hungryCodingSir/trade-agent-skills

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Última actividad de origen registrada: 5 de marzo de 2026 a las 15:01

Catálogo de SkillsMP actualizado: 26 de octubre de 2026 a las 11:37

skills recopiladas: 8

Estrellas en GitHub: 45
Forks en GitHub: 12

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# Trade Agent Skills — Cross-Border E-Commerce Multi-Agent Assistant A production-grade multi-agent assistant built on **LangChain 1.2.6 + Deep Agents + AgentSkills** architecture, designed for cross-border e-commerce scenarios. While e-commerce serves as the reference domain, the architecture is fully extensible to other conversational AI use cases. ## Tech Stack (Java + Python) This system adopts a **Java (MCP Server) + Python (Agent Brain)** heterogeneous architecture, deeply integrated with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) specification and built on the latest LangChain 1.x design principles. It provides a **production-grade Agent Skills reference architecture** with highly modular and extensible middleware lifecycle hooks, suitable for complex agent applications and task orchestration scenarios. | Layer | Technology | Description | |---|---|---| | **Agent Framework** | LangChain 1.2.6 + Deep Agents 0.3 + LangGraph 1.0.7 | Core orchestration & multi-agent collaboration | | **LLM Engine** | Alibaba Cloud Bailian (customizable) | Swap in any LLM provider as needed | | **Backend (Python)** | FastAPI + Uvicorn + SQLAlchemy | Agent Brain API & services | | **Backend (Java)** | Spring Boot 3.4.1 + Spring AI 1.0.0 | MCP Server tool services | | **Database** | MySQL 8.0+ | Business data persistence | | **Cache / State** | Redis | Session management & ephemeral state | | **Vector Search** | Milvus (BM25 + Dense) | Hybrid retrieval for memory augmentation | | **ORM (Java)** | MyBatis-Plus 3.5.9 | Java-side data access | | **Tool Protocol** | MCP (Model Context Protocol) | Cross-language tool invocation standard (SSE) | | **Email Service** | Spring Mail (SMTP) | Automated email notifications | | **Evaluation** | agentevals + LLM-as-Judge | Automated trajectory & quality assessment | ## Architecture Overview ``` ┌───────────────────────────────────────────────┐ │ FastAPI Gateway │ └────────────────────┬──────────────────────────┘ │ ┌───────────▼────────────┐ │ Deep Agent Orchestrator│ │ Planning / Skills / │ │ FileSystem Backend │ └───────┬────────────────┘ │ On-demand dispatch ┌────────────┼────────────────────────┐ ▼ ▼ ▼ ▼ ┌────────┐ ┌─────────┐ ┌─────────┐ ┌──────────┐ │ Order │ │Logistics│ │Comms │ │Analytics │ │ Agent │ │ Agent │ │ Agent │ │ Agent │ └───┬────┘ └───┬─────┘ └───┬─────┘ └───┬──────┘ └──────────┴─────┬─────┴────────────┘ │ ┌────────▼────────┐ │ MCP Protocol │ │ (SSE Client) │ └────────┬────────┘ │ ┌───────────▼────────────┐ │ Java MCP Server │ └────────────────────────┘ ``` ## Middleware Layer Design The system implements three middlewares on top of LangChain Deep Agent's middleware mechanism, covering **memory management**, **message persistence**, and **response quality guardrails**. They are mounted on the Orchestrator in sequence and intervene at different lifecycle hooks. ``` Request Incoming │ ▼ MemoryMiddleware.before_agent ← Restore history + retrieve context PersistenceMiddleware.before_agent ← Persist user message │ ▼ [Agent Reasoning / Tool Calls] │ ▼ (on each LLM call) MemoryMiddleware.before_model ← Trigger summarization if needed * QualityGuardMiddleware.wrap_model ← Evaluate & retry low-quality responses │ ▼ PersistenceMiddleware.after_agent ← Persist AI response ``` > \* A custom summarization strategy is used instead of the built-in `SummarizationMiddleware` to allow fine-grained control over summarization timing and storage logic. ## Human-in-the-Loop: Email Confirmation Email sending requires explicit human approval, implemented via LangGraph's `interrupt()`: ``` User requests email → Agent drafts → interrupt() pauses → Frontend shows preview ↓ User: approve / reject / edit ↓ POST /resume → Command(resume=decision) → Resume execution ``` ## Key Features | Feature | Description | |---|---| | **AgentSkills** | 7 domain skills loaded on demand to reduce token overhead | | **SubAgent Delegation** | Complex tasks are automatically dispatched to specialized sub-agents | | **Planning Tool** | Built-in task planner that decomposes multi-step requests | | **FileSystem Context** | Virtual file system for managing long documents and analysis reports | | **Hybrid Memory** | MySQL persistence + Milvus vector retrieval + Redis session cache | | **MCP Tool Calls** | Connects to the Java backend via SSE protocol | | **Human-in-the-Loop** | Mandatory human confirmation before sending emails | | **4-Layer Evaluation** | Trajectory + Quality + Safety + LLM-as-Judge | ## Project Structure ``` trade-agent-brain/ ├── app/ │ ├── agents/ │ │ ├── orchestrator.py # Deep Agent orchestrator │ │ └── subagents.py # Sub-agent definitions │ ├── config/ │ │ ├── settings.py # Configuration management │ │ ├── llm_config.py # LLM configuration │ │ ├── database.py # MySQL connection │ │ └── redis_config.py # Redis connection │ ├── middleware/ │ │ ├── memory_middleware.py # Unified memory middleware │ │ ├── persistence_middleware.py # Persistence middleware │ │ └── quality_guard_middleware.py # Quality guard middleware │ ├── models/ # Data models │ ├── routers/ # API routes │ ├── services/ # Business services │ ├── tools/ # MCP tools │ └── main.py # FastAPI entry point ├── skills/ # AgentSkills directory │ ├── order-management/ │ │ └── SKILL.md │ ├── logistics-tracking/ │ │ └── SKILL.md │ ├── cart-management/ │ │ └── SKILL.md │ ├── email-notification/ │ │ └── SKILL.md │ ├── customs-clearance/ │ │ └── SKILL.md │ ├── data-analytics/ │ │ └── SKILL.md │ └── dispute-resolution/ │ └── SKILL.md ├── tests/ ├── requirements.txt ├── .env.example └── README.md trade-mcp-server/ # Java MCP Server (Maven multi-module) ├── pom.xml ├── mcp-common/ │ ├── pom.xml │ └── src/main/java/com/cbec/mcp/common/ │ ├── entity/ │ ├── enums/ │ ├── result/ │ └── util/ ├── mcp-server/ # MCP Server main module │ ├── pom.xml │ └── src/main/ │ ├── java/com/cbec/mcp/server/ │ │ ├── McpServerApplication.java │ │ ├── config/ │ │ │ └── McpConfig.java # Unified MCP tool registration │ │ ├── dto/ # Data transfer objects │ │ ├── mapper/ # MyBatis-Plus mapper interfaces │ │ ├── service/ # Domain service layer │ │ └── tool/ # MCP Tool definitions (@Tool) │ └── resources/ │ ├── application.yml │ └── mapper/ └── sql/ # Database scripts sql/ # Global SQL scripts ├── schema.sql # Table creation script └── data-demo.sql # Demo data ``` ## trade-mcp-server ### Module Overview - **mcp-common** — Shared layer containing database entities, enums, the unified response wrapper `McpResult`, and JSON utilities. Depended on by `mcp-server`. - **mcp-server** — Core service module containing MCP Tool definitions, service logic, MyBatis mappers, and the Spring Boot entry point. ### MCP Tools All tools are registered centrally via `McpConfig` using the `@Tool` annotation, and are automatically exposed on the SSE endpoint for MCP Client discovery and invocation. ### Connecting to trade-agent-brain The Python side (`trade-agent-brain`) connects to `trade-mcp-server` via MCP SSE Client. The connection URL is configured in `.env`: ```env MCP_SERVER_URL=http://127.0.0.1:8081/sse MCP_CALL_TIMEOUT=30 ``` `app/tools/__init__.py` defines a generic `call_mcp_tool()` function that establishes an SSE connection using `mcp.client.sse.sse_client` and invokes remote Java-side `@Tool` methods via `ClientSession.call_tool()`. The full call chain is: ``` Agent Reasoning → Python @tool → MCP SSE Client → Java MCP Server (SSE endpoint) → @Tool method → Service → MyBatis Mapper → MySQL → McpResult JSON response ``` ## Getting Started > 🚧 The project is still under active development. A full setup guide will be provided soon. ## License [MIT](LICENSE)

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