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Feature(multi agent): new examples for multi agent (#20)
* update human-agent chat * update new method(human-agent and multi agents) * update --------- Co-authored-by: Yining Zhao <zhaoyining5445@gmail.com>
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‎README.md‎

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git```
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# Tiny-Chat
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```
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conda create -n tiny-chat python=3.10
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conda activate tiny-chat
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curl -sSL https://install.python-poetry.org | python3
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poetry install
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```
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```
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## What's new:
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1. You can find some examples [here](/tiny-chat/examples/README.md).

‎examples/README.md‎

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# Tiny-Chat Examples
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This directory showcases various usage examples for Tiny-Chat.
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## Example Files
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```
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examples/
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├── README.md
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├── two_agents_chat.py # Two-agent conversation
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├── human_agent_chat.py # Human-AI interactive chat
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└── multi_agents_chat.py # Multi-agent conversation
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```
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## Running Examples
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### Two-Agent Conversation
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```bash
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python examples/two_agents_chat.py
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```
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### Human-AI Interactive Chat
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```bash
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python examples/human_agent_chat.py
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```
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### Multi-Agent Conversation
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```bash
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python examples/multi_agents_chat.py
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```
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## Customizing Examples
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You can create your own examples by modifying the following parameters:
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### 1. Two-Agent Conversation Customization
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In `two_agents_chat.py`, you can modify:
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```python
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# Agent configurations
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agent_configs = [
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{
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"name": "Your Agent 1 Name",
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"agent_number": 1,
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"type": "llm",
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"model": "gpt-4o-mini",
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"goal": "Your Agent 1 Goal",
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},
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{
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"name": "Your Agent 2 Name",
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"agent_number": 2,
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"type": "llm",
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"model": "gpt-4o-mini",
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"goal": "Your Agent 2 Goal",
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},
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]
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# Background settings
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background = TwoAgentChatBackground(
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scenario="Your conversation scenario description",
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p1_background="Agent 1 background information",
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p2_background="Agent 2 background information",
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p1_goal="Agent 1 specific goal",
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p2_goal="Agent 2 specific goal",
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p1_name="Agent 1 Name",
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p2_name="Agent 2 Name",
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)
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# Conversation parameters
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await server.two_agent_run_conversation(
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agent_configs=agent_configs,
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background=background,
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max_turns=10, # Maximum conversation turns
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enable_evaluation=True, # Enable evaluation
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)
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```
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### 2. Human-AI Interaction Customization
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In `human_agent_chat.py`, you can modify:
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```python
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# AI agent configuration
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agent = LLMAgent(
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agent_name="Your AI Assistant Name",
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model="gpt-4o-mini",
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api_key=api_key,
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goal="Your AI Assistant Goal"
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)
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# Conversation background
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background = ChatBackground(
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scenario="Your conversation scenario",
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p1_background="AI Assistant background setting",
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p2_background="User background setting",
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p1_goal="AI Assistant goal",
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p2_goal="User goal",
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p1_name="AI Assistant Name",
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p2_name="User Name"
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)
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```
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### 3. Multi-Agent Conversation Customization
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Multi-agent conversations support 4 different action ordering modes. You can assign each one in [action_order](/tiny-chat/examples/multi_agents_chat.py#L74):
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- **Sequential**: Agents take turns one by one in a fixed order
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- **Round-robin**: Agents take turns in a rotating order (A→B→C→A→B→C...)
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- **Simultaneous**: All agents act at the same time in each turn
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- **Random**: A random agent is selected to act in each turn
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## <span style="color: red;">WARNING: Simultaneous mode is Incomplete</span>
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In `multi_agents_chat.py`, you can modify:
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```python
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# Agent configurations (supports 2-3 agents)
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agent_configs = [
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{
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"name": "Agent 1 Name",
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"type": "llm",
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"model": "gpt-4o-mini",
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"goal": "Agent 1 Goal"
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},
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{
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"name": "Agent 2 Name",
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"type": "llm",
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"model": "gpt-4o-mini",
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"goal": "Agent 2 Goal"
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},
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{
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"name": "Agent 3 Name",
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"type": "llm",
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"model": "gpt-4o-mini",
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"goal": "Agent 3 Goal"
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}
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]
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background = MultiAgentChatBackground(
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scenario="Your multi-agent scenario description",
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agent_configs=[
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{
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'name': 'Agent 1 Name',
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'background': 'Agent 1 detailed background',
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'goal': 'Agent 1 specific goal'
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},
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{
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'name': 'Agent 2 Name',
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'background': 'Agent 2 detailed background',
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'goal': 'Agent 2 specific goal'
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},
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{
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'name': 'Agent 3 Name',
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'background': 'Agent 3 detailed background',
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'goal': 'Agent 3 specific goal'
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}
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]
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)
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# Conversation parameters
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await server.multi_agent_run_conversation(
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agent_configs=agent_configs,
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background=background,
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action_order='simultaneous', # sequential, round-robin, simultaneous, random
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max_turns=12, # Maximum conversation turns
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enable_evaluation=True # Enable evaluation
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)
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```

‎examples/human_agent_chat.py‎

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#!/usr/bin/env python3
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"""
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Interactive chat script - Chat with AI agents
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Usage: python scripts/interactive_chat.py
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"""
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import asyncio
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import logging
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import os
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import sys
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from pathlib import Path
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from tiny_chat.agents import LLMAgent
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from tiny_chat.messages import Observation, TwoAgentChatBackground
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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async def interactive_chat() -> None:
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"""Interactive chat with an AI agent"""
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logging.getLogger().setLevel(logging.ERROR)
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for logger_name in logging.root.manager.loggerDict:
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logging.getLogger(logger_name).setLevel(logging.ERROR)
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api_key = os.getenv("OPENAI_API_KEY")
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if not api_key:
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print("Error: OPENAI_API_KEY environment variable is required")
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return
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agent = LLMAgent(
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agent_name="AI Assistant",
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model="gpt-4o-mini",
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api_key=api_key,
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goal="Be helpful, friendly, and engaging in conversation",
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)
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_background = TwoAgentChatBackground(
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scenario="A helpful AI assistant chatting with a human",
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p1_background="You are a helpful AI assistant",
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p2_background="A human user who wants to chat",
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p1_goal="Provide helpful and engaging responses",
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p2_goal="Have an interesting conversation",
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p1_name="AI Assistant",
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p2_name="Human",
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)
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print("🤖 AI Assistant: Hello! I'm your AI assistant. How can I help you today?")
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print("(Type 'quit' or 'q' to exit)")
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print("-" * 50)
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turn_number = 1
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while True:
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user_input = input("👤 You: ").strip()
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if user_input.lower() in ["quit", "exit", "bye", "q"]:
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print("🤖 AI Assistant: Goodbye! It was nice chatting with you!")
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break
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if not user_input:
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continue
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observation = Observation(
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last_turn=f"Human says: {user_input}",
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available_actions=["speak", "none"],
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turn_number=turn_number,
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)
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try:
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action = await agent.act(observation)
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if action.action_type == "speak":
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print(f"AI Assistant: {action.argument}")
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else:
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print("AI Assistant: [No response]")
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except Exception as e:
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print(f"AI Assistant: Sorry, I encountered an error: {e}")
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turn_number += 1
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print("-" * 50)
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if __name__ == "__main__":
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asyncio.run(interactive_chat())

‎examples/multi_agents_chat.py‎

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import asyncio
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import os
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import sys
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from pathlib import Path
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# Add the project root to Python path
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from tiny_chat.messages import MultiAgentChatBackground
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from tiny_chat.utils.server import TinyChatServer
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async def main():
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"""Run a 3-agent conversation"""
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# Get API key from environment variable
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api_key = os.getenv('OPENAI_API_KEY')
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if not api_key:
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print('Warning: OPENAI_API_KEY not set. Some features may not work.')
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# Create chat server
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server = TinyChatServer(api_key=api_key)
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# Define 3-agent configurations
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agent_configs = [
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{
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'name': 'Alice',
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'type': 'llm',
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'model': 'gpt-4o-mini',
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'goal': 'Talk about weekend hiking plans',
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},
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{
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'name': 'Bob',
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'type': 'llm',
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'model': 'gpt-4o-mini',
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'goal': 'Share thoughts on a new sci-fi book',
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},
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{
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'name': 'Carol',
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'type': 'llm',
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'model': 'gpt-4o-mini',
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'goal': 'Discuss travel experiences and make everyone laugh',
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},
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]
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# Create background object
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background = MultiAgentChatBackground(
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scenario='Three friends catching up over coffee',
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agent_configs=[
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{
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'name': 'Alice',
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'background': 'Alice is a software engineer who loves hiking',
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'goal': 'Talk about weekend hiking plans',
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},
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{
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'name': 'Bob',
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'background': 'Bob is a teacher who enjoys reading science fiction',
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'goal': 'Share thoughts on a new sci-fi book',
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},
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{
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'name': 'Carol',
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'background': 'Carol is a graphic designer who recently returned from a trip to Japan',
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'goal': 'Discuss travel experiences and make everyone laugh',
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},
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],
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)
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print('Starting multi-agent conversation...')
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print('=' * 50)
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await server.multi_agent_run_conversation(
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agent_configs=agent_configs,
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background=background,
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action_order='simultaneous', # sequential, round-robin, simultaneous, random
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max_turns=12,
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enable_evaluation=True,
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)
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if __name__ == '__main__':
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asyncio.run(main())

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