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docs: update quickstart and installation guides for improved clarity (#5301)

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* docs: update quickstart and installation guides for improved clarity

- Revised the quickstart guide to emphasize creating a Flow and running a single-agent crew that generates a report.
- Updated the installation documentation to reflect changes in the quickstart process and enhance user understanding.

* translations
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Lorenze Jay
2026-04-06 15:04:54 -07:00
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4
docs/ar/enterprise/guides/deploy-to-amp.mdx
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@@ -106,7 +106,7 @@ mode: "wide"
```
<Tip>
يستغرق النشر الأول عادة 10-15 دقيقة لبناء صور الحاويات. عمليات النشر اللاحقة أسرع بكثير.
يستغرق النشر الأول عادة حوالي دقيقة واحدة.
</Tip>
</Step>
@@ -188,7 +188,7 @@ crewai deploy remove <deployment_id>
1. انقر على زر "Deploy" لبدء عملية النشر
2. يمكنك مراقبة التقدم عبر شريط التقدم
3. يستغرق النشر الأول عادة حوالي 10-15 دقيقة؛ عمليات النشر اللاحقة ستكون أسرع
3. يستغرق النشر الأول عادة حوالي دقيقة واحدة
<Frame>
![تقدم النشر](/images/enterprise/deploy-progress.png)

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

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

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@@ -1,6 +1,6 @@
---
title: البدء السريع
description: ابنِ أول وكيل ذكاء اصطناعي مع CrewAI في أقل من 5 دقائق.
description: ابنِ أول Flow في CrewAI خلال دقائق — التنسيق والحالة وفريقًا بوكيل واحد ينتج تقريرًا فعليًا.
icon: rocket
mode: "wide"
---
@@ -13,376 +13,266 @@ mode: "wide"
<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
## ابنِ أول وكيل CrewAI
في هذا الدليل ستُنشئ **Flow** يحدد موضوع بحث، ويشغّل **طاقمًا بوكيل واحد** (باحث يستخدم البحث على الويب)، وينتهي بتقرير **Markdown** على القرص. يُعد Flow الطريقة الموصى بها لتنظيم التطبيقات الإنتاجية: يمتلك **الحالة** و**ترتيب التنفيذ**، بينما **الوكلاء** ينفّذون العمل داخل خطوة الطاقم.
لننشئ طاقماً بسيطاً يساعدنا في `البحث` و`إعداد التقارير` عن `أحدث تطورات الذكاء الاصطناعي` لموضوع أو مجال معين.
إذا لم تُكمل تثبيت CrewAI بعد، اتبع [دليل التثبيت](/ar/installation) أولًا.
قبل المتابعة، تأكد من إنهاء تثبيت CrewAI.
إذا لم تكن قد ثبّتها بعد، يمكنك القيام بذلك باتباع [دليل التثبيت](/ar/installation).
## المتطلبات الأساسية
اتبع الخطوات أدناه للبدء!
- بيئة Python وواجهة سطر أوامر CrewAI (راجع [التثبيت](/ar/installation))
- نموذج لغوي مهيأ بالمفاتيح الصحيحة — راجع [LLMs](/ar/concepts/llms#setting-up-your-llm)
- مفتاح API من [Serper.dev](https://serper.dev/) (`SERPER_API_KEY`) للبحث على الويب في هذا الدرس
## ابنِ أول Flow لك
<Steps>
<Step title="إنشاء طاقمك">
أنشئ مشروع طاقم جديد عبر تشغيل الأمر التالي في الطرفية.
سينشئ هذا مجلداً جديداً باسم `latest-ai-development` مع البنية الأساسية لطاقمك.
<Step title="أنشئ مشروع Flow">
من الطرفية، أنشئ مشروع Flow (اسم المجلد يستخدم شرطة سفلية، مثل `latest_ai_flow`):
<CodeGroup>
```shell Terminal
crewai create crew latest-ai-development
crewai create flow latest-ai-flow
cd latest_ai_flow
```
</CodeGroup>
يُنشئ ذلك تطبيق Flow ضمن `src/latest_ai_flow/`، بما في ذلك طاقمًا أوليًا في `crews/content_crew/` ستستبدله بطاقم بحث **بوكيل واحد** في الخطوات التالية.
</Step>
<Step title="الانتقال إلى مشروع الطاقم الجديد">
<CodeGroup>
```shell Terminal
cd latest_ai_development
```
</CodeGroup>
</Step>
<Step title="تعديل ملف `agents.yaml`">
<Tip>
يمكنك أيضاً تعديل الوكلاء حسب الحاجة ليناسبوا حالة الاستخدام الخاصة بك أو نسخ ولصق كما هو في مشروعك.
أي متغير مُستكمل في ملفات `agents.yaml` و`tasks.yaml` مثل `{topic}` سيُستبدل بقيمة المتغير في ملف `main.py`.
</Tip>
<Step title="اضبط وكيلًا واحدًا في `agents.yaml`">
استبدل محتوى `src/latest_ai_flow/crews/content_crew/config/agents.yaml` بباحث واحد. تُملأ المتغيرات مثل `{topic}` من `crew.kickoff(inputs=...)`.
```yaml agents.yaml
# src/latest_ai_development/config/agents.yaml
# src/latest_ai_flow/crews/content_crew/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
باحث بيانات أول في {topic}
goal: >
Uncover cutting-edge developments in {topic}
اكتشاف أحدث التطورات في {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
أنت باحث مخضرم تكشف أحدث المستجدات في {topic}.
تجد المعلومات الأكثر صلة وتعرضها بوضوح.
```
</Step>
<Step title="تعديل ملف `tasks.yaml`">
<Step title="اضبط مهمة واحدة في `tasks.yaml`">
```yaml tasks.yaml
# src/latest_ai_development/config/tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2025.
أجرِ بحثًا معمقًا عن {topic}. استخدم البحث على الويب للعثور على معلومات
حديثة وموثوقة. السنة الحالية 2026.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
تقرير بصيغة Markdown بأقسام واضحة: الاتجاهات الرئيسية، أدوات أو شركات بارزة،
والآثار. بين 800 و1200 كلمة تقريبًا. دون إحاطة المستند بأكمله بكتل كود.
agent: researcher
reporting_task:
description: >
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledge reports with the mains topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst
output_file: report.md
output_file: output/report.md
```
</Step>
<Step title="تعديل ملف `crew.py`">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
<Step title="اربط صف الطاقم (`content_crew.py`)">
اجعل الطاقم المُولَّد يشير إلى YAML وأرفق `SerperDevTool` بالباحث.
```python content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List
from crewai import Agent, Crew, Process, Task
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
class ResearchCrew:
"""طاقم بحث بوكيل واحد داخل Flow."""
agents: List[BaseAgent]
tasks: List[Task]
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
config=self.agents_config["researcher"], # type: ignore[index]
verbose=True,
tools=[SerperDevTool()]
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
tools=[SerperDevTool()],
)
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'], # type: ignore[index]
)
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'], # type: ignore[index]
output_file='output/report.md' # This is the file that will be contain the final report.
config=self.tasks_config["research_task"], # type: ignore[index]
)
@crew
def crew(self) -> Crew:
"""Creates the LatestAiDevelopment crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
)
```
</Step>
<Step title="[اختياري] إضافة دوال قبل وبعد تشغيل الطاقم">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
<Step title="عرّف Flow في `main.py`">
اربط الطاقم بـ Flow: خطوة `@start()` تضبط الموضوع في **الحالة**، وخطوة `@listen` تشغّل الطاقم. يظل `output_file` للمهمة يكتب `output/report.md`.
@before_kickoff
def before_kickoff_function(self, inputs):
print(f"Before kickoff function with inputs: {inputs}")
return inputs # You can return the inputs or modify them as needed
@after_kickoff
def after_kickoff_function(self, result):
print(f"After kickoff function with result: {result}")
return result # You can return the result or modify it as needed
# ... remaining code
```
</Step>
<Step title="لا تتردد في تمرير مدخلات مخصصة لطاقمك">
على سبيل المثال، يمكنك تمرير مدخل `topic` لطاقمك لتخصيص البحث وإعداد التقارير.
```python main.py
#!/usr/bin/env python
# src/latest_ai_development/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew
# src/latest_ai_flow/main.py
from pydantic import BaseModel
def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI Agents'
}
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
from crewai.flow import Flow, listen, start
from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
class ResearchFlowState(BaseModel):
topic: str = ""
report: str = ""
class LatestAiFlow(Flow[ResearchFlowState]):
@start()
def prepare_topic(self, crewai_trigger_payload: dict | None = None):
if crewai_trigger_payload:
self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
else:
self.state.topic = "AI Agents"
print(f"الموضوع: {self.state.topic}")
@listen(prepare_topic)
def run_research(self):
result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
self.state.report = result.raw
print("اكتمل طاقم البحث.")
@listen(run_research)
def summarize(self):
print("مسار التقرير: output/report.md")
def kickoff():
LatestAiFlow().kickoff()
def plot():
LatestAiFlow().plot()
if __name__ == "__main__":
kickoff()
```
</Step>
<Step title="تعيين متغيرات البيئة">
قبل تشغيل طاقمك، تأكد من تعيين المفاتيح التالية كمتغيرات بيئة في ملف `.env`:
- مفتاح API لـ [Serper.dev](https://serper.dev/): `SERPER_API_KEY=YOUR_KEY_HERE`
- إعداد النموذج الذي اخترته، مثل مفتاح API. راجع
[دليل إعداد LLM](/ar/concepts/llms#setting-up-your-llm) لمعرفة كيفية إعداد النماذج من أي مزود.
</Step>
<Step title="قفل وتثبيت التبعيات">
- اقفل التبعيات وثبّتها باستخدام أمر CLI:
<CodeGroup>
```shell Terminal
crewai install
```
</CodeGroup>
- إذا كانت لديك حزم إضافية تريد تثبيتها، يمكنك القيام بذلك عبر:
<CodeGroup>
```shell Terminal
uv add <package-name>
```
</CodeGroup>
</Step>
<Step title="تشغيل طاقمك">
- لتشغيل طاقمك، نفّذ الأمر التالي في جذر مشروعك:
<CodeGroup>
```bash Terminal
crewai run
```
</CodeGroup>
<Tip>
إذا كان اسم الحزمة ليس `latest_ai_flow`، عدّل استيراد `ResearchCrew` ليطابق مسار الوحدة في مشروعك.
</Tip>
</Step>
<Step title="البديل المؤسسي: الإنشاء في Crew Studio">
لمستخدمي CrewAI AMP، يمكنك إنشاء نفس الطاقم دون كتابة كود:
<Step title="متغيرات البيئة">
في جذر المشروع، ضبط `.env`:
1. سجّل الدخول إلى حساب CrewAI AMP (أنشئ حساباً مجانياً على [app.crewai.com](https://app.crewai.com))
2. افتح Crew Studio
3. اكتب ما هي الأتمتة التي تحاول بناءها
4. أنشئ مهامك بصرياً واربطها بالتسلسل
5. هيئ مدخلاتك وانقر "تحميل الكود" أو "نشر"
![واجهة Crew Studio للبدء السريع](/images/enterprise/crew-studio-interface.png)
<Card title="جرّب CrewAI AMP" icon="rocket" href="https://app.crewai.com">
ابدأ حسابك المجاني في CrewAI AMP
</Card>
- `SERPER_API_KEY` — من [Serper.dev](https://serper.dev/)
- مفاتيح مزوّد النموذج حسب الحاجة — راجع [إعداد LLM](/ar/concepts/llms#setting-up-your-llm)
</Step>
<Step title="عرض التقرير النهائي">
يجب أن ترى المخرجات في وحدة التحكم ويجب إنشاء ملف `report.md` في جذر مشروعك مع التقرير النهائي.
إليك مثالاً على شكل التقرير:
<Step title="التثبيت والتشغيل">
<CodeGroup>
```shell Terminal
crewai install
crewai run
```
</CodeGroup>
يُنفّذ `crewai run` نقطة دخول Flow المعرّفة في المشروع (نفس أمر الطواقم؛ نوع المشروع `"flow"` في `pyproject.toml`).
</Step>
<Step title="تحقق من المخرجات">
يجب أن ترى سجلات من Flow والطاقم. افتح **`output/report.md`** للتقرير المُولَّد (مقتطف):
<CodeGroup>
```markdown output/report.md
# Comprehensive Report on the Rise and Impact of AI Agents in 2025
# وكلاء الذكاء الاصطناعي في 2026: المشهد والاتجاهات
## 1. Introduction to AI Agents
In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
## ملخص تنفيذي
…
## 2. Benefits of AI Agents
AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
## أبرز الاتجاهات
- **استخدام الأدوات والتنسيق** — …
- **التبني المؤسسي** — …
- **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
- **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
- **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
## 3. Popular AI Agent Frameworks
Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
- **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
- **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
- **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
- **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
- **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
- **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
## 4. AI Agents in Human Resources
AI agents are revolutionizing HR practices by automating and optimizing key functions:
- **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
- **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
- **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
## 5. AI Agents in Finance
The finance sector is seeing extensive integration of AI agents that enhance financial practices:
- **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
- **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
- **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
## 6. Market Trends and Investments
The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
## 7. Future Predictions and Implications
Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
- Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
- Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
- Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
## 8. Conclusion
The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
## الآثار
…
```
</CodeGroup>
سيكون الملف الفعلي أطول ويعكس نتائج بحث مباشرة.
</Step>
</Steps>
## كيف يترابط هذا
1. **Flow** — يشغّل `LatestAiFlow` أولًا `prepare_topic` ثم `run_research` ثم `summarize`. الحالة (`topic`، `report`) على Flow.
2. **الطاقم** — يشغّل `ResearchCrew` مهمة واحدة بوكيل واحد: الباحث يستخدم **Serper** للبحث على الويب ثم يكتب التقرير.
3. **المُخرَج** — يكتب `output_file` للمهمة التقرير في `output/report.md`.
للتعمق في أنماط Flow (التوجيه، الاستمرارية، الإنسان في الحلقة)، راجع [ابنِ أول Flow](/ar/guides/flows/first-flow) و[Flows](/ar/concepts/flows). للطواقم دون Flow، راجع [Crews](/ar/concepts/crews). لوكيل `Agent` واحد و`kickoff()` بلا مهام، راجع [Agents](/ar/concepts/agents#direct-agent-interaction-with-kickoff).
<Check>
تهانينا!
لقد أعددت مشروع طاقمك بنجاح وأنت جاهز للبدء في بناء سير العمل الوكيلي الخاص بك!
أصبح لديك Flow كامل مع طاقم وكيل وتقرير محفوظ — قاعدة قوية لإضافة خطوات أو طواقم أو أدوات.
</Check>
### ملاحظة حول اتساق التسمية
### اتساق التسمية
يجب أن تتطابق الأسماء التي تستخدمها في ملفات YAML (`agents.yaml` و`tasks.yaml`) مع أسماء الدوال في كود Python الخاص بك.
على سبيل المثال، يمكنك الإشارة إلى الوكيل لمهام محددة من ملف `tasks.yaml`.
يتيح اتساق التسمية هذا لـ CrewAI ربط تكويناتك بكودك تلقائياً؛ وإلا فلن تتعرف مهمتك على المرجع بشكل صحيح.
يجب أن تطابق مفاتيح YAML (`researcher`، `research_task`) أسماء الدوال في صف `@CrewBase`. راجع [Crews](/ar/concepts/crews) لنمط الديكورات الكامل.
#### أمثلة على المراجع
## النشر
<Tip>
لاحظ كيف نستخدم نفس الاسم للوكيل في ملف `agents.yaml`
(`email_summarizer`) واسم الدالة في ملف `crew.py`
(`email_summarizer`).
</Tip>
ادفع Flow إلى **[CrewAI AMP](https://app.crewai.com)** بعد أن يعمل محليًا ويكون المشروع في مستودع **GitHub**. من جذر المشروع:
```yaml agents.yaml
email_summarizer:
role: >
Email Summarizer
goal: >
Summarize emails into a concise and clear summary
backstory: >
You will create a 5 bullet point summary of the report
llm: provider/model-id # Add your choice of model here
<CodeGroup>
```bash المصادقة
crewai login
```
<Tip>
لاحظ كيف نستخدم نفس الاسم للمهمة في ملف `tasks.yaml`
(`email_summarizer_task`) واسم الدالة في ملف `crew.py`
(`email_summarizer_task`).
</Tip>
```yaml tasks.yaml
email_summarizer_task:
description: >
Summarize the email into a 5 bullet point summary
expected_output: >
A 5 bullet point summary of the email
agent: email_summarizer
context:
- reporting_task
- research_task
```bash إنشاء نشر
crewai deploy create
```
## نشر طاقمك
```bash الحالة والسجلات
crewai deploy status
crewai deploy logs
```
أسهل طريقة لنشر طاقمك في الإنتاج هي من خلال [CrewAI AMP](http://app.crewai.com).
```bash إرسال التحديثات بعد تغيير الكود
crewai deploy push
```
شاهد هذا الفيديو التعليمي لعرض خطوة بخطوة لنشر طاقمك على [CrewAI AMP](http://app.crewai.com) باستخدام CLI.
```bash عرض النشرات أو حذفها
crewai deploy list
crewai deploy remove <deployment_id>
```
</CodeGroup>
<iframe
className="w-full aspect-video rounded-xl"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameBorder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowFullScreen
></iframe>
<Tip>
غالبًا ما يستغرق **النشر الأول حوالي دقيقة**. المتطلبات الكاملة ومسار الواجهة الويب في [النشر على AMP](/ar/enterprise/guides/deploy-to-amp).
</Tip>
<CardGroup cols={2}>
<Card title="النشر على المؤسسة" icon="rocket" href="http://app.crewai.com">
ابدأ مع CrewAI AMP وانشر طاقمك في بيئة إنتاج
بنقرات قليلة فقط.
<Card title="دليل النشر" icon="book" href="/ar/enterprise/guides/deploy-to-amp">
النشر على AMP خطوة بخطوة (CLI ولوحة التحكم).
</Card>
<Card
title="انضم إلى المجتمع"
title="المجتمع"
icon="comments"
href="https://community.crewai.com"
>
انضم إلى مجتمعنا مفتوح المصدر لمناقشة الأفكار ومشاركة مشاريعك والتواصل
مع مطورين آخرين لـ CrewAI.
ناقش الأفكار وشارك مشاريعك وتواصل مع مطوري CrewAI.
</Card>
</CardGroup>

4
docs/en/enterprise/guides/deploy-to-amp.mdx
View File

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

5
docs/en/installation.mdx
View File

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

2
docs/en/introduction.mdx
View File

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

438
docs/en/quickstart.mdx
View File

@@ -1,6 +1,6 @@
---
title: Quickstart
description: Build your first AI agent with CrewAI in under 5 minutes.
description: Build your first CrewAI Flow in minutes — orchestration, state, and an agent crew that produces a real report.
icon: rocket
mode: "wide"
---
@@ -13,39 +13,37 @@ You can install it with `npx skills add crewaiinc/skills`
<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
## Build your first CrewAI Agent
In this guide you will **create a Flow** that sets a research topic, runs a **crew with one agent** (a researcher using web search), and ends with a **markdown report** on disk. Flows are the recommended way to structure production apps: they own **state** and **execution order**, while **agents** do the work inside a crew step.
Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.
If you have not installed CrewAI yet, follow the [installation guide](/en/installation) first.
Before we proceed, make sure you have finished installing CrewAI.
If you haven't installed them yet, you can do so by following the [installation guide](/en/installation).
## Prerequisites
Follow the steps below to get Crewing! 🚣‍♂️
- Python environment and the CrewAI CLI (see [installation](/en/installation))
- An LLM configured with the right API keys — see [LLMs](/en/concepts/llms#setting-up-your-llm)
- A [Serper.dev](https://serper.dev/) API key (`SERPER_API_KEY`) for web search in this tutorial
## Build your first Flow
<Steps>
<Step title="Create your crew">
Create a new crew project by running the following command in your terminal.
This will create a new directory called `latest-ai-development` with the basic structure for your crew.
<Step title="Create a Flow project">
From your terminal, scaffold a Flow project (the folder name uses underscores, e.g. `latest_ai_flow`):
<CodeGroup>
```shell Terminal
crewai create crew latest-ai-development
crewai create flow latest-ai-flow
cd latest_ai_flow
```
</CodeGroup>
This creates a Flow app under `src/latest_ai_flow/`, including a starter crew under `crews/content_crew/` that you will replace with a minimal **single-agent** research crew in the next steps.
</Step>
<Step title="Navigate to your new crew project">
<CodeGroup>
```shell Terminal
cd latest_ai_development
```
</CodeGroup>
</Step>
<Step title="Modify your `agents.yaml` file">
<Tip>
You can also modify the agents as needed to fit your use case or copy and paste as is to your project.
Any variable interpolated in your `agents.yaml` and `tasks.yaml` files like `{topic}` will be replaced by the value of the variable in the `main.py` file.
</Tip>
<Step title="Configure one agent in `agents.yaml`">
Replace the contents of `src/latest_ai_flow/crews/content_crew/config/agents.yaml` with a single researcher. Variables like `{topic}` are filled from `crew.kickoff(inputs=...)`.
```yaml agents.yaml
# src/latest_ai_development/config/agents.yaml
# src/latest_ai_flow/crews/content_crew/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
@@ -53,336 +51,232 @@ Follow the steps below to get Crewing! 🚣‍♂️
Uncover cutting-edge developments in {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
developments in {topic}. You find the most relevant information and
present it clearly.
```
</Step>
<Step title="Modify your `tasks.yaml` file">
<Step title="Configure one task in `tasks.yaml`">
```yaml tasks.yaml
# src/latest_ai_development/config/tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2025.
Conduct thorough research about {topic}. Use web search to find current,
credible information. The current year is 2026.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
A markdown report with clear sections: key trends, notable tools or companies,
and implications. Aim for 800–1200 words. No fenced code blocks around the whole document.
agent: researcher
reporting_task:
description: >
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledge reports with the mains topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst
output_file: report.md
output_file: output/report.md
```
</Step>
<Step title="Modify your `crew.py` file">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
<Step title="Wire the crew class (`content_crew.py`)">
Point the generated crew at your YAML and attach `SerperDevTool` to the researcher.
```python content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List
from crewai import Agent, Crew, Process, Task
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
class ResearchCrew:
"""Single-agent research crew used inside the Flow."""
agents: List[BaseAgent]
tasks: List[Task]
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
config=self.agents_config["researcher"], # type: ignore[index]
verbose=True,
tools=[SerperDevTool()]
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
tools=[SerperDevTool()],
)
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'], # type: ignore[index]
)
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'], # type: ignore[index]
output_file='output/report.md' # This is the file that will be contain the final report.
config=self.tasks_config["research_task"], # type: ignore[index]
)
@crew
def crew(self) -> Crew:
"""Creates the LatestAiDevelopment crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
)
```
</Step>
<Step title="[Optional] Add before and after crew functions">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
<Step title="Define the Flow in `main.py`">
Connect the crew to a Flow: a `@start()` step sets the topic in **state**, and a `@listen` step runs the crew. The task’s `output_file` still writes `output/report.md`.
@before_kickoff
def before_kickoff_function(self, inputs):
print(f"Before kickoff function with inputs: {inputs}")
return inputs # You can return the inputs or modify them as needed
@after_kickoff
def after_kickoff_function(self, result):
print(f"After kickoff function with result: {result}")
return result # You can return the result or modify it as needed
# ... remaining code
```
</Step>
<Step title="Feel free to pass custom inputs to your crew">
For example, you can pass the `topic` input to your crew to customize the research and reporting.
```python main.py
#!/usr/bin/env python
# src/latest_ai_development/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew
# src/latest_ai_flow/main.py
from pydantic import BaseModel
def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI Agents'
}
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
from crewai.flow import Flow, listen, start
from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
class ResearchFlowState(BaseModel):
topic: str = ""
report: str = ""
class LatestAiFlow(Flow[ResearchFlowState]):
@start()
def prepare_topic(self, crewai_trigger_payload: dict | None = None):
if crewai_trigger_payload:
self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
else:
self.state.topic = "AI Agents"
print(f"Topic: {self.state.topic}")
@listen(prepare_topic)
def run_research(self):
result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
self.state.report = result.raw
print("Research crew finished.")
@listen(run_research)
def summarize(self):
print("Report path: output/report.md")
def kickoff():
LatestAiFlow().kickoff()
def plot():
LatestAiFlow().plot()
if __name__ == "__main__":
kickoff()
```
</Step>
<Step title="Set your environment variables">
Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
- The configuration for your choice of model, such as an API key. See the
[LLM setup guide](/en/concepts/llms#setting-up-your-llm) to learn how to configure models from any provider.
</Step>
<Step title="Lock and install the dependencies">
- Lock the dependencies and install them by using the CLI command:
<CodeGroup>
```shell Terminal
crewai install
```
</CodeGroup>
- If you have additional packages that you want to install, you can do so by running:
<CodeGroup>
```shell Terminal
uv add <package-name>
```
</CodeGroup>
</Step>
<Step title="Run your crew">
- To run your crew, execute the following command in the root of your project:
<CodeGroup>
```bash Terminal
crewai run
```
</CodeGroup>
<Tip>
If your package name differs from `latest_ai_flow`, change the import of `ResearchCrew` to match your project’s module path.
</Tip>
</Step>
<Step title="Enterprise Alternative: Create in Crew Studio">
For CrewAI AMP users, you can create the same crew without writing code:
<Step title="Set environment variables">
In `.env` at the project root, set:
1. Log in to your CrewAI AMP account (create a free account at [app.crewai.com](https://app.crewai.com))
2. Open Crew Studio
3. Type what is the automation you're trying to build
4. Create your tasks visually and connect them in sequence
5. Configure your inputs and click "Download Code" or "Deploy"
![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
<Card title="Try CrewAI AMP" icon="rocket" href="https://app.crewai.com">
Start your free account at CrewAI AMP
</Card>
- `SERPER_API_KEY` — from [Serper.dev](https://serper.dev/)
- Your model provider keys as required — see [LLM setup](/en/concepts/llms#setting-up-your-llm)
</Step>
<Step title="View your final report">
You should see the output in the console and the `report.md` file should be created in the root of your project with the final report.
Here's an example of what the report should look like:
<Step title="Install and run">
<CodeGroup>
```shell Terminal
crewai install
crewai run
```
</CodeGroup>
`crewai run` executes the Flow entrypoint defined in your project (same command as for crews; project type is `"flow"` in `pyproject.toml`).
</Step>
<Step title="Check the output">
You should see logs from the Flow and the crew. Open **`output/report.md`** for the generated report (excerpt):
<CodeGroup>
```markdown output/report.md
# Comprehensive Report on the Rise and Impact of AI Agents in 2025
# AI Agents in 2026: Landscape and Trends
## 1. Introduction to AI Agents
In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
## Executive summary
…
## 2. Benefits of AI Agents
AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
## Key trends
- **Tool use and orchestration** — …
- **Enterprise adoption** — …
- **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
- **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
- **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
## 3. Popular AI Agent Frameworks
Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
- **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
- **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
- **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
- **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
- **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
- **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
## 4. AI Agents in Human Resources
AI agents are revolutionizing HR practices by automating and optimizing key functions:
- **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
- **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
- **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
## 5. AI Agents in Finance
The finance sector is seeing extensive integration of AI agents that enhance financial practices:
- **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
- **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
- **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
## 6. Market Trends and Investments
The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
## 7. Future Predictions and Implications
Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
- Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
- Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
- Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
## 8. Conclusion
The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
## Implications
…
```
</CodeGroup>
Your actual file will be longer and reflect live search results.
</Step>
</Steps>
## How this run fits together
1. **Flow** — `LatestAiFlow` runs `prepare_topic` first, then `run_research`, then `summarize`. State (`topic`, `report`) lives on the Flow.
2. **Crew** — `ResearchCrew` runs one task with one agent: the researcher uses **Serper** to search the web, then writes the structured report.
3. **Artifact** — The task’s `output_file` writes the report under `output/report.md`.
To go deeper on Flow patterns (routing, persistence, human-in-the-loop), see [Build your first Flow](/en/guides/flows/first-flow) and [Flows](/en/concepts/flows). For crews without a Flow, see [Crews](/en/concepts/crews). For a single `Agent` and `kickoff()` without tasks, see [Agents](/en/concepts/agents#direct-agent-interaction-with-kickoff).
<Check>
Congratulations!
You have successfully set up your crew project and are ready to start building your own agentic workflows!
You now have an end-to-end Flow with an agent crew and a saved report — a solid base to add more steps, crews, or tools.
</Check>
### Note on Consistency in Naming
### Naming consistency
The names you use in your YAML files (`agents.yaml` and `tasks.yaml`) should match the method names in your Python code.
For example, you can reference the agent for specific tasks from `tasks.yaml` file.
This naming consistency allows CrewAI to automatically link your configurations with your code; otherwise, your task won't recognize the reference properly.
YAML keys (`researcher`, `research_task`) must match the method names on your `@CrewBase` class. See [Crews](/en/concepts/crews) for the full decorator pattern.
#### Example References
## Deploying
<Tip>
Note how we use the same name for the agent in the `agents.yaml`
(`email_summarizer`) file as the method name in the `crew.py`
(`email_summarizer`) file.
</Tip>
Push your Flow to **[CrewAI AMP](https://app.crewai.com)** once it runs locally and your project is in a **GitHub** repository. From the project root:
```yaml agents.yaml
email_summarizer:
role: >
Email Summarizer
goal: >
Summarize emails into a concise and clear summary
backstory: >
You will create a 5 bullet point summary of the report
llm: provider/model-id # Add your choice of model here
<CodeGroup>
```bash Authenticate
crewai login
```
<Tip>
Note how we use the same name for the task in the `tasks.yaml`
(`email_summarizer_task`) file as the method name in the `crew.py`
(`email_summarizer_task`) file.
</Tip>
```yaml tasks.yaml
email_summarizer_task:
description: >
Summarize the email into a 5 bullet point summary
expected_output: >
A 5 bullet point summary of the email
agent: email_summarizer
context:
- reporting_task
- research_task
```bash Create deployment
crewai deploy create
```
## Deploying Your Crew
```bash Check status & logs
crewai deploy status
crewai deploy logs
```
The easiest way to deploy your crew to production is through [CrewAI AMP](http://app.crewai.com).
```bash Ship updates after you change code
crewai deploy push
```
Watch this video tutorial for a step-by-step demonstration of deploying your crew to [CrewAI AMP](http://app.crewai.com) using the CLI.
```bash List or remove deployments
crewai deploy list
crewai deploy remove <deployment_id>
```
</CodeGroup>
<iframe
className="w-full aspect-video rounded-xl"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameBorder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowFullScreen
></iframe>
<Tip>
The first deploy usually takes **around 1 minute**. Full prerequisites and the web UI flow are in [Deploy to AMP](/en/enterprise/guides/deploy-to-amp).
</Tip>
<CardGroup cols={2}>
<Card title="Deploy on Enterprise" icon="rocket" href="http://app.crewai.com">
Get started with CrewAI AMP and deploy your crew in a production environment
with just a few clicks.
<Card title="Deploy guide" icon="book" href="/en/enterprise/guides/deploy-to-amp">
Step-by-step AMP deployment (CLI and dashboard).
</Card>
<Card
title="Join the Community"
icon="comments"
href="https://community.crewai.com"
>
Join our open source community to discuss ideas, share your projects, and
connect with other CrewAI developers.
Discuss ideas, share projects, and connect with other CrewAI developers.
</Card>
</CardGroup>

4
docs/ko/enterprise/guides/deploy-to-amp.mdx
View File

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

5
docs/ko/installation.mdx
View File

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

4
docs/ko/introduction.mdx
View File

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

441
docs/ko/quickstart.mdx
View File

@@ -1,6 +1,6 @@
---
title: 퀵스타트
description: 5분 이내에 CrewAI로 첫 번째 AI 에이전트를 구축해보세요.
description: 몇 분 안에 첫 CrewAI Flow를 만듭니다 — 오케스트레이션, 상태, 그리고 실제 보고서를 만드는 에이전트 crew까지.
icon: rocket
mode: "wide"
---
@@ -13,375 +13,266 @@ mode: "wide"
<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
## 첫 번째 CrewAI Agent 만들기
이 가이드에서는 **Flow**를 만들어 연구 주제를 정하고, **에이전트 한 명으로 구성된 crew**(웹 검색을 쓰는 연구원)를 실행한 뒤, 디스크에 **Markdown 보고서**를 남깁니다. Flow는 프로덕션 앱을 구성하는 권장 방식으로, **상태**와 **실행 순서**를 담당하고 **에이전트**는 crew 단계 안에서 실제 작업을 수행합니다.
이제 주어진 주제나 항목에 대해 `최신 AI 개발 동향`을 `연구`하고 `보고`하는 간단한 crew를 만들어보겠습니다.
CrewAI를 아직 설치하지 않았다면 먼저 [설치 가이드](/ko/installation)를 따르세요.
진행하기 전에 CrewAI 설치를 완료했는지 확인하세요.
아직 설치하지 않았다면, [설치 가이드](/ko/installation)를 참고해 설치할 수 있습니다.
## 사전 요건
아래 단계를 따라 Crewing을 시작하세요! 🚣‍♂️
- Python 환경과 CrewAI CLI([설치](/ko/installation) 참고)
- 올바른 API 키로 설정한 LLM — [LLM](/ko/concepts/llms#setting-up-your-llm) 참고
- 이 튜토리얼의 웹 검색용 [Serper.dev](https://serper.dev/) API 키(`SERPER_API_KEY`)
## 첫 번째 Flow 만들기
<Steps>
<Step title="crew 생성하기">
터미널에서 아래 명령어를 실행하여 새로운 crew 프로젝트를 만드세요.
이 작업은 `latest-ai-development`라는 새 디렉터리와 기본 구조를 생성합니다.
<Step title="Flow 프로젝트 생성">
터미널에서 Flow 프로젝트를 생성합니다(폴더 이름은 밑줄 형식입니다. 예: `latest_ai_flow`).
<CodeGroup>
```shell Terminal
crewai create crew latest-ai-development
crewai create flow latest-ai-flow
cd latest_ai_flow
```
</CodeGroup>
이렇게 하면 `src/latest_ai_flow/` 아래에 Flow 앱이 만들어지고, 다음 단계에서 **단일 에이전트** 연구 crew로 바꿀 시작용 crew가 `crews/content_crew/`에 포함됩니다.
</Step>
<Step title="새로운 crew 프로젝트로 이동하기">
<CodeGroup>
```shell Terminal
cd latest_ai_development
```
</CodeGroup>
</Step>
<Step title="`agents.yaml` 파일 수정하기">
<Tip>
프로젝트에 맞게 agent를 수정하거나 복사/붙여넣기를 할 수 있습니다.
`agents.yaml` 및 `tasks.yaml` 파일에서 `{topic}`과 같은 변수를 사용하면, 이는 `main.py` 파일의 변수 값으로 대체됩니다.
</Tip>
<Step title="`agents.yaml`에 에이전트 하나 설정">
`src/latest_ai_flow/crews/content_crew/config/agents.yaml` 내용을 한 명의 연구원만 남기도록 바꿉니다. `{topic}` 같은 변수는 `crew.kickoff(inputs=...)`로 채워집니다.
```yaml agents.yaml
# src/latest_ai_development/config/agents.yaml
# src/latest_ai_flow/crews/content_crew/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
{topic} 시니어 데이터 리서처
goal: >
Uncover cutting-edge developments in {topic}
{topic} 분야의 최신 동향을 파악한다
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
당신은 {topic}의 최신 흐름을 찾아내는 데 능숙한 연구원입니다.
가장 관련성 높은 정보를 찾아 명확하게 전달합니다.
```
</Step>
<Step title="`tasks.yaml` 파일 수정하기">
<Step title="`tasks.yaml`에 작업 하나 설정">
```yaml tasks.yaml
# src/latest_ai_development/config/tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2025.
{topic}에 대해 철저히 조사하세요. 웹 검색으로 최신이고 신뢰할 수 있는 정보를 찾으세요.
현재 연도는 2026년입니다.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
마크다운 보고서로, 주요 트렌드·주목할 도구나 기업·시사점 등으로 섹션을 나누세요.
분량은 약 800~1200단어. 문서 전체를 코드 펜스로 감싸지 마세요.
agent: researcher
reporting_task:
description: >
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledge reports with the mains topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst
output_file: report.md
output_file: output/report.md
```
</Step>
<Step title="`crew.py` 파일 수정하기">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
<Step title="crew 클래스 연결 (`content_crew.py`)">
생성된 crew가 YAML을 읽고 연구원에게 `SerperDevTool`을 붙이도록 합니다.
```python content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List
from crewai import Agent, Crew, Process, Task
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
class ResearchCrew:
"""Flow 안에서 사용하는 단일 에이전트 연구 crew."""
agents: List[BaseAgent]
tasks: List[Task]
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
config=self.agents_config["researcher"], # type: ignore[index]
verbose=True,
tools=[SerperDevTool()]
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
tools=[SerperDevTool()],
)
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'], # type: ignore[index]
)
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'], # type: ignore[index]
output_file='output/report.md' # This is the file that will be contain the final report.
config=self.tasks_config["research_task"], # type: ignore[index]
)
@crew
def crew(self) -> Crew:
"""Creates the LatestAiDevelopment crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
)
```
</Step>
<Step title="[선택 사항] crew 실행 전/후 함수 추가">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
<Step title="`main.py`에서 Flow 정의">
crew를 Flow에 연결합니다: `@start()` 단계에서 주제를 **상태**에 넣고, `@listen` 단계에서 crew를 실행합니다. 작업의 `output_file`은 그대로 `output/report.md`에 씁니다.
@before_kickoff
def before_kickoff_function(self, inputs):
print(f"Before kickoff function with inputs: {inputs}")
return inputs # You can return the inputs or modify them as needed
@after_kickoff
def after_kickoff_function(self, result):
print(f"After kickoff function with result: {result}")
return result # You can return the result or modify it as needed
# ... remaining code
```
</Step>
<Step title="crew에 커스텀 입력값 전달하기">
예를 들어, crew에 `topic` 입력값을 넘겨 연구 및 보고서 출력을 맞춤화할 수 있습니다.
```python main.py
#!/usr/bin/env python
# src/latest_ai_development/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew
# src/latest_ai_flow/main.py
from pydantic import BaseModel
def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI Agents'
}
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
from crewai.flow import Flow, listen, start
from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
class ResearchFlowState(BaseModel):
topic: str = ""
report: str = ""
class LatestAiFlow(Flow[ResearchFlowState]):
@start()
def prepare_topic(self, crewai_trigger_payload: dict | None = None):
if crewai_trigger_payload:
self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
else:
self.state.topic = "AI Agents"
print(f"주제: {self.state.topic}")
@listen(prepare_topic)
def run_research(self):
result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
self.state.report = result.raw
print("연구 crew 실행 완료.")
@listen(run_research)
def summarize(self):
print("보고서 경로: output/report.md")
def kickoff():
LatestAiFlow().kickoff()
def plot():
LatestAiFlow().plot()
if __name__ == "__main__":
kickoff()
```
</Step>
<Step title="환경 변수 설정">
crew를 실행하기 전에 `.env` 파일에 아래 키가 환경 변수로 설정되어 있는지 확인하세요:
- [Serper.dev](https://serper.dev/) API 키: `SERPER_API_KEY=YOUR_KEY_HERE`
- 사용하려는 모델의 설정, 예: API 키. 다양한 공급자의 모델 설정은
[LLM 설정 가이드](/ko/concepts/llms#setting-up-your-llm)를 참고하세요.
</Step>
<Step title="의존성 잠그고 설치하기">
- CLI 명령어로 의존성을 잠그고 설치하세요:
<CodeGroup>
```shell Terminal
crewai install
```
</CodeGroup>
- 추가 설치가 필요한 패키지가 있다면, 아래와 같이 실행하면 됩니다:
<CodeGroup>
```shell Terminal
uv add <package-name>
```
</CodeGroup>
</Step>
<Step title="crew 실행하기">
- 프로젝트 루트에서 다음 명령어로 crew를 실행하세요:
<CodeGroup>
```bash Terminal
crewai run
```
</CodeGroup>
<Tip>
패키지 이름이 `latest_ai_flow`가 아니면 `ResearchCrew` import 경로를 프로젝트 모듈 경로에 맞게 바꾸세요.
</Tip>
</Step>
<Step title="엔터프라이즈 대안: Crew Studio에서 생성">
CrewAI AMP 사용자는 코드를 작성하지 않고도 동일한 crew를 생성할 수 있습니다:
<Step title="환경 변수">
프로젝트 루트의 `.env`에 다음을 설정합니다.
1. CrewAI AMP 계정에 로그인하세요([app.crewai.com](https://app.crewai.com)에서 무료 계정 만들기)
2. Crew Studio 열기
3. 구현하려는 자동화 내용을 입력하세요
4. 미션을 시각적으로 생성하고 순차적으로 연결하세요
5. 입력값을 구성하고 "Download Code" 또는 "Deploy"를 클릭하세요
![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
<Card title="CrewAI AMP 체험하기" icon="rocket" href="https://app.crewai.com">
CrewAI AOP에서 무료 계정을 시작하세요
</Card>
- `SERPER_API_KEY` — [Serper.dev](https://serper.dev/)에서 발급
- 모델 제공자 키 — [LLM 설정](/ko/concepts/llms#setting-up-your-llm) 참고
</Step>
<Step title="최종 보고서 확인하기">
콘솔에서 출력 결과를 확인할 수 있으며 프로젝트 루트에 `report.md` 파일로 최종 보고서가 생성됩니다.
보고서 예시는 다음과 같습니다:
<Step title="설치 및 실행">
<CodeGroup>
```shell Terminal
crewai install
crewai run
```
</CodeGroup>
`crewai run`은 프로젝트에 정의된 Flow 진입점을 실행합니다(crew와 동일한 명령이며, `pyproject.toml`의 프로젝트 유형은 `"flow"`입니다).
</Step>
<Step title="결과 확인">
Flow와 crew 로그가 출력되어야 합니다. 생성된 보고서는 **`output/report.md`**에서 확인하세요(발췌):
<CodeGroup>
```markdown output/report.md
# Comprehensive Report on the Rise and Impact of AI Agents in 2025
# 2026년 AI 에이전트: 동향과 전망
## 1. Introduction to AI Agents
In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
## 요약
…
## 2. Benefits of AI Agents
AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
## 주요 트렌드
- **도구 사용과 오케스트레이션** — …
- **엔터프라이즈 도입** — …
- **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
- **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
- **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
## 3. Popular AI Agent Frameworks
Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
- **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
- **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
- **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
- **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
- **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
- **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
## 4. AI Agents in Human Resources
AI agents are revolutionizing HR practices by automating and optimizing key functions:
- **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
- **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
- **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
## 5. AI Agents in Finance
The finance sector is seeing extensive integration of AI agents that enhance financial practices:
- **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
- **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
- **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
## 6. Market Trends and Investments
The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
## 7. Future Predictions and Implications
Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
- Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
- Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
- Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
## 8. Conclusion
The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
## 시사점
…
```
</CodeGroup>
실제 파일은 더 길고 실시간 검색 결과를 반영합니다.
</Step>
</Steps>
## 한 번에 이해하기
1. **Flow** — `LatestAiFlow`는 `prepare_topic` → `run_research` → `summarize` 순으로 실행됩니다. 상태(`topic`, `report`)는 Flow에 있습니다.
2. **Crew** — `ResearchCrew`는 에이전트 한 명·작업 하나로 실행됩니다. 연구원이 **Serper**로 웹을 검색하고 구조화된 보고서를 씁니다.
3. **결과물** — 작업의 `output_file`이 `output/report.md`에 보고서를 씁니다.
Flow 패턴(라우팅, 지속성, human-in-the-loop)을 더 보려면 [첫 Flow 만들기](/ko/guides/flows/first-flow)와 [Flows](/ko/concepts/flows)를 참고하세요. Flow 없이 crew만 쓰려면 [Crews](/ko/concepts/crews)를, 작업 없이 단일 `Agent`의 `kickoff()`만 쓰려면 [Agents](/ko/concepts/agents#direct-agent-interaction-with-kickoff)를 참고하세요.
<Check>
축하합니다!
crew 프로젝트 설정이 완료되었으며, 이제 자신만의 agentic workflow 구축을 바로 시작하실 수 있습니다!
에이전트 crew와 저장된 보고서까지 이어진 Flow를 완성했습니다. 이제 단계·crew·도구를 더해 확장할 수 있습니다.
</Check>
### 명명 일관성에 대한 참고
### 이름 일치
YAML 파일(`agents.yaml` 및 `tasks.yaml`)에서 사용하는 이름은 Python 코드의 메서드 이름과 일치해야 합니다.
예를 들어, 특정 task에 대한 agent를 `tasks.yaml` 파일에서 참조할 수 있습니다.
이러한 명명 일관성을 지키면 CrewAI가 설정과 코드를 자동으로 연결할 수 있습니다. 그렇지 않으면 task가 참조를 제대로 인식하지 못할 수 있습니다.
YAML 키(`researcher`, `research_task`)는 `@CrewBase` 클래스의 메서드 이름과 같아야 합니다. 전체 데코레이터 패턴은 [Crews](/ko/concepts/crews)를 참고하세요.
#### 예시 참조
## 배포
<Tip>
`agents.yaml` (`email_summarizer`) 파일에서 에이전트 이름과 `crew.py`
(`email_summarizer`) 파일에서 메서드 이름이 동일하게 사용되는 점에 주목하세요.
</Tip>
로컬에서 정상 실행되고 프로젝트가 **GitHub** 저장소에 있으면 Flow를 **[CrewAI AMP](https://app.crewai.com)**에 올릴 수 있습니다. 프로젝트 루트에서:
```yaml agents.yaml
email_summarizer:
role: >
Email Summarizer
goal: >
Summarize emails into a concise and clear summary
backstory: >
You will create a 5 bullet point summary of the report
llm: provider/model-id # Add your choice of model here
<CodeGroup>
```bash 인증
crewai login
```
<Tip>
`tasks.yaml` (`email_summarizer_task`) 파일에서 태스크 이름과 `crew.py`
(`email_summarizer_task`) 파일에서 메서드 이름이 동일하게 사용되는 점에
주목하세요.
</Tip>
```yaml tasks.yaml
email_summarizer_task:
description: >
Summarize the email into a 5 bullet point summary
expected_output: >
A 5 bullet point summary of the email
agent: email_summarizer
context:
- reporting_task
- research_task
```bash 배포 생성
crewai deploy create
```
## Crew 배포하기
```bash 상태 및 로그
crewai deploy status
crewai deploy logs
```
production 환경에 crew를 배포하는 가장 쉬운 방법은 [CrewAI AMP](http://app.crewai.com)를 통해서입니다.
```bash 코드 변경 후 반영
crewai deploy push
```
CLI를 사용하여 [CrewAI AMP](http://app.crewai.com)에 crew를 배포하는 단계별 시연은 이 영상 튜토리얼을 참고하세요.
```bash 배포 목록 또는 삭제
crewai deploy list
crewai deploy remove <deployment_id>
```
</CodeGroup>
<iframe
className="w-full aspect-video rounded-xl"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameBorder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowFullScreen
></iframe>
<Tip>
첫 배포는 보통 **약 1분** 정도 걸립니다. 전체 사전 요건과 웹 UI 절차는 [AMP에 배포](/ko/enterprise/guides/deploy-to-amp)를 참고하세요.
</Tip>
<CardGroup cols={2}>
<Card title="Enterprise에 배포" icon="rocket" href="http://app.crewai.com">
CrewAI AOP로 시작하여 몇 번의 클릭만으로 production 환경에 crew를
배포하세요.
<Card title="배포 가이드" icon="book" href="/ko/enterprise/guides/deploy-to-amp">
AMP 배포 단계별 안내(CLI 및 대시보드).
</Card>
<Card
title="커뮤니티 참여하기"
title="커뮤니티"
icon="comments"
href="https://community.crewai.com"
>
오픈 소스 커뮤니티에 참여하여 아이디어를 나누고, 프로젝트를 공유하며, 다른
CrewAI 개발자들과 소통하세요.
아이디어를 나누고 프로젝트를 공유하며 다른 CrewAI 개발자와 소통하세요.
</Card>
</CardGroup>

4
docs/pt-BR/enterprise/guides/deploy-to-amp.mdx
View File

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

5
docs/pt-BR/installation.mdx
View File

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

2
docs/pt-BR/introduction.mdx
View File

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

436
docs/pt-BR/quickstart.mdx
View File

@@ -1,6 +1,6 @@
---
title: Guia Rápido
description: Construa seu primeiro agente de IA com a CrewAI em menos de 5 minutos.
description: Crie seu primeiro Flow CrewAI em minutos — orquestração, estado e um crew com um agente que gera um relatório real.
icon: rocket
mode: "wide"
---
@@ -13,370 +13,266 @@ Você pode instalar com `npx skills add crewaiinc/skills`
<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
## Construa seu primeiro Agente CrewAI
Neste guia você vai **criar um Flow** que define um tópico de pesquisa, executa um **crew com um agente** (um pesquisador com busca na web) e termina com um **relatório em Markdown** no disco. Flows são a forma recomendada de estruturar apps em produção: eles controlam **estado** e **ordem de execução**, enquanto os **agentes** fazem o trabalho dentro da etapa do crew.
Vamos criar uma tripulação simples que nos ajudará a `pesquisar` e `relatar` sobre os `últimos avanços em IA` para um determinado tópico ou assunto.
Se ainda não instalou o CrewAI, siga primeiro o [guia de instalação](/pt-BR/installation).
Antes de prosseguir, certifique-se de ter concluído a instalação da CrewAI.
Se ainda não instalou, faça isso seguindo o [guia de instalação](/pt-BR/installation).
## Pré-requisitos
Siga os passos abaixo para começar a tripular! 🚣‍♂️
- Ambiente Python e a CLI do CrewAI (veja [instalação](/pt-BR/installation))
- Um LLM configurado com as chaves corretas — veja [LLMs](/pt-BR/concepts/llms#setting-up-your-llm)
- Uma chave de API do [Serper.dev](https://serper.dev/) (`SERPER_API_KEY`) para busca na web neste tutorial
## Construa seu primeiro Flow
<Steps>
<Step title="Crie sua tripulação">
Crie um novo projeto de tripulação executando o comando abaixo em seu terminal.
Isso criará um novo diretório chamado `latest-ai-development` com a estrutura básica para sua tripulação.
<Step title="Crie um projeto Flow">
No terminal, gere um projeto Flow (o nome da pasta usa sublinhados, ex.: `latest_ai_flow`):
<CodeGroup>
```shell Terminal
crewai create crew latest-ai-development
crewai create flow latest-ai-flow
cd latest_ai_flow
```
</CodeGroup>
Isso cria um app Flow em `src/latest_ai_flow/`, incluindo um crew inicial em `crews/content_crew/` que você substituirá por um crew de pesquisa **com um único agente** nos próximos passos.
</Step>
<Step title="Navegue até o novo projeto da sua tripulação">
<CodeGroup>
```shell Terminal
cd latest_ai_development
```
</CodeGroup>
</Step>
<Step title="Modifique seu arquivo `agents.yaml`">
<Tip>
Você também pode modificar os agentes conforme necessário para atender ao seu caso de uso ou copiar e colar como está para seu projeto.
Qualquer variável interpolada nos seus arquivos `agents.yaml` e `tasks.yaml`, como `{topic}`, será substituída pelo valor da variável no arquivo `main.py`.
</Tip>
<Step title="Configure um agente em `agents.yaml`">
Substitua o conteúdo de `src/latest_ai_flow/crews/content_crew/config/agents.yaml` por um único pesquisador. Variáveis como `{topic}` são preenchidas a partir de `crew.kickoff(inputs=...)`.
```yaml agents.yaml
# src/latest_ai_development/config/agents.yaml
# src/latest_ai_flow/crews/content_crew/config/agents.yaml
researcher:
role: >
Pesquisador Sênior de Dados em {topic}
Pesquisador(a) Sênior de Dados em {topic}
goal: >
Descobrir os avanços mais recentes em {topic}
Descobrir os desenvolvimentos mais recentes em {topic}
backstory: >
Você é um pesquisador experiente com talento para descobrir os últimos avanços em {topic}. Conhecido por sua habilidade em encontrar as informações mais relevantes e apresentá-las de forma clara e concisa.
reporting_analyst:
role: >
Analista de Relatórios em {topic}
goal: >
Criar relatórios detalhados com base na análise de dados e descobertas de pesquisa em {topic}
backstory: >
Você é um analista meticuloso com um olhar atento aos detalhes. É conhecido por sua capacidade de transformar dados complexos em relatórios claros e concisos, facilitando o entendimento e a tomada de decisão por parte dos outros.
Você é um pesquisador experiente que descobre os últimos avanços em {topic}.
Encontra as informações mais relevantes e apresenta tudo com clareza.
```
</Step>
<Step title="Modifique seu arquivo `tasks.yaml`">
<Step title="Configure uma tarefa em `tasks.yaml`">
```yaml tasks.yaml
# src/latest_ai_development/config/tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
description: >
Realize uma pesquisa aprofundada sobre {topic}.
Certifique-se de encontrar informações interessantes e relevantes considerando que o ano atual é 2025.
Faça uma pesquisa aprofundada sobre {topic}. Use busca na web para obter
informações atuais e confiáveis. O ano atual é 2026.
expected_output: >
Uma lista com 10 tópicos dos dados mais relevantes sobre {topic}
Um relatório em markdown com seções claras: tendências principais, ferramentas
ou empresas relevantes e implicações. Entre 800 e 1200 palavras. Sem cercas de código em volta do documento inteiro.
agent: researcher
reporting_task:
description: >
Revise o contexto obtido e expanda cada tópico em uma seção completa para um relatório.
Certifique-se de que o relatório seja detalhado e contenha todas as informações relevantes.
expected_output: >
Um relatório completo com os principais tópicos, cada um com uma seção detalhada de informações.
Formate como markdown sem usar '```'
agent: reporting_analyst
output_file: report.md
output_file: output/report.md
```
</Step>
<Step title="Modifique seu arquivo `crew.py`">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
from crewai.agents.agent_builder.base_agent import BaseAgent
<Step title="Conecte a classe do crew (`content_crew.py`)">
Aponte o crew gerado para o YAML e anexe `SerperDevTool` ao pesquisador.
```python content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List
from crewai import Agent, Crew, Process, Task
from crewai.agents.agent_builder.base_agent import BaseAgent
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
class ResearchCrew:
"""Crew de pesquisa com um agente, usado dentro do Flow."""
agents: List[BaseAgent]
tasks: List[Task]
agents_config = "config/agents.yaml"
tasks_config = "config/tasks.yaml"
@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'], # type: ignore[index]
config=self.agents_config["researcher"], # type: ignore[index]
verbose=True,
tools=[SerperDevTool()]
)
@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'], # type: ignore[index]
verbose=True
tools=[SerperDevTool()],
)
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'], # type: ignore[index]
)
@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'], # type: ignore[index]
output_file='output/report.md' # Este é o arquivo que conterá o relatório final.
config=self.tasks_config["research_task"], # type: ignore[index]
)
@crew
def crew(self) -> Crew:
"""Creates the LatestAiDevelopment crew"""
return Crew(
agents=self.agents, # Criado automaticamente pelo decorador @agent
tasks=self.tasks, # Criado automaticamente pelo decorador @task
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
)
```
</Step>
<Step title="[Opcional] Adicione funções de pré e pós execução da tripulação">
```python crew.py
# src/latest_ai_development/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
from crewai_tools import SerperDevTool
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""
<Step title="Defina o Flow em `main.py`">
Conecte o crew a um Flow: um passo `@start()` define o tópico no **estado** e um `@listen` executa o crew. O `output_file` da tarefa continua gravando `output/report.md`.
@before_kickoff
def before_kickoff_function(self, inputs):
print(f"Before kickoff function with inputs: {inputs}")
return inputs # You can return the inputs or modify them as needed
@after_kickoff
def after_kickoff_function(self, result):
print(f"After kickoff function with result: {result}")
return result # You can return the result or modify it as needed
# ... remaining code
```
</Step>
<Step title="Fique à vontade para passar entradas personalizadas para sua tripulação">
Por exemplo, você pode passar o input `topic` para sua tripulação para personalizar a pesquisa e o relatório.
```python main.py
#!/usr/bin/env python
# src/latest_ai_development/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew
# src/latest_ai_flow/main.py
from pydantic import BaseModel
def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI Agents'
}
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
from crewai.flow import Flow, listen, start
from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
class ResearchFlowState(BaseModel):
topic: str = ""
report: str = ""
class LatestAiFlow(Flow[ResearchFlowState]):
@start()
def prepare_topic(self, crewai_trigger_payload: dict | None = None):
if crewai_trigger_payload:
self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
else:
self.state.topic = "AI Agents"
print(f"Tópico: {self.state.topic}")
@listen(prepare_topic)
def run_research(self):
result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
self.state.report = result.raw
print("Crew de pesquisa concluído.")
@listen(run_research)
def summarize(self):
print("Relatório em: output/report.md")
def kickoff():
LatestAiFlow().kickoff()
def plot():
LatestAiFlow().plot()
if __name__ == "__main__":
kickoff()
```
</Step>
<Step title="Defina suas variáveis de ambiente">
Antes de executar sua tripulação, certifique-se de ter as seguintes chaves configuradas como variáveis de ambiente no seu arquivo `.env`:
- Uma chave da API do [Serper.dev](https://serper.dev/): `SERPER_API_KEY=YOUR_KEY_HERE`
- A configuração do modelo de sua escolha, como uma chave de API. Veja o
[guia de configuração do LLM](/pt-BR/concepts/llms#setting-up-your-llm) para aprender como configurar modelos de qualquer provedor.
</Step>
<Step title="Trave e instale as dependências">
- Trave e instale as dependências utilizando o comando da CLI:
<CodeGroup>
```shell Terminal
crewai install
```
</CodeGroup>
- Se quiser instalar pacotes adicionais, faça isso executando:
<CodeGroup>
```shell Terminal
uv add <package-name>
```
</CodeGroup>
</Step>
<Step title="Execute sua tripulação">
- Para executar sua tripulação, rode o seguinte comando na raiz do projeto:
<CodeGroup>
```bash Terminal
crewai run
```
</CodeGroup>
<Tip>
Se o nome do pacote não for `latest_ai_flow`, ajuste o import de `ResearchCrew` para o caminho de módulo do seu projeto.
</Tip>
</Step>
<Step title="Alternativa para Empresas: Crie no Crew Studio">
Para usuários do CrewAI AMP, você pode criar a mesma tripulação sem escrever código:
<Step title="Variáveis de ambiente">
Na raiz do projeto, no arquivo `.env`, defina:
1. Faça login na sua conta CrewAI AMP (crie uma conta gratuita em [app.crewai.com](https://app.crewai.com))
2. Abra o Crew Studio
3. Digite qual automação deseja construir
4. Crie suas tarefas visualmente e conecte-as em sequência
5. Configure seus inputs e clique em "Download Code" ou "Deploy"
![Crew Studio Quickstart](/images/enterprise/crew-studio-interface.png)
<Card title="Experimente o CrewAI AMP" icon="rocket" href="https://app.crewai.com">
Comece sua conta gratuita no CrewAI AMP
</Card>
- `SERPER_API_KEY` — obtida em [Serper.dev](https://serper.dev/)
- As chaves do provedor de modelo conforme necessário — veja [configuração de LLM](/pt-BR/concepts/llms#setting-up-your-llm)
</Step>
<Step title="Veja seu relatório final">
Você verá a saída no console e o arquivo `report.md` deve ser criado na raiz do seu projeto com o relatório final.
Veja um exemplo de como o relatório deve ser:
<Step title="Instalar e executar">
<CodeGroup>
```shell Terminal
crewai install
crewai run
```
</CodeGroup>
O `crewai run` executa o ponto de entrada do Flow definido no projeto (o mesmo comando dos crews; o tipo do projeto é `"flow"` no `pyproject.toml`).
</Step>
<Step title="Confira o resultado">
Você deve ver logs do Flow e do crew. Abra **`output/report.md`** para o relatório gerado (trecho):
<CodeGroup>
```markdown output/report.md
# Relatório Abrangente sobre a Ascensão e o Impacto dos Agentes de IA em 2025
# Agentes de IA em 2026: panorama e tendências
## 1. Introduction to AI Agents
In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
## Resumo executivo
…
## 2. Benefits of AI Agents
AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
## Principais tendências
- **Uso de ferramentas e orquestração** — …
- **Adoção empresarial** — …
- **Task Automation**: AI agents can carry out repetitive tasks such as data entry, scheduling, and payroll processing without human intervention, greatly reducing the time and resources spent on these activities.
- **Improved Efficiency**: By quickly processing large datasets and performing analyses that would take humans significantly longer, AI agents enhance operational efficiency. This allows teams to focus on strategic tasks that require higher-level thinking.
- **Enhanced Decision-Making**: AI agents can analyze trends and patterns in data, provide insights, and even suggest actions, helping stakeholders make informed decisions based on factual data rather than intuition alone.
## 3. Popular AI Agent Frameworks
Several frameworks have emerged to facilitate the development of AI agents, each with its own unique features and capabilities. Some of the most popular frameworks include:
- **Autogen**: A framework designed to streamline the development of AI agents through automation of code generation.
- **Semantic Kernel**: Focuses on natural language processing and understanding, enabling agents to comprehend user intentions better.
- **Promptflow**: Provides tools for developers to create conversational agents that can navigate complex interactions seamlessly.
- **Langchain**: Specializes in leveraging various APIs to ensure agents can access and utilize external data effectively.
- **CrewAI**: Aimed at collaborative environments, CrewAI strengthens teamwork by facilitating communication through AI-driven insights.
- **MemGPT**: Combines memory-optimized architectures with generative capabilities, allowing for more personalized interactions with users.
These frameworks empower developers to build versatile and intelligent agents that can engage users, perform advanced analytics, and execute various tasks aligned with organizational goals.
## 4. AI Agents in Human Resources
AI agents are revolutionizing HR practices by automating and optimizing key functions:
- **Recruiting**: AI agents can screen resumes, schedule interviews, and even conduct initial assessments, thus accelerating the hiring process while minimizing biases.
- **Succession Planning**: AI systems analyze employee performance data and potential, helping organizations identify future leaders and plan appropriate training.
- **Employee Engagement**: Chatbots powered by AI can facilitate feedback loops between employees and management, promoting an open culture and addressing concerns promptly.
As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
## 5. AI Agents in Finance
The finance sector is seeing extensive integration of AI agents that enhance financial practices:
- **Expense Tracking**: Automated systems manage and monitor expenses, flagging anomalies and offering recommendations based on spending patterns.
- **Risk Assessment**: AI models assess credit risk and uncover potential fraud by analyzing transaction data and behavioral patterns.
- **Investment Decisions**: AI agents provide stock predictions and analytics based on historical data and current market conditions, empowering investors with informative insights.
The incorporation of AI agents into finance is fostering a more responsive and risk-aware financial landscape.
## 6. Market Trends and Investments
The growth of AI agents has attracted significant investment, especially amidst the rising popularity of chatbots and generative AI technologies. Companies and entrepreneurs are eager to explore the potential of these systems, recognizing their ability to streamline operations and improve customer engagement.
Conversely, corporations like Microsoft are taking strides to integrate AI agents into their product offerings, with enhancements to their Copilot 365 applications. This strategic move emphasizes the importance of AI literacy in the modern workplace and indicates the stabilizing of AI agents as essential business tools.
## 7. Future Predictions and Implications
Experts predict that AI agents will transform essential aspects of work life. As we look toward the future, several anticipated changes include:
- Enhanced integration of AI agents across all business functions, creating interconnected systems that leverage data from various departmental silos for comprehensive decision-making.
- Continued advancement of AI technologies, resulting in smarter, more adaptable agents capable of learning and evolving from user interactions.
- Increased regulatory scrutiny to ensure ethical use, especially concerning data privacy and employee surveillance as AI agents become more prevalent.
To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
## 8. Conclusion
The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
## Implicações
…
```
</CodeGroup>
O arquivo real será mais longo e refletirá resultados de busca ao vivo.
</Step>
</Steps>
## Como isso se encaixa
1. **Flow** — `LatestAiFlow` executa `prepare_topic`, depois `run_research`, depois `summarize`. O estado (`topic`, `report`) fica no Flow.
2. **Crew** — `ResearchCrew` executa uma tarefa com um agente: o pesquisador usa **Serper** na web e escreve o relatório.
3. **Artefato** — O `output_file` da tarefa grava o relatório em `output/report.md`.
Para ir além em Flows (roteamento, persistência, human-in-the-loop), veja [Construa seu primeiro Flow](/pt-BR/guides/flows/first-flow) e [Flows](/pt-BR/concepts/flows). Para crews sem Flow, veja [Crews](/pt-BR/concepts/crews). Para um único `Agent` com `kickoff()` sem tarefas, veja [Agents](/pt-BR/concepts/agents#direct-agent-interaction-with-kickoff).
<Check>
Parabéns!
Você configurou seu projeto de tripulação com sucesso e está pronto para começar a construir seus próprios fluxos de trabalho baseados em agentes!
Você tem um Flow ponta a ponta com um crew de agente e um relatório salvo — uma base sólida para novas etapas, crews ou ferramentas.
</Check>
### Observação sobre Consistência nos Nomes
### Consistência de nomes
Os nomes utilizados nos seus arquivos YAML (`agents.yaml` e `tasks.yaml`) devem corresponder aos nomes dos métodos no seu código Python.
Por exemplo, você pode referenciar o agente para tarefas específicas a partir do arquivo `tasks.yaml`.
Essa consistência de nomes permite que a CrewAI conecte automaticamente suas configurações ao seu código; caso contrário, sua tarefa não reconhecerá a referência corretamente.
As chaves do YAML (`researcher`, `research_task`) devem coincidir com os nomes dos métodos na classe `@CrewBase`. Veja [Crews](/pt-BR/concepts/crews) para o padrão completo com decoradores.
#### Exemplos de Referências
## Implantação
<Tip>
Observe como usamos o mesmo nome para o agente no arquivo `agents.yaml`
(`email_summarizer`) e no método do arquivo `crew.py` (`email_summarizer`).
</Tip>
Envie seu Flow para o **[CrewAI AMP](https://app.crewai.com)** quando rodar localmente e o projeto estiver em um repositório **GitHub**. Na raiz do projeto:
```yaml agents.yaml
email_summarizer:
role: >
Email Summarizer
goal: >
Summarize emails into a concise and clear summary
backstory: >
You will create a 5 bullet point summary of the report
llm: provider/model-id # Add your choice of model here
<CodeGroup>
```bash Autenticar
crewai login
```
<Tip>
Observe como usamos o mesmo nome para a tarefa no arquivo `tasks.yaml`
(`email_summarizer_task`) e no método no arquivo `crew.py`
(`email_summarizer_task`).
</Tip>
```yaml tasks.yaml
email_summarizer_task:
description: >
Summarize the email into a 5 bullet point summary
expected_output: >
A 5 bullet point summary of the email
agent: email_summarizer
context:
- reporting_task
- research_task
```bash Criar implantação
crewai deploy create
```
## Fazendo o Deploy da Sua Tripulação
```bash Status e logs
crewai deploy status
crewai deploy logs
```
A forma mais fácil de fazer deploy da sua tripulação em produção é através da [CrewAI AMP](http://app.crewai.com).
```bash Enviar atualizações após mudanças no código
crewai deploy push
```
Assista a este vídeo tutorial para uma demonstração detalhada de como fazer deploy da sua tripulação na [CrewAI AMP](http://app.crewai.com) usando a CLI.
```bash Listar ou remover implantações
crewai deploy list
crewai deploy remove <deployment_id>
```
</CodeGroup>
<iframe
className="w-full aspect-video rounded-xl"
src="https://www.youtube.com/embed/3EqSV-CYDZA"
title="CrewAI Deployment Guide"
frameBorder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowFullScreen
></iframe>
<Tip>
A primeira implantação costuma levar **cerca de 1 minuto**. Pré-requisitos completos e fluxo na interface web estão em [Implantar no AMP](/pt-BR/enterprise/guides/deploy-to-amp).
</Tip>
<CardGroup cols={2}>
<Card title="Deploy no Enterprise" icon="rocket" href="http://app.crewai.com">
Comece com o CrewAI AMP e faça o deploy da sua tripulação em ambiente de
produção com apenas alguns cliques.
<Card title="Guia de implantação" icon="book" href="/pt-BR/enterprise/guides/deploy-to-amp">
AMP passo a passo (CLI e painel).
</Card>
<Card
title="Junte-se à Comunidade"
title="Comunidade"
icon="comments"
href="https://community.crewai.com"
>
Participe da nossa comunidade open source para discutir ideias, compartilhar
seus projetos e conectar-se com outros desenvolvedores CrewAI.
Troque ideias, compartilhe projetos e conecte-se com outros desenvolvedores CrewAI.
</Card>
</CardGroup>