crewai-multi-agent

# 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",