Interview

This cookbook demonstrates how to use the INTERVIEW action type to conduct interviews with AI agents in a Twitter simulation. The interview functionality allows you to ask specific questions to agents and collect their responses, which is useful for research, opinion polling, and understanding agent behaviors.

Overview

The INTERVIEW action type enables you to:

Ask specific questions to individual agents
Collect structured responses from agents
Store interview data in the database for analysis
Conduct interviews alongside regular social media interactions

Key Features

Manual Interview Actions: Use ManualAction with ActionType.INTERVIEW to conduct interviews
Automatic Response Collection: The system automatically collects and stores agent responses
Database Storage: All interview data is stored in the trace table for later analysis
Concurrent Execution: Interviews can be conducted alongside other social media actions

Important Note

Do NOT include ActionType.INTERVIEW in the available_actions list when creating your agent graph. The interview action is designed to be used only manually by researchers/developers, not automatically selected by LLM agents. Including it in available_actions would allow agents to interview each other automatically, which is typically not desired behavior.

Complete Example

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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import asyncio
import os
import sqlite3
import json

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

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


async def main():
    openai_model = ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI,
        model_type=ModelType.GPT_4O_MINI,
    )

    # Define the available actions for the agents
    # Note: INTERVIEW is NOT included here to prevent LLM from automatically selecting it
    # INTERVIEW can still be used manually via ManualAction
    available_actions = [
        ActionType.CREATE_POST,
        ActionType.LIKE_POST,
        ActionType.REPOST,
        ActionType.FOLLOW,
        ActionType.DO_NOTHING,
        ActionType.QUOTE_POST,
        # ActionType.INTERVIEW,  # DO NOT include this - interviews should be manual only
    ]

    agent_graph = await generate_twitter_agent_graph(
        profile_path=("data/twitter_dataset/anonymous_topic_200_1h/"
                      "False_Business_0.csv"),
        model=openai_model,
        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()

    # First timestep: Agent 0 creates a post
    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)

    # Second timestep: Let some agents respond with LLM actions
    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)

    # Third timestep: Agent 1 creates a post, and we interview Agent 0
    actions_3 = {}
    actions_3[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.CREATE_POST,
        action_args={"content": "Earth is not flat."})

    # Create an interview action to ask Agent 0 about their views
    actions_3[env.agent_graph.get_agent(0)] = ManualAction(
        action_type=ActionType.INTERVIEW,
        action_args={"prompt": "What do you think about the shape of the Earth? Please explain your reasoning."})

    await env.step(actions_3)

    # Fourth timestep: Let some other agents respond
    actions_4 = {
        agent: LLMAction()
        for _, agent in env.agent_graph.get_agents([2, 4, 6, 8, 10])
    }
    await env.step(actions_4)

    # Fifth timestep: Interview multiple agents
    actions_5 = {}
    actions_5[env.agent_graph.get_agent(1)] = ManualAction(
        action_type=ActionType.INTERVIEW,
        action_args={"prompt": "Why do you believe the Earth is not flat?"})

    actions_5[env.agent_graph.get_agent(2)] = ManualAction(
        action_type=ActionType.INTERVIEW,
        action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})

    await env.step(actions_5)

    # Sixth timestep: Final LLM actions for remaining agents
    actions_6 = {
        agent: LLMAction()
        for _, agent in env.agent_graph.get_agents([3, 5, 7, 9])
    }
    await env.step(actions_6)

    # Close the environment
    await env.close()

    # visualize the interview results
    print("\n=== Interview Results ===")
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()
    # Here we query all interview records from the database
    # We use ActionType.INTERVIEW.value as the query condition to get all interview records
    # Each record contains user ID, interview information (in JSON format), and creation timestamp
    cursor.execute("""
        SELECT user_id, info, created_at
        FROM trace
        WHERE action = ?
    """, (ActionType.INTERVIEW.value,))

    # This query retrieves all interview records from the trace table
    # - user_id: the ID of the agent who was interviewed
    # - info: JSON string containing interview details (prompt, response, etc.)
    # - created_at: timestamp when the interview was conducted
    # We'll parse this data below to display the interview results
    for user_id, info_json, timestamp in cursor.fetchall():
        info = json.loads(info_json)
        print(f"\nAgent {user_id} (Timestep {timestamp}):")
        print(f"Prompt: {info.get('prompt', 'N/A')}")
        print(f"Interview ID: {info.get('interview_id', 'N/A')}")
        print(f"Response: {info.get('response', 'N/A')}")

    conn.close()


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

How It Works

1. Setup and Configuration

Important: Do NOT include ActionType.INTERVIEW in your available actions list. Interviews should only be conducted manually:

# Correct configuration - INTERVIEW is NOT included
available_actions = [
    ActionType.CREATE_POST,
    ActionType.LIKE_POST,
    ActionType.REPOST,
    ActionType.FOLLOW,
    ActionType.DO_NOTHING,
    ActionType.QUOTE_POST,
    # ActionType.INTERVIEW,  # DO NOT include - interviews are manual only
]

This prevents LLM agents from automatically selecting the interview action during their decision-making process. Interviews can still be conducted using ManualAction.

2. Conducting Interviews

Use ManualAction with ActionType.INTERVIEW to conduct interviews:

# Single interview
interview_action = ManualAction(
    action_type=ActionType.INTERVIEW,
    action_args={"prompt": "What are your thoughts on climate change?"})

actions = {env.agent_graph.get_agent(0): interview_action}
await env.step(actions)

3. Multiple Interviews in One Step

You can interview multiple agents simultaneously:

actions = {}
actions[env.agent_graph.get_agent(1)] = ManualAction(
    action_type=ActionType.INTERVIEW,
    action_args={"prompt": "Why do you believe the Earth is not flat?"})

actions[env.agent_graph.get_agent(2)] = ManualAction(
    action_type=ActionType.INTERVIEW,
    action_args={"prompt": "What are your thoughts on the debate about Earth's shape?"})

await env.step(actions)

4. Mixing Interviews with Other Actions

Interviews can be conducted alongside regular social media actions:

actions = {}
# Regular post creation
actions[env.agent_graph.get_agent(1)] = ManualAction(
    action_type=ActionType.CREATE_POST,
    action_args={"content": "Earth is not flat."})

# Interview action
actions[env.agent_graph.get_agent(0)] = ManualAction(
    action_type=ActionType.INTERVIEW,
    action_args={"prompt": "What do you think about the shape of the Earth?"})

await env.step(actions)

Data Storage and Retrieval

Database Schema

Interview data is stored in the trace table with the following structure:

user_id: The ID of the interviewed agent
action: Set to ActionType.INTERVIEW.value
info: JSON string containing interview details
created_at: Timestamp of the interview

Retrieving Interview Results

import sqlite3
import json

conn = sqlite3.connect(db_path)
cursor = conn.cursor()

# Query all interview records
cursor.execute("""
    SELECT user_id, info, created_at
    FROM trace
    WHERE action = ?
""", (ActionType.INTERVIEW.value,))

for user_id, info_json, timestamp in cursor.fetchall():
    info = json.loads(info_json)
    print(f"Agent {user_id}: {info.get('response', 'N/A')}")

conn.close()

Interview Data Structure

Each interview record contains:

prompt: The question asked to the agent
interview_id: Unique identifier for the interview
response: The agent’s response to the question

Best Practices

1. Strategic Interview Timing

Conduct interviews at strategic points in your simulation:

After controversial posts to gauge reactions
Before and after significant events
At regular intervals to track opinion changes

2. Question Design

Design effective interview questions:

Be specific and clear
Avoid leading questions
Ask open-ended questions for richer responses

# Good examples
"What are your thoughts on renewable energy?"
"How do you feel about the recent policy changes?"
"Can you explain your reasoning behind your last post?"

# Avoid
"Don't you think renewable energy is great?" # Leading
"Yes or no: Do you like cats?" # Too restrictive

Get Started

Overview

Key Modules

Cookbooks

Visualization

Interview

Interview

Overview

Key Features

Important Note

Complete Example

How It Works

1. Setup and Configuration

2. Conducting Interviews

3. Multiple Interviews in One Step

4. Mixing Interviews with Other Actions

Data Storage and Retrieval

Database Schema

Retrieving Interview Results

Interview Data Structure

Best Practices

1. Strategic Interview Timing

2. Question Design

Get Started

Overview

Key Modules

Cookbooks

Visualization

Documentation Index

​Interview

​Overview

​Key Features

​Important Note

​Complete Example

​How It Works

​1. Setup and Configuration

​2. Conducting Interviews

​3. Multiple Interviews in One Step

​4. Mixing Interviews with Other Actions

​Data Storage and Retrieval

​Database Schema

​Retrieving Interview Results

​Interview Data Structure

​Best Practices

​1. Strategic Interview Timing

​2. Question Design

Interview

Overview

Key Features

Important Note

Complete Example

How It Works

1. Setup and Configuration

2. Conducting Interviews

3. Multiple Interviews in One Step

4. Mixing Interviews with Other Actions

Data Storage and Retrieval

Database Schema

Retrieving Interview Results

Interview Data Structure

Best Practices

1. Strategic Interview Timing

2. Question Design