OpenClaw的配置文件配置
来源：互联网 2026-03-15 20:06:03参考 wsl安装OpenClaw openclaw -h OpenClaw spec 阿里百练 {"meta": {"lastTouchedVersion": "2026.3.8","lastTouchedAt": "2026-03-10T09:08:52.969Z"},"wizard": {"lastRunAt": "2026-01-29T02:13:14.153Z","
参考
说到WSL环境下的OpenClaw安装，其实步骤相当清晰。首先自然是执行标准的安装流程，接着通过运行openclaw -h命令来验证安装是否成功——这个命令不仅能确认基础功能正常，还能展示出完整的帮助信息。
至于OpenClaw的具体配置规格，需要仔细研究官方文档中的spec部分。这里包含了所有关键参数和技术要求，是后续配置的基础。
长期稳定更新的攒劲资源： >>>点此立即查看<<<
阿里百练
阿里百练的配置结构值得深入探讨。从整体框架来看，其配置分为几个核心模块：
{
"meta": {
"lastTouchedVersion": "2026.3.8",
"lastTouchedAt": "2026-03-10T09:08:52.969Z"
},
"wizard": {
"lastRunAt": "2026-01-29T02:13:14.153Z",
"lastRunVersion": "2026.1.24-3",
"lastRunCommand": "onboard",
"lastRunMode": "local"
},
"auth": {
"profiles": {
"anthropic:default": {
"provider": "anthropic",
"mode": "api_key"
}
}
},
"models": {
"mode": "merge",
"providers": {
"bailian": {
"baseUrl": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"apiKey": "你的apikey",
"api": "openai-completions",
"models": [
{
"id": "qwen3.5-flash",
"name": "qwen3.5-flash",
"reasoning": false,
"input": ["text", "image"],
"contextWindow": 1000000,
"maxTokens": 65536
},
{
"id": "qwen3-coder-next",
"name": "qwen3-coder-next",
"reasoning": false,
"input": ["text"],
"contextWindow": 262144,
"maxTokens": 65536
}
]
}
}
},
"agents": {
"defaults": {
"model": {
"primary": "bailian/qwen3.5-flash"
},
"models": {
"bailian/qwen3.5-flash": {},
"bailian/qwen3-coder-next": {}
},
"workspace": "/home/minglie/.openclaw/workspace",
"contextPruning": {
"mode": "cache-ttl",
"ttl": "1h"
},
"compaction": {
"mode": "safeguard"
},
"heartbeat": {
"every": "30m"
},
"maxConcurrent": 4,
"subagents": {
"maxConcurrent": 8
}
}
},
"messages": {
"ackReactionScope": "group-mentions"
},
"commands": {
"native": "auto",
"nativeSkills": "auto",
"restart": true,
"ownerDisplay": "raw"
},
"gateway": {
"port": 18789,
"mode": "local",
"bind": "loopback",
"auth": {
"mode": "token",
"token": "437e690bda93b04c41d195589590442021be6359447c57dc"
},
"tailscale": {
"mode": "off",
"resetOnExit": false
}
},
"skills": {
"install": {
"nodeManager": "npm"
}
}
}仔细分析这个配置架构，有几个关键点需要特别注意。模型配置采用了merge模式，这意味着可以灵活集成多个模型提供商。百练提供商配置了两个主力模型：qwen3.5-flash支持文本和图像输入，拥有惊人的100万上下文窗口；而qwen3-coder-next专攻文本处理，26万的上下文窗口同样不容小觑。
代理配置方面，默认使用qwen3.5-flash作为主模型，工作空间指向用户目录，上下文修剪采用缓存TTL机制，这种设计既保证了性能又确保了资源的合理利用。
longcat
LongCat的配置方案则展现了另一种技术思路。其配置结构虽然与阿里百练有相似之处，但在细节处理上独具特色：
{
"meta": {
"lastTouchedVersion": "2026.3.8",
"lastTouchedAt": "2026-03-10T09:08:52.969Z"
},
"wizard": {
"lastRunAt": "2026-01-29T02:13:14.153Z",
"lastRunVersion": "2026.1.24-3",
"lastRunCommand": "onboard",
"lastRunMode": "local"
},
"auth": {
"profiles": {
"anthropic:default": {
"provider": "anthropic",
"mode": "api_key"
}
}
},
"models": {
"mode": "merge",
"providers": {
"longCat": {
"baseUrl": "https://api.longcat.chat/openai",
"apiKey": "你的apikey",
"auth": "api-key",
"api": "openai-completions",
"authHeader": true,
"models": [
{
"id": "LongCat-Flash-Thinking-2601",
"name": "LongCat-Flash-Thinking-2601",
"reasoning": false,
"input": ["text"],
"contextWindow": 200000,
"maxTokens": 8192,
"compat": {
"maxTokensField": "max_tokens"
}
}
]
}
}
},
"agents": {
"defaults": {
"model": {
"primary": "longCat/LongCat-Flash-Thinking-2601"
},
"workspace": "/home/minglie/.openclaw/workspace",
"contextPruning": {
"mode": "cache-ttl",
"ttl": "1h"
},
"compaction": {
"mode": "safeguard"
},
"heartbeat": {
"every": "30m"
},
"maxConcurrent": 4,
"subagents": {
"maxConcurrent": 8
}
}
},
"messages": {
"ackReactionScope": "group-mentions"
},
"commands": {
"native": "auto",
"nativeSkills": "auto",
"restart": true,
"ownerDisplay": "raw"
},
"gateway": {
"port": 18789,
"mode": "local",
"bind": "loopback",
"auth": {
"mode": "token",
"token": "437e690bda93b04c41d195589590442021be6359447c57dc"
},
"tailscale": {
"mode": "off",
"resetOnExit": false
}
},
"skills": {
"install": {
"nodeManager": "npm"
}
}
}LongCat配置的核心在于其专用的Flash-Thinking模型。这个模型专注于文本处理，提供20万的上下文窗口和8192的最大输出标记数。特别值得注意的是兼容性配置部分，这里明确指定了max_tokens字段的映射关系，这种细致的设计避免了潜在的接口兼容性问题。
对比两种配置方案，虽然基础架构相似，但在模型选择、API端点配置和具体参数设置上存在明显差异。这种差异恰恰反映了不同服务提供商的技术特色和优化方向。话说回来，无论选择哪种配置，关键在于根据实际需求进行针对性调整，这样才能充分发挥OpenClaw框架的潜力。
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