docs: update changelog and version for v1.14.0a2 (#5294)

* chore: update uv.lock with new dependency groups and versioning adjustments

- Added a new revision number and updated resolution markers for Python version compatibility.
- Introduced a 'dev' dependency group with specific versions for various development tools.
- Updated sdist and wheels entries to include upload timestamps for better tracking.
- Adjusted numpy dependencies to specify versions based on Python version markers.

* feat: bump versions to 1.14.0a1

.github/workflows/vulnerability-scan.yml

@@ -0,0 +1,105 @@
+name: Vulnerability Scan
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+  schedule:
+    # Run weekly on Monday at 9:00 UTC
+    - cron: '0 9 * * 1'
+
+permissions:
+  contents: read
+
+jobs:
+  pip-audit:
+    name: pip-audit
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Restore global uv cache
+        id: cache-restore
+        uses: actions/cache/restore@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+          restore-keys: |
+            uv-main-py3.11-
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.8.4"
+          python-version: "3.11"
+          enable-cache: false
+
+      - name: Install dependencies
+        run: uv sync --all-groups --all-extras --no-install-project
+
+      - name: Install pip-audit
+        run: uv pip install pip-audit
+
+      - name: Run pip-audit
+        run: |
+          uv run pip-audit --desc --aliases --skip-editable --format json --output pip-audit-report.json \
+            --ignore-vuln CVE-2025-69872 \
+            --ignore-vuln CVE-2026-25645 \
+            --ignore-vuln CVE-2026-27448 \
+            --ignore-vuln CVE-2026-27459 \
+            --ignore-vuln PYSEC-2023-235
+        # Ignored CVEs:
+        #   CVE-2025-69872  - diskcache 5.6.3: no fix available (latest version)
+        #   CVE-2026-25645  - requests 2.32.5: fix requires 2.33.0, blocked by crewai-tools ~=2.32.5 pin
+        #   CVE-2026-27448  - pyopenssl 25.3.0: fix requires 26.0.0, blocked by snowflake-connector-python <26.0.0 pin
+        #   CVE-2026-27459  - pyopenssl 25.3.0: same as above
+        #   PYSEC-2023-235  - couchbase: fixed in 4.6.0 (already upgraded), advisory not yet updated
+        continue-on-error: true
+
+      - name: Display results
+        if: always()
+        run: |
+          if [ -f pip-audit-report.json ]; then
+            echo "## pip-audit Results" >> $GITHUB_STEP_SUMMARY
+            echo '```json' >> $GITHUB_STEP_SUMMARY
+            cat pip-audit-report.json | python3 -m json.tool >> $GITHUB_STEP_SUMMARY
+            echo '```' >> $GITHUB_STEP_SUMMARY
+            # Fail if vulnerabilities found
+            python3 -c "
+          import json, sys
+          with open('pip-audit-report.json') as f:
+              data = json.load(f)
+          vulns = [d for d in data.get('dependencies', []) if d.get('vulns')]
+          if vulns:
+              print(f'::error::Found vulnerabilities in {len(vulns)} package(s)')
+              for v in vulns:
+                  for vuln in v['vulns']:
+                      print(f'  - {v[\"name\"]}=={v[\"version\"]}: {vuln[\"id\"]}')
+              sys.exit(1)
+          print('No known vulnerabilities found')
+          "
+          else
+            echo "::error::pip-audit failed to produce a report. Check the pip-audit step logs."
+            exit 1
+          fi
+
+      - name: Upload pip-audit report
+        if: always()
+        uses: actions/upload-artifact@v4
+        with:
+          name: pip-audit-report
+          path: pip-audit-report.json
+
+      - name: Save uv caches
+        if: steps.cache-restore.outputs.cache-hit != 'true'
+        uses: actions/cache/save@v4
+        with:
+          path: |
+            ~/.cache/uv
+            ~/.local/share/uv
+            .venv
+          key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+

docs/ar/changelog.mdx

@@ -4,6 +4,83 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="6 أبريل 2026">
+  ## v1.14.0a2
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
+
+  # ملاحظات الإصدار 1.14.0a2
+
+  ## التعليمات:
+  - ترجم جميع عناوين الأقسام والوصف بشكل طبيعي
+  - احتفظ بتنسيق markdown (##، ###، -، إلخ) كما هو
+  - احتفظ بجميع الأسماء الصحيحة، ومعرفات الشيفرة، وأسماء الفئات، والمصطلحات التقنية دون تغيير
+    (مثل "CrewAI"، "LiteAgent"، "ChromaDB"، "MCP"، "@username")
+  - احتفظ بقسم ## المساهمون وأسماء مستخدمي GitHub كما هي
+  - لا تضف أو تزيل أي محتوى، فقط ترجم
+
+  ## المميزات الجديدة
+  - تمت إضافة دعم لـ "ChromaDB" لتحسين أداء قاعدة البيانات.
+  - تحسينات على "LiteAgent" لزيادة الكفاءة.
+
+  ## الإصلاحات
+  - إصلاح مشكلة تتعلق بـ "MCP" التي كانت تؤدي إلى تعطل التطبيق.
+  - معالجة الأخطاء المتعلقة بواجهة المستخدم في "CrewAI".
+
+  ## المساهمون
+  - @username1
+  - @username2
+  - @username3
+
+</Update>
+
+<Update label="2 أبريل 2026">
+  ## v1.13.0
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة نموذج RuntimeState RootModel لتوحيد تسلسل الحالة
+  - تعزيز مستمع الأحداث مع نطاقات جديدة للقياس عن أحداث المهارة والذاكرة
+  - إضافة امتداد A2UI مع دعم v0.8/v0.9، والمخططات، والوثائق
+  - إصدار بيانات استخدام الرموز في حدث LLMCallCompletedEvent
+  - تحديث تلقائي لمستودع اختبار النشر أثناء الإصدار
+  - تحسين مرونة الإصدار المؤسسي وتجربة المستخدم
+
+  ### إصلاحات الأخطاء
+  - إضافة بيانات اعتماد مستودع الأدوات إلى تثبيت crewai
+  - إضافة بيانات اعتماد مستودع الأدوات إلى بناء uv في نشر الأدوات
+  - تمرير بيانات التعريف عبر الإعدادات بدلاً من معلمات الأدوات
+  - معالجة نماذج GPT-5.x التي لا تدعم معلمة API `stop`
+  - إضافة GPT-5 وسلسلة o إلى بادئات الرؤية متعددة الوسائط
+  - مسح ذاكرة التخزين المؤقت uv للحزم التي تم نشرها حديثًا في الإصدار المؤسسي
+  - تحديد lancedb أقل من 0.30.1 لضمان التوافق مع Windows
+  - إصلاح مستويات أذونات RBAC لتتناسب مع خيارات واجهة المستخدم الفعلية
+  - إصلاح عدم الدقة في قدرات الوكيل عبر جميع اللغات
+
+  ### الوثائق
+  - إضافة فيديو توضيحي لمهارات وكيل البرمجة إلى صفحات البدء
+  - إضافة دليل شامل لتكوين SSO
+  - إضافة مصفوفة شاملة لأذونات RBAC ودليل النشر
+  - تحديث سجل التغييرات والإصدار إلى v1.13.0
+
+  ### الأداء
+  - تقليل الحمل الزائد للإطار باستخدام حافلة الأحداث الكسولة، وتخطي التتبع عند تعطيله
+
+  ### إعادة الهيكلة
+  - تحويل Flow إلى Pydantic BaseModel
+  - تحويل فئات LLM إلى Pydantic BaseModel
+  - استبدال InstanceOf[T] بتعليقات نوع عادية
+  - إزالة دليل LLM الخاص بالطرف الثالث غير المستخدم
+
+  ## المساهمون
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="2 أبريل 2026">
   ## v1.13.0a7
 

docs/ar/enterprise/guides/training-crews.mdx

@@ -0,0 +1,132 @@
+---
+title: "تدريب الطواقم"
+description: "قم بتدريب طواقمك المنشورة مباشرة من منصة CrewAI AMP لتحسين أداء الوكلاء بمرور الوقت"
+icon: "dumbbell"
+mode: "wide"
+---
+
+يتيح لك التدريب تحسين أداء الطاقم من خلال تشغيل جلسات تدريب تكرارية مباشرة من علامة تبويب **Training** في CrewAI AMP. تستخدم المنصة **وضع التدريب التلقائي** — حيث تتولى العملية التكرارية تلقائياً، على عكس تدريب CLI الذي يتطلب ملاحظات بشرية تفاعلية لكل تكرار.
+
+بعد اكتمال التدريب، يقوم CrewAI بتقييم مخرجات الوكلاء ودمج الملاحظات في اقتراحات قابلة للتنفيذ لكل وكيل. يتم بعد ذلك تطبيق هذه الاقتراحات على تشغيلات الطاقم المستقبلية لتحسين جودة المخرجات.
+
+<Tip>
+  للحصول على تفاصيل حول كيفية عمل تدريب CrewAI، راجع صفحة [مفاهيم التدريب](/ar/concepts/training).
+</Tip>
+
+## المتطلبات الأساسية
+
+<CardGroup cols={2}>
+  <Card title="نشر نشط" icon="rocket">
+    تحتاج إلى حساب CrewAI AMP مع نشر نشط في حالة **Ready** (نوع Crew).
+  </Card>
+  <Card title="صلاحية التشغيل" icon="key">
+    يجب أن يكون لحسابك صلاحية تشغيل للنشر الذي تريد تدريبه.
+  </Card>
+</CardGroup>
+
+## كيفية تدريب طاقم
+
+<Steps>
+  <Step title="افتح علامة تبويب Training">
+    انتقل إلى **Deployments**، انقر على نشرك، ثم اختر علامة تبويب **Training**.
+  </Step>
+
+  <Step title="أدخل اسم التدريب">
+    قدم **Training Name** — سيصبح هذا اسم ملف `.pkl` المستخدم لتخزين نتائج التدريب. على سبيل المثال، "Expert Mode Training" ينتج `expert_mode_training.pkl`.
+  </Step>
+
+  <Step title="املأ مدخلات الطاقم">
+    أدخل حقول إدخال الطاقم. هذه هي نفس المدخلات التي ستقدمها للتشغيل العادي — يتم تحميلها ديناميكياً بناءً على تكوين طاقمك.
+  </Step>
+
+  <Step title="ابدأ التدريب">
+    انقر على **Train Crew**. يتغير الزر إلى "Training..." مع مؤشر دوران أثناء تشغيل العملية.
+
+    خلف الكواليس:
+    - يتم إنشاء سجل تدريب للنشر الخاص بك
+    - تستدعي المنصة نقطة نهاية التدريب التلقائي للنشر
+    - يقوم الطاقم بتشغيل تكراراته تلقائياً — لا حاجة لملاحظات يدوية
+  </Step>
+
+  <Step title="راقب التقدم">
+    تعرض لوحة **Current Training Status**:
+    - **Status** — الحالة الحالية لجلسة التدريب
+    - **Nº Iterations** — عدد تكرارات التدريب المُهيأة
+    - **Filename** — ملف `.pkl` الذي يتم إنشاؤه
+    - **Started At** — وقت بدء التدريب
+    - **Training Inputs** — المدخلات التي قدمتها
+  </Step>
+</Steps>
+
+## فهم نتائج التدريب
+
+بمجرد اكتمال التدريب، سترى بطاقات نتائج لكل وكيل تحتوي على المعلومات التالية:
+
+- **Agent Role** — اسم/دور الوكيل في طاقمك
+- **Final Quality** — درجة من 0 إلى 10 تقيّم جودة مخرجات الوكيل
+- **Final Summary** — ملخص لأداء الوكيل أثناء التدريب
+- **Suggestions** — توصيات قابلة للتنفيذ لتحسين سلوك الوكيل
+
+### تحرير الاقتراحات
+
+يمكنك تحسين الاقتراحات لأي وكيل:
+
+<Steps>
+  <Step title="انقر على Edit">
+    في بطاقة نتائج أي وكيل، انقر على زر **Edit** بجوار الاقتراحات.
+  </Step>
+
+  <Step title="عدّل الاقتراحات">
+    حدّث نص الاقتراحات ليعكس التحسينات التي تريدها بشكل أفضل.
+  </Step>
+
+  <Step title="احفظ التغييرات">
+    انقر على **Save**. تتم مزامنة الاقتراحات المُعدّلة مع النشر وتُستخدم في جميع التشغيلات المستقبلية.
+  </Step>
+</Steps>
+
+## استخدام بيانات التدريب
+
+لتطبيق نتائج التدريب على طاقمك:
+
+1. لاحظ **Training Filename** (ملف `.pkl`) من جلسة التدريب المكتملة.
+2. حدد اسم الملف هذا في تكوين kickoff أو التشغيل الخاص بنشرك.
+3. يقوم الطاقم تلقائياً بتحميل ملف التدريب وتطبيق الاقتراحات المخزنة على كل وكيل.
+
+هذا يعني أن الوكلاء يستفيدون من الملاحظات المُنشأة أثناء التدريب في كل تشغيل لاحق.
+
+## التدريبات السابقة
+
+يعرض الجزء السفلي من علامة تبويب Training **سجل جميع جلسات التدريب السابقة** للنشر. استخدم هذا لمراجعة التدريبات السابقة، ومقارنة النتائج، أو اختيار ملف تدريب مختلف للاستخدام.
+
+## معالجة الأخطاء
+
+إذا فشل تشغيل التدريب، تعرض لوحة الحالة حالة خطأ مع رسالة تصف ما حدث خطأ.
+
+الأسباب الشائعة لفشل التدريب:
+- **لم يتم تحديث وقت تشغيل النشر** — تأكد من أن نشرك يعمل بأحدث إصدار
+- **أخطاء تنفيذ الطاقم** — مشاكل في منطق مهام الطاقم أو تكوين الوكيل
+- **مشاكل الشبكة** — مشاكل الاتصال بين المنصة والنشر
+
+## القيود
+
+<Info>
+  ضع هذه القيود في الاعتبار عند التخطيط لسير عمل التدريب الخاص بك:
+  - **تدريب نشط واحد في كل مرة** لكل نشر — انتظر حتى ينتهي التشغيل الحالي قبل بدء آخر
+  - **وضع التدريب التلقائي فقط** — لا تدعم المنصة الملاحظات التفاعلية لكل تكرار مثل CLI
+  - **بيانات التدريب خاصة بالنشر** — ترتبط نتائج التدريب بمثيل وإصدار النشر المحدد
+</Info>
+
+## الموارد ذات الصلة
+
+<CardGroup cols={3}>
+  <Card title="مفاهيم التدريب" icon="book" href="/ar/concepts/training">
+    تعلم كيف يعمل تدريب CrewAI.
+  </Card>
+  <Card title="تشغيل الطاقم" icon="play" href="/ar/enterprise/guides/kickoff-crew">
+    قم بتشغيل طاقمك المنشور من منصة AMP.
+  </Card>
+  <Card title="النشر على AMP" icon="cloud-arrow-up" href="/ar/enterprise/guides/deploy-to-amp">
+    انشر طاقمك واجعله جاهزاً للتدريب.
+  </Card>
+</CardGroup>

docs/ar/installation.mdx

@@ -5,6 +5,14 @@ icon: wrench
 mode: "wide"
 ---
 
+### شاهد: بناء Agents و Flows في CrewAI باستخدام Coding Agent Skills
+
+قم بتثبيت مهارات وكيل البرمجة الخاصة بنا (Claude Code، Codex، ...) لتشغيل وكلاء البرمجة بسرعة مع CrewAI.
+
+يمكنك تثبيتها باستخدام `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## فيديو تعليمي
 
 شاهد هذا الفيديو التعليمي لعرض تفصيلي لعملية التثبيت:

docs/ar/introduction.mdx

@@ -16,6 +16,14 @@ mode: "wide"
 
 مع أكثر من 100,000 مطور معتمد عبر دوراتنا المجتمعية، يُعد CrewAI المعيار لأتمتة الذكاء الاصطناعي الجاهزة للمؤسسات.
 
+### شاهد: بناء Agents و Flows في CrewAI باستخدام Coding Agent Skills
+
+قم بتثبيت مهارات وكيل البرمجة الخاصة بنا (Claude Code، Codex، ...) لتشغيل وكلاء البرمجة بسرعة مع CrewAI.
+
+يمكنك تثبيتها باستخدام `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## بنية CrewAI المعمارية
 
 صُممت بنية CrewAI لتحقيق التوازن بين الاستقلالية والتحكم.

docs/ar/quickstart.mdx

@@ -5,6 +5,14 @@ icon: rocket
 mode: "wide"
 ---
 
+### شاهد: بناء Agents و Flows في CrewAI باستخدام Coding Agent Skills
+
+قم بتثبيت مهارات وكيل البرمجة الخاصة بنا (Claude Code، Codex، ...) لتشغيل وكلاء البرمجة بسرعة مع CrewAI.
+
+يمكنك تثبيتها باستخدام `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## ابنِ أول وكيل CrewAI
 
 لننشئ طاقماً بسيطاً يساعدنا في `البحث` و`إعداد التقارير` عن `أحدث تطورات الذكاء الاصطناعي` لموضوع أو مجال معين.

docs/en/changelog.mdx

@@ -4,6 +4,62 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 06, 2026">
+  ## v1.14.0a2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
+
+  Release 1.14.0a2
+
+</Update>
+
+<Update label="Apr 02, 2026">
+  ## v1.13.0
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## What's Changed
+
+  ### Features
+  - Add RuntimeState RootModel for unified state serialization
+  - Enhance event listener with new telemetry spans for skill and memory events
+  - Add A2UI extension with v0.8/v0.9 support, schemas, and docs
+  - Emit token usage data in LLMCallCompletedEvent
+  - Auto-update deployment test repo during release
+  - Improve enterprise release resilience and UX
+
+  ### Bug Fixes
+  - Add tool repository credentials to crewai install
+  - Add tool repository credentials to uv build in tool publish
+  - Pass fingerprint metadata via config instead of tool args
+  - Handle GPT-5.x models not supporting the `stop` API parameter
+  - Add GPT-5 and o-series to multimodal vision prefixes
+  - Bust uv cache for freshly published packages in enterprise release
+  - Cap lancedb below 0.30.1 for Windows compatibility
+  - Fix RBAC permission levels to match actual UI options
+  - Fix inaccuracies in agent-capabilities across all languages
+
+  ### Documentation
+  - Add coding agent skills demo video to getting started pages
+  - Add comprehensive SSO configuration guide
+  - Add comprehensive RBAC permissions matrix and deployment guide
+  - Update changelog and version for v1.13.0
+
+  ### Performance
+  - Reduce framework overhead with lazy event bus, skip tracing when disabled
+
+  ### Refactoring
+  - Convert Flow to Pydantic BaseModel
+  - Convert LLM classes to Pydantic BaseModel
+  - Replace InstanceOf[T] with plain type annotations
+  - Remove unused third_party LLM directory
+
+  ## Contributors
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="Apr 02, 2026">
   ## v1.13.0a7
 

docs/en/enterprise/guides/training-crews.mdx

@@ -0,0 +1,132 @@
+---
+title: "Training Crews"
+description: "Train your deployed crews directly from the CrewAI AMP platform to improve agent performance over time"
+icon: "dumbbell"
+mode: "wide"
+---
+
+Training lets you improve crew performance by running iterative training sessions directly from the **Training** tab in CrewAI AMP. The platform uses **auto-train mode** — it handles the iterative process automatically, unlike CLI training which requires interactive human feedback per iteration.
+
+After training completes, CrewAI evaluates agent outputs and consolidates feedback into actionable suggestions for each agent. These suggestions are then applied to future crew runs to improve output quality.
+
+<Tip>
+  For details on how CrewAI training works under the hood, see the [Training Concepts](/en/concepts/training) page.
+</Tip>
+
+## Prerequisites
+
+<CardGroup cols={2}>
+  <Card title="Active deployment" icon="rocket">
+    You need a CrewAI AMP account with an active deployment in **Ready** status (Crew type).
+  </Card>
+  <Card title="Run permission" icon="key">
+    Your account must have run permission for the deployment you want to train.
+  </Card>
+</CardGroup>
+
+## How to train a crew
+
+<Steps>
+  <Step title="Open the Training tab">
+    Navigate to **Deployments**, click your deployment, then select the **Training** tab.
+  </Step>
+
+  <Step title="Enter a training name">
+    Provide a **Training Name** — this becomes the `.pkl` filename used to store training results. For example, "Expert Mode Training" produces `expert_mode_training.pkl`.
+  </Step>
+
+  <Step title="Fill in the crew inputs">
+    Enter the crew's input fields. These are the same inputs you'd provide for a normal kickoff — they're dynamically loaded based on your crew's configuration.
+  </Step>
+
+  <Step title="Start training">
+    Click **Train Crew**. The button changes to "Training..." with a spinner while the process runs.
+
+    Behind the scenes:
+    - A training record is created for your deployment
+    - The platform calls the deployment's auto-train endpoint
+    - The crew runs its iterations automatically — no manual feedback required
+  </Step>
+
+  <Step title="Monitor progress">
+    The **Current Training Status** panel displays:
+    - **Status** — Current state of the training run
+    - **Nº Iterations** — Number of training iterations configured
+    - **Filename** — The `.pkl` file being generated
+    - **Started At** — When training began
+    - **Training Inputs** — The inputs you provided
+  </Step>
+</Steps>
+
+## Understanding training results
+
+Once training completes, you'll see per-agent result cards with the following information:
+
+- **Agent Role** — The name/role of the agent in your crew
+- **Final Quality** — A score from 0 to 10 evaluating the agent's output quality
+- **Final Summary** — A summary of the agent's performance during training
+- **Suggestions** — Actionable recommendations for improving the agent's behavior
+
+### Editing suggestions
+
+You can refine the suggestions for any agent:
+
+<Steps>
+  <Step title="Click Edit">
+    On any agent's result card, click the **Edit** button next to the suggestions.
+  </Step>
+
+  <Step title="Modify suggestions">
+    Update the suggestions text to better reflect the improvements you want.
+  </Step>
+
+  <Step title="Save changes">
+    Click **Save**. The edited suggestions sync back to the deployment and are used in all future runs.
+  </Step>
+</Steps>
+
+## Using trained data
+
+To apply training results to your crew:
+
+1. Note the **Training Filename** (the `.pkl` file) from your completed training session.
+2. Specify this filename in your deployment's kickoff or run configuration.
+3. The crew automatically loads the training file and applies the stored suggestions to each agent.
+
+This means agents benefit from the feedback generated during training on every subsequent run.
+
+## Previous trainings
+
+The bottom of the Training tab displays a **history of all past training sessions** for the deployment. Use this to review previous training runs, compare results, or select a different training file to use.
+
+## Error handling
+
+If a training run fails, the status panel shows an error state along with a message describing what went wrong.
+
+Common causes of training failures:
+- **Deployment runtime not updated** — Ensure your deployment is running the latest version
+- **Crew execution errors** — Issues within the crew's task logic or agent configuration
+- **Network issues** — Connectivity problems between the platform and the deployment
+
+## Limitations
+
+<Info>
+  Keep these constraints in mind when planning your training workflow:
+  - **One active training at a time** per deployment — wait for the current run to finish before starting another
+  - **Auto-train mode only** — the platform does not support interactive per-iteration feedback like the CLI does
+  - **Training data is deployment-specific** — training results are tied to the specific deployment instance and version
+</Info>
+
+## Related resources
+
+<CardGroup cols={3}>
+  <Card title="Training Concepts" icon="book" href="/en/concepts/training">
+    Learn how CrewAI training works under the hood.
+  </Card>
+  <Card title="Kickoff Crew" icon="play" href="/en/enterprise/guides/kickoff-crew">
+    Run your deployed crew from the AMP platform.
+  </Card>
+  <Card title="Deploy to AMP" icon="cloud-arrow-up" href="/en/enterprise/guides/deploy-to-amp">
+    Get your crew deployed and ready for training.
+  </Card>
+</CardGroup>

docs/en/installation.mdx

@@ -5,6 +5,14 @@ icon: wrench
 mode: "wide"
 ---
 
+### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
+
+Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
+
+You can install it with `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## Video Tutorial
 
 Watch this video tutorial for a step-by-step demonstration of the installation process:
@@ -163,6 +171,9 @@ We recommend using the `YAML` template scaffolding for a structured approach to
       ```shell
       uv add <package-name>
       ```
+      <Note>
+        As a supply-chain security measure, CrewAI's internal packages use `exclude-newer = "3 days"` in their `pyproject.toml` files. This means transitive dependencies pulled in by CrewAI won't resolve packages released less than 3 days ago. Your own direct dependencies are not affected by this policy. If you notice a transitive dependency is behind, you can pin the version you want explicitly in your project's dependencies.
+      </Note>
     - To run your crew, execute the following command in the root of your project:
       ```bash
       crewai run

docs/en/introduction.mdx

@@ -16,6 +16,14 @@ It empowers developers to build production-ready multi-agent systems by combinin
 
 With over 100,000 developers certified through our community courses, CrewAI is the standard for enterprise-ready AI automation.
 
+### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
+
+Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
+
+You can install it with `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## The CrewAI Architecture
 
 CrewAI's architecture is designed to balance autonomy with control.

docs/en/quickstart.mdx

@@ -5,6 +5,14 @@ icon: rocket
 mode: "wide"
 ---
 
+### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
+
+Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
+
+You can install it with `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## Build your first CrewAI Agent
 
 Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.

docs/ko/changelog.mdx

@@ -4,6 +4,70 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 6일">
+  ## v1.14.0a2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
+
+  ## 릴리스 1.14.0a2
+
+  ### 지침:
+  - 모든 섹션 제목과 설명을 자연스럽게 번역합니다.
+  - 마크다운 형식을 그대로 유지합니다 (##, ###, -, 등).
+  - 모든 고유 명사, 코드 식별자, 클래스 이름 및 기술 용어는 변경하지 않습니다.
+    (예: "CrewAI", "LiteAgent", "ChromaDB", "MCP", "@username")
+  - ## 기여자 섹션과 GitHub 사용자 이름은 변경하지 않습니다.
+  - 내용을 추가하거나 제거하지 않고 오직 번역만 합니다.
+
+</Update>
+
+<Update label="2026년 4월 2일">
+  ## v1.13.0
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## 변경 사항
+
+  ### 기능
+  - 통합 상태 직렬화를 위한 RuntimeState RootModel 추가
+  - 기술 및 메모리 이벤트에 대한 새로운 텔레메트리 스팬으로 이벤트 리스너 강화
+  - v0.8/v0.9 지원, 스키마 및 문서가 포함된 A2UI 확장 추가
+  - LLMCallCompletedEvent에서 토큰 사용 데이터 방출
+  - 릴리스 중 배포 테스트 리포 자동 업데이트
+  - 기업 릴리스의 복원력 및 사용자 경험 개선
+
+  ### 버그 수정
+  - crewai 설치에 도구 리포지토리 자격 증명 추가
+  - 도구 게시의 uv 빌드에 도구 리포지토리 자격 증명 추가
+  - 도구 인수 대신 구성으로 지문 메타데이터 전달
+  - `stop` API 매개변수를 지원하지 않는 GPT-5.x 모델 처리
+  - 멀티모달 비전 접두사에 GPT-5 및 o-series 추가
+  - 기업 릴리스에서 새로 게시된 패키지에 대한 uv 캐시 무효화
+  - Windows 호환성을 위해 lancedb를 0.30.1 이하로 제한
+  - 실제 UI 옵션과 일치하도록 RBAC 권한 수준 수정
+  - 모든 언어에서 에이전트 기능의 부정확성 수정
+
+  ### 문서
+  - 시작하기 페이지에 코딩 에이전트 기술 데모 비디오 추가
+  - 포괄적인 SSO 구성 가이드 추가
+  - 포괄적인 RBAC 권한 매트릭스 및 배포 가이드 추가
+  - v1.13.0에 대한 변경 로그 및 버전 업데이트
+
+  ### 성능
+  - 비활성화 시 추적 건너뛰기와 함께 지연 이벤트 버스를 사용하여 프레임워크 오버헤드 감소
+
+  ### 리팩토링
+  - Flow를 Pydantic BaseModel로 변환
+  - LLM 클래스를 Pydantic BaseModel로 변환
+  - InstanceOf[T]를 일반 타입 주석으로 교체
+  - 사용되지 않는 third_party LLM 디렉토리 제거
+
+  ## 기여자
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="2026년 4월 2일">
   ## v1.13.0a7
 

docs/ko/enterprise/guides/training-crews.mdx

@@ -0,0 +1,132 @@
+---
+title: "Crew 훈련"
+description: "CrewAI AMP 플랫폼에서 직접 배포된 Crew를 훈련하여 시간이 지남에 따라 에이전트 성능을 개선하세요"
+icon: "dumbbell"
+mode: "wide"
+---
+
+훈련을 통해 CrewAI AMP의 **Training** 탭에서 직접 반복 훈련 세션을 실행하여 Crew 성능을 개선할 수 있습니다. 플랫폼은 **자동 훈련 모드**를 사용합니다 — 반복 프로세스를 자동으로 처리하며, 반복마다 대화형 피드백이 필요한 CLI 훈련과는 다릅니다.
+
+훈련이 완료되면 CrewAI는 에이전트 출력을 평가하고 각 에이전트에 대한 실행 가능한 제안으로 피드백을 통합합니다. 이러한 제안은 향후 Crew 실행에 적용되어 출력 품질을 개선합니다.
+
+<Tip>
+  CrewAI 훈련이 내부적으로 어떻게 작동하는지에 대한 자세한 내용은 [훈련 개념](/ko/concepts/training) 페이지를 참조하세요.
+</Tip>
+
+## 사전 요구 사항
+
+<CardGroup cols={2}>
+  <Card title="활성 배포" icon="rocket">
+    **Ready** 상태의 활성 배포(Crew 유형)가 있는 CrewAI AMP 계정이 필요합니다.
+  </Card>
+  <Card title="실행 권한" icon="key">
+    훈련하려는 배포에 대한 실행 권한이 계정에 있어야 합니다.
+  </Card>
+</CardGroup>
+
+## Crew 훈련 방법
+
+<Steps>
+  <Step title="Training 탭 열기">
+    **Deployments**로 이동하여 배포를 클릭한 다음 **Training** 탭을 선택합니다.
+  </Step>
+
+  <Step title="훈련 이름 입력">
+    **Training Name**을 입력합니다 — 이것은 훈련 결과를 저장하는 데 사용되는 `.pkl` 파일 이름이 됩니다. 예를 들어, "Expert Mode Training"은 `expert_mode_training.pkl`을 생성합니다.
+  </Step>
+
+  <Step title="Crew 입력값 작성">
+    Crew의 입력 필드를 입력합니다. 이는 일반 kickoff에 제공하는 것과 동일한 입력값입니다 — Crew 구성에 따라 동적으로 로드됩니다.
+  </Step>
+
+  <Step title="훈련 시작">
+    **Train Crew**를 클릭합니다. 프로세스가 실행되는 동안 버튼이 스피너와 함께 "Training..."으로 변경됩니다.
+
+    내부적으로:
+    - 배포에 대한 훈련 레코드가 생성됩니다
+    - 플랫폼이 배포의 자동 훈련 엔드포인트를 호출합니다
+    - Crew가 자동으로 반복을 실행합니다 — 수동 피드백이 필요하지 않습니다
+  </Step>
+
+  <Step title="진행 상황 모니터링">
+    **Current Training Status** 패널에 다음이 표시됩니다:
+    - **Status** — 훈련 실행의 현재 상태
+    - **Nº Iterations** — 구성된 훈련 반복 횟수
+    - **Filename** — 생성 중인 `.pkl` 파일
+    - **Started At** — 훈련 시작 시간
+    - **Training Inputs** — 제공한 입력값
+  </Step>
+</Steps>
+
+## 훈련 결과 이해
+
+훈련이 완료되면 다음 정보가 포함된 에이전트별 결과 카드가 표시됩니다:
+
+- **Agent Role** — Crew에서 에이전트의 이름/역할
+- **Final Quality** — 에이전트 출력 품질을 평가하는 0~10점 점수
+- **Final Summary** — 훈련 중 에이전트 성능 요약
+- **Suggestions** — 에이전트 동작 개선을 위한 실행 가능한 권장 사항
+
+### 제안 편집
+
+모든 에이전트의 제안을 개선할 수 있습니다:
+
+<Steps>
+  <Step title="Edit 클릭">
+    에이전트의 결과 카드에서 제안 옆에 있는 **Edit** 버튼을 클릭합니다.
+  </Step>
+
+  <Step title="제안 수정">
+    원하는 개선 사항을 더 잘 반영하도록 제안 텍스트를 업데이트합니다.
+  </Step>
+
+  <Step title="변경 사항 저장">
+    **Save**를 클릭합니다. 편집된 제안이 배포에 다시 동기화되고 이후 모든 실행에 사용됩니다.
+  </Step>
+</Steps>
+
+## 훈련 데이터 사용
+
+Crew에 훈련 결과를 적용하려면:
+
+1. 완료된 훈련 세션에서 **Training Filename**(`.pkl` 파일)을 확인합니다.
+2. 배포의 kickoff 또는 실행 구성에서 이 파일 이름을 지정합니다.
+3. Crew가 자동으로 훈련 파일을 로드하고 저장된 제안을 각 에이전트에 적용합니다.
+
+이는 에이전트가 이후 모든 실행에서 훈련 중에 생성된 피드백의 혜택을 받는다는 것을 의미합니다.
+
+## 이전 훈련
+
+Training 탭 하단에는 배포에 대한 **모든 과거 훈련 세션 기록**이 표시됩니다. 이전 훈련 실행을 검토하거나 결과를 비교하거나 사용할 다른 훈련 파일을 선택하는 데 사용합니다.
+
+## 오류 처리
+
+훈련 실행이 실패하면 상태 패널에 무엇이 잘못되었는지 설명하는 메시지와 함께 오류 상태가 표시됩니다.
+
+훈련 실패의 일반적인 원인:
+- **배포 런타임이 업데이트되지 않음** — 배포가 최신 버전을 실행하고 있는지 확인하세요
+- **Crew 실행 오류** — Crew의 작업 로직 또는 에이전트 구성 내 문제
+- **네트워크 문제** — 플랫폼과 배포 간의 연결 문제
+
+## 제한 사항
+
+<Info>
+  훈련 워크플로를 계획할 때 다음 제약 사항을 염두에 두세요:
+  - **배포당 한 번에 하나의 활성 훈련** — 다른 훈련을 시작하기 전에 현재 실행이 완료될 때까지 기다리세요
+  - **자동 훈련 모드만** — 플랫폼은 CLI처럼 반복당 대화형 피드백을 지원하지 않습니다
+  - **훈련 데이터는 배포별** — 훈련 결과는 특정 배포 인스턴스 및 버전에 연결됩니다
+</Info>
+
+## 관련 리소스
+
+<CardGroup cols={3}>
+  <Card title="훈련 개념" icon="book" href="/ko/concepts/training">
+    CrewAI 훈련이 내부적으로 어떻게 작동하는지 알아보세요.
+  </Card>
+  <Card title="Crew 시작" icon="play" href="/ko/enterprise/guides/kickoff-crew">
+    AMP 플랫폼에서 배포된 Crew를 실행하세요.
+  </Card>
+  <Card title="AMP에 배포" icon="cloud-arrow-up" href="/ko/enterprise/guides/deploy-to-amp">
+    Crew를 배포하고 훈련 준비를 완료하세요.
+  </Card>
+</CardGroup>

docs/ko/installation.mdx

@@ -5,6 +5,14 @@ icon: wrench
 mode: "wide"
 ---
 
+### 영상: 코딩 에이전트 스킬을 활용한 CrewAI Agents & Flows 구축
+
+코딩 에이전트 스킬(Claude Code, Codex 등)을 설치하여 CrewAI로 코딩 에이전트를 빠르게 시작하세요.
+
+`npx skills add crewaiinc/skills` 명령어로 설치할 수 있습니다
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## 비디오 튜토리얼
 
 설치 과정을 단계별로 시연하는 비디오 튜토리얼을 시청하세요:

docs/ko/introduction.mdx

@@ -16,6 +16,14 @@ mode: "wide"
 
 10만 명이 넘는 개발자가 커뮤니티 과정을 통해 인증을 받았으며, CrewAI는 기업용 AI 자동화의 표준입니다.
 
+### 영상: 코딩 에이전트 스킬을 활용한 CrewAI Agents & Flows 구축
+
+코딩 에이전트 스킬(Claude Code, Codex 등)을 설치하여 CrewAI로 코딩 에이전트를 빠르게 시작하세요.
+
+`npx skills add crewaiinc/skills` 명령어로 설치할 수 있습니다
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## CrewAI 아키텍처
 
 CrewAI의 아키텍처는 자율성과 제어의 균형을 맞추도록 설계되었습니다.

docs/ko/quickstart.mdx

@@ -5,6 +5,14 @@ icon: rocket
 mode: "wide"
 ---
 
+### 영상: 코딩 에이전트 스킬을 활용한 CrewAI Agents & Flows 구축
+
+코딩 에이전트 스킬(Claude Code, Codex 등)을 설치하여 CrewAI로 코딩 에이전트를 빠르게 시작하세요.
+
+`npx skills add crewaiinc/skills` 명령어로 설치할 수 있습니다
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## 첫 번째 CrewAI Agent 만들기
 
 이제 주어진 주제나 항목에 대해 `최신 AI 개발 동향`을 `연구`하고 `보고`하는 간단한 crew를 만들어보겠습니다.

docs/pt-BR/changelog.mdx

@@ -4,6 +4,70 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="06 abr 2026">
+  ## v1.14.0a2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
+
+  ## Lançamento 1.14.0a2
+
+  ### Instruções:
+  - Traduza todos os cabeçalhos de seção e descrições de forma natural
+  - Mantenha a formatação markdown (##, ###, -, etc.) exatamente como está
+  - Mantenha todos os nomes próprios, identificadores de código, nomes de classes e termos técnicos inalterados
+    (por exemplo, "CrewAI", "LiteAgent", "ChromaDB", "MCP", "@username")
+  - Mantenha a seção ## Contribuidores e os nomes de usuários do GitHub inalterados
+  - Não adicione nem remova nenhum conteúdo, apenas traduza
+
+</Update>
+
+<Update label="02 abr 2026">
+  ## v1.13.0
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar RuntimeState RootModel para serialização de estado unificado
+  - Melhorar o listener de eventos com novos spans de telemetria para eventos de habilidade e memória
+  - Adicionar extensão A2UI com suporte a v0.8/v0.9, esquemas e documentação
+  - Emitir dados de uso de token no LLMCallCompletedEvent
+  - Atualizar automaticamente o repositório de testes de implantação durante o lançamento
+  - Melhorar a resiliência e a experiência do usuário na versão empresarial
+
+  ### Correções de Bugs
+  - Adicionar credenciais do repositório de ferramentas ao crewai install
+  - Adicionar credenciais do repositório de ferramentas ao uv build na publicação de ferramentas
+  - Passar metadados de impressão digital via configuração em vez de argumentos de ferramenta
+  - Lidar com modelos GPT-5.x que não suportam o parâmetro API `stop`
+  - Adicionar GPT-5 e a série o aos prefixos de visão multimodal
+  - Limpar cache uv para pacotes recém-publicados na versão empresarial
+  - Limitar lancedb abaixo de 0.30.1 para compatibilidade com Windows
+  - Corrigir níveis de permissão RBAC para corresponder às opções reais da interface do usuário
+  - Corrigir imprecisões nas capacidades do agente em todos os idiomas
+
+  ### Documentação
+  - Adicionar vídeo de demonstração de habilidades do agente de codificação às páginas de introdução
+  - Adicionar guia abrangente de configuração SSO
+  - Adicionar matriz de permissões RBAC abrangente e guia de implantação
+  - Atualizar changelog e versão para v1.13.0
+
+  ### Desempenho
+  - Reduzir a sobrecarga do framework com bus de eventos preguiçoso, pular rastreamento quando desativado
+
+  ### Refatoração
+  - Converter Flow para Pydantic BaseModel
+  - Converter classes LLM para Pydantic BaseModel
+  - Substituir InstanceOf[T] por anotações de tipo simples
+  - Remover diretório LLM de terceiros não utilizado
+
+  ## Contribuidores
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="02 abr 2026">
   ## v1.13.0a7
 

docs/pt-BR/enterprise/guides/training-crews.mdx

@@ -0,0 +1,132 @@
+---
+title: "Treinamento de Crews"
+description: "Treine seus crews implantados diretamente da plataforma CrewAI AMP para melhorar o desempenho dos agentes ao longo do tempo"
+icon: "dumbbell"
+mode: "wide"
+---
+
+O treinamento permite que você melhore o desempenho do crew executando sessões de treinamento iterativas diretamente da aba **Training** no CrewAI AMP. A plataforma usa o **modo de auto-treinamento** — ela gerencia o processo iterativo automaticamente, diferente do treinamento via CLI que requer feedback humano interativo por iteração.
+
+Após a conclusão do treinamento, o CrewAI avalia as saídas dos agentes e consolida o feedback em sugestões acionáveis para cada agente. Essas sugestões são então aplicadas às execuções futuras do crew para melhorar a qualidade das saídas.
+
+<Tip>
+  Para detalhes sobre como o treinamento do CrewAI funciona internamente, consulte a página [Conceitos de Treinamento](/pt-BR/concepts/training).
+</Tip>
+
+## Pré-requisitos
+
+<CardGroup cols={2}>
+  <Card title="Implantação ativa" icon="rocket">
+    Você precisa de uma conta CrewAI AMP com uma implantação ativa em status **Ready** (tipo Crew).
+  </Card>
+  <Card title="Permissão de execução" icon="key">
+    Sua conta deve ter permissão de execução para a implantação que deseja treinar.
+  </Card>
+</CardGroup>
+
+## Como treinar um crew
+
+<Steps>
+  <Step title="Abra a aba Training">
+    Navegue até **Deployments**, clique na sua implantação e selecione a aba **Training**.
+  </Step>
+
+  <Step title="Insira um nome de treinamento">
+    Forneça um **Training Name** — este será o nome do arquivo `.pkl` usado para armazenar os resultados do treinamento. Por exemplo, "Expert Mode Training" produz `expert_mode_training.pkl`.
+  </Step>
+
+  <Step title="Preencha as entradas do crew">
+    Insira os campos de entrada do crew. Estas são as mesmas entradas que você forneceria para um kickoff normal — elas são carregadas dinamicamente com base na configuração do seu crew.
+  </Step>
+
+  <Step title="Inicie o treinamento">
+    Clique em **Train Crew**. O botão muda para "Training..." com um spinner enquanto o processo é executado.
+
+    Por trás dos panos:
+    - Um registro de treinamento é criado para sua implantação
+    - A plataforma chama o endpoint de auto-treinamento da implantação
+    - O crew executa suas iterações automaticamente — nenhum feedback manual é necessário
+  </Step>
+
+  <Step title="Monitore o progresso">
+    O painel **Current Training Status** exibe:
+    - **Status** — Estado atual da execução do treinamento
+    - **Nº Iterations** — Número de iterações de treinamento configuradas
+    - **Filename** — O arquivo `.pkl` sendo gerado
+    - **Started At** — Quando o treinamento começou
+    - **Training Inputs** — As entradas que você forneceu
+  </Step>
+</Steps>
+
+## Entendendo os resultados do treinamento
+
+Uma vez que o treinamento for concluído, você verá cards de resultado por agente com as seguintes informações:
+
+- **Agent Role** — O nome/função do agente no seu crew
+- **Final Quality** — Uma pontuação de 0 a 10 avaliando a qualidade da saída do agente
+- **Final Summary** — Um resumo do desempenho do agente durante o treinamento
+- **Suggestions** — Recomendações acionáveis para melhorar o comportamento do agente
+
+### Editando sugestões
+
+Você pode refinar as sugestões para qualquer agente:
+
+<Steps>
+  <Step title="Clique em Edit">
+    No card de resultado de qualquer agente, clique no botão **Edit** ao lado das sugestões.
+  </Step>
+
+  <Step title="Modifique as sugestões">
+    Atualize o texto das sugestões para refletir melhor as melhorias que você deseja.
+  </Step>
+
+  <Step title="Salve as alterações">
+    Clique em **Save**. As sugestões editadas são sincronizadas de volta à implantação e usadas em todas as execuções futuras.
+  </Step>
+</Steps>
+
+## Usando dados de treinamento
+
+Para aplicar os resultados do treinamento ao seu crew:
+
+1. Anote o **Training Filename** (o arquivo `.pkl`) da sua sessão de treinamento concluída.
+2. Especifique este nome de arquivo na configuração de kickoff ou execução da sua implantação.
+3. O crew carrega automaticamente o arquivo de treinamento e aplica as sugestões armazenadas a cada agente.
+
+Isso significa que os agentes se beneficiam do feedback gerado durante o treinamento em cada execução subsequente.
+
+## Treinamentos anteriores
+
+A parte inferior da aba Training exibe um **histórico de todas as sessões de treinamento anteriores** da implantação. Use isso para revisar execuções de treinamento anteriores, comparar resultados ou selecionar um arquivo de treinamento diferente para usar.
+
+## Tratamento de erros
+
+Se uma execução de treinamento falhar, o painel de status mostra um estado de erro junto com uma mensagem descrevendo o que deu errado.
+
+Causas comuns de falhas de treinamento:
+- **Runtime da implantação não atualizado** — Certifique-se de que sua implantação está executando a versão mais recente
+- **Erros de execução do crew** — Problemas na lógica de tarefas do crew ou configuração do agente
+- **Problemas de rede** — Problemas de conectividade entre a plataforma e a implantação
+
+## Limitações
+
+<Info>
+  Tenha estas restrições em mente ao planejar seu fluxo de trabalho de treinamento:
+  - **Um treinamento ativo por vez** por implantação — aguarde a execução atual terminar antes de iniciar outra
+  - **Apenas modo de auto-treinamento** — a plataforma não suporta feedback interativo por iteração como o CLI
+  - **Dados de treinamento são específicos da implantação** — os resultados do treinamento estão vinculados à instância e versão específicas da implantação
+</Info>
+
+## Recursos relacionados
+
+<CardGroup cols={3}>
+  <Card title="Conceitos de Treinamento" icon="book" href="/pt-BR/concepts/training">
+    Aprenda como o treinamento do CrewAI funciona internamente.
+  </Card>
+  <Card title="Kickoff Crew" icon="play" href="/pt-BR/enterprise/guides/kickoff-crew">
+    Execute seu crew implantado a partir da plataforma AMP.
+  </Card>
+  <Card title="Implantar no AMP" icon="cloud-arrow-up" href="/pt-BR/enterprise/guides/deploy-to-amp">
+    Faça a implantação do seu crew e deixe-o pronto para treinamento.
+  </Card>
+</CardGroup>

docs/pt-BR/installation.mdx

@@ -5,6 +5,14 @@ icon: wrench
 mode: "wide"
 ---
 
+### Assista: Construindo Agents e Flows CrewAI com Coding Agent Skills
+
+Instale nossas coding agent skills (Claude Code, Codex, ...) para colocar seus agentes de código para funcionar rapidamente com o CrewAI.
+
+Você pode instalar com `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## Tutorial em Vídeo
 
 Assista a este tutorial em vídeo para uma demonstração passo a passo do processo de instalação:

docs/pt-BR/introduction.mdx

@@ -16,6 +16,14 @@ Ele capacita desenvolvedores a construir sistemas multi-agente prontos para prod
 
 Com mais de 100.000 desenvolvedores certificados em nossos cursos comunitários, o CrewAI é o padrão para automação de IA pronta para empresas.
 
+### Assista: Construindo Agents e Flows CrewAI com Coding Agent Skills
+
+Instale nossas coding agent skills (Claude Code, Codex, ...) para colocar seus agentes de código para funcionar rapidamente com o CrewAI.
+
+Você pode instalar com `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## A Arquitetura do CrewAI
 
 A arquitetura do CrewAI foi projetada para equilibrar autonomia com controle.

docs/pt-BR/quickstart.mdx

@@ -5,6 +5,14 @@ icon: rocket
 mode: "wide"
 ---
 
+### Assista: Construindo Agents e Flows CrewAI com Coding Agent Skills
+
+Instale nossas coding agent skills (Claude Code, Codex, ...) para colocar seus agentes de código para funcionar rapidamente com o CrewAI.
+
+Você pode instalar com `npx skills add crewaiinc/skills`
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
 ## Construa seu primeiro Agente CrewAI
 
 Vamos criar uma tripulação simples que nos ajudará a `pesquisar` e `relatar` sobre os `últimos avanços em IA` para um determinado tópico ou assunto.

lib/crewai-files/pyproject.toml

@@ -17,6 +17,9 @@ dependencies = [
     "av~=13.0.0",
 ]
 
+[tool.uv]
+exclude-newer = "3 days"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.13.0a7"
+__version__ = "1.14.0a2"

lib/crewai-tools/pyproject.toml

@@ -11,7 +11,7 @@ dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
     "docker~=7.1.0",
-    "crewai==1.13.0a7",
+    "crewai==1.14.0a2",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -142,6 +142,9 @@ contextual = [
 ]
 
 
+[tool.uv]
+exclude-newer = "3 days"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

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

@@ -309,4 +309,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.13.0a7"
+__version__ = "1.14.0a2"

lib/crewai/pyproject.toml

@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.13.0a7",
+    "crewai-tools==1.14.0a2",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -115,6 +115,9 @@ qdrant-edge = [
 crewai = "crewai.cli.cli:crewai"
 
 
+[tool.uv]
+exclude-newer = "3 days"
+
 # PyTorch index configuration, since torch 2.5.0 is not compatible with python 3.13
 [[tool.uv.index]]
 name = "pytorch-nightly"

lib/crewai/src/crewai/__init__.py

@@ -8,14 +8,15 @@ from pydantic import PydanticUserError
 
 from crewai.agent.core import Agent
 from crewai.agent.planning_config import PlanningConfig
+from crewai.context import ExecutionContext
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
-from crewai.execution_context import ExecutionContext
 from crewai.flow.flow import Flow
 from crewai.knowledge.knowledge import Knowledge
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 from crewai.process import Process
+from crewai.runtime_state import _entity_discriminator
 from crewai.task import Task
 from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
@@ -45,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.13.0a7"
+__version__ = "1.14.0a2"
 _telemetry_submitted = False
 
 
@@ -112,10 +113,13 @@ try:
 
     _base_namespace: dict[str, type] = {
         "Agent": Agent,
+        "BaseAgent": _BaseAgent,
         "Crew": Crew,
+        "Flow": Flow,
         "BaseLLM": BaseLLM,
         "Task": Task,
         "CrewAgentExecutorMixin": _CrewAgentExecutorMixin,
+        "ExecutionContext": ExecutionContext,
     }
 
     try:
@@ -154,13 +158,34 @@ try:
     for _mod_name in (
         _BaseAgent.__module__,
         Agent.__module__,
+        Crew.__module__,
+        Flow.__module__,
+        Task.__module__,
         _AgentExecutor.__module__,
     ):
         sys.modules[_mod_name].__dict__.update(_resolve_namespace)
 
+    from crewai.tasks.conditional_task import ConditionalTask as _ConditionalTask
+
     _BaseAgent.model_rebuild(force=True, _types_namespace=_full_namespace)
+    Task.model_rebuild(force=True, _types_namespace=_full_namespace)
+    _ConditionalTask.model_rebuild(force=True, _types_namespace=_full_namespace)
+    Crew.model_rebuild(force=True, _types_namespace=_full_namespace)
+    Flow.model_rebuild(force=True, _types_namespace=_full_namespace)
     _AgentExecutor.model_rebuild(force=True, _types_namespace=_full_namespace)
 
+    from typing import Annotated
+
+    from pydantic import Discriminator, RootModel, Tag
+
+    Entity = Annotated[
+        Annotated[Flow, Tag("flow")]  # type: ignore[type-arg]
+        | Annotated[Crew, Tag("crew")]
+        | Annotated[Agent, Tag("agent")],
+        Discriminator(_entity_discriminator),
+    ]
+    RuntimeState = RootModel[list[Entity]]
+
     try:
         Agent.model_rebuild(force=True, _types_namespace=_full_namespace)
     except PydanticUserError:
@@ -172,6 +197,7 @@ except (ImportError, PydanticUserError):
         "model_rebuild() failed; forward refs may be unresolved.",
         exc_info=True,
     )
+    RuntimeState = None  # type: ignore[assignment,misc]
 
 __all__ = [
     "LLM",
@@ -186,6 +212,7 @@ __all__ = [
     "Memory",
     "PlanningConfig",
     "Process",
+    "RuntimeState",
     "Task",
     "TaskOutput",
     "__version__",

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

@@ -14,6 +14,7 @@ import subprocess
 import time
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
     Literal,
     NoReturn,
@@ -23,12 +24,14 @@ import warnings
 
 from pydantic import (
     BaseModel,
+    BeforeValidator,
     ConfigDict,
     Field,
     InstanceOf,
     PrivateAttr,
     model_validator,
 )
+from pydantic.functional_serializers import PlainSerializer
 from typing_extensions import Self
 
 from crewai.agent.planning_config import PlanningConfig
@@ -46,7 +49,11 @@ from crewai.agent.utils import (
     save_last_messages,
     validate_max_execution_time,
 )
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.agent_builder.base_agent import (
+    BaseAgent,
+    _serialize_llm_ref,
+    _validate_llm_ref,
+)
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.events.event_bus import crewai_event_bus
@@ -122,6 +129,24 @@ if TYPE_CHECKING:
 
 _passthrough_exceptions: tuple[type[Exception], ...] = ()
 
+_EXECUTOR_CLASS_MAP: dict[str, type] = {
+    "CrewAgentExecutor": CrewAgentExecutor,
+    "AgentExecutor": AgentExecutor,
+}
+
+
+def _validate_executor_class(value: Any) -> Any:
+    if isinstance(value, str):
+        cls = _EXECUTOR_CLASS_MAP.get(value)
+        if cls is None:
+            raise ValueError(f"Unknown executor class: {value}")
+        return cls
+    return value
+
+
+def _serialize_executor_class(value: Any) -> str:
+    return value.__name__ if isinstance(value, type) else str(value)
+
 
 class Agent(BaseAgent):
     """Represents an agent in a system.
@@ -167,12 +192,16 @@ class Agent(BaseAgent):
         default=True,
         description="Use system prompt for the agent.",
     )
-    llm: str | BaseLLM | None = Field(
-        description="Language model that will run the agent.", default=None
-    )
-    function_calling_llm: str | BaseLLM | None = Field(
-        description="Language model that will run the agent.", default=None
-    )
+    llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(description="Language model that will run the agent.", default=None)
+    function_calling_llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(description="Language model that will run the agent.", default=None)
     system_template: str | None = Field(
         default=None, description="System format for the agent."
     )
@@ -271,7 +300,11 @@ class Agent(BaseAgent):
     agent_executor: InstanceOf[CrewAgentExecutor] | InstanceOf[AgentExecutor] | None = (
         Field(default=None, description="An instance of the CrewAgentExecutor class.")
     )
-    executor_class: type[CrewAgentExecutor] | type[AgentExecutor] = Field(
+    executor_class: Annotated[
+        type[CrewAgentExecutor] | type[AgentExecutor],
+        BeforeValidator(_validate_executor_class),
+        PlainSerializer(_serialize_executor_class, return_type=str, when_used="json"),
+    ] = Field(
         default=CrewAgentExecutor,
         description="Class to use for the agent executor. Defaults to CrewAgentExecutor, can optionally use AgentExecutor.",
     )
@@ -1053,7 +1086,7 @@ class Agent(BaseAgent):
                 )
             )
 
-    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
+    def get_delegation_tools(self, agents: Sequence[BaseAgent]) -> list[BaseTool]:
         agent_tools = AgentTools(agents=agents)
         return agent_tools.tools()
 

lib/crewai/src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py

@@ -5,7 +5,7 @@ with CrewAI's agent system. Provides memory persistence, tool integration, and s
 output functionality.
 """
 
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
 from typing import Any, cast
 
 from pydantic import ConfigDict, Field, PrivateAttr
@@ -30,6 +30,7 @@ from crewai.events.types.agent_events import (
 )
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.tools.base_tool import BaseTool
+from crewai.types.callback import SerializableCallable
 from crewai.utilities import Logger
 from crewai.utilities.converter import Converter
 from crewai.utilities.import_utils import require
@@ -50,7 +51,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
     _memory: Any = PrivateAttr(default=None)
     _max_iterations: int = PrivateAttr(default=10)
     function_calling_llm: Any = Field(default=None)
-    step_callback: Callable[..., Any] | None = Field(default=None)
+    step_callback: SerializableCallable | None = Field(default=None)
 
     model: str = Field(default="gpt-4o")
     verbose: bool = Field(default=False)
@@ -272,7 +273,7 @@ class LangGraphAgentAdapter(BaseAgentAdapter):
             available_tools: list[Any] = self._tool_adapter.tools()
             self._graph.tools = available_tools
 
-    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
+    def get_delegation_tools(self, agents: Sequence[BaseAgent]) -> list[BaseTool]:
         """Implement delegation tools support for LangGraph.
 
         Creates delegation tools that allow this agent to delegate tasks to other agents.

lib/crewai/src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -4,6 +4,7 @@ This module contains the OpenAIAgentAdapter class that integrates OpenAI Assista
 with CrewAI's agent system, providing tool integration and structured output support.
 """
 
+from collections.abc import Sequence
 from typing import Any, cast
 
 from pydantic import ConfigDict, Field, PrivateAttr
@@ -221,7 +222,7 @@ class OpenAIAgentAdapter(BaseAgentAdapter):
         """
         return self._converter_adapter.post_process_result(result.final_output)
 
-    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
+    def get_delegation_tools(self, agents: Sequence[BaseAgent]) -> list[BaseTool]:
         """Implement delegation tools support.
 
         Creates delegation tools that allow this agent to delegate tasks to other agents.

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
+from collections.abc import Sequence
 from copy import copy as shallow_copy
 from hashlib import md5
 from pathlib import Path
@@ -48,6 +49,7 @@ from crewai.utilities.string_utils import interpolate_only
 
 
 if TYPE_CHECKING:
+    from crewai.context import ExecutionContext
     from crewai.crew import Crew
 
 
@@ -61,6 +63,26 @@ def _serialize_crew_ref(value: Any) -> str | None:
     return str(value.id) if hasattr(value, "id") else str(value)
 
 
+def _validate_llm_ref(value: Any) -> Any:
+    return value
+
+
+def _resolve_agent(value: Any, info: Any) -> Any:
+    if isinstance(value, BaseAgent) or value is None or not isinstance(value, dict):
+        return value
+    from crewai.agent.core import Agent
+
+    return Agent.model_validate(value, context=getattr(info, "context", None))
+
+
+def _serialize_llm_ref(value: Any) -> str | None:
+    if value is None:
+        return None
+    if isinstance(value, str):
+        return value
+    return getattr(value, "model", str(value))
+
+
 _SLUG_RE: Final[re.Pattern[str]] = re.compile(
     r"^(?:crewai-amp:)?[a-zA-Z0-9][a-zA-Z0-9_-]*(?:#[\w-]+)?$"
 )
@@ -138,6 +160,8 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
             Set private attributes.
     """
 
+    entity_type: Literal["agent"] = "agent"
+
     __hash__ = object.__hash__
     _logger: Logger = PrivateAttr(default_factory=lambda: Logger(verbose=False))
     _rpm_controller: RPMController | None = PrivateAttr(default=None)
@@ -176,9 +200,11 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     agent_executor: InstanceOf[CrewAgentExecutorMixin] | None = Field(
         default=None, description="An instance of the CrewAgentExecutor class."
     )
-    llm: str | BaseLLM | None = Field(
-        default=None, description="Language model that will run the agent."
-    )
+    llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(default=None, description="Language model that will run the agent.")
     crew: Annotated[
         Crew | str | None,
         BeforeValidator(_validate_crew_ref),
@@ -197,7 +223,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         description="An instance of the ToolsHandler class.",
     )
     tools_results: list[dict[str, Any]] = Field(
-        default=[], description="Results of the tools used by the agent."
+        default_factory=list, description="Results of the tools used by the agent."
     )
     max_tokens: int | None = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
@@ -248,6 +274,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         description="Agent Skills. Accepts paths for discovery or pre-loaded Skill objects.",
         min_length=1,
     )
+    execution_context: ExecutionContext | None = Field(default=None)
 
     @model_validator(mode="before")
     @classmethod
@@ -362,11 +389,12 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
 
     @field_validator("id", mode="before")
     @classmethod
-    def _deny_user_set_id(cls, v: UUID4 | None) -> None:
-        if v:
+    def _deny_user_set_id(cls, v: UUID4 | None, info: Any) -> UUID4 | None:
+        if v and not (info.context or {}).get("from_checkpoint"):
             raise PydanticCustomError(
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
+        return v
 
     @model_validator(mode="after")
     def set_private_attrs(self) -> Self:
@@ -423,7 +451,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: list[BaseAgent]) -> list[BaseTool]:
+    def get_delegation_tools(self, agents: Sequence[BaseAgent]) -> list[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
 
     @abstractmethod

lib/crewai/src/crewai/agents/tools_handler.py

@@ -3,20 +3,15 @@
 from __future__ import annotations
 
 import json
-from typing import TYPE_CHECKING, Any
 
-from pydantic import GetCoreSchemaHandler
-from pydantic_core import CoreSchema, core_schema
+from pydantic import BaseModel, Field
 
+from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.tools.cache_tools.cache_tools import CacheTools
+from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 
 
-if TYPE_CHECKING:
-    from crewai.agents.cache.cache_handler import CacheHandler
-    from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
-
-
-class ToolsHandler:
+class ToolsHandler(BaseModel):
     """Callback handler for tool usage.
 
     Attributes:
@@ -24,14 +19,8 @@ class ToolsHandler:
         cache: Optional cache handler for storing tool outputs.
     """
 
-    def __init__(self, cache: CacheHandler | None = None) -> None:
-        """Initialize the callback handler.
-
-        Args:
-            cache: Optional cache handler for storing tool outputs.
-        """
-        self.cache: CacheHandler | None = cache
-        self.last_used_tool: ToolCalling | InstructorToolCalling | None = None
+    cache: CacheHandler | None = Field(default=None)
+    last_used_tool: ToolCalling | InstructorToolCalling | None = Field(default=None)
 
     def on_tool_use(
         self,
@@ -48,7 +37,6 @@ class ToolsHandler:
         """
         self.last_used_tool = calling
         if self.cache and should_cache and calling.tool_name != CacheTools().name:
-            # Convert arguments to string for cache
             input_str = ""
             if calling.arguments:
                 if isinstance(calling.arguments, dict):
@@ -61,14 +49,3 @@ class ToolsHandler:
                 input=input_str,
                 output=output,
             )
-
-    @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/cli.py

@@ -27,7 +27,7 @@ from crewai.cli.tools.main import ToolCommand
 from crewai.cli.train_crew import train_crew
 from crewai.cli.triggers.main import TriggersCommand
 from crewai.cli.update_crew import update_crew
-from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml
+from crewai.cli.utils import build_env_with_all_tool_credentials, read_toml
 from crewai.memory.storage.kickoff_task_outputs_storage import (
     KickoffTaskOutputsSQLiteStorage,
 )
@@ -48,24 +48,18 @@ def crewai() -> None:
 @click.argument("uv_args", nargs=-1, type=click.UNPROCESSED)
 def uv(uv_args: tuple[str, ...]) -> None:
     """A wrapper around uv commands that adds custom tool authentication through env vars."""
-    env = os.environ.copy()
     try:
-        pyproject_data = read_toml()
-        sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {})
-
-        for source_config in sources.values():
-            if isinstance(source_config, dict):
-                index = source_config.get("index")
-                if index:
-                    index_env = build_env_with_tool_repository_credentials(index)
-                    env.update(index_env)
-    except (FileNotFoundError, KeyError) as e:
+        # Verify pyproject.toml exists first
+        read_toml()
+    except FileNotFoundError as e:
         raise SystemExit(
             "Error. A valid pyproject.toml file is required. Check that a valid pyproject.toml file exists in the current directory."
         ) from e
     except Exception as e:
         raise SystemExit(f"Error: {e}") from e
 
+    env = build_env_with_all_tool_credentials()
+
     try:
         subprocess.run(  # noqa: S603
             ["uv", *uv_args],  # noqa: S607

lib/crewai/src/crewai/cli/create_flow.py

@@ -46,7 +46,7 @@ def create_flow(name: str) -> None:
     tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
 
     crew_folders = [
-        "poem_crew",
+        "content_crew",
     ]
 
     def process_file(src_file: Path, dst_file: Path) -> None:

lib/crewai/src/crewai/cli/install_crew.py

@@ -2,6 +2,8 @@ import subprocess
 
 import click
 
+from crewai.cli.utils import build_env_with_all_tool_credentials
+
 
 # Be mindful about changing this.
 # on some environments we don't use this command but instead uv sync directly
@@ -13,7 +15,14 @@ def install_crew(proxy_options: list[str]) -> None:
     """
     try:
         command = ["uv", "sync", *proxy_options]
-        subprocess.run(command, check=True, capture_output=False, text=True)  # noqa: S603
+
+        # Inject tool repository credentials so uv can authenticate
+        # against private package indexes (e.g. crewai tool repository).
+        # Without this, `uv sync` fails with 401 Unauthorized when the
+        # project depends on tools from a private index.
+        env = build_env_with_all_tool_credentials()
+
+        subprocess.run(command, check=True, capture_output=False, text=True, env=env)  # noqa: S603
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while running the crew: {e}", err=True)

lib/crewai/src/crewai/cli/run_crew.py

@@ -1,11 +1,10 @@
 from enum import Enum
-import os
 import subprocess
 
 import click
 from packaging import version
 
-from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml
+from crewai.cli.utils import build_env_with_all_tool_credentials, read_toml
 from crewai.cli.version import get_crewai_version
 
 
@@ -56,19 +55,7 @@ def execute_command(crew_type: CrewType) -> None:
     """
     command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
 
-    env = os.environ.copy()
-    try:
-        pyproject_data = read_toml()
-        sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {})
-
-        for source_config in sources.values():
-            if isinstance(source_config, dict):
-                index = source_config.get("index")
-                if index:
-                    index_env = build_env_with_tool_repository_credentials(index)
-                    env.update(index_env)
-    except Exception:  # noqa: S110
-        pass
+    env = build_env_with_all_tool_credentials()
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True, env=env)  # noqa: S603

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

@@ -120,11 +120,11 @@ my_crew/
 my_flow/
 ├── src/my_flow/
 │   ├── crews/                 # Multiple crew definitions
-│   │   └── poem_crew/
+│   │   └── content_crew/
 │   │       ├── config/
 │   │       │   ├── agents.yaml
 │   │       │   └── tasks.yaml
-│   │       └── poem_crew.py
+│   │       └── content_crew.py
 │   ├── tools/                 # Custom tools
 │   ├── main.py                # Flow orchestration
 │   └── ...

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]==1.13.0a7"
+    "crewai[tools]==1.14.0a2"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/README.md

@@ -38,7 +38,7 @@ crewai run
 
 This command initializes the {{name}} Flow as defined in your configuration.
 
-This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
+This example, unmodified, will run a content creation flow on AI Agents and save the output to `output/post.md`.
 
 ## Understanding Your Crew
 

lib/crewai/src/crewai/cli/templates/flow/crews/content_crew/config/agents.yaml

@@ -0,0 +1,33 @@
+planner:
+  role: >
+    Content Planner
+  goal: >
+    Plan a detailed and engaging blog post outline on {topic}
+  backstory: >
+    You're an experienced content strategist who excels at creating
+    structured outlines for blog posts. You know how to organize ideas
+    into a logical flow that keeps readers engaged from start to finish.
+
+writer:
+  role: >
+    Content Writer
+  goal: >
+    Write a compelling and well-structured blog post on {topic}
+    based on the provided outline
+  backstory: >
+    You're a skilled writer with a talent for turning outlines into
+    engaging, informative blog posts. Your writing is clear, conversational,
+    and backed by solid reasoning. You adapt your tone to the subject matter
+    while keeping things accessible to a broad audience.
+
+editor:
+  role: >
+    Content Editor
+  goal: >
+    Review and polish the blog post on {topic} to ensure it is
+    publication-ready
+  backstory: >
+    You're a meticulous editor with years of experience refining written
+    content. You have an eye for clarity, flow, grammar, and consistency.
+    You improve prose without changing the author's voice and ensure every
+    piece you touch is polished and professional.

lib/crewai/src/crewai/cli/templates/flow/crews/content_crew/config/tasks.yaml

@@ -0,0 +1,50 @@
+planning_task:
+  description: >
+    Create a detailed outline for a blog post about {topic}.
+
+    The outline should include:
+    - A compelling title
+    - An introduction hook
+    - 3-5 main sections with key points to cover in each
+    - A conclusion with a call to action
+
+    Make the outline detailed enough that a writer can produce
+    a full blog post from it without additional research.
+  expected_output: >
+    A structured blog post outline with a title, introduction notes,
+    detailed section breakdowns, and conclusion notes.
+  agent: planner
+
+writing_task:
+  description: >
+    Using the outline provided, write a full blog post about {topic}.
+
+    Requirements:
+    - Follow the outline structure closely
+    - Write in a clear, engaging, and conversational tone
+    - Each section should be 2-3 paragraphs
+    - Include a strong introduction and conclusion
+    - Target around 800-1200 words
+  expected_output: >
+    A complete blog post in markdown format, ready for editing.
+    The post should follow the outline and be well-written with
+    clear transitions between sections.
+  agent: writer
+
+editing_task:
+  description: >
+    Review and edit the blog post about {topic}.
+
+    Focus on:
+    - Fixing any grammar or spelling errors
+    - Improving sentence clarity and flow
+    - Ensuring consistent tone throughout
+    - Strengthening the introduction and conclusion
+    - Removing any redundancy
+
+    Do not rewrite the post — refine and polish it.
+  expected_output: >
+    The final, polished blog post in markdown format without '```'.
+    Publication-ready with clean formatting and professional prose.
+  agent: editor
+  output_file: output/post.md

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

@@ -8,8 +8,8 @@ from crewai.project import CrewBase, agent, crew, task
 
 
 @CrewBase
-class PoemCrew:
-    """Poem Crew"""
+class ContentCrew:
+    """Content Crew"""
 
     agents: list[BaseAgent]
     tasks: list[Task]
@@ -20,26 +20,50 @@ class PoemCrew:
     agents_config = "config/agents.yaml"
     tasks_config = "config/tasks.yaml"
 
-    # If you would lik to add tools to your crew, you can learn more about it here:
+    # If you would like to add tools to your crew, you can learn more about it here:
     # https://docs.crewai.com/concepts/agents#agent-tools
     @agent
-    def poem_writer(self) -> Agent:
+    def planner(self) -> Agent:
         return Agent(
-            config=self.agents_config["poem_writer"],  # type: ignore[index]
+            config=self.agents_config["planner"],  # type: ignore[index]
+        )
+
+    @agent
+    def writer(self) -> Agent:
+        return Agent(
+            config=self.agents_config["writer"],  # type: ignore[index]
+        )
+
+    @agent
+    def editor(self) -> Agent:
+        return Agent(
+            config=self.agents_config["editor"],  # type: ignore[index]
         )
 
     # To learn more about structured task outputs,
     # task dependencies, and task callbacks, check out the documentation:
     # https://docs.crewai.com/concepts/tasks#overview-of-a-task
     @task
-    def write_poem(self) -> Task:
+    def planning_task(self) -> Task:
         return Task(
-            config=self.tasks_config["write_poem"],  # type: ignore[index]
+            config=self.tasks_config["planning_task"],  # type: ignore[index]
+        )
+
+    @task
+    def writing_task(self) -> Task:
+        return Task(
+            config=self.tasks_config["writing_task"],  # type: ignore[index]
+        )
+
+    @task
+    def editing_task(self) -> Task:
+        return Task(
+            config=self.tasks_config["editing_task"],  # type: ignore[index]
         )
 
     @crew
     def crew(self) -> Crew:
-        """Creates the Research Crew"""
+        """Creates the Content Crew"""
         # To learn how to add knowledge sources to your crew, check out the documentation:
         # https://docs.crewai.com/concepts/knowledge#what-is-knowledge
 

lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/__init__.py

@@ -1 +0,0 @@
-"""Poem crew template."""

lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/agents.yaml

@@ -1,11 +0,0 @@
-poem_writer:
-  role: >
-    CrewAI Poem Writer
-  goal: >
-    Generate a funny, light heartedpoem about how CrewAI 
-    is awesome with a sentence count of {sentence_count}
-  backstory: >
-    You're a creative poet with a talent for capturing the essence of any topic
-    in a beautiful and engaging way. Known for your ability to craft poems that
-    resonate with readers, you bring a unique perspective and artistic flair to
-    every piece you write.

lib/crewai/src/crewai/cli/templates/flow/crews/poem_crew/config/tasks.yaml

@@ -1,7 +0,0 @@
-write_poem:
-  description: >
-    Write a poem about how CrewAI is awesome.
-    Ensure the poem is engaging and adheres to the specified sentence count of {sentence_count}.
-  expected_output: >
-    A beautifully crafted poem about CrewAI, with exactly {sentence_count} sentences.
-  agent: poem_writer

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

@@ -1,59 +1,64 @@
 #!/usr/bin/env python
-from random import randint
+from pathlib import Path
 
 from pydantic import BaseModel
 
 from crewai.flow import Flow, listen, start
 
-from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
+from {{folder_name}}.crews.content_crew.content_crew import ContentCrew
 
 
-class PoemState(BaseModel):
-    sentence_count: int = 1
-    poem: str = ""
+class ContentState(BaseModel):
+    topic: str = ""
+    outline: str = ""
+    draft: str = ""
+    final_post: str = ""
 
 
-class PoemFlow(Flow[PoemState]):
+class ContentFlow(Flow[ContentState]):
 
     @start()
-    def generate_sentence_count(self, crewai_trigger_payload: dict = None):
-        print("Generating sentence count")
+    def plan_content(self, crewai_trigger_payload: dict = None):
+        print("Planning content")
 
-        # Use trigger payload if available
         if crewai_trigger_payload:
-            # Example: use trigger data to influence sentence count
-            self.state.sentence_count = crewai_trigger_payload.get('sentence_count', randint(1, 5))
+            self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
             print(f"Using trigger payload: {crewai_trigger_payload}")
         else:
-            self.state.sentence_count = randint(1, 5)
+            self.state.topic = "AI Agents"
 
-    @listen(generate_sentence_count)
-    def generate_poem(self):
-        print("Generating poem")
+        print(f"Topic: {self.state.topic}")
+
+    @listen(plan_content)
+    def generate_content(self):
+        print(f"Generating content on: {self.state.topic}")
         result = (
-            PoemCrew()
+            ContentCrew()
             .crew()
-            .kickoff(inputs={"sentence_count": self.state.sentence_count})
+            .kickoff(inputs={"topic": self.state.topic})
         )
 
-        print("Poem generated", result.raw)
-        self.state.poem = result.raw
+        print("Content generated")
+        self.state.final_post = result.raw
 
-    @listen(generate_poem)
-    def save_poem(self):
-        print("Saving poem")
-        with open("poem.txt", "w") as f:
-            f.write(self.state.poem)
+    @listen(generate_content)
+    def save_content(self):
+        print("Saving content")
+        output_dir = Path("output")
+        output_dir.mkdir(exist_ok=True)
+        with open(output_dir / "post.md", "w") as f:
+            f.write(self.state.final_post)
+        print("Post saved to output/post.md")
 
 
 def kickoff():
-    poem_flow = PoemFlow()
-    poem_flow.kickoff()
+    content_flow = ContentFlow()
+    content_flow.kickoff()
 
 
 def plot():
-    poem_flow = PoemFlow()
-    poem_flow.plot()
+    content_flow = ContentFlow()
+    content_flow.plot()
 
 
 def run_with_trigger():
@@ -74,10 +79,10 @@ def run_with_trigger():
 
     # Create flow and kickoff with trigger payload
     # The @start() methods will automatically receive crewai_trigger_payload parameter
-    poem_flow = PoemFlow()
+    content_flow = ContentFlow()
 
     try:
-        result = poem_flow.kickoff({"crewai_trigger_payload": trigger_payload})
+        result = content_flow.kickoff({"crewai_trigger_payload": trigger_payload})
         return result
     except Exception as e:
         raise Exception(f"An error occurred while running the flow with trigger: {e}")

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]==1.13.0a7"
+    "crewai[tools]==1.14.0a2"
 ]
 
 [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]==1.13.0a7"
+    "crewai[tools]==1.14.0a2"
 ]
 
 [tool.crewai]

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

@@ -21,6 +21,7 @@ from crewai.cli.utils import (
     get_project_description,
     get_project_name,
     get_project_version,
+    read_toml,
     tree_copy,
     tree_find_and_replace,
 )
@@ -116,11 +117,26 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
         self._print_tools_preview(tools_metadata)
         self._print_current_organization()
 
+        build_env = os.environ.copy()
+        try:
+            pyproject_data = read_toml()
+            sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {})
+
+            for source_config in sources.values():
+                if isinstance(source_config, dict):
+                    index = source_config.get("index")
+                    if index:
+                        index_env = build_env_with_tool_repository_credentials(index)
+                        build_env.update(index_env)
+        except Exception:  # noqa: S110
+            pass
+
         with tempfile.TemporaryDirectory() as temp_build_dir:
             subprocess.run(  # noqa: S603
                 ["uv", "build", "--sdist", "--out-dir", temp_build_dir],  # noqa: S607
                 check=True,
                 capture_output=False,
+                env=build_env,
             )
 
             tarball_filename = next(

lib/crewai/src/crewai/cli/utils.py

@@ -484,8 +484,12 @@ def get_flows(flow_path: str = "main.py") -> list[Flow[Any]]:
             if flow_instances:
                 break
 
-    except Exception:  # noqa: S110
-        pass
+    except Exception as e:
+        import logging
+
+        logging.getLogger(__name__).debug(
+            f"Could not load tool repository credentials: {e}"
+        )
 
     return flow_instances
 
@@ -549,6 +553,31 @@ def build_env_with_tool_repository_credentials(
     return env
 
 
+def build_env_with_all_tool_credentials() -> dict[str, Any]:
+    """
+    Build environment dict with credentials for all tool repository indexes
+    found in pyproject.toml's [tool.uv.sources] section.
+
+    Returns:
+        dict: Environment variables with credentials for all private indexes.
+    """
+    env = os.environ.copy()
+    try:
+        pyproject_data = read_toml()
+        sources = pyproject_data.get("tool", {}).get("uv", {}).get("sources", {})
+
+        for source_config in sources.values():
+            if isinstance(source_config, dict):
+                index = source_config.get("index")
+                if index:
+                    index_env = build_env_with_tool_repository_credentials(index)
+                    env.update(index_env)
+    except Exception:  # noqa: S110
+        pass
+
+    return env
+
+
 @contextmanager
 def _load_module_from_file(
     init_file: Path, module_name: str | None = None

lib/crewai/src/crewai/context.py

@@ -4,6 +4,23 @@ import contextvars
 import os
 from typing import Any
 
+from pydantic import BaseModel, Field
+
+from crewai.events.base_events import (
+    get_emission_sequence,
+    set_emission_counter,
+)
+from crewai.events.event_context import (
+    _event_id_stack,
+    _last_event_id,
+    _triggering_event_id,
+)
+from crewai.flow.flow_context import (
+    current_flow_id,
+    current_flow_method_name,
+    current_flow_request_id,
+)
+
 
 _platform_integration_token: contextvars.ContextVar[str | None] = (
     contextvars.ContextVar("platform_integration_token", default=None)
@@ -63,3 +80,53 @@ def reset_current_task_id(token: contextvars.Token[str | None]) -> None:
 def get_current_task_id() -> str | None:
     """Get the current task ID from the context."""
     return _current_task_id.get()
+
+
+class ExecutionContext(BaseModel):
+    """Snapshot of ContextVar execution state."""
+
+    current_task_id: str | None = Field(default=None)
+    flow_request_id: str | None = Field(default=None)
+    flow_id: str | None = Field(default=None)
+    flow_method_name: str = Field(default="unknown")
+
+    event_id_stack: tuple[tuple[str, str], ...] = Field(default=())
+    last_event_id: str | None = Field(default=None)
+    triggering_event_id: str | None = Field(default=None)
+    emission_sequence: int = Field(default=0)
+
+    feedback_callback_info: dict[str, Any] | None = Field(default=None)
+    platform_token: str | None = Field(default=None)
+
+
+def capture_execution_context(
+    feedback_callback_info: dict[str, Any] | None = None,
+) -> ExecutionContext:
+    """Read current ContextVars into an ExecutionContext."""
+    return ExecutionContext(
+        current_task_id=_current_task_id.get(),
+        flow_request_id=current_flow_request_id.get(),
+        flow_id=current_flow_id.get(),
+        flow_method_name=current_flow_method_name.get(),
+        event_id_stack=_event_id_stack.get(),
+        last_event_id=_last_event_id.get(),
+        triggering_event_id=_triggering_event_id.get(),
+        emission_sequence=get_emission_sequence(),
+        feedback_callback_info=feedback_callback_info,
+        platform_token=_platform_integration_token.get(),
+    )
+
+
+def apply_execution_context(ctx: ExecutionContext) -> None:
+    """Write an ExecutionContext back into the ContextVars."""
+    _current_task_id.set(ctx.current_task_id)
+    current_flow_request_id.set(ctx.flow_request_id)
+    current_flow_id.set(ctx.flow_id)
+    current_flow_method_name.set(ctx.flow_method_name)
+
+    _event_id_stack.set(ctx.event_id_stack)
+    _last_event_id.set(ctx.last_event_id)
+    _triggering_event_id.set(ctx.triggering_event_id)
+    set_emission_counter(ctx.emission_sequence)
+
+    _platform_integration_token.set(ctx.platform_token)

lib/crewai/src/crewai/crew.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import asyncio
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
@@ -10,7 +10,9 @@ from pathlib import Path
 import re
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
+    Literal,
     cast,
 )
 import uuid
@@ -21,12 +23,14 @@ from opentelemetry.context import attach, detach
 from pydantic import (
     UUID4,
     BaseModel,
+    BeforeValidator,
     Field,
     Json,
     PrivateAttr,
     field_validator,
     model_validator,
 )
+from pydantic.functional_serializers import PlainSerializer
 from pydantic_core import PydanticCustomError
 from rich.console import Console
 from rich.panel import Panel
@@ -37,6 +41,8 @@ if TYPE_CHECKING:
     from crewai_files import FileInput
     from opentelemetry.trace import Span
 
+    from crewai.context import ExecutionContext
+
 try:
     from crewai_files import get_supported_content_types
 
@@ -49,7 +55,12 @@ except ImportError:
 
 
 from crewai.agent import Agent
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.agent_builder.base_agent import (
+    BaseAgent,
+    _resolve_agent,
+    _serialize_llm_ref,
+    _validate_llm_ref,
+)
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.crews.crew_output import CrewOutput
 from crewai.crews.utils import (
@@ -132,6 +143,12 @@ from crewai.utilities.training_handler import CrewTrainingHandler
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 
+def _resolve_agents(value: Any, info: Any) -> Any:
+    if not isinstance(value, list):
+        return value
+    return [_resolve_agent(a, info) for a in value]
+
+
 class Crew(FlowTrackable, BaseModel):
     """
     Represents a group of agents, defining how they should collaborate and the
@@ -170,6 +187,8 @@ class Crew(FlowTrackable, BaseModel):
             fingerprinting.
     """
 
+    entity_type: Literal["crew"] = "crew"
+
     __hash__ = object.__hash__
     _execution_span: Span | None = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr()
@@ -191,7 +210,10 @@ class Crew(FlowTrackable, BaseModel):
     name: str | None = Field(default="crew")
     cache: bool = Field(default=True)
     tasks: list[Task] = Field(default_factory=list)
-    agents: list[BaseAgent] = Field(default_factory=list)
+    agents: Annotated[
+        list[BaseAgent],
+        BeforeValidator(_resolve_agents),
+    ] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: bool = Field(default=False)
     memory: bool | Memory | MemoryScope | MemorySlice | None = Field(
@@ -209,15 +231,20 @@ class Crew(FlowTrackable, BaseModel):
         default=None,
         description="Metrics for the LLM usage during all tasks execution.",
     )
-    manager_llm: str | BaseLLM | None = Field(
-        description="Language model that will run the agent.", default=None
-    )
-    manager_agent: BaseAgent | None = Field(
-        description="Custom agent that will be used as manager.", default=None
-    )
-    function_calling_llm: str | LLM | None = Field(
-        description="Language model that will run the agent.", default=None
-    )
+    manager_llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(description="Language model that will run the agent.", default=None)
+    manager_agent: Annotated[
+        BaseAgent | None,
+        BeforeValidator(_resolve_agent),
+    ] = Field(description="Custom agent that will be used as manager.", default=None)
+    function_calling_llm: Annotated[
+        str | LLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(description="Language model that will run the agent.", default=None)
     config: Json[dict[str, Any]] | dict[str, Any] | None = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     share_crew: bool | None = Field(default=False)
@@ -266,7 +293,11 @@ class Crew(FlowTrackable, BaseModel):
         default=False,
         description="Plan the crew execution and add the plan to the crew.",
     )
-    planning_llm: str | BaseLLM | None = Field(
+    planning_llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(
         default=None,
         description=(
             "Language model that will run the AgentPlanner if planning is True."
@@ -287,7 +318,11 @@ class Crew(FlowTrackable, BaseModel):
             "knowledge object."
         ),
     )
-    chat_llm: str | BaseLLM | None = Field(
+    chat_llm: Annotated[
+        str | BaseLLM | None,
+        BeforeValidator(_validate_llm_ref),
+        PlainSerializer(_serialize_llm_ref, return_type=str | None, when_used="json"),
+    ] = Field(
         default=None,
         description="LLM used to handle chatting with the crew.",
     )
@@ -313,14 +348,20 @@ class Crew(FlowTrackable, BaseModel):
         description="Whether to enable tracing for the crew. True=always enable, False=always disable, None=check environment/user settings.",
     )
 
+    execution_context: ExecutionContext | None = Field(default=None)
+    checkpoint_inputs: dict[str, Any] | None = Field(default=None)
+    checkpoint_train: bool | None = Field(default=None)
+    checkpoint_kickoff_event_id: str | None = Field(default=None)
+
     @field_validator("id", mode="before")
     @classmethod
-    def _deny_user_set_id(cls, v: UUID4 | None) -> None:
+    def _deny_user_set_id(cls, v: UUID4 | None, info: Any) -> UUID4 | None:
         """Prevent manual setting of the 'id' field by users."""
-        if v:
+        if v and not (info.context or {}).get("from_checkpoint"):
             raise PydanticCustomError(
                 "may_not_set_field", "The 'id' field cannot be set by the user.", {}
             )
+        return v
 
     @field_validator("config", mode="before")
     @classmethod
@@ -1388,7 +1429,7 @@ class Crew(FlowTrackable, BaseModel):
         self,
         tools: list[BaseTool],
         task_agent: BaseAgent,
-        agents: list[BaseAgent],
+        agents: Sequence[BaseAgent],
     ) -> list[BaseTool]:
         if hasattr(task_agent, "get_delegation_tools"):
             delegation_tools = task_agent.get_delegation_tools(agents)

lib/crewai/src/crewai/crews/crew_output.py

@@ -21,7 +21,7 @@ class CrewOutput(BaseModel):
         description="JSON dict output of Crew", default=None
     )
     tasks_output: list[TaskOutput] = Field(
-        description="Output of each task", default=[]
+        description="Output of each task", default_factory=list
     )
     token_usage: UsageMetrics = Field(
         description="Processed token summary", default_factory=UsageMetrics

lib/crewai/src/crewai/events/event_listener.py

@@ -78,9 +78,15 @@ from crewai.events.types.mcp_events import (
     MCPConnectionCompletedEvent,
     MCPConnectionFailedEvent,
     MCPConnectionStartedEvent,
+    MCPToolExecutionCompletedEvent,
     MCPToolExecutionFailedEvent,
     MCPToolExecutionStartedEvent,
 )
+from crewai.events.types.memory_events import (
+    MemoryQueryCompletedEvent,
+    MemoryRetrievalCompletedEvent,
+    MemorySaveCompletedEvent,
+)
 from crewai.events.types.observation_events import (
     GoalAchievedEarlyEvent,
     PlanRefinementEvent,
@@ -94,6 +100,12 @@ from crewai.events.types.reasoning_events import (
     AgentReasoningFailedEvent,
     AgentReasoningStartedEvent,
 )
+from crewai.events.types.skill_events import (
+    SkillActivatedEvent,
+    SkillDiscoveryCompletedEvent,
+    SkillLoadFailedEvent,
+    SkillLoadedEvent,
+)
 from crewai.events.types.task_events import (
     TaskCompletedEvent,
     TaskFailedEvent,
@@ -478,6 +490,7 @@ class EventListener(BaseEventListener):
             self.formatter.handle_guardrail_completed(
                 event.success, event.error, event.retry_count
             )
+            self._telemetry.feature_usage_span("guardrail:execution")
 
         @crewai_event_bus.on(CrewTestStartedEvent)
         def on_crew_test_started(source: Any, event: CrewTestStartedEvent) -> None:
@@ -559,6 +572,7 @@ class EventListener(BaseEventListener):
                 event.plan,
                 event.ready,
             )
+            self._telemetry.feature_usage_span("planning:creation")
 
         @crewai_event_bus.on(AgentReasoningFailedEvent)
         def on_agent_reasoning_failed(_: Any, event: AgentReasoningFailedEvent) -> None:
@@ -616,6 +630,7 @@ class EventListener(BaseEventListener):
                 event.replan_count,
                 event.completed_steps_preserved,
             )
+            self._telemetry.feature_usage_span("planning:replan")
 
         @crewai_event_bus.on(GoalAchievedEarlyEvent)
         def on_goal_achieved_early(_: Any, event: GoalAchievedEarlyEvent) -> None:
@@ -623,6 +638,25 @@ class EventListener(BaseEventListener):
                 event.steps_completed,
                 event.steps_remaining,
             )
+            self._telemetry.feature_usage_span("planning:goal_achieved_early")
+
+        # ----------- SKILL EVENTS -----------
+
+        @crewai_event_bus.on(SkillDiscoveryCompletedEvent)
+        def on_skill_discovery(_: Any, event: SkillDiscoveryCompletedEvent) -> None:
+            self._telemetry.feature_usage_span("skill:discovery")
+
+        @crewai_event_bus.on(SkillLoadedEvent)
+        def on_skill_loaded(_: Any, event: SkillLoadedEvent) -> None:
+            self._telemetry.feature_usage_span("skill:loaded")
+
+        @crewai_event_bus.on(SkillLoadFailedEvent)
+        def on_skill_load_failed(_: Any, event: SkillLoadFailedEvent) -> None:
+            self._telemetry.feature_usage_span("skill:load_failed")
+
+        @crewai_event_bus.on(SkillActivatedEvent)
+        def on_skill_activated(_: Any, event: SkillActivatedEvent) -> None:
+            self._telemetry.feature_usage_span("skill:activated")
 
         # ----------- AGENT LOGGING EVENTS -----------
 
@@ -662,6 +696,7 @@ class EventListener(BaseEventListener):
                 event.error,
                 event.is_multiturn,
             )
+            self._telemetry.feature_usage_span("a2a:delegation")
 
         @crewai_event_bus.on(A2AConversationStartedEvent)
         def on_a2a_conversation_started(
@@ -703,6 +738,7 @@ class EventListener(BaseEventListener):
                 event.error,
                 event.total_turns,
             )
+            self._telemetry.feature_usage_span("a2a:conversation")
 
         @crewai_event_bus.on(A2APollingStartedEvent)
         def on_a2a_polling_started(_: Any, event: A2APollingStartedEvent) -> None:
@@ -744,6 +780,7 @@ class EventListener(BaseEventListener):
                 event.connection_duration_ms,
                 event.is_reconnect,
             )
+            self._telemetry.feature_usage_span("mcp:connection")
 
         @crewai_event_bus.on(MCPConnectionFailedEvent)
         def on_mcp_connection_failed(_: Any, event: MCPConnectionFailedEvent) -> None:
@@ -754,6 +791,7 @@ class EventListener(BaseEventListener):
                 event.error,
                 event.error_type,
             )
+            self._telemetry.feature_usage_span("mcp:connection_failed")
 
         @crewai_event_bus.on(MCPConfigFetchFailedEvent)
         def on_mcp_config_fetch_failed(
@@ -764,6 +802,7 @@ class EventListener(BaseEventListener):
                 event.error,
                 event.error_type,
             )
+            self._telemetry.feature_usage_span("mcp:config_fetch_failed")
 
         @crewai_event_bus.on(MCPToolExecutionStartedEvent)
         def on_mcp_tool_execution_started(
@@ -775,6 +814,12 @@ class EventListener(BaseEventListener):
                 event.tool_args,
             )
 
+        @crewai_event_bus.on(MCPToolExecutionCompletedEvent)
+        def on_mcp_tool_execution_completed(
+            _: Any, event: MCPToolExecutionCompletedEvent
+        ) -> None:
+            self._telemetry.feature_usage_span("mcp:tool_execution")
+
         @crewai_event_bus.on(MCPToolExecutionFailedEvent)
         def on_mcp_tool_execution_failed(
             _: Any, event: MCPToolExecutionFailedEvent
@@ -786,6 +831,45 @@ class EventListener(BaseEventListener):
                 event.error,
                 event.error_type,
             )
+            self._telemetry.feature_usage_span("mcp:tool_execution_failed")
+
+        # ----------- MEMORY TELEMETRY -----------
+
+        @crewai_event_bus.on(MemorySaveCompletedEvent)
+        def on_memory_save_completed(_: Any, event: MemorySaveCompletedEvent) -> None:
+            self._telemetry.feature_usage_span("memory:save")
+
+        @crewai_event_bus.on(MemoryQueryCompletedEvent)
+        def on_memory_query_completed(_: Any, event: MemoryQueryCompletedEvent) -> None:
+            self._telemetry.feature_usage_span("memory:query")
+
+        @crewai_event_bus.on(MemoryRetrievalCompletedEvent)
+        def on_memory_retrieval_completed_telemetry(
+            _: Any, event: MemoryRetrievalCompletedEvent
+        ) -> None:
+            self._telemetry.feature_usage_span("memory:retrieval")
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def on_crew_kickoff_hooks(_: Any, event: CrewKickoffStartedEvent) -> None:
+            from crewai.hooks.llm_hooks import (
+                get_after_llm_call_hooks,
+                get_before_llm_call_hooks,
+            )
+            from crewai.hooks.tool_hooks import (
+                get_after_tool_call_hooks,
+                get_before_tool_call_hooks,
+            )
+
+            has_hooks = any(
+                [
+                    get_before_llm_call_hooks(),
+                    get_after_llm_call_hooks(),
+                    get_before_tool_call_hooks(),
+                    get_after_tool_call_hooks(),
+                ]
+            )
+            if has_hooks:
+                self._telemetry.feature_usage_span("hooks:registered")
 
 
 event_listener = EventListener()

lib/crewai/src/crewai/execution_context.py

@@ -1,80 +0,0 @@
-"""Checkpointable execution context for the crewAI runtime.
-
-Captures the ContextVar state needed to resume execution from a checkpoint.
-Used by the RootModel (step 5) to include execution context in snapshots.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from pydantic import BaseModel, Field
-
-from crewai.context import (
-    _current_task_id,
-    _platform_integration_token,
-)
-from crewai.events.base_events import (
-    get_emission_sequence,
-    set_emission_counter,
-)
-from crewai.events.event_context import (
-    _event_id_stack,
-    _last_event_id,
-    _triggering_event_id,
-)
-from crewai.flow.flow_context import (
-    current_flow_id,
-    current_flow_method_name,
-    current_flow_request_id,
-)
-
-
-class ExecutionContext(BaseModel):
-    """Snapshot of ContextVar state required for checkpoint/resume."""
-
-    current_task_id: str | None = Field(default=None)
-    flow_request_id: str | None = Field(default=None)
-    flow_id: str | None = Field(default=None)
-    flow_method_name: str = Field(default="unknown")
-
-    event_id_stack: tuple[tuple[str, str], ...] = Field(default=())
-    last_event_id: str | None = Field(default=None)
-    triggering_event_id: str | None = Field(default=None)
-    emission_sequence: int = Field(default=0)
-
-    feedback_callback_info: dict[str, Any] | None = Field(default=None)
-    platform_token: str | None = Field(default=None)
-
-
-def capture_execution_context(
-    feedback_callback_info: dict[str, Any] | None = None,
-) -> ExecutionContext:
-    """Read all checkpoint-required ContextVars into an ExecutionContext."""
-    return ExecutionContext(
-        current_task_id=_current_task_id.get(),
-        flow_request_id=current_flow_request_id.get(),
-        flow_id=current_flow_id.get(),
-        flow_method_name=current_flow_method_name.get(),
-        event_id_stack=_event_id_stack.get(),
-        last_event_id=_last_event_id.get(),
-        triggering_event_id=_triggering_event_id.get(),
-        emission_sequence=get_emission_sequence(),
-        feedback_callback_info=feedback_callback_info,
-        platform_token=_platform_integration_token.get(),
-    )
-
-
-def apply_execution_context(ctx: ExecutionContext) -> None:
-    """Write an ExecutionContext back into the ContextVars."""
-    _current_task_id.set(ctx.current_task_id)
-    current_flow_request_id.set(ctx.flow_request_id)
-    current_flow_id.set(ctx.flow_id)
-    current_flow_method_name.set(ctx.flow_method_name)
-
-    _event_id_stack.set(ctx.event_id_stack)
-    _last_event_id.set(ctx.last_event_id)
-    _triggering_event_id.set(ctx.triggering_event_id)
-    set_emission_counter(ctx.emission_sequence)
-
-    _platform_integration_token.set(ctx.platform_token)

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

@@ -1907,6 +1907,37 @@ class AgentExecutor(Flow[AgentExecutorState], CrewAgentExecutorMixin):
             "original_tool": original_tool,
         }
 
+    def _extract_tool_name(self, tool_call: Any) -> str:
+        """Extract tool name from various tool call formats."""
+        if hasattr(tool_call, "function"):
+            return sanitize_tool_name(tool_call.function.name)
+        if hasattr(tool_call, "function_call") and tool_call.function_call:
+            return sanitize_tool_name(tool_call.function_call.name)
+        if hasattr(tool_call, "name"):
+            return sanitize_tool_name(tool_call.name)
+        if isinstance(tool_call, dict):
+            func_info = tool_call.get("function", {})
+            return sanitize_tool_name(
+                func_info.get("name", "") or tool_call.get("name", "unknown")
+            )
+        return "unknown"
+
+    @router(execute_native_tool)
+    def check_native_todo_completion(
+        self,
+    ) -> Literal["todo_satisfied", "todo_not_satisfied"]:
+        """Check if the native tool execution satisfied the active todo.
+
+        Similar to check_todo_completion but for native tool execution path.
+        """
+        current_todo = self.state.todos.current_todo
+
+        if not current_todo:
+            return "todo_not_satisfied"
+
+        # For native tools, any tool execution satisfies the todo
+        return "todo_satisfied"
+
     @listen("initialized")
     def continue_iteration(self) -> Literal["check_iteration"]:
         """Bridge listener that connects iteration loop back to iteration check."""

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

@@ -25,6 +25,7 @@ import logging
 import threading
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
     ClassVar,
     Generic,
@@ -41,9 +42,11 @@ from opentelemetry import baggage
 from opentelemetry.context import attach, detach
 from pydantic import (
     BaseModel,
+    BeforeValidator,
     ConfigDict,
     Field,
     PrivateAttr,
+    SerializeAsAny,
     ValidationError,
 )
 from pydantic._internal._model_construction import ModelMetaclass
@@ -115,6 +118,7 @@ from crewai.memory.unified_memory import Memory
 if TYPE_CHECKING:
     from crewai_files import FileInput
 
+    from crewai.context import ExecutionContext
     from crewai.flow.async_feedback.types import PendingFeedbackContext
     from crewai.llms.base_llm import BaseLLM
 
@@ -134,6 +138,19 @@ from crewai.utilities.streaming import (
 logger = logging.getLogger(__name__)
 
 
+def _resolve_persistence(value: Any) -> Any:
+    if value is None or isinstance(value, FlowPersistence):
+        return value
+    if isinstance(value, dict):
+        from crewai.flow.persistence.base import _persistence_registry
+
+        type_name = value.get("persistence_type", "SQLiteFlowPersistence")
+        cls = _persistence_registry.get(type_name)
+        if cls is not None:
+            return cls.model_validate(value)
+    return value
+
+
 class FlowState(BaseModel):
     """Base model for all flow states, ensuring each state has a unique ID."""
 
@@ -883,6 +900,8 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
     _routers: ClassVar[set[FlowMethodName]] = set()
     _router_paths: ClassVar[dict[FlowMethodName, list[FlowMethodName]]] = {}
 
+    entity_type: Literal["flow"] = "flow"
+
     initial_state: Any = Field(default=None)
     name: str | None = Field(default=None)
     tracing: bool | None = Field(default=None)
@@ -893,8 +912,17 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
     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)
+    persistence: Annotated[
+        SerializeAsAny[FlowPersistence] | Any,
+        BeforeValidator(lambda v, _: _resolve_persistence(v)),
+    ] = Field(default=None)
+    max_method_calls: int = Field(default=100)
+
+    execution_context: ExecutionContext | None = Field(default=None)
+    checkpoint_completed_methods: set[str] | None = Field(default=None)
+    checkpoint_method_outputs: list[Any] | None = Field(default=None)
+    checkpoint_method_counts: dict[str, int] | None = Field(default=None)
+    checkpoint_state: dict[str, Any] | None = Field(default=None)
 
     _methods: dict[FlowMethodName, FlowMethod[Any, Any]] = PrivateAttr(
         default_factory=dict

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

@@ -5,14 +5,17 @@ from __future__ import annotations
 from abc import ABC, abstractmethod
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 if TYPE_CHECKING:
     from crewai.flow.async_feedback.types import PendingFeedbackContext
 
 
-class FlowPersistence(ABC):
+_persistence_registry: dict[str, type[FlowPersistence]] = {}
+
+
+class FlowPersistence(BaseModel, ABC):
     """Abstract base class for flow state persistence.
 
     This class defines the interface that all persistence implementations must follow.
@@ -24,6 +27,13 @@ class FlowPersistence(ABC):
     - clear_pending_feedback(): Clears pending feedback after resume
     """
 
+    persistence_type: str = Field(default="base")
+
+    def __init_subclass__(cls, **kwargs: Any) -> None:
+        super().__init_subclass__(**kwargs)
+        if not getattr(cls, "__abstractmethods__", set()):
+            _persistence_registry[cls.__name__] = cls
+
     @abstractmethod
     def init_db(self) -> None:
         """Initialize the persistence backend.
@@ -95,7 +105,7 @@ class FlowPersistence(ABC):
         """
         return None
 
-    def clear_pending_feedback(self, flow_uuid: str) -> None:  # noqa: B027
+    def clear_pending_feedback(self, flow_uuid: str) -> None:
         """Clear the pending feedback marker after successful resume.
 
         This is called after feedback is received and the flow resumes.

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

@@ -9,7 +9,8 @@ from pathlib import Path
 import sqlite3
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, PrivateAttr, model_validator
+from typing_extensions import Self
 
 from crewai.flow.persistence.base import FlowPersistence
 from crewai.utilities.lock_store import lock as store_lock
@@ -50,26 +51,22 @@ class SQLiteFlowPersistence(FlowPersistence):
         ```
     """
 
-    def __init__(self, db_path: str | None = None) -> None:
-        """Initialize SQLite persistence.
+    persistence_type: str = Field(default="SQLiteFlowPersistence")
+    db_path: str = Field(
+        default_factory=lambda: str(Path(db_storage_path()) / "flow_states.db")
+    )
+    _lock_name: str = PrivateAttr()
 
-        Args:
-            db_path: Path to the SQLite database file. If not provided, uses
-                    db_storage_path() from utilities.paths.
+    def __init__(self, db_path: str | None = None, /, **kwargs: Any) -> None:
+        if db_path is not None:
+            kwargs["db_path"] = db_path
+        super().__init__(**kwargs)
 
-        Raises:
-            ValueError: If db_path is invalid
-        """
-
-        # Get path from argument or default location
-        path = db_path or str(Path(db_storage_path()) / "flow_states.db")
-
-        if not path:
-            raise ValueError("Database path must be provided")
-
-        self.db_path = path  # Now mypy knows this is str
+    @model_validator(mode="after")
+    def _setup(self) -> Self:
         self._lock_name = f"sqlite:{os.path.realpath(self.db_path)}"
         self.init_db()
+        return self
 
     def init_db(self) -> None:
         """Create the necessary tables if they don't exist."""

lib/crewai/src/crewai/lite_agent_output.py

@@ -40,7 +40,9 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: dict[str, Any] | None = Field(
         description="Token usage metrics for this execution", default=None
     )
-    messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
+    messages: list[LLMMessage] = Field(
+        description="Messages of the agent", default_factory=list
+    )
 
     plan: str | None = Field(
         default=None, description="The execution plan that was generated, if any"

lib/crewai/src/crewai/memory/memory_scope.py

@@ -32,6 +32,10 @@ class MemoryScope(BaseModel):
         """Extract memory dependency and normalize root path before validation."""
         if isinstance(data, MemoryScope):
             return data
+        if not isinstance(data, dict):
+            raise ValueError(f"Expected dict or MemoryScope, got {type(data).__name__}")
+        if "memory" not in data:
+            raise ValueError("MemoryScope requires a 'memory' key")
         memory = data.pop("memory")
         instance: MemoryScope = handler(data)
         instance._memory = memory
@@ -199,6 +203,10 @@ class MemorySlice(BaseModel):
         """Extract memory dependency and normalize scopes before validation."""
         if isinstance(data, MemorySlice):
             return data
+        if not isinstance(data, dict):
+            raise ValueError(f"Expected dict or MemorySlice, got {type(data).__name__}")
+        if "memory" not in data:
+            raise ValueError("MemorySlice requires a 'memory' key")
         memory = data.pop("memory")
         data["scopes"] = [s.rstrip("/") or "/" for s in data.get("scopes", [])]
         instance: MemorySlice = handler(data)

lib/crewai/src/crewai/runtime_state.py

@@ -0,0 +1,18 @@
+"""Unified runtime state for crewAI.
+
+``RuntimeState`` is a ``RootModel`` whose ``model_dump_json()`` produces a
+complete, self-contained snapshot of every active entity in the program.
+
+The ``Entity`` type alias and ``RuntimeState`` model are built at import time
+in ``crewai/__init__.py`` after all forward references are resolved.
+"""
+
+from typing import Any
+
+
+def _entity_discriminator(v: dict[str, Any] | object) -> str:
+    if isinstance(v, dict):
+        raw = v.get("entity_type", "agent")
+    else:
+        raw = getattr(v, "entity_type", "agent")
+    return str(raw)

lib/crewai/src/crewai/task.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import asyncio
+from collections.abc import Sequence
 from concurrent.futures import Future
 import contextvars
 from copy import copy as shallow_copy
@@ -12,6 +13,7 @@ import logging
 from pathlib import Path
 import threading
 from typing import (
+    Annotated,
     Any,
     ClassVar,
     cast,
@@ -24,6 +26,7 @@ import warnings
 from pydantic import (
     UUID4,
     BaseModel,
+    BeforeValidator,
     Field,
     PrivateAttr,
     field_validator,
@@ -32,7 +35,7 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
-from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.agents.agent_builder.base_agent import BaseAgent, _resolve_agent
 from crewai.context import reset_current_task_id, set_current_task_id
 from crewai.core.providers.content_processor import process_content
 from crewai.events.event_bus import crewai_event_bus
@@ -129,9 +132,10 @@ class Task(BaseModel):
     callback: SerializableCallable | None = Field(
         description="Callback to be executed after the task is completed.", default=None
     )
-    agent: BaseAgent | None = Field(
-        description="Agent responsible for execution the task.", default=None
-    )
+    agent: Annotated[
+        BaseAgent | None,
+        BeforeValidator(_resolve_agent),
+    ] = Field(description="Agent responsible for execution the task.", default=None)
     context: list[Task] | None | _NotSpecified = Field(
         description="Other tasks that will have their output used as context for this task.",
         default=NOT_SPECIFIED,
@@ -392,11 +396,12 @@ class Task(BaseModel):
 
     @field_validator("id", mode="before")
     @classmethod
-    def _deny_user_set_id(cls, v: UUID4 | None) -> None:
-        if v:
+    def _deny_user_set_id(cls, v: UUID4 | None, info: Any) -> UUID4 | None:
+        if v and not (info.context or {}).get("from_checkpoint"):
             raise PydanticCustomError(
                 "may_not_set_field", "This field is not to be set by the user.", {}
             )
+        return v
 
     @field_validator("input_files", mode="before")
     @classmethod
@@ -997,7 +1002,7 @@ Follow these guidelines:
         self.delegations += 1
 
     def copy(  # type: ignore
-        self, agents: list[BaseAgent], task_mapping: dict[str, Task]
+        self, agents: Sequence[BaseAgent], task_mapping: dict[str, Task]
     ) -> Task:
         """Creates a deep copy of the Task while preserving its original class type.
 

lib/crewai/src/crewai/tasks/conditional_task.py

@@ -8,6 +8,7 @@ from pydantic import Field
 from crewai.task import Task
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
+from crewai.types.callback import SerializableCallable
 
 
 class ConditionalTask(Task):
@@ -24,7 +25,7 @@ class ConditionalTask(Task):
         - Cannot be the first task since it needs context from the previous task
     """
 
-    condition: Callable[[TaskOutput], bool] | None = Field(
+    condition: SerializableCallable | None = Field(
         default=None,
         description="Function that determines whether the task should be executed based on previous task output.",
     )
@@ -51,7 +52,7 @@ class ConditionalTask(Task):
         """
         if self.condition is None:
             raise ValueError("No condition function set for conditional task")
-        return self.condition(context)
+        return bool(self.condition(context))
 
     def get_skipped_task_output(self) -> TaskOutput:
         """Generate a TaskOutput for when the conditional task is skipped.

lib/crewai/src/crewai/tasks/task_output.py

@@ -43,7 +43,9 @@ class TaskOutput(BaseModel):
     output_format: OutputFormat = Field(
         description="Output format of the task", default=OutputFormat.RAW
     )
-    messages: list[LLMMessage] = Field(description="Messages of the task", default=[])
+    messages: list[LLMMessage] = Field(
+        description="Messages of the task", default_factory=list
+    )
 
     @model_validator(mode="after")
     def set_summary(self) -> TaskOutput:

lib/crewai/src/crewai/telemetry/telemetry.py

@@ -1040,3 +1040,20 @@ class Telemetry:
             close_span(span)
 
         self._safe_telemetry_operation(_operation)
+
+    def feature_usage_span(self, feature: str) -> None:
+        """Records that a feature was used. One span = one count.
+
+        Args:
+            feature: Feature identifier, e.g. "planning:creation",
+                     "mcp:connection", "a2a:delegation".
+        """
+
+        def _operation() -> None:
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Feature Usage")
+            self._add_attribute(span, "crewai_version", version("crewai"))
+            self._add_attribute(span, "feature", feature)
+            close_span(span)
+
+        self._safe_telemetry_operation(_operation)

lib/crewai/src/crewai/tools/agent_tools/agent_tools.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
 from crewai.tools.agent_tools.ask_question_tool import AskQuestionTool
@@ -16,7 +17,7 @@ if TYPE_CHECKING:
 class AgentTools:
     """Manager class for agent-related tools"""
 
-    def __init__(self, agents: list[BaseAgent], i18n: I18N | None = None) -> None:
+    def __init__(self, agents: Sequence[BaseAgent], i18n: I18N | None = None) -> None:
         self.agents = agents
         self.i18n = i18n if i18n is not None else get_i18n()
 

lib/crewai/src/crewai/tools/tool_usage.py

@@ -318,6 +318,8 @@ class ToolUsage:
                         if self.task:
                             self.task.increment_delegations(coworker)
 
+                    fingerprint_config = self._build_fingerprint_config()
+
                     if calling.arguments:
                         try:
                             acceptable_args = tool.args_schema.model_json_schema()[
@@ -328,15 +330,16 @@ class ToolUsage:
                                 for k, v in calling.arguments.items()
                                 if k in acceptable_args
                             }
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = await tool.ainvoke(input=arguments)
+                            result = await tool.ainvoke(
+                                input=arguments, config=fingerprint_config
+                            )
                         except Exception:
                             arguments = calling.arguments
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = await tool.ainvoke(input=arguments)
+                            result = await tool.ainvoke(
+                                input=arguments, config=fingerprint_config
+                            )
                     else:
-                        arguments = self._add_fingerprint_metadata({})
-                        result = await tool.ainvoke(input=arguments)
+                        result = await tool.ainvoke(input={}, config=fingerprint_config)
 
                     if self.tools_handler:
                         should_cache = True
@@ -550,6 +553,8 @@ class ToolUsage:
                         if self.task:
                             self.task.increment_delegations(coworker)
 
+                    fingerprint_config = self._build_fingerprint_config()
+
                     if calling.arguments:
                         try:
                             acceptable_args = tool.args_schema.model_json_schema()[
@@ -560,15 +565,16 @@ class ToolUsage:
                                 for k, v in calling.arguments.items()
                                 if k in acceptable_args
                             }
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = tool.invoke(input=arguments)
+                            result = tool.invoke(
+                                input=arguments, config=fingerprint_config
+                            )
                         except Exception:
                             arguments = calling.arguments
-                            arguments = self._add_fingerprint_metadata(arguments)
-                            result = tool.invoke(input=arguments)
+                            result = tool.invoke(
+                                input=arguments, config=fingerprint_config
+                            )
                     else:
-                        arguments = self._add_fingerprint_metadata({})
-                        result = tool.invoke(input=arguments)
+                        result = tool.invoke(input={}, config=fingerprint_config)
 
                     if self.tools_handler:
                         should_cache = True
@@ -1008,23 +1014,16 @@ class ToolUsage:
 
         return event_data
 
-    def _add_fingerprint_metadata(self, arguments: dict[str, Any]) -> dict[str, Any]:
-        """Add fingerprint metadata to tool arguments if available.
+    def _build_fingerprint_config(self) -> dict[str, Any]:
+        """Build fingerprint metadata as a config dict for tool invocation.
 
-        Args:
-            arguments: The original tool arguments
+        Returns the fingerprint data in a config dict rather than injecting it
+        into tool arguments, so it doesn't conflict with strict tool schemas.
 
         Returns:
-            Updated arguments dictionary with fingerprint metadata
+            Config dictionary with security_context metadata.
         """
-        # Create a shallow copy to avoid modifying the original
-        arguments = arguments.copy()
-
-        # Add security metadata under a designated key
-        if "security_context" not in arguments:
-            arguments["security_context"] = {}
-
-        security_context = arguments["security_context"]
+        security_context: dict[str, Any] = {}
 
         # Add agent fingerprint if available
         if self.agent and hasattr(self.agent, "security_config"):
@@ -1048,4 +1047,4 @@ class ToolUsage:
                 except AttributeError:
                     pass
 
-        return arguments
+        return {"security_context": security_context} if security_context else {}

lib/crewai/src/crewai/utilities/constants.py

@@ -1,5 +1,7 @@
 from typing import Annotated, Final
 
+from pydantic_core import CoreSchema
+
 from crewai.utilities.printer import PrinterColor
 
 
@@ -36,6 +38,25 @@ class _NotSpecified:
     def __repr__(self) -> str:
         return "NOT_SPECIFIED"
 
+    @classmethod
+    def __get_pydantic_core_schema__(
+        cls, _source_type: object, _handler: object
+    ) -> CoreSchema:
+        from pydantic_core import core_schema
+
+        def _validate(v: object) -> _NotSpecified:
+            if isinstance(v, _NotSpecified) or v == "NOT_SPECIFIED":
+                return NOT_SPECIFIED
+            raise ValueError(f"Expected NOT_SPECIFIED sentinel, got {type(v).__name__}")
+
+        return core_schema.no_info_plain_validator_function(
+            _validate,
+            serialization=core_schema.plain_serializer_function_ser_schema(
+                lambda v: "NOT_SPECIFIED",
+                info_arg=False,
+            ),
+        )
+
 
 NOT_SPECIFIED: Final[
     Annotated[

lib/crewai/src/crewai/utilities/pydantic_schema_utils.py

@@ -623,7 +623,7 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         for name, prop in (json_schema.get("properties", {}) or {}).items()
     }
 
-    effective_config = __config__ or ConfigDict(extra="ignore")
+    effective_config = __config__ or ConfigDict(extra="forbid")
 
     return create_model_base(
         effective_name,

lib/crewai/tests/agents/test_agent_executor.py

@@ -927,6 +927,30 @@ class TestNativeToolExecution:
         assert len(tool_messages) == 1
         assert tool_messages[0]["tool_call_id"] == "call_1"
 
+    def test_check_native_todo_completion_requires_current_todo(
+        self, mock_dependencies
+    ):
+        from crewai.utilities.planning_types import TodoList
+
+        executor = _build_executor(**mock_dependencies)
+
+        # No current todo → not satisfied
+        executor.state.todos = TodoList(items=[])
+        assert executor.check_native_todo_completion() == "todo_not_satisfied"
+
+        # With a current todo that has tool_to_use → satisfied
+        running = TodoItem(
+            step_number=1,
+            description="Use the expected tool",
+            tool_to_use="expected_tool",
+            status="running",
+        )
+        executor.state.todos = TodoList(items=[running])
+        assert executor.check_native_todo_completion() == "todo_satisfied"
+
+        # With a current todo without tool_to_use → still satisfied
+        running.tool_to_use = None
+        assert executor.check_native_todo_completion() == "todo_satisfied"
 
 
 class TestPlannerObserver:

lib/crewai/tests/cli/tools/test_main.py

@@ -218,6 +218,7 @@ def test_publish_when_not_in_sync_and_force(
         ["uv", "build", "--sdist", "--out-dir", unittest.mock.ANY],
         check=True,
         capture_output=False,
+        env=unittest.mock.ANY,
     )
     mock_open.assert_called_with(unittest.mock.ANY, "rb")
     mock_publish.assert_called_with(
@@ -279,6 +280,7 @@ def test_publish_success(
         ["uv", "build", "--sdist", "--out-dir", unittest.mock.ANY],
         check=True,
         capture_output=False,
+        env=unittest.mock.ANY,
     )
     mock_open.assert_called_with(unittest.mock.ANY, "rb")
     mock_publish.assert_called_with(

lib/crewai/tests/utilities/test_pydantic_schema_utils.py

@@ -882,129 +882,3 @@ class TestEndToEndMCPSchema:
         )
         assert obj.filters.date_from == datetime.date(2025, 1, 1)
         assert obj.filters.categories == ["news", "tech"]
-
-
-class TestExtraFieldsIgnored:
-    """Regression tests for OSS-9: security_context injection causing
-    extra_forbidden errors on MCP and integration tool schemas.
-
-    When the framework injects metadata like security_context into tool call
-    arguments, dynamically-created Pydantic models must ignore (not reject)
-    extra fields so that tool execution is not blocked.
-    """
-
-    SIMPLE_TOOL_SCHEMA: dict[str, Any] = {
-        "type": "object",
-        "title": "ExecuteSqlSchema",
-        "properties": {
-            "query": {
-                "type": "string",
-                "description": "The SQL query to execute.",
-            },
-        },
-        "required": ["query"],
-    }
-
-    OUTLOOK_TOOL_SCHEMA: dict[str, Any] = {
-        "type": "object",
-        "title": "MicrosoftOutlookSendEmailSchema",
-        "properties": {
-            "to_recipients": {
-                "type": "array",
-                "items": {"type": "string"},
-                "description": "Array of recipient email addresses.",
-            },
-            "subject": {
-                "type": "string",
-                "description": "Email subject line.",
-            },
-            "body": {
-                "type": "string",
-                "description": "Email body content.",
-            },
-        },
-        "required": ["to_recipients", "subject", "body"],
-    }
-
-    SECURITY_CONTEXT_PAYLOAD: dict[str, Any] = {
-        "agent_fingerprint": {
-            "user_id": "test-user-123",
-            "session_id": "test-session-456",
-            "metadata": {},
-        },
-    }
-
-    def test_mcp_tool_schema_ignores_security_context(self) -> None:
-        """Reproduces OSS-9 Case 1: Databricks MCP execute_sql fails when
-        security_context is injected into tool args."""
-        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
-        # This previously raised: Extra inputs are not permitted
-        # [type=extra_forbidden, input_value={'agent_fingerprint': ...}]
-        obj = Model.model_validate(
-            {
-                "query": "SELECT * FROM my_table",
-                "security_context": self.SECURITY_CONTEXT_PAYLOAD,
-            }
-        )
-        assert obj.query == "SELECT * FROM my_table"
-        # security_context should be silently dropped, not present on the model
-        assert not hasattr(obj, "security_context")
-
-    def test_integration_tool_schema_ignores_security_context(self) -> None:
-        """Reproduces OSS-9 Case 2: Microsoft Outlook send_email fails when
-        security_context is injected into tool args."""
-        Model = create_model_from_schema(self.OUTLOOK_TOOL_SCHEMA)
-        obj = Model.model_validate(
-            {
-                "to_recipients": ["user@example.com"],
-                "subject": "Test",
-                "body": "Hello",
-                "security_context": self.SECURITY_CONTEXT_PAYLOAD,
-            }
-        )
-        assert obj.to_recipients == ["user@example.com"]
-        assert obj.subject == "Test"
-        assert not hasattr(obj, "security_context")
-
-    def test_arbitrary_extra_fields_ignored(self) -> None:
-        """Any unexpected extra field should be silently ignored, not just
-        security_context."""
-        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
-        obj = Model.model_validate(
-            {
-                "query": "SELECT 1",
-                "some_unknown_field": "should be dropped",
-                "another_extra": 42,
-            }
-        )
-        assert obj.query == "SELECT 1"
-        assert not hasattr(obj, "some_unknown_field")
-        assert not hasattr(obj, "another_extra")
-
-    def test_required_fields_still_enforced(self) -> None:
-        """Changing to extra=ignore must NOT weaken required field validation."""
-        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
-        with pytest.raises(Exception):
-            Model.model_validate({"security_context": self.SECURITY_CONTEXT_PAYLOAD})
-
-    def test_type_validation_still_enforced(self) -> None:
-        """Changing to extra=ignore must NOT weaken type validation."""
-        Model = create_model_from_schema(self.SIMPLE_TOOL_SCHEMA)
-        with pytest.raises(Exception):
-            Model.model_validate({"query": 12345})  # should be string
-
-    def test_explicit_extra_forbid_still_works(self) -> None:
-        """Callers can still opt into extra=forbid via __config__."""
-        from pydantic import ConfigDict
-
-        Model = create_model_from_schema(
-            self.SIMPLE_TOOL_SCHEMA,
-            __config__=ConfigDict(extra="forbid"),
-        )
-        with pytest.raises(Exception):
-            Model.model_validate(
-                {
-                    "query": "SELECT 1",
-                    "security_context": self.SECURITY_CONTEXT_PAYLOAD,
-                }
-            )

lib/devtools/pyproject.toml

@@ -25,6 +25,9 @@ release = "crewai_devtools.cli:release"
 docs-check = "crewai_devtools.docs_check:docs_check"
 devtools = "crewai_devtools.cli:main"
 
+[tool.uv]
+exclude-newer = "3 days"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.13.0a7"
+__version__ = "1.14.0a2"

pyproject.toml

@@ -160,6 +160,7 @@ info = "Commits must follow Conventional Commits 1.0.0."
 
 
 [tool.uv]
+exclude-newer = "3 days"
 
 # composio-core pins rich<14 but textual requires rich>=14.
 # onnxruntime 1.24+ dropped Python 3.10 wheels; cap it so qdrant[fastembed] resolves on 3.10.