fix(tracing): stop nagging users who declined tracing (#5665 )

- When user explicitly declined tracing, skip the 'Tracing is disabled' message instead of showing it on every crew/flow execution - Add CREWAI_SUPPRESS_TRACING_MESSAGES env var to let users fully suppress the message - Remove duplicate identical if/else branches in all four _show_tracing_disabled_message implementations - Add 24 tests covering suppression via env var, context var, and user-declined scenarios Co-Authored-By: João <joao@crewai.com>
2026-05-07 10:12:38 +00:00 · 2026-04-30 04:52:51 +00:00
66 changed files with 853 additions and 3932 deletions
--- a/docs/ar/changelog.mdx
+++ b/docs/ar/changelog.mdx
@@ -4,71 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
-<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

--- a/docs/ar/concepts/flows.mdx
+++ b/docs/ar/concepts/flows.mdx
@@ -380,41 +380,32 @@ class AnotherFlow(Flow[dict]):
        print("Method-level persisted runs:", self.state["runs"])
 ```

-### تفرع الحالة المستمرة
+### مفتاح استمرارية مخصص

-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.
+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:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    turn: int = 0

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")  # استخدام حقل مخصص كمفتاح للاستمرارية
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
-        print(f"[id={self.state.id}] counter={self.state.counter}")
+    def begin(self):
+        self.state.turn += 1
+        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")

-# 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.
+# إعادة تشغيل المحادثة بنفس conversation_id يُعيد تحميل الحالة السابقة
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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.
+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`.

 ### كيف تعمل

--- a/docs/ar/concepts/production-architecture.mdx
+++ b/docs/ar/concepts/production-architecture.mdx
@@ -146,14 +146,15 @@ class ProductionFlow(Flow[AppState]):
    # ...
 ```

-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`:
+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.

 ```python
-flow.kickoff(restore_from_state_id="<previous-run-state-id>")
+@persist(key="conversation_id")
+class ProductionFlow(Flow[AppState]):
+    # يجب أن يحتوي AppState على conversation_id؛ استئناف الجلسة يُعيد تحميل الحالة السابقة
+    ...
 ```

-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.**
--- a/docs/ar/concepts/tools.mdx
+++ b/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.                                                  |
--- a/docs/ar/guides/flows/mastering-flow-state.mdx
+++ b/docs/ar/guides/flows/mastering-flow-state.mdx
@@ -116,47 +116,32 @@ class PersistentCounterFlow(Flow[CounterState]):
        return self.state.value
 ```

-#### تفرع الحالة المستمرة
+### استخدام مفتاح استمرارية مخصص

-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) |
+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`:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    history: list[str] = []

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
+    def greet(self):
+        self.state.history.append("hello")
+        return self.state.history

-# 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.
+# تشغيل ثانٍ بنفس conversation_id يُعيد تحميل الحالة السابقة
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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`.
+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`.

 ## أنماط حالة متقدمة

--- a/docs/ar/tools/search-research/exasearchtool.mdx
+++ b/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."
+description: "ابحث في الويب باستخدام Exa Search API للعثور على النتائج الأكثر صلة لأي استعلام، مع خيارات لمحتوى الصفحة الكامل والمقتطفات والملخصات."
 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 والمقتطفات الموفرة للرموز.
+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.

 ## 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.

@@ -86,52 +86,25 @@ print(result)
 يمكنك تكوين الأداة بمعاملات مخصصة للحصول على نتائج أغنى:

 ```python
-# Use 'deep' for thorough, multi-step searches
-exa_tool = ExaSearchTool(
-    highlights=True,
+# Get full page content with AI summaries
+exa_tool = EXASearchTool(
+    content=True,
+    summary=True,
    type="deep"
 )

 # Use it in an agent
 agent = Agent(
    role="Deep Researcher",
-    goal="Conduct thorough research",
+    goal="Conduct thorough research with full content and summaries",
    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
-
-## موارد
-
- [توثيق Exa](https://exa.ai/docs)
- [لوحة تحكم Exa — إدارة مفاتيح API والاستخدام](https://dashboard.exa.ai)
+- **تصفية النطاقات**: تقييد عمليات البحث على نطاقات محددة
--- a/docs/docs.json
+++ b/docs/docs.json
--- a/docs/en/changelog.mdx
+++ b/docs/en/changelog.mdx
@@ -4,71 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<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

--- a/docs/en/concepts/flows.mdx
+++ b/docs/en/concepts/flows.mdx
@@ -380,41 +380,32 @@ class AnotherFlow(Flow[dict]):
        print("Method-level persisted runs:", self.state["runs"])
 ```

-### Forking Persisted State
+### Custom Persistence Key

-`@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.
+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:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    turn: int = 0

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")  # Use a custom field as the persistence key
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
-        print(f"[id={self.state.id}] counter={self.state.counter}")
+    def begin(self):
+        self.state.turn += 1
+        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")

-# 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.
+# Resuming the same conversation reloads its prior state by conversation_id
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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`.
+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.

 ### How It Works

--- a/docs/en/concepts/production-architecture.mdx
+++ b/docs/en/concepts/production-architecture.mdx
@@ -146,14 +146,15 @@ class ProductionFlow(Flow[AppState]):
    # ...
 ```

-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`:
+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.

 ```python
-flow.kickoff(restore_from_state_id="<previous-run-state-id>")
+@persist(key="conversation_id")
+class ProductionFlow(Flow[AppState]):
+    # AppState must expose conversation_id; resuming a session reloads its prior state
+    ...
 ```

-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.**
--- a/docs/en/concepts/tools.mdx
+++ b/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**                | Search the web with Exa, the fastest and most accurate web search API. Supports token-efficient highlights and full page content. |
+| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
 | **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.                                                  |
--- a/docs/en/guides/flows/mastering-flow-state.mdx
+++ b/docs/en/guides/flows/mastering-flow-state.mdx
@@ -346,47 +346,32 @@ class SelectivePersistFlow(Flow):
        return f"Complete with count {self.state['count']}"
 ```

-#### Forking Persisted State
+#### Using a Custom Persistence Key

-`@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) |
+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`:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    history: list[str] = []

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
+    def greet(self):
+        self.state.history.append("hello")
+        return self.state.history

-# 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.
+# A second run with the same conversation_id reloads the prior state
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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`.
+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`.


 ## Advanced State Patterns
--- a/docs/en/tools/search-research/exasearchtool.mdx
+++ b/docs/en/tools/search-research/exasearchtool.mdx
@@ -1,11 +1,11 @@
 ---
 title: "Exa Search Tool"
-description: "Search the web with Exa, the fastest and most accurate web search API. Get token-efficient highlights and full page content."
+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."
 icon: "magnifying-glass"
 mode: "wide"
 ---

-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.
+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.

 ## 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.

@@ -83,70 +83,28 @@ When calling the tool (or when an agent invokes it), the following search parame

 ## Advanced Usage

-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:
+You can configure the tool with custom parameters for richer results:

 ```python
-# Get token-efficient excerpts most relevant to the query
-exa_tool = ExaSearchTool(
-    highlights=True,
-    type="auto",
+# Get full page content with AI summaries
+exa_tool = EXASearchTool(
+    content=True,
+    summary=True,
+    type="deep"
 )

 # Use it in an agent
 agent = Agent(
-    role="Researcher",
-    goal="Answer questions with current web data",
+    role="Deep Researcher",
+    goal="Conduct thorough research with full content and summaries",
    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)
--- a/docs/ko/changelog.mdx
+++ b/docs/ko/changelog.mdx
@@ -4,71 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<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

--- a/docs/ko/concepts/flows.mdx
+++ b/docs/ko/concepts/flows.mdx
@@ -373,41 +373,32 @@ class AnotherFlow(Flow[dict]):
        print("Method-level persisted runs:", self.state["runs"])
 ```

-### 영속 상태 포크하기
+### 사용자 지정 영속성 키

-`@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.
+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:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    turn: int = 0

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")  # 사용자 지정 필드를 영속성 키로 사용
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
-        print(f"[id={self.state.id}] counter={self.state.counter}")
+    def begin(self):
+        self.state.turn += 1
+        print(f"Conversation {self.state.conversation_id} turn {self.state.turn}")

-# 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.
+# 동일한 conversation_id로 다시 실행하면 이전 상태가 다시 로드됩니다
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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.
+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.

 ### 작동 방식

--- a/docs/ko/concepts/production-architecture.mdx
+++ b/docs/ko/concepts/production-architecture.mdx
@@ -146,14 +146,15 @@ class ProductionFlow(Flow[AppState]):
    # ...
 ```

-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:
+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.

 ```python
-flow.kickoff(restore_from_state_id="<previous-run-state-id>")
+@persist(key="conversation_id")
+class ProductionFlow(Flow[AppState]):
+    # AppState는 conversation_id를 노출해야 합니다; 세션을 재개하면 이전 상태가 다시 로드됩니다
+    ...
 ```

-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.**
--- a/docs/ko/concepts/tools.mdx
+++ b/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.                                                  |
--- a/docs/ko/guides/flows/mastering-flow-state.mdx
+++ b/docs/ko/guides/flows/mastering-flow-state.mdx
@@ -346,47 +346,32 @@ class SelectivePersistFlow(Flow):
        return f"Complete with count {self.state['count']}"
 ```

-#### 영속 상태 포크하기
+#### 사용자 지정 영속성 키 사용하기

-`@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) |
+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:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    history: list[str] = []

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
+    def greet(self):
+        self.state.history.append("hello")
+        return self.state.history

-# 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.
+# 동일한 conversation_id로 두 번째 실행 시 이전 상태가 다시 로드됩니다
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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.
+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.

 ## 고급 상태 패턴

--- a/docs/ko/tools/search-research/exasearchtool.mdx
+++ b/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">
@@ -47,35 +47,7 @@ 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)
--- a/docs/pt-BR/changelog.mdx
+++ b/docs/pt-BR/changelog.mdx
@@ -4,71 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<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

--- a/docs/pt-BR/concepts/flows.mdx
+++ b/docs/pt-BR/concepts/flows.mdx
@@ -193,41 +193,32 @@ Para um controle mais granular, você pode aplicar @persist em métodos específ
 # (O código não é traduzido)
 ```

-### Forking de Estado Persistido
+### Chave de Persistência Personalizada

-`@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.
+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:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    turn: int = 0

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")  # Usa um campo personalizado como chave de persistência
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
-        print(f"[id={self.state.id}] counter={self.state.counter}")
+    def begin(self):
+        self.state.turn += 1
+        print(f"Conversa {self.state.conversation_id} turno {self.state.turn}")

-# 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.
+# Retomar a mesma conversa recarrega o estado anterior pelo conversation_id
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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`.
+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.

 ### Como Funciona

--- a/docs/pt-BR/concepts/production-architecture.mdx
+++ b/docs/pt-BR/concepts/production-architecture.mdx
@@ -146,14 +146,15 @@ class ProductionFlow(Flow[AppState]):
    # ...
 ```

-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`:
+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.

 ```python
-flow.kickoff(restore_from_state_id="<previous-run-state-id>")
+@persist(key="conversation_id")
+class ProductionFlow(Flow[AppState]):
+    # AppState precisa expor conversation_id; retomar a sessão recarrega o estado anterior
+    ...
 ```

-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.**
--- a/docs/pt-BR/concepts/tools.mdx
+++ b/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.                                         |
--- a/docs/pt-BR/guides/flows/mastering-flow-state.mdx
+++ b/docs/pt-BR/guides/flows/mastering-flow-state.mdx
@@ -167,47 +167,32 @@ Para mais controle, você pode aplicar `@persist()` em métodos específicos:
 # código não traduzido
 ```

-#### Forking de Estado Persistido
+#### Usando uma Chave de Persistência Personalizada

-`@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) |
+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`:

 ```python
-from crewai.flow.flow import Flow, start
+from crewai.flow.flow import Flow, listen, start
 from crewai.flow.persistence import persist
 from pydantic import BaseModel

-class CounterState(BaseModel):
-    id: str = ""
-    counter: int = 0
+class ConversationState(BaseModel):
+    conversation_id: str
+    history: list[str] = []

-@persist
-class CounterFlow(Flow[CounterState]):
+@persist(key="conversation_id")
+class ConversationFlow(Flow[ConversationState]):
    @start()
-    def step(self):
-        self.state.counter += 1
+    def greet(self):
+        self.state.history.append("hello")
+        return self.state.history

-# 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.
+# Uma segunda execução com o mesmo conversation_id recarrega o estado anterior
+flow = ConversationFlow(conversation_id="user-42")
+flow.kickoff()
 ```

-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`.
+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`.

 ## Padrões Avançados de Estado

--- a/docs/pt-BR/tools/search-research/exasearchtool.mdx
+++ b/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">
@@ -47,35 +47,7 @@ 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.
-
-## Recursos
-
- [Documentação do Exa](https://exa.ai/docs)
- [Painel do Exa — gerenciar chaves de API e uso](https://dashboard.exa.ai)
+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.
--- a/lib/crewai-files/src/crewai_files/init.py
+++ b/lib/crewai-files/src/crewai_files/init.py
@@ -152,4 +152,4 @@ __all__ = [
    "wrap_file_source",
 ]

-__version__ = "1.14.5a1"
+__version__ = "1.14.4a1"
--- a/lib/crewai-tools/README.md
+++ b/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.
--- a/lib/crewai-tools/pyproject.toml
+++ b/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==1.14.5a1",
+    "crewai==1.14.4a1",
    "tiktoken>=0.8.0,<0.13",
    "beautifulsoup4~=4.13.4",
    "python-docx~=1.2.0",
--- a/lib/crewai-tools/src/crewai_tools/init.py
+++ b/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, ExaSearchTool
+from crewai_tools.tools.exa_tools.exa_search_tool import 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 (
@@ -258,7 +258,6 @@ __all__ = [
    "E2BPythonTool",
    "EXASearchTool",
    "EnterpriseActionTool",
-    "ExaSearchTool",
    "FileCompressorTool",
    "FileReadTool",
    "FileWriterTool",
@@ -330,4 +329,4 @@ __all__ = [
    "ZapierActionTools",
 ]

-__version__ = "1.14.5a1"
+__version__ = "1.14.4a1"
--- a/lib/crewai-tools/src/crewai_tools/tools/init.py
+++ b/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, ExaSearchTool
+from crewai_tools.tools.exa_tools.exa_search_tool import 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 (
@@ -242,7 +242,6 @@ __all__ = [
    "E2BFileTool",
    "E2BPythonTool",
    "EXASearchTool",
-    "ExaSearchTool",
    "FileCompressorTool",
    "FileReadTool",
    "FileWriterTool",
--- a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py
+++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_action_tool.py
@@ -26,8 +26,6 @@ class CrewAIPlatformActionTool(BaseTool):
        description: str,
        action_name: str,
        action_schema: dict[str, Any],
-        provider: str | None = None,
-        provider_id: str | None = None,
    ):
        parameters = action_schema.get("function", {}).get("parameters", {})

@@ -50,12 +48,6 @@ class CrewAIPlatformActionTool(BaseTool):
        )
        self.action_name = action_name
        self.action_schema = action_schema
-        # Private metadata used by enterprise tooling to recover the canonical
-        # tool_id (e.g. "crewai_oauth:google_drive" or "paragon:<uuid>") for
-        # ACP rule evaluation. Set by CrewaiPlatformToolBuilder; remains None
-        # for direct construction.
-        self._provider = provider
-        self._provider_id = provider_id

    def _run(self, **kwargs: Any) -> str:
        try:
--- a/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py
+++ b/lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py
@@ -75,7 +75,6 @@ class CrewaiPlatformToolBuilder:
                                ),
                                "parameters": action.get("parameters", {}),
                                "app": app,
-                                "provider": action.get("provider"),
                            }
                        }
                        self._actions_schema[action_name] = action_schema
@@ -92,8 +91,6 @@ class CrewaiPlatformToolBuilder:
                description=description,
                action_name=action_name,
                action_schema=action_schema,
-                provider=function_details.get("provider"),
-                provider_id=function_details.get("app"),
            )

            tools.append(tool)
--- a/lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md
+++ b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/README.md
@@ -1,7 +1,7 @@
-# ExaSearchTool Documentation
+# EXASearchTool Documentation

 ## Description
-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.
+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.

 ## Installation
 To incorporate this tool into your project, follow the installation instructions below:
@@ -10,23 +10,21 @@ uv add crewai[tools] exa_py
 ```

 ## Example
-The following example demonstrates how to initialize the tool and run a search:
+The following example demonstrates how to initialize the tool and execute a search with a given query:

 ```python
-from crewai_tools import ExaSearchTool
+from crewai_tools import EXASearchTool

-# Default: results with token-efficient highlights
-tool = ExaSearchTool(api_key="your_api_key", highlights=True)
+# Initialize the tool for internet searching capabilities
+tool = EXASearchTool(api_key="your_api_key")
 ```

 ## 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**: 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.
+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.

-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`.
+## 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.
--- a/lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py
+++ b/lib/crewai-tools/src/crewai_tools/tools/exa_tools/exa_search_tool.py
@@ -3,19 +3,12 @@ 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."""

@@ -25,7 +18,7 @@ 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"
    )
@@ -38,20 +31,14 @@ class ExaBaseToolSchema(BaseModel):
    )


-EXABaseToolSchema = ExaBaseToolSchema
-
-
-class ExaSearchTool(BaseTool):
+class EXASearchTool(BaseTool):
    model_config = ConfigDict(arbitrary_types_allowed=True)
-    name: str = "ExaSearchTool"
-    description: str = (
-        "Search the web with Exa, the fastest and most accurate web search API."
-    )
-    args_schema: type_[BaseModel] = ExaBaseToolSchema
+    name: str = "EXASearchTool"
+    description: str = "Search the internet using Exa"
+    args_schema: type_[BaseModel] = EXABaseToolSchema
    client: Any | None = None
-    content: bool | dict[str, Any] | None = False
-    summary: bool | dict[str, Any] | None = False
-    highlights: bool | dict[str, Any] | None = True
+    content: bool | None = False
+    summary: bool | None = False
    type: str | None = "auto"
    package_dependencies: list[str] = Field(default_factory=lambda: ["exa_py"])
    api_key: str | None = Field(
@@ -81,17 +68,17 @@ class ExaSearchTool(BaseTool):

    def __init__(
        self,
-        content: bool | dict[str, Any] | None = False,
-        summary: bool | dict[str, Any] | None = False,
-        highlights: bool | dict[str, Any] | None = True,
+        content: bool | None = False,
+        summary: bool | None = False,
        type: str | None = "auto",
        **kwargs: Any,
    ) -> None:
        super().__init__(
            **kwargs,
        )
-        global Exa
-        if Exa is None:
+        try:
+            from exa_py import Exa
+        except ImportError as e:
            import click

            if click.confirm(
@@ -101,13 +88,12 @@ class ExaSearchTool(BaseTool):

                subprocess.run(["uv", "add", "exa_py"], check=True)  # noqa: S607

-                from exa_py import Exa as _Exa
-
-                Exa = _Exa  # type: ignore[misc]
+                # Re-import after installation
+                from exa_py import Exa
            else:
                raise ImportError(
-                    "You are missing the 'exa_py' package. Please install it to use ExaSearchTool."
-                )
+                    "You are missing the 'exa_py' package. Would you like to install it?"
+                ) from e

        client_kwargs: dict[str, str] = {}
        if self.api_key:
@@ -115,10 +101,8 @@ 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(
@@ -142,31 +126,10 @@ class ExaSearchTool(BaseTool):
        if include_domains:
            search_params["include_domains"] = include_domains

-        contents_kwargs: dict[str, Any] = {}
        if self.content:
-            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
+            results = self.client.search_and_contents(
+                search_query, summary=self.summary, **search_params
            )
-        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)
+        else:
+            results = self.client.search(search_query, **search_params)
+        return results
--- a/lib/crewai-tools/tests/tools/crewai_platform_tools/test_provider_tagging.py
+++ b/lib/crewai-tools/tests/tools/crewai_platform_tools/test_provider_tagging.py
@@ -1,120 +0,0 @@
-"""Tests for the _provider / _provider_id metadata attached to platform tools.
-
-These attributes are private metadata used by enterprise tooling to recover
-the canonical tool_id (e.g. ``crewai_oauth:google_drive`` or
-``paragon:<uuid>``) for ACP rule evaluation.
-"""
-
-from unittest.mock import Mock, patch
-
-from crewai_tools.tools.crewai_platform_tools import (
-    CrewAIPlatformActionTool,
-    CrewaiPlatformToolBuilder,
-)
-
-
-class TestActionToolProviderAttrs:
-    def setup_method(self):
-        self.action_schema = {
-            "function": {
-                "name": "test_action",
-                "parameters": {"type": "object", "properties": {}},
-            }
-        }
-
-    def test_defaults_to_none_when_not_provided(self):
-        tool = CrewAIPlatformActionTool(
-            description="x",
-            action_name="test_action",
-            action_schema=self.action_schema,
-        )
-        assert tool._provider is None
-        assert tool._provider_id is None
-
-    def test_stores_explicit_values(self):
-        tool = CrewAIPlatformActionTool(
-            description="x",
-            action_name="test_action",
-            action_schema=self.action_schema,
-            provider="crewai_oauth",
-            provider_id="google_drive",
-        )
-        assert tool._provider == "crewai_oauth"
-        assert tool._provider_id == "google_drive"
-
-
-class TestBuilderProviderThreading:
-    @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
-    @patch(
-        "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
-    )
-    def test_builder_threads_provider_and_app_into_each_tool(self, mock_get):
-        mock_api_response = {
-            "actions": {
-                "google_drive": [
-                    {
-                        "name": "create_file",
-                        "description": "Create a file",
-                        "parameters": {"type": "object", "properties": {}},
-                        "provider": "crewai_oauth",
-                    }
-                ],
-                "1b5f2395-65a5-4da8-9b2f-c10eafc83a0b": [
-                    {
-                        "name": "send_invoice",
-                        "description": "Send an invoice",
-                        "parameters": {"type": "object", "properties": {}},
-                        "provider": "paragon",
-                    }
-                ],
-            }
-        }
-        mock_response = Mock()
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = mock_api_response
-        mock_get.return_value = mock_response
-
-        builder = CrewaiPlatformToolBuilder(
-            apps=["google_drive", "1b5f2395-65a5-4da8-9b2f-c10eafc83a0b"]
-        )
-        tools = builder.tools()
-
-        by_action = {tool.action_name: tool for tool in tools}
-
-        oauth_tool = by_action["create_file"]
-        assert oauth_tool._provider == "crewai_oauth"
-        assert oauth_tool._provider_id == "google_drive"
-
-        paragon_tool = by_action["send_invoice"]
-        assert paragon_tool._provider == "paragon"
-        assert paragon_tool._provider_id == "1b5f2395-65a5-4da8-9b2f-c10eafc83a0b"
-
-    @patch.dict("os.environ", {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"})
-    @patch(
-        "crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder.requests.get"
-    )
-    def test_builder_handles_response_without_provider_field(self, mock_get):
-        # Older crewai-plus versions return actions without a "provider" key.
-        # The builder must remain compatible: provider_id is set, provider is None.
-        mock_api_response = {
-            "actions": {
-                "github": [
-                    {
-                        "name": "create_issue",
-                        "description": "Create issue",
-                        "parameters": {"type": "object", "properties": {}},
-                    }
-                ]
-            }
-        }
-        mock_response = Mock()
-        mock_response.raise_for_status.return_value = None
-        mock_response.json.return_value = mock_api_response
-        mock_get.return_value = mock_response
-
-        builder = CrewaiPlatformToolBuilder(apps=["github"])
-        tools = builder.tools()
-
-        assert len(tools) == 1
-        assert tools[0]._provider is None
-        assert tools[0]._provider_id == "github"
--- a/lib/crewai-tools/tests/tools/exa_search_tool_test.py
+++ b/lib/crewai-tools/tests/tools/exa_search_tool_test.py
@@ -1,13 +1,13 @@
 import os
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch

-from crewai_tools import EXASearchTool, ExaSearchTool
+from crewai_tools import 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)
@@ -22,12 +22,11 @@ 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)

@@ -37,7 +36,7 @@ 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")


@@ -48,13 +47,12 @@ 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)

@@ -69,7 +67,7 @@ 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"
        )
@@ -81,33 +79,8 @@ 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)
--- a/lib/crewai-tools/tool.specs.json
+++ b/lib/crewai-tools/tool.specs.json
@@ -9397,7 +9397,7 @@
      }
    },
    {
-      "description": "Search the web with Exa, the fastest and most accurate web search API.",
+      "description": "Search the internet using Exa",
      "env_vars": [
        {
          "default": null,
@@ -9412,7 +9412,7 @@
          "required": false
        }
      ],
-      "humanized_name": "ExaSearchTool",
+      "humanized_name": "EXASearchTool",
      "init_params_schema": {
        "$defs": {
          "EnvVar": {
@@ -9451,7 +9451,6 @@
            "type": "object"
          }
        },
-        "description": "Deprecated alias for :class:`ExaSearchTool`. Kept for backwards compatibility.",
        "properties": {
          "api_key": {
            "anyOf": [
@@ -9494,10 +9493,6 @@
              {
                "type": "boolean"
              },
-              {
-                "additionalProperties": true,
-                "type": "object"
-              },
              {
                "type": "null"
              }
@@ -9505,31 +9500,11 @@
            "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"
              }
@@ -9611,225 +9586,7 @@
        "required": [
          "search_query"
        ],
-        "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",
+        "title": "EXABaseToolSchema",
        "type": "object"
      }
    },
--- a/lib/crewai/pyproject.toml
+++ b/lib/crewai/pyproject.toml
@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"

 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.14.5a1",
+    "crewai-tools==1.14.4a1",
 ]
 embeddings = [
    "tiktoken>=0.8.0,<0.13"
--- a/lib/crewai/src/crewai/init.py
+++ b/lib/crewai/src/crewai/init.py
@@ -48,7 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:

 _suppress_pydantic_deprecation_warnings()

-__version__ = "1.14.5a1"
+__version__ = "1.14.4a1"

 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
    "Memory": ("crewai.memory.unified_memory", "Memory"),
--- a/lib/crewai/src/crewai/cli/templates/AGENTS.md
+++ b/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
--- a/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml
+++ b/lib/crewai/src/crewai/cli/templates/crew/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.5a1"
+    "crewai[tools]==1.14.4a1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml
+++ b/lib/crewai/src/crewai/cli/templates/flow/pyproject.toml
@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.5a1"
+    "crewai[tools]==1.14.4a1"
 ]

 [project.scripts]
--- a/lib/crewai/src/crewai/cli/templates/tool/pyproject.toml
+++ b/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]==1.14.5a1"
+    "crewai[tools]==1.14.4a1"
 ]

 [tool.crewai]
--- a/lib/crewai/src/crewai/crew.py
+++ b/lib/crewai/src/crewai/crew.py
@@ -2272,17 +2272,13 @@ class Crew(FlowTrackable, BaseModel):
        if should_suppress_tracing_messages():
            return

+        # Don't nag users who have explicitly declined tracing
+        if has_user_declined_tracing():
+            return
+
        console = Console()

-        if has_user_declined_tracing():
-            message = """Info: Tracing is disabled.
-
-To enable tracing, do any one of these:
-• Set tracing=True in your Crew code
-• Set CREWAI_TRACING_ENABLED=true in your project's .env file
-• Run: crewai traces enable"""
-        else:
-            message = """Info: Tracing is disabled.
+        message = """Info: Tracing is disabled.

 To enable tracing, do any one of these:
 • Set tracing=True in your Crew code
--- a/lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py
+++ b/lib/crewai/src/crewai/events/listeners/tracing/trace_listener.py
@@ -108,13 +108,6 @@ 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,
@@ -537,30 +530,6 @@ 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."""

@@ -899,17 +868,13 @@ class TraceCollectionListener(BaseEventListener):
        if should_suppress_tracing_messages():
            return

+        # Don't nag users who have explicitly declined tracing
+        if has_user_declined_tracing():
+            return
+
        console = Console()

-        if has_user_declined_tracing():
-            message = """Info: Tracing is disabled.
-
-To enable tracing, do any one of these:
-• Set tracing=True in your Crew/Flow code
-• Set CREWAI_TRACING_ENABLED=true in your project's .env file
-• Run: crewai traces enable"""
-        else:
-            message = """Info: Tracing is disabled.
+        message = """Info: Tracing is disabled.

 To enable tracing, do any one of these:
 • Set tracing=True in your Crew/Flow code
--- a/lib/crewai/src/crewai/events/listeners/tracing/utils.py
+++ b/lib/crewai/src/crewai/events/listeners/tracing/utils.py
@@ -53,10 +53,19 @@ def set_suppress_tracing_messages(suppress: bool) -> object:
 def should_suppress_tracing_messages() -> bool:
    """Check if tracing messages should be suppressed.

+    Checks the context variable first, then falls back to the
+    CREWAI_SUPPRESS_TRACING_MESSAGES environment variable.
+
    Returns:
        True if messages should be suppressed, False otherwise.
    """
-    return _suppress_tracing_messages.get()
+    if _suppress_tracing_messages.get():
+        return True
+    return os.getenv("CREWAI_SUPPRESS_TRACING_MESSAGES", "false").lower() in (
+        "true",
+        "1",
+        "yes",
+    )


 def should_enable_tracing(*, override: bool | None = None) -> bool:
--- a/lib/crewai/src/crewai/events/utils/console_formatter.py
+++ b/lib/crewai/src/crewai/events/utils/console_formatter.py
@@ -145,16 +145,12 @@ To update, run: uv sync --upgrade-package crewai"""
        if listener and listener.first_time_handler.is_first_time:
            return

-        if not is_tracing_enabled_in_context():
-            if has_user_declined_tracing():
-                message = """Info: Tracing is disabled.
+        # Don't nag users who have explicitly declined tracing
+        if has_user_declined_tracing():
+            return

-To enable tracing, do any one of these:
-• Set tracing=True in your Crew/Flow code
-• Set CREWAI_TRACING_ENABLED=true in your project's .env file
-• Run: crewai traces enable"""
-            else:
-                message = """Info: Tracing is disabled.
+        if not is_tracing_enabled_in_context():
+            message = """Info: Tracing is disabled.

 To enable tracing, do any one of these:
 • Set tracing=True in your Crew/Flow code
--- a/lib/crewai/src/crewai/flow/flow.py
+++ b/lib/crewai/src/crewai/flow/flow.py
@@ -1074,6 +1074,7 @@ 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]
@@ -1864,6 +1865,27 @@ 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.

@@ -2032,7 +2054,6 @@ 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.

@@ -2044,24 +2065,10 @@ 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)
@@ -2083,11 +2090,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
            def run_flow() -> None:
                try:
                    self.stream = False
-                    result = self.kickoff(
-                        inputs=inputs,
-                        input_files=input_files,
-                        restore_from_state_id=restore_from_state_id,
-                    )
+                    result = self.kickoff(inputs=inputs, input_files=input_files)
                    result_holder.append(result)
                except Exception as e:
                    # HumanFeedbackPending is expected control flow, not an error
@@ -2110,11 +2113,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
            return streaming_output

        async def _run_flow() -> Any:
-            return await self.kickoff_async(
-                inputs,
-                input_files,
-                restore_from_state_id=restore_from_state_id,
-            )
+            return await self.kickoff_async(inputs, input_files)

        try:
            asyncio.get_running_loop()
@@ -2129,7 +2128,6 @@ 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.

@@ -2143,23 +2141,10 @@ 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)
@@ -2182,9 +2167,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                try:
                    self.stream = False
                    result = await self.kickoff_async(
-                        inputs=inputs,
-                        input_files=input_files,
-                        restore_from_state_id=restore_from_state_id,
+                        inputs=inputs, input_files=input_files
                    )
                    result_holder.append(result)
                except Exception as e:
@@ -2216,9 +2199,9 @@ 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.flow_id)
+            flow_id_token = current_flow_id.set(self.execution_id)
        if current_flow_request_id.get() is None:
-            request_id_token = current_flow_request_id.set(self.flow_id)
+            request_id_token = current_flow_request_id.set(self.execution_id)

        try:
            # Reset flow state for fresh execution unless restoring from persistence
@@ -2241,54 +2224,16 @@ 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.
-                # Skip when the fork already assigned state.id above.
-                if "id" in inputs and not fork_succeeded:
+                # Override the id in the state if it exists in inputs
+                if "id" in inputs:
                    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.
-                # Skip when the fork already restored self._state above.
-                if (
-                    "id" in inputs
-                    and self.persistence is not None
-                    and not fork_succeeded
-                ):
+                if "id" in inputs and self.persistence is not None:
                    restore_uuid = inputs["id"]
                    stored_state = self.persistence.load_state(restore_uuid)
                    if stored_state:
@@ -2471,7 +2416,6 @@ 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.

@@ -2480,19 +2424,11 @@ 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,
-            restore_from_state_id=restore_from_state_id,
-        )
+        return await self.kickoff_async(inputs, input_files, from_checkpoint)

    async def _replay_recorded_events(self) -> None:
        """Dispatch recorded ``MethodExecution*`` events from the event record."""
@@ -3610,17 +3546,13 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
        if should_suppress_tracing_messages():
            return

+        # Don't nag users who have explicitly declined tracing
+        if has_user_declined_tracing():
+            return
+
        console = Console()

-        if has_user_declined_tracing():
-            message = """Info: Tracing is disabled.
-
-To enable tracing, do any one of these:
-• Set tracing=True in your Flow code
-• Set CREWAI_TRACING_ENABLED=true in your project's .env file
-• Run: crewai traces enable"""
-        else:
-            message = """Info: Tracing is disabled.
+        message = """Info: Tracing is disabled.

 To enable tracing, do any one of these:
 • Set tracing=True in your Flow code
--- a/lib/crewai/src/crewai/flow/persistence/decorators.py
+++ b/lib/crewai/src/crewai/flow/persistence/decorators.py
@@ -50,6 +50,7 @@ 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 '{}'",
 }


@@ -63,6 +64,7 @@ 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.

@@ -74,9 +76,12 @@ 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 or state lacks an ID
+            ValueError: If flow has no state, state lacks an ID, or the
+                requested ``key`` is missing or falsy on state.
            RuntimeError: If state persistence fails
            AttributeError: If flow instance lacks required state attributes
        """
@@ -85,19 +90,22 @@ 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("id")
+                flow_uuid = state.get(lookup_key)
            elif hasattr(state, "_unwrap"):
                unwrapped = state._unwrap()
                if isinstance(unwrapped, dict):
-                    flow_uuid = unwrapped.get("id")
+                    flow_uuid = unwrapped.get(lookup_key)
                else:
-                    flow_uuid = getattr(unwrapped, "id", None)
-            elif isinstance(state, BaseModel) or hasattr(state, "id"):
-                flow_uuid = getattr(state, "id", None)
+                    flow_uuid = getattr(unwrapped, lookup_key, None)
+            elif isinstance(state, BaseModel) or hasattr(state, lookup_key):
+                flow_uuid = getattr(state, lookup_key, 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
@@ -127,7 +135,7 @@ class PersistenceDecorator:
            logger.error(error_msg)
            raise ValueError(error_msg) from e
        except (TypeError, ValueError) as e:
-            error_msg = LOG_MESSAGES["id_missing"]
+            error_msg = str(e) or LOG_MESSAGES["id_missing"]
            if verbose:
                PRINTER.print(error_msg, color="red")
            logger.error(error_msg)
@@ -135,7 +143,9 @@ class PersistenceDecorator:


 def persist(
-    persistence: FlowPersistence | None = None, verbose: bool = False
+    persistence: FlowPersistence | None = None,
+    verbose: bool = False,
+    key: str | None = None,
 ) -> Callable[[type | Callable[..., T]], type | Callable[..., T]]:
    """Decorator to persist flow state.

@@ -148,12 +158,16 @@ 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
+        ValueError: If the flow state doesn't have an 'id' field, or the
+            specified ``key`` is missing or falsy on state.
        RuntimeError: If state persistence fails

    Example:
@@ -162,6 +176,10 @@ 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]:
@@ -207,7 +225,7 @@ def persist(
                        ) -> Any:
                            result = await original_method(self, *args, **kwargs)
                            PersistenceDecorator.persist_state(
-                                self, method_name, actual_persistence, verbose
+                                self, method_name, actual_persistence, verbose, key
                            )
                            return result

@@ -237,7 +255,7 @@ 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
+                                self, method_name, actual_persistence, verbose, key
                            )
                            return result

@@ -276,7 +294,7 @@ def persist(
                else:
                    result = method_coro
                PersistenceDecorator.persist_state(
-                    flow_instance, method.__name__, actual_persistence, verbose
+                    flow_instance, method.__name__, actual_persistence, verbose, key
                )
                return cast(T, result)

@@ -295,7 +313,7 @@ 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
+                flow_instance, method.__name__, actual_persistence, verbose, key
            )
            return result

--- a/lib/crewai/src/crewai/llm.py
+++ b/lib/crewai/src/crewai/llm.py
@@ -1235,12 +1235,8 @@ class LLM(BaseLLM):
        # --- 4) Check for tool calls
        tool_calls = response_message.tool_calls or []

-        # --- 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:
+        # --- 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:
            self._handle_emit_call_events(
                response=text_response,
                call_type=LLMCallType.LLM_CALL,
@@ -1251,6 +1247,11 @@ 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(
@@ -1383,10 +1384,7 @@ class LLM(BaseLLM):

        tool_calls = response_message.tool_calls or []

-        if tool_calls and not available_functions:
-            return tool_calls
-
-        if not tool_calls and text_response:
+        if (not tool_calls or not available_functions) and text_response:
            self._handle_emit_call_events(
                response=text_response,
                call_type=LLMCallType.LLM_CALL,
@@ -1397,6 +1395,11 @@ 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(
--- a/lib/crewai/src/crewai/mcp/tool_resolver.py
+++ b/lib/crewai/src/crewai/mcp/tool_resolver.py
@@ -152,8 +152,6 @@ class MCPToolResolver:

            try:
                tools, clients = self._resolve_native(mcp_server_config)
-                for tool in tools:
-                    tool._amp_slug = slug  # type: ignore[attr-defined]
                resolved_cache[slug] = (tools, clients)
                all_clients.extend(clients)
            except Exception as e:
--- a/lib/crewai/src/crewai/task.py
+++ b/lib/crewai/src/crewai/task.py
@@ -1110,7 +1110,7 @@ Follow these guidelines:
        )

    def _export_output(
-        self, result: str | BaseModel
+        self, result: str
    ) -> tuple[BaseModel | None, dict[str, Any] | None]:
        pydantic_output: BaseModel | None = None
        json_output: dict[str, Any] | None = None
--- a/lib/crewai/src/crewai/tools/mcp_native_tool.py
+++ b/lib/crewai/src/crewai/tools/mcp_native_tool.py
@@ -59,11 +59,6 @@ class MCPNativeTool(BaseTool):
        self._client_factory = client_factory
        self._original_tool_name = original_tool_name or tool_name
        self._server_name = server_name
-        # Set by MCPToolResolver._resolve_amp when this tool is produced for
-        # an AMP slug; remains None for direct config / external URL refs.
-        # Consumed downstream by enterprise tooling to recover the canonical
-        # tool_id (e.g. "crewai_oauth:<slug>|mcp").
-        self._amp_slug: str | None = None

    @property
    def original_tool_name(self) -> str:
--- a/lib/crewai/src/crewai/tools/mcp_tool_wrapper.py
+++ b/lib/crewai/src/crewai/tools/mcp_tool_wrapper.py
@@ -54,11 +54,6 @@ class MCPToolWrapper(BaseTool):
        self._mcp_server_params = mcp_server_params
        self._original_tool_name = tool_name
        self._server_name = server_name
-        # Set by MCPToolResolver._resolve_amp when this wrapper is produced for
-        # an AMP slug; remains None for direct config / external URL refs.
-        # Consumed downstream by enterprise tooling to recover the canonical
-        # tool_id (e.g. "crewai_oauth:<slug>|mcp").
-        self._amp_slug: str | None = None

    @property
    def mcp_server_params(self) -> dict[str, Any]:
--- a/lib/crewai/src/crewai/utilities/converter.py
+++ b/lib/crewai/src/crewai/utilities/converter.py
@@ -153,18 +153,16 @@ class Converter(OutputConverter):


 def convert_to_model(
-    result: str | BaseModel,
+    result: str,
    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 to a Pydantic model or JSON.
+    """Convert a result string to a Pydantic model or JSON.

    Args:
-        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.
+        result: The result string to convert.
        output_pydantic: The Pydantic model class to convert to.
        output_json: The Pydantic model class to convert to JSON.
        agent: The agent instance.
@@ -177,11 +175,6 @@ 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,
@@ -264,21 +257,12 @@ def handle_partial_json(
    match = _JSON_PATTERN.search(result)
    if match:
        try:
-            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)
+            exported_result = model.model_validate_json(match.group())
            if is_json_output:
                return exported_result.model_dump()
            return exported_result
+        except json.JSONDecodeError:
+            pass
        except ValidationError:
            raise
        except Exception as e:
--- a/lib/crewai/tests/mcp/test_tool_resolver_amp_slug.py
+++ b/lib/crewai/tests/mcp/test_tool_resolver_amp_slug.py
@@ -1,135 +0,0 @@
-"""Tests for the _amp_slug attribute set by MCPToolResolver._resolve_amp.
-
-The slug is private metadata used downstream by enterprise tooling to recover
-the canonical tool_id (e.g. ``crewai_oauth:<slug>|mcp``) for ACP rule
-evaluation.
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-from crewai.agent.core import Agent
-from crewai.mcp.config import MCPServerHTTP
-from crewai.mcp.tool_resolver import MCPToolResolver
-from crewai.tools.mcp_native_tool import MCPNativeTool
-from crewai.tools.mcp_tool_wrapper import MCPToolWrapper
-
-
-@pytest.fixture
-def agent():
-    return Agent(
-        role="Test Agent",
-        goal="Test goal",
-        backstory="Test backstory",
-    )
-
-
-@pytest.fixture
-def resolver(agent):
-    return MCPToolResolver(agent=agent, logger=agent._logger)
-
-
-class TestAmpSlugDefaultsNone:
-    def test_native_tool_default_amp_slug_is_none(self):
-        tool = MCPNativeTool(
-            client_factory=lambda: None,
-            tool_name="search",
-            tool_schema={"description": "Search"},
-            server_name="notion",
-        )
-        assert tool._amp_slug is None
-
-    def test_wrapper_tool_default_amp_slug_is_none(self):
-        tool = MCPToolWrapper(
-            mcp_server_params={"url": "https://mcp.example.com"},
-            tool_name="search",
-            tool_schema={"description": "Search"},
-            server_name="notion",
-        )
-        assert tool._amp_slug is None
-
-
-class TestAmpSlugSetByResolveAmp:
-    @patch("crewai.mcp.tool_resolver.MCPToolResolver._resolve_native")
-    @patch("crewai.mcp.tool_resolver.MCPToolResolver._fetch_amp_mcp_configs")
-    def test_resolve_amp_tags_each_tool_with_its_slug(
-        self, mock_fetch_configs, mock_resolve_native, resolver
-    ):
-        mock_fetch_configs.return_value = {
-            "notion": {"url": "https://mcp.crewai.com/notion"},
-            "github": {"url": "https://mcp.crewai.com/github"},
-        }
-
-        notion_tool = MCPNativeTool(
-            client_factory=lambda: None,
-            tool_name="search",
-            tool_schema={"description": "search Notion"},
-            server_name="notion",
-        )
-        github_tool = MCPNativeTool(
-            client_factory=lambda: None,
-            tool_name="list_repos",
-            tool_schema={"description": "list github repos"},
-            server_name="github",
-        )
-
-        def fake_resolve_native(config):
-            url = config.url if hasattr(config, "url") else config["url"]
-            if "notion" in url:
-                return ([notion_tool], [MagicMock()])
-            return ([github_tool], [MagicMock()])
-
-        mock_resolve_native.side_effect = fake_resolve_native
-
-        tools, _ = resolver._resolve_amp(
-            [("notion", None), ("github", None)]
-        )
-
-        assert {tool._amp_slug for tool in tools} == {"notion", "github"}
-
-    @patch("crewai.mcp.tool_resolver.MCPToolResolver._fetch_amp_mcp_configs")
-    def test_resolve_amp_does_not_tag_when_config_missing(
-        self, mock_fetch_configs, resolver
-    ):
-        mock_fetch_configs.return_value = {}
-
-        tools, _ = resolver._resolve_amp([("unknown", None)])
-
-        assert tools == []
-
-
-class TestAmpSlugUntaggedForOtherPaths:
-    @patch("crewai.mcp.tool_resolver.MCPClient")
-    def test_resolve_external_does_not_set_amp_slug(self, mock_client_class, resolver):
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(
-            return_value=[{"name": "search", "description": "Search"}]
-        )
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        with patch.object(
-            resolver, "_get_mcp_tool_schemas", return_value={"search": {"description": "Search"}}
-        ):
-            tools = resolver._resolve_external("https://mcp.example.com/api")
-
-        assert len(tools) == 1
-        assert tools[0]._amp_slug is None
-
-    @patch("crewai.mcp.tool_resolver.MCPClient")
-    def test_resolve_native_does_not_set_amp_slug(self, mock_client_class, resolver):
-        mock_client = AsyncMock()
-        mock_client.list_tools = AsyncMock(
-            return_value=[{"name": "search", "description": "Search"}]
-        )
-        mock_client.connected = False
-        mock_client.connect = AsyncMock()
-        mock_client.disconnect = AsyncMock()
-        mock_client_class.return_value = mock_client
-
-        config = MCPServerHTTP(url="https://mcp.example.com/api")
-        tools, _ = resolver._resolve_native(config)
-
-        assert all(tool._amp_slug is None for tool in tools)
--- a/lib/crewai/tests/test_crew.py
+++ b/lib/crewai/tests/test_crew.py
@@ -4519,8 +4519,8 @@ 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.flow_id  # type: ignore[attr-defined]
-    assert captured_crew._request_id == flow.flow_id  # type: ignore[attr-defined]
+    assert captured_crew._flow_id == flow.execution_id  # type: ignore[attr-defined]
+    assert captured_crew._request_id == flow.execution_id  # type: ignore[attr-defined]


 def test_sets_flow_context_when_outside_flow(researcher, writer):
@@ -4554,8 +4554,8 @@ def test_sets_flow_context_when_inside_flow(researcher, writer):

    flow = MyFlow()
    result = flow.kickoff()
-    assert result._flow_id == flow.flow_id  # type: ignore[attr-defined]
-    assert result._request_id == flow.flow_id  # type: ignore[attr-defined]
+    assert result._flow_id == flow.execution_id  # type: ignore[attr-defined]
+    assert result._request_id == flow.execution_id  # type: ignore[attr-defined]


 def test_reset_knowledge_with_no_crew_knowledge(researcher, writer):
--- a/lib/crewai/tests/test_flow_execution_id.py
+++ b/lib/crewai/tests/test_flow_execution_id.py
@@ -0,0 +1,127 @@
+"""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
--- a/lib/crewai/tests/test_flow_persistence.py
+++ b/lib/crewai/tests/test_flow_persistence.py
@@ -251,240 +251,67 @@ def test_persistence_with_base_model(tmp_path):
    assert isinstance(flow.state._unwrap(), 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)."""
+def test_persist_custom_key_with_pydantic_state(tmp_path):
+    """`@persist(key=...)` uses the named attribute on a Pydantic state."""
    db_path = os.path.join(tmp_path, "test_flows.db")
    persistence = SQLiteFlowPersistence(db_path)

-    class ForkableFlow(Flow[TestState]):
+    class KeyedState(FlowState):
+        conversation_id: str = "conv-42"
+        message: str = ""
+
+    class KeyedFlow(Flow[KeyedState]):
        @start()
-        @persist(persistence)
-        def step(self):
-            self.state.counter += 1
+        @persist(persistence, key="conversation_id")
+        def init_step(self):
+            self.state.message = "hello"

-    # 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
+    flow = KeyedFlow(persistence=persistence)
+    flow.kickoff()

-    # 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
+    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


-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."""
+def test_persist_custom_key_with_dict_state(tmp_path):
+    """`@persist(key=...)` uses the named key on a dict state."""
    db_path = os.path.join(tmp_path, "test_flows.db")
    persistence = SQLiteFlowPersistence(db_path)

-    class PinnableFlow(Flow[TestState]):
+    class DictKeyedFlow(Flow[Dict[str, str]]):
+        initial_state = dict()
+
        @start()
-        @persist(persistence)
-        def step(self):
-            self.state.counter += 1
+        @persist(persistence, key="conversation_id")
+        def init_step(self):
+            self.state["conversation_id"] = "conv-dict-7"
+            self.state["message"] = "hi from dict"

-    flow1 = PinnableFlow(persistence=persistence)
-    flow1.kickoff()
-    source_uuid = flow1.state.id
-    assert flow1.state.counter == 1
+    flow = DictKeyedFlow(persistence=persistence)
+    flow.kickoff()

-    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
+    saved_state = persistence.load_state("conv-dict-7")
+    assert saved_state is not None
+    assert saved_state["message"] == "hi from dict"


-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."""
+def test_persist_custom_key_missing_raises(tmp_path):
+    """A missing/falsy custom key must raise a clear ValueError."""
    db_path = os.path.join(tmp_path, "test_flows.db")
    persistence = SQLiteFlowPersistence(db_path)

-    class FallbackFlow(Flow[TestState]):
+    class MissingKeyFlow(Flow[Dict[str, str]]):
+        initial_state = dict()
+
        @start()
-        @persist(persistence)
-        def step(self):
-            self.state.counter += 1
+        @persist(persistence, key="conversation_id")
+        def init_step(self):
+            # Intentionally do NOT set "conversation_id" on state.
+            self.state["message"] = "no key here"

-    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
+    flow = MissingKeyFlow(persistence=persistence)
+    with pytest.raises(ValueError, match="conversation_id"):
+        flow.kickoff()
--- a/lib/crewai/tests/test_llm.py
+++ b/lib/crewai/tests/test_llm.py
@@ -177,7 +177,6 @@ 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()
@@ -1147,52 +1146,3 @@ 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"
--- a/lib/crewai/tests/test_task_guardrails.py
+++ b/lib/crewai/tests/test_task_guardrails.py
@@ -690,27 +690,6 @@ 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."""

--- a/lib/crewai/tests/tracing/test_tracing_message_suppression.py
+++ b/lib/crewai/tests/tracing/test_tracing_message_suppression.py
@@ -0,0 +1,259 @@
+"""Tests for tracing disabled message suppression (issue #5665).
+
+Verifies that:
+- Users who explicitly declined tracing are NOT nagged with the message.
+- The CREWAI_SUPPRESS_TRACING_MESSAGES env var suppresses the message.
+- The message is shown only when tracing is disabled and user hasn't declined.
+"""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from crewai.events.listeners.tracing.utils import (
+    set_suppress_tracing_messages,
+    should_suppress_tracing_messages,
+)
+
+
+class TestShouldSuppressTracingMessages:
+    """Tests for the should_suppress_tracing_messages utility function."""
+
+    def test_suppress_false_by_default(self):
+        """By default, messages should NOT be suppressed."""
+        token = set_suppress_tracing_messages(False)
+        try:
+            assert should_suppress_tracing_messages() is False
+        finally:
+            from crewai.events.listeners.tracing.utils import (
+                _suppress_tracing_messages,
+            )
+            _suppress_tracing_messages.reset(token)
+
+    def test_suppress_via_context_var(self):
+        """Setting the context var should suppress messages."""
+        token = set_suppress_tracing_messages(True)
+        try:
+            assert should_suppress_tracing_messages() is True
+        finally:
+            from crewai.events.listeners.tracing.utils import (
+                _suppress_tracing_messages,
+            )
+            _suppress_tracing_messages.reset(token)
+
+    @pytest.mark.parametrize("env_value", ["true", "True", "TRUE", "1", "yes", "YES"])
+    def test_suppress_via_env_var(self, env_value, monkeypatch):
+        """CREWAI_SUPPRESS_TRACING_MESSAGES env var should suppress messages."""
+        token = set_suppress_tracing_messages(False)
+        try:
+            monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", env_value)
+            assert should_suppress_tracing_messages() is True
+        finally:
+            from crewai.events.listeners.tracing.utils import (
+                _suppress_tracing_messages,
+            )
+            _suppress_tracing_messages.reset(token)
+
+    @pytest.mark.parametrize("env_value", ["false", "False", "0", "no", ""])
+    def test_no_suppress_with_falsy_env_var(self, env_value, monkeypatch):
+        """Falsy values for the env var should NOT suppress messages."""
+        token = set_suppress_tracing_messages(False)
+        try:
+            monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", env_value)
+            assert should_suppress_tracing_messages() is False
+        finally:
+            from crewai.events.listeners.tracing.utils import (
+                _suppress_tracing_messages,
+            )
+            _suppress_tracing_messages.reset(token)
+
+    def test_context_var_takes_precedence_over_env(self, monkeypatch):
+        """Context var set to True should suppress even if env var is false."""
+        token = set_suppress_tracing_messages(True)
+        try:
+            monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", "false")
+            assert should_suppress_tracing_messages() is True
+        finally:
+            from crewai.events.listeners.tracing.utils import (
+                _suppress_tracing_messages,
+            )
+            _suppress_tracing_messages.reset(token)
+
+
+class TestShowTracingDisabledMessage:
+    """Tests that _show_tracing_disabled_message does not nag declined users."""
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={"first_execution_done": True, "trace_consent": False},
+    )
+    def test_crew_no_message_when_user_declined(self, mock_load):
+        """Crew._show_tracing_disabled_message should not print when user declined."""
+        from crewai.crew import Crew
+
+        with patch("crewai.crew.Console") as MockConsole:
+            Crew._show_tracing_disabled_message()
+            MockConsole.return_value.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={"first_execution_done": True, "trace_consent": False},
+    )
+    def test_flow_no_message_when_user_declined(self, mock_load):
+        """Flow._show_tracing_disabled_message should not print when user declined."""
+        from crewai.flow.flow import Flow
+
+        with patch("crewai.flow.flow.Console") as MockConsole:
+            Flow._show_tracing_disabled_message()
+            MockConsole.return_value.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={"first_execution_done": True, "trace_consent": False},
+    )
+    def test_trace_listener_no_message_when_user_declined(self, mock_load):
+        """TraceCollectionListener._show_tracing_disabled_message should not print when user declined."""
+        from crewai.events.listeners.tracing.trace_listener import (
+            TraceCollectionListener,
+        )
+
+        listener = TraceCollectionListener.__new__(TraceCollectionListener)
+        with patch("rich.console.Console") as MockConsole:
+            listener._show_tracing_disabled_message()
+            MockConsole.return_value.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    def test_crew_shows_message_when_user_has_not_decided(self, mock_load):
+        """Crew._show_tracing_disabled_message should print when user hasn't decided yet."""
+        from crewai.crew import Crew
+
+        with patch("crewai.crew.Console") as MockConsole:
+            mock_console_instance = MockConsole.return_value
+            Crew._show_tracing_disabled_message()
+            mock_console_instance.print.assert_called_once()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    def test_crew_no_message_when_suppress_env_set(self, mock_load, monkeypatch):
+        """Crew._show_tracing_disabled_message should not print when env var suppresses."""
+        from crewai.crew import Crew
+
+        monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", "true")
+        with patch("crewai.crew.Console") as MockConsole:
+            Crew._show_tracing_disabled_message()
+            MockConsole.return_value.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    def test_flow_no_message_when_suppress_env_set(self, mock_load, monkeypatch):
+        """Flow._show_tracing_disabled_message should not print when env var suppresses."""
+        from crewai.flow.flow import Flow
+
+        monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", "true")
+        with patch("crewai.flow.flow.Console") as MockConsole:
+            Flow._show_tracing_disabled_message()
+            MockConsole.return_value.print.assert_not_called()
+
+
+class TestConsoleFormatterTracingMessage:
+    """Tests for console_formatter._show_tracing_disabled_message_if_needed."""
+
+    def _make_formatter(self):
+        from crewai.events.utils.console_formatter import ConsoleFormatter
+
+        formatter = ConsoleFormatter.__new__(ConsoleFormatter)
+        formatter.console = MagicMock()
+        formatter.verbose = True
+        return formatter
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={"first_execution_done": True, "trace_consent": False},
+    )
+    def test_no_message_when_user_declined(self, mock_load):
+        """Console formatter should not show the message when user declined tracing."""
+        formatter = self._make_formatter()
+
+        with patch(
+            "crewai.events.listeners.tracing.trace_listener.TraceCollectionListener"
+        ) as mock_listener_cls:
+            mock_listener_cls._instance = None
+            formatter._show_tracing_disabled_message_if_needed()
+
+        formatter.console.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    def test_no_message_when_suppress_env_set(self, mock_load, monkeypatch):
+        """Console formatter should not show the message when env var is set."""
+        monkeypatch.setenv("CREWAI_SUPPRESS_TRACING_MESSAGES", "true")
+        formatter = self._make_formatter()
+
+        formatter._show_tracing_disabled_message_if_needed()
+
+        formatter.console.print.assert_not_called()
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    @patch(
+        "crewai.events.listeners.tracing.utils.is_tracing_enabled_in_context",
+        return_value=False,
+    )
+    def test_message_shown_when_tracing_disabled_and_not_declined(
+        self, mock_tracing_ctx, mock_load
+    ):
+        """Console formatter should show the message when tracing disabled and user hasn't declined."""
+        from crewai.events.listeners.tracing.trace_listener import (
+            TraceCollectionListener,
+        )
+
+        formatter = self._make_formatter()
+
+        mock_instance = MagicMock()
+        mock_instance.first_time_handler.is_first_time = False
+        original_instance = TraceCollectionListener._instance
+
+        try:
+            TraceCollectionListener._instance = mock_instance  # type: ignore[misc]
+            formatter._show_tracing_disabled_message_if_needed()
+            formatter.console.print.assert_called_once()
+        finally:
+            TraceCollectionListener._instance = original_instance  # type: ignore[misc]
+
+    @patch(
+        "crewai.events.listeners.tracing.utils._load_user_data",
+        return_value={},
+    )
+    @patch(
+        "crewai.events.listeners.tracing.utils.is_tracing_enabled_in_context",
+        return_value=True,
+    )
+    def test_no_message_when_tracing_enabled(self, mock_tracing_ctx, mock_load):
+        """Console formatter should not show the message when tracing is enabled."""
+        from crewai.events.listeners.tracing.trace_listener import (
+            TraceCollectionListener,
+        )
+
+        formatter = self._make_formatter()
+
+        mock_instance = MagicMock()
+        mock_instance.first_time_handler.is_first_time = False
+        original_instance = TraceCollectionListener._instance
+
+        try:
+            TraceCollectionListener._instance = mock_instance  # type: ignore[misc]
+            formatter._show_tracing_disabled_message_if_needed()
+            formatter.console.print.assert_not_called()
+        finally:
+            TraceCollectionListener._instance = original_instance  # type: ignore[misc]
--- a/lib/crewai/tests/utilities/test_converter.py
+++ b/lib/crewai/tests/utilities/test_converter.py
@@ -87,31 +87,6 @@ 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 = """
    {
@@ -202,34 +177,6 @@ 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")
--- a/lib/devtools/README.md
+++ b/lib/devtools/README.md
@@ -11,8 +11,6 @@ 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

--- a/lib/devtools/src/crewai_devtools/init.py
+++ b/lib/devtools/src/crewai_devtools/init.py
@@ -1,3 +1,3 @@
 """CrewAI development tools."""

-__version__ = "1.14.5a1"
+__version__ = "1.14.4a1"
--- a/lib/devtools/src/crewai_devtools/cli.py
+++ b/lib/devtools/src/crewai_devtools/cli.py
@@ -1207,12 +1207,7 @@ _ENTERPRISE_WORKFLOW_PATHS: Final[tuple[str, ...]] = tuple(


 def _update_enterprise_crewai_dep(pyproject_path: Path, version: str) -> bool:
-    """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.
+    """Update the crewai[tools] pin in an enterprise pyproject.toml.

    Args:
        pyproject_path: Path to the pyproject.toml file.
@@ -1224,57 +1219,20 @@ 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)
-        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
+        return True
+    return False


 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.
@@ -1285,31 +1243,29 @@ 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 _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"):
+        if not workflow.exists():
            continue
-        if _update_workflow_crewai_pins(workflow, version):
+
+        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)
            updated.append(workflow)
+
    return updated


@@ -1358,10 +1314,8 @@ _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
-    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).
+    Clones the repo, updates the crewai[tools] pin in pyproject.toml,
+    regenerates the lockfile, commits, and pushes directly to main.

    Args:
        version: New crewai version string.
@@ -1379,91 +1333,50 @@ 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)
-        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:
+        if new_content == content:
            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}")

-        paths_to_add: list[str] = [
-            str(wf.relative_to(repo_dir)) for wf in updated_workflows
+        lock_cmd = [
+            "uv",
+            "lock",
+            "--refresh-package",
+            "crewai",
+            "--refresh-package",
+            "crewai-tools",
        ]
+        if is_prerelease:
+            lock_cmd.append("--prerelease=allow")

-        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
+        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"[yellow]uv lock failed (attempt {attempt}/{max_retries}),"
-                        f" retrying in {_PYPI_POLL_INTERVAL}s...[/yellow]"
+                        f"[red]Error:[/red] uv lock failed after {max_retries} attempts"
                    )
-                    time.sleep(_PYPI_POLL_INTERVAL)
-            console.print("[green]✓[/green] Lockfile updated")
-            paths_to_add.extend(["pyproject.toml", "uv.lock"])
+                    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")

-        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", "add", "pyproject.toml", "uv.lock"], cwd=repo_dir)
        run_command(
            ["git", "commit", "-m", f"chore: bump crewai to {version}"],
            cwd=repo_dir,
        )
-        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)
+        run_command(["git", "push"], cwd=repo_dir)
+        console.print(f"[green]✓[/green] Pushed to {_DEPLOYMENT_TEST_REPO}")


 def _wait_for_pypi(package: str, version: str) -> None:
@@ -1495,37 +1408,6 @@ 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.