name: smart_sprint_planner
version: "1.1.0"
display_name: "Smart Sprint Planner"
description: >
  A real-world OpenEnv environment for agile sprint planning and dynamic replanning.
  Agents convert planning context into practical task assignments across a software team,
  balancing deadlines, skill fit, capacity, dependencies, and mid-sprint disruptions.

author: "Sprint Planning Team"
license: "MIT"

tasks:
  - task_id: easy
    name: "Static Sprint Planning"
    difficulty: easy
    baseline_expected_score: 0.833
  - task_id: medium
    name: "Single Disruption Replanning"
    difficulty: medium
    baseline_expected_score: 0.734
  - task_id: hard
    name: "Multi-Disruption Dynamic Planning"
    difficulty: hard
    baseline_expected_score: 0.761

tags:
  - openenv
  - agile
  - sprint-planning
  - scheduling
  - task-assignment
  - dynamic-replanning
  - rl-environment

sdk: docker
hardware: cpu-basic
base_image: "ghcr.io/meta-pytorch/openenv-base:latest"

endpoints:
  reset: "/reset"
  step: "/step"
  state: "/state"
  tasks: "/tasks"
  grader: "/grade"
  health: "/health"

spec:
  openenv_version: "0.3.0"
  gymnasium_compatible: true
  max_episodes_per_session: 50
  session_timeout_seconds: 3600

  tasks:
    - id: easy
      name: "Static Sprint Planning"
      difficulty: easy
      max_steps: 10
      baseline_score: 0.833
      grader_type: programmatic
    - id: medium
      name: "Single Disruption Replanning"
      difficulty: medium
      max_steps: 15
      baseline_score: 0.734
      grader_type: programmatic
    - id: hard
      name: "Multi-Disruption Dynamic Planning"
      difficulty: hard
      max_steps: 20
      baseline_score: 0.761
      grader_type: programmatic

  observation_space:
    type: structured
    schema: "Observation"
    contains:
      meeting_text: "str - planning transcript or transcribed meeting audio"
      extracted_items: "list[ExtractedItem] - structured planning items extracted from the meeting"
      jira_tickets: "list[Task] - active sprint backlog tickets"
      developers: "list[Developer] - available engineers with capacity and specialties"
      completed_task_ids: "list[str] - completed ticket ids"
      sprint_day: "int - current day within the simulated sprint"
      metrics: "SprintMetrics - reward and disruption statistics"
      difficulty: "Difficulty - active task difficulty"
      recent_events: "list[SprintEvent] - disruption events applied this step"
      pending_events: "list[SprintEvent] - scheduled future disruptions"

  action_space:
    type: discrete_typed
    schema: "Action"
    action_types:
      - task_assignment

  reward_function:
    type: dense
    range: [-0.5, 2.5]
    signals:
      on_time_completion: +0.50
      skill_match: +0.20
      high_priority: +0.10
      disruption_adaptation: +0.25
      invalid_assignment: -0.10
      blocked_assignment: -0.40
      over_capacity_assignment: -0.20

data:
  scenario_sources:
    - dataset.json
    - dataset_hard_extra.json
    - dataset_hard_extra2.json
    - built_in_fallbacks
  synthetic_data: true
  realism: high

baseline:
  model: "Qwen/Qwen2.5-72B-Instruct"
  api_compatible: "openai"
  temperature: 0.1
  max_tokens: 500
  seeds: [42]
  expected_scores:
    easy: 0.833
    medium: 0.734
    hard: 0.761
    overall_mean: 0.776
  api_key_env_var: "HF_TOKEN"
  script: "inference.py"

infrastructure:
  dockerfile: "Dockerfile"
  port: 7860
  build_command: "docker build -t smart-sprint-planner ."
  run_command: "docker run -p 7860:7860 smart-sprint-planner"
  framework: fastapi
  server: uvicorn
  workers: 1
  startup_command: "uvicorn server.app:app --host 0.0.0.0 --port 7860"
  session_backend: in_memory
  env_vars:
    required:
      - API_BASE_URL
      - MODEL_NAME
      - HF_TOKEN
    optional:
      - API_KEY

dependencies:
  - fastapi
  - uvicorn
  - pydantic
  - numpy
  - openai
  - tenacity