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Update installation and quickstart documentation for JSON-first crew projects

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- Revised the installation guide to reflect the new JSON-first project structure, detailing the creation of `crew.jsonc` and `agents/*.jsonc` files.
- Updated the quickstart guide to demonstrate setting up agents and tasks using JSONC format, replacing previous YAML examples.
- Enhanced the agents and tasks documentation to clarify the transition from YAML to JSONC, including examples and explanations of the new structure.
- Added notes on the classic YAML structure for legacy projects and provided guidance on migrating to the new format.
This commit is contained in:
Joao Moura
2026-06-16 08:28:37 -07:00
parent e9d568dc69
commit 5b766f999f
60 changed files with 2022 additions and 2814 deletions
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docs/en/concepts/cli.mdx
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@@ -52,6 +52,8 @@ crewai create crew my_new_crew
crewai create flow my_new_flow
```
By default, `crewai create crew` creates a JSON-first crew project with `crew.jsonc` and `agents/*.jsonc`. Use `crewai create crew my_new_crew --classic` only when you want the older Python/YAML scaffold with `crew.py`, `config/agents.yaml`, and `config/tasks.yaml`.
### 2. Version
Show the installed version of CrewAI.
@@ -185,7 +187,20 @@ crewai chat
Ensure you execute these commands from your CrewAI project's root directory.
</Note>
<Note>
IMPORTANT: Set the `chat_llm` property in your `crew.py` file to enable this command.
IMPORTANT: Set the `chat_llm` property in your crew definition to enable this command.
For JSON-first crews, add it to `crew.jsonc`:
```jsonc
{
"name": "My Crew",
"agents": ["researcher"],
"tasks": [],
"chat_llm": "openai/gpt-4o"
}
```
For classic Python/YAML crews, set it in `crew.py`:
```python
@crew
@@ -336,7 +351,7 @@ Notes:
### 12. API Keys
When running `crewai create crew` command, the CLI will show you a list of available LLM providers to choose from, followed by model selection for your chosen provider.
When running the `crewai create crew` command, the CLI shows a list of available LLM providers, followed by model selection for your chosen provider. The selected model is saved in the generated `.env` file and each generated agent JSONC file can set its own `llm`.
Once you've selected an LLM provider and model, you will be prompted for API keys.