email-triage-env

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title: Email Triage OpenEnv emoji: 📧 colorFrom: blue colorTo: green sdk: docker pinned: false tags:

• openenv

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📧 Email Triage — OpenEnv Environment

A real-world reinforcement learning environment where an AI agent triages a business inbox: classifying email priority, routing to the correct department, deciding if a response is needed, and summarizing the required action.

Why This Matters

Email triage is one of the most universal knowledge-work tasks — every company does it, it's well-defined enough to evaluate, and bad triage has real business costs (missed critical issues, wasted time on spam). This makes it an ideal benchmark for evaluating LLM agents on practical NLP reasoning.

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Environment Description

Task: Given an email (subject, body, sender, timestamp), the agent must output:

• Priority: low | medium | high | critical
• Department: sales | support | billing | engineering | hr | spam
• Summary: 1-2 sentence action summary
• Needs response: true | false

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Tasks

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Observation Space

{
  "email_id": "string",
  "subject": "string",
  "body": "string",
  "sender": "string",
  "timestamp": "string (YYYY-MM-DD HH:MM:SS)",
  "step_number": "integer",
  "task_id": "integer (1-3)",
  "instructions": "string (task-specific guidance)"
}

Action Space

{
  "priority": "low | medium | high | critical",
  "department": "sales | support | billing | engineering | hr | spam",
  "summary": "string (1-2 sentences)",
  "needs_response": "boolean"
}

Reward Function

Each step returns a reward in [0.0, 1.0] computed as:

Reward provides continuous partial credit throughout the episode, not just binary end-of-episode scores.

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API Endpoints

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Setup & Usage

Docker

docker build -t email-triage-env .
docker run -p 7860:7860 email-triage-env

Run baseline inference

export API_BASE_URL="https://api.openai.com/v1"
export MODEL_NAME="gpt-4o-mini"
export HF_TOKEN="your-token-here"
export ENV_URL="http://localhost:7860"

python inference.py

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Baseline Scores

Tested with gpt-4o-mini (temperature=0):

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Expected Log Format

The inference script emits structured JSON logs:

{"type": "START", "model": "...", "tasks": [1, 2, 3]}
{"type": "STEP", "task_id": 1, "step": 1, "reward": 0.92, ...}
{"type": "END", "overall_avg_reward": 0.64, "task_scores": {...}}