fix: resolve forward refs for CrewAgentExecutor BaseModel

docs/ar/changelog.mdx

@@ -4,70 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
-<Update label="31 مارس 2026">
-  ## v1.13.0a5
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
-
-  ## ما الذي تغير
-
-  ### الوثائق
-  - تحديث سجل التغييرات والإصدار لـ v1.13.0a4
-
-  ## المساهمون
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="1 أبريل 2026">
-  ## v1.13.0a4
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
-
-  ## ما الذي تغير
-
-  ### الوثائق
-  - تحديث سجل التغييرات والإصدار لـ v1.13.0a3
-
-  ## المساهمون
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="1 أبريل 2026">
-  ## v1.13.0a3
-
-  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a3)
-
-  ## ما الذي تغير
-
-  ### الميزات
-  - إصدار بيانات استخدام الرمز في LLMCallCompletedEvent
-  - استخراج ونشر بيانات الأداة إلى AMP
-
-  ### إصلاح الأخطاء
-  - التعامل مع نماذج GPT-5.x التي لا تدعم معلمة API `stop`
-
-  ### الوثائق
-  - إصلاح عدم الدقة في قدرات الوكيل عبر جميع اللغات
-  - إضافة نظرة عامة على قدرات الوكيل وتحسين وثائق المهارات
-  - إضافة دليل شامل لتكوين SSO
-  - تحديث سجل التغييرات والإصدار لـ v1.13.0rc1
-
-  ### إعادة الهيكلة
-  - تحويل Flow إلى Pydantic BaseModel
-  - تحويل فئات LLM إلى Pydantic BaseModel
-  - استبدال InstanceOf[T] بتعليقات نوع عادية
-  - إزالة الطرق غير المستخدمة
-
-  ## المساهمون
-
-  @dependabot[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide, @thiagomoretto
-
-</Update>
-
 <Update label="27 مارس 2026">
   ## v1.13.0rc1
 

docs/ar/concepts/agent-capabilities.mdx

@@ -1,147 +0,0 @@
----
-title: "قدرات الوكيل"
-description: "فهم الطرق الخمس لتوسيع وكلاء CrewAI: الأدوات، MCP، التطبيقات، المهارات، والمعرفة."
-icon: puzzle-piece
-mode: "wide"
----
-
-## نظرة عامة
-
-يمكن توسيع وكلاء CrewAI بـ **خمسة أنواع مميزة من القدرات**، كل منها يخدم غرضًا مختلفًا. فهم متى تستخدم كل نوع — وكيف يعملون معًا — هو المفتاح لبناء وكلاء فعّالين.
-
-<CardGroup cols={2}>
-  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools" color="#3B82F6">
-    **دوال قابلة للاستدعاء** — تمنح الوكلاء القدرة على اتخاذ إجراءات. البحث على الويب، عمليات الملفات، استدعاءات API، تنفيذ الكود.
-  </Card>
-  <Card title="خوادم MCP" icon="plug" href="/ar/mcp/overview" color="#8B5CF6">
-    **خوادم أدوات عن بُعد** — تربط الوكلاء بخوادم أدوات خارجية عبر Model Context Protocol. نفس تأثير الأدوات، لكن مستضافة خارجيًا.
-  </Card>
-  <Card title="التطبيقات" icon="grid-2" color="#EC4899">
-    **تكاملات المنصة** — تربط الوكلاء بتطبيقات SaaS (Gmail، Slack، Jira، Salesforce) عبر منصة CrewAI. تعمل محليًا مع رمز تكامل المنصة.
-  </Card>
-  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills" color="#F59E0B">
-    **خبرة المجال** — تحقن التعليمات والإرشادات والمواد المرجعية في إرشادات الوكلاء. المهارات تخبر الوكلاء *كيف يفكرون*.
-  </Card>
-  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge" color="#10B981">
-    **حقائق مُسترجعة** — توفر للوكلاء بيانات من المستندات والملفات وعناوين URL عبر البحث الدلالي (RAG). المعرفة تعطي الوكلاء *ما يحتاجون معرفته*.
-  </Card>
-</CardGroup>
-
----
-
-## التمييز الأساسي
-
-أهم شيء يجب فهمه: **هذه القدرات تنقسم إلى فئتين**.
-
-### قدرات الإجراء (الأدوات، MCP، التطبيقات)
-
-تمنح الوكلاء القدرة على **فعل أشياء** — استدعاء APIs، قراءة الملفات، البحث على الويب، إرسال رسائل البريد الإلكتروني. عند التنفيذ، تتحول الأنواع الثلاثة إلى نفس التنسيق الداخلي (مثيلات `BaseTool`) وتظهر في قائمة أدوات موحدة يمكن للوكيل استدعاؤها.
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool
-
-agent = Agent(
-    role="Researcher",
-    goal="Find and compile market data",
-    backstory="Expert market analyst",
-    tools=[SerperDevTool(), FileReadTool()],  # أدوات محلية
-    mcps=["https://mcp.example.com/sse"],     # أدوات خادم MCP عن بُعد
-    apps=["gmail", "google_sheets"],           # تكاملات المنصة
-)
-```
-
-### قدرات السياق (المهارات، المعرفة)
-
-تُعدّل **إرشادات** الوكيل — بحقن الخبرة أو التعليمات أو البيانات المُسترجعة قبل أن يبدأ الوكيل في التفكير. لا تمنح الوكلاء إجراءات جديدة؛ بل تُشكّل كيف يفكر الوكلاء وما هي المعلومات التي يمكنهم الوصول إليها.
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Security Auditor",
-    goal="Audit cloud infrastructure for vulnerabilities",
-    backstory="Expert in cloud security with 10 years of experience",
-    skills=["./skills/security-audit"],        # تعليمات المجال
-    knowledge_sources=[pdf_source, url_source], # حقائق مُسترجعة
-)
-```
-
----
-
-## متى تستخدم ماذا
-
-| تحتاج إلى...                                            | استخدم            | مثال                                     |
-| :------------------------------------------------------- | :---------------- | :--------------------------------------- |
-| الوكيل يبحث على الويب                                    | **الأدوات**       | `tools=[SerperDevTool()]`                |
-| الوكيل يستدعي API عن بُعد عبر MCP                        | **MCP**           | `mcps=["https://api.example.com/sse"]`   |
-| الوكيل يرسل بريد إلكتروني عبر Gmail                      | **التطبيقات**     | `apps=["gmail"]`                         |
-| الوكيل يتبع إجراءات محددة                                | **المهارات**      | `skills=["./skills/code-review"]`        |
-| الوكيل يرجع لمستندات الشركة                              | **المعرفة**       | `knowledge_sources=[pdf_source]`         |
-| الوكيل يبحث على الويب ويتبع إرشادات المراجعة              | **الأدوات + المهارات** | استخدم كليهما معًا                  |
-
----
-
-## دمج القدرات
-
-في الممارسة العملية، غالبًا ما يستخدم الوكلاء **أنواعًا متعددة من القدرات معًا**. إليك مثال واقعي:
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-# وكيل بحث مجهز بالكامل
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Produce comprehensive market analysis reports",
-    backstory="Expert analyst with deep industry knowledge",
-
-    # الإجراء: ما يمكن للوكيل فعله
-    tools=[
-        SerperDevTool(),         # البحث على الويب
-        FileReadTool(),          # قراءة الملفات المحلية
-        CodeInterpreterTool(),   # تشغيل كود Python للتحليل
-    ],
-    mcps=["https://data-api.example.com/sse"],  # الوصول لـ API بيانات عن بُعد
-    apps=["google_sheets"],                      # الكتابة في Google Sheets
-
-    # السياق: ما يعرفه الوكيل
-    skills=["./skills/research-methodology"],    # كيفية إجراء البحث
-    knowledge_sources=[company_docs],            # بيانات خاصة بالشركة
-)
-```
-
----
-
-## جدول المقارنة
-
-| الميزة | الأدوات | MCP | التطبيقات | المهارات | المعرفة |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **يمنح الوكيل إجراءات** | ✅ | ✅ | ✅ | ❌ | ❌ |
-| **يُعدّل الإرشادات** | ❌ | ❌ | ❌ | ✅ | ✅ |
-| **يتطلب كود** | نعم | إعداد فقط | إعداد فقط | Markdown فقط | إعداد فقط |
-| **يعمل محليًا** | نعم | يعتمد | نعم (مع متغير بيئة) | غير متاح | نعم |
-| **يحتاج مفاتيح API** | لكل أداة | لكل خادم | رمز التكامل | لا | المُضمّن فقط |
-| **يُعيَّن على Agent** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
-| **يُعيَّن على Crew** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
-
----
-
-## تعمّق أكثر
-
-هل أنت مستعد لمعرفة المزيد عن كل نوع من أنواع القدرات؟
-
-<CardGroup cols={2}>
-  <Card title="الأدوات" icon="wrench" href="/ar/concepts/tools">
-    إنشاء أدوات مخصصة، استخدام كتالوج OSS مع أكثر من 75 خيارًا، تكوين التخزين المؤقت والتنفيذ غير المتزامن.
-  </Card>
-  <Card title="تكامل MCP" icon="plug" href="/ar/mcp/overview">
-    الاتصال بخوادم MCP عبر stdio أو SSE أو HTTP. تصفية الأدوات، تكوين المصادقة.
-  </Card>
-  <Card title="المهارات" icon="bolt" href="/ar/concepts/skills">
-    بناء حزم المهارات مع SKILL.md، حقن خبرة المجال، استخدام الكشف التدريجي.
-  </Card>
-  <Card title="المعرفة" icon="book" href="/ar/concepts/knowledge">
-    إضافة المعرفة من ملفات PDF وCSV وعناوين URL والمزيد. تكوين المُضمّنات والاسترجاع.
-  </Card>
-</CardGroup>

docs/ar/concepts/skills.mdx

@@ -1,217 +1,15 @@
 ---
 title: المهارات
-description: حزم المهارات المبنية على نظام الملفات التي تحقن خبرة المجال والتعليمات في إرشادات الوكلاء.
+description: حزم المهارات المبنية على نظام الملفات التي تحقن السياق في إرشادات الوكيل.
 icon: bolt
 mode: "wide"
 ---
 
 ## نظرة عامة
 
-المهارات هي مجلدات مستقلة توفر للوكلاء **تعليمات وإرشادات ومواد مرجعية خاصة بالمجال**. تُعرّف كل مهارة بملف `SKILL.md` يحتوي على بيانات وصفية YAML ومحتوى Markdown.
+المهارات هي مجلدات مستقلة توفر للوكلاء تعليمات ومراجع وموارد خاصة بالمجال. تُعرّف كل مهارة بملف `SKILL.md` يحتوي على بيانات وصفية YAML ومحتوى Markdown.
 
-عند التفعيل، يتم حقن تعليمات المهارة مباشرة في إرشادات مهمة الوكيل — مما يمنح الوكيل خبرة دون الحاجة لأي تغييرات في الكود.
-
-<Note type="info" title="المهارات مقابل الأدوات — التمييز الأساسي">
-**المهارات ليست أدوات.** هذه هي نقطة الارتباك الأكثر شيوعًا.
-
-- **المهارات** تحقن *تعليمات وسياق* في إرشادات الوكيل. تخبر الوكيل *كيف يفكر* في مشكلة ما.
-- **الأدوات** تمنح الوكيل *دوال قابلة للاستدعاء* لاتخاذ إجراءات (البحث، قراءة الملفات، استدعاء APIs).
-
-غالبًا ما تحتاج **كليهما**: مهارات للخبرة، وأدوات للإجراء. يتم تكوينهما بشكل مستقل ويُكمّلان بعضهما.
-</Note>
-
----
-
-## البداية السريعة
-
-### 1. إنشاء مجلد المهارة
-
-```
-skills/
-└── code-review/
-    ├── SKILL.md          # مطلوب — التعليمات
-    ├── references/       # اختياري — مستندات مرجعية
-    │   └── style-guide.md
-    └── scripts/          # اختياري — سكربتات قابلة للتنفيذ
-```
-
-### 2. كتابة SKILL.md الخاص بك
-
-```markdown
----
-name: code-review
-description: Guidelines for conducting thorough code reviews with focus on security and performance.
-metadata:
-  author: your-team
-  version: "1.0"
----
-
-## إرشادات مراجعة الكود
-
-عند مراجعة الكود، اتبع قائمة التحقق هذه:
-
-1. **الأمان**: تحقق من ثغرات الحقن وتجاوز المصادقة وكشف البيانات
-2. **الأداء**: ابحث عن استعلامات N+1 والتخصيصات غير الضرورية والاستدعاءات المحظورة
-3. **القابلية للقراءة**: تأكد من وضوح التسمية والتعليقات المناسبة والأسلوب المتسق
-4. **الاختبارات**: تحقق من تغطية اختبار كافية للوظائف الجديدة
-
-### مستويات الخطورة
-- **حرج**: ثغرات أمنية، مخاطر فقدان البيانات → حظر الدمج
-- **رئيسي**: مشاكل أداء، أخطاء منطقية → طلب تغييرات
-- **ثانوي**: مسائل أسلوبية، اقتراحات تسمية → الموافقة مع تعليقات
-```
-
-### 3. ربطها بوكيل
-
-```python
-from crewai import Agent
-from crewai_tools import GithubSearchTool, FileReadTool
-
-reviewer = Agent(
-    role="Senior Code Reviewer",
-    goal="Review pull requests for quality and security issues",
-    backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["./skills"],                          # يحقن إرشادات المراجعة
-    tools=[GithubSearchTool(), FileReadTool()],   # يسمح للوكيل بقراءة الكود
-)
-```
-
-الوكيل الآن لديه **خبرة** (من المهارة) و**قدرات** (من الأدوات) معًا.
-
----
-
-## المهارات + الأدوات: العمل معًا
-
-إليك أنماط شائعة توضح كيف تُكمّل المهارات والأدوات بعضهما:
-
-### النمط 1: مهارات فقط (خبرة المجال، بدون إجراءات مطلوبة)
-
-استخدم عندما يحتاج الوكيل لتعليمات محددة لكن لا يحتاج لاستدعاء خدمات خارجية:
-
-```python
-agent = Agent(
-    role="Technical Writer",
-    goal="Write clear API documentation",
-    backstory="Expert technical writer",
-    skills=["./skills/api-docs-style"],  # إرشادات وقوالب الكتابة
-    # لا حاجة لأدوات — الوكيل يكتب بناءً على السياق المقدم
-)
-```
-
-### النمط 2: أدوات فقط (إجراءات، بدون خبرة خاصة)
-
-استخدم عندما يحتاج الوكيل لاتخاذ إجراءات لكن لا يحتاج لتعليمات مجال محددة:
-
-```python
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Find information about a topic",
-    backstory="Skilled at finding information online",
-    tools=[SerperDevTool(), ScrapeWebsiteTool()],  # يمكنه البحث والاستخراج
-    # لا حاجة لمهارات — البحث العام لا يحتاج إرشادات خاصة
-)
-```
-
-### النمط 3: مهارات + أدوات (خبرة وإجراءات)
-
-النمط الأكثر شيوعًا في العالم الحقيقي. المهارة توفر *كيف* تقترب من العمل؛ الأدوات توفر *ما* يمكن للوكيل فعله:
-
-```python
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-analyst = Agent(
-    role="Security Analyst",
-    goal="Audit infrastructure for vulnerabilities",
-    backstory="Expert in cloud security and compliance",
-    skills=["./skills/security-audit"],   # منهجية وقوائم تحقق التدقيق
-    tools=[
-        SerperDevTool(),                  # البحث عن ثغرات معروفة
-        FileReadTool(),                   # قراءة ملفات التكوين
-        CodeInterpreterTool(),            # تشغيل سكربتات التحليل
-    ],
-)
-```
-
-### النمط 4: مهارات + MCP
-
-المهارات تعمل مع خوادم MCP بنفس الطريقة التي تعمل بها مع الأدوات:
-
-```python
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze customer data and generate reports",
-    backstory="Expert data analyst with strong statistical background",
-    skills=["./skills/data-analysis"],           # منهجية التحليل
-    mcps=["https://data-warehouse.example.com/sse"],  # وصول بيانات عن بُعد
-)
-```
-
-### النمط 5: مهارات + تطبيقات
-
-المهارات يمكن أن توجّه كيف يستخدم الوكيل تكاملات المنصة:
-
-```python
-agent = Agent(
-    role="Customer Support Agent",
-    goal="Respond to customer inquiries professionally",
-    backstory="Experienced support representative",
-    skills=["./skills/support-playbook"],  # قوالب الردود وقواعد التصعيد
-    apps=["gmail", "zendesk"],             # يمكنه إرسال رسائل بريد وتحديث التذاكر
-)
-```
-
----
-
-## المهارات على مستوى الطاقم
-
-يمكن تعيين المهارات على الطاقم لتُطبّق على **جميع الوكلاء**:
-
-```python
-from crewai import Crew
-
-crew = Crew(
-    agents=[researcher, writer, reviewer],
-    tasks=[research_task, write_task, review_task],
-    skills=["./skills"],  # جميع الوكلاء يحصلون على هذه المهارات
-)
-```
-
-المهارات على مستوى الوكيل لها الأولوية — إذا تم اكتشاف نفس المهارة في كلا المستويين، يتم استخدام نسخة الوكيل.
-
----
-
-## تنسيق SKILL.md
-
-```markdown
----
-name: my-skill
-description: وصف قصير لما تفعله هذه المهارة ومتى تُستخدم.
-license: Apache-2.0                    # اختياري
-compatibility: crewai>=0.1.0          # اختياري
-metadata:                              # اختياري
-  author: your-name
-  version: "1.0"
-allowed-tools: web-search file-read   # اختياري، تجريبي
----
-
-التعليمات للوكيل تُكتب هنا. يتم حقن محتوى Markdown هذا
-في إرشادات الوكيل عند تفعيل المهارة.
-```
-
-### حقول البيانات الوصفية
-
-| الحقل           | مطلوب    | الوصف                                                                    |
-| :-------------- | :------- | :----------------------------------------------------------------------- |
-| `name`          | نعم      | 1-64 حرف. أحرف صغيرة أبجدية رقمية وشرطات. يجب أن يطابق اسم المجلد.     |
-| `description`   | نعم      | 1-1024 حرف. يصف ما تفعله المهارة ومتى تُستخدم.                          |
-| `license`       | لا       | اسم الترخيص أو مرجع لملف ترخيص مضمّن.                                   |
-| `compatibility` | لا       | حد أقصى 500 حرف. متطلبات البيئة (منتجات، حزم، شبكة).                    |
-| `metadata`      | لا       | تعيين مفتاح-قيمة نصي عشوائي.                                            |
-| `allowed-tools` | لا       | قائمة أدوات معتمدة مسبقًا مفصولة بمسافات. تجريبي.                        |
-
----
+تستخدم المهارات **الكشف التدريجي** — يتم تحميل البيانات الوصفية أولاً، ثم التعليمات الكاملة فقط عند التفعيل، وكتالوجات الموارد فقط عند الحاجة.
 
 ## هيكل المجلد
 
@@ -223,25 +21,79 @@ my-skill/
 └── assets/             # اختياري — ملفات ثابتة (إعدادات، بيانات)
 ```
 
-يجب أن يتطابق اسم المجلد مع حقل `name` في `SKILL.md`. مجلدات `scripts/` و `references/` و `assets/` متاحة في مسار المهارة `path` للوكلاء الذين يحتاجون للإشارة إلى الملفات مباشرة.
+يجب أن يتطابق اسم المجلد مع حقل `name` في `SKILL.md`.
 
+## تنسيق SKILL.md
+
+```markdown
+---
+name: my-skill
+description: Short description of what this skill does and when to use it.
+license: Apache-2.0                    # optional
+compatibility: crewai>=0.1.0          # optional
+metadata:                              # optional
+  author: your-name
+  version: "1.0"
+allowed-tools: web-search file-read   # optional, space-delimited
 ---
 
-## المهارات المحمّلة مسبقًا
+Instructions for the agent go here. This markdown body is injected
+into the agent's prompt when the skill is activated.
+```
 
-للمزيد من التحكم، يمكنك اكتشاف المهارات وتفعيلها برمجيًا:
+### حقول البيانات الوصفية
+
+| الحقل           | مطلوب | القيود                                                              |
+| :-------------- | :------- | :----------------------------------------------------------------------- |
+| `name`          | نعم      | 1-64 حرف. أحرف صغيرة أبجدية رقمية وشرطات. بدون شرطات بادئة/لاحقة/متتالية. يجب أن يطابق اسم المجلد. |
+| `description`   | نعم      | 1-1024 حرف. يصف ما تفعله المهارة ومتى تُستخدم.          |
+| `license`       | لا       | اسم الترخيص أو مرجع لملف ترخيص مضمّن.                     |
+| `compatibility` | لا       | حد أقصى 500 حرف. متطلبات البيئة (منتجات، حزم، شبكة).   |
+| `metadata`      | لا       | تعيين مفتاح-قيمة نصي عشوائي.                                      |
+| `allowed-tools` | لا       | قائمة أدوات معتمدة مسبقًا مفصولة بمسافات. تجريبي.                |
+
+## الاستخدام
+
+### المهارات على مستوى الوكيل
+
+مرر مسارات مجلدات المهارات إلى وكيل:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # يكتشف جميع المهارات في هذا المجلد
+)
+```
+
+### المهارات على مستوى الطاقم
+
+تُدمج مسارات المهارات في الطاقم مع كل وكيل:
+
+```python
+from crewai import Crew
+
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### المهارات المحمّلة مسبقًا
+
+يمكنك أيضًا تمرير كائنات `Skill` مباشرة:
 
 ```python
 from pathlib import Path
 from crewai.skills import discover_skills, activate_skill
 
-# اكتشاف جميع المهارات في مجلد
 skills = discover_skills(Path("./skills"))
-
-# تفعيلها (تحميل محتوى SKILL.md الكامل)
 activated = [activate_skill(s) for s in skills]
 
-# تمرير إلى وكيل
 agent = Agent(
     role="Researcher",
     goal="Find relevant information",
@@ -250,57 +102,13 @@ agent = Agent(
 )
 ```
 
----
-
 ## كيف يتم تحميل المهارات
 
-تستخدم المهارات **الكشف التدريجي** — تحمّل فقط ما هو مطلوب في كل مرحلة:
+يتم تحميل المهارات تدريجيًا — فقط البيانات المطلوبة في كل مرحلة يتم قراءتها:
 
-| المرحلة    | ما يتم تحميله                          | متى                |
-| :--------- | :------------------------------------ | :------------------ |
-| الاكتشاف  | الاسم، الوصف، حقول البيانات الوصفية   | `discover_skills()` |
-| التفعيل   | نص محتوى SKILL.md الكامل              | `activate_skill()`  |
+| المرحلة            | ما يتم تحميله                                     | متى               |
+| :--------------- | :------------------------------------------------ | :----------------- |
+| الاكتشاف        | الاسم، الوصف، حقول البيانات الوصفية             | `discover_skills()` |
+| التفعيل       | نص محتوى SKILL.md الكامل                           | `activate_skill()` |
 
-أثناء التنفيذ العادي للوكيل (تمرير مسارات المجلدات عبر `skills=["./skills"]`)، يتم اكتشاف المهارات وتفعيلها تلقائيًا. التحميل التدريجي مهم فقط عند استخدام الواجهة البرمجية.
-
----
-
-## المهارات مقابل المعرفة
-
-كلا المهارات والمعرفة تُعدّل إرشادات الوكيل، لكنهما يخدمان أغراضًا مختلفة:
-
-| الجانب | المهارات | المعرفة |
-| :--- | :--- | :--- |
-| **ما توفره** | تعليمات، إجراءات، إرشادات | حقائق، بيانات، معلومات |
-| **كيف تُخزّن** | ملفات Markdown (SKILL.md) | مُضمّنة في مخزن متجهي (ChromaDB) |
-| **كيف تُسترجع** | يتم حقن المحتوى الكامل في الإرشادات | البحث الدلالي يجد الأجزاء ذات الصلة |
-| **الأفضل لـ** | المنهجيات، قوائم التحقق، أدلة الأسلوب | مستندات الشركة، معلومات المنتج، بيانات مرجعية |
-| **يُعيّن عبر** | `skills=["./skills"]` | `knowledge_sources=[source]` |
-
-**القاعدة العامة:** إذا كان الوكيل يحتاج لاتباع *عملية*، استخدم مهارة. إذا كان يحتاج للرجوع إلى *بيانات*، استخدم المعرفة.
-
----
-
-## الأسئلة الشائعة
-
-<AccordionGroup>
-  <Accordion title="هل أحتاج لتعيين المهارات والأدوات معًا؟">
-    يعتمد على حالة الاستخدام. المهارات والأدوات **مستقلتان** — يمكنك استخدام أيّ منهما أو كليهما أو لا شيء.
-
-    - **مهارات فقط**: عندما يحتاج الوكيل خبرة لكن لا يحتاج إجراءات خارجية (مثال: الكتابة بإرشادات أسلوبية)
-    - **أدوات فقط**: عندما يحتاج الوكيل إجراءات لكن لا يحتاج منهجية خاصة (مثال: بحث بسيط على الويب)
-    - **كليهما**: عندما يحتاج الوكيل خبرة وإجراءات (مثال: تدقيق أمني بقوائم تحقق محددة وقدرة على فحص الكود)
-  </Accordion>
-
-  <Accordion title="هل توفر المهارات أدوات تلقائيًا؟">
-    **لا.** حقل `allowed-tools` في SKILL.md هو بيانات وصفية تجريبية فقط — لا يُنشئ أو يحقن أي أدوات. يجب عليك دائمًا تعيين الأدوات بشكل منفصل عبر `tools=[]` أو `mcps=[]` أو `apps=[]`.
-  </Accordion>
-
-  <Accordion title="ماذا يحدث إذا عيّنت نفس المهارة على كل من الوكيل والطاقم؟">
-    المهارة على مستوى الوكيل لها الأولوية. يتم إزالة التكرار حسب الاسم — مهارات الوكيل تُعالج أولاً، لذا إذا ظهر نفس اسم المهارة في كلا المستويين، تُستخدم نسخة الوكيل.
-  </Accordion>
-
-  <Accordion title="ما الحجم الأقصى لمحتوى SKILL.md؟">
-    هناك تحذير ناعم عند 50,000 حرف، لكن بدون حد صارم. حافظ على تركيز المهارات وإيجازها للحصول على أفضل النتائج — الحقن الكبيرة في الإرشادات قد تُشتت انتباه الوكيل.
-  </Accordion>
-</AccordionGroup>
+أثناء التنفيذ العادي للوكيل، يتم اكتشاف المهارات وتفعيلها تلقائيًا. مجلدات `scripts/` و `references/` و `assets/` متاحة في مسار المهارة `path` للوكلاء الذين يحتاجون للإشارة إلى الملفات مباشرة.

docs/ar/concepts/tools.mdx

@@ -10,10 +10,6 @@ mode: "wide"
 تُمكّن أدوات CrewAI الوكلاء بقدرات تتراوح من البحث على الويب وتحليل البيانات إلى التعاون وتفويض المهام بين الزملاء.
 توضح هذه الوثائق كيفية إنشاء هذه الأدوات ودمجها والاستفادة منها ضمن إطار عمل CrewAI، بما في ذلك التركيز على أدوات التعاون.
 
-<Note type="info" title="الأدوات هي أحد أنواع قدرات الوكيل الخمسة">
-  الأدوات تمنح الوكلاء **دوال قابلة للاستدعاء** لاتخاذ إجراءات. تعمل جنبًا إلى جنب مع [MCP](/ar/mcp/overview) (خوادم أدوات عن بُعد) و[التطبيقات](/ar/concepts/agent-capabilities) (تكاملات المنصة) و[المهارات](/ar/concepts/skills) (خبرة المجال) و[المعرفة](/ar/concepts/knowledge) (حقائق مُسترجعة). راجع نظرة عامة على [قدرات الوكيل](/ar/concepts/agent-capabilities) لفهم متى تستخدم كل نوع.
-</Note>
-
 ## ما هي الأداة؟
 
 الأداة في CrewAI هي مهارة أو وظيفة يمكن للوكلاء استخدامها لأداء إجراءات مختلفة.

docs/ar/enterprise/features/rbac.mdx

@@ -7,13 +7,11 @@ mode: "wide"
 
 ## نظرة عامة
 
-يتيح RBAC في CrewAI AMP إدارة وصول آمنة وقابلة للتوسع من خلال طبقتين:
-
-1. **صلاحيات الميزات** — تتحكم في ما يمكن لكل دور القيام به عبر المنصة (إدارة، قراءة، أو بدون وصول)
-2. **صلاحيات على مستوى الكيان** — وصول دقيق للأتمتات الفردية ومتغيرات البيئة واتصالات LLM ومستودعات Git
+يتيح RBAC في CrewAI AMP إدارة وصول آمنة وقابلة للتوسع من خلال مزيج من الأدوار على مستوى المؤسسة وعناصر التحكم في الرؤية على مستوى الأتمتة.
 
 <Frame>
   <img src="/images/enterprise/users_and_roles.png" alt="نظرة عامة على RBAC في CrewAI AMP" />
+
 </Frame>
 
 ## المستخدمون والأدوار
@@ -41,13 +39,6 @@ mode: "wide"
   </Step>
 </Steps>
 
-### الأدوار المحددة مسبقاً
-
-| الدور       | الوصف                                                                 |
-| :---------- | :-------------------------------------------------------------------- |
-| **Owner**   | وصول كامل لجميع الميزات والإعدادات. لا يمكن تقييده.                   |
-| **Member**  | وصول للقراءة لمعظم الميزات، وصول إدارة لمتغيرات البيئة واتصالات LLM ومشاريع Studio. لا يمكنه تعديل إعدادات المؤسسة أو الإعدادات الافتراضية. |
-
 ### ملخص التهيئة
 
 | المجال                | مكان التهيئة                       | الخيارات                                |
@@ -55,80 +46,23 @@ mode: "wide"
 | المستخدمون والأدوار   | Settings → Roles                   | محددة مسبقاً: Owner، Member؛ أدوار مخصصة |
 | رؤية الأتمتة         | Automation → Settings → Visibility | خاص؛ قائمة بيضاء للمستخدمين/الأدوار     |
 
----
+## التحكم في الوصول على مستوى الأتمتة
 
-## مصفوفة صلاحيات الميزات
+بالإضافة إلى الأدوار على مستوى المؤسسة، تدعم أتمتات CrewAI إعدادات رؤية دقيقة تتيح لك تقييد الوصول إلى أتمتات محددة حسب المستخدم أو الدور.
 
-لكل دور مستوى صلاحية لكل منطقة ميزة. المستويات الثلاثة هي:
-
-- **إدارة (Manage)** — وصول كامل للقراءة/الكتابة (إنشاء، تعديل، حذف)
-- **قراءة (Read)** — وصول للعرض فقط
-- **بدون وصول (No access)** — الميزة مخفية/غير قابلة للوصول
-
-| الميزة                    | Owner   | Member (افتراضي) | المستويات المتاحة                  | الوصف                                                           |
-| :------------------------ | :------ | :--------------- | :--------------------------------- | :-------------------------------------------------------------- |
-| `usage_dashboards`        | Manage  | Read             | Manage / Read / No access          | عرض مقاييس وتحليلات الاستخدام                                   |
-| `crews_dashboards`        | Manage  | Read             | Manage / Read / No access          | عرض لوحات النشر والوصول إلى تفاصيل الأتمتة                      |
-| `invitations`             | Manage  | Read             | Manage / Read / No access          | دعوة أعضاء جدد إلى المؤسسة                                      |
-| `training_ui`             | Manage  | Read             | Manage / Read / No access          | الوصول إلى واجهات التدريب/الضبط الدقيق                           |
-| `tools`                   | Manage  | Read             | Manage / Read / No access          | إنشاء وإدارة الأدوات                                            |
-| `agents`                  | Manage  | Read             | Manage / Read / No access          | إنشاء وإدارة الوكلاء                                            |
-| `environment_variables`   | Manage  | Manage           | Manage / No access                 | إنشاء وإدارة متغيرات البيئة                                     |
-| `llm_connections`         | Manage  | Manage           | Manage / No access                 | تهيئة اتصالات مزودي LLM                                         |
-| `default_settings`        | Manage  | No access        | Manage / No access                 | تعديل الإعدادات الافتراضية على مستوى المؤسسة                     |
-| `organization_settings`   | Manage  | No access        | Manage / No access                 | إدارة الفوترة والخطط وتهيئة المؤسسة                              |
-| `studio_projects`         | Manage  | Manage           | Manage / No access                 | إنشاء وتعديل المشاريع في Studio                                  |
-
-<Tip>
-  عند إنشاء دور مخصص، يمكن ضبط معظم الميزات على **Manage** أو **Read** أو **No access**. ومع ذلك، فإن `environment_variables` و`llm_connections` و`default_settings` و`organization_settings` و`studio_projects` تدعم فقط **Manage** أو **No access** — لا يوجد خيار للقراءة فقط لهذه الميزات.
-</Tip>
-
----
-
-## النشر من GitHub أو Zip
-
-من أكثر أسئلة RBAC شيوعاً: _"ما الصلاحيات التي يحتاجها عضو الفريق للنشر؟"_
-
-### النشر من GitHub
-
-لنشر أتمتة من مستودع GitHub، يحتاج المستخدم إلى:
-
-1. **`crews_dashboards`**: على الأقل `Read` — مطلوب للوصول إلى لوحة الأتمتات حيث يتم إنشاء عمليات النشر
-2. **الوصول إلى مستودع Git** (إذا كان RBAC على مستوى الكيان لمستودعات Git مفعلاً): يجب منح دور المستخدم الوصول إلى مستودع Git المحدد عبر صلاحيات مستوى الكيان
-3. **`studio_projects`: `Manage`** — إذا كان يبني الطاقم في Studio قبل النشر
-
-### النشر من Zip
-
-لنشر أتمتة من ملف Zip، يحتاج المستخدم إلى:
-
-1. **`crews_dashboards`**: على الأقل `Read` — مطلوب للوصول إلى لوحة الأتمتات
-2. **تفعيل نشر Zip**: يجب ألا تكون المؤسسة قد عطلت نشر Zip في إعدادات المؤسسة
-
-### مرجع سريع: الحد الأدنى من الصلاحيات للنشر
-
-| الإجراء              | صلاحيات الميزات المطلوبة             | متطلبات إضافية                                   |
-| :------------------- | :----------------------------------- | :----------------------------------------------- |
-| النشر من GitHub      | `crews_dashboards: Read`             | وصول كيان مستودع Git (إذا كان Git RBAC مفعلاً)   |
-| النشر من Zip         | `crews_dashboards: Read`             | يجب تفعيل نشر Zip على مستوى المؤسسة              |
-| البناء في Studio     | `studio_projects: Manage`            | —                                                |
-| تهيئة مفاتيح LLM     | `llm_connections: Manage`            | —                                                |
-| ضبط متغيرات البيئة   | `environment_variables: Manage`      | وصول مستوى الكيان (إذا كان RBAC الكيان مفعلاً)   |
-
----
-
-## التحكم في الوصول على مستوى الأتمتة (صلاحيات الكيان)
-
-بالإضافة إلى الأدوار على مستوى المؤسسة، يدعم CrewAI صلاحيات دقيقة على مستوى الكيان تقيد الوصول إلى موارد فردية.
-
-### رؤية الأتمتة
-
-تدعم الأتمتات إعدادات رؤية تقيد الوصول حسب المستخدم أو الدور. هذا مفيد لـ:
+هذا مفيد لـ:
 
 - الحفاظ على خصوصية الأتمتات الحساسة أو التجريبية
 - إدارة الرؤية عبر الفرق الكبيرة أو المتعاونين الخارجيين
 - اختبار الأتمتات في سياقات معزولة
 
-يمكن تهيئة عمليات النشر كخاصة، مما يعني أن المستخدمين والأدوار المدرجين في القائمة البيضاء فقط سيتمكنون من التفاعل معها.
+يمكن تهيئة عمليات النشر كخاصة، مما يعني أن المستخدمين والأدوار المدرجين في القائمة البيضاء فقط سيتمكنون من:
+
+- عرض عملية النشر
+- تشغيلها أو التفاعل مع API الخاص بها
+- الوصول إلى سجلاتها ومقاييسها وإعداداتها
+
+يتمتع مالك المؤسسة دائماً بالوصول، بغض النظر عن إعدادات الرؤية.
 
 يمكنك تهيئة التحكم في الوصول على مستوى الأتمتة في Automation → Settings → علامة تبويب Visibility.
 
@@ -165,92 +99,9 @@ mode: "wide"
 
 <Frame>
   <img src="/images/enterprise/visibility.png" alt="إعدادات رؤية الأتمتة في CrewAI AMP" />
+
 </Frame>
 
-### أنواع صلاحيات النشر
-
-عند منح وصول على مستوى الكيان لأتمتة محددة، يمكنك تعيين أنواع الصلاحيات التالية:
-
-| الصلاحية             | ما تسمح به                                          |
-| :------------------- | :-------------------------------------------------- |
-| `run`                | تنفيذ الأتمتة واستخدام API الخاص بها                 |
-| `traces`             | عرض تتبعات التنفيذ والسجلات                          |
-| `manage_settings`    | تعديل، إعادة نشر، استرجاع، أو حذف الأتمتة           |
-| `human_in_the_loop`  | الرد على طلبات الإنسان في الحلقة (HITL)              |
-| `full_access`        | جميع ما سبق                                         |
-
-### RBAC على مستوى الكيان لموارد أخرى
-
-عند تفعيل RBAC على مستوى الكيان، يمكن أيضاً التحكم في الوصول لهذه الموارد حسب المستخدم أو الدور:
-
-| المورد                | يتم التحكم فيه بواسطة              | الوصف                                                          |
-| :-------------------- | :--------------------------------- | :------------------------------------------------------------- |
-| متغيرات البيئة        | علامة ميزة RBAC الكيان             | تقييد أي الأدوار/المستخدمين يمكنهم عرض أو إدارة متغيرات بيئة محددة |
-| اتصالات LLM           | علامة ميزة RBAC الكيان             | تقييد الوصول لتهيئات مزودي LLM محددة                            |
-| مستودعات Git           | إعداد RBAC لمستودعات Git بالمؤسسة  | تقييد أي الأدوار/المستخدمين يمكنهم الوصول لمستودعات متصلة محددة  |
-
----
-
-## أنماط الأدوار الشائعة
-
-بينما يأتي CrewAI بدوري Owner وMember، تستفيد معظم الفرق من إنشاء أدوار مخصصة. إليك الأنماط الشائعة:
-
-### دور المطور
-
-دور لأعضاء الفريق الذين يبنون وينشرون الأتمتات لكن لا يديرون إعدادات المؤسسة.
-
-| الميزة                    | الصلاحية    |
-| :------------------------ | :---------- |
-| `usage_dashboards`        | Read        |
-| `crews_dashboards`        | Manage      |
-| `invitations`             | Read        |
-| `training_ui`             | Read        |
-| `tools`                   | Manage      |
-| `agents`                  | Manage      |
-| `environment_variables`   | Manage      |
-| `llm_connections`         | Manage      |
-| `default_settings`        | No access   |
-| `organization_settings`   | No access   |
-| `studio_projects`         | Manage      |
-
-### دور المشاهد / أصحاب المصلحة
-
-دور للمعنيين غير التقنيين الذين يحتاجون لمراقبة الأتمتات وعرض النتائج.
-
-| الميزة                    | الصلاحية    |
-| :------------------------ | :---------- |
-| `usage_dashboards`        | Read        |
-| `crews_dashboards`        | Read        |
-| `invitations`             | No access   |
-| `training_ui`             | Read        |
-| `tools`                   | Read        |
-| `agents`                  | Read        |
-| `environment_variables`   | No access   |
-| `llm_connections`         | No access   |
-| `default_settings`        | No access   |
-| `organization_settings`   | No access   |
-| `studio_projects`         | No access   |
-
-### دور مسؤول العمليات / المنصة
-
-دور لمشغلي المنصة الذين يديرون إعدادات البنية التحتية لكن قد لا يبنون الوكلاء.
-
-| الميزة                    | الصلاحية    |
-| :------------------------ | :---------- |
-| `usage_dashboards`        | Manage      |
-| `crews_dashboards`        | Manage      |
-| `invitations`             | Manage      |
-| `training_ui`             | Read        |
-| `tools`                   | Read        |
-| `agents`                  | Read        |
-| `environment_variables`   | Manage      |
-| `llm_connections`         | Manage      |
-| `default_settings`        | Manage      |
-| `organization_settings`   | Read        |
-| `studio_projects`         | No access   |
-
----
-
 <Card title="تحتاج مساعدة؟" icon="headset" href="mailto:support@crewai.com">
   تواصل مع فريق الدعم للمساعدة في أسئلة RBAC.
 </Card>

docs/docs.json

@@ -150,7 +150,6 @@
                     "group": "Core Concepts",
                     "pages": [
                       "en/concepts/agents",
-                      "en/concepts/agent-capabilities",
                       "en/concepts/tasks",
                       "en/concepts/crews",
                       "en/concepts/flows",
@@ -3463,7 +3462,6 @@
                     "group": "Conceitos-Chave",
                     "pages": [
                       "pt-BR/concepts/agents",
-                      "pt-BR/concepts/agent-capabilities",
                       "pt-BR/concepts/tasks",
                       "pt-BR/concepts/crews",
                       "pt-BR/concepts/flows",
@@ -6671,7 +6669,6 @@
                     "pages": [
                       "ko/concepts/agents",
                       "ko/concepts/tasks",
-                      "ko/concepts/agent-capabilities",
                       "ko/concepts/crews",
                       "ko/concepts/flows",
                       "ko/concepts/production-architecture",
@@ -9961,7 +9958,6 @@
                     "group": "\u0627\u0644\u0645\u0641\u0627\u0647\u064a\u0645 \u0627\u0644\u0623\u0633\u0627\u0633\u064a\u0629",
                     "pages": [
                       "ar/concepts/agents",
-                      "ar/concepts/agent-capabilities",
                       "ar/concepts/tasks",
                       "ar/concepts/crews",
                       "ar/concepts/flows",

docs/en/changelog.mdx

@@ -4,70 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="Mar 31, 2026">
-  ## v1.13.0a5
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
-
-  ## What's Changed
-
-  ### Documentation
-  - Update changelog and version for v1.13.0a4
-
-  ## Contributors
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="Apr 01, 2026">
-  ## v1.13.0a4
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
-
-  ## What's Changed
-
-  ### Documentation
-  - Update changelog and version for v1.13.0a3
-
-  ## Contributors
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="Apr 01, 2026">
-  ## v1.13.0a3
-
-  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a3)
-
-  ## What's Changed
-
-  ### Features
-  - Emit token usage data in LLMCallCompletedEvent
-  - Extract and publish tool metadata to AMP
-
-  ### Bug Fixes
-  - Handle GPT-5.x models not supporting the `stop` API parameter
-
-  ### Documentation
-  - Fix inaccuracies in agent-capabilities across all languages
-  - Add Agent Capabilities overview and improve Skills documentation
-  - Add comprehensive SSO configuration guide
-  - Update changelog and version for v1.13.0rc1
-
-  ### Refactoring
-  - Convert Flow to Pydantic BaseModel
-  - Convert LLM classes to Pydantic BaseModel
-  - Replace InstanceOf[T] with plain type annotations
-  - Remove unused methods
-
-  ## Contributors
-
-  @dependabot[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide, @thiagomoretto
-
-</Update>
-
 <Update label="Mar 27, 2026">
   ## v1.13.0rc1
 

docs/en/concepts/agent-capabilities.mdx

@@ -1,147 +0,0 @@
----
-title: "Agent Capabilities"
-description: "Understand the five ways to extend CrewAI agents: Tools, MCPs, Apps, Skills, and Knowledge."
-icon: puzzle-piece
-mode: "wide"
----
-
-## Overview
-
-CrewAI agents can be extended with **five distinct capability types**, each serving a different purpose. Understanding when to use each one — and how they work together — is key to building effective agents.
-
-<CardGroup cols={2}>
-  <Card title="Tools" icon="wrench" href="/en/concepts/tools" color="#3B82F6">
-    **Callable functions** — give agents the ability to take action. Web searches, file operations, API calls, code execution.
-  </Card>
-  <Card title="MCP Servers" icon="plug" href="/en/mcp/overview" color="#8B5CF6">
-    **Remote tool servers** — connect agents to external tool servers via the Model Context Protocol. Same effect as tools, but hosted externally.
-  </Card>
-  <Card title="Apps" icon="grid-2" color="#EC4899">
-    **Platform integrations** — connect agents to SaaS apps (Gmail, Slack, Jira, Salesforce) via CrewAI's platform. Runs locally with a platform integration token.
-  </Card>
-  <Card title="Skills" icon="bolt" href="/en/concepts/skills" color="#F59E0B">
-    **Domain expertise** — inject instructions, guidelines, and reference material into agent prompts. Skills tell agents *how to think*.
-  </Card>
-  <Card title="Knowledge" icon="book" href="/en/concepts/knowledge" color="#10B981">
-    **Retrieved facts** — provide agents with data from documents, files, and URLs via semantic search (RAG). Knowledge gives agents *what to know*.
-  </Card>
-</CardGroup>
-
----
-
-## The Key Distinction
-
-The most important thing to understand: **these capabilities fall into two categories**.
-
-### Action Capabilities (Tools, MCPs, Apps)
-
-These give agents the ability to **do things** — call APIs, read files, search the web, send emails. At execution time, all three resolve into the same internal format (`BaseTool` instances) and appear in a unified tool list the agent can call.
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool
-
-agent = Agent(
-    role="Researcher",
-    goal="Find and compile market data",
-    backstory="Expert market analyst",
-    tools=[SerperDevTool(), FileReadTool()],  # Local tools
-    mcps=["https://mcp.example.com/sse"],     # Remote MCP server tools
-    apps=["gmail", "google_sheets"],           # Platform integrations
-)
-```
-
-### Context Capabilities (Skills, Knowledge)
-
-These modify the agent's **prompt** — injecting expertise, instructions, or retrieved data before the agent starts reasoning. They don't give agents new actions; they shape how agents think and what information they have access to.
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Security Auditor",
-    goal="Audit cloud infrastructure for vulnerabilities",
-    backstory="Expert in cloud security with 10 years of experience",
-    skills=["./skills/security-audit"],        # Domain instructions
-    knowledge_sources=[pdf_source, url_source], # Retrieved facts
-)
-```
-
----
-
-## When to Use What
-
-| You need...                                       | Use               | Example                                  |
-| :------------------------------------------------ | :---------------- | :--------------------------------------- |
-| Agent to search the web                           | **Tools**         | `tools=[SerperDevTool()]`                |
-| Agent to call a remote API via MCP                | **MCPs**          | `mcps=["https://api.example.com/sse"]`   |
-| Agent to send emails via Gmail                    | **Apps**          | `apps=["gmail"]`                         |
-| Agent to follow specific procedures               | **Skills**        | `skills=["./skills/code-review"]`        |
-| Agent to reference company docs                   | **Knowledge**     | `knowledge_sources=[pdf_source]`         |
-| Agent to search the web AND follow review guidelines | **Tools + Skills** | Use both together                     |
-
----
-
-## Combining Capabilities
-
-In practice, agents often use **multiple capability types together**. Here's a realistic example:
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-# A fully-equipped research agent
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Produce comprehensive market analysis reports",
-    backstory="Expert analyst with deep industry knowledge",
-
-    # ACTION: What the agent can DO
-    tools=[
-        SerperDevTool(),         # Search the web
-        FileReadTool(),          # Read local files
-        CodeInterpreterTool(),   # Run Python code for analysis
-    ],
-    mcps=["https://data-api.example.com/sse"],  # Access remote data API
-    apps=["google_sheets"],                      # Write to Google Sheets
-
-    # CONTEXT: What the agent KNOWS
-    skills=["./skills/research-methodology"],    # How to conduct research
-    knowledge_sources=[company_docs],            # Company-specific data
-)
-```
-
----
-
-## Comparison Table
-
-| Feature | Tools | MCPs | Apps | Skills | Knowledge |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **Gives agent actions** | ✅ | ✅ | ✅ | ❌ | ❌ |
-| **Modifies prompt** | ❌ | ❌ | ❌ | ✅ | ✅ |
-| **Requires code** | Yes | Config only | Config only | Markdown only | Config only |
-| **Runs locally** | Yes | Depends | Yes (with env var) | N/A | Yes |
-| **Needs API keys** | Per tool | Per server | Integration token | No | Embedder only |
-| **Set on Agent** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
-| **Set on Crew** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
-
----
-
-## Deep Dives
-
-Ready to learn more about each capability type?
-
-<CardGroup cols={2}>
-  <Card title="Tools" icon="wrench" href="/en/concepts/tools">
-    Create custom tools, use the 75+ OSS catalog, configure caching and async execution.
-  </Card>
-  <Card title="MCP Integration" icon="plug" href="/en/mcp/overview">
-    Connect to MCP servers via stdio, SSE, or HTTP. Filter tools, configure auth.
-  </Card>
-  <Card title="Skills" icon="bolt" href="/en/concepts/skills">
-    Build skill packages with SKILL.md, inject domain expertise, use progressive disclosure.
-  </Card>
-  <Card title="Knowledge" icon="book" href="/en/concepts/knowledge">
-    Add knowledge from PDFs, CSVs, URLs, and more. Configure embedders and retrieval.
-  </Card>
-</CardGroup>

docs/en/concepts/skills.mdx

@@ -1,186 +1,27 @@
 ---
 title: Skills
-description: Filesystem-based skill packages that inject domain expertise and instructions into agent prompts.
+description: Filesystem-based skill packages that inject context into agent prompts.
 icon: bolt
 mode: "wide"
 ---
 
 ## Overview
 
-Skills are self-contained directories that provide agents with **domain-specific instructions, guidelines, and reference material**. Each skill is defined by a `SKILL.md` file with YAML frontmatter and a markdown body.
+Skills are self-contained directories that provide agents with domain-specific instructions, references, and assets. Each skill is defined by a `SKILL.md` file with YAML frontmatter and a markdown body.
 
-When activated, a skill's instructions are injected directly into the agent's task prompt — giving the agent expertise without requiring any code changes.
+Skills use **progressive disclosure** — metadata is loaded first, full instructions only when activated, and resource catalogs only when needed.
 
-<Note type="info" title="Skills vs Tools — The Key Distinction">
-**Skills are NOT tools.** This is the most common point of confusion.
-
-- **Skills** inject *instructions and context* into the agent's prompt. They tell the agent *how to think* about a problem.
-- **Tools** give the agent *callable functions* to take action (search, read files, call APIs).
-
-You often need **both**: skills for expertise, tools for action. They are configured independently and complement each other.
-</Note>
-
----
-
-## Quick Start
-
-### 1. Create a Skill Directory
+## Directory Structure
 
 ```
-skills/
-└── code-review/
-    ├── SKILL.md          # Required — instructions
-    ├── references/       # Optional — reference docs
-    │   └── style-guide.md
-    └── scripts/          # Optional — executable scripts
+my-skill/
+├── SKILL.md            # Required — frontmatter + instructions
+├── scripts/            # Optional — executable scripts
+├── references/         # Optional — reference documents
+└── assets/             # Optional — static files (configs, data)
 ```
 
-### 2. Write Your SKILL.md
-
-```markdown
----
-name: code-review
-description: Guidelines for conducting thorough code reviews with focus on security and performance.
-metadata:
-  author: your-team
-  version: "1.0"
----
-
-## Code Review Guidelines
-
-When reviewing code, follow this checklist:
-
-1. **Security**: Check for injection vulnerabilities, auth bypasses, and data exposure
-2. **Performance**: Look for N+1 queries, unnecessary allocations, and blocking calls
-3. **Readability**: Ensure clear naming, appropriate comments, and consistent style
-4. **Testing**: Verify adequate test coverage for new functionality
-
-### Severity Levels
-- **Critical**: Security vulnerabilities, data loss risks → block merge
-- **Major**: Performance issues, logic errors → request changes
-- **Minor**: Style issues, naming suggestions → approve with comments
-```
-
-### 3. Attach to an Agent
-
-```python
-from crewai import Agent
-from crewai_tools import GithubSearchTool, FileReadTool
-
-reviewer = Agent(
-    role="Senior Code Reviewer",
-    goal="Review pull requests for quality and security issues",
-    backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["./skills"],                          # Injects review guidelines
-    tools=[GithubSearchTool(), FileReadTool()],   # Lets agent read code
-)
-```
-
-The agent now has both **expertise** (from the skill) and **capabilities** (from the tools).
-
----
-
-## Skills + Tools: Working Together
-
-Here are common patterns showing how skills and tools complement each other:
-
-### Pattern 1: Skills Only (Domain Expertise, No Actions Needed)
-
-Use when the agent needs specific instructions but doesn't need to call external services:
-
-```python
-agent = Agent(
-    role="Technical Writer",
-    goal="Write clear API documentation",
-    backstory="Expert technical writer",
-    skills=["./skills/api-docs-style"],  # Writing guidelines and templates
-    # No tools needed — agent writes based on provided context
-)
-```
-
-### Pattern 2: Tools Only (Actions, No Special Expertise)
-
-Use when the agent needs to take action but doesn't need domain-specific instructions:
-
-```python
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Find information about a topic",
-    backstory="Skilled at finding information online",
-    tools=[SerperDevTool(), ScrapeWebsiteTool()],  # Can search and scrape
-    # No skills needed — general research doesn't need special guidelines
-)
-```
-
-### Pattern 3: Skills + Tools (Expertise AND Actions)
-
-The most common real-world pattern. The skill provides *how* to approach the work; tools provide *what* the agent can do:
-
-```python
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-analyst = Agent(
-    role="Security Analyst",
-    goal="Audit infrastructure for vulnerabilities",
-    backstory="Expert in cloud security and compliance",
-    skills=["./skills/security-audit"],   # Audit methodology and checklists
-    tools=[
-        SerperDevTool(),                  # Research known vulnerabilities
-        FileReadTool(),                   # Read config files
-        CodeInterpreterTool(),            # Run analysis scripts
-    ],
-)
-```
-
-### Pattern 4: Skills + MCPs
-
-Skills work alongside MCP servers the same way they work with tools:
-
-```python
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze customer data and generate reports",
-    backstory="Expert data analyst with strong statistical background",
-    skills=["./skills/data-analysis"],           # Analysis methodology
-    mcps=["https://data-warehouse.example.com/sse"],  # Remote data access
-)
-```
-
-### Pattern 5: Skills + Apps
-
-Skills can guide how an agent uses platform integrations:
-
-```python
-agent = Agent(
-    role="Customer Support Agent",
-    goal="Respond to customer inquiries professionally",
-    backstory="Experienced support representative",
-    skills=["./skills/support-playbook"],  # Response templates and escalation rules
-    apps=["gmail", "zendesk"],             # Can send emails and update tickets
-)
-```
-
----
-
-## Crew-Level Skills
-
-Skills can be set on a crew to apply to **all agents**:
-
-```python
-from crewai import Crew
-
-crew = Crew(
-    agents=[researcher, writer, reviewer],
-    tasks=[research_task, write_task, review_task],
-    skills=["./skills"],  # All agents get these skills
-)
-```
-
-Agent-level skills take priority — if the same skill is discovered at both levels, the agent's version is used.
-
----
+The directory name must match the `name` field in `SKILL.md`.
 
 ## SKILL.md Format
 
@@ -193,7 +34,7 @@ compatibility: crewai>=0.1.0          # optional
 metadata:                              # optional
   author: your-name
   version: "1.0"
-allowed-tools: web-search file-read   # optional, experimental
+allowed-tools: web-search file-read   # optional, space-delimited
 ---
 
 Instructions for the agent go here. This markdown body is injected
@@ -202,46 +43,57 @@ into the agent's prompt when the skill is activated.
 
 ### Frontmatter Fields
 
-| Field           | Required | Description                                                              |
+| Field           | Required | Constraints                                                              |
 | :-------------- | :------- | :----------------------------------------------------------------------- |
-| `name`          | Yes      | 1–64 chars. Lowercase alphanumeric and hyphens. Must match directory name. |
+| `name`          | Yes      | 1–64 chars. Lowercase alphanumeric and hyphens. No leading/trailing/consecutive hyphens. Must match directory name. |
 | `description`   | Yes      | 1–1024 chars. Describes what the skill does and when to use it.          |
 | `license`       | No       | License name or reference to a bundled license file.                     |
 | `compatibility` | No       | Max 500 chars. Environment requirements (products, packages, network).   |
 | `metadata`      | No       | Arbitrary string key-value mapping.                                      |
 | `allowed-tools` | No       | Space-delimited list of pre-approved tools. Experimental.                |
 
----
+## Usage
 
-## Directory Structure
+### Agent-level Skills
 
-```
-my-skill/
-├── SKILL.md            # Required — frontmatter + instructions
-├── scripts/            # Optional — executable scripts
-├── references/         # Optional — reference documents
-└── assets/             # Optional — static files (configs, data)
+Pass skill directory paths to an agent:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # discovers all skills in this directory
+)
 ```
 
-The directory name must match the `name` field in `SKILL.md`. The `scripts/`, `references/`, and `assets/` directories are available on the skill's `path` for agents that need to reference files directly.
+### Crew-level Skills
 
----
+Skill paths on a crew are merged into every agent:
 
-## Pre-loading Skills
+```python
+from crewai import Crew
 
-For more control, you can discover and activate skills programmatically:
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### Pre-loaded Skills
+
+You can also pass `Skill` objects directly:
 
 ```python
 from pathlib import Path
 from crewai.skills import discover_skills, activate_skill
 
-# Discover all skills in a directory
 skills = discover_skills(Path("./skills"))
-
-# Activate them (loads full SKILL.md body)
 activated = [activate_skill(s) for s in skills]
 
-# Pass to an agent
 agent = Agent(
     role="Researcher",
     goal="Find relevant information",
@@ -250,57 +102,14 @@ agent = Agent(
 )
 ```
 
----
-
 ## How Skills Are Loaded
 
-Skills use **progressive disclosure** — only loading what's needed at each stage:
+Skills load progressively — only the data needed at each stage is read:
 
-| Stage      | What's loaded                         | When                |
-| :--------- | :------------------------------------ | :------------------ |
-| Discovery  | Name, description, frontmatter fields | `discover_skills()` |
-| Activation | Full SKILL.md body text               | `activate_skill()`  |
+| Stage            | What's loaded                                     | When               |
+| :--------------- | :------------------------------------------------ | :----------------- |
+| Discovery        | Name, description, frontmatter fields             | `discover_skills()` |
+| Activation       | Full SKILL.md body text                           | `activate_skill()` |
 
-During normal agent execution (passing directory paths via `skills=["./skills"]`), skills are automatically discovered and activated. The progressive loading only matters when using the programmatic API.
+During normal agent execution, skills are automatically discovered and activated. The `scripts/`, `references/`, and `assets/` directories are available on the skill's `path` for agents that need to reference files directly.
 
----
-
-## Skills vs Knowledge
-
-Both skills and knowledge modify the agent's prompt, but they serve different purposes:
-
-| Aspect | Skills | Knowledge |
-| :--- | :--- | :--- |
-| **What it provides** | Instructions, procedures, guidelines | Facts, data, information |
-| **How it's stored** | Markdown files (SKILL.md) | Embedded in vector store (ChromaDB) |
-| **How it's retrieved** | Entire body injected into prompt | Semantic search finds relevant chunks |
-| **Best for** | Methodology, checklists, style guides | Company docs, product info, reference data |
-| **Set via** | `skills=["./skills"]` | `knowledge_sources=[source]` |
-
-**Rule of thumb:** If the agent needs to follow a *process*, use a skill. If the agent needs to reference *data*, use knowledge.
-
----
-
-## Common Questions
-
-<AccordionGroup>
-  <Accordion title="Do I need to set skills AND tools?">
-    It depends on your use case. Skills and tools are **independent** — you can use either, both, or neither.
-
-    - **Skills alone**: When the agent needs expertise but no external actions (e.g., writing with style guidelines)
-    - **Tools alone**: When the agent needs actions but no special methodology (e.g., simple web search)
-    - **Both**: When the agent needs expertise AND actions (e.g., security audit with specific checklists AND ability to scan code)
-  </Accordion>
-
-  <Accordion title="Do skills automatically provide tools?">
-    **No.** The `allowed-tools` field in SKILL.md is experimental metadata only — it does not provision or inject any tools. You must always set tools separately via `tools=[]`, `mcps=[]`, or `apps=[]`.
-  </Accordion>
-
-  <Accordion title="What happens if I set the same skill on both an agent and its crew?">
-    The agent-level skill takes priority. Skills are deduplicated by name — the agent's skills are processed first, so if the same skill name appears at both levels, the agent's version is used.
-  </Accordion>
-
-  <Accordion title="How large can a SKILL.md body be?">
-    There's a soft warning at 50,000 characters, but no hard limit. Keep skills focused and concise for best results — large prompt injections can dilute the agent's attention.
-  </Accordion>
-</AccordionGroup>

docs/en/concepts/tools.mdx

@@ -10,10 +10,6 @@ mode: "wide"
 CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
 
-<Note type="info" title="Tools are one of five agent capability types">
-  Tools give agents **callable functions** to take action. They work alongside [MCPs](/en/mcp/overview) (remote tool servers), [Apps](/en/concepts/agent-capabilities) (platform integrations), [Skills](/en/concepts/skills) (domain expertise), and [Knowledge](/en/concepts/knowledge) (retrieved facts). See the [Agent Capabilities](/en/concepts/agent-capabilities) overview to understand when to use each.
-</Note>
-
 ## What is a Tool?
 
 A tool in CrewAI is a skill or function that agents can utilize to perform various actions.

docs/en/enterprise/features/rbac.mdx

@@ -46,7 +46,7 @@ You can configure users and roles in Settings → Roles.
 | Role       | Description                                                                 |
 | :--------- | :-------------------------------------------------------------------------- |
 | **Owner**  | Full access to all features and settings. Cannot be restricted.             |
-| **Member** | Read access to most features, manage access to environment variables, LLM connections, and Studio projects. Cannot modify organization or default settings. |
+| **Member** | Read access to most features, manage access to Studio projects. Cannot modify organization or default settings. |
 
 ### Configuration summary
 
@@ -65,22 +65,22 @@ Every role has a permission level for each feature area. The three levels are:
 - **Read** — view-only access
 - **No access** — feature is hidden/inaccessible
 
-| Feature                   | Owner   | Member (default) | Available levels          | Description                                                     |
-| :------------------------ | :------ | :--------------- | :------------------------ | :-------------------------------------------------------------- |
-| `usage_dashboards`        | Manage  | Read             | Manage / Read / No access | View usage metrics and analytics                                |
-| `crews_dashboards`        | Manage  | Read             | Manage / Read / No access | View deployment dashboards, access automation details           |
-| `invitations`             | Manage  | Read             | Manage / Read / No access | Invite new members to the organization                          |
-| `training_ui`             | Manage  | Read             | Manage / Read / No access | Access training/fine-tuning interfaces                          |
-| `tools`                   | Manage  | Read             | Manage / Read / No access | Create and manage tools                                         |
-| `agents`                  | Manage  | Read             | Manage / Read / No access | Create and manage agents                                        |
-| `environment_variables`   | Manage  | Manage           | Manage / No access        | Create and manage environment variables                         |
-| `llm_connections`         | Manage  | Manage           | Manage / No access        | Configure LLM provider connections                              |
-| `default_settings`        | Manage  | No access        | Manage / No access        | Modify organization-wide default settings                       |
-| `organization_settings`   | Manage  | No access        | Manage / No access        | Manage billing, plans, and organization configuration           |
-| `studio_projects`         | Manage  | Manage           | Manage / No access        | Create and edit projects in Studio                              |
+| Feature                   | Owner   | Member (default) | Description                                                     |
+| :------------------------ | :------ | :--------------- | :-------------------------------------------------------------- |
+| `usage_dashboards`        | Manage  | Read             | View usage metrics and analytics                                |
+| `crews_dashboards`        | Manage  | Read             | View deployment dashboards, access automation details           |
+| `invitations`             | Manage  | Read             | Invite new members to the organization                          |
+| `training_ui`             | Manage  | Read             | Access training/fine-tuning interfaces                          |
+| `tools`                   | Manage  | Read             | Create and manage tools                                         |
+| `agents`                  | Manage  | Read             | Create and manage agents                                        |
+| `environment_variables`   | Manage  | Read             | Create and manage environment variables                         |
+| `llm_connections`         | Manage  | Read             | Configure LLM provider connections                              |
+| `default_settings`        | Manage  | No access        | Modify organization-wide default settings                       |
+| `organization_settings`   | Manage  | No access        | Manage billing, plans, and organization configuration           |
+| `studio_projects`         | Manage  | Manage           | Create and edit projects in Studio                              |
 
 <Tip>
-  When creating a custom role, most features can be set to **Manage**, **Read**, or **No access**. However, `environment_variables`, `llm_connections`, `default_settings`, `organization_settings`, and `studio_projects` only support **Manage** or **No access** — there is no read-only option for these features.
+  When creating a custom role, you can set each feature independently to **Manage**, **Read**, or **No access** to match your team's needs.
 </Tip>
 
 ---
@@ -208,7 +208,7 @@ A role for team members who build and deploy automations but don't manage organi
 | `tools`                   | Manage     |
 | `agents`                  | Manage     |
 | `environment_variables`   | Manage     |
-| `llm_connections`         | Manage     |
+| `llm_connections`         | Read       |
 | `default_settings`        | No access  |
 | `organization_settings`   | No access  |
 | `studio_projects`         | Manage     |
@@ -229,7 +229,7 @@ A role for non-technical stakeholders who need to monitor automations and view r
 | `llm_connections`         | No access  |
 | `default_settings`        | No access  |
 | `organization_settings`   | No access  |
-| `studio_projects`         | No access  |
+| `studio_projects`         | Read       |
 
 ### Ops / Platform Admin Role
 
@@ -247,7 +247,7 @@ A role for platform operators who manage infrastructure settings but may not bui
 | `llm_connections`         | Manage     |
 | `default_settings`        | Manage     |
 | `organization_settings`   | Read       |
-| `studio_projects`         | No access  |
+| `studio_projects`         | Read       |
 
 ---
 

docs/ko/changelog.mdx

@@ -4,70 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="2026년 3월 31일">
-  ## v1.13.0a5
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
-
-  ## 변경 사항
-
-  ### 문서
-  - v1.13.0a4에 대한 변경 로그 및 버전 업데이트
-
-  ## 기여자
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="2026년 4월 1일">
-  ## v1.13.0a4
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
-
-  ## 변경 사항
-
-  ### 문서
-  - v1.13.0a3에 대한 변경 로그 및 버전 업데이트
-
-  ## 기여자
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="2026년 4월 1일">
-  ## v1.13.0a3
-
-  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a3)
-
-  ## 변경 사항
-
-  ### 기능
-  - LLMCallCompletedEvent에서 토큰 사용 데이터 발행
-  - 도구 메타데이터를 AMP로 추출 및 게시
-
-  ### 버그 수정
-  - `stop` API 매개변수를 지원하지 않는 GPT-5.x 모델 처리
-
-  ### 문서
-  - 모든 언어에서 에이전트 기능의 부정확성 수정
-  - 에이전트 기능 개요 추가 및 기술 문서 개선
-  - 포괄적인 SSO 구성 가이드 추가
-  - v1.13.0rc1에 대한 변경 로그 및 버전 업데이트
-
-  ### 리팩토링
-  - Flow를 Pydantic BaseModel로 변환
-  - LLM 클래스를 Pydantic BaseModel로 변환
-  - InstanceOf[T]를 일반 타입 주석으로 교체
-  - 사용되지 않는 메서드 제거
-
-  ## 기여자
-
-  @dependabot[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide, @thiagomoretto
-
-</Update>
-
 <Update label="2026년 3월 27일">
   ## v1.13.0rc1
 

docs/ko/concepts/agent-capabilities.mdx

@@ -1,147 +0,0 @@
----
-title: "에이전트 기능"
-description: "CrewAI 에이전트를 확장하는 다섯 가지 방법 이해하기: 도구, MCP, 앱, 스킬, 지식."
-icon: puzzle-piece
-mode: "wide"
----
-
-## 개요
-
-CrewAI 에이전트는 **다섯 가지 고유한 기능 유형**으로 확장할 수 있으며, 각각 다른 목적을 가지고 있습니다. 각 유형을 언제 사용해야 하는지, 그리고 어떻게 함께 작동하는지 이해하는 것이 효과적인 에이전트를 구축하는 핵심입니다.
-
-<CardGroup cols={2}>
-  <Card title="도구" icon="wrench" href="/ko/concepts/tools" color="#3B82F6">
-    **호출 가능한 함수** — 에이전트가 행동을 취할 수 있게 합니다. 웹 검색, 파일 작업, API 호출, 코드 실행.
-  </Card>
-  <Card title="MCP 서버" icon="plug" href="/ko/mcp/overview" color="#8B5CF6">
-    **원격 도구 서버** — Model Context Protocol을 통해 에이전트를 외부 도구 서버에 연결합니다. 도구와 같은 효과이지만 외부에서 호스팅됩니다.
-  </Card>
-  <Card title="앱" icon="grid-2" color="#EC4899">
-    **플랫폼 통합** — CrewAI 플랫폼을 통해 에이전트를 SaaS 앱(Gmail, Slack, Jira, Salesforce)에 연결합니다. 플랫폼 통합 토큰으로 로컬에서 실행됩니다.
-  </Card>
-  <Card title="스킬" icon="bolt" href="/ko/concepts/skills" color="#F59E0B">
-    **도메인 전문성** — 에이전트 프롬프트에 지침, 가이드라인 및 참조 자료를 주입합니다. 스킬은 에이전트에게 *어떻게 생각할지*를 알려줍니다.
-  </Card>
-  <Card title="지식" icon="book" href="/ko/concepts/knowledge" color="#10B981">
-    **검색된 사실** — 시맨틱 검색(RAG)을 통해 문서, 파일 및 URL에서 에이전트에게 데이터를 제공합니다. 지식은 에이전트에게 *무엇을 알아야 하는지*를 제공합니다.
-  </Card>
-</CardGroup>
-
----
-
-## 핵심 구분
-
-가장 중요한 점: **이 기능들은 두 가지 범주로 나뉩니다**.
-
-### 액션 기능 (도구, MCP, 앱)
-
-에이전트에게 **무언가를 할 수 있는** 능력을 부여합니다 — API 호출, 파일 읽기, 웹 검색, 이메일 전송. 실행 시점에 세 가지 모두 동일한 내부 형식(`BaseTool` 인스턴스)으로 변환되며, 에이전트가 호출할 수 있는 통합 도구 목록에 나타납니다.
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool
-
-agent = Agent(
-    role="Researcher",
-    goal="Find and compile market data",
-    backstory="Expert market analyst",
-    tools=[SerperDevTool(), FileReadTool()],  # 로컬 도구
-    mcps=["https://mcp.example.com/sse"],     # 원격 MCP 서버 도구
-    apps=["gmail", "google_sheets"],           # 플랫폼 통합
-)
-```
-
-### 컨텍스트 기능 (스킬, 지식)
-
-에이전트의 **프롬프트**를 수정합니다 — 에이전트가 추론을 시작하기 전에 전문성, 지침 또는 검색된 데이터를 주입합니다. 에이전트에게 새로운 액션을 제공하는 것이 아니라, 에이전트가 어떻게 생각하고 어떤 정보에 접근할 수 있는지를 형성합니다.
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Security Auditor",
-    goal="Audit cloud infrastructure for vulnerabilities",
-    backstory="Expert in cloud security with 10 years of experience",
-    skills=["./skills/security-audit"],        # 도메인 지침
-    knowledge_sources=[pdf_source, url_source], # 검색된 사실
-)
-```
-
----
-
-## 언제 무엇을 사용할까
-
-| 필요한 것...                                             | 사용할 것          | 예시                                     |
-| :------------------------------------------------------- | :---------------- | :--------------------------------------- |
-| 에이전트가 웹을 검색                                      | **도구**          | `tools=[SerperDevTool()]`                |
-| 에이전트가 MCP를 통해 원격 API 호출                       | **MCP**           | `mcps=["https://api.example.com/sse"]`   |
-| 에이전트가 Gmail로 이메일 전송                            | **앱**            | `apps=["gmail"]`                         |
-| 에이전트가 특정 절차를 따름                               | **스킬**          | `skills=["./skills/code-review"]`        |
-| 에이전트가 회사 문서 참조                                 | **지식**          | `knowledge_sources=[pdf_source]`         |
-| 에이전트가 웹 검색 AND 리뷰 가이드라인 준수               | **도구 + 스킬**    | 둘 다 함께 사용                           |
-
----
-
-## 기능 조합하기
-
-실제로 에이전트는 종종 **여러 기능 유형을 함께** 사용합니다. 현실적인 예시입니다:
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-# 완전히 갖춘 리서치 에이전트
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Produce comprehensive market analysis reports",
-    backstory="Expert analyst with deep industry knowledge",
-
-    # 액션: 에이전트가 할 수 있는 것
-    tools=[
-        SerperDevTool(),         # 웹 검색
-        FileReadTool(),          # 로컬 파일 읽기
-        CodeInterpreterTool(),   # 분석을 위한 Python 코드 실행
-    ],
-    mcps=["https://data-api.example.com/sse"],  # 원격 데이터 API 접근
-    apps=["google_sheets"],                      # Google Sheets에 쓰기
-
-    # 컨텍스트: 에이전트가 아는 것
-    skills=["./skills/research-methodology"],    # 연구 수행 방법
-    knowledge_sources=[company_docs],            # 회사 특화 데이터
-)
-```
-
----
-
-## 비교 테이블
-
-| 특성 | 도구 | MCP | 앱 | 스킬 | 지식 |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **에이전트에게 액션 부여** | ✅ | ✅ | ✅ | ❌ | ❌ |
-| **프롬프트 수정** | ❌ | ❌ | ❌ | ✅ | ✅ |
-| **코드 필요** | 예 | 설정만 | 설정만 | 마크다운만 | 설정만 |
-| **로컬 실행** | 예 | 경우에 따라 | 예 (환경 변수 필요) | N/A | 예 |
-| **API 키 필요** | 도구별 | 서버별 | 통합 토큰 | 아니오 | 임베더만 |
-| **Agent에 설정** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
-| **Crew에 설정** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
-
----
-
-## 상세 가이드
-
-각 기능 유형에 대해 더 알아볼 준비가 되셨나요?
-
-<CardGroup cols={2}>
-  <Card title="도구" icon="wrench" href="/ko/concepts/tools">
-    맞춤형 도구 생성, 75개 이상의 OSS 카탈로그 사용, 캐싱 및 비동기 실행 설정.
-  </Card>
-  <Card title="MCP 통합" icon="plug" href="/ko/mcp/overview">
-    stdio, SSE 또는 HTTP를 통해 MCP 서버에 연결. 도구 필터링, 인증 설정.
-  </Card>
-  <Card title="스킬" icon="bolt" href="/ko/concepts/skills">
-    SKILL.md로 스킬 패키지 구축, 도메인 전문성 주입, 점진적 공개 사용.
-  </Card>
-  <Card title="지식" icon="book" href="/ko/concepts/knowledge">
-    PDF, CSV, URL 등에서 지식 추가. 임베더 및 검색 설정.
-  </Card>
-</CardGroup>

docs/ko/concepts/skills.mdx

@@ -1,186 +1,27 @@
 ---
 title: 스킬
-description: 에이전트 프롬프트에 도메인 전문성과 지침을 주입하는 파일 시스템 기반 스킬 패키지.
+description: 에이전트 프롬프트에 컨텍스트를 주입하는 파일 시스템 기반 스킬 패키지.
 icon: bolt
 mode: "wide"
 ---
 
 ## 개요
 
-스킬은 에이전트에게 **도메인별 지침, 가이드라인 및 참조 자료**를 제공하는 자체 포함 디렉터리입니다. 각 스킬은 YAML 프론트매터와 마크다운 본문이 포함된 `SKILL.md` 파일로 정의됩니다.
+스킬은 에이전트에게 도메인별 지침, 참조 자료, 에셋을 제공하는 자체 포함 디렉터리입니다. 각 스킬은 YAML 프론트매터와 마크다운 본문이 포함된 `SKILL.md` 파일로 정의됩니다.
 
-활성화되면 스킬의 지침이 에이전트의 작업 프롬프트에 직접 주입됩니다 — 코드 변경 없이 에이전트에게 전문성을 부여합니다.
+스킬은 **점진적 공개**를 사용합니다 — 메타데이터가 먼저 로드되고, 활성화 시에만 전체 지침이 로드되며, 필요할 때만 리소스 카탈로그가 로드됩니다.
 
-<Note type="info" title="스킬 vs 도구 — 핵심 구분">
-**스킬은 도구가 아닙니다.** 이것이 가장 흔한 혼동 포인트입니다.
-
-- **스킬**은 에이전트의 프롬프트에 *지침과 컨텍스트*를 주입합니다. 에이전트에게 문제에 대해 *어떻게 생각할지*를 알려줍니다.
-- **도구**는 에이전트에게 행동을 취할 수 있는 *호출 가능한 함수*를 제공합니다 (검색, 파일 읽기, API 호출).
-
-흔히 **둘 다** 필요합니다: 전문성을 위한 스킬과 행동을 위한 도구. 이들은 독립적으로 구성되며 서로 보완합니다.
-</Note>
-
----
-
-## 빠른 시작
-
-### 1. 스킬 디렉터리 생성
+## 디렉터리 구조
 
 ```
-skills/
-└── code-review/
-    ├── SKILL.md          # 필수 — 지침
-    ├── references/       # 선택 — 참조 문서
-    │   └── style-guide.md
-    └── scripts/          # 선택 — 실행 가능한 스크립트
+my-skill/
+├── SKILL.md            # 필수 — 프론트매터 + 지침
+├── scripts/            # 선택 — 실행 가능한 스크립트
+├── references/         # 선택 — 참조 문서
+└── assets/             # 선택 — 정적 파일 (설정, 데이터)
 ```
 
-### 2. SKILL.md 작성
-
-```markdown
----
-name: code-review
-description: Guidelines for conducting thorough code reviews with focus on security and performance.
-metadata:
-  author: your-team
-  version: "1.0"
----
-
-## 코드 리뷰 가이드라인
-
-코드를 리뷰할 때 이 체크리스트를 따르세요:
-
-1. **보안**: 인젝션 취약점, 인증 우회, 데이터 노출 확인
-2. **성능**: N+1 쿼리, 불필요한 할당, 블로킹 호출 확인
-3. **가독성**: 명확한 네이밍, 적절한 주석, 일관된 스타일 보장
-4. **테스트**: 새로운 기능에 대한 적절한 테스트 커버리지 확인
-
-### 심각도 수준
-- **크리티컬**: 보안 취약점, 데이터 손실 위험 → 머지 차단
-- **메이저**: 성능 문제, 로직 오류 → 변경 요청
-- **마이너**: 스타일 문제, 네이밍 제안 → 코멘트와 함께 승인
-```
-
-### 3. 에이전트에 연결
-
-```python
-from crewai import Agent
-from crewai_tools import GithubSearchTool, FileReadTool
-
-reviewer = Agent(
-    role="Senior Code Reviewer",
-    goal="Review pull requests for quality and security issues",
-    backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["./skills"],                          # 리뷰 가이드라인 주입
-    tools=[GithubSearchTool(), FileReadTool()],   # 에이전트가 코드를 읽을 수 있게 함
-)
-```
-
-이제 에이전트는 **전문성** (스킬에서)과 **기능** (도구에서) 모두를 갖추게 됩니다.
-
----
-
-## 스킬 + 도구: 함께 작동하기
-
-스킬과 도구가 어떻게 보완하는지 보여주는 일반적인 패턴입니다:
-
-### 패턴 1: 스킬만 (도메인 전문성, 액션 불필요)
-
-에이전트가 특정 지침이 필요하지만 외부 서비스를 호출할 필요가 없을 때 사용:
-
-```python
-agent = Agent(
-    role="Technical Writer",
-    goal="Write clear API documentation",
-    backstory="Expert technical writer",
-    skills=["./skills/api-docs-style"],  # 작성 가이드라인 및 템플릿
-    # 도구 불필요 — 에이전트가 제공된 컨텍스트를 기반으로 작성
-)
-```
-
-### 패턴 2: 도구만 (액션, 특별한 전문성 불필요)
-
-에이전트가 행동을 취해야 하지만 도메인별 지침이 필요 없을 때 사용:
-
-```python
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Find information about a topic",
-    backstory="Skilled at finding information online",
-    tools=[SerperDevTool(), ScrapeWebsiteTool()],  # 검색 및 스크래핑 가능
-    # 스킬 불필요 — 일반 연구에는 특별한 가이드라인이 필요 없음
-)
-```
-
-### 패턴 3: 스킬 + 도구 (전문성 AND 액션)
-
-가장 일반적인 실제 패턴. 스킬은 작업에 *어떻게* 접근할지를 제공하고, 도구는 에이전트가 *무엇을* 할 수 있는지를 제공합니다:
-
-```python
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-analyst = Agent(
-    role="Security Analyst",
-    goal="Audit infrastructure for vulnerabilities",
-    backstory="Expert in cloud security and compliance",
-    skills=["./skills/security-audit"],   # 감사 방법론 및 체크리스트
-    tools=[
-        SerperDevTool(),                  # 알려진 취약점 조사
-        FileReadTool(),                   # 설정 파일 읽기
-        CodeInterpreterTool(),            # 분석 스크립트 실행
-    ],
-)
-```
-
-### 패턴 4: 스킬 + MCP
-
-스킬은 도구와 마찬가지로 MCP 서버와 함께 작동합니다:
-
-```python
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze customer data and generate reports",
-    backstory="Expert data analyst with strong statistical background",
-    skills=["./skills/data-analysis"],           # 분석 방법론
-    mcps=["https://data-warehouse.example.com/sse"],  # 원격 데이터 접근
-)
-```
-
-### 패턴 5: 스킬 + 앱
-
-스킬은 에이전트가 플랫폼 통합을 사용하는 방법을 안내할 수 있습니다:
-
-```python
-agent = Agent(
-    role="Customer Support Agent",
-    goal="Respond to customer inquiries professionally",
-    backstory="Experienced support representative",
-    skills=["./skills/support-playbook"],  # 응답 템플릿 및 에스컬레이션 규칙
-    apps=["gmail", "zendesk"],             # 이메일 전송 및 티켓 업데이트 가능
-)
-```
-
----
-
-## 크루 레벨 스킬
-
-스킬을 크루에 설정하여 **모든 에이전트**에 적용할 수 있습니다:
-
-```python
-from crewai import Crew
-
-crew = Crew(
-    agents=[researcher, writer, reviewer],
-    tasks=[research_task, write_task, review_task],
-    skills=["./skills"],  # 모든 에이전트가 이 스킬을 받음
-)
-```
-
-에이전트 레벨 스킬이 우선합니다 — 동일한 스킬이 양쪽 레벨에서 발견되면 에이전트의 버전이 사용됩니다.
-
----
+디렉터리 이름은 `SKILL.md`의 `name` 필드와 일치해야 합니다.
 
 ## SKILL.md 형식
 
@@ -193,7 +34,7 @@ compatibility: crewai>=0.1.0          # 선택
 metadata:                              # 선택
   author: your-name
   version: "1.0"
-allowed-tools: web-search file-read   # 선택, 실험적
+allowed-tools: web-search file-read   # 선택, 공백으로 구분
 ---
 
 에이전트를 위한 지침이 여기에 들어갑니다. 이 마크다운 본문은
@@ -202,46 +43,57 @@ allowed-tools: web-search file-read   # 선택, 실험적
 
 ### 프론트매터 필드
 
-| 필드             | 필수   | 설명                                                                     |
+| 필드             | 필수   | 제약 조건                                                                |
 | :-------------- | :----- | :----------------------------------------------------------------------- |
-| `name`          | 예     | 1–64자. 소문자 영숫자와 하이픈. 디렉터리 이름과 일치 필수.                  |
+| `name`          | 예     | 1–64자. 소문자 영숫자와 하이픈. 선행/후행/연속 하이픈 불가. 디렉터리 이름과 일치 필수. |
 | `description`   | 예     | 1–1024자. 스킬이 무엇을 하고 언제 사용하는지 설명.                          |
 | `license`       | 아니오  | 라이선스 이름 또는 번들된 라이선스 파일 참조.                                |
 | `compatibility` | 아니오  | 최대 500자. 환경 요구 사항 (제품, 패키지, 네트워크).                         |
 | `metadata`      | 아니오  | 임의의 문자열 키-값 매핑.                                                  |
 | `allowed-tools` | 아니오  | 공백으로 구분된 사전 승인 도구 목록. 실험적.                                 |
 
----
+## 사용법
 
-## 디렉터리 구조
+### 에이전트 레벨 스킬
 
-```
-my-skill/
-├── SKILL.md            # 필수 — 프론트매터 + 지침
-├── scripts/            # 선택 — 실행 가능한 스크립트
-├── references/         # 선택 — 참조 문서
-└── assets/             # 선택 — 정적 파일 (설정, 데이터)
+에이전트에 스킬 디렉터리 경로를 전달합니다:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # 이 디렉터리의 모든 스킬을 검색
+)
 ```
 
-디렉터리 이름은 `SKILL.md`의 `name` 필드와 일치해야 합니다. `scripts/`, `references/`, `assets/` 디렉터리는 파일을 직접 참조해야 하는 에이전트를 위해 스킬의 `path`에서 사용할 수 있습니다.
+### 크루 레벨 스킬
 
----
+크루의 스킬 경로는 모든 에이전트에 병합됩니다:
 
-## 사전 로드된 스킬
+```python
+from crewai import Crew
 
-더 세밀한 제어를 위해 프로그래밍 방식으로 스킬을 검색하고 활성화할 수 있습니다:
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### 사전 로드된 스킬
+
+`Skill` 객체를 직접 전달할 수도 있습니다:
 
 ```python
 from pathlib import Path
 from crewai.skills import discover_skills, activate_skill
 
-# 디렉터리의 모든 스킬 검색
 skills = discover_skills(Path("./skills"))
-
-# 활성화 (전체 SKILL.md 본문 로드)
 activated = [activate_skill(s) for s in skills]
 
-# 에이전트에 전달
 agent = Agent(
     role="Researcher",
     goal="Find relevant information",
@@ -250,57 +102,13 @@ agent = Agent(
 )
 ```
 
----
-
 ## 스킬 로드 방식
 
-스킬은 **점진적 공개**를 사용합니다 — 각 단계에서 필요한 것만 로드합니다:
+스킬은 점진적으로 로드됩니다 — 각 단계에서 필요한 데이터만 읽습니다:
 
-| 단계     | 로드되는 내용                          | 시점                |
-| :------- | :------------------------------------ | :------------------ |
-| 검색     | 이름, 설명, 프론트매터 필드             | `discover_skills()` |
-| 활성화   | 전체 SKILL.md 본문 텍스트              | `activate_skill()`  |
+| 단계              | 로드되는 내용                                      | 시점                |
+| :--------------- | :------------------------------------------------ | :----------------- |
+| 검색              | 이름, 설명, 프론트매터 필드                          | `discover_skills()` |
+| 활성화            | 전체 SKILL.md 본문 텍스트                           | `activate_skill()` |
 
-일반적인 에이전트 실행 중(`skills=["./skills"]`로 디렉터리 경로 전달 시) 스킬은 자동으로 검색되고 활성화됩니다. 점진적 로딩은 프로그래밍 API를 사용할 때만 관련됩니다.
-
----
-
-## 스킬 vs 지식
-
-스킬과 지식 모두 에이전트의 프롬프트를 수정하지만, 서로 다른 목적을 가지고 있습니다:
-
-| 측면 | 스킬 | 지식 |
-| :--- | :--- | :--- |
-| **제공하는 것** | 지침, 절차, 가이드라인 | 사실, 데이터, 정보 |
-| **저장 방식** | 마크다운 파일 (SKILL.md) | 벡터 스토어에 임베딩 (ChromaDB) |
-| **검색 방식** | 전체 본문이 프롬프트에 주입 | 시맨틱 검색으로 관련 청크 찾기 |
-| **적합한 용도** | 방법론, 체크리스트, 스타일 가이드 | 회사 문서, 제품 정보, 참조 데이터 |
-| **설정 방법** | `skills=["./skills"]` | `knowledge_sources=[source]` |
-
-**경험 법칙:** 에이전트가 *프로세스*를 따라야 하면 스킬을 사용하세요. 에이전트가 *데이터*를 참조해야 하면 지식을 사용하세요.
-
----
-
-## 자주 묻는 질문
-
-<AccordionGroup>
-  <Accordion title="스킬과 도구를 모두 설정해야 하나요?">
-    사용 사례에 따라 다릅니다. 스킬과 도구는 **독립적**입니다 — 둘 중 하나, 둘 다, 또는 아무것도 사용하지 않을 수 있습니다.
-
-    - **스킬만**: 에이전트가 전문성은 필요하지만 외부 액션이 필요 없을 때 (예: 스타일 가이드라인으로 작성)
-    - **도구만**: 에이전트가 액션은 필요하지만 특별한 방법론이 필요 없을 때 (예: 간단한 웹 검색)
-    - **둘 다**: 에이전트가 전문성 AND 액션이 필요할 때 (예: 특정 체크리스트로 보안 감사 AND 코드 스캔 기능)
-  </Accordion>
-
-  <Accordion title="스킬이 자동으로 도구를 제공하나요?">
-    **아니요.** SKILL.md의 `allowed-tools` 필드는 실험적 메타데이터일 뿐 — 도구를 프로비저닝하거나 주입하지 않습니다. 항상 `tools=[]`, `mcps=[]` 또는 `apps=[]`를 통해 별도로 도구를 설정해야 합니다.
-  </Accordion>
-
-  <Accordion title="에이전트와 크루 모두에 같은 스킬을 설정하면 어떻게 되나요?">
-    에이전트 레벨 스킬이 우선합니다. 스킬은 이름으로 중복 제거됩니다 — 에이전트의 스킬이 먼저 처리되므로, 같은 스킬 이름이 양쪽 레벨에 나타나면 에이전트의 버전이 사용됩니다.
-  </Accordion>
-
-  <Accordion title="SKILL.md 본문의 최대 크기는 얼마인가요?">
-    50,000자에서 소프트 경고가 있지만 하드 리밋은 없습니다. 최상의 결과를 위해 스킬을 집중적이고 간결하게 유지하세요 — 너무 큰 프롬프트 주입은 에이전트의 주의를 분산시킬 수 있습니다.
-  </Accordion>
-</AccordionGroup>
+일반적인 에이전트 실행 중에 스킬은 자동으로 검색되고 활성화됩니다. `scripts/`, `references/`, `assets/` 디렉터리는 파일을 직접 참조해야 하는 에이전트를 위해 스킬의 `path`에서 사용할 수 있습니다.

docs/ko/concepts/tools.mdx

@@ -10,10 +10,6 @@ mode: "wide"
 CrewAI 도구는 에이전트에게 웹 검색, 데이터 분석부터 동료 간 협업 및 작업 위임에 이르기까지 다양한 기능을 제공합니다.  
 이 문서에서는 CrewAI 프레임워크 내에서 이러한 도구를 생성, 통합 및 활용하는 방법과, 협업 도구에 초점을 맞춘 새로운 기능에 대해 설명합니다.
 
-<Note type="info" title="도구는 다섯 가지 에이전트 기능 유형 중 하나입니다">
-  도구는 에이전트에게 행동을 취할 수 있는 **호출 가능한 함수**를 제공합니다. [MCP](/ko/mcp/overview) (원격 도구 서버), [앱](/ko/concepts/agent-capabilities) (플랫폼 통합), [스킬](/ko/concepts/skills) (도메인 전문성), [지식](/ko/concepts/knowledge) (검색된 사실)과 함께 작동합니다. 각 유형을 언제 사용해야 하는지 알아보려면 [에이전트 기능](/ko/concepts/agent-capabilities) 개요를 참조하세요.
-</Note>
-
 ## Tool이란 무엇인가?
 
 CrewAI에서 tool은 에이전트가 다양한 작업을 수행하기 위해 활용할 수 있는 기술 또는 기능입니다.

docs/ko/enterprise/features/rbac.mdx

@@ -1,260 +1,108 @@
 ---
 title: "역할 기반 접근 제어 (RBAC)"
-description: "역할, 범위, 세분화된 권한으로 crews, 도구, 데이터 접근을 제어합니다."
+description: "역할과 자동화별 가시성으로 crews, 도구, 데이터 접근을 제어합니다."
 icon: "shield"
 mode: "wide"
 ---
 
 ## 개요
 
-CrewAI AMP의 RBAC는 두 가지 계층을 통해 안전하고 확장 가능한 접근 관리를 제공합니다:
-
-1. **기능 권한** — 플랫폼 전반에서 각 역할이 수행할 수 있는 작업을 제어합니다 (관리, 읽기 또는 접근 불가)
-2. **엔티티 수준 권한** — 개별 자동화, 환경 변수, LLM 연결, Git 저장소에 대한 세분화된 접근 제어
+CrewAI AOP의 RBAC는 **조직 수준 역할**과 **자동화(Automation) 수준 가시성**을 결합하여 안전하고 확장 가능한 접근 제어를 제공합니다.
 
 <Frame>
   <img src="/images/enterprise/users_and_roles.png" alt="CrewAI AMP RBAC 개요" />
+
 </Frame>
 
 ## 사용자와 역할
 
-CrewAI 워크스페이스의 각 구성원에게는 역할이 할당되며, 이를 통해 다양한 기능에 대한 접근 범위가 결정됩니다.
+워크스페이스의 각 구성원은 역할이 있으며, 이는 기능 접근 범위를 결정합니다.
 
 가능한 작업:
 
 - 사전 정의된 역할 사용 (Owner, Member)
-- 특정 권한에 맞춘 커스텀 역할 생성
-- 설정 패널에서 언제든지 역할 할당
+- 권한을 세분화한 커스텀 역할 생성
+- 설정 화면에서 언제든 역할 할당/변경
 
 설정 위치: Settings → Roles
 
 <Steps>
-  <Step title="Roles 설정 열기">
-    CrewAI AMP에서 <b>Settings → Roles</b>로 이동합니다.
+  <Step title="Roles 열기">
+    <b>Settings → Roles</b>로 이동합니다.
   </Step>
-  <Step title="역할 유형 선택">
-    사전 정의된 역할(<b>Owner</b>, <b>Member</b>)을 사용하거나{" "}
-    <b>Create role</b>을 클릭하여 커스텀 역할을 만듭니다.
+  <Step title="역할 선택">
+    <b>Owner</b> 또는 <b>Member</b>를 사용하거나 <b>Create role</b>로 커스텀
+    역할을 만듭니다.
   </Step>
   <Step title="멤버에 할당">
-    사용자를 선택하고 역할을 할당합니다. 언제든지 변경할 수 있습니다.
+    사용자들을 선택하여 역할을 지정합니다. 언제든 변경할 수 있습니다.
   </Step>
 </Steps>
 
-### 사전 정의된 역할
-
-| 역할       | 설명                                                                 |
-| :--------- | :------------------------------------------------------------------- |
-| **Owner**  | 모든 기능 및 설정에 대한 전체 접근 권한. 제한할 수 없습니다.            |
-| **Member** | 대부분의 기능에 대한 읽기 접근, 환경 변수, LLM 연결, Studio 프로젝트에 대한 관리 접근. 조직 설정이나 기본 설정은 수정할 수 없습니다. |
-
 ### 구성 요약
 
-| 영역          | 설정 위치                          | 옵션                              |
+| 영역          | 위치                               | 옵션                              |
 | :------------ | :--------------------------------- | :-------------------------------- |
-| 사용자 & 역할 | Settings → Roles                   | 사전 정의: Owner, Member; 커스텀 역할 |
+| 사용자 & 역할 | Settings → Roles                   | Owner, Member; 커스텀 역할        |
 | 자동화 가시성 | Automation → Settings → Visibility | Private; 사용자/역할 화이트리스트 |
 
----
+## 자동화 수준 접근 제어
 
-## 기능 권한 매트릭스
+조직 역할과 별개로, **Automations**는 사용자/역할별로 특정 자동화 접근을 제한하는 가시성 설정을 제공합니다.
 
-각 역할에는 기능 영역별 권한 수준이 있습니다. 세 가지 수준은 다음과 같습니다:
+유용한 경우:
 
-- **Manage** — 전체 읽기/쓰기 접근 (생성, 편집, 삭제)
-- **Read** — 읽기 전용 접근
-- **No access** — 기능이 숨겨지거나 접근 불가
-
-| 기능                        | Owner   | Member (기본값)  | 사용 가능한 수준            | 설명                                                           |
-| :-------------------------- | :------ | :--------------- | :------------------------- | :------------------------------------------------------------- |
-| `usage_dashboards`          | Manage  | Read             | Manage / Read / No access  | 사용 메트릭 및 분석 보기                                        |
-| `crews_dashboards`          | Manage  | Read             | Manage / Read / No access  | 배포 대시보드 보기, 자동화 세부 정보 접근                        |
-| `invitations`               | Manage  | Read             | Manage / Read / No access  | 조직에 새 멤버 초대                                             |
-| `training_ui`               | Manage  | Read             | Manage / Read / No access  | 훈련/파인튜닝 인터페이스 접근                                    |
-| `tools`                     | Manage  | Read             | Manage / Read / No access  | 도구 생성 및 관리                                               |
-| `agents`                    | Manage  | Read             | Manage / Read / No access  | 에이전트 생성 및 관리                                           |
-| `environment_variables`     | Manage  | Manage           | Manage / No access         | 환경 변수 생성 및 관리                                          |
-| `llm_connections`           | Manage  | Manage           | Manage / No access         | LLM 제공자 연결 구성                                            |
-| `default_settings`          | Manage  | No access        | Manage / No access         | 조직 전체 기본 설정 수정                                        |
-| `organization_settings`     | Manage  | No access        | Manage / No access         | 결제, 플랜 및 조직 구성 관리                                    |
-| `studio_projects`           | Manage  | Manage           | Manage / No access         | Studio에서 프로젝트 생성 및 편집                                 |
-
-<Tip>
-  커스텀 역할을 만들 때 대부분의 기능은 **Manage**, **Read** 또는 **No access**로 설정할 수 있습니다. 그러나 `environment_variables`, `llm_connections`, `default_settings`, `organization_settings`, `studio_projects`는 **Manage** 또는 **No access**만 지원합니다 — 이 기능들에는 읽기 전용 옵션이 없습니다.
-</Tip>
-
----
-
-## GitHub 또는 Zip에서 배포
-
-가장 흔한 RBAC 질문 중 하나: _"팀원이 배포하려면 어떤 권한이 필요한가요?"_
-
-### GitHub에서 배포
-
-GitHub 저장소에서 자동화를 배포하려면 사용자에게 다음이 필요합니다:
-
-1. **`crews_dashboards`**: 최소 `Read` — 배포가 생성되는 자동화 대시보드에 접근하는 데 필요
-2. **Git 저장소 접근** (Git 저장소에 대한 엔티티 수준 RBAC가 활성화된 경우): 사용자의 역할에 엔티티 수준 권한을 통해 특정 Git 저장소에 대한 접근이 부여되어야 함
-3. **`studio_projects`: `Manage`** — 배포 전에 Studio에서 crew를 빌드하는 경우
-
-### Zip에서 배포
-
-Zip 파일 업로드로 자동화를 배포하려면 사용자에게 다음이 필요합니다:
-
-1. **`crews_dashboards`**: 최소 `Read` — 자동화 대시보드에 접근하는 데 필요
-2. **Zip 배포 활성화**: 조직이 조직 설정에서 Zip 배포를 비활성화하지 않아야 함
-
-### 빠른 참조: 배포에 필요한 최소 권한
-
-| 작업                 | 필요한 기능 권한                      | 추가 요구사항                                     |
-| :------------------- | :----------------------------------- | :----------------------------------------------- |
-| GitHub에서 배포      | `crews_dashboards: Read`             | Git 저장소 엔티티 접근 (Git RBAC 활성화 시)        |
-| Zip에서 배포         | `crews_dashboards: Read`             | 조직 수준에서 Zip 배포가 활성화되어야 함           |
-| Studio에서 빌드      | `studio_projects: Manage`            | —                                                |
-| LLM 키 구성          | `llm_connections: Manage`            | —                                                |
-| 환경 변수 설정        | `environment_variables: Manage`      | 엔티티 수준 접근 (엔티티 RBAC 활성화 시)           |
-
----
-
-## 자동화 수준 접근 제어 (엔티티 권한)
-
-조직 전체 역할 외에도, CrewAI는 개별 리소스에 대한 접근을 제한하는 세분화된 엔티티 수준 권한을 지원합니다.
-
-### 자동화 가시성
-
-자동화는 사용자 또는 역할별로 접근을 제한하는 가시성 설정을 지원합니다. 다음과 같은 경우에 유용합니다:
-
-- 민감하거나 실험적인 자동화를 비공개로 유지
-- 대규모 팀이나 외부 협업자의 가시성 관리
+- 민감/실험 자동화를 비공개로 유지
+- 대규모 팀/외부 협업에서 가시성 관리
 - 격리된 컨텍스트에서 자동화 테스트
 
-배포를 비공개로 구성할 수 있으며, 이 경우 화이트리스트에 포함된 사용자와 역할만 상호작용할 수 있습니다.
+Private 모드에서는 화이트리스트에 포함된 사용자/역할만 다음 작업이 가능합니다:
 
-설정 위치: Automation → Settings → Visibility 탭
+- 자동화 보기
+- 실행/API 사용
+- 로그, 메트릭, 설정 접근
+
+조직 Owner는 항상 접근 가능하며, 가시성 설정에 영향을 받지 않습니다.
+
+설정 위치: Automation → Settings → Visibility
 
 <Steps>
   <Step title="Visibility 탭 열기">
     <b>Automation → Settings → Visibility</b>로 이동합니다.
   </Step>
   <Step title="가시성 설정">
-    접근을 제한하려면 <b>Private</b>를 선택합니다. 조직 Owner는 항상
-    접근 권한을 유지합니다.
+    <b>Private</b>를 선택합니다. Owner는 항상 접근 가능합니다.
   </Step>
-  <Step title="접근 허용 대상 추가">
-    보기, 실행, 로그/메트릭/설정 접근이 허용된 특정 사용자와 역할을
-    추가합니다.
+  <Step title="허용 대상 추가">
+    보기/실행/로그·메트릭·설정 접근이 가능한 사용자/역할을 추가합니다.
   </Step>
   <Step title="저장 및 확인">
-    변경 사항을 저장한 후, 화이트리스트에 없는 사용자가 자동화를 보거나 실행할 수
-    없는지 확인합니다.
+    저장 후, 목록에 없는 사용자가 보거나 실행할 수 없는지 확인합니다.
   </Step>
 </Steps>
 
-### Private 가시성: 접근 결과
+### Private 모드 접근 결과
 
-| 동작                   | Owner | 화이트리스트 사용자/역할 | 비포함 |
-| :--------------------- | :---- | :----------------------- | :----- |
-| 자동화 보기            | ✓     | ✓                        | ✗      |
-| 자동화/API 실행        | ✓     | ✓                        | ✗      |
-| 로그/메트릭/설정 접근  | ✓     | ✓                        | ✗      |
+| 동작             | Owner | 화이트리스트 사용자/역할 | 비포함 |
+| :--------------- | :---- | :----------------------- | :----- |
+| 자동화 보기      | ✓     | ✓                        | ✗      |
+| 실행/API         | ✓     | ✓                        | ✗      |
+| 로그/메트릭/설정 | ✓     | ✓                        | ✗      |
 
 <Tip>
-  조직 Owner는 항상 접근 권한이 있습니다. Private 모드에서는 화이트리스트에 포함된
-  사용자/역할만 보기, 실행, 로그/메트릭/설정에 접근할 수 있습니다.
+  Owner는 항상 접근 가능하며, Private 모드에서는 화이트리스트에 포함된
+  사용자/역할만 권한이 부여됩니다.
 </Tip>
 
 <Frame>
-  <img src="/images/enterprise/visibility.png" alt="CrewAI AMP 자동화 가시성 설정" />
+  <img src="/images/enterprise/visibility.png" alt="CrewAI AMP 가시성 설정" />
+
 </Frame>
 
-### 배포 권한 유형
-
-특정 자동화에 엔티티 수준 접근을 부여할 때 다음 권한 유형을 할당할 수 있습니다:
-
-| 권한                 | 허용 범위                                            |
-| :------------------- | :-------------------------------------------------- |
-| `run`                | 자동화 실행 및 API 사용                               |
-| `traces`             | 실행 트레이스 및 로그 보기                             |
-| `manage_settings`    | 자동화 편집, 재배포, 롤백 또는 삭제                     |
-| `human_in_the_loop`  | HITL(human-in-the-loop) 요청에 응답                   |
-| `full_access`        | 위의 모든 권한                                        |
-
-### 기타 리소스에 대한 엔티티 수준 RBAC
-
-엔티티 수준 RBAC가 활성화되면 다음 리소스에 대한 접근도 사용자 또는 역할별로 제어할 수 있습니다:
-
-| 리소스             | 제어 방식                           | 설명                                                          |
-| :----------------- | :---------------------------------- | :------------------------------------------------------------ |
-| 환경 변수          | 엔티티 RBAC 기능 플래그              | 특정 환경 변수를 보거나 관리할 수 있는 역할/사용자 제한          |
-| LLM 연결           | 엔티티 RBAC 기능 플래그              | 특정 LLM 제공자 구성에 대한 접근 제한                           |
-| Git 저장소         | Git 저장소 RBAC 조직 설정            | 특정 연결된 저장소에 접근할 수 있는 역할/사용자 제한             |
-
----
-
-## 일반적인 역할 패턴
-
-CrewAI는 Owner와 Member 역할을 기본 제공하지만, 대부분의 팀은 커스텀 역할을 만들어 활용합니다. 일반적인 패턴은 다음과 같습니다:
-
-### Developer 역할
-
-자동화를 빌드하고 배포하지만 조직 설정을 관리하지 않는 팀원을 위한 역할입니다.
-
-| 기능                        | 권한        |
-| :-------------------------- | :---------- |
-| `usage_dashboards`          | Read        |
-| `crews_dashboards`          | Manage      |
-| `invitations`               | Read        |
-| `training_ui`               | Read        |
-| `tools`                     | Manage      |
-| `agents`                    | Manage      |
-| `environment_variables`     | Manage      |
-| `llm_connections`           | Manage      |
-| `default_settings`          | No access   |
-| `organization_settings`     | No access   |
-| `studio_projects`           | Manage      |
-
-### Viewer / Stakeholder 역할
-
-자동화를 모니터링하고 결과를 확인해야 하는 비기술 이해관계자를 위한 역할입니다.
-
-| 기능                        | 권한        |
-| :-------------------------- | :---------- |
-| `usage_dashboards`          | Read        |
-| `crews_dashboards`          | Read        |
-| `invitations`               | No access   |
-| `training_ui`               | Read        |
-| `tools`                     | Read        |
-| `agents`                    | Read        |
-| `environment_variables`     | No access   |
-| `llm_connections`           | No access   |
-| `default_settings`          | No access   |
-| `organization_settings`     | No access   |
-| `studio_projects`           | No access   |
-
-### Ops / Platform Admin 역할
-
-인프라 설정을 관리하지만 에이전트를 빌드하지 않을 수 있는 플랫폼 운영자를 위한 역할입니다.
-
-| 기능                        | 권한        |
-| :-------------------------- | :---------- |
-| `usage_dashboards`          | Manage      |
-| `crews_dashboards`          | Manage      |
-| `invitations`               | Manage      |
-| `training_ui`               | Read        |
-| `tools`                     | Read        |
-| `agents`                    | Read        |
-| `environment_variables`     | Manage      |
-| `llm_connections`           | Manage      |
-| `default_settings`          | Manage      |
-| `organization_settings`     | Read        |
-| `studio_projects`           | No access   |
-
----
-
 <Card
   title="도움이 필요하신가요?"
   icon="headset"
   href="mailto:support@crewai.com"
 >
-  RBAC 관련 질문은 지원팀에 문의해 주세요.
+  RBAC 구성과 점검에 대한 지원이 필요하면 연락해 주세요.
 </Card>

docs/pt-BR/changelog.mdx

@@ -4,70 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
-<Update label="31 mar 2026">
-  ## v1.13.0a5
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
-
-  ## O que Mudou
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.13.0a4
-
-  ## Contributors
-
-  @greysonlalonde, @joaomdmoura
-
-</Update>
-
-<Update label="01 abr 2026">
-  ## v1.13.0a4
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
-
-  ## O que Mudou
-
-  ### Documentação
-  - Atualizar changelog e versão para v1.13.0a3
-
-  ## Contribuidores
-
-  @greysonlalonde
-
-</Update>
-
-<Update label="01 abr 2026">
-  ## v1.13.0a3
-
-  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a3)
-
-  ## O que Mudou
-
-  ### Recursos
-  - Emitir dados de uso de token no LLMCallCompletedEvent
-  - Extrair e publicar metadados de ferramentas no AMP
-
-  ### Correções de Bugs
-  - Lidar com modelos GPT-5.x que não suportam o parâmetro de API `stop`
-
-  ### Documentação
-  - Corrigir imprecisões nas capacidades do agente em todas as línguas
-  - Adicionar visão geral das Capacidades do Agente e melhorar a documentação de Habilidades
-  - Adicionar um guia abrangente de configuração de SSO
-  - Atualizar o changelog e a versão para v1.13.0rc1
-
-  ### Refatoração
-  - Converter Flow para Pydantic BaseModel
-  - Converter classes LLM para Pydantic BaseModel
-  - Substituir InstanceOf[T] por anotações de tipo simples
-  - Remover métodos não utilizados
-
-  ## Contribuidores
-
-  @dependabot[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide, @thiagomoretto
-
-</Update>
-
 <Update label="27 mar 2026">
   ## v1.13.0rc1
 

docs/pt-BR/concepts/agent-capabilities.mdx

@@ -1,147 +0,0 @@
----
-title: "Capacidades do Agente"
-description: "Entenda as cinco formas de estender agentes CrewAI: Ferramentas, MCPs, Apps, Skills e Knowledge."
-icon: puzzle-piece
-mode: "wide"
----
-
-## Visão Geral
-
-Agentes CrewAI podem ser estendidos com **cinco tipos distintos de capacidades**, cada um servindo a um propósito diferente. Entender quando usar cada um — e como eles funcionam juntos — é fundamental para construir agentes eficazes.
-
-<CardGroup cols={2}>
-  <Card title="Ferramentas" icon="wrench" href="/pt-BR/concepts/tools" color="#3B82F6">
-    **Funções chamáveis** — permitem que agentes tomem ações. Buscas na web, operações com arquivos, chamadas de API, execução de código.
-  </Card>
-  <Card title="Servidores MCP" icon="plug" href="/pt-BR/mcp/overview" color="#8B5CF6">
-    **Servidores de ferramentas remotos** — conectam agentes a servidores de ferramentas externos via Model Context Protocol. Mesmo efeito de ferramentas, mas hospedados externamente.
-  </Card>
-  <Card title="Apps" icon="grid-2" color="#EC4899">
-    **Integrações com plataformas** — conectam agentes a aplicativos SaaS (Gmail, Slack, Jira, Salesforce) via plataforma CrewAI. Executa localmente com um token de integração.
-  </Card>
-  <Card title="Skills" icon="bolt" href="/pt-BR/concepts/skills" color="#F59E0B">
-    **Expertise de domínio** — injetam instruções, diretrizes e material de referência nos prompts dos agentes. Skills dizem aos agentes *como pensar*.
-  </Card>
-  <Card title="Knowledge" icon="book" href="/pt-BR/concepts/knowledge" color="#10B981">
-    **Fatos recuperados** — fornecem aos agentes dados de documentos, arquivos e URLs via busca semântica (RAG). Knowledge dá aos agentes *o que saber*.
-  </Card>
-</CardGroup>
-
----
-
-## A Distinção Fundamental
-
-O mais importante a entender: **essas capacidades se dividem em duas categorias**.
-
-### Capacidades de Ação (Ferramentas, MCPs, Apps)
-
-Estas dão aos agentes a capacidade de **fazer coisas** — chamar APIs, ler arquivos, buscar na web, enviar emails. No momento da execução, os três tipos se resolvem no mesmo formato interno (instâncias de `BaseTool`) e aparecem em uma lista unificada de ferramentas que o agente pode chamar.
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool
-
-agent = Agent(
-    role="Researcher",
-    goal="Find and compile market data",
-    backstory="Expert market analyst",
-    tools=[SerperDevTool(), FileReadTool()],  # Ferramentas locais
-    mcps=["https://mcp.example.com/sse"],     # Ferramentas de servidor MCP remoto
-    apps=["gmail", "google_sheets"],           # Integrações com plataformas
-)
-```
-
-### Capacidades de Contexto (Skills, Knowledge)
-
-Estas modificam o **prompt** do agente — injetando expertise, instruções ou dados recuperados antes do agente começar a raciocinar. Não dão aos agentes novas ações; elas moldam como os agentes pensam e a quais informações têm acesso.
-
-```python
-from crewai import Agent
-
-agent = Agent(
-    role="Security Auditor",
-    goal="Audit cloud infrastructure for vulnerabilities",
-    backstory="Expert in cloud security with 10 years of experience",
-    skills=["./skills/security-audit"],        # Instruções de domínio
-    knowledge_sources=[pdf_source, url_source], # Fatos recuperados
-)
-```
-
----
-
-## Quando Usar o Quê
-
-| Você precisa...                                          | Use               | Exemplo                                  |
-| :------------------------------------------------------- | :---------------- | :--------------------------------------- |
-| Agente buscar na web                                     | **Ferramentas**   | `tools=[SerperDevTool()]`                |
-| Agente chamar uma API remota via MCP                     | **MCPs**          | `mcps=["https://api.example.com/sse"]`   |
-| Agente enviar emails pelo Gmail                          | **Apps**          | `apps=["gmail"]`                         |
-| Agente seguir procedimentos específicos                  | **Skills**        | `skills=["./skills/code-review"]`        |
-| Agente consultar documentos da empresa                   | **Knowledge**     | `knowledge_sources=[pdf_source]`         |
-| Agente buscar na web E seguir diretrizes de revisão      | **Ferramentas + Skills** | Use ambos juntos                  |
-
----
-
-## Combinando Capacidades
-
-Na prática, agentes frequentemente usam **múltiplos tipos de capacidades juntos**. Aqui está um exemplo realista:
-
-```python
-from crewai import Agent
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-# Um agente de pesquisa totalmente equipado
-researcher = Agent(
-    role="Senior Research Analyst",
-    goal="Produce comprehensive market analysis reports",
-    backstory="Expert analyst with deep industry knowledge",
-
-    # AÇÃO: O que o agente pode FAZER
-    tools=[
-        SerperDevTool(),         # Buscar na web
-        FileReadTool(),          # Ler arquivos locais
-        CodeInterpreterTool(),   # Executar código Python para análise
-    ],
-    mcps=["https://data-api.example.com/sse"],  # Acessar API de dados remota
-    apps=["google_sheets"],                      # Escrever no Google Sheets
-
-    # CONTEXTO: O que o agente SABE
-    skills=["./skills/research-methodology"],    # Como conduzir pesquisas
-    knowledge_sources=[company_docs],            # Dados específicos da empresa
-)
-```
-
----
-
-## Tabela Comparativa
-
-| Característica | Ferramentas | MCPs | Apps | Skills | Knowledge |
-| :--- | :---: | :---: | :---: | :---: | :---: |
-| **Dá ações ao agente** | ✅ | ✅ | ✅ | ❌ | ❌ |
-| **Modifica o prompt** | ❌ | ❌ | ❌ | ✅ | ✅ |
-| **Requer código** | Sim | Apenas config | Apenas config | Apenas Markdown | Apenas config |
-| **Executa localmente** | Sim | Depende | Sim (com variável de ambiente) | N/A | Sim |
-| **Precisa de chaves API** | Por ferramenta | Por servidor | Token de integração | Não | Apenas embedder |
-| **Definido no Agent** | `tools=[]` | `mcps=[]` | `apps=[]` | `skills=[]` | `knowledge_sources=[]` |
-| **Definido no Crew** | ❌ | ❌ | ❌ | `skills=[]` | `knowledge_sources=[]` |
-
----
-
-## Aprofundamentos
-
-Pronto para aprender mais sobre cada tipo de capacidade?
-
-<CardGroup cols={2}>
-  <Card title="Ferramentas" icon="wrench" href="/pt-BR/concepts/tools">
-    Crie ferramentas personalizadas, use o catálogo OSS com 75+ opções, configure cache e execução assíncrona.
-  </Card>
-  <Card title="Integração MCP" icon="plug" href="/pt-BR/mcp/overview">
-    Conecte-se a servidores MCP via stdio, SSE ou HTTP. Filtre ferramentas, configure autenticação.
-  </Card>
-  <Card title="Skills" icon="bolt" href="/pt-BR/concepts/skills">
-    Construa pacotes de skills com SKILL.md, injete expertise de domínio, use divulgação progressiva.
-  </Card>
-  <Card title="Knowledge" icon="book" href="/pt-BR/concepts/knowledge">
-    Adicione conhecimento de PDFs, CSVs, URLs e mais. Configure embedders e recuperação.
-  </Card>
-</CardGroup>

docs/pt-BR/concepts/skills.mdx

@@ -1,186 +1,27 @@
 ---
 title: Skills
-description: Pacotes de skills baseados em sistema de arquivos que injetam expertise de domínio e instruções nos prompts dos agentes.
+description: Pacotes de skills baseados em sistema de arquivos que injetam contexto nos prompts dos agentes.
 icon: bolt
 mode: "wide"
 ---
 
 ## Visão Geral
 
-Skills são diretórios autocontidos que fornecem aos agentes **instruções, diretrizes e material de referência específicos de domínio**. Cada skill é definida por um arquivo `SKILL.md` com frontmatter YAML e um corpo em markdown.
+Skills são diretórios autocontidos que fornecem aos agentes instruções, referências e assets específicos de domínio. Cada skill é definida por um arquivo `SKILL.md` com frontmatter YAML e um corpo em markdown.
 
-Quando ativada, as instruções de uma skill são injetadas diretamente no prompt da tarefa do agente — dando ao agente expertise sem exigir alterações de código.
+Skills usam **divulgação progressiva** — metadados são carregados primeiro, instruções completas apenas quando ativadas, e catálogos de recursos apenas quando necessário.
 
-<Note type="info" title="Skills vs Ferramentas — A Distinção Fundamental">
-**Skills NÃO são ferramentas.** Este é o ponto de confusão mais comum.
-
-- **Skills** injetam *instruções e contexto* no prompt do agente. Elas dizem ao agente *como pensar* sobre um problema.
-- **Ferramentas** dão ao agente *funções chamáveis* para tomar ações (buscar, ler arquivos, chamar APIs).
-
-Frequentemente você precisa de **ambos**: skills para expertise, ferramentas para ação. Eles são configurados independentemente e se complementam.
-</Note>
-
----
-
-## Início Rápido
-
-### 1. Crie um Diretório de Skill
+## Estrutura de Diretório
 
 ```
-skills/
-└── code-review/
-    ├── SKILL.md          # Obrigatório — instruções
-    ├── references/       # Opcional — documentos de referência
-    │   └── style-guide.md
-    └── scripts/          # Opcional — scripts executáveis
+my-skill/
+├── SKILL.md            # Obrigatório — frontmatter + instruções
+├── scripts/            # Opcional — scripts executáveis
+├── references/         # Opcional — documentos de referência
+└── assets/             # Opcional — arquivos estáticos (configs, dados)
 ```
 
-### 2. Escreva seu SKILL.md
-
-```markdown
----
-name: code-review
-description: Guidelines for conducting thorough code reviews with focus on security and performance.
-metadata:
-  author: your-team
-  version: "1.0"
----
-
-## Diretrizes de Code Review
-
-Ao revisar código, siga esta checklist:
-
-1. **Segurança**: Verifique vulnerabilidades de injeção, bypasses de autenticação e exposição de dados
-2. **Performance**: Procure por queries N+1, alocações desnecessárias e chamadas bloqueantes
-3. **Legibilidade**: Garanta nomenclatura clara, comentários apropriados e estilo consistente
-4. **Testes**: Verifique cobertura adequada de testes para novas funcionalidades
-
-### Níveis de Severidade
-- **Crítico**: Vulnerabilidades de segurança, riscos de perda de dados → bloquear merge
-- **Major**: Problemas de performance, erros de lógica → solicitar alterações
-- **Minor**: Questões de estilo, sugestões de nomenclatura → aprovar com comentários
-```
-
-### 3. Anexe a um Agente
-
-```python
-from crewai import Agent
-from crewai_tools import GithubSearchTool, FileReadTool
-
-reviewer = Agent(
-    role="Senior Code Reviewer",
-    goal="Review pull requests for quality and security issues",
-    backstory="Staff engineer with expertise in secure coding practices.",
-    skills=["./skills"],                          # Injeta diretrizes de revisão
-    tools=[GithubSearchTool(), FileReadTool()],   # Permite ao agente ler código
-)
-```
-
-O agente agora tem tanto **expertise** (da skill) quanto **capacidades** (das ferramentas).
-
----
-
-## Skills + Ferramentas: Trabalhando Juntos
-
-Aqui estão padrões comuns mostrando como skills e ferramentas se complementam:
-
-### Padrão 1: Apenas Skills (Expertise de Domínio, Sem Ações Necessárias)
-
-Use quando o agente precisa de instruções específicas mas não precisa chamar serviços externos:
-
-```python
-agent = Agent(
-    role="Technical Writer",
-    goal="Write clear API documentation",
-    backstory="Expert technical writer",
-    skills=["./skills/api-docs-style"],  # Diretrizes e templates de escrita
-    # Sem ferramentas necessárias — agente escreve baseado no contexto fornecido
-)
-```
-
-### Padrão 2: Apenas Ferramentas (Ações, Sem Expertise Especial)
-
-Use quando o agente precisa tomar ações mas não precisa de instruções específicas de domínio:
-
-```python
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
-
-agent = Agent(
-    role="Web Researcher",
-    goal="Find information about a topic",
-    backstory="Skilled at finding information online",
-    tools=[SerperDevTool(), ScrapeWebsiteTool()],  # Pode buscar e extrair dados
-    # Sem skills necessárias — pesquisa geral não precisa de diretrizes especiais
-)
-```
-
-### Padrão 3: Skills + Ferramentas (Expertise E Ações)
-
-O padrão mais comum no mundo real. A skill fornece *como* abordar o trabalho; ferramentas fornecem *o que* o agente pode fazer:
-
-```python
-from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool
-
-analyst = Agent(
-    role="Security Analyst",
-    goal="Audit infrastructure for vulnerabilities",
-    backstory="Expert in cloud security and compliance",
-    skills=["./skills/security-audit"],   # Metodologia e checklists de auditoria
-    tools=[
-        SerperDevTool(),                  # Pesquisar vulnerabilidades conhecidas
-        FileReadTool(),                   # Ler arquivos de configuração
-        CodeInterpreterTool(),            # Executar scripts de análise
-    ],
-)
-```
-
-### Padrão 4: Skills + MCPs
-
-Skills funcionam junto com servidores MCP da mesma forma que com ferramentas:
-
-```python
-agent = Agent(
-    role="Data Analyst",
-    goal="Analyze customer data and generate reports",
-    backstory="Expert data analyst with strong statistical background",
-    skills=["./skills/data-analysis"],           # Metodologia de análise
-    mcps=["https://data-warehouse.example.com/sse"],  # Acesso remoto a dados
-)
-```
-
-### Padrão 5: Skills + Apps
-
-Skills podem guiar como um agente usa integrações de plataforma:
-
-```python
-agent = Agent(
-    role="Customer Support Agent",
-    goal="Respond to customer inquiries professionally",
-    backstory="Experienced support representative",
-    skills=["./skills/support-playbook"],  # Templates de resposta e regras de escalação
-    apps=["gmail", "zendesk"],             # Pode enviar emails e atualizar tickets
-)
-```
-
----
-
-## Skills no Nível do Crew
-
-Skills podem ser definidas no crew para aplicar a **todos os agentes**:
-
-```python
-from crewai import Crew
-
-crew = Crew(
-    agents=[researcher, writer, reviewer],
-    tasks=[research_task, write_task, review_task],
-    skills=["./skills"],  # Todos os agentes recebem essas skills
-)
-```
-
-Skills no nível do agente têm prioridade — se a mesma skill é descoberta em ambos os níveis, a versão do agente é usada.
-
----
+O nome do diretório deve corresponder ao campo `name` no `SKILL.md`.
 
 ## Formato do SKILL.md
 
@@ -193,7 +34,7 @@ compatibility: crewai>=0.1.0          # opcional
 metadata:                              # opcional
   author: your-name
   version: "1.0"
-allowed-tools: web-search file-read   # opcional, experimental
+allowed-tools: web-search file-read   # opcional, delimitado por espaços
 ---
 
 Instruções para o agente vão aqui. Este corpo em markdown é injetado
@@ -202,46 +43,57 @@ no prompt do agente quando a skill é ativada.
 
 ### Campos do Frontmatter
 
-| Campo           | Obrigatório | Descrição                                                                |
+| Campo           | Obrigatório | Restrições                                                               |
 | :-------------- | :---------- | :----------------------------------------------------------------------- |
-| `name`          | Sim         | 1–64 chars. Alfanumérico minúsculo e hifens. Deve corresponder ao nome do diretório. |
+| `name`          | Sim         | 1–64 chars. Alfanumérico minúsculo e hifens. Sem hifens iniciais/finais/consecutivos. Deve corresponder ao nome do diretório. |
 | `description`   | Sim         | 1–1024 chars. Descreve o que a skill faz e quando usá-la.                |
 | `license`       | Não         | Nome da licença ou referência a um arquivo de licença incluído.           |
 | `compatibility` | Não         | Máx 500 chars. Requisitos de ambiente (produtos, pacotes, rede).          |
 | `metadata`      | Não         | Mapeamento arbitrário de chave-valor string.                              |
 | `allowed-tools` | Não         | Lista de ferramentas pré-aprovadas delimitada por espaços. Experimental.  |
 
----
+## Uso
 
-## Estrutura de Diretório
+### Skills no Nível do Agente
 
-```
-my-skill/
-├── SKILL.md            # Obrigatório — frontmatter + instruções
-├── scripts/            # Opcional — scripts executáveis
-├── references/         # Opcional — documentos de referência
-└── assets/             # Opcional — arquivos estáticos (configs, dados)
+Passe caminhos de diretório de skills para um agente:
+
+```python
+from crewai import Agent
+
+agent = Agent(
+    role="Researcher",
+    goal="Find relevant information",
+    backstory="An expert researcher.",
+    skills=["./skills"],  # descobre todas as skills neste diretório
+)
 ```
 
-O nome do diretório deve corresponder ao campo `name` no `SKILL.md`. Os diretórios `scripts/`, `references/` e `assets/` estão disponíveis no `path` da skill para agentes que precisam referenciar arquivos diretamente.
+### Skills no Nível do Crew
 
----
+Caminhos de skills no crew são mesclados em todos os agentes:
 
-## Skills Pré-carregadas
+```python
+from crewai import Crew
 
-Para mais controle, você pode descobrir e ativar skills programaticamente:
+crew = Crew(
+    agents=[agent],
+    tasks=[task],
+    skills=["./skills"],
+)
+```
+
+### Skills Pré-carregadas
+
+Você também pode passar objetos `Skill` diretamente:
 
 ```python
 from pathlib import Path
 from crewai.skills import discover_skills, activate_skill
 
-# Descobrir todas as skills em um diretório
 skills = discover_skills(Path("./skills"))
-
-# Ativá-las (carrega o corpo completo do SKILL.md)
 activated = [activate_skill(s) for s in skills]
 
-# Passar para um agente
 agent = Agent(
     role="Researcher",
     goal="Find relevant information",
@@ -250,57 +102,13 @@ agent = Agent(
 )
 ```
 
----
-
 ## Como as Skills São Carregadas
 
-Skills usam **divulgação progressiva** — carregando apenas o necessário em cada estágio:
+Skills carregam progressivamente — apenas os dados necessários em cada etapa são lidos:
 
-| Estágio    | O que é carregado                     | Quando              |
-| :--------- | :------------------------------------ | :------------------ |
-| Descoberta | Nome, descrição, campos do frontmatter | `discover_skills()` |
-| Ativação   | Texto completo do corpo do SKILL.md    | `activate_skill()`  |
+| Etapa            | O que é carregado                                  | Quando              |
+| :--------------- | :------------------------------------------------ | :------------------ |
+| Descoberta       | Nome, descrição, campos do frontmatter             | `discover_skills()` |
+| Ativação         | Texto completo do corpo do SKILL.md                | `activate_skill()` |
 
-Durante a execução normal do agente (passando caminhos de diretório via `skills=["./skills"]`), skills são automaticamente descobertas e ativadas. O carregamento progressivo só importa quando usando a API programática.
-
----
-
-## Skills vs Knowledge
-
-Tanto skills quanto knowledge modificam o prompt do agente, mas servem propósitos diferentes:
-
-| Aspecto | Skills | Knowledge |
-| :--- | :--- | :--- |
-| **O que fornece** | Instruções, procedimentos, diretrizes | Fatos, dados, informações |
-| **Como é armazenado** | Arquivos Markdown (SKILL.md) | Embarcado em banco vetorial (ChromaDB) |
-| **Como é recuperado** | Corpo inteiro injetado no prompt | Busca semântica encontra trechos relevantes |
-| **Melhor para** | Metodologia, checklists, guias de estilo | Documentos da empresa, info de produto, dados de referência |
-| **Definido via** | `skills=["./skills"]` | `knowledge_sources=[source]` |
-
-**Regra prática:** Se o agente precisa seguir um *processo*, use uma skill. Se o agente precisa consultar *dados*, use knowledge.
-
----
-
-## Perguntas Frequentes
-
-<AccordionGroup>
-  <Accordion title="Preciso definir skills E ferramentas?">
-    Depende do seu caso de uso. Skills e ferramentas são **independentes** — você pode usar qualquer um, ambos ou nenhum.
-
-    - **Apenas skills**: Quando o agente precisa de expertise mas não de ações externas (ex: escrever com diretrizes de estilo)
-    - **Apenas ferramentas**: Quando o agente precisa de ações mas não de metodologia especial (ex: busca simples na web)
-    - **Ambos**: Quando o agente precisa de expertise E ações (ex: auditoria de segurança com checklists específicas E capacidade de escanear código)
-  </Accordion>
-
-  <Accordion title="Skills fornecem ferramentas automaticamente?">
-    **Não.** O campo `allowed-tools` no SKILL.md é apenas metadado experimental — ele não provisiona nem injeta nenhuma ferramenta. Você deve sempre definir ferramentas separadamente via `tools=[]`, `mcps=[]` ou `apps=[]`.
-  </Accordion>
-
-  <Accordion title="O que acontece se eu definir a mesma skill tanto no agente quanto no crew?">
-    A skill no nível do agente tem prioridade. Skills são deduplicadas por nome — as skills do agente são processadas primeiro, então se o mesmo nome de skill aparece em ambos os níveis, a versão do agente é usada.
-  </Accordion>
-
-  <Accordion title="Qual o tamanho máximo do corpo do SKILL.md?">
-    Há um aviso suave em 50.000 caracteres, mas sem limite rígido. Mantenha skills focadas e concisas para melhores resultados — injeções de prompt muito grandes podem diluir a atenção do agente.
-  </Accordion>
-</AccordionGroup>
+Durante a execução normal do agente, skills são automaticamente descobertas e ativadas. Os diretórios `scripts/`, `references/` e `assets/` estão disponíveis no `path` da skill para agentes que precisam referenciar arquivos diretamente.

docs/pt-BR/concepts/tools.mdx

@@ -10,10 +10,6 @@ mode: "wide"
 As ferramentas do CrewAI capacitam agentes com habilidades que vão desde busca na web e análise de dados até colaboração e delegação de tarefas entre colegas de trabalho.
 Esta documentação descreve como criar, integrar e aproveitar essas ferramentas dentro do framework CrewAI, incluindo um novo foco em ferramentas de colaboração.
 
-<Note type="info" title="Ferramentas são um dos cinco tipos de capacidades de agentes">
-  Ferramentas dão aos agentes **funções chamáveis** para tomar ações. Elas funcionam junto com [MCPs](/pt-BR/mcp/overview) (servidores de ferramentas remotos), [Apps](/pt-BR/concepts/agent-capabilities) (integrações com plataformas), [Skills](/pt-BR/concepts/skills) (expertise de domínio) e [Knowledge](/pt-BR/concepts/knowledge) (fatos recuperados). Veja a visão geral de [Capacidades do Agente](/pt-BR/concepts/agent-capabilities) para entender quando usar cada um.
-</Note>
-
 ## O que é uma Ferramenta?
 
 Uma ferramenta no CrewAI é uma habilidade ou função que os agentes podem utilizar para executar diversas ações.

docs/pt-BR/enterprise/features/rbac.mdx

@@ -1,24 +1,22 @@
 ---
 title: "Controle de Acesso Baseado em Funções (RBAC)"
-description: "Controle o acesso a crews, ferramentas e dados com funções, escopos e permissões granulares."
+description: "Controle o acesso a crews, ferramentas e dados com funções e visibilidade por automação."
 icon: "shield"
 mode: "wide"
 ---
 
 ## Visão Geral
 
-O RBAC no CrewAI AMP permite gerenciamento de acesso seguro e escalável através de duas camadas:
-
-1. **Permissões de funcionalidade** — controlam o que cada função pode fazer na plataforma (gerenciar, ler ou sem acesso)
-2. **Permissões em nível de entidade** — acesso granular em automações individuais, variáveis de ambiente, conexões LLM e repositórios Git
+O RBAC no CrewAI AMP permite gerenciar acesso de forma segura e escalável combinando **funções em nível de organização** com **controles de visibilidade em nível de automação**.
 
 <Frame>
   <img src="/images/enterprise/users_and_roles.png" alt="Visão geral de RBAC no CrewAI AMP" />
+
 </Frame>
 
 ## Usuários e Funções
 
-Cada membro da sua workspace CrewAI recebe uma função, que determina seu acesso aos diversos recursos.
+Cada membro da sua workspace possui uma função, que determina o acesso aos recursos.
 
 Você pode:
 
@@ -33,21 +31,14 @@ A configuração de usuários e funções é feita em Settings → Roles.
     Vá em <b>Settings → Roles</b> no CrewAI AMP.
   </Step>
   <Step title="Escolher a função">
-    Use uma função pré-definida (<b>Owner</b>, <b>Member</b>) ou clique em{" "}
-    <b>Create role</b> para criar uma personalizada.
+    Use <b>Owner</b> ou <b>Member</b>, ou clique em <b>Create role</b> para
+    criar uma função personalizada.
   </Step>
   <Step title="Atribuir aos membros">
     Selecione os usuários e atribua a função. Você pode alterar depois.
   </Step>
 </Steps>
 
-### Funções Pré-definidas
-
-| Função     | Descrição                                                                 |
-| :--------- | :------------------------------------------------------------------------ |
-| **Owner**  | Acesso total a todas as funcionalidades e configurações. Não pode ser restrito. |
-| **Member** | Acesso de leitura à maioria das funcionalidades, acesso de gerenciamento a variáveis de ambiente, conexões LLM e projetos Studio. Não pode modificar configurações da organização ou padrões. |
-
 ### Resumo de configuração
 
 | Área                      | Onde configurar                    | Opções                                               |
@@ -55,93 +46,35 @@ A configuração de usuários e funções é feita em Settings → Roles.
 | Usuários & Funções        | Settings → Roles                   | Pré-definidas: Owner, Member; Funções personalizadas |
 | Visibilidade da automação | Automation → Settings → Visibility | Private; Lista de usuários/funções                   |
 
----
+## Controle de Acesso em Nível de Automação
 
-## Matriz de Permissões de Funcionalidades
+Além das funções na organização, as **Automations** suportam visibilidade refinada para restringir acesso por usuário ou função.
 
-Cada função possui um nível de permissão para cada área de funcionalidade. Os três níveis são:
+Útil para:
 
-- **Manage** — acesso total de leitura/escrita (criar, editar, excluir)
-- **Read** — acesso somente leitura
-- **No access** — funcionalidade oculta/inacessível
-
-| Funcionalidade            | Owner   | Member (padrão)  | Níveis disponíveis        | Descrição                                                       |
-| :------------------------ | :------ | :--------------- | :------------------------ | :-------------------------------------------------------------- |
-| `usage_dashboards`        | Manage  | Read             | Manage / Read / No access | Visualizar métricas e análises de uso                           |
-| `crews_dashboards`        | Manage  | Read             | Manage / Read / No access | Visualizar dashboards de deploy, acessar detalhes de automações |
-| `invitations`             | Manage  | Read             | Manage / Read / No access | Convidar novos membros para a organização                       |
-| `training_ui`             | Manage  | Read             | Manage / Read / No access | Acessar interfaces de treinamento/fine-tuning                   |
-| `tools`                   | Manage  | Read             | Manage / Read / No access | Criar e gerenciar ferramentas                                   |
-| `agents`                  | Manage  | Read             | Manage / Read / No access | Criar e gerenciar agentes                                       |
-| `environment_variables`   | Manage  | Manage           | Manage / No access        | Criar e gerenciar variáveis de ambiente                         |
-| `llm_connections`         | Manage  | Manage           | Manage / No access        | Configurar conexões de provedores LLM                           |
-| `default_settings`        | Manage  | No access        | Manage / No access        | Modificar configurações padrão da organização                   |
-| `organization_settings`   | Manage  | No access        | Manage / No access        | Gerenciar cobrança, planos e configuração da organização        |
-| `studio_projects`         | Manage  | Manage           | Manage / No access        | Criar e editar projetos no Studio                               |
-
-<Tip>
-  Ao criar uma função personalizada, a maioria das funcionalidades pode ser definida como **Manage**, **Read** ou **No access**. No entanto, `environment_variables`, `llm_connections`, `default_settings`, `organization_settings` e `studio_projects` suportam apenas **Manage** ou **No access** — não há opção somente leitura para essas funcionalidades.
-</Tip>
-
----
-
-## Deploy via GitHub ou Zip
-
-Uma das perguntas mais comuns sobre RBAC é: _"Quais permissões um membro da equipe precisa para fazer deploy?"_
-
-### Deploy via GitHub
-
-Para fazer deploy de uma automação a partir de um repositório GitHub, o usuário precisa de:
-
-1. **`crews_dashboards`**: pelo menos `Read` — necessário para acessar o dashboard de automações onde os deploys são criados
-2. **Acesso ao repositório Git** (se RBAC em nível de entidade para repositórios Git estiver habilitado): a função do usuário deve ter acesso ao repositório Git específico via permissões de entidade
-3. **`studio_projects`: `Manage`** — se estiver construindo o crew no Studio antes do deploy
-
-### Deploy via Zip
-
-Para fazer deploy de uma automação via upload de arquivo Zip, o usuário precisa de:
-
-1. **`crews_dashboards`**: pelo menos `Read` — necessário para acessar o dashboard de automações
-2. **Deploys via Zip habilitados**: a organização não deve ter desabilitado deploys via Zip nas configurações da organização
-
-### Referência Rápida: Permissões Mínimas para Deploy
-
-| Ação                       | Permissões de funcionalidade necessárias | Requisitos adicionais                             |
-| :------------------------- | :--------------------------------------- | :------------------------------------------------ |
-| Deploy via GitHub          | `crews_dashboards: Read`                 | Acesso à entidade do repositório Git (se habilitado) |
-| Deploy via Zip             | `crews_dashboards: Read`                 | Deploys via Zip devem estar habilitados na organização |
-| Construir no Studio        | `studio_projects: Manage`                | —                                                 |
-| Configurar chaves LLM      | `llm_connections: Manage`                | —                                                 |
-| Definir variáveis de ambiente | `environment_variables: Manage`        | Acesso em nível de entidade (se habilitado)        |
-
----
-
-## Controle de Acesso em Nível de Automação (Permissões de Entidade)
-
-Além das funções em nível de organização, o CrewAI suporta permissões granulares em nível de entidade que restringem o acesso a recursos individuais.
-
-### Visibilidade da Automação
-
-Automações suportam configurações de visibilidade que restringem acesso por usuário ou função. Útil para:
-
-- Manter automações sensíveis ou experimentais privadas
+- Manter automações sensíveis/experimentais privadas
 - Gerenciar visibilidade em equipes grandes ou colaboradores externos
 - Testar automações em contexto isolado
 
-Deploys podem ser configurados como privados, significando que apenas usuários e funções na whitelist poderão interagir com eles.
+Em modo privado, somente usuários/funções na whitelist poderão:
 
-Configure em Automation → Settings → aba Visibility.
+- Ver a automação
+- Executar/usar a API
+- Acessar logs, métricas e configurações
+
+O owner da organização sempre tem acesso, independente da visibilidade.
+
+Configure em Automation → Settings → Visibility.
 
 <Steps>
   <Step title="Abrir a aba Visibility">
     Acesse <b>Automation → Settings → Visibility</b>.
   </Step>
   <Step title="Definir visibilidade">
-    Selecione <b>Private</b> para restringir o acesso. O owner da organização
-    mantém acesso sempre.
+    Selecione <b>Private</b> para restringir o acesso. O owner mantém acesso.
   </Step>
   <Step title="Permitir acesso">
-    Adicione usuários e funções que poderão ver, executar e acessar
+    Adicione usuários e funções que poderão ver/executar e acessar
     logs/métricas/configurações.
   </Step>
   <Step title="Salvar e verificar">
@@ -164,92 +97,9 @@ Configure em Automation → Settings → aba Visibility.
 
 <Frame>
   <img src="/images/enterprise/visibility.png" alt="Configuração de visibilidade no CrewAI AMP" />
+
 </Frame>
 
-### Tipos de Permissão de Deploy
-
-Ao conceder acesso em nível de entidade a uma automação específica, você pode atribuir estes tipos de permissão:
-
-| Permissão            | O que permite                                       |
-| :------------------- | :-------------------------------------------------- |
-| `run`                | Executar a automação e usar sua API                  |
-| `traces`             | Visualizar traces de execução e logs                 |
-| `manage_settings`    | Editar, reimplantar, reverter ou excluir a automação |
-| `human_in_the_loop`  | Responder a solicitações human-in-the-loop (HITL)    |
-| `full_access`        | Todos os anteriores                                  |
-
-### RBAC em Nível de Entidade para Outros Recursos
-
-Quando o RBAC em nível de entidade está habilitado, o acesso a estes recursos também pode ser controlado por usuário ou função:
-
-| Recurso                | Controlado por                         | Descrição                                                      |
-| :--------------------- | :------------------------------------- | :------------------------------------------------------------- |
-| Variáveis de ambiente  | Flag de funcionalidade RBAC de entidade | Restringir quais funções/usuários podem ver ou gerenciar variáveis específicas |
-| Conexões LLM           | Flag de funcionalidade RBAC de entidade | Restringir acesso a configurações de provedores LLM específicos |
-| Repositórios Git       | Configuração RBAC de repositórios Git   | Restringir quais funções/usuários podem acessar repositórios conectados específicos |
-
----
-
-## Padrões Comuns de Funções
-
-Embora o CrewAI venha com as funções Owner e Member, a maioria das equipes se beneficia da criação de funções personalizadas. Aqui estão os padrões comuns:
-
-### Função Developer
-
-Uma função para membros da equipe que constroem e fazem deploy de automações, mas não gerenciam configurações da organização.
-
-| Funcionalidade            | Permissão  |
-| :------------------------ | :--------- |
-| `usage_dashboards`        | Read       |
-| `crews_dashboards`        | Manage     |
-| `invitations`             | Read       |
-| `training_ui`             | Read       |
-| `tools`                   | Manage     |
-| `agents`                  | Manage     |
-| `environment_variables`   | Manage     |
-| `llm_connections`         | Manage     |
-| `default_settings`        | No access  |
-| `organization_settings`   | No access  |
-| `studio_projects`         | Manage     |
-
-### Função Viewer / Stakeholder
-
-Uma função para stakeholders não técnicos que precisam monitorar automações e visualizar resultados.
-
-| Funcionalidade            | Permissão  |
-| :------------------------ | :--------- |
-| `usage_dashboards`        | Read       |
-| `crews_dashboards`        | Read       |
-| `invitations`             | No access  |
-| `training_ui`             | Read       |
-| `tools`                   | Read       |
-| `agents`                  | Read       |
-| `environment_variables`   | No access  |
-| `llm_connections`         | No access  |
-| `default_settings`        | No access  |
-| `organization_settings`   | No access  |
-| `studio_projects`         | No access  |
-
-### Função Ops / Platform Admin
-
-Uma função para operadores de plataforma que gerenciam configurações de infraestrutura, mas podem não construir agentes.
-
-| Funcionalidade            | Permissão  |
-| :------------------------ | :--------- |
-| `usage_dashboards`        | Manage     |
-| `crews_dashboards`        | Manage     |
-| `invitations`             | Manage     |
-| `training_ui`             | Read       |
-| `tools`                   | Read       |
-| `agents`                  | Read       |
-| `environment_variables`   | Manage     |
-| `llm_connections`         | Manage     |
-| `default_settings`        | Manage     |
-| `organization_settings`   | Read       |
-| `studio_projects`         | No access  |
-
----
-
 <Card title="Precisa de Ajuda?" icon="headset" href="mailto:support@crewai.com">
-  Fale com o nosso time para suporte em configuração de RBAC.
+  Fale com o nosso time para suporte em configuração e auditoria de RBAC.
 </Card>

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.13.0a5"
+__version__ = "1.13.0rc1"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.13.0a5",
+    "crewai==1.13.0rc1",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",

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

@@ -309,4 +309,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.13.0a5"
+__version__ = "1.13.0rc1"

lib/crewai-tools/tool.specs.json

@@ -14281,349 +14281,10 @@
             ],
             "title": "EnvVar",
             "type": "object"
-          },
-          "JsonResponseFormat": {
-            "description": "Response format requesting raw JSON output (e.g. ``{\"type\": \"json_object\"}``).",
-            "properties": {
-              "type": {
-                "const": "json_object",
-                "title": "Type",
-                "type": "string"
-              }
-            },
-            "required": [
-              "type"
-            ],
-            "title": "JsonResponseFormat",
-            "type": "object"
-          },
-          "LLM": {
-            "properties": {
-              "additional_params": {
-                "additionalProperties": true,
-                "title": "Additional Params",
-                "type": "object"
-              },
-              "api_base": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Api Base"
-              },
-              "api_key": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Api Key"
-              },
-              "api_version": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Api Version"
-              },
-              "base_url": {
-                "anyOf": [
-                  {
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Base Url"
-              },
-              "callbacks": {
-                "anyOf": [
-                  {
-                    "items": {},
-                    "type": "array"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Callbacks"
-              },
-              "completion_cost": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Completion Cost"
-              },
-              "context_window_size": {
-                "default": 0,
-                "title": "Context Window Size",
-                "type": "integer"
-              },
-              "frequency_penalty": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Frequency Penalty"
-              },
-              "interceptor": {
-                "default": null,
-                "title": "Interceptor"
-              },
-              "is_anthropic": {
-                "default": false,
-                "title": "Is Anthropic",
-                "type": "boolean"
-              },
-              "is_litellm": {
-                "default": false,
-                "title": "Is Litellm",
-                "type": "boolean"
-              },
-              "logit_bias": {
-                "anyOf": [
-                  {
-                    "additionalProperties": {
-                      "type": "number"
-                    },
-                    "type": "object"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Logit Bias"
-              },
-              "logprobs": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Logprobs"
-              },
-              "max_completion_tokens": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Max Completion Tokens"
-              },
-              "max_tokens": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Max Tokens"
-              },
-              "model": {
-                "title": "Model",
-                "type": "string"
-              },
-              "n": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "N"
-              },
-              "prefer_upload": {
-                "default": false,
-                "title": "Prefer Upload",
-                "type": "boolean"
-              },
-              "presence_penalty": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Presence Penalty"
-              },
-              "provider": {
-                "default": "openai",
-                "title": "Provider",
-                "type": "string"
-              },
-              "reasoning_effort": {
-                "anyOf": [
-                  {
-                    "enum": [
-                      "none",
-                      "low",
-                      "medium",
-                      "high"
-                    ],
-                    "type": "string"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Reasoning Effort"
-              },
-              "response_format": {
-                "anyOf": [
-                  {
-                    "$ref": "#/$defs/JsonResponseFormat"
-                  },
-                  {},
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Response Format"
-              },
-              "seed": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Seed"
-              },
-              "stop": {
-                "items": {
-                  "type": "string"
-                },
-                "title": "Stop",
-                "type": "array"
-              },
-              "stream": {
-                "default": false,
-                "title": "Stream",
-                "type": "boolean"
-              },
-              "temperature": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Temperature"
-              },
-              "thinking": {
-                "default": null,
-                "title": "Thinking"
-              },
-              "timeout": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Timeout"
-              },
-              "top_logprobs": {
-                "anyOf": [
-                  {
-                    "type": "integer"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Top Logprobs"
-              },
-              "top_p": {
-                "anyOf": [
-                  {
-                    "type": "number"
-                  },
-                  {
-                    "type": "null"
-                  }
-                ],
-                "default": null,
-                "title": "Top P"
-              }
-            },
-            "required": [
-              "model"
-            ],
-            "title": "LLM",
-            "type": "object"
           }
         },
         "description": "A tool for performing Optical Character Recognition on images.\n\nThis tool leverages LLMs to extract text from images. It can process\nboth local image files and images available via URLs.\n\nAttributes:\n    name (str): Name of the tool.\n    description (str): Description of the tool's functionality.\n    args_schema (Type[BaseModel]): Pydantic schema for input validation.\n\nPrivate Attributes:\n    _llm (Optional[LLM]): Language model instance for making API calls.",
-        "properties": {
-          "llm": {
-            "$ref": "#/$defs/LLM"
-          }
-        },
+        "properties": {},
         "title": "OCRTool",
         "type": "object"
       },

lib/crewai/pyproject.toml

@@ -43,7 +43,7 @@ dependencies = [
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
     "pyyaml~=6.0",
-    "lancedb>=0.29.2,<0.30.1",
+    "lancedb>=0.29.2",
 ]
 
 [project.urls]
@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.13.0a5",
+    "crewai-tools==1.13.0rc1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -4,10 +4,9 @@ from typing import Any
 import urllib.request
 import warnings
 
-from pydantic import PydanticUserError
-
 from crewai.agent.core import Agent
 from crewai.agent.planning_config import PlanningConfig
+from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.flow.flow import Flow
@@ -21,6 +20,9 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
 
 
+CrewAgentExecutor.model_rebuild()
+
+
 def _suppress_pydantic_deprecation_warnings() -> None:
     """Suppress Pydantic deprecation warnings using targeted monkey patch."""
     original_warn = warnings.warn
@@ -44,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.13.0a5"
+__version__ = "1.13.0rc1"
 _telemetry_submitted = False
 
 
@@ -95,38 +97,6 @@ def __getattr__(name: str) -> Any:
     raise AttributeError(f"module 'crewai' has no attribute {name!r}")
 
 
-try:
-    from crewai.agents.tools_handler import ToolsHandler as _ToolsHandler
-    from crewai.experimental.agent_executor import AgentExecutor as _AgentExecutor
-    from crewai.hooks.llm_hooks import LLMCallHookContext as _LLMCallHookContext
-    from crewai.tools.tool_types import ToolResult as _ToolResult
-    from crewai.utilities.prompts import (
-        StandardPromptResult as _StandardPromptResult,
-        SystemPromptResult as _SystemPromptResult,
-    )
-
-    _AgentExecutor.model_rebuild(
-        force=True,
-        _types_namespace={
-            "Agent": Agent,
-            "ToolsHandler": _ToolsHandler,
-            "Crew": Crew,
-            "BaseLLM": BaseLLM,
-            "Task": Task,
-            "StandardPromptResult": _StandardPromptResult,
-            "SystemPromptResult": _SystemPromptResult,
-            "LLMCallHookContext": _LLMCallHookContext,
-            "ToolResult": _ToolResult,
-        },
-    )
-except (ImportError, PydanticUserError):
-    import logging as _logging
-
-    _logging.getLogger(__name__).warning(
-        "AgentExecutor.model_rebuild() failed; forward refs may be unresolved.",
-        exc_info=True,
-    )
-
 __all__ = [
     "LLM",
     "Agent",

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

@@ -1011,7 +1011,7 @@ class Agent(BaseAgent):
         self.agent_executor.tools = tools
         self.agent_executor.original_tools = raw_tools
         self.agent_executor.prompt = prompt
-        self.agent_executor.stop_words = stop_words
+        self.agent_executor.stop = stop_words
         self.agent_executor.tools_names = get_tool_names(tools)
         self.agent_executor.tools_description = render_text_description_and_args(tools)
         self.agent_executor.response_model = (

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

@@ -14,8 +14,15 @@ import inspect
 import logging
 from typing import TYPE_CHECKING, Any, Literal, cast
 
-from pydantic import BaseModel, GetCoreSchemaHandler, ValidationError
-from pydantic_core import CoreSchema, core_schema
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    PrivateAttr,
+    ValidationError,
+    model_validator,
+)
+from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -23,6 +30,7 @@ from crewai.agents.parser import (
     AgentFinish,
     OutputParserError,
 )
+from crewai.agents.tools_handler import ToolsHandler
 from crewai.core.providers.human_input import ExecutorContext, get_provider
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.logging_events import (
@@ -38,6 +46,9 @@ from crewai.hooks.tool_hooks import (
     get_after_tool_call_hooks,
     get_before_tool_call_hooks,
 )
+from crewai.llms.base_llm import BaseLLM
+from crewai.tools.base_tool import BaseTool
+from crewai.tools.structured_tool import CrewStructuredTool
 from crewai.utilities.agent_utils import (
     aget_llm_response,
     convert_tools_to_openai_schema,
@@ -59,106 +70,65 @@ from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.file_store import aget_all_files, get_all_files
 from crewai.utilities.i18n import I18N, get_i18n
 from crewai.utilities.printer import Printer
+from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
 from crewai.utilities.string_utils import sanitize_tool_name
 from crewai.utilities.tool_utils import (
     aexecute_tool_and_check_finality,
     execute_tool_and_check_finality,
 )
 from crewai.utilities.training_handler import CrewTrainingHandler
+from crewai.utilities.types import LLMMessage
 
 
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     from crewai.agent import Agent
-    from crewai.agents.tools_handler import ToolsHandler
     from crewai.crew import Crew
-    from crewai.llms.base_llm import BaseLLM
     from crewai.task import Task
-    from crewai.tools.base_tool import BaseTool
-    from crewai.tools.structured_tool import CrewStructuredTool
     from crewai.tools.tool_types import ToolResult
-    from crewai.utilities.prompts import StandardPromptResult, SystemPromptResult
-    from crewai.utilities.types import LLMMessage
 
 
-class CrewAgentExecutor(CrewAgentExecutorMixin):
+class CrewAgentExecutor(BaseModel, CrewAgentExecutorMixin):
     """Executor for crew agents.
 
     Manages the execution lifecycle of an agent including prompt formatting,
     LLM interactions, tool execution, and feedback handling.
     """
 
-    def __init__(
-        self,
-        llm: BaseLLM,
-        task: Task,
-        crew: Crew,
-        agent: Agent,
-        prompt: SystemPromptResult | StandardPromptResult,
-        max_iter: int,
-        tools: list[CrewStructuredTool],
-        tools_names: str,
-        stop_words: list[str],
-        tools_description: str,
-        tools_handler: ToolsHandler,
-        step_callback: Any = None,
-        original_tools: list[BaseTool] | None = None,
-        function_calling_llm: BaseLLM | Any | None = None,
-        respect_context_window: bool = False,
-        request_within_rpm_limit: Callable[[], bool] | None = None,
-        callbacks: list[Any] | None = None,
-        response_model: type[BaseModel] | None = None,
-        i18n: I18N | None = None,
-    ) -> None:
-        """Initialize executor.
+    model_config = ConfigDict(arbitrary_types_allowed=True, populate_by_name=True)
 
-        Args:
-            llm: Language model instance.
-            task: Task to execute.
-            crew: Crew instance.
-            agent: Agent to execute.
-            prompt: Prompt templates.
-            max_iter: Maximum iterations.
-            tools: Available tools.
-            tools_names: Tool names string.
-            stop_words: Stop word list.
-            tools_description: Tool descriptions.
-            tools_handler: Tool handler instance.
-            step_callback: Optional step callback.
-            original_tools: Original tool list.
-            function_calling_llm: Optional function calling LLM.
-            respect_context_window: Respect context limits.
-            request_within_rpm_limit: RPM limit check function.
-            callbacks: Optional callbacks list.
-            response_model: Optional Pydantic model for structured outputs.
-        """
-        self._i18n: I18N = i18n or get_i18n()
-        self.llm = llm
-        self.task = task
-        self.agent = agent
-        self.crew = crew
-        self.prompt = prompt
-        self.tools = tools
-        self.tools_names = tools_names
-        self.stop = stop_words
-        self.max_iter = max_iter
-        self.callbacks = callbacks or []
-        self._printer: Printer = Printer()
-        self.tools_handler = tools_handler
-        self.original_tools = original_tools or []
-        self.step_callback = step_callback
-        self.tools_description = tools_description
-        self.function_calling_llm = function_calling_llm
-        self.respect_context_window = respect_context_window
-        self.request_within_rpm_limit = request_within_rpm_limit
-        self.response_model = response_model
-        self.ask_for_human_input = False
-        self.messages: list[LLMMessage] = []
-        self.iterations = 0
-        self.log_error_after = 3
-        self.before_llm_call_hooks: list[Callable[..., Any]] = []
-        self.after_llm_call_hooks: list[Callable[..., Any]] = []
+    llm: BaseLLM
+    task: Task | None = None
+    crew: Crew | None = None
+    agent: Agent
+    prompt: SystemPromptResult | StandardPromptResult
+    max_iter: int
+    tools: list[CrewStructuredTool]
+    tools_names: str
+    stop: list[str] = Field(alias="stop_words")
+    tools_description: str
+    tools_handler: ToolsHandler
+    step_callback: Any = None
+    original_tools: list[BaseTool] = Field(default_factory=list)
+    function_calling_llm: BaseLLM | Any | None = None
+    respect_context_window: bool = False
+    request_within_rpm_limit: Callable[[], bool] | None = None
+    callbacks: list[Any] = Field(default_factory=list)
+    response_model: type[BaseModel] | None = None
+    i18n: I18N | None = Field(default=None, exclude=True)
+    ask_for_human_input: bool = False
+    messages: list[LLMMessage] = Field(default_factory=list)
+    iterations: int = 0
+    log_error_after: int = 3
+    before_llm_call_hooks: list[Callable[..., Any]] = Field(default_factory=list)
+    after_llm_call_hooks: list[Callable[..., Any]] = Field(default_factory=list)
+    _i18n: I18N = PrivateAttr()
+    _printer: Printer = PrivateAttr(default_factory=Printer)
+
+    @model_validator(mode="after")
+    def _init_executor(self) -> Self:
+        self._i18n = self.i18n or get_i18n()
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
         if self.llm:
@@ -171,6 +141,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     else self.stop
                 )
             )
+        return self
 
     @property
     def use_stop_words(self) -> bool:
@@ -1687,14 +1658,3 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return format_message_for_llm(
             self._i18n.slice("feedback_instructions").format(feedback=feedback)
         )
-
-    @classmethod
-    def __get_pydantic_core_schema__(
-        cls, _source_type: Any, _handler: GetCoreSchemaHandler
-    ) -> CoreSchema:
-        """Generate Pydantic core schema for BaseClient Protocol.
-
-        This allows the Protocol to be used in Pydantic models without
-        requiring arbitrary_types_allowed=True.
-        """
-        return core_schema.any_schema()

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a5"
+    "crewai[tools]==1.13.0rc1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a5"
+    "crewai[tools]==1.13.0rc1"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a5"
+    "crewai[tools]==1.13.0rc1"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/events/event_bus.py

@@ -85,8 +85,6 @@ class CrewAIEventsBus:
     _shutting_down: bool
     _pending_futures: set[Future[Any]]
     _futures_lock: threading.Lock
-    _executor_initialized: bool
-    _has_pending_events: bool
 
     def __new__(cls) -> Self:
         """Create or return the singleton instance.
@@ -104,9 +102,8 @@ class CrewAIEventsBus:
     def _initialize(self) -> None:
         """Initialize the event bus internal state.
 
-        Creates handler dictionaries. The thread pool executor and event loop
-        are lazily initialized on first emit() to avoid overhead when events
-        are never emitted.
+        Creates handler dictionaries and starts a dedicated background
+        event loop for async handler execution.
         """
         self._shutting_down = False
         self._rwlock = RWLock()
@@ -118,37 +115,19 @@ class CrewAIEventsBus:
             type[BaseEvent], dict[Handler, list[Depends[Any]]]
         ] = {}
         self._execution_plan_cache: dict[type[BaseEvent], ExecutionPlan] = {}
+        self._sync_executor = ThreadPoolExecutor(
+            max_workers=10,
+            thread_name_prefix="CrewAISyncHandler",
+        )
         self._console = ConsoleFormatter()
-        # Lazy initialization flags - executor and loop created on first emit
-        self._executor_initialized = False
-        self._has_pending_events = False
 
-    def _ensure_executor_initialized(self) -> None:
-        """Lazily initialize the thread pool executor and event loop.
-
-        Called on first emit() to avoid startup overhead when events are never used.
-        Thread-safe via double-checked locking.
-        """
-        if self._executor_initialized:
-            return
-
-        with self._instance_lock:
-            if self._executor_initialized:
-                return
-
-            self._sync_executor = ThreadPoolExecutor(
-                max_workers=10,
-                thread_name_prefix="CrewAISyncHandler",
-            )
-
-            self._loop = asyncio.new_event_loop()
-            self._loop_thread = threading.Thread(
-                target=self._run_loop,
-                name="CrewAIEventsLoop",
-                daemon=True,
-            )
-            self._loop_thread.start()
-            self._executor_initialized = True
+        self._loop = asyncio.new_event_loop()
+        self._loop_thread = threading.Thread(
+            target=self._run_loop,
+            name="CrewAIEventsLoop",
+            daemon=True,
+        )
+        self._loop_thread.start()
 
     def _track_future(self, future: Future[Any]) -> Future[Any]:
         """Track a future and set up automatic cleanup when it completes.
@@ -452,15 +431,6 @@ class CrewAIEventsBus:
             sync_handlers = self._sync_handlers.get(event_type, frozenset())
             async_handlers = self._async_handlers.get(event_type, frozenset())
 
-        # Skip executor initialization if no handlers exist for this event
-        if not sync_handlers and not async_handlers:
-            return None
-
-        # Lazily initialize executor and event loop only when handlers exist
-        self._ensure_executor_initialized()
-        # Track that we have pending events for flush optimization
-        self._has_pending_events = True
-
         if has_dependencies:
             return self._track_future(
                 asyncio.run_coroutine_threadsafe(
@@ -504,10 +474,6 @@ class CrewAIEventsBus:
         Returns:
             True if all handlers completed, False if timeout occurred.
         """
-        # Skip flush entirely if no events were ever emitted
-        if not self._has_pending_events:
-            return True
-
         with self._futures_lock:
             futures_to_wait = list(self._pending_futures)
 
@@ -663,9 +629,6 @@ class CrewAIEventsBus:
 
         with self._rwlock.w_locked():
             self._shutting_down = True
-            # Check if executor was ever initialized (lazy init optimization)
-            if not self._executor_initialized:
-                return
             loop = getattr(self, "_loop", None)
 
         if loop is None or loop.is_closed():

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

@@ -17,10 +17,7 @@ from crewai.events.listeners.tracing.first_time_trace_handler import (
 from crewai.events.listeners.tracing.trace_batch_manager import TraceBatchManager
 from crewai.events.listeners.tracing.types import TraceEvent
 from crewai.events.listeners.tracing.utils import (
-    is_tracing_enabled_in_context,
     safe_serialize_to_dict,
-    should_auto_collect_first_time_traces,
-    should_enable_tracing,
 )
 from crewai.events.types.a2a_events import (
     A2AAgentCardFetchedEvent,
@@ -201,17 +198,6 @@ class TraceCollectionListener(BaseEventListener):
         if self._listeners_setup:
             return
 
-        # Skip registration entirely if tracing is disabled and not first-time user
-        # This avoids overhead of 50+ handler registrations when tracing won't be used
-        # Also check is_tracing_enabled_in_context() so per-run overrides (Crew(tracing=True)) still work
-        if (
-            not should_enable_tracing()
-            and not is_tracing_enabled_in_context()
-            and not should_auto_collect_first_time_traces()
-        ):
-            self._listeners_setup = True
-            return
-
         self._register_env_event_handlers(crewai_event_bus)
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)

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

@@ -481,26 +481,6 @@ def should_auto_collect_first_time_traces() -> bool:
     return is_first_execution()
 
 
-def _is_interactive_terminal() -> bool:
-    """Check if stdin is an interactive terminal.
-
-    Returns False in non-interactive contexts (CI, API servers, Docker, etc.)
-    to avoid blocking on prompts that no one can respond to.
-    """
-    import sys
-
-    try:
-        stdin = getattr(sys, "stdin", None)
-        if stdin is None:
-            return False
-        isatty = getattr(stdin, "isatty", None)
-        if not callable(isatty):
-            return False
-        return bool(isatty())
-    except Exception:
-        return False
-
-
 def prompt_user_for_trace_viewing(timeout_seconds: int = 20) -> bool:
     """
     Prompt user if they want to see their traces with timeout.
@@ -512,11 +492,6 @@ def prompt_user_for_trace_viewing(timeout_seconds: int = 20) -> bool:
     if should_suppress_tracing_messages():
         return False
 
-    # Skip prompt in non-interactive contexts (CI, API servers, Docker, etc.)
-    # This avoids blocking for 20 seconds when no one can respond
-    if not _is_interactive_terminal():
-        return False
-
     try:
         import threading
 

lib/crewai/src/crewai/events/types/llm_events.py

@@ -57,7 +57,6 @@ class LLMCallCompletedEvent(LLMEventBase):
     messages: str | list[dict[str, Any]] | None = None
     response: Any
     call_type: LLMCallType
-    usage: dict[str, Any] | None = None
 
 
 class LLMCallFailedEvent(LLMEventBase):

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

@@ -11,15 +11,10 @@ import threading
 from typing import TYPE_CHECKING, Any, Literal, TypeVar, cast
 from uuid import uuid4
 
-from pydantic import (
-    BaseModel,
-    Field,
-    PrivateAttr,
-    model_validator,
-)
+from pydantic import BaseModel, Field, GetCoreSchemaHandler
+from pydantic_core import CoreSchema, core_schema
 from rich.console import Console
 from rich.text import Text
-from typing_extensions import Self
 
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -124,7 +119,6 @@ class AgentExecutorState(BaseModel):
     (todos, observations, replan tracking) in a single validated model.
     """
 
-    id: str = Field(default_factory=lambda: str(uuid4()))
     messages: list[LLMMessage] = Field(default_factory=list)
     iterations: int = Field(default=0)
     current_answer: AgentAction | AgentFinish | None = Field(default=None)
@@ -158,9 +152,6 @@ class AgentExecutorState(BaseModel):
 class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
     """Agent Executor for both standalone agents and crew-bound agents.
 
-    _skip_auto_memory prevents Flow from eagerly allocating a Memory
-    instance — the executor uses agent/crew memory, not its own.
-
     Inherits from:
     - Flow[AgentExecutorState]: Provides flow orchestration capabilities
     - CrewAgentExecutorMixin: Provides memory methods (short/long/external term)
@@ -168,74 +159,136 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
     This executor can operate in two modes:
     - Standalone mode: When crew and task are None (used by Agent.kickoff())
     - Crew mode: When crew and task are provided (used by Agent.execute_task())
+
+    Note: Multiple instances may be created during agent initialization
+    (cache setup, RPM controller setup, etc.) but only the final instance
+    should execute tasks via invoke().
     """
 
-    _skip_auto_memory: bool = True
+    def __init__(
+        self,
+        llm: BaseLLM,
+        agent: Agent,
+        prompt: SystemPromptResult | StandardPromptResult,
+        max_iter: int,
+        tools: list[CrewStructuredTool],
+        tools_names: str,
+        stop_words: list[str],
+        tools_description: str,
+        tools_handler: ToolsHandler,
+        task: Task | None = None,
+        crew: Crew | None = None,
+        step_callback: Any = None,
+        original_tools: list[BaseTool] | None = None,
+        function_calling_llm: BaseLLM | Any | None = None,
+        respect_context_window: bool = False,
+        request_within_rpm_limit: Callable[[], bool] | None = None,
+        callbacks: list[Any] | None = None,
+        response_model: type[BaseModel] | None = None,
+        i18n: I18N | None = None,
+    ) -> None:
+        """Initialize the flow-based agent executor.
 
-    suppress_flow_events: bool = True  # always suppress for executor
-    llm: BaseLLM = Field(exclude=True)
-    agent: Agent = Field(exclude=True)
-    prompt: SystemPromptResult | StandardPromptResult = Field(exclude=True)
-    max_iter: int = Field(default=25, exclude=True)
-    tools: list[CrewStructuredTool] = Field(default_factory=list, exclude=True)
-    tools_names: str = Field(default="", exclude=True)
-    stop_words: list[str] = Field(default_factory=list, exclude=True)
-    tools_description: str = Field(default="", exclude=True)
-    tools_handler: ToolsHandler | None = Field(default=None, exclude=True)
-    task: Task | None = Field(default=None, exclude=True)
-    crew: Crew | None = Field(default=None, exclude=True)
-    step_callback: Any = Field(default=None, exclude=True)
-    original_tools: list[BaseTool] = Field(default_factory=list, exclude=True)
-    function_calling_llm: BaseLLM | None = Field(default=None, exclude=True)
-    respect_context_window: bool = Field(default=False, exclude=True)
-    request_within_rpm_limit: Callable[[], bool] | None = Field(
-        default=None, exclude=True
-    )
-    callbacks: list[Any] = Field(default_factory=list, exclude=True)
-    response_model: type[BaseModel] | None = Field(default=None, exclude=True)
-    i18n: I18N | None = Field(default=None, exclude=True)
-    log_error_after: int = Field(default=3, exclude=True)
-    before_llm_call_hooks: list[BeforeLLMCallHookType | BeforeLLMCallHookCallable] = (
-        Field(default_factory=list, exclude=True)
-    )
-    after_llm_call_hooks: list[AfterLLMCallHookType | AfterLLMCallHookCallable] = Field(
-        default_factory=list, exclude=True
-    )
+        Args:
+            llm: Language model instance.
+            agent: Agent to execute.
+            prompt: Prompt templates.
+            max_iter: Maximum iterations.
+            tools: Available tools.
+            tools_names: Tool names string.
+            stop_words: Stop word list.
+            tools_description: Tool descriptions.
+            tools_handler: Tool handler instance.
+            task: Optional task to execute (None for standalone agent execution).
+            crew: Optional crew instance (None for standalone agent execution).
+            step_callback: Optional step callback.
+            original_tools: Original tool list.
+            function_calling_llm: Optional function calling LLM.
+            respect_context_window: Respect context limits.
+            request_within_rpm_limit: RPM limit check function.
+            callbacks: Optional callbacks list.
+            response_model: Optional Pydantic model for structured outputs.
+        """
+        self._i18n: I18N = i18n or get_i18n()
+        self.llm = llm
+        self.task: Task | None = task
+        self.agent = agent
+        self.crew: Crew | None = crew
+        self.prompt = prompt
+        self.tools = tools
+        self.tools_names = tools_names
+        self.stop = stop_words
+        self.max_iter = max_iter
+        self.callbacks = callbacks or []
+        self._printer: Printer = Printer()
+        self.tools_handler = tools_handler
+        self.original_tools = original_tools or []
+        self.step_callback = step_callback
+        self.tools_description = tools_description
+        self.function_calling_llm = function_calling_llm
+        self.respect_context_window = respect_context_window
+        self.request_within_rpm_limit = request_within_rpm_limit
+        self.response_model = response_model
+        self.log_error_after = 3
+        self._console: Console = Console()
 
-    _i18n: I18N = PrivateAttr(default_factory=get_i18n)
-    _printer: Printer = PrivateAttr(default_factory=Printer)
-    _console: Console = PrivateAttr(default_factory=Console)
-    _last_parser_error: OutputParserError | None = PrivateAttr(default=None)
-    _last_context_error: Exception | None = PrivateAttr(default=None)
-    _execution_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
-    _finalize_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
-    _finalize_called: bool = PrivateAttr(default=False)
-    _is_executing: bool = PrivateAttr(default=False)
-    _has_been_invoked: bool = PrivateAttr(default=False)
-    _instance_id: str = PrivateAttr(default_factory=lambda: str(uuid4())[:8])
-    _step_executor: Any = PrivateAttr(default=None)
-    _planner_observer: Any = PrivateAttr(default=None)
+        # Error context storage for recovery
+        self._last_parser_error: OutputParserError | None = None
+        self._last_context_error: Exception | None = None
 
-    @model_validator(mode="after")
-    def _setup_executor(self) -> Self:
-        """Configure executor after Pydantic field initialization."""
-        self._i18n = self.i18n or get_i18n()
+        # Execution guard to prevent concurrent/duplicate executions
+        self._execution_lock = threading.Lock()
+        self._finalize_lock = threading.Lock()
+        self._finalize_called: bool = False
+        self._is_executing: bool = False
+        self._has_been_invoked: bool = False
+        self._flow_initialized: bool = False
+
+        self._instance_id = str(uuid4())[:8]
+
+        self.before_llm_call_hooks: list[
+            BeforeLLMCallHookType | BeforeLLMCallHookCallable
+        ] = []
+        self.after_llm_call_hooks: list[
+            AfterLLMCallHookType | AfterLLMCallHookCallable
+        ] = []
         self.before_llm_call_hooks.extend(get_before_llm_call_hooks())
         self.after_llm_call_hooks.extend(get_after_llm_call_hooks())
 
         if self.llm:
             existing_stop = getattr(self.llm, "stop", [])
-            if not isinstance(existing_stop, list):
-                existing_stop = []
-            self.llm.stop = list(set(existing_stop + self.stop_words))
-
+            self.llm.stop = list(
+                set(
+                    existing_stop + self.stop
+                    if isinstance(existing_stop, list)
+                    else self.stop
+                )
+            )
         self._state = AgentExecutorState()
-        self.max_method_calls = self.max_iter * 10
 
-        current_tracing = is_tracing_enabled_in_context()
-        self.tracing = current_tracing if current_tracing else None
-        self._flow_post_init()
-        return self
+        # Plan-and-Execute components (Phase 2)
+        # Lazy-imported to avoid circular imports during module load
+        self._step_executor: Any = None
+        self._planner_observer: Any = None
+
+    def _ensure_flow_initialized(self) -> None:
+        """Ensure Flow.__init__() has been called.
+
+        This is deferred from __init__ to prevent FlowCreatedEvent emission
+        during agent setup when multiple executor instances are created.
+        Only the instance that actually executes via invoke() will emit events.
+        """
+        if not self._flow_initialized:
+            current_tracing = is_tracing_enabled_in_context()
+            # Now call Flow's __init__ which will replace self._state
+            # with Flow's managed state. Suppress flow events since this is
+            # an agent executor, not a user-facing flow.
+            super().__init__(
+                suppress_flow_events=True,
+                tracing=current_tracing if current_tracing else None,
+                max_method_calls=self.max_iter * 10,
+            )
+            self._flow_initialized = True
 
     def _check_native_tool_support(self) -> bool:
         """Check if LLM supports native function calling."""
@@ -265,13 +318,19 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
 
     @property
     def state(self) -> AgentExecutorState:
-        """Get thread-safe state proxy."""
-        return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
+        """Get state - returns temporary state if Flow not yet initialized.
+
+        Flow initialization is deferred to prevent event emission during agent setup.
+        Returns the temporary state until invoke() is called.
+        """
+        if self._flow_initialized and hasattr(self, "_state_lock"):
+            return StateProxy(self._state, self._state_lock)  # type: ignore[return-value]
+        return self._state
 
     @property
     def iterations(self) -> int:
         """Compatibility property for mixin - returns state iterations."""
-        return self._state.iterations  # type: ignore[no-any-return]
+        return self._state.iterations
 
     @iterations.setter
     def iterations(self, value: int) -> None:
@@ -281,7 +340,7 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
     @property
     def messages(self) -> list[LLMMessage]:
         """Compatibility property - returns state messages."""
-        return self._state.messages  # type: ignore[no-any-return]
+        return self._state.messages
 
     @messages.setter
     def messages(self, value: list[LLMMessage]) -> None:
@@ -1910,7 +1969,8 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
     @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""
-        self._discard_or_listener(FlowMethodName("continue_iteration"))
+        if self._flow_initialized:
+            self._discard_or_listener(FlowMethodName("continue_iteration"))
         return "check_iteration"
 
     @router(or_(initialize_reasoning, continue_iteration))
@@ -2538,6 +2598,8 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
         if is_inside_event_loop():
             return self.invoke_async(inputs)
 
+        self._ensure_flow_initialized()
+
         with self._execution_lock:
             if self._is_executing:
                 raise RuntimeError(
@@ -2628,6 +2690,8 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
         Returns:
             Dictionary with agent output.
         """
+        self._ensure_flow_initialized()
+
         with self._execution_lock:
             if self._is_executing:
                 raise RuntimeError(
@@ -2943,6 +3007,17 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
         """
         return bool(self.crew and self.crew._train)
 
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: Any, _handler: GetCoreSchemaHandler
+    ) -> CoreSchema:
+        """Generate Pydantic core schema for Protocol compatibility.
+
+        Allows the executor to be used in Pydantic models without
+        requiring arbitrary_types_allowed=True.
+        """
+        return core_schema.any_schema()
+
 
 # Backward compatibility alias (deprecated)
 CrewAgentExecutorFlow = AgentExecutor

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

@@ -39,14 +39,7 @@ from uuid import uuid4
 
 from opentelemetry import baggage
 from opentelemetry.context import attach, detach
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PrivateAttr,
-    ValidationError,
-)
-from pydantic._internal._model_construction import ModelMetaclass
+from pydantic import BaseModel, Field, ValidationError
 from rich.console import Console
 from rich.panel import Panel
 
@@ -88,7 +81,6 @@ from crewai.flow.flow_wrappers import (
     SimpleFlowCondition,
     StartMethod,
 )
-from crewai.flow.human_feedback import HumanFeedbackResult
 from crewai.flow.input_provider import InputProvider
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.flow.types import (
@@ -116,6 +108,7 @@ if TYPE_CHECKING:
     from crewai_files import FileInput
 
     from crewai.flow.async_feedback.types import PendingFeedbackContext
+    from crewai.flow.human_feedback import HumanFeedbackResult
     from crewai.llms.base_llm import BaseLLM
 
 from crewai.flow.visualization import build_flow_structure, render_interactive
@@ -735,7 +728,7 @@ class StateProxy(Generic[T]):
         return result
 
 
-class FlowMeta(ModelMetaclass):
+class FlowMeta(type):
     def __new__(
         mcs,
         name: str,
@@ -743,45 +736,6 @@ class FlowMeta(ModelMetaclass):
         namespace: dict[str, Any],
         **kwargs: Any,
     ) -> type:
-        parent_fields: set[str] = set()
-        for base in bases:
-            if hasattr(base, "model_fields"):
-                parent_fields.update(base.model_fields)
-
-        annotations = namespace.get("__annotations__", {})
-        _skip_types = (classmethod, staticmethod, property)
-
-        for base in bases:
-            if isinstance(base, ModelMetaclass):
-                continue
-            for attr_name in getattr(base, "__annotations__", {}):
-                if attr_name not in annotations and attr_name not in namespace:
-                    annotations[attr_name] = ClassVar
-
-        for attr_name, attr_value in namespace.items():
-            if isinstance(attr_value, property) and attr_name not in annotations:
-                for base in bases:
-                    base_ann = getattr(base, "__annotations__", {})
-                    if attr_name in base_ann:
-                        annotations[attr_name] = ClassVar
-
-        for attr_name, attr_value in list(namespace.items()):
-            if attr_name in annotations or attr_name.startswith("_"):
-                continue
-            if attr_name in parent_fields:
-                annotations[attr_name] = Any
-                if isinstance(attr_value, BaseModel):
-                    namespace[attr_name] = Field(
-                        default_factory=lambda v=attr_value: v, exclude=True
-                    )
-                continue
-            if callable(attr_value) or isinstance(
-                attr_value, (*_skip_types, FlowMethod)
-            ):
-                continue
-            annotations[attr_name] = ClassVar[type(attr_value)]
-        namespace["__annotations__"] = annotations
-
         cls = super().__new__(mcs, name, bases, namespace)
 
         start_methods = []
@@ -866,90 +820,88 @@ class FlowMeta(ModelMetaclass):
         return cls
 
 
-class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
+class Flow(Generic[T], metaclass=FlowMeta):
     """Base class for all flows.
 
     type parameter T must be either dict[str, Any] or a subclass of BaseModel."""
 
-    model_config = ConfigDict(
-        arbitrary_types_allowed=True,
-        ignored_types=(StartMethod, ListenMethod, RouterMethod),
-        revalidate_instances="never",
-    )
-    __hash__ = object.__hash__
-
     _start_methods: ClassVar[list[FlowMethodName]] = []
     _listeners: ClassVar[dict[FlowMethodName, SimpleFlowCondition | FlowCondition]] = {}
     _routers: ClassVar[set[FlowMethodName]] = set()
     _router_paths: ClassVar[dict[FlowMethodName, list[FlowMethodName]]] = {}
+    initial_state: type[T] | T | None = None
+    name: str | None = None
+    tracing: bool | None = None
+    stream: bool = False
+    memory: Memory | MemoryScope | MemorySlice | None = None
+    input_provider: InputProvider | None = None
 
-    initial_state: Any = Field(default=None)
-    name: str | None = Field(default=None)
-    tracing: bool | None = Field(default=None)
-    stream: bool = Field(default=False)
-    memory: Memory | MemoryScope | MemorySlice | None = Field(default=None)
-    input_provider: InputProvider | None = Field(default=None)
-    suppress_flow_events: bool = Field(default=False)
-    human_feedback_history: list[HumanFeedbackResult] = Field(default_factory=list)
-    last_human_feedback: HumanFeedbackResult | None = Field(default=None)
-
-    persistence: Any = Field(default=None, exclude=True)
-    max_method_calls: int = Field(default=100, exclude=True)
-
-    _methods: dict[FlowMethodName, FlowMethod[Any, Any]] = PrivateAttr(
-        default_factory=dict
-    )
-    _method_execution_counts: dict[FlowMethodName, int] = PrivateAttr(
-        default_factory=dict
-    )
-    _pending_and_listeners: dict[PendingListenerKey, set[FlowMethodName]] = PrivateAttr(
-        default_factory=dict
-    )
-    _fired_or_listeners: set[FlowMethodName] = PrivateAttr(default_factory=set)
-    _method_outputs: list[Any] = PrivateAttr(default_factory=list)
-    _state_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
-    _or_listeners_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
-    _completed_methods: set[FlowMethodName] = PrivateAttr(default_factory=set)
-    _method_call_counts: dict[FlowMethodName, int] = PrivateAttr(default_factory=dict)
-    _is_execution_resuming: bool = PrivateAttr(default=False)
-    _event_futures: list[Future[None]] = PrivateAttr(default_factory=list)
-    _pending_feedback_context: PendingFeedbackContext | None = PrivateAttr(default=None)
-    _human_feedback_method_outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
-    _input_history: list[InputHistoryEntry] = PrivateAttr(default_factory=list)
-    _state: Any = PrivateAttr(default=None)
-
-    def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:  # type: ignore[override]
-        class _FlowGeneric(cls):  # type: ignore[valid-type,misc]
-            pass
+    def __class_getitem__(cls: type[Flow[T]], item: type[T]) -> type[Flow[T]]:
+        class _FlowGeneric(cls):  # type: ignore
+            _initial_state_t = item
 
         _FlowGeneric.__name__ = f"{cls.__name__}[{item.__name__}]"
-        _FlowGeneric._initial_state_t = item
         return _FlowGeneric
 
-    def __setattr__(self, name: str, value: Any) -> None:
-        """Allow arbitrary attribute assignment for backward compat with plain class."""
-        if name in self.model_fields or name in self.__private_attributes__:
-            super().__setattr__(name, value)
-        else:
-            object.__setattr__(self, name, value)
+    def __init__(
+        self,
+        persistence: FlowPersistence | None = None,
+        tracing: bool | None = None,
+        suppress_flow_events: bool = False,
+        max_method_calls: int = 100,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new Flow instance.
 
-    def model_post_init(self, __context: Any) -> None:
-        self._flow_post_init()
+        Args:
+            persistence: Optional persistence backend for storing flow states
+            tracing: Whether to enable tracing. True=always enable, False=always disable, None=check environment/user settings
+            suppress_flow_events: Whether to suppress flow event emissions (internal use)
+            max_method_calls: Maximum times a single method can be called per execution before raising RecursionError
+            **kwargs: Additional state values to initialize or override
+        """
+        # Initialize basic instance attributes
+        self._methods: dict[FlowMethodName, FlowMethod[Any, Any]] = {}
+        self._method_execution_counts: dict[FlowMethodName, int] = {}
+        self._pending_and_listeners: dict[PendingListenerKey, set[FlowMethodName]] = {}
+        self._fired_or_listeners: set[FlowMethodName] = (
+            set()
+        )  # Track OR listeners that already fired
+        self._method_outputs: list[Any] = []  # list to store all method outputs
+        self._state_lock = threading.Lock()
+        self._or_listeners_lock = threading.Lock()
+        self._completed_methods: set[FlowMethodName] = (
+            set()
+        )  # Track completed methods for reload
+        self._method_call_counts: dict[FlowMethodName, int] = {}
+        self._max_method_calls = max_method_calls
+        self._persistence: FlowPersistence | None = persistence
+        self._is_execution_resuming: bool = False
+        self._event_futures: list[Future[None]] = []
 
-    def _flow_post_init(self) -> None:
-        """Heavy initialization: state creation, events, memory, method registration."""
-        if getattr(self, "_flow_post_init_done", False):
-            return
-        object.__setattr__(self, "_flow_post_init_done", True)
+        # Human feedback storage
+        self.human_feedback_history: list[HumanFeedbackResult] = []
+        self.last_human_feedback: HumanFeedbackResult | None = None
+        self._pending_feedback_context: PendingFeedbackContext | None = None
+        # Per-method stash for real @human_feedback output (keyed by method name)
+        # Used to decouple routing outcome from method return value when emit is set
+        self._human_feedback_method_outputs: dict[str, Any] = {}
+        self.suppress_flow_events: bool = suppress_flow_events
 
-        if self._state is None:
-            self._state = self._create_initial_state()
+        # User input history (for self.ask())
+        self._input_history: list[InputHistoryEntry] = []
 
+        # Initialize state with initial values
+        self._state = self._create_initial_state()
+        self.tracing = tracing
         tracing_enabled = should_enable_tracing(override=self.tracing)
         set_tracing_enabled(tracing_enabled)
 
         trace_listener = TraceCollectionListener()
         trace_listener.setup_listeners(crewai_event_bus)
+        # Apply any additional kwargs
+        if kwargs:
+            self._initialize_state(kwargs)
 
         if not self.suppress_flow_events:
             crewai_event_bus.emit(
@@ -1433,8 +1385,8 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         self._pending_feedback_context = None
 
         # Clear pending feedback from persistence
-        if self.persistence:
-            self.persistence.clear_pending_feedback(context.flow_id)
+        if self._persistence:
+            self._persistence.clear_pending_feedback(context.flow_id)
 
         # Emit feedback received event
         crewai_event_bus.emit(
@@ -1475,17 +1427,17 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             if isinstance(e, HumanFeedbackPending):
                 self._pending_feedback_context = e.context
 
-                if self.persistence is None:
+                if self._persistence is None:
                     from crewai.flow.persistence import SQLiteFlowPersistence
 
-                    self.persistence = SQLiteFlowPersistence()
+                    self._persistence = SQLiteFlowPersistence()
 
                 state_data = (
                     self._state
                     if isinstance(self._state, dict)
                     else self._state.model_dump()
                 )
-                self.persistence.save_pending_feedback(
+                self._persistence.save_pending_feedback(
                     flow_uuid=e.context.flow_id,
                     context=e.context,
                     state_data=state_data,
@@ -1535,33 +1487,39 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         """
         init_state = self.initial_state
 
+        # Handle case where initial_state is None but we have a type parameter
         if init_state is None and hasattr(self, "_initial_state_t"):
             state_type = self._initial_state_t
             if isinstance(state_type, type):
                 if issubclass(state_type, FlowState):
+                    # Create instance - FlowState auto-generates id via default_factory
                     instance = state_type()
+                    # Ensure id is set - generate UUID if empty
                     if not getattr(instance, "id", None):
                         object.__setattr__(instance, "id", str(uuid4()))
                     return cast(T, instance)
                 if issubclass(state_type, BaseModel):
-
+                    # Create a new type with FlowState first for proper id default
                     class StateWithId(FlowState, state_type):  # type: ignore
                         pass
 
                     instance = StateWithId()
+                    # Ensure id is set - generate UUID if empty
                     if not getattr(instance, "id", None):
                         object.__setattr__(instance, "id", str(uuid4()))
                     return cast(T, instance)
                 if state_type is dict:
                     return cast(T, {"id": str(uuid4())})
 
+        # Handle case where no initial state is provided
         if init_state is None:
             return cast(T, {"id": str(uuid4())})
 
+        # Handle case where initial_state is a type (class)
         if isinstance(init_state, type):
             state_class = init_state
             if issubclass(state_class, FlowState):
-                return cast(T, state_class())
+                return state_class()
             if issubclass(state_class, BaseModel):
                 model_fields = getattr(state_class, "model_fields", None)
                 if not model_fields or "id" not in model_fields:
@@ -1569,7 +1527,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 model_instance = state_class()
                 if not getattr(model_instance, "id", None):
                     object.__setattr__(model_instance, "id", str(uuid4()))
-                return cast(T, model_instance)
+                return model_instance
             if init_state is dict:
                 return cast(T, {"id": str(uuid4())})
 
@@ -1580,21 +1538,32 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 new_state["id"] = str(uuid4())
             return cast(T, new_state)
 
+        # Handle BaseModel instance case
         if isinstance(init_state, BaseModel):
-            model = init_state
-            if hasattr(model, "id"):
+            model = cast(BaseModel, init_state)
+            if not hasattr(model, "id"):
+                raise ValueError("Flow state model must have an 'id' field")
+
+            # Create new instance with same values to avoid mutations
+            if hasattr(model, "model_dump"):
+                # Pydantic v2
                 state_dict = model.model_dump()
-                if not state_dict.get("id"):
-                    state_dict["id"] = str(uuid4())
-                model_class = type(model)
-                return cast(T, model_class(**state_dict))
+            elif hasattr(model, "dict"):
+                # Pydantic v1
+                state_dict = model.dict()
+            else:
+                # Fallback for other BaseModel implementations
+                state_dict = {
+                    k: v for k, v in model.__dict__.items() if not k.startswith("_")
+                }
 
-            class StateWithId(FlowState, type(model)):  # type: ignore
-                pass
+            # Ensure id is set - generate UUID if empty
+            if not state_dict.get("id"):
+                state_dict["id"] = str(uuid4())
 
-            state_dict = model.model_dump()
-            state_dict["id"] = str(uuid4())
-            return cast(T, StateWithId(**state_dict))
+            # Create new instance of the same class
+            model_class = type(model)
+            return cast(T, model_class(**state_dict))
         raise TypeError(
             f"Initial state must be dict or BaseModel, got {type(self.initial_state)}"
         )
@@ -1607,17 +1576,17 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         """
         if isinstance(self._state, BaseModel):
             try:
-                return cast(T, self._state.model_copy(deep=True))
+                return self._state.model_copy(deep=True)
             except (TypeError, AttributeError):
                 try:
                     state_dict = self._state.model_dump()
                     model_class = type(self._state)
-                    return cast(T, model_class(**state_dict))
+                    return model_class(**state_dict)
                 except Exception:
-                    return cast(T, self._state.model_copy(deep=False))
+                    return self._state.model_copy(deep=False)
         else:
             try:
-                return cast(T, copy.deepcopy(self._state))
+                return copy.deepcopy(self._state)
             except (TypeError, AttributeError):
                 return cast(T, self._state.copy())
 
@@ -1693,7 +1662,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         elif isinstance(self._state, BaseModel):
             # For BaseModel states, preserve existing fields unless overridden
             try:
-                model = self._state
+                model = cast(BaseModel, self._state)
                 # Get current state as dict
                 if hasattr(model, "model_dump"):
                     current_state = model.model_dump()
@@ -1744,7 +1713,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             self._state.update(stored_state)
         elif isinstance(self._state, BaseModel):
             # For BaseModel states, create new instance with stored values
-            model = self._state
+            model = cast(BaseModel, self._state)
             if hasattr(model, "model_validate"):
                 # Pydantic v2
                 self._state = cast(T, type(model).model_validate(stored_state))
@@ -1969,7 +1938,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
         try:
             # Reset flow state for fresh execution unless restoring from persistence
-            is_restoring = inputs and "id" in inputs and self.persistence is not None
+            is_restoring = inputs and "id" in inputs and self._persistence is not None
             if not is_restoring:
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
@@ -1995,9 +1964,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                         setattr(self._state, "id", inputs["id"])  # noqa: B010
 
                 # If persistence is enabled, attempt to restore the stored state using the provided id.
-                if "id" in inputs and self.persistence is not None:
+                if "id" in inputs and self._persistence is not None:
                     restore_uuid = inputs["id"]
-                    stored_state = self.persistence.load_state(restore_uuid)
+                    stored_state = self._persistence.load_state(restore_uuid)
                     if stored_state:
                         self._log_flow_event(
                             f"Loading flow state from memory for UUID: {restore_uuid}"
@@ -2067,17 +2036,17 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
                 if isinstance(e, HumanFeedbackPending):
                     # Auto-save pending feedback (create default persistence if needed)
-                    if self.persistence is None:
+                    if self._persistence is None:
                         from crewai.flow.persistence import SQLiteFlowPersistence
 
-                        self.persistence = SQLiteFlowPersistence()
+                        self._persistence = SQLiteFlowPersistence()
 
                     state_data = (
                         self._state
                         if isinstance(self._state, dict)
                         else self._state.model_dump()
                     )
-                    self.persistence.save_pending_feedback(
+                    self._persistence.save_pending_feedback(
                         flow_uuid=e.context.flow_id,
                         context=e.context,
                         state_data=state_data,
@@ -2363,10 +2332,10 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             if isinstance(e, HumanFeedbackPending):
                 e.context.method_name = method_name
 
-                if self.persistence is None:
+                if self._persistence is None:
                     from crewai.flow.persistence import SQLiteFlowPersistence
 
-                    self.persistence = SQLiteFlowPersistence()
+                    self._persistence = SQLiteFlowPersistence()
 
                 # Emit paused event (not failed)
                 if not self.suppress_flow_events:
@@ -2727,9 +2696,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             - Catches and logs any exceptions during execution, preventing individual listener failures from breaking the entire flow
         """
         count = self._method_call_counts.get(listener_name, 0) + 1
-        if count > self.max_method_calls:
+        if count > self._max_method_calls:
             raise RecursionError(
-                f"Method '{listener_name}' has been called {self.max_method_calls} times in "
+                f"Method '{listener_name}' has been called {self._max_method_calls} times in "
                 f"this flow execution, which indicates an infinite loop. "
                 f"This commonly happens when a @listen label matches the "
                 f"method's own name."
@@ -2836,7 +2805,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
         This is best-effort: if persistence is not configured, this is a no-op.
         """
-        if self.persistence is None:
+        if self._persistence is None:
             return
         try:
             state_data = (
@@ -2844,7 +2813,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 if isinstance(self._state, dict)
                 else self._state.model_dump()
             )
-            self.persistence.save_state(
+            self._persistence.save_state(
                 flow_uuid=self.flow_id,
                 method_name="_ask_checkpoint",
                 state_data=state_data,

lib/crewai/src/crewai/llm.py

@@ -970,25 +970,21 @@ class LLM(BaseLLM):
                     )
                     result = instructor_instance.to_pydantic()
                     structured_response = result.model_dump_json()
-                    usage_dict = self._usage_to_dict(usage_info)
                     self._handle_emit_call_events(
                         response=structured_response,
                         call_type=LLMCallType.LLM_CALL,
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage_dict,
                     )
                     return structured_response
 
-                usage_dict = self._usage_to_dict(usage_info)
                 self._handle_emit_call_events(
                     response=full_response,
                     call_type=LLMCallType.LLM_CALL,
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage_dict,
                 )
                 return full_response
 
@@ -998,14 +994,12 @@ class LLM(BaseLLM):
                 return tool_result
 
             # --- 10) Emit completion event and return response
-            usage_dict = self._usage_to_dict(usage_info)
             self._handle_emit_call_events(
                 response=full_response,
                 call_type=LLMCallType.LLM_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage_dict,
             )
             return full_response
 
@@ -1027,7 +1021,6 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=self._usage_to_dict(usage_info),
                 )
                 return full_response
 
@@ -1179,7 +1172,6 @@ class LLM(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=None,
             )
             return structured_response
 
@@ -1210,8 +1202,6 @@ class LLM(BaseLLM):
                 raise LLMContextLengthExceededError(error_msg) from e
             raise
 
-        response_usage = self._usage_to_dict(getattr(response, "usage", None))
-
         # --- 2) Handle structured output response (when response_model is provided)
         if response_model is not None:
             # When using instructor/response_model, litellm returns a Pydantic model instance
@@ -1223,7 +1213,6 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=response_usage,
                 )
                 return structured_response
 
@@ -1255,7 +1244,6 @@ class LLM(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=response_usage,
             )
             return text_response
 
@@ -1279,7 +1267,6 @@ class LLM(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=response_usage,
         )
         return text_response
 
@@ -1329,7 +1316,6 @@ class LLM(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=None,
             )
             return structured_response
 
@@ -1356,8 +1342,6 @@ class LLM(BaseLLM):
                 raise LLMContextLengthExceededError(error_msg) from e
             raise
 
-        response_usage = self._usage_to_dict(getattr(response, "usage", None))
-
         if response_model is not None:
             if isinstance(response, BaseModel):
                 structured_response = response.model_dump_json()
@@ -1367,7 +1351,6 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=response_usage,
                 )
                 return structured_response
 
@@ -1397,7 +1380,6 @@ class LLM(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=response_usage,
             )
             return text_response
 
@@ -1420,7 +1402,6 @@ class LLM(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=response_usage,
         )
         return text_response
 
@@ -1567,14 +1548,12 @@ class LLM(BaseLLM):
                     if result is not None:
                         return result
 
-            usage_dict = self._usage_to_dict(usage_info)
             self._handle_emit_call_events(
                 response=full_response,
                 call_type=LLMCallType.LLM_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params.get("messages"),
-                usage=usage_dict,
             )
             return full_response
 
@@ -1596,7 +1575,6 @@ class LLM(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("messages"),
-                    usage=self._usage_to_dict(usage_info),
                 )
                 return full_response
             raise
@@ -1983,19 +1961,6 @@ class LLM(BaseLLM):
                     )
                     raise
 
-    @staticmethod
-    def _usage_to_dict(usage: Any) -> dict[str, Any] | None:
-        if usage is None:
-            return None
-        if isinstance(usage, dict):
-            return usage
-        if hasattr(usage, "model_dump"):
-            result: dict[str, Any] = usage.model_dump()
-            return result
-        if hasattr(usage, "__dict__"):
-            return {k: v for k, v in vars(usage).items() if not k.startswith("_")}
-        return None
-
     def _handle_emit_call_events(
         self,
         response: Any,
@@ -2003,7 +1968,6 @@ class LLM(BaseLLM):
         from_task: Task | None = None,
         from_agent: Agent | None = None,
         messages: str | list[LLMMessage] | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> None:
         """Handle the events for the LLM call.
 
@@ -2013,7 +1977,6 @@ class LLM(BaseLLM):
             from_task: Optional task object
             from_agent: Optional agent object
             messages: Optional messages object
-            usage: Optional token usage data
         """
         crewai_event_bus.emit(
             self,
@@ -2025,7 +1988,6 @@ class LLM(BaseLLM):
                 from_agent=from_agent,
                 model=self.model,
                 call_id=get_current_call_id(),
-                usage=usage,
             ),
         )
 

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

@@ -460,7 +460,6 @@ class BaseLLM(BaseModel, ABC):
         from_task: Task | None = None,
         from_agent: Agent | None = None,
         messages: str | list[LLMMessage] | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> None:
         """Emit LLM call completed event."""
         from crewai.utilities.serialization import to_serializable
@@ -475,7 +474,6 @@ class BaseLLM(BaseModel, ABC):
                 from_agent=from_agent,
                 model=self.model,
                 call_id=get_current_call_id(),
-                usage=usage,
             ),
         )
 

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

@@ -811,7 +811,6 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
-                            usage=usage,
                         )
                         return structured_data
             else:
@@ -827,7 +826,6 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
-                            usage=usage,
                         )
                         return structured_data
 
@@ -850,7 +848,6 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return list(tool_uses)
 
@@ -882,7 +879,6 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage,
         )
 
         if usage.get("total_tokens", 0) > 0:
@@ -1032,7 +1028,6 @@ class AnthropicCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage,
                 )
                 return structured_data
             for block in final_message.content:
@@ -1047,7 +1042,6 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return structured_data
 
@@ -1077,7 +1071,6 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -1248,7 +1241,6 @@ class AnthropicCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=follow_up_params["messages"],
-                usage=follow_up_usage,
             )
 
             # Log combined token usage
@@ -1340,7 +1332,6 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
-                            usage=usage,
                         )
                         return structured_data
             else:
@@ -1356,7 +1347,6 @@ class AnthropicCompletion(BaseLLM):
                             from_task=from_task,
                             from_agent=from_agent,
                             messages=params["messages"],
-                            usage=usage,
                         )
                         return structured_data
 
@@ -1377,7 +1367,6 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return list(tool_uses)
 
@@ -1401,7 +1390,6 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage,
         )
 
         if usage.get("total_tokens", 0) > 0:
@@ -1539,7 +1527,6 @@ class AnthropicCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage,
                 )
                 return structured_data
             for block in final_message.content:
@@ -1554,7 +1541,6 @@ class AnthropicCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return structured_data
 
@@ -1583,7 +1569,6 @@ class AnthropicCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage,
         )
 
         return full_response
@@ -1642,7 +1627,6 @@ class AnthropicCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=follow_up_params["messages"],
-                usage=follow_up_usage,
             )
 
             total_usage = {

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

@@ -569,7 +569,6 @@ class AzureCompletion(BaseLLM):
         params: AzureCompletionParams,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> BaseModel:
         """Validate content against response model and emit completion event.
 
@@ -595,7 +594,6 @@ class AzureCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage,
             )
 
             return structured_data
@@ -645,7 +643,6 @@ class AzureCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage,
             )
             return list(message.tool_calls)
 
@@ -683,7 +680,6 @@ class AzureCompletion(BaseLLM):
                 params=params,
                 from_task=from_task,
                 from_agent=from_agent,
-                usage=usage,
             )
 
         content = self._apply_stop_words(content)
@@ -695,7 +691,6 @@ class AzureCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -799,7 +794,7 @@ class AzureCompletion(BaseLLM):
         self,
         full_response: str,
         tool_calls: dict[int, dict[str, Any]],
-        usage_data: dict[str, Any] | None,
+        usage_data: dict[str, int],
         params: AzureCompletionParams,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -811,7 +806,7 @@ class AzureCompletion(BaseLLM):
         Args:
             full_response: The complete streamed response content
             tool_calls: Dictionary of tool calls accumulated during streaming
-            usage_data: Token usage data from the stream, or None if unavailable
+            usage_data: Token usage data from the stream
             params: Completion parameters containing messages
             available_functions: Available functions for tool calling
             from_task: Task that initiated the call
@@ -821,8 +816,7 @@ class AzureCompletion(BaseLLM):
         Returns:
             Final response content after processing, or structured output
         """
-        if usage_data:
-            self._track_token_usage_internal(usage_data)
+        self._track_token_usage_internal(usage_data)
 
         # Handle structured output validation
         if response_model and self.is_openai_model:
@@ -832,7 +826,6 @@ class AzureCompletion(BaseLLM):
                 params=params,
                 from_task=from_task,
                 from_agent=from_agent,
-                usage=usage_data,
             )
 
         # If there are tool_calls but no available_functions, return them
@@ -855,7 +848,6 @@ class AzureCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage_data,
             )
             return formatted_tool_calls
 
@@ -892,7 +884,6 @@ class AzureCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage_data,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -911,7 +902,7 @@ class AzureCompletion(BaseLLM):
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
 
-        usage_data: dict[str, Any] | None = None
+        usage_data = {"total_tokens": 0}
         for update in self._client.complete(**params):
             if isinstance(update, StreamingChatCompletionsUpdate):
                 if update.usage:
@@ -977,7 +968,7 @@ class AzureCompletion(BaseLLM):
         full_response = ""
         tool_calls: dict[int, dict[str, Any]] = {}
 
-        usage_data: dict[str, Any] | None = None
+        usage_data = {"total_tokens": 0}
 
         stream = await self._async_client.complete(**params)
         async for update in stream:

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

@@ -664,9 +664,8 @@ class BedrockCompletion(BaseLLM):
             )
 
             # Track token usage according to AWS response format
-            usage = response.get("usage")
-            if usage:
-                self._track_token_usage_internal(usage)
+            if "usage" in response:
+                self._track_token_usage_internal(response["usage"])
 
             stop_reason = response.get("stopReason")
             if stop_reason:
@@ -706,7 +705,6 @@ class BedrockCompletion(BaseLLM):
                                 from_task=from_task,
                                 from_agent=from_agent,
                                 messages=messages,
-                                usage=usage,
                             )
                             return result
                         except Exception as e:
@@ -729,7 +727,6 @@ class BedrockCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=messages,
-                    usage=usage,
                 )
                 return non_structured_output_tool_uses
 
@@ -809,7 +806,6 @@ class BedrockCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=messages,
-                usage=usage,
             )
 
             return self._invoke_after_llm_call_hooks(
@@ -940,7 +936,6 @@ class BedrockCompletion(BaseLLM):
         tool_use_id: str | None = None
         tool_use_index = 0
         accumulated_tool_input = ""
-        usage_data: dict[str, Any] | None = None
 
         try:
             response = self._client.converse_stream(
@@ -1050,7 +1045,6 @@ class BedrockCompletion(BaseLLM):
                                         from_task=from_task,
                                         from_agent=from_agent,
                                         messages=messages,
-                                        usage=usage_data,
                                     )
                                     return result  # type: ignore[return-value]
                                 except Exception as e:
@@ -1118,7 +1112,6 @@ class BedrockCompletion(BaseLLM):
                         metadata = event["metadata"]
                         if "usage" in metadata:
                             usage_metrics = metadata["usage"]
-                            usage_data = usage_metrics
                             self._track_token_usage_internal(usage_metrics)
                             logging.debug(f"Token usage: {usage_metrics}")
                             if "trace" in metadata:
@@ -1148,7 +1141,6 @@ class BedrockCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=messages,
-            usage=usage_data,
         )
 
         return full_response
@@ -1260,9 +1252,8 @@ class BedrockCompletion(BaseLLM):
                 **body,
             )
 
-            usage = response.get("usage")
-            if usage:
-                self._track_token_usage_internal(usage)
+            if "usage" in response:
+                self._track_token_usage_internal(response["usage"])
 
             stop_reason = response.get("stopReason")
             if stop_reason:
@@ -1301,7 +1292,6 @@ class BedrockCompletion(BaseLLM):
                                 from_task=from_task,
                                 from_agent=from_agent,
                                 messages=messages,
-                                usage=usage,
                             )
                             return result
                         except Exception as e:
@@ -1324,7 +1314,6 @@ class BedrockCompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=messages,
-                    usage=usage,
                 )
                 return non_structured_output_tool_uses
 
@@ -1399,7 +1388,6 @@ class BedrockCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=messages,
-                usage=usage,
             )
 
             return text_content
@@ -1520,7 +1508,6 @@ class BedrockCompletion(BaseLLM):
         tool_use_id: str | None = None
         tool_use_index = 0
         accumulated_tool_input = ""
-        usage_data: dict[str, Any] | None = None
 
         try:
             async_client = await self._ensure_async_client()
@@ -1632,7 +1619,6 @@ class BedrockCompletion(BaseLLM):
                                         from_task=from_task,
                                         from_agent=from_agent,
                                         messages=messages,
-                                        usage=usage_data,
                                     )
                                     return result  # type: ignore[return-value]
                                 except Exception as e:
@@ -1705,7 +1691,6 @@ class BedrockCompletion(BaseLLM):
                         metadata = event["metadata"]
                         if "usage" in metadata:
                             usage_metrics = metadata["usage"]
-                            usage_data = usage_metrics
                             self._track_token_usage_internal(usage_metrics)
                             logging.debug(f"Token usage: {usage_metrics}")
                         if "trace" in metadata:
@@ -1735,7 +1720,6 @@ class BedrockCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=messages,
-            usage=usage_data,
         )
 
         return self._invoke_after_llm_call_hooks(

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

@@ -665,7 +665,6 @@ class GeminiCompletion(BaseLLM):
         messages_for_event: list[LLMMessage],
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> BaseModel:
         """Validate content against response model and emit completion event.
 
@@ -691,7 +690,6 @@ class GeminiCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=messages_for_event,
-                usage=usage,
             )
 
             return structured_data
@@ -707,7 +705,6 @@ class GeminiCompletion(BaseLLM):
         response_model: type[BaseModel] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> str | BaseModel:
         """Finalize completion response with validation and event emission.
 
@@ -731,7 +728,6 @@ class GeminiCompletion(BaseLLM):
                 messages_for_event=messages_for_event,
                 from_task=from_task,
                 from_agent=from_agent,
-                usage=usage,
             )
 
         self._emit_call_completed_event(
@@ -740,7 +736,6 @@ class GeminiCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=messages_for_event,
-            usage=usage,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -754,7 +749,6 @@ class GeminiCompletion(BaseLLM):
         contents: list[types.Content],
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> BaseModel:
         """Validate and emit event for structured_output tool call.
 
@@ -779,7 +773,6 @@ class GeminiCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=self._convert_contents_to_dict(contents),
-                usage=usage,
             )
             return validated_data
         except Exception as e:
@@ -798,7 +791,6 @@ class GeminiCompletion(BaseLLM):
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-        usage: dict[str, Any] | None = None,
     ) -> str | Any:
         """Process response, execute function calls, and finalize completion.
 
@@ -839,7 +831,6 @@ class GeminiCompletion(BaseLLM):
                                 contents=contents,
                                 from_task=from_task,
                                 from_agent=from_agent,
-                                usage=usage,
                             )
 
                 # Filter out structured_output from function calls returned to executor
@@ -861,7 +852,6 @@ class GeminiCompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=self._convert_contents_to_dict(contents),
-                        usage=usage,
                     )
                     return non_structured_output_parts
 
@@ -903,7 +893,6 @@ class GeminiCompletion(BaseLLM):
             response_model=effective_response_model,
             from_task=from_task,
             from_agent=from_agent,
-            usage=usage,
         )
 
     def _process_stream_chunk(
@@ -911,10 +900,10 @@ class GeminiCompletion(BaseLLM):
         chunk: GenerateContentResponse,
         full_response: str,
         function_calls: dict[int, dict[str, Any]],
-        usage_data: dict[str, int] | None,
+        usage_data: dict[str, int],
         from_task: Any | None = None,
         from_agent: Any | None = None,
-    ) -> tuple[str, dict[int, dict[str, Any]], dict[str, int] | None]:
+    ) -> tuple[str, dict[int, dict[str, Any]], dict[str, int]]:
         """Process a single streaming chunk.
 
         Args:
@@ -990,7 +979,7 @@ class GeminiCompletion(BaseLLM):
         self,
         full_response: str,
         function_calls: dict[int, dict[str, Any]],
-        usage_data: dict[str, int] | None,
+        usage_data: dict[str, int],
         contents: list[types.Content],
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1002,7 +991,7 @@ class GeminiCompletion(BaseLLM):
         Args:
             full_response: The complete streamed response content
             function_calls: Dictionary of function calls accumulated during streaming
-            usage_data: Token usage data from the stream, or None if unavailable
+            usage_data: Token usage data from the stream
             contents: Original contents for event conversion
             available_functions: Available functions for function calling
             from_task: Task that initiated the call
@@ -1012,8 +1001,7 @@ class GeminiCompletion(BaseLLM):
         Returns:
             Final response content after processing
         """
-        if usage_data:
-            self._track_token_usage_internal(usage_data)
+        self._track_token_usage_internal(usage_data)
 
         if response_model and function_calls:
             for call_data in function_calls.values():
@@ -1025,7 +1013,6 @@ class GeminiCompletion(BaseLLM):
                         contents=contents,
                         from_task=from_task,
                         from_agent=from_agent,
-                        usage=usage_data,
                     )
 
         non_structured_output_calls = {
@@ -1054,7 +1041,6 @@ class GeminiCompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=self._convert_contents_to_dict(contents),
-                usage=usage_data,
             )
             return formatted_function_calls
 
@@ -1095,7 +1081,6 @@ class GeminiCompletion(BaseLLM):
             response_model=effective_response_model,
             from_task=from_task,
             from_agent=from_agent,
-            usage=usage_data,
         )
 
     def _handle_completion(
@@ -1133,7 +1118,6 @@ class GeminiCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             response_model=response_model,
-            usage=usage,
         )
 
     def _handle_streaming_completion(
@@ -1148,7 +1132,7 @@ class GeminiCompletion(BaseLLM):
         """Handle streaming content generation."""
         full_response = ""
         function_calls: dict[int, dict[str, Any]] = {}
-        usage_data: dict[str, int] | None = None
+        usage_data = {"total_tokens": 0}
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
@@ -1212,7 +1196,6 @@ class GeminiCompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             response_model=response_model,
-            usage=usage,
         )
 
     async def _ahandle_streaming_completion(
@@ -1227,7 +1210,7 @@ class GeminiCompletion(BaseLLM):
         """Handle async streaming content generation."""
         full_response = ""
         function_calls: dict[int, dict[str, Any]] = {}
-        usage_data: dict[str, int] | None = None
+        usage_data = {"total_tokens": 0}
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents

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

@@ -809,7 +809,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
 
                 return parsed_result
@@ -822,7 +821,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
                 return function_calls
 
@@ -860,7 +858,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params.get("input", []),
-                        usage=usage,
                     )
                     return structured_result
                 except ValueError as e:
@@ -874,7 +871,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params.get("input", []),
-                usage=usage,
             )
 
             content = self._invoke_after_llm_call_hooks(
@@ -945,7 +941,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
 
                 return parsed_result
@@ -958,7 +953,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
                 return function_calls
 
@@ -996,7 +990,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params.get("input", []),
-                        usage=usage,
                     )
                     return structured_result
                 except ValueError as e:
@@ -1010,7 +1003,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params.get("input", []),
-                usage=usage,
             )
 
         except NotFoundError as e:
@@ -1053,7 +1045,6 @@ class OpenAICompletion(BaseLLM):
         full_response = ""
         function_calls: list[dict[str, Any]] = []
         final_response: Response | None = None
-        usage: dict[str, Any] | None = None
 
         stream = self._client.responses.create(**params)
         response_id_stream = None
@@ -1111,7 +1102,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params.get("input", []),
-                usage=usage,
             )
 
             return parsed_result
@@ -1148,7 +1138,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
                 return structured_result
             except ValueError as e:
@@ -1162,7 +1151,6 @@ class OpenAICompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params.get("input", []),
-            usage=usage,
         )
 
         return self._invoke_after_llm_call_hooks(
@@ -1181,7 +1169,6 @@ class OpenAICompletion(BaseLLM):
         full_response = ""
         function_calls: list[dict[str, Any]] = []
         final_response: Response | None = None
-        usage: dict[str, Any] | None = None
 
         stream = await self._async_client.responses.create(**params)
         response_id_stream = None
@@ -1239,7 +1226,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params.get("input", []),
-                usage=usage,
             )
 
             return parsed_result
@@ -1276,7 +1262,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params.get("input", []),
-                    usage=usage,
                 )
                 return structured_result
             except ValueError as e:
@@ -1290,7 +1275,6 @@ class OpenAICompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params.get("input", []),
-            usage=usage,
         )
 
         return full_response
@@ -1596,7 +1580,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return parsed_object
 
@@ -1618,7 +1601,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage,
                 )
                 return list(message.tool_calls)
 
@@ -1657,7 +1639,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return structured_result
                 except ValueError as e:
@@ -1671,7 +1652,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage,
             )
 
             if usage.get("total_tokens", 0) > 0:
@@ -1713,7 +1693,7 @@ class OpenAICompletion(BaseLLM):
         self,
         full_response: str,
         tool_calls: dict[int, dict[str, Any]],
-        usage_data: dict[str, Any] | None,
+        usage_data: dict[str, int],
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -1724,7 +1704,7 @@ class OpenAICompletion(BaseLLM):
         Args:
             full_response: The accumulated text response from the stream.
             tool_calls: Accumulated tool calls from the stream, keyed by index.
-            usage_data: Token usage data from the stream, or None if unavailable.
+            usage_data: Token usage data from the stream.
             params: The completion parameters containing messages.
             available_functions: Available functions for tool calling.
             from_task: Task that initiated the call.
@@ -1735,8 +1715,7 @@ class OpenAICompletion(BaseLLM):
             tool execution result when available_functions is provided,
             or the text response string.
         """
-        if usage_data:
-            self._track_token_usage_internal(usage_data)
+        self._track_token_usage_internal(usage_data)
 
         if tool_calls and not available_functions:
             tool_calls_list = [
@@ -1757,7 +1736,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage_data,
             )
             return tool_calls_list
 
@@ -1800,7 +1778,6 @@ class OpenAICompletion(BaseLLM):
             from_task=from_task,
             from_agent=from_agent,
             messages=params["messages"],
-            usage=usage_data,
         )
 
         return full_response
@@ -1854,7 +1831,6 @@ class OpenAICompletion(BaseLLM):
                                 from_task=from_task,
                                 from_agent=from_agent,
                                 messages=params["messages"],
-                                usage=usage,
                             )
                             return parsed_result
 
@@ -1865,7 +1841,7 @@ class OpenAICompletion(BaseLLM):
             self._client.chat.completions.create(**params)
         )
 
-        usage_data: dict[str, Any] | None = None
+        usage_data = {"total_tokens": 0}
 
         for completion_chunk in completion_stream:
             response_id_stream = (
@@ -1979,7 +1955,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return parsed_object
 
@@ -2003,7 +1978,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage,
                 )
                 return list(message.tool_calls)
 
@@ -2042,7 +2016,6 @@ class OpenAICompletion(BaseLLM):
                         from_task=from_task,
                         from_agent=from_agent,
                         messages=params["messages"],
-                        usage=usage,
                     )
                     return structured_result
                 except ValueError as e:
@@ -2056,7 +2029,6 @@ class OpenAICompletion(BaseLLM):
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
-                usage=usage,
             )
 
             if usage.get("total_tokens", 0) > 0:
@@ -2107,7 +2079,7 @@ class OpenAICompletion(BaseLLM):
             ] = await self._async_client.chat.completions.create(**params)
 
             accumulated_content = ""
-            usage_data: dict[str, Any] | None = None
+            usage_data = {"total_tokens": 0}
             async for chunk in completion_stream:
                 response_id_stream = chunk.id if hasattr(chunk, "id") else None
 
@@ -2130,8 +2102,7 @@ class OpenAICompletion(BaseLLM):
                         response_id=response_id_stream,
                     )
 
-            if usage_data:
-                self._track_token_usage_internal(usage_data)
+            self._track_token_usage_internal(usage_data)
 
             try:
                 parsed_object = response_model.model_validate_json(accumulated_content)
@@ -2142,7 +2113,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage_data,
                 )
 
                 return parsed_object
@@ -2154,7 +2124,6 @@ class OpenAICompletion(BaseLLM):
                     from_task=from_task,
                     from_agent=from_agent,
                     messages=params["messages"],
-                    usage=usage_data,
                 )
                 return accumulated_content
 
@@ -2162,7 +2131,7 @@ class OpenAICompletion(BaseLLM):
             ChatCompletionChunk
         ] = await self._async_client.chat.completions.create(**params)
 
-        usage_data = None
+        usage_data = {"total_tokens": 0}
 
         async for chunk in stream:
             response_id_stream = chunk.id if hasattr(chunk, "id") else None

lib/crewai/src/crewai/memory/encoding_flow.py

@@ -98,7 +98,7 @@ class EncodingFlow(Flow[EncodingState]):
 
     _skip_auto_memory: bool = True
 
-    initial_state: type[EncodingState] = EncodingState
+    initial_state = EncodingState
 
     def __init__(
         self,

lib/crewai/src/crewai/memory/recall_flow.py

@@ -65,7 +65,7 @@ class RecallFlow(Flow[RecallState]):
 
     _skip_auto_memory: bool = True
 
-    initial_state: type[RecallState] = RecallState
+    initial_state = RecallState
 
     def __init__(
         self,

lib/crewai/src/crewai/memory/unified_memory.py

@@ -148,36 +148,6 @@ class Memory(BaseModel):
     _pending_saves: list[Future[Any]] = PrivateAttr(default_factory=list)
     _pending_lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
 
-    def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Memory:
-        """Deepcopy that handles unpickleable private attrs (ThreadPoolExecutor, Lock)."""
-        import copy as _copy
-
-        cls = type(self)
-        new = cls.__new__(cls)
-        if memo is None:
-            memo = {}
-        memo[id(self)] = new
-        object.__setattr__(new, "__dict__", _copy.deepcopy(self.__dict__, memo))
-        object.__setattr__(
-            new, "__pydantic_fields_set__", _copy.copy(self.__pydantic_fields_set__)
-        )
-        object.__setattr__(
-            new, "__pydantic_extra__", _copy.deepcopy(self.__pydantic_extra__, memo)
-        )
-        # Private attrs: create fresh pool/lock instead of deepcopying
-        private = {}
-        for k, v in (self.__pydantic_private__ or {}).items():
-            if isinstance(v, (ThreadPoolExecutor, threading.Lock)):
-                attr = self.__private_attributes__[k]
-                private[k] = attr.get_default()
-            else:
-                try:
-                    private[k] = _copy.deepcopy(v, memo)
-                except Exception:
-                    private[k] = v
-        object.__setattr__(new, "__pydantic_private__", private)
-        return new
-
     def model_post_init(self, __context: Any) -> None:
         """Initialize runtime state from field values."""
         self._config = MemoryConfig(

lib/crewai/src/crewai/utilities/prompts.py

@@ -2,10 +2,9 @@
 
 from __future__ import annotations
 
-from typing import Annotated, Any, Literal
+from typing import Annotated, Any, Literal, TypedDict
 
 from pydantic import BaseModel, Field
-from typing_extensions import TypedDict
 
 from crewai.utilities.i18n import I18N, get_i18n
 

lib/crewai/tests/agents/test_agent_executor.py

@@ -4,55 +4,13 @@ Tests the Flow-based agent executor implementation including state management,
 flow methods, routing logic, and error handling.
 """
 
-from __future__ import annotations
-
 import asyncio
 import time
-from typing import Any
 from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
-from crewai.agents.tools_handler import ToolsHandler as _ToolsHandler
 from crewai.agents.step_executor import StepExecutor
-
-
-def _build_executor(**kwargs: Any) -> AgentExecutor:
-    """Create an AgentExecutor without validation — for unit tests.
-
-    Uses model_construct to skip Pydantic validators so plain Mock()
-    objects are accepted for typed fields like llm, agent, crew, task.
-    """
-    executor = AgentExecutor.model_construct(**kwargs)
-    executor._state = AgentExecutorState()
-    executor._methods = {}
-    executor._method_outputs = []
-    executor._completed_methods = set()
-    executor._fired_or_listeners = set()
-    executor._pending_and_listeners = {}
-    executor._method_execution_counts = {}
-    executor._method_call_counts = {}
-    executor._event_futures = []
-    executor._human_feedback_method_outputs = {}
-    executor._input_history = []
-    executor._is_execution_resuming = False
-    import threading
-    executor._state_lock = threading.Lock()
-    executor._or_listeners_lock = threading.Lock()
-    executor._execution_lock = threading.Lock()
-    executor._finalize_lock = threading.Lock()
-    executor._finalize_called = False
-    executor._is_executing = False
-    executor._has_been_invoked = False
-    executor._last_parser_error = None
-    executor._last_context_error = None
-    executor._step_executor = None
-    executor._planner_observer = None
-    from crewai.utilities.printer import Printer
-    executor._printer = Printer()
-    from crewai.utilities.i18n import get_i18n
-    executor._i18n = kwargs.get("i18n") or get_i18n()
-    return executor
 from crewai.agents.planner_observer import PlannerObserver
 from crewai.experimental.agent_executor import (
     AgentExecutorState,
@@ -117,7 +75,6 @@ class TestAgentExecutor:
         """Create mock dependencies for executor."""
         llm = Mock()
         llm.supports_stop_words.return_value = True
-        llm.stop = []
 
         task = Mock()
         task.description = "Test task"
@@ -137,7 +94,7 @@ class TestAgentExecutor:
         prompt = {"prompt": "Test prompt with {input}, {tool_names}, {tools}"}
 
         tools = []
-        tools_handler = Mock(spec=_ToolsHandler)
+        tools_handler = Mock()
 
         return {
             "llm": llm,
@@ -155,7 +112,7 @@ class TestAgentExecutor:
 
     def test_executor_initialization(self, mock_dependencies):
         """Test AgentExecutor initialization."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         assert executor.llm == mock_dependencies["llm"]
         assert executor.task == mock_dependencies["task"]
@@ -169,7 +126,7 @@ class TestAgentExecutor:
         with patch.object(
             AgentExecutor, "_show_start_logs"
         ) as mock_show_start:
-            executor = _build_executor(**mock_dependencies)
+            executor = AgentExecutor(**mock_dependencies)
             result = executor.initialize_reasoning()
 
             assert result == "initialized"
@@ -177,7 +134,7 @@ class TestAgentExecutor:
 
     def test_check_max_iterations_not_reached(self, mock_dependencies):
         """Test routing when iterations < max."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.iterations = 5
 
         result = executor.check_max_iterations()
@@ -185,7 +142,7 @@ class TestAgentExecutor:
 
     def test_check_max_iterations_reached(self, mock_dependencies):
         """Test routing when iterations >= max."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.iterations = 10
 
         result = executor.check_max_iterations()
@@ -193,7 +150,7 @@ class TestAgentExecutor:
 
     def test_route_by_answer_type_action(self, mock_dependencies):
         """Test routing for AgentAction."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="search", tool_input="query", text="action text"
         )
@@ -203,7 +160,7 @@ class TestAgentExecutor:
 
     def test_route_by_answer_type_finish(self, mock_dependencies):
         """Test routing for AgentFinish."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.current_answer = AgentFinish(
             thought="final thoughts", output="Final answer", text="complete"
         )
@@ -213,7 +170,7 @@ class TestAgentExecutor:
 
     def test_continue_iteration(self, mock_dependencies):
         """Test iteration continuation."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         result = executor.continue_iteration()
 
@@ -222,7 +179,7 @@ class TestAgentExecutor:
     def test_finalize_success(self, mock_dependencies):
         """Test finalize with valid AgentFinish."""
         with patch.object(AgentExecutor, "_show_logs") as mock_show_logs:
-            executor = _build_executor(**mock_dependencies)
+            executor = AgentExecutor(**mock_dependencies)
             executor.state.current_answer = AgentFinish(
                 thought="final thinking", output="Done", text="complete"
             )
@@ -235,7 +192,7 @@ class TestAgentExecutor:
 
     def test_finalize_failure(self, mock_dependencies):
         """Test finalize skips when given AgentAction instead of AgentFinish."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="search", tool_input="query", text="action text"
         )
@@ -251,7 +208,7 @@ class TestAgentExecutor:
     ):
         """Finalize should skip synthesis when last todo is already a complete answer."""
         with patch.object(AgentExecutor, "_show_logs") as mock_show_logs:
-            executor = _build_executor(**mock_dependencies)
+            executor = AgentExecutor(**mock_dependencies)
             executor.state.todos.items = [
                 TodoItem(
                     step_number=1,
@@ -295,7 +252,7 @@ class TestAgentExecutor:
     ):
         """Finalize should still synthesize when response_model is configured."""
         with patch.object(AgentExecutor, "_show_logs"):
-            executor = _build_executor(**mock_dependencies)
+            executor = AgentExecutor(**mock_dependencies)
             executor.response_model = Mock()
             executor.state.todos.items = [
                 TodoItem(
@@ -330,7 +287,7 @@ class TestAgentExecutor:
 
     def test_format_prompt(self, mock_dependencies):
         """Test prompt formatting."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         inputs = {"input": "test input", "tool_names": "tool1, tool2", "tools": "desc"}
 
         result = executor._format_prompt("Prompt {input} {tool_names} {tools}", inputs)
@@ -341,18 +298,18 @@ class TestAgentExecutor:
 
     def test_is_training_mode_false(self, mock_dependencies):
         """Test training mode detection when not in training."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         assert executor._is_training_mode() is False
 
     def test_is_training_mode_true(self, mock_dependencies):
         """Test training mode detection when in training."""
         mock_dependencies["crew"]._train = True
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         assert executor._is_training_mode() is True
 
     def test_append_message_to_state(self, mock_dependencies):
         """Test message appending to state."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         initial_count = len(executor.state.messages)
 
         executor._append_message_to_state("test message")
@@ -365,7 +322,7 @@ class TestAgentExecutor:
         callback = Mock()
         mock_dependencies["step_callback"] = callback
 
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         answer = AgentFinish(thought="thinking", output="test", text="final")
 
         executor._invoke_step_callback(answer)
@@ -375,7 +332,7 @@ class TestAgentExecutor:
     def test_invoke_step_callback_none(self, mock_dependencies):
         """Test step callback when none provided."""
         mock_dependencies["step_callback"] = None
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         # Should not raise error
         executor._invoke_step_callback(
@@ -389,7 +346,7 @@ class TestAgentExecutor:
         """Test async step callback scheduling when already in an event loop."""
         callback = AsyncMock()
         mock_dependencies["step_callback"] = callback
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         answer = AgentFinish(thought="thinking", output="test", text="final")
         with patch("crewai.experimental.agent_executor.asyncio.run") as mock_run:
@@ -407,7 +364,6 @@ class TestStepExecutorCriticalFixes:
     def mock_dependencies(self):
         """Create mock dependencies for AgentExecutor tests in this class."""
         llm = Mock()
-        llm.stop = []
         llm.supports_stop_words.return_value = True
 
         task = Mock()
@@ -437,7 +393,6 @@ class TestStepExecutorCriticalFixes:
     @pytest.fixture
     def step_executor(self):
         llm = Mock()
-        llm.stop = []
         llm.supports_stop_words.return_value = True
 
         agent = Mock()
@@ -530,7 +485,7 @@ class TestStepExecutorCriticalFixes:
 
         mock_handle_exception.return_value = None
 
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor._last_parser_error = OutputParserError("test error")
         initial_iterations = executor.state.iterations
 
@@ -545,7 +500,7 @@ class TestStepExecutorCriticalFixes:
         self, mock_handle_context, mock_dependencies
     ):
         """Test recovery from context length error."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor._last_context_error = Exception("context too long")
         initial_iterations = executor.state.iterations
 
@@ -558,16 +513,16 @@ class TestStepExecutorCriticalFixes:
     def test_use_stop_words_property(self, mock_dependencies):
         """Test use_stop_words property."""
         mock_dependencies["llm"].supports_stop_words.return_value = True
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         assert executor.use_stop_words is True
 
         mock_dependencies["llm"].supports_stop_words.return_value = False
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         assert executor.use_stop_words is False
 
     def test_compatibility_properties(self, mock_dependencies):
         """Test compatibility properties for mixin."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.messages = [{"role": "user", "content": "test"}]
         executor.state.iterations = 5
 
@@ -583,7 +538,6 @@ class TestFlowErrorHandling:
     def mock_dependencies(self):
         """Create mock dependencies."""
         llm = Mock()
-        llm.stop = []
         llm.supports_stop_words.return_value = True
 
         task = Mock()
@@ -621,7 +575,7 @@ class TestFlowErrorHandling:
         mock_enforce_rpm.return_value = None
         mock_get_llm.side_effect = OutputParserError("parse failed")
 
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         result = executor.call_llm_and_parse()
 
         assert result == "parser_error"
@@ -642,7 +596,7 @@ class TestFlowErrorHandling:
         mock_get_llm.side_effect = Exception("context length")
         mock_is_context_exceeded.return_value = True
 
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         result = executor.call_llm_and_parse()
 
         assert result == "context_error"
@@ -656,7 +610,6 @@ class TestFlowInvoke:
     def mock_dependencies(self):
         """Create mock dependencies."""
         llm = Mock()
-        llm.stop = []
         task = Mock()
         task.description = "Test"
         task.human_input = False
@@ -693,7 +646,7 @@ class TestFlowInvoke:
         mock_dependencies,
     ):
         """Test successful invoke without human feedback."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         # Mock kickoff to set the final answer in state
         def mock_kickoff_side_effect():
@@ -713,7 +666,7 @@ class TestFlowInvoke:
     @patch.object(AgentExecutor, "kickoff")
     def test_invoke_failure_no_agent_finish(self, mock_kickoff, mock_dependencies):
         """Test invoke fails without AgentFinish."""
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         executor.state.current_answer = AgentAction(
             thought="thinking", tool="test", tool_input="test", text="action text"
         )
@@ -736,7 +689,7 @@ class TestFlowInvoke:
             "system": "System: {input}",
             "user": "User: {input} {tool_names} {tools}",
         }
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         def mock_kickoff_side_effect():
             executor.state.current_answer = AgentFinish(
@@ -760,7 +713,6 @@ class TestNativeToolExecution:
     @pytest.fixture
     def mock_dependencies(self):
         llm = Mock()
-        llm.stop = []
         llm.supports_stop_words.return_value = True
 
         task = Mock()
@@ -782,7 +734,7 @@ class TestNativeToolExecution:
 
         prompt = {"prompt": "Test {input} {tool_names} {tools}"}
 
-        tools_handler = Mock(spec=_ToolsHandler)
+        tools_handler = Mock()
         tools_handler.cache = None
 
         return {
@@ -802,7 +754,7 @@ class TestNativeToolExecution:
     def test_execute_native_tool_runs_parallel_for_multiple_calls(
         self, mock_dependencies
     ):
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         def slow_one() -> str:
             time.sleep(0.2)
@@ -838,7 +790,7 @@ class TestNativeToolExecution:
     def test_execute_native_tool_falls_back_to_sequential_for_result_as_answer(
         self, mock_dependencies
     ):
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
 
         def slow_one() -> str:
             time.sleep(0.2)
@@ -880,7 +832,7 @@ class TestNativeToolExecution:
     def test_execute_native_tool_result_as_answer_short_circuits_remaining_calls(
         self, mock_dependencies
     ):
-        executor = _build_executor(**mock_dependencies)
+        executor = AgentExecutor(**mock_dependencies)
         call_counts = {"slow_one": 0, "slow_two": 0}
 
         def slow_one() -> str:

lib/crewai/tests/cassettes/utilities/test_llm_completed_event_includes_usage.yaml

@@ -1,108 +0,0 @@
-interactions:
-- request:
-    body: '{"messages":[{"role":"user","content":"Say hello"}],"model":"gpt-4o-mini"}'
-    headers:
-      User-Agent:
-      - X-USER-AGENT-XXX
-      accept:
-      - application/json
-      accept-encoding:
-      - ACCEPT-ENCODING-XXX
-      authorization:
-      - AUTHORIZATION-XXX
-      connection:
-      - keep-alive
-      content-length:
-      - '74'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      x-stainless-arch:
-      - X-STAINLESS-ARCH-XXX
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - X-STAINLESS-OS-XXX
-      x-stainless-package-version:
-      - 1.83.0
-      x-stainless-read-timeout:
-      - X-STAINLESS-READ-TIMEOUT-XXX
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.13.2
-    method: POST
-    uri: https://api.openai.com/v1/chat/completions
-  response:
-    body:
-      string: "{\n  \"id\": \"chatcmpl-DPS8YQSwQ3pZKZztIoIe1eYodMqh2\",\n  \"object\":
-        \"chat.completion\",\n  \"created\": 1774958730,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n
-        \ \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\":
-        \"assistant\",\n        \"content\": \"Hello! How can I assist you today?\",\n
-        \       \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\":
-        null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\":
-        9,\n    \"completion_tokens\": 9,\n    \"total_tokens\": 18,\n    \"prompt_tokens_details\":
-        {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\":
-        {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\":
-        0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\":
-        \"default\",\n  \"system_fingerprint\": \"fp_709f182cb4\"\n}\n"
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-Ray:
-      - 9e4f38fc5d9d82e8-GIG
-      Connection:
-      - keep-alive
-      Content-Type:
-      - application/json
-      Date:
-      - Tue, 31 Mar 2026 12:05:30 GMT
-      Server:
-      - cloudflare
-      Strict-Transport-Security:
-      - STS-XXX
-      Transfer-Encoding:
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-      X-Content-Type-Options:
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-      access-control-expose-headers:
-      - ACCESS-CONTROL-XXX
-      alt-svc:
-      - h3=":443"; ma=86400
-      content-length:
-      - '839'
-      openai-organization:
-      - OPENAI-ORG-XXX
-      openai-processing-ms:
-      - '680'
-      openai-project:
-      - OPENAI-PROJECT-XXX
-      openai-version:
-      - '2020-10-01'
-      set-cookie:
-      - SET-COOKIE-XXX
-      x-openai-proxy-wasm:
-      - v0.1
-      x-ratelimit-limit-requests:
-      - X-RATELIMIT-LIMIT-REQUESTS-XXX
-      x-ratelimit-limit-tokens:
-      - X-RATELIMIT-LIMIT-TOKENS-XXX
-      x-ratelimit-remaining-requests:
-      - X-RATELIMIT-REMAINING-REQUESTS-XXX
-      x-ratelimit-remaining-tokens:
-      - X-RATELIMIT-REMAINING-TOKENS-XXX
-      x-ratelimit-reset-requests:
-      - X-RATELIMIT-RESET-REQUESTS-XXX
-      x-ratelimit-reset-tokens:
-      - X-RATELIMIT-RESET-TOKENS-XXX
-      x-request-id:
-      - X-REQUEST-ID-XXX
-    status:
-      code: 200
-      message: OK
-version: 1

lib/crewai/tests/events/test_llm_usage_event.py

@@ -1,176 +0,0 @@
-from typing import Any
-from unittest.mock import patch
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.events.event_bus import CrewAIEventsBus
-from crewai.events.types.llm_events import LLMCallCompletedEvent, LLMCallType
-from crewai.llm import LLM
-from crewai.llms.base_llm import BaseLLM
-
-
-class TestLLMCallCompletedEventUsageField:
-    def test_accepts_usage_dict(self):
-        event = LLMCallCompletedEvent(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            call_id="test-id",
-            usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
-        )
-        assert event.usage == {
-            "prompt_tokens": 10,
-            "completion_tokens": 20,
-            "total_tokens": 30,
-        }
-
-    def test_usage_defaults_to_none(self):
-        event = LLMCallCompletedEvent(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            call_id="test-id",
-        )
-        assert event.usage is None
-
-    def test_accepts_none_usage(self):
-        event = LLMCallCompletedEvent(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            call_id="test-id",
-            usage=None,
-        )
-        assert event.usage is None
-
-    def test_accepts_nested_usage_dict(self):
-        usage = {
-            "prompt_tokens": 100,
-            "completion_tokens": 200,
-            "total_tokens": 300,
-            "prompt_tokens_details": {"cached_tokens": 50},
-        }
-        event = LLMCallCompletedEvent(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            call_id="test-id",
-            usage=usage,
-        )
-        assert event.usage["prompt_tokens_details"]["cached_tokens"] == 50
-
-
-class TestUsageToDict:
-    def test_none_returns_none(self):
-        assert LLM._usage_to_dict(None) is None
-
-    def test_dict_passes_through(self):
-        usage = {"prompt_tokens": 10, "total_tokens": 30}
-        assert LLM._usage_to_dict(usage) is usage
-
-    def test_pydantic_model_uses_model_dump(self):
-        class Usage(BaseModel):
-            prompt_tokens: int = 10
-            completion_tokens: int = 20
-            total_tokens: int = 30
-
-        result = LLM._usage_to_dict(Usage())
-        assert result == {
-            "prompt_tokens": 10,
-            "completion_tokens": 20,
-            "total_tokens": 30,
-        }
-
-    def test_object_with_dict_attr(self):
-        class UsageObj:
-            def __init__(self):
-                self.prompt_tokens = 5
-                self.completion_tokens = 15
-                self.total_tokens = 20
-
-        result = LLM._usage_to_dict(UsageObj())
-        assert result == {
-            "prompt_tokens": 5,
-            "completion_tokens": 15,
-            "total_tokens": 20,
-        }
-
-    def test_object_with_dict_excludes_private_attrs(self):
-        class UsageObj:
-            def __init__(self):
-                self.total_tokens = 42
-                self._internal = "hidden"
-
-        result = LLM._usage_to_dict(UsageObj())
-        assert result == {"total_tokens": 42}
-        assert "_internal" not in result
-
-    def test_unsupported_type_returns_none(self):
-        assert LLM._usage_to_dict(42) is None
-        assert LLM._usage_to_dict("string") is None
-
-
-class _StubLLM(BaseLLM):
-    """Minimal concrete BaseLLM for testing event emission."""
-
-    model: str = "test-model"
-
-    def call(self, *args: Any, **kwargs: Any) -> str:
-        return ""
-
-    async def acall(self, *args: Any, **kwargs: Any) -> str:
-        return ""
-
-    def supports_function_calling(self) -> bool:
-        return False
-
-    def supports_stop_words(self) -> bool:
-        return True
-
-
-class TestEmitCallCompletedEventPassesUsage:
-    @pytest.fixture
-    def mock_emit(self):
-        with patch.object(CrewAIEventsBus, "emit") as mock:
-            yield mock
-
-    @pytest.fixture
-    def llm(self):
-        return _StubLLM(model="test-model")
-
-    def test_usage_is_passed_to_event(self, mock_emit, llm):
-        usage_data = {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}
-
-        llm._emit_call_completed_event(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            messages="test prompt",
-            usage=usage_data,
-        )
-
-        mock_emit.assert_called_once()
-        event = mock_emit.call_args[1]["event"]
-        assert isinstance(event, LLMCallCompletedEvent)
-        assert event.usage == usage_data
-
-    def test_none_usage_is_passed_to_event(self, mock_emit, llm):
-        llm._emit_call_completed_event(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            messages="test prompt",
-            usage=None,
-        )
-
-        mock_emit.assert_called_once()
-        event = mock_emit.call_args[1]["event"]
-        assert isinstance(event, LLMCallCompletedEvent)
-        assert event.usage is None
-
-    def test_usage_omitted_defaults_to_none(self, mock_emit, llm):
-        llm._emit_call_completed_event(
-            response="hello",
-            call_type=LLMCallType.LLM_CALL,
-            messages="test prompt",
-        )
-
-        mock_emit.assert_called_once()
-        event = mock_emit.call_args[1]["event"]
-        assert isinstance(event, LLMCallCompletedEvent)
-        assert event.usage is None

lib/crewai/tests/test_async_human_feedback.py

@@ -873,7 +873,7 @@ class TestAutoPersistence:
 
         # Create flow WITHOUT persistence
         flow = TestFlow()
-        assert flow.persistence is None  # No persistence initially
+        assert flow._persistence is None  # No persistence initially
 
         # kickoff should auto-create persistence when HumanFeedbackPending is raised
         result = flow.kickoff()
@@ -882,11 +882,11 @@ class TestAutoPersistence:
         assert isinstance(result, HumanFeedbackPending)
 
         # Persistence should have been auto-created
-        assert flow.persistence is not None
+        assert flow._persistence is not None
 
         # The pending feedback should be saved
         flow_id = result.context.flow_id
-        loaded = flow.persistence.load_pending_feedback(flow_id)
+        loaded = flow._persistence.load_pending_feedback(flow_id)
         assert loaded is not None
 
 

lib/crewai/tests/test_llm.py

@@ -752,7 +752,11 @@ def test_litellm_retry_catches_litellm_unsupported_params_error(caplog):
             raise litellm_error
         return MagicMock(
             choices=[MagicMock(message=MagicMock(content="Paris", tool_calls=None))],
-            usage={"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+            usage=MagicMock(
+                prompt_tokens=10,
+                completion_tokens=5,
+                total_tokens=15,
+            ),
         )
 
     with patch("litellm.completion", side_effect=mock_completion):
@@ -783,7 +787,11 @@ def test_litellm_retry_catches_openai_api_stop_error(caplog):
             raise api_error
         return MagicMock(
             choices=[MagicMock(message=MagicMock(content="Paris", tool_calls=None))],
-            usage={"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
+            usage=MagicMock(
+                prompt_tokens=10,
+                completion_tokens=5,
+                total_tokens=15,
+            ),
         )
 
     with patch("litellm.completion", side_effect=mock_completion):

lib/crewai/tests/tracing/test_tracing.py

@@ -793,10 +793,6 @@ class TestTraceListenerSetup:
                 "crewai.events.listeners.tracing.utils._is_test_environment",
                 return_value=False,
             ),
-            patch(
-                "crewai.events.listeners.tracing.utils._is_interactive_terminal",
-                return_value=True,
-            ),
             patch("threading.Thread") as mock_thread,
         ):
             from crewai.events.listeners.tracing.utils import (

lib/crewai/tests/utilities/test_events.py

@@ -879,35 +879,6 @@ def test_llm_emits_call_started_event():
     assert started_events[0].task_id is None
 
 
-@pytest.mark.vcr()
-def test_llm_completed_event_includes_usage():
-    completed_events: list[LLMCallCompletedEvent] = []
-    condition = threading.Condition()
-
-    @crewai_event_bus.on(LLMCallCompletedEvent)
-    def handle_llm_call_completed(source, event):
-        with condition:
-            completed_events.append(event)
-            condition.notify()
-
-    llm = LLM(model="gpt-4o-mini")
-    llm.call("Say hello")
-
-    with condition:
-        success = condition.wait_for(
-            lambda: len(completed_events) >= 1,
-            timeout=10,
-        )
-    assert success, "Timeout waiting for LLMCallCompletedEvent"
-
-    event = completed_events[0]
-    assert event.usage is not None
-    assert isinstance(event.usage, dict)
-    assert event.usage.get("prompt_tokens", 0) > 0
-    assert event.usage.get("completion_tokens", 0) > 0
-    assert event.usage.get("total_tokens", 0) > 0
-
-
 @pytest.mark.vcr()
 def test_llm_emits_call_failed_event():
     received_events = []

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.13.0a5"
+__version__ = "1.13.0rc1"

uv.lock

@@ -1243,7 +1243,7 @@ requires-dist = [
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     { name = "json5", specifier = "~=0.10.0" },
     { name = "jsonref", specifier = "~=1.1.0" },
-    { name = "lancedb", specifier = ">=0.29.2,<0.30.1" },
+    { name = "lancedb", specifier = ">=0.29.2" },
     { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9,<=1.82.6" },
     { name = "mcp", specifier = "~=1.26.0" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = "~=0.1.94" },
@@ -4275,7 +4275,7 @@ wheels = [
 
 [[package]]
 name = "nltk"
-version = "3.9.4"
+version = "3.9.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
@@ -4283,9 +4283,9 @@ dependencies = [
     { name = "regex" },
     { name = "tqdm" },
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+sdist = { url = "https://files.pythonhosted.org/packages/e1/8f/915e1c12df07c70ed779d18ab83d065718a926e70d3ea33eb0cd66ffb7c0/nltk-3.9.3.tar.gz", hash = "sha256:cb5945d6424a98d694c2b9a0264519fab4363711065a46aa0ae7a2195b92e71f", size = 2923673, upload-time = "2026-02-24T12:05:53.833Z" }
 wheels = [
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 ]
 
 [[package]]
@@ -6235,14 +6235,14 @@ wheels = [
 
 [[package]]
 name = "pypdf"
-version = "6.9.2"
+version = "6.9.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/31/83/691bdb309306232362503083cb15777491045dd54f45393a317dc7d8082f/pypdf-6.9.2.tar.gz", hash = "sha256:7f850faf2b0d4ab936582c05da32c52214c2b089d61a316627b5bfb5b0dab46c", size = 5311837, upload-time = "2026-03-23T14:53:27.983Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/f9/fb/dc2e8cb006e80b0020ed20d8649106fe4274e82d8e756ad3e24ade19c0df/pypdf-6.9.1.tar.gz", hash = "sha256:ae052407d33d34de0c86c5c729be6d51010bf36e03035a8f23ab449bca52377d", size = 5311551, upload-time = "2026-03-17T10:46:07.876Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/f9/f4/75543fa802b86e72f87e9395440fe1a89a6d149887e3e55745715c3352ac/pypdf-6.9.1-py3-none-any.whl", hash = "sha256:f35a6a022348fae47e092a908339a8f3dc993510c026bb39a96718fc7185e89f", size = 333661, upload-time = "2026-03-17T10:46:06.286Z" },
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 [[package]]