Quickstart

Setup your environment

Learn how to set up OASIS and run your first social simulation.

Installation

You can install OASIS in two ways:

Option 1: Install via pip

Option 2: Clone the repository

Running simulations

OASIS supports different types of LLM backends for running simulations. Choose the option that works best for your needs.

Using OpenAI API

Set up your API key

Add your OpenAI API key to your environment variables:

For Bash (Linux, macOS, Git Bash on Windows):

export OPENAI_API_KEY=<insert your OpenAI API key>
export OPENAI_API_BASE_URL=<insert your OpenAI API BASE URL>  # Optional: for proxy services

For Windows Command Prompt:

set OPENAI_API_KEY=<insert your OpenAI API key>
set OPENAI_API_BASE_URL=<insert your OpenAI API BASE URL>  # Optional: for proxy services

For Windows PowerShell:

$env:OPENAI_API_KEY="<insert your OpenAI API key>"
$env:OPENAI_API_BASE_URL="<insert your OpenAI API BASE URL>"  # Optional: for proxy services

Prepare the user profiles

Run a Reddit simulation

Execute the Reddit simulation script:

import asyncio
import os

from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

import oasis
from oasis import (ActionType, LLMAction, ManualAction,
                  generate_reddit_agent_graph)


async def main():
    # Define the model for the agents
    openai_model = ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI,
        model_type=ModelType.GPT_4O_MINI,
    )

    # Define the available actions for the agents
    available_actions = [
        ActionType.LIKE_POST,
        ActionType.DISLIKE_POST,
        ActionType.CREATE_POST,
        ActionType.CREATE_COMMENT,
        ActionType.LIKE_COMMENT,
        ActionType.DISLIKE_COMMENT,
        ActionType.SEARCH_POSTS,
        ActionType.SEARCH_USER,
        ActionType.TREND,
        ActionType.REFRESH,
        ActionType.DO_NOTHING,
        ActionType.FOLLOW,
        ActionType.MUTE,
    ]

    agent_graph = await generate_reddit_agent_graph(
        profile_path="./data/reddit/user_data_36.json",
        model=openai_model,
        available_actions=available_actions,
    )

    # Define the path to the database
    db_path = "./data/reddit_simulation.db"

    # Delete the old database
    if os.path.exists(db_path):
        os.remove(db_path)

    # Make the environment
    env = oasis.make(
        agent_graph=agent_graph,
        platform=oasis.DefaultPlatformType.REDDIT,
        database_path=db_path,
    )

    # Run the environment
    await env.reset()

    actions_1 = {}
    actions_1[env.agent_graph.get_agent(0)] = [
        ManualAction(action_type=ActionType.CREATE_POST,
                    action_args={"content": "Hello, world!"}),
        ManualAction(action_type=ActionType.CREATE_COMMENT,
                    action_args={
                        "post_id": "1",
                        "content": "Welcome to the OASIS World!"
                    })
    ]
    actions_1[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.CREATE_COMMENT,
        action_args={
            "post_id": "1",
            "content": "I like the OASIS world."
        })
    await env.step(actions_1)

    actions_2 = {
        agent: LLMAction()
        for _, agent in env.agent_graph.get_agents()
    }

    # Perform the actions
    await env.step(actions_2)

    # Close the environment
    await env.close()


if __name__ == "__main__":
    asyncio.run(main())

This will start a simulation of user interactions in a Reddit-like environment.

Using local open-source models with VLLM

Set up VLLM

Install VLLM by following the instructions in the VLLM repository
Download a model (e.g., Qwen 2.5) to your local machine:

pip install huggingface_hub

huggingface-cli download --resume-download "Qwen/Qwen2.5-7B-Instruct" \
  --local-dir "YOUR_LOCAL_MODEL_DIRECTORY" \
  --local-dir-use-symlinks False \
  --resume-download \
  --token "YOUR_HUGGING_FACE_TOKEN"

Deploy the VLLM API server:

vllm serve /path/to/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 8000 \
  --served-model-name 'qwen-2' \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Test if VLLM is correctly deployed:

curl http://$ip:$port/v1/models

Run a Twitter simulation with local models

Edit or write the scripts/environment/twitter_simulation.py file to use your VLLM deployment:

vllm_model_1 = ModelFactory.create(
    model_platform=ModelPlatformType.VLLM,
    model_type="qwen-2",
    url="http://$ip:$port",
)
vllm_model_2 = ModelFactory.create(
    model_platform=ModelPlatformType.VLLM,
    model_type="qwen-2",
    url="http://$ip:$port",
)
models = [vllm_model_1, vllm_model_2]

Prepare the user profiles: If you install with pip, download this file to your own data/twitter_dataset/anonymous_topic_200_1h/False_Business_0.csv directory.
Run the Twitter simulation:

import asyncio
import os

from camel.models import ModelFactory
from camel.types import ModelPlatformType

import oasis
from oasis import (ActionType, LLMAction, ManualAction,
                  generate_twitter_agent_graph)

async def main():
    # Define the models for agents. Agents will select models based on
    # pre-defined scheduling strategies
    vllm_model_1 = ModelFactory.create(
        model_platform=ModelPlatformType.VLLM,
        model_type="qwen-2",
        url="http://$ip:$port",
    )
    vllm_model_2 = ModelFactory.create(
        model_platform=ModelPlatformType.VLLM,
        model_type="qwen-2",
        url="http://$ip:$port",
    )
    models = [vllm_model_1, vllm_model_2]

    # Define the available actions for the agents
    available_actions = [
        ActionType.CREATE_POST,
        ActionType.LIKE_POST,
        ActionType.REPOST,
        ActionType.FOLLOW,
        ActionType.DO_NOTHING,
        ActionType.QUOTE_POST,
    ]

    agent_graph = await generate_twitter_agent_graph(
        profile_path="./data/reddit/user_data_36.json",
        model=models,
        available_actions=available_actions,
    )

    # Define the path to the database
    db_path = "./data/twitter_simulation.db"

    # Delete the old database
    if os.path.exists(db_path):
        os.remove(db_path)

    # Make the environment
    env = oasis.make(
        agent_graph=agent_graph,
        platform=oasis.DefaultPlatformType.TWITTER,
        database_path=db_path,
    )

    # Run the environment
    await env.reset()

    actions_1 = {}

    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
        action_type=ActionType.CREATE_POST,
        action_args={"content": "Earth is flat."})
    await env.step(actions_1)

    actions_2 = {
        agent: LLMAction()
        # Activate 5 agents with id 1, 3, 5, 7, 9
        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
    }

    await env.step(actions_2)

    actions_3 = {}

    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.CREATE_POST,
        action_args={"content": "Earth is not flat."})
    await env.step(actions_3)

    actions_4 = {
        agent: LLMAction()
        # get all agents
        for _, agent in env.agent_graph.get_agents()
    }
    await env.step(actions_4)

    # Close the environment
    await env.close()


if __name__ == "__main__":
    asyncio.run(main())

On this page

Setup your environment
Installation
Running simulations
Using OpenAI API
Using local open-source models with VLLM

Setup your environment

Learn how to set up OASIS and run your first social simulation.

Installation

You can install OASIS in two ways:

Option 1: Install via pip

Option 2: Clone the repository

Running simulations

OASIS supports different types of LLM backends for running simulations. Choose the option that works best for your needs.

Using OpenAI API

Set up your API key

Add your OpenAI API key to your environment variables:

For Bash (Linux, macOS, Git Bash on Windows):

export OPENAI_API_KEY=<insert your OpenAI API key>
export OPENAI_API_BASE_URL=<insert your OpenAI API BASE URL>  # Optional: for proxy services

For Windows Command Prompt:

set OPENAI_API_KEY=<insert your OpenAI API key>
set OPENAI_API_BASE_URL=<insert your OpenAI API BASE URL>  # Optional: for proxy services

For Windows PowerShell:

$env:OPENAI_API_KEY="<insert your OpenAI API key>"
$env:OPENAI_API_BASE_URL="<insert your OpenAI API BASE URL>"  # Optional: for proxy services

Prepare the user profiles

Run a Reddit simulation

Execute the Reddit simulation script:

import asyncio
import os

from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType

import oasis
from oasis import (ActionType, LLMAction, ManualAction,
                  generate_reddit_agent_graph)


async def main():
    # Define the model for the agents
    openai_model = ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI,
        model_type=ModelType.GPT_4O_MINI,
    )

    # Define the available actions for the agents
    available_actions = [
        ActionType.LIKE_POST,
        ActionType.DISLIKE_POST,
        ActionType.CREATE_POST,
        ActionType.CREATE_COMMENT,
        ActionType.LIKE_COMMENT,
        ActionType.DISLIKE_COMMENT,
        ActionType.SEARCH_POSTS,
        ActionType.SEARCH_USER,
        ActionType.TREND,
        ActionType.REFRESH,
        ActionType.DO_NOTHING,
        ActionType.FOLLOW,
        ActionType.MUTE,
    ]

    agent_graph = await generate_reddit_agent_graph(
        profile_path="./data/reddit/user_data_36.json",
        model=openai_model,
        available_actions=available_actions,
    )

    # Define the path to the database
    db_path = "./data/reddit_simulation.db"

    # Delete the old database
    if os.path.exists(db_path):
        os.remove(db_path)

    # Make the environment
    env = oasis.make(
        agent_graph=agent_graph,
        platform=oasis.DefaultPlatformType.REDDIT,
        database_path=db_path,
    )

    # Run the environment
    await env.reset()

    actions_1 = {}
    actions_1[env.agent_graph.get_agent(0)] = [
        ManualAction(action_type=ActionType.CREATE_POST,
                    action_args={"content": "Hello, world!"}),
        ManualAction(action_type=ActionType.CREATE_COMMENT,
                    action_args={
                        "post_id": "1",
                        "content": "Welcome to the OASIS World!"
                    })
    ]
    actions_1[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.CREATE_COMMENT,
        action_args={
            "post_id": "1",
            "content": "I like the OASIS world."
        })
    await env.step(actions_1)

    actions_2 = {
        agent: LLMAction()
        for _, agent in env.agent_graph.get_agents()
    }

    # Perform the actions
    await env.step(actions_2)

    # Close the environment
    await env.close()


if __name__ == "__main__":
    asyncio.run(main())

This will start a simulation of user interactions in a Reddit-like environment.

Using local open-source models with VLLM

Set up VLLM

Install VLLM by following the instructions in the VLLM repository
Download a model (e.g., Qwen 2.5) to your local machine:

pip install huggingface_hub

huggingface-cli download --resume-download "Qwen/Qwen2.5-7B-Instruct" \
  --local-dir "YOUR_LOCAL_MODEL_DIRECTORY" \
  --local-dir-use-symlinks False \
  --resume-download \
  --token "YOUR_HUGGING_FACE_TOKEN"

Deploy the VLLM API server:

vllm serve /path/to/Qwen2.5-7B-Instruct --host 0.0.0.0 --port 8000 \
  --served-model-name 'qwen-2' \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Test if VLLM is correctly deployed:

curl http://$ip:$port/v1/models

Run a Twitter simulation with local models

Edit or write the scripts/environment/twitter_simulation.py file to use your VLLM deployment:

vllm_model_1 = ModelFactory.create(
    model_platform=ModelPlatformType.VLLM,
    model_type="qwen-2",
    url="http://$ip:$port",
)
vllm_model_2 = ModelFactory.create(
    model_platform=ModelPlatformType.VLLM,
    model_type="qwen-2",
    url="http://$ip:$port",
)
models = [vllm_model_1, vllm_model_2]

Prepare the user profiles: If you install with pip, download this file to your own data/twitter_dataset/anonymous_topic_200_1h/False_Business_0.csv directory.
Run the Twitter simulation:

import asyncio
import os

from camel.models import ModelFactory
from camel.types import ModelPlatformType

import oasis
from oasis import (ActionType, LLMAction, ManualAction,
                  generate_twitter_agent_graph)

async def main():
    # Define the models for agents. Agents will select models based on
    # pre-defined scheduling strategies
    vllm_model_1 = ModelFactory.create(
        model_platform=ModelPlatformType.VLLM,
        model_type="qwen-2",
        url="http://$ip:$port",
    )
    vllm_model_2 = ModelFactory.create(
        model_platform=ModelPlatformType.VLLM,
        model_type="qwen-2",
        url="http://$ip:$port",
    )
    models = [vllm_model_1, vllm_model_2]

    # Define the available actions for the agents
    available_actions = [
        ActionType.CREATE_POST,
        ActionType.LIKE_POST,
        ActionType.REPOST,
        ActionType.FOLLOW,
        ActionType.DO_NOTHING,
        ActionType.QUOTE_POST,
    ]

    agent_graph = await generate_twitter_agent_graph(
        profile_path="./data/reddit/user_data_36.json",
        model=models,
        available_actions=available_actions,
    )

    # Define the path to the database
    db_path = "./data/twitter_simulation.db"

    # Delete the old database
    if os.path.exists(db_path):
        os.remove(db_path)

    # Make the environment
    env = oasis.make(
        agent_graph=agent_graph,
        platform=oasis.DefaultPlatformType.TWITTER,
        database_path=db_path,
    )

    # Run the environment
    await env.reset()

    actions_1 = {}

    actions_1[env.agent_graph.get_agent(0)] = ManualAction(
        action_type=ActionType.CREATE_POST,
        action_args={"content": "Earth is flat."})
    await env.step(actions_1)

    actions_2 = {
        agent: LLMAction()
        # Activate 5 agents with id 1, 3, 5, 7, 9
        for _, agent in env.agent_graph.get_agents([1, 3, 5, 7, 9])
    }

    await env.step(actions_2)

    actions_3 = {}

    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.CREATE_POST,
        action_args={"content": "Earth is not flat."})
    await env.step(actions_3)

    actions_4 = {
        agent: LLMAction()
        # get all agents
        for _, agent in env.agent_graph.get_agents()
    }
    await env.step(actions_4)

    # Close the environment
    await env.close()


if __name__ == "__main__":
    asyncio.run(main())

On this page

Setup your environment
Installation
Running simulations
Using OpenAI API
Using local open-source models with VLLM

Setup your environment

Installation

Running simulations

Using OpenAI API

Using local open-source models with VLLM

Get Started

Overview

Key Modules

Cookbooks

Visualization

Quickstart

Setup your environment

Installation

Running simulations

Using OpenAI API

Using local open-source models with VLLM

​Setup your environment

​Installation

​Running simulations

​Using OpenAI API

​Using local open-source models with VLLM

Get Started

Overview

Key Modules

Cookbooks

Visualization

​Setup your environment

​Installation

​Running simulations

​Using OpenAI API

​Using local open-source models with VLLM

Setup your environment

Installation

Running simulations

Using OpenAI API

Using local open-source models with VLLM

Setup your environment

Installation

Running simulations

Using OpenAI API

Using local open-source models with VLLM