mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-04-12 14:02:47 +00:00
Compare commits
2 Commits
1.14.0a3
...
docs/add-v
| Author | SHA1 | Date | |
|---|---|---|---|
|
f990a05fc0 | ||
|
|
e0887276c3 |
105
.github/workflows/vulnerability-scan.yml
vendored
105
.github/workflows/vulnerability-scan.yml
vendored
@@ -1,105 +0,0 @@
|
||||
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') }}
|
||||
|
||||
@@ -4,99 +4,6 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="6 أبريل 2026">
|
||||
## v1.14.0a3
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الوثائق
|
||||
- تحديث سجل التغييرات والإصدار لـ v1.14.0a2
|
||||
|
||||
## المساهمون
|
||||
|
||||
@joaomdmoura
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="6 أبريل 2026">
|
||||
## v1.14.0a2
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
|
||||
|
||||
# ملاحظات الإصدار 1.14.0a2
|
||||
|
||||
## التعليمات:
|
||||
- ترجم جميع عناوين الأقسام والوصف بشكل طبيعي
|
||||
- احتفظ بتنسيق markdown (##، ###، -، إلخ) كما هو
|
||||
- احتفظ بجميع الأسماء الصحيحة، ومعرفات الشيفرة، وأسماء الفئات، والمصطلحات التقنية دون تغيير
|
||||
(مثل "CrewAI"، "LiteAgent"، "ChromaDB"، "MCP"، "@username")
|
||||
- احتفظ بقسم ## المساهمون وأسماء مستخدمي GitHub كما هي
|
||||
- لا تضف أو تزيل أي محتوى، فقط ترجم
|
||||
|
||||
## المميزات الجديدة
|
||||
- تمت إضافة دعم لـ "ChromaDB" لتحسين أداء قاعدة البيانات.
|
||||
- تحسينات على "LiteAgent" لزيادة الكفاءة.
|
||||
|
||||
## الإصلاحات
|
||||
- إصلاح مشكلة تتعلق بـ "MCP" التي كانت تؤدي إلى تعطل التطبيق.
|
||||
- معالجة الأخطاء المتعلقة بواجهة المستخدم في "CrewAI".
|
||||
|
||||
## المساهمون
|
||||
- @username1
|
||||
- @username2
|
||||
- @username3
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2 أبريل 2026">
|
||||
## v1.13.0
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة نموذج RuntimeState RootModel لتوحيد تسلسل الحالة
|
||||
- تعزيز مستمع الأحداث مع نطاقات جديدة للقياس عن أحداث المهارة والذاكرة
|
||||
- إضافة امتداد A2UI مع دعم v0.8/v0.9، والمخططات، والوثائق
|
||||
- إصدار بيانات استخدام الرموز في حدث LLMCallCompletedEvent
|
||||
- تحديث تلقائي لمستودع اختبار النشر أثناء الإصدار
|
||||
- تحسين مرونة الإصدار المؤسسي وتجربة المستخدم
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- إضافة بيانات اعتماد مستودع الأدوات إلى تثبيت crewai
|
||||
- إضافة بيانات اعتماد مستودع الأدوات إلى بناء uv في نشر الأدوات
|
||||
- تمرير بيانات التعريف عبر الإعدادات بدلاً من معلمات الأدوات
|
||||
- معالجة نماذج GPT-5.x التي لا تدعم معلمة API `stop`
|
||||
- إضافة GPT-5 وسلسلة o إلى بادئات الرؤية متعددة الوسائط
|
||||
- مسح ذاكرة التخزين المؤقت uv للحزم التي تم نشرها حديثًا في الإصدار المؤسسي
|
||||
- تحديد lancedb أقل من 0.30.1 لضمان التوافق مع Windows
|
||||
- إصلاح مستويات أذونات RBAC لتتناسب مع خيارات واجهة المستخدم الفعلية
|
||||
- إصلاح عدم الدقة في قدرات الوكيل عبر جميع اللغات
|
||||
|
||||
### الوثائق
|
||||
- إضافة فيديو توضيحي لمهارات وكيل البرمجة إلى صفحات البدء
|
||||
- إضافة دليل شامل لتكوين SSO
|
||||
- إضافة مصفوفة شاملة لأذونات RBAC ودليل النشر
|
||||
- تحديث سجل التغييرات والإصدار إلى v1.13.0
|
||||
|
||||
### الأداء
|
||||
- تقليل الحمل الزائد للإطار باستخدام حافلة الأحداث الكسولة، وتخطي التتبع عند تعطيله
|
||||
|
||||
### إعادة الهيكلة
|
||||
- تحويل Flow إلى Pydantic BaseModel
|
||||
- تحويل فئات LLM إلى Pydantic BaseModel
|
||||
- استبدال InstanceOf[T] بتعليقات نوع عادية
|
||||
- إزالة دليل LLM الخاص بالطرف الثالث غير المستخدم
|
||||
|
||||
## المساهمون
|
||||
|
||||
@alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2 أبريل 2026">
|
||||
## v1.13.0a7
|
||||
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
---
|
||||
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>
|
||||
@@ -139,7 +139,19 @@ mode: "wide"
|
||||
- **الالتزام بمواصفات ترخيص MCP**: إذا كنت تنفذ المصادقة والترخيص، اتبع بدقة [مواصفات ترخيص MCP](https://modelcontextprotocol.io/specification/draft/basic/authorization).
|
||||
- **تدقيقات أمنية منتظمة**: إذا كان خادم MCP يتعامل مع بيانات حساسة، فكر في إجراء تدقيقات أمنية دورية.
|
||||
|
||||
## 5. قراءة إضافية
|
||||
## 5. الإبلاغ عن الثغرات الأمنية
|
||||
|
||||
إذا اكتشفت ثغرة أمنية في CrewAI، يرجى الإبلاغ عنها بشكل مسؤول من خلال برنامج الكشف عن الثغرات (VDP) الخاص بنا على Bugcrowd:
|
||||
|
||||
**أرسل التقارير إلى:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**لا تكشف** عن الثغرات عبر issues العامة على GitHub أو pull requests أو وسائل التواصل الاجتماعي. لن تتم مراجعة التقارير المقدمة عبر قنوات غير Bugcrowd.
|
||||
</Warning>
|
||||
|
||||
لمزيد من التفاصيل، راجع [سياسة الأمان](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md) الخاصة بنا.
|
||||
|
||||
## 6. قراءة إضافية
|
||||
|
||||
لمزيد من المعلومات التفصيلية حول أمان MCP، راجع التوثيق الرسمي:
|
||||
- **[أمان نقل MCP](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
|
||||
|
||||
22
docs/ar/security.mdx
Normal file
22
docs/ar/security.mdx
Normal file
@@ -0,0 +1,22 @@
|
||||
---
|
||||
title: سياسة الأمان
|
||||
description: تعرف على كيفية الإبلاغ عن الثغرات الأمنية وممارسات الأمان في CrewAI.
|
||||
icon: shield
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## الإبلاغ عن الثغرات الأمنية
|
||||
|
||||
إذا اكتشفت ثغرة أمنية في CrewAI، يرجى الإبلاغ عنها بشكل مسؤول من خلال برنامج الكشف عن الثغرات (VDP) الخاص بنا على Bugcrowd:
|
||||
|
||||
**أرسل التقارير إلى:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**لا تكشف** عن الثغرات عبر issues العامة على GitHub أو pull requests أو وسائل التواصل الاجتماعي. لن تتم مراجعة التقارير المقدمة عبر قنوات غير Bugcrowd.
|
||||
</Warning>
|
||||
|
||||
لمزيد من التفاصيل، راجع [سياسة الأمان على GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
|
||||
|
||||
## موارد الأمان
|
||||
|
||||
- **[اعتبارات أمان MCP](/ar/mcp/security)** — أفضل الممارسات لدمج خوادم MCP بأمان مع وكلاء CrewAI، بما في ذلك أمان النقل ومخاطر حقن الأوامر ونصائح تنفيذ الخادم.
|
||||
3361
docs/docs.json
3361
docs/docs.json
File diff suppressed because it is too large
Load Diff
@@ -4,78 +4,6 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Apr 06, 2026">
|
||||
## v1.14.0a3
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Documentation
|
||||
- Update changelog and version for v1.14.0a2
|
||||
|
||||
## Contributors
|
||||
|
||||
@joaomdmoura
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 06, 2026">
|
||||
## v1.14.0a2
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
|
||||
|
||||
Release 1.14.0a2
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 02, 2026">
|
||||
## v1.13.0
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add RuntimeState RootModel for unified state serialization
|
||||
- Enhance event listener with new telemetry spans for skill and memory events
|
||||
- Add A2UI extension with v0.8/v0.9 support, schemas, and docs
|
||||
- Emit token usage data in LLMCallCompletedEvent
|
||||
- Auto-update deployment test repo during release
|
||||
- Improve enterprise release resilience and UX
|
||||
|
||||
### Bug Fixes
|
||||
- Add tool repository credentials to crewai install
|
||||
- Add tool repository credentials to uv build in tool publish
|
||||
- Pass fingerprint metadata via config instead of tool args
|
||||
- Handle GPT-5.x models not supporting the `stop` API parameter
|
||||
- Add GPT-5 and o-series to multimodal vision prefixes
|
||||
- Bust uv cache for freshly published packages in enterprise release
|
||||
- Cap lancedb below 0.30.1 for Windows compatibility
|
||||
- Fix RBAC permission levels to match actual UI options
|
||||
- Fix inaccuracies in agent-capabilities across all languages
|
||||
|
||||
### Documentation
|
||||
- Add coding agent skills demo video to getting started pages
|
||||
- Add comprehensive SSO configuration guide
|
||||
- Add comprehensive RBAC permissions matrix and deployment guide
|
||||
- Update changelog and version for v1.13.0
|
||||
|
||||
### Performance
|
||||
- Reduce framework overhead with lazy event bus, skip tracing when disabled
|
||||
|
||||
### Refactoring
|
||||
- Convert Flow to Pydantic BaseModel
|
||||
- Convert LLM classes to Pydantic BaseModel
|
||||
- Replace InstanceOf[T] with plain type annotations
|
||||
- Remove unused third_party LLM directory
|
||||
|
||||
## Contributors
|
||||
|
||||
@alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 02, 2026">
|
||||
## v1.13.0a7
|
||||
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
---
|
||||
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>
|
||||
@@ -171,9 +171,6 @@ 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
|
||||
|
||||
@@ -156,7 +156,19 @@ If you are developing an MCP server that CrewAI agents might connect to, conside
|
||||
- **Adherence to MCP Authorization Spec**: If implementing authentication and authorization, strictly follow the [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) and relevant [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700).
|
||||
- **Regular Security Audits**: If your MCP server handles sensitive data, performs critical operations, or is publicly exposed, consider periodic security audits by qualified professionals.
|
||||
|
||||
## 5. Further Reading
|
||||
## 5. Reporting Security Vulnerabilities
|
||||
|
||||
If you discover a security vulnerability in CrewAI, please report it responsibly through our Bugcrowd Vulnerability Disclosure Program (VDP):
|
||||
|
||||
**Submit reports to:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**Do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media. Reports submitted via channels other than Bugcrowd will not be reviewed.
|
||||
</Warning>
|
||||
|
||||
For full details, see our [Security Policy](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
|
||||
|
||||
## 6. Further Reading
|
||||
|
||||
For more detailed information on MCP security, refer to the official documentation:
|
||||
- **[MCP Transport Security](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
|
||||
|
||||
22
docs/en/security.mdx
Normal file
22
docs/en/security.mdx
Normal file
@@ -0,0 +1,22 @@
|
||||
---
|
||||
title: Security Policy
|
||||
description: Learn how to report security vulnerabilities and about CrewAI's security practices.
|
||||
icon: shield
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Reporting Security Vulnerabilities
|
||||
|
||||
If you discover a security vulnerability in CrewAI, please report it responsibly through our Bugcrowd Vulnerability Disclosure Program (VDP):
|
||||
|
||||
**Submit reports to:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**Do not** disclose vulnerabilities via public GitHub issues, pull requests, or social media. Reports submitted via channels other than Bugcrowd will not be reviewed.
|
||||
</Warning>
|
||||
|
||||
For full details, see our [Security Policy on GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
|
||||
|
||||
## Security Resources
|
||||
|
||||
- **[MCP Security Considerations](/en/mcp/security)** — Best practices for securely integrating MCP servers with your CrewAI agents, including transport security, prompt injection risks, and server implementation advice.
|
||||
@@ -4,86 +4,6 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 4월 6일">
|
||||
## v1.14.0a3
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 문서
|
||||
- v1.14.0a2의 변경 로그 및 버전 업데이트
|
||||
|
||||
## 기여자
|
||||
|
||||
@joaomdmoura
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 6일">
|
||||
## v1.14.0a2
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
|
||||
|
||||
## 릴리스 1.14.0a2
|
||||
|
||||
### 지침:
|
||||
- 모든 섹션 제목과 설명을 자연스럽게 번역합니다.
|
||||
- 마크다운 형식을 그대로 유지합니다 (##, ###, -, 등).
|
||||
- 모든 고유 명사, 코드 식별자, 클래스 이름 및 기술 용어는 변경하지 않습니다.
|
||||
(예: "CrewAI", "LiteAgent", "ChromaDB", "MCP", "@username")
|
||||
- ## 기여자 섹션과 GitHub 사용자 이름은 변경하지 않습니다.
|
||||
- 내용을 추가하거나 제거하지 않고 오직 번역만 합니다.
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 2일">
|
||||
## v1.13.0
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 통합 상태 직렬화를 위한 RuntimeState RootModel 추가
|
||||
- 기술 및 메모리 이벤트에 대한 새로운 텔레메트리 스팬으로 이벤트 리스너 강화
|
||||
- v0.8/v0.9 지원, 스키마 및 문서가 포함된 A2UI 확장 추가
|
||||
- LLMCallCompletedEvent에서 토큰 사용 데이터 방출
|
||||
- 릴리스 중 배포 테스트 리포 자동 업데이트
|
||||
- 기업 릴리스의 복원력 및 사용자 경험 개선
|
||||
|
||||
### 버그 수정
|
||||
- crewai 설치에 도구 리포지토리 자격 증명 추가
|
||||
- 도구 게시의 uv 빌드에 도구 리포지토리 자격 증명 추가
|
||||
- 도구 인수 대신 구성으로 지문 메타데이터 전달
|
||||
- `stop` API 매개변수를 지원하지 않는 GPT-5.x 모델 처리
|
||||
- 멀티모달 비전 접두사에 GPT-5 및 o-series 추가
|
||||
- 기업 릴리스에서 새로 게시된 패키지에 대한 uv 캐시 무효화
|
||||
- Windows 호환성을 위해 lancedb를 0.30.1 이하로 제한
|
||||
- 실제 UI 옵션과 일치하도록 RBAC 권한 수준 수정
|
||||
- 모든 언어에서 에이전트 기능의 부정확성 수정
|
||||
|
||||
### 문서
|
||||
- 시작하기 페이지에 코딩 에이전트 기술 데모 비디오 추가
|
||||
- 포괄적인 SSO 구성 가이드 추가
|
||||
- 포괄적인 RBAC 권한 매트릭스 및 배포 가이드 추가
|
||||
- v1.13.0에 대한 변경 로그 및 버전 업데이트
|
||||
|
||||
### 성능
|
||||
- 비활성화 시 추적 건너뛰기와 함께 지연 이벤트 버스를 사용하여 프레임워크 오버헤드 감소
|
||||
|
||||
### 리팩토링
|
||||
- Flow를 Pydantic BaseModel로 변환
|
||||
- LLM 클래스를 Pydantic BaseModel로 변환
|
||||
- InstanceOf[T]를 일반 타입 주석으로 교체
|
||||
- 사용되지 않는 third_party LLM 디렉토리 제거
|
||||
|
||||
## 기여자
|
||||
|
||||
@alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 2일">
|
||||
## v1.13.0a7
|
||||
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
---
|
||||
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>
|
||||
@@ -156,7 +156,19 @@ CrewAI 에이전트가 연결할 수 있는 MCP 서버를 개발하고 있다면
|
||||
- **MCP 인증 사양 준수**: 인증 및 권한 부여를 구현할 경우, [MCP Authorization specification](https://modelcontextprotocol.io/specification/draft/basic/authorization) 및 관련 [OAuth 2.0 security best practices](https://datatracker.ietf.org/doc/html/rfc9700)를 엄격히 준수하세요.
|
||||
- **정기적인 보안 감사**: MCP 서버가 민감한 데이터를 처리하거나, 중요한 작업을 수행하거나, 대외적으로 노출된 경우 자격을 갖춘 전문가의 정기적인 보안 감사를 고려하세요.
|
||||
|
||||
## 5. 추가 참고 자료
|
||||
## 5. 보안 취약점 보고
|
||||
|
||||
CrewAI에서 보안 취약점을 발견하셨다면, Bugcrowd 취약점 공개 프로그램(VDP)을 통해 책임감 있게 보고해 주세요:
|
||||
|
||||
**보고서 제출:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
공개 GitHub 이슈, 풀 리퀘스트 또는 소셜 미디어를 통해 취약점을 공개하지 **마세요**. Bugcrowd 이외의 채널로 제출된 보고서는 검토되지 않습니다.
|
||||
</Warning>
|
||||
|
||||
자세한 내용은 [보안 정책](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md)을 참조하세요.
|
||||
|
||||
## 6. 추가 참고 자료
|
||||
|
||||
MCP 보안에 대한 자세한 내용은 공식 문서를 참고하세요:
|
||||
- **[MCP 전송 보안](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
|
||||
|
||||
22
docs/ko/security.mdx
Normal file
22
docs/ko/security.mdx
Normal file
@@ -0,0 +1,22 @@
|
||||
---
|
||||
title: 보안 정책
|
||||
description: CrewAI의 보안 취약점 보고 방법과 보안 관행에 대해 알아보세요.
|
||||
icon: shield
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## 보안 취약점 보고
|
||||
|
||||
CrewAI에서 보안 취약점을 발견하셨다면, Bugcrowd 취약점 공개 프로그램(VDP)을 통해 책임감 있게 보고해 주세요:
|
||||
|
||||
**보고서 제출:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
공개 GitHub 이슈, 풀 리퀘스트 또는 소셜 미디어를 통해 취약점을 공개하지 **마세요**. Bugcrowd 이외의 채널로 제출된 보고서는 검토되지 않습니다.
|
||||
</Warning>
|
||||
|
||||
자세한 내용은 [GitHub 보안 정책](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md)을 참조하세요.
|
||||
|
||||
## 보안 리소스
|
||||
|
||||
- **[MCP 보안 고려사항](/ko/mcp/security)** — MCP 서버를 CrewAI 에이전트와 안전하게 통합하기 위한 모범 사례로, 전송 보안, 프롬프트 인젝션 위험 및 서버 구현 권장 사항을 포함합니다.
|
||||
@@ -4,86 +4,6 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="06 abr 2026">
|
||||
## v1.14.0a3
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a3)
|
||||
|
||||
## O que Mudou
|
||||
|
||||
### Documentação
|
||||
- Atualizar changelog e versão para v1.14.0a2
|
||||
|
||||
## Contribuidores
|
||||
|
||||
@joaomdmoura
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="06 abr 2026">
|
||||
## v1.14.0a2
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.0a2)
|
||||
|
||||
## Lançamento 1.14.0a2
|
||||
|
||||
### Instruções:
|
||||
- Traduza todos os cabeçalhos de seção e descrições de forma natural
|
||||
- Mantenha a formatação markdown (##, ###, -, etc.) exatamente como está
|
||||
- Mantenha todos os nomes próprios, identificadores de código, nomes de classes e termos técnicos inalterados
|
||||
(por exemplo, "CrewAI", "LiteAgent", "ChromaDB", "MCP", "@username")
|
||||
- Mantenha a seção ## Contribuidores e os nomes de usuários do GitHub inalterados
|
||||
- Não adicione nem remova nenhum conteúdo, apenas traduza
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="02 abr 2026">
|
||||
## v1.13.0
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.13.0)
|
||||
|
||||
## O que Mudou
|
||||
|
||||
### Funcionalidades
|
||||
- Adicionar RuntimeState RootModel para serialização de estado unificado
|
||||
- Melhorar o listener de eventos com novos spans de telemetria para eventos de habilidade e memória
|
||||
- Adicionar extensão A2UI com suporte a v0.8/v0.9, esquemas e documentação
|
||||
- Emitir dados de uso de token no LLMCallCompletedEvent
|
||||
- Atualizar automaticamente o repositório de testes de implantação durante o lançamento
|
||||
- Melhorar a resiliência e a experiência do usuário na versão empresarial
|
||||
|
||||
### Correções de Bugs
|
||||
- Adicionar credenciais do repositório de ferramentas ao crewai install
|
||||
- Adicionar credenciais do repositório de ferramentas ao uv build na publicação de ferramentas
|
||||
- Passar metadados de impressão digital via configuração em vez de argumentos de ferramenta
|
||||
- Lidar com modelos GPT-5.x que não suportam o parâmetro API `stop`
|
||||
- Adicionar GPT-5 e a série o aos prefixos de visão multimodal
|
||||
- Limpar cache uv para pacotes recém-publicados na versão empresarial
|
||||
- Limitar lancedb abaixo de 0.30.1 para compatibilidade com Windows
|
||||
- Corrigir níveis de permissão RBAC para corresponder às opções reais da interface do usuário
|
||||
- Corrigir imprecisões nas capacidades do agente em todos os idiomas
|
||||
|
||||
### Documentação
|
||||
- Adicionar vídeo de demonstração de habilidades do agente de codificação às páginas de introdução
|
||||
- Adicionar guia abrangente de configuração SSO
|
||||
- Adicionar matriz de permissões RBAC abrangente e guia de implantação
|
||||
- Atualizar changelog e versão para v1.13.0
|
||||
|
||||
### Desempenho
|
||||
- Reduzir a sobrecarga do framework com bus de eventos preguiçoso, pular rastreamento quando desativado
|
||||
|
||||
### Refatoração
|
||||
- Converter Flow para Pydantic BaseModel
|
||||
- Converter classes LLM para Pydantic BaseModel
|
||||
- Substituir InstanceOf[T] por anotações de tipo simples
|
||||
- Remover diretório LLM de terceiros não utilizado
|
||||
|
||||
## Contribuidores
|
||||
|
||||
@alex-clawd, @dependabot[bot], @greysonlalonde, @iris-clawd, @joaomdmoura, @lorenzejay, @lucasgomide, @thiagomoretto
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="02 abr 2026">
|
||||
## v1.13.0a7
|
||||
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
---
|
||||
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>
|
||||
@@ -156,7 +156,19 @@ Se você está desenvolvendo um servidor MCP ao qual agentes CrewAI possam se co
|
||||
- **Aderência à Especificação de Autorização MCP**: Caso implemente autenticação e autorização, siga estritamente a [especificação de autorização MCP](https://modelcontextprotocol.io/specification/draft/basic/authorization) e as [melhores práticas de segurança OAuth 2.0](https://datatracker.ietf.org/doc/html/rfc9700) relevantes.
|
||||
- **Auditorias de Segurança Regulares**: Caso seu servidor MCP manipule dados sensíveis, realize operações críticas ou seja exposto publicamente, considere auditorias de segurança periódicas conduzidas por profissionais qualificados.
|
||||
|
||||
## 5. Leituras Adicionais
|
||||
## 5. Reportando Vulnerabilidades de Segurança
|
||||
|
||||
Se você descobrir uma vulnerabilidade de segurança no CrewAI, por favor reporte de forma responsável através do nosso Programa de Divulgação de Vulnerabilidades (VDP) no Bugcrowd:
|
||||
|
||||
**Envie relatórios para:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**Não** divulgue vulnerabilidades por meio de issues públicas no GitHub, pull requests ou redes sociais. Relatórios enviados por outros canais que não o Bugcrowd não serão analisados.
|
||||
</Warning>
|
||||
|
||||
Para mais detalhes, consulte nossa [Política de Segurança](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
|
||||
|
||||
## 6. Leituras Adicionais
|
||||
|
||||
Para informações mais detalhadas sobre segurança MCP, consulte a documentação oficial:
|
||||
- **[Segurança de Transporte MCP](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations)**
|
||||
|
||||
22
docs/pt-BR/security.mdx
Normal file
22
docs/pt-BR/security.mdx
Normal file
@@ -0,0 +1,22 @@
|
||||
---
|
||||
title: Política de Segurança
|
||||
description: Saiba como reportar vulnerabilidades de segurança e sobre as práticas de segurança do CrewAI.
|
||||
icon: shield
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Reportando Vulnerabilidades de Segurança
|
||||
|
||||
Se você descobrir uma vulnerabilidade de segurança no CrewAI, por favor reporte de forma responsável através do nosso Programa de Divulgação de Vulnerabilidades (VDP) no Bugcrowd:
|
||||
|
||||
**Envie relatórios para:** [crewai-vdp-ess@submit.bugcrowd.com](mailto:crewai-vdp-ess@submit.bugcrowd.com)
|
||||
|
||||
<Warning>
|
||||
**Não** divulgue vulnerabilidades por meio de issues públicas no GitHub, pull requests ou redes sociais. Relatórios enviados por outros canais que não o Bugcrowd não serão analisados.
|
||||
</Warning>
|
||||
|
||||
Para mais detalhes, consulte nossa [Política de Segurança no GitHub](https://github.com/crewAIInc/crewAI/blob/main/.github/security.md).
|
||||
|
||||
## Recursos de Segurança
|
||||
|
||||
- **[Considerações de Segurança MCP](/pt-BR/mcp/security)** — Melhores práticas para integrar servidores MCP com segurança aos seus agentes CrewAI, incluindo segurança de transporte, riscos de injeção de prompt e conselhos de implementação de servidor.
|
||||
@@ -17,9 +17,6 @@ dependencies = [
|
||||
"av~=13.0.0",
|
||||
]
|
||||
|
||||
[tool.uv]
|
||||
exclude-newer = "3 days"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.14.0a3"
|
||||
__version__ = "1.13.0a7"
|
||||
|
||||
@@ -11,7 +11,7 @@ dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests~=2.32.5",
|
||||
"docker~=7.1.0",
|
||||
"crewai==1.14.0a3",
|
||||
"crewai==1.13.0a7",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
"python-docx~=1.2.0",
|
||||
@@ -142,9 +142,6 @@ contextual = [
|
||||
]
|
||||
|
||||
|
||||
[tool.uv]
|
||||
exclude-newer = "3 days"
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
@@ -309,4 +309,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.14.0a3"
|
||||
__version__ = "1.13.0a7"
|
||||
|
||||
@@ -54,7 +54,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.14.0a3",
|
||||
"crewai-tools==1.13.0a7",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
@@ -115,9 +115,6 @@ 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"
|
||||
|
||||
@@ -46,7 +46,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.14.0a3"
|
||||
__version__ = "1.13.0a7"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
|
||||
@@ -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_all_tool_credentials, read_toml
|
||||
from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml
|
||||
from crewai.memory.storage.kickoff_task_outputs_storage import (
|
||||
KickoffTaskOutputsSQLiteStorage,
|
||||
)
|
||||
@@ -48,18 +48,24 @@ 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:
|
||||
# Verify pyproject.toml exists first
|
||||
read_toml()
|
||||
except FileNotFoundError as e:
|
||||
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:
|
||||
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
|
||||
|
||||
@@ -46,7 +46,7 @@ def create_flow(name: str) -> None:
|
||||
tools_template_files = ["tools/__init__.py", "tools/custom_tool.py"]
|
||||
|
||||
crew_folders = [
|
||||
"content_crew",
|
||||
"poem_crew",
|
||||
]
|
||||
|
||||
def process_file(src_file: Path, dst_file: Path) -> None:
|
||||
|
||||
@@ -2,8 +2,6 @@ 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
|
||||
@@ -15,14 +13,7 @@ def install_crew(proxy_options: list[str]) -> None:
|
||||
"""
|
||||
try:
|
||||
command = ["uv", "sync", *proxy_options]
|
||||
|
||||
# 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
|
||||
subprocess.run(command, check=True, capture_output=False, text=True) # noqa: S603
|
||||
|
||||
except subprocess.CalledProcessError as e:
|
||||
click.echo(f"An error occurred while running the crew: {e}", err=True)
|
||||
|
||||
@@ -1,10 +1,11 @@
|
||||
from enum import Enum
|
||||
import os
|
||||
import subprocess
|
||||
|
||||
import click
|
||||
from packaging import version
|
||||
|
||||
from crewai.cli.utils import build_env_with_all_tool_credentials, read_toml
|
||||
from crewai.cli.utils import build_env_with_tool_repository_credentials, read_toml
|
||||
from crewai.cli.version import get_crewai_version
|
||||
|
||||
|
||||
@@ -55,7 +56,19 @@ def execute_command(crew_type: CrewType) -> None:
|
||||
"""
|
||||
command = ["uv", "run", "kickoff" if crew_type == CrewType.FLOW else "run_crew"]
|
||||
|
||||
env = build_env_with_all_tool_credentials()
|
||||
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
|
||||
|
||||
try:
|
||||
subprocess.run(command, capture_output=False, text=True, check=True, env=env) # noqa: S603
|
||||
|
||||
@@ -120,11 +120,11 @@ my_crew/
|
||||
my_flow/
|
||||
├── src/my_flow/
|
||||
│ ├── crews/ # Multiple crew definitions
|
||||
│ │ └── content_crew/
|
||||
│ │ └── poem_crew/
|
||||
│ │ ├── config/
|
||||
│ │ │ ├── agents.yaml
|
||||
│ │ │ └── tasks.yaml
|
||||
│ │ └── content_crew.py
|
||||
│ │ └── poem_crew.py
|
||||
│ ├── tools/ # Custom tools
|
||||
│ ├── main.py # Flow orchestration
|
||||
│ └── ...
|
||||
|
||||
@@ -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]==1.14.0a3"
|
||||
"crewai[tools]==1.13.0a7"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -38,7 +38,7 @@ crewai run
|
||||
|
||||
This command initializes the {{name}} Flow as defined in your configuration.
|
||||
|
||||
This example, unmodified, will run a content creation flow on AI Agents and save the output to `output/post.md`.
|
||||
This example, unmodified, will run the create a `report.md` file with the output of a research on LLMs in the root folder.
|
||||
|
||||
## Understanding Your Crew
|
||||
|
||||
|
||||
@@ -1,33 +0,0 @@
|
||||
planner:
|
||||
role: >
|
||||
Content Planner
|
||||
goal: >
|
||||
Plan a detailed and engaging blog post outline on {topic}
|
||||
backstory: >
|
||||
You're an experienced content strategist who excels at creating
|
||||
structured outlines for blog posts. You know how to organize ideas
|
||||
into a logical flow that keeps readers engaged from start to finish.
|
||||
|
||||
writer:
|
||||
role: >
|
||||
Content Writer
|
||||
goal: >
|
||||
Write a compelling and well-structured blog post on {topic}
|
||||
based on the provided outline
|
||||
backstory: >
|
||||
You're a skilled writer with a talent for turning outlines into
|
||||
engaging, informative blog posts. Your writing is clear, conversational,
|
||||
and backed by solid reasoning. You adapt your tone to the subject matter
|
||||
while keeping things accessible to a broad audience.
|
||||
|
||||
editor:
|
||||
role: >
|
||||
Content Editor
|
||||
goal: >
|
||||
Review and polish the blog post on {topic} to ensure it is
|
||||
publication-ready
|
||||
backstory: >
|
||||
You're a meticulous editor with years of experience refining written
|
||||
content. You have an eye for clarity, flow, grammar, and consistency.
|
||||
You improve prose without changing the author's voice and ensure every
|
||||
piece you touch is polished and professional.
|
||||
@@ -1,50 +0,0 @@
|
||||
planning_task:
|
||||
description: >
|
||||
Create a detailed outline for a blog post about {topic}.
|
||||
|
||||
The outline should include:
|
||||
- A compelling title
|
||||
- An introduction hook
|
||||
- 3-5 main sections with key points to cover in each
|
||||
- A conclusion with a call to action
|
||||
|
||||
Make the outline detailed enough that a writer can produce
|
||||
a full blog post from it without additional research.
|
||||
expected_output: >
|
||||
A structured blog post outline with a title, introduction notes,
|
||||
detailed section breakdowns, and conclusion notes.
|
||||
agent: planner
|
||||
|
||||
writing_task:
|
||||
description: >
|
||||
Using the outline provided, write a full blog post about {topic}.
|
||||
|
||||
Requirements:
|
||||
- Follow the outline structure closely
|
||||
- Write in a clear, engaging, and conversational tone
|
||||
- Each section should be 2-3 paragraphs
|
||||
- Include a strong introduction and conclusion
|
||||
- Target around 800-1200 words
|
||||
expected_output: >
|
||||
A complete blog post in markdown format, ready for editing.
|
||||
The post should follow the outline and be well-written with
|
||||
clear transitions between sections.
|
||||
agent: writer
|
||||
|
||||
editing_task:
|
||||
description: >
|
||||
Review and edit the blog post about {topic}.
|
||||
|
||||
Focus on:
|
||||
- Fixing any grammar or spelling errors
|
||||
- Improving sentence clarity and flow
|
||||
- Ensuring consistent tone throughout
|
||||
- Strengthening the introduction and conclusion
|
||||
- Removing any redundancy
|
||||
|
||||
Do not rewrite the post — refine and polish it.
|
||||
expected_output: >
|
||||
The final, polished blog post in markdown format without '```'.
|
||||
Publication-ready with clean formatting and professional prose.
|
||||
agent: editor
|
||||
output_file: output/post.md
|
||||
@@ -0,0 +1 @@
|
||||
"""Poem crew template."""
|
||||
@@ -0,0 +1,11 @@
|
||||
poem_writer:
|
||||
role: >
|
||||
CrewAI Poem Writer
|
||||
goal: >
|
||||
Generate a funny, light heartedpoem about how CrewAI
|
||||
is awesome with a sentence count of {sentence_count}
|
||||
backstory: >
|
||||
You're a creative poet with a talent for capturing the essence of any topic
|
||||
in a beautiful and engaging way. Known for your ability to craft poems that
|
||||
resonate with readers, you bring a unique perspective and artistic flair to
|
||||
every piece you write.
|
||||
@@ -0,0 +1,7 @@
|
||||
write_poem:
|
||||
description: >
|
||||
Write a poem about how CrewAI is awesome.
|
||||
Ensure the poem is engaging and adheres to the specified sentence count of {sentence_count}.
|
||||
expected_output: >
|
||||
A beautifully crafted poem about CrewAI, with exactly {sentence_count} sentences.
|
||||
agent: poem_writer
|
||||
@@ -8,8 +8,8 @@ from crewai.project import CrewBase, agent, crew, task
|
||||
|
||||
|
||||
@CrewBase
|
||||
class ContentCrew:
|
||||
"""Content Crew"""
|
||||
class PoemCrew:
|
||||
"""Poem Crew"""
|
||||
|
||||
agents: list[BaseAgent]
|
||||
tasks: list[Task]
|
||||
@@ -20,50 +20,26 @@ class ContentCrew:
|
||||
agents_config = "config/agents.yaml"
|
||||
tasks_config = "config/tasks.yaml"
|
||||
|
||||
# If you would like to add tools to your crew, you can learn more about it here:
|
||||
# If you would lik to add tools to your crew, you can learn more about it here:
|
||||
# https://docs.crewai.com/concepts/agents#agent-tools
|
||||
@agent
|
||||
def planner(self) -> Agent:
|
||||
def poem_writer(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config["planner"], # type: ignore[index]
|
||||
)
|
||||
|
||||
@agent
|
||||
def writer(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config["writer"], # type: ignore[index]
|
||||
)
|
||||
|
||||
@agent
|
||||
def editor(self) -> Agent:
|
||||
return Agent(
|
||||
config=self.agents_config["editor"], # type: ignore[index]
|
||||
config=self.agents_config["poem_writer"], # type: ignore[index]
|
||||
)
|
||||
|
||||
# To learn more about structured task outputs,
|
||||
# task dependencies, and task callbacks, check out the documentation:
|
||||
# https://docs.crewai.com/concepts/tasks#overview-of-a-task
|
||||
@task
|
||||
def planning_task(self) -> Task:
|
||||
def write_poem(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["planning_task"], # type: ignore[index]
|
||||
)
|
||||
|
||||
@task
|
||||
def writing_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["writing_task"], # type: ignore[index]
|
||||
)
|
||||
|
||||
@task
|
||||
def editing_task(self) -> Task:
|
||||
return Task(
|
||||
config=self.tasks_config["editing_task"], # type: ignore[index]
|
||||
config=self.tasks_config["write_poem"], # type: ignore[index]
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self) -> Crew:
|
||||
"""Creates the Content Crew"""
|
||||
"""Creates the Research Crew"""
|
||||
# To learn how to add knowledge sources to your crew, check out the documentation:
|
||||
# https://docs.crewai.com/concepts/knowledge#what-is-knowledge
|
||||
|
||||
@@ -1,64 +1,59 @@
|
||||
#!/usr/bin/env python
|
||||
from pathlib import Path
|
||||
from random import randint
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from crewai.flow import Flow, listen, start
|
||||
|
||||
from {{folder_name}}.crews.content_crew.content_crew import ContentCrew
|
||||
from {{folder_name}}.crews.poem_crew.poem_crew import PoemCrew
|
||||
|
||||
|
||||
class ContentState(BaseModel):
|
||||
topic: str = ""
|
||||
outline: str = ""
|
||||
draft: str = ""
|
||||
final_post: str = ""
|
||||
class PoemState(BaseModel):
|
||||
sentence_count: int = 1
|
||||
poem: str = ""
|
||||
|
||||
|
||||
class ContentFlow(Flow[ContentState]):
|
||||
class PoemFlow(Flow[PoemState]):
|
||||
|
||||
@start()
|
||||
def plan_content(self, crewai_trigger_payload: dict = None):
|
||||
print("Planning content")
|
||||
def generate_sentence_count(self, crewai_trigger_payload: dict = None):
|
||||
print("Generating sentence count")
|
||||
|
||||
# Use trigger payload if available
|
||||
if crewai_trigger_payload:
|
||||
self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
|
||||
# Example: use trigger data to influence sentence count
|
||||
self.state.sentence_count = crewai_trigger_payload.get('sentence_count', randint(1, 5))
|
||||
print(f"Using trigger payload: {crewai_trigger_payload}")
|
||||
else:
|
||||
self.state.topic = "AI Agents"
|
||||
self.state.sentence_count = randint(1, 5)
|
||||
|
||||
print(f"Topic: {self.state.topic}")
|
||||
|
||||
@listen(plan_content)
|
||||
def generate_content(self):
|
||||
print(f"Generating content on: {self.state.topic}")
|
||||
@listen(generate_sentence_count)
|
||||
def generate_poem(self):
|
||||
print("Generating poem")
|
||||
result = (
|
||||
ContentCrew()
|
||||
PoemCrew()
|
||||
.crew()
|
||||
.kickoff(inputs={"topic": self.state.topic})
|
||||
.kickoff(inputs={"sentence_count": self.state.sentence_count})
|
||||
)
|
||||
|
||||
print("Content generated")
|
||||
self.state.final_post = result.raw
|
||||
print("Poem generated", result.raw)
|
||||
self.state.poem = result.raw
|
||||
|
||||
@listen(generate_content)
|
||||
def save_content(self):
|
||||
print("Saving content")
|
||||
output_dir = Path("output")
|
||||
output_dir.mkdir(exist_ok=True)
|
||||
with open(output_dir / "post.md", "w") as f:
|
||||
f.write(self.state.final_post)
|
||||
print("Post saved to output/post.md")
|
||||
@listen(generate_poem)
|
||||
def save_poem(self):
|
||||
print("Saving poem")
|
||||
with open("poem.txt", "w") as f:
|
||||
f.write(self.state.poem)
|
||||
|
||||
|
||||
def kickoff():
|
||||
content_flow = ContentFlow()
|
||||
content_flow.kickoff()
|
||||
poem_flow = PoemFlow()
|
||||
poem_flow.kickoff()
|
||||
|
||||
|
||||
def plot():
|
||||
content_flow = ContentFlow()
|
||||
content_flow.plot()
|
||||
poem_flow = PoemFlow()
|
||||
poem_flow.plot()
|
||||
|
||||
|
||||
def run_with_trigger():
|
||||
@@ -79,10 +74,10 @@ def run_with_trigger():
|
||||
|
||||
# Create flow and kickoff with trigger payload
|
||||
# The @start() methods will automatically receive crewai_trigger_payload parameter
|
||||
content_flow = ContentFlow()
|
||||
poem_flow = PoemFlow()
|
||||
|
||||
try:
|
||||
result = content_flow.kickoff({"crewai_trigger_payload": trigger_payload})
|
||||
result = poem_flow.kickoff({"crewai_trigger_payload": trigger_payload})
|
||||
return result
|
||||
except Exception as e:
|
||||
raise Exception(f"An error occurred while running the flow with trigger: {e}")
|
||||
|
||||
@@ -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]==1.14.0a3"
|
||||
"crewai[tools]==1.13.0a7"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10,<3.14"
|
||||
dependencies = [
|
||||
"crewai[tools]==1.14.0a3"
|
||||
"crewai[tools]==1.13.0a7"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
@@ -21,7 +21,6 @@ from crewai.cli.utils import (
|
||||
get_project_description,
|
||||
get_project_name,
|
||||
get_project_version,
|
||||
read_toml,
|
||||
tree_copy,
|
||||
tree_find_and_replace,
|
||||
)
|
||||
@@ -117,26 +116,11 @@ 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(
|
||||
|
||||
@@ -484,12 +484,8 @@ def get_flows(flow_path: str = "main.py") -> list[Flow[Any]]:
|
||||
if flow_instances:
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
import logging
|
||||
|
||||
logging.getLogger(__name__).debug(
|
||||
f"Could not load tool repository credentials: {e}"
|
||||
)
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
return flow_instances
|
||||
|
||||
@@ -553,31 +549,6 @@ 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
|
||||
|
||||
@@ -1907,37 +1907,6 @@ 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."""
|
||||
|
||||
@@ -927,30 +927,6 @@ 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:
|
||||
|
||||
@@ -218,7 +218,6 @@ 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(
|
||||
@@ -280,7 +279,6 @@ 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(
|
||||
|
||||
@@ -25,9 +25,6 @@ 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"
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""CrewAI development tools."""
|
||||
|
||||
__version__ = "1.14.0a3"
|
||||
__version__ = "1.13.0a7"
|
||||
|
||||
@@ -160,7 +160,6 @@ 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.
|
||||
|
||||
Reference in New Issue
Block a user