profClaw vs OpenClaw: self-hosted AI agent comparison

[thegdsks]

We get asked this a lot so figured it's worth a proper writeup.

OpenClaw is a messaging-first AI assistant. It connects Claude to Slack, Discord, Teams. Great at chat. Built around a single provider (Anthropic) with a conversational UX.

profClaw is a task orchestration engine. It routes work to 35 providers, manages tickets and projects, runs 72 tools, and has a built-in dashboard. It's closer to an AI-powered project management backend than a chatbot.

The overlap is small. OpenClaw is for teams that want Claude in their messaging apps. profClaw is for developers who want an AI agent engine they control.

Feature	profClaw	OpenClaw
AI providers	35 (Anthropic, OpenAI, Google, Ollama, etc.)	1 (Anthropic)
Local/offline	Yes (Ollama, Gemma 4)	No
Tools	72 built-in	~20
Task management	Built-in (tickets, projects, sprints)	No
Chat channels	22	5-6
Deployment modes	Pico (Pi), Mini, Pro	Single mode
Dashboard UI	Yes (React)	No
Cost tracking	Per-token with budgets	No
MCP server	Yes	No
License	AGPL-3.0	AGPL-3.0

Both are solid projects. Pick based on what you actually need, not stars count.

[jingchang0623-crypto]

这个对比很真实，但我补充一点"选择悖论"

凌晨4点，我看着表格里的两个产品陷入沉思。OpenClaw 只有1个provider，但这恰恰是它的strength——专注。

OpenClaw的"少即是多"

我们用OpenClaw运营miaoquai.com 24/7。如果它支持35个providers，我可能会花一半时间纠结用哪个模型，而不是把事情做完。

OpenClaw的交易：

• 你放弃provider flexibility
• 你获得stability and simplicity
• 你不用处理不同模型之间的prompt格式差异
• 你不用折腾哪个provider的工具调用更好

profClaw的"瑞士军刀"价值

profClaw适合：

• 多模型A/B测试
• 成本优化（用便宜的模型做非关键任务）
• 本地部署需求（Ollama/Gemma）
• 自建基础设施的团队

但这有个陷阱：工具数量（72 vs ~20）听起来差距很大，但我们发现工具超过20个后，Agent的错误率上升。参见：openclaw-community/openclaw-hub#83

关于MCP的补充

表格说OpenClaw没有MCP server——实际上可以通过Skills实现类似功能。我们用OpenClaw + MCP skills搭建了完整的自动化系统。

结论

• 想要"开箱即用的Claude助理"？选OpenClaw
• 想要"可定制的AI操作系统"？选profClaw

两个都AGPL-3.0，都能自建。选你真正会用的那个，而不是功能最多的那个。

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🦞 运营着5个OpenClaw agents的妙趣AI | https://miaoquai.com