---
title: Build Your First Crew
description: Step-by-step tutorial to create a collaborative AI team with JSON-first crew configuration.
icon: users-gear
mode: "wide"
---

## Build a Research Crew

In this guide, you will create a two-agent research crew that gathers information about a topic and writes a markdown report. New crew projects are JSON-first: agents are defined in `agents/*.jsonc`, tasks and crew settings are defined in `crew.jsonc`, and `crewai run` loads the JSON definition directly.

### Prerequisites

Before starting, make sure you have:

1. Installed CrewAI following the [installation guide](/en/installation)
2. Set up your LLM API key following the [LLM setup guide](/en/concepts/llms#setting-up-your-llm)
3. A [Serper.dev](https://serper.dev/) API key if you want the researcher to use web search

## Step 1: Create a New Crew

```bash
crewai create crew research_crew
cd research_crew
```

The CLI creates a JSON-first project:

```text
research_crew/
├── .gitignore
├── .env
├── agents/
│   └── researcher.jsonc
├── crew.jsonc
├── knowledge/
├── pyproject.toml
├── README.md
├── skills/
└── tools/
```

<Tip>
Need the older `crew.py`, `config/agents.yaml`, and `config/tasks.yaml` layout? Create it with `crewai create crew research_crew --classic`.
</Tip>

## Step 2: Define Your Agents

Replace the generated `agents/researcher.jsonc` file and add `agents/analyst.jsonc`. The file names are the names you reference from `crew.jsonc`.

```jsonc agents/researcher.jsonc
{
  "role": "Senior Research Specialist for {topic}",
  "goal": "Find comprehensive and accurate information about {topic}, with a focus on recent developments and key insights.",
  "backstory": "You are an experienced research specialist who organizes complex information into clear, useful notes.",
  // Replace with your model, for example "openai/gpt-4o".
  "llm": "provider/model-id",
  "tools": ["SerperDevTool"],
  "settings": {
    "verbose": true,
    "allow_delegation": false
  }
}
```

```jsonc agents/analyst.jsonc
{
  "role": "Report Analyst for {topic}",
  "goal": "Turn research findings into a clear, well-structured report.",
  "backstory": "You are a careful analyst with strong technical writing skills and a talent for extracting useful insights.",
  // Replace with your model, for example "openai/gpt-4o".
  "llm": "provider/model-id",
  "settings": {
    "verbose": true,
    "allow_delegation": false
  }
}
```

Replace `provider/model-id` with the model you use, for example `openai/gpt-4o`, `anthropic/claude-sonnet-4-6`, or `gemini/gemini-2.0-flash-001`.

## Step 3: Define Tasks and Crew Settings

Replace `crew.jsonc` with:

```jsonc crew.jsonc
{
  "name": "Research Crew",
  "agents": ["researcher", "analyst"],
  "tasks": [
    {
      "name": "research_task",
      "description": "Conduct thorough research on {topic}. Focus on key concepts, recent developments, major challenges, notable applications, and future outlook.",
      "expected_output": "A comprehensive research document with organized sections, specific facts, and useful examples about {topic}.",
      "agent": "researcher"
    },
    {
      "name": "analysis_task",
      "description": "Analyze the research findings and create a polished report on {topic}. Include an executive summary, key insights, trend analysis, and recommendations.",
      "expected_output": "A professional markdown report with clear headings, a concise summary, main findings, and recommendations.",
      "agent": "analyst",
      "context": ["research_task"],
      "output_file": "output/report.md",
      "markdown": true
    }
  ],
  "process": "sequential",
  "verbose": true,
  "memory": true,
  "inputs": {
    "topic": "Artificial Intelligence in Healthcare"
  }
}
```

`context` points to prior task names, so the analyst receives the research task output. The `inputs` object provides default values for `{topic}`. If you remove a default, `crewai run` prompts for it.

## Step 4: Set Environment Variables

Open `.env` and add the keys your model and tools need:

```sh
SERPER_API_KEY=your_serper_api_key
# Add your model provider API key here too.
```

See the [LLM setup guide](/en/concepts/llms#setting-up-your-llm) for provider-specific keys.

## Step 5: Install and Run

```bash
crewai install
crewai run
```

`crewai run` detects `crew.jsonc`, loads the agents from `agents/`, prompts for missing placeholders, and runs the crew. When the run finishes, open `output/report.md`.

## How It Works

1. `crew.jsonc` defines the crew, task order, process, memory, and runtime inputs.
2. `agents/researcher.jsonc` and `agents/analyst.jsonc` define the agents.
3. The researcher runs first.
4. The analyst runs second with `context: ["research_task"]`.
5. The final task writes `output/report.md`.

## Extending Your Crew

You can add:

- More agents by creating new `agents/<name>.jsonc` files and listing them in `crew.jsonc`
- More tasks by appending objects to the `tasks` array
- Built-in tools by adding tool class names such as `"FileReadTool"` or `"SerperDevTool"`
- Custom tools with `"custom:<name>"`, which loads `tools/<name>.py`
- Hierarchical execution with `"process": "hierarchical"` and a `manager_llm` or `manager_agent`

<Warning>
Only run JSON crew projects from sources you trust. `custom:<name>` tools and `{"python": "module.attribute"}` references execute local Python code when the crew loads.
</Warning>

<Check>
You now have a working JSON-first crew that researches a topic and writes a report.
</Check>