# Build a multi-agent browser workflow with CrewAI
URL: /cookbook/crewai
---
title: Build a multi-agent browser workflow with CrewAI
description: Integrate Steel with the CrewAI multi-agent framework.
---
CrewAI composes LLM work out of three primitives: an `Agent` (role, goal, tools), a `Task` (description + expected output), and a `Crew` that runs them in order. This recipe wires two agents, a `researcher` and a `reporting_analyst`, to a single custom tool that calls Steel's scrape API. The researcher gathers sources; the analyst turns them into `report.md`.
```python
@agent
def researcher(self) -> Agent:
return Agent(
role="Instruction-Following Web Researcher",
goal="Understand and execute: {task}. Find, verify, and extract ...",
backstory="You specialize in decomposing and executing complex ...",
tools=[SteelScrapeWebsiteTool()],
llm="gpt-5-nano",
verbose=True,
)
```
The `{task}` placeholder is interpolated from the `inputs` dict passed to `kickoff()`, so the same crew runs against any research prompt without a code edit.
`SteelScrapeWebsiteTool` subclasses `BaseTool`, declares `args_schema = SteelScrapeWebsiteToolSchema` (a single `url: str` field), and implements `_run`:
```python
class SteelScrapeWebsiteTool(BaseTool):
name: str = "Steel web scrape tool"
description: str = "Scrape webpages using Steel and return the contents"
args_schema: Type[BaseModel] = SteelScrapeWebsiteToolSchema
def _run(self, url: str):
return self._steel.scrape(
url=url, use_proxy=self.proxy, format=self.formats, region="iad",
)
```
No session lifecycle to manage: `scrape()` is one-shot and returns markdown by default.
## Run it
```bash
cd examples/crewai
cp .env.example .env # set STEEL_API_KEY and OPENAI_API_KEY
uv run main.py
```
Get keys from [app.steel.dev/settings/api-keys](https://app.steel.dev/settings/api-keys) and [platform.openai.com](https://platform.openai.com/api-keys). Default `TASK` is `"Research AI LLMs and summarize key developments"`; override via the `TASK` env var or edit `main.py`.
Your output varies. Structure looks like this:
```text
Steel + CrewAI Starter
============================================================
Running crew...
# Agent: Instruction-Following Web Researcher
## Task: Interpret and execute the following instruction...
## Using tool: Steel web scrape tool
## Tool Input: {"url": "https://..."}
## Tool Output: # Page title ... (markdown)
## Final Answer: - Finding 1 ... - Finding 2 ...
# Agent: Instruction-Following Reporting Analyst
## Task: Review the research context and produce a complete report...
## Final Answer: # AI LLMs ... (full markdown report)
Report written to report.md
```
A run takes ~60-90 seconds. `report.md` is overwritten each run.
## Make it yours
- **Change the task.** Set `TASK="Find the top 3 open-source vector databases and compare licensing"` in `.env` and rerun.
- **Add an agent.** Slot a fact-checker between researcher and analyst with a new `@agent` and `@task`. `Process.sequential` picks them up in declaration order.
- **Mix models.** The researcher can stay on `gpt-5-nano` while the analyst runs `gpt-5` or `claude-sonnet-4-6`. Set `llm=` independently on each `Agent`.
- **Tighten the scraper.** Pass `proxy=True` to `SteelScrapeWebsiteTool()` for sites that block datacenter IPs, or `formats=["html"]` if the markdown conversion strips something you need.
## Related
[CrewAI docs](https://docs.crewai.com) ยท [CrewAI tools reference](https://docs.crewai.com/en/concepts/tools)
## Related recipes