Workflow Editor

Build conversation flows with the visual workflow editor

Overview

The Workflow Editor is a visual node-based tool for designing conversation logic. Drag, drop, and connect nodes to create complex conversation flows.

Node Types

Start Node

The entry point for every workflow. Each workflow has exactly one Start node.

LLM Node

Sends a message to the configured AI model and returns the response.

Settings:

• Temperature (0.0 - 1.0)
• Max tokens
• Custom system prompt override

Condition Node

Branches the workflow based on conditions. Supports:

• Keyword matching
• Sentiment analysis
• Custom expressions

Tool Call Node

Executes an external function or API call. Results are passed back to the conversation context.

Voice Call Node

Handles voice-specific interactions — speech recognition and synthesis.

End Node

Terminates the conversation with an optional closing message.

Connecting Nodes

Click on a node’s output port and drag to another node’s input port to create a connection. Each connection represents a possible conversation path.

Best Practices

Always build explicit paths between nodes with clear conditions. However, in some cases the AI may autonomously route to a node that has no direct edge — this is called a synapse. Synapses occur when the AI determines that existing routing conditions are unclear or insufficient and decides to reach a node on its own. While this provides flexibility, frequent synapses usually indicate that your edge conditions need to be more specific.

• Keep workflows simple — complex flows are harder to debug
• Use Condition nodes to handle edge cases
• Test each path individually before publishing

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Generate Workflow with Claude Code

If you want to create a custom workflow for your AI agent based on your business requirements, you can use Claude Code to generate a ready-to-use workflow template JSON.

Prompt for workflow generation

# Instruction: Generate a Revol Workflow Template JSON

## Context

I use Revol — a platform for building AI sales agents. Each agent
has a visual workflow editor with nodes and edges that define
conversation logic.

I need you to generate a workflow template as a JSON object that
I can import into my Revol agent. The JSON follows a strict schema
used by Revol's template system.

## JSON Schema

The output must be a single JSON object with these top-level keys:

{
  "nodes_data": [...],
  "edges_data": [...],
  "tools_data": {...},
  "memory_fields_data": [...]   // optional
}

### nodes_data (array of objects)

Each node object has these fields:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| index | integer | yes | Unique index (0-based), used for edge references |
| node_type | string | yes | One of: start, custom, system_stt, system_tts, system_product, system_media, system_company, system_formatter |
| name | string | yes | Display name on the canvas |
| position_x | integer | yes | Canvas X coordinate |
| position_y | integer | yes | Canvas Y coordinate |
| is_active | boolean | yes | Whether this node is active |
| conversation_goal | string | no | System prompt for this node (custom nodes only) |
| config | object | no | Node-specific configuration (see below) |
| llm_provider_override | string | no | Override LLM provider: openai, anthropic, gemini, groq |
| llm_model_override | string | no | Override model name |
| temperature_override | number | no | Override temperature (0.0-2.0) |

Node types and their purpose:
- start — entry point, exactly one per workflow, no conversation_goal
- custom — LLM-powered node with its own prompt, tools, and knowledge base
- system_product — searches products, checks availability, shows details
- system_media — retrieves photos, videos, documents
- system_company — company info and support questions
- system_formatter — combines outputs from parallel nodes into final response
- system_stt — speech-to-text (voice input), usually inactive by default
- system_tts — text-to-speech (voice output), usually inactive by default

Config for system_stt:
{
  "provider": "openai",
  "language": "en",
  "greeting_text": "Hi! How can I help you?",
  "greeting_audio_url": null,
  "farewell_text": "Thanks! Goodbye!",
  "farewell_audio_url": null
}

Config for system_tts:
{
  "provider": "openai",
  "voice": "nova",
  "model": "tts-1",
  "speed": 1.0
}

Config for custom nodes (optional):
{
  "save_to_memory": ["field_key1", "field_key2"],
  "max_tool_rounds": 3,
  "tool_timeout_seconds": 30
}

### edges_data (array of objects)

Each edge object connects two nodes:

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| source_index | integer | yes | Index of the source node |
| target_index | integer | yes | Index of the target node |
| condition_type | string | yes | One of: always, keyword, intent, state, fallback |
| condition_value | object | no | Condition parameters (null for always/fallback) |
| priority | integer | no | Evaluation priority (default: 90 for always) |

Condition types and priorities:
- keyword (priority: 100) — matches words in the message
  condition_value: { "keywords": ["word1", "word2", ...] }
- state (priority: 95) — checks memory field values
  condition_value: { "conditions": [{"field": "name", "operator": "filled|empty|=|!=|contains|>|<", "value": ""}] }
- always (priority: 90) — unconditional, enables parallel fan-out
  condition_value: null
- intent (priority: 50) — LLM classifies the message intent
  condition_value: { "intents": ["intent_name"] }
- fallback (priority: 10) — routes only when nothing else matches
  condition_value: null

### tools_data (object, keys are node indexes as strings)

Maps node index to an array of tool names:

{
  "2": ["search_documents", "get_company_info"],
  "3": ["get_products", "get_product_details"]
}

Available built-in tools (9):
- get_products — search products by name/description
- get_product_details — full details for one product
- check_availability — check if product is in stock
- search_by_parameters — filter products by attributes
- get_company_info — company name, description, contacts
- search_documents — semantic RAG search in knowledge base
- get_photos — retrieve photos
- get_videos — retrieve videos
- get_documents — retrieve PDF/Word/Excel files

### memory_fields_data (optional array)

Define structured fields the agent should collect:

[
  {"key": "client_name", "label": "Client Name", "type": "text"},
  {"key": "client_phone", "label": "Phone", "type": "phone"},
  {"key": "topic", "label": "Topic", "type": "select", "options": ["sales", "support"]}
]

Field types: text, phone, email, number, select (with options array).

## Workflow Patterns

### Pattern 1: Simple (single expert)
Start → Custom Node → Formatter
Best for: FAQ bots, simple support, single-topic agents.

### Pattern 2: Parallel experts (always routing)
Start → Expert A → Formatter
      → Expert B → Formatter
      → Expert C → Formatter
Best for: agents that need to search products + documents + company info simultaneously.

### Pattern 3: Smart router (intent/keyword routing)
Start → (intent: sales) → Sales Expert → Formatter
      → (intent: support) → Support Expert → Formatter
      → (fallback) → General Expert → Formatter
Best for: multi-topic agents where different questions need different expertise.

### Pattern 4: Lead collection (state routing)
Start → (name empty) → Collect Name → Formatter
      → (name filled, phone empty) → Collect Phone → Formatter
      → (all filled) → Final Response → Formatter
Best for: lead qualification, appointment booking, form-filling flows.

### Pattern 5: Voice agent
STT → Start → Expert → Formatter → TTS
Same as any pattern above, but with STT/TTS nodes active.

## Layout Guidelines

- Start node at position (0, 0)
- STT node to the left: (-200, 0)
- Expert nodes to the right: (200, -100), (200, 0), (200, 100)
- Formatter further right: (400, 0)
- TTS node furthest right: (600, 0)
- Vertical spacing between parallel nodes: 100-200px

## Rules

1. Every workflow MUST have exactly one "start" node
2. Every workflow MUST have exactly one "system_formatter" node
3. STT and TTS nodes should be included but set to is_active: false
   unless voice is specifically requested
4. Custom nodes should have clear, focused conversation_goal text
5. Each custom node should have at least search_documents tool
6. Use intent routing instead of keyword for multilingual agents
7. The fallback edge ensures no message goes unanswered
8. Node indexes must be sequential starting from 0
9. All edge source_index and target_index must reference valid node indexes
10. conversation_goal should be written in the language the agent will use

## Output

Return ONLY the JSON object — no markdown, no explanation, no
code fences. The JSON must be valid and parseable.

## Input

My business: [DESCRIBE YOUR BUSINESS]
Agent role: [e.g., sales consultant, support agent, lead collector]
Languages: [e.g., English, Ukrainian, multilingual]
Voice enabled: [yes/no]
Key topics/departments: [e.g., products, billing, technical support]
Memory fields to collect: [e.g., name, phone, email, or none]
Special requirements: [any additional requirements]

After generating the JSON, you can review it and adjust node names, conversation goals, or tool assignments before importing. The workflow template format will be importable directly in a future Revol update.