TinyChat: A lightweight platform for realistic social simulations

Introduction

Tiny-Chat is a lightweight , user-friendly and highly extensible multi-agent stimulation framework. Designed for social stimulation research.

Core Features

• 🤖 Multi-Agent interactions: Support for multiple AI agents with distinct personalities and goals

• 🌍 Flexible Environments: Configurable conversation scenarios and relationship dynamics

• 📊 Built-in and Plugin Evaluation: Multi-dimensional conversation evaluation using LLM and rule-based methods and Extensible evaluator plugins for custom assessment strategies

• 🚀 Server Architecture: High-performance server with HTTP API and configuration management

Installation

Install from source

# clone the repository
[email protected]:ulab-uiuc/tiny-chat.git
cd tiny-chat

# create conda environment
conda create -n tiny-chat python=3.10
conda activate tiny-chat

# Install Poetry
curl -sSL https://install.python-poetry.org | python3
export PATH="$HOME/.local/bin:$PATH"

# Install dependencies
poetry install

Get Started

Before running any code, set your API key, you can also manage your api key in .yaml.

export OPENAI_API_KEY=your-key-here
# or use DEEPSEEK_API_KEY, ANTHROPIC_API_KEY, or OPENROUTER_API_KEY

Basic Usage

import asyncio
from tiny_chat.server import TinyChatServer, ServerConfig, ModelProviderConfig

async def basic_conversation():
    config = ServerConfig(
        models={
            'gpt-4o-mini': ModelProviderConfig(
                name='gpt-4o-mini',
                type='openai',
                temperature=0.7
            )
        },
        default_model='gpt-4o-mini'
    )

    async with TinyChatServer(config) as server:
        episode_log = await server.run_conversation(
            agent_configs=[
                {"name": "Alice", "type": "llm", "goal": "Convince Bob to join the project"},
                {"name": "Bob", "type": "llm", "goal": "Learn more about the project"}
            ],
            scenario="Two colleagues discussing a new project collaboration",
            max_turns=10
        )

        print(f"Conversation completed: {episode_log.reasoning}")

asyncio.run(basic_conversation())

Configuration in YAML

Create config/tiny_chat.yaml:

models:
  gpt-4o-mini:
    name: gpt-4o-mini
    type: openai
    temperature: 0.7

default_model: gpt-4o-mini
max_turns: 20

evaluators:
  - type: rule_based
    enabled: true
    config:
      max_turn_number: 20
  - type: llm
    enabled: true
    model: gpt-4o-mini

api:
  host: 0.0.0.0
  port: 8000

Start the server:

python -m tiny_chat.server.cli serve

Examples

For detailed examples and use cases, check the examples/ directory:

• examples/human_agent_chat_demo.py - Human-in-the-loop conversations
• examples/multi_agent_chat_demo.py - Multi-agent scenarios
• examples/multi_agent_chat_obs_control.py - Observation control examples

Advanced Usage

CLI Tools

# Generate environment profile
python -m tiny_chat.utils.cli env-profile "asking my boyfriend to stop being friends with his ex"

# Generate agent action
python -m tiny_chat.utils.cli action --agent Alice --goal "Convince Bob" --history "Previous conversation..."

# Generate script
python -m tiny_chat.utils.cli script --background-file background.json --agent-name Alice --agent-name Bob

Plugin System

from tiny_chat.server.plugins import PluginManager, EvaluatorConfig

# Create custom evaluator plugin
class CustomEvaluatorPlugin(EvaluatorPlugin):
    @property
    def plugin_type(self) -> str:
        return 'custom'

    def _create_evaluator(self) -> Evaluator:
        return CustomEvaluator()

# Register and use
manager = PluginManager()
manager.register_plugin('custom', CustomEvaluatorPlugin)