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README.md
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README.md
@@ -83,6 +83,7 @@ intelligent automations.
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|
||||
## Table of contents
|
||||
|
||||
- [Build with AI](#build-with-ai)
|
||||
- [Why CrewAI?](#why-crewai)
|
||||
- [Getting Started](#getting-started)
|
||||
- [Key Features](#key-features)
|
||||
@@ -101,6 +102,32 @@ intelligent automations.
|
||||
- [Telemetry](#telemetry)
|
||||
- [License](#license)
|
||||
|
||||
## Build with AI
|
||||
|
||||
Using an AI coding agent? Teach it CrewAI best practices in one command:
|
||||
|
||||
**Claude Code:**
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
Four skills that activate automatically when you ask relevant CrewAI questions:
|
||||
|
||||
| Skill | When it runs |
|
||||
|-------|--------------|
|
||||
| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
|
||||
| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
|
||||
| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
|
||||
| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
|
||||
|
||||
**Cursor, Codex, Windsurf, and others ([skills.sh](https://skills.sh/crewaiinc/skills)):**
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
|
||||
This installs the official [CrewAI Skills](https://github.com/crewAIInc/skills) — structured instructions that teach coding agents how to scaffold Flows, configure Crews, design agents and tasks, and follow CrewAI patterns.
|
||||
|
||||
## Why CrewAI?
|
||||
|
||||
<div align="center" style="margin-bottom: 30px;">
|
||||
|
||||
@@ -4,6 +4,36 @@ description: "تحديثات المنتج والتحسينات وإصلاحات
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="22 أبريل 2026">
|
||||
## v1.14.3a2
|
||||
|
||||
[عرض الإصدار على GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
|
||||
|
||||
## ما الذي تغير
|
||||
|
||||
### الميزات
|
||||
- إضافة دعم لـ bedrock V4
|
||||
- إضافة أدوات Daytona sandbox لوظائف محسّنة
|
||||
- إضافة صفحة "البناء باستخدام الذكاء الاصطناعي" — مستندات أصلية للذكاء الاصطناعي لوكلاء البرمجة
|
||||
- إضافة "البناء باستخدام الذكاء الاصطناعي" إلى التنقل في صفحة "البدء" وملفات الصفحات لجميع اللغات (en, ko, pt-BR, ar)
|
||||
|
||||
### إصلاحات الأخطاء
|
||||
- إصلاح انتشار أسماء @CrewBase الضمنية إلى أحداث الطاقم
|
||||
- حل مشكلة تكرار تهيئة الدفعات في دمج بيانات التنفيذ الوصفية
|
||||
- إصلاح تسلسل حقول مرجع فئة Task لعمليات التحقق من النقاط
|
||||
- التعامل مع نتيجة BaseModel في حلقة إعادة المحاولة للحدود
|
||||
- تحديث python-dotenv إلى الإصدار >=1.2.2 للامتثال الأمني
|
||||
|
||||
### الوثائق
|
||||
- تحديث سجل التغييرات والإصدار لـ v1.14.3a1
|
||||
- تحديث الأوصاف وتطبيق الترجمات الفعلية
|
||||
|
||||
## المساهمون
|
||||
|
||||
@MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="21 أبريل 2026">
|
||||
## v1.14.3a1
|
||||
|
||||
|
||||
217
docs/ar/guides/coding-tools/build-with-ai.mdx
Normal file
217
docs/ar/guides/coding-tools/build-with-ai.mdx
Normal file
@@ -0,0 +1,217 @@
|
||||
---
|
||||
title: "البناء باستخدام الذكاء الاصطناعي"
|
||||
description: "كل ما يحتاجه وكلاء البرمجة بالذكاء الاصطناعي للبناء والنشر والتوسع مع CrewAI — المهارات، وثائق مقروءة آلياً، النشر، وميزات المؤسسات."
|
||||
icon: robot
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# البناء باستخدام الذكاء الاصطناعي
|
||||
|
||||
CrewAI مُصمَّم أصلاً للعمل مع الذكاء الاصطناعي. تجمع هذه الصفحة ما يحتاجه وكيل البرمجة بالذكاء الاصطناعي للبناء مع CrewAI — سواءً كان Claude Code أو Codex أو Cursor أو Gemini CLI أو أي مساعد آخر يساعد المطوّر على إيصال الـ crews والـ flows.
|
||||
|
||||
### وكلاء البرمجة المدعومون
|
||||
|
||||
<CardGroup cols={5}>
|
||||
<Card title="Claude Code" icon="message-bot" color="#D97706" />
|
||||
<Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
|
||||
<Card title="Codex" icon="terminal" color="#10B981" />
|
||||
<Card title="Windsurf" icon="wind" color="#06B6D4" />
|
||||
<Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
|
||||
</CardGroup>
|
||||
|
||||
<Note>
|
||||
صُممت هذه الصفحة للبشر وللمساعدين الذكيين على حدٍّ سواء. إذا كنت وكيل برمجة، ابدأ بـ **Skills** للحصول على سياق CrewAI، ثم استخدم **llms.txt** للوصول الكامل إلى الوثائق.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## 1. Skills — علِّم وكيلك CrewAI
|
||||
|
||||
**Skills** حزم تعليمات تمنح وكلاء البرمجة معرفة عميقة بـ CrewAI — كيفية إنشاء هيكل Flows، وضبط Crews، استخدام الأدوات، واتباع اتفاقيات الإطار.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Claude Code (سوق الإضافات)">
|
||||
<img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
|
||||
مهارات CrewAI متاحة في **سوق إضافات Claude Code** — نفس قناة التوزيع التي تستخدمها شركات رائدة في مجال الذكاء الاصطناعي:
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
|
||||
تُفعَّل أربع مهارات تلقائياً عند طرح أسئلة متعلقة بـ CrewAI:
|
||||
|
||||
| المهارة | متى تُستخدم |
|
||||
|---------|-------------|
|
||||
| `getting-started` | مشاريع جديدة، الاختيار بين `LLM.call()` / `Agent` / `Crew` / `Flow`، ربط `crew.py` / `main.py` |
|
||||
| `design-agent` | ضبط الوكلاء — الدور، الهدف، الخلفية، الأدوات، نماذج اللغة، الذاكرة، الحدود الآمنة |
|
||||
| `design-task` | وصف المهام، التبعيات، المخرجات المنظمة (`output_pydantic`، `output_json`)، المراجعة البشرية |
|
||||
| `ask-docs` | الاستعلام من [خادم CrewAI docs MCP](https://docs.crewai.com/mcp) للحصول على تفاصيل واجهة البرمجة الحالية |
|
||||
</Tab>
|
||||
<Tab title="npx (أي وكيل)">
|
||||
يعمل مع Claude Code أو Codex أو Cursor أو Gemini CLI أو أي وكيل برمجة:
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
يُجلب من [سجل skills.sh](https://skills.sh/crewaiinc/skills).
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Steps>
|
||||
<Step title="ثبِّت حزمة المهارات الرسمية">
|
||||
استخدم إحدى الطريقتين أعلاه — سوق إضافات Claude Code أو `npx skills add`. كلاهما يثبّت الحزمة الرسمية [crewAIInc/skills](https://github.com/crewAIInc/skills).
|
||||
</Step>
|
||||
<Step title="يحصل وكيلك فوراً على خبرة CrewAI">
|
||||
تعلّم الحزمة وكيلك:
|
||||
- **Flows** — تطبيقات ذات حالة، خطوات، وتشغيل crews
|
||||
- **Crews والوكلاء** — أنماط YAML أولاً، الأدوار، المهام، التفويض
|
||||
- **الأدوات والتكاملات** — البحث، واجهات API، خوادم MCP، وأدوات CrewAI الشائعة
|
||||
- **هيكل المشروع** — هياكل CLI واتفاقيات المستودع
|
||||
- **أنماط محدثة** — يتماشى مع وثائق CrewAI الحالية وأفضل الممارسات
|
||||
</Step>
|
||||
<Step title="ابدأ البناء">
|
||||
يمكن لوكيلك الآن إنشاء هيكل وبناء مشاريع CrewAI دون أن تعيد شرح الإطار في كل جلسة.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="مفهوم Skills" icon="bolt" href="/ar/concepts/skills">
|
||||
كيف تعمل المهارات في وكلاء CrewAI — الحقن، التفعيل، والأنماط.
|
||||
</Card>
|
||||
<Card title="صفحة Skills" icon="wand-magic-sparkles" href="/ar/skills">
|
||||
نظرة على حزمة crewAIInc/skills وما تتضمنه.
|
||||
</Card>
|
||||
<Card title="AGENTS.md والأدوات" icon="terminal" href="/ar/guides/coding-tools/agents-md">
|
||||
إعداد AGENTS.md لـ Claude Code وCodex وCursor وGemini CLI.
|
||||
</Card>
|
||||
<Card title="سجل skills.sh" icon="globe" href="https://skills.sh/crewaiinc/skills">
|
||||
القائمة الرسمية — المهارات، إحصاءات التثبيت، والتدقيق.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 2. llms.txt — وثائق مقروءة آلياً
|
||||
|
||||
ينشر CrewAI ملف `llms.txt` يمنح المساعدين الذكيين وصولاً مباشراً إلى الوثائق الكاملة بصيغة مقروءة آلياً.
|
||||
|
||||
```
|
||||
https://docs.crewai.com/llms.txt
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="ما هو llms.txt؟">
|
||||
[`llms.txt`](https://llmstxt.org/) معيار ناشئ لجعل الوثائق قابلة للاستهلاك من قبل نماذج اللغة الكبيرة. بدلاً من استخراج HTML، يمكن لوكيلك جلب ملف نصي واحد منظم بكل المحتوى المطلوب.
|
||||
|
||||
ملف `llms.txt` الخاص بـ CrewAI **متاح فعلياً** — يمكن لوكيلك استخدامه الآن.
|
||||
</Tab>
|
||||
<Tab title="كيفية الاستخدام">
|
||||
وجِّه وكيل البرمجة إلى عنوان URL عندما يحتاج إلى مرجع CrewAI:
|
||||
|
||||
```
|
||||
Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
|
||||
```
|
||||
|
||||
يمكن للعديد من وكلاء البرمجة (Claude Code، Cursor، وغيرهما) جلب عناوين URL مباشرة. يحتوي الملف على وثائق منظمة تغطي مفاهيم CrewAI وواجهات البرمجة والأدلة.
|
||||
</Tab>
|
||||
<Tab title="لماذا يهم">
|
||||
- **دون استخراج ويب** — محتوى نظيف ومنظم في طلب واحد
|
||||
- **دائماً محدث** — يُقدَّم مباشرة من docs.crewai.com
|
||||
- **محسّن لنماذج اللغة** — مُنسَّق لنوافذ السياق لا للمتصفحات
|
||||
- **يُكمّل Skills** — المهارات تعلّم الأنماط، وllms.txt يوفّر المرجع
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
---
|
||||
|
||||
## 3. النشر للمؤسسات
|
||||
|
||||
انتقل من crew محلي إلى الإنتاج على **CrewAI AMP** (منصة إدارة الوكلاء) في دقائق.
|
||||
|
||||
<Steps>
|
||||
<Step title="ابنِ محلياً">
|
||||
أنشئ الهيكل واختبر crew أو flow:
|
||||
```bash
|
||||
crewai create crew my_crew
|
||||
cd my_crew
|
||||
crewai run
|
||||
```
|
||||
</Step>
|
||||
<Step title="جهّز للنشر">
|
||||
تأكد أن هيكل مشروعك جاهز:
|
||||
```bash
|
||||
crewai deploy --prepare
|
||||
```
|
||||
راجع [دليل التحضير](/ar/enterprise/guides/prepare-for-deployment) لتفاصيل الهيكل والمتطلبات.
|
||||
</Step>
|
||||
<Step title="انشر على AMP">
|
||||
ادفع إلى منصة CrewAI AMP:
|
||||
```bash
|
||||
crewai deploy
|
||||
```
|
||||
يمكنك أيضاً النشر عبر [تكامل GitHub](/ar/enterprise/guides/deploy-to-amp) أو [Crew Studio](/ar/enterprise/guides/enable-crew-studio).
|
||||
</Step>
|
||||
<Step title="الوصول عبر API">
|
||||
يحصل الـ crew المنشور على نقطة نهاية REST. دمجه في أي تطبيق:
|
||||
```bash
|
||||
curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
|
||||
-H "Authorization: Bearer $CREWAI_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"inputs": {"topic": "AI agents"}}'
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="النشر على AMP" icon="rocket" href="/ar/enterprise/guides/deploy-to-amp">
|
||||
دليل النشر الكامل — CLI وGitHub وCrew Studio.
|
||||
</Card>
|
||||
<Card title="مقدمة عن AMP" icon="globe" href="/ar/enterprise/introduction">
|
||||
نظرة على المنصة — ما يوفّره AMP لـ crews في الإنتاج.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 4. ميزات المؤسسات
|
||||
|
||||
CrewAI AMP مُصمَّم لفرق الإنتاج. إليك ما تحصل عليه بعد النشر.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="المراقبة والرصد" icon="chart-line">
|
||||
مسارات تنفيذ مفصّلة، وسجلات، ومقاييس أداء لكل تشغيل crew. راقب قرارات الوكلاء، استدعاءات الأدوات، وإكمال المهام في الوقت الفعلي.
|
||||
</Card>
|
||||
<Card title="Crew Studio" icon="paintbrush">
|
||||
واجهة منخفضة/بدون كود لإنشاء crews وتخصيصها ونشرها بصرياً — ثم التصدير إلى الشيفرة أو النشر مباشرة.
|
||||
</Card>
|
||||
<Card title="بث الويبهوك" icon="webhook">
|
||||
بث أحداث فورية من تنفيذات الـ crews إلى أنظمتك. تكامل مع Slack أو Zapier أو أي مستهلك ويبهوك.
|
||||
</Card>
|
||||
<Card title="إدارة الفريق" icon="users">
|
||||
SSO وRBAC وضوابط على مستوى المؤسسة. أدر من يمكنه إنشاء crews ونشرها والوصول إليها.
|
||||
</Card>
|
||||
<Card title="مستودع الأدوات" icon="toolbox">
|
||||
انشر وشارك أدواتاً مخصصة عبر مؤسستك. ثبّت أدوات المجتمع من السجل.
|
||||
</Card>
|
||||
<Card title="Factory (استضافة ذاتية)" icon="server">
|
||||
شغّل CrewAI AMP على بنيتك التحتية. قدرات المنصة كاملة مع ضوابط إقامة البيانات والامتثال.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="لمن مخصص AMP؟">
|
||||
لفرق تحتاج نقل سير عمل وكلاء الذكاء الاصطناعي من النماذج الأولية إلى الإنتاج — مع المراقبة وضوابط الوصول والبنية التحتية القابلة للتوسع. سواءً كنت ناشئاً أو مؤسسة كبيرة، يتولى AMP التعقيد التشغيلي لتتفرغ لبناء الوكلاء.
|
||||
</Accordion>
|
||||
<Accordion title="ما خيارات النشر المتاحة؟">
|
||||
- **السحابة (app.crewai.com)** — تُدار من CrewAI، أسرع طريق إلى الإنتاج
|
||||
- **Factory (استضافة ذاتية)** — على بنيتك التحتية لسيطرة كاملة على البيانات
|
||||
- **هجين** — دمج السحابة والاستضافة الذاتية حسب حساسية البيانات
|
||||
</Accordion>
|
||||
<Accordion title="كيف يعمل التسعير؟">
|
||||
سجّل في [app.crewai.com](https://app.crewai.com) لمعرفة الخطط الحالية. تسعير المؤسسات وFactory متاح عند الطلب.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Card title="استكشف CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
|
||||
سجّل وانشر أول crew لك في الإنتاج.
|
||||
</Card>
|
||||
@@ -79,6 +79,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -127,7 +128,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -553,6 +555,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -601,7 +604,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1027,6 +1031,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -1075,7 +1080,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1501,6 +1507,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -1549,7 +1556,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -1975,6 +1983,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -2023,7 +2032,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -2449,6 +2459,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -2497,7 +2508,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -2921,6 +2933,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -2969,7 +2982,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -3393,6 +3407,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -3441,7 +3456,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -3866,6 +3882,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -3914,7 +3931,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4340,6 +4358,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -4388,7 +4407,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -4812,6 +4832,7 @@
|
||||
"group": "Get Started",
|
||||
"pages": [
|
||||
"en/introduction",
|
||||
"en/guides/coding-tools/build-with-ai",
|
||||
"en/skills",
|
||||
"en/installation",
|
||||
"en/quickstart"
|
||||
@@ -4860,7 +4881,8 @@
|
||||
"group": "Coding Tools",
|
||||
"icon": "terminal",
|
||||
"pages": [
|
||||
"en/guides/coding-tools/agents-md"
|
||||
"en/guides/coding-tools/agents-md",
|
||||
"en/guides/coding-tools/build-with-ai"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -5317,6 +5339,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -5775,6 +5798,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -6233,6 +6257,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -6691,6 +6716,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -7149,6 +7175,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -7607,6 +7634,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -8064,6 +8092,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -8521,6 +8550,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -8978,6 +9008,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -9434,6 +9465,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -9890,6 +9922,7 @@
|
||||
"group": "Começando",
|
||||
"pages": [
|
||||
"pt-BR/introduction",
|
||||
"pt-BR/guides/coding-tools/build-with-ai",
|
||||
"pt-BR/skills",
|
||||
"pt-BR/installation",
|
||||
"pt-BR/quickstart"
|
||||
@@ -10377,6 +10410,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -10847,6 +10881,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -11317,6 +11352,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -11787,6 +11823,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -12257,6 +12294,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -12727,6 +12765,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -13196,6 +13235,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -13665,6 +13705,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -14134,6 +14175,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -14602,6 +14644,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -15070,6 +15113,7 @@
|
||||
"group": "시작 안내",
|
||||
"pages": [
|
||||
"ko/introduction",
|
||||
"ko/guides/coding-tools/build-with-ai",
|
||||
"ko/skills",
|
||||
"ko/installation",
|
||||
"ko/quickstart"
|
||||
@@ -15569,6 +15613,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -16039,6 +16084,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -16509,6 +16555,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -16979,6 +17026,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -17449,6 +17497,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -17919,6 +17968,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -18388,6 +18438,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -18857,6 +18908,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -19326,6 +19378,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -19794,6 +19847,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
@@ -20262,6 +20316,7 @@
|
||||
"group": "البدء",
|
||||
"pages": [
|
||||
"ar/introduction",
|
||||
"ar/guides/coding-tools/build-with-ai",
|
||||
"ar/skills",
|
||||
"ar/installation",
|
||||
"ar/quickstart"
|
||||
|
||||
@@ -4,6 +4,36 @@ description: "Product updates, improvements, and bug fixes for CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="Apr 22, 2026">
|
||||
## v1.14.3a2
|
||||
|
||||
[View release on GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
|
||||
|
||||
## What's Changed
|
||||
|
||||
### Features
|
||||
- Add support for bedrock V4
|
||||
- Add Daytona sandbox tools for enhanced functionality
|
||||
- Add 'Build with AI' page — AI-native docs for coding agents
|
||||
- Add Build with AI to Get Started navigation and page files for all languages (en, ko, pt-BR, ar)
|
||||
|
||||
### Bug Fixes
|
||||
- Fix propagation of implicit @CrewBase names to crew events
|
||||
- Resolve issue with duplicate batch initialization in execution metadata merge
|
||||
- Fix serialization of Task class-reference fields for checkpointing
|
||||
- Handle BaseModel result in guardrail retry loop
|
||||
- Bump python-dotenv to version >=1.2.2 for security compliance
|
||||
|
||||
### Documentation
|
||||
- Update changelog and version for v1.14.3a1
|
||||
- Update descriptions and apply actual translations
|
||||
|
||||
## Contributors
|
||||
|
||||
@MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="Apr 21, 2026">
|
||||
## v1.14.3a1
|
||||
|
||||
|
||||
@@ -1,18 +1,27 @@
|
||||
---
|
||||
title: Planning
|
||||
description: Learn how to add planning to your CrewAI Crew and improve their performance.
|
||||
description: Learn how to add planning to CrewAI at the crew level (sequential task planning) and the agent level (Plan-and-Act with PlanningConfig).
|
||||
icon: ruler-combined
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration,
|
||||
all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
|
||||
CrewAI provides two complementary planning systems:
|
||||
|
||||
- **Crew-level planning** — before each crew iteration, an `AgentPlanner` produces a step-by-step plan for every task and injects it into the task description. Useful when you want the crew to think through the *whole pipeline* before any agent starts working.
|
||||
- **Agent-level planning (Plan-and-Act)** — a single agent builds an explicit multi-step plan, executes it step by step, and observes/replans as it goes. Configured per-agent via `PlanningConfig`. Useful when you want one agent to tackle a complex task adaptively.
|
||||
|
||||
The two are independent and can be combined: a crew can have planning enabled, and individual agents in that crew can also use `planning_config`.
|
||||
|
||||
## Crew-Level Planning
|
||||
|
||||
The crew-level planning feature adds planning capability to your crew. When enabled, before each Crew iteration,
|
||||
all Crew information is sent to an `AgentPlanner` that will plan the tasks step by step, and this plan will be added to each task description.
|
||||
|
||||
### Using the Planning Feature
|
||||
|
||||
Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
|
||||
Getting started with crew-level planning is very easy, the only step required is to add `planning=True` to your Crew:
|
||||
|
||||
<CodeGroup>
|
||||
```python Code
|
||||
@@ -36,9 +45,9 @@ When planning is enabled, crewAI will use `gpt-4o-mini` as the default LLM for p
|
||||
|
||||
#### Planning LLM
|
||||
|
||||
Now you can define the LLM that will be used to plan the tasks.
|
||||
Now you can define the LLM that will be used to plan the tasks.
|
||||
|
||||
When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner`
|
||||
When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner`
|
||||
responsible for creating the step-by-step logic to add to the Agents' tasks.
|
||||
|
||||
<CodeGroup>
|
||||
@@ -152,4 +161,191 @@ A list with 10 bullet points of the most relevant information about AI LLMs.
|
||||
**Expected Output:**
|
||||
A fully fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
|
||||
```
|
||||
</CodeGroup>
|
||||
</CodeGroup>
|
||||
|
||||
## Agent-Level Planning (Plan-and-Act)
|
||||
|
||||
Agent-level planning gives a single agent an explicit Plan-and-Act loop: it builds a structured multi-step plan up front, executes each step, observes the result, and can replan or refine when reality diverges from the plan. It's configured per-agent through `PlanningConfig`.
|
||||
|
||||
### Enabling Agent Planning
|
||||
|
||||
Pass a `PlanningConfig` to the agent. The presence of a `PlanningConfig` enables planning — you don't need a separate flag.
|
||||
|
||||
<CodeGroup>
|
||||
```python Defaults
|
||||
from crewai import Agent, PlanningConfig
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze datasets and surface insights",
|
||||
backstory="You are an experienced data analyst.",
|
||||
planning_config=PlanningConfig(), # medium effort, defaults
|
||||
)
|
||||
```
|
||||
|
||||
```python Tuned
|
||||
from crewai import Agent, PlanningConfig
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze datasets and surface insights",
|
||||
backstory="You are an experienced data analyst.",
|
||||
planning_config=PlanningConfig(
|
||||
reasoning_effort="high",
|
||||
max_steps=10,
|
||||
max_replans=2,
|
||||
max_step_iterations=10,
|
||||
step_timeout=120,
|
||||
llm="gpt-4o-mini",
|
||||
),
|
||||
)
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
### Reasoning Effort
|
||||
|
||||
`reasoning_effort` controls what happens *between steps* — how aggressively the agent observes, replans, and refines as it executes the plan. It is the most important knob for tuning latency vs. adaptiveness.
|
||||
|
||||
<ParamField body="low" type="string">
|
||||
Observe each step for success validation only. Skip the decide/replan/refine pipeline; steps are marked complete and execution continues linearly. **Fastest option** — best when the plan is likely to be correct on the first try and you want minimal overhead per step.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="medium" type="string" default="default">
|
||||
Observe each step. On failure, trigger replanning. On success, skip refinement and continue. **Balanced option (default)** — replans only when something goes wrong, so you get adaptiveness without paying for it on the happy path.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="high" type="string">
|
||||
Full observation pipeline with `decide_next_action` after every step. Can trigger early goal achievement (finish before all steps run), full replanning, or lightweight step refinement. **Most adaptive, highest latency** — best for open-ended or exploratory tasks where the right path can't be predicted up front.
|
||||
</ParamField>
|
||||
|
||||
### PlanningConfig Fields
|
||||
|
||||
<ParamField body="reasoning_effort" type="Literal['low', 'medium', 'high']" default="medium">
|
||||
Post-step observation/replanning behavior. See above.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_attempts" type="int | None" default="None">
|
||||
Maximum number of planning refinement attempts during the initial plan creation. If `None`, the agent keeps refining until it indicates readiness.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_steps" type="int" default="20">
|
||||
Maximum number of steps in the generated plan. Must be `>= 1`. Lower this when you want concise plans; raise it for complex tasks that legitimately need many steps.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_replans" type="int" default="3">
|
||||
Maximum number of full replanning cycles allowed during execution. Must be `>= 0`. Set to `0` to forbid replanning entirely (the agent will stick to the original plan even if steps fail).
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_step_iterations" type="int" default="15">
|
||||
Maximum LLM iterations per step inside the `StepExecutor` multi-turn loop. Must be `>= 1`. Lower values make individual steps faster but less thorough — useful when each step is a small, well-scoped action.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="step_timeout" type="int | None" default="None">
|
||||
Wall-clock seconds for a single step. If exceeded, the step is marked failed and observation decides whether to continue or replan. `None` means no per-step timeout.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="system_prompt" type="str | None" default="None">
|
||||
Override the default planning system prompt. Use this to inject domain-specific instructions for how plans should be structured.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="plan_prompt" type="str | None" default="None">
|
||||
Override the prompt used to create the initial plan. Supports template variables like `{description}`.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="refine_prompt" type="str | None" default="None">
|
||||
Override the prompt used to refine the plan during the `max_attempts` refinement loop.
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="llm" type="str | BaseLLM | None" default="None">
|
||||
LLM used for planning. Falls back to the agent's own LLM if not provided. Pass either a model string (e.g., `"gpt-4o-mini"`) or a `BaseLLM` instance.
|
||||
</ParamField>
|
||||
|
||||
### How the Plan-and-Act Loop Works
|
||||
|
||||
When `planning_config` is set, the agent executes the task as follows:
|
||||
|
||||
1. **Plan** — build an initial multi-step plan, refining up to `max_attempts` times until ready.
|
||||
2. **Execute step** — run one step through the `StepExecutor` (up to `max_step_iterations` LLM turns, bounded by `step_timeout`).
|
||||
3. **Observe** — validate whether the step succeeded.
|
||||
4. **Decide next action** — depending on `reasoning_effort`:
|
||||
- `low`: continue to the next step.
|
||||
- `medium`: continue on success; replan on failure.
|
||||
- `high`: route through `decide_next_action`, which can finish early, replan, refine the next step, or continue.
|
||||
5. Repeat until the plan completes, the goal is achieved, or `max_replans` is exhausted.
|
||||
|
||||
### Custom Prompts Example
|
||||
|
||||
```python
|
||||
from crewai import Agent, PlanningConfig
|
||||
|
||||
agent = Agent(
|
||||
role="Researcher",
|
||||
goal="Research topics",
|
||||
backstory="Expert researcher",
|
||||
planning_config=PlanningConfig(
|
||||
reasoning_effort="high",
|
||||
max_attempts=3,
|
||||
max_steps=10,
|
||||
plan_prompt="Create a focused plan for: {description}",
|
||||
refine_prompt="Tighten this plan, removing any step that doesn't materially advance the goal.",
|
||||
llm="gpt-4o-mini",
|
||||
),
|
||||
)
|
||||
```
|
||||
|
||||
### Migration from `reasoning=True`
|
||||
|
||||
The original agent reasoning API used two fields directly on `Agent`:
|
||||
|
||||
- `reasoning: bool = False`
|
||||
- `max_reasoning_attempts: int | None = None`
|
||||
|
||||
Both are **deprecated**. They still work — passing them emits a `DeprecationWarning` and CrewAI auto-migrates them to an equivalent `PlanningConfig` — but new code should use `PlanningConfig` directly.
|
||||
|
||||
<Warning>
|
||||
`Agent(reasoning=True, ...)` and `Agent(max_reasoning_attempts=N, ...)` are deprecated and will be removed in a future release. Migrate to `planning_config=PlanningConfig(...)`.
|
||||
</Warning>
|
||||
|
||||
<CodeGroup>
|
||||
```python Before (deprecated)
|
||||
from crewai import Agent
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
backstory="Expert data analyst.",
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=3,
|
||||
)
|
||||
```
|
||||
|
||||
```python After
|
||||
from crewai import Agent, PlanningConfig
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
backstory="Expert data analyst.",
|
||||
planning_config=PlanningConfig(max_attempts=3),
|
||||
)
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
The mapping is direct:
|
||||
|
||||
- `reasoning=True` → presence of `planning_config` enables planning.
|
||||
- `max_reasoning_attempts=N` → `PlanningConfig(max_attempts=N)`.
|
||||
|
||||
Everything else (`reasoning_effort`, `max_steps`, `max_replans`, `max_step_iterations`, `step_timeout`, custom prompts, dedicated planning LLM) is new functionality only available through `PlanningConfig`.
|
||||
|
||||
## Choosing Between Crew-Level and Agent-Level Planning
|
||||
|
||||
| Concern | Crew-level (`Crew(planning=True)`) | Agent-level (`PlanningConfig`) |
|
||||
| --- | --- | --- |
|
||||
| Scope | Plans every task in the crew up front | Plans one agent's task adaptively |
|
||||
| When the plan is built | Once per crew iteration, before any task runs | At the start of each agent's task |
|
||||
| Adapts mid-execution | No — the plan is injected as guidance | Yes — observes, replans, and refines per step |
|
||||
| Best for | Multi-task pipelines where ordering and hand-offs matter | Open-ended tasks where the right path emerges as the agent works |
|
||||
| Configuration surface | `planning`, `planning_llm` on `Crew` | `PlanningConfig` on `Agent` |
|
||||
|
||||
The two are complementary — you can enable crew-level planning to coordinate the overall pipeline and use `planning_config` on individual agents that need to think adaptively while executing their step.
|
||||
|
||||
@@ -1,148 +1,59 @@
|
||||
---
|
||||
title: Reasoning
|
||||
description: "Learn how to enable and use agent reasoning to improve task execution."
|
||||
description: "Agent reasoning has been renamed to planning_config. See the Planning page for the current API."
|
||||
icon: brain
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
|
||||
<Warning>
|
||||
The `reasoning=True` and `max_reasoning_attempts=N` arguments on `Agent` are **deprecated**. They still work for now — passing them emits a `DeprecationWarning` and CrewAI auto-migrates the values into a `PlanningConfig` — but they will be removed in a future release.
|
||||
|
||||
## Usage
|
||||
The replacement is **`planning_config`**, documented in full on the [Planning](/en/concepts/planning) page.
|
||||
</Warning>
|
||||
|
||||
To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
|
||||
## Migration
|
||||
|
||||
```python
|
||||
The new API lives on `Agent.planning_config` and uses the `PlanningConfig` model. The presence of a `PlanningConfig` enables planning — there is no separate boolean flag.
|
||||
|
||||
<CodeGroup>
|
||||
```python Before (deprecated)
|
||||
from crewai import Agent
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze complex datasets and provide insights",
|
||||
backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
|
||||
reasoning=True, # Enable reasoning
|
||||
max_reasoning_attempts=3 # Optional: Set a maximum number of reasoning attempts
|
||||
)
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
When reasoning is enabled, before executing a task, the agent will:
|
||||
|
||||
1. Reflect on the task and create a detailed plan
|
||||
2. Evaluate whether it's ready to execute the task
|
||||
3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
|
||||
4. Inject the reasoning plan into the task description before execution
|
||||
|
||||
This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
|
||||
|
||||
## Configuration Options
|
||||
|
||||
<ParamField body="reasoning" type="bool" default="False">
|
||||
Enable or disable reasoning
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_reasoning_attempts" type="int" default="None">
|
||||
Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready.
|
||||
</ParamField>
|
||||
|
||||
## Example
|
||||
|
||||
Here's a complete example:
|
||||
|
||||
```python
|
||||
from crewai import Agent, Task, Crew
|
||||
|
||||
# Create an agent with reasoning enabled
|
||||
analyst = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
backstory="You are an expert data analyst.",
|
||||
backstory="Expert data analyst.",
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=3 # Optional: Set a limit on reasoning attempts
|
||||
max_reasoning_attempts=3,
|
||||
)
|
||||
|
||||
# Create a task
|
||||
analysis_task = Task(
|
||||
description="Analyze the provided sales data and identify key trends.",
|
||||
expected_output="A report highlighting the top 3 sales trends.",
|
||||
agent=analyst
|
||||
)
|
||||
|
||||
# Create a crew and run the task
|
||||
crew = Crew(agents=[analyst], tasks=[analysis_task])
|
||||
result = crew.kickoff()
|
||||
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
```python After
|
||||
from crewai import Agent, PlanningConfig
|
||||
|
||||
The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
|
||||
|
||||
Here's how to handle potential errors in your code:
|
||||
|
||||
```python
|
||||
from crewai import Agent, Task
|
||||
import logging
|
||||
|
||||
# Set up logging to capture any reasoning errors
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Create an agent with reasoning enabled
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=3
|
||||
backstory="Expert data analyst.",
|
||||
planning_config=PlanningConfig(max_attempts=3),
|
||||
)
|
||||
|
||||
# Create a task
|
||||
task = Task(
|
||||
description="Analyze the provided sales data and identify key trends.",
|
||||
expected_output="A report highlighting the top 3 sales trends.",
|
||||
agent=agent
|
||||
)
|
||||
|
||||
# Execute the task
|
||||
# If an error occurs during reasoning, it will be logged and execution will continue
|
||||
result = agent.execute_task(task)
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Example Reasoning Output
|
||||
Field mapping:
|
||||
|
||||
Here's an example of what a reasoning plan might look like for a data analysis task:
|
||||
- `reasoning=True` → presence of `planning_config` enables planning.
|
||||
- `max_reasoning_attempts=N` → `PlanningConfig(max_attempts=N)`.
|
||||
|
||||
```
|
||||
Task: Analyze the provided sales data and identify key trends.
|
||||
## What's New
|
||||
|
||||
Reasoning Plan:
|
||||
I'll analyze the sales data to identify the top 3 trends.
|
||||
`PlanningConfig` exposes capabilities that the old `reasoning` flag did not, including:
|
||||
|
||||
1. Understanding of the task:
|
||||
I need to analyze sales data to identify key trends that would be valuable for business decision-making.
|
||||
- `reasoning_effort` (`"low"` / `"medium"` / `"high"`) to control post-step observation, replanning, and refinement.
|
||||
- `max_steps`, `max_replans`, `max_step_iterations`, and `step_timeout` to bound plan size and execution.
|
||||
- A dedicated planning `llm` separate from the agent's execution LLM.
|
||||
- Custom `system_prompt`, `plan_prompt`, and `refine_prompt` overrides.
|
||||
|
||||
2. Key steps I'll take:
|
||||
- First, I'll examine the data structure to understand what fields are available
|
||||
- Then I'll perform exploratory data analysis to identify patterns
|
||||
- Next, I'll analyze sales by time periods to identify temporal trends
|
||||
- I'll also analyze sales by product categories and customer segments
|
||||
- Finally, I'll identify the top 3 most significant trends
|
||||
|
||||
3. Approach to challenges:
|
||||
- If the data has missing values, I'll decide whether to fill or filter them
|
||||
- If the data has outliers, I'll investigate whether they're valid data points or errors
|
||||
- If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
|
||||
|
||||
4. Use of available tools:
|
||||
- I'll use data analysis tools to explore and visualize the data
|
||||
- I'll use statistical tools to identify significant patterns
|
||||
- I'll use knowledge retrieval to access relevant information about sales analysis
|
||||
|
||||
5. Expected outcome:
|
||||
A concise report highlighting the top 3 sales trends with supporting evidence from the data.
|
||||
|
||||
READY: I am ready to execute the task.
|
||||
```
|
||||
|
||||
This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.
|
||||
For the full field reference, the Plan-and-Act loop, and guidance on when to use agent-level planning vs. crew-level planning, see [Planning](/en/concepts/planning).
|
||||
|
||||
217
docs/en/guides/coding-tools/build-with-ai.mdx
Normal file
217
docs/en/guides/coding-tools/build-with-ai.mdx
Normal file
@@ -0,0 +1,217 @@
|
||||
---
|
||||
title: "Build with AI"
|
||||
description: "Everything AI coding agents need to build, deploy, and scale with CrewAI — skills, machine-readable docs, deployment, and enterprise features."
|
||||
icon: robot
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# Build with AI
|
||||
|
||||
CrewAI is AI-native. This page brings together everything an AI coding agent needs to build with CrewAI — whether you're Claude Code, Codex, Cursor, Gemini CLI, or any other assistant helping a developer ship crews and flows.
|
||||
|
||||
### Supported Coding Agents
|
||||
|
||||
<CardGroup cols={5}>
|
||||
<Card title="Claude Code" icon="message-bot" color="#D97706" />
|
||||
<Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
|
||||
<Card title="Codex" icon="terminal" color="#10B981" />
|
||||
<Card title="Windsurf" icon="wind" color="#06B6D4" />
|
||||
<Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
|
||||
</CardGroup>
|
||||
|
||||
<Note>
|
||||
This page is designed to be consumed by both humans and AI assistants. If you're a coding agent, start with **Skills** to get CrewAI context, then use **llms.txt** for full docs access.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## 1. Skills — Teach Your Agent CrewAI
|
||||
|
||||
**Skills** are instruction packs that give coding agents deep CrewAI knowledge — how to scaffold Flows, configure Crews, use tools, and follow framework conventions.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Claude Code (Plugin Marketplace)">
|
||||
<img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
|
||||
CrewAI skills are available in the **Claude Code plugin marketplace** — the same distribution channel used by top AI-native companies:
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
|
||||
Four skills activate automatically when you ask relevant CrewAI questions:
|
||||
|
||||
| Skill | When it runs |
|
||||
|-------|--------------|
|
||||
| `getting-started` | Scaffolding new projects, choosing between `LLM.call()` / `Agent` / `Crew` / `Flow`, wiring `crew.py` / `main.py` |
|
||||
| `design-agent` | Configuring agents — role, goal, backstory, tools, LLMs, memory, guardrails |
|
||||
| `design-task` | Writing task descriptions, dependencies, structured output (`output_pydantic`, `output_json`), human review |
|
||||
| `ask-docs` | Querying the live [CrewAI docs MCP server](https://docs.crewai.com/mcp) for up-to-date API details |
|
||||
</Tab>
|
||||
<Tab title="npx (Any Agent)">
|
||||
Works with Claude Code, Codex, Cursor, Gemini CLI, or any coding agent:
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
Pulls from the [skills.sh registry](https://skills.sh/crewaiinc/skills).
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Steps>
|
||||
<Step title="Install the official skill pack">
|
||||
Use either method above — the Claude Code plugin marketplace or `npx skills add`. Both install the official [crewAIInc/skills](https://github.com/crewAIInc/skills) pack.
|
||||
</Step>
|
||||
<Step title="Your agent gets instant CrewAI expertise">
|
||||
The skill pack teaches your agent:
|
||||
- **Flows** — stateful apps, steps, and crew kickoffs
|
||||
- **Crews & Agents** — YAML-first patterns, roles, tasks, delegation
|
||||
- **Tools & Integrations** — search, APIs, MCP servers, and common CrewAI tools
|
||||
- **Project layout** — CLI scaffolds and repo conventions
|
||||
- **Up-to-date patterns** — tracks current CrewAI docs and best practices
|
||||
</Step>
|
||||
<Step title="Start building">
|
||||
Your agent can now scaffold and build CrewAI projects without you re-explaining the framework each session.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Skills concept" icon="bolt" href="/en/concepts/skills">
|
||||
How skills work in CrewAI agents — injection, activation, and patterns.
|
||||
</Card>
|
||||
<Card title="Skills landing page" icon="wand-magic-sparkles" href="/en/skills">
|
||||
Overview of the crewAIInc/skills pack and what it includes.
|
||||
</Card>
|
||||
<Card title="AGENTS.md & coding tools" icon="terminal" href="/en/guides/coding-tools/agents-md">
|
||||
Set up AGENTS.md for Claude Code, Codex, Cursor, and Gemini CLI.
|
||||
</Card>
|
||||
<Card title="Skills registry (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
|
||||
Official listing — skills, install stats, and audits.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 2. llms.txt — Machine-Readable Docs
|
||||
|
||||
CrewAI publishes an `llms.txt` file that gives AI assistants direct access to the full documentation in a machine-readable format.
|
||||
|
||||
```
|
||||
https://docs.crewai.com/llms.txt
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="What is llms.txt?">
|
||||
[`llms.txt`](https://llmstxt.org/) is an emerging standard for making documentation consumable by large language models. Instead of scraping HTML, your agent can fetch a single structured text file with all the content it needs.
|
||||
|
||||
CrewAI's `llms.txt` is **already live** — your agent can use it right now.
|
||||
</Tab>
|
||||
<Tab title="How to use it">
|
||||
Point your coding agent at the URL when it needs CrewAI reference docs:
|
||||
|
||||
```
|
||||
Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
|
||||
```
|
||||
|
||||
Many coding agents (Claude Code, Cursor, etc.) can fetch URLs directly. The file contains structured documentation covering all CrewAI concepts, APIs, and guides.
|
||||
</Tab>
|
||||
<Tab title="Why it matters">
|
||||
- **No scraping required** — clean, structured content in one request
|
||||
- **Always up-to-date** — served directly from docs.crewai.com
|
||||
- **Optimized for LLMs** — formatted for context windows, not browsers
|
||||
- **Complements skills** — skills teach patterns, llms.txt provides reference
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
---
|
||||
|
||||
## 3. Deploy to Enterprise
|
||||
|
||||
Go from a local crew to production on **CrewAI AMP** (Agent Management Platform) in minutes.
|
||||
|
||||
<Steps>
|
||||
<Step title="Build locally">
|
||||
Scaffold and test your crew or flow:
|
||||
```bash
|
||||
crewai create crew my_crew
|
||||
cd my_crew
|
||||
crewai run
|
||||
```
|
||||
</Step>
|
||||
<Step title="Prepare for deployment">
|
||||
Ensure your project structure is ready:
|
||||
```bash
|
||||
crewai deploy --prepare
|
||||
```
|
||||
See the [preparation guide](/en/enterprise/guides/prepare-for-deployment) for details on project structure and requirements.
|
||||
</Step>
|
||||
<Step title="Deploy to AMP">
|
||||
Push to the CrewAI AMP platform:
|
||||
```bash
|
||||
crewai deploy
|
||||
```
|
||||
You can also deploy via [GitHub integration](/en/enterprise/guides/deploy-to-amp) or [Crew Studio](/en/enterprise/guides/enable-crew-studio).
|
||||
</Step>
|
||||
<Step title="Access via API">
|
||||
Your deployed crew gets a REST API endpoint. Integrate it into any application:
|
||||
```bash
|
||||
curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
|
||||
-H "Authorization: Bearer $CREWAI_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"inputs": {"topic": "AI agents"}}'
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Deploy to AMP" icon="rocket" href="/en/enterprise/guides/deploy-to-amp">
|
||||
Full deployment guide — CLI, GitHub, and Crew Studio methods.
|
||||
</Card>
|
||||
<Card title="AMP introduction" icon="globe" href="/en/enterprise/introduction">
|
||||
Platform overview — what AMP provides for production crews.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 4. Enterprise Features
|
||||
|
||||
CrewAI AMP is built for production teams. Here's what you get beyond deployment.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Observability" icon="chart-line">
|
||||
Detailed execution traces, logs, and performance metrics for every crew run. Monitor agent decisions, tool calls, and task completion in real time.
|
||||
</Card>
|
||||
<Card title="Crew Studio" icon="paintbrush">
|
||||
No-code/low-code interface to create, customize, and deploy crews visually — then export to code or deploy directly.
|
||||
</Card>
|
||||
<Card title="Webhook Streaming" icon="webhook">
|
||||
Stream real-time events from crew executions to your systems. Integrate with Slack, Zapier, or any webhook consumer.
|
||||
</Card>
|
||||
<Card title="Team Management" icon="users">
|
||||
SSO, RBAC, and organization-level controls. Manage who can create, deploy, and access crews across your team.
|
||||
</Card>
|
||||
<Card title="Tool Repository" icon="toolbox">
|
||||
Publish and share custom tools across your organization. Install community tools from the registry.
|
||||
</Card>
|
||||
<Card title="Factory (Self-Hosted)" icon="server">
|
||||
Run CrewAI AMP on your own infrastructure. Full platform capabilities with data residency and compliance controls.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Who is AMP for?">
|
||||
AMP is for teams that need to move AI agent workflows from prototypes to production — with observability, access controls, and scalable infrastructure. Whether you're a startup or enterprise, AMP handles the operational complexity so you can focus on building agents.
|
||||
</Accordion>
|
||||
<Accordion title="What deployment options are available?">
|
||||
- **Cloud (app.crewai.com)** — managed by CrewAI, fastest path to production
|
||||
- **Factory (self-hosted)** — run on your own infrastructure for full data control
|
||||
- **Hybrid** — mix cloud and self-hosted based on sensitivity requirements
|
||||
</Accordion>
|
||||
<Accordion title="How does pricing work?">
|
||||
Sign up at [app.crewai.com](https://app.crewai.com) to see current plans. Enterprise and Factory pricing is available on request.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Card title="Explore CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
|
||||
Sign up and deploy your first crew to production.
|
||||
</Card>
|
||||
@@ -4,6 +4,36 @@ description: "CrewAI의 제품 업데이트, 개선 사항 및 버그 수정"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="2026년 4월 22일">
|
||||
## v1.14.3a2
|
||||
|
||||
[GitHub 릴리스 보기](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
|
||||
|
||||
## 변경 사항
|
||||
|
||||
### 기능
|
||||
- 베드록 V4 지원 추가
|
||||
- 향상된 기능을 위한 데이토나 샌드박스 도구 추가
|
||||
- 'AI와 함께 빌드' 페이지 추가 — 코딩 에이전트를 위한 AI 네이티브 문서
|
||||
- 모든 언어(en, ko, pt-BR, ar)에 대한 시작하기 탐색 및 페이지 파일에 AI와 함께 빌드 추가
|
||||
|
||||
### 버그 수정
|
||||
- 크루 이벤트에 대한 암묵적 @CrewBase 이름 전파 수정
|
||||
- 실행 메타데이터 병합에서 중복 배치 초기화 문제 해결
|
||||
- 체크포인트를 위한 Task 클래스 참조 필드 직렬화 수정
|
||||
- 가드레일 재시도 루프에서 BaseModel 결과 처리
|
||||
- 보안 준수를 위해 python-dotenv를 버전 >=1.2.2로 업데이트
|
||||
|
||||
### 문서
|
||||
- v1.14.3a1에 대한 변경 로그 및 버전 업데이트
|
||||
- 설명 업데이트 및 실제 번역 적용
|
||||
|
||||
## 기여자
|
||||
|
||||
@MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="2026년 4월 21일">
|
||||
## v1.14.3a1
|
||||
|
||||
|
||||
217
docs/ko/guides/coding-tools/build-with-ai.mdx
Normal file
217
docs/ko/guides/coding-tools/build-with-ai.mdx
Normal file
@@ -0,0 +1,217 @@
|
||||
---
|
||||
title: "AI와 함께 빌드하기"
|
||||
description: "CrewAI로 빌드·배포·확장하는 데 필요한 모든 것 — 스킬, 기계가 읽을 수 있는 문서, 배포, 엔터프라이즈 기능을 AI 코딩 에이전트용으로 정리했습니다."
|
||||
icon: robot
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# AI와 함께 빌드하기
|
||||
|
||||
CrewAI는 AI 네이티브입니다. 이 페이지는 Claude Code, Codex, Cursor, Gemini CLI 등 개발자가 crew와 flow를 배포하도록 돕는 코딩 에이전트가 CrewAI로 빌드할 때 필요한 내용을 한곳에 모았습니다.
|
||||
|
||||
### 지원 코딩 에이전트
|
||||
|
||||
<CardGroup cols={5}>
|
||||
<Card title="Claude Code" icon="message-bot" color="#D97706" />
|
||||
<Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
|
||||
<Card title="Codex" icon="terminal" color="#10B981" />
|
||||
<Card title="Windsurf" icon="wind" color="#06B6D4" />
|
||||
<Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
|
||||
</CardGroup>
|
||||
|
||||
<Note>
|
||||
이 페이지는 사람과 AI 어시스턴트 모두를 위해 작성되었습니다. 코딩 에이전트라면 CrewAI 맥락은 **Skills**부터, 전체 문서 접근은 **llms.txt**를 사용하세요.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## 1. Skills — 에이전트에게 CrewAI 가르치기
|
||||
|
||||
**Skills**는 코딩 에이전트에게 Flow 스캐폴딩, Crew 구성, 도구 사용, 프레임워크 관례 등 CrewAI에 대한 깊은 지식을 담은 지침 묶음입니다.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Claude Code (플러그인 마켓플레이스)">
|
||||
<img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
|
||||
CrewAI 스킬은 **Claude Code 플러그인 마켓플레이스**에서 제공됩니다. AI 네이티브 기업들이 쓰는 것과 같은 배포 채널입니다.
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
|
||||
CrewAI와 관련된 질문을 하면 다음 네 가지 스킬이 자동으로 활성화됩니다.
|
||||
|
||||
| 스킬 | 실행 시점 |
|
||||
|------|-------------|
|
||||
| `getting-started` | 새 프로젝트 스캐폴딩, `LLM.call()` / `Agent` / `Crew` / `Flow` 선택, `crew.py` / `main.py` 연결 |
|
||||
| `design-agent` | 에이전트 구성 — 역할, 목표, 배경 이야기, 도구, LLM, 메모리, 가드레일 |
|
||||
| `design-task` | 태스크 설명, 의존성, 구조화된 출력(`output_pydantic`, `output_json`), 사람 검토 |
|
||||
| `ask-docs` | 최신 API 정보를 위해 [CrewAI 문서 MCP 서버](https://docs.crewai.com/mcp) 조회 |
|
||||
</Tab>
|
||||
<Tab title="npx (모든 에이전트)">
|
||||
Claude Code, Codex, Cursor, Gemini CLI 등 모든 코딩 에이전트에서 사용할 수 있습니다.
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
[skills.sh 레지스트리](https://skills.sh/crewaiinc/skills)에서 가져옵니다.
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Steps>
|
||||
<Step title="공식 스킬 팩 설치">
|
||||
위 방법 중 하나를 사용하세요 — Claude Code 플러그인 마켓플레이스 또는 `npx skills add`. 둘 다 공식 [crewAIInc/skills](https://github.com/crewAIInc/skills) 팩을 설치합니다.
|
||||
</Step>
|
||||
<Step title="에이전트가 즉시 CrewAI 전문성을 갖춤">
|
||||
스킬 팩이 에이전트에게 알려 주는 내용:
|
||||
- **Flow** — 상태ful 앱, 단계, crew 킥오프
|
||||
- **Crew 및 에이전트** — YAML 우선 패턴, 역할, 태스크, 위임
|
||||
- **도구 및 통합** — 검색, API, MCP 서버, 일반적인 CrewAI 도구
|
||||
- **프로젝트 레이아웃** — CLI 스캐폴드와 저장소 관례
|
||||
- **최신 패턴** — 현재 CrewAI 문서와 모범 사례 반영
|
||||
</Step>
|
||||
<Step title="빌드 시작">
|
||||
매 세션마다 프레임워크를 다시 설명하지 않아도 에이전트가 CrewAI 프로젝트를 스캐폴딩하고 빌드할 수 있습니다.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Skills 개념" icon="bolt" href="/ko/concepts/skills">
|
||||
CrewAI 에이전트에서 스킬이 동작하는 방식 — 주입, 활성화, 패턴.
|
||||
</Card>
|
||||
<Card title="Skills 랜딩 페이지" icon="wand-magic-sparkles" href="/ko/skills">
|
||||
crewAIInc/skills 팩 개요와 포함 내용.
|
||||
</Card>
|
||||
<Card title="AGENTS.md 및 코딩 도구" icon="terminal" href="/ko/guides/coding-tools/agents-md">
|
||||
Claude Code, Codex, Cursor, Gemini CLI용 AGENTS.md 설정.
|
||||
</Card>
|
||||
<Card title="Skills 레지스트리 (skills.sh)" icon="globe" href="https://skills.sh/crewaiinc/skills">
|
||||
공식 목록 — 스킬, 설치 통계, 감사 정보.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 2. llms.txt — 기계가 읽을 수 있는 문서
|
||||
|
||||
CrewAI는 AI 어시스턴트가 전체 문서에 기계가 읽을 수 있는 형태로 바로 접근할 수 있도록 `llms.txt` 파일을 제공합니다.
|
||||
|
||||
```
|
||||
https://docs.crewai.com/llms.txt
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="llms.txt란?">
|
||||
[`llms.txt`](https://llmstxt.org/)는 문서를 대규모 언어 모델이 소비하기 쉽게 만드는 새로운 표준입니다. HTML을 스크래핑하는 대신, 필요한 내용이 담긴 하나의 구조화된 텍스트 파일을 가져올 수 있습니다.
|
||||
|
||||
CrewAI의 `llms.txt`는 **이미 제공 중**이며, 에이전트가 바로 사용할 수 있습니다.
|
||||
</Tab>
|
||||
<Tab title="사용 방법">
|
||||
CrewAI 참고 문서가 필요할 때 코딩 에이전트에 URL을 알려 주세요.
|
||||
|
||||
```
|
||||
Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
|
||||
```
|
||||
|
||||
Claude Code, Cursor 등 많은 코딩 에이전트가 URL을 직접 가져올 수 있습니다. 파일에는 CrewAI 개념, API, 가이드를 아우르는 구조화된 문서가 포함되어 있습니다.
|
||||
</Tab>
|
||||
<Tab title="왜 중요한가">
|
||||
- **스크래핑 불필요** — 한 번의 요청으로 깔끔한 구조화 콘텐츠
|
||||
- **항상 최신** — docs.crewai.com에서 직접 제공
|
||||
- **LLM에 최적화** — 브라우저가 아니라 컨텍스트 윈도우에 맞게 포맷
|
||||
- **스킬과 상호 보완** — 스킬은 패턴을, llms.txt는 참조를 제공
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
---
|
||||
|
||||
## 3. 엔터프라이즈에 배포
|
||||
|
||||
로컬 crew를 몇 분 안에 **CrewAI AMP**(Agent Management Platform) 프로덕션으로 가져가세요.
|
||||
|
||||
<Steps>
|
||||
<Step title="로컬에서 빌드">
|
||||
crew 또는 flow를 스캐폴딩하고 테스트합니다.
|
||||
```bash
|
||||
crewai create crew my_crew
|
||||
cd my_crew
|
||||
crewai run
|
||||
```
|
||||
</Step>
|
||||
<Step title="배포 준비">
|
||||
프로젝트 구조가 준비되었는지 확인합니다.
|
||||
```bash
|
||||
crewai deploy --prepare
|
||||
```
|
||||
구조와 요구 사항은 [준비 가이드](/ko/enterprise/guides/prepare-for-deployment)를 참고하세요.
|
||||
</Step>
|
||||
<Step title="AMP에 배포">
|
||||
CrewAI AMP 플랫폼으로 푸시합니다.
|
||||
```bash
|
||||
crewai deploy
|
||||
```
|
||||
[GitHub 연동](/ko/enterprise/guides/deploy-to-amp) 또는 [Crew Studio](/ko/enterprise/guides/enable-crew-studio)로도 배포할 수 있습니다.
|
||||
</Step>
|
||||
<Step title="API로 접근">
|
||||
배포된 crew는 REST API 엔드포인트를 받습니다. 모든 애플리케이션에 통합할 수 있습니다.
|
||||
```bash
|
||||
curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
|
||||
-H "Authorization: Bearer $CREWAI_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"inputs": {"topic": "AI agents"}}'
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="AMP에 배포" icon="rocket" href="/ko/enterprise/guides/deploy-to-amp">
|
||||
전체 배포 가이드 — CLI, GitHub, Crew Studio 방법.
|
||||
</Card>
|
||||
<Card title="AMP 소개" icon="globe" href="/ko/enterprise/introduction">
|
||||
플랫폼 개요 — 프로덕션 crew에 AMP가 제공하는 것.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 4. 엔터프라이즈 기능
|
||||
|
||||
CrewAI AMP는 프로덕션 팀을 위해 만들어졌습니다. 배포 외에 제공되는 것은 다음과 같습니다.
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="관측 가능성" icon="chart-line">
|
||||
모든 crew 실행에 대한 상세 실행 추적, 로그, 성능 지표. 에이전트 결정, 도구 호출, 태스크 완료를 실시간으로 모니터링합니다.
|
||||
</Card>
|
||||
<Card title="Crew Studio" icon="paintbrush">
|
||||
시각적으로 crew를 만들고, 맞춤 설정하고, 배포하는 노코드/로코드 인터페이스 — 코드로 보내거나 바로 배포할 수 있습니다.
|
||||
</Card>
|
||||
<Card title="웹훅 스트리밍" icon="webhook">
|
||||
crew 실행에서 실시간 이벤트를 시스템으로 스트리밍합니다. Slack, Zapier 등 웹훅 소비자와 연동할 수 있습니다.
|
||||
</Card>
|
||||
<Card title="팀 관리" icon="users">
|
||||
SSO, RBAC, 조직 단위 제어. 팀 전체에서 crew 생성·배포·접근 권한을 관리합니다.
|
||||
</Card>
|
||||
<Card title="도구 저장소" icon="toolbox">
|
||||
조직 전체에 맞춤 도구를 게시하고 공유합니다. 레지스트리에서 커뮤니티 도구를 설치합니다.
|
||||
</Card>
|
||||
<Card title="Factory(셀프 호스팅)" icon="server">
|
||||
자체 인프라에서 CrewAI AMP를 실행합니다. 데이터 상주와 규정 준수 제어와 함께 플랫폼 전체 기능을 사용할 수 있습니다.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="AMP는 누구를 위한 것인가요?">
|
||||
AI 에이전트 워크플로를 프로토타입에서 프로덕션으로 옮겨야 하는 팀을 위한 제품입니다. 관측 가능성, 접근 제어, 확장 가능한 인프라를 제공합니다. 스타트업이든 대기업이든 운영 복잡도는 AMP가 맡고, 에이전트 구축에 집중할 수 있습니다.
|
||||
</Accordion>
|
||||
<Accordion title="배포 옵션은 무엇이 있나요?">
|
||||
- **클라우드 (app.crewai.com)** — CrewAI가 관리, 프로덕션까지 가장 빠른 경로
|
||||
- **Factory(셀프 호스팅)** — 데이터 통제를 위해 자체 인프라에서 실행
|
||||
- **하이브리드** — 민감도에 따라 클라우드와 셀프 호스팅을 혼합
|
||||
</Accordion>
|
||||
<Accordion title="가격은 어떻게 되나요?">
|
||||
[app.crewai.com](https://app.crewai.com)에 가입하면 현재 요금제를 확인할 수 있습니다. 엔터프라이즈 및 Factory 가격은 문의 시 안내합니다.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Card title="CrewAI AMP 살펴보기 →" icon="arrow-right" href="https://app.crewai.com">
|
||||
가입하고 첫 crew를 프로덕션에 배포해 보세요.
|
||||
</Card>
|
||||
@@ -4,6 +4,36 @@ description: "Atualizações de produto, melhorias e correções do CrewAI"
|
||||
icon: "clock"
|
||||
mode: "wide"
|
||||
---
|
||||
<Update label="22 abr 2026">
|
||||
## v1.14.3a2
|
||||
|
||||
[Ver release no GitHub](https://github.com/crewAIInc/crewAI/releases/tag/1.14.3a2)
|
||||
|
||||
## O que mudou
|
||||
|
||||
### Recursos
|
||||
- Adicionar suporte para bedrock V4
|
||||
- Adicionar ferramentas de sandbox Daytona para funcionalidade aprimorada
|
||||
- Adicionar página 'Construir com IA' — documentação nativa de IA para agentes de codificação
|
||||
- Adicionar Construir com IA à navegação Começar e arquivos de página para todos os idiomas (en, ko, pt-BR, ar)
|
||||
|
||||
### Correções de Bugs
|
||||
- Corrigir a propagação de nomes implícitos @CrewBase para eventos da equipe
|
||||
- Resolver problema com inicialização de lote duplicada na mesclagem de metadados de execução
|
||||
- Corrigir a serialização de campos de referência de classe Task para checkpointing
|
||||
- Lidar com o resultado BaseModel no loop de repetição de guardrail
|
||||
- Atualizar python-dotenv para a versão >=1.2.2 para conformidade de segurança
|
||||
|
||||
### Documentação
|
||||
- Atualizar changelog e versão para v1.14.3a1
|
||||
- Atualizar descrições e aplicar traduções reais
|
||||
|
||||
## Contributors
|
||||
|
||||
@MatthiasHowellYopp, @github-actions[bot], @greysonlalonde, @iris-clawd, @lorenzejay, @renatonitta
|
||||
|
||||
</Update>
|
||||
|
||||
<Update label="21 abr 2026">
|
||||
## v1.14.3a1
|
||||
|
||||
|
||||
217
docs/pt-BR/guides/coding-tools/build-with-ai.mdx
Normal file
217
docs/pt-BR/guides/coding-tools/build-with-ai.mdx
Normal file
@@ -0,0 +1,217 @@
|
||||
---
|
||||
title: "Construa com IA"
|
||||
description: "Tudo o que agentes de codificação com IA precisam para criar, implantar e escalar com CrewAI — skills, documentação legível por máquina, implantação e recursos enterprise."
|
||||
icon: robot
|
||||
mode: "wide"
|
||||
---
|
||||
|
||||
# Construa com IA
|
||||
|
||||
O CrewAI é nativo de IA. Esta página reúne o que um agente de codificação com IA precisa para construir com CrewAI — seja Claude Code, Codex, Cursor, Gemini CLI ou qualquer outro assistente que ajude um desenvolvedor a entregar crews e flows.
|
||||
|
||||
### Agentes de codificação compatíveis
|
||||
|
||||
<CardGroup cols={5}>
|
||||
<Card title="Claude Code" icon="message-bot" color="#D97706" />
|
||||
<Card title="Cursor" icon="arrow-pointer" color="#3B82F6" />
|
||||
<Card title="Codex" icon="terminal" color="#10B981" />
|
||||
<Card title="Windsurf" icon="wind" color="#06B6D4" />
|
||||
<Card title="Gemini CLI" icon="sparkles" color="#8B5CF6" />
|
||||
</CardGroup>
|
||||
|
||||
<Note>
|
||||
Esta página serve para humanos e para assistentes de IA. Se você é um agente de codificação, comece por **Skills** para obter contexto do CrewAI e depois use **llms.txt** para acesso completo à documentação.
|
||||
</Note>
|
||||
|
||||
---
|
||||
|
||||
## 1. Skills — ensine CrewAI ao seu agente
|
||||
|
||||
**Skills** são pacotes de instruções que dão aos agentes de codificação conhecimento profundo do CrewAI — como estruturar Flows, configurar Crews, usar ferramentas e seguir convenções do framework.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="Claude Code (Plugin Marketplace)">
|
||||
<img src="https://cdn.simpleicons.org/anthropic/D97706" alt="Anthropic" width="28" style={{display: "inline", verticalAlign: "middle", marginRight: "8px"}} />
|
||||
As skills do CrewAI estão no **plugin marketplace do Claude Code** — o mesmo canal usado por empresas líderes em IA:
|
||||
```shell
|
||||
/plugin marketplace add crewAIInc/skills
|
||||
/plugin install crewai-skills@crewai-plugins
|
||||
/reload-plugins
|
||||
```
|
||||
|
||||
Quatro skills são ativadas automaticamente quando você faz perguntas relevantes sobre CrewAI:
|
||||
|
||||
| Skill | Quando é usada |
|
||||
|-------|----------------|
|
||||
| `getting-started` | Novos projetos, escolha entre `LLM.call()` / `Agent` / `Crew` / `Flow`, arquivos `crew.py` / `main.py` |
|
||||
| `design-agent` | Configurar agentes — papel, objetivo, história, ferramentas, LLMs, memória, guardrails |
|
||||
| `design-task` | Descrever tarefas, dependências, saída estruturada (`output_pydantic`, `output_json`), revisão humana |
|
||||
| `ask-docs` | Consultar o [servidor MCP da documentação CrewAI](https://docs.crewai.com/mcp) em tempo real para detalhes de API |
|
||||
</Tab>
|
||||
<Tab title="npx (qualquer agente)">
|
||||
Funciona com Claude Code, Codex, Cursor, Gemini CLI ou qualquer agente de codificação:
|
||||
```shell
|
||||
npx skills add crewaiinc/skills
|
||||
```
|
||||
Obtido do [registro skills.sh](https://skills.sh/crewaiinc/skills).
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
<Steps>
|
||||
<Step title="Instale o pacote oficial de skills">
|
||||
Use um dos métodos acima — o plugin marketplace do Claude Code ou `npx skills add`. Ambos instalam o pacote oficial [crewAIInc/skills](https://github.com/crewAIInc/skills).
|
||||
</Step>
|
||||
<Step title="Seu agente ganha expertise imediata em CrewAI">
|
||||
O pacote ensina ao seu agente:
|
||||
- **Flows** — apps com estado, passos e disparo de crews
|
||||
- **Crews e agentes** — padrões YAML-first, papéis, tarefas, delegação
|
||||
- **Ferramentas e integrações** — busca, APIs, servidores MCP e ferramentas comuns do CrewAI
|
||||
- **Estrutura do projeto** — scaffolds da CLI e convenções de repositório
|
||||
- **Padrões atualizados** — alinhado à documentação e às melhores práticas atuais do CrewAI
|
||||
</Step>
|
||||
<Step title="Comece a construir">
|
||||
Seu agente pode estruturar e construir projetos CrewAI sem você precisar reexplicar o framework a cada sessão.
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Conceito de skills" icon="bolt" href="/pt-BR/concepts/skills">
|
||||
Como skills funcionam em agentes CrewAI — injeção, ativação e padrões.
|
||||
</Card>
|
||||
<Card title="Página de skills" icon="wand-magic-sparkles" href="/pt-BR/skills">
|
||||
Visão geral do pacote crewAIInc/skills e do que ele inclui.
|
||||
</Card>
|
||||
<Card title="AGENTS.md e ferramentas" icon="terminal" href="/pt-BR/guides/coding-tools/agents-md">
|
||||
Configure o AGENTS.md para Claude Code, Codex, Cursor e Gemini CLI.
|
||||
</Card>
|
||||
<Card title="Registro skills.sh" icon="globe" href="https://skills.sh/crewaiinc/skills">
|
||||
Listagem oficial — skills, estatísticas de instalação e auditorias.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 2. llms.txt — documentação legível por máquina
|
||||
|
||||
O CrewAI publica um arquivo `llms.txt` que dá aos assistentes de IA acesso direto à documentação completa em formato legível por máquinas.
|
||||
|
||||
```
|
||||
https://docs.crewai.com/llms.txt
|
||||
```
|
||||
|
||||
<Tabs>
|
||||
<Tab title="O que é llms.txt?">
|
||||
[`llms.txt`](https://llmstxt.org/) é um padrão emergente para tornar a documentação consumível por grandes modelos de linguagem. Em vez de fazer scraping de HTML, seu agente pode buscar um único arquivo de texto estruturado com o conteúdo necessário.
|
||||
|
||||
O `llms.txt` do CrewAI **já está no ar** — seu agente pode usar agora.
|
||||
</Tab>
|
||||
<Tab title="Como usar">
|
||||
Indique ao agente de codificação a URL quando precisar da referência do CrewAI:
|
||||
|
||||
```
|
||||
Fetch https://docs.crewai.com/llms.txt for CrewAI documentation.
|
||||
```
|
||||
|
||||
Muitos agentes (Claude Code, Cursor etc.) conseguem buscar URLs diretamente. O arquivo contém documentação estruturada sobre conceitos, APIs e guias do CrewAI.
|
||||
</Tab>
|
||||
<Tab title="Por que importa">
|
||||
- **Sem scraping** — conteúdo limpo e estruturado em uma requisição
|
||||
- **Sempre atualizado** — servido diretamente de docs.crewai.com
|
||||
- **Otimizado para LLMs** — formatado para janelas de contexto, não para navegadores
|
||||
- **Complementa as skills** — skills ensinam padrões; llms.txt fornece referência
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
---
|
||||
|
||||
## 3. Implantação enterprise
|
||||
|
||||
Do crew local à produção no **CrewAI AMP** (Agent Management Platform) em minutos.
|
||||
|
||||
<Steps>
|
||||
<Step title="Construa localmente">
|
||||
Estruture e teste seu crew ou flow:
|
||||
```bash
|
||||
crewai create crew my_crew
|
||||
cd my_crew
|
||||
crewai run
|
||||
```
|
||||
</Step>
|
||||
<Step title="Prepare a implantação">
|
||||
Garanta que a estrutura do projeto está pronta:
|
||||
```bash
|
||||
crewai deploy --prepare
|
||||
```
|
||||
Veja o [guia de preparação](/pt-BR/enterprise/guides/prepare-for-deployment) para detalhes de estrutura e requisitos.
|
||||
</Step>
|
||||
<Step title="Implante no AMP">
|
||||
Envie para a plataforma CrewAI AMP:
|
||||
```bash
|
||||
crewai deploy
|
||||
```
|
||||
Também é possível implantar pela [integração com GitHub](/pt-BR/enterprise/guides/deploy-to-amp) ou pelo [Crew Studio](/pt-BR/enterprise/guides/enable-crew-studio).
|
||||
</Step>
|
||||
<Step title="Acesso via API">
|
||||
O crew implantado recebe um endpoint REST. Integre em qualquer aplicação:
|
||||
```bash
|
||||
curl -X POST https://app.crewai.com/api/v1/crews/<crew-id>/kickoff \
|
||||
-H "Authorization: Bearer $CREWAI_API_KEY" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"inputs": {"topic": "AI agents"}}'
|
||||
```
|
||||
</Step>
|
||||
</Steps>
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Implantar no AMP" icon="rocket" href="/pt-BR/enterprise/guides/deploy-to-amp">
|
||||
Guia completo de implantação — CLI, GitHub e Crew Studio.
|
||||
</Card>
|
||||
<Card title="Introdução ao AMP" icon="globe" href="/pt-BR/enterprise/introduction">
|
||||
Visão da plataforma — o que o AMP oferece para crews em produção.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
---
|
||||
|
||||
## 4. Recursos enterprise
|
||||
|
||||
O CrewAI AMP foi feito para equipes em produção. Além da implantação, você obtém:
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Observabilidade" icon="chart-line">
|
||||
Traces de execução, logs e métricas de desempenho para cada execução de crew. Monitore decisões de agentes, chamadas de ferramentas e conclusão de tarefas em tempo real.
|
||||
</Card>
|
||||
<Card title="Crew Studio" icon="paintbrush">
|
||||
Interface no-code/low-code para criar, personalizar e implantar crews visualmente — exporte para código ou implante direto.
|
||||
</Card>
|
||||
<Card title="Webhook streaming" icon="webhook">
|
||||
Transmita eventos em tempo real das execuções para seus sistemas. Integre com Slack, Zapier ou qualquer consumidor de webhook.
|
||||
</Card>
|
||||
<Card title="Gestão de equipe" icon="users">
|
||||
SSO, RBAC e controles em nível de organização. Gerencie quem pode criar, implantar e acessar crews.
|
||||
</Card>
|
||||
<Card title="Repositório de ferramentas" icon="toolbox">
|
||||
Publique e compartilhe ferramentas customizadas na organização. Instale ferramentas da comunidade a partir do registro.
|
||||
</Card>
|
||||
<Card title="Factory (self-hosted)" icon="server">
|
||||
Execute o CrewAI AMP na sua infraestrutura. Capacidades completas da plataforma com residência de dados e controles de conformidade.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
<AccordionGroup>
|
||||
<Accordion title="Para quem é o AMP?">
|
||||
Para equipes que precisam levar fluxos de agentes de IA do protótipo à produção — com observabilidade, controles de acesso e infraestrutura escalável. De startups a grandes empresas, o AMP cuida da complexidade operacional para você focar nos agentes.
|
||||
</Accordion>
|
||||
<Accordion title="Quais opções de implantação existem?">
|
||||
- **Nuvem (app.crewai.com)** — gerenciada pela CrewAI, caminho mais rápido para produção
|
||||
- **Factory (self-hosted)** — na sua infraestrutura para controle total dos dados
|
||||
- **Híbrido** — combine nuvem e self-hosted conforme a sensibilidade dos dados
|
||||
</Accordion>
|
||||
<Accordion title="Como funciona o preço?">
|
||||
Cadastre-se em [app.crewai.com](https://app.crewai.com) para ver os planos atuais. Preços enterprise e Factory sob consulta.
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
<Card title="Conheça o CrewAI AMP →" icon="arrow-right" href="https://app.crewai.com">
|
||||
Cadastre-se e leve seu primeiro crew à produção.
|
||||
</Card>
|
||||
@@ -152,4 +152,4 @@ __all__ = [
|
||||
"wrap_file_source",
|
||||
]
|
||||
|
||||
__version__ = "1.14.3a1"
|
||||
__version__ = "1.14.3a2"
|
||||
|
||||
@@ -10,7 +10,7 @@ requires-python = ">=3.10, <3.14"
|
||||
dependencies = [
|
||||
"pytube~=15.0.0",
|
||||
"requests>=2.33.0,<3",
|
||||
"crewai==1.14.3a1",
|
||||
"crewai==1.14.3a2",
|
||||
"tiktoken~=0.8.0",
|
||||
"beautifulsoup4~=4.13.4",
|
||||
"python-docx~=1.2.0",
|
||||
|
||||
@@ -313,4 +313,4 @@ __all__ = [
|
||||
"ZapierActionTools",
|
||||
]
|
||||
|
||||
__version__ = "1.14.3a1"
|
||||
__version__ = "1.14.3a2"
|
||||
|
||||
@@ -24,7 +24,7 @@ dependencies = [
|
||||
"tokenizers>=0.21,<1",
|
||||
"openpyxl~=3.1.5",
|
||||
# Authentication and Security
|
||||
"python-dotenv~=1.1.1",
|
||||
"python-dotenv>=1.2.2,<2",
|
||||
"pyjwt>=2.9.0,<3",
|
||||
# TUI
|
||||
"textual>=7.5.0",
|
||||
@@ -55,7 +55,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
|
||||
|
||||
[project.optional-dependencies]
|
||||
tools = [
|
||||
"crewai-tools==1.14.3a1",
|
||||
"crewai-tools==1.14.3a2",
|
||||
]
|
||||
embeddings = [
|
||||
"tiktoken~=0.8.0"
|
||||
|
||||
@@ -1,10 +1,9 @@
|
||||
import contextvars
|
||||
import threading
|
||||
from typing import Any
|
||||
import urllib.request
|
||||
import importlib
|
||||
import sys
|
||||
from typing import TYPE_CHECKING, Annotated, Any
|
||||
import warnings
|
||||
|
||||
from pydantic import PydanticUserError
|
||||
from pydantic import Field, PydanticUserError
|
||||
|
||||
from crewai.agent.core import Agent
|
||||
from crewai.agent.planning_config import PlanningConfig
|
||||
@@ -20,7 +19,10 @@ from crewai.state.checkpoint_config import CheckpointConfig # noqa: F401
|
||||
from crewai.task import Task
|
||||
from crewai.tasks.llm_guardrail import LLMGuardrail
|
||||
from crewai.tasks.task_output import TaskOutput
|
||||
from crewai.telemetry.telemetry import Telemetry
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from crewai.memory.unified_memory import Memory
|
||||
|
||||
|
||||
def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
@@ -46,38 +48,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
|
||||
|
||||
_suppress_pydantic_deprecation_warnings()
|
||||
|
||||
__version__ = "1.14.3a1"
|
||||
_telemetry_submitted = False
|
||||
|
||||
|
||||
def _track_install() -> None:
|
||||
"""Track package installation/first-use via Scarf analytics."""
|
||||
global _telemetry_submitted
|
||||
|
||||
if _telemetry_submitted or Telemetry._is_telemetry_disabled():
|
||||
return
|
||||
|
||||
try:
|
||||
pixel_url = "https://api.scarf.sh/v2/packages/CrewAI/crewai/docs/00f2dad1-8334-4a39-934e-003b2e1146db"
|
||||
|
||||
req = urllib.request.Request(pixel_url) # noqa: S310
|
||||
req.add_header("User-Agent", f"CrewAI-Python/{__version__}")
|
||||
|
||||
with urllib.request.urlopen(req, timeout=2): # noqa: S310
|
||||
_telemetry_submitted = True
|
||||
except Exception: # noqa: S110
|
||||
pass
|
||||
|
||||
|
||||
def _track_install_async() -> None:
|
||||
"""Track installation in background thread to avoid blocking imports."""
|
||||
if not Telemetry._is_telemetry_disabled():
|
||||
ctx = contextvars.copy_context()
|
||||
thread = threading.Thread(target=ctx.run, args=(_track_install,), daemon=True)
|
||||
thread.start()
|
||||
|
||||
|
||||
_track_install_async()
|
||||
__version__ = "1.14.3a2"
|
||||
|
||||
_LAZY_IMPORTS: dict[str, tuple[str, str]] = {
|
||||
"Memory": ("crewai.memory.unified_memory", "Memory"),
|
||||
@@ -88,8 +59,6 @@ def __getattr__(name: str) -> Any:
|
||||
"""Lazily import heavy modules (e.g. Memory → lancedb) on first access."""
|
||||
if name in _LAZY_IMPORTS:
|
||||
module_path, attr = _LAZY_IMPORTS[name]
|
||||
import importlib
|
||||
|
||||
mod = importlib.import_module(module_path)
|
||||
val = getattr(mod, attr)
|
||||
globals()[name] = val
|
||||
@@ -147,8 +116,6 @@ try:
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
import sys
|
||||
|
||||
_full_namespace = {
|
||||
**_base_namespace,
|
||||
"ToolsHandler": _ToolsHandler,
|
||||
@@ -191,10 +158,6 @@ try:
|
||||
Flow.model_rebuild(force=True, _types_namespace=_full_namespace)
|
||||
_AgentExecutor.model_rebuild(force=True, _types_namespace=_full_namespace)
|
||||
|
||||
from typing import Annotated
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
from crewai.state.runtime import RuntimeState
|
||||
|
||||
Entity = Annotated[
|
||||
|
||||
@@ -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]==1.14.3a1"
|
||||
"crewai[tools]==1.14.3a2"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
|
||||
@@ -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]==1.14.3a1"
|
||||
"crewai[tools]==1.14.3a2"
|
||||
]
|
||||
|
||||
[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]==1.14.3a1"
|
||||
"crewai[tools]==1.14.3a2"
|
||||
]
|
||||
|
||||
[tool.crewai]
|
||||
|
||||
@@ -81,8 +81,11 @@ class TraceBatchManager:
|
||||
"""Initialize a new trace batch (thread-safe)"""
|
||||
with self._batch_ready_cv:
|
||||
if self.current_batch is not None:
|
||||
# Lazy init (e.g. DefaultEnvEvent) may have created the batch without
|
||||
# execution_type; merge metadata from a later flow/crew initializer.
|
||||
self.current_batch.execution_metadata.update(execution_metadata)
|
||||
logger.debug(
|
||||
"Batch already initialized, skipping duplicate initialization"
|
||||
"Batch already initialized, merged execution metadata and skipped duplicate initialization"
|
||||
)
|
||||
return self.current_batch
|
||||
|
||||
|
||||
@@ -60,12 +60,6 @@ from crewai.events.types.crew_events import (
|
||||
CrewKickoffFailedEvent,
|
||||
CrewKickoffStartedEvent,
|
||||
)
|
||||
from crewai.events.types.env_events import (
|
||||
CCEnvEvent,
|
||||
CodexEnvEvent,
|
||||
CursorEnvEvent,
|
||||
DefaultEnvEvent,
|
||||
)
|
||||
from crewai.events.types.flow_events import (
|
||||
FlowCreatedEvent,
|
||||
FlowFinishedEvent,
|
||||
@@ -212,7 +206,6 @@ class TraceCollectionListener(BaseEventListener):
|
||||
self._listeners_setup = True
|
||||
return
|
||||
|
||||
self._register_env_event_handlers(crewai_event_bus)
|
||||
self._register_flow_event_handlers(crewai_event_bus)
|
||||
self._register_context_event_handlers(crewai_event_bus)
|
||||
self._register_action_event_handlers(crewai_event_bus)
|
||||
@@ -221,25 +214,6 @@ class TraceCollectionListener(BaseEventListener):
|
||||
|
||||
self._listeners_setup = True
|
||||
|
||||
def _register_env_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
|
||||
"""Register handlers for environment context events."""
|
||||
|
||||
@event_bus.on(CCEnvEvent)
|
||||
def on_cc_env(source: Any, event: CCEnvEvent) -> None:
|
||||
self._handle_action_event("cc_env", source, event)
|
||||
|
||||
@event_bus.on(CodexEnvEvent)
|
||||
def on_codex_env(source: Any, event: CodexEnvEvent) -> None:
|
||||
self._handle_action_event("codex_env", source, event)
|
||||
|
||||
@event_bus.on(CursorEnvEvent)
|
||||
def on_cursor_env(source: Any, event: CursorEnvEvent) -> None:
|
||||
self._handle_action_event("cursor_env", source, event)
|
||||
|
||||
@event_bus.on(DefaultEnvEvent)
|
||||
def on_default_env(source: Any, event: DefaultEnvEvent) -> None:
|
||||
self._handle_action_event("default_env", source, event)
|
||||
|
||||
def _register_flow_event_handlers(self, event_bus: CrewAIEventsBus) -> None:
|
||||
"""Register handlers for flow events."""
|
||||
|
||||
@@ -286,8 +260,8 @@ class TraceCollectionListener(BaseEventListener):
|
||||
if self.batch_manager.batch_owner_type != "flow":
|
||||
# Always call _initialize_crew_batch to claim ownership.
|
||||
# If batch was already initialized by a concurrent action event
|
||||
# (race condition with DefaultEnvEvent), initialize_batch() returns
|
||||
# early but batch_owner_type is still correctly set to "crew".
|
||||
# (e.g. LLM/tool before crew_kickoff_started), initialize_batch()
|
||||
# returns early but batch_owner_type is still correctly set to "crew".
|
||||
# Skip only when a parent flow already owns the batch.
|
||||
self._initialize_crew_batch(source, event)
|
||||
self._handle_trace_event("crew_kickoff_started", source, event)
|
||||
|
||||
@@ -1503,6 +1503,8 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
except Exception:
|
||||
logger.warning("FlowStartedEvent handler failed", exc_info=True)
|
||||
|
||||
get_env_context()
|
||||
|
||||
context = self._pending_feedback_context
|
||||
emit = context.emit
|
||||
default_outcome = context.default_outcome
|
||||
@@ -2004,7 +2006,6 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
restored = apply_checkpoint(self, from_checkpoint)
|
||||
if restored is not None:
|
||||
return restored.kickoff(inputs=inputs, input_files=input_files)
|
||||
get_env_context()
|
||||
if self.stream:
|
||||
result_holder: list[Any] = []
|
||||
current_task_info: TaskInfo = {
|
||||
@@ -2206,6 +2207,10 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
|
||||
f"Flow started with ID: {self.flow_id}", color="bold magenta"
|
||||
)
|
||||
|
||||
# After FlowStarted (when not suppressed): env events must not pre-empt
|
||||
# trace batch init with implicit "crew" execution_type.
|
||||
get_env_context()
|
||||
|
||||
if inputs is not None and "id" not in inputs:
|
||||
self._initialize_state(inputs)
|
||||
|
||||
|
||||
@@ -175,6 +175,16 @@ LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
|
||||
"us.amazon.nova-pro-v1:0": 300000,
|
||||
"us.amazon.nova-micro-v1:0": 128000,
|
||||
"us.amazon.nova-lite-v1:0": 300000,
|
||||
# Claude 4 models
|
||||
"us.anthropic.claude-opus-4-7": 1000000,
|
||||
"us.anthropic.claude-sonnet-4-6": 1000000,
|
||||
"us.anthropic.claude-opus-4-6-v1": 1000000,
|
||||
"us.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
|
||||
"us.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
|
||||
"us.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
|
||||
"us.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
|
||||
"us.anthropic.claude-opus-4-20250514-v1:0": 200000,
|
||||
"us.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
|
||||
"us.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
|
||||
"us.anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
|
||||
"us.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
|
||||
@@ -193,15 +203,44 @@ LLM_CONTEXT_WINDOW_SIZES: Final[dict[str, int]] = {
|
||||
"eu.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
|
||||
"eu.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
|
||||
"eu.anthropic.claude-3-haiku-20240307-v1:0": 200000,
|
||||
# Claude 4 EU
|
||||
"eu.anthropic.claude-opus-4-7": 1000000,
|
||||
"eu.anthropic.claude-sonnet-4-6": 1000000,
|
||||
"eu.anthropic.claude-opus-4-6-v1": 1000000,
|
||||
"eu.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
|
||||
"eu.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
|
||||
"eu.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
|
||||
"eu.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
|
||||
"eu.anthropic.claude-opus-4-20250514-v1:0": 200000,
|
||||
"eu.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
|
||||
"eu.meta.llama3-2-3b-instruct-v1:0": 131000,
|
||||
"eu.meta.llama3-2-1b-instruct-v1:0": 131000,
|
||||
"apac.anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
|
||||
"apac.anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
|
||||
"apac.anthropic.claude-3-sonnet-20240229-v1:0": 200000,
|
||||
"apac.anthropic.claude-3-haiku-20240307-v1:0": 200000,
|
||||
# Claude 4 APAC
|
||||
"apac.anthropic.claude-opus-4-7": 1000000,
|
||||
"apac.anthropic.claude-sonnet-4-6": 1000000,
|
||||
"apac.anthropic.claude-opus-4-6-v1": 1000000,
|
||||
"apac.anthropic.claude-opus-4-5-20251101-v1:0": 200000,
|
||||
"apac.anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
|
||||
"apac.anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
|
||||
"apac.anthropic.claude-opus-4-1-20250805-v1:0": 200000,
|
||||
"apac.anthropic.claude-opus-4-20250514-v1:0": 200000,
|
||||
"apac.anthropic.claude-sonnet-4-20250514-v1:0": 200000,
|
||||
"amazon.nova-pro-v1:0": 300000,
|
||||
"amazon.nova-micro-v1:0": 128000,
|
||||
"amazon.nova-lite-v1:0": 300000,
|
||||
"anthropic.claude-opus-4-7": 1000000,
|
||||
"anthropic.claude-sonnet-4-6": 1000000,
|
||||
"anthropic.claude-opus-4-6-v1": 1000000,
|
||||
"anthropic.claude-opus-4-5-20251101-v1:0": 200000,
|
||||
"anthropic.claude-haiku-4-5-20251001-v1:0": 200000,
|
||||
"anthropic.claude-sonnet-4-5-20250929-v1:0": 200000,
|
||||
"anthropic.claude-opus-4-1-20250805-v1:0": 200000,
|
||||
"anthropic.claude-opus-4-20250514-v1:0": 200000,
|
||||
"anthropic.claude-sonnet-4-20250514-v1:0": 200000,
|
||||
"anthropic.claude-3-5-sonnet-20240620-v1:0": 200000,
|
||||
"anthropic.claude-3-5-haiku-20241022-v1:0": 200000,
|
||||
"anthropic.claude-3-5-sonnet-20241022-v2:0": 200000,
|
||||
|
||||
@@ -423,6 +423,34 @@ AZURE_MODELS: list[AzureModels] = [
|
||||
|
||||
|
||||
BedrockModels: TypeAlias = Literal[
|
||||
# Inference profiles (regional) - Claude 4
|
||||
"us.anthropic.claude-sonnet-4-5-20250929-v1:0",
|
||||
"us.anthropic.claude-sonnet-4-20250514-v1:0",
|
||||
"us.anthropic.claude-opus-4-5-20251101-v1:0",
|
||||
"us.anthropic.claude-opus-4-20250514-v1:0",
|
||||
"us.anthropic.claude-opus-4-1-20250805-v1:0",
|
||||
"us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
"us.anthropic.claude-sonnet-4-6",
|
||||
"us.anthropic.claude-opus-4-6-v1",
|
||||
# Inference profiles - shorter versions
|
||||
"us.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"us.anthropic.claude-opus-4-5-v1:0",
|
||||
"us.anthropic.claude-opus-4-6-v1:0",
|
||||
"us.anthropic.claude-haiku-4-5-v1:0",
|
||||
"eu.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"eu.anthropic.claude-opus-4-5-v1:0",
|
||||
"eu.anthropic.claude-haiku-4-5-v1:0",
|
||||
"apac.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"apac.anthropic.claude-opus-4-5-v1:0",
|
||||
"apac.anthropic.claude-haiku-4-5-v1:0",
|
||||
# Global inference profiles
|
||||
"global.anthropic.claude-sonnet-4-5-20250929-v1:0",
|
||||
"global.anthropic.claude-sonnet-4-20250514-v1:0",
|
||||
"global.anthropic.claude-opus-4-5-20251101-v1:0",
|
||||
"global.anthropic.claude-opus-4-6-v1",
|
||||
"global.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
"global.anthropic.claude-sonnet-4-6",
|
||||
# Direct model IDs
|
||||
"ai21.jamba-1-5-large-v1:0",
|
||||
"ai21.jamba-1-5-mini-v1:0",
|
||||
"amazon.nova-lite-v1:0",
|
||||
@@ -496,6 +524,34 @@ BedrockModels: TypeAlias = Literal[
|
||||
"twelvelabs.pegasus-1-2-v1:0",
|
||||
]
|
||||
BEDROCK_MODELS: list[BedrockModels] = [
|
||||
# Inference profiles (regional) - Claude 4
|
||||
"us.anthropic.claude-sonnet-4-5-20250929-v1:0",
|
||||
"us.anthropic.claude-sonnet-4-20250514-v1:0",
|
||||
"us.anthropic.claude-opus-4-5-20251101-v1:0",
|
||||
"us.anthropic.claude-opus-4-20250514-v1:0",
|
||||
"us.anthropic.claude-opus-4-1-20250805-v1:0",
|
||||
"us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
"us.anthropic.claude-sonnet-4-6",
|
||||
"us.anthropic.claude-opus-4-6-v1",
|
||||
# Inference profiles - shorter versions
|
||||
"us.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"us.anthropic.claude-opus-4-5-v1:0",
|
||||
"us.anthropic.claude-opus-4-6-v1:0",
|
||||
"us.anthropic.claude-haiku-4-5-v1:0",
|
||||
"eu.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"eu.anthropic.claude-opus-4-5-v1:0",
|
||||
"eu.anthropic.claude-haiku-4-5-v1:0",
|
||||
"apac.anthropic.claude-sonnet-4-5-v1:0",
|
||||
"apac.anthropic.claude-opus-4-5-v1:0",
|
||||
"apac.anthropic.claude-haiku-4-5-v1:0",
|
||||
# Global inference profiles
|
||||
"global.anthropic.claude-sonnet-4-5-20250929-v1:0",
|
||||
"global.anthropic.claude-sonnet-4-20250514-v1:0",
|
||||
"global.anthropic.claude-opus-4-5-20251101-v1:0",
|
||||
"global.anthropic.claude-opus-4-6-v1",
|
||||
"global.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
"global.anthropic.claude-sonnet-4-6",
|
||||
# Direct model IDs
|
||||
"ai21.jamba-1-5-large-v1:0",
|
||||
"ai21.jamba-1-5-mini-v1:0",
|
||||
"amazon.nova-lite-v1:0",
|
||||
|
||||
@@ -2075,6 +2075,9 @@ class BedrockCompletion(BaseLLM):
|
||||
|
||||
# Context window sizes for common Bedrock models
|
||||
context_windows = {
|
||||
"anthropic.claude-sonnet-4": 200000,
|
||||
"anthropic.claude-opus-4": 200000,
|
||||
"anthropic.claude-haiku-4": 200000,
|
||||
"anthropic.claude-3-5-sonnet": 200000,
|
||||
"anthropic.claude-3-5-haiku": 200000,
|
||||
"anthropic.claude-3-opus": 200000,
|
||||
|
||||
@@ -237,6 +237,8 @@ def crew(
|
||||
self.tasks = instantiated_tasks
|
||||
|
||||
crew_instance: Crew = _call_method(meth, self, *args, **kwargs)
|
||||
if "name" not in crew_instance.model_fields_set:
|
||||
crew_instance.name = getattr(self, "_crew_name", None) or crew_instance.name
|
||||
|
||||
def callback_wrapper(
|
||||
hook: Callable[Concatenate[CrewInstance, P2], R2], instance: CrewInstance
|
||||
|
||||
@@ -32,6 +32,7 @@ from pydantic import (
|
||||
field_validator,
|
||||
model_validator,
|
||||
)
|
||||
from pydantic.functional_serializers import PlainSerializer
|
||||
from pydantic_core import PydanticCustomError
|
||||
from typing_extensions import Self
|
||||
|
||||
@@ -86,6 +87,22 @@ from crewai.utilities.printer import PRINTER
|
||||
from crewai.utilities.string_utils import interpolate_only
|
||||
|
||||
|
||||
def _serialize_model_class(v: type[BaseModel] | None) -> dict[str, Any] | None:
|
||||
"""Serialize a Pydantic model class reference to its JSON schema."""
|
||||
return v.model_json_schema() if v else None
|
||||
|
||||
|
||||
def _deserialize_model_class(v: Any) -> type[BaseModel] | None:
|
||||
"""Hydrate a model class reference from checkpoint data."""
|
||||
if v is None or isinstance(v, type):
|
||||
return v
|
||||
if isinstance(v, dict):
|
||||
from crewai.utilities.pydantic_schema_utils import create_model_from_schema
|
||||
|
||||
return create_model_from_schema(v)
|
||||
return None
|
||||
|
||||
|
||||
class Task(BaseModel):
|
||||
"""Class that represents a task to be executed.
|
||||
|
||||
@@ -141,15 +158,33 @@ class Task(BaseModel):
|
||||
description="Whether the task should be executed asynchronously or not.",
|
||||
default=False,
|
||||
)
|
||||
output_json: type[BaseModel] | None = Field(
|
||||
output_json: Annotated[
|
||||
type[BaseModel] | None,
|
||||
BeforeValidator(_deserialize_model_class),
|
||||
PlainSerializer(
|
||||
_serialize_model_class, return_type=dict | None, when_used="json"
|
||||
),
|
||||
] = Field(
|
||||
description="A Pydantic model to be used to create a JSON output.",
|
||||
default=None,
|
||||
)
|
||||
output_pydantic: type[BaseModel] | None = Field(
|
||||
output_pydantic: Annotated[
|
||||
type[BaseModel] | None,
|
||||
BeforeValidator(_deserialize_model_class),
|
||||
PlainSerializer(
|
||||
_serialize_model_class, return_type=dict | None, when_used="json"
|
||||
),
|
||||
] = Field(
|
||||
description="A Pydantic model to be used to create a Pydantic output.",
|
||||
default=None,
|
||||
)
|
||||
response_model: type[BaseModel] | None = Field(
|
||||
response_model: Annotated[
|
||||
type[BaseModel] | None,
|
||||
BeforeValidator(_deserialize_model_class),
|
||||
PlainSerializer(
|
||||
_serialize_model_class, return_type=dict | None, when_used="json"
|
||||
),
|
||||
] = Field(
|
||||
description="A Pydantic model for structured LLM outputs using native provider features.",
|
||||
default=None,
|
||||
)
|
||||
@@ -189,7 +224,13 @@ class Task(BaseModel):
|
||||
description="Whether the task should instruct the agent to return the final answer formatted in Markdown",
|
||||
default=False,
|
||||
)
|
||||
converter_cls: type[Converter] | None = Field(
|
||||
converter_cls: Annotated[
|
||||
type[Converter] | None,
|
||||
BeforeValidator(lambda v: v if v is None or isinstance(v, type) else None),
|
||||
PlainSerializer(
|
||||
_serialize_model_class, return_type=dict | None, when_used="json"
|
||||
),
|
||||
] = Field(
|
||||
description="A converter class used to export structured output",
|
||||
default=None,
|
||||
)
|
||||
|
||||
@@ -8,6 +8,7 @@ from concurrent.futures import Future
|
||||
from hashlib import md5
|
||||
import re
|
||||
import sys
|
||||
from typing import Any, cast
|
||||
from unittest.mock import ANY, MagicMock, call, patch
|
||||
|
||||
from crewai.agent import Agent
|
||||
@@ -17,6 +18,7 @@ from crewai.crew import Crew
|
||||
from crewai.crews.crew_output import CrewOutput
|
||||
from crewai.events.event_bus import crewai_event_bus
|
||||
from crewai.events.types.crew_events import (
|
||||
CrewKickoffStartedEvent,
|
||||
CrewTestCompletedEvent,
|
||||
CrewTestStartedEvent,
|
||||
CrewTrainCompletedEvent,
|
||||
@@ -4741,6 +4743,61 @@ def test_default_crew_name(researcher, writer):
|
||||
assert crew.name == "crew"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"explicit_name,expected",
|
||||
[
|
||||
(None, "ResearchAutomation"),
|
||||
("My Research Automation", "My Research Automation"),
|
||||
],
|
||||
ids=["class_name_from_decorator", "explicit_name_preserved"],
|
||||
)
|
||||
def test_crew_kickoff_started_emits_display_name(
|
||||
researcher, writer, explicit_name, expected
|
||||
):
|
||||
"""Kickoff events should use the decorator-provided display name when implicit."""
|
||||
from crewai.crews.utils import prepare_kickoff
|
||||
from crewai.project import CrewBase, agent, crew, task
|
||||
|
||||
@CrewBase
|
||||
class ResearchAutomation:
|
||||
agents_config = None
|
||||
tasks_config = None
|
||||
|
||||
@agent
|
||||
def researcher(self):
|
||||
return researcher
|
||||
|
||||
@task
|
||||
def first_task(self):
|
||||
return Task(
|
||||
description="Task 1",
|
||||
expected_output="output",
|
||||
agent=self.researcher(),
|
||||
)
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
crew_kwargs: dict[str, Any] = {
|
||||
"agents": self.agents,
|
||||
"tasks": self.tasks,
|
||||
}
|
||||
if explicit_name is not None:
|
||||
crew_kwargs["name"] = explicit_name
|
||||
return Crew(**crew_kwargs)
|
||||
|
||||
captured: list[str | None] = []
|
||||
with crewai_event_bus.scoped_handlers():
|
||||
|
||||
@crewai_event_bus.on(CrewKickoffStartedEvent)
|
||||
def _capture(_source: Any, event: CrewKickoffStartedEvent) -> None:
|
||||
captured.append(event.crew_name)
|
||||
|
||||
automation_cls = cast(type[Any], ResearchAutomation)
|
||||
prepare_kickoff(cast(Any, automation_cls()).crew(), inputs=None)
|
||||
|
||||
assert captured == [expected]
|
||||
|
||||
|
||||
@pytest.mark.vcr()
|
||||
def test_memory_remember_receives_task_content():
|
||||
"""With memory=True, extract_memories receives raw content with task, agent, expected output, and result."""
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from typing import Any, ClassVar
|
||||
from typing import Any, ClassVar, cast
|
||||
from unittest.mock import Mock, create_autospec, patch
|
||||
|
||||
import pytest
|
||||
@@ -261,6 +261,55 @@ def test_crew_name():
|
||||
assert crew._crew_name == "InternalCrew"
|
||||
|
||||
|
||||
def test_crew_decorator_propagates_class_name_to_instance():
|
||||
"""@crew-decorated factory method should set Crew.name to the decorated class name."""
|
||||
sample_agent = Agent(role="r", goal="g", backstory="b")
|
||||
sample_task = Task(description="d", expected_output="o", agent=sample_agent)
|
||||
|
||||
@CrewBase
|
||||
class ImplicitNameCrewFactory:
|
||||
agents_config = None
|
||||
tasks_config = None
|
||||
agents: list[BaseAgent] = [sample_agent]
|
||||
tasks: list[Task] = [sample_task]
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
return Crew(
|
||||
agents=[sample_agent],
|
||||
tasks=[sample_task],
|
||||
)
|
||||
|
||||
factory_cls = cast(type[Any], ImplicitNameCrewFactory)
|
||||
crew_instance: Crew = cast(Any, factory_cls()).crew()
|
||||
assert crew_instance.name == "ImplicitNameCrewFactory"
|
||||
|
||||
|
||||
def test_crew_decorator_preserves_explicit_name():
|
||||
"""Explicit Crew(name=...) inside @crew should win over the @CrewBase class name."""
|
||||
sample_agent = Agent(role="r", goal="g", backstory="b")
|
||||
sample_task = Task(description="d", expected_output="o", agent=sample_agent)
|
||||
|
||||
@CrewBase
|
||||
class NamedCrewFactory:
|
||||
agents_config = None
|
||||
tasks_config = None
|
||||
agents: list[BaseAgent] = [sample_agent]
|
||||
tasks: list[Task] = [sample_task]
|
||||
|
||||
@crew
|
||||
def crew(self):
|
||||
return Crew(
|
||||
name="My Explicit Name",
|
||||
agents=[sample_agent],
|
||||
tasks=[sample_task],
|
||||
)
|
||||
|
||||
factory_cls = cast(type[Any], NamedCrewFactory)
|
||||
crew_instance: Crew = cast(Any, factory_cls()).crew()
|
||||
assert crew_instance.name == "My Explicit Name"
|
||||
|
||||
|
||||
@tool
|
||||
def simple_tool():
|
||||
"""Return 'Hi!'"""
|
||||
|
||||
@@ -1640,3 +1640,43 @@ class TestBackendInitializedGatedOnSuccess:
|
||||
|
||||
assert bm.backend_initialized is False
|
||||
assert bm.trace_batch_id is None
|
||||
|
||||
|
||||
class TestTraceBatchManagerDuplicateInitMerge:
|
||||
"""Second initialize_batch call merges execution_metadata (flow after lazy action)."""
|
||||
|
||||
def test_duplicate_initialize_merges_execution_metadata(self):
|
||||
with (
|
||||
patch(
|
||||
"crewai.events.listeners.tracing.trace_batch_manager.should_auto_collect_first_time_traces",
|
||||
return_value=True,
|
||||
),
|
||||
patch(
|
||||
"crewai.events.listeners.tracing.trace_batch_manager.is_tracing_enabled_in_context",
|
||||
return_value=True,
|
||||
),
|
||||
):
|
||||
bm = TraceBatchManager()
|
||||
bm.initialize_batch(
|
||||
user_context={"privacy_level": "standard"},
|
||||
execution_metadata={
|
||||
"crew_name": "Unknown Crew",
|
||||
"crewai_version": "9.9.9",
|
||||
},
|
||||
)
|
||||
first_batch_id = bm.current_batch.batch_id
|
||||
bm.initialize_batch(
|
||||
user_context={"privacy_level": "standard"},
|
||||
execution_metadata={
|
||||
"flow_name": "ResearchFlow",
|
||||
"execution_type": "flow",
|
||||
"crewai_version": "9.9.9",
|
||||
"execution_start": "2026-01-01T00:00:00+00:00",
|
||||
},
|
||||
)
|
||||
|
||||
assert bm.current_batch.batch_id == first_batch_id
|
||||
meta = bm.current_batch.execution_metadata
|
||||
assert meta.get("execution_type") == "flow"
|
||||
assert meta.get("flow_name") == "ResearchFlow"
|
||||
assert meta.get("crew_name") == "Unknown Crew"
|
||||
|
||||
@@ -13,7 +13,7 @@ dependencies = [
|
||||
"click~=8.1.7",
|
||||
"tomlkit~=0.13.2",
|
||||
"openai>=1.83.0,<3",
|
||||
"python-dotenv~=1.1.1",
|
||||
"python-dotenv>=1.2.2,<2",
|
||||
"pygithub~=1.59.1",
|
||||
"rich>=13.9.4",
|
||||
]
|
||||
|
||||
@@ -1,3 +1,3 @@
|
||||
"""CrewAI development tools."""
|
||||
|
||||
__version__ = "1.14.3a1"
|
||||
__version__ = "1.14.3a2"
|
||||
|
||||
357
uv.lock
generated
357
uv.lock
generated
@@ -13,7 +13,7 @@ resolution-markers = [
|
||||
]
|
||||
|
||||
[options]
|
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[[package]]
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[[package]]
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Reference in New Issue
Block a user