diff --git a/README.md b/README.md index 5423dc6..b8c6757 100644 --- a/README.md +++ b/README.md @@ -2,41 +2,45 @@ [![Go](https://img.shields.io/badge/Go-1.25.6+-00ADD8?style=flat-square&logo=go)](https://go.dev/) [![Gin](https://img.shields.io/badge/Gin-HTTP-00ACD7?style=flat-square)](https://gin-gonic.com/) +[![Google ADK Go](https://img.shields.io/badge/Google%20ADK-Go-4285F4?style=flat-square)](https://google.github.io/adk-docs/) [![Eino](https://img.shields.io/badge/Eino-Agent-111827?style=flat-square)](https://github.com/cloudwego/eino) -[![ADK Go](https://img.shields.io/badge/Google%20ADK-Go-4285F4?style=flat-square)](https://github.com/google/adk-go) [![GORM](https://img.shields.io/badge/GORM-MySQL-2D3748?style=flat-square)](https://gorm.io/) -[![Redis](https://img.shields.io/badge/Redis-Session-DC382D?style=flat-square&logo=redis)](https://redis.io/) -[![Next.js](https://img.shields.io/badge/Next.js-Frontend-000000?style=flat-square&logo=nextdotjs)](https://nextjs.org/) +[![Redis](https://img.shields.io/badge/Redis-Optional-DC382D?style=flat-square&logo=redis)](https://redis.io/) +[![Next.js](https://img.shields.io/badge/Next.js-Demo%20UI-000000?style=flat-square&logo=nextdotjs)](https://nextjs.org/) AI Agent Scaffold Go 是一个面向 Agent 应用开发的 Go 脚手架，围绕 Gin、Eino、Google ADK Go、GORM、MySQL、Redis 构建，重点提供清晰的 DDD 分层、配置驱动的 Agent 装配、多 Agent 工作流编排和 HTTP 运行时接口。仓库同时附带一个基于 Next.js 的 `frontend/` 子项目，作为对接后端 API 的示例前端，这个场景是通过drawio组件搭建的，旨在通过多轮对话来画出理想中的流程图。 -这个项目适合作为 Agent 平台、智能助手后端、工作流 Agent 服务的起点。它把模型、工具、技能、Agent、Workflow、Runner 的装配逻辑收敛在领域层，通过端口隔离基础设施实现，让核心业务代码不直接依赖具体 SDK 或框架。 - ## 目录 -- [核心特性](#核心特性) +- [核心能力](#核心能力) - [技术栈](#技术栈) - [项目结构](#项目结构) -- [架构设计](#架构设计) +- [后端架构](#后端架构) +- [启动流程](#启动流程) - [Armory 装配流程](#armory-装配流程) +- [运行时对话流程](#运行时对话流程) - [快速开始](#快速开始) - [配置说明](#配置说明) - [HTTP API](#http-api) -- [开发命令](#开发命令) -- [当前实现状态](#当前实现状态) -- [路线图](#路线图) -- [Star 趋势](#star-趋势) -- [许可证](#许可证) +- [前端启动](#前端启动) +- [开发与验证](#开发与验证) +- [联系作者](#联系作者) -## 核心特性 +## 项目讲解文档 -- **配置驱动 Agent**：通过 YAML 定义模型 API、ChatModel、工具、技能、单 Agent、Workflow 和 Runner。 -- **多 Agent 工作流**：支持 `loop`、`parallel`、`sequential` 三类工作流 Agent 组装。 -- **Armory 规则树装配**：使用 `Root -> AiApi -> ChatModel -> Agent -> AgentWorkflow -> Runner` 的显式节点链路构建运行时 Agent。 -- **DDD 六层结构**：将 API 契约、应用启动、领域模型、触发器、基础设施和通用类型分离。 -- **端口隔离基础设施**：领域层依赖本地 ports，不直接耦合 Gin、GORM、Redis、Eino、ADK Go 等实现细节。 -- **HTTP 运行时接口**：提供查询 Agent、创建会话（POST/GET）、同步对话、流式对话四类接口。 -- **本地开发资产**：包含示例配置、MySQL/Redis Docker Compose、环境变量模板和可一键启动的 Next.js 示例前端。 +- 地址：[项目文档](https://my.feishu.cn/wiki/FbISwBnZPiGx0vkxyHFc8a1enig?fromScene=spaceOverview) +- 内容：简历写法、项目亮点与难点解析等 + +## 核心能力 + +- **配置驱动 Agent**：通过 `configs/agent/*.yaml` 声明模型 API、ChatModel、MCP 工具、skills、LLM Agent、workflow Agent 和 Runner。 +- **Armory 装配链**：按 `Root -> AiAPI -> ChatModel -> Agent -> AgentWorkflow -> Runner` 的顺序装配运行时对象。 +- **多 Agent 工作流**：支持 `loop`、`parallel`、`sequential` 三类 workflow Agent，串行工作流可通过 `output-key` 把上一步结果注入下一步指令。 +- **HTTP 运行时接口**：提供 Agent 列表、创建会话、同步聊天、SSE 流式聊天接口。 +- **OpenAI-compatible 模型调用**：基础设施层直接构造 Chat Completions 请求，支持普通响应和 `stream: true`。 +- **MCP SSE 工具调用**：装配阶段注册 SSE MCP 工具，运行时在模型触发 tool call 后执行 JSON-RPC `tools/call`，再把工具结果回灌给模型。 +- **Skill 资源扫描**：支持扫描 `SKILL.md`，把 skill 元数据转换为模型可见工具。 +- **分层边界清晰**：领域层依赖本地 `ports`，避免直接耦合 Gin、GORM、Redis、模型 provider 和 ADK 实现。 ## 技术栈 @@ -44,194 +48,262 @@ AI Agent Scaffold Go 是一个面向 Agent 应用开发的 Go 脚手架，围绕 | --- | --- | | 后端语言 | Go 1.25.6+ | | HTTP 框架 | Gin | -| Agent / 模型封装 | Eino | -| Agent 编排 | Google ADK Go | -| 持久化 | GORM + MySQL | -| 缓存 / 会话 | Redis | -| 配置 | YAML + 环境变量 | -| 日志 | zap | -| 前端 | Next.js + React + TailwindCSS | +| Agent 运行与插件 | Google ADK Go | +| 模型/工具抽象依赖 | Eino 相关依赖已纳入模块；当前模型调用由本地 OpenAI-compatible adapter 完成 | +| 配置 | YAML + dotenv + 环境变量占位符 | +| 模型协议 | OpenAI-compatible Chat Completions | +| 工具协议 | MCP SSE；stdio/local 配置结构已存在 | +| 持久化适配器 | GORM + MySQL，当前默认启动路径未强制启用 | +| 缓存适配器 | go-redis，当前默认启动路径未强制启用 | +| 日志 | zap + Gin logger | +| 前端 | Next.js 16 + React 19 + Tailwind CSS + react-drawio | ## 项目结构 ```text ai-agent-scaffold-go -├── cmd/server # 服务入口，当前负责启动日志初始化 +├── cmd/server +│ └── main.go # 后端入口：加载 .env、读取配置、构建应用、启动 HTTP 服务 ├── configs -│ ├── application.yaml # 应用、服务端口、MySQL、Redis、Agent 配置入口 +│ ├── application.yaml # 应用、端口、LLM 超时、Agent 配置路径、可选 DB/Redis 设置 │ └── agent -│ ├── only-one-agent.yaml # 单 Agent 示例配置（含 MCP 与 skills） -│ ├── agent-draw-io.yaml # draw.io Agent 示例配置 -│ └── skills/ # 内置 skill 资源（battle-plan、pdf 等） +│ ├── only-one-agent.yaml # 单 Agent 示例 +│ ├── agent-draw-io.yaml # draw.io 多 Agent 工作流示例 +│ └── skills/ # skill 资源，按 SKILL.md 扫描 ├── deployments -│ └── docker-compose.yml # 本地 MySQL / Redis -├── frontend # Next.js 示例前端，调用后端 /api/v1 +│ ├── docker-compose.yml # 本地 MySQL/Redis 参考服务 +│ ├── mysql/my.cnf +│ └── redis/redis.conf +├── frontend # Next.js draw.io 示例前端 ├── internal -│ ├── api # DTO 与统一响应封装 -│ ├── app # 应用装配与配置加载边界 +│ ├── api +│ │ ├── dto # HTTP 请求/响应 DTO +│ │ └── response # code/info/data 统一响应封装 +│ ├── app +│ │ ├── bootstrap # 应用装配：配置表加载、Armory、ChatService、Gin 路由 +│ │ └── config # application.yaml 与 Agent YAML 加载/校验 │ ├── domain │ │ ├── agent -│ │ │ ├── model # Agent 配置、聊天命令、Runner 等领域模型 -│ │ │ ├── ports # 模型、工具、Agent、Runner、Registry 等领域端口 +│ │ │ ├── model # Agent 配置模型、聊天命令、Runner 接口 +│ │ │ ├── ports # 模型、工具、Agent、Runner、Registry、SessionStore 端口 │ │ │ └── service -│ │ │ ├── armory # Agent 装配领域服务与核心节点 -│ │ │ │ ├── factory # 默认装配工厂 -│ │ │ │ └── workflow # loop / parallel / sequential 工作流节点 -│ │ │ └── chat # 会话与聊天运行时服务 +│ │ │ ├── armory # Agent 装配领域服务与节点链 +│ │ │ └── chat # 会话解析与聊天运行时服务 │ │ └── shared/tree # 泛型策略树路由框架 -│ ├── infrastructure # Eino、ADK、MySQL、Redis、日志等适配器 -│ └── trigger/http # Gin HTTP 入站适配器 -├── pkg/types # 响应码与应用错误 -├── .env.example # 环境变量示例 +│ ├── infrastructure +│ │ ├── adk # Agent/Runner/插件适配 +│ │ ├── ai # OpenAI-compatible client、MCP SSE client、tool/skill 工厂 +│ │ ├── cache # Redis client adapter +│ │ ├── logging # zap logger +│ │ └── persistence # MySQL/GORM adapter +│ └── trigger/http # Gin HTTP 入站路由 +├── pkg/types # 应用错误码与 AppError +├── .env.example ├── go.mod └── README.md ``` -## 架构设计 +## 后端架构 ```mermaid flowchart LR - Client["客户端 / frontend"] --> Trigger["trigger/http
Gin 路由"] - Trigger --> ChatService["domain/service/chat
聊天服务"] - ChatService --> Registry["AgentRegistry"] - Registry --> Runner["Runner"] + Client["Client / frontend"] --> Gin["trigger/http
Gin routes"] + Gin --> ChatService["domain/agent/service/chat"] + ChatService --> Registry["AgentRegistry
in-memory by default"] + ChatService --> SessionStore["SessionStore
in-memory by default"] + Registry --> Runner["model.Runner"] + Runner --> Agent["ADK adapter Agent"] + Agent --> Model["ports.ChatModel"] + Model --> OpenAI["OpenAI-compatible
chat/completions"] + Agent --> ToolRouter["MCPToolRouter"] + ToolRouter --> MCP["MCP SSE tools"] - Config["configs/*.yaml"] --> App["internal/app
配置加载与应用装配"] - App --> Armory["domain/service/armory
Agent 装配"] + Config["configs/application.yaml
configs/agent/*.yaml"] --> Bootstrap["internal/app/bootstrap"] + Bootstrap --> Armory["domain/agent/service/armory"] Armory --> Registry - - Armory --> Ports["domain/agent/ports"] - ChatService --> Ports - Ports --> Infra["internal/infrastructure"] - Infra --> Eino["Eino"] - Infra --> ADK["Google ADK Go"] - Infra --> MySQL["MySQL / GORM"] - Infra --> Redis["Redis"] ``` -### 分层边界 +后端按端口与适配器组织： -- `internal/api`：请求/响应 DTO 与统一响应结构。 -- `internal/app`：应用启动、配置加载、服务装配边界。 -- `internal/domain`：领域模型、端口、Armory 装配、聊天运行时、策略树框架。 -- `internal/trigger`：HTTP 等入站触发器。 -- `internal/infrastructure`：数据库、缓存、AI SDK、日志等外部依赖适配。 -- `pkg/types`：跨层可复用的错误码和应用错误。 +- `internal/domain` 定义核心模型、端口、Armory 装配和聊天服务。 +- `internal/app/bootstrap` 负责把配置、领域服务、基础设施 adapter 和 Gin 路由连起来。 +- `internal/infrastructure` 实现外部依赖适配，包括模型 client、MCP client、ADK Runner、Redis、MySQL 和日志。 +- `internal/trigger/http` 只处理 HTTP 入参、出参和路由注册。 +- `pkg/types` 提供跨层使用的错误码：`0000` 成功、`0001` 未知错误、`0002` 参数错误、`0003` Agent 不存在。 -领域层保持稳定，不直接导入 Gin、GORM、Redis、Eino、ADK Go 或 provider-specific 的基础设施包。 +## 启动流程 + +```mermaid +sequenceDiagram + participant Main as cmd/server + participant Config as app/config + participant Bootstrap as app/bootstrap + participant Armory as domain/armory + participant HTTP as Gin + + Main->>Main: parse -env and -config flags + Main->>Main: load dotenv file if present + Main->>Config: LoadApplication(configs/application.yaml) + Main->>Bootstrap: Build(context, appCfg, logger) + Bootstrap->>Config: LoadAgentTablesFile(configured paths) + Bootstrap->>Armory: AcceptArmoryAgents(tables) + Armory->>Bootstrap: registered runnable Agents + Bootstrap->>HTTP: create router and register /healthz + /api/v1 + Main->>HTTP: engine.Run() +``` + +入口文件是 `cmd/server/main.go`。默认行为： + +- `-env` 默认读取 `.env`，也可通过 `APP_ENV_FILE` 指定；传空字符串可跳过 dotenv。 +- `-config` 默认读取 `configs/application.yaml`，也可通过 `APP_CONFIG` 指定。 +- dotenv 中的变量会进入进程环境，Agent YAML 里的 `${VAR}` 和 `${VAR:-default}` 会在加载时展开。 +- `configs/application.yaml` 负责声明服务地址、LLM 请求超时、Agent 配置文件路径，以及当前可选的 MySQL/Redis 设置。 +- `bootstrap.Build` 会装配 Agent、创建 `ChatService`、注册 Gin 路由并返回可运行的 `Engine`。 ## Armory 装配流程 -Armory 是项目里的 Agent 装配链路。它接收 Agent 配置表，按节点顺序构建模型、工具、Agent、Workflow 和 Runner。 +Armory 是 Agent 配置到运行时对象的装配链。每个 Agent table 都会使用新的 `DynamicContext`，按节点顺序构建和传递装配状态。 ```mermaid flowchart TD - Root["RootNode
装配入口"] --> AiApi["AiAPINode
创建模型 API"] - AiApi --> ChatModel["ChatModelNode
创建 ChatModel 并挂载工具"] - ChatModel --> Agent["AgentNode
创建单 Agent"] - Agent --> Workflow["workflow.AgentWorkflowNode
创建工作流 Agent"] - Workflow --> Runner["RunnerNode
创建并注册 Runner"] + Root["RootNode"] --> Api["AiAPINode
build ModelAPI"] + Api --> ChatModel["ChatModelNode
build MCP/skill tools
build ChatModel"] + ChatModel --> Agent["AgentNode
build LLM Agents"] + Agent --> Workflow["AgentWorkflowNode
build loop/parallel/sequential Agents"] + Workflow --> Runner["RunnerNode
build Runner and register Agent"] ``` -最新代码已经按职责拆分： +关键文件： -```text -internal/domain/agent/service/armory -├── root_node.go -├── ai_api_node.go -├── chat_model_node.go -├── agent_node.go -├── runner_node.go -├── factory/factory.go -└── workflow - ├── agent_workflow_node.go - ├── loop_node.go - ├── parallel_node.go - └── sequential_node.go +- `internal/domain/agent/service/armory/factory/factory.go`：组合完整节点链。 +- `internal/domain/agent/service/armory/context.go`：保存 ModelAPI、ChatModel、已构建 Agent、当前 workflow 进度和临时值。 +- `internal/domain/agent/service/armory/chat_model_node.go`：构建 MCP 工具、skill 工具和 ChatModel。 +- `internal/domain/agent/service/armory/workflow/*.go`：按 `loop`、`parallel`、`sequential` 构建 workflow Agent。 +- `internal/domain/agent/service/armory/runner_node.go`：根据 `runner.agent-name` 找到入口 Agent，创建 Runner 并注册到 `AgentRegistry`。 + +Workflow 行为： + +- `sequential`：按配置顺序执行子 Agent，并用子 Agent 的 `output-key` 保存中间结果，后续 Agent 指令可用 `{output_key}` 引用。 +- `loop`：当前 adapter 中按 fan-out 方式执行配置的子 Agent；配置里保留 `max-iterations` 字段。 +- `parallel`：当前 adapter 中按 fan-out 方式聚合子 Agent 输出。 + +## 运行时对话流程 + +```mermaid +sequenceDiagram + participant Client + participant Handler as trigger/http + participant Chat as chat.Service + participant Registry as AgentRegistry + participant Runner + participant Agent + participant Model as OpenAI-compatible model + participant Tool as MCPToolRouter + + Client->>Handler: POST /api/v1/chat + Handler->>Chat: HandleMessage(agentId,userId,sessionId,message) + Chat->>Registry: Get(agentId) + Chat->>Runner: Run(userId,sessionId,content) + Runner->>Agent: run(content) + Agent->>Model: chat/completions + alt model requests tool calls + Agent->>Tool: CallTool(name,args) + Tool-->>Agent: tool result + Agent->>Model: next chat/completions with tool result + end + Model-->>Agent: final content + Agent-->>Runner: output + Runner-->>Handler: []string + Handler-->>Client: {code,info,data} ``` -Workflow 支持三种编排方式： - -- `loop`：循环执行子 Agent，支持最大迭代次数配置。 -- `parallel`：并行组合多个子 Agent。 -- `sequential`：按顺序串联单 Agent 或已装配的 Workflow Agent。 +同步聊天走 `Runner.Run`，流式聊天走 `Runner.Stream`。LLM Agent 最多允许 4 轮 tool-call 循环，避免工具调用无限递归。 ## 快速开始 ### 环境要求 - Go 1.25.6+ -- Node.js 18+ 与 npm（仅在启动 `frontend/` 时需要） -- Docker，可选，用于本地 MySQL / Redis +- Node.js 18+ 和 npm，仅启动 `frontend/` 时需要 +- Docker，可选，仅在本地启动 MySQL/Redis 参考服务时需要 +- 一个 OpenAI-compatible Chat Completions 服务 +- 如果启用示例里的百度搜索 MCP，还需要可访问的 MCP SSE endpoint -### 获取代码并编译 +### 1. 安装依赖并编译 ```bash -git clone cd ai-agent-scaffold-go go mod tidy go build ./... ``` -### 启动后端服务入口 +### 2. 准备环境变量 + +复制示例文件： ```bash -go run ./cmd/server +cp .env.example .env ``` -当前 `cmd/server` 会完成日志初始化并输出启动日志。完整运行时装配、Armory 初始化、Gin 路由挂载等能力已经按包结构准备好，后续可以继续在入口层串接。 +至少替换以下变量为自己的真实值： -### 启动本地基础设施 +```dotenv +OPENAI_BASE_URL=https://api.example.com/ +OPENAI_API_KEY=replace-with-your-key +BAIDU_SEARCH_MCP_BASE_URI=http://example.com/mcp/ +BAIDU_SEARCH_MCP_SSE_ENDPOINT=sse?api_key=replace-with-your-key +``` + +`configs/agent/*.yaml` 会读取这些变量来构建模型和 MCP 工具。 + +### 3. 可选：启动本地 MySQL/Redis + +当前默认后端启动路径使用内存态 Agent registry 和 session store；MySQL/Redis 适配器已经存在，但不是默认启动的硬依赖。需要本地服务时可以使用： ```bash docker compose -f deployments/docker-compose.yml up -d ``` -默认端口： +参考端口： - MySQL：`127.0.0.1:13306` - Redis：`127.0.0.1:16379` -### 启动前端 - -仓库内置一个 Next.js 示例前端，默认调用后端 `http://localhost:8091/api/v1`。最小启动方式： +### 4. 启动后端 ```bash -cd frontend -npm install -npm run dev +go run ./cmd/server ``` -访问： +也可以显式指定文件： + +```bash +go run ./cmd/server -env .env -config configs/application.yaml +``` + +默认监听地址来自 `configs/application.yaml`： ```text -http://localhost:3000 +:8091 ``` -更多前端使用细节见 `frontend/README.md`。 +健康检查： + +```bash +curl http://localhost:8091/healthz +``` + +预期响应： + +```json +{"status":"ok"} +``` ## 配置说明 -应用主配置： +### 应用配置 -```text -configs/application.yaml -``` - -Agent 示例配置： - -```text -configs/agent/only-one-agent.yaml -configs/agent/agent-draw-io.yaml -``` - -环境变量示例： - -```text -.env.example -``` - -`configs/application.yaml` 会声明服务端口、本地数据库、Redis 和 Agent 配置路径： +`configs/application.yaml` 控制应用启动参数： ```yaml app: @@ -241,16 +313,30 @@ server: addr: ":8091" database: required: false - dsn: "root:123456@tcp(127.0.0.1:13306)/ai_agent_scaffold_go?charset=utf8mb4&parseTime=True&loc=Local" + dsn: "root:@tcp(127.0.0.1:13306)/ai-agent-scaffold-go?charset=utf8mb4&parseTime=True&loc=Local" redis: required: false addr: "127.0.0.1:16379" + password: "" + db: 0 +llm: + request-timeout: 5m agent: config-paths: - configs/agent/only-one-agent.yaml + - configs/agent/agent-draw-io.yaml ``` -Agent 配置示例（节选自 `configs/agent/only-one-agent.yaml`）： +说明： + +- `server.addr` 是 Gin 监听地址。 +- `llm.request-timeout` 使用 Go `time.ParseDuration` 格式，例如 `30s`、`5m`、`1h`。 +- `agent.config-paths` 可配置多个 Agent YAML，启动时会合并所有 tables。 +- `database` 和 `redis` 设置目前主要对应基础设施 adapter；默认 bootstrap 没有强制打开它们。 + +### Agent 配置 + +Agent 配置文件结构位于 `configs/agent/*.yaml`： ```yaml ai: @@ -265,17 +351,17 @@ ai: agent-desc: "single agent demo" module: ai-api: - base-url: "https://apis.itedus.cn" - api-key: "${OPENAI_API_KEY}" + base-url: ${OPENAI_BASE_URL} + api-key: ${OPENAI_API_KEY} completions-path: "v1/chat/completions" embeddings-path: "v1/embeddings" chat-model: - model: "gpt-4.1" + model: "gpt-5.5" tool-mcp-list: - sse: name: baidu-search - base-uri: http://appbuilder.baidu.com - sse-endpoint: /v2/ai_search/mcp/sse?api_key=${BAIDU_SEARCH_MCP_API_KEY} + base-uri: ${BAIDU_SEARCH_MCP_BASE_URI} + sse-endpoint: ${BAIDU_SEARCH_MCP_SSE_ENDPOINT} request-timeout: 500000 tool-skills-list: - type: "resource" @@ -292,25 +378,77 @@ ai: - "myLogPlugin" ``` -连接真实模型服务前，需要复制 `.env.example` 并设置真实的模型 API Key。 +启动时会校验： + +- `app-name` +- `agent.agent-id` +- `module.ai-api.base-url` +- `module.ai-api.api-key` +- `module.chat-model.model` +- `module.agents[].name` +- `module.agents[].instruction` +- `module.runner.agent-name` +- 每个 MCP tool 必须且只能声明 `local`、`sse`、`stdio` 之一 +- workflow type 必须是 `loop`、`parallel` 或 `sequential` + +默认值： + +- `ai-api.completions-path` 默认 `v1/chat/completions` +- `ai-api.embeddings-path` 默认 `v1/embeddings` +- `tool-skills-list[].type` 默认 `directory` +- `agent-workflows[].max-iterations` 默认 `3` ## HTTP API -基础路径： +非流式接口统一返回： -```text -/api/v1 +```json +{ + "code": "0000", + "info": "success", + "data": {} +} ``` -这些路由由 `internal/trigger/http/agent_handler.go` 中的 `RegisterAgentRoutes` 注册。当前服务入口还没有把 Gin 路由完整挂到 `cmd/server`，接入时可复用 `RegisterAgentRoutes(router, chatService)`。 +### GET /healthz -### 查询 Agent 配置 +健康检查。 + +```bash +curl http://localhost:8091/healthz +``` + +```json +{"status":"ok"} +``` + +### GET /api/v1/query_ai_agent_config_list + +查询已经注册的 Agent。 ```bash curl http://localhost:8091/api/v1/query_ai_agent_config_list ``` -### 创建会话 +响应示例： + +```json +{ + "code": "0000", + "info": "success", + "data": [ + { + "agentId": "100003", + "agentName": "single agent", + "agentDesc": "single agent demo" + } + ] +} +``` + +### POST /api/v1/create_session + +创建或复用某个 `userId + agentId` 的会话。 ```bash curl -X POST http://localhost:8091/api/v1/create_session \ @@ -318,31 +456,107 @@ curl -X POST http://localhost:8091/api/v1/create_session \ -d '{"agentId":"100003","userId":"u1001"}' ``` -也支持 GET： +响应示例： + +```json +{ + "code": "0000", + "info": "success", + "data": { + "sessionId": "testAgent03:u1001:1" + } +} +``` + +同等 GET 形式： ```bash curl 'http://localhost:8091/api/v1/create_session?agentId=100003&userId=u1001' ``` -### 同步对话 +### POST /api/v1/chat + +同步聊天。`sessionId` 可为空；为空时服务会自动创建或复用会话。 ```bash curl -X POST http://localhost:8091/api/v1/chat \ -H 'Content-Type: application/json' \ - -d '{"agentId":"100003","userId":"u1001","message":"帮我制定一个学习计划"}' + -d '{ + "agentId": "100003", + "userId": "u1001", + "sessionId": "", + "message": "帮我制定一个 Go Agent 学习计划" + }' ``` -### 流式对话 +响应示例： -`/chat_stream` 通过 SSE（`text/event-stream`）推送结果，每个分片以 `event: message` 形式发送，错误以 `event: error` 结束。 +```json +{ + "code": "0000", + "info": "success", + "data": { + "content": "..." + } +} +``` + +### POST /api/v1/chat_stream + +流式聊天，响应类型为 `text/event-stream`。 ```bash curl -N -X POST http://localhost:8091/api/v1/chat_stream \ -H 'Content-Type: application/json' \ - -d '{"agentId":"100003","userId":"u1001","sessionId":"session-u1001","message":"继续"}' + -d '{ + "agentId": "100003", + "userId": "u1001", + "sessionId": "", + "message": "继续细化第一周计划" + }' ``` -## 开发命令 +流式片段示例： + +```text +event: message +data: 第一周可以从... + +event: message +data: 接下来... +``` + +如果执行出错，服务会发送： + +```text +event: error +data: +``` + +## 前端启动 + +`frontend/` 是一个 Next.js draw.io 示例前端，默认调用 `http://localhost:8091/api/v1`；启动方式是 `cd frontend && npm install && npm run dev`，然后访问 `http://localhost:3000`。 + +如需改后端地址，设置： + +```bash +NEXT_PUBLIC_API_BASE_URL=http://localhost:8091/api/v1 +``` + +## 开发与验证 + +后端常用命令： + +```bash +go test ./... +go build ./... +``` + +检查 Go 文件是否需要格式化： + +```bash +test -z "$(gofmt -l .)" +``` 格式化： @@ -350,45 +564,14 @@ curl -N -X POST http://localhost:8091/api/v1/chat_stream \ gofmt -w . ``` -编译： +前端常用命令： ```bash -go build ./... +cd frontend +npm run lint +npm run build ``` -启动前端开发服务器： +## 联系作者 -```bash -cd frontend && npm run dev -``` - -## 当前实现状态 - -- DDD 包结构、领域模型、端口、策略树和 Armory 装配链路已经建立。 -- Armory 核心节点、工厂、Workflow 节点已按职责拆分。 -- ChatService、AgentRegistry、SessionStore 和 HTTP 路由边界已在代码结构中分离。 -- Eino、ADK Go、GORM、Redis、zap 等依赖已经纳入模块，并通过本地端口和基础设施包隔离。 -- `cmd/server` 已串接配置加载、Armory 装配、Gin 路由和插件链路，启动后即对外提供 `/api/v1` 接口。 -- Runner 已接入真实 OpenAI 兼容 ChatModel：`/api/v1/chat` 调 `chat/completions` 同步接口，`/api/v1/chat_stream` 走 `stream: true` SSE 推送 delta。 -- SSE MCP 客户端可用：装配阶段连接 `tool-mcp-list[].sse`，运行时模型选中工具后真正发起 JSON-RPC `tools/call` 并把结果回灌给下一轮模型请求，工具调用循环上限为 4 轮。 -- Local MCP、stdio MCP 仍返回 unsupported 错误；Skill 工具当前只向模型暴露名字，不参与执行。 -- `frontend/` 提供基于 Next.js 的 draw.io 示例前端，默认对接后端 `/api/v1`。 - -## 路线图 - -- 接入更稳定的模型 backoff / 重试与请求级超时控制。 -- 补齐 stdio MCP 客户端实现，扩展 local MCP 真实执行入口。 -- 让 Skill 工具具备运行时调用能力，并把 skill 元数据注入模型 tool schema。 -- 增加基于 MySQL 的 Agent 配置仓储。 -- 增加基于 Redis 的分布式会话存储。 -- 增加请求追踪、Token 用量、Agent Workflow 事件等可观测能力。 - -## Star 趋势 - -[![Star History Chart](https://api.star-history.com/svg?repos=peakxy/ai-agent-scaffold-go&type=Date)](https://star-history.com/#peakxy/ai-agent-scaffold-go&Date) - -## 许可证 - -## 联系方式 - -- 邮箱: 2465549609@qq.com +- 邮箱：2465549609@qq.com diff --git a/go.sum b/go.sum new file mode 100644 index 0000000..993e4b7 --- /dev/null +++ b/go.sum @@ -0,0 +1,284 @@ +cloud.google.com/go v0.123.0 h1:2NAUJwPR47q+E35uaJeYoNhuNEM9kM8SjgRgdeOJUSE= +cloud.google.com/go v0.123.0/go.mod h1:xBoMV08QcqUGuPW65Qfm1o9Y4zKZBpGS+7bImXLTAZU= +cloud.google.com/go/auth v0.20.0 h1:kXTssoVb4azsVDoUiF8KvxAqrsQcQtB53DcSgta74CA= +cloud.google.com/go/auth v0.20.0/go.mod h1:942/yi/itH1SsmpyrbnTMDgGfdy2BUqIKyd0cyYLc5Q= +cloud.google.com/go/compute/metadata v0.9.0 h1:pDUj4QMoPejqq20dK0Pg2N4yG9zIkYGdBtwLoEkH9Zs= +cloud.google.com/go/compute/metadata v0.9.0/go.mod h1:E0bWwX5wTnLPedCKqk3pJmVgCBSM6qQI1yTBdEb3C10= +filippo.io/edwards25519 v1.1.0 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