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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
283 lines
9.0 KiB
Plaintext
283 lines
9.0 KiB
Plaintext
---
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title: Quickstart
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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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### Watch: Building CrewAI Agents & Flows with Coding Agent Skills
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Install our coding agent skills (Claude Code, Codex, ...) to quickly get your coding agents up and running with CrewAI.
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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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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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If you have not installed CrewAI yet, follow the [installation guide](/en/installation) first.
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## Prerequisites
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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 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 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="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_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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goal: >
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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}. 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="Configure one task in `tasks.yaml`">
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```yaml 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 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 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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output_file: output/report.md
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```
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</Step>
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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 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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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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@crew
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def crew(self) -> Crew:
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return Crew(
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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="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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```python main.py
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# src/latest_ai_flow/main.py
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from pydantic import BaseModel
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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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<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="Set environment variables">
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In `.env` at the project root, set:
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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="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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# AI Agents in 2026: Landscape and Trends
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## Executive summary
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…
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## Key trends
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- **Tool use and orchestration** — …
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- **Enterprise adoption** — …
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## Implications
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…
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```
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</CodeGroup>
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Your actual file will be longer and reflect live search results.
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</Step>
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</Steps>
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## How this run fits together
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1. **Flow** — `LatestAiFlow` runs `prepare_topic` first, then `run_research`, then `summarize`. State (`topic`, `report`) lives on the Flow.
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2. **Crew** — `ResearchCrew` runs one task with one agent: the researcher uses **Serper** to search the web, then writes the structured report.
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3. **Artifact** — The task’s `output_file` writes the report under `output/report.md`.
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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).
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<Check>
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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.
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</Check>
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### Naming consistency
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YAML keys (`researcher`, `research_task`) must match the method names on your `@CrewBase` class. See [Crews](/en/concepts/crews) for the full decorator pattern.
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## Deploying
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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:
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<CodeGroup>
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```bash Authenticate
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crewai login
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```
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```bash Create deployment
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crewai deploy create
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```
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```bash Check status & logs
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crewai deploy status
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crewai deploy logs
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```
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```bash Ship updates after you change code
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crewai deploy push
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```
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```bash List or remove deployments
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crewai deploy list
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crewai deploy remove <deployment_id>
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```
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</CodeGroup>
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<Tip>
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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).
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</Tip>
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<CardGroup cols={2}>
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<Card title="Deploy guide" icon="book" href="/en/enterprise/guides/deploy-to-amp">
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Step-by-step AMP deployment (CLI and dashboard).
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</Card>
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<Card
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title="Join the Community"
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icon="comments"
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href="https://community.crewai.com"
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>
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Discuss ideas, share projects, and connect with other CrewAI developers.
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</Card>
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</CardGroup>
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