CrewAI is an open-source Python framework for orchestrating teams of autonomous AI agents that work together on tasks. You give each agent a job, group them into a "crew," and the framework runs them as a coordinated workflow.
A multi-agent framework is software that lets several AI agents cooperate instead of relying on one agent to do everything. Orchestration just means coordinating who does what, in what order, and how results pass between agents.
CrewAI was created by João Moura and is fully open source. It has grown fast: according to CrewAI's own numbers, "With over 100,000 developers certified through our community courses, CrewAI is the standard for enterprise-ready AI automation," as stated in CrewAI's documentation.
Why Multi-Agent Systems Matter Right Now
One agent trying to do everything gets confused and slow. A team of focused agents works better because each one specializes, collaborates, and follows its own path to a result.
Think of it like a real crew: a researcher gathers facts, a writer drafts, and an editor reviews. Each does one job well, then hands off.
The timing matters too. Worldwide spending on AI is forecast to total nearly $1.5 trillion in 2025 according to Gartner, Inc., as shown in Gartner forecasts.
Enterprises are moving fast on agents specifically. Sixty-two percent of survey respondents say their organizations are at least experimenting with AI agents, per McKinsey's 2025 State of AI survey. And 55% of organizations are actively developing or deploying an agentic AI operating model, according to the IBM Institute for Business Value.
The Core Building Blocks of CrewAI
CrewAI is built on top of LangChain and organizes work around five simple pieces. Once you understand these, the whole framework clicks into place.
The five building blocks are agents, tasks, crews, processes, and tools. Here is what each one does.
Agents
An agent is an autonomous unit that handles part of the work. You define it with three plain attributes:
- Role: the job title, like "Senior Researcher" or "Report Writer."
- Goal: what the agent is trying to achieve.
- Backstory: context that shapes how the agent thinks and responds.
Agents can also delegate work and ask each other questions. That back-and-forth is what makes a crew feel like a team.
Tasks
A task is a specific assignment you give to an agent. Each task has three parts:
- Description: what needs to be done.
- Assigned agent: who does it.
- Expected output: what a finished result looks like.
One task's output can become the context for the next task. That lets information flow through the crew step by step.
Crews
A crew is the full team: your agents, their tasks, and a chosen process for running them. It is the container that ties everything together.
You start the workflow by "kicking off" the crew. From there, the agents run their tasks and produce a final result.
Processes: Sequential vs Hierarchical
A process decides how tasks get executed. CrewAI offers two main options.
Sequential runs tasks one after another in a fixed order. Hierarchical adds an automatically generated manager agent that assigns work and reviews it.
| Process | How it runs | Best for |
|---|---|---|
| Sequential | Tasks execute in order, one after another | Simple, linear workflows with clear steps |
| Hierarchical | A manager agent assigns tasks and reviews output | Complex work that needs coordination and oversight |
Tools
Tools are the skills agents call to actually do things. Examples include searching the web, scraping a page, running code, or querying a database.
Tools are what move an agent from "talking" to "doing." Without them, an agent can only reason over what it already knows. To understand why this matters for autonomous systems, see how web data in agentic workflows powers real decisions.
Crews vs Flows: Autonomy and Control
CrewAI gives you two ways to build. Crews let agents work autonomously, making their own decisions to reach a goal. Flows are an event-driven orchestration layer that adds state, conditional logic, and tighter control.
Use Crews when you want agents to figure out the path themselves. Use Flows when you need exact control over what happens and when.
| Feature | Crews | Flows |
|---|---|---|
| Style | Autonomous agent teams | Event-driven orchestration |
| Control | Agents decide the path | You define the exact path |
| State handling | Lighter | Built-in state and conditions |
| Best for | Open-ended research and creative work | Structured, repeatable pipelines |
Many production systems combine both. You let a crew handle open-ended reasoning, then wrap it in a flow for control.
Giving Your Crew Real-Time Web Data (Tools in Practice)
The hard part of production agents is not defining roles. It is feeding them fresh, reliable data. An agent's training data goes stale, so it needs live web content to research topics, verify facts, and act on current information.
This is where most guides stop short. Getting clean, structured web data into a crew at scale is a real engineering problem, not an afterthought.
A web data API solves this by acting as a tool your agents call. You point it at a URL, and it returns clean output your agent can read. Olostep integrates natively with agentic frameworks including CrewAI, LangChain, and AutoGPT, and provides structured scraping as a tool agents call programmatically.
Here is what that tool gives your crew:
- Clean, LLM-ready output: the Olostep web scraping API turns any URL into Markdown, HTML, PDF, or schema-defined JSON.
- Live search: a real-time web search API lets agents ground decisions in current results instead of stale training data.
- Reliability built in: full JavaScript rendering with premium residential proxy rotation handles anti-bot systems for you.
- Scale: batch runs handle 100k URLs in 5-7 minutes and roughly 1M requests in about 15 minutes.
The cost math works too. Olostep reports 50-90% cost savings versus building your own scraping infrastructure, with a free tier of 500 scrapes per month to start. For a practical walkthrough, learn how to add web search to an agent.
A Simple CrewAI Example: Building Your First Crew
Let's build a minimal crew. The flow is always the same: install CrewAI, define an agent, give it a task, assemble the crew, and kick it off.
In the example below, we also attach a web scraping tool to the agent so it can pull live data. This is the core pattern you will reuse for almost every project.
# 1. Install CrewAI
# pip install crewai crewai-tools
from crewai import Agent, Task, Crew, Process
from crewai_tools import ScrapeWebsiteTool
# 2. Give the agent a tool it can call
scrape_tool = ScrapeWebsiteTool()
# 3. Define an agent with a role, goal, and backstory
researcher = Agent(
role="Market Researcher",
goal="Find and summarize the latest facts about a topic",
backstory="An expert analyst who digs through live web pages for accurate data.",
tools=[scrape_tool],
verbose=True,
)
# 4. Define a task for the agent
research_task = Task(
description="Research the latest trends in AI agent frameworks.",
expected_output="A short, clear summary with three key findings.",
agent=researcher,
)
# 5. Assemble the crew and choose a process
crew = Crew(
agents=[researcher],
tasks=[research_task],
process=Process.sequential,
)
# 6. Kick off the crew
result = crew.kickoff()
print(result)That is a full, working crew. To grow it, add more agents and tasks, then let them hand off results to each other.
What You Can Build With CrewAI: Use Cases
CrewAI shines anywhere work can be split into specialized steps. Fresh web data makes each of these far more useful.
- Autonomous research agents: gather sources, summarize findings, and draft reports on live topics.
- Competitive and market intelligence: track competitor pages, pricing, and news as they change.
- Content planning: research a keyword, outline an article, and draft sections as a team.
- Lead enrichment: pull public data about companies and contacts to fill in your records.
- Monitoring: watch sites on a schedule and flag changes that matter.
Research and monitoring agents map cleanly to scheduled, autonomous work. The Olostep Research Agent shows this pattern running on its own. For more ideas, browse these production AI agent use cases.
CrewAI vs LangGraph vs AutoGen
CrewAI is not the only multi-agent framework. Here is a fair, short comparison of the three most common choices.
CrewAI is role-based and fast to a working prototype. LangGraph is graph-based with fine-grained control but more setup. AutoGen is conversation-first, though its status has changed recently.
| Framework | Style | Setup effort | Note |
|---|---|---|---|
| CrewAI | Role-based agent teams | Low, quick prototypes | Open source, built on LangChain |
| LangGraph | Graph-based control flow | Higher, more wiring | Precise control over each step |
| AutoGen | Conversational agents | Medium | See maintenance note below |
Popularity is one signal worth watching. CrewAI has over 55,000 GitHub stars as of 2026, according to CrewAI's GitHub repository.
Status matters when you pick a framework for the long term. AutoGen is now in maintenance mode, and it will not receive new features or enhancements, per AutoGen's GitHub repository. Treat stars as a popularity proxy, not a quality verdict.
How to Connect Tools to Your Agents
Wiring tools into agents is straightforward. You import a tool, pass it to an agent in the tools=[] list, and the agent calls it when a task needs it.
Tools can be simple functions, hosted APIs, or MCP-based connectors. MCP is a standard way for external tools to plug into agents and clients.
For a ready-made connection, the Olostep MCP server lets your agents call web data capabilities without custom glue code. That keeps your crew focused on reasoning while the tool handles the data.
Frequently Asked Questions
What is CrewAI used for?
CrewAI is used to build multi-agent systems where specialized agents collaborate on tasks using defined roles, goals, and workflows.
Is CrewAI free and open source?
Yes, CrewAI is a free, open-source Python framework, and it also offers paid enterprise options for teams that need more.
Do I need Python to use CrewAI?
Yes, CrewAI is a Python framework, but basic Python knowledge is enough to build your first crew.
What's the difference between Crews and Flows?
Crews are autonomous teams of agents, while Flows are an event-driven layer that adds state and control to orchestrate them precisely.
How do CrewAI agents use tools?
Agents call tools, which are functions like search, scrape, or run code, whenever a task needs an action beyond reasoning.
Can I add real-time web data to CrewAI?
Yes, you attach a web data or search API as a tool so agents pull live, structured data during a run.
Is CrewAI good for production systems?
Yes for many use cases, especially with Flows for control, though production reliability depends heavily on your tools and data sources.
