Agents

from ziet import Agent, Action, memory

@Agent(
    id="support_agent",
    name="CustomerSupport",
    description="Handle customer inquiries",
    instructions="""
    You are a customer support agent. Your workflow:
    1. Fetch customer data using fetch_customer action
    2. Get their latest order using get_order action
    3. Check shipment status using track_shipment action
    4. Store all results in memory
    5. Provide a helpful response to the customer
    
    Always be polite and provide tracking numbers when available.
    """,
    actions=["fetch_customer", "get_order", "track_shipment"],
    model="gpt-4o-mini"
)
class CustomerSupportAgent:
    pass  # Agent behavior is defined by instructions and strategies

@Agent(
    id="search_agent",                    # Required: Unique identifier (use <verb>_agent)
    name="SearchAgent",                   # Required: Human-readable name
    description="Search and analyze",     # Optional: Description
    instructions="""...""",               # Required: Instructions for agent behavior
    actions=["action1", "action2"],       # Required: Available action IDs
    model="gpt-4o-mini",                  # Optional: LLM model
    max_steps=40                          # Optional: Max action calls per run
)
class MyAgent:
    pass  # Agent behavior is defined by instructions

from ziet import Agent

@Agent(
    id="greeting_agent",
    name="GreetingAgent",
    instructions="Greet the user warmly and wish them a great day!",
    actions=[],
    model="gpt-4o-mini"
)
class GreetingAgent:
    pass  # Agent behavior is fully defined by instructions

from ziet import Agent, Action, memory
from ziet.integrations import google, openai

# Define actions
@Action(
    id="search_google",
    name="Search Google",
    description="Search Google for information"
)
def search_google(query: str) -> None:
    results = google.search(query, num_results=10)
    memory.add(key="search_results", value=results)

@Action(
    id="summarize_results",
    name="Summarize Results",
    description="Summarize search results using AI"
)
def summarize_results() -> None:
    results = memory.get("search_results")
    prompt = f"Summarize these search results: {results}"
    summary = openai.chat([{"role": "user", "content": prompt}])
    memory.add(key="final_summary", value=summary)

# Define agent
@Agent(
    id="research_agent",
    name="ResearchAgent",
    description="Research topics and provide summaries",
    instructions="""
    You are a research agent. Follow this workflow:
    1. Use search_google action to find information on the topic
    2. Store results in memory
    3. Use summarize_results action to create a concise summary
    4. All data should be stored in memory for retrieval
    
    Be thorough but concise. Focus on factual information.
    """,
    actions=["search_google", "summarize_results"],
    model="gpt-4o-mini",
    max_steps=20
)
class ResearchAgent:
    pass  # Agent orchestrates actions based on instructions

from ziet import Agent, Action, memory

@Action(
    id="fetch_user",
    name="Fetch User",
    description="Fetch user from database"
)
def fetch_user(user_id: str) -> None:
    user = database.get_user(user_id)
    memory.add(key="current_user", value=user)

@Agent(
    id="profile_agent",
    name="ProfileAgent",
    instructions="""
    1. Use fetch_user action to get user data
    2. User data will be stored in memory with key 'current_user'
    3. Analyze the user profile and provide insights
    4. Store your analysis in memory with key 'profile_analysis'
    """,
    actions=["fetch_user"]
)
class ProfileAgent:
    pass

from ziet import Agent, Action, memory
from ziet.integrations import google, openai, stripe, sendgrid

@Action(
    id="search_flights",
    name="Search Flights",
    description="Search for flights using Google"
)
def search_flights(origin: str, dest: str) -> None:
    results = google.search(f"flights {origin} to {dest}")
    memory.add(key="flight_results", value=results)

@Action(
    id="analyze_flights",
    name="Analyze Flights",
    description="Analyze flight options with AI"
)
def analyze_flights() -> None:
    flights = memory.get("flight_results")
    prompt = f"Which flight offers best value? {flights}"
    analysis = openai.chat([{"role": "user", "content": prompt}])
    memory.add(key="flight_analysis", value=analysis)

@Agent(
    id="booking_agent",
    name="BookingAgent",
    instructions="""
    1. Search for flights using search_flights action
    2. Analyze options using analyze_flights action
    3. Present the best recommendation to the user
    """,
    actions=["search_flights", "analyze_flights"]
)
class BookingAgent:
    pass

ziet run --local --agent research_agent

🧪 Running agent locally: research_agent

[INFO] Starting local run...
[INFO] Agent research_agent initialized
[INFO] Calling action: search_google
[INFO] Memory added: key=search_results
[INFO] Calling action: summarize_results
[INFO] Memory added: key=final_summary
✅ Local run completed

Agent completed successfully

ziet deploy