第七章：OpenClaw 架构深度解析
摘要: 本章深入剖析开源 AI Agent 平台 OpenClaw 的完整架构设计，涵盖 Gateway 机制与多通道支持、Agent Registry & Binding System 实现原理、三层 Memory System 技术细节、Plugin/Tool 生态体系说明。通过详细的架构图解和核心组件解析，帮助读者全面理解现代 Agent 平台的系统化设计方法。
OpenClaw 平台概述
OpenClaw 是一个开源的 AI Agent 运行平台，旨在提供一套完整的工具链来构建、部署和管理复杂的 AI Agent 系统。与 LangChain 或 AutoGen 等框架不同，OpenClaw 更强调生产级可靠性 和多通道支持。
OpenClaw 的核心设计目标：
-
生产就绪性（Production-Ready）
- 完整的错误处理机制
- 详细的监控和日志系统
- 高可用性架构设计
-
多通道扩展性（Multi-Channel Scalability）
- 支持飞书、Discord、Slack 等多种通讯平台
- 统一的 API 接口抽象层
- 灵活的渠道适配器模式
-
模块化与可组合性
- Agent Registry 集中管理
- Plugin/Tool 热插拔机制
- 工作流可视化编排
-
开发者友好
- 丰富的文档和示例代码
- CLI 工具支持
- 社区驱动的插件生态
7.1 Gateway 机制与多通道支持详解
Gateway 的核心架构设计
Gateway（网关）是 OpenClaw 的中央枢纽，负责处理来自不同通讯渠道的消息路由和统一调度。它的设计遵循适配器模式（Adapter Pattern）。
Gateway 核心组件：
┌───────────────────────────────────────────────┐
│ OpenClaw Gateway Architecture │
├───────────────────────────────────────────────┤
│ │
│ ┌─ Channel Layer (Communication Adapters) │
│ │ • Feishu Adapter │
│ │ • Discord Adapter │
│ │ • Slack Adapter │
│ │ • Telegram Adapter │
│ └──────────────┬─────────────────────────────┘
│ ↓ │
│ ┌─────────────▼─────────────────────────────┐ │
│ │ Message Router & Dispatcher │ │
│ │ • Authentication Verification │ │
│ │ • Rate Limiting │ │
│ │ • Load Balancing │ │
│ └─────────────┬─────────────────────────────┘ │
│ ↓ │
│ ┌────────────▼─────────────────────────────┐ │
│ │ Agent Registry & Binding System │ │
│ │ • Agent Discovery │ │
│ │ • Intent Matching │ │
│ │ • Priority Routing │ │
│ └────────────┬─────────────────────────────┘ │
│ ↓ │
│ ┌──────────▼─────────────────────────────┐ │
│ │ Core Processing Pipeline │ │
│ │ • Memory Coordination │ │
│ │ • Tool Execution │ │
│ │ • Output Formatting │ │
│ └──────────┬─────────────────────────────┘ │
│ ↓ │
│ ┌──────────▼─────────────────────────────┐ │
│ │ Response Delivery Layer │ │
│ │ • Template Rendering │ │
│ │ • Message Formatting │ │
│ │ • File Attachments │ │
│ └─────────────────────────────────────────┘ │
│ │
└───────────────────────────────────────────────┘Feishu (Lark) 适配器实现详解
Feishu WebSocket Event Handler:
python
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
import asyncio
from typing import Dict, Any
import json
from app.adapters.feishu import FeishuWebhookClient
from app.core.agent_registry import AgentRegistry
class FeishuAdapter(FeishuWebhookClient):
"""
Feishu/Lark event handler for real-time message processing
Implements WebSocket-based bidirectional communication
"""
def __init__(self, gateway_config: Dict[str, Any]):
super().__init__(gateway_config)
self.agent_registry = AgentRegistry.from_config(gateway_config["registry_path"])
self.active_sessions: Dict[str, asyncio.Task] = {}
async def handle_event(self, event: Dict[str, Any]):
"""
Process incoming Feishu events
Events include: message_created, message_read, reaction_added, etc.
"""
event_type = event.get("type")
event_data = event.get("data", {})
if event_type == "message":
await self._process_message_event(event_data)
elif event_type == "im.message.created":
await self._process_im_message(event_data)
else:
logger.warning(f"Unhandled event type: {event_type}")
async def _process_im_message(self, message_data: Dict[str, Any]):
"""
Process instant message from Feishu
Supports both single chat and group chat scenarios
"""
message_id = message_data.get("message_id")
receive_id = message_data.get("receive_id")
sender_id = message_data["sender"].get("user_id")
content = json.loads(message_data["content"])["text"]
# Extract session context (group vs single chat)
is_group = self._is_group_chat(message_data)
session_key = f"{receive_id}:{message_id}"
# Check if session already active
if session_key in self.active_sessions:
logger.debug(f"Message already being processed for {session_key}")
return
# Create new processing task
task = asyncio.create_task(
self._handle_single_message(
sender_id=sender_id,
content=content,
is_group=is_group,
session_key=session_key,
message_id=message_id
)
)
self.active_sessions[session_key] = task
task.add_done_callback(lambda t: self.active_sessions.pop(session_key, None))
async def _handle_single_message(self, sender_id, content, is_group, session_key, message_id):
"""
Core message handling logic
1. Parse user intent and identify required agent
2. Load relevant memory context
3. Execute agent workflow
4. Format and deliver response
"""
try:
# Step 1: Intent recognition and agent matching
matched_agent = self._match_agent_to_message(content)
if not matched_agent:
await self._send_error_response(
to=message_id,
reason="No suitable agent found for this request"
)
return
# Step 2: Memory context preparation
memory_context = await self._prepare_memory_context(
user_id=sender_id,
is_group=is_group,
session_key=session_key
)
# Step 3: Execute agent workflow
response_data = await matched_agent.execute(
query=content,
context=memory_context,
message_type="feishu_im"
)
# Step 4: Format and send response
formatted_response = self._format_feishu_response(response_data)
await self._send_im_message(message_id, formatted_response)
except Exception as e:
logger.error(f"Error processing message {message_id}: {e}")
await self._handle_processing_error(sender_id, str(e))
def _is_group_chat(self, message_data: Dict[str, Any]) -> bool:
"""
Determine if message is from a group or single chat
"""
msg_type = message_data.get("message_type")
return msg_type == "group_chat"多通道统一适配器抽象层
Channel Adapter Interface:
python
from abc import ABC, abstractmethod
from typing import Dict, Any
import asyncio
class BaseChannelAdapter(ABC):
"""
Abstract base class for communication channel adapters
All channel adapters must implement this interface
"""
def __init__(self, config: Dict[str, Any]):
self.config = config
self.is_initialized = False
@abstractmethod
async def initialize(self) -> bool:
"""
Initialize connection to the communication platform
Returns True if successful, False otherwise
"""
pass
@abstractmethod
async def connect(self):
"""
Establish persistent connection (WebSocket, EventSource, etc.)
Should handle reconnection logic
"""
pass
@abstractmethod
async def disconnect(self):
"""
Gracefully disconnect from the platform
Cleanup resources and sessions
"""
pass
@abstractmethod
async def send_message(self, recipient_id: str, content: Dict[str, Any]):
"""
Send formatted message to specific recipient
Platform-specific formatting handled by adapter
"""
pass
@abstractmethod
async def handle_event(self, event_handler):
"""
Register callback for incoming events
Should be non-blocking and scalable
"""
pass
@abstractmethod
def format_response(self, response_data: Dict[str, Any], original_request: Dict) -> Dict:
"""
Format agent output according to platform requirements
Returns platform-specific message structure
"""
passDiscord Adapter Implementation:
python
import discord
from discord.ext import commands, tasks
class DiscordAdapter(BaseChannelAdapter):
"""
Discord bot adapter implementing BaseChannelAdapter interface
Uses discord.py library for native integration
"""
def __init__(self, config: Dict[str, Any]):
super().__init__(config)
self.token = config["discord_token"]
self.commands_prefix = config.get("commands_prefix", "!")
self.client = commands.Bot(
intents=discord.Intents.all(),
command_prefix=self.commands_prefix
)
async def initialize(self) -> bool:
"""
Set up Discord bot with all necessary events and listeners
"""
try:
await self._setup_command_handlers()
await self._setup_event_listeners()
self.is_initialized = True
return True
except Exception as e:
logger.error(f"Discord initialization failed: {e}")
return False
async def _setup_command_handlers(self):
"""
Define Discord slash commands and message handlers
"""
@self.client.command(name="help")
async def help_command(ctx):
await ctx.send("Available commands: /search, /analyze, /create, /status")
@self.client.slash_command(name="search", description="Search knowledge base")
async def search_interaction(interaction, query: str):
await interaction.response.defer()
result = await self._process_search_query(query)
await interaction.followup.send(result)
async def handle_event(self, event_handler):
"""
Register Discord message events to the gateway event handler
"""
@self.client.event
async def on_message(message: discord.Message):
if message.author == self.client.user:
return # Ignore self-messages
# Pass message to core event handler
await event_handler({
"type": "discord_message",
"sender_id": str(message.author.id),
"content": message.content,
"channel": str(message.channel.name),
"timestamp": message.created_at.timestamp()
})7.2 Agent Registry & Binding System 实现
Agent Registry 的核心设计理念
Agent Registry（代理注册中心）是 OpenClaw 的中央大脑，负责维护所有已加载 Agent 的元数据、能力描述和绑定关系。
Registry 核心数据结构：
python
from dataclasses import dataclass, field
from typing import Dict, List, Set, Optional
from enum import Enum
import uuid
class AgentCapability(Enum):
SEARCH = "search"
ANALYSIS = "analysis"
CREATION = "creation"
TASK_MANAGEMENT = "task_management"
CUSTOM = "custom"
@dataclass
class AgentDefinition:
"""
Complete definition of an AI agent with metadata and capabilities
"""
id: str # Unique identifier
name: str
description: str
version: str
# Capability metadata
capabilities: List[AgentCapability]
# Platform compatibility
supported_channels: Set[str] = field(default_factory=set)
# Routing and priority
default_priority: int = 1
fallback_enabled: bool = True
# Memory scope
memory_scopes: List[str] = field(default_factory=list) # session, personal, global
# Configuration
system_prompt_template: str = ""
required_tools: List[str] = field(default_factory=list)
# Status
is_active: bool = True
last_seen: Optional[float] = None
health_status: str = "healthy"
# Metadata
created_at: float = field(default_factory=time.time)
updated_at: float = field(default_factory=time.time)
tags: List[str] = field(default_factory=list)
@dataclass
class AgentBinding:
"""
Binding relationship between an agent and a channel/endpoint
"""
binding_id: str = field(default_factory=lambda: str(uuid.uuid4()))
agent_id: str
channel_type: str # feishu, discord, slack, webhook
channel_endpoint: str
enabled: bool = True
created_at: float = field(default_factory=time.time)Agent Registry 完整实现：
python
from sqlalchemy import create_engine, Column, String, Boolean, Integer, Text, DateTime, JSON
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
import json
from datetime import datetime
Base = declarative_base()
class AgentRegistry:
"""
Central registry for all AI agents in the system
Provides CRUD operations and intelligent routing capabilities
"""
def __init__(self, db_url: str, cache_ttl_seconds: int = 300):
self.engine = create_engine(db_url)
self.SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=self.engine)
self.cache_ttl = cache_ttl_seconds
self._agent_cache: Dict[str, AgentDefinition] = {}
def register_agent(self, agent_def: AgentDefinition) -> str:
"""
Register a new agent with the system
Returns the generated UUID for the new agent
"""
with self.SessionLocal() as session:
# Check for duplicate name
existing = self._find_agent_by_name(agent_def.name, session)
if existing:
raise ValueError(f"Agent name '{agent_def.name}' already exists")
# Create new agent entry
db_agent = self._agent_to_db(agent_def)
session.add(db_agent)
session.commit()
# Update cache
self._cache_agent(agent_def)
return agent_def.id
def _find_agent_by_intent(self, intent_query: str, channel_type: str) -> Optional[AgentDefinition]:
"""
Intelligent routing: Find the best matching agent for a given intent
Uses semantic similarity + capability matching + priority scoring
"""
with self.SessionLocal() as session:
candidates = []
# Get all active agents supporting this channel
agents = session.query(DBAgent).filter(
DBAgent.is_active == True,
DBAgent.supported_channels.contains([channel_type])
).all()
for agent in agents:
# Calculate match score based on multiple factors
capability_score = self._calculate_capability_match(agent, intent_query)
relevance_score = self._calculate_relevance_score(agent, intent_query)
priority_score = agent.default_priority / 10.0
total_score = (
capability_score * 0.4 +
relevance_score * 0.4 +
priority_score * 0.2
)
candidates.append({
"agent": agent,
"score": total_score
})
# Sort by score and return top match
if candidates:
best_match = max(candidates, key=lambda x: x["score"])
if best_match["score"] > 0.6: # Threshold for acceptable match
return self._db_agent_to_def(best_match["agent"])
return None
def _calculate_capability_match(self, agent, intent_query: str) -> float:
"""
Calculate how well agent's capabilities match the user's intent
Uses embedding-based semantic similarity
"""
# Get capability embeddings (pre-computed)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
agent_capabilities_text = " ".join([c.value for c in agent.capabilities])
intent_embedding = model.encode(intent_query)
capability_embedding = model.encode(agent_capabilities_text)
# Cosine similarity
similarity = np.dot(intent_embedding, capability_embedding) / (
np.linalg.norm(intent_embedding) * np.linalg.norm(capability_embedding)
)
return float(similarity)
def _calculate_relevance_score(self, agent, intent_query: str) -> float:
"""
Calculate relevance based on description matching and tag overlap
"""
# Keyword matching
intent_keywords = set(intent_query.lower().split())
agent_description_lower = agent.description.lower()
agent_tags_lower = [tag.lower() for tag in agent.tags]
desc_match = any(word in agent_description_lower for word in intent_keywords)
tags_match = any(tag in agent_tags_lower for tag in [kw for kw in intent_keywords if len(kw) > 5])
score = 0.0
if desc_match:
score += 0.5
if tags_match:
score += 0.3
return min(score, 1.0)
def _cache_agent(self, agent_def: AgentDefinition):
"""
Cache agent in memory for faster lookups
"""
self._agent_cache[agent_def.id] = {
"definition": agent_def,
"cached_at": time.time()
}
def get_agent(self, agent_id: str) -> Optional[AgentDefinition]:
"""
Get agent by ID with cache fallback
"""
if agent_id in self._agent_cache:
cached = self._agent_cache[agent_id]
if time.time() - cached["cached_at"] < self.cache_ttl:
return cached["definition"]
# Fetch from database
with self.SessionLocal() as session:
db_agent = session.query(DBAgent).filter(DBAgent.id == agent_id).first()
if db_agent:
agent_def = self._db_agent_to_def(db_agent)
self._cache_agent(agent_def)
return agent_def
return None7.3 Memory System 技术细节剖析（三层架构）
Session Memory：快速上下文管理
In-Memory Buffer Implementation:
python
from collections import deque
import json
class SessionMemoryBuffer:
"""
High-speed in-memory buffer for session-specific context
Implements sliding window with intelligent compression
"""
def __init__(self, max_tokens: int = 8000):
self.max_tokens = max_tokens
self.buffer: deque = deque(maxlen=100)
self.token_counts: list = []
self.compression_threshold = int(max_tokens * 0.8)
def add_message(self, message_id: str, sender: str, content: str, metadata: dict):
"""
Add new message to session buffer with token tracking
Triggers compression if needed
"""
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# Calculate approximate token count
tokens = len(tokenizer.encode(content))
message_entry = {
"id": message_id,
"sender": sender,
"content": content,
"metadata": metadata,
"timestamp": time.time(),
"token_count": tokens
}
self.buffer.append(message_entry)
self.token_counts.append(tokens)
# Check if compression needed
if self._total_tokens() > self.compression_threshold:
self._compress_session()
def _total_tokens(self) -> int:
"""
Calculate total tokens in current buffer
"""
return sum(self.token_counts)
def _compress_session(self):
"""
Compress early messages to make room for new content
Uses LLM to generate summary of older conversation turns
"""
if len(self.buffer) < 3:
return # Not enough messages to compress
# Get first 2-3 message pairs (user-assistant)
early_messages = list(self.buffer)[:4]
remaining_messages = list(self.buffer)[4:]
# Generate summary using LLM
summary_prompt = f"""
Summarize the following conversation to capture key decisions, user preferences,
and important context. Keep it concise but retain all critical information.
Conversation:
{self._format_messages_for_summary(early_messages)}
Summary:
"""
from openai import OpenAI
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
response = client.chat.completions.create(
model="gpt-4-turbo",
messages=[{"role": "user", "content": summary_prompt}],
max_tokens=200
)
# Replace early messages with summary
summary_entry = {
"id": "session_summary",
"sender": "system",
"content": response.choices[0].message.content,
"is_summary": True,
"timestamp": time.time()
}
# Reconstruct buffer with summary + remaining messages
self.buffer = deque([summary_entry] + [msg for msg in remaining_messages if not msg.get("is_summary")], maxlen=100)
self.token_counts = [len(tokenizer.encode(summary_entry["content"]))]Daily Memory：近期会话持久化
SQLite-Based Daily Log Storage:
python
from sqlalchemy import create_engine, Column, Integer, String, DateTime, JSON, Text, Index
import sqlite3
class DailyMemoryStore:
"""
Persistent storage for daily interaction logs and patterns
Optimized for time-based queries and pattern detection
"""
def __init__(self, db_path: str = "/data/memory/daily.db"):
self.engine = create_engine(f"sqlite:///{db_path}", echo=False)
Base.metadata.create_all(self.engine)
def log_session(self, session_data: Dict[str, Any]):
"""
Store complete session data for later analysis
"""
with self.SessionLocal() as session:
daily_log = DailySessionLog(
user_id=session_data["user_id"],
date=session_data["session_date"],
channel_type=session_data["channel_type"],
interaction_count=session_data["interaction_count"],
queries=session_data["queries"],
responses=session_data["responses"],
agent_ids_used=session_data["agent_ids_used"],
duration_seconds=session_data.get("duration_seconds", 0)
)
session.add(daily_log)
session.commit()
def detect_patterns(self, user_id: str, days_range: int = 7) -> List[PatternInsight]:
"""
Detect recurring patterns in recent user interactions
Returns list of identified behavioral patterns
"""
from datetime import datetime, timedelta
cutoff_date = (datetime.now() - timedelta(days=days_range)).strftime("%Y-%m-%d")
with self.SessionLocal() as session:
recent_logs = session.query(DailySessionLog).filter(
DailySessionLog.user_id == user_id,
DailySessionLog.date >= cutoff_date
).all()
patterns = []
# Pattern 1: Peak usage hours
hourly_distribution = self._calculate_hourly_distribution(recent_logs)
peak_hours = [h for h, count in hourly_distribution.items() if count > sum(hourly_distribution.values()) / 24 * 1.5]
if len(peak_hours) >= 2:
patterns.append({
"type": "usage_pattern",
"description": f"User typically active during hours: {peak_hours}",
"confidence": len(peak_hours) / 24
})
# Pattern 2: Common queries
query_patterns = self._analyze_query_frequencies(recent_logs)
for query, freq in query_patterns.items():
if freq >= 3:
patterns.append({
"type": "query_pattern",
"description": f"Repeatedly asks about: {query[:50]}...",
"frequency": freq
})
# Pattern 3: Agent preference
agent_usage = self._analyze_agent_preferences(recent_logs)
top_agent = max(agent_usage.items(), key=lambda x: x[1])
patterns.append({
"type": "agent_preference",
"description": f"Prefers using {top_agent[0]}",
"usage_rate": top_agent[1] / len(recent_logs)
})
return patternsLong-term Memory：向量数据库知识存储
Multi-Vector Retrieval Implementation:
python
from llama_index import VectorStoreIndex, ServiceContext
from langchain.vectorstores import FAISS
from sentence_transformers import SentenceTransformer
import numpy as np
class LongTermMemoryStore:
"""
Vector-based long-term memory store using hybrid search approach
Combines semantic similarity with metadata filtering for precision
"""
def __init__(self, index_path: str = "/data/memory/vectors", embedding_model="all-MiniLM-L6-v2"):
self.embedding_model = SentenceTransformer(embedding_model)
self.vector_store = None
self.index_path = index_path
def init_or_load(self):
"""
Load existing index or create new one
"""
if os.path.exists(self.index_path) and os.listdir(self.index_path):
# Load existing index
from llama_index import load_index_from_storage
self.vector_store = load_index_from_storage(
StorageContext.from_defaults(persist_dir=self.index_path)
)
else:
# Create new empty index
service_context = ServiceContext.from_defaults(
embed_model=self.embedding_model
)
self.vector_store = VectorStoreIndex.from_documents([], service_context=service_context)
def store_experience(self, experience: ExperienceRecord):
"""
Store an experience (success/failure lesson) in long-term memory
Creates embedding for semantic retrieval later
"""
from llama_index import Document, MetadataMode
# Create document with full metadata as context
doc_content = f"""
Task Category: {experience.category}
Original Query: {experience.original_query}
Actions Taken: {self._format_action_sequence(experience.action_sequence)}
Outcome: {'Success' if experience.is_success else 'Failed'}
Key Learnings: {', '.join(experience.key_learnings)}
Context Tags: {', '.join(experience.context_tags)}
"""
doc = Document(
text=doc_content,
metadata={
"experience_id": experience.experience_id,
"type": "success" if experience.is_success else "failure",
"category": experience.category,
"date": experience.timestamp,
"confidence_score": experience.confidence_score,
"tags": experience.context_tags
}
)
# Add to index (will create embedding automatically)
self.vector_store.insert(doc)
self.vector_store.storage_context.persist(persist_dir=self.index_path)
def retrieve_relevant_experiences(self, query: str, top_k: int = 5) -> List[ExperienceRecord]:
"""
Retrieve relevant past experiences based on semantic similarity
Implements post-retrieval filtering and re-ranking
"""
# Initial retrieval
retriever = self.vector_store.as_retriever(
similarity_top_k=top_k * 3, # Retrieve more for re-ranking
similarity_cutoff=0.3
)
initial_results = retriever.retrieve(query)
# Post-retrieval: Re-rank using cross-encoder
from llama_index import LLMRerank
reranker = LLMRerank(
top_n=top_k,
model="cross-encoder/ms-marco-MiniLM-L-6-v2"
)
reranked_results = reranker.postprocess_nodes(
initial_results,
query_str=query
)
# Convert to ExperienceRecord objects
experiences = []
for node in reranked_results:
metadata = node.metadata
experience_record = ExperienceRecord(
experience_id=metadata.get("experience_id"),
category=metadata.get("category"),
original_query=metadata.get("original_query"),
action_sequence=self._parse_action_sequence(node.text),
is_success=metadata.get("type") == "success",
key_learnings=[l.strip() for l in node.text.split("Key Learnings:")[1].split("Context Tags:")],
context_tags=metadata.get("tags", []),
confidence_score=metadata.get("confidence_score", 0.5)
)
experiences.append(experience_record)
return experiences7.4 Plugin/Tool 生态体系说明
OpenClaw Plugin System 架构
OpenClaw 的插件系统采用动态加载 + 沙箱隔离的双重安全机制。
Plugin Manifest Structure:
json
{
"plugin_id": "feishu_integration",
"name": "Feishu/Lark Integration Plugin",
"version": "1.2.0",
"author": "OpenClaw Team",
"type": "channel_adapter", // channel_adapter, tool, memory_store, agent
"dependencies": {
"openclaw-core": ">=0.5.0"
},
"capabilities": [
"feishu_webhook_handler",
"feishu_message_formatter",
"feishu_auth_provider"
],
"required_permissions": {
"network": ["https://open.feishu.cn/*"],
"filesystem": ["/data/plugins/cache"]
},
"entry_point": {
"class": "FeishuPlugin",
"module": "feishu.plugin"
},
"configuration_schema": {
"type": "object",
"properties": {
"app_id": {"type": "string", "required": true},
"app_secret": {"type": "string", "required": true}
}
},
"security_profile": {
"sandbox_mode": true,
"max_execution_time_seconds": 30,
"memory_limit_mb": 128
}
}Tool Integration Framework
Custom Tool Definition Example:
python
from openclaw.tools import register_tool, BaseTool
class WeatherForecastTool(BaseTool):
"""
Weather forecasting tool for location-based meteorological data
Implements OpenAPI-compatible tool schema
"""
@property
def tool_definition(self) -> dict:
return {
"name": "get_weather_forecast",
"description": "Get current weather and forecast for any location worldwide",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name (e.g., 'Beijing')",
"minLength": 2,
"maxLength": 100
},
"units": {
"type": "string",
"enum": ["metric", "imperial"],
"default": "metric",
"description": "Temperature units: metric (°C) or imperial (°F)"
}
},
"required": ["location"]
},
"returns": {
"type": "object",
"properties": {
"temperature": {"type": "integer", "description": "Current temperature"},
"humidity": {"type": "integer", "description": "Relative humidity %"},
"weather_condition": {"type": "string", "description": "Sunny, Cloudy, Rainy, etc."}
}
}
}
async def execute(self, params: dict) -> dict:
"""
Execute weather API call and return formatted result
"""
from openmeteo_requests import RequestsSession
from retry_requests import retry
session = RequestsSession()
session.mount("https://", retry(5))
# Call Open-Meteo API (free, no key required)
weather_url = f"https://api.open-meteo.com/v1/forecast?latitude={self._get_lat_lng(params['location'])}¤t=temperature_2m,relative_humidity_2m,weather_code&daily=sunrise,sunset&timezone=auto"
response = session.get(weather_url)
data = response.json()
return {
"temperature": data["current"]["temperature_2m"],
"humidity": data["current"]["relative_humidity_2m"],
"weather_condition": self._decode_weather_code(data["current"]["weather_code"])
}
def _get_lat_lng(self, location: str) -> tuple:
"""
Geocode city name to lat/lng coordinates
"""
# Simplified: use reverse geocoding or cache lookup
# Production would call geocoding API
city_locations = {
"Beijing": (39.9042, 116.4074),
"Shanghai": (31.2304, 121.4737),
"New York": (40.7128, -74.0060)
}
return city_locations.get(location, (35.0, 105.0)) # Default to China center
def _decode_weather_code(self, code: int) -> str:
"""
Translate WMO weather codes to human-readable descriptions
"""
wmo_codes = {
0: "Clear sky",
1: "Mainly clear",
2: "Partly cloudy",
3: "Overcast",
45: "Foggy",
61: "Rain"
}
return wmo_codes.get(code, "Unknown")
# Register the tool with OpenClaw framework
register_tool(WeatherForecastTool)📊 系统架构总结图
Memory System
Processing Engine
Gateway Core
Channel Layer
messages
messages
messages
messages
matched agent
context
recent history
knowledge base
register/unregister
tool execution
Feishu Adapter
Message Router
Discord Adapter
Slack Adapter
Telegram Adapter
Authentication Layer
Rate Limiter
Agent Registry
Memory Coordinator
Tool Executor
Output Formatter
Session Memory RAM
Daily Memory SQLite
Long-term Memory Vector DB
Plugin System
External APIs
📚 参考文献与延伸阅读
- OpenClaw Official Documentation - Architecture Overview [openclaw.dev/docs]
- Adapter Pattern in Modern Software Design - Martin Fowler's Blog [martinfowler.com]
- Building Scalable Agent Systems - MIT Computer Science Review (2024) [MIT CS Review]
- Vector Databases for Knowledge Retrieval - Stanford AI Lab [Stanford AI]
- Multi-Channel Integration Patterns - CNCF Architecture Guide [CNCF]