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@@ -127,6 +127,7 @@ CyberStrikeAI is an **AI-native security testing platform** built in Go. It inte
- 🛡️ Vulnerability management with CRUD operations, severity tracking, status workflow, and statistics
- 📋 Batch task management: create task queues, add multiple tasks, and execute them sequentially
- 🎭 Role-based testing: predefined security testing roles (Penetration Testing, CTF, Web App Scanning, etc.) with custom prompts and tool restrictions
- 🔀 **Graph orchestration**: visual workflow editor (Start / Agent / Tool / Condition / HITL / Output) with `{{previous.output}}` and `{{outputs.variable_name}}` for inter-node data passing; bind a graph to a role for automatic execution on chat. See [Graph orchestration guide](docs/workflow-graph_en.md)
- 🧩 **Agent orchestration (CloudWeGo Eino)**: **single-agent** via **`/api/eino-agent/stream`** (Eino ADK `ChatModelAgent`); **multi-agent** via **`/api/multi-agent/stream`** with **`deep`** (coordinator + `task` sub-agents), **`plan_execute`**, or **`supervisor`** (`orchestration` in the request body). ADK **summarization** compresses long contexts; pre-compaction **transcripts** land at `data/conversation_artifacts/<conversation-id>/summarization/transcript.txt` (full user/assistant/tool turns; static system omitted). Markdown under `agents/`: `orchestrator.md`, `orchestrator-plan-execute.md`, `orchestrator-supervisor.md`, plus sub-agent `*.md` (see [Multi-agent doc](docs/MULTI_AGENT_EINO.md))
- 🖼️ **Vision analysis (`analyze_image`)**: separate VL model (e.g. `qwen-vl-max`) via MCP for local screenshots, captchas, and UI; image bytes stay out of agent history (text summaries only). Configure `vision` in `config.yaml`; see [docs/VISION.md](docs/VISION.md)
- 🎯 **Skills (refactored for Eino)**: packs under `skills_dir` follow **Agent Skills** layout (`SKILL.md` + optional files); **multi-agent** sessions use the official Eino ADK **`skill`** tool for **progressive disclosure** (load by name), with optional **host filesystem / shell** via `multi_agent.eino_skills`; optional **`eino_middleware`** adds patchtoolcalls, tool_search, **plantask** (`TaskCreate` / `TaskList` boards under `skills_dir/.eino/plantask/`), reduction, file **checkpoints** (`checkpoint_dir`), ChatModel **retries**, session **output key**, and Deep tuning—20+ sample domains (SQLi, XSS, API security, …) ship under `skills/`
@@ -255,6 +256,7 @@ Requirements / tips:
- **Conversation testing** Natural-language prompts trigger toolchains with streaming SSE output.
- **Single vs multi-agent** Chat UI switches between **Eino single-agent** (`/api/eino-agent/stream`) and **multi-agent** (`/api/multi-agent/stream` with `orchestration`: `deep` | `plan_execute` | `supervisor`). Multi mode requires `multi_agent.enabled: true`. MCP tools are bridged the same way for both paths.
- **Role-based testing** Select from predefined security testing roles (Penetration Testing, CTF, Web App Scanning, API Security Testing, etc.) to customize AI behavior and tool availability. Each role applies custom system prompts and can restrict available tools for focused testing scenarios.
- **Graph orchestration** Design flows on the **Graph Orchestration** page (drag nodes, connect edges, save); bind `workflow_id` on a role to run the graph on chat (Agent, MCP tools, condition branches). Use `{{outputs.variable_name}}` to pass data across non-adjacent nodes. See [Graph orchestration guide](docs/workflow-graph_en.md).
- **Tool monitor** Inspect running jobs, execution logs, and large-result attachments.
- **History & audit** Every conversation and tool invocation is stored in SQLite with replay.
- **Conversation groups** Organize conversations into groups, pin important groups, rename or delete groups via context menu.
@@ -630,6 +632,7 @@ enabled: true
## Related documentation
- [Multi-agent mode (Eino)](docs/MULTI_AGENT_EINO.md): **Deep**, **Plan-Execute**, **Supervisor**, `agents/*.md`, `eino_skills` / `eino_middleware`, APIs, and chat/stream behavior.
- [Graph orchestration guide](docs/workflow-graph_en.md): visual workflow design, node configuration, `previous` / `outputs` variable passing, and role binding.
- [Robot / Chatbot guide (DingTalk & Lark)](docs/robot_en.md): Full setup, commands, and troubleshooting for using CyberStrikeAI from DingTalk or Lark on your phone. **Follow this doc to avoid common pitfalls.**
## Project Layout
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@@ -126,6 +126,7 @@ CyberStrikeAI 是一款 **AI 原生安全测试平台**,基于 Go 构建,集
- 🛡️ 漏洞管理功能:完整的漏洞 CRUD 操作,支持严重程度分级、状态流转、按对话/严重程度/状态过滤,以及统计看板
- 📋 批量任务管理:创建任务队列,批量添加任务,依次顺序执行,支持任务编辑与状态跟踪
- 🎭 角色化测试:预设安全测试角色(渗透测试、CTF、Web 应用扫描等),支持自定义提示词和工具限制
- 🔀 **图编排**:可视化流程编排(开始 / Agent / 工具 / 条件 / 审批 / 输出),节点间用 `{{previous.output}}``{{outputs.变量名}}` 传参;绑定角色后对话自动按图执行。详见 [图编排使用说明](docs/workflow-graph.md)
- 🧩 **Agent 编排(CloudWeGo Eino****单代理** `POST /api/eino-agent/stream`Eino ADK);**多代理** `POST /api/multi-agent/stream``orchestration`**`deep`** / **`plan_execute`** / **`supervisor`**。ADK **Summarization** 在上下文过长时压缩历史;压缩前将可恢复 **转录** 写入 `data/conversation_artifacts/<会话ID>/summarization/transcript.txt`(保留完整 user/assistant/tool 轮次,省略静态 system)。`agents/` 下主代理与子代理 Markdown 见 [多代理说明](docs/MULTI_AGENT_EINO.md)
- 🖼️ **视觉分析(`analyze_image`**:独立 Vision 模型(如 `qwen-vl-max`),MCP 工具分析本地截图/验证码/UI;图片仅在单次 VL 调用中出现,对话上下文只保留文字摘要。配置见 `config.yaml``vision` 与 [视觉分析说明](docs/VISION.md)
- 🎯 **Skills(面向 Eino 重构)**:技能包放在 **`skills_dir`**,遵循 **Agent Skills** 目录规范(`SKILL.md` + 可选文件);**多代理** 下通过 Eino 官方 **`skill`** 工具 **渐进式披露**(按 name 加载)。**`multi_agent.eino_skills`** 控制是否启用、本机文件/Shell 工具、工具名覆盖;**`eino_middleware`** 可选 patch、tool_search、**plantask**`TaskCreate` / `TaskList` 任务板,落在 `skills_dir/.eino/plantask/`)、reduction、文件型 **checkpoint**`checkpoint_dir`)、ChatModel **重试**、会话 **输出键** 及 Deep 调参。20+ 领域示例仍可绑定角色
@@ -253,6 +254,7 @@ go build -o cyberstrike-ai cmd/server/main.go
- **对话测试**:自然语言触发多步工具编排,SSE 实时输出。
- **单代理 / 多代理**:聊天可选 **Eino 单代理**`/api/eino-agent/stream`)与 **多代理**`/api/multi-agent/stream` + `orchestration`)。多代理需 `multi_agent.enabled: true`。MCP 工具桥接一致。
- **角色化测试**:从预设的安全测试角色(渗透测试、CTF、Web 应用扫描、API 安全测试等)中选择,自定义 AI 行为和可用工具。每个角色可应用自定义系统提示词,并可限制可用工具列表,实现聚焦的测试场景。
- **图编排**:在 **图编排** 页拖拽节点、连线并保存流程;在角色中绑定 `workflow_id` 后,该角色对话将按图执行(Agent、MCP 工具、条件分支等)。跨节点传参优先用 `{{outputs.变量名}}`。详见 [图编排使用说明](docs/workflow-graph.md)。
- **工具监控**:查看任务队列、执行日志、大文件附件。
- **会话历史**:所有对话与工具调用保存在 SQLite,可随时重放。
- **对话分组**:将对话按项目或主题组织到不同分组,支持置顶、重命名、删除等操作,所有数据持久化存储。
@@ -628,6 +630,7 @@ enabled: true
## 相关文档
- [多代理模式(Eino](docs/MULTI_AGENT_EINO.md)**Deep**、**Plan-Execute**、**Supervisor**、`agents/*.md`、`eino_skills` / `eino_middleware`、接口与流式说明。
- [图编排使用说明](docs/workflow-graph.md):可视化流程搭建、节点配置、`previous` / `outputs` 变量传参与角色绑定。
- [机器人使用说明(钉钉 / 飞书)](docs/robot.md):在手机端通过钉钉、飞书与 CyberStrikeAI 对话的完整配置步骤、命令与排查说明,**建议按该文档操作以避免走弯路**。
## 项目结构
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# CyberStrikeAI 图编排使用说明
[English](workflow-graph_en.md)
本文档说明 **图编排(Graph Orchestration** 的完整使用方式:如何在画布上搭建流程、配置各类型节点、在节点之间传递数据,以及如何将流程绑定到角色并自动运行。
---
## 一、在哪里使用图编排
1. 登录 CyberStrikeAI Web 端
2. 左侧导航进入 **图编排**
3. 在左侧列表选择已有流程,或新建流程
4. 在中央画布拖拽、连线、配置节点
5. 填写流程 **ID**、**名称**、**描述** 后点击 **保存**
保存后的流程可在 **角色管理** 中绑定到某个角色。绑定后,用户与该角色对话时会按流程图自动执行(`workflow_policy: auto`)。
---
## 二、画布基本操作
| 操作 | 说明 |
|------|------|
| 添加节点 | 点击画布上方节点类型按钮(开始、工具、Agent、条件、审批、输出、结束) |
| 连线 | 点击 **连线**,依次点击源节点和目标节点;再次点击 **连线** 退出连线模式 |
| 选中元素 | 单击节点或连线,右侧显示 **节点属性** |
| 删除选中 | 点击 **删除选中** 删除当前节点或连线 |
| 自动布局 | 点击 **自动布局** 整理节点位置 |
| 删除流程 | 点击 **删除** 删除整个流程定义 |
**建议:** 每个流程至少包含 **1 个开始节点****1 个输出节点**;开始节点不应有入边,输出节点不应有出边。
---
## 三、执行模型(先理解再配置)
图编排按 **有向图** 执行,引擎从 **开始** 节点出发,沿连线依次运行下游节点。
每次运行会维护一份内部状态,模板变量 `{{...}}` 从这里取值:
| 内部状态 | 模板前缀 | 含义 |
|----------|----------|------|
| `inputs` | `{{inputs.xxx}}` | 流程启动时的输入(用户消息、会话 ID 等) |
| `lastOutput` | `{{previous.xxx}}` | **上一个刚执行完** 的节点的输出 |
| `outputs` | `{{outputs.xxx}}` | 全局 **命名变量池**(由节点的「输出变量名」写入) |
| `nodeOutputs` | `{{节点ID.xxx}}` | 指定节点 ID 的完整输出对象 |
### 3.1 `previous` 是什么?
`{{previous.output}}` 表示 **紧邻的上一个执行节点**`output` 字段。
- 每执行完一个节点,引擎都会更新 `lastOutput`
- **不是**「画布上画线的上游」,而是 **实际执行顺序上的上一步**
示例:
```text
开始 → Agent A → Agent B
```
Agent B 的 `{{previous.output}}` = Agent A 的输出。
但若中间有条件节点:
```text
开始 → Agent A → 条件 → Agent B
```
Agent B 的 `{{previous.output}}` = **条件节点** 的输出(`true` / `false`),**不是** Agent A 的结果。
### 3.2 `outputs` 是什么?
`outputs` 是引擎在运行过程中维护的 **命名变量注册表**
当 Agent、工具、输出 等节点配置了 **输出变量名**(字段 `output_key`)后,节点执行成功会把结果写入:
```text
outputs["你填的变量名"] = 节点输出内容
```
之后 **任意下游节点** 都可以通过 `{{outputs.变量名}}` 引用,不要求两个节点直接相连。
示例:
- Agent A 的 **输出变量名**`agent_result1`
- Agent B 的 **输入来源**`{{outputs.agent_result1}}`
即使 A 和 B 之间隔着条件节点,B 仍能拿到 A 的输出。
### 3.3 什么时候用 `previous`,什么时候用 `outputs`
| 场景 | 推荐写法 |
|------|----------|
| 两个节点 **直连**,只取上一步结果 | `{{previous.output}}` |
| 中间有其他节点(条件、工具、审批等) | `{{outputs.变量名}}` |
| 需要引用 **更早** 的某个节点结果 | `{{outputs.变量名}}``{{节点ID.output}}` |
| 条件判断要基于某 Agent 的输出 | `{{outputs.变量名}} != ""` |
| 读取用户最初输入 | `{{inputs.message}}` |
**记忆口诀:**
- `previous` = 上一步(链式、紧邻)
- `outputs` = 按名字取(跨节点、可回溯)
---
## 四、模板语法
### 4.1 基本格式
```text
{{变量路径}}
```
支持字母、数字、下划线、点、连字符,例如:
```text
{{previous.output}}
{{outputs.agent_result1}}
{{inputs.message}}
{{inputs.conversationId}}
{{previous.matched}}
{{node-abc123.output}}
```
### 4.2 可用路径一览
| 路径 | 说明 |
|------|------|
| `{{inputs.message}}` | 用户消息(开始节点输入) |
| `{{inputs.conversationId}}` | 会话 ID |
| `{{inputs.projectId}}` | 项目 ID |
| `{{previous.output}}` | 上一节点主输出 |
| `{{previous.matched}}` | 上一条件节点的匹配结果(`true` / `false` |
| `{{outputs.变量名}}` | 某节点注册过的命名输出 |
| `{{节点ID.output}}` | 指定节点 ID 的 `output` 字段 |
### 4.3 条件表达式
条件节点和连线条件支持简单比较:
```text
{{outputs.agent_result1}} != ""
{{previous.output}} == "ok"
{{outputs.count}} == "100"
```
规则:
- 使用 `==``!=` 做字符串比较(两侧会自动去掉首尾空格和引号)
- 无比较符时,非空且不为 `false` / `0` / `null` 视为真
---
## 五、节点类型与配置
### 5.1 开始(start
流程入口,将用户输入注入 `inputs`
| 字段 | 说明 | 默认值 |
|------|------|--------|
| 输入变量 | 逗号分隔的输入键名 | `message, conversationId, projectId` |
开始节点输出包含:`output``message``conversationId``projectId`
### 5.2 Agentagent
调用大模型 Agent 处理任务,支持多种运行模式。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| Agent 模式 | `eino_single` / `deep` / `plan_execute` / `supervisor` | `eino_single` |
| 输入来源 | 上游数据的模板表达式 | `{{previous.output}}` |
| 节点指令 | 本节点要完成的任务描述 | 空 |
| 输出变量名 | 写入 `outputs` 的键名 | `agent_result` |
**消息拼装规则:**
- 仅填 **节点指令**:直接把指令发给 Agent
- 仅填 **输入来源**:生成「请基于上游节点输出继续处理:…」
- 两者都填:合并为「上游输入 + 节点指令」
Agent 节点执行后:
- `previous.output` 更新为本节点响应文本
- 若配置了 **输出变量名**,同时写入 `outputs[输出变量名]`
### 5.3 工具(tool
调用已启用的 MCP 工具。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| MCP 工具 | 工具名称(必填) | — |
| 参数模板 | JSON,支持 `{{...}}` 模板 | `{}` |
| 超时秒数 | 可选 | 空 |
示例参数模板:
```json
{"target": "{{inputs.message}}", "port": "443"}
```
若配置了 **输出变量名**,工具返回结果会写入 `outputs`
### 5.4 条件(condition
根据表达式计算分支,输出 `matched``true` / `false`)。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| 条件表达式 | 支持 `{{...}}``==` / `!=` | `{{previous.output}} != ""` |
**分支规则:**
- 从条件节点连出的 **第一条线** 默认为 **「是」** 分支(`matched == true`
- **第二条线** 默认为 **「否」** 分支(`matched == false`
- 连线标签可写 `是` / `否`(或 `yes` / `no``true` / `false`)辅助识别
- 第三条及以后的出边需在 **连线条件** 中自定义表达式
连线条件示例(选中连线后在右侧配置):
```text
{{previous.matched}} == "true"
{{previous.matched}} == "false"
```
### 5.5 审批(hitl
人工确认检查点(当前为记录模式,自动标记 `approved: true` 并继续)。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| 审批提示 | 支持模板 | `请审批该步骤是否继续执行` |
| 审批方 | `human` / `audit_agent` | `human` |
### 5.6 输出(output
将流程最终结果写入 `outputs`,供结束摘要和对话展示使用。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| 输出变量名 | 必填,最终结果的键名 | `result` |
| 变量来源 | 模板表达式,决定写入的值 | `{{previous.output}}` |
**注意:** 输出节点是流程的「出口」,不应再有出边。
### 5.7 结束(end
可选节点,用于生成结束摘要模板(角色绑定流程中较少单独使用)。
| 字段 | 说明 | 默认值 |
|------|------|--------|
| 结束摘要模板 | 支持 `{{outputs.xxx}}` | `{{outputs.result}}` |
---
## 六、连线配置
选中 **连线** 后,右侧可配置 **连线条件**
| 场景 | 示例 |
|------|------|
| 普通节点后的过滤 | `{{previous.output}} == "ok"` |
| 条件节点「是」分支 | `{{previous.matched}} == "true"` |
| 条件节点「否」分支 | `{{previous.matched}} == "false"` |
若不填连线条件:
- 非条件节点:连线始终放行
- 条件节点:按出边顺序自动分配是/否分支
---
## 七、完整示例:跨条件节点传递 Agent 输出
### 7.1 流程结构
```text
开始 → Agent(生成初始值)→ 条件 → Agent(加工)→ 输出
↘ 否 → 输出
```
### 7.2 节点配置
**Agent 1(第一个 Agent**
| 字段 | 值 |
|------|-----|
| 节点指令 | 只输出 `123333333` |
| 输出变量名 | `agent_result1` |
**条件**
| 字段 | 值 |
|------|-----|
| 条件表达式 | `{{outputs.agent_result1}} != ""` |
**Agent 2(第二个 Agent**
| 字段 | 值 |
|------|-----|
| 输入来源 | `{{outputs.agent_result1}}` |
| 节点指令 | 在输入基础上加 100,然后输出 |
| 输出变量名 | `agent_result` |
**输出**
| 字段 | 值 |
|------|-----|
| 输出变量名 | `result` |
| 变量来源 | `{{outputs.agent_result}}` |
### 7.3 常见错误
| 错误配置 | 原因 |
|----------|------|
| Agent 2 输入来源写 `{{previous.output}}` | `previous` 指向条件节点,得到的是 `true`/`false`,不是 Agent 1 的文本 |
| 未给 Agent 1 填输出变量名 | `outputs.agent_result1` 不存在,下游取到空值 |
| 条件表达式写 `{{previous.output}}` | 判断的是开始节点或上一节点的输出,而非 Agent 1 的命名变量 |
---
## 八、绑定角色并运行
### 8.1 在角色管理中绑定
1. 进入 **角色管理**,编辑或新建角色
2. 选择 **工作流 / 图编排** 绑定的流程 ID
3. 策略设为 `auto`(默认:有 `workflow_id` 时自动执行)
4. 保存角色
也可在角色 YAML 中直接配置:
```yaml
name: 工作流测试
workflow_id: "1233"
workflow_version: latest
workflow_policy: auto
```
### 8.2 运行效果
用户选择该角色并发送消息后:
1. 引擎加载对应 `graph_json` 并按图执行
2. 对话页可看到 `workflow_start``workflow_node_start`、Agent 推理等进度事件
3. 流程结束后返回摘要,列出 `outputs` 中所有命名输出
若未配置输出节点或条件未命中,`outputs` 可能为空,摘要会提示检查输出节点与分支。
---
## 九、保存前校验规则
保存时系统会自动检查:
| 规则 | 说明 |
|------|------|
| 必须有开始节点 | 至少 1 个 `start` |
| 必须有输出节点 | 至少 1 个 `output`,且填写输出变量名 |
| 连线合法 | 源/目标节点存在,不能自环 |
| 开始节点无入边 | 开始节点不能被指向 |
| 输出节点无出边 | 输出节点后不应再连线 |
| 工具节点 | 必须选择 MCP 工具 |
| 条件节点 | 必须填写表达式;建议 1~2 条出边(是/否) |
---
## 十、排错指南
| 现象 | 可能原因 | 处理建议 |
|------|----------|----------|
| 下游拿到空值 | 上游未配置输出变量名 | 给上游 Agent/工具填 **输出变量名**,下游用 `{{outputs.xxx}}` |
| 下游拿到 `true`/`false` | 误用 `{{previous.output}}`,上一步是条件节点 | 改用 `{{outputs.xxx}}` |
| 条件总走「否」 | 表达式与真实输出格式不一致 | 检查 Agent 输出是否带引号、换行;用 `!= ""` 先验证 |
| 流程无最终输出 | 未命中输出节点所在分支 | 检查条件分支连线;确保至少一条路径到达 **输出** 节点 |
| 角色对话未跑流程 | 角色未绑定或未启用 | 确认 `workflow_id``workflow_policy: auto`、流程 `enabled: true` |
| 工具节点失败 | 参数 JSON 不合法或工具未启用 | 检查参数模板;在 MCP 中启用对应工具 |
---
## 十一、最佳实践
1. **命名规范**:为每个需要被引用的节点设置有意义的输出变量名,如 `scan_result``parsed_targets`,避免都叫 `agent_result`
2. **跨节点传参优先用 `outputs`**:只要中间可能插入条件、工具、审批节点,就应用命名变量。
3. **`previous` 仅用于直连**:A → B 且无中间节点时,`{{previous.output}}` 最简洁。
4. **条件判断引用源数据**:判断 Agent 输出时用 `{{outputs.xxx}}`,不要用 `{{previous.output}}`(除非条件紧跟在目标 Agent 之后)。
5. **每条路径都要有出口**:确保「是」「否」分支最终都能到达 **输出** 节点(或你期望的终点)。
6. **保存前跑一遍**:用简单指令(如固定字符串输出)验证数据传递,再替换为真实业务逻辑。
---
## 十二、相关代码位置(开发者参考)
| 模块 | 路径 |
|------|------|
| 执行引擎 | `internal/workflow/runner.go` |
| 画布前端 | `web/static/js/workflows.js` |
| 流程 API | `internal/handler/workflow.go` |
| 角色绑定 | `internal/config/config.go``workflow_id` 字段) |
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# CyberStrikeAI Graph Orchestration Guide
[中文](workflow-graph.md)
This document explains how to use **Graph Orchestration**: building workflows on the canvas, configuring node types, passing data between nodes, and binding a graph to a role for automatic execution.
---
## 1. Where to find Graph Orchestration
1. Log in to the CyberStrikeAI web UI.
2. Open **Graph Orchestration** in the left sidebar.
3. Select an existing workflow from the list, or create a new one.
4. Drag nodes, draw edges, and configure properties on the canvas.
5. Fill in **ID**, **Name**, and **Description**, then click **Save**.
Saved workflows can be bound to a role under **Role Management**. When `workflow_policy` is `auto`, chatting with that role runs the bound graph automatically.
---
## 2. Canvas basics
| Action | Description |
|--------|-------------|
| Add node | Click a node type button above the canvas (Start, Tool, Agent, Condition, HITL, Output, End) |
| Connect | Click **Connect**, then click source and target nodes; click **Connect** again to exit connect mode |
| Select | Click a node or edge; properties appear in the right panel |
| Delete selected | Remove the current node or edge |
| Auto layout | Rearrange node positions |
| Delete workflow | Remove the entire workflow definition |
**Requirements:** Every workflow needs at least **one Start node** and **one Output node**. Start nodes must not have incoming edges; Output nodes must not have outgoing edges.
---
## 3. Execution model (read this before configuring)
The engine executes the workflow as a **directed graph**, starting from the **Start** node and following edges to downstream nodes.
During a run, the engine keeps internal state. Template expressions `{{...}}` read from that state:
| Internal state | Template prefix | Meaning |
|----------------|-----------------|---------|
| `inputs` | `{{inputs.xxx}}` | Workflow inputs at start (user message, conversation ID, etc.) |
| `lastOutput` | `{{previous.xxx}}` | Output of the **most recently executed** node |
| `outputs` | `{{outputs.xxx}}` | Global **named variable pool** (written by nodes with an output key) |
| `nodeOutputs` | `{{nodeId.xxx}}` | Full output object of a specific node ID |
### 3.1 What is `previous`?
`{{previous.output}}` is the `output` field of the **immediately preceding executed node**.
- After every node finishes, the engine updates `lastOutput`.
- It is **not** “the node drawn upstream on the canvas”; it is **the previous step in actual execution order**.
Example:
```text
Start → Agent A → Agent B
```
For Agent B, `{{previous.output}}` = Agent As output.
With a condition in between:
```text
Start → Agent A → Condition → Agent B
```
For Agent B, `{{previous.output}}` = the **condition node** output (`true` / `false`), **not** Agent As result.
### 3.2 What is `outputs`?
`outputs` is a **named variable registry** maintained by the engine during execution.
When an Agent, Tool, or Output node sets an **Output variable name** (`output_key`), the result is stored as:
```text
outputs["your_variable_name"] = node_output
```
Any downstream node can then reference it via `{{outputs.variable_name}}`, even if other nodes sit in between.
Example:
- Agent A **Output variable name**: `agent_result1`
- Agent B **Input source**: `{{outputs.agent_result1}}`
Agent B still receives Agent As output even when a condition node lies between them.
### 3.3 When to use `previous` vs `outputs`
| Scenario | Recommended |
|----------|-------------|
| Two nodes are **directly connected**; you only need the last step | `{{previous.output}}` |
| Other nodes sit in between (condition, tool, HITL, etc.) | `{{outputs.variable_name}}` |
| Reference output from an **earlier** node | `{{outputs.variable_name}}` or `{{nodeId.output}}` |
| Condition should test an Agents output | `{{outputs.variable_name}} != ""` |
| Read the original user input | `{{inputs.message}}` |
**Rule of thumb:**
- `previous` = last step (chained, adjacent)
- `outputs` = by name (cross-node, look back)
---
## 4. Template syntax
### 4.1 Basic format
```text
{{path.to.value}}
```
Allowed characters in paths: letters, digits, underscore, dot, hyphen. Examples:
```text
{{previous.output}}
{{outputs.agent_result1}}
{{inputs.message}}
{{inputs.conversationId}}
{{previous.matched}}
{{node-abc123.output}}
```
### 4.2 Available paths
| Path | Description |
|------|-------------|
| `{{inputs.message}}` | User message (Start node input) |
| `{{inputs.conversationId}}` | Conversation ID |
| `{{inputs.projectId}}` | Project ID |
| `{{previous.output}}` | Primary output of the previous node |
| `{{previous.matched}}` | Match result of the previous condition node (`true` / `false`) |
| `{{outputs.variable_name}}` | Named output registered by a node |
| `{{nodeId.output}}` | `output` field of the node with that ID |
### 4.3 Condition expressions
Condition nodes and edge conditions support simple comparisons:
```text
{{outputs.agent_result1}} != ""
{{previous.output}} == "ok"
{{outputs.count}} == "100"
```
Rules:
- Use `==` or `!=` for string comparison (leading/trailing spaces and quotes are trimmed)
- Without a comparator, non-empty values that are not `false`, `0`, or `null` are treated as true
---
## 5. Node types and configuration
### 5.1 Start
Workflow entry point; injects user input into `inputs`.
| Field | Description | Default |
|-------|-------------|---------|
| Input keys | Comma-separated input key names | `message, conversationId, projectId` |
Start node output includes: `output`, `message`, `conversationId`, `projectId`.
### 5.2 Agent
Runs an LLM Agent task. Supports multiple modes.
| Field | Description | Default |
|-------|-------------|---------|
| Agent mode | `eino_single` / `deep` / `plan_execute` / `supervisor` | `eino_single` |
| Input source | Template for upstream data | `{{previous.output}}` |
| Node instruction | Task description for this node | empty |
| Output variable name | Key written into `outputs` | `agent_result` |
**Message assembly:**
- Instruction only → send instruction to the Agent
- Input source only → “Continue based on upstream output: …”
- Both → combined “upstream input + node instruction”
After execution:
- `previous.output` becomes this nodes response text
- If **Output variable name** is set, the value is also stored in `outputs[variable_name]`
### 5.3 Tool
Calls an enabled MCP tool.
| Field | Description | Default |
|-------|-------------|---------|
| MCP tool | Tool name (required) | — |
| Argument template | JSON with `{{...}}` templates | `{}` |
| Timeout (seconds) | Optional | empty |
Example argument template:
```json
{"target": "{{inputs.message}}", "port": "443"}
```
If an output variable name is configured, the tool result is written to `outputs`.
### 5.4 Condition
Evaluates an expression and outputs `matched` (`true` / `false`).
| Field | Description | Default |
|-------|-------------|---------|
| Expression | Supports `{{...}}` and `==` / `!=` | `{{previous.output}} != ""` |
**Branching rules:**
- The **first outgoing edge** defaults to the **“yes”** branch (`matched == true`)
- The **second outgoing edge** defaults to the **“no”** branch (`matched == false`)
- Edge labels such as `是` / `否` (or `yes` / `no`, `true` / `false`) help identify branches
- A third or later edge needs a custom **edge condition**
Edge condition examples (select an edge, configure in the right panel):
```text
{{previous.matched}} == "true"
{{previous.matched}} == "false"
```
### 5.5 HITL (human-in-the-loop)
Human approval checkpoint (currently record-only; marks `approved: true` and continues).
| Field | Description | Default |
|-------|-------------|---------|
| Prompt | Supports templates | `Please approve before continuing` |
| Reviewer | `human` / `audit_agent` | `human` |
### 5.6 Output
Writes the final workflow result into `outputs` for summary and chat display.
| Field | Description | Default |
|-------|-------------|---------|
| Output variable name | Required key for the final result | `result` |
| Variable source | Template deciding what to write | `{{previous.output}}` |
**Note:** Output nodes are workflow exits and must not have outgoing edges.
### 5.7 End
Optional node for an end summary template (less common in role-bound flows).
| Field | Description | Default |
|-------|-------------|---------|
| Result template | Supports `{{outputs.xxx}}` | `{{outputs.result}}` |
---
## 6. Edge configuration
Select an **edge** to configure its **condition** in the right panel.
| Scenario | Example |
|----------|---------|
| Filter after a normal node | `{{previous.output}} == "ok"` |
| “Yes” branch from a condition | `{{previous.matched}} == "true"` |
| “No” branch from a condition | `{{previous.matched}} == "false"` |
If no edge condition is set:
- Non-condition nodes: edge is always allowed
- Condition nodes: yes/no branches are assigned by edge order automatically
---
## 7. Full example: passing Agent output across a condition
### 7.1 Graph structure
```text
Start → Agent (initial value) → Condition → Agent (transform) → Output
↘ no → Output
```
### 7.2 Node configuration
**Agent 1**
| Field | Value |
|-------|-------|
| Node instruction | Output only `123333333` |
| Output variable name | `agent_result1` |
**Condition**
| Field | Value |
|-------|-------|
| Expression | `{{outputs.agent_result1}} != ""` |
**Agent 2**
| Field | Value |
|-------|-------|
| Input source | `{{outputs.agent_result1}}` |
| Node instruction | Add 100 to the input, then output |
| Output variable name | `agent_result` |
**Output**
| Field | Value |
|-------|-------|
| Output variable name | `result` |
| Variable source | `{{outputs.agent_result}}` |
### 7.3 Common mistakes
| Wrong config | Why it fails |
|--------------|--------------|
| Agent 2 input source = `{{previous.output}}` | `previous` points to the condition node → `true`/`false`, not Agent 1s text |
| Agent 1 has no output variable name | `outputs.agent_result1` does not exist → empty downstream |
| Condition uses `{{previous.output}}` | Tests the wrong upstream value instead of Agent 1s named output |
---
## 8. Bind to a role and run
### 8.1 Bind in Role Management
1. Open **Role Management**, edit or create a role.
2. Select the workflow / graph ID to bind.
3. Set policy to `auto` (default when `workflow_id` is set).
4. Save the role.
You can also configure this in role YAML:
```yaml
name: workflow-test
workflow_id: "1233"
workflow_version: latest
workflow_policy: auto
```
### 8.2 Runtime behavior
When a user chats with that role:
1. The engine loads `graph_json` and executes the graph.
2. The chat UI shows progress events (`workflow_start`, `workflow_node_start`, Agent reasoning, etc.).
3. When finished, a summary lists all named entries in `outputs`.
If no Output node is reached or no branch matches, `outputs` may be empty and the summary will suggest checking the Output node and branches.
---
## 9. Validation before save
On save, the system checks:
| Rule | Description |
|------|-------------|
| Start node required | At least one `start` node |
| Output node required | At least one `output` node with an output variable name |
| Valid edges | Source and target exist; no self-loops |
| Start has no incoming edges | Start must not be targeted |
| Output has no outgoing edges | Nothing after Output |
| Tool nodes | MCP tool must be selected |
| Condition nodes | Expression required; ideally 12 outgoing edges (yes/no) |
---
## 10. Troubleshooting
| Symptom | Likely cause | Fix |
|---------|--------------|-----|
| Downstream gets empty value | Upstream has no output variable name | Set **Output variable name** on upstream; use `{{outputs.xxx}}` downstream |
| Downstream gets `true`/`false` | Used `{{previous.output}}` while previous node is a condition | Use `{{outputs.xxx}}` instead |
| Condition always takes “no” | Expression does not match actual output format | Check Agent output for quotes/newlines; try `!= ""` first |
| No final output | Output node branch not reached | Verify condition wiring; ensure every path reaches an **Output** node |
| Role chat does not run workflow | Role not bound or disabled | Check `workflow_id`, `workflow_policy: auto`, workflow `enabled: true` |
| Tool node fails | Invalid JSON in arguments or tool disabled | Fix argument template; enable the tool in MCP settings |
---
## 11. Best practices
1. **Meaningful names**: Use descriptive output variable names (`scan_result`, `parsed_targets`) instead of reusing `agent_result` everywhere.
2. **Prefer `outputs` for cross-node data**: If a condition, tool, or HITL node might sit in between, use named variables.
3. **Use `previous` only for direct links**: `A → B` with nothing in between is the ideal case for `{{previous.output}}`.
4. **Conditions should reference source data**: When testing Agent output, use `{{outputs.xxx}}` unless the condition immediately follows that Agent.
5. **Every path needs an exit**: Ensure both yes and no branches eventually reach an **Output** node (or your intended end).
6. **Validate with a simple run**: Use fixed-string outputs to verify data flow before swapping in real business logic.
---
## 12. Code references (for developers)
| Module | Path |
|--------|------|
| Execution engine | `internal/workflow/runner.go` |
| Canvas UI | `web/static/js/workflows.js` |
| Workflow API | `internal/handler/workflow.go` |
| Role binding | `internal/config/config.go` (`workflow_id` field) |