205 lines
5.3 KiB
Markdown
205 lines
5.3 KiB
Markdown
# AgentLiving - 技术规格文档
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## 1. Godot 项目结构
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```
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ai-agent-living/
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├── assets/
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│ ├── sprites/
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│ │ ├── val/
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│ │ │ ├── idle.png
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│ │ │ ├── walk.png
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│ │ │ └── interact.png
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│ │ └── furniture/
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│ │ ├── sofa.png
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│ │ ├── fridge.png
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│ │ └── bed.png
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│ ├── tilesets/
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│ │ └── apartment_floor.tres
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│ └── audio/
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│ └── ambient.mp3
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├── scenes/
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│ ├── main.tscn
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│ ├── apartment.tscn
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│ ├── val.tscn
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│ └── furniture/
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│ ├── sofa.tscn
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│ └── fridge.tscn
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├── scripts/
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│ ├── val.gd
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│ ├── apartment.gd
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│ ├── bridge_client.gd
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│ └── state_manager.gd
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└── project.godot
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```
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## 2. 关键类设计
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### 2.1 Val (CharacterBody2D)
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```gdscript
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class_name Val
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extends CharacterBody2D
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@export var speed: float = 100.0
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@onready var sprite: AnimatedSprite2D = $AnimatedSprite2D
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@onready var thought_bubble: Label = $ThoughtBubble
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var current_state: String = "idle"
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var target_position: Vector2 = Vector2.ZERO
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var agent_id: String = "val"
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func _ready():
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BridgeClient.connect("action_received", _on_action)
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_connect_to_openclaw()
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func _physics_process(delta):
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match current_state:
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"walk": _handle_walk(delta)
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"interact": _handle_interact(delta)
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"think": _handle_think(delta)
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_: _handle_idle(delta)
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func move_to(pos: Vector2):
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target_position = pos
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current_state = "walk"
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sprite.play("walk")
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func _on_action(action: Dictionary):
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match action.cmd:
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"walk_to": move_to(Vector2(action.pos[0], action.pos[1]))
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"play_anim": sprite.play(action.anim)
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"show_thought": _show_thought(action.text)
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"update_state": _update_internal_state(action.key, action.value)
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```
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### 2.2 BridgeClient (WebSocket)
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```gdscript
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extends Node
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signal action_received(action: Dictionary)
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signal state_synced(state: Dictionary)
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var socket: WebSocketPeer
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var openclaw_url: String = "ws://127.0.0.1:18789"
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func _ready():
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_connect()
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func _connect():
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socket = WebSocketPeer.new()
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socket.connect_to_url(openclaw_url)
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func _process(delta):
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socket.poll()
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var state = socket.get_ready_state()
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if state == WebSocketPeer.STATE_OPEN:
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while socket.get_available_packets() > 0:
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var packet = socket.get_packet()
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var message = JSON.parse_string(packet.get_string_from_utf8())
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_handle_message(message)
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func send_command(command: Dictionary):
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if socket.get_ready_state() == WebSocketPeer.STATE_OPEN:
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socket.send_text(JSON.stringify(command))
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func _handle_message(msg: Dictionary):
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match msg.type:
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"action_sequence":
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for action in msg.actions:
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action_received.emit(action)
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"state_sync":
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state_synced.emit(msg.state)
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```
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## 3. OpenClaw端集成
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### 3.1 AgentLiving Skill
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创建 `~/.openclaw/skills/agent-living/`：
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```
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agent-living/
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├── SKILL.md
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├── scripts/
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│ └── bridge_server.py
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└── config.json
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```
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### 3.2 Bridge Server (Python)
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```python
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# bridge_server.py
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import asyncio
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import websockets
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import json
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from openclaw import session_manager
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class AgentLivingBridge:
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def __init__(self):
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self.agents = {}
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self.godot_clients = {}
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async def handle_godot(self, websocket, path):
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"""处理Godot客户端连接"""
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async for message in websocket:
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data = json.loads(message)
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response = await self._process_command(data)
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await websocket.send(json.dumps(response))
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async def _process_command(self, cmd: dict) -> dict:
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"""将Godot指令转换为Agent动作"""
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if cmd["type"] == "user_command":
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# 调用OpenClaw Agent处理
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agent_session = session_manager.get("val")
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result = await agent_session.send_message(
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f"用户在Godot中发出指令：{cmd}"
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)
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return self._parse_agent_response(result)
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def _parse_agent_response(self, response: str) -> dict:
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"""解析Agent回复为Godot动作序列"""
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# 提取动作序列，格式化为Godot可执行的JSON
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actions = []
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# ... 解析逻辑
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return {"type": "action_sequence", "actions": actions}
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# 启动服务器
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async def main():
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bridge = AgentLivingBridge()
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async with websockets.serve(
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bridge.handle_godot,
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"localhost",
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8765
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):
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await asyncio.Future() # 永久运行
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if __name__ == "__main__":
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asyncio.run(main())
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```
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## 4. 状态同步策略
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### 4.1 心跳机制
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- Godot每5秒发送状态查询
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- OpenClaw立即返回完整状态
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- 差异超过阈值时触发同步
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### 4.2 事件驱动
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- Agent状态变化 → 立即推送到Godot
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- 用户交互 → 立即发送到Agent
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- 避免轮询开销
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## 5. 性能优化
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### 5.1 Agent端
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- 简单行为（走动、idle）用本地规则
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- 复杂决策（任务分解）才调用LLM
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- 缓存常用响应
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### 5.2 Godot端
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- 对象池复用粒子效果
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- 远处角色降低动画帧率
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- 按需加载房间（视野外不渲染）
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---
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**下一步：** Week 1 开始Godot场景搭建
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