开源Discord AI助手Zauq v4发布：支持MCP与有界Agent
A month ago I shared my open-source Discord AI assistant... I just released v4 with MCP, bounded agents, multi-provider tool calling and sandboxed code verification
Agent工程实践干货，有界运行时与MCP权限设计的取舍值得参考，建议收藏研究其架构解耦思路。
A month ago I shared Zauq(ذوق) here... an open-source AI assistant I was building for Discord communities.
一个月前，我在这里分享了 Zauq(ذوق)……一个我为 Discord 社区构建的开源 AI 助手。
At the time, it already had multi-model routing, persistent memory/RAG, web search, file handling, and sandboxed code execution.
当时，它已经具备多模型路由、持久化记忆/RAG、网络搜索、文件处理和沙盒代码执行功能。
Since then, I ended up doing a fairly major architectural rewrite and have now released Zauq v4.
此后，我进行了一次相当重大的架构重写，并现已发布 Zauq v4。
The main goal was to make it more genuinely agentic without turning it into an uncontrolled plan → act → reflect → repeat loop.
主要目标是使其成为更真正的智能体（agentic），同时避免将其变成不受控制的计划→行动→反思→循环重复过程。
The biggest changes are:
最大的变化包括：
- Bounded agent runtime: tool usage now runs under hard step limits, deadlines, resource budgets, and duplicate-call protection.
- MCP client support: Zauq can connect to trusted MCP servers and dynamically discover tools, with guild scopes, allowlists, namespaces, and risk classification.
- Native multi-provider support: Gemini, Claude, Qwen, DeepSeek, and DigitalOcean APIs, plus Ollama and Kaggle workers.
- Unified tool layer: native tools and MCP tools go through the same registry, schema validation, policy, timeout, and execution pipeline.
- Serper-powered web research: search, page fetching, caching, source attribution, and bounded deep research are now separated into their own retrieval subsystem.
- Automatic sandboxed code verification: generated Python/JS/Bash code can optionally be tested in Docker with off, auto, or always modes.
- Human-in-the-loop actions: write/destructive tools can require explicit approval before execution.
- Separate sandbox runner: the main backend no longer needs direct Docker privileges in the recommended deployment.
- Improved observability: tool calls, search usage, sandbox runs, MCP calls, latency, tokens, and optional cost estimates can be tracked.
- 有界智能体运行时：工具使用现在在严格的步骤限制、截止时间、资源预算和重复调用保护下运行。
- MCP 客户端支持：Zauq 可以连接到受信任的 MCP 服务器并动态发现工具，支持公会范围、白名单、命名空间和风险分类。
- 原生多提供商支持：Gemini、Claude、Qwen、DeepSeek 和 DigitalOcean API，以及 Ollama 和 Kaggle 工作节点。
- 统一工具层：原生工具和 MCP 工具都通过相同的注册表、模式验证、策略、超时和执行管道。
- Serper 驱动的网络研究：搜索、页面抓取、缓存、来源归因和有界深度研究现在被分离到它们自己的检索子系统中。
- 自动沙盒代码验证：生成的 Python/JS/Bash 代码可以选择在 Docker 中测试，支持关闭、自动或始终模式。
- 人在回路操作：写入/破坏性工具可以在执行前要求明确批准。
- 独立沙盒运行器：推荐部署中，主后端不再需要直接的 Docker 权限。
- 改进的可观测性：可以跟踪工具调用、搜索使用情况、沙盒运行、MCP 调用、延迟、令牌数和可选的成本估算。
The architecture now looks roughly like:
现在的架构大致如下：
Discord ↓ FastAPI Orchestrator ↓ Bounded Agent Runtime ↓ Tool Registry / Policy ├── Web Search ├── Sandbox ├── MCP Tools └── Other Native Tools ↓ Gemini / Claude / Qwen / DeepSeek / DO / Ollama / KaggleOne principle I tried to keep throughout the update was:
在整个更新过程中，我试图保持的一个原则是：
The model can choose a tool, but it should never be the authority on whether that tool is allowed to execute.
模型可以选择一个工具，但它不应成为该工具是否允许执行的最终权威。
So reasoning, authorization, and execution are intentionally kept separate.
因此，推理、授权和执行被有意地分开处理。
I also avoided adding Redis, Celery, Chromium, local rerankers, or always-on local LLMs to the default architecture because I still want Zauq to be practical on a relatively small VPS.
我还避免在默认架构中添加 Redis、Celery、Chromium、本地重排序器或常驻本地 LLM，因为我仍希望 Zauq 在相对较小的 VPS 上保持实用性。
The project is still fully open source under Apache 2.0.
该项目仍在 Apache 2.0 许可下完全开源。
GitHub:
GitHub：
https://github.com/Muhammad-Hassan12/Zauq
I’d especially like feedback on:
我特别希望就以下方面获得反馈：
- the MCP permission model
- the bounded-agent design
- the sandbox architecture
- provider abstraction/tool calling
- whether this still feels appropriately scoped, or if I’m overengineering it now 😅
- MCP 权限模型
- 有界智能体设计
- 沙箱架构
- 提供者抽象/工具调用
- 这是否仍然感觉范围恰当，或者我现在是否过度设计了 😅
I’m not claiming this is production-perfect. I’m mainly interested in what people who build agent systems would simplify, redesign, or remove.
我并非声称这是生产环境完美的方案。我主要感兴趣的是，那些构建智能体系统的人会如何简化、重新设计或移除其中的内容。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力