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Update installation and quickstart documentation for JSON-first crew projects (#6181)

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* Update installation and quickstart documentation for JSON-first crew projects

- 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.

* docs: clarify json crew quickstart guidance

* docs: address json docs review feedback
This commit is contained in:
João Moura
2026-06-16 19:55:23 -03:00
committed by GitHub
parent ebbc0998ef
commit ee4f853270
64 changed files with 2168 additions and 2920 deletions
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docs/en/quickstart.mdx
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@@ -39,85 +39,60 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
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="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=...)`.
<Step title="Configure one agent in JSONC">
Create `src/latest_ai_flow/crews/content_crew/agents/researcher.jsonc` (create the `agents/` directory if needed). Variables like `{topic}` are filled from `crew.kickoff(inputs=...)`.
```yaml agents.yaml
# src/latest_ai_flow/crews/content_crew/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
goal: >
Uncover cutting-edge developments in {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. You find the most relevant information and
present it clearly.
```jsonc agents/researcher.jsonc
{
"role": "{topic} Senior Data Researcher",
"goal": "Uncover cutting-edge developments in {topic}",
"backstory": "You're a seasoned researcher who finds relevant information and presents it clearly.",
"tools": ["SerperDevTool"],
"settings": {
"verbose": true
}
}
```
</Step>
<Step title="Configure one task in `tasks.yaml`">
```yaml tasks.yaml
# src/latest_ai_flow/crews/content_crew/config/tasks.yaml
research_task:
description: >
Conduct thorough research about {topic}. Use web search to find current,
credible information. The current year is 2026.
expected_output: >
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
output_file: output/report.md
<Step title="Configure the crew in `crew.jsonc`">
Create `src/latest_ai_flow/crews/content_crew/crew.jsonc`:
```jsonc crew.jsonc
{
"name": "Research Crew",
"agents": ["researcher"],
"tasks": [
{
"name": "research_task",
"description": "Conduct thorough research about {topic}. Use web search to find recent, credible information.",
"expected_output": "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",
"output_file": "output/report.md",
"markdown": true
}
],
"process": "sequential",
"verbose": true
}
```
</Step>
<Step title="Wire the crew class (`content_crew.py`)">
Point the generated crew at your YAML and attach `SerperDevTool` to the researcher.
<Step title="Load the JSON crew (`content_crew.py`)">
Replace the generated `content_crew.py` with a small loader that turns `crew.jsonc` into a `Crew`.
```python content_crew.py
# src/latest_ai_flow/crews/content_crew/content_crew.py
from typing import List
from pathlib import Path
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
from crewai.project import load_crew
@CrewBase
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]
verbose=True,
tools=[SerperDevTool()],
)
@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config["research_task"], # type: ignore[index]
)
@crew
def crew(self) -> Crew:
return Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
verbose=True,
)
def kickoff_content_crew(inputs: dict):
crew, default_inputs = load_crew(Path(__file__).with_name("crew.jsonc"))
return crew.kickoff(inputs={**default_inputs, **inputs})
```
</Step>
@@ -131,7 +106,7 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
from crewai.flow import Flow, listen, start
from latest_ai_flow.crews.content_crew.content_crew import ResearchCrew
from latest_ai_flow.crews.content_crew.content_crew import kickoff_content_crew
class ResearchFlowState(BaseModel):
@@ -150,7 +125,7 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
@listen(prepare_topic)
def run_research(self):
result = ResearchCrew().crew().kickoff(inputs={"topic": self.state.topic})
result = kickoff_content_crew(inputs={"topic": self.state.topic})
self.state.report = result.raw
print("Research crew finished.")
@@ -172,7 +147,7 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
```
<Tip>
If your package name differs from `latest_ai_flow`, change the import of `ResearchCrew` to match your project’s module path.
If your package name differs from `latest_ai_flow`, change the `kickoff_content_crew` import to match your project’s module path.
</Tip>
</Step>
@@ -202,7 +177,7 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
<CodeGroup>
```markdown output/report.md
# AI Agents in 2026: Landscape and Trends
# AI Agents: Recent Landscape and Trends
## Executive summary
…
@@ -223,7 +198,7 @@ If you have not installed CrewAI yet, follow the [installation guide](/en/instal
## 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.
2. **Crew** — `kickoff_content_crew` loads `crew.jsonc` and 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).
@@ -234,7 +209,10 @@ You now have an end-to-end Flow with an agent crew and a saved report — a soli
### Naming consistency
YAML keys (`researcher`, `research_task`) must match the method names on your `@CrewBase` class. See [Crews](/en/concepts/crews) for the full decorator pattern.
The names in `crew.jsonc` must match the files and task references you use:
- `agents: ["researcher"]` loads `agents/researcher.jsonc`
- `tasks[].agent: "researcher"` assigns the task to that agent
## Deploying