Fastgpt Workflow Generator

FastGPT Workflow Generator

Automatically generate production-ready FastGPT workflow JSON from natural language requirements

When to Use This Skill

Use this skill when you need to:

• Create new workflows from scratch: User asks to "create a FastGPT workflow for X purpose"
• Generate based on templates: User wants to build workflows similar to existing patterns (document processing, AI chat, data analysis, multi-agent systems)
• Modify existing workflows: User needs to add/remove/update nodes in an existing workflow JSON
• Validate workflow JSON: User has a workflow JSON that needs verification or fixing
• Design multi-agent systems: User mentions parallel processing, agent coordination, or workflow orchestration
• Automate workflow creation: User provides requirements document and needs executable JSON
• Convert requirements to JSON: User has specifications and wants a FastGPT-compatible workflow

Trigger Keywords: FastGPT, workflow, JSON, multi-agent, 工作流, template matching, workflow automation, node configuration, workflow validation

Core Workflow

This skill follows a 5-phase process to generate production-ready workflow JSON:

Phase 1: Requirements Analysis

Goal: Extract structured requirements from natural language input

Process:

1. Identify request type:

   • Create from scratch
   • Based on template
   • Modify existing workflow
   • Validate/fix existing JSON

2. Extract key information using AI semantic analysis:

   {
     "purpose": "Workflow objective (e.g., 'Travel planning assistance')",
     "domain": "Application domain (travel/event/document/data/general)",
     "complexity": "simple | medium | complex",
     "features": ["aiChat", "knowledgeBase", "httpRequest", "parallel"],
     "inputs": ["userChatInput", "city", "date"],
     "outputs": ["Complete plan", "Recommendations"],
     "externalIntegrations": ["Weather API", "Feishu API"],
     "specialRequirements": ["Multi-agent", "Real-time data"]
   }
   

3. Completeness check: If information is insufficient, clarify through dialogue

Output: Structured requirements object

Phase 2: Template Matching

Goal: Find the most similar built-in template

Built-in Templates (stored in templates/ directory):

• templates/文档翻译助手.json - Simple workflow (document processing)
• templates/销售陪练大师.json - Medium complexity (conversational AI)
• templates/简历筛选助手_飞书.json - Complex workflow (data processing + external integration)
• templates/AI金融日报.json - Scheduled trigger + multi-agent (news aggregation)

Matching Strategy:

Step 1: Coarse Filtering (Metadata-based)

Calculate similarity scores:
- Domain match: travel vs travel = 1.0, travel vs event = 0.3
- Complexity match: simple vs simple = 1.0, simple vs complex = 0.3
- Feature overlap: Jaccard similarity of feature sets
- Node count similarity: 1 - |count1 - count2| / max(count1, count2)

Combined score = 0.3 * domain + 0.2 * complexity + 0.3 * features + 0.2 * nodeCount

Select Top 3 candidate templates

Step 2: Fine Filtering (Semantic Similarity)

For Top 3 candidates:
1. Analyze user requirements vs template characteristics
2. Evaluate workflow structure similarity
3. Calculate comprehensive score

Final score = 0.3 * domain + 0.2 * complexity + 0.3 * features + 0.2 * semantic

Step 3: Selection Strategy

- Highest score < 0.5: Start from blank template
- Highest score 0.5-0.7: Use template as reference, major modifications
- Highest score > 0.7: Use template as base, minor adjustments

Output:

• Best matching template JSON object
• Matching analysis report
• Modification suggestions list

Phase 3: JSON Generation

Scenario 1: Generate Based on Template

1. Copy template structure

2. Modify nodes
   - Keep: structurally similar nodes (workflowStart, userGuide)
   - Modify: nodes requiring prompt/parameter adjustments
   - Delete: unnecessary nodes
   - Add: new requirement nodes

3. Regenerate NodeId
   function generateNodeId(nodeType, nodeName, existingIds) {
     // Fixed ID mapping
     if (nodeType === 'workflowStart') return 'workflowStart';
     if (nodeType === 'userGuide' || nodeType === 'systemConfig') return 'userGuide';

     // Generate semantic ID (camelCase)
     const baseName = nodeName.replace(/[\s\u4e00-\u9fa5]+/g, '');
     let nodeId = baseName ? `${baseName}Node` : `${nodeType}Node`;

     // Ensure uniqueness
     let counter = 1;
     while (existingIds.has(nodeId)) {
       nodeId = `${baseName}Node_${counter}`;
       counter++;
     }

     return nodeId;
   }

4. Update references
   - Traverse all inputs, replace old nodeId with new nodeId
   - Update edges' source/target
   - Handle two reference formats:
     - Array: ["nodeId", "key"]
     - Template: {{$nodeId.key$}}  (Note: double braces with single $)

5. Auto-layout positions (hierarchical layout algorithm)
   function autoLayout(nodes, edges) {
     // Topological sort to determine layers
     const layers = topologicalLayering(nodes, edges);

     // Calculate positions for each layer
     const LAYER_GAP_X = 350;
     const NODE_GAP_Y = 150;

     layers.forEach((layer, layerIndex) => {
       const x = -200 + layerIndex * LAYER_GAP_X;
       const totalHeight = (layer.length - 1) * NODE_GAP_Y;
       const startY = -totalHeight / 2;

       layer.forEach((nodeId, nodeIndex) => {
         positions[nodeId] = {
           x: x,
           y: startY + nodeIndex * NODE_GAP_Y
         };
       });
     });

     // Fixed position for special nodes
     positions['userGuide'] = { x: -600, y: -250 };
   }

6. Update configuration
   - Modify chatConfig.welcomeText
   - Update chatConfig.variables

Scenario 2: Create from Scratch

1. Determine node list
   - Required: workflowStart, userGuide
   - Add based on features: chatNode, datasetSearchNode, httpRequest468, etc.
   - Required: answerNode (output node)

2. Generate nodes and connections
   - Use standard node templates
   - Fill required fields
   - Customize inputs/outputs based on requirements

3. Calculate positions and generate configuration

Output: Complete FastGPT workflow JSON

Phase 4: Validation

Level 1: JSON Format Validation

✅ JSON is parseable
✅ Top level contains nodes, edges, chatConfig
✅ Each node contains: nodeId, name, flowNodeType, position, inputs, outputs
✅ flowNodeType is in valid type list (40+ types)
✅ position contains x, y numeric coordinates

Level 2: Node Connection Validation

✅ edges' source/target nodes exist
✅ sourceHandle/targetHandle format correct (nodeId-source-right, nodeId-target-left)
✅ Node input references' nodes and output keys exist
✅ Reference types match (string → string)
✅ Template references {{$nodeId.key$}} nodes and keys exist
✅ No self-loops, no duplicate connections

Level 3: Logic Completeness Validation

✅ Required nodes exist (workflowStart, userGuide, at least one output node)
✅ All nodes reachable from workflowStart (connectivity)
✅ No illegal cycles (unless using loop node)
✅ loop nodes correctly configured with parentNodeId and childrenNodeIdList
✅ No dead ends (non-output nodes without outgoing edges)
✅ All required inputs have values

Output: Validation report (containing errors, warnings, fix suggestions)

Phase 5: Incremental Modification (Optional)

Use Cases: Add/delete/modify nodes

Processing Steps:

1. Understand modification intent

Use AI to analyze user request, extract:
{
  "action": "add" | "delete" | "modify" | "reconnect",
  "targetNodes": ["aiChatNode"],
  "insertBefore": "aiChatNode",
  "newNodes": [{ "type": "datasetSearchNode", "name": "Knowledge Base Search" }],
  "modifications": {
    "aiChatNode": {
      "inputs": { "quoteQA": ["knowledgeBaseSearch", "searchResult"] }
    }
  }
}

2. Execute modifications

- Add node: generate new node, reconnect, calculate position
- Delete node: remove node, bypass reconnect, clean references
- Modify node: update inputs/outputs, validate references

3. Re-layout and validate

Examples

Example 1: Simple AI Q&A Workflow

User Request:

"Create a simple AI Q&A workflow where users input questions and AI responds directly"

Skill Processing:

1. Requirements Analysis

   {
     "purpose": "AI question answering",
     "domain": "general",
     "complexity": "simple",
     "features": ["aiChat"],
     "inputs": ["userChatInput"],
     "outputs": ["AI response"]
   }
   

2. Template Matching

   • 文档翻译助手.json - Score: 0.85 (simple workflow, direct processing)

3. JSON Generation

   • Use template, modify systemPrompt and welcomeText

4. Validation Result

   • ✅ All three layers pass validation

Generated JSON (key parts):

{
  "nodes": [
    {
      "nodeId": "userGuide",
      "name": "System Configuration",
      "flowNodeType": "userGuide",
      "position": {"x": -600, "y": -250}
    },
    {
      "nodeId": "workflowStart",
      "name": "Start",
      "flowNodeType": "workflowStart",
      "position": {"x": -150, "y": 100},
      "outputs": [
        {"key": "userChatInput", "type": "static", "valueType": "string"}
      ]
    },
    {
      "nodeId": "aiChatNode",
      "name": "AI Response",
      "flowNodeType": "chatNode",
      "position": {"x": 200, "y": 100},
      "inputs": [
        {
          "key": "model",
          "valueType": "string",
          "value": "gpt-4"
        },
        {
          "key": "systemPrompt",
          "valueType": "string",
          "value": "You are a professional AI assistant that can answer various questions. Please provide accurate and helpful answers based on user questions."
        },
        {
          "key": "userChatInput",
          "valueType": "string",
          "value": ["workflowStart", "userChatInput"]
        }
      ],
      "outputs": [
        {"key": "answerText", "type": "static", "valueType": "string"}
      ]
    },
    {
      "nodeId": "outputNode",
      "name": "Output Answer",
      "flowNodeType": "answerNode",
      "position": {"x": 550, "y": 100},
      "inputs": [
        {
          "key": "text",
          "valueType": "string",
          "value": ["aiChatNode", "answerText"]
        }
      ]
    }
  ],
  "edges": [
    {
      "source": "workflowStart",
      "target": "aiChatNode",
      "sourceHandle": "workflowStart-source-right",
      "targetHandle": "aiChatNode-target-left"
    },
    {
      "source": "aiChatNode",
      "target": "outputNode",
      "sourceHandle": "aiChatNode-source-right",
      "targetHandle": "outputNode-target-left"
    }
  ],
  "chatConfig": {
    "welcomeText": "Welcome to the AI Q&A assistant! Please enter your question.",
    "variables": []
  }
}

Example 2: Document Translation Workflow (Based on Template)

User Request:

"Create a document translation workflow that translates user-uploaded documents from Chinese to English"

Skill Processing:

1. Requirements Analysis

   {
     "purpose": "Document translation",
     "domain": "document",
     "complexity": "medium",
     "features": ["readFiles", "aiChat", "textOutput"],
     "inputs": ["userFiles"],
     "outputs": ["translated document"]
   }
   

2. Template Matching

   • 文档翻译助手.json - Score: 0.95 (perfect match!)

3. JSON Generation

   • Use template directly, only adjust language direction in prompt

Generated Workflow Structure:

workflowStart → readFiles → translateNode → outputNode

Key Node Configuration:

• readFiles Node: Reads user-uploaded files
• translateNode (chatNode): AI translates with specialized prompt
• outputNode (answerNode): Outputs translated text

Example 3: Incremental Modification (Add Knowledge Base)

User Request:

"I have an existing AI Q&A workflow (simple_qa_workflow.json),
I want to search the knowledge base first before AI answers,
find relevant information then generate response"

Existing Workflow Structure:

workflowStart → aiChatNode → outputNode

Modification Goal:

workflowStart → knowledgeBaseSearch → aiChatNode → outputNode

Skill Processing:

1. Analyze Modification Intent

   {
     "action": "add",
     "targetNodes": ["aiChatNode"],
     "insertBefore": "aiChatNode",
     "newNodes": [
       {
         "type": "datasetSearchNode",
         "name": "Knowledge Base Search"
       }
     ],
     "modifications": {
       "aiChatNode": {
         "inputs": {
           "quoteQA": ["knowledgeBaseSearch", "searchResult"]
         }
       }
     }
   }
   

2. Execute Modification

   • Add knowledgeBaseSearch node
   • Modify edge: workflowStart → knowledgeBaseSearch
   • Add edge: knowledgeBaseSearch → aiChatNode
   • Modify aiChatNode's inputs (add quoteQA)

3. Re-layout Positions

   • workflowStart: (-150, 100)
   • knowledgeBaseSearch: (50, 100) ← newly inserted
   • aiChatNode: (400, 100) ← shifted right
   • outputNode: (750, 100) ← shifted right

4. Validation Result

   • ✅ All validations pass

Modified JSON (new and modified parts):

{
  "nodes": [
    {
      "nodeId": "knowledgeBaseSearch",
      "name": "Knowledge Base Search",
      "flowNodeType": "datasetSearchNode",
      "position": {"x": 50, "y": 100},
      "inputs": [
        {
          "key": "datasetIds",
          "valueType": "selectDataset",
          "value": [],
          "required": true
        },
        {
          "key": "searchQuery",
          "valueType": "string",
          "value": ["workflowStart", "userChatInput"],
          "required": true
        },
        {
          "key": "similarity",
          "valueType": "number",
          "value": 0.5
        },
        {
          "key": "limitCount",
          "valueType": "number",
          "value": 5
        }
      ],
      "outputs": [
        {
          "key": "searchResult",
          "type": "static",
          "valueType": "datasetQuote"
        }
      ]
    },
    {
      "nodeId": "aiChatNode",
      "inputs": [
        {
          "key": "quoteQA",
          "valueType": "datasetQuote",
          "value": ["knowledgeBaseSearch", "searchResult"]
        }
      ]
    }
  ],
  "edges": [
    {
      "source": "workflowStart",
      "target": "knowledgeBaseSearch"
    },
    {
      "source": "knowledgeBaseSearch",
      "target": "aiChatNode"
    },
    {
      "source": "aiChatNode",
      "target": "outputNode"
    }
  ]
}

Modification Summary Report:

• ✅ Added 1 node: knowledgeBaseSearch (datasetSearchNode)
• ✅ Modified 1 node: aiChatNode (added quoteQA input)
• ✅ Added 1 edge: knowledgeBaseSearch → aiChatNode
• ✅ Modified 1 edge: workflowStart → knowledgeBaseSearch (originally workflowStart → aiChatNode)
• ✅ Re-layouted all positions

Technical Implementation

NodeId Generation Algorithm

Rules:

1. Fixed IDs: workflowStart, userGuide (systemConfig)
2. Semantic naming: Generate based on node name (remove spaces and Chinese, convert to camelCase)
3. Uniqueness guarantee: If conflict, add _1, _2 suffix

Examples:

• generateNodeId('chatNode', 'Travel Planning Assistant') → TravelPlanningAssistantNode
• generateNodeId('httpRequest468', 'Weather Query') → WeatherQueryNode
• generateNodeId('chatNode', 'Assistant', {TravelPlanningAssistantNode}) → AssistantNode_1

Position Auto-Layout Algorithm

Algorithm: Hierarchical Layout

Steps:

1. Topological sort to determine layers (BFS)
2. Calculate horizontal position and vertical spacing for each layer
3. Fixed position for special nodes (userGuide: {x: -600, y: -250})

Parameters:

• LAYER_GAP_X = 350 (horizontal spacing between layers)
• NODE_GAP_Y = 150 (vertical spacing within layer)
• START_X = -200, START_Y = 0

Reference Format Description

Two Reference Formats:

1. Array Format (direct value reference):

"value": ["workflowStart", "userChatInput"]

2. Template Syntax (string concatenation):

"value": "Please create a plan for me.\n\nDestination: {{$workflowStart.userChatInput$}}\n\nWeather: {{$weatherQueryNode.httpRawResponse$}}"

Important: Template syntax is {{$nodeId.key$}} (double braces with single $)

Special Node Handling

loop Node:

• Must have childrenNodeIdList field
• Child nodes must have parentNodeId field
• Child nodes include: loopStart, [processing nodes...], loopEnd

ifElse Node:

• Has multiple output branches
• Each branch corresponds to different conditions

Best Practices

Do's (Recommended Practices)

• ✅ Always validate at three levels - format, connections, logic
• ✅ Use meaningful nodeIds - use semantic names (e.g., weatherQueryNode)
• ✅ Prefer template matching - template-based generation is more reliable than creating from scratch
• ✅ Use array references for direct values - ["nodeId", "key"]
• ✅ Use template references for string concatenation - {{$nodeId.key$}}
• ✅ Auto-layout positions - use auto-layout algorithm
• ✅ Include system config node - always include userGuide
• ✅ Test with validation - use built-in validation before importing to FastGPT
• ✅ Provide clear error messages - include location and fix suggestions
• ✅ Document modifications - generate modification summary report

Don'ts (Prohibited Practices)

• ❌ Don't skip validation - never skip validation
• ❌ Don't use invalid node types - check flowNodeType validity
• ❌ Don't create circular references without loop nodes - no illegal cycles
• ❌ Don't forget required fields - nodeId, name, flowNodeType, position, inputs, outputs
• ❌ Don't use wrong reference format - prohibited: {{nodeId.key}} (missing $)
• ❌ Don't ignore warnings - warnings should be fixed
• ❌ Don't hardcode positions - except userGuide, use auto-layout
• ❌ Don't create unreachable nodes - ensure reachable from workflowStart
• ❌ Don't generate overly complex workflows - workflows with >20 nodes should be split

Troubleshooting

FAQ

Q1: Import to FastGPT reports "Invalid node type"

A: Check the flowNodeType field, ensure using supported types. Reference references/node_types_reference.md. Common errors:

• chatNode is correct (not aiChat)
• Number suffixes (like httpRequest468) should be retained

Q2: References between nodes not working

A: Check reference format:

• ✅ Correct: ["workflowStart", "userChatInput"] or {{$workflowStart.userChatInput$}}
• ❌ Wrong: {$workflowStart.userChatInput$} (single brace, should be double)

Q3: Some nodes not executing at runtime

A: Use built-in validation to check Level 3, ensure all nodes reachable from workflowStart

Q4: Parallel nodes not executing in parallel

A: Ensure multiple nodes' targets are the same aggregation node, and these nodes have no dependencies

Q5: Loop workflow errors

A: Must use flowNodeType: "loop" node, configure parentNodeId and childrenNodeIdList

Debug Checklist

## Phase 1: JSON Format Check
- [ ] JSON is parseable
- [ ] Contains nodes, edges, chatConfig
- [ ] All strings use double quotes
- [ ] No trailing commas

## Phase 2: Node Check
- [ ] workflowStart node exists
- [ ] At least one output node exists
- [ ] All flowNodeType valid
- [ ] All nodeId unique
- [ ] All position contains x, y

## Phase 3: Connection Check
- [ ] All edges' source and target exist
- [ ] All handle format correct
- [ ] No duplicate edges, no self-loops

## Phase 4: Reference Check
- [ ] All array references' nodes and keys exist
- [ ] All template references' nodes and keys exist
- [ ] Reference types match

## Phase 5: Logic Check
- [ ] All nodes reachable from workflowStart
- [ ] No illegal cycles
- [ ] No dead-end nodes
- [ ] All required inputs have values

## Phase 6: Runtime Test
- [ ] Import to FastGPT without errors
- [ ] Configure necessary parameters
- [ ] Run test cases
- [ ] Check output meets expectations

Quick Reference

Built-in Template Files

• templates/文档翻译助手.json - Simple workflow, document processing
• templates/销售陪练大师.json - Medium complexity, conversational AI
• templates/简历筛选助手_飞书.json - Complex workflow, data + external integration
• templates/AI金融日报.json - Scheduled trigger, multi-agent

Detailed Documentation

• references/node_types_reference.md - Complete reference of 40+ node types
• references/validation_rules.md - Detailed three-layer validation rules
• references/template_matching.md - Template matching algorithm
• references/json_structure_spec.md - Complete FastGPT JSON structure specification

Example Documents

• examples/example1_simple_qa.md - Complete example: Simple Q&A workflow
• examples/example2_travel_planning.md - Complete example: Travel planning workflow
• examples/example3_incremental_modify.md - Complete example: Incremental modification

Common Commands

# Validate workflow JSON
node scripts/validate_workflow.js path/to/workflow.json

# Copy template
cp templates/文档翻译助手.json my_workflow.json

# View template list
ls -lh templates/

Version: 1.0 Last Updated: 2025-01-02 Compatibility: FastGPT v4.8+

Source: YYH211/Claude-meta-skill — distributed by TomeVault.