customer-support-env / openenv.yaml
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# openenv.yaml β€” OpenEnv manifest for the Customer Support Environment.
# Describes the environment, its models, available tasks, and deployment metadata.
name: customer_support_env
version: "0.2.0"
description: >
A realistic customer support simulation environment where an AI agent must
classify tickets, craft empathetic responses, and manage multi-turn
conversations to resolve customer issues across 5 real-world categories
and 15 distinct scenarios.
# Pydantic model names (defined in models.py)
action: SupportAction
observation: SupportObservation
state: SupportState
# Available task tiers
tasks:
- name: easy
description: >
Ticket Classification: given a single customer message, the agent must
output the correct issue category (refund, technical, shipping, billing,
or account). Scored 0.0, 0.5, or 1.0.
difficulty: easy
max_steps: 1
- name: medium
description: >
Single-Turn Response: the agent must write an empathetic, complete reply
that resolves the customer's issue in one message. Scored 0.0–1.0 based
on keyword coverage, empathy, response length, and escalation avoidance.
difficulty: medium
max_steps: 1
- name: hard
description: >
Multi-Turn Conversation: the agent handles a full 3-turn support
dialogue β€” ask a clarifying question, provide a resolution, and close
the ticket politely. Cumulative score across turns, 0.0–1.0.
difficulty: hard
max_steps: 10
# API endpoints
endpoints:
health: GET /
reset: POST /reset
step: POST /step
state: GET /state
tasks: GET /tasks
grader: POST /grader
baseline: POST /baseline
# Baseline model
baseline_model: llama-3.1-8b-instant
baseline_api: groq
# Placeholder baseline scores (run `python run_baseline.py` to update)
baseline_scores:
easy: 0.90
medium: 0.55
hard: 0.40
tags:
- customer-support
- nlp
- multi-turn
- classification
- reward-shaping
- openenv
author: "sanathkumarps"
hf_space: "sanathkumarps/customer_support_env"