flowise-designer

name

flowise-designer

description

Generate, design, and export valid Flowise Chatflow and AgentFlow JSON files ready to import into Flowise. Use this skill whenever the user wants to: build a Flowise flow, create a chatflow, create an agentflow, design an AI workflow in Flowise, generate Flowise JSON, build a RAG pipeline in Flowise, create a Flowise tool agent, connect LLMs/memory/vector stores/tools in Flowise, or export a flow to import into Flowise. Trigger even if the user says things like "make me a Flowise bot that does X", "I want a RAG flow in Flowise", "build me an agent in Flowise", or "generate Flowise JSON for Y".

Flowise Flow Generator

Generate production-ready Flowise Chatflow and AgentFlow V2 JSON files that can be imported directly into Flowise via Import Chatflow or the API (POST /api/v1/chatflows).

Quick Decision: Chatflow vs AgentFlow V2?

Use Chatflow when…	Use AgentFlow V2 when…
Simple LLM chain or RAG pipeline	Multi-step orchestration with branching
Single agent with tools	Human-in-the-loop / approval steps
Conversational memory + retrieval	Parallel paths / conditional logic
Standard LangChain pattern	Stateful flows using $flow.state
User doesn't specify	User says "agent flow", "multi-step", "branching"

If the user doesn't specify, default to Chatflow for simple use cases and AgentFlow V2 for anything with branching, conditions, or multi-step orchestration.

---

Step 1 — Understand the Request

Gather (ask if not provided):

• Goal: What should the flow do?
• LLM: Which model? (OpenAI, Anthropic, Ollama, Groq, etc.)
• Components needed: Memory? Vector store? Tools? Document loaders?
• Flow type: Chatflow or AgentFlow V2?
• Credentials: Which API keys will be needed (note them but leave credential: "")

---

Step 2 — Design the Architecture

Before writing JSON, sketch the node graph mentally:

1. Identify all nodes needed (LLM, chain/agent, memory, tools, retriever, embeddings, etc.)
2. Identify connections between nodes (which output plugs into which input)
3. Verify type compatibility — source baseClasses must intersect target anchor type
4. Assign node IDs following pattern {nodeName}_{index} (e.g. chatOpenAI_0, pinecone_0)
5. Plan layout positions — space nodes ~400px apart horizontally, arrange left-to-right by data flow

---

Step 3 — Generate the JSON

Top-Level Structure (always this shape)

{
  "nodes": [ /* array of node objects */ ],
  "edges": [ /* array of edge objects */ ],
  "viewport": { "x": 0, "y": 0, "zoom": 0.75 }
}

Node Object Template

{
  "id": "{nodeName}_{index}",
  "position": { "x": 0, "y": 0 },
  "type": "customNode",
  "data": {
    "id": "{nodeName}_{index}",
    "label": "Human Readable Label",
    "version": 1,
    "name": "{nodeName}",
    "type": "{ComponentType}",
    "baseClasses": ["{ComponentType}", "...parent classes..."],
    "category": "{Category}",
    "description": "What this node does",
    "inputParams": [ /* form fields — see references/schema.md */ ],
    "inputAnchors": [ /* connectable input sockets */ ],
    "inputs": { /* actual values + instance references */ },
    "outputAnchors": [ /* output sockets */ ],
    "outputs": {},
    "credential": "",
    "selected": false
  },
  "width": 300,
  "height": 500,
  "selected": false,
  "positionAbsolute": { "x": 0, "y": 0 },
  "dragging": false
}

Key rules:

• data.id must equal the node's id
• type is "customNode" for Chatflow; "agentFlow" for AgentFlow V2
• position and positionAbsolute must be identical
• Anchor references in inputs use: "{{otherNodeId.data.instance}}"
• Leave credential: "" — user connects credentials in the UI

Edge Object Template — Chatflow

{
  "source": "{sourceNodeId}",
  "sourceHandle": "{sourceNodeId}-output-{outputName}-{Type1|Type2|Type3}",
  "target": "{targetNodeId}",
  "targetHandle": "{targetNodeId}-input-{inputName}-{AcceptedType}",
  "type": "buttonedge",
  "id": "{sourceNodeId}-{sourceHandle}-{targetNodeId}-{targetHandle}"
}

Critical rules (Chatflow only):

• type MUST be "buttonedge" (not "default", not "smoothstep")
• sourceHandle format: {nodeId}-output-{name}-{BaseClass1|BaseClass2|...}
• targetHandle format: {nodeId}-input-{name}-{AcceptedBaseClass}
• id = concatenation of {source}-{sourceHandle}-{target}-{targetHandle}
• Connection is valid only if source baseClasses ∩ target anchor type is non-empty

Edge Object Template — AgentFlow V2

{
  "source": "{sourceNodeId}",
  "sourceHandle": "{sourceNodeId}-output-{outputName}",
  "target": "{targetNodeId}",
  "targetHandle": "{targetNodeId}",
  "data": { "sourceColor": "{hexColor}", "targetColor": "{hexColor}", "isHumanInput": false },
  "type": "agentFlow",
  "id": "{sourceNodeId}-{sourceHandle}-{targetNodeId}-{targetHandle}"
}

Critical rules (AgentFlow V2 only):

• type MUST be "agentFlow" — NOT "buttonedge"
• sourceHandle format: {nodeId}-output-{outputName} — no type classes appended
• targetHandle format: just the target node ID — no -input- path
• data object is required: set sourceColor/targetColor to node hex colors (see references/schema.md Section 8 table); set isHumanInput: true only for edges from a HumanInput node
• id = concatenation of {source}-{sourceHandle}-{target}-{targetHandle}

---

Step 4 — Output & Delivery

1. Generate the complete, valid JSON
2. Save it as {flow-name}.json using create_file to /mnt/user-data/outputs/
3. Present the file with present_files
4. Tell the user: Flowise → Add New → Import Chatflow → select the file (or drag-and-drop)
5. Remind them to connect their credentials in the node settings after import

---

Reference Files

Read these when you need detailed schema information:

• references/schema.md — Full node data schema, all inputParam types, anchor id formats, AgentFlow V2 differences, common gotchas
• references/nodes.md — Ready-to-use node templates for every major category: Chat Models, LLMs, Chains, Agents, Tools, Vector Stores, Memory, Embeddings, Document Loaders, Text Splitters, Output Parsers, AgentFlow V2 nodes

When to read them:

• references/schema.md → when you need to verify field formats, param types, or AgentFlow V2 specifics
• references/nodes.md → always — copy node templates from here rather than generating from memory. Node schemas must be exact.

---

Common Patterns (Quick Reference)

Pattern A: Simple Conversational RAG

ChatModel → ConversationalRetrievalQAChain ← VectorStoreRetriever ← [Embeddings + VectorStore ← DocumentLoader ← TextSplitter]

Pattern B: Tool Agent

ChatModel → ToolAgent ← [Tool1, Tool2, ...] ← Memory (optional)

Pattern C: AgentFlow V2 Linear

Start → LLM → DirectReply

Pattern D: AgentFlow V2 with Condition

Start → Agent → Condition → [Path A: DirectReply] / [Path B: HumanInput → Agent → DirectReply]

---

Validation Checklist

Before outputting the final JSON, verify:

• Every data.id matches its node id
• Every position matches positionAbsolute
• Chatflow edges: "type": "buttonedge" with full {nodeId}-output-{name}-{Types} / {nodeId}-input-{name}-{Type} handles
• AgentFlow V2 edges: "type": "agentFlow", targetHandle = node ID only, data color object present
• All edge sourceHandle / targetHandle ids are correct and consistent with their nodes' anchor ids
• All inputs references use {{nodeId.data.instance}} format
• credential: "" on all nodes (not a real credential value)
• No circular dependencies in Chatflows
• AgentFlow V2: type: "agentFlow" on node wrappers, includes a startAgentflow_0 node
• JSON is valid (balanced brackets, proper commas, no trailing commas)