crewai-multi-agent — Создание и управление командами AI агентов

--- name: crewai-multi-agent description: 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. version: 1.0.0 author: Orchestra Research license: MIT tags: [Agents, CrewAI, Multi-Agent, Orchestration, Collaboration, Role-Based, Autonomous, Workflows, Memory, Production] dependencies: [crewai>=1.2.0, crewai-tools>=1.2.0] --- # 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 ## Quick start ### Installation ```bash # Core framework pip install crewai # With 50+ built-in tools pip install 'crewai[tools]' ``` ### Create project with CLI ```bash # Create new crew project crewai create crew my_project cd my_project # Install dependencies crewai install # Run the crew crewai run ``` ### Simple crew (code-only) ```python from crewai import Agent, Task, Crew, Process # 1. Define agents researcher = Agent( role="Senior Research Analyst", goal="Discover cutting-edge developments in AI", backstory="You are an expert analyst with a keen eye for emerging trends.", verbose=True ) writer = Agent( role="Technical Writer", goal="Create clear, engaging content about technical topics", backstory="You excel at explaining complex concepts to general audiences.", verbose=True ) # 2. Define tasks research_task = Task( description="Research the latest developments in {topic}. Find 5 key trends.", expected_output="A detailed report with 5 bullet points on key trends.", agent=researcher ) write_task = Task( description="Write a blog post based on the research findings.", expected_output="A 500-word blog post in markdown format.", agent=writer, context=[research_task] # Uses research output ) # 3. Create and run crew crew = Crew( agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential, # Tasks run in order verbose=True ) # 4. Execute result = crew.kickoff(inputs={"topic": "AI Agents"}) print(result.raw) ``` ## Core concepts ### Agents - Autonomous workers ```python 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 ```python 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 ```python 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: ```python 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: ```python 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+) ```bash pip install 'crewai[tools]' ``` ```python 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 ```python 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()] ) ``` ## YAML configuration (recommended) ### Project structure ``` my_project/ ├── src/my_project/ │ ├── config/ │ │ ├── agents.yaml # Agent definitions │ │ └── tasks.yaml # Task definitions │ ├── crew.py # Crew assembly │ └── main.py # Entry point └── pyproject.toml ``` ### agents.yaml ```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}. Known for your ability to find relevant information and present it clearly. reporting_analyst: role: "Reporting Analyst" goal: "Create detailed reports based on research data" backstory: > You're a meticulous analyst who transforms raw data into actionable insights through well-structured reports. ``` ### tasks.yaml ```yaml research_task: description: > Conduct thorough research about {topic}. Find the most relevant information for {year}. expected_output: > A list with 10 bullet points of the most relevant information about {topic}. agent: researcher reporting_task: description: > Review the research and create a comprehensive report. Focus on key findings and recommendations. expected_output: > A detailed report in markdown format with executive summary, findings, and recommendations. agent: reporting_analyst output_file: report.md ``` ### crew.py ```python from crewai import Agent, Crew, Process, Task from crewai.project import CrewBase, agent, crew, task from crewai_tools import SerperDevTool @CrewBase class MyProjectCrew: """My Project crew""" @agent def researcher(self) -> Agent: return Agent( config=self.agents_config['researcher'], tools=[SerperDevTool()], verbose=True ) @agent def reporting_analyst(self) -> Agent: return Agent( config=self.agents_config['reporting_analyst'], verbose=True ) @task def research_task(self) -> Task: return Task(config=self.tasks_config['research_task']) @task def reporting_task(self) -> Task: return Task( config=self.tasks_config['reporting_task'], output_file='report.md' ) @crew def crew(self) -> Crew: return Crew( agents=self.agents, tasks=self.tasks, process=Process.sequential, verbose=True ) ``` ### main.py ```python from my_project.crew import MyProjectCrew def run(): inputs = { 'topic': 'AI Agents', 'year': 2025 } MyProjectCrew().crew().kickoff(inputs=inputs) if __name__ == "__main__": run() ``` ## Flows - Event-driven orchestration For complex workflows with conditional logic, use Flows: ```python 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](references/flows.md) for complete documentation. ## Memory system ```python # 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 ```python 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 | Feature | CrewAI | LangChain | LangGraph | |---------|--------|-----------|-----------| | **Best for** | Multi-agent teams | General LLM apps | Stateful workflows | | **Learning curve** | Low | Medium | Higher | | **Agent paradigm** | Role-based | Tool-based | Graph-based | | **Memory** | Built-in | Plugin-based | Custom | ## 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 ## Common issues **Agent stuck in loop:** ```python agent = Agent( role="...", max_iter=10, # Limit iterations max_rpm=5 # Rate limit ) ``` **Task not using context:** ```python task2 = Task( description="...", context=[task1], # Explicitly pass context agent=writer ) ``` **Memory errors:** ```python # Use environment variable for storage import os os.environ["CREWAI_STORAGE_DIR"] = "./my_storage" ``` ## References - **[Flows Guide](references/flows.md)** - Event-driven workflows, state management - **[Tools Guide](references/tools.md)** - Built-in tools, custom tools, MCP - **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging ## Resources - **GitHub**: https://github.com/crewAIInc/crewAI (25k+ stars) - **Docs**: https://docs.crewai.com - **Tools**: https://github.com/crewAIInc/crewAI-tools - **Examples**: https://github.com/crewAIInc/crewAI-examples - **Version**: 1.2.0+ - **License**: MIT