eptan/crisis_inbox

CrisisInbox

A reinforcement learning environment built on OpenEnv 0.2.1 for training language models to manage personal task overload during natural disasters.

Problem Statement 3.2 (Personalized Tasks) + Patronus AI Sub-Theme (Schema Drift)

HF Space: eptan-crisis-inbox.hf.space

The Problem

When disaster strikes, your phone explodes. Evacuation orders, panicked family texts, insurance deadlines, your boss demanding slides, your sister begging you to pick up her kids from school, your dad's heart medication left behind. Everything is urgent. Policies change mid-crisis. There are no clean answers — only tradeoffs.

CrisisInbox trains an agent to make those tradeoffs well.

How It Works

The agent manages a 48-hour post-disaster inbox as a working parent in Sacramento during a hurricane. Messages arrive in real time from 30+ senders across 6 channels. The agent must triage, respond, and adapt — while the rules keep changing underneath.

Three Layers of Difficulty

1. Cognitive Overload — 116 messages arrive over 48 hours. Reading costs time (6 min). Responding costs more (15 min). The agent can't handle everything — it must prioritize and let some things slide.

2. Conflicting Obligations — Your boss says come in. HR says take emergency leave. Your sister needs you to watch her kids. Mom wants you to drive to Tahoe. The evacuation shelter is full. A wheelchair-bound neighbor is trapped. A baby down the street needs your generator. There's no right answer, only better tradeoffs.

3. Scams & Misinformation — Fake FEMA texts ask for your SSN. A contractor demands cash upfront. Social media says the dam is failing. The agent must recognize and deprioritize traps while acting on real emergencies.

4. Schema Drift — Mid-episode, the rules change:

• —Insurance deadline shortened from 72h to 48h
• —Evacuation zone expands to include your workplace
• —Employer switches from "use PTO" to "5 days paid emergency leave"
• —Airline extends free rebooking from 48h to 7 days
• —FEMA adds new documentation requirements
• —Shelter closes due to structural damage — relocate immediately
• —County implements fuel rationing (odd/even plates)
• —Multiple pharmacies close — prescription transfers needed
• —Curfew extended from 9PM-6AM to 6PM-8AM due to looting

Each episode randomly fires 3 of 9 drift events. The agent must detect changes and reprioritize.

Sender Profiles

MCP Tools (Agent Actions)

Reward Function

An optimal agent responding to the critical evacuation alert at hour 0 earns 15 points. The same response at hour 10 (after the 6h deadline) earns only 2.5.

Episode Variation

Each episode has:

• —3 of 9 drift events randomly selected (seed-controlled)
• —+/-15% jitter on message arrival times
• —+/-10% jitter on deadlines
• —Dependency chains that gate actions (e.g., must handle sister's request before school pickup confirmation)

Quick Start (Hosted)

from crisis_inbox import CrisisInboxEnv

with CrisisInboxEnv(base_url="https://eptan-crisis-inbox.hf.space") as env:
    env.reset()
    inbox = env.call_tool("get_inbox")
    print(inbox)

Deployment

# 1. Create and activate a virtual environment
python3.12 -m venv .venv
source .venv/bin/activate
pip install -e .

# 2. Deploy to HF Spaces via OpenEnv CLI
openenv push -r eptan/crisis-inbox

Or run locally:

source .venv/bin/activate
uvicorn server.app:app --host 0.0.0.0 --port 8000

Local Development

git clone https://github.com/eptan/crisis-inbox.git
cd crisis-inbox

python3.12 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -e .

# Run server
uvicorn server.app:app --host 0.0.0.0 --port 8000

Test with the client:

from crisis_inbox import CrisisInboxEnv

with CrisisInboxEnv(base_url="http://localhost:8000") as env:
    env.reset()

    # View the inbox
    inbox = env.call_tool("get_inbox")
    print(inbox)

    # Read a message
    msg = env.call_tool("read_message", message_id="msg_001")
    print(msg)

    # Respond to it
    result = env.call_tool("respond_to_message",
        message_id="msg_001",
        response="Evacuating to Lincoln High School immediately with documents and medication.")
    print(result)

    # Advance time and check status
    env.call_tool("advance_time", hours=4.0)
    status = env.call_tool("get_status")
    print(status)

Repository Structure

crisis-inbox/
├── models.py                           # Message data model (Channel, Urgency, Message)
├── messages.py                         # 116 pre-written messages across 48h timeline
├── drift_events.py                     # 9 schema drift events (3 fire per episode)
├── client.py                           # MCPToolClient subclass
├── __init__.py                         # Package exports
├── server/
│   ├── crisis_inbox_environment.py     # MCPEnvironment with timeline engine
│   ├── app.py                          # FastAPI app with MCPAction workaround
│   └── Dockerfile                      # HF Spaces deployment
├── notebooks/
│   └── crisisinbox_grpo_simple.ipynb   # GRPO training notebook (Colab)
├── .episodes.json                      # Pre-generated training episodes (gitignored)
├── generate_episodes.py                # Episode generator script
├── pyproject.toml                      # Package config
├── openenv.yaml                        # OpenEnv environment spec
├── requirements.txt                    # Docker build dependencies
└── ROADMAP.md                          # Hackathon timeline and progress

Stack

• —Environment: OpenEnv 0.2.1 (MCPEnvironment + FastMCP)
• —Deployment: HF Spaces (Docker)
• —Training: Unsloth GRPO via Google Colab
• —Model: Qwen2.5-0.5B-Instruct

GRPO training (Colab)

Open the notebook with the latest fixes (context length, reward signature, left-padding, batch size) in Google Colab (T4 GPU runtime):

[Open in Colab](https://colab.research.google.com/github/eptan/crisis-inbox/blob/main/notebooks/crisisinbox_grpo_simple.ipynb)

Push your local changes to the main branch so the link above serves the updated notebook.

Team

Built at the OpenEnv Hackathon @ Shack15, SF — March 7-8, 2026