crewai

CrewAI — Multi-Agent Orchestration

Overview

CrewAI organizes LLM agents into a Crew: each Agent has a role, goal, backstory and tools, each Task has a description and expected output, and the crew runs the tasks in order (Process.sequential) or under an auto-created manager (Process.hierarchical). Besides crews, the package has Flow for event-driven, stateful pipelines that can call crews. Checked against crewai and crewai-tools 1.15.23 (28 Sep 2026): imports and constructors below were executed; model calls were not (no API key used).

Instructions

Step 1: Install and scaffold

pip install crewai crewai-tools          # or: uv tool install crewai
crewai --version
crewai create crew research-crew         # interactive wizard that writes agents.yaml, tasks.yaml, crew.py
cd research-crew && crewai install && crewai run

Set the provider key in a .env file or the environment (for OpenAI, OPENAI_API_KEY); never commit it. Scaffolded projects keep prompts in src/<name>/config/agents.yaml and tasks.yaml, with a @CrewBase class in crew.py using @agent, @task and @crew decorators; {topic} placeholders are filled by kickoff(inputs=...).

Step 2: Agents, tasks and a crew in plain Python

from crewai import Agent, Task, Crew, Process, LLM
from crewai_tools import SerperDevTool, WebsiteSearchTool, FileReadTool   # SerperDevTool needs SERPER_API_KEY

llm = LLM(model="openai/gpt-4o-mini", temperature=0.2)

researcher = Agent(
    role="Senior Research Analyst",
    goal="Find accurate, cross-checked facts about {topic}",
    backstory="You have 15 years in technology analysis and always verify claims against several sources.",
    tools=[SerperDevTool(), WebsiteSearchTool()],
    llm=llm,
    allow_delegation=False,
    verbose=True,
)
writer = Agent(
    role="Content Writer",
    goal="Turn research into a clear article for developers",
    backstory="A technical writer who explains complex topics with concrete examples.",
    tools=[FileReadTool()],
    llm=llm,
)

research_task = Task(
    description="Research {topic}. Find at least 5 credible sources, key numbers and recent developments.",
    expected_output="A research brief with source URLs",
    agent=researcher,
)
writing_task = Task(
    description="Write a 1200-word markdown article from the research brief.",
    expected_output="A markdown article with intro, 3 sections and conclusion",
    agent=writer,
    context=[research_task],               # output of research_task is passed in
    output_file="article.md",
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, writing_task],
            process=Process.sequential, verbose=True)
result = crew.kickoff(inputs={"topic": "AI agents in production"})
print(result.raw)                          # final text; also result.pydantic, result.json_dict, result.tasks_output
print(result.token_usage)

Task also accepts output_pydantic=MyModel for structured results, guardrail= for output checks and async_execution=True.

Step 3: Custom tools

import json
from crewai.tools import BaseTool
from pydantic import BaseModel, Field

class OrdersQueryInput(BaseModel):
    query: str = Field(description="Read-only SQL SELECT against the orders database")

class OrdersQueryTool(BaseTool):
    name: str = "orders_query"
    description: str = "Run a read-only SQL query on the orders analytics database"
    args_schema: type[BaseModel] = OrdersQueryInput

    def _run(self, query: str) -> str:
        rows = run_readonly_query(query)   # your own function
        return json.dumps(rows, default=str)

The @tool("name") decorator from crewai.tools works for simple functions. Agents can also take MCP servers through the mcps= field.

Step 4: Hierarchical process and memory

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    process=Process.hierarchical,          # requires manager_llm or manager_agent
    manager_llm="gpt-4o",
    memory=True,                           # or pass a Memory instance for custom storage
)

Memory is a unified store (Memory, with scoped views) and calls an embedding model; configure embedder= if you do not use OpenAI. Reset it with crewai reset-memories. Other useful CLI commands: crewai test, crewai train, crewai replay.

Examples

Example 1: "Set up a researcher and writer crew for blog posts"

Run the Step 2 script with OPENAI_API_KEY and SERPER_API_KEY exported. Result: verbose logs show the researcher's tool calls, then article.md is written and the token usage dictionary prints.

Example 2: "Let the crew query our orders database"

Add OrdersQueryTool() to a Data Analyst agent's tools, give it a Task such as "Summarize last month's refunds by reason", and run crew.kickoff(). Result: the agent calls orders_query with SQL, and the answer in result.raw cites the returned rows.

Guidelines

• Specific roles, goals and expected_output text drive quality; vague agents wander and burn tokens.
• Use Process.sequential by default; hierarchical adds a manager LLM and more calls, so use it only when task assignment must be dynamic.
• Pass data between tasks explicitly with context=[...]; set max_iter and max_rpm on agents to cap loops and rate-limit errors.
• Tools run with your credentials: give database tools read-only access and validate inputs; do not let agents run arbitrary shell or write code unless sandboxed (allow_code_execution runs code and should stay off unless needed).
• Delegation (allow_delegation=True) increases cost and can loop between agents; enable it only where needed.
• CrewAI collects anonymous telemetry by default; set CREWAI_DISABLE_TELEMETRY=true where policy requires.
• Old tutorials use crewai_tools imports that moved or LangChain tools; prefer crewai_tools and crewai.tools.BaseTool, and pin the version you tested.