- Add action economy system with free (LOOK, SPEAK) vs turn-ending (GO, WAIT, TAKE) actions - Implement LOOK action with detailed descriptions for doors, objects, entities, directions - Add SPEAK/ANNOUNCE speech system with room-wide and proximity-based message delivery - Create multi-tile pathing with FOV interrupt detection (path cancels when new entity visible) - Implement TAKE action with adjacency requirement and clear error messages - Add conversation history and error feedback loop so agents learn from failed actions - Create structured simulation logging for offline viewer replay - Document offline viewer requirements in OFFLINE_VIEWER_SPEC.md - Fix import path in 1_multi_agent_demo.py for standalone execution 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
4.3 KiB
4.3 KiB
Offline Viewer Specification
Status: Planned (issue #154) Priority: After core simulation features are stable
Overview
The Offline Viewer allows users to replay stored simulation logs in McRogueFace, stepping through turn-by-turn to review:
- Each agent's perspective (FOV, camera position)
- LLM chain-of-thought reasoning
- Actions taken and their results
- Speech between agents
Log Format
Simulation logs are stored as JSON with this structure:
{
"metadata": {
"total_turns": 5,
"num_agents": 2,
"agent_names": ["Wizard", "Knight"],
"timestamp_start": "2025-01-15T10:30:00",
"timestamp_end": "2025-01-15T10:32:45",
"world_rooms": ["guard_room", "armory"],
"screenshot_dir": "/tmp/vllm_enhanced_demo"
},
"steps": [
{
"turn": 1,
"agent_id": "Wizard",
"timestamp": "2025-01-15T10:30:15",
"position_start": [5, 4],
"position_end": [6, 4],
"room": "guard_room",
"visible_entities": ["rat_123", "knight_456"],
"visible_tiles": 42,
"points_of_interest": [
{"name": "door", "direction": "east", "distance": 4}
],
"location_description": "You are in the guard room...",
"available_actions": ["GO EAST", "LOOK", "WAIT"],
"pending_messages": [],
"poi_prompt": "Points of interest:\n - a door to the armory (east)",
"screenshot_path": "/tmp/.../turn1_wizard.png",
"llm_prompt_system": "You are a wizard...",
"llm_prompt_user": "You are in the guard room...",
"llm_response": "I see a door to the east. I should explore. Action: GO EAST",
"llm_was_queried": true,
"free_actions": [
{"action_type": "LOOK", "args": ["DOOR"], "result": {"description": "A wooden door..."}}
],
"final_action_type": "GO",
"final_action_args": ["EAST"],
"final_action_success": true,
"final_action_message": "Moved east to (6, 4)",
"path_taken": [[5, 4], [6, 4]],
"path_remaining": 0
}
],
"speech_log": [
{
"turn": 2,
"speaker": "Wizard",
"type": "announce",
"content": "Hello, is anyone there?",
"recipients": ["Knight"]
}
]
}
Viewer Features (Planned)
Core Features
-
Turn Navigation
- Step forward/backward through turns
- Jump to specific turn number
- Auto-play at configurable speed
-
Agent Perspective
- Reconstruct agent's FOV from stored data
- Center camera on current agent
- Show visible entities and tiles
-
LLM Review Panel
- Display system prompt
- Display user prompt (context)
- Display LLM response
- Highlight parsed action
-
Action Log
- Show free actions (LOOK, SPEAK)
- Show final action and result
- Color-code success/failure
-
Speech History
- Timeline of all speech events
- Filter by agent
- Show recipients
Implementation Notes
The viewer should:
- Load screenshots from
screenshot_path(if available) - OR reconstruct scene from WorldGraph + step data
- Support keyboard navigation (arrow keys)
- Display agent state in sidebar
UI Layout (Suggested)
+----------------------------------+------------------+
| | Turn: 3/10 |
| Main Viewport | Agent: Wizard |
| (Agent's Perspective) | Room: armory |
| +------------------+
| | LLM Response: |
| | "I see a rat |
| | to the east. |
| | Action: LOOK |
| | AT RAT" |
+----------------------------------+------------------+
| < Prev | Turn 3 | Next > | Actions: |
| [Agent: Wizard v] | - LOOK AT RAT |
| | - GO EAST [OK] |
+----------------------------------+------------------+
Files
enhanced_orchestrator.py- GeneratesEnhancedSimulationLog4_enhanced_action_demo.py- Demo with--replaymode for text preview- Logs stored in
/tmp/vllm_enhanced_demo/simulation_log.json
Future Enhancements
- Animated path replay (smooth entity movement)
- Side-by-side multi-agent view
- Diff view comparing agent perceptions
- Export to video/GIF
- Integration with annotation tools for research