How to Build AgentScope-Friendly Production Workflows with React Agents, Custom Tools, Multi-Agent Debugging, Scheduled Outputs and Similar Queues

import subprocess, sys


subprocess.check_call([
   sys.executable, "-m", "pip", "install", "-q",
   "agentscope", "openai", "pydantic", "nest_asyncio",
])


print("✅  All packages installed.n")


import nest_asyncio
nest_asyncio.apply()


import asyncio
import json
import getpass
import math
import datetime
from typing import Any


from pydantic import BaseModel, Field


from agentscope.agent import ReActAgent
from agentscope.formatter import OpenAIChatFormatter, OpenAIMultiAgentFormatter
from agentscope.memory import InMemoryMemory
from agentscope.message import Msg, TextBlock, ToolUseBlock
from agentscope.model import OpenAIChatModel
from agentscope.pipeline import MsgHub, sequential_pipeline
from agentscope.tool import Toolkit, ToolResponse


OPENAI_API_KEY = getpass.getpass("🔑  Enter your OpenAI API key: ")
MODEL_NAME = "gpt-4o-mini"


print(f"n✅  API key captured. Using model: {MODEL_NAME}n")
print("=" * 72)


def make_model(stream: bool = False) -> OpenAIChatModel:
   return OpenAIChatModel(
       model_name=MODEL_NAME,
       api_key=OPENAI_API_KEY,
       stream=stream,
       generate_kwargs={"temperature": 0.7, "max_tokens": 1024},
   )


print("n" + "═" * 72)
print("  PART 1: Basic Model Call")
print("═" * 72)


async def part1_basic_model_call():
   model = make_model()
   response = await model(
       messages=[{"role": "user", "content": "What is AgentScope in one sentence?"}],
   )
   text = response.content[0]["text"]
   print(f"n🤖  Model says: {text}")
   print(f"📊  Tokens used: {response.usage}")


asyncio.run(part1_basic_model_call())

print("n" + "═" * 72)
print("  PART 2: Custom Tool Functions & Toolkit")
print("═" * 72)


async def calculate_expression(expression: str) -> ToolResponse:
   allowed = {
       "abs": abs, "round": round, "min": min, "max": max,
       "sum": sum, "pow": pow, "int": int, "float": float,
       "sqrt": math.sqrt, "pi": math.pi, "e": math.e,
       "log": math.log, "sin": math.sin, "cos": math.cos,
       "tan": math.tan, "factorial": math.factorial,
   }
   try:
       result = eval(expression, {"__builtins__": {}}, allowed)
       return ToolResponse(content=[TextBlock(type="text", text=str(result))])
   except Exception as exc:
       return ToolResponse(content=[TextBlock(type="text", text=f"Error: {exc}")])


async def get_current_datetime(timezone_offset: int = 0) -> ToolResponse:
   now = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=timezone_offset)))
   return ToolResponse(
       content=[TextBlock(type="text", text=now.strftime("%Y-%m-%d %H:%M:%S %Z"))],
   )


toolkit = Toolkit()
toolkit.register_tool_function(calculate_expression)
toolkit.register_tool_function(get_current_datetime)


schemas = toolkit.get_json_schemas()
print("n📋  Auto-generated tool schemas:")
print(json.dumps(schemas, indent=2))


async def part2_test_tool():
   result_gen = await toolkit.call_tool_function(
       ToolUseBlock(
           type="tool_use", id="test-1",
           name="calculate_expression",
           input={"expression": "factorial(10)"},
       ),
   )
   async for resp in result_gen:
       print(f"n🔧  Tool result for factorial(10): {resp.content[0]['text']}")


asyncio.run(part2_test_tool())

print("n" + "═" * 72)
print("  PART 3: ReAct Agent with Tools")
print("═" * 72)


async def part3_react_agent():
   agent = ReActAgent(
       name="MathBot",
       sys_prompt=(
           "You are MathBot, a helpful assistant that solves math problems. "
           "Use the calculate_expression tool for any computation. "
           "Use get_current_datetime when asked about the time."
       ),
       model=make_model(),
       memory=InMemoryMemory(),
       formatter=OpenAIChatFormatter(),
       toolkit=toolkit,
       max_iters=5,
   )


   queries = [
       "What's the current time in UTC+5?",
   ]
   for q in queries:
       print(f"n👤  User: {q}")
       msg = Msg("user", q, "user")
       response = await agent(msg)
       print(f"🤖  MathBot: {response.get_text_content()}")
       agent.memory.clear()


asyncio.run(part3_react_agent())


print("n" + "═" * 72)
print("  PART 4: Multi-Agent Debate (MsgHub)")
print("═" * 72)


DEBATE_TOPIC = (
   "Should artificial general intelligence (AGI) research be open-sourced, "
   "or should it remain behind closed doors at major labs?"
)

async def part4_debate():
   proponent = ReActAgent(
       name="Proponent",
       sys_prompt=(
           f"You are the Proponent in a debate. You argue IN FAVOR of open-sourcing AGI research. "
           f"Topic: {DEBATE_TOPIC}n"
           "Keep each response to 2-3 concise paragraphs. Address the other side's points directly."
       ),
       model=make_model(),
       memory=InMemoryMemory(),
       formatter=OpenAIMultiAgentFormatter(),
   )


   opponent = ReActAgent(
       name="Opponent",
       sys_prompt=(
           f"You are the Opponent in a debate. You argue AGAINST open-sourcing AGI research. "
           f"Topic: {DEBATE_TOPIC}n"
           "Keep each response to 2-3 concise paragraphs. Address the other side's points directly."
       ),
       model=make_model(),
       memory=InMemoryMemory(),
       formatter=OpenAIMultiAgentFormatter(),
   )


   num_rounds = 2
   for rnd in range(1, num_rounds + 1):
       print(f"n{'─' * 60}")
       print(f"  ROUND {rnd}")
       print(f"{'─' * 60}")


       async with MsgHub(
           participants=[proponent, opponent],
           announcement=Msg("Moderator", f"Round {rnd} — begin. Topic: {DEBATE_TOPIC}", "assistant"),
       ):
           pro_msg = await proponent(
               Msg("Moderator", "Proponent, please present your argument.", "user"),
           )
           print(f"n✅  Proponent:n{pro_msg.get_text_content()}")


           opp_msg = await opponent(
               Msg("Moderator", "Opponent, please respond and present your counter-argument.", "user"),
           )
           print(f"n❌  Opponent:n{opp_msg.get_text_content()}")


   print(f"n{'─' * 60}")
   print("  DEBATE COMPLETE")
   print(f"{'─' * 60}")


asyncio.run(part4_debate())


print("n" + "═" * 72)
print("  PART 5: Structured Output with Pydantic")
print("═" * 72)


class MovieReview(BaseModel):
   year: int = Field(description="The release year.")
   genre: str = Field(description="Primary genre of the movie.")
   rating: float = Field(description="Rating from 0.0 to 10.0.")
   pros: list[str] = Field(description="List of 2-3 strengths of the movie.")
   cons: list[str] = Field(description="List of 1-2 weaknesses of the movie.")
   verdict: str = Field(description="A one-sentence final verdict.")

async def part5_structured_output():
   agent = ReActAgent(
       name="Critic",
       sys_prompt="You are a professional movie critic. When asked to review a movie, provide a thorough analysis.",
       model=make_model(),
       memory=InMemoryMemory(),
       formatter=OpenAIChatFormatter(),
   )


   msg = Msg("user", "Review the movie 'Inception' (2010) by Christopher Nolan.", "user")
   response = await agent(msg, structured_model=MovieReview)


   print("n🎬  Structured Movie Review:")
   print(f"    Title   : {response.metadata.get('title', 'N/A')}")
   print(f"    Year    : {response.metadata.get('year', 'N/A')}")
   print(f"    Genre   : {response.metadata.get('genre', 'N/A')}")
   print(f"    Rating  : {response.metadata.get('rating', 'N/A')}/10")
   pros = response.metadata.get('pros', [])
   cons = response.metadata.get('cons', [])
   if pros:
       print(f"    Pros    : {', '.join(str(p) for p in pros)}")
   if cons:
       print(f"    Cons    : {', '.join(str(c) for c in cons)}")
   print(f"    Verdict : {response.metadata.get('verdict', 'N/A')}")


   print(f"n📝  Full text response:n{response.get_text_content()}")


asyncio.run(part5_structured_output())


print("n" + "═" * 72)
print("  PART 6: Concurrent Multi-Agent Pipeline")
print("═" * 72)


async def part6_concurrent_agents():
   specialists = {
       "Economist": "You are an economist. Analyze the given topic from an economic perspective in 2-3 sentences.",
       "Ethicist": "You are an ethicist. Analyze the given topic from an ethical perspective in 2-3 sentences.",
       "Technologist": "You are a technologist. Analyze the given topic from a technology perspective in 2-3 sentences.",
   }


   agents = []
   for name, prompt in specialists.items():
       agents.append(
           ReActAgent(
               name=name,
               sys_prompt=prompt,
               model=make_model(),
               memory=InMemoryMemory(),
               formatter=OpenAIChatFormatter(),
           )
       )


   topic_msg = Msg(
       "user",
       "Analyze the impact of large language models on the global workforce.",
       "user",
   )


   print("n⏳  Running 3 specialist agents concurrently...")
   results = await asyncio.gather(*(agent(topic_msg) for agent in agents))


   for agent, result in zip(agents, results):
       print(f"n🧠  {agent.name}:n{result.get_text_content()}")


   synthesiser = ReActAgent(
       name="Synthesiser",
       sys_prompt=(
           "You are a synthesiser. You receive analyses from an Economist, "
           "an Ethicist, and a Technologist. Combine their perspectives into "
           "a single coherent summary of 3-4 sentences."
       ),
       model=make_model(),
       memory=InMemoryMemory(),
       formatter=OpenAIMultiAgentFormatter(),
   )


   combined_text = "nn".join(
       f"[{agent.name}]: {r.get_text_content()}" for agent, r in zip(agents, results)
   )
   synthesis = await synthesiser(
       Msg("user", f"Here are the specialist analyses:nn{combined_text}nnPlease synthesise.", "user"),
   )
   print(f"n🔗  Synthesised Summary:n{synthesis.get_text_content()}")


asyncio.run(part6_concurrent_agents())


print("n" + "═" * 72)
print("  🎉  TUTORIAL COMPLETE!")
print("  You have covered:")
print("    1. Basic model calls with OpenAIChatModel")
print("    2. Custom tool functions & auto-generated JSON schemas")
print("    3. ReAct Agent with tool use")
print("    4. Multi-agent debate with MsgHub")
print("    5. Structured output with Pydantic models")
print("    6. Concurrent multi-agent pipelines")
print("═" * 72)