docs: add file upload to kickoff guide, clarify flow state population, all languages

- Added new "File Uploads" section to kickoff-crew.mdx with multipart,
  JSON URL, and separate upload + kickoff examples
- Clarified that file-typed fields in flow state schema signal the
  Platform UI to render file dropzones
- Updated flows.mdx File Inputs section to show state population pattern
- Updated files.mdx With Flows section with state schema example
- Applied all changes to en, ar, ko, pt-BR translations

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.github/workflows/import-time.yml

@@ -0,0 +1,103 @@
+name: Import Time Guard
+
+on:
+  pull_request:
+    paths:
+      - "lib/crewai/src/**"
+      - "lib/crewai/pyproject.toml"
+      - "pyproject.toml"
+
+permissions:
+  contents: read
+
+jobs:
+  import-time:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.12"]
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - uses: astral-sh/setup-uv@v6
+        with:
+          version: "0.11.3"
+          enable-cache: true
+
+      - name: Install the project
+        run: uv sync --all-extras --no-dev
+        env:
+          UV_PYTHON: ${{ matrix.python-version }}
+
+      - name: Benchmark PR branch
+        id: pr
+        run: |
+          result=$(uv run python scripts/benchmark_import_time.py --runs 5 --json)
+          echo "result=$result" >> "$GITHUB_OUTPUT"
+          echo "pr_median=$(echo $result | python3 -c 'import sys,json; print(json.load(sys.stdin)["median_s"])')" >> "$GITHUB_OUTPUT"
+          echo "### PR Branch Import Time" >> "$GITHUB_STEP_SUMMARY"
+          echo "$result" | python3 -c "
+          import sys, json
+          d = json.load(sys.stdin)
+          print(f'- Median: {d[\"median_s\"]}s')
+          print(f'- Mean: {d[\"mean_s\"]}s ± {d[\"stdev_s\"]}s')
+          print(f'- Range: {d[\"min_s\"]}s – {d[\"max_s\"]}s')
+          " >> "$GITHUB_STEP_SUMMARY"
+        env:
+          UV_PYTHON: ${{ matrix.python-version }}
+
+      - name: Checkout base branch
+        run: git checkout ${{ github.event.pull_request.base.sha }}
+
+      - name: Install base branch
+        run: uv sync --all-extras --no-dev
+        env:
+          UV_PYTHON: ${{ matrix.python-version }}
+
+      - name: Benchmark base branch
+        id: base
+        run: |
+          result=$(uv run python scripts/benchmark_import_time.py --runs 5 --json 2>/dev/null || echo '{"median_s": 0}')
+          echo "result=$result" >> "$GITHUB_OUTPUT"
+          echo "base_median=$(echo $result | python3 -c 'import sys,json; print(json.load(sys.stdin)["median_s"])')" >> "$GITHUB_OUTPUT"
+          echo "### Base Branch Import Time" >> "$GITHUB_STEP_SUMMARY"
+          echo "$result" | python3 -c "
+          import sys, json
+          d = json.load(sys.stdin)
+          if d.get('median_s', 0) > 0:
+              print(f'- Median: {d[\"median_s\"]}s')
+          else:
+              print('- Benchmark script not present on base branch (skip comparison)')
+          " >> "$GITHUB_STEP_SUMMARY"
+        env:
+          UV_PYTHON: ${{ matrix.python-version }}
+
+      - name: Compare and gate
+        run: |
+          pr_median=${{ steps.pr.outputs.pr_median }}
+          base_median=${{ steps.base.outputs.base_median }}
+
+          python3 -c "
+          pr = float('$pr_median')
+          base = float('$base_median')
+
+          if base <= 0:
+              print('⏭️  No base benchmark available — skipping comparison.')
+              exit(0)
+
+          change_pct = ((pr - base) / base) * 100
+          print(f'Base: {base:.3f}s')
+          print(f'PR:   {pr:.3f}s')
+          print(f'Change: {change_pct:+.1f}%')
+          print()
+
+          if change_pct > 5:
+              print(f'❌ BLOCKED: Import time regressed by {change_pct:.1f}% (threshold: 5%)')
+              exit(1)
+          elif change_pct > 0:
+              print(f'⚠️  Slight regression ({change_pct:.1f}%) but within 5% threshold.')
+          else:
+              print(f'✅ Import time improved by {abs(change_pct):.1f}%')
+          "

.gitignore

@@ -30,3 +30,4 @@ chromadb-*.lock
 .crewai/memory
 blogs/*
 secrets/*
+UNKNOWN.egg-info/

README.md

@@ -83,6 +83,7 @@ intelligent automations.
 
 ## Table of contents
 
+- [Build with AI](#build-with-ai)
 - [Why CrewAI?](#why-crewai)
 - [Getting Started](#getting-started)
 - [Key Features](#key-features)
@@ -101,6 +102,32 @@ intelligent automations.
 - [Telemetry](#telemetry)
 - [License](#license)
 
+## Build with AI
+
+Using an AI coding agent? Teach it CrewAI best practices in one command:
+
+**Claude Code:**
+```shell
+/plugin marketplace add crewAIInc/skills
+/plugin install crewai-skills@crewai-plugins
+/reload-plugins
+```
+Four skills that activate automatically when you ask relevant CrewAI questions:
+
+| Skill | When it runs |
+|-------|--------------|
+| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
+| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
+| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
+| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
+
+**Cursor, Codex, Windsurf, and others ([skills.sh](https://skills.sh/crewaiinc/skills)):**
+```shell
+npx skills add crewaiinc/skills
+```
+
+This installs the official [CrewAI Skills](https://github.com/crewAIInc/skills) — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
+
 ## Why CrewAI?
 
 <div align="center" style="margin-bottom: 30px;">

docs/ar/changelog.mdx

@@ -4,6 +4,161 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="22 أبريل 2026">
+  ## v1.14.3a2
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة دعم لـ bedrock V4
+  - إضافة أدوات Daytona sandbox لوظائف محسّنة
+  - إضافة صفحة "البناء باستخدام الذكاء الاصطناعي" — مستندات أصلية للذكاء الاصطناعي لوكلاء البرمجة
+  - إضافة "البناء باستخدام الذكاء الاصطناعي" إلى التنقل في صفحة "البدء" وملفات الصفحات لجميع اللغات (en, ko, pt-BR, ar)
+
+  ### إصلاحات الأخطاء
+  - إصلاح انتشار أسماء @CrewBase الضمنية إلى أحداث الطاقم
+  - حل مشكلة تكرار تهيئة الدفعات في دمج بيانات التنفيذ الوصفية
+  - إصلاح تسلسل حقول مرجع فئة Task لعمليات التحقق من النقاط
+  - التعامل مع نتيجة BaseModel في حلقة إعادة المحاولة للحدود
+  - تحديث python-dotenv إلى الإصدار >=1.2.2 للامتثال الأمني
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.3a1
+  - تحديث الأوصاف وتطبيق الترجمات الفعلية
+
+  ## المساهمون
+
+  @MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
+
+</Update>
+
+<Update label="21 أبريل 2026">
+  ## v1.14.3a1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة دعم نقاط التحقق والفروع لوكلاء مستقلين
+
+  ### إصلاحات الأخطاء
+  - الحفاظ على thought_signature في استدعاءات أداة البث Gemini
+  - إصدار task_started عند استئناف الفرع وإعادة تصميم واجهة المستخدم النصية لنقاط التحقق
+  - تصحيح ترتيب التشغيل الجاف ومعالجة الفرع القديم الذي تم التحقق منه في إصدار أدوات التطوير
+  - استخدام تواريخ مستقبلية في اختبارات تقليم نقاط التحقق لمنع الفشل المعتمد على الوقت (#5543)
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.2
+
+  ## المساهمون
+
+  @alex-clawd, @greysonlalonde
+
+</Update>
+
+<Update label="17 أبريل 2026">
+  ## v1.14.2
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة أوامر استئناف النقاط التفتيش، والاختلاف، والتنظيف مع تحسين إمكانية الاكتشاف.
+  - إضافة معلمة `from_checkpoint` إلى `Agent.kickoff` والطرق ذات الصلة.
+  - إضافة أوامر إدارة القوالب لقوالب المشاريع.
+  - إضافة تلميحات استئناف إلى إصدار أدوات المطور عند الفشل.
+  - إضافة واجهة سطر الأوامر للتحقق من النشر وتعزيز سهولة استخدام تهيئة LLM.
+  - إضافة تقسيم النقاط التفتيشية مع تتبع النسب.
+  - إثراء تتبع رموز LLM مع رموز الاستدلال ورموز إنشاء التخزين المؤقت.
+
+  ### إصلاحات الأخطاء
+  - إصلاح المطالبة بشأن تعارضات الفروع القديمة في إصدار أدوات المطور.
+  - تصحيح الثغرات في `authlib` و `langchain-text-splitters` و `pypdf`.
+  - تحديد نطاق معالجات البث لمنع تلوث أجزاء التشغيل المتقاطعة.
+  - إرسال نقاط التفتيش عبر واجهات Flow في TUI.
+  - استخدام نمط البحث المتكرر لاكتشاف نقاط التفتيش بتنسيق JSON.
+  - التعامل مع مخططات JSON الدائرية في أداة حل MCP.
+  - الحفاظ على معلمات استدعاء أداة Bedrock من خلال إزالة القيمة الافتراضية الصحيحة.
+  - إصدار حدث flow_finished بعد استئناف HITL.
+  - إصلاح ثغرات متنوعة من خلال تحديث التبعيات، بما في ذلك `requests` و `cryptography` و `pytest`.
+  - إصلاح لإيقاف تمرير وضع صارم إلى واجهة برمجة التطبيقات Bedrock Converse.
+
+  ### الوثائق
+  - توثيق المعلمات المفقودة وإضافة قسم النقاط التفتيشية.
+  - تحديث سجل التغييرات والإصدار للإصدار v1.14.2 ومرشحي الإصدار السابقين.
+  - إضافة توثيق ميزة A2A الخاصة بالشركات وتحديث وثائق A2A المفتوحة المصدر.
+
+  ## المساهمون
+
+  @Yanhu007، @alex-clawd، @github-actions[bot]، @greysonlalonde، @iris-clawd، @lorenzejay، @lucasgomide
+
+</Update>
+
+<Update label="16 أبريل 2026">
+  ## v1.14.2rc1
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2rc1)
+
+  ## ما الذي تغير
+
+  ### إصلاحات الأخطاء
+  - إصلاح معالجة مخططات JSON الدائرية في أداة MCP
+  - إصلاح ثغرة أمنية من خلال تحديث python-multipart إلى 0.0.26
+  - إصلاح ثغرة أمنية من خلال تحديث pypdf إلى 6.10.1
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.2a5
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="15 أبريل 2026">
+  ## v1.14.2a5
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a5)
+
+  ## ما الذي تغير
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.2a4
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="15 أبريل 2026">
+  ## v1.14.2a4
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة تلميحات استئناف إلى إصدار أدوات المطورين عند الفشل
+
+  ### إصلاحات الأخطاء
+  - إصلاح توجيه وضع الصرامة إلى واجهة برمجة تطبيقات Bedrock Converse
+  - إصلاح إصدار pytest إلى 9.0.3 لثغرة الأمان GHSA-6w46-j5rx-g56g
+  - رفع الحد الأدنى لـ OpenAI إلى >=2.0.0
+
+  ### الوثائق
+  - تحديث سجل التغييرات والإصدار لـ v1.14.2a3
+
+  ## المساهمون
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="13 أبريل 2026">
   ## v1.14.2a3
 

docs/ar/concepts/files.mdx

@@ -117,23 +117,35 @@ task = Task(
 
 ### مع التدفقات
 
-مرر الملفات إلى التدفقات، والتي تنتقل تلقائيًا إلى الأطقم:
+تعمل الحقول من نوع الملف (`File`، `ImageFile`، `PDFFile`) في مخطط حالة التدفق كإشارة لواجهة المنصة. عند النشر، تُعرض هذه الحقول كمناطق سحب وإفلات لرفع الملفات. يمكن أيضًا تمرير الملفات عبر `input_files` في API.
 
 ```python
 from crewai.flow.flow import Flow, start
-from crewai_files import ImageFile
+from crewai_files import File, ImageFile
+from pydantic import BaseModel
 
-class AnalysisFlow(Flow):
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform UI
+    cover_image: ImageFile   # Image-specific dropzone
+    title: str = ""
+
+class AnalysisFlow(Flow[MyState]):
     @start()
     def analyze(self):
+        # Files are automatically populated in state
+        content = self.state.document.read()
         return self.analysis_crew.kickoff()
 
 flow = AnalysisFlow()
 result = flow.kickoff(
-    input_files={"image": ImageFile(source="data.png")}
+    input_files={"document": File(source="report.pdf")}
 )
 ```
 
+<Note type="info" title="تكامل منصة CrewAI">
+عند النشر على منصة CrewAI، تحصل الحقول من نوع الملف مثل `ImageFile` و `PDFFile` وغيرها في حالة التدفق تلقائيًا على واجهة رفع ملفات. يمكن للمستخدمين سحب وإفلات الملفات مباشرة في واجهة المنصة. يتم تخزين الملفات بشكل آمن وتمريرها إلى الوكلاء باستخدام تحسينات خاصة بالمزود (base64 مضمّن، أو واجهات برمجة لرفع الملفات، أو مراجع URL حسب المزود). للاطلاع على أمثلة استخدام API، راجع [مدخلات الملفات في التدفقات](/ar/concepts/flows#مدخلات-الملفات).
+</Note>
+
 ### مع الوكلاء المستقلين
 
 مرر الملفات مباشرة إلى تشغيل الوكيل:

docs/ar/concepts/flows.mdx

@@ -341,6 +341,90 @@ flow.kickoff()
 
 من خلال توفير خيارات إدارة الحالة غير المهيكلة والمهيكلة، تمكّن تدفقات CrewAI المطورين من بناء سير عمل ذكاء اصطناعي مرن ومتين في آن واحد، ملبيةً مجموعة واسعة من متطلبات التطبيقات.
 
+### مدخلات الملفات
+
+عند استخدام الحالة المهيكلة، يمكنك تضمين حقول من نوع الملف باستخدام فئات من `crewai-files`. تعمل الحقول من نوع الملف في حالة التدفق كإشارة للمنصة — فهي تُعرض تلقائيًا كمناطق سحب وإفلات لرفع الملفات في واجهة علامة تبويب Run ويتم تعبئتها عند رفع الملفات عبر المنصة أو تمريرها عبر `input_files` في API.
+
+```python
+from crewai.flow.flow import Flow, start
+from crewai_files import File, ImageFile, PDFFile
+from pydantic import BaseModel
+
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform
+    title: str = ""
+
+class MyFlow(Flow[MyState]):
+    @start()
+    def process(self):
+        # File object is automatically populated in state
+        # when uploaded via Platform UI or passed via API
+        content = self.state.document.read()
+        print(f"Processing {self.state.title}: {len(content)} bytes")
+        return content
+```
+
+عند النشر على **منصة CrewAI**، تُعرض الحقول من نوع الملف (`File`، `ImageFile`، `PDFFile` من `crewai-files`) تلقائيًا كمناطق سحب وإفلات لرفع الملفات في واجهة المستخدم. يمكن للمستخدمين سحب وإفلات الملفات، والتي تُملأ بعد ذلك في حالة التدفق الخاص بك.
+
+**بدء التشغيل مع الملفات عبر API:**
+
+تكتشف نقطة النهاية `/kickoff` تنسيق الطلب تلقائيًا:
+- **جسم JSON** ← بدء تشغيل عادي
+- **multipart/form-data** ← رفع ملف + بدء تشغيل
+
+يمكن لمستخدمي API أيضًا تمرير سلاسل URL مباشرة إلى الحقول من نوع الملف — يقوم Pydantic بتحويلها تلقائيًا.
+
+### استخدام API
+
+#### الخيار 1: بدء تشغيل multipart (موصى به)
+
+أرسل الملفات مباشرة مع طلب بدء التشغيل:
+
+```bash
+# With files (multipart) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"company_name": "Einstein"}' \
+  -F 'cover_image=@/path/to/photo.jpg'
+```
+
+يتم تخزين الملفات تلقائيًا وتحويلها إلى كائنات `FileInput`. يتلقى الوكيل الملف مع تحسين خاص بالمزود (base64 مضمّن، أو API لرفع الملفات، أو مرجع URL حسب مزود LLM).
+
+#### الخيار 2: بدء تشغيل JSON (بدون ملفات)
+
+```bash
+# Without files (JSON) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}}'
+```
+
+#### الخيار 3: رفع منفصل + بدء تشغيل
+
+هذا بديل لرفع multipart عندما تحتاج إلى رفع الملفات بشكل منفصل عن طلب بدء التشغيل. ارفع الملفات أولاً، ثم أشِر إليها بواسطة URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/photo.jpg' \
+  -F 'field_name=cover_image'
+# Returns: {"url": "https://...", "field_name": "cover_image"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}, "input_files": {"cover_image": "https://..."}}'
+```
+
+راجع وثائق Platform API للحصول على تفاصيل كاملة حول نقطة النهاية `/files`.
+
+#### على منصة CrewAI
+
+عند استخدام واجهة المنصة، تُعرض الحقول من نوع الملف تلقائيًا كمناطق سحب وإفلات للرفع. لا حاجة لاستدعاءات API — فقط أفلِت الملف وانقر على تشغيل.
+
 ## استمرارية التدفق
 
 يتيح مزخرف @persist الاستمرارية التلقائية للحالة في تدفقات CrewAI، مما يسمح لك بالحفاظ على حالة التدفق عبر عمليات إعادة التشغيل أو تنفيذات سير العمل المختلفة. يمكن تطبيق هذا المزخرف على مستوى الفئة أو مستوى الدالة، مما يوفر مرونة في كيفية إدارة استمرارية الحالة.

docs/ar/enterprise/guides/kickoff-crew.mdx

@@ -86,6 +86,60 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
 
 رمز الحامل متاح في علامة تبويب Status في صفحة تفاصيل طاقمك.
 
+## رفع الملفات
+
+عندما يتضمن طاقمك أو تدفقك حقول حالة من نوع الملف (باستخدام `ImageFile` أو `PDFFile` أو `File` من `crewai-files`)، تُعرض هذه الحقول تلقائيًا كمناطق سحب وإفلات لرفع الملفات في واجهة علامة تبويب Run. يمكن للمستخدمين سحب وإفلات الملفات مباشرة، وتتولى المنصة التخزين والتسليم إلى وكلائك.
+
+### بدء تشغيل Multipart (موصى به)
+
+أرسل الملفات مباشرة مع طلب بدء التشغيل باستخدام `multipart/form-data`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"title": "Report"}' \
+  -F 'document=@/path/to/file.pdf'
+```
+
+يتم تخزين الملفات تلقائيًا وتحويلها إلى كائنات ملفات. يتلقى الوكيل الملف مع تحسين خاص بالمزود (base64 مضمّن، أو API لرفع الملفات، أو مرجع URL حسب مزود LLM).
+
+### بدء تشغيل JSON مع عناوين URL للملفات
+
+إذا كانت لديك ملفات مستضافة بالفعل على عناوين URL، مررها عبر `input_files`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "inputs": {"title": "Report"},
+    "input_files": {"document": "https://example.com/file.pdf"}
+  }'
+```
+
+### رفع منفصل + بدء تشغيل
+
+ارفع الملفات أولاً، ثم أشِر إليها بواسطة URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/file.pdf' \
+  -F 'field_name=document'
+# Returns: {"url": "https://...", "field_name": "document"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"title": "Report"}, "input_files": {"document": "https://..."}}'
+```
+
+<Note type="info">
+يعمل رفع الملفات بنفس الطريقة لكل من الطواقم والتدفقات. عرّف حقول من نوع الملف في مخطط حالتك، وستتولى واجهة المنصة وAPI الرفع تلقائيًا.
+</Note>
+
 ### التحقق من صحة الطاقم
 
 قبل تنفيذ العمليات، يمكنك التحقق من أن طاقمك يعمل بشكل صحيح:

docs/ar/guides/coding-tools/build-with-ai.mdx

@@ -0,0 +1,214 @@
+---
+title: "البناء باستخدام الذكاء الاصطناعي"
+description: "كل ما يحتاجه وكلاء البرمجة بالذكاء الاصطناعي للبناء والنشر والتوسع مع CrewAI — المهارات، وثائق مقروءة آلياً، النشر، وميزات المؤسسات."
+icon: robot
+mode: "wide"
+---
+
+# البناء باستخدام الذكاء الاصطناعي
+
+CrewAI مُصمَّم أصلاً للعمل مع الذكاء الاصطناعي. تجمع هذه الصفحة ما يحتاجه وكيل البرمجة بالذكاء الاصطناعي للبناء مع CrewAI — سواءً كان Claude Code أو Codex أو Cursor أو Gemini CLI أو أي مساعد آخر يساعد المطوّر على إيصال الـ crews والـ flows.
+
+### وكلاء البرمجة المدعومون
+
+<CardGroup cols={5}>
+  <Card title="Claude Code" icon="message-bot" color="#D97706" />
+  <Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
+  <Card title="Codex" icon="terminal" color="#10B981" />
+  <Card title="Windsurf" icon="wind" color="#06B6D4" />
+  <Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
+</CardGroup>
+
+<Note>
+  صُممت هذه الصفحة للبشر وللمساعدين الذكيين على حدٍّ سواء. إذا كنت وكيل برمجة، ابدأ بـ **Skills** للحصول على سياق CrewAI، ثم استخدم **llms.txt** للوصول الكامل إلى الوثائق.
+</Note>
+
+---
+
+## 1. Skills — علِّم وكيلك CrewAI
+
+**Skills** حزم تعليمات تمنح وكلاء البرمجة معرفة عميقة بـ CrewAI — كيفية إنشاء هيكل Flows، وضبط Crews، استخدام الأدوات، واتباع اتفاقيات الإطار.
+
+<Tabs>
+  <Tab title="Claude Code (سوق الإضافات)">
+    <img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
+    مهارات CrewAI متاحة في **سوق إضافات Claude Code** — نفس قناة التوزيع التي تستخدمها شركات رائدة في مجال الذكاء الاصطناعي:
+    ```shell
+    /plugin marketplace add crewAIInc/skills
+    /plugin install crewai-skills@crewai-plugins
+    /reload-plugins
+    ```
+
+    تُفعَّل أربع مهارات تلقائياً عند طرح أسئلة متعلقة بـ CrewAI:
+
+    | المهارة | متى تُستخدم |
+    |---------|-------------|
+    | `getting-started` | مشاريع جديدة، الاختيار بين `LLM.call()` / `Agent` / `Crew` / `Flow`، ربط `crew.py` / `main.py` |
+    | `design-agent` | ضبط الوكلاء — الدور، الهدف، الخلفية، الأدوات، نماذج اللغة، الذاكرة، الحدود الآمنة |
+    | `design-task` | وصف المهام، التبعيات، المخرجات المنظمة (`output_pydantic`، `output_json`)، المراجعة البشرية |
+    | `ask-docs` | الاستعلام من [خادم CrewAI docs MCP](https://docs.crewai.com/mcp) للحصول على تفاصيل واجهة البرمجة الحالية |
+  </Tab>
+  <Tab title="npx (أي وكيل)">
+    يعمل مع Claude Code أو Codex أو Cursor أو Gemini CLI أو أي وكيل برمجة:
+    ```shell
+    npx skills add crewaiinc/skills
+    ```
+    يُجلب من [سجل skills.sh](https://skills.sh/crewaiinc/skills).
+  </Tab>
+</Tabs>
+
+<Steps>
+  <Step title="ثبِّت حزمة المهارات الرسمية">
+    استخدم إحدى الطريقتين أعلاه — سوق إضافات Claude Code أو `npx skills add`. كلاهما يثبّت الحزمة الرسمية [crewAIInc/skills](https://github.com/crewAIInc/skills).
+  </Step>
+  <Step title="يحصل وكيلك فوراً على خبرة CrewAI">
+    تعلّم الحزمة وكيلك:
+    - **Flows** — تطبيقات ذات حالة، خطوات، وتشغيل crews
+    - **Crews والوكلاء** — أنماط YAML أولاً، الأدوار، المهام، التفويض
+    - **الأدوات والتكاملات** — البحث، واجهات API، خوادم MCP، وأدوات CrewAI الشائعة
+    - **هيكل المشروع** — هياكل CLI واتفاقيات المستودع
+    - **أنماط محدثة** — يتماشى مع وثائق CrewAI الحالية وأفضل الممارسات
+  </Step>
+  <Step title="ابدأ البناء">
+    يمكن لوكيلك الآن إنشاء هيكل وبناء مشاريع CrewAI دون أن تعيد شرح الإطار في كل جلسة.
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="مفهوم Skills" icon="bolt" href="/ar/concepts/skills">
+    كيف تعمل المهارات في وكلاء CrewAI — الحقن، التفعيل، والأنماط.
+  </Card>
+  <Card title="صفحة Skills" icon="wand-magic-sparkles" href="/ar/skills">
+    نظرة على حزمة crewAIInc/skills وما تتضمنه.
+  </Card>
+  <Card title="AGENTS.md والأدوات" icon="terminal" href="/ar/guides/coding-tools/agents-md">
+    إعداد AGENTS.md لـ Claude Code وCodex وCursor وGemini CLI.
+  </Card>
+  <Card title="سجل skills.sh" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    القائمة الرسمية — المهارات، إحصاءات التثبيت، والتدقيق.
+  </Card>
+</CardGroup>
+
+---
+
+## 2. llms.txt — وثائق مقروءة آلياً
+
+ينشر CrewAI ملف `llms.txt` يمنح المساعدين الذكيين وصولاً مباشراً إلى الوثائق الكاملة بصيغة مقروءة آلياً.
+
+```
+https://docs.crewai.com/llms.txt
+```
+
+<Tabs>
+  <Tab title="ما هو llms.txt؟">
+    [`llms.txt`](https://llmstxt.org/) معيار ناشئ لجعل الوثائق قابلة للاستهلاك من قبل نماذج اللغة الكبيرة. بدلاً من استخراج HTML، يمكن لوكيلك جلب ملف نصي واحد منظم بكل المحتوى المطلوب.
+
+    ملف `llms.txt` الخاص بـ CrewAI **متاح فعلياً** — يمكن لوكيلك استخدامه الآن.
+  </Tab>
+  <Tab title="كيفية الاستخدام">
+    وجِّه وكيل البرمجة إلى عنوان URL عندما يحتاج إلى مرجع CrewAI:
+
+    ```
+    Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
+    ```
+
+    يمكن للعديد من وكلاء البرمجة (Claude Code، Cursor، وغيرهما) جلب عناوين URL مباشرة. يحتوي الملف على وثائق منظمة تغطي مفاهيم CrewAI وواجهات البرمجة والأدلة.
+  </Tab>
+  <Tab title="لماذا يهم">
+    - **دون استخراج ويب** — محتوى نظيف ومنظم في طلب واحد
+    - **دائماً محدث** — يُقدَّم مباشرة من docs.crewai.com
+    - **محسّن لنماذج اللغة** — مُنسَّق لنوافذ السياق لا للمتصفحات
+    - **يُكمّل Skills** — المهارات تعلّم الأنماط، وllms.txt يوفّر المرجع
+  </Tab>
+</Tabs>
+
+---
+
+## 3. النشر للمؤسسات
+
+انتقل من crew محلي إلى الإنتاج على **CrewAI AMP** (منصة إدارة الوكلاء) في دقائق.
+
+<Steps>
+  <Step title="ابنِ محلياً">
+    أنشئ الهيكل واختبر crew أو flow:
+    ```bash
+    crewai create crew my_crew
+    cd my_crew
+    crewai run
+    ```
+  </Step>
+  <Step title="جهّز للنشر">
+    تأكد أن هيكل مشروعك جاهز:
+    ```bash
+    crewai deploy --prepare
+    ```
+    راجع [دليل التحضير](/ar/enterprise/guides/prepare-for-deployment) لتفاصيل الهيكل والمتطلبات.
+  </Step>
+  <Step title="انشر على AMP">
+    ادفع إلى منصة CrewAI AMP:
+    ```bash
+    crewai deploy
+    ```
+    يمكنك أيضاً النشر عبر [تكامل GitHub](/ar/enterprise/guides/deploy-to-amp) أو [Crew Studio](/ar/enterprise/guides/enable-crew-studio).
+  </Step>
+  <Step title="الوصول عبر API">
+    يحصل الـ crew المنشور على نقطة نهاية REST. دمجه في أي تطبيق:
+    ```bash
+    curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
+      -H "Authorization: Bearer $CREWAI_API_KEY" \
+      -H "Content-Type: application/json" \
+      -d '{"inputs": {"topic": "AI agents"}}'
+    ```
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="النشر على AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
+    دليل النشر الكامل — CLI وGitHub وCrew Studio.
+  </Card>
+  <Card title="مقدمة عن AMP" icon="globe" href="/ar/enterprise/introduction">
+    نظرة على المنصة — ما يوفّره AMP لـ crews في الإنتاج.
+  </Card>
+</CardGroup>
+
+---
+
+## 4. ميزات المؤسسات
+
+CrewAI AMP مُصمَّم لفرق الإنتاج. إليك ما تحصل عليه بعد النشر.
+
+<CardGroup cols={2}>
+  <Card title="المراقبة والرصد" icon="chart-line">
+    مسارات تنفيذ مفصّلة، وسجلات، ومقاييس أداء لكل تشغيل crew. راقب قرارات الوكلاء، استدعاءات الأدوات، وإكمال المهام في الوقت الفعلي.
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    واجهة منخفضة/بدون كود لإنشاء crews وتخصيصها ونشرها بصرياً — ثم التصدير إلى الشيفرة أو النشر مباشرة.
+  </Card>
+  <Card title="بث الويبهوك" icon="webhook">
+    بث أحداث فورية من تنفيذات الـ crews إلى أنظمتك. تكامل مع Slack أو Zapier أو أي مستهلك ويبهوك.
+  </Card>
+  <Card title="إدارة الفريق" icon="users">
+    SSO وRBAC وضوابط على مستوى المؤسسة. أدر من يمكنه إنشاء crews ونشرها والوصول إليها.
+  </Card>
+  <Card title="مستودع الأدوات" icon="toolbox">
+    انشر وشارك أدواتاً مخصصة عبر مؤسستك. ثبّت أدوات المجتمع من السجل.
+  </Card>
+  <Card title="Factory (استضافة ذاتية)" icon="server">
+    شغّل CrewAI AMP على بنيتك التحتية. قدرات المنصة كاملة مع ضوابط إقامة البيانات والامتثال.
+  </Card>
+</CardGroup>
+
+<AccordionGroup>
+  <Accordion title="لمن مخصص AMP؟">
+    لفرق تحتاج نقل سير عمل وكلاء الذكاء الاصطناعي من النماذج الأولية إلى الإنتاج — مع المراقبة وضوابط الوصول والبنية التحتية القابلة للتوسع. سواءً كنت ناشئاً أو مؤسسة كبيرة، يتولى AMP التعقيد التشغيلي لتتفرغ لبناء الوكلاء.
+  </Accordion>
+  <Accordion title="ما خيارات النشر المتاحة؟">
+    - **السحابة (app.crewai.com)** — تُدار من CrewAI، أسرع طريق إلى الإنتاج
+    - **Factory (استضافة ذاتية)** — على بنيتك التحتية لسيطرة كاملة على البيانات
+    - **هجين** — دمج السحابة والاستضافة الذاتية حسب حساسية البيانات
+  </Accordion>
+</AccordionGroup>
+
+<Card title="استكشف CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
+  سجّل وانشر أول crew لك في الإنتاج.
+</Card>

docs/ar/installation.mdx

@@ -196,7 +196,7 @@ python3 --version
 - يدعم أي مزود سحابي بما في ذلك النشر المحلي
 - تكامل مع أنظمة الأمان الحالية
 
-<Card title="استكشف خيارات المؤسسات" icon="building" href="https://crewai.com/enterprise">
+<Card title="استكشف خيارات المؤسسات" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   تعرّف على عروض CrewAI للمؤسسات وجدول عرضًا توضيحيًا
 </Card>
 </Note>

docs/en/changelog.mdx

@@ -4,6 +4,161 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 22, 2026">
+  ## v1.14.3a2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
+
+  ## What's Changed
+
+  ### Features
+  - Add support for bedrock V4
+  - Add Daytona sandbox tools for enhanced functionality
+  - Add 'Build with AI' page — AI-native docs for coding agents
+  - Add Build with AI to Get Started navigation and page files for all languages (en, ko, pt-BR, ar)
+
+  ### Bug Fixes
+  - Fix propagation of implicit @CrewBase names to crew events
+  - Resolve issue with duplicate batch initialization in execution metadata merge
+  - Fix serialization of Task class-reference fields for checkpointing
+  - Handle BaseModel result in guardrail retry loop
+  - Bump python-dotenv to version >=1.2.2 for security compliance
+
+  ### Documentation
+  - Update changelog and version for v1.14.3a1
+  - Update descriptions and apply actual translations
+
+  ## Contributors
+
+  @MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
+
+</Update>
+
+<Update label="Apr 21, 2026">
+  ## v1.14.3a1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
+
+  ## What's Changed
+
+  ### Features
+  - Add checkpoint and fork support to standalone agents
+
+  ### Bug Fixes
+  - Preserve thought_signature in Gemini streaming tool calls
+  - Emit task_started on fork resume and redesign checkpoint TUI
+  - Correct dry-run order and handle checked-out stale branch in devtools release
+  - Use future dates in checkpoint prune tests to prevent time-dependent failures (#5543)
+
+  ### Documentation
+  - Update changelog and version for v1.14.2
+
+  ## Contributors
+
+  @alex-clawd, @greysonlalonde
+
+</Update>
+
+<Update label="Apr 17, 2026">
+  ## v1.14.2
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2)
+
+  ## What's Changed
+
+  ### Features
+  - Add checkpoint resume, diff, and prune commands with improved discoverability.
+  - Add `from_checkpoint` parameter to `Agent.kickoff` and related methods.
+  - Add template management commands for project templates.
+  - Add resume hints to devtools release on failure.
+  - Add deploy validation CLI and enhance LLM initialization ergonomics.
+  - Add checkpoint forking with lineage tracking.
+  - Enrich LLM token tracking with reasoning tokens and cache creation tokens.
+
+  ### Bug Fixes
+  - Fix prompt on stale branch conflicts in devtools release.
+  - Patch vulnerabilities in `authlib`, `langchain-text-splitters`, and `pypdf`.
+  - Scope streaming handlers to prevent cross-run chunk contamination.
+  - Dispatch Flow checkpoints through Flow APIs in TUI.
+  - Use recursive glob for JSON checkpoint discovery.
+  - Handle cyclic JSON schemas in MCP tool resolution.
+  - Preserve Bedrock tool call arguments by removing truthy default.
+  - Emit flow_finished event after HITL resume.
+  - Fix various vulnerabilities by updating dependencies, including `requests`, `cryptography`, and `pytest`.
+  - Fix to stop forwarding strict mode to Bedrock Converse API.
+
+  ### Documentation
+  - Document missing parameters and add Checkpointing section.
+  - Update changelog and version for v1.14.2 and previous release candidates.
+  - Add enterprise A2A feature documentation and update OSS A2A docs.
+
+  ## Contributors
+
+  @Yanhu007, @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="Apr 16, 2026">
+  ## v1.14.2rc1
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2rc1)
+
+  ## What's Changed
+
+  ### Bug Fixes
+  - Fix handling of cyclic JSON schemas in MCP tool resolution
+  - Fix vulnerability by bumping python-multipart to 0.0.26
+  - Fix vulnerability by bumping pypdf to 6.10.1
+
+  ### Documentation
+  - Update changelog and version for v1.14.2a5
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="Apr 15, 2026">
+  ## v1.14.2a5
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a5)
+
+  ## What's Changed
+
+  ### Documentation
+  - Update changelog and version for v1.14.2a4
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="Apr 15, 2026">
+  ## v1.14.2a4
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
+
+  ## What's Changed
+
+  ### Features
+  - Add resume hints to devtools release on failure
+
+  ### Bug Fixes
+  - Fix strict mode forwarding to Bedrock Converse API
+  - Fix pytest version to 9.0.3 for security vulnerability GHSA-6w46-j5rx-g56g
+  - Bump OpenAI lower bound to >=2.0.0
+
+  ### Documentation
+  - Update changelog and version for v1.14.2a3
+
+  ## Contributors
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="Apr 13, 2026">
   ## v1.14.2a3
 

docs/en/concepts/crews.mdx

@@ -33,7 +33,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
 | **Planning LLM** *(optional)*         | `planning_llm`         | The language model used by the AgentPlanner in a planning process.                                                                                                                                                                                        |
 | **Knowledge Sources** _(optional)_    | `knowledge_sources`    | Knowledge sources available at the crew level, accessible to all the agents.                                                                                                                                                                                    |
-| **Stream** _(optional)_               | `stream`               | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`.                                                                                    |
+| **Stream** _(optional)_                        | `stream`                      | Enable streaming output to receive real-time updates during crew execution. Returns a `CrewStreamingOutput` object that can be iterated for chunks. Defaults to `False`.                                                                                    |
+| **Chat LLM** _(optional)_                      | `chat_llm`                    | The language model used to orchestrate `crewai chat` CLI interactions with the crew. Accepts a model name string or `LLM` instance. Defaults to `None`.                                                                                                    |
+| **Before Kickoff Callbacks** _(optional)_      | `before_kickoff_callbacks`    | A list of callable functions executed **before** the crew starts. Each callback receives and can modify the inputs dict. Distinct from the `@before_kickoff` decorator. Defaults to `[]`.                                                                   |
+| **After Kickoff Callbacks** _(optional)_       | `after_kickoff_callbacks`     | A list of callable functions executed **after** the crew finishes. Each callback receives and can modify the `CrewOutput`. Distinct from the `@after_kickoff` decorator. Defaults to `[]`.                                                                  |
+| **Tracing** _(optional)_                       | `tracing`                     | Controls OpenTelemetry tracing for the crew. `True` = always enable, `False` = always disable, `None` = inherit from environment / user settings. Defaults to `None`.                                                                                      |
+| **Skills** _(optional)_                        | `skills`                      | A list of `Path` objects (skill search directories) or pre-loaded `Skill` objects applied to all agents in the crew. Defaults to `None`.                                                                                                                    |
+| **Security Config** _(optional)_               | `security_config`              | A `SecurityConfig` instance managing crew fingerprinting and identity. Defaults to `SecurityConfig()`.                                                                                                                                                      |
+| **Checkpoint** _(optional)_                    | `checkpoint`                  | Enables automatic checkpointing. Pass `True` for sensible defaults, a `CheckpointConfig` for full control, `False` to opt out, or `None` to inherit. See the [Checkpointing](#checkpointing) section below. Defaults to `None`.                             |
 
 <Tip>
 **Crew Max RPM**: The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
@@ -271,6 +278,72 @@ crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name
 
 
 
+## Checkpointing
+
+Checkpointing lets a crew automatically save its state after key events (e.g. task completion) so that long-running or interrupted runs can be resumed exactly where they left off without re-executing completed tasks.
+
+### Quick Start
+
+Pass `checkpoint=True` to enable checkpointing with sensible defaults (saves to `.checkpoints/` after every task):
+
+```python Code
+from crewai import Crew, Process
+
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task],
+    process=Process.sequential,
+    checkpoint=True,  # saves to .checkpoints/ after every task
+)
+
+crew.kickoff(inputs={"topic": "AI trends"})
+```
+
+### Full Control with `CheckpointConfig`
+
+Use `CheckpointConfig` for fine-grained control over location, trigger events, storage backend, and retention:
+
+```python Code
+from crewai import Crew, Process
+from crewai.state.checkpoint_config import CheckpointConfig
+
+crew = Crew(
+    agents=[researcher, writer],
+    tasks=[research_task, write_task],
+    process=Process.sequential,
+    checkpoint=CheckpointConfig(
+        location="./.checkpoints",       # directory for JSON files (default)
+        on_events=["task_completed"],    # trigger after each task (default)
+        max_checkpoints=5,               # keep only the 5 most recent checkpoints
+    ),
+)
+
+crew.kickoff(inputs={"topic": "AI trends"})
+```
+
+### Resuming from a Checkpoint
+
+Use `Crew.from_checkpoint()` to restore a crew from a saved checkpoint file, then call `kickoff()` to resume:
+
+```python Code
+# Resume from the most recent checkpoint
+crew = Crew.from_checkpoint(".checkpoints/latest.json")
+crew.kickoff()
+```
+
+<Note>
+When restoring from a checkpoint, `checkpoint_inputs`, `checkpoint_train`, and `checkpoint_kickoff_event_id` are automatically reconstructed — you do not need to set these manually.
+</Note>
+
+### `CheckpointConfig` Attributes
+
+| Attribute          | Type                                   | Default              | Description                                                                                   |
+| :----------------- | :------------------------------------- | :------------------- | :-------------------------------------------------------------------------------------------- |
+| `location`         | `str`                                  | `"./.checkpoints"`   | Storage destination. For `JsonProvider` this is a directory path; for `SqliteProvider` a database file path. |
+| `on_events`        | `list[str]`                            | `["task_completed"]` | Event types that trigger a checkpoint write. Use `["*"]` to checkpoint on every event.        |
+| `provider`         | `JsonProvider \| SqliteProvider`       | `JsonProvider()`     | Storage backend. Defaults to `JsonProvider` (plain JSON files).                               |
+| `max_checkpoints`  | `int \| None`                          | `None`               | Maximum checkpoints to keep. Oldest are pruned after each write. `None` keeps all.            |
+
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.

docs/en/concepts/files.mdx

@@ -117,23 +117,35 @@ task = Task(
 
 ### With Flows
 
-Pass files to flows, which automatically inherit to crews:
+File-typed fields (`File`, `ImageFile`, `PDFFile`) in your flow's state schema serve as the signal to the Platform UI. When deployed, these fields render as file upload dropzones. Files can also be passed via `input_files` in the API.
 
 ```python
 from crewai.flow.flow import Flow, start
-from crewai_files import ImageFile
+from crewai_files import File, ImageFile
+from pydantic import BaseModel
 
-class AnalysisFlow(Flow):
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform UI
+    cover_image: ImageFile   # Image-specific dropzone
+    title: str = ""
+
+class AnalysisFlow(Flow[MyState]):
     @start()
     def analyze(self):
+        # Files are automatically populated in state
+        content = self.state.document.read()
         return self.analysis_crew.kickoff()
 
 flow = AnalysisFlow()
 result = flow.kickoff(
-    input_files={"image": ImageFile(source="data.png")}
+    input_files={"document": File(source="report.pdf")}
 )
 ```
 
+<Note type="info" title="CrewAI Platform Integration">
+When deployed on CrewAI Platform, `ImageFile`, `PDFFile`, and other file-typed fields in your flow state automatically get a file upload UI. Users can drag and drop files directly in the Platform interface. Files are stored securely and passed to agents using provider-specific optimizations (inline base64, file upload APIs, or URL references depending on the provider). For API usage examples, see [File Inputs in Flows](/concepts/flows#file-inputs).
+</Note>
+
 ### With Standalone Agents
 
 Pass files directly to agent kickoff:

docs/en/concepts/flows.mdx

@@ -341,6 +341,90 @@ flow.kickoff()
 
 By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
 
+### File Inputs
+
+When using structured state, you can include file-typed fields using classes from `crewai-files`. File-typed fields in your flow state serve as the signal to the Platform—they automatically render as file upload dropzones in the Run tab UI and get populated when files are uploaded via the Platform or passed via `input_files` in the API.
+
+```python
+from crewai.flow.flow import Flow, start
+from crewai_files import File, ImageFile, PDFFile
+from pydantic import BaseModel
+
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform
+    title: str = ""
+
+class MyFlow(Flow[MyState]):
+    @start()
+    def process(self):
+        # File object is automatically populated in state
+        # when uploaded via Platform UI or passed via API
+        content = self.state.document.read()
+        print(f"Processing {self.state.title}: {len(content)} bytes")
+        return content
+```
+
+When deployed on **CrewAI Platform**, file-typed fields (`File`, `ImageFile`, `PDFFile` from `crewai-files`) automatically render as file upload dropzones in the UI. Users can drag and drop files, which are then populated into your flow's state.
+
+**Kicking off with files via API:**
+
+The `/kickoff` endpoint auto-detects the request format:
+- **JSON body** → normal kickoff
+- **multipart/form-data** → file upload + kickoff
+
+API users can also pass URL strings directly to file-typed fields—Pydantic coerces them automatically.
+
+### API Usage
+
+#### Option 1: Multipart kickoff (recommended)
+
+Send files directly with the kickoff request:
+
+```bash
+# With files (multipart) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"company_name": "Einstein"}' \
+  -F 'cover_image=@/path/to/photo.jpg'
+```
+
+Files are automatically stored and converted to `FileInput` objects. The agent receives the file with provider-specific optimization (inline base64, file upload API, or URL reference depending on the LLM provider).
+
+#### Option 2: JSON kickoff (no files)
+
+```bash
+# Without files (JSON) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}}'
+```
+
+#### Option 3: Separate upload + kickoff
+
+This is an alternative to multipart upload when you need to upload files separately from the kickoff request. Upload files first, then reference them by URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/photo.jpg' \
+  -F 'field_name=cover_image'
+# Returns: {"url": "https://...", "field_name": "cover_image"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}, "input_files": {"cover_image": "https://..."}}'
+```
+
+See the Platform API documentation for full `/files` endpoint details.
+
+#### On CrewAI Platform
+
+When using the Platform UI, file-typed fields automatically render as drag-and-drop upload zones. No API calls needed—just drop the file and click Run.
+
 ## Flow Persistence
 
 The @persist decorator enables automatic state persistence in CrewAI Flows, allowing you to maintain flow state across restarts or different workflow executions. This decorator can be applied at either the class level or method level, providing flexibility in how you manage state persistence.

docs/en/enterprise/guides/kickoff-crew.mdx

@@ -86,6 +86,60 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
 
 Your bearer token is available on the Status tab of your crew's detail page.
 
+## File Uploads
+
+When your crew or flow includes file-typed state fields (using `ImageFile`, `PDFFile`, or `File` from `crewai-files`), these fields automatically render as file upload dropzones in the Run tab UI. Users can drag and drop files directly, and the Platform handles storage and delivery to your agents.
+
+### Multipart Kickoff (Recommended)
+
+Send files directly with the kickoff request using `multipart/form-data`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"title": "Report"}' \
+  -F 'document=@/path/to/file.pdf'
+```
+
+Files are automatically stored and converted to file objects. The agent receives the file with provider-specific optimization (inline base64, file upload API, or URL reference depending on the LLM provider).
+
+### JSON Kickoff with File URLs
+
+If you have files already hosted at URLs, pass them via `input_files`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "inputs": {"title": "Report"},
+    "input_files": {"document": "https://example.com/file.pdf"}
+  }'
+```
+
+### Separate Upload + Kickoff
+
+Upload files first, then reference them by URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/file.pdf' \
+  -F 'field_name=document'
+# Returns: {"url": "https://...", "field_name": "document"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"title": "Report"}, "input_files": {"document": "https://..."}}'
+```
+
+<Note type="info">
+File uploads work the same way for both crews and flows. Define file-typed fields in your state schema, and the Platform UI and API will handle uploads automatically.
+</Note>
+
 ### Checking Crew Health
 
 Before executing operations, you can verify that your crew is running properly:

docs/en/guides/coding-tools/build-with-ai.mdx

@@ -0,0 +1,214 @@
+---
+title: "Build with AI"
+description: "Everything AI coding agents need to build, deploy, and scale with CrewAI — skills, machine-readable docs, deployment, and enterprise features."
+icon: robot
+mode: "wide"
+---
+
+# Build with AI
+
+CrewAI is AI-native. This page brings together everything an AI coding agent needs to build with CrewAI — whether you're Claude Code, Codex, Cursor, Gemini CLI, or any other assistant helping a developer ship crews and flows.
+
+### Supported Coding Agents
+
+<CardGroup cols={5}>
+  <Card title="Claude Code" icon="message-bot" color="#D97706" />
+  <Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
+  <Card title="Codex" icon="terminal" color="#10B981" />
+  <Card title="Windsurf" icon="wind" color="#06B6D4" />
+  <Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
+</CardGroup>
+
+<Note>
+  This page is designed to be consumed by both humans and AI assistants. If you're a coding agent, start with **Skills** to get CrewAI context, then use **llms.txt** for full docs access.
+</Note>
+
+---
+
+## 1. Skills — Teach Your Agent CrewAI
+
+**Skills** are instruction packs that give coding agents deep CrewAI knowledge — how to scaffold Flows, configure Crews, use tools, and follow framework conventions.
+
+<Tabs>
+  <Tab title="Claude Code (Plugin Marketplace)">
+    <img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
+    CrewAI skills are available in the **Claude Code plugin marketplace** — the same distribution channel used by top AI-native companies:
+    ```shell
+    /plugin marketplace add crewAIInc/skills
+    /plugin install crewai-skills@crewai-plugins
+    /reload-plugins
+    ```
+
+    Four skills activate automatically when you ask relevant CrewAI questions:
+
+    | Skill | When it runs |
+    |-------|--------------|
+    | `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
+    | `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
+    | `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
+    | `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
+  </Tab>
+  <Tab title="npx (Any Agent)">
+    Works with Claude Code, Codex, Cursor, Gemini CLI, or any coding agent:
+    ```shell
+    npx skills add crewaiinc/skills
+    ```
+    Pulls from the [skills.sh registry](https://skills.sh/crewaiinc/skills).
+  </Tab>
+</Tabs>
+
+<Steps>
+  <Step title="Install the official skill pack">
+    Use either method above — the Claude Code plugin marketplace or `npx skills add`. Both install the official [crewAIInc/skills](https://github.com/crewAIInc/skills) pack.
+  </Step>
+  <Step title="Your agent gets instant CrewAI expertise">
+    The skill pack teaches your agent:
+    - **Flows** — stateful apps, steps, and crew kickoffs
+    - **Crews & Agents** — YAML-first patterns, roles, tasks, delegation
+    - **Tools & Integrations** — search, APIs, MCP servers, and common CrewAI tools
+    - **Project layout** — CLI scaffolds and repo conventions
+    - **Up-to-date patterns** — tracks current CrewAI docs and best practices
+  </Step>
+  <Step title="Start building">
+    Your agent can now scaffold and build CrewAI projects without you re-explaining the framework each session.
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="Skills concept" icon="bolt" href="/en/concepts/skills">
+    How skills work in CrewAI agents — injection, activation, and patterns.
+  </Card>
+  <Card title="Skills landing page" icon="wand-magic-sparkles" href="/en/skills">
+    Overview of the crewAIInc/skills pack and what it includes.
+  </Card>
+  <Card title="AGENTS.md & coding tools" icon="terminal" href="/en/guides/coding-tools/agents-md">
+    Set up AGENTS.md for Claude Code, Codex, Cursor, and Gemini CLI.
+  </Card>
+  <Card title="Skills registry (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Official listing — skills, install stats, and audits.
+  </Card>
+</CardGroup>
+
+---
+
+## 2. llms.txt — Machine-Readable Docs
+
+CrewAI publishes an `llms.txt` file that gives AI assistants direct access to the full documentation in a machine-readable format.
+
+```
+https://docs.crewai.com/llms.txt
+```
+
+<Tabs>
+  <Tab title="What is llms.txt?">
+    [`llms.txt`](https://llmstxt.org/) is an emerging standard for making documentation consumable by large language models. Instead of scraping HTML, your agent can fetch a single structured text file with all the content it needs.
+
+    CrewAI's `llms.txt` is **already live** — your agent can use it right now.
+  </Tab>
+  <Tab title="How to use it">
+    Point your coding agent at the URL when it needs CrewAI reference docs:
+
+    ```
+    Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
+    ```
+
+    Many coding agents (Claude Code, Cursor, etc.) can fetch URLs directly. The file contains structured documentation covering all CrewAI concepts, APIs, and guides.
+  </Tab>
+  <Tab title="Why it matters">
+    - **No scraping required** — clean, structured content in one request
+    - **Always up-to-date** — served directly from docs.crewai.com
+    - **Optimized for LLMs** — formatted for context windows, not browsers
+    - **Complements skills** — skills teach patterns, llms.txt provides reference
+  </Tab>
+</Tabs>
+
+---
+
+## 3. Deploy to Enterprise
+
+Go from a local crew to production on **CrewAI AMP** (Agent Management Platform) in minutes.
+
+<Steps>
+  <Step title="Build locally">
+    Scaffold and test your crew or flow:
+    ```bash
+    crewai create crew my_crew
+    cd my_crew
+    crewai run
+    ```
+  </Step>
+  <Step title="Prepare for deployment">
+    Ensure your project structure is ready:
+    ```bash
+    crewai deploy --prepare
+    ```
+    See the [preparation guide](/en/enterprise/guides/prepare-for-deployment) for details on project structure and requirements.
+  </Step>
+  <Step title="Deploy to AMP">
+    Push to the CrewAI AMP platform:
+    ```bash
+    crewai deploy
+    ```
+    You can also deploy via [GitHub integration](/en/enterprise/guides/deploy-to-amp) or [Crew Studio](/en/enterprise/guides/enable-crew-studio).
+  </Step>
+  <Step title="Access via API">
+    Your deployed crew gets a REST API endpoint. Integrate it into any application:
+    ```bash
+    curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
+      -H "Authorization: Bearer $CREWAI_API_KEY" \
+      -H "Content-Type: application/json" \
+      -d '{"inputs": {"topic": "AI agents"}}'
+    ```
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
+    Full deployment guide — CLI, GitHub, and Crew Studio methods.
+  </Card>
+  <Card title="AMP introduction" icon="globe" href="/en/enterprise/introduction">
+    Platform overview — what AMP provides for production crews.
+  </Card>
+</CardGroup>
+
+---
+
+## 4. Enterprise Features
+
+CrewAI AMP is built for production teams. Here's what you get beyond deployment.
+
+<CardGroup cols={2}>
+  <Card title="Observability" icon="chart-line">
+    Detailed execution traces, logs, and performance metrics for every crew run. Monitor agent decisions, tool calls, and task completion in real time.
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    No-code/low-code interface to create, customize, and deploy crews visually — then export to code or deploy directly.
+  </Card>
+  <Card title="Webhook Streaming" icon="webhook">
+    Stream real-time events from crew executions to your systems. Integrate with Slack, Zapier, or any webhook consumer.
+  </Card>
+  <Card title="Team Management" icon="users">
+    SSO, RBAC, and organization-level controls. Manage who can create, deploy, and access crews across your team.
+  </Card>
+  <Card title="Tool Repository" icon="toolbox">
+    Publish and share custom tools across your organization. Install community tools from the registry.
+  </Card>
+  <Card title="Factory (Self-Hosted)" icon="server">
+    Run CrewAI AMP on your own infrastructure. Full platform capabilities with data residency and compliance controls.
+  </Card>
+</CardGroup>
+
+<AccordionGroup>
+  <Accordion title="Who is AMP for?">
+    AMP is for teams that need to move AI agent workflows from prototypes to production — with observability, access controls, and scalable infrastructure. Whether you're a startup or enterprise, AMP handles the operational complexity so you can focus on building agents.
+  </Accordion>
+  <Accordion title="What deployment options are available?">
+    - **Cloud (app.crewai.com)** — managed by CrewAI, fastest path to production
+    - **Factory (self-hosted)** — run on your own infrastructure for full data control
+    - **Hybrid** — mix cloud and self-hosted based on sensitivity requirements
+  </Accordion>
+</AccordionGroup>
+
+<Card title="Explore CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
+  Sign up and deploy your first crew to production.
+</Card>

docs/en/installation.mdx

@@ -199,7 +199,7 @@ For teams and organizations, CrewAI offers enterprise deployment options that el
 - Supports any hyperscaler including on prem deployments
 - Integration with your existing security systems
 
-<Card title="Explore Enterprise Options" icon="building" href="https://crewai.com/enterprise">
+<Card title="Explore Enterprise Options" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   Learn about CrewAI's enterprise offerings and schedule a demo
 </Card>
 </Note>

docs/ko/changelog.mdx

@@ -4,6 +4,161 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 22일">
+  ## v1.14.3a2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
+
+  ## 변경 사항
+
+  ### 기능
+  - 베드록 V4 지원 추가
+  - 향상된 기능을 위한 데이토나 샌드박스 도구 추가
+  - 'AI와 함께 빌드' 페이지 추가 — 코딩 에이전트를 위한 AI 네이티브 문서
+  - 모든 언어(en, ko, pt-BR, ar)에 대한 시작하기 탐색 및 페이지 파일에 AI와 함께 빌드 추가
+
+  ### 버그 수정
+  - 크루 이벤트에 대한 암묵적 @CrewBase 이름 전파 수정
+  - 실행 메타데이터 병합에서 중복 배치 초기화 문제 해결
+  - 체크포인트를 위한 Task 클래스 참조 필드 직렬화 수정
+  - 가드레일 재시도 루프에서 BaseModel 결과 처리
+  - 보안 준수를 위해 python-dotenv를 버전 >=1.2.2로 업데이트
+
+  ### 문서
+  - v1.14.3a1에 대한 변경 로그 및 버전 업데이트
+  - 설명 업데이트 및 실제 번역 적용
+
+  ## 기여자
+
+  @MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
+
+</Update>
+
+<Update label="2026년 4월 21일">
+  ## v1.14.3a1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
+
+  ## 변경 사항
+
+  ### 기능
+  - 독립형 에이전트에 체크포인트 및 포크 지원 추가
+
+  ### 버그 수정
+  - Gemini 스트리밍 도구 호출에서 thought_signature 보존
+  - 포크 재개 시 task_started 방출 및 체크포인트 TUI 재설계
+  - dry-run 순서 수정 및 devtools 릴리스에서 체크아웃된 오래된 브랜치 처리
+  - 체크포인트 가지치기 테스트에서 미래 날짜 사용하여 시간 의존성 실패 방지 (#5543)
+
+  ### 문서
+  - v1.14.2에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @alex-clawd, @greysonlalonde
+
+</Update>
+
+<Update label="2026년 4월 17일">
+  ## v1.14.2
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2)
+
+  ## 변경 사항
+
+  ### 기능
+  - 체크포인트 재개, 차이(diff), 및 가지치기(prune) 명령을 추가하여 가시성을 개선했습니다.
+  - `Agent.kickoff` 및 관련 메서드에 `from_checkpoint` 매개변수를 추가했습니다.
+  - 프로젝트 템플릿을 위한 템플릿 관리 명령을 추가했습니다.
+  - 실패 시 개발 도구 릴리스에 재개 힌트를 추가했습니다.
+  - 배포 검증 CLI를 추가하고 LLM 초기화의 사용 편의성을 향상시켰습니다.
+  - 계보 추적이 가능한 체크포인트 포킹을 추가했습니다.
+  - 추론 토큰 및 캐시 생성 토큰으로 LLM 토큰 추적을 풍부하게 했습니다.
+
+  ### 버그 수정
+  - 개발 도구 릴리스에서 오래된 브랜치 충돌에 대한 프롬프트를 수정했습니다.
+  - `authlib`, `langchain-text-splitters`, 및 `pypdf`의 취약점을 패치했습니다.
+  - 스트리밍 핸들러의 범위를 설정하여 교차 실행 청크 오염을 방지했습니다.
+  - TUI에서 Flow API를 통해 Flow 체크포인트를 전송했습니다.
+  - JSON 체크포인트 발견을 위해 재귀적 글로브를 사용했습니다.
+  - MCP 도구 해상도에서 순환 JSON 스키마를 처리했습니다.
+  - 진리값이 있는 기본값을 제거하여 Bedrock 도구 호출 인수를 보존했습니다.
+  - HITL 재개 후 flow_finished 이벤트를 발생시켰습니다.
+  - `requests`, `cryptography`, 및 `pytest`를 포함한 종속성을 업데이트하여 다양한 취약점을 수정했습니다.
+  - Bedrock Converse API에 엄격 모드를 전달하지 않도록 수정했습니다.
+
+  ### 문서
+  - 누락된 매개변수를 문서화하고 체크포인팅 섹션을 추가했습니다.
+  - v1.14.2 및 이전 릴리스 후보에 대한 변경 로그 및 버전을 업데이트했습니다.
+  - 기업 A2A 기능 문서를 추가하고 OSS A2A 문서를 업데이트했습니다.
+
+  ## 기여자
+
+  @Yanhu007, @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="2026년 4월 16일">
+  ## v1.14.2rc1
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2rc1)
+
+  ## 변경 사항
+
+  ### 버그 수정
+  - MCP 도구 해상도에서 순환 JSON 스키마 처리 수정
+  - python-multipart를 0.0.26으로 업데이트하여 취약점 수정
+  - pypdf를 6.10.1로 업데이트하여 취약점 수정
+
+  ### 문서
+  - v1.14.2a5에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="2026년 4월 15일">
+  ## v1.14.2a5
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a5)
+
+  ## 변경 사항
+
+  ### 문서
+  - v1.14.2a4의 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="2026년 4월 15일">
+  ## v1.14.2a4
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
+
+  ## 변경 사항
+
+  ### 기능
+  - 실패 시 devtools 릴리스에 이력서 힌트 추가
+
+  ### 버그 수정
+  - Bedrock Converse API로의 엄격 모드 포워딩 수정
+  - 보안 취약점 GHSA-6w46-j5rx-g56g에 대해 pytest 버전을 9.0.3으로 수정
+  - OpenAI 하한을 >=2.0.0으로 상향 조정
+
+  ### 문서
+  - v1.14.2a3에 대한 변경 로그 및 버전 업데이트
+
+  ## 기여자
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="2026년 4월 13일">
   ## v1.14.2a3
 

docs/ko/concepts/files.mdx

@@ -117,23 +117,35 @@ task = Task(
 
 ### Flow와 함께
 
-flow에 파일을 전달하면 자동으로 crew에 상속됩니다:
+flow의 상태 스키마에 있는 파일 타입 필드(`File`, `ImageFile`, `PDFFile`)는 플랫폼 UI에 대한 신호 역할을 합니다. 배포 시 이러한 필드는 파일 업로드 드롭존으로 렌더링됩니다. 파일은 API에서 `input_files`를 통해서도 전달할 수 있습니다.
 
 ```python
 from crewai.flow.flow import Flow, start
-from crewai_files import ImageFile
+from crewai_files import File, ImageFile
+from pydantic import BaseModel
 
-class AnalysisFlow(Flow):
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform UI
+    cover_image: ImageFile   # Image-specific dropzone
+    title: str = ""
+
+class AnalysisFlow(Flow[MyState]):
     @start()
     def analyze(self):
+        # Files are automatically populated in state
+        content = self.state.document.read()
         return self.analysis_crew.kickoff()
 
 flow = AnalysisFlow()
 result = flow.kickoff(
-    input_files={"image": ImageFile(source="data.png")}
+    input_files={"document": File(source="report.pdf")}
 )
 ```
 
+<Note type="info" title="CrewAI 플랫폼 통합">
+CrewAI 플랫폼에 배포하면 flow 상태의 `ImageFile`, `PDFFile` 및 기타 파일 타입 필드가 자동으로 파일 업로드 UI를 갖게 됩니다. 사용자는 플랫폼 인터페이스에서 직접 파일을 드래그 앤 드롭할 수 있습니다. 파일은 안전하게 저장되고 프로바이더별 최적화(인라인 base64, 파일 업로드 API 또는 프로바이더에 따른 URL 참조)를 사용하여 에이전트에 전달됩니다. API 사용 예제는 [Flows의 파일 입력](/ko/concepts/flows#파일-입력)을 참조하세요.
+</Note>
+
 ### 단독 에이전트와 함께
 
 에이전트 킥오프에 직접 파일을 전달합니다:

docs/ko/concepts/flows.mdx

@@ -334,6 +334,90 @@ flow.kickoff()
 
 CrewAI Flows는 비구조적 및 구조적 상태 관리 옵션을 모두 제공함으로써, 개발자들이 다양한 애플리케이션 요구 사항에 맞춰 유연하면서도 견고한 AI 워크플로를 구축할 수 있도록 지원합니다.
 
+### 파일 입력
+
+구조화된 상태를 사용할 때, `crewai-files`의 클래스를 사용하여 파일 타입 필드를 포함할 수 있습니다. flow 상태의 파일 타입 필드는 플랫폼에 대한 신호 역할을 합니다 — Run 탭 UI에서 자동으로 파일 업로드 드롭존으로 렌더링되며, 플랫폼을 통해 파일을 업로드하거나 API에서 `input_files`를 통해 전달할 때 자동으로 채워집니다.
+
+```python
+from crewai.flow.flow import Flow, start
+from crewai_files import File, ImageFile, PDFFile
+from pydantic import BaseModel
+
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform
+    title: str = ""
+
+class MyFlow(Flow[MyState]):
+    @start()
+    def process(self):
+        # File object is automatically populated in state
+        # when uploaded via Platform UI or passed via API
+        content = self.state.document.read()
+        print(f"Processing {self.state.title}: {len(content)} bytes")
+        return content
+```
+
+**CrewAI 플랫폼**에 배포하면 파일 타입 필드(`crewai-files`의 `File`, `ImageFile`, `PDFFile`)가 UI에서 자동으로 파일 업로드 드롭존으로 렌더링됩니다. 사용자는 파일을 드래그 앤 드롭할 수 있으며, 해당 파일은 flow의 상태에 자동으로 채워집니다.
+
+**API를 통한 파일 포함 시작:**
+
+`/kickoff` 엔드포인트는 요청 형식을 자동으로 감지합니다:
+- **JSON body** → 일반 kickoff
+- **multipart/form-data** → 파일 업로드 + kickoff
+
+API 사용자는 파일 타입 필드에 URL 문자열을 직접 전달할 수도 있습니다 — Pydantic이 자동으로 변환합니다.
+
+### API 사용법
+
+#### 옵션 1: Multipart kickoff (권장)
+
+kickoff 요청과 함께 파일을 직접 전송합니다:
+
+```bash
+# With files (multipart) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"company_name": "Einstein"}' \
+  -F 'cover_image=@/path/to/photo.jpg'
+```
+
+파일은 자동으로 저장되고 `FileInput` 객체로 변환됩니다. 에이전트는 프로바이더별 최적화(LLM 프로바이더에 따라 인라인 base64, 파일 업로드 API 또는 URL 참조)와 함께 파일을 수신합니다.
+
+#### 옵션 2: JSON kickoff (파일 없음)
+
+```bash
+# Without files (JSON) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}}'
+```
+
+#### 옵션 3: 분리된 업로드 + kickoff
+
+kickoff 요청과 별도로 파일을 업로드해야 할 때 multipart 업로드의 대안입니다. 먼저 파일을 업로드한 다음 URL로 참조합니다:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/photo.jpg' \
+  -F 'field_name=cover_image'
+# Returns: {"url": "https://...", "field_name": "cover_image"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}, "input_files": {"cover_image": "https://..."}}'
+```
+
+`/files` 엔드포인트에 대한 자세한 내용은 플랫폼 API 문서를 참조하세요.
+
+#### CrewAI 플랫폼에서
+
+플랫폼 UI를 사용할 때 파일 타입 필드는 자동으로 드래그 앤 드롭 업로드 영역으로 렌더링됩니다. API 호출이 필요 없습니다 — 파일을 드롭하고 실행을 클릭하면 됩니다.
+
 ## 플로우 지속성
 
 @persist 데코레이터는 CrewAI 플로우에서 자동 상태 지속성을 활성화하여, 플로우 상태를 재시작이나 다른 워크플로우 실행 간에도 유지할 수 있도록 합니다. 이 데코레이터는 클래스 수준이나 메서드 수준 모두에 적용할 수 있어, 상태 지속성을 관리하는 데 유연성을 제공합니다.

docs/ko/enterprise/guides/kickoff-crew.mdx

@@ -86,6 +86,60 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
 
 베어러 토큰은 crew의 상세 페이지의 Status 탭에서 확인할 수 있습니다.
 
+## 파일 업로드
+
+crew나 flow에 파일 타입 상태 필드(`crewai-files`의 `ImageFile`, `PDFFile`, 또는 `File` 사용)가 포함되어 있으면, 이러한 필드는 Run 탭 UI에서 자동으로 파일 업로드 드롭존으로 렌더링됩니다. 사용자는 파일을 직접 드래그 앤 드롭할 수 있으며, 플랫폼이 저장 및 에이전트 전달을 처리합니다.
+
+### Multipart Kickoff (권장)
+
+`multipart/form-data`를 사용하여 kickoff 요청과 함께 파일을 직접 전송합니다:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"title": "Report"}' \
+  -F 'document=@/path/to/file.pdf'
+```
+
+파일은 자동으로 저장되고 파일 객체로 변환됩니다. 에이전트는 프로바이더별 최적화(LLM 프로바이더에 따라 인라인 base64, 파일 업로드 API 또는 URL 참조)와 함께 파일을 수신합니다.
+
+### 파일 URL을 포함한 JSON Kickoff
+
+이미 URL에 호스팅된 파일이 있다면 `input_files`를 통해 전달합니다:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "inputs": {"title": "Report"},
+    "input_files": {"document": "https://example.com/file.pdf"}
+  }'
+```
+
+### 분리된 업로드 + Kickoff
+
+먼저 파일을 업로드한 다음 URL로 참조합니다:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/file.pdf' \
+  -F 'field_name=document'
+# Returns: {"url": "https://...", "field_name": "document"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"title": "Report"}, "input_files": {"document": "https://..."}}'
+```
+
+<Note type="info">
+파일 업로드는 crew와 flow 모두에서 동일하게 작동합니다. 상태 스키마에 파일 타입 필드를 정의하면 플랫폼 UI와 API가 자동으로 업로드를 처리합니다.
+</Note>
+
 ### 크루 상태 확인
 
 작업을 실행하기 전에 크루가 정상적으로 실행되고 있는지 확인할 수 있습니다:

docs/ko/guides/coding-tools/build-with-ai.mdx

@@ -0,0 +1,214 @@
+---
+title: "AI와 함께 빌드하기"
+description: "CrewAI로 빌드·배포·확장하는 데 필요한 모든 것 — 스킬, 기계가 읽을 수 있는 문서, 배포, 엔터프라이즈 기능을 AI 코딩 에이전트용으로 정리했습니다."
+icon: robot
+mode: "wide"
+---
+
+# AI와 함께 빌드하기
+
+CrewAI는 AI 네이티브입니다. 이 페이지는 Claude Code, Codex, Cursor, Gemini CLI 등 개발자가 crew와 flow를 배포하도록 돕는 코딩 에이전트가 CrewAI로 빌드할 때 필요한 내용을 한곳에 모았습니다.
+
+### 지원 코딩 에이전트
+
+<CardGroup cols={5}>
+  <Card title="Claude Code" icon="message-bot" color="#D97706" />
+  <Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
+  <Card title="Codex" icon="terminal" color="#10B981" />
+  <Card title="Windsurf" icon="wind" color="#06B6D4" />
+  <Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
+</CardGroup>
+
+<Note>
+  이 페이지는 사람과 AI 어시스턴트 모두를 위해 작성되었습니다. 코딩 에이전트라면 CrewAI 맥락은 **Skills**부터, 전체 문서 접근은 **llms.txt**를 사용하세요.
+</Note>
+
+---
+
+## 1. Skills — 에이전트에게 CrewAI 가르치기
+
+**Skills**는 코딩 에이전트에게 Flow 스캐폴딩, Crew 구성, 도구 사용, 프레임워크 관례 등 CrewAI에 대한 깊은 지식을 담은 지침 묶음입니다.
+
+<Tabs>
+  <Tab title="Claude Code (플러그인 마켓플레이스)">
+    <img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
+    CrewAI 스킬은 **Claude Code 플러그인 마켓플레이스**에서 제공됩니다. AI 네이티브 기업들이 쓰는 것과 같은 배포 채널입니다.
+    ```shell
+    /plugin marketplace add crewAIInc/skills
+    /plugin install crewai-skills@crewai-plugins
+    /reload-plugins
+    ```
+
+    CrewAI와 관련된 질문을 하면 다음 네 가지 스킬이 자동으로 활성화됩니다.
+
+    | 스킬 | 실행 시점 |
+    |------|-------------|
+    | `getting-started` | 새 프로젝트 스캐폴딩, `LLM.call()` / `Agent` / `Crew` / `Flow` 선택, `crew.py` / `main.py` 연결 |
+    | `design-agent` | 에이전트 구성 — 역할, 목표, 배경 이야기, 도구, LLM, 메모리, 가드레일 |
+    | `design-task` | 태스크 설명, 의존성, 구조화된 출력(`output_pydantic`, `output_json`), 사람 검토 |
+    | `ask-docs` | 최신 API 정보를 위해 [CrewAI 문서 MCP 서버](https://docs.crewai.com/mcp) 조회 |
+  </Tab>
+  <Tab title="npx (모든 에이전트)">
+    Claude Code, Codex, Cursor, Gemini CLI 등 모든 코딩 에이전트에서 사용할 수 있습니다.
+    ```shell
+    npx skills add crewaiinc/skills
+    ```
+    [skills.sh 레지스트리](https://skills.sh/crewaiinc/skills)에서 가져옵니다.
+  </Tab>
+</Tabs>
+
+<Steps>
+  <Step title="공식 스킬 팩 설치">
+    위 방법 중 하나를 사용하세요 — Claude Code 플러그인 마켓플레이스 또는 `npx skills add`. 둘 다 공식 [crewAIInc/skills](https://github.com/crewAIInc/skills) 팩을 설치합니다.
+  </Step>
+  <Step title="에이전트가 즉시 CrewAI 전문성을 갖춤">
+    스킬 팩이 에이전트에게 알려 주는 내용:
+    - **Flow** — 상태ful 앱, 단계, crew 킥오프
+    - **Crew 및 에이전트** — YAML 우선 패턴, 역할, 태스크, 위임
+    - **도구 및 통합** — 검색, API, MCP 서버, 일반적인 CrewAI 도구
+    - **프로젝트 레이아웃** — CLI 스캐폴드와 저장소 관례
+    - **최신 패턴** — 현재 CrewAI 문서와 모범 사례 반영
+  </Step>
+  <Step title="빌드 시작">
+    매 세션마다 프레임워크를 다시 설명하지 않아도 에이전트가 CrewAI 프로젝트를 스캐폴딩하고 빌드할 수 있습니다.
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="Skills 개념" icon="bolt" href="/ko/concepts/skills">
+    CrewAI 에이전트에서 스킬이 동작하는 방식 — 주입, 활성화, 패턴.
+  </Card>
+  <Card title="Skills 랜딩 페이지" icon="wand-magic-sparkles" href="/ko/skills">
+    crewAIInc/skills 팩 개요와 포함 내용.
+  </Card>
+  <Card title="AGENTS.md 및 코딩 도구" icon="terminal" href="/ko/guides/coding-tools/agents-md">
+    Claude Code, Codex, Cursor, Gemini CLI용 AGENTS.md 설정.
+  </Card>
+  <Card title="Skills 레지스트리 (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    공식 목록 — 스킬, 설치 통계, 감사 정보.
+  </Card>
+</CardGroup>
+
+---
+
+## 2. llms.txt — 기계가 읽을 수 있는 문서
+
+CrewAI는 AI 어시스턴트가 전체 문서에 기계가 읽을 수 있는 형태로 바로 접근할 수 있도록 `llms.txt` 파일을 제공합니다.
+
+```
+https://docs.crewai.com/llms.txt
+```
+
+<Tabs>
+  <Tab title="llms.txt란?">
+    [`llms.txt`](https://llmstxt.org/)는 문서를 대규모 언어 모델이 소비하기 쉽게 만드는 새로운 표준입니다. HTML을 스크래핑하는 대신, 필요한 내용이 담긴 하나의 구조화된 텍스트 파일을 가져올 수 있습니다.
+
+    CrewAI의 `llms.txt`는 **이미 제공 중**이며, 에이전트가 바로 사용할 수 있습니다.
+  </Tab>
+  <Tab title="사용 방법">
+    CrewAI 참고 문서가 필요할 때 코딩 에이전트에 URL을 알려 주세요.
+
+    ```
+    Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
+    ```
+
+    Claude Code, Cursor 등 많은 코딩 에이전트가 URL을 직접 가져올 수 있습니다. 파일에는 CrewAI 개념, API, 가이드를 아우르는 구조화된 문서가 포함되어 있습니다.
+  </Tab>
+  <Tab title="왜 중요한가">
+    - **스크래핑 불필요** — 한 번의 요청으로 깔끔한 구조화 콘텐츠
+    - **항상 최신** — docs.crewai.com에서 직접 제공
+    - **LLM에 최적화** — 브라우저가 아니라 컨텍스트 윈도우에 맞게 포맷
+    - **스킬과 상호 보완** — 스킬은 패턴을, llms.txt는 참조를 제공
+  </Tab>
+</Tabs>
+
+---
+
+## 3. 엔터프라이즈에 배포
+
+로컬 crew를 몇 분 안에 **CrewAI AMP**(Agent Management Platform) 프로덕션으로 가져가세요.
+
+<Steps>
+  <Step title="로컬에서 빌드">
+    crew 또는 flow를 스캐폴딩하고 테스트합니다.
+    ```bash
+    crewai create crew my_crew
+    cd my_crew
+    crewai run
+    ```
+  </Step>
+  <Step title="배포 준비">
+    프로젝트 구조가 준비되었는지 확인합니다.
+    ```bash
+    crewai deploy --prepare
+    ```
+    구조와 요구 사항은 [준비 가이드](/ko/enterprise/guides/prepare-for-deployment)를 참고하세요.
+  </Step>
+  <Step title="AMP에 배포">
+    CrewAI AMP 플랫폼으로 푸시합니다.
+    ```bash
+    crewai deploy
+    ```
+    [GitHub 연동](/ko/enterprise/guides/deploy-to-amp) 또는 [Crew Studio](/ko/enterprise/guides/enable-crew-studio)로도 배포할 수 있습니다.
+  </Step>
+  <Step title="API로 접근">
+    배포된 crew는 REST API 엔드포인트를 받습니다. 모든 애플리케이션에 통합할 수 있습니다.
+    ```bash
+    curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
+      -H "Authorization: Bearer $CREWAI_API_KEY" \
+      -H "Content-Type: application/json" \
+      -d '{"inputs": {"topic": "AI agents"}}'
+    ```
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="AMP에 배포" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
+    전체 배포 가이드 — CLI, GitHub, Crew Studio 방법.
+  </Card>
+  <Card title="AMP 소개" icon="globe" href="/ko/enterprise/introduction">
+    플랫폼 개요 — 프로덕션 crew에 AMP가 제공하는 것.
+  </Card>
+</CardGroup>
+
+---
+
+## 4. 엔터프라이즈 기능
+
+CrewAI AMP는 프로덕션 팀을 위해 만들어졌습니다. 배포 외에 제공되는 것은 다음과 같습니다.
+
+<CardGroup cols={2}>
+  <Card title="관측 가능성" icon="chart-line">
+    모든 crew 실행에 대한 상세 실행 추적, 로그, 성능 지표. 에이전트 결정, 도구 호출, 태스크 완료를 실시간으로 모니터링합니다.
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    시각적으로 crew를 만들고, 맞춤 설정하고, 배포하는 노코드/로코드 인터페이스 — 코드로 보내거나 바로 배포할 수 있습니다.
+  </Card>
+  <Card title="웹훅 스트리밍" icon="webhook">
+    crew 실행에서 실시간 이벤트를 시스템으로 스트리밍합니다. Slack, Zapier 등 웹훅 소비자와 연동할 수 있습니다.
+  </Card>
+  <Card title="팀 관리" icon="users">
+    SSO, RBAC, 조직 단위 제어. 팀 전체에서 crew 생성·배포·접근 권한을 관리합니다.
+  </Card>
+  <Card title="도구 저장소" icon="toolbox">
+    조직 전체에 맞춤 도구를 게시하고 공유합니다. 레지스트리에서 커뮤니티 도구를 설치합니다.
+  </Card>
+  <Card title="Factory(셀프 호스팅)" icon="server">
+    자체 인프라에서 CrewAI AMP를 실행합니다. 데이터 상주와 규정 준수 제어와 함께 플랫폼 전체 기능을 사용할 수 있습니다.
+  </Card>
+</CardGroup>
+
+<AccordionGroup>
+  <Accordion title="AMP는 누구를 위한 것인가요?">
+    AI 에이전트 워크플로를 프로토타입에서 프로덕션으로 옮겨야 하는 팀을 위한 제품입니다. 관측 가능성, 접근 제어, 확장 가능한 인프라를 제공합니다. 스타트업이든 대기업이든 운영 복잡도는 AMP가 맡고, 에이전트 구축에 집중할 수 있습니다.
+  </Accordion>
+  <Accordion title="배포 옵션은 무엇이 있나요?">
+    - **클라우드 (app.crewai.com)** — CrewAI가 관리, 프로덕션까지 가장 빠른 경로
+    - **Factory(셀프 호스팅)** — 데이터 통제를 위해 자체 인프라에서 실행
+    - **하이브리드** — 민감도에 따라 클라우드와 셀프 호스팅을 혼합
+  </Accordion>
+</AccordionGroup>
+
+<Card title="CrewAI AMP 살펴보기 →" icon="arrow-right" href="https://app.crewai.com">
+  가입하고 첫 crew를 프로덕션에 배포해 보세요.
+</Card>

docs/ko/installation.mdx

@@ -189,7 +189,7 @@ CrewAI는 의존성 관리와 패키지 처리를 위해 `uv`를 사용합니다
 - 온프레미스 배포를 포함하여 모든 하이퍼스케일러 지원
 - 기존 보안 시스템과의 통합
 
-<Card title="엔터프라이즈 옵션 살펴보기" icon="building" href="https://crewai.com/enterprise">
+<Card title="엔터프라이즈 옵션 살펴보기" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   CrewAI의 엔터프라이즈 서비스에 대해 알아보고 데모를 예약하세요
 </Card>
 </Note>

docs/pt-BR/changelog.mdx

@@ -4,6 +4,161 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="22 abr 2026">
+  ## v1.14.3a2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
+
+  ## O que mudou
+
+  ### Recursos
+  - Adicionar suporte para bedrock V4
+  - Adicionar ferramentas de sandbox Daytona para funcionalidade aprimorada
+  - Adicionar página 'Construir com IA' — documentação nativa de IA para agentes de codificação
+  - Adicionar Construir com IA à navegação Começar e arquivos de página para todos os idiomas (en, ko, pt-BR, ar)
+
+  ### Correções de Bugs
+  - Corrigir a propagação de nomes implícitos @CrewBase para eventos da equipe
+  - Resolver problema com inicialização de lote duplicada na mesclagem de metadados de execução
+  - Corrigir a serialização de campos de referência de classe Task para checkpointing
+  - Lidar com o resultado BaseModel no loop de repetição de guardrail
+  - Atualizar python-dotenv para a versão >=1.2.2 para conformidade de segurança
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.3a1
+  - Atualizar descrições e aplicar traduções reais
+
+  ## Contributors
+
+  @MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
+
+</Update>
+
+<Update label="21 abr 2026">
+  ## v1.14.3a1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a1)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar suporte a checkpoint e fork para agentes autônomos
+
+  ### Correções de Bugs
+  - Preservar thought_signature nas chamadas da ferramenta de streaming Gemini
+  - Emitir task_started na retomada do fork e redesenhar a TUI de checkpoint
+  - Corrigir a ordem do dry-run e lidar com branch desatualizada em release do devtools
+  - Usar datas futuras nos testes de poda de checkpoint para evitar falhas dependentes do tempo (#5543)
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.2
+
+  ## Contribuidores
+
+  @alex-clawd, @greysonlalonde
+
+</Update>
+
+<Update label="17 abr 2026">
+  ## v1.14.2
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar comandos de retomar, diferenciar e podar checkpoints com melhor descobribilidade.
+  - Adicionar o parâmetro `from_checkpoint` ao `Agent.kickoff` e métodos relacionados.
+  - Adicionar comandos de gerenciamento de templates para templates de projeto.
+  - Adicionar dicas de retomar na liberação de devtools em caso de falha.
+  - Adicionar CLI de validação de implantação e melhorar a ergonomia da inicialização do LLM.
+  - Adicionar bifurcação de checkpoints com rastreamento de linhagem.
+  - Enriquecer o rastreamento de tokens do LLM com tokens de raciocínio e tokens de criação de cache.
+
+  ### Correções de Bugs
+  - Corrigir prompt em conflitos de branch obsoletos na liberação de devtools.
+  - Corrigir vulnerabilidades em `authlib`, `langchain-text-splitters` e `pypdf`.
+  - Restringir manipuladores de streaming para evitar contaminação de chunks entre execuções.
+  - Despachar checkpoints de Flow através das APIs de Flow na TUI.
+  - Usar glob recursivo para descoberta de checkpoints JSON.
+  - Lidar com esquemas JSON cíclicos na resolução de ferramentas MCP.
+  - Preservar os argumentos de chamada da ferramenta Bedrock removendo o padrão truthy.
+  - Emitir evento flow_finished após retomar HITL.
+  - Corrigir várias vulnerabilidades atualizando dependências, incluindo `requests`, `cryptography` e `pytest`.
+  - Corrigir para parar de encaminhar o modo estrito para a API Bedrock Converse.
+
+  ### Documentação
+  - Documentar parâmetros ausentes e adicionar seção de Checkpointing.
+  - Atualizar changelog e versão para v1.14.2 e candidatos a liberação anteriores.
+  - Adicionar documentação da funcionalidade A2A empresarial e atualizar a documentação A2A OSS.
+
+  ## Contribuidores
+
+  @Yanhu007, @alex-clawd, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @lucasgomide
+
+</Update>
+
+<Update label="16 abr 2026">
+  ## v1.14.2rc1
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2rc1)
+
+  ## O que Mudou
+
+  ### Correções de Bugs
+  - Corrigir o manuseio de esquemas JSON cíclicos na resolução da ferramenta MCP
+  - Corrigir vulnerabilidade atualizando python-multipart para 0.0.26
+  - Corrigir vulnerabilidade atualizando pypdf para 6.10.1
+
+  ### Documentação
+  - Atualizar o changelog e a versão para v1.14.2a5
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="15 abr 2026">
+  ## v1.14.2a5
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a5)
+
+  ## O que Mudou
+
+  ### Documentação
+  - Atualizar changelog e versão para v1.14.2a4
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
+<Update label="15 abr 2026">
+  ## v1.14.2a4
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.2a4)
+
+  ## O que Mudou
+
+  ### Recursos
+  - Adicionar dicas de retomar ao release do devtools em caso de falha
+
+  ### Correções de Bugs
+  - Corrigir o encaminhamento do modo estrito para a API Bedrock Converse
+  - Corrigir a versão do pytest para 9.0.3 devido à vulnerabilidade de segurança GHSA-6w46-j5rx-g56g
+  - Aumentar o limite inferior do OpenAI para >=2.0.0
+
+  ### Documentação
+  - Atualizar o changelog e a versão para v1.14.2a3
+
+  ## Contribuidores
+
+  @greysonlalonde
+
+</Update>
+
 <Update label="13 abr 2026">
   ## v1.14.2a3
 

docs/pt-BR/concepts/files.mdx

@@ -117,23 +117,35 @@ task = Task(
 
 ### Com Flows
 
-Passe arquivos para flows, que automaticamente herdam para crews:
+Campos tipados como arquivo (`File`, `ImageFile`, `PDFFile`) no esquema de estado do seu flow servem como sinal para a interface da Plataforma. Quando implantado, esses campos são renderizados como zonas de upload de arquivos. Arquivos também podem ser passados via `input_files` na API.
 
 ```python
 from crewai.flow.flow import Flow, start
-from crewai_files import ImageFile
+from crewai_files import File, ImageFile
+from pydantic import BaseModel
 
-class AnalysisFlow(Flow):
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform UI
+    cover_image: ImageFile   # Image-specific dropzone
+    title: str = ""
+
+class AnalysisFlow(Flow[MyState]):
     @start()
     def analyze(self):
+        # Files are automatically populated in state
+        content = self.state.document.read()
         return self.analysis_crew.kickoff()
 
 flow = AnalysisFlow()
 result = flow.kickoff(
-    input_files={"image": ImageFile(source="data.png")}
+    input_files={"document": File(source="report.pdf")}
 )
 ```
 
+<Note type="info" title="Integração com a Plataforma CrewAI">
+Quando implantado na Plataforma CrewAI, campos tipados como arquivo como `ImageFile`, `PDFFile` e outros no estado do seu flow recebem automaticamente uma interface de upload de arquivos. Os usuários podem arrastar e soltar arquivos diretamente na interface da Plataforma. Os arquivos são armazenados de forma segura e passados para os agentes usando otimizações específicas do provedor (base64 inline, APIs de upload de arquivo ou referências por URL dependendo do provedor). Para exemplos de uso da API, consulte [Entradas de Arquivos em Flows](/pt-BR/concepts/flows#entradas-de-arquivos).
+</Note>
+
 ### Com Agentes Standalone
 
 Passe arquivos diretamente no kickoff do agente:

docs/pt-BR/concepts/flows.mdx

@@ -173,6 +173,90 @@ Cada estado nos flows do CrewAI recebe automaticamente um identificador único (
 
 Ao oferecer as duas opções de gerenciamento de estado, o CrewAI Flows permite que desenvolvedores criem fluxos de IA que sejam ao mesmo tempo flexíveis e robustos, atendendo a uma ampla variedade de requisitos de aplicação.
 
+### Entradas de Arquivos
+
+Ao usar estado estruturado, você pode incluir campos tipados como arquivo usando classes do `crewai-files`. Campos tipados como arquivo no estado do seu flow servem como sinal para a Plataforma — eles são renderizados automaticamente como zonas de upload de arquivos na aba Run da interface e são preenchidos quando arquivos são enviados via Plataforma ou passados via `input_files` na API.
+
+```python
+from crewai.flow.flow import Flow, start
+from crewai_files import File, ImageFile, PDFFile
+from pydantic import BaseModel
+
+class MyState(BaseModel):
+    document: File           # Renders as file dropzone in Platform
+    title: str = ""
+
+class MyFlow(Flow[MyState]):
+    @start()
+    def process(self):
+        # File object is automatically populated in state
+        # when uploaded via Platform UI or passed via API
+        content = self.state.document.read()
+        print(f"Processing {self.state.title}: {len(content)} bytes")
+        return content
+```
+
+Quando implantado na **Plataforma CrewAI**, campos tipados como arquivo (`File`, `ImageFile`, `PDFFile` do `crewai-files`) são renderizados automaticamente como zonas de upload de arquivos na interface. Os usuários podem arrastar e soltar arquivos, que são então preenchidos no estado do seu flow.
+
+**Iniciando com arquivos via API:**
+
+O endpoint `/kickoff` detecta automaticamente o formato da requisição:
+- **Corpo JSON** → kickoff normal
+- **multipart/form-data** → upload de arquivo + kickoff
+
+Usuários da API também podem passar strings de URL diretamente para campos tipados como arquivo — o Pydantic as converte automaticamente.
+
+### Uso da API
+
+#### Opção 1: Kickoff multipart (recomendado)
+
+Envie arquivos diretamente com a requisição de kickoff:
+
+```bash
+# With files (multipart) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"company_name": "Einstein"}' \
+  -F 'cover_image=@/path/to/photo.jpg'
+```
+
+Os arquivos são armazenados automaticamente e convertidos em objetos `FileInput`. O agente recebe o arquivo com otimização específica do provedor (base64 inline, API de upload de arquivo ou referência por URL dependendo do provedor LLM).
+
+#### Opção 2: Kickoff JSON (sem arquivos)
+
+```bash
+# Without files (JSON) — same endpoint
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}}'
+```
+
+#### Opção 3: Upload separado + kickoff
+
+Esta é uma alternativa ao upload multipart quando você precisa fazer upload dos arquivos separadamente da requisição de kickoff. Faça o upload dos arquivos primeiro e depois referencie-os por URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/photo.jpg' \
+  -F 'field_name=cover_image'
+# Returns: {"url": "https://...", "field_name": "cover_image"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"company_name": "Einstein"}, "input_files": {"cover_image": "https://..."}}'
+```
+
+Consulte a documentação da API da Plataforma para detalhes completos do endpoint `/files`.
+
+#### Na Plataforma CrewAI
+
+Ao usar a interface da Plataforma, campos tipados como arquivo são renderizados automaticamente como zonas de arrastar e soltar para upload. Nenhuma chamada de API é necessária — basta soltar o arquivo e clicar em Executar.
+
 ## Persistência de Flow
 
 O decorador @persist permite a persistência automática do estado nos flows do CrewAI, garantindo que você mantenha o estado do flow entre reinicializações ou execuções diferentes do workflow. Esse decorador pode ser aplicado tanto ao nível de classe, quanto ao nível de método, oferecendo flexibilidade sobre como gerenciar a persistência do estado.

docs/pt-BR/enterprise/guides/kickoff-crew.mdx

@@ -86,6 +86,60 @@ curl -H "Authorization: Bearer YOUR_CREW_TOKEN" https://your-crew-url.crewai.com
 
 Seu bearer token está disponível na aba Status na página de detalhes do seu crew.
 
+## Upload de Arquivos
+
+Quando seu crew ou flow inclui campos de estado tipados como arquivo (usando `ImageFile`, `PDFFile` ou `File` do `crewai-files`), esses campos são renderizados automaticamente como zonas de upload de arquivos na aba Run da interface. Os usuários podem arrastar e soltar arquivos diretamente, e a Plataforma gerencia o armazenamento e entrega para seus agentes.
+
+### Kickoff Multipart (Recomendado)
+
+Envie arquivos diretamente com a requisição de kickoff usando `multipart/form-data`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'inputs={"title": "Report"}' \
+  -F 'document=@/path/to/file.pdf'
+```
+
+Os arquivos são armazenados automaticamente e convertidos em objetos de arquivo. O agente recebe o arquivo com otimização específica do provedor (base64 inline, API de upload de arquivo ou referência por URL dependendo do provedor LLM).
+
+### Kickoff JSON com URLs de Arquivos
+
+Se você já tem arquivos hospedados em URLs, passe-os via `input_files`:
+
+```bash
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "inputs": {"title": "Report"},
+    "input_files": {"document": "https://example.com/file.pdf"}
+  }'
+```
+
+### Upload Separado + Kickoff
+
+Faça upload dos arquivos primeiro e depois referencie-os por URL:
+
+```bash
+# Step 1: Upload
+curl -X POST https://your-deployment.crewai.com/files \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -F 'file=@/path/to/file.pdf' \
+  -F 'field_name=document'
+# Returns: {"url": "https://...", "field_name": "document"}
+
+# Step 2: Kickoff with URL
+curl -X POST https://your-deployment.crewai.com/kickoff \
+  -H 'Authorization: Bearer YOUR_TOKEN' \
+  -H 'Content-Type: application/json' \
+  -d '{"inputs": {"title": "Report"}, "input_files": {"document": "https://..."}}'
+```
+
+<Note type="info">
+O upload de arquivos funciona da mesma forma tanto para crews quanto para flows. Defina campos tipados como arquivo no seu esquema de estado, e a interface da Plataforma e a API tratarão os uploads automaticamente.
+</Note>
+
 ### Verificando o Status do Crew
 
 Antes de executar operações, você pode verificar se seu crew está funcionando corretamente:

docs/pt-BR/guides/coding-tools/build-with-ai.mdx

@@ -0,0 +1,214 @@
+---
+title: "Construa com IA"
+description: "Tudo o que agentes de codificação com IA precisam para criar, implantar e escalar com CrewAI — skills, documentação legível por máquina, implantação e recursos enterprise."
+icon: robot
+mode: "wide"
+---
+
+# Construa com IA
+
+O CrewAI é nativo de IA. Esta página reúne o que um agente de codificação com IA precisa para construir com CrewAI — seja Claude Code, Codex, Cursor, Gemini CLI ou qualquer outro assistente que ajude um desenvolvedor a entregar crews e flows.
+
+### Agentes de codificação compatíveis
+
+<CardGroup cols={5}>
+  <Card title="Claude Code" icon="message-bot" color="#D97706" />
+  <Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
+  <Card title="Codex" icon="terminal" color="#10B981" />
+  <Card title="Windsurf" icon="wind" color="#06B6D4" />
+  <Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
+</CardGroup>
+
+<Note>
+  Esta página serve para humanos e para assistentes de IA. Se você é um agente de codificação, comece por **Skills** para obter contexto do CrewAI e depois use **llms.txt** para acesso completo à documentação.
+</Note>
+
+---
+
+## 1. Skills — ensine CrewAI ao seu agente
+
+**Skills** são pacotes de instruções que dão aos agentes de codificação conhecimento profundo do CrewAI — como estruturar Flows, configurar Crews, usar ferramentas e seguir convenções do framework.
+
+<Tabs>
+  <Tab title="Claude Code (Plugin Marketplace)">
+    <img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
+    As skills do CrewAI estão no **plugin marketplace do Claude Code** — o mesmo canal usado por empresas líderes em IA:
+    ```shell
+    /plugin marketplace add crewAIInc/skills
+    /plugin install crewai-skills@crewai-plugins
+    /reload-plugins
+    ```
+
+    Quatro skills são ativadas automaticamente quando você faz perguntas relevantes sobre CrewAI:
+
+    | Skill | Quando é usada |
+    |-------|----------------|
+    | `getting-started` | Novos projetos, escolha entre `LLM.call()` / `Agent` / `Crew` / `Flow`, arquivos `crew.py` / `main.py` |
+    | `design-agent` | Configurar agentes — papel, objetivo, história, ferramentas, LLMs, memória, guardrails |
+    | `design-task` | Descrever tarefas, dependências, saída estruturada (`output_pydantic`, `output_json`), revisão humana |
+    | `ask-docs` | Consultar o [servidor MCP da documentação CrewAI](https://docs.crewai.com/mcp) em tempo real para detalhes de API |
+  </Tab>
+  <Tab title="npx (qualquer agente)">
+    Funciona com Claude Code, Codex, Cursor, Gemini CLI ou qualquer agente de codificação:
+    ```shell
+    npx skills add crewaiinc/skills
+    ```
+    Obtido do [registro skills.sh](https://skills.sh/crewaiinc/skills).
+  </Tab>
+</Tabs>
+
+<Steps>
+  <Step title="Instale o pacote oficial de skills">
+    Use um dos métodos acima — o plugin marketplace do Claude Code ou `npx skills add`. Ambos instalam o pacote oficial [crewAIInc/skills](https://github.com/crewAIInc/skills).
+  </Step>
+  <Step title="Seu agente ganha expertise imediata em CrewAI">
+    O pacote ensina ao seu agente:
+    - **Flows** — apps com estado, passos e disparo de crews
+    - **Crews e agentes** — padrões YAML-first, papéis, tarefas, delegação
+    - **Ferramentas e integrações** — busca, APIs, servidores MCP e ferramentas comuns do CrewAI
+    - **Estrutura do projeto** — scaffolds da CLI e convenções de repositório
+    - **Padrões atualizados** — alinhado à documentação e às melhores práticas atuais do CrewAI
+  </Step>
+  <Step title="Comece a construir">
+    Seu agente pode estruturar e construir projetos CrewAI sem você precisar reexplicar o framework a cada sessão.
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="Conceito de skills" icon="bolt" href="/pt-BR/concepts/skills">
+    Como skills funcionam em agentes CrewAI — injeção, ativação e padrões.
+  </Card>
+  <Card title="Página de skills" icon="wand-magic-sparkles" href="/pt-BR/skills">
+    Visão geral do pacote crewAIInc/skills e do que ele inclui.
+  </Card>
+  <Card title="AGENTS.md e ferramentas" icon="terminal" href="/pt-BR/guides/coding-tools/agents-md">
+    Configure o AGENTS.md para Claude Code, Codex, Cursor e Gemini CLI.
+  </Card>
+  <Card title="Registro skills.sh" icon="globe" href="https://skills.sh/crewaiinc/skills">
+    Listagem oficial — skills, estatísticas de instalação e auditorias.
+  </Card>
+</CardGroup>
+
+---
+
+## 2. llms.txt — documentação legível por máquina
+
+O CrewAI publica um arquivo `llms.txt` que dá aos assistentes de IA acesso direto à documentação completa em formato legível por máquinas.
+
+```
+https://docs.crewai.com/llms.txt
+```
+
+<Tabs>
+  <Tab title="O que é llms.txt?">
+    [`llms.txt`](https://llmstxt.org/) é um padrão emergente para tornar a documentação consumível por grandes modelos de linguagem. Em vez de fazer scraping de HTML, seu agente pode buscar um único arquivo de texto estruturado com o conteúdo necessário.
+
+    O `llms.txt` do CrewAI **já está no ar** — seu agente pode usar agora.
+  </Tab>
+  <Tab title="Como usar">
+    Indique ao agente de codificação a URL quando precisar da referência do CrewAI:
+
+    ```
+    Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
+    ```
+
+    Muitos agentes (Claude Code, Cursor etc.) conseguem buscar URLs diretamente. O arquivo contém documentação estruturada sobre conceitos, APIs e guias do CrewAI.
+  </Tab>
+  <Tab title="Por que importa">
+    - **Sem scraping** — conteúdo limpo e estruturado em uma requisição
+    - **Sempre atualizado** — servido diretamente de docs.crewai.com
+    - **Otimizado para LLMs** — formatado para janelas de contexto, não para navegadores
+    - **Complementa as skills** — skills ensinam padrões; llms.txt fornece referência
+  </Tab>
+</Tabs>
+
+---
+
+## 3. Implantação enterprise
+
+Do crew local à produção no **CrewAI AMP** (Agent Management Platform) em minutos.
+
+<Steps>
+  <Step title="Construa localmente">
+    Estruture e teste seu crew ou flow:
+    ```bash
+    crewai create crew my_crew
+    cd my_crew
+    crewai run
+    ```
+  </Step>
+  <Step title="Prepare a implantação">
+    Garanta que a estrutura do projeto está pronta:
+    ```bash
+    crewai deploy --prepare
+    ```
+    Veja o [guia de preparação](/pt-BR/enterprise/guides/prepare-for-deployment) para detalhes de estrutura e requisitos.
+  </Step>
+  <Step title="Implante no AMP">
+    Envie para a plataforma CrewAI AMP:
+    ```bash
+    crewai deploy
+    ```
+    Também é possível implantar pela [integração com GitHub](/pt-BR/enterprise/guides/deploy-to-amp) ou pelo [Crew Studio](/pt-BR/enterprise/guides/enable-crew-studio).
+  </Step>
+  <Step title="Acesso via API">
+    O crew implantado recebe um endpoint REST. Integre em qualquer aplicação:
+    ```bash
+    curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
+      -H "Authorization: Bearer $CREWAI_API_KEY" \
+      -H "Content-Type: application/json" \
+      -d '{"inputs": {"topic": "AI agents"}}'
+    ```
+  </Step>
+</Steps>
+
+<CardGroup cols={2}>
+  <Card title="Implantar no AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
+    Guia completo de implantação — CLI, GitHub e Crew Studio.
+  </Card>
+  <Card title="Introdução ao AMP" icon="globe" href="/pt-BR/enterprise/introduction">
+    Visão da plataforma — o que o AMP oferece para crews em produção.
+  </Card>
+</CardGroup>
+
+---
+
+## 4. Recursos enterprise
+
+O CrewAI AMP foi feito para equipes em produção. Além da implantação, você obtém:
+
+<CardGroup cols={2}>
+  <Card title="Observabilidade" icon="chart-line">
+    Traces de execução, logs e métricas de desempenho para cada execução de crew. Monitore decisões de agentes, chamadas de ferramentas e conclusão de tarefas em tempo real.
+  </Card>
+  <Card title="Crew Studio" icon="paintbrush">
+    Interface no-code/low-code para criar, personalizar e implantar crews visualmente — exporte para código ou implante direto.
+  </Card>
+  <Card title="Webhook streaming" icon="webhook">
+    Transmita eventos em tempo real das execuções para seus sistemas. Integre com Slack, Zapier ou qualquer consumidor de webhook.
+  </Card>
+  <Card title="Gestão de equipe" icon="users">
+    SSO, RBAC e controles em nível de organização. Gerencie quem pode criar, implantar e acessar crews.
+  </Card>
+  <Card title="Repositório de ferramentas" icon="toolbox">
+    Publique e compartilhe ferramentas customizadas na organização. Instale ferramentas da comunidade a partir do registro.
+  </Card>
+  <Card title="Factory (self-hosted)" icon="server">
+    Execute o CrewAI AMP na sua infraestrutura. Capacidades completas da plataforma com residência de dados e controles de conformidade.
+  </Card>
+</CardGroup>
+
+<AccordionGroup>
+  <Accordion title="Para quem é o AMP?">
+    Para equipes que precisam levar fluxos de agentes de IA do protótipo à produção — com observabilidade, controles de acesso e infraestrutura escalável. De startups a grandes empresas, o AMP cuida da complexidade operacional para você focar nos agentes.
+  </Accordion>
+  <Accordion title="Quais opções de implantação existem?">
+    - **Nuvem (app.crewai.com)** — gerenciada pela CrewAI, caminho mais rápido para produção
+    - **Factory (self-hosted)** — na sua infraestrutura para controle total dos dados
+    - **Híbrido** — combine nuvem e self-hosted conforme a sensibilidade dos dados
+  </Accordion>
+</AccordionGroup>
+
+<Card title="Conheça o CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
+  Cadastre-se e leve seu primeiro crew à produção.
+</Card>

docs/pt-BR/installation.mdx

@@ -191,7 +191,7 @@ Para equipes e organizações, o CrewAI oferece opções de implantação corpor
 - Compatível com qualquer hyperscaler, incluindo ambientes on-premises
 - Integração com seus sistemas de segurança existentes
 
-<Card title="Explore as Opções Enterprise" icon="building" href="https://crewai.com/enterprise">
+<Card title="Explore as Opções Enterprise" icon="building" href="https://share.hsforms.com/1Ooo2UViKQ22UOzdr7i77iwr87kg">
   Saiba mais sobre as soluções enterprise do CrewAI e agende uma demonstração
 </Card>
 </Note>

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

@@ -152,4 +152,4 @@ __all__ = [
     "wrap_file_source",
 ]
 
-__version__ = "1.14.2a3"
+__version__ = "1.14.3a2"

lib/crewai-tools/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     "pytube~=15.0.0",
     "requests>=2.33.0,<3",
-    "crewai==1.14.2a3",
+    "crewai==1.14.3a2",
     "tiktoken~=0.8.0",
     "beautifulsoup4~=4.13.4",
     "python-docx~=1.2.0",
@@ -112,7 +112,7 @@ github = [
 ]
 rag = [
     "python-docx>=1.1.0",
-    "lxml>=5.3.0,<5.4.0",  # Pin to avoid etree import issues in 5.4.0
+    "lxml>=6.1.0,<7",  # 6.1.0+ required for GHSA-vfmq-68hx-4jfw (XXE in iterparse)
 ]
 xml = [
     "unstructured[local-inference, all-docs]>=0.17.2"
@@ -139,6 +139,9 @@ contextual = [
     "contextual-client>=0.1.0",
     "nest-asyncio>=1.6.0",
 ]
+daytona = [
+    "daytona~=0.140.0",
+]
 
 
 [tool.uv]

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

@@ -59,6 +59,11 @@ from crewai_tools.tools.dalle_tool.dalle_tool import DallETool
 from crewai_tools.tools.databricks_query_tool.databricks_query_tool import (
     DatabricksQueryTool,
 )
+from crewai_tools.tools.daytona_sandbox_tool import (
+    DaytonaExecTool,
+    DaytonaFileTool,
+    DaytonaPythonTool,
+)
 from crewai_tools.tools.directory_read_tool.directory_read_tool import (
     DirectoryReadTool,
 )
@@ -232,6 +237,9 @@ __all__ = [
     "DOCXSearchTool",
     "DallETool",
     "DatabricksQueryTool",
+    "DaytonaExecTool",
+    "DaytonaFileTool",
+    "DaytonaPythonTool",
     "DirectoryReadTool",
     "DirectorySearchTool",
     "EXASearchTool",
@@ -305,4 +313,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.14.2a3"
+__version__ = "1.14.3a2"

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

@@ -48,6 +48,11 @@ from crewai_tools.tools.dalle_tool.dalle_tool import DallETool
 from crewai_tools.tools.databricks_query_tool.databricks_query_tool import (
     DatabricksQueryTool,
 )
+from crewai_tools.tools.daytona_sandbox_tool import (
+    DaytonaExecTool,
+    DaytonaFileTool,
+    DaytonaPythonTool,
+)
 from crewai_tools.tools.directory_read_tool.directory_read_tool import (
     DirectoryReadTool,
 )
@@ -217,6 +222,9 @@ __all__ = [
     "DOCXSearchTool",
     "DallETool",
     "DatabricksQueryTool",
+    "DaytonaExecTool",
+    "DaytonaFileTool",
+    "DaytonaPythonTool",
     "DirectoryReadTool",
     "DirectorySearchTool",
     "EXASearchTool",

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

@@ -0,0 +1,107 @@
+# Daytona Sandbox Tools
+
+Run shell commands, execute Python, and manage files inside a [Daytona](https://www.daytona.io/) sandbox. Daytona provides isolated, ephemeral compute environments suitable for agent-driven code execution.
+
+Three tools are provided so you can pick what the agent actually needs:
+
+- **`DaytonaExecTool`** — run a shell command (`sandbox.process.exec`).
+- **`DaytonaPythonTool`** — run a Python script (`sandbox.process.code_run`).
+- **`DaytonaFileTool`** — read / write / list / delete files (`sandbox.fs.*`).
+
+## Installation
+
+```shell
+uv add "crewai-tools[daytona]"
+# or
+pip install "crewai-tools[daytona]"
+```
+
+Set the API key:
+
+```shell
+export DAYTONA_API_KEY="..."
+```
+
+`DAYTONA_API_URL` and `DAYTONA_TARGET` are also respected if set.
+
+## Sandbox lifecycle
+
+All three tools share the same lifecycle controls from `DaytonaBaseTool`:
+
+| Mode | When the sandbox is created | When it is deleted |
+| --- | --- | --- |
+| **Ephemeral** (default, `persistent=False`) | On every `_run` call | At the end of that same call |
+| **Persistent** (`persistent=True`) | Lazily on first use | At process exit (via `atexit`), or manually via `tool.close()` |
+| **Attach** (`sandbox_id="…"`) | Never — the tool attaches to an existing sandbox | Never — the tool will not delete a sandbox it did not create |
+
+Ephemeral mode is the safe default: nothing leaks if the agent forgets to clean up. Use persistent mode when you want filesystem state or installed packages to carry across steps — this is typical when pairing `DaytonaFileTool` with `DaytonaExecTool`.
+
+## Examples
+
+### One-shot Python execution (ephemeral)
+
+```python
+from crewai_tools import DaytonaPythonTool
+
+tool = DaytonaPythonTool()
+result = tool.run(code="print(sum(range(10)))")
+```
+
+### Multi-step shell session (persistent)
+
+```python
+from crewai_tools import DaytonaExecTool, DaytonaFileTool
+
+exec_tool = DaytonaExecTool(persistent=True)
+file_tool = DaytonaFileTool(persistent=True)
+
+# Agent writes a script, then runs it — both share the same sandbox instance
+# because they each keep their own persistent sandbox. If you need the *same*
+# sandbox across two tools, create one tool, grab the sandbox id via
+# `tool._persistent_sandbox.id`, and pass it to the other via `sandbox_id=...`.
+```
+
+### Attach to an existing sandbox
+
+```python
+from crewai_tools import DaytonaExecTool
+
+tool = DaytonaExecTool(sandbox_id="my-long-lived-sandbox")
+```
+
+### Custom create params
+
+Pass Daytona's `CreateSandboxFromSnapshotParams` kwargs via `create_params`:
+
+```python
+tool = DaytonaExecTool(
+    persistent=True,
+    create_params={
+        "language": "python",
+        "env_vars": {"MY_FLAG": "1"},
+        "labels": {"owner": "crewai-agent"},
+    },
+)
+```
+
+## Tool arguments
+
+### `DaytonaExecTool`
+- `command: str` — shell command to run.
+- `cwd: str | None` — working directory.
+- `env: dict[str, str] | None` — extra env vars for this command.
+- `timeout: int | None` — seconds.
+
+### `DaytonaPythonTool`
+- `code: str` — Python source to execute.
+- `argv: list[str] | None` — argv forwarded via `CodeRunParams`.
+- `env: dict[str, str] | None` — env vars forwarded via `CodeRunParams`.
+- `timeout: int | None` — seconds.
+
+### `DaytonaFileTool`
+- `action: "read" | "write" | "list" | "delete" | "mkdir" | "info"`
+- `path: str` — absolute path inside the sandbox.
+- `content: str | None` — required for `write`.
+- `binary: bool` — if `True`, `content` is base64 on write / returned as base64 on read.
+- `recursive: bool` — for `delete`, removes directories recursively.
+- `mode: str` — for `mkdir`, octal permission string (default `"0755"`).

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

@@ -0,0 +1,13 @@
+from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
+from crewai_tools.tools.daytona_sandbox_tool.daytona_exec_tool import DaytonaExecTool
+from crewai_tools.tools.daytona_sandbox_tool.daytona_file_tool import DaytonaFileTool
+from crewai_tools.tools.daytona_sandbox_tool.daytona_python_tool import (
+    DaytonaPythonTool,
+)
+
+__all__ = [
+    "DaytonaBaseTool",
+    "DaytonaExecTool",
+    "DaytonaFileTool",
+    "DaytonaPythonTool",
+]

lib/crewai-tools/src/crewai_tools/tools/daytona_sandbox_tool/daytona_base_tool.py

@@ -0,0 +1,198 @@
+from __future__ import annotations
+
+import atexit
+import logging
+import os
+import threading
+from typing import Any, ClassVar
+
+from crewai.tools import BaseTool, EnvVar
+from pydantic import ConfigDict, Field, PrivateAttr
+
+
+logger = logging.getLogger(__name__)
+
+
+class DaytonaBaseTool(BaseTool):
+    """Shared base for tools that act on a Daytona sandbox.
+
+    Lifecycle modes:
+      - persistent=False (default): create a fresh sandbox per `_run` call and
+        delete it when the call returns. Safer and stateless — nothing leaks if
+        the agent forgets cleanup.
+      - persistent=True: lazily create a single sandbox on first use, cache it
+        on the instance, and register an atexit hook to delete it at process
+        exit. Cheaper across many calls and lets files/state carry over.
+      - sandbox_id=<existing>: attach to a sandbox the caller already owns.
+        Never deleted by the tool.
+    """
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    package_dependencies: list[str] = Field(default_factory=lambda: ["daytona"])
+
+    api_key: str | None = Field(
+        default_factory=lambda: os.getenv("DAYTONA_API_KEY"),
+        description="Daytona API key. Falls back to DAYTONA_API_KEY env var.",
+        json_schema_extra={"required": False},
+    )
+    api_url: str | None = Field(
+        default_factory=lambda: os.getenv("DAYTONA_API_URL"),
+        description="Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
+        json_schema_extra={"required": False},
+    )
+    target: str | None = Field(
+        default_factory=lambda: os.getenv("DAYTONA_TARGET"),
+        description="Daytona target region. Falls back to DAYTONA_TARGET env var.",
+        json_schema_extra={"required": False},
+    )
+
+    persistent: bool = Field(
+        default=False,
+        description=(
+            "If True, reuse one sandbox across all calls to this tool instance "
+            "and delete it at process exit. Default False creates and deletes a "
+            "fresh sandbox per call."
+        ),
+    )
+    sandbox_id: str | None = Field(
+        default=None,
+        description=(
+            "Attach to an existing sandbox by id or name instead of creating a "
+            "new one. The tool will never delete a sandbox it did not create."
+        ),
+    )
+    create_params: dict[str, Any] | None = Field(
+        default=None,
+        description=(
+            "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when "
+            "creating a sandbox (e.g. language, snapshot, env_vars, labels)."
+        ),
+    )
+    sandbox_timeout: float = Field(
+        default=60.0,
+        description="Timeout in seconds for sandbox create/delete operations.",
+    )
+
+    env_vars: list[EnvVar] = Field(
+        default_factory=lambda: [
+            EnvVar(
+                name="DAYTONA_API_KEY",
+                description="API key for Daytona sandbox service",
+                required=False,
+            ),
+            EnvVar(
+                name="DAYTONA_API_URL",
+                description="Daytona API base URL (optional)",
+                required=False,
+            ),
+            EnvVar(
+                name="DAYTONA_TARGET",
+                description="Daytona target region (optional)",
+                required=False,
+            ),
+        ]
+    )
+
+    _client: Any | None = PrivateAttr(default=None)
+    _persistent_sandbox: Any | None = PrivateAttr(default=None)
+    _lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
+    _cleanup_registered: bool = PrivateAttr(default=False)
+
+    _sdk_cache: ClassVar[dict[str, Any]] = {}
+
+    @classmethod
+    def _import_sdk(cls) -> dict[str, Any]:
+        if cls._sdk_cache:
+            return cls._sdk_cache
+        try:
+            from daytona import (
+                CreateSandboxFromSnapshotParams,
+                Daytona,
+                DaytonaConfig,
+            )
+        except ImportError as exc:
+            raise ImportError(
+                "The 'daytona' package is required for Daytona sandbox tools. "
+                "Install it with: uv add daytona  (or) pip install daytona"
+            ) from exc
+        cls._sdk_cache = {
+            "Daytona": Daytona,
+            "DaytonaConfig": DaytonaConfig,
+            "CreateSandboxFromSnapshotParams": CreateSandboxFromSnapshotParams,
+        }
+        return cls._sdk_cache
+
+    def _get_client(self) -> Any:
+        if self._client is not None:
+            return self._client
+        sdk = self._import_sdk()
+        config_kwargs: dict[str, Any] = {}
+        if self.api_key:
+            config_kwargs["api_key"] = self.api_key
+        if self.api_url:
+            config_kwargs["api_url"] = self.api_url
+        if self.target:
+            config_kwargs["target"] = self.target
+        config = sdk["DaytonaConfig"](**config_kwargs) if config_kwargs else None
+        self._client = sdk["Daytona"](config) if config else sdk["Daytona"]()
+        return self._client
+
+    def _build_create_params(self) -> Any | None:
+        if not self.create_params:
+            return None
+        sdk = self._import_sdk()
+        return sdk["CreateSandboxFromSnapshotParams"](**self.create_params)
+
+    def _acquire_sandbox(self) -> tuple[Any, bool]:
+        """Return (sandbox, should_delete_after_use)."""
+        client = self._get_client()
+
+        if self.sandbox_id:
+            return client.get(self.sandbox_id), False
+
+        if self.persistent:
+            with self._lock:
+                if self._persistent_sandbox is None:
+                    self._persistent_sandbox = client.create(
+                        self._build_create_params(),
+                        timeout=self.sandbox_timeout,
+                    )
+                    if not self._cleanup_registered:
+                        atexit.register(self.close)
+                        self._cleanup_registered = True
+                return self._persistent_sandbox, False
+
+        sandbox = client.create(
+            self._build_create_params(),
+            timeout=self.sandbox_timeout,
+        )
+        return sandbox, True
+
+    def _release_sandbox(self, sandbox: Any, should_delete: bool) -> None:
+        if not should_delete:
+            return
+        try:
+            sandbox.delete(timeout=self.sandbox_timeout)
+        except Exception:
+            logger.debug(
+                "Best-effort sandbox cleanup failed after ephemeral use; "
+                "the sandbox may need manual deletion.",
+                exc_info=True,
+            )
+
+    def close(self) -> None:
+        """Delete the cached persistent sandbox if one exists."""
+        with self._lock:
+            sandbox = self._persistent_sandbox
+            self._persistent_sandbox = None
+        if sandbox is None:
+            return
+        try:
+            sandbox.delete(timeout=self.sandbox_timeout)
+        except Exception:
+            logger.debug(
+                "Best-effort persistent sandbox cleanup failed at close(); "
+                "the sandbox may need manual deletion.",
+                exc_info=True,
+            )

lib/crewai-tools/src/crewai_tools/tools/daytona_sandbox_tool/daytona_exec_tool.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+from builtins import type as type_
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
+
+
+class DaytonaExecToolSchema(BaseModel):
+    command: str = Field(..., description="Shell command to execute in the sandbox.")
+    cwd: str | None = Field(
+        default=None,
+        description="Working directory to run the command in. Defaults to the sandbox work dir.",
+    )
+    env: dict[str, str] | None = Field(
+        default=None,
+        description="Optional environment variables to set for this command.",
+    )
+    timeout: int | None = Field(
+        default=None,
+        description="Maximum seconds to wait for the command to finish.",
+    )
+
+
+class DaytonaExecTool(DaytonaBaseTool):
+    """Run a shell command inside a Daytona sandbox."""
+
+    name: str = "Daytona Sandbox Exec"
+    description: str = (
+        "Execute a shell command inside a Daytona sandbox and return the exit "
+        "code and combined output. Use this to run builds, package installs, "
+        "git operations, or any one-off shell command."
+    )
+    args_schema: type_[BaseModel] = DaytonaExecToolSchema
+
+    def _run(
+        self,
+        command: str,
+        cwd: str | None = None,
+        env: dict[str, str] | None = None,
+        timeout: int | None = None,
+    ) -> Any:
+        sandbox, should_delete = self._acquire_sandbox()
+        try:
+            response = sandbox.process.exec(
+                command,
+                cwd=cwd,
+                env=env,
+                timeout=timeout,
+            )
+            return {
+                "exit_code": getattr(response, "exit_code", None),
+                "result": getattr(response, "result", None),
+                "artifacts": getattr(response, "artifacts", None),
+            }
+        finally:
+            self._release_sandbox(sandbox, should_delete)

lib/crewai-tools/src/crewai_tools/tools/daytona_sandbox_tool/daytona_file_tool.py

@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+import base64
+from builtins import type as type_
+import logging
+import posixpath
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field, model_validator
+
+from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
+
+
+logger = logging.getLogger(__name__)
+
+
+FileAction = Literal["read", "write", "append", "list", "delete", "mkdir", "info"]
+
+
+class DaytonaFileToolSchema(BaseModel):
+    action: FileAction = Field(
+        ...,
+        description=(
+            "The filesystem action to perform: 'read' (returns file contents), "
+            "'write' (create or replace a file with content), 'append' (append "
+            "content to an existing file — use this for writing large files in "
+            "chunks to avoid hitting tool-call size limits), 'list' (lists a "
+            "directory), 'delete' (removes a file/dir), 'mkdir' (creates a "
+            "directory), 'info' (returns file metadata)."
+        ),
+    )
+    path: str = Field(..., description="Absolute path inside the sandbox.")
+    content: str | None = Field(
+        default=None,
+        description=(
+            "Content to write or append. If omitted for 'write', an empty file "
+            "is created. For files larger than a few KB, prefer one 'write' "
+            "with empty content followed by multiple 'append' calls of ~4KB "
+            "each to stay within tool-call payload limits."
+        ),
+    )
+    binary: bool = Field(
+        default=False,
+        description=(
+            "For 'write': treat content as base64 and upload raw bytes. "
+            "For 'read': return contents as base64 instead of decoded utf-8."
+        ),
+    )
+    recursive: bool = Field(
+        default=False,
+        description="For action='delete': remove directories recursively.",
+    )
+    mode: str = Field(
+        default="0755",
+        description="For action='mkdir': octal permission string (default 0755).",
+    )
+
+    @model_validator(mode="after")
+    def _validate_action_args(self) -> DaytonaFileToolSchema:
+        if self.action == "append" and self.content is None:
+            raise ValueError(
+                "action='append' requires 'content'. Pass the chunk to append "
+                "in the 'content' field."
+            )
+        return self
+
+
+class DaytonaFileTool(DaytonaBaseTool):
+    """Read, write, and manage files inside a Daytona sandbox.
+
+    Notes:
+      - Most useful with `persistent=True` or an explicit `sandbox_id`. With the
+        default ephemeral mode, files disappear when this tool call finishes.
+    """
+
+    name: str = "Daytona Sandbox Files"
+    description: str = (
+        "Perform filesystem operations inside a Daytona sandbox: read a file, "
+        "write content to a path, append content to an existing file, list a "
+        "directory, delete a path, make a directory, or fetch file metadata. "
+        "For files larger than a few KB, create the file with action='write' "
+        "and empty content, then send the body via multiple 'append' calls of "
+        "~4KB each to stay within tool-call payload limits."
+    )
+    args_schema: type_[BaseModel] = DaytonaFileToolSchema
+
+    def _run(
+        self,
+        action: FileAction,
+        path: str,
+        content: str | None = None,
+        binary: bool = False,
+        recursive: bool = False,
+        mode: str = "0755",
+    ) -> Any:
+        sandbox, should_delete = self._acquire_sandbox()
+        try:
+            if action == "read":
+                return self._read(sandbox, path, binary=binary)
+            if action == "write":
+                return self._write(sandbox, path, content or "", binary=binary)
+            if action == "append":
+                return self._append(sandbox, path, content or "", binary=binary)
+            if action == "list":
+                return self._list(sandbox, path)
+            if action == "delete":
+                sandbox.fs.delete_file(path, recursive=recursive)
+                return {"status": "deleted", "path": path}
+            if action == "mkdir":
+                sandbox.fs.create_folder(path, mode)
+                return {"status": "created", "path": path, "mode": mode}
+            if action == "info":
+                return self._info(sandbox, path)
+            raise ValueError(f"Unknown action: {action}")
+        finally:
+            self._release_sandbox(sandbox, should_delete)
+
+    def _read(self, sandbox: Any, path: str, *, binary: bool) -> dict[str, Any]:
+        data: bytes = sandbox.fs.download_file(path)
+        if binary:
+            return {
+                "path": path,
+                "encoding": "base64",
+                "content": base64.b64encode(data).decode("ascii"),
+            }
+        try:
+            return {"path": path, "encoding": "utf-8", "content": data.decode("utf-8")}
+        except UnicodeDecodeError:
+            return {
+                "path": path,
+                "encoding": "base64",
+                "content": base64.b64encode(data).decode("ascii"),
+                "note": "File was not valid utf-8; returned as base64.",
+            }
+
+    def _write(
+        self, sandbox: Any, path: str, content: str, *, binary: bool
+    ) -> dict[str, Any]:
+        payload = base64.b64decode(content) if binary else content.encode("utf-8")
+        self._ensure_parent_dir(sandbox, path)
+        sandbox.fs.upload_file(payload, path)
+        return {"status": "written", "path": path, "bytes": len(payload)}
+
+    def _append(
+        self, sandbox: Any, path: str, content: str, *, binary: bool
+    ) -> dict[str, Any]:
+        chunk = base64.b64decode(content) if binary else content.encode("utf-8")
+        self._ensure_parent_dir(sandbox, path)
+        try:
+            existing: bytes = sandbox.fs.download_file(path)
+        except Exception:
+            existing = b""
+        payload = existing + chunk
+        sandbox.fs.upload_file(payload, path)
+        return {
+            "status": "appended",
+            "path": path,
+            "appended_bytes": len(chunk),
+            "total_bytes": len(payload),
+        }
+
+    @staticmethod
+    def _ensure_parent_dir(sandbox: Any, path: str) -> None:
+        """Make sure the parent directory of `path` exists.
+
+        Daytona's upload returns 400 if the parent directory is missing. We
+        best-effort mkdir the parent; any error (e.g. already exists) is
+        swallowed because `create_folder` is not idempotent on the server.
+        """
+        parent = posixpath.dirname(path)
+        if not parent or parent in ("/", "."):
+            return
+        try:
+            sandbox.fs.create_folder(parent, "0755")
+        except Exception:
+            logger.debug(
+                "Best-effort parent-directory create failed for %s; "
+                "assuming it already exists and proceeding with the write.",
+                parent,
+                exc_info=True,
+            )
+
+    def _list(self, sandbox: Any, path: str) -> dict[str, Any]:
+        entries = sandbox.fs.list_files(path)
+        return {
+            "path": path,
+            "entries": [self._file_info_to_dict(entry) for entry in entries],
+        }
+
+    def _info(self, sandbox: Any, path: str) -> dict[str, Any]:
+        return self._file_info_to_dict(sandbox.fs.get_file_info(path))
+
+    @staticmethod
+    def _file_info_to_dict(info: Any) -> dict[str, Any]:
+        fields = (
+            "name",
+            "size",
+            "mode",
+            "permissions",
+            "is_dir",
+            "mod_time",
+            "owner",
+            "group",
+        )
+        return {field: getattr(info, field, None) for field in fields}

lib/crewai-tools/src/crewai_tools/tools/daytona_sandbox_tool/daytona_python_tool.py

@@ -0,0 +1,82 @@
+from __future__ import annotations
+
+from builtins import type as type_
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from crewai_tools.tools.daytona_sandbox_tool.daytona_base_tool import DaytonaBaseTool
+
+
+class DaytonaPythonToolSchema(BaseModel):
+    code: str = Field(
+        ...,
+        description="Python source to execute inside the sandbox.",
+    )
+    argv: list[str] | None = Field(
+        default=None,
+        description="Optional argv passed to the script (forwarded as params.argv).",
+    )
+    env: dict[str, str] | None = Field(
+        default=None,
+        description="Optional environment variables for the run (forwarded as params.env).",
+    )
+    timeout: int | None = Field(
+        default=None,
+        description="Maximum seconds to wait for the code to finish.",
+    )
+
+
+class DaytonaPythonTool(DaytonaBaseTool):
+    """Run Python source inside a Daytona sandbox."""
+
+    name: str = "Daytona Sandbox Python"
+    description: str = (
+        "Execute a block of Python code inside a Daytona sandbox and return the "
+        "exit code, captured stdout, and any produced artifacts. Use this for "
+        "data processing, quick scripts, or analysis that should run in an "
+        "isolated environment."
+    )
+    args_schema: type_[BaseModel] = DaytonaPythonToolSchema
+
+    def _run(
+        self,
+        code: str,
+        argv: list[str] | None = None,
+        env: dict[str, str] | None = None,
+        timeout: int | None = None,
+    ) -> Any:
+        sandbox, should_delete = self._acquire_sandbox()
+        try:
+            params = self._build_code_run_params(argv=argv, env=env)
+            response = sandbox.process.code_run(code, params=params, timeout=timeout)
+            return {
+                "exit_code": getattr(response, "exit_code", None),
+                "result": getattr(response, "result", None),
+                "artifacts": getattr(response, "artifacts", None),
+            }
+        finally:
+            self._release_sandbox(sandbox, should_delete)
+
+    def _build_code_run_params(
+        self,
+        argv: list[str] | None,
+        env: dict[str, str] | None,
+    ) -> Any | None:
+        if argv is None and env is None:
+            return None
+        try:
+            from daytona import CodeRunParams
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import daytona.CodeRunParams while building "
+                "argv/env for sandbox.process.code_run. This usually means the "
+                "installed 'daytona' SDK is too old or incompatible. Upgrade "
+                "with: pip install -U 'crewai-tools[daytona]'"
+            ) from exc
+        kwargs: dict[str, Any] = {}
+        if argv is not None:
+            kwargs["argv"] = argv
+        if env is not None:
+            kwargs["env"] = env
+        return CodeRunParams(**kwargs)

lib/crewai-tools/tool.specs.json

@@ -6976,6 +6976,634 @@
         "type": "object"
       }
     },
+    {
+      "description": "Execute a shell command inside a Daytona sandbox and return the exit code and combined output. Use this to run builds, package installs, git operations, or any one-off shell command.",
+      "env_vars": [
+        {
+          "default": null,
+          "description": "API key for Daytona sandbox service",
+          "name": "DAYTONA_API_KEY",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona API base URL (optional)",
+          "name": "DAYTONA_API_URL",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona target region (optional)",
+          "name": "DAYTONA_TARGET",
+          "required": false
+        }
+      ],
+      "humanized_name": "Daytona Sandbox Exec",
+      "init_params_schema": {
+        "$defs": {
+          "EnvVar": {
+            "properties": {
+              "default": {
+                "anyOf": [
+                  {
+                    "type": "string"
+                  },
+                  {
+                    "type": "null"
+                  }
+                ],
+                "default": null,
+                "title": "Default"
+              },
+              "description": {
+                "title": "Description",
+                "type": "string"
+              },
+              "name": {
+                "title": "Name",
+                "type": "string"
+              },
+              "required": {
+                "default": true,
+                "title": "Required",
+                "type": "boolean"
+              }
+            },
+            "required": [
+              "name",
+              "description"
+            ],
+            "title": "EnvVar",
+            "type": "object"
+          }
+        },
+        "description": "Run a shell command inside a Daytona sandbox.",
+        "properties": {
+          "api_key": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
+            "required": false,
+            "title": "Api Key"
+          },
+          "api_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
+            "required": false,
+            "title": "Api Url"
+          },
+          "create_params": {
+            "anyOf": [
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
+            "title": "Create Params"
+          },
+          "persistent": {
+            "default": false,
+            "description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes a fresh sandbox per call.",
+            "title": "Persistent",
+            "type": "boolean"
+          },
+          "sandbox_id": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Attach to an existing sandbox by id or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
+            "title": "Sandbox Id"
+          },
+          "sandbox_timeout": {
+            "default": 60.0,
+            "description": "Timeout in seconds for sandbox create/delete operations.",
+            "title": "Sandbox Timeout",
+            "type": "number"
+          },
+          "target": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
+            "required": false,
+            "title": "Target"
+          }
+        },
+        "required": [],
+        "title": "DaytonaExecTool",
+        "type": "object"
+      },
+      "name": "DaytonaExecTool",
+      "package_dependencies": [
+        "daytona"
+      ],
+      "run_params_schema": {
+        "properties": {
+          "command": {
+            "description": "Shell command to execute in the sandbox.",
+            "title": "Command",
+            "type": "string"
+          },
+          "cwd": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Working directory to run the command in. Defaults to the sandbox work dir.",
+            "title": "Cwd"
+          },
+          "env": {
+            "anyOf": [
+              {
+                "additionalProperties": {
+                  "type": "string"
+                },
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional environment variables to set for this command.",
+            "title": "Env"
+          },
+          "timeout": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Maximum seconds to wait for the command to finish.",
+            "title": "Timeout"
+          }
+        },
+        "required": [
+          "command"
+        ],
+        "title": "DaytonaExecToolSchema",
+        "type": "object"
+      }
+    },
+    {
+      "description": "Perform filesystem operations inside a Daytona sandbox: read a file, write content to a path, append content to an existing file, list a directory, delete a path, make a directory, or fetch file metadata. For files larger than a few KB, create the file with action='write' and empty content, then send the body via multiple 'append' calls of ~4KB each to stay within tool-call payload limits.",
+      "env_vars": [
+        {
+          "default": null,
+          "description": "API key for Daytona sandbox service",
+          "name": "DAYTONA_API_KEY",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona API base URL (optional)",
+          "name": "DAYTONA_API_URL",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona target region (optional)",
+          "name": "DAYTONA_TARGET",
+          "required": false
+        }
+      ],
+      "humanized_name": "Daytona Sandbox Files",
+      "init_params_schema": {
+        "$defs": {
+          "EnvVar": {
+            "properties": {
+              "default": {
+                "anyOf": [
+                  {
+                    "type": "string"
+                  },
+                  {
+                    "type": "null"
+                  }
+                ],
+                "default": null,
+                "title": "Default"
+              },
+              "description": {
+                "title": "Description",
+                "type": "string"
+              },
+              "name": {
+                "title": "Name",
+                "type": "string"
+              },
+              "required": {
+                "default": true,
+                "title": "Required",
+                "type": "boolean"
+              }
+            },
+            "required": [
+              "name",
+              "description"
+            ],
+            "title": "EnvVar",
+            "type": "object"
+          }
+        },
+        "description": "Read, write, and manage files inside a Daytona sandbox.\n\nNotes:\n  - Most useful with `persistent=True` or an explicit `sandbox_id`. With the\n    default ephemeral mode, files disappear when this tool call finishes.",
+        "properties": {
+          "api_key": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
+            "required": false,
+            "title": "Api Key"
+          },
+          "api_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
+            "required": false,
+            "title": "Api Url"
+          },
+          "create_params": {
+            "anyOf": [
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
+            "title": "Create Params"
+          },
+          "persistent": {
+            "default": false,
+            "description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes a fresh sandbox per call.",
+            "title": "Persistent",
+            "type": "boolean"
+          },
+          "sandbox_id": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Attach to an existing sandbox by id or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
+            "title": "Sandbox Id"
+          },
+          "sandbox_timeout": {
+            "default": 60.0,
+            "description": "Timeout in seconds for sandbox create/delete operations.",
+            "title": "Sandbox Timeout",
+            "type": "number"
+          },
+          "target": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
+            "required": false,
+            "title": "Target"
+          }
+        },
+        "required": [],
+        "title": "DaytonaFileTool",
+        "type": "object"
+      },
+      "name": "DaytonaFileTool",
+      "package_dependencies": [
+        "daytona"
+      ],
+      "run_params_schema": {
+        "properties": {
+          "action": {
+            "description": "The filesystem action to perform: 'read' (returns file contents), 'write' (create or replace a file with content), 'append' (append content to an existing file \u2014 use this for writing large files in chunks to avoid hitting tool-call size limits), 'list' (lists a directory), 'delete' (removes a file/dir), 'mkdir' (creates a directory), 'info' (returns file metadata).",
+            "enum": [
+              "read",
+              "write",
+              "append",
+              "list",
+              "delete",
+              "mkdir",
+              "info"
+            ],
+            "title": "Action",
+            "type": "string"
+          },
+          "binary": {
+            "default": false,
+            "description": "For 'write': treat content as base64 and upload raw bytes. For 'read': return contents as base64 instead of decoded utf-8.",
+            "title": "Binary",
+            "type": "boolean"
+          },
+          "content": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Content to write or append. If omitted for 'write', an empty file is created. For files larger than a few KB, prefer one 'write' with empty content followed by multiple 'append' calls of ~4KB each to stay within tool-call payload limits.",
+            "title": "Content"
+          },
+          "mode": {
+            "default": "0755",
+            "description": "For action='mkdir': octal permission string (default 0755).",
+            "title": "Mode",
+            "type": "string"
+          },
+          "path": {
+            "description": "Absolute path inside the sandbox.",
+            "title": "Path",
+            "type": "string"
+          },
+          "recursive": {
+            "default": false,
+            "description": "For action='delete': remove directories recursively.",
+            "title": "Recursive",
+            "type": "boolean"
+          }
+        },
+        "required": [
+          "action",
+          "path"
+        ],
+        "title": "DaytonaFileToolSchema",
+        "type": "object"
+      }
+    },
+    {
+      "description": "Execute a block of Python code inside a Daytona sandbox and return the exit code, captured stdout, and any produced artifacts. Use this for data processing, quick scripts, or analysis that should run in an isolated environment.",
+      "env_vars": [
+        {
+          "default": null,
+          "description": "API key for Daytona sandbox service",
+          "name": "DAYTONA_API_KEY",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona API base URL (optional)",
+          "name": "DAYTONA_API_URL",
+          "required": false
+        },
+        {
+          "default": null,
+          "description": "Daytona target region (optional)",
+          "name": "DAYTONA_TARGET",
+          "required": false
+        }
+      ],
+      "humanized_name": "Daytona Sandbox Python",
+      "init_params_schema": {
+        "$defs": {
+          "EnvVar": {
+            "properties": {
+              "default": {
+                "anyOf": [
+                  {
+                    "type": "string"
+                  },
+                  {
+                    "type": "null"
+                  }
+                ],
+                "default": null,
+                "title": "Default"
+              },
+              "description": {
+                "title": "Description",
+                "type": "string"
+              },
+              "name": {
+                "title": "Name",
+                "type": "string"
+              },
+              "required": {
+                "default": true,
+                "title": "Required",
+                "type": "boolean"
+              }
+            },
+            "required": [
+              "name",
+              "description"
+            ],
+            "title": "EnvVar",
+            "type": "object"
+          }
+        },
+        "description": "Run Python source inside a Daytona sandbox.",
+        "properties": {
+          "api_key": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API key. Falls back to DAYTONA_API_KEY env var.",
+            "required": false,
+            "title": "Api Key"
+          },
+          "api_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona API URL override. Falls back to DAYTONA_API_URL env var.",
+            "required": false,
+            "title": "Api Url"
+          },
+          "create_params": {
+            "anyOf": [
+              {
+                "additionalProperties": true,
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional kwargs forwarded to CreateSandboxFromSnapshotParams when creating a sandbox (e.g. language, snapshot, env_vars, labels).",
+            "title": "Create Params"
+          },
+          "persistent": {
+            "default": false,
+            "description": "If True, reuse one sandbox across all calls to this tool instance and delete it at process exit. Default False creates and deletes a fresh sandbox per call.",
+            "title": "Persistent",
+            "type": "boolean"
+          },
+          "sandbox_id": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Attach to an existing sandbox by id or name instead of creating a new one. The tool will never delete a sandbox it did not create.",
+            "title": "Sandbox Id"
+          },
+          "sandbox_timeout": {
+            "default": 60.0,
+            "description": "Timeout in seconds for sandbox create/delete operations.",
+            "title": "Sandbox Timeout",
+            "type": "number"
+          },
+          "target": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Daytona target region. Falls back to DAYTONA_TARGET env var.",
+            "required": false,
+            "title": "Target"
+          }
+        },
+        "required": [],
+        "title": "DaytonaPythonTool",
+        "type": "object"
+      },
+      "name": "DaytonaPythonTool",
+      "package_dependencies": [
+        "daytona"
+      ],
+      "run_params_schema": {
+        "properties": {
+          "argv": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "string"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional argv passed to the script (forwarded as params.argv).",
+            "title": "Argv"
+          },
+          "code": {
+            "description": "Python source to execute inside the sandbox.",
+            "title": "Code",
+            "type": "string"
+          },
+          "env": {
+            "anyOf": [
+              {
+                "additionalProperties": {
+                  "type": "string"
+                },
+                "type": "object"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Optional environment variables for the run (forwarded as params.env).",
+            "title": "Env"
+          },
+          "timeout": {
+            "anyOf": [
+              {
+                "type": "integer"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Maximum seconds to wait for the code to finish.",
+            "title": "Timeout"
+          }
+        },
+        "required": [
+          "code"
+        ],
+        "title": "DaytonaPythonToolSchema",
+        "type": "object"
+      }
+    },
     {
       "description": "A tool that can be used to recursively list a directory's content.",
       "env_vars": [],

lib/crewai/pyproject.toml

@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
 dependencies = [
     # Core Dependencies
     "pydantic~=2.11.9",
-    "openai>=1.83.0,<3",
+    "openai>=2.0.0,<3",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
@@ -24,7 +24,7 @@ dependencies = [
     "tokenizers>=0.21,<1",
     "openpyxl~=3.1.5",
     # Authentication and Security
-    "python-dotenv~=1.1.1",
+    "python-dotenv>=1.2.2,<2",
     "pyjwt>=2.9.0,<3",
     # TUI
     "textual>=7.5.0",
@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.14.2a3",
+    "crewai-tools==1.14.3a2",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -1,10 +1,9 @@
-import contextvars
-import threading
-from typing import Any
-import urllib.request
+import importlib
+import sys
+from typing import TYPE_CHECKING, Annotated, Any
 import warnings
 
-from pydantic import PydanticUserError
+from pydantic import Field, PydanticUserError
 
 from crewai.agent.core import Agent
 from crewai.agent.planning_config import PlanningConfig
@@ -20,7 +19,10 @@ from crewai.state.checkpoint_config import CheckpointConfig  # noqa: F401
 from crewai.task import Task
 from crewai.tasks.llm_guardrail import LLMGuardrail
 from crewai.tasks.task_output import TaskOutput
-from crewai.telemetry.telemetry import Telemetry
+
+
+if TYPE_CHECKING:
+    from crewai.memory.unified_memory import Memory
 
 
 def _suppress_pydantic_deprecation_warnings() -> None:
@@ -46,38 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.14.2a3"
-_telemetry_submitted = False
-
-
-def _track_install() -> None:
-    """Track package installation/first-use via Scarf analytics."""
-    global _telemetry_submitted
-
-    if _telemetry_submitted or Telemetry._is_telemetry_disabled():
-        return
-
-    try:
-        pixel_url = "https://api.scarf.sh/v2/packages/CrewAI/crewai/docs/00f2dad1-8334-4a39-934e-003b2e1146db"
-
-        req = urllib.request.Request(pixel_url)  # noqa: S310
-        req.add_header("User-Agent", f"CrewAI-Python/{__version__}")
-
-        with urllib.request.urlopen(req, timeout=2):  # noqa: S310
-            _telemetry_submitted = True
-    except Exception:  # noqa: S110
-        pass
-
-
-def _track_install_async() -> None:
-    """Track installation in background thread to avoid blocking imports."""
-    if not Telemetry._is_telemetry_disabled():
-        ctx = contextvars.copy_context()
-        thread = threading.Thread(target=ctx.run, args=(_track_install,), daemon=True)
-        thread.start()
-
-
-_track_install_async()
+__version__ = "1.14.3a2"
 
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "Memory": ("crewai.memory.unified_memory", "Memory"),
@@ -88,8 +59,6 @@ def __getattr__(name: str) -> Any:
     """Lazily import heavy modules (e.g. Memory → lancedb) on first access."""
     if name in _LAZY_IMPORTS:
         module_path, attr = _LAZY_IMPORTS[name]
-        import importlib
-
         mod = importlib.import_module(module_path)
         val = getattr(mod, attr)
         globals()[name] = val
@@ -147,8 +116,6 @@ try:
     except ImportError:
         pass
 
-    import sys
-
     _full_namespace = {
         **_base_namespace,
         "ToolsHandler": _ToolsHandler,
@@ -191,10 +158,6 @@ try:
     Flow.model_rebuild(force=True, _types_namespace=_full_namespace)
     _AgentExecutor.model_rebuild(force=True, _types_namespace=_full_namespace)
 
-    from typing import Annotated
-
-    from pydantic import Field
-
     from crewai.state.runtime import RuntimeState
 
     Entity = Annotated[

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

@@ -29,7 +29,7 @@ from pydantic import (
     model_validator,
 )
 from pydantic.functional_serializers import PlainSerializer
-from typing_extensions import Self
+from typing_extensions import Self, TypeIs
 
 from crewai.agent.planning_config import PlanningConfig
 from crewai.agent.utils import (
@@ -78,12 +78,12 @@ from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.lite_agent_output import LiteAgentOutput
 from crewai.llms.base_llm import BaseLLM
-from crewai.mcp import MCPServerConfig
-from crewai.mcp.tool_resolver import MCPToolResolver
+from crewai.mcp.config import MCPServerConfig
 from crewai.rag.embeddings.types import EmbedderConfig
 from crewai.security.fingerprint import Fingerprint
 from crewai.skills.loader import activate_skill, discover_skills
 from crewai.skills.models import INSTRUCTIONS, Skill as SkillModel
+from crewai.state.checkpoint_config import CheckpointConfig, apply_checkpoint
 from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.callback import SerializableCallable
 from crewai.utilities.agent_utils import (
@@ -118,6 +118,7 @@ if TYPE_CHECKING:
 
     from crewai.a2a.config import A2AClientConfig, A2AConfig, A2AServerConfig
     from crewai.agents.agent_builder.base_agent import PlatformAppOrAction
+    from crewai.mcp.tool_resolver import MCPToolResolver
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.tools.structured_tool import CrewStructuredTool
@@ -132,6 +133,13 @@ _EXECUTOR_CLASS_MAP: dict[str, type] = {
 }
 
 
+def _is_resuming_agent_executor(
+    executor: CrewAgentExecutor | AgentExecutor | None,
+) -> TypeIs[AgentExecutor]:
+    """Type guard: True when the executor is resuming from a checkpoint."""
+    return isinstance(executor, AgentExecutor) and executor._resuming
+
+
 def _validate_executor_class(value: Any) -> Any:
     if isinstance(value, str):
         cls = _EXECUTOR_CLASS_MAP.get(value)
@@ -1112,6 +1120,8 @@ class Agent(BaseAgent):
         Delegates to :class:`~crewai.mcp.tool_resolver.MCPToolResolver`.
         """
         self._cleanup_mcp_clients()
+        from crewai.mcp.tool_resolver import MCPToolResolver
+
         self._mcp_resolver = MCPToolResolver(agent=self, logger=self._logger)
         return self._mcp_resolver.resolve(mcps)
 
@@ -1365,24 +1375,42 @@ class Agent(BaseAgent):
 
         prompt, stop_words, rpm_limit_fn = self._build_execution_prompt(raw_tools)
 
-        executor = AgentExecutor(
-            llm=cast(BaseLLM, self.llm),
-            agent=self,
-            prompt=prompt,
-            max_iter=self.max_iter,
-            tools=parsed_tools,
-            tools_names=get_tool_names(parsed_tools),
-            stop_words=stop_words,
-            tools_description=render_text_description_and_args(parsed_tools),
-            tools_handler=self.tools_handler,
-            original_tools=raw_tools,
-            step_callback=self.step_callback,
-            function_calling_llm=self.function_calling_llm,
-            respect_context_window=self.respect_context_window,
-            request_within_rpm_limit=rpm_limit_fn,
-            callbacks=[TokenCalcHandler(self._token_process)],
-            response_model=response_format,
-        )
+        if _is_resuming_agent_executor(self.agent_executor):
+            executor = self.agent_executor
+            executor.tools = parsed_tools
+            executor.tools_names = get_tool_names(parsed_tools)
+            executor.tools_description = render_text_description_and_args(parsed_tools)
+            executor.original_tools = raw_tools
+            executor.prompt = prompt
+            executor.response_model = response_format
+            executor.stop_words = stop_words
+            executor.tools_handler = self.tools_handler
+            executor.step_callback = self.step_callback
+            executor.function_calling_llm = cast(
+                BaseLLM | None, self.function_calling_llm
+            )
+            executor.respect_context_window = self.respect_context_window
+            executor.request_within_rpm_limit = rpm_limit_fn
+            executor.callbacks = [TokenCalcHandler(self._token_process)]
+        else:
+            executor = AgentExecutor(
+                llm=cast(BaseLLM, self.llm),
+                agent=self,
+                prompt=prompt,
+                max_iter=self.max_iter,
+                tools=parsed_tools,
+                tools_names=get_tool_names(parsed_tools),
+                stop_words=stop_words,
+                tools_description=render_text_description_and_args(parsed_tools),
+                tools_handler=self.tools_handler,
+                original_tools=raw_tools,
+                step_callback=self.step_callback,
+                function_calling_llm=self.function_calling_llm,
+                respect_context_window=self.respect_context_window,
+                request_within_rpm_limit=rpm_limit_fn,
+                callbacks=[TokenCalcHandler(self._token_process)],
+                response_model=response_format,
+            )
 
         all_files: dict[str, Any] = {}
         if isinstance(messages, str):
@@ -1457,6 +1485,7 @@ class Agent(BaseAgent):
         messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> LiteAgentOutput | Coroutine[Any, Any, LiteAgentOutput]:
         """Execute the agent with the given messages using the AgentExecutor.
 
@@ -1475,6 +1504,9 @@ class Agent(BaseAgent):
             response_format: Optional Pydantic model for structured output.
             input_files: Optional dict of named files to attach to the message.
                    Files can be paths, bytes, or File objects from crewai_files.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the agent resumes from that checkpoint. Remaining
+                config fields enable checkpointing for the run.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
@@ -1483,6 +1515,14 @@ class Agent(BaseAgent):
         Note:
             For explicit async usage outside of Flow, use kickoff_async() directly.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return restored.kickoff(  # type: ignore[no-any-return]
+                messages=messages,
+                response_format=response_format,
+                input_files=input_files,
+            )
+
         if is_inside_event_loop():
             return self.kickoff_async(messages, response_format, input_files)
 
@@ -1491,14 +1531,17 @@ class Agent(BaseAgent):
         )
 
         try:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionStartedEvent(
+            if self.checkpoint_kickoff_event_id is not None:
+                self._kickoff_event_id = self.checkpoint_kickoff_event_id
+                self.checkpoint_kickoff_event_id = None
+            else:
+                started_event = LiteAgentExecutionStartedEvent(
                     agent_info=agent_info,
                     tools=parsed_tools,
                     messages=messages,
-                ),
-            )
+                )
+                crewai_event_bus.emit(self, event=started_event)
+                self._kickoff_event_id = started_event.event_id
 
             output = self._execute_and_build_output(executor, inputs, response_format)
             return self._finalize_kickoff(
@@ -1760,6 +1803,7 @@ class Agent(BaseAgent):
         messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> LiteAgentOutput:
         """Execute the agent asynchronously with the given messages.
 
@@ -1775,23 +1819,36 @@ class Agent(BaseAgent):
             response_format: Optional Pydantic model for structured output.
             input_files: Optional dict of named files to attach to the message.
                    Files can be paths, bytes, or File objects from crewai_files.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the agent resumes from that checkpoint.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
         """
+        restored = apply_checkpoint(self, from_checkpoint)
+        if restored is not None:
+            return await restored.kickoff_async(  # type: ignore[no-any-return]
+                messages=messages,
+                response_format=response_format,
+                input_files=input_files,
+            )
+
         executor, inputs, agent_info, parsed_tools = self._prepare_kickoff(
             messages, response_format, input_files
         )
 
         try:
-            crewai_event_bus.emit(
-                self,
-                event=LiteAgentExecutionStartedEvent(
+            if self.checkpoint_kickoff_event_id is not None:
+                self._kickoff_event_id = self.checkpoint_kickoff_event_id
+                self.checkpoint_kickoff_event_id = None
+            else:
+                started_event = LiteAgentExecutionStartedEvent(
                     agent_info=agent_info,
                     tools=parsed_tools,
                     messages=messages,
-                ),
-            )
+                )
+                crewai_event_bus.emit(self, event=started_event)
+                self._kickoff_event_id = started_event.event_id
 
             output = await self._execute_and_build_output_async(
                 executor, inputs, response_format
@@ -1808,6 +1865,7 @@ class Agent(BaseAgent):
         messages: str | list[LLMMessage],
         response_format: type[Any] | None = None,
         input_files: dict[str, FileInput] | None = None,
+        from_checkpoint: CheckpointConfig | None = None,
     ) -> LiteAgentOutput:
         """Async version of kickoff. Alias for kickoff_async.
 
@@ -1815,8 +1873,12 @@ class Agent(BaseAgent):
             messages: Either a string query or a list of message dictionaries.
             response_format: Optional Pydantic model for structured output.
             input_files: Optional dict of named files to attach to the message.
+            from_checkpoint: Optional checkpoint config. If ``restore_from``
+                is set, the agent resumes from that checkpoint.
 
         Returns:
             LiteAgentOutput: The result of the agent execution.
         """
-        return await self.kickoff_async(messages, response_format, input_files)
+        return await self.kickoff_async(
+            messages, response_format, input_files, from_checkpoint
+        )

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

@@ -28,6 +28,9 @@ from crewai.agents.agent_builder.base_agent_executor import BaseAgentExecutor
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.events.base_events import set_emission_counter
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.event_context import restore_event_scope, set_last_event_id
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
@@ -51,6 +54,7 @@ from crewai.utilities.string_utils import interpolate_only
 if TYPE_CHECKING:
     from crewai.context import ExecutionContext
     from crewai.crew import Crew
+    from crewai.state.runtime import RuntimeState
 
 
 def _validate_crew_ref(value: Any) -> Any:
@@ -219,6 +223,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
     _original_goal: str | None = PrivateAttr(default=None)
     _original_backstory: str | None = PrivateAttr(default=None)
     _token_process: TokenProcess = PrivateAttr(default_factory=TokenProcess)
+    _kickoff_event_id: str | None = PrivateAttr(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
     role: str = Field(description="Role of the agent")
     goal: str = Field(description="Objective of the agent")
@@ -335,30 +340,90 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         min_length=1,
     )
     execution_context: ExecutionContext | None = Field(default=None)
+    checkpoint_kickoff_event_id: str | None = Field(default=None)
 
     @classmethod
     def from_checkpoint(cls, config: CheckpointConfig) -> Self:
-        """Restore an Agent from a checkpoint.
+        """Restore an Agent from a checkpoint, ready to resume via kickoff().
 
         Args:
-            config: Checkpoint configuration with ``restore_from`` set.
+            config: Checkpoint configuration with ``restore_from`` set to
+                the path of the checkpoint to load.
+
+        Returns:
+            An Agent instance. Call kickoff() to resume execution.
         """
         from crewai.context import apply_execution_context
         from crewai.state.runtime import RuntimeState
 
         state = RuntimeState.from_checkpoint(config, context={"from_checkpoint": True})
+        crewai_event_bus.set_runtime_state(state)
         for entity in state.root:
             if isinstance(entity, cls):
                 if entity.execution_context is not None:
                     apply_execution_context(entity.execution_context)
-                if entity.agent_executor is not None:
-                    entity.agent_executor.agent = entity
-                    entity.agent_executor._resuming = True
+                entity._restore_runtime(state)
                 return entity
         raise ValueError(
             f"No {cls.__name__} found in checkpoint: {config.restore_from}"
         )
 
+    @classmethod
+    def fork(cls, config: CheckpointConfig, branch: str | None = None) -> Self:
+        """Fork an Agent from a checkpoint, creating a new execution branch.
+
+        Args:
+            config: Checkpoint configuration with ``restore_from`` set.
+            branch: Branch label for the fork. Auto-generated if not provided.
+
+        Returns:
+            An Agent instance on the new branch. Call kickoff() to run.
+        """
+        agent = cls.from_checkpoint(config)
+        state = crewai_event_bus._runtime_state
+        if state is None:
+            raise RuntimeError("Cannot fork: no runtime state on the event bus.")
+        state.fork(branch)
+        return agent
+
+    def _restore_runtime(self, state: RuntimeState) -> None:
+        """Re-create runtime objects after restoring from a checkpoint.
+
+        Args:
+            state: The RuntimeState containing the event record.
+        """
+        if self.agent_executor is not None:
+            self.agent_executor.agent = self
+            self.agent_executor._resuming = True
+        if self.checkpoint_kickoff_event_id is not None:
+            self._kickoff_event_id = self.checkpoint_kickoff_event_id
+        self._restore_event_scope(state)
+
+    def _restore_event_scope(self, state: RuntimeState) -> None:
+        """Rebuild the event scope stack from the checkpoint's event record.
+
+        Args:
+            state: The RuntimeState containing the event record.
+        """
+        stack: list[tuple[str, str]] = []
+        kickoff_id = self._kickoff_event_id
+        if kickoff_id:
+            stack.append((kickoff_id, "lite_agent_execution_started"))
+
+        restore_event_scope(tuple(stack))
+
+        last_event_id: str | None = None
+        max_seq = 0
+        for node in state.event_record.nodes.values():
+            seq = node.event.emission_sequence or 0
+            if seq > max_seq:
+                max_seq = seq
+                last_event_id = node.event.event_id
+        if last_event_id is not None:
+            set_last_event_id(last_event_id)
+        if max_seq > 0:
+            set_emission_counter(max_seq)
+
     @model_validator(mode="before")
     @classmethod
     def process_model_config(cls, values: Any) -> dict[str, Any]:

lib/crewai/src/crewai/cli/checkpoint_cli.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from datetime import datetime
+from datetime import datetime, timedelta, timezone
 import glob
 import json
 import os
@@ -37,6 +37,26 @@ ORDER BY rowid DESC
 LIMIT 1
 """
 
+_DELETE_OLDER_THAN = """
+DELETE FROM checkpoints
+WHERE created_at < ?
+"""
+
+_DELETE_KEEP_N = """
+DELETE FROM checkpoints WHERE rowid NOT IN (
+    SELECT rowid FROM checkpoints ORDER BY rowid DESC LIMIT ?
+)
+"""
+
+_COUNT_CHECKPOINTS = "SELECT COUNT(*) FROM checkpoints"
+
+_SELECT_LIKE = """
+SELECT id, created_at, json(data)
+FROM checkpoints
+WHERE id LIKE ?
+ORDER BY rowid DESC
+"""
+
 
 _DEFAULT_DIR = "./.checkpoints"
 _DEFAULT_DB = "./.checkpoints.db"
@@ -86,17 +106,50 @@ def _parse_checkpoint_json(raw: str, source: str) -> dict[str, Any]:
             "name": entity.get("name"),
             "id": entity.get("id"),
         }
+
+        raw_agents = entity.get("agents", [])
+        agents_by_id: dict[str, dict[str, Any]] = {}
+        parsed_agents: list[dict[str, Any]] = []
+        for ag in raw_agents:
+            agent_info: dict[str, Any] = {
+                "id": ag.get("id", ""),
+                "role": ag.get("role", ""),
+                "goal": ag.get("goal", ""),
+            }
+            parsed_agents.append(agent_info)
+            if ag.get("id"):
+                agents_by_id[str(ag["id"])] = agent_info
+        if parsed_agents:
+            info["agents"] = parsed_agents
+
         if tasks:
             info["tasks_completed"] = completed
             info["tasks_total"] = len(tasks)
-            info["tasks"] = [
-                {
+            parsed_tasks: list[dict[str, Any]] = []
+            for t in tasks:
+                task_info: dict[str, Any] = {
                     "description": t.get("description", ""),
                     "completed": t.get("output") is not None,
                     "output": (t.get("output") or {}).get("raw", ""),
                 }
-                for t in tasks
-            ]
+                task_agent = t.get("agent")
+                if isinstance(task_agent, dict):
+                    task_info["agent_role"] = task_agent.get("role", "")
+                    task_info["agent_id"] = task_agent.get("id", "")
+                elif isinstance(task_agent, str) and task_agent in agents_by_id:
+                    task_info["agent_role"] = agents_by_id[task_agent].get("role", "")
+                    task_info["agent_id"] = task_agent
+                parsed_tasks.append(task_info)
+            info["tasks"] = parsed_tasks
+
+        if entity.get("entity_type") == "flow":
+            completed_methods = entity.get("checkpoint_completed_methods")
+            if completed_methods:
+                info["completed_methods"] = sorted(completed_methods)
+            state = entity.get("checkpoint_state")
+            if isinstance(state, dict):
+                info["flow_state"] = state
+
         parsed_entities.append(info)
 
     inputs: dict[str, Any] = {}
@@ -173,9 +226,11 @@ def _entity_summary(entities: list[dict[str, Any]]) -> str:
 
 
 def _list_json(location: str) -> list[dict[str, Any]]:
-    pattern = os.path.join(location, "*.json")
+    pattern = os.path.join(location, "**", "*.json")
     results = []
-    for path in sorted(glob.glob(pattern), key=os.path.getmtime, reverse=True):
+    for path in sorted(
+        glob.glob(pattern, recursive=True), key=os.path.getmtime, reverse=True
+    ):
         name = os.path.basename(path)
         try:
             with open(path) as f:
@@ -192,8 +247,10 @@ def _list_json(location: str) -> list[dict[str, Any]]:
 
 
 def _info_json_latest(location: str) -> dict[str, Any] | None:
-    pattern = os.path.join(location, "*.json")
-    files = sorted(glob.glob(pattern), key=os.path.getmtime, reverse=True)
+    pattern = os.path.join(location, "**", "*.json")
+    files = sorted(
+        glob.glob(pattern, recursive=True), key=os.path.getmtime, reverse=True
+    )
     if not files:
         return None
     path = files[0]
@@ -258,6 +315,8 @@ def _info_sqlite_latest(db_path: str) -> dict[str, Any] | None:
 def _info_sqlite_id(db_path: str, checkpoint_id: str) -> dict[str, Any] | None:
     with sqlite3.connect(db_path) as conn:
         row = conn.execute(_SELECT_ONE, (checkpoint_id,)).fetchone()
+        if not row:
+            row = conn.execute(_SELECT_LIKE, (f"%{checkpoint_id}%",)).fetchone()
     if not row:
         return None
     cid, created_at, raw = row
@@ -380,3 +439,294 @@ def _print_info(meta: dict[str, Any]) -> None:
                 if len(desc) > 70:
                     desc = desc[:67] + "..."
                 click.echo(f"    {i + 1}. [{status}] {desc}")
+
+
+def _resolve_checkpoint(
+    location: str, checkpoint_id: str | None
+) -> dict[str, Any] | None:
+    if _is_sqlite(location):
+        if checkpoint_id:
+            return _info_sqlite_id(location, checkpoint_id)
+        return _info_sqlite_latest(location)
+    if os.path.isdir(location):
+        if checkpoint_id:
+            from crewai.state.provider.json_provider import JsonProvider
+
+            _json_provider: JsonProvider = JsonProvider()
+            pattern: str = os.path.join(location, "**", "*.json")
+            all_files: list[str] = glob.glob(pattern, recursive=True)
+            matches: list[str] = [
+                f for f in all_files if checkpoint_id in _json_provider.extract_id(f)
+            ]
+            matches.sort(key=os.path.getmtime, reverse=True)
+            if matches:
+                return _info_json_file(matches[0])
+            return None
+        return _info_json_latest(location)
+    if os.path.isfile(location):
+        return _info_json_file(location)
+    return None
+
+
+def _entity_type_from_meta(meta: dict[str, Any]) -> str:
+    for ent in meta.get("entities", []):
+        if ent.get("type") == "flow":
+            return "flow"
+        if ent.get("type") == "agent":
+            return "agent"
+    return "crew"
+
+
+def resume_checkpoint(location: str, checkpoint_id: str | None) -> None:
+    import asyncio
+
+    meta: dict[str, Any] | None = _resolve_checkpoint(location, checkpoint_id)
+    if meta is None:
+        if checkpoint_id:
+            click.echo(f"Checkpoint not found: {checkpoint_id}")
+        else:
+            click.echo(f"No checkpoints found in {location}")
+        return
+
+    restore_path: str = meta.get("path") or meta.get("source", "")
+    if meta.get("db"):
+        restore_path = f"{meta['db']}#{meta['name']}"
+
+    click.echo(f"Resuming from: {meta.get('name', restore_path)}")
+    _print_info(meta)
+    click.echo()
+
+    from crewai.state.checkpoint_config import CheckpointConfig
+
+    config: CheckpointConfig = CheckpointConfig(restore_from=restore_path)
+    entity_type: str = _entity_type_from_meta(meta)
+    inputs: dict[str, Any] | None = meta.get("inputs") or None
+
+    if entity_type == "flow":
+        from crewai.flow.flow import Flow
+
+        flow = Flow.from_checkpoint(config)
+        result = asyncio.run(flow.kickoff_async(inputs=inputs))
+    elif entity_type == "agent":
+        from crewai.agent import Agent
+
+        agent = Agent.from_checkpoint(config)
+        result = asyncio.run(agent.akickoff(messages="Resume execution."))
+    else:
+        from crewai.crew import Crew
+
+        crew = Crew.from_checkpoint(config)
+        result = asyncio.run(crew.akickoff(inputs=inputs))
+
+    click.echo(f"\nResult: {getattr(result, 'raw', result)}")
+
+
+def _task_list_from_meta(meta: dict[str, Any]) -> list[dict[str, Any]]:
+    tasks: list[dict[str, Any]] = []
+    for ent in meta.get("entities", []):
+        tasks.extend(
+            {
+                "entity": ent.get("name", "unnamed"),
+                "description": t.get("description", ""),
+                "completed": t.get("completed", False),
+                "output": t.get("output", ""),
+            }
+            for t in ent.get("tasks", [])
+        )
+    return tasks
+
+
+def diff_checkpoints(location: str, id1: str, id2: str) -> None:
+    meta1: dict[str, Any] | None = _resolve_checkpoint(location, id1)
+    meta2: dict[str, Any] | None = _resolve_checkpoint(location, id2)
+
+    if meta1 is None:
+        click.echo(f"Checkpoint not found: {id1}")
+        return
+    if meta2 is None:
+        click.echo(f"Checkpoint not found: {id2}")
+        return
+
+    name1: str = meta1.get("name", id1)
+    name2: str = meta2.get("name", id2)
+
+    click.echo(f"--- {name1}")
+    click.echo(f"+++ {name2}")
+    click.echo()
+
+    fields: list[tuple[str, str]] = [
+        ("Time", "ts"),
+        ("Branch", "branch"),
+        ("Trigger", "trigger"),
+        ("Events", "event_count"),
+    ]
+    for label, key in fields:
+        v1: str = str(meta1.get(key, ""))
+        v2: str = str(meta2.get(key, ""))
+        if v1 != v2:
+            click.echo(f"  {label}:")
+            click.echo(f"    - {v1}")
+            click.echo(f"    + {v2}")
+
+    inputs1: dict[str, Any] = meta1.get("inputs", {})
+    inputs2: dict[str, Any] = meta2.get("inputs", {})
+    all_keys: list[str] = sorted(set(list(inputs1.keys()) + list(inputs2.keys())))
+    changed_inputs: list[tuple[str, Any, Any]] = [
+        (k, inputs1.get(k, ""), inputs2.get(k, ""))
+        for k in all_keys
+        if inputs1.get(k) != inputs2.get(k)
+    ]
+    if changed_inputs:
+        click.echo("\n  Inputs:")
+        for key, v1, v2 in changed_inputs:
+            click.echo(f"    {key}:")
+            click.echo(f"      - {v1}")
+            click.echo(f"      + {v2}")
+
+    tasks1: list[dict[str, Any]] = _task_list_from_meta(meta1)
+    tasks2: list[dict[str, Any]] = _task_list_from_meta(meta2)
+
+    max_tasks: int = max(len(tasks1), len(tasks2))
+    if max_tasks == 0:
+        return
+
+    click.echo("\n  Tasks:")
+    for i in range(max_tasks):
+        t1: dict[str, Any] | None = tasks1[i] if i < len(tasks1) else None
+        t2: dict[str, Any] | None = tasks2[i] if i < len(tasks2) else None
+
+        if t1 is None:
+            desc: str = t2["description"][:60] if t2 else ""
+            click.echo(f"    + {i + 1}. [new] {desc}")
+            continue
+        if t2 is None:
+            desc = t1["description"][:60]
+            click.echo(f"    - {i + 1}. [removed] {desc}")
+            continue
+
+        desc = str(t1["description"][:60])
+        s1: str = "done" if t1["completed"] else "pending"
+        s2: str = "done" if t2["completed"] else "pending"
+
+        if s1 != s2:
+            click.echo(f"    {i + 1}. {desc}")
+            click.echo(f"      status: {s1} -> {s2}")
+
+        out1: str = (t1.get("output") or "").strip()
+        out2: str = (t2.get("output") or "").strip()
+        if out1 != out2:
+            if s1 == s2:
+                click.echo(f"    {i + 1}. {desc}")
+            preview1: str = (
+                out1[:80] + ("..." if len(out1) > 80 else "") if out1 else "(empty)"
+            )
+            preview2: str = (
+                out2[:80] + ("..." if len(out2) > 80 else "") if out2 else "(empty)"
+            )
+            click.echo("      output:")
+            click.echo(f"        - {preview1}")
+            click.echo(f"        + {preview2}")
+
+
+def _parse_duration(value: str) -> timedelta:
+    match: re.Match[str] | None = re.match(r"^(\d+)([dhm])$", value.strip())
+    if not match:
+        raise click.BadParameter(
+            f"Invalid duration: {value!r}. Use format like '7d', '24h', or '30m'."
+        )
+    amount: int = int(match.group(1))
+    unit: str = match.group(2)
+    if unit == "d":
+        return timedelta(days=amount)
+    if unit == "h":
+        return timedelta(hours=amount)
+    return timedelta(minutes=amount)
+
+
+def _prune_json(location: str, keep: int | None, older_than: timedelta | None) -> int:
+    pattern: str = os.path.join(location, "**", "*.json")
+    files: list[str] = sorted(
+        glob.glob(pattern, recursive=True), key=os.path.getmtime, reverse=True
+    )
+    if not files:
+        return 0
+
+    to_delete: set[str] = set()
+
+    if keep is not None and len(files) > keep:
+        to_delete.update(files[keep:])
+
+    if older_than is not None:
+        cutoff: datetime = datetime.now(timezone.utc) - older_than
+        for path in files:
+            mtime: datetime = datetime.fromtimestamp(
+                os.path.getmtime(path), tz=timezone.utc
+            )
+            if mtime < cutoff:
+                to_delete.add(path)
+
+    deleted: int = 0
+    for path in to_delete:
+        try:
+            os.remove(path)
+            deleted += 1
+        except OSError:  # noqa: PERF203
+            pass
+
+    for dirpath, dirnames, filenames in os.walk(location, topdown=False):
+        if dirpath != location and not filenames and not dirnames:
+            try:
+                os.rmdir(dirpath)
+            except OSError:
+                pass
+
+    return deleted
+
+
+def _prune_sqlite(db_path: str, keep: int | None, older_than: timedelta | None) -> int:
+    deleted: int = 0
+    with sqlite3.connect(db_path) as conn:
+        if older_than is not None:
+            cutoff: str = (datetime.now(timezone.utc) - older_than).strftime(
+                "%Y%m%dT%H%M%S"
+            )
+            cursor: sqlite3.Cursor = conn.execute(_DELETE_OLDER_THAN, (cutoff,))
+            deleted += cursor.rowcount
+
+        if keep is not None:
+            cursor = conn.execute(_DELETE_KEEP_N, (keep,))
+            deleted += cursor.rowcount
+
+        conn.commit()
+    return deleted
+
+
+def prune_checkpoints(
+    location: str, keep: int | None, older_than: str | None, dry_run: bool = False
+) -> None:
+    if keep is None and older_than is None:
+        click.echo("Specify --keep N and/or --older-than DURATION (e.g. 7d, 24h)")
+        return
+
+    duration: timedelta | None = _parse_duration(older_than) if older_than else None
+
+    deleted: int
+    if _is_sqlite(location):
+        if dry_run:
+            with sqlite3.connect(location) as conn:
+                total: int = conn.execute(_COUNT_CHECKPOINTS).fetchone()[0]
+            click.echo(f"Would prune from {total} checkpoint(s) in {location}")
+            return
+        deleted = _prune_sqlite(location, keep, duration)
+    elif os.path.isdir(location):
+        if dry_run:
+            files: list[str] = glob.glob(
+                os.path.join(location, "**", "*.json"), recursive=True
+            )
+            click.echo(f"Would prune from {len(files)} checkpoint(s) in {location}")
+            return
+        deleted = _prune_json(location, keep, duration)
+    else:
+        click.echo(f"Not a directory or SQLite database: {location}")
+        return
+    click.echo(f"Pruned {deleted} checkpoint(s) from {location}")

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

@@ -18,6 +18,7 @@ from crewai.cli.install_crew import install_crew
 from crewai.cli.kickoff_flow import kickoff_flow
 from crewai.cli.organization.main import OrganizationCommand
 from crewai.cli.plot_flow import plot_flow
+from crewai.cli.remote_template.main import TemplateCommand
 from crewai.cli.replay_from_task import replay_task_command
 from crewai.cli.reset_memories_command import reset_memories_command
 from crewai.cli.run_crew import run_crew
@@ -496,6 +497,33 @@ def tool_publish(is_public: bool, force: bool) -> None:
     tool_cmd.publish(is_public, force)
 
 
+@crewai.group()
+def template() -> None:
+    """Browse and install project templates."""
+
+
+@template.command(name="list")
+def template_list() -> None:
+    """List available templates and select one to install."""
+    template_cmd = TemplateCommand()
+    template_cmd.list_templates()
+
+
+@template.command(name="add")
+@click.argument("name")
+@click.option(
+    "-o",
+    "--output-dir",
+    type=str,
+    default=None,
+    help="Directory name for the template (defaults to template name)",
+)
+def template_add(name: str, output_dir: str | None) -> None:
+    """Add a template to the current directory."""
+    template_cmd = TemplateCommand()
+    template_cmd.add_template(name, output_dir)
+
+
 @crewai.group()
 def flow() -> None:
     """Flow related commands."""
@@ -845,5 +873,48 @@ def checkpoint_info(path: str) -> None:
     info_checkpoint(_detect_location(path))
 
 
+@checkpoint.command("resume")
+@click.argument("checkpoint_id", required=False, default=None)
+@click.pass_context
+def checkpoint_resume(ctx: click.Context, checkpoint_id: str | None) -> None:
+    """Resume from a checkpoint. Defaults to the most recent."""
+    from crewai.cli.checkpoint_cli import resume_checkpoint
+
+    resume_checkpoint(ctx.obj["location"], checkpoint_id)
+
+
+@checkpoint.command("diff")
+@click.argument("id1")
+@click.argument("id2")
+@click.pass_context
+def checkpoint_diff(ctx: click.Context, id1: str, id2: str) -> None:
+    """Compare two checkpoints side-by-side."""
+    from crewai.cli.checkpoint_cli import diff_checkpoints
+
+    diff_checkpoints(ctx.obj["location"], id1, id2)
+
+
+@checkpoint.command("prune")
+@click.option(
+    "--keep", type=int, default=None, help="Keep the N most recent checkpoints."
+)
+@click.option(
+    "--older-than",
+    default=None,
+    help="Remove checkpoints older than duration (e.g. 7d, 24h, 30m).",
+)
+@click.option(
+    "--dry-run", is_flag=True, help="Show what would be pruned without deleting."
+)
+@click.pass_context
+def checkpoint_prune(
+    ctx: click.Context, keep: int | None, older_than: str | None, dry_run: bool
+) -> None:
+    """Remove old checkpoints."""
+    from crewai.cli.checkpoint_cli import prune_checkpoints
+
+    prune_checkpoints(ctx.obj["location"], keep, older_than, dry_run)
+
+
 if __name__ == "__main__":
     crewai()

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

@@ -0,0 +1,250 @@
+import io
+import logging
+import os
+import shutil
+from typing import Any
+import zipfile
+
+import click
+import httpx
+from rich.console import Console
+from rich.panel import Panel
+from rich.text import Text
+
+from crewai.cli.command import BaseCommand
+
+
+logger = logging.getLogger(__name__)
+console = Console()
+
+GITHUB_ORG = "crewAIInc"
+TEMPLATE_PREFIX = "template_"
+GITHUB_API_BASE = "https://api.github.com"
+
+BANNER = """\
+[bold white] ██████╗██████╗ ███████╗██╗    ██╗[/bold white] [bold red] █████╗ ██╗[/bold red]
+[bold white]██╔════╝██╔══██╗██╔════╝██║    ██║[/bold white] [bold red]██╔══██╗██║[/bold red]
+[bold white]██║     ██████╔╝█████╗  ██║ █╗ ██║[/bold white] [bold red]███████║██║[/bold red]
+[bold white]██║     ██╔══██╗██╔══╝  ██║███╗██║[/bold white] [bold red]██╔══██║██║[/bold red]
+[bold white]╚██████╗██║  ██║███████╗╚███╔███╔╝[/bold white] [bold red]██║  ██║██║[/bold red]
+[bold white] ╚═════╝╚═╝  ╚═╝╚══════╝ ╚══╝╚══╝[/bold white] [bold red]╚═╝  ╚═╝╚═╝[/bold red]
+[dim white]████████╗███████╗███╗   ███╗██████╗ ██╗      █████╗ ████████╗███████╗███████╗[/dim white]
+[dim white]╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██║     ██╔══██╗╚══██╔══╝██╔════╝██╔════╝[/dim white]
+[dim white]   ██║   █████╗  ██╔████╔██║██████╔╝██║     ███████║   ██║   █████╗  ███████╗[/dim white]
+[dim white]   ██║   ██╔══╝  ██║╚██╔╝██║██╔═══╝ ██║     ██╔══██║   ██║   ██╔══╝  ╚════██║[/dim white]
+[dim white]   ██║   ███████╗██║ ╚═╝ ██║██║     ███████╗██║  ██║   ██║   ███████╗███████║[/dim white]
+[dim white]   ╚═╝   ╚══════╝╚═╝     ╚═╝╚═╝     ╚══════╝╚═╝  ╚═╝   ╚═╝   ╚══════╝╚══════╝[/dim white]"""
+
+
+class TemplateCommand(BaseCommand):
+    """Handle template-related operations for CrewAI projects."""
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def list_templates(self) -> None:
+        """List available templates with an interactive selector to install."""
+        templates = self._fetch_templates()
+        if not templates:
+            click.echo("No templates found.")
+            return
+
+        console.print(f"\n{BANNER}\n")
+        console.print(" [on cyan] templates [/on cyan]\n")
+        console.print(f" [green]o[/green]  Source: https://github.com/{GITHUB_ORG}")
+        console.print(
+            f" [green]o[/green]  Found [bold]{len(templates)}[/bold] templates\n"
+        )
+        console.print(" [green]o[/green]  Select a template to install")
+
+        for idx, repo in enumerate(templates, start=1):
+            name = repo["name"].removeprefix(TEMPLATE_PREFIX)
+            description = repo.get("description") or ""
+            if description:
+                console.print(
+                    f"      [bold cyan]{idx}.[/bold cyan] [bold white]{name}[/bold white] [dim]({description})[/dim]"
+                )
+            else:
+                console.print(
+                    f"      [bold cyan]{idx}.[/bold cyan] [bold white]{name}[/bold white]"
+                )
+
+        console.print("      [bold cyan]q.[/bold cyan] [dim]Quit[/dim]\n")
+
+        while True:
+            choice = click.prompt("Enter your choice", type=str)
+
+            if choice.lower() == "q":
+                return
+
+            if choice.isdigit() and 1 <= int(choice) <= len(templates):
+                selected_index = int(choice) - 1
+                break
+
+            click.secho(
+                f"Please enter a number between 1 and {len(templates)}, or 'q' to quit.",
+                fg="yellow",
+            )
+
+        selected = templates[selected_index]
+        repo_name = selected["name"]
+        self._install_repo(repo_name)
+
+    def add_template(self, name: str, output_dir: str | None = None) -> None:
+        """Download a template and copy it into the current working directory.
+
+        Args:
+            name: Template name (with or without the template_ prefix).
+            output_dir: Optional directory name. Defaults to the template name.
+        """
+        repo_name = self._resolve_repo_name(name)
+        if repo_name is None:
+            click.secho(f"Template '{name}' not found.", fg="red")
+            click.echo("Run 'crewai template list' to see available templates.")
+            raise SystemExit(1)
+
+        self._install_repo(repo_name, output_dir)
+
+    def _install_repo(self, repo_name: str, output_dir: str | None = None) -> None:
+        """Download and extract a template repo into the current directory.
+
+        Args:
+            repo_name: Full GitHub repo name (e.g. template_deep_research).
+            output_dir: Optional directory name. Defaults to the template name.
+        """
+        folder_name = output_dir or repo_name.removeprefix(TEMPLATE_PREFIX)
+        dest = os.path.join(os.getcwd(), folder_name)
+
+        while os.path.exists(dest):
+            click.secho(f"Directory '{folder_name}' already exists.", fg="yellow")
+            folder_name = click.prompt(
+                "Enter a different directory name (or 'q' to quit)", type=str
+            )
+            if folder_name.lower() == "q":
+                return
+            dest = os.path.join(os.getcwd(), folder_name)
+
+        click.echo(
+            f"Downloading template '{repo_name.removeprefix(TEMPLATE_PREFIX)}'..."
+        )
+
+        zip_bytes = self._download_zip(repo_name)
+        self._extract_zip(zip_bytes, dest)
+
+        self._telemetry.template_installed_span(repo_name.removeprefix(TEMPLATE_PREFIX))
+
+        console.print(
+            f"\n [green]\u2713[/green]  Installed template [bold white]{folder_name}[/bold white]"
+            f" [dim](source: github.com/{GITHUB_ORG}/{repo_name})[/dim]\n"
+        )
+
+        next_steps = Text()
+        next_steps.append(f"  cd {folder_name}\n", style="bold white")
+        next_steps.append("  crewai install", style="bold white")
+
+        panel = Panel(
+            next_steps,
+            title="[green]\u25c7  Next steps[/green]",
+            title_align="left",
+            border_style="dim",
+            padding=(1, 2),
+        )
+        console.print(panel)
+
+    def _fetch_templates(self) -> list[dict[str, Any]]:
+        """Fetch all template repos from the GitHub org."""
+        templates: list[dict[str, Any]] = []
+        page = 1
+        while True:
+            url = f"{GITHUB_API_BASE}/orgs/{GITHUB_ORG}/repos"
+            params: dict[str, str | int] = {
+                "per_page": 100,
+                "page": page,
+                "type": "public",
+            }
+            try:
+                response = httpx.get(url, params=params, timeout=15)
+                response.raise_for_status()
+            except httpx.HTTPError as e:
+                click.secho(f"Failed to fetch templates from GitHub: {e}", fg="red")
+                raise SystemExit(1) from e
+
+            repos = response.json()
+            if not repos:
+                break
+
+            templates.extend(
+                repo
+                for repo in repos
+                if repo["name"].startswith(TEMPLATE_PREFIX) and not repo.get("private")
+            )
+
+            page += 1
+
+        templates.sort(key=lambda r: r["name"])
+        return templates
+
+    def _resolve_repo_name(self, name: str) -> str | None:
+        """Resolve user input to a full repo name, or None if not found."""
+        # Accept both 'deep_research' and 'template_deep_research'
+        candidates = [
+            f"{TEMPLATE_PREFIX}{name}"
+            if not name.startswith(TEMPLATE_PREFIX)
+            else name,
+            name,
+        ]
+
+        templates = self._fetch_templates()
+        template_names = {t["name"] for t in templates}
+
+        for candidate in candidates:
+            if candidate in template_names:
+                return candidate
+
+        return None
+
+    def _download_zip(self, repo_name: str) -> bytes:
+        """Download the default branch zipball for a repo."""
+        url = f"{GITHUB_API_BASE}/repos/{GITHUB_ORG}/{repo_name}/zipball"
+        try:
+            response = httpx.get(url, follow_redirects=True, timeout=60)
+            response.raise_for_status()
+        except httpx.HTTPError as e:
+            click.secho(f"Failed to download template: {e}", fg="red")
+            raise SystemExit(1) from e
+
+        return response.content
+
+    def _extract_zip(self, zip_bytes: bytes, dest: str) -> None:
+        """Extract a GitHub zipball into dest, stripping the top-level directory."""
+        with zipfile.ZipFile(io.BytesIO(zip_bytes)) as zf:
+            # GitHub zipballs have a single top-level dir like 'crewAIInc-template_xxx-<sha>/'
+            members = zf.namelist()
+            if not members:
+                click.secho("Downloaded archive is empty.", fg="red")
+                raise SystemExit(1)
+
+            top_dir = members[0].split("/")[0] + "/"
+
+            os.makedirs(dest, exist_ok=True)
+
+            for member in members:
+                if member == top_dir or not member.startswith(top_dir):
+                    continue
+
+                relative_path = member[len(top_dir) :]
+                if not relative_path:
+                    continue
+
+                target = os.path.realpath(os.path.join(dest, relative_path))
+                if not target.startswith(
+                    os.path.realpath(dest) + os.sep
+                ) and target != os.path.realpath(dest):
+                    continue
+
+                if member.endswith("/"):
+                    os.makedirs(target, exist_ok=True)
+                else:
+                    os.makedirs(os.path.dirname(target), exist_ok=True)
+                    with zf.open(member) as src, open(target, "wb") as dst:
+                        shutil.copyfileobj(src, dst)

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.2a3"
+    "crewai[tools]==1.14.3a2"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.2a3"
+    "crewai[tools]==1.14.3a2"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.14.2a3"
+    "crewai[tools]==1.14.3a2"
 ]
 
 [tool.crewai]

lib/crewai/src/crewai/crew.py

@@ -419,10 +419,32 @@ class Crew(FlowTrackable, BaseModel):
 
     def _restore_runtime(self) -> None:
         """Re-create runtime objects after restoring from a checkpoint."""
+        from crewai.events.event_bus import crewai_event_bus
+
+        started_task_ids: set[str] = set()
+        state = crewai_event_bus._runtime_state
+        if state is not None:
+            for node in state.event_record.nodes.values():
+                if node.event.type == "task_started" and node.event.task_id:
+                    started_task_ids.add(node.event.task_id)
+
+        resuming_task_agent_roles: set[str] = set()
+        for task in self.tasks:
+            if (
+                task.output is None
+                and task.agent is not None
+                and str(task.id) in started_task_ids
+            ):
+                resuming_task_agent_roles.add(task.agent.role)
+
         for agent in self.agents:
             agent.crew = self
             executor = agent.agent_executor
-            if executor and executor.messages:
+            if (
+                executor
+                and executor.messages
+                and agent.role in resuming_task_agent_roles
+            ):
                 executor.crew = self
                 executor.agent = agent
                 executor._resuming = True

lib/crewai/src/crewai/events/__init__.py

@@ -6,112 +6,20 @@ This module provides the event infrastructure that allows users to:
 - Build custom logging and analytics
 - Extend CrewAI with custom event handlers
 - Declare handler dependencies for ordered execution
+
+Event type classes are lazy-loaded on first access to avoid importing
+~12 Pydantic model modules (and their transitive deps) at package init time.
 """
 
 from __future__ import annotations
 
+import importlib
 from typing import TYPE_CHECKING, Any
 
 from crewai.events.base_event_listener import BaseEventListener
 from crewai.events.depends import Depends
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.handler_graph import CircularDependencyError
-from crewai.events.types.crew_events import (
-    CrewKickoffCompletedEvent,
-    CrewKickoffFailedEvent,
-    CrewKickoffStartedEvent,
-    CrewTestCompletedEvent,
-    CrewTestFailedEvent,
-    CrewTestResultEvent,
-    CrewTestStartedEvent,
-    CrewTrainCompletedEvent,
-    CrewTrainFailedEvent,
-    CrewTrainStartedEvent,
-)
-from crewai.events.types.flow_events import (
-    FlowCreatedEvent,
-    FlowEvent,
-    FlowFinishedEvent,
-    FlowPlotEvent,
-    FlowStartedEvent,
-    HumanFeedbackReceivedEvent,
-    HumanFeedbackRequestedEvent,
-    MethodExecutionFailedEvent,
-    MethodExecutionFinishedEvent,
-    MethodExecutionStartedEvent,
-)
-from crewai.events.types.knowledge_events import (
-    KnowledgeQueryCompletedEvent,
-    KnowledgeQueryFailedEvent,
-    KnowledgeQueryStartedEvent,
-    KnowledgeRetrievalCompletedEvent,
-    KnowledgeRetrievalStartedEvent,
-    KnowledgeSearchQueryFailedEvent,
-)
-from crewai.events.types.llm_events import (
-    LLMCallCompletedEvent,
-    LLMCallFailedEvent,
-    LLMCallStartedEvent,
-    LLMStreamChunkEvent,
-)
-from crewai.events.types.llm_guardrail_events import (
-    LLMGuardrailCompletedEvent,
-    LLMGuardrailStartedEvent,
-)
-from crewai.events.types.logging_events import (
-    AgentLogsExecutionEvent,
-    AgentLogsStartedEvent,
-)
-from crewai.events.types.mcp_events import (
-    MCPConfigFetchFailedEvent,
-    MCPConnectionCompletedEvent,
-    MCPConnectionFailedEvent,
-    MCPConnectionStartedEvent,
-    MCPToolExecutionCompletedEvent,
-    MCPToolExecutionFailedEvent,
-    MCPToolExecutionStartedEvent,
-)
-from crewai.events.types.memory_events import (
-    MemoryQueryCompletedEvent,
-    MemoryQueryFailedEvent,
-    MemoryQueryStartedEvent,
-    MemoryRetrievalCompletedEvent,
-    MemoryRetrievalFailedEvent,
-    MemoryRetrievalStartedEvent,
-    MemorySaveCompletedEvent,
-    MemorySaveFailedEvent,
-    MemorySaveStartedEvent,
-)
-from crewai.events.types.reasoning_events import (
-    AgentReasoningCompletedEvent,
-    AgentReasoningFailedEvent,
-    AgentReasoningStartedEvent,
-    ReasoningEvent,
-)
-from crewai.events.types.skill_events import (
-    SkillActivatedEvent,
-    SkillDiscoveryCompletedEvent,
-    SkillDiscoveryStartedEvent,
-    SkillEvent,
-    SkillLoadFailedEvent,
-    SkillLoadedEvent,
-)
-from crewai.events.types.task_events import (
-    TaskCompletedEvent,
-    TaskEvaluationEvent,
-    TaskFailedEvent,
-    TaskStartedEvent,
-)
-from crewai.events.types.tool_usage_events import (
-    ToolExecutionErrorEvent,
-    ToolSelectionErrorEvent,
-    ToolUsageErrorEvent,
-    ToolUsageEvent,
-    ToolUsageFinishedEvent,
-    ToolUsageStartedEvent,
-    ToolValidateInputErrorEvent,
-)
-
 
 if TYPE_CHECKING:
     from crewai.events.types.agent_events import (
@@ -125,6 +33,223 @@ if TYPE_CHECKING:
         LiteAgentExecutionErrorEvent,
         LiteAgentExecutionStartedEvent,
     )
+    from crewai.events.types.crew_events import (
+        CrewKickoffCompletedEvent,
+        CrewKickoffFailedEvent,
+        CrewKickoffStartedEvent,
+        CrewTestCompletedEvent,
+        CrewTestFailedEvent,
+        CrewTestResultEvent,
+        CrewTestStartedEvent,
+        CrewTrainCompletedEvent,
+        CrewTrainFailedEvent,
+        CrewTrainStartedEvent,
+    )
+    from crewai.events.types.flow_events import (
+        FlowCreatedEvent,
+        FlowEvent,
+        FlowFinishedEvent,
+        FlowPlotEvent,
+        FlowStartedEvent,
+        HumanFeedbackReceivedEvent,
+        HumanFeedbackRequestedEvent,
+        MethodExecutionFailedEvent,
+        MethodExecutionFinishedEvent,
+        MethodExecutionStartedEvent,
+    )
+    from crewai.events.types.knowledge_events import (
+        KnowledgeQueryCompletedEvent,
+        KnowledgeQueryFailedEvent,
+        KnowledgeQueryStartedEvent,
+        KnowledgeRetrievalCompletedEvent,
+        KnowledgeRetrievalStartedEvent,
+        KnowledgeSearchQueryFailedEvent,
+    )
+    from crewai.events.types.llm_events import (
+        LLMCallCompletedEvent,
+        LLMCallFailedEvent,
+        LLMCallStartedEvent,
+        LLMStreamChunkEvent,
+    )
+    from crewai.events.types.llm_guardrail_events import (
+        LLMGuardrailCompletedEvent,
+        LLMGuardrailStartedEvent,
+    )
+    from crewai.events.types.logging_events import (
+        AgentLogsExecutionEvent,
+        AgentLogsStartedEvent,
+    )
+    from crewai.events.types.mcp_events import (
+        MCPConfigFetchFailedEvent,
+        MCPConnectionCompletedEvent,
+        MCPConnectionFailedEvent,
+        MCPConnectionStartedEvent,
+        MCPToolExecutionCompletedEvent,
+        MCPToolExecutionFailedEvent,
+        MCPToolExecutionStartedEvent,
+    )
+    from crewai.events.types.memory_events import (
+        MemoryQueryCompletedEvent,
+        MemoryQueryFailedEvent,
+        MemoryQueryStartedEvent,
+        MemoryRetrievalCompletedEvent,
+        MemoryRetrievalFailedEvent,
+        MemoryRetrievalStartedEvent,
+        MemorySaveCompletedEvent,
+        MemorySaveFailedEvent,
+        MemorySaveStartedEvent,
+    )
+    from crewai.events.types.reasoning_events import (
+        AgentReasoningCompletedEvent,
+        AgentReasoningFailedEvent,
+        AgentReasoningStartedEvent,
+        ReasoningEvent,
+    )
+    from crewai.events.types.skill_events import (
+        SkillActivatedEvent,
+        SkillDiscoveryCompletedEvent,
+        SkillDiscoveryStartedEvent,
+        SkillEvent,
+        SkillLoadFailedEvent,
+        SkillLoadedEvent,
+    )
+    from crewai.events.types.task_events import (
+        TaskCompletedEvent,
+        TaskEvaluationEvent,
+        TaskFailedEvent,
+        TaskStartedEvent,
+    )
+    from crewai.events.types.tool_usage_events import (
+        ToolExecutionErrorEvent,
+        ToolSelectionErrorEvent,
+        ToolUsageErrorEvent,
+        ToolUsageEvent,
+        ToolUsageFinishedEvent,
+        ToolUsageStartedEvent,
+        ToolValidateInputErrorEvent,
+    )
+
+# Map every event class name → its module path for lazy loading
+_LAZY_EVENT_MAPPING: dict[str, str] = {
+    # agent_events
+    "AgentEvaluationCompletedEvent": "crewai.events.types.agent_events",
+    "AgentEvaluationFailedEvent": "crewai.events.types.agent_events",
+    "AgentEvaluationStartedEvent": "crewai.events.types.agent_events",
+    "AgentExecutionCompletedEvent": "crewai.events.types.agent_events",
+    "AgentExecutionErrorEvent": "crewai.events.types.agent_events",
+    "AgentExecutionStartedEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionCompletedEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionErrorEvent": "crewai.events.types.agent_events",
+    "LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
+    # crew_events
+    "CrewKickoffCompletedEvent": "crewai.events.types.crew_events",
+    "CrewKickoffFailedEvent": "crewai.events.types.crew_events",
+    "CrewKickoffStartedEvent": "crewai.events.types.crew_events",
+    "CrewTestCompletedEvent": "crewai.events.types.crew_events",
+    "CrewTestFailedEvent": "crewai.events.types.crew_events",
+    "CrewTestResultEvent": "crewai.events.types.crew_events",
+    "CrewTestStartedEvent": "crewai.events.types.crew_events",
+    "CrewTrainCompletedEvent": "crewai.events.types.crew_events",
+    "CrewTrainFailedEvent": "crewai.events.types.crew_events",
+    "CrewTrainStartedEvent": "crewai.events.types.crew_events",
+    # flow_events
+    "FlowCreatedEvent": "crewai.events.types.flow_events",
+    "FlowEvent": "crewai.events.types.flow_events",
+    "FlowFinishedEvent": "crewai.events.types.flow_events",
+    "FlowPlotEvent": "crewai.events.types.flow_events",
+    "FlowStartedEvent": "crewai.events.types.flow_events",
+    "HumanFeedbackReceivedEvent": "crewai.events.types.flow_events",
+    "HumanFeedbackRequestedEvent": "crewai.events.types.flow_events",
+    "MethodExecutionFailedEvent": "crewai.events.types.flow_events",
+    "MethodExecutionFinishedEvent": "crewai.events.types.flow_events",
+    "MethodExecutionStartedEvent": "crewai.events.types.flow_events",
+    # knowledge_events
+    "KnowledgeQueryCompletedEvent": "crewai.events.types.knowledge_events",
+    "KnowledgeQueryFailedEvent": "crewai.events.types.knowledge_events",
+    "KnowledgeQueryStartedEvent": "crewai.events.types.knowledge_events",
+    "KnowledgeRetrievalCompletedEvent": "crewai.events.types.knowledge_events",
+    "KnowledgeRetrievalStartedEvent": "crewai.events.types.knowledge_events",
+    "KnowledgeSearchQueryFailedEvent": "crewai.events.types.knowledge_events",
+    # llm_events
+    "LLMCallCompletedEvent": "crewai.events.types.llm_events",
+    "LLMCallFailedEvent": "crewai.events.types.llm_events",
+    "LLMCallStartedEvent": "crewai.events.types.llm_events",
+    "LLMStreamChunkEvent": "crewai.events.types.llm_events",
+    # llm_guardrail_events
+    "LLMGuardrailCompletedEvent": "crewai.events.types.llm_guardrail_events",
+    "LLMGuardrailStartedEvent": "crewai.events.types.llm_guardrail_events",
+    # logging_events
+    "AgentLogsExecutionEvent": "crewai.events.types.logging_events",
+    "AgentLogsStartedEvent": "crewai.events.types.logging_events",
+    # mcp_events
+    "MCPConfigFetchFailedEvent": "crewai.events.types.mcp_events",
+    "MCPConnectionCompletedEvent": "crewai.events.types.mcp_events",
+    "MCPConnectionFailedEvent": "crewai.events.types.mcp_events",
+    "MCPConnectionStartedEvent": "crewai.events.types.mcp_events",
+    "MCPToolExecutionCompletedEvent": "crewai.events.types.mcp_events",
+    "MCPToolExecutionFailedEvent": "crewai.events.types.mcp_events",
+    "MCPToolExecutionStartedEvent": "crewai.events.types.mcp_events",
+    # memory_events
+    "MemoryQueryCompletedEvent": "crewai.events.types.memory_events",
+    "MemoryQueryFailedEvent": "crewai.events.types.memory_events",
+    "MemoryQueryStartedEvent": "crewai.events.types.memory_events",
+    "MemoryRetrievalCompletedEvent": "crewai.events.types.memory_events",
+    "MemoryRetrievalFailedEvent": "crewai.events.types.memory_events",
+    "MemoryRetrievalStartedEvent": "crewai.events.types.memory_events",
+    "MemorySaveCompletedEvent": "crewai.events.types.memory_events",
+    "MemorySaveFailedEvent": "crewai.events.types.memory_events",
+    "MemorySaveStartedEvent": "crewai.events.types.memory_events",
+    # reasoning_events
+    "AgentReasoningCompletedEvent": "crewai.events.types.reasoning_events",
+    "AgentReasoningFailedEvent": "crewai.events.types.reasoning_events",
+    "AgentReasoningStartedEvent": "crewai.events.types.reasoning_events",
+    "ReasoningEvent": "crewai.events.types.reasoning_events",
+    # skill_events
+    "SkillActivatedEvent": "crewai.events.types.skill_events",
+    "SkillDiscoveryCompletedEvent": "crewai.events.types.skill_events",
+    "SkillDiscoveryStartedEvent": "crewai.events.types.skill_events",
+    "SkillEvent": "crewai.events.types.skill_events",
+    "SkillLoadFailedEvent": "crewai.events.types.skill_events",
+    "SkillLoadedEvent": "crewai.events.types.skill_events",
+    # task_events
+    "TaskCompletedEvent": "crewai.events.types.task_events",
+    "TaskEvaluationEvent": "crewai.events.types.task_events",
+    "TaskFailedEvent": "crewai.events.types.task_events",
+    "TaskStartedEvent": "crewai.events.types.task_events",
+    # tool_usage_events
+    "ToolExecutionErrorEvent": "crewai.events.types.tool_usage_events",
+    "ToolSelectionErrorEvent": "crewai.events.types.tool_usage_events",
+    "ToolUsageErrorEvent": "crewai.events.types.tool_usage_events",
+    "ToolUsageEvent": "crewai.events.types.tool_usage_events",
+    "ToolUsageFinishedEvent": "crewai.events.types.tool_usage_events",
+    "ToolUsageStartedEvent": "crewai.events.types.tool_usage_events",
+    "ToolValidateInputErrorEvent": "crewai.events.types.tool_usage_events",
+}
+
+_extension_exports: dict[str, Any] = {}
+
+
+def __getattr__(name: str) -> Any:
+    """Lazy import for event types and registered extensions."""
+    if name in _LAZY_EVENT_MAPPING:
+        module_path = _LAZY_EVENT_MAPPING[name]
+        module = importlib.import_module(module_path)
+        val = getattr(module, name)
+        globals()[name] = val  # cache for subsequent access
+        return val
+
+    if name in _extension_exports:
+        value = _extension_exports[name]
+        if isinstance(value, str):
+            module_path, _, attr_name = value.rpartition(".")
+            if module_path:
+                module = importlib.import_module(module_path)
+                return getattr(module, attr_name)
+            return importlib.import_module(value)
+        return value
+
+    msg = f"module {__name__!r} has no attribute {name!r}"
+    raise AttributeError(msg)
 
 
 __all__ = [
@@ -214,42 +339,3 @@ __all__ = [
     "_extension_exports",
     "crewai_event_bus",
 ]
-
-_AGENT_EVENT_MAPPING = {
-    "AgentEvaluationCompletedEvent": "crewai.events.types.agent_events",
-    "AgentEvaluationFailedEvent": "crewai.events.types.agent_events",
-    "AgentEvaluationStartedEvent": "crewai.events.types.agent_events",
-    "AgentExecutionCompletedEvent": "crewai.events.types.agent_events",
-    "AgentExecutionErrorEvent": "crewai.events.types.agent_events",
-    "AgentExecutionStartedEvent": "crewai.events.types.agent_events",
-    "LiteAgentExecutionCompletedEvent": "crewai.events.types.agent_events",
-    "LiteAgentExecutionErrorEvent": "crewai.events.types.agent_events",
-    "LiteAgentExecutionStartedEvent": "crewai.events.types.agent_events",
-}
-
-_extension_exports: dict[str, Any] = {}
-
-
-def __getattr__(name: str) -> Any:
-    """Lazy import for agent events and registered extensions."""
-    if name in _AGENT_EVENT_MAPPING:
-        import importlib
-
-        module_path = _AGENT_EVENT_MAPPING[name]
-        module = importlib.import_module(module_path)
-        return getattr(module, name)
-
-    if name in _extension_exports:
-        import importlib
-
-        value = _extension_exports[name]
-        if isinstance(value, str):
-            module_path, _, attr_name = value.rpartition(".")
-            if module_path:
-                module = importlib.import_module(module_path)
-                return getattr(module, attr_name)
-            return importlib.import_module(value)
-        return value
-
-    msg = f"module {__name__!r} has no attribute {name!r}"
-    raise AttributeError(msg)

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

@@ -81,8 +81,11 @@ class TraceBatchManager:
         """Initialize a new trace batch (thread-safe)"""
         with self._batch_ready_cv:
             if self.current_batch is not None:
+                # Lazy init (e.g. DefaultEnvEvent) may have created the batch without
+                # execution_type; merge metadata from a later flow/crew initializer.
+                self.current_batch.execution_metadata.update(execution_metadata)
                 logger.debug(
-                    "Batch already initialized, skipping duplicate initialization"
+                    "Batch already initialized, merged execution metadata and skipped duplicate initialization"
                 )
                 return self.current_batch
 

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

@@ -60,12 +60,6 @@ from crewai.events.types.crew_events import (
     CrewKickoffFailedEvent,
     CrewKickoffStartedEvent,
 )
-from crewai.events.types.env_events import (
-    CCEnvEvent,
-    CodexEnvEvent,
-    CursorEnvEvent,
-    DefaultEnvEvent,
-)
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -212,7 +206,6 @@ class TraceCollectionListener(BaseEventListener):
             self._listeners_setup = True
             return
 
-        self._register_env_event_handlers(crewai_event_bus)
         self._register_flow_event_handlers(crewai_event_bus)
         self._register_context_event_handlers(crewai_event_bus)
         self._register_action_event_handlers(crewai_event_bus)
@@ -221,25 +214,6 @@ class TraceCollectionListener(BaseEventListener):
 
         self._listeners_setup = True
 
-    def _register_env_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
-        """Register handlers for environment context events."""
-
-        @event_bus.on(CCEnvEvent)
-        def on_cc_env(source: Any, event: CCEnvEvent) -> None:
-            self._handle_action_event("cc_env", source, event)
-
-        @event_bus.on(CodexEnvEvent)
-        def on_codex_env(source: Any, event: CodexEnvEvent) -> None:
-            self._handle_action_event("codex_env", source, event)
-
-        @event_bus.on(CursorEnvEvent)
-        def on_cursor_env(source: Any, event: CursorEnvEvent) -> None:
-            self._handle_action_event("cursor_env", source, event)
-
-        @event_bus.on(DefaultEnvEvent)
-        def on_default_env(source: Any, event: DefaultEnvEvent) -> None:
-            self._handle_action_event("default_env", source, event)
-
     def _register_flow_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
         """Register handlers for flow events."""
 
@@ -286,8 +260,8 @@ class TraceCollectionListener(BaseEventListener):
             if self.batch_manager.batch_owner_type != "flow":
                 # Always call _initialize_crew_batch to claim ownership.
                 # If batch was already initialized by a concurrent action event
-                # (race condition with DefaultEnvEvent), initialize_batch() returns
-                # early but batch_owner_type is still correctly set to "crew".
+                # (e.g. LLM/tool before crew_kickoff_started), initialize_batch()
+                # returns early but batch_owner_type is still correctly set to "crew".
                 # Skip only when a parent flow already owns the batch.
                 self._initialize_crew_batch(source, event)
             self._handle_trace_event("crew_kickoff_started", source, event)

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

@@ -1503,6 +1503,8 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 except Exception:
                     logger.warning("FlowStartedEvent handler failed", exc_info=True)
 
+        get_env_context()
+
         context = self._pending_feedback_context
         emit = context.emit
         default_outcome = context.default_outcome
@@ -2004,7 +2006,6 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
         restored = apply_checkpoint(self, from_checkpoint)
         if restored is not None:
             return restored.kickoff(inputs=inputs, input_files=input_files)
-        get_env_context()
         if self.stream:
             result_holder: list[Any] = []
             current_task_info: TaskInfo = {
@@ -2138,7 +2139,9 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
 
         try:
             # Reset flow state for fresh execution unless restoring from persistence
-            is_restoring = inputs and "id" in inputs and self.persistence is not None
+            is_restoring = (
+                inputs and "id" in inputs and self.persistence is not None
+            ) or self.checkpoint_completed_methods is not None
             if not is_restoring:
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
@@ -2204,6 +2207,10 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                     f"Flow started with ID: {self.flow_id}", color="bold magenta"
                 )
 
+            # After FlowStarted (when not suppressed): env events must not pre-empt
+            # trace batch init with implicit "crew" execution_type.
+            get_env_context()
+
             if inputs is not None and "id" not in inputs:
                 self._initialize_state(inputs)
 

lib/crewai/src/crewai/lite_agent.py

@@ -16,7 +16,6 @@ from typing import (
     get_origin,
 )
 import uuid
-import warnings
 
 from pydantic import (
     UUID4,
@@ -26,7 +25,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
-from typing_extensions import Self
+from typing_extensions import Self, deprecated
 
 
 if TYPE_CHECKING:
@@ -173,9 +172,12 @@ def _kickoff_with_a2a_support(
     )
 
 
+@deprecated(
+    "LiteAgent is deprecated and will be removed in v2.0.0.",
+    category=FutureWarning,
+)
 class LiteAgent(FlowTrackable, BaseModel):
-    """
-    A lightweight agent that can process messages and use tools.
+    """A lightweight agent that can process messages and use tools.
 
     .. deprecated::
         LiteAgent is deprecated and will be removed in a future version.
@@ -278,18 +280,6 @@ class LiteAgent(FlowTrackable, BaseModel):
     )
     _memory: Any = PrivateAttr(default=None)
 
-    @model_validator(mode="after")
-    def emit_deprecation_warning(self) -> Self:
-        """Emit deprecation warning for LiteAgent usage."""
-        warnings.warn(
-            "LiteAgent is deprecated and will be removed in a future version. "
-            "Use Agent().kickoff(messages) instead, which provides the same "
-            "functionality with additional features like memory and knowledge support.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self
-
     @model_validator(mode="after")
     def setup_llm(self) -> Self:
         """Set up the LLM and other components after initialization."""

lib/crewai/src/crewai/llm.py

@@ -175,6 +175,16 @@ LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
     "us.amazon.nova-pro-v1:0": 300000,
     "us.amazon.nova-micro-v1:0": 128000,
     "us.amazon.nova-lite-v1:0": 300000,
+    # Claude 4 models
+    "us.anthropic.claude-opus-4-7": 1000000,
+    "us.anthropic.claude-sonnet-4-6": 1000000,
+    "us.anthropic.claude-opus-4-6-v1": 1000000,
+    "us.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
+    "us.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
+    "us.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
+    "us.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
+    "us.anthropic.claude-opus-4-20250514-v1:0": 200000,
+    "us.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
     "us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
     "us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
     "us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
@@ -193,15 +203,44 @@ LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
     "eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
     "eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
     "eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    # Claude 4 EU
+    "eu.anthropic.claude-opus-4-7": 1000000,
+    "eu.anthropic.claude-sonnet-4-6": 1000000,
+    "eu.anthropic.claude-opus-4-6-v1": 1000000,
+    "eu.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
+    "eu.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
+    "eu.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
+    "eu.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
+    "eu.anthropic.claude-opus-4-20250514-v1:0": 200000,
+    "eu.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
     "eu.meta.llama3-2-3b-instruct-v1:0": 131000,
     "eu.meta.llama3-2-1b-instruct-v1:0": 131000,
     "apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
     "apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
     "apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
     "apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
+    # Claude 4 APAC
+    "apac.anthropic.claude-opus-4-7": 1000000,
+    "apac.anthropic.claude-sonnet-4-6": 1000000,
+    "apac.anthropic.claude-opus-4-6-v1": 1000000,
+    "apac.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
+    "apac.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
+    "apac.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
+    "apac.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
+    "apac.anthropic.claude-opus-4-20250514-v1:0": 200000,
+    "apac.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
     "amazon.nova-pro-v1:0": 300000,
     "amazon.nova-micro-v1:0": 128000,
     "amazon.nova-lite-v1:0": 300000,
+    "anthropic.claude-opus-4-7": 1000000,
+    "anthropic.claude-sonnet-4-6": 1000000,
+    "anthropic.claude-opus-4-6-v1": 1000000,
+    "anthropic.claude-opus-4-5-20251101-v1:0": 200000,
+    "anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
+    "anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
+    "anthropic.claude-opus-4-1-20250805-v1:0": 200000,
+    "anthropic.claude-opus-4-20250514-v1:0": 200000,
+    "anthropic.claude-sonnet-4-20250514-v1:0": 200000,
     "anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
     "anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
     "anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,

lib/crewai/src/crewai/llms/constants.py

@@ -423,6 +423,34 @@ AZURE_MODELS: list[AzureModels] = [
 
 
 BedrockModels: TypeAlias = Literal[
+    # Inference profiles (regional) - Claude 4
+    "us.anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "us.anthropic.claude-sonnet-4-20250514-v1:0",
+    "us.anthropic.claude-opus-4-5-20251101-v1:0",
+    "us.anthropic.claude-opus-4-20250514-v1:0",
+    "us.anthropic.claude-opus-4-1-20250805-v1:0",
+    "us.anthropic.claude-haiku-4-5-20251001-v1:0",
+    "us.anthropic.claude-sonnet-4-6",
+    "us.anthropic.claude-opus-4-6-v1",
+    # Inference profiles - shorter versions
+    "us.anthropic.claude-sonnet-4-5-v1:0",
+    "us.anthropic.claude-opus-4-5-v1:0",
+    "us.anthropic.claude-opus-4-6-v1:0",
+    "us.anthropic.claude-haiku-4-5-v1:0",
+    "eu.anthropic.claude-sonnet-4-5-v1:0",
+    "eu.anthropic.claude-opus-4-5-v1:0",
+    "eu.anthropic.claude-haiku-4-5-v1:0",
+    "apac.anthropic.claude-sonnet-4-5-v1:0",
+    "apac.anthropic.claude-opus-4-5-v1:0",
+    "apac.anthropic.claude-haiku-4-5-v1:0",
+    # Global inference profiles
+    "global.anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "global.anthropic.claude-sonnet-4-20250514-v1:0",
+    "global.anthropic.claude-opus-4-5-20251101-v1:0",
+    "global.anthropic.claude-opus-4-6-v1",
+    "global.anthropic.claude-haiku-4-5-20251001-v1:0",
+    "global.anthropic.claude-sonnet-4-6",
+    # Direct model IDs
     "ai21.jamba-1-5-large-v1:0",
     "ai21.jamba-1-5-mini-v1:0",
     "amazon.nova-lite-v1:0",
@@ -496,6 +524,34 @@ BedrockModels: TypeAlias = Literal[
     "twelvelabs.pegasus-1-2-v1:0",
 ]
 BEDROCK_MODELS: list[BedrockModels] = [
+    # Inference profiles (regional) - Claude 4
+    "us.anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "us.anthropic.claude-sonnet-4-20250514-v1:0",
+    "us.anthropic.claude-opus-4-5-20251101-v1:0",
+    "us.anthropic.claude-opus-4-20250514-v1:0",
+    "us.anthropic.claude-opus-4-1-20250805-v1:0",
+    "us.anthropic.claude-haiku-4-5-20251001-v1:0",
+    "us.anthropic.claude-sonnet-4-6",
+    "us.anthropic.claude-opus-4-6-v1",
+    # Inference profiles - shorter versions
+    "us.anthropic.claude-sonnet-4-5-v1:0",
+    "us.anthropic.claude-opus-4-5-v1:0",
+    "us.anthropic.claude-opus-4-6-v1:0",
+    "us.anthropic.claude-haiku-4-5-v1:0",
+    "eu.anthropic.claude-sonnet-4-5-v1:0",
+    "eu.anthropic.claude-opus-4-5-v1:0",
+    "eu.anthropic.claude-haiku-4-5-v1:0",
+    "apac.anthropic.claude-sonnet-4-5-v1:0",
+    "apac.anthropic.claude-opus-4-5-v1:0",
+    "apac.anthropic.claude-haiku-4-5-v1:0",
+    # Global inference profiles
+    "global.anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "global.anthropic.claude-sonnet-4-20250514-v1:0",
+    "global.anthropic.claude-opus-4-5-20251101-v1:0",
+    "global.anthropic.claude-opus-4-6-v1",
+    "global.anthropic.claude-haiku-4-5-20251001-v1:0",
+    "global.anthropic.claude-sonnet-4-6",
+    # Direct model IDs
     "ai21.jamba-1-5-large-v1:0",
     "ai21.jamba-1-5-mini-v1:0",
     "amazon.nova-lite-v1:0",

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

@@ -17,10 +17,7 @@ from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
 )
-from crewai.utilities.pydantic_schema_utils import (
-    generate_model_description,
-    sanitize_tool_params_for_bedrock_strict,
-)
+from crewai.utilities.pydantic_schema_utils import generate_model_description
 from crewai.utilities.types import LLMMessage
 
 
@@ -173,7 +170,6 @@ class ToolSpec(TypedDict, total=False):
     name: Required[str]
     description: Required[str]
     inputSchema: ToolInputSchema
-    strict: bool
 
 
 class ConverseToolTypeDef(TypedDict):
@@ -1988,21 +1984,10 @@ class BedrockCompletion(BaseLLM):
                     "description": description,
                 }
 
-                func_info = tool.get("function", {})
-                strict_enabled = bool(func_info.get("strict"))
-
                 if parameters and isinstance(parameters, dict):
-                    schema_params = (
-                        sanitize_tool_params_for_bedrock_strict(parameters)
-                        if strict_enabled
-                        else parameters
-                    )
-                    input_schema: ToolInputSchema = {"json": schema_params}
+                    input_schema: ToolInputSchema = {"json": parameters}
                     tool_spec["inputSchema"] = input_schema
 
-                if strict_enabled:
-                    tool_spec["strict"] = True
-
                 converse_tool: ConverseToolTypeDef = {"toolSpec": tool_spec}
 
                 converse_tools.append(converse_tool)
@@ -2090,6 +2075,9 @@ class BedrockCompletion(BaseLLM):
 
         # Context window sizes for common Bedrock models
         context_windows = {
+            "anthropic.claude-sonnet-4": 200000,
+            "anthropic.claude-opus-4": 200000,
+            "anthropic.claude-haiku-4": 200000,
             "anthropic.claude-3-5-sonnet": 200000,
             "anthropic.claude-3-5-haiku": 200000,
             "anthropic.claude-3-opus": 200000,

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

@@ -976,6 +976,7 @@ class GeminiCompletion(BaseLLM):
                             "id": call_id,
                             "name": part.function_call.name,
                             "args": args_dict,
+                            "raw_part": part,
                         }
 
                         self._emit_stream_chunk_event(
@@ -1060,29 +1061,20 @@ class GeminiCompletion(BaseLLM):
             if call_data.get("name") != STRUCTURED_OUTPUT_TOOL_NAME
         }
 
-        # If there are function calls but no available_functions,
-        # return them for the executor to handle
         if non_structured_output_calls and not available_functions:
-            formatted_function_calls = [
-                {
-                    "id": call_data["id"],
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": json.dumps(call_data["args"]),
-                    },
-                    "type": "function",
-                }
+            raw_parts = [
+                call_data["raw_part"]
                 for call_data in non_structured_output_calls.values()
             ]
             self._emit_call_completed_event(
-                response=formatted_function_calls,
+                response=raw_parts,
                 call_type=LLMCallType.TOOL_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=self._convert_contents_to_dict(contents),
                 usage=usage_data,
             )
-            return formatted_function_calls
+            return raw_parts
 
         # Handle completed function calls (excluding structured_output)
         if non_structured_output_calls and available_functions:

lib/crewai/src/crewai/mcp/__init__.py

@@ -2,9 +2,17 @@
 
 This module provides native MCP client functionality, allowing CrewAI agents
 to connect to any MCP-compliant server using various transport types.
+
+Heavy imports (MCPClient, MCPToolResolver, BaseTransport, TransportType) are
+lazy-loaded on first access to avoid pulling in the ``mcp`` SDK (~400ms)
+when only lightweight config/filter types are needed.
 """
 
-from crewai.mcp.client import MCPClient
+from __future__ import annotations
+
+import importlib
+from typing import TYPE_CHECKING, Any
+
 from crewai.mcp.config import (
     MCPServerConfig,
     MCPServerHTTP,
@@ -18,8 +26,28 @@ from crewai.mcp.filters import (
     create_dynamic_tool_filter,
     create_static_tool_filter,
 )
-from crewai.mcp.tool_resolver import MCPToolResolver
-from crewai.mcp.transports.base import BaseTransport, TransportType
+
+if TYPE_CHECKING:
+    from crewai.mcp.client import MCPClient
+    from crewai.mcp.tool_resolver import MCPToolResolver
+    from crewai.mcp.transports.base import BaseTransport, TransportType
+
+_LAZY: dict[str, tuple[str, str]] = {
+    "MCPClient": ("crewai.mcp.client", "MCPClient"),
+    "MCPToolResolver": ("crewai.mcp.tool_resolver", "MCPToolResolver"),
+    "BaseTransport": ("crewai.mcp.transports.base", "BaseTransport"),
+    "TransportType": ("crewai.mcp.transports.base", "TransportType"),
+}
+
+
+def __getattr__(name: str) -> Any:
+    if name in _LAZY:
+        mod_path, attr = _LAZY[name]
+        mod = importlib.import_module(mod_path)
+        val = getattr(mod, attr)
+        globals()[name] = val  # cache for subsequent access
+        return val
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
 __all__ = [

lib/crewai/src/crewai/mcp/tool_resolver.py

@@ -417,9 +417,18 @@ class MCPToolResolver:
 
                 args_schema = None
                 if tool_def.get("inputSchema"):
-                    args_schema = self._json_schema_to_pydantic(
-                        tool_name, tool_def["inputSchema"]
-                    )
+                    try:
+                        args_schema = self._json_schema_to_pydantic(
+                            tool_name, tool_def["inputSchema"]
+                        )
+                    except Exception as e:
+                        self._logger.log(
+                            "warning",
+                            f"Failed to build args schema for MCP tool "
+                            f"'{tool_name}': {e}. Registering tool without a "
+                            "typed schema.",
+                        )
+                        args_schema = None
 
                 tool_schema = {
                     "description": tool_def.get("description", ""),

lib/crewai/src/crewai/project/annotations.py

@@ -237,6 +237,8 @@ def crew(
         self.tasks = instantiated_tasks
 
         crew_instance: Crew = _call_method(meth, self, *args, **kwargs)
+        if "name" not in crew_instance.model_fields_set:
+            crew_instance.name = getattr(self, "_crew_name", None) or crew_instance.name
 
         def callback_wrapper(
             hook: Callable[Concatenate[CrewInstance, P2], R2], instance: CrewInstance

lib/crewai/src/crewai/state/checkpoint_listener.py

@@ -120,6 +120,12 @@ def _do_checkpoint(
     )
     state._chain_lineage(cfg.provider, location)
 
+    checkpoint_id: str = cfg.provider.extract_id(location)
+    msg: str = (
+        f"Checkpoint saved. Resume with: crewai checkpoint resume {checkpoint_id}"
+    )
+    logger.info(msg)
+
     if cfg.max_checkpoints is not None:
         cfg.provider.prune(cfg.location, cfg.max_checkpoints, branch=state._branch)
 

lib/crewai/src/crewai/state/runtime.py

@@ -44,9 +44,12 @@ def _sync_checkpoint_fields(entity: object) -> None:
         entity: The entity whose private runtime attributes will be
             copied into its public checkpoint fields.
     """
+    from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.crew import Crew
     from crewai.flow.flow import Flow
 
+    if isinstance(entity, BaseAgent):
+        entity.checkpoint_kickoff_event_id = entity._kickoff_event_id
     if isinstance(entity, Flow):
         entity.checkpoint_completed_methods = (
             set(entity._completed_methods) if entity._completed_methods else None

lib/crewai/src/crewai/task.py

@@ -32,6 +32,7 @@ from pydantic import (
     field_validator,
     model_validator,
 )
+from pydantic.functional_serializers import PlainSerializer
 from pydantic_core import PydanticCustomError
 from typing_extensions import Self
 
@@ -86,6 +87,22 @@ from crewai.utilities.printer import PRINTER
 from crewai.utilities.string_utils import interpolate_only
 
 
+def _serialize_model_class(v: type[BaseModel] | None) -> dict[str, Any] | None:
+    """Serialize a Pydantic model class reference to its JSON schema."""
+    return v.model_json_schema() if v else None
+
+
+def _deserialize_model_class(v: Any) -> type[BaseModel] | None:
+    """Hydrate a model class reference from checkpoint data."""
+    if v is None or isinstance(v, type):
+        return v
+    if isinstance(v, dict):
+        from crewai.utilities.pydantic_schema_utils import create_model_from_schema
+
+        return create_model_from_schema(v)
+    return None
+
+
 class Task(BaseModel):
     """Class that represents a task to be executed.
 
@@ -141,15 +158,33 @@ class Task(BaseModel):
         description="Whether the task should be executed asynchronously or not.",
         default=False,
     )
-    output_json: type[BaseModel] | None = Field(
+    output_json: Annotated[
+        type[BaseModel] | None,
+        BeforeValidator(_deserialize_model_class),
+        PlainSerializer(
+            _serialize_model_class, return_type=dict | None, when_used="json"
+        ),
+    ] = Field(
         description="A Pydantic model to be used to create a JSON output.",
         default=None,
     )
-    output_pydantic: type[BaseModel] | None = Field(
+    output_pydantic: Annotated[
+        type[BaseModel] | None,
+        BeforeValidator(_deserialize_model_class),
+        PlainSerializer(
+            _serialize_model_class, return_type=dict | None, when_used="json"
+        ),
+    ] = Field(
         description="A Pydantic model to be used to create a Pydantic output.",
         default=None,
     )
-    response_model: type[BaseModel] | None = Field(
+    response_model: Annotated[
+        type[BaseModel] | None,
+        BeforeValidator(_deserialize_model_class),
+        PlainSerializer(
+            _serialize_model_class, return_type=dict | None, when_used="json"
+        ),
+    ] = Field(
         description="A Pydantic model for structured LLM outputs using native provider features.",
         default=None,
     )
@@ -189,7 +224,13 @@ class Task(BaseModel):
         description="Whether the task should instruct the agent to return the final answer formatted in Markdown",
         default=False,
     )
-    converter_cls: type[Converter] | None = Field(
+    converter_cls: Annotated[
+        type[Converter] | None,
+        BeforeValidator(lambda v: v if v is None or isinstance(v, type) else None),
+        PlainSerializer(
+            _serialize_model_class, return_type=dict | None, when_used="json"
+        ),
+    ] = Field(
         description="A converter class used to export structured output",
         default=None,
     )
@@ -1241,12 +1282,26 @@ Follow these guidelines:
                 tools=tools,
             )
 
-            pydantic_output, json_output = self._export_output(result)
+            if isinstance(result, BaseModel):
+                raw = result.model_dump_json()
+                if self.output_pydantic:
+                    pydantic_output = result
+                    json_output = None
+                elif self.output_json:
+                    pydantic_output = None
+                    json_output = result.model_dump()
+                else:
+                    pydantic_output = None
+                    json_output = None
+            else:
+                raw = result
+                pydantic_output, json_output = self._export_output(result)
+
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
                 expected_output=self.expected_output,
-                raw=result,
+                raw=raw,
                 pydantic=pydantic_output,
                 json_dict=json_output,
                 agent=agent.role,
@@ -1337,12 +1392,26 @@ Follow these guidelines:
                 tools=tools,
             )
 
-            pydantic_output, json_output = self._export_output(result)
+            if isinstance(result, BaseModel):
+                raw = result.model_dump_json()
+                if self.output_pydantic:
+                    pydantic_output = result
+                    json_output = None
+                elif self.output_json:
+                    pydantic_output = None
+                    json_output = result.model_dump()
+                else:
+                    pydantic_output = None
+                    json_output = None
+            else:
+                raw = result
+                pydantic_output, json_output = self._export_output(result)
+
             task_output = TaskOutput(
                 name=self.name or self.description,
                 description=self.description,
                 expected_output=self.expected_output,
-                raw=result,
+                raw=raw,
                 pydantic=pydantic_output,
                 json_dict=json_output,
                 agent=agent.role,

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

@@ -1058,3 +1058,20 @@ class Telemetry:
             close_span(span)
 
         self._safe_telemetry_operation(_operation)
+
+    def template_installed_span(self, template_name: str) -> None:
+        """Records when a template is downloaded and installed.
+
+        Args:
+            template_name: Name of the template that was installed
+                (without the template_ prefix).
+        """
+
+        def _operation() -> None:
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Template Installed")
+            self._add_attribute(span, "crewai_version", version("crewai"))
+            self._add_attribute(span, "template_name", template_name)
+            close_span(span)
+
+        self._safe_telemetry_operation(_operation)

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

@@ -19,7 +19,18 @@ from collections.abc import Callable
 from copy import deepcopy
 import datetime
 import logging
-from typing import TYPE_CHECKING, Annotated, Any, Final, Literal, TypedDict, Union, cast
+from typing import (
+    TYPE_CHECKING,
+    Annotated,
+    Any,
+    Final,
+    ForwardRef,
+    Literal,
+    Optional,
+    TypedDict,
+    Union,
+    cast,
+)
 import uuid
 
 import jsonref  # type: ignore[import-untyped]
@@ -99,15 +110,26 @@ def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
     """
     defs = schema.get("$defs", {})
     schema_copy = deepcopy(schema)
+    expanding: set[str] = set()
 
     def _resolve(node: Any) -> Any:
         if isinstance(node, dict):
             ref = node.get("$ref")
             if isinstance(ref, str) and ref.startswith("#/$defs/"):
                 def_name = ref.replace("#/$defs/", "")
-                if def_name in defs:
+                if def_name not in defs:
+                    raise KeyError(f"Definition '{def_name}' not found in $defs.")
+                if def_name in expanding:
+                    def_schema = defs[def_name]
+                    stub: dict[str, Any] = {"type": def_schema.get("type", "object")}
+                    if "description" in def_schema:
+                        stub["description"] = def_schema["description"]
+                    return stub
+                expanding.add(def_name)
+                try:
                     return _resolve(deepcopy(defs[def_name]))
-                raise KeyError(f"Definition '{def_name}' not found in $defs.")
+                finally:
+                    expanding.discard(def_name)
             return {k: _resolve(v) for k, v in node.items()}
 
         if isinstance(node, list):
@@ -119,7 +141,11 @@ def resolve_refs(schema: dict[str, Any]) -> dict[str, Any]:
 
 
 def add_key_in_dict_recursively(
-    d: dict[str, Any], key: str, value: Any, criteria: Callable[[dict[str, Any]], bool]
+    d: dict[str, Any],
+    key: str,
+    value: Any,
+    criteria: Callable[[dict[str, Any]], bool],
+    _seen: set[int] | None = None,
 ) -> dict[str, Any]:
     """Recursively adds a key/value pair to all nested dicts matching `criteria`.
 
@@ -128,22 +154,31 @@ def add_key_in_dict_recursively(
         key: The key to add.
         value: The value to add.
         criteria: A function that returns True for dicts that should receive the key.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         The modified dictionary.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(d, dict):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         if criteria(d) and key not in d:
             d[key] = value
         for v in d.values():
-            add_key_in_dict_recursively(v, key, value, criteria)
+            add_key_in_dict_recursively(v, key, value, criteria, _seen)
     elif isinstance(d, list):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for i in d:
-            add_key_in_dict_recursively(i, key, value, criteria)
+            add_key_in_dict_recursively(i, key, value, criteria, _seen)
     return d
 
 
-def force_additional_properties_false(d: Any) -> Any:
+def force_additional_properties_false(d: Any, _seen: set[int] | None = None) -> Any:
     """Force additionalProperties=false on all object-type dicts recursively.
 
     OpenAI strict mode requires all objects to have additionalProperties=false.
@@ -154,11 +189,17 @@ def force_additional_properties_false(d: Any) -> Any:
 
     Args:
         d: The dictionary/list to modify.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         The modified dictionary/list.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(d, dict):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         if d.get("type") == "object":
             d["additionalProperties"] = False
             if "properties" not in d:
@@ -166,10 +207,13 @@ def force_additional_properties_false(d: Any) -> Any:
             if "required" not in d:
                 d["required"] = []
         for v in d.values():
-            force_additional_properties_false(v)
+            force_additional_properties_false(v, _seen)
     elif isinstance(d, list):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for i in d:
-            force_additional_properties_false(i)
+            force_additional_properties_false(i, _seen)
     return d
 
 
@@ -183,7 +227,7 @@ OPENAI_SUPPORTED_FORMATS: Final[
 }
 
 
-def strip_unsupported_formats(d: Any) -> Any:
+def strip_unsupported_formats(d: Any, _seen: set[int] | None = None) -> Any:
     """Remove format annotations that OpenAI strict mode doesn't support.
 
     OpenAI only supports: date-time, date, time, duration.
@@ -191,11 +235,17 @@ def strip_unsupported_formats(d: Any) -> Any:
 
     Args:
         d: The dictionary/list to modify.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         The modified dictionary/list.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(d, dict):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         format_value = d.get("format")
         if (
             isinstance(format_value, str)
@@ -203,14 +253,17 @@ def strip_unsupported_formats(d: Any) -> Any:
         ):
             del d["format"]
         for v in d.values():
-            strip_unsupported_formats(v)
+            strip_unsupported_formats(v, _seen)
     elif isinstance(d, list):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for i in d:
-            strip_unsupported_formats(i)
+            strip_unsupported_formats(i, _seen)
     return d
 
 
-def ensure_type_in_schemas(d: Any) -> Any:
+def ensure_type_in_schemas(d: Any, _seen: set[int] | None = None) -> Any:
     """Ensure all schema objects in anyOf/oneOf have a 'type' key.
 
     OpenAI strict mode requires every schema to have a 'type' key.
@@ -218,11 +271,17 @@ def ensure_type_in_schemas(d: Any) -> Any:
 
     Args:
         d: The dictionary/list to modify.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         The modified dictionary/list.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(d, dict):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for key in ("anyOf", "oneOf"):
             if key in d:
                 schema_list = d[key]
@@ -230,12 +289,15 @@ def ensure_type_in_schemas(d: Any) -> Any:
                     if isinstance(schema, dict) and schema == {}:
                         schema_list[i] = {"type": "object"}
                     else:
-                        ensure_type_in_schemas(schema)
+                        ensure_type_in_schemas(schema, _seen)
         for v in d.values():
-            ensure_type_in_schemas(v)
+            ensure_type_in_schemas(v, _seen)
     elif isinstance(d, list):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for item in d:
-            ensure_type_in_schemas(item)
+            ensure_type_in_schemas(item, _seen)
     return d
 
 
@@ -318,7 +380,9 @@ def add_const_to_oneof_variants(schema: dict[str, Any]) -> dict[str, Any]:
     return _process_oneof(deepcopy(schema))
 
 
-def convert_oneof_to_anyof(schema: dict[str, Any]) -> dict[str, Any]:
+def convert_oneof_to_anyof(
+    schema: dict[str, Any], _seen: set[int] | None = None
+) -> dict[str, Any]:
     """Convert oneOf to anyOf for OpenAI compatibility.
 
     OpenAI's Structured Outputs support anyOf better than oneOf.
@@ -326,26 +390,37 @@ def convert_oneof_to_anyof(schema: dict[str, Any]) -> dict[str, Any]:
 
     Args:
         schema: JSON schema dictionary.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         Modified schema with anyOf instead of oneOf.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(schema, dict):
+        if id(schema) in _seen:
+            return schema
+        _seen.add(id(schema))
         if "oneOf" in schema:
             schema["anyOf"] = schema.pop("oneOf")
 
         for value in schema.values():
             if isinstance(value, dict):
-                convert_oneof_to_anyof(value)
+                convert_oneof_to_anyof(value, _seen)
             elif isinstance(value, list):
+                if id(value) in _seen:
+                    continue
+                _seen.add(id(value))
                 for item in value:
                     if isinstance(item, dict):
-                        convert_oneof_to_anyof(item)
+                        convert_oneof_to_anyof(item, _seen)
 
     return schema
 
 
-def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
+def ensure_all_properties_required(
+    schema: dict[str, Any], _seen: set[int] | None = None
+) -> dict[str, Any]:
     """Ensure all properties are in the required array for OpenAI strict mode.
 
     OpenAI's strict structured outputs require all properties to be listed
@@ -354,11 +429,17 @@ def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
 
     Args:
         schema: JSON schema dictionary.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         Modified schema with all properties marked as required.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(schema, dict):
+        if id(schema) in _seen:
+            return schema
+        _seen.add(id(schema))
         if schema.get("type") == "object" and "properties" in schema:
             properties = schema["properties"]
             if properties:
@@ -366,16 +447,21 @@ def ensure_all_properties_required(schema: dict[str, Any]) -> dict[str, Any]:
 
         for value in schema.values():
             if isinstance(value, dict):
-                ensure_all_properties_required(value)
+                ensure_all_properties_required(value, _seen)
             elif isinstance(value, list):
+                if id(value) in _seen:
+                    continue
+                _seen.add(id(value))
                 for item in value:
                     if isinstance(item, dict):
-                        ensure_all_properties_required(item)
+                        ensure_all_properties_required(item, _seen)
 
     return schema
 
 
-def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
+def strip_null_from_types(
+    schema: dict[str, Any], _seen: set[int] | None = None
+) -> dict[str, Any]:
     """Remove null type from anyOf/type arrays.
 
     Pydantic generates `T | None` for optional fields, which creates schemas with
@@ -384,11 +470,17 @@ def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
 
     Args:
         schema: JSON schema dictionary.
+        _seen: Internal set of visited ``id()``s, used to guard cyclic schemas.
 
     Returns:
         Modified schema with null types removed.
     """
+    if _seen is None:
+        _seen = set()
     if isinstance(schema, dict):
+        if id(schema) in _seen:
+            return schema
+        _seen.add(id(schema))
         if "anyOf" in schema:
             any_of = schema["anyOf"]
             non_null = [opt for opt in any_of if opt.get("type") != "null"]
@@ -408,11 +500,14 @@ def strip_null_from_types(schema: dict[str, Any]) -> dict[str, Any]:
 
         for value in schema.values():
             if isinstance(value, dict):
-                strip_null_from_types(value)
+                strip_null_from_types(value, _seen)
             elif isinstance(value, list):
+                if id(value) in _seen:
+                    continue
+                _seen.add(id(value))
                 for item in value:
                     if isinstance(item, dict):
-                        strip_null_from_types(item)
+                        strip_null_from_types(item, _seen)
 
     return schema
 
@@ -451,16 +546,26 @@ _CLAUDE_STRICT_UNSUPPORTED: Final[tuple[str, ...]] = (
 )
 
 
-def _strip_keys_recursive(d: Any, keys: tuple[str, ...]) -> Any:
+def _strip_keys_recursive(
+    d: Any, keys: tuple[str, ...], _seen: set[int] | None = None
+) -> Any:
     """Recursively delete a fixed set of keys from a schema."""
+    if _seen is None:
+        _seen = set()
     if isinstance(d, dict):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for key in keys:
             d.pop(key, None)
         for v in d.values():
-            _strip_keys_recursive(v, keys)
+            _strip_keys_recursive(v, keys, _seen)
     elif isinstance(d, list):
+        if id(d) in _seen:
+            return d
+        _seen.add(id(d))
         for i in d:
-            _strip_keys_recursive(i, keys)
+            _strip_keys_recursive(i, keys, _seen)
     return d
 
 
@@ -658,6 +763,25 @@ def build_rich_field_description(prop_schema: dict[str, Any]) -> str:
     return ". ".join(parts) if parts else ""
 
 
+def _inline_top_level_ref(schema: dict[str, Any]) -> dict[str, Any]:
+    """Resolve only the top-level ``$ref``, preserving ``$defs`` for lazy inner resolution.
+
+    Used as a fallback when ``jsonref.replace_refs`` fails on circular schemas.
+    Inner ``$ref`` pointers are left intact so that :func:`_resolve_ref` can
+    resolve them during model construction, with cycle detection via ``in_progress``.
+    """
+    schema = deepcopy(schema)
+    ref = schema.get("$ref")
+    if isinstance(ref, str) and ref.startswith("#/$defs/"):
+        def_name = ref[len("#/$defs/") :]
+        defs = schema.get("$defs", {})
+        if def_name in defs:
+            resolved: dict[str, Any] = deepcopy(defs[def_name])
+            resolved.setdefault("$defs", defs)
+            return resolved
+    return schema
+
+
 def create_model_from_schema(  # type: ignore[no-any-unimported]
     json_schema: dict[str, Any],
     *,
@@ -712,19 +836,80 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         >>> person.name
         'John'
     """
-    json_schema = dict(jsonref.replace_refs(json_schema, proxies=False))
+    try:
+        json_schema = dict(jsonref.replace_refs(json_schema, proxies=False))
+    except (jsonref.JsonRefError, RecursionError):
+        json_schema = _inline_top_level_ref(json_schema)
 
     effective_root = root_schema or json_schema
 
     json_schema = force_additional_properties_false(json_schema)
     effective_root = force_additional_properties_false(effective_root)
 
+    in_progress: dict[int, Any] = {}
+    model = _build_model_from_schema(
+        json_schema,
+        effective_root,
+        model_name=model_name,
+        enrich_descriptions=enrich_descriptions,
+        in_progress=in_progress,
+        __config__=__config__,
+        __base__=__base__,
+        __module__=__module__,
+        __validators__=__validators__,
+        __cls_kwargs__=__cls_kwargs__,
+    )
+
+    types_namespace: dict[str, Any] = {
+        entry.__name__: entry
+        for entry in in_progress.values()
+        if isinstance(entry, type) and issubclass(entry, BaseModel)
+    }
+    for entry in in_progress.values():
+        if (
+            isinstance(entry, type)
+            and issubclass(entry, BaseModel)
+            and not getattr(entry, "__pydantic_complete__", True)
+        ):
+            try:
+                entry.model_rebuild(_types_namespace=types_namespace)
+            except Exception as e:
+                logger.debug("model_rebuild failed for %s: %s", entry.__name__, e)
+    return model
+
+
+def _build_model_from_schema(  # type: ignore[no-any-unimported]
+    json_schema: dict[str, Any],
+    effective_root: dict[str, Any],
+    *,
+    model_name: str | None,
+    enrich_descriptions: bool,
+    in_progress: dict[int, Any],
+    __config__: ConfigDict | None = None,
+    __base__: type[BaseModel] | None = None,
+    __module__: str = __name__,
+    __validators__: dict[str, AnyClassMethod] | None = None,
+    __cls_kwargs__: dict[str, Any] | None = None,
+) -> type[BaseModel]:
+    """Inner builder shared by the public entry point and recursive nested-object creation.
+
+    Preprocessing via ``jsonref.replace_refs`` and the sanitization walkers is
+    run once by the public entry; this helper walks the already-normalized
+    schema and emits Pydantic models. ``in_progress`` maps ``id(schema)`` to
+    the model being built for that schema, so a cyclic ``$ref`` graph
+    degrades to a ``ForwardRef`` back-edge instead of blowing the stack.
+    """
+    original_id = id(json_schema)
     if "allOf" in json_schema:
         json_schema = _merge_all_of_schemas(json_schema["allOf"], effective_root)
-        if "title" not in json_schema and "title" in (root_schema or {}):
-            json_schema["title"] = (root_schema or {}).get("title")
 
     effective_name = model_name or json_schema.get("title") or "DynamicModel"
+
+    schema_id = id(json_schema)
+    in_progress[original_id] = effective_name
+    if schema_id != original_id:
+        in_progress[schema_id] = effective_name
+
     field_definitions = {
         name: _json_schema_to_pydantic_field(
             name,
@@ -732,13 +917,14 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
             json_schema.get("required", []),
             effective_root,
             enrich_descriptions=enrich_descriptions,
+            in_progress=in_progress,
         )
         for name, prop in (json_schema.get("properties", {}) or {}).items()
     }
 
     effective_config = __config__ or ConfigDict(extra="forbid")
 
-    return create_model_base(
+    model = create_model_base(
         effective_name,
         __config__=effective_config,
         __base__=__base__,
@@ -747,6 +933,10 @@ def create_model_from_schema(  # type: ignore[no-any-unimported]
         __cls_kwargs__=__cls_kwargs__,
         **field_definitions,
     )
+    in_progress[original_id] = model
+    if schema_id != original_id:
+        in_progress[schema_id] = model
+    return model
 
 
 def _json_schema_to_pydantic_field(
@@ -756,6 +946,7 @@ def _json_schema_to_pydantic_field(
     root_schema: dict[str, Any],
     *,
     enrich_descriptions: bool = False,
+    in_progress: dict[int, Any] | None = None,
 ) -> Any:
     """Convert a JSON schema property to a Pydantic field definition.
 
@@ -774,6 +965,7 @@ def _json_schema_to_pydantic_field(
         root_schema,
         name_=name.title(),
         enrich_descriptions=enrich_descriptions,
+        in_progress=in_progress,
     )
     is_required = name in required
 
@@ -833,7 +1025,7 @@ def _json_schema_to_pydantic_field(
             field_params["pattern"] = json_schema["pattern"]
 
     if not is_required:
-        type_ = type_ | None
+        type_ = Optional[type_]  # noqa: UP045 - ForwardRef does not support `|`
 
     if schema_extra:
         field_params["json_schema_extra"] = schema_extra
@@ -906,6 +1098,7 @@ def _json_schema_to_pydantic_type(
     *,
     name_: str | None = None,
     enrich_descriptions: bool = False,
+    in_progress: dict[int, Any] | None = None,
 ) -> Any:
     """Convert a JSON schema to a Python/Pydantic type.
 
@@ -914,10 +1107,23 @@ def _json_schema_to_pydantic_type(
         root_schema: The root schema for resolving $ref.
         name_: Optional name for nested models.
         enrich_descriptions: Propagated to nested model creation.
+        in_progress: Map of ``id(schema_dict)`` to the Pydantic model
+            currently being built for that schema, or to a placeholder name
+            as a plain ``str`` while the model is still being constructed.
+            Populated by :func:`_build_model_from_schema`. Enables cycle
+            detection so a self-referential ``$ref`` graph resolves to a
+            :class:`ForwardRef` back-edge rather than recursing forever.
 
     Returns:
         A Python type corresponding to the JSON schema.
     """
+    if in_progress is not None:
+        cached = in_progress.get(id(json_schema))
+        if isinstance(cached, str):
+            return ForwardRef(cached)
+        if cached is not None:
+            return cached
+
     ref = json_schema.get("$ref")
     if ref:
         ref_schema = _resolve_ref(ref, root_schema)
@@ -926,6 +1132,7 @@ def _json_schema_to_pydantic_type(
             root_schema,
             name_=name_,
             enrich_descriptions=enrich_descriptions,
+            in_progress=in_progress,
         )
 
     enum_values = json_schema.get("enum")
@@ -945,6 +1152,7 @@ def _json_schema_to_pydantic_type(
                 root_schema,
                 name_=f"{name_ or 'Union'}Option{i}",
                 enrich_descriptions=enrich_descriptions,
+                in_progress=in_progress,
             )
             for i, schema in enumerate(any_of_schemas)
         ]
@@ -958,6 +1166,15 @@ def _json_schema_to_pydantic_type(
                 root_schema,
                 name_=name_,
                 enrich_descriptions=enrich_descriptions,
+                in_progress=in_progress,
+            )
+        if in_progress is not None:
+            return _build_model_from_schema(
+                json_schema,
+                root_schema,
+                model_name=name_,
+                enrich_descriptions=enrich_descriptions,
+                in_progress=in_progress,
             )
         merged = _merge_all_of_schemas(all_of_schemas, root_schema)
         return _json_schema_to_pydantic_type(
@@ -965,6 +1182,7 @@ def _json_schema_to_pydantic_type(
             root_schema,
             name_=name_,
             enrich_descriptions=enrich_descriptions,
+            in_progress=in_progress,
         )
 
     type_ = json_schema.get("type")
@@ -985,12 +1203,21 @@ def _json_schema_to_pydantic_type(
                 root_schema,
                 name_=name_,
                 enrich_descriptions=enrich_descriptions,
+                in_progress=in_progress,
             )
             return list[item_type]  # type: ignore[valid-type]
         return list
     if type_ == "object":
         properties = json_schema.get("properties")
         if properties:
+            if in_progress is not None:
+                return _build_model_from_schema(
+                    json_schema,
+                    root_schema,
+                    model_name=name_,
+                    enrich_descriptions=enrich_descriptions,
+                    in_progress=in_progress,
+                )
             json_schema_ = json_schema.copy()
             if json_schema_.get("title") is None:
                 json_schema_["title"] = name_ or "DynamicModel"

lib/crewai/src/crewai/utilities/streaming.py

@@ -7,6 +7,7 @@ import logging
 import queue
 import threading
 from typing import Any, NamedTuple
+import uuid
 
 from typing_extensions import TypedDict
 
@@ -25,6 +26,10 @@ from crewai.utilities.string_utils import sanitize_tool_name
 
 logger = logging.getLogger(__name__)
 
+_current_stream_ids: contextvars.ContextVar[tuple[str, ...]] = contextvars.ContextVar(
+    "_current_stream_ids", default=()
+)
+
 
 class TaskInfo(TypedDict):
     """Task context information for streaming."""
@@ -45,6 +50,7 @@ class StreamingState(NamedTuple):
     async_queue: asyncio.Queue[StreamChunk | None | Exception] | None
     loop: asyncio.AbstractEventLoop | None
     handler: Callable[[Any, BaseEvent], None]
+    stream_id: str | None = None
 
 
 def _extract_tool_call_info(
@@ -106,6 +112,7 @@ def _create_stream_handler(
     sync_queue: queue.Queue[StreamChunk | None | Exception],
     async_queue: asyncio.Queue[StreamChunk | None | Exception] | None = None,
     loop: asyncio.AbstractEventLoop | None = None,
+    stream_id: str | None = None,
 ) -> Callable[[Any, BaseEvent], None]:
     """Create a stream handler function.
 
@@ -114,21 +121,19 @@ def _create_stream_handler(
         sync_queue: Synchronous queue for chunks.
         async_queue: Optional async queue for chunks.
         loop: Optional event loop for async operations.
+        stream_id: Stream scope ID for concurrent isolation.
 
     Returns:
         Handler function that can be registered with the event bus.
     """
 
     def stream_handler(_: Any, event: BaseEvent) -> None:
-        """Handle LLM stream chunk events and enqueue them.
-
-        Args:
-            _: Event source (unused).
-            event: The event to process.
-        """
         if not isinstance(event, LLMStreamChunkEvent):
             return
 
+        if stream_id is not None and stream_id not in _current_stream_ids.get():
+            return
+
         chunk = _create_stream_chunk(event, current_task_info)
 
         if async_queue is not None and loop is not None:
@@ -203,7 +208,11 @@ def create_streaming_state(
         async_queue = asyncio.Queue()
         loop = asyncio.get_event_loop()
 
-    handler = _create_stream_handler(current_task_info, sync_queue, async_queue, loop)
+    stream_id = str(uuid.uuid4())
+
+    handler = _create_stream_handler(
+        current_task_info, sync_queue, async_queue, loop, stream_id=stream_id
+    )
     crewai_event_bus.register_handler(LLMStreamChunkEvent, handler)
 
     return StreamingState(
@@ -213,6 +222,7 @@ def create_streaming_state(
         async_queue=async_queue,
         loop=loop,
         handler=handler,
+        stream_id=stream_id,
     )
 
 
@@ -260,7 +270,12 @@ def create_chunk_generator(
     Yields:
         StreamChunk objects as they arrive.
     """
-    ctx = contextvars.copy_context()
+    if state.stream_id is not None:
+        token = _current_stream_ids.set((*_current_stream_ids.get(), state.stream_id))
+        ctx = contextvars.copy_context()
+        _current_stream_ids.reset(token)
+    else:
+        ctx = contextvars.copy_context()
     thread = threading.Thread(target=ctx.run, args=(run_func,), daemon=True)
     thread.start()
 
@@ -300,7 +315,12 @@ async def create_async_chunk_generator(
             "Async queue not initialized. Use create_streaming_state(use_async=True)."
         )
 
-    task = asyncio.create_task(run_coro())
+    if state.stream_id is not None:
+        token = _current_stream_ids.set((*_current_stream_ids.get(), state.stream_id))
+        task = asyncio.create_task(run_coro())
+        _current_stream_ids.reset(token)
+    else:
+        task = asyncio.create_task(run_coro())
 
     try:
         while True:

lib/crewai/tests/agents/test_lite_agent.py

@@ -1051,7 +1051,7 @@ def test_lite_agent_verbose_false_suppresses_printer_output():
         successful_requests=1,
     )
 
-    with pytest.warns(DeprecationWarning):
+    with pytest.warns(FutureWarning):
         agent = LiteAgent(
             role="Test Agent",
             goal="Test goal",

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

@@ -0,0 +1,283 @@
+import io
+import os
+import zipfile
+from unittest.mock import MagicMock, patch
+
+import httpx
+import pytest
+from click.testing import CliRunner
+
+from crewai.cli.cli import template_add, template_list
+from crewai.cli.remote_template.main import TemplateCommand
+
+
+@pytest.fixture
+def runner():
+    return CliRunner()
+
+
+SAMPLE_REPOS = [
+    {"name": "template_deep_research", "description": "Deep research template", "private": False},
+    {"name": "template_pull_request_review", "description": "PR review template", "private": False},
+    {"name": "template_conversational_example", "description": "Conversational demo", "private": False},
+    {"name": "crewai", "description": "Main repo", "private": False},
+    {"name": "marketplace-crew-template", "description": "Marketplace", "private": False},
+]
+
+
+def _make_zipball(files: dict[str, str], top_dir: str = "crewAIInc-template_test-abc123") -> bytes:
+    """Create an in-memory zipball mimicking GitHub's format."""
+    buf = io.BytesIO()
+    with zipfile.ZipFile(buf, "w") as zf:
+        zf.writestr(f"{top_dir}/", "")
+        for path, content in files.items():
+            zf.writestr(f"{top_dir}/{path}", content)
+    return buf.getvalue()
+
+
+# --- CLI command tests ---
+
+
+@patch("crewai.cli.cli.TemplateCommand")
+def test_template_list_command(mock_cls, runner):
+    mock_instance = MagicMock()
+    mock_cls.return_value = mock_instance
+
+    result = runner.invoke(template_list)
+
+    assert result.exit_code == 0
+    mock_cls.assert_called_once()
+    mock_instance.list_templates.assert_called_once()
+
+
+@patch("crewai.cli.cli.TemplateCommand")
+def test_template_add_command(mock_cls, runner):
+    mock_instance = MagicMock()
+    mock_cls.return_value = mock_instance
+
+    result = runner.invoke(template_add, ["deep_research"])
+
+    assert result.exit_code == 0
+    mock_cls.assert_called_once()
+    mock_instance.add_template.assert_called_once_with("deep_research", None)
+
+
+@patch("crewai.cli.cli.TemplateCommand")
+def test_template_add_with_output_dir(mock_cls, runner):
+    mock_instance = MagicMock()
+    mock_cls.return_value = mock_instance
+
+    result = runner.invoke(template_add, ["deep_research", "-o", "my_project"])
+
+    assert result.exit_code == 0
+    mock_instance.add_template.assert_called_once_with("deep_research", "my_project")
+
+
+# --- TemplateCommand unit tests ---
+
+
+class TestTemplateCommand:
+    @pytest.fixture
+    def cmd(self):
+        with patch.object(TemplateCommand, "__init__", return_value=None):
+            instance = TemplateCommand()
+            instance._telemetry = MagicMock()
+            return instance
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_fetch_templates_filters_by_prefix(self, mock_get, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        # Return empty on page 2 to stop pagination
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        templates = cmd._fetch_templates()
+
+        assert len(templates) == 3
+        assert all(t["name"].startswith("template_") for t in templates)
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_fetch_templates_excludes_private(self, mock_get, cmd):
+        repos = [
+            {"name": "template_private_one", "description": "", "private": True},
+            {"name": "template_public_one", "description": "", "private": False},
+        ]
+        mock_response = MagicMock()
+        mock_response.json.return_value = repos
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        templates = cmd._fetch_templates()
+
+        assert len(templates) == 1
+        assert templates[0]["name"] == "template_public_one"
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_fetch_templates_api_error(self, mock_get, cmd):
+        mock_get.side_effect = httpx.HTTPError("connection error")
+
+        with pytest.raises(SystemExit):
+            cmd._fetch_templates()
+
+    @patch("crewai.cli.remote_template.main.click.prompt", return_value="q")
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_list_templates_prints_output(self, mock_get, mock_prompt, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        with patch("crewai.cli.remote_template.main.console") as mock_console:
+            cmd.list_templates()
+            assert mock_console.print.call_count > 0
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_resolve_repo_name_with_prefix(self, mock_get, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        result = cmd._resolve_repo_name("template_deep_research")
+        assert result == "template_deep_research"
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_resolve_repo_name_without_prefix(self, mock_get, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        result = cmd._resolve_repo_name("deep_research")
+        assert result == "template_deep_research"
+
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_resolve_repo_name_not_found(self, mock_get, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        result = cmd._resolve_repo_name("nonexistent")
+        assert result is None
+
+    def test_extract_zip(self, cmd, tmp_path):
+        files = {
+            "README.md": "# Test Template",
+            "src/main.py": "print('hello')",
+            "config/settings.yaml": "key: value",
+        }
+        zip_bytes = _make_zipball(files)
+        dest = str(tmp_path / "output")
+
+        cmd._extract_zip(zip_bytes, dest)
+
+        assert os.path.isfile(os.path.join(dest, "README.md"))
+        assert os.path.isfile(os.path.join(dest, "src", "main.py"))
+        assert os.path.isfile(os.path.join(dest, "config", "settings.yaml"))
+
+        with open(os.path.join(dest, "src", "main.py")) as f:
+            assert f.read() == "print('hello')"
+
+    @patch.object(TemplateCommand, "_extract_zip")
+    @patch.object(TemplateCommand, "_download_zip")
+    @patch.object(TemplateCommand, "_resolve_repo_name")
+    def test_add_template_success(self, mock_resolve, mock_download, mock_extract, cmd, tmp_path):
+        mock_resolve.return_value = "template_deep_research"
+        mock_download.return_value = b"fake-zip-bytes"
+
+        os.chdir(tmp_path)
+        cmd.add_template("deep_research")
+
+        mock_resolve.assert_called_once_with("deep_research")
+        mock_download.assert_called_once_with("template_deep_research")
+        expected_dest = os.path.join(str(tmp_path), "deep_research")
+        mock_extract.assert_called_once_with(b"fake-zip-bytes", expected_dest)
+
+    @patch.object(TemplateCommand, "_resolve_repo_name")
+    def test_add_template_not_found(self, mock_resolve, cmd):
+        mock_resolve.return_value = None
+
+        with pytest.raises(SystemExit):
+            cmd.add_template("nonexistent")
+
+    @patch.object(TemplateCommand, "_extract_zip")
+    @patch.object(TemplateCommand, "_download_zip")
+    @patch("crewai.cli.remote_template.main.click.prompt", return_value="my_project")
+    @patch.object(TemplateCommand, "_resolve_repo_name")
+    def test_add_template_dir_exists_prompts_rename(self, mock_resolve, mock_prompt, mock_download, mock_extract, cmd, tmp_path):
+        mock_resolve.return_value = "template_deep_research"
+        mock_download.return_value = b"fake-zip-bytes"
+        existing = tmp_path / "deep_research"
+        existing.mkdir()
+
+        os.chdir(tmp_path)
+        cmd.add_template("deep_research")
+
+        expected_dest = os.path.join(str(tmp_path), "my_project")
+        mock_extract.assert_called_once_with(b"fake-zip-bytes", expected_dest)
+
+    @patch.object(TemplateCommand, "_resolve_repo_name")
+    @patch("crewai.cli.remote_template.main.click.prompt", return_value="q")
+    def test_add_template_dir_exists_quit(self, mock_prompt, mock_resolve, cmd, tmp_path):
+        mock_resolve.return_value = "template_deep_research"
+        existing = tmp_path / "deep_research"
+        existing.mkdir()
+
+        os.chdir(tmp_path)
+        cmd.add_template("deep_research")
+        # Should return without downloading
+
+    @patch.object(TemplateCommand, "_install_repo")
+    @patch("crewai.cli.remote_template.main.click.prompt", return_value="2")
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_list_templates_selects_and_installs(self, mock_get, mock_prompt, mock_install, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        with patch("crewai.cli.remote_template.main.console"):
+            cmd.list_templates()
+
+        # Templates are sorted by name; index 1 (choice "2") = template_deep_research
+        mock_install.assert_called_once_with("template_deep_research")
+
+    @patch.object(TemplateCommand, "_install_repo")
+    @patch("crewai.cli.remote_template.main.click.prompt", return_value="q")
+    @patch("crewai.cli.remote_template.main.httpx.get")
+    def test_list_templates_quit(self, mock_get, mock_prompt, mock_install, cmd):
+        mock_response = MagicMock()
+        mock_response.json.return_value = SAMPLE_REPOS
+        mock_response.raise_for_status = MagicMock()
+        mock_empty = MagicMock()
+        mock_empty.json.return_value = []
+        mock_empty.raise_for_status = MagicMock()
+        mock_get.side_effect = [mock_response, mock_empty]
+
+        with patch("crewai.cli.remote_template.main.console"):
+            cmd.list_templates()
+
+        mock_install.assert_not_called()

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

@@ -161,7 +161,8 @@ def test_install_api_error(mock_get, capsys, tool_command):
 
 
 @patch("crewai.cli.tools.main.git.Repository.is_synced", return_value=False)
-def test_publish_when_not_in_sync(mock_is_synced, capsys, tool_command):
+@patch("crewai.cli.tools.main.git.Repository.__init__", return_value=None)
+def test_publish_when_not_in_sync(mock_init, mock_is_synced, capsys, tool_command):
     with raises(SystemExit):
         tool_command.publish(is_public=True)
 

lib/crewai/tests/test_checkpoint.py

@@ -523,6 +523,31 @@ class TestKickoffFromCheckpoint:
         assert isinstance(crew.checkpoint, CheckpointConfig)
         assert crew.checkpoint.on_events == ["task_completed"]
 
+    def test_agent_kickoff_delegates_to_from_checkpoint(self) -> None:
+        mock_restored = MagicMock(spec=Agent)
+        mock_restored.kickoff.return_value = "agent_result"
+
+        cfg = CheckpointConfig(restore_from="/path/to/agent_cp.json")
+        with patch.object(Agent, "from_checkpoint", return_value=mock_restored):
+            agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
+            result = agent.kickoff(messages="hello", from_checkpoint=cfg)
+
+        mock_restored.kickoff.assert_called_once_with(
+            messages="hello", response_format=None, input_files=None
+        )
+        assert mock_restored.checkpoint.restore_from is None
+        assert result == "agent_result"
+
+    def test_agent_kickoff_config_only_sets_checkpoint(self) -> None:
+        cfg = CheckpointConfig(on_events=["lite_agent_execution_completed"])
+        agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
+        assert agent.checkpoint is None
+        with patch.object(Agent, "_prepare_kickoff", side_effect=RuntimeError("stop")):
+            with pytest.raises(RuntimeError, match="stop"):
+                agent.kickoff(messages="hello", from_checkpoint=cfg)
+        assert isinstance(agent.checkpoint, CheckpointConfig)
+        assert agent.checkpoint.on_events == ["lite_agent_execution_completed"]
+
     def test_flow_kickoff_delegates_to_from_checkpoint(self) -> None:
         mock_restored = MagicMock(spec=Flow)
         mock_restored.kickoff.return_value = "flow_result"
@@ -537,3 +562,75 @@ class TestKickoffFromCheckpoint:
         )
         assert mock_restored.checkpoint.restore_from is None
         assert result == "flow_result"
+
+
+# ---------- Agent checkpoint/fork ----------
+
+
+class TestAgentCheckpoint:
+    def _make_agent_state(self) -> RuntimeState:
+        agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
+        return RuntimeState(root=[agent])
+
+    def test_agent_from_checkpoint_sets_runtime_state(self) -> None:
+        state = self._make_agent_state()
+        state._provider = JsonProvider()
+        with tempfile.TemporaryDirectory() as d:
+            loc = state.checkpoint(d)
+            cfg = CheckpointConfig(restore_from=loc)
+
+            from crewai.events.event_bus import crewai_event_bus
+
+            crewai_event_bus._runtime_state = None
+            Agent.from_checkpoint(cfg)
+            assert crewai_event_bus._runtime_state is not None
+
+    def test_agent_fork_sets_branch(self) -> None:
+        state = self._make_agent_state()
+        state._provider = JsonProvider()
+        with tempfile.TemporaryDirectory() as d:
+            loc = state.checkpoint(d)
+            cfg = CheckpointConfig(restore_from=loc)
+
+            from crewai.events.event_bus import crewai_event_bus
+
+            Agent.fork(cfg, branch="agent-experiment")
+            rt = crewai_event_bus._runtime_state
+            assert rt is not None
+            assert rt._branch == "agent-experiment"
+
+    def test_agent_fork_auto_branch(self) -> None:
+        state = self._make_agent_state()
+        state._provider = JsonProvider()
+        with tempfile.TemporaryDirectory() as d:
+            loc = state.checkpoint(d)
+            cfg = CheckpointConfig(restore_from=loc)
+
+            from crewai.events.event_bus import crewai_event_bus
+
+            Agent.fork(cfg)
+            rt = crewai_event_bus._runtime_state
+            assert rt is not None
+            assert rt._branch.startswith("fork/")
+
+    def test_sync_checkpoint_fields_agent(self) -> None:
+        from crewai.state.runtime import _sync_checkpoint_fields
+
+        agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
+        agent._kickoff_event_id = "evt-123"
+        _sync_checkpoint_fields(agent)
+        assert agent.checkpoint_kickoff_event_id == "evt-123"
+
+    def test_agent_restore_kickoff_event_id(self) -> None:
+        agent = Agent(role="r", goal="g", backstory="b", llm="gpt-4o-mini")
+        agent._kickoff_event_id = "evt-456"
+        state = RuntimeState(root=[agent])
+        state._provider = JsonProvider()
+        with tempfile.TemporaryDirectory() as d:
+            from crewai.state.runtime import _prepare_entities
+
+            _prepare_entities(state.root)
+            loc = state.checkpoint(d)
+            cfg = CheckpointConfig(restore_from=loc)
+            restored = Agent.from_checkpoint(cfg)
+            assert restored._kickoff_event_id == "evt-456"

lib/crewai/tests/test_checkpoint_cli.py

@@ -0,0 +1,402 @@
+"""Tests for checkpoint CLI commands."""
+
+from __future__ import annotations
+
+import json
+import os
+import sqlite3
+import tempfile
+import time
+from datetime import datetime, timedelta, timezone
+from typing import Any
+from unittest.mock import MagicMock, patch
+
+import pytest
+from crewai.cli.checkpoint_cli import (
+    _parse_checkpoint_json,
+    _parse_duration,
+    _prune_json,
+    _prune_sqlite,
+    _resolve_checkpoint,
+    _task_list_from_meta,
+    diff_checkpoints,
+    prune_checkpoints,
+    resume_checkpoint,
+)
+
+
+def _make_checkpoint_data(
+    tasks_completed: int = 2,
+    tasks_total: int = 4,
+    trigger: str = "task_completed",
+    branch: str = "main",
+    parent_id: str | None = None,
+    entity_type: str = "crew",
+    name: str = "test_crew",
+    inputs: dict[str, Any] | None = None,
+) -> str:
+    tasks: list[dict[str, Any]] = []
+    for i in range(tasks_total):
+        t: dict[str, Any] = {
+            "description": f"Task {i + 1} description",
+            "expected_output": f"Output {i + 1}",
+        }
+        if i < tasks_completed:
+            t["output"] = {"raw": f"Result of task {i + 1}"}
+        else:
+            t["output"] = None
+        tasks.append(t)
+
+    data: dict[str, Any] = {
+        "entities": [
+            {
+                "entity_type": entity_type,
+                "name": name,
+                "id": "abc12345-1234-1234-1234-abcdef012345",
+                "tasks": tasks,
+                "agents": [],
+                "checkpoint_inputs": inputs or {},
+            }
+        ],
+        "event_record": {"nodes": {f"node_{i}": {} for i in range(3)}},
+        "trigger": trigger,
+        "branch": branch,
+        "parent_id": parent_id,
+    }
+    return json.dumps(data)
+
+
+def _write_json_checkpoint(
+    base_dir: str,
+    branch: str = "main",
+    name: str | None = None,
+    data: str | None = None,
+    tasks_completed: int = 2,
+    inputs: dict[str, Any] | None = None,
+) -> str:
+    branch_dir = os.path.join(base_dir, branch)
+    os.makedirs(branch_dir, exist_ok=True)
+    if name is None:
+        ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S")
+        name = f"{ts}_abcd1234_p-none.json"
+    path = os.path.join(branch_dir, name)
+    if data is None:
+        data = _make_checkpoint_data(tasks_completed=tasks_completed, inputs=inputs)
+    with open(path, "w") as f:
+        f.write(data)
+    return path
+
+
+def _create_sqlite_checkpoint(
+    db_path: str,
+    checkpoint_id: str | None = None,
+    data: str | None = None,
+    tasks_completed: int = 2,
+    branch: str = "main",
+    inputs: dict[str, Any] | None = None,
+) -> str:
+    if checkpoint_id is None:
+        ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%S")
+        checkpoint_id = f"{ts}_abcd1234"
+    if data is None:
+        data = _make_checkpoint_data(
+            tasks_completed=tasks_completed, branch=branch, inputs=inputs
+        )
+    with sqlite3.connect(db_path) as conn:
+        conn.execute(
+            """CREATE TABLE IF NOT EXISTS checkpoints (
+                id TEXT PRIMARY KEY,
+                created_at TEXT NOT NULL,
+                parent_id TEXT,
+                branch TEXT NOT NULL DEFAULT 'main',
+                data JSONB NOT NULL
+            )"""
+        )
+        conn.execute(
+            "INSERT INTO checkpoints (id, created_at, parent_id, branch, data) "
+            "VALUES (?, ?, ?, ?, jsonb(?))",
+            (checkpoint_id, checkpoint_id.split("_")[0], None, branch, data),
+        )
+        conn.commit()
+    return checkpoint_id
+
+
+class TestParseDuration:
+    def test_days(self) -> None:
+        assert _parse_duration("7d") == timedelta(days=7)
+
+    def test_hours(self) -> None:
+        assert _parse_duration("24h") == timedelta(hours=24)
+
+    def test_minutes(self) -> None:
+        assert _parse_duration("30m") == timedelta(minutes=30)
+
+    def test_invalid_raises(self) -> None:
+        with pytest.raises(Exception):
+            _parse_duration("abc")
+
+    def test_no_unit_raises(self) -> None:
+        with pytest.raises(Exception):
+            _parse_duration("7")
+
+
+class TestResolveCheckpoint:
+    def test_json_latest(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(d, name="20260101T000000_aaaa1111_p-none.json")
+            time.sleep(0.01)
+            path2 = _write_json_checkpoint(
+                d, name="20260102T000000_bbbb2222_p-none.json", tasks_completed=3
+            )
+            meta = _resolve_checkpoint(d, None)
+            assert meta is not None
+            assert meta["path"] == path2
+
+    def test_json_by_id(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(d, name="20260101T000000_aaaa1111_p-none.json")
+            _write_json_checkpoint(d, name="20260102T000000_bbbb2222_p-none.json")
+            meta = _resolve_checkpoint(d, "aaaa1111")
+            assert meta is not None
+            assert "aaaa1111" in meta["name"]
+
+    def test_json_not_found(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(d)
+            assert _resolve_checkpoint(d, "nonexistent") is None
+
+    def test_sqlite_latest(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            db_path = os.path.join(d, "test.db")
+            _create_sqlite_checkpoint(db_path, "20260101T000000_aaaa1111")
+            _create_sqlite_checkpoint(
+                db_path, "20260102T000000_bbbb2222", tasks_completed=3
+            )
+            meta = _resolve_checkpoint(db_path, None)
+            assert meta is not None
+            assert "bbbb2222" in meta["name"]
+
+    def test_sqlite_by_id(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            db_path = os.path.join(d, "test.db")
+            _create_sqlite_checkpoint(db_path, "20260101T000000_aaaa1111")
+            _create_sqlite_checkpoint(db_path, "20260102T000000_bbbb2222")
+            meta = _resolve_checkpoint(db_path, "20260101T000000_aaaa1111")
+            assert meta is not None
+            assert "aaaa1111" in meta["name"]
+
+    def test_sqlite_partial_id(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            db_path = os.path.join(d, "test.db")
+            _create_sqlite_checkpoint(db_path, "20260101T000000_aaaa1111")
+            _create_sqlite_checkpoint(db_path, "20260102T000000_bbbb2222")
+            meta = _resolve_checkpoint(db_path, "aaaa1111")
+            assert meta is not None
+            assert "aaaa1111" in meta["name"]
+
+    def test_nonexistent(self) -> None:
+        assert _resolve_checkpoint("/nonexistent/path", None) is None
+
+
+class TestTaskListFromMeta:
+    def test_flattens_tasks(self) -> None:
+        data = _make_checkpoint_data(tasks_completed=2, tasks_total=3)
+        meta = _parse_checkpoint_json(data, "test")
+        tasks = _task_list_from_meta(meta)
+        assert len(tasks) == 3
+        assert tasks[0]["completed"] is True
+        assert tasks[2]["completed"] is False
+
+    def test_empty_entities(self) -> None:
+        assert _task_list_from_meta({"entities": []}) == []
+
+
+class TestDiffCheckpoints:
+    def test_diff_shows_status_change(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(
+                d, name="20260101T000000_aaaa1111_p-none.json", tasks_completed=1
+            )
+            _write_json_checkpoint(
+                d, name="20260102T000000_bbbb2222_p-none.json", tasks_completed=3
+            )
+            diff_checkpoints(d, "aaaa1111", "bbbb2222")
+            out = capsys.readouterr().out
+            assert "---" in out
+            assert "+++" in out
+            assert "status:" in out or "pending -> done" in out
+
+    def test_diff_shows_output_change(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            data1 = _make_checkpoint_data(tasks_completed=2)
+            data2 = json.loads(data1)
+            data2["entities"][0]["tasks"][0]["output"]["raw"] = "Updated result"
+            _write_json_checkpoint(
+                d,
+                name="20260101T000000_aaaa1111_p-none.json",
+                data=json.dumps(json.loads(data1)),
+            )
+            _write_json_checkpoint(
+                d,
+                name="20260102T000000_bbbb2222_p-none.json",
+                data=json.dumps(data2),
+            )
+            diff_checkpoints(d, "aaaa1111", "bbbb2222")
+            out = capsys.readouterr().out
+            assert "output:" in out
+
+    def test_diff_not_found(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(d, name="20260101T000000_aaaa1111_p-none.json")
+            diff_checkpoints(d, "aaaa1111", "nonexistent")
+            out = capsys.readouterr().out
+            assert "not found" in out
+
+    def test_diff_input_change(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(
+                d,
+                name="20260101T000000_aaaa1111_p-none.json",
+                inputs={"topic": "AI"},
+            )
+            _write_json_checkpoint(
+                d,
+                name="20260102T000000_bbbb2222_p-none.json",
+                inputs={"topic": "ML"},
+            )
+            diff_checkpoints(d, "aaaa1111", "bbbb2222")
+            out = capsys.readouterr().out
+            assert "Inputs:" in out
+            assert "AI" in out
+            assert "ML" in out
+
+
+class TestPruneJson:
+    def test_keep_n(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            for i in range(5):
+                _write_json_checkpoint(
+                    d, name=f"2026010{i + 1}T000000_aaa{i}1111_p-none.json"
+                )
+                time.sleep(0.01)
+            deleted = _prune_json(d, keep=2, older_than=None)
+            assert deleted == 3
+            remaining = []
+            for root, _, files in os.walk(d):
+                remaining.extend(files)
+            assert len(remaining) == 2
+
+    def test_older_than(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            old_path = _write_json_checkpoint(
+                d, name="20250101T000000_old01111_p-none.json"
+            )
+            os.utime(old_path, (0, 0))
+            _write_json_checkpoint(d, name="20990101T000000_new01111_p-none.json")
+            deleted = _prune_json(d, keep=None, older_than=timedelta(days=1))
+            assert deleted == 1
+
+    def test_empty_dir(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            assert _prune_json(d, keep=2, older_than=None) == 0
+
+    def test_removes_empty_branch_dirs(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            path = _write_json_checkpoint(
+                d,
+                branch="feature",
+                name="20260101T000000_aaaa1111_p-none.json",
+            )
+            os.utime(path, (0, 0))
+            _prune_json(d, keep=None, older_than=timedelta(days=1))
+            assert not os.path.exists(os.path.join(d, "feature"))
+
+
+class TestPruneSqlite:
+    def test_keep_n(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            db_path = os.path.join(d, "test.db")
+            for i in range(5):
+                _create_sqlite_checkpoint(
+                    db_path, f"2026010{i + 1}T000000_aaa{i}1111"
+                )
+            deleted = _prune_sqlite(db_path, keep=2, older_than=None)
+            assert deleted == 3
+            with sqlite3.connect(db_path) as conn:
+                count = conn.execute("SELECT COUNT(*) FROM checkpoints").fetchone()[0]
+            assert count == 2
+
+    def test_older_than(self) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            db_path = os.path.join(d, "test.db")
+            _create_sqlite_checkpoint(db_path, "20200101T000000_old01111")
+            _create_sqlite_checkpoint(db_path, "20990101T000000_new01111")
+            deleted = _prune_sqlite(db_path, keep=None, older_than=timedelta(days=1))
+            assert deleted >= 1
+            with sqlite3.connect(db_path) as conn:
+                count = conn.execute("SELECT COUNT(*) FROM checkpoints").fetchone()[0]
+            assert count >= 1
+
+
+class TestPruneCommand:
+    def test_no_options_shows_help(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            prune_checkpoints(d, keep=None, older_than=None)
+            out = capsys.readouterr().out
+            assert "Specify" in out
+
+    def test_dry_run_json(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            _write_json_checkpoint(d)
+            prune_checkpoints(d, keep=1, older_than=None, dry_run=True)
+            out = capsys.readouterr().out
+            assert "Would prune" in out
+
+    def test_not_found(self, capsys: pytest.CaptureFixture[str]) -> None:
+        prune_checkpoints("/nonexistent", keep=1, older_than=None)
+        out = capsys.readouterr().out
+        assert "Not a directory" in out
+
+
+class TestResumeCheckpoint:
+    def test_not_found(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            resume_checkpoint(d, "nonexistent")
+            out = capsys.readouterr().out
+            assert "not found" in out
+
+    def test_no_checkpoints(self, capsys: pytest.CaptureFixture[str]) -> None:
+        with tempfile.TemporaryDirectory() as d:
+            resume_checkpoint(d, None)
+            out = capsys.readouterr().out
+            assert "No checkpoints" in out
+
+
+class TestDiscoverabilityMessage:
+    def test_checkpoint_listener_logs_resume_hint(self) -> None:
+        from crewai.state.checkpoint_listener import _do_checkpoint
+        from crewai.state.runtime import RuntimeState
+
+        state = MagicMock(spec=RuntimeState)
+        state.root = []
+        state.model_dump.return_value = {"entities": [], "event_record": {"nodes": {}}}
+        state._parent_id = None
+        state._branch = "main"
+
+        cfg = MagicMock()
+        cfg.location = "/tmp/cp"
+        cfg.max_checkpoints = None
+        cfg.provider.checkpoint.return_value = "/tmp/cp/main/20260101T000000_test1234_p-none.json"
+        cfg.provider.extract_id.return_value = "20260101T000000_test1234"
+
+        with (
+            patch("crewai.state.checkpoint_listener._prepare_entities"),
+            patch("crewai.state.checkpoint_listener.logger") as mock_logger,
+        ):
+            _do_checkpoint(state, cfg)
+
+        cfg.provider.extract_id.assert_called_once()
+        mock_logger.info.assert_called_once()
+        logged: str = mock_logger.info.call_args[0][0]
+        assert "crewai checkpoint resume" in logged
+        assert "20260101T000000_test1234" in logged

lib/crewai/tests/test_crew.py

@@ -8,6 +8,7 @@ from concurrent.futures import Future
 from hashlib import md5
 import re
 import sys
+from typing import Any, cast
 from unittest.mock import ANY, MagicMock, call, patch
 
 from crewai.agent import Agent
@@ -17,6 +18,7 @@ from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.crew_events import (
+    CrewKickoffStartedEvent,
     CrewTestCompletedEvent,
     CrewTestStartedEvent,
     CrewTrainCompletedEvent,
@@ -4741,6 +4743,61 @@ def test_default_crew_name(researcher, writer):
     assert crew.name == "crew"
 
 
+@pytest.mark.parametrize(
+    "explicit_name,expected",
+    [
+        (None, "ResearchAutomation"),
+        ("My Research Automation", "My Research Automation"),
+    ],
+    ids=["class_name_from_decorator", "explicit_name_preserved"],
+)
+def test_crew_kickoff_started_emits_display_name(
+    researcher, writer, explicit_name, expected
+):
+    """Kickoff events should use the decorator-provided display name when implicit."""
+    from crewai.crews.utils import prepare_kickoff
+    from crewai.project import CrewBase, agent, crew, task
+
+    @CrewBase
+    class ResearchAutomation:
+        agents_config = None
+        tasks_config = None
+
+        @agent
+        def researcher(self):
+            return researcher
+
+        @task
+        def first_task(self):
+            return Task(
+                description="Task 1",
+                expected_output="output",
+                agent=self.researcher(),
+            )
+
+        @crew
+        def crew(self):
+            crew_kwargs: dict[str, Any] = {
+                "agents": self.agents,
+                "tasks": self.tasks,
+            }
+            if explicit_name is not None:
+                crew_kwargs["name"] = explicit_name
+            return Crew(**crew_kwargs)
+
+    captured: list[str | None] = []
+    with crewai_event_bus.scoped_handlers():
+
+        @crewai_event_bus.on(CrewKickoffStartedEvent)
+        def _capture(_source: Any, event: CrewKickoffStartedEvent) -> None:
+            captured.append(event.crew_name)
+
+        automation_cls = cast(type[Any], ResearchAutomation)
+        prepare_kickoff(cast(Any, automation_cls()).crew(), inputs=None)
+
+    assert captured == [expected]
+
+
 @pytest.mark.vcr()
 def test_memory_remember_receives_task_content():
     """With memory=True, extract_memories receives raw content with task, agent, expected output, and result."""

lib/crewai/tests/test_project.py

@@ -1,4 +1,4 @@
-from typing import Any, ClassVar
+from typing import Any, ClassVar, cast
 from unittest.mock import Mock, create_autospec, patch
 
 import pytest
@@ -261,6 +261,55 @@ def test_crew_name():
     assert crew._crew_name == "InternalCrew"
 
 
+def test_crew_decorator_propagates_class_name_to_instance():
+    """@crew-decorated factory method should set Crew.name to the decorated class name."""
+    sample_agent = Agent(role="r", goal="g", backstory="b")
+    sample_task = Task(description="d", expected_output="o", agent=sample_agent)
+
+    @CrewBase
+    class ImplicitNameCrewFactory:
+        agents_config = None
+        tasks_config = None
+        agents: list[BaseAgent] = [sample_agent]
+        tasks: list[Task] = [sample_task]
+
+        @crew
+        def crew(self):
+            return Crew(
+                agents=[sample_agent],
+                tasks=[sample_task],
+            )
+
+    factory_cls = cast(type[Any], ImplicitNameCrewFactory)
+    crew_instance: Crew = cast(Any, factory_cls()).crew()
+    assert crew_instance.name == "ImplicitNameCrewFactory"
+
+
+def test_crew_decorator_preserves_explicit_name():
+    """Explicit Crew(name=...) inside @crew should win over the @CrewBase class name."""
+    sample_agent = Agent(role="r", goal="g", backstory="b")
+    sample_task = Task(description="d", expected_output="o", agent=sample_agent)
+
+    @CrewBase
+    class NamedCrewFactory:
+        agents_config = None
+        tasks_config = None
+        agents: list[BaseAgent] = [sample_agent]
+        tasks: list[Task] = [sample_task]
+
+        @crew
+        def crew(self):
+            return Crew(
+                name="My Explicit Name",
+                agents=[sample_agent],
+                tasks=[sample_task],
+            )
+
+    factory_cls = cast(type[Any], NamedCrewFactory)
+    crew_instance: Crew = cast(Any, factory_cls()).crew()
+    assert crew_instance.name == "My Explicit Name"
+
+
 @tool
 def simple_tool():
     """Return 'Hi!'"""

lib/crewai/tests/test_streaming.py

@@ -879,3 +879,91 @@ class TestStreamingImports:
         assert StreamChunk is not None
         assert StreamChunkType is not None
         assert ToolCallChunk is not None
+
+
+class TestConcurrentStreamIsolation:
+    """Regression tests for concurrent streaming isolation (issue #5376)."""
+
+    def test_concurrent_streams_do_not_cross_contaminate(self) -> None:
+        """Two concurrent streaming runs must each receive only their own chunks.
+
+        Mirrors the real production path: create_streaming_state in the caller,
+        then temporarily push the stream_id into the ContextVar, copy_context,
+        and reset — exactly as create_chunk_generator does.
+        """
+        import contextvars
+        import threading
+
+        from crewai.utilities.streaming import (
+            TaskInfo,
+            _current_stream_ids,
+            _unregister_handler,
+            create_streaming_state,
+        )
+
+        task_info_a: TaskInfo = {
+            "index": 0,
+            "name": "task_a",
+            "id": "a",
+            "agent_role": "A",
+            "agent_id": "a",
+        }
+        task_info_b: TaskInfo = {
+            "index": 1,
+            "name": "task_b",
+            "id": "b",
+            "agent_role": "B",
+            "agent_id": "b",
+        }
+
+        state_a = create_streaming_state(task_info_a, [])
+        state_b = create_streaming_state(task_info_b, [])
+
+        def make_emitter_ctx(state: Any) -> contextvars.Context:
+            token = _current_stream_ids.set(
+                (*_current_stream_ids.get(), state.stream_id)
+            )
+            ctx = contextvars.copy_context()
+            _current_stream_ids.reset(token)
+            return ctx
+
+        ctx_a = make_emitter_ctx(state_a)
+        ctx_b = make_emitter_ctx(state_b)
+
+        def emit_chunks(prefix: str, call_id: str) -> None:
+            for text in [f"{prefix}1", f"{prefix}2", f"{prefix}3"]:
+                crewai_event_bus.emit(
+                    None,
+                    event=LLMStreamChunkEvent(
+                        chunk=text, call_id=call_id, response_id="r"
+                    ),
+                )
+
+        t_a = threading.Thread(target=ctx_a.run, args=(lambda: emit_chunks("A", "ca"),))
+        t_b = threading.Thread(target=ctx_b.run, args=(lambda: emit_chunks("B", "cb"),))
+        t_a.start()
+        t_b.start()
+        t_a.join()
+        t_b.join()
+
+        chunks_a: list[str] = []
+        while not state_a.sync_queue.empty():
+            item = state_a.sync_queue.get_nowait()
+            if isinstance(item, StreamChunk):
+                chunks_a.append(item.content)
+
+        chunks_b: list[str] = []
+        while not state_b.sync_queue.empty():
+            item = state_b.sync_queue.get_nowait()
+            if isinstance(item, StreamChunk):
+                chunks_b.append(item.content)
+
+        assert set(chunks_a) == {"A1", "A2", "A3"}, (
+            f"Stream A received unexpected chunks: {chunks_a}"
+        )
+        assert set(chunks_b) == {"B1", "B2", "B3"}, (
+            f"Stream B received unexpected chunks: {chunks_b}"
+        )
+
+        _unregister_handler(state_a.handler)
+        _unregister_handler(state_b.handler)

lib/crewai/tests/tracing/test_tracing.py

@@ -1640,3 +1640,43 @@ class TestBackendInitializedGatedOnSuccess:
 
         assert bm.backend_initialized is False
         assert bm.trace_batch_id is None
+
+
+class TestTraceBatchManagerDuplicateInitMerge:
+    """Second initialize_batch call merges execution_metadata (flow after lazy action)."""
+
+    def test_duplicate_initialize_merges_execution_metadata(self):
+        with (
+            patch(
+                "crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
+                return_value=True,
+            ),
+            patch(
+                "crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
+                return_value=True,
+            ),
+        ):
+            bm = TraceBatchManager()
+            bm.initialize_batch(
+                user_context={"privacy_level": "standard"},
+                execution_metadata={
+                    "crew_name": "Unknown Crew",
+                    "crewai_version": "9.9.9",
+                },
+            )
+            first_batch_id = bm.current_batch.batch_id
+            bm.initialize_batch(
+                user_context={"privacy_level": "standard"},
+                execution_metadata={
+                    "flow_name": "ResearchFlow",
+                    "execution_type": "flow",
+                    "crewai_version": "9.9.9",
+                    "execution_start": "2026-01-01T00:00:00+00:00",
+                },
+            )
+
+        assert bm.current_batch.batch_id == first_batch_id
+        meta = bm.current_batch.execution_metadata
+        assert meta.get("execution_type") == "flow"
+        assert meta.get("flow_name") == "ResearchFlow"
+        assert meta.get("crew_name") == "Unknown Crew"

lib/crewai/tests/utilities/test_pydantic_schema_utils.py

@@ -882,3 +882,110 @@ class TestEndToEndMCPSchema:
         )
         assert obj.filters.date_from == datetime.date(2025, 1, 1)
         assert obj.filters.categories == ["news", "tech"]
+
+
+# ---------------------------------------------------------------------------
+# Recursive / circular $ref schemas (GH-5490)
+# ---------------------------------------------------------------------------
+
+RECURSIVE_NODE_SCHEMA: dict = {
+    "$defs": {
+        "Node": {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string"},
+                "children": {
+                    "type": "array",
+                    "items": {"$ref": "#/$defs/Node"},
+                },
+            },
+            "required": ["name"],
+        }
+    },
+    "$ref": "#/$defs/Node",
+}
+
+MUTUAL_RECURSION_SCHEMA: dict = {
+    "$defs": {
+        "A": {
+            "type": "object",
+            "properties": {
+                "val": {"type": "string"},
+                "b": {"$ref": "#/$defs/B"},
+            },
+            "required": ["val"],
+        },
+        "B": {
+            "type": "object",
+            "properties": {
+                "val": {"type": "integer"},
+                "a": {"$ref": "#/$defs/A"},
+            },
+            "required": ["val"],
+        },
+    },
+    "$ref": "#/$defs/A",
+}
+
+
+class TestResolveRefsRecursive:
+    def test_circular_ref_preserves_type(self) -> None:
+        from crewai.utilities.pydantic_schema_utils import resolve_refs
+
+        resolved = resolve_refs(deepcopy(RECURSIVE_NODE_SCHEMA))
+        items = resolved["properties"]["children"]["items"]
+        assert items != {}, "Circular ref should not degrade to {}"
+        assert items.get("type") == "object"
+
+    def test_non_recursive_schema_still_resolves(self) -> None:
+        from crewai.utilities.pydantic_schema_utils import resolve_refs
+
+        schema = {
+            "$defs": {"Foo": {"type": "object", "properties": {"x": {"type": "integer"}}}},
+            "$ref": "#/$defs/Foo",
+        }
+        resolved = resolve_refs(schema)
+        assert resolved["properties"]["x"]["type"] == "integer"
+
+
+class TestSanitizeRecursiveSchemas:
+    def test_anthropic_strict_preserves_recursive_type(self) -> None:
+        from crewai.utilities.pydantic_schema_utils import sanitize_tool_params_for_anthropic_strict
+
+        san = sanitize_tool_params_for_anthropic_strict(deepcopy(RECURSIVE_NODE_SCHEMA))
+        items = san["properties"]["children"]["items"]
+        assert items != {}
+        assert items.get("type") == "object"
+
+    def test_openai_strict_preserves_recursive_type(self) -> None:
+        from crewai.utilities.pydantic_schema_utils import sanitize_tool_params_for_openai_strict
+
+        san = sanitize_tool_params_for_openai_strict(deepcopy(RECURSIVE_NODE_SCHEMA))
+        items = san["properties"]["children"]["items"]
+        assert items != {}
+        assert items.get("type") == "object"
+
+
+class TestCreateModelFromSchemaRecursive:
+    def test_model_creation_succeeds(self) -> None:
+        model = create_model_from_schema(deepcopy(RECURSIVE_NODE_SCHEMA), model_name="Node")
+        assert model is not None
+        assert model.__name__ == "Node"
+
+    def test_model_accepts_valid_recursive_data(self) -> None:
+        model = create_model_from_schema(deepcopy(RECURSIVE_NODE_SCHEMA), model_name="Node")
+        instance = model(name="root", children=[{"name": "child", "children": []}])
+        assert instance.name == "root"
+        assert len(instance.children) == 1
+
+    def test_model_rejects_missing_required_field(self) -> None:
+        import pytest
+
+        model = create_model_from_schema(deepcopy(RECURSIVE_NODE_SCHEMA), model_name="Node")
+        with pytest.raises(Exception):
+            model(children=[])
+
+    def test_mutual_recursion_schema(self) -> None:
+        model = create_model_from_schema(deepcopy(MUTUAL_RECURSION_SCHEMA), model_name="A")
+        instance = model(val="hello", b={"val": 42})
+        assert instance.val == "hello"

lib/devtools/pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     "click~=8.1.7",
     "tomlkit~=0.13.2",
     "openai>=1.83.0,<3",
-    "python-dotenv~=1.1.1",
+    "python-dotenv>=1.2.2,<2",
     "pygithub~=1.59.1",
     "rich>=13.9.4",
 ]

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.14.2a3"
+__version__ = "1.14.3a2"

lib/devtools/src/crewai_devtools/cli.py

@@ -29,6 +29,33 @@ load_dotenv()
 console = Console()
 
 
+def _resume_hint(message: str) -> None:
+    """Print a boxed resume hint after a failure."""
+    console.print()
+    console.print(
+        Panel(
+            message,
+            title="[bold yellow]How to resume[/bold yellow]",
+            border_style="yellow",
+            padding=(1, 2),
+        )
+    )
+
+
+def _print_release_error(e: BaseException) -> None:
+    """Print a release error with stderr if available."""
+    if isinstance(e, KeyboardInterrupt):
+        raise
+    if isinstance(e, SystemExit):
+        return
+    if isinstance(e, subprocess.CalledProcessError):
+        console.print(f"[red]Error running command:[/red] {e}")
+        if e.stderr:
+            console.print(e.stderr)
+    else:
+        console.print(f"[red]Error:[/red] {e}")
+
+
 def run_command(cmd: list[str], cwd: Path | None = None) -> str:
     """Run a shell command and return output.
 
@@ -127,6 +154,117 @@ def check_git_clean() -> None:
         sys.exit(1)
 
 
+def _branch_exists_local(branch: str, cwd: Path | None = None) -> bool:
+    try:
+        subprocess.run(  # noqa: S603
+            ["git", "show-ref", "--verify", "--quiet", f"refs/heads/{branch}"],  # noqa: S607
+            cwd=cwd,
+            check=True,
+            capture_output=True,
+        )
+        return True
+    except subprocess.CalledProcessError:
+        return False
+
+
+def _branch_exists_remote(branch: str, cwd: Path | None = None) -> bool:
+    try:
+        output = run_command(["git", "ls-remote", "--heads", "origin", branch], cwd=cwd)
+        return bool(output.strip())
+    except subprocess.CalledProcessError:
+        return False
+
+
+def _open_pr_url_for_branch(branch: str, cwd: Path | None = None) -> str | None:
+    """Return URL of open PR for branch, or None if no open PR exists."""
+    try:
+        url = run_command(
+            [
+                "gh",
+                "pr",
+                "list",
+                "--head",
+                branch,
+                "--state",
+                "open",
+                "--json",
+                "url",
+                "--jq",
+                ".[0].url // empty",
+            ],
+            cwd=cwd,
+        )
+        return url or None
+    except subprocess.CalledProcessError:
+        return None
+
+
+def create_or_reset_branch(branch: str, cwd: Path | None = None) -> None:
+    """Create ``branch`` from current HEAD, resetting any stale copy.
+
+    If the branch exists locally or on origin, prompts the user to
+    choose between resetting it or aborting. If an open PR exists on
+    the branch, the prompt surfaces the PR URL and includes a
+    close-and-reset option so in-flight work isn't silently clobbered.
+
+    Raises:
+        SystemExit: If the user declines to reset.
+    """
+    local_exists = _branch_exists_local(branch, cwd=cwd)
+    remote_exists = _branch_exists_remote(branch, cwd=cwd)
+    open_pr = _open_pr_url_for_branch(branch, cwd=cwd) if remote_exists else None
+
+    if local_exists or remote_exists:
+        if open_pr:
+            console.print(
+                f"\n[yellow]![/yellow] Branch [bold]{branch}[/bold] already has an open PR: {open_pr}"
+            )
+            prompt = "Close the PR, reset the branch, and continue?"
+        else:
+            where = []
+            if local_exists:
+                where.append("local")
+            if remote_exists:
+                where.append("remote")
+            console.print(
+                f"\n[yellow]![/yellow] Branch [bold]{branch}[/bold] already exists ({', '.join(where)}) with no open PR"
+            )
+            prompt = "Delete it and recreate?"
+
+        if not Confirm.ask(prompt, default=False):
+            console.print("[red]Aborted.[/red]")
+            sys.exit(1)
+
+        if open_pr:
+            console.print(f"Closing PR {open_pr}...")
+            run_command(
+                ["gh", "pr", "close", branch, "--delete-branch"],
+                cwd=cwd,
+            )
+            # `gh pr close --delete-branch` removes the remote branch
+            # and, when checked out, the local branch too.
+            local_exists = _branch_exists_local(branch, cwd=cwd)
+            remote_exists = False
+
+        if local_exists:
+            current = run_command(
+                ["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd=cwd
+            ).strip()
+            if current == branch:
+                console.print(
+                    f"[yellow]![/yellow] Currently on {branch}, switching to main before delete"
+                )
+                run_command(["git", "checkout", "main"], cwd=cwd)
+            console.print(f"[yellow]![/yellow] Deleting local branch {branch}")
+            run_command(["git", "branch", "-D", branch], cwd=cwd)
+
+        if remote_exists:
+            console.print(f"[yellow]![/yellow] Deleting remote branch {branch}")
+            run_command(["git", "push", "origin", "--delete", branch], cwd=cwd)
+
+    run_command(["git", "checkout", "-b", branch], cwd=cwd)
+
+
 def update_version_in_file(file_path: Path, new_version: str) -> bool:
     """Update __version__ attribute in a Python file.
 
@@ -264,11 +402,9 @@ def add_docs_version(docs_json_path: Path, version: str) -> bool:
         if not versions:
             continue
 
-        # Skip if this version already exists for this language
         if any(v.get("version") == version_label for v in versions):
             continue
 
-        # Find the current default and copy its tabs
         default_version = next(
             (v for v in versions if v.get("default")),
             versions[0],
@@ -280,10 +416,7 @@ def add_docs_version(docs_json_path: Path, version: str) -> bool:
             "tabs": default_version.get("tabs", []),
         }
 
-        # Remove default flag from old default
         default_version.pop("default", None)
-
-        # Insert new version at the beginning
         versions.insert(0, new_version)
         updated = True
 
@@ -477,7 +610,7 @@ def _is_crewai_dep(spec: str) -> bool:
     """Return True if *spec* is a ``crewai`` or ``crewai[...]`` dependency."""
     if not spec.startswith("crewai"):
         return False
-    rest = spec[6:]  # after "crewai"
+    rest = spec[6:]
     return len(rest) > 0 and rest[0] in ("[", "=", ">", "<", "~", "!")
 
 
@@ -499,7 +632,6 @@ def _pin_crewai_deps(content: str, version: str) -> str:
         deps = doc.get("project", {}).get(key)
         if deps is None:
             continue
-        # optional-dependencies is a table of lists; dependencies is a list
         dep_lists = deps.values() if isinstance(deps, Mapping) else [deps]
         for dep_list in dep_lists:
             for i, dep in enumerate(dep_list):
@@ -638,7 +770,6 @@ def get_github_contributors(commit_range: str) -> list[str]:
         List of GitHub usernames sorted alphabetically.
     """
     try:
-        # Get GitHub token from gh CLI
         try:
             gh_token = run_command(["gh", "auth", "token"])
         except subprocess.CalledProcessError:
@@ -680,11 +811,6 @@ def get_github_contributors(commit_range: str) -> list[str]:
         return []
 
 
-# ---------------------------------------------------------------------------
-# Shared workflow helpers
-# ---------------------------------------------------------------------------
-
-
 def _poll_pr_until_merged(
     branch_name: str, label: str, repo: str | None = None
 ) -> None:
@@ -764,7 +890,6 @@ def _update_all_versions(
             "[yellow]Warning:[/yellow] No __version__ attributes found to update"
         )
 
-    # Update CLI template pyproject.toml files
     templates_dir = lib_dir / "crewai" / "src" / "crewai" / "cli" / "templates"
     if templates_dir.exists():
         if dry_run:
@@ -966,7 +1091,7 @@ def _update_docs_and_create_pr(
 
         if docs_files_staged:
             docs_branch = f"docs/changelog-v{version}"
-            run_command(["git", "checkout", "-b", docs_branch])
+            create_or_reset_branch(docs_branch)
             for f in docs_files_staged:
                 run_command(["git", "add", f])
             run_command(
@@ -1163,13 +1288,11 @@ def _repin_crewai_install(run_value: str, version: str) -> str:
     while marker in remainder:
         before, _, after = remainder.partition(marker)
         result.append(before)
-        # after looks like: a2a]==1.14.0" ...
         bracket_end = after.index("]")
         extras = after[:bracket_end]
         rest = after[bracket_end + 1 :]
         if rest.startswith("=="):
-            # Find end of version — next quote or whitespace
-            ver_start = 2  # len("==")
+            ver_start = 2
             ver_end = ver_start
             while ver_end < len(rest) and rest[ver_end] not in ('"', "'", " ", "\n"):
                 ver_end += 1
@@ -1331,7 +1454,6 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
         run_command(["gh", "repo", "clone", enterprise_repo, str(repo_dir)])
         console.print(f"[green]✓[/green] Cloned {enterprise_repo}")
 
-        # --- bump versions ---
         for rel_dir in _ENTERPRISE_VERSION_DIRS:
             pkg_dir = repo_dir / rel_dir
             if not pkg_dir.exists():
@@ -1361,14 +1483,12 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
                         f"{pyproject.relative_to(repo_dir)}"
                     )
 
-        # --- update crewai[tools] pin ---
         enterprise_pyproject = repo_dir / enterprise_dep_path
         if _update_enterprise_crewai_dep(enterprise_pyproject, version):
             console.print(
                 f"[green]✓[/green] Updated crewai[tools] dep in {enterprise_dep_path}"
             )
 
-        # --- update crewai pins in CI workflows ---
         for wf in _update_enterprise_workflows(repo_dir, version):
             console.print(
                 f"[green]✓[/green] Updated crewai pin in {wf.relative_to(repo_dir)}"
@@ -1408,9 +1528,8 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
                 time.sleep(_PYPI_POLL_INTERVAL)
         console.print("[green]✓[/green] Workspace synced")
 
-        # --- branch, commit, push, PR ---
         branch_name = f"feat/bump-version-{version}"
-        run_command(["git", "checkout", "-b", branch_name], cwd=repo_dir)
+        create_or_reset_branch(branch_name, cwd=repo_dir)
         run_command(["git", "add", "."], cwd=repo_dir)
         run_command(
             ["git", "commit", "-m", f"feat: bump versions to {version}"],
@@ -1442,7 +1561,6 @@ def _release_enterprise(version: str, is_prerelease: bool, dry_run: bool) -> Non
 
         _poll_pr_until_merged(branch_name, "enterprise bump PR", repo=enterprise_repo)
 
-        # --- tag and release ---
         run_command(["git", "checkout", "main"], cwd=repo_dir)
         run_command(["git", "pull"], cwd=repo_dir)
 
@@ -1484,7 +1602,6 @@ def _trigger_pypi_publish(tag_name: str, wait: bool = False) -> None:
         tag_name: The release tag to publish.
         wait: Block until the workflow run completes.
     """
-    # Capture the latest run ID before triggering so we can detect the new one
     prev_run_id = ""
     if wait:
         try:
@@ -1559,11 +1676,6 @@ def _trigger_pypi_publish(tag_name: str, wait: bool = False) -> None:
         console.print("[green]✓[/green] PyPI publish workflow completed")
 
 
-# ---------------------------------------------------------------------------
-# CLI commands
-# ---------------------------------------------------------------------------
-
-
 @click.group()
 def cli() -> None:
     """Development tools for version bumping and git automation."""
@@ -1615,18 +1727,20 @@ def bump(version: str, dry_run: bool, no_push: bool, no_commit: bool) -> None:
         for pkg in packages:
             console.print(f"  - {pkg.name}")
 
-        console.print(f"\nUpdating version to {version}...")
-        _update_all_versions(cwd, lib_dir, version, packages, dry_run)
-
         if no_commit:
+            console.print(f"\nUpdating version to {version}...")
+            _update_all_versions(cwd, lib_dir, version, packages, dry_run)
             console.print("\nSkipping git operations (--no-commit flag set)")
         else:
             branch_name = f"feat/bump-version-{version}"
             if not dry_run:
                 console.print(f"\nCreating branch {branch_name}...")
-                run_command(["git", "checkout", "-b", branch_name])
+                create_or_reset_branch(branch_name)
                 console.print("[green]✓[/green] Branch created")
 
+                console.print(f"\nUpdating version to {version}...")
+                _update_all_versions(cwd, lib_dir, version, packages, dry_run)
+
                 console.print("\nCommitting changes...")
                 run_command(["git", "add", "."])
                 run_command(
@@ -1642,6 +1756,8 @@ def bump(version: str, dry_run: bool, no_push: bool, no_commit: bool) -> None:
                 console.print(
                     f"[dim][DRY RUN][/dim] Would create branch: {branch_name}"
                 )
+                console.print(f"\nUpdating version to {version}...")
+                _update_all_versions(cwd, lib_dir, version, packages, dry_run)
                 console.print(
                     f"[dim][DRY RUN][/dim] Would commit: feat: bump versions to {version}"
                 )
@@ -1831,70 +1947,88 @@ def release(
         skip_enterprise: Skip the enterprise release phase.
         skip_to_enterprise: Skip phases 1 & 2, run only the enterprise release phase.
     """
-    try:
-        check_gh_installed()
+    flags: list[str] = []
+    if no_edit:
+        flags.append("--no-edit")
+    if skip_enterprise:
+        flags.append("--skip-enterprise")
+    flag_suffix = (" " + " ".join(flags)) if flags else ""
+    enterprise_hint = (
+        ""
+        if skip_enterprise
+        else f"\n\nThen release enterprise:\n\n"
+        f"  devtools release {version} --skip-to-enterprise"
+    )
 
-        if skip_enterprise and skip_to_enterprise:
+    check_gh_installed()
+
+    if skip_enterprise and skip_to_enterprise:
+        console.print(
+            "[red]Error:[/red] Cannot use both --skip-enterprise "
+            "and --skip-to-enterprise"
+        )
+        sys.exit(1)
+
+    if not skip_enterprise or skip_to_enterprise:
+        missing: list[str] = []
+        if not _ENTERPRISE_REPO:
+            missing.append("ENTERPRISE_REPO")
+        if not _ENTERPRISE_VERSION_DIRS:
+            missing.append("ENTERPRISE_VERSION_DIRS")
+        if not _ENTERPRISE_CREWAI_DEP_PATH:
+            missing.append("ENTERPRISE_CREWAI_DEP_PATH")
+        if missing:
             console.print(
-                "[red]Error:[/red] Cannot use both --skip-enterprise "
-                "and --skip-to-enterprise"
+                f"[red]Error:[/red] Missing required environment variable(s): "
+                f"{', '.join(missing)}\n"
+                f"Set them or pass --skip-enterprise to skip the enterprise release."
             )
             sys.exit(1)
 
-        if not skip_enterprise or skip_to_enterprise:
-            missing: list[str] = []
-            if not _ENTERPRISE_REPO:
-                missing.append("ENTERPRISE_REPO")
-            if not _ENTERPRISE_VERSION_DIRS:
-                missing.append("ENTERPRISE_VERSION_DIRS")
-            if not _ENTERPRISE_CREWAI_DEP_PATH:
-                missing.append("ENTERPRISE_CREWAI_DEP_PATH")
-            if missing:
-                console.print(
-                    f"[red]Error:[/red] Missing required environment variable(s): "
-                    f"{', '.join(missing)}\n"
-                    f"Set them or pass --skip-enterprise to skip the enterprise release."
-                )
-                sys.exit(1)
+    cwd = Path.cwd()
+    lib_dir = cwd / "lib"
 
-        cwd = Path.cwd()
-        lib_dir = cwd / "lib"
+    is_prerelease = _is_prerelease(version)
 
-        is_prerelease = _is_prerelease(version)
-
-        if skip_to_enterprise:
+    if skip_to_enterprise:
+        try:
             _release_enterprise(version, is_prerelease, dry_run)
-            console.print(
-                f"\n[green]✓[/green] Enterprise release [bold]{version}[/bold] complete!"
+        except BaseException as e:
+            _print_release_error(e)
+            _resume_hint(
+                f"Fix the issue, then re-run:\n\n"
+                f"  devtools release {version} --skip-to-enterprise"
             )
-            return
-
-        if not dry_run:
-            console.print("Checking git status...")
-            check_git_clean()
-            console.print("[green]✓[/green] Working directory is clean")
-        else:
-            console.print("[dim][DRY RUN][/dim] Would check git status")
-
-        packages = get_packages(lib_dir)
-
-        console.print(f"\nFound {len(packages)} package(s) to update:")
-        for pkg in packages:
-            console.print(f"  - {pkg.name}")
-
-        # --- Phase 1: Bump versions ---
+            sys.exit(1)
         console.print(
-            f"\n[bold cyan]Phase 1: Bumping versions to {version}[/bold cyan]"
+            f"\n[green]✓[/green] Enterprise release [bold]{version}[/bold] complete!"
         )
+        return
 
-        _update_all_versions(cwd, lib_dir, version, packages, dry_run)
+    if not dry_run:
+        console.print("Checking git status...")
+        check_git_clean()
+        console.print("[green]✓[/green] Working directory is clean")
+    else:
+        console.print("[dim][DRY RUN][/dim] Would check git status")
 
+    packages = get_packages(lib_dir)
+
+    console.print(f"\nFound {len(packages)} package(s) to update:")
+    for pkg in packages:
+        console.print(f"  - {pkg.name}")
+
+    console.print(f"\n[bold cyan]Phase 1: Bumping versions to {version}[/bold cyan]")
+
+    try:
         branch_name = f"feat/bump-version-{version}"
         if not dry_run:
             console.print(f"\nCreating branch {branch_name}...")
-            run_command(["git", "checkout", "-b", branch_name])
+            create_or_reset_branch(branch_name)
             console.print("[green]✓[/green] Branch created")
 
+            _update_all_versions(cwd, lib_dir, version, packages, dry_run)
+
             console.print("\nCommitting changes...")
             run_command(["git", "add", "."])
             run_command(["git", "commit", "-m", f"feat: bump versions to {version}"])
@@ -1924,18 +2058,24 @@ def release(
             _poll_pr_until_merged(branch_name, "bump PR")
         else:
             console.print(f"[dim][DRY RUN][/dim] Would create branch: {branch_name}")
+            _update_all_versions(cwd, lib_dir, version, packages, dry_run)
             console.print(
                 f"[dim][DRY RUN][/dim] Would commit: feat: bump versions to {version}"
             )
             console.print(
                 "[dim][DRY RUN][/dim] Would push branch, create PR, and wait for merge"
             )
-
-        # --- Phase 2: Tag and release ---
-        console.print(
-            f"\n[bold cyan]Phase 2: Tagging and releasing {version}[/bold cyan]"
+    except BaseException as e:
+        _print_release_error(e)
+        _resume_hint(
+            f"Phase 1 failed. Fix the issue, then re-run:\n\n"
+            f"  devtools release {version}{flag_suffix}"
         )
+        sys.exit(1)
 
+    console.print(f"\n[bold cyan]Phase 2: Tagging and releasing {version}[/bold cyan]")
+
+    try:
         tag_name = version
 
         if not dry_run:
@@ -1962,22 +2102,57 @@ def release(
 
         if not dry_run:
             _create_tag_and_release(tag_name, release_notes, is_prerelease)
+    except BaseException as e:
+        _print_release_error(e)
+        _resume_hint(
+            "Phase 2 failed before PyPI publish. The bump PR is already merged.\n"
+            "Fix the issue, then resume with:\n\n"
+            "  devtools tag"
+            f"\n\nAfter tagging, publish to PyPI and update deployment test:\n\n"
+            f"  gh workflow run publish.yml -f release_tag={version}"
+            f"{enterprise_hint}"
+        )
+        sys.exit(1)
+
+    try:
+        if not dry_run:
             _trigger_pypi_publish(tag_name, wait=True)
+    except BaseException as e:
+        _print_release_error(e)
+        _resume_hint(
+            f"Phase 2 failed at PyPI publish. Tag and GitHub release already exist.\n"
+            f"Retry PyPI publish manually:\n\n"
+            f"  gh workflow run publish.yml -f release_tag={version}"
+            f"{enterprise_hint}"
+        )
+        sys.exit(1)
+
+    try:
+        if not dry_run:
             _update_deployment_test_repo(version, is_prerelease)
+    except BaseException as e:
+        _print_release_error(e)
+        _resume_hint(
+            f"Phase 2 failed updating deployment test repo. "
+            f"Tag, release, and PyPI are done.\n"
+            f"Fix the issue and update {_DEPLOYMENT_TEST_REPO} manually."
+            f"{enterprise_hint}"
+        )
+        sys.exit(1)
 
-        if not skip_enterprise:
+    if not skip_enterprise:
+        try:
             _release_enterprise(version, is_prerelease, dry_run)
+        except BaseException as e:
+            _print_release_error(e)
+            _resume_hint(
+                f"Phase 3 (enterprise) failed. Phases 1 & 2 completed successfully.\n"
+                f"Fix the issue, then resume:\n\n"
+                f"  devtools release {version} --skip-to-enterprise"
+            )
+            sys.exit(1)
 
-        console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")
-
-    except subprocess.CalledProcessError as e:
-        console.print(f"[red]Error running command:[/red] {e}")
-        if e.stderr:
-            console.print(e.stderr)
-        sys.exit(1)
-    except Exception as e:
-        console.print(f"[red]Error:[/red] {e}")
-        sys.exit(1)
+    console.print(f"\n[green]✓[/green] Release [bold]{version}[/bold] complete!")
 
 
 cli.add_command(bump)

pyproject.toml

@@ -12,7 +12,7 @@ dev = [
     "mypy==1.19.1",
     "pre-commit==4.5.1",
     "bandit==1.9.2",
-    "pytest==8.4.2",
+    "pytest==9.0.3",
     "pytest-asyncio==1.3.0",
     "pytest-subprocess==1.5.3",
     "vcrpy==7.0.0", # pinned, less versions break pytest-recording
@@ -20,7 +20,7 @@ dev = [
     "pytest-randomly==4.0.1",
     "pytest-timeout==2.4.0",
     "pytest-xdist==3.8.0",
-    "pytest-split==0.10.0",
+    "pytest-split==0.11.0",
     "types-requests~=2.31.0.6",
     "types-pyyaml==6.0.*",
     "types-regex==2026.1.15.*",
@@ -162,26 +162,36 @@ info = "Commits must follow Conventional Commits 1.0.0."
 
 
 [tool.uv]
-exclude-newer = "3 days"
+# Pinned to include the security patch releases (authlib 1.6.11,
+# langchain-text-splitters 1.1.2) uploaded on 2026-04-16.
+exclude-newer = "2026-04-22"
 
 # composio-core pins rich<14 but textual requires rich>=14.
 # onnxruntime 1.24+ dropped Python 3.10 wheels; cap it so qdrant[fastembed] resolves on 3.10.
 # fastembed 0.7.x and docling 2.63 cap pillow<12; the removed APIs don't affect them.
-# langchain-core <1.2.28 has GHSA-926x-3r5x-gfhw (incomplete f-string validation).
+# langchain-core <1.2.31 has GHSA-926x-3r5x-gfhw and is required by langchain-text-splitters 1.1.2+.
+# langchain-text-splitters <1.1.2 has GHSA-fv5p-p927-qmxr (SSRF bypass in split_text_from_url).
 # transformers 4.57.6 has CVE-2026-1839; force 5.4+ (docling 2.84 allows huggingface-hub>=1).
 # cryptography 46.0.6 has CVE-2026-39892; force 46.0.7+.
-# pypdf <6.10.0 has CVE-2026-40260; force 6.10.0+.
+# pypdf <6.10.2 has GHSA-4pxv-j86v-mhcw, GHSA-7gw9-cf7v-778f, GHSA-x284-j5p8-9c5p; force 6.10.2+.
 # uv <0.11.6 has GHSA-pjjw-68hj-v9mw; force 0.11.6+.
+# python-multipart <0.0.26 has GHSA-mj87-hwqh-73pj; force 0.0.26+.
+# langsmith <0.7.31 has GHSA-rr7j-v2q5-chgv (streaming token redaction bypass); force 0.7.31+.
+# authlib <1.6.11 has GHSA-jj8c-mmj3-mmgv (CSRF bypass in cache-based state storage).
 override-dependencies = [
     "rich>=13.7.1",
     "onnxruntime<1.24; python_version < '3.11'",
     "pillow>=12.1.1",
-    "langchain-core>=1.2.28,<2",
+    "langchain-core>=1.2.31,<2",
+    "langchain-text-splitters>=1.1.2,<2",
     "urllib3>=2.6.3",
     "transformers>=5.4.0; python_version >= '3.10'",
     "cryptography>=46.0.7",
-    "pypdf>=6.10.0,<7",
+    "pypdf>=6.10.2,<7",
     "uv>=0.11.6,<1",
+    "python-multipart>=0.0.26,<1",
+    "langsmith>=0.7.31,<0.8",
+    "authlib>=1.6.11",
 ]
 
 [tool.uv.workspace]

scripts/benchmark_import_time.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python3
+"""Benchmark `import crewai` cold start time.
+
+Usage:
+    python scripts/benchmark_import_time.py [--runs N] [--json]
+
+Spawns a fresh Python subprocess for each run to ensure cold imports.
+Prints median, mean, min, max across all runs.
+With --json, outputs machine-readable results for CI.
+"""
+import argparse
+import json
+import statistics
+import subprocess
+import sys
+
+
+IMPORT_SCRIPT = "import time; t0 = time.perf_counter(); import crewai; print(time.perf_counter() - t0)"
+
+
+def measure_import(python: str = sys.executable) -> float:
+    """Run a single cold-import measurement in a subprocess."""
+    result = subprocess.run(
+        [python, "-c", IMPORT_SCRIPT],
+        capture_output=True,
+        text=True,
+        env={"PATH": "", "VIRTUAL_ENV": "", "PYTHONPATH": ""},
+        timeout=30,
+    )
+    if result.returncode != 0:
+        raise RuntimeError(f"Import failed: {result.stderr.strip()}")
+    return float(result.stdout.strip())
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Benchmark crewai import time")
+    parser.add_argument("--runs", type=int, default=5, help="Number of runs (default: 5)")
+    parser.add_argument("--json", action="store_true", help="Output JSON for CI")
+    parser.add_argument("--threshold", type=float, default=None,
+                        help="Fail if median exceeds this value (seconds)")
+    args = parser.parse_args()
+
+    times = []
+    for i in range(args.runs):
+        t = measure_import()
+        times.append(t)
+        if not args.json:
+            print(f"  Run {i + 1}: {t:.3f}s")
+
+    median = statistics.median(times)
+    mean = statistics.mean(times)
+    stdev = statistics.stdev(times) if len(times) > 1 else 0.0
+
+    result = {
+        "runs": args.runs,
+        "median_s": round(median, 3),
+        "mean_s": round(mean, 3),
+        "stdev_s": round(stdev, 3),
+        "min_s": round(min(times), 3),
+        "max_s": round(max(times), 3),
+    }
+
+    if args.json:
+        print(json.dumps(result))
+    else:
+        print(f"\n  Median: {median:.3f}s")
+        print(f"  Mean:   {mean:.3f}s ± {stdev:.3f}s")
+        print(f"  Range:  {min(times):.3f}s – {max(times):.3f}s")
+
+    if args.threshold and median > args.threshold:
+        print(f"\n  ❌ FAILED: median {median:.3f}s exceeds threshold {args.threshold:.3f}s")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()