Multi-Agent Teams

# Agent team coordination (20 lines of code!)
from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.tools.yfinance import YFinanceTools
from phi.tools.duckduckgo import DuckDuckGoTools
from phi.storage.agent.sqlite import SqliteAgentStorage

# Web research agent
web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    tools=[DuckDuckGoTools()],
    storage=SqliteAgentStorage()
)

# Financial analysis agent
finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data and analysis",
    tools=[YFinanceTools()],
    storage=SqliteAgentStorage()
)

# Coordinating team lead
team_agent = Agent(
    team=[web_agent, finance_agent],
    model=OpenAIChat(id="gpt-4o"),
    show_tool_calls=True,
    markdown=True
)

cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_finance_agent_team
pip install -r requirements.txt
export OPENAI_API_KEY='your-api-key-here'
python3 finance_agent_team.py

Candidate Upload Resume
    ↓
Technical Recruiter Agent
├─ Parse resume
├─ Extract skills & experience
├─ Match with job requirements
└─ Make selection decision
    ↓
Communication Agent
├─ Draft personalized email
├─ Include decision & feedback
└─ Send via Gmail
    ↓
Scheduling Coordinator (if selected)
├─ Create Zoom meeting
├─ Generate meeting link
└─ Include in email
    ↓
Candidate Receives Complete Response

cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_recruitment_agent_team
pip install -r requirements.txt
streamlit run ai_recruitment_agent_team.py

cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_legal_agent_team
pip install -r requirements.txt
streamlit run legal_agent_team.py

Input Your Vision
├─ Destination and dates
├─ Budget constraints
├─ Travel style preferences
└─ Special interests
    ↓
AI Agents Collaborate (Parallel Processing)
├─ Flight Search Agent → Best flight options
├─ Hotel Search Agent → Accommodation recommendations
├─ Destination Explorer → Attractions and activities
├─ Dining Agent → Restaurant suggestions
├─ Budget Agent → Cost optimization
└─ All feed into ↓
    ↓
Itinerary Specialist
├─ Day-by-day schedule
├─ Booking recommendations
├─ Cost breakdown
└─ Hidden gem discoveries
    ↓
Complete Travel Itinerary

cd advanced_ai_agents/multi_agent_apps/agent_teams/multimodal_coding_agent_team
pip install -r requirements.txt
streamlit run ai_coding_agent_o3.py

from openai import OpenAI
from agents import Agent, Runner

# Triage agent plans the research
triage_agent = Agent(
    name="Triage",
    instructions="Plan research approach and coordinate workflow",
    model="gpt-4o"
)

# Research agent gathers information
research_agent = Agent(
    name="Researcher",
    instructions="Search web and gather relevant information",
    tools=[web_search_tool],
    model="gpt-4o"
)

# Editor compiles the final report
editor_agent = Agent(
    name="Editor",
    instructions="Compile collected facts into comprehensive report",
    model="gpt-4o"
)

# Runner orchestrates the workflow
runner = Runner(
    agents=[triage_agent, research_agent, editor_agent],
    handoffs=True
)

cd starter_ai_agents/openai_research_agent
pip install -r requirements.txt
export OPENAI_API_KEY='your-api-key-here'
streamlit run openai_researcher_agent.py

cd starter_ai_agents/ai_breakup_recovery_agent
pip install -r requirements.txt
streamlit run ai_breakup_recovery_agent.py

# Team lead coordinates specialist agents
team_lead = Agent(
    name="Team Lead",
    team=[specialist_1, specialist_2, specialist_3],
    instructions="Coordinate team and synthesize results"
)

# Agents work simultaneously
results = await asyncio.gather(
    agent_1.run_async(task_1),
    agent_2.run_async(task_2),
    agent_3.run_async(task_3)
)

# Agents pass work in sequence
runner = Runner(
    agents=[triage, research, analysis, editor],
    handoffs=True
)

# Specialists collaborate on complex tasks
analyst = Agent(role="Data Analyst", tools=[...])
researcher = Agent(role="Researcher", tools=[...])
strategist = Agent(role="Strategist", tools=[...])

team = AgentTeam(
    agents=[analyst, researcher, strategist],
    collaboration_mode="consensus"
)