@persist()` usa o `state.id` gerado automaticamente como chave do estado persistido. Quando seu domínio já possui um identificador natural — por exemplo um `conversation_id` que liga várias execuções do flow à mesma sessão de usuário — passe-o como argumento `key` e `@persist` usará esse atributo como UUID do flow em vez de `id`:
+`@persist` suporta dois modos distintos de hidratação em `kickoff` / `kickoff_async`. Use **resume** (`inputs["id"]`) para continuar a mesma linhagem; use **fork** (`restore_from_state_id`) para iniciar uma nova linhagem a partir de um snapshot:
+
+| | `state.id` após o kickoff | Escritas do `@persist` vão para |
+|---|---|---|
+| `inputs["id"]` (resume) | id informado | id informado (estende o histórico) |
+| `restore_from_state_id` (fork) | id novo, ou `inputs["id"]` se fixado | id novo (origem preservada) |
 
 ```python
-from crewai.flow.flow import Flow, listen, start
+from crewai.flow.flow import Flow, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel
 
-class ConversationState(BaseModel):
-    conversation_id: str
-    history: list[str] = []
+class CounterState(BaseModel):
+    id: str = ""
+    counter: int = 0
 
-@persist(key="conversation_id")
-class ConversationFlow(Flow[ConversationState]):
+@persist
+class CounterFlow(Flow[CounterState]):
     @start()
-    def greet(self):
-        self.state.history.append("hello")
-        return self.state.history
+    def step(self):
+        self.state.counter += 1
 
-# Uma segunda execução com o mesmo conversation_id recarrega o estado anterior
-flow = ConversationFlow(conversation_id="user-42")
-flow.kickoff()
+# Execução 1: estado novo, counter 0 -> 1
+flow_1 = CounterFlow()
+flow_1.kickoff()
+
+# Fork: hidrata do snapshot mais recente de flow_1, mas escreve sob um state.id NOVO
+flow_2 = CounterFlow()
+flow_2.kickoff(restore_from_state_id=flow_1.state.id)
+# flow_2 começa com counter=1 (hidratado), e step() incrementa para 2.
+# O histórico do flow_uuid de flow_1 não é alterado.
 ```
 
-Para estados baseados em dicionário `@persist` lê `state[key]`, e para estados Pydantic / objetos lê `getattr(state, key)`. Se o atributo informado estiver ausente ou for *falsy* no momento em que o estado for salvo, `@persist` lança um `ValueError` como `Flow state is missing required persistence key 'conversation_id'`, fazendo com que a falha apareça imediatamente em vez de descartar silenciosamente os dados persistidos. Chamar `@persist()` sem `key` mantém o comportamento original de usar `state.id`.
+Notas sobre o comportamento:
+
+- `restore_from_state_id` não encontrado na persistência → o kickoff retorna silenciosamente ao comportamento padrão (espelha o comportamento de `inputs["id"]` quando não encontrado). Nenhuma exceção é lançada.
+- Combinar `restore_from_state_id` com `from_checkpoint` lança um `ValueError` — eles miram sistemas de estado diferentes (`@persist` vs. Checkpointing) e não podem ser combinados.
+- `restore_from_state_id=None` (padrão) é byte-idêntico a um kickoff sem o parâmetro.
+- Fixar `inputs["id"]` durante o fork significa que a nova execução compartilha uma chave de persistência com outro flow — geralmente você quer apenas `restore_from_state_id`.
 
 ## Padrões Avançados de Estado
 

docs/pt-BR/tools/search-research/exasearchtool.mdx

@@ -1,15 +1,15 @@
 ---
 title: Carregador Web EXA Search
-description: O `EXASearchTool` foi projetado para realizar uma busca semântica para uma consulta especificada a partir do conteúdo de um texto em toda a internet.
+description: O `ExaSearchTool` foi projetado para realizar uma busca semântica para uma consulta especificada a partir do conteúdo de um texto em toda a internet.
 icon: globe-pointer
 mode: "wide"
 ---
 
-# `EXASearchTool`
+# `ExaSearchTool`
 
 ## Descrição
 
-O EXASearchTool foi projetado para realizar uma busca semântica para uma consulta especificada a partir do conteúdo de um texto em toda a internet.
+O ExaSearchTool foi projetado para realizar uma busca semântica para uma consulta especificada a partir do conteúdo de um texto em toda a internet.
 Ele utiliza a API da [exa.ai](https://exa.ai/) para buscar e exibir os resultados de pesquisa mais relevantes com base na consulta fornecida pelo usuário.
 
 ## Instalação
@@ -25,15 +25,15 @@ pip install 'crewai[tools]'
 O exemplo a seguir demonstra como inicializar a ferramenta e executar uma busca com uma consulta determinada:
 
 ```python Code
-from crewai_tools import EXASearchTool
+from crewai_tools import ExaSearchTool
 
 # Initialize the tool for internet searching capabilities
-tool = EXASearchTool()
+tool = ExaSearchTool()
 ```
 
 ## Etapas para Começar
 
-Para usar o EXASearchTool de forma eficaz, siga estas etapas:
+Para usar o ExaSearchTool de forma eficaz, siga estas etapas:
 
 <Steps>
     <Step title="Instalação do Pacote">
@@ -47,7 +47,35 @@ Para usar o EXASearchTool de forma eficaz, siga estas etapas:
     </Step>
 </Steps>
 
+## Usando o Exa via MCP
+
+Você também pode conectar seu agente ao servidor MCP hospedado pelo Exa. Passe sua chave de API no cabeçalho `x-api-key`:
+
+```python
+from crewai import Agent
+from crewai.mcp import MCPServerHTTP
+
+agent = Agent(
+    role="Research Analyst",
+    goal="Find and analyze information on the web",
+    backstory="Expert researcher with access to Exa's tools",
+    mcps=[
+        MCPServerHTTP(
+            url="https://mcp.exa.ai/mcp",
+            headers={"x-api-key": "YOUR_EXA_API_KEY"},
+        ),
+    ],
+)
+```
+
+Obtenha sua chave de API no [painel da Exa](https://dashboard.exa.ai/api-keys). Para mais informações sobre MCP no CrewAI, consulte a [visão geral do MCP](/pt-BR/mcp/overview).
+
 ## Conclusão
 
-Ao integrar o `EXASearchTool` em projetos Python, os usuários ganham a capacidade de realizar buscas relevantes e em tempo real pela internet diretamente de suas aplicações.
-Seguindo as orientações de configuração e uso fornecidas, a incorporação desta ferramenta em projetos torna-se simples e direta.
+Ao integrar o `ExaSearchTool` em projetos Python, os usuários ganham a capacidade de realizar buscas relevantes e em tempo real pela internet diretamente de suas aplicações.
+Seguindo as orientações de configuração e uso fornecidas, a incorporação desta ferramenta em projetos torna-se simples e direta.
+
+## Recursos
+
+- [Documentação do Exa](https://exa.ai/docs)
+- [Painel do Exa — gerenciar chaves de API e uso](https://dashboard.exa.ai)

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.14.4a1"
+__version__ = "1.14.5a2"

lib/crewai-tools/README.md

@@ -26,7 +26,7 @@ CrewAI provides an extensive collection of powerful tools ready to enhance your
 - **Web Scraping**: `ScrapeWebsiteTool`, `SeleniumScrapingTool`
 - **Database Integrations**: `MySQLSearchTool`
 - **Vector Database Integrations**: `MongoDBVectorSearchTool`, `QdrantVectorSearchTool`, `WeaviateVectorSearchTool`
-- **API Integrations**: `SerperApiTool`, `EXASearchTool`
+- **API Integrations**: `SerperApiTool`, `ExaSearchTool`
 - **AI-powered Tools**: `DallETool`, `VisionTool`, `StagehandTool`
 
 And many more robust tools to simplify your agent integrations.

lib/crewai-tools/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
     "requests>=2.33.0,<3",
-    "crewai==1.14.4a1",
+    "crewai==1.14.5a2",
     "tiktoken>=0.8.0,<0.13",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -107,7 +107,7 @@ stagehand = [
     "stagehand>=0.4.1",
 ]
 github = [
-    "gitpython>=3.1.41,<4",
+    "gitpython>=3.1.47,<4",
     "PyGithub==1.59.1",
 ]
 rag = [

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

@@ -76,7 +76,7 @@ from crewai_tools.tools.e2b_sandbox_tool import (
     E2BFileTool,
     E2BPythonTool,
 )
-from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool
+from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool, ExaSearchTool
 from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool
 from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool
 from crewai_tools.tools.files_compressor_tool.files_compressor_tool import (
@@ -258,6 +258,7 @@ __all__ = [
     "E2BPythonTool",
     "EXASearchTool",
     "EnterpriseActionTool",
+    "ExaSearchTool",
     "FileCompressorTool",
     "FileReadTool",
     "FileWriterTool",
@@ -329,4 +330,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.14.4a1"
+__version__ = "1.14.5a2"

lib/crewai-tools/src/crewai_tools/adapters/crewai_rag_adapter.py

@@ -268,7 +268,9 @@ class CrewAIRagAdapter(Adapter):
                             file_chunker = file_data_type.get_chunker()
 
                             file_source = SourceContent(file_path)
-                            file_result: LoaderResult = file_loader.load(file_source)
+                            file_result: LoaderResult = file_loader.load(
+                                file_source, **kwargs
+                            )
 
                             file_chunks = file_chunker.chunk(file_result.content)
 
@@ -319,7 +321,7 @@ class CrewAIRagAdapter(Adapter):
                 loader = data_type.get_loader()
                 chunker = data_type.get_chunker()
 
-                loader_result: LoaderResult = loader.load(source_content)
+                loader_result: LoaderResult = loader.load(source_content, **kwargs)
 
                 chunks = chunker.chunk(loader_result.content)
 

lib/crewai-tools/src/crewai_tools/rag/loaders/webpage_loader.py

@@ -2,9 +2,8 @@ import re
 from typing import Any, Final
 
 from bs4 import BeautifulSoup
-import requests
-
 from crewai_tools.rag.base_loader import BaseLoader, LoaderResult
+from crewai_tools.security.safe_path import safe_get
 from crewai_tools.rag.source_content import SourceContent
 
 
@@ -25,7 +24,7 @@ class WebPageLoader(BaseLoader):
         )
 
         try:
-            response = requests.get(url, timeout=15, headers=headers)
+            response = safe_get(url, timeout=15, headers=headers)
             response.encoding = response.apparent_encoding
 
             soup = BeautifulSoup(response.text, "html.parser")

lib/crewai-tools/src/crewai_tools/security/safe_path.py

@@ -14,8 +14,12 @@ import ipaddress
 import logging
 import os
 import socket
+from typing import Any
 from urllib.parse import urlparse
 
+import requests
+from requests.adapters import HTTPAdapter
+
 
 logger = logging.getLogger(__name__)
 
@@ -203,3 +207,72 @@ def validate_url(url: str) -> str:
             )
 
     return url
+
+
+# ---------------------------------------------------------------------------
+# SSRF-safe HTTP requests (validates IPs on every redirect hop)
+# ---------------------------------------------------------------------------
+
+
+class _SSRFSafeAdapter(HTTPAdapter):
+    """HTTPAdapter that validates the resolved IP of every request — including
+    redirect hops — against the private/reserved blocklist before the
+    connection is made."""
+
+    def send(  # type: ignore[override]
+        self, request: requests.PreparedRequest, **kwargs: Any
+    ) -> requests.Response:
+        parsed = urlparse(request.url)
+        if not _is_escape_hatch_enabled() and parsed.hostname:
+            try:
+                port = parsed.port or (443 if parsed.scheme == "https" else 80)
+                addrinfos = socket.getaddrinfo(parsed.hostname, port)
+            except socket.gaierror as exc:
+                raise ValueError(
+                    f"Could not resolve hostname: '{parsed.hostname}'"
+                ) from exc
+
+            for _family, _, _, _, sockaddr in addrinfos:
+                ip_str = str(sockaddr[0])
+                if _is_private_or_reserved(ip_str):
+                    raise ValueError(
+                        f"Redirect to '{request.url}' blocked: resolves to "
+                        f"private/reserved IP {ip_str}. Access to internal "
+                        f"networks is not allowed. "
+                        f"Set {_UNSAFE_PATHS_ENV}=true to bypass."
+                    )
+
+        return super().send(request, **kwargs)
+
+
+def safe_request_session() -> requests.Session:
+    """Return a :class:`requests.Session` that validates every connection
+    target (including redirect destinations) against the SSRF blocklist."""
+    session = requests.Session()
+    adapter = _SSRFSafeAdapter()
+    session.mount("http://", adapter)
+    session.mount("https://", adapter)
+    return session
+
+
+def safe_get(url: str, **kwargs: Any) -> requests.Response:
+    """Drop-in replacement for ``requests.get()`` with SSRF protection.
+
+    Validates the initial URL via :func:`validate_url`, then executes the
+    request through a session whose adapter re-checks every redirect hop.
+
+    Args:
+        url: The URL to fetch.
+        **kwargs: Passed through to ``session.get()`` (headers, cookies,
+            timeout, etc.).
+
+    Returns:
+        The :class:`requests.Response`.
+
+    Raises:
+        ValueError: If the initial URL or any redirect target resolves to
+            a private/reserved IP.
+    """
+    validate_url(url)
+    session = safe_request_session()
+    return session.get(url, **kwargs)

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

@@ -65,7 +65,7 @@ from crewai_tools.tools.e2b_sandbox_tool import (
     E2BFileTool,
     E2BPythonTool,
 )
-from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool
+from crewai_tools.tools.exa_tools.exa_search_tool import EXASearchTool, ExaSearchTool
 from crewai_tools.tools.file_read_tool.file_read_tool import FileReadTool
 from crewai_tools.tools.file_writer_tool.file_writer_tool import FileWriterTool
 from crewai_tools.tools.files_compressor_tool.files_compressor_tool import (
@@ -242,6 +242,7 @@ __all__ = [
     "E2BFileTool",
     "E2BPythonTool",
     "EXASearchTool",
+    "ExaSearchTool",
     "FileCompressorTool",
     "FileReadTool",
     "FileWriterTool",

lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md

@@ -1,7 +1,7 @@
-# EXASearchTool Documentation
+# ExaSearchTool Documentation
 
 ## Description
-This tool is designed to perform a semantic search for a specified query from a text's content across the internet. It utilizes the `https://exa.ai/` API to fetch and display the most relevant search results based on the query provided by the user.
+This tool lets CrewAI agents search the web using [Exa](https://exa.ai/), the fastest and most accurate web search API. By default the tool returns token-efficient highlights of the most relevant results for any query; you can also opt in to full page content.
 
 ## Installation
 To incorporate this tool into your project, follow the installation instructions below:
@@ -10,21 +10,23 @@ uv add crewai[tools] exa_py
 ```
 
 ## Example
-The following example demonstrates how to initialize the tool and execute a search with a given query:
+The following example demonstrates how to initialize the tool and run a search:
 
 ```python
-from crewai_tools import EXASearchTool
+from crewai_tools import ExaSearchTool
 
-# Initialize the tool for internet searching capabilities
-tool = EXASearchTool(api_key="your_api_key")
+# Default: results with token-efficient highlights
+tool = ExaSearchTool(api_key="your_api_key", highlights=True)
 ```
 
 ## Steps to Get Started
-To effectively use the `EXASearchTool`, follow these steps:
+To effectively use the `ExaSearchTool`, follow these steps:
 
 1. **Package Installation**: Confirm that the `crewai[tools]` package is installed in your Python environment.
-2. **API Key Acquisition**: Acquire a `https://exa.ai/` API key by registering for a free account at `https://exa.ai/`.
-3. **Environment Configuration**: Store your obtained API key in an environment variable named `EXA_API_KEY` to facilitate its use by the tool.
+2. **API Key Acquisition**: Get an Exa API key from the [Exa dashboard](https://dashboard.exa.ai/api-keys).
+3. **Environment Configuration**: Store your API key in an environment variable named `EXA_API_KEY` so the tool can pick it up automatically.
 
-## Conclusion
-By integrating the `EXASearchTool` into Python projects, users gain the ability to conduct real-time, relevant searches across the internet directly from their applications. By adhering to the setup and usage guidelines provided, incorporating this tool into projects is streamlined and straightforward.
+For details on choosing between highlights and full content, see the [Exa search best practices](https://exa.ai/docs/reference/search-best-practices).
+
+## Note
+`EXASearchTool` is a deprecated alias for `ExaSearchTool`. Existing imports continue to work but emit a deprecation warning; please migrate to `ExaSearchTool`.

lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py

@@ -3,12 +3,19 @@ from __future__ import annotations
 from builtins import type as type_
 import os
 from typing import Any, TypedDict
+import warnings
 
 from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import Required
 
 
+try:
+    from exa_py import Exa
+except ImportError:
+    Exa = None  # type: ignore[assignment,misc]
+
+
 class SearchParams(TypedDict, total=False):
     """Parameters for Exa search API."""
 
@@ -18,7 +25,7 @@ class SearchParams(TypedDict, total=False):
     include_domains: list[str]
 
 
-class EXABaseToolSchema(BaseModel):
+class ExaBaseToolSchema(BaseModel):
     search_query: str = Field(
         ..., description="Mandatory search query you want to use to search the internet"
     )
@@ -31,14 +38,20 @@ class EXABaseToolSchema(BaseModel):
     )
 
 
-class EXASearchTool(BaseTool):
+EXABaseToolSchema = ExaBaseToolSchema
+
+
+class ExaSearchTool(BaseTool):
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    name: str = "EXASearchTool"
-    description: str = "Search the internet using Exa"
-    args_schema: type_[BaseModel] = EXABaseToolSchema
+    name: str = "ExaSearchTool"
+    description: str = (
+        "Search the web with Exa, the fastest and most accurate web search API."
+    )
+    args_schema: type_[BaseModel] = ExaBaseToolSchema
     client: Any | None = None
-    content: bool | None = False
-    summary: bool | None = False
+    content: bool | dict[str, Any] | None = False
+    summary: bool | dict[str, Any] | None = False
+    highlights: bool | dict[str, Any] | None = True
     type: str | None = "auto"
     package_dependencies: list[str] = Field(default_factory=lambda: ["exa_py"])
     api_key: str | None = Field(
@@ -68,17 +81,17 @@ class EXASearchTool(BaseTool):
 
     def __init__(
         self,
-        content: bool | None = False,
-        summary: bool | None = False,
+        content: bool | dict[str, Any] | None = False,
+        summary: bool | dict[str, Any] | None = False,
+        highlights: bool | dict[str, Any] | None = True,
         type: str | None = "auto",
         **kwargs: Any,
     ) -> None:
         super().__init__(
             **kwargs,
         )
-        try:
-            from exa_py import Exa
-        except ImportError as e:
+        global Exa
+        if Exa is None:
             import click
 
             if click.confirm(
@@ -88,12 +101,13 @@ class EXASearchTool(BaseTool):
 
                 subprocess.run(["uv", "add", "exa_py"], check=True)  # noqa: S607
 
-                # Re-import after installation
-                from exa_py import Exa
+                from exa_py import Exa as _Exa
+
+                Exa = _Exa  # type: ignore[misc]
             else:
                 raise ImportError(
-                    "You are missing the 'exa_py' package. Would you like to install it?"
-                ) from e
+                    "You are missing the 'exa_py' package. Please install it to use ExaSearchTool."
+                )
 
         client_kwargs: dict[str, str] = {}
         if self.api_key:
@@ -101,8 +115,10 @@ class EXASearchTool(BaseTool):
         if self.base_url:
             client_kwargs["base_url"] = self.base_url
         self.client = Exa(**client_kwargs)
+        self.client.headers["x-exa-integration"] = "crewai"
         self.content = content
         self.summary = summary
+        self.highlights = highlights
         self.type = type
 
     def _run(
@@ -126,10 +142,31 @@ class EXASearchTool(BaseTool):
         if include_domains:
             search_params["include_domains"] = include_domains
 
+        contents_kwargs: dict[str, Any] = {}
         if self.content:
-            results = self.client.search_and_contents(
-                search_query, summary=self.summary, **search_params
+            contents_kwargs["text"] = self.content
+        if self.highlights:
+            contents_kwargs["highlights"] = self.highlights
+        if self.summary:
+            contents_kwargs["summary"] = self.summary
+
+        if contents_kwargs:
+            return self.client.search_and_contents(
+                search_query, **contents_kwargs, **search_params
             )
-        else:
-            results = self.client.search(search_query, **search_params)
-        return results
+        return self.client.search(search_query, **search_params)
+
+
+class EXASearchTool(ExaSearchTool):
+    """Deprecated alias for :class:`ExaSearchTool`. Kept for backwards compatibility."""
+
+    name: str = "ExaSearchTool"
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        warnings.warn(
+            "EXASearchTool is deprecated and will be removed in a future release; "
+            "use ExaSearchTool instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        super().__init__(*args, **kwargs)

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

@@ -3,9 +3,7 @@ from typing import Any
 
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
-import requests
-
-from crewai_tools.security.safe_path import validate_url
+from crewai_tools.security.safe_path import safe_get
 
 
 try:
@@ -83,8 +81,7 @@ class ScrapeElementFromWebsiteTool(BaseTool):
         if website_url is None or css_element is None:
             raise ValueError("Both website_url and css_element must be provided.")
 
-        website_url = validate_url(website_url)
-        page = requests.get(
+        page = safe_get(
             website_url,
             headers=self.headers,
             cookies=self.cookies if self.cookies else {},

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

@@ -3,9 +3,7 @@ import re
 from typing import Any
 
 from pydantic import Field
-import requests
-
-from crewai_tools.security.safe_path import validate_url
+from crewai_tools.security.safe_path import safe_get
 
 
 try:
@@ -75,8 +73,7 @@ class ScrapeWebsiteTool(BaseTool):
         if website_url is None:
             raise ValueError("Website URL must be provided.")
 
-        website_url = validate_url(website_url)
-        page = requests.get(
+        page = safe_get(
             website_url,
             timeout=15,
             headers=self.headers,

lib/crewai-tools/tests/rag/test_webpage_loader.py

@@ -22,7 +22,7 @@ class TestWebPageLoader:
         soup.return_value = script_style_elements or []
         return soup
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_load_basic_webpage(self, mock_bs, mock_get):
         mock_get.return_value = self.setup_mock_response(
@@ -37,7 +37,7 @@ class TestWebPageLoader:
         assert result.content == "Test content"
         assert result.metadata["title"] == "Test Page"
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_load_webpage_with_scripts_and_styles(self, mock_bs, mock_get):
         html = """
@@ -62,7 +62,7 @@ class TestWebPageLoader:
         for el in scripts + styles:
             el.decompose.assert_called_once()
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_text_cleaning_and_title_handling(self, mock_bs, mock_get):
         mock_get.return_value = self.setup_mock_response(
@@ -77,7 +77,7 @@ class TestWebPageLoader:
         assert result.content is not None
         assert result.metadata["title"] == ""
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_empty_or_missing_title(self, mock_bs, mock_get):
         for title in [None, ""]:
@@ -90,7 +90,7 @@ class TestWebPageLoader:
             result = loader.load(SourceContent("https://example.com"))
             assert result.metadata["title"] == ""
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     def test_custom_and_default_headers(self, mock_get):
         mock_get.return_value = self.setup_mock_response(
             "<html><body>Test</body></html>"
@@ -109,14 +109,14 @@ class TestWebPageLoader:
 
         assert mock_get.call_args[1]["headers"] == custom_headers
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     def test_error_handling(self, mock_get):
         for error in [Exception("Fail"), ValueError("Bad"), ImportError("Oops")]:
             mock_get.side_effect = error
             with pytest.raises(ValueError, match="Error loading webpage"):
                 WebPageLoader().load(SourceContent("https://example.com"))
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     def test_timeout_and_http_error(self, mock_get):
         import requests
 
@@ -131,7 +131,7 @@ class TestWebPageLoader:
         with pytest.raises(ValueError):
             WebPageLoader().load(SourceContent("https://example.com/404"))
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_doc_id_consistency(self, mock_bs, mock_get):
         mock_get.return_value = self.setup_mock_response(
@@ -145,7 +145,7 @@ class TestWebPageLoader:
 
         assert result1.doc_id == result2.doc_id
 
-    @patch("requests.get")
+    @patch("crewai_tools.rag.loaders.webpage_loader.safe_get")
     @patch("crewai_tools.rag.loaders.webpage_loader.BeautifulSoup")
     def test_status_code_and_content_type(self, mock_bs, mock_get):
         for status in [200, 201, 301]:

lib/crewai-tools/tests/tools/exa_search_tool_test.py

@@ -1,13 +1,13 @@
 import os
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
-from crewai_tools import EXASearchTool
+from crewai_tools import EXASearchTool, ExaSearchTool
 import pytest
 
 
 @pytest.fixture
 def exa_search_tool():
-    return EXASearchTool(api_key="test_api_key")
+    return ExaSearchTool(api_key="test_api_key")
 
 
 @pytest.fixture(autouse=True)
@@ -22,11 +22,12 @@ def test_exa_search_tool_initialization():
             "crewai_tools.tools.exa_tools.exa_search_tool.Exa"
         ) as mock_exa_class:
             api_key = "test_api_key"
-            tool = EXASearchTool(api_key=api_key)
+            tool = ExaSearchTool(api_key=api_key)
 
             assert tool.api_key == api_key
             assert tool.content is False
             assert tool.summary is False
+            assert tool.highlights is True
             assert tool.type == "auto"
             mock_exa_class.assert_called_once_with(api_key=api_key)
 
@@ -36,7 +37,7 @@ def test_exa_search_tool_initialization_with_env(mock_exa_api_key):
         with patch(
             "crewai_tools.tools.exa_tools.exa_search_tool.Exa"
         ) as mock_exa_class:
-            EXASearchTool()
+            ExaSearchTool()
             mock_exa_class.assert_called_once_with(api_key="test_key_from_env")
 
 
@@ -47,12 +48,13 @@ def test_exa_search_tool_initialization_with_base_url():
         ) as mock_exa_class:
             api_key = "test_api_key"
             base_url = "https://custom.exa.api.com"
-            tool = EXASearchTool(api_key=api_key, base_url=base_url)
+            tool = ExaSearchTool(api_key=api_key, base_url=base_url)
 
             assert tool.api_key == api_key
             assert tool.base_url == base_url
             assert tool.content is False
             assert tool.summary is False
+            assert tool.highlights is True
             assert tool.type == "auto"
             mock_exa_class.assert_called_once_with(api_key=api_key, base_url=base_url)
 
@@ -67,7 +69,7 @@ def test_exa_search_tool_initialization_with_env_base_url(
     mock_exa_api_key, mock_exa_base_url
 ):
     with patch("crewai_tools.tools.exa_tools.exa_search_tool.Exa") as mock_exa_class:
-        EXASearchTool()
+        ExaSearchTool()
         mock_exa_class.assert_called_once_with(
             api_key="test_key_from_env", base_url="https://env.exa.api.com"
         )
@@ -79,8 +81,33 @@ def test_exa_search_tool_initialization_without_base_url():
             "crewai_tools.tools.exa_tools.exa_search_tool.Exa"
         ) as mock_exa_class:
             api_key = "test_api_key"
-            tool = EXASearchTool(api_key=api_key)
+            tool = ExaSearchTool(api_key=api_key)
 
             assert tool.api_key == api_key
             assert tool.base_url is None
             mock_exa_class.assert_called_once_with(api_key=api_key)
+
+
+def test_exa_search_tool_highlights_uses_search_and_contents():
+    with patch("crewai_tools.tools.exa_tools.exa_search_tool.Exa") as mock_exa_class:
+        mock_client = MagicMock()
+        mock_exa_class.return_value = mock_client
+        tool = ExaSearchTool(
+            api_key="test_api_key", highlights={"max_characters": 4000}
+        )
+
+        tool._run(search_query="hello world")
+
+        mock_client.search_and_contents.assert_called_once_with(
+            "hello world",
+            highlights={"max_characters": 4000},
+            type="auto",
+        )
+        mock_client.search.assert_not_called()
+
+
+def test_exasearchtool_alias_is_deprecated():
+    with patch("crewai_tools.tools.exa_tools.exa_search_tool.Exa"):
+        with pytest.warns(DeprecationWarning, match="ExaSearchTool"):
+            tool = EXASearchTool(api_key="test_api_key")
+        assert isinstance(tool, ExaSearchTool)

lib/crewai-tools/tests/utilities/test_safe_path.py

@@ -6,7 +6,10 @@ import os
 
 import pytest
 
+from unittest.mock import MagicMock, patch
+
 from crewai_tools.security.safe_path import (
+    safe_get,
     validate_directory_path,
     validate_file_path,
     validate_url,
@@ -168,3 +171,62 @@ class TestValidateUrl:
         # file:// would normally be blocked
         result = validate_url("file:///etc/passwd")
         assert result == "file:///etc/passwd"
+
+
+# ---------------------------------------------------------------------------
+# safe_get — redirect-aware SSRF protection
+# ---------------------------------------------------------------------------
+
+
+def _fake_getaddrinfo_factory(ip: str):
+    """Return a getaddrinfo replacement that always resolves to *ip*."""
+    def _fake(host, port, *args, **kwargs):
+        return [(2, 1, 6, "", (ip, port or 80))]
+    return _fake
+
+
+class TestSafeGet:
+    """Tests for safe_get (validates IPs on every redirect hop)."""
+
+    @patch("crewai_tools.security.safe_path.socket.getaddrinfo",
+           side_effect=_fake_getaddrinfo_factory("93.184.216.34"))
+    @patch("requests.adapters.HTTPAdapter.send")
+    def test_allows_public_url(self, mock_send, mock_dns):
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.is_redirect = False
+        mock_response.headers = {}
+        mock_send.return_value = mock_response
+        resp = safe_get("https://example.com/page")
+        assert resp.status_code == 200
+
+    @patch("crewai_tools.security.safe_path.socket.getaddrinfo",
+           side_effect=_fake_getaddrinfo_factory("127.0.0.1"))
+    def test_blocks_redirect_to_localhost(self, mock_dns):
+        with pytest.raises(ValueError, match="private/reserved IP"):
+            safe_get("http://evil.com/redirect")
+
+    @patch("crewai_tools.security.safe_path.socket.getaddrinfo",
+           side_effect=_fake_getaddrinfo_factory("169.254.169.254"))
+    def test_blocks_redirect_to_metadata(self, mock_dns):
+        with pytest.raises(ValueError, match="private/reserved IP"):
+            safe_get("http://evil.com/metadata")
+
+    @patch("crewai_tools.security.safe_path.socket.getaddrinfo",
+           side_effect=_fake_getaddrinfo_factory("10.0.0.1"))
+    def test_blocks_redirect_to_private_range(self, mock_dns):
+        with pytest.raises(ValueError, match="private/reserved IP"):
+            safe_get("http://evil.com/internal")
+
+    @patch("crewai_tools.security.safe_path.socket.getaddrinfo",
+           side_effect=_fake_getaddrinfo_factory("169.254.169.254"))
+    @patch("requests.adapters.HTTPAdapter.send")
+    def test_escape_hatch_bypasses_redirect_check(self, mock_send, mock_dns, monkeypatch):
+        monkeypatch.setenv("CREWAI_TOOLS_ALLOW_UNSAFE_PATHS", "true")
+        mock_response = MagicMock()
+        mock_response.status_code = 200
+        mock_response.is_redirect = False
+        mock_response.headers = {}
+        mock_send.return_value = mock_response
+        resp = safe_get("http://evil.com/metadata")
+        assert resp.status_code == 200

lib/crewai-tools/tool.specs.json

@@ -9397,7 +9397,7 @@
       }
     },
     {
-      "description": "Search the internet using Exa",
+      "description": "Search the web with Exa, the fastest and most accurate web search API.",
       "env_vars": [
         {
           "default": null,
@@ -9412,7 +9412,7 @@
           "required": false
         }
       ],
-      "humanized_name": "EXASearchTool",
+      "humanized_name": "ExaSearchTool",
       "init_params_schema": {
         "$defs": {
           "EnvVar": {
@@ -9451,6 +9451,7 @@
             "type": "object"
           }
         },
+        "description": "Deprecated alias for :class:`ExaSearchTool`. Kept for backwards compatibility.",
         "properties": {
           "api_key": {
             "anyOf": [
@@ -9493,6 +9494,10 @@
               {
                 "type": "boolean"
               },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
               {
                 "type": "null"
               }
@@ -9500,11 +9505,31 @@
             "default": false,
             "title": "Content"
           },
+          "highlights": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": true,
+            "title": "Highlights"
+          },
           "summary": {
             "anyOf": [
               {
                 "type": "boolean"
               },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
               {
                 "type": "null"
               }
@@ -9586,7 +9611,225 @@
         "required": [
           "search_query"
         ],
-        "title": "EXABaseToolSchema",
+        "title": "ExaBaseToolSchema",
+        "type": "object"
+      }
+    },
+    {
+      "description": "Search the web with Exa, the fastest and most accurate web search API.",
+      "env_vars": [
+        {
+          "default": null,
+          "description": "API key for Exa services",
+          "name": "EXA_API_KEY",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "API url for the Exa services",
+          "name": "EXA_BASE_URL",
+          "required": false
+        }
+      ],
+      "humanized_name": "ExaSearchTool",
+      "init_params_schema": {
+        "$defs": {
+          "EnvVar": {
+            "properties": {
+              "default": {
+                "anyOf": [
+                  {
+                    "type": "string"
+                  },
+                  {
+                    "type": "null"
+                  }
+                ],
+                "default": null,
+                "title": "Default"
+              },
+              "description": {
+                "title": "Description",
+                "type": "string"
+              },
+              "name": {
+                "title": "Name",
+                "type": "string"
+              },
+              "required": {
+                "default": true,
+                "title": "Required",
+                "type": "boolean"
+              }
+            },
+            "required": [
+              "name",
+              "description"
+            ],
+            "title": "EnvVar",
+            "type": "object"
+          }
+        },
+        "properties": {
+          "api_key": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "API key for Exa services",
+            "required": false,
+            "title": "Api Key"
+          },
+          "base_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "API server url",
+            "required": false,
+            "title": "Base Url"
+          },
+          "client": {
+            "anyOf": [
+              {},
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Client"
+          },
+          "content": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": false,
+            "title": "Content"
+          },
+          "highlights": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": true,
+            "title": "Highlights"
+          },
+          "summary": {
+            "anyOf": [
+              {
+                "type": "boolean"
+              },
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": false,
+            "title": "Summary"
+          },
+          "type": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": "auto",
+            "title": "Type"
+          }
+        },
+        "required": [],
+        "title": "ExaSearchTool",
+        "type": "object"
+      },
+      "name": "ExaSearchTool",
+      "package_dependencies": [
+        "exa_py"
+      ],
+      "run_params_schema": {
+        "properties": {
+          "end_published_date": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "End date for the search",
+            "title": "End Published Date"
+          },
+          "include_domains": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "string"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "List of domains to include in the search",
+            "title": "Include Domains"
+          },
+          "search_query": {
+            "description": "Mandatory search query you want to use to search the internet",
+            "title": "Search Query",
+            "type": "string"
+          },
+          "start_published_date": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Start date for the search",
+            "title": "Start Published Date"
+          }
+        },
+        "required": [
+          "search_query"
+        ],
+        "title": "ExaBaseToolSchema",
         "type": "object"
       }
     },

lib/crewai/pyproject.toml

@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.14.4a1",
+    "crewai-tools==1.14.5a2",
 ]
 embeddings = [
     "tiktoken>=0.8.0,<0.13"

lib/crewai/src/crewai/__init__.py

@@ -48,7 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.14.4a1"
+__version__ = "1.14.5a2"
 
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),

lib/crewai/src/crewai/a2a/wrapper.py

@@ -386,8 +386,7 @@ def _execute_task_with_a2a(
         return raw_result
     finally:
         task.description = original_description
-        if task.output_pydantic is not None:
-            task.output_pydantic = original_output_pydantic
+        task.output_pydantic = original_output_pydantic
         task.response_model = original_response_model
 
 
@@ -1534,8 +1533,7 @@ async def _aexecute_task_with_a2a(
         return raw_result
     finally:
         task.description = original_description
-        if task.output_pydantic is not None:
-            task.output_pydantic = original_output_pydantic
+        task.output_pydantic = original_output_pydantic
         task.response_model = original_response_model
 
 

lib/crewai/src/crewai/agent/core.py

@@ -1102,16 +1102,6 @@ class Agent(BaseAgent):
         self.agent_executor.tools_handler = self.tools_handler
         self.agent_executor.request_within_rpm_limit = rpm_limit_fn
 
-        if isinstance(self.agent_executor.llm, BaseLLM):
-            existing_stop = getattr(self.agent_executor.llm, "stop", [])
-            self.agent_executor.llm.stop = list(
-                set(
-                    existing_stop + stop_words
-                    if isinstance(existing_stop, list)
-                    else stop_words
-                )
-            )
-
     def get_delegation_tools(self, agents: Sequence[BaseAgent]) -> list[BaseTool]:
         agent_tools = AgentTools(agents=agents)
         return agent_tools.tools()

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

@@ -49,6 +49,7 @@ from crewai.hooks.tool_hooks import (
 )
 from crewai.types.callback import SerializableCallable
 from crewai.utilities.agent_utils import (
+    _llm_stop_words_applied,
     aget_llm_response,
     convert_tools_to_openai_schema,
     enforce_rpm_limit,
@@ -141,15 +142,6 @@ class CrewAgentExecutor(BaseAgentExecutor):
             self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         if not self.after_llm_call_hooks:
             self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
-        if self.llm and not isinstance(self.llm, str):
-            existing_stop = getattr(self.llm, "stop", [])
-            self.llm.stop = list(
-                set(
-                    existing_stop + self.stop
-                    if isinstance(existing_stop, list)
-                    else self.stop
-                )
-            )
 
     @property
     def use_stop_words(self) -> bool:
@@ -210,21 +202,22 @@ class CrewAgentExecutor(BaseAgentExecutor):
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
 
-        try:
-            formatted_answer = self._invoke_loop()
-        except AssertionError:
-            if self.agent.verbose:
-                PRINTER.print(
-                    content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                    color="red",
-                )
-            raise
-        except Exception as e:
-            handle_unknown_error(PRINTER, e, verbose=self.agent.verbose)
-            raise
+        with _llm_stop_words_applied(self.llm, self):
+            try:
+                formatted_answer = self._invoke_loop()
+            except AssertionError:
+                if self.agent.verbose:
+                    PRINTER.print(
+                        content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                        color="red",
+                    )
+                raise
+            except Exception as e:
+                handle_unknown_error(PRINTER, e, verbose=self.agent.verbose)
+                raise
 
-        if self.ask_for_human_input:
-            formatted_answer = self._handle_human_feedback(formatted_answer)
+            if self.ask_for_human_input:
+                formatted_answer = self._handle_human_feedback(formatted_answer)
 
         self._save_to_memory(formatted_answer)
         return {"output": formatted_answer.output}
@@ -1082,21 +1075,22 @@ class CrewAgentExecutor(BaseAgentExecutor):
 
         self.ask_for_human_input = bool(inputs.get("ask_for_human_input", False))
 
-        try:
-            formatted_answer = await self._ainvoke_loop()
-        except AssertionError:
-            if self.agent.verbose:
-                PRINTER.print(
-                    content="Agent failed to reach a final answer. This is likely a bug - please report it.",
-                    color="red",
-                )
-            raise
-        except Exception as e:
-            handle_unknown_error(PRINTER, e, verbose=self.agent.verbose)
-            raise
+        with _llm_stop_words_applied(self.llm, self):
+            try:
+                formatted_answer = await self._ainvoke_loop()
+            except AssertionError:
+                if self.agent.verbose:
+                    PRINTER.print(
+                        content="Agent failed to reach a final answer. This is likely a bug - please report it.",
+                        color="red",
+                    )
+                raise
+            except Exception as e:
+                handle_unknown_error(PRINTER, e, verbose=self.agent.verbose)
+                raise
 
-        if self.ask_for_human_input:
-            formatted_answer = await self._ahandle_human_feedback(formatted_answer)
+            if self.ask_for_human_input:
+                formatted_answer = await self._ahandle_human_feedback(formatted_answer)
 
         self._save_to_memory(formatted_answer)
         return {"output": formatted_answer.output}

lib/crewai/src/crewai/cli/templates/AGENTS.md

@@ -774,7 +774,7 @@ def calculator(expression: str) -> str:
 ```
 
 ### Built-in Tools (install with `uv add crewai-tools`)
-Web/Search: SerperDevTool, ScrapeWebsiteTool, WebsiteSearchTool, EXASearchTool, FirecrawlSearchTool
+Web/Search: SerperDevTool, ScrapeWebsiteTool, WebsiteSearchTool, ExaSearchTool, FirecrawlSearchTool
 Documents: FileReadTool, DirectoryReadTool, PDFSearchTool, DOCXSearchTool, CSVSearchTool, JSONSearchTool, XMLSearchTool, MDXSearchTool
 Code: CodeInterpreterTool, CodeDocsSearchTool, GithubSearchTool
 Media: DALL-E Tool, YoutubeChannelSearchTool, YoutubeVideoSearchTool

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]==1.14.4a1"
+    "crewai[tools]==1.14.5a2"
 ]
 
 [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]==1.14.4a1"
+    "crewai[tools]==1.14.5a2"
 ]
 
 [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]==1.14.4a1"
+    "crewai[tools]==1.14.5a2"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/crew.py

@@ -1283,8 +1283,8 @@ class Crew(FlowTrackable, BaseModel):
                 pending_tasks.append((task, async_task, task_index))
             else:
                 if pending_tasks:
-                    task_outputs = await self._aprocess_async_tasks(
-                        pending_tasks, was_replayed
+                    task_outputs.extend(
+                        await self._aprocess_async_tasks(pending_tasks, was_replayed)
                     )
                     pending_tasks.clear()
 
@@ -1299,7 +1299,9 @@ class Crew(FlowTrackable, BaseModel):
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
         if pending_tasks:
-            task_outputs = await self._aprocess_async_tasks(pending_tasks, was_replayed)
+            task_outputs.extend(
+                await self._aprocess_async_tasks(pending_tasks, was_replayed)
+            )
 
         return self._create_crew_output(task_outputs)
 
@@ -1313,7 +1315,9 @@ class Crew(FlowTrackable, BaseModel):
     ) -> TaskOutput | None:
         """Handle conditional task evaluation using native async."""
         if pending_tasks:
-            task_outputs = await self._aprocess_async_tasks(pending_tasks, was_replayed)
+            task_outputs.extend(
+                await self._aprocess_async_tasks(pending_tasks, was_replayed)
+            )
             pending_tasks.clear()
 
         return check_conditional_skip(
@@ -1489,7 +1493,9 @@ class Crew(FlowTrackable, BaseModel):
                 futures.append((task, future, task_index))
             else:
                 if futures:
-                    task_outputs = self._process_async_tasks(futures, was_replayed)
+                    task_outputs.extend(
+                        self._process_async_tasks(futures, was_replayed)
+                    )
                     futures.clear()
 
                 context = self._get_context(task, task_outputs)
@@ -1503,7 +1509,7 @@ class Crew(FlowTrackable, BaseModel):
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
         if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
+            task_outputs.extend(self._process_async_tasks(futures, was_replayed))
 
         return self._create_crew_output(task_outputs)
 
@@ -1516,7 +1522,7 @@ class Crew(FlowTrackable, BaseModel):
         was_replayed: bool,
     ) -> TaskOutput | None:
         if futures:
-            task_outputs = self._process_async_tasks(futures, was_replayed)
+            task_outputs.extend(self._process_async_tasks(futures, was_replayed))
             futures.clear()
 
         return check_conditional_skip(

lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py

@@ -108,6 +108,13 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningFailedEvent,
     AgentReasoningStartedEvent,
 )
+from crewai.events.types.skill_events import (
+    SkillActivatedEvent,
+    SkillDiscoveryCompletedEvent,
+    SkillDiscoveryStartedEvent,
+    SkillLoadFailedEvent,
+    SkillLoadedEvent,
+)
 from crewai.events.types.system_events import SignalEvent, on_signal
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
@@ -530,6 +537,30 @@ class TraceCollectionListener(BaseEventListener):
         ) -> None:
             self._handle_action_event("knowledge_query_failed", source, event)
 
+        @event_bus.on(SkillDiscoveryStartedEvent)
+        def on_skill_discovery_started(
+            source: Any, event: SkillDiscoveryStartedEvent
+        ) -> None:
+            self._handle_action_event("skill_discovery_started", source, event)
+
+        @event_bus.on(SkillDiscoveryCompletedEvent)
+        def on_skill_discovery_completed(
+            source: Any, event: SkillDiscoveryCompletedEvent
+        ) -> None:
+            self._handle_action_event("skill_discovery_completed", source, event)
+
+        @event_bus.on(SkillLoadedEvent)
+        def on_skill_loaded(source: Any, event: SkillLoadedEvent) -> None:
+            self._handle_action_event("skill_loaded", source, event)
+
+        @event_bus.on(SkillActivatedEvent)
+        def on_skill_activated(source: Any, event: SkillActivatedEvent) -> None:
+            self._handle_action_event("skill_activated", source, event)
+
+        @event_bus.on(SkillLoadFailedEvent)
+        def on_skill_load_failed(source: Any, event: SkillLoadFailedEvent) -> None:
+            self._handle_action_event("skill_load_failed", source, event)
+
     def _register_a2a_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for A2A (Agent-to-Agent) events."""
 

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

@@ -71,6 +71,7 @@ from crewai.hooks.types import (
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.agent_utils import (
+    _llm_stop_words_applied,
     check_native_tool_support,
     enforce_rpm_limit,
     extract_tool_call_info,
@@ -215,12 +216,6 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
 
-        if self.llm:
-            existing_stop = getattr(self.llm, "stop", [])
-            if not isinstance(existing_stop, list):
-                existing_stop = []
-            self.llm.stop = list(set(existing_stop + self.stop_words))
-
         self._state = AgentExecutorState()
         self.max_method_calls = self.max_iter * 10
 
@@ -2601,17 +2596,18 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                 inputs.get("ask_for_human_input", False)
             )
 
-            self.kickoff()
+            with _llm_stop_words_applied(self.llm, self):
+                self.kickoff()
 
-            formatted_answer = self.state.current_answer
+                formatted_answer = self.state.current_answer
 
-            if not isinstance(formatted_answer, AgentFinish):
-                raise RuntimeError(
-                    "Agent execution ended without reaching a final answer."
-                )
+                if not isinstance(formatted_answer, AgentFinish):
+                    raise RuntimeError(
+                        "Agent execution ended without reaching a final answer."
+                    )
 
-            if self.state.ask_for_human_input:
-                formatted_answer = self._handle_human_feedback(formatted_answer)
+                if self.state.ask_for_human_input:
+                    formatted_answer = self._handle_human_feedback(formatted_answer)
 
             self._save_to_memory(formatted_answer)
 
@@ -2691,18 +2687,20 @@ class AgentExecutor(Flow[AgentExecutorState], BaseAgentExecutor):
                 inputs.get("ask_for_human_input", False)
             )
 
-            # Use async kickoff directly since we're already in an async context
-            await self.kickoff_async()
+            with _llm_stop_words_applied(self.llm, self):
+                await self.kickoff_async()
 
-            formatted_answer = self.state.current_answer
+                formatted_answer = self.state.current_answer
 
-            if not isinstance(formatted_answer, AgentFinish):
-                raise RuntimeError(
-                    "Agent execution ended without reaching a final answer."
-                )
+                if not isinstance(formatted_answer, AgentFinish):
+                    raise RuntimeError(
+                        "Agent execution ended without reaching a final answer."
+                    )
 
-            if self.state.ask_for_human_input:
-                formatted_answer = await self._ahandle_human_feedback(formatted_answer)
+                if self.state.ask_for_human_input:
+                    formatted_answer = await self._ahandle_human_feedback(
+                        formatted_answer
+                    )
 
             self._save_to_memory(formatted_answer)
 

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

@@ -1074,7 +1074,6 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
     _human_feedback_method_outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
     _input_history: list[InputHistoryEntry] = PrivateAttr(default_factory=list)
     _state: Any = PrivateAttr(default=None)
-    _execution_id: str = PrivateAttr(default_factory=lambda: str(uuid4()))
 
     def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:  # type: ignore[override]
         class _FlowGeneric(cls):  # type: ignore[valid-type,misc]
@@ -1865,27 +1864,6 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         except (AttributeError, TypeError):
             return ""  # Safely handle any unexpected attribute access issues
 
-    @property
-    def execution_id(self) -> str:
-        """Stable identifier for this flow execution.
-
-        Separate from ``flow_id`` / ``state.id``, which consumers may
-        override via ``kickoff(inputs={"id": ...})`` to resume a persisted
-        flow. ``execution_id`` is never affected by ``inputs`` and stays
-        stable for the lifetime of a single run, so it is the correct key
-        for telemetry, tracing, and any external correlation that must
-        uniquely identify a single execution even when callers pass an
-        ``id`` in ``inputs``.
-
-        Defaults to a fresh ``uuid4`` per ``Flow`` instance; assign to
-        override when an outer system already has an execution identity.
-        """
-        return self._execution_id
-
-    @execution_id.setter
-    def execution_id(self, value: str) -> None:
-        self._execution_id = value
-
     def _initialize_state(self, inputs: dict[str, Any]) -> None:
         """Initialize or update flow state with new inputs.
 
@@ -2054,6 +2032,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
         from_checkpoint: CheckpointConfig | None = None,
+        restore_from_state_id: str | None = None,
     ) -> Any | FlowStreamingOutput:
         """Start the flow execution in a synchronous context.
 
@@ -2065,10 +2044,24 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             input_files: Optional dict of named file inputs for the flow.
             from_checkpoint: Optional checkpoint config. If ``restore_from``
                 is set, the flow resumes from that checkpoint.
+            restore_from_state_id: Optional UUID of a previously-persisted flow
+                whose latest snapshot should hydrate this run's state. The new
+                run is assigned a fresh ``state.id`` (or ``inputs["id"]`` if
+                pinned), so its ``@persist`` writes land under a separate
+                persistence key and the source flow's history is preserved.
+                If the referenced state is not found, the kickoff falls back
+                silently to baseline behavior. Cannot be combined with
+                ``from_checkpoint``; passing both raises ``ValueError``.
 
         Returns:
             The final output from the flow or FlowStreamingOutput if streaming.
         """
+        if from_checkpoint is not None and restore_from_state_id is not None:
+            raise ValueError(
+                "Cannot combine `from_checkpoint` and `restore_from_state_id`. "
+                "These parameters target different state systems "
+                "(Checkpointing and @persist) and cannot be used together."
+            )
         restored = apply_checkpoint(self, from_checkpoint)
         if restored is not None:
             return restored.kickoff(inputs=inputs, input_files=input_files)
@@ -2090,7 +2083,11 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             def run_flow() -> None:
                 try:
                     self.stream = False
-                    result = self.kickoff(inputs=inputs, input_files=input_files)
+                    result = self.kickoff(
+                        inputs=inputs,
+                        input_files=input_files,
+                        restore_from_state_id=restore_from_state_id,
+                    )
                     result_holder.append(result)
                 except Exception as e:
                     # HumanFeedbackPending is expected control flow, not an error
@@ -2113,7 +2110,11 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             return streaming_output
 
         async def _run_flow() -> Any:
-            return await self.kickoff_async(inputs, input_files)
+            return await self.kickoff_async(
+                inputs,
+                input_files,
+                restore_from_state_id=restore_from_state_id,
+            )
 
         try:
             asyncio.get_running_loop()
@@ -2128,6 +2129,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
         from_checkpoint: CheckpointConfig | None = None,
+        restore_from_state_id: str | None = None,
     ) -> Any | FlowStreamingOutput:
         """Start the flow execution asynchronously.
 
@@ -2141,10 +2143,23 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             input_files: Optional dict of named file inputs for the flow.
             from_checkpoint: Optional checkpoint config. If ``restore_from``
                 is set, the flow resumes from that checkpoint.
+            restore_from_state_id: Optional UUID of a previously-persisted flow
+                whose latest snapshot should hydrate this run's state. The new
+                run is assigned a fresh ``state.id`` (or ``inputs["id"]`` if
+                pinned), so subsequent ``@persist`` writes land under a
+                separate persistence key. If the referenced state is not
+                found, falls back silently to baseline. Cannot be combined
+                with ``from_checkpoint``; passing both raises ``ValueError``.
 
         Returns:
             The final output from the flow, which is the result of the last executed method.
         """
+        if from_checkpoint is not None and restore_from_state_id is not None:
+            raise ValueError(
+                "Cannot combine `from_checkpoint` and `restore_from_state_id`. "
+                "These parameters target different state systems "
+                "(Checkpointing and @persist) and cannot be used together."
+            )
         restored = apply_checkpoint(self, from_checkpoint)
         if restored is not None:
             return await restored.kickoff_async(inputs=inputs, input_files=input_files)
@@ -2167,7 +2182,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 try:
                     self.stream = False
                     result = await self.kickoff_async(
-                        inputs=inputs, input_files=input_files
+                        inputs=inputs,
+                        input_files=input_files,
+                        restore_from_state_id=restore_from_state_id,
                     )
                     result_holder.append(result)
                 except Exception as e:
@@ -2199,9 +2216,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         flow_id_token = None
         request_id_token = None
         if current_flow_id.get() is None:
-            flow_id_token = current_flow_id.set(self.execution_id)
+            flow_id_token = current_flow_id.set(self.flow_id)
         if current_flow_request_id.get() is None:
-            request_id_token = current_flow_request_id.set(self.execution_id)
+            request_id_token = current_flow_request_id.set(self.flow_id)
 
         try:
             # Reset flow state for fresh execution unless restoring from persistence
@@ -2224,16 +2241,54 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 if self._completed_methods:
                     self._is_execution_resuming = True
 
+            # Fork hydration: when restore_from_state_id is set and persistence is
+            # available, hydrate self._state from the source UUID's latest snapshot
+            # and reassign state.id to a fresh value so subsequent @persist writes
+            # don't extend the source flow's history. If the source state is not
+            # found, fall through silently to the existing inputs handling.
+            fork_succeeded = False
+            if restore_from_state_id is not None and self.persistence is not None:
+                stored_state = self.persistence.load_state(restore_from_state_id)
+                if stored_state:
+                    self._log_flow_event(
+                        f"Forking flow state from UUID: {restore_from_state_id}"
+                    )
+                    self._restore_state(stored_state)
+                    # Pin to inputs["id"] when provided, otherwise mint a fresh
+                    # UUID. NOTE: pinning inputs.id while forking shares a
+                    # persistence key with another flow — usually you want only
+                    # restore_from_state_id.
+                    new_state_id = (inputs.get("id") if inputs else None) or str(
+                        uuid4()
+                    )
+                    if isinstance(self._state, dict):
+                        self._state["id"] = new_state_id
+                    elif isinstance(self._state, BaseModel):
+                        setattr(self._state, "id", new_state_id)  # noqa: B010
+                    fork_succeeded = True
+                else:
+                    self._log_flow_event(
+                        "No flow state found for restore_from_state_id: "
+                        f"{restore_from_state_id}; proceeding without hydration",
+                        color="yellow",
+                    )
+
             if inputs:
-                # Override the id in the state if it exists in inputs
-                if "id" in inputs:
+                # Override the id in the state if it exists in inputs.
+                # Skip when the fork already assigned state.id above.
+                if "id" in inputs and not fork_succeeded:
                     if isinstance(self._state, dict):
                         self._state["id"] = inputs["id"]
                     elif isinstance(self._state, BaseModel):
                         setattr(self._state, "id", inputs["id"])  # noqa: B010
 
                 # If persistence is enabled, attempt to restore the stored state using the provided id.
-                if "id" in inputs and self.persistence is not None:
+                # Skip when the fork already restored self._state above.
+                if (
+                    "id" in inputs
+                    and self.persistence is not None
+                    and not fork_succeeded
+                ):
                     restore_uuid = inputs["id"]
                     stored_state = self.persistence.load_state(restore_uuid)
                     if stored_state:
@@ -2416,6 +2471,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         inputs: dict[str, Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
         from_checkpoint: CheckpointConfig | None = None,
+        restore_from_state_id: str | None = None,
     ) -> Any | FlowStreamingOutput:
         """Native async method to start the flow execution. Alias for kickoff_async.
 
@@ -2424,11 +2480,19 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             input_files: Optional dict of named file inputs for the flow.
             from_checkpoint: Optional checkpoint config. If ``restore_from``
                 is set, the flow resumes from that checkpoint.
+            restore_from_state_id: Optional UUID of a previously-persisted flow
+                whose latest snapshot should hydrate this run's state. See
+                ``kickoff_async`` for full semantics.
 
         Returns:
             The final output from the flow, which is the result of the last executed method.
         """
-        return await self.kickoff_async(inputs, input_files, from_checkpoint)
+        return await self.kickoff_async(
+            inputs,
+            input_files,
+            from_checkpoint,
+            restore_from_state_id=restore_from_state_id,
+        )
 
     async def _replay_recorded_events(self) -> None:
         """Dispatch recorded ``MethodExecution*`` events from the event record."""

lib/crewai/src/crewai/flow/persistence/decorators.py

@@ -50,7 +50,6 @@ LOG_MESSAGES: Final[dict[str, str]] = {
     "save_error": "Failed to persist state for method {}: {}",
     "state_missing": "Flow instance has no state",
     "id_missing": "Flow state must have an 'id' field for persistence",
-    "key_missing": "Flow state is missing required persistence key '{}'",
 }
 
 
@@ -64,7 +63,6 @@ class PersistenceDecorator:
         method_name: str,
         persistence_instance: FlowPersistence,
         verbose: bool = False,
-        key: str | None = None,
     ) -> None:
         """Persist flow state with proper error handling and logging.
 
@@ -76,12 +74,9 @@ class PersistenceDecorator:
             method_name: Name of the method that triggered persistence
             persistence_instance: The persistence backend to use
             verbose: Whether to log persistence operations
-            key: Optional state attribute/key to use as the persistence key.
-                When None, falls back to ``state.id``.
 
         Raises:
-            ValueError: If flow has no state, state lacks an ID, or the
-                requested ``key`` is missing or falsy on state.
+            ValueError: If flow has no state or state lacks an ID
             RuntimeError: If state persistence fails
             AttributeError: If flow instance lacks required state attributes
         """
@@ -90,22 +85,19 @@ class PersistenceDecorator:
             if state is None:
                 raise ValueError("Flow instance has no state")
 
-            lookup_key = key if key is not None else "id"
             flow_uuid: str | None = None
             if isinstance(state, dict):
-                flow_uuid = state.get(lookup_key)
+                flow_uuid = state.get("id")
             elif hasattr(state, "_unwrap"):
                 unwrapped = state._unwrap()
                 if isinstance(unwrapped, dict):
-                    flow_uuid = unwrapped.get(lookup_key)
+                    flow_uuid = unwrapped.get("id")
                 else:
-                    flow_uuid = getattr(unwrapped, lookup_key, None)
-            elif isinstance(state, BaseModel) or hasattr(state, lookup_key):
-                flow_uuid = getattr(state, lookup_key, None)
+                    flow_uuid = getattr(unwrapped, "id", None)
+            elif isinstance(state, BaseModel) or hasattr(state, "id"):
+                flow_uuid = getattr(state, "id", None)
 
             if not flow_uuid:
-                if key is not None:
-                    raise ValueError(LOG_MESSAGES["key_missing"].format(key))
                 raise ValueError("Flow state must have an 'id' field for persistence")
 
             # Log state saving only if verbose is True
@@ -135,7 +127,7 @@ class PersistenceDecorator:
             logger.error(error_msg)
             raise ValueError(error_msg) from e
         except (TypeError, ValueError) as e:
-            error_msg = str(e) or LOG_MESSAGES["id_missing"]
+            error_msg = LOG_MESSAGES["id_missing"]
             if verbose:
                 PRINTER.print(error_msg, color="red")
             logger.error(error_msg)
@@ -143,9 +135,7 @@ class PersistenceDecorator:
 
 
 def persist(
-    persistence: FlowPersistence | None = None,
-    verbose: bool = False,
-    key: str | None = None,
+    persistence: FlowPersistence | None = None, verbose: bool = False
 ) -> Callable[[type | Callable[..., T]], type | Callable[..., T]]:
     """Decorator to persist flow state.
 
@@ -158,16 +148,12 @@ def persist(
         persistence: Optional FlowPersistence implementation to use.
                     If not provided, uses SQLiteFlowPersistence.
         verbose: Whether to log persistence operations. Defaults to False.
-        key: Optional name of the state attribute (for Pydantic/object states)
-            or dict key (for dict states) to use as the persistence key. When
-            ``None`` (default) the decorator falls back to ``state.id``.
 
     Returns:
         A decorator that can be applied to either a class or method
 
     Raises:
-        ValueError: If the flow state doesn't have an 'id' field, or the
-            specified ``key`` is missing or falsy on state.
+        ValueError: If the flow state doesn't have an 'id' field
         RuntimeError: If state persistence fails
 
     Example:
@@ -176,10 +162,6 @@ def persist(
             @start()
             def begin(self):
                 pass
-
-        @persist(key="conversation_id")  # Custom persistence key
-        class MyFlow(Flow[MyState]):
-            ...
     """
 
     def decorator(target: type | Callable[..., T]) -> type | Callable[..., T]:
@@ -225,7 +207,7 @@ def persist(
                         ) -> Any:
                             result = await original_method(self, *args, **kwargs)
                             PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose, key
+                                self, method_name, actual_persistence, verbose
                             )
                             return result
 
@@ -255,7 +237,7 @@ def persist(
                         def method_wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
                             result = original_method(self, *args, **kwargs)
                             PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose, key
+                                self, method_name, actual_persistence, verbose
                             )
                             return result
 
@@ -294,7 +276,7 @@ def persist(
                 else:
                     result = method_coro
                 PersistenceDecorator.persist_state(
-                    flow_instance, method.__name__, actual_persistence, verbose, key
+                    flow_instance, method.__name__, actual_persistence, verbose
                 )
                 return cast(T, result)
 
@@ -313,7 +295,7 @@ def persist(
         def method_sync_wrapper(flow_instance: Any, *args: Any, **kwargs: Any) -> T:
             result = method(flow_instance, *args, **kwargs)
             PersistenceDecorator.persist_state(
-                flow_instance, method.__name__, actual_persistence, verbose, key
+                flow_instance, method.__name__, actual_persistence, verbose
             )
             return result
 

lib/crewai/src/crewai/llm.py

@@ -688,7 +688,9 @@ class LLM(BaseLLM):
             "temperature": self.temperature,
             "top_p": self.top_p,
             "n": self.n,
-            "stop": (self.stop or None) if self.supports_stop_words() else None,
+            "stop": (self.stop_sequences or None)
+            if self.supports_stop_words()
+            else None,
             "max_tokens": self.max_tokens or self.max_completion_tokens,
             "presence_penalty": self.presence_penalty,
             "frequency_penalty": self.frequency_penalty,
@@ -1235,8 +1237,12 @@ class LLM(BaseLLM):
         # --- 4) Check for tool calls
         tool_calls = response_message.tool_calls or []
 
-        # --- 5) If no tool calls or no available functions, return the text response directly as long as there is a text response
-        if (not tool_calls or not available_functions) and text_response:
+        # --- 5) If there are tool calls but no available functions, return the tool calls
+        if tool_calls and not available_functions:
+            return tool_calls
+
+        # --- 6) If there are no tool calls to execute, return the text response directly
+        if not tool_calls and text_response:
             self._handle_emit_call_events(
                 response=text_response,
                 call_type=LLMCallType.LLM_CALL,
@@ -1247,11 +1253,6 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # --- 6) If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
-            return tool_calls
-
         # --- 7) Handle tool calls if present (execute when available_functions provided)
         if tool_calls and available_functions:
             tool_result = self._handle_tool_call(
@@ -1384,7 +1385,10 @@ class LLM(BaseLLM):
 
         tool_calls = response_message.tool_calls or []
 
-        if (not tool_calls or not available_functions) and text_response:
+        if tool_calls and not available_functions:
+            return tool_calls
+
+        if not tool_calls and text_response:
             self._handle_emit_call_events(
                 response=text_response,
                 call_type=LLMCallType.LLM_CALL,
@@ -1395,11 +1399,6 @@ class LLM(BaseLLM):
             )
             return text_response
 
-        # If there are tool calls but no available functions, return the tool calls
-        # This allows the caller (e.g., executor) to handle tool execution
-        if tool_calls and not available_functions:
-            return tool_calls
-
         # Handle tool calls if present (execute when available_functions provided)
         if tool_calls and available_functions:
             tool_result = self._handle_tool_call(

lib/crewai/src/crewai/llms/base_llm.py

@@ -72,6 +72,9 @@ _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTAL
 _current_call_id: contextvars.ContextVar[str | None] = contextvars.ContextVar(
     "_current_call_id", default=None
 )
+_call_stop_override_var: contextvars.ContextVar[dict[int, list[str]] | None] = (
+    contextvars.ContextVar("_call_stop_override_var", default=None)
+)
 
 
 @contextmanager
@@ -85,6 +88,31 @@ def llm_call_context() -> Generator[str, None, None]:
         _current_call_id.reset(token)
 
 
+@contextmanager
+def call_stop_override(
+    llm: BaseLLM, stop: list[str] | None
+) -> Generator[None, None, None]:
+    """Override the stop list for ``llm`` within the current call scope.
+
+    Only ``llm``'s reads via :attr:`BaseLLM.stop_sequences` see ``stop``;
+    other LLM instances (e.g. an agent's ``function_calling_llm``) keep their
+    own ``stop`` field. Passing ``None`` clears any prior override for ``llm``
+    in the same scope. The instance-level ``stop`` field is never mutated,
+    so the override is safe under concurrent execution.
+    """
+    current = _call_stop_override_var.get()
+    new_overrides: dict[int, list[str]] = dict(current) if current else {}
+    if stop is None:
+        new_overrides.pop(id(llm), None)
+    else:
+        new_overrides[id(llm)] = stop
+    token = _call_stop_override_var.set(new_overrides)
+    try:
+        yield
+    finally:
+        _call_stop_override_var.reset(token)
+
+
 def get_current_call_id() -> str:
     """Get current call_id from context"""
     call_id = _current_call_id.get()
@@ -158,11 +186,18 @@ class BaseLLM(BaseModel, ABC):
 
     @property
     def stop_sequences(self) -> list[str]:
-        """Alias for ``stop`` — kept for backward compatibility with provider APIs.
+        """Stop list active for the current call.
 
-        Writes are handled by ``__setattr__``, which normalizes and redirects
-        ``stop_sequences`` assignments to the ``stop`` field.
+        Returns the per-instance override set via :func:`call_stop_override`
+        when one is in effect for this LLM; otherwise the instance-level
+        ``stop`` field. Kept under this name for backward compatibility with
+        provider APIs that already read ``stop_sequences``.
         """
+        overrides = _call_stop_override_var.get()
+        if overrides is not None:
+            override = overrides.get(id(self))
+            if override is not None:
+                return override
         return self.stop
 
     _token_usage: dict[str, int] = PrivateAttr(
@@ -341,7 +376,7 @@ class BaseLLM(BaseModel, ABC):
         Returns:
             True if stop words are configured and can be applied
         """
-        return bool(self.stop)
+        return bool(self.stop_sequences)
 
     def _apply_stop_words(self, content: str) -> str:
         """Apply stop words to truncate response content.
@@ -363,14 +398,14 @@ class BaseLLM(BaseModel, ABC):
             >>> llm._apply_stop_words(response)
             "I need to search.\\n\\nAction: search"
         """
-        if not self.stop or not content:
+        stops = self.stop_sequences
+        if not stops or not content:
             return content
 
-        # Find the earliest occurrence of any stop word
         earliest_stop_pos = len(content)
         found_stop_word = None
 
-        for stop_word in self.stop:
+        for stop_word in stops:
             stop_pos = content.find(stop_word)
             if stop_pos != -1 and stop_pos < earliest_stop_pos:
                 earliest_stop_pos = stop_pos

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -679,8 +679,9 @@ class AzureCompletion(BaseLLM):
             params["presence_penalty"] = self.presence_penalty
         if self.max_tokens is not None:
             params["max_tokens"] = self.max_tokens
-        if self.stop and self.supports_stop_words():
-            params["stop"] = self.stop
+        stops = self.stop_sequences
+        if stops and self.supports_stop_words():
+            params["stop"] = stops
 
         # Handle tools/functions for Azure OpenAI models
         if tools and self.is_openai_model:

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -1328,9 +1328,11 @@ class GeminiCompletion(BaseLLM):
             usage = response.usage_metadata
             cached_tokens = getattr(usage, "cached_content_token_count", 0) or 0
             thinking_tokens = getattr(usage, "thoughts_token_count", 0) or 0
+            candidates_tokens = getattr(usage, "candidates_token_count", 0) or 0
             result: dict[str, Any] = {
                 "prompt_token_count": getattr(usage, "prompt_token_count", 0),
-                "candidates_token_count": getattr(usage, "candidates_token_count", 0),
+                "candidates_token_count": candidates_tokens,
+                "completion_tokens": candidates_tokens + thinking_tokens,
                 "total_token_count": getattr(usage, "total_token_count", 0),
                 "total_tokens": getattr(usage, "total_token_count", 0),
                 "cached_prompt_tokens": cached_tokens,

lib/crewai/src/crewai/task.py

@@ -53,7 +53,11 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.tools.base_tool import BaseTool
 from crewai.utilities.config import process_config
 from crewai.utilities.constants import NOT_SPECIFIED, _NotSpecified
-from crewai.utilities.converter import Converter, convert_to_model
+from crewai.utilities.converter import (
+    Converter,
+    async_convert_to_model,
+    convert_to_model,
+)
 from crewai.utilities.file_store import (
     clear_task_files,
     get_all_files,
@@ -681,7 +685,7 @@ class Task(BaseModel):
                     json_output = None
             elif not self._guardrails and not self._guardrail:
                 raw = result
-                pydantic_output, json_output = self._export_output(result)
+                pydantic_output, json_output = await self._aexport_output(result)
             else:
                 raw = result
                 pydantic_output, json_output = None, None
@@ -1110,7 +1114,7 @@ Follow these guidelines:
         )
 
     def _export_output(
-        self, result: str
+        self, result: str | BaseModel
     ) -> tuple[BaseModel | None, dict[str, Any] | None]:
         pydantic_output: BaseModel | None = None
         json_output: dict[str, Any] | None = None
@@ -1123,19 +1127,44 @@ Follow these guidelines:
                 self.agent,
                 self.converter_cls,
             )
-
-            if isinstance(model_output, BaseModel):
-                pydantic_output = model_output
-            elif isinstance(model_output, dict):
-                json_output = model_output
-            elif isinstance(model_output, str):
-                try:
-                    json_output = json.loads(model_output)
-                except json.JSONDecodeError:
-                    json_output = None
+            pydantic_output, json_output = self._unpack_model_output(model_output)
 
         return pydantic_output, json_output
 
+    async def _aexport_output(
+        self, result: str | BaseModel
+    ) -> tuple[BaseModel | None, dict[str, Any] | None]:
+        """Async equivalent of ``_export_output`` — uses ``acall`` so the event loop is not blocked."""
+        pydantic_output: BaseModel | None = None
+        json_output: dict[str, Any] | None = None
+
+        if self.output_pydantic or self.output_json:
+            model_output = await async_convert_to_model(
+                result,
+                self.output_pydantic,
+                self.output_json,
+                self.agent,
+                self.converter_cls,
+            )
+            pydantic_output, json_output = self._unpack_model_output(model_output)
+
+        return pydantic_output, json_output
+
+    @staticmethod
+    def _unpack_model_output(
+        model_output: dict[str, Any] | BaseModel | str,
+    ) -> tuple[BaseModel | None, dict[str, Any] | None]:
+        if isinstance(model_output, BaseModel):
+            return model_output, None
+        if isinstance(model_output, dict):
+            return None, model_output
+        if isinstance(model_output, str):
+            try:
+                return None, json.loads(model_output)
+            except json.JSONDecodeError:
+                return None, None
+        return None, None
+
     def _get_output_format(self) -> OutputFormat:
         if self.output_json:
             return OutputFormat.JSON
@@ -1364,7 +1393,7 @@ Follow these guidelines:
 
                 if isinstance(guardrail_result.result, str):
                     task_output.raw = guardrail_result.result
-                    pydantic_output, json_output = self._export_output(
+                    pydantic_output, json_output = await self._aexport_output(
                         guardrail_result.result
                     )
                     task_output.pydantic = pydantic_output
@@ -1421,7 +1450,7 @@ Follow these guidelines:
                     json_output = None
             else:
                 raw = result
-                pydantic_output, json_output = self._export_output(result)
+                pydantic_output, json_output = await self._aexport_output(result)
 
             task_output = TaskOutput(
                 name=self.name or self.description,

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

@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable, Sequence
+from collections.abc import Callable, Iterator, Sequence
 import concurrent.futures
+import contextlib
 import contextvars
 from dataclasses import dataclass, field
 from datetime import datetime
@@ -22,7 +23,7 @@ from crewai.agents.parser import (
     parse,
 )
 from crewai.cli.config import Settings
-from crewai.llms.base_llm import BaseLLM
+from crewai.llms.base_llm import BaseLLM, call_stop_override
 from crewai.tools import BaseTool as CrewAITool
 from crewai.tools.base_tool import BaseTool
 from crewai.tools.structured_tool import CrewStructuredTool
@@ -238,6 +239,38 @@ def extract_task_section(text: str) -> str:
     return text
 
 
+def _executor_stop_words(
+    executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
+) -> list[str]:
+    """Return the executor's stop words, regardless of which field name it uses."""
+    if executor_context is None:
+        return []
+    stops = getattr(executor_context, "stop", None)
+    if stops is None:
+        stops = getattr(executor_context, "stop_words", None)
+    return list(stops) if stops else []
+
+
+@contextlib.contextmanager
+def _llm_stop_words_applied(
+    llm: LLM | BaseLLM,
+    executor_context: CrewAgentExecutor | AgentExecutor | LiteAgent | None,
+) -> Iterator[None]:
+    """Apply the executor's stop words to the LLM for the duration of one call.
+
+    Uses :func:`crewai.llms.base_llm.call_stop_override` so the LLM's stop
+    field is never mutated. Safe under concurrent execution: the override is
+    propagated via a :class:`contextvars.ContextVar` and is scoped to this
+    call's task / thread context.
+    """
+    extra = _executor_stop_words(executor_context)
+    if not extra or not isinstance(llm, BaseLLM) or set(extra).issubset(llm.stop):
+        yield
+        return
+    with call_stop_override(llm, list(set(llm.stop + extra))):
+        yield
+
+
 def has_reached_max_iterations(iterations: int, max_iterations: int) -> bool:
     """Check if the maximum number of iterations has been reached.
 
@@ -459,18 +492,15 @@ def get_llm_response(
     """
     messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
 
-    try:
-        answer = llm.call(
-            messages,
-            tools=tools,
-            callbacks=callbacks,
-            available_functions=available_functions,
-            from_task=from_task,
-            from_agent=from_agent,
-            response_model=response_model,
-        )
-    except Exception as e:
-        raise e
+    answer = llm.call(
+        messages,
+        tools=tools,
+        callbacks=callbacks,
+        available_functions=available_functions,
+        from_task=from_task,
+        from_agent=from_agent,
+        response_model=response_model,
+    )
 
     return _validate_and_finalize_llm_response(
         answer, executor_context, printer, verbose=verbose
@@ -515,18 +545,15 @@ async def aget_llm_response(
     """
     messages = _prepare_llm_call(executor_context, messages, printer, verbose=verbose)
 
-    try:
-        answer = await llm.acall(
-            messages,
-            tools=tools,
-            callbacks=callbacks,
-            available_functions=available_functions,
-            from_task=from_task,
-            from_agent=from_agent,
-            response_model=response_model,
-        )
-    except Exception as e:
-        raise e
+    answer = await llm.acall(
+        messages,
+        tools=tools,
+        callbacks=callbacks,
+        available_functions=available_functions,
+        from_task=from_task,
+        from_agent=from_agent,
+        response_model=response_model,
+    )
 
     return _validate_and_finalize_llm_response(
         answer, executor_context, printer, verbose=verbose
@@ -1565,11 +1592,12 @@ def execute_single_native_tool_call(
             color="green",
         )
 
-    # Check result_as_answer
     is_result_as_answer = bool(
         original_tool
         and hasattr(original_tool, "result_as_answer")
         and original_tool.result_as_answer
+        and not error_event_emitted
+        and not hook_blocked
     )
 
     return NativeToolCallResult(

lib/crewai/src/crewai/utilities/converter.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import asyncio
 import json
 import re
 from typing import TYPE_CHECKING, Any, Final, TypedDict
@@ -41,6 +42,45 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
+    def _build_messages(self) -> list[dict[str, str]]:
+        return [
+            {"role": "system", "content": self.instructions},
+            {"role": "user", "content": self.text},
+        ]
+
+    def _coerce_response_to_pydantic(self, response: Any) -> BaseModel:
+        """Validate an LLM response into the configured Pydantic model.
+
+        Pure post-processing — performs no I/O. Shared by ``to_pydantic`` and
+        ``ato_pydantic`` so the validation/partial-JSON fallback logic stays in
+        a single place.
+        """
+        if isinstance(response, BaseModel):
+            return response
+        try:
+            return self.model.model_validate_json(response)
+        except ValidationError:
+            partial = handle_partial_json(
+                result=response,
+                model=self.model,
+                is_json_output=False,
+                agent=None,
+            )
+            if isinstance(partial, BaseModel):
+                return partial
+            if isinstance(partial, dict):
+                return self.model.model_validate(partial)
+            if isinstance(partial, str):
+                try:
+                    return self.model.model_validate_json(partial)
+                except Exception as parse_err:
+                    raise ConverterError(
+                        f"Failed to convert partial JSON result into Pydantic: {parse_err}"
+                    ) from parse_err
+            raise ConverterError(
+                "handle_partial_json returned an unexpected type."
+            ) from None
+
     def to_pydantic(self, current_attempt: int = 1) -> BaseModel:
         """Convert text to pydantic.
 
@@ -56,50 +96,12 @@ class Converter(OutputConverter):
         try:
             if self.llm.supports_function_calling():
                 response = self.llm.call(
-                    messages=[
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ],
+                    messages=self._build_messages(),
                     response_model=self.model,
                 )
-                if isinstance(response, BaseModel):
-                    result = response
-                else:
-                    result = self.model.model_validate_json(response)
             else:
-                response = self.llm.call(
-                    [
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ]
-                )
-                try:
-                    # Try to directly validate the response JSON
-                    result = self.model.model_validate_json(response)
-                except ValidationError:
-                    # If direct validation fails, attempt to extract valid JSON
-                    result = handle_partial_json(  # type: ignore[assignment]
-                        result=response,
-                        model=self.model,
-                        is_json_output=False,
-                        agent=None,
-                    )
-                    # Ensure result is a BaseModel instance
-                    if not isinstance(result, BaseModel):
-                        if isinstance(result, dict):
-                            result = self.model.model_validate(result)
-                        elif isinstance(result, str):
-                            try:
-                                result = self.model.model_validate_json(result)
-                            except Exception as parse_err:
-                                raise ConverterError(
-                                    f"Failed to convert partial JSON result into Pydantic: {parse_err}"
-                                ) from parse_err
-                        else:
-                            raise ConverterError(
-                                "handle_partial_json returned an unexpected type."
-                            ) from None
-            return result
+                response = self.llm.call(self._build_messages())
+            return self._coerce_response_to_pydantic(response)
         except ValidationError as e:
             if current_attempt < self.max_attempts:
                 return self.to_pydantic(current_attempt + 1)
@@ -113,6 +115,30 @@ class Converter(OutputConverter):
                 f"Failed to convert text into a Pydantic model due to error: {e}"
             ) from e
 
+    async def ato_pydantic(self, current_attempt: int = 1) -> BaseModel:
+        """Async equivalent of ``to_pydantic`` — uses ``acall`` so the event loop is not blocked."""
+        try:
+            if self.llm.supports_function_calling():
+                response = await self.llm.acall(
+                    messages=self._build_messages(),
+                    response_model=self.model,
+                )
+            else:
+                response = await self.llm.acall(self._build_messages())
+            return self._coerce_response_to_pydantic(response)
+        except ValidationError as e:
+            if current_attempt < self.max_attempts:
+                return await self.ato_pydantic(current_attempt + 1)
+            raise ConverterError(
+                f"Failed to convert text into a Pydantic model due to validation error: {e}"
+            ) from e
+        except Exception as e:
+            if current_attempt < self.max_attempts:
+                return await self.ato_pydantic(current_attempt + 1)
+            raise ConverterError(
+                f"Failed to convert text into a Pydantic model due to error: {e}"
+            ) from e
+
     def to_json(self, current_attempt: int = 1) -> str | ConverterError | Any:  # type: ignore[override]
         """Convert text to json.
 
@@ -129,19 +155,28 @@ class Converter(OutputConverter):
         try:
             if self.llm.supports_function_calling():
                 return self._create_instructor().to_json()
-            return json.dumps(
-                self.llm.call(
-                    [
-                        {"role": "system", "content": self.instructions},
-                        {"role": "user", "content": self.text},
-                    ]
-                )
-            )
+            return json.dumps(self.llm.call(self._build_messages()))
         except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_json(current_attempt + 1)
             return ConverterError(f"Failed to convert text into JSON, error: {e}.")
 
+    async def ato_json(self, current_attempt: int = 1) -> str | ConverterError | Any:
+        """Async equivalent of ``to_json``.
+
+        The function-calling path delegates to ``InternalInstructor`` (currently
+        sync-only); we run it via ``asyncio.to_thread`` so the event loop stays
+        free.
+        """
+        try:
+            if self.llm.supports_function_calling():
+                return await asyncio.to_thread(self._create_instructor().to_json)
+            return json.dumps(await self.llm.acall(self._build_messages()))
+        except Exception as e:
+            if current_attempt < self.max_attempts:
+                return await self.ato_json(current_attempt + 1)
+            return ConverterError(f"Failed to convert text into JSON, error: {e}.")
+
     def _create_instructor(self) -> InternalInstructor[Any]:
         """Create an instructor."""
 
@@ -153,16 +188,18 @@ class Converter(OutputConverter):
 
 
 def convert_to_model(
-    result: str,
+    result: str | BaseModel,
     output_pydantic: type[BaseModel] | None,
     output_json: type[BaseModel] | None,
     agent: Agent | BaseAgent | None = None,
     converter_cls: type[Converter] | None = None,
 ) -> dict[str, Any] | BaseModel | str:
-    """Convert a result string to a Pydantic model or JSON.
+    """Convert a result to a Pydantic model or JSON.
 
     Args:
-        result: The result string to convert.
+        result: The result to convert. Usually a JSON string, but a Pydantic
+            instance is also accepted when an upstream caller already produced
+            a structured object.
         output_pydantic: The Pydantic model class to convert to.
         output_json: The Pydantic model class to convert to JSON.
         agent: The agent instance.
@@ -175,6 +212,11 @@ def convert_to_model(
     if model is None:
         return result
 
+    if isinstance(result, BaseModel):
+        if isinstance(result, model):
+            return result.model_dump() if output_json else result
+        result = result.model_dump_json()
+
     if converter_cls:
         return convert_with_instructions(
             result=result,
@@ -257,12 +299,21 @@ def handle_partial_json(
     match = _JSON_PATTERN.search(result)
     if match:
         try:
-            exported_result = model.model_validate_json(match.group())
+            parsed = json.loads(match.group(), strict=False)
+        except json.JSONDecodeError:
+            return convert_with_instructions(
+                result=result,
+                model=model,
+                is_json_output=is_json_output,
+                agent=agent,
+                converter_cls=converter_cls,
+            )
+
+        try:
+            exported_result = model.model_validate(parsed)
             if is_json_output:
                 return exported_result.model_dump()
             return exported_result
-        except json.JSONDecodeError:
-            pass
         except ValidationError:
             raise
         except Exception as e:
@@ -338,6 +389,144 @@ def convert_with_instructions(
     return exported_result
 
 
+async def async_convert_to_model(
+    result: str | BaseModel,
+    output_pydantic: type[BaseModel] | None,
+    output_json: type[BaseModel] | None,
+    agent: Agent | BaseAgent | None = None,
+    converter_cls: type[Converter] | None = None,
+) -> dict[str, Any] | BaseModel | str:
+    """Async equivalent of ``convert_to_model`` — uses native ``acall``.
+
+    Mirrors the dispatch semantics of the sync version exactly; the only
+    difference is that LLM-bearing branches are awaited.
+    """
+    model = output_pydantic or output_json
+    if model is None:
+        return result
+
+    if isinstance(result, BaseModel):
+        if isinstance(result, model):
+            return result.model_dump() if output_json else result
+        result = result.model_dump_json()
+
+    if converter_cls:
+        return await async_convert_with_instructions(
+            result=result,
+            model=model,
+            is_json_output=bool(output_json),
+            agent=agent,
+            converter_cls=converter_cls,
+        )
+
+    try:
+        escaped_result = json.dumps(json.loads(result, strict=False))
+        return validate_model(
+            result=escaped_result, model=model, is_json_output=bool(output_json)
+        )
+    except (json.JSONDecodeError, ValidationError):
+        return await async_handle_partial_json(
+            result=result,
+            model=model,
+            is_json_output=bool(output_json),
+            agent=agent,
+            converter_cls=converter_cls,
+        )
+    except Exception as e:
+        if agent and getattr(agent, "verbose", True):
+            PRINTER.print(
+                content=f"Unexpected error during model conversion: {type(e).__name__}: {e}. Returning original result.",
+                color="red",
+            )
+        return result
+
+
+async def async_handle_partial_json(
+    result: str,
+    model: type[BaseModel],
+    is_json_output: bool,
+    agent: Agent | BaseAgent | None,
+    converter_cls: type[Converter] | None = None,
+) -> dict[str, Any] | BaseModel | str:
+    """Async equivalent of ``handle_partial_json`` — defers LLM fallback to ``acall``."""
+    match = _JSON_PATTERN.search(result)
+    if match:
+        try:
+            parsed = json.loads(match.group(), strict=False)
+        except json.JSONDecodeError:
+            return await async_convert_with_instructions(
+                result=result,
+                model=model,
+                is_json_output=is_json_output,
+                agent=agent,
+                converter_cls=converter_cls,
+            )
+
+        try:
+            exported_result = model.model_validate(parsed)
+            if is_json_output:
+                return exported_result.model_dump()
+            return exported_result
+        except ValidationError:
+            raise
+        except Exception as e:
+            if agent and getattr(agent, "verbose", True):
+                PRINTER.print(
+                    content=f"Unexpected error during partial JSON handling: {type(e).__name__}: {e}. Attempting alternative conversion method.",
+                    color="red",
+                )
+
+    return await async_convert_with_instructions(
+        result=result,
+        model=model,
+        is_json_output=is_json_output,
+        agent=agent,
+        converter_cls=converter_cls,
+    )
+
+
+async def async_convert_with_instructions(
+    result: str,
+    model: type[BaseModel],
+    is_json_output: bool,
+    agent: Agent | BaseAgent | None,
+    converter_cls: type[Converter] | None = None,
+) -> dict[str, Any] | BaseModel | str:
+    """Async equivalent of ``convert_with_instructions`` — calls ``ato_pydantic``/``ato_json``."""
+    if agent is None:
+        raise TypeError("Agent must be provided if converter_cls is not specified.")
+
+    llm = getattr(agent, "function_calling_llm", None) or agent.llm
+
+    if llm is None:
+        raise ValueError("Agent must have a valid LLM instance for conversion")
+
+    instructions = get_conversion_instructions(model=model, llm=llm)
+    converter = create_converter(
+        agent=agent,
+        converter_cls=converter_cls,
+        llm=llm,
+        text=result,
+        model=model,
+        instructions=instructions,
+    )
+    exported_result = (
+        await converter.ato_pydantic()
+        if not is_json_output
+        else await converter.ato_json()
+    )
+
+    if isinstance(exported_result, ConverterError):
+        if agent and getattr(agent, "verbose", True):
+            PRINTER.print(
+                content=f"Failed to convert result to model: {exported_result}",
+                color="red",
+            )
+        return result
+
+    return exported_result
+
+
 def get_conversion_instructions(
     model: type[BaseModel], llm: BaseLLM | LLM | str | Any
 ) -> str:

lib/crewai/tests/agents/test_agent.py

@@ -2452,3 +2452,167 @@ def test_agent_mcps_accepts_legacy_prefix_with_tool():
         mcps=["crewai-amp:notion#get_page"],
     )
     assert agent.mcps == ["crewai-amp:notion#get_page"]
+
+
+class TestSharedLLMStopWords:
+    """Regression tests for shared LLM stop words mutation (issue #5141).
+
+    Stop words from one executor must not leak into the shared LLM permanently
+    or pollute other agents sharing that LLM.
+    """
+
+    @staticmethod
+    def _make_executor(llm: LLM, stop_words: list[str]) -> CrewAgentExecutor:
+        """Build a CrewAgentExecutor with minimal deps."""
+        from crewai.agents.tools_handler import ToolsHandler
+
+        agent = Agent(role="r", goal="g", backstory="b")
+        task = Task(description="d", expected_output="o", agent=agent)
+        return CrewAgentExecutor(
+            agent=agent,
+            task=task,
+            llm=llm,
+            crew=None,
+            prompt={"prompt": "p {input} {tool_names} {tools}"},
+            max_iter=5,
+            tools=[],
+            tools_names="",
+            stop_words=stop_words,
+            tools_description="",
+            tools_handler=ToolsHandler(),
+        )
+
+    def test_executor_init_does_not_mutate_shared_llm(self) -> None:
+        """Constructing executors must not touch the shared LLM's stop list."""
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        original = list(shared.stop)
+
+        a = self._make_executor(shared, stop_words=["StopA:"])
+        b = self._make_executor(shared, stop_words=["StopB:"])
+
+        assert shared.stop == original
+        assert a.llm is shared
+        assert b.llm is shared
+
+    def test_effective_stop_reflects_override_inside_context(self) -> None:
+        """Inside the helper, the effective stop list includes the executor's words."""
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        executor = self._make_executor(shared, stop_words=["Observation:"])
+
+        with _llm_stop_words_applied(shared, executor):
+            assert set(shared.stop_sequences) == {"Original:", "Observation:"}
+            assert shared.stop == ["Original:"]
+
+        assert shared.stop == ["Original:"]
+        assert shared.stop_sequences == ["Original:"]
+
+    def test_override_cleared_when_context_raises(self) -> None:
+        """A failed call must still clear the per-call stop override."""
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        executor = self._make_executor(shared, stop_words=["Observation:"])
+
+        try:
+            with _llm_stop_words_applied(shared, executor):
+                raise RuntimeError("boom")
+        except RuntimeError:
+            pass
+
+        assert shared.stop == ["Original:"]
+        assert shared.stop_sequences == ["Original:"]
+
+    def test_override_applies_for_post_processing_when_api_lacks_stop_support(
+        self,
+    ) -> None:
+        """Models that lack API-level stop support still need the override.
+
+        Native providers (e.g. Azure on gpt-5/o-series) read ``stop_sequences``
+        in ``_apply_stop_words`` to truncate the response post-hoc even when
+        ``supports_stop_words()`` returns False, so the override must be set
+        regardless of API-level support. (Issue raised by Cursor Bugbot.)
+        """
+        from unittest.mock import patch
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        executor = self._make_executor(shared, stop_words=["Observation:"])
+
+        with patch.object(shared, "supports_stop_words", return_value=False):
+            with _llm_stop_words_applied(shared, executor):
+                assert set(shared.stop_sequences) == {"Original:", "Observation:"}
+
+        assert shared.stop == ["Original:"]
+        assert shared.stop_sequences == ["Original:"]
+
+    def test_concurrent_overrides_do_not_collide(self) -> None:
+        """Concurrent agents on a shared LLM must each see their own effective stop."""
+        import asyncio
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        exec_a = self._make_executor(shared, stop_words=["StopA:"])
+        exec_b = self._make_executor(shared, stop_words=["StopB:"])
+
+        async def run(executor: CrewAgentExecutor, expected: str) -> set[str]:
+            with _llm_stop_words_applied(shared, executor):
+                await asyncio.sleep(0)
+                seen = set(shared.stop_sequences)
+                assert expected in seen
+                return seen
+
+        async def main() -> tuple[set[str], set[str]]:
+            return await asyncio.gather(
+                run(exec_a, "StopA:"), run(exec_b, "StopB:")
+            )
+
+        a_seen, b_seen = asyncio.run(main())
+        assert a_seen == {"Original:", "StopA:"}
+        assert b_seen == {"Original:", "StopB:"}
+        assert shared.stop == ["Original:"]
+        assert shared.stop_sequences == ["Original:"]
+
+    def test_override_does_not_leak_to_other_llm_instances(self) -> None:
+        """Override for one LLM must not affect another LLM (e.g. function_calling_llm).
+
+        Regression for Cursor Bugbot: a global ContextVar would leak the
+        override to every BaseLLM that reads stop_sequences during the scope.
+        """
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        target = LLM(model="gpt-4", stop=["TargetStop:"])
+        other = LLM(model="gpt-4", stop=["OtherStop:"])
+        executor = self._make_executor(target, stop_words=["Observation:"])
+
+        with _llm_stop_words_applied(target, executor):
+            assert set(target.stop_sequences) == {"TargetStop:", "Observation:"}
+            assert other.stop_sequences == ["OtherStop:"]
+
+        assert target.stop_sequences == ["TargetStop:"]
+        assert other.stop_sequences == ["OtherStop:"]
+
+    def test_override_propagates_to_nested_direct_llm_calls(self) -> None:
+        """Once invoke wraps with the override, nested direct llm.call sites
+        (StepExecutor, handle_max_iterations_exceeded) see the merged stops.
+
+        Regression for Cursor Bugbot: those direct call sites bypass
+        get_llm_response, so the override must be set at executor entry, not
+        only around get_llm_response.
+        """
+        from crewai.utilities.agent_utils import _llm_stop_words_applied
+
+        shared = LLM(model="gpt-4", stop=["Original:"])
+        executor = self._make_executor(shared, stop_words=["Observation:"])
+
+        seen: list[set[str]] = []
+
+        def nested_direct_call() -> None:
+            seen.append(set(shared.stop_sequences))
+
+        with _llm_stop_words_applied(shared, executor):
+            nested_direct_call()
+
+        assert seen == [{"Original:", "Observation:"}]
+        assert shared.stop == ["Original:"]

lib/crewai/tests/llms/google/test_google.py

@@ -596,6 +596,35 @@ def test_gemini_token_usage_tracking():
     assert usage.total_tokens > 0
 
 
+def test_gemini_thoughts_tokens_counted_in_completion_and_total():
+    """Gemini's thoughts_token_count must be folded into completion_tokens so the
+    tracked total matches the API's total_token_count for thinking models."""
+    from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+    llm = GeminiCompletion(model="gemini-2.0-flash-001")
+
+    response = MagicMock()
+    response.usage_metadata = MagicMock(
+        prompt_token_count=100,
+        candidates_token_count=50,
+        thoughts_token_count=25,
+        total_token_count=175,
+        cached_content_token_count=0,
+    )
+
+    usage = llm._extract_token_usage(response)
+    assert usage["candidates_token_count"] == 50
+    assert usage["completion_tokens"] == 75
+    assert usage["reasoning_tokens"] == 25
+
+    llm._track_token_usage_internal(usage)
+    summary = llm.get_token_usage_summary()
+    assert summary.prompt_tokens == 100
+    assert summary.completion_tokens == 75
+    assert summary.total_tokens == 175
+    assert summary.reasoning_tokens == 25
+
+
 @pytest.mark.vcr()
 def test_gemini_tool_returning_float():
     """

lib/crewai/tests/task/test_async_task.py

@@ -1,12 +1,14 @@
 """Tests for async task execution."""
 
 import pytest
+from pydantic import BaseModel
 from unittest.mock import AsyncMock, MagicMock, patch
 
 from crewai.agent import Agent
 from crewai.task import Task
 from crewai.tasks.task_output import TaskOutput
 from crewai.tasks.output_format import OutputFormat
+from crewai.utilities.converter import Converter
 
 
 @pytest.fixture
@@ -383,4 +385,73 @@ class TestAsyncTaskOutput:
         assert result.description == "Test description"
         assert result.expected_output == "Test expected"
         assert result.raw == "Test result"
-        assert result.agent == "Test Agent"
+        assert result.agent == "Test Agent"
+
+
+class _AsyncOnlyOutput(BaseModel):
+    value: str
+
+
+class TestAsyncOutputConversion:
+    """Regression tests for native-async output conversion (issue #5230).
+
+    Ensures `_aexport_output` reaches the LLM via `acall` and never via the
+    blocking `call` method.
+    """
+
+    @pytest.mark.asyncio
+    async def test_aexport_output_uses_acall_not_call(self) -> None:
+        mock_llm = MagicMock()
+        mock_llm.supports_function_calling.return_value = False
+        mock_llm.acall = AsyncMock(return_value='{"value": "ok"}')
+        mock_llm.call = MagicMock(
+            side_effect=AssertionError("call() must NOT be invoked from async path")
+        )
+
+        converter = Converter(
+            llm=mock_llm,
+            model=_AsyncOnlyOutput,
+            text="raw",
+            instructions="convert",
+            max_attempts=1,
+        )
+        result = await converter.ato_pydantic()
+
+        assert isinstance(result, _AsyncOnlyOutput)
+        assert result.value == "ok"
+        mock_llm.acall.assert_awaited_once()
+        mock_llm.call.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_ato_json_function_calling_does_not_block_event_loop(self) -> None:
+        """The function-calling JSON path must run via asyncio.to_thread.
+
+        ``InternalInstructor`` is sync-only; `ato_json` should offload it so the
+        event loop is not blocked.
+        """
+        mock_llm = MagicMock()
+        mock_llm.supports_function_calling.return_value = True
+
+        converter = Converter(
+            llm=mock_llm,
+            model=_AsyncOnlyOutput,
+            text="raw",
+            instructions="convert",
+            max_attempts=1,
+        )
+
+        sentinel = '{"value": "ok"}'
+        with patch.object(
+            converter, "_create_instructor"
+        ) as mock_create, patch(
+            "crewai.utilities.converter.asyncio.to_thread", new_callable=AsyncMock
+        ) as mock_to_thread:
+            instructor = MagicMock()
+            instructor.to_json = MagicMock(return_value=sentinel)
+            mock_create.return_value = instructor
+            mock_to_thread.return_value = sentinel
+
+            result = await converter.ato_json()
+
+        assert result == sentinel
+        mock_to_thread.assert_awaited_once_with(instructor.to_json)

lib/crewai/tests/test_crew.py

@@ -1254,6 +1254,119 @@ async def test_async_task_execution_call_count(researcher, writer):
         assert mock_execute_sync.call_count == 1
 
 
+def test_mixed_sync_async_task_outputs_not_dropped(researcher, writer):
+    """Sync outputs accumulated before a pending async batch must survive the flush."""
+    sync1_output = TaskOutput(description="sync1", raw="s1", agent="researcher")
+    async1_output = TaskOutput(description="async1", raw="a1", agent="researcher")
+    sync2_output = TaskOutput(description="sync2", raw="s2", agent="writer")
+
+    sync1 = Task(description="sync1", expected_output="x", agent=researcher)
+    async1 = Task(
+        description="async1",
+        expected_output="x",
+        agent=researcher,
+        async_execution=True,
+    )
+    sync2 = Task(description="sync2", expected_output="x", agent=writer)
+
+    sync1.output = sync1_output
+    async1.output = async1_output
+    sync2.output = sync2_output
+
+    crew = Crew(agents=[researcher, writer], tasks=[sync1, async1, sync2])
+
+    mock_future = MagicMock(spec=Future)
+    mock_future.result.return_value = async1_output
+
+    with (
+        patch.object(
+            Task, "execute_sync", side_effect=[sync1_output, sync2_output]
+        ),
+        patch.object(Task, "execute_async", return_value=mock_future),
+    ):
+        result = crew.kickoff()
+
+    assert [o.raw for o in result.tasks_output] == ["s1", "a1", "s2"]
+
+
+@pytest.mark.asyncio
+async def test_mixed_sync_async_task_outputs_not_dropped_native_async(
+    researcher, writer
+):
+    """Same regression as the sync path, exercised via akickoff (native async)."""
+    sync1_output = TaskOutput(description="sync1", raw="s1", agent="researcher")
+    async1_output = TaskOutput(description="async1", raw="a1", agent="researcher")
+    sync2_output = TaskOutput(description="sync2", raw="s2", agent="writer")
+
+    sync1 = Task(description="sync1", expected_output="x", agent=researcher)
+    async1 = Task(
+        description="async1",
+        expected_output="x",
+        agent=researcher,
+        async_execution=True,
+    )
+    sync2 = Task(description="sync2", expected_output="x", agent=writer)
+
+    sync1.output = sync1_output
+    async1.output = async1_output
+    sync2.output = sync2_output
+
+    crew = Crew(agents=[researcher, writer], tasks=[sync1, async1, sync2])
+
+    aexecute_outputs = iter([sync1_output, async1_output, sync2_output])
+
+    async def fake_aexecute_sync(*_args: Any, **_kwargs: Any) -> TaskOutput:
+        return next(aexecute_outputs)
+
+    with patch.object(Task, "aexecute_sync", side_effect=fake_aexecute_sync):
+        result = await crew.akickoff()
+
+    assert [o.raw for o in result.tasks_output] == ["s1", "a1", "s2"]
+
+
+def test_pending_async_outputs_preserved_through_conditional_task(researcher, writer):
+    """A conditional task encountered after a pending async batch must not silently drop the async output."""
+    sync1_output = TaskOutput(description="sync1", raw="s1", agent="researcher")
+    async1_output = TaskOutput(description="async1", raw="a1", agent="researcher")
+
+    def always_skip(_: TaskOutput) -> bool:
+        return False
+
+    sync1 = Task(description="sync1", expected_output="x", agent=researcher)
+    async1 = Task(
+        description="async1",
+        expected_output="x",
+        agent=researcher,
+        async_execution=True,
+    )
+    conditional = ConditionalTask(
+        description="conditional",
+        expected_output="x",
+        agent=writer,
+        condition=always_skip,
+    )
+
+    sync1.output = sync1_output
+    async1.output = async1_output
+
+    crew = Crew(
+        agents=[researcher, writer], tasks=[sync1, async1, conditional]
+    )
+
+    mock_future = MagicMock(spec=Future)
+    mock_future.result.return_value = async1_output
+
+    with (
+        patch.object(Task, "execute_sync", return_value=sync1_output),
+        patch.object(Task, "execute_async", return_value=mock_future),
+    ):
+        result = crew.kickoff()
+
+    raws = [o.raw for o in result.tasks_output]
+    assert raws[:2] == ["s1", "a1"]
+    assert len(result.tasks_output) == 3
+
+
 @pytest.mark.vcr()
 def test_kickoff_for_each_single_input():
     """Tests if kickoff_for_each works with a single input."""
@@ -4519,8 +4632,8 @@ def test_sets_flow_context_when_using_crewbase_pattern_inside_flow():
     flow.kickoff()
 
     assert captured_crew is not None
-    assert captured_crew._flow_id == flow.execution_id  # type: ignore[attr-defined]
-    assert captured_crew._request_id == flow.execution_id  # type: ignore[attr-defined]
+    assert captured_crew._flow_id == flow.flow_id  # type: ignore[attr-defined]
+    assert captured_crew._request_id == flow.flow_id  # type: ignore[attr-defined]
 
 
 def test_sets_flow_context_when_outside_flow(researcher, writer):
@@ -4554,8 +4667,8 @@ def test_sets_flow_context_when_inside_flow(researcher, writer):
 
     flow = MyFlow()
     result = flow.kickoff()
-    assert result._flow_id == flow.execution_id  # type: ignore[attr-defined]
-    assert result._request_id == flow.execution_id  # type: ignore[attr-defined]
+    assert result._flow_id == flow.flow_id  # type: ignore[attr-defined]
+    assert result._request_id == flow.flow_id  # type: ignore[attr-defined]
 
 
 def test_reset_knowledge_with_no_crew_knowledge(researcher, writer):

lib/crewai/tests/test_flow_execution_id.py

@@ -1,127 +0,0 @@
-"""Regression tests for ``Flow.execution_id``.
-
-``execution_id`` is the stable tracking identifier for a single flow run.
-It must stay independent of ``state.id`` so that consumers passing an
-``id`` in ``inputs`` (used for persistence restore) cannot destabilize
-the identity used by telemetry, tracing, and external correlation.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-import pytest
-from crewai.flow.flow import Flow, FlowState, start
-from crewai.flow.flow_context import current_flow_id, current_flow_request_id
-
-
-class _CaptureState(FlowState):
-    captured_flow_id: str = ""
-    captured_state_id: str = ""
-    captured_current_flow_id: str = ""
-    captured_execution_id: str = ""
-
-
-class _IdentityCaptureFlow(Flow[_CaptureState]):
-    initial_state = _CaptureState
-
-    @start()
-    def capture(self) -> None:
-        self.state.captured_flow_id = self.flow_id
-        self.state.captured_state_id = self.state.id
-        self.state.captured_current_flow_id = current_flow_id.get() or ""
-        self.state.captured_execution_id = self.execution_id
-
-
-def test_execution_id_defaults_to_fresh_uuid_per_instance() -> None:
-    a = _IdentityCaptureFlow()
-    b = _IdentityCaptureFlow()
-
-    assert a.execution_id
-    assert b.execution_id
-    assert a.execution_id != b.execution_id
-
-
-def test_execution_id_survives_consumer_id_in_inputs() -> None:
-    flow = _IdentityCaptureFlow()
-    original_execution_id = flow.execution_id
-
-    flow.kickoff(inputs={"id": "consumer-supplied-id"})
-
-    assert flow.state.id == "consumer-supplied-id"
-    assert flow.flow_id == "consumer-supplied-id"
-    assert flow.execution_id == original_execution_id
-    assert flow.execution_id != "consumer-supplied-id"
-
-
-def test_two_runs_with_same_consumer_id_have_distinct_execution_ids() -> None:
-    flow_a = _IdentityCaptureFlow()
-    flow_b = _IdentityCaptureFlow()
-
-    colliding_id = "shared-consumer-id"
-    flow_a.kickoff(inputs={"id": colliding_id})
-    flow_b.kickoff(inputs={"id": colliding_id})
-
-    assert flow_a.state.id == colliding_id
-    assert flow_b.state.id == colliding_id
-    assert flow_a.execution_id != flow_b.execution_id
-
-
-def test_execution_id_is_writable() -> None:
-    flow = _IdentityCaptureFlow()
-    flow.execution_id = "external-task-id"
-
-    assert flow.execution_id == "external-task-id"
-
-    flow.kickoff(inputs={"id": "consumer-supplied-id"})
-    assert flow.execution_id == "external-task-id"
-    assert flow.state.id == "consumer-supplied-id"
-
-
-def test_current_flow_id_context_var_matches_execution_id() -> None:
-    flow = _IdentityCaptureFlow()
-    flow.execution_id = "external-task-id"
-
-    flow.kickoff(inputs={"id": "consumer-supplied-id"})
-
-    assert flow.state.captured_current_flow_id == "external-task-id"
-    assert flow.state.captured_flow_id == "consumer-supplied-id"
-    assert flow.state.captured_execution_id == "external-task-id"
-
-
-def test_execution_id_not_included_in_serialized_state() -> None:
-    flow = _IdentityCaptureFlow()
-    flow.execution_id = "external-task-id"
-    flow.kickoff()
-
-    dumped = flow.state.model_dump()
-    assert "execution_id" not in dumped
-    assert "_execution_id" not in dumped
-    assert dumped["id"] == flow.state.id
-
-
-def test_dict_state_flow_also_exposes_stable_execution_id() -> None:
-    class DictFlow(Flow[dict[str, Any]]):
-        initial_state = dict  # type: ignore[assignment]
-
-        @start()
-        def noop(self) -> None:
-            pass
-
-    flow = DictFlow()
-    original = flow.execution_id
-    flow.kickoff(inputs={"id": "consumer-supplied-id"})
-
-    assert flow.state["id"] == "consumer-supplied-id"
-    assert flow.execution_id == original
-
-
-@pytest.fixture(autouse=True)
-def _reset_flow_context_vars():
-    yield
-    for var in (current_flow_id, current_flow_request_id):
-        try:
-            var.set(None)
-        except LookupError:
-            # ContextVar was never set in this context; nothing to reset.
-            pass

lib/crewai/tests/test_flow_persistence.py

@@ -251,67 +251,240 @@ def test_persistence_with_base_model(tmp_path):
     assert isinstance(flow.state._unwrap(), State)
 
 
-def test_persist_custom_key_with_pydantic_state(tmp_path):
-    """`@persist(key=...)` uses the named attribute on a Pydantic state."""
+def test_fork_with_restore_from_state_id(tmp_path):
+    """Fork: restore_from_state_id hydrates state from source flow_uuid; new run gets a
+    fresh state.id; source's history is preserved (the fork's @persist writes go under
+    the new state.id, not the source's)."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
 
-    class KeyedState(FlowState):
-        conversation_id: str = "conv-42"
-        message: str = ""
-
-    class KeyedFlow(Flow[KeyedState]):
+    class ForkableFlow(Flow[TestState]):
         @start()
-        @persist(persistence, key="conversation_id")
-        def init_step(self):
-            self.state.message = "hello"
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
 
-    flow = KeyedFlow(persistence=persistence)
-    flow.kickoff()
+    # Run 1: build up source state. counter goes 0 -> 1.
+    flow1 = ForkableFlow(persistence=persistence)
+    flow1.kickoff()
+    source_uuid = flow1.state.id
+    assert flow1.state.counter == 1
 
-    saved_state = persistence.load_state("conv-42")
-    assert saved_state is not None
-    assert saved_state["message"] == "hello"
-    # The default `state.id` lookup must NOT have been used as the key.
-    assert persistence.load_state(flow.state.id) is None
+    # Resume on the same uuid bumps counter to 2 in the SAME flow_uuid history.
+    flow1b = ForkableFlow(persistence=persistence)
+    flow1b.kickoff(inputs={"id": source_uuid})
+    assert flow1b.state.counter == 2
+    assert persistence.load_state(source_uuid)["counter"] == 2
+
+    # Fork: hydrate from source, but persist under a fresh state.id.
+    flow2 = ForkableFlow(persistence=persistence)
+    flow2.kickoff(restore_from_state_id=source_uuid)
+
+    # Fork has a different state.id from the source.
+    assert flow2.state.id != source_uuid
+    # Hydrated from source's latest snapshot (counter=2), then incremented to 3.
+    assert flow2.state.counter == 3
+
+    # Source's history is unchanged after the fork.
+    assert persistence.load_state(source_uuid)["counter"] == 2
+
+    # Fork's writes landed under its own state.id.
+    assert persistence.load_state(flow2.state.id)["counter"] == 3
 
 
-def test_persist_custom_key_with_dict_state(tmp_path):
-    """`@persist(key=...)` uses the named key on a dict state."""
+def test_fork_with_pinned_state_id(tmp_path):
+    """Fork into a pinned state.id (inputs.id supplied alongside restore_from_state_id):
+    the new run uses inputs.id as state.id and hydrates from restore_from_state_id."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
 
-    class DictKeyedFlow(Flow[Dict[str, str]]):
-        initial_state = dict()
-
+    class PinnableFlow(Flow[TestState]):
         @start()
-        @persist(persistence, key="conversation_id")
-        def init_step(self):
-            self.state["conversation_id"] = "conv-dict-7"
-            self.state["message"] = "hi from dict"
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
 
-    flow = DictKeyedFlow(persistence=persistence)
-    flow.kickoff()
+    flow1 = PinnableFlow(persistence=persistence)
+    flow1.kickoff()
+    source_uuid = flow1.state.id
+    assert flow1.state.counter == 1
 
-    saved_state = persistence.load_state("conv-dict-7")
-    assert saved_state is not None
-    assert saved_state["message"] == "hi from dict"
+    pinned_uuid = "pinned-fork-uuid-1234"
+    flow2 = PinnableFlow(persistence=persistence)
+    flow2.kickoff(
+        inputs={"id": pinned_uuid},
+        restore_from_state_id=source_uuid,
+    )
+
+    # state.id pinned to inputs.id, NOT the source uuid.
+    assert flow2.state.id == pinned_uuid
+    # Hydrated from source: counter started at 1, step incremented to 2.
+    assert flow2.state.counter == 2
+    # Source's history is unchanged.
+    assert persistence.load_state(source_uuid)["counter"] == 1
+    # Fork's writes are under the pinned uuid.
+    assert persistence.load_state(pinned_uuid)["counter"] == 2
 
 
-def test_persist_custom_key_missing_raises(tmp_path):
-    """A missing/falsy custom key must raise a clear ValueError."""
+def test_restore_from_state_id_not_found_silent_fallback(tmp_path):
+    """Lookup miss on restore_from_state_id silently falls through to default behavior."""
     db_path = os.path.join(tmp_path, "test_flows.db")
     persistence = SQLiteFlowPersistence(db_path)
 
-    class MissingKeyFlow(Flow[Dict[str, str]]):
-        initial_state = dict()
-
+    class FallbackFlow(Flow[TestState]):
         @start()
-        @persist(persistence, key="conversation_id")
-        def init_step(self):
-            # Intentionally do NOT set "conversation_id" on state.
-            self.state["message"] = "no key here"
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
 
-    flow = MissingKeyFlow(persistence=persistence)
-    with pytest.raises(ValueError, match="conversation_id"):
-        flow.kickoff()
+    flow = FallbackFlow(persistence=persistence)
+    # No source UUID exists — should not raise.
+    flow.kickoff(restore_from_state_id="no-such-uuid")
+
+    # Default state path: counter starts at 0 and step increments to 1.
+    assert flow.state.counter == 1
+    # state.id is the auto-generated one, NOT the missing source.
+    assert flow.state.id != "no-such-uuid"
+
+
+def test_restore_from_state_id_none_is_no_op(tmp_path):
+    """restore_from_state_id=None (default) preserves baseline kickoff behavior."""
+    db_path = os.path.join(tmp_path, "test_flows.db")
+    persistence = SQLiteFlowPersistence(db_path)
+
+    class BaselineFlow(Flow[TestState]):
+        @start()
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
+
+    flow = BaselineFlow(persistence=persistence)
+    flow.kickoff(restore_from_state_id=None)
+    assert flow.state.counter == 1
+
+
+def test_fork_conflict_with_from_checkpoint_raises():
+    """Passing both from_checkpoint and restore_from_state_id raises ValueError, naming
+    both parameters."""
+    from crewai.state import CheckpointConfig
+
+    class ConflictFlow(Flow[TestState]):
+        @start()
+        def step(self):
+            pass
+
+    flow = ConflictFlow()
+    with pytest.raises(ValueError) as excinfo:
+        flow.kickoff(
+            from_checkpoint=CheckpointConfig(),
+            restore_from_state_id="some-uuid",
+        )
+    msg = str(excinfo.value)
+    assert "from_checkpoint" in msg
+    assert "restore_from_state_id" in msg
+
+
+@pytest.mark.asyncio
+async def test_fork_via_kickoff_async(tmp_path):
+    """kickoff_async honors restore_from_state_id: hydrates from source, mints fresh
+    state.id, persists under the new id, source history preserved."""
+    db_path = os.path.join(tmp_path, "test_flows.db")
+    persistence = SQLiteFlowPersistence(db_path)
+
+    class AsyncForkableFlow(Flow[TestState]):
+        @start()
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
+
+    flow1 = AsyncForkableFlow(persistence=persistence)
+    await flow1.kickoff_async()
+    source_uuid = flow1.state.id
+    assert flow1.state.counter == 1
+
+    flow2 = AsyncForkableFlow(persistence=persistence)
+    await flow2.kickoff_async(restore_from_state_id=source_uuid)
+
+    assert flow2.state.id != source_uuid
+    assert flow2.state.counter == 2
+    assert persistence.load_state(source_uuid)["counter"] == 1
+    assert persistence.load_state(flow2.state.id)["counter"] == 2
+
+
+@pytest.mark.asyncio
+async def test_fork_via_akickoff(tmp_path):
+    """akickoff is the public async alias and must accept restore_from_state_id with
+    the same semantics as kickoff_async."""
+    db_path = os.path.join(tmp_path, "test_flows.db")
+    persistence = SQLiteFlowPersistence(db_path)
+
+    class AkickoffForkableFlow(Flow[TestState]):
+        @start()
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
+
+    flow1 = AkickoffForkableFlow(persistence=persistence)
+    await flow1.akickoff()
+    source_uuid = flow1.state.id
+    assert flow1.state.counter == 1
+
+    flow2 = AkickoffForkableFlow(persistence=persistence)
+    await flow2.akickoff(restore_from_state_id=source_uuid)
+
+    assert flow2.state.id != source_uuid
+    assert flow2.state.counter == 2
+    assert persistence.load_state(source_uuid)["counter"] == 1
+    assert persistence.load_state(flow2.state.id)["counter"] == 2
+
+
+@pytest.mark.asyncio
+async def test_akickoff_pinned_fork(tmp_path):
+    """akickoff with both inputs.id and restore_from_state_id pins state.id while
+    hydrating from the source."""
+    db_path = os.path.join(tmp_path, "test_flows.db")
+    persistence = SQLiteFlowPersistence(db_path)
+
+    class PinnableAsyncFlow(Flow[TestState]):
+        @start()
+        @persist(persistence)
+        def step(self):
+            self.state.counter += 1
+
+    flow1 = PinnableAsyncFlow(persistence=persistence)
+    await flow1.akickoff()
+    source_uuid = flow1.state.id
+
+    pinned_uuid = "pinned-akickoff-fork-uuid"
+    flow2 = PinnableAsyncFlow(persistence=persistence)
+    await flow2.akickoff(
+        inputs={"id": pinned_uuid},
+        restore_from_state_id=source_uuid,
+    )
+
+    assert flow2.state.id == pinned_uuid
+    assert flow2.state.counter == 2
+    assert persistence.load_state(source_uuid)["counter"] == 1
+    assert persistence.load_state(pinned_uuid)["counter"] == 2
+
+
+@pytest.mark.asyncio
+async def test_akickoff_fork_conflict_with_from_checkpoint_raises():
+    """akickoff must raise the same conflict ValueError as kickoff/kickoff_async when
+    both from_checkpoint and restore_from_state_id are set."""
+    from crewai.state import CheckpointConfig
+
+    class AsyncConflictFlow(Flow[TestState]):
+        @start()
+        def step(self):
+            pass
+
+    flow = AsyncConflictFlow()
+    with pytest.raises(ValueError) as excinfo:
+        await flow.akickoff(
+            from_checkpoint=CheckpointConfig(),
+            restore_from_state_id="some-uuid",
+        )
+    msg = str(excinfo.value)
+    assert "from_checkpoint" in msg
+    assert "restore_from_state_id" in msg

lib/crewai/tests/test_llm.py

@@ -177,6 +177,7 @@ def test_llm_passes_additional_params():
         # Create mocks for response structure
         mock_message = MagicMock()
         mock_message.content = "Test response"
+        mock_message.tool_calls = None
         mock_choice = MagicMock()
         mock_choice.message = mock_message
         mock_response = MagicMock()
@@ -1146,3 +1147,52 @@ async def test_usage_info_streaming_with_acall():
     assert llm._token_usage["total_tokens"] > 0
 
     assert len(result) > 0
+
+
+def _build_response_with_text_and_tool_calls():
+    """Mimic a litellm ModelResponse that contains both content and tool_calls."""
+    from litellm.types.utils import ChatCompletionMessageToolCall, Function
+
+    response_message = MagicMock()
+    response_message.content = "I will search for the given query."
+    response_message.tool_calls = [
+        ChatCompletionMessageToolCall(
+            id="call_123",
+            type="function",
+            function=Function(name="search", arguments='{"q": "x"}'),
+        )
+    ]
+    choice = MagicMock(message=response_message)
+    response = MagicMock(choices=[choice], model_extra=None)
+    return response
+
+
+def test_non_streaming_returns_tool_calls_when_text_also_present():
+    """A response with both text and tool_calls must not drop the tool_calls
+    when available_functions is None (executor-managed tool execution path).
+    """
+    llm = LLM(model="gpt-4o-mini", is_litellm=True)
+    response = _build_response_with_text_and_tool_calls()
+
+    with patch("crewai.llm.litellm.completion", return_value=response):
+        result = llm.call("anything", available_functions=None)
+
+    assert isinstance(result, list)
+    assert len(result) == 1
+    assert result[0].function.name == "search"
+
+
+@pytest.mark.asyncio
+async def test_non_streaming_async_returns_tool_calls_when_text_also_present():
+    llm = LLM(model="openai/gpt-4o-mini", is_litellm=True, stream=False)
+    response = _build_response_with_text_and_tool_calls()
+
+    async def _ret(*args, **kwargs):
+        return response
+
+    with patch("crewai.llm.litellm.acompletion", side_effect=_ret):
+        result = await llm.acall("anything", available_functions=None)
+
+    assert isinstance(result, list)
+    assert len(result) == 1
+    assert result[0].function.name == "search"

lib/crewai/tests/test_task_guardrails.py

@@ -690,6 +690,27 @@ def test_multiple_guardrails_with_pydantic_output():
     assert parsed["processed"] is True
 
 
+def test_export_output_accepts_pydantic_input():
+    """Regression test for #5458: _export_output must not crash with TypeError
+    when called with a Pydantic instance (e.g. when an upstream caller passes
+    an already-converted model from a context task)."""
+    from pydantic import BaseModel
+
+    class StructuredResult(BaseModel):
+        value: str
+
+    task = create_smart_task(
+        description="Test pydantic export",
+        expected_output="Structured output",
+        output_pydantic=StructuredResult,
+    )
+
+    instance = StructuredResult(value="ok")
+    pydantic_output, json_output = task._export_output(instance)
+    assert pydantic_output is instance
+    assert json_output is None
+
+
 def test_guardrails_vs_single_guardrail_mutual_exclusion():
     """Test that guardrails list nullifies single guardrail."""
 

lib/crewai/tests/utilities/test_agent_utils.py

@@ -17,6 +17,8 @@ from crewai.utilities.agent_utils import (
     _format_messages_for_summary,
     _split_messages_into_chunks,
     convert_tools_to_openai_schema,
+    execute_single_native_tool_call,
+    NativeToolCallResult,
     parse_tool_call_args,
     summarize_messages,
 )
@@ -1033,3 +1035,91 @@ class TestParseToolCallArgs:
         _, error = parse_tool_call_args("{bad json}", "tool", "call_7")
         assert error is not None
         assert set(error.keys()) == {"call_id", "func_name", "result", "from_cache", "original_tool"}
+
+
+class TestExecuteSingleNativeToolCall:
+    """Tests for execute_single_native_tool_call."""
+
+    def test_result_as_answer_false_on_tool_error(self) -> None:
+        """When a tool with result_as_answer=True raises, result_as_answer must be False.
+
+        Regression test for https://github.com/crewAIInc/crewAI/issues/5156
+        """
+        from unittest.mock import MagicMock
+
+        class FailingTool(BaseTool):
+            name: str = "failing_tool"
+            description: str = "A tool that always fails"
+            result_as_answer: bool = True
+
+            def _run(self, **kwargs: Any) -> str:
+                raise RuntimeError("intentional failure")
+
+        tool = FailingTool()
+        tool_call = MagicMock()
+        tool_call.id = "call_1"
+        tool_call.function.name = "failing_tool"
+        tool_call.function.arguments = "{}"
+
+        result = execute_single_native_tool_call(
+            tool_call,
+            available_functions={"failing_tool": tool._run},
+            original_tools=[tool],
+            structured_tools=None,
+            tools_handler=None,
+            agent=None,
+            task=None,
+            crew=None,
+            event_source=MagicMock(),
+            printer=None,
+            verbose=False,
+        )
+
+        assert isinstance(result, NativeToolCallResult)
+        assert result.result_as_answer is False
+        assert "Error executing tool" in result.result
+
+    def test_result_as_answer_false_when_hook_blocks(self) -> None:
+        """When a before-hook blocks a tool with result_as_answer=True, result_as_answer must be False."""
+        from unittest.mock import MagicMock
+
+        from crewai.hooks.tool_hooks import (
+            clear_before_tool_call_hooks,
+            register_before_tool_call_hook,
+        )
+
+        class BlockedTool(BaseTool):
+            name: str = "blocked_tool"
+            description: str = "A tool whose execution will be blocked by a hook"
+            result_as_answer: bool = True
+
+            def _run(self, **kwargs: Any) -> str:
+                return "should not run"
+
+        tool = BlockedTool()
+        tool_call = MagicMock()
+        tool_call.id = "call_1"
+        tool_call.function.name = "blocked_tool"
+        tool_call.function.arguments = "{}"
+
+        register_before_tool_call_hook(lambda _ctx: False)
+        try:
+            result = execute_single_native_tool_call(
+                tool_call,
+                available_functions={"blocked_tool": tool._run},
+                original_tools=[tool],
+                structured_tools=None,
+                tools_handler=None,
+                agent=None,
+                task=None,
+                crew=None,
+                event_source=MagicMock(),
+                printer=None,
+                verbose=False,
+            )
+        finally:
+            clear_before_tool_call_hooks()
+
+        assert isinstance(result, NativeToolCallResult)
+        assert result.result_as_answer is False
+        assert "blocked by hook" in result.result

lib/crewai/tests/utilities/test_converter.py

@@ -87,6 +87,31 @@ def test_convert_to_model_with_no_model() -> None:
     assert output == "Plain text"
 
 
+def test_convert_to_model_with_basemodel_input_matching_pydantic() -> None:
+    instance = SimpleModel(name="John", age=30)
+    output = convert_to_model(instance, SimpleModel, None, None)
+    assert output is instance
+
+
+def test_convert_to_model_with_basemodel_input_matching_json() -> None:
+    instance = SimpleModel(name="John", age=30)
+    output = convert_to_model(instance, None, SimpleModel, None)
+    assert output == {"name": "John", "age": 30}
+
+
+def test_convert_to_model_with_basemodel_input_different_class() -> None:
+    class OtherModel(BaseModel):
+        name: str
+        age: int
+        extra: str = "default"
+
+    instance = OtherModel(name="John", age=30, extra="ignored")
+    output = convert_to_model(instance, SimpleModel, None, None)
+    assert isinstance(output, SimpleModel)
+    assert output.name == "John"
+    assert output.age == 30
+
+
 def test_convert_to_model_with_special_characters() -> None:
     json_string_test = """
     {
@@ -177,6 +202,34 @@ def test_handle_partial_json_with_invalid_partial(mock_agent: Mock) -> None:
         assert output == "Converted result"
 
 
+def test_handle_partial_json_accepts_literal_control_chars_in_strings() -> None:
+    """JSON values with literal newlines/tabs (lenient parsing) must still
+    validate, matching the prior model_validate_json behavior.
+    """
+    result = 'prefix {"name": "Charlie\nDoe", "age": 35} suffix'
+    output = handle_partial_json(result, SimpleModel, False, None)
+    assert isinstance(output, SimpleModel)
+    assert output.name == "Charlie\nDoe"
+    assert output.age == 35
+
+
+def test_handle_partial_json_falls_through_for_non_json_curly_blocks(
+    mock_agent: Mock,
+) -> None:
+    """A regex match that is not actually JSON (e.g. GraphQL) must fall through
+    to convert_with_instructions instead of raising a ValidationError.
+    """
+    result = (
+        "type Query {\n  countries: [Country]\n}\n\n"
+        "type Country {\n  code: String\n  name: String\n}"
+    )
+    with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
+        mock_convert.return_value = "Converted result"
+        output = handle_partial_json(result, SimpleModel, False, mock_agent)
+        assert output == "Converted result"
+        mock_convert.assert_called_once()
+
+
 # Tests for convert_with_instructions
 @patch("crewai.utilities.converter.create_converter")
 @patch("crewai.utilities.converter.get_conversion_instructions")

lib/devtools/README.md

@@ -11,6 +11,8 @@ Installed automatically via the workspace (`uv sync`). Requires:
 - `ENTERPRISE_REPO` env var — GitHub repo for enterprise releases
 - `ENTERPRISE_VERSION_DIRS` env var — comma-separated directories to bump in the enterprise repo
 - `ENTERPRISE_CREWAI_DEP_PATH` env var — path to the pyproject.toml with the `crewai[tools]` pin in the enterprise repo
+- `ENTERPRISE_WORKFLOW_PATHS` env var — comma-separated workflow file paths in the enterprise repo whose `crewai[extras]==<version>` pins should be rewritten on each release (e.g. `.github/workflows/tests.yml`)
+- `ENTERPRISE_EXTRA_PACKAGES` env var — comma-separated packages to also pin in enterprise pyproject files, in addition to `crewai` / `crewai[extras]`
 
 ## Commands
 

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.14.4a1"
+__version__ = "1.14.5a2"

lib/devtools/src/crewai_devtools/cli.py

@@ -1207,7 +1207,12 @@ _ENTERPRISE_WORKFLOW_PATHS: Final[tuple[str, ...]] = tuple(
 
 
 def _update_enterprise_crewai_dep(pyproject_path: Path, version: str) -> bool:
-    """Update the crewai[tools] pin in an enterprise pyproject.toml.
+    """Update crewai pins in an enterprise pyproject.toml.
+
+    Pins ``crewai`` / ``crewai[extras]`` via ``_pin_crewai_deps`` and
+    additionally pins any dashed ``crewai-*`` packages configured via
+    ``ENTERPRISE_EXTRA_PACKAGES`` (e.g. ``crewai-enterprise``), which
+    ``_pin_crewai_deps`` does not cover.
 
     Args:
         pyproject_path: Path to the pyproject.toml file.
@@ -1219,20 +1224,57 @@ def _update_enterprise_crewai_dep(pyproject_path: Path, version: str) -> bool:
     if not pyproject_path.exists():
         return False
 
+    changed = False
     content = pyproject_path.read_text()
     new_content = _pin_crewai_deps(content, version)
     if new_content != content:
         pyproject_path.write_text(new_content)
-        return True
-    return False
+        changed = True
+
+    if update_pyproject_dependencies(
+        pyproject_path, version, extra_packages=list(_ENTERPRISE_EXTRA_PACKAGES)
+    ):
+        changed = True
+
+    return changed
+
+
+def _update_workflow_crewai_pins(workflow_path: Path, version: str) -> bool:
+    """Rewrite ``crewai[extras]==<old>`` pins in a single workflow file.
+
+    Operates line-by-line on the raw file via ``_repin_crewai_install``
+    so only version numbers change and all formatting is preserved.
+
+    Args:
+        workflow_path: Path to a workflow YAML file.
+        version: New crewai version string.
+
+    Returns:
+        True if the file was modified.
+    """
+    if not workflow_path.exists():
+        return False
+
+    raw = workflow_path.read_text()
+    lines = raw.splitlines(keepends=True)
+    changed = False
+    for i, line in enumerate(lines):
+        if "crewai[" not in line:
+            continue
+        new_line = _repin_crewai_install(line, version)
+        if new_line != line:
+            lines[i] = new_line
+            changed = True
+
+    if not changed:
+        return False
+    workflow_path.write_text("".join(lines))
+    return True
 
 
 def _update_enterprise_workflows(repo_dir: Path, version: str) -> list[Path]:
     """Update crewai version pins in enterprise CI workflow files.
 
-    Applies ``_repin_crewai_install`` line-by-line on the raw file so
-    only version numbers change and all formatting is preserved.
-
     Args:
         repo_dir: Root of the cloned enterprise repo.
         version: New crewai version string.
@@ -1243,29 +1285,31 @@ def _update_enterprise_workflows(repo_dir: Path, version: str) -> list[Path]:
     updated: list[Path] = []
     for rel_path in _ENTERPRISE_WORKFLOW_PATHS:
         workflow = repo_dir / rel_path
-        if not workflow.exists():
-            continue
-
-        raw = workflow.read_text()
-        lines = raw.splitlines(keepends=True)
-        changed = False
-        for i, line in enumerate(lines):
-            if "crewai[" not in line:
-                continue
-            new_line = _repin_crewai_install(line, version)
-            if new_line != line:
-                lines[i] = new_line
-                changed = True
-
-        if changed:
-            new_raw = "".join(lines)
-        else:
-            new_raw = raw
-
-        if new_raw != raw:
-            workflow.write_text(new_raw)
+        if _update_workflow_crewai_pins(workflow, version):
             updated.append(workflow)
+    return updated
 
+
+def _update_repo_workflows_crewai_pins(repo_dir: Path, version: str) -> list[Path]:
+    """Update crewai pins across all GitHub workflow files in a repo.
+
+    Args:
+        repo_dir: Root of the cloned repo.
+        version: New crewai version string.
+
+    Returns:
+        List of workflow paths that were modified.
+    """
+    workflows_dir = repo_dir / ".github" / "workflows"
+    if not workflows_dir.exists():
+        return []
+
+    updated: list[Path] = []
+    for workflow in sorted(workflows_dir.iterdir()):
+        if workflow.suffix not in (".yml", ".yaml"):
+            continue
+        if _update_workflow_crewai_pins(workflow, version):
+            updated.append(workflow)
     return updated
 
 
@@ -1314,8 +1358,10 @@ _PYPI_POLL_TIMEOUT: Final[int] = 600
 def _update_deployment_test_repo(version: str, is_prerelease: bool) -> None:
     """Update the deployment test repo to pin the new crewai version.
 
-    Clones the repo, updates the crewai[tools] pin in pyproject.toml,
-    regenerates the lockfile, commits, and pushes directly to main.
+    Clones the repo, updates the crewai[tools] pin in pyproject.toml
+    and any crewai[extras] pins in .github/workflows, regenerates the
+    lockfile, commits to a branch, pushes, opens a PR against main,
+    then polls until the PR is merged (or closed).
 
     Args:
         version: New crewai version string.
@@ -1333,50 +1379,91 @@ def _update_deployment_test_repo(version: str, is_prerelease: bool) -> None:
         pyproject = repo_dir / "pyproject.toml"
         content = pyproject.read_text()
         new_content = _pin_crewai_deps(content, version)
-        if new_content == content:
+        pyproject_changed = new_content != content
+        if pyproject_changed:
+            pyproject.write_text(new_content)
+            console.print(f"[green]✓[/green] Updated crewai[tools] pin to {version}")
+        else:
             console.print(
                 "[yellow]Warning:[/yellow] No crewai[tools] pin found to update"
             )
+
+        updated_workflows = _update_repo_workflows_crewai_pins(repo_dir, version)
+        for wf in updated_workflows:
+            console.print(
+                f"[green]✓[/green] Updated crewai pin in {wf.relative_to(repo_dir)}"
+            )
+
+        if not pyproject_changed and not updated_workflows:
+            console.print("[yellow]Nothing to update; skipping commit and PR.[/yellow]")
             return
-        pyproject.write_text(new_content)
-        console.print(f"[green]✓[/green] Updated crewai[tools] pin to {version}")
 
-        lock_cmd = [
-            "uv",
-            "lock",
-            "--refresh-package",
-            "crewai",
-            "--refresh-package",
-            "crewai-tools",
+        paths_to_add: list[str] = [
+            str(wf.relative_to(repo_dir)) for wf in updated_workflows
         ]
-        if is_prerelease:
-            lock_cmd.append("--prerelease=allow")
 
-        max_retries = 10
-        for attempt in range(1, max_retries + 1):
-            try:
-                run_command(lock_cmd, cwd=repo_dir)
-                break
-            except subprocess.CalledProcessError:
-                if attempt == max_retries:
+        if pyproject_changed:
+            lock_cmd = [
+                "uv",
+                "lock",
+                "--refresh-package",
+                "crewai",
+                "--refresh-package",
+                "crewai-tools",
+            ]
+            if is_prerelease:
+                lock_cmd.append("--prerelease=allow")
+
+            max_retries = 10
+            for attempt in range(1, max_retries + 1):
+                try:
+                    run_command(lock_cmd, cwd=repo_dir)
+                    break
+                except subprocess.CalledProcessError:
+                    if attempt == max_retries:
+                        console.print(
+                            f"[red]Error:[/red] uv lock failed after {max_retries} attempts"
+                        )
+                        raise
                     console.print(
-                        f"[red]Error:[/red] uv lock failed after {max_retries} attempts"
+                        f"[yellow]uv lock failed (attempt {attempt}/{max_retries}),"
+                        f" retrying in {_PYPI_POLL_INTERVAL}s...[/yellow]"
                     )
-                    raise
-                console.print(
-                    f"[yellow]uv lock failed (attempt {attempt}/{max_retries}),"
-                    f" retrying in {_PYPI_POLL_INTERVAL}s...[/yellow]"
-                )
-                time.sleep(_PYPI_POLL_INTERVAL)
-        console.print("[green]✓[/green] Lockfile updated")
+                    time.sleep(_PYPI_POLL_INTERVAL)
+            console.print("[green]✓[/green] Lockfile updated")
+            paths_to_add.extend(["pyproject.toml", "uv.lock"])
 
-        run_command(["git", "add", "pyproject.toml", "uv.lock"], cwd=repo_dir)
+        branch = f"chore/bump-crewai-v{version}"
+        create_or_reset_branch(branch, cwd=repo_dir)
+
+        run_command(["git", "add", *paths_to_add], cwd=repo_dir)
         run_command(
             ["git", "commit", "-m", f"chore: bump crewai to {version}"],
             cwd=repo_dir,
         )
-        run_command(["git", "push"], cwd=repo_dir)
-        console.print(f"[green]✓[/green] Pushed to {_DEPLOYMENT_TEST_REPO}")
+        run_command(["git", "push", "-u", "origin", branch], cwd=repo_dir)
+        console.print(f"[green]✓[/green] Pushed branch {branch}")
+
+        pr_url = run_command(
+            [
+                "gh",
+                "pr",
+                "create",
+                "--base",
+                "main",
+                "--head",
+                branch,
+                "--title",
+                f"chore: bump crewai to {version}",
+                "--body",
+                "",
+            ],
+            cwd=repo_dir,
+        )
+        console.print(f"[green]✓[/green] Opened PR on {_DEPLOYMENT_TEST_REPO}")
+        console.print(f"[cyan]PR URL:[/cyan] {pr_url.strip()}")
+
+        _wait_for_pr_merged(branch, repo_dir)
 
 
 def _wait_for_pypi(package: str, version: str) -> None:
@@ -1408,6 +1495,37 @@ def _wait_for_pypi(package: str, version: str) -> None:
     sys.exit(1)
 
 
+_PR_MERGE_POLL_INTERVAL: Final[int] = 30
+
+
+def _wait_for_pr_merged(branch: str, cwd: Path) -> None:
+    """Poll a PR until it is merged, exiting on close-without-merge.
+
+    Args:
+        branch: Head branch name of the PR to watch.
+        cwd: Working directory of the cloned repo (so ``gh`` resolves
+            the right remote).
+
+    Raises:
+        SystemExit: If the PR is closed without being merged.
+    """
+    console.print(f"[cyan]Waiting for PR on branch {branch} to be merged...[/cyan]")
+    while True:
+        state = run_command(
+            ["gh", "pr", "view", branch, "--json", "state", "--jq", ".state"],
+            cwd=cwd,
+        ).strip()
+        if state == "MERGED":
+            console.print(f"[green]✓[/green] PR for {branch} merged")
+            return
+        if state == "CLOSED":
+            console.print(
+                f"[red]Error:[/red] PR for {branch} was closed without merging"
+            )
+            sys.exit(1)
+        time.sleep(_PR_MERGE_POLL_INTERVAL)
+
+
 def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> None:
     """Clone the enterprise repo, bump versions, and create a release PR.
 

uv.lock

@@ -1626,7 +1626,7 @@ requires-dist = [
     { name = "e2b-code-interpreter", marker = "extra == 'e2b'", specifier = "~=2.6.0" },
     { name = "exa-py", marker = "extra == 'exa-py'", specifier = ">=1.8.7" },
     { name = "firecrawl-py", marker = "extra == 'firecrawl-py'", specifier = ">=1.8.0" },
-    { name = "gitpython", marker = "extra == 'github'", specifier = ">=3.1.41,<4" },
+    { name = "gitpython", marker = "extra == 'github'", specifier = ">=3.1.47,<4" },
     { name = "hyperbrowser", marker = "extra == 'hyperbrowser'", specifier = ">=0.18.0" },
     { name = "langchain-apify", marker = "extra == 'apify'", specifier = ">=0.1.2,<1.0.0" },
     { name = "linkup-sdk", marker = "extra == 'linkup-sdk'", specifier = ">=0.2.2" },
@@ -2619,14 +2619,14 @@ wheels = [
 
 [[package]]
 name = "gitpython"
-version = "3.1.46"
+version = "3.1.47"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "gitdb" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/df/b5/59d16470a1f0dfe8c793f9ef56fd3826093fc52b3bd96d6b9d6c26c7e27b/gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f", size = 215371, upload-time = "2026-01-01T15:37:32.073Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c1/bd/50db468e9b1310529a19fce651b3b0e753b5c07954d486cba31bbee9a5d5/gitpython-3.1.47.tar.gz", hash = "sha256:dba27f922bd2b42cb54c87a8ab3cb6beb6bf07f3d564e21ac848913a05a8a3cd", size = 216978, upload-time = "2026-04-22T02:44:44.059Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6a/09/e21df6aef1e1ffc0c816f0522ddc3f6dcded766c3261813131c78a704470/gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058", size = 208620, upload-time = "2026-01-01T15:37:30.574Z" },
+    { url = "https://files.pythonhosted.org/packages/f2/c5/a1bc0996af85757903cf2bf444a7824e68e0035ce63fb41d6f76f9def68b/gitpython-3.1.47-py3-none-any.whl", hash = "sha256:489f590edfd6d20571b2c0e72c6a6ac6915ee8b8cd04572330e3842207a78905", size = 209547, upload-time = "2026-04-22T02:44:41.271Z" },
 ]
 
 [[package]]