Merge branch 'main' into docs/checkpointing-restructure

* docs: document one-time admin package install step

The previous revision described a manual "install in Salesforce first,
then connect from AMP" flow that nobody actually follows, and linked to
a private repo customers can't access.

* docs: point Integrations link at crewai_plus/unified_tools

docs/ar/concepts/checkpointing.mdx

@@ -5,225 +5,419 @@ icon: floppy-disk
 mode: "wide"
 ---
 
-<Warning>
-الـ Checkpointing في اصدار مبكر. قد تتغير واجهات البرمجة في الاصدارات المستقبلية.
-</Warning>
+الـ Checkpointing يحفظ لقطة من حالة التنفيذ أثناء التشغيل بحيث يمكن لطاقم أو تدفق أو وكيل الاستئناف بعد الفشل أو التفرع إلى فرع بديل.
 
-## نظرة عامة
+<CardGroup cols={2}>
+  <Card title="الشرح" icon="lightbulb" href="#الشرح">
+    كيف يعمل الـ Checkpointing: الأحداث والتخزين والوراثة.
+  </Card>
+  <Card title="درس تطبيقي" icon="graduation-cap" href="#درس-تطبيقي-استئناف-طاقم-فاشل">
+    دليل 5 دقائق: تشغيل، إيقاف، استئناف.
+  </Card>
+  <Card title="ادلة عملية" icon="screwdriver-wrench" href="#ادلة-عملية">
+    وصفات مركزة على المهام لسير العمل الشائع.
+  </Card>
+  <Card title="المرجع" icon="book" href="#المرجع">
+    `CheckpointConfig` والأحداث والمزودات وسطر الأوامر.
+  </Card>
+</CardGroup>
 
-يقوم الـ Checkpointing بحفظ حالة التنفيذ تلقائيا اثناء التشغيل. اذا فشل طاقم او تدفق او وكيل اثناء التنفيذ، يمكنك الاستعادة من اخر نقطة حفظ والاستئناف دون اعادة تنفيذ العمل المكتمل.
+## الشرح
 
-## البداية السريعة
+### ما هي نقطة الحفظ
 
-```python
-from crewai import Crew, CheckpointConfig
+تلتقط نقطة الحفظ كل ما يحتاجه CrewAI لإعادة إنشاء تشغيل أثناء سيره: الحالة الكاملة للطاقم أو التدفق أو الوكيل — التكوين، وذاكرة الوكلاء ومصادر المعرفة، وتقدم المهام، والمخرجات الوسيطة، والحالة الداخلية والسمات — إلى جانب مدخلات الـ kickoff، وسجل الأحداث حتى تلك النقطة، ومعرف نسب يربط نقطة الحفظ بالتشغيل الذي جاءت منه.
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=True,  # يستخدم الافتراضيات: ./.checkpoints, عند task_completed
-)
-result = crew.kickoff()
-```
+الاستعادة تعيد بناء تلك الحالة وتستمر. تتخطى المهام المكتملة، وتعاد ترطيب الذاكرة والمعرفة، ويعمل العمل التابع على نفس المخرجات التي أنتجها التشغيل الأصلي. التفرع يجري نفس الاستعادة تحت نسب جديد، بحيث يكتب الفرع الجديد والتشغيل الأصلي نقاط الحفظ جنبا إلى جنب دون أن يطمس أحدهما الآخر.
 
-تتم كتابة ملفات نقاط الحفظ في `./.checkpoints/` بعد اكتمال كل مهمة.
+### متى تكتب نقاط الحفظ
 
-## التكوين
+الـ Checkpointing مدفوع بالأحداث. يشترك وقت التشغيل في الأحداث التي تحددها عبر `on_events` ويكتب نقطة حفظ عند إطلاق أحدها. الافتراضي `task_completed` ينتج نقطة حفظ لكل مهمة منتهية — توازن معقول بين الدقة واستخدام القرص. الأحداث عالية التردد مثل `llm_call_completed` متاحة للاستعادة الدقيقة لكنها تكتب ملفات أكثر بكثير.
 
-استخدم `CheckpointConfig` للتحكم الكامل:
+### التخزين
 
-```python
-from crewai import Crew, CheckpointConfig
+يتضمن CrewAI مزودين:
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        on_events=["task_completed", "crew_kickoff_completed"],
-        max_checkpoints=5,
-    ),
-)
-```
+- `JsonProvider` يكتب ملفا لكل نقطة حفظ. قابل للقراءة وسهل التفقد.
+- `SqliteProvider` يكتب إلى قاعدة بيانات SQLite واحدة. أفضل لنقاط الحفظ عالية التردد.
 
-### حقول CheckpointConfig
+كلاهما يحذف أقدم نقاط الحفظ عند تحديد `max_checkpoints`.
 
-| الحقل | النوع | الافتراضي | الوصف |
-|:------|:------|:----------|:------|
-| `location` | `str` | `"./.checkpoints"` | مسار ملفات نقاط الحفظ |
-| `on_events` | `list[str]` | `["task_completed"]` | انواع الاحداث التي تطلق نقطة حفظ |
-| `provider` | `BaseProvider` | `JsonProvider()` | واجهة التخزين |
-| `max_checkpoints` | `int \| None` | `None` | الحد الاقصى للملفات؛ يتم حذف الاقدم اولا |
+<Note>
+كتابة نقاط الحفظ بأفضل جهد. فشل نقطة حفظ يسجل لكنه لا يقاطع التشغيل.
+</Note>
 
-### الوراثة والانسحاب
+### نموذج الوراثة
 
-يقبل حقل `checkpoint` في Crew و Flow و Agent قيم `CheckpointConfig` او `True` او `False` او `None`:
+`Crew` و`Flow` و`Agent` كلها تقبل وسيط `checkpoint`. يرث الأبناء من الأب ما لم يحددوا قيمتهم الخاصة أو يمرروا `False` للانسحاب. فعل الـ Checkpointing مرة واحدة على الطاقم وتشارك كل الوكلاء، أو استبعد وكيلا واحدا بشكل انتقائي.
 
-| القيمة | السلوك |
-|:-------|:-------|
-| `None` (افتراضي) | يرث من الاصل. الوكيل يرث اعدادات الطاقم. |
-| `True` | تفعيل بالاعدادات الافتراضية. |
-| `False` | انسحاب صريح. يوقف الوراثة من الاصل. |
-| `CheckpointConfig(...)` | اعدادات مخصصة. |
+## درس تطبيقي: استئناف طاقم فاشل
 
-```python
-crew = Crew(
-    agents=[
-        Agent(role="Researcher", ...),                  # يرث checkpoint من الطاقم
-        Agent(role="Writer", ..., checkpoint=False),     # منسحب، بدون نقاط حفظ
-    ],
-    tasks=[...],
-    checkpoint=True,
-)
-```
+هذا الدليل يستغرق حوالي 5 دقائق. ستشغل طاقما بمهمتين، توقفه في المنتصف، ثم تستأنف من نقطة الحفظ المحفوظة.
 
-## الاستئناف من نقطة حفظ
+<Steps>
+  <Step title="أنشئ الطاقم مع تفعيل الـ Checkpointing">
+    ```python
+    from crewai import Agent, Crew, Task
 
-```python
-# استعادة واستئناف
-crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
-result = crew.kickoff()  # يستأنف من اخر مهمة مكتملة
-```
+    researcher = Agent(role="Researcher", goal="Research", backstory="Expert")
+    writer = Agent(role="Writer", goal="Write", backstory="Expert")
 
-يتخطى الطاقم المستعاد المهام المكتملة ويستأنف من اول مهمة غير مكتملة.
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[
+            Task(description="Research AI trends", agent=researcher, expected_output="bullets"),
+            Task(description="Write a summary", agent=writer, expected_output="paragraph"),
+        ],
+        checkpoint=True,
+    )
+    ```
+  </Step>
+  <Step title="شغله وأوقفه بعد المهمة الأولى">
+    ```python
+    result = crew.kickoff()
+    ```
 
-## يعمل على Crew و Flow و Agent
+    اضغط `Ctrl+C` بعد انتهاء المهمة الأولى. في `./.checkpoints/`، الملف بصيغة `<timestamp>_<uuid>.json` هو نقطة الحفظ.
+  </Step>
+  <Step title="استأنف من نقطة الحفظ">
+    ```python
+    from crewai import CheckpointConfig
 
-### Crew
+    result = crew.kickoff(
+        from_checkpoint=CheckpointConfig(
+            restore_from="./.checkpoints/<timestamp>_<uuid>.json",
+        ),
+    )
+    ```
 
-```python
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_task, review_task],
-    checkpoint=CheckpointConfig(location="./crew_cp"),
-)
-```
+    يتم تخطي مهمة البحث، ويعمل الكاتب على مخرجات البحث المحفوظة، وينتهي الطاقم.
+  </Step>
+</Steps>
 
-المشغل الافتراضي: `task_completed` (نقطة حفظ واحدة لكل مهمة مكتملة).
+## ادلة عملية
 
-### Flow
+<AccordionGroup>
+  <Accordion title="تفعيل الـ Checkpointing بالإعدادات الافتراضية" icon="play">
+    ```python
+    crew = Crew(agents=[...], tasks=[...], checkpoint=True)
+    ```
 
-```python
-from crewai.flow.flow import Flow, start, listen
-from crewai import CheckpointConfig
+    يكتب إلى `./.checkpoints/` عند كل `task_completed`.
+  </Accordion>
 
-class MyFlow(Flow):
-    @start()
-    def step_one(self):
-        return "data"
+  <Accordion title="تخصيص التخزين والتردد" icon="sliders">
+    ```python
+    from crewai import Crew, CheckpointConfig
 
-    @listen(step_one)
-    def step_two(self, data):
-        return process(data)
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            on_events=["task_completed", "crew_kickoff_completed"],
+            max_checkpoints=5,
+        ),
+    )
+    ```
+  </Accordion>
 
-flow = MyFlow(
-    checkpoint=CheckpointConfig(
-        location="./flow_cp",
-        on_events=["method_execution_finished"],
-    ),
-)
-result = flow.kickoff()
+  <Accordion title="اختيار مزود التخزين" icon="database">
+    <CodeGroup>
+    ```python JsonProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import JsonProvider
 
-# استئناف
-flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
-result = flow.kickoff()
-```
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            provider=JsonProvider(),
+            max_checkpoints=5,
+        ),
+    )
+    ```
+    ```python SqliteProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import SqliteProvider
 
-### Agent
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./.checkpoints.db",
+            provider=SqliteProvider(),
+            max_checkpoints=50,
+        ),
+    )
+    ```
+    </CodeGroup>
 
-```python
-agent = Agent(
-    role="Researcher",
-    goal="Research topics",
-    backstory="Expert researcher",
-    checkpoint=CheckpointConfig(
-        location="./agent_cp",
-        on_events=["lite_agent_execution_completed"],
-    ),
-)
-result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
-```
+    <Tip>
+    SQLite يفعل وضع journal WAL للقراءات المتزامنة. يفضل لنقاط الحفظ عالية التردد.
+    </Tip>
+  </Accordion>
 
-## مزودات التخزين
+  <Accordion title="استبعاد وكيل واحد" icon="user-slash">
+    ```python
+    crew = Crew(
+        agents=[
+            Agent(role="Researcher", ...),
+            Agent(role="Writer", ..., checkpoint=False),
+        ],
+        tasks=[...],
+        checkpoint=True,
+    )
+    ```
+  </Accordion>
 
-يتضمن CrewAI مزودي تخزين لنقاط الحفظ.
+  <Accordion title="التفرع إلى فرع جديد" icon="code-branch">
+    `fork()` يستعيد نقطة حفظ تحت نسب جديد بحيث لا يتصادم التشغيل الجديد مع الأصلي.
 
-### JsonProvider (افتراضي)
+    ```python
+    config = CheckpointConfig(restore_from="./my_checkpoints/<file>.json")
+    crew = Crew.fork(config, branch="experiment-a")
+    result = crew.kickoff(inputs={"strategy": "aggressive"})
+    ```
 
-يكتب كل نقطة حفظ كملف JSON منفصل.
+    تسمية `branch` اختيارية؛ يتم إنشاء واحدة إذا أغفلت.
+  </Accordion>
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import JsonProvider
+  <Accordion title="Checkpointing لـ Crew أو Flow أو Agent" icon="cubes">
+    <Tabs>
+      <Tab title="Crew">
+        ```python
+        crew = Crew(
+            agents=[researcher, writer],
+            tasks=[research_task, write_task, review_task],
+            checkpoint=CheckpointConfig(location="./crew_cp"),
+        )
+        ```
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        provider=JsonProvider(),
-        max_checkpoints=5,
-    ),
-)
-```
+        المشغل الافتراضي: `task_completed`.
+      </Tab>
+      <Tab title="Flow">
+        ```python
+        from crewai.flow.flow import Flow, start, listen
+        from crewai import CheckpointConfig
 
-### SqliteProvider
+        class MyFlow(Flow):
+            @start()
+            def step_one(self):
+                return "data"
 
-يخزن جميع نقاط الحفظ في ملف قاعدة بيانات SQLite واحد.
+            @listen(step_one)
+            def step_two(self, data):
+                return process(data)
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import SqliteProvider
+        flow = MyFlow(
+            checkpoint=CheckpointConfig(
+                location="./flow_cp",
+                on_events=["method_execution_finished"],
+            ),
+        )
+        result = flow.kickoff()
+        ```
+      </Tab>
+      <Tab title="Agent">
+        ```python
+        agent = Agent(
+            role="Researcher",
+            goal="Research topics",
+            backstory="Expert researcher",
+            checkpoint=CheckpointConfig(
+                location="./agent_cp",
+                on_events=["lite_agent_execution_completed"],
+            ),
+        )
+        result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+        ```
+      </Tab>
+    </Tabs>
+  </Accordion>
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./.checkpoints.db",
-        provider=SqliteProvider(),
-    ),
-)
-```
+  <Accordion title="كتابة نقطة حفظ يدويا" icon="code">
+    سجل معالجا على أي حدث واستدع `state.checkpoint()`.
+
+    <CodeGroup>
+    ```python Sync
+    from __future__ import annotations
+
+    from typing import TYPE_CHECKING, Any
+
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
 
 
-## انواع الاحداث
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_llm_done(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = state.checkpoint("./my_checkpoints")
+        print(f"تم حفظ نقطة الحفظ: {path}")
+    ```
+    ```python Async
+    from __future__ import annotations
 
-يقبل حقل `on_events` اي مجموعة من سلاسل انواع الاحداث. الخيارات الشائعة:
+    from typing import TYPE_CHECKING, Any
 
-| حالة الاستخدام | الاحداث |
-|:---------------|:--------|
-| بعد كل مهمة (Crew) | `["task_completed"]` |
-| بعد كل طريقة في التدفق | `["method_execution_finished"]` |
-| بعد تنفيذ الوكيل | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
-| عند اكتمال الطاقم فقط | `["crew_kickoff_completed"]` |
-| بعد كل استدعاء LLM | `["llm_call_completed"]` |
-| على كل شيء | `["*"]` |
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
+
+
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    async def on_llm_done_async(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = await state.acheckpoint("./my_checkpoints")
+        print(f"تم حفظ نقطة الحفظ: {path}")
+    ```
+    </CodeGroup>
+
+    يتم تمرير وسيط `state` تلقائيا عندما يقبل المعالج ثلاثة معاملات. راجع [Event Listeners](/ar/concepts/event-listener) لقائمة الأحداث الكاملة.
+  </Accordion>
+
+  <Accordion title="التصفح والاستئناف والتفرع من سطر الأوامر" icon="terminal">
+    ```bash
+    crewai checkpoint
+    crewai checkpoint --location ./my_checkpoints
+    crewai checkpoint --location ./.checkpoints.db
+    ```
+
+    <Frame caption="شجرة نقاط الحفظ — الفروع والتفرعات تتداخل تحت أبيها.">
+      <img src="/images/checkpoint-tui-tree.png" alt="Checkpoint TUI tree view" />
+    </Frame>
+
+    اللوحة اليسرى تجمع نقاط الحفظ حسب الفرع؛ التفرعات تتداخل تحت أبيها. اختيار نقطة حفظ يفتح لوحة التفاصيل مع بياناتها الوصفية وحالة الكيان وتقدم المهام. **Resume** يكمل التشغيل؛ **Fork** يبدأ فرعا جديدا.
+
+    <Frame caption="تبويب النظرة العامة — البيانات الوصفية وحالة الكيان وملخص التشغيل.">
+      <img src="/images/checkpoint-tui-detail-overview.png" alt="Checkpoint detail overview tab" />
+    </Frame>
+
+    لوحة التفاصيل تعرض منطقتين قابلتين للتحرير:
+
+    - **Inputs** — مدخلات الـ kickoff الأصلية، معبأة مسبقا وقابلة للتحرير.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-inputs.png" alt="Editable kickoff inputs" />
+      </Frame>
+
+    - **مخرجات المهام** — مخرجات المهام المكتملة. تحرير مخرج والضغط على **Fork** يبطل المهام التابعة لتعاد بالسياق المعدل.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-tasks.png" alt="Editable task outputs" />
+      </Frame>
+
+    <Frame caption="عرض التفرع — تأكيد فرع جديد من نقطة الحفظ المختارة.">
+      <img src="/images/checkpoint-tui-details-fork.png" alt="Fork confirmation panel" />
+    </Frame>
+
+    <Tip>
+    مفيد لاستكشاف "ماذا لو": تفرع، عدل، راقب.
+    </Tip>
+  </Accordion>
+
+  <Accordion title="تفقد نقاط الحفظ بدون TUI" icon="magnifying-glass">
+    ```bash
+    crewai checkpoint list ./my_checkpoints
+    crewai checkpoint info ./my_checkpoints/<file>.json
+    crewai checkpoint info ./.checkpoints.db
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## المرجع
+
+### `CheckpointConfig`
+
+<ParamField path="location" type="str" default='"./.checkpoints"'>
+  وجهة التخزين. مجلد لـ `JsonProvider`، مسار ملف قاعدة بيانات لـ `SqliteProvider`.
+</ParamField>
+
+<ParamField path="on_events" type='list[CheckpointEventType | Literal["*"]]' default='["task_completed"]'>
+  أنواع الأحداث التي تطلق نقطة حفظ. `CheckpointEventType` هو `Literal` — مدقق الأنواع يكمل تلقائيا ويرفض القيم غير المدعومة. راجع [أنواع الأحداث](#أنواع-الأحداث) للقائمة الكاملة.
+</ParamField>
+
+<ParamField path="provider" type="BaseProvider" default="JsonProvider()">
+  واجهة التخزين. `JsonProvider` أو `SqliteProvider`.
+</ParamField>
+
+<ParamField path="max_checkpoints" type="int | None" default="None">
+  الحد الاقصى لنقاط الحفظ المحتفظ بها. الأقدم تحذف بعد كل كتابة.
+</ParamField>
+
+<ParamField path="restore_from" type="Path | str | None" default="None">
+  نقطة الحفظ المراد استعادتها عند تمريرها عبر `from_checkpoint`.
+</ParamField>
+
+### قيم حقل `checkpoint`
+
+مقبولة في `Crew` و`Flow` و`Agent`.
+
+<ParamField path="None" type="افتراضي">
+  يرث من الأب.
+</ParamField>
+
+<ParamField path="True" type="bool">
+  تفعيل بالإعدادات الافتراضية.
+</ParamField>
+
+<ParamField path="False" type="bool">
+  انسحاب صريح. يوقف الوراثة.
+</ParamField>
+
+<ParamField path="CheckpointConfig(...)" type="CheckpointConfig">
+  إعدادات مخصصة.
+</ParamField>
+
+### أنواع الأحداث
+
+يقبل `on_events` أي مجموعة من قيم `CheckpointEventType`. الافتراضي `["task_completed"]` يكتب نقطة حفظ لكل مهمة منتهية، و`["*"]` يطابق جميع الأحداث.
 
 <Warning>
-استخدام `["*"]` او احداث عالية التردد مثل `llm_call_completed` سيكتب العديد من ملفات نقاط الحفظ وقد يؤثر على الاداء. استخدم `max_checkpoints` للحد من استخدام المساحة.
+`["*"]` والأحداث عالية التردد مثل `llm_call_completed` تكتب نقاط حفظ كثيرة وقد تضر بالاداء. استخدمها مع `max_checkpoints`.
 </Warning>
 
-## نقاط الحفظ اليدوية
+<Expandable title="جميع الأحداث المدعومة">
 
-للتحكم الكامل، سجل معالج الاحداث الخاص بك واستدع `state.checkpoint()` مباشرة:
+- **Task** — `task_started`, `task_completed`, `task_failed`, `task_evaluation`
+- **Crew** — `crew_kickoff_started`, `crew_kickoff_completed`, `crew_kickoff_failed`, `crew_train_started`, `crew_train_completed`, `crew_train_failed`, `crew_test_started`, `crew_test_completed`, `crew_test_failed`, `crew_test_result`
+- **Agent** — `agent_execution_started`, `agent_execution_completed`, `agent_execution_error`, `lite_agent_execution_started`, `lite_agent_execution_completed`, `lite_agent_execution_error`, `agent_evaluation_started`, `agent_evaluation_completed`, `agent_evaluation_failed`
+- **Flow** — `flow_created`, `flow_started`, `flow_finished`, `flow_paused`, `method_execution_started`, `method_execution_finished`, `method_execution_failed`, `method_execution_paused`, `human_feedback_requested`, `human_feedback_received`, `flow_input_requested`, `flow_input_received`
+- **LLM** — `llm_call_started`, `llm_call_completed`, `llm_call_failed`, `llm_stream_chunk`, `llm_thinking_chunk`
+- **LLM Guardrail** — `llm_guardrail_started`, `llm_guardrail_completed`, `llm_guardrail_failed`
+- **Tool** — `tool_usage_started`, `tool_usage_finished`, `tool_usage_error`, `tool_validate_input_error`, `tool_selection_error`, `tool_execution_error`
+- **Memory** — `memory_save_started`, `memory_save_completed`, `memory_save_failed`, `memory_query_started`, `memory_query_completed`, `memory_query_failed`, `memory_retrieval_started`, `memory_retrieval_completed`, `memory_retrieval_failed`
+- **Knowledge** — `knowledge_search_query_started`, `knowledge_search_query_completed`, `knowledge_query_started`, `knowledge_query_completed`, `knowledge_query_failed`, `knowledge_search_query_failed`
+- **Reasoning** — `agent_reasoning_started`, `agent_reasoning_completed`, `agent_reasoning_failed`
+- **MCP** — `mcp_connection_started`, `mcp_connection_completed`, `mcp_connection_failed`, `mcp_tool_execution_started`, `mcp_tool_execution_completed`, `mcp_tool_execution_failed`, `mcp_config_fetch_failed`
+- **Observation** — `step_observation_started`, `step_observation_completed`, `step_observation_failed`, `plan_refinement`, `plan_replan_triggered`, `goal_achieved_early`
+- **Skill** — `skill_discovery_started`, `skill_discovery_completed`, `skill_loaded`, `skill_activated`, `skill_load_failed`
+- **Logging** — `agent_logs_started`, `agent_logs_execution`
+- **A2A** — `a2a_delegation_started`, `a2a_delegation_completed`, `a2a_conversation_started`, `a2a_conversation_completed`, `a2a_message_sent`, `a2a_response_received`, `a2a_polling_started`, `a2a_polling_status`, `a2a_push_notification_registered`, `a2a_push_notification_received`, `a2a_push_notification_sent`, `a2a_push_notification_timeout`, `a2a_streaming_started`, `a2a_streaming_chunk`, `a2a_agent_card_fetched`, `a2a_authentication_failed`, `a2a_artifact_received`, `a2a_connection_error`, `a2a_server_task_started`, `a2a_server_task_completed`, `a2a_server_task_canceled`, `a2a_server_task_failed`, `a2a_parallel_delegation_started`, `a2a_parallel_delegation_completed`, `a2a_transport_negotiated`, `a2a_content_type_negotiated`, `a2a_context_created`, `a2a_context_expired`, `a2a_context_idle`, `a2a_context_completed`, `a2a_context_pruned`
+- **إشارات النظام** — `SIGTERM`, `SIGINT`, `SIGHUP`, `SIGTSTP`, `SIGCONT`
+- **حرف بدل** — `"*"` يطابق جميع الأحداث.
 
-```python
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.llm_events import LLMCallCompletedEvent
+</Expandable>
 
-# معالج متزامن
-@crewai_event_bus.on(LLMCallCompletedEvent)
-def on_llm_done(source, event, state):
-    path = state.checkpoint("./my_checkpoints")
-    print(f"تم حفظ نقطة الحفظ: {path}")
+### مزودات التخزين
 
-# معالج غير متزامن
-@crewai_event_bus.on(LLMCallCompletedEvent)
-async def on_llm_done_async(source, event, state):
-    path = await state.acheckpoint("./my_checkpoints")
-    print(f"تم حفظ نقطة الحفظ: {path}")
-```
+<ParamField path="JsonProvider" type="provider">
+  ملف واحد لكل نقطة حفظ بصيغة `<timestamp>_<uuid>.json` داخل `location`.
+</ParamField>
 
-وسيط `state` هو `RuntimeState` الذي يتم تمريره تلقائيا بواسطة ناقل الاحداث عندما يقبل المعالج 3 معاملات. يمكنك تسجيل معالجات على اي نوع حدث مدرج في وثائق [Event Listeners](/ar/concepts/event-listener).
+<ParamField path="SqliteProvider" type="provider">
+  ملف قاعدة بيانات واحد في `location` مع journaling WAL.
+</ParamField>
 
-الـ Checkpointing يعمل بافضل جهد: اذا فشلت كتابة نقطة حفظ، يتم تسجيل الخطأ ولكن التنفيذ يستمر دون انقطاع.
+### سطر الأوامر
+
+| الامر | الغرض |
+|:------|:------|
+| `crewai checkpoint` | تشغيل TUI؛ كشف التخزين تلقائيا. |
+| `crewai checkpoint --location <path>` | تشغيل TUI على موقع محدد. |
+| `crewai checkpoint list <path>` | سرد نقاط الحفظ. |
+| `crewai checkpoint info <path>` | تفقد ملف نقطة حفظ أو آخر مدخل في قاعدة بيانات SQLite. |

docs/ar/enterprise/features/agent-control-plane/monitoring.mdx

@@ -0,0 +1,104 @@
+---
+title: "راقب أتمتاتك"
+description: "راقب صحة الأسطول واستهلاك LLM وسلوك كل أتمتة من تبويب Automations."
+sidebarTitle: "المراقبة"
+icon: "gauge"
+mode: "wide"
+---
+
+## نظرة عامة
+
+تبويب **Automations** هو عرض العمليات للقراءة فقط في [Agent Control Plane](/ar/enterprise/features/agent-control-plane/overview). يجمع بين بطاقتَي مقاييس و sankey تفاعلي وجدولين فرعيين — **Automations** و **Consumption** — يمكنك البحث والتصفية والفرز فيهما.
+
+<Frame>
+  ![نظرة عامة على Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+تحترم جميع المخططات والجداول مُحدّد **آخر 24 ساعة / الأسبوع الماضي / آخر 30 يوماً** في أعلى اليمين. تقارن قيم الفرق النافذة المختارة بالنافذة السابقة بنفس الطول.
+
+<Note>
+  تعرض الصفوف بيانات فقط لعمليات النشر على **crewAI v1.13 أو أحدث** — تظهر عمليات النشر الأقدم في لافتة *"We've detected N other automations that we can't display"* أسفل sankey ولا تساهم بأي مقاييس حتى يتم تحديثها وإعادة نشرها. راجع [نظرة عامة — المتطلبات](/ar/enterprise/features/agent-control-plane/overview#المتطلبات).
+</Note>
+
+## لوحة المعلومات
+
+يحتوي رأس الصفحة على بطاقتَي مقاييس و sankey تفاعلي. النقر على أي من البطاقتين يبدّل sankey بين وضعَين:
+
+- **وضع الصحة** — `إجمالي الأتمتات → حِزم الحالة (Critical / Warning / Healthy)`. انقر على حِزمة لتصفية جدول Automations إلى عمليات النشر تلك فقط.
+- **وضع الاستهلاك** — `مزودو النماذج → الأتمتات → التكلفة الإجمالية`. انقر على مزود لتصفية جدول Consumption إلى ذلك المزود.
+
+| البطاقة | ما تعرضه |
+|------|---------------|
+| **Automations** | الأتمتات `active` (والعدد الإجمالي)، إجمالي `errors` في النافذة، `active executions` الحالية (والإجمالي في النافذة)، مع الفرق مقابل الفترة السابقة. |
+| **Consumption** | إجمالي `cost` و `tokens used`، مع فرق التكلفة مقابل الفترة السابقة. |
+
+<Frame>
+  ![نظرة عامة مع sankey الاستهلاك](/images/enterprise/acp-overview-consumption-sankey.png)
+</Frame>
+
+## جدول Automations
+
+التبويب الفرعي **Automations** هو تفصيل صحة الأسطول لكل deployment. كل صف هو crew أو flow منشور.
+
+<Frame>
+  ![جدول الأتمتات مع تفصيل حالة الصحة](/images/enterprise/acp-automations-table.png)
+</Frame>
+
+| العمود | ما يعرضه |
+|--------|---------------|
+| **Automation** | اسم الـ deployment وأي وسوم مُسنَدة إليه (مثل `production`، `financial`). |
+| **Last execution** | الوقت المنقضي منذ آخر تنفيذ. |
+| **Health Status Breakdown** | شريط مكدّس بنسب `Critical` / `Warning` / `Healthy` لعمليات التنفيذ في النافذة. |
+| **Executions with Errors** | إجمالي عمليات التنفيذ الفاشلة في النافذة. |
+| **PII detection applied** | `Yes` إذا كان هناك تكوين PII لكل deployment أو [قاعدة PII](/ar/enterprise/features/agent-control-plane/rules) مطابِقة نشطة. |
+| **Executions** | إجمالي عمليات التنفيذ في النافذة. |
+| **Last updated** | متى أُعيد نشر الـ deployment آخر مرة. |
+| **Crew Version** | إصدار `crewai` الذي يُبلِّغ عنه الـ deployment. يشير أيقونة المعلومات بجانب الإصدارات الأقل من `1.13` إلى صفوف لا يمكنها المساهمة بالمقاييس. |
+
+ابحث بالاسم، صفِّ حسب `Status` (`Healthy` / `Warning` / `Critical`)، وافرز بأي رأس عمود. انقر على اسم الـ deployment لفتح **لوحة الأتمتة**.
+
+## جدول Consumption
+
+التبويب الفرعي **Consumption** هو تفصيل إنفاق LLM واستخدام الرموز لكل deployment.
+
+<Frame>
+  ![جدول الاستهلاك مُفصَّل حسب مزود LLM](/images/enterprise/acp-consumption-table.png)
+</Frame>
+
+| العمود | ما يعرضه |
+|--------|---------------|
+| **Automation** | اسم الـ deployment. |
+| **Last execution** | الوقت المنقضي منذ آخر تنفيذ. |
+| **Tokens used** | صف واحد لكل مزود LLM تستخدمه هذه الأتمتة، مع الفرق مقابل الفترة السابقة. |
+| **Cost** | التكلفة لكل مزود LLM، مع الفرق مقابل الفترة السابقة. |
+| **Total cost** | المجموع عبر جميع المزودين، مع الفرق. |
+| **Executions** | إجمالي عمليات التنفيذ في النافذة. |
+| **Last updated** | متى أُعيد نشر الـ deployment آخر مرة. |
+| **Crew Version** | إصدار `crewai` الذي يُبلِّغ عنه الـ deployment. |
+
+صفِّ حسب **LLM provider** وافرز حسب `Cost` أو `Executions` أو `Last run`.
+
+<Info>
+  **عادة ما تعني الخلايا الفارغة (`—` أو `$0.00`) أن الـ deployment أدنى من crewAI v1.13.** في اللقطة أعلاه، تظهر *Automation F* (`1.7.0`) و *Automation I* (`1.12.2`) فارغة في الرموز والتكلفة — لا تزال عمليات التنفيذ تعمل، لكنها لا تُصدِر التليمتري على مستوى المزود الذي يُغذِّي هذا الجدول. حدّث هذه الـ crews وأعد نشرها لبدء جمع بيانات الاستهلاك.
+</Info>
+
+## ذو صلة
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — نظرة عامة" icon="book-open" href="/ar/enterprise/features/agent-control-plane/overview">
+    ما هو ACP، المتطلبات، مستويات الخطط، و RBAC.
+  </Card>
+  <Card title="Agent Control Plane — القواعد" icon="shield-check" href="/ar/enterprise/features/agent-control-plane/rules">
+    طبّق قواعد PII Redaction على مستوى المؤسسة عبر العديد من الأتمتات.
+  </Card>
+  <Card title="Traces" icon="timeline" href="/ar/enterprise/features/traces">
+    تعمّق في تنفيذ واحد لرؤية تفكير الوكيل واستدعاءات الأدوات واستخدام الرموز.
+  </Card>
+  <Card title="النشر إلى AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
+    انشر crew على إصدار crewAI يدعم Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
+  تواصل مع فريق الدعم للمساعدة في تفسير المقاييس داخل Agent Control Plane.
+</Card>

docs/ar/enterprise/features/agent-control-plane/overview.mdx

@@ -0,0 +1,74 @@
+---
+title: نظرة عامة على Agent Control Plane
+description: "مركز عمليات موحّد للأتمتات الجارية — صحة الأسطول واستهلاك LLM والسياسات على مستوى المؤسسة في مكان واحد."
+sidebarTitle: نظرة عامة
+icon: "book-open"
+---
+
+## نظرة عامة
+
+**Agent Control Plane** (ACP) هو مركز العمليات لكل ما يعمل لديك على CrewAI AMP. إنها شاشة واحدة — مقسّمة إلى تبويبَي **Automations** و **Rules** — تمنح فريقك القدرة على:
+
+- مراقبة **حالة (الصحة)** كل أتمتة حيّة (crew أو flow) بتفصيل `Critical` / `Warning` / `Healthy` وعدد عمليات التنفيذ.
+- تتبع **استهلاك LLM** — الرموز (tokens) والتكلفة — لكل أتمتة ولكل مزود ولكل نموذج، مع الفرق مقابل الفترة السابقة.
+- التعمّق في أي أتمتة منفردة أو مزود نماذج لرؤية المخططات الزمنية وتفصيل البيانات لكل مزود.
+- تطبيق **قواعد (Rules)** على مستوى المؤسسة (اليوم: PII Redaction) عبر العديد من الأتمتات دفعة واحدة بدلاً من تعديل كل deployment على حدة.
+
+<Frame>
+  ![نظرة عامة على Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+<Note>
+  Agent Control Plane مُوسوم حالياً بـ **Beta** في CrewAI Platform.
+</Note>
+
+يجيب التبويبان عن سؤالَين مختلفَين:
+
+- **Automations** — *"كيف يتصرف أسطولي الآن، وكم يكلّفني؟"* راجع [المراقبة](/ar/enterprise/features/agent-control-plane/monitoring).
+- **Rules** — *"كيف أفرض سياسة (مثل PII redaction) عبر العديد من عمليات النشر دون إعادة نشر كل واحدة؟"* راجع [القواعد](/ar/enterprise/features/agent-control-plane/rules).
+
+## المتطلبات
+
+<Warning>
+  يُشترط **crewAI v1.13 أو أحدث** ليتمكن أي أتمتة من تعبئة أي بيانات على هذه الصفحة — تمر بيانات الصحة وعمليات التنفيذ والأخطاء والرموز والتكلفة عبر التليمتري الذي تم تفعيله في `crewai==1.13`. تظهر عمليات النشر الأقدم في لافتة *"We've detected N other automations that we can't display"* ولا تساهم بأي صفوف حتى يتم تحديثها وإعادة نشرها.
+</Warning>
+
+<Warning>
+  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل [القواعد](/ar/enterprise/features/agent-control-plane/rules). يمكن للمؤسسات على الخطط الأدنى فتح تبويب Rules وعرض القواعد الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction rules require an Enterprise plan."*. المراقبة (تبويب Automations) متاحة في جميع الخطط حيث يكون هذا الميزة مفعّلة.
+</Warning>
+
+- يجب أن تكون ميزة **Agent Control Plane** مفعّلة لمؤسستك. إن لم ترها في الشريط الجانبي، اطلب من مالك الحساب تفعيلها.
+- داخل ACP، يحكم [RBAC](/ar/enterprise/features/rbac) الوصول: `read` للعرض في لوحة المعلومات والقواعد، و`manage` لإنشاء وتعديل وتشغيل/إيقاف وحذف القواعد.
+- يمكن ضبط نطاق جميع المخططات والجداول إلى **آخر 24 ساعة** أو **الأسبوع الماضي** أو **آخر 30 يوماً** عبر مُحدّد الوقت في أعلى اليمين. تقارن قيم الفرق (`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday` وغيرها) النافذة المختارة بالنافذة السابقة بنفس الطول.
+
+## ما يمكنك فعله هنا
+
+<CardGroup cols={2}>
+  <Card title="المراقبة" icon="gauge" href="/ar/enterprise/features/agent-control-plane/monitoring">
+    راقب صحة الأسطول وإنفاق LLM عبر بطاقات المقاييس و sankey التفاعلي وجداول لكل أتمتة ولوحات جانبية للتعمق في أي أتمتة أو مزود.
+  </Card>
+  <Card title="القواعد" icon="shield-check" href="/ar/enterprise/features/agent-control-plane/rules">
+    طبّق سياسات PII Redaction على مستوى المؤسسة بنطاق محدد بالأدوات والوسوم. تسري التغييرات في التنفيذ التالي — دون الحاجة لإعادة نشر.
+  </Card>
+</CardGroup>
+
+## ذو صلة
+
+<CardGroup cols={2}>
+  <Card title="Traces" icon="timeline" href="/ar/enterprise/features/traces">
+    تعمّق في تنفيذ واحد لرؤية تفكير الوكيل واستدعاءات الأدوات واستخدام الرموز.
+  </Card>
+  <Card title="RBAC" icon="users" href="/ar/enterprise/features/rbac">
+    أدِر من يمكنه قراءة Agent Control Plane ومن يمكنه تعديل القواعد.
+  </Card>
+  <Card title="PII Redaction للـ Traces" icon="lock" href="/ar/enterprise/features/pii-trace-redactions">
+    كتالوج الكيانات وضبط PII لكل deployment التي تستند إليها القواعد.
+  </Card>
+  <Card title="النشر إلى AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
+    انشر crew على إصدار crewAI يدعم Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
+  تواصل مع فريق الدعم للمساعدة في تفسير المقاييس أو تصميم القواعد.
+</Card>

docs/ar/enterprise/features/agent-control-plane/rules.mdx

@@ -0,0 +1,114 @@
+---
+title: "إعداد القواعد"
+description: "طبّق سياسات على مستوى المؤسسة عبر العديد من الأتمتات من مكان واحد."
+sidebarTitle: "القواعد"
+icon: "shield-check"
+mode: "wide"
+---
+
+## نظرة عامة
+
+تتيح لك القواعد تطبيق سياسات — اليوم: **PII Redaction** — عبر العديد من الأتمتات دفعة واحدة، بدلاً من ضبط كل deployment على حدة. افتح تبويب **Rules** في [Agent Control Plane](/ar/enterprise/features/agent-control-plane/overview) لإدارتها.
+
+<Frame>
+  ![قائمة القواعد](/images/enterprise/acp-rules-list.png)
+</Frame>
+
+تعرض كل بطاقة قاعدة الاسم والوصف و**النطاق (scope)** الذي تنطبق عليه القاعدة (الأدوات والوسوم المختارة) وعدد **الأتمتات المُفعَّلة** — عمليات النشر التي تطابق النطاق حالياً. يقوم المُفتاح على اليمين بتشغيل القاعدة أو إيقافها دون حذفها.
+
+## المتطلبات
+
+<Warning>
+  يُشترط **خطة Enterprise أو Ultra** لإنشاء أو تعديل قواعد PII Redaction. يمكن للمؤسسات على الخطط الأدنى فتح تبويب Rules وعرض القواعد الموجودة، ولكن يُعرض المحرر للقراءة فقط مع شارة قفل "Enterprise" والتنبيه *"PII Redaction rules require an Enterprise plan."* — تواصل مع مالك حسابك أو المبيعات للترقية.
+</Warning>
+
+- يجب أن تكون ميزة **Agent Control Plane** مفعّلة لمؤسستك. راجع [نظرة عامة — المتطلبات](/ar/enterprise/features/agent-control-plane/overview#المتطلبات).
+- تحتاج إلى صلاحية `manage` ضمن [RBAC](/ar/enterprise/features/rbac) على Agent Control Plane لإنشاء وتعديل وتشغيل/إيقاف وحذف القواعد. صلاحية `read` كافية لعرضها.
+- تُسجَّل جميع تغييرات القواعد بإصدارات للتدقيق.
+
+## أنواع القواعد المتاحة
+
+| النوع | ما تفعله |
+|------|---------------|
+| **PII Redaction** | تطبّق PII redaction على عمليات التنفيذ لكل أتمتة مطابِقة، باستخدام نفس كتالوج الكيانات و recognizers المخصصة الموثَّقة في [PII Redaction للـ Traces](/ar/enterprise/features/pii-trace-redactions). |
+
+سيتم إضافة أنواع قواعد أخرى مع الوقت.
+
+## إنشاء قاعدة
+
+<Frame>
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="لوحة تعديل قاعدة جانبية بالشروط ونوع قناع PII" width="450" />
+</Frame>
+
+<Steps>
+  <Step title="افتح المحرر">
+    انقر على **+ Create new** في أعلى يمين تبويب Rules، أو على **View Details** في بطاقة قاعدة موجودة.
+  </Step>
+
+  <Step title="سَمِّ القاعدة وصِفها">
+    أعطِ القاعدة اسماً واضحاً (مثل *Mask PII (CC)*) ووصفاً يشرح متى تنطبق. يظهر كلاهما على بطاقة القاعدة وفي مودال Engaged Automations.
+  </Step>
+
+  <Step title="اختر النوع">
+    اليوم **PII Redaction** فقط متاحة.
+  </Step>
+
+  <Step title="حدّد الشروط">
+    تحدد الشروط الأتمتات التي تنخرط معها القاعدة. كلاهما اختياري ويستخدم دلالات **مساواة المجموعات (set-equality)**:
+
+    - **Tools** — تنخرط فقط الأتمتات التي تتطابق مجموعة أدواتها **تطابقاً تامّاً** مع الأدوات المختارة. اختر من تطبيقات Studio و MCPs والأدوات مفتوحة المصدر وأدوات سجل Tool Repository.
+    - **Automations** — تنخرط فقط الأتمتات التي تتطابق مجموعة وسومها **تطابقاً تامّاً** مع الوسوم المختارة.
+
+    ترك مُحدِّد فارغ يعني "بدون تصفية على هذا البعد". ترك كليهما فارغَين يعني أن القاعدة تنطبق على **كل** أتمتة في المؤسسة.
+  </Step>
+
+  <Step title="اضبط جدول PII Mask Type">
+    حدّد كل نوع كيان تريد تغطيته واختر **Mask** (يستبدل بتسمية الكيان مثل `<CREDIT_CARD>`) أو **Redact** (يحذف النص المطابِق بالكامل). راجع [PII Redaction للـ Traces](/ar/enterprise/features/pii-trace-redactions) للاطلاع على كتالوج الكيانات الكامل وكيفية إضافة recognizers مخصصة على مستوى المؤسسة.
+  </Step>
+
+  <Step title="احفظ">
+    تنطبق القاعدة على عمليات التنفيذ **المستقبلية** لكل أتمتة مُفعَّلة بمجرد الحفظ. لا حاجة لإعادة النشر.
+  </Step>
+</Steps>
+
+## الأتمتات المُفعَّلة
+
+انقر على **Engaged N automations** في أي بطاقة قاعدة لرؤية أي عمليات النشر تطابقها القاعدة حالياً بالضبط، إلى جانب آخر تنفيذ لكل منها.
+
+<Frame>
+  ![مودال الأتمتات المُفعَّلة](/images/enterprise/acp-rules-engaged-modal.png)
+</Frame>
+
+هذه هي أسرع طريقة للتحقق من نطاق قاعدة قبل تمكينها — على سبيل المثال، للتأكد من أن قاعدة محدَّدة بنطاق وسم `production` لا تطابق عن طريق الخطأ deployment تجريبي.
+
+## قواعد على مستوى المؤسسة مقابل إعدادات لكل deployment
+
+يمكن ضبط PII Redaction في مكانين:
+
+- **لكل deployment** — ضمن **Settings → PII Protection** على كل deployment على حدة ([الدليل](/ar/enterprise/features/pii-trace-redactions))
+- **على مستوى المؤسسة** — كقاعدة في هذه الصفحة
+
+عندما يتطابق نطاق قاعدة مُفعَّلة على مستوى المؤسسة مع deployment، يُجاوز تكوين الكيانات الخاص بالقاعدة **إعدادات PII المملوكة من قبل الـ deployment** لعمليات تنفيذ ذلك الـ deployment — تصبح القاعدة المصدر الوحيد للحقيقة طالما هي مرتبطة. عطّل القاعدة أو فُكَّ ارتباطها (أو غيِّر نطاقها بحيث لا تتطابق بعد الآن) ويعود الـ deployment إلى إعدادات PII Protection الخاصة به.
+
+فضّل القواعد على مستوى المؤسسة عندما تريد فرض سياسة متسقة عبر العديد من عمليات النشر؛ احتفظ بالضبط لكل deployment للاستثناءات الفردية.
+
+## ذو صلة
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — نظرة عامة" icon="book-open" href="/ar/enterprise/features/agent-control-plane/overview">
+    ما هو ACP، المتطلبات، مستويات الخطط، و RBAC.
+  </Card>
+  <Card title="Agent Control Plane — المراقبة" icon="gauge" href="/ar/enterprise/features/agent-control-plane/monitoring">
+    راقب الأتمتات واستهلاك LLM عبر أسطولك.
+  </Card>
+  <Card title="PII Redaction للـ Traces" icon="lock" href="/ar/enterprise/features/pii-trace-redactions">
+    كتالوج الكيانات، recognizers المخصصة، والضبط لكل deployment.
+  </Card>
+  <Card title="RBAC" icon="users" href="/ar/enterprise/features/rbac">
+    أدِر من يمكنه إنشاء أو تعديل القواعد.
+  </Card>
+</CardGroup>
+
+<Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
+  تواصل مع فريق الدعم للمساعدة في تصميم قواعد لمؤسستك.
+</Card>

docs/ar/enterprise/integrations/salesforce.mdx

@@ -17,15 +17,62 @@ mode: "wide"
 - حساب Salesforce بالصلاحيات المناسبة
 - ربط حساب Salesforce الخاص بك عبر [صفحة التكاملات](https://app.crewai.com/integrations)
 
+<Note>
+  يتطلب Salesforce **تثبيتًا واحدًا يقوم به مسؤول النظام (admin)** لحزمة
+  CrewAI في مؤسستك قبل أن يتمكن أي مستخدم من الاتصال. هذا متطلب من منصة
+  Salesforce لجميع التكاملات المعتمدة على ExternalClientApp اعتبارًا من
+  إصدار Spring '26 — وليس خطوة خاصة بـ CrewAI. تدليلك خطوة Connect
+  Salesforce في CrewAI AMP خلال هذه العملية عند المحاولة الأولى.
+</Note>
+
 ## إعداد تكامل Salesforce
 
 ### 1. ربط حساب Salesforce الخاص بك
 
-1. انتقل إلى [تكاملات CrewAI AMP](https://app.crewai.com/crewai_plus/connectors)
-2. ابحث عن **Salesforce** في قسم تكاملات المصادقة
-3. انقر على **Connect** وأكمل عملية OAuth
-4. امنح الصلاحيات اللازمة لإدارة CRM والمبيعات
-5. انسخ رمز المؤسسة من [إعدادات التكامل](https://app.crewai.com/crewai_plus/settings/integrations)
+1. انتقل إلى [تكاملات CrewAI AMP](https://app.crewai.com/crewai_plus/unified_tools).
+2. ابحث عن **Salesforce** في قسم تكاملات المصادقة.
+3. انقر على **Connect**.
+
+ما يحدث بعد ذلك يعتمد على ما إذا كان مسؤول Salesforce في مؤسستك قد ثبّت
+حزمة CrewAI بالفعل:
+
+- **الحزمة مثبتة بالفعل:** سيتم نقلك مباشرة إلى شاشة موافقة OAuth في
+  Salesforce — اعتمدها وسيكتمل الاتصال.
+- **الحزمة غير مثبتة بعد:** سترى صفحة **Install CrewAI in Salesforce**.
+  اتبع خطوات التثبيت لمرة واحدة أدناه، ثم عُد إلى CrewAI AMP وانقر على
+  **Connect** مرة أخرى.
+
+4. امنح الصلاحيات اللازمة لإدارة CRM والمبيعات.
+5. انسخ رمز المؤسسة من [إعدادات التكامل](https://app.crewai.com/crewai_plus/settings/integrations).
+
+#### تثبيت لمرة واحدة بواسطة المسؤول (مسؤول Salesforce فقط)
+
+عند أول نقرة على **Connect Salesforce** من أي مستخدم في مؤسستك، تقوم CrewAI
+بإعادة توجيهك إلى صفحة تثبيت تُشير إلى حزمة CrewAI المُدارة. يحتاج مسؤول
+Salesforce إلى تثبيتها مرة واحدة فقط لكامل المؤسسة.
+
+1. في صفحة التثبيت داخل CrewAI، انقر على **Install in Salesforce**. (يمكنك
+   أيضًا مشاركة عنوان URL لتلك الصفحة مع المسؤول — رابط التثبيت يعمل لأي
+   شخص يفتحه.)
+2. سجّل الدخول إلى Salesforce بصلاحيات مسؤول. لبيئات Sandbox، استبدل
+   `login.salesforce.com` بـ `test.salesforce.com` في الرابط قبل فتحه.
+3. اختر **Install for All Users**، ووافق على إشعار تطبيقات الجهات
+   الخارجية، ثم انقر **Install**.
+4. من Setup في Salesforce، ابحث عن **External Client App Manager** ←
+   **CrewAI App** ← افتح علامة التبويب **Policies** ← **Edit**، واضبط
+   القيم التالية:
+   - **Permitted Users:** All users may self-authorize
+   - **IP Relaxation:** Relax IP restrictions
+   - **Refresh Token Policy:** Refresh token is valid until revoked
+5. احفظ التغييرات.
+6. عُد إلى CrewAI AMP وانقر على **Connect Salesforce** مرة أخرى. سيكتمل
+   OAuth هذه المرة.
+
+<Note>
+  **لست مسؤول Salesforce؟** أعِد توجيه عنوان URL لصفحة التثبيت (أو رابط
+  التثبيت نفسه) إلى مسؤول Salesforce لديكم واطلب منه إكمال الخطوات أعلاه.
+  بمجرد انتهائه، عُد إلى CrewAI AMP وانقر على **Connect** مرة أخرى.
+</Note>
 
 ### 2. تثبيت الحزمة المطلوبة
 

docs/docs.json

@@ -2384,6 +2384,15 @@
                   {
                     "group": "Manage",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "en/enterprise/features/agent-control-plane/overview",
+                          "en/enterprise/features/agent-control-plane/monitoring",
+                          "en/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "en/enterprise/features/sso",
                       "en/enterprise/features/rbac"
                     ]
@@ -2868,6 +2877,15 @@
                   {
                     "group": "Manage",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "en/enterprise/features/agent-control-plane/overview",
+                          "en/enterprise/features/agent-control-plane/monitoring",
+                          "en/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "en/enterprise/features/sso",
                       "en/enterprise/features/rbac"
                     ]
@@ -3352,6 +3370,15 @@
                   {
                     "group": "Manage",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "en/enterprise/features/agent-control-plane/overview",
+                          "en/enterprise/features/agent-control-plane/monitoring",
+                          "en/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "en/enterprise/features/sso",
                       "en/enterprise/features/rbac"
                     ]
@@ -3836,6 +3863,15 @@
                   {
                     "group": "Manage",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "en/enterprise/features/agent-control-plane/overview",
+                          "en/enterprise/features/agent-control-plane/monitoring",
+                          "en/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "en/enterprise/features/sso",
                       "en/enterprise/features/rbac"
                     ]
@@ -9101,6 +9137,15 @@
                   {
                     "group": "Gerenciar",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "pt-BR/enterprise/features/agent-control-plane/overview",
+                          "pt-BR/enterprise/features/agent-control-plane/monitoring",
+                          "pt-BR/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "pt-BR/enterprise/features/rbac"
                     ]
                   },
@@ -9562,6 +9607,15 @@
                   {
                     "group": "Gerenciar",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "pt-BR/enterprise/features/agent-control-plane/overview",
+                          "pt-BR/enterprise/features/agent-control-plane/monitoring",
+                          "pt-BR/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "pt-BR/enterprise/features/rbac"
                     ]
                   },
@@ -10023,6 +10077,15 @@
                   {
                     "group": "Gerenciar",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "pt-BR/enterprise/features/agent-control-plane/overview",
+                          "pt-BR/enterprise/features/agent-control-plane/monitoring",
+                          "pt-BR/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "pt-BR/enterprise/features/rbac"
                     ]
                   },
@@ -10484,6 +10547,15 @@
                   {
                     "group": "Gerenciar",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "pt-BR/enterprise/features/agent-control-plane/overview",
+                          "pt-BR/enterprise/features/agent-control-plane/monitoring",
+                          "pt-BR/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "pt-BR/enterprise/features/rbac"
                     ]
                   },
@@ -15671,6 +15743,15 @@
                   {
                     "group": "관리",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ko/enterprise/features/agent-control-plane/overview",
+                          "ko/enterprise/features/agent-control-plane/monitoring",
+                          "ko/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ko/enterprise/features/rbac"
                     ]
                   },
@@ -16145,6 +16226,15 @@
                   {
                     "group": "관리",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ko/enterprise/features/agent-control-plane/overview",
+                          "ko/enterprise/features/agent-control-plane/monitoring",
+                          "ko/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ko/enterprise/features/rbac"
                     ]
                   },
@@ -16619,6 +16709,15 @@
                   {
                     "group": "관리",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ko/enterprise/features/agent-control-plane/overview",
+                          "ko/enterprise/features/agent-control-plane/monitoring",
+                          "ko/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ko/enterprise/features/rbac"
                     ]
                   },
@@ -17093,6 +17192,15 @@
                   {
                     "group": "관리",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ko/enterprise/features/agent-control-plane/overview",
+                          "ko/enterprise/features/agent-control-plane/monitoring",
+                          "ko/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ko/enterprise/features/rbac"
                     ]
                   },
@@ -22358,6 +22466,15 @@
                   {
                     "group": "الإدارة",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ar/enterprise/features/agent-control-plane/overview",
+                          "ar/enterprise/features/agent-control-plane/monitoring",
+                          "ar/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ar/enterprise/features/rbac"
                     ]
                   },
@@ -22832,6 +22949,15 @@
                   {
                     "group": "الإدارة",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ar/enterprise/features/agent-control-plane/overview",
+                          "ar/enterprise/features/agent-control-plane/monitoring",
+                          "ar/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ar/enterprise/features/rbac"
                     ]
                   },
@@ -23306,6 +23432,15 @@
                   {
                     "group": "الإدارة",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ar/enterprise/features/agent-control-plane/overview",
+                          "ar/enterprise/features/agent-control-plane/monitoring",
+                          "ar/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ar/enterprise/features/rbac"
                     ]
                   },
@@ -23780,6 +23915,15 @@
                   {
                     "group": "الإدارة",
                     "pages": [
+                      {
+                        "group": "Agent Control Plane",
+                        "icon": "gauge",
+                        "pages": [
+                          "ar/enterprise/features/agent-control-plane/overview",
+                          "ar/enterprise/features/agent-control-plane/monitoring",
+                          "ar/enterprise/features/agent-control-plane/rules"
+                        ]
+                      },
                       "ar/enterprise/features/rbac"
                     ]
                   },

docs/en/concepts/checkpointing.mdx

@@ -5,301 +5,419 @@ icon: floppy-disk
 mode: "wide"
 ---
 
-<Warning>
-Checkpointing is in early release. APIs may change in future versions.
-</Warning>
+Checkpointing saves a snapshot of execution state during a run so a crew, flow, or agent can resume after a failure or be forked into an alternate branch.
 
-## Overview
+<CardGroup cols={2}>
+  <Card title="Explanation" icon="lightbulb" href="#explanation">
+    How checkpointing works: events, storage, and inheritance.
+  </Card>
+  <Card title="Tutorial" icon="graduation-cap" href="#tutorial-resume-a-failing-crew">
+    A 5-minute walkthrough: run, interrupt, resume.
+  </Card>
+  <Card title="How-to guides" icon="screwdriver-wrench" href="#how-to-guides">
+    Task-focused recipes for common workflows.
+  </Card>
+  <Card title="Reference" icon="book" href="#reference">
+    `CheckpointConfig`, events, providers, and CLI.
+  </Card>
+</CardGroup>
 
-Checkpointing automatically saves execution state during a run. If a crew, flow, or agent fails mid-execution, you can restore from the last checkpoint and resume without re-running completed work.
+## Explanation
 
-## Quick Start
+### What a checkpoint is
 
-```python
-from crewai import Crew, CheckpointConfig
+A checkpoint captures everything CrewAI needs to recreate a run mid-flight: the full state of the crew, flow, or agent — configuration, agent memory and knowledge sources, task progress, intermediate outputs, internal state and attributes — alongside the kickoff inputs, the event history up to that point, and a lineage ID that ties the checkpoint to the run it came from.
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=True,  # uses defaults: ./.checkpoints, on task_completed
-)
-result = crew.kickoff()
-```
+Restoring rebuilds that state and continues. Completed tasks are skipped, memory and knowledge are rehydrated, and downstream work runs against the same outputs the original run produced. Forking does the same restore under a new lineage, so the new branch and the original run can write checkpoints side by side without overwriting each other.
 
-Checkpoint files are written to `./.checkpoints/` after each completed task.
+### When checkpoints are written
 
-## Configuration
+Checkpointing is event-driven. The runtime subscribes to events you select via `on_events` and writes a checkpoint each time one fires. The default `task_completed` produces one checkpoint per finished task — a sensible tradeoff between granularity and disk use. Higher-frequency events like `llm_call_completed` are available for fine-grained recovery but write far more files.
 
-Use `CheckpointConfig` for full control:
+### Storage
 
-```python
-from crewai import Crew, CheckpointConfig
+Two providers ship with CrewAI:
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        on_events=["task_completed", "crew_kickoff_completed"],
-        max_checkpoints=5,
-    ),
-)
-```
+- `JsonProvider` writes one file per checkpoint. Human-readable and easy to inspect.
+- `SqliteProvider` writes to a single SQLite database. Better for high-frequency checkpointing.
 
-### CheckpointConfig Fields
+Both prune oldest checkpoints when `max_checkpoints` is set.
 
-| Field | Type | Default | Description |
-|:------|:-----|:--------|:------------|
-| `location` | `str` | `"./.checkpoints"` | Storage destination — a directory for `JsonProvider`, a database file path for `SqliteProvider` |
-| `on_events` | `list[str]` | `["task_completed"]` | Event types that trigger a checkpoint |
-| `provider` | `BaseProvider` | `JsonProvider()` | Storage backend |
-| `max_checkpoints` | `int \| None` | `None` | Max checkpoints to keep. Oldest are pruned after each write. Pruning is handled by the provider. |
-| `restore_from` | `Path \| str \| None` | `None` | Path to a checkpoint to restore from. Used when passing config via a kickoff method's `from_checkpoint` parameter. |
+<Note>
+Auto-checkpoint writes (event-driven) are best-effort: a failed write is logged and the run continues. Manual `state.checkpoint()` and `state.acheckpoint()` calls re-raise on failure.
+</Note>
 
-### Inheritance and Opt-Out
+### Inheritance model
 
-The `checkpoint` field on Crew, Flow, and Agent accepts `CheckpointConfig`, `True`, `False`, or `None`:
+`Crew`, `Flow`, and `Agent` all accept a `checkpoint` argument. Children inherit from their parent unless they set their own value or pass `False` to opt out. Enable checkpointing once on the crew and every agent participates, or selectively exclude one agent.
 
-| Value | Behavior |
-|:------|:---------|
-| `None` (default) | Inherit from parent. An agent inherits its crew's config. |
-| `True` | Enable with defaults. |
-| `False` | Explicit opt-out. Stops inheritance from parent. |
-| `CheckpointConfig(...)` | Custom configuration. |
+## Tutorial: Resume a failing crew
 
-```python
-crew = Crew(
-    agents=[
-        Agent(role="Researcher", ...),                  # inherits crew's checkpoint
-        Agent(role="Writer", ..., checkpoint=False),     # opted out, no checkpoints
-    ],
-    tasks=[...],
-    checkpoint=True,
-)
-```
+This walkthrough takes ~5 minutes. You will run a two-task crew, kill it midway, and resume from the saved checkpoint.
 
-## Resuming from a Checkpoint
+<Steps>
+  <Step title="Create the crew with checkpointing enabled">
+    ```python
+    from crewai import Agent, Crew, Task
 
-Pass a `CheckpointConfig` with `restore_from` to any kickoff method. The crew restores from that checkpoint, skips completed tasks, and resumes.
+    researcher = Agent(role="Researcher", goal="Research", backstory="Expert")
+    writer = Agent(role="Writer", goal="Write", backstory="Expert")
 
-```python
-from crewai import Crew, CheckpointConfig
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[
+            Task(description="Research AI trends", agent=researcher, expected_output="bullets"),
+            Task(description="Write a summary", agent=writer, expected_output="paragraph"),
+        ],
+        checkpoint=True,
+    )
+    ```
+  </Step>
+  <Step title="Run it and interrupt after the first task">
+    ```python
+    result = crew.kickoff()
+    ```
 
-crew = Crew(agents=[...], tasks=[...])
-result = crew.kickoff(
-    from_checkpoint=CheckpointConfig(
-        restore_from="./my_checkpoints/20260407T120000_abc123.json",
-    ),
-)
-```
+    Press `Ctrl+C` after the first task finishes. Look in `./.checkpoints/` — a file named `<timestamp>_<uuid>.json` is the checkpoint.
+  </Step>
+  <Step title="Resume from the checkpoint">
+    ```python
+    from crewai import CheckpointConfig
 
-Remaining `CheckpointConfig` fields apply to the new run, so checkpointing continues after the restore.
+    result = crew.kickoff(
+        from_checkpoint=CheckpointConfig(
+            restore_from="./.checkpoints/<timestamp>_<uuid>.json",
+        ),
+    )
+    ```
 
-You can also use the classmethod directly:
+    The research task is skipped, the writer runs against the saved research output, and the crew finishes.
+  </Step>
+</Steps>
 
-```python
-config = CheckpointConfig(restore_from="./my_checkpoints/20260407T120000_abc123.json")
-crew = Crew.from_checkpoint(config)
-result = crew.kickoff()
-```
+## How-to guides
 
-## Forking from a Checkpoint
+<AccordionGroup>
+  <Accordion title="Enable checkpointing with defaults" icon="play">
+    ```python
+    crew = Crew(agents=[...], tasks=[...], checkpoint=True)
+    ```
 
-`fork()` restores a checkpoint and starts a new execution branch. Useful for exploring alternative paths from the same point.
+    Writes to `./.checkpoints/` on every `task_completed`.
+  </Accordion>
 
-```python
-from crewai import Crew, CheckpointConfig
+  <Accordion title="Customize storage and frequency" icon="sliders">
+    ```python
+    from crewai import Crew, CheckpointConfig
 
-config = CheckpointConfig(restore_from="./my_checkpoints/20260407T120000_abc123.json")
-crew = Crew.fork(config, branch="experiment-a")
-result = crew.kickoff(inputs={"strategy": "aggressive"})
-```
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            on_events=["task_completed", "crew_kickoff_completed"],
+            max_checkpoints=5,
+        ),
+    )
+    ```
+  </Accordion>
 
-Each fork gets a unique lineage ID so checkpoints from different branches don't collide. The `branch` label is optional and auto-generated if omitted.
+  <Accordion title="Choose a storage provider" icon="database">
+    <CodeGroup>
+    ```python JsonProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import JsonProvider
 
-## Works on Crew, Flow, and Agent
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            provider=JsonProvider(),
+            max_checkpoints=5,
+        ),
+    )
+    ```
+    ```python SqliteProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import SqliteProvider
 
-### Crew
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./.checkpoints.db",
+            provider=SqliteProvider(),
+            max_checkpoints=50,
+        ),
+    )
+    ```
+    </CodeGroup>
 
-```python
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_task, review_task],
-    checkpoint=CheckpointConfig(location="./crew_cp"),
-)
-```
+    <Tip>
+    SQLite enables WAL journal mode for concurrent reads. Prefer it for high-frequency checkpointing.
+    </Tip>
+  </Accordion>
 
-Default trigger: `task_completed` (one checkpoint per finished task).
+  <Accordion title="Opt one agent out" icon="user-slash">
+    ```python
+    crew = Crew(
+        agents=[
+            Agent(role="Researcher", ...),
+            Agent(role="Writer", ..., checkpoint=False),
+        ],
+        tasks=[...],
+        checkpoint=True,
+    )
+    ```
+  </Accordion>
 
-### Flow
+  <Accordion title="Fork into a new branch" icon="code-branch">
+    `fork()` restores a checkpoint under a fresh lineage so the new run does not collide with the original.
 
-```python
-from crewai.flow.flow import Flow, start, listen
-from crewai import CheckpointConfig
+    ```python
+    config = CheckpointConfig(restore_from="./my_checkpoints/<file>.json")
+    crew = Crew.fork(config, branch="experiment-a")
+    result = crew.kickoff(inputs={"strategy": "aggressive"})
+    ```
 
-class MyFlow(Flow):
-    @start()
-    def step_one(self):
-        return "data"
+    The `branch` label is optional; one is generated if omitted.
+  </Accordion>
 
-    @listen(step_one)
-    def step_two(self, data):
-        return process(data)
+  <Accordion title="Checkpoint a Crew, Flow, or Agent" icon="cubes">
+    <Tabs>
+      <Tab title="Crew">
+        ```python
+        crew = Crew(
+            agents=[researcher, writer],
+            tasks=[research_task, write_task, review_task],
+            checkpoint=CheckpointConfig(location="./crew_cp"),
+        )
+        ```
 
-flow = MyFlow(
-    checkpoint=CheckpointConfig(
-        location="./flow_cp",
-        on_events=["method_execution_finished"],
-    ),
-)
-result = flow.kickoff()
+        Default trigger: `task_completed`.
+      </Tab>
+      <Tab title="Flow">
+        ```python
+        from crewai.flow.flow import Flow, start, listen
+        from crewai import CheckpointConfig
 
-# Resume
-config = CheckpointConfig(restore_from="./flow_cp/20260407T120000_abc123.json")
-flow = MyFlow.from_checkpoint(config)
-result = flow.kickoff()
-```
+        class MyFlow(Flow):
+            @start()
+            def step_one(self):
+                return "data"
 
-### Agent
+            @listen(step_one)
+            def step_two(self, data):
+                return process(data)
 
-```python
-agent = Agent(
-    role="Researcher",
-    goal="Research topics",
-    backstory="Expert researcher",
-    checkpoint=CheckpointConfig(
-        location="./agent_cp",
-        on_events=["lite_agent_execution_completed"],
-    ),
-)
-result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
-```
+        flow = MyFlow(
+            checkpoint=CheckpointConfig(
+                location="./flow_cp",
+                on_events=["method_execution_finished"],
+            ),
+        )
+        result = flow.kickoff()
+        ```
+      </Tab>
+      <Tab title="Agent">
+        ```python
+        agent = Agent(
+            role="Researcher",
+            goal="Research topics",
+            backstory="Expert researcher",
+            checkpoint=CheckpointConfig(
+                location="./agent_cp",
+                on_events=["lite_agent_execution_completed"],
+            ),
+        )
+        result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+        ```
+      </Tab>
+    </Tabs>
+  </Accordion>
 
-## Storage Providers
+  <Accordion title="Write a checkpoint manually" icon="code">
+    Register a handler on any event and call `state.checkpoint()`.
 
-CrewAI ships with two checkpoint storage providers.
+    <CodeGroup>
+    ```python Sync
+    from __future__ import annotations
 
-### JsonProvider (default)
+    from typing import TYPE_CHECKING, Any
 
-Writes each checkpoint as a separate JSON file. Simple, human-readable, easy to inspect.
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import JsonProvider
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        provider=JsonProvider(),       # this is the default
-        max_checkpoints=5,             # prunes oldest files
-    ),
-)
-```
 
-Files are named `<timestamp>_<uuid>.json` inside the location directory.
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_llm_done(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = state.checkpoint("./my_checkpoints")
+        print(f"Saved checkpoint: {path}")
+    ```
+    ```python Async
+    from __future__ import annotations
 
-### SqliteProvider
+    from typing import TYPE_CHECKING, Any
 
-Stores all checkpoints in a single SQLite database file. Better for high-frequency checkpointing and avoids many small files.
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import SqliteProvider
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./.checkpoints.db",
-        provider=SqliteProvider(),
-        max_checkpoints=50,
-    ),
-)
-```
 
-WAL journal mode is enabled for concurrent read access.
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    async def on_llm_done_async(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = await state.acheckpoint("./my_checkpoints")
+        print(f"Saved checkpoint: {path}")
+    ```
+    </CodeGroup>
 
-## Event Types
+    A `state` argument is supplied automatically when the handler takes three parameters. See [Event Listeners](/en/concepts/event-listener) for the full event catalog.
+  </Accordion>
 
-The `on_events` field accepts any combination of event type strings. Common choices:
+  <Accordion title="Browse, resume, and fork from the CLI" icon="terminal">
+    ```bash
+    crewai checkpoint
+    crewai checkpoint --location ./my_checkpoints
+    crewai checkpoint --location ./.checkpoints.db
+    ```
 
-| Use Case | Events |
-|:---------|:-------|
-| After each task (Crew) | `["task_completed"]` |
-| After each flow method | `["method_execution_finished"]` |
-| After agent execution | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
-| On crew completion only | `["crew_kickoff_completed"]` |
-| After every LLM call | `["llm_call_completed"]` |
-| On everything | `["*"]` |
+    <Frame caption="Checkpoint tree — branches and forks nest under their parent.">
+      <img src="/images/checkpoint-tui-tree.png" alt="Checkpoint TUI tree view" />
+    </Frame>
+
+    The left panel groups checkpoints by branch; forks nest under their parent. Selecting a checkpoint opens the detail panel with metadata, entity state, and task progress. **Resume** continues the run; **Fork** starts a new branch.
+
+    <Frame caption="Overview tab — metadata, entity state, and run summary.">
+      <img src="/images/checkpoint-tui-detail-overview.png" alt="Checkpoint detail overview tab" />
+    </Frame>
+
+    The detail panel exposes two editable areas:
+
+    - **Inputs** — original kickoff inputs, pre-filled and editable.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-inputs.png" alt="Editable kickoff inputs" />
+      </Frame>
+
+    - **Task outputs** — outputs of completed tasks. Editing an output and hitting **Fork** invalidates downstream tasks so they re-run against the modified context.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-tasks.png" alt="Editable task outputs" />
+      </Frame>
+
+    <Frame caption="Fork view — confirm a new branch from the selected checkpoint.">
+      <img src="/images/checkpoint-tui-details-fork.png" alt="Fork confirmation panel" />
+    </Frame>
+
+    <Tip>
+    Useful for "what if" exploration: fork, tweak, observe.
+    </Tip>
+  </Accordion>
+
+  <Accordion title="Inspect checkpoints without the TUI" icon="magnifying-glass">
+    ```bash
+    crewai checkpoint list ./my_checkpoints
+    crewai checkpoint info ./my_checkpoints/<file>.json
+    crewai checkpoint info ./.checkpoints.db
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Reference
+
+### `CheckpointConfig`
+
+<ParamField path="location" type="str" default='"./.checkpoints"'>
+  Storage destination. A directory for `JsonProvider`, a database file path for `SqliteProvider`.
+</ParamField>
+
+<ParamField path="on_events" type='list[CheckpointEventType | Literal["*"]]' default='["task_completed"]'>
+  Event types that trigger a checkpoint. `CheckpointEventType` is a `Literal` — your type checker will autocomplete and reject unsupported values. See [event types](#event-types) for the full list.
+</ParamField>
+
+<ParamField path="provider" type="BaseProvider" default="JsonProvider()">
+  Storage backend. Either `JsonProvider` or `SqliteProvider`.
+</ParamField>
+
+<ParamField path="max_checkpoints" type="int | None" default="None">
+  Maximum checkpoints to retain. Oldest are pruned after each write.
+</ParamField>
+
+<ParamField path="restore_from" type="Path | str | None" default="None">
+  Checkpoint to restore from when passed via `from_checkpoint`.
+</ParamField>
+
+### `checkpoint` field values
+
+Accepted by `Crew`, `Flow`, and `Agent`.
+
+<ParamField path="None" type="default">
+  Inherit from parent.
+</ParamField>
+
+<ParamField path="True" type="bool">
+  Enable with defaults.
+</ParamField>
+
+<ParamField path="False" type="bool">
+  Explicit opt-out. Stops inheritance.
+</ParamField>
+
+<ParamField path="CheckpointConfig(...)" type="CheckpointConfig">
+  Custom configuration.
+</ParamField>
+
+### Event types
+
+`on_events` accepts any combination of `CheckpointEventType` values. The default `["task_completed"]` writes one checkpoint per finished task; `["*"]` matches every event.
 
 <Warning>
-Using `["*"]` or high-frequency events like `llm_call_completed` will write many checkpoint files and may impact performance. Use `max_checkpoints` to limit disk usage.
+`["*"]` and high-frequency events like `llm_call_completed` write many checkpoints and can degrade performance. Pair them with `max_checkpoints`.
 </Warning>
 
-## Manual Checkpointing
+<Expandable title="All supported events">
 
-For full control, register your own event handler and call `state.checkpoint()` directly:
+- **Task** — `task_started`, `task_completed`, `task_failed`, `task_evaluation`
+- **Crew** — `crew_kickoff_started`, `crew_kickoff_completed`, `crew_kickoff_failed`, `crew_train_started`, `crew_train_completed`, `crew_train_failed`, `crew_test_started`, `crew_test_completed`, `crew_test_failed`, `crew_test_result`
+- **Agent** — `agent_execution_started`, `agent_execution_completed`, `agent_execution_error`, `lite_agent_execution_started`, `lite_agent_execution_completed`, `lite_agent_execution_error`, `agent_evaluation_started`, `agent_evaluation_completed`, `agent_evaluation_failed`
+- **Flow** — `flow_created`, `flow_started`, `flow_finished`, `flow_paused`, `method_execution_started`, `method_execution_finished`, `method_execution_failed`, `method_execution_paused`, `human_feedback_requested`, `human_feedback_received`, `flow_input_requested`, `flow_input_received`
+- **LLM** — `llm_call_started`, `llm_call_completed`, `llm_call_failed`, `llm_stream_chunk`, `llm_thinking_chunk`
+- **LLM Guardrail** — `llm_guardrail_started`, `llm_guardrail_completed`, `llm_guardrail_failed`
+- **Tool** — `tool_usage_started`, `tool_usage_finished`, `tool_usage_error`, `tool_validate_input_error`, `tool_selection_error`, `tool_execution_error`
+- **Memory** — `memory_save_started`, `memory_save_completed`, `memory_save_failed`, `memory_query_started`, `memory_query_completed`, `memory_query_failed`, `memory_retrieval_started`, `memory_retrieval_completed`, `memory_retrieval_failed`
+- **Knowledge** — `knowledge_search_query_started`, `knowledge_search_query_completed`, `knowledge_query_started`, `knowledge_query_completed`, `knowledge_query_failed`, `knowledge_search_query_failed`
+- **Reasoning** — `agent_reasoning_started`, `agent_reasoning_completed`, `agent_reasoning_failed`
+- **MCP** — `mcp_connection_started`, `mcp_connection_completed`, `mcp_connection_failed`, `mcp_tool_execution_started`, `mcp_tool_execution_completed`, `mcp_tool_execution_failed`, `mcp_config_fetch_failed`
+- **Observation** — `step_observation_started`, `step_observation_completed`, `step_observation_failed`, `plan_refinement`, `plan_replan_triggered`, `goal_achieved_early`
+- **Skill** — `skill_discovery_started`, `skill_discovery_completed`, `skill_loaded`, `skill_activated`, `skill_load_failed`
+- **Logging** — `agent_logs_started`, `agent_logs_execution`
+- **A2A** — `a2a_delegation_started`, `a2a_delegation_completed`, `a2a_conversation_started`, `a2a_conversation_completed`, `a2a_message_sent`, `a2a_response_received`, `a2a_polling_started`, `a2a_polling_status`, `a2a_push_notification_registered`, `a2a_push_notification_received`, `a2a_push_notification_sent`, `a2a_push_notification_timeout`, `a2a_streaming_started`, `a2a_streaming_chunk`, `a2a_agent_card_fetched`, `a2a_authentication_failed`, `a2a_artifact_received`, `a2a_connection_error`, `a2a_server_task_started`, `a2a_server_task_completed`, `a2a_server_task_canceled`, `a2a_server_task_failed`, `a2a_parallel_delegation_started`, `a2a_parallel_delegation_completed`, `a2a_transport_negotiated`, `a2a_content_type_negotiated`, `a2a_context_created`, `a2a_context_expired`, `a2a_context_idle`, `a2a_context_completed`, `a2a_context_pruned`
+- **System signals** — `SIGTERM`, `SIGINT`, `SIGHUP`, `SIGTSTP`, `SIGCONT`
+- **Wildcard** — `"*"` matches every event.
 
-```python
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.llm_events import LLMCallCompletedEvent
+</Expandable>
 
-# Sync handler
-@crewai_event_bus.on(LLMCallCompletedEvent)
-def on_llm_done(source, event, state):
-    path = state.checkpoint("./my_checkpoints")
-    print(f"Saved checkpoint: {path}")
+### Storage providers
 
-# Async handler
-@crewai_event_bus.on(LLMCallCompletedEvent)
-async def on_llm_done_async(source, event, state):
-    path = await state.acheckpoint("./my_checkpoints")
-    print(f"Saved checkpoint: {path}")
-```
+<ParamField path="JsonProvider" type="provider">
+  One file per checkpoint, named `<timestamp>_<uuid>.json` inside `location`.
+</ParamField>
 
-The `state` argument is the `RuntimeState` passed automatically by the event bus when your handler accepts 3 parameters. You can register handlers on any event type listed in the [Event Listeners](/en/concepts/event-listener) documentation.
+<ParamField path="SqliteProvider" type="provider">
+  Single database file at `location` with WAL journaling.
+</ParamField>
 
-Checkpointing is best-effort: if a checkpoint write fails, the error is logged but execution continues uninterrupted.
+### CLI
 
-## CLI
-
-The `crewai checkpoint` command gives you a TUI for browsing, inspecting, resuming, and forking checkpoints. It auto-detects whether your checkpoints are JSON files or a SQLite database.
-
-```bash
-# Launch the TUI — auto-detects .checkpoints/ or .checkpoints.db
-crewai checkpoint
-
-# Point at a specific location
-crewai checkpoint --location ./my_checkpoints
-crewai checkpoint --location ./.checkpoints.db
-```
-
-<Frame>
-  <img src="/images/checkpointing.png" alt="Checkpoint TUI" />
-</Frame>
-
-The left panel is a tree view. Checkpoints are grouped by branch, and forks nest under the checkpoint they diverged from. Select a checkpoint to see its metadata, entity state, and task progress in the detail panel. Hit **Resume** to pick up where it left off, or **Fork** to start a new branch from that point.
-
-### Editing inputs and task outputs
-
-When a checkpoint is selected, the detail panel shows:
-
-- **Inputs** — if the original kickoff had inputs (e.g. `{topic}`), they appear as editable fields pre-filled with the original values. Change them before resuming or forking.
-- **Task outputs** — completed tasks show their output in editable text areas. Edit a task's output to change the context that downstream tasks receive. When you modify a task output and hit Fork, all subsequent tasks are invalidated and re-run with the new context.
-
-This is useful for "what if" exploration — fork from a checkpoint, tweak a task's result, and see how it changes downstream behavior.
-
-### Subcommands
-
-```bash
-# List all checkpoints
-crewai checkpoint list ./my_checkpoints
-
-# Inspect a specific checkpoint
-crewai checkpoint info ./my_checkpoints/20260407T120000_abc123.json
-
-# Inspect latest in a SQLite database
-crewai checkpoint info ./.checkpoints.db
-```
+| Command | Purpose |
+|:--------|:--------|
+| `crewai checkpoint` | Launch the TUI; auto-detect storage. |
+| `crewai checkpoint --location <path>` | Launch the TUI against a specific location. |
+| `crewai checkpoint list <path>` | List checkpoints. |
+| `crewai checkpoint info <path>` | Inspect a checkpoint file or the latest entry in a SQLite database. |

docs/en/enterprise/features/agent-control-plane/monitoring.mdx

@@ -0,0 +1,104 @@
+---
+title: "Watch your Automations"
+description: "Watch fleet health, LLM consumption, and per-automation behavior from the Automations tab."
+sidebarTitle: "Monitoring"
+icon: "gauge"
+mode: "wide"
+---
+
+## Overview
+
+The **Automations** tab is the read-only operations view of the [Agent Control Plane](/en/enterprise/features/agent-control-plane/overview). It combines two metric cards, an interactive sankey, and two sub-tables — **Automations** and **Consumption** — that you can search, filter, and sort.
+
+<Frame>
+  ![Agent Control Plane overview](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+All charts and tables respect the **Last 24 hours / Last Week / Last 30 days** selector at the top right. Deltas compare the selected window against the previous one of the same length.
+
+<Note>
+  Rows only show data for deployments on **crewAI v1.13 or higher** — older deployments appear in the *"We've detected N other automations that we can't display"* banner under the sankey and contribute zero metrics until they're updated and re-deployed. See [Overview — Requirements](/en/enterprise/features/agent-control-plane/overview#requirements).
+</Note>
+
+## Dashboard
+
+The header of the page has two metric cards and an interactive sankey. Clicking either card switches the sankey between two modes:
+
+- **Health mode** — `Total Automations → status buckets (Critical / Warning / Healthy)`. Click a bucket to filter the Automations table to just those deployments.
+- **Consumption mode** — `Model Providers → Automations → Total Cost`. Click a provider to filter the Consumption table to that provider.
+
+| Card | What it shows |
+|------|---------------|
+| **Automations** | `active` automations (and total count), total `errors` in the window, currently `active executions` (and total in the window), with a delta vs the previous period. |
+| **Consumption** | Total `cost` and `tokens used`, with a cost delta vs the previous period. |
+
+<Frame>
+  ![Overview with consumption sankey](/images/enterprise/acp-overview-consumption-sankey.png)
+</Frame>
+
+## Automations table
+
+The **Automations** sub-tab is the per-deployment breakdown of fleet health. Each row is one deployed crew or flow.
+
+<Frame>
+  ![Automations table with health status breakdown](/images/enterprise/acp-automations-table.png)
+</Frame>
+
+| Column | What it shows |
+|--------|---------------|
+| **Automation** | Deployment name and any tags assigned to it (e.g. `production`, `financial`). |
+| **Last execution** | Time since the most recent run. |
+| **Health Status Breakdown** | Stacked bar of `Critical` / `Warning` / `Healthy` percentages for executions in the window. |
+| **Executions with Errors** | Total failed executions in the window. |
+| **PII detection applied** | `Yes` if a per-deployment PII config or a matching [PII rule](/en/enterprise/features/agent-control-plane/rules) is active. |
+| **Executions** | Total executions in the window. |
+| **Last updated** | When the deployment was last re-deployed. |
+| **Crew Version** | The `crewai` version reported by the deployment. An info icon next to versions below `1.13` flags rows that can't contribute metrics. |
+
+Search by name, filter by `Status` (`Healthy` / `Warning` / `Critical`), and sort by any column header. Click a deployment name to open the **Automation panel** (see below).
+
+## Consumption table
+
+The **Consumption** sub-tab is the per-deployment breakdown of LLM spend and token usage.
+
+<Frame>
+  ![Consumption table broken down by LLM provider](/images/enterprise/acp-consumption-table.png)
+</Frame>
+
+| Column | What it shows |
+|--------|---------------|
+| **Automation** | Deployment name. |
+| **Last execution** | Time since the most recent run. |
+| **Tokens used** | One row per LLM provider used by this automation, with the delta vs the previous period. |
+| **Cost** | Cost per LLM provider, with the delta vs the previous period. |
+| **Total cost** | Sum across all providers, with the delta. |
+| **Executions** | Total executions in the window. |
+| **Last updated** | When the deployment was last re-deployed. |
+| **Crew Version** | The `crewai` version reported by the deployment. |
+
+Filter by **LLM provider** and sort by `Cost`, `Executions`, or `Last run`.
+
+<Info>
+  **Empty cells (`—` or `$0.00`) usually mean the deployment is below crewAI v1.13.** In the screenshot above, *Automation F* (`1.7.0`) and *Automation I* (`1.12.2`) show blanks for tokens and cost — their executions still run, but they don't emit the provider-level telemetry that powers this table. Update and re-deploy these crews to start collecting consumption data.
+</Info>
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — Overview" icon="book-open" href="/en/enterprise/features/agent-control-plane/overview">
+    What ACP is, requirements, plan tiers, and RBAC.
+  </Card>
+  <Card title="Agent Control Plane — Rules" icon="shield-check" href="/en/enterprise/features/agent-control-plane/rules">
+    Apply organization-wide PII Redaction rules across many automations.
+  </Card>
+  <Card title="Traces" icon="timeline" href="/en/enterprise/features/traces">
+    Drill into a single execution to see agent reasoning, tool calls, and token usage.
+  </Card>
+  <Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
+    Deploy a crew on a crewAI version that supports the Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for help interpreting metrics in the Agent Control Plane.
+</Card>

docs/en/enterprise/features/agent-control-plane/overview.mdx

@@ -0,0 +1,74 @@
+---
+title: Agent Control Plane Overview
+description: "Single operations hub for live automations — fleet health, LLM consumption, and organization-wide policies in one place."
+sidebarTitle: Overview
+icon: "book-open"
+---
+
+## Overview
+
+The **Agent Control Plane** (ACP) is the operations hub for everything you have running on CrewAI AMP. It is a single screen — split into **Automations** and **Rules** tabs — that lets your team:
+
+- Monitor the **health** of every live automation (crew or flow), with `Critical` / `Warning` / `Healthy` breakdowns and execution counts.
+- Track **LLM consumption** — tokens and cost — per automation, per provider, and per model, with a delta vs the previous period.
+- Drill into any single automation or model provider for time-series charts and per-provider breakdowns.
+- Apply organization-wide **Rules** (today: PII Redaction) across many automations at once instead of editing each deployment individually.
+
+<Frame>
+  ![Agent Control Plane overview](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+<Note>
+  The Agent Control Plane is currently labeled **Beta** in CrewAI Platform.
+</Note>
+
+The two tabs answer two different questions:
+
+- **Automations** — *"How is my fleet behaving right now, and what is it costing me?"* See [Monitoring](/en/enterprise/features/agent-control-plane/monitoring).
+- **Rules** — *"How do I enforce a policy (e.g. PII redaction) across many deployments without re-deploying each one?"* See [Rules](/en/enterprise/features/agent-control-plane/rules).
+
+## Requirements
+
+<Warning>
+  **crewAI v1.13 or higher** is required for an automation to populate any data on this page — health, executions, errors, tokens, and cost all flow through telemetry that lit up in `crewai==1.13`. Older deployments appear in the *"We've detected N other automations that we can't display"* banner and contribute zero rows until they are updated and re-deployed.
+</Warning>
+
+<Warning>
+  **Enterprise Plan or Ultra Plan** is required to create or edit [Rules](/en/enterprise/features/agent-control-plane/rules). Lower-tier organizations can open the Rules tab and view existing rules, but the editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction rules require an Enterprise plan."* Monitoring (the Automations tab) is available on all plans where the feature is enabled.
+</Warning>
+
+- The **Agent Control Plane** feature must be enabled for your organization. If you don't see it in the sidebar, ask your account owner to request enablement.
+- Inside ACP, [RBAC](/en/enterprise/features/rbac) governs access: `read` to view the dashboard and rules, `manage` to create, edit, toggle, or delete rules.
+- All charts and tables can be scoped to the **Last 24 hours**, **Last Week**, or **Last 30 days** using the time selector at the top right. Deltas (`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday`, etc.) compare the selected window against the previous one of the same length.
+
+## What you can do here
+
+<CardGroup cols={2}>
+  <Card title="Monitoring" icon="gauge" href="/en/enterprise/features/agent-control-plane/monitoring">
+    Watch fleet health and LLM spend with metric cards, an interactive sankey, per-automation tables, and drill-down side panels for any automation or provider.
+  </Card>
+  <Card title="Rules" icon="shield-check" href="/en/enterprise/features/agent-control-plane/rules">
+    Apply organization-wide PII Redaction policies scoped by tools and tags. Changes take effect on the next execution — no re-deploy required.
+  </Card>
+</CardGroup>
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Traces" icon="timeline" href="/en/enterprise/features/traces">
+    Drill into a single execution to see agent reasoning, tool calls, and token usage.
+  </Card>
+  <Card title="RBAC" icon="users" href="/en/enterprise/features/rbac">
+    Manage who can read the Agent Control Plane and who can edit rules.
+  </Card>
+  <Card title="PII Redaction for Traces" icon="lock" href="/en/enterprise/features/pii-trace-redactions">
+    Entity catalog and per-deployment PII configuration referenced by Rules.
+  </Card>
+  <Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
+    Deploy a crew on a crewAI version that supports the Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for help interpreting metrics or designing rules.
+</Card>

docs/en/enterprise/features/agent-control-plane/rules.mdx

@@ -0,0 +1,114 @@
+---
+title: "Set up the Rules"
+description: "Apply organization-wide policies across many automations from a single place."
+sidebarTitle: "Rules"
+icon: "shield-check"
+mode: "wide"
+---
+
+## Overview
+
+Rules let you apply policies — today: **PII Redaction** — across many automations at once, instead of configuring each deployment individually. Open the **Rules** tab in the [Agent Control Plane](/en/enterprise/features/agent-control-plane/overview) to manage them.
+
+<Frame>
+  ![Rules list](/images/enterprise/acp-rules-list.png)
+</Frame>
+
+Each rule card shows the name, description, the **scope** the rule applies to (selected tools and tags), and a count of **engaged automations** — deployments that currently match the scope. The toggle on the right enables or disables the rule without deleting it.
+
+## Requirements
+
+<Warning>
+  **Enterprise Plan or Ultra Plan** is required to create or edit PII Redaction rules. Lower-tier organizations can still open the Rules tab and view existing rules, but the editor renders read-only with an "Enterprise" lock pill and the alert *"PII Redaction rules require an Enterprise plan."* — contact your account owner or sales to upgrade.
+</Warning>
+
+- The **Agent Control Plane** feature must be enabled for your organization. See [Overview — Requirements](/en/enterprise/features/agent-control-plane/overview#requirements).
+- The `manage` [RBAC permission](/en/enterprise/features/rbac) on Agent Control Plane is required to create, edit, toggle, or delete rules. The `read` permission is enough to view them.
+- All rule changes are versioned for auditing.
+
+## Available rule types
+
+| Type | What it does |
+|------|---------------|
+| **PII Redaction** | Applies PII redaction to executions of every matching automation, using the same entity catalog and custom recognizers documented in [PII Redaction for Traces](/en/enterprise/features/pii-trace-redactions). |
+
+More rule types will be added over time.
+
+## Creating a rule
+
+<Frame>
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="Rule edit side panel with conditions and PII mask type" width="450" />
+</Frame>
+
+<Steps>
+  <Step title="Open the editor">
+    Click **+ Create new** at the top-right of the Rules tab, or **View Details** on an existing rule card.
+  </Step>
+
+  <Step title="Name and describe the rule">
+    Give the rule a clear name (e.g. *Mask PII (CC)*) and a description explaining when it applies. Both show up on the rule card and in the Engaged Automations modal.
+  </Step>
+
+  <Step title="Pick the type">
+    Today only **PII Redaction** is available.
+  </Step>
+
+  <Step title="Set the conditions">
+    Conditions decide which automations the rule engages with. Both are optional and use **set-equality** semantics:
+
+    - **Tools** — only automations whose tool set **exactly matches** the selected tools will engage. Picks from Studio apps, MCPs, OSS tools, and Tool Repository registry tools.
+    - **Automations** — only automations whose tag set **exactly matches** the selected tags will engage.
+
+    Leaving a picker empty means "no filter on this dimension". Leaving both empty means the rule applies to **every** automation in the organization.
+  </Step>
+
+  <Step title="Configure the PII Mask Type table">
+    Check each entity type you want covered and choose **Mask** (replaces with the entity label, e.g. `<CREDIT_CARD>`) or **Redact** (removes the matched text entirely). See [PII Redaction for Traces](/en/enterprise/features/pii-trace-redactions) for the full entity catalog and how to add organization-level custom recognizers.
+  </Step>
+
+  <Step title="Save">
+    The rule applies to **future** executions of every engaged automation as soon as you save. No re-deploy is needed.
+  </Step>
+</Steps>
+
+## Engaged automations
+
+Click **Engaged N automations** on any rule card to see exactly which deployments the rule is currently matching, along with each one's last execution.
+
+<Frame>
+  ![Engaged automations modal](/images/enterprise/acp-rules-engaged-modal.png)
+</Frame>
+
+This is the fastest way to sanity-check a rule's scope before enabling it — for example, to confirm that a rule scoped to the `production` tag isn't accidentally matching a staging deployment.
+
+## Org-wide rules vs per-deployment settings
+
+PII Redaction can be configured in two places:
+
+- **Per-deployment** — under **Settings → PII Protection** on each individual deployment ([guide](/en/enterprise/features/pii-trace-redactions))
+- **Org-wide** — as a Rule on this page
+
+When an enabled org-wide rule's scope matches a deployment, the rule's entity configuration **overrides** the deployment-owned PII settings for that deployment's executions — the rule becomes the single source of truth while it's attached. Disable or detach the rule (or change its scope so it no longer matches) and the deployment falls back to its own PII Protection settings.
+
+Prefer org-wide rules when you want to enforce a consistent policy across many deployments; reserve per-deployment configuration for one-off exceptions.
+
+## Related
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — Overview" icon="book-open" href="/en/enterprise/features/agent-control-plane/overview">
+    What ACP is, requirements, plan tiers, and RBAC.
+  </Card>
+  <Card title="Agent Control Plane — Monitoring" icon="gauge" href="/en/enterprise/features/agent-control-plane/monitoring">
+    Monitor automations and LLM consumption across your fleet.
+  </Card>
+  <Card title="PII Redaction for Traces" icon="lock" href="/en/enterprise/features/pii-trace-redactions">
+    Entity catalog, custom recognizers, and per-deployment configuration.
+  </Card>
+  <Card title="RBAC" icon="users" href="/en/enterprise/features/rbac">
+    Manage who can create or edit rules.
+  </Card>
+</CardGroup>
+
+<Card title="Need Help?" icon="headset" href="mailto:support@crewai.com">
+  Contact our support team for help designing rules for your organization.
+</Card>

docs/en/enterprise/integrations/salesforce.mdx

@@ -17,15 +17,61 @@ Before using the Salesforce integration, ensure you have:
 - A Salesforce account with appropriate permissions
 - Connected your Salesforce account through the [Integrations page](https://app.crewai.com/integrations)
 
+<Note>
+  Salesforce requires a **one-time admin install** of the CrewAI package in
+  your org before any user can connect. This is a Salesforce platform
+  requirement for all ExternalClientApp-based integrations as of the Spring
+  '26 release — not a CrewAI-specific step. The Connect Salesforce flow in
+  CrewAI AMP walks you through it the first time.
+</Note>
+
 ## Setting Up Salesforce Integration
 
 ### 1. Connect Your Salesforce Account
 
-1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/connectors)
-2. Find **Salesforce** in the Authentication Integrations section
-3. Click **Connect** and complete the OAuth flow
-4. Grant the necessary permissions for CRM and sales management
-5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations)
+1. Navigate to [CrewAI AMP Integrations](https://app.crewai.com/crewai_plus/unified_tools).
+2. Find **Salesforce** in the Authentication Integrations section.
+3. Click **Connect**.
+
+What happens next depends on whether a Salesforce admin in your org has
+already installed the CrewAI package:
+
+- **Package already installed:** you're taken straight to the Salesforce
+  OAuth consent screen — approve it and you're connected.
+- **Package not installed yet:** you'll see an **Install CrewAI in
+  Salesforce** page. Follow the one-time install steps below, then come
+  back to CrewAI AMP and click **Connect** again.
+
+4. Grant the necessary permissions for CRM and sales management.
+5. Copy your Enterprise Token from [Integration Settings](https://app.crewai.com/crewai_plus/settings/integrations).
+
+#### One-time admin install (Salesforce admin only)
+
+The first time anyone in your org clicks **Connect Salesforce**, CrewAI
+redirects them to an install page that points at the CrewAI managed package.
+A Salesforce admin needs to install it once for the whole org.
+
+1. On the install page in CrewAI, click **Install in Salesforce**. (You can
+   also share the page URL with your admin — the install link works for
+   anyone who opens it.)
+2. Sign in to Salesforce as an admin. For sandboxes, swap `login.salesforce.com`
+   for `test.salesforce.com` in the URL before opening it.
+3. Choose **Install for All Users**, acknowledge the third-party app prompt,
+   and click **Install**.
+4. In Salesforce Setup, search **External Client App Manager** → **CrewAI
+   App** → open the **Policies** tab → **Edit** and set:
+   - **Permitted Users:** All users may self-authorize
+   - **IP Relaxation:** Relax IP restrictions
+   - **Refresh Token Policy:** Refresh token is valid until revoked
+5. Save.
+6. Return to CrewAI AMP and click **Connect Salesforce** again. OAuth will
+   complete this time.
+
+<Note>
+  **Not a Salesforce admin?** Forward the install page URL (or the install
+  link itself) to your Salesforce admin and ask them to complete the steps
+  above. Once they're done, return to CrewAI AMP and click **Connect** again.
+</Note>
 
 ### 2. Install Required Package
 

docs/ko/concepts/checkpointing.mdx

@@ -5,225 +5,419 @@ icon: floppy-disk
 mode: "wide"
 ---
 
-<Warning>
-체크포인팅은 초기 릴리스 단계입니다. API는 향후 버전에서 변경될 수 있습니다.
-</Warning>
+체크포인팅은 실행 중 실행 상태의 스냅샷을 저장하여 크루, 플로우, 에이전트가 실패 후 재개하거나 대체 브랜치로 분기될 수 있도록 합니다.
 
-## 개요
+<CardGroup cols={2}>
+  <Card title="설명" icon="lightbulb" href="#설명">
+    체크포인팅의 작동 방식: 이벤트, 스토리지, 상속.
+  </Card>
+  <Card title="튜토리얼" icon="graduation-cap" href="#튜토리얼-실패한-크루-재개하기">
+    5분 가이드: 실행, 중단, 재개.
+  </Card>
+  <Card title="사용 방법" icon="screwdriver-wrench" href="#사용-방법">
+    일반적인 워크플로우를 위한 작업 중심 레시피.
+  </Card>
+  <Card title="레퍼런스" icon="book" href="#레퍼런스">
+    `CheckpointConfig`, 이벤트, 프로바이더, CLI.
+  </Card>
+</CardGroup>
 
-체크포인팅은 실행 중 자동으로 실행 상태를 저장합니다. 크루, 플로우 또는 에이전트가 실행 도중 실패하면 마지막 체크포인트에서 복원하여 이미 완료된 작업을 다시 실행하지 않고 재개할 수 있습니다.
+## 설명
 
-## 빠른 시작
+### 체크포인트란
 
-```python
-from crewai import Crew, CheckpointConfig
+체크포인트는 실행 중인 작업을 재현하기 위해 CrewAI가 필요한 모든 것을 캡처합니다: 크루, 플로우 또는 에이전트의 전체 상태 — 구성, 에이전트의 메모리 및 지식 소스, 태스크 진행 상황, 중간 출력값, 내부 상태 및 속성 — 그리고 kickoff 입력, 해당 시점까지의 이벤트 기록, 그리고 체크포인트를 원본 실행에 연결하는 lineage ID를 포함합니다.
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=True,  # 기본값 사용: ./.checkpoints, task_completed 이벤트
-)
-result = crew.kickoff()
-```
+복원하면 해당 상태를 재구성하고 계속 진행합니다. 완료된 태스크는 건너뛰고, 메모리와 지식은 재수화되며, 다운스트림 작업은 원본 실행이 생성한 동일한 출력을 기반으로 실행됩니다. 포크하면 새 lineage 아래에서 동일한 복원을 수행하여 새 브랜치와 원본 실행이 서로 덮어쓰지 않고 나란히 체크포인트를 기록할 수 있습니다.
 
-각 태스크가 완료된 후 `./.checkpoints/`에 체크포인트 파일이 기록됩니다.
+### 체크포인트가 기록되는 시점
 
-## 설정
+체크포인팅은 이벤트 기반입니다. 런타임은 `on_events`로 선택한 이벤트를 구독하고, 이벤트가 발생할 때마다 체크포인트를 기록합니다. 기본값 `task_completed`는 완료된 태스크당 하나의 체크포인트를 생성합니다 — 세분화와 디스크 사용의 합리적인 균형입니다. `llm_call_completed`와 같은 고빈도 이벤트는 더 세밀한 복구를 위해 사용 가능하지만 훨씬 많은 파일을 기록합니다.
 
-`CheckpointConfig`를 사용하여 세부 설정을 제어합니다:
+### 스토리지
 
-```python
-from crewai import Crew, CheckpointConfig
+CrewAI에는 두 가지 프로바이더가 포함되어 있습니다:
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        on_events=["task_completed", "crew_kickoff_completed"],
-        max_checkpoints=5,
-    ),
-)
-```
+- `JsonProvider`는 체크포인트당 하나의 파일을 기록합니다. 사람이 읽기 쉽고 검사하기 편리합니다.
+- `SqliteProvider`는 단일 SQLite 데이터베이스에 기록합니다. 고빈도 체크포인팅에 적합합니다.
 
-### CheckpointConfig 필드
+`max_checkpoints`가 설정되면 두 프로바이더 모두 가장 오래된 체크포인트를 자동으로 제거합니다.
 
-| 필드 | 타입 | 기본값 | 설명 |
-|:-----|:-----|:-------|:-----|
-| `location` | `str` | `"./.checkpoints"` | 체크포인트 파일 경로 |
-| `on_events` | `list[str]` | `["task_completed"]` | 체크포인트를 트리거하는 이벤트 타입 |
-| `provider` | `BaseProvider` | `JsonProvider()` | 스토리지 백엔드 |
-| `max_checkpoints` | `int \| None` | `None` | 보관할 최대 파일 수; 오래된 것부터 삭제 |
+<Note>
+체크포인트 기록은 best-effort 방식입니다. 실패한 체크포인트는 로그에 기록되지만 실행을 중단시키지 않습니다.
+</Note>
 
-### 상속 및 옵트아웃
+### 상속 모델
 
-Crew, Flow, Agent의 `checkpoint` 필드는 `CheckpointConfig`, `True`, `False`, `None`을 받습니다:
+`Crew`, `Flow`, `Agent` 모두 `checkpoint` 인수를 받습니다. 자식은 자체 값을 설정하거나 `False`를 전달하여 옵트아웃하지 않는 한 부모로부터 상속합니다. 크루에서 체크포인팅을 한 번 활성화하면 모든 에이전트가 참여하거나, 특정 에이전트만 선택적으로 제외할 수 있습니다.
 
-| 값 | 동작 |
-|:---|:-----|
-| `None` (기본값) | 부모에서 상속. 에이전트는 크루의 설정을 상속합니다. |
-| `True` | 기본값으로 활성화. |
-| `False` | 명시적 옵트아웃. 부모 상속을 중단합니다. |
-| `CheckpointConfig(...)` | 사용자 정의 설정. |
+## 튜토리얼: 실패한 크루 재개하기
 
-```python
-crew = Crew(
-    agents=[
-        Agent(role="Researcher", ...),                  # 크루의 checkpoint 상속
-        Agent(role="Writer", ..., checkpoint=False),     # 옵트아웃, 체크포인트 없음
-    ],
-    tasks=[...],
-    checkpoint=True,
-)
-```
+이 가이드는 약 5분이 소요됩니다. 두 개의 태스크가 있는 크루를 실행하고 중간에 종료한 다음, 저장된 체크포인트에서 재개합니다.
 
-## 체크포인트에서 재개
+<Steps>
+  <Step title="체크포인팅이 활성화된 크루를 생성합니다">
+    ```python
+    from crewai import Agent, Crew, Task
 
-```python
-# 복원 및 재개
-crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
-result = crew.kickoff()  # 마지막으로 완료된 태스크부터 재개
-```
+    researcher = Agent(role="Researcher", goal="Research", backstory="Expert")
+    writer = Agent(role="Writer", goal="Write", backstory="Expert")
 
-복원된 크루는 이미 완료된 태스크를 건너뛰고 첫 번째 미완료 태스크부터 재개합니다.
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[
+            Task(description="Research AI trends", agent=researcher, expected_output="bullets"),
+            Task(description="Write a summary", agent=writer, expected_output="paragraph"),
+        ],
+        checkpoint=True,
+    )
+    ```
+  </Step>
+  <Step title="실행하고 첫 번째 태스크 후에 중단합니다">
+    ```python
+    result = crew.kickoff()
+    ```
 
-## Crew, Flow, Agent에서 사용 가능
+    첫 번째 태스크가 완료된 후 `Ctrl+C`를 누릅니다. `./.checkpoints/` 디렉토리에서 `<timestamp>_<uuid>.json` 형식의 파일이 체크포인트입니다.
+  </Step>
+  <Step title="체크포인트에서 재개합니다">
+    ```python
+    from crewai import CheckpointConfig
 
-### Crew
+    result = crew.kickoff(
+        from_checkpoint=CheckpointConfig(
+            restore_from="./.checkpoints/<timestamp>_<uuid>.json",
+        ),
+    )
+    ```
 
-```python
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_task, review_task],
-    checkpoint=CheckpointConfig(location="./crew_cp"),
-)
-```
+    연구 태스크는 건너뛰고, 작성자는 저장된 연구 출력에 대해 실행되며, 크루가 완료됩니다.
+  </Step>
+</Steps>
 
-기본 트리거: `task_completed` (완료된 태스크당 하나의 체크포인트).
+## 사용 방법
 
-### Flow
+<AccordionGroup>
+  <Accordion title="기본값으로 체크포인팅 활성화" icon="play">
+    ```python
+    crew = Crew(agents=[...], tasks=[...], checkpoint=True)
+    ```
 
-```python
-from crewai.flow.flow import Flow, start, listen
-from crewai import CheckpointConfig
+    `task_completed` 이벤트마다 `./.checkpoints/`에 기록합니다.
+  </Accordion>
 
-class MyFlow(Flow):
-    @start()
-    def step_one(self):
-        return "data"
+  <Accordion title="스토리지와 빈도 사용자 정의" icon="sliders">
+    ```python
+    from crewai import Crew, CheckpointConfig
 
-    @listen(step_one)
-    def step_two(self, data):
-        return process(data)
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            on_events=["task_completed", "crew_kickoff_completed"],
+            max_checkpoints=5,
+        ),
+    )
+    ```
+  </Accordion>
 
-flow = MyFlow(
-    checkpoint=CheckpointConfig(
-        location="./flow_cp",
-        on_events=["method_execution_finished"],
-    ),
-)
-result = flow.kickoff()
+  <Accordion title="스토리지 프로바이더 선택" icon="database">
+    <CodeGroup>
+    ```python JsonProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import JsonProvider
 
-# 재개
-flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
-result = flow.kickoff()
-```
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            provider=JsonProvider(),
+            max_checkpoints=5,
+        ),
+    )
+    ```
+    ```python SqliteProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import SqliteProvider
 
-### Agent
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./.checkpoints.db",
+            provider=SqliteProvider(),
+            max_checkpoints=50,
+        ),
+    )
+    ```
+    </CodeGroup>
 
-```python
-agent = Agent(
-    role="Researcher",
-    goal="Research topics",
-    backstory="Expert researcher",
-    checkpoint=CheckpointConfig(
-        location="./agent_cp",
-        on_events=["lite_agent_execution_completed"],
-    ),
-)
-result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
-```
+    <Tip>
+    SQLite는 동시 읽기를 위해 WAL 저널 모드를 활성화합니다. 고빈도 체크포인팅에는 SQLite를 선호하세요.
+    </Tip>
+  </Accordion>
 
-## 스토리지 프로바이더
+  <Accordion title="특정 에이전트 옵트아웃" icon="user-slash">
+    ```python
+    crew = Crew(
+        agents=[
+            Agent(role="Researcher", ...),
+            Agent(role="Writer", ..., checkpoint=False),
+        ],
+        tasks=[...],
+        checkpoint=True,
+    )
+    ```
+  </Accordion>
 
-CrewAI는 두 가지 체크포인트 스토리지 프로바이더를 제공합니다.
+  <Accordion title="새 브랜치로 포크" icon="code-branch">
+    `fork()`는 새 lineage 아래에 체크포인트를 복원하여 새 실행이 원본과 충돌하지 않도록 합니다.
 
-### JsonProvider (기본값)
+    ```python
+    config = CheckpointConfig(restore_from="./my_checkpoints/<file>.json")
+    crew = Crew.fork(config, branch="experiment-a")
+    result = crew.kickoff(inputs={"strategy": "aggressive"})
+    ```
 
-각 체크포인트를 별도의 JSON 파일로 저장합니다.
+    `branch` 레이블은 선택 사항이며, 생략하면 자동 생성됩니다.
+  </Accordion>
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import JsonProvider
+  <Accordion title="Crew, Flow, Agent 체크포인트" icon="cubes">
+    <Tabs>
+      <Tab title="Crew">
+        ```python
+        crew = Crew(
+            agents=[researcher, writer],
+            tasks=[research_task, write_task, review_task],
+            checkpoint=CheckpointConfig(location="./crew_cp"),
+        )
+        ```
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        provider=JsonProvider(),
-        max_checkpoints=5,
-    ),
-)
-```
+        기본 트리거: `task_completed`.
+      </Tab>
+      <Tab title="Flow">
+        ```python
+        from crewai.flow.flow import Flow, start, listen
+        from crewai import CheckpointConfig
 
-### SqliteProvider
+        class MyFlow(Flow):
+            @start()
+            def step_one(self):
+                return "data"
 
-모든 체크포인트를 단일 SQLite 데이터베이스 파일에 저장합니다.
+            @listen(step_one)
+            def step_two(self, data):
+                return process(data)
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import SqliteProvider
+        flow = MyFlow(
+            checkpoint=CheckpointConfig(
+                location="./flow_cp",
+                on_events=["method_execution_finished"],
+            ),
+        )
+        result = flow.kickoff()
+        ```
+      </Tab>
+      <Tab title="Agent">
+        ```python
+        agent = Agent(
+            role="Researcher",
+            goal="Research topics",
+            backstory="Expert researcher",
+            checkpoint=CheckpointConfig(
+                location="./agent_cp",
+                on_events=["lite_agent_execution_completed"],
+            ),
+        )
+        result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+        ```
+      </Tab>
+    </Tabs>
+  </Accordion>
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./.checkpoints.db",
-        provider=SqliteProvider(),
-    ),
-)
-```
+  <Accordion title="수동으로 체크포인트 기록" icon="code">
+    모든 이벤트에 핸들러를 등록하고 `state.checkpoint()`를 호출합니다.
+
+    <CodeGroup>
+    ```python Sync
+    from __future__ import annotations
+
+    from typing import TYPE_CHECKING, Any
+
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
 
 
-## 이벤트 타입
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_llm_done(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = state.checkpoint("./my_checkpoints")
+        print(f"체크포인트 저장: {path}")
+    ```
+    ```python Async
+    from __future__ import annotations
 
-`on_events` 필드는 이벤트 타입 문자열의 조합을 받습니다. 일반적인 선택:
+    from typing import TYPE_CHECKING, Any
 
-| 사용 사례 | 이벤트 |
-|:----------|:-------|
-| 각 태스크 완료 후 (Crew) | `["task_completed"]` |
-| 각 플로우 메서드 완료 후 | `["method_execution_finished"]` |
-| 에이전트 실행 완료 후 | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
-| 크루 완료 시에만 | `["crew_kickoff_completed"]` |
-| 모든 LLM 호출 후 | `["llm_call_completed"]` |
-| 모든 이벤트 | `["*"]` |
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
+
+
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    async def on_llm_done_async(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = await state.acheckpoint("./my_checkpoints")
+        print(f"체크포인트 저장: {path}")
+    ```
+    </CodeGroup>
+
+    핸들러가 세 개의 매개변수를 받을 때 `state` 인수가 자동으로 제공됩니다. 전체 이벤트 카탈로그는 [Event Listeners](/ko/concepts/event-listener) 문서를 참조하세요.
+  </Accordion>
+
+  <Accordion title="CLI에서 탐색, 재개, 포크" icon="terminal">
+    ```bash
+    crewai checkpoint
+    crewai checkpoint --location ./my_checkpoints
+    crewai checkpoint --location ./.checkpoints.db
+    ```
+
+    <Frame caption="체크포인트 트리 — 브랜치와 포크가 부모 아래에 중첩됩니다.">
+      <img src="/images/checkpoint-tui-tree.png" alt="Checkpoint TUI tree view" />
+    </Frame>
+
+    왼쪽 패널은 체크포인트를 브랜치별로 그룹화하며, 포크는 부모 아래에 중첩됩니다. 체크포인트를 선택하면 메타데이터, 엔티티 상태, 태스크 진행 상황이 있는 세부 정보 패널이 열립니다. **Resume**은 실행을 계속하고, **Fork**는 새 브랜치를 시작합니다.
+
+    <Frame caption="개요 탭 — 메타데이터, 엔티티 상태, 실행 요약.">
+      <img src="/images/checkpoint-tui-detail-overview.png" alt="Checkpoint detail overview tab" />
+    </Frame>
+
+    세부 정보 패널에는 두 개의 편집 가능한 영역이 있습니다:
+
+    - **Inputs** — 원래 kickoff의 입력으로, 미리 채워져 있으며 편집 가능합니다.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-inputs.png" alt="Editable kickoff inputs" />
+      </Frame>
+
+    - **태스크 출력** — 완료된 태스크의 출력. 출력을 편집하고 **Fork**를 누르면 다운스트림 태스크가 무효화되어 수정된 컨텍스트로 다시 실행됩니다.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-tasks.png" alt="Editable task outputs" />
+      </Frame>
+
+    <Frame caption="포크 화면 — 선택한 체크포인트에서 새 브랜치를 확인합니다.">
+      <img src="/images/checkpoint-tui-details-fork.png" alt="Fork confirmation panel" />
+    </Frame>
+
+    <Tip>
+    "what if" 탐색에 유용합니다: 포크, 조정, 관찰.
+    </Tip>
+  </Accordion>
+
+  <Accordion title="TUI 없이 체크포인트 검사" icon="magnifying-glass">
+    ```bash
+    crewai checkpoint list ./my_checkpoints
+    crewai checkpoint info ./my_checkpoints/<file>.json
+    crewai checkpoint info ./.checkpoints.db
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## 레퍼런스
+
+### `CheckpointConfig`
+
+<ParamField path="location" type="str" default='"./.checkpoints"'>
+  스토리지 대상. `JsonProvider`는 디렉토리, `SqliteProvider`는 데이터베이스 파일 경로.
+</ParamField>
+
+<ParamField path="on_events" type='list[CheckpointEventType | Literal["*"]]' default='["task_completed"]'>
+  체크포인트를 트리거하는 이벤트 타입. `CheckpointEventType`은 `Literal`이므로 타입 체커가 자동 완성하고 지원되지 않는 값을 거부합니다. 전체 목록은 [이벤트 타입](#이벤트-타입) 참조.
+</ParamField>
+
+<ParamField path="provider" type="BaseProvider" default="JsonProvider()">
+  스토리지 백엔드. `JsonProvider` 또는 `SqliteProvider`.
+</ParamField>
+
+<ParamField path="max_checkpoints" type="int | None" default="None">
+  보관할 최대 체크포인트 수. 각 기록 후 가장 오래된 것이 제거됩니다.
+</ParamField>
+
+<ParamField path="restore_from" type="Path | str | None" default="None">
+  `from_checkpoint`를 통해 전달될 때 복원할 체크포인트.
+</ParamField>
+
+### `checkpoint` 필드 값
+
+`Crew`, `Flow`, `Agent`에서 사용 가능.
+
+<ParamField path="None" type="기본값">
+  부모에서 상속.
+</ParamField>
+
+<ParamField path="True" type="bool">
+  기본값으로 활성화.
+</ParamField>
+
+<ParamField path="False" type="bool">
+  명시적 옵트아웃. 상속을 중단합니다.
+</ParamField>
+
+<ParamField path="CheckpointConfig(...)" type="CheckpointConfig">
+  사용자 정의 설정.
+</ParamField>
+
+### 이벤트 타입
+
+`on_events`는 `CheckpointEventType` 값의 임의 조합을 받습니다. 기본값 `["task_completed"]`는 완료된 태스크당 하나의 체크포인트를 기록하며, `["*"]`는 모든 이벤트와 일치합니다.
 
 <Warning>
-`["*"]` 또는 `llm_call_completed`와 같은 고빈도 이벤트를 사용하면 많은 체크포인트 파일이 생성되어 성능에 영향을 줄 수 있습니다. `max_checkpoints`를 사용하여 디스크 사용량을 제한하세요.
+`["*"]` 및 `llm_call_completed`와 같은 고빈도 이벤트는 많은 체크포인트를 기록하고 성능을 저하시킬 수 있습니다. `max_checkpoints`와 함께 사용하세요.
 </Warning>
 
-## 수동 체크포인팅
+<Expandable title="지원되는 모든 이벤트">
 
-완전한 제어를 위해 자체 이벤트 핸들러를 등록하고 `state.checkpoint()`를 직접 호출할 수 있습니다:
+- **Task** — `task_started`, `task_completed`, `task_failed`, `task_evaluation`
+- **Crew** — `crew_kickoff_started`, `crew_kickoff_completed`, `crew_kickoff_failed`, `crew_train_started`, `crew_train_completed`, `crew_train_failed`, `crew_test_started`, `crew_test_completed`, `crew_test_failed`, `crew_test_result`
+- **Agent** — `agent_execution_started`, `agent_execution_completed`, `agent_execution_error`, `lite_agent_execution_started`, `lite_agent_execution_completed`, `lite_agent_execution_error`, `agent_evaluation_started`, `agent_evaluation_completed`, `agent_evaluation_failed`
+- **Flow** — `flow_created`, `flow_started`, `flow_finished`, `flow_paused`, `method_execution_started`, `method_execution_finished`, `method_execution_failed`, `method_execution_paused`, `human_feedback_requested`, `human_feedback_received`, `flow_input_requested`, `flow_input_received`
+- **LLM** — `llm_call_started`, `llm_call_completed`, `llm_call_failed`, `llm_stream_chunk`, `llm_thinking_chunk`
+- **LLM Guardrail** — `llm_guardrail_started`, `llm_guardrail_completed`, `llm_guardrail_failed`
+- **Tool** — `tool_usage_started`, `tool_usage_finished`, `tool_usage_error`, `tool_validate_input_error`, `tool_selection_error`, `tool_execution_error`
+- **Memory** — `memory_save_started`, `memory_save_completed`, `memory_save_failed`, `memory_query_started`, `memory_query_completed`, `memory_query_failed`, `memory_retrieval_started`, `memory_retrieval_completed`, `memory_retrieval_failed`
+- **Knowledge** — `knowledge_search_query_started`, `knowledge_search_query_completed`, `knowledge_query_started`, `knowledge_query_completed`, `knowledge_query_failed`, `knowledge_search_query_failed`
+- **Reasoning** — `agent_reasoning_started`, `agent_reasoning_completed`, `agent_reasoning_failed`
+- **MCP** — `mcp_connection_started`, `mcp_connection_completed`, `mcp_connection_failed`, `mcp_tool_execution_started`, `mcp_tool_execution_completed`, `mcp_tool_execution_failed`, `mcp_config_fetch_failed`
+- **Observation** — `step_observation_started`, `step_observation_completed`, `step_observation_failed`, `plan_refinement`, `plan_replan_triggered`, `goal_achieved_early`
+- **Skill** — `skill_discovery_started`, `skill_discovery_completed`, `skill_loaded`, `skill_activated`, `skill_load_failed`
+- **Logging** — `agent_logs_started`, `agent_logs_execution`
+- **A2A** — `a2a_delegation_started`, `a2a_delegation_completed`, `a2a_conversation_started`, `a2a_conversation_completed`, `a2a_message_sent`, `a2a_response_received`, `a2a_polling_started`, `a2a_polling_status`, `a2a_push_notification_registered`, `a2a_push_notification_received`, `a2a_push_notification_sent`, `a2a_push_notification_timeout`, `a2a_streaming_started`, `a2a_streaming_chunk`, `a2a_agent_card_fetched`, `a2a_authentication_failed`, `a2a_artifact_received`, `a2a_connection_error`, `a2a_server_task_started`, `a2a_server_task_completed`, `a2a_server_task_canceled`, `a2a_server_task_failed`, `a2a_parallel_delegation_started`, `a2a_parallel_delegation_completed`, `a2a_transport_negotiated`, `a2a_content_type_negotiated`, `a2a_context_created`, `a2a_context_expired`, `a2a_context_idle`, `a2a_context_completed`, `a2a_context_pruned`
+- **시스템 시그널** — `SIGTERM`, `SIGINT`, `SIGHUP`, `SIGTSTP`, `SIGCONT`
+- **와일드카드** — `"*"`는 모든 이벤트와 일치합니다.
 
-```python
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.llm_events import LLMCallCompletedEvent
+</Expandable>
 
-# 동기 핸들러
-@crewai_event_bus.on(LLMCallCompletedEvent)
-def on_llm_done(source, event, state):
-    path = state.checkpoint("./my_checkpoints")
-    print(f"체크포인트 저장: {path}")
+### 스토리지 프로바이더
 
-# 비동기 핸들러
-@crewai_event_bus.on(LLMCallCompletedEvent)
-async def on_llm_done_async(source, event, state):
-    path = await state.acheckpoint("./my_checkpoints")
-    print(f"체크포인트 저장: {path}")
-```
+<ParamField path="JsonProvider" type="provider">
+  체크포인트당 하나의 파일, `location` 내부에 `<timestamp>_<uuid>.json` 형식으로 명명.
+</ParamField>
 
-`state` 인수는 핸들러가 3개의 매개변수를 받을 때 이벤트 버스가 자동으로 전달하는 `RuntimeState`입니다. [Event Listeners](/ko/concepts/event-listener) 문서에 나열된 모든 이벤트 타입에 핸들러를 등록할 수 있습니다.
+<ParamField path="SqliteProvider" type="provider">
+  WAL 저널링이 있는 `location`의 단일 데이터베이스 파일.
+</ParamField>
 
-체크포인팅은 best-effort입니다: 체크포인트 기록이 실패하면 오류가 로그에 기록되지만 실행은 중단 없이 계속됩니다.
+### CLI
+
+| 명령 | 목적 |
+|:-----|:-----|
+| `crewai checkpoint` | TUI 실행; 스토리지 자동 감지. |
+| `crewai checkpoint --location <path>` | 특정 위치에 대해 TUI 실행. |
+| `crewai checkpoint list <path>` | 체크포인트 나열. |
+| `crewai checkpoint info <path>` | 체크포인트 파일 또는 SQLite 데이터베이스의 최신 항목 검사. |

docs/ko/enterprise/features/agent-control-plane/monitoring.mdx

@@ -0,0 +1,104 @@
+---
+title: "자동화 살펴보기"
+description: "Automations 탭에서 플릿 상태, LLM 소비, 자동화별 동작을 확인합니다."
+sidebarTitle: "모니터링"
+icon: "gauge"
+mode: "wide"
+---
+
+## 개요
+
+**Automations** 탭은 [Agent Control Plane](/ko/enterprise/features/agent-control-plane/overview)의 읽기 전용 운영 뷰입니다. 두 개의 메트릭 카드, 인터랙티브 sankey, 그리고 **Automations**와 **Consumption** 두 개의 서브 테이블을 결합해 검색·필터·정렬을 지원합니다.
+
+<Frame>
+  ![Agent Control Plane 개요](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+모든 차트와 테이블은 오른쪽 상단의 **지난 24시간 / 지난 1주 / 지난 30일** 선택기를 따릅니다. 변화량은 선택한 윈도우를 같은 길이의 이전 윈도우와 비교합니다.
+
+<Note>
+  행은 **crewAI v1.13 이상**의 deployment에 대해서만 데이터를 표시합니다. 그 이전 deployment는 sankey 아래 *"We've detected N other automations that we can't display"* 배너에 나타나며, 업데이트하고 재배포할 때까지 어떤 메트릭에도 기여하지 않습니다. [개요 — 요구사항](/ko/enterprise/features/agent-control-plane/overview#요구사항)을 참고하세요.
+</Note>
+
+## 대시보드
+
+페이지 상단에는 두 개의 메트릭 카드와 인터랙티브 sankey가 있습니다. 어느 카드를 클릭해도 sankey가 다음 두 모드 사이를 전환합니다:
+
+- **상태 모드** — `전체 자동화 → 상태 버킷(Critical / Warning / Healthy)`. 버킷을 클릭하면 Automations 테이블이 해당 deployment로 필터링됩니다.
+- **소비 모드** — `모델 공급자 → 자동화 → 총 비용`. 공급자를 클릭하면 Consumption 테이블이 해당 공급자로 필터링됩니다.
+
+| 카드 | 표시 내용 |
+|------|---------------|
+| **Automations** | `active` 자동화(및 전체 개수), 윈도우 내 총 `errors`, 현재 `active executions`(및 윈도우 내 총합), 이전 기간 대비 변화량. |
+| **Consumption** | 총 `cost`와 `tokens used`, 이전 기간 대비 비용 변화량. |
+
+<Frame>
+  ![소비 sankey가 적용된 개요](/images/enterprise/acp-overview-consumption-sankey.png)
+</Frame>
+
+## Automations 테이블
+
+**Automations** 서브 탭은 deployment 단위의 플릿 상태 분해입니다. 각 행은 배포된 하나의 crew 또는 flow입니다.
+
+<Frame>
+  ![상태 분해가 있는 자동화 테이블](/images/enterprise/acp-automations-table.png)
+</Frame>
+
+| 열 | 표시 내용 |
+|--------|---------------|
+| **Automation** | deployment 이름과 할당된 태그(예: `production`, `financial`). |
+| **Last execution** | 가장 최근 실행 이후 경과 시간. |
+| **Health Status Breakdown** | 윈도우 내 실행에 대한 `Critical` / `Warning` / `Healthy` 비율의 누적 막대. |
+| **Executions with Errors** | 윈도우 내 총 실패 실행 수. |
+| **PII detection applied** | deployment별 PII 설정 또는 일치하는 [PII 규칙](/ko/enterprise/features/agent-control-plane/rules)이 활성화된 경우 `Yes`. |
+| **Executions** | 윈도우 내 총 실행 수. |
+| **Last updated** | deployment의 마지막 재배포 시점. |
+| **Crew Version** | deployment가 보고한 `crewai` 버전. `1.13` 미만 버전 옆의 정보 아이콘은 메트릭에 기여할 수 없는 행을 표시합니다. |
+
+이름으로 검색하고 `Status`(`Healthy` / `Warning` / `Critical`)로 필터링하며, 컬럼 헤더로 정렬할 수 있습니다. deployment 이름을 클릭하면 **Automation 패널**이 열립니다.
+
+## Consumption 테이블
+
+**Consumption** 서브 탭은 deployment 단위의 LLM 지출 및 토큰 사용량 분해입니다.
+
+<Frame>
+  ![LLM 공급자별로 분해된 소비 테이블](/images/enterprise/acp-consumption-table.png)
+</Frame>
+
+| 열 | 표시 내용 |
+|--------|---------------|
+| **Automation** | deployment 이름. |
+| **Last execution** | 가장 최근 실행 이후 경과 시간. |
+| **Tokens used** | 이 자동화가 사용한 LLM 공급자별로 한 행, 이전 기간 대비 변화량 포함. |
+| **Cost** | LLM 공급자별 비용, 이전 기간 대비 변화량 포함. |
+| **Total cost** | 모든 공급자의 합계, 변화량 포함. |
+| **Executions** | 윈도우 내 총 실행 수. |
+| **Last updated** | deployment의 마지막 재배포 시점. |
+| **Crew Version** | deployment가 보고한 `crewai` 버전. |
+
+**LLM provider**로 필터링하고 `Cost`, `Executions` 또는 `Last run`으로 정렬할 수 있습니다.
+
+<Info>
+  **빈 셀(`—` 또는 `$0.00`)은 보통 deployment가 crewAI v1.13 미만임을 의미합니다.** 위 스크린샷에서 *Automation F*(`1.7.0`)와 *Automation I*(`1.12.2`)는 토큰과 비용이 비어 있습니다. 실행은 여전히 동작하지만, 이 테이블을 채우는 공급자 수준 텔레메트리를 emit하지 않습니다. 이 crew들을 업데이트하고 재배포하면 소비 데이터가 수집되기 시작합니다.
+</Info>
+
+## 관련 문서
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — 개요" icon="book-open" href="/ko/enterprise/features/agent-control-plane/overview">
+    ACP란 무엇이며, 요구사항, 플랜 등급, RBAC에 대해.
+  </Card>
+  <Card title="Agent Control Plane — 규칙" icon="shield-check" href="/ko/enterprise/features/agent-control-plane/rules">
+    조직 단위 PII Redaction 규칙을 여러 자동화에 적용합니다.
+  </Card>
+  <Card title="Traces" icon="timeline" href="/ko/enterprise/features/traces">
+    개별 실행을 드릴다운하여 에이전트의 추론, 도구 호출, 토큰 사용량을 확인합니다.
+  </Card>
+  <Card title="AMP에 배포" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
+    Agent Control Plane을 지원하는 crewAI 버전으로 crew를 배포합니다.
+  </Card>
+</CardGroup>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  Agent Control Plane에서 메트릭을 해석하는 데 도움이 필요하시면 지원 팀에 문의하세요.
+</Card>

docs/ko/enterprise/features/agent-control-plane/overview.mdx

@@ -0,0 +1,74 @@
+---
+title: Agent Control Plane 개요
+description: "라이브 자동화의 통합 운영 허브 — 플릿 상태, LLM 소비, 조직 단위 정책을 한 화면에서."
+sidebarTitle: 개요
+icon: "book-open"
+---
+
+## 개요
+
+**Agent Control Plane**(ACP)은 CrewAI AMP에서 실행 중인 모든 워크로드를 위한 운영 허브입니다. **Automations**와 **Rules** 두 개의 탭으로 구성된 단일 화면에서 다음 작업을 할 수 있습니다:
+
+- 모든 라이브 자동화(crew 또는 flow)의 **상태(health)**를 `Critical` / `Warning` / `Healthy` 분포와 실행 횟수로 모니터링합니다.
+- 자동화별·공급자별·모델별 **LLM 소비**(토큰 및 비용)를 추적하고, 이전 기간 대비 변화량을 확인합니다.
+- 임의의 자동화 또는 모델 공급자를 드릴다운하여 시계열 차트와 공급자별 분해를 살펴봅니다.
+- 조직 전체에 **규칙(Rules)**(현재: PII Redaction)을 적용하여 각 deployment를 개별 편집하지 않고 한 번에 여러 자동화에 정책을 강제합니다.
+
+<Frame>
+  ![Agent Control Plane 개요](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+<Note>
+  Agent Control Plane은 현재 CrewAI Platform에서 **Beta**로 표시되어 있습니다.
+</Note>
+
+두 탭은 서로 다른 두 가지 질문에 답합니다:
+
+- **Automations** — *"지금 내 플릿은 어떻게 동작하고 있고, 얼마나 비용이 들고 있는가?"* [모니터링](/ko/enterprise/features/agent-control-plane/monitoring)을 참고하세요.
+- **Rules** — *"정책(예: PII redaction)을 매번 재배포하지 않고 여러 deployment에 어떻게 강제할 수 있는가?"* [규칙](/ko/enterprise/features/agent-control-plane/rules)을 참고하세요.
+
+## 요구사항
+
+<Warning>
+  이 페이지에서 자동화에 대한 데이터(상태, 실행, 오류, 토큰, 비용)를 채우려면 **crewAI v1.13 이상**이 필요합니다. 모든 데이터는 `crewai==1.13`에서 활성화된 텔레메트리를 통해 흘러갑니다. 그 이전 deployment는 *"We've detected N other automations that we can't display"* 배너에 나타나며, 업데이트하고 재배포할 때까지 어떤 행에도 데이터를 기여하지 않습니다.
+</Warning>
+
+<Warning>
+  [규칙](/ko/enterprise/features/agent-control-plane/rules)을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Rules 탭을 열고 기존 규칙을 볼 수 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction rules require an Enterprise plan."* 경고와 함께 읽기 전용으로 표시됩니다. 모니터링(Automations 탭)은 기능이 활성화된 모든 플랜에서 사용할 수 있습니다.
+</Warning>
+
+- **Agent Control Plane** 기능이 조직에 대해 활성화되어 있어야 합니다. 사이드바에 보이지 않으면 계정 오너에게 활성화를 요청하세요.
+- ACP 내부에서는 [RBAC](/ko/enterprise/features/rbac)가 접근 권한을 관리합니다: 대시보드 및 규칙을 보려면 `read`, 규칙을 생성·편집·토글·삭제하려면 `manage` 권한이 필요합니다.
+- 모든 차트와 테이블은 오른쪽 상단의 시간 선택기를 통해 **지난 24시간**, **지난 1주**, **지난 30일**로 범위를 조정할 수 있습니다. 변화량(`↑ 8 vs yesterday`, `↓ $20.57 vs yesterday` 등)은 선택한 윈도우를 같은 길이의 이전 윈도우와 비교합니다.
+
+## 여기에서 할 수 있는 일
+
+<CardGroup cols={2}>
+  <Card title="모니터링" icon="gauge" href="/ko/enterprise/features/agent-control-plane/monitoring">
+    메트릭 카드, 인터랙티브 sankey, 자동화별 테이블, 자동화 또는 공급자별 드릴다운 사이드 패널로 플릿 상태와 LLM 지출을 살펴봅니다.
+  </Card>
+  <Card title="규칙" icon="shield-check" href="/ko/enterprise/features/agent-control-plane/rules">
+    도구와 태그로 범위를 지정한 PII Redaction 정책을 조직 단위로 적용합니다. 변경 사항은 다음 실행부터 적용되며 재배포가 필요 없습니다.
+  </Card>
+</CardGroup>
+
+## 관련 문서
+
+<CardGroup cols={2}>
+  <Card title="Traces" icon="timeline" href="/ko/enterprise/features/traces">
+    개별 실행을 드릴다운하여 에이전트의 추론, 도구 호출, 토큰 사용량을 확인합니다.
+  </Card>
+  <Card title="RBAC" icon="users" href="/ko/enterprise/features/rbac">
+    누가 Agent Control Plane을 읽을 수 있고 누가 규칙을 편집할 수 있는지 관리합니다.
+  </Card>
+  <Card title="Traces용 PII Redaction" icon="lock" href="/ko/enterprise/features/pii-trace-redactions">
+    규칙이 참조하는 엔티티 카탈로그 및 deployment 단위 PII 설정.
+  </Card>
+  <Card title="AMP에 배포" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
+    Agent Control Plane을 지원하는 crewAI 버전으로 crew를 배포합니다.
+  </Card>
+</CardGroup>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  메트릭 해석 또는 규칙 설계에 도움이 필요하시면 지원 팀에 문의하세요.
+</Card>

docs/ko/enterprise/features/agent-control-plane/rules.mdx

@@ -0,0 +1,114 @@
+---
+title: "규칙 설정하기"
+description: "한 곳에서 조직 단위 정책을 여러 자동화에 적용합니다."
+sidebarTitle: "규칙"
+icon: "shield-check"
+mode: "wide"
+---
+
+## 개요
+
+규칙(Rules)은 각 deployment를 개별 설정하는 대신, 정책 — 현재: **PII Redaction** — 을 한 번에 여러 자동화에 적용할 수 있게 해줍니다. 관리하려면 [Agent Control Plane](/ko/enterprise/features/agent-control-plane/overview)에서 **Rules** 탭을 엽니다.
+
+<Frame>
+  ![규칙 목록](/images/enterprise/acp-rules-list.png)
+</Frame>
+
+각 규칙 카드에는 이름, 설명, 규칙이 적용되는 **범위(scope)**(선택된 도구와 태그), 그리고 현재 범위와 일치하는 deployment의 수인 **engaged automations**가 표시됩니다. 오른쪽 토글로 규칙을 삭제하지 않고 활성/비활성할 수 있습니다.
+
+## 요구사항
+
+<Warning>
+  PII Redaction 규칙을 생성하거나 편집하려면 **Enterprise 또는 Ultra 플랜**이 필요합니다. 하위 플랜의 조직도 Rules 탭을 열고 기존 규칙을 볼 수는 있지만, 편집기는 "Enterprise" 잠금 핀과 *"PII Redaction rules require an Enterprise plan."* 경고와 함께 읽기 전용으로 렌더링됩니다. 업그레이드하려면 계정 오너 또는 영업팀에 문의하세요.
+</Warning>
+
+- **Agent Control Plane** 기능이 조직에 대해 활성화되어 있어야 합니다. [개요 — 요구사항](/ko/enterprise/features/agent-control-plane/overview#요구사항)을 참고하세요.
+- 규칙을 생성·편집·토글·삭제하려면 Agent Control Plane에 대한 [RBAC](/ko/enterprise/features/rbac)의 `manage` 권한이 필요합니다. 보려면 `read` 권한만으로 충분합니다.
+- 모든 규칙 변경은 감사를 위해 버전 관리됩니다.
+
+## 사용 가능한 규칙 유형
+
+| 유형 | 동작 |
+|------|---------------|
+| **PII Redaction** | 일치하는 모든 자동화의 실행에 PII redaction을 적용합니다. [Traces용 PII Redaction](/ko/enterprise/features/pii-trace-redactions)에 문서화된 동일한 엔티티 카탈로그와 커스텀 recognizer를 사용합니다. |
+
+향후 더 많은 규칙 유형이 추가될 예정입니다.
+
+## 규칙 만들기
+
+<Frame>
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="조건 및 PII 마스크 유형이 있는 규칙 편집 사이드 패널" width="450" />
+</Frame>
+
+<Steps>
+  <Step title="편집기 열기">
+    Rules 탭 오른쪽 상단의 **+ Create new**를 클릭하거나, 기존 규칙 카드의 **View Details**를 클릭합니다.
+  </Step>
+
+  <Step title="규칙 이름과 설명 작성">
+    규칙에 명확한 이름(예: *Mask PII (CC)*)과 적용 시점을 설명하는 description을 부여합니다. 둘 다 규칙 카드와 Engaged Automations 모달에 표시됩니다.
+  </Step>
+
+  <Step title="유형 선택">
+    현재 **PII Redaction**만 사용할 수 있습니다.
+  </Step>
+
+  <Step title="조건 설정">
+    조건은 규칙이 어떤 자동화에 engage 할지 결정합니다. 둘 다 선택 사항이며 **집합 동일성(set-equality)** 의미론을 사용합니다:
+
+    - **Tools** — 도구 집합이 선택된 도구와 **정확히 일치**하는 자동화만 engage 됩니다. Studio 앱, MCP, OSS 도구, Tool Repository registry 도구 중에서 선택합니다.
+    - **Automations** — 태그 집합이 선택된 태그와 **정확히 일치**하는 자동화만 engage 됩니다.
+
+    피커를 비워두면 "이 차원에서 필터링하지 않음"을 의미합니다. 두 피커를 모두 비워두면 규칙이 조직의 **모든** 자동화에 적용됩니다.
+  </Step>
+
+  <Step title="PII Mask Type 테이블 구성">
+    적용할 각 엔티티 유형을 체크하고 **Mask**(엔티티 레이블로 치환, 예: `<CREDIT_CARD>`) 또는 **Redact**(일치하는 텍스트를 완전히 제거) 중에서 선택합니다. 전체 엔티티 카탈로그와 조직 단위 커스텀 recognizer 추가 방법은 [Traces용 PII Redaction](/ko/enterprise/features/pii-trace-redactions)을 참고하세요.
+  </Step>
+
+  <Step title="저장">
+    저장하는 즉시 engage 된 모든 자동화의 **향후** 실행에 규칙이 적용됩니다. 재배포는 필요 없습니다.
+  </Step>
+</Steps>
+
+## Engaged automations
+
+규칙 카드의 **Engaged N automations**를 클릭하면 현재 규칙이 일치시키고 있는 deployment와 각 deployment의 마지막 실행을 정확히 확인할 수 있습니다.
+
+<Frame>
+  ![Engaged automations 모달](/images/enterprise/acp-rules-engaged-modal.png)
+</Frame>
+
+규칙을 활성화하기 전에 범위를 빠르게 점검하는 가장 좋은 방법입니다. 예를 들어, `production` 태그로 범위를 지정한 규칙이 의도치 않게 staging deployment를 일치시키지 않는지 확인할 수 있습니다.
+
+## 조직 단위 규칙 vs deployment 단위 설정
+
+PII Redaction은 두 곳에서 설정할 수 있습니다:
+
+- **deployment 단위** — 각 deployment의 **Settings → PII Protection** ([가이드](/ko/enterprise/features/pii-trace-redactions))
+- **조직 단위** — 이 페이지의 규칙
+
+활성화된 조직 단위 규칙의 범위가 어떤 deployment와 일치하면, 규칙의 엔티티 구성이 그 deployment의 실행에 대해 **deployment가 소유한 PII 설정을 덮어씁니다**. 규칙이 연결된 동안에는 규칙이 단일 진실 공급원이 됩니다. 규칙을 비활성화하거나 분리하면(또는 범위를 변경하여 더 이상 일치하지 않게 만들면) deployment는 자체 PII Protection 설정으로 되돌아갑니다.
+
+여러 deployment에 걸쳐 일관된 정책을 강제하고 싶을 때는 조직 단위 규칙을 선호하고, 일회성 예외에 대해서는 deployment 단위 설정을 사용하세요.
+
+## 관련 문서
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — 개요" icon="book-open" href="/ko/enterprise/features/agent-control-plane/overview">
+    ACP란 무엇이며, 요구사항, 플랜 등급, RBAC에 대해.
+  </Card>
+  <Card title="Agent Control Plane — 모니터링" icon="gauge" href="/ko/enterprise/features/agent-control-plane/monitoring">
+    플릿 전반의 자동화와 LLM 소비를 모니터링합니다.
+  </Card>
+  <Card title="Traces용 PII Redaction" icon="lock" href="/ko/enterprise/features/pii-trace-redactions">
+    엔티티 카탈로그, 커스텀 recognizer, deployment 단위 구성.
+  </Card>
+  <Card title="RBAC" icon="users" href="/ko/enterprise/features/rbac">
+    누가 규칙을 만들거나 편집할 수 있는지 관리합니다.
+  </Card>
+</CardGroup>
+
+<Card title="도움이 필요하신가요?" icon="headset" href="mailto:support@crewai.com">
+  조직의 규칙을 설계하는 데 도움이 필요하시면 지원 팀에 문의하세요.
+</Card>

docs/ko/enterprise/integrations/salesforce.mdx

@@ -17,16 +17,60 @@ Salesforce 통합을 사용하기 전에 다음을 확인하세요:
 - 적절한 권한이 있는 Salesforce 계정
 - [통합 페이지](https://app.crewai.com/integrations)를 통해 Salesforce 계정 연결
 
+<Note>
+  Salesforce는 사용자가 연결하기 전에 **관리자가 CrewAI 패키지를 한 번 설치**
+  해야 합니다. 이는 Spring '26 릴리스부터 모든 ExternalClientApp 기반 통합에
+  적용되는 Salesforce 플랫폼의 요구 사항이며, CrewAI 고유의 단계가 아닙니다.
+  CrewAI AMP의 Connect Salesforce 플로우가 첫 연결 시 이 과정을 안내합니다.
+</Note>
+
 ## Salesforce 통합 설정
 
 ### 1. Salesforce 계정 연결
 
-1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/connectors)으로 이동합니다.
+1. [CrewAI AMP 통합](https://app.crewai.com/crewai_plus/unified_tools)으로 이동합니다.
 2. 인증 통합 섹션에서 **Salesforce**를 찾습니다.
-3. **연결**을 클릭하고 OAuth 과정을 완료합니다.
+3. **연결**을 클릭합니다.
+
+이후 동작은 관리자가 조직에 CrewAI 패키지를 이미 설치했는지에 따라 달라집니다:
+
+- **패키지가 이미 설치된 경우:** 곧바로 Salesforce OAuth 동의 화면으로
+  이동합니다. 승인하면 연결이 완료됩니다.
+- **패키지가 아직 설치되지 않은 경우:** **Install CrewAI in Salesforce**
+  페이지가 표시됩니다. 아래의 일회성 설치 단계를 따른 뒤, CrewAI AMP로
+  돌아와 **연결**을 다시 클릭하세요.
+
 4. CRM 및 영업 관리에 필요한 권한을 부여합니다.
 5. [통합 설정](https://app.crewai.com/crewai_plus/settings/integrations)에서 Enterprise Token을 복사합니다.
 
+#### 일회성 관리자 설치 (Salesforce 관리자 전용)
+
+조직 내 누군가 **Connect Salesforce**를 처음 클릭하면, CrewAI는 CrewAI
+관리형 패키지의 설치 페이지로 리디렉션합니다. Salesforce 관리자가 조직
+전체를 위해 한 번만 설치하면 됩니다.
+
+1. CrewAI 내 설치 페이지에서 **Install in Salesforce**를 클릭합니다.
+   (해당 페이지 URL을 관리자에게 공유해도 됩니다. 설치 링크는 누구든 열 수
+   있도록 동작합니다.)
+2. 관리자 권한으로 Salesforce에 로그인합니다. 샌드박스 환경에서는 URL의
+   `login.salesforce.com`을 `test.salesforce.com`으로 바꾼 뒤 엽니다.
+3. **Install for All Users**를 선택하고, 서드파티 앱 동의 항목을 확인한 뒤
+   **Install**을 클릭합니다.
+4. Salesforce Setup에서 **External Client App Manager** → **CrewAI App** →
+   **Policies** 탭 → **Edit**로 이동하여 다음과 같이 설정합니다:
+   - **Permitted Users:** All users may self-authorize
+   - **IP Relaxation:** Relax IP restrictions
+   - **Refresh Token Policy:** Refresh token is valid until revoked
+5. 저장합니다.
+6. CrewAI AMP로 돌아가 **Connect Salesforce**를 다시 클릭합니다. 이번에는
+   OAuth가 정상적으로 완료됩니다.
+
+<Note>
+  **Salesforce 관리자가 아니신가요?** 설치 페이지의 URL(또는 설치 링크 자체)
+  을 Salesforce 관리자에게 전달하고 위 단계를 진행해 달라고 요청하세요.
+  관리자가 완료하면 CrewAI AMP로 돌아와 **연결**을 다시 클릭하면 됩니다.
+</Note>
+
 ### 2. 필수 패키지 설치
 
 ```bash

docs/pt-BR/concepts/checkpointing.mdx

@@ -1,229 +1,423 @@
 ---
 title: Checkpointing
-description: Salve automaticamente o estado de execucao para que crews, flows e agentes possam retomar apos falhas.
+description: Salve automaticamente o estado de execução para que crews, flows e agentes possam retomar após falhas.
 icon: floppy-disk
 mode: "wide"
 ---
 
-<Warning>
-O checkpointing esta em versao inicial. As APIs podem mudar em versoes futuras.
-</Warning>
+O checkpointing salva um snapshot do estado de execução durante uma execução para que uma crew, flow ou agente possa retomar após uma falha ou ser bifurcado em uma branch alternativa.
 
-## Visao Geral
+<CardGroup cols={2}>
+  <Card title="Explicação" icon="lightbulb" href="#explicacao">
+    Como o checkpointing funciona: eventos, armazenamento e herança.
+  </Card>
+  <Card title="Tutorial" icon="graduation-cap" href="#tutorial-retomar-uma-crew-com-falha">
+    Um passo a passo de 5 minutos: executar, interromper, retomar.
+  </Card>
+  <Card title="Guias de uso" icon="screwdriver-wrench" href="#guias-de-uso">
+    Receitas focadas em tarefas para fluxos comuns.
+  </Card>
+  <Card title="Referência" icon="book" href="#referencia">
+    `CheckpointConfig`, eventos, provedores e CLI.
+  </Card>
+</CardGroup>
 
-O checkpointing salva automaticamente o estado de execucao durante uma execucao. Se uma crew, flow ou agente falhar no meio da execucao, voce pode restaurar a partir do ultimo checkpoint e retomar sem reexecutar o trabalho ja concluido.
+## Explicação
 
-## Inicio Rapido
+### O que é um checkpoint
 
-```python
-from crewai import Crew, CheckpointConfig
+Um checkpoint captura tudo o que o CrewAI precisa para recriar uma execução em andamento: o estado completo da crew, flow ou agente — configuração, memória e fontes de conhecimento dos agentes, progresso das tarefas, saídas intermediárias, estado interno e atributos — junto com os inputs do kickoff, o histórico de eventos até aquele ponto e um ID de linhagem que liga o checkpoint à execução de origem.
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=True,  # usa padroes: ./.checkpoints, em task_completed
-)
-result = crew.kickoff()
-```
+Restaurar reconstrói esse estado e continua. Tarefas concluídas são puladas, memória e conhecimento são reidratados, e o trabalho downstream roda contra as mesmas saídas que a execução original produziu. Fazer fork executa a mesma restauração sob uma nova linhagem, para que a nova branch e a execução original gravem checkpoints lado a lado sem sobrescrever uma a outra.
 
-Os arquivos de checkpoint sao gravados em `./.checkpoints/` apos cada tarefa concluida.
+### Quando os checkpoints são gravados
 
-## Configuracao
+O checkpointing é orientado a eventos. O runtime se inscreve nos eventos selecionados em `on_events` e grava um checkpoint sempre que um é disparado. O padrão `task_completed` produz um checkpoint por tarefa finalizada — um equilíbrio razoável entre granularidade e uso de disco. Eventos de alta frequência como `llm_call_completed` estão disponíveis para recuperação mais granular, mas gravam muito mais arquivos.
 
-Use `CheckpointConfig` para controle total:
+### Armazenamento
 
-```python
-from crewai import Crew, CheckpointConfig
+Dois provedores acompanham o CrewAI:
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        on_events=["task_completed", "crew_kickoff_completed"],
-        max_checkpoints=5,
-    ),
-)
-```
+- `JsonProvider` grava um arquivo por checkpoint. Legível e fácil de inspecionar.
+- `SqliteProvider` grava em um único banco SQLite. Melhor para checkpointing de alta frequência.
 
-### Campos do CheckpointConfig
+Ambos removem os checkpoints mais antigos quando `max_checkpoints` está definido.
 
-| Campo | Tipo | Padrao | Descricao |
-|:------|:-----|:-------|:----------|
-| `location` | `str` | `"./.checkpoints"` | Caminho para os arquivos de checkpoint |
-| `on_events` | `list[str]` | `["task_completed"]` | Tipos de evento que acionam um checkpoint |
-| `provider` | `BaseProvider` | `JsonProvider()` | Backend de armazenamento |
-| `max_checkpoints` | `int \| None` | `None` | Maximo de arquivos a manter; os mais antigos sao removidos primeiro |
+<Note>
+Gravações de checkpoint automáticas (acionadas por evento) são best-effort: uma falha é registrada em log e a execução continua. Chamadas manuais a `state.checkpoint()` e `state.acheckpoint()` relançam a exceção.
+</Note>
 
-### Heranca e Desativacao
+### Modelo de herança
 
-O campo `checkpoint` em Crew, Flow e Agent aceita `CheckpointConfig`, `True`, `False` ou `None`:
+`Crew`, `Flow` e `Agent` aceitam um argumento `checkpoint`. Filhos herdam do pai a menos que definam seu próprio valor ou passem `False` para desativar. Ative o checkpointing uma vez na crew e todos os agentes participam, ou exclua um agente seletivamente.
 
-| Valor | Comportamento |
-|:------|:--------------|
-| `None` (padrao) | Herda do pai. Um agente herda a configuracao da crew. |
-| `True` | Ativa com padroes. |
-| `False` | Desativacao explicita. Interrompe a heranca do pai. |
-| `CheckpointConfig(...)` | Configuracao personalizada. |
+## Tutorial: Retomar uma crew com falha
 
-```python
-crew = Crew(
-    agents=[
-        Agent(role="Researcher", ...),                  # herda checkpoint da crew
-        Agent(role="Writer", ..., checkpoint=False),     # desativado, sem checkpoints
-    ],
-    tasks=[...],
-    checkpoint=True,
-)
-```
+Este passo a passo leva cerca de 5 minutos. Você executará uma crew de duas tarefas, a interromperá no meio e a retomará a partir do checkpoint salvo.
 
-## Retomando a partir de um Checkpoint
+<Steps>
+  <Step title="Crie a crew com checkpointing ativado">
+    ```python
+    from crewai import Agent, Crew, Task
 
-```python
-# Restaurar e retomar
-crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
-result = crew.kickoff()  # retoma a partir da ultima tarefa concluida
-```
+    researcher = Agent(role="Researcher", goal="Research", backstory="Expert")
+    writer = Agent(role="Writer", goal="Write", backstory="Expert")
 
-A crew restaurada pula tarefas ja concluidas e retoma a partir da primeira incompleta.
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[
+            Task(description="Research AI trends", agent=researcher, expected_output="bullets"),
+            Task(description="Write a summary", agent=writer, expected_output="paragraph"),
+        ],
+        checkpoint=True,
+    )
+    ```
+  </Step>
+  <Step title="Execute e interrompa após a primeira tarefa">
+    ```python
+    result = crew.kickoff()
+    ```
 
-## Funciona em Crew, Flow e Agent
+    Pressione `Ctrl+C` após a primeira tarefa concluir. Em `./.checkpoints/`, um arquivo `<timestamp>_<uuid>.json` é o checkpoint.
+  </Step>
+  <Step title="Retome a partir do checkpoint">
+    ```python
+    from crewai import CheckpointConfig
 
-### Crew
+    result = crew.kickoff(
+        from_checkpoint=CheckpointConfig(
+            restore_from="./.checkpoints/<timestamp>_<uuid>.json",
+        ),
+    )
+    ```
 
-```python
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research_task, write_task, review_task],
-    checkpoint=CheckpointConfig(location="./crew_cp"),
-)
-```
+    A tarefa de pesquisa é pulada, o escritor executa contra a saída de pesquisa salva e a crew finaliza.
+  </Step>
+</Steps>
 
-Gatilho padrao: `task_completed` (um checkpoint por tarefa finalizada).
+## Guias de uso
 
-### Flow
+<AccordionGroup>
+  <Accordion title="Ativar checkpointing com padrões" icon="play">
+    ```python
+    crew = Crew(agents=[...], tasks=[...], checkpoint=True)
+    ```
 
-```python
-from crewai.flow.flow import Flow, start, listen
-from crewai import CheckpointConfig
+    Grava em `./.checkpoints/` em cada `task_completed`.
+  </Accordion>
 
-class MyFlow(Flow):
-    @start()
-    def step_one(self):
-        return "data"
+  <Accordion title="Personalizar armazenamento e frequência" icon="sliders">
+    ```python
+    from crewai import Crew, CheckpointConfig
 
-    @listen(step_one)
-    def step_two(self, data):
-        return process(data)
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            on_events=["task_completed", "crew_kickoff_completed"],
+            max_checkpoints=5,
+        ),
+    )
+    ```
+  </Accordion>
 
-flow = MyFlow(
-    checkpoint=CheckpointConfig(
-        location="./flow_cp",
-        on_events=["method_execution_finished"],
-    ),
-)
-result = flow.kickoff()
+  <Accordion title="Escolher um provedor de armazenamento" icon="database">
+    <CodeGroup>
+    ```python JsonProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import JsonProvider
 
-# Retomar
-flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
-result = flow.kickoff()
-```
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./my_checkpoints",
+            provider=JsonProvider(),
+            max_checkpoints=5,
+        ),
+    )
+    ```
+    ```python SqliteProvider
+    from crewai import Crew, CheckpointConfig
+    from crewai.state import SqliteProvider
 
-### Agent
+    crew = Crew(
+        agents=[...],
+        tasks=[...],
+        checkpoint=CheckpointConfig(
+            location="./.checkpoints.db",
+            provider=SqliteProvider(),
+            max_checkpoints=50,
+        ),
+    )
+    ```
+    </CodeGroup>
 
-```python
-agent = Agent(
-    role="Researcher",
-    goal="Research topics",
-    backstory="Expert researcher",
-    checkpoint=CheckpointConfig(
-        location="./agent_cp",
-        on_events=["lite_agent_execution_completed"],
-    ),
-)
-result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
-```
+    <Tip>
+    O SQLite ativa o modo journal WAL para leituras concorrentes. Prefira-o para checkpointing de alta frequência.
+    </Tip>
+  </Accordion>
 
-## Provedores de Armazenamento
+  <Accordion title="Desativar um agente específico" icon="user-slash">
+    ```python
+    crew = Crew(
+        agents=[
+            Agent(role="Researcher", ...),
+            Agent(role="Writer", ..., checkpoint=False),
+        ],
+        tasks=[...],
+        checkpoint=True,
+    )
+    ```
+  </Accordion>
 
-O CrewAI inclui dois provedores de armazenamento para checkpoints.
+  <Accordion title="Fazer fork em uma nova branch" icon="code-branch">
+    `fork()` restaura um checkpoint sob uma nova linhagem para que a nova execução não colida com a original.
 
-### JsonProvider (padrao)
+    ```python
+    config = CheckpointConfig(restore_from="./my_checkpoints/<file>.json")
+    crew = Crew.fork(config, branch="experiment-a")
+    result = crew.kickoff(inputs={"strategy": "aggressive"})
+    ```
 
-Grava cada checkpoint como um arquivo JSON separado.
+    O label `branch` é opcional; um é gerado se omitido.
+  </Accordion>
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import JsonProvider
+  <Accordion title="Checkpoint em Crew, Flow ou Agent" icon="cubes">
+    <Tabs>
+      <Tab title="Crew">
+        ```python
+        crew = Crew(
+            agents=[researcher, writer],
+            tasks=[research_task, write_task, review_task],
+            checkpoint=CheckpointConfig(location="./crew_cp"),
+        )
+        ```
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./my_checkpoints",
-        provider=JsonProvider(),
-        max_checkpoints=5,
-    ),
-)
-```
+        Gatilho padrão: `task_completed`.
+      </Tab>
+      <Tab title="Flow">
+        ```python
+        from crewai.flow.flow import Flow, start, listen
+        from crewai import CheckpointConfig
 
-### SqliteProvider
+        class MyFlow(Flow):
+            @start()
+            def step_one(self):
+                return "data"
 
-Armazena todos os checkpoints em um unico arquivo SQLite.
+            @listen(step_one)
+            def step_two(self, data):
+                return process(data)
 
-```python
-from crewai import Crew, CheckpointConfig
-from crewai.state import SqliteProvider
+        flow = MyFlow(
+            checkpoint=CheckpointConfig(
+                location="./flow_cp",
+                on_events=["method_execution_finished"],
+            ),
+        )
+        result = flow.kickoff()
+        ```
+      </Tab>
+      <Tab title="Agent">
+        ```python
+        agent = Agent(
+            role="Researcher",
+            goal="Research topics",
+            backstory="Expert researcher",
+            checkpoint=CheckpointConfig(
+                location="./agent_cp",
+                on_events=["lite_agent_execution_completed"],
+            ),
+        )
+        result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+        ```
+      </Tab>
+    </Tabs>
+  </Accordion>
 
-crew = Crew(
-    agents=[...],
-    tasks=[...],
-    checkpoint=CheckpointConfig(
-        location="./.checkpoints.db",
-        provider=SqliteProvider(),
-    ),
-)
-```
+  <Accordion title="Gravar um checkpoint manualmente" icon="code">
+    Registre um handler em qualquer evento e chame `state.checkpoint()`.
+
+    <CodeGroup>
+    ```python Sync
+    from __future__ import annotations
+
+    from typing import TYPE_CHECKING, Any
+
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
 
 
-## Tipos de Evento
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    def on_llm_done(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = state.checkpoint("./my_checkpoints")
+        print(f"Checkpoint salvo: {path}")
+    ```
+    ```python Async
+    from __future__ import annotations
 
-O campo `on_events` aceita qualquer combinacao de strings de tipo de evento. Escolhas comuns:
+    from typing import TYPE_CHECKING, Any
 
-| Caso de Uso | Eventos |
-|:------------|:--------|
-| Apos cada tarefa (Crew) | `["task_completed"]` |
-| Apos cada metodo do flow | `["method_execution_finished"]` |
-| Apos execucao do agente | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
-| Apenas na conclusao da crew | `["crew_kickoff_completed"]` |
-| Apos cada chamada LLM | `["llm_call_completed"]` |
-| Em tudo | `["*"]` |
+    from crewai.events.event_bus import crewai_event_bus
+    from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+    if TYPE_CHECKING:
+        from crewai.state.runtime import RuntimeState
+
+
+    @crewai_event_bus.on(LLMCallCompletedEvent)
+    async def on_llm_done_async(source: Any, event: LLMCallCompletedEvent, state: RuntimeState) -> None:
+        path = await state.acheckpoint("./my_checkpoints")
+        print(f"Checkpoint salvo: {path}")
+    ```
+    </CodeGroup>
+
+    Um argumento `state` é fornecido automaticamente quando o handler recebe três parâmetros. Veja [Event Listeners](/pt-BR/concepts/event-listener) para o catálogo completo de eventos.
+  </Accordion>
+
+  <Accordion title="Navegar, retomar e fazer fork pela CLI" icon="terminal">
+    ```bash
+    crewai checkpoint
+    crewai checkpoint --location ./my_checkpoints
+    crewai checkpoint --location ./.checkpoints.db
+    ```
+
+    <Frame caption="Árvore de checkpoints — branches e forks aninham sob seu pai.">
+      <img src="/images/checkpoint-tui-tree.png" alt="Checkpoint TUI tree view" />
+    </Frame>
+
+    O painel esquerdo agrupa checkpoints por branch; forks aninham sob seu pai. Selecionar um checkpoint abre o painel de detalhes com metadados, estado da entidade e progresso das tarefas. **Resume** continua a execução; **Fork** inicia uma nova branch.
+
+    <Frame caption="Aba de visão geral — metadados, estado da entidade e resumo da execução.">
+      <img src="/images/checkpoint-tui-detail-overview.png" alt="Checkpoint detail overview tab" />
+    </Frame>
+
+    O painel de detalhes expõe duas áreas editáveis:
+
+    - **Inputs** — os inputs originais do kickoff, preenchidos e editáveis.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-inputs.png" alt="Editable kickoff inputs" />
+      </Frame>
+
+    - **Saídas das tarefas** — saídas das tarefas concluídas. Editar uma saída e pressionar **Fork** invalida tarefas downstream para que sejam reexecutadas com o contexto modificado.
+
+      <Frame>
+        <img src="/images/checkpoint-tui-detail-tasks.png" alt="Editable task outputs" />
+      </Frame>
+
+    <Frame caption="Tela de fork — confirme uma nova branch a partir do checkpoint selecionado.">
+      <img src="/images/checkpoint-tui-details-fork.png" alt="Fork confirmation panel" />
+    </Frame>
+
+    <Tip>
+    Útil para exploração de cenários: fork, ajuste, observe.
+    </Tip>
+  </Accordion>
+
+  <Accordion title="Inspecionar checkpoints sem a TUI" icon="magnifying-glass">
+    ```bash
+    crewai checkpoint list ./my_checkpoints
+    crewai checkpoint info ./my_checkpoints/<file>.json
+    crewai checkpoint info ./.checkpoints.db
+    ```
+  </Accordion>
+</AccordionGroup>
+
+## Referência
+
+### `CheckpointConfig`
+
+<ParamField path="location" type="str" default='"./.checkpoints"'>
+  Destino do armazenamento. Diretório para `JsonProvider`, caminho de arquivo de banco para `SqliteProvider`.
+</ParamField>
+
+<ParamField path="on_events" type='list[CheckpointEventType | Literal["*"]]' default='["task_completed"]'>
+  Tipos de evento que disparam um checkpoint. `CheckpointEventType` é um `Literal` — seu type checker autocompleta e rejeita valores não suportados. Veja [tipos de evento](#tipos-de-evento) para a lista completa.
+</ParamField>
+
+<ParamField path="provider" type="BaseProvider" default="JsonProvider()">
+  Backend de armazenamento. `JsonProvider` ou `SqliteProvider`.
+</ParamField>
+
+<ParamField path="max_checkpoints" type="int | None" default="None">
+  Máximo de checkpoints a reter. Os mais antigos são removidos após cada gravação.
+</ParamField>
+
+<ParamField path="restore_from" type="Path | str | None" default="None">
+  Checkpoint a restaurar quando passado via `from_checkpoint`.
+</ParamField>
+
+### Valores do campo `checkpoint`
+
+Aceito por `Crew`, `Flow` e `Agent`.
+
+<ParamField path="None" type="padrão">
+  Herda do pai.
+</ParamField>
+
+<ParamField path="True" type="bool">
+  Ativa com padrões.
+</ParamField>
+
+<ParamField path="False" type="bool">
+  Desativação explícita. Interrompe a herança.
+</ParamField>
+
+<ParamField path="CheckpointConfig(...)" type="CheckpointConfig">
+  Configuração personalizada.
+</ParamField>
+
+### Tipos de evento
+
+`on_events` aceita qualquer combinação de valores `CheckpointEventType`. O padrão `["task_completed"]` grava um checkpoint por tarefa finalizada; `["*"]` corresponde a todos os eventos.
 
 <Warning>
-Usar `["*"]` ou eventos de alta frequencia como `llm_call_completed` gravara muitos arquivos de checkpoint e pode impactar o desempenho. Use `max_checkpoints` para limitar o uso de disco.
+`["*"]` e eventos de alta frequência como `llm_call_completed` gravam muitos checkpoints e podem degradar o desempenho. Combine com `max_checkpoints`.
 </Warning>
 
-## Checkpointing Manual
+<Expandable title="Todos os eventos suportados">
 
-Para controle total, registre seu proprio handler de evento e chame `state.checkpoint()` diretamente:
+- **Task** — `task_started`, `task_completed`, `task_failed`, `task_evaluation`
+- **Crew** — `crew_kickoff_started`, `crew_kickoff_completed`, `crew_kickoff_failed`, `crew_train_started`, `crew_train_completed`, `crew_train_failed`, `crew_test_started`, `crew_test_completed`, `crew_test_failed`, `crew_test_result`
+- **Agent** — `agent_execution_started`, `agent_execution_completed`, `agent_execution_error`, `lite_agent_execution_started`, `lite_agent_execution_completed`, `lite_agent_execution_error`, `agent_evaluation_started`, `agent_evaluation_completed`, `agent_evaluation_failed`
+- **Flow** — `flow_created`, `flow_started`, `flow_finished`, `flow_paused`, `method_execution_started`, `method_execution_finished`, `method_execution_failed`, `method_execution_paused`, `human_feedback_requested`, `human_feedback_received`, `flow_input_requested`, `flow_input_received`
+- **LLM** — `llm_call_started`, `llm_call_completed`, `llm_call_failed`, `llm_stream_chunk`, `llm_thinking_chunk`
+- **LLM Guardrail** — `llm_guardrail_started`, `llm_guardrail_completed`, `llm_guardrail_failed`
+- **Tool** — `tool_usage_started`, `tool_usage_finished`, `tool_usage_error`, `tool_validate_input_error`, `tool_selection_error`, `tool_execution_error`
+- **Memory** — `memory_save_started`, `memory_save_completed`, `memory_save_failed`, `memory_query_started`, `memory_query_completed`, `memory_query_failed`, `memory_retrieval_started`, `memory_retrieval_completed`, `memory_retrieval_failed`
+- **Knowledge** — `knowledge_search_query_started`, `knowledge_search_query_completed`, `knowledge_query_started`, `knowledge_query_completed`, `knowledge_query_failed`, `knowledge_search_query_failed`
+- **Reasoning** — `agent_reasoning_started`, `agent_reasoning_completed`, `agent_reasoning_failed`
+- **MCP** — `mcp_connection_started`, `mcp_connection_completed`, `mcp_connection_failed`, `mcp_tool_execution_started`, `mcp_tool_execution_completed`, `mcp_tool_execution_failed`, `mcp_config_fetch_failed`
+- **Observation** — `step_observation_started`, `step_observation_completed`, `step_observation_failed`, `plan_refinement`, `plan_replan_triggered`, `goal_achieved_early`
+- **Skill** — `skill_discovery_started`, `skill_discovery_completed`, `skill_loaded`, `skill_activated`, `skill_load_failed`
+- **Logging** — `agent_logs_started`, `agent_logs_execution`
+- **A2A** — `a2a_delegation_started`, `a2a_delegation_completed`, `a2a_conversation_started`, `a2a_conversation_completed`, `a2a_message_sent`, `a2a_response_received`, `a2a_polling_started`, `a2a_polling_status`, `a2a_push_notification_registered`, `a2a_push_notification_received`, `a2a_push_notification_sent`, `a2a_push_notification_timeout`, `a2a_streaming_started`, `a2a_streaming_chunk`, `a2a_agent_card_fetched`, `a2a_authentication_failed`, `a2a_artifact_received`, `a2a_connection_error`, `a2a_server_task_started`, `a2a_server_task_completed`, `a2a_server_task_canceled`, `a2a_server_task_failed`, `a2a_parallel_delegation_started`, `a2a_parallel_delegation_completed`, `a2a_transport_negotiated`, `a2a_content_type_negotiated`, `a2a_context_created`, `a2a_context_expired`, `a2a_context_idle`, `a2a_context_completed`, `a2a_context_pruned`
+- **Sinais de sistema** — `SIGTERM`, `SIGINT`, `SIGHUP`, `SIGTSTP`, `SIGCONT`
+- **Wildcard** — `"*"` corresponde a todos os eventos.
 
-```python
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.types.llm_events import LLMCallCompletedEvent
+</Expandable>
 
-# Handler sincrono
-@crewai_event_bus.on(LLMCallCompletedEvent)
-def on_llm_done(source, event, state):
-    path = state.checkpoint("./my_checkpoints")
-    print(f"Checkpoint salvo: {path}")
+### Provedores de armazenamento
 
-# Handler assincrono
-@crewai_event_bus.on(LLMCallCompletedEvent)
-async def on_llm_done_async(source, event, state):
-    path = await state.acheckpoint("./my_checkpoints")
-    print(f"Checkpoint salvo: {path}")
-```
+<ParamField path="JsonProvider" type="provider">
+  Um arquivo por checkpoint, nomeado `<timestamp>_<uuid>.json` dentro de `location`.
+</ParamField>
 
-O argumento `state` e o `RuntimeState` passado automaticamente pelo barramento de eventos quando seu handler aceita 3 parametros. Voce pode registrar handlers em qualquer tipo de evento listado na documentacao de [Event Listeners](/pt-BR/concepts/event-listener).
+<ParamField path="SqliteProvider" type="provider">
+  Arquivo de banco único em `location` com journaling WAL.
+</ParamField>
 
-O checkpointing e best-effort: se uma gravacao de checkpoint falhar, o erro e registrado no log, mas a execucao continua sem interrupcao.
+### CLI
+
+| Comando | Propósito |
+|:--------|:----------|
+| `crewai checkpoint` | Inicia a TUI; detecta o armazenamento automaticamente. |
+| `crewai checkpoint --location <path>` | Inicia a TUI em uma localização específica. |
+| `crewai checkpoint list <path>` | Lista checkpoints. |
+| `crewai checkpoint info <path>` | Inspeciona um arquivo de checkpoint ou a entrada mais recente em um banco SQLite. |

docs/pt-BR/enterprise/features/agent-control-plane/monitoring.mdx

@@ -0,0 +1,104 @@
+---
+title: "Observe suas Automações"
+description: "Acompanhe a saúde da frota, o consumo de LLM e o comportamento por automação a partir da aba Automações."
+sidebarTitle: "Monitoramento"
+icon: "gauge"
+mode: "wide"
+---
+
+## Visão Geral
+
+A aba **Automações** é a visão de operações somente leitura do [Agent Control Plane](/pt-BR/enterprise/features/agent-control-plane/overview). Ela combina dois cards de métricas, um sankey interativo e duas sub-tabelas — **Automações** e **Consumo** — nas quais você pode buscar, filtrar e ordenar.
+
+<Frame>
+  ![Visão geral do Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+Todos os gráficos e tabelas respeitam o seletor **Últimas 24 horas / Última Semana / Últimos 30 dias** no canto superior direito. As variações comparam a janela selecionada com a janela anterior de mesma duração.
+
+<Note>
+  As linhas só mostram dados de deployments em **crewAI v1.13 ou superior** — deployments mais antigos aparecem no banner *"We've detected N other automations that we can't display"* abaixo do sankey e não contribuem com nenhuma métrica até que sejam atualizados e re-implantados. Veja [Visão Geral — Requisitos](/pt-BR/enterprise/features/agent-control-plane/overview#requisitos).
+</Note>
+
+## Dashboard
+
+O topo da página tem dois cards de métricas e um sankey interativo. Clicar em qualquer um dos cards alterna o sankey entre dois modos:
+
+- **Modo Saúde** — `Total de Automações → buckets de status (Critical / Warning / Healthy)`. Clique em um bucket para filtrar a tabela Automações para esses deployments.
+- **Modo Consumo** — `Provedores de Modelo → Automações → Custo Total`. Clique em um provedor para filtrar a tabela Consumo para esse provedor.
+
+| Card | O que mostra |
+|------|---------------|
+| **Automações** | Automações `active` (e total), total de `errors` na janela, `active executions` no momento (e total na janela), com variação em relação ao período anterior. |
+| **Consumo** | `cost` total e `tokens used`, com variação de custo em relação ao período anterior. |
+
+<Frame>
+  ![Visão geral com o sankey de consumo](/images/enterprise/acp-overview-consumption-sankey.png)
+</Frame>
+
+## Tabela de Automações
+
+A sub-aba **Automações** é o detalhamento por deployment da saúde da frota. Cada linha é uma crew ou flow implantada.
+
+<Frame>
+  ![Tabela de automações com o detalhamento de status de saúde](/images/enterprise/acp-automations-table.png)
+</Frame>
+
+| Coluna | O que mostra |
+|--------|---------------|
+| **Automação** | Nome do deployment e quaisquer tags atribuídas a ele (ex.: `production`, `financial`). |
+| **Última execução** | Tempo decorrido desde a execução mais recente. |
+| **Health Status Breakdown** | Barra empilhada com percentuais de `Critical` / `Warning` / `Healthy` para as execuções na janela. |
+| **Executions with Errors** | Total de execuções com falha na janela. |
+| **PII detection applied** | `Yes` se houver configuração de PII por deployment ou uma [regra de PII](/pt-BR/enterprise/features/agent-control-plane/rules) correspondente ativa. |
+| **Executions** | Total de execuções na janela. |
+| **Last updated** | Quando o deployment foi re-implantado pela última vez. |
+| **Crew Version** | A versão do `crewai` reportada pelo deployment. Um ícone de informação ao lado de versões abaixo de `1.13` indica linhas que não conseguem contribuir com métricas. |
+
+Busque por nome, filtre por `Status` (`Healthy` / `Warning` / `Critical`) e ordene por qualquer cabeçalho de coluna. Clique no nome de um deployment para abrir o **painel da Automação**.
+
+## Tabela de Consumo
+
+A sub-aba **Consumo** é o detalhamento por deployment de gasto com LLM e uso de tokens.
+
+<Frame>
+  ![Tabela de consumo detalhada por provedor de LLM](/images/enterprise/acp-consumption-table.png)
+</Frame>
+
+| Coluna | O que mostra |
+|--------|---------------|
+| **Automação** | Nome do deployment. |
+| **Última execução** | Tempo decorrido desde a execução mais recente. |
+| **Tokens used** | Uma linha por provedor de LLM usado por esta automação, com variação em relação ao período anterior. |
+| **Cost** | Custo por provedor de LLM, com variação em relação ao período anterior. |
+| **Total cost** | Soma entre todos os provedores, com a variação. |
+| **Executions** | Total de execuções na janela. |
+| **Last updated** | Quando o deployment foi re-implantado pela última vez. |
+| **Crew Version** | A versão do `crewai` reportada pelo deployment. |
+
+Filtre por **LLM provider** e ordene por `Cost`, `Executions` ou `Last run`.
+
+<Info>
+  **Células vazias (`—` ou `$0.00`) normalmente significam que o deployment está abaixo do crewAI v1.13.** Na captura acima, *Automation F* (`1.7.0`) e *Automation I* (`1.12.2`) mostram valores em branco para tokens e custo — suas execuções ainda rodam, mas não emitem a telemetria a nível de provedor que alimenta esta tabela. Atualize e re-implante essas crews para começar a coletar dados de consumo.
+</Info>
+
+## Relacionados
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — Visão Geral" icon="book-open" href="/pt-BR/enterprise/features/agent-control-plane/overview">
+    O que é o ACP, requisitos, planos suportados e RBAC.
+  </Card>
+  <Card title="Agent Control Plane — Regras" icon="shield-check" href="/pt-BR/enterprise/features/agent-control-plane/rules">
+    Aplique regras de PII Redaction em nível de organização em muitas automações.
+  </Card>
+  <Card title="Traces" icon="timeline" href="/pt-BR/enterprise/features/traces">
+    Aprofunde em uma única execução para ver o raciocínio do agente, chamadas de ferramentas e uso de tokens.
+  </Card>
+  <Card title="Deploy no AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
+    Implante uma crew em uma versão do crewAI que suporta o Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nosso time de suporte para ajudar a interpretar métricas no Agent Control Plane.
+</Card>

docs/pt-BR/enterprise/features/agent-control-plane/overview.mdx

@@ -0,0 +1,74 @@
+---
+title: Visão Geral do Agent Control Plane
+description: "Hub único de operações para automações ao vivo — saúde da frota, consumo de LLM e políticas em nível de organização em um só lugar."
+sidebarTitle: Visão Geral
+icon: "book-open"
+---
+
+## Visão Geral
+
+O **Agent Control Plane** (ACP) é o hub de operações para tudo que você tem rodando no CrewAI AMP. É uma tela única — dividida nas abas **Automações** e **Regras** — que permite à sua equipe:
+
+- Monitorar a **saúde** de cada automação ao vivo (crew ou flow), com detalhamentos `Critical` / `Warning` / `Healthy` e contagem de execuções.
+- Acompanhar o **consumo de LLM** — tokens e custo — por automação, por provedor e por modelo, com a variação em relação ao período anterior.
+- Aprofundar em qualquer automação ou provedor de modelo para ver gráficos de série temporal e detalhamentos por provedor.
+- Aplicar **Regras** em nível de organização (hoje: PII Redaction) em muitas automações de uma só vez, em vez de editar cada deployment individualmente.
+
+<Frame>
+  ![Visão geral do Agent Control Plane](/images/enterprise/acp-overview-automations-sankey.png)
+</Frame>
+
+<Note>
+  O Agent Control Plane está atualmente marcado como **Beta** na CrewAI Platform.
+</Note>
+
+As duas abas respondem a duas perguntas distintas:
+
+- **Automações** — *"Como minha frota está se comportando agora e quanto está me custando?"* Veja [Monitoramento](/pt-BR/enterprise/features/agent-control-plane/monitoring).
+- **Regras** — *"Como aplico uma política (por exemplo, PII redaction) em muitos deployments sem precisar reimplantar cada um?"* Veja [Regras](/pt-BR/enterprise/features/agent-control-plane/rules).
+
+## Requisitos
+
+<Warning>
+  **crewAI v1.13 ou superior** é necessário para que uma automação preencha qualquer dado nesta página — saúde, execuções, erros, tokens e custo passam por telemetria que foi habilitada em `crewai==1.13`. Deployments mais antigos aparecem no banner *"We've detected N other automations that we can't display"* e não contribuem com nenhum dado até que sejam atualizados e re-implantados.
+</Warning>
+
+<Warning>
+  **Plano Enterprise ou Ultra** é necessário para criar ou editar [Regras](/pt-BR/enterprise/features/agent-control-plane/rules). Organizações em planos inferiores ainda podem abrir a aba Regras e visualizar regras existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction rules require an Enterprise plan."* O Monitoramento (a aba Automações) está disponível em todos os planos em que o recurso esteja habilitado.
+</Warning>
+
+- O recurso **Agent Control Plane** precisa estar habilitado para sua organização. Se você não o vê na barra lateral, peça ao owner da conta para solicitar a habilitação.
+- Dentro do ACP, o [RBAC](/pt-BR/enterprise/features/rbac) controla o acesso: `read` para visualizar o dashboard e as regras, `manage` para criar, editar, ligar/desligar ou excluir regras.
+- Todos os gráficos e tabelas podem ser ajustados para **Últimas 24 horas**, **Última Semana** ou **Últimos 30 dias** usando o seletor de tempo no canto superior direito. As variações (`↑ 8 vs ontem`, `↓ $20.57 vs ontem`, etc.) comparam a janela selecionada com a janela anterior de mesma duração.
+
+## O que você pode fazer aqui
+
+<CardGroup cols={2}>
+  <Card title="Monitoramento" icon="gauge" href="/pt-BR/enterprise/features/agent-control-plane/monitoring">
+    Acompanhe a saúde da frota e o gasto com LLM com cards de métricas, um sankey interativo, tabelas por automação e painéis laterais de detalhamento para qualquer automação ou provedor.
+  </Card>
+  <Card title="Regras" icon="shield-check" href="/pt-BR/enterprise/features/agent-control-plane/rules">
+    Aplique políticas de PII Redaction em nível de organização, com escopo por ferramentas e tags. As mudanças entram em vigor na próxima execução — sem necessidade de re-implantação.
+  </Card>
+</CardGroup>
+
+## Relacionados
+
+<CardGroup cols={2}>
+  <Card title="Traces" icon="timeline" href="/pt-BR/enterprise/features/traces">
+    Aprofunde em uma única execução para ver o raciocínio do agente, chamadas de ferramentas e uso de tokens.
+  </Card>
+  <Card title="RBAC" icon="users" href="/pt-BR/enterprise/features/rbac">
+    Gerencie quem pode ler o Agent Control Plane e quem pode editar regras.
+  </Card>
+  <Card title="PII Redaction para Traces" icon="lock" href="/pt-BR/enterprise/features/pii-trace-redactions">
+    Catálogo de entidades e configuração de PII por deployment, referenciados pelas Regras.
+  </Card>
+  <Card title="Deploy no AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
+    Implante uma crew em uma versão do crewAI que suporta o Agent Control Plane.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nosso time de suporte para interpretar métricas ou desenhar regras.
+</Card>

docs/pt-BR/enterprise/features/agent-control-plane/rules.mdx

@@ -0,0 +1,114 @@
+---
+title: "Configure as Regras"
+description: "Aplique políticas em nível de organização em muitas automações a partir de um único lugar."
+sidebarTitle: "Regras"
+icon: "shield-check"
+mode: "wide"
+---
+
+## Visão Geral
+
+As Regras permitem aplicar políticas — hoje: **PII Redaction** — em muitas automações de uma só vez, em vez de configurar cada deployment individualmente. Abra a aba **Regras** no [Agent Control Plane](/pt-BR/enterprise/features/agent-control-plane/overview) para gerenciá-las.
+
+<Frame>
+  ![Lista de regras](/images/enterprise/acp-rules-list.png)
+</Frame>
+
+Cada card de regra mostra o nome, a descrição, o **escopo** ao qual a regra se aplica (ferramentas e tags selecionadas) e a contagem de **automações engajadas** — deployments que atualmente correspondem ao escopo. O toggle à direita ativa ou desativa a regra sem excluí-la.
+
+## Requisitos
+
+<Warning>
+  **Plano Enterprise ou Ultra** é necessário para criar ou editar regras de PII Redaction. Organizações em planos inferiores ainda podem abrir a aba Regras e visualizar regras existentes, mas o editor é renderizado em modo somente leitura, com um selo "Enterprise" de bloqueio e o alerta *"PII Redaction rules require an Enterprise plan."* — entre em contato com o owner da sua conta ou com vendas para fazer upgrade.
+</Warning>
+
+- O recurso **Agent Control Plane** precisa estar habilitado para sua organização. Veja [Visão Geral — Requisitos](/pt-BR/enterprise/features/agent-control-plane/overview#requisitos).
+- A permissão `manage` no [RBAC](/pt-BR/enterprise/features/rbac) sobre o Agent Control Plane é necessária para criar, editar, ligar/desligar ou excluir regras. A permissão `read` é suficiente para visualizá-las.
+- Todas as mudanças de regras são versionadas para auditoria.
+
+## Tipos de regra disponíveis
+
+| Tipo | O que faz |
+|------|---------------|
+| **PII Redaction** | Aplica PII redaction às execuções de cada automação correspondente, usando o mesmo catálogo de entidades e recognizers customizados documentados em [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions). |
+
+Mais tipos de regras serão adicionados ao longo do tempo.
+
+## Criando uma regra
+
+<Frame>
+  <img src="/images/enterprise/acp-rules-edit-side-panel.png" alt="Painel lateral de edição de regra com condições e tipo de máscara de PII" width="450" />
+</Frame>
+
+<Steps>
+  <Step title="Abra o editor">
+    Clique em **+ Create new** no canto superior direito da aba Regras, ou em **View Details** em um card de regra existente.
+  </Step>
+
+  <Step title="Dê um nome e descreva a regra">
+    Dê à regra um nome claro (ex.: *Mask PII (CC)*) e uma descrição explicando quando ela se aplica. Ambos aparecem no card da regra e no modal de Automações Engajadas.
+  </Step>
+
+  <Step title="Escolha o tipo">
+    Hoje só **PII Redaction** está disponível.
+  </Step>
+
+  <Step title="Defina as condições">
+    As condições decidem quais automações a regra engaja. Ambas são opcionais e usam a semântica de **igualdade de conjuntos**:
+
+    - **Tools** — apenas automações cujo conjunto de ferramentas **corresponde exatamente** às ferramentas selecionadas serão engajadas. Selecione entre apps do Studio, MCPs, ferramentas OSS e ferramentas do Tool Repository.
+    - **Automations** — apenas automações cujo conjunto de tags **corresponde exatamente** às tags selecionadas serão engajadas.
+
+    Deixar um seletor vazio significa "sem filtro nesta dimensão". Deixar ambos vazios significa que a regra se aplica a **todas** as automações da organização.
+  </Step>
+
+  <Step title="Configure a tabela PII Mask Type">
+    Marque cada tipo de entidade que deseja cobrir e escolha **Mask** (substitui pelo rótulo da entidade, ex.: `<CREDIT_CARD>`) ou **Redact** (remove o texto correspondente por completo). Veja [PII Redaction para Traces](/pt-BR/enterprise/features/pii-trace-redactions) para o catálogo completo de entidades e como adicionar recognizers customizados em nível de organização.
+  </Step>
+
+  <Step title="Salve">
+    A regra se aplica a **futuras** execuções de cada automação engajada assim que você salva. Nenhuma re-implantação é necessária.
+  </Step>
+</Steps>
+
+## Automações engajadas
+
+Clique em **Engaged N automations** em qualquer card de regra para ver exatamente quais deployments a regra está correspondendo no momento, junto com a última execução de cada um.
+
+<Frame>
+  ![Modal de automações engajadas](/images/enterprise/acp-rules-engaged-modal.png)
+</Frame>
+
+Esta é a forma mais rápida de validar o escopo de uma regra antes de habilitá-la — por exemplo, para confirmar que uma regra com escopo na tag `production` não está acidentalmente correspondendo a um deployment de staging.
+
+## Regras em nível de organização vs configurações por deployment
+
+A PII Redaction pode ser configurada em dois lugares:
+
+- **Por deployment** — em **Settings → PII Protection** em cada deployment individual ([guia](/pt-BR/enterprise/features/pii-trace-redactions))
+- **Em nível de organização** — como uma Regra nesta página
+
+Quando o escopo de uma regra habilitada em nível de organização corresponde a um deployment, a configuração de entidades da regra **sobrescreve** as configurações de PII pertencentes ao deployment para as execuções daquele deployment — a regra se torna a fonte única da verdade enquanto está vinculada. Desabilite ou desvincule a regra (ou altere o escopo para que ela não corresponda mais) e o deployment volta às suas próprias configurações de PII Protection.
+
+Prefira regras em nível de organização quando quiser impor uma política consistente em muitos deployments; reserve a configuração por deployment para exceções pontuais.
+
+## Relacionados
+
+<CardGroup cols={2}>
+  <Card title="Agent Control Plane — Visão Geral" icon="book-open" href="/pt-BR/enterprise/features/agent-control-plane/overview">
+    O que é o ACP, requisitos, planos suportados e RBAC.
+  </Card>
+  <Card title="Agent Control Plane — Monitoramento" icon="gauge" href="/pt-BR/enterprise/features/agent-control-plane/monitoring">
+    Acompanhe automações e consumo de LLM em toda a sua frota.
+  </Card>
+  <Card title="PII Redaction para Traces" icon="lock" href="/pt-BR/enterprise/features/pii-trace-redactions">
+    Catálogo de entidades, recognizers customizados e configuração por deployment.
+  </Card>
+  <Card title="RBAC" icon="users" href="/pt-BR/enterprise/features/rbac">
+    Gerencie quem pode criar ou editar regras.
+  </Card>
+</CardGroup>
+
+<Card title="Precisa de ajuda?" icon="headset" href="mailto:support@crewai.com">
+  Entre em contato com nosso time de suporte para ajudar a desenhar regras para a sua organização.
+</Card>

docs/pt-BR/enterprise/integrations/salesforce.mdx

@@ -17,15 +17,65 @@ Antes de usar a integração Salesforce, certifique-se de que você possui:
 - Uma conta Salesforce com permissões apropriadas
 - Sua conta Salesforce conectada via a [página de Integrações](https://app.crewai.com/integrations)
 
+<Note>
+  O Salesforce exige uma **instalação única feita por um administrador** do
+  pacote CrewAI na sua organização antes que qualquer usuário possa se
+  conectar. Isso é uma exigência da plataforma Salesforce para todas as
+  integrações baseadas em ExternalClientApp a partir da release Spring '26 —
+  não é uma etapa específica da CrewAI. O fluxo Connect Salesforce na CrewAI
+  AMP guia você por esta etapa na primeira vez.
+</Note>
+
 ## Configurando a Integração Salesforce
 
 ### 1. Conecte sua Conta Salesforce
 
-1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/connectors)
-2. Encontre **Salesforce** na seção Integrações de Autenticação
-3. Clique em **Conectar** e complete o fluxo OAuth
-4. Conceda as permissões necessárias para gerenciamento de CRM e vendas
-5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations)
+1. Acesse [CrewAI AMP Integrações](https://app.crewai.com/crewai_plus/unified_tools).
+2. Encontre **Salesforce** na seção Integrações de Autenticação.
+3. Clique em **Conectar**.
+
+O que acontece em seguida depende de o administrador Salesforce já ter
+instalado o pacote CrewAI na sua organização:
+
+- **Pacote já instalado:** você será levado diretamente à tela de consentimento
+  OAuth do Salesforce — aprove e a conexão estará feita.
+- **Pacote ainda não instalado:** você verá uma página **Install CrewAI in
+  Salesforce**. Siga as etapas de instalação única abaixo e, depois, volte à
+  CrewAI AMP e clique em **Conectar** novamente.
+
+4. Conceda as permissões necessárias para gerenciamento de CRM e vendas.
+5. Copie seu Token Enterprise em [Configurações de Integração](https://app.crewai.com/crewai_plus/settings/integrations).
+
+#### Instalação única pelo administrador (apenas admin Salesforce)
+
+Na primeira vez que alguém na sua organização clica em **Connect Salesforce**,
+a CrewAI redireciona para uma página de instalação que aponta para o pacote
+gerenciado CrewAI. Um administrador Salesforce precisa instalá-lo uma única
+vez para toda a organização.
+
+1. Na página de instalação dentro da CrewAI, clique em **Install in
+   Salesforce**. (Você também pode compartilhar a URL dessa página com seu
+   administrador — o link de instalação funciona para qualquer pessoa que o
+   abra.)
+2. Entre no Salesforce como administrador. Para sandboxes, troque
+   `login.salesforce.com` por `test.salesforce.com` na URL antes de abrir.
+3. Escolha **Install for All Users**, confirme o aviso sobre aplicativos de
+   terceiros e clique em **Install**.
+4. No Setup do Salesforce, busque **External Client App Manager** → **CrewAI
+   App** → abra a aba **Policies** → **Edit** e configure:
+   - **Permitted Users:** All users may self-authorize
+   - **IP Relaxation:** Relax IP restrictions
+   - **Refresh Token Policy:** Refresh token is valid until revoked
+5. Salve.
+6. Volte à CrewAI AMP e clique em **Connect Salesforce** novamente. Desta vez
+   o OAuth será concluído.
+
+<Note>
+  **Não é administrador Salesforce?** Encaminhe a URL da página de instalação
+  (ou o link de instalação em si) para o seu administrador e peça que ele
+  conclua as etapas acima. Quando ele terminar, volte à CrewAI AMP e clique
+  em **Conectar** novamente.
+</Note>
 
 ### 2. Instale o Pacote Necessário
 

lib/cli/src/crewai_cli/add_crew_to_flow.py

@@ -8,7 +8,6 @@ from crewai_cli.utils import copy_template
 
 def add_crew_to_flow(crew_name: str) -> None:
     """Add a new crew to the current flow."""
-    # Check if pyproject.toml exists in the current directory
     if not Path("pyproject.toml").exists():
         PRINTER.print(
             "This command must be run from the root of a flow project.", color="red"
@@ -17,7 +16,6 @@ def add_crew_to_flow(crew_name: str) -> None:
             "This command must be run from the root of a flow project."
         )
 
-    # Determine the flow folder based on the current directory
     flow_folder = Path.cwd()
     crews_folder = flow_folder / "src" / flow_folder.name / "crews"
 
@@ -25,7 +23,6 @@ def add_crew_to_flow(crew_name: str) -> None:
         PRINTER.print("Crews folder does not exist in the current flow.", color="red")
         raise click.ClickException("Crews folder does not exist in the current flow.")
 
-    # Create the crew within the flow's crews directory
     create_embedded_crew(crew_name, parent_folder=crews_folder)
 
     click.echo(
@@ -51,13 +48,12 @@ def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
         click.secho(f"Creating crew {folder_name}...", fg="green", bold=True)
         crew_folder.mkdir(parents=True)
 
-    # Create config and crew.py files
     config_folder = crew_folder / "config"
     config_folder.mkdir(exist_ok=True)
 
     templates_dir = Path(__file__).parent / "templates" / "crew"
     config_template_files = ["agents.yaml", "tasks.yaml"]
-    crew_template_file = f"{folder_name}.py"  # Updated file name
+    crew_template_file = f"{folder_name}.py"
 
     for file_name in config_template_files:
         src_file = templates_dir / "config" / file_name

lib/cli/src/crewai_cli/checkpoint_cli.py

@@ -222,9 +222,6 @@ def _entity_summary(entities: list[dict[str, Any]]) -> str:
     return ", ".join(parts) if parts else "empty"
 
 
-# --- JSON directory ---
-
-
 def _list_json(location: str) -> list[dict[str, Any]]:
     pattern = os.path.join(location, "**", "*.json")
     results = []
@@ -275,9 +272,6 @@ def _info_json_file(path: str) -> dict[str, Any]:
     return meta
 
 
-# --- SQLite ---
-
-
 def _list_sqlite(db_path: str) -> list[dict[str, Any]]:
     results = []
     with sqlite3.connect(db_path) as conn:
@@ -327,9 +321,6 @@ def _info_sqlite_id(db_path: str, checkpoint_id: str) -> dict[str, Any] | None:
     return meta
 
 
-# --- Public API ---
-
-
 def list_checkpoints(location: str) -> None:
     """List all checkpoints at a location."""
     if _is_sqlite(location):
@@ -367,7 +358,6 @@ def info_checkpoint(path: str) -> None:
     """Show details of a single checkpoint."""
     meta: dict[str, Any] | None = None
 
-    # db_path#checkpoint_id format
     if "#" in path:
         db_path, checkpoint_id = path.rsplit("#", 1)
         if _is_sqlite(db_path):
@@ -376,7 +366,6 @@ def info_checkpoint(path: str) -> None:
                 click.echo(f"Checkpoint not found: {checkpoint_id}")
                 return
 
-    # SQLite file — show latest
     if meta is None and _is_sqlite(path):
         meta = _info_sqlite_latest(path)
         if not meta:
@@ -384,7 +373,6 @@ def info_checkpoint(path: str) -> None:
             return
         click.echo(f"Latest checkpoint: {meta['name']}\n")
 
-    # Directory — show latest JSON
     if meta is None and os.path.isdir(path):
         meta = _info_json_latest(path)
         if not meta:
@@ -392,7 +380,6 @@ def info_checkpoint(path: str) -> None:
             return
         click.echo(f"Latest checkpoint: {meta['name']}\n")
 
-    # Specific JSON file
     if meta is None and os.path.isfile(path):
         try:
             meta = _info_json_file(path)

lib/cli/src/crewai_cli/checkpoint_tui.py

@@ -320,8 +320,6 @@ class CheckpointTUI(App[_TuiResult]):
         self._refresh_tree()
         self.query_one("#tree-panel", Tree).root.expand()
 
-    # ── Tree building ──────────────────────────────────────────────
-
     @staticmethod
     def _top_level_entity(entry: dict[str, Any]) -> tuple[str, str]:
         etype, ename = "unknown", ""
@@ -473,8 +471,6 @@ class CheckpointTUI(App[_TuiResult]):
         self.sub_title = self._location
         self.query_one("#status", Static).update(f" {count} checkpoint(s) | {storage}")
 
-    # ── Detail panel ───────────────────────────────────────────────
-
     async def _clear_scroll(self, tab_id: str) -> VerticalScroll:
         tab = self.query_one(f"#{tab_id}", TabPane)
         scroll = tab.query_one(VerticalScroll)
@@ -661,8 +657,6 @@ class CheckpointTUI(App[_TuiResult]):
             )
             await scroll.mount(row)
 
-    # ── Data collection ────────────────────────────────────────────
-
     def _collect_inputs(self) -> dict[str, Any] | None:
         if not self._input_keys:
             return None
@@ -699,8 +693,6 @@ class CheckpointTUI(App[_TuiResult]):
             return f"{self._location}#{entry['name']}"
         return str(entry.get("name", ""))
 
-    # ── Events ─────────────────────────────────────────────────────
-
     async def on_tree_node_highlighted(
         self, event: Tree.NodeHighlighted[dict[str, Any]]
     ) -> None:

lib/cli/src/crewai_cli/cli.py

@@ -42,7 +42,6 @@ from crewai_cli.utils import build_env_with_all_tool_credentials, read_toml
 
 def _get_cli_version() -> str:
     """Return the best available version string for the CLI."""
-    # Prefer crewai version if installed (keeps existing UX)
     try:
         return get_version("crewai")
     except Exception:  # noqa: S110
@@ -67,7 +66,6 @@ def crewai() -> None:
 def uv(uv_args: tuple[str, ...]) -> None:
     """A wrapper around uv commands that adds custom tool authentication through env vars."""
     try:
-        # Verify pyproject.toml exists first
         read_toml()
     except FileNotFoundError as e:
         raise SystemExit(
@@ -321,7 +319,6 @@ def memory(
         )
         raise SystemExit(1) from exc
 
-    # Build embedder spec from CLI flags.
     embedder_spec: dict[str, Any] | None = None
     if embedder_config:
         import json as _json
@@ -435,7 +432,6 @@ def logout(reset: bool) -> None:
         click.echo("Successfully logged out from CrewAI AMP.")
 
 
-# DEPLOY CREWAI+ COMMANDS
 @crewai.group()
 def deploy() -> None:
     """Deploy the Crew CLI group."""
@@ -766,17 +762,14 @@ def env_view() -> None:
 
     console = Console()
 
-    # Check for .env file
     env_file = Path(".env")
     env_file_exists = env_file.exists()
 
-    # Create table for environment variables
     table = Table(show_header=True, header_style="bold cyan", expand=True)
     table.add_column("Environment Variable", style="cyan", width=30)
     table.add_column("Value", style="white", width=20)
     table.add_column("Source", style="yellow", width=20)
 
-    # Check CREWAI_TRACING_ENABLED
     crewai_tracing = os.getenv("CREWAI_TRACING_ENABLED", "")
     if crewai_tracing:
         table.add_row(
@@ -791,7 +784,6 @@ def env_view() -> None:
             "[dim]—[/dim]",
         )
 
-    # Check other related env vars
     crewai_testing = os.getenv("CREWAI_TESTING", "")
     if crewai_testing:
         table.add_row("CREWAI_TESTING", crewai_testing, "Environment/Shell")
@@ -804,7 +796,6 @@ def env_view() -> None:
     if crewai_org_id:
         table.add_row("CREWAI_ORG_ID", crewai_org_id, "Environment/Shell")
 
-    # Check if .env file exists
     table.add_row(
         ".env file",
         "✅ Found" if env_file_exists else "❌ Not found",
@@ -820,7 +811,6 @@ def env_view() -> None:
     console.print("\n")
     console.print(panel)
 
-    # Show helpful message
     if env_file_exists:
         console.print(
             "\n[dim]💡 Tip: To enable tracing via .env, add: CREWAI_TRACING_ENABLED=true[/dim]"
@@ -896,11 +886,9 @@ def traces_status() -> None:
     table.add_column("Setting", style="cyan")
     table.add_column("Value", style="white")
 
-    # Check environment variable
     env_enabled = os.getenv("CREWAI_TRACING_ENABLED", "false")
     table.add_row("CREWAI_TRACING_ENABLED", env_enabled)
 
-    # Check user consent
     trace_consent = user_data.get("trace_consent")
     if trace_consent is True:
         consent_status = "✅ Enabled (user consented)"
@@ -910,7 +898,6 @@ def traces_status() -> None:
         consent_status = "⚪ Not set (first-time user)"
     table.add_row("User Consent", consent_status)
 
-    # Check overall status
     if is_tracing_enabled():
         overall_status = "✅ ENABLED"
         border_style = "green"

lib/cli/src/crewai_cli/create_crew.py

@@ -50,7 +50,6 @@ def create_folder_structure(
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
     folder_name = re.sub(r"[^a-zA-Z0-9_]", "", folder_name)
 
-    # Check if the name starts with invalid characters or is primarily invalid
     if re.match(r"^[^a-zA-Z0-9_-]+", name):
         raise ValueError(
             f"Project name '{name}' contains no valid characters for a Python module name"
@@ -98,7 +97,6 @@ def create_folder_structure(
             f"Project name '{name}' would generate class name '{class_name}' which cannot start with a digit"
         )
 
-    # Check if the original name (before title casing) is a keyword
     original_name_clean = re.sub(
         r"[^a-zA-Z0-9_]", "", name.replace("_", "").replace("-", "").lower()
     )
@@ -128,7 +126,7 @@ def create_folder_structure(
             click.secho("Operation cancelled.", fg="yellow")
             sys.exit(0)
         click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True)
-        shutil.rmtree(folder_path)  # Delete the existing folder and its contents
+        shutil.rmtree(folder_path)
 
     click.secho(
         f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
@@ -144,7 +142,6 @@ def create_folder_structure(
         (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
         (folder_path / "src" / folder_name / "config").mkdir(parents=True)
 
-        # Copy AGENTS.md to project root (top-level projects only)
         package_dir = Path(__file__).parent
         agents_md_src = package_dir / "templates" / "AGENTS.md"
         if agents_md_src.exists():
@@ -232,25 +229,22 @@ def create_crew(
 
         while True:
             selected_provider = select_provider(provider_models)
-            if selected_provider is None:  # User typed 'q'
+            if selected_provider is None:
                 click.secho("Exiting...", fg="yellow")
                 sys.exit(0)
-            if selected_provider and isinstance(
-                selected_provider, str
-            ):  # Valid selection
+            if selected_provider and isinstance(selected_provider, str):
                 break
             click.secho(
                 "No provider selected. Please try again or press 'q' to exit.", fg="red"
             )
 
-        # Check if the selected provider has predefined models
         if MODELS.get(selected_provider):
             while True:
                 selected_model = select_model(selected_provider, provider_models)
-                if selected_model is None:  # User typed 'q'
+                if selected_model is None:
                     click.secho("Exiting...", fg="yellow")
                     sys.exit(0)
-                if selected_model:  # Valid selection
+                if selected_model:
                     break
                 click.secho(
                     "No model selected. Please try again or press 'q' to exit.",
@@ -258,17 +252,14 @@ def create_crew(
                 )
             env_vars["MODEL"] = selected_model
 
-        # Check if the selected provider requires API keys
         if selected_provider in ENV_VARS:
             provider_env_vars = ENV_VARS[selected_provider]
             for details in provider_env_vars:
                 if details.get("default", False):
-                    # Automatically add default key-value pairs
                     for key, value in details.items():
                         if key not in ["prompt", "key_name", "default"]:
                             env_vars[key] = value
                 elif "key_name" in details:
-                    # Prompt for non-default key-value pairs
                     prompt = details["prompt"]
                     key_name = details["key_name"]
                     api_key_value = click.prompt(prompt, default="", show_default=False)

lib/cli/src/crewai_cli/create_flow.py

@@ -20,25 +20,21 @@ def create_flow(name: str) -> None:
     telemetry = Telemetry()
     telemetry.flow_creation_span(class_name)
 
-    # Create directory structure
     (project_root / "src" / folder_name).mkdir(parents=True)
     (project_root / "src" / folder_name / "crews").mkdir(parents=True)
     (project_root / "src" / folder_name / "tools").mkdir(parents=True)
     (project_root / "tests").mkdir(exist_ok=True)
 
-    # Create .env file
     with open(project_root / ".env", "w") as file:
         file.write("OPENAI_API_KEY=YOUR_API_KEY")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "flow"
 
-    # Copy AGENTS.md to project root
     agents_md_src = package_dir / "templates" / "AGENTS.md"
     if agents_md_src.exists():
         shutil.copy2(agents_md_src, project_root / "AGENTS.md")
 
-    # List of template files to copy
     root_template_files = [".gitignore", "pyproject.toml", "README.md"]
     src_template_files = ["__init__.py", "main.py"]
     tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
@@ -65,25 +61,21 @@ def create_flow(name: str) -> None:
         with open(dst_file, "w") as file:
             file.write(content)
 
-    # Copy and process root template files
     for file_name in root_template_files:
         src_file = templates_dir / file_name
         dst_file = project_root / file_name
         process_file(src_file, dst_file)
 
-    # Copy and process src template files
     for file_name in src_template_files:
         src_file = templates_dir / file_name
         dst_file = project_root / "src" / folder_name / file_name
         process_file(src_file, dst_file)
 
-    # Copy tools files
     for file_name in tools_template_files:
         src_file = templates_dir / file_name
         dst_file = project_root / "src" / folder_name / file_name
         process_file(src_file, dst_file)
 
-    # Copy crew folders
     for crew_folder in crew_folders:
         src_crew_folder = templates_dir / "crews" / crew_folder
         dst_crew_folder = project_root / "src" / folder_name / "crews" / crew_folder

lib/cli/src/crewai_cli/deploy/validate.py

@@ -74,7 +74,6 @@ class ValidationResult:
     hint: str = ""
 
 
-# Maps known provider env var names → label used in hint messages.
 _KNOWN_API_KEY_HINTS: dict[str, str] = {
     "OPENAI_API_KEY": "OpenAI",
     "ANTHROPIC_API_KEY": "Anthropic",

lib/cli/src/crewai_cli/memory_tui.py

@@ -10,10 +10,9 @@ from textual.containers import Horizontal, Vertical
 from textual.widgets import Footer, Header, Input, OptionList, Static, Tree
 
 
-# -- CrewAI brand palette --
-_PRIMARY = "#eb6658"  # coral
-_SECONDARY = "#1F7982"  # teal
-_TERTIARY = "#ffffff"  # white
+_PRIMARY = "#eb6658"
+_SECONDARY = "#1F7982"
+_TERTIARY = "#ffffff"
 
 
 def _format_scope_info(info: Any) -> str:
@@ -193,8 +192,6 @@ class MemoryTUI(App[None]):
             node = parent_node.add(label, data=child)
             self._add_scope_children(node, child, depth + 1, max_depth)
 
-    # -- Populating the OptionList -------------------------------------------
-
     def _populate_entry_list(self) -> None:
         """Clear the OptionList and fill it with the current scope's entries."""
         option_list = self.query_one("#entry-list", OptionList)
@@ -226,8 +223,6 @@ class MemoryTUI(App[None]):
             )
             option_list.add_option(label)
 
-    # -- Detail rendering ----------------------------------------------------
-
     def _format_record_detail(self, record: Any, context_line: str = "") -> str:
         """Format a full MemoryRecord as Rich markup for the detail view.
 
@@ -246,7 +241,6 @@ class MemoryTUI(App[None]):
             lines.append(context_line)
             lines.append("")
 
-        # -- Fields block --
         lines.append(f"[dim]ID:[/]             {record.id}")
         lines.append(f"[dim]Scope:[/]          [bold]{record.scope}[/]")
         lines.append(f"[dim]Importance:[/]      [bold]{record.importance:.2f}[/]")
@@ -264,12 +258,10 @@ class MemoryTUI(App[None]):
         lines.append(f"[dim]Source:[/]         {record.source or '-'}")
         lines.append(f"[dim]Private:[/]        {'Yes' if record.private else 'No'}")
 
-        # -- Content block --
         lines.append(f"\n{sep}")
         lines.append("[bold]Content[/]\n")
         lines.append(record.content)
 
-        # -- Metadata block --
         if record.metadata:
             lines.append(f"\n{sep}")
             lines.append("[bold]Metadata[/]\n")
@@ -278,8 +270,6 @@ class MemoryTUI(App[None]):
 
         return "\n".join(lines)
 
-    # -- Event handlers ------------------------------------------------------
-
     def on_tree_node_selected(self, event: Tree.NodeSelected[str]) -> None:
         """Load entries for the selected scope and populate the OptionList."""
         path = event.node.data if event.node.data is not None else "/"

lib/cli/src/crewai_cli/provider.py

@@ -68,12 +68,12 @@ def select_provider(provider_models: dict[str, list[str]]) -> str | None | bool:
     provider = select_choice(
         "Select a provider to set up:", [*predefined_providers, "other"]
     )
-    if provider is None:  # User typed 'q'
+    if provider is None:
         return None
 
     if provider == "other":
         provider = select_choice("Select a provider from the full list:", all_providers)
-        if provider is None:  # User typed 'q'
+        if provider is None:
             return None
 
     return provider.lower() if provider else False

lib/cli/src/crewai_cli/run_crew.py

@@ -31,7 +31,6 @@ def run_crew(trained_agents_file: str | None = None) -> None:
     min_required_version = "0.71.0"
     pyproject_data = read_toml()
 
-    # Check for legacy poetry configuration
     if pyproject_data.get("tool", {}).get("poetry") and (
         version.parse(crewai_version) < version.parse(min_required_version)
     ):
@@ -41,14 +40,11 @@ def run_crew(trained_agents_file: str | None = None) -> None:
             fg="red",
         )
 
-    # Determine crew type
     is_flow = pyproject_data.get("tool", {}).get("crewai", {}).get("type") == "flow"
     crew_type = CrewType.FLOW if is_flow else CrewType.STANDARD
 
-    # Display appropriate message
     click.echo(f"Running the {'Flow' if is_flow else 'Crew'}")
 
-    # Execute the appropriate command
     execute_command(crew_type, trained_agents_file=trained_agents_file)
 
 

lib/cli/src/crewai_cli/settings/main.py

@@ -28,10 +28,8 @@ class SettingsCommand(BaseCommand):
         table.add_column("Value", style="green")
         table.add_column("Description", style="yellow")
 
-        # Add all settings to the table
         for field_name, field_info in Settings.model_fields.items():
             if field_name in HIDDEN_SETTINGS_KEYS:
-                # Do not display hidden settings
                 continue
 
             current_value = getattr(self.settings, field_name)
@@ -42,10 +40,8 @@ class SettingsCommand(BaseCommand):
 
             table.add_row(field_name, display_value, description)
 
-        # Add trace-related settings from user data
         user_data = _load_user_data()
 
-        # CREWAI_TRACING_ENABLED environment variable
         env_tracing = os.getenv("CREWAI_TRACING_ENABLED", "")
         env_tracing_display = env_tracing if env_tracing else "Not set"
         table.add_row(
@@ -54,7 +50,6 @@ class SettingsCommand(BaseCommand):
             "Environment variable to enable/disable tracing",
         )
 
-        # Trace consent status
         trace_consent = user_data.get("trace_consent")
         if trace_consent is True:
             consent_display = "✅ Enabled"
@@ -66,7 +61,6 @@ class SettingsCommand(BaseCommand):
             "trace_consent", consent_display, "Whether trace collection is enabled"
         )
 
-        # First execution timestamp
         if user_data.get("first_execution_at"):
             timestamp = datetime.fromtimestamp(user_data["first_execution_at"])
             first_exec_display = timestamp.strftime("%Y-%m-%d %H:%M:%S")

lib/cli/src/crewai_cli/skills/main.py

@@ -41,10 +41,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
         BaseCommand.__init__(self)
         PlusAPIMixin.__init__(self, telemetry=self._telemetry)
 
-    # ------------------------------------------------------------------
-    # create
-    # ------------------------------------------------------------------
-
     def create(self, name: str, in_project: bool = True) -> None:
         """Scaffold a new skill directory.
 
@@ -73,10 +69,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
         )
         console.print(f"Edit [bold]{skill_md}[/bold] to define the skill instructions.")
 
-    # ------------------------------------------------------------------
-    # install
-    # ------------------------------------------------------------------
-
     def install(self, ref: str) -> None:
         """Download and install a registry skill.
 
@@ -182,10 +174,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
                 f"[green]Installed [bold]{ref}[/bold]{' (' + version + ')' if version else ''} to global cache.[/green]"
             )
 
-    # ------------------------------------------------------------------
-    # publish
-    # ------------------------------------------------------------------
-
     def publish(self, is_public: bool, org: str | None, force: bool = False) -> None:
         """Publish the skill in the current directory to the registry."""
         skill_md = Path("SKILL.md")
@@ -196,7 +184,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
             )
             raise SystemExit(1)
 
-        # Parse frontmatter to extract name + version
         try:
             frontmatter = self._parse_frontmatter(skill_md.read_text())
         except ValueError as exc:
@@ -257,10 +244,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
             f"Monitor status at: {base_url}/crewai_plus/skills/{effective_org}/{name}[/green]"
         )
 
-    # ------------------------------------------------------------------
-    # list_cached
-    # ------------------------------------------------------------------
-
     def list_cached(self) -> None:
         """Show locally installed skills."""
         table = Table(title="Installed Skills", show_lines=True)
@@ -269,7 +252,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
         table.add_column("Version")
         table.add_column("Path")
 
-        # Project-local ./skills/
         local_skills_dir = Path("skills")
         if local_skills_dir.is_dir():
             for skill_dir in sorted(local_skills_dir.iterdir()):
@@ -282,7 +264,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
                         str(skill_dir),
                     )
 
-        # Global cache
         cache_root = Path.home() / ".crewai" / "skills"
         if cache_root.exists():
             for org_dir in sorted(cache_root.iterdir()):
@@ -306,10 +287,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
 
         console.print(table)
 
-    # ------------------------------------------------------------------
-    # internal helpers
-    # ------------------------------------------------------------------
-
     def _print_current_organization(self) -> None:
         settings = Settings()
         if settings.org_uuid:
@@ -326,7 +303,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
 
     def _unpack_archive(self, archive_bytes: bytes, dest: Path) -> None:
         """Unpack a .tar.gz or .zip archive into dest."""
-        # Try tar first, then zip
         try:
             with tarfile.open(fileobj=io.BytesIO(archive_bytes), mode="r:gz") as tf:
                 try:
@@ -337,7 +313,6 @@ class SkillCommand(BaseCommand, PlusAPIMixin):
         except tarfile.TarError:
             pass
 
-        # Fallback: zip
         with zipfile.ZipFile(io.BytesIO(archive_bytes)) as zf:
             _safe_extract_zip(zf, dest)
 

lib/cli/src/crewai_cli/templates/crew/crew.py

@@ -1,9 +1,7 @@
 from crewai import Agent, Crew, Process, Task
 from crewai.project import CrewBase, agent, crew, task
 from crewai.agents.agent_builder.base_agent import BaseAgent
-# If you want to run a snippet of code before or after the crew starts,
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
+
 
 @CrewBase
 class {{crew_name}}():
@@ -12,12 +10,6 @@ class {{crew_name}}():
     agents: list[BaseAgent]
     tasks: list[Task]
 
-    # Learn more about YAML configuration files here:
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
-    
-    # If you would like to add tools to your agents, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
     @agent
     def researcher(self) -> Agent:
         return Agent(
@@ -32,9 +24,6 @@ class {{crew_name}}():
             verbose=True
         )
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
     @task
     def research_task(self) -> Task:
         return Task(
@@ -51,13 +40,9 @@ class {{crew_name}}():
     @crew
     def crew(self) -> Crew:
         """Creates the {{crew_name}} crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-
         return Crew(
-            agents=self.agents, # Automatically created by the @agent decorator
-            tasks=self.tasks, # Automatically created by the @task decorator
+            agents=self.agents,
+            tasks=self.tasks,
             process=Process.sequential,
             verbose=True,
-            # process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
         )

lib/cli/src/crewai_cli/templates/crew/main.py

@@ -8,10 +8,6 @@ from {{folder_name}}.crew import {{crew_name}}
 
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
-# This main file is intended to be a way for you to run your
-# crew locally, so refrain from adding unnecessary logic into this file.
-# Replace with inputs you want to test with, it will automatically
-# interpolate any tasks and agents information
 
 def run():
     """

lib/cli/src/crewai_cli/templates/crew/tools/custom_tool.py

@@ -15,5 +15,4 @@ class MyCustomTool(BaseTool):
     args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
-        # Implementation goes here
         return "this is an example of a tool output, ignore it and move along."

lib/cli/src/crewai_cli/templates/flow/crews/content_crew/content_crew.py

@@ -2,10 +2,6 @@ from crewai import Agent, Crew, Process, Task
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.project import CrewBase, agent, crew, task
 
-# If you want to run a snippet of code before or after the crew starts,
-# you can use the @before_kickoff and @after_kickoff decorators
-# https://docs.crewai.com/concepts/crews#example-crew-class-with-decorators
-
 
 @CrewBase
 class ContentCrew:
@@ -14,14 +10,9 @@ class ContentCrew:
     agents: list[BaseAgent]
     tasks: list[Task]
 
-    # Learn more about YAML configuration files here:
-    # Agents: https://docs.crewai.com/concepts/agents#yaml-configuration-recommended
-    # Tasks: https://docs.crewai.com/concepts/tasks#yaml-configuration-recommended
     agents_config = "config/agents.yaml"
     tasks_config = "config/tasks.yaml"
 
-    # If you would like to add tools to your crew, you can learn more about it here:
-    # https://docs.crewai.com/concepts/agents#agent-tools
     @agent
     def planner(self) -> Agent:
         return Agent(
@@ -40,9 +31,6 @@ class ContentCrew:
             config=self.agents_config["editor"],  # type: ignore[index]
         )
 
-    # To learn more about structured task outputs,
-    # task dependencies, and task callbacks, check out the documentation:
-    # https://docs.crewai.com/concepts/tasks#overview-of-a-task
     @task
     def planning_task(self) -> Task:
         return Task(
@@ -64,12 +52,9 @@ class ContentCrew:
     @crew
     def crew(self) -> Crew:
         """Creates the Content Crew"""
-        # To learn how to add knowledge sources to your crew, check out the documentation:
-        # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
-
         return Crew(
-            agents=self.agents,  # Automatically created by the @agent decorator
-            tasks=self.tasks,  # Automatically created by the @task decorator
+            agents=self.agents,
+            tasks=self.tasks,
             process=Process.sequential,
             verbose=True,
         )

lib/cli/src/crewai_cli/templates/flow/main.py

@@ -68,7 +68,6 @@ def run_with_trigger():
     import json
     import sys
 
-    # Get trigger payload from command line argument
     if len(sys.argv) < 2:
         raise Exception("No trigger payload provided. Please provide JSON payload as argument.")
 
@@ -77,8 +76,6 @@ def run_with_trigger():
     except json.JSONDecodeError:
         raise Exception("Invalid JSON payload provided as argument")
 
-    # Create flow and kickoff with trigger payload
-    # The @start() methods will automatically receive crewai_trigger_payload parameter
     content_flow = ContentFlow()
 
     try:

lib/cli/src/crewai_cli/templates/flow/tools/custom_tool.py

@@ -17,5 +17,4 @@ class MyCustomTool(BaseTool):
     args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
-        # Implementation goes here
         return "this is an example of a tool output, ignore it and move along."

lib/cli/src/crewai_cli/templates/tool/src/{{folder_name}}/tool.py

@@ -6,5 +6,4 @@ class {{class_name}}(BaseTool):
     description: str = "What this tool does. It's vital for effective utilization."
 
     def _run(self, argument: str) -> str:
-        # Your tool's logic here
         return "Tool's result"

lib/cli/src/crewai_cli/tools/main.py

@@ -82,7 +82,6 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         tree_find_and_replace(project_root, "{{folder_name}}", folder_name)
         tree_find_and_replace(project_root, "{{class_name}}", class_name)
 
-        # Copy AGENTS.md to project root
         agents_md_src = Path(__file__).parent.parent / "templates" / "AGENTS.md"
         if agents_md_src.exists():
             shutil.copy2(agents_md_src, project_root / "AGENTS.md")

lib/cli/src/crewai_cli/triggers/main.py

@@ -37,7 +37,6 @@ class TriggersCommand(BaseCommand, PlusAPIMixin):
     def execute_with_trigger(self, trigger_path: str) -> None:
         """Execute crew with trigger payload."""
         try:
-            # Parse app_slug/trigger_slug
             if "/" not in trigger_path:
                 console.print(
                     "[bold red]Error: Trigger must be in format 'app_slug/trigger_slug'[/bold red]"
@@ -63,7 +62,6 @@ class TriggersCommand(BaseCommand, PlusAPIMixin):
             trigger_data = response.json()
             self._display_trigger_info(trigger_data)
 
-            # Run crew with trigger payload
             self._run_crew_with_payload(trigger_data.get("sample_payload", {}))
 
         except Exception as e:

lib/cli/src/crewai_cli/update_crew.py

@@ -21,7 +21,6 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
     When the time comes that uv supports the new format, this function will be deprecated.
     """
     poetry_data = {}
-    # Read the input pyproject.toml
     pyproject_data = read_toml()
 
     new_pyproject: dict[str, Any] = {
@@ -29,7 +28,6 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
         "build-system": {"requires": ["hatchling"], "build-backend": "hatchling.build"},
     }
 
-    # Migrate project metadata
     if "tool" in pyproject_data and "poetry" in pyproject_data["tool"]:
         poetry_data = pyproject_data["tool"]["poetry"]
         new_pyproject["project"]["name"] = poetry_data.get("name")
@@ -44,18 +42,15 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
         ]
         new_pyproject["project"]["requires-python"] = poetry_data.get("python")
     else:
-        # If it's already in the new format, just copy the project and tool sections
         new_pyproject["project"] = pyproject_data.get("project", {})
         new_pyproject["tool"] = pyproject_data.get("tool", {})
 
-    # Migrate or copy dependencies
     if "dependencies" in new_pyproject["project"]:
-        # If dependencies are already in the new format, keep them as is
         pass
     elif poetry_data and "dependencies" in poetry_data:
         new_pyproject["project"]["dependencies"] = []
         for dep, version in poetry_data["dependencies"].items():
-            if isinstance(version, dict):  # Handle extras
+            if isinstance(version, dict):
                 extras = ",".join(version.get("extras", []))
                 new_dep = f"{dep}[{extras}]"
                 if "version" in version:
@@ -67,7 +62,6 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
                 new_dep = f"{dep}{parse_version(version)}"
             new_pyproject["project"]["dependencies"].append(new_dep)
 
-    # Migrate or copy scripts
     if poetry_data and "scripts" in poetry_data:
         new_pyproject["project"]["scripts"] = poetry_data["scripts"]
     elif pyproject_data.get("project", {}) and "scripts" in pyproject_data["project"]:
@@ -79,7 +73,6 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
         "run_crew" not in new_pyproject["project"]["scripts"]
         and len(new_pyproject["project"]["scripts"]) > 0
     ):
-        # Extract the module name from any existing script
         existing_scripts = new_pyproject["project"]["scripts"]
         module_name = next(
             (value.split(".")[0] for value in existing_scripts.values() if "." in value)
@@ -87,15 +80,12 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
 
         new_pyproject["project"]["scripts"]["run_crew"] = f"{module_name}.main:run"
 
-    # Migrate optional dependencies
     if poetry_data and "extras" in poetry_data:
         new_pyproject["project"]["optional-dependencies"] = poetry_data["extras"]
 
-    # Backup the old pyproject.toml
     backup_file = "pyproject-old.toml"
     shutil.copy2(input_file, backup_file)
 
-    # Rename the poetry.lock file
     lock_file = "poetry.lock"
     lock_backup = "poetry-old.lock"
     if os.path.exists(lock_file):
@@ -103,7 +93,6 @@ def migrate_pyproject(input_file: str, output_file: str) -> None:
     else:
         pass
 
-    # Write the new pyproject.toml
     with open(output_file, "wb") as f:
         tomli_w.dump(new_pyproject, f)
 

lib/cli/tests/authentication/test_auth_main.py

@@ -333,7 +333,6 @@ class TestAuthenticationCommand:
     @patch("crewai_core.auth.oauth2.httpx.post")
     def test_poll_for_token_error(self, mock_post):
         """Test the method to poll for token (error path)."""
-        # Setup mock to return error
         mock_response_error = MagicMock()
         mock_response_error.status_code = 400
         mock_response_error.json.return_value = {

lib/cli/tests/authentication/test_utils.py

@@ -12,7 +12,6 @@ class TestUtils(unittest.TestCase):
     def test_validate_jwt_token(self, mock_jwt, mock_pyjwkclient):
         mock_jwt.decode.return_value = {"exp": 1719859200}
 
-        # Create signing key object mock with a .key attribute
         mock_pyjwkclient.return_value.get_signing_key_from_jwt.return_value = MagicMock(
             key="mock_signing_key"
         )

lib/cli/tests/deploy/test_validate.py

@@ -164,7 +164,6 @@ def test_poetry_lock_is_accepted(tmp_path: Path) -> None:
 
 def test_stale_lockfile_warns(tmp_path: Path) -> None:
     _scaffold_standard_crew(tmp_path)
-    # Make lockfile older than pyproject.
     lock = tmp_path / "uv.lock"
     pyproject = tmp_path / "pyproject.toml"
     old_time = pyproject.stat().st_mtime - 60

lib/cli/tests/skills/test_main.py

@@ -41,14 +41,9 @@ def skill_command():
             yield cmd
 
 
-# ---------------------------------------------------------------------------
-# create
-# ---------------------------------------------------------------------------
-
 class TestSkillCreate:
     def test_create_in_project(self, skill_command, tmp_path):
         with in_temp_dir():
-            # Simulate being inside a project
             Path("pyproject.toml").write_text("[tool.poetry]\nname = 'test'\n")
             skill_command.create("my-skill")
             assert Path("skills/my-skill/SKILL.md").exists()
@@ -75,10 +70,6 @@ class TestSkillCreate:
                 skill_command.create("existing-skill", in_project=False)
 
 
-# ---------------------------------------------------------------------------
-# install
-# ---------------------------------------------------------------------------
-
 class TestSkillInstall:
     def _zip_skill(self, name: str) -> bytes:
         buf = io.BytesIO()
@@ -118,10 +109,6 @@ class TestSkillInstall:
             assert Path("skills/my-skill/SKILL.md").exists()
 
 
-# ---------------------------------------------------------------------------
-# publish
-# ---------------------------------------------------------------------------
-
 class TestSkillPublish:
     def test_publish_no_skill_md(self, skill_command):
         with in_temp_dir():
@@ -155,7 +142,6 @@ class TestSkillPublish:
                 mock_resp.status_code = 200
                 mock_resp.json.return_value = {}
                 mock_client.publish_skill.return_value = mock_resp
-                # No org set → should SystemExit (no org_name in settings)
                 with patch("crewai_cli.skills.main.Settings") as mock_settings_cls:
                     mock_settings_cls.return_value.org_name = None
                     mock_settings_cls.return_value.enterprise_base_url = None
@@ -184,15 +170,10 @@ class TestSkillPublish:
             assert call_kwargs.kwargs["version"] == "1.0.0"
 
 
-# ---------------------------------------------------------------------------
-# list_cached
-# ---------------------------------------------------------------------------
-
 class TestSkillListCached:
     def test_list_cached_empty(self, skill_command, capsys):
         with in_temp_dir():
             skill_command.list_cached()
-            # Should not raise
 
     def test_list_cached_shows_project_skills(self, skill_command, capsys):
         with in_temp_dir():
@@ -202,4 +183,3 @@ class TestSkillListCached:
                 "---\nname: my-skill\nversion: 0.5.0\ndescription: A skill.\n---\nBody."
             )
             skill_command.list_cached()
-            # Should complete without error

lib/cli/tests/test_crew_test.py

@@ -83,7 +83,6 @@ def test_test_crew_called_process_error(mock_subprocess_run, click):
 @mock.patch("crewai_cli.evaluate_crew.click")
 @mock.patch("crewai_cli.evaluate_crew.subprocess.run")
 def test_test_crew_unexpected_exception(mock_subprocess_run, click):
-    # Arrange
     n_iterations = 5
     mock_subprocess_run.side_effect = Exception("Unexpected error")
     evaluate_crew.evaluate_crew(n_iterations, "gpt-4o")

lib/cli/tests/test_settings_command.py

@@ -35,7 +35,6 @@ class TestSettingsCommand(unittest.TestCase):
 
         self.settings_command.list()
 
-        # Tests that the table is created skipping hidden settings
         mock_table_instance.add_row.assert_has_calls(
             [
                 call(
@@ -48,7 +47,6 @@ class TestSettingsCommand(unittest.TestCase):
             ]
         )
 
-        # Tests that the table is printed
         mock_console.print.assert_called_once_with(mock_table_instance)
 
     def test_set_valid_keys(self):

lib/cli/tests/test_token_manager.py

@@ -146,7 +146,6 @@ class TestAtomicFileOperations(unittest.TestCase):
         self.temp_dir = tempfile.mkdtemp()
         self.original_get_path = TokenManager._get_secure_storage_path
 
-        # Patch to use temp directory
         def mock_get_path() -> Path:
             return Path(self.temp_dir)
 
@@ -182,7 +181,6 @@ class TestAtomicFileOperations(unittest.TestCase):
         mock_get_key.return_value = Fernet.generate_key()
         tm = TokenManager()
 
-        # Create file first
         file_path = Path(self.temp_dir) / "test.txt"
         file_path.write_bytes(b"original")
 
@@ -231,7 +229,6 @@ class TestAtomicFileOperations(unittest.TestCase):
 
         tm._atomic_write_secure_file("test.txt", b"content")
 
-        # Check no temp files remain
         temp_files = list(Path(self.temp_dir).glob(".test.txt.*"))
         self.assertEqual(len(temp_files), 0)
 
@@ -285,7 +282,6 @@ class TestAtomicFileOperations(unittest.TestCase):
         mock_get_key.return_value = Fernet.generate_key()
         tm = TokenManager()
 
-        # Should not raise
         tm._delete_secure_file("nonexistent.txt")
 
 

lib/cli/tests/tools/test_main.py

@@ -27,9 +27,7 @@ def in_temp_dir():
 
 @pytest.fixture
 def tool_command():
-    # Create a temporary directory for each test to avoid token storage conflicts
     with tempfile.TemporaryDirectory() as temp_dir:
-        # Mock the secure storage path to use the temp directory
         with patch.object(
             TokenManager, "_get_secure_storage_path", return_value=Path(temp_dir)
         ):
@@ -97,7 +95,6 @@ def test_install_success(
         env=unittest.mock.ANY,
     )
 
-    # Verify _print_current_organization was called
     mock_print_org.assert_called_once()
 
 

lib/crewai-core/src/crewai_core/telemetry.py

@@ -186,8 +186,6 @@ class Telemetry:
 
         self._safe_telemetry_procedure(_operation)
 
-    # --- CLI-facing spans ---------------------------------------------------
-
     def deploy_signup_error_span(self) -> None:
         """Records when an error occurs during the deployment signup process."""
 

lib/crewai-files/src/crewai_files/formatting/api.py

@@ -114,14 +114,12 @@ def format_multimodal_content(
     content_blocks: list[dict[str, Any]] = []
     provider_type = _normalize_provider(provider)
 
-    # Add text block first if provided
     if text:
         content_blocks.append(_format_text_block(text, provider_type, api))
 
     if not files:
         return content_blocks
 
-    # Use API-specific constraints for OpenAI
     constraints_key: str = provider_type
     if api == "responses" and "openai" in provider_type.lower():
         constraints_key = "openai_responses"
@@ -186,7 +184,6 @@ async def aformat_multimodal_content(
     if not files:
         return content_blocks
 
-    # Use API-specific constraints for OpenAI
     constraints_key: str = provider_type
     if api == "responses" and "openai" in provider_type.lower():
         constraints_key = "openai_responses"

lib/crewai-files/src/crewai_files/resolution/resolver.py

@@ -245,7 +245,6 @@ class FileResolver:
         type_constraint = self._get_type_constraint(content_type, constraints)
 
         if type_constraint is not None:
-            # Check if file exceeds type-specific inline limit
             if file_size > type_constraint.max_size_bytes:
                 logger.debug(
                     f"File {file.filename} ({file_size}B) exceeds {content_type} "

lib/crewai-files/tests/processing/test_processor.py

@@ -162,7 +162,6 @@ class TestFileProcessorValidate:
             image=ImageConstraints(max_size_bytes=10),
         )
         processor = FileProcessor(constraints=constraints)
-        # Set mode to strict on the file
         file = ImageFile(
             source=FileBytes(data=MINIMAL_PNG, filename="test.png"), mode="strict"
         )
@@ -199,7 +198,6 @@ class TestFileProcessorProcess:
             image=ImageConstraints(max_size_bytes=10),
         )
         processor = FileProcessor(constraints=constraints)
-        # Set mode to strict on the file
         file = ImageFile(
             source=FileBytes(data=MINIMAL_PNG, filename="test.png"), mode="strict"
         )
@@ -214,7 +212,6 @@ class TestFileProcessorProcess:
             image=ImageConstraints(max_size_bytes=10),
         )
         processor = FileProcessor(constraints=constraints)
-        # Set mode to warn on the file
         file = ImageFile(
             source=FileBytes(data=MINIMAL_PNG, filename="test.png"), mode="warn"
         )

lib/crewai-files/tests/test_resolver.py

@@ -93,14 +93,11 @@ class TestFileResolver:
         resolver = FileResolver(upload_cache=cache)
         file = ImageFile(source=FileBytes(data=MINIMAL_PNG, filename="test.png"))
 
-        # First resolution
         resolved1 = resolver.resolve(file, "openai")
-        # Second resolution (should use same base64 encoding)
         resolved2 = resolver.resolve(file, "openai")
 
         assert isinstance(resolved1, InlineBase64)
         assert isinstance(resolved2, InlineBase64)
-        # Data should be identical
         assert resolved1.data == resolved2.data
 
     def test_clear_cache(self):
@@ -108,7 +105,6 @@ class TestFileResolver:
         cache = UploadCache()
         file = ImageFile(source=FileBytes(data=MINIMAL_PNG, filename="test.png"))
 
-        # Add something to cache manually
         cache.set(file=file, provider="gemini", file_id="test")
 
         resolver = FileResolver(upload_cache=cache)

lib/crewai-files/tests/test_upload_cache.py

@@ -162,7 +162,6 @@ class TestUploadCache:
             source=FileBytes(data=MINIMAL_PNG + b"x", filename="test2.png")
         )
 
-        # Add one expired and one valid entry
         past = datetime.now(timezone.utc) - timedelta(hours=1)
         future = datetime.now(timezone.utc) + timedelta(hours=24)
 

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

@@ -252,12 +252,10 @@ class CrewAIRagAdapter(Adapter):
                         if filename.startswith("."):
                             continue
 
-                        # Skip binary files based on extension
                         file_ext = os.path.splitext(filename)[1].lower()
                         if file_ext in binary_extensions:
                             continue
 
-                        # Skip __pycache__ directories
                         if "__pycache__" in root:
                             continue
 

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

@@ -46,7 +46,6 @@ class EnterpriseActionTool(BaseTool):
 
         schema_props, required = self._extract_schema_info(action_schema)
 
-        # Define field definitions for the model
         field_definitions = {}
         for param_name, param_details in schema_props.items():
             param_desc = param_details.get("description", "")
@@ -59,12 +58,10 @@ class EnterpriseActionTool(BaseTool):
             except Exception:
                 field_type = str
 
-            # Create field definition based on requirement
             field_definitions[param_name] = self._create_field_definition(
                 field_type, is_required, param_desc
             )
 
-        # Create the model
         if field_definitions:
             try:
                 args_schema = create_model(  # type: ignore[call-overload]

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

@@ -16,7 +16,6 @@ class RAGAdapter(Adapter):
     ):
         super().__init__()
 
-        # Prepare embedding configuration
         embedding_config = {"api_key": embedding_api_key, **embedding_kwargs}
 
         self._adapter = RAG(

lib/crewai-tools/src/crewai_tools/aws/bedrock/agents/invoke_agent_tool.py

@@ -14,7 +14,6 @@ from crewai_tools.aws.bedrock.exceptions import (
 )
 
 
-# Load environment variables from .env file
 load_dotenv()
 
 
@@ -66,29 +65,24 @@ class BedrockInvokeAgentTool(BaseTool):
         self.enable_trace = enable_trace
         self.end_session = end_session
 
-        # Update the description if provided
         if description:
             self.description = description
 
-        # Validate parameters
         self._validate_parameters()
 
     def _validate_parameters(self) -> None:
         """Validate the parameters according to AWS API requirements."""
         try:
-            # Validate agent_id
             if not self.agent_id:
                 raise BedrockValidationError("agent_id cannot be empty")
             if not isinstance(self.agent_id, str):
                 raise BedrockValidationError("agent_id must be a string")
 
-            # Validate agent_alias_id
             if not self.agent_alias_id:
                 raise BedrockValidationError("agent_alias_id cannot be empty")
             if not isinstance(self.agent_alias_id, str):
                 raise BedrockValidationError("agent_alias_id must be a string")
 
-            # Validate session_id if provided
             if self.session_id and not isinstance(self.session_id, str):
                 raise BedrockValidationError("session_id must be a string")
 
@@ -113,7 +107,6 @@ class BedrockInvokeAgentTool(BaseTool):
                 ),
             )
 
-            # Format the prompt with current time
             current_utc = datetime.now(timezone.utc)
             prompt = f"""
 The current time is: {current_utc}
@@ -132,12 +125,9 @@ Below is the users query or task. Complete it and answer it consicely and to the
                 endSession=self.end_session,
             )
 
-            # Process the response
             completion = ""
 
-            # Check if response contains a completion field
             if "completion" in response:
-                # Process streaming response format
                 for event in response.get("completion", []):
                     if "chunk" in event and "bytes" in event["chunk"]:
                         chunk_bytes = event["chunk"]["bytes"]
@@ -161,7 +151,6 @@ Below is the users query or task. Complete it and answer it consicely and to the
                     "response_keys": list(response.keys()),
                 }
 
-                # Add more debug info
                 if "chunk" in response:
                     debug_info["chunk_keys"] = list(response["chunk"].keys())
 

lib/crewai-tools/src/crewai_tools/aws/bedrock/browser/browser_toolkit.py

@@ -135,10 +135,8 @@ class NavigateTool(BrowserBaseTool):
     def _run(self, url: str, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Get page for this thread
             page = self.get_sync_page(thread_id)
 
-            # Validate URL scheme
             parsed_url = urlparse(url)
             if parsed_url.scheme not in ("http", "https"):
                 raise ValueError("URL scheme must be 'http' or 'https'")
@@ -153,10 +151,8 @@ class NavigateTool(BrowserBaseTool):
     async def _arun(self, url: str, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the async tool."""
         try:
-            # Get page for this thread
             page = await self.get_async_page(thread_id)
 
-            # Validate URL scheme
             parsed_url = urlparse(url)
             if parsed_url.scheme not in ("http", "https"):
                 raise ValueError("URL scheme must be 'http' or 'https'")
@@ -191,7 +187,6 @@ class ClickTool(BrowserBaseTool):
     def _run(self, selector: str, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
             # Click on the element
@@ -218,7 +213,6 @@ class ClickTool(BrowserBaseTool):
     ) -> str:
         """Use the async tool."""
         try:
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
             # Click on the element
@@ -251,7 +245,6 @@ class NavigateBackTool(BrowserBaseTool):
     def _run(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
             # Navigate back
@@ -266,7 +259,6 @@ class NavigateBackTool(BrowserBaseTool):
     async def _arun(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the async tool."""
         try:
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
             # Navigate back
@@ -289,7 +281,6 @@ class ExtractTextTool(BrowserBaseTool):
     def _run(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Import BeautifulSoup
             try:
                 from bs4 import BeautifulSoup
             except ImportError:
@@ -298,10 +289,8 @@ class ExtractTextTool(BrowserBaseTool):
                     " Please install it with 'pip install beautifulsoup4'."
                 )
 
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
-            # Extract text
             content = page.content()
             soup = BeautifulSoup(content, "html.parser")
             return soup.get_text(separator="\n").strip()
@@ -311,7 +300,6 @@ class ExtractTextTool(BrowserBaseTool):
     async def _arun(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the async tool."""
         try:
-            # Import BeautifulSoup
             try:
                 from bs4 import BeautifulSoup
             except ImportError:
@@ -320,10 +308,8 @@ class ExtractTextTool(BrowserBaseTool):
                     " Please install it with 'pip install beautifulsoup4'."
                 )
 
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
-            # Extract text
             content = await page.content()
             soup = BeautifulSoup(content, "html.parser")
             return soup.get_text(separator="\n").strip()
@@ -341,7 +327,6 @@ class ExtractHyperlinksTool(BrowserBaseTool):
     def _run(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Import BeautifulSoup
             try:
                 from bs4 import BeautifulSoup, Tag
             except ImportError:
@@ -350,10 +335,8 @@ class ExtractHyperlinksTool(BrowserBaseTool):
                     " Please install it with 'pip install beautifulsoup4'."
                 )
 
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
-            # Extract hyperlinks
             content = page.content()
             soup = BeautifulSoup(content, "html.parser")
             links = []
@@ -374,7 +357,6 @@ class ExtractHyperlinksTool(BrowserBaseTool):
     async def _arun(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the async tool."""
         try:
-            # Import BeautifulSoup
             try:
                 from bs4 import BeautifulSoup, Tag
             except ImportError:
@@ -383,10 +365,8 @@ class ExtractHyperlinksTool(BrowserBaseTool):
                     " Please install it with 'pip install beautifulsoup4'."
                 )
 
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
-            # Extract hyperlinks
             content = await page.content()
             soup = BeautifulSoup(content, "html.parser")
             links = []
@@ -415,10 +395,8 @@ class GetElementsTool(BrowserBaseTool):
     def _run(self, selector: str, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
-            # Get elements
             elements = page.query_selector_all(selector)
             if not elements:
                 return f"No elements found with selector '{selector}'"
@@ -437,10 +415,8 @@ class GetElementsTool(BrowserBaseTool):
     ) -> str:
         """Use the async tool."""
         try:
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
-            # Get elements
             elements = await page.query_selector_all(selector)
             if not elements:
                 return f"No elements found with selector '{selector}'"
@@ -465,10 +441,8 @@ class CurrentWebPageTool(BrowserBaseTool):
     def _run(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the sync tool."""
         try:
-            # Get the current page
             page = self.get_sync_page(thread_id)
 
-            # Get information
             url = page.url
             title = page.title()
             return f"URL: {url}\nTitle: {title}"
@@ -478,10 +452,8 @@ class CurrentWebPageTool(BrowserBaseTool):
     async def _arun(self, thread_id: str = "default", **kwargs: Any) -> str:
         """Use the async tool."""
         try:
-            # Get the current page
             page = await self.get_async_page(thread_id)
 
-            # Get information
             url = page.url
             title = await page.title()
             return f"URL: {url}\nTitle: {title}"

lib/crewai-tools/src/crewai_tools/aws/bedrock/code_interpreter/code_interpreter_toolkit.py

@@ -155,12 +155,10 @@ class ExecuteCodeTool(BaseTool):
         thread_id: str = "default",
     ) -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Execute code
             response = code_interpreter.invoke(
                 method="executeCode",
                 params={
@@ -204,12 +202,10 @@ class ExecuteCommandTool(BaseTool):
 
     def _run(self, command: str, thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Execute command
             response = code_interpreter.invoke(
                 method="executeCommand", params={"command": command}
             )
@@ -237,12 +233,10 @@ class ReadFilesTool(BaseTool):
 
     def _run(self, paths: list[str], thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Read files
             response = code_interpreter.invoke(
                 method="readFiles", params={"paths": paths}
             )
@@ -270,7 +264,6 @@ class ListFilesTool(BaseTool):
 
     def _run(self, directory_path: str = "", thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
@@ -303,12 +296,10 @@ class DeleteFilesTool(BaseTool):
 
     def _run(self, paths: list[str], thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Remove files
             response = code_interpreter.invoke(
                 method="removeFiles", params={"paths": paths}
             )
@@ -336,12 +327,10 @@ class WriteFilesTool(BaseTool):
 
     def _run(self, files: list[dict[str, str]], thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Write files
             response = code_interpreter.invoke(
                 method="writeFiles", params={"content": files}
             )
@@ -371,12 +360,10 @@ class StartCommandTool(BaseTool):
 
     def _run(self, command: str, thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Start command execution
             response = code_interpreter.invoke(
                 method="startCommandExecution", params={"command": command}
             )
@@ -404,12 +391,10 @@ class GetTaskTool(BaseTool):
 
     def _run(self, task_id: str, thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Get task status
             response = code_interpreter.invoke(
                 method="getTask", params={"taskId": task_id}
             )
@@ -437,12 +422,10 @@ class StopTaskTool(BaseTool):
 
     def _run(self, task_id: str, thread_id: str = "default") -> str:
         try:
-            # Get or create code interpreter
             code_interpreter = self.toolkit._get_or_create_interpreter(
                 thread_id=thread_id
             )
 
-            # Stop task
             response = code_interpreter.invoke(
                 method="stopTask", params={"taskId": task_id}
             )
@@ -555,7 +538,6 @@ class CodeInterpreterToolkit:
             f"Started code interpreter with session_id:{code_interpreter.session_id} for thread:{thread_id}"
         )
 
-        # Store the interpreter
         self._code_interpreters[thread_id] = code_interpreter
         return code_interpreter
 
@@ -582,7 +564,6 @@ class CodeInterpreterToolkit:
             thread_id: Optional thread ID to clean up. If None, cleans up all sessions.
         """
         if thread_id:
-            # Clean up a specific thread's session
             if thread_id in self._code_interpreters:
                 try:
                     self._code_interpreters[thread_id].stop()
@@ -595,7 +576,6 @@ class CodeInterpreterToolkit:
                         f"Error stopping code interpreter for thread {thread_id}: {e}"
                     )
         else:
-            # Clean up all sessions
             thread_ids = list(self._code_interpreters.keys())
             for tid in thread_ids:
                 try:

lib/crewai-tools/src/crewai_tools/aws/bedrock/knowledge_base/retriever_tool.py

@@ -12,7 +12,6 @@ from crewai_tools.aws.bedrock.exceptions import (
 )
 
 
-# Load environment variables from .env file
 load_dotenv()
 
 
@@ -69,7 +68,6 @@ class BedrockKBRetrieverTool(BaseTool):
         else:
             self.retrieval_configuration = retrieval_configuration
 
-        # Validate parameters
         self._validate_parameters()
 
         # Update the description to include the knowledge base details
@@ -83,7 +81,6 @@ class BedrockKBRetrieverTool(BaseTool):
         """
         vector_search_config = {}
 
-        # Add number of results if provided
         if self.number_of_results is not None:
             vector_search_config["numberOfResults"] = self.number_of_results
 
@@ -92,7 +89,6 @@ class BedrockKBRetrieverTool(BaseTool):
     def _validate_parameters(self) -> None:
         """Validate the parameters according to AWS API requirements."""
         try:
-            # Validate knowledge_base_id
             if not self.knowledge_base_id:
                 raise BedrockValidationError("knowledge_base_id cannot be empty")
             if not isinstance(self.knowledge_base_id, str):
@@ -106,7 +102,6 @@ class BedrockKBRetrieverTool(BaseTool):
                     "knowledge_base_id must contain only alphanumeric characters"
                 )
 
-            # Validate next_token if provided
             if self.next_token:
                 if not isinstance(self.next_token, str):
                     raise BedrockValidationError("next_token must be a string")
@@ -117,7 +112,6 @@ class BedrockKBRetrieverTool(BaseTool):
                 if " " in self.next_token:
                     raise BedrockValidationError("next_token cannot contain spaces")
 
-            # Validate number_of_results if provided
             if self.number_of_results is not None:
                 if not isinstance(self.number_of_results, int):
                     raise BedrockValidationError("number_of_results must be an integer")
@@ -138,12 +132,10 @@ class BedrockKBRetrieverTool(BaseTool):
         Returns:
             Dict[str, Any]: Processed result with standardized format
         """
-        # Extract content
         content_obj = result.get("content", {})
         content = content_obj.get("text", "")
         content_type = content_obj.get("type", "text")
 
-        # Extract location information
         location = result.get("location", {})
         location_type = location.get("type", "unknown")
         source_uri = None
@@ -160,7 +152,6 @@ class BedrockKBRetrieverTool(BaseTool):
             "sqlLocation": {"field": "query", "type": "SQL"},
         }
 
-        # Extract the URI based on location type
         for loc_key, config in location_mapping.items():
             if loc_key in location:
                 source_uri = location[loc_key].get(config["field"])
@@ -168,7 +159,6 @@ class BedrockKBRetrieverTool(BaseTool):
                     location_type = config["type"]
                 break
 
-        # Create result object
         result_object = {
             "content": content,
             "content_type": content_type,
@@ -176,18 +166,15 @@ class BedrockKBRetrieverTool(BaseTool):
             "source_uri": source_uri,
         }
 
-        # Add optional fields if available
         if "score" in result:
             result_object["score"] = result["score"]
 
         if "metadata" in result:
             result_object["metadata"] = result["metadata"]
 
-        # Handle byte content if present
         if "byteContent" in content_obj:
             result_object["byte_content"] = content_obj["byteContent"]
 
-        # Handle row content if present
         if "row" in content_obj:
             result_object["row_content"] = content_obj["row"]
 
@@ -212,13 +199,11 @@ class BedrockKBRetrieverTool(BaseTool):
                 # AWS SDK will automatically use AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY from environment
             )
 
-            # Prepare the request parameters
             retrieve_params = {
                 "knowledgeBaseId": self.knowledge_base_id,
                 "retrievalQuery": {"text": query},
             }
 
-            # Add optional parameters if provided
             if self.retrieval_configuration:
                 retrieve_params["retrievalConfiguration"] = self.retrieval_configuration
 
@@ -228,16 +213,13 @@ class BedrockKBRetrieverTool(BaseTool):
             if self.next_token:
                 retrieve_params["nextToken"] = self.next_token
 
-            # Make the retrieve API call
             response = bedrock_agent_runtime.retrieve(**retrieve_params)
 
-            # Process the response
             results = []
             for result in response.get("retrievalResults", []):
                 processed_result = self._process_retrieval_result(result)
                 results.append(processed_result)
 
-            # Build the response object
             response_object = {}
             if results:
                 response_object["results"] = results
@@ -250,7 +232,6 @@ class BedrockKBRetrieverTool(BaseTool):
             if "guardrailAction" in response:
                 response_object["guardrailAction"] = response["guardrailAction"]
 
-            # Return the results as a JSON string
             return json.dumps(response_object, indent=2)
 
         except ClientError as e:

lib/crewai-tools/src/crewai_tools/aws/s3/reader_tool.py

@@ -37,7 +37,6 @@ class S3ReaderTool(BaseTool):
                 aws_secret_access_key=os.getenv("CREW_AWS_SEC_ACCESS_KEY"),
             )
 
-            # Read file content from S3
             response = s3.get_object(Bucket=bucket_name, Key=object_key)
             result: str = response["Body"].read().decode("utf-8")
             return result

lib/crewai-tools/src/crewai_tools/rag/chunkers/text_chunker.py

@@ -12,15 +12,15 @@ class TextChunker(BaseChunker):
         if separators is None:
             separators = [
                 "\n\n\n",  # Multiple line breaks (sections)
-                "\n\n",  # Paragraph breaks
-                "\n",  # Line breaks
-                ". ",  # Sentence endings
-                "! ",  # Exclamation endings
-                "? ",  # Question endings
-                "; ",  # Semicolon breaks
-                ", ",  # Comma breaks
-                " ",  # Word breaks
-                "",  # Character level
+                "\n\n",
+                "\n",
+                ". ",
+                "! ",
+                "? ",
+                "; ",
+                ", ",
+                " ",
+                "",
             ]
         super().__init__(chunk_size, chunk_overlap, separators, keep_separator)
 
@@ -36,15 +36,15 @@ class DocxChunker(BaseChunker):
         if separators is None:
             separators = [
                 "\n\n\n",  # Multiple line breaks (major sections)
-                "\n\n",  # Paragraph breaks
-                "\n",  # Line breaks
-                ". ",  # Sentence endings
-                "! ",  # Exclamation endings
-                "? ",  # Question endings
-                "; ",  # Semicolon breaks
-                ", ",  # Comma breaks
-                " ",  # Word breaks
-                "",  # Character level
+                "\n\n",
+                "\n",
+                ". ",
+                "! ",
+                "? ",
+                "; ",
+                ", ",
+                " ",
+                "",
             ]
         super().__init__(chunk_size, chunk_overlap, separators, keep_separator)
 
@@ -62,15 +62,15 @@ class MdxChunker(BaseChunker):
                 "\n## ",  # H2 headers (major sections)
                 "\n### ",  # H3 headers (subsections)
                 "\n#### ",  # H4 headers (sub-subsections)
-                "\n\n",  # Paragraph breaks
-                "\n```",  # Code block boundaries
-                "\n",  # Line breaks
-                ". ",  # Sentence endings
-                "! ",  # Exclamation endings
-                "? ",  # Question endings
-                "; ",  # Semicolon breaks
-                ", ",  # Comma breaks
-                " ",  # Word breaks
-                "",  # Character level
+                "\n\n",
+                "\n```",
+                "\n",
+                ". ",
+                "! ",
+                "? ",
+                "; ",
+                ", ",
+                " ",
+                "",
             ]
         super().__init__(chunk_size, chunk_overlap, separators, keep_separator)

lib/crewai-tools/src/crewai_tools/rag/chunkers/web_chunker.py

@@ -11,15 +11,15 @@ class WebsiteChunker(BaseChunker):
     ):
         if separators is None:
             separators = [
-                "\n\n\n",  # Major section breaks
-                "\n\n",  # Paragraph breaks
-                "\n",  # Line breaks
-                ". ",  # Sentence endings
-                "! ",  # Exclamation endings
-                "? ",  # Question endings
-                "; ",  # Semicolon breaks
-                ", ",  # Comma breaks
-                " ",  # Word breaks
-                "",  # Character level
+                "\n\n\n",
+                "\n\n",
+                "\n",
+                ". ",
+                "! ",
+                "? ",
+                "; ",
+                ", ",
+                " ",
+                "",
             ]
         super().__init__(chunk_size, chunk_overlap, separators, keep_separator)

lib/crewai-tools/src/crewai_tools/rag/core.py

@@ -191,7 +191,6 @@ class RAG(Adapter):
             metadatas = results.get("metadatas", [None])[0] or []
             distances = results.get("distances", [None])[0] or []
 
-            # Return sources with relevance scores
             formatted_results = []
             for i, doc in enumerate(documents):
                 metadata = metadatas[i] if i < len(metadatas) else {}

lib/crewai-tools/src/crewai_tools/rag/data_types.py

@@ -37,7 +37,6 @@ class DataType(str, Enum):
             DataType.TEXT: ("text_chunker", "TextChunker"),
             DataType.DOCX: ("text_chunker", "DocxChunker"),
             DataType.MDX: ("text_chunker", "MdxChunker"),
-            # Structured formats
             DataType.CSV: ("structured_chunker", "CsvChunker"),
             DataType.JSON: ("structured_chunker", "JsonChunker"),
             DataType.XML: ("structured_chunker", "XmlChunker"),

lib/crewai-tools/src/crewai_tools/rag/embedding_service.py

@@ -113,10 +113,8 @@ class EmbeddingService:
         try:
             from crewai.rag.embeddings.factory import build_embedder
 
-            # Build the configuration for CrewAI's factory
             config = self._build_provider_config()
 
-            # Create the embedding function
             self._embedding_function = build_embedder(config)
 
             logger.info(
@@ -287,7 +285,6 @@ class EmbeddingService:
         if not texts:
             return []
 
-        # Filter out empty texts
         valid_texts = [text for text in texts if text and text.strip()]
         if not valid_texts:
             logger.warning("No valid texts provided for batch embedding")

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

@@ -39,16 +39,12 @@ class MDXLoader(BaseLoader):
     def _parse_mdx(self, content: str, source_ref: str) -> LoaderResult:
         cleaned_content = content
 
-        # Remove import statements
         cleaned_content = _IMPORT_PATTERN.sub("", cleaned_content)
 
-        # Remove export statements
         cleaned_content = _EXPORT_PATTERN.sub("", cleaned_content)
 
-        # Remove JSX tags (simple approach)
         cleaned_content = _JSX_TAG_PATTERN.sub("", cleaned_content)
 
-        # Clean up extra whitespace
         cleaned_content = _EXTRA_NEWLINES_PATTERN.sub("\n\n", cleaned_content)
         cleaned_content = cleaned_content.strip()
 

lib/crewai-tools/src/crewai_tools/rag/misc.py

@@ -31,9 +31,7 @@ def sanitize_metadata_for_chromadb(metadata: dict[str, Any]) -> dict[str, Any]:
         if isinstance(value, (str, int, float, bool)) or value is None:
             sanitized[key] = value
         elif isinstance(value, (list, tuple)):
-            # Convert lists/tuples to pipe-separated strings
             sanitized[key] = " | ".join(str(v) for v in value)
         else:
-            # Convert other types to string
             sanitized[key] = str(value)
     return sanitized

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

@@ -27,11 +27,6 @@ def _is_escape_hatch_enabled() -> bool:
     return os.environ.get(_UNSAFE_PATHS_ENV, "").lower() in ("true", "1", "yes")
 
 
-# ---------------------------------------------------------------------------
-# File path validation
-# ---------------------------------------------------------------------------
-
-
 def validate_file_path(path: str, base_dir: str | None = None) -> str:
     """Validate that a file path is safe to read.
 
@@ -101,10 +96,6 @@ def validate_directory_path(path: str, base_dir: str | None = None) -> str:
     return validated
 
 
-# ---------------------------------------------------------------------------
-# URL validation
-# ---------------------------------------------------------------------------
-
 # Private and reserved IP ranges that should not be accessed
 _BLOCKED_IPV4_NETWORKS = [
     ipaddress.ip_network("10.0.0.0/8"),
@@ -185,7 +176,6 @@ def validate_url(url: str) -> str:
     if not parsed.hostname:
         raise ValueError(f"URL has no hostname: '{url}'")
 
-    # Resolve DNS and check IPs
     try:
         addrinfos = socket.getaddrinfo(
             parsed.hostname, parsed.port or (443 if parsed.scheme == "https" else 80)

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

@@ -62,7 +62,6 @@ class AIMindTool(BaseTool):
 
         minds_client = Client(api_key=self.api_key)
 
-        # Convert the datasources to DatabaseConfig objects.
         datasources = []
         for datasource in self.datasources:
             config = DatabaseConfig(
@@ -74,7 +73,6 @@ class AIMindTool(BaseTool):
             )
             datasources.append(config)
 
-        # Generate a random name for the Mind.
         name = f"{AIMindToolConstants.MIND_NAME_PREFIX}_{secrets.token_hex(5)}"
 
         mind = minds_client.minds.create(
@@ -84,7 +82,6 @@ class AIMindTool(BaseTool):
         self.mind_name = mind.name
 
     def _run(self, query: str) -> str | None:
-        # Run the query on the AI-Mind.
         # The Minds API is OpenAI compatible and therefore, the OpenAI client can be used.
         openai_client = OpenAI(
             base_url=AIMindToolConstants.MINDS_API_BASE_URL, api_key=self.api_key

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/base.py

@@ -186,7 +186,6 @@ class BraveSearchToolBase(BaseTool, ABC):
         for attempt in range(_max_retries):
             self._rate_limit()
 
-            # Make the request
             try:
                 resp = requests.get(
                     self.search_url,
@@ -203,7 +202,6 @@ class BraveSearchToolBase(BaseTool, ABC):
                     f"Brave Search API request timed out after {self._timeout}s: {exc}"
                 ) from exc
 
-            # Log the rate limit headers and request details
             logger.debug(
                 "Brave Search API request: %s %s -> %d",
                 "GET",
@@ -251,7 +249,6 @@ class BraveSearchToolBase(BaseTool, ABC):
 
         params = self._common_payload_refinement(params)
 
-        # Validate only schema fields
         schema_keys = self.args_schema.model_fields
         payload_in = {k: v for k, v in params.items() if k in schema_keys}
 
@@ -301,7 +298,6 @@ class BraveSearchToolBase(BaseTool, ABC):
             if k not in fields or fields[k].is_required() or v not in self._EMPTY_VALUES
         }
 
-        # Make sure params has "q" for query instead of "query" or "search_query"
         query = params.get("query") or params.get("search_query")
         if query is not None and "q" not in params:
             params["q"] = query

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_image_tool.py

@@ -27,7 +27,6 @@ class BraveImageSearchTool(BraveSearchToolBase):
         return params
 
     def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
         results = response.get("results", [])
         return [
             {

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_news_tool.py

@@ -27,7 +27,6 @@ class BraveNewsSearchTool(BraveSearchToolBase):
         return params
 
     def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
         results = response.get("results", [])
         return [
             {

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

@@ -68,7 +68,6 @@ class BraveSearchTool(BaseTool):
             )
         BraveSearchTool._last_request_time = time.time()
 
-        # Construct and send the request
         try:
             # Fallback to "query" or "search_query" for backwards compatibility
             query = kwargs.get("q") or kwargs.get("query") or kwargs.get("search_query")
@@ -123,11 +122,9 @@ class BraveSearchTool(BaseTool):
                 payload["operators"] = operators
 
             # Limit the result types to "web" since there is presently no
-            # handling of other types like "discussions", "faq", "infobox",
             # "news", "videos", or "locations".
             payload["result_filter"] = "web"
 
-            # Setup Request Headers
             headers = {
                 "X-Subscription-Token": os.environ["BRAVE_API_KEY"],
                 "Accept": "application/json",
@@ -136,7 +133,7 @@ class BraveSearchTool(BaseTool):
             response = requests.get(
                 self.search_url, headers=headers, params=payload, timeout=30
             )
-            response.raise_for_status()  # Handle non-200 responses
+            response.raise_for_status()
             results = response.json()
 
             # TODO: Handle other result types like "discussions", "faq", etc.

lib/crewai-tools/src/crewai_tools/tools/brave_search_tool/brave_video_tool.py

@@ -27,7 +27,6 @@ class BraveVideoSearchTool(BraveSearchToolBase):
         return params
 
     def _refine_response(self, response: dict[str, Any]) -> list[dict[str, Any]]:
-        # Make the response more concise, and easier to consume
         results = response.get("results", [])
         return [
             {

lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_dataset.py

@@ -496,7 +496,6 @@ class BrightDataDatasetTool(BaseTool):
             )
 
         async with aiohttp.ClientSession() as session:
-            # Step 1: Trigger job
             async with session.post(
                 f"{BRIGHTDATA_API_URL}/datasets/v3/trigger",
                 params={"dataset_id": dataset_id, "include_errors": "true"},
@@ -511,7 +510,6 @@ class BrightDataDatasetTool(BaseTool):
                 trigger_data = await trigger_response.json()
                 snapshot_id = trigger_data.get("snapshot_id")
 
-            # Step 2: Poll for completion
             elapsed = 0
             while elapsed < timeout:
                 await asyncio.sleep(polling_interval)
@@ -536,7 +534,6 @@ class BrightDataDatasetTool(BaseTool):
             else:
                 raise TimeoutError("Polling timed out before job completed.")
 
-            # Step 3: Retrieve result
             async with session.get(
                 f"{BRIGHTDATA_API_URL}/datasets/v3/snapshot/{snapshot_id}",
                 params={"format": output_format},

lib/crewai-tools/src/crewai_tools/tools/brightdata_tool/brightdata_serp.py

@@ -173,15 +173,12 @@ class BrightDataSearchTool(BaseTool):
         )
         results_count = kwargs.get("results_count", "10")
 
-        # Validate required parameters
         if not query:
             raise ValueError("query is required either in constructor or method call")
 
-        # Build the search URL
         query = urllib.parse.quote(query)
         url = self.get_search_url(search_engine, query)
 
-        # Add parameters to the URL
         params = []
 
         if country:
@@ -214,7 +211,6 @@ class BrightDataSearchTool(BaseTool):
         if params:
             url += "&" + "&".join(params)
 
-        # Set up the API request parameters
         request_params = {"zone": self.zone, "url": url, "format": "raw"}
 
         request_params = {k: v for k, v in request_params.items() if v is not None}

lib/crewai-tools/src/crewai_tools/tools/contextualai_create_agent_tool/contextual_create_agent_tool.py

@@ -53,7 +53,6 @@ class ContextualAICreateAgentTool(BaseTool):
         try:
             import os
 
-            # Create datastore
             datastore = self.contextual_client.datastores.create(name=datastore_name)
             datastore_id = datastore.id
 
@@ -71,7 +70,6 @@ class ContextualAICreateAgentTool(BaseTool):
                     )
                     document_ids.append(ingestion_result.id)
 
-            # Create agent
             agent = self.contextual_client.agents.create(
                 name=agent_name,
                 description=agent_description,

lib/crewai-tools/src/crewai_tools/tools/contextualai_parse_tool/contextual_parse_tool.py

@@ -96,7 +96,6 @@ class ContextualAIParseTool(BaseTool):
 
                 sleep(5)
 
-            # Get parse results
             results_url = f"{base_url}/parse/jobs/{job_id}/results"
             result = requests.get(
                 results_url,