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crewai-multi-agent

✓ Official★ 11

by firecrawl · part of firecrawl/ai-research-skills

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

🔥🔥🔥FreeQuick setup
#scraping#git
🧩 One of 7 skills in the firecrawl/ai-research-skills package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

CrewAI - Multi-Agent Orchestration Framework

Build teams of autonomous AI agents that collaborate to solve complex tasks.

When to use CrewAI

Use CrewAI when:

  • Building multi-agent systems with specialized roles
  • Need autonomous collaboration between agents
  • Want role-based task delegation (researcher, writer, analyst)
  • Require sequential or hierarchical process execution
  • Building production workflows with memory and observability
  • Need simpler setup than LangChain/LangGraph

Key features:

  • Standalone: No LangChain dependencies, lean footprint
  • Role-based: Agents have roles, goals, and backstories
  • Dual paradigm: Crews (autonomous) + Flows (event-driven)
  • 50+ tools: Web scraping, search, databases, AI services
  • Memory: Short-term, long-term, and entity memory
  • Production-ready: Tracing, enterprise features

Use alternatives instead:

  • LangChain: General-purpose LLM apps, RAG pipelines
  • LangGraph: Complex stateful workflows with cycles
  • AutoGen: Microsoft ecosystem, multi-agent conversations
  • LlamaIndex: Document Q&A, knowledge retrieval

Core concepts

Agents - Autonomous workers

from crewai import Agent

agent = Agent(
    role="Data Scientist",                    # Job title/role
    goal="Analyze data to find insights",     # What they aim to achieve
    backstory="PhD in statistics...",         # Background context
    llm="gpt-4o",                             # LLM to use
    tools=[],                                 # Tools available
    memory=True,                              # Enable memory
    verbose=True,                             # Show reasoning
    allow_delegation=True,                    # Can delegate to others
    max_iter=15,                              # Max reasoning iterations
    max_rpm=10                                # Rate limit
)

Tasks - Units of work

from crewai import Task

task = Task(
    description="Analyze the sales data for Q4 2024. {context}",
    expected_output="A summary report with key metrics and trends.",
    agent=analyst,                            # Assigned agent
    context=[previous_task],                  # Input from other tasks
    output_file="report.md",                  # Save to file
    async_execution=False,                    # Run synchronously
    human_input=False                         # No human approval needed
)

Crews - Teams of agents

from crewai import Crew, Process

crew = Crew(
    agents=[researcher, writer, editor],      # Team members
    tasks=[research, write, edit],            # Tasks to complete
    process=Process.sequential,               # Or Process.hierarchical
    verbose=True,
    memory=True,                              # Enable crew memory
    cache=True,                               # Cache tool results
    max_rpm=10,                               # Rate limit
    share_crew=False                          # Opt-in telemetry
)

# Execute with inputs
result = crew.kickoff(inputs={"topic": "AI trends"})

# Access results
print(result.raw)                             # Final output
print(result.tasks_output)                    # All task outputs
print(result.token_usage)                     # Token consumption

Process types

Sequential (default)

Tasks execute in order, each agent completing their task before the next:

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential  # Task 1 → Task 2 → Task 3
)

Hierarchical

Auto-creates a manager agent that delegates and coordinates:

crew = Crew(
    agents=[researcher, writer, analyst],
    tasks=[research_task, write_task, analyze_task],
    process=Process.hierarchical,  # Manager delegates tasks
    manager_llm="gpt-4o"           # LLM for manager
)

Using tools

Built-in tools (50+)

pip install 'crewai[tools]'
from crewai_tools import (
    SerperDevTool,           # Web search
    ScrapeWebsiteTool,       # Web scraping
    FileReadTool,            # Read files
    PDFSearchTool,           # Search PDFs
    WebsiteSearchTool,       # Search websites
    CodeDocsSearchTool,      # Search code docs
    YoutubeVideoSearchTool,  # Search YouTube
)

# Assign tools to agent
researcher = Agent(
    role="Researcher",
    goal="Find accurate information",
    backstory="Expert at finding data online.",
    tools=[SerperDevTool(), ScrapeWebsiteTool()]
)

Custom tools

from crewai.tools import BaseTool
from pydantic import Field

class CalculatorTool(BaseTool):
    name: str = "Calculator"
    description: str = "Performs mathematical calculations. Input: expression"

    def _run(self, expression: str) -> str:
        try:
            result = eval(expression)
            return f"Result: {result}"
        except Exception as e:
            return f"Error: {str(e)}"

# Use custom tool
agent = Agent(
    role="Analyst",
    goal="Perform calculations",
    tools=[CalculatorTool()]
)

Flows - Event-driven orchestration

For complex workflows with conditional logic, use Flows:

from crewai.flow.flow import Flow, listen, start, router
from pydantic import BaseModel

class MyState(BaseModel):
    confidence: float = 0.0

class MyFlow(Flow[MyState]):
    @start()
    def gather_data(self):
        return {"data": "collected"}

    @listen(gather_data)
    def analyze(self, data):
        self.state.confidence = 0.85
        return analysis_crew.kickoff(inputs=data)

    @router(analyze)
    def decide(self):
        return "high" if self.state.confidence > 0.8 else "low"

    @listen("high")
    def generate_report(self):
        return report_crew.kickoff()

# Run flow
flow = MyFlow()
result = flow.kickoff()

See Flows Guide for complete documentation.

Memory system

# Enable all memory types
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    memory=True,           # Enable memory
    embedder={             # Custom embeddings
        "provider": "openai",
        "config": {"model": "text-embedding-3-small"}
    }
)

Memory types: Short-term (ChromaDB), Long-term (SQLite), Entity (ChromaDB)

LLM providers

from crewai import LLM

llm = LLM(model="gpt-4o")                              # OpenAI (default)
llm = LLM(model="claude-sonnet-4-5-20250929")                       # Anthropic
llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")  # Local
llm = LLM(model="azure/gpt-4o", base_url="https://...")              # Azure

agent = Agent(role="Analyst", goal="Analyze data", llm=llm)

CrewAI vs alternatives

FeatureCrewAILangChainLangGraph
Best forMulti-agent teamsGeneral LLM appsStateful workflows
Learning curveLowMediumHigher
Agent paradigmRole-basedTool-basedGraph-based
MemoryBuilt-inPlugin-basedCustom

Best practices

  1. Clear roles - Each agent should have a distinct specialty
  2. YAML config - Better organization for larger projects
  3. Enable memory - Improves context across tasks
  4. Set max_iter - Prevent infinite loops (default 15)
  5. Limit tools - 3-5 tools per agent max
  6. Rate limiting - Set max_rpm to avoid API limits

References

Resources