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docs: update quickstart and installation guides for improved clarity (#5301)
* 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
This commit is contained in:
Lorenze Jay
@@ -1,6 +1,6 @@
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---
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title: Quickstart
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description: Build your first AI agent with CrewAI in under 5 minutes.
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description: Build your first CrewAI Flow in minutes — orchestration, state, and an agent crew that produces a real report.
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icon: rocket
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mode: "wide"
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---
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@@ -13,39 +13,37 @@ You can install it with `npx skills add crewaiinc/skills`
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<iframe src="https://www.loom.com/embed/befb9f68b81f42ad8112bfdd95a780af" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen style={{width: "100%", height: "400px"}}></iframe>
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## Build your first CrewAI Agent
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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.
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Let's create a simple crew that will help us `research` and `report` on the `latest AI developments` for a given topic or subject.
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If you have not installed CrewAI yet, follow the [installation guide](/en/installation) first.
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Before we proceed, make sure you have finished installing CrewAI.
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If you haven't installed them yet, you can do so by following the [installation guide](/en/installation).
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## Prerequisites
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Follow the steps below to get Crewing! 🚣♂️
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- Python environment and the CrewAI CLI (see [installation](/en/installation))
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- An LLM configured with the right API keys — see [LLMs](/en/concepts/llms#setting-up-your-llm)
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- A [Serper.dev](https://serper.dev/) API key (`SERPER_API_KEY`) for web search in this tutorial
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## Build your first Flow
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<Steps>
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<Step title="Create your crew">
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Create a new crew project by running the following command in your terminal.
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This will create a new directory called `latest-ai-development` with the basic structure for your crew.
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<Step title="Create a Flow project">
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From your terminal, scaffold a Flow project (the folder name uses underscores, e.g. `latest_ai_flow`):
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<CodeGroup>
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```shell Terminal
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crewai create crew latest-ai-development
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crewai create flow latest-ai-flow
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cd latest_ai_flow
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```
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</CodeGroup>
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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.
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</Step>
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<Step title="Navigate to your new crew project">
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<CodeGroup>
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```shell Terminal
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cd latest_ai_development
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```
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</CodeGroup>
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</Step>
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<Step title="Modify your `agents.yaml` file">
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<Tip>
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You can also modify the agents as needed to fit your use case or copy and paste as is to your project.
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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.
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</Tip>
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<Step title="Configure one agent in `agents.yaml`">
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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=...)`.
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```yaml agents.yaml
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# src/latest_ai_development/config/agents.yaml
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# src/latest_ai_flow/crews/content_crew/config/agents.yaml
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researcher:
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role: >
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{topic} Senior Data Researcher
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@@ -53,336 +51,232 @@ Follow the steps below to get Crewing! 🚣♂️
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Uncover cutting-edge developments in {topic}
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backstory: >
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You're a seasoned researcher with a knack for uncovering the latest
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developments in {topic}. Known for your ability to find the most relevant
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information and present it in a clear and concise manner.
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reporting_analyst:
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role: >
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{topic} Reporting Analyst
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goal: >
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Create detailed reports based on {topic} data analysis and research findings
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backstory: >
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You're a meticulous analyst with a keen eye for detail. You're known for
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your ability to turn complex data into clear and concise reports, making
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it easy for others to understand and act on the information you provide.
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developments in {topic}. You find the most relevant information and
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present it clearly.
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```
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</Step>
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<Step title="Modify your `tasks.yaml` file">
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<Step title="Configure one task in `tasks.yaml`">
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```yaml tasks.yaml
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# src/latest_ai_development/config/tasks.yaml
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# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
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research_task:
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description: >
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Conduct a thorough research about {topic}
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Make sure you find any interesting and relevant information given
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the current year is 2025.
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Conduct thorough research about {topic}. Use web search to find current,
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credible information. The current year is 2026.
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expected_output: >
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A list with 10 bullet points of the most relevant information about {topic}
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A markdown report with clear sections: key trends, notable tools or companies,
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and implications. Aim for 800–1200 words. No fenced code blocks around the whole document.
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agent: researcher
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reporting_task:
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description: >
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Review the context you got and expand each topic into a full section for a report.
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Make sure the report is detailed and contains any and all relevant information.
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expected_output: >
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A fully fledge reports with the mains topics, each with a full section of information.
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Formatted as markdown without '```'
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agent: reporting_analyst
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output_file: report.md
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output_file: output/report.md
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```
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</Step>
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<Step title="Modify your `crew.py` file">
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```python crew.py
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# src/latest_ai_development/crew.py
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task
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from crewai_tools import SerperDevTool
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from crewai.agents.agent_builder.base_agent import BaseAgent
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<Step title="Wire the crew class (`content_crew.py`)">
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Point the generated crew at your YAML and attach `SerperDevTool` to the researcher.
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```python content_crew.py
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# src/latest_ai_flow/crews/content_crew/content_crew.py
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from typing import List
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from crewai import Agent, Crew, Process, Task
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from crewai.agents.agent_builder.base_agent import BaseAgent
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from crewai.project import CrewBase, agent, crew, task
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from crewai_tools import SerperDevTool
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@CrewBase
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class LatestAiDevelopmentCrew():
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"""LatestAiDevelopment crew"""
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class ResearchCrew:
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"""Single-agent research crew used inside the Flow."""
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agents: List[BaseAgent]
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tasks: List[Task]
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agents_config = "config/agents.yaml"
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tasks_config = "config/tasks.yaml"
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@agent
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def researcher(self) -> Agent:
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return Agent(
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config=self.agents_config['researcher'], # type: ignore[index]
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config=self.agents_config["researcher"], # type: ignore[index]
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verbose=True,
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tools=[SerperDevTool()]
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)
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@agent
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def reporting_analyst(self) -> Agent:
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return Agent(
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config=self.agents_config['reporting_analyst'], # type: ignore[index]
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verbose=True
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tools=[SerperDevTool()],
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)
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@task
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def research_task(self) -> Task:
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return Task(
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config=self.tasks_config['research_task'], # type: ignore[index]
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)
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@task
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def reporting_task(self) -> Task:
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return Task(
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config=self.tasks_config['reporting_task'], # type: ignore[index]
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output_file='output/report.md' # This is the file that will be contain the final report.
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config=self.tasks_config["research_task"], # type: ignore[index]
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)
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@crew
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def crew(self) -> Crew:
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"""Creates the LatestAiDevelopment crew"""
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return Crew(
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agents=self.agents, # Automatically created by the @agent decorator
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tasks=self.tasks, # Automatically created by the @task decorator
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agents=self.agents,
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tasks=self.tasks,
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process=Process.sequential,
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verbose=True,
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)
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```
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</Step>
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<Step title="[Optional] Add before and after crew functions">
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```python crew.py
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# src/latest_ai_development/crew.py
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from crewai import Agent, Crew, Process, Task
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from crewai.project import CrewBase, agent, crew, task, before_kickoff, after_kickoff
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from crewai_tools import SerperDevTool
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@CrewBase
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class LatestAiDevelopmentCrew():
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"""LatestAiDevelopment crew"""
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<Step title="Define the Flow in `main.py`">
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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`.
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@before_kickoff
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def before_kickoff_function(self, inputs):
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print(f"Before kickoff function with inputs: {inputs}")
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return inputs # You can return the inputs or modify them as needed
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@after_kickoff
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def after_kickoff_function(self, result):
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print(f"After kickoff function with result: {result}")
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return result # You can return the result or modify it as needed
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# ... remaining code
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```
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</Step>
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<Step title="Feel free to pass custom inputs to your crew">
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For example, you can pass the `topic` input to your crew to customize the research and reporting.
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```python main.py
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#!/usr/bin/env python
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# src/latest_ai_development/main.py
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import sys
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from latest_ai_development.crew import LatestAiDevelopmentCrew
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# src/latest_ai_flow/main.py
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from pydantic import BaseModel
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def run():
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"""
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Run the crew.
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"""
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inputs = {
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'topic': 'AI Agents'
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}
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LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
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from crewai.flow import Flow, listen, start
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from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
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class ResearchFlowState(BaseModel):
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topic: str = ""
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report: str = ""
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class LatestAiFlow(Flow[ResearchFlowState]):
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@start()
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def prepare_topic(self, crewai_trigger_payload: dict | None = None):
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if crewai_trigger_payload:
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self.state.topic = crewai_trigger_payload.get("topic", "AI Agents")
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else:
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self.state.topic = "AI Agents"
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print(f"Topic: {self.state.topic}")
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@listen(prepare_topic)
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def run_research(self):
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result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
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self.state.report = result.raw
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print("Research crew finished.")
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@listen(run_research)
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def summarize(self):
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print("Report path: output/report.md")
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def kickoff():
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LatestAiFlow().kickoff()
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def plot():
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LatestAiFlow().plot()
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if __name__ == "__main__":
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kickoff()
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```
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</Step>
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<Step title="Set your environment variables">
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Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:
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- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`
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- The configuration for your choice of model, such as an API key. See the
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[LLM setup guide](/en/concepts/llms#setting-up-your-llm) to learn how to configure models from any provider.
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</Step>
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<Step title="Lock and install the dependencies">
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- Lock the dependencies and install them by using the CLI command:
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<CodeGroup>
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```shell Terminal
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crewai install
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```
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</CodeGroup>
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- If you have additional packages that you want to install, you can do so by running:
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<CodeGroup>
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```shell Terminal
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uv add <package-name>
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```
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</CodeGroup>
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</Step>
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<Step title="Run your crew">
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- To run your crew, execute the following command in the root of your project:
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<CodeGroup>
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```bash Terminal
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crewai run
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```
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</CodeGroup>
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<Tip>
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If your package name differs from `latest_ai_flow`, change the import of `ResearchCrew` to match your project’s module path.
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</Tip>
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</Step>
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<Step title="Enterprise Alternative: Create in Crew Studio">
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For CrewAI AMP users, you can create the same crew without writing code:
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<Step title="Set environment variables">
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In `.env` at the project root, set:
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1. Log in to your CrewAI AMP account (create a free account at [app.crewai.com](https://app.crewai.com))
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2. Open Crew Studio
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3. Type what is the automation you're trying to build
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4. Create your tasks visually and connect them in sequence
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5. Configure your inputs and click "Download Code" or "Deploy"
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<Card title="Try CrewAI AMP" icon="rocket" href="https://app.crewai.com">
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Start your free account at CrewAI AMP
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</Card>
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- `SERPER_API_KEY` — from [Serper.dev](https://serper.dev/)
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- Your model provider keys as required — see [LLM setup](/en/concepts/llms#setting-up-your-llm)
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</Step>
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<Step title="View your final report">
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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.
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Here's an example of what the report should look like:
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<Step title="Install and run">
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<CodeGroup>
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```shell Terminal
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crewai install
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crewai run
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```
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</CodeGroup>
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`crewai run` executes the Flow entrypoint defined in your project (same command as for crews; project type is `"flow"` in `pyproject.toml`).
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</Step>
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<Step title="Check the output">
|
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You should see logs from the Flow and the crew. Open **`output/report.md`** for the generated report (excerpt):
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<CodeGroup>
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```markdown output/report.md
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# Comprehensive Report on the Rise and Impact of AI Agents in 2025
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# AI Agents in 2026: Landscape and Trends
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## 1. Introduction to AI Agents
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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.
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## Executive summary
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…
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## 2. Benefits of AI Agents
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AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
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## Key trends
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||||
- **Tool use and orchestration** — …
|
||||
- **Enterprise adoption** — …
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||||
|
||||
- **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.
|
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- **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.
|
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- **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.
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|
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## 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.
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||||
|
||||
## 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.
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||||
|
||||
As AI continues to evolve, HR departments leveraging these agents can realize substantial improvements in both efficiency and employee satisfaction.
|
||||
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||||
## 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.
|
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- **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.
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## 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.
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||||
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||||
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.
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||||
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||||
## 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:
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||||
|
||||
- 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.
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||||
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||||
## 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>
|
||||
|
||||
Reference in New Issue
Block a user