Merge pull request #126 from Madhuvod/ag2-magnetic

Added new Demo: AI Mental Health Crisis Navigator with AG2 Swarms
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Shubham Saboo 2025-02-26 22:43:32 -06:00 committed by GitHub
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# AI Mental Wellbeing Agent Team 🧠
The AI Mental Wellbeing Agent Team is a supportive mental health assessment and guidance system powered by [AG2](https://github.com/ag2ai/ag2?tab=readme-ov-file)(formerly AutoGen)'s AI Agent framework. This app provides personalized mental health support through the coordination of specialized AI agents, each focusing on different aspects of mental health care based on user inputs such as emotional state, stress levels, sleep patterns, and current symptoms. This is built on AG2's new swarm feature run through initiate_swarm_chat() method.
## Features
- **Specialized Mental Wellbeing Support Team**
- 🧠 **Assessment Agent**: Analyzes emotional state and psychological needs with clinical precision and empathy
- 🎯 **Action Agent**: Creates immediate action plans and connects users with appropriate resources
- 🔄 **Follow-up Agent**: Designs long-term support strategies and prevention plans
- **Comprehensive Mental Wellbeing Support**:
- Detailed psychological assessment
- Immediate coping strategies
- Resource recommendations
- Long-term support planning
- Crisis prevention strategies
- Progress monitoring systems
- **Customizable Input Parameters**:
- Current emotional state
- Sleep patterns
- Stress levels
- Support system information
- Recent life changes
- Current symptoms
- **Interactive Results**:
- Real-time assessment summaries
- Detailed recommendations in expandable sections
- Clear action steps and resources
- Long-term support strategies
## How to Run
Follow these steps to set up and run the application:
1. **Clone the Repository**:
```bash
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd ai_agent_tutorials/ai_mental_wellbeing_agent
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Create Environment File**:
Create a `.env` file in the project directory:
```bash
echo "AUTOGEN_USE_DOCKER=0" > .env
```
This disables Docker requirement for code execution in AutoGen.
4. **Set Up OpenAI API Key**:
- Obtain an OpenAI API key from [OpenAI's platform](https://platform.openai.com)
- You'll input this key in the app's sidebar when running
5. **Run the Streamlit App**:
```bash
streamlit run ai_mental_wellbeing_agent.py
```
## ⚠️ Important Notice
This application is a supportive tool and does not replace professional mental health care. If you're experiencing thoughts of self-harm or severe crisis:
- Call National Crisis Hotline: 988
- Call Emergency Services: 911
- Seek immediate professional help

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import streamlit as st
from autogen import (SwarmAgent, SwarmResult, initiate_swarm_chat, OpenAIWrapper,AFTER_WORK,UPDATE_SYSTEM_MESSAGE)
import os
os.environ["AUTOGEN_USE_DOCKER"] = "0"
if 'output' not in st.session_state:
st.session_state.output = {
'assessment': '',
'action': '',
'followup': ''
}
st.sidebar.title("OpenAI API Key")
api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
st.sidebar.warning("""
## ⚠️ Important Notice
This application is a supportive tool and does not replace professional mental health care. If you're experiencing thoughts of self-harm or severe crisis:
- Call National Crisis Hotline: 988
- Call Emergency Services: 911
- Seek immediate professional help
""")
st.title("🧠 Mental Wellbeing Agent")
st.info("""
**Meet Your Mental Wellbeing Agent Team:**
🧠 **Assessment Agent** - Analyzes your situation and emotional needs
🎯 **Action Agent** - Creates immediate action plan and connects you with resources
🔄 **Follow-up Agent** - Designs your long-term support strategy
""")
st.subheader("Personal Information")
col1, col2 = st.columns(2)
with col1:
mental_state = st.text_area("How have you been feeling recently?",
placeholder="Describe your emotional state, thoughts, or concerns...")
sleep_pattern = st.select_slider(
"Sleep Pattern (hours per night)",
options=[f"{i}" for i in range(0, 13)],
value="7"
)
with col2:
stress_level = st.slider("Current Stress Level (1-10)", 1, 10, 5)
support_system = st.multiselect(
"Current Support System",
["Family", "Friends", "Therapist", "Support Groups", "None"]
)
recent_changes = st.text_area(
"Any significant life changes or events recently?",
placeholder="Job changes, relationships, losses, etc..."
)
current_symptoms = st.multiselect(
"Current Symptoms",
["Anxiety", "Depression", "Insomnia", "Fatigue", "Loss of Interest",
"Difficulty Concentrating", "Changes in Appetite", "Social Withdrawal",
"Mood Swings", "Physical Discomfort"]
)
if st.button("Get Support Plan"):
if not api_key:
st.error("Please enter your OpenAI API key.")
else:
with st.spinner('🤖 AI Agents are analyzing your situation...'):
try:
task = f"""
Create a comprehensive mental health support plan based on:
Emotional State: {mental_state}
Sleep: {sleep_pattern} hours per night
Stress Level: {stress_level}/10
Support System: {', '.join(support_system) if support_system else 'None reported'}
Recent Changes: {recent_changes}
Current Symptoms: {', '.join(current_symptoms) if current_symptoms else 'None reported'}
"""
system_messages = {
"assessment_agent": """
You are an experienced mental health professional speaking directly to the user. Your task is to:
1. Create a safe space by acknowledging their courage in seeking support
2. Analyze their emotional state with clinical precision and genuine empathy
3. Ask targeted follow-up questions to understand their full situation
4. Identify patterns in their thoughts, behaviors, and relationships
5. Assess risk levels with validated screening approaches
6. Help them understand their current mental health in accessible language
7. Validate their experiences without minimizing or catastrophizing
Always use "you" and "your" when addressing the user. Blend clinical expertise with genuine warmth and never rush to conclusions.
""",
"action_agent": """
You are a crisis intervention and resource specialist speaking directly to the user. Your task is to:
1. Provide immediate evidence-based coping strategies tailored to their specific situation
2. Prioritize interventions based on urgency and effectiveness
3. Connect them with appropriate mental health services while acknowledging barriers (cost, access, stigma)
4. Create a concrete daily wellness plan with specific times and activities
5. Suggest specific support communities with details on how to join
6. Balance crisis resources with empowerment techniques
7. Teach simple self-regulation techniques they can use immediately
Focus on practical, achievable steps that respect their current capacity and energy levels. Provide options ranging from minimal effort to more involved actions.
""",
"followup_agent": """
You are a mental health recovery planner speaking directly to the user. Your task is to:
1. Design a personalized long-term support strategy with milestone markers
2. Create a progress monitoring system that matches their preferences and habits
3. Develop specific relapse prevention strategies based on their unique triggers
4. Establish a support network mapping exercise to identify existing resources
5. Build a graduated self-care routine that evolves with their recovery
6. Plan for setbacks with self-compassion techniques
7. Set up a maintenance schedule with clear check-in mechanisms
Focus on building sustainable habits that integrate with their lifestyle and values. Emphasize progress over perfection and teach skills for self-directed care.
"""
}
llm_config = {
"config_list": [{"model": "gpt-4o", "api_key": api_key}]
}
context_variables = {
"assessment": None,
"action": None,
"followup": None,
}
def update_assessment_overview(assessment_summary: str, context_variables: dict) -> SwarmResult:
context_variables["assessment"] = assessment_summary
st.sidebar.success('Assessment: ' + assessment_summary)
return SwarmResult(agent="action_agent", context_variables=context_variables)
def update_action_overview(action_summary: str, context_variables: dict) -> SwarmResult:
context_variables["action"] = action_summary
st.sidebar.success('Action Plan: ' + action_summary)
return SwarmResult(agent="followup_agent", context_variables=context_variables)
def update_followup_overview(followup_summary: str, context_variables: dict) -> SwarmResult:
context_variables["followup"] = followup_summary
st.sidebar.success('Follow-up Strategy: ' + followup_summary)
return SwarmResult(agent="assessment_agent", context_variables=context_variables)
def update_system_message_func(agent: SwarmAgent, messages) -> str:
system_prompt = system_messages[agent.name]
current_gen = agent.name.split("_")[0]
if agent._context_variables.get(current_gen) is None:
system_prompt += f"Call the update function provided to first provide a 2-3 sentence summary of your ideas on {current_gen.upper()} based on the context provided."
agent.llm_config['tool_choice'] = {"type": "function", "function": {"name": f"update_{current_gen}_overview"}}
else:
agent.llm_config["tools"] = None
agent.llm_config['tool_choice'] = None
system_prompt += f"\n\nYour task\nYou task is write the {current_gen} part of the report. Do not include any other parts. Do not use XML tags.\nStart your reponse with: '## {current_gen.capitalize()} Design'."
k = list(agent._oai_messages.keys())[-1]
agent._oai_messages[k] = agent._oai_messages[k][:1]
system_prompt += f"\n\n\nBelow are some context for you to refer to:"
for k, v in agent._context_variables.items():
if v is not None:
system_prompt += f"\n{k.capitalize()} Summary:\n{v}"
agent.client = OpenAIWrapper(**agent.llm_config)
return system_prompt
state_update = UPDATE_SYSTEM_MESSAGE(update_system_message_func)
assessment_agent = SwarmAgent(
"assessment_agent",
llm_config=llm_config,
functions=update_assessment_overview,
update_agent_state_before_reply=[state_update]
)
action_agent = SwarmAgent(
"action_agent",
llm_config=llm_config,
functions=update_action_overview,
update_agent_state_before_reply=[state_update]
)
followup_agent = SwarmAgent(
"followup_agent",
llm_config=llm_config,
functions=update_followup_overview,
update_agent_state_before_reply=[state_update]
)
assessment_agent.register_hand_off(AFTER_WORK(action_agent))
action_agent.register_hand_off(AFTER_WORK(followup_agent))
followup_agent.register_hand_off(AFTER_WORK(assessment_agent))
result, _, _ = initiate_swarm_chat(
initial_agent=assessment_agent,
agents=[assessment_agent, action_agent, followup_agent],
user_agent=None,
messages=task,
max_rounds=13,
)
st.session_state.output = {
'assessment': result.chat_history[-3]['content'],
'action': result.chat_history[-2]['content'],
'followup': result.chat_history[-1]['content']
}
with st.expander("Situation Assessment"):
st.markdown(st.session_state.output['assessment'])
with st.expander("Action Plan & Resources"):
st.markdown(st.session_state.output['action'])
with st.expander("Long-term Support Strategy"):
st.markdown(st.session_state.output['followup'])
st.success('✨ Mental health support plan generated successfully!')
except Exception as e:
st.error(f"An error occurred: {str(e)}")

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autogen-agentchat
autogen-ext
pyautogen
streamlit