feat: add google_drive/upload_from_file action for token-efficient file uploads

Add a new CrewAIPlatformFileUploadTool that reads files directly from disk
and uploads to Google Drive via the platform API, bypassing the LLM context
window entirely. This solves two problems:

1. File content no longer consumes LLM tokens
2. Binary/large files no longer risk exceeding the 128k context limit

The new action accepts a file_path parameter instead of content. It handles:
- Auto-detection of MIME type from file extension
- Optional custom file name (defaults to local filename)
- File size validation (50 MB limit for simple uploads)
- Base64 encoding of file content before sending to API

The existing google_drive/upload_file action is unchanged — full backwards
compatibility with the 10k+ existing executions.

Changes:
- New tool: CrewAIPlatformFileUploadTool
- Builder auto-injects the tool when apps include google_drive or
  google_drive/upload_from_file
- 14 unit tests covering upload, error handling, MIME detection, SSL
- Updated docs with new action reference and usage examples

.github/workflows/docs-broken-links.yml

@@ -4,13 +4,13 @@ on:
   pull_request:
     paths:
       - "docs/**"
-      - "docs/docs.json"
+      - "docs.json"
   push:
     branches:
       - main
     paths:
       - "docs/**"
-      - "docs/docs.json"
+      - "docs.json"
   workflow_dispatch:
 
 jobs:
@@ -25,12 +25,11 @@ jobs:
         with:
           node-version: "22"
 
-      - name: Install libsecret for Mintlify CLI
-        run: sudo apt-get update && sudo apt-get install -y libsecret-1-0
-
       - name: Install Mintlify CLI
-        run: npm install -g mint@latest
+        run: npm i -g mintlify
 
       - name: Run broken link checker
+        run: |
+          # Auto-answer the prompt with yes command
+          yes "" | mintlify broken-links || test $? -eq 141
         working-directory: ./docs
-        run: mint broken-links

.github/workflows/vulnerability-scan.yml

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

docs/ar/changelog.mdx

@@ -4,6 +4,53 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2 أبريل 2026">
+  ## v1.13.0
+
+  [عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## ما الذي تغير
+
+  ### الميزات
+  - إضافة نموذج RuntimeState RootModel لتوحيد تسلسل الحالة
+  - تعزيز مستمع الأحداث مع نطاقات جديدة للقياس عن أحداث المهارة والذاكرة
+  - إضافة امتداد A2UI مع دعم v0.8/v0.9، والمخططات، والوثائق
+  - إصدار بيانات استخدام الرموز في حدث LLMCallCompletedEvent
+  - تحديث تلقائي لمستودع اختبار النشر أثناء الإصدار
+  - تحسين مرونة الإصدار المؤسسي وتجربة المستخدم
+
+  ### إصلاحات الأخطاء
+  - إضافة بيانات اعتماد مستودع الأدوات إلى تثبيت crewai
+  - إضافة بيانات اعتماد مستودع الأدوات إلى بناء uv في نشر الأدوات
+  - تمرير بيانات التعريف عبر الإعدادات بدلاً من معلمات الأدوات
+  - معالجة نماذج GPT-5.x التي لا تدعم معلمة API `stop`
+  - إضافة GPT-5 وسلسلة o إلى بادئات الرؤية متعددة الوسائط
+  - مسح ذاكرة التخزين المؤقت uv للحزم التي تم نشرها حديثًا في الإصدار المؤسسي
+  - تحديد lancedb أقل من 0.30.1 لضمان التوافق مع Windows
+  - إصلاح مستويات أذونات RBAC لتتناسب مع خيارات واجهة المستخدم الفعلية
+  - إصلاح عدم الدقة في قدرات الوكيل عبر جميع اللغات
+
+  ### الوثائق
+  - إضافة فيديو توضيحي لمهارات وكيل البرمجة إلى صفحات البدء
+  - إضافة دليل شامل لتكوين SSO
+  - إضافة مصفوفة شاملة لأذونات RBAC ودليل النشر
+  - تحديث سجل التغييرات والإصدار إلى v1.13.0
+
+  ### الأداء
+  - تقليل الحمل الزائد للإطار باستخدام حافلة الأحداث الكسولة، وتخطي التتبع عند تعطيله
+
+  ### إعادة الهيكلة
+  - تحويل Flow إلى Pydantic BaseModel
+  - تحويل فئات LLM إلى Pydantic BaseModel
+  - استبدال InstanceOf[T] بتعليقات نوع عادية
+  - إزالة دليل LLM الخاص بالطرف الثالث غير المستخدم
+
+  ## المساهمون
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="2 أبريل 2026">
   ## v1.13.0a7
 

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

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

docs/en/changelog.mdx

@@ -4,6 +4,53 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="Apr 02, 2026">
+  ## v1.13.0
+
+  [View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## What's Changed
+
+  ### Features
+  - Add RuntimeState RootModel for unified state serialization
+  - Enhance event listener with new telemetry spans for skill and memory events
+  - Add A2UI extension with v0.8/v0.9 support, schemas, and docs
+  - Emit token usage data in LLMCallCompletedEvent
+  - Auto-update deployment test repo during release
+  - Improve enterprise release resilience and UX
+
+  ### Bug Fixes
+  - Add tool repository credentials to crewai install
+  - Add tool repository credentials to uv build in tool publish
+  - Pass fingerprint metadata via config instead of tool args
+  - Handle GPT-5.x models not supporting the `stop` API parameter
+  - Add GPT-5 and o-series to multimodal vision prefixes
+  - Bust uv cache for freshly published packages in enterprise release
+  - Cap lancedb below 0.30.1 for Windows compatibility
+  - Fix RBAC permission levels to match actual UI options
+  - Fix inaccuracies in agent-capabilities across all languages
+
+  ### Documentation
+  - Add coding agent skills demo video to getting started pages
+  - Add comprehensive SSO configuration guide
+  - Add comprehensive RBAC permissions matrix and deployment guide
+  - Update changelog and version for v1.13.0
+
+  ### Performance
+  - Reduce framework overhead with lazy event bus, skip tracing when disabled
+
+  ### Refactoring
+  - Convert Flow to Pydantic BaseModel
+  - Convert LLM classes to Pydantic BaseModel
+  - Replace InstanceOf[T] with plain type annotations
+  - Remove unused third_party LLM directory
+
+  ## Contributors
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="Apr 02, 2026">
   ## v1.13.0a7
 

docs/en/concepts/flows.mdx

@@ -572,176 +572,6 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
-### Conversational Flows (User Input)
-
-The `self.ask()` method pauses flow execution to request input from a user inline, then returns their response as a string. This enables conversational, interactive flows where the AI can gather information, ask clarifying questions, or request approvals during execution.
-
-#### Basic Usage
-
-```python Code
-from crewai.flow.flow import Flow, start, listen
-
-class GreetingFlow(Flow):
-    @start()
-    def greet(self):
-        name = self.ask("What's your name?")
-        self.state["name"] = name
-
-    @listen(greet)
-    def welcome(self):
-        print(f"Welcome, {self.state['name']}!")
-
-flow = GreetingFlow()
-flow.kickoff()
-```
-
-By default, `self.ask()` uses a `ConsoleProvider` that prompts via Python's built-in `input()`.
-
-#### Multiple Asks in One Method
-
-You can call `self.ask()` multiple times within a single method to gather several inputs:
-
-```python Code
-from crewai.flow.flow import Flow, start
-
-class OnboardingFlow(Flow):
-    @start()
-    def collect_info(self):
-        name = self.ask("What's your name?")
-        role = self.ask("What's your role?")
-        team = self.ask("Which team are you joining?")
-        self.state["profile"] = {"name": name, "role": role, "team": team}
-        print(f"Welcome {name}, {role} on {team}!")
-
-flow = OnboardingFlow()
-flow.kickoff()
-```
-
-#### Timeout Support
-
-Pass `timeout=` (in seconds) to avoid blocking indefinitely. If the user doesn't respond in time, `self.ask()` returns `None`:
-
-```python Code
-from crewai.flow.flow import Flow, start
-
-class ApprovalFlow(Flow):
-    @start()
-    def request_approval(self):
-        response = self.ask("Approve deployment? (yes/no)", timeout=120)
-
-        if response is None:
-            print("No response received — timed out.")
-            self.state["approved"] = False
-            return
-
-        self.state["approved"] = response.strip().lower() == "yes"
-```
-
-Use a `while` loop to retry on timeout:
-
-```python Code
-from crewai.flow.flow import Flow, start
-
-class RetryFlow(Flow):
-    @start()
-    def ask_with_retry(self):
-        answer = None
-        while answer is None:
-            answer = self.ask("Please confirm (yes/no):", timeout=60)
-            if answer is None:
-                print("Timed out, asking again...")
-        self.state["confirmed"] = answer.strip().lower() == "yes"
-```
-
-#### Metadata Support
-
-The `metadata` parameter enables bidirectional context passing between the flow and the input provider. Send context to the provider, and receive structured context back:
-
-```python Code
-from crewai.flow.flow import Flow, start
-
-class ContextualFlow(Flow):
-    @start()
-    def gather_feedback(self):
-        response = self.ask(
-            "Rate this output (1-5):",
-            metadata={
-                "step": "quality_review",
-                "output_id": "abc-123",
-                "options": ["1", "2", "3", "4", "5"],
-            },
-        )
-        self.state["rating"] = int(response) if response else None
-```
-
-When a custom provider returns an `InputResponse`, it can include its own metadata (e.g., user identity, timestamp, channel info) that your flow can process.
-
-#### Custom InputProvider
-
-For production use cases (Slack bots, web UIs, webhooks), implement the `InputProvider` protocol:
-
-```python Code
-from crewai.flow.flow import Flow, start
-from crewai.flow.input_provider import InputProvider, InputResponse
-import requests
-
-class SlackInputProvider(InputProvider):
-    def __init__(self, channel_id: str, bot_token: str):
-        self.channel_id = channel_id
-        self.bot_token = bot_token
-
-    def request_input(self, message, flow, metadata=None):
-        # Post the question to Slack
-        requests.post(
-            "https://slack.com/api/chat.postMessage",
-            headers={"Authorization": f"Bearer {self.bot_token}"},
-            json={"channel": self.channel_id, "text": message},
-        )
-        # Wait for and return the user's reply (simplified)
-        reply = self.poll_for_reply()
-        return InputResponse(
-            value=reply["text"],
-            metadata={"user": reply["user"], "ts": reply["ts"]},
-        )
-
-    def poll_for_reply(self):
-        # Your implementation to wait for a Slack reply
-        ...
-
-# Use the custom provider
-flow = Flow(input_provider=SlackInputProvider(
-    channel_id="C01ABC123",
-    bot_token="xoxb-...",
-))
-flow.kickoff()
-```
-
-The `request_input` method can return:
-- A **string** — used directly as the user's response
-- An **`InputResponse`** — includes `value` (the response string) and optional `metadata`
-- **`None`** — treated as a timeout / no response
-
-#### Auto-Checkpoint Behavior
-
-<Note>
-When persistence is configured, the flow state is automatically saved **before** each `self.ask()` call. If the process restarts while waiting for input, the flow can resume from the checkpoint without losing progress.
-</Note>
-
-#### `self.ask()` vs `@human_feedback`
-
-| | `self.ask()` | `@human_feedback` |
-|---|---|---|
-| **Purpose** | Inline user input during execution | Approval gates and review feedback |
-| **Returns** | `str \| None` | `HumanFeedbackResult` with structured fields |
-| **Timeout** | Built-in `timeout=` parameter | Not built-in |
-| **Provider** | Pluggable `InputProvider` protocol | Console-based |
-| **Use when** | Gathering data, clarifications, confirmations | Review/approval workflows with structured feedback |
-| **Decorator** | None — call `self.ask()` anywhere | `@human_feedback` on the method |
-
-<Note>
-Both features coexist — you can use `self.ask()` and `@human_feedback` in the same flow. Use `self.ask()` for inline data gathering and `@human_feedback` for structured review gates.
-</Note>
-
 ### Human in the Loop (human feedback)
 
 <Note>

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

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

docs/en/enterprise/integrations/google_drive.mdx

@@ -86,6 +86,22 @@ CREWAI_PLATFORM_INTEGRATION_TOKEN=your_enterprise_token
 
   </Accordion>
 
+  <Accordion title="google_drive/upload_from_file">
+    **Description:** Upload a file from a local path to Google Drive. The file is read directly from disk — its content never passes through the LLM context window, making this efficient for large or binary files.
+
+    <Note>
+      Unlike `google_drive/upload_file` which requires passing file content as a parameter (consuming LLM tokens), this action takes a file path and reads the file directly. Use this for binary files, large documents, or any case where you want to avoid token overhead.
+    </Note>
+
+    **Parameters:**
+    - `file_path` (string, required): Path to the local file to upload.
+    - `name` (string, optional): Name for the file in Google Drive (defaults to the local filename).
+    - `mime_type` (string, optional): MIME type of the file (auto-detected from file extension if not provided).
+    - `parent_folder_id` (string, optional): ID of the parent folder where the file should be created.
+    - `description` (string, optional): Description of the file.
+
+  </Accordion>
+
   <Accordion title="google_drive/download_file">
     **Description:** Download a file from Google Drive.
 
@@ -205,6 +221,42 @@ crew = Crew(
 crew.kickoff()
 ```
 
+### Uploading Files from Disk (Token-Efficient)
+
+```python
+from crewai import Agent, Task, Crew
+
+# Use upload_from_file to avoid passing file content through the LLM context
+upload_agent = Agent(
+    role="File Uploader",
+    goal="Upload local files to Google Drive efficiently",
+    backstory="An AI assistant that uploads files without wasting context tokens.",
+    apps=[
+        'google_drive/upload_from_file',
+        'google_drive/list_files'
+    ]
+)
+
+# The agent only needs to specify the file path — the file is read directly
+# from disk and never passes through the LLM context window
+upload_task = Task(
+    description="Upload the file at /data/reports/quarterly_report.pdf to Google Drive in the Reports folder",
+    agent=upload_agent,
+    expected_output="File uploaded successfully with file ID"
+)
+
+crew = Crew(
+    agents=[upload_agent],
+    tasks=[upload_task]
+)
+
+crew.kickoff()
+```
+
+<Tip>
+  Use `google_drive/upload_from_file` instead of `google_drive/upload_file` when uploading existing files from disk. This is especially important for binary files (PDFs, images, etc.) or large files that would otherwise consume significant LLM context tokens or exceed context limits.
+</Tip>
+
 ### Advanced File Management
 
 ```python

docs/en/installation.mdx

@@ -171,6 +171,9 @@ We recommend using the `YAML` template scaffolding for a structured approach to
       ```shell
       uv add <package-name>
       ```
+      <Note>
+        As a supply-chain security measure, CrewAI's internal packages use `exclude-newer = "3 days"` in their `pyproject.toml` files. This means transitive dependencies pulled in by CrewAI won't resolve packages released less than 3 days ago. Your own direct dependencies are not affected by this policy. If you notice a transitive dependency is behind, you can pin the version you want explicitly in your project's dependencies.
+      </Note>
     - To run your crew, execute the following command in the root of your project:
       ```bash
       crewai run

docs/ko/changelog.mdx

@@ -4,6 +4,53 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="2026년 4월 2일">
+  ## v1.13.0
+
+  [GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## 변경 사항
+
+  ### 기능
+  - 통합 상태 직렬화를 위한 RuntimeState RootModel 추가
+  - 기술 및 메모리 이벤트에 대한 새로운 텔레메트리 스팬으로 이벤트 리스너 강화
+  - v0.8/v0.9 지원, 스키마 및 문서가 포함된 A2UI 확장 추가
+  - LLMCallCompletedEvent에서 토큰 사용 데이터 방출
+  - 릴리스 중 배포 테스트 리포 자동 업데이트
+  - 기업 릴리스의 복원력 및 사용자 경험 개선
+
+  ### 버그 수정
+  - crewai 설치에 도구 리포지토리 자격 증명 추가
+  - 도구 게시의 uv 빌드에 도구 리포지토리 자격 증명 추가
+  - 도구 인수 대신 구성으로 지문 메타데이터 전달
+  - `stop` API 매개변수를 지원하지 않는 GPT-5.x 모델 처리
+  - 멀티모달 비전 접두사에 GPT-5 및 o-series 추가
+  - 기업 릴리스에서 새로 게시된 패키지에 대한 uv 캐시 무효화
+  - Windows 호환성을 위해 lancedb를 0.30.1 이하로 제한
+  - 실제 UI 옵션과 일치하도록 RBAC 권한 수준 수정
+  - 모든 언어에서 에이전트 기능의 부정확성 수정
+
+  ### 문서
+  - 시작하기 페이지에 코딩 에이전트 기술 데모 비디오 추가
+  - 포괄적인 SSO 구성 가이드 추가
+  - 포괄적인 RBAC 권한 매트릭스 및 배포 가이드 추가
+  - v1.13.0에 대한 변경 로그 및 버전 업데이트
+
+  ### 성능
+  - 비활성화 시 추적 건너뛰기와 함께 지연 이벤트 버스를 사용하여 프레임워크 오버헤드 감소
+
+  ### 리팩토링
+  - Flow를 Pydantic BaseModel로 변환
+  - LLM 클래스를 Pydantic BaseModel로 변환
+  - InstanceOf[T]를 일반 타입 주석으로 교체
+  - 사용되지 않는 third_party LLM 디렉토리 제거
+
+  ## 기여자
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="2026년 4월 2일">
   ## v1.13.0a7
 

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

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

docs/pt-BR/changelog.mdx

@@ -4,6 +4,53 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
 icon: "clock"
 mode: "wide"
 ---
+<Update label="02 abr 2026">
+  ## v1.13.0
+
+  [Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
+
+  ## O que Mudou
+
+  ### Funcionalidades
+  - Adicionar RuntimeState RootModel para serialização de estado unificado
+  - Melhorar o listener de eventos com novos spans de telemetria para eventos de habilidade e memória
+  - Adicionar extensão A2UI com suporte a v0.8/v0.9, esquemas e documentação
+  - Emitir dados de uso de token no LLMCallCompletedEvent
+  - Atualizar automaticamente o repositório de testes de implantação durante o lançamento
+  - Melhorar a resiliência e a experiência do usuário na versão empresarial
+
+  ### Correções de Bugs
+  - Adicionar credenciais do repositório de ferramentas ao crewai install
+  - Adicionar credenciais do repositório de ferramentas ao uv build na publicação de ferramentas
+  - Passar metadados de impressão digital via configuração em vez de argumentos de ferramenta
+  - Lidar com modelos GPT-5.x que não suportam o parâmetro API `stop`
+  - Adicionar GPT-5 e a série o aos prefixos de visão multimodal
+  - Limpar cache uv para pacotes recém-publicados na versão empresarial
+  - Limitar lancedb abaixo de 0.30.1 para compatibilidade com Windows
+  - Corrigir níveis de permissão RBAC para corresponder às opções reais da interface do usuário
+  - Corrigir imprecisões nas capacidades do agente em todos os idiomas
+
+  ### Documentação
+  - Adicionar vídeo de demonstração de habilidades do agente de codificação às páginas de introdução
+  - Adicionar guia abrangente de configuração SSO
+  - Adicionar matriz de permissões RBAC abrangente e guia de implantação
+  - Atualizar changelog e versão para v1.13.0
+
+  ### Desempenho
+  - Reduzir a sobrecarga do framework com bus de eventos preguiçoso, pular rastreamento quando desativado
+
+  ### Refatoração
+  - Converter Flow para Pydantic BaseModel
+  - Converter classes LLM para Pydantic BaseModel
+  - Substituir InstanceOf[T] por anotações de tipo simples
+  - Remover diretório LLM de terceiros não utilizado
+
+  ## Contribuidores
+
+  @alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
+
+</Update>
+
 <Update label="02 abr 2026">
   ## v1.13.0a7
 

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

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

lib/crewai-files/pyproject.toml

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

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

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

lib/crewai-tools/pyproject.toml

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

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

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

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

@@ -7,6 +7,9 @@ through the CrewAI platform API.
 from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import (
     CrewAIPlatformActionTool,
 )
+from crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool import (
+    CrewAIPlatformFileUploadTool,
+)
 from crewai_tools.tools.crewai_platform_tools.crewai_platform_tool_builder import (
     CrewaiPlatformToolBuilder,
 )
@@ -17,6 +20,7 @@ from crewai_tools.tools.crewai_platform_tools.crewai_platform_tools import (
 
 __all__ = [
     "CrewAIPlatformActionTool",
+    "CrewAIPlatformFileUploadTool",
     "CrewaiPlatformToolBuilder",
     "CrewaiPlatformTools",
 ]

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_file_upload_tool.py

@@ -0,0 +1,165 @@
+"""CrewAI Platform File Upload Tool.
+
+Uploads a file from disk to Google Drive without passing file content
+through the LLM context window. This avoids token waste and context
+limit issues with large or binary files.
+"""
+
+import base64
+import json
+import logging
+import mimetypes
+import os
+from pathlib import Path
+from typing import Any, Optional
+
+from crewai.tools import BaseTool
+from pydantic import Field, create_model
+
+from crewai_tools.tools.crewai_platform_tools.misc import (
+    get_platform_api_base_url,
+    get_platform_integration_token,
+)
+
+import requests
+
+
+logger = logging.getLogger(__name__)
+
+# Google Drive simple upload limit
+MAX_FILE_SIZE_BYTES = 50 * 1024 * 1024  # 50 MB
+
+
+_UploadFromFileSchema = create_model(
+    "UploadFromFileSchema",
+    file_path=(str, Field(description="Path to the local file to upload")),
+    name=(
+        Optional[str],
+        Field(
+            default=None,
+            description="Name for the file in Google Drive (defaults to the local filename)",
+        ),
+    ),
+    mime_type=(
+        Optional[str],
+        Field(
+            default=None,
+            description="MIME type of the file (auto-detected if not provided)",
+        ),
+    ),
+    parent_folder_id=(
+        Optional[str],
+        Field(
+            default=None,
+            description="ID of the parent folder where the file should be created",
+        ),
+    ),
+    description=(
+        Optional[str],
+        Field(default=None, description="Description of the file"),
+    ),
+)
+
+
+class CrewAIPlatformFileUploadTool(BaseTool):
+    """Upload a file from disk to Google Drive.
+
+    Reads the file locally and sends it to the platform API, bypassing
+    the LLM context window entirely. Supports auto-detection of MIME type
+    and optional file naming.
+    """
+
+    name: str = "google_drive_upload_from_file"
+    description: str = (
+        "Upload a file from a local path to Google Drive. "
+        "The file is read directly from disk — its content never passes "
+        "through the LLM context, making this efficient for large or binary files."
+    )
+    args_schema: type = _UploadFromFileSchema
+
+    def _run(
+        self,
+        file_path: str,
+        name: str | None = None,
+        mime_type: str | None = None,
+        parent_folder_id: str | None = None,
+        description: str | None = None,
+        **kwargs: Any,
+    ) -> str:
+        try:
+            path = Path(file_path).expanduser().resolve()
+
+            if not path.exists():
+                return f"Error: File not found: {file_path}"
+
+            if not path.is_file():
+                return f"Error: Path is not a file: {file_path}"
+
+            file_size = path.stat().st_size
+            if file_size > MAX_FILE_SIZE_BYTES:
+                return (
+                    f"Error: File size ({file_size / (1024 * 1024):.1f} MB) exceeds "
+                    f"the 50 MB limit for simple uploads. Consider splitting the file "
+                    f"or using a resumable upload method."
+                )
+
+            # Read and encode file content
+            content_bytes = path.read_bytes()
+            content_b64 = base64.b64encode(content_bytes).decode("utf-8")
+
+            # Auto-detect MIME type if not provided
+            if mime_type is None:
+                guessed_type, _ = mimetypes.guess_type(str(path))
+                mime_type = guessed_type or "application/octet-stream"
+
+            # Use filename if name not provided
+            upload_name = name or path.name
+
+            # Build payload matching the existing upload_file action format
+            payload_data: dict[str, Any] = {
+                "name": upload_name,
+                "content": content_b64,
+            }
+            if mime_type:
+                payload_data["mime_type"] = mime_type
+            if parent_folder_id:
+                payload_data["parent_folder_id"] = parent_folder_id
+            if description:
+                payload_data["description"] = description
+
+            api_url = (
+                f"{get_platform_api_base_url()}/actions/GOOGLE_DRIVE_SAVE_FILE/execute"
+            )
+            token = get_platform_integration_token()
+            headers = {
+                "Authorization": f"Bearer {token}",
+                "Content-Type": "application/json",
+            }
+            payload = {"integration": payload_data}
+
+            response = requests.post(
+                url=api_url,
+                headers=headers,
+                json=payload,
+                timeout=120,  # Longer timeout for file uploads
+                verify=os.environ.get("CREWAI_FACTORY", "false").lower() != "true",
+            )
+
+            data = response.json()
+            if not response.ok:
+                if isinstance(data, dict):
+                    error_info = data.get("error", {})
+                    if isinstance(error_info, dict):
+                        error_message = error_info.get("message", json.dumps(data))
+                    else:
+                        error_message = str(error_info)
+                else:
+                    error_message = str(data)
+                return f"API request failed: {error_message}"
+
+            return json.dumps(data, indent=2)
+
+        except PermissionError:
+            return f"Error: Permission denied reading file: {file_path}"
+        except Exception as e:
+            return f"Error uploading file: {e!s}"

lib/crewai-tools/src/crewai_tools/tools/crewai_platform_tools/crewai_platform_tool_builder.py

@@ -11,11 +11,22 @@ import requests
 from crewai_tools.tools.crewai_platform_tools.crewai_platform_action_tool import (
     CrewAIPlatformActionTool,
 )
+from crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool import (
+    CrewAIPlatformFileUploadTool,
+)
 from crewai_tools.tools.crewai_platform_tools.misc import (
     get_platform_api_base_url,
     get_platform_integration_token,
 )
 
+# Apps that have client-side local tools (e.g. file-path-based upload)
+_LOCAL_TOOL_APPS = {
+    "google_drive": [CrewAIPlatformFileUploadTool],
+}
+_LOCAL_TOOL_ACTIONS = {
+    "google_drive/upload_from_file": CrewAIPlatformFileUploadTool,
+}
+
 
 logger = logging.getLogger(__name__)
 
@@ -95,6 +106,23 @@ class CrewaiPlatformToolBuilder:
 
             tools.append(tool)
 
+        # Inject client-side local tools based on requested apps
+        added_local_tools: set[type] = set()
+        for app in self._apps:
+            # Check for specific action (e.g. "google_drive/upload_from_file")
+            if app in _LOCAL_TOOL_ACTIONS:
+                tool_cls = _LOCAL_TOOL_ACTIONS[app]
+                if tool_cls not in added_local_tools:
+                    tools.append(tool_cls())
+                    added_local_tools.add(tool_cls)
+            # Check for full app (e.g. "google_drive") — inject all local tools
+            app_base = app.split("/")[0]
+            if app_base in _LOCAL_TOOL_APPS:
+                for tool_cls in _LOCAL_TOOL_APPS[app_base]:
+                    if tool_cls not in added_local_tools:
+                        tools.append(tool_cls())
+                        added_local_tools.add(tool_cls)
+
         self._tools = tools
 
     def __enter__(self) -> list[BaseTool]:

lib/crewai-tools/tests/tools/crewai_platform_tools/test_crewai_platform_file_upload_tool.py

@@ -0,0 +1,363 @@
+"""Tests for CrewAIPlatformFileUploadTool."""
+
+import base64
+import json
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+
+from crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool import (
+    CrewAIPlatformFileUploadTool,
+    MAX_FILE_SIZE_BYTES,
+)
+
+
+class TestCrewAIPlatformFileUploadTool:
+    """Test suite for the file upload tool."""
+
+    def setup_method(self):
+        self.tool = CrewAIPlatformFileUploadTool()
+
+    def test_tool_name_and_description(self):
+        assert self.tool.name == "google_drive_upload_from_file"
+        assert "local path" in self.tool.description.lower()
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_successful_upload(self, mock_post):
+        """Test uploading a file successfully."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {
+            "id": "file123",
+            "name": "test.txt",
+        }
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("Hello, Google Drive!")
+            temp_path = f.name
+
+        try:
+            result = self.tool._run(file_path=temp_path)
+
+            assert "file123" in result
+            mock_post.assert_called_once()
+
+            call_kwargs = mock_post.call_args
+            payload = call_kwargs.kwargs["json"]["integration"]
+            assert payload["name"] == Path(temp_path).name
+            assert payload["mime_type"] == "text/plain"
+            # Verify content is base64-encoded
+            decoded = base64.b64decode(payload["content"]).decode("utf-8")
+            assert decoded == "Hello, Google Drive!"
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_custom_name_override(self, mock_post):
+        """Test that a custom name overrides the filename."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path, name="custom_report.txt")
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert payload["name"] == "custom_report.txt"
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_mime_type_auto_detection(self, mock_post):
+        """Test MIME type is auto-detected from file extension."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".json", delete=False
+        ) as f:
+            f.write("{}")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path)
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert payload["mime_type"] == "application/json"
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_custom_mime_type(self, mock_post):
+        """Test that a custom MIME type overrides auto-detection."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path, mime_type="application/pdf")
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert payload["mime_type"] == "application/pdf"
+        finally:
+            os.unlink(temp_path)
+
+    def test_file_not_found(self):
+        """Test error when file does not exist."""
+        result = self.tool._run(file_path="/nonexistent/path/file.txt")
+        assert "Error: File not found" in result
+
+    def test_path_is_directory(self):
+        """Test error when path is a directory."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            result = self.tool._run(file_path=tmpdir)
+            assert "Error: Path is not a file" in result
+
+    def test_file_too_large(self):
+        """Test error when file exceeds 50 MB limit."""
+        with tempfile.NamedTemporaryFile(delete=False) as f:
+            temp_path = f.name
+            # Create a sparse file that reports as > 50 MB
+            f.seek(MAX_FILE_SIZE_BYTES + 1)
+            f.write(b"\0")
+
+        try:
+            result = self.tool._run(file_path=temp_path)
+            assert "exceeds" in result
+            assert "50 MB" in result
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_optional_params_included(self, mock_post):
+        """Test that optional params are included in payload when provided."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(
+                file_path=temp_path,
+                parent_folder_id="folder456",
+                description="My report",
+            )
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert payload["parent_folder_id"] == "folder456"
+            assert payload["description"] == "My report"
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_optional_params_excluded_when_none(self, mock_post):
+        """Test that optional params are NOT in payload when not provided."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path)
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert "parent_folder_id" not in payload
+            assert "description" not in payload
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_api_error_response(self, mock_post):
+        """Test handling of API error responses."""
+        mock_response = Mock()
+        mock_response.ok = False
+        mock_response.json.return_value = {
+            "error": {"message": "Unauthorized"}
+        }
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            result = self.tool._run(file_path=temp_path)
+            assert "API request failed" in result
+            assert "Unauthorized" in result
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {
+            "CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token",
+            "CREWAI_FACTORY": "true",
+        },
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_ssl_verification_disabled_for_factory(self, mock_post):
+        """Test that SSL verification is disabled when CREWAI_FACTORY=true."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path)
+            assert mock_post.call_args.kwargs["verify"] is False
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_ssl_verification_enabled_by_default(self, mock_post):
+        """Test that SSL verification is enabled by default."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".txt", delete=False
+        ) as f:
+            f.write("content")
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path)
+            assert mock_post.call_args.kwargs["verify"] is True
+        finally:
+            os.unlink(temp_path)
+
+    @patch.dict(
+        "os.environ",
+        {"CREWAI_PLATFORM_INTEGRATION_TOKEN": "test_token"},
+        clear=True,
+    )
+    @patch(
+        "crewai_tools.tools.crewai_platform_tools.crewai_platform_file_upload_tool.requests.post"
+    )
+    def test_binary_file_upload(self, mock_post):
+        """Test uploading a binary file (e.g. PNG)."""
+        mock_response = Mock()
+        mock_response.ok = True
+        mock_response.json.return_value = {"id": "file123"}
+        mock_post.return_value = mock_response
+
+        binary_content = bytes(range(256))
+
+        with tempfile.NamedTemporaryFile(
+            suffix=".png", delete=False
+        ) as f:
+            f.write(binary_content)
+            temp_path = f.name
+
+        try:
+            self.tool._run(file_path=temp_path)
+
+            payload = mock_post.call_args.kwargs["json"]["integration"]
+            assert payload["mime_type"] == "image/png"
+            decoded = base64.b64decode(payload["content"])
+            assert decoded == binary_content
+        finally:
+            os.unlink(temp_path)

lib/crewai/pyproject.toml

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

lib/crewai/src/crewai/__init__.py

@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.13.0a7"
+__version__ = "1.13.0"
 _telemetry_submitted = False
 
 

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

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

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

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

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

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

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a7"
+    "crewai[tools]==1.13.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a7"
+    "crewai[tools]==1.13.0"
 ]
 
 [project.scripts]

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

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.13.0a7"
+    "crewai[tools]==1.13.0"
 ]
 
 [tool.crewai]

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

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

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

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

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

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

lib/crewai/tests/agents/test_agent_executor.py

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

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

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

lib/devtools/pyproject.toml

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

lib/devtools/src/crewai_devtools/__init__.py

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

pyproject.toml

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

uv.lock

@@ -2,14 +2,22 @@ version = 1
 revision = 3
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
-    "python_full_version < '3.11' and platform_python_implementation == 'PyPy'",
-    "python_full_version < '3.11' and platform_python_implementation != 'PyPy'",
-    "python_full_version == '3.11.*' and platform_python_implementation == 'PyPy'",
-    "python_full_version == '3.11.*' and platform_python_implementation != 'PyPy'",
-    "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy'",
-    "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy'",
-    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'",
-    "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'",
+    "python_full_version < '3.11' and platform_machine != 's390x' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.11' and platform_machine == 's390x' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.11' and platform_machine != 's390x' and platform_python_implementation != 'PyPy'",
+    "python_full_version < '3.11' and platform_machine == 's390x' and platform_python_implementation != 'PyPy'",
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