docs: update changelog and version for v1.14.2a1

resume_async() was missing trace infrastructure that kickoff_async()
sets up, causing flow_finished to never reach the platform after HITL
feedback. Add FlowStartedEvent emission to initialize the trace batch,
await event futures, finalize the trace batch, and guard with
suppress_flow_events.

.github/workflows/build-uv-cache.yml

@@ -28,7 +28,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 

.github/workflows/generate-tool-specs.yml

@@ -35,7 +35,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: "3.12"
           enable-cache: true
 

.github/workflows/linter.yml

@@ -6,7 +6,24 @@ permissions:
   contents: read
 
 jobs:
-  lint:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
+  lint-run:
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
@@ -26,7 +43,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: "3.11"
           enable-cache: false
 
@@ -48,3 +65,23 @@ jobs:
             ~/.local/share/uv
             .venv
           key: uv-main-py3.11-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  lint:
+    name: lint
+    runs-on: ubuntu-latest
+    needs: [changes, lint-run]
+    if: always()
+    steps:
+      - name: Check results
+        run: |
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping lint"
+            exit 0
+          fi
+          if [ "${{ needs.lint-run.result }}" == "success" ]; then
+            echo "Lint passed"
+          else
+            echo "Lint failed"
+            exit 1
+          fi

.github/workflows/nightly.yml

@@ -95,7 +95,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: "3.12"
           enable-cache: false
 

.github/workflows/publish.yml

@@ -65,7 +65,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: "3.12"
           enable-cache: false
 

.github/workflows/tests.yml

@@ -6,8 +6,25 @@ permissions:
   contents: read
 
 jobs:
-  tests:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
+  tests-matrix:
     name: tests (${{ matrix.python-version }})
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     timeout-minutes: 15
     strategy:
@@ -36,7 +53,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
@@ -98,3 +115,23 @@ jobs:
             ~/.local/share/uv
             .venv
           key: uv-main-py${{ matrix.python-version }}-${{ hashFiles('uv.lock') }}
+
+  # Summary job to provide single status for branch protection
+  tests:
+    name: tests
+    runs-on: ubuntu-latest
+    needs: [changes, tests-matrix]
+    if: always()
+    steps:
+      - name: Check results
+        run: |
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping tests"
+            exit 0
+          fi
+          if [ "${{ needs.tests-matrix.result }}" == "success" ]; then
+            echo "All tests passed"
+          else
+            echo "Tests failed"
+            exit 1
+          fi

.github/workflows/type-checker.yml

@@ -6,8 +6,25 @@ permissions:
   contents: read
 
 jobs:
+  changes:
+    name: Detect changes
+    runs-on: ubuntu-latest
+    outputs:
+      code: ${{ steps.filter.outputs.code }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: |
+            code:
+              - '!docs/**'
+              - '!**/*.md'
+
   type-checker-matrix:
     name: type-checker (${{ matrix.python-version }})
+    needs: changes
+    if: needs.changes.outputs.code == 'true'
     runs-on: ubuntu-latest
     strategy:
       fail-fast: false
@@ -33,7 +50,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 
@@ -57,14 +74,18 @@ jobs:
   type-checker:
     name: type-checker
     runs-on: ubuntu-latest
-    needs: type-checker-matrix
+    needs: [changes, type-checker-matrix]
     if: always()
     steps:
-      - name: Check matrix results
+      - name: Check results
         run: |
-          if [ "${{ needs.type-checker-matrix.result }}" == "success" ] || [ "${{ needs.type-checker-matrix.result }}" == "skipped" ]; then
-            echo "✅ All type checks passed"
+          if [ "${{ needs.changes.outputs.code }}" != "true" ]; then
+            echo "Docs-only change, skipping type checks"
+            exit 0
+          fi
+          if [ "${{ needs.type-checker-matrix.result }}" == "success" ]; then
+            echo "All type checks passed"
           else
-            echo "❌ Type checks failed"
+            echo "Type checks failed"
             exit 1
           fi

.github/workflows/update-test-durations.yml

@@ -40,7 +40,7 @@ jobs:
       - name: Install uv
         uses: astral-sh/setup-uv@v6
         with:
-          version: "0.8.4"
+          version: "0.11.3"
           python-version: ${{ matrix.python-version }}
           enable-cache: false
 

.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: "0.11.3"
+          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') }}
+

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
         types: [python]
         exclude: ^(lib/crewai/src/crewai/cli/templates/|lib/crewai/tests/|lib/crewai-tools/tests/|lib/crewai-files/tests/)
   - repo: https://github.com/astral-sh/uv-pre-commit
-    rev: 0.9.3
+    rev: 0.11.3
     hooks:
       - id: uv-lock
   - repo: https://github.com/commitizen-tools/commitizen

docs/ar/changelog.mdx

@@ -4,6 +4,334 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="9 أبريل 2026">
+  ## v1.14.2a1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## ما الذي تغير
+
+  ### إصلاحات الأخطاء
+  - إصلاح إصدار حدث flow_finished بعد استئناف HITL
+  - إصلاح إصدار التشفير إلى 46.0.7 لمعالجة CVE-2026-39892
+
+  ### إعادة هيكلة
+  - إعادة هيكلة لاستخدام I18N_DEFAULT المشترك
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.1
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="9 أبريل 2026">
+  ## v1.14.1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة متصفح TUI لنقاط التفتيش غير المتزامنة
+  - إضافة دالة aclose()/close() ومدير سياق غير متزامن لمخرجات البث
+
+  ### إصلاحات الأخطاء
+  - إصلاح التعبير النمطي لزيادة إصدار pyproject.toml
+  - تنظيف أسماء الأدوات في مرشحات زخرفة الخطاف
+  - إصلاح تسجيل معالجات نقاط التفتيش عند إنشاء CheckpointConfig
+  - رفع إصدار transformers إلى 5.5.0 لحل CVE-2026-1839
+  - إزالة غلاف FilteredStream لـ stdout/stderr
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.1rc1
+
+  ### إعادة الهيكلة
+  - استبدال القائمة المحظورة الثابتة باستبعاد حقل BaseTool الديناميكي في توليد المواصفات
+  - استبدال التعبير النمطي بـ tomlkit في واجهة سطر أوامر أدوات التطوير
+  - استخدام كائن PRINTER المشترك
+  - جعل BaseProvider نموذجاً أساسياً مع مميز نوع المزود
+
+  ## المساهمون
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="9 أبريل 2026">
+  ## v1.14.1rc1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة متصفح TUI لنقطة التحقق غير المتزامنة
+  - إضافة aclose()/close() ومدير سياق غير متزامن لمخرجات البث
+
+  ### إصلاحات الأخطاء
+  - إصلاح زيادة إصدارات pyproject.toml باستخدام التعبيرات العادية
+  - تنظيف أسماء الأدوات في مرشحات ديكور المكونات
+  - زيادة إصدار transformers إلى 5.5.0 لحل CVE-2026-1839
+  - تسجيل معالجات نقطة التحقق عند إنشاء CheckpointConfig
+
+  ### إعادة الهيكلة
+  - استبدال القائمة المحظورة الثابتة باستبعاد حقل BaseTool الديناميكي في توليد المواصفات
+  - استبدال التعبيرات العادية بـ tomlkit في واجهة سطر الأوامر devtools
+  - استخدام كائن PRINTER المشترك
+  - جعل BaseProvider نموذجًا أساسيًا مع مميز نوع المزود
+  - إزالة غلاف stdout/stderr لـ FilteredStream
+  - إزالة flow/config.py غير المستخدمة
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.0
+
+  ## المساهمون
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
+<Update label="7 أبريل 2026">
+  ## v1.14.0
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة أوامر CLI لقائمة/معلومات نقاط التحقق
+  - إضافة guardrail_type و name لتمييز التتبع
+  - إضافة SqliteProvider لتخزين نقاط التحقق
+  - إضافة CheckpointConfig للتسجيل التلقائي لنقاط التحقق
+  - تنفيذ تسجيل حالة وقت التشغيل، نظام الأحداث، وإعادة هيكلة المنفذ
+
+  ### إصلاحات الأخطاء
+  - إضافة حماية من SSRF وتجاوز المسار
+  - إضافة التحقق من المسار وعنوان URL لأدوات RAG
+  - استبعاد متجهات التضمين من تسلسل الذاكرة لتوفير الرموز
+  - التأكد من وجود دليل الإخراج قبل الكتابة في قالب التدفق
+  - رفع litellm إلى >=1.83.0 لمعالجة CVE-2026-35030
+  - إزالة حقل فهرسة SEO الذي يتسبب في عرض الصفحة العربية بشكل غير صحيح
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.0
+  - تحديث أدلة البدء السريع والتثبيت لتحسين الوضوح
+  - إضافة قسم مزودي التخزين، تصدير JsonProvider
+  - إضافة دليل علامة AMP التدريبية
+
+  ### إعادة الهيكلة
+  - تنظيف واجهة برمجة تطبيقات نقاط التحقق
+  - إزالة CodeInterpreterTool وإهمال معلمات تنفيذ الكود
+
+  ## المساهمون
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="7 أبريل 2026">
+  ## v1.14.0a4
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a4)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة guardrail_type و name لتمييز الآثار
+  - إضافة SqliteProvider لتخزين نقاط التحقق
+  - إضافة CheckpointConfig للتخزين التلقائي لنقاط التحقق
+  - تنفيذ نقاط التحقق لحالة التشغيل، نظام الأحداث، وإعادة هيكلة المنفذ
+
+  ### إصلاحات الأخطاء
+  - استبعاد متجهات التضمين من تسلسل الذاكرة لتوفير الرموز
+  - رفع litellm إلى >=1.83.0 لمعالجة CVE-2026-35030
+
+  ### الوثائق
+  - تحديث أدلة البدء السريع والتثبيت لتحسين الوضوح
+  - إضافة قسم مقدمي التخزين وتصدير JsonProvider
+
+  ### الأداء
+  - استخدام JSONB لعمود بيانات نقاط التحقق
+
+  ### إعادة الهيكلة
+  - إزالة CodeInterpreterTool وإهمال معلمات تنفيذ الكود
+
+  ## المساهمون
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="6 أبريل 2026">
+  ## v1.14.0a3
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
+
+  ## ما الذي تغير
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.0a2
+
+  ## المساهمون
+
+  @joaomdmoura
+
+</Update>
+
+<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
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة امتداد A2UI مع دعم v0.8/v0.9، والمخططات، والوثائق
+
+  ### إصلاحات الأخطاء
+  - إصلاح بادئات الرؤية متعددة الأنماط عن طريق إضافة GPT-5 وسلسلة o
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.13.0a6
+
+  ## المساهمون
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="1 أبريل 2026">
+  ## v1.13.0a6
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a6)
+
+  ## ما الذي تغير
+
+  ### الوثائق
+  - إصلاح مستويات أذونات RBAC لتتوافق مع خيارات واجهة المستخدم الفعلية (#5210)
+  - تحديث سجل التغييرات والإصدار لـ v1.13.0a5 (#5200)
+
+  ### الأداء
+  - تقليل عبء العمل على الإطار من خلال تنفيذ حافلة أحداث كسولة وتجاوز التتبع عند تعطيله (#5187)
+
+  ## المساهمون
+
+  @alex-clawd, @joaomdmoura, @lucasgomide
+
+</Update>
+
+<Update label="31 مارس 2026">
+  ## v1.13.0a5
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
+
+  ## ما الذي تغير
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.13.0a4
+
+  ## المساهمون
+
+  @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="1 أبريل 2026">
+  ## v1.13.0a4
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
+
+  ## ما الذي تغير
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.13.0a3
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="1 أبريل 2026">
   ## v1.13.0a3
 

docs/ar/concepts/agents.mdx

@@ -250,16 +250,12 @@ analysis_agent = Agent(
 
 #### تنفيذ الكود
 
-- `allow_code_execution`: يجب أن يكون True لتشغيل الكود
-- `code_execution_mode`:
-  - `"safe"`: يستخدم Docker (موصى به للإنتاج)
-  - `"unsafe"`: تنفيذ مباشر (استخدم فقط في بيئات موثوقة)
+<Warning>
+  `allow_code_execution` و`code_execution_mode` مهجوران. تمت إزالة `CodeInterpreterTool` من `crewai-tools`. استخدم خدمة بيئة معزولة مخصصة مثل [E2B](https://e2b.dev) أو [Modal](https://modal.com) لتنفيذ الكود بأمان.
+</Warning>
 
-<Note>
-  يشغّل هذا صورة Docker افتراضية. إذا أردت تهيئة صورة Docker،
-  راجع أداة Code Interpreter في قسم الأدوات. أضف أداة
-  مفسر الكود كأداة في معامل أداة الوكيل.
-</Note>
+- `allow_code_execution` _(مهجور)_: كان يُمكّن تنفيذ الكود المدمج عبر `CodeInterpreterTool`.
+- `code_execution_mode` _(مهجور)_: كان يتحكم في وضع التنفيذ (`"safe"` لـ Docker، `"unsafe"` للتنفيذ المباشر).
 
 #### الميزات المتقدمة
 
@@ -332,9 +328,9 @@ print(result.raw)
 
 ### الأمان وتنفيذ الكود
 
-- عند استخدام `allow_code_execution`، كن حذرًا مع مدخلات المستخدم وتحقق منها دائمًا
-- استخدم `code_execution_mode: "safe"` (Docker) في بيئات الإنتاج
-- فكّر في تعيين حدود `max_execution_time` مناسبة لمنع الحلقات اللانهائية
+<Warning>
+  `allow_code_execution` و`code_execution_mode` مهجوران وتمت إزالة `CodeInterpreterTool`. استخدم خدمة بيئة معزولة مخصصة مثل [E2B](https://e2b.dev) أو [Modal](https://modal.com) لتنفيذ الكود بأمان.
+</Warning>
 
 ### تحسين الأداء
 

docs/ar/concepts/checkpointing.mdx

@@ -0,0 +1,229 @@
+---
+title: Checkpointing
+description: حفظ حالة التنفيذ تلقائيا حتى تتمكن الطواقم والتدفقات والوكلاء من الاستئناف بعد الفشل.
+icon: floppy-disk
+mode: "wide"
+---
+
+<Warning>
+الـ Checkpointing في اصدار مبكر. قد تتغير واجهات البرمجة في الاصدارات المستقبلية.
+</Warning>
+
+## نظرة عامة
+
+يقوم الـ Checkpointing بحفظ حالة التنفيذ تلقائيا اثناء التشغيل. اذا فشل طاقم او تدفق او وكيل اثناء التنفيذ، يمكنك الاستعادة من اخر نقطة حفظ والاستئناف دون اعادة تنفيذ العمل المكتمل.
+
+## البداية السريعة
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=True,  # يستخدم الافتراضيات: ./.checkpoints, عند task_completed
+)
+result = crew.kickoff()
+```
+
+تتم كتابة ملفات نقاط الحفظ في `./.checkpoints/` بعد اكتمال كل مهمة.
+
+## التكوين
+
+استخدم `CheckpointConfig` للتحكم الكامل:
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        on_events=["task_completed", "crew_kickoff_completed"],
+        max_checkpoints=5,
+    ),
+)
+```
+
+### حقول CheckpointConfig
+
+| الحقل | النوع | الافتراضي | الوصف |
+|:------|:------|:----------|:------|
+| `location` | `str` | `"./.checkpoints"` | مسار ملفات نقاط الحفظ |
+| `on_events` | `list[str]` | `["task_completed"]` | انواع الاحداث التي تطلق نقطة حفظ |
+| `provider` | `BaseProvider` | `JsonProvider()` | واجهة التخزين |
+| `max_checkpoints` | `int \| None` | `None` | الحد الاقصى للملفات؛ يتم حذف الاقدم اولا |
+
+### الوراثة والانسحاب
+
+يقبل حقل `checkpoint` في Crew و Flow و Agent قيم `CheckpointConfig` او `True` او `False` او `None`:
+
+| القيمة | السلوك |
+|:-------|:-------|
+| `None` (افتراضي) | يرث من الاصل. الوكيل يرث اعدادات الطاقم. |
+| `True` | تفعيل بالاعدادات الافتراضية. |
+| `False` | انسحاب صريح. يوقف الوراثة من الاصل. |
+| `CheckpointConfig(...)` | اعدادات مخصصة. |
+
+```python
+crew = Crew(
+    agents=[
+        Agent(role="Researcher", ...),                  # يرث checkpoint من الطاقم
+        Agent(role="Writer", ..., checkpoint=False),     # منسحب، بدون نقاط حفظ
+    ],
+    tasks=[...],
+    checkpoint=True,
+)
+```
+
+## الاستئناف من نقطة حفظ
+
+```python
+# استعادة واستئناف
+crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
+result = crew.kickoff()  # يستأنف من اخر مهمة مكتملة
+```
+
+يتخطى الطاقم المستعاد المهام المكتملة ويستأنف من اول مهمة غير مكتملة.
+
+## يعمل على Crew و Flow و Agent
+
+### Crew
+
+```python
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task, review_task],
+    checkpoint=CheckpointConfig(location="./crew_cp"),
+)
+```
+
+المشغل الافتراضي: `task_completed` (نقطة حفظ واحدة لكل مهمة مكتملة).
+
+### Flow
+
+```python
+from crewai.flow.flow import Flow, start, listen
+from crewai import CheckpointConfig
+
+class MyFlow(Flow):
+    @start()
+    def step_one(self):
+        return "data"
+
+    @listen(step_one)
+    def step_two(self, data):
+        return process(data)
+
+flow = MyFlow(
+    checkpoint=CheckpointConfig(
+        location="./flow_cp",
+        on_events=["method_execution_finished"],
+    ),
+)
+result = flow.kickoff()
+
+# استئناف
+flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
+result = flow.kickoff()
+```
+
+### Agent
+
+```python
+agent = Agent(
+    role="Researcher",
+    goal="Research topics",
+    backstory="Expert researcher",
+    checkpoint=CheckpointConfig(
+        location="./agent_cp",
+        on_events=["lite_agent_execution_completed"],
+    ),
+)
+result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+```
+
+## مزودات التخزين
+
+يتضمن CrewAI مزودي تخزين لنقاط الحفظ.
+
+### JsonProvider (افتراضي)
+
+يكتب كل نقطة حفظ كملف JSON منفصل.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import JsonProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        provider=JsonProvider(),
+        max_checkpoints=5,
+    ),
+)
+```
+
+### SqliteProvider
+
+يخزن جميع نقاط الحفظ في ملف قاعدة بيانات SQLite واحد.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import SqliteProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./.checkpoints.db",
+        provider=SqliteProvider(),
+    ),
+)
+```
+
+
+## انواع الاحداث
+
+يقبل حقل `on_events` اي مجموعة من سلاسل انواع الاحداث. الخيارات الشائعة:
+
+| حالة الاستخدام | الاحداث |
+|:---------------|:--------|
+| بعد كل مهمة (Crew) | `["task_completed"]` |
+| بعد كل طريقة في التدفق | `["method_execution_finished"]` |
+| بعد تنفيذ الوكيل | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
+| عند اكتمال الطاقم فقط | `["crew_kickoff_completed"]` |
+| بعد كل استدعاء LLM | `["llm_call_completed"]` |
+| على كل شيء | `["*"]` |
+
+<Warning>
+استخدام `["*"]` او احداث عالية التردد مثل `llm_call_completed` سيكتب العديد من ملفات نقاط الحفظ وقد يؤثر على الاداء. استخدم `max_checkpoints` للحد من استخدام المساحة.
+</Warning>
+
+## نقاط الحفظ اليدوية
+
+للتحكم الكامل، سجل معالج الاحداث الخاص بك واستدع `state.checkpoint()` مباشرة:
+
+```python
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+# معالج متزامن
+@crewai_event_bus.on(LLMCallCompletedEvent)
+def on_llm_done(source, event, state):
+    path = state.checkpoint("./my_checkpoints")
+    print(f"تم حفظ نقطة الحفظ: {path}")
+
+# معالج غير متزامن
+@crewai_event_bus.on(LLMCallCompletedEvent)
+async def on_llm_done_async(source, event, state):
+    path = await state.acheckpoint("./my_checkpoints")
+    print(f"تم حفظ نقطة الحفظ: {path}")
+```
+
+وسيط `state` هو `RuntimeState` الذي يتم تمريره تلقائيا بواسطة ناقل الاحداث عندما يقبل المعالج 3 معاملات. يمكنك تسجيل معالجات على اي نوع حدث مدرج في وثائق [Event Listeners](/ar/concepts/event-listener).
+
+الـ Checkpointing يعمل بافضل جهد: اذا فشلت كتابة نقطة حفظ، يتم تسجيل الخطأ ولكن التنفيذ يستمر دون انقطاع.

docs/ar/enterprise/features/rbac.mdx

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

docs/ar/enterprise/guides/deploy-to-amp.mdx

@@ -106,7 +106,7 @@ mode: "wide"
     ```
 
     <Tip>
-    يستغرق النشر الأول عادة 10-15 دقيقة لبناء صور الحاويات. عمليات النشر اللاحقة أسرع بكثير.
+    يستغرق النشر الأول عادة حوالي دقيقة واحدة.
     </Tip>
 
   </Step>
@@ -188,7 +188,7 @@ crewai deploy remove <deployment_id>
 
     1. انقر على زر "Deploy" لبدء عملية النشر
     2. يمكنك مراقبة التقدم عبر شريط التقدم
-    3. يستغرق النشر الأول عادة حوالي 10-15 دقيقة؛ عمليات النشر اللاحقة ستكون أسرع
+    3. يستغرق النشر الأول عادة حوالي دقيقة واحدة
 
     <Frame>
       ![تقدم النشر](/images/enterprise/deploy-progress.png)

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:
@@ -196,8 +204,8 @@ python3 --version
 ## الخطوات التالية
 
 <CardGroup cols={2}>
-  <Card title="ابنِ أول Agent لك" icon="code" href="/ar/quickstart">
-    اتبع دليل البداية السريعة لإنشاء أول Agent في CrewAI والحصول على تجربة عملية.
+  <Card title="بدء سريع: Flow + وكيل" icon="code" href="/ar/quickstart">
+    اتبع البداية السريعة لإنشاء Flow وتشغيل طاقم بوكيل واحد وإنتاج تقرير.
   </Card>
   <Card
     title="انضم إلى المجتمع"

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.
@@ -130,9 +138,9 @@ mode: "wide"
   <Card
     title="البداية السريعة"
     icon="bolt"
-    href="en/quickstart"
+    href="/ar/quickstart"
   >
-    اتبع دليل البداية السريعة لإنشاء أول Agent في CrewAI والحصول على تجربة عملية.
+    أنشئ Flow وشغّل طاقمًا بوكيل واحد وأنشئ تقريرًا من البداية للنهاية.
   </Card>
   <Card
     title="انضم إلى المجتمع"

docs/ar/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ asyncio.run(interactive_research())
 - **تجربة المستخدم**: تقليل زمن الاستجابة المتصور بعرض نتائج تدريجية
 - **لوحات المعلومات الحية**: بناء واجهات مراقبة تعرض حالة تنفيذ الطاقم
 
+## الإلغاء وتنظيف الموارد
+
+يدعم `CrewStreamingOutput` الإلغاء السلس بحيث يتوقف العمل الجاري فوراً عند انقطاع اتصال المستهلك.
+
+### مدير السياق غير المتزامن
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### الإلغاء الصريح
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # غير متزامن
+    # streaming.close()       # المكافئ المتزامن
+```
+
+بعد الإلغاء، يكون كل من `streaming.is_cancelled` و `streaming.is_completed` بقيمة `True`. كل من `aclose()` و `close()` متساويان القوة.
+
 ## ملاحظات مهمة
 
 - يفعّل البث تلقائياً بث LLM لجميع الوكلاء في الطاقم

docs/ar/learn/streaming-flow-execution.mdx

@@ -420,6 +420,34 @@ except Exception as e:
         print("Streaming completed but flow encountered an error")
 ```
 
+## الإلغاء وتنظيف الموارد
+
+يدعم `FlowStreamingOutput` الإلغاء السلس بحيث يتوقف العمل الجاري فوراً عند انقطاع اتصال المستهلك.
+
+### مدير السياق غير المتزامن
+
+```python Code
+streaming = await flow.kickoff_async()
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### الإلغاء الصريح
+
+```python Code
+streaming = await flow.kickoff_async()
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # غير متزامن
+    # streaming.close()       # المكافئ المتزامن
+```
+
+بعد الإلغاء، يكون كل من `streaming.is_cancelled` و `streaming.is_completed` بقيمة `True`. كل من `aclose()` و `close()` متساويان القوة.
+
 ## ملاحظات مهمة
 
 - يفعّل البث تلقائياً بث LLM لأي أطقم مستخدمة داخل التدفق

docs/ar/quickstart.mdx

@@ -1,380 +1,278 @@
 ---
 title: البدء السريع
-description: ابنِ أول وكيل ذكاء اصطناعي مع CrewAI في أقل من 5 دقائق.
+description: ابنِ أول Flow في CrewAI خلال دقائق — التنسيق والحالة وفريقًا بوكيل واحد ينتج تقريرًا فعليًا.
 icon: rocket
 mode: "wide"
 ---
 
-## ابنِ أول وكيل CrewAI
+### شاهد: بناء Agents و Flows في CrewAI باستخدام Coding Agent Skills
 
-لننشئ طاقماً بسيطاً يساعدنا في `البحث` و`إعداد التقارير` عن `أحدث تطورات الذكاء الاصطناعي` لموضوع أو مجال معين.
+قم بتثبيت مهارات وكيل البرمجة الخاصة بنا (Claude Code، Codex، ...) لتشغيل وكلاء البرمجة بسرعة مع CrewAI.
 
-قبل المتابعة، تأكد من إنهاء تثبيت CrewAI.
-إذا لم تكن قد ثبّتها بعد، يمكنك القيام بذلك باتباع [دليل التثبيت](/ar/installation).
+يمكنك تثبيتها باستخدام `npx skills add crewaiinc/skills`
 
-اتبع الخطوات أدناه للبدء!
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
+في هذا الدليل ستُنشئ **Flow** يحدد موضوع بحث، ويشغّل **طاقمًا بوكيل واحد** (باحث يستخدم البحث على الويب)، وينتهي بتقرير **Markdown** على القرص. يُعد Flow الطريقة الموصى بها لتنظيم التطبيقات الإنتاجية: يمتلك **الحالة** و**ترتيب التنفيذ**، بينما **الوكلاء** ينفّذون العمل داخل خطوة الطاقم.
+
+إذا لم تُكمل تثبيت CrewAI بعد، اتبع [دليل التثبيت](/ar/installation) أولًا.
+
+## المتطلبات الأساسية
+
+- بيئة Python وواجهة سطر أوامر CrewAI (راجع [التثبيت](/ar/installation))
+- نموذج لغوي مهيأ بالمفاتيح الصحيحة — راجع [LLMs](/ar/concepts/llms#setting-up-your-llm)
+- مفتاح API من [Serper.dev](https://serper.dev/) (`SERPER_API_KEY`) للبحث على الويب في هذا الدرس
+
+## ابنِ أول Flow لك
 
 <Steps>
-  <Step title="إنشاء طاقمك">
-  أنشئ مشروع طاقم جديد عبر تشغيل الأمر التالي في الطرفية.
-  سينشئ هذا مجلداً جديداً باسم `latest-ai-development` مع البنية الأساسية لطاقمك.
+  <Step title="أنشئ مشروع Flow">
+  من الطرفية، أنشئ مشروع Flow (اسم المجلد يستخدم شرطة سفلية، مثل `latest_ai_flow`):
+
     <CodeGroup>
       ```shell Terminal
-      crewai create crew latest-ai-development
+      crewai create flow latest-ai-flow
+      cd latest_ai_flow
       ```
     </CodeGroup>
+
+  يُنشئ ذلك تطبيق Flow ضمن `src/latest_ai_flow/`، بما في ذلك طاقمًا أوليًا في `crews/content_crew/` ستستبدله بطاقم بحث **بوكيل واحد** في الخطوات التالية.
   </Step>
-  <Step title="الانتقال إلى مشروع الطاقم الجديد">
-    <CodeGroup>
-      ```shell Terminal
-      cd latest_ai_development
-      ```
-    </CodeGroup>
-  </Step>
-  <Step title="تعديل ملف `agents.yaml`">
-  <Tip>
-  يمكنك أيضاً تعديل الوكلاء حسب الحاجة ليناسبوا حالة الاستخدام الخاصة بك أو نسخ ولصق كما هو في مشروعك.
-  أي متغير مُستكمل في ملفات `agents.yaml` و`tasks.yaml` مثل `{topic}` سيُستبدل بقيمة المتغير في ملف `main.py`.
-  </Tip>
+
+  <Step title="اضبط وكيلًا واحدًا في `agents.yaml`">
+  استبدل محتوى `src/latest_ai_flow/crews/content_crew/config/agents.yaml` بباحث واحد. تُملأ المتغيرات مثل `{topic}` من `crew.kickoff(inputs=...)`.
+
     ```yaml agents.yaml
-    # src/latest_ai_development/config/agents.yaml
+    # src/latest_ai_flow/crews/content_crew/config/agents.yaml
     researcher:
       role: >
-        {topic} Senior Data Researcher
+        باحث بيانات أول في {topic}
       goal: >
-        Uncover cutting-edge developments in {topic}
+        اكتشاف أحدث التطورات في {topic}
       backstory: >
-        You're a seasoned researcher with a knack for uncovering the latest
-        developments in {topic}. Known for your ability to find the most relevant
-        information and present it in a clear and concise manner.
-
-    reporting_analyst:
-      role: >
-        {topic} Reporting Analyst
-      goal: >
-        Create detailed reports based on {topic} data analysis and research findings
-      backstory: >
-        You're a meticulous analyst with a keen eye for detail. You're known for
-        your ability to turn complex data into clear and concise reports, making
-        it easy for others to understand and act on the information you provide.
+        أنت باحث مخضرم تكشف أحدث المستجدات في {topic}.
+        تجد المعلومات الأكثر صلة وتعرضها بوضوح.
     ```
 
   </Step>
-  <Step title="تعديل ملف `tasks.yaml`">
+
+  <Step title="اضبط مهمة واحدة في `tasks.yaml`">
     ```yaml tasks.yaml
-    # src/latest_ai_development/config/tasks.yaml
+    # src/latest_ai_flow/crews/content_crew/config/tasks.yaml
     research_task:
       description: >
-        Conduct a thorough research about {topic}
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        أجرِ بحثًا معمقًا عن {topic}. استخدم البحث على الويب للعثور على معلومات
+        حديثة وموثوقة. السنة الحالية 2026.
       expected_output: >
-        A list with 10 bullet points of the most relevant information about {topic}
+        تقرير بصيغة Markdown بأقسام واضحة: الاتجاهات الرئيسية، أدوات أو شركات بارزة،
+        والآثار. بين 800 و1200 كلمة تقريبًا. دون إحاطة المستند بأكمله بكتل كود.
       agent: researcher
-
-    reporting_task:
-      description: >
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
-      expected_output: >
-        A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
-      agent: reporting_analyst
-      output_file: report.md
+      output_file: output/report.md
     ```
 
   </Step>
-  <Step title="تعديل ملف `crew.py`">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task
-    from crewai_tools import SerperDevTool
-    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+  <Step title="اربط صف الطاقم (`content_crew.py`)">
+  اجعل الطاقم المُولَّد يشير إلى YAML وأرفق `SerperDevTool` بالباحث.
+
+    ```python content_crew.py
+    # src/latest_ai_flow/crews/content_crew/content_crew.py
     from typing import List
 
+    from crewai import Agent, Crew, Process, Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.project import CrewBase, agent, crew, task
+    from crewai_tools import SerperDevTool
+
+
     @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+    class ResearchCrew:
+      """طاقم بحث بوكيل واحد داخل Flow."""
 
       agents: List[BaseAgent]
       tasks: List[Task]
 
+      agents_config = "config/agents.yaml"
+      tasks_config = "config/tasks.yaml"
+
       @agent
       def researcher(self) -> Agent:
         return Agent(
-          config=self.agents_config['researcher'], # type: ignore[index]
+          config=self.agents_config["researcher"],  # type: ignore[index]
           verbose=True,
-          tools=[SerperDevTool()]
-        )
-
-      @agent
-      def reporting_analyst(self) -> Agent:
-        return Agent(
-          config=self.agents_config['reporting_analyst'], # type: ignore[index]
-          verbose=True
+          tools=[SerperDevTool()],
         )
 
       @task
       def research_task(self) -> Task:
         return Task(
-          config=self.tasks_config['research_task'], # type: ignore[index]
-        )
-
-      @task
-      def reporting_task(self) -> Task:
-        return Task(
-          config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # This is the file that will be contain the final report.
+          config=self.tasks_config["research_task"],  # type: ignore[index]
         )
 
       @crew
       def crew(self) -> Crew:
-        """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Automatically created by the @agent decorator
-          tasks=self.tasks, # Automatically created by the @task decorator
+          agents=self.agents,
+          tasks=self.tasks,
           process=Process.sequential,
           verbose=True,
         )
     ```
 
   </Step>
-  <Step title="[اختياري] إضافة دوال قبل وبعد تشغيل الطاقم">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
-    from crewai_tools import SerperDevTool
 
-    @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+  <Step title="عرّف Flow في `main.py`">
+  اربط الطاقم بـ Flow: خطوة `@start()` تضبط الموضوع في **الحالة**، وخطوة `@listen` تشغّل الطاقم. يظل `output_file` للمهمة يكتب `output/report.md`.
 
-      @before_kickoff
-      def before_kickoff_function(self, inputs):
-        print(f"Before kickoff function with inputs: {inputs}")
-        return inputs # You can return the inputs or modify them as needed
-
-      @after_kickoff
-      def after_kickoff_function(self, result):
-        print(f"After kickoff function with result: {result}")
-        return result # You can return the result or modify it as needed
-
-      # ... remaining code
-    ```
-
-  </Step>
-  <Step title="لا تتردد في تمرير مدخلات مخصصة لطاقمك">
-  على سبيل المثال، يمكنك تمرير مدخل `topic` لطاقمك لتخصيص البحث وإعداد التقارير.
     ```python main.py
-    #!/usr/bin/env python
-    # src/latest_ai_development/main.py
-    import sys
-    from latest_ai_development.crew import LatestAiDevelopmentCrew
+    # src/latest_ai_flow/main.py
+    from pydantic import BaseModel
 
-    def run():
-      """
-      Run the crew.
-      """
-      inputs = {
-        'topic': 'AI Agents'
-      }
-      LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+    from crewai.flow import Flow, listen, start
+
+    from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
+
+
+    class ResearchFlowState(BaseModel):
+      topic: str = ""
+      report: str = ""
+
+
+    class LatestAiFlow(Flow[ResearchFlowState]):
+      @start()
+      def prepare_topic(self, crewai_trigger_payload: dict | None = None):
+        if crewai_trigger_payload:
+          self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
+        else:
+          self.state.topic = "AI Agents"
+        print(f"الموضوع: {self.state.topic}")
+
+      @listen(prepare_topic)
+      def run_research(self):
+        result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
+        self.state.report = result.raw
+        print("اكتمل طاقم البحث.")
+
+      @listen(run_research)
+      def summarize(self):
+        print("مسار التقرير: output/report.md")
+
+
+    def kickoff():
+      LatestAiFlow().kickoff()
+
+
+    def plot():
+      LatestAiFlow().plot()
+
+
+    if __name__ == "__main__":
+      kickoff()
     ```
 
-  </Step>
-  <Step title="تعيين متغيرات البيئة">
-  قبل تشغيل طاقمك، تأكد من تعيين المفاتيح التالية كمتغيرات بيئة في ملف `.env`:
-    - مفتاح API لـ [Serper.dev](https://serper.dev/): `SERPER_API_KEY=YOUR_KEY_HERE`
-    - إعداد النموذج الذي اخترته، مثل مفتاح API. راجع
-        [دليل إعداد LLM](/ar/concepts/llms#setting-up-your-llm) لمعرفة كيفية إعداد النماذج من أي مزود.
-  </Step>
-  <Step title="قفل وتثبيت التبعيات">
-    - اقفل التبعيات وثبّتها باستخدام أمر CLI:
-      <CodeGroup>
-        ```shell Terminal
-        crewai install
-        ```
-      </CodeGroup>
-    - إذا كانت لديك حزم إضافية تريد تثبيتها، يمكنك القيام بذلك عبر:
-      <CodeGroup>
-        ```shell Terminal
-        uv add <package-name>
-        ```
-      </CodeGroup>
-  </Step>
-  <Step title="تشغيل طاقمك">
-    - لتشغيل طاقمك، نفّذ الأمر التالي في جذر مشروعك:
-      <CodeGroup>
-        ```bash Terminal
-        crewai run
-        ```
-      </CodeGroup>
+  <Tip>
+  إذا كان اسم الحزمة ليس `latest_ai_flow`، عدّل استيراد `ResearchCrew` ليطابق مسار الوحدة في مشروعك.
+  </Tip>
   </Step>
 
-  <Step title="البديل المؤسسي: الإنشاء في Crew Studio">
-  لمستخدمي CrewAI AMP، يمكنك إنشاء نفس الطاقم دون كتابة كود:
+  <Step title="متغيرات البيئة">
+  في جذر المشروع، ضبط `.env`:
 
-1. سجّل الدخول إلى حساب CrewAI AMP (أنشئ حساباً مجانياً على [app.crewai.com](https://app.crewai.com))
-2. افتح Crew Studio
-3. اكتب ما هي الأتمتة التي تحاول بناءها
-4. أنشئ مهامك بصرياً واربطها بالتسلسل
-5. هيئ مدخلاتك وانقر "تحميل الكود" أو "نشر"
-
-![واجهة Crew Studio للبدء السريع](/images/enterprise/crew-studio-interface.png)
-
-  <Card title="جرّب CrewAI AMP" icon="rocket" href="https://app.crewai.com">
-    ابدأ حسابك المجاني في CrewAI AMP
-  </Card>
+    - `SERPER_API_KEY` — من [Serper.dev](https://serper.dev/)
+    - مفاتيح مزوّد النموذج حسب الحاجة — راجع [إعداد LLM](/ar/concepts/llms#setting-up-your-llm)
   </Step>
-  <Step title="عرض التقرير النهائي">
-  يجب أن ترى المخرجات في وحدة التحكم ويجب إنشاء ملف `report.md` في جذر مشروعك مع التقرير النهائي.
 
-إليك مثالاً على شكل التقرير:
+  <Step title="التثبيت والتشغيل">
+    <CodeGroup>
+      ```shell Terminal
+      crewai install
+      crewai run
+      ```
+    </CodeGroup>
+
+  يُنفّذ `crewai run` نقطة دخول Flow المعرّفة في المشروع (نفس أمر الطواقم؛ نوع المشروع `"flow"` في `pyproject.toml`).
+  </Step>
+
+  <Step title="تحقق من المخرجات">
+  يجب أن ترى سجلات من Flow والطاقم. افتح **`output/report.md`** للتقرير المُولَّد (مقتطف):
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # وكلاء الذكاء الاصطناعي في 2026: المشهد والاتجاهات
 
-    ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    ## ملخص تنفيذي
+    …
 
-    ## 2. Benefits of AI Agents
-    AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
+    ## أبرز الاتجاهات
+    - **استخدام الأدوات والتنسيق** — …
+    - **التبني المؤسسي** — …
 
-    - **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
-    - **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
-    - **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
-
-    ## 3. Popular AI Agent Frameworks
-    Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
-
-    - **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
-    - **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
-    - **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
-    - **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
-    - **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
-    - **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
-
-    These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
-
-    ## 4. AI Agents in Human Resources
-    AI agents are revolutionizing HR practices by automating and optimizing key functions:
-
-    - **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
-    - **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
-    - **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
-
-    As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
-
-    ## 5. AI Agents in Finance
-    The finance sector is seeing extensive integration of AI agents that enhance financial practices:
-
-    - **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
-    - **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
-    - **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
-
-    The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
-
-    ## 6. Market Trends and Investments
-    The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
-
-    Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
-
-    ## 7. Future Predictions and Implications
-    Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
-
-    - Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
-    - Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
-    - Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
-
-    To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
-
-    ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    ## الآثار
+    …
     ```
-
   </CodeGroup>
+
+  سيكون الملف الفعلي أطول ويعكس نتائج بحث مباشرة.
   </Step>
 </Steps>
 
+## كيف يترابط هذا
+
+1. **Flow** — يشغّل `LatestAiFlow` أولًا `prepare_topic` ثم `run_research` ثم `summarize`. الحالة (`topic`، `report`) على Flow.
+2. **الطاقم** — يشغّل `ResearchCrew` مهمة واحدة بوكيل واحد: الباحث يستخدم **Serper** للبحث على الويب ثم يكتب التقرير.
+3. **المُخرَج** — يكتب `output_file` للمهمة التقرير في `output/report.md`.
+
+للتعمق في أنماط Flow (التوجيه، الاستمرارية، الإنسان في الحلقة)، راجع [ابنِ أول Flow](/ar/guides/flows/first-flow) و[Flows](/ar/concepts/flows). للطواقم دون Flow، راجع [Crews](/ar/concepts/crews). لوكيل `Agent` واحد و`kickoff()` بلا مهام، راجع [Agents](/ar/concepts/agents#direct-agent-interaction-with-kickoff).
+
 <Check>
-تهانينا!
-
-لقد أعددت مشروع طاقمك بنجاح وأنت جاهز للبدء في بناء سير العمل الوكيلي الخاص بك!
-
+أصبح لديك Flow كامل مع طاقم وكيل وتقرير محفوظ — قاعدة قوية لإضافة خطوات أو طواقم أو أدوات.
 </Check>
 
-### ملاحظة حول اتساق التسمية
+### اتساق التسمية
 
-يجب أن تتطابق الأسماء التي تستخدمها في ملفات YAML (`agents.yaml` و`tasks.yaml`) مع أسماء الدوال في كود Python الخاص بك.
-على سبيل المثال، يمكنك الإشارة إلى الوكيل لمهام محددة من ملف `tasks.yaml`.
-يتيح اتساق التسمية هذا لـ CrewAI ربط تكويناتك بكودك تلقائياً؛ وإلا فلن تتعرف مهمتك على المرجع بشكل صحيح.
+يجب أن تطابق مفاتيح YAML (`researcher`، `research_task`) أسماء الدوال في صف `@CrewBase`. راجع [Crews](/ar/concepts/crews) لنمط الديكورات الكامل.
 
-#### أمثلة على المراجع
+## النشر
 
-<Tip>
-  لاحظ كيف نستخدم نفس الاسم للوكيل في ملف `agents.yaml`
-  (`email_summarizer`) واسم الدالة في ملف `crew.py`
-  (`email_summarizer`).
-</Tip>
+ادفع Flow إلى **[CrewAI AMP](https://app.crewai.com)** بعد أن يعمل محليًا ويكون المشروع في مستودع **GitHub**. من جذر المشروع:
 
-```yaml agents.yaml
-email_summarizer:
-  role: >
-    Email Summarizer
-  goal: >
-    Summarize emails into a concise and clear summary
-  backstory: >
-    You will create a 5 bullet point summary of the report
-  llm: provider/model-id # Add your choice of model here
+<CodeGroup>
+```bash المصادقة
+crewai login
 ```
 
-<Tip>
-  لاحظ كيف نستخدم نفس الاسم للمهمة في ملف `tasks.yaml`
-  (`email_summarizer_task`) واسم الدالة في ملف `crew.py`
-  (`email_summarizer_task`).
-</Tip>
-
-```yaml tasks.yaml
-email_summarizer_task:
-  description: >
-    Summarize the email into a 5 bullet point summary
-  expected_output: >
-    A 5 bullet point summary of the email
-  agent: email_summarizer
-  context:
-    - reporting_task
-    - research_task
+```bash إنشاء نشر
+crewai deploy create
 ```
 
-## نشر طاقمك
+```bash الحالة والسجلات
+crewai deploy status
+crewai deploy logs
+```
 
-أسهل طريقة لنشر طاقمك في الإنتاج هي من خلال [CrewAI AMP](http://app.crewai.com).
+```bash إرسال التحديثات بعد تغيير الكود
+crewai deploy push
+```
 
-شاهد هذا الفيديو التعليمي لعرض خطوة بخطوة لنشر طاقمك على [CrewAI AMP](http://app.crewai.com) باستخدام CLI.
+```bash عرض النشرات أو حذفها
+crewai deploy list
+crewai deploy remove <deployment_id>
+```
+</CodeGroup>
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
+<Tip>
+  غالبًا ما يستغرق **النشر الأول حوالي دقيقة**. المتطلبات الكاملة ومسار الواجهة الويب في [النشر على AMP](/ar/enterprise/guides/deploy-to-amp).
+</Tip>
 
 <CardGroup cols={2}>
-  <Card title="النشر على المؤسسة" icon="rocket" href="http://app.crewai.com">
-    ابدأ مع CrewAI AMP وانشر طاقمك في بيئة إنتاج
-    بنقرات قليلة فقط.
+  <Card title="دليل النشر" icon="book" href="/ar/enterprise/guides/deploy-to-amp">
+    النشر على AMP خطوة بخطوة (CLI ولوحة التحكم).
   </Card>
   <Card
-    title="انضم إلى المجتمع"
+    title="المجتمع"
     icon="comments"
     href="https://community.crewai.com"
   >
-    انضم إلى مجتمعنا مفتوح المصدر لمناقشة الأفكار ومشاركة مشاريعك والتواصل
-    مع مطورين آخرين لـ CrewAI.
+    ناقش الأفكار وشارك مشاريعك وتواصل مع مطوري CrewAI.
   </Card>
 </CardGroup>

docs/ar/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: ثبّت crewaiinc/skills من السجل الرسمي على skills.sh—Flows وCrews ووكلاء مرتبطون بالوثائق لـ Claude Code وCursor وCodex وغيرها.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**امنح وكيل البرمجة سياق CrewAI في أمر واحد.**
+
+تُنشر **Skills** الخاصة بـ CrewAI على **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—السجل الرسمي لـ `crewaiinc/skills`، بما في ذلك كل مهارة (مثل **design-agent** و**getting-started** و**design-task** و**ask-docs**) وإحصاءات التثبيت والتدقيقات. تعلّم وكلاء البرمجة—مثل Claude Code وCursor وCodex—هيكلة Flows وضبط Crews واستخدام الأدوات واتباع أنماط CrewAI. نفّذ الأمر أدناه (أو الصقه في الوكيل).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+يضيف ذلك حزمة المهارات إلى سير عمل الوكيل لتطبيق اتفاقيات CrewAI دون إعادة شرح الإطار في كل جلسة. المصدر والقضايا على [GitHub](https://github.com/crewAIInc/skills).
+
+## ما يحصل عليه الوكيل
+
+- **Flows** — تطبيقات ذات حالة وخطوات وkickoffs للـ crew على نمط CrewAI
+- **Crews والوكلاء** — أنماط YAML أولاً، أدوار، مهام، وتفويض
+- **الأدوات والتكاملات** — ربط الوكلاء بالبحث وواجهات API وأدوات CrewAI الشائعة
+- **هيكل المشروع** — مواءمة مع قوالب CLI واتفاقيات المستودع
+- **أنماط محدثة** — تتبع المهارات وثائق CrewAI والممارسات الموصى بها
+
+## تعرّف أكثر على هذا الموقع
+
+<CardGroup cols={2}>
+  <Card title="أدوات البرمجة و AGENTS.md" icon="terminal" href="/ar/guides/coding-tools/agents-md">
+    استخدام `AGENTS.md` وسير عمل وكلاء البرمجة مع CrewAI.
+  </Card>
+  <Card title="البداية السريعة" icon="rocket" href="/ar/quickstart">
+    ابنِ أول Flow وcrew من البداية للنهاية.
+  </Card>
+  <Card title="التثبيت" icon="download" href="/ar/installation">
+    ثبّت CrewAI CLI وحزمة Python.
+  </Card>
+  <Card title="سجل Skills (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    القائمة الرسمية لـ `crewaiinc/skills`—المهارات والتثبيتات والتدقيقات.
+  </Card>
+  <Card title="المصدر على GitHub" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    مصدر الحزمة والتحديثات والقضايا.
+  </Card>
+</CardGroup>
+
+### فيديو: CrewAI مع مهارات وكلاء البرمجة
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/ar/tools/ai-ml/codeinterpretertool.mdx

@@ -7,6 +7,10 @@ mode: "wide"
 
 # `CodeInterpreterTool`
 
+<Warning>
+  **مهجور:** تمت إزالة `CodeInterpreterTool` من `crewai-tools`. كما أن معاملَي `allow_code_execution` و`code_execution_mode` على `Agent` أصبحا مهجورَين. استخدم خدمة بيئة معزولة مخصصة — [E2B](https://e2b.dev) أو [Modal](https://modal.com) — لتنفيذ الكود بشكل آمن ومعزول.
+</Warning>
+
 ## الوصف
 
 تمكّن `CodeInterpreterTool` وكلاء CrewAI من تنفيذ كود Python 3 الذي يولّدونه بشكل مستقل. هذه الوظيفة ذات قيمة خاصة لأنها تتيح للوكلاء إنشاء الكود وتنفيذه والحصول على النتائج واستخدام تلك المعلومات لاتخاذ القرارات والإجراءات اللاحقة.

docs/ar/tools/file-document/csvsearchtool.mdx

@@ -74,3 +74,19 @@ tool = CSVSearchTool(
     }
 )
 ```
+
+## الأمان
+
+### التحقق من صحة المسارات
+
+يتم التحقق من مسارات الملفات المقدمة لهذه الأداة مقابل مجلد العمل الحالي. يتم رفض المسارات التي تحل خارج مجلد العمل وإطلاق `ValueError`.
+
+للسماح بالمسارات خارج مجلد العمل (مثلاً في الاختبارات أو خطوط الأنابيب الموثوقة)، عيّن متغير البيئة التالي:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### التحقق من صحة الروابط
+
+يتم التحقق من مدخلات الروابط: يتم حظر مخطط `file://` والطلبات التي تستهدف نطاقات IP الخاصة أو المحجوزة لمنع هجمات تزوير الطلبات من جانب الخادم (SSRF).

docs/ar/tools/file-document/directorysearchtool.mdx

@@ -68,3 +68,15 @@ tool = DirectorySearchTool(
     }
 )
 ```
+
+## الأمان
+
+### التحقق من صحة المسارات
+
+يتم التحقق من مسارات المجلدات المقدمة لهذه الأداة مقابل مجلد العمل الحالي. يتم رفض المسارات التي تحل خارج مجلد العمل وإطلاق `ValueError`.
+
+للسماح بالمسارات خارج مجلد العمل (مثلاً في الاختبارات أو خطوط الأنابيب الموثوقة)، عيّن متغير البيئة التالي:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```

docs/ar/tools/file-document/jsonsearchtool.mdx

@@ -73,3 +73,19 @@ tool = JSONSearchTool(
     }
 )
 ```
+
+## الأمان
+
+### التحقق من صحة المسارات
+
+يتم التحقق من مسارات الملفات المقدمة لهذه الأداة مقابل مجلد العمل الحالي. يتم رفض المسارات التي تحل خارج مجلد العمل وإطلاق `ValueError`.
+
+للسماح بالمسارات خارج مجلد العمل (مثلاً في الاختبارات أو خطوط الأنابيب الموثوقة)، عيّن متغير البيئة التالي:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### التحقق من صحة الروابط
+
+يتم التحقق من مدخلات الروابط: يتم حظر مخطط `file://` والطلبات التي تستهدف نطاقات IP الخاصة أو المحجوزة لمنع هجمات تزوير الطلبات من جانب الخادم (SSRF).

docs/ar/tools/file-document/pdfsearchtool.mdx

@@ -105,3 +105,19 @@ tool = PDFSearchTool(
     }
 )
 ```
+
+## الأمان
+
+### التحقق من صحة المسارات
+
+يتم التحقق من مسارات الملفات المقدمة لهذه الأداة مقابل مجلد العمل الحالي. يتم رفض المسارات التي تحل خارج مجلد العمل وإطلاق `ValueError`.
+
+للسماح بالمسارات خارج مجلد العمل (مثلاً في الاختبارات أو خطوط الأنابيب الموثوقة)، عيّن متغير البيئة التالي:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### التحقق من صحة الروابط
+
+يتم التحقق من مدخلات الروابط: يتم حظر مخطط `file://` والطلبات التي تستهدف نطاقات IP الخاصة أو المحجوزة لمنع هجمات تزوير الطلبات من جانب الخادم (SSRF).

docs/en/changelog.mdx

@@ -4,6 +4,313 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 09, 2026">
+  ## v1.14.2a1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Fix emission of flow_finished event after HITL resume
+  - Fix cryptography version to 46.0.7 to address CVE-2026-39892
+
+  ### Refactoring
+  - Refactor to use shared I18N_DEFAULT singleton
+
+  ### Documentation
+  - Update changelog and version for v1.14.1
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="Apr 09, 2026">
+  ## v1.14.1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## What's Changed
+
+  ### Features
+  - Add async checkpoint TUI browser
+  - Add aclose()/close() and async context manager to streaming outputs
+
+  ### Bug Fixes
+  - Fix regex for template pyproject.toml version bumps
+  - Sanitize tool names in hook decorator filters
+  - Fix checkpoint handlers registration when CheckpointConfig is created
+  - Bump transformers to 5.5.0 to resolve CVE-2026-1839
+  - Remove FilteredStream stdout/stderr wrapper
+
+  ### Documentation
+  - Update changelog and version for v1.14.1rc1
+
+  ### Refactoring
+  - Replace hardcoded denylist with dynamic BaseTool field exclusion in spec gen
+  - Replace regex with tomlkit in devtools CLI
+  - Use shared PRINTER singleton
+  - Make BaseProvider a BaseModel with provider_type discriminator
+
+  ## Contributors
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="Apr 09, 2026">
+  ## v1.14.1rc1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## What's Changed
+
+  ### Features
+  - Add async checkpoint TUI browser
+  - Add aclose()/close() and async context manager to streaming outputs
+
+  ### Bug Fixes
+  - Fix template pyproject.toml version bumps using regex
+  - Sanitize tool names in hook decorator filters
+  - Bump transformers to 5.5.0 to resolve CVE-2026-1839
+  - Register checkpoint handlers when CheckpointConfig is created
+
+  ### Refactoring
+  - Replace hardcoded denylist with dynamic BaseTool field exclusion in spec gen
+  - Replace regex with tomlkit in devtools CLI
+  - Use shared PRINTER singleton
+  - Make BaseProvider a BaseModel with provider_type discriminator
+  - Remove FilteredStream stdout/stderr wrapper
+  - Remove unused flow/config.py
+
+  ### Documentation
+  - Update changelog and version for v1.14.0
+
+  ## Contributors
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
+<Update label="Apr 07, 2026">
+  ## v1.14.0
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0)
+
+  ## What's Changed
+
+  ### Features
+  - Add checkpoint list/info CLI commands
+  - Add guardrail_type and name to distinguish traces
+  - Add SqliteProvider for checkpoint storage
+  - Add CheckpointConfig for automatic checkpointing
+  - Implement runtime state checkpointing, event system, and executor refactor
+
+  ### Bug Fixes
+  - Add SSRF and path traversal protections
+  - Add path and URL validation to RAG tools
+  - Exclude embedding vectors from memory serialization to save tokens
+  - Ensure output directory exists before writing in flow template
+  - Bump litellm to >=1.83.0 to address CVE-2026-35030
+  - Remove SEO indexing field causing Arabic page rendering
+
+  ### Documentation
+  - Update changelog and version for v1.14.0
+  - Update quickstart and installation guides for improved clarity
+  - Add storage providers section, export JsonProvider
+  - Add AMP Training Tab guide
+
+  ### Refactoring
+  - Clean up checkpoint API
+  - Remove CodeInterpreterTool and deprecate code execution parameters
+
+  ## Contributors
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="Apr 07, 2026">
+  ## v1.14.0a4
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a4)
+
+  ## What's Changed
+
+  ### Features
+  - Add guardrail_type and name to distinguish traces
+  - Add SqliteProvider for checkpoint storage
+  - Add CheckpointConfig for automatic checkpointing
+  - Implement runtime state checkpointing, event system, and executor refactor
+
+  ### Bug Fixes
+  - Exclude embedding vectors from memory serialization to save tokens
+  - Bump litellm to >=1.83.0 to address CVE-2026-35030
+
+  ### Documentation
+  - Update quickstart and installation guides for improved clarity
+  - Add storage providers section and export JsonProvider
+
+  ### Performance
+  - Use JSONB for checkpoint data column
+
+  ### Refactoring
+  - Remove CodeInterpreterTool and deprecate code execution params
+
+  ## Contributors
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="Apr 06, 2026">
+  ## v1.14.0a3
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
+
+  ## What's Changed
+
+  ### Documentation
+  - Update changelog and version for v1.14.0a2
+
+  ## Contributors
+
+  @joaomdmoura
+
+</Update>
+
+<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
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## What's Changed
+
+  ### Features
+  - Add A2UI extension with v0.8/v0.9 support, schemas, and docs
+
+  ### Bug Fixes
+  - Fix multimodal vision prefixes by adding GPT-5 and o-series
+
+  ### Documentation
+  - Update changelog and version for v1.13.0a6
+
+  ## Contributors
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="Apr 01, 2026">
+  ## v1.13.0a6
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a6)
+
+  ## What's Changed
+
+  ### Documentation
+  - Fix RBAC permission levels to match actual UI options (#5210)
+  - Update changelog and version for v1.13.0a5 (#5200)
+
+  ### Performance
+  - Reduce framework overhead by implementing a lazy event bus and skipping tracing when disabled (#5187)
+
+  ## Contributors
+
+  @alex-clawd, @joaomdmoura, @lucasgomide
+
+</Update>
+
+<Update label="Mar 31, 2026">
+  ## v1.13.0a5
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
+
+  ## What's Changed
+
+  ### Documentation
+  - Update changelog and version for v1.13.0a4
+
+  ## Contributors
+
+  @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="Apr 01, 2026">
+  ## v1.13.0a4
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
+
+  ## What's Changed
+
+  ### Documentation
+  - Update changelog and version for v1.13.0a3
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="Apr 01, 2026">
   ## v1.13.0a3
 

docs/en/concepts/agents.mdx

@@ -308,16 +308,12 @@ multimodal_agent = Agent(
 
 #### Code Execution
 
-- `allow_code_execution`: Must be True to run code
-- `code_execution_mode`:
-  - `"safe"`: Uses Docker (recommended for production)
-  - `"unsafe"`: Direct execution (use only in trusted environments)
+<Warning>
+  `allow_code_execution` and `code_execution_mode` are deprecated. `CodeInterpreterTool` has been removed from `crewai-tools`. Use a dedicated sandbox service such as [E2B](https://e2b.dev) or [Modal](https://modal.com) for secure code execution.
+</Warning>
 
-<Note>
-  This runs a default Docker image. If you want to configure the docker image,
-  the checkout the Code Interpreter Tool in the tools section. Add the code
-  interpreter tool as a tool in the agent as a tool parameter.
-</Note>
+- `allow_code_execution` _(deprecated)_: Previously enabled built-in code execution via `CodeInterpreterTool`.
+- `code_execution_mode` _(deprecated)_: Previously controlled execution mode (`"safe"` for Docker, `"unsafe"` for direct execution).
 
 #### Advanced Features
 
@@ -667,9 +663,9 @@ asyncio.run(main())
 
 ### Security and Code Execution
 
-- When using `allow_code_execution`, be cautious with user input and always validate it
-- Use `code_execution_mode: "safe"` (Docker) in production environments
-- Consider setting appropriate `max_execution_time` limits to prevent infinite loops
+<Warning>
+  `allow_code_execution` and `code_execution_mode` are deprecated and `CodeInterpreterTool` has been removed. Use a dedicated sandbox service such as [E2B](https://e2b.dev) or [Modal](https://modal.com) for secure code execution.
+</Warning>
 
 ### Performance Optimization
 

docs/en/concepts/checkpointing.mdx

@@ -0,0 +1,233 @@
+---
+title: Checkpointing
+description: Automatically save execution state so crews, flows, and agents can resume after failures.
+icon: floppy-disk
+mode: "wide"
+---
+
+<Warning>
+Checkpointing is in early release. APIs may change in future versions.
+</Warning>
+
+## Overview
+
+Checkpointing automatically saves execution state during a run. If a crew, flow, or agent fails mid-execution, you can restore from the last checkpoint and resume without re-running completed work.
+
+## Quick Start
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=True,  # uses defaults: ./.checkpoints, on task_completed
+)
+result = crew.kickoff()
+```
+
+Checkpoint files are written to `./.checkpoints/` after each completed task.
+
+## Configuration
+
+Use `CheckpointConfig` for full control:
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        on_events=["task_completed", "crew_kickoff_completed"],
+        max_checkpoints=5,
+    ),
+)
+```
+
+### CheckpointConfig Fields
+
+| Field | Type | Default | Description |
+|:------|:-----|:--------|:------------|
+| `location` | `str` | `"./.checkpoints"` | Storage destination — a directory for `JsonProvider`, a database file path for `SqliteProvider` |
+| `on_events` | `list[str]` | `["task_completed"]` | Event types that trigger a checkpoint |
+| `provider` | `BaseProvider` | `JsonProvider()` | Storage backend |
+| `max_checkpoints` | `int \| None` | `None` | Max checkpoints to keep. Oldest are pruned after each write. Pruning is handled by the provider. |
+
+### Inheritance and Opt-Out
+
+The `checkpoint` field on Crew, Flow, and Agent accepts `CheckpointConfig`, `True`, `False`, or `None`:
+
+| Value | Behavior |
+|:------|:---------|
+| `None` (default) | Inherit from parent. An agent inherits its crew's config. |
+| `True` | Enable with defaults. |
+| `False` | Explicit opt-out. Stops inheritance from parent. |
+| `CheckpointConfig(...)` | Custom configuration. |
+
+```python
+crew = Crew(
+    agents=[
+        Agent(role="Researcher", ...),                  # inherits crew's checkpoint
+        Agent(role="Writer", ..., checkpoint=False),     # opted out, no checkpoints
+    ],
+    tasks=[...],
+    checkpoint=True,
+)
+```
+
+## Resuming from a Checkpoint
+
+```python
+# Restore and resume
+crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
+result = crew.kickoff()  # picks up from last completed task
+```
+
+The restored crew skips already-completed tasks and resumes from the first incomplete one.
+
+## Works on Crew, Flow, and Agent
+
+### Crew
+
+```python
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task, review_task],
+    checkpoint=CheckpointConfig(location="./crew_cp"),
+)
+```
+
+Default trigger: `task_completed` (one checkpoint per finished task).
+
+### Flow
+
+```python
+from crewai.flow.flow import Flow, start, listen
+from crewai import CheckpointConfig
+
+class MyFlow(Flow):
+    @start()
+    def step_one(self):
+        return "data"
+
+    @listen(step_one)
+    def step_two(self, data):
+        return process(data)
+
+flow = MyFlow(
+    checkpoint=CheckpointConfig(
+        location="./flow_cp",
+        on_events=["method_execution_finished"],
+    ),
+)
+result = flow.kickoff()
+
+# Resume
+flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
+result = flow.kickoff()
+```
+
+### Agent
+
+```python
+agent = Agent(
+    role="Researcher",
+    goal="Research topics",
+    backstory="Expert researcher",
+    checkpoint=CheckpointConfig(
+        location="./agent_cp",
+        on_events=["lite_agent_execution_completed"],
+    ),
+)
+result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+```
+
+## Storage Providers
+
+CrewAI ships with two checkpoint storage providers.
+
+### JsonProvider (default)
+
+Writes each checkpoint as a separate JSON file. Simple, human-readable, easy to inspect.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import JsonProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        provider=JsonProvider(),       # this is the default
+        max_checkpoints=5,             # prunes oldest files
+    ),
+)
+```
+
+Files are named `<timestamp>_<uuid>.json` inside the location directory.
+
+### SqliteProvider
+
+Stores all checkpoints in a single SQLite database file. Better for high-frequency checkpointing and avoids many small files.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import SqliteProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./.checkpoints.db",
+        provider=SqliteProvider(),
+        max_checkpoints=50,
+    ),
+)
+```
+
+WAL journal mode is enabled for concurrent read access.
+
+## Event Types
+
+The `on_events` field accepts any combination of event type strings. Common choices:
+
+| Use Case | Events |
+|:---------|:-------|
+| After each task (Crew) | `["task_completed"]` |
+| After each flow method | `["method_execution_finished"]` |
+| After agent execution | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
+| On crew completion only | `["crew_kickoff_completed"]` |
+| After every LLM call | `["llm_call_completed"]` |
+| On everything | `["*"]` |
+
+<Warning>
+Using `["*"]` or high-frequency events like `llm_call_completed` will write many checkpoint files and may impact performance. Use `max_checkpoints` to limit disk usage.
+</Warning>
+
+## Manual Checkpointing
+
+For full control, register your own event handler and call `state.checkpoint()` directly:
+
+```python
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+# Sync handler
+@crewai_event_bus.on(LLMCallCompletedEvent)
+def on_llm_done(source, event, state):
+    path = state.checkpoint("./my_checkpoints")
+    print(f"Saved checkpoint: {path}")
+
+# Async handler
+@crewai_event_bus.on(LLMCallCompletedEvent)
+async def on_llm_done_async(source, event, state):
+    path = await state.acheckpoint("./my_checkpoints")
+    print(f"Saved checkpoint: {path}")
+```
+
+The `state` argument is the `RuntimeState` passed automatically by the event bus when your handler accepts 3 parameters. You can register handlers on any event type listed in the [Event Listeners](/en/concepts/event-listener) documentation.
+
+Checkpointing is best-effort: if a checkpoint write fails, the error is logged but execution continues uninterrupted.

docs/en/enterprise/features/rbac.mdx

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

docs/en/enterprise/guides/deploy-to-amp.mdx

@@ -106,7 +106,7 @@ The CLI automatically detects your project type from `pyproject.toml` and builds
     ```
 
     <Tip>
-    The first deployment typically takes 10-15 minutes as it builds the container images. Subsequent deployments are much faster.
+    The first deployment typically takes around 1 minute.
     </Tip>
 
   </Step>
@@ -188,7 +188,7 @@ You need to push your crew to a GitHub repository. If you haven't created a crew
 
     1. Click the "Deploy" button to start the deployment process
     2. You can monitor the progress through the progress bar
-    3. The first deployment typically takes around 10-15 minutes; subsequent deployments will be faster
+    3. The first deployment typically takes around 1 minute
 
     <Frame>
       ![Deploy Progress](/images/enterprise/deploy-progress.png)

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
@@ -196,9 +207,8 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
 ## Next Steps
 
 <CardGroup cols={2}>
-  <Card title="Build Your First Agent" icon="code" href="/en/quickstart">
-    Follow our quickstart guide to create your first CrewAI agent and get
-    hands-on experience.
+  <Card title="Quickstart: Flow + agent" icon="code" href="/en/quickstart">
+    Follow the quickstart to scaffold a Flow, run a one-agent crew, and produce a report.
   </Card>
   <Card
     title="Join the Community"

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.
@@ -132,7 +140,7 @@ For any production-ready application, **start with a Flow**.
     icon="bolt"
     href="en/quickstart"
   >
-    Follow our quickstart guide to create your first CrewAI agent and get hands-on experience.
+    Scaffold a Flow, run a crew with one agent, and generate a report end to end.
   </Card>
   <Card
     title="Join the Community"

docs/en/learn/a2ui.mdx

@@ -0,0 +1,344 @@
+---
+title: Agent-to-UI (A2UI) Protocol
+description: Enable agents to generate declarative UI surfaces for rich client rendering via the A2UI extension.
+icon: window-restore
+mode: "wide"
+---
+
+## A2UI Overview
+
+A2UI is a declarative UI protocol extension for [A2A](/en/learn/a2a-agent-delegation) that lets agents emit structured JSON messages describing interactive surfaces. Clients receive these messages and render them as rich UI components — forms, cards, lists, modals, and more — without the agent needing to know anything about the client's rendering stack.
+
+A2UI is built on the A2A extension mechanism and identified by the URI `https://a2ui.org/a2a-extension/a2ui/v0.8`.
+
+<Note>
+  A2UI requires the `a2a-sdk` package. Install with: `uv add 'crewai[a2a]'` or `pip install 'crewai[a2a]'`
+</Note>
+
+## How It Works
+
+1. The **server extension** scans agent output for A2UI JSON objects
+2. Valid messages are wrapped as `DataPart` entries with the `application/json+a2ui` MIME type
+3. The **client extension** augments the agent's system prompt with A2UI instructions and the component catalog
+4. The client tracks surface state (active surfaces and data models) across conversation turns
+
+## Server Setup
+
+Add `A2UIServerExtension` to your `A2AServerConfig` to enable A2UI output:
+
+```python Code
+from crewai import Agent
+from crewai.a2a import A2AServerConfig
+from crewai.a2a.extensions.a2ui import A2UIServerExtension
+
+agent = Agent(
+    role="Dashboard Agent",
+    goal="Present data through interactive UI surfaces",
+    backstory="Expert at building clear, actionable dashboards",
+    llm="gpt-4o",
+    a2a=A2AServerConfig(
+        url="https://your-server.com",
+        server_extensions=[A2UIServerExtension()],
+    ),
+)
+```
+
+### Server Extension Options
+
+<ParamField path="catalog_ids" type="list[str] | None" default="None">
+  Component catalog identifiers the server supports. When set, only these catalogs are advertised to clients.
+</ParamField>
+
+<ParamField path="accept_inline_catalogs" type="bool" default="False">
+  Whether to accept inline catalog definitions from clients in addition to named catalogs.
+</ParamField>
+
+## Client Setup
+
+Add `A2UIClientExtension` to your `A2AClientConfig` to enable A2UI rendering:
+
+```python Code
+from crewai import Agent
+from crewai.a2a import A2AClientConfig
+from crewai.a2a.extensions.a2ui import A2UIClientExtension
+
+agent = Agent(
+    role="UI Coordinator",
+    goal="Coordinate tasks and render agent responses as rich UI",
+    backstory="Expert at presenting agent output in interactive formats",
+    llm="gpt-4o",
+    a2a=A2AClientConfig(
+        endpoint="https://dashboard-agent.example.com/.well-known/agent-card.json",
+        client_extensions=[A2UIClientExtension()],
+    ),
+)
+```
+
+### Client Extension Options
+
+<ParamField path="catalog_id" type="str | None" default="None">
+  Preferred component catalog identifier. Defaults to `"standard (v0.8)"` when not set.
+</ParamField>
+
+<ParamField path="allowed_components" type="list[str] | None" default="None">
+  Restrict which components the agent may use. When `None`, all 18 standard catalog components are available.
+</ParamField>
+
+## Message Types
+
+A2UI defines four server-to-client message types. Each message targets a **surface** identified by `surfaceId`.
+
+<Tabs>
+  <Tab title="beginRendering">
+  Initializes a new surface with a root component and optional styles.
+
+  ```json
+  {
+    "beginRendering": {
+      "surfaceId": "dashboard-1",
+      "root": "main-column",
+      "catalogId": "standard (v0.8)",
+      "styles": {
+        "primaryColor": "#EB6658"
+      }
+    }
+  }
+  ```
+  </Tab>
+
+  <Tab title="surfaceUpdate">
+  Sends or updates one or more components on an existing surface.
+
+  ```json
+  {
+    "surfaceUpdate": {
+      "surfaceId": "dashboard-1",
+      "components": [
+        {
+          "id": "main-column",
+          "component": {
+            "Column": {
+              "children": { "explicitList": ["title", "content"] },
+              "alignment": "start"
+            }
+          }
+        },
+        {
+          "id": "title",
+          "component": {
+            "Text": {
+              "text": { "literalString": "Dashboard" },
+              "usageHint": "h1"
+            }
+          }
+        }
+      ]
+    }
+  }
+  ```
+  </Tab>
+
+  <Tab title="dataModelUpdate">
+  Updates the data model bound to a surface, enabling dynamic content.
+
+  ```json
+  {
+    "dataModelUpdate": {
+      "surfaceId": "dashboard-1",
+      "path": "/data/model",
+      "contents": [
+        {
+          "key": "userName",
+          "valueString": "Alice"
+        },
+        {
+          "key": "score",
+          "valueNumber": 42
+        }
+      ]
+    }
+  }
+  ```
+  </Tab>
+
+  <Tab title="deleteSurface">
+  Removes a surface and all its components.
+
+  ```json
+  {
+    "deleteSurface": {
+      "surfaceId": "dashboard-1"
+    }
+  }
+  ```
+  </Tab>
+</Tabs>
+
+## Component Catalog
+
+A2UI ships with 18 standard components organized into three categories:
+
+### Content
+
+| Component | Description | Required Fields |
+|-----------|-------------|-----------------|
+| **Text** | Renders text with optional heading/body hints | `text` (StringBinding) |
+| **Image** | Displays an image with fit and size options | `url` (StringBinding) |
+| **Icon** | Renders a named icon from a set of 47 icons | `name` (IconBinding) |
+| **Video** | Embeds a video player | `url` (StringBinding) |
+| **AudioPlayer** | Embeds an audio player with optional description | `url` (StringBinding) |
+
+### Layout
+
+| Component | Description | Required Fields |
+|-----------|-------------|-----------------|
+| **Row** | Horizontal flex container | `children` (ChildrenDef) |
+| **Column** | Vertical flex container | `children` (ChildrenDef) |
+| **List** | Scrollable list (vertical or horizontal) | `children` (ChildrenDef) |
+| **Card** | Elevated container for a single child | `child` (str) |
+| **Tabs** | Tabbed container | `tabItems` (list of TabItem) |
+| **Divider** | Visual separator (horizontal or vertical) | — |
+| **Modal** | Overlay triggered by an entry point | `entryPointChild`, `contentChild` (str) |
+
+### Interactive
+
+| Component | Description | Required Fields |
+|-----------|-------------|-----------------|
+| **Button** | Clickable button that triggers an action | `child` (str), `action` (Action) |
+| **CheckBox** | Boolean toggle with a label | `label` (StringBinding), `value` (BooleanBinding) |
+| **TextField** | Text input with type and validation options | `label` (StringBinding) |
+| **DateTimeInput** | Date and/or time picker | `value` (StringBinding) |
+| **MultipleChoice** | Selection from a list of options | `selections` (ArrayBinding), `options` (list) |
+| **Slider** | Numeric range slider | `value` (NumberBinding) |
+
+## Data Binding
+
+Components reference values through **bindings** rather than raw literals. This allows surfaces to update dynamically when the data model changes.
+
+There are two ways to bind a value:
+
+- **Literal values** — hardcoded directly in the component definition
+- **Path references** — point to a key in the surface's data model
+
+```json
+{
+  "surfaceUpdate": {
+    "surfaceId": "profile-1",
+    "components": [
+      {
+        "id": "greeting",
+        "component": {
+          "Text": {
+            "text": { "path": "/data/model/userName" },
+            "usageHint": "h2"
+          }
+        }
+      },
+      {
+        "id": "status",
+        "component": {
+          "Text": {
+            "text": { "literalString": "Online" },
+            "usageHint": "caption"
+          }
+        }
+      }
+    ]
+  }
+}
+```
+
+In this example, `greeting` reads the user's name from the data model (updated via `dataModelUpdate`), while `status` uses a hardcoded literal.
+
+## Handling User Actions
+
+Interactive components like `Button` trigger `userAction` events that flow back to the server. Each action includes a `name`, the originating `surfaceId` and `sourceComponentId`, and an optional `context` with key-value pairs.
+
+```json
+{
+  "userAction": {
+    "name": "submitForm",
+    "surfaceId": "form-1",
+    "sourceComponentId": "submit-btn",
+    "timestamp": "2026-03-12T10:00:00Z",
+    "context": {
+      "selectedOption": "optionA"
+    }
+  }
+}
+```
+
+Action context values can also use path bindings to send current data model values back to the server:
+
+```json
+{
+  "Button": {
+    "child": "confirm-label",
+    "action": {
+      "name": "confirm",
+      "context": [
+        {
+          "key": "currentScore",
+          "value": { "path": "/data/model/score" }
+        }
+      ]
+    }
+  }
+}
+```
+
+## Validation
+
+Use `validate_a2ui_message` to validate server-to-client messages and `validate_a2ui_event` for client-to-server events:
+
+```python Code
+from crewai.a2a.extensions.a2ui import validate_a2ui_message
+from crewai.a2a.extensions.a2ui.validator import (
+    validate_a2ui_event,
+    A2UIValidationError,
+)
+
+# Validate a server message
+try:
+    msg = validate_a2ui_message({"beginRendering": {"surfaceId": "s1", "root": "r1"}})
+except A2UIValidationError as exc:
+    print(exc.errors)
+
+# Validate a client event
+try:
+    event = validate_a2ui_event({
+        "userAction": {
+            "name": "click",
+            "surfaceId": "s1",
+            "sourceComponentId": "btn-1",
+            "timestamp": "2026-03-12T10:00:00Z",
+        }
+    })
+except A2UIValidationError as exc:
+    print(exc.errors)
+```
+
+## Best Practices
+
+<CardGroup cols={2}>
+  <Card title="Start Simple" icon="play">
+    Begin with a `beginRendering` message and a single `surfaceUpdate`. Add data binding and interactivity once the basic flow works.
+  </Card>
+
+  <Card title="Use Data Binding for Dynamic Content" icon="arrows-rotate">
+    Prefer path bindings over literal values for content that changes. Use `dataModelUpdate` to push new values without resending the full component tree.
+  </Card>
+
+  <Card title="Filter Components" icon="filter">
+    Use the `allowed_components` option on `A2UIClientExtension` to restrict which components the agent may emit, reducing prompt size and keeping output predictable.
+  </Card>
+
+  <Card title="Validate Messages" icon="check">
+    Use `validate_a2ui_message` and `validate_a2ui_event` to catch malformed payloads early, especially when building custom integrations.
+  </Card>
+</CardGroup>
+
+## Learn More
+
+- [A2A Agent Delegation](/en/learn/a2a-agent-delegation) — configure agents for remote delegation via the A2A protocol
+- [A2A Protocol Documentation](https://a2a-protocol.org) — official protocol specification

docs/en/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ Streaming is particularly valuable for:
 - **User Experience**: Reduce perceived latency by showing incremental results
 - **Live Dashboards**: Build monitoring interfaces that display crew execution status
 
+## Cancellation and Resource Cleanup
+
+`CrewStreamingOutput` supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
+
+### Async Context Manager
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Explicit Cancellation
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # async
+    # streaming.close()       # sync equivalent
+```
+
+After cancellation, `streaming.is_cancelled` and `streaming.is_completed` are both `True`. Both `aclose()` and `close()` are idempotent.
+
 ## Important Notes
 
 - Streaming automatically enables LLM streaming for all agents in the crew

docs/en/learn/streaming-flow-execution.mdx

@@ -420,6 +420,34 @@ except Exception as e:
         print("Streaming completed but flow encountered an error")
 ```
 
+## Cancellation and Resource Cleanup
+
+`FlowStreamingOutput` supports graceful cancellation so that in-flight work stops promptly when the consumer disconnects.
+
+### Async Context Manager
+
+```python Code
+streaming = await flow.kickoff_async()
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Explicit Cancellation
+
+```python Code
+streaming = await flow.kickoff_async()
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # async
+    # streaming.close()       # sync equivalent
+```
+
+After cancellation, `streaming.is_cancelled` and `streaming.is_completed` are both `True`. Both `aclose()` and `close()` are idempotent.
+
 ## Important Notes
 
 - Streaming automatically enables LLM streaming for any crews used within the flow

docs/en/quickstart.mdx

@@ -1,43 +1,49 @@
 ---
 title: Quickstart
-description: Build your first AI agent with CrewAI in under 5 minutes.
+description: Build your first CrewAI Flow in minutes — orchestration, state, and an agent crew that produces a real report.
 icon: rocket
 mode: "wide"
 ---
 
-## Build your first CrewAI Agent
+### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
 
-Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.
+Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
 
-Before we proceed, make sure you have finished installing CrewAI.
-If you haven't installed them yet, you can do so by following the [installation guide](/en/installation).
+You can install it with `npx skills add crewaiinc/skills`
 
-Follow the steps below to get Crewing! 🚣‍♂️
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
+In this guide you will **create a Flow** that sets a research topic, runs a **crew with one agent** (a researcher using web search), and ends with a **markdown report** on disk. Flows are the recommended way to structure production apps: they own **state** and **execution order**, while **agents** do the work inside a crew step.
+
+If you have not installed CrewAI yet, follow the [installation guide](/en/installation) first.
+
+## Prerequisites
+
+- Python environment and the CrewAI CLI (see [installation](/en/installation))
+- An LLM configured with the right API keys — see [LLMs](/en/concepts/llms#setting-up-your-llm)
+- A [Serper.dev](https://serper.dev/) API key (`SERPER_API_KEY`) for web search in this tutorial
+
+## Build your first Flow
 
 <Steps>
-  <Step title="Create your crew">
-  Create a new crew project by running the following command in your terminal.
-  This will create a new directory called `latest-ai-development` with the basic structure for your crew.
+  <Step title="Create a Flow project">
+  From your terminal, scaffold a Flow project (the folder name uses underscores, e.g. `latest_ai_flow`):
+
     <CodeGroup>
       ```shell Terminal
-      crewai create crew latest-ai-development
+      crewai create flow latest-ai-flow
+      cd latest_ai_flow
       ```
     </CodeGroup>
+
+  This creates a Flow app under `src/latest_ai_flow/`, including a starter crew under `crews/content_crew/` that you will replace with a minimal **single-agent** research crew in the next steps.
   </Step>
-  <Step title="Navigate to your new crew project">
-    <CodeGroup>
-      ```shell Terminal
-      cd latest_ai_development
-      ```
-    </CodeGroup>
-  </Step>
-  <Step title="Modify your `agents.yaml` file">
-  <Tip>
-  You can also modify the agents as needed to fit your use case or copy and paste as is to your project.
-  Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{topic}` will be replaced by the value of the variable in the `main.py` file.
-  </Tip>
+
+  <Step title="Configure one agent in `agents.yaml`">
+  Replace the contents of `src/latest_ai_flow/crews/content_crew/config/agents.yaml` with a single researcher. Variables like `{topic}` are filled from `crew.kickoff(inputs=...)`.
+
     ```yaml agents.yaml
-    # src/latest_ai_development/config/agents.yaml
+    # src/latest_ai_flow/crews/content_crew/config/agents.yaml
     researcher:
       role: >
         {topic} Senior Data Researcher
@@ -45,336 +51,232 @@ Follow the steps below to get Crewing! 🚣‍♂️
         Uncover cutting-edge developments in {topic}
       backstory: >
         You're a seasoned researcher with a knack for uncovering the latest
-        developments in {topic}. Known for your ability to find the most relevant
-        information and present it in a clear and concise manner.
-
-    reporting_analyst:
-      role: >
-        {topic} Reporting Analyst
-      goal: >
-        Create detailed reports based on {topic} data analysis and research findings
-      backstory: >
-        You're a meticulous analyst with a keen eye for detail. You're known for
-        your ability to turn complex data into clear and concise reports, making
-        it easy for others to understand and act on the information you provide.
+        developments in {topic}. You find the most relevant information and
+        present it clearly.
     ```
 
   </Step>
-  <Step title="Modify your `tasks.yaml` file">
+
+  <Step title="Configure one task in `tasks.yaml`">
     ```yaml tasks.yaml
-    # src/latest_ai_development/config/tasks.yaml
+    # src/latest_ai_flow/crews/content_crew/config/tasks.yaml
     research_task:
       description: >
-        Conduct a thorough research about {topic}
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        Conduct thorough research about {topic}. Use web search to find current,
+        credible information. The current year is 2026.
       expected_output: >
-        A list with 10 bullet points of the most relevant information about {topic}
+        A markdown report with clear sections: key trends, notable tools or companies,
+        and implications. Aim for 800–1200 words. No fenced code blocks around the whole document.
       agent: researcher
-
-    reporting_task:
-      description: >
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
-      expected_output: >
-        A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
-      agent: reporting_analyst
-      output_file: report.md
+      output_file: output/report.md
     ```
 
   </Step>
-  <Step title="Modify your `crew.py` file">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task
-    from crewai_tools import SerperDevTool
-    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+  <Step title="Wire the crew class (`content_crew.py`)">
+  Point the generated crew at your YAML and attach `SerperDevTool` to the researcher.
+
+    ```python content_crew.py
+    # src/latest_ai_flow/crews/content_crew/content_crew.py
     from typing import List
 
+    from crewai import Agent, Crew, Process, Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.project import CrewBase, agent, crew, task
+    from crewai_tools import SerperDevTool
+
+
     @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+    class ResearchCrew:
+      """Single-agent research crew used inside the Flow."""
 
       agents: List[BaseAgent]
       tasks: List[Task]
 
+      agents_config = "config/agents.yaml"
+      tasks_config = "config/tasks.yaml"
+
       @agent
       def researcher(self) -> Agent:
         return Agent(
-          config=self.agents_config['researcher'], # type: ignore[index]
+          config=self.agents_config["researcher"],  # type: ignore[index]
           verbose=True,
-          tools=[SerperDevTool()]
-        )
-
-      @agent
-      def reporting_analyst(self) -> Agent:
-        return Agent(
-          config=self.agents_config['reporting_analyst'], # type: ignore[index]
-          verbose=True
+          tools=[SerperDevTool()],
         )
 
       @task
       def research_task(self) -> Task:
         return Task(
-          config=self.tasks_config['research_task'], # type: ignore[index]
-        )
-
-      @task
-      def reporting_task(self) -> Task:
-        return Task(
-          config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # This is the file that will be contain the final report.
+          config=self.tasks_config["research_task"],  # type: ignore[index]
         )
 
       @crew
       def crew(self) -> Crew:
-        """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Automatically created by the @agent decorator
-          tasks=self.tasks, # Automatically created by the @task decorator
+          agents=self.agents,
+          tasks=self.tasks,
           process=Process.sequential,
           verbose=True,
         )
     ```
 
   </Step>
-  <Step title="[Optional] Add before and after crew functions">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
-    from crewai_tools import SerperDevTool
 
-    @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+  <Step title="Define the Flow in `main.py`">
+  Connect the crew to a Flow: a `@start()` step sets the topic in **state**, and a `@listen` step runs the crew. The task’s `output_file` still writes `output/report.md`.
 
-      @before_kickoff
-      def before_kickoff_function(self, inputs):
-        print(f"Before kickoff function with inputs: {inputs}")
-        return inputs # You can return the inputs or modify them as needed
-
-      @after_kickoff
-      def after_kickoff_function(self, result):
-        print(f"After kickoff function with result: {result}")
-        return result # You can return the result or modify it as needed
-
-      # ... remaining code
-    ```
-
-  </Step>
-  <Step title="Feel free to pass custom inputs to your crew">
-  For example, you can pass the `topic` input to your crew to customize the research and reporting.
     ```python main.py
-    #!/usr/bin/env python
-    # src/latest_ai_development/main.py
-    import sys
-    from latest_ai_development.crew import LatestAiDevelopmentCrew
+    # src/latest_ai_flow/main.py
+    from pydantic import BaseModel
 
-    def run():
-      """
-      Run the crew.
-      """
-      inputs = {
-        'topic': 'AI Agents'
-      }
-      LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+    from crewai.flow import Flow, listen, start
+
+    from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
+
+
+    class ResearchFlowState(BaseModel):
+      topic: str = ""
+      report: str = ""
+
+
+    class LatestAiFlow(Flow[ResearchFlowState]):
+      @start()
+      def prepare_topic(self, crewai_trigger_payload: dict | None = None):
+        if crewai_trigger_payload:
+          self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
+        else:
+          self.state.topic = "AI Agents"
+        print(f"Topic: {self.state.topic}")
+
+      @listen(prepare_topic)
+      def run_research(self):
+        result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
+        self.state.report = result.raw
+        print("Research crew finished.")
+
+      @listen(run_research)
+      def summarize(self):
+        print("Report path: output/report.md")
+
+
+    def kickoff():
+      LatestAiFlow().kickoff()
+
+
+    def plot():
+      LatestAiFlow().plot()
+
+
+    if __name__ == "__main__":
+      kickoff()
     ```
 
-  </Step>
-  <Step title="Set your environment variables">
-  Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
-    - A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
-    - The configuration for your choice of model, such as an API key. See the
-        [LLM setup guide](/en/concepts/llms#setting-up-your-llm) to learn how to configure models from any provider.
-  </Step>
-  <Step title="Lock and install the dependencies">
-    - Lock the dependencies and install them by using the CLI command:
-      <CodeGroup>
-        ```shell Terminal
-        crewai install
-        ```
-      </CodeGroup>
-    - If you have additional packages that you want to install, you can do so by running:
-      <CodeGroup>
-        ```shell Terminal
-        uv add <package-name>
-        ```
-      </CodeGroup>
-  </Step>
-  <Step title="Run your crew">
-    - To run your crew, execute the following command in the root of your project:
-      <CodeGroup>
-        ```bash Terminal
-        crewai run
-        ```
-      </CodeGroup>
+  <Tip>
+  If your package name differs from `latest_ai_flow`, change the import of `ResearchCrew` to match your project’s module path.
+  </Tip>
   </Step>
 
-  <Step title="Enterprise Alternative: Create in Crew Studio">
-  For CrewAI AMP users, you can create the same crew without writing code:
+  <Step title="Set environment variables">
+  In `.env` at the project root, set:
 
-1. Log in to your CrewAI AMP account (create a free account at [app.crewai.com](https://app.crewai.com))
-2. Open Crew Studio
-3. Type what is the automation you're trying to build
-4. Create your tasks visually and connect them in sequence
-5. Configure your inputs and click "Download Code" or "Deploy"
-
-![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
-
-  <Card title="Try CrewAI AMP" icon="rocket" href="https://app.crewai.com">
-    Start your free account at CrewAI AMP
-  </Card>
+    - `SERPER_API_KEY` — from [Serper.dev](https://serper.dev/)
+    - Your model provider keys as required — see [LLM setup](/en/concepts/llms#setting-up-your-llm)
   </Step>
-  <Step title="View your final report">
-  You should see the output in the console and the `report.md` file should be created in the root of your project with the final report.
 
-Here's an example of what the report should look like:
+  <Step title="Install and run">
+    <CodeGroup>
+      ```shell Terminal
+      crewai install
+      crewai run
+      ```
+    </CodeGroup>
+
+  `crewai run` executes the Flow entrypoint defined in your project (same command as for crews; project type is `"flow"` in `pyproject.toml`).
+  </Step>
+
+
+
+
+  <Step title="Check the output">
+  You should see logs from the Flow and the crew. Open **`output/report.md`** for the generated report (excerpt):
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # AI Agents in 2026: Landscape and Trends
 
-    ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    ## Executive summary
+    …
 
-    ## 2. Benefits of AI Agents
-    AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
+    ## Key trends
+    - **Tool use and orchestration** — …
+    - **Enterprise adoption** — …
 
-    - **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
-    - **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
-    - **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
-
-    ## 3. Popular AI Agent Frameworks
-    Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
-
-    - **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
-    - **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
-    - **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
-    - **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
-    - **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
-    - **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
-
-    These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
-
-    ## 4. AI Agents in Human Resources
-    AI agents are revolutionizing HR practices by automating and optimizing key functions:
-
-    - **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
-    - **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
-    - **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
-
-    As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
-
-    ## 5. AI Agents in Finance
-    The finance sector is seeing extensive integration of AI agents that enhance financial practices:
-
-    - **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
-    - **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
-    - **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
-
-    The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
-
-    ## 6. Market Trends and Investments
-    The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
-
-    Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
-
-    ## 7. Future Predictions and Implications
-    Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
-
-    - Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
-    - Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
-    - Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
-
-    To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
-
-    ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    ## Implications
+    …
     ```
-
   </CodeGroup>
+
+  Your actual file will be longer and reflect live search results.
   </Step>
 </Steps>
 
+## How this run fits together
+
+1. **Flow** — `LatestAiFlow` runs `prepare_topic` first, then `run_research`, then `summarize`. State (`topic`, `report`) lives on the Flow.
+2. **Crew** — `ResearchCrew` runs one task with one agent: the researcher uses **Serper** to search the web, then writes the structured report.
+3. **Artifact** — The task’s `output_file` writes the report under `output/report.md`.
+
+To go deeper on Flow patterns (routing, persistence, human-in-the-loop), see [Build your first Flow](/en/guides/flows/first-flow) and [Flows](/en/concepts/flows). For crews without a Flow, see [Crews](/en/concepts/crews). For a single `Agent` and `kickoff()` without tasks, see [Agents](/en/concepts/agents#direct-agent-interaction-with-kickoff).
+
 <Check>
-Congratulations!
-
-You have successfully set up your crew project and are ready to start building your own agentic workflows!
-
+You now have an end-to-end Flow with an agent crew and a saved report — a solid base to add more steps, crews, or tools.
 </Check>
 
-### Note on Consistency in Naming
+### Naming consistency
 
-The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
-For example, you can reference the agent for specific tasks from `tasks.yaml` file.
-This naming consistency allows CrewAI to automatically link your configurations with your code; otherwise, your task won't recognize the reference properly.
+YAML keys (`researcher`, `research_task`) must match the method names on your `@CrewBase` class. See [Crews](/en/concepts/crews) for the full decorator pattern.
 
-#### Example References
+## Deploying
 
-<Tip>
-  Note how we use the same name for the agent in the `agents.yaml`
-  (`email_summarizer`) file as the method name in the `crew.py`
-  (`email_summarizer`) file.
-</Tip>
+Push your Flow to **[CrewAI AMP](https://app.crewai.com)** once it runs locally and your project is in a **GitHub** repository. From the project root:
 
-```yaml agents.yaml
-email_summarizer:
-  role: >
-    Email Summarizer
-  goal: >
-    Summarize emails into a concise and clear summary
-  backstory: >
-    You will create a 5 bullet point summary of the report
-  llm: provider/model-id # Add your choice of model here
+<CodeGroup>
+```bash Authenticate
+crewai login
 ```
 
-<Tip>
-  Note how we use the same name for the task in the `tasks.yaml`
-  (`email_summarizer_task`) file as the method name in the `crew.py`
-  (`email_summarizer_task`) file.
-</Tip>
-
-```yaml tasks.yaml
-email_summarizer_task:
-  description: >
-    Summarize the email into a 5 bullet point summary
-  expected_output: >
-    A 5 bullet point summary of the email
-  agent: email_summarizer
-  context:
-    - reporting_task
-    - research_task
+```bash Create deployment
+crewai deploy create
 ```
 
-## Deploying Your Crew
+```bash Check status & logs
+crewai deploy status
+crewai deploy logs
+```
 
-The easiest way to deploy your crew to production is through [CrewAI AMP](http://app.crewai.com).
+```bash Ship updates after you change code
+crewai deploy push
+```
 
-Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
+```bash List or remove deployments
+crewai deploy list
+crewai deploy remove <deployment_id>
+```
+</CodeGroup>
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
+<Tip>
+  The first deploy usually takes **around 1 minute**. Full prerequisites and the web UI flow are in [Deploy to AMP](/en/enterprise/guides/deploy-to-amp).
+</Tip>
 
 <CardGroup cols={2}>
-  <Card title="Deploy on Enterprise" icon="rocket" href="http://app.crewai.com">
-    Get started with CrewAI AMP and deploy your crew in a production environment
-    with just a few clicks.
+  <Card title="Deploy guide" icon="book" href="/en/enterprise/guides/deploy-to-amp">
+    Step-by-step AMP deployment (CLI and dashboard).
   </Card>
   <Card
     title="Join the Community"
     icon="comments"
     href="https://community.crewai.com"
   >
-    Join our open source community to discuss ideas, share your projects, and
-    connect with other CrewAI developers.
+    Discuss ideas, share projects, and connect with other CrewAI developers.
   </Card>
 </CardGroup>

docs/en/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: Install crewaiinc/skills from the official registry at skills.sh—Flows, Crews, and docs-aware agents for Claude Code, Cursor, Codex, and more.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**Give your AI coding agent CrewAI context in one command.**
+
+CrewAI **Skills** are published on **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—the official registry for `crewaiinc/skills`, including individual skills (for example **design-agent**, **getting-started**, **design-task**, and **ask-docs**), install stats, and audits. They teach coding agents—like Claude Code, Cursor, and Codex—how to scaffold Flows, configure Crews, use tools, and follow CrewAI patterns. Run the install below (or paste it into your agent).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+That pulls the official skill pack into your agent workflow so it can apply CrewAI conventions without you re-explaining the framework each session. Source code and issues live on [GitHub](https://github.com/crewAIInc/skills).
+
+## What your agent gets
+
+- **Flows** — structure stateful apps, steps, and crew kickoffs the CrewAI way
+- **Crews & agents** — YAML-first patterns, roles, tasks, and delegation
+- **Tools & integrations** — hook agents to search, APIs, and common CrewAI tools
+- **Project layout** — align with CLI scaffolds and repo conventions
+- **Up-to-date patterns** — skills track current CrewAI docs and recommended practices
+
+## Learn more on this site
+
+<CardGroup cols={2}>
+  <Card title="Coding tools & AGENTS.md" icon="terminal" href="/en/guides/coding-tools/agents-md">
+    How to use `AGENTS.md` and coding-agent workflows with CrewAI.
+  </Card>
+  <Card title="Quickstart" icon="rocket" href="/en/quickstart">
+    Build your first Flow and crew end-to-end.
+  </Card>
+  <Card title="Installation" icon="download" href="/en/installation">
+    Install the CrewAI CLI and Python package.
+  </Card>
+  <Card title="Skills registry (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Official listing for `crewaiinc/skills`—skills, installs, and audits.
+  </Card>
+  <Card title="GitHub source" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    Source, updates, and issues for the skill pack.
+  </Card>
+</CardGroup>
+
+### Video: CrewAI with coding agent skills
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/en/tools/ai-ml/codeinterpretertool.mdx

@@ -7,6 +7,10 @@ mode: "wide"
 
 # `CodeInterpreterTool`
 
+<Warning>
+  **Deprecated:** `CodeInterpreterTool` has been removed from `crewai-tools`. The `allow_code_execution` and `code_execution_mode` parameters on `Agent` are also deprecated. Use a dedicated sandbox service — [E2B](https://e2b.dev) or [Modal](https://modal.com) — for secure, isolated code execution.
+</Warning>
+
 ## Description
 
 The `CodeInterpreterTool` enables CrewAI agents to execute Python 3 code that they generate autonomously. This functionality is particularly valuable as it allows agents to create code, execute it, obtain the results, and utilize that information to inform subsequent decisions and actions.

docs/en/tools/file-document/csvsearchtool.mdx

@@ -75,4 +75,20 @@ tool = CSVSearchTool(
         },
     }
 )
+
+## Security
+
+### Path Validation
+
+File paths provided to this tool are validated against the current working directory. Paths that resolve outside the working directory are rejected with a `ValueError`.
+
+To allow paths outside the working directory (for example, in tests or trusted pipelines), set the environment variable:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL Validation
+
+URL inputs are validated: `file://` URIs and requests targeting private or reserved IP ranges are blocked to prevent server-side request forgery (SSRF) attacks.
 ```

docs/en/tools/file-document/directorysearchtool.mdx

@@ -67,4 +67,16 @@ tool = DirectorySearchTool(
         },
     }
 )
+
+## Security
+
+### Path Validation
+
+Directory paths provided to this tool are validated against the current working directory. Paths that resolve outside the working directory are rejected with a `ValueError`.
+
+To allow paths outside the working directory (for example, in tests or trusted pipelines), set the environment variable:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
 ```

docs/en/tools/file-document/jsonsearchtool.mdx

@@ -74,3 +74,19 @@ tool = JSONSearchTool(
     }
 )
 ```
+
+## Security
+
+### Path Validation
+
+File paths provided to this tool are validated against the current working directory. Paths that resolve outside the working directory are rejected with a `ValueError`.
+
+To allow paths outside the working directory (for example, in tests or trusted pipelines), set the environment variable:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL Validation
+
+URL inputs are validated: `file://` URIs and requests targeting private or reserved IP ranges are blocked to prevent server-side request forgery (SSRF) attacks.

docs/en/tools/file-document/pdfsearchtool.mdx

@@ -105,4 +105,20 @@ tool = PDFSearchTool(
         },
     }
 )
+
+## Security
+
+### Path Validation
+
+File paths provided to this tool are validated against the current working directory. Paths that resolve outside the working directory are rejected with a `ValueError`.
+
+To allow paths outside the working directory (for example, in tests or trusted pipelines), set the environment variable:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL Validation
+
+URL inputs are validated: `file://` URIs and requests targeting private or reserved IP ranges are blocked to prevent server-side request forgery (SSRF) attacks.
 ```

docs/ko/changelog.mdx

@@ -4,6 +4,321 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 9일">
+  ## v1.14.2a1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - HITL 재개 후 flow_finished 이벤트 방출 수정
+  - CVE-2026-39892 문제를 해결하기 위해 암호화 버전을 46.0.7로 수정
+
+  ### 리팩토링
+  - 공유 I18N_DEFAULT 싱글톤을 사용하도록 리팩토링
+
+  ### 문서
+  - v1.14.1에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="2026년 4월 9일">
+  ## v1.14.1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 비동기 체크포인트 TUI 브라우저 추가
+  - 스트리밍 출력에 aclose()/close() 및 비동기 컨텍스트 관리자 추가
+
+  ### 버그 수정
+  - 템플릿 pyproject.toml 버전 증가를 위한 정규 표현식 수정
+  - 훅 데코레이터 필터에서 도구 이름 정리
+  - CheckpointConfig 생성 시 체크포인트 핸들러 등록 수정
+  - CVE-2026-1839 해결을 위해 transformers를 5.5.0으로 업데이트
+  - FilteredStream stdout/stderr 래퍼 제거
+
+  ### 문서
+  - v1.14.1rc1에 대한 변경 로그 및 버전 업데이트
+
+  ### 리팩토링
+  - 하드코딩된 거부 목록을 동적 BaseTool 필드 제외로 교체
+  - devtools CLI에서 정규 표현식을 tomlkit으로 교체
+  - 공유 PRINTER 싱글톤 사용
+  - BaseProvider를 provider_type 식별자가 있는 BaseModel로 변경
+
+  ## 기여자
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="2026년 4월 9일">
+  ## v1.14.1rc1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 비동기 체크포인트 TUI 브라우저 추가
+  - 스트리밍 출력에 aclose()/close() 및 비동기 컨텍스트 관리자 추가
+
+  ### 버그 수정
+  - 정규 표현식을 사용하여 템플릿 pyproject.toml 버전 증가 수정
+  - 후크 데코레이터 필터에서 도구 이름 정리
+  - CVE-2026-1839 해결을 위해 transformers를 5.5.0으로 업데이트
+  - CheckpointConfig가 생성될 때 체크포인트 핸들러 등록
+
+  ### 리팩토링
+  - 하드코딩된 거부 목록을 동적 BaseTool 필드 제외로 교체
+  - devtools CLI에서 정규 표현식을 tomlkit으로 교체
+  - 공유 PRINTER 싱글톤 사용
+  - BaseProvider를 provider_type 구분자가 있는 BaseModel로 변경
+  - FilteredStream stdout/stderr 래퍼 제거
+  - 사용되지 않는 flow/config.py 제거
+
+  ### 문서
+  - v1.14.0에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
+<Update label="2026년 4월 7일">
+  ## v1.14.0
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0)
+
+  ## 변경 사항
+
+  ### 기능
+  - 체크포인트 목록/정보 CLI 명령 추가
+  - 추적을 구분하기 위한 guardrail_type 및 이름 추가
+  - 체크포인트 저장을 위한 SqliteProvider 추가
+  - 자동 체크포인트 생성을 위한 CheckpointConfig 추가
+  - 런타임 상태 체크포인트, 이벤트 시스템 및 실행기 리팩토링 구현
+
+  ### 버그 수정
+  - SSRF 및 경로 탐색 보호 추가
+  - RAG 도구에 경로 및 URL 유효성 검사 추가
+  - 토큰 절약을 위해 메모리 직렬화에서 임베딩 벡터 제외
+  - 흐름 템플릿에 쓰기 전에 출력 디렉토리가 존재하는지 확인
+  - CVE-2026-35030 문제를 해결하기 위해 litellm을 >=1.83.0으로 업데이트
+  - 아랍어 페이지 렌더링을 유발하는 SEO 인덱싱 필드 제거
+
+  ### 문서
+  - v1.14.0에 대한 변경 로그 및 버전 업데이트
+  - 명확성을 개선하기 위해 빠른 시작 및 설치 가이드 업데이트
+  - 저장소 제공자 섹션 추가, JsonProvider 내보내기
+  - AMP 교육 탭 가이드 추가
+
+  ### 리팩토링
+  - 체크포인트 API 정리
+  - CodeInterpreterTool 제거 및 코드 실행 매개변수 사용 중단
+
+  ## 기여자
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="2026년 4월 7일">
+  ## v1.14.0a4
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a4)
+
+  ## 변경 사항
+
+  ### 기능
+  - 추적을 구분하기 위해 guardrail_type 및 이름 추가
+  - 체크포인트 저장을 위한 SqliteProvider 추가
+  - 자동 체크포인트 생성을 위한 CheckpointConfig 추가
+  - 런타임 상태 체크포인트, 이벤트 시스템 및 실행기 리팩토링 구현
+
+  ### 버그 수정
+  - 토큰 절약을 위해 메모리 직렬화에서 임베딩 벡터 제외
+  - CVE-2026-35030 문제를 해결하기 위해 litellm을 >=1.83.0으로 업데이트
+
+  ### 문서
+  - 명확성을 개선하기 위해 빠른 시작 및 설치 가이드 업데이트
+  - 저장소 제공자 섹션 추가 및 JsonProvider 내보내기
+
+  ### 성능
+  - 체크포인트 데이터 열에 JSONB 사용
+
+  ### 리팩토링
+  - CodeInterpreterTool 제거 및 코드 실행 매개변수 사용 중단
+
+  ## 기여자
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="2026년 4월 6일">
+  ## v1.14.0a3
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
+
+  ## 변경 사항
+
+  ### 문서
+  - v1.14.0a2의 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @joaomdmoura
+
+</Update>
+
+<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
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## 변경 사항
+
+  ### 기능
+  - v0.8/v0.9 지원, 스키마 및 문서가 포함된 A2UI 확장 추가
+
+  ### 버그 수정
+  - GPT-5 및 o-series를 추가하여 다중 모드 비전 접두사 수정
+
+  ### 문서
+  - v1.13.0a6에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="2026년 4월 1일">
+  ## v1.13.0a6
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a6)
+
+  ## 변경 사항
+
+  ### 문서
+  - 실제 UI 옵션에 맞게 RBAC 권한 수준 수정 (#5210)
+  - v1.13.0a5에 대한 변경 로그 및 버전 업데이트 (#5200)
+
+  ### 성능
+  - 지연 이벤트 버스를 구현하고 비활성화 시 추적을 건너뛰어 프레임워크 오버헤드 감소 (#5187)
+
+  ## 기여자
+
+  @alex-clawd, @joaomdmoura, @lucasgomide
+
+</Update>
+
+<Update label="2026년 3월 31일">
+  ## v1.13.0a5
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
+
+  ## 변경 사항
+
+  ### 문서
+  - v1.13.0a4에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="2026년 4월 1일">
+  ## v1.13.0a4
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
+
+  ## 변경 사항
+
+  ### 문서
+  - v1.13.0a3에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="2026년 4월 1일">
   ## v1.13.0a3
 

docs/ko/concepts/agents.mdx

@@ -291,15 +291,13 @@ multimodal_agent = Agent(
 - `max_retry_limit`: 오류 발생 시 재시도 횟수
 
 #### 코드 실행
-- `allow_code_execution`: 코드를 실행하려면 True여야 합니다
-- `code_execution_mode`:
-  - `"safe"`: Docker를 사용합니다 (프로덕션에 권장)
-  - `"unsafe"`: 직접 실행 (신뢰할 수 있는 환경에서만 사용)
 
-<Note>
-  이 옵션은 기본 Docker 이미지를 실행합니다. Docker 이미지를 구성하려면 도구 섹션에 있는 Code Interpreter Tool을 확인하십시오.
-  Code Interpreter Tool을 에이전트의 도구 파라미터로 추가하십시오.
-</Note>
+<Warning>
+  `allow_code_execution` 및 `code_execution_mode`는 더 이상 사용되지 않습니다. `CodeInterpreterTool`이 `crewai-tools`에서 제거되었습니다. 안전한 코드 실행을 위해 [E2B](https://e2b.dev) 또는 [Modal](https://modal.com)과 같은 전용 샌드박스 서비스를 사용하세요.
+</Warning>
+
+- `allow_code_execution` _(지원 중단)_: 이전에 `CodeInterpreterTool`을 통한 내장 코드 실행을 활성화했습니다.
+- `code_execution_mode` _(지원 중단)_: 이전에 실행 모드를 제어했습니다 (Docker의 경우 `"safe"`, 직접 실행의 경우 `"unsafe"`).
 
 #### 고급 기능
 - `multimodal`: 텍스트와 시각적 콘텐츠 처리를 위한 멀티모달 기능 활성화
@@ -627,9 +625,10 @@ asyncio.run(main())
 ## 중요한 고려사항 및 모범 사례
 
 ### 보안 및 코드 실행
-- `allow_code_execution`을 사용할 때는 사용자 입력에 주의하고 항상 입력 값을 검증하세요
-- 운영 환경에서는 `code_execution_mode: "safe"`(Docker)를 사용하세요
-- 무한 루프를 방지하기 위해 적절한 `max_execution_time` 제한을 설정하는 것을 고려하세요
+
+<Warning>
+  `allow_code_execution` 및 `code_execution_mode`는 더 이상 사용되지 않으며 `CodeInterpreterTool`이 제거되었습니다. 안전한 코드 실행을 위해 [E2B](https://e2b.dev) 또는 [Modal](https://modal.com)과 같은 전용 샌드박스 서비스를 사용하세요.
+</Warning>
 
 ### 성능 최적화
 - `respect_context_window: true`를 사용하여 토큰 제한 문제를 방지하세요.

docs/ko/concepts/checkpointing.mdx

@@ -0,0 +1,229 @@
+---
+title: Checkpointing
+description: 실행 상태를 자동으로 저장하여 크루, 플로우, 에이전트가 실패 후 재개할 수 있습니다.
+icon: floppy-disk
+mode: "wide"
+---
+
+<Warning>
+체크포인팅은 초기 릴리스 단계입니다. API는 향후 버전에서 변경될 수 있습니다.
+</Warning>
+
+## 개요
+
+체크포인팅은 실행 중 자동으로 실행 상태를 저장합니다. 크루, 플로우 또는 에이전트가 실행 도중 실패하면 마지막 체크포인트에서 복원하여 이미 완료된 작업을 다시 실행하지 않고 재개할 수 있습니다.
+
+## 빠른 시작
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=True,  # 기본값 사용: ./.checkpoints, task_completed 이벤트
+)
+result = crew.kickoff()
+```
+
+각 태스크가 완료된 후 `./.checkpoints/`에 체크포인트 파일이 기록됩니다.
+
+## 설정
+
+`CheckpointConfig`를 사용하여 세부 설정을 제어합니다:
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        on_events=["task_completed", "crew_kickoff_completed"],
+        max_checkpoints=5,
+    ),
+)
+```
+
+### CheckpointConfig 필드
+
+| 필드 | 타입 | 기본값 | 설명 |
+|:-----|:-----|:-------|:-----|
+| `location` | `str` | `"./.checkpoints"` | 체크포인트 파일 경로 |
+| `on_events` | `list[str]` | `["task_completed"]` | 체크포인트를 트리거하는 이벤트 타입 |
+| `provider` | `BaseProvider` | `JsonProvider()` | 스토리지 백엔드 |
+| `max_checkpoints` | `int \| None` | `None` | 보관할 최대 파일 수; 오래된 것부터 삭제 |
+
+### 상속 및 옵트아웃
+
+Crew, Flow, Agent의 `checkpoint` 필드는 `CheckpointConfig`, `True`, `False`, `None`을 받습니다:
+
+| 값 | 동작 |
+|:---|:-----|
+| `None` (기본값) | 부모에서 상속. 에이전트는 크루의 설정을 상속합니다. |
+| `True` | 기본값으로 활성화. |
+| `False` | 명시적 옵트아웃. 부모 상속을 중단합니다. |
+| `CheckpointConfig(...)` | 사용자 정의 설정. |
+
+```python
+crew = Crew(
+    agents=[
+        Agent(role="Researcher", ...),                  # 크루의 checkpoint 상속
+        Agent(role="Writer", ..., checkpoint=False),     # 옵트아웃, 체크포인트 없음
+    ],
+    tasks=[...],
+    checkpoint=True,
+)
+```
+
+## 체크포인트에서 재개
+
+```python
+# 복원 및 재개
+crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
+result = crew.kickoff()  # 마지막으로 완료된 태스크부터 재개
+```
+
+복원된 크루는 이미 완료된 태스크를 건너뛰고 첫 번째 미완료 태스크부터 재개합니다.
+
+## Crew, Flow, Agent에서 사용 가능
+
+### Crew
+
+```python
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task, review_task],
+    checkpoint=CheckpointConfig(location="./crew_cp"),
+)
+```
+
+기본 트리거: `task_completed` (완료된 태스크당 하나의 체크포인트).
+
+### Flow
+
+```python
+from crewai.flow.flow import Flow, start, listen
+from crewai import CheckpointConfig
+
+class MyFlow(Flow):
+    @start()
+    def step_one(self):
+        return "data"
+
+    @listen(step_one)
+    def step_two(self, data):
+        return process(data)
+
+flow = MyFlow(
+    checkpoint=CheckpointConfig(
+        location="./flow_cp",
+        on_events=["method_execution_finished"],
+    ),
+)
+result = flow.kickoff()
+
+# 재개
+flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
+result = flow.kickoff()
+```
+
+### Agent
+
+```python
+agent = Agent(
+    role="Researcher",
+    goal="Research topics",
+    backstory="Expert researcher",
+    checkpoint=CheckpointConfig(
+        location="./agent_cp",
+        on_events=["lite_agent_execution_completed"],
+    ),
+)
+result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+```
+
+## 스토리지 프로바이더
+
+CrewAI는 두 가지 체크포인트 스토리지 프로바이더를 제공합니다.
+
+### JsonProvider (기본값)
+
+각 체크포인트를 별도의 JSON 파일로 저장합니다.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import JsonProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        provider=JsonProvider(),
+        max_checkpoints=5,
+    ),
+)
+```
+
+### SqliteProvider
+
+모든 체크포인트를 단일 SQLite 데이터베이스 파일에 저장합니다.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import SqliteProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./.checkpoints.db",
+        provider=SqliteProvider(),
+    ),
+)
+```
+
+
+## 이벤트 타입
+
+`on_events` 필드는 이벤트 타입 문자열의 조합을 받습니다. 일반적인 선택:
+
+| 사용 사례 | 이벤트 |
+|:----------|:-------|
+| 각 태스크 완료 후 (Crew) | `["task_completed"]` |
+| 각 플로우 메서드 완료 후 | `["method_execution_finished"]` |
+| 에이전트 실행 완료 후 | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
+| 크루 완료 시에만 | `["crew_kickoff_completed"]` |
+| 모든 LLM 호출 후 | `["llm_call_completed"]` |
+| 모든 이벤트 | `["*"]` |
+
+<Warning>
+`["*"]` 또는 `llm_call_completed`와 같은 고빈도 이벤트를 사용하면 많은 체크포인트 파일이 생성되어 성능에 영향을 줄 수 있습니다. `max_checkpoints`를 사용하여 디스크 사용량을 제한하세요.
+</Warning>
+
+## 수동 체크포인팅
+
+완전한 제어를 위해 자체 이벤트 핸들러를 등록하고 `state.checkpoint()`를 직접 호출할 수 있습니다:
+
+```python
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+# 동기 핸들러
+@crewai_event_bus.on(LLMCallCompletedEvent)
+def on_llm_done(source, event, state):
+    path = state.checkpoint("./my_checkpoints")
+    print(f"체크포인트 저장: {path}")
+
+# 비동기 핸들러
+@crewai_event_bus.on(LLMCallCompletedEvent)
+async def on_llm_done_async(source, event, state):
+    path = await state.acheckpoint("./my_checkpoints")
+    print(f"체크포인트 저장: {path}")
+```
+
+`state` 인수는 핸들러가 3개의 매개변수를 받을 때 이벤트 버스가 자동으로 전달하는 `RuntimeState`입니다. [Event Listeners](/ko/concepts/event-listener) 문서에 나열된 모든 이벤트 타입에 핸들러를 등록할 수 있습니다.
+
+체크포인팅은 best-effort입니다: 체크포인트 기록이 실패하면 오류가 로그에 기록되지만 실행은 중단 없이 계속됩니다.

docs/ko/enterprise/features/rbac.mdx

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

docs/ko/enterprise/guides/deploy-to-amp.mdx

@@ -105,7 +105,7 @@ CLI는 `pyproject.toml`에서 프로젝트 유형을 자동으로 감지하고
     ```
 
     <Tip>
-    첫 배포는 컨테이너 이미지를 빌드하므로 일반적으로 10~15분 정도 소요됩니다. 이후 배포는 훨씬 빠릅니다.
+    첫 배포는 보통 약 1분 정도 소요됩니다.
     </Tip>
 
   </Step>
@@ -187,7 +187,7 @@ Crew를 GitHub 저장소에 푸시해야 합니다. 아직 Crew를 만들지 않
 
     1. "Deploy" 버튼을 클릭하여 배포 프로세스를 시작합니다.
     2. 진행 바를 통해 진행 상황을 모니터링할 수 있습니다.
-    3. 첫 번째 배포에는 일반적으로 약 10-15분 정도 소요되며, 이후 배포는 더 빠릅니다.
+    3. 첫 번째 배포에는 일반적으로 약 1분 정도 소요됩니다
 
     <Frame>
       ![Deploy Progress](/images/enterprise/deploy-progress.png)

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:
@@ -189,9 +197,8 @@ CrewAI는 의존성 관리와 패키지 처리를 위해 `uv`를 사용합니다
 ## 다음 단계
 
 <CardGroup cols={2}>
-  <Card title="첫 번째 Agent 만들기" icon="code" href="/ko/quickstart">
-    빠른 시작 가이드를 따라 CrewAI 에이전트를 처음 만들어보고 직접 경험해
-    보세요.
+  <Card title="퀵스타트: Flow + 에이전트" icon="code" href="/ko/quickstart">
+    Flow를 만들고 에이전트 한 명짜리 crew를 실행해 보고서까지 만드는 방법을 따라 해 보세요.
   </Card>
   <Card
     title="커뮤니티 참여하기"

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.
@@ -130,9 +138,9 @@ Crews의 기능:
   <Card
     title="빠른 시작"
     icon="bolt"
-    href="ko/quickstart"
+    href="/ko/quickstart"
   >
-    빠른 시작 가이드를 따라 첫 번째 CrewAI agent를 만들고 직접 경험해 보세요.
+    Flow를 만들고 에이전트 한 명 crew를 실행해 끝까지 보고서를 생성해 보세요.
   </Card>
   <Card
     title="커뮤니티 가입하기"

docs/ko/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ asyncio.run(interactive_research())
 - **사용자 경험**: 점진적인 결과를 표시하여 체감 지연 시간 감소
 - **라이브 대시보드**: crew 실행 상태를 표시하는 모니터링 인터페이스 구축
 
+## 취소 및 리소스 정리
+
+`CrewStreamingOutput`은 소비자가 연결을 끊을 때 진행 중인 작업을 즉시 중단하는 정상적인 취소를 지원합니다.
+
+### 비동기 컨텍스트 매니저
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### 명시적 취소
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # 비동기
+    # streaming.close()       # 동기 버전
+```
+
+취소 후 `streaming.is_cancelled`와 `streaming.is_completed`는 모두 `True`입니다. `aclose()`와 `close()` 모두 멱등성을 가집니다.
+
 ## 중요 사항
 
 - 스트리밍은 crew의 모든 에이전트에 대해 자동으로 LLM 스트리밍을 활성화합니다

docs/ko/quickstart.mdx

@@ -1,379 +1,278 @@
 ---
 title: 퀵스타트
-description: 5분 이내에 CrewAI로 첫 번째 AI 에이전트를 구축해보세요.
+description: 몇 분 안에 첫 CrewAI Flow를 만듭니다 — 오케스트레이션, 상태, 그리고 실제 보고서를 만드는 에이전트 crew까지.
 icon: rocket
 mode: "wide"
 ---
 
-## 첫 번째 CrewAI Agent 만들기
+### 영상: 코딩 에이전트 스킬을 활용한 CrewAI Agents & Flows 구축
 
-이제 주어진 주제나 항목에 대해 `최신 AI 개발 동향`을 `연구`하고 `보고`하는 간단한 crew를 만들어보겠습니다.
+코딩 에이전트 스킬(Claude Code, Codex 등)을 설치하여 CrewAI로 코딩 에이전트를 빠르게 시작하세요.
 
-진행하기 전에 CrewAI 설치를 완료했는지 확인하세요.
-아직 설치하지 않았다면, [설치 가이드](/ko/installation)를 참고해 설치할 수 있습니다.
+`npx skills add crewaiinc/skills` 명령어로 설치할 수 있습니다
 
-아래 단계를 따라 Crewing을 시작하세요! 🚣‍♂️
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
+이 가이드에서는 **Flow**를 만들어 연구 주제를 정하고, **에이전트 한 명으로 구성된 crew**(웹 검색을 쓰는 연구원)를 실행한 뒤, 디스크에 **Markdown 보고서**를 남깁니다. Flow는 프로덕션 앱을 구성하는 권장 방식으로, **상태**와 **실행 순서**를 담당하고 **에이전트**는 crew 단계 안에서 실제 작업을 수행합니다.
+
+CrewAI를 아직 설치하지 않았다면 먼저 [설치 가이드](/ko/installation)를 따르세요.
+
+## 사전 요건
+
+- Python 환경과 CrewAI CLI([설치](/ko/installation) 참고)
+- 올바른 API 키로 설정한 LLM — [LLM](/ko/concepts/llms#setting-up-your-llm) 참고
+- 이 튜토리얼의 웹 검색용 [Serper.dev](https://serper.dev/) API 키(`SERPER_API_KEY`)
+
+## 첫 번째 Flow 만들기
 
 <Steps>
-  <Step title="crew 생성하기">
-  터미널에서 아래 명령어를 실행하여 새로운 crew 프로젝트를 만드세요.
-  이 작업은 `latest-ai-development`라는 새 디렉터리와 기본 구조를 생성합니다.
+  <Step title="Flow 프로젝트 생성">
+  터미널에서 Flow 프로젝트를 생성합니다(폴더 이름은 밑줄 형식입니다. 예: `latest_ai_flow`).
+
     <CodeGroup>
       ```shell Terminal
-      crewai create crew latest-ai-development
+      crewai create flow latest-ai-flow
+      cd latest_ai_flow
       ```
     </CodeGroup>
+
+  이렇게 하면 `src/latest_ai_flow/` 아래에 Flow 앱이 만들어지고, 다음 단계에서 **단일 에이전트** 연구 crew로 바꿀 시작용 crew가 `crews/content_crew/`에 포함됩니다.
   </Step>
-  <Step title="새로운 crew 프로젝트로 이동하기">
-    <CodeGroup>
-      ```shell Terminal
-      cd latest_ai_development
-      ```
-    </CodeGroup>
-  </Step>
-  <Step title="`agents.yaml` 파일 수정하기">
-  <Tip>
-  프로젝트에 맞게 agent를 수정하거나 복사/붙여넣기를 할 수 있습니다.
-  `agents.yaml` 및 `tasks.yaml` 파일에서 `{topic}`과 같은 변수를 사용하면, 이는 `main.py` 파일의 변수 값으로 대체됩니다.
-  </Tip>
+
+  <Step title="`agents.yaml`에 에이전트 하나 설정">
+  `src/latest_ai_flow/crews/content_crew/config/agents.yaml` 내용을 한 명의 연구원만 남기도록 바꿉니다. `{topic}` 같은 변수는 `crew.kickoff(inputs=...)`로 채워집니다.
+
     ```yaml agents.yaml
-    # src/latest_ai_development/config/agents.yaml
+    # src/latest_ai_flow/crews/content_crew/config/agents.yaml
     researcher:
       role: >
-        {topic} Senior Data Researcher
+        {topic} 시니어 데이터 리서처
       goal: >
-        Uncover cutting-edge developments in {topic}
+        {topic} 분야의 최신 동향을 파악한다
       backstory: >
-        You're a seasoned researcher with a knack for uncovering the latest
-        developments in {topic}. Known for your ability to find the most relevant
-        information and present it in a clear and concise manner.
-
-    reporting_analyst:
-      role: >
-        {topic} Reporting Analyst
-      goal: >
-        Create detailed reports based on {topic} data analysis and research findings
-      backstory: >
-        You're a meticulous analyst with a keen eye for detail. You're known for
-        your ability to turn complex data into clear and concise reports, making
-        it easy for others to understand and act on the information you provide.
+        당신은 {topic}의 최신 흐름을 찾아내는 데 능숙한 연구원입니다.
+        가장 관련성 높은 정보를 찾아 명확하게 전달합니다.
     ```
 
   </Step>
-  <Step title="`tasks.yaml` 파일 수정하기">
+
+  <Step title="`tasks.yaml`에 작업 하나 설정">
     ```yaml tasks.yaml
-    # src/latest_ai_development/config/tasks.yaml
+    # src/latest_ai_flow/crews/content_crew/config/tasks.yaml
     research_task:
       description: >
-        Conduct a thorough research about {topic}
-        Make sure you find any interesting and relevant information given
-        the current year is 2025.
+        {topic}에 대해 철저히 조사하세요. 웹 검색으로 최신이고 신뢰할 수 있는 정보를 찾으세요.
+        현재 연도는 2026년입니다.
       expected_output: >
-        A list with 10 bullet points of the most relevant information about {topic}
+        마크다운 보고서로, 주요 트렌드·주목할 도구나 기업·시사점 등으로 섹션을 나누세요.
+        분량은 약 800~1200단어. 문서 전체를 코드 펜스로 감싸지 마세요.
       agent: researcher
-
-    reporting_task:
-      description: >
-        Review the context you got and expand each topic into a full section for a report.
-        Make sure the report is detailed and contains any and all relevant information.
-      expected_output: >
-        A fully fledge reports with the mains topics, each with a full section of information.
-        Formatted as markdown without '```'
-      agent: reporting_analyst
-      output_file: report.md
+      output_file: output/report.md
     ```
 
   </Step>
-  <Step title="`crew.py` 파일 수정하기">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task
-    from crewai_tools import SerperDevTool
-    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+  <Step title="crew 클래스 연결 (`content_crew.py`)">
+  생성된 crew가 YAML을 읽고 연구원에게 `SerperDevTool`을 붙이도록 합니다.
+
+    ```python content_crew.py
+    # src/latest_ai_flow/crews/content_crew/content_crew.py
     from typing import List
 
+    from crewai import Agent, Crew, Process, Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.project import CrewBase, agent, crew, task
+    from crewai_tools import SerperDevTool
+
+
     @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+    class ResearchCrew:
+      """Flow 안에서 사용하는 단일 에이전트 연구 crew."""
 
       agents: List[BaseAgent]
       tasks: List[Task]
 
+      agents_config = "config/agents.yaml"
+      tasks_config = "config/tasks.yaml"
+
       @agent
       def researcher(self) -> Agent:
         return Agent(
-          config=self.agents_config['researcher'], # type: ignore[index]
+          config=self.agents_config["researcher"],  # type: ignore[index]
           verbose=True,
-          tools=[SerperDevTool()]
-        )
-
-      @agent
-      def reporting_analyst(self) -> Agent:
-        return Agent(
-          config=self.agents_config['reporting_analyst'], # type: ignore[index]
-          verbose=True
+          tools=[SerperDevTool()],
         )
 
       @task
       def research_task(self) -> Task:
         return Task(
-          config=self.tasks_config['research_task'], # type: ignore[index]
-        )
-
-      @task
-      def reporting_task(self) -> Task:
-        return Task(
-          config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # This is the file that will be contain the final report.
+          config=self.tasks_config["research_task"],  # type: ignore[index]
         )
 
       @crew
       def crew(self) -> Crew:
-        """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Automatically created by the @agent decorator
-          tasks=self.tasks, # Automatically created by the @task decorator
+          agents=self.agents,
+          tasks=self.tasks,
           process=Process.sequential,
           verbose=True,
         )
     ```
 
   </Step>
-  <Step title="[선택 사항] crew 실행 전/후 함수 추가">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
-    from crewai_tools import SerperDevTool
 
-    @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+  <Step title="`main.py`에서 Flow 정의">
+  crew를 Flow에 연결합니다: `@start()` 단계에서 주제를 **상태**에 넣고, `@listen` 단계에서 crew를 실행합니다. 작업의 `output_file`은 그대로 `output/report.md`에 씁니다.
 
-      @before_kickoff
-      def before_kickoff_function(self, inputs):
-        print(f"Before kickoff function with inputs: {inputs}")
-        return inputs # You can return the inputs or modify them as needed
-
-      @after_kickoff
-      def after_kickoff_function(self, result):
-        print(f"After kickoff function with result: {result}")
-        return result # You can return the result or modify it as needed
-
-      # ... remaining code
-    ```
-
-  </Step>
-  <Step title="crew에 커스텀 입력값 전달하기">
-  예를 들어, crew에 `topic` 입력값을 넘겨 연구 및 보고서 출력을 맞춤화할 수 있습니다.
     ```python main.py
-    #!/usr/bin/env python
-    # src/latest_ai_development/main.py
-    import sys
-    from latest_ai_development.crew import LatestAiDevelopmentCrew
+    # src/latest_ai_flow/main.py
+    from pydantic import BaseModel
 
-    def run():
-      """
-      Run the crew.
-      """
-      inputs = {
-        'topic': 'AI Agents'
-      }
-      LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+    from crewai.flow import Flow, listen, start
+
+    from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
+
+
+    class ResearchFlowState(BaseModel):
+      topic: str = ""
+      report: str = ""
+
+
+    class LatestAiFlow(Flow[ResearchFlowState]):
+      @start()
+      def prepare_topic(self, crewai_trigger_payload: dict | None = None):
+        if crewai_trigger_payload:
+          self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
+        else:
+          self.state.topic = "AI Agents"
+        print(f"주제: {self.state.topic}")
+
+      @listen(prepare_topic)
+      def run_research(self):
+        result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
+        self.state.report = result.raw
+        print("연구 crew 실행 완료.")
+
+      @listen(run_research)
+      def summarize(self):
+        print("보고서 경로: output/report.md")
+
+
+    def kickoff():
+      LatestAiFlow().kickoff()
+
+
+    def plot():
+      LatestAiFlow().plot()
+
+
+    if __name__ == "__main__":
+      kickoff()
     ```
 
-  </Step>
-  <Step title="환경 변수 설정">
-  crew를 실행하기 전에 `.env` 파일에 아래 키가 환경 변수로 설정되어 있는지 확인하세요:
-    - [Serper.dev](https://serper.dev/) API 키: `SERPER_API_KEY=YOUR_KEY_HERE`
-    - 사용하려는 모델의 설정, 예: API 키. 다양한 공급자의 모델 설정은
-        [LLM 설정 가이드](/ko/concepts/llms#setting-up-your-llm)를 참고하세요.
-  </Step>
-  <Step title="의존성 잠그고 설치하기">
-    - CLI 명령어로 의존성을 잠그고 설치하세요:
-      <CodeGroup>
-        ```shell Terminal
-        crewai install
-        ```
-      </CodeGroup>
-    - 추가 설치가 필요한 패키지가 있다면, 아래와 같이 실행하면 됩니다:
-      <CodeGroup>
-        ```shell Terminal
-        uv add <package-name>
-        ```
-      </CodeGroup>
-  </Step>
-  <Step title="crew 실행하기">
-    - 프로젝트 루트에서 다음 명령어로 crew를 실행하세요:
-      <CodeGroup>
-        ```bash Terminal
-        crewai run
-        ```
-      </CodeGroup>
+  <Tip>
+  패키지 이름이 `latest_ai_flow`가 아니면 `ResearchCrew` import 경로를 프로젝트 모듈 경로에 맞게 바꾸세요.
+  </Tip>
   </Step>
 
-  <Step title="엔터프라이즈 대안: Crew Studio에서 생성">
-  CrewAI AMP 사용자는 코드를 작성하지 않고도 동일한 crew를 생성할 수 있습니다:
+  <Step title="환경 변수">
+  프로젝트 루트의 `.env`에 다음을 설정합니다.
 
-1. CrewAI AMP 계정에 로그인하세요([app.crewai.com](https://app.crewai.com)에서 무료 계정 만들기)
-2. Crew Studio 열기
-3. 구현하려는 자동화 내용을 입력하세요
-4. 미션을 시각적으로 생성하고 순차적으로 연결하세요
-5. 입력값을 구성하고 "Download Code" 또는 "Deploy"를 클릭하세요
-
-![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
-
-  <Card title="CrewAI AMP 체험하기" icon="rocket" href="https://app.crewai.com">
-    CrewAI AOP에서 무료 계정을 시작하세요
-  </Card>
+    - `SERPER_API_KEY` — [Serper.dev](https://serper.dev/)에서 발급
+    - 모델 제공자 키 — [LLM 설정](/ko/concepts/llms#setting-up-your-llm) 참고
   </Step>
-  <Step title="최종 보고서 확인하기">
-  콘솔에서 출력 결과를 확인할 수 있으며 프로젝트 루트에 `report.md` 파일로 최종 보고서가 생성됩니다.
 
-보고서 예시는 다음과 같습니다:
+  <Step title="설치 및 실행">
+    <CodeGroup>
+      ```shell Terminal
+      crewai install
+      crewai run
+      ```
+    </CodeGroup>
+
+  `crewai run`은 프로젝트에 정의된 Flow 진입점을 실행합니다(crew와 동일한 명령이며, `pyproject.toml`의 프로젝트 유형은 `"flow"`입니다).
+  </Step>
+
+  <Step title="결과 확인">
+  Flow와 crew 로그가 출력되어야 합니다. 생성된 보고서는 **`output/report.md`**에서 확인하세요(발췌):
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
+    # 2026년 AI 에이전트: 동향과 전망
 
-    ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    ## 요약
+    …
 
-    ## 2. Benefits of AI Agents
-    AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
+    ## 주요 트렌드
+    - **도구 사용과 오케스트레이션** — …
+    - **엔터프라이즈 도입** — …
 
-    - **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
-    - **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
-    - **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
-
-    ## 3. Popular AI Agent Frameworks
-    Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
-
-    - **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
-    - **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
-    - **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
-    - **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
-    - **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
-    - **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
-
-    These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
-
-    ## 4. AI Agents in Human Resources
-    AI agents are revolutionizing HR practices by automating and optimizing key functions:
-
-    - **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
-    - **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
-    - **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
-
-    As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
-
-    ## 5. AI Agents in Finance
-    The finance sector is seeing extensive integration of AI agents that enhance financial practices:
-
-    - **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
-    - **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
-    - **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
-
-    The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
-
-    ## 6. Market Trends and Investments
-    The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
-
-    Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
-
-    ## 7. Future Predictions and Implications
-    Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
-
-    - Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
-    - Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
-    - Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
-
-    To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
-
-    ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    ## 시사점
+    …
     ```
-
   </CodeGroup>
+
+  실제 파일은 더 길고 실시간 검색 결과를 반영합니다.
   </Step>
 </Steps>
 
+## 한 번에 이해하기
+
+1. **Flow** — `LatestAiFlow`는 `prepare_topic` → `run_research` → `summarize` 순으로 실행됩니다. 상태(`topic`, `report`)는 Flow에 있습니다.
+2. **Crew** — `ResearchCrew`는 에이전트 한 명·작업 하나로 실행됩니다. 연구원이 **Serper**로 웹을 검색하고 구조화된 보고서를 씁니다.
+3. **결과물** — 작업의 `output_file`이 `output/report.md`에 보고서를 씁니다.
+
+Flow 패턴(라우팅, 지속성, human-in-the-loop)을 더 보려면 [첫 Flow 만들기](/ko/guides/flows/first-flow)와 [Flows](/ko/concepts/flows)를 참고하세요. Flow 없이 crew만 쓰려면 [Crews](/ko/concepts/crews)를, 작업 없이 단일 `Agent`의 `kickoff()`만 쓰려면 [Agents](/ko/concepts/agents#direct-agent-interaction-with-kickoff)를 참고하세요.
+
 <Check>
-축하합니다!
-
-crew 프로젝트 설정이 완료되었으며, 이제 자신만의 agentic workflow 구축을 바로 시작하실 수 있습니다!
-
+에이전트 crew와 저장된 보고서까지 이어진 Flow를 완성했습니다. 이제 단계·crew·도구를 더해 확장할 수 있습니다.
 </Check>
 
-### 명명 일관성에 대한 참고
+### 이름 일치
 
-YAML 파일(`agents.yaml` 및 `tasks.yaml`)에서 사용하는 이름은 Python 코드의 메서드 이름과 일치해야 합니다.
-예를 들어, 특정 task에 대한 agent를 `tasks.yaml` 파일에서 참조할 수 있습니다.
-이러한 명명 일관성을 지키면 CrewAI가 설정과 코드를 자동으로 연결할 수 있습니다. 그렇지 않으면 task가 참조를 제대로 인식하지 못할 수 있습니다.
+YAML 키(`researcher`, `research_task`)는 `@CrewBase` 클래스의 메서드 이름과 같아야 합니다. 전체 데코레이터 패턴은 [Crews](/ko/concepts/crews)를 참고하세요.
 
-#### 예시 참조
+## 배포
 
-<Tip>
-  `agents.yaml` (`email_summarizer`) 파일에서 에이전트 이름과 `crew.py`
-  (`email_summarizer`) 파일에서 메서드 이름이 동일하게 사용되는 점에 주목하세요.
-</Tip>
+로컬에서 정상 실행되고 프로젝트가 **GitHub** 저장소에 있으면 Flow를 **[CrewAI AMP](https://app.crewai.com)**에 올릴 수 있습니다. 프로젝트 루트에서:
 
-```yaml agents.yaml
-email_summarizer:
-  role: >
-    Email Summarizer
-  goal: >
-    Summarize emails into a concise and clear summary
-  backstory: >
-    You will create a 5 bullet point summary of the report
-  llm: provider/model-id # Add your choice of model here
+<CodeGroup>
+```bash 인증
+crewai login
 ```
 
-<Tip>
-  `tasks.yaml` (`email_summarizer_task`) 파일에서 태스크 이름과 `crew.py`
-  (`email_summarizer_task`) 파일에서 메서드 이름이 동일하게 사용되는 점에
-  주목하세요.
-</Tip>
-
-```yaml tasks.yaml
-email_summarizer_task:
-  description: >
-    Summarize the email into a 5 bullet point summary
-  expected_output: >
-    A 5 bullet point summary of the email
-  agent: email_summarizer
-  context:
-    - reporting_task
-    - research_task
+```bash 배포 생성
+crewai deploy create
 ```
 
-## Crew 배포하기
+```bash 상태 및 로그
+crewai deploy status
+crewai deploy logs
+```
 
-production 환경에 crew를 배포하는 가장 쉬운 방법은 [CrewAI AMP](http://app.crewai.com)를 통해서입니다.
+```bash 코드 변경 후 반영
+crewai deploy push
+```
 
-CLI를 사용하여 [CrewAI AMP](http://app.crewai.com)에 crew를 배포하는 단계별 시연은 이 영상 튜토리얼을 참고하세요.
+```bash 배포 목록 또는 삭제
+crewai deploy list
+crewai deploy remove <deployment_id>
+```
+</CodeGroup>
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
+<Tip>
+  첫 배포는 보통 **약 1분** 정도 걸립니다. 전체 사전 요건과 웹 UI 절차는 [AMP에 배포](/ko/enterprise/guides/deploy-to-amp)를 참고하세요.
+</Tip>
 
 <CardGroup cols={2}>
-  <Card title="Enterprise에 배포" icon="rocket" href="http://app.crewai.com">
-    CrewAI AOP로 시작하여 몇 번의 클릭만으로 production 환경에 crew를
-    배포하세요.
+  <Card title="배포 가이드" icon="book" href="/ko/enterprise/guides/deploy-to-amp">
+    AMP 배포 단계별 안내(CLI 및 대시보드).
   </Card>
   <Card
-    title="커뮤니티 참여하기"
+    title="커뮤니티"
     icon="comments"
     href="https://community.crewai.com"
   >
-    오픈 소스 커뮤니티에 참여하여 아이디어를 나누고, 프로젝트를 공유하며, 다른
-    CrewAI 개발자들과 소통하세요.
+    아이디어를 나누고 프로젝트를 공유하며 다른 CrewAI 개발자와 소통하세요.
   </Card>
 </CardGroup>

docs/ko/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: skills.sh의 공식 레지스트리에서 crewaiinc/skills를 설치하세요. Claude Code, Cursor, Codex 등을 위한 Flow, Crew, 문서 연동 스킬.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**한 번의 명령으로 코딩 에이전트에 CrewAI 컨텍스트를 제공하세요.**
+
+CrewAI **Skills**는 **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**에 게시됩니다. `crewaiinc/skills`의 공식 레지스트리로, 개별 스킬(예: **design-agent**, **getting-started**, **design-task**, **ask-docs**), 설치 수, 감사 정보를 확인할 수 있습니다. Claude Code, Cursor, Codex 같은 코딩 에이전트에게 Flow 구성, Crew 설정, 도구 사용, CrewAI 패턴을 가르칩니다. 아래를 실행하거나 에이전트에 붙여 넣으세요.
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+에이전트 워크플로에 스킬 팩이 추가되어 세션마다 프레임워크를 다시 설명하지 않아도 CrewAI 관례를 적용할 수 있습니다. 소스와 이슈는 [GitHub](https://github.com/crewAIInc/skills)에서 관리합니다.
+
+## 에이전트가 얻는 것
+
+- **Flows** — CrewAI 방식의 상태ful 앱, 단계, crew kickoff
+- **Crew & 에이전트** — YAML 우선 패턴, 역할, 작업, 위임
+- **도구 & 통합** — 검색, API, 일반적인 CrewAI 도구 연결
+- **프로젝트 구조** — CLI 스캐폴드 및 저장소 관례와 정렬
+- **최신 패턴** — 스킬이 현재 CrewAI 문서 및 권장 사항을 반영
+
+## 이 사이트에서 더 알아보기
+
+<CardGroup cols={2}>
+  <Card title="코딩 도구 & AGENTS.md" icon="terminal" href="/ko/guides/coding-tools/agents-md">
+    CrewAI와 `AGENTS.md`, 코딩 에이전트 워크플로 사용법.
+  </Card>
+  <Card title="빠른 시작" icon="rocket" href="/ko/quickstart">
+    첫 Flow와 crew를 처음부터 끝까지 구축합니다.
+  </Card>
+  <Card title="설치" icon="download" href="/ko/installation">
+    CrewAI CLI와 Python 패키지를 설치합니다.
+  </Card>
+  <Card title="Skills 레지스트리 (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    `crewaiinc/skills` 공식 목록—스킬, 설치 수, 감사.
+  </Card>
+  <Card title="GitHub 소스" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    스킬 팩 소스, 업데이트, 이슈.
+  </Card>
+</CardGroup>
+
+### 영상: 코딩 에이전트 스킬과 CrewAI
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/ko/tools/ai-ml/codeinterpretertool.mdx

@@ -7,6 +7,10 @@ mode: "wide"
 
 # `CodeInterpreterTool`
 
+<Warning>
+  **지원 중단:** `CodeInterpreterTool`이 `crewai-tools`에서 제거되었습니다. `Agent`의 `allow_code_execution` 및 `code_execution_mode` 파라미터도 더 이상 사용되지 않습니다. 안전하고 격리된 코드 실행을 위해 전용 샌드박스 서비스 — [E2B](https://e2b.dev) 또는 [Modal](https://modal.com) — 을 사용하세요.
+</Warning>
+
 ## 설명
 
 `CodeInterpreterTool`은 CrewAI 에이전트가 자율적으로 생성한 Python 3 코드를 실행할 수 있도록 합니다. 이 기능은 에이전트가 코드를 생성하고, 실행하며, 결과를 얻고, 그 정보를 활용하여 이후의 결정과 행동에 반영할 수 있다는 점에서 특히 유용합니다.

docs/ko/tools/file-document/csvsearchtool.mdx

@@ -76,3 +76,19 @@ tool = CSVSearchTool(
     }
 )
 ```
+
+## 보안
+
+### 경로 유효성 검사
+
+이 도구에 제공되는 파일 경로는 현재 작업 디렉터리에 대해 검증됩니다. 작업 디렉터리 외부로 확인되는 경로는 `ValueError`로 거부됩니다.
+
+작업 디렉터리 외부의 경로를 허용하려면 (예: 테스트 또는 신뢰할 수 있는 파이프라인), 다음 환경 변수를 설정하세요:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL 유효성 검사
+
+URL 입력도 검증됩니다: `file://` URI와 사설 또는 예약된 IP 범위를 대상으로 하는 요청은 서버 측 요청 위조(SSRF) 공격을 방지하기 위해 차단됩니다.

docs/ko/tools/file-document/directorysearchtool.mdx

@@ -68,3 +68,15 @@ tool = DirectorySearchTool(
     }
 )
 ```
+
+## 보안
+
+### 경로 유효성 검사
+
+이 도구에 제공되는 디렉터리 경로는 현재 작업 디렉터리에 대해 검증됩니다. 작업 디렉터리 외부로 확인되는 경로는 `ValueError`로 거부됩니다.
+
+작업 디렉터리 외부의 경로를 허용하려면 (예: 테스트 또는 신뢰할 수 있는 파이프라인), 다음 환경 변수를 설정하세요:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```

docs/ko/tools/file-document/jsonsearchtool.mdx

@@ -71,3 +71,19 @@ tool = JSONSearchTool(
     }
 )
 ```
+
+## 보안
+
+### 경로 유효성 검사
+
+이 도구에 제공되는 파일 경로는 현재 작업 디렉터리에 대해 검증됩니다. 작업 디렉터리 외부로 확인되는 경로는 `ValueError`로 거부됩니다.
+
+작업 디렉터리 외부의 경로를 허용하려면 (예: 테스트 또는 신뢰할 수 있는 파이프라인), 다음 환경 변수를 설정하세요:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL 유효성 검사
+
+URL 입력도 검증됩니다: `file://` URI와 사설 또는 예약된 IP 범위를 대상으로 하는 요청은 서버 측 요청 위조(SSRF) 공격을 방지하기 위해 차단됩니다.

docs/ko/tools/file-document/pdfsearchtool.mdx

@@ -102,3 +102,19 @@ tool = PDFSearchTool(
     }
 )
 ```
+
+## 보안
+
+### 경로 유효성 검사
+
+이 도구에 제공되는 파일 경로는 현재 작업 디렉터리에 대해 검증됩니다. 작업 디렉터리 외부로 확인되는 경로는 `ValueError`로 거부됩니다.
+
+작업 디렉터리 외부의 경로를 허용하려면 (예: 테스트 또는 신뢰할 수 있는 파이프라인), 다음 환경 변수를 설정하세요:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### URL 유효성 검사
+
+URL 입력도 검증됩니다: `file://` URI와 사설 또는 예약된 IP 범위를 대상으로 하는 요청은 서버 측 요청 위조(SSRF) 공격을 방지하기 위해 차단됩니다.

docs/pt-BR/changelog.mdx

@@ -4,6 +4,321 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="09 abr 2026">
+  ## v1.14.2a1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a1)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Corrigir a emissão do evento flow_finished após a retomada do HITL
+  - Corrigir a versão da criptografia para 46.0.7 para resolver o CVE-2026-39892
+
+  ### Refatoração
+  - Refatorar para usar o singleton I18N_DEFAULT compartilhado
+
+  ### Documentação
+  - Atualizar o changelog e a versão para v1.14.1
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="09 abr 2026">
+  ## v1.14.1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar navegador TUI de ponto de verificação assíncrono
+  - Adicionar aclose()/close() e gerenciador de contexto assíncrono para saídas de streaming
+
+  ### Correções de Bugs
+  - Corrigir regex para aumentos de versão do template pyproject.toml
+  - Sanitizar nomes de ferramentas nos filtros do decorador de hook
+  - Corrigir registro de manipuladores de ponto de verificação quando CheckpointConfig é criado
+  - Atualizar transformers para 5.5.0 para resolver CVE-2026-1839
+  - Remover wrapper stdout/stderr de FilteredStream
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.1rc1
+
+  ### Refatoração
+  - Substituir lista de negação codificada por exclusão dinâmica de campo BaseTool na geração de especificações
+  - Substituir regex por tomlkit na CLI do devtools
+  - Usar singleton PRINTER compartilhado
+  - Fazer BaseProvider um BaseModel com discriminador provider_type
+
+  ## Contribuidores
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay
+
+</Update>
+
+<Update label="09 abr 2026">
+  ## v1.14.1rc1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.1rc1)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar navegador TUI de ponto de verificação assíncrono
+  - Adicionar aclose()/close() e gerenciador de contexto assíncrono para saídas de streaming
+
+  ### Correções de Bugs
+  - Corrigir aumentos de versão do template pyproject.toml usando regex
+  - Sanitizar nomes de ferramentas nos filtros do decorador de hook
+  - Atualizar transformers para 5.5.0 para resolver CVE-2026-1839
+  - Registrar manipuladores de ponto de verificação quando CheckpointConfig é criado
+
+  ### Refatoração
+  - Substituir lista de negação codificada por exclusão dinâmica de campo BaseTool na geração de especificações
+  - Substituir regex por tomlkit na CLI do devtools
+  - Usar singleton PRINTER compartilhado
+  - Tornar BaseProvider um BaseModel com discriminador de tipo de provedor
+  - Remover wrapper stdout/stderr de FilteredStream
+  - Remover flow/config.py não utilizado
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.0
+
+  ## Contribuidores
+
+  @greysonlalonde, @iris-clawd, @joaomdmoura
+
+</Update>
+
+<Update label="07 abr 2026">
+  ## v1.14.0
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar comandos CLI de lista/informações de checkpoint
+  - Adicionar guardrail_type e nome para distinguir rastros
+  - Adicionar SqliteProvider para armazenamento de checkpoints
+  - Adicionar CheckpointConfig para checkpointing automático
+  - Implementar checkpointing de estado em tempo de execução, sistema de eventos e refatoração do executor
+
+  ### Correções de Bugs
+  - Adicionar proteções contra SSRF e travessia de caminho
+  - Adicionar validação de caminho e URL às ferramentas RAG
+  - Excluir vetores de incorporação da serialização de memória para economizar tokens
+  - Garantir que o diretório de saída exista antes de escrever no modelo de fluxo
+  - Atualizar litellm para >=1.83.0 para resolver CVE-2026-35030
+  - Remover campo de indexação SEO que causava renderização de página em árabe
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.0
+  - Atualizar guias de início rápido e instalação para maior clareza
+  - Adicionar seção de provedores de armazenamento, exportar JsonProvider
+  - Adicionar guia da aba de Treinamento AMP
+
+  ### Refatoração
+  - Limpar API de checkpoint
+  - Remover CodeInterpreterTool e descontinuar parâmetros de execução de código
+
+  ## Contribuidores
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="07 abr 2026">
+  ## v1.14.0a4
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a4)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar guardrail_type e nome para distinguir rastros
+  - Adicionar SqliteProvider para armazenamento de checkpoints
+  - Adicionar CheckpointConfig para checkpointing automático
+  - Implementar checkpointing de estado em tempo de execução, sistema de eventos e refatoração do executor
+
+  ### Correções de Bugs
+  - Excluir vetores de incorporação da serialização de memória para economizar tokens
+  - Atualizar litellm para >=1.83.0 para resolver CVE-2026-35030
+
+  ### Documentação
+  - Atualizar guias de início rápido e instalação para melhor clareza
+  - Adicionar seção de provedores de armazenamento e exportar JsonProvider
+
+  ### Desempenho
+  - Usar JSONB para a coluna de dados de checkpoint
+
+  ### Refatoração
+  - Remover CodeInterpreterTool e descontinuar parâmetros de execução de código
+
+  ## Contribuidores
+
+  @alex-clawd, @github-actions[bot], @greysonlalonde, @joaomdmoura, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="06 abr 2026">
+  ## v1.14.0a3
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.0a2
+
+  ## Contribuidores
+
+  @joaomdmoura
+
+</Update>
+
+<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
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a7)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar a extensão A2UI com suporte a v0.8/v0.9, esquemas e documentação
+
+  ### Correções de Bugs
+  - Corrigir prefixos de visão multimodal adicionando GPT-5 e o-series
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.13.0a6
+
+  ## Contribuidores
+
+  @alex-clawd, @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="01 abr 2026">
+  ## v1.13.0a6
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a6)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Corrigir níveis de permissão RBAC para corresponder às opções reais da interface do usuário (#5210)
+  - Atualizar changelog e versão para v1.13.0a5 (#5200)
+
+  ### Desempenho
+  - Reduzir a sobrecarga do framework implementando um barramento de eventos preguiçoso e pulando o rastreamento quando desativado (#5187)
+
+  ## Contributors
+
+  @alex-clawd, @joaomdmoura, @lucasgomide
+
+</Update>
+
+<Update label="31 mar 2026">
+  ## v1.13.0a5
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a5)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.13.0a4
+
+  ## Contributors
+
+  @greysonlalonde, @joaomdmoura
+
+</Update>
+
+<Update label="01 abr 2026">
+  ## v1.13.0a4
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0a4)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.13.0a3
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="01 abr 2026">
   ## v1.13.0a3
 

docs/pt-BR/concepts/agents.mdx

@@ -304,17 +304,12 @@ multimodal_agent = Agent(
 
 #### Execução de Código
 
-- `allow_code_execution`: Deve ser True para permitir execução de código
-- `code_execution_mode`:
-  - `"safe"`: Usa Docker (recomendado para produção)
-  - `"unsafe"`: Execução direta (apenas em ambientes confiáveis)
+<Warning>
+  `allow_code_execution` e `code_execution_mode` estão depreciados. O `CodeInterpreterTool` foi removido do `crewai-tools`. Use um serviço de sandbox dedicado como [E2B](https://e2b.dev) ou [Modal](https://modal.com) para execução segura de código.
+</Warning>
 
-<Note>
-  Isso executa uma imagem Docker padrão. Se você deseja configurar a imagem
-  Docker, veja a ferramenta Code Interpreter na seção de ferramentas. Adicione a
-  ferramenta de interpretação de código como um parâmetro em ferramentas no
-  agente.
-</Note>
+- `allow_code_execution` _(depreciado)_: Anteriormente habilitava a execução de código embutida via `CodeInterpreterTool`.
+- `code_execution_mode` _(depreciado)_: Anteriormente controlava o modo de execução (`"safe"` para Docker, `"unsafe"` para execução direta).
 
 #### Funcionalidades Avançadas
 
@@ -565,9 +560,9 @@ agent = Agent(
 
 ### Segurança e Execução de Código
 
-- Ao usar `allow_code_execution`, seja cauteloso com entradas do usuário e sempre as valide
-- Use `code_execution_mode: "safe"` (Docker) em ambientes de produção
-- Considere definir limites adequados de `max_execution_time` para evitar loops infinitos
+<Warning>
+  `allow_code_execution` e `code_execution_mode` estão depreciados e o `CodeInterpreterTool` foi removido. Use um serviço de sandbox dedicado como [E2B](https://e2b.dev) ou [Modal](https://modal.com) para execução segura de código.
+</Warning>
 
 ### Otimização de Performance
 

docs/pt-BR/concepts/checkpointing.mdx

@@ -0,0 +1,229 @@
+---
+title: Checkpointing
+description: Salve automaticamente o estado de execucao para que crews, flows e agentes possam retomar apos falhas.
+icon: floppy-disk
+mode: "wide"
+---
+
+<Warning>
+O checkpointing esta em versao inicial. As APIs podem mudar em versoes futuras.
+</Warning>
+
+## Visao Geral
+
+O checkpointing salva automaticamente o estado de execucao durante uma execucao. Se uma crew, flow ou agente falhar no meio da execucao, voce pode restaurar a partir do ultimo checkpoint e retomar sem reexecutar o trabalho ja concluido.
+
+## Inicio Rapido
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=True,  # usa padroes: ./.checkpoints, em task_completed
+)
+result = crew.kickoff()
+```
+
+Os arquivos de checkpoint sao gravados em `./.checkpoints/` apos cada tarefa concluida.
+
+## Configuracao
+
+Use `CheckpointConfig` para controle total:
+
+```python
+from crewai import Crew, CheckpointConfig
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        on_events=["task_completed", "crew_kickoff_completed"],
+        max_checkpoints=5,
+    ),
+)
+```
+
+### Campos do CheckpointConfig
+
+| Campo | Tipo | Padrao | Descricao |
+|:------|:-----|:-------|:----------|
+| `location` | `str` | `"./.checkpoints"` | Caminho para os arquivos de checkpoint |
+| `on_events` | `list[str]` | `["task_completed"]` | Tipos de evento que acionam um checkpoint |
+| `provider` | `BaseProvider` | `JsonProvider()` | Backend de armazenamento |
+| `max_checkpoints` | `int \| None` | `None` | Maximo de arquivos a manter; os mais antigos sao removidos primeiro |
+
+### Heranca e Desativacao
+
+O campo `checkpoint` em Crew, Flow e Agent aceita `CheckpointConfig`, `True`, `False` ou `None`:
+
+| Valor | Comportamento |
+|:------|:--------------|
+| `None` (padrao) | Herda do pai. Um agente herda a configuracao da crew. |
+| `True` | Ativa com padroes. |
+| `False` | Desativacao explicita. Interrompe a heranca do pai. |
+| `CheckpointConfig(...)` | Configuracao personalizada. |
+
+```python
+crew = Crew(
+    agents=[
+        Agent(role="Researcher", ...),                  # herda checkpoint da crew
+        Agent(role="Writer", ..., checkpoint=False),     # desativado, sem checkpoints
+    ],
+    tasks=[...],
+    checkpoint=True,
+)
+```
+
+## Retomando a partir de um Checkpoint
+
+```python
+# Restaurar e retomar
+crew = Crew.from_checkpoint("./my_checkpoints/20260407T120000_abc123.json")
+result = crew.kickoff()  # retoma a partir da ultima tarefa concluida
+```
+
+A crew restaurada pula tarefas ja concluidas e retoma a partir da primeira incompleta.
+
+## Funciona em Crew, Flow e Agent
+
+### Crew
+
+```python
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task, review_task],
+    checkpoint=CheckpointConfig(location="./crew_cp"),
+)
+```
+
+Gatilho padrao: `task_completed` (um checkpoint por tarefa finalizada).
+
+### Flow
+
+```python
+from crewai.flow.flow import Flow, start, listen
+from crewai import CheckpointConfig
+
+class MyFlow(Flow):
+    @start()
+    def step_one(self):
+        return "data"
+
+    @listen(step_one)
+    def step_two(self, data):
+        return process(data)
+
+flow = MyFlow(
+    checkpoint=CheckpointConfig(
+        location="./flow_cp",
+        on_events=["method_execution_finished"],
+    ),
+)
+result = flow.kickoff()
+
+# Retomar
+flow = MyFlow.from_checkpoint("./flow_cp/20260407T120000_abc123.json")
+result = flow.kickoff()
+```
+
+### Agent
+
+```python
+agent = Agent(
+    role="Researcher",
+    goal="Research topics",
+    backstory="Expert researcher",
+    checkpoint=CheckpointConfig(
+        location="./agent_cp",
+        on_events=["lite_agent_execution_completed"],
+    ),
+)
+result = agent.kickoff(messages=[{"role": "user", "content": "Research AI trends"}])
+```
+
+## Provedores de Armazenamento
+
+O CrewAI inclui dois provedores de armazenamento para checkpoints.
+
+### JsonProvider (padrao)
+
+Grava cada checkpoint como um arquivo JSON separado.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import JsonProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./my_checkpoints",
+        provider=JsonProvider(),
+        max_checkpoints=5,
+    ),
+)
+```
+
+### SqliteProvider
+
+Armazena todos os checkpoints em um unico arquivo SQLite.
+
+```python
+from crewai import Crew, CheckpointConfig
+from crewai.state import SqliteProvider
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    checkpoint=CheckpointConfig(
+        location="./.checkpoints.db",
+        provider=SqliteProvider(),
+    ),
+)
+```
+
+
+## Tipos de Evento
+
+O campo `on_events` aceita qualquer combinacao de strings de tipo de evento. Escolhas comuns:
+
+| Caso de Uso | Eventos |
+|:------------|:--------|
+| Apos cada tarefa (Crew) | `["task_completed"]` |
+| Apos cada metodo do flow | `["method_execution_finished"]` |
+| Apos execucao do agente | `["agent_execution_completed"]`, `["lite_agent_execution_completed"]` |
+| Apenas na conclusao da crew | `["crew_kickoff_completed"]` |
+| Apos cada chamada LLM | `["llm_call_completed"]` |
+| Em tudo | `["*"]` |
+
+<Warning>
+Usar `["*"]` ou eventos de alta frequencia como `llm_call_completed` gravara muitos arquivos de checkpoint e pode impactar o desempenho. Use `max_checkpoints` para limitar o uso de disco.
+</Warning>
+
+## Checkpointing Manual
+
+Para controle total, registre seu proprio handler de evento e chame `state.checkpoint()` diretamente:
+
+```python
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.llm_events import LLMCallCompletedEvent
+
+# Handler sincrono
+@crewai_event_bus.on(LLMCallCompletedEvent)
+def on_llm_done(source, event, state):
+    path = state.checkpoint("./my_checkpoints")
+    print(f"Checkpoint salvo: {path}")
+
+# Handler assincrono
+@crewai_event_bus.on(LLMCallCompletedEvent)
+async def on_llm_done_async(source, event, state):
+    path = await state.acheckpoint("./my_checkpoints")
+    print(f"Checkpoint salvo: {path}")
+```
+
+O argumento `state` e o `RuntimeState` passado automaticamente pelo barramento de eventos quando seu handler aceita 3 parametros. Voce pode registrar handlers em qualquer tipo de evento listado na documentacao de [Event Listeners](/pt-BR/concepts/event-listener).
+
+O checkpointing e best-effort: se uma gravacao de checkpoint falhar, o erro e registrado no log, mas a execucao continua sem interrupcao.

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

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

docs/pt-BR/enterprise/guides/deploy-to-amp.mdx

@@ -105,7 +105,7 @@ A CLI detecta automaticamente o tipo do seu projeto a partir do `pyproject.toml`
     ```
 
     <Tip>
-    A primeira implantação normalmente leva de 10 a 15 minutos, pois as imagens dos containers são construídas. As próximas implantações são bem mais rápidas.
+    A primeira implantação normalmente leva cerca de 1 minuto.
     </Tip>
 
   </Step>
@@ -187,7 +187,7 @@ Você precisa enviar seu crew para um repositório do GitHub. Caso ainda não te
 
     1. Clique no botão "Deploy" para iniciar o processo de implantação
     2. Você pode monitorar o progresso pela barra de progresso
-    3. A primeira implantação geralmente demora de 10 a 15 minutos; as próximas serão mais rápidas
+    3. A primeira implantação geralmente demora cerca de 1 minuto
 
     <Frame>
       ![Progresso da Implantação](/images/enterprise/deploy-progress.png)

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:
@@ -192,12 +200,11 @@ Para equipes e organizações, o CrewAI oferece opções de implantação corpor
 
 <CardGroup cols={2}>
   <Card
-    title="Construa Seu Primeiro Agente"
+    title="Início rápido: Flow + agente"
     icon="code"
     href="/pt-BR/quickstart"
   >
-    Siga nosso guia de início rápido para criar seu primeiro agente CrewAI e
-    obter experiência prática.
+    Siga o guia rápido para gerar um Flow, executar um crew com um agente e produzir um relatório.
   </Card>
   <Card
     title="Junte-se à Comunidade"

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.
@@ -132,7 +140,7 @@ Para qualquer aplicação pronta para produção, **comece com um Flow**.
     icon="bolt"
     href="/pt-BR/quickstart"
   >
-    Siga nosso guia rápido para criar seu primeiro agente CrewAI e colocar a mão na massa.
+    Gere um Flow, execute um crew com um agente e produza um relatório ponta a ponta.
   </Card>
   <Card
     title="Junte-se à Comunidade"

docs/pt-BR/learn/streaming-crew-execution.mdx

@@ -325,6 +325,34 @@ O streaming é particularmente valioso para:
 - **Experiência do Usuário**: Reduzir latência percebida mostrando resultados incrementais
 - **Dashboards ao Vivo**: Construir interfaces de monitoramento que exibem status de execução da crew
 
+## Cancelamento e Limpeza de Recursos
+
+`CrewStreamingOutput` suporta cancelamento gracioso para que o trabalho em andamento pare imediatamente quando o consumidor desconecta.
+
+### Gerenciador de Contexto Assíncrono
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+
+async with streaming:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+```
+
+### Cancelamento Explícito
+
+```python Code
+streaming = await crew.akickoff(inputs={"topic": "AI"})
+try:
+    async for chunk in streaming:
+        print(chunk.content, end="", flush=True)
+finally:
+    await streaming.aclose()  # assíncrono
+    # streaming.close()       # equivalente síncrono
+```
+
+Após o cancelamento, `streaming.is_cancelled` e `streaming.is_completed` são ambos `True`. Tanto `aclose()` quanto `close()` são idempotentes.
+
 ## Notas Importantes
 
 - O streaming ativa automaticamente o streaming do LLM para todos os agentes na crew

docs/pt-BR/quickstart.mdx

@@ -1,374 +1,278 @@
 ---
 title: Guia Rápido
-description: Construa seu primeiro agente de IA com a CrewAI em menos de 5 minutos.
+description: Crie seu primeiro Flow CrewAI em minutos — orquestração, estado e um crew com um agente que gera um relatório real.
 icon: rocket
 mode: "wide"
 ---
 
-## Construa seu primeiro Agente CrewAI
+### Assista: Construindo Agents e Flows CrewAI com Coding Agent Skills
 
-Vamos criar uma tripulação simples que nos ajudará a `pesquisar` e `relatar` sobre os `últimos avanços em IA` para um determinado tópico ou assunto.
+Instale nossas coding agent skills (Claude Code, Codex, ...) para colocar seus agentes de código para funcionar rapidamente com o CrewAI.
 
-Antes de prosseguir, certifique-se de ter concluído a instalação da CrewAI.
-Se ainda não instalou, faça isso seguindo o [guia de instalação](/pt-BR/installation).
+Você pode instalar com `npx skills add crewaiinc/skills`
 
-Siga os passos abaixo para começar a tripular! 🚣‍♂️
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
+
+Neste guia você vai **criar um Flow** que define um tópico de pesquisa, executa um **crew com um agente** (um pesquisador com busca na web) e termina com um **relatório em Markdown** no disco. Flows são a forma recomendada de estruturar apps em produção: eles controlam **estado** e **ordem de execução**, enquanto os **agentes** fazem o trabalho dentro da etapa do crew.
+
+Se ainda não instalou o CrewAI, siga primeiro o [guia de instalação](/pt-BR/installation).
+
+## Pré-requisitos
+
+- Ambiente Python e a CLI do CrewAI (veja [instalação](/pt-BR/installation))
+- Um LLM configurado com as chaves corretas — veja [LLMs](/pt-BR/concepts/llms#setting-up-your-llm)
+- Uma chave de API do [Serper.dev](https://serper.dev/) (`SERPER_API_KEY`) para busca na web neste tutorial
+
+## Construa seu primeiro Flow
 
 <Steps>
-  <Step title="Crie sua tripulação">
-  Crie um novo projeto de tripulação executando o comando abaixo em seu terminal.
-  Isso criará um novo diretório chamado `latest-ai-development` com a estrutura básica para sua tripulação.
+  <Step title="Crie um projeto Flow">
+  No terminal, gere um projeto Flow (o nome da pasta usa sublinhados, ex.: `latest_ai_flow`):
+
     <CodeGroup>
       ```shell Terminal
-      crewai create crew latest-ai-development
+      crewai create flow latest-ai-flow
+      cd latest_ai_flow
       ```
     </CodeGroup>
+
+  Isso cria um app Flow em `src/latest_ai_flow/`, incluindo um crew inicial em `crews/content_crew/` que você substituirá por um crew de pesquisa **com um único agente** nos próximos passos.
   </Step>
-  <Step title="Navegue até o novo projeto da sua tripulação">
-    <CodeGroup>
-      ```shell Terminal
-      cd latest_ai_development
-      ```
-    </CodeGroup>
-  </Step>
-  <Step title="Modifique seu arquivo `agents.yaml`">
-  <Tip>
-  Você também pode modificar os agentes conforme necessário para atender ao seu caso de uso ou copiar e colar como está para seu projeto.
-  Qualquer variável interpolada nos seus arquivos `agents.yaml` e `tasks.yaml`, como `{topic}`, será substituída pelo valor da variável no arquivo `main.py`.
-  </Tip>
+
+  <Step title="Configure um agente em `agents.yaml`">
+  Substitua o conteúdo de `src/latest_ai_flow/crews/content_crew/config/agents.yaml` por um único pesquisador. Variáveis como `{topic}` são preenchidas a partir de `crew.kickoff(inputs=...)`.
+
     ```yaml agents.yaml
-    # src/latest_ai_development/config/agents.yaml
+    # src/latest_ai_flow/crews/content_crew/config/agents.yaml
     researcher:
       role: >
-        Pesquisador Sênior de Dados em {topic}
+        Pesquisador(a) Sênior de Dados em {topic}
       goal: >
-        Descobrir os avanços mais recentes em {topic}
+        Descobrir os desenvolvimentos mais recentes em {topic}
       backstory: >
-        Você é um pesquisador experiente com talento para descobrir os últimos avanços em {topic}. Conhecido por sua habilidade em encontrar as informações mais relevantes e apresentá-las de forma clara e concisa.
-
-    reporting_analyst:
-      role: >
-        Analista de Relatórios em {topic}
-      goal: >
-        Criar relatórios detalhados com base na análise de dados e descobertas de pesquisa em {topic}
-      backstory: >
-        Você é um analista meticuloso com um olhar atento aos detalhes. É conhecido por sua capacidade de transformar dados complexos em relatórios claros e concisos, facilitando o entendimento e a tomada de decisão por parte dos outros.
+        Você é um pesquisador experiente que descobre os últimos avanços em {topic}.
+        Encontra as informações mais relevantes e apresenta tudo com clareza.
     ```
 
   </Step>
-  <Step title="Modifique seu arquivo `tasks.yaml`">
+
+  <Step title="Configure uma tarefa em `tasks.yaml`">
     ```yaml tasks.yaml
-    # src/latest_ai_development/config/tasks.yaml
+    # src/latest_ai_flow/crews/content_crew/config/tasks.yaml
     research_task:
       description: >
-        Realize uma pesquisa aprofundada sobre {topic}.
-        Certifique-se de encontrar informações interessantes e relevantes considerando que o ano atual é 2025.
+        Faça uma pesquisa aprofundada sobre {topic}. Use busca na web para obter
+        informações atuais e confiáveis. O ano atual é 2026.
       expected_output: >
-        Uma lista com 10 tópicos dos dados mais relevantes sobre {topic}
+        Um relatório em markdown com seções claras: tendências principais, ferramentas
+        ou empresas relevantes e implicações. Entre 800 e 1200 palavras. Sem cercas de código em volta do documento inteiro.
       agent: researcher
-
-    reporting_task:
-      description: >
-        Revise o contexto obtido e expanda cada tópico em uma seção completa para um relatório.
-        Certifique-se de que o relatório seja detalhado e contenha todas as informações relevantes.
-      expected_output: >
-        Um relatório completo com os principais tópicos, cada um com uma seção detalhada de informações.
-        Formate como markdown sem usar '```'
-      agent: reporting_analyst
-      output_file: report.md
+      output_file: output/report.md
     ```
 
   </Step>
-  <Step title="Modifique seu arquivo `crew.py`">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task
-    from crewai_tools import SerperDevTool
-    from crewai.agents.agent_builder.base_agent import BaseAgent
+
+  <Step title="Conecte a classe do crew (`content_crew.py`)">
+  Aponte o crew gerado para o YAML e anexe `SerperDevTool` ao pesquisador.
+
+    ```python content_crew.py
+    # src/latest_ai_flow/crews/content_crew/content_crew.py
     from typing import List
 
+    from crewai import Agent, Crew, Process, Task
+    from crewai.agents.agent_builder.base_agent import BaseAgent
+    from crewai.project import CrewBase, agent, crew, task
+    from crewai_tools import SerperDevTool
+
+
     @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+    class ResearchCrew:
+      """Crew de pesquisa com um agente, usado dentro do Flow."""
 
       agents: List[BaseAgent]
       tasks: List[Task]
 
+      agents_config = "config/agents.yaml"
+      tasks_config = "config/tasks.yaml"
+
       @agent
       def researcher(self) -> Agent:
         return Agent(
-          config=self.agents_config['researcher'], # type: ignore[index]
+          config=self.agents_config["researcher"],  # type: ignore[index]
           verbose=True,
-          tools=[SerperDevTool()]
-        )
-
-      @agent
-      def reporting_analyst(self) -> Agent:
-        return Agent(
-          config=self.agents_config['reporting_analyst'], # type: ignore[index]
-          verbose=True
+          tools=[SerperDevTool()],
         )
 
       @task
       def research_task(self) -> Task:
         return Task(
-          config=self.tasks_config['research_task'], # type: ignore[index]
-        )
-
-      @task
-      def reporting_task(self) -> Task:
-        return Task(
-          config=self.tasks_config['reporting_task'], # type: ignore[index]
-          output_file='output/report.md' # Este é o arquivo que conterá o relatório final.
+          config=self.tasks_config["research_task"],  # type: ignore[index]
         )
 
       @crew
       def crew(self) -> Crew:
-        """Creates the LatestAiDevelopment crew"""
         return Crew(
-          agents=self.agents, # Criado automaticamente pelo decorador @agent
-          tasks=self.tasks, # Criado automaticamente pelo decorador @task
+          agents=self.agents,
+          tasks=self.tasks,
           process=Process.sequential,
           verbose=True,
         )
     ```
 
   </Step>
-  <Step title="[Opcional] Adicione funções de pré e pós execução da tripulação">
-    ```python crew.py
-    # src/latest_ai_development/crew.py
-    from crewai import Agent, Crew, Process, Task
-    from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
-    from crewai_tools import SerperDevTool
 
-    @CrewBase
-    class LatestAiDevelopmentCrew():
-      """LatestAiDevelopment crew"""
+  <Step title="Defina o Flow em `main.py`">
+  Conecte o crew a um Flow: um passo `@start()` define o tópico no **estado** e um `@listen` executa o crew. O `output_file` da tarefa continua gravando `output/report.md`.
 
-      @before_kickoff
-      def before_kickoff_function(self, inputs):
-        print(f"Before kickoff function with inputs: {inputs}")
-        return inputs # You can return the inputs or modify them as needed
-
-      @after_kickoff
-      def after_kickoff_function(self, result):
-        print(f"After kickoff function with result: {result}")
-        return result # You can return the result or modify it as needed
-
-      # ... remaining code
-    ```
-
-  </Step>
-  <Step title="Fique à vontade para passar entradas personalizadas para sua tripulação">
-  Por exemplo, você pode passar o input `topic` para sua tripulação para personalizar a pesquisa e o relatório.
     ```python main.py
-    #!/usr/bin/env python
-    # src/latest_ai_development/main.py
-    import sys
-    from latest_ai_development.crew import LatestAiDevelopmentCrew
+    # src/latest_ai_flow/main.py
+    from pydantic import BaseModel
 
-    def run():
-      """
-      Run the crew.
-      """
-      inputs = {
-        'topic': 'AI Agents'
-      }
-      LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
+    from crewai.flow import Flow, listen, start
+
+    from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
+
+
+    class ResearchFlowState(BaseModel):
+      topic: str = ""
+      report: str = ""
+
+
+    class LatestAiFlow(Flow[ResearchFlowState]):
+      @start()
+      def prepare_topic(self, crewai_trigger_payload: dict | None = None):
+        if crewai_trigger_payload:
+          self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
+        else:
+          self.state.topic = "AI Agents"
+        print(f"Tópico: {self.state.topic}")
+
+      @listen(prepare_topic)
+      def run_research(self):
+        result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
+        self.state.report = result.raw
+        print("Crew de pesquisa concluído.")
+
+      @listen(run_research)
+      def summarize(self):
+        print("Relatório em: output/report.md")
+
+
+    def kickoff():
+      LatestAiFlow().kickoff()
+
+
+    def plot():
+      LatestAiFlow().plot()
+
+
+    if __name__ == "__main__":
+      kickoff()
     ```
 
-  </Step>
-  <Step title="Defina suas variáveis de ambiente">
-  Antes de executar sua tripulação, certifique-se de ter as seguintes chaves configuradas como variáveis de ambiente no seu arquivo `.env`:
-    - Uma chave da API do [Serper.dev](https://serper.dev/): `SERPER_API_KEY=YOUR_KEY_HERE`
-    - A configuração do modelo de sua escolha, como uma chave de API. Veja o
-        [guia de configuração do LLM](/pt-BR/concepts/llms#setting-up-your-llm) para aprender como configurar modelos de qualquer provedor.
-  </Step>
-  <Step title="Trave e instale as dependências">
-    - Trave e instale as dependências utilizando o comando da CLI:
-      <CodeGroup>
-        ```shell Terminal
-        crewai install
-        ```
-      </CodeGroup>
-    - Se quiser instalar pacotes adicionais, faça isso executando:
-      <CodeGroup>
-        ```shell Terminal
-        uv add <package-name>
-        ```
-      </CodeGroup>
-  </Step>
-  <Step title="Execute sua tripulação">
-    - Para executar sua tripulação, rode o seguinte comando na raiz do projeto:
-      <CodeGroup>
-        ```bash Terminal
-        crewai run
-        ```
-      </CodeGroup>
+  <Tip>
+  Se o nome do pacote não for `latest_ai_flow`, ajuste o import de `ResearchCrew` para o caminho de módulo do seu projeto.
+  </Tip>
   </Step>
 
-  <Step title="Alternativa para Empresas: Crie no Crew Studio">
-  Para usuários do CrewAI AMP, você pode criar a mesma tripulação sem escrever código:
+  <Step title="Variáveis de ambiente">
+  Na raiz do projeto, no arquivo `.env`, defina:
 
-1. Faça login na sua conta CrewAI AMP (crie uma conta gratuita em [app.crewai.com](https://app.crewai.com))
-2. Abra o Crew Studio
-3. Digite qual automação deseja construir
-4. Crie suas tarefas visualmente e conecte-as em sequência
-5. Configure seus inputs e clique em "Download Code" ou "Deploy"
-
-![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
-
-  <Card title="Experimente o CrewAI AMP" icon="rocket" href="https://app.crewai.com">
-    Comece sua conta gratuita no CrewAI AMP
-  </Card>
+    - `SERPER_API_KEY` — obtida em [Serper.dev](https://serper.dev/)
+    - As chaves do provedor de modelo conforme necessário — veja [configuração de LLM](/pt-BR/concepts/llms#setting-up-your-llm)
   </Step>
-  <Step title="Veja seu relatório final">
-  Você verá a saída no console e o arquivo `report.md` deve ser criado na raiz do seu projeto com o relatório final.
 
-Veja um exemplo de como o relatório deve ser:
+  <Step title="Instalar e executar">
+    <CodeGroup>
+      ```shell Terminal
+      crewai install
+      crewai run
+      ```
+    </CodeGroup>
+
+  O `crewai run` executa o ponto de entrada do Flow definido no projeto (o mesmo comando dos crews; o tipo do projeto é `"flow"` no `pyproject.toml`).
+  </Step>
+
+  <Step title="Confira o resultado">
+  Você deve ver logs do Flow e do crew. Abra **`output/report.md`** para o relatório gerado (trecho):
 
   <CodeGroup>
     ```markdown output/report.md
-    # Relatório Abrangente sobre a Ascensão e o Impacto dos Agentes de IA em 2025
+    # Agentes de IA em 2026: panorama e tendências
 
-    ## 1. Introduction to AI Agents
-    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    ## Resumo executivo
+    …
 
-    ## 2. Benefits of AI Agents
-    AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
+    ## Principais tendências
+    - **Uso de ferramentas e orquestração** — …
+    - **Adoção empresarial** — …
 
-    - **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
-    - **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
-    - **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
-
-    ## 3. Popular AI Agent Frameworks
-    Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
-
-    - **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
-    - **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
-    - **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
-    - **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
-    - **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
-    - **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
-
-    These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
-
-    ## 4. AI Agents in Human Resources
-    AI agents are revolutionizing HR practices by automating and optimizing key functions:
-
-    - **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
-    - **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
-    - **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
-
-    As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
-
-    ## 5. AI Agents in Finance
-    The finance sector is seeing extensive integration of AI agents that enhance financial practices:
-
-    - **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
-    - **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
-    - **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
-
-    The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
-
-    ## 6. Market Trends and Investments
-    The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
-
-    Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
-
-    ## 7. Future Predictions and Implications
-    Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
-
-    - Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
-    - Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
-    - Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
-
-    To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
-
-    ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    ## Implicações
+    …
     ```
-
   </CodeGroup>
+
+  O arquivo real será mais longo e refletirá resultados de busca ao vivo.
   </Step>
 </Steps>
 
+## Como isso se encaixa
+
+1. **Flow** — `LatestAiFlow` executa `prepare_topic`, depois `run_research`, depois `summarize`. O estado (`topic`, `report`) fica no Flow.
+2. **Crew** — `ResearchCrew` executa uma tarefa com um agente: o pesquisador usa **Serper** na web e escreve o relatório.
+3. **Artefato** — O `output_file` da tarefa grava o relatório em `output/report.md`.
+
+Para ir além em Flows (roteamento, persistência, human-in-the-loop), veja [Construa seu primeiro Flow](/pt-BR/guides/flows/first-flow) e [Flows](/pt-BR/concepts/flows). Para crews sem Flow, veja [Crews](/pt-BR/concepts/crews). Para um único `Agent` com `kickoff()` sem tarefas, veja [Agents](/pt-BR/concepts/agents#direct-agent-interaction-with-kickoff).
+
 <Check>
-Parabéns!
-
-Você configurou seu projeto de tripulação com sucesso e está pronto para começar a construir seus próprios fluxos de trabalho baseados em agentes!
-
+Você tem um Flow ponta a ponta com um crew de agente e um relatório salvo — uma base sólida para novas etapas, crews ou ferramentas.
 </Check>
 
-### Observação sobre Consistência nos Nomes
+### Consistência de nomes
 
-Os nomes utilizados nos seus arquivos YAML (`agents.yaml` e `tasks.yaml`) devem corresponder aos nomes dos métodos no seu código Python.
-Por exemplo, você pode referenciar o agente para tarefas específicas a partir do arquivo `tasks.yaml`.
-Essa consistência de nomes permite que a CrewAI conecte automaticamente suas configurações ao seu código; caso contrário, sua tarefa não reconhecerá a referência corretamente.
+As chaves do YAML (`researcher`, `research_task`) devem coincidir com os nomes dos métodos na classe `@CrewBase`. Veja [Crews](/pt-BR/concepts/crews) para o padrão completo com decoradores.
 
-#### Exemplos de Referências
+## Implantação
 
-<Tip>
-  Observe como usamos o mesmo nome para o agente no arquivo `agents.yaml`
-  (`email_summarizer`) e no método do arquivo `crew.py` (`email_summarizer`).
-</Tip>
+Envie seu Flow para o **[CrewAI AMP](https://app.crewai.com)** quando rodar localmente e o projeto estiver em um repositório **GitHub**. Na raiz do projeto:
 
-```yaml agents.yaml
-email_summarizer:
-  role: >
-    Email Summarizer
-  goal: >
-    Summarize emails into a concise and clear summary
-  backstory: >
-    You will create a 5 bullet point summary of the report
-  llm: provider/model-id # Add your choice of model here
+<CodeGroup>
+```bash Autenticar
+crewai login
 ```
 
-<Tip>
-  Observe como usamos o mesmo nome para a tarefa no arquivo `tasks.yaml`
-  (`email_summarizer_task`) e no método no arquivo `crew.py`
-  (`email_summarizer_task`).
-</Tip>
-
-```yaml tasks.yaml
-email_summarizer_task:
-  description: >
-    Summarize the email into a 5 bullet point summary
-  expected_output: >
-    A 5 bullet point summary of the email
-  agent: email_summarizer
-  context:
-    - reporting_task
-    - research_task
+```bash Criar implantação
+crewai deploy create
 ```
 
-## Fazendo o Deploy da Sua Tripulação
+```bash Status e logs
+crewai deploy status
+crewai deploy logs
+```
 
-A forma mais fácil de fazer deploy da sua tripulação em produção é através da [CrewAI AMP](http://app.crewai.com).
+```bash Enviar atualizações após mudanças no código
+crewai deploy push
+```
 
-Assista a este vídeo tutorial para uma demonstração detalhada de como fazer deploy da sua tripulação na [CrewAI AMP](http://app.crewai.com) usando a CLI.
+```bash Listar ou remover implantações
+crewai deploy list
+crewai deploy remove <deployment_id>
+```
+</CodeGroup>
 
-<iframe
-  className="w-full aspect-video rounded-xl"
-  src="https://www.youtube.com/embed/3EqSV-CYDZA"
-  title="CrewAI Deployment Guide"
-  frameBorder="0"
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowFullScreen
-></iframe>
+<Tip>
+  A primeira implantação costuma levar **cerca de 1 minuto**. Pré-requisitos completos e fluxo na interface web estão em [Implantar no AMP](/pt-BR/enterprise/guides/deploy-to-amp).
+</Tip>
 
 <CardGroup cols={2}>
-  <Card title="Deploy no Enterprise" icon="rocket" href="http://app.crewai.com">
-    Comece com o CrewAI AMP e faça o deploy da sua tripulação em ambiente de
-    produção com apenas alguns cliques.
+  <Card title="Guia de implantação" icon="book" href="/pt-BR/enterprise/guides/deploy-to-amp">
+    AMP passo a passo (CLI e painel).
   </Card>
   <Card
-    title="Junte-se à Comunidade"
+    title="Comunidade"
     icon="comments"
     href="https://community.crewai.com"
   >
-    Participe da nossa comunidade open source para discutir ideias, compartilhar
-    seus projetos e conectar-se com outros desenvolvedores CrewAI.
+    Troque ideias, compartilhe projetos e conecte-se com outros desenvolvedores CrewAI.
   </Card>
 </CardGroup>

docs/pt-BR/skills.mdx

@@ -0,0 +1,50 @@
+---
+title: Skills
+description: Instale crewaiinc/skills pelo registro oficial em skills.sh—Flows, Crews e agentes alinhados à documentação para Claude Code, Cursor, Codex e outros.
+icon: wand-magic-sparkles
+mode: "wide"
+---
+
+# Skills
+
+**Dê ao seu agente de código o contexto do CrewAI em um comando.**
+
+As **Skills** do CrewAI são publicadas em **[skills.sh/crewaiinc/skills](https://skills.sh/crewaiinc/skills)**—o registro oficial de `crewaiinc/skills`, com cada skill (por exemplo **design-agent**, **getting-started**, **design-task** e **ask-docs**), estatísticas de instalação e auditorias. Ensinam agentes de código—como Claude Code, Cursor e Codex—a estruturar Flows, configurar Crews, usar ferramentas e seguir os padrões do CrewAI. Execute o comando abaixo (ou cole no seu agente).
+
+```shell Terminal
+npx skills add crewaiinc/skills
+```
+
+Isso adiciona o pacote de skills ao fluxo do seu agente para aplicar convenções do CrewAI sem precisar reexplicar o framework a cada sessão. Código-fonte e issues ficam no [GitHub](https://github.com/crewAIInc/skills).
+
+## O que seu agente ganha
+
+- **Flows** — apps com estado, passos e kickoffs de crew no estilo CrewAI
+- **Crews e agentes** — padrões YAML-first, papéis, tarefas e delegação
+- **Ferramentas e integrações** — conectar agentes a busca, APIs e ferramentas comuns
+- **Layout de projeto** — alinhar com scaffolds da CLI e convenções do repositório
+- **Padrões atualizados** — skills acompanham a documentação e as práticas recomendadas
+
+## Saiba mais neste site
+
+<CardGroup cols={2}>
+  <Card title="Ferramentas de codificação e AGENTS.md" icon="terminal" href="/pt-BR/guides/coding-tools/agents-md">
+    Como usar `AGENTS.md` e fluxos de agente de código com o CrewAI.
+  </Card>
+  <Card title="Início rápido" icon="rocket" href="/pt-BR/quickstart">
+    Construa seu primeiro Flow e crew ponta a ponta.
+  </Card>
+  <Card title="Instalação" icon="download" href="/pt-BR/installation">
+    Instale a CLI e o pacote Python do CrewAI.
+  </Card>
+  <Card title="Registro de skills (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Listagem oficial de `crewaiinc/skills`—skills, instalações e auditorias.
+  </Card>
+  <Card title="Código no GitHub" icon="code-branch" href="https://github.com/crewAIInc/skills">
+    Fonte, atualizações e issues do pacote de skills.
+  </Card>
+</CardGroup>
+
+### Vídeo: CrewAI com coding agent skills
+
+<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{ width: "100%", height: "400px" }} />

docs/pt-BR/tools/ai-ml/codeinterpretertool.mdx

@@ -7,6 +7,10 @@ mode: "wide"
 
 # `CodeInterpreterTool`
 
+<Warning>
+  **Depreciado:** O `CodeInterpreterTool` foi removido do `crewai-tools`. Os parâmetros `allow_code_execution` e `code_execution_mode` do `Agent` também estão depreciados. Use um serviço de sandbox dedicado — [E2B](https://e2b.dev) ou [Modal](https://modal.com) — para execução de código segura e isolada.
+</Warning>
+
 ## Descrição
 
 O `CodeInterpreterTool` permite que agentes CrewAI executem códigos Python 3 gerados autonomamente. Essa funcionalidade é particularmente valiosa, pois permite que os agentes criem códigos, os executem, obtenham os resultados e usem essas informações para orientar decisões e ações subsequentes.

docs/pt-BR/tools/file-document/csvsearchtool.mdx

@@ -75,4 +75,20 @@ tool = CSVSearchTool(
         ),
     )
 )
+
+## Segurança
+
+### Validação de Caminhos
+
+Os caminhos de arquivo fornecidos a esta ferramenta são validados em relação ao diretório de trabalho atual. Caminhos que resolvem fora do diretório de trabalho são rejeitados com um `ValueError`.
+
+Para permitir caminhos fora do diretório de trabalho (por exemplo, em testes ou pipelines confiáveis), defina a variável de ambiente:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### Validação de URLs
+
+Entradas de URL também são validadas: URIs `file://` e requisições direcionadas a faixas de IP privadas ou reservadas são bloqueadas para prevenir ataques de falsificação de requisições do lado do servidor (SSRF).
 ```

docs/pt-BR/tools/file-document/directorysearchtool.mdx

@@ -67,4 +67,16 @@ tool = DirectorySearchTool(
         },
     }
 )
+```
+
+## Segurança
+
+### Validação de Caminhos
+
+Os caminhos de diretório fornecidos a esta ferramenta são validados em relação ao diretório de trabalho atual. Caminhos que resolvem fora do diretório de trabalho são rejeitados com um `ValueError`.
+
+Para permitir caminhos fora do diretório de trabalho (por exemplo, em testes ou pipelines confiáveis), defina a variável de ambiente:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
 ```

docs/pt-BR/tools/file-document/jsonsearchtool.mdx

@@ -73,4 +73,20 @@ tool = JSONSearchTool(
         },
     }
 )
+
+## Segurança
+
+### Validação de Caminhos
+
+Os caminhos de arquivo fornecidos a esta ferramenta são validados em relação ao diretório de trabalho atual. Caminhos que resolvem fora do diretório de trabalho são rejeitados com um `ValueError`.
+
+Para permitir caminhos fora do diretório de trabalho (por exemplo, em testes ou pipelines confiáveis), defina a variável de ambiente:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### Validação de URLs
+
+Entradas de URL também são validadas: URIs `file://` e requisições direcionadas a faixas de IP privadas ou reservadas são bloqueadas para prevenir ataques de falsificação de requisições do lado do servidor (SSRF).
 ```

docs/pt-BR/tools/file-document/pdfsearchtool.mdx

@@ -101,4 +101,20 @@ tool = PDFSearchTool(
         },
     }
 )
-```
+```
+
+## Segurança
+
+### Validação de Caminhos
+
+Os caminhos de arquivo fornecidos a esta ferramenta são validados em relação ao diretório de trabalho atual. Caminhos que resolvem fora do diretório de trabalho são rejeitados com um `ValueError`.
+
+Para permitir caminhos fora do diretório de trabalho (por exemplo, em testes ou pipelines confiáveis), defina a variável de ambiente:
+
+```shell
+CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true
+```
+
+### Validação de URLs
+
+Entradas de URL também são validadas: URIs `file://` e requisições direcionadas a faixas de IP privadas ou reservadas são bloqueadas para prevenir ataques de falsificação de requisições do lado do servidor (SSRF).

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__ = "1.13.0a3"
+__version__ = "1.14.2a1"

lib/crewai-tools/pyproject.toml

@@ -10,8 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
     "requests~=2.32.5",
-    "docker~=7.1.0",
-    "crewai==1.13.0a3",
+    "crewai==1.14.2a1",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -142,6 +141,9 @@ contextual = [
 ]
 
 
+[tool.uv]
+exclude-newer = "3 days"
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

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

@@ -35,9 +35,6 @@ from crewai_tools.tools.browserbase_load_tool.browserbase_load_tool import (
 from crewai_tools.tools.code_docs_search_tool.code_docs_search_tool import (
     CodeDocsSearchTool,
 )
-from crewai_tools.tools.code_interpreter_tool.code_interpreter_tool import (
-    CodeInterpreterTool,
-)
 from crewai_tools.tools.composio_tool.composio_tool import ComposioTool
 from crewai_tools.tools.contextualai_create_agent_tool.contextual_create_agent_tool import (
     ContextualAICreateAgentTool,
@@ -225,7 +222,6 @@ __all__ = [
     "BrowserbaseLoadTool",
     "CSVSearchTool",
     "CodeDocsSearchTool",
-    "CodeInterpreterTool",
     "ComposioTool",
     "ContextualAICreateAgentTool",
     "ContextualAIParseTool",
@@ -309,4 +305,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.13.0a3"
+__version__ = "1.14.2a1"

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

@@ -154,21 +154,19 @@ class ToolSpecExtractor:
 
         return default_value
 
+    # Dynamically computed from BaseTool so that any future fields or
+    # computed_fields added to BaseTool are automatically excluded from
+    # the generated spec — no hardcoded denylist to maintain.
+    # ``package_dependencies`` is not a BaseTool field but is extracted
+    # into its own top-level key, so it's also excluded from init_params.
+    _BASE_TOOL_FIELDS: set[str] = (
+        set(BaseTool.model_fields)
+        | set(BaseTool.model_computed_fields)
+        | {"package_dependencies"}
+    )
+
     @staticmethod
     def _extract_init_params(tool_class: type[BaseTool]) -> dict[str, Any]:
-        ignored_init_params = [
-            "name",
-            "description",
-            "env_vars",
-            "args_schema",
-            "description_updated",
-            "cache_function",
-            "result_as_answer",
-            "max_usage_count",
-            "current_usage_count",
-            "package_dependencies",
-        ]
-
         json_schema = tool_class.model_json_schema(
             schema_generator=SchemaGenerator, mode="serialization"
         )
@@ -176,8 +174,14 @@ class ToolSpecExtractor:
         json_schema["properties"] = {
             key: value
             for key, value in json_schema["properties"].items()
-            if key not in ignored_init_params
+            if key not in ToolSpecExtractor._BASE_TOOL_FIELDS
         }
+        if "required" in json_schema:
+            json_schema["required"] = [
+                key
+                for key in json_schema["required"]
+                if key not in ToolSpecExtractor._BASE_TOOL_FIELDS
+            ]
         return json_schema
 
     def save_to_json(self, output_path: str) -> None:

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

@@ -109,7 +109,7 @@ class DataTypes:
         if isinstance(content, str):
             try:
                 url = urlparse(content)
-                is_url = bool(url.scheme and url.netloc) or url.scheme == "file"
+                is_url = bool(url.scheme in ("http", "https") and url.netloc)
             except Exception:  # noqa: S110
                 pass
 

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

@@ -0,0 +1,205 @@
+"""Path and URL validation utilities for crewai-tools.
+
+Provides validation for file paths and URLs to prevent unauthorized
+file access and server-side request forgery (SSRF) when tools accept
+user-controlled or LLM-controlled inputs at runtime.
+
+Set CREWAI_TOOLS_ALLOW_UNSAFE_PATHS=true to bypass validation (not
+recommended for production).
+"""
+
+from __future__ import annotations
+
+import ipaddress
+import logging
+import os
+import socket
+from urllib.parse import urlparse
+
+
+logger = logging.getLogger(__name__)
+
+_UNSAFE_PATHS_ENV = "CREWAI_TOOLS_ALLOW_UNSAFE_PATHS"
+
+
+def _is_escape_hatch_enabled() -> bool:
+    """Check if the unsafe paths escape hatch is enabled."""
+    return os.environ.get(_UNSAFE_PATHS_ENV, "").lower() in ("true", "1", "yes")
+
+
+# ---------------------------------------------------------------------------
+# File path validation
+# ---------------------------------------------------------------------------
+
+
+def validate_file_path(path: str, base_dir: str | None = None) -> str:
+    """Validate that a file path is safe to read.
+
+    Resolves symlinks and ``..`` components, then checks that the resolved
+    path falls within *base_dir* (defaults to the current working directory).
+
+    Args:
+        path: The file path to validate.
+        base_dir: Allowed root directory. Defaults to ``os.getcwd()``.
+
+    Returns:
+        The resolved, validated absolute path.
+
+    Raises:
+        ValueError: If the path escapes the allowed directory.
+    """
+    if _is_escape_hatch_enabled():
+        logger.warning(
+            "%s is enabled — skipping file path validation for: %s",
+            _UNSAFE_PATHS_ENV,
+            path,
+        )
+        return os.path.realpath(path)
+
+    if base_dir is None:
+        base_dir = os.getcwd()
+
+    resolved_base = os.path.realpath(base_dir)
+    resolved_path = os.path.realpath(
+        os.path.join(resolved_base, path) if not os.path.isabs(path) else path
+    )
+
+    # Ensure the resolved path is within the base directory.
+    # When resolved_base already ends with a separator (e.g. the filesystem
+    # root "/"), appending os.sep would double it ("//"), so use the base
+    # as-is in that case.
+    prefix = resolved_base if resolved_base.endswith(os.sep) else resolved_base + os.sep
+    if not resolved_path.startswith(prefix) and resolved_path != resolved_base:
+        raise ValueError(
+            f"Path '{path}' resolves to '{resolved_path}' which is outside "
+            f"the allowed directory '{resolved_base}'. "
+            f"Set {_UNSAFE_PATHS_ENV}=true to bypass this check."
+        )
+
+    return resolved_path
+
+
+def validate_directory_path(path: str, base_dir: str | None = None) -> str:
+    """Validate that a directory path is safe to read.
+
+    Same as :func:`validate_file_path` but also checks that the path
+    is an existing directory.
+
+    Args:
+        path: The directory path to validate.
+        base_dir: Allowed root directory. Defaults to ``os.getcwd()``.
+
+    Returns:
+        The resolved, validated absolute path.
+
+    Raises:
+        ValueError: If the path escapes the allowed directory or is not a directory.
+    """
+    validated = validate_file_path(path, base_dir)
+    if not os.path.isdir(validated):
+        raise ValueError(f"Path '{validated}' is not a directory.")
+    return validated
+
+
+# ---------------------------------------------------------------------------
+# URL validation
+# ---------------------------------------------------------------------------
+
+# Private and reserved IP ranges that should not be accessed
+_BLOCKED_IPV4_NETWORKS = [
+    ipaddress.ip_network("10.0.0.0/8"),
+    ipaddress.ip_network("172.16.0.0/12"),
+    ipaddress.ip_network("192.168.0.0/16"),
+    ipaddress.ip_network("127.0.0.0/8"),
+    ipaddress.ip_network("169.254.0.0/16"),  # Link-local / cloud metadata
+    ipaddress.ip_network("0.0.0.0/32"),
+]
+
+_BLOCKED_IPV6_NETWORKS = [
+    ipaddress.ip_network("::1/128"),
+    ipaddress.ip_network("::/128"),
+    ipaddress.ip_network("fc00::/7"),  # Unique local addresses
+    ipaddress.ip_network("fe80::/10"),  # Link-local IPv6
+]
+
+
+def _is_private_or_reserved(ip_str: str) -> bool:
+    """Check if an IP address is private, reserved, or otherwise unsafe."""
+    try:
+        addr = ipaddress.ip_address(ip_str)
+        # Unwrap IPv4-mapped IPv6 addresses (e.g., ::ffff:127.0.0.1) to IPv4
+        # so they are only checked against IPv4 networks (avoids TypeError when
+        # an IPv4Address is compared against an IPv6Network).
+        if isinstance(addr, ipaddress.IPv6Address) and addr.ipv4_mapped:
+            addr = addr.ipv4_mapped
+        networks = (
+            _BLOCKED_IPV4_NETWORKS
+            if isinstance(addr, ipaddress.IPv4Address)
+            else _BLOCKED_IPV6_NETWORKS
+        )
+        return any(addr in network for network in networks)
+    except ValueError:
+        return True  # If we can't parse, block it
+
+
+def validate_url(url: str) -> str:
+    """Validate that a URL is safe to fetch.
+
+    Blocks ``file://`` scheme entirely. For ``http``/``https``, resolves
+    DNS and checks that the target IP is not private or reserved (prevents
+    SSRF to internal services and cloud metadata endpoints).
+
+    Args:
+        url: The URL to validate.
+
+    Returns:
+        The validated URL string.
+
+    Raises:
+        ValueError: If the URL uses a blocked scheme or resolves to a
+            private/reserved IP address.
+    """
+    if _is_escape_hatch_enabled():
+        logger.warning(
+            "%s is enabled — skipping URL validation for: %s",
+            _UNSAFE_PATHS_ENV,
+            url,
+        )
+        return url
+
+    parsed = urlparse(url)
+
+    # Block file:// scheme
+    if parsed.scheme == "file":
+        raise ValueError(
+            f"file:// URLs are not allowed: '{url}'. "
+            f"Use a file path instead, or set {_UNSAFE_PATHS_ENV}=true to bypass."
+        )
+
+    # Only allow http and https
+    if parsed.scheme not in ("http", "https"):
+        raise ValueError(
+            f"URL scheme '{parsed.scheme}' is not allowed. Only http and https are supported."
+        )
+
+    if not parsed.hostname:
+        raise ValueError(f"URL has no hostname: '{url}'")
+
+    # Resolve DNS and check IPs
+    try:
+        addrinfos = socket.getaddrinfo(
+            parsed.hostname, parsed.port or (443 if parsed.scheme == "https" else 80)
+        )
+    except socket.gaierror as exc:
+        raise ValueError(f"Could not resolve hostname: '{parsed.hostname}'") from exc
+
+    for _family, _, _, _, sockaddr in addrinfos:
+        ip_str = str(sockaddr[0])
+        if _is_private_or_reserved(ip_str):
+            raise ValueError(
+                f"URL '{url}' resolves to private/reserved IP {ip_str}. "
+                f"Access to internal networks is not allowed. "
+                f"Set {_UNSAFE_PATHS_ENV}=true to bypass."
+            )
+
+    return url

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

@@ -24,9 +24,6 @@ from crewai_tools.tools.browserbase_load_tool.browserbase_load_tool import (
 from crewai_tools.tools.code_docs_search_tool.code_docs_search_tool import (
     CodeDocsSearchTool,
 )
-from crewai_tools.tools.code_interpreter_tool.code_interpreter_tool import (
-    CodeInterpreterTool,
-)
 from crewai_tools.tools.composio_tool.composio_tool import ComposioTool
 from crewai_tools.tools.contextualai_create_agent_tool.contextual_create_agent_tool import (
     ContextualAICreateAgentTool,
@@ -210,7 +207,6 @@ __all__ = [
     "BrowserbaseLoadTool",
     "CSVSearchTool",
     "CodeDocsSearchTool",
-    "CodeInterpreterTool",
     "ComposioTool",
     "ContextualAICreateAgentTool",
     "ContextualAIParseTool",

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

@@ -7,6 +7,8 @@ from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, Field
 import requests
 
+from crewai_tools.security.safe_path import validate_url
+
 
 class BrightDataConfig(BaseModel):
     API_URL: str = "https://api.brightdata.com/request"
@@ -134,6 +136,7 @@ class BrightDataWebUnlockerTool(BaseTool):
             "Content-Type": "application/json",
         }
 
+        validate_url(url)
         try:
             response = requests.post(
                 self.base_url, json=payload, headers=headers, timeout=30

lib/crewai-tools/src/crewai_tools/tools/code_interpreter_tool/Dockerfile

@@ -1,6 +0,0 @@
-FROM python:3.12-alpine
-
-RUN pip install requests beautifulsoup4 
-
-# Set the working directory
-WORKDIR /workspace

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

@@ -1,95 +0,0 @@
-# CodeInterpreterTool
-
-## Description
-This tool is used to give the Agent the ability to run code (Python3) from the code generated by the Agent itself. The code is executed in a Docker container for secure isolation.
-
-It is incredibly useful since it allows the Agent to generate code, run it in an isolated environment, get the result and use it to make decisions.
-
-## ⚠️ Security Requirements
-
-**Docker is REQUIRED** for safe code execution. The tool will refuse to execute code without Docker to prevent security vulnerabilities.
-
-### Why Docker is Required
-
-Previous versions included a "restricted sandbox" fallback when Docker was unavailable. This has been **removed** due to critical security vulnerabilities:
-
-- The Python-based sandbox could be escaped via object introspection
-- Attackers could recover the original `__import__` function and access any module
-- This allowed arbitrary command execution on the host system
-
-**Docker provides real process isolation** and is the only secure way to execute untrusted code.
-
-## Requirements
-
-- **Docker (REQUIRED)** - Install from [docker.com](https://docs.docker.com/get-docker/)
-
-## Installation
-Install the crewai_tools package
-```shell
-pip install 'crewai[tools]'
-```
-
-## Example
-
-Remember that when using this tool, the code must be generated by the Agent itself. The code must be Python3 code. It will take some time the first time to run because it needs to build the Docker image.
-
-### Basic Usage (Docker Container - Recommended)
-
-```python
-from crewai_tools import CodeInterpreterTool
-
-Agent(
-    ...
-    tools=[CodeInterpreterTool()],
-)
-```
-
-### Custom Dockerfile
-
-If you need to pass your own Dockerfile:
-
-```python
-from crewai_tools import CodeInterpreterTool
-
-Agent(
-    ...
-    tools=[CodeInterpreterTool(user_dockerfile_path="<Dockerfile_path>")],
-)
-```
-
-### Manual Docker Host Configuration
-
-If it is difficult to connect to the Docker daemon automatically (especially for macOS users), you can set up the Docker host manually:
-
-```python 
-from crewai_tools import CodeInterpreterTool
-
-Agent(
-    ...
-    tools=[CodeInterpreterTool(
-        user_docker_base_url="<Docker Host Base Url>",
-        user_dockerfile_path="<Dockerfile_path>"
-    )],
-)
-```
-
-### Unsafe Mode (NOT RECOMMENDED)
-
-If you absolutely cannot use Docker and **fully trust the code source**, you can use unsafe mode:
-
-```python
-from crewai_tools import CodeInterpreterTool
-
-# WARNING: Only use with fully trusted code!
-Agent(
-    ...
-    tools=[CodeInterpreterTool(unsafe_mode=True)],
-)
-```
-
-**⚠️ SECURITY WARNING:** `unsafe_mode=True` executes code directly on the host without any isolation. Only use this if:
-- You completely trust the code being executed
-- You understand the security risks
-- You cannot install Docker in your environment
-
-For production use, **always use Docker** (the default mode).

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

@@ -1,424 +0,0 @@
-"""Code Interpreter Tool for executing Python code in isolated environments.
-
-This module provides a tool for executing Python code either in a Docker container for
-safe isolation or directly in a restricted sandbox. It includes mechanisms for blocking
-potentially unsafe operations and importing restricted modules.
-"""
-
-import importlib.util
-import os
-import subprocess
-import sys
-from types import ModuleType
-from typing import Any, ClassVar, TypedDict
-
-from crewai.tools import BaseTool
-from docker import (  # type: ignore[import-untyped]
-    DockerClient,
-    from_env as docker_from_env,
-)
-from docker.errors import ImageNotFound, NotFound  # type: ignore[import-untyped]
-from pydantic import BaseModel, Field
-from typing_extensions import Unpack
-
-from crewai_tools.printer import Printer
-
-
-class RunKwargs(TypedDict, total=False):
-    """Keyword arguments for the _run method."""
-
-    code: str
-    libraries_used: list[str]
-
-
-class CodeInterpreterSchema(BaseModel):
-    """Schema for defining inputs to the CodeInterpreterTool.
-
-    This schema defines the required parameters for code execution,
-    including the code to run and any libraries that need to be installed.
-    """
-
-    code: str = Field(
-        ...,
-        description="Python3 code used to be interpreted in the Docker container. ALWAYS PRINT the final result and the output of the code",
-    )
-
-    libraries_used: list[str] = Field(
-        ...,
-        description="List of libraries used in the code with proper installing names separated by commas. Example: numpy,pandas,beautifulsoup4",
-    )
-
-
-class SandboxPython:
-    """INSECURE: A restricted Python execution environment with known vulnerabilities.
-
-    WARNING: This class does NOT provide real security isolation and is vulnerable to
-    sandbox escape attacks via Python object introspection. Attackers can recover the
-    original __import__ function and bypass all restrictions.
-
-    DO NOT USE for untrusted code execution. Use Docker containers instead.
-
-    This class attempts to restrict access to dangerous modules and built-in functions
-    but provides no real security boundary against a motivated attacker.
-    """
-
-    BLOCKED_MODULES: ClassVar[set[str]] = {
-        "os",
-        "sys",
-        "subprocess",
-        "shutil",
-        "importlib",
-        "inspect",
-        "tempfile",
-        "sysconfig",
-        "builtins",
-    }
-
-    UNSAFE_BUILTINS: ClassVar[set[str]] = {
-        "exec",
-        "eval",
-        "open",
-        "compile",
-        "input",
-        "globals",
-        "locals",
-        "vars",
-        "help",
-        "dir",
-    }
-
-    @staticmethod
-    def restricted_import(
-        name: str,
-        custom_globals: dict[str, Any] | None = None,
-        custom_locals: dict[str, Any] | None = None,
-        fromlist: list[str] | None = None,
-        level: int = 0,
-    ) -> ModuleType:
-        """A restricted import function that blocks importing of unsafe modules.
-
-        Args:
-            name: The name of the module to import.
-            custom_globals: Global namespace to use.
-            custom_locals: Local namespace to use.
-            fromlist: List of items to import from the module.
-            level: The level value passed to __import__.
-
-        Returns:
-            The imported module if allowed.
-
-        Raises:
-            ImportError: If the module is in the blocked modules list.
-        """
-        if name in SandboxPython.BLOCKED_MODULES:
-            raise ImportError(f"Importing '{name}' is not allowed.")
-        return __import__(name, custom_globals, custom_locals, fromlist or (), level)
-
-    @staticmethod
-    def safe_builtins() -> dict[str, Any]:
-        """Creates a dictionary of built-in functions with unsafe ones removed.
-
-        Returns:
-            A dictionary of safe built-in functions and objects.
-        """
-        import builtins
-
-        safe_builtins = {
-            k: v
-            for k, v in builtins.__dict__.items()
-            if k not in SandboxPython.UNSAFE_BUILTINS
-        }
-        safe_builtins["__import__"] = SandboxPython.restricted_import
-        return safe_builtins
-
-    @staticmethod
-    def exec(code: str, locals_: dict[str, Any]) -> None:
-        """Executes Python code in a restricted environment.
-
-        Args:
-            code: The Python code to execute as a string.
-            locals_: A dictionary that will be used for local variable storage.
-        """
-        exec(code, {"__builtins__": SandboxPython.safe_builtins()}, locals_)  # noqa: S102
-
-
-class CodeInterpreterTool(BaseTool):
-    """A tool for executing Python code in isolated environments.
-
-    This tool provides functionality to run Python code either in a Docker container
-    for safe isolation or directly in a restricted sandbox. It can handle installing
-    Python packages and executing arbitrary Python code.
-    """
-
-    name: str = "Code Interpreter"
-    description: str = "Interprets Python3 code strings with a final print statement."
-    args_schema: type[BaseModel] = CodeInterpreterSchema
-    default_image_tag: str = "code-interpreter:latest"
-    code: str | None = None
-    user_dockerfile_path: str | None = None
-    user_docker_base_url: str | None = None
-    unsafe_mode: bool = False
-
-    @staticmethod
-    def _get_installed_package_path() -> str:
-        """Gets the installation path of the crewai_tools package.
-
-        Returns:
-            The directory path where the package is installed.
-
-        Raises:
-            RuntimeError: If the package cannot be found.
-        """
-        spec = importlib.util.find_spec("crewai_tools")
-        if spec is None or spec.origin is None:
-            raise RuntimeError("Cannot find crewai_tools package installation path")
-        return os.path.dirname(spec.origin)
-
-    def _verify_docker_image(self) -> None:
-        """Verifies if the Docker image is available or builds it if necessary.
-
-        Checks if the required Docker image exists. If not, builds it using either a
-        user-provided Dockerfile or the default one included with the package.
-
-        Raises:
-            FileNotFoundError: If the Dockerfile cannot be found.
-        """
-        client = (
-            docker_from_env()
-            if self.user_docker_base_url is None
-            else DockerClient(base_url=self.user_docker_base_url)
-        )
-
-        try:
-            client.images.get(self.default_image_tag)
-
-        except ImageNotFound:
-            if self.user_dockerfile_path and os.path.exists(self.user_dockerfile_path):
-                dockerfile_path = self.user_dockerfile_path
-            else:
-                package_path = self._get_installed_package_path()
-                dockerfile_path = os.path.join(
-                    package_path, "tools/code_interpreter_tool"
-                )
-                if not os.path.exists(dockerfile_path):
-                    raise FileNotFoundError(
-                        f"Dockerfile not found in {dockerfile_path}"
-                    ) from None
-
-            client.images.build(
-                path=dockerfile_path,
-                tag=self.default_image_tag,
-                rm=True,
-            )
-
-    def _run(self, **kwargs: Unpack[RunKwargs]) -> str:
-        """Runs the code interpreter tool with the provided arguments.
-
-        Args:
-            **kwargs: Keyword arguments that should include 'code' and 'libraries_used'.
-
-        Returns:
-            The output of the executed code as a string.
-        """
-        code: str | None = kwargs.get("code", self.code)
-        libraries_used: list[str] = kwargs.get("libraries_used", [])
-
-        if not code:
-            return "No code provided to execute."
-
-        if self.unsafe_mode:
-            return self.run_code_unsafe(code, libraries_used)
-        return self.run_code_safety(code, libraries_used)
-
-    @staticmethod
-    def _install_libraries(container: Any, libraries: list[str]) -> None:
-        """Installs required Python libraries in the Docker container.
-
-        Args:
-            container: The Docker container where libraries will be installed.
-            libraries: A list of library names to install using pip.
-        """
-        for library in libraries:
-            container.exec_run(["pip", "install", library])
-
-    def _init_docker_container(self) -> Any:
-        """Initializes and returns a Docker container for code execution.
-
-        Stops and removes any existing container with the same name before creating
-        a new one. Maps the current working directory to /workspace in the container.
-
-        Returns:
-            A Docker container object ready for code execution.
-        """
-        container_name = "code-interpreter"
-        client = docker_from_env()
-        current_path = os.getcwd()
-
-        # Check if the container is already running
-        try:
-            existing_container = client.containers.get(container_name)
-            existing_container.stop()
-            existing_container.remove()
-        except NotFound:
-            pass  # Container does not exist, no need to remove
-
-        return client.containers.run(
-            self.default_image_tag,
-            detach=True,
-            tty=True,
-            working_dir="/workspace",
-            name=container_name,
-            volumes={current_path: {"bind": "/workspace", "mode": "rw"}},
-        )
-
-    @staticmethod
-    def _check_docker_available() -> bool:
-        """Checks if Docker is available and running on the system.
-
-        Attempts to run the 'docker info' command to verify Docker availability.
-        Prints appropriate messages if Docker is not installed or not running.
-
-        Returns:
-            True if Docker is available and running, False otherwise.
-        """
-
-        try:
-            subprocess.run(
-                ["docker", "info"],  # noqa: S607
-                check=True,
-                stdout=subprocess.DEVNULL,
-                stderr=subprocess.DEVNULL,
-                timeout=1,
-            )
-            return True
-        except (subprocess.CalledProcessError, subprocess.TimeoutExpired):
-            Printer.print(
-                "Docker is installed but not running or inaccessible.",
-                color="bold_purple",
-            )
-            return False
-        except FileNotFoundError:
-            Printer.print("Docker is not installed", color="bold_purple")
-            return False
-
-    def run_code_safety(self, code: str, libraries_used: list[str]) -> str:
-        """Runs code in the safest available environment.
-
-        Requires Docker to be available for secure code execution. Fails closed
-        if Docker is not available to prevent sandbox escape vulnerabilities.
-
-        Args:
-            code: The Python code to execute as a string.
-            libraries_used: A list of Python library names to install before execution.
-
-        Returns:
-            The output of the executed code as a string.
-
-        Raises:
-            RuntimeError: If Docker is not available, as the restricted sandbox
-                         is vulnerable to escape attacks and should not be used
-                         for untrusted code execution.
-        """
-        if self._check_docker_available():
-            return self.run_code_in_docker(code, libraries_used)
-
-        error_msg = (
-            "Docker is required for safe code execution but is not available. "
-            "The restricted sandbox fallback has been removed due to security vulnerabilities "
-            "that allow sandbox escape via Python object introspection. "
-            "Please install Docker (https://docs.docker.com/get-docker/) or use unsafe_mode=True "
-            "if you trust the code source and understand the security risks."
-        )
-        Printer.print(error_msg, color="bold_red")
-        raise RuntimeError(error_msg)
-
-    def run_code_in_docker(self, code: str, libraries_used: list[str]) -> str:
-        """Runs Python code in a Docker container for safe isolation.
-
-        Creates a Docker container, installs the required libraries, executes the code,
-        and then cleans up by stopping and removing the container.
-
-        Args:
-            code: The Python code to execute as a string.
-            libraries_used: A list of Python library names to install before execution.
-
-        Returns:
-            The output of the executed code as a string, or an error message if execution failed.
-        """
-        Printer.print("Running code in Docker environment", color="bold_blue")
-        self._verify_docker_image()
-        container = self._init_docker_container()
-        self._install_libraries(container, libraries_used)
-
-        exec_result: Any = container.exec_run(["python3", "-c", code])
-
-        container.stop()
-        container.remove()
-
-        if exec_result.exit_code != 0:
-            return f"Something went wrong while running the code: \n{exec_result.output.decode('utf-8')}"
-        return str(exec_result.output.decode("utf-8"))
-
-    @staticmethod
-    def run_code_in_restricted_sandbox(code: str) -> str:
-        """DEPRECATED AND INSECURE: Runs Python code in a restricted sandbox environment.
-
-        WARNING: This method is vulnerable to sandbox escape attacks via Python object
-        introspection and should NOT be used for untrusted code execution. It has been
-        deprecated and is only kept for backward compatibility with trusted code.
-
-        The "restricted" environment can be bypassed by attackers who can:
-        - Use object graph introspection to recover the original __import__ function
-        - Access any Python module including os, subprocess, sys, etc.
-        - Execute arbitrary commands on the host system
-
-        Use run_code_in_docker() for secure code execution, or run_code_unsafe()
-        if you explicitly acknowledge the security risks.
-
-        Args:
-            code: The Python code to execute as a string.
-
-        Returns:
-            The value of the 'result' variable from the executed code,
-            or an error message if execution failed.
-        """
-        Printer.print(
-            "WARNING: Running code in INSECURE restricted sandbox (vulnerable to escape attacks)",
-            color="bold_red",
-        )
-        exec_locals: dict[str, Any] = {}
-        try:
-            SandboxPython.exec(code=code, locals_=exec_locals)
-            return exec_locals.get("result", "No result variable found.")  # type: ignore[no-any-return]
-        except Exception as e:
-            return f"An error occurred: {e!s}"
-
-    @staticmethod
-    def run_code_unsafe(code: str, libraries_used: list[str]) -> str:
-        """Runs code directly on the host machine without any safety restrictions.
-
-        WARNING: This mode is unsafe and should only be used in trusted environments
-        with code from trusted sources.
-
-        Args:
-            code: The Python code to execute as a string.
-            libraries_used: A list of Python library names to install before execution.
-
-        Returns:
-            The value of the 'result' variable from the executed code,
-            or an error message if execution failed.
-        """
-        Printer.print("WARNING: Running code in unsafe mode", color="bold_magenta")
-        # Install libraries on the host machine
-        for library in libraries_used:
-            subprocess.run(  # noqa: S603
-                [sys.executable, "-m", "pip", "install", library], check=False
-            )
-
-        # Execute the code
-        try:
-            exec_locals: dict[str, Any] = {}
-            exec(code, {}, exec_locals)  # noqa: S102
-            return exec_locals.get("result", "No result variable found.")  # type: ignore[no-any-return]
-        except Exception as e:
-            return f"An error occurred: {e!s}"

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

@@ -3,6 +3,8 @@ from typing import Any
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_file_path
+
 
 class ContextualAICreateAgentSchema(BaseModel):
     """Schema for contextual create agent tool."""
@@ -47,6 +49,7 @@ class ContextualAICreateAgentTool(BaseTool):
         document_paths: list[str],
     ) -> str:
         """Create a complete RAG pipeline with documents."""
+        resolved_paths = [validate_file_path(doc_path) for doc_path in document_paths]
         try:
             import os
 
@@ -56,7 +59,7 @@ class ContextualAICreateAgentTool(BaseTool):
 
             # Upload documents
             document_ids = []
-            for doc_path in document_paths:
+            for doc_path in resolved_paths:
                 if not os.path.exists(doc_path):
                     raise FileNotFoundError(f"Document not found: {doc_path}")
 

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

@@ -1,6 +1,8 @@
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_file_path
+
 
 class ContextualAIParseSchema(BaseModel):
     """Schema for contextual parse tool."""
@@ -45,6 +47,7 @@ class ContextualAIParseTool(BaseTool):
         """Parse a document using Contextual AI's parser."""
         if output_types is None:
             output_types = ["markdown-per-page"]
+        file_path = validate_file_path(file_path)
         try:
             import json
             import os

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

@@ -4,6 +4,8 @@ from typing import Any
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_directory_path
+
 
 class FixedDirectoryReadToolSchema(BaseModel):
     """Input for DirectoryReadTool."""
@@ -39,6 +41,7 @@ class DirectoryReadTool(BaseTool):
         if directory is None:
             raise ValueError("Directory must be provided.")
 
+        directory = validate_directory_path(directory)
         if directory[-1] == "/":
             directory = directory[:-1]
         files_list = [

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

@@ -3,6 +3,7 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from crewai_tools.rag.data_types import DataType
+from crewai_tools.security.safe_path import validate_directory_path
 from crewai_tools.tools.rag.rag_tool import RagTool
 
 
@@ -37,6 +38,7 @@ class DirectorySearchTool(RagTool):
             self._generate_description()
 
     def add(self, directory: str) -> None:  # type: ignore[override]
+        directory = validate_directory_path(directory)
         super().add(directory, data_type=DataType.DIRECTORY)
 
     def _run(  # type: ignore[override]

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

@@ -3,6 +3,8 @@ from typing import Any
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_file_path
+
 
 class FileReadToolSchema(BaseModel):
     """Input for FileReadTool."""
@@ -76,6 +78,7 @@ class FileReadTool(BaseTool):
         if file_path is None:
             return "Error: No file path provided. Please provide a file path either in the constructor or as an argument."
 
+        file_path = validate_file_path(file_path)
         try:
             with open(file_path, "r") as file:
                 if start_line == 1 and line_count is None:

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

@@ -5,6 +5,8 @@ import zipfile
 from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_file_path
+
 
 class FileCompressorToolInput(BaseModel):
     """Input schema for FileCompressorTool."""
@@ -40,12 +42,15 @@ class FileCompressorTool(BaseTool):
         overwrite: bool = False,
         format: str = "zip",
     ) -> str:
+        input_path = validate_file_path(input_path)
         if not os.path.exists(input_path):
             return f"Input path '{input_path}' does not exist."
 
         if not output_path:
             output_path = self._generate_output_path(input_path, format)
 
+        output_path = validate_file_path(output_path)
+
         format_extension = {
             "zip": ".zip",
             "tar": ".tar",

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

@@ -5,6 +5,8 @@ from typing import Any
 from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
 
+from crewai_tools.security.safe_path import validate_url
+
 
 try:
     from firecrawl import FirecrawlApp  # type: ignore[import-untyped]
@@ -106,6 +108,7 @@ class FirecrawlCrawlWebsiteTool(BaseTool):
         if not self._firecrawl:
             raise RuntimeError("FirecrawlApp not properly initialized")
 
+        url = validate_url(url)
         return self._firecrawl.crawl(url=url, poll_interval=2, **self.config)
 
 

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

@@ -5,6 +5,8 @@ from typing import Any
 from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
 
+from crewai_tools.security.safe_path import validate_url
+
 
 try:
     from firecrawl import FirecrawlApp  # type: ignore[import-untyped]
@@ -106,6 +108,7 @@ class FirecrawlScrapeWebsiteTool(BaseTool):
         if not self._firecrawl:
             raise RuntimeError("FirecrawlApp not properly initialized")
 
+        url = validate_url(url)
         return self._firecrawl.scrape(url=url, **self.config)
 
 

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

@@ -4,6 +4,8 @@ from typing import Any, Literal
 from crewai.tools import BaseTool, EnvVar
 from pydantic import BaseModel, Field
 
+from crewai_tools.security.safe_path import validate_url
+
 
 class HyperbrowserLoadToolSchema(BaseModel):
     url: str = Field(description="Website URL")
@@ -119,6 +121,7 @@ class HyperbrowserLoadTool(BaseTool):
             ) from e
 
         params = self._prepare_params(params)
+        url = validate_url(url)
 
         if operation == "scrape":
             scrape_params = StartScrapeJobParams(url=url, **params)